From 9834142e9673019e8e3b0625b72a48a6201f3934 Mon Sep 17 00:00:00 2001
From: JTran-IDM <nhat.tran@mavs.uta.edu>
Date: Wed, 13 Aug 2025 13:22:56 -0700
Subject: [PATCH 01/20] feat: shouldShowDocumentPanel

---
 .../container/mode/FocusAnnotation.vue        |  10 +-
 .../mode/useDocumentViewModel.spec.js         | 208 ++++++++++++++++++
 .../container/mode/useDocumentViewModel.ts    |  11 +-
 3 files changed, 218 insertions(+), 11 deletions(-)
 create mode 100644 extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js

diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index e6d52f96f..8af25fc84 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -1,7 +1,7 @@
 <template>
   <VerticalResizable class="wrapper" :id="`${recordCriteria.datasetId}-r-v-rz`">
     <template #left>
-      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" collapsable>
+      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" :collapsable="shouldShowDocumentPanel">
         <template #up>
           <section class="wrapper__records" aria-label="Focus Annotation View">
             <DatasetFilters :recordCriteria="recordCriteria">
@@ -25,13 +25,13 @@
             <Record v-else :datasetVectors="datasetVectors" :recordCriteria="recordCriteria" :record="record" />
           </section>
         </template>
-        <template #downHeader>
+        <template #downHeader v-if="shouldShowDocumentPanel">
           <p v-text="$t('document')" />
         </template>
-        <template #downHeaderExpanded>
+        <template #downHeaderExpanded v-if="shouldShowDocumentPanel">
           <p v-text="$t('document')" />
         </template>
-        <template #downContent>
+        <template #downContent v-if="shouldShowDocumentPanel">
           <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
         </template>
       </HorizontalResizable>
@@ -69,6 +69,7 @@
       </HorizontalResizable>
     </template>
     <BaseCollapsablePanel
+      v-if="shouldShowDocumentPanel"
       hideOnDesktop
       :isExpanded="expandedGuidelines"
       @toggle-expand="expandedGuidelines = !expandedGuidelines"
@@ -146,6 +147,7 @@ export default {
         this.isDraftSaving = false;
       }
     },
+
   },
   setup(props) {
     return {
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js
new file mode 100644
index 000000000..8841c692f
--- /dev/null
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js
@@ -0,0 +1,208 @@
+import { useDocumentViewModel } from "./useDocumentViewModel";
+
+// Mock dependencies inline to avoid hoisting issues
+jest.mock("ts-injecty", () => ({
+  useResolve: jest.fn(() => mockGetDocumentUseCase),
+}));
+
+jest.mock("@/v1/infrastructure/storage/DocumentStorage", () => ({
+  useDocument: jest.fn(() => ({
+    state: mockDocument,
+    set: mockSetDocument,
+    clear: mockClearDocument,
+  })),
+}));
+
+jest.mock("@/v1/infrastructure/storage/DatasetStorage", () => ({
+  useDataset: jest.fn(() => ({
+    state: mockDataset,
+  })),
+}));
+
+jest.mock("@/v1/infrastructure/storage/WorkspaceStorage", () => ({
+  useWorkspaces: jest.fn(() => ({
+    state: mockWorkspaces,
+  })),
+}));
+
+jest.mock("~/v1/infrastructure/services/useNotifications", () => ({
+  useNotifications: jest.fn(() => mockNotifications),
+}));
+
+jest.mock("@/v1/infrastructure/services/useWait", () => ({
+  waitForAsyncValue: jest.fn(() => Promise.resolve()),
+}));
+
+jest.mock("vue-demi", () => ({
+  ref: jest.fn(),
+  watch: jest.fn(),
+  computed: jest.fn(),
+}));
+
+// Mock objects
+let mockDocument;
+let mockSetDocument;
+let mockClearDocument;
+let mockDataset;
+let mockWorkspaces;
+let mockNotifications;
+let mockGetDocumentUseCase;
+let mockRef;
+let mockWatch;
+let mockComputed;
+
+describe("useDocumentViewModel", () => {
+  beforeEach(() => {
+    jest.clearAllMocks();
+
+    // Setup mock document state
+    mockDocument = {
+      id: null,
+      url: null,
+      pmid: null,
+      doi: null,
+      file_name: null,
+      page_number: null,
+    };
+
+    mockSetDocument = jest.fn();
+    mockClearDocument = jest.fn();
+
+    mockDataset = {
+      workspaceName: "test-workspace",
+    };
+
+    mockWorkspaces = {
+      selectedWorkspace: {
+        id: "workspace-123",
+      },
+    };
+
+    mockNotifications = {
+      notify: jest.fn(),
+    };
+
+    mockGetDocumentUseCase = {
+      setDocument: jest.fn(),
+      setSegments: jest.fn(),
+    };
+
+    // Setup Vue composition API mocks
+    const vueDemi = require("vue-demi");
+    mockRef = vueDemi.ref;
+    mockWatch = vueDemi.watch;
+    mockComputed = vueDemi.computed;
+
+    mockRef.mockImplementation((value) => ({ value }));
+    mockWatch.mockImplementation(() => {});
+    mockComputed.mockImplementation((fn) => ({ value: fn() }));
+  });
+
+  afterEach(() => {
+    jest.restoreAllMocks();
+  });
+
+  describe("shouldShowDocumentPanel computed property", () => {
+    it("should return false when document has no id", () => {
+      mockDocument.id = null;
+      mockDocument.url = "http://example.com/doc.pdf";
+
+      const viewModel = useDocumentViewModel({ record: {} });
+
+      // Get the computed function that was passed to mockComputed
+      const computedCalls = mockComputed.mock.calls;
+      const shouldShowDocumentPanelCall = computedCalls.find(
+        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
+      );
+
+      expect(shouldShowDocumentPanelCall).toBeDefined();
+      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
+      expect(shouldShowDocumentPanel).toBe(false);
+    });
+
+    it("should return false when document has no url", () => {
+      mockDocument.id = "doc-123";
+      mockDocument.url = null;
+
+      const viewModel = useDocumentViewModel({ record: {} });
+
+      // Get the computed function that was passed to mockComputed
+      const computedCalls = mockComputed.mock.calls;
+      const shouldShowDocumentPanelCall = computedCalls.find(
+        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
+      );
+
+      expect(shouldShowDocumentPanelCall).toBeDefined();
+      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
+      expect(shouldShowDocumentPanel).toBe(false);
+    });
+
+    it("should return true when document has both id and url", () => {
+      mockDocument.id = "doc-123";
+      mockDocument.url = "http://example.com/doc.pdf";
+
+      const viewModel = useDocumentViewModel({ record: {} });
+
+      // Get the computed function that was passed to mockComputed
+      const computedCalls = mockComputed.mock.calls;
+      const shouldShowDocumentPanelCall = computedCalls.find(
+        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
+      );
+
+      expect(shouldShowDocumentPanelCall).toBeDefined();
+      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
+      expect(shouldShowDocumentPanel).toBe(true);
+    });
+  });
+
+  describe("clearDocument behavior", () => {
+    it("should call clearDocument when no document identifiers are present", () => {
+      mockDocument.id = "doc-123";
+
+      const viewModel = useDocumentViewModel({
+        record: {
+          metadata: {}, // No pmid, doi, doc_id, or reference
+        },
+      });
+
+      // Verify that watch was called for props.record?.metadata
+      expect(mockWatch).toHaveBeenCalled();
+
+      // Find the watch call for metadata
+      const watchCalls = mockWatch.mock.calls;
+      const metadataWatchCall = watchCalls.find(
+        (call) => call[0].toString().includes("props.record") && call[0].toString().includes("metadata")
+      );
+
+      expect(metadataWatchCall).toBeDefined();
+
+      // Simulate the watch callback being called with empty metadata
+      const watchCallback = metadataWatchCall[1];
+      watchCallback({}, null);
+
+      // Since we can't easily test the async updateDocument function,
+      // we'll verify that the watch was set up correctly
+      expect(mockWatch).toHaveBeenCalledWith(expect.any(Function), expect.any(Function), { immediate: true });
+    });
+  });
+
+  describe("return values", () => {
+    it("should return all expected properties and methods", () => {
+      const viewModel = useDocumentViewModel({ record: {} });
+
+      expect(viewModel).toHaveProperty("document");
+      expect(viewModel).toHaveProperty("fetchDocumentSegments");
+      expect(viewModel).toHaveProperty("focusDocumentPageNumber");
+      expect(viewModel).toHaveProperty("clearDocument");
+      expect(viewModel).toHaveProperty("shouldShowDocumentPanel");
+    });
+
+    it("should return functions for methods", () => {
+      const viewModel = useDocumentViewModel({ record: {} });
+
+      expect(typeof viewModel.fetchDocumentSegments).toBe("function");
+      expect(typeof viewModel.focusDocumentPageNumber).toBe("function");
+      expect(typeof viewModel.clearDocument).toBe("function");
+    });
+  });
+});
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
index 03d4ab8e7..8ca022fe7 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
@@ -19,10 +19,9 @@ export const useDocumentViewModel = (props: { record: any }) => {
   const hasDocumentLoaded = computed(() => {
     return document.id !== null;
   });
-  const hasDocument = computed(() => {
-    return (
-      props.record.metadata === null || props.record.metadata?.reference != null || props.record.metadata?.pmid != null
-    );
+
+  const shouldShowDocumentPanel = computed(() => {
+    return hasDocumentLoaded.value && document.url !== null;
   });
 
   const fetchDocument = async (metadata: any) => {
@@ -105,9 +104,6 @@ export const useDocumentViewModel = (props: { record: any }) => {
           console.log(error);
         } finally {
           isLoading.value = false;
-          if (!hasDocumentLoaded.value) {
-            // TODO closePanel();
-          }
         }
       }
       if (newMetadata?.reference && oldMetadata?.reference !== newMetadata.reference) {
@@ -122,6 +118,7 @@ export const useDocumentViewModel = (props: { record: any }) => {
     fetchDocumentSegments,
     focusDocumentPageNumber,
     clearDocument,
+    shouldShowDocumentPanel,
   };
 };
 

From 58e23fa82007d3f7b42405e0d47e83fb14dae832 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 13:23:09 -0700
Subject: [PATCH 02/20] chore

---
 .kiro/specs/papers-library-importer/design.md |     2 +-
 .../document_extraction/LLM_extractions.ipynb | 19233 ----------------
 .../PDF_preprocessing.ipynb                   |  2908 ---
 .../document_extraction/setup_workspace.ipynb |  1127 +-
 .../base/base-breadcrumbs/BaseBreadcrumbs.vue |     4 +-
 .../components/base/base-code/BaseCode.vue    |     4 +-
 .../container/mode/FocusAnnotation.vue        |     6 +-
 .../annotation/settings/SettingsInfo.vue      |     4 +-
 .../settings/SettingsInfoReadOnly.vue         |     4 +-
 .../user-token/UserToken.component.vue        |     4 +-
 10 files changed, 563 insertions(+), 22733 deletions(-)
 delete mode 100644 examples/document_extraction/LLM_extractions.ipynb
 delete mode 100644 examples/document_extraction/PDF_preprocessing.ipynb

diff --git a/.kiro/specs/papers-library-importer/design.md b/.kiro/specs/papers-library-importer/design.md
index 6a2995055..fa90f3016 100644
--- a/.kiro/specs/papers-library-importer/design.md
+++ b/.kiro/specs/papers-library-importer/design.md
@@ -134,7 +134,7 @@ async def upload_reference_documents_job(
 Note to reuse existing styles in extralit-frontend/assets/scss/base/base.scss, extralit-frontend/assets/scss/abstract/variables/_variables.scss and existing components in `components/base` where possible to keep similar the design system and code reuse and best practices.
 
 - `extralit-frontend/components/base`:
-    base-action-tooltip, base-badge, base-banner, base-brand-icon, base-breadcrumbs, base-button, base-card, base-checkbox, base-code, base-collpasable-panel, base-date, base-documentation-viewer, base-dropdown, base-feedback, base-icon, base-input, base-loading, base-modal, base-pdf-viewer, base-progress, base-radio-button, base-range, base-render-html, base-render-markdown, base-render-table, base-resizable, base-scroll, base-search-bar, base-separator, base-shapes, base-slider, base-spinner, base-switch, base-tabs, base-tag, base-toast, base-tooltip, base-topbar-brand
+    BaseActionTooltip, base-badge, base-banner, base-brand-icon, base-breadcrumbs, base-button, base-card, base-checkbox, base-code, base-collpasable-panel, base-date, base-documentation-viewer, base-dropdown, base-feedback, base-icon, base-input, base-loading, base-modal, base-pdf-viewer, base-progress, base-radio-button, base-range, base-render-html, base-render-markdown, base-render-table, base-resizable, base-scroll, base-search-bar, base-separator, base-shapes, base-slider, base-spinner, base-switch, base-tabs, base-tag, base-toast, base-tooltip, base-topbar-brand
 
 
 #### 1. Home Page Integration (`extralit-frontend/pages/index.vue`)
diff --git a/examples/document_extraction/LLM_extractions.ipynb b/examples/document_extraction/LLM_extractions.ipynb
deleted file mode 100644
index fe1bbd227..000000000
--- a/examples/document_extraction/LLM_extractions.ipynb
+++ /dev/null
@@ -1,19233 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "25e3624f-beb0-4a8a-a02f-20d7525d9d5b",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "# %load_ext heat"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "32c1921b-c936-4a0d-90d7-eb02cfcedc68",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/var/folders/1_/cmltqwn11bqfmnz786mfyhgc0000gn/T/ipykernel_19247/3721118118.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from tqdm.autonotebook import tqdm\n",
-                        "/Users/jonny/micromamba/envs/extralit/lib/python3.9/site-packages/pydantic/_internal/_config.py:295: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/\n",
-                        "  warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)\n"
-                    ]
-                },
-                {
-                    "ename": "ModuleNotFoundError",
-                    "evalue": "No module named 'argilla.client.feedback'; 'argilla.client' is not a package",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-                        "Cell \u001b[0;32mIn[3], line 14\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01margilla\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrg\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mextralit\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mextralit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpipeline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mingest\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpaper\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_paper_extractions\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# TEMP fix to use create_extraction_records from v0.2.3\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# from extralit.pipeline.export.record import create_extraction_records\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpatch_pipeline_export_record\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m create_extraction_records\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla-v1/src/extralit/pipeline/ingest/paper.py:6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01margilla\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrg\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01margilla\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeedback\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschemas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mremote\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrecords\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RemoteFeedbackRecord\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mextralit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mjson_table\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m json_to_df, is_json_table\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mextralit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mextraction\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpaper\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PaperExtraction\n",
-                        "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'argilla.client.feedback'; 'argilla.client' is not a package"
-                    ]
-                }
-            ],
-            "source": [
-                "import uuid, sys, os, io, json, re, rich, glob, dill, itertools, ast\n",
-                "from IPython.display import HTML, JSON\n",
-                "from collections import Counter, defaultdict\n",
-                "from tqdm.autonotebook import tqdm\n",
-                "from dotenv import load_dotenv\n",
-                "load_dotenv(override=True)\n",
-                "# sys.path.insert(0, 'src')\n",
-                "\n",
-                "import pandas as pd\n",
-                "# import plotly.express as px\n",
-                "import extralit as ex\n",
-                "\n",
-                "import extralit\n",
-                "from extralit.pipeline.ingest.paper import get_paper_extractions\n",
-                "\n",
-                "# TEMP fix to use create_extraction_records from v0.2.3\n",
-                "# from extralit.pipeline.export.record import create_extraction_records\n",
-                "from patch_pipeline_export_record import create_extraction_records\n",
-                "\n",
-                "from extralit.pipeline.ingest.record import get_record_data\n",
-                "#from extralit.pipeline.ingest.trace import get_langfuse_traces\n",
-                "from extralit.pipeline.update import *\n",
-                "from extralit.metrics.extraction import grits_from_pandas, grits_from_batch, grits_paper, grits_multi_tables\n",
-                "from extralit.convert.json_table import df_to_json\n",
-                "from extralit.convert.markdown import read_markdown_table_to_df\n",
-                "from extralit.extraction.models import PaperExtraction, SchemaStructure\n",
-                "from extralit.convert.json_table import schema_to_json, json_to_df\n",
-                "\n",
-                "from extralit.extraction.schema import get_extraction_schema_model\n",
-                "from extralit.extraction.prompts import *\n",
-                "from extralit.extraction.extraction import *\n",
-                "from extralit.extraction.vector_index import *\n",
-                "\n",
-                "from extralit.server.models.extraction import ExtractionRequest\n",
-                "from extralit.server.context.datasets import get_argilla_dataset\n",
-                "from extralit.server.context.files import get_minio_client\n",
-                "from extralit.server.context.vectordb import get_weaviate_client\n",
-                "from extralit.server.context.llamaindex import get_langfuse_callback\n",
-                "\n",
-                "from llama_index.core.output_parsers import PydanticOutputParser\n",
-                "from llama_index.core.response.notebook_utils import display_source_node, display_response\n",
-                "from llama_index.core import set_global_handler, Settings, callbacks, PromptTemplate"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "ec9a9f71-1b32-4f36-8f29-12ec428dcac9",
-            "metadata": {},
-            "source": [
-                "## Connect clients"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "1a209a15",
-            "metadata": {},
-            "outputs": [
-                {
-                    "ename": "KeyboardInterrupt",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-                        "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mweaviate\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvector_stores\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mweaviate\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m WeaviateVectorStore\n\u001b[1;32m      4\u001b[0m weaviate_client \u001b[38;5;241m=\u001b[39m get_weaviate_client()\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/vector_stores/weaviate/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvector_stores\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mweaviate\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m WeaviateVectorStore\n\u001b[1;32m      3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeaviateVectorStore\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/vector_stores/weaviate/base.py:11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, List, Optional, Union, cast\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01muuid\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m uuid4\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbridge\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpydantic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Field, PrivateAttr\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschema\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseNode\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvector_stores\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     14\u001b[0m     BasePydanticVectorStore,\n\u001b[1;32m     15\u001b[0m     MetadataFilters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     18\u001b[0m     VectorStoreQueryResult,\n\u001b[1;32m     19\u001b[0m )\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/core/__init__.py:10\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Callable, Optional\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# response\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponse\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschema\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Response\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# import global eval handler\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcallbacks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mglobal_handlers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m set_global_handler\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/core/base/response/schema.py:9\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01masync_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m asyncio_run\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbridge\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpydantic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseModel\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschema\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m NodeWithScore\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TokenGen, TokenAsyncGen\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m truncate_text\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/core/schema.py:18\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbridge\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpydantic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseModel, Field\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minstrumentation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DispatcherSpanMixin\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mllama_index\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SAMPLE_TEXT, truncate_text\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping_extensions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Self\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/core/utils.py:89\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopwords \u001b[38;5;241m=\u001b[39m stopwords\u001b[38;5;241m.\u001b[39mwords(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menglish\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     86\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopwords\n\u001b[0;32m---> 89\u001b[0m globals_helper \u001b[38;5;241m=\u001b[39m \u001b[43mGlobalsHelper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;66;03m# Global Tokenizer\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@runtime_checkable\u001b[39m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mTokenizer\u001b[39;00m(Protocol):\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/llama_index/core/utils.py:45\u001b[0m, in \u001b[0;36mGlobalsHelper.__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     44\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Initialize NLTK stopwords and punkt.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 45\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nltk_data_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39menviron\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNLTK_DATA\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     49\u001b[0m         os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     52\u001b[0m         ),\n\u001b[1;32m     53\u001b[0m     )\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nltk_data_dir \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m nltk\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mpath:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/__init__.py:146\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mjsontags\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;66;03m###########################################################\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;66;03m# PACKAGES\u001b[39;00m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m###########################################################\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minference\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/chunk/__init__.py:155\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Natural Language Toolkit: Chunkers\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Copyright (C) 2001-2024 NLTK Project\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# For license information, see LICENSE.TXT\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124;03mClasses and interfaces for identifying non-overlapping linguistic\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124;03mgroups (such as base noun phrases) in unrestricted text.  This task is\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;124;03m     pattern is valid.\u001b[39;00m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 155\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChunkParserI\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnamed_entity\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Maxent_NE_Chunker\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregexp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RegexpChunkParser, RegexpParser\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/chunk/api.py:13\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Natural Language Toolkit: Chunk parsing API\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Copyright (C) 2001-2024 NLTK Project\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m##  Chunk Parser Interface\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m##//////////////////////////////////////////////////////\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchunk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChunkScore\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deprecated\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ParserI\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/chunk/util.py:12\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mre\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m accuracy \u001b[38;5;28;01mas\u001b[39;00m _accuracy\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmapping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m map_tag\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m str2tuple\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Tree\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/tag/__init__.py:72\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TaggerI\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m str2tuple, tuple2str, untag\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msequential\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     73\u001b[0m     SequentialBackoffTagger,\n\u001b[1;32m     74\u001b[0m     ContextTagger,\n\u001b[1;32m     75\u001b[0m     DefaultTagger,\n\u001b[1;32m     76\u001b[0m     NgramTagger,\n\u001b[1;32m     77\u001b[0m     UnigramTagger,\n\u001b[1;32m     78\u001b[0m     BigramTagger,\n\u001b[1;32m     79\u001b[0m     TrigramTagger,\n\u001b[1;32m     80\u001b[0m     AffixTagger,\n\u001b[1;32m     81\u001b[0m     RegexpTagger,\n\u001b[1;32m     82\u001b[0m     ClassifierBasedTagger,\n\u001b[1;32m     83\u001b[0m     ClassifierBasedPOSTagger,\n\u001b[1;32m     84\u001b[0m )\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbrill\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BrillTagger\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbrill_trainer\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BrillTaggerTrainer\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/tag/sequential.py:26\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m List, Optional, Tuple\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m jsontags\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m NaiveBayesClassifier\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprobability\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ConditionalFreqDist\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtag\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FeaturesetTaggerI, TaggerI\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/classify/__init__.py:97\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpositivenaivebayes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PositiveNaiveBayesClassifier\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrte_classify\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RTEFeatureExtractor, rte_classifier, rte_features\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mscikitlearn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SklearnClassifier\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msenna\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Senna\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclassify\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtextcat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TextCat\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/nltk/classify/scikitlearn.py:38\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprobability\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DictionaryProbDist\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DictVectorizer\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LabelEncoder\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/__init__.py:84\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[38;5;66;03m# We are not importing the rest of scikit-learn during the build\u001b[39;00m\n\u001b[1;32m     71\u001b[0m     \u001b[38;5;66;03m# process, as it may not be compiled yet\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;66;03m# later is linked to the OpenMP runtime to make it possible to introspect\u001b[39;00m\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;66;03m# it and importing it first would fail if the OpenMP dll cannot be found.\u001b[39;00m\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     81\u001b[0m         __check_build,  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     82\u001b[0m         _distributor_init,  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     83\u001b[0m     )\n\u001b[0;32m---> 84\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clone\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_show_versions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m show_versions\n\u001b[1;32m     87\u001b[0m     __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     88\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalibration\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     89\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcluster\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshow_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    131\u001b[0m     ]\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/base.py:19\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m InconsistentVersionWarning\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_estimator_html_repr\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _HTMLDocumentationLinkMixin, estimator_html_repr\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_metadata_requests\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _MetadataRequester, _routing_enabled\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m validate_parameter_constraints\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/__init__.py:11\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _joblib, metadata_routing\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_bunch\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Bunch\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_chunking\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m gen_batches, gen_even_slices\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_estimator_html_repr\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m estimator_html_repr\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# Make _safe_indexing importable from here for backward compat as this particular\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# helper is considered semi-private and typically very useful for third-party\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# libraries that want to comply with scikit-learn's estimator API. In particular,\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# _safe_indexing was included in our public API documentation despite the leading\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# `_` in its name.\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/_chunking.py:8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Interval, validate_params\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mchunk_generator\u001b[39m(gen, chunksize):\n\u001b[1;32m     12\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Chunk generator, ``gen`` into lists of length ``chunksize``. The last\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;124;03m    chunk may have a length less than ``chunksize``.\"\"\"\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/_param_validation.py:14\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msparse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m csr_matrix, issparse\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvalidation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _is_arraylike_not_scalar\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mInvalidParameterError\u001b[39;00m(\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m     18\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Custom exception to be raised when the parameter of a class/method/function\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;124;03m    does not have a valid type or value.\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/validation.py:26\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config \u001b[38;5;28;01mas\u001b[39;00m _get_config\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataConversionWarning, NotFittedError, PositiveSpectrumWarning\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _asarray_with_order, _is_numpy_namespace, get_namespace\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfixes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ComplexWarning, _preserve_dia_indices_dtype\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_isfinite\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FiniteStatus, cy_isfinite\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/_array_api.py:11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspecial\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mspecial\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfixes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m parse_version\n\u001b[1;32m     13\u001b[0m _NUMPY_NAMESPACE_NAMES \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray_api_compat.numpy\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21myield_namespaces\u001b[39m(include_numpy_namespaces\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/sklearn/utils/fixes.py:24\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 24\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m     26\u001b[0m     pd \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/pandas/__init__.py:26\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m _hard_dependencies, _dependency, _missing_dependencies\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;66;03m# numpy compat\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     27\u001b[0m         is_numpy_dev \u001b[38;5;28;01mas\u001b[39;00m _is_numpy_dev,  \u001b[38;5;66;03m# pyright: ignore[reportUnusedImport] # noqa: F401\u001b[39;00m\n\u001b[1;32m     28\u001b[0m     )\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m _err:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m     30\u001b[0m     _module \u001b[38;5;241m=\u001b[39m _err\u001b[38;5;241m.\u001b[39mname\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/pandas/compat/__init__.py:27\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompressors\u001b[39;00m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m is_numpy_dev\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyarrow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     28\u001b[0m     pa_version_under10p1,\n\u001b[1;32m     29\u001b[0m     pa_version_under11p0,\n\u001b[1;32m     30\u001b[0m     pa_version_under13p0,\n\u001b[1;32m     31\u001b[0m     pa_version_under14p0,\n\u001b[1;32m     32\u001b[0m     pa_version_under14p1,\n\u001b[1;32m     33\u001b[0m     pa_version_under16p0,\n\u001b[1;32m     34\u001b[0m     pa_version_under17p0,\n\u001b[1;32m     35\u001b[0m )\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_typing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m F\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/pandas/compat/pyarrow.py:8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Version\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 8\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpyarrow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpa\u001b[39;00m\n\u001b[1;32m     10\u001b[0m     _palv \u001b[38;5;241m=\u001b[39m Version(Version(pa\u001b[38;5;241m.\u001b[39m__version__)\u001b[38;5;241m.\u001b[39mbase_version)\n\u001b[1;32m     11\u001b[0m     pa_version_under10p1 \u001b[38;5;241m=\u001b[39m _palv \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m10.0.1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/pyarrow/__init__.py:65\u001b[0m\n\u001b[1;32m     63\u001b[0m _gc_enabled \u001b[38;5;241m=\u001b[39m _gc\u001b[38;5;241m.\u001b[39misenabled()\n\u001b[1;32m     64\u001b[0m _gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpyarrow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m_lib\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _gc_enabled:\n\u001b[1;32m     67\u001b[0m     _gc\u001b[38;5;241m.\u001b[39menable()\n",
-                        "File \u001b[0;32m<frozen importlib._bootstrap>:398\u001b[0m, in \u001b[0;36mparent\u001b[0;34m(self)\u001b[0m\n",
-                        "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-                    ]
-                }
-            ],
-            "source": [
-                "import weaviate\n",
-                "from llama_index.vector_stores.weaviate import WeaviateVectorStore\n",
-                "\n",
-                "weaviate_client = get_weaviate_client()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "d8998e65-c4f8-4af2-8ef7-a8d822288307",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "dict_keys(['Extralit_itn_recalibration', 'LlamaIndexDocumentSections', 'Test_DSM_Embeddings'])"
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Confirm weaviate connection and display available indexes\n",
-                "weaviate_client.is_connected()\n",
-                "collections = weaviate_client.collections.list_all(simple=False)\n",
-                "collections.keys()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "3db50810-1602-4ad8-8b12-8c3ab04a68bf",
-            "metadata": {
-                "jupyter": {
-                    "is_executing": true
-                }
-            },
-            "outputs": [],
-            "source": [
-                "#len(set([x['ref_doc_id'] for x in res.get['Extralit_itn_recalibration']]))\n",
-                "#res.get['Extralit_itn_recalibration']"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "4dc7bb0d-18e4-4dc5-8671-2229e39d611d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# minio_client = get_minio_client()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "ba6e8fae-c005-40c4-92c8-a6b966fb60d8",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "### comment out langfuse code \n",
-                "# import langfuse\n",
-                "# from langfuse.llama_index import LlamaIndexCallbackHandler\n",
-                "\n",
-                "# langfuse_callback_handler = LlamaIndexCallbackHandler(\n",
-                "#     public_key=os.environ['LANGFUSE_PUBLIC_KEY'],\n",
-                "#     secret_key=os.environ['LANGFUSE_SECRET_KEY'],\n",
-                "#     host=os.environ['LANGFUSE_HOST'],\n",
-                "# )\n",
-                "# if not Settings.callback_manager.handlers:\n",
-                "#     Settings.callback_manager.add_handler(langfuse_callback_handler)\n",
-                "# set_global_handler(\"langfuse\")\n",
-                "\n",
-                "# from llama_index.core import global_handler\n",
-                "# global_handler\n",
-                "\n",
-                "# Ensure the global handler is NOT set to langfuse\n",
-                "set_global_handler(\"simple\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "80fd1176-dfb1-4dfe-b318-94bce659b1a8",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "ex.init(\n",
-                "    api_url='http://extralit-public-demo.hf.space/',\n",
-                "    api_key=\"386a5806-0182-41f6-8076-f4b557818f0b\", #argilla\n",
-                "    # api_key='9df5c40e-b5ce-4d19-ac07-50381a5c79aa', #abv \n",
-                "    workspace='itn-recalibration',\n",
-                ")\n",
-                "papers_dataset = ex.FeedbackDataset.from_argilla(name=\"1-Master-Paper-List\", workspace=\"itn-recalibration\", )\n",
-                "extraction_dataset = ex.FeedbackDataset.from_argilla(name=\"2-Data-Extractions\", workspace=\"itn-recalibration\", )\n",
-                "preprocessing_dataset = ex.FeedbackDataset.from_argilla(name=\"Table-Preprocessing\", workspace=\"itn-recalibration\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "054f734f-a69e-4d5d-8f1a-e4ffed74d18a",
-            "metadata": {},
-            "source": [
-                "## Load papers"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "8ac5c2db-ea2c-44d6-bf4a-cf9e632df058",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "sys:1: ResourceWarning: unclosed <socket.socket fd=93, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('192.168.1.208', 54920), raddr=('18.209.85.126', 80)>\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
-                    ]
-                }
-            ],
-            "source": [
-                "papers = pd.read_parquet('config/extraction_queue_197papers_2024-04-03.parquet')\n",
-                "\n",
-                "# papers_preprocessed = list({r.metadata['reference'] \\\n",
-                "#                             for r in preprocessing_dataset.filter_by(\n",
-                "#                                 response_status=['submitted'], \n",
-                "#                                 metadata_filters=ex.TermsMetadataFilter(name='type', values=['table', 'Table'])).records})\n",
-                "# papers_uploaded = list({r.metadata['reference'] \\\n",
-                "#                         for r in extraction_dataset.filter_by(response_status=['submitted', 'draft']).records})\n",
-                "\n",
-                "# papers_queue = papers_preprocessed\n",
-                "# len(papers_preprocessed), len(papers_uploaded)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "a21a61ef-7ec3-437a-93fe-370a683d79c7",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(91, 32)"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers_queue = papers.loc[papers.index.isin(papers_preprocessed)]\n",
-                "papers_queue.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "28993312-b191-47a0-9fdd-a278853342f2",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Mendeley Reference Key</th>\n",
-                            "      <th>Measured_outcome</th>\n",
-                            "      <th>Approx_num_rows</th>\n",
-                            "      <th>Tables_count</th>\n",
-                            "      <th>Notes_on_approx_num_rows</th>\n",
-                            "      <th>Check_out_by</th>\n",
-                            "      <th>Check_in_by</th>\n",
-                            "      <th>Needs_digitization</th>\n",
-                            "      <th>Notes_on_extraction</th>\n",
-                            "      <th>title</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>issn</th>\n",
-                            "      <th>pmid</th>\n",
-                            "      <th>doi</th>\n",
-                            "      <th>collections</th>\n",
-                            "      <th>file_id</th>\n",
-                            "      <th>mime_type</th>\n",
-                            "      <th>file_name</th>\n",
-                            "      <th>size</th>\n",
-                            "      <th>filehash</th>\n",
-                            "      <th>file_path</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>reference</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>abdella2009does</th>\n",
-                            "      <td>Abdella2009</td>\n",
-                            "      <td>Incidence (u5)</td>\n",
-                            "      <td>4</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>CB</td>\n",
-                            "      <td>CB</td>\n",
-                            "      <td>False</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Does Insecticide Treated Mosquito Nets (ITNs) ...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1573-3610 (Electronic)</td>\n",
-                            "      <td>18958607</td>\n",
-                            "      <td>10.1007/s10900-008-9132-6</td>\n",
-                            "      <td>[Human Health Outcomes]</td>\n",
-                            "      <td>6428579b-3ee9-2f91-2ec9-66250ba74834</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Adbella_et_al_2009_J_Community_Health.pdf</td>\n",
-                            "      <td>213444</td>\n",
-                            "      <td>cada6d5613eff1204416c1e2a63038931eff40f2</td>\n",
-                            "      <td>data/pdf/Adbella_et_al_2009_J_Community_Health...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>abdulla2001impact</th>\n",
-                            "      <td>Abdulla2001</td>\n",
-                            "      <td>Parasitemia (u2)</td>\n",
-                            "      <td>4</td>\n",
-                            "      <td>3</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>JS</td>\n",
-                            "      <td>JS</td>\n",
-                            "      <td>False</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Impact on malaria morbidity of a programme sup...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0959-8138 (Print)</td>\n",
-                            "      <td>11157527</td>\n",
-                            "      <td>10.1136/bmj.322.7281.270</td>\n",
-                            "      <td>[Human Health Outcomes]</td>\n",
-                            "      <td>177972f6-b129-e7c1-220d-03e95856f758</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Abdulla_et_al___2001___Impact_on_malaria_morbi...</td>\n",
-                            "      <td>285528</td>\n",
-                            "      <td>ed77ef48bf6ca690b635d080eb3e1f38b8438fa5</td>\n",
-                            "      <td>data/pdf/Abdulla_et_al___2001___Impact_on_mala...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>abilio2015bio</th>\n",
-                            "      <td>Abilio2015</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>20</td>\n",
-                            "      <td>1-3, 5, 6</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>False</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Bio-efficacy of new long-lasting insecticide-t...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>10.1186/s12936-015-0885-y</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>54ede4f1-a9e8-5e79-6cab-5b66c556bf18</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>12936_2015_Article_885_pdf.pdf</td>\n",
-                            "      <td>2577795</td>\n",
-                            "      <td>a507d55efd4cb88d2ebaf5cf8508a12dd92b1332</td>\n",
-                            "      <td>data/pdf/12936_2015_Article_885_pdf.pdf</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>agossa2014laboratory</th>\n",
-                            "      <td>Agossa2014</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>37</td>\n",
-                            "      <td>1-4</td>\n",
-                            "      <td>Large row count due to repetition of experieme...</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>True</td>\n",
-                            "      <td>digitization updated</td>\n",
-                            "      <td>Laboratory and field evaluation of the impact ...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1475-2875 (Electronic)</td>\n",
-                            "      <td>24884502</td>\n",
-                            "      <td>10.1186/1475-2875-13-193</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>64a054f4-66a9-8885-2345-13b33b3671c0</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Agossa_et_al_2014_Mal_J.pdf</td>\n",
-                            "      <td>616175</td>\n",
-                            "      <td>8b4d0a6f39dc33ec547170e3c2629501a6960d64</td>\n",
-                            "      <td>data/pdf/Agossa_et_al_2014_Mal_J.pdf</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>agossa2015impact</th>\n",
-                            "      <td>Agossa2015</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>23</td>\n",
-                            "      <td>1-4</td>\n",
-                            "      <td>Maybe need to digitize/extract from fig 3</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>KB</td>\n",
-                            "      <td>False</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Impact of Insecticide Resistance on the Effect...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1932-6203 (Electronic)</td>\n",
-                            "      <td>26674643</td>\n",
-                            "      <td>10.1371/journal.pone.0145207</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>0aa9c155-7306-a71f-52fe-944db8e633a4</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>2015-Impact_of_Insecticide_Resistance_on_the_E...</td>\n",
-                            "      <td>764912</td>\n",
-                            "      <td>d4b9abf675a1604a02dd66231b62816d790b533a</td>\n",
-                            "      <td>data/pdf/2015-Impact_of_Insecticide_Resistance...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>lindblade2005evaluation</th>\n",
-                            "      <td>Lindblade2005</td>\n",
-                            "      <td>Net durability</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Evaluation of long-lasting insecticidal nets a...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1360-2276</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>10.1111/j.1365-3156.2005.01501.x</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>15294800-a4a4-d26d-14b4-99c8b396e948</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>lindblade2005evaluation.pdf</td>\n",
-                            "      <td>121140</td>\n",
-                            "      <td>5cfed926ae656cc33fb366423a257059585a1e4b</td>\n",
-                            "      <td>data/pdf/lindblade2005evaluation.pdf</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>norris2011efficacy</th>\n",
-                            "      <td>Norris2011</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Efficacy of long-lasting insecticidal nets in ...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1475-2875 (Electronic)</td>\n",
-                            "      <td>21880143</td>\n",
-                            "      <td>10.1186/1475-2875-10-254</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>11ea9233-51f9-4cff-377d-b585d8267094</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Efficacy_of_long_lasting_insecticidal_nets_in_...</td>\n",
-                            "      <td>1439742</td>\n",
-                            "      <td>96a5f52cd8d7ec1aba424a881c5d01aa940ca5fb</td>\n",
-                            "      <td>data/pdf/Efficacy_of_long_lasting_insecticidal...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ohashi2012efficacy</th>\n",
-                            "      <td>Ohashi2012</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Efficacy of pyriproxyfen-treated nets in steri...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0022-2585 (Print)</td>\n",
-                            "      <td>23025186</td>\n",
-                            "      <td>10.1603/me12006</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>37e8cc37-85fa-6c5b-3e5d-ed37d0566158</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Efficacy_of_pyriproxyfen_treated_nets_in_steri...</td>\n",
-                            "      <td>284647</td>\n",
-                            "      <td>a30942a6ef6ce47354e34ab23d4a0a22e6cdb179</td>\n",
-                            "      <td>data/pdf/Efficacy_of_pyriproxyfen_treated_nets...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>okumu2012implications</th>\n",
-                            "      <td>Okumu2012</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Implications of bio-efficacy and persistence o...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1475-2875 (Electronic)</td>\n",
-                            "      <td>23164062</td>\n",
-                            "      <td>10.1186/1475-2875-11-378</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>2525cc93-97af-6ef1-0860-3cd04be66be4</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Implications_of_bio_efficacy_and_persistence_o...</td>\n",
-                            "      <td>1032638</td>\n",
-                            "      <td>e452bf88ce21236d63599f701156fab11520b304</td>\n",
-                            "      <td>data/pdf/Implications_of_bio_efficacy_and_pers...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>riveron2018high</th>\n",
-                            "      <td>Riveron2018</td>\n",
-                            "      <td>Vector susceptibility</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>High Plasmodium Infection Rate and Reduced Bed...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1537-6613 (Electronic)</td>\n",
-                            "      <td>29087484</td>\n",
-                            "      <td>10.1093/infdis/jix570</td>\n",
-                            "      <td>[Entomological Outcomes]</td>\n",
-                            "      <td>7ce097cb-884a-6401-3dc1-e552dee6a424</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>High_Plasmodium_Infection_Rate_and_Reduced_Bed...</td>\n",
-                            "      <td>1131111</td>\n",
-                            "      <td>cb0a6a5a59693e4406af3defddb2d223878592de</td>\n",
-                            "      <td>data/pdf/High_Plasmodium_Infection_Rate_and_Re...</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>111 rows × 32 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                        Mendeley Reference Key       Measured_outcome  \\\n",
-                            "reference                                                               \n",
-                            "abdella2009does                    Abdella2009         Incidence (u5)   \n",
-                            "abdulla2001impact                  Abdulla2001       Parasitemia (u2)   \n",
-                            "abilio2015bio                       Abilio2015  Vector susceptibility   \n",
-                            "agossa2014laboratory                Agossa2014  Vector susceptibility   \n",
-                            "agossa2015impact                    Agossa2015  Vector susceptibility   \n",
-                            "...                                        ...                    ...   \n",
-                            "lindblade2005evaluation          Lindblade2005         Net durability   \n",
-                            "norris2011efficacy                  Norris2011  Vector susceptibility   \n",
-                            "ohashi2012efficacy                  Ohashi2012  Vector susceptibility   \n",
-                            "okumu2012implications                Okumu2012  Vector susceptibility   \n",
-                            "riveron2018high                    Riveron2018  Vector susceptibility   \n",
-                            "\n",
-                            "                        Approx_num_rows Tables_count  \\\n",
-                            "reference                                              \n",
-                            "abdella2009does                       4            2   \n",
-                            "abdulla2001impact                     4            3   \n",
-                            "abilio2015bio                        20    1-3, 5, 6   \n",
-                            "agossa2014laboratory                 37          1-4   \n",
-                            "agossa2015impact                     23          1-4   \n",
-                            "...                                 ...          ...   \n",
-                            "lindblade2005evaluation            None         None   \n",
-                            "norris2011efficacy                 None         None   \n",
-                            "ohashi2012efficacy                 None         None   \n",
-                            "okumu2012implications              None         None   \n",
-                            "riveron2018high                    None         None   \n",
-                            "\n",
-                            "                                                  Notes_on_approx_num_rows  \\\n",
-                            "reference                                                                    \n",
-                            "abdella2009does                                                       None   \n",
-                            "abdulla2001impact                                                     None   \n",
-                            "abilio2015bio                                                         None   \n",
-                            "agossa2014laboratory     Large row count due to repetition of experieme...   \n",
-                            "agossa2015impact                 Maybe need to digitize/extract from fig 3   \n",
-                            "...                                                                    ...   \n",
-                            "lindblade2005evaluation                                               None   \n",
-                            "norris2011efficacy                                                    None   \n",
-                            "ohashi2012efficacy                                                    None   \n",
-                            "okumu2012implications                                                 None   \n",
-                            "riveron2018high                                                       None   \n",
-                            "\n",
-                            "                        Check_out_by Check_in_by Needs_digitization  \\\n",
-                            "reference                                                             \n",
-                            "abdella2009does                   CB          CB              False   \n",
-                            "abdulla2001impact                 JS          JS              False   \n",
-                            "abilio2015bio                     KB          KB              False   \n",
-                            "agossa2014laboratory              KB          KB               True   \n",
-                            "agossa2015impact                  KB          KB              False   \n",
-                            "...                              ...         ...                ...   \n",
-                            "lindblade2005evaluation         None        None               None   \n",
-                            "norris2011efficacy              None        None               None   \n",
-                            "ohashi2012efficacy              None        None               None   \n",
-                            "okumu2012implications           None        None               None   \n",
-                            "riveron2018high                 None        None               None   \n",
-                            "\n",
-                            "                          Notes_on_extraction  \\\n",
-                            "reference                                       \n",
-                            "abdella2009does                          None   \n",
-                            "abdulla2001impact                        None   \n",
-                            "abilio2015bio                            None   \n",
-                            "agossa2014laboratory     digitization updated   \n",
-                            "agossa2015impact                         None   \n",
-                            "...                                       ...   \n",
-                            "lindblade2005evaluation                  None   \n",
-                            "norris2011efficacy                       None   \n",
-                            "ohashi2012efficacy                       None   \n",
-                            "okumu2012implications                    None   \n",
-                            "riveron2018high                          None   \n",
-                            "\n",
-                            "                                                                     title  \\\n",
-                            "reference                                                                    \n",
-                            "abdella2009does          Does Insecticide Treated Mosquito Nets (ITNs) ...   \n",
-                            "abdulla2001impact        Impact on malaria morbidity of a programme sup...   \n",
-                            "abilio2015bio            Bio-efficacy of new long-lasting insecticide-t...   \n",
-                            "agossa2014laboratory     Laboratory and field evaluation of the impact ...   \n",
-                            "agossa2015impact         Impact of Insecticide Resistance on the Effect...   \n",
-                            "...                                                                    ...   \n",
-                            "lindblade2005evaluation  Evaluation of long-lasting insecticidal nets a...   \n",
-                            "norris2011efficacy       Efficacy of long-lasting insecticidal nets in ...   \n",
-                            "ohashi2012efficacy       Efficacy of pyriproxyfen-treated nets in steri...   \n",
-                            "okumu2012implications    Implications of bio-efficacy and persistence o...   \n",
-                            "riveron2018high          High Plasmodium Infection Rate and Reduced Bed...   \n",
-                            "\n",
-                            "                         ...                    issn      pmid  \\\n",
-                            "reference                ...                                     \n",
-                            "abdella2009does          ...  1573-3610 (Electronic)  18958607   \n",
-                            "abdulla2001impact        ...       0959-8138 (Print)  11157527   \n",
-                            "abilio2015bio            ...                    None      None   \n",
-                            "agossa2014laboratory     ...  1475-2875 (Electronic)  24884502   \n",
-                            "agossa2015impact         ...  1932-6203 (Electronic)  26674643   \n",
-                            "...                      ...                     ...       ...   \n",
-                            "lindblade2005evaluation  ...               1360-2276      None   \n",
-                            "norris2011efficacy       ...  1475-2875 (Electronic)  21880143   \n",
-                            "ohashi2012efficacy       ...       0022-2585 (Print)  23025186   \n",
-                            "okumu2012implications    ...  1475-2875 (Electronic)  23164062   \n",
-                            "riveron2018high          ...  1537-6613 (Electronic)  29087484   \n",
-                            "\n",
-                            "                                                      doi  \\\n",
-                            "reference                                                   \n",
-                            "abdella2009does                 10.1007/s10900-008-9132-6   \n",
-                            "abdulla2001impact                10.1136/bmj.322.7281.270   \n",
-                            "abilio2015bio                   10.1186/s12936-015-0885-y   \n",
-                            "agossa2014laboratory             10.1186/1475-2875-13-193   \n",
-                            "agossa2015impact             10.1371/journal.pone.0145207   \n",
-                            "...                                                   ...   \n",
-                            "lindblade2005evaluation  10.1111/j.1365-3156.2005.01501.x   \n",
-                            "norris2011efficacy               10.1186/1475-2875-10-254   \n",
-                            "ohashi2012efficacy                        10.1603/me12006   \n",
-                            "okumu2012implications            10.1186/1475-2875-11-378   \n",
-                            "riveron2018high                     10.1093/infdis/jix570   \n",
-                            "\n",
-                            "                                      collections  \\\n",
-                            "reference                                           \n",
-                            "abdella2009does           [Human Health Outcomes]   \n",
-                            "abdulla2001impact         [Human Health Outcomes]   \n",
-                            "abilio2015bio            [Entomological Outcomes]   \n",
-                            "agossa2014laboratory     [Entomological Outcomes]   \n",
-                            "agossa2015impact         [Entomological Outcomes]   \n",
-                            "...                                           ...   \n",
-                            "lindblade2005evaluation  [Entomological Outcomes]   \n",
-                            "norris2011efficacy       [Entomological Outcomes]   \n",
-                            "ohashi2012efficacy       [Entomological Outcomes]   \n",
-                            "okumu2012implications    [Entomological Outcomes]   \n",
-                            "riveron2018high          [Entomological Outcomes]   \n",
-                            "\n",
-                            "                                                      file_id  \\\n",
-                            "reference                                                       \n",
-                            "abdella2009does          6428579b-3ee9-2f91-2ec9-66250ba74834   \n",
-                            "abdulla2001impact        177972f6-b129-e7c1-220d-03e95856f758   \n",
-                            "abilio2015bio            54ede4f1-a9e8-5e79-6cab-5b66c556bf18   \n",
-                            "agossa2014laboratory     64a054f4-66a9-8885-2345-13b33b3671c0   \n",
-                            "agossa2015impact         0aa9c155-7306-a71f-52fe-944db8e633a4   \n",
-                            "...                                                       ...   \n",
-                            "lindblade2005evaluation  15294800-a4a4-d26d-14b4-99c8b396e948   \n",
-                            "norris2011efficacy       11ea9233-51f9-4cff-377d-b585d8267094   \n",
-                            "ohashi2012efficacy       37e8cc37-85fa-6c5b-3e5d-ed37d0566158   \n",
-                            "okumu2012implications    2525cc93-97af-6ef1-0860-3cd04be66be4   \n",
-                            "riveron2018high          7ce097cb-884a-6401-3dc1-e552dee6a424   \n",
-                            "\n",
-                            "                               mime_type  \\\n",
-                            "reference                                  \n",
-                            "abdella2009does          application/pdf   \n",
-                            "abdulla2001impact        application/pdf   \n",
-                            "abilio2015bio            application/pdf   \n",
-                            "agossa2014laboratory     application/pdf   \n",
-                            "agossa2015impact         application/pdf   \n",
-                            "...                                  ...   \n",
-                            "lindblade2005evaluation  application/pdf   \n",
-                            "norris2011efficacy       application/pdf   \n",
-                            "ohashi2012efficacy       application/pdf   \n",
-                            "okumu2012implications    application/pdf   \n",
-                            "riveron2018high          application/pdf   \n",
-                            "\n",
-                            "                                                                 file_name  \\\n",
-                            "reference                                                                    \n",
-                            "abdella2009does                  Adbella_et_al_2009_J_Community_Health.pdf   \n",
-                            "abdulla2001impact        Abdulla_et_al___2001___Impact_on_malaria_morbi...   \n",
-                            "abilio2015bio                               12936_2015_Article_885_pdf.pdf   \n",
-                            "agossa2014laboratory                           Agossa_et_al_2014_Mal_J.pdf   \n",
-                            "agossa2015impact         2015-Impact_of_Insecticide_Resistance_on_the_E...   \n",
-                            "...                                                                    ...   \n",
-                            "lindblade2005evaluation                        lindblade2005evaluation.pdf   \n",
-                            "norris2011efficacy       Efficacy_of_long_lasting_insecticidal_nets_in_...   \n",
-                            "ohashi2012efficacy       Efficacy_of_pyriproxyfen_treated_nets_in_steri...   \n",
-                            "okumu2012implications    Implications_of_bio_efficacy_and_persistence_o...   \n",
-                            "riveron2018high          High_Plasmodium_Infection_Rate_and_Reduced_Bed...   \n",
-                            "\n",
-                            "                            size                                  filehash  \\\n",
-                            "reference                                                                    \n",
-                            "abdella2009does           213444  cada6d5613eff1204416c1e2a63038931eff40f2   \n",
-                            "abdulla2001impact         285528  ed77ef48bf6ca690b635d080eb3e1f38b8438fa5   \n",
-                            "abilio2015bio            2577795  a507d55efd4cb88d2ebaf5cf8508a12dd92b1332   \n",
-                            "agossa2014laboratory      616175  8b4d0a6f39dc33ec547170e3c2629501a6960d64   \n",
-                            "agossa2015impact          764912  d4b9abf675a1604a02dd66231b62816d790b533a   \n",
-                            "...                          ...                                       ...   \n",
-                            "lindblade2005evaluation   121140  5cfed926ae656cc33fb366423a257059585a1e4b   \n",
-                            "norris2011efficacy       1439742  96a5f52cd8d7ec1aba424a881c5d01aa940ca5fb   \n",
-                            "ohashi2012efficacy        284647  a30942a6ef6ce47354e34ab23d4a0a22e6cdb179   \n",
-                            "okumu2012implications    1032638  e452bf88ce21236d63599f701156fab11520b304   \n",
-                            "riveron2018high          1131111  cb0a6a5a59693e4406af3defddb2d223878592de   \n",
-                            "\n",
-                            "                                                                 file_path  \n",
-                            "reference                                                                   \n",
-                            "abdella2009does          data/pdf/Adbella_et_al_2009_J_Community_Health...  \n",
-                            "abdulla2001impact        data/pdf/Abdulla_et_al___2001___Impact_on_mala...  \n",
-                            "abilio2015bio                      data/pdf/12936_2015_Article_885_pdf.pdf  \n",
-                            "agossa2014laboratory                  data/pdf/Agossa_et_al_2014_Mal_J.pdf  \n",
-                            "agossa2015impact         data/pdf/2015-Impact_of_Insecticide_Resistance...  \n",
-                            "...                                                                    ...  \n",
-                            "lindblade2005evaluation               data/pdf/lindblade2005evaluation.pdf  \n",
-                            "norris2011efficacy       data/pdf/Efficacy_of_long_lasting_insecticidal...  \n",
-                            "ohashi2012efficacy       data/pdf/Efficacy_of_pyriproxyfen_treated_nets...  \n",
-                            "okumu2012implications    data/pdf/Implications_of_bio_efficacy_and_pers...  \n",
-                            "riveron2018high          data/pdf/High_Plasmodium_Infection_Rate_and_Re...  \n",
-                            "\n",
-                            "[111 rows x 32 columns]"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# prejonny papers\n",
-                "references = ['abdella2009does','abdulla2001impact','abilio2015bio','agossa2014laboratory','agossa2015impact','akoton2018experimental','allossogbe2017who','alonso1993malaria','anshebo2014estimation','asale2014evaluation','asidi2004experimental','asidi2005experimental','asidi2012loss','atieli2010effect','awolola2014impact','badolo2012experimental','bayili2017evaluation','bayili2019experimental','birhanu2019bio','bobanga2013field','bogh1998permethrin','boussougou2017physical','camara2018efficacy','chandre2010field','chouaibou2006efficacy','corbel2010field','curtis2003insecticide','curtis1998comparison','dabire2006personal','dalessandro1995mortality','darriet2011combining','djenontin2009managing','djenontin2010indoor','djenontin2015insecticidal','djenontin2018field','etang2004reduced','etang2007preliminary','fadel2019combination','fane2012anopheles','glunt2015long','guillet2001combined','hassan2008retention','hassan2012bioassay','hauser2019ability','hawley2003community','henry2005protective','hougard2002useful','howard2000evidence','hughes2020anopheles','ibrahim2019high','jones2012aging','kilian2015field','kolaczinski2000experimental','koudou2011efficacy','kulkarni2007efficacy','kweka2011durability','kweka2017efficacy','kweka2019bio','lines1987experimental','magbity1997effects','mahande2018bio','malima2008experimental','malima2009behavioural','malima2013evaluation','masalu2018potential','massue2016durability','maxwell1999comparison','maxwell2002effect','maxwell2003variation','maxwell2006tests','mosha2008experimental','msangi2008effects','murray2020barrier','musa2020long','nguessan2007reduced','nguessan2016chlorfenapyr','ngufor2014olyset','ngufor2014combining','ngufor2017which','obi2020monitoring','ochomo2017insecticide','okoyo2015comparing','omondi2017quantifying','oumbouke2019evaluation','oxborough2013itn','oxborough2015new','pmi2019tanzania','pmi2017madagascar','randriamaherijaona2017durability','sheikhi2017wash','solomon2018bed','soremekun2004measuring','spitzen2017effect','tami2004evaluation','tiono2018efficacy','tungu2015evaluation','vatandoost2006comparative','vatandoost2013wash','pmi2018mozambique','winkler2012efficacy','zhou2016insecticide','duchon2009mixture','etang2013evaluation','etang2016when','kayedi2015entomological','kolaczinski2000comparison','lindblade2005evaluation','norris2011efficacy','ohashi2012efficacy','okumu2012implications','riveron2018high']\n",
-                "papers_queue = papers.loc[references]\n",
-                "papers_queue"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "69e52c30-f07e-4341-976d-45e1c76725aa",
-            "metadata": {},
-            "source": [
-                "# Predict extractions"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7c611c47-8c41-4dbb-a30c-7861d7a37914",
-            "metadata": {},
-            "source": [
-                "## Load schemas"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c27c5844-09c4-410d-9704-4c50f3bf4f80",
-            "metadata": {},
-            "source": [
-                "### Fetch latest schema version\n",
-                "Loading schemas into `SchemaStructure` is needed so the LLM can extract a table for each schemas in `schemas` argument"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "84797356-25f1-429f-a302-017adf890643",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "# ignore this until fetching schemas from minio works\n",
-                "\n",
-                "# ss = SchemaStructure.from_s3(workspace='itn-recalibration', minio_client=get_minio_client())\n",
-                "# ss.ordering"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "373baa81-6b3c-43f7-ac25-77100842faa2",
-            "metadata": {},
-            "source": [
-                "### How to update schemas\n",
-                "\n",
-                "These steps are to easily make versioned updates to *existing* schemas or to add new schemas:\n",
-                "1. Update the schema specs in the `pandera.DataFrameSchema` classes located in the `schemas/*.py` files, then import the classes to load the changes.\n",
-                "2. Apply the changes to the server using `.to_s3`, which uploads these schema .json files to a MinIO file object storage server. If a schema already exists, then it would create a new version of the schema file, while keeping the old versions intact.\n",
-                "   - Make sures that the `workspace` argument matches the Extralit workspace name for your project.\n",
-                "   - You may browse and manipulate all uploaded files in the MinIO Console, if connected to the K8s cluster\n",
-                "3. The extraction tables in the Extralit web UI will automatically fetch the latest version of the schemas that were uploaded, or allow you to update to a new schema version if the table was built with an older version."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "87394620-24db-478d-8ad1-3134b60ec29b",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['ITNCondition', 'Observation', 'ClinicalOutcome', 'EntomologicalOutcome']"
-                        ]
-                    },
-                    "execution_count": 12,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from schemas import Observation, ITNCondition, EntomologicalOutcome, ClinicalOutcome, Publication\n",
-                "\n",
-                "ss = SchemaStructure(schemas=[Observation, ITNCondition, EntomologicalOutcome, ClinicalOutcome])\n",
-                "ss.ordering"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "b94adb50-694d-4b6c-8243-a8b62f09cf15",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "# also ignore?\n",
-                "\n",
-                "# ss.to_s3(workspace='itn-recalibration', minio_client=minio_client)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "04c20e3d-a40d-42b3-ac0b-dbaff2a9a144",
-            "metadata": {},
-            "source": [
-                "## Select extraction references"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7b577217-8ff2-4b02-99e4-baff82c25eb1",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "'pinder2015efficacy, terlouw2010impact, tokponnon2014impact'"
-                        ]
-                    },
-                    "execution_count": 14,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# references = ['Atieli2010effectgambiaeMalaria', 'Dabire2006personalpyrethroidsMalaria', 'Ochomo2017insecticidekenyaEmerging', 'Vatandoost2013washtestArthropod']\n",
-                "# references = ['apetogbo2022insecticide', 'azizi2022implementing', 'bamou2021increased', 'clegban2021evaluation',]\n",
-                "# references = ['padonou2012decreased', 'robert1991influence', 'curtis2000comparison', 'pwalia2019high', 'darriet2002experimental', 'mosqueira2015pilot']\n",
-                "# references = ['ketoh2018efficacy', 'darriet2005pyrethoid', 'magesa1991trial', 'quinones1998permethrin', 'kawada2014small', 'kawada2014preventive', 'randriamaherijaona2015do', 'kilian2011evidence',]\n",
-                "# references = ['mbogo1996impact', 'menze2020experimental', 'kitau2012species', 'kilian2008long', 'mnzava2001malaria', 'hougard2003efficacy', 'pennetier2013efficacy',]\n",
-                "# references = ['ngufor2016efficacy', 'okia2013bioefficacy', 'mosha2008comparative', 'quinones1997anopheles', 'ahoua2012status', 'sreehari2009wash', 'abdulla2005spatial', 'tan2016longitudinal',\n",
-                "#               'wills2013physical', 'marchant2002socially', 'nevill1996insecticide', 'msuya1991trial', 'tungu2021efficacy']\n",
-                "# references = ['hamel2011combination', 'ter2003impact', 'moiroux2014human', 'schellenberg2001effect', 'tamari2020protective', 'yewhalaw2022experimental', 'tungu2021field', 'sanou2021insecticide', 'kouassi2020susceptibility', 'mulatier2019prior', \n",
-                "#               'ngongang2022reduced', 'lorenz2020comparative', 'hien2021evidence', 'ngufor2020efficacy', 'sovi2022physical', 'adageba2022bio', 'githinji2020impact', 'grisales2021pyriproxyfen', 'tungu2021bio', 'tungu2021effectiveness',]\n",
-                "\n",
-                "references = ['pinder2015efficacy', 'terlouw2010impact', 'tokponnon2014impact']\n",
-                "\n",
-                "# references = [ 'accrombessi2023efficacy', 'mieguim2021insights', 'gebremariam2021evaluation', 'gichuki2021bioefficacy', 'syme2021which', 'zahouli2023small', 'diouf2022evaluation', 'ibrahim2020exploring', 'kibondo2022influence', 'meiwald2022association', 'menze2022experimental', 'syme2022pyrethroid', 'toe2018do', 'yewhalaw2022experimental', 'ngufor2022comparative' ]\n",
-                "\n",
-                "\n",
-                "', '.join(references)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "9db560c2-f130-45d5-8b0e-46b5083d24d0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "15"
-                        ]
-                    },
-                    "execution_count": 16,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "len(references)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "508c3959-e4c9-44e8-a3bb-f8c258ebccf0",
-            "metadata": {},
-            "source": [
-                "## Run LLM extractions"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "5143f215-378b-4ded-bee0-65366b7ca2ca",
-            "metadata": {},
-            "source": [
-                "### Select LLM and embedding models"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "d2938dbc-a94e-47f3-8349-9f6ea9eca993",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# llm_model = 'gpt-3.5-turbo'\n",
-                "# llm_model = 'gpt-4-turbo'\n",
-                "llm_model = 'gpt-4o'\n",
-                "\n",
-                "embed_model = 'text-embedding-3-small'\n",
-                "# embed_model = 'text-embedding-3-large'\n",
-                "\n",
-                "pred_extractions = defaultdict(lambda: {})\n",
-                "indexes = {}\n",
-                "responses = {}"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "8183742a-9082-47f0-b502-2f9daadccc3f",
-            "metadata": {},
-            "source": [
-                "### Add model segments to Weaviate vector db"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "24742f9a-efcf-4bb2-b54f-74b17e13d827",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "_RawGQLReturn(aggregate={'LlamaIndexDocumentSections': [{'meta': {'count': 3828}}]}, explore={}, get={}, errors=None)"
-                        ]
-                    },
-                    "execution_count": 18,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Verify the number of items in the index before\n",
-                "weaviate_client.graphql_raw_query(\"\"\"\n",
-                "{\n",
-                "  Aggregate {\n",
-                "    LlamaIndexDocumentSections {\n",
-                "      meta {\n",
-                "        count\n",
-                "      }\n",
-                "    }\n",
-                "  }\n",
-                "}\n",
-                "\"\"\"\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "0bc3ddf2-bd99-4423-964c-33fd1d262f76",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "a22619b907694ee79b5b0a0993f5b91a",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/15 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "for ref, paper in tqdm(papers.loc[references].iterrows(), total=len(references)):\n",
-                "    ### comment out langfuse code \n",
-                "    # global_handler.set_trace_params(\n",
-                "    #     name=f\"embed-{ref}\",\n",
-                "    #     session_id=ref,\n",
-                "    #     user_id='jonnytr',\n",
-                "    #     tags=['itn-recalibration', ref, 'embed']\n",
-                "    # )\n",
-                "\n",
-                "    create_vector_index(\n",
-                "        paper, \n",
-                "        weaviate_client=weaviate_client, \n",
-                "        preprocessing_dataset=preprocessing_dataset, # Setting this will only fetch the tables from submitted table correction records\n",
-                "        preprocessing_path='data/preprocessing/nougat/',\n",
-                "        overwrite='table')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7f364543-e543-4e74-89d3-e061f64ec290",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "_RawGQLReturn(aggregate={'LlamaIndexDocumentSections': [{'meta': {'count': 3862}}]}, explore={}, get={}, errors=None)"
-                        ]
-                    },
-                    "execution_count": 20,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Verify the number of items in the index after\n",
-                "\n",
-                "weaviate_client.graphql_raw_query(\"\"\"\n",
-                "{\n",
-                "  Aggregate {\n",
-                "    LlamaIndexDocumentSections {\n",
-                "      meta {\n",
-                "        count\n",
-                "      }\n",
-                "    }\n",
-                "  }\n",
-                "}\n",
-                "\"\"\"\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "9e3c15d1d0bd849",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# More graphql query examples\n",
-                "\n",
-                "# res = weaviate_client.graphql_raw_query(\"\"\"\n",
-                "# {\n",
-                "#   Get {\n",
-                "#     Extralit_itn_recalibration(limit: 200) \n",
-                "#     # (\n",
-                "#     #   where: {\n",
-                "#     #     operator: And\n",
-                "#     #     operands: [\n",
-                "#     #       {\n",
-                "#     #         path: [\"ref_doc_id\"]\n",
-                "#     #         operator: Equal\n",
-                "#     #         valueText: \"e4ff64c9-349a-4d7e-af35-690a4a7feb02\"\n",
-                "#     #       }\n",
-                "#     #     ]\n",
-                "#     #   }\n",
-                "#     # )    \n",
-                "#     {\n",
-                "#       ref_doc_id\n",
-                "#       page_number\n",
-                "#       text\n",
-                "#     }\n",
-                "#   }\n",
-                "# }\n",
-                "# \n",
-                "# \"\"\")\n",
-                "\n",
-                "# Display the number of documents in the index\n",
-                "#len([x['ref_doc_id'] for x in res.get['Extralit_itn_recalibration']])\n",
-                "\n",
-                "# Display Extralit_itn_recalibration index class\n",
-                "# res.get['Extralit_itn_recalibration']"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "b36376b8-6bf9-4b9e-8dea-f620e410ffbc",
-            "metadata": {},
-            "source": [
-                "### Load RAG index and run schema extractions"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "54fdd92b-895b-47b8-bb7b-e6976bb0b80b",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "6cc9bd1172344b4f9ba5c841d87db51e",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/15 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Data sharing: Acknowledgments\n",
-                        "\n",
-                        "This trial was funded by a grant to the London School of Hygiene & Tropical Medicine from UNITAD and The Global Fund via the Innovative Vector Control Consortium. This trial is part of a larger project. The New Nets project: We thank the communities from the three study districts (Cow, Zagnanado, Ouinhi), particularly the participants, children and their parents, community health workers, staff in health clinics, and the regional study team. We thank colleagues and staff at the Centre de Recherche Entomologique de Cotonou and those at the National Malaria Control Program. We also thank the trial system committee, the data safety monitoring board, and Charles Dickinson (School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada) for supporting the mapping and cluster delineation.\n",
-                        "\n",
-                        "header: Outcomes\n",
-                        "\n",
-                        "The primary outcome was malaria case incidence (infrared frontal temperature >37.5degC or reported fever in the previous 48 h, and positive malaria rapid diagnostic test) in children enrolled in the active case detection cohort in the 2 years after net distribution. Visits of children in the cohort at health facilities were monitored and the results of malaria rapid diagnostic tests and any treatment given were recorded. However, it was difficult to assess with certainty if malaria rapid diagnostic tests were used only if the child was symptomatic (as our protocol specified); therefore, the passive data was not included in the primary analyses.\n",
-                        "\n",
-                        "Secondary clinical outcomes were malaria infection prevalence in all age groups and anaemia (defined as haemoglobin concentration <10 g/dL) prevalence in children aged 5 years or younger at 6 months and 18 months after net distribution.\n",
-                        "\n",
-                        "Type and duration of adverse events related to usage of nets were recorded using a prespecified questionnaire at each cohort visit and during net usage and cross-sectional surveys. Data on hospitalisation and death in children in the cohort were collected by interviewing the child's guardian and reviewing the hospital record, following receipt of consent.\n",
-                        "\n",
-                        "The primary entomological outcome was entomological inoculation rate, measured as the mean number of _Plasmodium_ spp infective malaria vectors collected per person per night measured indoors and outdoors. Secondary entomological outcomes were vector density (number of mosquitoes caught per person per night), sporozoite rate, and species composition. Centers for Disease Control and Prevention bottle bioassays were performed by exposing adult female _An gambiae_, collected as larvae, to alpha-cypermethrin for 30 min (1, 2, 5, and 10 times the diagnostic dose), and to chlorfenrapy (100 mg/bottle) and pyriproxyfen (100 mg/bottle) for 60 min each year.(r) Mortality was recorded at 30 min for all four doses of alpha-cypermethrin (insectidic resistance intensity measurement), and at 24, 48, and 72 h after exposure for chlorfenapyr. The reduction in fecundity rate induced by pyriproxyfen relative to unexposed mosquitoes was assessed by ovarian dissection, 3 days after pyriproxyfen exposure. Other outcomes included in the protocol (parity, resting behaviour, survivorship, and other resistance measures) are still to be analysed and will be published elsewhere.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The sample size calculations for epidemiological data collection were calculated using the method of Hayes and Moulton.(r) The study was designed to detect a 30% difference in malaria case incidence, assuming a control group incidence of 1 malaria case per child-year and a coefficient of variation of 0-3 between clusters. This design required 20 clusters per group and 25 (20+5 allowing for loss to follow-up) children per cluster followed up for 2 years to give 80% power. Due to the delay in enrolment, which resulted in a shorter follow-up time, the number of children per cluster was increased to 30 (1800 children total). This sample size calculation includes adjustment for multiple testing (allowing for the three groups) using a Bonferroni-corrected two-sided a of 2.5%.\n",
-                        "\n",
-                        "For malaria infection prevalence, it was assumed that the prevalence in the reference group was 40%, with a coefficient of variation between clusters of 0-3. With 72 individuals per cluster, the study had 80% power to detect a 30% lower prevalence in the intervention groups compared with the reference group, using a Bonferroni-corrected a for multiple comparisons.\n",
-                        "\n",
-                        "The primary intention-to-treat analysis was a comparison of incidence of clinical malaria episodes between each dual active-ingredient LLIN group and the\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidalnets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial\n",
-                        "\n",
-                        "Manfred Accomphesi\n",
-                        "\n",
-                        "\n",
-                        "Background New classes of long-lasting insecticidal nets (LLINs) combining mixtures of insecticides with different modes of action could put malaria control back on track after rebounds in transmission across sub-Saharan Africa. We evaluated the relative efficacy of pyriproxyfen-pyrethroid LLINs and chlorfenapyr-pyrethroid LLINs compared with standard LLINs against malaria transmission in an area of high pyrethroid resistance in Benin.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Between May 23 and June 24, 2019, 53854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. The households were delineated into 61 clusters, with one subsequently excluded due to extensive seasonal flooding (figure). Baseline cross-sectional epidemiological and entomological surveys were conducted between October and November, 2019 (table 1). The malaria infection prevalence was 43 5% (1924 of 4428 participants, cluster range 15 1-72 796) and population self-reported LLIN usage was 95 7% (3913 of 4088 participants). Cluster demographics and malaria prevalence were similar between groups (table 1). Entomological inoculation rate was 0 68 _Plasmodium_ spp infective bites per person per night (cluster range 0 00-3 80) indoors and 0 26 per person per night (0 00-1 88) outdoors.\n",
-                        "\n",
-                        "Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54 300 households in an updated census, with 97 1% of households receiving at least one net per household. Active ingredients in the new nets were found to be within or higher than the acceptable target limits depending on LLIN brand (appendix p 4). Study net usage was highest (5532 [76 8%] of 7206 participants) at 9 months after distribution, following a second hang-up campaign, but had decreased by 24 months (4032 [60 6%] of 6654). Study net coverage and usage indicators were similar between study groups up to 18 months after net distribution. At 24 months, pyriproxyfen-pyrethroid LLIN usage and ownership was the lowest (appendix pp 5-6). Use of any type of LLIN (study LLINs and others) remained greater than 80% up to 24 months after distribution (appendix p 7).\n",
-                        "\n",
-                        "In the 2283 households that were randomly selected for post-intervention cohort follow-up, 3129 children were eligible, 1829 of whom were randomly selected, and consent was obtained for 1806 (figure). Among the 2849 and 2771 households randomly selected at 6 months and 18 months after LLIN distribution for the malaria prevalence cross-sectional surveys, 4781 (85 1%) of 5620 households consent, with a similar proportion in the two surveys. The remaining households were either not available during the visit (775 [13 8%] of) or declined to participate (64 [1 1%]; appendix p 8).\n",
-                        "\n",
-                        "Children aged 6 months-9 years enrolled for active case detection were monitored for 21 months, for a total follow-up time of 2645 4- child-years at risk. Loss to follow-up was similar between groups (figure). At enrolment, children were balanced on age, sex, and net usage. During the 21-month follow-up period, we detected 2135 malaria cases through active case detection (table 2). The mean malaria case incidence over 21 months of follow-up was 1 03 cases per child-year (95% CI 0 96-1 09) in the pyrethroid-only LLIN reference group, 0 84 cases per child-year (0 78-70 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 86, 95% CI 0 65-1 46; p = 0 - 28), and 0 56 cases per child-year (0 51-0 64) in the chlorfenapry-pyrethroid LLIN group (HR 0 54, 95% CI 0 - 42-0 70; p < 0 0001; table 2). The strongest effect was observed in the first year of follow-up in the chlorfenapry-pyrethroid group (HR 0 46, 95% CI 0 30-0 72; p = 0 - 0005; table 2). When combining active and passive visits, the number of cases detected nearly doubled; however, the effect size in comparison to the reference group was similar for both intervention nets (appendix p 9).\n",
-                        "\n",
-                        "Malaria infection prevalence was 23 5% (1037 of 4409 participants) at 6 months and 34 9% (1554 of 4450) at 18 months after net distribution (table 3). There was strong evidence for a reduction of malaria infection prevalence in the chlorfenapry-pyrethroid LLIN group at 6 months (15 7%; odds ratio [OR] 0 47, 95% CI 0 32-0 69; p = 0 0002) and at 18 months (27 9%; OR 0 60 - 60, 0 - 43-0 85; p = 0 004) compared with the reference group (28 - 0% at 6 months and 38 7% at 18 months; table 3). In the pyriproxyfen-pyrethroid group, there was no evidence for a reduction in malaria prevalence at 6 months (26 9%; OR 0 - 92, 95% CI 0 63-1 35; p = 0 - 67) or 18 months (38 2%; OR 0 97 0, 0 69-1 37; p = 0 87) compared with the reference group. Results were similar for the per-protocol analysis (appendix p 1). There was no evidence of a reduction in moderate to severe anaemia prevalence in either intervention group (table 3). The post-hoc analysis adjusting for covariates used in the randomisation did not change the interpretation of the results (appendix pp 12-13).\n",
-                        "\n",
-                        "\n",
-                        "Adverse events related to study nets were reported in 528 (45-496) of 1162 participants surveyed at 1 month after net distribution. The highest proportion of adverse events was reported in the pyrethroid-only LLIN group (247 [63-8%] of 387 participants), followed by the pyriproyfen-pyrethroid LLIN group (212 [52-5%] of 404) and then the chlorfenapy-pyrethroid LLIN group (69 [18-6%] of 371). Facial burning (392 [33-7%] of 1162 participants), and skin irritation or itchiness (244 [20-9%]) were the most common adverse events in all groups. Adverse events were rare in all three groups at all later timepoints (appendix P 14-15). We recorded 44 serious adverse events (including three deaths) in the cohort children, with 32 (72-7%) documented as severe malaria (11 cases in the pyriproyfen-pyrethroid LLIN group, ten in the chlorfenapy-pyrethroid LLIN group, and 11 in the pyrethroid-only LLIN group). No serious adverse events related to net use were reported.\n",
-                        "\n",
-                        "A total of 259265 mosquitoes were collected over 3840 collection nights indoors and outdoors, of which 20-9% (29814 from indoors and 24436 from outdoors) were malaria vectors, with _An gambiae_ sensu that the most predominant. Overall, indoor entomological inoculation rate was lower in both intervention groups than in the reference group, with a mean entomological inoculation rate of 0-90 infectious bites per night per person in the chlorfenapy-pyrethroid LLIN group (rate ratio [RR] 0-34, 95% CI 0-18-0-62; p=0-0005), 0-12 in the pyriproyfen-pyrethroid LLIN group (RR 0-42, 0-2-3-0-74; p=0-0028), and 0-28 in the pyrethroid-only LLIN group (table 4). Mean indoor vector density appeared to be lower in the chlorfenapy-pyrethroid LLIN group (10-18 bites per person per night, density ratio 0-44, 95% CI 0-2-30-84; p=0-014), and in the pyriproyfen-pyrethroid LLIN group (13-6 bites per person per night, density ratio 0-58, 0-30-1-12; p=0-011) than in the reference group (23-0 bites per person per night, but the latter difference was not statistically significant (table 4). Adjusting for baseline vector density in the models gave similar results (appendix P 16). There was no significant difference in sporozoite rate in any of the intervention groups compared with the reference group (table 4).\n",
-                        "\n",
-                        "Reduction in outdoor entomological inoculation rate compared with the reference group was observed only in the chlorfenapy-pyrethroid LLIN group (RR 0-30, 95% CI 0-13-0-67; p=0-0035; appendix P 17). There was very weak evidence for a reduction in outdoor entomological inoculation rate in the pyriproyfen-pyrethroid LLIN group compared with the pyrethroid-only group (RR 0-58, 0-30-1-13; p=0-11). Similar effects were observed for outdoor vector density for both intervention nets (appendix P 17).\n",
-                        "\n",
-                        "Post-intervention pyrethrold resistance intensity was high across the study groups in _An gambiae_ sensu lato, with mean mortality of 85% or less after exposure to 10 times the diagnostic concentrations of alpha-cypentrohin in year 2 (appendix P 18). No resistance to chlorfenapy was observed during either year after net distribution (appendix P 18). Exposure of _An gambiae_ sensu lato to pyriproyfen led to a high reduction in fecundity rate relative to unexposed control mosquitoes over the 2 years after net distribution (73-2%, 95% CI 62-2-82-4 in year 1, and 76-6%, 66-6-84-9 in year 2).\n",
-                        "\n",
-                        "header: Implications of all the available evidence\n",
-                        "\n",
-                        "Given the positive findings, both in Benin and Tanzania, for chlorfenapyr-pyrethroid LLINs, they are likely to become the first WHO-recommended LLINs impregnated with an insecticide class other than pyrethroids. The absence of superior efficacy of pyrioxyfen-pyrethroid LLINs compared with standard pyrethroid-only LLINs is consistent with the results of the previous Tanzania study and calls into question the role of the current pyrioxyfen-pyrethroid LLINs in future malaria vector strategies.\n",
-                        "\n",
-                        "header: Role of the funding source\n",
-                        "\n",
-                        "The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication.\n",
-                        "\n",
-                        "header: Procedures\n",
-                        "\n",
-                        "The nets tested in the trial were: Royal Guard (Disease Control Technologies, Greer, SC, USA), polyethylene netting (I20 deniers incorporating 220 mg/m2 pyriproyfen and 220 mg/m2 alpha-cypermethrin); Interceptor G2 (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 chlorfenapyr and 100 mg/m2 alpha-cypermethrin); and the reference net, Interceptor (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 of alpha-cypermethrin). Nets were distributed in collaboration with the Benin National Malaria Control Program. Households were asked to collect their nets from a central point and received one net per every two residents in their household, rounded up for odd numbers. Nets already in houses were not removed but households were encouraged to use the new study nets. Additionally, net hang-up campaigns to encourage net use took place at 1 month and 7 months after distribution. Net coverage surveys to assess net ownership and usage were done at 1, 9, and 24 months after distribution. Throughout the follow-up period, children enrolled in the analysis cohort and participants enrolled in the cross-sectional surveys were also asked about their net use.\n",
-                        "\n",
-                        "Insecticide content at baseline was assessed on 30 randomly selected new nets per LLIN brand, by gas chromatography with Flame Ionisation Detection, at the\n",
-                        "Centre Wallon de Recherches Agronomiques, Gembloux, Belgium. The insecticidal and physical durability of the study nets is being assessed in a separate study.\n",
-                        "\n",
-                        "Due to the COVID-19 pandemic, there was a 3-month gap between the distribution of the nets and the enrolment of children in the cohort. At enrolment and at 1 year after distribution (April, 2021), children were treated with antimalarial drugs (atremether-lumefantrinity to clear any underlying infection. The cohort was monitored from August, 2020, to April, 2022, a 21-month period, which encompassed the first 2 years after net distribution. Study nurses visited children every 2 weeks during the transmission season (April-October) and every 1 month in the dry season (November-March). At each visit, children were clinically examined and if they were febrile or had a history of fever in the past 48 h, they were tested for malaria using a malaria rapid diagnostic test. If the test was positive, the child was treated with artemether-lumefantrine, according to national guidelines.\n",
-                        "\n",
-                        "During cross-sectional surveys, all participants were tested for malaria using a malaria rapid diagnostic test and received treatment if the test was positive, and children younger than 5 years were tested for anaemia. Information regarding net ownership and usage, and other household-level indicators were collected at the same time.\n",
-                        "\n",
-                        "Entomological monitoring was also delayed and took place every 3 months between June, 2020, and April, 2022. Volunteers recruited from study clusters collected mosquitoes that landed on their legs between 1900 h and 0700 h for 1 night at four randomly selected houses in each cluster at each timepoint. Mosquitoes were morphologically identified to species and a subsample of _Anopheles_ spp was tested for molecular species using PCR.(r) A random sample of _Anopheles_ spp (up to 30% from each nightly catch in each cluster) were tested for sporozoites using the ELISA circumsporozoite protein technique.(r)\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "This trial assessed the efficacy of two dual active-ingredient LLINs in an area of Benin with malaria vectors that are highly resistant to pyrethroids and found that chlorfenapy-pyrethroid LLINs provided significantly better protection against malaria for up to 2 years after net distribution compared with pyrethroid-only LLINs. Children aged 6 months-10 years living in clusters that received chlorfenapy-pyrethroid LLINs had a 46% lower incidence of malaria over 2 years after LLIN distribution;\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c c c}  & **Malaria infection** & \\multicolumn{6}{c}{**Anaemia in children younger than 5 years**} \\\\ \\hline n/N & Prevalence & OR & 95\\% CI & p valuea & n/N & Prevalence & OR & 95\\% CI & p valuea \\\\ \\hline\n",
-                        "**6 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 412/1471 & 28\\(\\,\\)0\\% & 1 (ref) & – & – & 992/41 & 41\\(\\,\\)1\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 394/1463 & 26\\(\\,\\)9\\% & 0\\(\\,\\)92 & 0\\(\\,\\)\n",
-                        "\n",
-                        "63\\(\\,\\)45 & 0\\(\\,\\)67 & 117/250 & 46\\(\\,\\)8\\% & 124 & 0.71–218 & 0.45 \\\\ LLIN group & & & & & & & & & \\\\ Chlorfenapy-pyrethroid & 231/1475 & 157\\(\\,\\)\\% & 0\\(\\,\\)47 & 0\\(\\,\\)32\\(\\,\\)0\\(\\,\\)669 & 0\\(\\,\\)0002 & 82/241 & 34\\(\\,\\)0\\% & 0.71 & 0.40–126 & 0.24 \\\\ LLIN group & & & & & & & & & \\\\\n",
-                        "**18 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 576/14/849 & 38\\(\\,\\)7\\% & 1 (ref) & – & – & 118/25 & 46\\(\\,\\)8\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 564/14/83 & 38\\(\\,\\)2\\% & 0\\(\\,\\)97 & 0\\(\\,\\)69\\(\\,\\)13\\(\\,\\)07 & 0\\(\\,\\)87 & 108/245 & 44\\(\\,\\)1\\% & 0.84 & 0.40–179\n",
-                        "\n",
-                        "\n",
-                        "participants of any age had 53% lower odds of malaria infection at 6 months and 40% lower odds at 18 months after LLIN distribution, and were exposed to a 66% reduction in entomological inoculation rate compared with those living in clusters that received pyrethroid-only LLINs. Similar gains were not observed with the pyriproxyfen-pyrethroid LLIN, with a small reduction in malaria incidence in the first year of the study that was attenuated in year 2, and no effect on malaria infection prevalence in either year, compared with the pyrethroid-only reference group. However, a 58% reduction in indoor entomological inoculation rate was detected with the pyriproxyfen-pyrethroid LLIN. Both dual active-ingredient LLINs provided a similar safety profile compared with standard pyrethroid-only LLINs, with short-lasting skin irritation and facial burning (commonly associated with the pyrethroid alpha-Cybermethrin) most frequently reported in participants using pyrethroid-only LLINs and pyriproxyfen-pyrethroid LLINs. A similar observation has been reported in another randomised controlled trial evaluating the same LLINs, and this is likely to be associated with the high concentration of alpha-Cybermethrin in those nets compared with the chlorfenapy-pyrethroid LLINs.\n",
-                        "\n",
-                        "To our knowledge, this is the second cluster-randomised trial to provide strong evidence of increased efficacy of chlorfenapy-pyrethroid LLINs relative to pyrethroid-only LLINs for malaria control in areas with pyrethroid-resistant vectors. Chlorfenapyr insecticide on nets has already shown improved control of pyrethroid-resistant _An gambiae_ in laboratory and semi-field experimental huts against resistant malaria vectors.3- The previous trial in Tanzania reported a malaria case incidence reduction of 44%, a 55% decrease in odds of malaria prevalence, and 85% reduction in entomological inoculation rate compared with the pyrethroid-only group, consistent with these results, despite the differing malaria vector populations and intensity of pyrethroid resistance in the mosquitoes.4 In both trials, per-protocol analyses suggested that individuals living in the chlorfenapy-pyrethroid clusters benefited regardless of whether they were using a study net, suggesting a community effect of the net, which was likely to be obtained by the overall reduction of mosquito density, which remained considerably lower than in the pyrethroid-only group in both years of this trial after net distribution. Community effect is also indicated by the reduction in both indoor and outdoor entomological indices in the chlorfenapy-pyrethroid group in the present study.\n",
-                        "\n",
-                        "Similar to the trial in Tanzania,4 the pyriproxyfen-pyrethroid LLINs we tested did not provide additional protection against malaria infection or disease compared with pyrethroid-only LLINs. However, there was evidence of an effect on indoor transmission, with the strongest effect seen in the first year after net distribution. Tino and colleagues5 have previously reported a 12% reduction in malaria incidence and 49% reduction in entomological \n",
-                        "inoculation rate with a pyriproxyfen-pyrethroid LLIN compared with pyrethroid-only LLINs over 18 months in a stepped-wedge randomised trial in Burkina Faso. The different study design, length of follow-up, and brand of net might explain the differences seen between the two studies. In Benin, laboratory and semi-field experimental hut studies have shown the superior efficacy of pyriproxyfen-pyrethroid nets on entomological indicators compared with standard pyrethroid-only LLINs,2,3 and are consistent with the decrease in indoor vector density observed in our trial. However, the decrease in indoor vector density did not translate into significant disease reduction, suggesting that a larger effect on malaria transmission (entomological inoculation rate) is crucial to provide community protection. Although lower net usage in the pyriproxyfen-pyrethroid LLIN group could have partially contributed to the lack of effect, we are also assessing textile durability, sterilisation effects, and chemical content of pyriproxyfen-pyrethroid LLINs to fully understand these results.\n",
-                        "\n",
-                        "With several trials now showing the superior efficacy of next-generation LLINs over pyrethroid-only nets, the importance of developing new active ingredients to use on nets in the future is brought to the fore. The distribution of next-generation LLINs across sub-Saharan Africa has already begun, with the development of the brands of chlorfenapry-pyrethroid nets underway,3 as well as the development of chlorfenapry product formulations for indoor residual spraying.3 Although chlorfenapry-pyrethroid nets offer a superior alternative to pyrethroid-only nets in areas of pyrethroid resistance, to preserve their effectiveness optimal resistance-management strategies should be used. There was no evidence of the development of resistance to chlorfenapry during the 2 years of this trial; however, the wide-scale deployment of one type of insecticide risks the rapid development of resistance, which could result in a similar situation to the current widespread resistance to pyrethroids. The nets should be deployed ideally alongside other insecticides as part of a strategy aimed to reduce selection pressure for development of resistance in mosquito vectors. Given the significant effect of the pyriproxyfen-pyrethroid LLINs on malaria transmission during the first year after net distribution, additional studies are necessary to evaluate the potential value of active ingredients such as pyriproxyfen, which are not primarily intended to kill resistant adult mosquitoes but rather to sterilise them. There might still be a role for these hormone growth-regulator insecticides in combination with other active ingredients for net treatment in long-term resistance management.\n",
-                        "\n",
-                        "If WHO policy recommendations are made on the basis of this trial's results, future chlorfenapry-pyrethroid nets might not have to undergo the rigorous trial testing that Interceptor G2 has. Caution should, however, be taken to assess the comparative efficacy, quality, and durability of the second-in-class chlorfenapry-pyrethroid nets as they might use different concentrations of chlorfenapry, different pyrethroids, or different net materials. Considering the time and resources required to generate evidence of epidemiological effect using randomised trials, non-inferiority experimental hut trials, recently proposed by WHO,3 might be a useful alternative for second-in-class chlorfenapry-pyrethroid LLINs. Further work to investigate the capacity of such entomological studies to predict the performance of the trial nets against clinical malaria is ongoing.3\n",
-                        "\n",
-                        "Footnote 3: endnote: [https://www.thelanct.com/Vol.401](https://www.thelanct.com/Vol.401). February 11, 2023\n",
-                        "\n",
-                        "Our study has some limitations. First, net ownership and use were high throughout the study; however, this did not always equate to study net use, which was approximately 60% at 2 years after net distribution, with overall usage of greater than 80%. Similar findings have been observed in other bednet trials, and probably indicate populations choosing to discard damaged nets when other nets are readily available. Second, the three types of nets were not completely identical and differences in textile material and size might have resulted in differential net usage between the groups. As our net use indicator was primarily self-reporting, it is also possible that net usage was overestimated. Third, our primary measure of incidence was based on active detection, involving visits every 2 weeks or every month. The passive data collected alongside the trial suggests that this frequency of visit resulted in some cases being missed, meaning the absolute effect of the nets might be greater than reported here. However, the relative effect of the nets remained similar when the active and passive data were combined. Finally, cost-effectiveness was not assessed; however, the trial in Tanzania' showed that dual active-ingredient LLINs can be highly cost-effective and even cost-saving compared with pyrethroid-only LLINs when providing sufficient protection.\n",
-                        "\n",
-                        "New classes of vector control interventions currently require clinical trial evaluation in two different geographical settings, after 24 months of community use.4 Currently, the only class of next-generation LLIN to receive a WHO recommendation are the PBO synergism nets. However, previous publications have shown concerns about the durability of these nets.5 This trial provides the second key evidence for the effectiveness of chlorfenapry-pyrethroid nets in an area with pyrethroid-resistant vectors and will therefore support a WHO policy recommendation. However, the effect of the nets was reduced in the second year of the trial, and there is no evidence for the efficacy of the nets in their third year of use. Many studies report that the durability of nets is much less than the 3 years required to be designated as long lasting.5 The next-generation LLINs might face the same problems of fabric integrity and durability of insecticidal content unless standards of manufacture are improved. Different channels of distribution (eg, school-based, antenatal care visits, or expanded programme immunisation visits) could play a key role in maintaining high levels of net use in communities.5\n",
-                        "Although generating this evidence to support a WHO recommendation for another class of bednet is a key turning point in malaria control, without new insecticides and new ways to deploy them, we risk repeating the mistakes of the past. Now is the time for more innovation and less complacency.\n",
-                        "\n",
-                        "header: Study design and participants: Randomisation and masking\n",
-                        "\n",
-                        "We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (1:1:1), to receive nets containing either pyriproxyfen and alpha-cypermethrin (pyrethroid), chlorfenapyr and alpha-cypermethrin, or alpha-cypermethrin only (reference). Restricted randomisation was used to ensure balanced cluster allocation between study groups with respect to population size, malaria infection prevalence (measured in the baseline survey), district (n=3), and socioeconomic status.\n",
-                        "\n",
-                        "To mask the net types from the participants and the field workers, the nets were designed to look as similar as possible. Each net was rectangular, was requested to be the same size (1-8 m length, 1-9 m width, and 1-8 m height), and blue. To differentiate the nets in the field, a colour-coded loop was attached to the net. All data analyses were performed masked.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a cluster-randomised, superiority trial in Zou Department, Benin. Clusters were villages or groups of villages with a minimum of 100 houses. We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (I:1:1): to receive nets containing either pyriproxyfen and alpha-Cypermethrin (pyrethroid), chlorfenapyr and alpha-Cypermethrin only (reference). Households received one LLIN for every two people. The field team, laboratory staff, analyses team, and community members were masked to the group allocation. The primary outcome was malaria case incidence measured over 2 years after net distribution in a cohort of children aged 6 months-10 years, in the intention-to-treat population. This study is ongoing and is registered with ClinicalTrials.gov, NCT03931473.\n",
-                        "\n",
-                        "Findings Between May 23 and June 24, 2019, 53 854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54030 households in an updated census. A cross-sectional survey showed that study LLIN usage was highest 9 months after distribution (5532 [76 * 986] of 7206 participants), but decreased by 24 months (4032 [60 * 696] of 6654). Mean malaria incidence over 2 years after LLIN distribution was 1-03 cases per child-year (95% CI 0 * 96-1 * 09) in the pyrethroid-only LLIN reference group. 0 * 84 cases per child-year (0.78-0 * 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 * 86, 95% CI 0 * 65-1* 14; p=0 * 28), and 0 * 56 cases per child-year (0 * 51-0 * 61) in the chlorfenapyr-pyrethroid LLIN group (HR 0 * 54, 95% CI 0 * 42-0 * 70; p<0 * 0001).\n",
-                        "\n",
-                        "Interpretation Over 2 years, chlorfenapyr-pyrethroid LLINs provided greater protection from malaria than pyrethroid-only LLINs in an area with pyrethroid-resistant mosquitoes. Pyriproxyfen-pyrethroid LLINs conferred protection similar to pyrethroid-only LLINs. These findings provide crucial second-trial evidence to enable WHO to make policy recommendations on these new LLIN classes. This study confirms the importance of chlorfenapyr as an LLIN treatment to control malaria in areas with pyrethroid-resistant vectors. However, an arsenal of new active ingredients is required for successful long-term resistance management, and additional innovations, including pyriproxyfen, need to be further investigated for effective vector control strategies.\n",
-                        "\n",
-                        "header: Contributions\n",
-                        "\n",
-                        "NP, JC, and CN conceived and designed the study, with contributions from MR, IK, LAM, and MCA, JC and MA led the development of the analysis plan, with input from BA, Aso, and NP, MA, CA, JC, NP, FT, and MCA coordinated the trial implementation with local and national authorities, RA, and AO-H. MA, BF, HA, Aso, and LA led the data collection in the field with and Aso, and AP, each of them, MCA, JC, and NP and NP. Also, BC, BA, and KAB led the molecular laboratory work.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a three-group, cluster-randomised, superiority trial in three districts (Cove, Zagnanado, and Ouinhi) in Zou Department, central Benin. Malaria is highly endemic in the region, with a peak during the wet season (May-October). The main vector control strategy in the study area is use of pyrethroid-only LLINs distributed en masse once every 3 years, and through routine service delivery by antenatal clinics and vaccination programmes. The primary vectors in the setting are _Anopheles coluzzi_ and _Anopheles gambiae_. There is a high intensity of pyrethroid resistance in the main local vector populations.11\n",
-                        "\n",
-                        "A demographic census of all 123 villages in the study area was done in June, 2019. Clusters consisted of villages, or several villages. To reduce contamination between study groups, clusters consisted of a core (minimum 100 households) surrounded by a buffer, ensuring that core areas of neighbouring clusters were separated by at least 1000 m. Households in the buffer and core received the intervention, but measurement of outcomes only took place in core areas. A detailed description of the study protocol is published elsewhere.\"\n",
-                        "\n",
-                        "Ethics approval was obtained from the Benin Ministry of Health ethics committee (6/30/MS/DC/SGM/DRFMT/CNERS/SA), the London School of Hygiene & Tropical Medicine ethics committee (16237), and the WHO Research Ethics Review Committee (ERC.0003153). The trial was independently monitored by a data safety monitoring board and a trial steering committee.\n",
-                        "\n",
-                        "Epidemiological effect was estimated through measuring malaria case incidence in the 2 years after net distribution by active case detection in a cohort of children. A cohort of approximately 30 children aged 6 months-9 years randomly selected (by simple random sampling using a random number generator) from each cluster (1800 in total) was enrolled in July, 2020. Children were eligible for inclusion if they were permanent residents in the cluster, had no serious illnesses, and written informed consent was obtained from their guardians.\n",
-                        "\n",
-                        "Malaria infection prevalence (in all ages) was measured by cross-sectional surveys at 6 months and 18 months after net distribution. 72 individuals residing in the core of each cluster were randomly selected (using a random number generator) from the census for each cross-sectional survey. Each prevalence surveyed collected data on malaria infection, measured using malaria rapid diagnostic tests (CareStart malaria HRP2/PLDH [pf/pan] combo, DiaSy, UK), net ownership and use, sex, and household assets (as a proxy for socioeconomic status).\n",
-                        "\n",
-                        "Entomological effects were measured using human landing catches in four randomly selected houses every 3 months in each cluster. Insecticide resistance intensity was measured in two clusters per group (six clusters total) at baseline and in each follow-up year.\n",
-                        "\n",
-                        "Written informed consent was obtained from all participants, or from guardians for participants younger than 18 years. Assent was sought for children aged between 10 and 18 years. Written consent was also obtained from volunteer mosquito collectors who were all aged 18 years or older and who were vaccinated against yellow fever. All participation was voluntary, and participants could withdraw at any time. Study investigators sought consent in French or local languages.\n",
-                        "\n",
-                        "header: Table 1: Baseline characteristics\n",
-                        "footer: Data are n or %; n/N unless otherwise stated. EIR=entomological inoculation rate. LLIN=long-lasting insecticidal net. *Proportion of households in the poorest tercile based on the wealth index of the entire study area. †Anaemia defined as haemoglobin concentration of <10 g/dL. ‡Malaria vectors included Anopheles gambiae, Anophelesfunestus, and Anopheles nili\n",
-                        "columns: ['Unnamed: 0_level_0 - Clusters', 'Pyriproxyfen-pyrethroid LLIN group - Clusters', 'Chlorfenapyr-pyrethroid LLIN group - Clusters', 'Pyrethroid-only LLIN group - Clusters']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of clusters\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"20\",\"Pyrethroid-only LLIN group - Clusters\":\"20\"},\"1\":{\"Unnamed: 0_level_0 - Clusters\":\"Total population in core and buffer areas\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"74822\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"70989\",\"Pyrethroid-only LLIN group - Clusters\":\"69239\"},\"2\":{\"Unnamed: 0_level_0 - Clusters\":\"Mean population in core area of clusters (range)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"1096.1 (225-4524)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"1120.5 (243-5040)\",\"Pyrethroid-only LLIN group - Clusters\":\"1058.1 (252-5217)\"},\"3\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of people per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"4.0 (2.3)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"3.9 (2.3)\",\"Pyrethroid-only LLIN group - Clusters\":\"4.1 (2.4)\"},\"4\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of sleeping spaces per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"2.2 (1.2)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"2.1 (1.1)\",\"Pyrethroid-only LLIN group - Clusters\":\"2.2 (1.2)\"},\"5\":{\"Unnamed: 0_level_0 - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyrethroid-only LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\"},\"6\":{\"Unnamed: 0_level_0 - Clusters\":\"Low socioeconomic status*\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"35.8%; 529\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"36.2%; 533\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"29.0%; 431\\/1487\"},\"7\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN ownership (at least one LLIN in the household)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"96.4%; 1426\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"97.1%; 1431\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"95.1%; 1415\\/1488\"},\"8\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN usage in all age groups the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"95.8%; 1312\\/1370\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"94.9%; 1258\\/1326\",\"Pyrethroid-only LLIN group - Clusters\":\"96.5%; 1343\\/1392\"},\"9\":{\"Unnamed: 0_level_0 - Clusters\":\"Malaria infection prevalence in all age groups\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"43.1%; 636\\/1475\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"40.7%; 598\\/1468\",\"Pyrethroid-only LLIN group - Clusters\":\"46.5%; 690\\/1485\"},\"10\":{\"Unnamed: 0_level_0 - Clusters\":\"Anaemia prevalence in children aged 6 months-4 years\\u2020\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"53.3%; 136\\/255\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"53.3%; 131\\/246\",\"Pyrethroid-only LLIN group - Clusters\":\"50.2%; 122\\/243\"},\"11\":{\"Unnamed: 0_level_0 - Clusters\":\"Entomological characteristics\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Pyrethroid-only LLIN group - Clusters\":\"Entomological characteristics\"},\"12\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night indoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"32.9 (28.9)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"21.6 (21.0)\",\"Pyrethroid-only LLIN group - Clusters\":\"29.2 (25.3)\"},\"13\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night outdoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20.3 (17.6)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"16.4 (17.4)\",\"Pyrethroid-only LLIN group - Clusters\":\"20.6 (20.2)\"},\"14\":{\"Unnamed: 0_level_0 - Clusters\":\"Indoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.62 (0.93)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.48 (0.65)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.96 (1.18)\"},\"15\":{\"Unnamed: 0_level_0 - Clusters\":\"Outdoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.27 (0.45)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.17 (0.44)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.33 (0.45)\"},\"16\":{\"Unnamed: 0_level_0 - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyrethroid-only LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\"},\"17\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of children younger than 5 years\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"52.3%; 316\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"50.6%; 304\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"53.6%; 322\\/601\"},\"18\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of female children\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"47.2%; 285\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"48.4%; 291\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"48.8%; 293\\/601\"},\"19\":{\"Unnamed: 0_level_0 - Clusters\":\"Net usage the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"99.0%; 598\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"98.7%; 593\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"99.2%; 596\\/601\"}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "Data collected for the study, including deidentified participant data and data dictionaries, might be made available at the end of the third year of trial follow-up upon reasonable request to the corresponding author.\n",
-                        "\n",
-                        "header: Funding UNITAID, The Global Fund.\n",
-                        "\n",
-                        "Pyrethroid-treated long-lasting insecticidal nets (LLINs) are the main malaria prevention intervention in sub-Saharan Africa and have been the major contributor to the estimated 1 * 5 billion malaria cases and 7 * 6 million malaria deaths averted in the past two decades. However, since 2015, the decline in malaria cases has stalled, and between 2019 and 2020, a rebound in malaria transmission was reported in some areas of sub-Saharan Africa.1 This rebound is likely to be a consequence of the continued spread of resistance to pyrethroid insecticides in malaria-transmitting mosquitoes, coinciding with a plateau in malaria control investment, leading to suboptimal coverage of interventions. Urgent actions are needed to prevent malaria resurgences, which were previously observed in sub-Saharan Africa in the 1980s after malaria control measures were scaled down.2\n",
-                        "In the past 10 years, new insecticide-treated nets containing non-pyrethroid insecticides and insecticide synergists, in combination with pyrethroids, have been shown to be safe and efficacious against malaria mosquito vectors.14 The first net combined a pyrethroid insecticide and the synergistic piperonyl butoxide (PBO) and, following a randomised controlled trial,1 received a WHO policy recommendation in 2017. Other next-generation LLINs have shown encouraging results against resistant vectors in laboratory and small-scale entomological studies.47 One of these nets is a dual active-ingredient LLIN combining a pyrethroid and a pyrrole (chlorfenapyr). Both insecticides lead to mosquito mortality, with chlorfenapyr disrupting the production of cellular energy rather than targeting the nervous system, as pyrethroids do. Another dual active-ingredient LLIN combines a pyrethroid with an insect growth regulator (pyriprosyfen), which leads to sterility in exposed adult mosquitoes.\n",
-                        "\n",
-                        "The first randomised trial of these two products was conducted in Tanzania and showed that the chlorfenapyr-pyrethroid LLINs nearly halved malaria infection over 2 years in villages that received chlorfenapyr-pyrethroid LLINs compared with villages that received pyrethroid-only LLINs. PBO-pyrethroid LLINs were consistently found to be more effective than pyrethroid-only LLINs; however, the duration of the superior effect varied from 12 months to 21 months according to the different trials.\n",
-                        "\n",
-                        "header: Table 2: Malaria case incidence in children aged 6 months–10 years per year of follow-up and overall (including active visits only)\n",
-                        "footer: Each intervention was compared with the pyrethroid-only LLIN group for the same timepoint. LLIN=long-lasting insecticidal net. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Overall', 'Number of clinical malaria episodes - Overall', 'Child- years of follow-up - Overall', 'Incidence, cases per child-year (95% Cl) - Overall', 'Hazard ratio - Overall', '95%Cl - Overall', 'p value* - Overall']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"897\",\"Child- years of follow-up - Overall\":\"874.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.03 (0.96-1.09)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"1\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"744\",\"Child- years of follow-up - Overall\":\"883.8\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.84 (0.78-0.90)\",\"Hazard ratio - Overall\":\"0.86\",\"95%Cl - Overall\":\"0.65-1.14\",\"p value* - Overall\":\"0.28\"},\"2\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"494\",\"Child- years of follow-up - Overall\":\"887.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.56 (0.51-0.61)\",\"Hazard ratio - Overall\":\"0.54\",\"95%Cl - Overall\":\"0.42-0.70\",\"p value* - Overall\":\"<0.0001\"},\"3\":{\"Unnamed: 0_level_0 - Overall\":\"Year 1\",\"Number of clinical malaria episodes - Overall\":\"Year 1\",\"Child- years of follow-up - Overall\":\"Year 1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 1\",\"Hazard ratio - Overall\":\"Year 1\",\"95%Cl - Overall\":\"Year 1\",\"p value* - Overall\":\"Year 1\"},\"4\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"267\",\"Child- years of follow-up - Overall\":\"344.4\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.77 (0.69-0.87)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"5\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"211\",\"Child- years of follow-up - Overall\":\"342.7\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.62 (0.54-0.70)\",\"Hazard ratio - Overall\":\"0.83\",\"95%Cl - Overall\":\"0.51-1.35\",\"p value* - Overall\":\"0.46\"},\"6\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"124\",\"Child- years of follow-up - Overall\":\"349.2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.36 (0.30-0.42)\",\"Hazard ratio - Overall\":\"0.46\",\"95%Cl - Overall\":\"0.30-0.72\",\"p value* - Overall\":\"0.0005\"},\"7\":{\"Unnamed: 0_level_0 - Overall\":\"Year 2\",\"Number of clinical malaria episodes - Overall\":\"Year 2\",\"Child- years of follow-up - Overall\":\"Year 2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 2\",\"Hazard ratio - Overall\":\"Year 2\",\"95%Cl - Overall\":\"Year 2\",\"p value* - Overall\":\"Year 2\"},\"8\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"630\",\"Child- years of follow-up - Overall\":\"529.9\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.19 (1.10-1.29)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"9\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"533\",\"Child- years of follow-up - Overall\":\"541.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.98 (0.90-1.07)\",\"Hazard ratio - Overall\":\"0.88\",\"95%Cl - Overall\":\"0.68-1.13\",\"p value* - Overall\":\"0.31\"},\"10\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"370\",\"Child- years of follow-up - Overall\":\"538.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.69 (0.62-0.76)\",\"Hazard ratio - Overall\":\"0.57\",\"95%Cl - Overall\":\"0.45-0.73\",\"p value* - Overall\":\"<0.0001\"}}\n",
-                        "\n",
-                        "header: Table 3: Malaria infection and anaemia prevalence in the study population at 6 months and 18 months after net distribution (intention-to-treat analysis)\n",
-                        "footer: Anaemia was defined as a haemogloblin concentration of <10 g/dL. LLIN=long-lasting insecticidal net. OR=odds ratio. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution', 'Malaria infection - n/N - 6 months after net distribution', 'Malaria infection - Prevalence - 6 months after net distribution', 'Malaria infection - OR - 6 months after net distribution', 'Malaria infection - 95% Cl - 6 months after net distribution', 'Malaria infection - p value* - 6 months after net distribution', 'Anaemia in children younger than 5 years - n/N - 6 months after net distribution', 'Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution', 'Anaemia in children younger than 5 years - OR - 6 months after net distribution', 'Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution', 'Anaemia in children younger than 5 years - p value* - 6 months after net distribution']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"412\\/1471\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"28.0%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"99\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"41.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"394\\/1463\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"26.9 %\",\"Malaria infection - OR - 6 months after net distribution\":\"0.92\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.63-1.35\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.67\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"117\\/250\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.24\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.71-2.18\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.45\"},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"231\\/1475\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"15.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.47\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.32-0.69\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0002\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"82\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"34.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.71\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.26\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0-24\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - p value* - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"18 months after net distribution\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"576\\/1489\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/252\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"564\\/1478\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.2%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.97\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.69-1.37.\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.87\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"108\\/245\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"44.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.84\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.79\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.65\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"414\\/1483\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"27.9%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.60\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.43-0.85\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0041\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/246\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"48.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.08\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.51-2.28\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.84\"}}\n",
-                        "\n",
-                        "header: Introduction: Added value of this study\n",
-                        "\n",
-                        "To our knowledge, this is the second study aiming to compare the efficacy of the next generation of LLINs combining _p_iprosyfen or chlorfenapyr and pyrethroid versus standard pyrethroid-only LLINs, and the first of its kind to be conducted in west Africa. This trial confirmed the superior efficacy of chlorfenapyr-pyrethroid LLINs, in terms of malaria case incidence, prevalence, and transmission in children, over 2 years of use in the community, in an area of moderate malaria transmission and with high insecticide resistance intensity in malaria vectors, in Benin. However, pyrioxyfen-pyrethroid LLINs did not offer additional protection against malaria outcomes compared with pyrethroid-only LLINs.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Data sharing: Acknowledgments\n",
-                        "\n",
-                        "This trial was funded by a grant to the London School of Hygiene & Tropical Medicine from UNITAD and The Global Fund via the Innovative Vector Control Consortium. This trial is part of a larger project. The New Nets project: We thank the communities from the three study districts (Cow, Zagnanado, Ouinhi), particularly the participants, children and their parents, community health workers, staff in health clinics, and the regional study team. We thank colleagues and staff at the Centre de Recherche Entomologique de Cotonou and those at the National Malaria Control Program. We also thank the trial system committee, the data safety monitoring board, and Charles Dickinson (School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada) for supporting the mapping and cluster delineation.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "Data collected for the study, including deidentified participant data and data dictionaries, might be made available at the end of the third year of trial follow-up upon reasonable request to the corresponding author.\n",
-                        "\n",
-                        "header: Outcomes\n",
-                        "\n",
-                        "The primary outcome was malaria case incidence (infrared frontal temperature >37.5degC or reported fever in the previous 48 h, and positive malaria rapid diagnostic test) in children enrolled in the active case detection cohort in the 2 years after net distribution. Visits of children in the cohort at health facilities were monitored and the results of malaria rapid diagnostic tests and any treatment given were recorded. However, it was difficult to assess with certainty if malaria rapid diagnostic tests were used only if the child was symptomatic (as our protocol specified); therefore, the passive data was not included in the primary analyses.\n",
-                        "\n",
-                        "Secondary clinical outcomes were malaria infection prevalence in all age groups and anaemia (defined as haemoglobin concentration <10 g/dL) prevalence in children aged 5 years or younger at 6 months and 18 months after net distribution.\n",
-                        "\n",
-                        "Type and duration of adverse events related to usage of nets were recorded using a prespecified questionnaire at each cohort visit and during net usage and cross-sectional surveys. Data on hospitalisation and death in children in the cohort were collected by interviewing the child's guardian and reviewing the hospital record, following receipt of consent.\n",
-                        "\n",
-                        "The primary entomological outcome was entomological inoculation rate, measured as the mean number of _Plasmodium_ spp infective malaria vectors collected per person per night measured indoors and outdoors. Secondary entomological outcomes were vector density (number of mosquitoes caught per person per night), sporozoite rate, and species composition. Centers for Disease Control and Prevention bottle bioassays were performed by exposing adult female _An gambiae_, collected as larvae, to alpha-cypermethrin for 30 min (1, 2, 5, and 10 times the diagnostic dose), and to chlorfenrapy (100 mg/bottle) and pyriproxyfen (100 mg/bottle) for 60 min each year.(r) Mortality was recorded at 30 min for all four doses of alpha-cypermethrin (insectidic resistance intensity measurement), and at 24, 48, and 72 h after exposure for chlorfenapyr. The reduction in fecundity rate induced by pyriproxyfen relative to unexposed mosquitoes was assessed by ovarian dissection, 3 days after pyriproxyfen exposure. Other outcomes included in the protocol (parity, resting behaviour, survivorship, and other resistance measures) are still to be analysed and will be published elsewhere.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The sample size calculations for epidemiological data collection were calculated using the method of Hayes and Moulton.(r) The study was designed to detect a 30% difference in malaria case incidence, assuming a control group incidence of 1 malaria case per child-year and a coefficient of variation of 0-3 between clusters. This design required 20 clusters per group and 25 (20+5 allowing for loss to follow-up) children per cluster followed up for 2 years to give 80% power. Due to the delay in enrolment, which resulted in a shorter follow-up time, the number of children per cluster was increased to 30 (1800 children total). This sample size calculation includes adjustment for multiple testing (allowing for the three groups) using a Bonferroni-corrected two-sided a of 2.5%.\n",
-                        "\n",
-                        "For malaria infection prevalence, it was assumed that the prevalence in the reference group was 40%, with a coefficient of variation between clusters of 0-3. With 72 individuals per cluster, the study had 80% power to detect a 30% lower prevalence in the intervention groups compared with the reference group, using a Bonferroni-corrected a for multiple comparisons.\n",
-                        "\n",
-                        "The primary intention-to-treat analysis was a comparison of incidence of clinical malaria episodes between each dual active-ingredient LLIN group and the\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidalnets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial\n",
-                        "\n",
-                        "Manfred Accomphesi\n",
-                        "\n",
-                        "\n",
-                        "Background New classes of long-lasting insecticidal nets (LLINs) combining mixtures of insecticides with different modes of action could put malaria control back on track after rebounds in transmission across sub-Saharan Africa. We evaluated the relative efficacy of pyriproxyfen-pyrethroid LLINs and chlorfenapyr-pyrethroid LLINs compared with standard LLINs against malaria transmission in an area of high pyrethroid resistance in Benin.\n",
-                        "\n",
-                        "header: Implications of all the available evidence\n",
-                        "\n",
-                        "Given the positive findings, both in Benin and Tanzania, for chlorfenapyr-pyrethroid LLINs, they are likely to become the first WHO-recommended LLINs impregnated with an insecticide class other than pyrethroids. The absence of superior efficacy of pyrioxyfen-pyrethroid LLINs compared with standard pyrethroid-only LLINs is consistent with the results of the previous Tanzania study and calls into question the role of the current pyrioxyfen-pyrethroid LLINs in future malaria vector strategies.\n",
-                        "\n",
-                        "header: Study design and participants: Randomisation and masking\n",
-                        "\n",
-                        "We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (1:1:1), to receive nets containing either pyriproxyfen and alpha-cypermethrin (pyrethroid), chlorfenapyr and alpha-cypermethrin, or alpha-cypermethrin only (reference). Restricted randomisation was used to ensure balanced cluster allocation between study groups with respect to population size, malaria infection prevalence (measured in the baseline survey), district (n=3), and socioeconomic status.\n",
-                        "\n",
-                        "To mask the net types from the participants and the field workers, the nets were designed to look as similar as possible. Each net was rectangular, was requested to be the same size (1-8 m length, 1-9 m width, and 1-8 m height), and blue. To differentiate the nets in the field, a colour-coded loop was attached to the net. All data analyses were performed masked.\n",
-                        "\n",
-                        "header: Role of the funding source\n",
-                        "\n",
-                        "The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Between May 23 and June 24, 2019, 53854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. The households were delineated into 61 clusters, with one subsequently excluded due to extensive seasonal flooding (figure). Baseline cross-sectional epidemiological and entomological surveys were conducted between October and November, 2019 (table 1). The malaria infection prevalence was 43 5% (1924 of 4428 participants, cluster range 15 1-72 796) and population self-reported LLIN usage was 95 7% (3913 of 4088 participants). Cluster demographics and malaria prevalence were similar between groups (table 1). Entomological inoculation rate was 0 68 _Plasmodium_ spp infective bites per person per night (cluster range 0 00-3 80) indoors and 0 26 per person per night (0 00-1 88) outdoors.\n",
-                        "\n",
-                        "Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54 300 households in an updated census, with 97 1% of households receiving at least one net per household. Active ingredients in the new nets were found to be within or higher than the acceptable target limits depending on LLIN brand (appendix p 4). Study net usage was highest (5532 [76 8%] of 7206 participants) at 9 months after distribution, following a second hang-up campaign, but had decreased by 24 months (4032 [60 6%] of 6654). Study net coverage and usage indicators were similar between study groups up to 18 months after net distribution. At 24 months, pyriproxyfen-pyrethroid LLIN usage and ownership was the lowest (appendix pp 5-6). Use of any type of LLIN (study LLINs and others) remained greater than 80% up to 24 months after distribution (appendix p 7).\n",
-                        "\n",
-                        "In the 2283 households that were randomly selected for post-intervention cohort follow-up, 3129 children were eligible, 1829 of whom were randomly selected, and consent was obtained for 1806 (figure). Among the 2849 and 2771 households randomly selected at 6 months and 18 months after LLIN distribution for the malaria prevalence cross-sectional surveys, 4781 (85 1%) of 5620 households consent, with a similar proportion in the two surveys. The remaining households were either not available during the visit (775 [13 8%] of) or declined to participate (64 [1 1%]; appendix p 8).\n",
-                        "\n",
-                        "Children aged 6 months-9 years enrolled for active case detection were monitored for 21 months, for a total follow-up time of 2645 4- child-years at risk. Loss to follow-up was similar between groups (figure). At enrolment, children were balanced on age, sex, and net usage. During the 21-month follow-up period, we detected 2135 malaria cases through active case detection (table 2). The mean malaria case incidence over 21 months of follow-up was 1 03 cases per child-year (95% CI 0 96-1 09) in the pyrethroid-only LLIN reference group, 0 84 cases per child-year (0 78-70 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 86, 95% CI 0 65-1 46; p = 0 - 28), and 0 56 cases per child-year (0 51-0 64) in the chlorfenapry-pyrethroid LLIN group (HR 0 54, 95% CI 0 - 42-0 70; p < 0 0001; table 2). The strongest effect was observed in the first year of follow-up in the chlorfenapry-pyrethroid group (HR 0 46, 95% CI 0 30-0 72; p = 0 - 0005; table 2). When combining active and passive visits, the number of cases detected nearly doubled; however, the effect size in comparison to the reference group was similar for both intervention nets (appendix p 9).\n",
-                        "\n",
-                        "Malaria infection prevalence was 23 5% (1037 of 4409 participants) at 6 months and 34 9% (1554 of 4450) at 18 months after net distribution (table 3). There was strong evidence for a reduction of malaria infection prevalence in the chlorfenapry-pyrethroid LLIN group at 6 months (15 7%; odds ratio [OR] 0 47, 95% CI 0 32-0 69; p = 0 0002) and at 18 months (27 9%; OR 0 60 - 60, 0 - 43-0 85; p = 0 004) compared with the reference group (28 - 0% at 6 months and 38 7% at 18 months; table 3). In the pyriproxyfen-pyrethroid group, there was no evidence for a reduction in malaria prevalence at 6 months (26 9%; OR 0 - 92, 95% CI 0 63-1 35; p = 0 - 67) or 18 months (38 2%; OR 0 97 0, 0 69-1 37; p = 0 87) compared with the reference group. Results were similar for the per-protocol analysis (appendix p 1). There was no evidence of a reduction in moderate to severe anaemia prevalence in either intervention group (table 3). The post-hoc analysis adjusting for covariates used in the randomisation did not change the interpretation of the results (appendix pp 12-13).\n",
-                        "\n",
-                        "\n",
-                        "Adverse events related to study nets were reported in 528 (45-496) of 1162 participants surveyed at 1 month after net distribution. The highest proportion of adverse events was reported in the pyrethroid-only LLIN group (247 [63-8%] of 387 participants), followed by the pyriproyfen-pyrethroid LLIN group (212 [52-5%] of 404) and then the chlorfenapy-pyrethroid LLIN group (69 [18-6%] of 371). Facial burning (392 [33-7%] of 1162 participants), and skin irritation or itchiness (244 [20-9%]) were the most common adverse events in all groups. Adverse events were rare in all three groups at all later timepoints (appendix P 14-15). We recorded 44 serious adverse events (including three deaths) in the cohort children, with 32 (72-7%) documented as severe malaria (11 cases in the pyriproyfen-pyrethroid LLIN group, ten in the chlorfenapy-pyrethroid LLIN group, and 11 in the pyrethroid-only LLIN group). No serious adverse events related to net use were reported.\n",
-                        "\n",
-                        "A total of 259265 mosquitoes were collected over 3840 collection nights indoors and outdoors, of which 20-9% (29814 from indoors and 24436 from outdoors) were malaria vectors, with _An gambiae_ sensu that the most predominant. Overall, indoor entomological inoculation rate was lower in both intervention groups than in the reference group, with a mean entomological inoculation rate of 0-90 infectious bites per night per person in the chlorfenapy-pyrethroid LLIN group (rate ratio [RR] 0-34, 95% CI 0-18-0-62; p=0-0005), 0-12 in the pyriproyfen-pyrethroid LLIN group (RR 0-42, 0-2-3-0-74; p=0-0028), and 0-28 in the pyrethroid-only LLIN group (table 4). Mean indoor vector density appeared to be lower in the chlorfenapy-pyrethroid LLIN group (10-18 bites per person per night, density ratio 0-44, 95% CI 0-2-30-84; p=0-014), and in the pyriproyfen-pyrethroid LLIN group (13-6 bites per person per night, density ratio 0-58, 0-30-1-12; p=0-011) than in the reference group (23-0 bites per person per night, but the latter difference was not statistically significant (table 4). Adjusting for baseline vector density in the models gave similar results (appendix P 16). There was no significant difference in sporozoite rate in any of the intervention groups compared with the reference group (table 4).\n",
-                        "\n",
-                        "Reduction in outdoor entomological inoculation rate compared with the reference group was observed only in the chlorfenapy-pyrethroid LLIN group (RR 0-30, 95% CI 0-13-0-67; p=0-0035; appendix P 17). There was very weak evidence for a reduction in outdoor entomological inoculation rate in the pyriproyfen-pyrethroid LLIN group compared with the pyrethroid-only group (RR 0-58, 0-30-1-13; p=0-11). Similar effects were observed for outdoor vector density for both intervention nets (appendix P 17).\n",
-                        "\n",
-                        "Post-intervention pyrethrold resistance intensity was high across the study groups in _An gambiae_ sensu lato, with mean mortality of 85% or less after exposure to 10 times the diagnostic concentrations of alpha-cypentrohin in year 2 (appendix P 18). No resistance to chlorfenapy was observed during either year after net distribution (appendix P 18). Exposure of _An gambiae_ sensu lato to pyriproyfen led to a high reduction in fecundity rate relative to unexposed control mosquitoes over the 2 years after net distribution (73-2%, 95% CI 62-2-82-4 in year 1, and 76-6%, 66-6-84-9 in year 2).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "This trial assessed the efficacy of two dual active-ingredient LLINs in an area of Benin with malaria vectors that are highly resistant to pyrethroids and found that chlorfenapy-pyrethroid LLINs provided significantly better protection against malaria for up to 2 years after net distribution compared with pyrethroid-only LLINs. Children aged 6 months-10 years living in clusters that received chlorfenapy-pyrethroid LLINs had a 46% lower incidence of malaria over 2 years after LLIN distribution;\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c c c}  & **Malaria infection** & \\multicolumn{6}{c}{**Anaemia in children younger than 5 years**} \\\\ \\hline n/N & Prevalence & OR & 95\\% CI & p valuea & n/N & Prevalence & OR & 95\\% CI & p valuea \\\\ \\hline\n",
-                        "**6 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 412/1471 & 28\\(\\,\\)0\\% & 1 (ref) & – & – & 992/41 & 41\\(\\,\\)1\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 394/1463 & 26\\(\\,\\)9\\% & 0\\(\\,\\)92 & 0\\(\\,\\)\n",
-                        "\n",
-                        "63\\(\\,\\)45 & 0\\(\\,\\)67 & 117/250 & 46\\(\\,\\)8\\% & 124 & 0.71–218 & 0.45 \\\\ LLIN group & & & & & & & & & \\\\ Chlorfenapy-pyrethroid & 231/1475 & 157\\(\\,\\)\\% & 0\\(\\,\\)47 & 0\\(\\,\\)32\\(\\,\\)0\\(\\,\\)669 & 0\\(\\,\\)0002 & 82/241 & 34\\(\\,\\)0\\% & 0.71 & 0.40–126 & 0.24 \\\\ LLIN group & & & & & & & & & \\\\\n",
-                        "**18 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 576/14/849 & 38\\(\\,\\)7\\% & 1 (ref) & – & – & 118/25 & 46\\(\\,\\)8\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 564/14/83 & 38\\(\\,\\)2\\% & 0\\(\\,\\)97 & 0\\(\\,\\)69\\(\\,\\)13\\(\\,\\)07 & 0\\(\\,\\)87 & 108/245 & 44\\(\\,\\)1\\% & 0.84 & 0.40–179\n",
-                        "\n",
-                        "\n",
-                        "participants of any age had 53% lower odds of malaria infection at 6 months and 40% lower odds at 18 months after LLIN distribution, and were exposed to a 66% reduction in entomological inoculation rate compared with those living in clusters that received pyrethroid-only LLINs. Similar gains were not observed with the pyriproxyfen-pyrethroid LLIN, with a small reduction in malaria incidence in the first year of the study that was attenuated in year 2, and no effect on malaria infection prevalence in either year, compared with the pyrethroid-only reference group. However, a 58% reduction in indoor entomological inoculation rate was detected with the pyriproxyfen-pyrethroid LLIN. Both dual active-ingredient LLINs provided a similar safety profile compared with standard pyrethroid-only LLINs, with short-lasting skin irritation and facial burning (commonly associated with the pyrethroid alpha-Cybermethrin) most frequently reported in participants using pyrethroid-only LLINs and pyriproxyfen-pyrethroid LLINs. A similar observation has been reported in another randomised controlled trial evaluating the same LLINs, and this is likely to be associated with the high concentration of alpha-Cybermethrin in those nets compared with the chlorfenapy-pyrethroid LLINs.\n",
-                        "\n",
-                        "To our knowledge, this is the second cluster-randomised trial to provide strong evidence of increased efficacy of chlorfenapy-pyrethroid LLINs relative to pyrethroid-only LLINs for malaria control in areas with pyrethroid-resistant vectors. Chlorfenapyr insecticide on nets has already shown improved control of pyrethroid-resistant _An gambiae_ in laboratory and semi-field experimental huts against resistant malaria vectors.3- The previous trial in Tanzania reported a malaria case incidence reduction of 44%, a 55% decrease in odds of malaria prevalence, and 85% reduction in entomological inoculation rate compared with the pyrethroid-only group, consistent with these results, despite the differing malaria vector populations and intensity of pyrethroid resistance in the mosquitoes.4 In both trials, per-protocol analyses suggested that individuals living in the chlorfenapy-pyrethroid clusters benefited regardless of whether they were using a study net, suggesting a community effect of the net, which was likely to be obtained by the overall reduction of mosquito density, which remained considerably lower than in the pyrethroid-only group in both years of this trial after net distribution. Community effect is also indicated by the reduction in both indoor and outdoor entomological indices in the chlorfenapy-pyrethroid group in the present study.\n",
-                        "\n",
-                        "Similar to the trial in Tanzania,4 the pyriproxyfen-pyrethroid LLINs we tested did not provide additional protection against malaria infection or disease compared with pyrethroid-only LLINs. However, there was evidence of an effect on indoor transmission, with the strongest effect seen in the first year after net distribution. Tino and colleagues5 have previously reported a 12% reduction in malaria incidence and 49% reduction in entomological \n",
-                        "inoculation rate with a pyriproxyfen-pyrethroid LLIN compared with pyrethroid-only LLINs over 18 months in a stepped-wedge randomised trial in Burkina Faso. The different study design, length of follow-up, and brand of net might explain the differences seen between the two studies. In Benin, laboratory and semi-field experimental hut studies have shown the superior efficacy of pyriproxyfen-pyrethroid nets on entomological indicators compared with standard pyrethroid-only LLINs,2,3 and are consistent with the decrease in indoor vector density observed in our trial. However, the decrease in indoor vector density did not translate into significant disease reduction, suggesting that a larger effect on malaria transmission (entomological inoculation rate) is crucial to provide community protection. Although lower net usage in the pyriproxyfen-pyrethroid LLIN group could have partially contributed to the lack of effect, we are also assessing textile durability, sterilisation effects, and chemical content of pyriproxyfen-pyrethroid LLINs to fully understand these results.\n",
-                        "\n",
-                        "With several trials now showing the superior efficacy of next-generation LLINs over pyrethroid-only nets, the importance of developing new active ingredients to use on nets in the future is brought to the fore. The distribution of next-generation LLINs across sub-Saharan Africa has already begun, with the development of the brands of chlorfenapry-pyrethroid nets underway,3 as well as the development of chlorfenapry product formulations for indoor residual spraying.3 Although chlorfenapry-pyrethroid nets offer a superior alternative to pyrethroid-only nets in areas of pyrethroid resistance, to preserve their effectiveness optimal resistance-management strategies should be used. There was no evidence of the development of resistance to chlorfenapry during the 2 years of this trial; however, the wide-scale deployment of one type of insecticide risks the rapid development of resistance, which could result in a similar situation to the current widespread resistance to pyrethroids. The nets should be deployed ideally alongside other insecticides as part of a strategy aimed to reduce selection pressure for development of resistance in mosquito vectors. Given the significant effect of the pyriproxyfen-pyrethroid LLINs on malaria transmission during the first year after net distribution, additional studies are necessary to evaluate the potential value of active ingredients such as pyriproxyfen, which are not primarily intended to kill resistant adult mosquitoes but rather to sterilise them. There might still be a role for these hormone growth-regulator insecticides in combination with other active ingredients for net treatment in long-term resistance management.\n",
-                        "\n",
-                        "If WHO policy recommendations are made on the basis of this trial's results, future chlorfenapry-pyrethroid nets might not have to undergo the rigorous trial testing that Interceptor G2 has. Caution should, however, be taken to assess the comparative efficacy, quality, and durability of the second-in-class chlorfenapry-pyrethroid nets as they might use different concentrations of chlorfenapry, different pyrethroids, or different net materials. Considering the time and resources required to generate evidence of epidemiological effect using randomised trials, non-inferiority experimental hut trials, recently proposed by WHO,3 might be a useful alternative for second-in-class chlorfenapry-pyrethroid LLINs. Further work to investigate the capacity of such entomological studies to predict the performance of the trial nets against clinical malaria is ongoing.3\n",
-                        "\n",
-                        "Footnote 3: endnote: [https://www.thelanct.com/Vol.401](https://www.thelanct.com/Vol.401). February 11, 2023\n",
-                        "\n",
-                        "Our study has some limitations. First, net ownership and use were high throughout the study; however, this did not always equate to study net use, which was approximately 60% at 2 years after net distribution, with overall usage of greater than 80%. Similar findings have been observed in other bednet trials, and probably indicate populations choosing to discard damaged nets when other nets are readily available. Second, the three types of nets were not completely identical and differences in textile material and size might have resulted in differential net usage between the groups. As our net use indicator was primarily self-reporting, it is also possible that net usage was overestimated. Third, our primary measure of incidence was based on active detection, involving visits every 2 weeks or every month. The passive data collected alongside the trial suggests that this frequency of visit resulted in some cases being missed, meaning the absolute effect of the nets might be greater than reported here. However, the relative effect of the nets remained similar when the active and passive data were combined. Finally, cost-effectiveness was not assessed; however, the trial in Tanzania' showed that dual active-ingredient LLINs can be highly cost-effective and even cost-saving compared with pyrethroid-only LLINs when providing sufficient protection.\n",
-                        "\n",
-                        "New classes of vector control interventions currently require clinical trial evaluation in two different geographical settings, after 24 months of community use.4 Currently, the only class of next-generation LLIN to receive a WHO recommendation are the PBO synergism nets. However, previous publications have shown concerns about the durability of these nets.5 This trial provides the second key evidence for the effectiveness of chlorfenapry-pyrethroid nets in an area with pyrethroid-resistant vectors and will therefore support a WHO policy recommendation. However, the effect of the nets was reduced in the second year of the trial, and there is no evidence for the efficacy of the nets in their third year of use. Many studies report that the durability of nets is much less than the 3 years required to be designated as long lasting.5 The next-generation LLINs might face the same problems of fabric integrity and durability of insecticidal content unless standards of manufacture are improved. Different channels of distribution (eg, school-based, antenatal care visits, or expanded programme immunisation visits) could play a key role in maintaining high levels of net use in communities.5\n",
-                        "Although generating this evidence to support a WHO recommendation for another class of bednet is a key turning point in malaria control, without new insecticides and new ways to deploy them, we risk repeating the mistakes of the past. Now is the time for more innovation and less complacency.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a cluster-randomised, superiority trial in Zou Department, Benin. Clusters were villages or groups of villages with a minimum of 100 houses. We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (I:1:1): to receive nets containing either pyriproxyfen and alpha-Cypermethrin (pyrethroid), chlorfenapyr and alpha-Cypermethrin only (reference). Households received one LLIN for every two people. The field team, laboratory staff, analyses team, and community members were masked to the group allocation. The primary outcome was malaria case incidence measured over 2 years after net distribution in a cohort of children aged 6 months-10 years, in the intention-to-treat population. This study is ongoing and is registered with ClinicalTrials.gov, NCT03931473.\n",
-                        "\n",
-                        "Findings Between May 23 and June 24, 2019, 53 854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54030 households in an updated census. A cross-sectional survey showed that study LLIN usage was highest 9 months after distribution (5532 [76 * 986] of 7206 participants), but decreased by 24 months (4032 [60 * 696] of 6654). Mean malaria incidence over 2 years after LLIN distribution was 1-03 cases per child-year (95% CI 0 * 96-1 * 09) in the pyrethroid-only LLIN reference group. 0 * 84 cases per child-year (0.78-0 * 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 * 86, 95% CI 0 * 65-1* 14; p=0 * 28), and 0 * 56 cases per child-year (0 * 51-0 * 61) in the chlorfenapyr-pyrethroid LLIN group (HR 0 * 54, 95% CI 0 * 42-0 * 70; p<0 * 0001).\n",
-                        "\n",
-                        "Interpretation Over 2 years, chlorfenapyr-pyrethroid LLINs provided greater protection from malaria than pyrethroid-only LLINs in an area with pyrethroid-resistant mosquitoes. Pyriproxyfen-pyrethroid LLINs conferred protection similar to pyrethroid-only LLINs. These findings provide crucial second-trial evidence to enable WHO to make policy recommendations on these new LLIN classes. This study confirms the importance of chlorfenapyr as an LLIN treatment to control malaria in areas with pyrethroid-resistant vectors. However, an arsenal of new active ingredients is required for successful long-term resistance management, and additional innovations, including pyriproxyfen, need to be further investigated for effective vector control strategies.\n",
-                        "\n",
-                        "header: Contributions\n",
-                        "\n",
-                        "NP, JC, and CN conceived and designed the study, with contributions from MR, IK, LAM, and MCA, JC and MA led the development of the analysis plan, with input from BA, Aso, and NP, MA, CA, JC, NP, FT, and MCA coordinated the trial implementation with local and national authorities, RA, and AO-H. MA, BF, HA, Aso, and LA led the data collection in the field with and Aso, and AP, each of them, MCA, JC, and NP and NP. Also, BC, BA, and KAB led the molecular laboratory work.\n",
-                        "\n",
-                        "header: Funding UNITAID, The Global Fund.\n",
-                        "\n",
-                        "Pyrethroid-treated long-lasting insecticidal nets (LLINs) are the main malaria prevention intervention in sub-Saharan Africa and have been the major contributor to the estimated 1 * 5 billion malaria cases and 7 * 6 million malaria deaths averted in the past two decades. However, since 2015, the decline in malaria cases has stalled, and between 2019 and 2020, a rebound in malaria transmission was reported in some areas of sub-Saharan Africa.1 This rebound is likely to be a consequence of the continued spread of resistance to pyrethroid insecticides in malaria-transmitting mosquitoes, coinciding with a plateau in malaria control investment, leading to suboptimal coverage of interventions. Urgent actions are needed to prevent malaria resurgences, which were previously observed in sub-Saharan Africa in the 1980s after malaria control measures were scaled down.2\n",
-                        "In the past 10 years, new insecticide-treated nets containing non-pyrethroid insecticides and insecticide synergists, in combination with pyrethroids, have been shown to be safe and efficacious against malaria mosquito vectors.14 The first net combined a pyrethroid insecticide and the synergistic piperonyl butoxide (PBO) and, following a randomised controlled trial,1 received a WHO policy recommendation in 2017. Other next-generation LLINs have shown encouraging results against resistant vectors in laboratory and small-scale entomological studies.47 One of these nets is a dual active-ingredient LLIN combining a pyrethroid and a pyrrole (chlorfenapyr). Both insecticides lead to mosquito mortality, with chlorfenapyr disrupting the production of cellular energy rather than targeting the nervous system, as pyrethroids do. Another dual active-ingredient LLIN combines a pyrethroid with an insect growth regulator (pyriprosyfen), which leads to sterility in exposed adult mosquitoes.\n",
-                        "\n",
-                        "The first randomised trial of these two products was conducted in Tanzania and showed that the chlorfenapyr-pyrethroid LLINs nearly halved malaria infection over 2 years in villages that received chlorfenapyr-pyrethroid LLINs compared with villages that received pyrethroid-only LLINs. PBO-pyrethroid LLINs were consistently found to be more effective than pyrethroid-only LLINs; however, the duration of the superior effect varied from 12 months to 21 months according to the different trials.\n",
-                        "\n",
-                        "header: Procedures\n",
-                        "\n",
-                        "The nets tested in the trial were: Royal Guard (Disease Control Technologies, Greer, SC, USA), polyethylene netting (I20 deniers incorporating 220 mg/m2 pyriproyfen and 220 mg/m2 alpha-cypermethrin); Interceptor G2 (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 chlorfenapyr and 100 mg/m2 alpha-cypermethrin); and the reference net, Interceptor (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 of alpha-cypermethrin). Nets were distributed in collaboration with the Benin National Malaria Control Program. Households were asked to collect their nets from a central point and received one net per every two residents in their household, rounded up for odd numbers. Nets already in houses were not removed but households were encouraged to use the new study nets. Additionally, net hang-up campaigns to encourage net use took place at 1 month and 7 months after distribution. Net coverage surveys to assess net ownership and usage were done at 1, 9, and 24 months after distribution. Throughout the follow-up period, children enrolled in the analysis cohort and participants enrolled in the cross-sectional surveys were also asked about their net use.\n",
-                        "\n",
-                        "Insecticide content at baseline was assessed on 30 randomly selected new nets per LLIN brand, by gas chromatography with Flame Ionisation Detection, at the\n",
-                        "Centre Wallon de Recherches Agronomiques, Gembloux, Belgium. The insecticidal and physical durability of the study nets is being assessed in a separate study.\n",
-                        "\n",
-                        "Due to the COVID-19 pandemic, there was a 3-month gap between the distribution of the nets and the enrolment of children in the cohort. At enrolment and at 1 year after distribution (April, 2021), children were treated with antimalarial drugs (atremether-lumefantrinity to clear any underlying infection. The cohort was monitored from August, 2020, to April, 2022, a 21-month period, which encompassed the first 2 years after net distribution. Study nurses visited children every 2 weeks during the transmission season (April-October) and every 1 month in the dry season (November-March). At each visit, children were clinically examined and if they were febrile or had a history of fever in the past 48 h, they were tested for malaria using a malaria rapid diagnostic test. If the test was positive, the child was treated with artemether-lumefantrine, according to national guidelines.\n",
-                        "\n",
-                        "During cross-sectional surveys, all participants were tested for malaria using a malaria rapid diagnostic test and received treatment if the test was positive, and children younger than 5 years were tested for anaemia. Information regarding net ownership and usage, and other household-level indicators were collected at the same time.\n",
-                        "\n",
-                        "Entomological monitoring was also delayed and took place every 3 months between June, 2020, and April, 2022. Volunteers recruited from study clusters collected mosquitoes that landed on their legs between 1900 h and 0700 h for 1 night at four randomly selected houses in each cluster at each timepoint. Mosquitoes were morphologically identified to species and a subsample of _Anopheles_ spp was tested for molecular species using PCR.(r) A random sample of _Anopheles_ spp (up to 30% from each nightly catch in each cluster) were tested for sporozoites using the ELISA circumsporozoite protein technique.(r)\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a three-group, cluster-randomised, superiority trial in three districts (Cove, Zagnanado, and Ouinhi) in Zou Department, central Benin. Malaria is highly endemic in the region, with a peak during the wet season (May-October). The main vector control strategy in the study area is use of pyrethroid-only LLINs distributed en masse once every 3 years, and through routine service delivery by antenatal clinics and vaccination programmes. The primary vectors in the setting are _Anopheles coluzzi_ and _Anopheles gambiae_. There is a high intensity of pyrethroid resistance in the main local vector populations.11\n",
-                        "\n",
-                        "A demographic census of all 123 villages in the study area was done in June, 2019. Clusters consisted of villages, or several villages. To reduce contamination between study groups, clusters consisted of a core (minimum 100 households) surrounded by a buffer, ensuring that core areas of neighbouring clusters were separated by at least 1000 m. Households in the buffer and core received the intervention, but measurement of outcomes only took place in core areas. A detailed description of the study protocol is published elsewhere.\"\n",
-                        "\n",
-                        "Ethics approval was obtained from the Benin Ministry of Health ethics committee (6/30/MS/DC/SGM/DRFMT/CNERS/SA), the London School of Hygiene & Tropical Medicine ethics committee (16237), and the WHO Research Ethics Review Committee (ERC.0003153). The trial was independently monitored by a data safety monitoring board and a trial steering committee.\n",
-                        "\n",
-                        "Epidemiological effect was estimated through measuring malaria case incidence in the 2 years after net distribution by active case detection in a cohort of children. A cohort of approximately 30 children aged 6 months-9 years randomly selected (by simple random sampling using a random number generator) from each cluster (1800 in total) was enrolled in July, 2020. Children were eligible for inclusion if they were permanent residents in the cluster, had no serious illnesses, and written informed consent was obtained from their guardians.\n",
-                        "\n",
-                        "Malaria infection prevalence (in all ages) was measured by cross-sectional surveys at 6 months and 18 months after net distribution. 72 individuals residing in the core of each cluster were randomly selected (using a random number generator) from the census for each cross-sectional survey. Each prevalence surveyed collected data on malaria infection, measured using malaria rapid diagnostic tests (CareStart malaria HRP2/PLDH [pf/pan] combo, DiaSy, UK), net ownership and use, sex, and household assets (as a proxy for socioeconomic status).\n",
-                        "\n",
-                        "Entomological effects were measured using human landing catches in four randomly selected houses every 3 months in each cluster. Insecticide resistance intensity was measured in two clusters per group (six clusters total) at baseline and in each follow-up year.\n",
-                        "\n",
-                        "Written informed consent was obtained from all participants, or from guardians for participants younger than 18 years. Assent was sought for children aged between 10 and 18 years. Written consent was also obtained from volunteer mosquito collectors who were all aged 18 years or older and who were vaccinated against yellow fever. All participation was voluntary, and participants could withdraw at any time. Study investigators sought consent in French or local languages.\n",
-                        "\n",
-                        "header: Table 2: Malaria case incidence in children aged 6 months–10 years per year of follow-up and overall (including active visits only)\n",
-                        "footer: Each intervention was compared with the pyrethroid-only LLIN group for the same timepoint. LLIN=long-lasting insecticidal net. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Overall', 'Number of clinical malaria episodes - Overall', 'Child- years of follow-up - Overall', 'Incidence, cases per child-year (95% Cl) - Overall', 'Hazard ratio - Overall', '95%Cl - Overall', 'p value* - Overall']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"897\",\"Child- years of follow-up - Overall\":\"874.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.03 (0.96-1.09)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"1\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"744\",\"Child- years of follow-up - Overall\":\"883.8\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.84 (0.78-0.90)\",\"Hazard ratio - Overall\":\"0.86\",\"95%Cl - Overall\":\"0.65-1.14\",\"p value* - Overall\":\"0.28\"},\"2\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"494\",\"Child- years of follow-up - Overall\":\"887.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.56 (0.51-0.61)\",\"Hazard ratio - Overall\":\"0.54\",\"95%Cl - Overall\":\"0.42-0.70\",\"p value* - Overall\":\"<0.0001\"},\"3\":{\"Unnamed: 0_level_0 - Overall\":\"Year 1\",\"Number of clinical malaria episodes - Overall\":\"Year 1\",\"Child- years of follow-up - Overall\":\"Year 1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 1\",\"Hazard ratio - Overall\":\"Year 1\",\"95%Cl - Overall\":\"Year 1\",\"p value* - Overall\":\"Year 1\"},\"4\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"267\",\"Child- years of follow-up - Overall\":\"344.4\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.77 (0.69-0.87)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"5\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"211\",\"Child- years of follow-up - Overall\":\"342.7\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.62 (0.54-0.70)\",\"Hazard ratio - Overall\":\"0.83\",\"95%Cl - Overall\":\"0.51-1.35\",\"p value* - Overall\":\"0.46\"},\"6\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"124\",\"Child- years of follow-up - Overall\":\"349.2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.36 (0.30-0.42)\",\"Hazard ratio - Overall\":\"0.46\",\"95%Cl - Overall\":\"0.30-0.72\",\"p value* - Overall\":\"0.0005\"},\"7\":{\"Unnamed: 0_level_0 - Overall\":\"Year 2\",\"Number of clinical malaria episodes - Overall\":\"Year 2\",\"Child- years of follow-up - Overall\":\"Year 2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 2\",\"Hazard ratio - Overall\":\"Year 2\",\"95%Cl - Overall\":\"Year 2\",\"p value* - Overall\":\"Year 2\"},\"8\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"630\",\"Child- years of follow-up - Overall\":\"529.9\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.19 (1.10-1.29)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"9\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"533\",\"Child- years of follow-up - Overall\":\"541.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.98 (0.90-1.07)\",\"Hazard ratio - Overall\":\"0.88\",\"95%Cl - Overall\":\"0.68-1.13\",\"p value* - Overall\":\"0.31\"},\"10\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"370\",\"Child- years of follow-up - Overall\":\"538.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.69 (0.62-0.76)\",\"Hazard ratio - Overall\":\"0.57\",\"95%Cl - Overall\":\"0.45-0.73\",\"p value* - Overall\":\"<0.0001\"}}\n",
-                        "\n",
-                        "header: Table 1: Baseline characteristics\n",
-                        "footer: Data are n or %; n/N unless otherwise stated. EIR=entomological inoculation rate. LLIN=long-lasting insecticidal net. *Proportion of households in the poorest tercile based on the wealth index of the entire study area. †Anaemia defined as haemoglobin concentration of <10 g/dL. ‡Malaria vectors included Anopheles gambiae, Anophelesfunestus, and Anopheles nili\n",
-                        "columns: ['Unnamed: 0_level_0 - Clusters', 'Pyriproxyfen-pyrethroid LLIN group - Clusters', 'Chlorfenapyr-pyrethroid LLIN group - Clusters', 'Pyrethroid-only LLIN group - Clusters']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of clusters\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"20\",\"Pyrethroid-only LLIN group - Clusters\":\"20\"},\"1\":{\"Unnamed: 0_level_0 - Clusters\":\"Total population in core and buffer areas\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"74822\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"70989\",\"Pyrethroid-only LLIN group - Clusters\":\"69239\"},\"2\":{\"Unnamed: 0_level_0 - Clusters\":\"Mean population in core area of clusters (range)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"1096.1 (225-4524)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"1120.5 (243-5040)\",\"Pyrethroid-only LLIN group - Clusters\":\"1058.1 (252-5217)\"},\"3\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of people per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"4.0 (2.3)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"3.9 (2.3)\",\"Pyrethroid-only LLIN group - Clusters\":\"4.1 (2.4)\"},\"4\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of sleeping spaces per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"2.2 (1.2)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"2.1 (1.1)\",\"Pyrethroid-only LLIN group - Clusters\":\"2.2 (1.2)\"},\"5\":{\"Unnamed: 0_level_0 - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyrethroid-only LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\"},\"6\":{\"Unnamed: 0_level_0 - Clusters\":\"Low socioeconomic status*\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"35.8%; 529\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"36.2%; 533\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"29.0%; 431\\/1487\"},\"7\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN ownership (at least one LLIN in the household)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"96.4%; 1426\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"97.1%; 1431\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"95.1%; 1415\\/1488\"},\"8\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN usage in all age groups the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"95.8%; 1312\\/1370\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"94.9%; 1258\\/1326\",\"Pyrethroid-only LLIN group - Clusters\":\"96.5%; 1343\\/1392\"},\"9\":{\"Unnamed: 0_level_0 - Clusters\":\"Malaria infection prevalence in all age groups\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"43.1%; 636\\/1475\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"40.7%; 598\\/1468\",\"Pyrethroid-only LLIN group - Clusters\":\"46.5%; 690\\/1485\"},\"10\":{\"Unnamed: 0_level_0 - Clusters\":\"Anaemia prevalence in children aged 6 months-4 years\\u2020\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"53.3%; 136\\/255\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"53.3%; 131\\/246\",\"Pyrethroid-only LLIN group - Clusters\":\"50.2%; 122\\/243\"},\"11\":{\"Unnamed: 0_level_0 - Clusters\":\"Entomological characteristics\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Pyrethroid-only LLIN group - Clusters\":\"Entomological characteristics\"},\"12\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night indoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"32.9 (28.9)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"21.6 (21.0)\",\"Pyrethroid-only LLIN group - Clusters\":\"29.2 (25.3)\"},\"13\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night outdoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20.3 (17.6)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"16.4 (17.4)\",\"Pyrethroid-only LLIN group - Clusters\":\"20.6 (20.2)\"},\"14\":{\"Unnamed: 0_level_0 - Clusters\":\"Indoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.62 (0.93)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.48 (0.65)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.96 (1.18)\"},\"15\":{\"Unnamed: 0_level_0 - Clusters\":\"Outdoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.27 (0.45)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.17 (0.44)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.33 (0.45)\"},\"16\":{\"Unnamed: 0_level_0 - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyrethroid-only LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\"},\"17\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of children younger than 5 years\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"52.3%; 316\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"50.6%; 304\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"53.6%; 322\\/601\"},\"18\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of female children\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"47.2%; 285\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"48.4%; 291\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"48.8%; 293\\/601\"},\"19\":{\"Unnamed: 0_level_0 - Clusters\":\"Net usage the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"99.0%; 598\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"98.7%; 593\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"99.2%; 596\\/601\"}}\n",
-                        "\n",
-                        "header: Introduction: Added value of this study\n",
-                        "\n",
-                        "To our knowledge, this is the second study aiming to compare the efficacy of the next generation of LLINs combining _p_iprosyfen or chlorfenapyr and pyrethroid versus standard pyrethroid-only LLINs, and the first of its kind to be conducted in west Africa. This trial confirmed the superior efficacy of chlorfenapyr-pyrethroid LLINs, in terms of malaria case incidence, prevalence, and transmission in children, over 2 years of use in the community, in an area of moderate malaria transmission and with high insecticide resistance intensity in malaria vectors, in Benin. However, pyrioxyfen-pyrethroid LLINs did not offer additional protection against malaria outcomes compared with pyrethroid-only LLINs.\n",
-                        "\n",
-                        "header: Table 3: Malaria infection and anaemia prevalence in the study population at 6 months and 18 months after net distribution (intention-to-treat analysis)\n",
-                        "footer: Anaemia was defined as a haemogloblin concentration of <10 g/dL. LLIN=long-lasting insecticidal net. OR=odds ratio. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution', 'Malaria infection - n/N - 6 months after net distribution', 'Malaria infection - Prevalence - 6 months after net distribution', 'Malaria infection - OR - 6 months after net distribution', 'Malaria infection - 95% Cl - 6 months after net distribution', 'Malaria infection - p value* - 6 months after net distribution', 'Anaemia in children younger than 5 years - n/N - 6 months after net distribution', 'Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution', 'Anaemia in children younger than 5 years - OR - 6 months after net distribution', 'Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution', 'Anaemia in children younger than 5 years - p value* - 6 months after net distribution']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"412\\/1471\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"28.0%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"99\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"41.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"394\\/1463\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"26.9 %\",\"Malaria infection - OR - 6 months after net distribution\":\"0.92\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.63-1.35\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.67\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"117\\/250\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.24\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.71-2.18\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.45\"},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"231\\/1475\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"15.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.47\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.32-0.69\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0002\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"82\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"34.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.71\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.26\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0-24\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - p value* - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"18 months after net distribution\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"576\\/1489\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/252\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"564\\/1478\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.2%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.97\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.69-1.37.\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.87\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"108\\/245\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"44.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.84\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.79\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.65\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"414\\/1483\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"27.9%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.60\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.43-0.85\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0041\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/246\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"48.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.08\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.51-2.28\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.84\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Data sharing: Acknowledgments\n",
-                        "\n",
-                        "This trial was funded by a grant to the London School of Hygiene & Tropical Medicine from UNITAD and The Global Fund via the Innovative Vector Control Consortium. This trial is part of a larger project. The New Nets project: We thank the communities from the three study districts (Cow, Zagnanado, Ouinhi), particularly the participants, children and their parents, community health workers, staff in health clinics, and the regional study team. We thank colleagues and staff at the Centre de Recherche Entomologique de Cotonou and those at the National Malaria Control Program. We also thank the trial system committee, the data safety monitoring board, and Charles Dickinson (School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada) for supporting the mapping and cluster delineation.\n",
-                        "\n",
-                        "header: Role of the funding source\n",
-                        "\n",
-                        "The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication.\n",
-                        "\n",
-                        "header: Outcomes\n",
-                        "\n",
-                        "The primary outcome was malaria case incidence (infrared frontal temperature >37.5degC or reported fever in the previous 48 h, and positive malaria rapid diagnostic test) in children enrolled in the active case detection cohort in the 2 years after net distribution. Visits of children in the cohort at health facilities were monitored and the results of malaria rapid diagnostic tests and any treatment given were recorded. However, it was difficult to assess with certainty if malaria rapid diagnostic tests were used only if the child was symptomatic (as our protocol specified); therefore, the passive data was not included in the primary analyses.\n",
-                        "\n",
-                        "Secondary clinical outcomes were malaria infection prevalence in all age groups and anaemia (defined as haemoglobin concentration <10 g/dL) prevalence in children aged 5 years or younger at 6 months and 18 months after net distribution.\n",
-                        "\n",
-                        "Type and duration of adverse events related to usage of nets were recorded using a prespecified questionnaire at each cohort visit and during net usage and cross-sectional surveys. Data on hospitalisation and death in children in the cohort were collected by interviewing the child's guardian and reviewing the hospital record, following receipt of consent.\n",
-                        "\n",
-                        "The primary entomological outcome was entomological inoculation rate, measured as the mean number of _Plasmodium_ spp infective malaria vectors collected per person per night measured indoors and outdoors. Secondary entomological outcomes were vector density (number of mosquitoes caught per person per night), sporozoite rate, and species composition. Centers for Disease Control and Prevention bottle bioassays were performed by exposing adult female _An gambiae_, collected as larvae, to alpha-cypermethrin for 30 min (1, 2, 5, and 10 times the diagnostic dose), and to chlorfenrapy (100 mg/bottle) and pyriproxyfen (100 mg/bottle) for 60 min each year.(r) Mortality was recorded at 30 min for all four doses of alpha-cypermethrin (insectidic resistance intensity measurement), and at 24, 48, and 72 h after exposure for chlorfenapyr. The reduction in fecundity rate induced by pyriproxyfen relative to unexposed mosquitoes was assessed by ovarian dissection, 3 days after pyriproxyfen exposure. Other outcomes included in the protocol (parity, resting behaviour, survivorship, and other resistance measures) are still to be analysed and will be published elsewhere.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The sample size calculations for epidemiological data collection were calculated using the method of Hayes and Moulton.(r) The study was designed to detect a 30% difference in malaria case incidence, assuming a control group incidence of 1 malaria case per child-year and a coefficient of variation of 0-3 between clusters. This design required 20 clusters per group and 25 (20+5 allowing for loss to follow-up) children per cluster followed up for 2 years to give 80% power. Due to the delay in enrolment, which resulted in a shorter follow-up time, the number of children per cluster was increased to 30 (1800 children total). This sample size calculation includes adjustment for multiple testing (allowing for the three groups) using a Bonferroni-corrected two-sided a of 2.5%.\n",
-                        "\n",
-                        "For malaria infection prevalence, it was assumed that the prevalence in the reference group was 40%, with a coefficient of variation between clusters of 0-3. With 72 individuals per cluster, the study had 80% power to detect a 30% lower prevalence in the intervention groups compared with the reference group, using a Bonferroni-corrected a for multiple comparisons.\n",
-                        "\n",
-                        "The primary intention-to-treat analysis was a comparison of incidence of clinical malaria episodes between each dual active-ingredient LLIN group and the\n",
-                        "\n",
-                        "header: Implications of all the available evidence\n",
-                        "\n",
-                        "Given the positive findings, both in Benin and Tanzania, for chlorfenapyr-pyrethroid LLINs, they are likely to become the first WHO-recommended LLINs impregnated with an insecticide class other than pyrethroids. The absence of superior efficacy of pyrioxyfen-pyrethroid LLINs compared with standard pyrethroid-only LLINs is consistent with the results of the previous Tanzania study and calls into question the role of the current pyrioxyfen-pyrethroid LLINs in future malaria vector strategies.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "Data collected for the study, including deidentified participant data and data dictionaries, might be made available at the end of the third year of trial follow-up upon reasonable request to the corresponding author.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Between May 23 and June 24, 2019, 53854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. The households were delineated into 61 clusters, with one subsequently excluded due to extensive seasonal flooding (figure). Baseline cross-sectional epidemiological and entomological surveys were conducted between October and November, 2019 (table 1). The malaria infection prevalence was 43 5% (1924 of 4428 participants, cluster range 15 1-72 796) and population self-reported LLIN usage was 95 7% (3913 of 4088 participants). Cluster demographics and malaria prevalence were similar between groups (table 1). Entomological inoculation rate was 0 68 _Plasmodium_ spp infective bites per person per night (cluster range 0 00-3 80) indoors and 0 26 per person per night (0 00-1 88) outdoors.\n",
-                        "\n",
-                        "Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54 300 households in an updated census, with 97 1% of households receiving at least one net per household. Active ingredients in the new nets were found to be within or higher than the acceptable target limits depending on LLIN brand (appendix p 4). Study net usage was highest (5532 [76 8%] of 7206 participants) at 9 months after distribution, following a second hang-up campaign, but had decreased by 24 months (4032 [60 6%] of 6654). Study net coverage and usage indicators were similar between study groups up to 18 months after net distribution. At 24 months, pyriproxyfen-pyrethroid LLIN usage and ownership was the lowest (appendix pp 5-6). Use of any type of LLIN (study LLINs and others) remained greater than 80% up to 24 months after distribution (appendix p 7).\n",
-                        "\n",
-                        "In the 2283 households that were randomly selected for post-intervention cohort follow-up, 3129 children were eligible, 1829 of whom were randomly selected, and consent was obtained for 1806 (figure). Among the 2849 and 2771 households randomly selected at 6 months and 18 months after LLIN distribution for the malaria prevalence cross-sectional surveys, 4781 (85 1%) of 5620 households consent, with a similar proportion in the two surveys. The remaining households were either not available during the visit (775 [13 8%] of) or declined to participate (64 [1 1%]; appendix p 8).\n",
-                        "\n",
-                        "Children aged 6 months-9 years enrolled for active case detection were monitored for 21 months, for a total follow-up time of 2645 4- child-years at risk. Loss to follow-up was similar between groups (figure). At enrolment, children were balanced on age, sex, and net usage. During the 21-month follow-up period, we detected 2135 malaria cases through active case detection (table 2). The mean malaria case incidence over 21 months of follow-up was 1 03 cases per child-year (95% CI 0 96-1 09) in the pyrethroid-only LLIN reference group, 0 84 cases per child-year (0 78-70 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 86, 95% CI 0 65-1 46; p = 0 - 28), and 0 56 cases per child-year (0 51-0 64) in the chlorfenapry-pyrethroid LLIN group (HR 0 54, 95% CI 0 - 42-0 70; p < 0 0001; table 2). The strongest effect was observed in the first year of follow-up in the chlorfenapry-pyrethroid group (HR 0 46, 95% CI 0 30-0 72; p = 0 - 0005; table 2). When combining active and passive visits, the number of cases detected nearly doubled; however, the effect size in comparison to the reference group was similar for both intervention nets (appendix p 9).\n",
-                        "\n",
-                        "Malaria infection prevalence was 23 5% (1037 of 4409 participants) at 6 months and 34 9% (1554 of 4450) at 18 months after net distribution (table 3). There was strong evidence for a reduction of malaria infection prevalence in the chlorfenapry-pyrethroid LLIN group at 6 months (15 7%; odds ratio [OR] 0 47, 95% CI 0 32-0 69; p = 0 0002) and at 18 months (27 9%; OR 0 60 - 60, 0 - 43-0 85; p = 0 004) compared with the reference group (28 - 0% at 6 months and 38 7% at 18 months; table 3). In the pyriproxyfen-pyrethroid group, there was no evidence for a reduction in malaria prevalence at 6 months (26 9%; OR 0 - 92, 95% CI 0 63-1 35; p = 0 - 67) or 18 months (38 2%; OR 0 97 0, 0 69-1 37; p = 0 87) compared with the reference group. Results were similar for the per-protocol analysis (appendix p 1). There was no evidence of a reduction in moderate to severe anaemia prevalence in either intervention group (table 3). The post-hoc analysis adjusting for covariates used in the randomisation did not change the interpretation of the results (appendix pp 12-13).\n",
-                        "\n",
-                        "\n",
-                        "Adverse events related to study nets were reported in 528 (45-496) of 1162 participants surveyed at 1 month after net distribution. The highest proportion of adverse events was reported in the pyrethroid-only LLIN group (247 [63-8%] of 387 participants), followed by the pyriproyfen-pyrethroid LLIN group (212 [52-5%] of 404) and then the chlorfenapy-pyrethroid LLIN group (69 [18-6%] of 371). Facial burning (392 [33-7%] of 1162 participants), and skin irritation or itchiness (244 [20-9%]) were the most common adverse events in all groups. Adverse events were rare in all three groups at all later timepoints (appendix P 14-15). We recorded 44 serious adverse events (including three deaths) in the cohort children, with 32 (72-7%) documented as severe malaria (11 cases in the pyriproyfen-pyrethroid LLIN group, ten in the chlorfenapy-pyrethroid LLIN group, and 11 in the pyrethroid-only LLIN group). No serious adverse events related to net use were reported.\n",
-                        "\n",
-                        "A total of 259265 mosquitoes were collected over 3840 collection nights indoors and outdoors, of which 20-9% (29814 from indoors and 24436 from outdoors) were malaria vectors, with _An gambiae_ sensu that the most predominant. Overall, indoor entomological inoculation rate was lower in both intervention groups than in the reference group, with a mean entomological inoculation rate of 0-90 infectious bites per night per person in the chlorfenapy-pyrethroid LLIN group (rate ratio [RR] 0-34, 95% CI 0-18-0-62; p=0-0005), 0-12 in the pyriproyfen-pyrethroid LLIN group (RR 0-42, 0-2-3-0-74; p=0-0028), and 0-28 in the pyrethroid-only LLIN group (table 4). Mean indoor vector density appeared to be lower in the chlorfenapy-pyrethroid LLIN group (10-18 bites per person per night, density ratio 0-44, 95% CI 0-2-30-84; p=0-014), and in the pyriproyfen-pyrethroid LLIN group (13-6 bites per person per night, density ratio 0-58, 0-30-1-12; p=0-011) than in the reference group (23-0 bites per person per night, but the latter difference was not statistically significant (table 4). Adjusting for baseline vector density in the models gave similar results (appendix P 16). There was no significant difference in sporozoite rate in any of the intervention groups compared with the reference group (table 4).\n",
-                        "\n",
-                        "Reduction in outdoor entomological inoculation rate compared with the reference group was observed only in the chlorfenapy-pyrethroid LLIN group (RR 0-30, 95% CI 0-13-0-67; p=0-0035; appendix P 17). There was very weak evidence for a reduction in outdoor entomological inoculation rate in the pyriproyfen-pyrethroid LLIN group compared with the pyrethroid-only group (RR 0-58, 0-30-1-13; p=0-11). Similar effects were observed for outdoor vector density for both intervention nets (appendix P 17).\n",
-                        "\n",
-                        "Post-intervention pyrethrold resistance intensity was high across the study groups in _An gambiae_ sensu lato, with mean mortality of 85% or less after exposure to 10 times the diagnostic concentrations of alpha-cypentrohin in year 2 (appendix P 18). No resistance to chlorfenapy was observed during either year after net distribution (appendix P 18). Exposure of _An gambiae_ sensu lato to pyriproyfen led to a high reduction in fecundity rate relative to unexposed control mosquitoes over the 2 years after net distribution (73-2%, 95% CI 62-2-82-4 in year 1, and 76-6%, 66-6-84-9 in year 2).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "This trial assessed the efficacy of two dual active-ingredient LLINs in an area of Benin with malaria vectors that are highly resistant to pyrethroids and found that chlorfenapy-pyrethroid LLINs provided significantly better protection against malaria for up to 2 years after net distribution compared with pyrethroid-only LLINs. Children aged 6 months-10 years living in clusters that received chlorfenapy-pyrethroid LLINs had a 46% lower incidence of malaria over 2 years after LLIN distribution;\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c c c}  & **Malaria infection** & \\multicolumn{6}{c}{**Anaemia in children younger than 5 years**} \\\\ \\hline n/N & Prevalence & OR & 95\\% CI & p valuea & n/N & Prevalence & OR & 95\\% CI & p valuea \\\\ \\hline\n",
-                        "**6 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 412/1471 & 28\\(\\,\\)0\\% & 1 (ref) & – & – & 992/41 & 41\\(\\,\\)1\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 394/1463 & 26\\(\\,\\)9\\% & 0\\(\\,\\)92 & 0\\(\\,\\)\n",
-                        "\n",
-                        "63\\(\\,\\)45 & 0\\(\\,\\)67 & 117/250 & 46\\(\\,\\)8\\% & 124 & 0.71–218 & 0.45 \\\\ LLIN group & & & & & & & & & \\\\ Chlorfenapy-pyrethroid & 231/1475 & 157\\(\\,\\)\\% & 0\\(\\,\\)47 & 0\\(\\,\\)32\\(\\,\\)0\\(\\,\\)669 & 0\\(\\,\\)0002 & 82/241 & 34\\(\\,\\)0\\% & 0.71 & 0.40–126 & 0.24 \\\\ LLIN group & & & & & & & & & \\\\\n",
-                        "**18 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 576/14/849 & 38\\(\\,\\)7\\% & 1 (ref) & – & – & 118/25 & 46\\(\\,\\)8\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 564/14/83 & 38\\(\\,\\)2\\% & 0\\(\\,\\)97 & 0\\(\\,\\)69\\(\\,\\)13\\(\\,\\)07 & 0\\(\\,\\)87 & 108/245 & 44\\(\\,\\)1\\% & 0.84 & 0.40–179\n",
-                        "\n",
-                        "\n",
-                        "participants of any age had 53% lower odds of malaria infection at 6 months and 40% lower odds at 18 months after LLIN distribution, and were exposed to a 66% reduction in entomological inoculation rate compared with those living in clusters that received pyrethroid-only LLINs. Similar gains were not observed with the pyriproxyfen-pyrethroid LLIN, with a small reduction in malaria incidence in the first year of the study that was attenuated in year 2, and no effect on malaria infection prevalence in either year, compared with the pyrethroid-only reference group. However, a 58% reduction in indoor entomological inoculation rate was detected with the pyriproxyfen-pyrethroid LLIN. Both dual active-ingredient LLINs provided a similar safety profile compared with standard pyrethroid-only LLINs, with short-lasting skin irritation and facial burning (commonly associated with the pyrethroid alpha-Cybermethrin) most frequently reported in participants using pyrethroid-only LLINs and pyriproxyfen-pyrethroid LLINs. A similar observation has been reported in another randomised controlled trial evaluating the same LLINs, and this is likely to be associated with the high concentration of alpha-Cybermethrin in those nets compared with the chlorfenapy-pyrethroid LLINs.\n",
-                        "\n",
-                        "To our knowledge, this is the second cluster-randomised trial to provide strong evidence of increased efficacy of chlorfenapy-pyrethroid LLINs relative to pyrethroid-only LLINs for malaria control in areas with pyrethroid-resistant vectors. Chlorfenapyr insecticide on nets has already shown improved control of pyrethroid-resistant _An gambiae_ in laboratory and semi-field experimental huts against resistant malaria vectors.3- The previous trial in Tanzania reported a malaria case incidence reduction of 44%, a 55% decrease in odds of malaria prevalence, and 85% reduction in entomological inoculation rate compared with the pyrethroid-only group, consistent with these results, despite the differing malaria vector populations and intensity of pyrethroid resistance in the mosquitoes.4 In both trials, per-protocol analyses suggested that individuals living in the chlorfenapy-pyrethroid clusters benefited regardless of whether they were using a study net, suggesting a community effect of the net, which was likely to be obtained by the overall reduction of mosquito density, which remained considerably lower than in the pyrethroid-only group in both years of this trial after net distribution. Community effect is also indicated by the reduction in both indoor and outdoor entomological indices in the chlorfenapy-pyrethroid group in the present study.\n",
-                        "\n",
-                        "Similar to the trial in Tanzania,4 the pyriproxyfen-pyrethroid LLINs we tested did not provide additional protection against malaria infection or disease compared with pyrethroid-only LLINs. However, there was evidence of an effect on indoor transmission, with the strongest effect seen in the first year after net distribution. Tino and colleagues5 have previously reported a 12% reduction in malaria incidence and 49% reduction in entomological \n",
-                        "inoculation rate with a pyriproxyfen-pyrethroid LLIN compared with pyrethroid-only LLINs over 18 months in a stepped-wedge randomised trial in Burkina Faso. The different study design, length of follow-up, and brand of net might explain the differences seen between the two studies. In Benin, laboratory and semi-field experimental hut studies have shown the superior efficacy of pyriproxyfen-pyrethroid nets on entomological indicators compared with standard pyrethroid-only LLINs,2,3 and are consistent with the decrease in indoor vector density observed in our trial. However, the decrease in indoor vector density did not translate into significant disease reduction, suggesting that a larger effect on malaria transmission (entomological inoculation rate) is crucial to provide community protection. Although lower net usage in the pyriproxyfen-pyrethroid LLIN group could have partially contributed to the lack of effect, we are also assessing textile durability, sterilisation effects, and chemical content of pyriproxyfen-pyrethroid LLINs to fully understand these results.\n",
-                        "\n",
-                        "With several trials now showing the superior efficacy of next-generation LLINs over pyrethroid-only nets, the importance of developing new active ingredients to use on nets in the future is brought to the fore. The distribution of next-generation LLINs across sub-Saharan Africa has already begun, with the development of the brands of chlorfenapry-pyrethroid nets underway,3 as well as the development of chlorfenapry product formulations for indoor residual spraying.3 Although chlorfenapry-pyrethroid nets offer a superior alternative to pyrethroid-only nets in areas of pyrethroid resistance, to preserve their effectiveness optimal resistance-management strategies should be used. There was no evidence of the development of resistance to chlorfenapry during the 2 years of this trial; however, the wide-scale deployment of one type of insecticide risks the rapid development of resistance, which could result in a similar situation to the current widespread resistance to pyrethroids. The nets should be deployed ideally alongside other insecticides as part of a strategy aimed to reduce selection pressure for development of resistance in mosquito vectors. Given the significant effect of the pyriproxyfen-pyrethroid LLINs on malaria transmission during the first year after net distribution, additional studies are necessary to evaluate the potential value of active ingredients such as pyriproxyfen, which are not primarily intended to kill resistant adult mosquitoes but rather to sterilise them. There might still be a role for these hormone growth-regulator insecticides in combination with other active ingredients for net treatment in long-term resistance management.\n",
-                        "\n",
-                        "If WHO policy recommendations are made on the basis of this trial's results, future chlorfenapry-pyrethroid nets might not have to undergo the rigorous trial testing that Interceptor G2 has. Caution should, however, be taken to assess the comparative efficacy, quality, and durability of the second-in-class chlorfenapry-pyrethroid nets as they might use different concentrations of chlorfenapry, different pyrethroids, or different net materials. Considering the time and resources required to generate evidence of epidemiological effect using randomised trials, non-inferiority experimental hut trials, recently proposed by WHO,3 might be a useful alternative for second-in-class chlorfenapry-pyrethroid LLINs. Further work to investigate the capacity of such entomological studies to predict the performance of the trial nets against clinical malaria is ongoing.3\n",
-                        "\n",
-                        "Footnote 3: endnote: [https://www.thelanct.com/Vol.401](https://www.thelanct.com/Vol.401). February 11, 2023\n",
-                        "\n",
-                        "Our study has some limitations. First, net ownership and use were high throughout the study; however, this did not always equate to study net use, which was approximately 60% at 2 years after net distribution, with overall usage of greater than 80%. Similar findings have been observed in other bednet trials, and probably indicate populations choosing to discard damaged nets when other nets are readily available. Second, the three types of nets were not completely identical and differences in textile material and size might have resulted in differential net usage between the groups. As our net use indicator was primarily self-reporting, it is also possible that net usage was overestimated. Third, our primary measure of incidence was based on active detection, involving visits every 2 weeks or every month. The passive data collected alongside the trial suggests that this frequency of visit resulted in some cases being missed, meaning the absolute effect of the nets might be greater than reported here. However, the relative effect of the nets remained similar when the active and passive data were combined. Finally, cost-effectiveness was not assessed; however, the trial in Tanzania' showed that dual active-ingredient LLINs can be highly cost-effective and even cost-saving compared with pyrethroid-only LLINs when providing sufficient protection.\n",
-                        "\n",
-                        "New classes of vector control interventions currently require clinical trial evaluation in two different geographical settings, after 24 months of community use.4 Currently, the only class of next-generation LLIN to receive a WHO recommendation are the PBO synergism nets. However, previous publications have shown concerns about the durability of these nets.5 This trial provides the second key evidence for the effectiveness of chlorfenapry-pyrethroid nets in an area with pyrethroid-resistant vectors and will therefore support a WHO policy recommendation. However, the effect of the nets was reduced in the second year of the trial, and there is no evidence for the efficacy of the nets in their third year of use. Many studies report that the durability of nets is much less than the 3 years required to be designated as long lasting.5 The next-generation LLINs might face the same problems of fabric integrity and durability of insecticidal content unless standards of manufacture are improved. Different channels of distribution (eg, school-based, antenatal care visits, or expanded programme immunisation visits) could play a key role in maintaining high levels of net use in communities.5\n",
-                        "Although generating this evidence to support a WHO recommendation for another class of bednet is a key turning point in malaria control, without new insecticides and new ways to deploy them, we risk repeating the mistakes of the past. Now is the time for more innovation and less complacency.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidalnets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial\n",
-                        "\n",
-                        "Manfred Accomphesi\n",
-                        "\n",
-                        "\n",
-                        "Background New classes of long-lasting insecticidal nets (LLINs) combining mixtures of insecticides with different modes of action could put malaria control back on track after rebounds in transmission across sub-Saharan Africa. We evaluated the relative efficacy of pyriproxyfen-pyrethroid LLINs and chlorfenapyr-pyrethroid LLINs compared with standard LLINs against malaria transmission in an area of high pyrethroid resistance in Benin.\n",
-                        "\n",
-                        "header: Procedures\n",
-                        "\n",
-                        "The nets tested in the trial were: Royal Guard (Disease Control Technologies, Greer, SC, USA), polyethylene netting (I20 deniers incorporating 220 mg/m2 pyriproyfen and 220 mg/m2 alpha-cypermethrin); Interceptor G2 (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 chlorfenapyr and 100 mg/m2 alpha-cypermethrin); and the reference net, Interceptor (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 of alpha-cypermethrin). Nets were distributed in collaboration with the Benin National Malaria Control Program. Households were asked to collect their nets from a central point and received one net per every two residents in their household, rounded up for odd numbers. Nets already in houses were not removed but households were encouraged to use the new study nets. Additionally, net hang-up campaigns to encourage net use took place at 1 month and 7 months after distribution. Net coverage surveys to assess net ownership and usage were done at 1, 9, and 24 months after distribution. Throughout the follow-up period, children enrolled in the analysis cohort and participants enrolled in the cross-sectional surveys were also asked about their net use.\n",
-                        "\n",
-                        "Insecticide content at baseline was assessed on 30 randomly selected new nets per LLIN brand, by gas chromatography with Flame Ionisation Detection, at the\n",
-                        "Centre Wallon de Recherches Agronomiques, Gembloux, Belgium. The insecticidal and physical durability of the study nets is being assessed in a separate study.\n",
-                        "\n",
-                        "Due to the COVID-19 pandemic, there was a 3-month gap between the distribution of the nets and the enrolment of children in the cohort. At enrolment and at 1 year after distribution (April, 2021), children were treated with antimalarial drugs (atremether-lumefantrinity to clear any underlying infection. The cohort was monitored from August, 2020, to April, 2022, a 21-month period, which encompassed the first 2 years after net distribution. Study nurses visited children every 2 weeks during the transmission season (April-October) and every 1 month in the dry season (November-March). At each visit, children were clinically examined and if they were febrile or had a history of fever in the past 48 h, they were tested for malaria using a malaria rapid diagnostic test. If the test was positive, the child was treated with artemether-lumefantrine, according to national guidelines.\n",
-                        "\n",
-                        "During cross-sectional surveys, all participants were tested for malaria using a malaria rapid diagnostic test and received treatment if the test was positive, and children younger than 5 years were tested for anaemia. Information regarding net ownership and usage, and other household-level indicators were collected at the same time.\n",
-                        "\n",
-                        "Entomological monitoring was also delayed and took place every 3 months between June, 2020, and April, 2022. Volunteers recruited from study clusters collected mosquitoes that landed on their legs between 1900 h and 0700 h for 1 night at four randomly selected houses in each cluster at each timepoint. Mosquitoes were morphologically identified to species and a subsample of _Anopheles_ spp was tested for molecular species using PCR.(r) A random sample of _Anopheles_ spp (up to 30% from each nightly catch in each cluster) were tested for sporozoites using the ELISA circumsporozoite protein technique.(r)\n",
-                        "\n",
-                        "header: Contributions\n",
-                        "\n",
-                        "NP, JC, and CN conceived and designed the study, with contributions from MR, IK, LAM, and MCA, JC and MA led the development of the analysis plan, with input from BA, Aso, and NP, MA, CA, JC, NP, FT, and MCA coordinated the trial implementation with local and national authorities, RA, and AO-H. MA, BF, HA, Aso, and LA led the data collection in the field with and Aso, and AP, each of them, MCA, JC, and NP and NP. Also, BC, BA, and KAB led the molecular laboratory work.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a cluster-randomised, superiority trial in Zou Department, Benin. Clusters were villages or groups of villages with a minimum of 100 houses. We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (I:1:1): to receive nets containing either pyriproxyfen and alpha-Cypermethrin (pyrethroid), chlorfenapyr and alpha-Cypermethrin only (reference). Households received one LLIN for every two people. The field team, laboratory staff, analyses team, and community members were masked to the group allocation. The primary outcome was malaria case incidence measured over 2 years after net distribution in a cohort of children aged 6 months-10 years, in the intention-to-treat population. This study is ongoing and is registered with ClinicalTrials.gov, NCT03931473.\n",
-                        "\n",
-                        "Findings Between May 23 and June 24, 2019, 53 854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54030 households in an updated census. A cross-sectional survey showed that study LLIN usage was highest 9 months after distribution (5532 [76 * 986] of 7206 participants), but decreased by 24 months (4032 [60 * 696] of 6654). Mean malaria incidence over 2 years after LLIN distribution was 1-03 cases per child-year (95% CI 0 * 96-1 * 09) in the pyrethroid-only LLIN reference group. 0 * 84 cases per child-year (0.78-0 * 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 * 86, 95% CI 0 * 65-1* 14; p=0 * 28), and 0 * 56 cases per child-year (0 * 51-0 * 61) in the chlorfenapyr-pyrethroid LLIN group (HR 0 * 54, 95% CI 0 * 42-0 * 70; p<0 * 0001).\n",
-                        "\n",
-                        "Interpretation Over 2 years, chlorfenapyr-pyrethroid LLINs provided greater protection from malaria than pyrethroid-only LLINs in an area with pyrethroid-resistant mosquitoes. Pyriproxyfen-pyrethroid LLINs conferred protection similar to pyrethroid-only LLINs. These findings provide crucial second-trial evidence to enable WHO to make policy recommendations on these new LLIN classes. This study confirms the importance of chlorfenapyr as an LLIN treatment to control malaria in areas with pyrethroid-resistant vectors. However, an arsenal of new active ingredients is required for successful long-term resistance management, and additional innovations, including pyriproxyfen, need to be further investigated for effective vector control strategies.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a three-group, cluster-randomised, superiority trial in three districts (Cove, Zagnanado, and Ouinhi) in Zou Department, central Benin. Malaria is highly endemic in the region, with a peak during the wet season (May-October). The main vector control strategy in the study area is use of pyrethroid-only LLINs distributed en masse once every 3 years, and through routine service delivery by antenatal clinics and vaccination programmes. The primary vectors in the setting are _Anopheles coluzzi_ and _Anopheles gambiae_. There is a high intensity of pyrethroid resistance in the main local vector populations.11\n",
-                        "\n",
-                        "A demographic census of all 123 villages in the study area was done in June, 2019. Clusters consisted of villages, or several villages. To reduce contamination between study groups, clusters consisted of a core (minimum 100 households) surrounded by a buffer, ensuring that core areas of neighbouring clusters were separated by at least 1000 m. Households in the buffer and core received the intervention, but measurement of outcomes only took place in core areas. A detailed description of the study protocol is published elsewhere.\"\n",
-                        "\n",
-                        "Ethics approval was obtained from the Benin Ministry of Health ethics committee (6/30/MS/DC/SGM/DRFMT/CNERS/SA), the London School of Hygiene & Tropical Medicine ethics committee (16237), and the WHO Research Ethics Review Committee (ERC.0003153). The trial was independently monitored by a data safety monitoring board and a trial steering committee.\n",
-                        "\n",
-                        "Epidemiological effect was estimated through measuring malaria case incidence in the 2 years after net distribution by active case detection in a cohort of children. A cohort of approximately 30 children aged 6 months-9 years randomly selected (by simple random sampling using a random number generator) from each cluster (1800 in total) was enrolled in July, 2020. Children were eligible for inclusion if they were permanent residents in the cluster, had no serious illnesses, and written informed consent was obtained from their guardians.\n",
-                        "\n",
-                        "Malaria infection prevalence (in all ages) was measured by cross-sectional surveys at 6 months and 18 months after net distribution. 72 individuals residing in the core of each cluster were randomly selected (using a random number generator) from the census for each cross-sectional survey. Each prevalence surveyed collected data on malaria infection, measured using malaria rapid diagnostic tests (CareStart malaria HRP2/PLDH [pf/pan] combo, DiaSy, UK), net ownership and use, sex, and household assets (as a proxy for socioeconomic status).\n",
-                        "\n",
-                        "Entomological effects were measured using human landing catches in four randomly selected houses every 3 months in each cluster. Insecticide resistance intensity was measured in two clusters per group (six clusters total) at baseline and in each follow-up year.\n",
-                        "\n",
-                        "Written informed consent was obtained from all participants, or from guardians for participants younger than 18 years. Assent was sought for children aged between 10 and 18 years. Written consent was also obtained from volunteer mosquito collectors who were all aged 18 years or older and who were vaccinated against yellow fever. All participation was voluntary, and participants could withdraw at any time. Study investigators sought consent in French or local languages.\n",
-                        "\n",
-                        "header: Funding UNITAID, The Global Fund.\n",
-                        "\n",
-                        "Pyrethroid-treated long-lasting insecticidal nets (LLINs) are the main malaria prevention intervention in sub-Saharan Africa and have been the major contributor to the estimated 1 * 5 billion malaria cases and 7 * 6 million malaria deaths averted in the past two decades. However, since 2015, the decline in malaria cases has stalled, and between 2019 and 2020, a rebound in malaria transmission was reported in some areas of sub-Saharan Africa.1 This rebound is likely to be a consequence of the continued spread of resistance to pyrethroid insecticides in malaria-transmitting mosquitoes, coinciding with a plateau in malaria control investment, leading to suboptimal coverage of interventions. Urgent actions are needed to prevent malaria resurgences, which were previously observed in sub-Saharan Africa in the 1980s after malaria control measures were scaled down.2\n",
-                        "In the past 10 years, new insecticide-treated nets containing non-pyrethroid insecticides and insecticide synergists, in combination with pyrethroids, have been shown to be safe and efficacious against malaria mosquito vectors.14 The first net combined a pyrethroid insecticide and the synergistic piperonyl butoxide (PBO) and, following a randomised controlled trial,1 received a WHO policy recommendation in 2017. Other next-generation LLINs have shown encouraging results against resistant vectors in laboratory and small-scale entomological studies.47 One of these nets is a dual active-ingredient LLIN combining a pyrethroid and a pyrrole (chlorfenapyr). Both insecticides lead to mosquito mortality, with chlorfenapyr disrupting the production of cellular energy rather than targeting the nervous system, as pyrethroids do. Another dual active-ingredient LLIN combines a pyrethroid with an insect growth regulator (pyriprosyfen), which leads to sterility in exposed adult mosquitoes.\n",
-                        "\n",
-                        "The first randomised trial of these two products was conducted in Tanzania and showed that the chlorfenapyr-pyrethroid LLINs nearly halved malaria infection over 2 years in villages that received chlorfenapyr-pyrethroid LLINs compared with villages that received pyrethroid-only LLINs. PBO-pyrethroid LLINs were consistently found to be more effective than pyrethroid-only LLINs; however, the duration of the superior effect varied from 12 months to 21 months according to the different trials.\n",
-                        "\n",
-                        "header: Table 1: Baseline characteristics\n",
-                        "footer: Data are n or %; n/N unless otherwise stated. EIR=entomological inoculation rate. LLIN=long-lasting insecticidal net. *Proportion of households in the poorest tercile based on the wealth index of the entire study area. †Anaemia defined as haemoglobin concentration of <10 g/dL. ‡Malaria vectors included Anopheles gambiae, Anophelesfunestus, and Anopheles nili\n",
-                        "columns: ['Unnamed: 0_level_0 - Clusters', 'Pyriproxyfen-pyrethroid LLIN group - Clusters', 'Chlorfenapyr-pyrethroid LLIN group - Clusters', 'Pyrethroid-only LLIN group - Clusters']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of clusters\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"20\",\"Pyrethroid-only LLIN group - Clusters\":\"20\"},\"1\":{\"Unnamed: 0_level_0 - Clusters\":\"Total population in core and buffer areas\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"74822\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"70989\",\"Pyrethroid-only LLIN group - Clusters\":\"69239\"},\"2\":{\"Unnamed: 0_level_0 - Clusters\":\"Mean population in core area of clusters (range)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"1096.1 (225-4524)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"1120.5 (243-5040)\",\"Pyrethroid-only LLIN group - Clusters\":\"1058.1 (252-5217)\"},\"3\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of people per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"4.0 (2.3)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"3.9 (2.3)\",\"Pyrethroid-only LLIN group - Clusters\":\"4.1 (2.4)\"},\"4\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of sleeping spaces per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"2.2 (1.2)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"2.1 (1.1)\",\"Pyrethroid-only LLIN group - Clusters\":\"2.2 (1.2)\"},\"5\":{\"Unnamed: 0_level_0 - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyrethroid-only LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\"},\"6\":{\"Unnamed: 0_level_0 - Clusters\":\"Low socioeconomic status*\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"35.8%; 529\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"36.2%; 533\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"29.0%; 431\\/1487\"},\"7\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN ownership (at least one LLIN in the household)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"96.4%; 1426\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"97.1%; 1431\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"95.1%; 1415\\/1488\"},\"8\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN usage in all age groups the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"95.8%; 1312\\/1370\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"94.9%; 1258\\/1326\",\"Pyrethroid-only LLIN group - Clusters\":\"96.5%; 1343\\/1392\"},\"9\":{\"Unnamed: 0_level_0 - Clusters\":\"Malaria infection prevalence in all age groups\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"43.1%; 636\\/1475\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"40.7%; 598\\/1468\",\"Pyrethroid-only LLIN group - Clusters\":\"46.5%; 690\\/1485\"},\"10\":{\"Unnamed: 0_level_0 - Clusters\":\"Anaemia prevalence in children aged 6 months-4 years\\u2020\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"53.3%; 136\\/255\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"53.3%; 131\\/246\",\"Pyrethroid-only LLIN group - Clusters\":\"50.2%; 122\\/243\"},\"11\":{\"Unnamed: 0_level_0 - Clusters\":\"Entomological characteristics\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Pyrethroid-only LLIN group - Clusters\":\"Entomological characteristics\"},\"12\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night indoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"32.9 (28.9)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"21.6 (21.0)\",\"Pyrethroid-only LLIN group - Clusters\":\"29.2 (25.3)\"},\"13\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night outdoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20.3 (17.6)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"16.4 (17.4)\",\"Pyrethroid-only LLIN group - Clusters\":\"20.6 (20.2)\"},\"14\":{\"Unnamed: 0_level_0 - Clusters\":\"Indoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.62 (0.93)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.48 (0.65)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.96 (1.18)\"},\"15\":{\"Unnamed: 0_level_0 - Clusters\":\"Outdoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.27 (0.45)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.17 (0.44)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.33 (0.45)\"},\"16\":{\"Unnamed: 0_level_0 - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyrethroid-only LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\"},\"17\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of children younger than 5 years\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"52.3%; 316\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"50.6%; 304\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"53.6%; 322\\/601\"},\"18\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of female children\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"47.2%; 285\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"48.4%; 291\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"48.8%; 293\\/601\"},\"19\":{\"Unnamed: 0_level_0 - Clusters\":\"Net usage the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"99.0%; 598\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"98.7%; 593\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"99.2%; 596\\/601\"}}\n",
-                        "\n",
-                        "header: Study design and participants: Randomisation and masking\n",
-                        "\n",
-                        "We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (1:1:1), to receive nets containing either pyriproxyfen and alpha-cypermethrin (pyrethroid), chlorfenapyr and alpha-cypermethrin, or alpha-cypermethrin only (reference). Restricted randomisation was used to ensure balanced cluster allocation between study groups with respect to population size, malaria infection prevalence (measured in the baseline survey), district (n=3), and socioeconomic status.\n",
-                        "\n",
-                        "To mask the net types from the participants and the field workers, the nets were designed to look as similar as possible. Each net was rectangular, was requested to be the same size (1-8 m length, 1-9 m width, and 1-8 m height), and blue. To differentiate the nets in the field, a colour-coded loop was attached to the net. All data analyses were performed masked.\n",
-                        "\n",
-                        "header: Table 2: Malaria case incidence in children aged 6 months–10 years per year of follow-up and overall (including active visits only)\n",
-                        "footer: Each intervention was compared with the pyrethroid-only LLIN group for the same timepoint. LLIN=long-lasting insecticidal net. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Overall', 'Number of clinical malaria episodes - Overall', 'Child- years of follow-up - Overall', 'Incidence, cases per child-year (95% Cl) - Overall', 'Hazard ratio - Overall', '95%Cl - Overall', 'p value* - Overall']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"897\",\"Child- years of follow-up - Overall\":\"874.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.03 (0.96-1.09)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"1\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"744\",\"Child- years of follow-up - Overall\":\"883.8\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.84 (0.78-0.90)\",\"Hazard ratio - Overall\":\"0.86\",\"95%Cl - Overall\":\"0.65-1.14\",\"p value* - Overall\":\"0.28\"},\"2\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"494\",\"Child- years of follow-up - Overall\":\"887.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.56 (0.51-0.61)\",\"Hazard ratio - Overall\":\"0.54\",\"95%Cl - Overall\":\"0.42-0.70\",\"p value* - Overall\":\"<0.0001\"},\"3\":{\"Unnamed: 0_level_0 - Overall\":\"Year 1\",\"Number of clinical malaria episodes - Overall\":\"Year 1\",\"Child- years of follow-up - Overall\":\"Year 1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 1\",\"Hazard ratio - Overall\":\"Year 1\",\"95%Cl - Overall\":\"Year 1\",\"p value* - Overall\":\"Year 1\"},\"4\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"267\",\"Child- years of follow-up - Overall\":\"344.4\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.77 (0.69-0.87)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"5\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"211\",\"Child- years of follow-up - Overall\":\"342.7\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.62 (0.54-0.70)\",\"Hazard ratio - Overall\":\"0.83\",\"95%Cl - Overall\":\"0.51-1.35\",\"p value* - Overall\":\"0.46\"},\"6\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"124\",\"Child- years of follow-up - Overall\":\"349.2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.36 (0.30-0.42)\",\"Hazard ratio - Overall\":\"0.46\",\"95%Cl - Overall\":\"0.30-0.72\",\"p value* - Overall\":\"0.0005\"},\"7\":{\"Unnamed: 0_level_0 - Overall\":\"Year 2\",\"Number of clinical malaria episodes - Overall\":\"Year 2\",\"Child- years of follow-up - Overall\":\"Year 2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 2\",\"Hazard ratio - Overall\":\"Year 2\",\"95%Cl - Overall\":\"Year 2\",\"p value* - Overall\":\"Year 2\"},\"8\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"630\",\"Child- years of follow-up - Overall\":\"529.9\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.19 (1.10-1.29)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"9\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"533\",\"Child- years of follow-up - Overall\":\"541.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.98 (0.90-1.07)\",\"Hazard ratio - Overall\":\"0.88\",\"95%Cl - Overall\":\"0.68-1.13\",\"p value* - Overall\":\"0.31\"},\"10\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"370\",\"Child- years of follow-up - Overall\":\"538.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.69 (0.62-0.76)\",\"Hazard ratio - Overall\":\"0.57\",\"95%Cl - Overall\":\"0.45-0.73\",\"p value* - Overall\":\"<0.0001\"}}\n",
-                        "\n",
-                        "header: Table 3: Malaria infection and anaemia prevalence in the study population at 6 months and 18 months after net distribution (intention-to-treat analysis)\n",
-                        "footer: Anaemia was defined as a haemogloblin concentration of <10 g/dL. LLIN=long-lasting insecticidal net. OR=odds ratio. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution', 'Malaria infection - n/N - 6 months after net distribution', 'Malaria infection - Prevalence - 6 months after net distribution', 'Malaria infection - OR - 6 months after net distribution', 'Malaria infection - 95% Cl - 6 months after net distribution', 'Malaria infection - p value* - 6 months after net distribution', 'Anaemia in children younger than 5 years - n/N - 6 months after net distribution', 'Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution', 'Anaemia in children younger than 5 years - OR - 6 months after net distribution', 'Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution', 'Anaemia in children younger than 5 years - p value* - 6 months after net distribution']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"412\\/1471\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"28.0%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"99\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"41.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"394\\/1463\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"26.9 %\",\"Malaria infection - OR - 6 months after net distribution\":\"0.92\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.63-1.35\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.67\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"117\\/250\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.24\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.71-2.18\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.45\"},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"231\\/1475\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"15.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.47\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.32-0.69\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0002\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"82\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"34.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.71\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.26\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0-24\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - p value* - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"18 months after net distribution\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"576\\/1489\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/252\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"564\\/1478\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.2%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.97\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.69-1.37.\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.87\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"108\\/245\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"44.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.84\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.79\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.65\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"414\\/1483\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"27.9%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.60\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.43-0.85\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0041\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/246\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"48.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.08\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.51-2.28\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.84\"}}\n",
-                        "\n",
-                        "header: Introduction: Added value of this study\n",
-                        "\n",
-                        "To our knowledge, this is the second study aiming to compare the efficacy of the next generation of LLINs combining _p_iprosyfen or chlorfenapyr and pyrethroid versus standard pyrethroid-only LLINs, and the first of its kind to be conducted in west Africa. This trial confirmed the superior efficacy of chlorfenapyr-pyrethroid LLINs, in terms of malaria case incidence, prevalence, and transmission in children, over 2 years of use in the community, in an area of moderate malaria transmission and with high insecticide resistance intensity in malaria vectors, in Benin. However, pyrioxyfen-pyrethroid LLINs did not offer additional protection against malaria outcomes compared with pyrethroid-only LLINs.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Cove\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019},\"S02\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Zagnanado\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019},\"S03\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Ouinhi\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Royal Guard\",\"Insecticide\":\"pyriproyfen, alpha-cypermethrin\",\"Concentration_initial\":\"220 mg\\/m2, 220 mg\\/m2\"},\"N02\":{\"Net_type\":\"Interceptor G2\",\"Insecticide\":\"chlorfenapyr, alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2, 100 mg\\/m2\"},\"N03\":{\"Net_type\":\"Interceptor\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Data sharing: Acknowledgments\n",
-                        "\n",
-                        "This trial was funded by a grant to the London School of Hygiene & Tropical Medicine from UNITAD and The Global Fund via the Innovative Vector Control Consortium. This trial is part of a larger project. The New Nets project: We thank the communities from the three study districts (Cow, Zagnanado, Ouinhi), particularly the participants, children and their parents, community health workers, staff in health clinics, and the regional study team. We thank colleagues and staff at the Centre de Recherche Entomologique de Cotonou and those at the National Malaria Control Program. We also thank the trial system committee, the data safety monitoring board, and Charles Dickinson (School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada) for supporting the mapping and cluster delineation.\n",
-                        "\n",
-                        "header: Role of the funding source\n",
-                        "\n",
-                        "The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication.\n",
-                        "\n",
-                        "header: Outcomes\n",
-                        "\n",
-                        "The primary outcome was malaria case incidence (infrared frontal temperature >37.5degC or reported fever in the previous 48 h, and positive malaria rapid diagnostic test) in children enrolled in the active case detection cohort in the 2 years after net distribution. Visits of children in the cohort at health facilities were monitored and the results of malaria rapid diagnostic tests and any treatment given were recorded. However, it was difficult to assess with certainty if malaria rapid diagnostic tests were used only if the child was symptomatic (as our protocol specified); therefore, the passive data was not included in the primary analyses.\n",
-                        "\n",
-                        "Secondary clinical outcomes were malaria infection prevalence in all age groups and anaemia (defined as haemoglobin concentration <10 g/dL) prevalence in children aged 5 years or younger at 6 months and 18 months after net distribution.\n",
-                        "\n",
-                        "Type and duration of adverse events related to usage of nets were recorded using a prespecified questionnaire at each cohort visit and during net usage and cross-sectional surveys. Data on hospitalisation and death in children in the cohort were collected by interviewing the child's guardian and reviewing the hospital record, following receipt of consent.\n",
-                        "\n",
-                        "The primary entomological outcome was entomological inoculation rate, measured as the mean number of _Plasmodium_ spp infective malaria vectors collected per person per night measured indoors and outdoors. Secondary entomological outcomes were vector density (number of mosquitoes caught per person per night), sporozoite rate, and species composition. Centers for Disease Control and Prevention bottle bioassays were performed by exposing adult female _An gambiae_, collected as larvae, to alpha-cypermethrin for 30 min (1, 2, 5, and 10 times the diagnostic dose), and to chlorfenrapy (100 mg/bottle) and pyriproxyfen (100 mg/bottle) for 60 min each year.(r) Mortality was recorded at 30 min for all four doses of alpha-cypermethrin (insectidic resistance intensity measurement), and at 24, 48, and 72 h after exposure for chlorfenapyr. The reduction in fecundity rate induced by pyriproxyfen relative to unexposed mosquitoes was assessed by ovarian dissection, 3 days after pyriproxyfen exposure. Other outcomes included in the protocol (parity, resting behaviour, survivorship, and other resistance measures) are still to be analysed and will be published elsewhere.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "Data collected for the study, including deidentified participant data and data dictionaries, might be made available at the end of the third year of trial follow-up upon reasonable request to the corresponding author.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The sample size calculations for epidemiological data collection were calculated using the method of Hayes and Moulton.(r) The study was designed to detect a 30% difference in malaria case incidence, assuming a control group incidence of 1 malaria case per child-year and a coefficient of variation of 0-3 between clusters. This design required 20 clusters per group and 25 (20+5 allowing for loss to follow-up) children per cluster followed up for 2 years to give 80% power. Due to the delay in enrolment, which resulted in a shorter follow-up time, the number of children per cluster was increased to 30 (1800 children total). This sample size calculation includes adjustment for multiple testing (allowing for the three groups) using a Bonferroni-corrected two-sided a of 2.5%.\n",
-                        "\n",
-                        "For malaria infection prevalence, it was assumed that the prevalence in the reference group was 40%, with a coefficient of variation between clusters of 0-3. With 72 individuals per cluster, the study had 80% power to detect a 30% lower prevalence in the intervention groups compared with the reference group, using a Bonferroni-corrected a for multiple comparisons.\n",
-                        "\n",
-                        "The primary intention-to-treat analysis was a comparison of incidence of clinical malaria episodes between each dual active-ingredient LLIN group and the\n",
-                        "\n",
-                        "header: Contributions\n",
-                        "\n",
-                        "NP, JC, and CN conceived and designed the study, with contributions from MR, IK, LAM, and MCA, JC and MA led the development of the analysis plan, with input from BA, Aso, and NP, MA, CA, JC, NP, FT, and MCA coordinated the trial implementation with local and national authorities, RA, and AO-H. MA, BF, HA, Aso, and LA led the data collection in the field with and Aso, and AP, each of them, MCA, JC, and NP and NP. Also, BC, BA, and KAB led the molecular laboratory work.\n",
-                        "\n",
-                        "header: Implications of all the available evidence\n",
-                        "\n",
-                        "Given the positive findings, both in Benin and Tanzania, for chlorfenapyr-pyrethroid LLINs, they are likely to become the first WHO-recommended LLINs impregnated with an insecticide class other than pyrethroids. The absence of superior efficacy of pyrioxyfen-pyrethroid LLINs compared with standard pyrethroid-only LLINs is consistent with the results of the previous Tanzania study and calls into question the role of the current pyrioxyfen-pyrethroid LLINs in future malaria vector strategies.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Between May 23 and June 24, 2019, 53854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. The households were delineated into 61 clusters, with one subsequently excluded due to extensive seasonal flooding (figure). Baseline cross-sectional epidemiological and entomological surveys were conducted between October and November, 2019 (table 1). The malaria infection prevalence was 43 5% (1924 of 4428 participants, cluster range 15 1-72 796) and population self-reported LLIN usage was 95 7% (3913 of 4088 participants). Cluster demographics and malaria prevalence were similar between groups (table 1). Entomological inoculation rate was 0 68 _Plasmodium_ spp infective bites per person per night (cluster range 0 00-3 80) indoors and 0 26 per person per night (0 00-1 88) outdoors.\n",
-                        "\n",
-                        "Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54 300 households in an updated census, with 97 1% of households receiving at least one net per household. Active ingredients in the new nets were found to be within or higher than the acceptable target limits depending on LLIN brand (appendix p 4). Study net usage was highest (5532 [76 8%] of 7206 participants) at 9 months after distribution, following a second hang-up campaign, but had decreased by 24 months (4032 [60 6%] of 6654). Study net coverage and usage indicators were similar between study groups up to 18 months after net distribution. At 24 months, pyriproxyfen-pyrethroid LLIN usage and ownership was the lowest (appendix pp 5-6). Use of any type of LLIN (study LLINs and others) remained greater than 80% up to 24 months after distribution (appendix p 7).\n",
-                        "\n",
-                        "In the 2283 households that were randomly selected for post-intervention cohort follow-up, 3129 children were eligible, 1829 of whom were randomly selected, and consent was obtained for 1806 (figure). Among the 2849 and 2771 households randomly selected at 6 months and 18 months after LLIN distribution for the malaria prevalence cross-sectional surveys, 4781 (85 1%) of 5620 households consent, with a similar proportion in the two surveys. The remaining households were either not available during the visit (775 [13 8%] of) or declined to participate (64 [1 1%]; appendix p 8).\n",
-                        "\n",
-                        "Children aged 6 months-9 years enrolled for active case detection were monitored for 21 months, for a total follow-up time of 2645 4- child-years at risk. Loss to follow-up was similar between groups (figure). At enrolment, children were balanced on age, sex, and net usage. During the 21-month follow-up period, we detected 2135 malaria cases through active case detection (table 2). The mean malaria case incidence over 21 months of follow-up was 1 03 cases per child-year (95% CI 0 96-1 09) in the pyrethroid-only LLIN reference group, 0 84 cases per child-year (0 78-70 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 86, 95% CI 0 65-1 46; p = 0 - 28), and 0 56 cases per child-year (0 51-0 64) in the chlorfenapry-pyrethroid LLIN group (HR 0 54, 95% CI 0 - 42-0 70; p < 0 0001; table 2). The strongest effect was observed in the first year of follow-up in the chlorfenapry-pyrethroid group (HR 0 46, 95% CI 0 30-0 72; p = 0 - 0005; table 2). When combining active and passive visits, the number of cases detected nearly doubled; however, the effect size in comparison to the reference group was similar for both intervention nets (appendix p 9).\n",
-                        "\n",
-                        "Malaria infection prevalence was 23 5% (1037 of 4409 participants) at 6 months and 34 9% (1554 of 4450) at 18 months after net distribution (table 3). There was strong evidence for a reduction of malaria infection prevalence in the chlorfenapry-pyrethroid LLIN group at 6 months (15 7%; odds ratio [OR] 0 47, 95% CI 0 32-0 69; p = 0 0002) and at 18 months (27 9%; OR 0 60 - 60, 0 - 43-0 85; p = 0 004) compared with the reference group (28 - 0% at 6 months and 38 7% at 18 months; table 3). In the pyriproxyfen-pyrethroid group, there was no evidence for a reduction in malaria prevalence at 6 months (26 9%; OR 0 - 92, 95% CI 0 63-1 35; p = 0 - 67) or 18 months (38 2%; OR 0 97 0, 0 69-1 37; p = 0 87) compared with the reference group. Results were similar for the per-protocol analysis (appendix p 1). There was no evidence of a reduction in moderate to severe anaemia prevalence in either intervention group (table 3). The post-hoc analysis adjusting for covariates used in the randomisation did not change the interpretation of the results (appendix pp 12-13).\n",
-                        "\n",
-                        "\n",
-                        "Adverse events related to study nets were reported in 528 (45-496) of 1162 participants surveyed at 1 month after net distribution. The highest proportion of adverse events was reported in the pyrethroid-only LLIN group (247 [63-8%] of 387 participants), followed by the pyriproyfen-pyrethroid LLIN group (212 [52-5%] of 404) and then the chlorfenapy-pyrethroid LLIN group (69 [18-6%] of 371). Facial burning (392 [33-7%] of 1162 participants), and skin irritation or itchiness (244 [20-9%]) were the most common adverse events in all groups. Adverse events were rare in all three groups at all later timepoints (appendix P 14-15). We recorded 44 serious adverse events (including three deaths) in the cohort children, with 32 (72-7%) documented as severe malaria (11 cases in the pyriproyfen-pyrethroid LLIN group, ten in the chlorfenapy-pyrethroid LLIN group, and 11 in the pyrethroid-only LLIN group). No serious adverse events related to net use were reported.\n",
-                        "\n",
-                        "A total of 259265 mosquitoes were collected over 3840 collection nights indoors and outdoors, of which 20-9% (29814 from indoors and 24436 from outdoors) were malaria vectors, with _An gambiae_ sensu that the most predominant. Overall, indoor entomological inoculation rate was lower in both intervention groups than in the reference group, with a mean entomological inoculation rate of 0-90 infectious bites per night per person in the chlorfenapy-pyrethroid LLIN group (rate ratio [RR] 0-34, 95% CI 0-18-0-62; p=0-0005), 0-12 in the pyriproyfen-pyrethroid LLIN group (RR 0-42, 0-2-3-0-74; p=0-0028), and 0-28 in the pyrethroid-only LLIN group (table 4). Mean indoor vector density appeared to be lower in the chlorfenapy-pyrethroid LLIN group (10-18 bites per person per night, density ratio 0-44, 95% CI 0-2-30-84; p=0-014), and in the pyriproyfen-pyrethroid LLIN group (13-6 bites per person per night, density ratio 0-58, 0-30-1-12; p=0-011) than in the reference group (23-0 bites per person per night, but the latter difference was not statistically significant (table 4). Adjusting for baseline vector density in the models gave similar results (appendix P 16). There was no significant difference in sporozoite rate in any of the intervention groups compared with the reference group (table 4).\n",
-                        "\n",
-                        "Reduction in outdoor entomological inoculation rate compared with the reference group was observed only in the chlorfenapy-pyrethroid LLIN group (RR 0-30, 95% CI 0-13-0-67; p=0-0035; appendix P 17). There was very weak evidence for a reduction in outdoor entomological inoculation rate in the pyriproyfen-pyrethroid LLIN group compared with the pyrethroid-only group (RR 0-58, 0-30-1-13; p=0-11). Similar effects were observed for outdoor vector density for both intervention nets (appendix P 17).\n",
-                        "\n",
-                        "Post-intervention pyrethrold resistance intensity was high across the study groups in _An gambiae_ sensu lato, with mean mortality of 85% or less after exposure to 10 times the diagnostic concentrations of alpha-cypentrohin in year 2 (appendix P 18). No resistance to chlorfenapy was observed during either year after net distribution (appendix P 18). Exposure of _An gambiae_ sensu lato to pyriproyfen led to a high reduction in fecundity rate relative to unexposed control mosquitoes over the 2 years after net distribution (73-2%, 95% CI 62-2-82-4 in year 1, and 76-6%, 66-6-84-9 in year 2).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "This trial assessed the efficacy of two dual active-ingredient LLINs in an area of Benin with malaria vectors that are highly resistant to pyrethroids and found that chlorfenapy-pyrethroid LLINs provided significantly better protection against malaria for up to 2 years after net distribution compared with pyrethroid-only LLINs. Children aged 6 months-10 years living in clusters that received chlorfenapy-pyrethroid LLINs had a 46% lower incidence of malaria over 2 years after LLIN distribution;\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c c c}  & **Malaria infection** & \\multicolumn{6}{c}{**Anaemia in children younger than 5 years**} \\\\ \\hline n/N & Prevalence & OR & 95\\% CI & p valuea & n/N & Prevalence & OR & 95\\% CI & p valuea \\\\ \\hline\n",
-                        "**6 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 412/1471 & 28\\(\\,\\)0\\% & 1 (ref) & – & – & 992/41 & 41\\(\\,\\)1\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 394/1463 & 26\\(\\,\\)9\\% & 0\\(\\,\\)92 & 0\\(\\,\\)\n",
-                        "\n",
-                        "63\\(\\,\\)45 & 0\\(\\,\\)67 & 117/250 & 46\\(\\,\\)8\\% & 124 & 0.71–218 & 0.45 \\\\ LLIN group & & & & & & & & & \\\\ Chlorfenapy-pyrethroid & 231/1475 & 157\\(\\,\\)\\% & 0\\(\\,\\)47 & 0\\(\\,\\)32\\(\\,\\)0\\(\\,\\)669 & 0\\(\\,\\)0002 & 82/241 & 34\\(\\,\\)0\\% & 0.71 & 0.40–126 & 0.24 \\\\ LLIN group & & & & & & & & & \\\\\n",
-                        "**18 months after net distribution** & & & & & & & & \\\\ Pyrethroid-only LLIN group & 576/14/849 & 38\\(\\,\\)7\\% & 1 (ref) & – & – & 118/25 & 46\\(\\,\\)8\\% & 1 (ref) & – & – \\\\ Pyriproyfen-pyrethroid & 564/14/83 & 38\\(\\,\\)2\\% & 0\\(\\,\\)97 & 0\\(\\,\\)69\\(\\,\\)13\\(\\,\\)07 & 0\\(\\,\\)87 & 108/245 & 44\\(\\,\\)1\\% & 0.84 & 0.40–179\n",
-                        "\n",
-                        "\n",
-                        "participants of any age had 53% lower odds of malaria infection at 6 months and 40% lower odds at 18 months after LLIN distribution, and were exposed to a 66% reduction in entomological inoculation rate compared with those living in clusters that received pyrethroid-only LLINs. Similar gains were not observed with the pyriproxyfen-pyrethroid LLIN, with a small reduction in malaria incidence in the first year of the study that was attenuated in year 2, and no effect on malaria infection prevalence in either year, compared with the pyrethroid-only reference group. However, a 58% reduction in indoor entomological inoculation rate was detected with the pyriproxyfen-pyrethroid LLIN. Both dual active-ingredient LLINs provided a similar safety profile compared with standard pyrethroid-only LLINs, with short-lasting skin irritation and facial burning (commonly associated with the pyrethroid alpha-Cybermethrin) most frequently reported in participants using pyrethroid-only LLINs and pyriproxyfen-pyrethroid LLINs. A similar observation has been reported in another randomised controlled trial evaluating the same LLINs, and this is likely to be associated with the high concentration of alpha-Cybermethrin in those nets compared with the chlorfenapy-pyrethroid LLINs.\n",
-                        "\n",
-                        "To our knowledge, this is the second cluster-randomised trial to provide strong evidence of increased efficacy of chlorfenapy-pyrethroid LLINs relative to pyrethroid-only LLINs for malaria control in areas with pyrethroid-resistant vectors. Chlorfenapyr insecticide on nets has already shown improved control of pyrethroid-resistant _An gambiae_ in laboratory and semi-field experimental huts against resistant malaria vectors.3- The previous trial in Tanzania reported a malaria case incidence reduction of 44%, a 55% decrease in odds of malaria prevalence, and 85% reduction in entomological inoculation rate compared with the pyrethroid-only group, consistent with these results, despite the differing malaria vector populations and intensity of pyrethroid resistance in the mosquitoes.4 In both trials, per-protocol analyses suggested that individuals living in the chlorfenapy-pyrethroid clusters benefited regardless of whether they were using a study net, suggesting a community effect of the net, which was likely to be obtained by the overall reduction of mosquito density, which remained considerably lower than in the pyrethroid-only group in both years of this trial after net distribution. Community effect is also indicated by the reduction in both indoor and outdoor entomological indices in the chlorfenapy-pyrethroid group in the present study.\n",
-                        "\n",
-                        "Similar to the trial in Tanzania,4 the pyriproxyfen-pyrethroid LLINs we tested did not provide additional protection against malaria infection or disease compared with pyrethroid-only LLINs. However, there was evidence of an effect on indoor transmission, with the strongest effect seen in the first year after net distribution. Tino and colleagues5 have previously reported a 12% reduction in malaria incidence and 49% reduction in entomological \n",
-                        "inoculation rate with a pyriproxyfen-pyrethroid LLIN compared with pyrethroid-only LLINs over 18 months in a stepped-wedge randomised trial in Burkina Faso. The different study design, length of follow-up, and brand of net might explain the differences seen between the two studies. In Benin, laboratory and semi-field experimental hut studies have shown the superior efficacy of pyriproxyfen-pyrethroid nets on entomological indicators compared with standard pyrethroid-only LLINs,2,3 and are consistent with the decrease in indoor vector density observed in our trial. However, the decrease in indoor vector density did not translate into significant disease reduction, suggesting that a larger effect on malaria transmission (entomological inoculation rate) is crucial to provide community protection. Although lower net usage in the pyriproxyfen-pyrethroid LLIN group could have partially contributed to the lack of effect, we are also assessing textile durability, sterilisation effects, and chemical content of pyriproxyfen-pyrethroid LLINs to fully understand these results.\n",
-                        "\n",
-                        "With several trials now showing the superior efficacy of next-generation LLINs over pyrethroid-only nets, the importance of developing new active ingredients to use on nets in the future is brought to the fore. The distribution of next-generation LLINs across sub-Saharan Africa has already begun, with the development of the brands of chlorfenapry-pyrethroid nets underway,3 as well as the development of chlorfenapry product formulations for indoor residual spraying.3 Although chlorfenapry-pyrethroid nets offer a superior alternative to pyrethroid-only nets in areas of pyrethroid resistance, to preserve their effectiveness optimal resistance-management strategies should be used. There was no evidence of the development of resistance to chlorfenapry during the 2 years of this trial; however, the wide-scale deployment of one type of insecticide risks the rapid development of resistance, which could result in a similar situation to the current widespread resistance to pyrethroids. The nets should be deployed ideally alongside other insecticides as part of a strategy aimed to reduce selection pressure for development of resistance in mosquito vectors. Given the significant effect of the pyriproxyfen-pyrethroid LLINs on malaria transmission during the first year after net distribution, additional studies are necessary to evaluate the potential value of active ingredients such as pyriproxyfen, which are not primarily intended to kill resistant adult mosquitoes but rather to sterilise them. There might still be a role for these hormone growth-regulator insecticides in combination with other active ingredients for net treatment in long-term resistance management.\n",
-                        "\n",
-                        "If WHO policy recommendations are made on the basis of this trial's results, future chlorfenapry-pyrethroid nets might not have to undergo the rigorous trial testing that Interceptor G2 has. Caution should, however, be taken to assess the comparative efficacy, quality, and durability of the second-in-class chlorfenapry-pyrethroid nets as they might use different concentrations of chlorfenapry, different pyrethroids, or different net materials. Considering the time and resources required to generate evidence of epidemiological effect using randomised trials, non-inferiority experimental hut trials, recently proposed by WHO,3 might be a useful alternative for second-in-class chlorfenapry-pyrethroid LLINs. Further work to investigate the capacity of such entomological studies to predict the performance of the trial nets against clinical malaria is ongoing.3\n",
-                        "\n",
-                        "Footnote 3: endnote: [https://www.thelanct.com/Vol.401](https://www.thelanct.com/Vol.401). February 11, 2023\n",
-                        "\n",
-                        "Our study has some limitations. First, net ownership and use were high throughout the study; however, this did not always equate to study net use, which was approximately 60% at 2 years after net distribution, with overall usage of greater than 80%. Similar findings have been observed in other bednet trials, and probably indicate populations choosing to discard damaged nets when other nets are readily available. Second, the three types of nets were not completely identical and differences in textile material and size might have resulted in differential net usage between the groups. As our net use indicator was primarily self-reporting, it is also possible that net usage was overestimated. Third, our primary measure of incidence was based on active detection, involving visits every 2 weeks or every month. The passive data collected alongside the trial suggests that this frequency of visit resulted in some cases being missed, meaning the absolute effect of the nets might be greater than reported here. However, the relative effect of the nets remained similar when the active and passive data were combined. Finally, cost-effectiveness was not assessed; however, the trial in Tanzania' showed that dual active-ingredient LLINs can be highly cost-effective and even cost-saving compared with pyrethroid-only LLINs when providing sufficient protection.\n",
-                        "\n",
-                        "New classes of vector control interventions currently require clinical trial evaluation in two different geographical settings, after 24 months of community use.4 Currently, the only class of next-generation LLIN to receive a WHO recommendation are the PBO synergism nets. However, previous publications have shown concerns about the durability of these nets.5 This trial provides the second key evidence for the effectiveness of chlorfenapry-pyrethroid nets in an area with pyrethroid-resistant vectors and will therefore support a WHO policy recommendation. However, the effect of the nets was reduced in the second year of the trial, and there is no evidence for the efficacy of the nets in their third year of use. Many studies report that the durability of nets is much less than the 3 years required to be designated as long lasting.5 The next-generation LLINs might face the same problems of fabric integrity and durability of insecticidal content unless standards of manufacture are improved. Different channels of distribution (eg, school-based, antenatal care visits, or expanded programme immunisation visits) could play a key role in maintaining high levels of net use in communities.5\n",
-                        "Although generating this evidence to support a WHO recommendation for another class of bednet is a key turning point in malaria control, without new insecticides and new ways to deploy them, we risk repeating the mistakes of the past. Now is the time for more innovation and less complacency.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Efficacy of pyriproxyfen-pyrethroid long-lasting insecticidalnets (LLINs) and chlorfenapyr-pyrethroid LLINs compared with pyrethroid-only LLINs for malaria control in Benin: a cluster-randomised, superiority trial\n",
-                        "\n",
-                        "Manfred Accomphesi\n",
-                        "\n",
-                        "\n",
-                        "Background New classes of long-lasting insecticidal nets (LLINs) combining mixtures of insecticides with different modes of action could put malaria control back on track after rebounds in transmission across sub-Saharan Africa. We evaluated the relative efficacy of pyriproxyfen-pyrethroid LLINs and chlorfenapyr-pyrethroid LLINs compared with standard LLINs against malaria transmission in an area of high pyrethroid resistance in Benin.\n",
-                        "\n",
-                        "header: Procedures\n",
-                        "\n",
-                        "The nets tested in the trial were: Royal Guard (Disease Control Technologies, Greer, SC, USA), polyethylene netting (I20 deniers incorporating 220 mg/m2 pyriproyfen and 220 mg/m2 alpha-cypermethrin); Interceptor G2 (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 chlorfenapyr and 100 mg/m2 alpha-cypermethrin); and the reference net, Interceptor (BASF SE, Ludwigshafen, Germany), polyester netting (100 deniers coated with 200 mg/m2 of alpha-cypermethrin). Nets were distributed in collaboration with the Benin National Malaria Control Program. Households were asked to collect their nets from a central point and received one net per every two residents in their household, rounded up for odd numbers. Nets already in houses were not removed but households were encouraged to use the new study nets. Additionally, net hang-up campaigns to encourage net use took place at 1 month and 7 months after distribution. Net coverage surveys to assess net ownership and usage were done at 1, 9, and 24 months after distribution. Throughout the follow-up period, children enrolled in the analysis cohort and participants enrolled in the cross-sectional surveys were also asked about their net use.\n",
-                        "\n",
-                        "Insecticide content at baseline was assessed on 30 randomly selected new nets per LLIN brand, by gas chromatography with Flame Ionisation Detection, at the\n",
-                        "Centre Wallon de Recherches Agronomiques, Gembloux, Belgium. The insecticidal and physical durability of the study nets is being assessed in a separate study.\n",
-                        "\n",
-                        "Due to the COVID-19 pandemic, there was a 3-month gap between the distribution of the nets and the enrolment of children in the cohort. At enrolment and at 1 year after distribution (April, 2021), children were treated with antimalarial drugs (atremether-lumefantrinity to clear any underlying infection. The cohort was monitored from August, 2020, to April, 2022, a 21-month period, which encompassed the first 2 years after net distribution. Study nurses visited children every 2 weeks during the transmission season (April-October) and every 1 month in the dry season (November-March). At each visit, children were clinically examined and if they were febrile or had a history of fever in the past 48 h, they were tested for malaria using a malaria rapid diagnostic test. If the test was positive, the child was treated with artemether-lumefantrine, according to national guidelines.\n",
-                        "\n",
-                        "During cross-sectional surveys, all participants were tested for malaria using a malaria rapid diagnostic test and received treatment if the test was positive, and children younger than 5 years were tested for anaemia. Information regarding net ownership and usage, and other household-level indicators were collected at the same time.\n",
-                        "\n",
-                        "Entomological monitoring was also delayed and took place every 3 months between June, 2020, and April, 2022. Volunteers recruited from study clusters collected mosquitoes that landed on their legs between 1900 h and 0700 h for 1 night at four randomly selected houses in each cluster at each timepoint. Mosquitoes were morphologically identified to species and a subsample of _Anopheles_ spp was tested for molecular species using PCR.(r) A random sample of _Anopheles_ spp (up to 30% from each nightly catch in each cluster) were tested for sporozoites using the ELISA circumsporozoite protein technique.(r)\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a cluster-randomised, superiority trial in Zou Department, Benin. Clusters were villages or groups of villages with a minimum of 100 houses. We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (I:1:1): to receive nets containing either pyriproxyfen and alpha-Cypermethrin (pyrethroid), chlorfenapyr and alpha-Cypermethrin only (reference). Households received one LLIN for every two people. The field team, laboratory staff, analyses team, and community members were masked to the group allocation. The primary outcome was malaria case incidence measured over 2 years after net distribution in a cohort of children aged 6 months-10 years, in the intention-to-treat population. This study is ongoing and is registered with ClinicalTrials.gov, NCT03931473.\n",
-                        "\n",
-                        "Findings Between May 23 and June 24, 2019, 53 854 households and 216 289 inhabitants were accounted for in the initial census and included in the study. Between March 19 and 22, 2020, 115 323 LLINs were distributed to 54030 households in an updated census. A cross-sectional survey showed that study LLIN usage was highest 9 months after distribution (5532 [76 * 986] of 7206 participants), but decreased by 24 months (4032 [60 * 696] of 6654). Mean malaria incidence over 2 years after LLIN distribution was 1-03 cases per child-year (95% CI 0 * 96-1 * 09) in the pyrethroid-only LLIN reference group. 0 * 84 cases per child-year (0.78-0 * 90) in the pyriproxyfen-pyrethroid LLIN group (hazard ratio [HR] 0 * 86, 95% CI 0 * 65-1* 14; p=0 * 28), and 0 * 56 cases per child-year (0 * 51-0 * 61) in the chlorfenapyr-pyrethroid LLIN group (HR 0 * 54, 95% CI 0 * 42-0 * 70; p<0 * 0001).\n",
-                        "\n",
-                        "Interpretation Over 2 years, chlorfenapyr-pyrethroid LLINs provided greater protection from malaria than pyrethroid-only LLINs in an area with pyrethroid-resistant mosquitoes. Pyriproxyfen-pyrethroid LLINs conferred protection similar to pyrethroid-only LLINs. These findings provide crucial second-trial evidence to enable WHO to make policy recommendations on these new LLIN classes. This study confirms the importance of chlorfenapyr as an LLIN treatment to control malaria in areas with pyrethroid-resistant vectors. However, an arsenal of new active ingredients is required for successful long-term resistance management, and additional innovations, including pyriproxyfen, need to be further investigated for effective vector control strategies.\n",
-                        "\n",
-                        "header: Funding UNITAID, The Global Fund.\n",
-                        "\n",
-                        "Pyrethroid-treated long-lasting insecticidal nets (LLINs) are the main malaria prevention intervention in sub-Saharan Africa and have been the major contributor to the estimated 1 * 5 billion malaria cases and 7 * 6 million malaria deaths averted in the past two decades. However, since 2015, the decline in malaria cases has stalled, and between 2019 and 2020, a rebound in malaria transmission was reported in some areas of sub-Saharan Africa.1 This rebound is likely to be a consequence of the continued spread of resistance to pyrethroid insecticides in malaria-transmitting mosquitoes, coinciding with a plateau in malaria control investment, leading to suboptimal coverage of interventions. Urgent actions are needed to prevent malaria resurgences, which were previously observed in sub-Saharan Africa in the 1980s after malaria control measures were scaled down.2\n",
-                        "In the past 10 years, new insecticide-treated nets containing non-pyrethroid insecticides and insecticide synergists, in combination with pyrethroids, have been shown to be safe and efficacious against malaria mosquito vectors.14 The first net combined a pyrethroid insecticide and the synergistic piperonyl butoxide (PBO) and, following a randomised controlled trial,1 received a WHO policy recommendation in 2017. Other next-generation LLINs have shown encouraging results against resistant vectors in laboratory and small-scale entomological studies.47 One of these nets is a dual active-ingredient LLIN combining a pyrethroid and a pyrrole (chlorfenapyr). Both insecticides lead to mosquito mortality, with chlorfenapyr disrupting the production of cellular energy rather than targeting the nervous system, as pyrethroids do. Another dual active-ingredient LLIN combines a pyrethroid with an insect growth regulator (pyriprosyfen), which leads to sterility in exposed adult mosquitoes.\n",
-                        "\n",
-                        "The first randomised trial of these two products was conducted in Tanzania and showed that the chlorfenapyr-pyrethroid LLINs nearly halved malaria infection over 2 years in villages that received chlorfenapyr-pyrethroid LLINs compared with villages that received pyrethroid-only LLINs. PBO-pyrethroid LLINs were consistently found to be more effective than pyrethroid-only LLINs; however, the duration of the superior effect varied from 12 months to 21 months according to the different trials.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "We conducted a three-group, cluster-randomised, superiority trial in three districts (Cove, Zagnanado, and Ouinhi) in Zou Department, central Benin. Malaria is highly endemic in the region, with a peak during the wet season (May-October). The main vector control strategy in the study area is use of pyrethroid-only LLINs distributed en masse once every 3 years, and through routine service delivery by antenatal clinics and vaccination programmes. The primary vectors in the setting are _Anopheles coluzzi_ and _Anopheles gambiae_. There is a high intensity of pyrethroid resistance in the main local vector populations.11\n",
-                        "\n",
-                        "A demographic census of all 123 villages in the study area was done in June, 2019. Clusters consisted of villages, or several villages. To reduce contamination between study groups, clusters consisted of a core (minimum 100 households) surrounded by a buffer, ensuring that core areas of neighbouring clusters were separated by at least 1000 m. Households in the buffer and core received the intervention, but measurement of outcomes only took place in core areas. A detailed description of the study protocol is published elsewhere.\"\n",
-                        "\n",
-                        "Ethics approval was obtained from the Benin Ministry of Health ethics committee (6/30/MS/DC/SGM/DRFMT/CNERS/SA), the London School of Hygiene & Tropical Medicine ethics committee (16237), and the WHO Research Ethics Review Committee (ERC.0003153). The trial was independently monitored by a data safety monitoring board and a trial steering committee.\n",
-                        "\n",
-                        "Epidemiological effect was estimated through measuring malaria case incidence in the 2 years after net distribution by active case detection in a cohort of children. A cohort of approximately 30 children aged 6 months-9 years randomly selected (by simple random sampling using a random number generator) from each cluster (1800 in total) was enrolled in July, 2020. Children were eligible for inclusion if they were permanent residents in the cluster, had no serious illnesses, and written informed consent was obtained from their guardians.\n",
-                        "\n",
-                        "Malaria infection prevalence (in all ages) was measured by cross-sectional surveys at 6 months and 18 months after net distribution. 72 individuals residing in the core of each cluster were randomly selected (using a random number generator) from the census for each cross-sectional survey. Each prevalence surveyed collected data on malaria infection, measured using malaria rapid diagnostic tests (CareStart malaria HRP2/PLDH [pf/pan] combo, DiaSy, UK), net ownership and use, sex, and household assets (as a proxy for socioeconomic status).\n",
-                        "\n",
-                        "Entomological effects were measured using human landing catches in four randomly selected houses every 3 months in each cluster. Insecticide resistance intensity was measured in two clusters per group (six clusters total) at baseline and in each follow-up year.\n",
-                        "\n",
-                        "Written informed consent was obtained from all participants, or from guardians for participants younger than 18 years. Assent was sought for children aged between 10 and 18 years. Written consent was also obtained from volunteer mosquito collectors who were all aged 18 years or older and who were vaccinated against yellow fever. All participation was voluntary, and participants could withdraw at any time. Study investigators sought consent in French or local languages.\n",
-                        "\n",
-                        "header: Study design and participants: Randomisation and masking\n",
-                        "\n",
-                        "We used restricted randomisation to randomly assign 60 clusters to one of three LLIN groups (1:1:1), to receive nets containing either pyriproxyfen and alpha-cypermethrin (pyrethroid), chlorfenapyr and alpha-cypermethrin, or alpha-cypermethrin only (reference). Restricted randomisation was used to ensure balanced cluster allocation between study groups with respect to population size, malaria infection prevalence (measured in the baseline survey), district (n=3), and socioeconomic status.\n",
-                        "\n",
-                        "To mask the net types from the participants and the field workers, the nets were designed to look as similar as possible. Each net was rectangular, was requested to be the same size (1-8 m length, 1-9 m width, and 1-8 m height), and blue. To differentiate the nets in the field, a colour-coded loop was attached to the net. All data analyses were performed masked.\n",
-                        "\n",
-                        "header: Table 1: Baseline characteristics\n",
-                        "footer: Data are n or %; n/N unless otherwise stated. EIR=entomological inoculation rate. LLIN=long-lasting insecticidal net. *Proportion of households in the poorest tercile based on the wealth index of the entire study area. †Anaemia defined as haemoglobin concentration of <10 g/dL. ‡Malaria vectors included Anopheles gambiae, Anophelesfunestus, and Anopheles nili\n",
-                        "columns: ['Unnamed: 0_level_0 - Clusters', 'Pyriproxyfen-pyrethroid LLIN group - Clusters', 'Chlorfenapyr-pyrethroid LLIN group - Clusters', 'Pyrethroid-only LLIN group - Clusters']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of clusters\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"20\",\"Pyrethroid-only LLIN group - Clusters\":\"20\"},\"1\":{\"Unnamed: 0_level_0 - Clusters\":\"Total population in core and buffer areas\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"74822\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"70989\",\"Pyrethroid-only LLIN group - Clusters\":\"69239\"},\"2\":{\"Unnamed: 0_level_0 - Clusters\":\"Mean population in core area of clusters (range)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"1096.1 (225-4524)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"1120.5 (243-5040)\",\"Pyrethroid-only LLIN group - Clusters\":\"1058.1 (252-5217)\"},\"3\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of people per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"4.0 (2.3)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"3.9 (2.3)\",\"Pyrethroid-only LLIN group - Clusters\":\"4.1 (2.4)\"},\"4\":{\"Unnamed: 0_level_0 - Clusters\":\"Number of sleeping spaces per household, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"2.2 (1.2)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"2.1 (1.1)\",\"Pyrethroid-only LLIN group - Clusters\":\"2.2 (1.2)\"},\"5\":{\"Unnamed: 0_level_0 - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\",\"Pyrethroid-only LLIN group - Clusters\":\"Household and participant characteristics in the baseline cross-sectional survey\"},\"6\":{\"Unnamed: 0_level_0 - Clusters\":\"Low socioeconomic status*\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"35.8%; 529\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"36.2%; 533\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"29.0%; 431\\/1487\"},\"7\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN ownership (at least one LLIN in the household)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"96.4%; 1426\\/1479\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"97.1%; 1431\\/1474\",\"Pyrethroid-only LLIN group - Clusters\":\"95.1%; 1415\\/1488\"},\"8\":{\"Unnamed: 0_level_0 - Clusters\":\"LLIN usage in all age groups the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"95.8%; 1312\\/1370\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"94.9%; 1258\\/1326\",\"Pyrethroid-only LLIN group - Clusters\":\"96.5%; 1343\\/1392\"},\"9\":{\"Unnamed: 0_level_0 - Clusters\":\"Malaria infection prevalence in all age groups\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"43.1%; 636\\/1475\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"40.7%; 598\\/1468\",\"Pyrethroid-only LLIN group - Clusters\":\"46.5%; 690\\/1485\"},\"10\":{\"Unnamed: 0_level_0 - Clusters\":\"Anaemia prevalence in children aged 6 months-4 years\\u2020\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"53.3%; 136\\/255\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"53.3%; 131\\/246\",\"Pyrethroid-only LLIN group - Clusters\":\"50.2%; 122\\/243\"},\"11\":{\"Unnamed: 0_level_0 - Clusters\":\"Entomological characteristics\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Entomological characteristics\",\"Pyrethroid-only LLIN group - Clusters\":\"Entomological characteristics\"},\"12\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night indoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"32.9 (28.9)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"21.6 (21.0)\",\"Pyrethroid-only LLIN group - Clusters\":\"29.2 (25.3)\"},\"13\":{\"Unnamed: 0_level_0 - Clusters\":\"Human biting density per person per night outdoors\\u2021, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"20.3 (17.6)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"16.4 (17.4)\",\"Pyrethroid-only LLIN group - Clusters\":\"20.6 (20.2)\"},\"14\":{\"Unnamed: 0_level_0 - Clusters\":\"Indoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.62 (0.93)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.48 (0.65)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.96 (1.18)\"},\"15\":{\"Unnamed: 0_level_0 - Clusters\":\"Outdoor EIR per person per night, mean (SD)\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"0.27 (0.45)\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"0.17 (0.44)\",\"Pyrethroid-only LLIN group - Clusters\":\"0.33 (0.45)\"},\"16\":{\"Unnamed: 0_level_0 - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\",\"Pyrethroid-only LLIN group - Clusters\":\"Children (aged 6 months-10 years) at cohort enrolment\"},\"17\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of children younger than 5 years\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"52.3%; 316\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"50.6%; 304\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"53.6%; 322\\/601\"},\"18\":{\"Unnamed: 0_level_0 - Clusters\":\"Proportion of female children\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"47.2%; 285\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"48.4%; 291\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"48.8%; 293\\/601\"},\"19\":{\"Unnamed: 0_level_0 - Clusters\":\"Net usage the night before survey\",\"Pyriproxyfen-pyrethroid LLIN group - Clusters\":\"99.0%; 598\\/604\",\"Chlorfenapyr-pyrethroid LLIN group - Clusters\":\"98.7%; 593\\/601\",\"Pyrethroid-only LLIN group - Clusters\":\"99.2%; 596\\/601\"}}\n",
-                        "\n",
-                        "header: Table 2: Malaria case incidence in children aged 6 months–10 years per year of follow-up and overall (including active visits only)\n",
-                        "footer: Each intervention was compared with the pyrethroid-only LLIN group for the same timepoint. LLIN=long-lasting insecticidal net. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Overall', 'Number of clinical malaria episodes - Overall', 'Child- years of follow-up - Overall', 'Incidence, cases per child-year (95% Cl) - Overall', 'Hazard ratio - Overall', '95%Cl - Overall', 'p value* - Overall']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"897\",\"Child- years of follow-up - Overall\":\"874.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.03 (0.96-1.09)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"1\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"744\",\"Child- years of follow-up - Overall\":\"883.8\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.84 (0.78-0.90)\",\"Hazard ratio - Overall\":\"0.86\",\"95%Cl - Overall\":\"0.65-1.14\",\"p value* - Overall\":\"0.28\"},\"2\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"494\",\"Child- years of follow-up - Overall\":\"887.3\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.56 (0.51-0.61)\",\"Hazard ratio - Overall\":\"0.54\",\"95%Cl - Overall\":\"0.42-0.70\",\"p value* - Overall\":\"<0.0001\"},\"3\":{\"Unnamed: 0_level_0 - Overall\":\"Year 1\",\"Number of clinical malaria episodes - Overall\":\"Year 1\",\"Child- years of follow-up - Overall\":\"Year 1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 1\",\"Hazard ratio - Overall\":\"Year 1\",\"95%Cl - Overall\":\"Year 1\",\"p value* - Overall\":\"Year 1\"},\"4\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"267\",\"Child- years of follow-up - Overall\":\"344.4\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.77 (0.69-0.87)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"5\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"211\",\"Child- years of follow-up - Overall\":\"342.7\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.62 (0.54-0.70)\",\"Hazard ratio - Overall\":\"0.83\",\"95%Cl - Overall\":\"0.51-1.35\",\"p value* - Overall\":\"0.46\"},\"6\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"124\",\"Child- years of follow-up - Overall\":\"349.2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.36 (0.30-0.42)\",\"Hazard ratio - Overall\":\"0.46\",\"95%Cl - Overall\":\"0.30-0.72\",\"p value* - Overall\":\"0.0005\"},\"7\":{\"Unnamed: 0_level_0 - Overall\":\"Year 2\",\"Number of clinical malaria episodes - Overall\":\"Year 2\",\"Child- years of follow-up - Overall\":\"Year 2\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"Year 2\",\"Hazard ratio - Overall\":\"Year 2\",\"95%Cl - Overall\":\"Year 2\",\"p value* - Overall\":\"Year 2\"},\"8\":{\"Unnamed: 0_level_0 - Overall\":\"Pyrethroid-only LLIN group\",\"Number of clinical malaria episodes - Overall\":\"630\",\"Child- years of follow-up - Overall\":\"529.9\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"1.19 (1.10-1.29)\",\"Hazard ratio - Overall\":\"1 (ref)\",\"95%Cl - Overall\":null,\"p value* - Overall\":null},\"9\":{\"Unnamed: 0_level_0 - Overall\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"533\",\"Child- years of follow-up - Overall\":\"541.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.98 (0.90-1.07)\",\"Hazard ratio - Overall\":\"0.88\",\"95%Cl - Overall\":\"0.68-1.13\",\"p value* - Overall\":\"0.31\"},\"10\":{\"Unnamed: 0_level_0 - Overall\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Number of clinical malaria episodes - Overall\":\"370\",\"Child- years of follow-up - Overall\":\"538.1\",\"Incidence, cases per child-year (95% Cl) - Overall\":\"0.69 (0.62-0.76)\",\"Hazard ratio - Overall\":\"0.57\",\"95%Cl - Overall\":\"0.45-0.73\",\"p value* - Overall\":\"<0.0001\"}}\n",
-                        "\n",
-                        "header: Table 3: Malaria infection and anaemia prevalence in the study population at 6 months and 18 months after net distribution (intention-to-treat analysis)\n",
-                        "footer: Anaemia was defined as a haemogloblin concentration of <10 g/dL. LLIN=long-lasting insecticidal net. OR=odds ratio. *A p value of <0·025 was considered significant after Bonferroni correction\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution', 'Malaria infection - n/N - 6 months after net distribution', 'Malaria infection - Prevalence - 6 months after net distribution', 'Malaria infection - OR - 6 months after net distribution', 'Malaria infection - 95% Cl - 6 months after net distribution', 'Malaria infection - p value* - 6 months after net distribution', 'Anaemia in children younger than 5 years - n/N - 6 months after net distribution', 'Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution', 'Anaemia in children younger than 5 years - OR - 6 months after net distribution', 'Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution', 'Anaemia in children younger than 5 years - p value* - 6 months after net distribution']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"412\\/1471\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"28.0%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"99\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"41.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"394\\/1463\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"26.9 %\",\"Malaria infection - OR - 6 months after net distribution\":\"0.92\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.63-1.35\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.67\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"117\\/250\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.24\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.71-2.18\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.45\"},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"231\\/1475\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"15.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.47\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.32-0.69\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0002\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"82\\/241\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"34.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.71\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.26\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0-24\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Malaria infection - p value* - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"18 months after net distribution\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"18 months after net distribution\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyrethroid-only LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"576\\/1489\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.7%\",\"Malaria infection - OR - 6 months after net distribution\":\"1 (ref)\",\"Malaria infection - 95% Cl - 6 months after net distribution\":null,\"Malaria infection - p value* - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/252\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"46.8%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1 (ref)\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":null,\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Pyriproxyfen-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"564\\/1478\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"38.2%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.97\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.69-1.37.\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.87\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"108\\/245\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"44.1%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"0.84\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.40-1.79\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.65\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1 - 6 months after net distribution\":\"Chlorfenapyr-pyrethroid LLIN group\",\"Malaria infection - n\\/N - 6 months after net distribution\":\"414\\/1483\",\"Malaria infection - Prevalence - 6 months after net distribution\":\"27.9%\",\"Malaria infection - OR - 6 months after net distribution\":\"0.60\",\"Malaria infection - 95% Cl - 6 months after net distribution\":\"0.43-0.85\",\"Malaria infection - p value* - 6 months after net distribution\":\"0.0041\",\"Anaemia in children younger than 5 years - n\\/N - 6 months after net distribution\":\"118\\/246\",\"Anaemia in children younger than 5 years - Prevalence - 6 months after net distribution\":\"48.0%\",\"Anaemia in children younger than 5 years - OR - 6 months after net distribution\":\"1.08\",\"Anaemia in children younger than 5 years - 95% Cl - 6 months after net distribution\":\"0.51-2.28\",\"Anaemia in children younger than 5 years - p value* - 6 months after net distribution\":\"0.84\"}}\n",
-                        "\n",
-                        "header: Introduction: Added value of this study\n",
-                        "\n",
-                        "To our knowledge, this is the second study aiming to compare the efficacy of the next generation of LLINs combining _p_iprosyfen or chlorfenapyr and pyrethroid versus standard pyrethroid-only LLINs, and the first of its kind to be conducted in west Africa. This trial confirmed the superior efficacy of chlorfenapyr-pyrethroid LLINs, in terms of malaria case incidence, prevalence, and transmission in children, over 2 years of use in the community, in an area of moderate malaria transmission and with high insecticide resistance intensity in malaria vectors, in Benin. However, pyrioxyfen-pyrethroid LLINs did not offer additional protection against malaria outcomes compared with pyrethroid-only LLINs.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Cove\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019},\"S02\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Zagnanado\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019},\"S03\":{\"Study_type\":\"Village trial\",\"Country\":\"Benin\",\"Site\":\"Ouinhi\",\"Start_month\":5,\"Start_year\":2019,\"End_month\":6,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Royal Guard\",\"Insecticide\":\"pyriproyfen, alpha-cypermethrin\",\"Concentration_initial\":\"220 mg\\/m2, 220 mg\\/m2\"},\"N02\":{\"Net_type\":\"Interceptor G2\",\"Insecticide\":\"chlorfenapyr, alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2, 100 mg\\/m2\"},\"N03\":{\"Net_type\":\"Interceptor\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This research was funded by the Royal Society of Tropical Medicine and Hygiene (RSTM#) through a small grant awarded to DM and the Wellcome Trust through a Wellcome Trust Training fellowship in public health and tropical medicine to CN (102543/2/13/2). The RSTM# and the Wellcome Trust have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "1 Laboratory of Parasitology and Ecology, Faculty of Sciences, University of Yaoundet i P.O. Box 812, Yaoundet, Cameroon. 1 Institute for the Recherche de Yaoundet (FR), Organizing the Coordination pour la Tutte Contre les Endrines en Afrique Centrale (OCEAC), PO. Box 283, Yaoundet, Cameroon. 3 Department of Biological Sciences, Faculty of Medicine and Pharmaceutical Sciences, University of Ouala, PO. Box 2415, Ouala, Cameroon. 4 Department of Parasitology and Microbiology, Centre for Research in Infectious Diseases (RDI), PO. Box 13591, Yaounde, Cameroon. 5 Animal Organisms Laboratory, Faculty of Sciences, University of Ouala, PO. Box 24157, Douala, Cameroon.\n",
-                        "\n",
-                        "header: Bed net coverage, use and maintenance\n",
-                        "\n",
-                        "Bed net ownership was investigated in 80 of 97 the households that currently make up the village through visual inspection. The heads of these households or their representatives were questioned on their use of nets and their maintenance. The number of people living in each house was recorded to estimate net coverage. Household net ownership was defined as the percentage of households owning at least one LLIN. Net coverage defined as the percentage of households with at least one LLIN for every two people was also determined.\n",
-                        "\n",
-                        "header: Insecticide bioassays and knockdown resistance detection\n",
-                        "\n",
-                        "Larvae of _An. gambiae_ (_s.l._) were collected in stagnant pools of water and reared in the insectary of the medical entomology laboratory of OCEAC. F1 adults that emerged from those larvae were fed with 10% sucrose solution made by dissolving 100 g of ordinary white sugar in 1 L of water [22]. _Anopheles funestus_ was not tested due to the low number of adults obtained following larval collections and rearing. The susceptibility of the F1 adults to 0.75% permethrin and 0.05% deltamethrin, both pyrethroid pesticides, was assessed using a World Health Organization (WHO) standard test procedure [23]. Tests were performed at 25 +- 2 degC, 806 +- 10% relative humidity (RH). For each insecticide, four batches of 20-25 field F1 females, aged between 2 and 5 days, were exposed to insecticide-impregnated papers in WHO test-tube for 1 h. At the same time, two batches of the same number of mosquitoes were exposed to untreated papers as control. At the end of the insecticide exposure period, the number of knocked-down mosquitoes was recorded, following which the mosquitoes were transferred into holding tubes. Cotton balls that had first been soaked in a 10% sugar solution and then the moisture squeezed out were placed at the mouth of the tubes. The mortality was recorded 24 h later. Mortality rate in the tested samples was corrected using Abbott's formula [24], when the mortality in the control tubes varied between 5 to 20%. The knockdown resistance (_kdr_) mutation L1014F, which is responsible of cross resistance to DDT (dichlorodiphenyl-trichloroethane) and pyrethroids was genotyped using the protocol described by Martinez-Tores et al. [25].\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichlorodiphenyltrichloroethane; ER: Entomological inoculation rate; ELISA CSP: Enzyme-linked immunosorbent assay to detect the circumsporolite protein; HL: Human landing catch method; Adv: Knockdown resistance; LINK: long-lasting insecticidal net; pH: Proportionate hole index; WHORES: World Health Organization Pesticides Evaluation Scheme\n",
-                        "\n",
-                        "header: Possession, coverage and use of LLINs\n",
-                        "\n",
-                        "In general, 81.25% of the households inspected possessed at least one LLIN. Despite this high level of ownership, the proportion of households that met the ratio of one net for two persons was only 47.69%. Fortunately, 81.54% of the people interviewed affirmed they used their bed nets every night (Table 3). Of the LLINs available, 92.3% were acquired during the government free mass distribution campaigns of 2011-2012 and 2015-2016. Although almost 70% of the study population affirmed that they have been educated by the public health community agents during the distribution campaigns, only 10.52% knew bed nets should be dried in the shade before usage or after washing. Moreover, 56.46% of respondents had washed their bet nets at least once; 63.15% used water and ordinary soap while 36.85% used detergents soap and bleach (Table 3).\n",
-                        "\n",
-                        "header: Physical integrity and effectiveness of LLINs\n",
-                        "\n",
-                        "Ten LLINs aged 3-7 years old were sampled to assess their physical integrity and bio-efficacy. A total of 161 holes belonging to all the four different types described by WHOPES (the size of a person's thumb, fist or head, or larger than a head) were counted, with a mean of 17 holes per bed net. The proportion of these holes per type was 52.79% (thumb size), 32.29% (fist size), 9.93% (head size) and 4.96% (large than head size). Of the ten LLINs examined, seven (70%) were damaged and were found to be in poor condition (pHI > 300) (Table 4).\n",
-                        "\n",
-                        "A total of 2000 specimens of the _An. gambiae_ (_s.l._) susceptible Kisumu strain were exposed to the ten bed nets. Eight of these nets were effective against this strain, with mortality rates > 80%. Exposure of the same number of _An. gambiae_ (_s.l._) field F1 mosquitoes to these LLINs revealed that they were all ineffective, with mortalities ranging from 0 to 11.5% (Table 4).\n",
-                        "\n",
-                        "header: Bed net integrity and bio-efficacy\n",
-                        "\n",
-                        "Ten bed nets were randomly collected from ten selected houses and immediately replaced by new ones. The physical integrity of the nets collected was determined by counting, per category, the number of holes that were approximately the size of a person's thumb, fist or head, or larger than a head, on any of the four faces and the top of the bed net [26, 27]. The proportionate hole index (pH) was calculated using the WHO Pesticide Evaluation Scheme (WHOPES ) guidelines [11] and nets were classified into four classes accordingly [28, 29]. Nets with a pHI <25 were classified as \"good\"; those with a pH ranging between 25 and 174, as \"fair\"; those nets with a pHI ranging from 175 to 299, as \"mediocre\"; and those with a pHI > 300, as \"poor\".\n",
-                        "\n",
-                        "\n",
-                        "Cone assays were performed on each net to test for its bio-efficacy as described in WHO guidelines [30]. Four cones were fixed by their widest opening onto four different parts of each face (4 sides and 1 roof) of each net. Ten unfed _An. gambiae_ (_s.l._) female mosquitoes aged 2-5 days were introduced into each cone using a mouth aspirator, for a total of 200 specimens per net. After 3 min of exposure, the mosquitoes were removed from the cones, then transferred into paper cups and fed with a 10% sugar solution. The assay was conducted at 25 +- 1 degC, 80% +- 5% RH, and the number of mosquitoes knocked-down and dead were recorded after 60 min and 24 h, respectively. An untreated net was used as the negative control.\n",
-                        "\n",
-                        "header: Table 3 Origin, use and maintenance of long‑lasting insecticidal nets\n",
-                        "footer: LLIN, Long-lasting insecticidal net\n",
-                        "columns: ['Variables related to LLIN', 'N', 'Frequency (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Variables related to LLIN\":\"Possession\",\"N\":\"Possession\",\"Frequency (%)\":\"Possession\"},\"1\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"65\",\"Frequency (%)\":\"81.25\"},\"2\":{\"Variables related to LLIN\":\"No\",\"N\":\"15\",\"Frequency (%)\":\"18.75\"},\"3\":{\"Variables related to LLIN\":\"Origin\",\"N\":\"Origin\",\"Frequency (%)\":\"Origin\"},\"4\":{\"Variables related to LLIN\":\"Government\",\"N\":\"60\",\"Frequency (%)\":\"92.3\"},\"5\":{\"Variables related to LLIN\":\"Market\",\"N\":\"5\",\"Frequency (%)\":\"7.7\"},\"6\":{\"Variables related to LLIN\":\"Installed\",\"N\":\"Installed\",\"Frequency (%)\":\"Installed\"},\"7\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"59\",\"Frequency (%)\":\"90.76\"},\"8\":{\"Variables related to LLIN\":\"No\",\"N\":\"6\",\"Frequency (%)\":\"9.24\"},\"9\":{\"Variables related to LLIN\":\"Presence per 2 persons\",\"N\":\"Presence per 2 persons\",\"Frequency (%)\":\"Presence per 2 persons\"},\"10\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"31\",\"Frequency (%)\":\"47.7\"},\"11\":{\"Variables related to LLIN\":\"No\",\"N\":\"34\",\"Frequency (%)\":\"52.3\"},\"12\":{\"Variables related to LLIN\":\"Usage\",\"N\":\"Usage\",\"Frequency (%)\":\"Usage\"},\"13\":{\"Variables related to LLIN\":\"Every night\",\"N\":\"53\",\"Frequency (%)\":\"81.54\"},\"14\":{\"Variables related to LLIN\":\"Not every night\",\"N\":\"12\",\"Frequency (%)\":\"18.46\"},\"15\":{\"Variables related to LLIN\":\"Education received on LLINs during distribution\",\"N\":\"Education received on LLINs during distribution\",\"Frequency (%)\":\"Education received on LLINs during distribution\"},\"16\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"45\",\"Frequency (%)\":\"69.92\"},\"17\":{\"Variables related to LLIN\":\"No\",\"N\":\"20\",\"Frequency (%)\":\"30.78\"},\"18\":{\"Variables related to LLIN\":\"Had LLIN been washed\",\"N\":\"Had LLIN been washed\",\"Frequency (%)\":\"Had LLIN been washed\"},\"19\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"38\",\"Frequency (%)\":\"58.46\"},\"20\":{\"Variables related to LLIN\":\"No\",\"N\":\"27\",\"Frequency (%)\":\"41.54\"},\"21\":{\"Variables related to LLIN\":\"Substance used for washing\",\"N\":\"Substance used for washing\",\"Frequency (%)\":\"Substance used for washing\"},\"22\":{\"Variables related to LLIN\":\"Ordinary soap\",\"N\":\"24\",\"Frequency (%)\":\"63.15\"},\"23\":{\"Variables related to LLIN\":\"Detergent and bleach\",\"N\":\"14\",\"Frequency (%)\":\"36.85\"},\"24\":{\"Variables related to LLIN\":\"Drying place\",\"N\":\"Drying place\",\"Frequency (%)\":\"Drying place\"},\"25\":{\"Variables related to LLIN\":\"In sun\",\"N\":\"34\",\"Frequency (%)\":\"89.48\"},\"26\":{\"Variables related to LLIN\":\"In shade\",\"N\":\"4\",\"Frequency (%)\":\"10.52\"}}\n",
-                        "\n",
-                        "header: Table 1 Plasmodium circumsporozoite index, human biting rate and monthly entomological inoculation rate of Anopheles species in Mvoua between August 2018 and April 2019\n",
-                        "footer: Human-biting rate (HBR) is the average number of bites received per person per night; (ii) Plasmodium circumsporozoite index (infection rate) is the proportion of mosquitoes found with Plasmodium circumsporozoite antigen in the heads and thoraces; entomological inoculation rate is the product of the HBR and circumsporozoite rateNC not calculated\n",
-                        "columns: ['Anopheles species', 'Tested (N)', 'Positive (N)', 'Entomological parameters - Plasmodium circumsporozoite index (%)', 'Entomological parameters - Human biting rate', 'Entomological parameters - Human biting rate.1', 'Entomological parameters - Entomological inoculation rate/ month']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species\":\"An. funestus (s.l.)\",\"Tested (N)\":65,\"Positive (N)\":7,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"10.77\",\"Entomological parameters - Human biting rate\":0.55,\"Entomological parameters - Human biting rate.1\":0.55,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.5\"},\"1\":{\"Anopheles species\":\"An. gambiae (s.l.)\",\"Tested (N)\":42,\"Positive (N)\":6,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"14.29\",\"Entomological parameters - Human biting rate\":0.35,\"Entomological parameters - Human biting rate.1\":0.35,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.2\"},\"2\":{\"Anopheles species\":\"An. ziemanii\",\"Tested (N)\":3,\"Positive (N)\":0,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"0\",\"Entomological parameters - Human biting rate\":0.02,\"Entomological parameters - Human biting rate.1\":0.02,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"NC\"},\"3\":{\"Anopheles species\":\"Total\",\"Tested (N)\":110,\"Positive (N)\":13,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"11.81%\",\"Entomological parameters - Human biting rate\":0.93,\"Entomological parameters - Human biting rate.1\":0.93,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"2.7\"}}\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare they have no competing interests.\n",
-                        "\n",
-                        "header: Table 4 Hole index and mortality of An. gambiae (s.l.) after being exposed to the long‑lasting insecticidal nets\n",
-                        "footer: Not eff., not e\u001d\n",
-                        "fficient; PHI, proportionate hole index\n",
-                        "columns: ['Nets', 'Date of impregnation', 'Brand', 'Physical integrity of LLIN - pHI', 'Physical integrity of LLIN - Status', 'Bio‑efficacy of LLIN (%) - Kisumu control strain', 'Bio‑efficacy of LLIN (%) - Field strain', 'Decision - Unnamed: 7_level_1']\n",
-                        "\n",
-                        "{\"0\":{\"Nets\":\"Net 1\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":0,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":83.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"1\":{\"Nets\":\"Net 2\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":916,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":80.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":0.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"2\":{\"Nets\":\"Net 3\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1939,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":69.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"3\":{\"Nets\":\"Net 4\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":581,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":88.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"4\":{\"Nets\":\"Net 5\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":13,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":99.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":8.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"5\":{\"Nets\":\"Net 6\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":651,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":85.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"6\":{\"Nets\":\"Net 7\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":2225,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":72.4,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"7\":{\"Nets\":\"Net 8\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":98.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":9.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"8\":{\"Nets\":\"Net 9\",\"Date of impregnation\":\"May 2014\",\"Brand\":\"Royal sentry\",\"Physical integrity of LLIN - pHI\":1180,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":95.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"9\":{\"Nets\":\"Net 10\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":1395,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":87.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"}}\n",
-                        "\n",
-                        "header: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate\n",
-                        "\n",
-                        "The study was approved by the Carneroonian National Ethical Committee for Research on Human Health (Statement No 2018/07/20/CE/NIRER/SFD). One week before the beginning of field activities, the population received a notice of information explaining the objectives, methodology, expected benefits and possible risks of the study. A consent form was sought from the head of households and for participants aged > 18 years for mosquito collection. They were given the opportunity to ask questions and they were informed that they are free to withdraw from the study at any time, without penalty or loss of benefits. At the end of each sampling period, all mosquito collectors were given anti-malaria prophylaxis based on artesunate/amodiaquine according to the national guide for malaria treatment.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data were analyzed using SPSS software version 25 (IBM Corp., Armonk, NY, USA). The entomological parameters that were considered were: (i) human-biting rate (HBR), i.e. the average number of bites received per person per night; (ii) infection rate, i.e. the proportion of mosquitoes found with _Plasmodium_ circumsporozoite antigen in the heads and thoraces; (iii) entomological inoculation rate (EIR), i.e. the product of the HBR and circumsporozoite rate. Chi-square statistics was used to compare mosquito densities between seasons while mosquitoes' seasonal aggressiveness (HBR) were compared by the Kruskal-Wallis test. Differences were considered statistically significant at \\(P\\) < 0.05.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Insights into factors sustaining persistence of high malaria transmission in forested areas of sub-Saharan Africa: the case of Mvoua, South Cameroon\n",
-                        "\n",
-                        "Dominique Mieguim Ngninpogni\n",
-                        "\n",
-                        "\n",
-                        "**Background:** In Mvoua, a village situated in a forested area of Cameroon, recent studies have reported high prevalence of _Plasmodium falciparum_ infection among the population. In order to understand factors that can sustain such a high malaria transmission, we investigated the biology of _Anopheles_ vectors and its susceptibility to insecticides, as well as long-lasting insecticidal net (LLIN) coverage, use and bio-efficacy.\n",
-                        "\n",
-                        "**Methods:** A longitudinal entomological survey was conducted from July 2018 to April 2019. Adult mosquitoes were collected using the human landing catch (HLC) method and identified using morphological and molecular techniques. _Anopheles gambiae (s.l)_ larvae were sampled from several stagnant water pools throughout the village and reared to generate F1 adults. The presence of _P. falciparum_ circumsporozoite antigen was detected in the heads and thoraces of mosquitoes collected as adults using an enzyme-linked immunosorbent assay. The insecticide susceptibility status of the local _An. gambiae (s.l)_ F1 population to the pyrethroid insecticides deltamethrin 0.5% and permethrin 0.75% was determined using World Health Organization-tube bioassays, while the frequency of the knockdown resistance (_kdt_) mutation was determined by PCR. Coverage, use and physical integrity of LLINs were assessed in households, then cone assays were used to test for their bio-efficacy on both the reference insecticide-susceptible Kisumu strain and on field F1 _An. gambiae (s.l)_\n",
-                        "\n",
-                        "**Results:** In total, 110 _Anopheles_ mosquitoes were collected, of which 59.1% were identified as _Anopheles funestus (s.l)_, 38.18% as _An. gambiae (s.l)_ and 2.72% as _An. Ziemanili. Anopheles funestus_ was the most abundant species except in the long rainy season, when _An. gambiae (s.l)_ predominated (65.8%). In the dry seasons, vectors were principally endophagous (76% of those collected indoors) while they tended to be esophagus (66% of those collected outdoors) in rainy seasons. High _Plasmodium_ infection was observed in _An. gambiae (s.l)_ and _An. funestus_, with a circumsporozoite rate of 14.29 and 10.77%, respectively. _Anopheles gambiae (s.l)_ was highly resistant to pyrethroid insecticides (mortality rates: 32% for permethrin and 5% for deltamethrin) and harbored the _kdt_-L1014F mutation at a high frequency (89.74%). Of the 80 households surveyed, only 47.69% had achieved universal coverage with LLINs. Around 70% of the LLINs\n",
-                        "sampled were in poor physical condition, with a proportionate hole index > 300. Of the ten LLNs tested, eight were effective against the _An. gambiae_ reference insecticide-susceptible Kisumu strain, showing mortality rate of > 80%, while none of these LLINs were efficient against local _An. gamabie_ (\\(x_{l}\\)) populations (mortality rates < 11.5%).\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Table 2 Anopheles gambiae complex species composition and the frequency of the knockdown resistance L1014F mutation\n",
-                        "footer: kdr-L1014F, Knockdown resistance L1014F mutation; R resistance; S, susceptible\n",
-                        "columns: ['Anopheles species - Unnamed: 0_level_1', 'Composition - N', 'Composition - %', 'kdr-L1014F genotypes - RR', 'kdr-L1014F genotypes - RS', 'kdr-L1014F genotypes - SS', 'kdr-L1014F alleles (%) - R', 'kdr-L1014F alleles (%) - S']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. gambiae\",\"Composition - N\":28,\"Composition - %\":71.8,\"kdr-L1014F genotypes - RR\":27,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":0,\"kdr-L1014F alleles (%) - R\":98.21,\"kdr-L1014F alleles (%) - S\":1.79},\"1\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. coluzzii\",\"Composition - N\":11,\"Composition - %\":28.2,\"kdr-L1014F genotypes - RR\":8,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":77.27,\"kdr-L1014F alleles (%) - S\":22.73},\"2\":{\"Anopheles species - Unnamed: 0_level_1\":\"Total\",\"Composition - N\":39,\"Composition - %\":100.0,\"kdr-L1014F genotypes - RR\":35,\"kdr-L1014F genotypes - RS\":2,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":92.31,\"kdr-L1014F alleles (%) - S\":7.69}}\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In total, 129 adult mosquitoes were collected during eight nights. These belonged to four genera, of which _Anopheles_ (_N_ = 110; 85.27%) was the most prevalent, followed in decreasing order of prevalence by _Mansonia_ (_N_ = 11; 8.53%), _Aedes_ (_N_ = 6; 4.65%) and _Culex_ (_N_ = 2; 1.55%). Three _Anopheles_ species were collected namely _An. funestus_ (_s.l._) (59.1%), _An. gambiae_ (_s.l._) (38.18%) and _An. zienanii_ (2.72%). _Anopheles. sp_ were most abundant in the short dry season followed by the long rainy season (49.09% of the total _Anopheles_ collected) and the long rainy season (34.54%).\n",
-                        "\n",
-                        "In terms of seasonal species abundance, _An. funestus_ was the most abundant species collected (59.1%) over the period of study, with the exception of the long rainy season when _An. gambiae_ (_s.l._) predominated (Fig. 1).\n",
-                        "\n",
-                        "header: Study area: Adult mosquito collection\n",
-                        "\n",
-                        "Collections of adult host-seeking mosquitoes were undertaken employing both the CDC light trap (CDC-LT) and human landing catch (HLC) methods. Because no mosquito was collected using the CDC-LTs after two consecutive nights of sampling, this method was discarded.\n",
-                        "\n",
-                        "Sampling using the HLC method was performed in ten randomly selected houses situated at least 100 m apart. The method involved persons sitting with their lower legs exposed and then collecting mosquitoes that just landed on them [14]. Adult mosquitoes were sampled both indoors and outdoors during two consecutive nights, once per each of the four seasons. Mosquitoes collected each hour were placed into separate bags, labeled accordingly and brought back to the laboratory for further analysis. To avoid bias due to sleep and tiredness, one team of collectors worked from 18:00 h to midnight, and was replaced by another team which worked from midnight to 6:00 h.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Author details\n",
-                        "\n",
-                        "1 Laboratory of Parasitology and Ecology, Faculty of Sciences, University of Yaoundet i P.O. Box 812, Yaoundet, Cameroon. 1 Institute for the Recherche de Yaoundet (FR), Organizing the Coordination pour la Tutte Contre les Endrines en Afrique Centrale (OCEAC), PO. Box 283, Yaoundet, Cameroon. 3 Department of Biological Sciences, Faculty of Medicine and Pharmaceutical Sciences, University of Ouala, PO. Box 2415, Ouala, Cameroon. 4 Department of Parasitology and Microbiology, Centre for Research in Infectious Diseases (RDI), PO. Box 13591, Yaounde, Cameroon. 5 Animal Organisms Laboratory, Faculty of Sciences, University of Ouala, PO. Box 24157, Douala, Cameroon.\n",
-                        "\n",
-                        "header: Insecticide bioassays and knockdown resistance detection\n",
-                        "\n",
-                        "Larvae of _An. gambiae_ (_s.l._) were collected in stagnant pools of water and reared in the insectary of the medical entomology laboratory of OCEAC. F1 adults that emerged from those larvae were fed with 10% sucrose solution made by dissolving 100 g of ordinary white sugar in 1 L of water [22]. _Anopheles funestus_ was not tested due to the low number of adults obtained following larval collections and rearing. The susceptibility of the F1 adults to 0.75% permethrin and 0.05% deltamethrin, both pyrethroid pesticides, was assessed using a World Health Organization (WHO) standard test procedure [23]. Tests were performed at 25 +- 2 degC, 806 +- 10% relative humidity (RH). For each insecticide, four batches of 20-25 field F1 females, aged between 2 and 5 days, were exposed to insecticide-impregnated papers in WHO test-tube for 1 h. At the same time, two batches of the same number of mosquitoes were exposed to untreated papers as control. At the end of the insecticide exposure period, the number of knocked-down mosquitoes was recorded, following which the mosquitoes were transferred into holding tubes. Cotton balls that had first been soaked in a 10% sugar solution and then the moisture squeezed out were placed at the mouth of the tubes. The mortality was recorded 24 h later. Mortality rate in the tested samples was corrected using Abbott's formula [24], when the mortality in the control tubes varied between 5 to 20%. The knockdown resistance (_kdr_) mutation L1014F, which is responsible of cross resistance to DDT (dichlorodiphenyl-trichloroethane) and pyrethroids was genotyped using the protocol described by Martinez-Tores et al. [25].\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This research was funded by the Royal Society of Tropical Medicine and Hygiene (RSTM#) through a small grant awarded to DM and the Wellcome Trust through a Wellcome Trust Training fellowship in public health and tropical medicine to CN (102543/2/13/2). The RSTM# and the Wellcome Trust have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\n",
-                        "\n",
-                        "header: Bed net coverage, use and maintenance\n",
-                        "\n",
-                        "Bed net ownership was investigated in 80 of 97 the households that currently make up the village through visual inspection. The heads of these households or their representatives were questioned on their use of nets and their maintenance. The number of people living in each house was recorded to estimate net coverage. Household net ownership was defined as the percentage of households owning at least one LLIN. Net coverage defined as the percentage of households with at least one LLIN for every two people was also determined.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichlorodiphenyltrichloroethane; ER: Entomological inoculation rate; ELISA CSP: Enzyme-linked immunosorbent assay to detect the circumsporolite protein; HL: Human landing catch method; Adv: Knockdown resistance; LINK: long-lasting insecticidal net; pH: Proportionate hole index; WHORES: World Health Organization Pesticides Evaluation Scheme\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "A combination of elevated _P. falciparum_ infection in _Anopheles_ vector populations, insufficient coverage and loss of effectiveness of LLINs due to physical degradation, as well as high resistance to pyrethroid insecticides is responsible for the persistence of high malaria transmission in forested rural area of Mvoua, Cameroon.\n",
-                        "\n",
-                        "header: Physical integrity and effectiveness of LLINs\n",
-                        "\n",
-                        "Ten LLINs aged 3-7 years old were sampled to assess their physical integrity and bio-efficacy. A total of 161 holes belonging to all the four different types described by WHOPES (the size of a person's thumb, fist or head, or larger than a head) were counted, with a mean of 17 holes per bed net. The proportion of these holes per type was 52.79% (thumb size), 32.29% (fist size), 9.93% (head size) and 4.96% (large than head size). Of the ten LLINs examined, seven (70%) were damaged and were found to be in poor condition (pHI > 300) (Table 4).\n",
-                        "\n",
-                        "A total of 2000 specimens of the _An. gambiae_ (_s.l._) susceptible Kisumu strain were exposed to the ten bed nets. Eight of these nets were effective against this strain, with mortality rates > 80%. Exposure of the same number of _An. gambiae_ (_s.l._) field F1 mosquitoes to these LLINs revealed that they were all ineffective, with mortalities ranging from 0 to 11.5% (Table 4).\n",
-                        "\n",
-                        "header: Possession, coverage and use of LLINs\n",
-                        "\n",
-                        "In general, 81.25% of the households inspected possessed at least one LLIN. Despite this high level of ownership, the proportion of households that met the ratio of one net for two persons was only 47.69%. Fortunately, 81.54% of the people interviewed affirmed they used their bed nets every night (Table 3). Of the LLINs available, 92.3% were acquired during the government free mass distribution campaigns of 2011-2012 and 2015-2016. Although almost 70% of the study population affirmed that they have been educated by the public health community agents during the distribution campaigns, only 10.52% knew bed nets should be dried in the shade before usage or after washing. Moreover, 56.46% of respondents had washed their bet nets at least once; 63.15% used water and ordinary soap while 36.85% used detergents soap and bleach (Table 3).\n",
-                        "\n",
-                        "header: Bed net integrity and bio-efficacy\n",
-                        "\n",
-                        "Ten bed nets were randomly collected from ten selected houses and immediately replaced by new ones. The physical integrity of the nets collected was determined by counting, per category, the number of holes that were approximately the size of a person's thumb, fist or head, or larger than a head, on any of the four faces and the top of the bed net [26, 27]. The proportionate hole index (pH) was calculated using the WHO Pesticide Evaluation Scheme (WHOPES ) guidelines [11] and nets were classified into four classes accordingly [28, 29]. Nets with a pHI <25 were classified as \"good\"; those with a pH ranging between 25 and 174, as \"fair\"; those nets with a pHI ranging from 175 to 299, as \"mediocre\"; and those with a pHI > 300, as \"poor\".\n",
-                        "\n",
-                        "\n",
-                        "Cone assays were performed on each net to test for its bio-efficacy as described in WHO guidelines [30]. Four cones were fixed by their widest opening onto four different parts of each face (4 sides and 1 roof) of each net. Ten unfed _An. gambiae_ (_s.l._) female mosquitoes aged 2-5 days were introduced into each cone using a mouth aspirator, for a total of 200 specimens per net. After 3 min of exposure, the mosquitoes were removed from the cones, then transferred into paper cups and fed with a 10% sugar solution. The assay was conducted at 25 +- 1 degC, 80% +- 5% RH, and the number of mosquitoes knocked-down and dead were recorded after 60 min and 24 h, respectively. An untreated net was used as the negative control.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data were analyzed using SPSS software version 25 (IBM Corp., Armonk, NY, USA). The entomological parameters that were considered were: (i) human-biting rate (HBR), i.e. the average number of bites received per person per night; (ii) infection rate, i.e. the proportion of mosquitoes found with _Plasmodium_ circumsporozoite antigen in the heads and thoraces; (iii) entomological inoculation rate (EIR), i.e. the product of the HBR and circumsporozoite rate. Chi-square statistics was used to compare mosquito densities between seasons while mosquitoes' seasonal aggressiveness (HBR) were compared by the Kruskal-Wallis test. Differences were considered statistically significant at \\(P\\) < 0.05.\n",
-                        "\n",
-                        "header: Table 1 Plasmodium circumsporozoite index, human biting rate and monthly entomological inoculation rate of Anopheles species in Mvoua between August 2018 and April 2019\n",
-                        "footer: Human-biting rate (HBR) is the average number of bites received per person per night; (ii) Plasmodium circumsporozoite index (infection rate) is the proportion of mosquitoes found with Plasmodium circumsporozoite antigen in the heads and thoraces; entomological inoculation rate is the product of the HBR and circumsporozoite rateNC not calculated\n",
-                        "columns: ['Anopheles species', 'Tested (N)', 'Positive (N)', 'Entomological parameters - Plasmodium circumsporozoite index (%)', 'Entomological parameters - Human biting rate', 'Entomological parameters - Human biting rate.1', 'Entomological parameters - Entomological inoculation rate/ month']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species\":\"An. funestus (s.l.)\",\"Tested (N)\":65,\"Positive (N)\":7,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"10.77\",\"Entomological parameters - Human biting rate\":0.55,\"Entomological parameters - Human biting rate.1\":0.55,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.5\"},\"1\":{\"Anopheles species\":\"An. gambiae (s.l.)\",\"Tested (N)\":42,\"Positive (N)\":6,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"14.29\",\"Entomological parameters - Human biting rate\":0.35,\"Entomological parameters - Human biting rate.1\":0.35,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.2\"},\"2\":{\"Anopheles species\":\"An. ziemanii\",\"Tested (N)\":3,\"Positive (N)\":0,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"0\",\"Entomological parameters - Human biting rate\":0.02,\"Entomological parameters - Human biting rate.1\":0.02,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"NC\"},\"3\":{\"Anopheles species\":\"Total\",\"Tested (N)\":110,\"Positive (N)\":13,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"11.81%\",\"Entomological parameters - Human biting rate\":0.93,\"Entomological parameters - Human biting rate.1\":0.93,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"2.7\"}}\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare they have no competing interests.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate\n",
-                        "\n",
-                        "The study was approved by the Carneroonian National Ethical Committee for Research on Human Health (Statement No 2018/07/20/CE/NIRER/SFD). One week before the beginning of field activities, the population received a notice of information explaining the objectives, methodology, expected benefits and possible risks of the study. A consent form was sought from the head of households and for participants aged > 18 years for mosquito collection. They were given the opportunity to ask questions and they were informed that they are free to withdraw from the study at any time, without penalty or loss of benefits. At the end of each sampling period, all mosquito collectors were given anti-malaria prophylaxis based on artesunate/amodiaquine according to the national guide for malaria treatment.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The findings of this study highlight high and perennial malaria transmission in Mvoua, with the highest infection rate in _Anopheles_ vectors observed in the short dry season. Three major vectors, namely _An. funestus_, _An. coluzzizi_ and _An. gambiae_, were responsible for the transmission of the disease. This vector displayed a high level of resistance to pyrethroid insecticides due to the _kdr_ mutation, but also probably due to metabolic mechanisms. Although there was a high level of LLIN possession among the households, universal coverage was not reached and LLINs found in the locality were no longer effective against the local _An. gambiae_ (_s.l._) strain. The National Malaria Control Program should provide the locality with new LLINs every 3 years, as recommended by WHO. Preferably, to manage the high pyrethroid resistance observed, these LLINs should incorporate a synergism to block the action of detoxification enzymes. People should be educated on the use and maintenance of LLINs, with an emphasis on the importance of sleeping under LLINs irrespective of season, as well as on the damaging impact of detergents used for their washing.\n",
-                        "\n",
-                        "header: Table 2 Anopheles gambiae complex species composition and the frequency of the knockdown resistance L1014F mutation\n",
-                        "footer: kdr-L1014F, Knockdown resistance L1014F mutation; R resistance; S, susceptible\n",
-                        "columns: ['Anopheles species - Unnamed: 0_level_1', 'Composition - N', 'Composition - %', 'kdr-L1014F genotypes - RR', 'kdr-L1014F genotypes - RS', 'kdr-L1014F genotypes - SS', 'kdr-L1014F alleles (%) - R', 'kdr-L1014F alleles (%) - S']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. gambiae\",\"Composition - N\":28,\"Composition - %\":71.8,\"kdr-L1014F genotypes - RR\":27,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":0,\"kdr-L1014F alleles (%) - R\":98.21,\"kdr-L1014F alleles (%) - S\":1.79},\"1\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. coluzzii\",\"Composition - N\":11,\"Composition - %\":28.2,\"kdr-L1014F genotypes - RR\":8,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":77.27,\"kdr-L1014F alleles (%) - S\":22.73},\"2\":{\"Anopheles species - Unnamed: 0_level_1\":\"Total\",\"Composition - N\":39,\"Composition - %\":100.0,\"kdr-L1014F genotypes - RR\":35,\"kdr-L1014F genotypes - RS\":2,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":92.31,\"kdr-L1014F alleles (%) - S\":7.69}}\n",
-                        "\n",
-                        "header: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Insights into factors sustaining persistence of high malaria transmission in forested areas of sub-Saharan Africa: the case of Mvoua, South Cameroon\n",
-                        "\n",
-                        "Dominique Mieguim Ngninpogni\n",
-                        "\n",
-                        "\n",
-                        "**Background:** In Mvoua, a village situated in a forested area of Cameroon, recent studies have reported high prevalence of _Plasmodium falciparum_ infection among the population. In order to understand factors that can sustain such a high malaria transmission, we investigated the biology of _Anopheles_ vectors and its susceptibility to insecticides, as well as long-lasting insecticidal net (LLIN) coverage, use and bio-efficacy.\n",
-                        "\n",
-                        "**Methods:** A longitudinal entomological survey was conducted from July 2018 to April 2019. Adult mosquitoes were collected using the human landing catch (HLC) method and identified using morphological and molecular techniques. _Anopheles gambiae (s.l)_ larvae were sampled from several stagnant water pools throughout the village and reared to generate F1 adults. The presence of _P. falciparum_ circumsporozoite antigen was detected in the heads and thoraces of mosquitoes collected as adults using an enzyme-linked immunosorbent assay. The insecticide susceptibility status of the local _An. gambiae (s.l)_ F1 population to the pyrethroid insecticides deltamethrin 0.5% and permethrin 0.75% was determined using World Health Organization-tube bioassays, while the frequency of the knockdown resistance (_kdt_) mutation was determined by PCR. Coverage, use and physical integrity of LLINs were assessed in households, then cone assays were used to test for their bio-efficacy on both the reference insecticide-susceptible Kisumu strain and on field F1 _An. gambiae (s.l)_\n",
-                        "\n",
-                        "**Results:** In total, 110 _Anopheles_ mosquitoes were collected, of which 59.1% were identified as _Anopheles funestus (s.l)_, 38.18% as _An. gambiae (s.l)_ and 2.72% as _An. Ziemanili. Anopheles funestus_ was the most abundant species except in the long rainy season, when _An. gambiae (s.l)_ predominated (65.8%). In the dry seasons, vectors were principally endophagous (76% of those collected indoors) while they tended to be esophagus (66% of those collected outdoors) in rainy seasons. High _Plasmodium_ infection was observed in _An. gambiae (s.l)_ and _An. funestus_, with a circumsporozoite rate of 14.29 and 10.77%, respectively. _Anopheles gambiae (s.l)_ was highly resistant to pyrethroid insecticides (mortality rates: 32% for permethrin and 5% for deltamethrin) and harbored the _kdt_-L1014F mutation at a high frequency (89.74%). Of the 80 households surveyed, only 47.69% had achieved universal coverage with LLINs. Around 70% of the LLINs\n",
-                        "sampled were in poor physical condition, with a proportionate hole index > 300. Of the ten LLNs tested, eight were effective against the _An. gambiae_ reference insecticide-susceptible Kisumu strain, showing mortality rate of > 80%, while none of these LLINs were efficient against local _An. gamabie_ (\\(x_{l}\\)) populations (mortality rates < 11.5%).\n",
-                        "\n",
-                        "header: Mosquito identification and detection of _Plasmodium infection_\n",
-                        "\n",
-                        "Adult mosquitoes collected using the HLC method were identified based on morphological criteria following the identification keys of Gillies and De Meillon [15] and Gillies and Coetzee [16]. Female _Anopheles_ mosquitoes were sorted from other _Culicinae_, stored in Eppendorf tubes with silica gel (desiccant) and taken to the medical entomology laboratory of OCEAC (Organisation de Coordination pour la lutte Contre les Endemies en Afrique Centrale) for subsequent analyses. Heads and thoraces were processed for detection of _P. falciparum_ circumsporozoite protein (CSP) using an enzyme-linked immunosorbent assay (ELISA) method as previously described [17, 18]. DNA extracted from the abdomen and legs [19] was used for the molecular identification of sibling species by PCR, as described previously [20, 21].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In total, 129 adult mosquitoes were collected during eight nights. These belonged to four genera, of which _Anopheles_ (_N_ = 110; 85.27%) was the most prevalent, followed in decreasing order of prevalence by _Mansonia_ (_N_ = 11; 8.53%), _Aedes_ (_N_ = 6; 4.65%) and _Culex_ (_N_ = 2; 1.55%). Three _Anopheles_ species were collected namely _An. funestus_ (_s.l._) (59.1%), _An. gambiae_ (_s.l._) (38.18%) and _An. zienanii_ (2.72%). _Anopheles. sp_ were most abundant in the short dry season followed by the long rainy season (49.09% of the total _Anopheles_ collected) and the long rainy season (34.54%).\n",
-                        "\n",
-                        "In terms of seasonal species abundance, _An. funestus_ was the most abundant species collected (59.1%) over the period of study, with the exception of the long rainy season when _An. gambiae_ (_s.l._) predominated (Fig. 1).\n",
-                        "\n",
-                        "header: Table 4 Hole index and mortality of An. gambiae (s.l.) after being exposed to the long‑lasting insecticidal nets\n",
-                        "footer: Not eff., not e\u001d\n",
-                        "fficient; PHI, proportionate hole index\n",
-                        "columns: ['Nets', 'Date of impregnation', 'Brand', 'Physical integrity of LLIN - pHI', 'Physical integrity of LLIN - Status', 'Bio‑efficacy of LLIN (%) - Kisumu control strain', 'Bio‑efficacy of LLIN (%) - Field strain', 'Decision - Unnamed: 7_level_1']\n",
-                        "\n",
-                        "{\"0\":{\"Nets\":\"Net 1\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":0,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":83.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"1\":{\"Nets\":\"Net 2\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":916,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":80.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":0.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"2\":{\"Nets\":\"Net 3\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1939,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":69.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"3\":{\"Nets\":\"Net 4\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":581,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":88.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"4\":{\"Nets\":\"Net 5\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":13,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":99.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":8.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"5\":{\"Nets\":\"Net 6\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":651,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":85.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"6\":{\"Nets\":\"Net 7\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":2225,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":72.4,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"7\":{\"Nets\":\"Net 8\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":98.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":9.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"8\":{\"Nets\":\"Net 9\",\"Date of impregnation\":\"May 2014\",\"Brand\":\"Royal sentry\",\"Physical integrity of LLIN - pHI\":1180,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":95.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"9\":{\"Nets\":\"Net 10\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":1395,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":87.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This research was funded by the Royal Society of Tropical Medicine and Hygiene (RSTM#) through a small grant awarded to DM and the Wellcome Trust through a Wellcome Trust Training fellowship in public health and tropical medicine to CN (102543/2/13/2). The RSTM# and the Wellcome Trust have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichlorodiphenyltrichloroethane; ER: Entomological inoculation rate; ELISA CSP: Enzyme-linked immunosorbent assay to detect the circumsporolite protein; HL: Human landing catch method; Adv: Knockdown resistance; LINK: long-lasting insecticidal net; pH: Proportionate hole index; WHORES: World Health Organization Pesticides Evaluation Scheme\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "1 Laboratory of Parasitology and Ecology, Faculty of Sciences, University of Yaoundet i P.O. Box 812, Yaoundet, Cameroon. 1 Institute for the Recherche de Yaoundet (FR), Organizing the Coordination pour la Tutte Contre les Endrines en Afrique Centrale (OCEAC), PO. Box 283, Yaoundet, Cameroon. 3 Department of Biological Sciences, Faculty of Medicine and Pharmaceutical Sciences, University of Ouala, PO. Box 2415, Ouala, Cameroon. 4 Department of Parasitology and Microbiology, Centre for Research in Infectious Diseases (RDI), PO. Box 13591, Yaounde, Cameroon. 5 Animal Organisms Laboratory, Faculty of Sciences, University of Ouala, PO. Box 24157, Douala, Cameroon.\n",
-                        "\n",
-                        "header: Insecticide bioassays and knockdown resistance detection\n",
-                        "\n",
-                        "Larvae of _An. gambiae_ (_s.l._) were collected in stagnant pools of water and reared in the insectary of the medical entomology laboratory of OCEAC. F1 adults that emerged from those larvae were fed with 10% sucrose solution made by dissolving 100 g of ordinary white sugar in 1 L of water [22]. _Anopheles funestus_ was not tested due to the low number of adults obtained following larval collections and rearing. The susceptibility of the F1 adults to 0.75% permethrin and 0.05% deltamethrin, both pyrethroid pesticides, was assessed using a World Health Organization (WHO) standard test procedure [23]. Tests were performed at 25 +- 2 degC, 806 +- 10% relative humidity (RH). For each insecticide, four batches of 20-25 field F1 females, aged between 2 and 5 days, were exposed to insecticide-impregnated papers in WHO test-tube for 1 h. At the same time, two batches of the same number of mosquitoes were exposed to untreated papers as control. At the end of the insecticide exposure period, the number of knocked-down mosquitoes was recorded, following which the mosquitoes were transferred into holding tubes. Cotton balls that had first been soaked in a 10% sugar solution and then the moisture squeezed out were placed at the mouth of the tubes. The mortality was recorded 24 h later. Mortality rate in the tested samples was corrected using Abbott's formula [24], when the mortality in the control tubes varied between 5 to 20%. The knockdown resistance (_kdr_) mutation L1014F, which is responsible of cross resistance to DDT (dichlorodiphenyl-trichloroethane) and pyrethroids was genotyped using the protocol described by Martinez-Tores et al. [25].\n",
-                        "\n",
-                        "header: Table 1 Plasmodium circumsporozoite index, human biting rate and monthly entomological inoculation rate of Anopheles species in Mvoua between August 2018 and April 2019\n",
-                        "footer: Human-biting rate (HBR) is the average number of bites received per person per night; (ii) Plasmodium circumsporozoite index (infection rate) is the proportion of mosquitoes found with Plasmodium circumsporozoite antigen in the heads and thoraces; entomological inoculation rate is the product of the HBR and circumsporozoite rateNC not calculated\n",
-                        "columns: ['Anopheles species', 'Tested (N)', 'Positive (N)', 'Entomological parameters - Plasmodium circumsporozoite index (%)', 'Entomological parameters - Human biting rate', 'Entomological parameters - Human biting rate.1', 'Entomological parameters - Entomological inoculation rate/ month']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species\":\"An. funestus (s.l.)\",\"Tested (N)\":65,\"Positive (N)\":7,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"10.77\",\"Entomological parameters - Human biting rate\":0.55,\"Entomological parameters - Human biting rate.1\":0.55,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.5\"},\"1\":{\"Anopheles species\":\"An. gambiae (s.l.)\",\"Tested (N)\":42,\"Positive (N)\":6,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"14.29\",\"Entomological parameters - Human biting rate\":0.35,\"Entomological parameters - Human biting rate.1\":0.35,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.2\"},\"2\":{\"Anopheles species\":\"An. ziemanii\",\"Tested (N)\":3,\"Positive (N)\":0,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"0\",\"Entomological parameters - Human biting rate\":0.02,\"Entomological parameters - Human biting rate.1\":0.02,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"NC\"},\"3\":{\"Anopheles species\":\"Total\",\"Tested (N)\":110,\"Positive (N)\":13,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"11.81%\",\"Entomological parameters - Human biting rate\":0.93,\"Entomological parameters - Human biting rate.1\":0.93,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"2.7\"}}\n",
-                        "\n",
-                        "header: Bed net coverage, use and maintenance\n",
-                        "\n",
-                        "Bed net ownership was investigated in 80 of 97 the households that currently make up the village through visual inspection. The heads of these households or their representatives were questioned on their use of nets and their maintenance. The number of people living in each house was recorded to estimate net coverage. Household net ownership was defined as the percentage of households owning at least one LLIN. Net coverage defined as the percentage of households with at least one LLIN for every two people was also determined.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate\n",
-                        "\n",
-                        "The study was approved by the Carneroonian National Ethical Committee for Research on Human Health (Statement No 2018/07/20/CE/NIRER/SFD). One week before the beginning of field activities, the population received a notice of information explaining the objectives, methodology, expected benefits and possible risks of the study. A consent form was sought from the head of households and for participants aged > 18 years for mosquito collection. They were given the opportunity to ask questions and they were informed that they are free to withdraw from the study at any time, without penalty or loss of benefits. At the end of each sampling period, all mosquito collectors were given anti-malaria prophylaxis based on artesunate/amodiaquine according to the national guide for malaria treatment.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare they have no competing interests.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data were analyzed using SPSS software version 25 (IBM Corp., Armonk, NY, USA). The entomological parameters that were considered were: (i) human-biting rate (HBR), i.e. the average number of bites received per person per night; (ii) infection rate, i.e. the proportion of mosquitoes found with _Plasmodium_ circumsporozoite antigen in the heads and thoraces; (iii) entomological inoculation rate (EIR), i.e. the product of the HBR and circumsporozoite rate. Chi-square statistics was used to compare mosquito densities between seasons while mosquitoes' seasonal aggressiveness (HBR) were compared by the Kruskal-Wallis test. Differences were considered statistically significant at \\(P\\) < 0.05.\n",
-                        "\n",
-                        "header: Possession, coverage and use of LLINs\n",
-                        "\n",
-                        "In general, 81.25% of the households inspected possessed at least one LLIN. Despite this high level of ownership, the proportion of households that met the ratio of one net for two persons was only 47.69%. Fortunately, 81.54% of the people interviewed affirmed they used their bed nets every night (Table 3). Of the LLINs available, 92.3% were acquired during the government free mass distribution campaigns of 2011-2012 and 2015-2016. Although almost 70% of the study population affirmed that they have been educated by the public health community agents during the distribution campaigns, only 10.52% knew bed nets should be dried in the shade before usage or after washing. Moreover, 56.46% of respondents had washed their bet nets at least once; 63.15% used water and ordinary soap while 36.85% used detergents soap and bleach (Table 3).\n",
-                        "\n",
-                        "header: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Table 2 Anopheles gambiae complex species composition and the frequency of the knockdown resistance L1014F mutation\n",
-                        "footer: kdr-L1014F, Knockdown resistance L1014F mutation; R resistance; S, susceptible\n",
-                        "columns: ['Anopheles species - Unnamed: 0_level_1', 'Composition - N', 'Composition - %', 'kdr-L1014F genotypes - RR', 'kdr-L1014F genotypes - RS', 'kdr-L1014F genotypes - SS', 'kdr-L1014F alleles (%) - R', 'kdr-L1014F alleles (%) - S']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. gambiae\",\"Composition - N\":28,\"Composition - %\":71.8,\"kdr-L1014F genotypes - RR\":27,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":0,\"kdr-L1014F alleles (%) - R\":98.21,\"kdr-L1014F alleles (%) - S\":1.79},\"1\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. coluzzii\",\"Composition - N\":11,\"Composition - %\":28.2,\"kdr-L1014F genotypes - RR\":8,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":77.27,\"kdr-L1014F alleles (%) - S\":22.73},\"2\":{\"Anopheles species - Unnamed: 0_level_1\":\"Total\",\"Composition - N\":39,\"Composition - %\":100.0,\"kdr-L1014F genotypes - RR\":35,\"kdr-L1014F genotypes - RS\":2,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":92.31,\"kdr-L1014F alleles (%) - S\":7.69}}\n",
-                        "\n",
-                        "header: Bed net integrity and bio-efficacy\n",
-                        "\n",
-                        "Ten bed nets were randomly collected from ten selected houses and immediately replaced by new ones. The physical integrity of the nets collected was determined by counting, per category, the number of holes that were approximately the size of a person's thumb, fist or head, or larger than a head, on any of the four faces and the top of the bed net [26, 27]. The proportionate hole index (pH) was calculated using the WHO Pesticide Evaluation Scheme (WHOPES ) guidelines [11] and nets were classified into four classes accordingly [28, 29]. Nets with a pHI <25 were classified as \"good\"; those with a pH ranging between 25 and 174, as \"fair\"; those nets with a pHI ranging from 175 to 299, as \"mediocre\"; and those with a pHI > 300, as \"poor\".\n",
-                        "\n",
-                        "\n",
-                        "Cone assays were performed on each net to test for its bio-efficacy as described in WHO guidelines [30]. Four cones were fixed by their widest opening onto four different parts of each face (4 sides and 1 roof) of each net. Ten unfed _An. gambiae_ (_s.l._) female mosquitoes aged 2-5 days were introduced into each cone using a mouth aspirator, for a total of 200 specimens per net. After 3 min of exposure, the mosquitoes were removed from the cones, then transferred into paper cups and fed with a 10% sugar solution. The assay was conducted at 25 +- 1 degC, 80% +- 5% RH, and the number of mosquitoes knocked-down and dead were recorded after 60 min and 24 h, respectively. An untreated net was used as the negative control.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Physical integrity and effectiveness of LLINs\n",
-                        "\n",
-                        "Ten LLINs aged 3-7 years old were sampled to assess their physical integrity and bio-efficacy. A total of 161 holes belonging to all the four different types described by WHOPES (the size of a person's thumb, fist or head, or larger than a head) were counted, with a mean of 17 holes per bed net. The proportion of these holes per type was 52.79% (thumb size), 32.29% (fist size), 9.93% (head size) and 4.96% (large than head size). Of the ten LLINs examined, seven (70%) were damaged and were found to be in poor condition (pHI > 300) (Table 4).\n",
-                        "\n",
-                        "A total of 2000 specimens of the _An. gambiae_ (_s.l._) susceptible Kisumu strain were exposed to the ten bed nets. Eight of these nets were effective against this strain, with mortality rates > 80%. Exposure of the same number of _An. gambiae_ (_s.l._) field F1 mosquitoes to these LLINs revealed that they were all ineffective, with mortalities ranging from 0 to 11.5% (Table 4).\n",
-                        "\n",
-                        "header: Study area: Adult mosquito collection\n",
-                        "\n",
-                        "Collections of adult host-seeking mosquitoes were undertaken employing both the CDC light trap (CDC-LT) and human landing catch (HLC) methods. Because no mosquito was collected using the CDC-LTs after two consecutive nights of sampling, this method was discarded.\n",
-                        "\n",
-                        "Sampling using the HLC method was performed in ten randomly selected houses situated at least 100 m apart. The method involved persons sitting with their lower legs exposed and then collecting mosquitoes that just landed on them [14]. Adult mosquitoes were sampled both indoors and outdoors during two consecutive nights, once per each of the four seasons. Mosquitoes collected each hour were placed into separate bags, labeled accordingly and brought back to the laboratory for further analysis. To avoid bias due to sleep and tiredness, one team of collectors worked from 18:00 h to midnight, and was replaced by another team which worked from midnight to 6:00 h.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "A combination of elevated _P. falciparum_ infection in _Anopheles_ vector populations, insufficient coverage and loss of effectiveness of LLINs due to physical degradation, as well as high resistance to pyrethroid insecticides is responsible for the persistence of high malaria transmission in forested rural area of Mvoua, Cameroon.\n",
-                        "\n",
-                        "header: Table 4 Hole index and mortality of An. gambiae (s.l.) after being exposed to the long‑lasting insecticidal nets\n",
-                        "footer: Not eff., not e\u001d\n",
-                        "fficient; PHI, proportionate hole index\n",
-                        "columns: ['Nets', 'Date of impregnation', 'Brand', 'Physical integrity of LLIN - pHI', 'Physical integrity of LLIN - Status', 'Bio‑efficacy of LLIN (%) - Kisumu control strain', 'Bio‑efficacy of LLIN (%) - Field strain', 'Decision - Unnamed: 7_level_1']\n",
-                        "\n",
-                        "{\"0\":{\"Nets\":\"Net 1\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":0,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":83.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"1\":{\"Nets\":\"Net 2\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":916,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":80.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":0.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"2\":{\"Nets\":\"Net 3\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1939,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":69.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"3\":{\"Nets\":\"Net 4\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":581,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":88.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"4\":{\"Nets\":\"Net 5\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":13,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":99.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":8.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"5\":{\"Nets\":\"Net 6\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":651,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":85.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"6\":{\"Nets\":\"Net 7\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":2225,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":72.4,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"7\":{\"Nets\":\"Net 8\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":98.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":9.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"8\":{\"Nets\":\"Net 9\",\"Date of impregnation\":\"May 2014\",\"Brand\":\"Royal sentry\",\"Physical integrity of LLIN - pHI\":1180,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":95.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"9\":{\"Nets\":\"Net 10\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":1395,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":87.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"}}\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In total, 129 adult mosquitoes were collected during eight nights. These belonged to four genera, of which _Anopheles_ (_N_ = 110; 85.27%) was the most prevalent, followed in decreasing order of prevalence by _Mansonia_ (_N_ = 11; 8.53%), _Aedes_ (_N_ = 6; 4.65%) and _Culex_ (_N_ = 2; 1.55%). Three _Anopheles_ species were collected namely _An. funestus_ (_s.l._) (59.1%), _An. gambiae_ (_s.l._) (38.18%) and _An. zienanii_ (2.72%). _Anopheles. sp_ were most abundant in the short dry season followed by the long rainy season (49.09% of the total _Anopheles_ collected) and the long rainy season (34.54%).\n",
-                        "\n",
-                        "In terms of seasonal species abundance, _An. funestus_ was the most abundant species collected (59.1%) over the period of study, with the exception of the long rainy season when _An. gambiae_ (_s.l._) predominated (Fig. 1).\n",
-                        "\n",
-                        "header: Table 3 Origin, use and maintenance of long‑lasting insecticidal nets\n",
-                        "footer: LLIN, Long-lasting insecticidal net\n",
-                        "columns: ['Variables related to LLIN', 'N', 'Frequency (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Variables related to LLIN\":\"Possession\",\"N\":\"Possession\",\"Frequency (%)\":\"Possession\"},\"1\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"65\",\"Frequency (%)\":\"81.25\"},\"2\":{\"Variables related to LLIN\":\"No\",\"N\":\"15\",\"Frequency (%)\":\"18.75\"},\"3\":{\"Variables related to LLIN\":\"Origin\",\"N\":\"Origin\",\"Frequency (%)\":\"Origin\"},\"4\":{\"Variables related to LLIN\":\"Government\",\"N\":\"60\",\"Frequency (%)\":\"92.3\"},\"5\":{\"Variables related to LLIN\":\"Market\",\"N\":\"5\",\"Frequency (%)\":\"7.7\"},\"6\":{\"Variables related to LLIN\":\"Installed\",\"N\":\"Installed\",\"Frequency (%)\":\"Installed\"},\"7\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"59\",\"Frequency (%)\":\"90.76\"},\"8\":{\"Variables related to LLIN\":\"No\",\"N\":\"6\",\"Frequency (%)\":\"9.24\"},\"9\":{\"Variables related to LLIN\":\"Presence per 2 persons\",\"N\":\"Presence per 2 persons\",\"Frequency (%)\":\"Presence per 2 persons\"},\"10\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"31\",\"Frequency (%)\":\"47.7\"},\"11\":{\"Variables related to LLIN\":\"No\",\"N\":\"34\",\"Frequency (%)\":\"52.3\"},\"12\":{\"Variables related to LLIN\":\"Usage\",\"N\":\"Usage\",\"Frequency (%)\":\"Usage\"},\"13\":{\"Variables related to LLIN\":\"Every night\",\"N\":\"53\",\"Frequency (%)\":\"81.54\"},\"14\":{\"Variables related to LLIN\":\"Not every night\",\"N\":\"12\",\"Frequency (%)\":\"18.46\"},\"15\":{\"Variables related to LLIN\":\"Education received on LLINs during distribution\",\"N\":\"Education received on LLINs during distribution\",\"Frequency (%)\":\"Education received on LLINs during distribution\"},\"16\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"45\",\"Frequency (%)\":\"69.92\"},\"17\":{\"Variables related to LLIN\":\"No\",\"N\":\"20\",\"Frequency (%)\":\"30.78\"},\"18\":{\"Variables related to LLIN\":\"Had LLIN been washed\",\"N\":\"Had LLIN been washed\",\"Frequency (%)\":\"Had LLIN been washed\"},\"19\":{\"Variables related to LLIN\":\"Yes\",\"N\":\"38\",\"Frequency (%)\":\"58.46\"},\"20\":{\"Variables related to LLIN\":\"No\",\"N\":\"27\",\"Frequency (%)\":\"41.54\"},\"21\":{\"Variables related to LLIN\":\"Substance used for washing\",\"N\":\"Substance used for washing\",\"Frequency (%)\":\"Substance used for washing\"},\"22\":{\"Variables related to LLIN\":\"Ordinary soap\",\"N\":\"24\",\"Frequency (%)\":\"63.15\"},\"23\":{\"Variables related to LLIN\":\"Detergent and bleach\",\"N\":\"14\",\"Frequency (%)\":\"36.85\"},\"24\":{\"Variables related to LLIN\":\"Drying place\",\"N\":\"Drying place\",\"Frequency (%)\":\"Drying place\"},\"25\":{\"Variables related to LLIN\":\"In sun\",\"N\":\"34\",\"Frequency (%)\":\"89.48\"},\"26\":{\"Variables related to LLIN\":\"In shade\",\"N\":\"4\",\"Frequency (%)\":\"10.52\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Cameroon\",\"Site\":\"Mvoua\",\"Start_month\":7,\"Start_year\":2018,\"End_month\":4,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}, \"pHI_lower_bound\": {\"title\": \"Phi Lower Bound\", \"description\": \"lowest pHI detected.\"}, \"pHI_upper_bound\": {\"title\": \"Phi Upper Bound\", \"description\": \"lowest pHI detected.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":0.0,\"pHI_upper_bound\":916.0},\"N02\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":0.0,\"pHI_upper_bound\":916.0},\"N03\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":581.0,\"pHI_upper_bound\":1939.0},\"N05\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":13.0,\"pHI_upper_bound\":13.0},\"N06\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":651.0,\"pHI_upper_bound\":651.0},\"N07\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":2225.0,\"pHI_upper_bound\":2225.0},\"N08\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":1.0,\"pHI_upper_bound\":1.0},\"N09\":{\"Net_type\":\"Royal sentry\",\"Net_age\":9.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":1180.0,\"pHI_upper_bound\":1180.0},\"N10\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":1395.0,\"pHI_upper_bound\":1395.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This research was funded by the Royal Society of Tropical Medicine and Hygiene (RSTM#) through a small grant awarded to DM and the Wellcome Trust through a Wellcome Trust Training fellowship in public health and tropical medicine to CN (102543/2/13/2). The RSTM# and the Wellcome Trust have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichlorodiphenyltrichloroethane; ER: Entomological inoculation rate; ELISA CSP: Enzyme-linked immunosorbent assay to detect the circumsporolite protein; HL: Human landing catch method; Adv: Knockdown resistance; LINK: long-lasting insecticidal net; pH: Proportionate hole index; WHORES: World Health Organization Pesticides Evaluation Scheme\n",
-                        "\n",
-                        "header: Insecticide bioassays and knockdown resistance detection\n",
-                        "\n",
-                        "Larvae of _An. gambiae_ (_s.l._) were collected in stagnant pools of water and reared in the insectary of the medical entomology laboratory of OCEAC. F1 adults that emerged from those larvae were fed with 10% sucrose solution made by dissolving 100 g of ordinary white sugar in 1 L of water [22]. _Anopheles funestus_ was not tested due to the low number of adults obtained following larval collections and rearing. The susceptibility of the F1 adults to 0.75% permethrin and 0.05% deltamethrin, both pyrethroid pesticides, was assessed using a World Health Organization (WHO) standard test procedure [23]. Tests were performed at 25 +- 2 degC, 806 +- 10% relative humidity (RH). For each insecticide, four batches of 20-25 field F1 females, aged between 2 and 5 days, were exposed to insecticide-impregnated papers in WHO test-tube for 1 h. At the same time, two batches of the same number of mosquitoes were exposed to untreated papers as control. At the end of the insecticide exposure period, the number of knocked-down mosquitoes was recorded, following which the mosquitoes were transferred into holding tubes. Cotton balls that had first been soaked in a 10% sugar solution and then the moisture squeezed out were placed at the mouth of the tubes. The mortality was recorded 24 h later. Mortality rate in the tested samples was corrected using Abbott's formula [24], when the mortality in the control tubes varied between 5 to 20%. The knockdown resistance (_kdr_) mutation L1014F, which is responsible of cross resistance to DDT (dichlorodiphenyl-trichloroethane) and pyrethroids was genotyped using the protocol described by Martinez-Tores et al. [25].\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "1 Laboratory of Parasitology and Ecology, Faculty of Sciences, University of Yaoundet i P.O. Box 812, Yaoundet, Cameroon. 1 Institute for the Recherche de Yaoundet (FR), Organizing the Coordination pour la Tutte Contre les Endrines en Afrique Centrale (OCEAC), PO. Box 283, Yaoundet, Cameroon. 3 Department of Biological Sciences, Faculty of Medicine and Pharmaceutical Sciences, University of Ouala, PO. Box 2415, Ouala, Cameroon. 4 Department of Parasitology and Microbiology, Centre for Research in Infectious Diseases (RDI), PO. Box 13591, Yaounde, Cameroon. 5 Animal Organisms Laboratory, Faculty of Sciences, University of Ouala, PO. Box 24157, Douala, Cameroon.\n",
-                        "\n",
-                        "header: Table 1 Plasmodium circumsporozoite index, human biting rate and monthly entomological inoculation rate of Anopheles species in Mvoua between August 2018 and April 2019\n",
-                        "footer: Human-biting rate (HBR) is the average number of bites received per person per night; (ii) Plasmodium circumsporozoite index (infection rate) is the proportion of mosquitoes found with Plasmodium circumsporozoite antigen in the heads and thoraces; entomological inoculation rate is the product of the HBR and circumsporozoite rateNC not calculated\n",
-                        "columns: ['Anopheles species', 'Tested (N)', 'Positive (N)', 'Entomological parameters - Plasmodium circumsporozoite index (%)', 'Entomological parameters - Human biting rate', 'Entomological parameters - Human biting rate.1', 'Entomological parameters - Entomological inoculation rate/ month']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species\":\"An. funestus (s.l.)\",\"Tested (N)\":65,\"Positive (N)\":7,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"10.77\",\"Entomological parameters - Human biting rate\":0.55,\"Entomological parameters - Human biting rate.1\":0.55,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.5\"},\"1\":{\"Anopheles species\":\"An. gambiae (s.l.)\",\"Tested (N)\":42,\"Positive (N)\":6,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"14.29\",\"Entomological parameters - Human biting rate\":0.35,\"Entomological parameters - Human biting rate.1\":0.35,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"1.2\"},\"2\":{\"Anopheles species\":\"An. ziemanii\",\"Tested (N)\":3,\"Positive (N)\":0,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"0\",\"Entomological parameters - Human biting rate\":0.02,\"Entomological parameters - Human biting rate.1\":0.02,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"NC\"},\"3\":{\"Anopheles species\":\"Total\",\"Tested (N)\":110,\"Positive (N)\":13,\"Entomological parameters - Plasmodium circumsporozoite index (%)\":\"11.81%\",\"Entomological parameters - Human biting rate\":0.93,\"Entomological parameters - Human biting rate.1\":0.93,\"Entomological parameters - Entomological inoculation rate\\/ month\":\"2.7\"}}\n",
-                        "\n",
-                        "header: Bed net coverage, use and maintenance\n",
-                        "\n",
-                        "Bed net ownership was investigated in 80 of 97 the households that currently make up the village through visual inspection. The heads of these households or their representatives were questioned on their use of nets and their maintenance. The number of people living in each house was recorded to estimate net coverage. Household net ownership was defined as the percentage of households owning at least one LLIN. Net coverage defined as the percentage of households with at least one LLIN for every two people was also determined.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare they have no competing interests.\n",
-                        "\n",
-                        "header: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate\n",
-                        "\n",
-                        "The study was approved by the Carneroonian National Ethical Committee for Research on Human Health (Statement No 2018/07/20/CE/NIRER/SFD). One week before the beginning of field activities, the population received a notice of information explaining the objectives, methodology, expected benefits and possible risks of the study. A consent form was sought from the head of households and for participants aged > 18 years for mosquito collection. They were given the opportunity to ask questions and they were informed that they are free to withdraw from the study at any time, without penalty or loss of benefits. At the end of each sampling period, all mosquito collectors were given anti-malaria prophylaxis based on artesunate/amodiaquine according to the national guide for malaria treatment.\n",
-                        "\n",
-                        "header: Possession, coverage and use of LLINs\n",
-                        "\n",
-                        "In general, 81.25% of the households inspected possessed at least one LLIN. Despite this high level of ownership, the proportion of households that met the ratio of one net for two persons was only 47.69%. Fortunately, 81.54% of the people interviewed affirmed they used their bed nets every night (Table 3). Of the LLINs available, 92.3% were acquired during the government free mass distribution campaigns of 2011-2012 and 2015-2016. Although almost 70% of the study population affirmed that they have been educated by the public health community agents during the distribution campaigns, only 10.52% knew bed nets should be dried in the shade before usage or after washing. Moreover, 56.46% of respondents had washed their bet nets at least once; 63.15% used water and ordinary soap while 36.85% used detergents soap and bleach (Table 3).\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data were analyzed using SPSS software version 25 (IBM Corp., Armonk, NY, USA). The entomological parameters that were considered were: (i) human-biting rate (HBR), i.e. the average number of bites received per person per night; (ii) infection rate, i.e. the proportion of mosquitoes found with _Plasmodium_ circumsporozoite antigen in the heads and thoraces; (iii) entomological inoculation rate (EIR), i.e. the product of the HBR and circumsporozoite rate. Chi-square statistics was used to compare mosquito densities between seasons while mosquitoes' seasonal aggressiveness (HBR) were compared by the Kruskal-Wallis test. Differences were considered statistically significant at \\(P\\) < 0.05.\n",
-                        "\n",
-                        "header: Bed net integrity and bio-efficacy\n",
-                        "\n",
-                        "Ten bed nets were randomly collected from ten selected houses and immediately replaced by new ones. The physical integrity of the nets collected was determined by counting, per category, the number of holes that were approximately the size of a person's thumb, fist or head, or larger than a head, on any of the four faces and the top of the bed net [26, 27]. The proportionate hole index (pH) was calculated using the WHO Pesticide Evaluation Scheme (WHOPES ) guidelines [11] and nets were classified into four classes accordingly [28, 29]. Nets with a pHI <25 were classified as \"good\"; those with a pH ranging between 25 and 174, as \"fair\"; those nets with a pHI ranging from 175 to 299, as \"mediocre\"; and those with a pHI > 300, as \"poor\".\n",
-                        "\n",
-                        "\n",
-                        "Cone assays were performed on each net to test for its bio-efficacy as described in WHO guidelines [30]. Four cones were fixed by their widest opening onto four different parts of each face (4 sides and 1 roof) of each net. Ten unfed _An. gambiae_ (_s.l._) female mosquitoes aged 2-5 days were introduced into each cone using a mouth aspirator, for a total of 200 specimens per net. After 3 min of exposure, the mosquitoes were removed from the cones, then transferred into paper cups and fed with a 10% sugar solution. The assay was conducted at 25 +- 1 degC, 80% +- 5% RH, and the number of mosquitoes knocked-down and dead were recorded after 60 min and 24 h, respectively. An untreated net was used as the negative control.\n",
-                        "\n",
-                        "header: Table 2 Anopheles gambiae complex species composition and the frequency of the knockdown resistance L1014F mutation\n",
-                        "footer: kdr-L1014F, Knockdown resistance L1014F mutation; R resistance; S, susceptible\n",
-                        "columns: ['Anopheles species - Unnamed: 0_level_1', 'Composition - N', 'Composition - %', 'kdr-L1014F genotypes - RR', 'kdr-L1014F genotypes - RS', 'kdr-L1014F genotypes - SS', 'kdr-L1014F alleles (%) - R', 'kdr-L1014F alleles (%) - S']\n",
-                        "\n",
-                        "{\"0\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. gambiae\",\"Composition - N\":28,\"Composition - %\":71.8,\"kdr-L1014F genotypes - RR\":27,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":0,\"kdr-L1014F alleles (%) - R\":98.21,\"kdr-L1014F alleles (%) - S\":1.79},\"1\":{\"Anopheles species - Unnamed: 0_level_1\":\"An. coluzzii\",\"Composition - N\":11,\"Composition - %\":28.2,\"kdr-L1014F genotypes - RR\":8,\"kdr-L1014F genotypes - RS\":1,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":77.27,\"kdr-L1014F alleles (%) - S\":22.73},\"2\":{\"Anopheles species - Unnamed: 0_level_1\":\"Total\",\"Composition - N\":39,\"Composition - %\":100.0,\"kdr-L1014F genotypes - RR\":35,\"kdr-L1014F genotypes - RS\":2,\"kdr-L1014F genotypes - SS\":2,\"kdr-L1014F alleles (%) - R\":92.31,\"kdr-L1014F alleles (%) - S\":7.69}}\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "A combination of elevated _P. falciparum_ infection in _Anopheles_ vector populations, insufficient coverage and loss of effectiveness of LLINs due to physical degradation, as well as high resistance to pyrethroid insecticides is responsible for the persistence of high malaria transmission in forested rural area of Mvoua, Cameroon.\n",
-                        "\n",
-                        "header: Physical integrity and effectiveness of LLINs\n",
-                        "\n",
-                        "Ten LLINs aged 3-7 years old were sampled to assess their physical integrity and bio-efficacy. A total of 161 holes belonging to all the four different types described by WHOPES (the size of a person's thumb, fist or head, or larger than a head) were counted, with a mean of 17 holes per bed net. The proportion of these holes per type was 52.79% (thumb size), 32.29% (fist size), 9.93% (head size) and 4.96% (large than head size). Of the ten LLINs examined, seven (70%) were damaged and were found to be in poor condition (pHI > 300) (Table 4).\n",
-                        "\n",
-                        "A total of 2000 specimens of the _An. gambiae_ (_s.l._) susceptible Kisumu strain were exposed to the ten bed nets. Eight of these nets were effective against this strain, with mortality rates > 80%. Exposure of the same number of _An. gambiae_ (_s.l._) field F1 mosquitoes to these LLINs revealed that they were all ineffective, with mortalities ranging from 0 to 11.5% (Table 4).\n",
-                        "\n",
-                        "header: Table 4 Hole index and mortality of An. gambiae (s.l.) after being exposed to the long‑lasting insecticidal nets\n",
-                        "footer: Not eff., not e\u001d\n",
-                        "fficient; PHI, proportionate hole index\n",
-                        "columns: ['Nets', 'Date of impregnation', 'Brand', 'Physical integrity of LLIN - pHI', 'Physical integrity of LLIN - Status', 'Bio‑efficacy of LLIN (%) - Kisumu control strain', 'Bio‑efficacy of LLIN (%) - Field strain', 'Decision - Unnamed: 7_level_1']\n",
-                        "\n",
-                        "{\"0\":{\"Nets\":\"Net 1\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":0,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":83.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"1\":{\"Nets\":\"Net 2\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":916,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":80.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":0.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"2\":{\"Nets\":\"Net 3\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1939,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":69.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"3\":{\"Nets\":\"Net 4\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":581,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":88.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"4\":{\"Nets\":\"Net 5\",\"Date of impregnation\":\"February 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":13,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":99.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":8.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"5\":{\"Nets\":\"Net 6\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":651,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":85.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"6\":{\"Nets\":\"Net 7\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":2225,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":72.4,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":3.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"7\":{\"Nets\":\"Net 8\",\"Date of impregnation\":\"October 2011\",\"Brand\":\"Permanet\",\"Physical integrity of LLIN - pHI\":1,\"Physical integrity of LLIN - Status\":\"Good\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":98.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":9.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"8\":{\"Nets\":\"Net 9\",\"Date of impregnation\":\"May 2014\",\"Brand\":\"Royal sentry\",\"Physical integrity of LLIN - pHI\":1180,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":95.0,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":11.5,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"},\"9\":{\"Nets\":\"Net 10\",\"Date of impregnation\":\"October 2015\",\"Brand\":\"Olyset\",\"Physical integrity of LLIN - pHI\":1395,\"Physical integrity of LLIN - Status\":\"Poor\",\"Bio\\u2011efficacy of LLIN (%) - Kisumu control strain\":87.5,\"Bio\\u2011efficacy of LLIN (%) - Field strain\":2.0,\"Decision - Unnamed: 7_level_1\":\"Not efficient\"}}\n",
-                        "\n",
-                        "header: Study area: Adult mosquito collection\n",
-                        "\n",
-                        "Collections of adult host-seeking mosquitoes were undertaken employing both the CDC light trap (CDC-LT) and human landing catch (HLC) methods. Because no mosquito was collected using the CDC-LTs after two consecutive nights of sampling, this method was discarded.\n",
-                        "\n",
-                        "Sampling using the HLC method was performed in ten randomly selected houses situated at least 100 m apart. The method involved persons sitting with their lower legs exposed and then collecting mosquitoes that just landed on them [14]. Adult mosquitoes were sampled both indoors and outdoors during two consecutive nights, once per each of the four seasons. Mosquitoes collected each hour were placed into separate bags, labeled accordingly and brought back to the laboratory for further analysis. To avoid bias due to sleep and tiredness, one team of collectors worked from 18:00 h to midnight, and was replaced by another team which worked from midnight to 6:00 h.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In total, 129 adult mosquitoes were collected during eight nights. These belonged to four genera, of which _Anopheles_ (_N_ = 110; 85.27%) was the most prevalent, followed in decreasing order of prevalence by _Mansonia_ (_N_ = 11; 8.53%), _Aedes_ (_N_ = 6; 4.65%) and _Culex_ (_N_ = 2; 1.55%). Three _Anopheles_ species were collected namely _An. funestus_ (_s.l._) (59.1%), _An. gambiae_ (_s.l._) (38.18%) and _An. zienanii_ (2.72%). _Anopheles. sp_ were most abundant in the short dry season followed by the long rainy season (49.09% of the total _Anopheles_ collected) and the long rainy season (34.54%).\n",
-                        "\n",
-                        "In terms of seasonal species abundance, _An. funestus_ was the most abundant species collected (59.1%) over the period of study, with the exception of the long rainy season when _An. gambiae_ (_s.l._) predominated (Fig. 1).\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The findings of this study highlight high and perennial malaria transmission in Mvoua, with the highest infection rate in _Anopheles_ vectors observed in the short dry season. Three major vectors, namely _An. funestus_, _An. coluzzizi_ and _An. gambiae_, were responsible for the transmission of the disease. This vector displayed a high level of resistance to pyrethroid insecticides due to the _kdr_ mutation, but also probably due to metabolic mechanisms. Although there was a high level of LLIN possession among the households, universal coverage was not reached and LLINs found in the locality were no longer effective against the local _An. gambiae_ (_s.l._) strain. The National Malaria Control Program should provide the locality with new LLINs every 3 years, as recommended by WHO. Preferably, to manage the high pyrethroid resistance observed, these LLINs should incorporate a synergism to block the action of detoxification enzymes. People should be educated on the use and maintenance of LLINs, with an emphasis on the importance of sleeping under LLINs irrespective of season, as well as on the damaging impact of detergents used for their washing.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Cameroon\",\"Site\":\"Mvoua\",\"Start_month\":7,\"Start_year\":2018,\"End_month\":4,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}, \"pHI_lower_bound\": {\"title\": \"Phi Lower Bound\", \"description\": \"lowest pHI detected.\"}, \"pHI_upper_bound\": {\"title\": \"Phi Upper Bound\", \"description\": \"lowest pHI detected.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":0.0,\"pHI_upper_bound\":916.0},\"N02\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":0.0,\"pHI_upper_bound\":916.0},\"N03\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":581.0,\"pHI_upper_bound\":1939.0},\"N05\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":13.0,\"pHI_upper_bound\":13.0},\"N06\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":651.0,\"pHI_upper_bound\":651.0},\"N07\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":2225.0,\"pHI_upper_bound\":2225.0},\"N08\":{\"Net_type\":\"Permanet\",\"Net_age\":11.0,\"pHI_category\":\"Good\",\"pHI_lower_bound\":1.0,\"pHI_upper_bound\":1.0},\"N09\":{\"Net_type\":\"Royal sentry\",\"Net_age\":9.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":1180.0,\"pHI_upper_bound\":1180.0},\"N10\":{\"Net_type\":\"Olyset\",\"Net_age\":7.0,\"pHI_category\":\"Poor\",\"pHI_lower_bound\":1395.0,\"pHI_upper_bound\":1395.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A laboratory and a semi-field conditions experimental huts against _Anopheles_ Mosquitoes were conducted in southwestern Ethiopia from September 2015 to January 2016. The bio efficacy of DuraNet(r) was evaluated using the WHO cone bioassay test and then its field efficacy was evaluated using experimental huts against the malaria vector population.\n",
-                        "\n",
-                        "header: CONCLUSION\n",
-                        "\n",
-                        "Both DuraNet(r) and PermanNet 2.0 moderate efficacy against a pyrethroid-resistant population of _An. araabensis_ from Ethiopia. The bio efficacy of DuraNet(r) was found below the WHO recommendation. Therefore, the real impact of the observed insecticide resistance against DuraNet(r) to be further studied under phase-III trials, the need for new alternative vector control tools remains critical.\n",
-                        "\n",
-                        "header: Study area and period: Mosquito rearing and cone bioassay testing.\n",
-                        "\n",
-                        "Anopheles mosquito larvae were collected from field sites near Gilgel-Gibe hydroelectric power production reservoir and reared to adults under standard conditions (25 \\(\\pm\\) 20*C temperature,80 \\(\\pm\\) 4% relative humidity) in tropical and infectious diseases research institute, Sekoru campus, Jimma University, Ethiopia. For WHO cone bioassay test, five 2-5 days age unfed female _An. gambiae s.l._ (presumably _An. ankiensis_ according to Yewhalaw et al., (2009))\\(\\%\\) mosquitoes were used per cone. Twenty mosquitoes (5 mosquito\\({}^{\\text{\\textregistered}}\\) per-cone \\(\\times\\) 4 cone-per sub-sample net piece) were introduced into the cone facing net sample for 3 minutes and then moved to holding paper cups. Then mosquitoes were supplied with a 10% sucrose solution. The number of mosquitoes knocked down and the number of dead mosquitoes were recorded every 10 minute within 60 minutes and 24 hours, respectively. Mosquitoes exposed to untreated DuraNet\\({}^{*}\\) pieces were used as controls and experiment conditions were set to be 27 \\(\\pm\\) 2*C temperature and 75 \\(\\pm\\) 10% relative humidity (RH) throughout the study. Thus, a total of 1260 mosquitoes (63 net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used for complete bioassay testing and another 180 mosquitoes (9 untreated net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used as control.\n",
-                        "\n",
-                        "For net pieces washing each net sub-samples (25 cm \\(\\times\\) 25 cm) were introduced individually into 1-1 beakers containing 0.5 l deionized water, with 2 g/l WHO recommended soap (Savon de Marseille; pH 10-11) added and fully dissolved just before washing. The beakers were then introduced into a water-bath at 30*C and shaken for 10 min at 155 movements per minute. The samples were then removed, rinsed twice for 10 min in clean, deionized water under the same shaking conditions as above, dried at room temperature and stored at 30*C in the dark between washes.\n",
-                        "\n",
-                        "header: Limitation\n",
-                        "\n",
-                        "In this study, supplementary test like chemical assays were not conducted to measure the total insecticide content of the netting before and after wash resistance studies that support better interpretation of the results.\n",
-                        "\n",
-                        "header: Mosquito mortality, blood feeding and exit rates.\n",
-                        "\n",
-                        "Mosquito blood feeding rates, mortality rates and exit rates of the 5 treatments are presented in Table 1. The mean blood feeding rate was significantly lower (_P_ <.001) in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. However, there was no significance difference (_P_ >.05) in blood feeding rate among huts containing untreated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet*2.0. The mean mortality rate was significantly higher (_P_ <.001) among huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*. Higher mean number of mosquitoes were caught while exiting the huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*, however, there was no significant difference (_P_ >.05) among the treatments.\n",
-                        "\n",
-                        "header: Blood feeding inhibition and personal protection.\n",
-                        "\n",
-                        "Unwashed DuraNet* showed strong and better bio-efficacy in terms of blood feeding inhibition when compared to untreated DuraNet* and the rest of the treatment arms. The mean blood feeding rate of mosquitoes collected from a hut with unwashed DuraNet*, Unwashed PernaNet 2.0, twenty times washed DuraNet*, 20 times washed PernaNet 2.0 and Untreated DuraNet*, was 0.8/person-night, 4.1/person-night, 5.7/person-night, 6.0/person-night, and 7.0/person-night respectively. There was significant reduction (_P_ <.001) in blood feeding inhibition in all the treatment arms (hut with unwashed DuraNet*, but with 20 times washed DuraNet*, but with\n",
-                        "\n",
-                        "Figure 2: Mean percent knockdown and mortality of _An. arabiensis_ exposed in 3 minutes cone bioassay to DuraNet®, dimma, and southwestern Ethiopia.\n",
-                        "\n",
-                        "\n",
-                        "unwashed PermaNet 2.0, hut with 20 times washed permanent 2.0) when compared to hut with untreated DuraNet*(control). Blood feeding inhibition can be also expressed as personal protection and unwashed DuraNet* showed the highest personal protection 88.7%, 100%, and 93.3% against _An. arabiensis, An. pbaroensis_, and _An. coustani_, respectively. Both PermaNet 2.0 twenty times washed and DuraNet* washed twenty times showed low to moderate personal protection (45.6% against _An. arabiensis_ to 83% against _An. pbaroensis_) when compared to untreated DuraNet* (Table 2).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Insecticide resistance remains major threat against the efficacy of long-lasting insecticidal nets, which are key intervention tools in controlling malaria vector. In this study laboratory bioassay was initially conducted using field collected larvae raised to adult population of _An. arabiensis_ to assess the bio-efficacy of unwashed DuraNet* (candidate bed net) and untreated DuraNet* (negative control). Furthermore, field experimental study was conducted on bio-efficacy of unwashed DuraNet* and PermaNet 2.0 following WHOPES standard guidelines in order to corroborate the laboratory findings.5\n",
-                        "\n",
-                        "The cone bioassay tests indicated that exposure of population of _An. arabiensis_ mosquitoes from Gilgel-Gibe area, southwestern Ethiopia, to sections of DuraNet* LLIN resulted in mean knockdown and mean mortality of 93% and 78%, respectively. The mortality result is slightly lower than the range of WHO recommendation (>80%) for public health application. The knockdown effect also is slightly below the required WHOPES recommended levels of 95% and above. In contrast to the above results earlier bio efficacy tests conducted using unwashed DuraNet* in India (Sood et al10; Gunasekaran et al11) showed 100% efficacy when tested against populations of different species of _amopheles_. The slight decline in bio-efficacy of DuraNet* in this study may be due to resistance development of vector populations of _An. arabiensis_ in the study area.8,12 In Ethiopia, Previous studies indicate that populations of _An. Arabiensis_ have developed resistance against insecticides used for net impregnation.13-17\n",
-                        "\n",
-                        "Wash resistant long-lasting insecticidal nets treated with pyrethroids are viewed as an important device in the area of vector control that would lessen the difficulties related to retreating conventional insecticide treated nets.[18] DuraNet*, the alpha-cypermethrin treated net, has similar conditions as that of Interceptor* LLIN and PermaNet* 3.0, which had been given interim but full recommendation since June 2017 by WHOPES.[19]\n",
-                        "\n",
-                        "In current study, DuraNet* showed reduced washing resistance. This is reflected in continues decrease of both mean percent knockdown and mean percent mortality of population of _An. arabiensis_ on exposure to the DuraNet* washed 0 and 20 times, respectively. This is in contrast to the study done by Sood et al[10] which showed no significant difference in percent mortality and percent knockdown of population of _An. caliticiacis_ between unwashed and 20 times washed DuraNet* in India. However, in the same study by Sood et al,[10] DuraNet* showed 100% knockdown but only 45% mortality after 20 washes against _Anopheles gambiae_ which is also consistent with current results. Wash resistance technology is added to the LLIN's materials with the assumption of pyrethroid chemicals such as Alphacypermethrin should have a strong affinity to the polyester netting fibers so that even after forceful washing a thin layer of pyrethroid, practically undetectable by High Performance Liquid Chromatography yet adequately bio active to encourage knockdown and mortality should still continue bound to the fibers.\n",
-                        "\n",
-                        "Comparison of mosquito density collected from experimental huts with (unwashed DuraNet*, 20 times washed DuraNet*, unwashed PermaNet*2.0, 20 times washed PermaNet*2.0, and untreated DuraNet*) showed no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the unwashed DuraNet* compared to untreated DuraNet*. Likewise, a study carried out by Asale et al[8] using experimental huts in the study area showed the absence of significance variation in deterrence among huts containing unwashed PermaNet*2.0, untreated net and DDT sprayed hut. The possible explanation for the absence of significant variation in mosquito density among treatment arms could be accounted to low number of mosquito catches during the study period but the impact of insecticide resistance on the efficacy of the nets to be further studied in phase-III trials.\n",
-                        "\n",
-                        "In this study significant reduction in blood feeding rate was observed in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. Similarly, higher mean mosquito mortality rate was recorded among huts containing treatment arms when compared to hut containing control net (untreated DuraNet*). The results for DuraNet* are new report from Jimma area thus we could not corroborate but, Vector population of _An. arabiensis_ from the study area showed no significant reduction of mortality rate, exit pattern and blood feeding inhibition when tested against PermaNet 2.0.[8]\n",
-                        "\n",
-                        "In this study, DuraNet* and PermaNet 2.0 showed low to moderate killing efficacy against vector population of _An. arabiensis_. Moreover, both DuraNet* and PermaNet 2.0 showed no evidence of killing effect against _An. pbarensis_. According to Kitau et al[20] LLINs and ITNs treated with pyrethroids were more effective at killing _An. gambiae_ and _An. fintetus_ than _An. arabiensis_. Thus, the variations in the outcome variables of Anopheles shown above (DuraNet* and PermaNet 2.0) may be due to different susceptibility status and species differences.\n",
-                        "\n",
-                        "header: Table 1: Mean blood feeding, mortality and exit rates of populations of An. arabiensis exposed to different bed net types in field experimental huts in southwestern Ethiopia.\n",
-                        "footer: Means within the same column and with different letters are signi\u001fcantly different using Tukey means separation test.**Significant at P<.001. *Significant at P<.05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['TREATMENTS', 'BLOOD-FEEDING RATE MEAN ± SE', 'MORTALITY RATE MEAN ± SE', 'EXIT RATE MEAN ± SE']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENTS\":\"Untreated DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"77.70 \\u00b1 7.30\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"6.61 \\u00b1 3.80**\",\"EXIT RATE MEAN \\u00b1 SE\":\"26.77 \\u00b1 5.51\"},\"1\":{\"TREATMENTS\":\"Unwashed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"5.80 \\u00b1 2.32**\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"52.38 \\u00b1 5.62\",\"EXIT RATE MEAN \\u00b1 SE\":\"40.42 \\u00b1 5.22\"},\"2\":{\"TREATMENTS\":\"20\\u00d7 washed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"55.94 \\u00b1 6.78\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"28.17 \\u00b1 6.57*\",\"EXIT RATE MEAN \\u00b1 SE\":\"36.24 \\u00b1 5.50\"},\"3\":{\"TREATMENTS\":\"Unwashed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.39 \\u00b1 5.89\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"61.74 \\u00b1 6.09\",\"EXIT RATE MEAN \\u00b1 SE\":\"48.16 \\u00b1 5.53\"},\"4\":{\"TREATMENTS\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.35 \\u00b1 7.01\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"39.87 \\u00b1 6.34\",\"EXIT RATE MEAN \\u00b1 SE\":\"35.32 \\u00b1 5.78\"}}\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Evaluation of Long-Lasting Insecticidal Nets (DuraNet(r)) Under laboratory and Semi-Field Conditions Using Experimental Huts Against _Anopheles Mosquitoes_ in\n",
-                        "\n",
-                        "Jimma Zone\n",
-                        "\n",
-                        " Southwestern Ethiopia\n",
-                        "\n",
-                        "B. Gebremariam(r), W. Birke, W. Zeine, A. Ambelu\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) efficacy could be compromised due to a lot of influences together with user compliance and vector population insecticide resistance status. Thus, this study was to assess the biological efficacy of DuraNet(r) with the help of the World Health Organization cone bioassay and field experimental hut.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "World Health Organization cone bioassay tests against pyrethroid-resistant _An. araabensis_ led to mean percent mortality and knockdown of 78% and 93%, respectively. Washing of DuraNet(r) successively reduced its efficacy from 93% knockdown (0 wash) to 45% knockdown (20 washes). Similarly, mean mortality decreased from 84% (wash) to 47% (20 washes). A total of 1575 female mosquitoes were collected over 40 nights out of which 13738(87.8%) were _An. amabise._ Is Title (74%) were _An. amabensis_. The mean blood-feeding rate was significantly lower (_P_<.001) in hut containing unwashed DuraNet(r) when compared to hut containing untreated DuraNet(r). The mean mortality rate was significantly higher (_P_<.001) in hut containing DuraNet(r) when compared to hut containing untreated DuraNet(r). Unwashed DuraNet(r) showed the highest personal protection 88.7% and 100% against _An. araabensis_ and _An. araabensis_, respectively.\n",
-                        "\n",
-                        "header: The killing effect DuraNet*\n",
-                        "\n",
-                        "DuraNet* (both unwashed and 20 times washed) showed low to moderate killing efficacy (65.93% to 54.03%) against vector population of _An. arabiensis_. Whereas, against _An. pbaroensis_, both unwashed and washed DuraNet* showed no evidence of killing effect (0.00 to 9.0%). Similarly, PermaNet 2.0 (unwashed and washed) showed low to moderate killing effect against population of _An. arabiensis_ (Table 3).\n",
-                        "\n",
-                        "header: Experimental but establishment.: Treatment arms and sleepers rotation\n",
-                        "\n",
-                        "In this experiment, 5 different treatment arms were used. These include (1) Untreated unwashed DuraNet(Negative control) (2) Unwashed treated DuraNet(3) treated DuraNet(20 times washed (4) PermaNet(4) 2.0 20 times washed, and (5) unwashed PermaNet(4) 2.0 (positive control). All nets were 75 denier polyethylene and polyester nets respectively. To simulate wear and tear condition of nets under usage 6 (4 cm \\(\\times\\) 4 cm size) holes were cut in each net (2 holes on each of the sides and 1 hole at each end). The DuraNet(4) LLIN and PermaNet(4) 2.0 were washed according to World Health Organization Stage II washing procedure [5]. For washing, the nets were put in to aluminum bowl with 10 l of tap water and 2g/l of savon de Marseille soap. The nets were agitated for 3 minutes, then soaked for 4 minutes, and agitated again for 3 minutes. The nets were agitated manually by stirring them with a pole at 20 rotations per minute. Thus, each net was washed for a total of 10 minute and then rinsed with clean water by a similar procedure, dried horizontally in the shade and stored at ambient temperature between washes. WHO recognized PermaNet(4) 2.0 LLIN washed 20 times, was used as a positive control to assess DuraNet(4) LLIN performance.\n",
-                        "\n",
-                        "In each data collection night, 2 non-smoker male volunteers aged 20 to 25 were allowed to sleep in each experimental hut with its door remain closed between 19:00 and 07:00 hours. Each team was rotated between treatments on successive nights within a week to avoid possible bias which could arise due to individual attractiveness to mosquitoes. There were 5 successive collection nights (Monday to Friday) and 2 successive break nights for hut ventilation per week. Thus, there were 25 collection nights in order to complete the whole study. Informed written consent was obtained from each sleeper.\n",
-                        "\n",
-                        "header: Cone bioassay and washing resistance: Experimental but trial\n",
-                        "\n",
-                        "_Mosquito entry into the bus._ A total of 1575 Anopheles mosquitoes were entered and collected in the 40 nights during the trial. These consisted of 1373 (87.8%) _An. gambiae_ s.l. presumably _An. arabiensis_ (Yewhalaw et al., 2009), 116 (7.4%) _Angelo custani_ (Laveran), and 107 (6.8%) _An. phareensis_ (Theobald). The mean number of mosquitoes caught per night was 8.75 for _An. arabiensis_, 5.52 for _An. Coastani_, and 6.33 for _An. pharensis._ The mean mosquito entry is not significantly different among the treatment arms (F = 1.277, \\(P\\) >.05). There was no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the Unwashed DuraNet* compared to treated DuraNet* and PernaNet* 2.0 (Table 2).\n",
-                        "\n",
-                        "header: Table 2. Blood-feeding inhibition and personal protection due to DuraNet® LLIN in the experimental huts, southwestern Ethiopia.\n",
-                        "footer: *Significant at P<.05. **Significant at P<.01.\n",
-                        "columns: ['TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2', 'PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Untreated DuraNet\\u00ae (NC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"232 (0**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"43 (0**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"30 (0*)\"},\"1\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"24 (88.7**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"00 (100**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"2 (93.3*)\"},\"2\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"176 (42.4)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"27 (62.8)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"5 (83.3*)\"},\"3\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"131 (45.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"11 (83**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"16 (46.6)\"},\"4\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"192 (38.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"6 (75)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"33 (0*)\"}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "This study was conducted from September 2015 to January 2016 near the Gilgel-Gibe hydroelectric dam area, southwestern Ethiopia. The hydroelectric dam is one of the major hydroelectric dams in Ethiopia with artificial reservoir occupying an estimated area of 62 kmsq,6 It produces around 184 MWh and is located 260 km southwest of the capital Addis Ababa. It has been functional since 2004. The study area is located between latitudes 7*42*50*7N and 07*53*50*7N and longitudes 37*11*22*7E and 37*20*36*E with an altitude ranging from 1672-1864 m above sea level. The area has a sub-humid, warm to hot climate, obtains between 1300 and 1800 mm of rainfall annually and has a mean annual temperature of 19*C. Mostly, the 2 peak seasonal transmissions of malaria occur during the months of September to December and March to May in the study area.\n",
-                        "\n",
-                        "Bio-efficacy testing of LLIN (DuraNet\\({}^{\\text{\\text{\\textregistered}}}\\)) under laboratory setting\n",
-                        "\n",
-                        "\\(LLIN\\)_sample preparation and washing process._ DuraNet\\({}^{*}\\) LLINs (Karur, Tamil Nadu 639006, and India) is treated with alpha-cypermethrin at a target dose of 250 mg/m\\({}^{2}\\) of netting material. It contains 0.55% W/W \\(\\pm\\) 15% alpha-cypermethrin. The alpha-cypermethrin chemical is coated onto filaments at bursting strength of 450 kPa of netting material and of 145 \\(\\pm\\) 5% for seam sub-section per denier yarn. Prior to testing the production date and batch number of all nets were recorded. Seven sub-samples per net (1 from the roof, the rest from side of the net) were taken from each net and prepared for standard LLINs cone tests by cutting 25 cm \\(\\times\\) 25 cm pieces following WHO protocol.7 In this study, 9 candidate nets were used for bio-efficacy testing. Thus, a total of 63 sub-sample net pieces (7 sub-sample pieces from each net \\(\\times\\) 9 DuraNet\\({}^{*}\\)s) individually rolled up in aluminum foil, labeled (by net type, net number and sample area) and kept in a refrigerator prior to the assay. Concurrently 9 (1 sub-sample piece from each untreated DuraNet\\({}^{*}\\)\\(\\times\\) 9 untreated DuraNet\\({}^{*}\\)s) sub-samples (30 cm \\(\\times\\) 30 cm) were individually rolled up in aluminum foil, labeled and kept in a refrigerator prior to the assay.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Exposure of wild population of _An. arabiensis_ mosquitoes to net sections of DuraNet* LLIN in WHO bioassay tests gives mean mortality rate of 78% as well as mean knockdown effect of 93%. Mean percent knockdown and percent mortality of DuraNet* LLIN showed significant relationship (\\(R^{2}\\) = 0.797, n = 260, \\(P\\) <.001). The wash resistance activity of DuraNet* LLINs, measured in terms of percent knockdown and mortality is presented in Figure 2. The mean percent knockdown of wild population of _An. arabiensis_ decreased from 93% to 45% up on exposure to the DuraNet* washed 0 and 20 times respectively. The mean percent mortality decreased from 84% to 47% on exposure to the DuraNet* washed 0 and 20 times, respectively. Wash resistance has negative correlation with percent knockdown and percent mortality which means as number of wash increase, percent knockdown and percent mortality declined (\\(r^{2}\\) = 0.333, 96KD = 89.16 \\(\\pm\\) 2.15, \\(P\\) <.001 and \\(r^{2}\\) = 0.236, 90Mt = 81.94 \\(\\pm\\) 1.68, \\(P\\) <.001), respectively.\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Malaria is still one of highest health risks in developing countries with high morbidity and mortality in under 5 children. Assessment of overall economic impact of the disease shows that it accounts for 40% of health cost spending, 30% to 50% of inpatient charges, and up to 50% of outpatient official visits in areas with great malaria.1 According to the world health organization, there were 216 million new incidents of malaria leading to 445 000 death, of which 90% occurred in African countries.2\n",
-                        "\n",
-                        "Footnote 1: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) are uppermost community health apparatuses and, when used by children and pregnant women, subsidize to improving motherly, neonatal, and infant health, with long-lasting reimbursements to the emerging child.3 These nets provide personal barrier from bite by mosquitoes in addition those nets lessen also the transmission of malaria and have excito-repellency effect.4 A total of 505 million insecticidal nets (ITNs) were distributed in developing countries between 2014 and 2016 with household ownership of at least 1 insecticidal net, improved from 50% in 2010 to 80% in 2016. Nevertheless, the ratio of houses with adequate bed nets (ie, 1 net for every 2 people) is very low (43%).2 Following the high demand of bed nets as key vector control intervention, companies are ramping up production and new brand nets are being introduced for public health utilization.\n",
-                        "\n",
-                        "Footnote 2: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "In order for any new long-lasting insecticidal nets to be used in public health, it must gain WHOPES approval.5 The approval process passes through 3 stages of efficacy testing (laboratory, small-scale semi-field, and large-scale field trial). To be classified as a long-lasting insecticidal net, it must \n",
-                        "maintain their effective biological activity for at least 20 World Health Organization recommended washes typical washes under laboratory conditions and 3 years of suggested use under semi-field and field condition.\n",
-                        "\n",
-                        "DuraNet\\({}^{*}\\) is an LLIN developed by the Shobikaa Impex pvt Ltd (Karur, Tamil Nadu 639006, and India). It contains 0.55% w/w \\(\\pm\\) 15% alpha-cypermethrin. The net is a polyethylene fiber coated with a proprietary polymer containing target dose of alpha-cypermethrin at 250 mg/m\\({}^{2}\\). The polymer binds to the fiber and can withstand multiple washings, the active ingredient diffusing in a controlled manner to the surface of the polymer coat to maintain insecticidal efficacy.\n",
-                        "\n",
-                        "Thus, this study was conducted to verify the intended efficacy of the product in the presence of pyrethroid resistance vector population of _An. gambiae_ s.l. using cone bioassay and semi-field experimental huts in southwestern Ethiopia. The cone bioassays were conducted in Tropical and Infectious diseases research institute, Jimma University. The experimental hut study trials were undertaken near the Gilgel-Gibe hydroelectric power dam-I.\n",
-                        "\n",
-                        "header: Mosquito collection, identification and key parameters measured in determination of LLINs efficacy\n",
-                        "\n",
-                        "Mosquitoes were collected from 6:00 to 7:00 each morning inside bed nets, floors, walls, ceilings, and verandas of each experimental hut by the help of mouth aspirators and torches. Then the collected mosquitoes were recorded as dead or alive. Live mosquitoes were held in paper cups and supplied with 10% sucrose solution. The collected mosquitoes were transported to Asendabo Vector Biology Laboratory, Jimma University, where mosquitoes were sorted by genus, sex and morphologically identified using taxonomic keys [9]. Mosquitoes were also scored for their physiological state as unfed, fed, half gravid and gravid. Delayed mortality was recorded after 24 hours. To evaluate the efficacy of DuraNet LLIN against the resistant populations of _An. arabiensis_, different entomological parameters (deterrence, exit, blood feeding inhibition, and mortality rates) were derived from basic measurements.\n",
-                        "\n",
-                        "The primary outcomes were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in entry in to treatment hut relative to the control hut (ie, huts holding untreated nets);\n",
-                        "2. Mortality--the proportion of mosquitoes killed relative to the total catch size;\n",
-                        "3. Killing effect--the numbers killed by a treatment relative to the untreated control, as derived from the formula; \\[\\text{Killing effect}\\left( \\% \\right) = \\left( \\frac{Kt-Ku}{Tu} \\right) \\ast 100\\]\n",
-                        "\n",
-                        "Where **Kt** is the number dead mosquitoes in the huts with treated nets, **Ku** is the number dead mosquitoes in the huts with untreated nets, and **Tu** is the total entering the huts with untreated nets.\n",
-                        "\n",
-                        "1. Blood Feeding Inhibition-The proportional reduction in blood feeding in huts with treated nets relative to controls with untreated nets.\n",
-                        "\n",
-                        "Figure 1: Experimental hut design of the study.\n",
-                        "\n",
-                        "\n",
-                        "5. Personal Protection--The reduction in mosquito biting by treated nets relative to untreated nets, as derived from the formula \\[\\%\\text{personal protection} = (\\frac{Bu-Bt}{Bu})*100\\]\n",
-                        "\n",
-                        "Where Bu is the total number blood fed mosquitoes in the huts with untreated nets, and Bt is the total number blood fed in the huts with treated nets.\n",
-                        "\n",
-                        "header: Bio-efficacy testing of DuraNet\\({}^{*}\\) LLIN using experimental but\n",
-                        "\n",
-                        "Five experimental huts of West African style, each with 1 room and screened veranda trap were established approximately 500 m from Gilgel-Gibe reservoir shore, southwestern Ethiopia following world health protocol developed for field evaluation of long-lasting insecticidal nets.5 The huts were made from concrete bricks with a corrugated iron roof, a ceiling of white cotton sheeting and a concrete base surrounded by a water filled moat to prevent the entry of ants. Details of the dimensions of the hut, the Verenda trap and \n",
-                        "materials from which the tukuls made were described elsewhere [8]. The slits were constructed from pieces of metal shutters, fixed at an angle of 45\\({}^{\\circ}\\) to create a funnel of 1 cm between slits. The design of window slit allows the inward flight of mosquitoes coming from filed but it will be hardly possible for them to escape once they entered the hut. A veranda trap made of iron mesh (22 mm diameter) was set at the back of each hut for trapping exophilic mosquitoes (Figure 1). It is presumed that some mosquitoes (inherently exophilic or mosquitoes repelled due to the exit-repellency effect of the chemicals impregnated into LLINs) will exit the hut once they enter and then after feeding on their host of choice. Thus, the verenda trap was designed to trap and quantify the proportion of mosquitoes exit the experimental hut and by virtue the efficacy of LLINs under test for its exito-repellent effect. Each night mosquitoes were allowed to enter into the hut via the window slits from the environment and freely move between the room and the verenda trap.\n",
-                        "\n",
-                        "header: Table 3. The overall killing effect of DuraNet® LLIN in experimental hut.\n",
-                        "footer: None\n",
-                        "columns: ['TREATMENT', 'OVERALL KILLING EFFECT (%) - AN. ARABIENSIS', 'OVERALL KILLING EFFECT (%) - AN. PHAROENSIS', 'OVERALL KILLING EFFECT (%) - AN. COUSTANI']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT\":\"Unwashed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":65.93,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"0.0*\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":12.33},\"1\":{\"TREATMENT\":\"20\\u00d7washed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":54.03,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":13.4},\"2\":{\"TREATMENT\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":64.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"2.25\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":39.7},\"3\":{\"TREATMENT\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":39.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":20.0}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: CONCLUSION\n",
-                        "\n",
-                        "Both DuraNet(r) and PermanNet 2.0 moderate efficacy against a pyrethroid-resistant population of _An. araabensis_ from Ethiopia. The bio efficacy of DuraNet(r) was found below the WHO recommendation. Therefore, the real impact of the observed insecticide resistance against DuraNet(r) to be further studied under phase-III trials, the need for new alternative vector control tools remains critical.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A laboratory and a semi-field conditions experimental huts against _Anopheles_ Mosquitoes were conducted in southwestern Ethiopia from September 2015 to January 2016. The bio efficacy of DuraNet(r) was evaluated using the WHO cone bioassay test and then its field efficacy was evaluated using experimental huts against the malaria vector population.\n",
-                        "\n",
-                        "header: Limitation\n",
-                        "\n",
-                        "In this study, supplementary test like chemical assays were not conducted to measure the total insecticide content of the netting before and after wash resistance studies that support better interpretation of the results.\n",
-                        "\n",
-                        "header: Mosquito mortality, blood feeding and exit rates.\n",
-                        "\n",
-                        "Mosquito blood feeding rates, mortality rates and exit rates of the 5 treatments are presented in Table 1. The mean blood feeding rate was significantly lower (_P_ <.001) in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. However, there was no significance difference (_P_ >.05) in blood feeding rate among huts containing untreated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet*2.0. The mean mortality rate was significantly higher (_P_ <.001) among huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*. Higher mean number of mosquitoes were caught while exiting the huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*, however, there was no significant difference (_P_ >.05) among the treatments.\n",
-                        "\n",
-                        "header: Blood feeding inhibition and personal protection.\n",
-                        "\n",
-                        "Unwashed DuraNet* showed strong and better bio-efficacy in terms of blood feeding inhibition when compared to untreated DuraNet* and the rest of the treatment arms. The mean blood feeding rate of mosquitoes collected from a hut with unwashed DuraNet*, Unwashed PernaNet 2.0, twenty times washed DuraNet*, 20 times washed PernaNet 2.0 and Untreated DuraNet*, was 0.8/person-night, 4.1/person-night, 5.7/person-night, 6.0/person-night, and 7.0/person-night respectively. There was significant reduction (_P_ <.001) in blood feeding inhibition in all the treatment arms (hut with unwashed DuraNet*, but with 20 times washed DuraNet*, but with\n",
-                        "\n",
-                        "Figure 2: Mean percent knockdown and mortality of _An. arabiensis_ exposed in 3 minutes cone bioassay to DuraNet®, dimma, and southwestern Ethiopia.\n",
-                        "\n",
-                        "\n",
-                        "unwashed PermaNet 2.0, hut with 20 times washed permanent 2.0) when compared to hut with untreated DuraNet*(control). Blood feeding inhibition can be also expressed as personal protection and unwashed DuraNet* showed the highest personal protection 88.7%, 100%, and 93.3% against _An. arabiensis, An. pbaroensis_, and _An. coustani_, respectively. Both PermaNet 2.0 twenty times washed and DuraNet* washed twenty times showed low to moderate personal protection (45.6% against _An. arabiensis_ to 83% against _An. pbaroensis_) when compared to untreated DuraNet* (Table 2).\n",
-                        "\n",
-                        "header: Table 1: Mean blood feeding, mortality and exit rates of populations of An. arabiensis exposed to different bed net types in field experimental huts in southwestern Ethiopia.\n",
-                        "footer: Means within the same column and with different letters are signi\u001fcantly different using Tukey means separation test.**Significant at P<.001. *Significant at P<.05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['TREATMENTS', 'BLOOD-FEEDING RATE MEAN ± SE', 'MORTALITY RATE MEAN ± SE', 'EXIT RATE MEAN ± SE']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENTS\":\"Untreated DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"77.70 \\u00b1 7.30\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"6.61 \\u00b1 3.80**\",\"EXIT RATE MEAN \\u00b1 SE\":\"26.77 \\u00b1 5.51\"},\"1\":{\"TREATMENTS\":\"Unwashed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"5.80 \\u00b1 2.32**\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"52.38 \\u00b1 5.62\",\"EXIT RATE MEAN \\u00b1 SE\":\"40.42 \\u00b1 5.22\"},\"2\":{\"TREATMENTS\":\"20\\u00d7 washed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"55.94 \\u00b1 6.78\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"28.17 \\u00b1 6.57*\",\"EXIT RATE MEAN \\u00b1 SE\":\"36.24 \\u00b1 5.50\"},\"3\":{\"TREATMENTS\":\"Unwashed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.39 \\u00b1 5.89\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"61.74 \\u00b1 6.09\",\"EXIT RATE MEAN \\u00b1 SE\":\"48.16 \\u00b1 5.53\"},\"4\":{\"TREATMENTS\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.35 \\u00b1 7.01\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"39.87 \\u00b1 6.34\",\"EXIT RATE MEAN \\u00b1 SE\":\"35.32 \\u00b1 5.78\"}}\n",
-                        "\n",
-                        "header: Cone bioassay and washing resistance: Experimental but trial\n",
-                        "\n",
-                        "_Mosquito entry into the bus._ A total of 1575 Anopheles mosquitoes were entered and collected in the 40 nights during the trial. These consisted of 1373 (87.8%) _An. gambiae_ s.l. presumably _An. arabiensis_ (Yewhalaw et al., 2009), 116 (7.4%) _Angelo custani_ (Laveran), and 107 (6.8%) _An. phareensis_ (Theobald). The mean number of mosquitoes caught per night was 8.75 for _An. arabiensis_, 5.52 for _An. Coastani_, and 6.33 for _An. pharensis._ The mean mosquito entry is not significantly different among the treatment arms (F = 1.277, \\(P\\) >.05). There was no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the Unwashed DuraNet* compared to treated DuraNet* and PernaNet* 2.0 (Table 2).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "World Health Organization cone bioassay tests against pyrethroid-resistant _An. araabensis_ led to mean percent mortality and knockdown of 78% and 93%, respectively. Washing of DuraNet(r) successively reduced its efficacy from 93% knockdown (0 wash) to 45% knockdown (20 washes). Similarly, mean mortality decreased from 84% (wash) to 47% (20 washes). A total of 1575 female mosquitoes were collected over 40 nights out of which 13738(87.8%) were _An. amabise._ Is Title (74%) were _An. amabensis_. The mean blood-feeding rate was significantly lower (_P_<.001) in hut containing unwashed DuraNet(r) when compared to hut containing untreated DuraNet(r). The mean mortality rate was significantly higher (_P_<.001) in hut containing DuraNet(r) when compared to hut containing untreated DuraNet(r). Unwashed DuraNet(r) showed the highest personal protection 88.7% and 100% against _An. araabensis_ and _An. araabensis_, respectively.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Insecticide resistance remains major threat against the efficacy of long-lasting insecticidal nets, which are key intervention tools in controlling malaria vector. In this study laboratory bioassay was initially conducted using field collected larvae raised to adult population of _An. arabiensis_ to assess the bio-efficacy of unwashed DuraNet* (candidate bed net) and untreated DuraNet* (negative control). Furthermore, field experimental study was conducted on bio-efficacy of unwashed DuraNet* and PermaNet 2.0 following WHOPES standard guidelines in order to corroborate the laboratory findings.5\n",
-                        "\n",
-                        "The cone bioassay tests indicated that exposure of population of _An. arabiensis_ mosquitoes from Gilgel-Gibe area, southwestern Ethiopia, to sections of DuraNet* LLIN resulted in mean knockdown and mean mortality of 93% and 78%, respectively. The mortality result is slightly lower than the range of WHO recommendation (>80%) for public health application. The knockdown effect also is slightly below the required WHOPES recommended levels of 95% and above. In contrast to the above results earlier bio efficacy tests conducted using unwashed DuraNet* in India (Sood et al10; Gunasekaran et al11) showed 100% efficacy when tested against populations of different species of _amopheles_. The slight decline in bio-efficacy of DuraNet* in this study may be due to resistance development of vector populations of _An. arabiensis_ in the study area.8,12 In Ethiopia, Previous studies indicate that populations of _An. Arabiensis_ have developed resistance against insecticides used for net impregnation.13-17\n",
-                        "\n",
-                        "Wash resistant long-lasting insecticidal nets treated with pyrethroids are viewed as an important device in the area of vector control that would lessen the difficulties related to retreating conventional insecticide treated nets.[18] DuraNet*, the alpha-cypermethrin treated net, has similar conditions as that of Interceptor* LLIN and PermaNet* 3.0, which had been given interim but full recommendation since June 2017 by WHOPES.[19]\n",
-                        "\n",
-                        "In current study, DuraNet* showed reduced washing resistance. This is reflected in continues decrease of both mean percent knockdown and mean percent mortality of population of _An. arabiensis_ on exposure to the DuraNet* washed 0 and 20 times, respectively. This is in contrast to the study done by Sood et al[10] which showed no significant difference in percent mortality and percent knockdown of population of _An. caliticiacis_ between unwashed and 20 times washed DuraNet* in India. However, in the same study by Sood et al,[10] DuraNet* showed 100% knockdown but only 45% mortality after 20 washes against _Anopheles gambiae_ which is also consistent with current results. Wash resistance technology is added to the LLIN's materials with the assumption of pyrethroid chemicals such as Alphacypermethrin should have a strong affinity to the polyester netting fibers so that even after forceful washing a thin layer of pyrethroid, practically undetectable by High Performance Liquid Chromatography yet adequately bio active to encourage knockdown and mortality should still continue bound to the fibers.\n",
-                        "\n",
-                        "Comparison of mosquito density collected from experimental huts with (unwashed DuraNet*, 20 times washed DuraNet*, unwashed PermaNet*2.0, 20 times washed PermaNet*2.0, and untreated DuraNet*) showed no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the unwashed DuraNet* compared to untreated DuraNet*. Likewise, a study carried out by Asale et al[8] using experimental huts in the study area showed the absence of significance variation in deterrence among huts containing unwashed PermaNet*2.0, untreated net and DDT sprayed hut. The possible explanation for the absence of significant variation in mosquito density among treatment arms could be accounted to low number of mosquito catches during the study period but the impact of insecticide resistance on the efficacy of the nets to be further studied in phase-III trials.\n",
-                        "\n",
-                        "In this study significant reduction in blood feeding rate was observed in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. Similarly, higher mean mosquito mortality rate was recorded among huts containing treatment arms when compared to hut containing control net (untreated DuraNet*). The results for DuraNet* are new report from Jimma area thus we could not corroborate but, Vector population of _An. arabiensis_ from the study area showed no significant reduction of mortality rate, exit pattern and blood feeding inhibition when tested against PermaNet 2.0.[8]\n",
-                        "\n",
-                        "In this study, DuraNet* and PermaNet 2.0 showed low to moderate killing efficacy against vector population of _An. arabiensis_. Moreover, both DuraNet* and PermaNet 2.0 showed no evidence of killing effect against _An. pbarensis_. According to Kitau et al[20] LLINs and ITNs treated with pyrethroids were more effective at killing _An. gambiae_ and _An. fintetus_ than _An. arabiensis_. Thus, the variations in the outcome variables of Anopheles shown above (DuraNet* and PermaNet 2.0) may be due to different susceptibility status and species differences.\n",
-                        "\n",
-                        "header: The killing effect DuraNet*\n",
-                        "\n",
-                        "DuraNet* (both unwashed and 20 times washed) showed low to moderate killing efficacy (65.93% to 54.03%) against vector population of _An. arabiensis_. Whereas, against _An. pbaroensis_, both unwashed and washed DuraNet* showed no evidence of killing effect (0.00 to 9.0%). Similarly, PermaNet 2.0 (unwashed and washed) showed low to moderate killing effect against population of _An. arabiensis_ (Table 3).\n",
-                        "\n",
-                        "header: Study area and period: Mosquito rearing and cone bioassay testing.\n",
-                        "\n",
-                        "Anopheles mosquito larvae were collected from field sites near Gilgel-Gibe hydroelectric power production reservoir and reared to adults under standard conditions (25 \\(\\pm\\) 20*C temperature,80 \\(\\pm\\) 4% relative humidity) in tropical and infectious diseases research institute, Sekoru campus, Jimma University, Ethiopia. For WHO cone bioassay test, five 2-5 days age unfed female _An. gambiae s.l._ (presumably _An. ankiensis_ according to Yewhalaw et al., (2009))\\(\\%\\) mosquitoes were used per cone. Twenty mosquitoes (5 mosquito\\({}^{\\text{\\textregistered}}\\) per-cone \\(\\times\\) 4 cone-per sub-sample net piece) were introduced into the cone facing net sample for 3 minutes and then moved to holding paper cups. Then mosquitoes were supplied with a 10% sucrose solution. The number of mosquitoes knocked down and the number of dead mosquitoes were recorded every 10 minute within 60 minutes and 24 hours, respectively. Mosquitoes exposed to untreated DuraNet\\({}^{*}\\) pieces were used as controls and experiment conditions were set to be 27 \\(\\pm\\) 2*C temperature and 75 \\(\\pm\\) 10% relative humidity (RH) throughout the study. Thus, a total of 1260 mosquitoes (63 net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used for complete bioassay testing and another 180 mosquitoes (9 untreated net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used as control.\n",
-                        "\n",
-                        "For net pieces washing each net sub-samples (25 cm \\(\\times\\) 25 cm) were introduced individually into 1-1 beakers containing 0.5 l deionized water, with 2 g/l WHO recommended soap (Savon de Marseille; pH 10-11) added and fully dissolved just before washing. The beakers were then introduced into a water-bath at 30*C and shaken for 10 min at 155 movements per minute. The samples were then removed, rinsed twice for 10 min in clean, deionized water under the same shaking conditions as above, dried at room temperature and stored at 30*C in the dark between washes.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Evaluation of Long-Lasting Insecticidal Nets (DuraNet(r)) Under laboratory and Semi-Field Conditions Using Experimental Huts Against _Anopheles Mosquitoes_ in\n",
-                        "\n",
-                        "Jimma Zone\n",
-                        "\n",
-                        " Southwestern Ethiopia\n",
-                        "\n",
-                        "B. Gebremariam(r), W. Birke, W. Zeine, A. Ambelu\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) efficacy could be compromised due to a lot of influences together with user compliance and vector population insecticide resistance status. Thus, this study was to assess the biological efficacy of DuraNet(r) with the help of the World Health Organization cone bioassay and field experimental hut.\n",
-                        "\n",
-                        "header: ORCID iD\n",
-                        "\n",
-                        "Brhane Gebremariam (c) [https://orcid.org/0000-0002-0824-3221](https://orcid.org/0000-0002-0824-3221)\n",
-                        "\n",
-                        "header: Table 2. Blood-feeding inhibition and personal protection due to DuraNet® LLIN in the experimental huts, southwestern Ethiopia.\n",
-                        "footer: *Significant at P<.05. **Significant at P<.01.\n",
-                        "columns: ['TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2', 'PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Untreated DuraNet\\u00ae (NC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"232 (0**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"43 (0**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"30 (0*)\"},\"1\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"24 (88.7**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"00 (100**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"2 (93.3*)\"},\"2\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"176 (42.4)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"27 (62.8)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"5 (83.3*)\"},\"3\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"131 (45.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"11 (83**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"16 (46.6)\"},\"4\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"192 (38.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"6 (75)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"33 (0*)\"}}\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Exposure of wild population of _An. arabiensis_ mosquitoes to net sections of DuraNet* LLIN in WHO bioassay tests gives mean mortality rate of 78% as well as mean knockdown effect of 93%. Mean percent knockdown and percent mortality of DuraNet* LLIN showed significant relationship (\\(R^{2}\\) = 0.797, n = 260, \\(P\\) <.001). The wash resistance activity of DuraNet* LLINs, measured in terms of percent knockdown and mortality is presented in Figure 2. The mean percent knockdown of wild population of _An. arabiensis_ decreased from 93% to 45% up on exposure to the DuraNet* washed 0 and 20 times respectively. The mean percent mortality decreased from 84% to 47% on exposure to the DuraNet* washed 0 and 20 times, respectively. Wash resistance has negative correlation with percent knockdown and percent mortality which means as number of wash increase, percent knockdown and percent mortality declined (\\(r^{2}\\) = 0.333, 96KD = 89.16 \\(\\pm\\) 2.15, \\(P\\) <.001 and \\(r^{2}\\) = 0.236, 90Mt = 81.94 \\(\\pm\\) 1.68, \\(P\\) <.001), respectively.\n",
-                        "\n",
-                        "header: Experimental but establishment.: Treatment arms and sleepers rotation\n",
-                        "\n",
-                        "In this experiment, 5 different treatment arms were used. These include (1) Untreated unwashed DuraNet(Negative control) (2) Unwashed treated DuraNet(3) treated DuraNet(20 times washed (4) PermaNet(4) 2.0 20 times washed, and (5) unwashed PermaNet(4) 2.0 (positive control). All nets were 75 denier polyethylene and polyester nets respectively. To simulate wear and tear condition of nets under usage 6 (4 cm \\(\\times\\) 4 cm size) holes were cut in each net (2 holes on each of the sides and 1 hole at each end). The DuraNet(4) LLIN and PermaNet(4) 2.0 were washed according to World Health Organization Stage II washing procedure [5]. For washing, the nets were put in to aluminum bowl with 10 l of tap water and 2g/l of savon de Marseille soap. The nets were agitated for 3 minutes, then soaked for 4 minutes, and agitated again for 3 minutes. The nets were agitated manually by stirring them with a pole at 20 rotations per minute. Thus, each net was washed for a total of 10 minute and then rinsed with clean water by a similar procedure, dried horizontally in the shade and stored at ambient temperature between washes. WHO recognized PermaNet(4) 2.0 LLIN washed 20 times, was used as a positive control to assess DuraNet(4) LLIN performance.\n",
-                        "\n",
-                        "In each data collection night, 2 non-smoker male volunteers aged 20 to 25 were allowed to sleep in each experimental hut with its door remain closed between 19:00 and 07:00 hours. Each team was rotated between treatments on successive nights within a week to avoid possible bias which could arise due to individual attractiveness to mosquitoes. There were 5 successive collection nights (Monday to Friday) and 2 successive break nights for hut ventilation per week. Thus, there were 25 collection nights in order to complete the whole study. Informed written consent was obtained from each sleeper.\n",
-                        "\n",
-                        "header: Mosquito collection, identification and key parameters measured in determination of LLINs efficacy\n",
-                        "\n",
-                        "Mosquitoes were collected from 6:00 to 7:00 each morning inside bed nets, floors, walls, ceilings, and verandas of each experimental hut by the help of mouth aspirators and torches. Then the collected mosquitoes were recorded as dead or alive. Live mosquitoes were held in paper cups and supplied with 10% sucrose solution. The collected mosquitoes were transported to Asendabo Vector Biology Laboratory, Jimma University, where mosquitoes were sorted by genus, sex and morphologically identified using taxonomic keys [9]. Mosquitoes were also scored for their physiological state as unfed, fed, half gravid and gravid. Delayed mortality was recorded after 24 hours. To evaluate the efficacy of DuraNet LLIN against the resistant populations of _An. arabiensis_, different entomological parameters (deterrence, exit, blood feeding inhibition, and mortality rates) were derived from basic measurements.\n",
-                        "\n",
-                        "The primary outcomes were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in entry in to treatment hut relative to the control hut (ie, huts holding untreated nets);\n",
-                        "2. Mortality--the proportion of mosquitoes killed relative to the total catch size;\n",
-                        "3. Killing effect--the numbers killed by a treatment relative to the untreated control, as derived from the formula; \\[\\text{Killing effect}\\left( \\% \\right) = \\left( \\frac{Kt-Ku}{Tu} \\right) \\ast 100\\]\n",
-                        "\n",
-                        "Where **Kt** is the number dead mosquitoes in the huts with treated nets, **Ku** is the number dead mosquitoes in the huts with untreated nets, and **Tu** is the total entering the huts with untreated nets.\n",
-                        "\n",
-                        "1. Blood Feeding Inhibition-The proportional reduction in blood feeding in huts with treated nets relative to controls with untreated nets.\n",
-                        "\n",
-                        "Figure 1: Experimental hut design of the study.\n",
-                        "\n",
-                        "\n",
-                        "5. Personal Protection--The reduction in mosquito biting by treated nets relative to untreated nets, as derived from the formula \\[\\%\\text{personal protection} = (\\frac{Bu-Bt}{Bu})*100\\]\n",
-                        "\n",
-                        "Where Bu is the total number blood fed mosquitoes in the huts with untreated nets, and Bt is the total number blood fed in the huts with treated nets.\n",
-                        "\n",
-                        "header: Table 3. The overall killing effect of DuraNet® LLIN in experimental hut.\n",
-                        "footer: None\n",
-                        "columns: ['TREATMENT', 'OVERALL KILLING EFFECT (%) - AN. ARABIENSIS', 'OVERALL KILLING EFFECT (%) - AN. PHAROENSIS', 'OVERALL KILLING EFFECT (%) - AN. COUSTANI']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT\":\"Unwashed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":65.93,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"0.0*\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":12.33},\"1\":{\"TREATMENT\":\"20\\u00d7washed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":54.03,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":13.4},\"2\":{\"TREATMENT\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":64.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"2.25\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":39.7},\"3\":{\"TREATMENT\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":39.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":20.0}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "This study was conducted from September 2015 to January 2016 near the Gilgel-Gibe hydroelectric dam area, southwestern Ethiopia. The hydroelectric dam is one of the major hydroelectric dams in Ethiopia with artificial reservoir occupying an estimated area of 62 kmsq,6 It produces around 184 MWh and is located 260 km southwest of the capital Addis Ababa. It has been functional since 2004. The study area is located between latitudes 7*42*50*7N and 07*53*50*7N and longitudes 37*11*22*7E and 37*20*36*E with an altitude ranging from 1672-1864 m above sea level. The area has a sub-humid, warm to hot climate, obtains between 1300 and 1800 mm of rainfall annually and has a mean annual temperature of 19*C. Mostly, the 2 peak seasonal transmissions of malaria occur during the months of September to December and March to May in the study area.\n",
-                        "\n",
-                        "Bio-efficacy testing of LLIN (DuraNet\\({}^{\\text{\\text{\\textregistered}}}\\)) under laboratory setting\n",
-                        "\n",
-                        "\\(LLIN\\)_sample preparation and washing process._ DuraNet\\({}^{*}\\) LLINs (Karur, Tamil Nadu 639006, and India) is treated with alpha-cypermethrin at a target dose of 250 mg/m\\({}^{2}\\) of netting material. It contains 0.55% W/W \\(\\pm\\) 15% alpha-cypermethrin. The alpha-cypermethrin chemical is coated onto filaments at bursting strength of 450 kPa of netting material and of 145 \\(\\pm\\) 5% for seam sub-section per denier yarn. Prior to testing the production date and batch number of all nets were recorded. Seven sub-samples per net (1 from the roof, the rest from side of the net) were taken from each net and prepared for standard LLINs cone tests by cutting 25 cm \\(\\times\\) 25 cm pieces following WHO protocol.7 In this study, 9 candidate nets were used for bio-efficacy testing. Thus, a total of 63 sub-sample net pieces (7 sub-sample pieces from each net \\(\\times\\) 9 DuraNet\\({}^{*}\\)s) individually rolled up in aluminum foil, labeled (by net type, net number and sample area) and kept in a refrigerator prior to the assay. Concurrently 9 (1 sub-sample piece from each untreated DuraNet\\({}^{*}\\)\\(\\times\\) 9 untreated DuraNet\\({}^{*}\\)s) sub-samples (30 cm \\(\\times\\) 30 cm) were individually rolled up in aluminum foil, labeled and kept in a refrigerator prior to the assay.\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Malaria is still one of highest health risks in developing countries with high morbidity and mortality in under 5 children. Assessment of overall economic impact of the disease shows that it accounts for 40% of health cost spending, 30% to 50% of inpatient charges, and up to 50% of outpatient official visits in areas with great malaria.1 According to the world health organization, there were 216 million new incidents of malaria leading to 445 000 death, of which 90% occurred in African countries.2\n",
-                        "\n",
-                        "Footnote 1: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) are uppermost community health apparatuses and, when used by children and pregnant women, subsidize to improving motherly, neonatal, and infant health, with long-lasting reimbursements to the emerging child.3 These nets provide personal barrier from bite by mosquitoes in addition those nets lessen also the transmission of malaria and have excito-repellency effect.4 A total of 505 million insecticidal nets (ITNs) were distributed in developing countries between 2014 and 2016 with household ownership of at least 1 insecticidal net, improved from 50% in 2010 to 80% in 2016. Nevertheless, the ratio of houses with adequate bed nets (ie, 1 net for every 2 people) is very low (43%).2 Following the high demand of bed nets as key vector control intervention, companies are ramping up production and new brand nets are being introduced for public health utilization.\n",
-                        "\n",
-                        "Footnote 2: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "In order for any new long-lasting insecticidal nets to be used in public health, it must gain WHOPES approval.5 The approval process passes through 3 stages of efficacy testing (laboratory, small-scale semi-field, and large-scale field trial). To be classified as a long-lasting insecticidal net, it must \n",
-                        "maintain their effective biological activity for at least 20 World Health Organization recommended washes typical washes under laboratory conditions and 3 years of suggested use under semi-field and field condition.\n",
-                        "\n",
-                        "DuraNet\\({}^{*}\\) is an LLIN developed by the Shobikaa Impex pvt Ltd (Karur, Tamil Nadu 639006, and India). It contains 0.55% w/w \\(\\pm\\) 15% alpha-cypermethrin. The net is a polyethylene fiber coated with a proprietary polymer containing target dose of alpha-cypermethrin at 250 mg/m\\({}^{2}\\). The polymer binds to the fiber and can withstand multiple washings, the active ingredient diffusing in a controlled manner to the surface of the polymer coat to maintain insecticidal efficacy.\n",
-                        "\n",
-                        "Thus, this study was conducted to verify the intended efficacy of the product in the presence of pyrethroid resistance vector population of _An. gambiae_ s.l. using cone bioassay and semi-field experimental huts in southwestern Ethiopia. The cone bioassays were conducted in Tropical and Infectious diseases research institute, Jimma University. The experimental hut study trials were undertaken near the Gilgel-Gibe hydroelectric power dam-I.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Limitation\n",
-                        "\n",
-                        "In this study, supplementary test like chemical assays were not conducted to measure the total insecticide content of the netting before and after wash resistance studies that support better interpretation of the results.\n",
-                        "\n",
-                        "header: Blood feeding inhibition and personal protection.\n",
-                        "\n",
-                        "Unwashed DuraNet* showed strong and better bio-efficacy in terms of blood feeding inhibition when compared to untreated DuraNet* and the rest of the treatment arms. The mean blood feeding rate of mosquitoes collected from a hut with unwashed DuraNet*, Unwashed PernaNet 2.0, twenty times washed DuraNet*, 20 times washed PernaNet 2.0 and Untreated DuraNet*, was 0.8/person-night, 4.1/person-night, 5.7/person-night, 6.0/person-night, and 7.0/person-night respectively. There was significant reduction (_P_ <.001) in blood feeding inhibition in all the treatment arms (hut with unwashed DuraNet*, but with 20 times washed DuraNet*, but with\n",
-                        "\n",
-                        "Figure 2: Mean percent knockdown and mortality of _An. arabiensis_ exposed in 3 minutes cone bioassay to DuraNet®, dimma, and southwestern Ethiopia.\n",
-                        "\n",
-                        "\n",
-                        "unwashed PermaNet 2.0, hut with 20 times washed permanent 2.0) when compared to hut with untreated DuraNet*(control). Blood feeding inhibition can be also expressed as personal protection and unwashed DuraNet* showed the highest personal protection 88.7%, 100%, and 93.3% against _An. arabiensis, An. pbaroensis_, and _An. coustani_, respectively. Both PermaNet 2.0 twenty times washed and DuraNet* washed twenty times showed low to moderate personal protection (45.6% against _An. arabiensis_ to 83% against _An. pbaroensis_) when compared to untreated DuraNet* (Table 2).\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A laboratory and a semi-field conditions experimental huts against _Anopheles_ Mosquitoes were conducted in southwestern Ethiopia from September 2015 to January 2016. The bio efficacy of DuraNet(r) was evaluated using the WHO cone bioassay test and then its field efficacy was evaluated using experimental huts against the malaria vector population.\n",
-                        "\n",
-                        "header: CONCLUSION\n",
-                        "\n",
-                        "Both DuraNet(r) and PermanNet 2.0 moderate efficacy against a pyrethroid-resistant population of _An. araabensis_ from Ethiopia. The bio efficacy of DuraNet(r) was found below the WHO recommendation. Therefore, the real impact of the observed insecticide resistance against DuraNet(r) to be further studied under phase-III trials, the need for new alternative vector control tools remains critical.\n",
-                        "\n",
-                        "header: The killing effect DuraNet*\n",
-                        "\n",
-                        "DuraNet* (both unwashed and 20 times washed) showed low to moderate killing efficacy (65.93% to 54.03%) against vector population of _An. arabiensis_. Whereas, against _An. pbaroensis_, both unwashed and washed DuraNet* showed no evidence of killing effect (0.00 to 9.0%). Similarly, PermaNet 2.0 (unwashed and washed) showed low to moderate killing effect against population of _An. arabiensis_ (Table 3).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "World Health Organization cone bioassay tests against pyrethroid-resistant _An. araabensis_ led to mean percent mortality and knockdown of 78% and 93%, respectively. Washing of DuraNet(r) successively reduced its efficacy from 93% knockdown (0 wash) to 45% knockdown (20 washes). Similarly, mean mortality decreased from 84% (wash) to 47% (20 washes). A total of 1575 female mosquitoes were collected over 40 nights out of which 13738(87.8%) were _An. amabise._ Is Title (74%) were _An. amabensis_. The mean blood-feeding rate was significantly lower (_P_<.001) in hut containing unwashed DuraNet(r) when compared to hut containing untreated DuraNet(r). The mean mortality rate was significantly higher (_P_<.001) in hut containing DuraNet(r) when compared to hut containing untreated DuraNet(r). Unwashed DuraNet(r) showed the highest personal protection 88.7% and 100% against _An. araabensis_ and _An. araabensis_, respectively.\n",
-                        "\n",
-                        "header: Mosquito mortality, blood feeding and exit rates.\n",
-                        "\n",
-                        "Mosquito blood feeding rates, mortality rates and exit rates of the 5 treatments are presented in Table 1. The mean blood feeding rate was significantly lower (_P_ <.001) in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. However, there was no significance difference (_P_ >.05) in blood feeding rate among huts containing untreated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet*2.0. The mean mortality rate was significantly higher (_P_ <.001) among huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*. Higher mean number of mosquitoes were caught while exiting the huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*, however, there was no significant difference (_P_ >.05) among the treatments.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Insecticide resistance remains major threat against the efficacy of long-lasting insecticidal nets, which are key intervention tools in controlling malaria vector. In this study laboratory bioassay was initially conducted using field collected larvae raised to adult population of _An. arabiensis_ to assess the bio-efficacy of unwashed DuraNet* (candidate bed net) and untreated DuraNet* (negative control). Furthermore, field experimental study was conducted on bio-efficacy of unwashed DuraNet* and PermaNet 2.0 following WHOPES standard guidelines in order to corroborate the laboratory findings.5\n",
-                        "\n",
-                        "The cone bioassay tests indicated that exposure of population of _An. arabiensis_ mosquitoes from Gilgel-Gibe area, southwestern Ethiopia, to sections of DuraNet* LLIN resulted in mean knockdown and mean mortality of 93% and 78%, respectively. The mortality result is slightly lower than the range of WHO recommendation (>80%) for public health application. The knockdown effect also is slightly below the required WHOPES recommended levels of 95% and above. In contrast to the above results earlier bio efficacy tests conducted using unwashed DuraNet* in India (Sood et al10; Gunasekaran et al11) showed 100% efficacy when tested against populations of different species of _amopheles_. The slight decline in bio-efficacy of DuraNet* in this study may be due to resistance development of vector populations of _An. arabiensis_ in the study area.8,12 In Ethiopia, Previous studies indicate that populations of _An. Arabiensis_ have developed resistance against insecticides used for net impregnation.13-17\n",
-                        "\n",
-                        "Wash resistant long-lasting insecticidal nets treated with pyrethroids are viewed as an important device in the area of vector control that would lessen the difficulties related to retreating conventional insecticide treated nets.[18] DuraNet*, the alpha-cypermethrin treated net, has similar conditions as that of Interceptor* LLIN and PermaNet* 3.0, which had been given interim but full recommendation since June 2017 by WHOPES.[19]\n",
-                        "\n",
-                        "In current study, DuraNet* showed reduced washing resistance. This is reflected in continues decrease of both mean percent knockdown and mean percent mortality of population of _An. arabiensis_ on exposure to the DuraNet* washed 0 and 20 times, respectively. This is in contrast to the study done by Sood et al[10] which showed no significant difference in percent mortality and percent knockdown of population of _An. caliticiacis_ between unwashed and 20 times washed DuraNet* in India. However, in the same study by Sood et al,[10] DuraNet* showed 100% knockdown but only 45% mortality after 20 washes against _Anopheles gambiae_ which is also consistent with current results. Wash resistance technology is added to the LLIN's materials with the assumption of pyrethroid chemicals such as Alphacypermethrin should have a strong affinity to the polyester netting fibers so that even after forceful washing a thin layer of pyrethroid, practically undetectable by High Performance Liquid Chromatography yet adequately bio active to encourage knockdown and mortality should still continue bound to the fibers.\n",
-                        "\n",
-                        "Comparison of mosquito density collected from experimental huts with (unwashed DuraNet*, 20 times washed DuraNet*, unwashed PermaNet*2.0, 20 times washed PermaNet*2.0, and untreated DuraNet*) showed no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the unwashed DuraNet* compared to untreated DuraNet*. Likewise, a study carried out by Asale et al[8] using experimental huts in the study area showed the absence of significance variation in deterrence among huts containing unwashed PermaNet*2.0, untreated net and DDT sprayed hut. The possible explanation for the absence of significant variation in mosquito density among treatment arms could be accounted to low number of mosquito catches during the study period but the impact of insecticide resistance on the efficacy of the nets to be further studied in phase-III trials.\n",
-                        "\n",
-                        "In this study significant reduction in blood feeding rate was observed in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. Similarly, higher mean mosquito mortality rate was recorded among huts containing treatment arms when compared to hut containing control net (untreated DuraNet*). The results for DuraNet* are new report from Jimma area thus we could not corroborate but, Vector population of _An. arabiensis_ from the study area showed no significant reduction of mortality rate, exit pattern and blood feeding inhibition when tested against PermaNet 2.0.[8]\n",
-                        "\n",
-                        "In this study, DuraNet* and PermaNet 2.0 showed low to moderate killing efficacy against vector population of _An. arabiensis_. Moreover, both DuraNet* and PermaNet 2.0 showed no evidence of killing effect against _An. pbarensis_. According to Kitau et al[20] LLINs and ITNs treated with pyrethroids were more effective at killing _An. gambiae_ and _An. fintetus_ than _An. arabiensis_. Thus, the variations in the outcome variables of Anopheles shown above (DuraNet* and PermaNet 2.0) may be due to different susceptibility status and species differences.\n",
-                        "\n",
-                        "header: Study area and period: Mosquito rearing and cone bioassay testing.\n",
-                        "\n",
-                        "Anopheles mosquito larvae were collected from field sites near Gilgel-Gibe hydroelectric power production reservoir and reared to adults under standard conditions (25 \\(\\pm\\) 20*C temperature,80 \\(\\pm\\) 4% relative humidity) in tropical and infectious diseases research institute, Sekoru campus, Jimma University, Ethiopia. For WHO cone bioassay test, five 2-5 days age unfed female _An. gambiae s.l._ (presumably _An. ankiensis_ according to Yewhalaw et al., (2009))\\(\\%\\) mosquitoes were used per cone. Twenty mosquitoes (5 mosquito\\({}^{\\text{\\textregistered}}\\) per-cone \\(\\times\\) 4 cone-per sub-sample net piece) were introduced into the cone facing net sample for 3 minutes and then moved to holding paper cups. Then mosquitoes were supplied with a 10% sucrose solution. The number of mosquitoes knocked down and the number of dead mosquitoes were recorded every 10 minute within 60 minutes and 24 hours, respectively. Mosquitoes exposed to untreated DuraNet\\({}^{*}\\) pieces were used as controls and experiment conditions were set to be 27 \\(\\pm\\) 2*C temperature and 75 \\(\\pm\\) 10% relative humidity (RH) throughout the study. Thus, a total of 1260 mosquitoes (63 net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used for complete bioassay testing and another 180 mosquitoes (9 untreated net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used as control.\n",
-                        "\n",
-                        "For net pieces washing each net sub-samples (25 cm \\(\\times\\) 25 cm) were introduced individually into 1-1 beakers containing 0.5 l deionized water, with 2 g/l WHO recommended soap (Savon de Marseille; pH 10-11) added and fully dissolved just before washing. The beakers were then introduced into a water-bath at 30*C and shaken for 10 min at 155 movements per minute. The samples were then removed, rinsed twice for 10 min in clean, deionized water under the same shaking conditions as above, dried at room temperature and stored at 30*C in the dark between washes.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Exposure of wild population of _An. arabiensis_ mosquitoes to net sections of DuraNet* LLIN in WHO bioassay tests gives mean mortality rate of 78% as well as mean knockdown effect of 93%. Mean percent knockdown and percent mortality of DuraNet* LLIN showed significant relationship (\\(R^{2}\\) = 0.797, n = 260, \\(P\\) <.001). The wash resistance activity of DuraNet* LLINs, measured in terms of percent knockdown and mortality is presented in Figure 2. The mean percent knockdown of wild population of _An. arabiensis_ decreased from 93% to 45% up on exposure to the DuraNet* washed 0 and 20 times respectively. The mean percent mortality decreased from 84% to 47% on exposure to the DuraNet* washed 0 and 20 times, respectively. Wash resistance has negative correlation with percent knockdown and percent mortality which means as number of wash increase, percent knockdown and percent mortality declined (\\(r^{2}\\) = 0.333, 96KD = 89.16 \\(\\pm\\) 2.15, \\(P\\) <.001 and \\(r^{2}\\) = 0.236, 90Mt = 81.94 \\(\\pm\\) 1.68, \\(P\\) <.001), respectively.\n",
-                        "\n",
-                        "header: Table 1: Mean blood feeding, mortality and exit rates of populations of An. arabiensis exposed to different bed net types in field experimental huts in southwestern Ethiopia.\n",
-                        "footer: Means within the same column and with different letters are signi\u001fcantly different using Tukey means separation test.**Significant at P<.001. *Significant at P<.05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['TREATMENTS', 'BLOOD-FEEDING RATE MEAN ± SE', 'MORTALITY RATE MEAN ± SE', 'EXIT RATE MEAN ± SE']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENTS\":\"Untreated DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"77.70 \\u00b1 7.30\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"6.61 \\u00b1 3.80**\",\"EXIT RATE MEAN \\u00b1 SE\":\"26.77 \\u00b1 5.51\"},\"1\":{\"TREATMENTS\":\"Unwashed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"5.80 \\u00b1 2.32**\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"52.38 \\u00b1 5.62\",\"EXIT RATE MEAN \\u00b1 SE\":\"40.42 \\u00b1 5.22\"},\"2\":{\"TREATMENTS\":\"20\\u00d7 washed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"55.94 \\u00b1 6.78\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"28.17 \\u00b1 6.57*\",\"EXIT RATE MEAN \\u00b1 SE\":\"36.24 \\u00b1 5.50\"},\"3\":{\"TREATMENTS\":\"Unwashed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.39 \\u00b1 5.89\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"61.74 \\u00b1 6.09\",\"EXIT RATE MEAN \\u00b1 SE\":\"48.16 \\u00b1 5.53\"},\"4\":{\"TREATMENTS\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.35 \\u00b1 7.01\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"39.87 \\u00b1 6.34\",\"EXIT RATE MEAN \\u00b1 SE\":\"35.32 \\u00b1 5.78\"}}\n",
-                        "\n",
-                        "header: Cone bioassay and washing resistance: Experimental but trial\n",
-                        "\n",
-                        "_Mosquito entry into the bus._ A total of 1575 Anopheles mosquitoes were entered and collected in the 40 nights during the trial. These consisted of 1373 (87.8%) _An. gambiae_ s.l. presumably _An. arabiensis_ (Yewhalaw et al., 2009), 116 (7.4%) _Angelo custani_ (Laveran), and 107 (6.8%) _An. phareensis_ (Theobald). The mean number of mosquitoes caught per night was 8.75 for _An. arabiensis_, 5.52 for _An. Coastani_, and 6.33 for _An. pharensis._ The mean mosquito entry is not significantly different among the treatment arms (F = 1.277, \\(P\\) >.05). There was no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the Unwashed DuraNet* compared to treated DuraNet* and PernaNet* 2.0 (Table 2).\n",
-                        "\n",
-                        "header: Table 2. Blood-feeding inhibition and personal protection due to DuraNet® LLIN in the experimental huts, southwestern Ethiopia.\n",
-                        "footer: *Significant at P<.05. **Significant at P<.01.\n",
-                        "columns: ['TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2', 'PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Untreated DuraNet\\u00ae (NC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"232 (0**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"43 (0**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"30 (0*)\"},\"1\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"24 (88.7**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"00 (100**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"2 (93.3*)\"},\"2\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"176 (42.4)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"27 (62.8)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"5 (83.3*)\"},\"3\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"131 (45.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"11 (83**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"16 (46.6)\"},\"4\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"192 (38.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"6 (75)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"33 (0*)\"}}\n",
-                        "\n",
-                        "header: Experimental but establishment.: Treatment arms and sleepers rotation\n",
-                        "\n",
-                        "In this experiment, 5 different treatment arms were used. These include (1) Untreated unwashed DuraNet(Negative control) (2) Unwashed treated DuraNet(3) treated DuraNet(20 times washed (4) PermaNet(4) 2.0 20 times washed, and (5) unwashed PermaNet(4) 2.0 (positive control). All nets were 75 denier polyethylene and polyester nets respectively. To simulate wear and tear condition of nets under usage 6 (4 cm \\(\\times\\) 4 cm size) holes were cut in each net (2 holes on each of the sides and 1 hole at each end). The DuraNet(4) LLIN and PermaNet(4) 2.0 were washed according to World Health Organization Stage II washing procedure [5]. For washing, the nets were put in to aluminum bowl with 10 l of tap water and 2g/l of savon de Marseille soap. The nets were agitated for 3 minutes, then soaked for 4 minutes, and agitated again for 3 minutes. The nets were agitated manually by stirring them with a pole at 20 rotations per minute. Thus, each net was washed for a total of 10 minute and then rinsed with clean water by a similar procedure, dried horizontally in the shade and stored at ambient temperature between washes. WHO recognized PermaNet(4) 2.0 LLIN washed 20 times, was used as a positive control to assess DuraNet(4) LLIN performance.\n",
-                        "\n",
-                        "In each data collection night, 2 non-smoker male volunteers aged 20 to 25 were allowed to sleep in each experimental hut with its door remain closed between 19:00 and 07:00 hours. Each team was rotated between treatments on successive nights within a week to avoid possible bias which could arise due to individual attractiveness to mosquitoes. There were 5 successive collection nights (Monday to Friday) and 2 successive break nights for hut ventilation per week. Thus, there were 25 collection nights in order to complete the whole study. Informed written consent was obtained from each sleeper.\n",
-                        "\n",
-                        "header: Table 3. The overall killing effect of DuraNet® LLIN in experimental hut.\n",
-                        "footer: None\n",
-                        "columns: ['TREATMENT', 'OVERALL KILLING EFFECT (%) - AN. ARABIENSIS', 'OVERALL KILLING EFFECT (%) - AN. PHAROENSIS', 'OVERALL KILLING EFFECT (%) - AN. COUSTANI']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT\":\"Unwashed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":65.93,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"0.0*\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":12.33},\"1\":{\"TREATMENT\":\"20\\u00d7washed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":54.03,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":13.4},\"2\":{\"TREATMENT\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":64.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"2.25\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":39.7},\"3\":{\"TREATMENT\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":39.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":20.0}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "This study was conducted from September 2015 to January 2016 near the Gilgel-Gibe hydroelectric dam area, southwestern Ethiopia. The hydroelectric dam is one of the major hydroelectric dams in Ethiopia with artificial reservoir occupying an estimated area of 62 kmsq,6 It produces around 184 MWh and is located 260 km southwest of the capital Addis Ababa. It has been functional since 2004. The study area is located between latitudes 7*42*50*7N and 07*53*50*7N and longitudes 37*11*22*7E and 37*20*36*E with an altitude ranging from 1672-1864 m above sea level. The area has a sub-humid, warm to hot climate, obtains between 1300 and 1800 mm of rainfall annually and has a mean annual temperature of 19*C. Mostly, the 2 peak seasonal transmissions of malaria occur during the months of September to December and March to May in the study area.\n",
-                        "\n",
-                        "Bio-efficacy testing of LLIN (DuraNet\\({}^{\\text{\\text{\\textregistered}}}\\)) under laboratory setting\n",
-                        "\n",
-                        "\\(LLIN\\)_sample preparation and washing process._ DuraNet\\({}^{*}\\) LLINs (Karur, Tamil Nadu 639006, and India) is treated with alpha-cypermethrin at a target dose of 250 mg/m\\({}^{2}\\) of netting material. It contains 0.55% W/W \\(\\pm\\) 15% alpha-cypermethrin. The alpha-cypermethrin chemical is coated onto filaments at bursting strength of 450 kPa of netting material and of 145 \\(\\pm\\) 5% for seam sub-section per denier yarn. Prior to testing the production date and batch number of all nets were recorded. Seven sub-samples per net (1 from the roof, the rest from side of the net) were taken from each net and prepared for standard LLINs cone tests by cutting 25 cm \\(\\times\\) 25 cm pieces following WHO protocol.7 In this study, 9 candidate nets were used for bio-efficacy testing. Thus, a total of 63 sub-sample net pieces (7 sub-sample pieces from each net \\(\\times\\) 9 DuraNet\\({}^{*}\\)s) individually rolled up in aluminum foil, labeled (by net type, net number and sample area) and kept in a refrigerator prior to the assay. Concurrently 9 (1 sub-sample piece from each untreated DuraNet\\({}^{*}\\)\\(\\times\\) 9 untreated DuraNet\\({}^{*}\\)s) sub-samples (30 cm \\(\\times\\) 30 cm) were individually rolled up in aluminum foil, labeled and kept in a refrigerator prior to the assay.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Evaluation of Long-Lasting Insecticidal Nets (DuraNet(r)) Under laboratory and Semi-Field Conditions Using Experimental Huts Against _Anopheles Mosquitoes_ in\n",
-                        "\n",
-                        "Jimma Zone\n",
-                        "\n",
-                        " Southwestern Ethiopia\n",
-                        "\n",
-                        "B. Gebremariam(r), W. Birke, W. Zeine, A. Ambelu\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) efficacy could be compromised due to a lot of influences together with user compliance and vector population insecticide resistance status. Thus, this study was to assess the biological efficacy of DuraNet(r) with the help of the World Health Organization cone bioassay and field experimental hut.\n",
-                        "\n",
-                        "header: Mosquito collection, identification and key parameters measured in determination of LLINs efficacy\n",
-                        "\n",
-                        "Mosquitoes were collected from 6:00 to 7:00 each morning inside bed nets, floors, walls, ceilings, and verandas of each experimental hut by the help of mouth aspirators and torches. Then the collected mosquitoes were recorded as dead or alive. Live mosquitoes were held in paper cups and supplied with 10% sucrose solution. The collected mosquitoes were transported to Asendabo Vector Biology Laboratory, Jimma University, where mosquitoes were sorted by genus, sex and morphologically identified using taxonomic keys [9]. Mosquitoes were also scored for their physiological state as unfed, fed, half gravid and gravid. Delayed mortality was recorded after 24 hours. To evaluate the efficacy of DuraNet LLIN against the resistant populations of _An. arabiensis_, different entomological parameters (deterrence, exit, blood feeding inhibition, and mortality rates) were derived from basic measurements.\n",
-                        "\n",
-                        "The primary outcomes were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in entry in to treatment hut relative to the control hut (ie, huts holding untreated nets);\n",
-                        "2. Mortality--the proportion of mosquitoes killed relative to the total catch size;\n",
-                        "3. Killing effect--the numbers killed by a treatment relative to the untreated control, as derived from the formula; \\[\\text{Killing effect}\\left( \\% \\right) = \\left( \\frac{Kt-Ku}{Tu} \\right) \\ast 100\\]\n",
-                        "\n",
-                        "Where **Kt** is the number dead mosquitoes in the huts with treated nets, **Ku** is the number dead mosquitoes in the huts with untreated nets, and **Tu** is the total entering the huts with untreated nets.\n",
-                        "\n",
-                        "1. Blood Feeding Inhibition-The proportional reduction in blood feeding in huts with treated nets relative to controls with untreated nets.\n",
-                        "\n",
-                        "Figure 1: Experimental hut design of the study.\n",
-                        "\n",
-                        "\n",
-                        "5. Personal Protection--The reduction in mosquito biting by treated nets relative to untreated nets, as derived from the formula \\[\\%\\text{personal protection} = (\\frac{Bu-Bt}{Bu})*100\\]\n",
-                        "\n",
-                        "Where Bu is the total number blood fed mosquitoes in the huts with untreated nets, and Bt is the total number blood fed in the huts with treated nets.\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Malaria is still one of highest health risks in developing countries with high morbidity and mortality in under 5 children. Assessment of overall economic impact of the disease shows that it accounts for 40% of health cost spending, 30% to 50% of inpatient charges, and up to 50% of outpatient official visits in areas with great malaria.1 According to the world health organization, there were 216 million new incidents of malaria leading to 445 000 death, of which 90% occurred in African countries.2\n",
-                        "\n",
-                        "Footnote 1: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) are uppermost community health apparatuses and, when used by children and pregnant women, subsidize to improving motherly, neonatal, and infant health, with long-lasting reimbursements to the emerging child.3 These nets provide personal barrier from bite by mosquitoes in addition those nets lessen also the transmission of malaria and have excito-repellency effect.4 A total of 505 million insecticidal nets (ITNs) were distributed in developing countries between 2014 and 2016 with household ownership of at least 1 insecticidal net, improved from 50% in 2010 to 80% in 2016. Nevertheless, the ratio of houses with adequate bed nets (ie, 1 net for every 2 people) is very low (43%).2 Following the high demand of bed nets as key vector control intervention, companies are ramping up production and new brand nets are being introduced for public health utilization.\n",
-                        "\n",
-                        "Footnote 2: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "In order for any new long-lasting insecticidal nets to be used in public health, it must gain WHOPES approval.5 The approval process passes through 3 stages of efficacy testing (laboratory, small-scale semi-field, and large-scale field trial). To be classified as a long-lasting insecticidal net, it must \n",
-                        "maintain their effective biological activity for at least 20 World Health Organization recommended washes typical washes under laboratory conditions and 3 years of suggested use under semi-field and field condition.\n",
-                        "\n",
-                        "DuraNet\\({}^{*}\\) is an LLIN developed by the Shobikaa Impex pvt Ltd (Karur, Tamil Nadu 639006, and India). It contains 0.55% w/w \\(\\pm\\) 15% alpha-cypermethrin. The net is a polyethylene fiber coated with a proprietary polymer containing target dose of alpha-cypermethrin at 250 mg/m\\({}^{2}\\). The polymer binds to the fiber and can withstand multiple washings, the active ingredient diffusing in a controlled manner to the surface of the polymer coat to maintain insecticidal efficacy.\n",
-                        "\n",
-                        "Thus, this study was conducted to verify the intended efficacy of the product in the presence of pyrethroid resistance vector population of _An. gambiae_ s.l. using cone bioassay and semi-field experimental huts in southwestern Ethiopia. The cone bioassays were conducted in Tropical and Infectious diseases research institute, Jimma University. The experimental hut study trials were undertaken near the Gilgel-Gibe hydroelectric power dam-I.\n",
-                        "\n",
-                        "header: ORCID iD\n",
-                        "\n",
-                        "Brhane Gebremariam (c) [https://orcid.org/0000-0002-0824-3221](https://orcid.org/0000-0002-0824-3221)\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Ethiopia\",\"Site\":\"Southwestern Ethiopia\",\"Start_month\":9,\"Start_year\":2015,\"End_month\":1,\"End_year\":2016}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"0.55% W\\/W \\u00b1 15%\",\"Net_washed\":0.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N02\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"0.55% W\\/W \\u00b1 15%\",\"Net_washed\":20.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N03\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"pyrethroid\",\"Concentration_initial\":null,\"Net_washed\":0.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N04\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"pyrethroid\",\"Concentration_initial\":null,\"Net_washed\":20.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A laboratory and a semi-field conditions experimental huts against _Anopheles_ Mosquitoes were conducted in southwestern Ethiopia from September 2015 to January 2016. The bio efficacy of DuraNet(r) was evaluated using the WHO cone bioassay test and then its field efficacy was evaluated using experimental huts against the malaria vector population.\n",
-                        "\n",
-                        "header: Blood feeding inhibition and personal protection.\n",
-                        "\n",
-                        "Unwashed DuraNet* showed strong and better bio-efficacy in terms of blood feeding inhibition when compared to untreated DuraNet* and the rest of the treatment arms. The mean blood feeding rate of mosquitoes collected from a hut with unwashed DuraNet*, Unwashed PernaNet 2.0, twenty times washed DuraNet*, 20 times washed PernaNet 2.0 and Untreated DuraNet*, was 0.8/person-night, 4.1/person-night, 5.7/person-night, 6.0/person-night, and 7.0/person-night respectively. There was significant reduction (_P_ <.001) in blood feeding inhibition in all the treatment arms (hut with unwashed DuraNet*, but with 20 times washed DuraNet*, but with\n",
-                        "\n",
-                        "Figure 2: Mean percent knockdown and mortality of _An. arabiensis_ exposed in 3 minutes cone bioassay to DuraNet®, dimma, and southwestern Ethiopia.\n",
-                        "\n",
-                        "\n",
-                        "unwashed PermaNet 2.0, hut with 20 times washed permanent 2.0) when compared to hut with untreated DuraNet*(control). Blood feeding inhibition can be also expressed as personal protection and unwashed DuraNet* showed the highest personal protection 88.7%, 100%, and 93.3% against _An. arabiensis, An. pbaroensis_, and _An. coustani_, respectively. Both PermaNet 2.0 twenty times washed and DuraNet* washed twenty times showed low to moderate personal protection (45.6% against _An. arabiensis_ to 83% against _An. pbaroensis_) when compared to untreated DuraNet* (Table 2).\n",
-                        "\n",
-                        "header: Limitation\n",
-                        "\n",
-                        "In this study, supplementary test like chemical assays were not conducted to measure the total insecticide content of the netting before and after wash resistance studies that support better interpretation of the results.\n",
-                        "\n",
-                        "header: CONCLUSION\n",
-                        "\n",
-                        "Both DuraNet(r) and PermanNet 2.0 moderate efficacy against a pyrethroid-resistant population of _An. araabensis_ from Ethiopia. The bio efficacy of DuraNet(r) was found below the WHO recommendation. Therefore, the real impact of the observed insecticide resistance against DuraNet(r) to be further studied under phase-III trials, the need for new alternative vector control tools remains critical.\n",
-                        "\n",
-                        "header: Mosquito mortality, blood feeding and exit rates.\n",
-                        "\n",
-                        "Mosquito blood feeding rates, mortality rates and exit rates of the 5 treatments are presented in Table 1. The mean blood feeding rate was significantly lower (_P_ <.001) in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. However, there was no significance difference (_P_ >.05) in blood feeding rate among huts containing untreated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet*2.0. The mean mortality rate was significantly higher (_P_ <.001) among huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*. Higher mean number of mosquitoes were caught while exiting the huts containing treated DuraNet*, 20 times washed DuraNet*, unwashed PernaNet* 2.0 and 20 times washed PernaNet* 2.0 when compared to hut containing untreated DuraNet*, however, there was no significant difference (_P_ >.05) among the treatments.\n",
-                        "\n",
-                        "header: The killing effect DuraNet*\n",
-                        "\n",
-                        "DuraNet* (both unwashed and 20 times washed) showed low to moderate killing efficacy (65.93% to 54.03%) against vector population of _An. arabiensis_. Whereas, against _An. pbaroensis_, both unwashed and washed DuraNet* showed no evidence of killing effect (0.00 to 9.0%). Similarly, PermaNet 2.0 (unwashed and washed) showed low to moderate killing effect against population of _An. arabiensis_ (Table 3).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "World Health Organization cone bioassay tests against pyrethroid-resistant _An. araabensis_ led to mean percent mortality and knockdown of 78% and 93%, respectively. Washing of DuraNet(r) successively reduced its efficacy from 93% knockdown (0 wash) to 45% knockdown (20 washes). Similarly, mean mortality decreased from 84% (wash) to 47% (20 washes). A total of 1575 female mosquitoes were collected over 40 nights out of which 13738(87.8%) were _An. amabise._ Is Title (74%) were _An. amabensis_. The mean blood-feeding rate was significantly lower (_P_<.001) in hut containing unwashed DuraNet(r) when compared to hut containing untreated DuraNet(r). The mean mortality rate was significantly higher (_P_<.001) in hut containing DuraNet(r) when compared to hut containing untreated DuraNet(r). Unwashed DuraNet(r) showed the highest personal protection 88.7% and 100% against _An. araabensis_ and _An. araabensis_, respectively.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Exposure of wild population of _An. arabiensis_ mosquitoes to net sections of DuraNet* LLIN in WHO bioassay tests gives mean mortality rate of 78% as well as mean knockdown effect of 93%. Mean percent knockdown and percent mortality of DuraNet* LLIN showed significant relationship (\\(R^{2}\\) = 0.797, n = 260, \\(P\\) <.001). The wash resistance activity of DuraNet* LLINs, measured in terms of percent knockdown and mortality is presented in Figure 2. The mean percent knockdown of wild population of _An. arabiensis_ decreased from 93% to 45% up on exposure to the DuraNet* washed 0 and 20 times respectively. The mean percent mortality decreased from 84% to 47% on exposure to the DuraNet* washed 0 and 20 times, respectively. Wash resistance has negative correlation with percent knockdown and percent mortality which means as number of wash increase, percent knockdown and percent mortality declined (\\(r^{2}\\) = 0.333, 96KD = 89.16 \\(\\pm\\) 2.15, \\(P\\) <.001 and \\(r^{2}\\) = 0.236, 90Mt = 81.94 \\(\\pm\\) 1.68, \\(P\\) <.001), respectively.\n",
-                        "\n",
-                        "header: Table 1: Mean blood feeding, mortality and exit rates of populations of An. arabiensis exposed to different bed net types in field experimental huts in southwestern Ethiopia.\n",
-                        "footer: Means within the same column and with different letters are signi\u001fcantly different using Tukey means separation test.**Significant at P<.001. *Significant at P<.05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['TREATMENTS', 'BLOOD-FEEDING RATE MEAN ± SE', 'MORTALITY RATE MEAN ± SE', 'EXIT RATE MEAN ± SE']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENTS\":\"Untreated DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"77.70 \\u00b1 7.30\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"6.61 \\u00b1 3.80**\",\"EXIT RATE MEAN \\u00b1 SE\":\"26.77 \\u00b1 5.51\"},\"1\":{\"TREATMENTS\":\"Unwashed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"5.80 \\u00b1 2.32**\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"52.38 \\u00b1 5.62\",\"EXIT RATE MEAN \\u00b1 SE\":\"40.42 \\u00b1 5.22\"},\"2\":{\"TREATMENTS\":\"20\\u00d7 washed DuraNet\\u00ae\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"55.94 \\u00b1 6.78\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"28.17 \\u00b1 6.57*\",\"EXIT RATE MEAN \\u00b1 SE\":\"36.24 \\u00b1 5.50\"},\"3\":{\"TREATMENTS\":\"Unwashed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.39 \\u00b1 5.89\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"61.74 \\u00b1 6.09\",\"EXIT RATE MEAN \\u00b1 SE\":\"48.16 \\u00b1 5.53\"},\"4\":{\"TREATMENTS\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"BLOOD-FEEDING RATE MEAN \\u00b1 SE\":\"53.35 \\u00b1 7.01\",\"MORTALITY RATE MEAN \\u00b1 SE\":\"39.87 \\u00b1 6.34\",\"EXIT RATE MEAN \\u00b1 SE\":\"35.32 \\u00b1 5.78\"}}\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Insecticide resistance remains major threat against the efficacy of long-lasting insecticidal nets, which are key intervention tools in controlling malaria vector. In this study laboratory bioassay was initially conducted using field collected larvae raised to adult population of _An. arabiensis_ to assess the bio-efficacy of unwashed DuraNet* (candidate bed net) and untreated DuraNet* (negative control). Furthermore, field experimental study was conducted on bio-efficacy of unwashed DuraNet* and PermaNet 2.0 following WHOPES standard guidelines in order to corroborate the laboratory findings.5\n",
-                        "\n",
-                        "The cone bioassay tests indicated that exposure of population of _An. arabiensis_ mosquitoes from Gilgel-Gibe area, southwestern Ethiopia, to sections of DuraNet* LLIN resulted in mean knockdown and mean mortality of 93% and 78%, respectively. The mortality result is slightly lower than the range of WHO recommendation (>80%) for public health application. The knockdown effect also is slightly below the required WHOPES recommended levels of 95% and above. In contrast to the above results earlier bio efficacy tests conducted using unwashed DuraNet* in India (Sood et al10; Gunasekaran et al11) showed 100% efficacy when tested against populations of different species of _amopheles_. The slight decline in bio-efficacy of DuraNet* in this study may be due to resistance development of vector populations of _An. arabiensis_ in the study area.8,12 In Ethiopia, Previous studies indicate that populations of _An. Arabiensis_ have developed resistance against insecticides used for net impregnation.13-17\n",
-                        "\n",
-                        "Wash resistant long-lasting insecticidal nets treated with pyrethroids are viewed as an important device in the area of vector control that would lessen the difficulties related to retreating conventional insecticide treated nets.[18] DuraNet*, the alpha-cypermethrin treated net, has similar conditions as that of Interceptor* LLIN and PermaNet* 3.0, which had been given interim but full recommendation since June 2017 by WHOPES.[19]\n",
-                        "\n",
-                        "In current study, DuraNet* showed reduced washing resistance. This is reflected in continues decrease of both mean percent knockdown and mean percent mortality of population of _An. arabiensis_ on exposure to the DuraNet* washed 0 and 20 times, respectively. This is in contrast to the study done by Sood et al[10] which showed no significant difference in percent mortality and percent knockdown of population of _An. caliticiacis_ between unwashed and 20 times washed DuraNet* in India. However, in the same study by Sood et al,[10] DuraNet* showed 100% knockdown but only 45% mortality after 20 washes against _Anopheles gambiae_ which is also consistent with current results. Wash resistance technology is added to the LLIN's materials with the assumption of pyrethroid chemicals such as Alphacypermethrin should have a strong affinity to the polyester netting fibers so that even after forceful washing a thin layer of pyrethroid, practically undetectable by High Performance Liquid Chromatography yet adequately bio active to encourage knockdown and mortality should still continue bound to the fibers.\n",
-                        "\n",
-                        "Comparison of mosquito density collected from experimental huts with (unwashed DuraNet*, 20 times washed DuraNet*, unwashed PermaNet*2.0, 20 times washed PermaNet*2.0, and untreated DuraNet*) showed no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the unwashed DuraNet* compared to untreated DuraNet*. Likewise, a study carried out by Asale et al[8] using experimental huts in the study area showed the absence of significance variation in deterrence among huts containing unwashed PermaNet*2.0, untreated net and DDT sprayed hut. The possible explanation for the absence of significant variation in mosquito density among treatment arms could be accounted to low number of mosquito catches during the study period but the impact of insecticide resistance on the efficacy of the nets to be further studied in phase-III trials.\n",
-                        "\n",
-                        "In this study significant reduction in blood feeding rate was observed in hut containing unwashed DuraNet* when compared to hut containing untreated DuraNet*. Similarly, higher mean mosquito mortality rate was recorded among huts containing treatment arms when compared to hut containing control net (untreated DuraNet*). The results for DuraNet* are new report from Jimma area thus we could not corroborate but, Vector population of _An. arabiensis_ from the study area showed no significant reduction of mortality rate, exit pattern and blood feeding inhibition when tested against PermaNet 2.0.[8]\n",
-                        "\n",
-                        "In this study, DuraNet* and PermaNet 2.0 showed low to moderate killing efficacy against vector population of _An. arabiensis_. Moreover, both DuraNet* and PermaNet 2.0 showed no evidence of killing effect against _An. pbarensis_. According to Kitau et al[20] LLINs and ITNs treated with pyrethroids were more effective at killing _An. gambiae_ and _An. fintetus_ than _An. arabiensis_. Thus, the variations in the outcome variables of Anopheles shown above (DuraNet* and PermaNet 2.0) may be due to different susceptibility status and species differences.\n",
-                        "\n",
-                        "header: Study area and period: Mosquito rearing and cone bioassay testing.\n",
-                        "\n",
-                        "Anopheles mosquito larvae were collected from field sites near Gilgel-Gibe hydroelectric power production reservoir and reared to adults under standard conditions (25 \\(\\pm\\) 20*C temperature,80 \\(\\pm\\) 4% relative humidity) in tropical and infectious diseases research institute, Sekoru campus, Jimma University, Ethiopia. For WHO cone bioassay test, five 2-5 days age unfed female _An. gambiae s.l._ (presumably _An. ankiensis_ according to Yewhalaw et al., (2009))\\(\\%\\) mosquitoes were used per cone. Twenty mosquitoes (5 mosquito\\({}^{\\text{\\textregistered}}\\) per-cone \\(\\times\\) 4 cone-per sub-sample net piece) were introduced into the cone facing net sample for 3 minutes and then moved to holding paper cups. Then mosquitoes were supplied with a 10% sucrose solution. The number of mosquitoes knocked down and the number of dead mosquitoes were recorded every 10 minute within 60 minutes and 24 hours, respectively. Mosquitoes exposed to untreated DuraNet\\({}^{*}\\) pieces were used as controls and experiment conditions were set to be 27 \\(\\pm\\) 2*C temperature and 75 \\(\\pm\\) 10% relative humidity (RH) throughout the study. Thus, a total of 1260 mosquitoes (63 net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used for complete bioassay testing and another 180 mosquitoes (9 untreated net sub-samples \\(\\times\\) 20 mosquitoes per sub-sample net) were used as control.\n",
-                        "\n",
-                        "For net pieces washing each net sub-samples (25 cm \\(\\times\\) 25 cm) were introduced individually into 1-1 beakers containing 0.5 l deionized water, with 2 g/l WHO recommended soap (Savon de Marseille; pH 10-11) added and fully dissolved just before washing. The beakers were then introduced into a water-bath at 30*C and shaken for 10 min at 155 movements per minute. The samples were then removed, rinsed twice for 10 min in clean, deionized water under the same shaking conditions as above, dried at room temperature and stored at 30*C in the dark between washes.\n",
-                        "\n",
-                        "header: Table 2. Blood-feeding inhibition and personal protection due to DuraNet® LLIN in the experimental huts, southwestern Ethiopia.\n",
-                        "footer: *Significant at P<.05. **Significant at P<.01.\n",
-                        "columns: ['TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2', 'PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)', 'PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Untreated DuraNet\\u00ae (NC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"232 (0**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"43 (0**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"30 (0*)\"},\"1\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"24 (88.7**)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"00 (100**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"2 (93.3*)\"},\"2\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed DuraNet\\u00ae\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"176 (42.4)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"27 (62.8)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"5 (83.3*)\"},\"3\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"131 (45.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"11 (83**)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"16 (46.6)\"},\"4\":{\"TREATMENT - Unnamed: 0_level_1 - Unnamed: 0_level_2\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"PERSONAL PROTECTION - AN. ARABIENSIS (WILD) - N (%)\":\"192 (38.6)\",\"PERSONAL PROTECTION - AN. PHAROENSIS (WILD) - N (%)\":\"6 (75)\",\"PERSONAL PROTECTION - AN. COUSTANI (WILD) - N (%)\":\"33 (0*)\"}}\n",
-                        "\n",
-                        "header: Cone bioassay and washing resistance: Experimental but trial\n",
-                        "\n",
-                        "_Mosquito entry into the bus._ A total of 1575 Anopheles mosquitoes were entered and collected in the 40 nights during the trial. These consisted of 1373 (87.8%) _An. gambiae_ s.l. presumably _An. arabiensis_ (Yewhalaw et al., 2009), 116 (7.4%) _Angelo custani_ (Laveran), and 107 (6.8%) _An. phareensis_ (Theobald). The mean number of mosquitoes caught per night was 8.75 for _An. arabiensis_, 5.52 for _An. Coastani_, and 6.33 for _An. pharensis._ The mean mosquito entry is not significantly different among the treatment arms (F = 1.277, \\(P\\) >.05). There was no clear evidence of deterrence associated with any of the treatments however; there were fewer _An. Arabiensis_ in huts with the Unwashed DuraNet* compared to treated DuraNet* and PernaNet* 2.0 (Table 2).\n",
-                        "\n",
-                        "header: Experimental but establishment.: Treatment arms and sleepers rotation\n",
-                        "\n",
-                        "In this experiment, 5 different treatment arms were used. These include (1) Untreated unwashed DuraNet(Negative control) (2) Unwashed treated DuraNet(3) treated DuraNet(20 times washed (4) PermaNet(4) 2.0 20 times washed, and (5) unwashed PermaNet(4) 2.0 (positive control). All nets were 75 denier polyethylene and polyester nets respectively. To simulate wear and tear condition of nets under usage 6 (4 cm \\(\\times\\) 4 cm size) holes were cut in each net (2 holes on each of the sides and 1 hole at each end). The DuraNet(4) LLIN and PermaNet(4) 2.0 were washed according to World Health Organization Stage II washing procedure [5]. For washing, the nets were put in to aluminum bowl with 10 l of tap water and 2g/l of savon de Marseille soap. The nets were agitated for 3 minutes, then soaked for 4 minutes, and agitated again for 3 minutes. The nets were agitated manually by stirring them with a pole at 20 rotations per minute. Thus, each net was washed for a total of 10 minute and then rinsed with clean water by a similar procedure, dried horizontally in the shade and stored at ambient temperature between washes. WHO recognized PermaNet(4) 2.0 LLIN washed 20 times, was used as a positive control to assess DuraNet(4) LLIN performance.\n",
-                        "\n",
-                        "In each data collection night, 2 non-smoker male volunteers aged 20 to 25 were allowed to sleep in each experimental hut with its door remain closed between 19:00 and 07:00 hours. Each team was rotated between treatments on successive nights within a week to avoid possible bias which could arise due to individual attractiveness to mosquitoes. There were 5 successive collection nights (Monday to Friday) and 2 successive break nights for hut ventilation per week. Thus, there were 25 collection nights in order to complete the whole study. Informed written consent was obtained from each sleeper.\n",
-                        "\n",
-                        "header: ORCID iD\n",
-                        "\n",
-                        "Brhane Gebremariam (c) [https://orcid.org/0000-0002-0824-3221](https://orcid.org/0000-0002-0824-3221)\n",
-                        "\n",
-                        "header: Table 3. The overall killing effect of DuraNet® LLIN in experimental hut.\n",
-                        "footer: None\n",
-                        "columns: ['TREATMENT', 'OVERALL KILLING EFFECT (%) - AN. ARABIENSIS', 'OVERALL KILLING EFFECT (%) - AN. PHAROENSIS', 'OVERALL KILLING EFFECT (%) - AN. COUSTANI']\n",
-                        "\n",
-                        "{\"0\":{\"TREATMENT\":\"Unwashed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":65.93,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"0.0*\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":12.33},\"1\":{\"TREATMENT\":\"20\\u00d7washed DuraNet\\u00ae\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":54.03,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":13.4},\"2\":{\"TREATMENT\":\"Unwashed PermaNet\\u00ae 2.0 (PC)\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":64.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"2.25\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":39.7},\"3\":{\"TREATMENT\":\"20\\u00d7 washed PermaNet\\u00ae 2.0\",\"OVERALL KILLING EFFECT (%) - AN. ARABIENSIS\":39.81,\"OVERALL KILLING EFFECT (%) - AN. PHAROENSIS\":\"9.0\",\"OVERALL KILLING EFFECT (%) - AN. COUSTANI\":20.0}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "This study was conducted from September 2015 to January 2016 near the Gilgel-Gibe hydroelectric dam area, southwestern Ethiopia. The hydroelectric dam is one of the major hydroelectric dams in Ethiopia with artificial reservoir occupying an estimated area of 62 kmsq,6 It produces around 184 MWh and is located 260 km southwest of the capital Addis Ababa. It has been functional since 2004. The study area is located between latitudes 7*42*50*7N and 07*53*50*7N and longitudes 37*11*22*7E and 37*20*36*E with an altitude ranging from 1672-1864 m above sea level. The area has a sub-humid, warm to hot climate, obtains between 1300 and 1800 mm of rainfall annually and has a mean annual temperature of 19*C. Mostly, the 2 peak seasonal transmissions of malaria occur during the months of September to December and March to May in the study area.\n",
-                        "\n",
-                        "Bio-efficacy testing of LLIN (DuraNet\\({}^{\\text{\\text{\\textregistered}}}\\)) under laboratory setting\n",
-                        "\n",
-                        "\\(LLIN\\)_sample preparation and washing process._ DuraNet\\({}^{*}\\) LLINs (Karur, Tamil Nadu 639006, and India) is treated with alpha-cypermethrin at a target dose of 250 mg/m\\({}^{2}\\) of netting material. It contains 0.55% W/W \\(\\pm\\) 15% alpha-cypermethrin. The alpha-cypermethrin chemical is coated onto filaments at bursting strength of 450 kPa of netting material and of 145 \\(\\pm\\) 5% for seam sub-section per denier yarn. Prior to testing the production date and batch number of all nets were recorded. Seven sub-samples per net (1 from the roof, the rest from side of the net) were taken from each net and prepared for standard LLINs cone tests by cutting 25 cm \\(\\times\\) 25 cm pieces following WHO protocol.7 In this study, 9 candidate nets were used for bio-efficacy testing. Thus, a total of 63 sub-sample net pieces (7 sub-sample pieces from each net \\(\\times\\) 9 DuraNet\\({}^{*}\\)s) individually rolled up in aluminum foil, labeled (by net type, net number and sample area) and kept in a refrigerator prior to the assay. Concurrently 9 (1 sub-sample piece from each untreated DuraNet\\({}^{*}\\)\\(\\times\\) 9 untreated DuraNet\\({}^{*}\\)s) sub-samples (30 cm \\(\\times\\) 30 cm) were individually rolled up in aluminum foil, labeled and kept in a refrigerator prior to the assay.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Evaluation of Long-Lasting Insecticidal Nets (DuraNet(r)) Under laboratory and Semi-Field Conditions Using Experimental Huts Against _Anopheles Mosquitoes_ in\n",
-                        "\n",
-                        "Jimma Zone\n",
-                        "\n",
-                        " Southwestern Ethiopia\n",
-                        "\n",
-                        "B. Gebremariam(r), W. Birke, W. Zeine, A. Ambelu\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) efficacy could be compromised due to a lot of influences together with user compliance and vector population insecticide resistance status. Thus, this study was to assess the biological efficacy of DuraNet(r) with the help of the World Health Organization cone bioassay and field experimental hut.\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Malaria is still one of highest health risks in developing countries with high morbidity and mortality in under 5 children. Assessment of overall economic impact of the disease shows that it accounts for 40% of health cost spending, 30% to 50% of inpatient charges, and up to 50% of outpatient official visits in areas with great malaria.1 According to the world health organization, there were 216 million new incidents of malaria leading to 445 000 death, of which 90% occurred in African countries.2\n",
-                        "\n",
-                        "Footnote 1: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "Long-Lasting insecticidal Nets (LLINs) are uppermost community health apparatuses and, when used by children and pregnant women, subsidize to improving motherly, neonatal, and infant health, with long-lasting reimbursements to the emerging child.3 These nets provide personal barrier from bite by mosquitoes in addition those nets lessen also the transmission of malaria and have excito-repellency effect.4 A total of 505 million insecticidal nets (ITNs) were distributed in developing countries between 2014 and 2016 with household ownership of at least 1 insecticidal net, improved from 50% in 2010 to 80% in 2016. Nevertheless, the ratio of houses with adequate bed nets (ie, 1 net for every 2 people) is very low (43%).2 Following the high demand of bed nets as key vector control intervention, companies are ramping up production and new brand nets are being introduced for public health utilization.\n",
-                        "\n",
-                        "Footnote 2: [https://developers.com/en/article/](https://developers.com/en/article/)\n",
-                        "\n",
-                        "In order for any new long-lasting insecticidal nets to be used in public health, it must gain WHOPES approval.5 The approval process passes through 3 stages of efficacy testing (laboratory, small-scale semi-field, and large-scale field trial). To be classified as a long-lasting insecticidal net, it must \n",
-                        "maintain their effective biological activity for at least 20 World Health Organization recommended washes typical washes under laboratory conditions and 3 years of suggested use under semi-field and field condition.\n",
-                        "\n",
-                        "DuraNet\\({}^{*}\\) is an LLIN developed by the Shobikaa Impex pvt Ltd (Karur, Tamil Nadu 639006, and India). It contains 0.55% w/w \\(\\pm\\) 15% alpha-cypermethrin. The net is a polyethylene fiber coated with a proprietary polymer containing target dose of alpha-cypermethrin at 250 mg/m\\({}^{2}\\). The polymer binds to the fiber and can withstand multiple washings, the active ingredient diffusing in a controlled manner to the surface of the polymer coat to maintain insecticidal efficacy.\n",
-                        "\n",
-                        "Thus, this study was conducted to verify the intended efficacy of the product in the presence of pyrethroid resistance vector population of _An. gambiae_ s.l. using cone bioassay and semi-field experimental huts in southwestern Ethiopia. The cone bioassays were conducted in Tropical and Infectious diseases research institute, Jimma University. The experimental hut study trials were undertaken near the Gilgel-Gibe hydroelectric power dam-I.\n",
-                        "\n",
-                        "header: Mosquito collection, identification and key parameters measured in determination of LLINs efficacy\n",
-                        "\n",
-                        "Mosquitoes were collected from 6:00 to 7:00 each morning inside bed nets, floors, walls, ceilings, and verandas of each experimental hut by the help of mouth aspirators and torches. Then the collected mosquitoes were recorded as dead or alive. Live mosquitoes were held in paper cups and supplied with 10% sucrose solution. The collected mosquitoes were transported to Asendabo Vector Biology Laboratory, Jimma University, where mosquitoes were sorted by genus, sex and morphologically identified using taxonomic keys [9]. Mosquitoes were also scored for their physiological state as unfed, fed, half gravid and gravid. Delayed mortality was recorded after 24 hours. To evaluate the efficacy of DuraNet LLIN against the resistant populations of _An. arabiensis_, different entomological parameters (deterrence, exit, blood feeding inhibition, and mortality rates) were derived from basic measurements.\n",
-                        "\n",
-                        "The primary outcomes were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in entry in to treatment hut relative to the control hut (ie, huts holding untreated nets);\n",
-                        "2. Mortality--the proportion of mosquitoes killed relative to the total catch size;\n",
-                        "3. Killing effect--the numbers killed by a treatment relative to the untreated control, as derived from the formula; \\[\\text{Killing effect}\\left( \\% \\right) = \\left( \\frac{Kt-Ku}{Tu} \\right) \\ast 100\\]\n",
-                        "\n",
-                        "Where **Kt** is the number dead mosquitoes in the huts with treated nets, **Ku** is the number dead mosquitoes in the huts with untreated nets, and **Tu** is the total entering the huts with untreated nets.\n",
-                        "\n",
-                        "1. Blood Feeding Inhibition-The proportional reduction in blood feeding in huts with treated nets relative to controls with untreated nets.\n",
-                        "\n",
-                        "Figure 1: Experimental hut design of the study.\n",
-                        "\n",
-                        "\n",
-                        "5. Personal Protection--The reduction in mosquito biting by treated nets relative to untreated nets, as derived from the formula \\[\\%\\text{personal protection} = (\\frac{Bu-Bt}{Bu})*100\\]\n",
-                        "\n",
-                        "Where Bu is the total number blood fed mosquitoes in the huts with untreated nets, and Bt is the total number blood fed in the huts with treated nets.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Ethiopia\",\"Site\":\"Southwestern Ethiopia\",\"Start_month\":9,\"Start_year\":2015,\"End_month\":1,\"End_year\":2016}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"0.55% W\\/W \\u00b1 15%\",\"Net_washed\":0.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N02\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"0.55% W\\/W \\u00b1 15%\",\"Net_washed\":20.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N03\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"pyrethroid\",\"Concentration_initial\":null,\"Net_washed\":0.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"},\"N04\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"pyrethroid\",\"Concentration_initial\":null,\"Net_washed\":20.0,\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"NA\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Results\n",
-                        "\n",
-                        "Among the 1784 participating homesteads with a population of 10 325, a total of 1551 households participated in the study (Table 1). Most of the heads of the households (age: 17-96 years) had received formal education and practiced farming (96.6%).\n",
-                        "\n",
-                        "Out of 100 heads of households with Olyset(r) Plus interviewed for adverse events, 44% reported itching, 4% eye irritation, and 7% unpleasant smell. Among the Olyset(r) Net users, 7% each reported nasal discharge and eye irritation. These were reported to be mild symptoms lasting for one or two days. To reduce these effects, 43% users of Olyset(r) Plus ventilated nets for a day in the open under the sun and 33% under the shade. Among Olyset(r) Net users, similar actions were taken by 44% and 39%, respectively.\n",
-                        "\n",
-                        "header: Table 7 Comparison of the estimates of the mean pHI and the mean hole area for O lyset® Plus and Olyset® Net\n",
-                        "footer: pHI proportionate Hole Index\n",
-                        "columns: ['Survey month', 'Olyset® Plus - No. of nets inspected', 'Olyset® Plus - Mean hole area in cm2 (± SD)', 'Olyset® Plus - Mean pHI (± SD)', 'Olyset® Net - No. of nets inspected', 'Olyset® Net - Mean hole area in cm2 (± SD)', 'Olyset® Net - Mean pHI (± SD)']\n",
-                        "\n",
-                        "{\"0\":{\"Survey month\":6,\"Olyset\\u00ae Plus - No. of nets inspected\":250,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"52.3 \\u00b1 230.5\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"93.3 \\u00b1 313.6\",\"Olyset\\u00ae Net - No. of nets inspected\":250,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"123.3 \\u00b1 272.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"110.5 \\u00b1 221.8\"},\"1\":{\"Survey month\":12,\"Olyset\\u00ae Plus - No. of nets inspected\":243,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"229.2 \\u00b1 574.2\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"153.6 \\u00b1 437.0\",\"Olyset\\u00ae Net - No. of nets inspected\":249,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"192.5 \\u00b1 376.2\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"156.8 \\u00b1 306.5\"},\"2\":{\"Survey month\":24,\"Olyset\\u00ae Plus - No. of nets inspected\":231,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"389.8 \\u00b1 975.4\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"317.6 \\u00b1 794.7\",\"Olyset\\u00ae Net - No. of nets inspected\":226,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"873.9 \\u00b1 353.4\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"279.4 \\u00b1 428.1\"},\"3\":{\"Survey month\":36,\"Olyset\\u00ae Plus - No. of nets inspected\":228,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"630.7 \\u00b1 1191.8\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"513.9 \\u00b1 970.9\",\"Olyset\\u00ae Net - No. of nets inspected\":216,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"621.5 \\u00b1 1710.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"506.4 \\u00b1 1393.2\"}}\n",
-                        "\n",
-                        "header: Table 5 Mean permethrin and PBO contents (relative standard deviation) in nets at baseline, and at 12, 24 and 36 months\n",
-                        "footer: a  Within net variation (relative standard deviation) in permethrin and PBO contents at baseline was 2.2% and 3.7%, respectively (Olyset® Plus). – not applicable, PBOpiperonyl butoxideb Within net variation (relative standard deviation) in permethrin content at baseline was 1.2% (Olyset® Net)\n",
-                        "columns: ['Sampling month', 'Olyset® Plus - Mean permethrin content in g/kg (95% CI)', 'Olyset® Plus - Permethrin content lost (%)', 'Olyset® Plus - PBO content in g/ kg (95% CI)', 'Olyset® Plus - PBO content lost (%)', 'Olyset® Net - Mean permethrin content in g/kg (95% CI)', 'Olyset® Net - Permethrin content lost (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Sampling month\":0,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"18.4 (18.3\\u201318.6)a\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"8.98 (8.86\\u20139.10)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"19.6 (19.5\\u201319.7)b\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"\\u2013\"},\"1\":{\"Sampling month\":12,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.7 (10.6\\u201312.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"36\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"3.01 (2.30\\u20133.80)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"66\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"16.5 (16.0\\u201317.0)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"16\"},\"2\":{\"Sampling month\":24,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.1 (10.2\\u201312.0)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"40\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.70 (1.20\\u20132.20)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"81\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"15.9 (15.1\\u201316.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"19\"},\"3\":{\"Sampling month\":36,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"8.8 (7.9\\u20139.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"52\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.16 (0.8\\u20131.5)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"87\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"14.8 (13.9\\u201315.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"24\"}}\n",
-                        "\n",
-                        "header: Net sampling scheme\n",
-                        "\n",
-                        "The scheme of sampling nets at different time points for bioassays and chemical assays is given in Table 2. The netting pieces were cut from the sampled nets from positions 1-9 for bioassays, and positions HP1-HP5 (at time 0) or positions HP2-HP5 (at time 12, 24 and 36 months) for chemical content assays (Fig. 1). All the nets which were sampled and retrieved at the given survey points were replaced with new ones.\n",
-                        "\n",
-                        "header: Chemical contents\n",
-                        "\n",
-                        "At time 0 (baseline), the contents of permethrin and PBO in Olyset(r) Plus were within the target tolerance limits of 20 +- 5 g/kg and 10 +- 2.5 g/kg, respectively; while the permethrin content in Olyset(r) Net was also within the target tolerance limit of 20 +- 3 g/kg (Table 5). The within net variation (relative standard deviation, RSD) in permethrin and PBO contents for Olyset(r) Plus at baseline were 2.2% and 3.7%, respectively, and were within the acceptable tolerance limits. The within net variation in the permethrin content in Olyset(r) Net at the baseline was 1.2% and was also within the acceptable tolerance limit. At the end of years 1, 2 and 3 of net usage, the permethrin content in Olyset(r) Plus decreased by 36%, 40% and 52%, and the PBO content showed a marked reduction of 66%, 81% and 87%, respectively. The loss of the permethrin in Olyset(r) Net\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c} \\hline\n",
-                        "**Survey month** & **No. of** & **Mean 1 h** & **Mean 24 h** & **\\% of nets passed by** & **\\% of nets passed by** & **No. of nets passed by** & **Overall pass rate in** \\\\\n",
-                        "**nets** & **KD (\\%)** & **mortality** & **KD criteria alone(r)** & **mortality criteria** & **requiring tunnel** & **percentage (95\\% CI)(r)** \\\\\n",
-                        "**Olyset(r) Plus** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.5 & 100 & 100 & 100 & 0 & 100 (80–100) \\\\\n",
-                        "6 & 30 & 97.0 & 88.2 & 83.3 & 73.3 & 4 & 96.7 (82–99) \\\\\n",
-                        "12 & 30 & 9\n",
-                        "\n",
-                        "4.2 & 74.3 & 76.7 & 43.3 & 6 & 93.3 (78–99) \\\\\n",
-                        "18 & 30 & 82.9 & 65.9 & 36.7 & 36.7 & 16 & 86.7 (69–96) \\\\\n",
-                        "24 & 30 & 77.4 & 75.3 & 0.0 & 46.7 & 15 & 76.7 (57–90) \\\\\n",
-                        "30 & 30 & 60.4 & 56.9 & 0.0 & 10.0 & 27 & 70.0 (50–85) \\\\\n",
-                        "**Olyset(r) Net** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.1 & 100 & 100 & 100 & 0 & 100 (88–100) \\\\\n",
-                        "6 & 30 & 96.9 & 79.8 & 80.0 & 60.0 & 4 & 93.3 (77–99) \\\\\n",
-                        "12 & 30 & 68.5 & 54.7 & 20.0 & 3.3 & 24 & 83.3 (65–94) \\\\\n",
-                        "18 & 30 & 72.1 & 50.2 & 30.0 & 20.0 & 18 & 73.3 (54–87) \\\\\n",
-                        "24 & 30 & 79.8 & 68.9 & 3.3 & 46.7 & 18 & 66.7 (47–83) \\\\\n",
-                        "30 & 30 & 50.9 & 50.9 & 0.0 & 3.3 & 29 & 60.0\n",
-                        "\n",
-                        "\n",
-                        "was 16% at year 1 and did not change much at year 2 and 3 (19 and 24%, respectively).\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Bioefficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide-treated insecticidal net in a 3-year long trial in Kenya\n",
-                        "\n",
-                        "Paul M. Gichuki, Anna Kamau, Kiambo Niagi, Solomon Karoki, Njoroge Muigai, Damaris Matoke-Muhia, Nabie Bayoh, Evan Mathenge\n",
-                        "\n",
-                        "\n",
-                        "**Background:** Long-lasting insecticide nets (LLINs) are a core malaria intervention. LLINs should retain efficacy against mosquito vectors for a minimum of three years. Efficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide (PBO) treated LLIN, was evaluated versus permethrin treated Olyset(r) Net. In the absence of WHO guidelines of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated as a pyrethroid LILN.\n",
-                        "\n",
-                        "**Methods:** This was a household randomized controlled trial in a malaria endemic rice cultivation zone of Kirinyaga County, Kenya between 2014 and 2017. Cone bioassays and tunnel tests were done against _Anopheles gambiae_ Kisumu. The chemical content, fabric integrity and LLIN survivorship were monitored. Comparisons between nets were tested for significance using the Chi-square test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. The WHO efficacy criteria used were >= 95% knockdown and/or >= 80% mortality rate in cone bioassays and >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests.\n",
-                        "\n",
-                        "**Results:** At 36 months, Olyset(r) Plus lost 52% permethrin and 87% PBO content; Olyset(r) Net lost 24% permethrin. Over 80% of Olyset(r) Plus and Olyset(r) Net passed the WHO efficacy criteria for LLINs up to 18 and 12 months, respectively. At month 36, 91.2% Olyset(r) Plus and 86.4% Olyset(r) Net survived, while 72% and 63% developed at least one hole. The proportionate Hole Index (pHI) values representing nets in good, serviceable and torn condition were 49.6%, 27.1% and 23.2%, respectively for Olyset(r) Plus, and 44.9%, 32.8% and 22.2%, respectively for Olyset(r) Net but were not significantly different.\n",
-                        "\n",
-                        "**Conclusions:** Olyset(r) Plus retained efficacy above or close to the WHO efficacy criteria for about 2 years than Olyset(r) Net (1-1.5 years). Both nets did not meet the 3-year WHO efficacy criteria, and showed little attrition, comparable physical durability and survivorship, with 50% of Olyset(r) Plus having good and serviceable condition after 3 years. Better community education on appropriate use and upkeep of LLINs is essential to ensure effectiveness of LLIN based malaria interventions.\n",
-                        "\n",
-                        "**Keywords:**_Anopheles gambiae, Bioefficacy, Durability, Kenya, Long-lasting insecticidal net, Olyset(r) Net, Olyset(r) Plus, Permeability, Piperonyl butoxide_\n",
-                        "\n",
-                        "header: Assessment of durability of cohort nets\n",
-                        "\n",
-                        "To monitor durability of nets (i.e. survivorship and changes in physical integrity) during their routine use, 250 each of Olyset(r) Plus and Olyset(r) Net were distributed in separate households (2 nets per household) in the same villages by simple randomization. From these cohorts of nets, all nets available at the households at the time of surveys at 6, 12, 24 and 36 months were inspected for their presence and physical integrity. For this, the nets were retracted from the sleeping places, individually hung up on a rectangular frame held outside the house and inspected for presence of any holes on the side and roof panels. The holes were counted for each net, their diameter was measured, and they were classified in the following categories according to the WHO criteria for hole sizes [3, 11]:\n",
-                        "\n",
-                        "Size A1: 0.5-2 cm diameter, Size A2: 2-10 cm diameter, Size A3: 10-25 cm diameter, Size A4: 25 cm diameter.\n",
-                        "\n",
-                        "The parameters for assessing the integrity of nets included the proportions of Olyset(r) Plus and Olyset(r) Net with any size holes or tears, and the proportionate Hole Index (pHI) for each net, which was calculated as follows [11]:\n",
-                        "\n",
-                        "\\[\\text{pH} = \\left( {1.23 \\times \\text{ No.~of~size~A1~holes}} \\right) + \\left( {28.28 \\times \\text{ No.~of~size~A2~holes}} \\right) + \\left( {240.56 \\times \\text{ No.~of~size~A3~holes}} \\right) + \\left( {706.95 \\times \\text{ No.~of~size~A4~holes}} \\right)\\]\n",
-                        "\n",
-                        "Using the pHI values, Olyset(r) Plus and Olyset(r) Net were categorized as those in 'good condition' (pHI: 0-64), nets in 'acceptable' condition (pHI: 65-642), and torn nets that were likely to provide no protective efficacy (pHI > 642) [15]. The number of nets in 'good' and\n",
-                        "\n",
-                        "Fig. 1: A rectangular net and its individual panels showing positions for cutting netting pieces (positions 1–9 for bioassays; HP1–HPS for chemical assays)\n",
-                        "'acceptable condition' together were taken to estimate the survival of nets over time.\n",
-                        "\n",
-                        "The sampled nets were also inspected for any repairs of holes or tears done by the households. After inspection, the nets were returned to the same sleeping place in the same household.\n",
-                        "\n",
-                        "header: Net survivorship and fabric integrity\n",
-                        "\n",
-                        "At the end of year 1, Olyset(r) Plus reported survivorship rates of 97.2% (_n_ = 243), with Olyset(r) Net recording 99.6% (_n_ = 249). At year 2, there were 92.4% (_n_ = 231) Olyset(r) Plus and 90.4% (_n_ = 226) of Olyset(r) Nets still surviving. At the end of the 3 years of study, the survivorship rates for Olyset(r) Plus and Olyset(r) Net were at 91.2% (_n_ = 228) and 86.4% (_n_ = 216), respectively.\n",
-                        "\n",
-                        "The pH values of the nets showed that 87.6% of Olyset(r) Plus were in good condition at 6 months, 7.6% were in acceptable/serviceable condition, and 4.8% were too torn (Table 6). These proportions increased gradually during the study and at 36 months, 49.6% were in good condition, 27.1% in acceptable/serviceable condition, and 23.2% were too torn. A similar trend of attrition was seen for Olyset(r) Net, with 44.9%, 32.8% and 22.2% nets being in good condition, acceptable condition or too torn at 36 months, respectively.\n",
-                        "\n",
-                        "Data on the comparison of estimates of the mean pH values and the mean hole area for Olyset(r) Plus and Olyset(r) Net are given in Table 7.\n",
-                        "\n",
-                        "The proportion of Olyset(r) Plus nets having been repaired by households increased from 6% (95% CI: 3.4-9.7) at month 6 to 17.9% (95% CI: 13.2-23.6) at months\n",
-                        "\n",
-                        "36, while that of Olyset(r) Net increased from 5% (95% CI: 2.8-8.7) to 12% (95% CI: 8.0-17.1) in the same period.\n",
-                        "\n",
-                        "\n",
-                        "Olyset(r) Net incorporated with permethrin alone was included in the trial as a positive control net. A net was considered to have passed the WHO efficacy criteria if it caused >= 80% mortality and/or >= 95% knockdown in cone bioassays or >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests [3]. Most of the Olyset(r) Plus and Olyset(r) Net lost the knockdown effect during the 24-36 months follow-up. Overall, neither Olyset(r) Plus nor Olyset(r) Net performed as they should even after 2 years and did not meet the WHO efficacy criteria of a 3-year LLIN.\n",
-                        "\n",
-                        "Previous studies have demonstrated enhanced efficacy of Olyset(r) Plus against resistant _An. gambiae_ attributable to PBO [16-18]. A recent randomised controlled field trial in Tanzania with high levels of pyrethroid resistance in malaria vectors _An. gambiae_ and _An. arabiensis_ also attributed higher impact on malaria of Olyset(r) Plus over Olyset(r) Net to the presence of PBO that was sustained after 21 months of the trial [19]. However, there is a view that these two nets are not comparable and that the higher efficacy of Olyset(r) Plus was due to much more bleed rate of permethrin into its surface and not due to PBO that was incorporated in small amount and dwindled rapidly over time [20]. In this trial, Olyset(r) Plus was evaluated as a pyrethroid LLIN since there were no guidelines published by WHO of how to evaluate PBO nets at the time of this trial, and considering the manufacturer's product claim.\n",
-                        "\n",
-                        "While, it was reported that high intensity insecticide resistance will reduce the level of personal and community protection of pyrethroid LLINs [21] and PBO nets could be effective against malaria in some resistance scenarios [22], a recent WHO multi-country study found no evidence of an association between insecticide resistance and malaria infection prevalence or incidence [23]. There is also a view that at present there is limited evidence that recent reduction in the impact on malaria in sub-Saharan Africa was due to increasing resistance in malaria vectors to the pyrethroid insecticides used in treating nets [24].\n",
-                        "\n",
-                        "The initial permethrin content in both nets was more or less similar but the enhanced efficacy of Olyset(r) Plus was mainly due to the higher release of permethrin into the surface fibres and presence of PBO than Olyset(r) Net. The gradual decline in efficacy of both nets is consistent with the loss of permethrin and PBO in Olyset(r) Plus and of permethrin in Olyset(r) Net over time during their routine usage by the community. The decline in permethrin content was more rapid in Olyset(r) Plus than Olyset(r) Net. This explains why Olyset(r) Plus having permethrin and PBO was relatively superior than Olyset(r) Net initially, i.e., until 18 months, but after that their performance was below the WHO efficacy cut off. While the PBO content in Olyset(r) Plus at the baseline was within the target tolerance limits, 66% of the PBO content was lost within one year of net usage, and the loss increased to 81% at year 2 and 87% at year 3. Previous studies have reported that the insecticidal content in LLINs decay over time [6]. While permethrin and PBO contents may be lost due to natural decay and evaporation, washing of nets frequently and drying them under direct sunlight are known to contribute to such losses [25, 26]. In this study, despite manufacturer's label recommendation not to expose nets to direct sunlight, a good proportion of both type of nets were exposed to bright sunlight before use and left to dry in the sun after washing. This would have reduced both permethrin and PBO contents and consequently reduced the biological efficacy of these nets and should be borne in mind when considering these results. Even though Kisumu strain was tested, the loss of PBO would have affected efficacy of Olyset Plus as PBO can improve net performance even against susceptible strains, which too have P450s, and PBO is also known to act as a good solvent that can aid cuticular penetration and help improve insecticidal activity. The operational implication of rapid loss of PBO is that Olyset(r) Plus was unlikely to remain effective even against pyrethroid resistant mosquitoes for 2 or 3 years of use.\n",
-                        "\n",
-                        "Community net wash practices were comparable between the two nets. During the first 6 months, no net was reported to have been washed. At 1 year, 7% of Olyset(r) Plus and 7% of Olyset(r) Net were reported to have been washed at least once. At month 36 months, about 17% and 21% of Olyset(r) Plus and Olyset(r) Net\n",
-                        "respectfully had been washed at least once. LLINs are usually recommended to be washed at most every 3 months [5], although this frequency depends on local cultural practices and water availability. Different net washing frequencies have been reported in other studies spanning from a high of 8 washes per month in Mali [27] to 4-7 times in Tanzania [28] to a low average of 1.5 washes per year in Uganda [29]. In coastal Kenya, on average nets were washed twice in 6 months, with blue colour nets least often washed than white or green colour nets, and older nets washed more frequently [30]. In western Kenya lowlands, three quarter of nets were washed once within 3 months and a third of them were dried under the sun [31]. The main reasons for low reported wash rates during the annual surveys in our study could be attributed to net users' poor recall of the net washing frequencies, and people's apprehension that washing of nets reduce net efficacy, although there was no water scarcity in the trial area being located in a rice irrigation area.\n",
-                        "\n",
-                        "The study participants reported experiencing certain transient adverse effects during the first two days of use of nets, but these could be prompted reactions since the community members had been informed about the possibility of such effects at the time of net distribution. The higher side effects from use of Olyset(r) Plus is in line with the higher release of permethrin as mentioned above. Users resorted to ventilating their nets in direct sunlight to reduce the effects although our project team had advised to dry up and keep the nets under shade. Ventilating new nets or drying washed nets under direct sunlight might have contributed to a significant reduction in active ingredient content and bio-efficacy of nets.\n",
-                        "\n",
-                        "At the end of the 3-year period, among the two cohorts of 250 nets each, only about 9% Olyset(r) Plus and about 14% Olyset(r) Net were reported to have been lost showing a high net survivorship. These results are in line with the expected 75% net survivorship rate reported by the NetCALC 3-year serviceable prediction model [32]. Previous studies have reported varying rate of net survivorship in African countries, such as 58% nets were lost within two years in Rwanda [33], only 39% of distributed nets remained both present and in serviceable physical condition 2-4 years after a mass campaign in Tanzania [34], but in Western Uganda an estimated attrition rate of just 12% after three years of use was observed for a polyester net [35]. In four African countries of Mozambique, Nigeria, DRC and Zanzibar, Tanzania, survival in serviceable condition of polyethylene and polyester LLINs after 31-37 months of use varied between different sites from 17 to 80% with median survival from 1.6 to 5.3 years [36]. In our study, the observed low attrition rate of nets appears to be also due to the 'Hawthorne effect' as the households included in the two cohorts of nets were visited every 6 or 12 months to inspect the nets. So, the awareness among study participants that the nets were regularly being followed could have modified their behaviour in favour of more careful upkeep and maintenance of the nets and as such may have been a limitation in this study.\n",
-                        "\n",
-                        "Studies have shown that despite small to medium size holes (64 < PHI < 642), LLINs are still protective against mosquito bites due to the excito-repellent effects of insecticides [35]. However, the nets may become ineffective when the overall hole area in nets reaches a certain threshold [37]. In our study, both Olyset(r) Plus and Olyset(r) Net recorded high fabric integrity up to the 3-year trial period. At 12 months after distribution, 78.1% of Olyset Plus and 63.8% of Olyset(r) Net were in good/acceptable condition, 12.3% Olyset(r) Plus and 30.5% Olyset(r) Net were in serviceable condition, while 9.4% Olyset(r) Plus and 5.6% Olyset(r) Net were completely torn and required to be replaced. At the end of the 3-year trial period, 49.5% of Olyset(r) Plus and 44.9% of Olyset(r) Net were in good/acceptable condition, 27.1% Olyset(r) Plus and 32.8% Olyset(r) Net in serviceable condition, while 23.2% Olyset(r) Plus and 22.2% Olyset(r) Net were completely torn and required replacement.\n",
-                        "\n",
-                        "Although Olyset(r) Net retained a higher permethrin content that Olyset(r) Plus, the latter showed longer duration of efficacy than the former, i.e., 18 months compared with 12 months above the WHO efficacy cut off, and higher efficacy thereafter i.e., the efficacy of Olyset(r) Plus declined to 42% at the end of 3-year trial period and that of Olyset(r) Net to 36% over the same period. This could be attributed to higher release of permethrin into the netting surface, and also because PBO can make nets perform better even against susceptible mosquitoes. Pyrethroid-PBO nets have previously been associated with higher mosquito mortality and lower blood-feeding rates in areas of high-level insecticide resistance than were non-PBO LLINs [38, 39]. Although in our study we did not test nets with a pyrethroid resistant mosquito strain, it is likely that the fast decline of PBO in Olyset(r) Plus was caused by the inappropriate exposure to sunlight, as well as the normal loss through washing would have reduced efficacy against resistant mosquitoes in the same way that it reduced efficacy against the susceptible mosquitoes tested here.\n",
-                        "\n",
-                        "Appropriate testing guidelines and field studies are thus required to evaluate the operational effectiveness of PBO nets against pyrethroid resistant mosquitoes and considering the PBO retention and release properties. The revised WHO LLIN guidelines should clarify for how long PBO nets should be effective and if the \n",
-                        "current WHO efficacy criteria for a 3-year LLIN should be reduced to fit the chemistry.\n",
-                        "\n",
-                        "A limitation of our trial was that we did not evaluate efficacy of Olysset(r) Plus against resistant _An. gambling_ mosquitoes. Instead, we tested its efficacy for the presence of permethrin alone using a susceptible malaria vector strain, although inclusion of PBO in nets has the potential of increasing insecticidal efficacy due to increased cuticular penetration and presence of P450s in susceptible insects. There is thus a need for suitable testing guidelines and a much more validation of the impact of PBO in pyrethroid-PBO nets.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All the relevant datasets supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Table 6 Progression of hole formation and proportionate Hole Index (pHI) values for Olyset® Plus and Olyset® Net\n",
-                        "footer: None\n",
-                        "columns: ['Net brand', 'Follow up months', 'No. of nets sampled', 'Number of nets with any size holes (%; 95% CI)', 'No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)', 'No. of nets by pHI values (%; 95% CI) - pHI 65–642 (in acceptable condition)', 'No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)']\n",
-                        "\n",
-                        "{\"0\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":6,\"No. of nets sampled\":250,\"Number of nets with any size holes (%; 95% CI)\":\"115 (46.0; 39.9\\u201352.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"219 (87.6; 82.9\\u201391.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"19 (7.6; 4.9\\u201311.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"12 (4.8; 2.8\\u20138.2)\"},\"1\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":12,\"No. of nets sampled\":243,\"Number of nets with any size holes (%; 95% CI)\":\"131 (54.0; 47.6\\u201360.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"190 (78.2; 72.6\\u201382.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"30 (12.4; 8.8\\u201317.0)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"23 (9.5; 6.3\\u201313.8)\"},\"2\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":24,\"No. of nets sampled\":231,\"Number of nets with any size holes (%; 95% CI)\":\"120 (52.0; 45.5\\u201358.3)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"145 (62.8; 56.7\\u201368.7)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"58 (25.1; 19.9\\u201331.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"28 (12.0; 8.5\\u201316.9)\"},\"3\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":36,\"No. of nets sampled\":228,\"Number of nets with any size holes (%; 95% CI)\":\"144 (63.0; 56.7\\u201369.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"113 (49.6; 43.1\\u201356.0)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"62 (27.1; 21.8\\u201333.3)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"53 (23.2; 18.2\\u201329.2)\"},\"4\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":6,\"No. of nets sampled\":250,\"Number of nets with any size holes (%; 95% CI)\":\"157 (63.0; 56.7\\u201368.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"186 (74.4; 68.7\\u201379.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"57 (22.8; 18.1\\u201328.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"7 (2.8; 1.3\\u20135.7)\"},\"5\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":12,\"No. of nets sampled\":249,\"Number of nets with any size holes (%; 95% CI)\":\"169 (68.0; 61.8\\u201373.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"159 (63.9; 57.7\\u201369.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"76 (30.5; 25.1\\u201336.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"14 (5.6; 3.4\\u20139.2)\"},\"6\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":24,\"No. of nets sampled\":226,\"Number of nets with any size holes (%; 95% CI)\":\"147 (65.0; 58.6\\u201370.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"120 (53.1; 46.6\\u201359.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"71 (31.4; 25.7\\u201337.7)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"35 (15.5; 11.4\\u201320.8)\"},\"7\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":36,\"No. of nets sampled\":216,\"Number of nets with any size holes (%; 95% CI)\":\"156 (72.0; 65.9\\u201377.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"97 (44.9; 38.4\\u201351.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"71 (32.8; 26.9\\u201339.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"48 (22.2; 17.2\\u201328.2)\"}}\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The data collected were entered in excel spreadsheets and cross-checked for accuracy. Data were analyzed using STATA version 14.0 (Stata Corporation, College Station, TX, USA). Comparisons between the two types of nets were tested for significance using the Chi-square (kh2) test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. For continuous variables, the arithmetic mean was used depending on the distribution of values compared to a normal distribution.\n",
-                        "\n",
-                        "The nets were considered to have passed the WHO efficacy criteria if the mosquito knockdown rate was found to be >= 95% and/or mortality rate >= 80% in cone bioassays. For the tunnel tests, nets were considered to have passed the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90% [3]. The procedure for calculation of proportionate Hole Index (pHI) for nets is already described above.\n",
-                        "\n",
-                        "header: Bioassays\n",
-                        "\n",
-                        "The insecticidal effect of the nets was evaluated using cone bioassays and where required tunnel tests, as per the WHO guidelines [3]. The bioassays were done at time zero (baseline) and then at every 6 months up to 36 months. On each of the netting pieces cut for bioassays, standard WHO plastic cones were held in place using a plastic manifold and a total of 50 laboratory bred, susceptible Kisumu strain _Anopheles gambiae_ (non-blood fed, 2-5 day old) were exposed for 3 min (5 pieces per net x 5 mosquitoes per test x 2 replicates). After the exposure, the mosquitoes were removed gently from\n",
-                        "the cones and kept separately in plastic cups provided with cotton-wool moistened with 10% glucose solution. Knockdown (KD) was recorded at 60 min and mortality at 24 h after exposure. Mosquitoes exposed to untreated polyester net pieces were used as untreated controls. The bioassays were carried out at 27 +- 2 degC temperature and 80% +- 10% relative humidity. When the mosquito knockdown rate was < 95% and mortality < 80%, the mortality and blood-feeding inhibition effect of such nets was assessed in a tunnel test according to the WHO guidelines [3].\n",
-                        "\n",
-                        "For each net, only one netting piece with which mosquito mortality was found closest to the average mortality in the cone test was tested in the tunnel test [3]. One hundred, non-blood fed female _An. gambiae_ Kisumu mosquitoes, aged 5-8 days were released at the longer end of a tunnel equipment made of glass. At each end of the tunnel, a 25 cm square cage covered with a polyester netting was fitted. At the shorter end of the tunnel, a rabbit which could not move but was available for mosquito biting was placed. The mosquitoes were released at 18:00 and mosquitoes recovered at 09:00 on the following morning. During the test, the tunnel was kept in a place maintained at 27 +- 2 degC and 80% +- 10% relative humidity in full darkness. Another tunnel with untreated netting was used as control. The mean mortality rate and percentage blood-feeding inhibition were recorded, as follows:\n",
-                        "\n",
-                        "1. Mortality = this was measured by pooling the mortality rates of mosquitoes from the two sections of the tunnel. If mortalities in the control recorded more than 10%, the test was considered invalid.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding\\,inhibition}(\\%) = \\left[ {100 \\times \\left( {\\text{Bfc} - \\text{Bfc}} \\right)} \\right]/\\text{Bfc}\\]\n",
-                        "\n",
-                        "where, Bfc is the proportion of blood-fed mosquitoes in the control tunnel and Bft is the proportion of blood-fed mosquitoes in the tunnel with the treated net. The treated net was considered to have met the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90%.\n",
-                        "\n",
-                        "header: Chemical content analysis\n",
-                        "\n",
-                        "The chemical contents in the nets were determined at the beginning and at years 1, 2 and 3 by samplings nets according to the scheme described earlier. The netting pieces cut for determination of active ingredient content were individually rolled up and placed in new, clean and labeled aluminum foils and sent to the Phyto-pharmacy Department of the Walloon Agricultural Research Centre, Gembloux, Belgium, a WHO Collaborating Centre, for chemical analysis.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "The work was funded as a collaborative project by the WHO Pesticide Evaluation Scheme, Geneva, Switzerland.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Olysset(r) Plus did not meet the WHO criteria for efficacy of LLINs in this 3-year Phase III study, which could have resulted from faster loss of permethrin and PBO and users not following the manufacturer's recommendations to not leave the nets under direct sunlight. Better community education is therefore essential to ensure appropriate use and upkeep of nets in areas with LLIN-based interventions in order to maximize their operational impact on the prevention, control and elimination of malaria. Failure to do this would result in operational implications requiring early replenishment with new nets to ensure that such an LLIN based malaria intervention remains effective.\n",
-                        "\n",
-                        "header: Trial design and study area\n",
-                        "\n",
-                        "We did a prospective, household randomized controlled, large-scale 3-year field trial in a rice irrigation area of Kirinyaga County, Kenya from 2014 to 2017. The area is located about 100 km northeast of Nairobi at the base of Mt Kenya at an altitude of about 1200 m above sea level [14]. It receives mean annual rainfall of 1200-1600 mm with long rains in March-June and short rains in October-December. Due to high densities of mosquitoes year-round, many local people use mosquito nets. Several bed net trials have previously been conducted here for national product registration. Over 80% of the inhabitants of the area have only primary level education. Most of the houses in the area are made of mud walls with roofs of corrugated iron sheets [14].\n",
-                        "\n",
-                        "Four villages of Maendeleo, Huruma, Kiratina and Kasarani were selected by a simple randomization, of which the first two villages were randomly allocated Olyset(r) Plus and the other two villages received Olyset(r) Net (Table 1). In each of the selected village, households were randomly selected.\n",
-                        "\n",
-                        "header: Table 3 Frequency of washing nets by the users at different time points\n",
-                        "footer: The reported wash rate was zero at 6 months\n",
-                        "columns: ['Period (months)', 'Olyset® Plus - No. of nets sampled', 'Olyset® Plus - No. of nets washed once (%)', 'Olyset® Plus - No. of nets washed twice (%)', 'Olyset® Plus - No. of nets washed > 2 times (%)', 'Olyset® Net - No. of nets sampled', 'Olyset® Net - No. of nets washed at once (%)', 'Olyset® Net - No. of nets washed twice (%)', 'Olyset® Net - No. of nets washed > 2 times (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Period (months)\":6,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"\\u2013\"},\"1\":{\"Period (months)\":12,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"1 (3)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"2\":{\"Period (months)\":24,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"4 (12)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"1 (3)\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"3 (11)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"3\":{\"Period (months)\":36,\"Olyset\\u00ae Plus - No. of nets sampled\":50,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"9 (17)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"5 (10)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"3 (6)\",\"Olyset\\u00ae Net - No. of nets sampled\":50,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"11 (21)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"8 (6)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"3 (6)\"}}\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets reduce child mortality, number of _Plasmodium falciparum_ cases and probably the number of _P. vivax_ cases per person/year [1]. Factory-produced long-lasting insecticide nets (LLINs) are a core malaria intervention for the global elimination of malaria led by the World Health Organization (WHO) [2]. According to the WHO definition, LLINs should retain efficacy against mosquito vectors for a minimum of 20 standard washes under laboratory conditions and a minimum of 3 years of use under field conditions [3]. When used by most of the people in a population, insecticide-treated nets can protect all people in the community, including those who do not sleep under nets [4]. Currently, the National Malaria Control Programme (NMCP) in Kenya has been scaling up the use of LLINs in malaria endemic and high-risk areas to achieve universal coverage, which is defined by WHO as access to one LLIN for every 1.8 persons in each household [2, 5].\n",
-                        "\n",
-                        "Pyrethroid LLINs were the first brands of treated nets recommended by WHO for malaria control. However, it was believed that insecticide resistance was worsening in Africa and would present a major threat to malaria control [6-8], and that pyrethroid-LLINs were becoming less effective at killing mosquitoes in household conditions when pyrethroid resistance develops [9, 10]. Consequently, piperonyl butoxide (PBO) was incorporated in nets with the intension to improve their efficacy against pyrethroid resistant mosquitoes by acting as a metabolic enzyme inhibitor targeting the P450 cytochrome or mixed function oxidases that metabolise pyrethroids and enhance the efficacy of pyrethroid treated nets. Olyset(r) Plus has been prequalified by WHO as the first in class PBO net.\n",
-                        "\n",
-                        "However, at the time of beginning the trial due to the absence of guidelines published by WHO of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated only as a pyrethroid LLIN against a pyrethroid susceptible mosquito strain rather than against a resistant strain. This paper presents results of a 3-year long-term (Phase III) field trial of the candidate product, Olyset(r) Plus, along with a positive control product Olyset(r) Net, in a malaria endemic rice cultivation area of Kirinyaga County, Kenya.\n",
-                        "\n",
-                        "header: Test products\n",
-                        "\n",
-                        "Bioefficacy and physical durability of the Olyset(r) Plus and Olyset(r) Net both manufactured by Sumitomo Chemical were evaluated. The former is a polyethylene mono-filament net incorporated with 2% permethrin corresponding to 20 g permethrin active ingredient (AI)/kg or 800 mg permethrin AI/m\\({}^{2}\\) and 1% PBO (corresponding to 10 g PBO AI/kg or 400 mg PBO AI/m\\({}^{2}\\)). The latter is a polyethylene mono-filament net incorporated with 2% permethrin (w/w) (corresponding to 800 mg permethrin AI/m\\({}^{2}\\)). Olyset(r) Net was recommended in 2009 by the WHO Pesticide Evaluation Scheme (WHOPES) for use in malaria control [12], while Olyset(r) Plus was given interim recommendation by WHO in 2012 with the requirement to evaluate the product in a 3-year long-term trial to confirm its long-lasting efficacy in field [13].\n",
-                        "\n",
-                        "header: Table 4 Bioefficacy of Olyset® Plus and Olyset® Net against Anopheles. gambiae Kisumu in cone and tunnel tests\n",
-                        "footer: a KD, knockdown≥95%; b mortality≥80%; c taking into account the KD and mortality rates in cone bioassays, and blood-feeding inhibition and mortality rates in tunnel tests\n",
-                        "columns: ['Survey month', 'No. of nets tested', 'Mean 1 h KD (%)', 'Mean 24 h mortality (%)', '% of nets passed by KD criteria alonea', '% of nets passed mortality criteria aloneb', 'No. of nets requiring tunnel test', 'Overall pass rate in percentage (95% CI)c']\n",
-                        "\n",
-                        "{\"0\":{\"Survey month\":\"Olyset\\u00ae Plus\",\"No. of nets tested\":\"Olyset\\u00ae Plus\",\"Mean 1 h KD (%)\":\"Olyset\\u00ae Plus\",\"Mean 24 h mortality (%)\":\"Olyset\\u00ae Plus\",\"% of nets passed by KD criteria alonea\":\"Olyset\\u00ae Plus\",\"% of nets passed mortality criteria aloneb\":\"Olyset\\u00ae Plus\",\"No. of nets requiring tunnel test\":\"Olyset\\u00ae Plus\",\"Overall pass rate in percentage (95% CI)c\":\"Olyset\\u00ae Plus\"},\"1\":{\"Survey month\":\"0\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"99.5\",\"Mean 24 h mortality (%)\":\"100\",\"% of nets passed by KD criteria alonea\":\"100\",\"% of nets passed mortality criteria aloneb\":\"100\",\"No. of nets requiring tunnel test\":\"0\",\"Overall pass rate in percentage (95% CI)c\":\"100 (80\\u2013100)\"},\"2\":{\"Survey month\":\"6\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"97.0\",\"Mean 24 h mortality (%)\":\"88.2\",\"% of nets passed by KD criteria alonea\":\"83.3\",\"% of nets passed mortality criteria aloneb\":\"73.3\",\"No. of nets requiring tunnel test\":\"4\",\"Overall pass rate in percentage (95% CI)c\":\"96.7 (82\\u201399)\"},\"3\":{\"Survey month\":\"12\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"94.2\",\"Mean 24 h mortality (%)\":\"74.3\",\"% of nets passed by KD criteria alonea\":\"76.7\",\"% of nets passed mortality criteria aloneb\":\"43.3\",\"No. of nets requiring tunnel test\":\"6\",\"Overall pass rate in percentage (95% CI)c\":\"93.3 (78\\u201399)\"},\"4\":{\"Survey month\":\"18\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"82.9\",\"Mean 24 h mortality (%)\":\"65.9\",\"% of nets passed by KD criteria alonea\":\"36.7\",\"% of nets passed mortality criteria aloneb\":\"36.7\",\"No. of nets requiring tunnel test\":\"16\",\"Overall pass rate in percentage (95% CI)c\":\"86.7 (69\\u201396)\"},\"5\":{\"Survey month\":\"24\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"77.4\",\"Mean 24 h mortality (%)\":\"75.3\",\"% of nets passed by KD criteria alonea\":\"0.0\",\"% of nets passed mortality criteria aloneb\":\"46.7\",\"No. of nets requiring tunnel test\":\"15\",\"Overall pass rate in percentage (95% CI)c\":\"76.7 (57\\u201390)\"},\"6\":{\"Survey month\":\"30\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"60.4\",\"Mean 24 h mortality (%)\":\"56.9\",\"% of nets passed by KD criteria alonea\":\"0.0\",\"% of nets passed mortality criteria aloneb\":\"10.0\",\"No. of nets requiring tunnel test\":\"27\",\"Overall pass rate in percentage (95% CI)c\":\"70.0 (50\\u201385)\"},\"7\":{\"Survey month\":\"36\",\"No. of nets tested\":\"50\",\"Mean 1 h KD (%)\":\"66.4\",\"Mean 24 h mortality (%)\":\"51.5\",\"% of nets passed by KD criteria alonea\":\"2.0\",\"% of nets passed mortality criteria aloneb\":\"8.0\",\"No. of nets requiring tunnel test\":\"24\",\"Overall pass rate in percentage (95% CI)c\":\"42.0 (28\\u201356)\"},\"8\":{\"Survey month\":\"Olyset\\u00ae Net\",\"No. of nets tested\":\"Olyset\\u00ae Net\",\"Mean 1 h KD (%)\":\"Olyset\\u00ae Net\",\"Mean 24 h mortality (%)\":\"Olyset\\u00ae Net\",\"% of nets passed by KD criteria alonea\":\"Olyset\\u00ae Net\",\"% of nets passed mortality criteria aloneb\":\"Olyset\\u00ae Net\",\"No. of nets requiring tunnel test\":\"Olyset\\u00ae Net\",\"Overall pass rate in percentage (95% CI)c\":\"Olyset\\u00ae Net\"},\"9\":{\"Survey month\":\"0\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"99.1\",\"Mean 24 h mortality (%)\":\"100\",\"% of nets passed by KD criteria alonea\":\"100\",\"% of nets passed mortality criteria aloneb\":\"100\",\"No. of nets requiring tunnel test\":\"0\",\"Overall pass rate in percentage (95% CI)c\":\"100 (88\\u2013100)\"},\"10\":{\"Survey month\":\"6\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"96.9\",\"Mean 24 h mortality (%)\":\"79.8\",\"% of nets passed by KD criteria alonea\":\"80.0\",\"% of nets passed mortality criteria aloneb\":\"60.0\",\"No. of nets requiring tunnel test\":\"4\",\"Overall pass rate in percentage (95% CI)c\":\"93.3 (77\\u201399)\"},\"11\":{\"Survey month\":\"12\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"68.5\",\"Mean 24 h mortality (%)\":\"54.7\",\"% of nets passed by KD criteria alonea\":\"20.0\",\"% of nets passed mortality criteria aloneb\":\"3.3\",\"No. of nets requiring tunnel test\":\"24\",\"Overall pass rate in percentage (95% CI)c\":\"83.3 (65\\u201394)\"},\"12\":{\"Survey month\":\"18\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"72.1\",\"Mean 24 h mortality (%)\":\"50.2\",\"% of nets passed by KD criteria alonea\":\"30.0\",\"% of nets passed mortality criteria aloneb\":\"20.0\",\"No. of nets requiring tunnel test\":\"18\",\"Overall pass rate in percentage (95% CI)c\":\"73.3 (54\\u201387)\"},\"13\":{\"Survey month\":\"24\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"79.8\",\"Mean 24 h mortality (%)\":\"68.9\",\"% of nets passed by KD criteria alonea\":\"3.3\",\"% of nets passed mortality criteria aloneb\":\"46.7\",\"No. of nets requiring tunnel test\":\"18\",\"Overall pass rate in percentage (95% CI)c\":\"66.7 (47\\u201383)\"},\"14\":{\"Survey month\":\"30\",\"No. of nets tested\":\"30\",\"Mean 1 h KD (%)\":\"50.9\",\"Mean 24 h mortality (%)\":\"50.9\",\"% of nets passed by KD criteria alonea\":\"0.0\",\"% of nets passed mortality criteria aloneb\":\"3.3\",\"No. of nets requiring tunnel test\":\"29\",\"Overall pass rate in percentage (95% CI)c\":\"60.0 (40\\u201377)\"},\"15\":{\"Survey month\":\"36\",\"No. of nets tested\":\"50\",\"Mean 1 h KD (%)\":\"66.3\",\"Mean 24 h mortality (%)\":\"51.9\",\"% of nets passed by KD criteria alonea\":\"0.0\",\"% of nets passed mortality criteria aloneb\":\"8.0\",\"No. of nets requiring tunnel test\":\"26\",\"Overall pass rate in percentage (95% CI)c\":\"36.0 (23\\u201350)\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Results\n",
-                        "\n",
-                        "Among the 1784 participating homesteads with a population of 10 325, a total of 1551 households participated in the study (Table 1). Most of the heads of the households (age: 17-96 years) had received formal education and practiced farming (96.6%).\n",
-                        "\n",
-                        "Out of 100 heads of households with Olyset(r) Plus interviewed for adverse events, 44% reported itching, 4% eye irritation, and 7% unpleasant smell. Among the Olyset(r) Net users, 7% each reported nasal discharge and eye irritation. These were reported to be mild symptoms lasting for one or two days. To reduce these effects, 43% users of Olyset(r) Plus ventilated nets for a day in the open under the sun and 33% under the shade. Among Olyset(r) Net users, similar actions were taken by 44% and 39%, respectively.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Bioefficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide-treated insecticidal net in a 3-year long trial in Kenya\n",
-                        "\n",
-                        "Paul M. Gichuki, Anna Kamau, Kiambo Niagi, Solomon Karoki, Njoroge Muigai, Damaris Matoke-Muhia, Nabie Bayoh, Evan Mathenge\n",
-                        "\n",
-                        "\n",
-                        "**Background:** Long-lasting insecticide nets (LLINs) are a core malaria intervention. LLINs should retain efficacy against mosquito vectors for a minimum of three years. Efficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide (PBO) treated LLIN, was evaluated versus permethrin treated Olyset(r) Net. In the absence of WHO guidelines of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated as a pyrethroid LILN.\n",
-                        "\n",
-                        "**Methods:** This was a household randomized controlled trial in a malaria endemic rice cultivation zone of Kirinyaga County, Kenya between 2014 and 2017. Cone bioassays and tunnel tests were done against _Anopheles gambiae_ Kisumu. The chemical content, fabric integrity and LLIN survivorship were monitored. Comparisons between nets were tested for significance using the Chi-square test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. The WHO efficacy criteria used were >= 95% knockdown and/or >= 80% mortality rate in cone bioassays and >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests.\n",
-                        "\n",
-                        "**Results:** At 36 months, Olyset(r) Plus lost 52% permethrin and 87% PBO content; Olyset(r) Net lost 24% permethrin. Over 80% of Olyset(r) Plus and Olyset(r) Net passed the WHO efficacy criteria for LLINs up to 18 and 12 months, respectively. At month 36, 91.2% Olyset(r) Plus and 86.4% Olyset(r) Net survived, while 72% and 63% developed at least one hole. The proportionate Hole Index (pHI) values representing nets in good, serviceable and torn condition were 49.6%, 27.1% and 23.2%, respectively for Olyset(r) Plus, and 44.9%, 32.8% and 22.2%, respectively for Olyset(r) Net but were not significantly different.\n",
-                        "\n",
-                        "**Conclusions:** Olyset(r) Plus retained efficacy above or close to the WHO efficacy criteria for about 2 years than Olyset(r) Net (1-1.5 years). Both nets did not meet the 3-year WHO efficacy criteria, and showed little attrition, comparable physical durability and survivorship, with 50% of Olyset(r) Plus having good and serviceable condition after 3 years. Better community education on appropriate use and upkeep of LLINs is essential to ensure effectiveness of LLIN based malaria interventions.\n",
-                        "\n",
-                        "**Keywords:**_Anopheles gambiae, Bioefficacy, Durability, Kenya, Long-lasting insecticidal net, Olyset(r) Net, Olyset(r) Plus, Permeability, Piperonyl butoxide_\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Olysset(r) Plus did not meet the WHO criteria for efficacy of LLINs in this 3-year Phase III study, which could have resulted from faster loss of permethrin and PBO and users not following the manufacturer's recommendations to not leave the nets under direct sunlight. Better community education is therefore essential to ensure appropriate use and upkeep of nets in areas with LLIN-based interventions in order to maximize their operational impact on the prevention, control and elimination of malaria. Failure to do this would result in operational implications requiring early replenishment with new nets to ensure that such an LLIN based malaria intervention remains effective.\n",
-                        "\n",
-                        "header: Chemical contents\n",
-                        "\n",
-                        "At time 0 (baseline), the contents of permethrin and PBO in Olyset(r) Plus were within the target tolerance limits of 20 +- 5 g/kg and 10 +- 2.5 g/kg, respectively; while the permethrin content in Olyset(r) Net was also within the target tolerance limit of 20 +- 3 g/kg (Table 5). The within net variation (relative standard deviation, RSD) in permethrin and PBO contents for Olyset(r) Plus at baseline were 2.2% and 3.7%, respectively, and were within the acceptable tolerance limits. The within net variation in the permethrin content in Olyset(r) Net at the baseline was 1.2% and was also within the acceptable tolerance limit. At the end of years 1, 2 and 3 of net usage, the permethrin content in Olyset(r) Plus decreased by 36%, 40% and 52%, and the PBO content showed a marked reduction of 66%, 81% and 87%, respectively. The loss of the permethrin in Olyset(r) Net\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c} \\hline\n",
-                        "**Survey month** & **No. of** & **Mean 1 h** & **Mean 24 h** & **\\% of nets passed by** & **\\% of nets passed by** & **No. of nets passed by** & **Overall pass rate in** \\\\\n",
-                        "**nets** & **KD (\\%)** & **mortality** & **KD criteria alone(r)** & **mortality criteria** & **requiring tunnel** & **percentage (95\\% CI)(r)** \\\\\n",
-                        "**Olyset(r) Plus** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.5 & 100 & 100 & 100 & 0 & 100 (80–100) \\\\\n",
-                        "6 & 30 & 97.0 & 88.2 & 83.3 & 73.3 & 4 & 96.7 (82–99) \\\\\n",
-                        "12 & 30 & 9\n",
-                        "\n",
-                        "4.2 & 74.3 & 76.7 & 43.3 & 6 & 93.3 (78–99) \\\\\n",
-                        "18 & 30 & 82.9 & 65.9 & 36.7 & 36.7 & 16 & 86.7 (69–96) \\\\\n",
-                        "24 & 30 & 77.4 & 75.3 & 0.0 & 46.7 & 15 & 76.7 (57–90) \\\\\n",
-                        "30 & 30 & 60.4 & 56.9 & 0.0 & 10.0 & 27 & 70.0 (50–85) \\\\\n",
-                        "**Olyset(r) Net** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.1 & 100 & 100 & 100 & 0 & 100 (88–100) \\\\\n",
-                        "6 & 30 & 96.9 & 79.8 & 80.0 & 60.0 & 4 & 93.3 (77–99) \\\\\n",
-                        "12 & 30 & 68.5 & 54.7 & 20.0 & 3.3 & 24 & 83.3 (65–94) \\\\\n",
-                        "18 & 30 & 72.1 & 50.2 & 30.0 & 20.0 & 18 & 73.3 (54–87) \\\\\n",
-                        "24 & 30 & 79.8 & 68.9 & 3.3 & 46.7 & 18 & 66.7 (47–83) \\\\\n",
-                        "30 & 30 & 50.9 & 50.9 & 0.0 & 3.3 & 29 & 60.0\n",
-                        "\n",
-                        "\n",
-                        "was 16% at year 1 and did not change much at year 2 and 3 (19 and 24%, respectively).\n",
-                        "\n",
-                        "header: Table 5 Mean permethrin and PBO contents (relative standard deviation) in nets at baseline, and at 12, 24 and 36 months\n",
-                        "footer: a  Within net variation (relative standard deviation) in permethrin and PBO contents at baseline was 2.2% and 3.7%, respectively (Olyset® Plus). – not applicable, PBOpiperonyl butoxideb Within net variation (relative standard deviation) in permethrin content at baseline was 1.2% (Olyset® Net)\n",
-                        "columns: ['Sampling month', 'Olyset® Plus - Mean permethrin content in g/kg (95% CI)', 'Olyset® Plus - Permethrin content lost (%)', 'Olyset® Plus - PBO content in g/ kg (95% CI)', 'Olyset® Plus - PBO content lost (%)', 'Olyset® Net - Mean permethrin content in g/kg (95% CI)', 'Olyset® Net - Permethrin content lost (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Sampling month\":0,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"18.4 (18.3\\u201318.6)a\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"8.98 (8.86\\u20139.10)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"19.6 (19.5\\u201319.7)b\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"\\u2013\"},\"1\":{\"Sampling month\":12,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.7 (10.6\\u201312.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"36\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"3.01 (2.30\\u20133.80)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"66\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"16.5 (16.0\\u201317.0)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"16\"},\"2\":{\"Sampling month\":24,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.1 (10.2\\u201312.0)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"40\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.70 (1.20\\u20132.20)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"81\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"15.9 (15.1\\u201316.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"19\"},\"3\":{\"Sampling month\":36,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"8.8 (7.9\\u20139.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"52\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.16 (0.8\\u20131.5)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"87\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"14.8 (13.9\\u201315.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"24\"}}\n",
-                        "\n",
-                        "header: Net survivorship and fabric integrity\n",
-                        "\n",
-                        "At the end of year 1, Olyset(r) Plus reported survivorship rates of 97.2% (_n_ = 243), with Olyset(r) Net recording 99.6% (_n_ = 249). At year 2, there were 92.4% (_n_ = 231) Olyset(r) Plus and 90.4% (_n_ = 226) of Olyset(r) Nets still surviving. At the end of the 3 years of study, the survivorship rates for Olyset(r) Plus and Olyset(r) Net were at 91.2% (_n_ = 228) and 86.4% (_n_ = 216), respectively.\n",
-                        "\n",
-                        "The pH values of the nets showed that 87.6% of Olyset(r) Plus were in good condition at 6 months, 7.6% were in acceptable/serviceable condition, and 4.8% were too torn (Table 6). These proportions increased gradually during the study and at 36 months, 49.6% were in good condition, 27.1% in acceptable/serviceable condition, and 23.2% were too torn. A similar trend of attrition was seen for Olyset(r) Net, with 44.9%, 32.8% and 22.2% nets being in good condition, acceptable condition or too torn at 36 months, respectively.\n",
-                        "\n",
-                        "Data on the comparison of estimates of the mean pH values and the mean hole area for Olyset(r) Plus and Olyset(r) Net are given in Table 7.\n",
-                        "\n",
-                        "The proportion of Olyset(r) Plus nets having been repaired by households increased from 6% (95% CI: 3.4-9.7) at month 6 to 17.9% (95% CI: 13.2-23.6) at months\n",
-                        "\n",
-                        "36, while that of Olyset(r) Net increased from 5% (95% CI: 2.8-8.7) to 12% (95% CI: 8.0-17.1) in the same period.\n",
-                        "\n",
-                        "\n",
-                        "Olyset(r) Net incorporated with permethrin alone was included in the trial as a positive control net. A net was considered to have passed the WHO efficacy criteria if it caused >= 80% mortality and/or >= 95% knockdown in cone bioassays or >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests [3]. Most of the Olyset(r) Plus and Olyset(r) Net lost the knockdown effect during the 24-36 months follow-up. Overall, neither Olyset(r) Plus nor Olyset(r) Net performed as they should even after 2 years and did not meet the WHO efficacy criteria of a 3-year LLIN.\n",
-                        "\n",
-                        "Previous studies have demonstrated enhanced efficacy of Olyset(r) Plus against resistant _An. gambiae_ attributable to PBO [16-18]. A recent randomised controlled field trial in Tanzania with high levels of pyrethroid resistance in malaria vectors _An. gambiae_ and _An. arabiensis_ also attributed higher impact on malaria of Olyset(r) Plus over Olyset(r) Net to the presence of PBO that was sustained after 21 months of the trial [19]. However, there is a view that these two nets are not comparable and that the higher efficacy of Olyset(r) Plus was due to much more bleed rate of permethrin into its surface and not due to PBO that was incorporated in small amount and dwindled rapidly over time [20]. In this trial, Olyset(r) Plus was evaluated as a pyrethroid LLIN since there were no guidelines published by WHO of how to evaluate PBO nets at the time of this trial, and considering the manufacturer's product claim.\n",
-                        "\n",
-                        "While, it was reported that high intensity insecticide resistance will reduce the level of personal and community protection of pyrethroid LLINs [21] and PBO nets could be effective against malaria in some resistance scenarios [22], a recent WHO multi-country study found no evidence of an association between insecticide resistance and malaria infection prevalence or incidence [23]. There is also a view that at present there is limited evidence that recent reduction in the impact on malaria in sub-Saharan Africa was due to increasing resistance in malaria vectors to the pyrethroid insecticides used in treating nets [24].\n",
-                        "\n",
-                        "The initial permethrin content in both nets was more or less similar but the enhanced efficacy of Olyset(r) Plus was mainly due to the higher release of permethrin into the surface fibres and presence of PBO than Olyset(r) Net. The gradual decline in efficacy of both nets is consistent with the loss of permethrin and PBO in Olyset(r) Plus and of permethrin in Olyset(r) Net over time during their routine usage by the community. The decline in permethrin content was more rapid in Olyset(r) Plus than Olyset(r) Net. This explains why Olyset(r) Plus having permethrin and PBO was relatively superior than Olyset(r) Net initially, i.e., until 18 months, but after that their performance was below the WHO efficacy cut off. While the PBO content in Olyset(r) Plus at the baseline was within the target tolerance limits, 66% of the PBO content was lost within one year of net usage, and the loss increased to 81% at year 2 and 87% at year 3. Previous studies have reported that the insecticidal content in LLINs decay over time [6]. While permethrin and PBO contents may be lost due to natural decay and evaporation, washing of nets frequently and drying them under direct sunlight are known to contribute to such losses [25, 26]. In this study, despite manufacturer's label recommendation not to expose nets to direct sunlight, a good proportion of both type of nets were exposed to bright sunlight before use and left to dry in the sun after washing. This would have reduced both permethrin and PBO contents and consequently reduced the biological efficacy of these nets and should be borne in mind when considering these results. Even though Kisumu strain was tested, the loss of PBO would have affected efficacy of Olyset Plus as PBO can improve net performance even against susceptible strains, which too have P450s, and PBO is also known to act as a good solvent that can aid cuticular penetration and help improve insecticidal activity. The operational implication of rapid loss of PBO is that Olyset(r) Plus was unlikely to remain effective even against pyrethroid resistant mosquitoes for 2 or 3 years of use.\n",
-                        "\n",
-                        "Community net wash practices were comparable between the two nets. During the first 6 months, no net was reported to have been washed. At 1 year, 7% of Olyset(r) Plus and 7% of Olyset(r) Net were reported to have been washed at least once. At month 36 months, about 17% and 21% of Olyset(r) Plus and Olyset(r) Net\n",
-                        "respectfully had been washed at least once. LLINs are usually recommended to be washed at most every 3 months [5], although this frequency depends on local cultural practices and water availability. Different net washing frequencies have been reported in other studies spanning from a high of 8 washes per month in Mali [27] to 4-7 times in Tanzania [28] to a low average of 1.5 washes per year in Uganda [29]. In coastal Kenya, on average nets were washed twice in 6 months, with blue colour nets least often washed than white or green colour nets, and older nets washed more frequently [30]. In western Kenya lowlands, three quarter of nets were washed once within 3 months and a third of them were dried under the sun [31]. The main reasons for low reported wash rates during the annual surveys in our study could be attributed to net users' poor recall of the net washing frequencies, and people's apprehension that washing of nets reduce net efficacy, although there was no water scarcity in the trial area being located in a rice irrigation area.\n",
-                        "\n",
-                        "The study participants reported experiencing certain transient adverse effects during the first two days of use of nets, but these could be prompted reactions since the community members had been informed about the possibility of such effects at the time of net distribution. The higher side effects from use of Olyset(r) Plus is in line with the higher release of permethrin as mentioned above. Users resorted to ventilating their nets in direct sunlight to reduce the effects although our project team had advised to dry up and keep the nets under shade. Ventilating new nets or drying washed nets under direct sunlight might have contributed to a significant reduction in active ingredient content and bio-efficacy of nets.\n",
-                        "\n",
-                        "At the end of the 3-year period, among the two cohorts of 250 nets each, only about 9% Olyset(r) Plus and about 14% Olyset(r) Net were reported to have been lost showing a high net survivorship. These results are in line with the expected 75% net survivorship rate reported by the NetCALC 3-year serviceable prediction model [32]. Previous studies have reported varying rate of net survivorship in African countries, such as 58% nets were lost within two years in Rwanda [33], only 39% of distributed nets remained both present and in serviceable physical condition 2-4 years after a mass campaign in Tanzania [34], but in Western Uganda an estimated attrition rate of just 12% after three years of use was observed for a polyester net [35]. In four African countries of Mozambique, Nigeria, DRC and Zanzibar, Tanzania, survival in serviceable condition of polyethylene and polyester LLINs after 31-37 months of use varied between different sites from 17 to 80% with median survival from 1.6 to 5.3 years [36]. In our study, the observed low attrition rate of nets appears to be also due to the 'Hawthorne effect' as the households included in the two cohorts of nets were visited every 6 or 12 months to inspect the nets. So, the awareness among study participants that the nets were regularly being followed could have modified their behaviour in favour of more careful upkeep and maintenance of the nets and as such may have been a limitation in this study.\n",
-                        "\n",
-                        "Studies have shown that despite small to medium size holes (64 < PHI < 642), LLINs are still protective against mosquito bites due to the excito-repellent effects of insecticides [35]. However, the nets may become ineffective when the overall hole area in nets reaches a certain threshold [37]. In our study, both Olyset(r) Plus and Olyset(r) Net recorded high fabric integrity up to the 3-year trial period. At 12 months after distribution, 78.1% of Olyset Plus and 63.8% of Olyset(r) Net were in good/acceptable condition, 12.3% Olyset(r) Plus and 30.5% Olyset(r) Net were in serviceable condition, while 9.4% Olyset(r) Plus and 5.6% Olyset(r) Net were completely torn and required to be replaced. At the end of the 3-year trial period, 49.5% of Olyset(r) Plus and 44.9% of Olyset(r) Net were in good/acceptable condition, 27.1% Olyset(r) Plus and 32.8% Olyset(r) Net in serviceable condition, while 23.2% Olyset(r) Plus and 22.2% Olyset(r) Net were completely torn and required replacement.\n",
-                        "\n",
-                        "Although Olyset(r) Net retained a higher permethrin content that Olyset(r) Plus, the latter showed longer duration of efficacy than the former, i.e., 18 months compared with 12 months above the WHO efficacy cut off, and higher efficacy thereafter i.e., the efficacy of Olyset(r) Plus declined to 42% at the end of 3-year trial period and that of Olyset(r) Net to 36% over the same period. This could be attributed to higher release of permethrin into the netting surface, and also because PBO can make nets perform better even against susceptible mosquitoes. Pyrethroid-PBO nets have previously been associated with higher mosquito mortality and lower blood-feeding rates in areas of high-level insecticide resistance than were non-PBO LLINs [38, 39]. Although in our study we did not test nets with a pyrethroid resistant mosquito strain, it is likely that the fast decline of PBO in Olyset(r) Plus was caused by the inappropriate exposure to sunlight, as well as the normal loss through washing would have reduced efficacy against resistant mosquitoes in the same way that it reduced efficacy against the susceptible mosquitoes tested here.\n",
-                        "\n",
-                        "Appropriate testing guidelines and field studies are thus required to evaluate the operational effectiveness of PBO nets against pyrethroid resistant mosquitoes and considering the PBO retention and release properties. The revised WHO LLIN guidelines should clarify for how long PBO nets should be effective and if the \n",
-                        "current WHO efficacy criteria for a 3-year LLIN should be reduced to fit the chemistry.\n",
-                        "\n",
-                        "A limitation of our trial was that we did not evaluate efficacy of Olysset(r) Plus against resistant _An. gambling_ mosquitoes. Instead, we tested its efficacy for the presence of permethrin alone using a susceptible malaria vector strain, although inclusion of PBO in nets has the potential of increasing insecticidal efficacy due to increased cuticular penetration and presence of P450s in susceptible insects. There is thus a need for suitable testing guidelines and a much more validation of the impact of PBO in pyrethroid-PBO nets.\n",
-                        "\n",
-                        "header: Bioassays\n",
-                        "\n",
-                        "The insecticidal effect of the nets was evaluated using cone bioassays and where required tunnel tests, as per the WHO guidelines [3]. The bioassays were done at time zero (baseline) and then at every 6 months up to 36 months. On each of the netting pieces cut for bioassays, standard WHO plastic cones were held in place using a plastic manifold and a total of 50 laboratory bred, susceptible Kisumu strain _Anopheles gambiae_ (non-blood fed, 2-5 day old) were exposed for 3 min (5 pieces per net x 5 mosquitoes per test x 2 replicates). After the exposure, the mosquitoes were removed gently from\n",
-                        "the cones and kept separately in plastic cups provided with cotton-wool moistened with 10% glucose solution. Knockdown (KD) was recorded at 60 min and mortality at 24 h after exposure. Mosquitoes exposed to untreated polyester net pieces were used as untreated controls. The bioassays were carried out at 27 +- 2 degC temperature and 80% +- 10% relative humidity. When the mosquito knockdown rate was < 95% and mortality < 80%, the mortality and blood-feeding inhibition effect of such nets was assessed in a tunnel test according to the WHO guidelines [3].\n",
-                        "\n",
-                        "For each net, only one netting piece with which mosquito mortality was found closest to the average mortality in the cone test was tested in the tunnel test [3]. One hundred, non-blood fed female _An. gambiae_ Kisumu mosquitoes, aged 5-8 days were released at the longer end of a tunnel equipment made of glass. At each end of the tunnel, a 25 cm square cage covered with a polyester netting was fitted. At the shorter end of the tunnel, a rabbit which could not move but was available for mosquito biting was placed. The mosquitoes were released at 18:00 and mosquitoes recovered at 09:00 on the following morning. During the test, the tunnel was kept in a place maintained at 27 +- 2 degC and 80% +- 10% relative humidity in full darkness. Another tunnel with untreated netting was used as control. The mean mortality rate and percentage blood-feeding inhibition were recorded, as follows:\n",
-                        "\n",
-                        "1. Mortality = this was measured by pooling the mortality rates of mosquitoes from the two sections of the tunnel. If mortalities in the control recorded more than 10%, the test was considered invalid.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding\\,inhibition}(\\%) = \\left[ {100 \\times \\left( {\\text{Bfc} - \\text{Bfc}} \\right)} \\right]/\\text{Bfc}\\]\n",
-                        "\n",
-                        "where, Bfc is the proportion of blood-fed mosquitoes in the control tunnel and Bft is the proportion of blood-fed mosquitoes in the tunnel with the treated net. The treated net was considered to have met the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90%.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All the relevant datasets supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Table 7 Comparison of the estimates of the mean pHI and the mean hole area for O lyset® Plus and Olyset® Net\n",
-                        "footer: pHI proportionate Hole Index\n",
-                        "columns: ['Survey month', 'Olyset® Plus - No. of nets inspected', 'Olyset® Plus - Mean hole area in cm2 (± SD)', 'Olyset® Plus - Mean pHI (± SD)', 'Olyset® Net - No. of nets inspected', 'Olyset® Net - Mean hole area in cm2 (± SD)', 'Olyset® Net - Mean pHI (± SD)']\n",
-                        "\n",
-                        "{\"0\":{\"Survey month\":6,\"Olyset\\u00ae Plus - No. of nets inspected\":250,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"52.3 \\u00b1 230.5\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"93.3 \\u00b1 313.6\",\"Olyset\\u00ae Net - No. of nets inspected\":250,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"123.3 \\u00b1 272.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"110.5 \\u00b1 221.8\"},\"1\":{\"Survey month\":12,\"Olyset\\u00ae Plus - No. of nets inspected\":243,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"229.2 \\u00b1 574.2\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"153.6 \\u00b1 437.0\",\"Olyset\\u00ae Net - No. of nets inspected\":249,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"192.5 \\u00b1 376.2\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"156.8 \\u00b1 306.5\"},\"2\":{\"Survey month\":24,\"Olyset\\u00ae Plus - No. of nets inspected\":231,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"389.8 \\u00b1 975.4\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"317.6 \\u00b1 794.7\",\"Olyset\\u00ae Net - No. of nets inspected\":226,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"873.9 \\u00b1 353.4\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"279.4 \\u00b1 428.1\"},\"3\":{\"Survey month\":36,\"Olyset\\u00ae Plus - No. of nets inspected\":228,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"630.7 \\u00b1 1191.8\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"513.9 \\u00b1 970.9\",\"Olyset\\u00ae Net - No. of nets inspected\":216,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"621.5 \\u00b1 1710.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"506.4 \\u00b1 1393.2\"}}\n",
-                        "\n",
-                        "header: Chemical content analysis\n",
-                        "\n",
-                        "The chemical contents in the nets were determined at the beginning and at years 1, 2 and 3 by samplings nets according to the scheme described earlier. The netting pieces cut for determination of active ingredient content were individually rolled up and placed in new, clean and labeled aluminum foils and sent to the Phyto-pharmacy Department of the Walloon Agricultural Research Centre, Gembloux, Belgium, a WHO Collaborating Centre, for chemical analysis.\n",
-                        "\n",
-                        "header: Trial design and study area\n",
-                        "\n",
-                        "We did a prospective, household randomized controlled, large-scale 3-year field trial in a rice irrigation area of Kirinyaga County, Kenya from 2014 to 2017. The area is located about 100 km northeast of Nairobi at the base of Mt Kenya at an altitude of about 1200 m above sea level [14]. It receives mean annual rainfall of 1200-1600 mm with long rains in March-June and short rains in October-December. Due to high densities of mosquitoes year-round, many local people use mosquito nets. Several bed net trials have previously been conducted here for national product registration. Over 80% of the inhabitants of the area have only primary level education. Most of the houses in the area are made of mud walls with roofs of corrugated iron sheets [14].\n",
-                        "\n",
-                        "Four villages of Maendeleo, Huruma, Kiratina and Kasarani were selected by a simple randomization, of which the first two villages were randomly allocated Olyset(r) Plus and the other two villages received Olyset(r) Net (Table 1). In each of the selected village, households were randomly selected.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The field trial was conducted according to the WHO guidelines [3, 11]. The description of the test products and trial procedures are described below.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The data collected were entered in excel spreadsheets and cross-checked for accuracy. Data were analyzed using STATA version 14.0 (Stata Corporation, College Station, TX, USA). Comparisons between the two types of nets were tested for significance using the Chi-square (kh2) test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. For continuous variables, the arithmetic mean was used depending on the distribution of values compared to a normal distribution.\n",
-                        "\n",
-                        "The nets were considered to have passed the WHO efficacy criteria if the mosquito knockdown rate was found to be >= 95% and/or mortality rate >= 80% in cone bioassays. For the tunnel tests, nets were considered to have passed the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90% [3]. The procedure for calculation of proportionate Hole Index (pHI) for nets is already described above.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "_Cl_ Confidence interval; CHW: Community Health Workers; KD: Knockdown; LINK: long-lasting insecticidal nets; NMD: National Malaria Control Programme; pH: proportionate Hole Index; PBO: Papernot by duakoc. Relative standard deviation; SD: Standard deviation; WHO: World Health Organization.\n",
-                        "\n",
-                        "header: Table 6 Progression of hole formation and proportionate Hole Index (pHI) values for Olyset® Plus and Olyset® Net\n",
-                        "footer: None\n",
-                        "columns: ['Net brand', 'Follow up months', 'No. of nets sampled', 'Number of nets with any size holes (%; 95% CI)', 'No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)', 'No. of nets by pHI values (%; 95% CI) - pHI 65–642 (in acceptable condition)', 'No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)']\n",
-                        "\n",
-                        "{\"0\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":6,\"No. of nets sampled\":250,\"Number of nets with any size holes (%; 95% CI)\":\"115 (46.0; 39.9\\u201352.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"219 (87.6; 82.9\\u201391.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"19 (7.6; 4.9\\u201311.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"12 (4.8; 2.8\\u20138.2)\"},\"1\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":12,\"No. of nets sampled\":243,\"Number of nets with any size holes (%; 95% CI)\":\"131 (54.0; 47.6\\u201360.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"190 (78.2; 72.6\\u201382.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"30 (12.4; 8.8\\u201317.0)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"23 (9.5; 6.3\\u201313.8)\"},\"2\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":24,\"No. of nets sampled\":231,\"Number of nets with any size holes (%; 95% CI)\":\"120 (52.0; 45.5\\u201358.3)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"145 (62.8; 56.7\\u201368.7)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"58 (25.1; 19.9\\u201331.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"28 (12.0; 8.5\\u201316.9)\"},\"3\":{\"Net brand\":\"Olyset\\u00ae Plus\",\"Follow up months\":36,\"No. of nets sampled\":228,\"Number of nets with any size holes (%; 95% CI)\":\"144 (63.0; 56.7\\u201369.1)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"113 (49.6; 43.1\\u201356.0)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"62 (27.1; 21.8\\u201333.3)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"53 (23.2; 18.2\\u201329.2)\"},\"4\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":6,\"No. of nets sampled\":250,\"Number of nets with any size holes (%; 95% CI)\":\"157 (63.0; 56.7\\u201368.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"186 (74.4; 68.7\\u201379.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"57 (22.8; 18.1\\u201328.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"7 (2.8; 1.3\\u20135.7)\"},\"5\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":12,\"No. of nets sampled\":249,\"Number of nets with any size holes (%; 95% CI)\":\"169 (68.0; 61.8\\u201373.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"159 (63.9; 57.7\\u201369.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"76 (30.5; 25.1\\u201336.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"14 (5.6; 3.4\\u20139.2)\"},\"6\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":24,\"No. of nets sampled\":226,\"Number of nets with any size holes (%; 95% CI)\":\"147 (65.0; 58.6\\u201370.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"120 (53.1; 46.6\\u201359.5)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"71 (31.4; 25.7\\u201337.7)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"35 (15.5; 11.4\\u201320.8)\"},\"7\":{\"Net brand\":\"Olyset\\u00ae Net\",\"Follow up months\":36,\"No. of nets sampled\":216,\"Number of nets with any size holes (%; 95% CI)\":\"156 (72.0; 65.9\\u201377.9)\",\"No. of nets by pHI values (%; 95% CI) - pHI < 65 (in good condition)\":\"97 (44.9; 38.4\\u201351.6)\",\"No. of nets by pHI values (%; 95% CI) - pHI 65\\u2013642 (in acceptable condition)\":\"71 (32.8; 26.9\\u201339.4)\",\"No. of nets by pHI values (%; 95% CI) - pHI > 642 (too torn nets)\":\"48 (22.2; 17.2\\u201328.2)\"}}\n",
-                        "\n",
-                        "header: Net sampling scheme\n",
-                        "\n",
-                        "The scheme of sampling nets at different time points for bioassays and chemical assays is given in Table 2. The netting pieces were cut from the sampled nets from positions 1-9 for bioassays, and positions HP1-HP5 (at time 0) or positions HP2-HP5 (at time 12, 24 and 36 months) for chemical content assays (Fig. 1). All the nets which were sampled and retrieved at the given survey points were replaced with new ones.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent to publication\n",
-                        "\n",
-                        "This paper is published with the authority from the Director General, Kenya Medical Research Institute (KEMR).\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets reduce child mortality, number of _Plasmodium falciparum_ cases and probably the number of _P. vivax_ cases per person/year [1]. Factory-produced long-lasting insecticide nets (LLINs) are a core malaria intervention for the global elimination of malaria led by the World Health Organization (WHO) [2]. According to the WHO definition, LLINs should retain efficacy against mosquito vectors for a minimum of 20 standard washes under laboratory conditions and a minimum of 3 years of use under field conditions [3]. When used by most of the people in a population, insecticide-treated nets can protect all people in the community, including those who do not sleep under nets [4]. Currently, the National Malaria Control Programme (NMCP) in Kenya has been scaling up the use of LLINs in malaria endemic and high-risk areas to achieve universal coverage, which is defined by WHO as access to one LLIN for every 1.8 persons in each household [2, 5].\n",
-                        "\n",
-                        "Pyrethroid LLINs were the first brands of treated nets recommended by WHO for malaria control. However, it was believed that insecticide resistance was worsening in Africa and would present a major threat to malaria control [6-8], and that pyrethroid-LLINs were becoming less effective at killing mosquitoes in household conditions when pyrethroid resistance develops [9, 10]. Consequently, piperonyl butoxide (PBO) was incorporated in nets with the intension to improve their efficacy against pyrethroid resistant mosquitoes by acting as a metabolic enzyme inhibitor targeting the P450 cytochrome or mixed function oxidases that metabolise pyrethroids and enhance the efficacy of pyrethroid treated nets. Olyset(r) Plus has been prequalified by WHO as the first in class PBO net.\n",
-                        "\n",
-                        "However, at the time of beginning the trial due to the absence of guidelines published by WHO of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated only as a pyrethroid LLIN against a pyrethroid susceptible mosquito strain rather than against a resistant strain. This paper presents results of a 3-year long-term (Phase III) field trial of the candidate product, Olyset(r) Plus, along with a positive control product Olyset(r) Net, in a malaria endemic rice cultivation area of Kirinyaga County, Kenya.\n",
-                        "\n",
-                        "header: Test products\n",
-                        "\n",
-                        "Bioefficacy and physical durability of the Olyset(r) Plus and Olyset(r) Net both manufactured by Sumitomo Chemical were evaluated. The former is a polyethylene mono-filament net incorporated with 2% permethrin corresponding to 20 g permethrin active ingredient (AI)/kg or 800 mg permethrin AI/m\\({}^{2}\\) and 1% PBO (corresponding to 10 g PBO AI/kg or 400 mg PBO AI/m\\({}^{2}\\)). The latter is a polyethylene mono-filament net incorporated with 2% permethrin (w/w) (corresponding to 800 mg permethrin AI/m\\({}^{2}\\)). Olyset(r) Net was recommended in 2009 by the WHO Pesticide Evaluation Scheme (WHOPES) for use in malaria control [12], while Olyset(r) Plus was given interim recommendation by WHO in 2012 with the requirement to evaluate the product in a 3-year long-term trial to confirm its long-lasting efficacy in field [13].\n",
-                        "\n",
-                        "header: Assessment of durability of cohort nets\n",
-                        "\n",
-                        "To monitor durability of nets (i.e. survivorship and changes in physical integrity) during their routine use, 250 each of Olyset(r) Plus and Olyset(r) Net were distributed in separate households (2 nets per household) in the same villages by simple randomization. From these cohorts of nets, all nets available at the households at the time of surveys at 6, 12, 24 and 36 months were inspected for their presence and physical integrity. For this, the nets were retracted from the sleeping places, individually hung up on a rectangular frame held outside the house and inspected for presence of any holes on the side and roof panels. The holes were counted for each net, their diameter was measured, and they were classified in the following categories according to the WHO criteria for hole sizes [3, 11]:\n",
-                        "\n",
-                        "Size A1: 0.5-2 cm diameter, Size A2: 2-10 cm diameter, Size A3: 10-25 cm diameter, Size A4: 25 cm diameter.\n",
-                        "\n",
-                        "The parameters for assessing the integrity of nets included the proportions of Olyset(r) Plus and Olyset(r) Net with any size holes or tears, and the proportionate Hole Index (pHI) for each net, which was calculated as follows [11]:\n",
-                        "\n",
-                        "\\[\\text{pH} = \\left( {1.23 \\times \\text{ No.~of~size~A1~holes}} \\right) + \\left( {28.28 \\times \\text{ No.~of~size~A2~holes}} \\right) + \\left( {240.56 \\times \\text{ No.~of~size~A3~holes}} \\right) + \\left( {706.95 \\times \\text{ No.~of~size~A4~holes}} \\right)\\]\n",
-                        "\n",
-                        "Using the pHI values, Olyset(r) Plus and Olyset(r) Net were categorized as those in 'good condition' (pHI: 0-64), nets in 'acceptable' condition (pHI: 65-642), and torn nets that were likely to provide no protective efficacy (pHI > 642) [15]. The number of nets in 'good' and\n",
-                        "\n",
-                        "Fig. 1: A rectangular net and its individual panels showing positions for cutting netting pieces (positions 1–9 for bioassays; HP1–HPS for chemical assays)\n",
-                        "'acceptable condition' together were taken to estimate the survival of nets over time.\n",
-                        "\n",
-                        "The sampled nets were also inspected for any repairs of holes or tears done by the households. After inspection, the nets were returned to the same sleeping place in the same household.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Results\n",
-                        "\n",
-                        "Among the 1784 participating homesteads with a population of 10 325, a total of 1551 households participated in the study (Table 1). Most of the heads of the households (age: 17-96 years) had received formal education and practiced farming (96.6%).\n",
-                        "\n",
-                        "Out of 100 heads of households with Olyset(r) Plus interviewed for adverse events, 44% reported itching, 4% eye irritation, and 7% unpleasant smell. Among the Olyset(r) Net users, 7% each reported nasal discharge and eye irritation. These were reported to be mild symptoms lasting for one or two days. To reduce these effects, 43% users of Olyset(r) Plus ventilated nets for a day in the open under the sun and 33% under the shade. Among Olyset(r) Net users, similar actions were taken by 44% and 39%, respectively.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "The work was funded as a collaborative project by the WHO Pesticide Evaluation Scheme, Geneva, Switzerland.\n",
-                        "\n",
-                        "header: Chemical contents\n",
-                        "\n",
-                        "At time 0 (baseline), the contents of permethrin and PBO in Olyset(r) Plus were within the target tolerance limits of 20 +- 5 g/kg and 10 +- 2.5 g/kg, respectively; while the permethrin content in Olyset(r) Net was also within the target tolerance limit of 20 +- 3 g/kg (Table 5). The within net variation (relative standard deviation, RSD) in permethrin and PBO contents for Olyset(r) Plus at baseline were 2.2% and 3.7%, respectively, and were within the acceptable tolerance limits. The within net variation in the permethrin content in Olyset(r) Net at the baseline was 1.2% and was also within the acceptable tolerance limit. At the end of years 1, 2 and 3 of net usage, the permethrin content in Olyset(r) Plus decreased by 36%, 40% and 52%, and the PBO content showed a marked reduction of 66%, 81% and 87%, respectively. The loss of the permethrin in Olyset(r) Net\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c} \\hline\n",
-                        "**Survey month** & **No. of** & **Mean 1 h** & **Mean 24 h** & **\\% of nets passed by** & **\\% of nets passed by** & **No. of nets passed by** & **Overall pass rate in** \\\\\n",
-                        "**nets** & **KD (\\%)** & **mortality** & **KD criteria alone(r)** & **mortality criteria** & **requiring tunnel** & **percentage (95\\% CI)(r)** \\\\\n",
-                        "**Olyset(r) Plus** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.5 & 100 & 100 & 100 & 0 & 100 (80–100) \\\\\n",
-                        "6 & 30 & 97.0 & 88.2 & 83.3 & 73.3 & 4 & 96.7 (82–99) \\\\\n",
-                        "12 & 30 & 9\n",
-                        "\n",
-                        "4.2 & 74.3 & 76.7 & 43.3 & 6 & 93.3 (78–99) \\\\\n",
-                        "18 & 30 & 82.9 & 65.9 & 36.7 & 36.7 & 16 & 86.7 (69–96) \\\\\n",
-                        "24 & 30 & 77.4 & 75.3 & 0.0 & 46.7 & 15 & 76.7 (57–90) \\\\\n",
-                        "30 & 30 & 60.4 & 56.9 & 0.0 & 10.0 & 27 & 70.0 (50–85) \\\\\n",
-                        "**Olyset(r) Net** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.1 & 100 & 100 & 100 & 0 & 100 (88–100) \\\\\n",
-                        "6 & 30 & 96.9 & 79.8 & 80.0 & 60.0 & 4 & 93.3 (77–99) \\\\\n",
-                        "12 & 30 & 68.5 & 54.7 & 20.0 & 3.3 & 24 & 83.3 (65–94) \\\\\n",
-                        "18 & 30 & 72.1 & 50.2 & 30.0 & 20.0 & 18 & 73.3 (54–87) \\\\\n",
-                        "24 & 30 & 79.8 & 68.9 & 3.3 & 46.7 & 18 & 66.7 (47–83) \\\\\n",
-                        "30 & 30 & 50.9 & 50.9 & 0.0 & 3.3 & 29 & 60.0\n",
-                        "\n",
-                        "\n",
-                        "was 16% at year 1 and did not change much at year 2 and 3 (19 and 24%, respectively).\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "_Cl_ Confidence interval; CHW: Community Health Workers; KD: Knockdown; LINK: long-lasting insecticidal nets; NMD: National Malaria Control Programme; pH: proportionate Hole Index; PBO: Papernot by duakoc. Relative standard deviation; SD: Standard deviation; WHO: World Health Organization.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All the relevant datasets supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Net sampling scheme\n",
-                        "\n",
-                        "The scheme of sampling nets at different time points for bioassays and chemical assays is given in Table 2. The netting pieces were cut from the sampled nets from positions 1-9 for bioassays, and positions HP1-HP5 (at time 0) or positions HP2-HP5 (at time 12, 24 and 36 months) for chemical content assays (Fig. 1). All the nets which were sampled and retrieved at the given survey points were replaced with new ones.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Olysset(r) Plus did not meet the WHO criteria for efficacy of LLINs in this 3-year Phase III study, which could have resulted from faster loss of permethrin and PBO and users not following the manufacturer's recommendations to not leave the nets under direct sunlight. Better community education is therefore essential to ensure appropriate use and upkeep of nets in areas with LLIN-based interventions in order to maximize their operational impact on the prevention, control and elimination of malaria. Failure to do this would result in operational implications requiring early replenishment with new nets to ensure that such an LLIN based malaria intervention remains effective.\n",
-                        "\n",
-                        "header: Table 5 Mean permethrin and PBO contents (relative standard deviation) in nets at baseline, and at 12, 24 and 36 months\n",
-                        "footer: a  Within net variation (relative standard deviation) in permethrin and PBO contents at baseline was 2.2% and 3.7%, respectively (Olyset® Plus). – not applicable, PBOpiperonyl butoxideb Within net variation (relative standard deviation) in permethrin content at baseline was 1.2% (Olyset® Net)\n",
-                        "columns: ['Sampling month', 'Olyset® Plus - Mean permethrin content in g/kg (95% CI)', 'Olyset® Plus - Permethrin content lost (%)', 'Olyset® Plus - PBO content in g/ kg (95% CI)', 'Olyset® Plus - PBO content lost (%)', 'Olyset® Net - Mean permethrin content in g/kg (95% CI)', 'Olyset® Net - Permethrin content lost (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Sampling month\":0,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"18.4 (18.3\\u201318.6)a\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"8.98 (8.86\\u20139.10)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"19.6 (19.5\\u201319.7)b\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"\\u2013\"},\"1\":{\"Sampling month\":12,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.7 (10.6\\u201312.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"36\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"3.01 (2.30\\u20133.80)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"66\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"16.5 (16.0\\u201317.0)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"16\"},\"2\":{\"Sampling month\":24,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.1 (10.2\\u201312.0)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"40\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.70 (1.20\\u20132.20)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"81\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"15.9 (15.1\\u201316.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"19\"},\"3\":{\"Sampling month\":36,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"8.8 (7.9\\u20139.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"52\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.16 (0.8\\u20131.5)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"87\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"14.8 (13.9\\u201315.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"24\"}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The field trial was conducted according to the WHO guidelines [3, 11]. The description of the test products and trial procedures are described below.\n",
-                        "\n",
-                        "header: Recording of adverse events\n",
-                        "\n",
-                        "One month after net distribution, a survey was carried out to record adverse events reported by net users, and overall experiences of net usage. A pre-tested questionnaire adapted from the WHO guidelines [11] was administered to the heads or an adult person of the households after obtaining informed consent.\n",
-                        "\n",
-                        "header: Chemical content analysis\n",
-                        "\n",
-                        "The chemical contents in the nets were determined at the beginning and at years 1, 2 and 3 by samplings nets according to the scheme described earlier. The netting pieces cut for determination of active ingredient content were individually rolled up and placed in new, clean and labeled aluminum foils and sent to the Phyto-pharmacy Department of the Walloon Agricultural Research Centre, Gembloux, Belgium, a WHO Collaborating Centre, for chemical analysis.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The data collected were entered in excel spreadsheets and cross-checked for accuracy. Data were analyzed using STATA version 14.0 (Stata Corporation, College Station, TX, USA). Comparisons between the two types of nets were tested for significance using the Chi-square (kh2) test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. For continuous variables, the arithmetic mean was used depending on the distribution of values compared to a normal distribution.\n",
-                        "\n",
-                        "The nets were considered to have passed the WHO efficacy criteria if the mosquito knockdown rate was found to be >= 95% and/or mortality rate >= 80% in cone bioassays. For the tunnel tests, nets were considered to have passed the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90% [3]. The procedure for calculation of proportionate Hole Index (pHI) for nets is already described above.\n",
-                        "\n",
-                        "header: Table 3 Frequency of washing nets by the users at different time points\n",
-                        "footer: The reported wash rate was zero at 6 months\n",
-                        "columns: ['Period (months)', 'Olyset® Plus - No. of nets sampled', 'Olyset® Plus - No. of nets washed once (%)', 'Olyset® Plus - No. of nets washed twice (%)', 'Olyset® Plus - No. of nets washed > 2 times (%)', 'Olyset® Net - No. of nets sampled', 'Olyset® Net - No. of nets washed at once (%)', 'Olyset® Net - No. of nets washed twice (%)', 'Olyset® Net - No. of nets washed > 2 times (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Period (months)\":6,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"\\u2013\"},\"1\":{\"Period (months)\":12,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"1 (3)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"2\":{\"Period (months)\":24,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"4 (12)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"1 (3)\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"3 (11)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"3\":{\"Period (months)\":36,\"Olyset\\u00ae Plus - No. of nets sampled\":50,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"9 (17)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"5 (10)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"3 (6)\",\"Olyset\\u00ae Net - No. of nets sampled\":50,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"11 (21)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"8 (6)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"3 (6)\"}}\n",
-                        "\n",
-                        "header: Household net washing practices\n",
-                        "\n",
-                        "The net washing practices by households were evaluated using a questionnaire at 6, 12, 24 and 36 months after net distribution.\n",
-                        "\n",
-                        "header: Bioassays\n",
-                        "\n",
-                        "The insecticidal effect of the nets was evaluated using cone bioassays and where required tunnel tests, as per the WHO guidelines [3]. The bioassays were done at time zero (baseline) and then at every 6 months up to 36 months. On each of the netting pieces cut for bioassays, standard WHO plastic cones were held in place using a plastic manifold and a total of 50 laboratory bred, susceptible Kisumu strain _Anopheles gambiae_ (non-blood fed, 2-5 day old) were exposed for 3 min (5 pieces per net x 5 mosquitoes per test x 2 replicates). After the exposure, the mosquitoes were removed gently from\n",
-                        "the cones and kept separately in plastic cups provided with cotton-wool moistened with 10% glucose solution. Knockdown (KD) was recorded at 60 min and mortality at 24 h after exposure. Mosquitoes exposed to untreated polyester net pieces were used as untreated controls. The bioassays were carried out at 27 +- 2 degC temperature and 80% +- 10% relative humidity. When the mosquito knockdown rate was < 95% and mortality < 80%, the mortality and blood-feeding inhibition effect of such nets was assessed in a tunnel test according to the WHO guidelines [3].\n",
-                        "\n",
-                        "For each net, only one netting piece with which mosquito mortality was found closest to the average mortality in the cone test was tested in the tunnel test [3]. One hundred, non-blood fed female _An. gambiae_ Kisumu mosquitoes, aged 5-8 days were released at the longer end of a tunnel equipment made of glass. At each end of the tunnel, a 25 cm square cage covered with a polyester netting was fitted. At the shorter end of the tunnel, a rabbit which could not move but was available for mosquito biting was placed. The mosquitoes were released at 18:00 and mosquitoes recovered at 09:00 on the following morning. During the test, the tunnel was kept in a place maintained at 27 +- 2 degC and 80% +- 10% relative humidity in full darkness. Another tunnel with untreated netting was used as control. The mean mortality rate and percentage blood-feeding inhibition were recorded, as follows:\n",
-                        "\n",
-                        "1. Mortality = this was measured by pooling the mortality rates of mosquitoes from the two sections of the tunnel. If mortalities in the control recorded more than 10%, the test was considered invalid.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding\\,inhibition}(\\%) = \\left[ {100 \\times \\left( {\\text{Bfc} - \\text{Bfc}} \\right)} \\right]/\\text{Bfc}\\]\n",
-                        "\n",
-                        "where, Bfc is the proportion of blood-fed mosquitoes in the control tunnel and Bft is the proportion of blood-fed mosquitoes in the tunnel with the treated net. The treated net was considered to have met the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90%.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare no competing interests. As a collaborative project, RSY, a WHO professional staff, reviewed and agreed with the study design and contributed to writing the manuscript.\n",
-                        "\n",
-                        "header: Net survivorship and fabric integrity\n",
-                        "\n",
-                        "At the end of year 1, Olyset(r) Plus reported survivorship rates of 97.2% (_n_ = 243), with Olyset(r) Net recording 99.6% (_n_ = 249). At year 2, there were 92.4% (_n_ = 231) Olyset(r) Plus and 90.4% (_n_ = 226) of Olyset(r) Nets still surviving. At the end of the 3 years of study, the survivorship rates for Olyset(r) Plus and Olyset(r) Net were at 91.2% (_n_ = 228) and 86.4% (_n_ = 216), respectively.\n",
-                        "\n",
-                        "The pH values of the nets showed that 87.6% of Olyset(r) Plus were in good condition at 6 months, 7.6% were in acceptable/serviceable condition, and 4.8% were too torn (Table 6). These proportions increased gradually during the study and at 36 months, 49.6% were in good condition, 27.1% in acceptable/serviceable condition, and 23.2% were too torn. A similar trend of attrition was seen for Olyset(r) Net, with 44.9%, 32.8% and 22.2% nets being in good condition, acceptable condition or too torn at 36 months, respectively.\n",
-                        "\n",
-                        "Data on the comparison of estimates of the mean pH values and the mean hole area for Olyset(r) Plus and Olyset(r) Net are given in Table 7.\n",
-                        "\n",
-                        "The proportion of Olyset(r) Plus nets having been repaired by households increased from 6% (95% CI: 3.4-9.7) at month 6 to 17.9% (95% CI: 13.2-23.6) at months\n",
-                        "\n",
-                        "36, while that of Olyset(r) Net increased from 5% (95% CI: 2.8-8.7) to 12% (95% CI: 8.0-17.1) in the same period.\n",
-                        "\n",
-                        "\n",
-                        "Olyset(r) Net incorporated with permethrin alone was included in the trial as a positive control net. A net was considered to have passed the WHO efficacy criteria if it caused >= 80% mortality and/or >= 95% knockdown in cone bioassays or >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests [3]. Most of the Olyset(r) Plus and Olyset(r) Net lost the knockdown effect during the 24-36 months follow-up. Overall, neither Olyset(r) Plus nor Olyset(r) Net performed as they should even after 2 years and did not meet the WHO efficacy criteria of a 3-year LLIN.\n",
-                        "\n",
-                        "Previous studies have demonstrated enhanced efficacy of Olyset(r) Plus against resistant _An. gambiae_ attributable to PBO [16-18]. A recent randomised controlled field trial in Tanzania with high levels of pyrethroid resistance in malaria vectors _An. gambiae_ and _An. arabiensis_ also attributed higher impact on malaria of Olyset(r) Plus over Olyset(r) Net to the presence of PBO that was sustained after 21 months of the trial [19]. However, there is a view that these two nets are not comparable and that the higher efficacy of Olyset(r) Plus was due to much more bleed rate of permethrin into its surface and not due to PBO that was incorporated in small amount and dwindled rapidly over time [20]. In this trial, Olyset(r) Plus was evaluated as a pyrethroid LLIN since there were no guidelines published by WHO of how to evaluate PBO nets at the time of this trial, and considering the manufacturer's product claim.\n",
-                        "\n",
-                        "While, it was reported that high intensity insecticide resistance will reduce the level of personal and community protection of pyrethroid LLINs [21] and PBO nets could be effective against malaria in some resistance scenarios [22], a recent WHO multi-country study found no evidence of an association between insecticide resistance and malaria infection prevalence or incidence [23]. There is also a view that at present there is limited evidence that recent reduction in the impact on malaria in sub-Saharan Africa was due to increasing resistance in malaria vectors to the pyrethroid insecticides used in treating nets [24].\n",
-                        "\n",
-                        "The initial permethrin content in both nets was more or less similar but the enhanced efficacy of Olyset(r) Plus was mainly due to the higher release of permethrin into the surface fibres and presence of PBO than Olyset(r) Net. The gradual decline in efficacy of both nets is consistent with the loss of permethrin and PBO in Olyset(r) Plus and of permethrin in Olyset(r) Net over time during their routine usage by the community. The decline in permethrin content was more rapid in Olyset(r) Plus than Olyset(r) Net. This explains why Olyset(r) Plus having permethrin and PBO was relatively superior than Olyset(r) Net initially, i.e., until 18 months, but after that their performance was below the WHO efficacy cut off. While the PBO content in Olyset(r) Plus at the baseline was within the target tolerance limits, 66% of the PBO content was lost within one year of net usage, and the loss increased to 81% at year 2 and 87% at year 3. Previous studies have reported that the insecticidal content in LLINs decay over time [6]. While permethrin and PBO contents may be lost due to natural decay and evaporation, washing of nets frequently and drying them under direct sunlight are known to contribute to such losses [25, 26]. In this study, despite manufacturer's label recommendation not to expose nets to direct sunlight, a good proportion of both type of nets were exposed to bright sunlight before use and left to dry in the sun after washing. This would have reduced both permethrin and PBO contents and consequently reduced the biological efficacy of these nets and should be borne in mind when considering these results. Even though Kisumu strain was tested, the loss of PBO would have affected efficacy of Olyset Plus as PBO can improve net performance even against susceptible strains, which too have P450s, and PBO is also known to act as a good solvent that can aid cuticular penetration and help improve insecticidal activity. The operational implication of rapid loss of PBO is that Olyset(r) Plus was unlikely to remain effective even against pyrethroid resistant mosquitoes for 2 or 3 years of use.\n",
-                        "\n",
-                        "Community net wash practices were comparable between the two nets. During the first 6 months, no net was reported to have been washed. At 1 year, 7% of Olyset(r) Plus and 7% of Olyset(r) Net were reported to have been washed at least once. At month 36 months, about 17% and 21% of Olyset(r) Plus and Olyset(r) Net\n",
-                        "respectfully had been washed at least once. LLINs are usually recommended to be washed at most every 3 months [5], although this frequency depends on local cultural practices and water availability. Different net washing frequencies have been reported in other studies spanning from a high of 8 washes per month in Mali [27] to 4-7 times in Tanzania [28] to a low average of 1.5 washes per year in Uganda [29]. In coastal Kenya, on average nets were washed twice in 6 months, with blue colour nets least often washed than white or green colour nets, and older nets washed more frequently [30]. In western Kenya lowlands, three quarter of nets were washed once within 3 months and a third of them were dried under the sun [31]. The main reasons for low reported wash rates during the annual surveys in our study could be attributed to net users' poor recall of the net washing frequencies, and people's apprehension that washing of nets reduce net efficacy, although there was no water scarcity in the trial area being located in a rice irrigation area.\n",
-                        "\n",
-                        "The study participants reported experiencing certain transient adverse effects during the first two days of use of nets, but these could be prompted reactions since the community members had been informed about the possibility of such effects at the time of net distribution. The higher side effects from use of Olyset(r) Plus is in line with the higher release of permethrin as mentioned above. Users resorted to ventilating their nets in direct sunlight to reduce the effects although our project team had advised to dry up and keep the nets under shade. Ventilating new nets or drying washed nets under direct sunlight might have contributed to a significant reduction in active ingredient content and bio-efficacy of nets.\n",
-                        "\n",
-                        "At the end of the 3-year period, among the two cohorts of 250 nets each, only about 9% Olyset(r) Plus and about 14% Olyset(r) Net were reported to have been lost showing a high net survivorship. These results are in line with the expected 75% net survivorship rate reported by the NetCALC 3-year serviceable prediction model [32]. Previous studies have reported varying rate of net survivorship in African countries, such as 58% nets were lost within two years in Rwanda [33], only 39% of distributed nets remained both present and in serviceable physical condition 2-4 years after a mass campaign in Tanzania [34], but in Western Uganda an estimated attrition rate of just 12% after three years of use was observed for a polyester net [35]. In four African countries of Mozambique, Nigeria, DRC and Zanzibar, Tanzania, survival in serviceable condition of polyethylene and polyester LLINs after 31-37 months of use varied between different sites from 17 to 80% with median survival from 1.6 to 5.3 years [36]. In our study, the observed low attrition rate of nets appears to be also due to the 'Hawthorne effect' as the households included in the two cohorts of nets were visited every 6 or 12 months to inspect the nets. So, the awareness among study participants that the nets were regularly being followed could have modified their behaviour in favour of more careful upkeep and maintenance of the nets and as such may have been a limitation in this study.\n",
-                        "\n",
-                        "Studies have shown that despite small to medium size holes (64 < PHI < 642), LLINs are still protective against mosquito bites due to the excito-repellent effects of insecticides [35]. However, the nets may become ineffective when the overall hole area in nets reaches a certain threshold [37]. In our study, both Olyset(r) Plus and Olyset(r) Net recorded high fabric integrity up to the 3-year trial period. At 12 months after distribution, 78.1% of Olyset Plus and 63.8% of Olyset(r) Net were in good/acceptable condition, 12.3% Olyset(r) Plus and 30.5% Olyset(r) Net were in serviceable condition, while 9.4% Olyset(r) Plus and 5.6% Olyset(r) Net were completely torn and required to be replaced. At the end of the 3-year trial period, 49.5% of Olyset(r) Plus and 44.9% of Olyset(r) Net were in good/acceptable condition, 27.1% Olyset(r) Plus and 32.8% Olyset(r) Net in serviceable condition, while 23.2% Olyset(r) Plus and 22.2% Olyset(r) Net were completely torn and required replacement.\n",
-                        "\n",
-                        "Although Olyset(r) Net retained a higher permethrin content that Olyset(r) Plus, the latter showed longer duration of efficacy than the former, i.e., 18 months compared with 12 months above the WHO efficacy cut off, and higher efficacy thereafter i.e., the efficacy of Olyset(r) Plus declined to 42% at the end of 3-year trial period and that of Olyset(r) Net to 36% over the same period. This could be attributed to higher release of permethrin into the netting surface, and also because PBO can make nets perform better even against susceptible mosquitoes. Pyrethroid-PBO nets have previously been associated with higher mosquito mortality and lower blood-feeding rates in areas of high-level insecticide resistance than were non-PBO LLINs [38, 39]. Although in our study we did not test nets with a pyrethroid resistant mosquito strain, it is likely that the fast decline of PBO in Olyset(r) Plus was caused by the inappropriate exposure to sunlight, as well as the normal loss through washing would have reduced efficacy against resistant mosquitoes in the same way that it reduced efficacy against the susceptible mosquitoes tested here.\n",
-                        "\n",
-                        "Appropriate testing guidelines and field studies are thus required to evaluate the operational effectiveness of PBO nets against pyrethroid resistant mosquitoes and considering the PBO retention and release properties. The revised WHO LLIN guidelines should clarify for how long PBO nets should be effective and if the \n",
-                        "current WHO efficacy criteria for a 3-year LLIN should be reduced to fit the chemistry.\n",
-                        "\n",
-                        "A limitation of our trial was that we did not evaluate efficacy of Olysset(r) Plus against resistant _An. gambling_ mosquitoes. Instead, we tested its efficacy for the presence of permethrin alone using a susceptible malaria vector strain, although inclusion of PBO in nets has the potential of increasing insecticidal efficacy due to increased cuticular penetration and presence of P450s in susceptible insects. There is thus a need for suitable testing guidelines and a much more validation of the impact of PBO in pyrethroid-PBO nets.\n",
-                        "\n",
-                        "header: Table 7 Comparison of the estimates of the mean pHI and the mean hole area for O lyset® Plus and Olyset® Net\n",
-                        "footer: pHI proportionate Hole Index\n",
-                        "columns: ['Survey month', 'Olyset® Plus - No. of nets inspected', 'Olyset® Plus - Mean hole area in cm2 (± SD)', 'Olyset® Plus - Mean pHI (± SD)', 'Olyset® Net - No. of nets inspected', 'Olyset® Net - Mean hole area in cm2 (± SD)', 'Olyset® Net - Mean pHI (± SD)']\n",
-                        "\n",
-                        "{\"0\":{\"Survey month\":6,\"Olyset\\u00ae Plus - No. of nets inspected\":250,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"52.3 \\u00b1 230.5\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"93.3 \\u00b1 313.6\",\"Olyset\\u00ae Net - No. of nets inspected\":250,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"123.3 \\u00b1 272.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"110.5 \\u00b1 221.8\"},\"1\":{\"Survey month\":12,\"Olyset\\u00ae Plus - No. of nets inspected\":243,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"229.2 \\u00b1 574.2\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"153.6 \\u00b1 437.0\",\"Olyset\\u00ae Net - No. of nets inspected\":249,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"192.5 \\u00b1 376.2\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"156.8 \\u00b1 306.5\"},\"2\":{\"Survey month\":24,\"Olyset\\u00ae Plus - No. of nets inspected\":231,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"389.8 \\u00b1 975.4\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"317.6 \\u00b1 794.7\",\"Olyset\\u00ae Net - No. of nets inspected\":226,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"873.9 \\u00b1 353.4\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"279.4 \\u00b1 428.1\"},\"3\":{\"Survey month\":36,\"Olyset\\u00ae Plus - No. of nets inspected\":228,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"630.7 \\u00b1 1191.8\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"513.9 \\u00b1 970.9\",\"Olyset\\u00ae Net - No. of nets inspected\":216,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"621.5 \\u00b1 1710.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"506.4 \\u00b1 1393.2\"}}\n",
-                        "\n",
-                        "header: Test products\n",
-                        "\n",
-                        "Bioefficacy and physical durability of the Olyset(r) Plus and Olyset(r) Net both manufactured by Sumitomo Chemical were evaluated. The former is a polyethylene mono-filament net incorporated with 2% permethrin corresponding to 20 g permethrin active ingredient (AI)/kg or 800 mg permethrin AI/m\\({}^{2}\\) and 1% PBO (corresponding to 10 g PBO AI/kg or 400 mg PBO AI/m\\({}^{2}\\)). The latter is a polyethylene mono-filament net incorporated with 2% permethrin (w/w) (corresponding to 800 mg permethrin AI/m\\({}^{2}\\)). Olyset(r) Net was recommended in 2009 by the WHO Pesticide Evaluation Scheme (WHOPES) for use in malaria control [12], while Olyset(r) Plus was given interim recommendation by WHO in 2012 with the requirement to evaluate the product in a 3-year long-term trial to confirm its long-lasting efficacy in field [13].\n",
-                        "\n",
-                        "header: Assessment of durability of cohort nets\n",
-                        "\n",
-                        "To monitor durability of nets (i.e. survivorship and changes in physical integrity) during their routine use, 250 each of Olyset(r) Plus and Olyset(r) Net were distributed in separate households (2 nets per household) in the same villages by simple randomization. From these cohorts of nets, all nets available at the households at the time of surveys at 6, 12, 24 and 36 months were inspected for their presence and physical integrity. For this, the nets were retracted from the sleeping places, individually hung up on a rectangular frame held outside the house and inspected for presence of any holes on the side and roof panels. The holes were counted for each net, their diameter was measured, and they were classified in the following categories according to the WHO criteria for hole sizes [3, 11]:\n",
-                        "\n",
-                        "Size A1: 0.5-2 cm diameter, Size A2: 2-10 cm diameter, Size A3: 10-25 cm diameter, Size A4: 25 cm diameter.\n",
-                        "\n",
-                        "The parameters for assessing the integrity of nets included the proportions of Olyset(r) Plus and Olyset(r) Net with any size holes or tears, and the proportionate Hole Index (pHI) for each net, which was calculated as follows [11]:\n",
-                        "\n",
-                        "\\[\\text{pH} = \\left( {1.23 \\times \\text{ No.~of~size~A1~holes}} \\right) + \\left( {28.28 \\times \\text{ No.~of~size~A2~holes}} \\right) + \\left( {240.56 \\times \\text{ No.~of~size~A3~holes}} \\right) + \\left( {706.95 \\times \\text{ No.~of~size~A4~holes}} \\right)\\]\n",
-                        "\n",
-                        "Using the pHI values, Olyset(r) Plus and Olyset(r) Net were categorized as those in 'good condition' (pHI: 0-64), nets in 'acceptable' condition (pHI: 65-642), and torn nets that were likely to provide no protective efficacy (pHI > 642) [15]. The number of nets in 'good' and\n",
-                        "\n",
-                        "Fig. 1: A rectangular net and its individual panels showing positions for cutting netting pieces (positions 1–9 for bioassays; HP1–HPS for chemical assays)\n",
-                        "'acceptable condition' together were taken to estimate the survival of nets over time.\n",
-                        "\n",
-                        "The sampled nets were also inspected for any repairs of holes or tears done by the households. After inspection, the nets were returned to the same sleeping place in the same household.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Village trial\",\"Country\":\"Kenya\",\"Site\":\"Kirinyaga County\",\"Start_year\":2014,\"End_month\":12,\"End_year\":2017}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Concentration\": {\"title\": \"Insecticide concentration (measured mean)\", \"description\": \"Enter the measured concentration of the insecticide(s) for the active ingredient(s), in the net type.If multiple sides (e.g. roof or sides) of the net were tested, provide the mean value.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}, \"median_pHI\": {\"title\": \"Median Proportional hole index\", \"description\": \"Usually provided only for those nets with holes. This value should be provided in the text or a table of the paper. If it requires your own calculations, skip it. \\nDefinition: Numerator is total number of each LN product with at least one hole of size 1\\u20134; Denominator is Total number of each LN product found and assessed in surveyed households.\"}, \"pHI_lower_IQR\": {\"title\": \"pHI lower inter-quartile\", \"description\": \"Lower inter-quartile range of `pHI_median`.\"}, \"pHI_upper_IQR\": {\"title\": \"pHI upper inter-quartile\", \"description\": \"Upper inter-quartile range of `pHI_median`.\"}, \"pHI_upper_bound\": {\"title\": \"Phi Upper Bound\", \"description\": \"lowest pHI detected.\"}, \"median_hole_size\": {\"title\": \"Median hole surface area in cm^2\", \"description\": \"Usually provided only for those nets with holes. This value should be provided in the text or a table of the paper. If it requires your own calculations, skip it.\"}, \"hole_size_upper_IQR\": {\"title\": \"hole size upper inter-quartile\", \"description\": \"Upper inter-quartile range of `hole_size_median`.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset\\u00ae Plus\",\"Insecticide\":\"Permethrin, Piperonyl Butoxide\",\"Concentration_initial\":\"18.4, 8.98\",\"Concentration\":\"8.8, 1.16\",\"pHI_category\":\"Good\",\"median_pHI\":49.6,\"pHI_lower_IQR\":43.1,\"pHI_upper_IQR\":56.0,\"pHI_upper_bound\":642.0,\"median_hole_size\":52.3,\"hole_size_upper_IQR\":230.5},\"N02\":{\"Net_type\":\"Olyset\\u00ae Net\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"19.6\",\"Concentration\":\"14.8\",\"pHI_category\":\"Good\",\"median_pHI\":44.9,\"pHI_lower_IQR\":38.4,\"pHI_upper_IQR\":51.6,\"pHI_upper_bound\":642.0,\"median_hole_size\":123.3,\"hole_size_upper_IQR\":272.3}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Results\n",
-                        "\n",
-                        "Among the 1784 participating homesteads with a population of 10 325, a total of 1551 households participated in the study (Table 1). Most of the heads of the households (age: 17-96 years) had received formal education and practiced farming (96.6%).\n",
-                        "\n",
-                        "Out of 100 heads of households with Olyset(r) Plus interviewed for adverse events, 44% reported itching, 4% eye irritation, and 7% unpleasant smell. Among the Olyset(r) Net users, 7% each reported nasal discharge and eye irritation. These were reported to be mild symptoms lasting for one or two days. To reduce these effects, 43% users of Olyset(r) Plus ventilated nets for a day in the open under the sun and 33% under the shade. Among Olyset(r) Net users, similar actions were taken by 44% and 39%, respectively.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All the relevant datasets supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Chemical contents\n",
-                        "\n",
-                        "At time 0 (baseline), the contents of permethrin and PBO in Olyset(r) Plus were within the target tolerance limits of 20 +- 5 g/kg and 10 +- 2.5 g/kg, respectively; while the permethrin content in Olyset(r) Net was also within the target tolerance limit of 20 +- 3 g/kg (Table 5). The within net variation (relative standard deviation, RSD) in permethrin and PBO contents for Olyset(r) Plus at baseline were 2.2% and 3.7%, respectively, and were within the acceptable tolerance limits. The within net variation in the permethrin content in Olyset(r) Net at the baseline was 1.2% and was also within the acceptable tolerance limit. At the end of years 1, 2 and 3 of net usage, the permethrin content in Olyset(r) Plus decreased by 36%, 40% and 52%, and the PBO content showed a marked reduction of 66%, 81% and 87%, respectively. The loss of the permethrin in Olyset(r) Net\n",
-                        "\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{c c c c c c c c} \\hline\n",
-                        "**Survey month** & **No. of** & **Mean 1 h** & **Mean 24 h** & **\\% of nets passed by** & **\\% of nets passed by** & **No. of nets passed by** & **Overall pass rate in** \\\\\n",
-                        "**nets** & **KD (\\%)** & **mortality** & **KD criteria alone(r)** & **mortality criteria** & **requiring tunnel** & **percentage (95\\% CI)(r)** \\\\\n",
-                        "**Olyset(r) Plus** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.5 & 100 & 100 & 100 & 0 & 100 (80–100) \\\\\n",
-                        "6 & 30 & 97.0 & 88.2 & 83.3 & 73.3 & 4 & 96.7 (82–99) \\\\\n",
-                        "12 & 30 & 9\n",
-                        "\n",
-                        "4.2 & 74.3 & 76.7 & 43.3 & 6 & 93.3 (78–99) \\\\\n",
-                        "18 & 30 & 82.9 & 65.9 & 36.7 & 36.7 & 16 & 86.7 (69–96) \\\\\n",
-                        "24 & 30 & 77.4 & 75.3 & 0.0 & 46.7 & 15 & 76.7 (57–90) \\\\\n",
-                        "30 & 30 & 60.4 & 56.9 & 0.0 & 10.0 & 27 & 70.0 (50–85) \\\\\n",
-                        "**Olyset(r) Net** & & & & & & & \\\\\n",
-                        "0 & 30 & 99.1 & 100 & 100 & 100 & 0 & 100 (88–100) \\\\\n",
-                        "6 & 30 & 96.9 & 79.8 & 80.0 & 60.0 & 4 & 93.3 (77–99) \\\\\n",
-                        "12 & 30 & 68.5 & 54.7 & 20.0 & 3.3 & 24 & 83.3 (65–94) \\\\\n",
-                        "18 & 30 & 72.1 & 50.2 & 30.0 & 20.0 & 18 & 73.3 (54–87) \\\\\n",
-                        "24 & 30 & 79.8 & 68.9 & 3.3 & 46.7 & 18 & 66.7 (47–83) \\\\\n",
-                        "30 & 30 & 50.9 & 50.9 & 0.0 & 3.3 & 29 & 60.0\n",
-                        "\n",
-                        "\n",
-                        "was 16% at year 1 and did not change much at year 2 and 3 (19 and 24%, respectively).\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Olysset(r) Plus did not meet the WHO criteria for efficacy of LLINs in this 3-year Phase III study, which could have resulted from faster loss of permethrin and PBO and users not following the manufacturer's recommendations to not leave the nets under direct sunlight. Better community education is therefore essential to ensure appropriate use and upkeep of nets in areas with LLIN-based interventions in order to maximize their operational impact on the prevention, control and elimination of malaria. Failure to do this would result in operational implications requiring early replenishment with new nets to ensure that such an LLIN based malaria intervention remains effective.\n",
-                        "\n",
-                        "header: Recording of adverse events\n",
-                        "\n",
-                        "One month after net distribution, a survey was carried out to record adverse events reported by net users, and overall experiences of net usage. A pre-tested questionnaire adapted from the WHO guidelines [11] was administered to the heads or an adult person of the households after obtaining informed consent.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "The work was funded as a collaborative project by the WHO Pesticide Evaluation Scheme, Geneva, Switzerland.\n",
-                        "\n",
-                        "header: Table 5 Mean permethrin and PBO contents (relative standard deviation) in nets at baseline, and at 12, 24 and 36 months\n",
-                        "footer: a  Within net variation (relative standard deviation) in permethrin and PBO contents at baseline was 2.2% and 3.7%, respectively (Olyset® Plus). – not applicable, PBOpiperonyl butoxideb Within net variation (relative standard deviation) in permethrin content at baseline was 1.2% (Olyset® Net)\n",
-                        "columns: ['Sampling month', 'Olyset® Plus - Mean permethrin content in g/kg (95% CI)', 'Olyset® Plus - Permethrin content lost (%)', 'Olyset® Plus - PBO content in g/ kg (95% CI)', 'Olyset® Plus - PBO content lost (%)', 'Olyset® Net - Mean permethrin content in g/kg (95% CI)', 'Olyset® Net - Permethrin content lost (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Sampling month\":0,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"18.4 (18.3\\u201318.6)a\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"8.98 (8.86\\u20139.10)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"\\u2013\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"19.6 (19.5\\u201319.7)b\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"\\u2013\"},\"1\":{\"Sampling month\":12,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.7 (10.6\\u201312.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"36\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"3.01 (2.30\\u20133.80)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"66\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"16.5 (16.0\\u201317.0)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"16\"},\"2\":{\"Sampling month\":24,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"11.1 (10.2\\u201312.0)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"40\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.70 (1.20\\u20132.20)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"81\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"15.9 (15.1\\u201316.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"19\"},\"3\":{\"Sampling month\":36,\"Olyset\\u00ae Plus - Mean permethrin content in g\\/kg (95% CI)\":\"8.8 (7.9\\u20139.7)\",\"Olyset\\u00ae Plus - Permethrin content lost (%)\":\"52\",\"Olyset\\u00ae Plus - PBO content in g\\/ kg (95% CI)\":\"1.16 (0.8\\u20131.5)\",\"Olyset\\u00ae Plus - PBO content lost (%)\":\"87\",\"Olyset\\u00ae Net - Mean permethrin content in g\\/kg (95% CI)\":\"14.8 (13.9\\u201315.7)\",\"Olyset\\u00ae Net - Permethrin content lost (%)\":\"24\"}}\n",
-                        "\n",
-                        "header: Net sampling scheme\n",
-                        "\n",
-                        "The scheme of sampling nets at different time points for bioassays and chemical assays is given in Table 2. The netting pieces were cut from the sampled nets from positions 1-9 for bioassays, and positions HP1-HP5 (at time 0) or positions HP2-HP5 (at time 12, 24 and 36 months) for chemical content assays (Fig. 1). All the nets which were sampled and retrieved at the given survey points were replaced with new ones.\n",
-                        "\n",
-                        "header: Bioassays\n",
-                        "\n",
-                        "The insecticidal effect of the nets was evaluated using cone bioassays and where required tunnel tests, as per the WHO guidelines [3]. The bioassays were done at time zero (baseline) and then at every 6 months up to 36 months. On each of the netting pieces cut for bioassays, standard WHO plastic cones were held in place using a plastic manifold and a total of 50 laboratory bred, susceptible Kisumu strain _Anopheles gambiae_ (non-blood fed, 2-5 day old) were exposed for 3 min (5 pieces per net x 5 mosquitoes per test x 2 replicates). After the exposure, the mosquitoes were removed gently from\n",
-                        "the cones and kept separately in plastic cups provided with cotton-wool moistened with 10% glucose solution. Knockdown (KD) was recorded at 60 min and mortality at 24 h after exposure. Mosquitoes exposed to untreated polyester net pieces were used as untreated controls. The bioassays were carried out at 27 +- 2 degC temperature and 80% +- 10% relative humidity. When the mosquito knockdown rate was < 95% and mortality < 80%, the mortality and blood-feeding inhibition effect of such nets was assessed in a tunnel test according to the WHO guidelines [3].\n",
-                        "\n",
-                        "For each net, only one netting piece with which mosquito mortality was found closest to the average mortality in the cone test was tested in the tunnel test [3]. One hundred, non-blood fed female _An. gambiae_ Kisumu mosquitoes, aged 5-8 days were released at the longer end of a tunnel equipment made of glass. At each end of the tunnel, a 25 cm square cage covered with a polyester netting was fitted. At the shorter end of the tunnel, a rabbit which could not move but was available for mosquito biting was placed. The mosquitoes were released at 18:00 and mosquitoes recovered at 09:00 on the following morning. During the test, the tunnel was kept in a place maintained at 27 +- 2 degC and 80% +- 10% relative humidity in full darkness. Another tunnel with untreated netting was used as control. The mean mortality rate and percentage blood-feeding inhibition were recorded, as follows:\n",
-                        "\n",
-                        "1. Mortality = this was measured by pooling the mortality rates of mosquitoes from the two sections of the tunnel. If mortalities in the control recorded more than 10%, the test was considered invalid.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding\\,inhibition}(\\%) = \\left[ {100 \\times \\left( {\\text{Bfc} - \\text{Bfc}} \\right)} \\right]/\\text{Bfc}\\]\n",
-                        "\n",
-                        "where, Bfc is the proportion of blood-fed mosquitoes in the control tunnel and Bft is the proportion of blood-fed mosquitoes in the tunnel with the treated net. The treated net was considered to have met the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90%.\n",
-                        "\n",
-                        "header: Table 3 Frequency of washing nets by the users at different time points\n",
-                        "footer: The reported wash rate was zero at 6 months\n",
-                        "columns: ['Period (months)', 'Olyset® Plus - No. of nets sampled', 'Olyset® Plus - No. of nets washed once (%)', 'Olyset® Plus - No. of nets washed twice (%)', 'Olyset® Plus - No. of nets washed > 2 times (%)', 'Olyset® Net - No. of nets sampled', 'Olyset® Net - No. of nets washed at once (%)', 'Olyset® Net - No. of nets washed twice (%)', 'Olyset® Net - No. of nets washed > 2 times (%)']\n",
-                        "\n",
-                        "{\"0\":{\"Period (months)\":6,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"\\u2013\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"\\u2013\"},\"1\":{\"Period (months)\":12,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"1 (3)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"0\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"2\":{\"Period (months)\":24,\"Olyset\\u00ae Plus - No. of nets sampled\":30,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"4 (12)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"1 (3)\",\"Olyset\\u00ae Net - No. of nets sampled\":30,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"3 (11)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"2 (7)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"0\"},\"3\":{\"Period (months)\":36,\"Olyset\\u00ae Plus - No. of nets sampled\":50,\"Olyset\\u00ae Plus - No. of nets washed once (%)\":\"9 (17)\",\"Olyset\\u00ae Plus - No. of nets washed twice (%)\":\"5 (10)\",\"Olyset\\u00ae Plus - No. of nets washed > 2 times (%)\":\"3 (6)\",\"Olyset\\u00ae Net - No. of nets sampled\":50,\"Olyset\\u00ae Net - No. of nets washed at once (%)\":\"11 (21)\",\"Olyset\\u00ae Net - No. of nets washed twice (%)\":\"8 (6)\",\"Olyset\\u00ae Net - No. of nets washed > 2 times (%)\":\"3 (6)\"}}\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "The data collected were entered in excel spreadsheets and cross-checked for accuracy. Data were analyzed using STATA version 14.0 (Stata Corporation, College Station, TX, USA). Comparisons between the two types of nets were tested for significance using the Chi-square (kh2) test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. For continuous variables, the arithmetic mean was used depending on the distribution of values compared to a normal distribution.\n",
-                        "\n",
-                        "The nets were considered to have passed the WHO efficacy criteria if the mosquito knockdown rate was found to be >= 95% and/or mortality rate >= 80% in cone bioassays. For the tunnel tests, nets were considered to have passed the WHO efficacy criteria when mortality in mosquitoes was >= 80% and/or blood-feeding inhibition was >= 90% [3]. The procedure for calculation of proportionate Hole Index (pHI) for nets is already described above.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "_Cl_ Confidence interval; CHW: Community Health Workers; KD: Knockdown; LINK: long-lasting insecticidal nets; NMD: National Malaria Control Programme; pH: proportionate Hole Index; PBO: Papernot by duakoc. Relative standard deviation; SD: Standard deviation; WHO: World Health Organization.\n",
-                        "\n",
-                        "header: Table 7 Comparison of the estimates of the mean pHI and the mean hole area for O lyset® Plus and Olyset® Net\n",
-                        "footer: pHI proportionate Hole Index\n",
-                        "columns: ['Survey month', 'Olyset® Plus - No. of nets inspected', 'Olyset® Plus - Mean hole area in cm2 (± SD)', 'Olyset® Plus - Mean pHI (± SD)', 'Olyset® Net - No. of nets inspected', 'Olyset® Net - Mean hole area in cm2 (± SD)', 'Olyset® Net - Mean pHI (± SD)']\n",
-                        "\n",
-                        "{\"0\":{\"Survey month\":6,\"Olyset\\u00ae Plus - No. of nets inspected\":250,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"52.3 \\u00b1 230.5\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"93.3 \\u00b1 313.6\",\"Olyset\\u00ae Net - No. of nets inspected\":250,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"123.3 \\u00b1 272.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"110.5 \\u00b1 221.8\"},\"1\":{\"Survey month\":12,\"Olyset\\u00ae Plus - No. of nets inspected\":243,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"229.2 \\u00b1 574.2\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"153.6 \\u00b1 437.0\",\"Olyset\\u00ae Net - No. of nets inspected\":249,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"192.5 \\u00b1 376.2\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"156.8 \\u00b1 306.5\"},\"2\":{\"Survey month\":24,\"Olyset\\u00ae Plus - No. of nets inspected\":231,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"389.8 \\u00b1 975.4\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"317.6 \\u00b1 794.7\",\"Olyset\\u00ae Net - No. of nets inspected\":226,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"873.9 \\u00b1 353.4\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"279.4 \\u00b1 428.1\"},\"3\":{\"Survey month\":36,\"Olyset\\u00ae Plus - No. of nets inspected\":228,\"Olyset\\u00ae Plus - Mean hole area in cm2 (\\u00b1 SD)\":\"630.7 \\u00b1 1191.8\",\"Olyset\\u00ae Plus - Mean pHI (\\u00b1 SD)\":\"513.9 \\u00b1 970.9\",\"Olyset\\u00ae Net - No. of nets inspected\":216,\"Olyset\\u00ae Net - Mean hole area in cm2 (\\u00b1 SD)\":\"621.5 \\u00b1 1710.3\",\"Olyset\\u00ae Net - Mean pHI (\\u00b1 SD)\":\"506.4 \\u00b1 1393.2\"}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The field trial was conducted according to the WHO guidelines [3, 11]. The description of the test products and trial procedures are described below.\n",
-                        "\n",
-                        "header: Household net washing practices\n",
-                        "\n",
-                        "The net washing practices by households were evaluated using a questionnaire at 6, 12, 24 and 36 months after net distribution.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare no competing interests. As a collaborative project, RSY, a WHO professional staff, reviewed and agreed with the study design and contributed to writing the manuscript.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Bioefficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide-treated insecticidal net in a 3-year long trial in Kenya\n",
-                        "\n",
-                        "Paul M. Gichuki, Anna Kamau, Kiambo Niagi, Solomon Karoki, Njoroge Muigai, Damaris Matoke-Muhia, Nabie Bayoh, Evan Mathenge\n",
-                        "\n",
-                        "\n",
-                        "**Background:** Long-lasting insecticide nets (LLINs) are a core malaria intervention. LLINs should retain efficacy against mosquito vectors for a minimum of three years. Efficacy and durability of Olyset(r) Plus, a permethrin and piperonyl butoxide (PBO) treated LLIN, was evaluated versus permethrin treated Olyset(r) Net. In the absence of WHO guidelines of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated as a pyrethroid LILN.\n",
-                        "\n",
-                        "**Methods:** This was a household randomized controlled trial in a malaria endemic rice cultivation zone of Kirinyaga County, Kenya between 2014 and 2017. Cone bioassays and tunnel tests were done against _Anopheles gambiae_ Kisumu. The chemical content, fabric integrity and LLIN survivorship were monitored. Comparisons between nets were tested for significance using the Chi-square test. Exact binomial distribution with 95% confidence intervals (95% CI) was used for percentages. The WHO efficacy criteria used were >= 95% knockdown and/or >= 80% mortality rate in cone bioassays and >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests.\n",
-                        "\n",
-                        "**Results:** At 36 months, Olyset(r) Plus lost 52% permethrin and 87% PBO content; Olyset(r) Net lost 24% permethrin. Over 80% of Olyset(r) Plus and Olyset(r) Net passed the WHO efficacy criteria for LLINs up to 18 and 12 months, respectively. At month 36, 91.2% Olyset(r) Plus and 86.4% Olyset(r) Net survived, while 72% and 63% developed at least one hole. The proportionate Hole Index (pHI) values representing nets in good, serviceable and torn condition were 49.6%, 27.1% and 23.2%, respectively for Olyset(r) Plus, and 44.9%, 32.8% and 22.2%, respectively for Olyset(r) Net but were not significantly different.\n",
-                        "\n",
-                        "**Conclusions:** Olyset(r) Plus retained efficacy above or close to the WHO efficacy criteria for about 2 years than Olyset(r) Net (1-1.5 years). Both nets did not meet the 3-year WHO efficacy criteria, and showed little attrition, comparable physical durability and survivorship, with 50% of Olyset(r) Plus having good and serviceable condition after 3 years. Better community education on appropriate use and upkeep of LLINs is essential to ensure effectiveness of LLIN based malaria interventions.\n",
-                        "\n",
-                        "**Keywords:**_Anopheles gambiae, Bioefficacy, Durability, Kenya, Long-lasting insecticidal net, Olyset(r) Net, Olyset(r) Plus, Permeability, Piperonyl butoxide_\n",
-                        "\n",
-                        "header: Net survivorship and fabric integrity\n",
-                        "\n",
-                        "At the end of year 1, Olyset(r) Plus reported survivorship rates of 97.2% (_n_ = 243), with Olyset(r) Net recording 99.6% (_n_ = 249). At year 2, there were 92.4% (_n_ = 231) Olyset(r) Plus and 90.4% (_n_ = 226) of Olyset(r) Nets still surviving. At the end of the 3 years of study, the survivorship rates for Olyset(r) Plus and Olyset(r) Net were at 91.2% (_n_ = 228) and 86.4% (_n_ = 216), respectively.\n",
-                        "\n",
-                        "The pH values of the nets showed that 87.6% of Olyset(r) Plus were in good condition at 6 months, 7.6% were in acceptable/serviceable condition, and 4.8% were too torn (Table 6). These proportions increased gradually during the study and at 36 months, 49.6% were in good condition, 27.1% in acceptable/serviceable condition, and 23.2% were too torn. A similar trend of attrition was seen for Olyset(r) Net, with 44.9%, 32.8% and 22.2% nets being in good condition, acceptable condition or too torn at 36 months, respectively.\n",
-                        "\n",
-                        "Data on the comparison of estimates of the mean pH values and the mean hole area for Olyset(r) Plus and Olyset(r) Net are given in Table 7.\n",
-                        "\n",
-                        "The proportion of Olyset(r) Plus nets having been repaired by households increased from 6% (95% CI: 3.4-9.7) at month 6 to 17.9% (95% CI: 13.2-23.6) at months\n",
-                        "\n",
-                        "36, while that of Olyset(r) Net increased from 5% (95% CI: 2.8-8.7) to 12% (95% CI: 8.0-17.1) in the same period.\n",
-                        "\n",
-                        "\n",
-                        "Olyset(r) Net incorporated with permethrin alone was included in the trial as a positive control net. A net was considered to have passed the WHO efficacy criteria if it caused >= 80% mortality and/or >= 95% knockdown in cone bioassays or >= 80% mortality and/or >= 90% blood-feeding inhibition in tunnel tests [3]. Most of the Olyset(r) Plus and Olyset(r) Net lost the knockdown effect during the 24-36 months follow-up. Overall, neither Olyset(r) Plus nor Olyset(r) Net performed as they should even after 2 years and did not meet the WHO efficacy criteria of a 3-year LLIN.\n",
-                        "\n",
-                        "Previous studies have demonstrated enhanced efficacy of Olyset(r) Plus against resistant _An. gambiae_ attributable to PBO [16-18]. A recent randomised controlled field trial in Tanzania with high levels of pyrethroid resistance in malaria vectors _An. gambiae_ and _An. arabiensis_ also attributed higher impact on malaria of Olyset(r) Plus over Olyset(r) Net to the presence of PBO that was sustained after 21 months of the trial [19]. However, there is a view that these two nets are not comparable and that the higher efficacy of Olyset(r) Plus was due to much more bleed rate of permethrin into its surface and not due to PBO that was incorporated in small amount and dwindled rapidly over time [20]. In this trial, Olyset(r) Plus was evaluated as a pyrethroid LLIN since there were no guidelines published by WHO of how to evaluate PBO nets at the time of this trial, and considering the manufacturer's product claim.\n",
-                        "\n",
-                        "While, it was reported that high intensity insecticide resistance will reduce the level of personal and community protection of pyrethroid LLINs [21] and PBO nets could be effective against malaria in some resistance scenarios [22], a recent WHO multi-country study found no evidence of an association between insecticide resistance and malaria infection prevalence or incidence [23]. There is also a view that at present there is limited evidence that recent reduction in the impact on malaria in sub-Saharan Africa was due to increasing resistance in malaria vectors to the pyrethroid insecticides used in treating nets [24].\n",
-                        "\n",
-                        "The initial permethrin content in both nets was more or less similar but the enhanced efficacy of Olyset(r) Plus was mainly due to the higher release of permethrin into the surface fibres and presence of PBO than Olyset(r) Net. The gradual decline in efficacy of both nets is consistent with the loss of permethrin and PBO in Olyset(r) Plus and of permethrin in Olyset(r) Net over time during their routine usage by the community. The decline in permethrin content was more rapid in Olyset(r) Plus than Olyset(r) Net. This explains why Olyset(r) Plus having permethrin and PBO was relatively superior than Olyset(r) Net initially, i.e., until 18 months, but after that their performance was below the WHO efficacy cut off. While the PBO content in Olyset(r) Plus at the baseline was within the target tolerance limits, 66% of the PBO content was lost within one year of net usage, and the loss increased to 81% at year 2 and 87% at year 3. Previous studies have reported that the insecticidal content in LLINs decay over time [6]. While permethrin and PBO contents may be lost due to natural decay and evaporation, washing of nets frequently and drying them under direct sunlight are known to contribute to such losses [25, 26]. In this study, despite manufacturer's label recommendation not to expose nets to direct sunlight, a good proportion of both type of nets were exposed to bright sunlight before use and left to dry in the sun after washing. This would have reduced both permethrin and PBO contents and consequently reduced the biological efficacy of these nets and should be borne in mind when considering these results. Even though Kisumu strain was tested, the loss of PBO would have affected efficacy of Olyset Plus as PBO can improve net performance even against susceptible strains, which too have P450s, and PBO is also known to act as a good solvent that can aid cuticular penetration and help improve insecticidal activity. The operational implication of rapid loss of PBO is that Olyset(r) Plus was unlikely to remain effective even against pyrethroid resistant mosquitoes for 2 or 3 years of use.\n",
-                        "\n",
-                        "Community net wash practices were comparable between the two nets. During the first 6 months, no net was reported to have been washed. At 1 year, 7% of Olyset(r) Plus and 7% of Olyset(r) Net were reported to have been washed at least once. At month 36 months, about 17% and 21% of Olyset(r) Plus and Olyset(r) Net\n",
-                        "respectfully had been washed at least once. LLINs are usually recommended to be washed at most every 3 months [5], although this frequency depends on local cultural practices and water availability. Different net washing frequencies have been reported in other studies spanning from a high of 8 washes per month in Mali [27] to 4-7 times in Tanzania [28] to a low average of 1.5 washes per year in Uganda [29]. In coastal Kenya, on average nets were washed twice in 6 months, with blue colour nets least often washed than white or green colour nets, and older nets washed more frequently [30]. In western Kenya lowlands, three quarter of nets were washed once within 3 months and a third of them were dried under the sun [31]. The main reasons for low reported wash rates during the annual surveys in our study could be attributed to net users' poor recall of the net washing frequencies, and people's apprehension that washing of nets reduce net efficacy, although there was no water scarcity in the trial area being located in a rice irrigation area.\n",
-                        "\n",
-                        "The study participants reported experiencing certain transient adverse effects during the first two days of use of nets, but these could be prompted reactions since the community members had been informed about the possibility of such effects at the time of net distribution. The higher side effects from use of Olyset(r) Plus is in line with the higher release of permethrin as mentioned above. Users resorted to ventilating their nets in direct sunlight to reduce the effects although our project team had advised to dry up and keep the nets under shade. Ventilating new nets or drying washed nets under direct sunlight might have contributed to a significant reduction in active ingredient content and bio-efficacy of nets.\n",
-                        "\n",
-                        "At the end of the 3-year period, among the two cohorts of 250 nets each, only about 9% Olyset(r) Plus and about 14% Olyset(r) Net were reported to have been lost showing a high net survivorship. These results are in line with the expected 75% net survivorship rate reported by the NetCALC 3-year serviceable prediction model [32]. Previous studies have reported varying rate of net survivorship in African countries, such as 58% nets were lost within two years in Rwanda [33], only 39% of distributed nets remained both present and in serviceable physical condition 2-4 years after a mass campaign in Tanzania [34], but in Western Uganda an estimated attrition rate of just 12% after three years of use was observed for a polyester net [35]. In four African countries of Mozambique, Nigeria, DRC and Zanzibar, Tanzania, survival in serviceable condition of polyethylene and polyester LLINs after 31-37 months of use varied between different sites from 17 to 80% with median survival from 1.6 to 5.3 years [36]. In our study, the observed low attrition rate of nets appears to be also due to the 'Hawthorne effect' as the households included in the two cohorts of nets were visited every 6 or 12 months to inspect the nets. So, the awareness among study participants that the nets were regularly being followed could have modified their behaviour in favour of more careful upkeep and maintenance of the nets and as such may have been a limitation in this study.\n",
-                        "\n",
-                        "Studies have shown that despite small to medium size holes (64 < PHI < 642), LLINs are still protective against mosquito bites due to the excito-repellent effects of insecticides [35]. However, the nets may become ineffective when the overall hole area in nets reaches a certain threshold [37]. In our study, both Olyset(r) Plus and Olyset(r) Net recorded high fabric integrity up to the 3-year trial period. At 12 months after distribution, 78.1% of Olyset Plus and 63.8% of Olyset(r) Net were in good/acceptable condition, 12.3% Olyset(r) Plus and 30.5% Olyset(r) Net were in serviceable condition, while 9.4% Olyset(r) Plus and 5.6% Olyset(r) Net were completely torn and required to be replaced. At the end of the 3-year trial period, 49.5% of Olyset(r) Plus and 44.9% of Olyset(r) Net were in good/acceptable condition, 27.1% Olyset(r) Plus and 32.8% Olyset(r) Net in serviceable condition, while 23.2% Olyset(r) Plus and 22.2% Olyset(r) Net were completely torn and required replacement.\n",
-                        "\n",
-                        "Although Olyset(r) Net retained a higher permethrin content that Olyset(r) Plus, the latter showed longer duration of efficacy than the former, i.e., 18 months compared with 12 months above the WHO efficacy cut off, and higher efficacy thereafter i.e., the efficacy of Olyset(r) Plus declined to 42% at the end of 3-year trial period and that of Olyset(r) Net to 36% over the same period. This could be attributed to higher release of permethrin into the netting surface, and also because PBO can make nets perform better even against susceptible mosquitoes. Pyrethroid-PBO nets have previously been associated with higher mosquito mortality and lower blood-feeding rates in areas of high-level insecticide resistance than were non-PBO LLINs [38, 39]. Although in our study we did not test nets with a pyrethroid resistant mosquito strain, it is likely that the fast decline of PBO in Olyset(r) Plus was caused by the inappropriate exposure to sunlight, as well as the normal loss through washing would have reduced efficacy against resistant mosquitoes in the same way that it reduced efficacy against the susceptible mosquitoes tested here.\n",
-                        "\n",
-                        "Appropriate testing guidelines and field studies are thus required to evaluate the operational effectiveness of PBO nets against pyrethroid resistant mosquitoes and considering the PBO retention and release properties. The revised WHO LLIN guidelines should clarify for how long PBO nets should be effective and if the \n",
-                        "current WHO efficacy criteria for a 3-year LLIN should be reduced to fit the chemistry.\n",
-                        "\n",
-                        "A limitation of our trial was that we did not evaluate efficacy of Olysset(r) Plus against resistant _An. gambling_ mosquitoes. Instead, we tested its efficacy for the presence of permethrin alone using a susceptible malaria vector strain, although inclusion of PBO in nets has the potential of increasing insecticidal efficacy due to increased cuticular penetration and presence of P450s in susceptible insects. There is thus a need for suitable testing guidelines and a much more validation of the impact of PBO in pyrethroid-PBO nets.\n",
-                        "\n",
-                        "header: Test products\n",
-                        "\n",
-                        "Bioefficacy and physical durability of the Olyset(r) Plus and Olyset(r) Net both manufactured by Sumitomo Chemical were evaluated. The former is a polyethylene mono-filament net incorporated with 2% permethrin corresponding to 20 g permethrin active ingredient (AI)/kg or 800 mg permethrin AI/m\\({}^{2}\\) and 1% PBO (corresponding to 10 g PBO AI/kg or 400 mg PBO AI/m\\({}^{2}\\)). The latter is a polyethylene mono-filament net incorporated with 2% permethrin (w/w) (corresponding to 800 mg permethrin AI/m\\({}^{2}\\)). Olyset(r) Net was recommended in 2009 by the WHO Pesticide Evaluation Scheme (WHOPES) for use in malaria control [12], while Olyset(r) Plus was given interim recommendation by WHO in 2012 with the requirement to evaluate the product in a 3-year long-term trial to confirm its long-lasting efficacy in field [13].\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets reduce child mortality, number of _Plasmodium falciparum_ cases and probably the number of _P. vivax_ cases per person/year [1]. Factory-produced long-lasting insecticide nets (LLINs) are a core malaria intervention for the global elimination of malaria led by the World Health Organization (WHO) [2]. According to the WHO definition, LLINs should retain efficacy against mosquito vectors for a minimum of 20 standard washes under laboratory conditions and a minimum of 3 years of use under field conditions [3]. When used by most of the people in a population, insecticide-treated nets can protect all people in the community, including those who do not sleep under nets [4]. Currently, the National Malaria Control Programme (NMCP) in Kenya has been scaling up the use of LLINs in malaria endemic and high-risk areas to achieve universal coverage, which is defined by WHO as access to one LLIN for every 1.8 persons in each household [2, 5].\n",
-                        "\n",
-                        "Pyrethroid LLINs were the first brands of treated nets recommended by WHO for malaria control. However, it was believed that insecticide resistance was worsening in Africa and would present a major threat to malaria control [6-8], and that pyrethroid-LLINs were becoming less effective at killing mosquitoes in household conditions when pyrethroid resistance develops [9, 10]. Consequently, piperonyl butoxide (PBO) was incorporated in nets with the intension to improve their efficacy against pyrethroid resistant mosquitoes by acting as a metabolic enzyme inhibitor targeting the P450 cytochrome or mixed function oxidases that metabolise pyrethroids and enhance the efficacy of pyrethroid treated nets. Olyset(r) Plus has been prequalified by WHO as the first in class PBO net.\n",
-                        "\n",
-                        "However, at the time of beginning the trial due to the absence of guidelines published by WHO of how to evaluate PBO nets, and considering the manufacturer's product claim, Olyset(r) Plus was evaluated only as a pyrethroid LLIN against a pyrethroid susceptible mosquito strain rather than against a resistant strain. This paper presents results of a 3-year long-term (Phase III) field trial of the candidate product, Olyset(r) Plus, along with a positive control product Olyset(r) Net, in a malaria endemic rice cultivation area of Kirinyaga County, Kenya.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Village trial\",\"Country\":\"Kenya\",\"Site\":\"Kirinyaga County\",\"Start_year\":2014,\"End_month\":12,\"End_year\":2017}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Concentration\": {\"title\": \"Insecticide concentration (measured mean)\", \"description\": \"Enter the measured concentration of the insecticide(s) for the active ingredient(s), in the net type.If multiple sides (e.g. roof or sides) of the net were tested, provide the mean value.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}, \"median_pHI\": {\"title\": \"Median Proportional hole index\", \"description\": \"Usually provided only for those nets with holes. This value should be provided in the text or a table of the paper. If it requires your own calculations, skip it. \\nDefinition: Numerator is total number of each LN product with at least one hole of size 1\\u20134; Denominator is Total number of each LN product found and assessed in surveyed households.\"}, \"pHI_lower_IQR\": {\"title\": \"pHI lower inter-quartile\", \"description\": \"Lower inter-quartile range of `pHI_median`.\"}, \"pHI_upper_IQR\": {\"title\": \"pHI upper inter-quartile\", \"description\": \"Upper inter-quartile range of `pHI_median`.\"}, \"pHI_upper_bound\": {\"title\": \"Phi Upper Bound\", \"description\": \"lowest pHI detected.\"}, \"median_hole_size\": {\"title\": \"Median hole surface area in cm^2\", \"description\": \"Usually provided only for those nets with holes. This value should be provided in the text or a table of the paper. If it requires your own calculations, skip it.\"}, \"hole_size_upper_IQR\": {\"title\": \"hole size upper inter-quartile\", \"description\": \"Upper inter-quartile range of `hole_size_median`.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset\\u00ae Plus\",\"Insecticide\":\"Permethrin, Piperonyl Butoxide\",\"Concentration_initial\":\"18.4, 8.98\",\"Concentration\":\"8.8, 1.16\",\"pHI_category\":\"Good\",\"median_pHI\":49.6,\"pHI_lower_IQR\":43.1,\"pHI_upper_IQR\":56.0,\"pHI_upper_bound\":642.0,\"median_hole_size\":52.3,\"hole_size_upper_IQR\":230.5},\"N02\":{\"Net_type\":\"Olyset\\u00ae Net\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"19.6\",\"Concentration\":\"14.8\",\"pHI_category\":\"Good\",\"median_pHI\":44.9,\"pHI_lower_IQR\":38.4,\"pHI_upper_IQR\":51.6,\"pHI_upper_bound\":642.0,\"median_hole_size\":123.3,\"hole_size_upper_IQR\":272.3}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Acknowledgments\n",
-                        "\n",
-                        "We thank BASF and Bayer for providing the insecticides. We also thank the technical staff of CREC (Abibath Odojo, Estelle Vigninou, Josias Fagbohoun, Edwige Kpanou etc) for their assistance. We are grateful to the rice farmers at Cove for their support in the hut study.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "The efficacy of combining a standard pyrethroid LLIN (DuraNet(r)) with IRS insecticides from three chemical classes (bendicocarb, chlorfenapyr and pirimiphos-methyl CS) was evaluated in an experimental hut trial against wild pyrethroid-resistant _Anopheles gambiae_ s.l. in Cove, Benin. The combinations were also compared to each intervention alone. WHO cylinder and CDC bottle bioassays were performed to assess susceptibility of the local _An. gambiae_ s.l. vector population at the Cove hut site to insecticides used in the combinations.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Susceptibility bioassays revealed that the vector population at Cove, was resistant to pyrethroids (<20% mortality) but susceptible to carbamates, chlorfenapyr and organophosphates (>=98% mortality). Mortality of wild free-flying pyrethroid resistant _An. gambiae_ s.l. entering the hut with the untreated net control (4%) did not differ significantly from DuraNet(r) alone (8%, p = 0.169). Pirimiphos-methyl CS IRS induced the highest mortality both on its own (85%) and in combination with DuraNet(r) (81%). Mortality with the DuraNet(r) + chlorfenapyr IRS combination was significantly higher than each intervention alone (46% vs. 33% and 8%, p<0.05) demonstrating an additive effect. The DuraNet(r) + bendicocarb IRS combination induced significantly lower mortality compared to the other combinations (32%, p<0.05). Blood-feeding inhibition was very low with the IRS treatments alone (3-5%) but \n",
-                        "increased significantly when they were combined with DuraNet(r) (61% - 71%, p<0.05). Blood-feeding rates in the combinations were similar to the net alone. Adding bendicorarb IRS to DuraNet(r) induced significantly lower levels of mosquito feeding compared to adding chlorfenapyr IRS (28% vs. 37%, p = 0.015).\n",
-                        "\n",
-                        "header: Susceptibility bioassays\n",
-                        "\n",
-                        "WHO susceptibility cylinder bioassays [20] and CDC bottle bioassays [21] were performed in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove experimental hut site to the constituent insecticides in the hut treatments. Adult F1 progeny of field-collected _An. gambiae_ s.l. were exposed to filter papers treated with discriminating doses of alpha-cypermethrin (0.05%), permethrin (0.75%), bendiocarb (0.1%) and fenitrothion (1.0%) in WHO cylinders and CDC bottles impregnated with 100 mg of chlorfenapyr. Comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu colony. For each insecticide and strain, 100 adult female mosquitoes aged 2-3 days were introduced to four insecticide-treated cylinders/bottles in batches of 25. In the control arms, mosquitoes were exposed to silicone oil-treated filter papers and acetone-treated bottles. After 1 h exposure, mosquitoes were transferred to holding tubes and observation cups and provided access to 10% glucose solution (w/v). Mortality was recorded after 24 h for mosquitoes exposed to permethrin, alpha-cypermethrin, bendiocarb and fenitrothion in WHO cylinder bioassays and every 24 h up to 72 h for mosquitoes exposed to chlorfenapyr in CDC bottles. The insecticide treated filter papers used for the WHO cylinder bioassays were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Table 2: Susceptibility of field-collected Anopheles gambiae sensu late from Cove to chlorfenapyr in CDC bottle bioassays.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment', 'Strain', 'N exposed', 'N dead', '% Mortality�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Control\",\"Strain\":\"Kisumu\",\"N exposed\":101,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"1\":{\"Treatment\":\"Control\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":100,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"2\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Kisumu\",\"N exposed\":103,\"N dead\":103,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"},\"3\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":102,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"}}\n",
-                        "\n",
-                        "header: Table 4. Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total blood-fed', '% Blood-fed*', '95% CI', '% Blood-feeding inhibition', '% Personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total blood-fed\":675,\"% Blood-fed*\":\"95a\",\"95% CI\":\"93\\u201397\",\"% Blood-feeding inhibition\":\"\\u2013\",\"% Personal protection\":\"\\u2013\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total blood-fed\":231,\"% Blood-fed*\":\"55b\",\"95% CI\":\"50\\u201360\",\"% Blood-feeding inhibition\":\"42\",\"% Personal protection\":\"66\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total blood-fed\":202,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201336\",\"% Blood-feeding inhibition\":\"66\",\"% Personal protection\":\"70\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total blood-fed\":460,\"% Blood-fed*\":\"91e\",\"95% CI\":\"88\\u201393\",\"% Blood-feeding inhibition\":\"5\",\"% Personal protection\":\"32\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total blood-fed\":150,\"% Blood-fed*\":\"28c\",\"95% CI\":\"24\\u201332\",\"% Blood-feeding inhibition\":\"71\",\"% Personal protection\":\"78\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total blood-fed\":440,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201394\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"35\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total blood-fed\":153,\"% Blood-fed*\":\"37d\",\"95% CI\":\"33\\u201342\",\"% Blood-feeding inhibition\":\"61\",\"% Personal protection\":\"77\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total blood-fed\":488,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201395\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"28\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total blood-fed\":160,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201337\",\"% Blood-feeding inhibition\":\"65\",\"% Personal protection\":\"76\"}}\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only LLINs against a pyrethroid-resistant vector population which is susceptible to the IRS insecticide, can provide higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone. Adding pirimphos-methyl CS IRS may provide substantial improvements in vector control while adding chlorfenapyr IRS can demonstrate an additive effect relative to both interventions alone. Adding bendicorarb IRS may show limited enhancements in vector control owing to its short residual effect.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality rates of wild _An. gambiae_ s.l. from Cove in WHO susceptibility and CDC bottle bioassays are presented in Tables 1 and 2 respectively. Low proportions of adult F1 progeny of field-collected _An. gambiae_ s.l. were killed following exposure to alpha-cypermethrin (11%) and permethrin (20%); demonstrating pyrethroid resistance in the vector population. In contrast, the vast majority if not all mosquitoes from Cove were killed following exposure to bendiocarb (98%), fenitrothion (100%) and chlorfenapyr (100%); demonstrating susceptibility to carbamates, organophosphates and chlorfenapyr. With the insecticide susceptible _An. gambiae_ s.s. Kisumu colony, mortality was 100% with all insecticides.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only nets against a pyrethroid-resistant vector population which was susceptible to the IRS insecticide, generally provided higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone; the IRS intervention was responsible for improved mosquito mortality while the pyrethroid net provided blood-feeding inhibition. The combination with pirimiphos-methyl CS IRS was the most efficacious. Adding chlorfenapyr IRS to the pyrethroid LLIN demonstrated an additive mortality effect relative to both interventions alone. The combination with benidocarb IRS provided the \n",
-                        "lowest levels of mortality owing to its short residual effect but also induced lower levels of mosquito feeding compared to combinations with the other IRS insecticides.\n",
-                        "\n",
-                        "header: Study site and experimental huts: Experimental hut treatments\n",
-                        "\n",
-                        "The following treatments were assessed in 9 experimental huts:\n",
-                        "\n",
-                        "1. Control (untreated hut)\n",
-                        "2. Control (untreated net)\n",
-                        "3. DuraNet(r) (Alpha-cypermethrin LLIN, 261 mg/m2, Shobikaa Impex)\n",
-                        "4. Bendiocarb IRS applied at 400 mg/m2 (Ficam(r) WP, Bayer)\n",
-                        "5. DuraNet(r) + Bendiocarb IRS applied at 400 mg/m2\n",
-                        "6. Chlorfenapyr IRS applied at 250 mg/m2 (Sylando(r) 240SC, BASF)\n",
-                        "7. DuraNet(r) + Chlorfenapryr IRS applied at 250 mg/m2\n",
-                        "8. Pirimiphos-methyl CS IRS applied at 1000 mg/m2 (Actellic(r) 300CS, Syngenta)\n",
-                        "9. DuraNet(r) + Pirimiphos-methyl CS IRS applied at 1000 mg/m2\n",
-                        "\n",
-                        "Interior hut walls were sprayed from top to bottom with IRS insecticide solutions using a Hudson X-pert(r) compression sprayer equipped with a flat-fan nozzle. To improve spraying accuracy, spray swaths were pre-marked on hut walls and a guidance pole was attached to the end of the spray lance to maintain a fixed distance to the wall. After spraying each hut, the volume remaining in the spray tank was measured to assess the overall volume sprayed. All spray volumes were within 30% of the target. The palm match used on the ceiling was sprayed flat on the ground outside the experimental hut and allowed to dry for 2 hours prior to being fitted on the ceiling of the hut. Three replicate nets were used per LLIN treatment and these were rotated every 2 nights within the same hut. To simulate wear and tear from field use, 6 holes each measuring 16 cm2 were cut into each bed net (one per panel) used in the trial.\n",
-                        "\n",
-                        "header: Ethical considerations\n",
-                        "\n",
-                        "Ethical approval was obtained from the ethics review committees of the London School of Hygiene & Tropical Medicine (LSHTM) (Reference No. 15265) and the local Ministry of Health in Benin (Reference No. N'39/CEIC/CREC). All volunteers provided informed written consent and were offered a course of chemoprophylaxis (doxycycline) prior to their participation in the study and up to four weeks following its completion. A nurse was available throughout the study to assess any cases of fever. In the event a volunteer tested positive for malaria, they were immediately withdrawn from the trial and administered effective treatment.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are core interventions for preventing and controlling malaria [1]. Both methods have independently proven to be highly effective in reducing the burden of disease in diverse epidemiological settings. The substantial reductions in malaria morbidity and mortality observed in endemic countries over the last two decades, has been attributed to a significant increase in the roll out of ITNs and IRS during this period [2]. Where resources are available, both interventions have been deployed together in the same geographical location, with the primary aim of reducing the disease burden to a greater extent than could be achieved with either method alone [3,4].\n",
-                        "\n",
-                        "The benefits of combining these interventions is, however, contentious. Some community-randomised controlled trials (RCTs) have compared epidemiological outcomes in communities receiving pyrethroid long-lasting insecticidal nets (LLINs) plus IRS versus LLINs alone, yielding conflicting results. Whilst trials in Tanzania [5] and Sudan [6] associated combined use of LLINs and IRS with significant reductions in malaria infection prevalence, similar studies in Benin [7], Eritrea [8], Ethiopia [9] and The Gambla [10] reported no such effect. Based on a Cochrane review demonstrating the inconsistencies in epidemiological evidence [4], the World Health Organisation (WHO) has recently issued a provisional recommendation against combining LLINs and IRS as a means of reducing malaria-associated morbidity and mortality [1]. National Malaria Control Programmes are encouraged to prioritise delivering either ITNs or IRS at high coverage and to a high standard, rather than introducing the second intervention as a means to compensate for deficiencies in the implementation of the first. However, this should not be interpreted to mean that the combined IRS and ITN approach is redundant in all settings. Considering the recent stall in progress against malaria [11] and increasing prevalence of pyrethroid resistance in malaria vector populations across Africa [12], combining interventions may be vital for high transmission settings and insecticide resistance management. Vector control programmes are expected to be guided by local evidence to decide when, where and how to combine IRS with ITNs for different epidemiological settings [1].\n",
-                        "\n",
-                        "Evidence from RCTs and operational studies suggests that the impact of co-implementing LLINs and IRS may depend on a number of location-specific factors including: intervention coverage, transmission intensity, vector behaviour, choice of IRS insecticide and insecticide resistance [13,14]. The choice of IRS insecticide for a specific geographical setting whether deployed alone or together with LLINs will be influenced largely by the susceptibility of the target vector population to the insecticide, its mode of action, chemical properties and residual \n",
-                        "activity on wall substrates. To maximise the impact of the combined intervention approach, it is important to consider the interactions that may occur between the insecticide in the LLIN and the chosen IRS insecticide. Where the mode of action of the IRS insecticides is not complementary to the insecticide on the net, the combination might be redundant or result in lower levels of vector control than what is achievable with either intervention alone. Although pyrethroids are not recommended for IRS, the redundancy of combining pyrethroid IRS or wall linings with pyrethroid LLINs has been clearly demonstrated in experimental but studies against pyrethroid-resistant malaria vectors in West Africa [15,16].\n",
-                        "\n",
-                        "Insecticides belonging to four classes of compounds have been approved by the WHO for IRS against malaria vectors; pyrethroids, organophosphates, carbamates and more recently, the neonicotinoid clothianidin [1,17]. While RCTs are the most robust method for generating evidence on the impact of combining vector control interventions, they are often time-consuming, expensive and unrealistic in some settings. Evidence from experimental but trials may provide some insights on what to expect when different types IRS insecticides are deployed to complement pyrethroid-only nets. In this study, we compared the impact of combining pyrethroid LLINs with IRS insecticides from three distinct chemical classes in experimental huts against wild free-flying pyrethroid-resistant malaria vectors in southern Benin.\n",
-                        "\n",
-                        "header: Trial procedure\n",
-                        "\n",
-                        "The trial began three days after IRS application and continued for 4 months, between July and October 2018 and followed WHO guidelines [19]. Nine (9) consenting human volunteers kept in experimental huts each trial night to attract mosquitoes. To account for bias due to differential attractiveness to mosquitoes, volunteers were rotated through experimental huts daily in accordance with a predetermined Latin square design. To mitigate the effect of hut position on mosquito entry, bed nets were rotated through experimental huts on a weekly basis. IRS treatments could not be rotated thus remained fixed throughout the trial. At dawn, sleepers collected all mosquitoes from under the net, the sleeping room and the veranda using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were subsequently transferred to the laboratory for morphological identification and scoring of blood-feeding and mortality. To account for the slow-action of chlorfenapyr, delayed mortality was recorded every 24 h up to 72 h after collection for all treatments. All alive _An. gambiae_ s.l. were held at 27 +- 2deg C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution.\n",
-                        "\n",
-                        "header: Table 3. Overall entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total females caught', 'Average catch per night', '% Deterrence', 'Total exiting', '% Exophily�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total females caught\":711,\"Average catch per night\":7,\"% Deterrence\":\"\\u2013\",\"Total exiting\":322,\"% Exophily\\ufffd\":\"45a\",\"95% CI\":\"42\\u201349\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total females caught\":420,\"Average catch per night\":4,\"% Deterrence\":\"41\",\"Total exiting\":165,\"% Exophily\\ufffd\":\"39a\",\"95% CI\":\"35\\u201344\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total females caught\":619,\"Average catch per night\":6,\"% Deterrence\":\"13\",\"Total exiting\":350,\"% Exophily\\ufffd\":\"57b\",\"95% CI\":\"53\\u201361\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":508,\"Average catch per night\":5,\"% Deterrence\":\"29\",\"Total exiting\":389,\"% Exophily\\ufffd\":\"77c\",\"95% CI\":\"73\\u201380\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total females caught\":537,\"Average catch per night\":6,\"% Deterrence\":\"25\",\"Total exiting\":373,\"% Exophily\\ufffd\":\"70cd\",\"95% CI\":\"66\\u201373\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total females caught\":478,\"Average catch per night\":5,\"% Deterrence\":\"33\",\"Total exiting\":297,\"% Exophily\\ufffd\":\"62bd\",\"95% CI\":\"58\\u201367\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total females caught\":411,\"Average catch per night\":4,\"% Deterrence\":\"42\",\"Total exiting\":241,\"% Exophily\\ufffd\":\"59b\",\"95% CI\":\"54\\u201363\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total females caught\":528,\"Average catch per night\":6,\"% Deterrence\":\"26\",\"Total exiting\":356,\"% Exophily\\ufffd\":\"67d\",\"95% CI\":\"63\\u201371\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total females caught\":486,\"Average catch per night\":5,\"% Deterrence\":\"32\",\"Total exiting\":298,\"% Exophily\\ufffd\":\"61bd\",\"95% CI\":\"57\\u201366\"}}\n",
-                        "\n",
-                        "header: Outcome measures\n",
-                        "\n",
-                        "The efficacy of each experimental hut treatment was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "* Exophily-exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda\n",
-                        "* Deterrence-defined as the percentage reduction in numbers of mosquitoes collected in a treated hut relative to the untreated control\n",
-                        "* Blood-feeding-proportion of blood-fed mosquitoes at the time of collection\n",
-                        "\n",
-                        "Where _B__\\({}_{\\mu}\\)_ is the proportion of blood-fed mosquitoes in the untreated control and _B__\\({}_{\\beta}\\)_ is the proportion of blood-fed mosquitoes in the treated hut.\n",
-                        "\n",
-                        "\n",
-                        "- B_{i})}{B_{u}}\\] Where \\(B_{u}\\) is the number of blood-fed mosquitoes in the untreated control and \\(B_{i}\\) is the number of blood-fed mosquitoes in the huts with insecticide treatments.\n",
-                        "* Mortality-proportion of dead mosquitoes after a 72 h holding period Mosquito deterrence, blood-feeding inhibition and personal protection were calculated relative to the untreated net control hut for treatments which involved the LLIN and relative to the untreated hut control for IRS-only treatments.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Which indoor residual spraying insecticide best complements standard pyrethroid long-lasting insecticidal nets for improved control of pyrethroid resistant malaria vectors?\n",
-                        "\n",
-                        "Thomas Syme, Augustin Fongnikin, Damien Todjinou, Renaud Goveetchan, Mark Rowland, Martin Akogbeto, Corine Nguforo\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Where resources are available, non-pyrethroid IRS can be deployed to complement standard pyrethroid LLINs with the aim of achieving improved vector control and managing insecticide resistance. The impact of the combination may however depend on the type of IRS insecticide deployed. Studies comparing combinations of pyrethroid LLINs with different types of non-pyrethroid IRS products will be necessary for decision making.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study was conducted at the CREC/LSHTM experimental hut station located in Cove, southern Benin (7deg14degN2deg18degE). The experimental huts are situated in a vast area of rice irrigation, which provide extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzeii_ and _An. gambiae_ sensu stricto (s.s.) occur in sympatry, with the latter present at lower densities and predominantly in the dry season [18]. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold) [18]. Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (>90%) and overexpression of the cytochrome P450, CYP6P3, associated with pyrethroid detoxification [18]. The experimental huts used were of the West African design [19], made from concrete bricks with a corrugated iron roof and a ceiling with palm-thatch mats. Interior walls were cement plastered and each hut was constructed on a concrete base containing a water-filled moat to preclude predators. Entry of mosquitoes occurred via four window slits, 1 cm wide and 30 cm long, positioned on two sides of the hut. A wooden framed veranda made of polyethylene sheeting and screening mesh projected from the rear wall of each hut to capture exiting mosquitoes.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental hut treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to differential attractiveness of the volunteer sleepers and huts.\n",
-                        "\n",
-                        "\n",
-                        "Mortality in susceptibility bioassays was interpreted according to WHO criteria. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Susceptibility bioassay results: Experimental hut results\n",
-                        "\n",
-                        "A total of 4,698 female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trial period (Tables 3 and S1). Due to the inability to\n",
-                        "\n",
-                        "rotate IRS, it was not possible to distinguish treatment-induced deterrence for the IRS treatments from differential attractiveness due to hut position. The proportion of mosquitoes exiting into the veranda for the untreated hut and untreated net controls were 45% and 39% respectively. Exiting was significantly higher in all experimental hut treatments relative to both controls (p<0.01), suggesting the insecticide treatments induced an irritant response which caused mosquitoes to seek refuge in the veranda. The highest level of mosquito exiting was achieved with beniodicarb IRS alone (77%). Adding beniodicarb IRS to DuraNet(r) also induced significantly higher levels of mosquito exiting compared to DuraNet(r) alone (57% with DuraNet(r) vs. 70% with DuraNet(r) + beniodicarb IRS, p = 0.001). This effect was, however, not observed with chlorfenapyr IRS and pirimiphos-methyl CS IRS; exiting rates were similar between the combinations with these IRS insecticides and DuraNet(r) alone, (59% - 61% vs 57%, p>0.05).\n",
-                        "\n",
-                        "**Blood-feeding and personal protection.** Blood-feeding and personal protection results of wild, pyrethroid-resistant _An. gambiae_ s.l. in the experimental huts are presented in Table 4. Blood-feeding rates with the untreated hut and untreated net controls were 95% and 55% respectively. DuraNet(r) induced significantly lower blood-feeding rates than the control untreated net (33% with DuraNet(r) vs. 55% with untreated net, p<0.001). Meanwhile, blood-feeding was high across all IRS treatments (91-92%) thus providing very limited blood-feeding inhibition (3% - 5%) compared to the untreated hut control (Table 4). All three combinations provided significantly higher levels of blood-feeding inhibition relative to the IRS treatments\n",
-                        "\n",
-                        "alone (60% - 71% vs. 3% - 5%, p<0.001). Between the combinations, DuraNet(r) + bendiocarb IRS induced the lowest blood-feeding rate (28%) and offered personal protection of 78% while DuraNet(r) + chlorfenapyr IRS induced a higher blood-feeding rate (37%, p = 0.015) though providing about the same levels of personal protection (77%).\n",
-                        "\n",
-                        "**Mortality rates in experimental huts.** Overall mortality rates of wild, pyrethroid-resistant _An. gambiae_ s.l. in the untreated hut and untreated net controls were 2% and 4% respectively (Table 5). Mortality with DuraNet(r) was low and did not differ significantly from the untreated net control (8% vs. 4%, p = 0.169). The IRS treatments alone and the combinations induced significantly higher mortality than DuraNet(r) and the untreated controls (p < 0.001); adding any IRS treatment onto DuraNet(r) therefore always significantly improved vector mortality compared to DuraNet(r) alone. Between the IRS only treatments, the highest mortality was achieved with pirimiphos-methyl CS (85%, p < 0.001) while chlorfenapyr and bendiocarb induced fairly similar mortality rates (34% and 28%, p = 0.09). Combining DuraNet(r) with bendiocarb and pirimiphos-methyl CS IRS treatments did not improve mortality compared to the corresponding IRS alone (bendiocarb: 28% vs. 32%, p = 0.265 and pirimiphos-methyl CS: 85% vs. 81%, p = 0.129) (Table 5). With chlorfenapyr IRS, mortality was significantly higher in the combination compared to chlorfenapyr IRS alone over the 4-month trial (34% vs. 46%, p < 0.001) (Fig 3). Between the combinations, the highest mortality was achieved with DuraNet(r) + pirimiphos-methyl CS IRS (81%). Mortality was significantly higher with the DuraNet(r) + chlorfenapyr IRS combination compared to DuraNet(r) + bendiocarb IRS (46% vs. 32%, p < 0.001) (Table 5).\n",
-                        "\n",
-                        "Initial monthly wild mosquito mortality with pirimiphos-methyl CS IRS and chlorfenapyr IRS was steady and did not decline substantially over the course of the 4 months trial both when applied alone and in combination with DuraNet(r) (Figs 1 and 2). By contrast, wild mosquito mortality with bendiocarb IRS declined sharply from ~55% to about 28-32% by the last month of the trial both when applied alone and in combination with DuraNet(r) (Fig 3).\n",
-                        "\n",
-                        "**Wall cone bioassay results.** Mortality rates of the laboratory maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu colony following exposure to IRS-treated walls in monthly, 30 mins wall cone bioassays are presented in Fig 4 with more details in supporting information (S2 Table). On pirimiphos-methyl CS-treated surfaces, cone bioassay mortality was very high (> 85%) throughout all 4 months of the trial. As observed with wild free-flying mosquitoes, a progressive decline in cone bioassay mortality with increasing months elapsed from spraying was observed with bendiocarb, beginning at 63% in month 1 and decreasing to 19% by month 4. Cone bioassay mortality with chlorfenapyr IRS was consistently low throughout the trial,\n",
-                        "ranging from 11-31%. Cone bioassay mortality on untreated control walls did not exceed 10% in any month of the trial (Fig 4).\n",
-                        "\n",
-                        "header: Mosquito entry and exiting.: Discussion\n",
-                        "\n",
-                        "The ultimate purpose of combining IRS with LLINs is to achieve greater levels of vector control through the differential effects of the interventions and modes of action of the insecticides involved than what is achievable with a single intervention on its own. Transmission dynamics models have suggested that the levels of vector mosquito mortality and blood-feeding inhibition observed in an experimental hut setting could be predictive of the capacity of an indoor vector control intervention or a combination of these to control vector populations, reduce vector biting and improve public health impact when deployed on a large scale [22,23]. In this study, we demonstrate that where vectors are resistant to pyrethroids but susceptible to a given non-pyrethroid IRS insecticide, it should be possible to achieve substantially improved vector control by adding the non-pyrethroid IRS to a standard pyrethroid-only LLIN to boost\n",
-                        "\n",
-                        "Fig 1: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pirimphos-methyl CS IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "Fig 2: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with chlorfenapyr IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "\n",
-                        "mortality rates through the IRS intervention, while benefiting from the blood-feeding inhibition provided by the LLIN.\n",
-                        "\n",
-                        "The level of improved mortality achieved in the pyrethroid-only LLIN plus non-pyrethroid IRS combinations relative to the pyrethroid LLIN alone was clearly dependent on the type of non-pyrethroid IRS insecticide used. The combination with pirimiphos-methyl CS IRS induced the highest mortality rate (81%) and this can be attributed to the high and prolonged activity of the pirimiphos-methyl CS IRS formulation on cement walls as demonstrated in this study and previous studies in Benin [24]. This substantiates findings from community trials and operational studies demonstrating significant reductions in transmission of malaria by pyrethroid-resistant vector populations when a single round of pirimiphos-methyl CS IRS was deployed against a background of moderate to high coverage with standard pyrethroid nets [25-27]. By contrast the levels of mortality achieved in the combination with bendiocarb IRS was much lower (32%); attributable to the fast decline in efficacy with the insecticide as observed monthly in the experimental huts and in cone bioassays. The poor residual effect of\n",
-                        "\n",
-                        "Fig 4: **Mortality (72 h) of laboratory maintained, insecticide-susceptible _Anopheles gambiae_ sensu stricto Kisumu colony exposed to IRS-treated hut walls in monthly, 30 mins wall cone bioassays in experimental huts in Cove, southern Benin. Error bars represent 95% CI. Approximately fifty (50) 2–3 days old mosquitoes were exposed for 30 mins to each IRS treated hut in batches of 10 per cone and mortality recorded after 72 h.**\n",
-                        "\n",
-                        "Fig 3: **Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato entering experimental huts with bendiocarb IRS applied alone and in combination with DuraNet® in Cove, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.**\n",
-                        "bendiocarb for IRS is well documented [28, 29, 30]. The failure of a combination of bendiocarb treated walls and pyrethroid nets to provide improved control of clinical malaria compared to either intervention alone in a previous RCT in Benin [7] was also largely attributed to the short-lived efficacy of bendiocarb on the treated walls [13, 14]. Multiple IRS campaign rounds may be necessary to achieve sustained levels of improved vector control when bendiocarb IRS is deployed to complement standard pyrethroid LLINs. Given the significant costs associated with running IRS campaigns, these findings raise questions over the suitability of currently available formulations of bendiocarb for IRS and the cost-effectiveness of its combination with pyrethroid nets. Considering the high initial mortality rates usually achieved with the insecticide, reformulation chemistry may help address the short residual efficacy of bendiocarb and improve the utility of this insecticide for vector control.\n",
-                        "\n",
-                        "While mortality in the combinations with pirimiphos-methyl CS IRS and bendiocarb IRS were similar to each IRS alone, the combination with chlorfenapyr IRS induced significantly higher levels of mortality compared to chlorfenapyr IRS alone. This demonstrates an additive effect of the chlorfenapyr IRS plus pyrethroid LLINs combination which was not observed with the other IRS insecticides tested. This trend is consistent with previous experimental hut studies in Benin [31, 32] though the levels of mortality achieved with the combination were substantially higher (73%-83% vs. 46%). The difference in mortality could be due to changes in the composition of the vector population and/or increasing pyrethroid resistance levels over time. Chlorfenapyr is a new repurposed pyrrole insecticide with a unique mode of action that has shown potential for IRS but often inducing moderate mortality rates when applied alone in experimental huts against pyrethroid-resistant malaria vectors [29, 33]. It acts on the oxidative/metabolic pathway of insects hence its action can be enhanced by physical activity in the insect [34]. The additive mortality observed in the chlorfenapyr IRS plus pyrethroid net combination may therefore be attributed to the irritant effect of the pyrethroid in the LLIN on mosquitoes as they alight on the chlorfenapyr-treated wall after failed attempts to feed on the sleeper under the net. In addition, owing to the non-neurotoxic and non-irritant effect of chlorfenapyr [35], mosquitoes may have alighted on chlorfenapyr-treated walls for longer, leading to increased insecticide pick-up, potentially contributing to the additional mortality observed with this combination. The inability of the WHO 30-min cone bioassays to predict wild mosquito mortality with chlorfenapyr IRS in experimental huts [29, 34] is further demonstrated in this study; while cone bioassay mortality of the susceptible Kisumu strain was very low, wild mosquito mortality was higher and consistent throughout the trial confirming previous findings [29]. Considering the prolonged effect of chlorfenapyr IRS on wild mosquitoes [29], substantial and sustained improvements in the control of pyrethroid-resistant vector populations can be expected if the IRS insecticide is deployed to complement pyrethroid LLINs.\n",
-                        "\n",
-                        "IRS is highly effective for malaria vector control when deployed on its own but provides limited personal protection from mosquito biting and this is evident from the high blood-feeding rates observed with the IRS treatments alone. In the absence of a bed net, mosquitoes will usually feed on the human occupants in an IRS treated home before resting on the wall where they pick up the insecticide, consequently, direct blood-feeding inhibition is not expected when the intervention is applied independently. By contrast, pyrethroid LLINs reduce mosquito biting and provide substantial levels of personal protection for the user; an effect which is attributable to the physical barrier of the net and the excito-repellent effect of the pyrethroid insecticide. The findings from this study demonstrate that this effect can persist even against a vector population that has developed intense resistance to pyrethroids as observed in Cove [18]. By combining the IRS treatments with a pyrethroid LLIN, it was possible to achieve substantially improved levels of blood-feeding inhibition and personal protection compared to \n",
-                        "the IRS alone. The reduced exposure to infective bites constitutes a crucial advantage of the combination strategy over IRS alone. The blood-feeding rate in the combination appeared to depend on the type of IRS insecticide used albeit to a lesser extent than the levels of mortality achieved; lower blood-feeding rates and thus higher levels of blood-feeding inhibition were achieved in the combination with benidocarb IRS compared to the combination with chlorfenapyr IRS. This can be due to differences in the mode of action of both IRS insecticides; in contrast to chlorfenapyr, benidocarb acts on the insect's nervous system eliciting an irritant rapid knockdown effect on mosquitoes [35] and this together with the excito-repellent effect of the pyrethroid in the standard pyrethroid LLIN could have contributed to the high levels of early exiting and reduced blood-feeding rates observed when both interventions were combined in a hut.\n",
-                        "\n",
-                        "Overall, the results demonstrate the superiority of the organophosphate, pirimiphos-methyl CS for IRS on its own and in combination with pyrethroid LLINs over benidocarb and the pyrrole, chlorfenapyr. However, this finding may not be generalisable to areas where vectors are resistant to organophosphates. Indeed, previous experimental hut studies in Cote d'Ivoire failed to demonstrate improved vector control with the combination compared to the net alone when standard pyrethroid LLINs were combined with pirimiphos-methyl CS-treated wall linings against a vector population that was resistant to pyrethroids and organophosphates [36]. Resistance to organophosphates and carbamates is unfortunately increasing in malaria vector populations especially in West Africa [37, 38, 39, 40]. To mitigate its impact, vector control programmes are encouraged to rotate between IRS insecticides with different modes of action [41]. Considering that most IRS campaigns will usually be deployed against a background of high LLIN coverage, effective IRS rotation plans should prioritise IRS insecticides which in addition to providing improved and prolonged vector control, can efficiently complement LLINs. Based on the findings of this study, chlorfenapyr IRS shows some potential to be a useful addition to such IRS rotation plans in pyrethroid-resistant areas with high pyrethroid LLIN coverage. It will be interesting to investigate the impact of combining pyrethroid LLINs with other newly approved IRS formulations containing clothianidin [17].\n",
-                        "\n",
-                        "The low mortality rates achieved with the standard pyrethroid net in this study (8%), further demonstrates the threat of pyrethroid resistance on the efficacy of standard pyrethroids. Pyrethroid resistance is widespread in malaria vectors across Africa and increasing in intensity the more they are used [12]. A new generation of LLINs treated with a pyrethroid and non-pyrethroid insecticide are now in advanced development. Studies have demonstrated the potential of some of these nets to provide improved control of clinical malaria transmitted by pyrethroid-resistant malaria vectors compared to standard pyrethroid nets [42, 43, 44]. As next generation nets come into the market, and are rolled out in endemic countries, it will be essential to explore any possible interactions with the different types of IRS insecticides that could be deployed together with them in a combined intervention approach.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "The efficacy of combining a standard pyrethroid LLIN (DuraNet(r)) with IRS insecticides from three chemical classes (bendicocarb, chlorfenapyr and pirimiphos-methyl CS) was evaluated in an experimental hut trial against wild pyrethroid-resistant _Anopheles gambiae_ s.l. in Cove, Benin. The combinations were also compared to each intervention alone. WHO cylinder and CDC bottle bioassays were performed to assess susceptibility of the local _An. gambiae_ s.l. vector population at the Cove hut site to insecticides used in the combinations.\n",
-                        "\n",
-                        "header: Acknowledgments\n",
-                        "\n",
-                        "We thank BASF and Bayer for providing the insecticides. We also thank the technical staff of CREC (Abibath Odojo, Estelle Vigninou, Josias Fagbohoun, Edwige Kpanou etc) for their assistance. We are grateful to the rice farmers at Cove for their support in the hut study.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality rates of wild _An. gambiae_ s.l. from Cove in WHO susceptibility and CDC bottle bioassays are presented in Tables 1 and 2 respectively. Low proportions of adult F1 progeny of field-collected _An. gambiae_ s.l. were killed following exposure to alpha-cypermethrin (11%) and permethrin (20%); demonstrating pyrethroid resistance in the vector population. In contrast, the vast majority if not all mosquitoes from Cove were killed following exposure to bendiocarb (98%), fenitrothion (100%) and chlorfenapyr (100%); demonstrating susceptibility to carbamates, organophosphates and chlorfenapyr. With the insecticide susceptible _An. gambiae_ s.s. Kisumu colony, mortality was 100% with all insecticides.\n",
-                        "\n",
-                        "header: Table 2: Susceptibility of field-collected Anopheles gambiae sensu late from Cove to chlorfenapyr in CDC bottle bioassays.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment', 'Strain', 'N exposed', 'N dead', '% Mortality�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Control\",\"Strain\":\"Kisumu\",\"N exposed\":101,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"1\":{\"Treatment\":\"Control\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":100,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"2\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Kisumu\",\"N exposed\":103,\"N dead\":103,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"},\"3\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":102,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"}}\n",
-                        "\n",
-                        "header: Susceptibility bioassays\n",
-                        "\n",
-                        "WHO susceptibility cylinder bioassays [20] and CDC bottle bioassays [21] were performed in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove experimental hut site to the constituent insecticides in the hut treatments. Adult F1 progeny of field-collected _An. gambiae_ s.l. were exposed to filter papers treated with discriminating doses of alpha-cypermethrin (0.05%), permethrin (0.75%), bendiocarb (0.1%) and fenitrothion (1.0%) in WHO cylinders and CDC bottles impregnated with 100 mg of chlorfenapyr. Comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu colony. For each insecticide and strain, 100 adult female mosquitoes aged 2-3 days were introduced to four insecticide-treated cylinders/bottles in batches of 25. In the control arms, mosquitoes were exposed to silicone oil-treated filter papers and acetone-treated bottles. After 1 h exposure, mosquitoes were transferred to holding tubes and observation cups and provided access to 10% glucose solution (w/v). Mortality was recorded after 24 h for mosquitoes exposed to permethrin, alpha-cypermethrin, bendiocarb and fenitrothion in WHO cylinder bioassays and every 24 h up to 72 h for mosquitoes exposed to chlorfenapyr in CDC bottles. The insecticide treated filter papers used for the WHO cylinder bioassays were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Susceptibility bioassays revealed that the vector population at Cove, was resistant to pyrethroids (<20% mortality) but susceptible to carbamates, chlorfenapyr and organophosphates (>=98% mortality). Mortality of wild free-flying pyrethroid resistant _An. gambiae_ s.l. entering the hut with the untreated net control (4%) did not differ significantly from DuraNet(r) alone (8%, p = 0.169). Pirimiphos-methyl CS IRS induced the highest mortality both on its own (85%) and in combination with DuraNet(r) (81%). Mortality with the DuraNet(r) + chlorfenapyr IRS combination was significantly higher than each intervention alone (46% vs. 33% and 8%, p<0.05) demonstrating an additive effect. The DuraNet(r) + bendicocarb IRS combination induced significantly lower mortality compared to the other combinations (32%, p<0.05). Blood-feeding inhibition was very low with the IRS treatments alone (3-5%) but \n",
-                        "increased significantly when they were combined with DuraNet(r) (61% - 71%, p<0.05). Blood-feeding rates in the combinations were similar to the net alone. Adding bendicorarb IRS to DuraNet(r) induced significantly lower levels of mosquito feeding compared to adding chlorfenapyr IRS (28% vs. 37%, p = 0.015).\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only LLINs against a pyrethroid-resistant vector population which is susceptible to the IRS insecticide, can provide higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone. Adding pirimphos-methyl CS IRS may provide substantial improvements in vector control while adding chlorfenapyr IRS can demonstrate an additive effect relative to both interventions alone. Adding bendicorarb IRS may show limited enhancements in vector control owing to its short residual effect.\n",
-                        "\n",
-                        "header: Table 4. Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total blood-fed', '% Blood-fed*', '95% CI', '% Blood-feeding inhibition', '% Personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total blood-fed\":675,\"% Blood-fed*\":\"95a\",\"95% CI\":\"93\\u201397\",\"% Blood-feeding inhibition\":\"\\u2013\",\"% Personal protection\":\"\\u2013\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total blood-fed\":231,\"% Blood-fed*\":\"55b\",\"95% CI\":\"50\\u201360\",\"% Blood-feeding inhibition\":\"42\",\"% Personal protection\":\"66\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total blood-fed\":202,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201336\",\"% Blood-feeding inhibition\":\"66\",\"% Personal protection\":\"70\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total blood-fed\":460,\"% Blood-fed*\":\"91e\",\"95% CI\":\"88\\u201393\",\"% Blood-feeding inhibition\":\"5\",\"% Personal protection\":\"32\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total blood-fed\":150,\"% Blood-fed*\":\"28c\",\"95% CI\":\"24\\u201332\",\"% Blood-feeding inhibition\":\"71\",\"% Personal protection\":\"78\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total blood-fed\":440,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201394\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"35\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total blood-fed\":153,\"% Blood-fed*\":\"37d\",\"95% CI\":\"33\\u201342\",\"% Blood-feeding inhibition\":\"61\",\"% Personal protection\":\"77\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total blood-fed\":488,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201395\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"28\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total blood-fed\":160,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201337\",\"% Blood-feeding inhibition\":\"65\",\"% Personal protection\":\"76\"}}\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only nets against a pyrethroid-resistant vector population which was susceptible to the IRS insecticide, generally provided higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone; the IRS intervention was responsible for improved mosquito mortality while the pyrethroid net provided blood-feeding inhibition. The combination with pirimiphos-methyl CS IRS was the most efficacious. Adding chlorfenapyr IRS to the pyrethroid LLIN demonstrated an additive mortality effect relative to both interventions alone. The combination with benidocarb IRS provided the \n",
-                        "lowest levels of mortality owing to its short residual effect but also induced lower levels of mosquito feeding compared to combinations with the other IRS insecticides.\n",
-                        "\n",
-                        "header: Ethical considerations\n",
-                        "\n",
-                        "Ethical approval was obtained from the ethics review committees of the London School of Hygiene & Tropical Medicine (LSHTM) (Reference No. 15265) and the local Ministry of Health in Benin (Reference No. N'39/CEIC/CREC). All volunteers provided informed written consent and were offered a course of chemoprophylaxis (doxycycline) prior to their participation in the study and up to four weeks following its completion. A nurse was available throughout the study to assess any cases of fever. In the event a volunteer tested positive for malaria, they were immediately withdrawn from the trial and administered effective treatment.\n",
-                        "\n",
-                        "header: Outcome measures\n",
-                        "\n",
-                        "The efficacy of each experimental hut treatment was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "* Exophily-exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda\n",
-                        "* Deterrence-defined as the percentage reduction in numbers of mosquitoes collected in a treated hut relative to the untreated control\n",
-                        "* Blood-feeding-proportion of blood-fed mosquitoes at the time of collection\n",
-                        "\n",
-                        "Where _B__\\({}_{\\mu}\\)_ is the proportion of blood-fed mosquitoes in the untreated control and _B__\\({}_{\\beta}\\)_ is the proportion of blood-fed mosquitoes in the treated hut.\n",
-                        "\n",
-                        "\n",
-                        "- B_{i})}{B_{u}}\\] Where \\(B_{u}\\) is the number of blood-fed mosquitoes in the untreated control and \\(B_{i}\\) is the number of blood-fed mosquitoes in the huts with insecticide treatments.\n",
-                        "* Mortality-proportion of dead mosquitoes after a 72 h holding period Mosquito deterrence, blood-feeding inhibition and personal protection were calculated relative to the untreated net control hut for treatments which involved the LLIN and relative to the untreated hut control for IRS-only treatments.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are core interventions for preventing and controlling malaria [1]. Both methods have independently proven to be highly effective in reducing the burden of disease in diverse epidemiological settings. The substantial reductions in malaria morbidity and mortality observed in endemic countries over the last two decades, has been attributed to a significant increase in the roll out of ITNs and IRS during this period [2]. Where resources are available, both interventions have been deployed together in the same geographical location, with the primary aim of reducing the disease burden to a greater extent than could be achieved with either method alone [3,4].\n",
-                        "\n",
-                        "The benefits of combining these interventions is, however, contentious. Some community-randomised controlled trials (RCTs) have compared epidemiological outcomes in communities receiving pyrethroid long-lasting insecticidal nets (LLINs) plus IRS versus LLINs alone, yielding conflicting results. Whilst trials in Tanzania [5] and Sudan [6] associated combined use of LLINs and IRS with significant reductions in malaria infection prevalence, similar studies in Benin [7], Eritrea [8], Ethiopia [9] and The Gambla [10] reported no such effect. Based on a Cochrane review demonstrating the inconsistencies in epidemiological evidence [4], the World Health Organisation (WHO) has recently issued a provisional recommendation against combining LLINs and IRS as a means of reducing malaria-associated morbidity and mortality [1]. National Malaria Control Programmes are encouraged to prioritise delivering either ITNs or IRS at high coverage and to a high standard, rather than introducing the second intervention as a means to compensate for deficiencies in the implementation of the first. However, this should not be interpreted to mean that the combined IRS and ITN approach is redundant in all settings. Considering the recent stall in progress against malaria [11] and increasing prevalence of pyrethroid resistance in malaria vector populations across Africa [12], combining interventions may be vital for high transmission settings and insecticide resistance management. Vector control programmes are expected to be guided by local evidence to decide when, where and how to combine IRS with ITNs for different epidemiological settings [1].\n",
-                        "\n",
-                        "Evidence from RCTs and operational studies suggests that the impact of co-implementing LLINs and IRS may depend on a number of location-specific factors including: intervention coverage, transmission intensity, vector behaviour, choice of IRS insecticide and insecticide resistance [13,14]. The choice of IRS insecticide for a specific geographical setting whether deployed alone or together with LLINs will be influenced largely by the susceptibility of the target vector population to the insecticide, its mode of action, chemical properties and residual \n",
-                        "activity on wall substrates. To maximise the impact of the combined intervention approach, it is important to consider the interactions that may occur between the insecticide in the LLIN and the chosen IRS insecticide. Where the mode of action of the IRS insecticides is not complementary to the insecticide on the net, the combination might be redundant or result in lower levels of vector control than what is achievable with either intervention alone. Although pyrethroids are not recommended for IRS, the redundancy of combining pyrethroid IRS or wall linings with pyrethroid LLINs has been clearly demonstrated in experimental but studies against pyrethroid-resistant malaria vectors in West Africa [15,16].\n",
-                        "\n",
-                        "Insecticides belonging to four classes of compounds have been approved by the WHO for IRS against malaria vectors; pyrethroids, organophosphates, carbamates and more recently, the neonicotinoid clothianidin [1,17]. While RCTs are the most robust method for generating evidence on the impact of combining vector control interventions, they are often time-consuming, expensive and unrealistic in some settings. Evidence from experimental but trials may provide some insights on what to expect when different types IRS insecticides are deployed to complement pyrethroid-only nets. In this study, we compared the impact of combining pyrethroid LLINs with IRS insecticides from three distinct chemical classes in experimental huts against wild free-flying pyrethroid-resistant malaria vectors in southern Benin.\n",
-                        "\n",
-                        "header: Study site and experimental huts: Experimental hut treatments\n",
-                        "\n",
-                        "The following treatments were assessed in 9 experimental huts:\n",
-                        "\n",
-                        "1. Control (untreated hut)\n",
-                        "2. Control (untreated net)\n",
-                        "3. DuraNet(r) (Alpha-cypermethrin LLIN, 261 mg/m2, Shobikaa Impex)\n",
-                        "4. Bendiocarb IRS applied at 400 mg/m2 (Ficam(r) WP, Bayer)\n",
-                        "5. DuraNet(r) + Bendiocarb IRS applied at 400 mg/m2\n",
-                        "6. Chlorfenapyr IRS applied at 250 mg/m2 (Sylando(r) 240SC, BASF)\n",
-                        "7. DuraNet(r) + Chlorfenapryr IRS applied at 250 mg/m2\n",
-                        "8. Pirimiphos-methyl CS IRS applied at 1000 mg/m2 (Actellic(r) 300CS, Syngenta)\n",
-                        "9. DuraNet(r) + Pirimiphos-methyl CS IRS applied at 1000 mg/m2\n",
-                        "\n",
-                        "Interior hut walls were sprayed from top to bottom with IRS insecticide solutions using a Hudson X-pert(r) compression sprayer equipped with a flat-fan nozzle. To improve spraying accuracy, spray swaths were pre-marked on hut walls and a guidance pole was attached to the end of the spray lance to maintain a fixed distance to the wall. After spraying each hut, the volume remaining in the spray tank was measured to assess the overall volume sprayed. All spray volumes were within 30% of the target. The palm match used on the ceiling was sprayed flat on the ground outside the experimental hut and allowed to dry for 2 hours prior to being fitted on the ceiling of the hut. Three replicate nets were used per LLIN treatment and these were rotated every 2 nights within the same hut. To simulate wear and tear from field use, 6 holes each measuring 16 cm2 were cut into each bed net (one per panel) used in the trial.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental hut treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to differential attractiveness of the volunteer sleepers and huts.\n",
-                        "\n",
-                        "\n",
-                        "Mortality in susceptibility bioassays was interpreted according to WHO criteria. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Trial procedure\n",
-                        "\n",
-                        "The trial began three days after IRS application and continued for 4 months, between July and October 2018 and followed WHO guidelines [19]. Nine (9) consenting human volunteers kept in experimental huts each trial night to attract mosquitoes. To account for bias due to differential attractiveness to mosquitoes, volunteers were rotated through experimental huts daily in accordance with a predetermined Latin square design. To mitigate the effect of hut position on mosquito entry, bed nets were rotated through experimental huts on a weekly basis. IRS treatments could not be rotated thus remained fixed throughout the trial. At dawn, sleepers collected all mosquitoes from under the net, the sleeping room and the veranda using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were subsequently transferred to the laboratory for morphological identification and scoring of blood-feeding and mortality. To account for the slow-action of chlorfenapyr, delayed mortality was recorded every 24 h up to 72 h after collection for all treatments. All alive _An. gambiae_ s.l. were held at 27 +- 2deg C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution.\n",
-                        "\n",
-                        "header: Susceptibility bioassay results: Experimental hut results\n",
-                        "\n",
-                        "A total of 4,698 female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trial period (Tables 3 and S1). Due to the inability to\n",
-                        "\n",
-                        "rotate IRS, it was not possible to distinguish treatment-induced deterrence for the IRS treatments from differential attractiveness due to hut position. The proportion of mosquitoes exiting into the veranda for the untreated hut and untreated net controls were 45% and 39% respectively. Exiting was significantly higher in all experimental hut treatments relative to both controls (p<0.01), suggesting the insecticide treatments induced an irritant response which caused mosquitoes to seek refuge in the veranda. The highest level of mosquito exiting was achieved with beniodicarb IRS alone (77%). Adding beniodicarb IRS to DuraNet(r) also induced significantly higher levels of mosquito exiting compared to DuraNet(r) alone (57% with DuraNet(r) vs. 70% with DuraNet(r) + beniodicarb IRS, p = 0.001). This effect was, however, not observed with chlorfenapyr IRS and pirimiphos-methyl CS IRS; exiting rates were similar between the combinations with these IRS insecticides and DuraNet(r) alone, (59% - 61% vs 57%, p>0.05).\n",
-                        "\n",
-                        "**Blood-feeding and personal protection.** Blood-feeding and personal protection results of wild, pyrethroid-resistant _An. gambiae_ s.l. in the experimental huts are presented in Table 4. Blood-feeding rates with the untreated hut and untreated net controls were 95% and 55% respectively. DuraNet(r) induced significantly lower blood-feeding rates than the control untreated net (33% with DuraNet(r) vs. 55% with untreated net, p<0.001). Meanwhile, blood-feeding was high across all IRS treatments (91-92%) thus providing very limited blood-feeding inhibition (3% - 5%) compared to the untreated hut control (Table 4). All three combinations provided significantly higher levels of blood-feeding inhibition relative to the IRS treatments\n",
-                        "\n",
-                        "alone (60% - 71% vs. 3% - 5%, p<0.001). Between the combinations, DuraNet(r) + bendiocarb IRS induced the lowest blood-feeding rate (28%) and offered personal protection of 78% while DuraNet(r) + chlorfenapyr IRS induced a higher blood-feeding rate (37%, p = 0.015) though providing about the same levels of personal protection (77%).\n",
-                        "\n",
-                        "**Mortality rates in experimental huts.** Overall mortality rates of wild, pyrethroid-resistant _An. gambiae_ s.l. in the untreated hut and untreated net controls were 2% and 4% respectively (Table 5). Mortality with DuraNet(r) was low and did not differ significantly from the untreated net control (8% vs. 4%, p = 0.169). The IRS treatments alone and the combinations induced significantly higher mortality than DuraNet(r) and the untreated controls (p < 0.001); adding any IRS treatment onto DuraNet(r) therefore always significantly improved vector mortality compared to DuraNet(r) alone. Between the IRS only treatments, the highest mortality was achieved with pirimiphos-methyl CS (85%, p < 0.001) while chlorfenapyr and bendiocarb induced fairly similar mortality rates (34% and 28%, p = 0.09). Combining DuraNet(r) with bendiocarb and pirimiphos-methyl CS IRS treatments did not improve mortality compared to the corresponding IRS alone (bendiocarb: 28% vs. 32%, p = 0.265 and pirimiphos-methyl CS: 85% vs. 81%, p = 0.129) (Table 5). With chlorfenapyr IRS, mortality was significantly higher in the combination compared to chlorfenapyr IRS alone over the 4-month trial (34% vs. 46%, p < 0.001) (Fig 3). Between the combinations, the highest mortality was achieved with DuraNet(r) + pirimiphos-methyl CS IRS (81%). Mortality was significantly higher with the DuraNet(r) + chlorfenapyr IRS combination compared to DuraNet(r) + bendiocarb IRS (46% vs. 32%, p < 0.001) (Table 5).\n",
-                        "\n",
-                        "Initial monthly wild mosquito mortality with pirimiphos-methyl CS IRS and chlorfenapyr IRS was steady and did not decline substantially over the course of the 4 months trial both when applied alone and in combination with DuraNet(r) (Figs 1 and 2). By contrast, wild mosquito mortality with bendiocarb IRS declined sharply from ~55% to about 28-32% by the last month of the trial both when applied alone and in combination with DuraNet(r) (Fig 3).\n",
-                        "\n",
-                        "**Wall cone bioassay results.** Mortality rates of the laboratory maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu colony following exposure to IRS-treated walls in monthly, 30 mins wall cone bioassays are presented in Fig 4 with more details in supporting information (S2 Table). On pirimiphos-methyl CS-treated surfaces, cone bioassay mortality was very high (> 85%) throughout all 4 months of the trial. As observed with wild free-flying mosquitoes, a progressive decline in cone bioassay mortality with increasing months elapsed from spraying was observed with bendiocarb, beginning at 63% in month 1 and decreasing to 19% by month 4. Cone bioassay mortality with chlorfenapyr IRS was consistently low throughout the trial,\n",
-                        "ranging from 11-31%. Cone bioassay mortality on untreated control walls did not exceed 10% in any month of the trial (Fig 4).\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Which indoor residual spraying insecticide best complements standard pyrethroid long-lasting insecticidal nets for improved control of pyrethroid resistant malaria vectors?\n",
-                        "\n",
-                        "Thomas Syme, Augustin Fongnikin, Damien Todjinou, Renaud Goveetchan, Mark Rowland, Martin Akogbeto, Corine Nguforo\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Where resources are available, non-pyrethroid IRS can be deployed to complement standard pyrethroid LLINs with the aim of achieving improved vector control and managing insecticide resistance. The impact of the combination may however depend on the type of IRS insecticide deployed. Studies comparing combinations of pyrethroid LLINs with different types of non-pyrethroid IRS products will be necessary for decision making.\n",
-                        "\n",
-                        "header: Table 1: WHO susceptibility cylinder bioassay results with Anopheles gambiae sensu late from Cove.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment', 'Strain', 'N exposed', 'N dead', '% Mortality', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Control\",\"Strain\":\"Kisumu\",\"N exposed\":102,\"N dead\":0,\"% Mortality\":0,\"95% CI\":\"\\u2013\"},\"1\":{\"Treatment\":null,\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":0,\"% Mortality\":0,\"95% CI\":\"\\u2013\"},\"2\":{\"Treatment\":\"Alpha-cypermethrin (0.05%)\",\"Strain\":\"Kisumu\",\"N exposed\":102,\"N dead\":102,\"% Mortality\":100,\"95% CI\":\"\\u2013\"},\"3\":{\"Treatment\":null,\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":11,\"% Mortality\":11,\"95% CI\":\"5\\u201317\"},\"4\":{\"Treatment\":\"Permethrin (0.75%)\",\"Strain\":\"Kisumu\",\"N exposed\":104,\"N dead\":104,\"% Mortality\":100,\"95% CI\":\"\\u2013\"},\"5\":{\"Treatment\":null,\"Strain\":\"Cov\\u00e8\",\"N exposed\":105,\"N dead\":21,\"% Mortality\":20,\"95% CI\":\"12\\u201328\"},\"6\":{\"Treatment\":\"Bendiocarb (0.1%)\",\"Strain\":\"Kisumu\",\"N exposed\":104,\"N dead\":104,\"% Mortality\":100,\"95% CI\":\"\\u2013\"},\"7\":{\"Treatment\":null,\"Strain\":\"Cov\\u00e8\",\"N exposed\":100,\"N dead\":96,\"% Mortality\":98,\"95% CI\":\"96\\u2013100\"},\"8\":{\"Treatment\":\"Fenitrothion (1%)\",\"Strain\":\"Kisumu\",\"N exposed\":103,\"N dead\":103,\"% Mortality\":100,\"95% CI\":\"\\u2013\"},\"9\":{\"Treatment\":null,\"Strain\":\"Cov\\u00e8\",\"N exposed\":101,\"N dead\":101,\"% Mortality\":100,\"95% CI\":\"\\u2013\"}}\n",
-                        "\n",
-                        "header: Table 3. Overall entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total females caught', 'Average catch per night', '% Deterrence', 'Total exiting', '% Exophily�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total females caught\":711,\"Average catch per night\":7,\"% Deterrence\":\"\\u2013\",\"Total exiting\":322,\"% Exophily\\ufffd\":\"45a\",\"95% CI\":\"42\\u201349\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total females caught\":420,\"Average catch per night\":4,\"% Deterrence\":\"41\",\"Total exiting\":165,\"% Exophily\\ufffd\":\"39a\",\"95% CI\":\"35\\u201344\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total females caught\":619,\"Average catch per night\":6,\"% Deterrence\":\"13\",\"Total exiting\":350,\"% Exophily\\ufffd\":\"57b\",\"95% CI\":\"53\\u201361\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":508,\"Average catch per night\":5,\"% Deterrence\":\"29\",\"Total exiting\":389,\"% Exophily\\ufffd\":\"77c\",\"95% CI\":\"73\\u201380\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total females caught\":537,\"Average catch per night\":6,\"% Deterrence\":\"25\",\"Total exiting\":373,\"% Exophily\\ufffd\":\"70cd\",\"95% CI\":\"66\\u201373\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total females caught\":478,\"Average catch per night\":5,\"% Deterrence\":\"33\",\"Total exiting\":297,\"% Exophily\\ufffd\":\"62bd\",\"95% CI\":\"58\\u201367\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total females caught\":411,\"Average catch per night\":4,\"% Deterrence\":\"42\",\"Total exiting\":241,\"% Exophily\\ufffd\":\"59b\",\"95% CI\":\"54\\u201363\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total females caught\":528,\"Average catch per night\":6,\"% Deterrence\":\"26\",\"Total exiting\":356,\"% Exophily\\ufffd\":\"67d\",\"95% CI\":\"63\\u201371\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total females caught\":486,\"Average catch per night\":5,\"% Deterrence\":\"32\",\"Total exiting\":298,\"% Exophily\\ufffd\":\"61bd\",\"95% CI\":\"57\\u201366\"}}\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study was conducted at the CREC/LSHTM experimental hut station located in Cove, southern Benin (7deg14degN2deg18degE). The experimental huts are situated in a vast area of rice irrigation, which provide extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzeii_ and _An. gambiae_ sensu stricto (s.s.) occur in sympatry, with the latter present at lower densities and predominantly in the dry season [18]. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold) [18]. Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (>90%) and overexpression of the cytochrome P450, CYP6P3, associated with pyrethroid detoxification [18]. The experimental huts used were of the West African design [19], made from concrete bricks with a corrugated iron roof and a ceiling with palm-thatch mats. Interior walls were cement plastered and each hut was constructed on a concrete base containing a water-filled moat to preclude predators. Entry of mosquitoes occurred via four window slits, 1 cm wide and 30 cm long, positioned on two sides of the hut. A wooden framed veranda made of polyethylene sheeting and screening mesh projected from the rear wall of each hut to capture exiting mosquitoes.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Acknowledgments\n",
-                        "\n",
-                        "We thank BASF and Bayer for providing the insecticides. We also thank the technical staff of CREC (Abibath Odojo, Estelle Vigninou, Josias Fagbohoun, Edwige Kpanou etc) for their assistance. We are grateful to the rice farmers at Cove for their support in the hut study.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "The efficacy of combining a standard pyrethroid LLIN (DuraNet(r)) with IRS insecticides from three chemical classes (bendicocarb, chlorfenapyr and pirimiphos-methyl CS) was evaluated in an experimental hut trial against wild pyrethroid-resistant _Anopheles gambiae_ s.l. in Cove, Benin. The combinations were also compared to each intervention alone. WHO cylinder and CDC bottle bioassays were performed to assess susceptibility of the local _An. gambiae_ s.l. vector population at the Cove hut site to insecticides used in the combinations.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Susceptibility bioassays revealed that the vector population at Cove, was resistant to pyrethroids (<20% mortality) but susceptible to carbamates, chlorfenapyr and organophosphates (>=98% mortality). Mortality of wild free-flying pyrethroid resistant _An. gambiae_ s.l. entering the hut with the untreated net control (4%) did not differ significantly from DuraNet(r) alone (8%, p = 0.169). Pirimiphos-methyl CS IRS induced the highest mortality both on its own (85%) and in combination with DuraNet(r) (81%). Mortality with the DuraNet(r) + chlorfenapyr IRS combination was significantly higher than each intervention alone (46% vs. 33% and 8%, p<0.05) demonstrating an additive effect. The DuraNet(r) + bendicocarb IRS combination induced significantly lower mortality compared to the other combinations (32%, p<0.05). Blood-feeding inhibition was very low with the IRS treatments alone (3-5%) but \n",
-                        "increased significantly when they were combined with DuraNet(r) (61% - 71%, p<0.05). Blood-feeding rates in the combinations were similar to the net alone. Adding bendicorarb IRS to DuraNet(r) induced significantly lower levels of mosquito feeding compared to adding chlorfenapyr IRS (28% vs. 37%, p = 0.015).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality rates of wild _An. gambiae_ s.l. from Cove in WHO susceptibility and CDC bottle bioassays are presented in Tables 1 and 2 respectively. Low proportions of adult F1 progeny of field-collected _An. gambiae_ s.l. were killed following exposure to alpha-cypermethrin (11%) and permethrin (20%); demonstrating pyrethroid resistance in the vector population. In contrast, the vast majority if not all mosquitoes from Cove were killed following exposure to bendiocarb (98%), fenitrothion (100%) and chlorfenapyr (100%); demonstrating susceptibility to carbamates, organophosphates and chlorfenapyr. With the insecticide susceptible _An. gambiae_ s.s. Kisumu colony, mortality was 100% with all insecticides.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only LLINs against a pyrethroid-resistant vector population which is susceptible to the IRS insecticide, can provide higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone. Adding pirimphos-methyl CS IRS may provide substantial improvements in vector control while adding chlorfenapyr IRS can demonstrate an additive effect relative to both interventions alone. Adding bendicorarb IRS may show limited enhancements in vector control owing to its short residual effect.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only nets against a pyrethroid-resistant vector population which was susceptible to the IRS insecticide, generally provided higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone; the IRS intervention was responsible for improved mosquito mortality while the pyrethroid net provided blood-feeding inhibition. The combination with pirimiphos-methyl CS IRS was the most efficacious. Adding chlorfenapyr IRS to the pyrethroid LLIN demonstrated an additive mortality effect relative to both interventions alone. The combination with benidocarb IRS provided the \n",
-                        "lowest levels of mortality owing to its short residual effect but also induced lower levels of mosquito feeding compared to combinations with the other IRS insecticides.\n",
-                        "\n",
-                        "header: Susceptibility bioassays\n",
-                        "\n",
-                        "WHO susceptibility cylinder bioassays [20] and CDC bottle bioassays [21] were performed in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove experimental hut site to the constituent insecticides in the hut treatments. Adult F1 progeny of field-collected _An. gambiae_ s.l. were exposed to filter papers treated with discriminating doses of alpha-cypermethrin (0.05%), permethrin (0.75%), bendiocarb (0.1%) and fenitrothion (1.0%) in WHO cylinders and CDC bottles impregnated with 100 mg of chlorfenapyr. Comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu colony. For each insecticide and strain, 100 adult female mosquitoes aged 2-3 days were introduced to four insecticide-treated cylinders/bottles in batches of 25. In the control arms, mosquitoes were exposed to silicone oil-treated filter papers and acetone-treated bottles. After 1 h exposure, mosquitoes were transferred to holding tubes and observation cups and provided access to 10% glucose solution (w/v). Mortality was recorded after 24 h for mosquitoes exposed to permethrin, alpha-cypermethrin, bendiocarb and fenitrothion in WHO cylinder bioassays and every 24 h up to 72 h for mosquitoes exposed to chlorfenapyr in CDC bottles. The insecticide treated filter papers used for the WHO cylinder bioassays were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Table 4. Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total blood-fed', '% Blood-fed*', '95% CI', '% Blood-feeding inhibition', '% Personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total blood-fed\":675,\"% Blood-fed*\":\"95a\",\"95% CI\":\"93\\u201397\",\"% Blood-feeding inhibition\":\"\\u2013\",\"% Personal protection\":\"\\u2013\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total blood-fed\":231,\"% Blood-fed*\":\"55b\",\"95% CI\":\"50\\u201360\",\"% Blood-feeding inhibition\":\"42\",\"% Personal protection\":\"66\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total blood-fed\":202,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201336\",\"% Blood-feeding inhibition\":\"66\",\"% Personal protection\":\"70\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total blood-fed\":460,\"% Blood-fed*\":\"91e\",\"95% CI\":\"88\\u201393\",\"% Blood-feeding inhibition\":\"5\",\"% Personal protection\":\"32\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total blood-fed\":150,\"% Blood-fed*\":\"28c\",\"95% CI\":\"24\\u201332\",\"% Blood-feeding inhibition\":\"71\",\"% Personal protection\":\"78\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total blood-fed\":440,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201394\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"35\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total blood-fed\":153,\"% Blood-fed*\":\"37d\",\"95% CI\":\"33\\u201342\",\"% Blood-feeding inhibition\":\"61\",\"% Personal protection\":\"77\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total blood-fed\":488,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201395\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"28\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total blood-fed\":160,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201337\",\"% Blood-feeding inhibition\":\"65\",\"% Personal protection\":\"76\"}}\n",
-                        "\n",
-                        "header: Ethical considerations\n",
-                        "\n",
-                        "Ethical approval was obtained from the ethics review committees of the London School of Hygiene & Tropical Medicine (LSHTM) (Reference No. 15265) and the local Ministry of Health in Benin (Reference No. N'39/CEIC/CREC). All volunteers provided informed written consent and were offered a course of chemoprophylaxis (doxycycline) prior to their participation in the study and up to four weeks following its completion. A nurse was available throughout the study to assess any cases of fever. In the event a volunteer tested positive for malaria, they were immediately withdrawn from the trial and administered effective treatment.\n",
-                        "\n",
-                        "header: Outcome measures\n",
-                        "\n",
-                        "The efficacy of each experimental hut treatment was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "* Exophily-exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda\n",
-                        "* Deterrence-defined as the percentage reduction in numbers of mosquitoes collected in a treated hut relative to the untreated control\n",
-                        "* Blood-feeding-proportion of blood-fed mosquitoes at the time of collection\n",
-                        "\n",
-                        "Where _B__\\({}_{\\mu}\\)_ is the proportion of blood-fed mosquitoes in the untreated control and _B__\\({}_{\\beta}\\)_ is the proportion of blood-fed mosquitoes in the treated hut.\n",
-                        "\n",
-                        "\n",
-                        "- B_{i})}{B_{u}}\\] Where \\(B_{u}\\) is the number of blood-fed mosquitoes in the untreated control and \\(B_{i}\\) is the number of blood-fed mosquitoes in the huts with insecticide treatments.\n",
-                        "* Mortality-proportion of dead mosquitoes after a 72 h holding period Mosquito deterrence, blood-feeding inhibition and personal protection were calculated relative to the untreated net control hut for treatments which involved the LLIN and relative to the untreated hut control for IRS-only treatments.\n",
-                        "\n",
-                        "header: Table 2: Susceptibility of field-collected Anopheles gambiae sensu late from Cove to chlorfenapyr in CDC bottle bioassays.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment', 'Strain', 'N exposed', 'N dead', '% Mortality�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Control\",\"Strain\":\"Kisumu\",\"N exposed\":101,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"1\":{\"Treatment\":\"Control\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":100,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"2\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Kisumu\",\"N exposed\":103,\"N dead\":103,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"},\"3\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":102,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"}}\n",
-                        "\n",
-                        "header: Study site and experimental huts: Experimental hut treatments\n",
-                        "\n",
-                        "The following treatments were assessed in 9 experimental huts:\n",
-                        "\n",
-                        "1. Control (untreated hut)\n",
-                        "2. Control (untreated net)\n",
-                        "3. DuraNet(r) (Alpha-cypermethrin LLIN, 261 mg/m2, Shobikaa Impex)\n",
-                        "4. Bendiocarb IRS applied at 400 mg/m2 (Ficam(r) WP, Bayer)\n",
-                        "5. DuraNet(r) + Bendiocarb IRS applied at 400 mg/m2\n",
-                        "6. Chlorfenapyr IRS applied at 250 mg/m2 (Sylando(r) 240SC, BASF)\n",
-                        "7. DuraNet(r) + Chlorfenapryr IRS applied at 250 mg/m2\n",
-                        "8. Pirimiphos-methyl CS IRS applied at 1000 mg/m2 (Actellic(r) 300CS, Syngenta)\n",
-                        "9. DuraNet(r) + Pirimiphos-methyl CS IRS applied at 1000 mg/m2\n",
-                        "\n",
-                        "Interior hut walls were sprayed from top to bottom with IRS insecticide solutions using a Hudson X-pert(r) compression sprayer equipped with a flat-fan nozzle. To improve spraying accuracy, spray swaths were pre-marked on hut walls and a guidance pole was attached to the end of the spray lance to maintain a fixed distance to the wall. After spraying each hut, the volume remaining in the spray tank was measured to assess the overall volume sprayed. All spray volumes were within 30% of the target. The palm match used on the ceiling was sprayed flat on the ground outside the experimental hut and allowed to dry for 2 hours prior to being fitted on the ceiling of the hut. Three replicate nets were used per LLIN treatment and these were rotated every 2 nights within the same hut. To simulate wear and tear from field use, 6 holes each measuring 16 cm2 were cut into each bed net (one per panel) used in the trial.\n",
-                        "\n",
-                        "header: Susceptibility bioassay results: Experimental hut results\n",
-                        "\n",
-                        "A total of 4,698 female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trial period (Tables 3 and S1). Due to the inability to\n",
-                        "\n",
-                        "rotate IRS, it was not possible to distinguish treatment-induced deterrence for the IRS treatments from differential attractiveness due to hut position. The proportion of mosquitoes exiting into the veranda for the untreated hut and untreated net controls were 45% and 39% respectively. Exiting was significantly higher in all experimental hut treatments relative to both controls (p<0.01), suggesting the insecticide treatments induced an irritant response which caused mosquitoes to seek refuge in the veranda. The highest level of mosquito exiting was achieved with beniodicarb IRS alone (77%). Adding beniodicarb IRS to DuraNet(r) also induced significantly higher levels of mosquito exiting compared to DuraNet(r) alone (57% with DuraNet(r) vs. 70% with DuraNet(r) + beniodicarb IRS, p = 0.001). This effect was, however, not observed with chlorfenapyr IRS and pirimiphos-methyl CS IRS; exiting rates were similar between the combinations with these IRS insecticides and DuraNet(r) alone, (59% - 61% vs 57%, p>0.05).\n",
-                        "\n",
-                        "**Blood-feeding and personal protection.** Blood-feeding and personal protection results of wild, pyrethroid-resistant _An. gambiae_ s.l. in the experimental huts are presented in Table 4. Blood-feeding rates with the untreated hut and untreated net controls were 95% and 55% respectively. DuraNet(r) induced significantly lower blood-feeding rates than the control untreated net (33% with DuraNet(r) vs. 55% with untreated net, p<0.001). Meanwhile, blood-feeding was high across all IRS treatments (91-92%) thus providing very limited blood-feeding inhibition (3% - 5%) compared to the untreated hut control (Table 4). All three combinations provided significantly higher levels of blood-feeding inhibition relative to the IRS treatments\n",
-                        "\n",
-                        "alone (60% - 71% vs. 3% - 5%, p<0.001). Between the combinations, DuraNet(r) + bendiocarb IRS induced the lowest blood-feeding rate (28%) and offered personal protection of 78% while DuraNet(r) + chlorfenapyr IRS induced a higher blood-feeding rate (37%, p = 0.015) though providing about the same levels of personal protection (77%).\n",
-                        "\n",
-                        "**Mortality rates in experimental huts.** Overall mortality rates of wild, pyrethroid-resistant _An. gambiae_ s.l. in the untreated hut and untreated net controls were 2% and 4% respectively (Table 5). Mortality with DuraNet(r) was low and did not differ significantly from the untreated net control (8% vs. 4%, p = 0.169). The IRS treatments alone and the combinations induced significantly higher mortality than DuraNet(r) and the untreated controls (p < 0.001); adding any IRS treatment onto DuraNet(r) therefore always significantly improved vector mortality compared to DuraNet(r) alone. Between the IRS only treatments, the highest mortality was achieved with pirimiphos-methyl CS (85%, p < 0.001) while chlorfenapyr and bendiocarb induced fairly similar mortality rates (34% and 28%, p = 0.09). Combining DuraNet(r) with bendiocarb and pirimiphos-methyl CS IRS treatments did not improve mortality compared to the corresponding IRS alone (bendiocarb: 28% vs. 32%, p = 0.265 and pirimiphos-methyl CS: 85% vs. 81%, p = 0.129) (Table 5). With chlorfenapyr IRS, mortality was significantly higher in the combination compared to chlorfenapyr IRS alone over the 4-month trial (34% vs. 46%, p < 0.001) (Fig 3). Between the combinations, the highest mortality was achieved with DuraNet(r) + pirimiphos-methyl CS IRS (81%). Mortality was significantly higher with the DuraNet(r) + chlorfenapyr IRS combination compared to DuraNet(r) + bendiocarb IRS (46% vs. 32%, p < 0.001) (Table 5).\n",
-                        "\n",
-                        "Initial monthly wild mosquito mortality with pirimiphos-methyl CS IRS and chlorfenapyr IRS was steady and did not decline substantially over the course of the 4 months trial both when applied alone and in combination with DuraNet(r) (Figs 1 and 2). By contrast, wild mosquito mortality with bendiocarb IRS declined sharply from ~55% to about 28-32% by the last month of the trial both when applied alone and in combination with DuraNet(r) (Fig 3).\n",
-                        "\n",
-                        "**Wall cone bioassay results.** Mortality rates of the laboratory maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu colony following exposure to IRS-treated walls in monthly, 30 mins wall cone bioassays are presented in Fig 4 with more details in supporting information (S2 Table). On pirimiphos-methyl CS-treated surfaces, cone bioassay mortality was very high (> 85%) throughout all 4 months of the trial. As observed with wild free-flying mosquitoes, a progressive decline in cone bioassay mortality with increasing months elapsed from spraying was observed with bendiocarb, beginning at 63% in month 1 and decreasing to 19% by month 4. Cone bioassay mortality with chlorfenapyr IRS was consistently low throughout the trial,\n",
-                        "ranging from 11-31%. Cone bioassay mortality on untreated control walls did not exceed 10% in any month of the trial (Fig 4).\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are core interventions for preventing and controlling malaria [1]. Both methods have independently proven to be highly effective in reducing the burden of disease in diverse epidemiological settings. The substantial reductions in malaria morbidity and mortality observed in endemic countries over the last two decades, has been attributed to a significant increase in the roll out of ITNs and IRS during this period [2]. Where resources are available, both interventions have been deployed together in the same geographical location, with the primary aim of reducing the disease burden to a greater extent than could be achieved with either method alone [3,4].\n",
-                        "\n",
-                        "The benefits of combining these interventions is, however, contentious. Some community-randomised controlled trials (RCTs) have compared epidemiological outcomes in communities receiving pyrethroid long-lasting insecticidal nets (LLINs) plus IRS versus LLINs alone, yielding conflicting results. Whilst trials in Tanzania [5] and Sudan [6] associated combined use of LLINs and IRS with significant reductions in malaria infection prevalence, similar studies in Benin [7], Eritrea [8], Ethiopia [9] and The Gambla [10] reported no such effect. Based on a Cochrane review demonstrating the inconsistencies in epidemiological evidence [4], the World Health Organisation (WHO) has recently issued a provisional recommendation against combining LLINs and IRS as a means of reducing malaria-associated morbidity and mortality [1]. National Malaria Control Programmes are encouraged to prioritise delivering either ITNs or IRS at high coverage and to a high standard, rather than introducing the second intervention as a means to compensate for deficiencies in the implementation of the first. However, this should not be interpreted to mean that the combined IRS and ITN approach is redundant in all settings. Considering the recent stall in progress against malaria [11] and increasing prevalence of pyrethroid resistance in malaria vector populations across Africa [12], combining interventions may be vital for high transmission settings and insecticide resistance management. Vector control programmes are expected to be guided by local evidence to decide when, where and how to combine IRS with ITNs for different epidemiological settings [1].\n",
-                        "\n",
-                        "Evidence from RCTs and operational studies suggests that the impact of co-implementing LLINs and IRS may depend on a number of location-specific factors including: intervention coverage, transmission intensity, vector behaviour, choice of IRS insecticide and insecticide resistance [13,14]. The choice of IRS insecticide for a specific geographical setting whether deployed alone or together with LLINs will be influenced largely by the susceptibility of the target vector population to the insecticide, its mode of action, chemical properties and residual \n",
-                        "activity on wall substrates. To maximise the impact of the combined intervention approach, it is important to consider the interactions that may occur between the insecticide in the LLIN and the chosen IRS insecticide. Where the mode of action of the IRS insecticides is not complementary to the insecticide on the net, the combination might be redundant or result in lower levels of vector control than what is achievable with either intervention alone. Although pyrethroids are not recommended for IRS, the redundancy of combining pyrethroid IRS or wall linings with pyrethroid LLINs has been clearly demonstrated in experimental but studies against pyrethroid-resistant malaria vectors in West Africa [15,16].\n",
-                        "\n",
-                        "Insecticides belonging to four classes of compounds have been approved by the WHO for IRS against malaria vectors; pyrethroids, organophosphates, carbamates and more recently, the neonicotinoid clothianidin [1,17]. While RCTs are the most robust method for generating evidence on the impact of combining vector control interventions, they are often time-consuming, expensive and unrealistic in some settings. Evidence from experimental but trials may provide some insights on what to expect when different types IRS insecticides are deployed to complement pyrethroid-only nets. In this study, we compared the impact of combining pyrethroid LLINs with IRS insecticides from three distinct chemical classes in experimental huts against wild free-flying pyrethroid-resistant malaria vectors in southern Benin.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental hut treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to differential attractiveness of the volunteer sleepers and huts.\n",
-                        "\n",
-                        "\n",
-                        "Mortality in susceptibility bioassays was interpreted according to WHO criteria. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Trial procedure\n",
-                        "\n",
-                        "The trial began three days after IRS application and continued for 4 months, between July and October 2018 and followed WHO guidelines [19]. Nine (9) consenting human volunteers kept in experimental huts each trial night to attract mosquitoes. To account for bias due to differential attractiveness to mosquitoes, volunteers were rotated through experimental huts daily in accordance with a predetermined Latin square design. To mitigate the effect of hut position on mosquito entry, bed nets were rotated through experimental huts on a weekly basis. IRS treatments could not be rotated thus remained fixed throughout the trial. At dawn, sleepers collected all mosquitoes from under the net, the sleeping room and the veranda using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were subsequently transferred to the laboratory for morphological identification and scoring of blood-feeding and mortality. To account for the slow-action of chlorfenapyr, delayed mortality was recorded every 24 h up to 72 h after collection for all treatments. All alive _An. gambiae_ s.l. were held at 27 +- 2deg C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution.\n",
-                        "\n",
-                        "header: Table 3. Overall entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total females caught', 'Average catch per night', '% Deterrence', 'Total exiting', '% Exophily�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total females caught\":711,\"Average catch per night\":7,\"% Deterrence\":\"\\u2013\",\"Total exiting\":322,\"% Exophily\\ufffd\":\"45a\",\"95% CI\":\"42\\u201349\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total females caught\":420,\"Average catch per night\":4,\"% Deterrence\":\"41\",\"Total exiting\":165,\"% Exophily\\ufffd\":\"39a\",\"95% CI\":\"35\\u201344\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total females caught\":619,\"Average catch per night\":6,\"% Deterrence\":\"13\",\"Total exiting\":350,\"% Exophily\\ufffd\":\"57b\",\"95% CI\":\"53\\u201361\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":508,\"Average catch per night\":5,\"% Deterrence\":\"29\",\"Total exiting\":389,\"% Exophily\\ufffd\":\"77c\",\"95% CI\":\"73\\u201380\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total females caught\":537,\"Average catch per night\":6,\"% Deterrence\":\"25\",\"Total exiting\":373,\"% Exophily\\ufffd\":\"70cd\",\"95% CI\":\"66\\u201373\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total females caught\":478,\"Average catch per night\":5,\"% Deterrence\":\"33\",\"Total exiting\":297,\"% Exophily\\ufffd\":\"62bd\",\"95% CI\":\"58\\u201367\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total females caught\":411,\"Average catch per night\":4,\"% Deterrence\":\"42\",\"Total exiting\":241,\"% Exophily\\ufffd\":\"59b\",\"95% CI\":\"54\\u201363\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total females caught\":528,\"Average catch per night\":6,\"% Deterrence\":\"26\",\"Total exiting\":356,\"% Exophily\\ufffd\":\"67d\",\"95% CI\":\"63\\u201371\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total females caught\":486,\"Average catch per night\":5,\"% Deterrence\":\"32\",\"Total exiting\":298,\"% Exophily\\ufffd\":\"61bd\",\"95% CI\":\"57\\u201366\"}}\n",
-                        "\n",
-                        "header: Residual activity of IRS treatments\n",
-                        "\n",
-                        "Cone bioassays were performed at monthly intervals after spraying to assess the residual activity of IRS treatments on hut walls following WHO guidelines [19]. Laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu mosquitoes were used for this purpose. In each IRS-treated hut, 50, 3-5-day old adult female mosquitoes were transferred to five cones attached to the walls and ceiling in batches of 10. As a control, mosquitoes were introduced into cones attached to an unsprayed wall. Mosquitoes were exposed to the surfaces for 30 mins before being transferred to netted cups. Mosquitoes were held at 27 +- 2' C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution. Knockdown was recorded 1 h post-exposure and delayed mortality every 24 h up to 72 h thereafter.\n",
-                        "\n",
-                        "header: Mosquito entry and exiting.: Discussion\n",
-                        "\n",
-                        "The ultimate purpose of combining IRS with LLINs is to achieve greater levels of vector control through the differential effects of the interventions and modes of action of the insecticides involved than what is achievable with a single intervention on its own. Transmission dynamics models have suggested that the levels of vector mosquito mortality and blood-feeding inhibition observed in an experimental hut setting could be predictive of the capacity of an indoor vector control intervention or a combination of these to control vector populations, reduce vector biting and improve public health impact when deployed on a large scale [22,23]. In this study, we demonstrate that where vectors are resistant to pyrethroids but susceptible to a given non-pyrethroid IRS insecticide, it should be possible to achieve substantially improved vector control by adding the non-pyrethroid IRS to a standard pyrethroid-only LLIN to boost\n",
-                        "\n",
-                        "Fig 1: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pirimphos-methyl CS IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "Fig 2: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with chlorfenapyr IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "\n",
-                        "mortality rates through the IRS intervention, while benefiting from the blood-feeding inhibition provided by the LLIN.\n",
-                        "\n",
-                        "The level of improved mortality achieved in the pyrethroid-only LLIN plus non-pyrethroid IRS combinations relative to the pyrethroid LLIN alone was clearly dependent on the type of non-pyrethroid IRS insecticide used. The combination with pirimiphos-methyl CS IRS induced the highest mortality rate (81%) and this can be attributed to the high and prolonged activity of the pirimiphos-methyl CS IRS formulation on cement walls as demonstrated in this study and previous studies in Benin [24]. This substantiates findings from community trials and operational studies demonstrating significant reductions in transmission of malaria by pyrethroid-resistant vector populations when a single round of pirimiphos-methyl CS IRS was deployed against a background of moderate to high coverage with standard pyrethroid nets [25-27]. By contrast the levels of mortality achieved in the combination with bendiocarb IRS was much lower (32%); attributable to the fast decline in efficacy with the insecticide as observed monthly in the experimental huts and in cone bioassays. The poor residual effect of\n",
-                        "\n",
-                        "Fig 4: **Mortality (72 h) of laboratory maintained, insecticide-susceptible _Anopheles gambiae_ sensu stricto Kisumu colony exposed to IRS-treated hut walls in monthly, 30 mins wall cone bioassays in experimental huts in Cove, southern Benin. Error bars represent 95% CI. Approximately fifty (50) 2–3 days old mosquitoes were exposed for 30 mins to each IRS treated hut in batches of 10 per cone and mortality recorded after 72 h.**\n",
-                        "\n",
-                        "Fig 3: **Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato entering experimental huts with bendiocarb IRS applied alone and in combination with DuraNet® in Cove, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.**\n",
-                        "bendiocarb for IRS is well documented [28, 29, 30]. The failure of a combination of bendiocarb treated walls and pyrethroid nets to provide improved control of clinical malaria compared to either intervention alone in a previous RCT in Benin [7] was also largely attributed to the short-lived efficacy of bendiocarb on the treated walls [13, 14]. Multiple IRS campaign rounds may be necessary to achieve sustained levels of improved vector control when bendiocarb IRS is deployed to complement standard pyrethroid LLINs. Given the significant costs associated with running IRS campaigns, these findings raise questions over the suitability of currently available formulations of bendiocarb for IRS and the cost-effectiveness of its combination with pyrethroid nets. Considering the high initial mortality rates usually achieved with the insecticide, reformulation chemistry may help address the short residual efficacy of bendiocarb and improve the utility of this insecticide for vector control.\n",
-                        "\n",
-                        "While mortality in the combinations with pirimiphos-methyl CS IRS and bendiocarb IRS were similar to each IRS alone, the combination with chlorfenapyr IRS induced significantly higher levels of mortality compared to chlorfenapyr IRS alone. This demonstrates an additive effect of the chlorfenapyr IRS plus pyrethroid LLINs combination which was not observed with the other IRS insecticides tested. This trend is consistent with previous experimental hut studies in Benin [31, 32] though the levels of mortality achieved with the combination were substantially higher (73%-83% vs. 46%). The difference in mortality could be due to changes in the composition of the vector population and/or increasing pyrethroid resistance levels over time. Chlorfenapyr is a new repurposed pyrrole insecticide with a unique mode of action that has shown potential for IRS but often inducing moderate mortality rates when applied alone in experimental huts against pyrethroid-resistant malaria vectors [29, 33]. It acts on the oxidative/metabolic pathway of insects hence its action can be enhanced by physical activity in the insect [34]. The additive mortality observed in the chlorfenapyr IRS plus pyrethroid net combination may therefore be attributed to the irritant effect of the pyrethroid in the LLIN on mosquitoes as they alight on the chlorfenapyr-treated wall after failed attempts to feed on the sleeper under the net. In addition, owing to the non-neurotoxic and non-irritant effect of chlorfenapyr [35], mosquitoes may have alighted on chlorfenapyr-treated walls for longer, leading to increased insecticide pick-up, potentially contributing to the additional mortality observed with this combination. The inability of the WHO 30-min cone bioassays to predict wild mosquito mortality with chlorfenapyr IRS in experimental huts [29, 34] is further demonstrated in this study; while cone bioassay mortality of the susceptible Kisumu strain was very low, wild mosquito mortality was higher and consistent throughout the trial confirming previous findings [29]. Considering the prolonged effect of chlorfenapyr IRS on wild mosquitoes [29], substantial and sustained improvements in the control of pyrethroid-resistant vector populations can be expected if the IRS insecticide is deployed to complement pyrethroid LLINs.\n",
-                        "\n",
-                        "IRS is highly effective for malaria vector control when deployed on its own but provides limited personal protection from mosquito biting and this is evident from the high blood-feeding rates observed with the IRS treatments alone. In the absence of a bed net, mosquitoes will usually feed on the human occupants in an IRS treated home before resting on the wall where they pick up the insecticide, consequently, direct blood-feeding inhibition is not expected when the intervention is applied independently. By contrast, pyrethroid LLINs reduce mosquito biting and provide substantial levels of personal protection for the user; an effect which is attributable to the physical barrier of the net and the excito-repellent effect of the pyrethroid insecticide. The findings from this study demonstrate that this effect can persist even against a vector population that has developed intense resistance to pyrethroids as observed in Cove [18]. By combining the IRS treatments with a pyrethroid LLIN, it was possible to achieve substantially improved levels of blood-feeding inhibition and personal protection compared to \n",
-                        "the IRS alone. The reduced exposure to infective bites constitutes a crucial advantage of the combination strategy over IRS alone. The blood-feeding rate in the combination appeared to depend on the type of IRS insecticide used albeit to a lesser extent than the levels of mortality achieved; lower blood-feeding rates and thus higher levels of blood-feeding inhibition were achieved in the combination with benidocarb IRS compared to the combination with chlorfenapyr IRS. This can be due to differences in the mode of action of both IRS insecticides; in contrast to chlorfenapyr, benidocarb acts on the insect's nervous system eliciting an irritant rapid knockdown effect on mosquitoes [35] and this together with the excito-repellent effect of the pyrethroid in the standard pyrethroid LLIN could have contributed to the high levels of early exiting and reduced blood-feeding rates observed when both interventions were combined in a hut.\n",
-                        "\n",
-                        "Overall, the results demonstrate the superiority of the organophosphate, pirimiphos-methyl CS for IRS on its own and in combination with pyrethroid LLINs over benidocarb and the pyrrole, chlorfenapyr. However, this finding may not be generalisable to areas where vectors are resistant to organophosphates. Indeed, previous experimental hut studies in Cote d'Ivoire failed to demonstrate improved vector control with the combination compared to the net alone when standard pyrethroid LLINs were combined with pirimiphos-methyl CS-treated wall linings against a vector population that was resistant to pyrethroids and organophosphates [36]. Resistance to organophosphates and carbamates is unfortunately increasing in malaria vector populations especially in West Africa [37, 38, 39, 40]. To mitigate its impact, vector control programmes are encouraged to rotate between IRS insecticides with different modes of action [41]. Considering that most IRS campaigns will usually be deployed against a background of high LLIN coverage, effective IRS rotation plans should prioritise IRS insecticides which in addition to providing improved and prolonged vector control, can efficiently complement LLINs. Based on the findings of this study, chlorfenapyr IRS shows some potential to be a useful addition to such IRS rotation plans in pyrethroid-resistant areas with high pyrethroid LLIN coverage. It will be interesting to investigate the impact of combining pyrethroid LLINs with other newly approved IRS formulations containing clothianidin [17].\n",
-                        "\n",
-                        "The low mortality rates achieved with the standard pyrethroid net in this study (8%), further demonstrates the threat of pyrethroid resistance on the efficacy of standard pyrethroids. Pyrethroid resistance is widespread in malaria vectors across Africa and increasing in intensity the more they are used [12]. A new generation of LLINs treated with a pyrethroid and non-pyrethroid insecticide are now in advanced development. Studies have demonstrated the potential of some of these nets to provide improved control of clinical malaria transmitted by pyrethroid-resistant malaria vectors compared to standard pyrethroid nets [42, 43, 44]. As next generation nets come into the market, and are rolled out in endemic countries, it will be essential to explore any possible interactions with the different types of IRS insecticides that could be deployed together with them in a combined intervention approach.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Which indoor residual spraying insecticide best complements standard pyrethroid long-lasting insecticidal nets for improved control of pyrethroid resistant malaria vectors?\n",
-                        "\n",
-                        "Thomas Syme, Augustin Fongnikin, Damien Todjinou, Renaud Goveetchan, Mark Rowland, Martin Akogbeto, Corine Nguforo\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Where resources are available, non-pyrethroid IRS can be deployed to complement standard pyrethroid LLINs with the aim of achieving improved vector control and managing insecticide resistance. The impact of the combination may however depend on the type of IRS insecticide deployed. Studies comparing combinations of pyrethroid LLINs with different types of non-pyrethroid IRS products will be necessary for decision making.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cove\",\"Start_month\":7,\"Start_year\":2018,\"End_month\":10,\"End_year\":2018}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"Alpha-cypermethrin\",\"Concentration_initial\":\"261 mg\\/m2\",\"Net_holed\":6.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Acknowledgments\n",
-                        "\n",
-                        "We thank BASF and Bayer for providing the insecticides. We also thank the technical staff of CREC (Abibath Odojo, Estelle Vigninou, Josias Fagbohoun, Edwige Kpanou etc) for their assistance. We are grateful to the rice farmers at Cove for their support in the hut study.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Susceptibility bioassays revealed that the vector population at Cove, was resistant to pyrethroids (<20% mortality) but susceptible to carbamates, chlorfenapyr and organophosphates (>=98% mortality). Mortality of wild free-flying pyrethroid resistant _An. gambiae_ s.l. entering the hut with the untreated net control (4%) did not differ significantly from DuraNet(r) alone (8%, p = 0.169). Pirimiphos-methyl CS IRS induced the highest mortality both on its own (85%) and in combination with DuraNet(r) (81%). Mortality with the DuraNet(r) + chlorfenapyr IRS combination was significantly higher than each intervention alone (46% vs. 33% and 8%, p<0.05) demonstrating an additive effect. The DuraNet(r) + bendicocarb IRS combination induced significantly lower mortality compared to the other combinations (32%, p<0.05). Blood-feeding inhibition was very low with the IRS treatments alone (3-5%) but \n",
-                        "increased significantly when they were combined with DuraNet(r) (61% - 71%, p<0.05). Blood-feeding rates in the combinations were similar to the net alone. Adding bendicorarb IRS to DuraNet(r) induced significantly lower levels of mosquito feeding compared to adding chlorfenapyr IRS (28% vs. 37%, p = 0.015).\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only LLINs against a pyrethroid-resistant vector population which is susceptible to the IRS insecticide, can provide higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone. Adding pirimphos-methyl CS IRS may provide substantial improvements in vector control while adding chlorfenapyr IRS can demonstrate an additive effect relative to both interventions alone. Adding bendicorarb IRS may show limited enhancements in vector control owing to its short residual effect.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "The efficacy of combining a standard pyrethroid LLIN (DuraNet(r)) with IRS insecticides from three chemical classes (bendicocarb, chlorfenapyr and pirimiphos-methyl CS) was evaluated in an experimental hut trial against wild pyrethroid-resistant _Anopheles gambiae_ s.l. in Cove, Benin. The combinations were also compared to each intervention alone. WHO cylinder and CDC bottle bioassays were performed to assess susceptibility of the local _An. gambiae_ s.l. vector population at the Cove hut site to insecticides used in the combinations.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Adding non-pyrethroid IRS to standard pyrethroid-only nets against a pyrethroid-resistant vector population which was susceptible to the IRS insecticide, generally provided higher levels of vector mosquito control compared to the pyrethroid net alone or IRS alone; the IRS intervention was responsible for improved mosquito mortality while the pyrethroid net provided blood-feeding inhibition. The combination with pirimiphos-methyl CS IRS was the most efficacious. Adding chlorfenapyr IRS to the pyrethroid LLIN demonstrated an additive mortality effect relative to both interventions alone. The combination with benidocarb IRS provided the \n",
-                        "lowest levels of mortality owing to its short residual effect but also induced lower levels of mosquito feeding compared to combinations with the other IRS insecticides.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality rates of wild _An. gambiae_ s.l. from Cove in WHO susceptibility and CDC bottle bioassays are presented in Tables 1 and 2 respectively. Low proportions of adult F1 progeny of field-collected _An. gambiae_ s.l. were killed following exposure to alpha-cypermethrin (11%) and permethrin (20%); demonstrating pyrethroid resistance in the vector population. In contrast, the vast majority if not all mosquitoes from Cove were killed following exposure to bendiocarb (98%), fenitrothion (100%) and chlorfenapyr (100%); demonstrating susceptibility to carbamates, organophosphates and chlorfenapyr. With the insecticide susceptible _An. gambiae_ s.s. Kisumu colony, mortality was 100% with all insecticides.\n",
-                        "\n",
-                        "header: Ethical considerations\n",
-                        "\n",
-                        "Ethical approval was obtained from the ethics review committees of the London School of Hygiene & Tropical Medicine (LSHTM) (Reference No. 15265) and the local Ministry of Health in Benin (Reference No. N'39/CEIC/CREC). All volunteers provided informed written consent and were offered a course of chemoprophylaxis (doxycycline) prior to their participation in the study and up to four weeks following its completion. A nurse was available throughout the study to assess any cases of fever. In the event a volunteer tested positive for malaria, they were immediately withdrawn from the trial and administered effective treatment.\n",
-                        "\n",
-                        "header: Table 4. Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total blood-fed', '% Blood-fed*', '95% CI', '% Blood-feeding inhibition', '% Personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total blood-fed\":675,\"% Blood-fed*\":\"95a\",\"95% CI\":\"93\\u201397\",\"% Blood-feeding inhibition\":\"\\u2013\",\"% Personal protection\":\"\\u2013\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total blood-fed\":231,\"% Blood-fed*\":\"55b\",\"95% CI\":\"50\\u201360\",\"% Blood-feeding inhibition\":\"42\",\"% Personal protection\":\"66\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total blood-fed\":202,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201336\",\"% Blood-feeding inhibition\":\"66\",\"% Personal protection\":\"70\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total blood-fed\":460,\"% Blood-fed*\":\"91e\",\"95% CI\":\"88\\u201393\",\"% Blood-feeding inhibition\":\"5\",\"% Personal protection\":\"32\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total blood-fed\":150,\"% Blood-fed*\":\"28c\",\"95% CI\":\"24\\u201332\",\"% Blood-feeding inhibition\":\"71\",\"% Personal protection\":\"78\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total blood-fed\":440,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201394\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"35\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total blood-fed\":153,\"% Blood-fed*\":\"37d\",\"95% CI\":\"33\\u201342\",\"% Blood-feeding inhibition\":\"61\",\"% Personal protection\":\"77\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total blood-fed\":488,\"% Blood-fed*\":\"92ae\",\"95% CI\":\"90\\u201395\",\"% Blood-feeding inhibition\":\"3\",\"% Personal protection\":\"28\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total blood-fed\":160,\"% Blood-fed*\":\"33cd\",\"95% CI\":\"29\\u201337\",\"% Blood-feeding inhibition\":\"65\",\"% Personal protection\":\"76\"}}\n",
-                        "\n",
-                        "header: Susceptibility bioassays\n",
-                        "\n",
-                        "WHO susceptibility cylinder bioassays [20] and CDC bottle bioassays [21] were performed in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove experimental hut site to the constituent insecticides in the hut treatments. Adult F1 progeny of field-collected _An. gambiae_ s.l. were exposed to filter papers treated with discriminating doses of alpha-cypermethrin (0.05%), permethrin (0.75%), bendiocarb (0.1%) and fenitrothion (1.0%) in WHO cylinders and CDC bottles impregnated with 100 mg of chlorfenapyr. Comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu colony. For each insecticide and strain, 100 adult female mosquitoes aged 2-3 days were introduced to four insecticide-treated cylinders/bottles in batches of 25. In the control arms, mosquitoes were exposed to silicone oil-treated filter papers and acetone-treated bottles. After 1 h exposure, mosquitoes were transferred to holding tubes and observation cups and provided access to 10% glucose solution (w/v). Mortality was recorded after 24 h for mosquitoes exposed to permethrin, alpha-cypermethrin, bendiocarb and fenitrothion in WHO cylinder bioassays and every 24 h up to 72 h for mosquitoes exposed to chlorfenapyr in CDC bottles. The insecticide treated filter papers used for the WHO cylinder bioassays were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Outcome measures\n",
-                        "\n",
-                        "The efficacy of each experimental hut treatment was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "* Exophily-exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda\n",
-                        "* Deterrence-defined as the percentage reduction in numbers of mosquitoes collected in a treated hut relative to the untreated control\n",
-                        "* Blood-feeding-proportion of blood-fed mosquitoes at the time of collection\n",
-                        "\n",
-                        "Where _B__\\({}_{\\mu}\\)_ is the proportion of blood-fed mosquitoes in the untreated control and _B__\\({}_{\\beta}\\)_ is the proportion of blood-fed mosquitoes in the treated hut.\n",
-                        "\n",
-                        "\n",
-                        "- B_{i})}{B_{u}}\\] Where \\(B_{u}\\) is the number of blood-fed mosquitoes in the untreated control and \\(B_{i}\\) is the number of blood-fed mosquitoes in the huts with insecticide treatments.\n",
-                        "* Mortality-proportion of dead mosquitoes after a 72 h holding period Mosquito deterrence, blood-feeding inhibition and personal protection were calculated relative to the untreated net control hut for treatments which involved the LLIN and relative to the untreated hut control for IRS-only treatments.\n",
-                        "\n",
-                        "header: Susceptibility bioassay results: Experimental hut results\n",
-                        "\n",
-                        "A total of 4,698 female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trial period (Tables 3 and S1). Due to the inability to\n",
-                        "\n",
-                        "rotate IRS, it was not possible to distinguish treatment-induced deterrence for the IRS treatments from differential attractiveness due to hut position. The proportion of mosquitoes exiting into the veranda for the untreated hut and untreated net controls were 45% and 39% respectively. Exiting was significantly higher in all experimental hut treatments relative to both controls (p<0.01), suggesting the insecticide treatments induced an irritant response which caused mosquitoes to seek refuge in the veranda. The highest level of mosquito exiting was achieved with beniodicarb IRS alone (77%). Adding beniodicarb IRS to DuraNet(r) also induced significantly higher levels of mosquito exiting compared to DuraNet(r) alone (57% with DuraNet(r) vs. 70% with DuraNet(r) + beniodicarb IRS, p = 0.001). This effect was, however, not observed with chlorfenapyr IRS and pirimiphos-methyl CS IRS; exiting rates were similar between the combinations with these IRS insecticides and DuraNet(r) alone, (59% - 61% vs 57%, p>0.05).\n",
-                        "\n",
-                        "**Blood-feeding and personal protection.** Blood-feeding and personal protection results of wild, pyrethroid-resistant _An. gambiae_ s.l. in the experimental huts are presented in Table 4. Blood-feeding rates with the untreated hut and untreated net controls were 95% and 55% respectively. DuraNet(r) induced significantly lower blood-feeding rates than the control untreated net (33% with DuraNet(r) vs. 55% with untreated net, p<0.001). Meanwhile, blood-feeding was high across all IRS treatments (91-92%) thus providing very limited blood-feeding inhibition (3% - 5%) compared to the untreated hut control (Table 4). All three combinations provided significantly higher levels of blood-feeding inhibition relative to the IRS treatments\n",
-                        "\n",
-                        "alone (60% - 71% vs. 3% - 5%, p<0.001). Between the combinations, DuraNet(r) + bendiocarb IRS induced the lowest blood-feeding rate (28%) and offered personal protection of 78% while DuraNet(r) + chlorfenapyr IRS induced a higher blood-feeding rate (37%, p = 0.015) though providing about the same levels of personal protection (77%).\n",
-                        "\n",
-                        "**Mortality rates in experimental huts.** Overall mortality rates of wild, pyrethroid-resistant _An. gambiae_ s.l. in the untreated hut and untreated net controls were 2% and 4% respectively (Table 5). Mortality with DuraNet(r) was low and did not differ significantly from the untreated net control (8% vs. 4%, p = 0.169). The IRS treatments alone and the combinations induced significantly higher mortality than DuraNet(r) and the untreated controls (p < 0.001); adding any IRS treatment onto DuraNet(r) therefore always significantly improved vector mortality compared to DuraNet(r) alone. Between the IRS only treatments, the highest mortality was achieved with pirimiphos-methyl CS (85%, p < 0.001) while chlorfenapyr and bendiocarb induced fairly similar mortality rates (34% and 28%, p = 0.09). Combining DuraNet(r) with bendiocarb and pirimiphos-methyl CS IRS treatments did not improve mortality compared to the corresponding IRS alone (bendiocarb: 28% vs. 32%, p = 0.265 and pirimiphos-methyl CS: 85% vs. 81%, p = 0.129) (Table 5). With chlorfenapyr IRS, mortality was significantly higher in the combination compared to chlorfenapyr IRS alone over the 4-month trial (34% vs. 46%, p < 0.001) (Fig 3). Between the combinations, the highest mortality was achieved with DuraNet(r) + pirimiphos-methyl CS IRS (81%). Mortality was significantly higher with the DuraNet(r) + chlorfenapyr IRS combination compared to DuraNet(r) + bendiocarb IRS (46% vs. 32%, p < 0.001) (Table 5).\n",
-                        "\n",
-                        "Initial monthly wild mosquito mortality with pirimiphos-methyl CS IRS and chlorfenapyr IRS was steady and did not decline substantially over the course of the 4 months trial both when applied alone and in combination with DuraNet(r) (Figs 1 and 2). By contrast, wild mosquito mortality with bendiocarb IRS declined sharply from ~55% to about 28-32% by the last month of the trial both when applied alone and in combination with DuraNet(r) (Fig 3).\n",
-                        "\n",
-                        "**Wall cone bioassay results.** Mortality rates of the laboratory maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu colony following exposure to IRS-treated walls in monthly, 30 mins wall cone bioassays are presented in Fig 4 with more details in supporting information (S2 Table). On pirimiphos-methyl CS-treated surfaces, cone bioassay mortality was very high (> 85%) throughout all 4 months of the trial. As observed with wild free-flying mosquitoes, a progressive decline in cone bioassay mortality with increasing months elapsed from spraying was observed with bendiocarb, beginning at 63% in month 1 and decreasing to 19% by month 4. Cone bioassay mortality with chlorfenapyr IRS was consistently low throughout the trial,\n",
-                        "ranging from 11-31%. Cone bioassay mortality on untreated control walls did not exceed 10% in any month of the trial (Fig 4).\n",
-                        "\n",
-                        "header: Table 2: Susceptibility of field-collected Anopheles gambiae sensu late from Cove to chlorfenapyr in CDC bottle bioassays.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment', 'Strain', 'N exposed', 'N dead', '% Mortality�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Control\",\"Strain\":\"Kisumu\",\"N exposed\":101,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"1\":{\"Treatment\":\"Control\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":100,\"N dead\":0,\"% Mortality\\ufffd\":0,\"95% CI\":\"\\u2013\"},\"2\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Kisumu\",\"N exposed\":103,\"N dead\":103,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"},\"3\":{\"Treatment\":\"Chlorfenapyr (100 \\u03bcg)\",\"Strain\":\"Cov\\u00e8\",\"N exposed\":102,\"N dead\":102,\"% Mortality\\ufffd\":100,\"95% CI\":\"\\u2013\"}}\n",
-                        "\n",
-                        "header: Study site and experimental huts: Experimental hut treatments\n",
-                        "\n",
-                        "The following treatments were assessed in 9 experimental huts:\n",
-                        "\n",
-                        "1. Control (untreated hut)\n",
-                        "2. Control (untreated net)\n",
-                        "3. DuraNet(r) (Alpha-cypermethrin LLIN, 261 mg/m2, Shobikaa Impex)\n",
-                        "4. Bendiocarb IRS applied at 400 mg/m2 (Ficam(r) WP, Bayer)\n",
-                        "5. DuraNet(r) + Bendiocarb IRS applied at 400 mg/m2\n",
-                        "6. Chlorfenapyr IRS applied at 250 mg/m2 (Sylando(r) 240SC, BASF)\n",
-                        "7. DuraNet(r) + Chlorfenapryr IRS applied at 250 mg/m2\n",
-                        "8. Pirimiphos-methyl CS IRS applied at 1000 mg/m2 (Actellic(r) 300CS, Syngenta)\n",
-                        "9. DuraNet(r) + Pirimiphos-methyl CS IRS applied at 1000 mg/m2\n",
-                        "\n",
-                        "Interior hut walls were sprayed from top to bottom with IRS insecticide solutions using a Hudson X-pert(r) compression sprayer equipped with a flat-fan nozzle. To improve spraying accuracy, spray swaths were pre-marked on hut walls and a guidance pole was attached to the end of the spray lance to maintain a fixed distance to the wall. After spraying each hut, the volume remaining in the spray tank was measured to assess the overall volume sprayed. All spray volumes were within 30% of the target. The palm match used on the ceiling was sprayed flat on the ground outside the experimental hut and allowed to dry for 2 hours prior to being fitted on the ceiling of the hut. Three replicate nets were used per LLIN treatment and these were rotated every 2 nights within the same hut. To simulate wear and tear from field use, 6 holes each measuring 16 cm2 were cut into each bed net (one per panel) used in the trial.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are core interventions for preventing and controlling malaria [1]. Both methods have independently proven to be highly effective in reducing the burden of disease in diverse epidemiological settings. The substantial reductions in malaria morbidity and mortality observed in endemic countries over the last two decades, has been attributed to a significant increase in the roll out of ITNs and IRS during this period [2]. Where resources are available, both interventions have been deployed together in the same geographical location, with the primary aim of reducing the disease burden to a greater extent than could be achieved with either method alone [3,4].\n",
-                        "\n",
-                        "The benefits of combining these interventions is, however, contentious. Some community-randomised controlled trials (RCTs) have compared epidemiological outcomes in communities receiving pyrethroid long-lasting insecticidal nets (LLINs) plus IRS versus LLINs alone, yielding conflicting results. Whilst trials in Tanzania [5] and Sudan [6] associated combined use of LLINs and IRS with significant reductions in malaria infection prevalence, similar studies in Benin [7], Eritrea [8], Ethiopia [9] and The Gambla [10] reported no such effect. Based on a Cochrane review demonstrating the inconsistencies in epidemiological evidence [4], the World Health Organisation (WHO) has recently issued a provisional recommendation against combining LLINs and IRS as a means of reducing malaria-associated morbidity and mortality [1]. National Malaria Control Programmes are encouraged to prioritise delivering either ITNs or IRS at high coverage and to a high standard, rather than introducing the second intervention as a means to compensate for deficiencies in the implementation of the first. However, this should not be interpreted to mean that the combined IRS and ITN approach is redundant in all settings. Considering the recent stall in progress against malaria [11] and increasing prevalence of pyrethroid resistance in malaria vector populations across Africa [12], combining interventions may be vital for high transmission settings and insecticide resistance management. Vector control programmes are expected to be guided by local evidence to decide when, where and how to combine IRS with ITNs for different epidemiological settings [1].\n",
-                        "\n",
-                        "Evidence from RCTs and operational studies suggests that the impact of co-implementing LLINs and IRS may depend on a number of location-specific factors including: intervention coverage, transmission intensity, vector behaviour, choice of IRS insecticide and insecticide resistance [13,14]. The choice of IRS insecticide for a specific geographical setting whether deployed alone or together with LLINs will be influenced largely by the susceptibility of the target vector population to the insecticide, its mode of action, chemical properties and residual \n",
-                        "activity on wall substrates. To maximise the impact of the combined intervention approach, it is important to consider the interactions that may occur between the insecticide in the LLIN and the chosen IRS insecticide. Where the mode of action of the IRS insecticides is not complementary to the insecticide on the net, the combination might be redundant or result in lower levels of vector control than what is achievable with either intervention alone. Although pyrethroids are not recommended for IRS, the redundancy of combining pyrethroid IRS or wall linings with pyrethroid LLINs has been clearly demonstrated in experimental but studies against pyrethroid-resistant malaria vectors in West Africa [15,16].\n",
-                        "\n",
-                        "Insecticides belonging to four classes of compounds have been approved by the WHO for IRS against malaria vectors; pyrethroids, organophosphates, carbamates and more recently, the neonicotinoid clothianidin [1,17]. While RCTs are the most robust method for generating evidence on the impact of combining vector control interventions, they are often time-consuming, expensive and unrealistic in some settings. Evidence from experimental but trials may provide some insights on what to expect when different types IRS insecticides are deployed to complement pyrethroid-only nets. In this study, we compared the impact of combining pyrethroid LLINs with IRS insecticides from three distinct chemical classes in experimental huts against wild free-flying pyrethroid-resistant malaria vectors in southern Benin.\n",
-                        "\n",
-                        "header: Trial procedure\n",
-                        "\n",
-                        "The trial began three days after IRS application and continued for 4 months, between July and October 2018 and followed WHO guidelines [19]. Nine (9) consenting human volunteers kept in experimental huts each trial night to attract mosquitoes. To account for bias due to differential attractiveness to mosquitoes, volunteers were rotated through experimental huts daily in accordance with a predetermined Latin square design. To mitigate the effect of hut position on mosquito entry, bed nets were rotated through experimental huts on a weekly basis. IRS treatments could not be rotated thus remained fixed throughout the trial. At dawn, sleepers collected all mosquitoes from under the net, the sleeping room and the veranda using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were subsequently transferred to the laboratory for morphological identification and scoring of blood-feeding and mortality. To account for the slow-action of chlorfenapyr, delayed mortality was recorded every 24 h up to 72 h after collection for all treatments. All alive _An. gambiae_ s.l. were held at 27 +- 2deg C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution.\n",
-                        "\n",
-                        "header: Residual activity of IRS treatments\n",
-                        "\n",
-                        "Cone bioassays were performed at monthly intervals after spraying to assess the residual activity of IRS treatments on hut walls following WHO guidelines [19]. Laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu mosquitoes were used for this purpose. In each IRS-treated hut, 50, 3-5-day old adult female mosquitoes were transferred to five cones attached to the walls and ceiling in batches of 10. As a control, mosquitoes were introduced into cones attached to an unsprayed wall. Mosquitoes were exposed to the surfaces for 30 mins before being transferred to netted cups. Mosquitoes were held at 27 +- 2' C and 75 +- 10% RH and provided access to 10% (w/v) glucose solution. Knockdown was recorded 1 h post-exposure and delayed mortality every 24 h up to 72 h thereafter.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Which indoor residual spraying insecticide best complements standard pyrethroid long-lasting insecticidal nets for improved control of pyrethroid resistant malaria vectors?\n",
-                        "\n",
-                        "Thomas Syme, Augustin Fongnikin, Damien Todjinou, Renaud Goveetchan, Mark Rowland, Martin Akogbeto, Corine Nguforo\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Where resources are available, non-pyrethroid IRS can be deployed to complement standard pyrethroid LLINs with the aim of achieving improved vector control and managing insecticide resistance. The impact of the combination may however depend on the type of IRS insecticide deployed. Studies comparing combinations of pyrethroid LLINs with different types of non-pyrethroid IRS products will be necessary for decision making.\n",
-                        "\n",
-                        "header: Mosquito entry and exiting.: Discussion\n",
-                        "\n",
-                        "The ultimate purpose of combining IRS with LLINs is to achieve greater levels of vector control through the differential effects of the interventions and modes of action of the insecticides involved than what is achievable with a single intervention on its own. Transmission dynamics models have suggested that the levels of vector mosquito mortality and blood-feeding inhibition observed in an experimental hut setting could be predictive of the capacity of an indoor vector control intervention or a combination of these to control vector populations, reduce vector biting and improve public health impact when deployed on a large scale [22,23]. In this study, we demonstrate that where vectors are resistant to pyrethroids but susceptible to a given non-pyrethroid IRS insecticide, it should be possible to achieve substantially improved vector control by adding the non-pyrethroid IRS to a standard pyrethroid-only LLIN to boost\n",
-                        "\n",
-                        "Fig 1: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pirimphos-methyl CS IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "Fig 2: Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with chlorfenapyr IRS applied alone and in combination with DuraNet(r) in Cové, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\n",
-                        "\n",
-                        "\n",
-                        "mortality rates through the IRS intervention, while benefiting from the blood-feeding inhibition provided by the LLIN.\n",
-                        "\n",
-                        "The level of improved mortality achieved in the pyrethroid-only LLIN plus non-pyrethroid IRS combinations relative to the pyrethroid LLIN alone was clearly dependent on the type of non-pyrethroid IRS insecticide used. The combination with pirimiphos-methyl CS IRS induced the highest mortality rate (81%) and this can be attributed to the high and prolonged activity of the pirimiphos-methyl CS IRS formulation on cement walls as demonstrated in this study and previous studies in Benin [24]. This substantiates findings from community trials and operational studies demonstrating significant reductions in transmission of malaria by pyrethroid-resistant vector populations when a single round of pirimiphos-methyl CS IRS was deployed against a background of moderate to high coverage with standard pyrethroid nets [25-27]. By contrast the levels of mortality achieved in the combination with bendiocarb IRS was much lower (32%); attributable to the fast decline in efficacy with the insecticide as observed monthly in the experimental huts and in cone bioassays. The poor residual effect of\n",
-                        "\n",
-                        "Fig 4: **Mortality (72 h) of laboratory maintained, insecticide-susceptible _Anopheles gambiae_ sensu stricto Kisumu colony exposed to IRS-treated hut walls in monthly, 30 mins wall cone bioassays in experimental huts in Cove, southern Benin. Error bars represent 95% CI. Approximately fifty (50) 2–3 days old mosquitoes were exposed for 30 mins to each IRS treated hut in batches of 10 per cone and mortality recorded after 72 h.**\n",
-                        "\n",
-                        "Fig 3: **Monthly mortality rates of wild, free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato entering experimental huts with bendiocarb IRS applied alone and in combination with DuraNet® in Cove, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.**\n",
-                        "bendiocarb for IRS is well documented [28, 29, 30]. The failure of a combination of bendiocarb treated walls and pyrethroid nets to provide improved control of clinical malaria compared to either intervention alone in a previous RCT in Benin [7] was also largely attributed to the short-lived efficacy of bendiocarb on the treated walls [13, 14]. Multiple IRS campaign rounds may be necessary to achieve sustained levels of improved vector control when bendiocarb IRS is deployed to complement standard pyrethroid LLINs. Given the significant costs associated with running IRS campaigns, these findings raise questions over the suitability of currently available formulations of bendiocarb for IRS and the cost-effectiveness of its combination with pyrethroid nets. Considering the high initial mortality rates usually achieved with the insecticide, reformulation chemistry may help address the short residual efficacy of bendiocarb and improve the utility of this insecticide for vector control.\n",
-                        "\n",
-                        "While mortality in the combinations with pirimiphos-methyl CS IRS and bendiocarb IRS were similar to each IRS alone, the combination with chlorfenapyr IRS induced significantly higher levels of mortality compared to chlorfenapyr IRS alone. This demonstrates an additive effect of the chlorfenapyr IRS plus pyrethroid LLINs combination which was not observed with the other IRS insecticides tested. This trend is consistent with previous experimental hut studies in Benin [31, 32] though the levels of mortality achieved with the combination were substantially higher (73%-83% vs. 46%). The difference in mortality could be due to changes in the composition of the vector population and/or increasing pyrethroid resistance levels over time. Chlorfenapyr is a new repurposed pyrrole insecticide with a unique mode of action that has shown potential for IRS but often inducing moderate mortality rates when applied alone in experimental huts against pyrethroid-resistant malaria vectors [29, 33]. It acts on the oxidative/metabolic pathway of insects hence its action can be enhanced by physical activity in the insect [34]. The additive mortality observed in the chlorfenapyr IRS plus pyrethroid net combination may therefore be attributed to the irritant effect of the pyrethroid in the LLIN on mosquitoes as they alight on the chlorfenapyr-treated wall after failed attempts to feed on the sleeper under the net. In addition, owing to the non-neurotoxic and non-irritant effect of chlorfenapyr [35], mosquitoes may have alighted on chlorfenapyr-treated walls for longer, leading to increased insecticide pick-up, potentially contributing to the additional mortality observed with this combination. The inability of the WHO 30-min cone bioassays to predict wild mosquito mortality with chlorfenapyr IRS in experimental huts [29, 34] is further demonstrated in this study; while cone bioassay mortality of the susceptible Kisumu strain was very low, wild mosquito mortality was higher and consistent throughout the trial confirming previous findings [29]. Considering the prolonged effect of chlorfenapyr IRS on wild mosquitoes [29], substantial and sustained improvements in the control of pyrethroid-resistant vector populations can be expected if the IRS insecticide is deployed to complement pyrethroid LLINs.\n",
-                        "\n",
-                        "IRS is highly effective for malaria vector control when deployed on its own but provides limited personal protection from mosquito biting and this is evident from the high blood-feeding rates observed with the IRS treatments alone. In the absence of a bed net, mosquitoes will usually feed on the human occupants in an IRS treated home before resting on the wall where they pick up the insecticide, consequently, direct blood-feeding inhibition is not expected when the intervention is applied independently. By contrast, pyrethroid LLINs reduce mosquito biting and provide substantial levels of personal protection for the user; an effect which is attributable to the physical barrier of the net and the excito-repellent effect of the pyrethroid insecticide. The findings from this study demonstrate that this effect can persist even against a vector population that has developed intense resistance to pyrethroids as observed in Cove [18]. By combining the IRS treatments with a pyrethroid LLIN, it was possible to achieve substantially improved levels of blood-feeding inhibition and personal protection compared to \n",
-                        "the IRS alone. The reduced exposure to infective bites constitutes a crucial advantage of the combination strategy over IRS alone. The blood-feeding rate in the combination appeared to depend on the type of IRS insecticide used albeit to a lesser extent than the levels of mortality achieved; lower blood-feeding rates and thus higher levels of blood-feeding inhibition were achieved in the combination with benidocarb IRS compared to the combination with chlorfenapyr IRS. This can be due to differences in the mode of action of both IRS insecticides; in contrast to chlorfenapyr, benidocarb acts on the insect's nervous system eliciting an irritant rapid knockdown effect on mosquitoes [35] and this together with the excito-repellent effect of the pyrethroid in the standard pyrethroid LLIN could have contributed to the high levels of early exiting and reduced blood-feeding rates observed when both interventions were combined in a hut.\n",
-                        "\n",
-                        "Overall, the results demonstrate the superiority of the organophosphate, pirimiphos-methyl CS for IRS on its own and in combination with pyrethroid LLINs over benidocarb and the pyrrole, chlorfenapyr. However, this finding may not be generalisable to areas where vectors are resistant to organophosphates. Indeed, previous experimental hut studies in Cote d'Ivoire failed to demonstrate improved vector control with the combination compared to the net alone when standard pyrethroid LLINs were combined with pirimiphos-methyl CS-treated wall linings against a vector population that was resistant to pyrethroids and organophosphates [36]. Resistance to organophosphates and carbamates is unfortunately increasing in malaria vector populations especially in West Africa [37, 38, 39, 40]. To mitigate its impact, vector control programmes are encouraged to rotate between IRS insecticides with different modes of action [41]. Considering that most IRS campaigns will usually be deployed against a background of high LLIN coverage, effective IRS rotation plans should prioritise IRS insecticides which in addition to providing improved and prolonged vector control, can efficiently complement LLINs. Based on the findings of this study, chlorfenapyr IRS shows some potential to be a useful addition to such IRS rotation plans in pyrethroid-resistant areas with high pyrethroid LLIN coverage. It will be interesting to investigate the impact of combining pyrethroid LLINs with other newly approved IRS formulations containing clothianidin [17].\n",
-                        "\n",
-                        "The low mortality rates achieved with the standard pyrethroid net in this study (8%), further demonstrates the threat of pyrethroid resistance on the efficacy of standard pyrethroids. Pyrethroid resistance is widespread in malaria vectors across Africa and increasing in intensity the more they are used [12]. A new generation of LLINs treated with a pyrethroid and non-pyrethroid insecticide are now in advanced development. Studies have demonstrated the potential of some of these nets to provide improved control of clinical malaria transmitted by pyrethroid-resistant malaria vectors compared to standard pyrethroid nets [42, 43, 44]. As next generation nets come into the market, and are rolled out in endemic countries, it will be essential to explore any possible interactions with the different types of IRS insecticides that could be deployed together with them in a combined intervention approach.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental hut treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to differential attractiveness of the volunteer sleepers and huts.\n",
-                        "\n",
-                        "\n",
-                        "Mortality in susceptibility bioassays was interpreted according to WHO criteria. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Table 3. Overall entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts in Covè, southern Benin.\n",
-                        "footer: * Values in the same column sharing a superscript letter do not differ significantly (P>0.05).\n",
-                        "columns: ['Treatment', 'Total females caught', 'Average catch per night', '% Deterrence', 'Total exiting', '% Exophily�', '95% CI']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Untreated hut (control)\",\"Total females caught\":711,\"Average catch per night\":7,\"% Deterrence\":\"\\u2013\",\"Total exiting\":322,\"% Exophily\\ufffd\":\"45a\",\"95% CI\":\"42\\u201349\"},\"1\":{\"Treatment\":\"Untreated net (control)\",\"Total females caught\":420,\"Average catch per night\":4,\"% Deterrence\":\"41\",\"Total exiting\":165,\"% Exophily\\ufffd\":\"39a\",\"95% CI\":\"35\\u201344\"},\"2\":{\"Treatment\":\"DuraNet\",\"Total females caught\":619,\"Average catch per night\":6,\"% Deterrence\":\"13\",\"Total exiting\":350,\"% Exophily\\ufffd\":\"57b\",\"95% CI\":\"53\\u201361\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":508,\"Average catch per night\":5,\"% Deterrence\":\"29\",\"Total exiting\":389,\"% Exophily\\ufffd\":\"77c\",\"95% CI\":\"73\\u201380\"},\"4\":{\"Treatment\":\"DuraNet + Bendiocarb IRS\",\"Total females caught\":537,\"Average catch per night\":6,\"% Deterrence\":\"25\",\"Total exiting\":373,\"% Exophily\\ufffd\":\"70cd\",\"95% CI\":\"66\\u201373\"},\"5\":{\"Treatment\":\"Chlorfenapyr IRS\",\"Total females caught\":478,\"Average catch per night\":5,\"% Deterrence\":\"33\",\"Total exiting\":297,\"% Exophily\\ufffd\":\"62bd\",\"95% CI\":\"58\\u201367\"},\"6\":{\"Treatment\":\"DuraNet + Chlorfenapyr IRS\",\"Total females caught\":411,\"Average catch per night\":4,\"% Deterrence\":\"42\",\"Total exiting\":241,\"% Exophily\\ufffd\":\"59b\",\"95% CI\":\"54\\u201363\"},\"7\":{\"Treatment\":\"Pirimiphos-methyl CS IRS\",\"Total females caught\":528,\"Average catch per night\":6,\"% Deterrence\":\"26\",\"Total exiting\":356,\"% Exophily\\ufffd\":\"67d\",\"95% CI\":\"63\\u201371\"},\"8\":{\"Treatment\":\"DuraNet + Pirimiphos-methyl CS IRS\",\"Total females caught\":486,\"Average catch per night\":5,\"% Deterrence\":\"32\",\"Total exiting\":298,\"% Exophily\\ufffd\":\"61bd\",\"95% CI\":\"57\\u201366\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cove\",\"Start_month\":7,\"Start_year\":2018,\"End_month\":10,\"End_year\":2018}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"DuraNet\",\"Insecticide\":\"Alpha-cypermethrin\",\"Concentration_initial\":\"261 mg\\/m2\",\"Net_holed\":6.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Tunnel test\n",
-                        "\n",
-                        "The tunnel test outcomes confirmed the cone bioassay results for the susceptible _An. gambiae s.s._ Kisumu strain, with strong blood-feeding inhibition and high mortality in all nets being above the WHO tunnel test thresholds (>= 90% blood-feeding inhibition or >= 80% mortality) (Fig. 4). The blood-feeding inhibition was high only in unwashed and used samples of PermaNet(r) Dual (A) (92.4%), PermaNet(r) Dual (B) (91.6%) and PermaNet(r) 3.0 (92.3%) (Fig. 4A). The mortality was very high (100%) in unwashed or washed and unused or used samples of all nets (PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0) (Fig. 4B), thus showing their good efficacy against this pyrethroid-susceptible _An. gambiae_ Kisumu strain. Additional file shows the Kisumu strain mortality with tunnel tests in more detail (see Additional file 4: Table S4). For the pyrethroid-resistant _An. gambiae s.l._, only washed and unused PermaNet(r) Dual (A) induced a strong blood-feeding inhibition (91.3%) being above the WHO tunnel cut-off of 90% (Fig. 5A). However, the mortality with all samples (unwashed or washed and unused) of PermaNet(r) Dual (85.2-94.9%) were higher compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 5B), and consistent with the higher efficacy of PermaNet(r) Dual against the free-flying pyrethroid-resistant _An. gambiae s.l._ Tiassale strain populations observed in the concurrent experimental hut trials. Additional file shows the pyrethroid-resistant Tiassale strain mortality with tunnel tests in more detail (see Additional file 5: Table S5).\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0, unwashed and washed (20 washes), were tested against free-flying pyrethroid-resistant _An. gambiae s.l._ in the experimental huts in Tiassale, Cote d'lvoire from March to August 2020. Complementary laboratory cone bioassays (daytime and 3-min exposure) and tunnel tests (nightly and 15-h exposure) were performed against pyrethroid-susceptible _An. gambiae_ sensu stricto (_S.S._) (Kisumu strain) and pyrethroid-resistant _An. gambiae s.l._ (Tiassale strain).\n",
-                        "\n",
-                        "header: Small-scale field evaluation of PermaNet(r): Abstract\n",
-                        "\n",
-                        "Dual (a long-lasting net coated with a mixture of chlorfenapyr and deltamethrin) against pyrethroid-resistant _Anopheles gambiae_ mosquitoes from Tiassale, Cote d'lvoire\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien.zahouli@crsrs.ci\n",
-                        "\n",
-                        "Constant A. V. Edi\n",
-                        "\n",
-                        "1Laurence A. Yao\n",
-                        "\n",
-                        "1Emmanuelle G. Lisro\n",
-                        "\n",
-                        "1Marc Adou\n",
-                        "\n",
-                        "1Inza Kone\n",
-                        "\n",
-                        "1Graham Small\n",
-                        "\n",
-                        "2Leanore D. Sternberg\n",
-                        "\n",
-                        "28Benjamin G. Koudou\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Due to the rapid expansion of pyrethroid-resistance in malaria vectors in Africa, Global Plan for insecticide Resistance Management (GIPRM) has recommended the development of long-lasting insecticidal nets (LLINs), containing insecticide mixtures of active ingredients with different modes of action to mitigate resistance and improve LLIN efficacy. This good laboratory practice (GLP) study evaluated the efficacy of the chlorfenapyr and deltamethrin-coated PermaNet(r) Dual, in comparison with the deltamethrin and synergismt piperonyl butoxide (PBO)-treated PermaNet(r) 3.0 and the deltamethrin-coated PermaNet(r) 2.0, against wild free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato (_S.I._), in experimental huts in Tiassale, Cote d'lvoire (West Africa).\n",
-                        "\n",
-                        "header: Comparison of laboratory bioassay results with hut trial results\n",
-                        "\n",
-                        "Comparing the laboratory cone bioassay and tunnel test results with the experimental hut trial results with the same pyrethroid-resistant _An. gambiae s.l._ strain showed that these laboratory bioassays predicted the response in the huts for PermaNet(r) 3.0 and PermaNet(r) 2.0. Indeed, the unwashed and washed PermaNet(r) 3.0 and PermaNet(r) 2.0 induced relatively lower mortality in the pyrethroid-resistant _An. gambiae_ strain with the cone bioassays, the tunnel test and the hut trial. Focussing on the bioefficacy of PermaNet(r) Dual, the respective mortality rates in the cone, tunnel and hut were 14.0%, 84.5% and 94.9% for unwashed PermaNet(r) Dual (A), 24.0%, 86.5% and 90.2% for unwashed PermaNet(r) Dual (B), and 10.0%, 87.5% and 90.4% for washed PermaNet(r) Dual (B). This suggests that laboratory tunnel tests are more predictive of the performance of PermaNet(r) Dual in experimental huts. Additionally, there were correlations in the blood-feeding rates and blood-feeding inhibition between the tunnel test results and the hut trial results. The high mortality and strong blood-feeding inhibition of PermaNet(r) Dual in tunnel tests with the pyrethroid-resistant _An. gambiae_ Tiassale strain shows that the chlorfenapyr component of PermaNet(r) Dual provided good efficacy against pyrethroid-resistant _Anopheles_.\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "Pieces of netting were sampled from unwashed and washed PermaNet(r) Dual (A and B), PermaNet(r) 3.0 and PermaNet(r) 2.0 LLINs before and after being used in the hut trial, in accordance with the WHO protocol for chemical analysis [25]. All sampled net pieces were labelled and stored individually in aluminium foil at 3.7-4.2 degC. The net pieces were then shipped to the Vestergaard's ISO IEC17025 laboratory in Vietnam for chemical analysis of chlorfenapyr and deltamethrin. Deltamethrin and PBO contents were determined following the Collaborative International Pesticides Analytical Council Ltd (CIPAC) (i.e. ClPAC 33/LN/(M2)/3) methods [26]. Briefly, chlorfenapyr content was analysed using in-house method VCL-098-20 that was undergoing ClIPAC validation and was still to be published. Chlorfenapyr content was extracted from the PermaNet(r) Dual net using a mixture of n-hexane and 1,4-dioxane (95:5, v:v) solvents with an internal standard of dibutylphthalate added. The mixture was shaken by shaking machine to extract chlorfenapyr. Extracted solution was filtered through 0.45 mm Teflon membrane and analysed by a normal phase high performance liquid chromatography for chlorfenapyr concentration, detector UV 236 nm.\n",
-                        "\n",
-                        "header: Supporting laboratory testing of net samples\n",
-                        "\n",
-                        "Standard WHO cone bioassays and tunnel tests were conducted with unwashed and washed nets under laboratory conditions, to predict their responses in the field experimental huts. The insecticide susceptible Kisumu strains and field-collected F0 generation of pyrethroid-resistant Tiassale strain of _An. gambiae_ were tested with samples (25 cm x 25 cm) cut from each of the eight net \n",
-                        "types (unwashed and washed net samples) before and after their inclusion into the hut trial [23]. For the tunnel tests, tunnels were divided into two sections by netting samples held in a frame. Nine holes of 1 cm in diameter were cut in each net sample (with one hole located at the centre of the square, and the other eight equidistant and located 5 cm from the border), and the surface of netting available to the mosquitoes was 400 cm\\({}^{2}\\) (20 cm \\(\\times\\) 20 cm).\n",
-                        "\n",
-                        "For each type of net and wash status, 100 non-blood-fed, 2-5-day-old females per strain were subjected to 3-min exposure in cone bioassays in replicates of five mosquitoes per cone according to the WHO guidelines [22]. For the tunnel tests, 100 non-blood-fed, 5-8-day-old mosquito females per strain were subjected to a 15-h exposure overnight using guinea pig as host, following the WHO guidelines [23]. Testing and holding conditions were 27 +- 2 degC and 80 +- 10% relative humidity. The 60-min knockdown for cone bioassays, blood-feeding status for tunnel tests and 24, 48 and 72-h mortality for both methods were recorded.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All relevant data are within the manuscript.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The deltamethrin-chlorfenapyr-coated PermaNet(r) Dual induced a high efficacy and performed better than the deltamethrin-PBO PermaNet(r) 3.0 and the deltamethrin-only PermaNet(r) 2.0, testing both unwashed and 20 times washed samples against the pyrethroid-susceptible and resistant strains of _An. gambiae s.l._ The inclusion of chlorfenapyr with deltamethrin in PermaNet(r) Dual net greatly improved protection and control of pyrethroid-resistant _An. gambiae_ populations. PermaNet(r) Dual thus represents a promising tool, with a high potential to reduce malaria transmission and provide community protection in areas compromised by mosquito vector resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Blood-feeding and personal protection\n",
-                        "\n",
-                        "The blood-feeding effect of the nets in wild pyrethroid-resistant _An. gambiae_ that entered the trial huts and personal protection are presented in Fig. 1A and Table 1. Before washing, the percentage blood-feeding with PernaNet(r) Dual (A) (36.7 +- 4.0%) did not differ significantly from PernaNet(r) Dual (B) (37.5 +- 4.4%) and PernaNet(r) 2.0 (55.7 +- 3.7%), but was significantly higher than PernaNet(r) 2.0 (31.3 +- 4.4%) (z = - 2.17; p = 0.030). The percentage blood-feeding for unwashed PernaNet(r) Dual (B) was similar to unwashed samples of PernaNet(r) 2.0 (z = 1.74; p = 0.081) and PernaNet(r) 3.0 (z = - 1.49; p = 0.136). Washing 20 times did not influence significantly the percentage blood-feeding in PernaNet(r) Dual (B) (z = 0.10; p = 0.921). After washing,\n",
-                        "the percentage blood-feeding of PermaNet(r) Dual (B) (36.0 +- 3.7%) was substantially lower, but without significant differences when compared with PermaNet(r) 3.0 (57.7 +- 3.7%) (z = 1.71; p = 0.087), PermaNet(r) 2.0 (72.3 +- 3.2%) (z = 1.78; p = 0.076) and the untreated control net (z = 1.72; p = 0.085). For blood-feeding inhibition, unwashed PermaNet(r) Dual (A) (42.5 +- 6.8%) was similar to unwashed PermaNet(r) Dual (B) (41.4 +- 6.9%) (z = 0.25; p = 0.802) and washed PermaNet(r) Dual (B) (43.7 +- 4.8%) that was not affected by washing (z = - 0.41; p = 0.682). Compared with PermaNet(r) 3.0, the blood-feeding inhibition of PermaNet(r) Dual (B) was significantly higher before washing (z = - 0.72; p = 0.469), and significantly lower after washing (z = - 1.92; p = 0.045). However, both unwashed and washed PermaNet(r) Dual (B) provided, respectively, significantly higher blood-feeding inhibition than unwashed and washed PermaNet(r) 2.0 (all p < 0.05). The personal protection was similar between unwashed samples of PermaNet(r) Dual (A) (58.8 +- 3.5%) and PermaNet(r) Dual (B) (60.8 +- 3.8%) (z = - 0.04; p = 0.968). Unwashed PermaNet(r) Dual (B) resulted in a personal protection that was similar to unwashed PermaNet(r) 3.0 (62.9 +- 4.2%) (z = 0.21; p = 0.738), and significantly different from unwashed PermaNet(r) 2.0 (28.8 +- 1.9%) (z = 3.21; p = 0.001). With 20 washes, the personal protection with PermaNet(r) Dual (B) declined significantly from 60.8 +- 3.8% to 20.2 +- 1.1% (z = - 2.96; p = 0.003). However, washed PermaNet(r) Dual (B) provided significantly higher personal protection compared with their washed counterparts of PermaNet(r) 3.0 (9.8 +- 0.7%) (z = 2.89; p = 0.004) and PermaNet(r) 2.0 (- 32.6 +- 2.2%) (z = 2.93; p = 0.003).\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "According to the latest World Malaria Report 2022 of the World Health Organization (WHO), there were 247 million malaria cases and 619,000 malaria deaths in 84 malaria endemic countries worldwide in 2021 [1, 2]. This represents about 13.4 million more cases in 2021 compared to 2019 attributable to disruptions to essential malaria services during the COVID-18 pandemic [1]. The WHO African Region, with an estimated 234 million cases in 2021, accounted for about 95% of global cases malaria [1]. The recent decline of malaria burden from 2000 to 2019 was largely due to the massive distribution and use of long-lasting insecticidal nets (LLINs) (2.5 billion LLINs were delivered from 2004 to 2021) for _Anopheles_ mosquito vector control [1]. The malaria burden reduction is now slowing down and is threatened by the spread of resistance to pyrethroids (78 of 88 endemic countries have detected resistance to at least one insecticide class reported to the WHO from 2010 to 2020) [1]. The WHO Global Plan for Insecticide Resistance Management (GPIRM) has recommended the development of LLINs with insecticide mixtures of active ingredients with different modes of action to mitigate resistance [3]. The WHO Global Technical Strategy (GTS) aims for a reduction of malaria case incidence and mortality rate of at least 40% by 2020, 75% by 2025 and 90% by 2030 from the 2015 baseline [1, 4]. To meet these targets, GTS has called for the development of new tools, with combined or more effective insecticide molecules to control insecticide-resistant _Anopheles_ vectors [4]. These new vector control tools must incorporate new insecticide molecules and/or insecticide mixtures containing at least two active ingredients with different modes of action for the management of malaria vector resistance to insecticides [1, 3].\n",
-                        "\n",
-                        "Malaria is endemic throughout Cote d'Ivoire and represents the leading cause of mortality and morbidity in the country. Several studies have shown a wide spread resistance in local _Anopheles_ to most of the insecticide classes currently used in malaria vector control (e.g. pyrethroids, DDT and carbamates) [5-8]. The existence of multiple mechanisms of resistance in the main vector, _Anopheles gambiae_ sensu lato (_s.l._) threatens the efficacy of vector control tools currently used in Cote d'Ivoire, including LLINs [9-11]. Meiwald et al. [11] found that pyrethroid resistance is associated with significant overexpression of _CYP6P4, CYP6P3,_ and _CYP6Z1_ in _Anopheles coluzzii_ in Cote d'Ivoire. High allelic frequencies of knock-down resistance (_kdr_) _L1014F_ mutation (range: 0.46-1), relatively low frequencies of the _ace-1R_ mutation in the acetylcholinesterase gene associated with target-site insensitivity to carbamates and organophosphates (<0.5), and elevated activity of insecticide detoxifying enzymes (mainly mixed function oxidases (MFOs), esterase and glutathione S-transferase (GST)), have been reported in Cote d'Ivoire [5, 7, 10]. Recent laboratory (WHO tube and CDC bottle) bioassays against local pyrethroid-resistant _An. gambiae_ from Cote d'Ivoire have shown that pyrethroids induce low mortality, whilst pre-exposure to the synergist piperonyl butoxide (PBO) increases the mortality but does not restore fully the susceptibility due to resistance [9]. However, the pyrrole insecticide chlorfenapyr induces higher mortality in resistant _An. gambiae_ compared with pyrethroids alone or combined with PBO in Cote d'Ivoire [9].\n",
-                        "\n",
-                        "The present good laboratory practice (GLP) study evaluated the bio-efficacy of a new candidate LLIN, PermaNet(r) Dual, against pyrethroid-resistant _An. gambiae s.l._ in comparison with the WHO Prequalification Unit, Vector Control Product Assessment Team (PQT/VCP) listed standard LLINs, PermaNet(r) 2.0 and PermaNet(r) 3.0, through the conduct of an experimental hut trial and laboratory bioassays in Tiassale, Cote d'Ivoire. PermaNet(r) Dual has a mixture of the pyrrole chlorfenapyr and the pyrethroid deltamethrin coated onto a polyester fabric. PermaNet(r) 3.0 is treated with deltamethrin and PBO, and PermaNet(r) 2.0 is treated with deltamethrin only. Chlorfenapyr is a pro-insecticide activated by the oxygenase function of cytochrome P450s and oxidative removal of the N-ethoxymethyl\n",
-                        "group leads to a toxic form identified as CL 303,268. The toxic form uncouples oxidative phosphorylation in the mitochondria, resulting in disruption of the production of adenosine triphosphate and loss of energy, leading to cell dysfunction and ultimately death of the insect [12]. The WHO has received some resistance monitoring data for chlorfenapyr, but these data are insufficient to assess the potential presence of resistance to this insecticide [1]. Deltamethrin is a neurotoxic insecticide and has excito-repellent effects on mosquitoes. PBO, used on PermaNet(r) 3.0, is a synergist which inhibits mixed function oxidases, blocking the detoxification of pyrethroids and at least partially restoring pyrethroid susceptibility [1, 13]. Pyrethroid-resistant strains of _Anopheles_ have so far been found to be susceptible to chlorfenapyr [9, 14-18]. Thus, the hypothesis of the current study was that chlorfenapyr would kill the pyrethroid-resistant _An. gambiae_ population in Tiassale, and PermaNet(r) Dual would induce higher mortality compared to both PermaNet(r) 3.0 and PermaNet(r) 2.0 in the experimental huts, and the supplementary laboratory cone and tunnel bioassays could predict these hut trial outcomes.\n",
-                        "\n",
-                        "header: Supplementary Information\n",
-                        "\n",
-                        "The online version contains supplementary material available at [https://doi.org/10.11016/s12936-023-04455-z](https://doi.org/10.11016/s12936-023-04455-z).\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This study was funded by a Grant from Vestergaard Sarl, Lausanne, Switzerland.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The outcomes of the current experimental hut trial showed that the parameters of the net efficacy against the pyrethroid-resistant _An. gambiae s.l._ Tiassale population varied as a function of net type and wash status, with the performance of PermaNet(r) Dual being better overall compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 1 and Table 1).\n",
-                        "\n",
-                        "header: Experimental huts and mosquitoes: Net treatments and treatment arms\n",
-                        "\n",
-                        "This study included the PermaNet(r) Dual (candidate net), PermaNet(r) 3.0 and PermaNet(r) 2.0 (reference nets) and untreated net (negative control). All mosquito nets were new and unwashed nets supplied by Vestergaard Sarl (Lausanne, Switzerland). The untreated nets were made of polyester fabric without any insecticide. PermaNet(r) Dual was made of polyester fabric coated with chlorfenapyr at 5.0 g/kg +- 25% and deltamethrin at 2.1 g/kg +- 25%. The unwashed PermaNet(r) Dual arm was replicated using nets from two different production batches (referred to as A and B). PermaNet(r) 3.0 was made of polyester fabric coated with 2.1 g/kg +- 25% of deltamethrin on the sides, and polyethylene incorporated with 4.0 g/kg +- 25% of deltamethrin and 25.0 g/kg +- 25% of PBO on the roof. PermaNet(r) 2.0 was made of polyester fabric coated with 1.4 g/kg +- 25% of deltamethrin.\n",
-                        "\n",
-                        "Eight treatment arms were compared in the experimental huts and laboratory: PermaNet(r) Dual (A) unwashed; PermaNet(r) Dual (B) unwashed and washed 20 times; PermaNet(r) 3.0 unwashed and washed 20 times; PermaNet(r) 2.0 unwashed and washed 20 times; and untreated control net. The nets were prepared and washed using a soap called \"Savon de Marseille\" and according to the WHO guidelines on small-scale field hut trials and laboratory testing [23]. The interval of time between two consecutive washes was one day, corresponding to the regeneration time of all the nets. The regeneration time of the PermaNet(r) Dual (i.e. 1 day) was supplied by Vestergaard. The nets were washed 20 times. Before testing them in the experimental huts, all the unwashed, washed and untreated control nets were deliberately holed with six holes of 4 cm in diameter to stimulate the conditions of torn nets according to the WHO guidelines [23].\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Additional file 3: Table 53.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (Tissale strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The present small-scale GLP experimental hut study demonstrated that the deltamethrin- chlorfenapyr PermaNet(r) Dual net has high bio-efficacy, inducing significantly higher mortality and blood-feeding inhibition effects in the wild insecticide-resistant _An. gambiae s.l._ when compared with the deltamethrin-PBO PermaNet(r) 3.0 and deltamethrin-only PermaNet(r) 2.0. The inclusion of chlorfenapyr with pyrethroid in PermaNet(r) Dual net has greatly improved protection and control of free-flying wild pyrethroid-resistant _An. gambiae_ populations. The chlorfenapyr-deltamethrin-coated PermaNet(r) Dual net has great potential to reduce malaria transmission, particularly in areas compromised by high level of pyrethroid resistance in _Anopheles_ mosquitoes. Further validations in large-scale field trials are required to assess the effectiveness, the durability and the acceptability of this new tool for malaria vector control.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The current study evaluated the efficacy of PermaNet(r) Dual, a new candidate net coated with a mixture of chlorfenapyr and deltamethrin, against _An. gambiae_ in a small-scale GLP but study in Tiassale, Cote d'Ivoire, with supporting exploratory laboratory cone and tunnel\n",
-                        "\n",
-                        "Fig. 4: Mean blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the susceptible _Anopheles gambiae_ s.s. Kisumu strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassale, Côte d’Ivoire. Error bars show the standard error of the mean\n",
-                        "bioassays. WHO-prequalified PermaNet(r) 3.0 (co-treated with deltamethrin and PBO) and PermaNet(r) 2.0 (treated with deltamethrin only) were used as the reference nets. In this GLP but study, PermaNet(r) Dual performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0, in terms of mortality and blood-feeding inhibition, for both unwashed and unstressed cases, respectively.\n",
-                        "\n",
-                        "Fig. 5: Mean of blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the pyrethroid-resistant _Anopheles gambiae s1_. Tassale strain exposed to net samples using tunnel tests before and after experimental hut trial in Tassale, Côte d’thovie. Error bars show the standard error of the mean \n",
-                        "and washed samples. Unlike the cone bioassays, the tunnel tests successfully confirmed the field efficacy of the nets. Briefly, this hut trial showed the benefit of mixing deltamethrin and chlorfenapyr together in a long-lasting net, PermaNet(r) Dual, for an effective control of pyrethroid-resistant _Anopheles._\n",
-                        "\n",
-                        "The current experimental hut study demonstrated that PermaNet(r) Dual had increased efficacy against highly pyrethroid-resistant _An. gambiae._ Indeed, hut mortality of _An. gambiae_ Tiassale strain was significantly higher with PermaNet(r) Dual (>83%) in comparison with PermaNet(r) 3.0 (<38%) and PermaNet(r) 2.0 (<12%), for both unwashed and 20 times washed samples (Fig. 1B and Table 1). This PermaNet(r) Dual efficacy was similar to chlorfenapyr and alpha-cypermethrin mixture-coated Intercept G2 in West and East Africa [16, 17, 27, 28, 29, 30, 31, 32]. The good performance of PermaNet(r) Dual might be attributed to the susceptibility of the highly pyrethroid-resistant _An. gambiae_ s.l. to chlorfenapyr [9, 11]. Due to the novel mode of action of chlorfenapyr, the present pyrethroid-resistance mechanisms did not provide any cross-resistance to this insecticide [12]. Indeed, chlorfenapyr-treated tool has been shown to control a number of different multiple-insecticide-resistant _Anopheles_ populations [14, 15, 27, 28, 29, 30, 31, 32].\n",
-                        "\n",
-                        "This hut study showed that the percentage blood-feeding in wild pyrethroid-resistant _An. gambiae_ population was lower for PermaNet(r) Dual (unwashed and washed) compared with PermaNet(r) 2.0 (unwashed and washed) and washed PermaNet(r) 3.0, but slightly higher than\n",
-                        "unwashed PermaNet(r) 3.0 (Fig. 1A and Table 1). Likewise, blood-feeding inhibition with PermaNet(r) Dual was comparable or stronger than with PermaNet(r) 2.0 and washed PermaNet(r) 3.0. This PermaNet(r) Dual blood-feeding inhibition outcome is consistent with that caused by Intercept G2 in earlier hut trials in West Africa [27-31], and may be explained by the irritant effects of the deltamethrin [32-35]. While unwashed PermaNet(r) Dual and PermaNet(r) 3.0 produced similar levels of blood-feeding inhibition, the 20 times washed PermaNet(r) Dual induced a statistically greater blood-feeding inhibition in comparison with both PermaNet(r) 3.0 and PermaNet(r) 2.0 washed 20 times. This suggests that PermaNet(r) Dual may have performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0 over time. PermaNet(r) Dual effectiveness for inhibiting blood-feeding could imply its good potential for reducing the human-biting and blood-feeding, adding value into its killing effects in malaria vectors.\n",
-                        "\n",
-                        "In the present hut study, unwashed PermaNet(r) Dual deterrence effect against the wild pyrethroid-resistant _An. gambiae_ population was comparable to that recorded in PermaNet(r) 2.0 and PermaNet(r) 3.0. The deltamethrin component of PermaNet(r) Dual has a deterrence effect and may have induced exiting in mosquitoes. However, the 20 times washed PermaNet(r) Dual did not have a deterrence effect (Table 1), probably due to a reduction of the chemical contents (Table 2). A dipped chlorfenapyr net did seem to have a deterrence effect, but did not have a significant effect on exiting rates in West Africa [15, 16, 26-31]. The higher deterrence effect may be due to both the chlorfenapyr and deltamethrin components, but higher exiting rate would likely be due only to the deltamethrin component. Thus, PermaNet(r) Dual may have both deterrent and excito-repellency effects, providing personal protection against pyrethroid-resistant _An. gambiae_ mosquitoes, and reducing human-vector contacts, which may, in turn, lead to an increased user acceptance [36].\n",
-                        "\n",
-                        "While standard laboratory cone bioassays failed to predict PermaNet(r) Dual efficacy against pyrethroid-resistant _An. gambiae s.l.,_ tunnel tests successfully predicted its efficacy against the same strain in the semi-field experimental huts. Both cone and tunnel bioassays met the WHO criteria [44], with the susceptible _An. gambiae s.s._ Kisumu strain. However, _An. gambiae s.s._ Kisumu strain mortality against unwashed and used PermaNet(r) Dual (A) was lower (34%) with cone bioassay (Fig. 2B) probably due to the reduction of the chemical bioavailability on netting fibre surface of net samples tested, or the inappropriateness of cone bioassay method for evaluation of the chlorfenapyr-coated PermaNet(r) Dual as the mortality was higher (100%) with tunnel test (Fig. 4B). With the wild pyrethroid-resistant _An. gambiae s.l._ Tiassale strain, only the tunnel tests achieved high efficacy that was consistent with that observed in the huts with PermaNet(r) Dual. The difference in PermaNet(r) Dual efficacy between both _Anopheles_ Kiumu and Tiassale strains may be explained by the difference in the levels of their resistance to insecticides. However, the lower efficacy of PermaNet(r) Dual against pyrethroid-resistant _An. gambiae s.l._ with cone bioassays (daytime and 3-min exposure) compared with tunnel tests (night-time and 15-h exposure) may be attributable to the slow mode of action of chlorfenapyr and that mosquitoes need to be metabolically active for the activation of the chlorfenapyr pro-insecticide [12, 13, 37]. Indeed, chlorfenapyr has a previously been observed to have a slower action and delayed toxic activity of 2-3 days post-exposure compared to other insecticides (pyrethroids and organophosphates) used in mosquito vector control [14, 28, 29]. The cone bioassay method was developed to assess the bioefficacy of pyrethroid-only LLINs, and the use of this method to test LLINs containing non-neurotoxic insecticides, such as chlorfenapyr, may not necessarily be predictive of field impact, even with increased exposure time of mosquitoes inside the cones [16, 27]. In contrast, the tunnel test results with pyrethroid-resistant _An. gambiae s.l._ Tiassale strain (field-collected F0 generation) were more predictive of PermaNet(r) Dual efficacy in huts, as observed for other chlorfenapyr-coated nets, such as Interceptor(r) G2 [16, 27, 28]. Tunnel tests provide an increased exposure time of mosquitoes to the LLIN samples being tested, and being an overnight exposure. As the female mosquitoes in the tunnel tests are also exhibiting host-seeking behaviour and are, therefore, metabolically active, the activation of chlorfenapyr following tarsal pickup by mosquitoes may be more effective. The tunnel test was a better predictor of PermaNet(r) Dual field efficacy because exposure occurred at night when host-seeking mosquitoes are more vulnerable to chlorfenapyr. Tunnel test thus is a more reliable technique to assess the efficacy of a chlorfenapyr-treated net prior to field trials against free-flying mosquitoes [16, 38, 39].\n",
-                        "\n",
-                        "In PermaNet(r) Dual, deltamethrin and chlorfenapyr contents varied slightly, but complied with the dose interval limits of target specification [23]. This revealed a good homogeneity of the active ingredients' distribution over and a good retention of these active ingredients in PermaNet(r) Dual, thus resulting in high and similar mortality and blood-feeding inhibition between unwashed samples of PermaNet(r) Dual (A) and PermaNet(r) Dual (B). The reduction of chemical contents due to the loss of chlorfenapyr and deltamethrin with 20 washes had no apparent effect on PermaNet(r) Dual efficacy as the washed samples were still producing high mortality against the pyrethroid-resistant mosquitoes. Overall,\n",
-                        "PernaNet(r) Dual chemical bioavailability was sufficient and produced high mortality in pyrethroid-resistant _Anopheles_ mosquitoes after 20 standardized washes (corresponding to a 3-year use) and over the hut trial.\n",
-                        "\n",
-                        "The high vector mortality and personal protection effect of the PernaNet(r) Dual found in this study are the desired outcomes of any vector control tool. The high mortality effects and the additive blood-feeding inhibition impacts induced by PernaNet(r) Dual in this hut trial are expected to substantially diminish the malaria vector density and biting rates in the field, and hence the transmission of malaria in the areas where vectors are resistant to insecticides [40-47]. Additionally, PernaNet(r) Dual performed better than the reference PernaNet(r) 3.0 and PernaNet(r) 2.0 nets, when washed 20 times, thus meeting the WHO hut trial criteria [23].\n",
-                        "\n",
-                        "However, these results need to be further validated in a large-scale field trial to assess the durability and acceptability of this new tool for malaria vector control [48-50]. A community trial would be the best way to evaluate the community level effect of this promising candidate net (i.e. PernaNet(r) Dual) against malaria transmission, by monitoring entomological infection rate, _Plasmodium_ prevalence and disease incidence [48-50]. Such a community trial could be a randomized controlled trial as with dual-active-ingredient LLINs (e.g. Interceptor G2) in Tanzania [49], soon in Benin [48, 50], and the pilot deployments that are part of the New Nets Project led by PATH [51]. Furthermore, PernaNet(r) Dual should be tested against wild populations of _Anopheles funestus_[32], or other main or secondary vectors of malaria in Africa [52-56]. Ultimately, in the present study, the series of hut and laboratory tests demonstrated that the chlorfenapyr component of PernaNet(r) Dual could make a major contribution to controlling the pyrethroid-resistant _An. gambiae_ populations. Furthermore, there were no adverse effects reported among hut sleepers and mosquito collectors during the trial in the huts where PernaNet(r) Dual were used, and this may possibly increase the rate of future user acceptability and improve the usage of this net for malaria vector control.\n",
-                        "\n",
-                        "header: Outcomes measures\n",
-                        "\n",
-                        "The following entomological outcomes were used to evaluate the efficacy of each treatment arm in the current experimental hut trial [23]:\n",
-                        "\n",
-                        "1. Deterrence: proportional reduction in the number of mosquitoes caught in treated hut relative to the number caught in the control hut.\n",
-                        "2. Exiting rate: percentage of the mosquitoes collected from the veranda trap out of all mosquitoes collected.\n",
-                        "3. Induced exophily: proportional reduction of mosquitoes found in the exit and veranda traps relative to control hut.\n",
-                        "4. Blood-feeding: percentage of blood-fed mosquitoes relative to the total collected.\n",
-                        "5. Blood-feeding inhibition: proportional reduction in blood feeding percentage in treated huts relative to the control hut.\n",
-                        "6. Mortality: percentage of dead mosquitoes found dead in hut in the morning (immediate mortality) or after being caught alive and dead during holding (delayed mortality) in treatment huts out of mosquitoes collected, and corrected for control mortality.\n",
-                        "7. Personal protection: the proportional reduction in the number of blood-fed mosquitoes in the treated huts relative to the number of blood-fed mosquitoes in the untreated control.\n",
-                        "8. Killing effect: the proportional reduction in the number of mosquitoes killed in the treated huts relative to the number of mosquitoes killed in the untreated control.\n",
-                        "\n",
-                        "The formulas of key entomological outcomes measured in this study are [23]:\n",
-                        "\n",
-                        "\\[\\text{Deterrence}(\\%) = \\frac{\\text{Nc} - \\text{Nt}}{\\text{Nc}} \\times 100 = \\left( 1 - \\frac{\\text{Nt}}{\\text{Nc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Nt the total number of mosquitoes collected in the treatment hut and veranda/exit traps and Nc the total number of mosquitoes in the control hut and veranda/exit traps.\n",
-                        "\n",
-                        "\\[\\text{Exiting}\\,\\text{rate}(\\%) = \\frac{\\text{n}}{\\text{N}} \\times 100,\\]\n",
-                        "\n",
-                        "where n is the number of mosquitoes from veranda and window traps, while N is the total number of mosquitoes collected in the hut.\n",
-                        "\n",
-                        "\\[\\text{Induced}\\,\\text{exophily}(\\%) = \\frac{\\text{Pt} - \\text{Pc}}{\\text{Pc}} \\times 100\\] \\[= \\left( \\frac{\\text{Pt}}{\\text{Pc}} - 1 \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Pt is the proportion of mosquitoes from veranda and window traps of treated hut while Pc is the number of mosquitoes from veranda and window traps of untreated control hut.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding}\\,\\text{inhibition}(\\%) = \\frac{\\text{Pc} - \\text{Pt}}{\\text{Pc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Pt the proportion of blood-fed mosquitoes in the treatment hut, and Pc the proportion of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Personal}\\,\\text{protection}(\\%) = \\frac{\\text{Bc} - \\text{Bt}}{\\text{Bc}} \\times 100\\] \\[= \\left( 1 - \\frac{\\text{Bt}}{\\text{Bc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Bt the number of blood-fed mosquitoes in the treatment hut and Bc the number of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Killing}\\,\\text{effect}(\\%) = \\frac{\\text{Kt} - \\text{Kc}}{\\text{Tc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Kt is the number of mosquitoes killed in the treatment huts, Kc is the number of mosquitoes killed in the control huts, and Tc is the total number of mosquitoes collected from the control.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "PermaNet(r) Dual demonstrated significantly improved efficacy, compared to PermaNet(r) 3.0 and PermaNet(r) 2.0, against the pyrethroid-resistant _An. gambiae s.l._ Indeed, the experimental hut trial data showed that the mortality and blood-feeding inhibition in the wild pyrethroid-resistant _An. gambiae s.l._ were overall significantly higher with PermaNet(r) Dual compared with PermaNet(r) 3.0 and PermaNet(r) 2.0, for both unwashed and washed samples. The mortality with unwashed and washed samples were 93.6 +- 0.2% and 83.2 +- 0.9% for PermaNet(r) Dual, 37.5 +- 2.9% and 14.4 +- 3.3% for PermaNet(r) 3.0, and 7.4 +- 5.1% and 11.7 +- 3.4% for PermaNet(r) 2.0, respectively. Moreover, unwashed and washed samples produced the respective percentage blood-feeding inhibition of 41.4 +- 6.9% and 43.7 +- 4.8% with PermaNet(r) Dual, 51.0 +- 5.7% and 9.8 +- 3.6% with PermaNet(r) 3.0, and 12.8 +- 4.3% and - 13.0 +- 3.6% with PermaNet(r) 2.0. Overall, PermaNet(r) Dual also induced higher or similar deterrence, exophily and personal protection when compared with the standard PermaNet(r) 3.0 and PermaNet(r) 2.0 reference nets, with both unwashed and\n",
-                        "washed net samples. In contrast to cone bioassays, tunnel tests predicted the efficacy of PermaNet(r) Dual seen in the current experimental hut trial.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Tunnel test\n",
-                        "\n",
-                        "The tunnel test outcomes confirmed the cone bioassay results for the susceptible _An. gambiae s.s._ Kisumu strain, with strong blood-feeding inhibition and high mortality in all nets being above the WHO tunnel test thresholds (>= 90% blood-feeding inhibition or >= 80% mortality) (Fig. 4). The blood-feeding inhibition was high only in unwashed and used samples of PermaNet(r) Dual (A) (92.4%), PermaNet(r) Dual (B) (91.6%) and PermaNet(r) 3.0 (92.3%) (Fig. 4A). The mortality was very high (100%) in unwashed or washed and unused or used samples of all nets (PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0) (Fig. 4B), thus showing their good efficacy against this pyrethroid-susceptible _An. gambiae_ Kisumu strain. Additional file shows the Kisumu strain mortality with tunnel tests in more detail (see Additional file 4: Table S4). For the pyrethroid-resistant _An. gambiae s.l._, only washed and unused PermaNet(r) Dual (A) induced a strong blood-feeding inhibition (91.3%) being above the WHO tunnel cut-off of 90% (Fig. 5A). However, the mortality with all samples (unwashed or washed and unused) of PermaNet(r) Dual (85.2-94.9%) were higher compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 5B), and consistent with the higher efficacy of PermaNet(r) Dual against the free-flying pyrethroid-resistant _An. gambiae s.l._ Tiassale strain populations observed in the concurrent experimental hut trials. Additional file shows the pyrethroid-resistant Tiassale strain mortality with tunnel tests in more detail (see Additional file 5: Table S5).\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0, unwashed and washed (20 washes), were tested against free-flying pyrethroid-resistant _An. gambiae s.l._ in the experimental huts in Tiassale, Cote d'lvoire from March to August 2020. Complementary laboratory cone bioassays (daytime and 3-min exposure) and tunnel tests (nightly and 15-h exposure) were performed against pyrethroid-susceptible _An. gambiae_ sensu stricto (_S.S._) (Kisumu strain) and pyrethroid-resistant _An. gambiae s.l._ (Tiassale strain).\n",
-                        "\n",
-                        "header: Comparison of laboratory bioassay results with hut trial results\n",
-                        "\n",
-                        "Comparing the laboratory cone bioassay and tunnel test results with the experimental hut trial results with the same pyrethroid-resistant _An. gambiae s.l._ strain showed that these laboratory bioassays predicted the response in the huts for PermaNet(r) 3.0 and PermaNet(r) 2.0. Indeed, the unwashed and washed PermaNet(r) 3.0 and PermaNet(r) 2.0 induced relatively lower mortality in the pyrethroid-resistant _An. gambiae_ strain with the cone bioassays, the tunnel test and the hut trial. Focussing on the bioefficacy of PermaNet(r) Dual, the respective mortality rates in the cone, tunnel and hut were 14.0%, 84.5% and 94.9% for unwashed PermaNet(r) Dual (A), 24.0%, 86.5% and 90.2% for unwashed PermaNet(r) Dual (B), and 10.0%, 87.5% and 90.4% for washed PermaNet(r) Dual (B). This suggests that laboratory tunnel tests are more predictive of the performance of PermaNet(r) Dual in experimental huts. Additionally, there were correlations in the blood-feeding rates and blood-feeding inhibition between the tunnel test results and the hut trial results. The high mortality and strong blood-feeding inhibition of PermaNet(r) Dual in tunnel tests with the pyrethroid-resistant _An. gambiae_ Tiassale strain shows that the chlorfenapyr component of PermaNet(r) Dual provided good efficacy against pyrethroid-resistant _Anopheles_.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The deltamethrin-chlorfenapyr-coated PermaNet(r) Dual induced a high efficacy and performed better than the deltamethrin-PBO PermaNet(r) 3.0 and the deltamethrin-only PermaNet(r) 2.0, testing both unwashed and 20 times washed samples against the pyrethroid-susceptible and resistant strains of _An. gambiae s.l._ The inclusion of chlorfenapyr with deltamethrin in PermaNet(r) Dual net greatly improved protection and control of pyrethroid-resistant _An. gambiae_ populations. PermaNet(r) Dual thus represents a promising tool, with a high potential to reduce malaria transmission and provide community protection in areas compromised by mosquito vector resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All relevant data are within the manuscript.\n",
-                        "\n",
-                        "header: Small-scale field evaluation of PermaNet(r): Abstract\n",
-                        "\n",
-                        "Dual (a long-lasting net coated with a mixture of chlorfenapyr and deltamethrin) against pyrethroid-resistant _Anopheles gambiae_ mosquitoes from Tiassale, Cote d'lvoire\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien.zahouli@crsrs.ci\n",
-                        "\n",
-                        "Constant A. V. Edi\n",
-                        "\n",
-                        "1Laurence A. Yao\n",
-                        "\n",
-                        "1Emmanuelle G. Lisro\n",
-                        "\n",
-                        "1Marc Adou\n",
-                        "\n",
-                        "1Inza Kone\n",
-                        "\n",
-                        "1Graham Small\n",
-                        "\n",
-                        "2Leanore D. Sternberg\n",
-                        "\n",
-                        "28Benjamin G. Koudou\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Due to the rapid expansion of pyrethroid-resistance in malaria vectors in Africa, Global Plan for insecticide Resistance Management (GIPRM) has recommended the development of long-lasting insecticidal nets (LLINs), containing insecticide mixtures of active ingredients with different modes of action to mitigate resistance and improve LLIN efficacy. This good laboratory practice (GLP) study evaluated the efficacy of the chlorfenapyr and deltamethrin-coated PermaNet(r) Dual, in comparison with the deltamethrin and synergismt piperonyl butoxide (PBO)-treated PermaNet(r) 3.0 and the deltamethrin-coated PermaNet(r) 2.0, against wild free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato (_S.I._), in experimental huts in Tiassale, Cote d'lvoire (West Africa).\n",
-                        "\n",
-                        "header: Supplementary Information\n",
-                        "\n",
-                        "The online version contains supplementary material available at [https://doi.org/10.11016/s12936-023-04455-z](https://doi.org/10.11016/s12936-023-04455-z).\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "According to the latest World Malaria Report 2022 of the World Health Organization (WHO), there were 247 million malaria cases and 619,000 malaria deaths in 84 malaria endemic countries worldwide in 2021 [1, 2]. This represents about 13.4 million more cases in 2021 compared to 2019 attributable to disruptions to essential malaria services during the COVID-18 pandemic [1]. The WHO African Region, with an estimated 234 million cases in 2021, accounted for about 95% of global cases malaria [1]. The recent decline of malaria burden from 2000 to 2019 was largely due to the massive distribution and use of long-lasting insecticidal nets (LLINs) (2.5 billion LLINs were delivered from 2004 to 2021) for _Anopheles_ mosquito vector control [1]. The malaria burden reduction is now slowing down and is threatened by the spread of resistance to pyrethroids (78 of 88 endemic countries have detected resistance to at least one insecticide class reported to the WHO from 2010 to 2020) [1]. The WHO Global Plan for Insecticide Resistance Management (GPIRM) has recommended the development of LLINs with insecticide mixtures of active ingredients with different modes of action to mitigate resistance [3]. The WHO Global Technical Strategy (GTS) aims for a reduction of malaria case incidence and mortality rate of at least 40% by 2020, 75% by 2025 and 90% by 2030 from the 2015 baseline [1, 4]. To meet these targets, GTS has called for the development of new tools, with combined or more effective insecticide molecules to control insecticide-resistant _Anopheles_ vectors [4]. These new vector control tools must incorporate new insecticide molecules and/or insecticide mixtures containing at least two active ingredients with different modes of action for the management of malaria vector resistance to insecticides [1, 3].\n",
-                        "\n",
-                        "Malaria is endemic throughout Cote d'Ivoire and represents the leading cause of mortality and morbidity in the country. Several studies have shown a wide spread resistance in local _Anopheles_ to most of the insecticide classes currently used in malaria vector control (e.g. pyrethroids, DDT and carbamates) [5-8]. The existence of multiple mechanisms of resistance in the main vector, _Anopheles gambiae_ sensu lato (_s.l._) threatens the efficacy of vector control tools currently used in Cote d'Ivoire, including LLINs [9-11]. Meiwald et al. [11] found that pyrethroid resistance is associated with significant overexpression of _CYP6P4, CYP6P3,_ and _CYP6Z1_ in _Anopheles coluzzii_ in Cote d'Ivoire. High allelic frequencies of knock-down resistance (_kdr_) _L1014F_ mutation (range: 0.46-1), relatively low frequencies of the _ace-1R_ mutation in the acetylcholinesterase gene associated with target-site insensitivity to carbamates and organophosphates (<0.5), and elevated activity of insecticide detoxifying enzymes (mainly mixed function oxidases (MFOs), esterase and glutathione S-transferase (GST)), have been reported in Cote d'Ivoire [5, 7, 10]. Recent laboratory (WHO tube and CDC bottle) bioassays against local pyrethroid-resistant _An. gambiae_ from Cote d'Ivoire have shown that pyrethroids induce low mortality, whilst pre-exposure to the synergist piperonyl butoxide (PBO) increases the mortality but does not restore fully the susceptibility due to resistance [9]. However, the pyrrole insecticide chlorfenapyr induces higher mortality in resistant _An. gambiae_ compared with pyrethroids alone or combined with PBO in Cote d'Ivoire [9].\n",
-                        "\n",
-                        "The present good laboratory practice (GLP) study evaluated the bio-efficacy of a new candidate LLIN, PermaNet(r) Dual, against pyrethroid-resistant _An. gambiae s.l._ in comparison with the WHO Prequalification Unit, Vector Control Product Assessment Team (PQT/VCP) listed standard LLINs, PermaNet(r) 2.0 and PermaNet(r) 3.0, through the conduct of an experimental hut trial and laboratory bioassays in Tiassale, Cote d'Ivoire. PermaNet(r) Dual has a mixture of the pyrrole chlorfenapyr and the pyrethroid deltamethrin coated onto a polyester fabric. PermaNet(r) 3.0 is treated with deltamethrin and PBO, and PermaNet(r) 2.0 is treated with deltamethrin only. Chlorfenapyr is a pro-insecticide activated by the oxygenase function of cytochrome P450s and oxidative removal of the N-ethoxymethyl\n",
-                        "group leads to a toxic form identified as CL 303,268. The toxic form uncouples oxidative phosphorylation in the mitochondria, resulting in disruption of the production of adenosine triphosphate and loss of energy, leading to cell dysfunction and ultimately death of the insect [12]. The WHO has received some resistance monitoring data for chlorfenapyr, but these data are insufficient to assess the potential presence of resistance to this insecticide [1]. Deltamethrin is a neurotoxic insecticide and has excito-repellent effects on mosquitoes. PBO, used on PermaNet(r) 3.0, is a synergist which inhibits mixed function oxidases, blocking the detoxification of pyrethroids and at least partially restoring pyrethroid susceptibility [1, 13]. Pyrethroid-resistant strains of _Anopheles_ have so far been found to be susceptible to chlorfenapyr [9, 14-18]. Thus, the hypothesis of the current study was that chlorfenapyr would kill the pyrethroid-resistant _An. gambiae_ population in Tiassale, and PermaNet(r) Dual would induce higher mortality compared to both PermaNet(r) 3.0 and PermaNet(r) 2.0 in the experimental huts, and the supplementary laboratory cone and tunnel bioassays could predict these hut trial outcomes.\n",
-                        "\n",
-                        "header: Supporting laboratory testing of net samples\n",
-                        "\n",
-                        "Standard WHO cone bioassays and tunnel tests were conducted with unwashed and washed nets under laboratory conditions, to predict their responses in the field experimental huts. The insecticide susceptible Kisumu strains and field-collected F0 generation of pyrethroid-resistant Tiassale strain of _An. gambiae_ were tested with samples (25 cm x 25 cm) cut from each of the eight net \n",
-                        "types (unwashed and washed net samples) before and after their inclusion into the hut trial [23]. For the tunnel tests, tunnels were divided into two sections by netting samples held in a frame. Nine holes of 1 cm in diameter were cut in each net sample (with one hole located at the centre of the square, and the other eight equidistant and located 5 cm from the border), and the surface of netting available to the mosquitoes was 400 cm\\({}^{2}\\) (20 cm \\(\\times\\) 20 cm).\n",
-                        "\n",
-                        "For each type of net and wash status, 100 non-blood-fed, 2-5-day-old females per strain were subjected to 3-min exposure in cone bioassays in replicates of five mosquitoes per cone according to the WHO guidelines [22]. For the tunnel tests, 100 non-blood-fed, 5-8-day-old mosquito females per strain were subjected to a 15-h exposure overnight using guinea pig as host, following the WHO guidelines [23]. Testing and holding conditions were 27 +- 2 degC and 80 +- 10% relative humidity. The 60-min knockdown for cone bioassays, blood-feeding status for tunnel tests and 24, 48 and 72-h mortality for both methods were recorded.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The outcomes of the current experimental hut trial showed that the parameters of the net efficacy against the pyrethroid-resistant _An. gambiae s.l._ Tiassale population varied as a function of net type and wash status, with the performance of PermaNet(r) Dual being better overall compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 1 and Table 1).\n",
-                        "\n",
-                        "header: Additional file 3: Table 53.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (Tissale strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "Pieces of netting were sampled from unwashed and washed PermaNet(r) Dual (A and B), PermaNet(r) 3.0 and PermaNet(r) 2.0 LLINs before and after being used in the hut trial, in accordance with the WHO protocol for chemical analysis [25]. All sampled net pieces were labelled and stored individually in aluminium foil at 3.7-4.2 degC. The net pieces were then shipped to the Vestergaard's ISO IEC17025 laboratory in Vietnam for chemical analysis of chlorfenapyr and deltamethrin. Deltamethrin and PBO contents were determined following the Collaborative International Pesticides Analytical Council Ltd (CIPAC) (i.e. ClPAC 33/LN/(M2)/3) methods [26]. Briefly, chlorfenapyr content was analysed using in-house method VCL-098-20 that was undergoing ClIPAC validation and was still to be published. Chlorfenapyr content was extracted from the PermaNet(r) Dual net using a mixture of n-hexane and 1,4-dioxane (95:5, v:v) solvents with an internal standard of dibutylphthalate added. The mixture was shaken by shaking machine to extract chlorfenapyr. Extracted solution was filtered through 0.45 mm Teflon membrane and analysed by a normal phase high performance liquid chromatography for chlorfenapyr concentration, detector UV 236 nm.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Blood-feeding and personal protection\n",
-                        "\n",
-                        "The blood-feeding effect of the nets in wild pyrethroid-resistant _An. gambiae_ that entered the trial huts and personal protection are presented in Fig. 1A and Table 1. Before washing, the percentage blood-feeding with PernaNet(r) Dual (A) (36.7 +- 4.0%) did not differ significantly from PernaNet(r) Dual (B) (37.5 +- 4.4%) and PernaNet(r) 2.0 (55.7 +- 3.7%), but was significantly higher than PernaNet(r) 2.0 (31.3 +- 4.4%) (z = - 2.17; p = 0.030). The percentage blood-feeding for unwashed PernaNet(r) Dual (B) was similar to unwashed samples of PernaNet(r) 2.0 (z = 1.74; p = 0.081) and PernaNet(r) 3.0 (z = - 1.49; p = 0.136). Washing 20 times did not influence significantly the percentage blood-feeding in PernaNet(r) Dual (B) (z = 0.10; p = 0.921). After washing,\n",
-                        "the percentage blood-feeding of PermaNet(r) Dual (B) (36.0 +- 3.7%) was substantially lower, but without significant differences when compared with PermaNet(r) 3.0 (57.7 +- 3.7%) (z = 1.71; p = 0.087), PermaNet(r) 2.0 (72.3 +- 3.2%) (z = 1.78; p = 0.076) and the untreated control net (z = 1.72; p = 0.085). For blood-feeding inhibition, unwashed PermaNet(r) Dual (A) (42.5 +- 6.8%) was similar to unwashed PermaNet(r) Dual (B) (41.4 +- 6.9%) (z = 0.25; p = 0.802) and washed PermaNet(r) Dual (B) (43.7 +- 4.8%) that was not affected by washing (z = - 0.41; p = 0.682). Compared with PermaNet(r) 3.0, the blood-feeding inhibition of PermaNet(r) Dual (B) was significantly higher before washing (z = - 0.72; p = 0.469), and significantly lower after washing (z = - 1.92; p = 0.045). However, both unwashed and washed PermaNet(r) Dual (B) provided, respectively, significantly higher blood-feeding inhibition than unwashed and washed PermaNet(r) 2.0 (all p < 0.05). The personal protection was similar between unwashed samples of PermaNet(r) Dual (A) (58.8 +- 3.5%) and PermaNet(r) Dual (B) (60.8 +- 3.8%) (z = - 0.04; p = 0.968). Unwashed PermaNet(r) Dual (B) resulted in a personal protection that was similar to unwashed PermaNet(r) 3.0 (62.9 +- 4.2%) (z = 0.21; p = 0.738), and significantly different from unwashed PermaNet(r) 2.0 (28.8 +- 1.9%) (z = 3.21; p = 0.001). With 20 washes, the personal protection with PermaNet(r) Dual (B) declined significantly from 60.8 +- 3.8% to 20.2 +- 1.1% (z = - 2.96; p = 0.003). However, washed PermaNet(r) Dual (B) provided significantly higher personal protection compared with their washed counterparts of PermaNet(r) 3.0 (9.8 +- 0.7%) (z = 2.89; p = 0.004) and PermaNet(r) 2.0 (- 32.6 +- 2.2%) (z = 2.93; p = 0.003).\n",
-                        "\n",
-                        "header: Additional file 2: Table 52.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (bisumu strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The present small-scale GLP experimental hut study demonstrated that the deltamethrin- chlorfenapyr PermaNet(r) Dual net has high bio-efficacy, inducing significantly higher mortality and blood-feeding inhibition effects in the wild insecticide-resistant _An. gambiae s.l._ when compared with the deltamethrin-PBO PermaNet(r) 3.0 and deltamethrin-only PermaNet(r) 2.0. The inclusion of chlorfenapyr with pyrethroid in PermaNet(r) Dual net has greatly improved protection and control of free-flying wild pyrethroid-resistant _An. gambiae_ populations. The chlorfenapyr-deltamethrin-coated PermaNet(r) Dual net has great potential to reduce malaria transmission, particularly in areas compromised by high level of pyrethroid resistance in _Anopheles_ mosquitoes. Further validations in large-scale field trials are required to assess the effectiveness, the durability and the acceptability of this new tool for malaria vector control.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "PermaNet(r) Dual demonstrated significantly improved efficacy, compared to PermaNet(r) 3.0 and PermaNet(r) 2.0, against the pyrethroid-resistant _An. gambiae s.l._ Indeed, the experimental hut trial data showed that the mortality and blood-feeding inhibition in the wild pyrethroid-resistant _An. gambiae s.l._ were overall significantly higher with PermaNet(r) Dual compared with PermaNet(r) 3.0 and PermaNet(r) 2.0, for both unwashed and washed samples. The mortality with unwashed and washed samples were 93.6 +- 0.2% and 83.2 +- 0.9% for PermaNet(r) Dual, 37.5 +- 2.9% and 14.4 +- 3.3% for PermaNet(r) 3.0, and 7.4 +- 5.1% and 11.7 +- 3.4% for PermaNet(r) 2.0, respectively. Moreover, unwashed and washed samples produced the respective percentage blood-feeding inhibition of 41.4 +- 6.9% and 43.7 +- 4.8% with PermaNet(r) Dual, 51.0 +- 5.7% and 9.8 +- 3.6% with PermaNet(r) 3.0, and 12.8 +- 4.3% and - 13.0 +- 3.6% with PermaNet(r) 2.0. Overall, PermaNet(r) Dual also induced higher or similar deterrence, exophily and personal protection when compared with the standard PermaNet(r) 3.0 and PermaNet(r) 2.0 reference nets, with both unwashed and\n",
-                        "washed net samples. In contrast to cone bioassays, tunnel tests predicted the efficacy of PermaNet(r) Dual seen in the current experimental hut trial.\n",
-                        "\n",
-                        "header: Outcomes measures\n",
-                        "\n",
-                        "The following entomological outcomes were used to evaluate the efficacy of each treatment arm in the current experimental hut trial [23]:\n",
-                        "\n",
-                        "1. Deterrence: proportional reduction in the number of mosquitoes caught in treated hut relative to the number caught in the control hut.\n",
-                        "2. Exiting rate: percentage of the mosquitoes collected from the veranda trap out of all mosquitoes collected.\n",
-                        "3. Induced exophily: proportional reduction of mosquitoes found in the exit and veranda traps relative to control hut.\n",
-                        "4. Blood-feeding: percentage of blood-fed mosquitoes relative to the total collected.\n",
-                        "5. Blood-feeding inhibition: proportional reduction in blood feeding percentage in treated huts relative to the control hut.\n",
-                        "6. Mortality: percentage of dead mosquitoes found dead in hut in the morning (immediate mortality) or after being caught alive and dead during holding (delayed mortality) in treatment huts out of mosquitoes collected, and corrected for control mortality.\n",
-                        "7. Personal protection: the proportional reduction in the number of blood-fed mosquitoes in the treated huts relative to the number of blood-fed mosquitoes in the untreated control.\n",
-                        "8. Killing effect: the proportional reduction in the number of mosquitoes killed in the treated huts relative to the number of mosquitoes killed in the untreated control.\n",
-                        "\n",
-                        "The formulas of key entomological outcomes measured in this study are [23]:\n",
-                        "\n",
-                        "\\[\\text{Deterrence}(\\%) = \\frac{\\text{Nc} - \\text{Nt}}{\\text{Nc}} \\times 100 = \\left( 1 - \\frac{\\text{Nt}}{\\text{Nc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Nt the total number of mosquitoes collected in the treatment hut and veranda/exit traps and Nc the total number of mosquitoes in the control hut and veranda/exit traps.\n",
-                        "\n",
-                        "\\[\\text{Exiting}\\,\\text{rate}(\\%) = \\frac{\\text{n}}{\\text{N}} \\times 100,\\]\n",
-                        "\n",
-                        "where n is the number of mosquitoes from veranda and window traps, while N is the total number of mosquitoes collected in the hut.\n",
-                        "\n",
-                        "\\[\\text{Induced}\\,\\text{exophily}(\\%) = \\frac{\\text{Pt} - \\text{Pc}}{\\text{Pc}} \\times 100\\] \\[= \\left( \\frac{\\text{Pt}}{\\text{Pc}} - 1 \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Pt is the proportion of mosquitoes from veranda and window traps of treated hut while Pc is the number of mosquitoes from veranda and window traps of untreated control hut.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding}\\,\\text{inhibition}(\\%) = \\frac{\\text{Pc} - \\text{Pt}}{\\text{Pc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Pt the proportion of blood-fed mosquitoes in the treatment hut, and Pc the proportion of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Personal}\\,\\text{protection}(\\%) = \\frac{\\text{Bc} - \\text{Bt}}{\\text{Bc}} \\times 100\\] \\[= \\left( 1 - \\frac{\\text{Bt}}{\\text{Bc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Bt the number of blood-fed mosquitoes in the treatment hut and Bc the number of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Killing}\\,\\text{effect}(\\%) = \\frac{\\text{Kt} - \\text{Kc}}{\\text{Tc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Kt is the number of mosquitoes killed in the treatment huts, Kc is the number of mosquitoes killed in the control huts, and Tc is the total number of mosquitoes collected from the control.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The current study evaluated the efficacy of PermaNet(r) Dual, a new candidate net coated with a mixture of chlorfenapyr and deltamethrin, against _An. gambiae_ in a small-scale GLP but study in Tiassale, Cote d'Ivoire, with supporting exploratory laboratory cone and tunnel\n",
-                        "\n",
-                        "Fig. 4: Mean blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the susceptible _Anopheles gambiae_ s.s. Kisumu strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassale, Côte d’Ivoire. Error bars show the standard error of the mean\n",
-                        "bioassays. WHO-prequalified PermaNet(r) 3.0 (co-treated with deltamethrin and PBO) and PermaNet(r) 2.0 (treated with deltamethrin only) were used as the reference nets. In this GLP but study, PermaNet(r) Dual performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0, in terms of mortality and blood-feeding inhibition, for both unwashed and unstressed cases, respectively.\n",
-                        "\n",
-                        "Fig. 5: Mean of blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the pyrethroid-resistant _Anopheles gambiae s1_. Tassale strain exposed to net samples using tunnel tests before and after experimental hut trial in Tassale, Côte d’thovie. Error bars show the standard error of the mean \n",
-                        "and washed samples. Unlike the cone bioassays, the tunnel tests successfully confirmed the field efficacy of the nets. Briefly, this hut trial showed the benefit of mixing deltamethrin and chlorfenapyr together in a long-lasting net, PermaNet(r) Dual, for an effective control of pyrethroid-resistant _Anopheles._\n",
-                        "\n",
-                        "The current experimental hut study demonstrated that PermaNet(r) Dual had increased efficacy against highly pyrethroid-resistant _An. gambiae._ Indeed, hut mortality of _An. gambiae_ Tiassale strain was significantly higher with PermaNet(r) Dual (>83%) in comparison with PermaNet(r) 3.0 (<38%) and PermaNet(r) 2.0 (<12%), for both unwashed and 20 times washed samples (Fig. 1B and Table 1). This PermaNet(r) Dual efficacy was similar to chlorfenapyr and alpha-cypermethrin mixture-coated Intercept G2 in West and East Africa [16, 17, 27, 28, 29, 30, 31, 32]. The good performance of PermaNet(r) Dual might be attributed to the susceptibility of the highly pyrethroid-resistant _An. gambiae_ s.l. to chlorfenapyr [9, 11]. Due to the novel mode of action of chlorfenapyr, the present pyrethroid-resistance mechanisms did not provide any cross-resistance to this insecticide [12]. Indeed, chlorfenapyr-treated tool has been shown to control a number of different multiple-insecticide-resistant _Anopheles_ populations [14, 15, 27, 28, 29, 30, 31, 32].\n",
-                        "\n",
-                        "This hut study showed that the percentage blood-feeding in wild pyrethroid-resistant _An. gambiae_ population was lower for PermaNet(r) Dual (unwashed and washed) compared with PermaNet(r) 2.0 (unwashed and washed) and washed PermaNet(r) 3.0, but slightly higher than\n",
-                        "unwashed PermaNet(r) 3.0 (Fig. 1A and Table 1). Likewise, blood-feeding inhibition with PermaNet(r) Dual was comparable or stronger than with PermaNet(r) 2.0 and washed PermaNet(r) 3.0. This PermaNet(r) Dual blood-feeding inhibition outcome is consistent with that caused by Intercept G2 in earlier hut trials in West Africa [27-31], and may be explained by the irritant effects of the deltamethrin [32-35]. While unwashed PermaNet(r) Dual and PermaNet(r) 3.0 produced similar levels of blood-feeding inhibition, the 20 times washed PermaNet(r) Dual induced a statistically greater blood-feeding inhibition in comparison with both PermaNet(r) 3.0 and PermaNet(r) 2.0 washed 20 times. This suggests that PermaNet(r) Dual may have performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0 over time. PermaNet(r) Dual effectiveness for inhibiting blood-feeding could imply its good potential for reducing the human-biting and blood-feeding, adding value into its killing effects in malaria vectors.\n",
-                        "\n",
-                        "In the present hut study, unwashed PermaNet(r) Dual deterrence effect against the wild pyrethroid-resistant _An. gambiae_ population was comparable to that recorded in PermaNet(r) 2.0 and PermaNet(r) 3.0. The deltamethrin component of PermaNet(r) Dual has a deterrence effect and may have induced exiting in mosquitoes. However, the 20 times washed PermaNet(r) Dual did not have a deterrence effect (Table 1), probably due to a reduction of the chemical contents (Table 2). A dipped chlorfenapyr net did seem to have a deterrence effect, but did not have a significant effect on exiting rates in West Africa [15, 16, 26-31]. The higher deterrence effect may be due to both the chlorfenapyr and deltamethrin components, but higher exiting rate would likely be due only to the deltamethrin component. Thus, PermaNet(r) Dual may have both deterrent and excito-repellency effects, providing personal protection against pyrethroid-resistant _An. gambiae_ mosquitoes, and reducing human-vector contacts, which may, in turn, lead to an increased user acceptance [36].\n",
-                        "\n",
-                        "While standard laboratory cone bioassays failed to predict PermaNet(r) Dual efficacy against pyrethroid-resistant _An. gambiae s.l.,_ tunnel tests successfully predicted its efficacy against the same strain in the semi-field experimental huts. Both cone and tunnel bioassays met the WHO criteria [44], with the susceptible _An. gambiae s.s._ Kisumu strain. However, _An. gambiae s.s._ Kisumu strain mortality against unwashed and used PermaNet(r) Dual (A) was lower (34%) with cone bioassay (Fig. 2B) probably due to the reduction of the chemical bioavailability on netting fibre surface of net samples tested, or the inappropriateness of cone bioassay method for evaluation of the chlorfenapyr-coated PermaNet(r) Dual as the mortality was higher (100%) with tunnel test (Fig. 4B). With the wild pyrethroid-resistant _An. gambiae s.l._ Tiassale strain, only the tunnel tests achieved high efficacy that was consistent with that observed in the huts with PermaNet(r) Dual. The difference in PermaNet(r) Dual efficacy between both _Anopheles_ Kiumu and Tiassale strains may be explained by the difference in the levels of their resistance to insecticides. However, the lower efficacy of PermaNet(r) Dual against pyrethroid-resistant _An. gambiae s.l._ with cone bioassays (daytime and 3-min exposure) compared with tunnel tests (night-time and 15-h exposure) may be attributable to the slow mode of action of chlorfenapyr and that mosquitoes need to be metabolically active for the activation of the chlorfenapyr pro-insecticide [12, 13, 37]. Indeed, chlorfenapyr has a previously been observed to have a slower action and delayed toxic activity of 2-3 days post-exposure compared to other insecticides (pyrethroids and organophosphates) used in mosquito vector control [14, 28, 29]. The cone bioassay method was developed to assess the bioefficacy of pyrethroid-only LLINs, and the use of this method to test LLINs containing non-neurotoxic insecticides, such as chlorfenapyr, may not necessarily be predictive of field impact, even with increased exposure time of mosquitoes inside the cones [16, 27]. In contrast, the tunnel test results with pyrethroid-resistant _An. gambiae s.l._ Tiassale strain (field-collected F0 generation) were more predictive of PermaNet(r) Dual efficacy in huts, as observed for other chlorfenapyr-coated nets, such as Interceptor(r) G2 [16, 27, 28]. Tunnel tests provide an increased exposure time of mosquitoes to the LLIN samples being tested, and being an overnight exposure. As the female mosquitoes in the tunnel tests are also exhibiting host-seeking behaviour and are, therefore, metabolically active, the activation of chlorfenapyr following tarsal pickup by mosquitoes may be more effective. The tunnel test was a better predictor of PermaNet(r) Dual field efficacy because exposure occurred at night when host-seeking mosquitoes are more vulnerable to chlorfenapyr. Tunnel test thus is a more reliable technique to assess the efficacy of a chlorfenapyr-treated net prior to field trials against free-flying mosquitoes [16, 38, 39].\n",
-                        "\n",
-                        "In PermaNet(r) Dual, deltamethrin and chlorfenapyr contents varied slightly, but complied with the dose interval limits of target specification [23]. This revealed a good homogeneity of the active ingredients' distribution over and a good retention of these active ingredients in PermaNet(r) Dual, thus resulting in high and similar mortality and blood-feeding inhibition between unwashed samples of PermaNet(r) Dual (A) and PermaNet(r) Dual (B). The reduction of chemical contents due to the loss of chlorfenapyr and deltamethrin with 20 washes had no apparent effect on PermaNet(r) Dual efficacy as the washed samples were still producing high mortality against the pyrethroid-resistant mosquitoes. Overall,\n",
-                        "PernaNet(r) Dual chemical bioavailability was sufficient and produced high mortality in pyrethroid-resistant _Anopheles_ mosquitoes after 20 standardized washes (corresponding to a 3-year use) and over the hut trial.\n",
-                        "\n",
-                        "The high vector mortality and personal protection effect of the PernaNet(r) Dual found in this study are the desired outcomes of any vector control tool. The high mortality effects and the additive blood-feeding inhibition impacts induced by PernaNet(r) Dual in this hut trial are expected to substantially diminish the malaria vector density and biting rates in the field, and hence the transmission of malaria in the areas where vectors are resistant to insecticides [40-47]. Additionally, PernaNet(r) Dual performed better than the reference PernaNet(r) 3.0 and PernaNet(r) 2.0 nets, when washed 20 times, thus meeting the WHO hut trial criteria [23].\n",
-                        "\n",
-                        "However, these results need to be further validated in a large-scale field trial to assess the durability and acceptability of this new tool for malaria vector control [48-50]. A community trial would be the best way to evaluate the community level effect of this promising candidate net (i.e. PernaNet(r) Dual) against malaria transmission, by monitoring entomological infection rate, _Plasmodium_ prevalence and disease incidence [48-50]. Such a community trial could be a randomized controlled trial as with dual-active-ingredient LLINs (e.g. Interceptor G2) in Tanzania [49], soon in Benin [48, 50], and the pilot deployments that are part of the New Nets Project led by PATH [51]. Furthermore, PernaNet(r) Dual should be tested against wild populations of _Anopheles funestus_[32], or other main or secondary vectors of malaria in Africa [52-56]. Ultimately, in the present study, the series of hut and laboratory tests demonstrated that the chlorfenapyr component of PernaNet(r) Dual could make a major contribution to controlling the pyrethroid-resistant _An. gambiae_ populations. Furthermore, there were no adverse effects reported among hut sleepers and mosquito collectors during the trial in the huts where PernaNet(r) Dual were used, and this may possibly increase the rate of future user acceptability and improve the usage of this net for malaria vector control.\n",
-                        "\n",
-                        "header: Additional file 55.\n",
-                        "\n",
-                        "Up to 72-h mortality in multiple-resistant populations of _Anopheles gambiae s.l._ (Tissale strain) exposed to long-lasting insecticidal nets using tunnel tests before and after the experimental hut trial.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Tunnel test\n",
-                        "\n",
-                        "The tunnel test outcomes confirmed the cone bioassay results for the susceptible _An. gambiae s.s._ Kisumu strain, with strong blood-feeding inhibition and high mortality in all nets being above the WHO tunnel test thresholds (>= 90% blood-feeding inhibition or >= 80% mortality) (Fig. 4). The blood-feeding inhibition was high only in unwashed and used samples of PermaNet(r) Dual (A) (92.4%), PermaNet(r) Dual (B) (91.6%) and PermaNet(r) 3.0 (92.3%) (Fig. 4A). The mortality was very high (100%) in unwashed or washed and unused or used samples of all nets (PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0) (Fig. 4B), thus showing their good efficacy against this pyrethroid-susceptible _An. gambiae_ Kisumu strain. Additional file shows the Kisumu strain mortality with tunnel tests in more detail (see Additional file 4: Table S4). For the pyrethroid-resistant _An. gambiae s.l._, only washed and unused PermaNet(r) Dual (A) induced a strong blood-feeding inhibition (91.3%) being above the WHO tunnel cut-off of 90% (Fig. 5A). However, the mortality with all samples (unwashed or washed and unused) of PermaNet(r) Dual (85.2-94.9%) were higher compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 5B), and consistent with the higher efficacy of PermaNet(r) Dual against the free-flying pyrethroid-resistant _An. gambiae s.l._ Tiassale strain populations observed in the concurrent experimental hut trials. Additional file shows the pyrethroid-resistant Tiassale strain mortality with tunnel tests in more detail (see Additional file 5: Table S5).\n",
-                        "\n",
-                        "header: Comparison of laboratory bioassay results with hut trial results\n",
-                        "\n",
-                        "Comparing the laboratory cone bioassay and tunnel test results with the experimental hut trial results with the same pyrethroid-resistant _An. gambiae s.l._ strain showed that these laboratory bioassays predicted the response in the huts for PermaNet(r) 3.0 and PermaNet(r) 2.0. Indeed, the unwashed and washed PermaNet(r) 3.0 and PermaNet(r) 2.0 induced relatively lower mortality in the pyrethroid-resistant _An. gambiae_ strain with the cone bioassays, the tunnel test and the hut trial. Focussing on the bioefficacy of PermaNet(r) Dual, the respective mortality rates in the cone, tunnel and hut were 14.0%, 84.5% and 94.9% for unwashed PermaNet(r) Dual (A), 24.0%, 86.5% and 90.2% for unwashed PermaNet(r) Dual (B), and 10.0%, 87.5% and 90.4% for washed PermaNet(r) Dual (B). This suggests that laboratory tunnel tests are more predictive of the performance of PermaNet(r) Dual in experimental huts. Additionally, there were correlations in the blood-feeding rates and blood-feeding inhibition between the tunnel test results and the hut trial results. The high mortality and strong blood-feeding inhibition of PermaNet(r) Dual in tunnel tests with the pyrethroid-resistant _An. gambiae_ Tiassale strain shows that the chlorfenapyr component of PermaNet(r) Dual provided good efficacy against pyrethroid-resistant _Anopheles_.\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "Pieces of netting were sampled from unwashed and washed PermaNet(r) Dual (A and B), PermaNet(r) 3.0 and PermaNet(r) 2.0 LLINs before and after being used in the hut trial, in accordance with the WHO protocol for chemical analysis [25]. All sampled net pieces were labelled and stored individually in aluminium foil at 3.7-4.2 degC. The net pieces were then shipped to the Vestergaard's ISO IEC17025 laboratory in Vietnam for chemical analysis of chlorfenapyr and deltamethrin. Deltamethrin and PBO contents were determined following the Collaborative International Pesticides Analytical Council Ltd (CIPAC) (i.e. ClPAC 33/LN/(M2)/3) methods [26]. Briefly, chlorfenapyr content was analysed using in-house method VCL-098-20 that was undergoing ClIPAC validation and was still to be published. Chlorfenapyr content was extracted from the PermaNet(r) Dual net using a mixture of n-hexane and 1,4-dioxane (95:5, v:v) solvents with an internal standard of dibutylphthalate added. The mixture was shaken by shaking machine to extract chlorfenapyr. Extracted solution was filtered through 0.45 mm Teflon membrane and analysed by a normal phase high performance liquid chromatography for chlorfenapyr concentration, detector UV 236 nm.\n",
-                        "\n",
-                        "header: Supplementary Information\n",
-                        "\n",
-                        "The online version contains supplementary material available at [https://doi.org/10.11016/s12936-023-04455-z](https://doi.org/10.11016/s12936-023-04455-z).\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0, unwashed and washed (20 washes), were tested against free-flying pyrethroid-resistant _An. gambiae s.l._ in the experimental huts in Tiassale, Cote d'lvoire from March to August 2020. Complementary laboratory cone bioassays (daytime and 3-min exposure) and tunnel tests (nightly and 15-h exposure) were performed against pyrethroid-susceptible _An. gambiae_ sensu stricto (_S.S._) (Kisumu strain) and pyrethroid-resistant _An. gambiae s.l._ (Tiassale strain).\n",
-                        "\n",
-                        "header: Small-scale field evaluation of PermaNet(r): Abstract\n",
-                        "\n",
-                        "Dual (a long-lasting net coated with a mixture of chlorfenapyr and deltamethrin) against pyrethroid-resistant _Anopheles gambiae_ mosquitoes from Tiassale, Cote d'lvoire\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien.zahouli@crsrs.ci\n",
-                        "\n",
-                        "Constant A. V. Edi\n",
-                        "\n",
-                        "1Laurence A. Yao\n",
-                        "\n",
-                        "1Emmanuelle G. Lisro\n",
-                        "\n",
-                        "1Marc Adou\n",
-                        "\n",
-                        "1Inza Kone\n",
-                        "\n",
-                        "1Graham Small\n",
-                        "\n",
-                        "2Leanore D. Sternberg\n",
-                        "\n",
-                        "28Benjamin G. Koudou\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Due to the rapid expansion of pyrethroid-resistance in malaria vectors in Africa, Global Plan for insecticide Resistance Management (GIPRM) has recommended the development of long-lasting insecticidal nets (LLINs), containing insecticide mixtures of active ingredients with different modes of action to mitigate resistance and improve LLIN efficacy. This good laboratory practice (GLP) study evaluated the efficacy of the chlorfenapyr and deltamethrin-coated PermaNet(r) Dual, in comparison with the deltamethrin and synergismt piperonyl butoxide (PBO)-treated PermaNet(r) 3.0 and the deltamethrin-coated PermaNet(r) 2.0, against wild free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato (_S.I._), in experimental huts in Tiassale, Cote d'lvoire (West Africa).\n",
-                        "\n",
-                        "header: Outcomes measures\n",
-                        "\n",
-                        "The following entomological outcomes were used to evaluate the efficacy of each treatment arm in the current experimental hut trial [23]:\n",
-                        "\n",
-                        "1. Deterrence: proportional reduction in the number of mosquitoes caught in treated hut relative to the number caught in the control hut.\n",
-                        "2. Exiting rate: percentage of the mosquitoes collected from the veranda trap out of all mosquitoes collected.\n",
-                        "3. Induced exophily: proportional reduction of mosquitoes found in the exit and veranda traps relative to control hut.\n",
-                        "4. Blood-feeding: percentage of blood-fed mosquitoes relative to the total collected.\n",
-                        "5. Blood-feeding inhibition: proportional reduction in blood feeding percentage in treated huts relative to the control hut.\n",
-                        "6. Mortality: percentage of dead mosquitoes found dead in hut in the morning (immediate mortality) or after being caught alive and dead during holding (delayed mortality) in treatment huts out of mosquitoes collected, and corrected for control mortality.\n",
-                        "7. Personal protection: the proportional reduction in the number of blood-fed mosquitoes in the treated huts relative to the number of blood-fed mosquitoes in the untreated control.\n",
-                        "8. Killing effect: the proportional reduction in the number of mosquitoes killed in the treated huts relative to the number of mosquitoes killed in the untreated control.\n",
-                        "\n",
-                        "The formulas of key entomological outcomes measured in this study are [23]:\n",
-                        "\n",
-                        "\\[\\text{Deterrence}(\\%) = \\frac{\\text{Nc} - \\text{Nt}}{\\text{Nc}} \\times 100 = \\left( 1 - \\frac{\\text{Nt}}{\\text{Nc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Nt the total number of mosquitoes collected in the treatment hut and veranda/exit traps and Nc the total number of mosquitoes in the control hut and veranda/exit traps.\n",
-                        "\n",
-                        "\\[\\text{Exiting}\\,\\text{rate}(\\%) = \\frac{\\text{n}}{\\text{N}} \\times 100,\\]\n",
-                        "\n",
-                        "where n is the number of mosquitoes from veranda and window traps, while N is the total number of mosquitoes collected in the hut.\n",
-                        "\n",
-                        "\\[\\text{Induced}\\,\\text{exophily}(\\%) = \\frac{\\text{Pt} - \\text{Pc}}{\\text{Pc}} \\times 100\\] \\[= \\left( \\frac{\\text{Pt}}{\\text{Pc}} - 1 \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Pt is the proportion of mosquitoes from veranda and window traps of treated hut while Pc is the number of mosquitoes from veranda and window traps of untreated control hut.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding}\\,\\text{inhibition}(\\%) = \\frac{\\text{Pc} - \\text{Pt}}{\\text{Pc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Pt the proportion of blood-fed mosquitoes in the treatment hut, and Pc the proportion of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Personal}\\,\\text{protection}(\\%) = \\frac{\\text{Bc} - \\text{Bt}}{\\text{Bc}} \\times 100\\] \\[= \\left( 1 - \\frac{\\text{Bt}}{\\text{Bc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Bt the number of blood-fed mosquitoes in the treatment hut and Bc the number of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Killing}\\,\\text{effect}(\\%) = \\frac{\\text{Kt} - \\text{Kc}}{\\text{Tc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Kt is the number of mosquitoes killed in the treatment huts, Kc is the number of mosquitoes killed in the control huts, and Tc is the total number of mosquitoes collected from the control.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All relevant data are within the manuscript.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The deltamethrin-chlorfenapyr-coated PermaNet(r) Dual induced a high efficacy and performed better than the deltamethrin-PBO PermaNet(r) 3.0 and the deltamethrin-only PermaNet(r) 2.0, testing both unwashed and 20 times washed samples against the pyrethroid-susceptible and resistant strains of _An. gambiae s.l._ The inclusion of chlorfenapyr with deltamethrin in PermaNet(r) Dual net greatly improved protection and control of pyrethroid-resistant _An. gambiae_ populations. PermaNet(r) Dual thus represents a promising tool, with a high potential to reduce malaria transmission and provide community protection in areas compromised by mosquito vector resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "According to the latest World Malaria Report 2022 of the World Health Organization (WHO), there were 247 million malaria cases and 619,000 malaria deaths in 84 malaria endemic countries worldwide in 2021 [1, 2]. This represents about 13.4 million more cases in 2021 compared to 2019 attributable to disruptions to essential malaria services during the COVID-18 pandemic [1]. The WHO African Region, with an estimated 234 million cases in 2021, accounted for about 95% of global cases malaria [1]. The recent decline of malaria burden from 2000 to 2019 was largely due to the massive distribution and use of long-lasting insecticidal nets (LLINs) (2.5 billion LLINs were delivered from 2004 to 2021) for _Anopheles_ mosquito vector control [1]. The malaria burden reduction is now slowing down and is threatened by the spread of resistance to pyrethroids (78 of 88 endemic countries have detected resistance to at least one insecticide class reported to the WHO from 2010 to 2020) [1]. The WHO Global Plan for Insecticide Resistance Management (GPIRM) has recommended the development of LLINs with insecticide mixtures of active ingredients with different modes of action to mitigate resistance [3]. The WHO Global Technical Strategy (GTS) aims for a reduction of malaria case incidence and mortality rate of at least 40% by 2020, 75% by 2025 and 90% by 2030 from the 2015 baseline [1, 4]. To meet these targets, GTS has called for the development of new tools, with combined or more effective insecticide molecules to control insecticide-resistant _Anopheles_ vectors [4]. These new vector control tools must incorporate new insecticide molecules and/or insecticide mixtures containing at least two active ingredients with different modes of action for the management of malaria vector resistance to insecticides [1, 3].\n",
-                        "\n",
-                        "Malaria is endemic throughout Cote d'Ivoire and represents the leading cause of mortality and morbidity in the country. Several studies have shown a wide spread resistance in local _Anopheles_ to most of the insecticide classes currently used in malaria vector control (e.g. pyrethroids, DDT and carbamates) [5-8]. The existence of multiple mechanisms of resistance in the main vector, _Anopheles gambiae_ sensu lato (_s.l._) threatens the efficacy of vector control tools currently used in Cote d'Ivoire, including LLINs [9-11]. Meiwald et al. [11] found that pyrethroid resistance is associated with significant overexpression of _CYP6P4, CYP6P3,_ and _CYP6Z1_ in _Anopheles coluzzii_ in Cote d'Ivoire. High allelic frequencies of knock-down resistance (_kdr_) _L1014F_ mutation (range: 0.46-1), relatively low frequencies of the _ace-1R_ mutation in the acetylcholinesterase gene associated with target-site insensitivity to carbamates and organophosphates (<0.5), and elevated activity of insecticide detoxifying enzymes (mainly mixed function oxidases (MFOs), esterase and glutathione S-transferase (GST)), have been reported in Cote d'Ivoire [5, 7, 10]. Recent laboratory (WHO tube and CDC bottle) bioassays against local pyrethroid-resistant _An. gambiae_ from Cote d'Ivoire have shown that pyrethroids induce low mortality, whilst pre-exposure to the synergist piperonyl butoxide (PBO) increases the mortality but does not restore fully the susceptibility due to resistance [9]. However, the pyrrole insecticide chlorfenapyr induces higher mortality in resistant _An. gambiae_ compared with pyrethroids alone or combined with PBO in Cote d'Ivoire [9].\n",
-                        "\n",
-                        "The present good laboratory practice (GLP) study evaluated the bio-efficacy of a new candidate LLIN, PermaNet(r) Dual, against pyrethroid-resistant _An. gambiae s.l._ in comparison with the WHO Prequalification Unit, Vector Control Product Assessment Team (PQT/VCP) listed standard LLINs, PermaNet(r) 2.0 and PermaNet(r) 3.0, through the conduct of an experimental hut trial and laboratory bioassays in Tiassale, Cote d'Ivoire. PermaNet(r) Dual has a mixture of the pyrrole chlorfenapyr and the pyrethroid deltamethrin coated onto a polyester fabric. PermaNet(r) 3.0 is treated with deltamethrin and PBO, and PermaNet(r) 2.0 is treated with deltamethrin only. Chlorfenapyr is a pro-insecticide activated by the oxygenase function of cytochrome P450s and oxidative removal of the N-ethoxymethyl\n",
-                        "group leads to a toxic form identified as CL 303,268. The toxic form uncouples oxidative phosphorylation in the mitochondria, resulting in disruption of the production of adenosine triphosphate and loss of energy, leading to cell dysfunction and ultimately death of the insect [12]. The WHO has received some resistance monitoring data for chlorfenapyr, but these data are insufficient to assess the potential presence of resistance to this insecticide [1]. Deltamethrin is a neurotoxic insecticide and has excito-repellent effects on mosquitoes. PBO, used on PermaNet(r) 3.0, is a synergist which inhibits mixed function oxidases, blocking the detoxification of pyrethroids and at least partially restoring pyrethroid susceptibility [1, 13]. Pyrethroid-resistant strains of _Anopheles_ have so far been found to be susceptible to chlorfenapyr [9, 14-18]. Thus, the hypothesis of the current study was that chlorfenapyr would kill the pyrethroid-resistant _An. gambiae_ population in Tiassale, and PermaNet(r) Dual would induce higher mortality compared to both PermaNet(r) 3.0 and PermaNet(r) 2.0 in the experimental huts, and the supplementary laboratory cone and tunnel bioassays could predict these hut trial outcomes.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Supporting laboratory testing of net samples\n",
-                        "\n",
-                        "Standard WHO cone bioassays and tunnel tests were conducted with unwashed and washed nets under laboratory conditions, to predict their responses in the field experimental huts. The insecticide susceptible Kisumu strains and field-collected F0 generation of pyrethroid-resistant Tiassale strain of _An. gambiae_ were tested with samples (25 cm x 25 cm) cut from each of the eight net \n",
-                        "types (unwashed and washed net samples) before and after their inclusion into the hut trial [23]. For the tunnel tests, tunnels were divided into two sections by netting samples held in a frame. Nine holes of 1 cm in diameter were cut in each net sample (with one hole located at the centre of the square, and the other eight equidistant and located 5 cm from the border), and the surface of netting available to the mosquitoes was 400 cm\\({}^{2}\\) (20 cm \\(\\times\\) 20 cm).\n",
-                        "\n",
-                        "For each type of net and wash status, 100 non-blood-fed, 2-5-day-old females per strain were subjected to 3-min exposure in cone bioassays in replicates of five mosquitoes per cone according to the WHO guidelines [22]. For the tunnel tests, 100 non-blood-fed, 5-8-day-old mosquito females per strain were subjected to a 15-h exposure overnight using guinea pig as host, following the WHO guidelines [23]. Testing and holding conditions were 27 +- 2 degC and 80 +- 10% relative humidity. The 60-min knockdown for cone bioassays, blood-feeding status for tunnel tests and 24, 48 and 72-h mortality for both methods were recorded.\n",
-                        "\n",
-                        "header: Blood-feeding and personal protection\n",
-                        "\n",
-                        "The blood-feeding effect of the nets in wild pyrethroid-resistant _An. gambiae_ that entered the trial huts and personal protection are presented in Fig. 1A and Table 1. Before washing, the percentage blood-feeding with PernaNet(r) Dual (A) (36.7 +- 4.0%) did not differ significantly from PernaNet(r) Dual (B) (37.5 +- 4.4%) and PernaNet(r) 2.0 (55.7 +- 3.7%), but was significantly higher than PernaNet(r) 2.0 (31.3 +- 4.4%) (z = - 2.17; p = 0.030). The percentage blood-feeding for unwashed PernaNet(r) Dual (B) was similar to unwashed samples of PernaNet(r) 2.0 (z = 1.74; p = 0.081) and PernaNet(r) 3.0 (z = - 1.49; p = 0.136). Washing 20 times did not influence significantly the percentage blood-feeding in PernaNet(r) Dual (B) (z = 0.10; p = 0.921). After washing,\n",
-                        "the percentage blood-feeding of PermaNet(r) Dual (B) (36.0 +- 3.7%) was substantially lower, but without significant differences when compared with PermaNet(r) 3.0 (57.7 +- 3.7%) (z = 1.71; p = 0.087), PermaNet(r) 2.0 (72.3 +- 3.2%) (z = 1.78; p = 0.076) and the untreated control net (z = 1.72; p = 0.085). For blood-feeding inhibition, unwashed PermaNet(r) Dual (A) (42.5 +- 6.8%) was similar to unwashed PermaNet(r) Dual (B) (41.4 +- 6.9%) (z = 0.25; p = 0.802) and washed PermaNet(r) Dual (B) (43.7 +- 4.8%) that was not affected by washing (z = - 0.41; p = 0.682). Compared with PermaNet(r) 3.0, the blood-feeding inhibition of PermaNet(r) Dual (B) was significantly higher before washing (z = - 0.72; p = 0.469), and significantly lower after washing (z = - 1.92; p = 0.045). However, both unwashed and washed PermaNet(r) Dual (B) provided, respectively, significantly higher blood-feeding inhibition than unwashed and washed PermaNet(r) 2.0 (all p < 0.05). The personal protection was similar between unwashed samples of PermaNet(r) Dual (A) (58.8 +- 3.5%) and PermaNet(r) Dual (B) (60.8 +- 3.8%) (z = - 0.04; p = 0.968). Unwashed PermaNet(r) Dual (B) resulted in a personal protection that was similar to unwashed PermaNet(r) 3.0 (62.9 +- 4.2%) (z = 0.21; p = 0.738), and significantly different from unwashed PermaNet(r) 2.0 (28.8 +- 1.9%) (z = 3.21; p = 0.001). With 20 washes, the personal protection with PermaNet(r) Dual (B) declined significantly from 60.8 +- 3.8% to 20.2 +- 1.1% (z = - 2.96; p = 0.003). However, washed PermaNet(r) Dual (B) provided significantly higher personal protection compared with their washed counterparts of PermaNet(r) 3.0 (9.8 +- 0.7%) (z = 2.89; p = 0.004) and PermaNet(r) 2.0 (- 32.6 +- 2.2%) (z = 2.93; p = 0.003).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The outcomes of the current experimental hut trial showed that the parameters of the net efficacy against the pyrethroid-resistant _An. gambiae s.l._ Tiassale population varied as a function of net type and wash status, with the performance of PermaNet(r) Dual being better overall compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 1 and Table 1).\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This study was funded by a Grant from Vestergaard Sarl, Lausanne, Switzerland.\n",
-                        "\n",
-                        "header: Additional file 3: Table 53.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (Tissale strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Additional file 2: Table 52.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (bisumu strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "PermaNet(r) Dual demonstrated significantly improved efficacy, compared to PermaNet(r) 3.0 and PermaNet(r) 2.0, against the pyrethroid-resistant _An. gambiae s.l._ Indeed, the experimental hut trial data showed that the mortality and blood-feeding inhibition in the wild pyrethroid-resistant _An. gambiae s.l._ were overall significantly higher with PermaNet(r) Dual compared with PermaNet(r) 3.0 and PermaNet(r) 2.0, for both unwashed and washed samples. The mortality with unwashed and washed samples were 93.6 +- 0.2% and 83.2 +- 0.9% for PermaNet(r) Dual, 37.5 +- 2.9% and 14.4 +- 3.3% for PermaNet(r) 3.0, and 7.4 +- 5.1% and 11.7 +- 3.4% for PermaNet(r) 2.0, respectively. Moreover, unwashed and washed samples produced the respective percentage blood-feeding inhibition of 41.4 +- 6.9% and 43.7 +- 4.8% with PermaNet(r) Dual, 51.0 +- 5.7% and 9.8 +- 3.6% with PermaNet(r) 3.0, and 12.8 +- 4.3% and - 13.0 +- 3.6% with PermaNet(r) 2.0. Overall, PermaNet(r) Dual also induced higher or similar deterrence, exophily and personal protection when compared with the standard PermaNet(r) 3.0 and PermaNet(r) 2.0 reference nets, with both unwashed and\n",
-                        "washed net samples. In contrast to cone bioassays, tunnel tests predicted the efficacy of PermaNet(r) Dual seen in the current experimental hut trial.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The present small-scale GLP experimental hut study demonstrated that the deltamethrin- chlorfenapyr PermaNet(r) Dual net has high bio-efficacy, inducing significantly higher mortality and blood-feeding inhibition effects in the wild insecticide-resistant _An. gambiae s.l._ when compared with the deltamethrin-PBO PermaNet(r) 3.0 and deltamethrin-only PermaNet(r) 2.0. The inclusion of chlorfenapyr with pyrethroid in PermaNet(r) Dual net has greatly improved protection and control of free-flying wild pyrethroid-resistant _An. gambiae_ populations. The chlorfenapyr-deltamethrin-coated PermaNet(r) Dual net has great potential to reduce malaria transmission, particularly in areas compromised by high level of pyrethroid resistance in _Anopheles_ mosquitoes. Further validations in large-scale field trials are required to assess the effectiveness, the durability and the acceptability of this new tool for malaria vector control.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The current study evaluated the efficacy of PermaNet(r) Dual, a new candidate net coated with a mixture of chlorfenapyr and deltamethrin, against _An. gambiae_ in a small-scale GLP but study in Tiassale, Cote d'Ivoire, with supporting exploratory laboratory cone and tunnel\n",
-                        "\n",
-                        "Fig. 4: Mean blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the susceptible _Anopheles gambiae_ s.s. Kisumu strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassale, Côte d’Ivoire. Error bars show the standard error of the mean\n",
-                        "bioassays. WHO-prequalified PermaNet(r) 3.0 (co-treated with deltamethrin and PBO) and PermaNet(r) 2.0 (treated with deltamethrin only) were used as the reference nets. In this GLP but study, PermaNet(r) Dual performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0, in terms of mortality and blood-feeding inhibition, for both unwashed and unstressed cases, respectively.\n",
-                        "\n",
-                        "Fig. 5: Mean of blood-feeding inhibition (**A**) and corrected mortality (**B**) rates in the pyrethroid-resistant _Anopheles gambiae s1_. Tassale strain exposed to net samples using tunnel tests before and after experimental hut trial in Tassale, Côte d’thovie. Error bars show the standard error of the mean \n",
-                        "and washed samples. Unlike the cone bioassays, the tunnel tests successfully confirmed the field efficacy of the nets. Briefly, this hut trial showed the benefit of mixing deltamethrin and chlorfenapyr together in a long-lasting net, PermaNet(r) Dual, for an effective control of pyrethroid-resistant _Anopheles._\n",
-                        "\n",
-                        "The current experimental hut study demonstrated that PermaNet(r) Dual had increased efficacy against highly pyrethroid-resistant _An. gambiae._ Indeed, hut mortality of _An. gambiae_ Tiassale strain was significantly higher with PermaNet(r) Dual (>83%) in comparison with PermaNet(r) 3.0 (<38%) and PermaNet(r) 2.0 (<12%), for both unwashed and 20 times washed samples (Fig. 1B and Table 1). This PermaNet(r) Dual efficacy was similar to chlorfenapyr and alpha-cypermethrin mixture-coated Intercept G2 in West and East Africa [16, 17, 27, 28, 29, 30, 31, 32]. The good performance of PermaNet(r) Dual might be attributed to the susceptibility of the highly pyrethroid-resistant _An. gambiae_ s.l. to chlorfenapyr [9, 11]. Due to the novel mode of action of chlorfenapyr, the present pyrethroid-resistance mechanisms did not provide any cross-resistance to this insecticide [12]. Indeed, chlorfenapyr-treated tool has been shown to control a number of different multiple-insecticide-resistant _Anopheles_ populations [14, 15, 27, 28, 29, 30, 31, 32].\n",
-                        "\n",
-                        "This hut study showed that the percentage blood-feeding in wild pyrethroid-resistant _An. gambiae_ population was lower for PermaNet(r) Dual (unwashed and washed) compared with PermaNet(r) 2.0 (unwashed and washed) and washed PermaNet(r) 3.0, but slightly higher than\n",
-                        "unwashed PermaNet(r) 3.0 (Fig. 1A and Table 1). Likewise, blood-feeding inhibition with PermaNet(r) Dual was comparable or stronger than with PermaNet(r) 2.0 and washed PermaNet(r) 3.0. This PermaNet(r) Dual blood-feeding inhibition outcome is consistent with that caused by Intercept G2 in earlier hut trials in West Africa [27-31], and may be explained by the irritant effects of the deltamethrin [32-35]. While unwashed PermaNet(r) Dual and PermaNet(r) 3.0 produced similar levels of blood-feeding inhibition, the 20 times washed PermaNet(r) Dual induced a statistically greater blood-feeding inhibition in comparison with both PermaNet(r) 3.0 and PermaNet(r) 2.0 washed 20 times. This suggests that PermaNet(r) Dual may have performed better than PermaNet(r) 3.0 and PermaNet(r) 2.0 over time. PermaNet(r) Dual effectiveness for inhibiting blood-feeding could imply its good potential for reducing the human-biting and blood-feeding, adding value into its killing effects in malaria vectors.\n",
-                        "\n",
-                        "In the present hut study, unwashed PermaNet(r) Dual deterrence effect against the wild pyrethroid-resistant _An. gambiae_ population was comparable to that recorded in PermaNet(r) 2.0 and PermaNet(r) 3.0. The deltamethrin component of PermaNet(r) Dual has a deterrence effect and may have induced exiting in mosquitoes. However, the 20 times washed PermaNet(r) Dual did not have a deterrence effect (Table 1), probably due to a reduction of the chemical contents (Table 2). A dipped chlorfenapyr net did seem to have a deterrence effect, but did not have a significant effect on exiting rates in West Africa [15, 16, 26-31]. The higher deterrence effect may be due to both the chlorfenapyr and deltamethrin components, but higher exiting rate would likely be due only to the deltamethrin component. Thus, PermaNet(r) Dual may have both deterrent and excito-repellency effects, providing personal protection against pyrethroid-resistant _An. gambiae_ mosquitoes, and reducing human-vector contacts, which may, in turn, lead to an increased user acceptance [36].\n",
-                        "\n",
-                        "While standard laboratory cone bioassays failed to predict PermaNet(r) Dual efficacy against pyrethroid-resistant _An. gambiae s.l.,_ tunnel tests successfully predicted its efficacy against the same strain in the semi-field experimental huts. Both cone and tunnel bioassays met the WHO criteria [44], with the susceptible _An. gambiae s.s._ Kisumu strain. However, _An. gambiae s.s._ Kisumu strain mortality against unwashed and used PermaNet(r) Dual (A) was lower (34%) with cone bioassay (Fig. 2B) probably due to the reduction of the chemical bioavailability on netting fibre surface of net samples tested, or the inappropriateness of cone bioassay method for evaluation of the chlorfenapyr-coated PermaNet(r) Dual as the mortality was higher (100%) with tunnel test (Fig. 4B). With the wild pyrethroid-resistant _An. gambiae s.l._ Tiassale strain, only the tunnel tests achieved high efficacy that was consistent with that observed in the huts with PermaNet(r) Dual. The difference in PermaNet(r) Dual efficacy between both _Anopheles_ Kiumu and Tiassale strains may be explained by the difference in the levels of their resistance to insecticides. However, the lower efficacy of PermaNet(r) Dual against pyrethroid-resistant _An. gambiae s.l._ with cone bioassays (daytime and 3-min exposure) compared with tunnel tests (night-time and 15-h exposure) may be attributable to the slow mode of action of chlorfenapyr and that mosquitoes need to be metabolically active for the activation of the chlorfenapyr pro-insecticide [12, 13, 37]. Indeed, chlorfenapyr has a previously been observed to have a slower action and delayed toxic activity of 2-3 days post-exposure compared to other insecticides (pyrethroids and organophosphates) used in mosquito vector control [14, 28, 29]. The cone bioassay method was developed to assess the bioefficacy of pyrethroid-only LLINs, and the use of this method to test LLINs containing non-neurotoxic insecticides, such as chlorfenapyr, may not necessarily be predictive of field impact, even with increased exposure time of mosquitoes inside the cones [16, 27]. In contrast, the tunnel test results with pyrethroid-resistant _An. gambiae s.l._ Tiassale strain (field-collected F0 generation) were more predictive of PermaNet(r) Dual efficacy in huts, as observed for other chlorfenapyr-coated nets, such as Interceptor(r) G2 [16, 27, 28]. Tunnel tests provide an increased exposure time of mosquitoes to the LLIN samples being tested, and being an overnight exposure. As the female mosquitoes in the tunnel tests are also exhibiting host-seeking behaviour and are, therefore, metabolically active, the activation of chlorfenapyr following tarsal pickup by mosquitoes may be more effective. The tunnel test was a better predictor of PermaNet(r) Dual field efficacy because exposure occurred at night when host-seeking mosquitoes are more vulnerable to chlorfenapyr. Tunnel test thus is a more reliable technique to assess the efficacy of a chlorfenapyr-treated net prior to field trials against free-flying mosquitoes [16, 38, 39].\n",
-                        "\n",
-                        "In PermaNet(r) Dual, deltamethrin and chlorfenapyr contents varied slightly, but complied with the dose interval limits of target specification [23]. This revealed a good homogeneity of the active ingredients' distribution over and a good retention of these active ingredients in PermaNet(r) Dual, thus resulting in high and similar mortality and blood-feeding inhibition between unwashed samples of PermaNet(r) Dual (A) and PermaNet(r) Dual (B). The reduction of chemical contents due to the loss of chlorfenapyr and deltamethrin with 20 washes had no apparent effect on PermaNet(r) Dual efficacy as the washed samples were still producing high mortality against the pyrethroid-resistant mosquitoes. Overall,\n",
-                        "PernaNet(r) Dual chemical bioavailability was sufficient and produced high mortality in pyrethroid-resistant _Anopheles_ mosquitoes after 20 standardized washes (corresponding to a 3-year use) and over the hut trial.\n",
-                        "\n",
-                        "The high vector mortality and personal protection effect of the PernaNet(r) Dual found in this study are the desired outcomes of any vector control tool. The high mortality effects and the additive blood-feeding inhibition impacts induced by PernaNet(r) Dual in this hut trial are expected to substantially diminish the malaria vector density and biting rates in the field, and hence the transmission of malaria in the areas where vectors are resistant to insecticides [40-47]. Additionally, PernaNet(r) Dual performed better than the reference PernaNet(r) 3.0 and PernaNet(r) 2.0 nets, when washed 20 times, thus meeting the WHO hut trial criteria [23].\n",
-                        "\n",
-                        "However, these results need to be further validated in a large-scale field trial to assess the durability and acceptability of this new tool for malaria vector control [48-50]. A community trial would be the best way to evaluate the community level effect of this promising candidate net (i.e. PernaNet(r) Dual) against malaria transmission, by monitoring entomological infection rate, _Plasmodium_ prevalence and disease incidence [48-50]. Such a community trial could be a randomized controlled trial as with dual-active-ingredient LLINs (e.g. Interceptor G2) in Tanzania [49], soon in Benin [48, 50], and the pilot deployments that are part of the New Nets Project led by PATH [51]. Furthermore, PernaNet(r) Dual should be tested against wild populations of _Anopheles funestus_[32], or other main or secondary vectors of malaria in Africa [52-56]. Ultimately, in the present study, the series of hut and laboratory tests demonstrated that the chlorfenapyr component of PernaNet(r) Dual could make a major contribution to controlling the pyrethroid-resistant _An. gambiae_ populations. Furthermore, there were no adverse effects reported among hut sleepers and mosquito collectors during the trial in the huts where PernaNet(r) Dual were used, and this may possibly increase the rate of future user acceptability and improve the usage of this net for malaria vector control.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"tunnel test\",\"Country\":\"Cote d'Ivoire\",\"Site\":\"Tiassale\",\"Start_month\":3,\"Start_year\":2020,\"End_month\":8,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet(r) Dual\",\"Insecticide\":\"chlorfenapyr, deltamethrin\",\"Net_washed\":0.0},\"N02\":{\"Net_type\":\"PermaNet(r) Dual\",\"Insecticide\":\"chlorfenapyr, deltamethrin\",\"Net_washed\":20.0},\"N03\":{\"Net_type\":\"PermaNet(r) 3.0\",\"Insecticide\":\"deltamethrin, PBO\",\"Net_washed\":0.0},\"N04\":{\"Net_type\":\"PermaNet(r) 3.0\",\"Insecticide\":\"deltamethrin, PBO\",\"Net_washed\":20.0},\"N05\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_washed\":0.0},\"N06\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_washed\":20.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Tunnel test\n",
-                        "\n",
-                        "The tunnel test outcomes confirmed the cone bioassay results for the susceptible _An. gambiae s.s._ Kisumu strain, with strong blood-feeding inhibition and high mortality in all nets being above the WHO tunnel test thresholds (>= 90% blood-feeding inhibition or >= 80% mortality) (Fig. 4). The blood-feeding inhibition was high only in unwashed and used samples of PermaNet(r) Dual (A) (92.4%), PermaNet(r) Dual (B) (91.6%) and PermaNet(r) 3.0 (92.3%) (Fig. 4A). The mortality was very high (100%) in unwashed or washed and unused or used samples of all nets (PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0) (Fig. 4B), thus showing their good efficacy against this pyrethroid-susceptible _An. gambiae_ Kisumu strain. Additional file shows the Kisumu strain mortality with tunnel tests in more detail (see Additional file 4: Table S4). For the pyrethroid-resistant _An. gambiae s.l._, only washed and unused PermaNet(r) Dual (A) induced a strong blood-feeding inhibition (91.3%) being above the WHO tunnel cut-off of 90% (Fig. 5A). However, the mortality with all samples (unwashed or washed and unused) of PermaNet(r) Dual (85.2-94.9%) were higher compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 5B), and consistent with the higher efficacy of PermaNet(r) Dual against the free-flying pyrethroid-resistant _An. gambiae s.l._ Tiassale strain populations observed in the concurrent experimental hut trials. Additional file shows the pyrethroid-resistant Tiassale strain mortality with tunnel tests in more detail (see Additional file 5: Table S5).\n",
-                        "\n",
-                        "header: Comparison of laboratory bioassay results with hut trial results\n",
-                        "\n",
-                        "Comparing the laboratory cone bioassay and tunnel test results with the experimental hut trial results with the same pyrethroid-resistant _An. gambiae s.l._ strain showed that these laboratory bioassays predicted the response in the huts for PermaNet(r) 3.0 and PermaNet(r) 2.0. Indeed, the unwashed and washed PermaNet(r) 3.0 and PermaNet(r) 2.0 induced relatively lower mortality in the pyrethroid-resistant _An. gambiae_ strain with the cone bioassays, the tunnel test and the hut trial. Focussing on the bioefficacy of PermaNet(r) Dual, the respective mortality rates in the cone, tunnel and hut were 14.0%, 84.5% and 94.9% for unwashed PermaNet(r) Dual (A), 24.0%, 86.5% and 90.2% for unwashed PermaNet(r) Dual (B), and 10.0%, 87.5% and 90.4% for washed PermaNet(r) Dual (B). This suggests that laboratory tunnel tests are more predictive of the performance of PermaNet(r) Dual in experimental huts. Additionally, there were correlations in the blood-feeding rates and blood-feeding inhibition between the tunnel test results and the hut trial results. The high mortality and strong blood-feeding inhibition of PermaNet(r) Dual in tunnel tests with the pyrethroid-resistant _An. gambiae_ Tiassale strain shows that the chlorfenapyr component of PermaNet(r) Dual provided good efficacy against pyrethroid-resistant _Anopheles_.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "PermaNet(r) Dual, PermaNet(r) 3.0 and PermaNet(r) 2.0, unwashed and washed (20 washes), were tested against free-flying pyrethroid-resistant _An. gambiae s.l._ in the experimental huts in Tiassale, Cote d'lvoire from March to August 2020. Complementary laboratory cone bioassays (daytime and 3-min exposure) and tunnel tests (nightly and 15-h exposure) were performed against pyrethroid-susceptible _An. gambiae_ sensu stricto (_S.S._) (Kisumu strain) and pyrethroid-resistant _An. gambiae s.l._ (Tiassale strain).\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "Pieces of netting were sampled from unwashed and washed PermaNet(r) Dual (A and B), PermaNet(r) 3.0 and PermaNet(r) 2.0 LLINs before and after being used in the hut trial, in accordance with the WHO protocol for chemical analysis [25]. All sampled net pieces were labelled and stored individually in aluminium foil at 3.7-4.2 degC. The net pieces were then shipped to the Vestergaard's ISO IEC17025 laboratory in Vietnam for chemical analysis of chlorfenapyr and deltamethrin. Deltamethrin and PBO contents were determined following the Collaborative International Pesticides Analytical Council Ltd (CIPAC) (i.e. ClPAC 33/LN/(M2)/3) methods [26]. Briefly, chlorfenapyr content was analysed using in-house method VCL-098-20 that was undergoing ClIPAC validation and was still to be published. Chlorfenapyr content was extracted from the PermaNet(r) Dual net using a mixture of n-hexane and 1,4-dioxane (95:5, v:v) solvents with an internal standard of dibutylphthalate added. The mixture was shaken by shaking machine to extract chlorfenapyr. Extracted solution was filtered through 0.45 mm Teflon membrane and analysed by a normal phase high performance liquid chromatography for chlorfenapyr concentration, detector UV 236 nm.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The deltamethrin-chlorfenapyr-coated PermaNet(r) Dual induced a high efficacy and performed better than the deltamethrin-PBO PermaNet(r) 3.0 and the deltamethrin-only PermaNet(r) 2.0, testing both unwashed and 20 times washed samples against the pyrethroid-susceptible and resistant strains of _An. gambiae s.l._ The inclusion of chlorfenapyr with deltamethrin in PermaNet(r) Dual net greatly improved protection and control of pyrethroid-resistant _An. gambiae_ populations. PermaNet(r) Dual thus represents a promising tool, with a high potential to reduce malaria transmission and provide community protection in areas compromised by mosquito vector resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Small-scale field evaluation of PermaNet(r): Abstract\n",
-                        "\n",
-                        "Dual (a long-lasting net coated with a mixture of chlorfenapyr and deltamethrin) against pyrethroid-resistant _Anopheles gambiae_ mosquitoes from Tiassale, Cote d'lvoire\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien Z. B. Zahouli\n",
-                        "\n",
-                        "Julien.zahouli@crsrs.ci\n",
-                        "\n",
-                        "Constant A. V. Edi\n",
-                        "\n",
-                        "1Laurence A. Yao\n",
-                        "\n",
-                        "1Emmanuelle G. Lisro\n",
-                        "\n",
-                        "1Marc Adou\n",
-                        "\n",
-                        "1Inza Kone\n",
-                        "\n",
-                        "1Graham Small\n",
-                        "\n",
-                        "2Leanore D. Sternberg\n",
-                        "\n",
-                        "28Benjamin G. Koudou\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Due to the rapid expansion of pyrethroid-resistance in malaria vectors in Africa, Global Plan for insecticide Resistance Management (GIPRM) has recommended the development of long-lasting insecticidal nets (LLINs), containing insecticide mixtures of active ingredients with different modes of action to mitigate resistance and improve LLIN efficacy. This good laboratory practice (GLP) study evaluated the efficacy of the chlorfenapyr and deltamethrin-coated PermaNet(r) Dual, in comparison with the deltamethrin and synergismt piperonyl butoxide (PBO)-treated PermaNet(r) 3.0 and the deltamethrin-coated PermaNet(r) 2.0, against wild free-flying pyrethroid-resistant _Anopheles gambiae_ sensu lato (_S.I._), in experimental huts in Tiassale, Cote d'lvoire (West Africa).\n",
-                        "\n",
-                        "header: Supplementary Information\n",
-                        "\n",
-                        "The online version contains supplementary material available at [https://doi.org/10.11016/s12936-023-04455-z](https://doi.org/10.11016/s12936-023-04455-z).\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "All relevant data are within the manuscript.\n",
-                        "\n",
-                        "header: Supporting laboratory testing of net samples\n",
-                        "\n",
-                        "Standard WHO cone bioassays and tunnel tests were conducted with unwashed and washed nets under laboratory conditions, to predict their responses in the field experimental huts. The insecticide susceptible Kisumu strains and field-collected F0 generation of pyrethroid-resistant Tiassale strain of _An. gambiae_ were tested with samples (25 cm x 25 cm) cut from each of the eight net \n",
-                        "types (unwashed and washed net samples) before and after their inclusion into the hut trial [23]. For the tunnel tests, tunnels were divided into two sections by netting samples held in a frame. Nine holes of 1 cm in diameter were cut in each net sample (with one hole located at the centre of the square, and the other eight equidistant and located 5 cm from the border), and the surface of netting available to the mosquitoes was 400 cm\\({}^{2}\\) (20 cm \\(\\times\\) 20 cm).\n",
-                        "\n",
-                        "For each type of net and wash status, 100 non-blood-fed, 2-5-day-old females per strain were subjected to 3-min exposure in cone bioassays in replicates of five mosquitoes per cone according to the WHO guidelines [22]. For the tunnel tests, 100 non-blood-fed, 5-8-day-old mosquito females per strain were subjected to a 15-h exposure overnight using guinea pig as host, following the WHO guidelines [23]. Testing and holding conditions were 27 +- 2 degC and 80 +- 10% relative humidity. The 60-min knockdown for cone bioassays, blood-feeding status for tunnel tests and 24, 48 and 72-h mortality for both methods were recorded.\n",
-                        "\n",
-                        "header: Blood-feeding and personal protection\n",
-                        "\n",
-                        "The blood-feeding effect of the nets in wild pyrethroid-resistant _An. gambiae_ that entered the trial huts and personal protection are presented in Fig. 1A and Table 1. Before washing, the percentage blood-feeding with PernaNet(r) Dual (A) (36.7 +- 4.0%) did not differ significantly from PernaNet(r) Dual (B) (37.5 +- 4.4%) and PernaNet(r) 2.0 (55.7 +- 3.7%), but was significantly higher than PernaNet(r) 2.0 (31.3 +- 4.4%) (z = - 2.17; p = 0.030). The percentage blood-feeding for unwashed PernaNet(r) Dual (B) was similar to unwashed samples of PernaNet(r) 2.0 (z = 1.74; p = 0.081) and PernaNet(r) 3.0 (z = - 1.49; p = 0.136). Washing 20 times did not influence significantly the percentage blood-feeding in PernaNet(r) Dual (B) (z = 0.10; p = 0.921). After washing,\n",
-                        "the percentage blood-feeding of PermaNet(r) Dual (B) (36.0 +- 3.7%) was substantially lower, but without significant differences when compared with PermaNet(r) 3.0 (57.7 +- 3.7%) (z = 1.71; p = 0.087), PermaNet(r) 2.0 (72.3 +- 3.2%) (z = 1.78; p = 0.076) and the untreated control net (z = 1.72; p = 0.085). For blood-feeding inhibition, unwashed PermaNet(r) Dual (A) (42.5 +- 6.8%) was similar to unwashed PermaNet(r) Dual (B) (41.4 +- 6.9%) (z = 0.25; p = 0.802) and washed PermaNet(r) Dual (B) (43.7 +- 4.8%) that was not affected by washing (z = - 0.41; p = 0.682). Compared with PermaNet(r) 3.0, the blood-feeding inhibition of PermaNet(r) Dual (B) was significantly higher before washing (z = - 0.72; p = 0.469), and significantly lower after washing (z = - 1.92; p = 0.045). However, both unwashed and washed PermaNet(r) Dual (B) provided, respectively, significantly higher blood-feeding inhibition than unwashed and washed PermaNet(r) 2.0 (all p < 0.05). The personal protection was similar between unwashed samples of PermaNet(r) Dual (A) (58.8 +- 3.5%) and PermaNet(r) Dual (B) (60.8 +- 3.8%) (z = - 0.04; p = 0.968). Unwashed PermaNet(r) Dual (B) resulted in a personal protection that was similar to unwashed PermaNet(r) 3.0 (62.9 +- 4.2%) (z = 0.21; p = 0.738), and significantly different from unwashed PermaNet(r) 2.0 (28.8 +- 1.9%) (z = 3.21; p = 0.001). With 20 washes, the personal protection with PermaNet(r) Dual (B) declined significantly from 60.8 +- 3.8% to 20.2 +- 1.1% (z = - 2.96; p = 0.003). However, washed PermaNet(r) Dual (B) provided significantly higher personal protection compared with their washed counterparts of PermaNet(r) 3.0 (9.8 +- 0.7%) (z = 2.89; p = 0.004) and PermaNet(r) 2.0 (- 32.6 +- 2.2%) (z = 2.93; p = 0.003).\n",
-                        "\n",
-                        "header: Outcomes measures\n",
-                        "\n",
-                        "The following entomological outcomes were used to evaluate the efficacy of each treatment arm in the current experimental hut trial [23]:\n",
-                        "\n",
-                        "1. Deterrence: proportional reduction in the number of mosquitoes caught in treated hut relative to the number caught in the control hut.\n",
-                        "2. Exiting rate: percentage of the mosquitoes collected from the veranda trap out of all mosquitoes collected.\n",
-                        "3. Induced exophily: proportional reduction of mosquitoes found in the exit and veranda traps relative to control hut.\n",
-                        "4. Blood-feeding: percentage of blood-fed mosquitoes relative to the total collected.\n",
-                        "5. Blood-feeding inhibition: proportional reduction in blood feeding percentage in treated huts relative to the control hut.\n",
-                        "6. Mortality: percentage of dead mosquitoes found dead in hut in the morning (immediate mortality) or after being caught alive and dead during holding (delayed mortality) in treatment huts out of mosquitoes collected, and corrected for control mortality.\n",
-                        "7. Personal protection: the proportional reduction in the number of blood-fed mosquitoes in the treated huts relative to the number of blood-fed mosquitoes in the untreated control.\n",
-                        "8. Killing effect: the proportional reduction in the number of mosquitoes killed in the treated huts relative to the number of mosquitoes killed in the untreated control.\n",
-                        "\n",
-                        "The formulas of key entomological outcomes measured in this study are [23]:\n",
-                        "\n",
-                        "\\[\\text{Deterrence}(\\%) = \\frac{\\text{Nc} - \\text{Nt}}{\\text{Nc}} \\times 100 = \\left( 1 - \\frac{\\text{Nt}}{\\text{Nc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Nt the total number of mosquitoes collected in the treatment hut and veranda/exit traps and Nc the total number of mosquitoes in the control hut and veranda/exit traps.\n",
-                        "\n",
-                        "\\[\\text{Exiting}\\,\\text{rate}(\\%) = \\frac{\\text{n}}{\\text{N}} \\times 100,\\]\n",
-                        "\n",
-                        "where n is the number of mosquitoes from veranda and window traps, while N is the total number of mosquitoes collected in the hut.\n",
-                        "\n",
-                        "\\[\\text{Induced}\\,\\text{exophily}(\\%) = \\frac{\\text{Pt} - \\text{Pc}}{\\text{Pc}} \\times 100\\] \\[= \\left( \\frac{\\text{Pt}}{\\text{Pc}} - 1 \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Pt is the proportion of mosquitoes from veranda and window traps of treated hut while Pc is the number of mosquitoes from veranda and window traps of untreated control hut.\n",
-                        "\n",
-                        "\\[\\text{Blood} - \\text{feeding}\\,\\text{inhibition}(\\%) = \\frac{\\text{Pc} - \\text{Pt}}{\\text{Pc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Pt the proportion of blood-fed mosquitoes in the treatment hut, and Pc the proportion of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Personal}\\,\\text{protection}(\\%) = \\frac{\\text{Bc} - \\text{Bt}}{\\text{Bc}} \\times 100\\] \\[= \\left( 1 - \\frac{\\text{Bt}}{\\text{Bc}} \\right) \\times 100,\\]\n",
-                        "\n",
-                        "where Bt the number of blood-fed mosquitoes in the treatment hut and Bc the number of blood-fed mosquitoes in the control hut.\n",
-                        "\n",
-                        "\\[\\text{Killing}\\,\\text{effect}(\\%) = \\frac{\\text{Kt} - \\text{Kc}}{\\text{Tc}} \\times 100,\\]\n",
-                        "\n",
-                        "where Kt is the number of mosquitoes killed in the treatment huts, Kc is the number of mosquitoes killed in the control huts, and Tc is the total number of mosquitoes collected from the control.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The outcomes of the current experimental hut trial showed that the parameters of the net efficacy against the pyrethroid-resistant _An. gambiae s.l._ Tiassale population varied as a function of net type and wash status, with the performance of PermaNet(r) Dual being better overall compared with PermaNet(r) 3.0 and PermaNet(r) 2.0 (Fig. 1 and Table 1).\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "According to the latest World Malaria Report 2022 of the World Health Organization (WHO), there were 247 million malaria cases and 619,000 malaria deaths in 84 malaria endemic countries worldwide in 2021 [1, 2]. This represents about 13.4 million more cases in 2021 compared to 2019 attributable to disruptions to essential malaria services during the COVID-18 pandemic [1]. The WHO African Region, with an estimated 234 million cases in 2021, accounted for about 95% of global cases malaria [1]. The recent decline of malaria burden from 2000 to 2019 was largely due to the massive distribution and use of long-lasting insecticidal nets (LLINs) (2.5 billion LLINs were delivered from 2004 to 2021) for _Anopheles_ mosquito vector control [1]. The malaria burden reduction is now slowing down and is threatened by the spread of resistance to pyrethroids (78 of 88 endemic countries have detected resistance to at least one insecticide class reported to the WHO from 2010 to 2020) [1]. The WHO Global Plan for Insecticide Resistance Management (GPIRM) has recommended the development of LLINs with insecticide mixtures of active ingredients with different modes of action to mitigate resistance [3]. The WHO Global Technical Strategy (GTS) aims for a reduction of malaria case incidence and mortality rate of at least 40% by 2020, 75% by 2025 and 90% by 2030 from the 2015 baseline [1, 4]. To meet these targets, GTS has called for the development of new tools, with combined or more effective insecticide molecules to control insecticide-resistant _Anopheles_ vectors [4]. These new vector control tools must incorporate new insecticide molecules and/or insecticide mixtures containing at least two active ingredients with different modes of action for the management of malaria vector resistance to insecticides [1, 3].\n",
-                        "\n",
-                        "Malaria is endemic throughout Cote d'Ivoire and represents the leading cause of mortality and morbidity in the country. Several studies have shown a wide spread resistance in local _Anopheles_ to most of the insecticide classes currently used in malaria vector control (e.g. pyrethroids, DDT and carbamates) [5-8]. The existence of multiple mechanisms of resistance in the main vector, _Anopheles gambiae_ sensu lato (_s.l._) threatens the efficacy of vector control tools currently used in Cote d'Ivoire, including LLINs [9-11]. Meiwald et al. [11] found that pyrethroid resistance is associated with significant overexpression of _CYP6P4, CYP6P3,_ and _CYP6Z1_ in _Anopheles coluzzii_ in Cote d'Ivoire. High allelic frequencies of knock-down resistance (_kdr_) _L1014F_ mutation (range: 0.46-1), relatively low frequencies of the _ace-1R_ mutation in the acetylcholinesterase gene associated with target-site insensitivity to carbamates and organophosphates (<0.5), and elevated activity of insecticide detoxifying enzymes (mainly mixed function oxidases (MFOs), esterase and glutathione S-transferase (GST)), have been reported in Cote d'Ivoire [5, 7, 10]. Recent laboratory (WHO tube and CDC bottle) bioassays against local pyrethroid-resistant _An. gambiae_ from Cote d'Ivoire have shown that pyrethroids induce low mortality, whilst pre-exposure to the synergist piperonyl butoxide (PBO) increases the mortality but does not restore fully the susceptibility due to resistance [9]. However, the pyrrole insecticide chlorfenapyr induces higher mortality in resistant _An. gambiae_ compared with pyrethroids alone or combined with PBO in Cote d'Ivoire [9].\n",
-                        "\n",
-                        "The present good laboratory practice (GLP) study evaluated the bio-efficacy of a new candidate LLIN, PermaNet(r) Dual, against pyrethroid-resistant _An. gambiae s.l._ in comparison with the WHO Prequalification Unit, Vector Control Product Assessment Team (PQT/VCP) listed standard LLINs, PermaNet(r) 2.0 and PermaNet(r) 3.0, through the conduct of an experimental hut trial and laboratory bioassays in Tiassale, Cote d'Ivoire. PermaNet(r) Dual has a mixture of the pyrrole chlorfenapyr and the pyrethroid deltamethrin coated onto a polyester fabric. PermaNet(r) 3.0 is treated with deltamethrin and PBO, and PermaNet(r) 2.0 is treated with deltamethrin only. Chlorfenapyr is a pro-insecticide activated by the oxygenase function of cytochrome P450s and oxidative removal of the N-ethoxymethyl\n",
-                        "group leads to a toxic form identified as CL 303,268. The toxic form uncouples oxidative phosphorylation in the mitochondria, resulting in disruption of the production of adenosine triphosphate and loss of energy, leading to cell dysfunction and ultimately death of the insect [12]. The WHO has received some resistance monitoring data for chlorfenapyr, but these data are insufficient to assess the potential presence of resistance to this insecticide [1]. Deltamethrin is a neurotoxic insecticide and has excito-repellent effects on mosquitoes. PBO, used on PermaNet(r) 3.0, is a synergist which inhibits mixed function oxidases, blocking the detoxification of pyrethroids and at least partially restoring pyrethroid susceptibility [1, 13]. Pyrethroid-resistant strains of _Anopheles_ have so far been found to be susceptible to chlorfenapyr [9, 14-18]. Thus, the hypothesis of the current study was that chlorfenapyr would kill the pyrethroid-resistant _An. gambiae_ population in Tiassale, and PermaNet(r) Dual would induce higher mortality compared to both PermaNet(r) 3.0 and PermaNet(r) 2.0 in the experimental huts, and the supplementary laboratory cone and tunnel bioassays could predict these hut trial outcomes.\n",
-                        "\n",
-                        "header: Additional file 3: Table 53.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (Tissale strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "PermaNet(r) Dual demonstrated significantly improved efficacy, compared to PermaNet(r) 3.0 and PermaNet(r) 2.0, against the pyrethroid-resistant _An. gambiae s.l._ Indeed, the experimental hut trial data showed that the mortality and blood-feeding inhibition in the wild pyrethroid-resistant _An. gambiae s.l._ were overall significantly higher with PermaNet(r) Dual compared with PermaNet(r) 3.0 and PermaNet(r) 2.0, for both unwashed and washed samples. The mortality with unwashed and washed samples were 93.6 +- 0.2% and 83.2 +- 0.9% for PermaNet(r) Dual, 37.5 +- 2.9% and 14.4 +- 3.3% for PermaNet(r) 3.0, and 7.4 +- 5.1% and 11.7 +- 3.4% for PermaNet(r) 2.0, respectively. Moreover, unwashed and washed samples produced the respective percentage blood-feeding inhibition of 41.4 +- 6.9% and 43.7 +- 4.8% with PermaNet(r) Dual, 51.0 +- 5.7% and 9.8 +- 3.6% with PermaNet(r) 3.0, and 12.8 +- 4.3% and - 13.0 +- 3.6% with PermaNet(r) 2.0. Overall, PermaNet(r) Dual also induced higher or similar deterrence, exophily and personal protection when compared with the standard PermaNet(r) 3.0 and PermaNet(r) 2.0 reference nets, with both unwashed and\n",
-                        "washed net samples. In contrast to cone bioassays, tunnel tests predicted the efficacy of PermaNet(r) Dual seen in the current experimental hut trial.\n",
-                        "\n",
-                        "header: Additional file 2: Table 52.\n",
-                        "\n",
-                        "Mean knock-down and mortality rates in multi-resistant _Anopheles gambiae s.l._ (bisumu strain) exposed to long-lasting insecticidal nets using cone bioassays before and after experimental hut trial in Tassale, Cape d'hore.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This study was funded by a Grant from Vestergaard Sarl, Lausanne, Switzerland.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "The present small-scale GLP experimental hut study demonstrated that the deltamethrin- chlorfenapyr PermaNet(r) Dual net has high bio-efficacy, inducing significantly higher mortality and blood-feeding inhibition effects in the wild insecticide-resistant _An. gambiae s.l._ when compared with the deltamethrin-PBO PermaNet(r) 3.0 and deltamethrin-only PermaNet(r) 2.0. The inclusion of chlorfenapyr with pyrethroid in PermaNet(r) Dual net has greatly improved protection and control of free-flying wild pyrethroid-resistant _An. gambiae_ populations. The chlorfenapyr-deltamethrin-coated PermaNet(r) Dual net has great potential to reduce malaria transmission, particularly in areas compromised by high level of pyrethroid resistance in _Anopheles_ mosquitoes. Further validations in large-scale field trials are required to assess the effectiveness, the durability and the acceptability of this new tool for malaria vector control.\n",
-                        "\n",
-                        "header: Experimental huts and mosquitoes: Net treatments and treatment arms\n",
-                        "\n",
-                        "This study included the PermaNet(r) Dual (candidate net), PermaNet(r) 3.0 and PermaNet(r) 2.0 (reference nets) and untreated net (negative control). All mosquito nets were new and unwashed nets supplied by Vestergaard Sarl (Lausanne, Switzerland). The untreated nets were made of polyester fabric without any insecticide. PermaNet(r) Dual was made of polyester fabric coated with chlorfenapyr at 5.0 g/kg +- 25% and deltamethrin at 2.1 g/kg +- 25%. The unwashed PermaNet(r) Dual arm was replicated using nets from two different production batches (referred to as A and B). PermaNet(r) 3.0 was made of polyester fabric coated with 2.1 g/kg +- 25% of deltamethrin on the sides, and polyethylene incorporated with 4.0 g/kg +- 25% of deltamethrin and 25.0 g/kg +- 25% of PBO on the roof. PermaNet(r) 2.0 was made of polyester fabric coated with 1.4 g/kg +- 25% of deltamethrin.\n",
-                        "\n",
-                        "Eight treatment arms were compared in the experimental huts and laboratory: PermaNet(r) Dual (A) unwashed; PermaNet(r) Dual (B) unwashed and washed 20 times; PermaNet(r) 3.0 unwashed and washed 20 times; PermaNet(r) 2.0 unwashed and washed 20 times; and untreated control net. The nets were prepared and washed using a soap called \"Savon de Marseille\" and according to the WHO guidelines on small-scale field hut trials and laboratory testing [23]. The interval of time between two consecutive washes was one day, corresponding to the regeneration time of all the nets. The regeneration time of the PermaNet(r) Dual (i.e. 1 day) was supplied by Vestergaard. The nets were washed 20 times. Before testing them in the experimental huts, all the unwashed, washed and untreated control nets were deliberately holed with six holes of 4 cm in diameter to stimulate the conditions of torn nets according to the WHO guidelines [23].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"tunnel test\",\"Country\":\"Cote d'Ivoire\",\"Site\":\"Tiassale\",\"Start_month\":3,\"Start_year\":2020,\"End_month\":8,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet(r) Dual\",\"Insecticide\":\"chlorfenapyr, deltamethrin\",\"Net_washed\":0.0},\"N02\":{\"Net_type\":\"PermaNet(r) Dual\",\"Insecticide\":\"chlorfenapyr, deltamethrin\",\"Net_washed\":20.0},\"N03\":{\"Net_type\":\"PermaNet(r) 3.0\",\"Insecticide\":\"deltamethrin, PBO\",\"Net_washed\":0.0},\"N04\":{\"Net_type\":\"PermaNet(r) 3.0\",\"Insecticide\":\"deltamethrin, PBO\",\"Net_washed\":20.0},\"N05\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_washed\":0.0},\"N06\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_washed\":20.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: PermaNet(r) 2.0 and Dawa Plus(r) 2.0: Life Net(r) and NetProtect(r)\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 250 ml round-bottom boiling flask followed by the addition of 95 ml of xylene and 5 ml of a known concentration of internal standard dipropyl phthalate, (2 mg/ml). The flask was fitted with a reflux condenser and heated to boiling for 30 min. After cooling, approximately 2.5 ml of the extract was filtered and transferred to a glass tube and evaporated to dryness under a stream of dry nitrogen for 30 min at 60 degC. A known volume (0.75 ml) of the mobile phase (94/6 (v/v) isooctane/1, 4-dioxane) was used to reconstitute the residue and was transferred to a sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter. The filtrate was centrifuged (500g) for 2 min and transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Extraction of permethrin for Olyset Net(r)\n",
-                        "\n",
-                        "Permethrin analysis of Olyset Net(r) samples was based on CIPAC Method 331 [31]. Each specimen set was weighed and placed in a 100 ml round-bottom boiling flask, followed by heptane (45 ml) and triphenyl phosphate internal standard (5.0 ml of known concentration in heptane). The flask was fitted with a reflux condenser and heated to boiling for 45 min. After cooling, approximately 1.5 ml of the extract was transferred to a chromatographic sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "- Laboratoire d'Ecologie Vectorieile et Parastaire, Département de Biologie Animal, Faculte Des Sciences et Techniques, Universite Cheikh Anta Diop, Dakar, Senegal. 2institut Pasteur de Dakar, Dakar, Senegal. 3Laboratoire de Télédétection Appliquee, (ITA/IST/FST/UCAD, Dakar, Senegal. 4Division of Parabolic Diseases and Malaria, Centers for Disease Control (CDC) and Prevention, Atlanta, GA, USA.\n",
-                        "\n",
-                        "header: Mosquito net coverage\n",
-                        "\n",
-                        "A total of 3012 nets were distributed in 1249 households (Table 2). The overall average household coverage, 94% (95% CI 92.68-95.3), was 68.2% (95% CI 63.63-72.77) and 92% (95% CI 90-94) in Mbagame and Thies area villages, respectively. Moreover, the average percentage of households receiving only one mosquito net was 26% (95% CI 23-28) in all areas. The average percentage of households in Mbagame and Thies receiving only one mosquito net was estimated as 41.8% (95% CI 36.9-46.8) and 17.4% (95% CI 15-20.6), respectively.\n",
-                        "\n",
-                        "The global coverage of sleeping spaces (3,012/3,840) was 78% (95% CI 77-78). However, the sleeping space coverage in Mbagame area was estimated at 68.2% (95% CI: 65-70), and 95.3% (95% CI 94-96) in the villages surrounding Thies.\n",
-                        "\n",
-                        "header: Gas chromatography analysis\n",
-                        "\n",
-                        "The extracts were analysed using an Agilent 6890 N chromatograph fitted with a 30 m x 0.25 mm (i.d.) fused silica DB-1 capillary column coated with 0.25 mm cross linked polydimethylsiloxane stationary phase. Ultra-high purity nitrogen (1.2 ml/min) was used as the carrier gas. Injector port, column oven, and detector temperatures were 265 degC, 240 degC, and 300 degC, respectively. Flame ionization (FID) was used for analyte detection. Two injections were used for each sample and the results averaged. Permethrin concentration was calculated by comparing permethrin/triphenyl phosphate peak area ratios against a calibration curve generated from solutions containing known permethrin /triphenyl phosphate mass ratios.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "A total of five brands of LLINs were distributed to households: NetProtect(r), PermaNet(r) 2.0, Dawa Plus(r) 2.0, Olyset Net(r) and Life Net(r) (Table 1). NetProtect(r) mosquito nets were white, rectangular (length = 190, width = 180 and height = 150 cm) made of 118 denier polyethylene mono filaments and 136 holes/in2 mesh. PermaNet(r) 2.0 mosquito nets were white, rectangular (length = 190, width = 180 and height = 200 cm), made with 75 denier polyester filament tulle and 156 holes/inch2 mesh. Dawa Plus(r) 2.0 were white, rectangular (length = 200, width = 160 and height = 180 cm), made of polyester multi-filament tulle, 75 deniers and 156 holes/inch2 mesh. Olyset Net(r) were white, conical (circumference 1250 cm, height = 250 cm), made with a denier polyethylene mono filament tulle greater than 150 cm and 56 holes/inch2 mesh. Life Net(r) nets were the only mosquito nets made from polypropylene, they were white, rectangular (length = 190, width = 180 and height = 150 cm) made with multi-filament tulle with 110 denier and 156 holes/inch2 mesh.\n",
-                        "\n",
-                        "Aside from the Olyset Net(r) mosquito nets which were treated with permethrin, all other nets distributed were impregnated with deltamethrin. Olyset Net(r) and Life Net(r) were donated by OMVS (Senegal River Basin Development Organization) and BAYER AG (Germany),\n",
-                        "respectively, while the three other brands (PermaNet(r) 2.0, Dawa Plus(r) 2.0 and NetProtect(r)) were purchased from vendors approved by manufacturers.\n",
-                        "\n",
-                        "header: Extraction of deltamethrin\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 125 ml screw capped Erlenmeyer flask and 50 ml of the extraction solvent mixture was added making sure that the net was completely submerged in the mixture. The flask was tightly capped and sealed with paraffin film before placing in an ultrasonic bath for 15 min. Later the flask was shaken in a bath at 25 degC for 30 min at a frequency of 155 cycles per min. The extract was transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Physical integrity\n",
-                        "\n",
-                        "At least 83% (95% CI 80-90) of mosquito nets had holes (all types of holes) at the end of the study. Except the Life Net(r), the average number of holes increased with time for all LLINs. For the LLINs inspected in laboratory, at the end of the 6th semester of follow-up, the majority of them had holes, with average proportions of 95.2% 95%, 93.3%, 100% and 60% for Dawa Plus(r) 2.0, NetProtect(r) PermaNet(r) 2.0, Olyset Net(r) and Life Net(r), respectively. The average number of holes remained relatively low for Life Net(r) (Fisher's exact test, OR: 12.02; 95% CI 0.96-684.89, P = 0.027).\n",
-                        "\n",
-                        "At each semester, size 1 holes (Fig. 3) were predominant for all types of mosquito nets. The average number of holes (all types) per mosquito net was 39 per LLIN at the end of six semesters of study. It was 13, 39, 15, 48 and 54 respectively for Life Net(r), NetProtect(r) Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Olyset Net(r), respectively. Only NetProtect(r) and PermaNet(r) 2.0 types were still in good condition at the end of the three-year study (Table 3). Proportions of mosquito nets in good condition in\n",
-                        "\n",
-                        "Figure 2: Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\n",
-                        "households were 71% (NetProtect(r)) and 50% (PernaNet(r) 2.0) in both mosquito nets.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Rate of sampling mosquito nets, retention of LLINs\n",
-                        "\n",
-                        "From the beginning of the study in 2011 to the end in 2014 (6 semesters), 839 mosquito nets (all types) were removed representing 93.2% of the expected 900 LLINs to be removed (Fig. 2, Table 3). Of the 180 mosquito nets of each brand expected to be sampled, 94.4%, 79.4%, 94.4%, 98.9% and 98.3% of the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r), Olyset Net(r) and PermaNet(r) 2.0, respectively, were removed from the field. The number of mosquito nets sampled was less than the 28-30 units expected in the 5th and 6th semester for Life Net(r), and in the 6th semester for Dawa Plus(r) 2.0 and NetProtect(r). At the end of the study, the retention rate of all five LLIN types was 12.5% (95% CI 11.4-13.7) based on the total number of nets distributed and was relatively better for Olyset Net(r) at 20.5% (95% CI 17.8-23.4) and to a lesser extent for Dawa Plus(r) 2.0 with 15.1% (95% CI 12.4-18.2). With more than half of the mosquito nets not found after only two semesters of follow-up, the average percentage of Life Net(r) found in households fell to about one in five mosquito nets distributed remaining in the third semester.\n",
-                        "\n",
-                        "header: Table 3 Physical integrity of the LLINs inspected in the laboratory per type and semester after three years\n",
-                        "footer: N: Number of samples \n",
-                        "columns: ['Semesters', 'Types', 'N', 'General State - Good condition - N', 'General State - Good condition - %', 'General State - Serviceable condition - N', 'General State - Serviceable condition - %', 'General State - Tear - N', 'General State - Tear - %']\n",
-                        "\n",
-                        "{\"0\":{\"Semesters\":1,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":21,\"General State - Good condition - %\":70.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":7,\"General State - Tear - %\":23.0},\"1\":{\"Semesters\":1,\"Types\":\"Life Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":28,\"General State - Good condition - %\":93.3,\"General State - Serviceable condition - N\":1,\"General State - Serviceable condition - %\":3.3,\"General State - Tear - N\":1,\"General State - Tear - %\":3.3},\"2\":{\"Semesters\":1,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":26,\"General State - Good condition - %\":86.7,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":4,\"General State - Tear - %\":13.3},\"3\":{\"Semesters\":1,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":13,\"General State - Good condition - %\":43.3,\"General State - Serviceable condition - N\":7,\"General State - Serviceable condition - %\":23.3,\"General State - Tear - N\":10,\"General State - Tear - %\":33.4},\"4\":{\"Semesters\":1,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":19,\"General State - Good condition - %\":63.3,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":11,\"General State - Tear - %\":36.7},\"5\":{\"Semesters\":2,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":18,\"General State - Good condition - %\":60.0,\"General State - Serviceable condition - N\":5,\"General State - Serviceable condition - %\":16.7,\"General State - Tear - N\":7,\"General State - Tear - %\":23.3},\"6\":{\"Semesters\":2,\"Types\":\"Life Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":24,\"General State - Good condition - %\":80.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"7\":{\"Semesters\":2,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":22,\"General State - Good condition - %\":73.3,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":6.7,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"8\":{\"Semesters\":2,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":13,\"General State - Good condition - %\":43.3,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":33.4,\"General State - Tear - N\":7,\"General State - Tear - %\":23.3},\"9\":{\"Semesters\":2,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":28,\"General State - Good condition - N\":10,\"General State - Good condition - %\":35.7,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.1,\"General State - Tear - N\":16,\"General State - Tear - %\":57.2},\"10\":{\"Semesters\":3,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":15,\"General State - Good condition - %\":50.0,\"General State - Serviceable condition - N\":5,\"General State - Serviceable condition - %\":17.0,\"General State - Tear - N\":10,\"General State - Tear - %\":33.0},\"11\":{\"Semesters\":3,\"Types\":\"Life Net\\u00ae\",\"N\":29,\"General State - Good condition - N\":18,\"General State - Good condition - %\":62.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":9,\"General State - Tear - %\":31.0},\"12\":{\"Semesters\":3,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":18,\"General State - Good condition - %\":60.0,\"General State - Serviceable condition - N\":12,\"General State - Serviceable condition - %\":40.0,\"General State - Tear - N\":0,\"General State - Tear - %\":0.0},\"13\":{\"Semesters\":3,\"Types\":\"Olyset Net\\u00ae\",\"N\":28,\"General State - Good condition - N\":7,\"General State - Good condition - %\":25.0,\"General State - Serviceable condition - N\":9,\"General State - Serviceable condition - %\":32.0,\"General State - Tear - N\":12,\"General State - Tear - %\":43.0},\"14\":{\"Semesters\":3,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":19,\"General State - Good condition - %\":63.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":9,\"General State - Tear - %\":30.0},\"15\":{\"Semesters\":4,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":1,\"General State - Good condition - %\":3.0,\"General State - Serviceable condition - N\":20,\"General State - Serviceable condition - %\":67.0,\"General State - Tear - N\":9,\"General State - Tear - %\":30.0},\"16\":{\"Semesters\":4,\"Types\":\"Life Net\\u00ae\",\"N\":29,\"General State - Good condition - N\":25,\"General State - Good condition - %\":86.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":1,\"General State - Tear - %\":3.0},\"17\":{\"Semesters\":4,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":14,\"General State - Good condition - %\":47.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":13,\"General State - Tear - %\":43.0},\"18\":{\"Semesters\":4,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":0,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":24,\"General State - Serviceable condition - %\":80.0,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"19\":{\"Semesters\":4,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":11,\"General State - Good condition - %\":37.0,\"General State - Serviceable condition - N\":11,\"General State - Serviceable condition - %\":37.0,\"General State - Tear - N\":8,\"General State - Tear - %\":27.0},\"20\":{\"Semesters\":5,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":29,\"General State - Good condition - N\":10,\"General State - Good condition - %\":34.0,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":34.0,\"General State - Tear - N\":9,\"General State - Tear - %\":31.0},\"21\":{\"Semesters\":5,\"Types\":\"Life Net\\u00ae\",\"N\":15,\"General State - Good condition - N\":15,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":0,\"General State - Tear - %\":0.0},\"22\":{\"Semesters\":5,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":16,\"General State - Good condition - %\":53.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":11,\"General State - Tear - %\":37.0},\"23\":{\"Semesters\":5,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":5,\"General State - Good condition - %\":17.0,\"General State - Serviceable condition - N\":18,\"General State - Serviceable condition - %\":60.0,\"General State - Tear - N\":7,\"General State - Tear - %\":23.0},\"24\":{\"Semesters\":5,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":29,\"General State - Good condition - N\":16,\"General State - Good condition - %\":55.0,\"General State - Serviceable condition - N\":6,\"General State - Serviceable condition - %\":21.0,\"General State - Tear - N\":7,\"General State - Tear - %\":24.0},\"25\":{\"Semesters\":6,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":21,\"General State - Good condition - N\":1,\"General State - Good condition - %\":5.0,\"General State - Serviceable condition - N\":18,\"General State - Serviceable condition - %\":86.0,\"General State - Tear - N\":3,\"General State - Tear - %\":14.0},\"26\":{\"Semesters\":6,\"Types\":\"Life Net\\u00ae\",\"N\":10,\"General State - Good condition - N\":0,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":10,\"General State - Tear - %\":100.0},\"27\":{\"Semesters\":6,\"Types\":\"NetProtect\\u00ae\",\"N\":21,\"General State - Good condition - N\":15,\"General State - Good condition - %\":71.0,\"General State - Serviceable condition - N\":1,\"General State - Serviceable condition - %\":5.0,\"General State - Tear - N\":5,\"General State - Tear - %\":24.0},\"28\":{\"Semesters\":6,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":2,\"General State - Good condition - %\":7.0,\"General State - Serviceable condition - N\":24,\"General State - Serviceable condition - %\":80.0,\"General State - Tear - N\":4,\"General State - Tear - %\":13.0},\"29\":{\"Semesters\":6,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":15,\"General State - Good condition - %\":50.0,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":33.0,\"General State - Tear - N\":5,\"General State - Tear - %\":17.0}}\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare that they have no competing interests.\n",
-                        "\n",
-                        "header: Physical integrity of LLINs\n",
-                        "\n",
-                        "The condition of each LLIN was checked every six months. Physical integrity of sampled LLINs was assessed in the laboratory by searching for holes or tears on 170 Dawa Plus(r) 2.0, 171 NetProtect(r), 143 Life Net(r), 178 Olyset Net(r) and 177 PermaNet(r) 2.0. Holes were classified as size 1 (0.5-2 cm diameter), size 2 (2-10 cm diameter) or size 3 (diameter > 10 cm) [28]. The proportional hole index (pHI) value was calculated on each LLIN inspected per WHO guidelines [28, 29]. It is the sum of the proportional indexes by categories. The proportional index for a category was the product of the number of holes in category and the index attributed to that size:\n",
-                        "\n",
-                        "\\[\\begin{array}{l} {\\text{pH}} \\equiv \\neq \\text{size}\\;1\\;\\text{holes}\\;\\times\\;1+(\\text{\\#size}\\;2\\;\\text{holes}\\; \\times\\;23)\\\\ {}\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "{\n",
-                        "\n",
-                        "\n",
-                        "sampling pattern recommended by WHOPES [28]. For all the net samples, insecticide from five swatches of all the four sides and one from the top were extracted and analysed. Each specimen was trimmed to a 10 cm x 10 cm square (0.010m2) using a die cutting in a pneumatic press. During the cutting operation, each specimen was sandwiched between layers of aluminum foil to prevent cross-contamination. The specimen set from each net was analysed as a group to yield an average value of insecticide concentration for the whole net. Chemical analysis was based on methods published by the Collaborative International Pesticides Analytical Council (CIPAC). Deltamethrin analysis of PermaNet(r)2.0 and Dawa Plus(r) samples was based on CIPAC Method 333 [32-34].\n",
-                        "\n",
-                        "header: Data entry and statistical analysis\n",
-                        "\n",
-                        "Household survey data were entered on EPI data Entry 3.0, exported to Excel Microsoft office 2010 and analysed with R software. The calculation of proportions and averages was done according to the normal law by 95% Confidence Intervals (CI). Comparisons and proportions were made by Chi-squared tests (kh2) of homogeneity. Generalized linear models were performed to understand the relationship between mosquito mortality and LLIN washing and mortality bioassay results and insecticide chemical content. The analysis of the condition of the mosquito net, the values obtained for pHI, the torn surface and its shape was used to establish one of the criteria of durability [28]; a mosquito net was in good condition when the pHI was between 0 and 64, is usable when pHI was in 65-642 and was too torn when the pHI was greater than 643 [30]. The acceptable residual efficacy of each type of LLIN was determined based on WHO criteria [30].\n",
-                        "\n",
-                        "Residual efficacy was optimal if the KD 60 or the mortality rate (MR), defined as the number of dead females\n",
-                        "relative to the total number of mosquitoes exposed per mosquito net was >= 95% or >= 80%, respectively, after 20 washes in the laboratory or after three years of use [28]. The proportion of mosquito nets meeting optimal bio efficacy levels of KD60 and mortality rates were determined for each type of LLINs by performing cone bioassays in the laboratory. A minimal bio-efficacy criteria of >= 75% for KD or >= 50% mortality rate was used to determine the efficacy status of LLINs [28]. The MR was corrected by the Abbott formula [35] if the mortality rate for controls was less than 20% and the bioassay was redone if it was greater than 20%. Generalized linear regression models were run to estimate the relationship in mortality rate between washed and non-washed mosquito nets.\n",
-                        "\n",
-                        "The retention rate was calculated for each type of LLIN at each follow up period. It was defined as the ratio of the number of the original study mosquito nets available in households to the total number of mosquito nets initially distributed. The mosquito nets declared lost but found in the following survey were included in the calculation of the retention rate of the previous semester.\n",
-                        "\n",
-                        "header: Table 1: Characteristics of types of LLINs distributed in study areas\n",
-                        "footer: None\n",
-                        "columns: ['Type of LLINs', 'Manufacturers', 'Material', 'Mesh (holes/ square inch2)', 'Denier', 'Insecticide', 'Standard Dose (mg/ m2)']\n",
-                        "\n",
-                        "{\"0\":{\"Type of LLINs\":\"NetProtect\\u00ae\",\"Manufacturers\":\"Intelligent Insect Control, France\",\"Material\":\"Polyethylene\",\"Mesh (holes\\/ square inch2)\":136,\"Denier\":\"118\",\"Insecticide\":\"Deltamethrin\",\"Standard Dose (mg\\/ m2)\":68},\"1\":{\"Type of LLINs\":\"PermaNet\\u00ae 2.0\",\"Manufacturers\":\"Vestergaard-Frandsen, Suisse\",\"Material\":\"Polyester\",\"Mesh (holes\\/ square inch2)\":156,\"Denier\":\"75\",\"Insecticide\":\"Deltamethrin\",\"Standard Dose (mg\\/ m2)\":55},\"2\":{\"Type of LLINs\":\"Dawa Plus\\u00ae 2.0\",\"Manufacturers\":\"Tana Netting, CO,LTD,Thailand\",\"Material\":\"Polyester\",\"Mesh (holes\\/ square inch2)\":156,\"Denier\":\"75\",\"Insecticide\":\"Deltamethrin\",\"Standard Dose (mg\\/ m2)\":80},\"3\":{\"Type of LLINs\":\"Olyset Net\\u00ae\",\"Manufacturers\":\"Sumitomo Chemical, Japon\",\"Material\":\"Polyethylene\",\"Mesh (holes\\/ square inch2)\":56,\"Denier\":\"> 150\",\"Insecticide\":\"Permethrin\",\"Standard Dose (mg\\/ m2)\":1000},\"4\":{\"Type of LLINs\":\"Life Net\\u00ae\",\"Manufacturers\":\"Bayer CropSciences, France\",\"Material\":\"Polypropylene\",\"Mesh (holes\\/ square inch2)\":156,\"Denier\":\"110\",\"Insecticide\":\"Deltamethrin\",\"Standard Dose (mg\\/ m2)\":340}}\n",
-                        "\n",
-                        "header: Use and maintenance of LLINs\n",
-                        "\n",
-                        "The regular use of the mosquito nets (use the night before the survey) at the end of the follow up was 23.3%, 38.5%, 7.3%, 78.3% and 16.1% for the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r) Olyset Net(r) and PermaNet(r) 2.0 types, respectively. For the NetProtect(r) this rate was statistically lower compared to the Life Net(r) and Olyset Net(r) (Fisher's exact test, OR: 0.13; 95% CI: 0.017-0.83, P = 0.014; Fisher's exact test, OR: 0.022; 95% CI 0.017-0.83, P < 0.001).\n",
-                        "\n",
-                        "Dawa Plus(r) 2.0 and PermaNet(r) 2.0 used the night before the survey consistently exceeded 50% during the first four semesters. For Olyset Net(r), this rate was always above 60% during the three years of follow-up. In contrast, the NetProtect(r) and Life Net(r) types were the least used in households. Their regular use rates were only satisfactory in the first two semesters following LLINs distribution, with 52.6% (95% CI 47.7-57.5) for NetProtect(r) in semester one and 53% (95% CI 46-59.6) for Life Net(r) in the second.\n",
-                        "\n",
-                        "For all LLIN types distributed, only 19% (95% CI 18-20) were washed. The results obtained with washing habits showed that 66% (95% CI 64-69) of the washed mosquito nets were washed with warm water. In addition, 73% (95% CI 71-76) of the cases used local soap and 50% of the mosquito net was dried in shade after the washing.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The evaluation of LLINs after three years of use revealed a loss of integrity of almost all types of nets distributed. Despite this loss of integrity, biological efficacy was maintained for the Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Life Net(r) brands.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "Cl 95%: Confidence interval 95%: CIO: Collaborative international pesticides analytical council; KD60: Proportion of mosquitoes knocked down at 60 min post-exposure; HPLC: High performance liquid chromatography; IQR: Intra quarter-time; LINK: Long-lasting insecticidal nets; pH: Proportional hole index; MW: World Health Organization; WHOPS: World Health Organization pesticide evaluation scheme; UV: Ultraviolet; PVC: Polyvinyl chloride; CDC: Centers for disease control and prevention; MR: Mortality rate; CNRS: National research on contract for health; OMVS: Senegal river basin development organization; NMC: National malaria control programme; P: Probability; OR: Odd ratio.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The data used and analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "- This work was supported by President's Malaria Initiative and Universite Cheikh Anta Diop (Dakar).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: PermaNet(r) 2.0 and Dawa Plus(r) 2.0: Life Net(r) and NetProtect(r)\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 250 ml round-bottom boiling flask followed by the addition of 95 ml of xylene and 5 ml of a known concentration of internal standard dipropyl phthalate, (2 mg/ml). The flask was fitted with a reflux condenser and heated to boiling for 30 min. After cooling, approximately 2.5 ml of the extract was filtered and transferred to a glass tube and evaporated to dryness under a stream of dry nitrogen for 30 min at 60 degC. A known volume (0.75 ml) of the mobile phase (94/6 (v/v) isooctane/1, 4-dioxane) was used to reconstitute the residue and was transferred to a sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter. The filtrate was centrifuged (500g) for 2 min and transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Gas chromatography analysis\n",
-                        "\n",
-                        "The extracts were analysed using an Agilent 6890 N chromatograph fitted with a 30 m x 0.25 mm (i.d.) fused silica DB-1 capillary column coated with 0.25 mm cross linked polydimethylsiloxane stationary phase. Ultra-high purity nitrogen (1.2 ml/min) was used as the carrier gas. Injector port, column oven, and detector temperatures were 265 degC, 240 degC, and 300 degC, respectively. Flame ionization (FID) was used for analyte detection. Two injections were used for each sample and the results averaged. Permethrin concentration was calculated by comparing permethrin/triphenyl phosphate peak area ratios against a calibration curve generated from solutions containing known permethrin /triphenyl phosphate mass ratios.\n",
-                        "\n",
-                        "header: Extraction of permethrin for Olyset Net(r)\n",
-                        "\n",
-                        "Permethrin analysis of Olyset Net(r) samples was based on CIPAC Method 331 [31]. Each specimen set was weighed and placed in a 100 ml round-bottom boiling flask, followed by heptane (45 ml) and triphenyl phosphate internal standard (5.0 ml of known concentration in heptane). The flask was fitted with a reflux condenser and heated to boiling for 45 min. After cooling, approximately 1.5 ml of the extract was transferred to a chromatographic sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "- Laboratoire d'Ecologie Vectorieile et Parastaire, Département de Biologie Animal, Faculte Des Sciences et Techniques, Universite Cheikh Anta Diop, Dakar, Senegal. 2institut Pasteur de Dakar, Dakar, Senegal. 3Laboratoire de Télédétection Appliquee, (ITA/IST/FST/UCAD, Dakar, Senegal. 4Division of Parabolic Diseases and Malaria, Centers for Disease Control (CDC) and Prevention, Atlanta, GA, USA.\n",
-                        "\n",
-                        "header: Mosquito net coverage\n",
-                        "\n",
-                        "A total of 3012 nets were distributed in 1249 households (Table 2). The overall average household coverage, 94% (95% CI 92.68-95.3), was 68.2% (95% CI 63.63-72.77) and 92% (95% CI 90-94) in Mbagame and Thies area villages, respectively. Moreover, the average percentage of households receiving only one mosquito net was 26% (95% CI 23-28) in all areas. The average percentage of households in Mbagame and Thies receiving only one mosquito net was estimated as 41.8% (95% CI 36.9-46.8) and 17.4% (95% CI 15-20.6), respectively.\n",
-                        "\n",
-                        "The global coverage of sleeping spaces (3,012/3,840) was 78% (95% CI 77-78). However, the sleeping space coverage in Mbagame area was estimated at 68.2% (95% CI: 65-70), and 95.3% (95% CI 94-96) in the villages surrounding Thies.\n",
-                        "\n",
-                        "header: Extraction of deltamethrin\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 125 ml screw capped Erlenmeyer flask and 50 ml of the extraction solvent mixture was added making sure that the net was completely submerged in the mixture. The flask was tightly capped and sealed with paraffin film before placing in an ultrasonic bath for 15 min. Later the flask was shaken in a bath at 25 degC for 30 min at a frequency of 155 cycles per min. The extract was transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare that they have no competing interests.\n",
-                        "\n",
-                        "header: High performance liquid chromatography (HPLC) analysis\n",
-                        "\n",
-                        "The deltamethrin content was analysed using Agilent 1200 HPLC equipped with a UV detector set at 230 nm and a 150 x 4.6 mm (i.d.) Ascents Si 5 mm column held at 40 degC. The mobile phase was 94/6 (v/v) isooctane/1, 4-dioxane with a flow rate of 1.5 ml/min. For each extract, three injections of 20 ml were made. Injections, calibrations, and quantification of the deltamethrin content were followed as per the procedure in the Collaborative International Pesticides Analytical Council (CIPAC) 333 [32-34].\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "A total of five brands of LLINs were distributed to households: NetProtect(r), PermaNet(r) 2.0, Dawa Plus(r) 2.0, Olyset Net(r) and Life Net(r) (Table 1). NetProtect(r) mosquito nets were white, rectangular (length = 190, width = 180 and height = 150 cm) made of 118 denier polyethylene mono filaments and 136 holes/in2 mesh. PermaNet(r) 2.0 mosquito nets were white, rectangular (length = 190, width = 180 and height = 200 cm), made with 75 denier polyester filament tulle and 156 holes/inch2 mesh. Dawa Plus(r) 2.0 were white, rectangular (length = 200, width = 160 and height = 180 cm), made of polyester multi-filament tulle, 75 deniers and 156 holes/inch2 mesh. Olyset Net(r) were white, conical (circumference 1250 cm, height = 250 cm), made with a denier polyethylene mono filament tulle greater than 150 cm and 56 holes/inch2 mesh. Life Net(r) nets were the only mosquito nets made from polypropylene, they were white, rectangular (length = 190, width = 180 and height = 150 cm) made with multi-filament tulle with 110 denier and 156 holes/inch2 mesh.\n",
-                        "\n",
-                        "Aside from the Olyset Net(r) mosquito nets which were treated with permethrin, all other nets distributed were impregnated with deltamethrin. Olyset Net(r) and Life Net(r) were donated by OMVS (Senegal River Basin Development Organization) and BAYER AG (Germany),\n",
-                        "respectively, while the three other brands (PermaNet(r) 2.0, Dawa Plus(r) 2.0 and NetProtect(r)) were purchased from vendors approved by manufacturers.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "Cl 95%: Confidence interval 95%: CIO: Collaborative international pesticides analytical council; KD60: Proportion of mosquitoes knocked down at 60 min post-exposure; HPLC: High performance liquid chromatography; IQR: Intra quarter-time; LINK: Long-lasting insecticidal nets; pH: Proportional hole index; MW: World Health Organization; WHOPS: World Health Organization pesticide evaluation scheme; UV: Ultraviolet; PVC: Polyvinyl chloride; CDC: Centers for disease control and prevention; MR: Mortality rate; CNRS: National research on contract for health; OMVS: Senegal river basin development organization; NMC: National malaria control programme; P: Probability; OR: Odd ratio.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The evaluation of LLINs after three years of use revealed a loss of integrity of almost all types of nets distributed. Despite this loss of integrity, biological efficacy was maintained for the Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Life Net(r) brands.\n",
-                        "\n",
-                        "header: Physical integrity\n",
-                        "\n",
-                        "At least 83% (95% CI 80-90) of mosquito nets had holes (all types of holes) at the end of the study. Except the Life Net(r), the average number of holes increased with time for all LLINs. For the LLINs inspected in laboratory, at the end of the 6th semester of follow-up, the majority of them had holes, with average proportions of 95.2% 95%, 93.3%, 100% and 60% for Dawa Plus(r) 2.0, NetProtect(r) PermaNet(r) 2.0, Olyset Net(r) and Life Net(r), respectively. The average number of holes remained relatively low for Life Net(r) (Fisher's exact test, OR: 12.02; 95% CI 0.96-684.89, P = 0.027).\n",
-                        "\n",
-                        "At each semester, size 1 holes (Fig. 3) were predominant for all types of mosquito nets. The average number of holes (all types) per mosquito net was 39 per LLIN at the end of six semesters of study. It was 13, 39, 15, 48 and 54 respectively for Life Net(r), NetProtect(r) Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Olyset Net(r), respectively. Only NetProtect(r) and PermaNet(r) 2.0 types were still in good condition at the end of the three-year study (Table 3). Proportions of mosquito nets in good condition in\n",
-                        "\n",
-                        "Figure 2: Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\n",
-                        "households were 71% (NetProtect(r)) and 50% (PernaNet(r) 2.0) in both mosquito nets.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "A total of 1,249 households were enrolled in both study areas. On average, 92.7% (95% CI 90-94) of heads of households were men, with an average 53 years of age. This average percentage was 78.2% in the Mbagame area with an average age of 52 years (95% CI 49-55) and 94.8% in the villages around Thies with a mean age of 53 years (95% CI 51.6-54.4) (P < 0.001). The average percentage of heads of households with any schooling was 38.4% (95% CI 34-42). The gender breakdown was 96% for men (n = 456) and 4% for women (n = 19).\n",
-                        "\n",
-                        "Of 604 households, 37.7% (228/604) had means of transportation among which 68% (95% CI 61-74) used carts while only 14% (95% CI 8-20) owned cars. The overall average percentage of households with electricity was 31% (95% CI 27-35). In Mbagame village, 87.2% households had electricity compared to an average of 22.6% for the study villages near Thies (_kh_2 = 132.4377, df = 1, P < 0.0001). Wood was the main form of fuel for cooking in 97.7% of households in both study areas.\n",
-                        "\n",
-                        "header: Rate of sampling mosquito nets, retention of LLINs\n",
-                        "\n",
-                        "From the beginning of the study in 2011 to the end in 2014 (6 semesters), 839 mosquito nets (all types) were removed representing 93.2% of the expected 900 LLINs to be removed (Fig. 2, Table 3). Of the 180 mosquito nets of each brand expected to be sampled, 94.4%, 79.4%, 94.4%, 98.9% and 98.3% of the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r), Olyset Net(r) and PermaNet(r) 2.0, respectively, were removed from the field. The number of mosquito nets sampled was less than the 28-30 units expected in the 5th and 6th semester for Life Net(r), and in the 6th semester for Dawa Plus(r) 2.0 and NetProtect(r). At the end of the study, the retention rate of all five LLIN types was 12.5% (95% CI 11.4-13.7) based on the total number of nets distributed and was relatively better for Olyset Net(r) at 20.5% (95% CI 17.8-23.4) and to a lesser extent for Dawa Plus(r) 2.0 with 15.1% (95% CI 12.4-18.2). With more than half of the mosquito nets not found after only two semesters of follow-up, the average percentage of Life Net(r) found in households fell to about one in five mosquito nets distributed remaining in the third semester.\n",
-                        "\n",
-                        "header: Data entry and statistical analysis\n",
-                        "\n",
-                        "Household survey data were entered on EPI data Entry 3.0, exported to Excel Microsoft office 2010 and analysed with R software. The calculation of proportions and averages was done according to the normal law by 95% Confidence Intervals (CI). Comparisons and proportions were made by Chi-squared tests (kh2) of homogeneity. Generalized linear models were performed to understand the relationship between mosquito mortality and LLIN washing and mortality bioassay results and insecticide chemical content. The analysis of the condition of the mosquito net, the values obtained for pHI, the torn surface and its shape was used to establish one of the criteria of durability [28]; a mosquito net was in good condition when the pHI was between 0 and 64, is usable when pHI was in 65-642 and was too torn when the pHI was greater than 643 [30]. The acceptable residual efficacy of each type of LLIN was determined based on WHO criteria [30].\n",
-                        "\n",
-                        "Residual efficacy was optimal if the KD 60 or the mortality rate (MR), defined as the number of dead females\n",
-                        "relative to the total number of mosquitoes exposed per mosquito net was >= 95% or >= 80%, respectively, after 20 washes in the laboratory or after three years of use [28]. The proportion of mosquito nets meeting optimal bio efficacy levels of KD60 and mortality rates were determined for each type of LLINs by performing cone bioassays in the laboratory. A minimal bio-efficacy criteria of >= 75% for KD or >= 50% mortality rate was used to determine the efficacy status of LLINs [28]. The MR was corrected by the Abbott formula [35] if the mortality rate for controls was less than 20% and the bioassay was redone if it was greater than 20%. Generalized linear regression models were run to estimate the relationship in mortality rate between washed and non-washed mosquito nets.\n",
-                        "\n",
-                        "The retention rate was calculated for each type of LLIN at each follow up period. It was defined as the ratio of the number of the original study mosquito nets available in households to the total number of mosquito nets initially distributed. The mosquito nets declared lost but found in the following survey were included in the calculation of the retention rate of the previous semester.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The data used and analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Table 3 Physical integrity of the LLINs inspected in the laboratory per type and semester after three years\n",
-                        "footer: N: Number of samples \n",
-                        "columns: ['Semesters', 'Types', 'N', 'General State - Good condition - N', 'General State - Good condition - %', 'General State - Serviceable condition - N', 'General State - Serviceable condition - %', 'General State - Tear - N', 'General State - Tear - %']\n",
-                        "\n",
-                        "{\"0\":{\"Semesters\":1,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":21,\"General State - Good condition - %\":70.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":7,\"General State - Tear - %\":23.0},\"1\":{\"Semesters\":1,\"Types\":\"Life Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":28,\"General State - Good condition - %\":93.3,\"General State - Serviceable condition - N\":1,\"General State - Serviceable condition - %\":3.3,\"General State - Tear - N\":1,\"General State - Tear - %\":3.3},\"2\":{\"Semesters\":1,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":26,\"General State - Good condition - %\":86.7,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":4,\"General State - Tear - %\":13.3},\"3\":{\"Semesters\":1,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":13,\"General State - Good condition - %\":43.3,\"General State - Serviceable condition - N\":7,\"General State - Serviceable condition - %\":23.3,\"General State - Tear - N\":10,\"General State - Tear - %\":33.4},\"4\":{\"Semesters\":1,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":19,\"General State - Good condition - %\":63.3,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":11,\"General State - Tear - %\":36.7},\"5\":{\"Semesters\":2,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":18,\"General State - Good condition - %\":60.0,\"General State - Serviceable condition - N\":5,\"General State - Serviceable condition - %\":16.7,\"General State - Tear - N\":7,\"General State - Tear - %\":23.3},\"6\":{\"Semesters\":2,\"Types\":\"Life Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":24,\"General State - Good condition - %\":80.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"7\":{\"Semesters\":2,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":22,\"General State - Good condition - %\":73.3,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":6.7,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"8\":{\"Semesters\":2,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":13,\"General State - Good condition - %\":43.3,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":33.4,\"General State - Tear - N\":7,\"General State - Tear - %\":23.3},\"9\":{\"Semesters\":2,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":28,\"General State - Good condition - N\":10,\"General State - Good condition - %\":35.7,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.1,\"General State - Tear - N\":16,\"General State - Tear - %\":57.2},\"10\":{\"Semesters\":3,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":15,\"General State - Good condition - %\":50.0,\"General State - Serviceable condition - N\":5,\"General State - Serviceable condition - %\":17.0,\"General State - Tear - N\":10,\"General State - Tear - %\":33.0},\"11\":{\"Semesters\":3,\"Types\":\"Life Net\\u00ae\",\"N\":29,\"General State - Good condition - N\":18,\"General State - Good condition - %\":62.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":9,\"General State - Tear - %\":31.0},\"12\":{\"Semesters\":3,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":18,\"General State - Good condition - %\":60.0,\"General State - Serviceable condition - N\":12,\"General State - Serviceable condition - %\":40.0,\"General State - Tear - N\":0,\"General State - Tear - %\":0.0},\"13\":{\"Semesters\":3,\"Types\":\"Olyset Net\\u00ae\",\"N\":28,\"General State - Good condition - N\":7,\"General State - Good condition - %\":25.0,\"General State - Serviceable condition - N\":9,\"General State - Serviceable condition - %\":32.0,\"General State - Tear - N\":12,\"General State - Tear - %\":43.0},\"14\":{\"Semesters\":3,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":19,\"General State - Good condition - %\":63.0,\"General State - Serviceable condition - N\":2,\"General State - Serviceable condition - %\":7.0,\"General State - Tear - N\":9,\"General State - Tear - %\":30.0},\"15\":{\"Semesters\":4,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":30,\"General State - Good condition - N\":1,\"General State - Good condition - %\":3.0,\"General State - Serviceable condition - N\":20,\"General State - Serviceable condition - %\":67.0,\"General State - Tear - N\":9,\"General State - Tear - %\":30.0},\"16\":{\"Semesters\":4,\"Types\":\"Life Net\\u00ae\",\"N\":29,\"General State - Good condition - N\":25,\"General State - Good condition - %\":86.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":1,\"General State - Tear - %\":3.0},\"17\":{\"Semesters\":4,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":14,\"General State - Good condition - %\":47.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":13,\"General State - Tear - %\":43.0},\"18\":{\"Semesters\":4,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":0,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":24,\"General State - Serviceable condition - %\":80.0,\"General State - Tear - N\":6,\"General State - Tear - %\":20.0},\"19\":{\"Semesters\":4,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":11,\"General State - Good condition - %\":37.0,\"General State - Serviceable condition - N\":11,\"General State - Serviceable condition - %\":37.0,\"General State - Tear - N\":8,\"General State - Tear - %\":27.0},\"20\":{\"Semesters\":5,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":29,\"General State - Good condition - N\":10,\"General State - Good condition - %\":34.0,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":34.0,\"General State - Tear - N\":9,\"General State - Tear - %\":31.0},\"21\":{\"Semesters\":5,\"Types\":\"Life Net\\u00ae\",\"N\":15,\"General State - Good condition - N\":15,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":0,\"General State - Tear - %\":0.0},\"22\":{\"Semesters\":5,\"Types\":\"NetProtect\\u00ae\",\"N\":30,\"General State - Good condition - N\":16,\"General State - Good condition - %\":53.0,\"General State - Serviceable condition - N\":3,\"General State - Serviceable condition - %\":10.0,\"General State - Tear - N\":11,\"General State - Tear - %\":37.0},\"23\":{\"Semesters\":5,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":5,\"General State - Good condition - %\":17.0,\"General State - Serviceable condition - N\":18,\"General State - Serviceable condition - %\":60.0,\"General State - Tear - N\":7,\"General State - Tear - %\":23.0},\"24\":{\"Semesters\":5,\"Types\":\"PermaNet\\u00ae 2.0\",\"N\":29,\"General State - Good condition - N\":16,\"General State - Good condition - %\":55.0,\"General State - Serviceable condition - N\":6,\"General State - Serviceable condition - %\":21.0,\"General State - Tear - N\":7,\"General State - Tear - %\":24.0},\"25\":{\"Semesters\":6,\"Types\":\"Dawa Plus\\u00ae 2.0\",\"N\":21,\"General State - Good condition - N\":1,\"General State - Good condition - %\":5.0,\"General State - Serviceable condition - N\":18,\"General State - Serviceable condition - %\":86.0,\"General State - Tear - N\":3,\"General State - Tear - %\":14.0},\"26\":{\"Semesters\":6,\"Types\":\"Life Net\\u00ae\",\"N\":10,\"General State - Good condition - N\":0,\"General State - Good condition - %\":0.0,\"General State - Serviceable condition - N\":0,\"General State - Serviceable condition - %\":0.0,\"General State - Tear - N\":10,\"General State - Tear - %\":100.0},\"27\":{\"Semesters\":6,\"Types\":\"NetProtect\\u00ae\",\"N\":21,\"General State - Good condition - N\":15,\"General State - Good condition - %\":71.0,\"General State - Serviceable condition - N\":1,\"General State - Serviceable condition - %\":5.0,\"General State - Tear - N\":5,\"General State - Tear - %\":24.0},\"28\":{\"Semesters\":6,\"Types\":\"Olyset Net\\u00ae\",\"N\":30,\"General State - Good condition - N\":2,\"General State - Good condition - %\":7.0,\"General State - Serviceable condition - N\":24,\"General State - Serviceable condition - %\":80.0,\"General State - Tear - N\":4,\"General State - Tear - %\":13.0},\"29\":{\"Semesters\":6,\"Types\":\"PermaNet \\u00ae2.0\",\"N\":30,\"General State - Good condition - N\":15,\"General State - Good condition - %\":50.0,\"General State - Serviceable condition - N\":10,\"General State - Serviceable condition - %\":33.0,\"General State - Tear - N\":5,\"General State - Tear - %\":17.0}}\n",
-                        "\n",
-                        "header: Table 5 Multiple regression washing and efficiency of LLINs\n",
-                        "footer: CI: Confidence interval\n",
-                        "columns: ['Parameter - Unnamed: 0_level_1', '95% Cl for odds Ratio - p-value', '95% Cl for odds Ratio - Lower', '95% Cl for odds Ratio - Odds ratio', '95% Cl for odds Ratio - Upper']\n",
-                        "\n",
-                        "{\"0\":{\"Parameter - Unnamed: 0_level_1\":\"Mortality (Intercept)\",\"95% Cl for odds Ratio - p-value\":\"<2.10\\u221216\",\"95% Cl for odds Ratio - Lower\":\"\\u2013\",\"95% Cl for odds Ratio - Odds ratio\":\"\\u2013\",\"95% Cl for odds Ratio - Upper\":\"\\u2013\"},\"1\":{\"Parameter - Unnamed: 0_level_1\":\"Ever Washed\",\"95% Cl for odds Ratio - p-value\":\"2.18 10\\u221211\",\"95% Cl for odds Ratio - Lower\":\"0.89\",\"95% Cl for odds Ratio - Odds ratio\":\"0.91\",\"95% Cl for odds Ratio - Upper\":\"0.94\"},\"2\":{\"Parameter - Unnamed: 0_level_1\":\"Insecticides\",\"95% Cl for odds Ratio - p-value\":\"2 10\\u221216\",\"95% Cl for odds Ratio - Lower\":\"1.0005\",\"95% Cl for odds Ratio - Odds ratio\":\"1.0006\",\"95% Cl for odds Ratio - Upper\":\"1.0007\"}}\n",
-                        "\n",
-                        "header: Physical integrity of LLINs\n",
-                        "\n",
-                        "The condition of each LLIN was checked every six months. Physical integrity of sampled LLINs was assessed in the laboratory by searching for holes or tears on 170 Dawa Plus(r) 2.0, 171 NetProtect(r), 143 Life Net(r), 178 Olyset Net(r) and 177 PermaNet(r) 2.0. Holes were classified as size 1 (0.5-2 cm diameter), size 2 (2-10 cm diameter) or size 3 (diameter > 10 cm) [28]. The proportional hole index (pHI) value was calculated on each LLIN inspected per WHO guidelines [28, 29]. It is the sum of the proportional indexes by categories. The proportional index for a category was the product of the number of holes in category and the index attributed to that size:\n",
-                        "\n",
-                        "\\[\\begin{array}{l} {\\text{pH}} \\equiv \\neq \\text{size}\\;1\\;\\text{holes}\\;\\times\\;1+(\\text{\\#size}\\;2\\;\\text{holes}\\; \\times\\;23)\\\\ {}\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "{\n",
-                        "\n",
-                        "\n",
-                        "sampling pattern recommended by WHOPES [28]. For all the net samples, insecticide from five swatches of all the four sides and one from the top were extracted and analysed. Each specimen was trimmed to a 10 cm x 10 cm square (0.010m2) using a die cutting in a pneumatic press. During the cutting operation, each specimen was sandwiched between layers of aluminum foil to prevent cross-contamination. The specimen set from each net was analysed as a group to yield an average value of insecticide concentration for the whole net. Chemical analysis was based on methods published by the Collaborative International Pesticides Analytical Council (CIPAC). Deltamethrin analysis of PermaNet(r)2.0 and Dawa Plus(r) samples was based on CIPAC Method 333 [32-34].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: PermaNet(r) 2.0 and Dawa Plus(r) 2.0: Life Net(r) and NetProtect(r)\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 250 ml round-bottom boiling flask followed by the addition of 95 ml of xylene and 5 ml of a known concentration of internal standard dipropyl phthalate, (2 mg/ml). The flask was fitted with a reflux condenser and heated to boiling for 30 min. After cooling, approximately 2.5 ml of the extract was filtered and transferred to a glass tube and evaporated to dryness under a stream of dry nitrogen for 30 min at 60 degC. A known volume (0.75 ml) of the mobile phase (94/6 (v/v) isooctane/1, 4-dioxane) was used to reconstitute the residue and was transferred to a sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter. The filtrate was centrifuged (500g) for 2 min and transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Extraction of permethrin for Olyset Net(r)\n",
-                        "\n",
-                        "Permethrin analysis of Olyset Net(r) samples was based on CIPAC Method 331 [31]. Each specimen set was weighed and placed in a 100 ml round-bottom boiling flask, followed by heptane (45 ml) and triphenyl phosphate internal standard (5.0 ml of known concentration in heptane). The flask was fitted with a reflux condenser and heated to boiling for 45 min. After cooling, approximately 1.5 ml of the extract was transferred to a chromatographic sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Gas chromatography analysis\n",
-                        "\n",
-                        "The extracts were analysed using an Agilent 6890 N chromatograph fitted with a 30 m x 0.25 mm (i.d.) fused silica DB-1 capillary column coated with 0.25 mm cross linked polydimethylsiloxane stationary phase. Ultra-high purity nitrogen (1.2 ml/min) was used as the carrier gas. Injector port, column oven, and detector temperatures were 265 degC, 240 degC, and 300 degC, respectively. Flame ionization (FID) was used for analyte detection. Two injections were used for each sample and the results averaged. Permethrin concentration was calculated by comparing permethrin/triphenyl phosphate peak area ratios against a calibration curve generated from solutions containing known permethrin /triphenyl phosphate mass ratios.\n",
-                        "\n",
-                        "header: Extraction of deltamethrin\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 125 ml screw capped Erlenmeyer flask and 50 ml of the extraction solvent mixture was added making sure that the net was completely submerged in the mixture. The flask was tightly capped and sealed with paraffin film before placing in an ultrasonic bath for 15 min. Later the flask was shaken in a bath at 25 degC for 30 min at a frequency of 155 cycles per min. The extract was transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "- Laboratoire d'Ecologie Vectorieile et Parastaire, Département de Biologie Animal, Faculte Des Sciences et Techniques, Universite Cheikh Anta Diop, Dakar, Senegal. 2institut Pasteur de Dakar, Dakar, Senegal. 3Laboratoire de Télédétection Appliquee, (ITA/IST/FST/UCAD, Dakar, Senegal. 4Division of Parabolic Diseases and Malaria, Centers for Disease Control (CDC) and Prevention, Atlanta, GA, USA.\n",
-                        "\n",
-                        "header: Mosquito net coverage\n",
-                        "\n",
-                        "A total of 3012 nets were distributed in 1249 households (Table 2). The overall average household coverage, 94% (95% CI 92.68-95.3), was 68.2% (95% CI 63.63-72.77) and 92% (95% CI 90-94) in Mbagame and Thies area villages, respectively. Moreover, the average percentage of households receiving only one mosquito net was 26% (95% CI 23-28) in all areas. The average percentage of households in Mbagame and Thies receiving only one mosquito net was estimated as 41.8% (95% CI 36.9-46.8) and 17.4% (95% CI 15-20.6), respectively.\n",
-                        "\n",
-                        "The global coverage of sleeping spaces (3,012/3,840) was 78% (95% CI 77-78). However, the sleeping space coverage in Mbagame area was estimated at 68.2% (95% CI: 65-70), and 95.3% (95% CI 94-96) in the villages surrounding Thies.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "Cl 95%: Confidence interval 95%: CIO: Collaborative international pesticides analytical council; KD60: Proportion of mosquitoes knocked down at 60 min post-exposure; HPLC: High performance liquid chromatography; IQR: Intra quarter-time; LINK: Long-lasting insecticidal nets; pH: Proportional hole index; MW: World Health Organization; WHOPS: World Health Organization pesticide evaluation scheme; UV: Ultraviolet; PVC: Polyvinyl chloride; CDC: Centers for disease control and prevention; MR: Mortality rate; CNRS: National research on contract for health; OMVS: Senegal river basin development organization; NMC: National malaria control programme; P: Probability; OR: Odd ratio.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "A total of 1,249 households were enrolled in both study areas. On average, 92.7% (95% CI 90-94) of heads of households were men, with an average 53 years of age. This average percentage was 78.2% in the Mbagame area with an average age of 52 years (95% CI 49-55) and 94.8% in the villages around Thies with a mean age of 53 years (95% CI 51.6-54.4) (P < 0.001). The average percentage of heads of households with any schooling was 38.4% (95% CI 34-42). The gender breakdown was 96% for men (n = 456) and 4% for women (n = 19).\n",
-                        "\n",
-                        "Of 604 households, 37.7% (228/604) had means of transportation among which 68% (95% CI 61-74) used carts while only 14% (95% CI 8-20) owned cars. The overall average percentage of households with electricity was 31% (95% CI 27-35). In Mbagame village, 87.2% households had electricity compared to an average of 22.6% for the study villages near Thies (_kh_2 = 132.4377, df = 1, P < 0.0001). Wood was the main form of fuel for cooking in 97.7% of households in both study areas.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "A total of five brands of LLINs were distributed to households: NetProtect(r), PermaNet(r) 2.0, Dawa Plus(r) 2.0, Olyset Net(r) and Life Net(r) (Table 1). NetProtect(r) mosquito nets were white, rectangular (length = 190, width = 180 and height = 150 cm) made of 118 denier polyethylene mono filaments and 136 holes/in2 mesh. PermaNet(r) 2.0 mosquito nets were white, rectangular (length = 190, width = 180 and height = 200 cm), made with 75 denier polyester filament tulle and 156 holes/inch2 mesh. Dawa Plus(r) 2.0 were white, rectangular (length = 200, width = 160 and height = 180 cm), made of polyester multi-filament tulle, 75 deniers and 156 holes/inch2 mesh. Olyset Net(r) were white, conical (circumference 1250 cm, height = 250 cm), made with a denier polyethylene mono filament tulle greater than 150 cm and 56 holes/inch2 mesh. Life Net(r) nets were the only mosquito nets made from polypropylene, they were white, rectangular (length = 190, width = 180 and height = 150 cm) made with multi-filament tulle with 110 denier and 156 holes/inch2 mesh.\n",
-                        "\n",
-                        "Aside from the Olyset Net(r) mosquito nets which were treated with permethrin, all other nets distributed were impregnated with deltamethrin. Olyset Net(r) and Life Net(r) were donated by OMVS (Senegal River Basin Development Organization) and BAYER AG (Germany),\n",
-                        "respectively, while the three other brands (PermaNet(r) 2.0, Dawa Plus(r) 2.0 and NetProtect(r)) were purchased from vendors approved by manufacturers.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare that they have no competing interests.\n",
-                        "\n",
-                        "header: High performance liquid chromatography (HPLC) analysis\n",
-                        "\n",
-                        "The deltamethrin content was analysed using Agilent 1200 HPLC equipped with a UV detector set at 230 nm and a 150 x 4.6 mm (i.d.) Ascents Si 5 mm column held at 40 degC. The mobile phase was 94/6 (v/v) isooctane/1, 4-dioxane with a flow rate of 1.5 ml/min. For each extract, three injections of 20 ml were made. Injections, calibrations, and quantification of the deltamethrin content were followed as per the procedure in the Collaborative International Pesticides Analytical Council (CIPAC) 333 [32-34].\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "- This work was supported by President's Malaria Initiative and Universite Cheikh Anta Diop (Dakar).\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The data used and analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: Physical integrity\n",
-                        "\n",
-                        "At least 83% (95% CI 80-90) of mosquito nets had holes (all types of holes) at the end of the study. Except the Life Net(r), the average number of holes increased with time for all LLINs. For the LLINs inspected in laboratory, at the end of the 6th semester of follow-up, the majority of them had holes, with average proportions of 95.2% 95%, 93.3%, 100% and 60% for Dawa Plus(r) 2.0, NetProtect(r) PermaNet(r) 2.0, Olyset Net(r) and Life Net(r), respectively. The average number of holes remained relatively low for Life Net(r) (Fisher's exact test, OR: 12.02; 95% CI 0.96-684.89, P = 0.027).\n",
-                        "\n",
-                        "At each semester, size 1 holes (Fig. 3) were predominant for all types of mosquito nets. The average number of holes (all types) per mosquito net was 39 per LLIN at the end of six semesters of study. It was 13, 39, 15, 48 and 54 respectively for Life Net(r), NetProtect(r) Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Olyset Net(r), respectively. Only NetProtect(r) and PermaNet(r) 2.0 types were still in good condition at the end of the three-year study (Table 3). Proportions of mosquito nets in good condition in\n",
-                        "\n",
-                        "Figure 2: Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\n",
-                        "households were 71% (NetProtect(r)) and 50% (PernaNet(r) 2.0) in both mosquito nets.\n",
-                        "\n",
-                        "header: Data entry and statistical analysis\n",
-                        "\n",
-                        "Household survey data were entered on EPI data Entry 3.0, exported to Excel Microsoft office 2010 and analysed with R software. The calculation of proportions and averages was done according to the normal law by 95% Confidence Intervals (CI). Comparisons and proportions were made by Chi-squared tests (kh2) of homogeneity. Generalized linear models were performed to understand the relationship between mosquito mortality and LLIN washing and mortality bioassay results and insecticide chemical content. The analysis of the condition of the mosquito net, the values obtained for pHI, the torn surface and its shape was used to establish one of the criteria of durability [28]; a mosquito net was in good condition when the pHI was between 0 and 64, is usable when pHI was in 65-642 and was too torn when the pHI was greater than 643 [30]. The acceptable residual efficacy of each type of LLIN was determined based on WHO criteria [30].\n",
-                        "\n",
-                        "Residual efficacy was optimal if the KD 60 or the mortality rate (MR), defined as the number of dead females\n",
-                        "relative to the total number of mosquitoes exposed per mosquito net was >= 95% or >= 80%, respectively, after 20 washes in the laboratory or after three years of use [28]. The proportion of mosquito nets meeting optimal bio efficacy levels of KD60 and mortality rates were determined for each type of LLINs by performing cone bioassays in the laboratory. A minimal bio-efficacy criteria of >= 75% for KD or >= 50% mortality rate was used to determine the efficacy status of LLINs [28]. The MR was corrected by the Abbott formula [35] if the mortality rate for controls was less than 20% and the bioassay was redone if it was greater than 20%. Generalized linear regression models were run to estimate the relationship in mortality rate between washed and non-washed mosquito nets.\n",
-                        "\n",
-                        "The retention rate was calculated for each type of LLIN at each follow up period. It was defined as the ratio of the number of the original study mosquito nets available in households to the total number of mosquito nets initially distributed. The mosquito nets declared lost but found in the following survey were included in the calculation of the retention rate of the previous semester.\n",
-                        "\n",
-                        "header: Rate of sampling mosquito nets, retention of LLINs\n",
-                        "\n",
-                        "From the beginning of the study in 2011 to the end in 2014 (6 semesters), 839 mosquito nets (all types) were removed representing 93.2% of the expected 900 LLINs to be removed (Fig. 2, Table 3). Of the 180 mosquito nets of each brand expected to be sampled, 94.4%, 79.4%, 94.4%, 98.9% and 98.3% of the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r), Olyset Net(r) and PermaNet(r) 2.0, respectively, were removed from the field. The number of mosquito nets sampled was less than the 28-30 units expected in the 5th and 6th semester for Life Net(r), and in the 6th semester for Dawa Plus(r) 2.0 and NetProtect(r). At the end of the study, the retention rate of all five LLIN types was 12.5% (95% CI 11.4-13.7) based on the total number of nets distributed and was relatively better for Olyset Net(r) at 20.5% (95% CI 17.8-23.4) and to a lesser extent for Dawa Plus(r) 2.0 with 15.1% (95% CI 12.4-18.2). With more than half of the mosquito nets not found after only two semesters of follow-up, the average percentage of Life Net(r) found in households fell to about one in five mosquito nets distributed remaining in the third semester.\n",
-                        "\n",
-                        "header: Mortality rate associated with insecticide content\n",
-                        "\n",
-                        "Residual efficacy greater than 95% knock down was probably correlated with the content of insecticide. The overall correlation coefficients were: r = 0.08, r = 0.08, r = 0.22, r = 0.09 and r = 0.08 for Dawa Plus(r) 2.0, NetProtect(r) Life Net(r), Olyset Net(r) and PermaNet(r) 2.0, respectively (Fig. 5). For mortality rate, the correlation coefficient was more important for Life Net(r)(r = 0.43) when compared to other LLIN types.\n",
-                        "\n",
-                        "The generalized linear regression models have indicated that the mortality rate was significantly decreased with washing mosquito nets (Table 5). A significant difference (p < 0.001) in mortality rate was observed between washed and unwashed mosquito nets. This mortality rate was also associated with the average the amount of insecticide content present on the net.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The evaluation of LLINs after three years of use revealed a loss of integrity of almost all types of nets distributed. Despite this loss of integrity, biological efficacy was maintained for the Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Life Net(r) brands.\n",
-                        "\n",
-                        "header: Use and maintenance of LLINs\n",
-                        "\n",
-                        "The regular use of the mosquito nets (use the night before the survey) at the end of the follow up was 23.3%, 38.5%, 7.3%, 78.3% and 16.1% for the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r) Olyset Net(r) and PermaNet(r) 2.0 types, respectively. For the NetProtect(r) this rate was statistically lower compared to the Life Net(r) and Olyset Net(r) (Fisher's exact test, OR: 0.13; 95% CI: 0.017-0.83, P = 0.014; Fisher's exact test, OR: 0.022; 95% CI 0.017-0.83, P < 0.001).\n",
-                        "\n",
-                        "Dawa Plus(r) 2.0 and PermaNet(r) 2.0 used the night before the survey consistently exceeded 50% during the first four semesters. For Olyset Net(r), this rate was always above 60% during the three years of follow-up. In contrast, the NetProtect(r) and Life Net(r) types were the least used in households. Their regular use rates were only satisfactory in the first two semesters following LLINs distribution, with 52.6% (95% CI 47.7-57.5) for NetProtect(r) in semester one and 53% (95% CI 46-59.6) for Life Net(r) in the second.\n",
-                        "\n",
-                        "For all LLIN types distributed, only 19% (95% CI 18-20) were washed. The results obtained with washing habits showed that 66% (95% CI 64-69) of the washed mosquito nets were washed with warm water. In addition, 73% (95% CI 71-76) of the cases used local soap and 50% of the mosquito net was dried in shade after the washing.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Country\":\"Senegal\",\"Site\":\"Mbagame\",\"Start_month\":1,\"Start_year\":2011,\"End_month\":12,\"End_year\":2014},\"S02\":{\"Country\":\"Senegal\",\"Site\":\"Thies\",\"Start_month\":1,\"Start_year\":2011,\"End_month\":12,\"End_year\":2014}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"NetProtect(r)\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":39.0,\"pHI_category\":\"Good\"},\"N02\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":48.0,\"pHI_category\":\"Good\"},\"N03\":{\"Net_type\":\"Dawa Plus(r) 2.0\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":15.0,\"pHI_category\":\"Good\"},\"N04\":{\"Net_type\":\"Olyset Net(r)\",\"Insecticide\":\"Permethrin\",\"Net_washed\":19.0,\"Net_holed\":54.0,\"pHI_category\":\"Good\"},\"N05\":{\"Net_type\":\"Life Net(r)\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":13.0,\"pHI_category\":\"Good\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: PermaNet(r) 2.0 and Dawa Plus(r) 2.0: Life Net(r) and NetProtect(r)\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 250 ml round-bottom boiling flask followed by the addition of 95 ml of xylene and 5 ml of a known concentration of internal standard dipropyl phthalate, (2 mg/ml). The flask was fitted with a reflux condenser and heated to boiling for 30 min. After cooling, approximately 2.5 ml of the extract was filtered and transferred to a glass tube and evaporated to dryness under a stream of dry nitrogen for 30 min at 60 degC. A known volume (0.75 ml) of the mobile phase (94/6 (v/v) isooctane/1, 4-dioxane) was used to reconstitute the residue and was transferred to a sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter. The filtrate was centrifuged (500g) for 2 min and transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Extraction of permethrin for Olyset Net(r)\n",
-                        "\n",
-                        "Permethrin analysis of Olyset Net(r) samples was based on CIPAC Method 331 [31]. Each specimen set was weighed and placed in a 100 ml round-bottom boiling flask, followed by heptane (45 ml) and triphenyl phosphate internal standard (5.0 ml of known concentration in heptane). The flask was fitted with a reflux condenser and heated to boiling for 45 min. After cooling, approximately 1.5 ml of the extract was transferred to a chromatographic sample vial using a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Extraction of deltamethrin\n",
-                        "\n",
-                        "Each specimen set was weighed and placed in a 125 ml screw capped Erlenmeyer flask and 50 ml of the extraction solvent mixture was added making sure that the net was completely submerged in the mixture. The flask was tightly capped and sealed with paraffin film before placing in an ultrasonic bath for 15 min. Later the flask was shaken in a bath at 25 degC for 30 min at a frequency of 155 cycles per min. The extract was transferred into a chromatographic vial after filtering through a glass syringe fitted with a 0.45 mm reconstituted cellulose syringe filter.\n",
-                        "\n",
-                        "header: Gas chromatography analysis\n",
-                        "\n",
-                        "The extracts were analysed using an Agilent 6890 N chromatograph fitted with a 30 m x 0.25 mm (i.d.) fused silica DB-1 capillary column coated with 0.25 mm cross linked polydimethylsiloxane stationary phase. Ultra-high purity nitrogen (1.2 ml/min) was used as the carrier gas. Injector port, column oven, and detector temperatures were 265 degC, 240 degC, and 300 degC, respectively. Flame ionization (FID) was used for analyte detection. Two injections were used for each sample and the results averaged. Permethrin concentration was calculated by comparing permethrin/triphenyl phosphate peak area ratios against a calibration curve generated from solutions containing known permethrin /triphenyl phosphate mass ratios.\n",
-                        "\n",
-                        "header: Mosquito net coverage\n",
-                        "\n",
-                        "A total of 3012 nets were distributed in 1249 households (Table 2). The overall average household coverage, 94% (95% CI 92.68-95.3), was 68.2% (95% CI 63.63-72.77) and 92% (95% CI 90-94) in Mbagame and Thies area villages, respectively. Moreover, the average percentage of households receiving only one mosquito net was 26% (95% CI 23-28) in all areas. The average percentage of households in Mbagame and Thies receiving only one mosquito net was estimated as 41.8% (95% CI 36.9-46.8) and 17.4% (95% CI 15-20.6), respectively.\n",
-                        "\n",
-                        "The global coverage of sleeping spaces (3,012/3,840) was 78% (95% CI 77-78). However, the sleeping space coverage in Mbagame area was estimated at 68.2% (95% CI: 65-70), and 95.3% (95% CI 94-96) in the villages surrounding Thies.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "A total of five brands of LLINs were distributed to households: NetProtect(r), PermaNet(r) 2.0, Dawa Plus(r) 2.0, Olyset Net(r) and Life Net(r) (Table 1). NetProtect(r) mosquito nets were white, rectangular (length = 190, width = 180 and height = 150 cm) made of 118 denier polyethylene mono filaments and 136 holes/in2 mesh. PermaNet(r) 2.0 mosquito nets were white, rectangular (length = 190, width = 180 and height = 200 cm), made with 75 denier polyester filament tulle and 156 holes/inch2 mesh. Dawa Plus(r) 2.0 were white, rectangular (length = 200, width = 160 and height = 180 cm), made of polyester multi-filament tulle, 75 deniers and 156 holes/inch2 mesh. Olyset Net(r) were white, conical (circumference 1250 cm, height = 250 cm), made with a denier polyethylene mono filament tulle greater than 150 cm and 56 holes/inch2 mesh. Life Net(r) nets were the only mosquito nets made from polypropylene, they were white, rectangular (length = 190, width = 180 and height = 150 cm) made with multi-filament tulle with 110 denier and 156 holes/inch2 mesh.\n",
-                        "\n",
-                        "Aside from the Olyset Net(r) mosquito nets which were treated with permethrin, all other nets distributed were impregnated with deltamethrin. Olyset Net(r) and Life Net(r) were donated by OMVS (Senegal River Basin Development Organization) and BAYER AG (Germany),\n",
-                        "respectively, while the three other brands (PermaNet(r) 2.0, Dawa Plus(r) 2.0 and NetProtect(r)) were purchased from vendors approved by manufacturers.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "- Laboratoire d'Ecologie Vectorieile et Parastaire, Département de Biologie Animal, Faculte Des Sciences et Techniques, Universite Cheikh Anta Diop, Dakar, Senegal. 2institut Pasteur de Dakar, Dakar, Senegal. 3Laboratoire de Télédétection Appliquee, (ITA/IST/FST/UCAD, Dakar, Senegal. 4Division of Parabolic Diseases and Malaria, Centers for Disease Control (CDC) and Prevention, Atlanta, GA, USA.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "A total of 1,249 households were enrolled in both study areas. On average, 92.7% (95% CI 90-94) of heads of households were men, with an average 53 years of age. This average percentage was 78.2% in the Mbagame area with an average age of 52 years (95% CI 49-55) and 94.8% in the villages around Thies with a mean age of 53 years (95% CI 51.6-54.4) (P < 0.001). The average percentage of heads of households with any schooling was 38.4% (95% CI 34-42). The gender breakdown was 96% for men (n = 456) and 4% for women (n = 19).\n",
-                        "\n",
-                        "Of 604 households, 37.7% (228/604) had means of transportation among which 68% (95% CI 61-74) used carts while only 14% (95% CI 8-20) owned cars. The overall average percentage of households with electricity was 31% (95% CI 27-35). In Mbagame village, 87.2% households had electricity compared to an average of 22.6% for the study villages near Thies (_kh_2 = 132.4377, df = 1, P < 0.0001). Wood was the main form of fuel for cooking in 97.7% of households in both study areas.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "Cl 95%: Confidence interval 95%: CIO: Collaborative international pesticides analytical council; KD60: Proportion of mosquitoes knocked down at 60 min post-exposure; HPLC: High performance liquid chromatography; IQR: Intra quarter-time; LINK: Long-lasting insecticidal nets; pH: Proportional hole index; MW: World Health Organization; WHOPS: World Health Organization pesticide evaluation scheme; UV: Ultraviolet; PVC: Polyvinyl chloride; CDC: Centers for disease control and prevention; MR: Mortality rate; CNRS: National research on contract for health; OMVS: Senegal river basin development organization; NMC: National malaria control programme; P: Probability; OR: Odd ratio.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "The authors declare that they have no competing interests.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "The data used and analysed during the current study are available from the corresponding author on reasonable request.\n",
-                        "\n",
-                        "header: High performance liquid chromatography (HPLC) analysis\n",
-                        "\n",
-                        "The deltamethrin content was analysed using Agilent 1200 HPLC equipped with a UV detector set at 230 nm and a 150 x 4.6 mm (i.d.) Ascents Si 5 mm column held at 40 degC. The mobile phase was 94/6 (v/v) isooctane/1, 4-dioxane with a flow rate of 1.5 ml/min. For each extract, three injections of 20 ml were made. Injections, calibrations, and quantification of the deltamethrin content were followed as per the procedure in the Collaborative International Pesticides Analytical Council (CIPAC) 333 [32-34].\n",
-                        "\n",
-                        "header: Rate of sampling mosquito nets, retention of LLINs\n",
-                        "\n",
-                        "From the beginning of the study in 2011 to the end in 2014 (6 semesters), 839 mosquito nets (all types) were removed representing 93.2% of the expected 900 LLINs to be removed (Fig. 2, Table 3). Of the 180 mosquito nets of each brand expected to be sampled, 94.4%, 79.4%, 94.4%, 98.9% and 98.3% of the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r), Olyset Net(r) and PermaNet(r) 2.0, respectively, were removed from the field. The number of mosquito nets sampled was less than the 28-30 units expected in the 5th and 6th semester for Life Net(r), and in the 6th semester for Dawa Plus(r) 2.0 and NetProtect(r). At the end of the study, the retention rate of all five LLIN types was 12.5% (95% CI 11.4-13.7) based on the total number of nets distributed and was relatively better for Olyset Net(r) at 20.5% (95% CI 17.8-23.4) and to a lesser extent for Dawa Plus(r) 2.0 with 15.1% (95% CI 12.4-18.2). With more than half of the mosquito nets not found after only two semesters of follow-up, the average percentage of Life Net(r) found in households fell to about one in five mosquito nets distributed remaining in the third semester.\n",
-                        "\n",
-                        "header: Physical integrity\n",
-                        "\n",
-                        "At least 83% (95% CI 80-90) of mosquito nets had holes (all types of holes) at the end of the study. Except the Life Net(r), the average number of holes increased with time for all LLINs. For the LLINs inspected in laboratory, at the end of the 6th semester of follow-up, the majority of them had holes, with average proportions of 95.2% 95%, 93.3%, 100% and 60% for Dawa Plus(r) 2.0, NetProtect(r) PermaNet(r) 2.0, Olyset Net(r) and Life Net(r), respectively. The average number of holes remained relatively low for Life Net(r) (Fisher's exact test, OR: 12.02; 95% CI 0.96-684.89, P = 0.027).\n",
-                        "\n",
-                        "At each semester, size 1 holes (Fig. 3) were predominant for all types of mosquito nets. The average number of holes (all types) per mosquito net was 39 per LLIN at the end of six semesters of study. It was 13, 39, 15, 48 and 54 respectively for Life Net(r), NetProtect(r) Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Olyset Net(r), respectively. Only NetProtect(r) and PermaNet(r) 2.0 types were still in good condition at the end of the three-year study (Table 3). Proportions of mosquito nets in good condition in\n",
-                        "\n",
-                        "Figure 2: Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\n",
-                        "households were 71% (NetProtect(r)) and 50% (PernaNet(r) 2.0) in both mosquito nets.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "- This work was supported by President's Malaria Initiative and Universite Cheikh Anta Diop (Dakar).\n",
-                        "\n",
-                        "header: Mortality rate associated with insecticide content\n",
-                        "\n",
-                        "Residual efficacy greater than 95% knock down was probably correlated with the content of insecticide. The overall correlation coefficients were: r = 0.08, r = 0.08, r = 0.22, r = 0.09 and r = 0.08 for Dawa Plus(r) 2.0, NetProtect(r) Life Net(r), Olyset Net(r) and PermaNet(r) 2.0, respectively (Fig. 5). For mortality rate, the correlation coefficient was more important for Life Net(r)(r = 0.43) when compared to other LLIN types.\n",
-                        "\n",
-                        "The generalized linear regression models have indicated that the mortality rate was significantly decreased with washing mosquito nets (Table 5). A significant difference (p < 0.001) in mortality rate was observed between washed and unwashed mosquito nets. This mortality rate was also associated with the average the amount of insecticide content present on the net.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The evaluation of LLINs after three years of use revealed a loss of integrity of almost all types of nets distributed. Despite this loss of integrity, biological efficacy was maintained for the Dawa Plus(r) 2.0, PermaNet(r) 2.0 and Life Net(r) brands.\n",
-                        "\n",
-                        "header: Use and maintenance of LLINs\n",
-                        "\n",
-                        "The regular use of the mosquito nets (use the night before the survey) at the end of the follow up was 23.3%, 38.5%, 7.3%, 78.3% and 16.1% for the Dawa Plus(r) 2.0, Life Net(r), NetProtect(r) Olyset Net(r) and PermaNet(r) 2.0 types, respectively. For the NetProtect(r) this rate was statistically lower compared to the Life Net(r) and Olyset Net(r) (Fisher's exact test, OR: 0.13; 95% CI: 0.017-0.83, P = 0.014; Fisher's exact test, OR: 0.022; 95% CI 0.017-0.83, P < 0.001).\n",
-                        "\n",
-                        "Dawa Plus(r) 2.0 and PermaNet(r) 2.0 used the night before the survey consistently exceeded 50% during the first four semesters. For Olyset Net(r), this rate was always above 60% during the three years of follow-up. In contrast, the NetProtect(r) and Life Net(r) types were the least used in households. Their regular use rates were only satisfactory in the first two semesters following LLINs distribution, with 52.6% (95% CI 47.7-57.5) for NetProtect(r) in semester one and 53% (95% CI 46-59.6) for Life Net(r) in the second.\n",
-                        "\n",
-                        "For all LLIN types distributed, only 19% (95% CI 18-20) were washed. The results obtained with washing habits showed that 66% (95% CI 64-69) of the washed mosquito nets were washed with warm water. In addition, 73% (95% CI 71-76) of the cases used local soap and 50% of the mosquito net was dried in shade after the washing.\n",
-                        "\n",
-                        "header: Data entry and statistical analysis\n",
-                        "\n",
-                        "Household survey data were entered on EPI data Entry 3.0, exported to Excel Microsoft office 2010 and analysed with R software. The calculation of proportions and averages was done according to the normal law by 95% Confidence Intervals (CI). Comparisons and proportions were made by Chi-squared tests (kh2) of homogeneity. Generalized linear models were performed to understand the relationship between mosquito mortality and LLIN washing and mortality bioassay results and insecticide chemical content. The analysis of the condition of the mosquito net, the values obtained for pHI, the torn surface and its shape was used to establish one of the criteria of durability [28]; a mosquito net was in good condition when the pHI was between 0 and 64, is usable when pHI was in 65-642 and was too torn when the pHI was greater than 643 [30]. The acceptable residual efficacy of each type of LLIN was determined based on WHO criteria [30].\n",
-                        "\n",
-                        "Residual efficacy was optimal if the KD 60 or the mortality rate (MR), defined as the number of dead females\n",
-                        "relative to the total number of mosquitoes exposed per mosquito net was >= 95% or >= 80%, respectively, after 20 washes in the laboratory or after three years of use [28]. The proportion of mosquito nets meeting optimal bio efficacy levels of KD60 and mortality rates were determined for each type of LLINs by performing cone bioassays in the laboratory. A minimal bio-efficacy criteria of >= 75% for KD or >= 50% mortality rate was used to determine the efficacy status of LLINs [28]. The MR was corrected by the Abbott formula [35] if the mortality rate for controls was less than 20% and the bioassay was redone if it was greater than 20%. Generalized linear regression models were run to estimate the relationship in mortality rate between washed and non-washed mosquito nets.\n",
-                        "\n",
-                        "The retention rate was calculated for each type of LLIN at each follow up period. It was defined as the ratio of the number of the original study mosquito nets available in households to the total number of mosquito nets initially distributed. The mosquito nets declared lost but found in the following survey were included in the calculation of the retention rate of the previous semester.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Country\":\"Senegal\",\"Site\":\"Mbagame\",\"Start_month\":1,\"Start_year\":2011,\"End_month\":12,\"End_year\":2014},\"S02\":{\"Country\":\"Senegal\",\"Site\":\"Thies\",\"Start_month\":1,\"Start_year\":2011,\"End_month\":12,\"End_year\":2014}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"NetProtect(r)\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":39.0,\"pHI_category\":\"Good\"},\"N02\":{\"Net_type\":\"PermaNet(r) 2.0\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":48.0,\"pHI_category\":\"Good\"},\"N03\":{\"Net_type\":\"Dawa Plus(r) 2.0\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":15.0,\"pHI_category\":\"Good\"},\"N04\":{\"Net_type\":\"Olyset Net(r)\",\"Insecticide\":\"Permethrin\",\"Net_washed\":19.0,\"Net_holed\":54.0,\"pHI_category\":\"Good\"},\"N05\":{\"Net_type\":\"Life Net(r)\",\"Insecticide\":\"Deltamethrin\",\"Net_washed\":19.0,\"Net_holed\":13.0,\"pHI_category\":\"Good\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 3.2.1 Indoor Resting Density, Human Blood Index and Biting Rate: 3.2.2 Estimation of Sporozoite Infection Rate\n",
-                        "\n",
-                        "A total of 36 out of 66 heads/thoraces were positive for _P. falciparum_ (F\\({}_{+}\\) = 36), corresponding to an unusually high sporozoite rate of 54.55% \\(\\pm\\) 13.63. Validation of the TaqMan results using nested PCR [34] confirmed the _P. falciparum_ infection in the 36 samples.\n",
-                        "\n",
-                        "header: Composition of the Mosquito Species Collected Indoor: Entomological and Parasitological Parameters of Transmission\n",
-                        "\n",
-                        "The indoor resting density of the _An. funestus_ was estimated as 14 from the 112 blood females collected in eight houses. Analysis of the blood source of these 112 females by PCR revealed that they all fed on human blood, leading to a human blood index of 100%. The human biting rate was estimated as ~5.3 bites per person per night (CI: 4.91-5.69).\n",
-                        "\n",
-                        "header: Estimation of Entomological and Parasitological Parameters\n",
-                        "\n",
-                        "The indoor resting density (IRD) was calculated from the number of _An. funestus_ collected on the 4th d, relative to the number of houses assessed, as advised by the WHO [30; 31]. A total of 112 blood-fed females were dissected <48 h after collection, separating heads/thoraces from the abdomens. The abdomens were used for gDNA extraction as outlined in WHO guidelines [30] using the DNeasy Blood and Tissue Kit (QIAGEN, Hilden, Germany), according to manufacturer's protocol. Identification of the source of blood was conducted using the cocktail PCR of Kent et al. [32]. The human blood index was calculated from the number of females that have fed on humans, relative to the total number of females caught [30; 31]. The human biting rate was estimated from the number of the females that have fed on humans, relative to the total number of people in the houses sampled.\n",
-                        "\n",
-                        "header: 2.3.1 Estimation of Indoor Resting Density, Human Blood Index and Biting Rate: 2.3.2 Estimation of Sporozoite Rate\n",
-                        "\n",
-                        "Sixty-six females (22 individuals randomly selected from each of the three days of indoor collection) which laid eggs successfully were dissected, and the heads/thoraces separated from the abdomens immediately, as explained above. The presence of sporozoite was investigated using a TaqMan genotyping protocol, established by Bass and colleagues [33]. Real-time PCR MX 3005 (Agilent, Santa Clara, CA, USA) was used for the amplification. A total of 1 uL of gDNA, extracted from each female head/thorax, was used for PCR, with an initial denaturation at 95 \\({}^{\\circ}\\)C for 10 min, followed by 40 cycles each of 15 s at 95 \\({}^{\\circ}\\)C and 1 min at 60 \\({}^{\\circ}\\)C. Primers described by Bass (PlasF_GCTTAGTTACGATTAATAGGAGTAGCTTG and PlasR_GAAAATCTAAGAATTTCAC CTCTGACA) were used, together with two probes labelled with fluorophores, FAM (Falcip+_TCTGAAT ACGAATGTC) to detect _Plasmodium falciparum_, and HEX (OVM+_CTGAATACAAATGCC) to detect the combination of _P. ovale_, _P. vivax_ and _P. malariae_. Positive controls (known FAM+ and OVM+) were used, in addition to a negative control, in which 1 uL of ddH2O was added. To validate findings of the TaqMan assay, a nested PCR of Snounou et al. [34] was carried out, using all the samples that tested positive with TaqMan. The sporozoite rate was calculated as the percentage of females positive, relative to the total number of the females examined [30].\n",
-                        "\n",
-                        "header: Synergist Bioassay with Piperonyl Butoxide (PBO) and Diethyl Malate\n",
-                        "\n",
-                        "To investigate the potential role of P450 monooxygenases in pyrethroid resistance, a synergist bioassay was carried out using 4% PBO [an inhibitor of CYP450s [38]] against permethrin. The role of glutathione S-transferases (GSTs) in DDT resistance was also investigated by pre-exposure to 8% diethyl maleate (DEM). The insecticides and the synergists PBO were sourced from the WHO/Vector Control Research Unit (VCRU) of the University of Sains Malaysia (Penang, Malaysia). Four replicates of 22-26 F1 females (3-4 d old) were pre-exposed to PBO or DEM for 1 h, and then transferred to tubes containing permethrin [35] or DDT, respectively. Mosquitoes were treated as in the WHO bioassays described above, and mortalities scored after 24 h. Two replicates of 25 females each were exposed to PBO only, as a control.\n",
-                        "\n",
-                        "Genotyping of L119F Glutathione S-Transferase (GSTe2) Mutation Associated with DDT/Permethrin Resistance\n",
-                        "\n",
-                        "To detect the L119F _GSTe2_ mutation previously linked to DDT/pyrethroid metabolic resistance [20], TaqMan genotyping was conducted using 44 F0 females collected from the field. This was carried out using a real-time PCR thermocycler (Agilent Mx3005) following an established protocol [20]. The primers L119F_GSTe2F (5'-AACAATTTTTCATTTCTTATTCTCATTAC-3') and L119F_GSTe2R (5'- CGACTCGATCTTCGGGAATGTC-3') were utilised with the following probes: reporter L119 (VIC_AGGACGGTATTCTTTTTCTA) to detect the susceptibility allele, and reporter 119F (FAM_AGGAGCGTATTTTTTTCTA) for the resistance allele. The assay was performed in a 10 mL final volume comprise of 5 mL of 1x Sensimix (Bioline, London, UK), 800 nM each of primer, 200 nM of each probe, and 1 mL of gDNA. Thermocycling conditions were initial denaturation of 10 min at 95 \\({}^{\\circ}\\)C, followed by 40 cycles each of 92 \\({}^{\\circ}\\)C for 10 s, and 60 \\({}^{\\circ}\\)C for 45 s. Genotypes were scored from scatter plots of results produced by the Mx3005 v4.10 software (Agilent, Santa Clara, CA, USA). Three positive samples of known genotypes [20]--(i) homozygote resistant 1014F/1014F; (ii) heterozygote L1014/1014F; and (iii) susceptible L1014/L1014--were used as positive controls. For the negative control, 1 mL of ddH2O was incorporated into the control well. To assess the correlation between the presence of the 119F mutation and the resistance phenotype, 12 each of DDT-alive and -dead females were genotyped using the above protocol. In addition, these 12 alive and 12 dead females were also screened, using the recently established allele-specific PCR [39].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "_Anopheles funestus_ is the only mosquito species caught, a total of 721 in 4 d. Out of these, 590 were caught in the first 3 d (52 \\(\\circ\\), 538 \\(\\circ\\) [487 blood fed and 51 unfed]). A total of 312 \\(\\mathrm{\\SIUnitSymbolOhm}\\)ial eggs and 288 egg batches hatched successfully. A total of 131 mosquitoes were caught on the 4th day: 12 \\(\\circ\\) and 119 \\(\\circ\\) (112 of them blood fed and 7 unfed).\n",
-                        "\n",
-                        "header: Supplementary Materials:\n",
-                        "\n",
-                        "The following are available online at [http://www.mdpi.com/2073-4425/11/4/454/s1](http://www.mdpi.com/2073-4425/11/4/454/s1), Figure S1: Knockdown profiles of _An. funestus females_ exposed to pyrethroids and DDT. Each bar is a percentage knockdown of values from four different replicates from bioassays at different time points, Figure S2: Analysis of the _acc-_I fragment encompassing the 119 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S3: Analysis of the _acc-_I fragment encompassing the 485 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S4: Analysis of the _VGSC_ fragment encompassing the 1014 _kdr_ mutation codon. (**a**). pattern of genetic variability of 30 sequences from randomly selected F0 females; (**b**). a maximum likelihood phylogenetic tree of the sequences, Table S1: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 119 codon for alive and dead mosquitoes, Table S2: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 485 codon for alive and dead mosquitoes, Table S3: Summary statistics for polymorphism of a fragment of voltage-gated sodium channel encompassing the 1014 codon for the field F0 females, File S1: Analyzed sequences (gDNA partial fragments) from encompassing the G119S and N485I of _acetylcholinesterase_-1 gene and sequences from the partial fragment of the voltage-gated sodium channel spanning the 1014th codon.\n",
-                        "\n",
-                        "header: Investigating the Role of L119F-GSTe2 Mutation in DDT Resistance\n",
-                        "\n",
-                        "Initial genotyping of 43 F0 females revealed a high frequency of the L119F-GSTe2 mutation with 30.2% (13 individuals) heterozygotes (RS, L119/F119), 32.6% (14 individuals) homozygote resistant (RR, 119F/119F), and 16.3% (7 individuals) homozygote susceptible (SS, L119/L119) (Figure 3a).\n",
-                        "\n",
-                        "Figure 2: Resistance profiles of F1 _An. funestus_ females. (**a**). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (**b**). Results of the cone bioassays with PermaNet®3.0 (side and roof), PermaNet®2.0, Olyset®Plus and Olyset®Net. Results are the average of percentage mortalities ± SEM of five replicates; (**c**). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at \\(P\\) < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (**d**). Time-course bioassay for LTs0 estimation/test for strength of permethrin resistance.\n",
-                        "\n",
-                        "\n",
-                        "The 119F allele frequency f(R) was calculated as ~0.84. 12 each of DDT-alive and -dead F1 females were used for allele-specific PCR to detect the L119F GSTe2 mutation. 8 of the DDT-alive (8/12, 66.66%) were homozygote resistant (RR) for the mutation (Figure 3b,c), and 4 females (4/12, 33.33%) were heterozygote resistant. From the dead females, one sample failed (Figure 3c), only one female was homozygote resistant (1/11, 9.09%), 8 were heterozygotes (8/11, 72.72%), and 2 were susceptible (2/11, 18.18%). A difference was observed in the distribution of the 119F mutation [Odds Ratio of 16.00 (95% CI: 6.67-38.3, \\(\\chi^{2}\\) = 3.70, \\(p\\) = 0.05)] when comparing the frequency of the homozygote resistant allele (RR) with susceptible allele (SS) in the alive and dead females. A comparison of all resistant (RR + RS) with susceptible (SS) individuals from alive and dead females revealed no genotype-phenotype association [OR = 0.49 (CI: 0.19-3.15, \\(\\chi^{2}\\) = 1.2, \\(p\\) = 0.27)]. No association was observed when comparing the heterozygote individuals (RS) from both alive and dead to the susceptible (SS) [OR = 2.44 (CI: 0.9-13.53, \\(\\chi^{2}\\) = 0.49, \\(p\\) = 0.48)].\n",
-                        "\n",
-                        "header: Investigating the Role of Metabolic Resistance Using Synergist Bioassays\n",
-                        "\n",
-                        "Pre-exposure to PBO and DEM recovered some susceptibility to both permethrin and DDT, respectively (Figure 2c). A significant increase in mortality was observed when comparing results of a repeat conventional bioassay with permethrin [mortality = 43.9% (95% CI: 40.93-46.90)] to the results from the PBO-synergised bioassay [mortality = 78.73% (95% CI: 69.53-87.94), kh2 = 22.33, df = 1, \\(P\\) < 0.0001]. Pre-exposure to DEM recovered DDT susceptibility as well, with repeat exposure to DDT producing mortality of 49.82% (95% CI: 45.54-54.9), compared to a synergised experiment with a mortality of 81.44% (95% CI: 74.36-88.52, kh2 = 19.12, df = 1, \\(P\\) < 0.0001). In both cases, the doubling of mortalities from synergist pre-exposure suggests the possible role of cytochrome P450s and GSTs, respectively, in the observed permethrin and DDT resistance. No mortality was obtained from the control individuals.\n",
-                        "\n",
-                        "header: Polymorphism Analysis of Voltage-Gated Sodium Channel Gene Spanning the 1014 kdr Locus\n",
-                        "\n",
-                        "A fragment of voltage-gated sodium channel spanning intron 19 and the exon 20 containing the 1014F/S mutations associated with knockdown resistance (_kdr_) in _An. gambiae_[21,22] was amplified and sequenced. This was done with DNA extracted from 30 F0 females that laid eggs successfully. PCR was carried using primers: AfunkdrExon19-20F: GTTCAATGAAAGCCCCTCAAA and Afunkdr Exon19-20R: CCGAAATTTGACAAAGCAAA, as described in previous works [43]. The PCR products were purified using the Qiaquick Purification Kit (QIAGEN, Hilden, Germany), sequenced and aligned using BioEdit. Polymorphisms analysis and maximum likelihood phylogenetic tree construction were as described above. All DNA sequences from the alive and dead females are provided in File S1.\n",
-                        "\n",
-                        "header: Insecticide Resistance Profile of An. funestus Population\n",
-                        "\n",
-                        "The _An. funestus_ population demonstrated high knockdown resistance, with ~5% of them knocked down at 1 h exposure with deltamethrin and DDT (Figure S1), and ~10% knocked down after 1 h exposure with permethrin. High resistance was observed towards the pyrethroids, with a higher mortality of 48.30% (95% CI: 41-55) for permethrin, compared with 29.44% (95% CI: 24-34) for deltamethrin (Figure 2a). A 100% mortality rate was obtained in the FANG susceptible colony. Significant resistance was also observed towards DDT and bendiocarb, with mortalities of 56.34% (95% CI: 51-62) and 54.05% (95% CI: 49-59), respectively. Moderate resistance was observed from exposure to fenitrothion with mortality of 94.22% (95% CI: 88-101).\n",
-                        "\n",
-                        "header: Estimation of Resistance Intensity with Time-Course Bioassay\n",
-                        "\n",
-                        "To establish the strength of pyrethroid resistance with time, additional bioassays were performed, with 0.75% permethrin, at varying times of exposure. Four replicates of 20-25 F1 females were exposed in time-course spanning 60, 120, 180, 240 and 300 min, to establish the time required to kill 50%\n",
-                        "of them (LT50). Protocol was as described above in conventional bioassays, except for variation in time. Resistance intensity was established by comparing the LT50 from the Gajerar Giwa _An. fintestus_ populations to the LT50 previously established for the fully susceptible _Anopheles coluzzi_ lab colony [5]. This type of inter-species comparison of using _An. gambiae_ (Kisumu colony) LT50s had been done before in the absence of LT50 data from FANG colony [37].\n",
-                        "\n",
-                        "header: Study Site and Mosquito Sampling: Molecular Identification of Mosquito Species\n",
-                        "\n",
-                        "Genomic DNA (gDNA) was extracted from the females which laid eggs, using the Livak protocol [29], and the mosquitoes identified to species using the cocktail PCR of Koekemoer, et al. [28]. These include all the females from the first 3 days of collections, as well as all the females collected on day 4, which were used for the estimation of indoor resting density and identification of blood meal source.\n",
-                        "\n",
-                        "Figure 1: A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\n",
-                        "\n",
-                        "header: Determination of LLIN Efficacy with Cone Bioassays\n",
-                        "\n",
-                        "To investigate the efficacy of commonly distributed long-lasting insecticidal nets (LLINs), cone bioassays were conducted following the WHO protocol [36], using 3-4 d old F1 females. Five replicates of 9-12 mosquitoes were placed in a plastic cone attached to a fresh insecticide-containing bed net and tested. The LLINs include: the Olyset(r)Net (containing 2% permethrin), Olyset(r)Plus (containing 2% permethrin combined with 1% of the synergist, piperonyl butoxide, PBO), PermaNet(r)2.0 (containing 1.4-1.8 g/kg +-25% deltamethrin), PermaNet(r)3.0 side panel (containing 2.1-2.8 g/kg +-25% deltamethrin), and PermaNet(r)3.0 roof (containing 4.0 g/kg +-25% deltamethrin, combined with 25 g/kg +-25% of PBO). For each experiment, five different pieces cut from an LLIN brand were used for the five technical replicates. Mosquitoes were exposed for 3 min and immediately transferred to paper cups. They were supplied with 10% sucrose and mortalities recorded after 24 h. For the control, five replicates of ten mosquitoes were exposed to untreated net.\n",
-                        "\n",
-                        "header: A Low-Efficacy of Long-Lasting Insecticidal Nets Is a Serious Challenge to Malaria Control\n",
-                        "\n",
-                        "The high pyrethroid resistance was also evident in the low efficacy of insecticide-treated bed nets, most especially the Olyset(r)Net and the PermaNet(r)3.0 (the side panel). Except for PermaNet(r)3.0, profoundly lower efficacies were seen with all the nets, in line with observations in same species from southern Africa [46], and a recent report from northern Cameroon [50]. The recovery of susceptibilities from exposure to the roof of PermaNet(r)3.0 and the PBO-synergist bioassays suggest the preeminent role of CYP450s in the pyrethroid resistance in this population. This agrees with several studies carried out across Africa [46; 51; 52]. Low efficacy seen with Olyset(r)Plus net have been described in several studies with both _An. funestus_ and _An. coluzzii_[50; 51; 52; 53].\n",
-                        "\n",
-                        "Insecticide Resistance Mechanism Is Driven by Metabolic Resistance Mechanisms in Gajerar Giwa An. funestus Population\n",
-                        "\n",
-                        "The recovery of susceptibilities from pre-exposure to PBO and DEM suggests the preeminent role of CYP450s and GSTs in pyrethroid and DDT resistance, respectively. Indeed, several studies have used these synergists to establish the potential role of the above metabolic enzymes in resistance in _An. funestus_[51; 52; 54]. The role of metabolic resistance is strengthened by (i) the finding of high frequency of the 119F-_GSTe2_ mutation in the Gajerar Giwa population and its possible link to DDT resistance, as documented in several studies [20; 47; 51; 54]; (ii) the absence of the G119S and N485I mutations in the acetylcholinesterase gene; and (iii) the absence of the 1014F/_S kdr_ mutation in the voltage-gated sodium channel from the Gajerar Giwa population, in line with the observations across Africa [37; 52; 55]. The high diversity seen in the fragment spanning the exon 20 of the VGSC suggests lack of reduced diversity due to absence of selection.\n",
-                        "\n",
-                        "The high bendiocarb resistance prompted sequencing of the _ace-1_ fragment for the G119S and N485I mutations previously associated with carbamate/organophosphate resistance. The absence of these mutations in the highly bendiocarb-resistant Gajerar Giwa populations, and the observed relative fenitrothion susceptibility, suggests no cross resistance, and points to metabolic mechanism responsible for the bendiocarb resistance. Several studies have reported absence of the G119S mutation in various populations of _An. funestus_ across Africa [24; 43; 52]. The high diversity seen in the _ace-1_ fragment suggests a lack of selection in the gene. However, with only 12 females (low sample size) each of alive and dead used, the presence of this mutation at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "The absence of the N485I mutation in Gajerar Giwa populations confirmed previous observations of the mutation present only in southern African populations [24; 46]. However, with 12 females each of the alive and dead used, the presence of these mutations or others at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "header: Data Analysis\n",
-                        "\n",
-                        "The results of bioassays were interpreted as continuous variables, with normal distributions and percentage mortalities +- standard error of mean (SEM) calculated, based on the WHO protocol [35]. Results of mortalities from synergism-pyrethroid exposure were compared with values obtained from exposure to pyrethroid alone using a two-tailed Chi-Square test of independence, with the level of significance pegged as \\(P\\) < 0.05, as implemented in GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA). For genotyping of 119F _GSTe2_ mutation allele frequency was calculated using the formula f(R) = (2 x RR + RS)/2\\(N\\) for individuals carrying the mutations, and f(S) = 1 - f(R) for the susceptible individuals; where RR = total number of homozygote resistant; RS = total number heterozygote resistant; \\(N\\), total number of individuals investigated. Genotype frequency was calculated as relative frequencies of the homozygote resistant and heterozygote resistant individuals. The correlation between the genotypes and resistance phenotypes was estimated by estimating the odds ratio using the epiR package [R version 3.5.0 ([https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/))] with statistical significance established based on the Fisher's exact probability test (_P_ < 0.05). For the estimation of LT50, a probit analysis was carried out using glm with a MASS package of R [44].\n",
-                        "\n",
-                        "header: WHO Insecticides Susceptibility Tests\n",
-                        "\n",
-                        "The bioassays were performed following the protocol of WHO [35], with various insecticides from four major public health classes. These include (i) the type I pyrethroid: permethrin (0.75%); (ii) the type II pyrethroid: deltamethrin (0.05%); (iii) the organochloride: DDT (4%); (iv) the carbamate: benodiocarb (0.1%); and (v) the organophosphate: fenitrothion (1%). All insecticide impregnated papers (reference: WHO/VBC/81.806) were sourced from the WHO/Vector Control Research Unit (VCRU) of University of Sains Malaysia (Penang, Malaysia). Four replicates of 24-26 F1 females (3-4 d old) per tube were used for each insecticide. The mosquitoes were transferred to tubes lined with insecticide papers and exposed for 1 h, after which they were transferred back to the holding tubes, supplied with 10% sugar, and mortality recorded at 24 h. For each bioassay, one replicate of 20-25 females unexposed to any insecticides were used as a control. The fully susceptible _An. funestus_ (FANG colony) [36] was tested, alongside the field populations, for the pyrethroid papers to ascertain the potency of the papers. The mosquitoes were deemed susceptible to an insecticide where mortality was >98%, suspected to be moderately resistant where mortality is between 90-98%, and resistant where mortality was <90% [35]. Figures were prepared using GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA).\n",
-                        "\n",
-                        "header: Bed Net Efficacy Using Cone Bioassay\n",
-                        "\n",
-                        "A low efficacy was observed with the pyrethroid-based Olyset(r)Net (mortality = 22.22, 95% CI: 14.37-30.07) and the Perma(r)Net2.0 (mortality = 38.05%, CI: 19.12-56.98) (Figure 2b). The side panels of PermaNet(r)3.0 induced higher mortality of 67.41% (CI: 44.87-89.95). This is more than the mortality\n",
-                        "observed with the PBO-containing Olyset(r)Plus (mortality = 52.31%, CI: 41.85-62.78). A 100% mortality rate was seen from exposure to the roof of PermaNet(r)3.0 (containing PBO). No mortality was obtained from the control populations, which were exposed to untreated nets.\n",
-                        "\n",
-                        "header: Materials and Methods\n",
-                        "\n",
-                        "Exploring the Mechanisms of Multiple Insecticide Resistance in a Highly _Plasmodium_-Infected Malaria Vector _Anopheles funestus_ Sensu Stricto from Sahel of Northern Nigeria\n",
-                        "\n",
-                        "Sulaiman S. Ibrahim, Muhamad M. Mukhtar, Helen Irving, Jacob M. Riveron, Amen N. Fadel, Williams Tchapga, Jack Hearn, Abdullahi Muhammad, Faruk Sarkinfada, Charles S. Wondji\n",
-                        "\n",
-                        "1Vector Biology Department, Liverpool School of Tropical Medicine (LSTM), Liverpool L3 5QA, UK; Helen.Irving@lstmed.ac.uk (H.I.); jack.hearn@lstmed.ac.uk (J.H.); Abdullahi.Muhammad@lstmed.ac.uk (A.M.); charles.wondji@lstmed.ac.uk (C.S.W.) Department of Biochemistry, Bayero University, PMB 3011 Kano, Nigeria; muhammadmaehuhtar@gmail.com (LSTM Research Unit, Centre for Research in Infectious Diseases (CRID), P.O. Box 13591 Yaounde, Cameroon; Jacob.Riveron_Miranda@syngenta.com (J.M.R.); amen.fadel@crid-cam.net (A.N.F.); williams.tchapga@crid-cam.net (W.T.) Centre for Biotechnology Research, Bayero University, PMB 3011 Kano, Nigeria; fstada.mcp@buk.edu.ng (Center for Biotechnology Research, Bayero University, PMB 3011 Kano, Nigeria; fstada.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "ng (Center for Biotechnology Research, Bayero University, PMB 30\n",
-                        "\n",
-                        "\n",
-                        "countries like Nigeria, which reported increased cases of more than half a million in 2017, compared with 2016 [1]. With _Anopheles gambiae_ sensu lato and _Anopheles funestus_ sensu structo (_An. funestus_) as the major malaria vectors, and _Plasmodium falciparum_ as the major malaria-causing species (100% of all cases in 2017), it is not surprising that this disease accounts for ~60% of outpatient visits to health facilities and 30% of child mortality in Nigeria [3]. The lack of progress towards malaria pre-elimination in Nigeria is partly due to insufficient and/or discordant entomological and active case surveillance data [4], which are important guides to identify priority areas and the most vulnerable populations to implement data-driven decisions. In stark contrast to _An. gambiae_ s.l. [5-7], the major malaria vector _An. funestus_ from the Sudan/Sahel savannah of northern Nigeria has been neglected for decades, after comprehensive works conducted by several pioneers, before 1960. These almost seven decades old studies include (i) the work of Bruce-Chwatt and Haworth (carried out in 1955-56), which described _An. funestus_ populations from Sokoto, north-western Nigeria, highly resistant to DDT (dichlorodiphenyltrichloroethane), dieldrin, and benzene hexachloride [8]; (ii) a detailed examination of _An. funestus_ species, published in 1959 by W.M. Service [9] and its role in transmission in northern Nigeria [10]; as well as (iii) a 1964 small-scale hut trials to establish impact of DDT and malathion exposure on behaviour of _An. funestus_ and _An. gambiae_[11]. After the Garki Project (1960-1970) [12], interest in _An. funestus_ waned in northern Nigeria, though it is the chief vector in the dry season [13], extending the period of malaria transmission when densities of _An. gambiae_ s.l. have declined [14]. Due to its high vectorial capacity, conferred by its unusually high anthropophilic and endophilic behaviour [14,15], this species is very important target, which should not be neglected if the ambitious target of the WHO to reduce global malaria case incidence by 90% is to be realised [16]. Contrary to the north, several studies have characterised _An. funestus_ populations from southern Nigeria. For example, the role of this vector in malaria transmission was established in populations from four sites in southwest Nigeria [17], and recently its role in transmission and insecticide resistance profile was investigated by Djouaka and colleagues [18]. Unfortunately, information on this vector species from southern Nigeria cannot be extrapolated to the north, because Nigeria has five ecological zones which define intensity and seasonality of transmission and heterogeneity in mosquito vector compositions [19].\n",
-                        "\n",
-                        "Here, a primary data from study of the major malaria vector _An. funestus_ is presented. The role of this vector from Sahel of northern Nigeria in malaria transmission was investigated, and its resistance status to the major public health insecticides in use for bed nets and indoor-residual spraying established. The possible mechanisms driving metabolic resistance in the field were also investigated using the synergist bioassays and TaqMan genotyping for the 119F glutathione S-transferase (_GSTe2_) mutation, which confers resistance to DDT and pyrethroids [20]. The presence of the 1014F/S knockdown resistance (_kdr_) mutations previously associated with pyrethroid resistance [21,22], and the _acetylcholinesterase-1_ (_ace-I_) G119S [23] and N485I [24] mutations associated with carbamate/organophosphate resistance, was also assessed through sequencing. Findings of this study could guide the Nigerian National Malaria Elimination Program to implement evidence-based resistance management and malaria control measures in northern Nigeria as it scales up distribution of insecticide treated net (ITNs) as part of the goals of the National Malaria Strategic Plan (NMSP) 2014-2020 [25].\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Blood-fed female _Anopheles_ mosquitoes resting indoors were collected between 17-20 November 2018 using battery-operated aspirators (John. W. Hock, Gainesville, FL, USA). Collection was done in eight randomly selected houses (among those who consented), in the morning hours (6:00-7:00 a.m.) at Gajerar Giwa (13deg11'57.1'' N 7deg45'53.5'' E), a village in Katsina State, north-western Nigeria. Located in the semi-arid savannah, Gajerar Giwa (Figure 1) neighbors Ajiwa Dam, built in 1975 and is used by nearby communities, for domestic purposes, fishing, and the year-round\n",
-                        "irrigation of vegetables, including tomato, lettuce, pepper, etc. Farmers apply quantities of pesticides mainly organophosphate-based, as well as pyrethroids and carbamates for the control of insect pests, undesirable herbs and fungi ([http://documents.worldbank.org/curated/pt/244751486100486129/pdf/SFG2945-EA-P148616-Box402883B-PUBLIC-Disclosed-1-31-2017.pdf](http://documents.worldbank.org/curated/pt/244751486100486129/pdf/SFG2945-EA-P148616-Box402883B-PUBLIC-Disclosed-1-31-2017.pdf)).\n",
-                        "\n",
-                        "Clearance for indoor collection previously obtained from Operational Research Advisory Committee, Ministry of Health (with reference number MOH/off/797/TI/402) was used. The blood fed females obtained were maintained on 10% sugar at 25 \\({}^{\\circ}\\)C +- 2 and 70-80% relative humidity for 6-7 d. Gravid females were transferred into 1.5 mL tubes individually and forced to lay eggs, using established protocols [26]. The F0 parents were identified as belonging to the _An. funestus_ group using morphological keys [27] and confirmed as _An. funestus_ s.s. using the cocktail polymerase chain reaction PCR [28]. Egg batches were transferred into paper cups for hatching in the insectary located at Bayero University Kano, Nigeria. Eggs that hatched were pooled into bowls and supplemented with Tetramin(tm) baby fish food. The 3- to 4-days old F1 females that emerged were mixed in cages and used for bioassays. DNA extracted from all F0 and F1 females and to be used for molecular analyses were transported to the Liverpool School of Tropical Medicine (LSTM), UK, under the DEFRA license (PATH/125/2012, UK). To establish the indoor resting densities of the _An. funestus_ and the source of blood meal, a pyrethrum spray collection was conducted indoor in the eight houses on 21 November 2018.\n",
-                        "\n",
-                        "header: Polymorphism Analysis of Acetylcholinesterase-1 (ace-1) for G119S and N485I Target-Site Mutations\n",
-                        "\n",
-                        "To detect the 119S _ace-1_ mutation linked to bendiocarb/organophosphate resistance [23] a fragment of the gene spanning exons 4 to 5 was amplified from 12 bendiocarb-alive and -12 dead females. The following primers spanning exon 4, through intron 4_5 and exon 5: AFUNace1_ex4-5F: 5'-ACGCTAACGATAATGATCCGCT-3' and AFUNace1_ex4-5R:5'AGTAGCTTCTTCGCGTGATA CA-3' were utilised. A 12.5 mL premix comprise of 2x AccuStar II PCR SuperMix (QuantaBio, Beverly, Massachusetts, USA), containing optimised concentrations of MgCl2 and dNTPs, 0.2 mmol/L each of the forward and reverse primer was prepared. A total of 1 mL gDNA from individual female mosquitoes was added, followed by 10.5 mL of ddH2O (final volume = 25 mL). Amplification was carried out using the following condition: initial denaturation of 1 cycle at 94 degC for 3 min; followed by 35 cycles each of 94 degC for 30 s (denaturation); 60 degC for 30 s (annealing); extension at 72 degC for 1 min; and final elongation of one cycle at 72 degC for 5 min. PCR products were cleaned with QIAquick(r) PCR Purification Kit (QIAGEN, Hilden, Germany) and sequenced on both strands, using the above primers.\n",
-                        "\n",
-                        "For the N485I mutation the same samples as above were used. A forward primer AfunExon6-7ace_F: 5'-AACAATGATACATGTACCT-3' was utilised together with a previously described reverse primer AfunExon4-7ace R: 5'-TGACACTAGCACACAACCA-3' [24] to amplify a fragment spanning exons 6-7. Thermocycling condition was as described above.\n",
-                        "\n",
-                        "Polymorphisms were detected through manual examination of sequence traces using BioEdit version 7.2.3.0 ([http://www.mbio.ncsu.edu/BioEdit/bioedit.html](http://www.mbio.ncsu.edu/BioEdit/bioedit.html)) [40], and analyses of genetic parameters of polymorphism were done using the DnaSP 5.10 [41]. Different sequences were compared by constructing a maximum likelihood phylogenetic tree using MEGA 6.0 [42]. All DNA sequences from the alive and dead females are provided in File S1.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 3.2.1 Indoor Resting Density, Human Blood Index and Biting Rate: 3.2.2 Estimation of Sporozoite Infection Rate\n",
-                        "\n",
-                        "A total of 36 out of 66 heads/thoraces were positive for _P. falciparum_ (F\\({}_{+}\\) = 36), corresponding to an unusually high sporozoite rate of 54.55% \\(\\pm\\) 13.63. Validation of the TaqMan results using nested PCR [34] confirmed the _P. falciparum_ infection in the 36 samples.\n",
-                        "\n",
-                        "header: 2.3.1 Estimation of Indoor Resting Density, Human Blood Index and Biting Rate: 2.3.2 Estimation of Sporozoite Rate\n",
-                        "\n",
-                        "Sixty-six females (22 individuals randomly selected from each of the three days of indoor collection) which laid eggs successfully were dissected, and the heads/thoraces separated from the abdomens immediately, as explained above. The presence of sporozoite was investigated using a TaqMan genotyping protocol, established by Bass and colleagues [33]. Real-time PCR MX 3005 (Agilent, Santa Clara, CA, USA) was used for the amplification. A total of 1 uL of gDNA, extracted from each female head/thorax, was used for PCR, with an initial denaturation at 95 \\({}^{\\circ}\\)C for 10 min, followed by 40 cycles each of 15 s at 95 \\({}^{\\circ}\\)C and 1 min at 60 \\({}^{\\circ}\\)C. Primers described by Bass (PlasF_GCTTAGTTACGATTAATAGGAGTAGCTTG and PlasR_GAAAATCTAAGAATTTCAC CTCTGACA) were used, together with two probes labelled with fluorophores, FAM (Falcip+_TCTGAAT ACGAATGTC) to detect _Plasmodium falciparum_, and HEX (OVM+_CTGAATACAAATGCC) to detect the combination of _P. ovale_, _P. vivax_ and _P. malariae_. Positive controls (known FAM+ and OVM+) were used, in addition to a negative control, in which 1 uL of ddH2O was added. To validate findings of the TaqMan assay, a nested PCR of Snounou et al. [34] was carried out, using all the samples that tested positive with TaqMan. The sporozoite rate was calculated as the percentage of females positive, relative to the total number of the females examined [30].\n",
-                        "\n",
-                        "header: Estimation of Entomological and Parasitological Parameters\n",
-                        "\n",
-                        "The indoor resting density (IRD) was calculated from the number of _An. funestus_ collected on the 4th d, relative to the number of houses assessed, as advised by the WHO [30; 31]. A total of 112 blood-fed females were dissected <48 h after collection, separating heads/thoraces from the abdomens. The abdomens were used for gDNA extraction as outlined in WHO guidelines [30] using the DNeasy Blood and Tissue Kit (QIAGEN, Hilden, Germany), according to manufacturer's protocol. Identification of the source of blood was conducted using the cocktail PCR of Kent et al. [32]. The human blood index was calculated from the number of females that have fed on humans, relative to the total number of females caught [30; 31]. The human biting rate was estimated from the number of the females that have fed on humans, relative to the total number of people in the houses sampled.\n",
-                        "\n",
-                        "header: Composition of the Mosquito Species Collected Indoor: Entomological and Parasitological Parameters of Transmission\n",
-                        "\n",
-                        "The indoor resting density of the _An. funestus_ was estimated as 14 from the 112 blood females collected in eight houses. Analysis of the blood source of these 112 females by PCR revealed that they all fed on human blood, leading to a human blood index of 100%. The human biting rate was estimated as ~5.3 bites per person per night (CI: 4.91-5.69).\n",
-                        "\n",
-                        "header: Investigating the Role of Metabolic Resistance Using Synergist Bioassays\n",
-                        "\n",
-                        "Pre-exposure to PBO and DEM recovered some susceptibility to both permethrin and DDT, respectively (Figure 2c). A significant increase in mortality was observed when comparing results of a repeat conventional bioassay with permethrin [mortality = 43.9% (95% CI: 40.93-46.90)] to the results from the PBO-synergised bioassay [mortality = 78.73% (95% CI: 69.53-87.94), kh2 = 22.33, df = 1, \\(P\\) < 0.0001]. Pre-exposure to DEM recovered DDT susceptibility as well, with repeat exposure to DDT producing mortality of 49.82% (95% CI: 45.54-54.9), compared to a synergised experiment with a mortality of 81.44% (95% CI: 74.36-88.52, kh2 = 19.12, df = 1, \\(P\\) < 0.0001). In both cases, the doubling of mortalities from synergist pre-exposure suggests the possible role of cytochrome P450s and GSTs, respectively, in the observed permethrin and DDT resistance. No mortality was obtained from the control individuals.\n",
-                        "\n",
-                        "header: Synergist Bioassay with Piperonyl Butoxide (PBO) and Diethyl Malate\n",
-                        "\n",
-                        "To investigate the potential role of P450 monooxygenases in pyrethroid resistance, a synergist bioassay was carried out using 4% PBO [an inhibitor of CYP450s [38]] against permethrin. The role of glutathione S-transferases (GSTs) in DDT resistance was also investigated by pre-exposure to 8% diethyl maleate (DEM). The insecticides and the synergists PBO were sourced from the WHO/Vector Control Research Unit (VCRU) of the University of Sains Malaysia (Penang, Malaysia). Four replicates of 22-26 F1 females (3-4 d old) were pre-exposed to PBO or DEM for 1 h, and then transferred to tubes containing permethrin [35] or DDT, respectively. Mosquitoes were treated as in the WHO bioassays described above, and mortalities scored after 24 h. Two replicates of 25 females each were exposed to PBO only, as a control.\n",
-                        "\n",
-                        "Genotyping of L119F Glutathione S-Transferase (GSTe2) Mutation Associated with DDT/Permethrin Resistance\n",
-                        "\n",
-                        "To detect the L119F _GSTe2_ mutation previously linked to DDT/pyrethroid metabolic resistance [20], TaqMan genotyping was conducted using 44 F0 females collected from the field. This was carried out using a real-time PCR thermocycler (Agilent Mx3005) following an established protocol [20]. The primers L119F_GSTe2F (5'-AACAATTTTTCATTTCTTATTCTCATTAC-3') and L119F_GSTe2R (5'- CGACTCGATCTTCGGGAATGTC-3') were utilised with the following probes: reporter L119 (VIC_AGGACGGTATTCTTTTTCTA) to detect the susceptibility allele, and reporter 119F (FAM_AGGAGCGTATTTTTTTCTA) for the resistance allele. The assay was performed in a 10 mL final volume comprise of 5 mL of 1x Sensimix (Bioline, London, UK), 800 nM each of primer, 200 nM of each probe, and 1 mL of gDNA. Thermocycling conditions were initial denaturation of 10 min at 95 \\({}^{\\circ}\\)C, followed by 40 cycles each of 92 \\({}^{\\circ}\\)C for 10 s, and 60 \\({}^{\\circ}\\)C for 45 s. Genotypes were scored from scatter plots of results produced by the Mx3005 v4.10 software (Agilent, Santa Clara, CA, USA). Three positive samples of known genotypes [20]--(i) homozygote resistant 1014F/1014F; (ii) heterozygote L1014/1014F; and (iii) susceptible L1014/L1014--were used as positive controls. For the negative control, 1 mL of ddH2O was incorporated into the control well. To assess the correlation between the presence of the 119F mutation and the resistance phenotype, 12 each of DDT-alive and -dead females were genotyped using the above protocol. In addition, these 12 alive and 12 dead females were also screened, using the recently established allele-specific PCR [39].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "_Anopheles funestus_ is the only mosquito species caught, a total of 721 in 4 d. Out of these, 590 were caught in the first 3 d (52 \\(\\circ\\), 538 \\(\\circ\\) [487 blood fed and 51 unfed]). A total of 312 \\(\\mathrm{\\SIUnitSymbolOhm}\\)ial eggs and 288 egg batches hatched successfully. A total of 131 mosquitoes were caught on the 4th day: 12 \\(\\circ\\) and 119 \\(\\circ\\) (112 of them blood fed and 7 unfed).\n",
-                        "\n",
-                        "header: Data Analysis\n",
-                        "\n",
-                        "The results of bioassays were interpreted as continuous variables, with normal distributions and percentage mortalities +- standard error of mean (SEM) calculated, based on the WHO protocol [35]. Results of mortalities from synergism-pyrethroid exposure were compared with values obtained from exposure to pyrethroid alone using a two-tailed Chi-Square test of independence, with the level of significance pegged as \\(P\\) < 0.05, as implemented in GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA). For genotyping of 119F _GSTe2_ mutation allele frequency was calculated using the formula f(R) = (2 x RR + RS)/2\\(N\\) for individuals carrying the mutations, and f(S) = 1 - f(R) for the susceptible individuals; where RR = total number of homozygote resistant; RS = total number heterozygote resistant; \\(N\\), total number of individuals investigated. Genotype frequency was calculated as relative frequencies of the homozygote resistant and heterozygote resistant individuals. The correlation between the genotypes and resistance phenotypes was estimated by estimating the odds ratio using the epiR package [R version 3.5.0 ([https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/))] with statistical significance established based on the Fisher's exact probability test (_P_ < 0.05). For the estimation of LT50, a probit analysis was carried out using glm with a MASS package of R [44].\n",
-                        "\n",
-                        "header: Study Site and Mosquito Sampling: Molecular Identification of Mosquito Species\n",
-                        "\n",
-                        "Genomic DNA (gDNA) was extracted from the females which laid eggs, using the Livak protocol [29], and the mosquitoes identified to species using the cocktail PCR of Koekemoer, et al. [28]. These include all the females from the first 3 days of collections, as well as all the females collected on day 4, which were used for the estimation of indoor resting density and identification of blood meal source.\n",
-                        "\n",
-                        "Figure 1: A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\n",
-                        "\n",
-                        "header: Supplementary Materials:\n",
-                        "\n",
-                        "The following are available online at [http://www.mdpi.com/2073-4425/11/4/454/s1](http://www.mdpi.com/2073-4425/11/4/454/s1), Figure S1: Knockdown profiles of _An. funestus females_ exposed to pyrethroids and DDT. Each bar is a percentage knockdown of values from four different replicates from bioassays at different time points, Figure S2: Analysis of the _acc-_I fragment encompassing the 119 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S3: Analysis of the _acc-_I fragment encompassing the 485 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S4: Analysis of the _VGSC_ fragment encompassing the 1014 _kdr_ mutation codon. (**a**). pattern of genetic variability of 30 sequences from randomly selected F0 females; (**b**). a maximum likelihood phylogenetic tree of the sequences, Table S1: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 119 codon for alive and dead mosquitoes, Table S2: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 485 codon for alive and dead mosquitoes, Table S3: Summary statistics for polymorphism of a fragment of voltage-gated sodium channel encompassing the 1014 codon for the field F0 females, File S1: Analyzed sequences (gDNA partial fragments) from encompassing the G119S and N485I of _acetylcholinesterase_-1 gene and sequences from the partial fragment of the voltage-gated sodium channel spanning the 1014th codon.\n",
-                        "\n",
-                        "header: Insecticide Resistance Profile of An. funestus Population\n",
-                        "\n",
-                        "The _An. funestus_ population demonstrated high knockdown resistance, with ~5% of them knocked down at 1 h exposure with deltamethrin and DDT (Figure S1), and ~10% knocked down after 1 h exposure with permethrin. High resistance was observed towards the pyrethroids, with a higher mortality of 48.30% (95% CI: 41-55) for permethrin, compared with 29.44% (95% CI: 24-34) for deltamethrin (Figure 2a). A 100% mortality rate was obtained in the FANG susceptible colony. Significant resistance was also observed towards DDT and bendiocarb, with mortalities of 56.34% (95% CI: 51-62) and 54.05% (95% CI: 49-59), respectively. Moderate resistance was observed from exposure to fenitrothion with mortality of 94.22% (95% CI: 88-101).\n",
-                        "\n",
-                        "header: Polymorphism Analysis of Voltage-Gated Sodium Channel Gene Spanning the 1014 kdr Locus\n",
-                        "\n",
-                        "A fragment of voltage-gated sodium channel spanning intron 19 and the exon 20 containing the 1014F/S mutations associated with knockdown resistance (_kdr_) in _An. gambiae_[21,22] was amplified and sequenced. This was done with DNA extracted from 30 F0 females that laid eggs successfully. PCR was carried using primers: AfunkdrExon19-20F: GTTCAATGAAAGCCCCTCAAA and Afunkdr Exon19-20R: CCGAAATTTGACAAAGCAAA, as described in previous works [43]. The PCR products were purified using the Qiaquick Purification Kit (QIAGEN, Hilden, Germany), sequenced and aligned using BioEdit. Polymorphisms analysis and maximum likelihood phylogenetic tree construction were as described above. All DNA sequences from the alive and dead females are provided in File S1.\n",
-                        "\n",
-                        "header: Bed Net Efficacy Using Cone Bioassay\n",
-                        "\n",
-                        "A low efficacy was observed with the pyrethroid-based Olyset(r)Net (mortality = 22.22, 95% CI: 14.37-30.07) and the Perma(r)Net2.0 (mortality = 38.05%, CI: 19.12-56.98) (Figure 2b). The side panels of PermaNet(r)3.0 induced higher mortality of 67.41% (CI: 44.87-89.95). This is more than the mortality\n",
-                        "observed with the PBO-containing Olyset(r)Plus (mortality = 52.31%, CI: 41.85-62.78). A 100% mortality rate was seen from exposure to the roof of PermaNet(r)3.0 (containing PBO). No mortality was obtained from the control populations, which were exposed to untreated nets.\n",
-                        "\n",
-                        "header: Investigating the Role of L119F-GSTe2 Mutation in DDT Resistance\n",
-                        "\n",
-                        "Initial genotyping of 43 F0 females revealed a high frequency of the L119F-GSTe2 mutation with 30.2% (13 individuals) heterozygotes (RS, L119/F119), 32.6% (14 individuals) homozygote resistant (RR, 119F/119F), and 16.3% (7 individuals) homozygote susceptible (SS, L119/L119) (Figure 3a).\n",
-                        "\n",
-                        "Figure 2: Resistance profiles of F1 _An. funestus_ females. (**a**). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (**b**). Results of the cone bioassays with PermaNet®3.0 (side and roof), PermaNet®2.0, Olyset®Plus and Olyset®Net. Results are the average of percentage mortalities ± SEM of five replicates; (**c**). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at \\(P\\) < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (**d**). Time-course bioassay for LTs0 estimation/test for strength of permethrin resistance.\n",
-                        "\n",
-                        "\n",
-                        "The 119F allele frequency f(R) was calculated as ~0.84. 12 each of DDT-alive and -dead F1 females were used for allele-specific PCR to detect the L119F GSTe2 mutation. 8 of the DDT-alive (8/12, 66.66%) were homozygote resistant (RR) for the mutation (Figure 3b,c), and 4 females (4/12, 33.33%) were heterozygote resistant. From the dead females, one sample failed (Figure 3c), only one female was homozygote resistant (1/11, 9.09%), 8 were heterozygotes (8/11, 72.72%), and 2 were susceptible (2/11, 18.18%). A difference was observed in the distribution of the 119F mutation [Odds Ratio of 16.00 (95% CI: 6.67-38.3, \\(\\chi^{2}\\) = 3.70, \\(p\\) = 0.05)] when comparing the frequency of the homozygote resistant allele (RR) with susceptible allele (SS) in the alive and dead females. A comparison of all resistant (RR + RS) with susceptible (SS) individuals from alive and dead females revealed no genotype-phenotype association [OR = 0.49 (CI: 0.19-3.15, \\(\\chi^{2}\\) = 1.2, \\(p\\) = 0.27)]. No association was observed when comparing the heterozygote individuals (RS) from both alive and dead to the susceptible (SS) [OR = 2.44 (CI: 0.9-13.53, \\(\\chi^{2}\\) = 0.49, \\(p\\) = 0.48)].\n",
-                        "\n",
-                        "header: Estimation of Resistance Intensity with Time-Course Bioassay\n",
-                        "\n",
-                        "To establish the strength of pyrethroid resistance with time, additional bioassays were performed, with 0.75% permethrin, at varying times of exposure. Four replicates of 20-25 F1 females were exposed in time-course spanning 60, 120, 180, 240 and 300 min, to establish the time required to kill 50%\n",
-                        "of them (LT50). Protocol was as described above in conventional bioassays, except for variation in time. Resistance intensity was established by comparing the LT50 from the Gajerar Giwa _An. fintestus_ populations to the LT50 previously established for the fully susceptible _Anopheles coluzzi_ lab colony [5]. This type of inter-species comparison of using _An. gambiae_ (Kisumu colony) LT50s had been done before in the absence of LT50 data from FANG colony [37].\n",
-                        "\n",
-                        "header: A Low-Efficacy of Long-Lasting Insecticidal Nets Is a Serious Challenge to Malaria Control\n",
-                        "\n",
-                        "The high pyrethroid resistance was also evident in the low efficacy of insecticide-treated bed nets, most especially the Olyset(r)Net and the PermaNet(r)3.0 (the side panel). Except for PermaNet(r)3.0, profoundly lower efficacies were seen with all the nets, in line with observations in same species from southern Africa [46], and a recent report from northern Cameroon [50]. The recovery of susceptibilities from exposure to the roof of PermaNet(r)3.0 and the PBO-synergist bioassays suggest the preeminent role of CYP450s in the pyrethroid resistance in this population. This agrees with several studies carried out across Africa [46; 51; 52]. Low efficacy seen with Olyset(r)Plus net have been described in several studies with both _An. funestus_ and _An. coluzzii_[50; 51; 52; 53].\n",
-                        "\n",
-                        "Insecticide Resistance Mechanism Is Driven by Metabolic Resistance Mechanisms in Gajerar Giwa An. funestus Population\n",
-                        "\n",
-                        "The recovery of susceptibilities from pre-exposure to PBO and DEM suggests the preeminent role of CYP450s and GSTs in pyrethroid and DDT resistance, respectively. Indeed, several studies have used these synergists to establish the potential role of the above metabolic enzymes in resistance in _An. funestus_[51; 52; 54]. The role of metabolic resistance is strengthened by (i) the finding of high frequency of the 119F-_GSTe2_ mutation in the Gajerar Giwa population and its possible link to DDT resistance, as documented in several studies [20; 47; 51; 54]; (ii) the absence of the G119S and N485I mutations in the acetylcholinesterase gene; and (iii) the absence of the 1014F/_S kdr_ mutation in the voltage-gated sodium channel from the Gajerar Giwa population, in line with the observations across Africa [37; 52; 55]. The high diversity seen in the fragment spanning the exon 20 of the VGSC suggests lack of reduced diversity due to absence of selection.\n",
-                        "\n",
-                        "The high bendiocarb resistance prompted sequencing of the _ace-1_ fragment for the G119S and N485I mutations previously associated with carbamate/organophosphate resistance. The absence of these mutations in the highly bendiocarb-resistant Gajerar Giwa populations, and the observed relative fenitrothion susceptibility, suggests no cross resistance, and points to metabolic mechanism responsible for the bendiocarb resistance. Several studies have reported absence of the G119S mutation in various populations of _An. funestus_ across Africa [24; 43; 52]. The high diversity seen in the _ace-1_ fragment suggests a lack of selection in the gene. However, with only 12 females (low sample size) each of alive and dead used, the presence of this mutation at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "The absence of the N485I mutation in Gajerar Giwa populations confirmed previous observations of the mutation present only in southern African populations [24; 46]. However, with 12 females each of the alive and dead used, the presence of these mutations or others at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "header: Determination of LLIN Efficacy with Cone Bioassays\n",
-                        "\n",
-                        "To investigate the efficacy of commonly distributed long-lasting insecticidal nets (LLINs), cone bioassays were conducted following the WHO protocol [36], using 3-4 d old F1 females. Five replicates of 9-12 mosquitoes were placed in a plastic cone attached to a fresh insecticide-containing bed net and tested. The LLINs include: the Olyset(r)Net (containing 2% permethrin), Olyset(r)Plus (containing 2% permethrin combined with 1% of the synergist, piperonyl butoxide, PBO), PermaNet(r)2.0 (containing 1.4-1.8 g/kg +-25% deltamethrin), PermaNet(r)3.0 side panel (containing 2.1-2.8 g/kg +-25% deltamethrin), and PermaNet(r)3.0 roof (containing 4.0 g/kg +-25% deltamethrin, combined with 25 g/kg +-25% of PBO). For each experiment, five different pieces cut from an LLIN brand were used for the five technical replicates. Mosquitoes were exposed for 3 min and immediately transferred to paper cups. They were supplied with 10% sucrose and mortalities recorded after 24 h. For the control, five replicates of ten mosquitoes were exposed to untreated net.\n",
-                        "\n",
-                        "header: 5 Conclusions\n",
-                        "\n",
-                        "This study discovered a disproportionately high _Plasmodium_ infection in a major malaria vector _An. funestus_ from Nigeria, which will pose a serious threat to malaria control, and highlights the extent \n",
-                        "of the efforts needed to reach the pre-elimination stage in this region of Nigeria. Pyrethroid and DDT resistance was seen at high levels which could compromise control tools using the LLINs and indoor residual spraying (IRS). The high bendiocarb resistance observed in the field populations of this vector may affect the future control measures in northern Nigeria, using the carbamate-based IRS. However, the PBO-containing combination bed net PermaNet(r)3.0 was found to be still effective in killing this species. The discovery of a high susceptibility to fenitrothion suggests that organophosphates could also be alternatives for IRS. Finally, all evidences pointed to the preeminent role of metabolic mechanisms in the multiple resistance in this populations of _An. funestus_. This should be considered by Nigeria's National Malaria Elimination Program when deploying control tools/interventions into this area.\n",
-                        "\n",
-                        "header: WHO Insecticides Susceptibility Tests\n",
-                        "\n",
-                        "The bioassays were performed following the protocol of WHO [35], with various insecticides from four major public health classes. These include (i) the type I pyrethroid: permethrin (0.75%); (ii) the type II pyrethroid: deltamethrin (0.05%); (iii) the organochloride: DDT (4%); (iv) the carbamate: benodiocarb (0.1%); and (v) the organophosphate: fenitrothion (1%). All insecticide impregnated papers (reference: WHO/VBC/81.806) were sourced from the WHO/Vector Control Research Unit (VCRU) of University of Sains Malaysia (Penang, Malaysia). Four replicates of 24-26 F1 females (3-4 d old) per tube were used for each insecticide. The mosquitoes were transferred to tubes lined with insecticide papers and exposed for 1 h, after which they were transferred back to the holding tubes, supplied with 10% sugar, and mortality recorded at 24 h. For each bioassay, one replicate of 20-25 females unexposed to any insecticides were used as a control. The fully susceptible _An. funestus_ (FANG colony) [36] was tested, alongside the field populations, for the pyrethroid papers to ascertain the potency of the papers. The mosquitoes were deemed susceptible to an insecticide where mortality was >98%, suspected to be moderately resistant where mortality is between 90-98%, and resistant where mortality was <90% [35]. Figures were prepared using GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA).\n",
-                        "\n",
-                        "header: Evidence of Multiple Resistance in the Gajerar Giwa An. funestus\n",
-                        "\n",
-                        "The multiple insecticide resistance seen in the _An. funestus_ in this study is in line with reported data from several studies across Africa [20,46-48], though with the intensity of the resistance higher in southern Africa. However, the level of pyrethroid resistance observed in this study was higher than reported in the same species from southern Nigeria by Djouaka et al. [18], especially for deltamethrin,\n",
-                        "where mortalities in the population from southern Nigeria was on average three times lower compared to the Gajerar Giwa population. In contrast, the extremely high DDT resistance reported in southern Nigeria [18] was not seen in the Gajerar Giwa population, though the DDT resistance is higher compared to the values reported for the same species in Sahel of northern Senegal [48]. The high bendiocarb resistance seen in the Gajerar Giwa population (54% mortality) is lower than carbamate resistance in southern African populations (30-42% mortality) [37; 46], where carbamate resistance is established to be very high. However, the bendiocarb resistance in the Gajerar Giwa is higher than what was reported for the southern Nigerian population of _An. funestus_ (84% mortality) [18], or the population from Senegal (94% mortality) [48], and even in northern Cameroon (89% mortality) [49]. The Gajerar Giwa _An. funestus_ population exhibited a high LT50 for permethrin, reflecting high resistance intensity, though on average less than half of what was documented for the southern African populations [37; 46].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 3.2.1 Indoor Resting Density, Human Blood Index and Biting Rate: 3.2.2 Estimation of Sporozoite Infection Rate\n",
-                        "\n",
-                        "A total of 36 out of 66 heads/thoraces were positive for _P. falciparum_ (F\\({}_{+}\\) = 36), corresponding to an unusually high sporozoite rate of 54.55% \\(\\pm\\) 13.63. Validation of the TaqMan results using nested PCR [34] confirmed the _P. falciparum_ infection in the 36 samples.\n",
-                        "\n",
-                        "header: Estimation of Entomological and Parasitological Parameters\n",
-                        "\n",
-                        "The indoor resting density (IRD) was calculated from the number of _An. funestus_ collected on the 4th d, relative to the number of houses assessed, as advised by the WHO [30; 31]. A total of 112 blood-fed females were dissected <48 h after collection, separating heads/thoraces from the abdomens. The abdomens were used for gDNA extraction as outlined in WHO guidelines [30] using the DNeasy Blood and Tissue Kit (QIAGEN, Hilden, Germany), according to manufacturer's protocol. Identification of the source of blood was conducted using the cocktail PCR of Kent et al. [32]. The human blood index was calculated from the number of females that have fed on humans, relative to the total number of females caught [30; 31]. The human biting rate was estimated from the number of the females that have fed on humans, relative to the total number of people in the houses sampled.\n",
-                        "\n",
-                        "header: Composition of the Mosquito Species Collected Indoor: Entomological and Parasitological Parameters of Transmission\n",
-                        "\n",
-                        "The indoor resting density of the _An. funestus_ was estimated as 14 from the 112 blood females collected in eight houses. Analysis of the blood source of these 112 females by PCR revealed that they all fed on human blood, leading to a human blood index of 100%. The human biting rate was estimated as ~5.3 bites per person per night (CI: 4.91-5.69).\n",
-                        "\n",
-                        "header: 2.3.1 Estimation of Indoor Resting Density, Human Blood Index and Biting Rate: 2.3.2 Estimation of Sporozoite Rate\n",
-                        "\n",
-                        "Sixty-six females (22 individuals randomly selected from each of the three days of indoor collection) which laid eggs successfully were dissected, and the heads/thoraces separated from the abdomens immediately, as explained above. The presence of sporozoite was investigated using a TaqMan genotyping protocol, established by Bass and colleagues [33]. Real-time PCR MX 3005 (Agilent, Santa Clara, CA, USA) was used for the amplification. A total of 1 uL of gDNA, extracted from each female head/thorax, was used for PCR, with an initial denaturation at 95 \\({}^{\\circ}\\)C for 10 min, followed by 40 cycles each of 15 s at 95 \\({}^{\\circ}\\)C and 1 min at 60 \\({}^{\\circ}\\)C. Primers described by Bass (PlasF_GCTTAGTTACGATTAATAGGAGTAGCTTG and PlasR_GAAAATCTAAGAATTTCAC CTCTGACA) were used, together with two probes labelled with fluorophores, FAM (Falcip+_TCTGAAT ACGAATGTC) to detect _Plasmodium falciparum_, and HEX (OVM+_CTGAATACAAATGCC) to detect the combination of _P. ovale_, _P. vivax_ and _P. malariae_. Positive controls (known FAM+ and OVM+) were used, in addition to a negative control, in which 1 uL of ddH2O was added. To validate findings of the TaqMan assay, a nested PCR of Snounou et al. [34] was carried out, using all the samples that tested positive with TaqMan. The sporozoite rate was calculated as the percentage of females positive, relative to the total number of the females examined [30].\n",
-                        "\n",
-                        "header: Investigating the Role of Metabolic Resistance Using Synergist Bioassays\n",
-                        "\n",
-                        "Pre-exposure to PBO and DEM recovered some susceptibility to both permethrin and DDT, respectively (Figure 2c). A significant increase in mortality was observed when comparing results of a repeat conventional bioassay with permethrin [mortality = 43.9% (95% CI: 40.93-46.90)] to the results from the PBO-synergised bioassay [mortality = 78.73% (95% CI: 69.53-87.94), kh2 = 22.33, df = 1, \\(P\\) < 0.0001]. Pre-exposure to DEM recovered DDT susceptibility as well, with repeat exposure to DDT producing mortality of 49.82% (95% CI: 45.54-54.9), compared to a synergised experiment with a mortality of 81.44% (95% CI: 74.36-88.52, kh2 = 19.12, df = 1, \\(P\\) < 0.0001). In both cases, the doubling of mortalities from synergist pre-exposure suggests the possible role of cytochrome P450s and GSTs, respectively, in the observed permethrin and DDT resistance. No mortality was obtained from the control individuals.\n",
-                        "\n",
-                        "header: Synergist Bioassay with Piperonyl Butoxide (PBO) and Diethyl Malate\n",
-                        "\n",
-                        "To investigate the potential role of P450 monooxygenases in pyrethroid resistance, a synergist bioassay was carried out using 4% PBO [an inhibitor of CYP450s [38]] against permethrin. The role of glutathione S-transferases (GSTs) in DDT resistance was also investigated by pre-exposure to 8% diethyl maleate (DEM). The insecticides and the synergists PBO were sourced from the WHO/Vector Control Research Unit (VCRU) of the University of Sains Malaysia (Penang, Malaysia). Four replicates of 22-26 F1 females (3-4 d old) were pre-exposed to PBO or DEM for 1 h, and then transferred to tubes containing permethrin [35] or DDT, respectively. Mosquitoes were treated as in the WHO bioassays described above, and mortalities scored after 24 h. Two replicates of 25 females each were exposed to PBO only, as a control.\n",
-                        "\n",
-                        "Genotyping of L119F Glutathione S-Transferase (GSTe2) Mutation Associated with DDT/Permethrin Resistance\n",
-                        "\n",
-                        "To detect the L119F _GSTe2_ mutation previously linked to DDT/pyrethroid metabolic resistance [20], TaqMan genotyping was conducted using 44 F0 females collected from the field. This was carried out using a real-time PCR thermocycler (Agilent Mx3005) following an established protocol [20]. The primers L119F_GSTe2F (5'-AACAATTTTTCATTTCTTATTCTCATTAC-3') and L119F_GSTe2R (5'- CGACTCGATCTTCGGGAATGTC-3') were utilised with the following probes: reporter L119 (VIC_AGGACGGTATTCTTTTTCTA) to detect the susceptibility allele, and reporter 119F (FAM_AGGAGCGTATTTTTTTCTA) for the resistance allele. The assay was performed in a 10 mL final volume comprise of 5 mL of 1x Sensimix (Bioline, London, UK), 800 nM each of primer, 200 nM of each probe, and 1 mL of gDNA. Thermocycling conditions were initial denaturation of 10 min at 95 \\({}^{\\circ}\\)C, followed by 40 cycles each of 92 \\({}^{\\circ}\\)C for 10 s, and 60 \\({}^{\\circ}\\)C for 45 s. Genotypes were scored from scatter plots of results produced by the Mx3005 v4.10 software (Agilent, Santa Clara, CA, USA). Three positive samples of known genotypes [20]--(i) homozygote resistant 1014F/1014F; (ii) heterozygote L1014/1014F; and (iii) susceptible L1014/L1014--were used as positive controls. For the negative control, 1 mL of ddH2O was incorporated into the control well. To assess the correlation between the presence of the 119F mutation and the resistance phenotype, 12 each of DDT-alive and -dead females were genotyped using the above protocol. In addition, these 12 alive and 12 dead females were also screened, using the recently established allele-specific PCR [39].\n",
-                        "\n",
-                        "header: Insecticide Resistance Profile of An. funestus Population\n",
-                        "\n",
-                        "The _An. funestus_ population demonstrated high knockdown resistance, with ~5% of them knocked down at 1 h exposure with deltamethrin and DDT (Figure S1), and ~10% knocked down after 1 h exposure with permethrin. High resistance was observed towards the pyrethroids, with a higher mortality of 48.30% (95% CI: 41-55) for permethrin, compared with 29.44% (95% CI: 24-34) for deltamethrin (Figure 2a). A 100% mortality rate was obtained in the FANG susceptible colony. Significant resistance was also observed towards DDT and bendiocarb, with mortalities of 56.34% (95% CI: 51-62) and 54.05% (95% CI: 49-59), respectively. Moderate resistance was observed from exposure to fenitrothion with mortality of 94.22% (95% CI: 88-101).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "_Anopheles funestus_ is the only mosquito species caught, a total of 721 in 4 d. Out of these, 590 were caught in the first 3 d (52 \\(\\circ\\), 538 \\(\\circ\\) [487 blood fed and 51 unfed]). A total of 312 \\(\\mathrm{\\SIUnitSymbolOhm}\\)ial eggs and 288 egg batches hatched successfully. A total of 131 mosquitoes were caught on the 4th day: 12 \\(\\circ\\) and 119 \\(\\circ\\) (112 of them blood fed and 7 unfed).\n",
-                        "\n",
-                        "header: Polymorphism Analysis of Voltage-Gated Sodium Channel Gene Spanning the 1014 kdr Locus\n",
-                        "\n",
-                        "A fragment of voltage-gated sodium channel spanning intron 19 and the exon 20 containing the 1014F/S mutations associated with knockdown resistance (_kdr_) in _An. gambiae_[21,22] was amplified and sequenced. This was done with DNA extracted from 30 F0 females that laid eggs successfully. PCR was carried using primers: AfunkdrExon19-20F: GTTCAATGAAAGCCCCTCAAA and Afunkdr Exon19-20R: CCGAAATTTGACAAAGCAAA, as described in previous works [43]. The PCR products were purified using the Qiaquick Purification Kit (QIAGEN, Hilden, Germany), sequenced and aligned using BioEdit. Polymorphisms analysis and maximum likelihood phylogenetic tree construction were as described above. All DNA sequences from the alive and dead females are provided in File S1.\n",
-                        "\n",
-                        "header: Bed Net Efficacy Using Cone Bioassay\n",
-                        "\n",
-                        "A low efficacy was observed with the pyrethroid-based Olyset(r)Net (mortality = 22.22, 95% CI: 14.37-30.07) and the Perma(r)Net2.0 (mortality = 38.05%, CI: 19.12-56.98) (Figure 2b). The side panels of PermaNet(r)3.0 induced higher mortality of 67.41% (CI: 44.87-89.95). This is more than the mortality\n",
-                        "observed with the PBO-containing Olyset(r)Plus (mortality = 52.31%, CI: 41.85-62.78). A 100% mortality rate was seen from exposure to the roof of PermaNet(r)3.0 (containing PBO). No mortality was obtained from the control populations, which were exposed to untreated nets.\n",
-                        "\n",
-                        "header: Supplementary Materials:\n",
-                        "\n",
-                        "The following are available online at [http://www.mdpi.com/2073-4425/11/4/454/s1](http://www.mdpi.com/2073-4425/11/4/454/s1), Figure S1: Knockdown profiles of _An. funestus females_ exposed to pyrethroids and DDT. Each bar is a percentage knockdown of values from four different replicates from bioassays at different time points, Figure S2: Analysis of the _acc-_I fragment encompassing the 119 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S3: Analysis of the _acc-_I fragment encompassing the 485 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S4: Analysis of the _VGSC_ fragment encompassing the 1014 _kdr_ mutation codon. (**a**). pattern of genetic variability of 30 sequences from randomly selected F0 females; (**b**). a maximum likelihood phylogenetic tree of the sequences, Table S1: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 119 codon for alive and dead mosquitoes, Table S2: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 485 codon for alive and dead mosquitoes, Table S3: Summary statistics for polymorphism of a fragment of voltage-gated sodium channel encompassing the 1014 codon for the field F0 females, File S1: Analyzed sequences (gDNA partial fragments) from encompassing the G119S and N485I of _acetylcholinesterase_-1 gene and sequences from the partial fragment of the voltage-gated sodium channel spanning the 1014th codon.\n",
-                        "\n",
-                        "header: Data Analysis\n",
-                        "\n",
-                        "The results of bioassays were interpreted as continuous variables, with normal distributions and percentage mortalities +- standard error of mean (SEM) calculated, based on the WHO protocol [35]. Results of mortalities from synergism-pyrethroid exposure were compared with values obtained from exposure to pyrethroid alone using a two-tailed Chi-Square test of independence, with the level of significance pegged as \\(P\\) < 0.05, as implemented in GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA). For genotyping of 119F _GSTe2_ mutation allele frequency was calculated using the formula f(R) = (2 x RR + RS)/2\\(N\\) for individuals carrying the mutations, and f(S) = 1 - f(R) for the susceptible individuals; where RR = total number of homozygote resistant; RS = total number heterozygote resistant; \\(N\\), total number of individuals investigated. Genotype frequency was calculated as relative frequencies of the homozygote resistant and heterozygote resistant individuals. The correlation between the genotypes and resistance phenotypes was estimated by estimating the odds ratio using the epiR package [R version 3.5.0 ([https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/))] with statistical significance established based on the Fisher's exact probability test (_P_ < 0.05). For the estimation of LT50, a probit analysis was carried out using glm with a MASS package of R [44].\n",
-                        "\n",
-                        "header: WHO Insecticides Susceptibility Tests\n",
-                        "\n",
-                        "The bioassays were performed following the protocol of WHO [35], with various insecticides from four major public health classes. These include (i) the type I pyrethroid: permethrin (0.75%); (ii) the type II pyrethroid: deltamethrin (0.05%); (iii) the organochloride: DDT (4%); (iv) the carbamate: benodiocarb (0.1%); and (v) the organophosphate: fenitrothion (1%). All insecticide impregnated papers (reference: WHO/VBC/81.806) were sourced from the WHO/Vector Control Research Unit (VCRU) of University of Sains Malaysia (Penang, Malaysia). Four replicates of 24-26 F1 females (3-4 d old) per tube were used for each insecticide. The mosquitoes were transferred to tubes lined with insecticide papers and exposed for 1 h, after which they were transferred back to the holding tubes, supplied with 10% sugar, and mortality recorded at 24 h. For each bioassay, one replicate of 20-25 females unexposed to any insecticides were used as a control. The fully susceptible _An. funestus_ (FANG colony) [36] was tested, alongside the field populations, for the pyrethroid papers to ascertain the potency of the papers. The mosquitoes were deemed susceptible to an insecticide where mortality was >98%, suspected to be moderately resistant where mortality is between 90-98%, and resistant where mortality was <90% [35]. Figures were prepared using GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA).\n",
-                        "\n",
-                        "header: Investigating the Role of L119F-GSTe2 Mutation in DDT Resistance\n",
-                        "\n",
-                        "Initial genotyping of 43 F0 females revealed a high frequency of the L119F-GSTe2 mutation with 30.2% (13 individuals) heterozygotes (RS, L119/F119), 32.6% (14 individuals) homozygote resistant (RR, 119F/119F), and 16.3% (7 individuals) homozygote susceptible (SS, L119/L119) (Figure 3a).\n",
-                        "\n",
-                        "Figure 2: Resistance profiles of F1 _An. funestus_ females. (**a**). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (**b**). Results of the cone bioassays with PermaNet®3.0 (side and roof), PermaNet®2.0, Olyset®Plus and Olyset®Net. Results are the average of percentage mortalities ± SEM of five replicates; (**c**). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at \\(P\\) < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (**d**). Time-course bioassay for LTs0 estimation/test for strength of permethrin resistance.\n",
-                        "\n",
-                        "\n",
-                        "The 119F allele frequency f(R) was calculated as ~0.84. 12 each of DDT-alive and -dead F1 females were used for allele-specific PCR to detect the L119F GSTe2 mutation. 8 of the DDT-alive (8/12, 66.66%) were homozygote resistant (RR) for the mutation (Figure 3b,c), and 4 females (4/12, 33.33%) were heterozygote resistant. From the dead females, one sample failed (Figure 3c), only one female was homozygote resistant (1/11, 9.09%), 8 were heterozygotes (8/11, 72.72%), and 2 were susceptible (2/11, 18.18%). A difference was observed in the distribution of the 119F mutation [Odds Ratio of 16.00 (95% CI: 6.67-38.3, \\(\\chi^{2}\\) = 3.70, \\(p\\) = 0.05)] when comparing the frequency of the homozygote resistant allele (RR) with susceptible allele (SS) in the alive and dead females. A comparison of all resistant (RR + RS) with susceptible (SS) individuals from alive and dead females revealed no genotype-phenotype association [OR = 0.49 (CI: 0.19-3.15, \\(\\chi^{2}\\) = 1.2, \\(p\\) = 0.27)]. No association was observed when comparing the heterozygote individuals (RS) from both alive and dead to the susceptible (SS) [OR = 2.44 (CI: 0.9-13.53, \\(\\chi^{2}\\) = 0.49, \\(p\\) = 0.48)].\n",
-                        "\n",
-                        "header: Estimation of Resistance Intensity with Time-Course Bioassay\n",
-                        "\n",
-                        "To establish the strength of pyrethroid resistance with time, additional bioassays were performed, with 0.75% permethrin, at varying times of exposure. Four replicates of 20-25 F1 females were exposed in time-course spanning 60, 120, 180, 240 and 300 min, to establish the time required to kill 50%\n",
-                        "of them (LT50). Protocol was as described above in conventional bioassays, except for variation in time. Resistance intensity was established by comparing the LT50 from the Gajerar Giwa _An. fintestus_ populations to the LT50 previously established for the fully susceptible _Anopheles coluzzi_ lab colony [5]. This type of inter-species comparison of using _An. gambiae_ (Kisumu colony) LT50s had been done before in the absence of LT50 data from FANG colony [37].\n",
-                        "\n",
-                        "header: Pyetthroid Resistance Intensity\n",
-                        "\n",
-                        "The LT50 for permethrin was estimated as 64.76 min [95% CI: 59.17-70.35, Fiducial] (Figure 2d) after assessing mortality from 30 to 300 min exposure. To calculate the resistance intensity, the LT50 from Gajerar Giwa _An. funestus_ was compared to LT50 (3.320 min), previously established for the fully susceptible _An. coluzzii_ Ngoussou lab colony [5]. The resistance ratio was estimated as 19.51.\n",
-                        "\n",
-                        "header: Study Site and Mosquito Sampling: Molecular Identification of Mosquito Species\n",
-                        "\n",
-                        "Genomic DNA (gDNA) was extracted from the females which laid eggs, using the Livak protocol [29], and the mosquitoes identified to species using the cocktail PCR of Koekemoer, et al. [28]. These include all the females from the first 3 days of collections, as well as all the females collected on day 4, which were used for the estimation of indoor resting density and identification of blood meal source.\n",
-                        "\n",
-                        "Figure 1: A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\n",
-                        "\n",
-                        "header: Determination of LLIN Efficacy with Cone Bioassays\n",
-                        "\n",
-                        "To investigate the efficacy of commonly distributed long-lasting insecticidal nets (LLINs), cone bioassays were conducted following the WHO protocol [36], using 3-4 d old F1 females. Five replicates of 9-12 mosquitoes were placed in a plastic cone attached to a fresh insecticide-containing bed net and tested. The LLINs include: the Olyset(r)Net (containing 2% permethrin), Olyset(r)Plus (containing 2% permethrin combined with 1% of the synergist, piperonyl butoxide, PBO), PermaNet(r)2.0 (containing 1.4-1.8 g/kg +-25% deltamethrin), PermaNet(r)3.0 side panel (containing 2.1-2.8 g/kg +-25% deltamethrin), and PermaNet(r)3.0 roof (containing 4.0 g/kg +-25% deltamethrin, combined with 25 g/kg +-25% of PBO). For each experiment, five different pieces cut from an LLIN brand were used for the five technical replicates. Mosquitoes were exposed for 3 min and immediately transferred to paper cups. They were supplied with 10% sucrose and mortalities recorded after 24 h. For the control, five replicates of ten mosquitoes were exposed to untreated net.\n",
-                        "\n",
-                        "header: A Low-Efficacy of Long-Lasting Insecticidal Nets Is a Serious Challenge to Malaria Control\n",
-                        "\n",
-                        "The high pyrethroid resistance was also evident in the low efficacy of insecticide-treated bed nets, most especially the Olyset(r)Net and the PermaNet(r)3.0 (the side panel). Except for PermaNet(r)3.0, profoundly lower efficacies were seen with all the nets, in line with observations in same species from southern Africa [46], and a recent report from northern Cameroon [50]. The recovery of susceptibilities from exposure to the roof of PermaNet(r)3.0 and the PBO-synergist bioassays suggest the preeminent role of CYP450s in the pyrethroid resistance in this population. This agrees with several studies carried out across Africa [46; 51; 52]. Low efficacy seen with Olyset(r)Plus net have been described in several studies with both _An. funestus_ and _An. coluzzii_[50; 51; 52; 53].\n",
-                        "\n",
-                        "Insecticide Resistance Mechanism Is Driven by Metabolic Resistance Mechanisms in Gajerar Giwa An. funestus Population\n",
-                        "\n",
-                        "The recovery of susceptibilities from pre-exposure to PBO and DEM suggests the preeminent role of CYP450s and GSTs in pyrethroid and DDT resistance, respectively. Indeed, several studies have used these synergists to establish the potential role of the above metabolic enzymes in resistance in _An. funestus_[51; 52; 54]. The role of metabolic resistance is strengthened by (i) the finding of high frequency of the 119F-_GSTe2_ mutation in the Gajerar Giwa population and its possible link to DDT resistance, as documented in several studies [20; 47; 51; 54]; (ii) the absence of the G119S and N485I mutations in the acetylcholinesterase gene; and (iii) the absence of the 1014F/_S kdr_ mutation in the voltage-gated sodium channel from the Gajerar Giwa population, in line with the observations across Africa [37; 52; 55]. The high diversity seen in the fragment spanning the exon 20 of the VGSC suggests lack of reduced diversity due to absence of selection.\n",
-                        "\n",
-                        "The high bendiocarb resistance prompted sequencing of the _ace-1_ fragment for the G119S and N485I mutations previously associated with carbamate/organophosphate resistance. The absence of these mutations in the highly bendiocarb-resistant Gajerar Giwa populations, and the observed relative fenitrothion susceptibility, suggests no cross resistance, and points to metabolic mechanism responsible for the bendiocarb resistance. Several studies have reported absence of the G119S mutation in various populations of _An. funestus_ across Africa [24; 43; 52]. The high diversity seen in the _ace-1_ fragment suggests a lack of selection in the gene. However, with only 12 females (low sample size) each of alive and dead used, the presence of this mutation at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "The absence of the N485I mutation in Gajerar Giwa populations confirmed previous observations of the mutation present only in southern African populations [24; 46]. However, with 12 females each of the alive and dead used, the presence of these mutations or others at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "header: Ethical Approval\n",
-                        "\n",
-                        "Clearance for indoor collection previously obtained on 12/01/2017 from Operational Research Advisory Committee, Ministry of Health (with reference number MOH/off/797/TI/402), Kano state was used. A meeting was held with the village head and household members at Gajerar Giwa; the scope of the study and its benefits to malaria control was explained. A written and signed informed consent was obtained from individuals who agreed to indoor collection in their houses. All communication was carried out in the local language (Hausa).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Nigeria\",\"Site\":\"Gajerar Giwa\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset(r)Net\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"2%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N02\":{\"Net_type\":\"Olyset(r)Plus\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"2%,1%\",\"pHI_category\":\"Good\",\"Synergist\":\"piperonyl butoxide\"},\"N03\":{\"Net_type\":\"PermaNet(r)2.0\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"1.4-1.8 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N04\":{\"Net_type\":\"PermaNet(r)3.0 side panel\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"2.1-2.8 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N05\":{\"Net_type\":\"PermaNet(r)3.0 roof\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"4.0 g\\/kg +-25%,25 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":\"piperonyl butoxide\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 3.2.1 Indoor Resting Density, Human Blood Index and Biting Rate: 3.2.2 Estimation of Sporozoite Infection Rate\n",
-                        "\n",
-                        "A total of 36 out of 66 heads/thoraces were positive for _P. falciparum_ (F\\({}_{+}\\) = 36), corresponding to an unusually high sporozoite rate of 54.55% \\(\\pm\\) 13.63. Validation of the TaqMan results using nested PCR [34] confirmed the _P. falciparum_ infection in the 36 samples.\n",
-                        "\n",
-                        "header: Composition of the Mosquito Species Collected Indoor: Entomological and Parasitological Parameters of Transmission\n",
-                        "\n",
-                        "The indoor resting density of the _An. funestus_ was estimated as 14 from the 112 blood females collected in eight houses. Analysis of the blood source of these 112 females by PCR revealed that they all fed on human blood, leading to a human blood index of 100%. The human biting rate was estimated as ~5.3 bites per person per night (CI: 4.91-5.69).\n",
-                        "\n",
-                        "header: Estimation of Entomological and Parasitological Parameters\n",
-                        "\n",
-                        "The indoor resting density (IRD) was calculated from the number of _An. funestus_ collected on the 4th d, relative to the number of houses assessed, as advised by the WHO [30; 31]. A total of 112 blood-fed females were dissected <48 h after collection, separating heads/thoraces from the abdomens. The abdomens were used for gDNA extraction as outlined in WHO guidelines [30] using the DNeasy Blood and Tissue Kit (QIAGEN, Hilden, Germany), according to manufacturer's protocol. Identification of the source of blood was conducted using the cocktail PCR of Kent et al. [32]. The human blood index was calculated from the number of females that have fed on humans, relative to the total number of females caught [30; 31]. The human biting rate was estimated from the number of the females that have fed on humans, relative to the total number of people in the houses sampled.\n",
-                        "\n",
-                        "header: 2.3.1 Estimation of Indoor Resting Density, Human Blood Index and Biting Rate: 2.3.2 Estimation of Sporozoite Rate\n",
-                        "\n",
-                        "Sixty-six females (22 individuals randomly selected from each of the three days of indoor collection) which laid eggs successfully were dissected, and the heads/thoraces separated from the abdomens immediately, as explained above. The presence of sporozoite was investigated using a TaqMan genotyping protocol, established by Bass and colleagues [33]. Real-time PCR MX 3005 (Agilent, Santa Clara, CA, USA) was used for the amplification. A total of 1 uL of gDNA, extracted from each female head/thorax, was used for PCR, with an initial denaturation at 95 \\({}^{\\circ}\\)C for 10 min, followed by 40 cycles each of 15 s at 95 \\({}^{\\circ}\\)C and 1 min at 60 \\({}^{\\circ}\\)C. Primers described by Bass (PlasF_GCTTAGTTACGATTAATAGGAGTAGCTTG and PlasR_GAAAATCTAAGAATTTCAC CTCTGACA) were used, together with two probes labelled with fluorophores, FAM (Falcip+_TCTGAAT ACGAATGTC) to detect _Plasmodium falciparum_, and HEX (OVM+_CTGAATACAAATGCC) to detect the combination of _P. ovale_, _P. vivax_ and _P. malariae_. Positive controls (known FAM+ and OVM+) were used, in addition to a negative control, in which 1 uL of ddH2O was added. To validate findings of the TaqMan assay, a nested PCR of Snounou et al. [34] was carried out, using all the samples that tested positive with TaqMan. The sporozoite rate was calculated as the percentage of females positive, relative to the total number of the females examined [30].\n",
-                        "\n",
-                        "header: Investigating the Role of Metabolic Resistance Using Synergist Bioassays\n",
-                        "\n",
-                        "Pre-exposure to PBO and DEM recovered some susceptibility to both permethrin and DDT, respectively (Figure 2c). A significant increase in mortality was observed when comparing results of a repeat conventional bioassay with permethrin [mortality = 43.9% (95% CI: 40.93-46.90)] to the results from the PBO-synergised bioassay [mortality = 78.73% (95% CI: 69.53-87.94), kh2 = 22.33, df = 1, \\(P\\) < 0.0001]. Pre-exposure to DEM recovered DDT susceptibility as well, with repeat exposure to DDT producing mortality of 49.82% (95% CI: 45.54-54.9), compared to a synergised experiment with a mortality of 81.44% (95% CI: 74.36-88.52, kh2 = 19.12, df = 1, \\(P\\) < 0.0001). In both cases, the doubling of mortalities from synergist pre-exposure suggests the possible role of cytochrome P450s and GSTs, respectively, in the observed permethrin and DDT resistance. No mortality was obtained from the control individuals.\n",
-                        "\n",
-                        "header: Insecticide Resistance Profile of An. funestus Population\n",
-                        "\n",
-                        "The _An. funestus_ population demonstrated high knockdown resistance, with ~5% of them knocked down at 1 h exposure with deltamethrin and DDT (Figure S1), and ~10% knocked down after 1 h exposure with permethrin. High resistance was observed towards the pyrethroids, with a higher mortality of 48.30% (95% CI: 41-55) for permethrin, compared with 29.44% (95% CI: 24-34) for deltamethrin (Figure 2a). A 100% mortality rate was obtained in the FANG susceptible colony. Significant resistance was also observed towards DDT and bendiocarb, with mortalities of 56.34% (95% CI: 51-62) and 54.05% (95% CI: 49-59), respectively. Moderate resistance was observed from exposure to fenitrothion with mortality of 94.22% (95% CI: 88-101).\n",
-                        "\n",
-                        "header: Polymorphism Analysis of Voltage-Gated Sodium Channel Gene Spanning the 1014 kdr Locus\n",
-                        "\n",
-                        "A fragment of voltage-gated sodium channel spanning intron 19 and the exon 20 containing the 1014F/S mutations associated with knockdown resistance (_kdr_) in _An. gambiae_[21,22] was amplified and sequenced. This was done with DNA extracted from 30 F0 females that laid eggs successfully. PCR was carried using primers: AfunkdrExon19-20F: GTTCAATGAAAGCCCCTCAAA and Afunkdr Exon19-20R: CCGAAATTTGACAAAGCAAA, as described in previous works [43]. The PCR products were purified using the Qiaquick Purification Kit (QIAGEN, Hilden, Germany), sequenced and aligned using BioEdit. Polymorphisms analysis and maximum likelihood phylogenetic tree construction were as described above. All DNA sequences from the alive and dead females are provided in File S1.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "_Anopheles funestus_ is the only mosquito species caught, a total of 721 in 4 d. Out of these, 590 were caught in the first 3 d (52 \\(\\circ\\), 538 \\(\\circ\\) [487 blood fed and 51 unfed]). A total of 312 \\(\\mathrm{\\SIUnitSymbolOhm}\\)ial eggs and 288 egg batches hatched successfully. A total of 131 mosquitoes were caught on the 4th day: 12 \\(\\circ\\) and 119 \\(\\circ\\) (112 of them blood fed and 7 unfed).\n",
-                        "\n",
-                        "header: Synergist Bioassay with Piperonyl Butoxide (PBO) and Diethyl Malate\n",
-                        "\n",
-                        "To investigate the potential role of P450 monooxygenases in pyrethroid resistance, a synergist bioassay was carried out using 4% PBO [an inhibitor of CYP450s [38]] against permethrin. The role of glutathione S-transferases (GSTs) in DDT resistance was also investigated by pre-exposure to 8% diethyl maleate (DEM). The insecticides and the synergists PBO were sourced from the WHO/Vector Control Research Unit (VCRU) of the University of Sains Malaysia (Penang, Malaysia). Four replicates of 22-26 F1 females (3-4 d old) were pre-exposed to PBO or DEM for 1 h, and then transferred to tubes containing permethrin [35] or DDT, respectively. Mosquitoes were treated as in the WHO bioassays described above, and mortalities scored after 24 h. Two replicates of 25 females each were exposed to PBO only, as a control.\n",
-                        "\n",
-                        "Genotyping of L119F Glutathione S-Transferase (GSTe2) Mutation Associated with DDT/Permethrin Resistance\n",
-                        "\n",
-                        "To detect the L119F _GSTe2_ mutation previously linked to DDT/pyrethroid metabolic resistance [20], TaqMan genotyping was conducted using 44 F0 females collected from the field. This was carried out using a real-time PCR thermocycler (Agilent Mx3005) following an established protocol [20]. The primers L119F_GSTe2F (5'-AACAATTTTTCATTTCTTATTCTCATTAC-3') and L119F_GSTe2R (5'- CGACTCGATCTTCGGGAATGTC-3') were utilised with the following probes: reporter L119 (VIC_AGGACGGTATTCTTTTTCTA) to detect the susceptibility allele, and reporter 119F (FAM_AGGAGCGTATTTTTTTCTA) for the resistance allele. The assay was performed in a 10 mL final volume comprise of 5 mL of 1x Sensimix (Bioline, London, UK), 800 nM each of primer, 200 nM of each probe, and 1 mL of gDNA. Thermocycling conditions were initial denaturation of 10 min at 95 \\({}^{\\circ}\\)C, followed by 40 cycles each of 92 \\({}^{\\circ}\\)C for 10 s, and 60 \\({}^{\\circ}\\)C for 45 s. Genotypes were scored from scatter plots of results produced by the Mx3005 v4.10 software (Agilent, Santa Clara, CA, USA). Three positive samples of known genotypes [20]--(i) homozygote resistant 1014F/1014F; (ii) heterozygote L1014/1014F; and (iii) susceptible L1014/L1014--were used as positive controls. For the negative control, 1 mL of ddH2O was incorporated into the control well. To assess the correlation between the presence of the 119F mutation and the resistance phenotype, 12 each of DDT-alive and -dead females were genotyped using the above protocol. In addition, these 12 alive and 12 dead females were also screened, using the recently established allele-specific PCR [39].\n",
-                        "\n",
-                        "header: Bed Net Efficacy Using Cone Bioassay\n",
-                        "\n",
-                        "A low efficacy was observed with the pyrethroid-based Olyset(r)Net (mortality = 22.22, 95% CI: 14.37-30.07) and the Perma(r)Net2.0 (mortality = 38.05%, CI: 19.12-56.98) (Figure 2b). The side panels of PermaNet(r)3.0 induced higher mortality of 67.41% (CI: 44.87-89.95). This is more than the mortality\n",
-                        "observed with the PBO-containing Olyset(r)Plus (mortality = 52.31%, CI: 41.85-62.78). A 100% mortality rate was seen from exposure to the roof of PermaNet(r)3.0 (containing PBO). No mortality was obtained from the control populations, which were exposed to untreated nets.\n",
-                        "\n",
-                        "header: Investigating the Role of L119F-GSTe2 Mutation in DDT Resistance\n",
-                        "\n",
-                        "Initial genotyping of 43 F0 females revealed a high frequency of the L119F-GSTe2 mutation with 30.2% (13 individuals) heterozygotes (RS, L119/F119), 32.6% (14 individuals) homozygote resistant (RR, 119F/119F), and 16.3% (7 individuals) homozygote susceptible (SS, L119/L119) (Figure 3a).\n",
-                        "\n",
-                        "Figure 2: Resistance profiles of F1 _An. funestus_ females. (**a**). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (**b**). Results of the cone bioassays with PermaNet®3.0 (side and roof), PermaNet®2.0, Olyset®Plus and Olyset®Net. Results are the average of percentage mortalities ± SEM of five replicates; (**c**). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at \\(P\\) < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (**d**). Time-course bioassay for LTs0 estimation/test for strength of permethrin resistance.\n",
-                        "\n",
-                        "\n",
-                        "The 119F allele frequency f(R) was calculated as ~0.84. 12 each of DDT-alive and -dead F1 females were used for allele-specific PCR to detect the L119F GSTe2 mutation. 8 of the DDT-alive (8/12, 66.66%) were homozygote resistant (RR) for the mutation (Figure 3b,c), and 4 females (4/12, 33.33%) were heterozygote resistant. From the dead females, one sample failed (Figure 3c), only one female was homozygote resistant (1/11, 9.09%), 8 were heterozygotes (8/11, 72.72%), and 2 were susceptible (2/11, 18.18%). A difference was observed in the distribution of the 119F mutation [Odds Ratio of 16.00 (95% CI: 6.67-38.3, \\(\\chi^{2}\\) = 3.70, \\(p\\) = 0.05)] when comparing the frequency of the homozygote resistant allele (RR) with susceptible allele (SS) in the alive and dead females. A comparison of all resistant (RR + RS) with susceptible (SS) individuals from alive and dead females revealed no genotype-phenotype association [OR = 0.49 (CI: 0.19-3.15, \\(\\chi^{2}\\) = 1.2, \\(p\\) = 0.27)]. No association was observed when comparing the heterozygote individuals (RS) from both alive and dead to the susceptible (SS) [OR = 2.44 (CI: 0.9-13.53, \\(\\chi^{2}\\) = 0.49, \\(p\\) = 0.48)].\n",
-                        "\n",
-                        "header: Data Analysis\n",
-                        "\n",
-                        "The results of bioassays were interpreted as continuous variables, with normal distributions and percentage mortalities +- standard error of mean (SEM) calculated, based on the WHO protocol [35]. Results of mortalities from synergism-pyrethroid exposure were compared with values obtained from exposure to pyrethroid alone using a two-tailed Chi-Square test of independence, with the level of significance pegged as \\(P\\) < 0.05, as implemented in GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA). For genotyping of 119F _GSTe2_ mutation allele frequency was calculated using the formula f(R) = (2 x RR + RS)/2\\(N\\) for individuals carrying the mutations, and f(S) = 1 - f(R) for the susceptible individuals; where RR = total number of homozygote resistant; RS = total number heterozygote resistant; \\(N\\), total number of individuals investigated. Genotype frequency was calculated as relative frequencies of the homozygote resistant and heterozygote resistant individuals. The correlation between the genotypes and resistance phenotypes was estimated by estimating the odds ratio using the epiR package [R version 3.5.0 ([https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/))] with statistical significance established based on the Fisher's exact probability test (_P_ < 0.05). For the estimation of LT50, a probit analysis was carried out using glm with a MASS package of R [44].\n",
-                        "\n",
-                        "header: Supplementary Materials:\n",
-                        "\n",
-                        "The following are available online at [http://www.mdpi.com/2073-4425/11/4/454/s1](http://www.mdpi.com/2073-4425/11/4/454/s1), Figure S1: Knockdown profiles of _An. funestus females_ exposed to pyrethroids and DDT. Each bar is a percentage knockdown of values from four different replicates from bioassays at different time points, Figure S2: Analysis of the _acc-_I fragment encompassing the 119 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S3: Analysis of the _acc-_I fragment encompassing the 485 codon. (**a**). pattern of genetic variability of sequences from 12 bendiocarb-alive and -12 dead females; (**b**). a maximum likelihood phylogenetic tree of the sequences., Figure S4: Analysis of the _VGSC_ fragment encompassing the 1014 _kdr_ mutation codon. (**a**). pattern of genetic variability of 30 sequences from randomly selected F0 females; (**b**). a maximum likelihood phylogenetic tree of the sequences, Table S1: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 119 codon for alive and dead mosquitoes, Table S2: Summary statistics for polymorphism of a fragment of _acetylcholinesterase_-1 encompassing the 485 codon for alive and dead mosquitoes, Table S3: Summary statistics for polymorphism of a fragment of voltage-gated sodium channel encompassing the 1014 codon for the field F0 females, File S1: Analyzed sequences (gDNA partial fragments) from encompassing the G119S and N485I of _acetylcholinesterase_-1 gene and sequences from the partial fragment of the voltage-gated sodium channel spanning the 1014th codon.\n",
-                        "\n",
-                        "header: WHO Insecticides Susceptibility Tests\n",
-                        "\n",
-                        "The bioassays were performed following the protocol of WHO [35], with various insecticides from four major public health classes. These include (i) the type I pyrethroid: permethrin (0.75%); (ii) the type II pyrethroid: deltamethrin (0.05%); (iii) the organochloride: DDT (4%); (iv) the carbamate: benodiocarb (0.1%); and (v) the organophosphate: fenitrothion (1%). All insecticide impregnated papers (reference: WHO/VBC/81.806) were sourced from the WHO/Vector Control Research Unit (VCRU) of University of Sains Malaysia (Penang, Malaysia). Four replicates of 24-26 F1 females (3-4 d old) per tube were used for each insecticide. The mosquitoes were transferred to tubes lined with insecticide papers and exposed for 1 h, after which they were transferred back to the holding tubes, supplied with 10% sugar, and mortality recorded at 24 h. For each bioassay, one replicate of 20-25 females unexposed to any insecticides were used as a control. The fully susceptible _An. funestus_ (FANG colony) [36] was tested, alongside the field populations, for the pyrethroid papers to ascertain the potency of the papers. The mosquitoes were deemed susceptible to an insecticide where mortality was >98%, suspected to be moderately resistant where mortality is between 90-98%, and resistant where mortality was <90% [35]. Figures were prepared using GraphPad Prism 7.02 (GraphPad Inc., La Jolla, CA, USA).\n",
-                        "\n",
-                        "header: Estimation of Resistance Intensity with Time-Course Bioassay\n",
-                        "\n",
-                        "To establish the strength of pyrethroid resistance with time, additional bioassays were performed, with 0.75% permethrin, at varying times of exposure. Four replicates of 20-25 F1 females were exposed in time-course spanning 60, 120, 180, 240 and 300 min, to establish the time required to kill 50%\n",
-                        "of them (LT50). Protocol was as described above in conventional bioassays, except for variation in time. Resistance intensity was established by comparing the LT50 from the Gajerar Giwa _An. fintestus_ populations to the LT50 previously established for the fully susceptible _Anopheles coluzzi_ lab colony [5]. This type of inter-species comparison of using _An. gambiae_ (Kisumu colony) LT50s had been done before in the absence of LT50 data from FANG colony [37].\n",
-                        "\n",
-                        "header: Determination of LLIN Efficacy with Cone Bioassays\n",
-                        "\n",
-                        "To investigate the efficacy of commonly distributed long-lasting insecticidal nets (LLINs), cone bioassays were conducted following the WHO protocol [36], using 3-4 d old F1 females. Five replicates of 9-12 mosquitoes were placed in a plastic cone attached to a fresh insecticide-containing bed net and tested. The LLINs include: the Olyset(r)Net (containing 2% permethrin), Olyset(r)Plus (containing 2% permethrin combined with 1% of the synergist, piperonyl butoxide, PBO), PermaNet(r)2.0 (containing 1.4-1.8 g/kg +-25% deltamethrin), PermaNet(r)3.0 side panel (containing 2.1-2.8 g/kg +-25% deltamethrin), and PermaNet(r)3.0 roof (containing 4.0 g/kg +-25% deltamethrin, combined with 25 g/kg +-25% of PBO). For each experiment, five different pieces cut from an LLIN brand were used for the five technical replicates. Mosquitoes were exposed for 3 min and immediately transferred to paper cups. They were supplied with 10% sucrose and mortalities recorded after 24 h. For the control, five replicates of ten mosquitoes were exposed to untreated net.\n",
-                        "\n",
-                        "header: Study Site and Mosquito Sampling: Molecular Identification of Mosquito Species\n",
-                        "\n",
-                        "Genomic DNA (gDNA) was extracted from the females which laid eggs, using the Livak protocol [29], and the mosquitoes identified to species using the cocktail PCR of Koekemoer, et al. [28]. These include all the females from the first 3 days of collections, as well as all the females collected on day 4, which were used for the estimation of indoor resting density and identification of blood meal source.\n",
-                        "\n",
-                        "Figure 1: A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\n",
-                        "\n",
-                        "header: Pyetthroid Resistance Intensity\n",
-                        "\n",
-                        "The LT50 for permethrin was estimated as 64.76 min [95% CI: 59.17-70.35, Fiducial] (Figure 2d) after assessing mortality from 30 to 300 min exposure. To calculate the resistance intensity, the LT50 from Gajerar Giwa _An. funestus_ was compared to LT50 (3.320 min), previously established for the fully susceptible _An. coluzzii_ Ngoussou lab colony [5]. The resistance ratio was estimated as 19.51.\n",
-                        "\n",
-                        "header: A Low-Efficacy of Long-Lasting Insecticidal Nets Is a Serious Challenge to Malaria Control\n",
-                        "\n",
-                        "The high pyrethroid resistance was also evident in the low efficacy of insecticide-treated bed nets, most especially the Olyset(r)Net and the PermaNet(r)3.0 (the side panel). Except for PermaNet(r)3.0, profoundly lower efficacies were seen with all the nets, in line with observations in same species from southern Africa [46], and a recent report from northern Cameroon [50]. The recovery of susceptibilities from exposure to the roof of PermaNet(r)3.0 and the PBO-synergist bioassays suggest the preeminent role of CYP450s in the pyrethroid resistance in this population. This agrees with several studies carried out across Africa [46; 51; 52]. Low efficacy seen with Olyset(r)Plus net have been described in several studies with both _An. funestus_ and _An. coluzzii_[50; 51; 52; 53].\n",
-                        "\n",
-                        "Insecticide Resistance Mechanism Is Driven by Metabolic Resistance Mechanisms in Gajerar Giwa An. funestus Population\n",
-                        "\n",
-                        "The recovery of susceptibilities from pre-exposure to PBO and DEM suggests the preeminent role of CYP450s and GSTs in pyrethroid and DDT resistance, respectively. Indeed, several studies have used these synergists to establish the potential role of the above metabolic enzymes in resistance in _An. funestus_[51; 52; 54]. The role of metabolic resistance is strengthened by (i) the finding of high frequency of the 119F-_GSTe2_ mutation in the Gajerar Giwa population and its possible link to DDT resistance, as documented in several studies [20; 47; 51; 54]; (ii) the absence of the G119S and N485I mutations in the acetylcholinesterase gene; and (iii) the absence of the 1014F/_S kdr_ mutation in the voltage-gated sodium channel from the Gajerar Giwa population, in line with the observations across Africa [37; 52; 55]. The high diversity seen in the fragment spanning the exon 20 of the VGSC suggests lack of reduced diversity due to absence of selection.\n",
-                        "\n",
-                        "The high bendiocarb resistance prompted sequencing of the _ace-1_ fragment for the G119S and N485I mutations previously associated with carbamate/organophosphate resistance. The absence of these mutations in the highly bendiocarb-resistant Gajerar Giwa populations, and the observed relative fenitrothion susceptibility, suggests no cross resistance, and points to metabolic mechanism responsible for the bendiocarb resistance. Several studies have reported absence of the G119S mutation in various populations of _An. funestus_ across Africa [24; 43; 52]. The high diversity seen in the _ace-1_ fragment suggests a lack of selection in the gene. However, with only 12 females (low sample size) each of alive and dead used, the presence of this mutation at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "The absence of the N485I mutation in Gajerar Giwa populations confirmed previous observations of the mutation present only in southern African populations [24; 46]. However, with 12 females each of the alive and dead used, the presence of these mutations or others at a low frequency cannot be ruled out.\n",
-                        "\n",
-                        "header: Ethical Approval\n",
-                        "\n",
-                        "Clearance for indoor collection previously obtained on 12/01/2017 from Operational Research Advisory Committee, Ministry of Health (with reference number MOH/off/797/TI/402), Kano state was used. A meeting was held with the village head and household members at Gajerar Giwa; the scope of the study and its benefits to malaria control was explained. A written and signed informed consent was obtained from individuals who agreed to indoor collection in their houses. All communication was carried out in the local language (Hausa).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Nigeria\",\"Site\":\"Gajerar Giwa\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Olyset(r)Net\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"2%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N02\":{\"Net_type\":\"Olyset(r)Plus\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"2%,1%\",\"pHI_category\":\"Good\",\"Synergist\":\"piperonyl butoxide\"},\"N03\":{\"Net_type\":\"PermaNet(r)2.0\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"1.4-1.8 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N04\":{\"Net_type\":\"PermaNet(r)3.0 side panel\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"2.1-2.8 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":null},\"N05\":{\"Net_type\":\"PermaNet(r)3.0 roof\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"4.0 g\\/kg +-25%,25 g\\/kg +-25%\",\"pHI_category\":\"Good\",\"Synergist\":\"piperonyl butoxide\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: WHO tunnel test\n",
-                        "\n",
-                        "The tunnel tests were carried out from August to November 2020 according to standard WHO procedures [9]. Five 25 cm x 25 cm net pieces per ITN/control were cut adjacent to the swatches cut for cone assays from three nets per treatment arm to make a total of 15 pieces per study arm and to account for possible intra-net variability of insecticide loadings. Five tunnels were run with one sample from each treatment arm with one mosquito strain on a single night. Over 60 nights, all 15 pieces of ITNs per study arm were tested with four mosquito strains.\n",
-                        "\n",
-                        "Non-blood-fed nulliparous females, 5-8 days old, sugar starved for 6-8 h were released in a 60-cm-long glass tunnel. At each end of the tunnel, a 25-cm square mosquito cage covered with polyester netting was fitted. At one third of the length, the netting sample was affixed. The surface of netting \"available\" to mosquitoes is 400 cm2 (20 cm x 20 cm), with 9 x 1-cm-diameter holes: one hole is located at the centre of the square; the other eight were equidistant and located 5 cm from the border. In the shorter section of the tunnel, a small rabbit, its back shaved and restrained in a mesh tunnel, was placed as bait (Fig. 1). In the cage at the end of the longer section of the tunnel, 100 female mosquitoes (one strain \n",
-                        "per replicate) were introduced at 21:00. The following morning at 09:00, the mosquitoes were removed using a mouth aspirator and counted separately from each section of the tunnel, and mortality and blood-feeding rates were recorded. The mosquitoes were placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Mortality was recoded at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Overall mortality was measured by pooling the mortalities of mosquitoes from the two sections of the tunnel. Acceptable feeding success and mortality in controls were 50% and 10%, respectively. Any tests not meeting the specified control cut-off were repeated.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding in WHO tunnel, I-ACT and experimental hut\n",
-                        "\n",
-                        "For all bioassays and strains, blood-feeding inhibition was very high (Fig. 3, Table 3). Both Interceptor(r) G2 and Interceptor(r) gave high levels of blood-feeding inhibition in all \"free-flying\" bioassays.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "As funding for malaria control falls and mosquito resistance to current public health insecticides increases, new and durable vector control tools that utilise new insecticide classes are needed [1]. For insecticide resistance management, a new Insecticide Treated Net (ITN) Interceptor(r) G2 has been developed, coated with a mixture of the pyrethroid alpha-cypermethrin and the pro-insecticide chlorfenapyr [2]. Chlorfenapyr is an Insecticide Resistance Action Committee (IRAC) Group 13 insecticide, having a pyrrole chemistry that uncouples oxidative phosphorylation via disruption of the proton gradient (as a protonophore) to short circuit mitochondrial respiration through inner mitochondrial membranes of insect cells so that ATP cannot be synthesized, subsequently robbing insects of energy, resulting in death [3, 4]. Metabolic resistance is one of the main mechanisms of resistance observed in malaria vectors [5] where one or several detoxification gene families--cytochrome P450s (P450s), esterases and glutathione S-transferases (GSTs)--are overproduced to detoxify insecticides [6]. While this metabolism is a detoxification process, it can increase the potency of a pro-insecticide and may therefore be exploited as a means to control metabolically resistant insect populations [3]. The unique mode of action of chlorfenapyr on an insect's metabolism is particularly relevant for the control of vectors harboring insecticide resistance mechanisms, as increased metabolic activity increases the conversion of the pro-insecticide into its potent n-dealkylated form and will consequently increase mosquito mortality [7].\n",
-                        "\n",
-                        "For any new ITN to be used in public health, a thorough evaluation of its safety, efficacy and effectiveness is conducted based on World Health Organisation (WHO) standards and criteria [8]. The methods and scope of the laboratory tests and field trials required to attain WHO Prequalification (PQ) listing for ITNs are outlined in a set of WHO guidelines, last updated in 2013 [9]. As part of these guidelines, WHO recommends a set of standardised laboratory tests to ascertain the bioefficacy of pyrethroid ITNs, i.e., the ability of ITN products to kill, incapacitate (knock down) and prevent mosquitoes from blood-feeding. Laboratory bioefficacy tests are also a critical component of ITN durability evaluation used to confirm continued ITN bioefficacy after long-term use in the community [10]. The simplest and most commonly applied ITN bioefficacy test is the WHO cone bioassay where mosquitoes are held close to ITN in plastic cones and the number of mosquitoes knocked down (incapacitated) or killed is counted [9]. For ITNs with feeding inhibition mode of action, the WHO tunnel test is used, where a swatch of ITN with small holes (9 x 1-cm diameter) is made and is placed between mosquitoes and small animal bait overnight [11]. The WHO tunnel test has been shown to agree with experimental but data, using laboratory-reared mosquitoes released into the huts [12]. Thresholds for the mosquito knockdown rate (95%) and mosquito mortality rate (80%) and blood-feeding inhibition (90%) using pyrethroid-susceptible _Anopheles_ mosquitoes serve as performance benchmarks; candidate products are required to fulfill minimum performance standards, which have been established by WHO for qualification to be listed/recommended for their public health values to sustain and protect users from disease transmissions. This includes their physical durability and chemical contents recovered from nets replaced in the field over 3 years or as predicted from wash-resistance testing. For a net to be classified as a long-lasting ITN, i.e., LLIN, > 80% of nets tested should pass WHO cone/tunnel performance benchmarks after 3 years of use [13].\n",
-                        "\n",
-                        "Cone tests are relatively easy to perform, high throughput and sensitive to detecting changes in bioavailable pyrethroids that act through rapid contact neurotoxicity. However, chlorfenapyr requires the mosquito to be metabolically and/or physiologically active (as it would be when encountering the ITN under user conditions) to bioactivate into the potent n-dealkylated form which elicits increased mosquito mortality. As mosquitoes are more metabolically active at night when flying and host-seeking during their typical circadian rhythms, the tunnel test may be more appropriate [14].\n",
-                        "\n",
-                        "The President's Malaria Initiative (PMI) currently uses WHO Tunnel tests for the durability monitoring of chlorfenapyr nets with 4 samples per net evaluated and 48 nets per sampling point [15]. This requires large numbers of mosquitoes and access to small animals for testing, so it cannot be done at all facilities in malaria-endemic areas. To accommodate high-throughput evaluation of whole ITNs for durability evaluation, the Ifakara ambient chamber test (I-ACT) was developed [16]. The Ifakara ambient chamber test (I-ACT) is not currently a recognized method \n",
-                        "approved by the WHO. However, it is currently seeking confirmation by direct comparison to approved methods. This is of particular importance where novel slow-acting chemistries cannot achieve the benchmark standards established for conventional pyrethroid ITN exposures yet may prove to be highly efficacious when tested according to their discrete modes of action. Like the experimental hut, I-ACT makes use of whole nets and human hosts to evaluate bioefficacy of field-used ITNs, but the assay is done under controlled conditions with laboratory-reared mosquitoes. Mosquitoes are released into net chambers within which the test net is hung with a volunteer sleeping beneath, and all mosquitoes are recaptured in the morning. The use of laboratory mosquitoes (rather than conducting experimental hut trials with wild mosquitoes) is done to improve the precision of estimates by releasing mosquito cohorts of a defined number of mosquitoes with high recapture rate (99%) at the conclusion of exposure intervals.\n",
-                        "\n",
-                        "I-ACT bioassay has been used for evaluation of pyrethroid ITNs, was able to discriminate between products [17] and agreed with results of combined WHO cone and tunnel tests [16]. The I-ACT may show suitability for use in evaluation of pro-insecticides because: (i) the assay is run overnight, favouring malarial mosquitoes' circadian rhythms, (ii) the mosquitoes have a large arena to fly in, allowing them to be metabolically active, (iii) the mosquitoes have the opportunity to feed ad libitum and (iv) the I-ACT test eliminates infected mosquitoes that may represent a malaria transmission potential that cannot be completely excluded in WHO experimental huts--a significant safety benefit to volunteers. Therefore, the I-ACT method was evaluated alongside standard methods (i.e., experimental huts, WHO cone and tunnel tests) to provide direct evidence of its comparability. Interceptor(r) (alpha-cypermethrin only) and Interceptor(r) G2 (alpha-cypermethrin and chlorfenapyr) were evaluated to compare the performance of each assay for durability monitoring of pro-insecticidal ITNs.\n",
-                        "\n",
-                        "header: Test nets\n",
-                        "\n",
-                        "ITNs were supplied by BASF in November 2019 and stored at optimal conditions (25 to 32 degC) before testing and during the experimental phase. Interceptor(r) G2 nets were from two different production batches (two batches were used for experimental hut only and one batch for other biossays) and Interceptor(r) from one production batch. Interceptor(r) G2 is made from 100-denier polyester coated with a mixture of wash-resistant formulation containing 200 mg/m2 chlorfenapyr and 100 mg/m2alpha-cypermethrin. Interceptor(r) LN is made from 100-denier polyester coated with 200 mg/m2 alpha-cypermethrin. SafiNet is an untreated polyester net manufactured by A to Z. Textiles Mills, Ltd., Tanzania, and was used as a control. The nets were washed 20 times according to a protocol adapted from the standard WHO washing procedure [9] using 20 g/l palm soap (Jamaa) and dried flat in a shaded area. The interval of time used between two washes (i.e. regeneration time) was 1 day for both Interceptor(r) G2 and Interceptor(r) TTNs.\n",
-                        "\n",
-                        "header: Selection of the correct strain for bioassay\n",
-                        "\n",
-                        "The current study demonstrated that using the correct mosquito strain when evaluating ITNs is a critical consideration. The benefit of chlorfenapyr was only seen against the resistant strains, and against the susceptible strains Interceptor(r) was more efficacious because it contains a higher dose of alphacypermethrin. Comparing between the products using both a susceptible and a resistant strain revealed the different modes of action of Interceptor(r) and Interceptor(r) G2.\n",
-                        "\n",
-                        "header: Sample size and power\n",
-                        "\n",
-                        "A sample size calculation for generalized linear mixed effects models (GLMMs) through simulation [23] in R statistical software 3.02 [https://www.r-project.org/](https://www.r-project.org/) was performed for the I-ACT and experimental huts. For the I-ACT, to detect a 10% effect difference between the nets, simulations were performed using an estimated mosquito mortality of 80% for unwashed Interceptor(r) G2, 70% for unwashed Interceptor(r) and 10% for SafiNet(r) (deliberately holed). The design was for five arms replicated in two groups, with each volunteer testing each treatment one time over 20 replicates within its group (i.e. 40 replicates per arm), with an inter-observational variance of 0.42 for the night of observation based on the variance of the random effects observed in a previous study. The evaluation was powered at 81% for 15 mosquitoes of each strain released per chamber using 1000 simulations.\n",
-                        "\n",
-                        "For experimental huts, simulation was performed using a Latin square design with volunteers rotating nightly and accounted for as a fixed effect for 25 nights of data collection in 10 huts. The study had 84% power to detect the difference between Interceptor(r) G2 LN and Interceptor(r) LN on mosquito mortality endpoints, with two huts per treatment arm (i.e. 50 replicates per arm). The study power was calculated based on a previous study of pyrethroid nets conducted in the same area, with the estimation of 20 _An. arabiensis_ mosquitoes per night per hut, 21% mortality in unwashed Interceptor(r) G2, 7% in 20x washed Interceptor(r) G2 and 10% in unwashed Interceptor(r) vs. 4% in 20 times washed and 1% in negative control, and overdispersion parameter for daily variation was set at intermediate 0.44.\n",
-                        "\n",
-                        "To test whether I-ACT measures similarly to the WHO tunnels (H0\\(m\\)2=_m_1) a power calculation using Satterthwaites _t_-test was conducted in STATA 16 software (StataCorp LLC, College Station TX, USA) for two unpaired sample means assuming unequal variance. The power estimated was > 90% based on estimates from previous studies conducted in the same setting: mean mortality of 81.5% for WHO tunnel test with an assumed daily variation of 0.5 and 15 replicates per arm and mean\n",
-                        "mortality estimates of 86.5% and an assumed daily variation of 0.42 with 40 replicates per arm were considered for I-ACT.\n",
-                        "\n",
-                        "header: Considerations for each bioassay\n",
-                        "\n",
-                        "Both I-ACT and experimental huts use the whole bednet and human host, which represent user conditions; however, there is no risk of disease for human participants in I-ACT because the mosquitoes used are laboratory reared. The WHO tunnel test is a well-established bioassay that has been shown to agree with experimental hut tests in this evaluation as well as others [12, 14]. However, it tests only a sample of net and is therefore only able to accurately measure the chemical durability of an ITN and not the chemical _and_ physical durability. In addition, it requires a high number of mosquitoes (100 per replicate) and more testing days compared to I-ACT. In I-ACT there is possibility of testing more than one species or strain per chamber compared to tunnel test, which makes results more comparable. The I-ACT is a new assay that consistently predicts the results of experimental hut tests, measures with a similar magnitude of difference as a tunnel test and provides high-throughput and precise estimates of whole ITN protective efficacy in this study with chlorfenapyr as well as in previous studies with pyrethroid nets [17] at lower cost than tunnel tests. However, this method is yet to become a WHO/PQ-accepted testing modality despite the observed higher precision vs. current WHO-recommended modalities. The detailed descriptions of cost implications for each bioassay and how to build an I-ACT with the cost of \n",
-                        "establishment are described in Additional file 1: Table S4 and Additional file 2 respectively.\n",
-                        "\n",
-                        "Laboratory and/or semi-field bioassays are not replacements for field evaluation, but they are useful if they can predict the results of field tests because they are substantially cheaper and more standardised. Experimental hut tests remain the gold standard test because they represent field conditions and can be related to public health impact [48]. However, variation in mosquito entrance in huts per night is highly heterogeneous and requires a high level of replication to achieve precision [23]. In addition, variation in hut designs affects outcome measurement [34] and should be considered when interpreting results. However, comparing between products, the same trends were consistently seen: Interceptor(r) G2 was superior to Interceptor(r) against resistant mosquitoes when they were tested in a \"free-flying\" scenario and Interceptor(r) was superior to Interceptor(r) G2 against susceptible strains, while the cone test was suitable for evaluating pyrethroids but not pro-insecticides such as chlorfenapyr.\n",
-                        "\n",
-                        "header: Cone test results: Use of I-ACT for durability monitoring\n",
-                        "\n",
-                        "I-ACT is designed to be a bridging bioassay that reproduces a more natural interaction between the mosquito and human hosts beneath a bednet [16]. Blood-feeding inhibition and 72-h mortality measured by WHO tunnel and I-ACT were similar, meaning that the use of I-ACT for durability monitoring using WHO thresholds of 80% mortality and 90% feeding inhibition is appropriate. We suggest that I-ACT can be a useful alternative to tunnel tests for bioefficacy monitoring and upstream product evaluation as both the bioassays measure a similar odds ratio and I-ACT gives a precise estimation of bioefficacy because it uses laboratory-reared mosquitoes. This is particularly important when considering the longitudinal evaluation of product performance as occurs in durability bioefficacy evaluation for two reasons. Currently, experimental huts tests are being used to evaluate the insecticidal durability of Interceptor(r) G2 [33]. It was been observed at all experimental hut testing sites that mosquito resistance to insecticides has intensified through time [34]. This needs to be factored into considerations about durability testing. ITN protection may wane more quickly against more resistant mosquito populations as nets age [35], and if older ITNs are tested 3 years after baseline efficacy has been calculated, it is possible that they will be tested against a more resistant mosquito population than that at baseline. Therefore, use of carefully maintained laboratory strains may be helpful to minimise differences in insecticide resistance levels. Second, a previous longitudinal durability trial of ITNs [17] using standard WHO cone and tunnel tests as well as I-ACT shows that ITNs are highly heterogeneous, with up to 100% variance after use (John Bradley personal communication), because of variable use practices (washing, drying, care, sleeping space) [36, 37] that result in differential levels of damage and insecticide content [38]. This fact, coupled with WHO/PQ accepted intra-net insecticidal manufacturing heterogeneities [39], shows the need to expedite testing modalities that improve comprehension of net performances by donor organisations, national malaria control programmes (NMCPs) and manufacturers alike.\n",
-                        "\n",
-                        "header: Table 4: Number and proportion of net pieces passing using each laboratory bioassay based on WHO criteria of 80% control corrected mortality, 95% knockdown in WHO cone test, 90% blood-feeding inhibition or 80% control corrected mortality in tunnel test\n",
-                        "footer: The WHO criteria for tunnel were adopted for I-ACT. Whole nets were used for I-ACT\n",
-                        "columns: ['ITNs type - Unnamed: 0_level_1', 'Number of net pieces passing - Cone (N = 60)', 'Number of net pieces passing - Tunnel (N = 15)', 'Number of net pieces passing - I-ACT (N = 40)']\n",
-                        "\n",
-                        "{\"0\":{\"ITNs type - Unnamed: 0_level_1\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Cone (N = 60)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"Anopheles arabiensis (Kingani strain, resistant)\"},\"1\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":\"n (%)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"n (%)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"n (%)\"},\"2\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"11 (73)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"3\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"22 (37)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"13 (87)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"4\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"5\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"12 (80)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"36 (90)\"},\"6\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"21 (35)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"10 (67)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"7\":{\"ITNs type - Unnamed: 0_level_1\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Cone (N = 60)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"An. gambiae ss. (Kisumu strain, susceptible)\"},\"8\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"9\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"50 (83)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"10\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"2 (3)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"14 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"11\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"12\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"38 (63)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"13\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"10 (17)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"}}\n",
-                        "\n",
-                        "header: Publisher's Note\n",
-                        "\n",
-                        "Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n",
-                        "\n",
-                        "header: Table 2 Logistic regression analysis to compare mosquito mortality in Interceptor® and Interceptor® G2 ITNs after exposure in WHO cone and tunnel tests, Ifakara ambient chamber test and experimental hut, Tanzania\n",
-                        "footer: Odds ratio adjusted for net type, volunteer, hut position/number and day of the experiment\n",
-                        "Laboratory-reared mosquitoes were used for WHO cone, WHO tunnel and I-ACT\n",
-                        "For the experimental hut trial, the ITNs were tested against free-flying wild pyrethroid-resistant Anopheles arabiensis and Culex quinquefasciatus in Lupiro, Ifakara\n",
-                        "*p-value>0.05\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1', 'WHO cone - OR (95% CI)', 'Tunnel - OR (95% CI)', 'I-ACT - OR (95% CI)', 'Hut - OR (95% CI)', 'Huts with netting window - OR (95% CI)']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Anopheles anabiensis (resistant)\",\"WHO cone - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Tunnel - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"I-ACT - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Hut - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Anopheles anabiensis (resistant)\"},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.03, 0.07)\",\"Tunnel - OR (95% CI)\":\"1.33 (1.17, 1.51)\",\"I-ACT - OR (95% CI)\":\"1.55 (1.16, 2.07)\",\"Hut - OR (95% CI)\":\"2.38 (2.09, 2.72)\",\"Huts with netting window - OR (95% CI)\":\"2.43 (1.88, 3.14)\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.05 (0.20, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.42 (1.19, 1.70)\",\"I-ACT - OR (95% CI)\":\"1.61 (1.05, 2.49)\",\"Hut - OR (95% CI)\":\"2.53 (1.96, 3.26)\",\"Huts with netting window - OR (95% CI)\":\"2.55 (1.76, 3.68)\"},\"7\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"8\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"9\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.2, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.23 (1.03, 1.48)\",\"I-ACT - OR (95% CI)\":\"1.50 (1.02, 2.23)\",\"Hut - OR (95% CI)\":\"2.36 (2.02, 2.77)\",\"Huts with netting window - OR (95% CI)\":\"2.32 (1.63, 3.31)\"},\"10\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Culex quinquefasciatus (resistant)\",\"WHO cone - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Tunnel - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"I-ACT - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Hut - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\"},\"11\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"12\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"13\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.05 (0.57, 1.93)*\",\"Tunnel - OR (95% CI)\":\"2.55 (2.26, 2.55)\",\"I-ACT - OR (95% CI)\":\"13.40 (10.75, 16.67)\",\"Hut - OR (95% CI)\":\"1.50 (1.31, 1.73)\",\"Huts with netting window - OR (95% CI)\":\"1.40 (1.19, 1.63)\"},\"14\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"15\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"16\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.74 (0.31, 1.79)*\",\"Tunnel - OR (95% CI)\":\"2.52 (2.13, 3.00)\",\"I-ACT - OR (95% CI)\":\"12.71 (9.43, 17.14)\",\"Hut - OR (95% CI)\":\"1.52 (1.23, 1.88)\",\"Huts with netting window - OR (95% CI)\":\"1.25 (1.00, 1.57)\"},\"17\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"18\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"19\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.46 (0.62, 3.50)*\",\"Tunnel - OR (95% CI)\":\"2.58 (2.17, 3.06)\",\"I-ACT - OR (95% CI)\":\"14.11 (10.42, 19.11)\",\"Hut - OR (95% CI)\":\"1.50 (1.25, 1.80)\",\"Huts with netting window - OR (95% CI)\":\"1.55 (1.24, 1.94)\"},\"20\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"An gambiae s.s. (susceptible)\",\"WHO cone - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Tunnel - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"I-ACT - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Hut - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"An gambiae s.s.\n",
-                        "\n",
-                        "header: Mosquito test systems\n",
-                        "\n",
-                        "IHI laboratory maintains local mosquito strains with resistant mechanisms present in the local population to\n",
-                        "avoid accidental release of new resistance alleles into the wild population. Four types of laboratory-reared mosquitoes with different resistance levels (confirmed at the time of testing) were used in the WHO cone, WHO tunnel and I-ACT tests: _Anopheles arabiensis_ (Kingani strain, upregulation of cytochrome p450s, 14% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is reversed by piperonyl butoxide (PBO) pre-exposure, which acts to block enzymatic detoxification mechanisms by inhibiting their metabolism), _Anopheles gambiae_ s.s. (Kisumu strain, fully susceptible to all insecticide classes at WHO discriminating doses), _Aedes aegypti_ (Bagamoyo strain, fully susceptible to all insecticide classes at WHO discriminating doses) and _Culex quinquefasciatus_ (Bagamoyo strain, 6% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is partially reversed (only moderate susceptibility is restored from inhibition of detoxifying mechanism) by PBO pre-exposure). In I-ACT and tunnel tests, sugar-starved (8 to 9 h) nulliparous female mosquitoes, 5-8 days old, were used. For cone bioassay, 2-5-day-old nulliparous sugar-fed female mosquitoes were challenged. Laboratory colonies were maintained by feeding larvae Tetramin(r) tropical fish food and adults on blood between 3 and 6 days after emergence and 10% sugar solution ad libitum. Temperature and humidity within the insectary are maintained between 27 degC+-5 degC and 40%-100% RH, relatively following MR4 guidelines [18]. For the experimental hut assays, only wild populations of _An. arabiensis_ and _Culex quinquefasciatus_ were collected in sufficient numbers for evaluation.\n",
-                        "\n",
-                        "header: WHO cone bioassays\n",
-                        "\n",
-                        "WHO cone tests were performed between October 2020 and February 2021 according to standard WHO procedures [9], with two modifications: the test board was set at 60deg tilt [19] and holes were cut in the boards (Fig. 1) to maximise mosquito contact with the test nets during exposures. From each treatment arm, three nets were randomly sampled, and five net swatches of 25 cm x 25 cm size were cut from positions 1 to 5. On each netting sample, four standard WHO cones were positioned over net swatches and secured in place using tape. Five laboratory-bred mosquitoes were introduced into each cone and exposed for 3 min, and four replicates were conducted per net piece (20 mosquitoes were exposed per net piece).\n",
-                        "\n",
-                        "After each exposure, the mosquitoes were removed gently from the cones (by mouth aspiration), placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Knockdown (KD60) was recorded after 60 min and mortality at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Acceptable control mortality was <= 10% after 72 h holding time. Any tests exceeding the specified control cut off were repeated.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Interceptor(r) G2 clearly demonstrated superior bio-efficacy against resistant mosquitoes compared to Interceptor(r) when mosquitoes were challenged in free-flying bioassays. The I-ACT measured similar odds ratios as the WHO tunnel, currently used for testing of ITNs with chlorfenapyr. Both free-flying laboratory bioassays (WHO tunnel and I-ACT) predicted the results of the experimental hut test. Experimental hut design has an influence on mosquito mortality; however, using the odds ratio, all free-flying tests gave consistent findings. In this setting, I-ACT was a reliable bioassay for bioefficacy testing of Interceptor(r) G2 and may be a useful additional bioassay for durability monitoring of ITNs treated with pro-insecticides.\n",
-                        "\n",
-                        "header: Resistant mosquitoes\n",
-                        "\n",
-                        "For pyrethroid-resistant _An. arabiensis_, blood-feeding inhibition was lower with Interceptor(r) G2 than Interceptor(r) in WHO tunnel: 87.8% (95% CI: 82.9- 92.7) vs. 92.9% (95% CI: 89.8-96.0) OR blood-fed = 1.92 [95% CI:14.8-2.48], \\(p\\) < 0.001 and I-ACT: 89.8% (95% CI: 85.8-93.7) vs. 93.9% (95% CI: 91.5-96.3) (OR blood-fed = 1.67 [95% CI: 1.07-2.62], \\(p\\) = 0.024). There was no difference between the two products measured in experimental huts: 95.9% (95% CI: 93.7-98.2) vs. 96.3% (95% CI: 93.0-99.7) (OR blood-fed = 1.06 [95% CI: 0.54-2.10], \\(p\\) = 0.857). A similar trend was observed for the 20x washed nets.\n",
-                        "\n",
-                        "Blood-feeding inhibition was similar for Interceptor(r) G2 compared to Interceptor(r) with _Cx. Quinquefasciatus_ in WHO tunnel: 97.8% (95CI: 96.1-99.5) vs. 97.1% (95% CI: 95.9-98.3) (OR blood fed = 1.36 [95% CI: 0.85-2.17], \\(p\\) = 0.196) and experimental huts: 97.2% (95% CI: 95.9-98.4) vs. 97.2% (95% CI: 95.8-98.7) (OR = 1.04 [95% CI: 0.76-1.42], \\(p\\) = 0.827), whilst in I-ACT the difference was significantly different: 99.0% (95% CI: 98.1-99.9) vs. 91.3% (95% CI: 88.6-93.9) (OR blood fed = 0.11 [95% CI: 0.05-0.26], \\(p\\) < 0.001). A similar trend was observed for the 20x washed nets (Fig. 3a, b, Table 3). The percentage blood-feeding inhibition and mean difference between bioassays for all mosquito species are presented in Additional file 1: Table S2.\n",
-                        "\n",
-                        "header: Study area: Study design\n",
-                        "\n",
-                        "The study was a five-arm comparative efficacy study to determine the performances of Interceptor(r) G2 ITNs and Interceptor(r) against pyrethroid-susceptible and -resistant mosquitoes measured by WHO cone bioassay, WHO tunnel test, Ifakara ambient chamber test (I-ACT) and experimental huts. Study arms were: (i) Interceptor(r) G2, unwashed; (ii) Interceptor(r) G2, washed 20 times; (iii) Interceptor(r), unwashed; (iv) Interceptor(r), washed 20 times; (v) SafNet(r) (negative control). The primary performance metric upon which the study was powered is 72-h mortality (M72), which is measured in all four bioassays. A secondary outcome was blood-feeding, which was measured in the three free-flying bioassays. Additionally, knockdown at 60 min (KD60) was measured in cone tests (Table 1).\n",
-                        "\n",
-                        "header: Mosquito retention\n",
-                        "\n",
-                        "Immediately after the main experimental hut trial, a retention test was conducted by releasing wild _An. ara-biensis_ mosquitoes that were collected from the area by human landing catch (HLC). Fifteen mosquitoes marked with non-toxic fluorescent powder were released in each of the ten huts with the same treatment arms as the main experiment hut design. Five huts were completely closed while the other five were left with open eaves (with 10-cm baffles to reduce egress) and two window exit traps as per main experimental hut study. This experiment was conducted for 5 nights. Each morning mosquitoes were sorted, scored and held for 72 h to observe delayed mortality for each treatment arm.\n",
-                        "\n",
-                        "header: Susceptible mosquitoes\n",
-                        "\n",
-                        "For unwashed Interceptor(r), blood-feeding inhibition was measured in tunnel and I-ACT and a similar trend was observed in susceptible mosquitoes as was seen in the resistant laboratory strains (Fig. 3c, d, Table 3). For susceptible _An. gambiae_, blood-feeding inhibition was lower with Interceptor(r) G2 than for Interceptor(r) WHO tunnel: 89.7% (95% CI: 83.3-96.1) vs. 94.5% (95% CI: 91.9-97.2), (OR blood fed = 1.95 [95% CI: 1.46, 2.61], \\(p\\) < 0.001) and similar in I-ACT: 97.4% (95% CI: 95.7-99.1) vs. 98.2% (95% CI: 96.8-99.5), (OR blood fed = 1.41 [95% CI: 0.63-3.17], \\(p\\) = 0.407. For susceptible _Ae. Aggyti_ in WHO tunnel, results showed: 93.8% (95% CI:90.6-97.1) vs. 96.3% (95% CI: 94.0-98.7) (OR blood fed = 2.03 [95% CI: 1.40-2.93], \\(p\\) < 0.001) and in I-ACT: 96.8% (95% CI: 94.8-98.9) vs. 98.3% (95% CI: 97.1-99.5) (OR blood fed = 1.92 [95% CI: 0.87-4.24],\n",
-                        "\\(p=0.107\\)). As before, for both species a similar pattern was seen for the 20\\(\\times\\) washed nets.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The laboratory bioassays (I-ACT, WHO cone and WHO tunnel tests) were performed at the Vector Control Product Testing Unit (VCPTU) testing facility located at the Bagamoyo branch of Ifakara Health Institute (IHI), Tanzania (6.446deg S and 38.901deg E). The district experiences average annual rainfall of 800 mm-1000 mm, average temperatures between 24 degC and 29 degC and average annual humidity of 73%. The experimental hut study was conducted in Lupiro village (8.385deg S and 36.673deg E) in Ulanga District, southeastern Tanzania. The village is bordered by irrigated rice fields with average annual rainfall of 1200-1800 mm, average temperatures between 20 and 34 degC and average annual humidity of 69%. The main malaria vector is _Anopheles arabiensis_, constituting > 99.9% of the _An. gambiae_ complex species in the last test conducted in November 2020, and resistance to alphabcypermethrin was recorded (57% mortality at 1 x WHO discriminating concentration) at the time of testing.\n",
-                        "\n",
-                        "header: Table 3 Logistic regression analysis to compare mosquito blood-feeding success in Interceptor and Interceptor G2 ITNs after exposure in WHO tunnel test, Ifakara ambient chamber test and experimental hut, Tanzania\n",
-                        "footer: Odds ratio adjusted for net type, volunteer, hut position/number and day of the experiment\n",
-                        "Laboratory-reared mosquitoes were used for tunnel and I-ACT\n",
-                        "For the experimental hut trial, the ITNs were tested against free-\u001e\n",
-                        "flying wild pyrethroid-resistant Anopheles arabiensis and Culex quinquefasciatus in Lupiro, Ifakara\n",
-                        "*p-value<0.05\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1', 'Tunnel - OR (95% CI)', 'I-ACT - OR (95% CI)', 'Hutt - OR (95% CI)', 'Huts with netting window - OR (95% CI)']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Anopheles anabiensis (resistant)\",\"Tunnel - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"I-ACT - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Hutt - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Anopheles anabiensis (resistant)\"},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.75 (1.46, 2.09)*\",\"I-ACT - OR (95% CI)\":\"1.53 (1.10, 2.13)*\",\"Hutt - OR (95% CI)\":\"1.04 (0.74, 1.47)\",\"Huts with netting window - OR (95% CI)\":\"1.42 (0.80, 2.53)\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":\"1\"},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":\"1\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.92 (1.48, 2.48)*\",\"I-ACT - OR (95% CI)\":\"1.67 (1.07, 2.62)*\",\"Hutt - OR (95% CI)\":\"1.06 (0.54, 2.10)\",\"Huts with netting window - OR (95% CI)\":\"1.81 (0.79, 4.14)\"},\"7\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20x\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"8\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"9\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.60 (1.24, 2.05)*\",\"I-ACT - OR (95% CI)\":\"1.37 (0.83, 2.25)\",\"Hutt - OR (95% CI)\":\"1.09 (0.72, 1.63)\",\"Huts with netting window - OR (95% CI)\":\"1.15 (0.53, 2.50)\"},\"10\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Culex quinquefasciatus (resistant)\",\"Tunnel - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"I-ACT - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Hutt - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\"},\"11\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"12\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"13\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"0.73 (0.53, 1.00)\",\"I-ACT - OR (95% CI)\":\"0.13 (0.07, 0.23)*\",\"Hutt - OR (95% CI)\":\"1.34 (0.88, 2.04)\",\"Huts with netting window - OR (95% CI)\":\"0.63 (0.49, 0.81)*\"},\"14\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":\"1\"},\"15\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":\"1\"},\"16\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.36 (0.85, 2.17)\",\"I-ACT - OR (95% CI)\":\"0.11 (0.05, 0.26)*\",\"Hutt - OR (95% CI)\":\"1.04 (0.76, 1.42)\",\"Huts with netting window - OR (95% CI)\":\"0.67 (0.45, 1.02)\"},\"17\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20x\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"18\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"19\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"0.41 (0.26, 0.65)*\",\"I-ACT - OR (95% CI)\":\"0.14 (0.06, 0.33)*\",\"Hutt - OR (95% CI)\":\"1.11 (0.86, 1.42)\",\"Huts with netting window - OR (95% CI)\":\"1.59 (0.43, 0.82)*\"},\"20\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"An gambiae s.s. (susceptible)\",\"Tunnel - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"I-ACT - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Hutt - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"An gambiae s.s. (susceptible)\"},\"21\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"22\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"23\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.28 (1.03, 1.59)*\",\"I-ACT - OR (95% CI)\":\"1.55 (0.86, 2.80)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"},\"24\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"25\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.95 (1.46, 2.61)*\",\"I-ACT - OR (95% CI)\":\"1.41 (0.63, 3.17)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"},\"26\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20x\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"27\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"28\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.81 (1.35, 2.41)*\",\"I-ACT - OR (95% CI)\":\"1.75 (0.69, 4.41)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"},\"29\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Aedes aegypti (susceptible)\",\"Tunnel - OR (95% CI)\":\"Aedes aegypti (susceptible)\",\"I-ACT - OR (95% CI)\":\"Aedes aegypti (susceptible)\",\"Hutt - OR (95% CI)\":\"Aedes aegypti (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"Aedes aegypti (susceptible)\"},\"30\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"31\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"32\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.46 (1.14, 1.87)*\",\"I-ACT - OR (95% CI)\":\"1.63 (0.87, 3.04)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"},\"33\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"34\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"35\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"2.03 (1.40, 2.93)*\",\"I-ACT - OR (95% CI)\":\"1.92 (0.87, 4.24)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"},\"36\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20x\",\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"37\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hutt - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"38\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"Tunnel - OR (95% CI)\":\"1.10 (0.79, 1.54)\",\"I-ACT - OR (95% CI)\":\"1.20 (0.43, 3.36)\",\"Hutt - OR (95% CI)\":\"\\u2013\",\"Huts with netting window - OR (95% CI)\":\"\\u2013\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: WHO tunnel test\n",
-                        "\n",
-                        "The tunnel tests were carried out from August to November 2020 according to standard WHO procedures [9]. Five 25 cm x 25 cm net pieces per ITN/control were cut adjacent to the swatches cut for cone assays from three nets per treatment arm to make a total of 15 pieces per study arm and to account for possible intra-net variability of insecticide loadings. Five tunnels were run with one sample from each treatment arm with one mosquito strain on a single night. Over 60 nights, all 15 pieces of ITNs per study arm were tested with four mosquito strains.\n",
-                        "\n",
-                        "Non-blood-fed nulliparous females, 5-8 days old, sugar starved for 6-8 h were released in a 60-cm-long glass tunnel. At each end of the tunnel, a 25-cm square mosquito cage covered with polyester netting was fitted. At one third of the length, the netting sample was affixed. The surface of netting \"available\" to mosquitoes is 400 cm2 (20 cm x 20 cm), with 9 x 1-cm-diameter holes: one hole is located at the centre of the square; the other eight were equidistant and located 5 cm from the border. In the shorter section of the tunnel, a small rabbit, its back shaved and restrained in a mesh tunnel, was placed as bait (Fig. 1). In the cage at the end of the longer section of the tunnel, 100 female mosquitoes (one strain \n",
-                        "per replicate) were introduced at 21:00. The following morning at 09:00, the mosquitoes were removed using a mouth aspirator and counted separately from each section of the tunnel, and mortality and blood-feeding rates were recorded. The mosquitoes were placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Mortality was recoded at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Overall mortality was measured by pooling the mortalities of mosquitoes from the two sections of the tunnel. Acceptable feeding success and mortality in controls were 50% and 10%, respectively. Any tests not meeting the specified control cut-off were repeated.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding in WHO tunnel, I-ACT and experimental hut\n",
-                        "\n",
-                        "For all bioassays and strains, blood-feeding inhibition was very high (Fig. 3, Table 3). Both Interceptor(r) G2 and Interceptor(r) gave high levels of blood-feeding inhibition in all \"free-flying\" bioassays.\n",
-                        "\n",
-                        "header: Selection of the correct strain for bioassay\n",
-                        "\n",
-                        "The current study demonstrated that using the correct mosquito strain when evaluating ITNs is a critical consideration. The benefit of chlorfenapyr was only seen against the resistant strains, and against the susceptible strains Interceptor(r) was more efficacious because it contains a higher dose of alphacypermethrin. Comparing between the products using both a susceptible and a resistant strain revealed the different modes of action of Interceptor(r) and Interceptor(r) G2.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "As funding for malaria control falls and mosquito resistance to current public health insecticides increases, new and durable vector control tools that utilise new insecticide classes are needed [1]. For insecticide resistance management, a new Insecticide Treated Net (ITN) Interceptor(r) G2 has been developed, coated with a mixture of the pyrethroid alpha-cypermethrin and the pro-insecticide chlorfenapyr [2]. Chlorfenapyr is an Insecticide Resistance Action Committee (IRAC) Group 13 insecticide, having a pyrrole chemistry that uncouples oxidative phosphorylation via disruption of the proton gradient (as a protonophore) to short circuit mitochondrial respiration through inner mitochondrial membranes of insect cells so that ATP cannot be synthesized, subsequently robbing insects of energy, resulting in death [3, 4]. Metabolic resistance is one of the main mechanisms of resistance observed in malaria vectors [5] where one or several detoxification gene families--cytochrome P450s (P450s), esterases and glutathione S-transferases (GSTs)--are overproduced to detoxify insecticides [6]. While this metabolism is a detoxification process, it can increase the potency of a pro-insecticide and may therefore be exploited as a means to control metabolically resistant insect populations [3]. The unique mode of action of chlorfenapyr on an insect's metabolism is particularly relevant for the control of vectors harboring insecticide resistance mechanisms, as increased metabolic activity increases the conversion of the pro-insecticide into its potent n-dealkylated form and will consequently increase mosquito mortality [7].\n",
-                        "\n",
-                        "For any new ITN to be used in public health, a thorough evaluation of its safety, efficacy and effectiveness is conducted based on World Health Organisation (WHO) standards and criteria [8]. The methods and scope of the laboratory tests and field trials required to attain WHO Prequalification (PQ) listing for ITNs are outlined in a set of WHO guidelines, last updated in 2013 [9]. As part of these guidelines, WHO recommends a set of standardised laboratory tests to ascertain the bioefficacy of pyrethroid ITNs, i.e., the ability of ITN products to kill, incapacitate (knock down) and prevent mosquitoes from blood-feeding. Laboratory bioefficacy tests are also a critical component of ITN durability evaluation used to confirm continued ITN bioefficacy after long-term use in the community [10]. The simplest and most commonly applied ITN bioefficacy test is the WHO cone bioassay where mosquitoes are held close to ITN in plastic cones and the number of mosquitoes knocked down (incapacitated) or killed is counted [9]. For ITNs with feeding inhibition mode of action, the WHO tunnel test is used, where a swatch of ITN with small holes (9 x 1-cm diameter) is made and is placed between mosquitoes and small animal bait overnight [11]. The WHO tunnel test has been shown to agree with experimental but data, using laboratory-reared mosquitoes released into the huts [12]. Thresholds for the mosquito knockdown rate (95%) and mosquito mortality rate (80%) and blood-feeding inhibition (90%) using pyrethroid-susceptible _Anopheles_ mosquitoes serve as performance benchmarks; candidate products are required to fulfill minimum performance standards, which have been established by WHO for qualification to be listed/recommended for their public health values to sustain and protect users from disease transmissions. This includes their physical durability and chemical contents recovered from nets replaced in the field over 3 years or as predicted from wash-resistance testing. For a net to be classified as a long-lasting ITN, i.e., LLIN, > 80% of nets tested should pass WHO cone/tunnel performance benchmarks after 3 years of use [13].\n",
-                        "\n",
-                        "Cone tests are relatively easy to perform, high throughput and sensitive to detecting changes in bioavailable pyrethroids that act through rapid contact neurotoxicity. However, chlorfenapyr requires the mosquito to be metabolically and/or physiologically active (as it would be when encountering the ITN under user conditions) to bioactivate into the potent n-dealkylated form which elicits increased mosquito mortality. As mosquitoes are more metabolically active at night when flying and host-seeking during their typical circadian rhythms, the tunnel test may be more appropriate [14].\n",
-                        "\n",
-                        "The President's Malaria Initiative (PMI) currently uses WHO Tunnel tests for the durability monitoring of chlorfenapyr nets with 4 samples per net evaluated and 48 nets per sampling point [15]. This requires large numbers of mosquitoes and access to small animals for testing, so it cannot be done at all facilities in malaria-endemic areas. To accommodate high-throughput evaluation of whole ITNs for durability evaluation, the Ifakara ambient chamber test (I-ACT) was developed [16]. The Ifakara ambient chamber test (I-ACT) is not currently a recognized method \n",
-                        "approved by the WHO. However, it is currently seeking confirmation by direct comparison to approved methods. This is of particular importance where novel slow-acting chemistries cannot achieve the benchmark standards established for conventional pyrethroid ITN exposures yet may prove to be highly efficacious when tested according to their discrete modes of action. Like the experimental hut, I-ACT makes use of whole nets and human hosts to evaluate bioefficacy of field-used ITNs, but the assay is done under controlled conditions with laboratory-reared mosquitoes. Mosquitoes are released into net chambers within which the test net is hung with a volunteer sleeping beneath, and all mosquitoes are recaptured in the morning. The use of laboratory mosquitoes (rather than conducting experimental hut trials with wild mosquitoes) is done to improve the precision of estimates by releasing mosquito cohorts of a defined number of mosquitoes with high recapture rate (99%) at the conclusion of exposure intervals.\n",
-                        "\n",
-                        "I-ACT bioassay has been used for evaluation of pyrethroid ITNs, was able to discriminate between products [17] and agreed with results of combined WHO cone and tunnel tests [16]. The I-ACT may show suitability for use in evaluation of pro-insecticides because: (i) the assay is run overnight, favouring malarial mosquitoes' circadian rhythms, (ii) the mosquitoes have a large arena to fly in, allowing them to be metabolically active, (iii) the mosquitoes have the opportunity to feed ad libitum and (iv) the I-ACT test eliminates infected mosquitoes that may represent a malaria transmission potential that cannot be completely excluded in WHO experimental huts--a significant safety benefit to volunteers. Therefore, the I-ACT method was evaluated alongside standard methods (i.e., experimental huts, WHO cone and tunnel tests) to provide direct evidence of its comparability. Interceptor(r) (alpha-cypermethrin only) and Interceptor(r) G2 (alpha-cypermethrin and chlorfenapyr) were evaluated to compare the performance of each assay for durability monitoring of pro-insecticidal ITNs.\n",
-                        "\n",
-                        "header: Considerations for each bioassay\n",
-                        "\n",
-                        "Both I-ACT and experimental huts use the whole bednet and human host, which represent user conditions; however, there is no risk of disease for human participants in I-ACT because the mosquitoes used are laboratory reared. The WHO tunnel test is a well-established bioassay that has been shown to agree with experimental hut tests in this evaluation as well as others [12, 14]. However, it tests only a sample of net and is therefore only able to accurately measure the chemical durability of an ITN and not the chemical _and_ physical durability. In addition, it requires a high number of mosquitoes (100 per replicate) and more testing days compared to I-ACT. In I-ACT there is possibility of testing more than one species or strain per chamber compared to tunnel test, which makes results more comparable. The I-ACT is a new assay that consistently predicts the results of experimental hut tests, measures with a similar magnitude of difference as a tunnel test and provides high-throughput and precise estimates of whole ITN protective efficacy in this study with chlorfenapyr as well as in previous studies with pyrethroid nets [17] at lower cost than tunnel tests. However, this method is yet to become a WHO/PQ-accepted testing modality despite the observed higher precision vs. current WHO-recommended modalities. The detailed descriptions of cost implications for each bioassay and how to build an I-ACT with the cost of \n",
-                        "establishment are described in Additional file 1: Table S4 and Additional file 2 respectively.\n",
-                        "\n",
-                        "Laboratory and/or semi-field bioassays are not replacements for field evaluation, but they are useful if they can predict the results of field tests because they are substantially cheaper and more standardised. Experimental hut tests remain the gold standard test because they represent field conditions and can be related to public health impact [48]. However, variation in mosquito entrance in huts per night is highly heterogeneous and requires a high level of replication to achieve precision [23]. In addition, variation in hut designs affects outcome measurement [34] and should be considered when interpreting results. However, comparing between products, the same trends were consistently seen: Interceptor(r) G2 was superior to Interceptor(r) against resistant mosquitoes when they were tested in a \"free-flying\" scenario and Interceptor(r) was superior to Interceptor(r) G2 against susceptible strains, while the cone test was suitable for evaluating pyrethroids but not pro-insecticides such as chlorfenapyr.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The superiority of Interceptor(r) G2 over Interceptor(r) for 72-h mortality endpoints, which challenged metabolically resistant _An. arabiensis_ and _Cx. quinquefasciatus_, was clearly seen in all \"free-flying\" bioassays but not in the WHO cone test where mosquitoes are unable to fly around and be metabolically active. This observation has been seen by several other authors [14, 25-27] leading to a consensus that the overnight tunnel test using a 72-h mortality endpoint is a superior laboratory bioassay for evaluation of chlorfenapyr [2, 28] relative to the cone test, which was designed to test contact insecticides that do not require metabolic conversion into a secondary metabolite (most ITN insecticides are classified as IRAC Group 3 sodium channel modulators). It has been routinely observed in experimental hut bioassays that Interceptor(r) G2 gives greater mortality that Interceptor(r) among pyrethroid-resistant mosquitoes [14, 25-27], while mosquitoes are foraging at night when their metabolic rate is high. Both the tunnel test and the I-ACT consistently predicted the superiority of Interceptor(r) G2 over Interceptor(r) observed in experimental hut tests.\n",
-                        "\n",
-                        "The results of this study's mortality elicited on _Cx. quinqufaciatus_ from chlorfenapyr is consistent with other studies over the past decade [29, 30]. Preliminary investigations for chlorfenapyr intoxication in metabolically resistant _Cx. quinquefasciatus_ in experimental huts by [31] demonstrated relatively high levels of control in both ITNs and for IRS in Benin. Later, [27] observed 3 x mortality with chlorfenapyr + alpha-cypermethrin-treated nets compared to pyrethroid-only nets demonstrating the effect of chlorfenapyr on free-flying exposures that optimize conversion rates of the pro-insecticide. However, it is also known that once ITNs develop holes, a pyrethroid treatment offers little or no protection against pyrethroid-resistant _Cu. quinquefaciatus_[32]. The relatively high level of control sustained in _Cx. quinqufaciatus_ in this study and relatively good mortality are important resistance management attributes that make inclusion of chlorfenapyr into vector control interventions a significant advancement for active ingredient options.\n",
-                        "\n",
-                        "header: Cone test results: Use of I-ACT for durability monitoring\n",
-                        "\n",
-                        "I-ACT is designed to be a bridging bioassay that reproduces a more natural interaction between the mosquito and human hosts beneath a bednet [16]. Blood-feeding inhibition and 72-h mortality measured by WHO tunnel and I-ACT were similar, meaning that the use of I-ACT for durability monitoring using WHO thresholds of 80% mortality and 90% feeding inhibition is appropriate. We suggest that I-ACT can be a useful alternative to tunnel tests for bioefficacy monitoring and upstream product evaluation as both the bioassays measure a similar odds ratio and I-ACT gives a precise estimation of bioefficacy because it uses laboratory-reared mosquitoes. This is particularly important when considering the longitudinal evaluation of product performance as occurs in durability bioefficacy evaluation for two reasons. Currently, experimental huts tests are being used to evaluate the insecticidal durability of Interceptor(r) G2 [33]. It was been observed at all experimental hut testing sites that mosquito resistance to insecticides has intensified through time [34]. This needs to be factored into considerations about durability testing. ITN protection may wane more quickly against more resistant mosquito populations as nets age [35], and if older ITNs are tested 3 years after baseline efficacy has been calculated, it is possible that they will be tested against a more resistant mosquito population than that at baseline. Therefore, use of carefully maintained laboratory strains may be helpful to minimise differences in insecticide resistance levels. Second, a previous longitudinal durability trial of ITNs [17] using standard WHO cone and tunnel tests as well as I-ACT shows that ITNs are highly heterogeneous, with up to 100% variance after use (John Bradley personal communication), because of variable use practices (washing, drying, care, sleeping space) [36, 37] that result in differential levels of damage and insecticide content [38]. This fact, coupled with WHO/PQ accepted intra-net insecticidal manufacturing heterogeneities [39], shows the need to expedite testing modalities that improve comprehension of net performances by donor organisations, national malaria control programmes (NMCPs) and manufacturers alike.\n",
-                        "\n",
-                        "header: WHO cone bioassays\n",
-                        "\n",
-                        "WHO cone tests were performed between October 2020 and February 2021 according to standard WHO procedures [9], with two modifications: the test board was set at 60deg tilt [19] and holes were cut in the boards (Fig. 1) to maximise mosquito contact with the test nets during exposures. From each treatment arm, three nets were randomly sampled, and five net swatches of 25 cm x 25 cm size were cut from positions 1 to 5. On each netting sample, four standard WHO cones were positioned over net swatches and secured in place using tape. Five laboratory-bred mosquitoes were introduced into each cone and exposed for 3 min, and four replicates were conducted per net piece (20 mosquitoes were exposed per net piece).\n",
-                        "\n",
-                        "After each exposure, the mosquitoes were removed gently from the cones (by mouth aspiration), placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Knockdown (KD60) was recorded after 60 min and mortality at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Acceptable control mortality was <= 10% after 72 h holding time. Any tests exceeding the specified control cut off were repeated.\n",
-                        "\n",
-                        "header: Sample size and power\n",
-                        "\n",
-                        "A sample size calculation for generalized linear mixed effects models (GLMMs) through simulation [23] in R statistical software 3.02 [https://www.r-project.org/](https://www.r-project.org/) was performed for the I-ACT and experimental huts. For the I-ACT, to detect a 10% effect difference between the nets, simulations were performed using an estimated mosquito mortality of 80% for unwashed Interceptor(r) G2, 70% for unwashed Interceptor(r) and 10% for SafiNet(r) (deliberately holed). The design was for five arms replicated in two groups, with each volunteer testing each treatment one time over 20 replicates within its group (i.e. 40 replicates per arm), with an inter-observational variance of 0.42 for the night of observation based on the variance of the random effects observed in a previous study. The evaluation was powered at 81% for 15 mosquitoes of each strain released per chamber using 1000 simulations.\n",
-                        "\n",
-                        "For experimental huts, simulation was performed using a Latin square design with volunteers rotating nightly and accounted for as a fixed effect for 25 nights of data collection in 10 huts. The study had 84% power to detect the difference between Interceptor(r) G2 LN and Interceptor(r) LN on mosquito mortality endpoints, with two huts per treatment arm (i.e. 50 replicates per arm). The study power was calculated based on a previous study of pyrethroid nets conducted in the same area, with the estimation of 20 _An. arabiensis_ mosquitoes per night per hut, 21% mortality in unwashed Interceptor(r) G2, 7% in 20x washed Interceptor(r) G2 and 10% in unwashed Interceptor(r) vs. 4% in 20 times washed and 1% in negative control, and overdispersion parameter for daily variation was set at intermediate 0.44.\n",
-                        "\n",
-                        "To test whether I-ACT measures similarly to the WHO tunnels (H0\\(m\\)2=_m_1) a power calculation using Satterthwaites _t_-test was conducted in STATA 16 software (StataCorp LLC, College Station TX, USA) for two unpaired sample means assuming unequal variance. The power estimated was > 90% based on estimates from previous studies conducted in the same setting: mean mortality of 81.5% for WHO tunnel test with an assumed daily variation of 0.5 and 15 replicates per arm and mean\n",
-                        "mortality estimates of 86.5% and an assumed daily variation of 0.42 with 40 replicates per arm were considered for I-ACT.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The laboratory bioassays (I-ACT, WHO cone and WHO tunnel tests) were performed at the Vector Control Product Testing Unit (VCPTU) testing facility located at the Bagamoyo branch of Ifakara Health Institute (IHI), Tanzania (6.446deg S and 38.901deg E). The district experiences average annual rainfall of 800 mm-1000 mm, average temperatures between 24 degC and 29 degC and average annual humidity of 73%. The experimental hut study was conducted in Lupiro village (8.385deg S and 36.673deg E) in Ulanga District, southeastern Tanzania. The village is bordered by irrigated rice fields with average annual rainfall of 1200-1800 mm, average temperatures between 20 and 34 degC and average annual humidity of 69%. The main malaria vector is _Anopheles arabiensis_, constituting > 99.9% of the _An. gambiae_ complex species in the last test conducted in November 2020, and resistance to alphabcypermethrin was recorded (57% mortality at 1 x WHO discriminating concentration) at the time of testing.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Interceptor(r) G2 clearly demonstrated superior bio-efficacy against resistant mosquitoes compared to Interceptor(r) when mosquitoes were challenged in free-flying bioassays. The I-ACT measured similar odds ratios as the WHO tunnel, currently used for testing of ITNs with chlorfenapyr. Both free-flying laboratory bioassays (WHO tunnel and I-ACT) predicted the results of the experimental hut test. Experimental hut design has an influence on mosquito mortality; however, using the odds ratio, all free-flying tests gave consistent findings. In this setting, I-ACT was a reliable bioassay for bioefficacy testing of Interceptor(r) G2 and may be a useful additional bioassay for durability monitoring of ITNs treated with pro-insecticides.\n",
-                        "\n",
-                        "header: Publisher's Note\n",
-                        "\n",
-                        "Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n",
-                        "\n",
-                        "header: Test nets\n",
-                        "\n",
-                        "ITNs were supplied by BASF in November 2019 and stored at optimal conditions (25 to 32 degC) before testing and during the experimental phase. Interceptor(r) G2 nets were from two different production batches (two batches were used for experimental hut only and one batch for other biossays) and Interceptor(r) from one production batch. Interceptor(r) G2 is made from 100-denier polyester coated with a mixture of wash-resistant formulation containing 200 mg/m2 chlorfenapyr and 100 mg/m2alpha-cypermethrin. Interceptor(r) LN is made from 100-denier polyester coated with 200 mg/m2 alpha-cypermethrin. SafiNet is an untreated polyester net manufactured by A to Z. Textiles Mills, Ltd., Tanzania, and was used as a control. The nets were washed 20 times according to a protocol adapted from the standard WHO washing procedure [9] using 20 g/l palm soap (Jamaa) and dried flat in a shaded area. The interval of time used between two washes (i.e. regeneration time) was 1 day for both Interceptor(r) G2 and Interceptor(r) TTNs.\n",
-                        "\n",
-                        "header: Mosquito test systems\n",
-                        "\n",
-                        "IHI laboratory maintains local mosquito strains with resistant mechanisms present in the local population to\n",
-                        "avoid accidental release of new resistance alleles into the wild population. Four types of laboratory-reared mosquitoes with different resistance levels (confirmed at the time of testing) were used in the WHO cone, WHO tunnel and I-ACT tests: _Anopheles arabiensis_ (Kingani strain, upregulation of cytochrome p450s, 14% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is reversed by piperonyl butoxide (PBO) pre-exposure, which acts to block enzymatic detoxification mechanisms by inhibiting their metabolism), _Anopheles gambiae_ s.s. (Kisumu strain, fully susceptible to all insecticide classes at WHO discriminating doses), _Aedes aegypti_ (Bagamoyo strain, fully susceptible to all insecticide classes at WHO discriminating doses) and _Culex quinquefasciatus_ (Bagamoyo strain, 6% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is partially reversed (only moderate susceptibility is restored from inhibition of detoxifying mechanism) by PBO pre-exposure). In I-ACT and tunnel tests, sugar-starved (8 to 9 h) nulliparous female mosquitoes, 5-8 days old, were used. For cone bioassay, 2-5-day-old nulliparous sugar-fed female mosquitoes were challenged. Laboratory colonies were maintained by feeding larvae Tetramin(r) tropical fish food and adults on blood between 3 and 6 days after emergence and 10% sugar solution ad libitum. Temperature and humidity within the insectary are maintained between 27 degC+-5 degC and 40%-100% RH, relatively following MR4 guidelines [18]. For the experimental hut assays, only wild populations of _An. arabiensis_ and _Culex quinquefasciatus_ were collected in sufficient numbers for evaluation.\n",
-                        "\n",
-                        "header: Table 4: Number and proportion of net pieces passing using each laboratory bioassay based on WHO criteria of 80% control corrected mortality, 95% knockdown in WHO cone test, 90% blood-feeding inhibition or 80% control corrected mortality in tunnel test\n",
-                        "footer: The WHO criteria for tunnel were adopted for I-ACT. Whole nets were used for I-ACT\n",
-                        "columns: ['ITNs type - Unnamed: 0_level_1', 'Number of net pieces passing - Cone (N = 60)', 'Number of net pieces passing - Tunnel (N = 15)', 'Number of net pieces passing - I-ACT (N = 40)']\n",
-                        "\n",
-                        "{\"0\":{\"ITNs type - Unnamed: 0_level_1\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Cone (N = 60)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"Anopheles arabiensis (Kingani strain, resistant)\"},\"1\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":\"n (%)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"n (%)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"n (%)\"},\"2\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"11 (73)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"3\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"22 (37)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"13 (87)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"4\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"5\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"12 (80)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"36 (90)\"},\"6\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"21 (35)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"10 (67)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"7\":{\"ITNs type - Unnamed: 0_level_1\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Cone (N = 60)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"An. gambiae ss. (Kisumu strain, susceptible)\"},\"8\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"9\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"50 (83)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"10\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"2 (3)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"14 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"11\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"12\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"38 (63)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"13\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"10 (17)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"}}\n",
-                        "\n",
-                        "header: Resistant mosquitoes\n",
-                        "\n",
-                        "For pyrethroid-resistant _An. arabiensis_, blood-feeding inhibition was lower with Interceptor(r) G2 than Interceptor(r) in WHO tunnel: 87.8% (95% CI: 82.9- 92.7) vs. 92.9% (95% CI: 89.8-96.0) OR blood-fed = 1.92 [95% CI:14.8-2.48], \\(p\\) < 0.001 and I-ACT: 89.8% (95% CI: 85.8-93.7) vs. 93.9% (95% CI: 91.5-96.3) (OR blood-fed = 1.67 [95% CI: 1.07-2.62], \\(p\\) = 0.024). There was no difference between the two products measured in experimental huts: 95.9% (95% CI: 93.7-98.2) vs. 96.3% (95% CI: 93.0-99.7) (OR blood-fed = 1.06 [95% CI: 0.54-2.10], \\(p\\) = 0.857). A similar trend was observed for the 20x washed nets.\n",
-                        "\n",
-                        "Blood-feeding inhibition was similar for Interceptor(r) G2 compared to Interceptor(r) with _Cx. Quinquefasciatus_ in WHO tunnel: 97.8% (95CI: 96.1-99.5) vs. 97.1% (95% CI: 95.9-98.3) (OR blood fed = 1.36 [95% CI: 0.85-2.17], \\(p\\) = 0.196) and experimental huts: 97.2% (95% CI: 95.9-98.4) vs. 97.2% (95% CI: 95.8-98.7) (OR = 1.04 [95% CI: 0.76-1.42], \\(p\\) = 0.827), whilst in I-ACT the difference was significantly different: 99.0% (95% CI: 98.1-99.9) vs. 91.3% (95% CI: 88.6-93.9) (OR blood fed = 0.11 [95% CI: 0.05-0.26], \\(p\\) < 0.001). A similar trend was observed for the 20x washed nets (Fig. 3a, b, Table 3). The percentage blood-feeding inhibition and mean difference between bioassays for all mosquito species are presented in Additional file 1: Table S2.\n",
-                        "\n",
-                        "header: Table 2 Logistic regression analysis to compare mosquito mortality in Interceptor® and Interceptor® G2 ITNs after exposure in WHO cone and tunnel tests, Ifakara ambient chamber test and experimental hut, Tanzania\n",
-                        "footer: Odds ratio adjusted for net type, volunteer, hut position/number and day of the experiment\n",
-                        "Laboratory-reared mosquitoes were used for WHO cone, WHO tunnel and I-ACT\n",
-                        "For the experimental hut trial, the ITNs were tested against free-flying wild pyrethroid-resistant Anopheles arabiensis and Culex quinquefasciatus in Lupiro, Ifakara\n",
-                        "*p-value>0.05\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1', 'WHO cone - OR (95% CI)', 'Tunnel - OR (95% CI)', 'I-ACT - OR (95% CI)', 'Hut - OR (95% CI)', 'Huts with netting window - OR (95% CI)']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Anopheles anabiensis (resistant)\",\"WHO cone - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Tunnel - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"I-ACT - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Hut - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Anopheles anabiensis (resistant)\"},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.03, 0.07)\",\"Tunnel - OR (95% CI)\":\"1.33 (1.17, 1.51)\",\"I-ACT - OR (95% CI)\":\"1.55 (1.16, 2.07)\",\"Hut - OR (95% CI)\":\"2.38 (2.09, 2.72)\",\"Huts with netting window - OR (95% CI)\":\"2.43 (1.88, 3.14)\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.05 (0.20, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.42 (1.19, 1.70)\",\"I-ACT - OR (95% CI)\":\"1.61 (1.05, 2.49)\",\"Hut - OR (95% CI)\":\"2.53 (1.96, 3.26)\",\"Huts with netting window - OR (95% CI)\":\"2.55 (1.76, 3.68)\"},\"7\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"8\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"9\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.2, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.23 (1.03, 1.48)\",\"I-ACT - OR (95% CI)\":\"1.50 (1.02, 2.23)\",\"Hut - OR (95% CI)\":\"2.36 (2.02, 2.77)\",\"Huts with netting window - OR (95% CI)\":\"2.32 (1.63, 3.31)\"},\"10\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Culex quinquefasciatus (resistant)\",\"WHO cone - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Tunnel - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"I-ACT - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Hut - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\"},\"11\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"12\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"13\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.05 (0.57, 1.93)*\",\"Tunnel - OR (95% CI)\":\"2.55 (2.26, 2.55)\",\"I-ACT - OR (95% CI)\":\"13.40 (10.75, 16.67)\",\"Hut - OR (95% CI)\":\"1.50 (1.31, 1.73)\",\"Huts with netting window - OR (95% CI)\":\"1.40 (1.19, 1.63)\"},\"14\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"15\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"16\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.74 (0.31, 1.79)*\",\"Tunnel - OR (95% CI)\":\"2.52 (2.13, 3.00)\",\"I-ACT - OR (95% CI)\":\"12.71 (9.43, 17.14)\",\"Hut - OR (95% CI)\":\"1.52 (1.23, 1.88)\",\"Huts with netting window - OR (95% CI)\":\"1.25 (1.00, 1.57)\"},\"17\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"18\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"19\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.46 (0.62, 3.50)*\",\"Tunnel - OR (95% CI)\":\"2.58 (2.17, 3.06)\",\"I-ACT - OR (95% CI)\":\"14.11 (10.42, 19.11)\",\"Hut - OR (95% CI)\":\"1.50 (1.25, 1.80)\",\"Huts with netting window - OR (95% CI)\":\"1.55 (1.24, 1.94)\"},\"20\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"An gambiae s.s. (susceptible)\",\"WHO cone - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Tunnel - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"I-ACT - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Hut - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"An gambiae s.s.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "Data were entered and validated in Excel using double entry system and exported into STATA 16 software (StataCorp LLC, College Station TX, USA) for further cleaning and analysis. Descriptive statistics were used for data summarization, where arithmetic mean percentage of mosquito control corrected mortality at 72 h and arithmetic mean percentage blood-feeding inhibition for each test and species was presented. Control corrected mortality was calculated by using Abbott's formula: (treatment mortality - control mortality/(1 - control mortality)*100% and blood-feeding inhibition was calculated by taking the total number of unfed mosquitoes divided by total mosquito recapture per hut night [24].\n",
-                        "\n",
-                        "Multivariable mixed-effect logistic regression with binomial error and log link was used to compare the performance of Interceptor(r) G2 to Interceptor(r) on the mortality and blood-feeding endpoints for each species in WHO cone (mortality only), tunnel, I-ACT and experimental huts. Fixed effects were treatment, volunteer, hut number/position (for experimental hut) and night of the experiment. The same regression was used for the comparison of hut design, whereby an interaction term between hut design and ITN type was also included in the model.\n",
-                        "\n",
-                        "header: Table 1: Outcomes measured in WHO cone bioassays, tunnel, I-ACT and experimental hut tests\n",
-                        "footer: a  Not reported\n",
-                        "columns: ['Variable', 'Cone bioassay', 'WHO tunnel test', 'I-ACT', 'Experimental hut']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Primary outcome\",\"Cone bioassay\":\"72-h mortality\",\"WHO tunnel test\":\"72-h mortality\",\"I-ACT\":\"72-h mortality\",\"Experimental hut\":\"72-h mortality\"},\"1\":{\"Variable\":\"Secondary outcome\",\"Cone bioassay\":null,\"WHO tunnel test\":\"Blood-feeding\",\"I-ACT\":\"Blood-feeding\",\"Experimental hut\":\"Blood-feeding\"},\"2\":{\"Variable\":\"Other outcomes\",\"Cone bioassay\":\"Knockdown at 60 min (KD60)\",\"WHO tunnel test\":null,\"I-ACT\":null,\"Experimental hut\":\"Deterrence a and induced exophily a\"}}\n",
-                        "\n",
-                        "header: Impact of hut design on mosquito mortality estimates\n",
-                        "\n",
-                        "To explore the effect of experimental hut design on the measured efficacy of the ITNs, two experiments were conducted between July and August 2021 using ten huts with the same five treatment arms as per the main experimental huts experiment. For the first experiment, conducted over 5 nights, windows were completely covered with white cloth to block light, ventilation and exit. For the remaining 25 nights, 5 huts had windows blocked with netting to allow light and ventilation but to block exit and five huts had exit traps as per main trial. Like the main experimental hut trial, the design was a fully balanced 5 x 5 Latin square with five huts per condition (window exit traps vs. netting windows). Each morning mosquitoes were sorted, scored and held for 72 h to observe delayed mortality for each treatment arm.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: WHO tunnel test\n",
-                        "\n",
-                        "The tunnel tests were carried out from August to November 2020 according to standard WHO procedures [9]. Five 25 cm x 25 cm net pieces per ITN/control were cut adjacent to the swatches cut for cone assays from three nets per treatment arm to make a total of 15 pieces per study arm and to account for possible intra-net variability of insecticide loadings. Five tunnels were run with one sample from each treatment arm with one mosquito strain on a single night. Over 60 nights, all 15 pieces of ITNs per study arm were tested with four mosquito strains.\n",
-                        "\n",
-                        "Non-blood-fed nulliparous females, 5-8 days old, sugar starved for 6-8 h were released in a 60-cm-long glass tunnel. At each end of the tunnel, a 25-cm square mosquito cage covered with polyester netting was fitted. At one third of the length, the netting sample was affixed. The surface of netting \"available\" to mosquitoes is 400 cm2 (20 cm x 20 cm), with 9 x 1-cm-diameter holes: one hole is located at the centre of the square; the other eight were equidistant and located 5 cm from the border. In the shorter section of the tunnel, a small rabbit, its back shaved and restrained in a mesh tunnel, was placed as bait (Fig. 1). In the cage at the end of the longer section of the tunnel, 100 female mosquitoes (one strain \n",
-                        "per replicate) were introduced at 21:00. The following morning at 09:00, the mosquitoes were removed using a mouth aspirator and counted separately from each section of the tunnel, and mortality and blood-feeding rates were recorded. The mosquitoes were placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Mortality was recoded at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Overall mortality was measured by pooling the mortalities of mosquitoes from the two sections of the tunnel. Acceptable feeding success and mortality in controls were 50% and 10%, respectively. Any tests not meeting the specified control cut-off were repeated.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding in WHO tunnel, I-ACT and experimental hut\n",
-                        "\n",
-                        "For all bioassays and strains, blood-feeding inhibition was very high (Fig. 3, Table 3). Both Interceptor(r) G2 and Interceptor(r) gave high levels of blood-feeding inhibition in all \"free-flying\" bioassays.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "As funding for malaria control falls and mosquito resistance to current public health insecticides increases, new and durable vector control tools that utilise new insecticide classes are needed [1]. For insecticide resistance management, a new Insecticide Treated Net (ITN) Interceptor(r) G2 has been developed, coated with a mixture of the pyrethroid alpha-cypermethrin and the pro-insecticide chlorfenapyr [2]. Chlorfenapyr is an Insecticide Resistance Action Committee (IRAC) Group 13 insecticide, having a pyrrole chemistry that uncouples oxidative phosphorylation via disruption of the proton gradient (as a protonophore) to short circuit mitochondrial respiration through inner mitochondrial membranes of insect cells so that ATP cannot be synthesized, subsequently robbing insects of energy, resulting in death [3, 4]. Metabolic resistance is one of the main mechanisms of resistance observed in malaria vectors [5] where one or several detoxification gene families--cytochrome P450s (P450s), esterases and glutathione S-transferases (GSTs)--are overproduced to detoxify insecticides [6]. While this metabolism is a detoxification process, it can increase the potency of a pro-insecticide and may therefore be exploited as a means to control metabolically resistant insect populations [3]. The unique mode of action of chlorfenapyr on an insect's metabolism is particularly relevant for the control of vectors harboring insecticide resistance mechanisms, as increased metabolic activity increases the conversion of the pro-insecticide into its potent n-dealkylated form and will consequently increase mosquito mortality [7].\n",
-                        "\n",
-                        "For any new ITN to be used in public health, a thorough evaluation of its safety, efficacy and effectiveness is conducted based on World Health Organisation (WHO) standards and criteria [8]. The methods and scope of the laboratory tests and field trials required to attain WHO Prequalification (PQ) listing for ITNs are outlined in a set of WHO guidelines, last updated in 2013 [9]. As part of these guidelines, WHO recommends a set of standardised laboratory tests to ascertain the bioefficacy of pyrethroid ITNs, i.e., the ability of ITN products to kill, incapacitate (knock down) and prevent mosquitoes from blood-feeding. Laboratory bioefficacy tests are also a critical component of ITN durability evaluation used to confirm continued ITN bioefficacy after long-term use in the community [10]. The simplest and most commonly applied ITN bioefficacy test is the WHO cone bioassay where mosquitoes are held close to ITN in plastic cones and the number of mosquitoes knocked down (incapacitated) or killed is counted [9]. For ITNs with feeding inhibition mode of action, the WHO tunnel test is used, where a swatch of ITN with small holes (9 x 1-cm diameter) is made and is placed between mosquitoes and small animal bait overnight [11]. The WHO tunnel test has been shown to agree with experimental but data, using laboratory-reared mosquitoes released into the huts [12]. Thresholds for the mosquito knockdown rate (95%) and mosquito mortality rate (80%) and blood-feeding inhibition (90%) using pyrethroid-susceptible _Anopheles_ mosquitoes serve as performance benchmarks; candidate products are required to fulfill minimum performance standards, which have been established by WHO for qualification to be listed/recommended for their public health values to sustain and protect users from disease transmissions. This includes their physical durability and chemical contents recovered from nets replaced in the field over 3 years or as predicted from wash-resistance testing. For a net to be classified as a long-lasting ITN, i.e., LLIN, > 80% of nets tested should pass WHO cone/tunnel performance benchmarks after 3 years of use [13].\n",
-                        "\n",
-                        "Cone tests are relatively easy to perform, high throughput and sensitive to detecting changes in bioavailable pyrethroids that act through rapid contact neurotoxicity. However, chlorfenapyr requires the mosquito to be metabolically and/or physiologically active (as it would be when encountering the ITN under user conditions) to bioactivate into the potent n-dealkylated form which elicits increased mosquito mortality. As mosquitoes are more metabolically active at night when flying and host-seeking during their typical circadian rhythms, the tunnel test may be more appropriate [14].\n",
-                        "\n",
-                        "The President's Malaria Initiative (PMI) currently uses WHO Tunnel tests for the durability monitoring of chlorfenapyr nets with 4 samples per net evaluated and 48 nets per sampling point [15]. This requires large numbers of mosquitoes and access to small animals for testing, so it cannot be done at all facilities in malaria-endemic areas. To accommodate high-throughput evaluation of whole ITNs for durability evaluation, the Ifakara ambient chamber test (I-ACT) was developed [16]. The Ifakara ambient chamber test (I-ACT) is not currently a recognized method \n",
-                        "approved by the WHO. However, it is currently seeking confirmation by direct comparison to approved methods. This is of particular importance where novel slow-acting chemistries cannot achieve the benchmark standards established for conventional pyrethroid ITN exposures yet may prove to be highly efficacious when tested according to their discrete modes of action. Like the experimental hut, I-ACT makes use of whole nets and human hosts to evaluate bioefficacy of field-used ITNs, but the assay is done under controlled conditions with laboratory-reared mosquitoes. Mosquitoes are released into net chambers within which the test net is hung with a volunteer sleeping beneath, and all mosquitoes are recaptured in the morning. The use of laboratory mosquitoes (rather than conducting experimental hut trials with wild mosquitoes) is done to improve the precision of estimates by releasing mosquito cohorts of a defined number of mosquitoes with high recapture rate (99%) at the conclusion of exposure intervals.\n",
-                        "\n",
-                        "I-ACT bioassay has been used for evaluation of pyrethroid ITNs, was able to discriminate between products [17] and agreed with results of combined WHO cone and tunnel tests [16]. The I-ACT may show suitability for use in evaluation of pro-insecticides because: (i) the assay is run overnight, favouring malarial mosquitoes' circadian rhythms, (ii) the mosquitoes have a large arena to fly in, allowing them to be metabolically active, (iii) the mosquitoes have the opportunity to feed ad libitum and (iv) the I-ACT test eliminates infected mosquitoes that may represent a malaria transmission potential that cannot be completely excluded in WHO experimental huts--a significant safety benefit to volunteers. Therefore, the I-ACT method was evaluated alongside standard methods (i.e., experimental huts, WHO cone and tunnel tests) to provide direct evidence of its comparability. Interceptor(r) (alpha-cypermethrin only) and Interceptor(r) G2 (alpha-cypermethrin and chlorfenapyr) were evaluated to compare the performance of each assay for durability monitoring of pro-insecticidal ITNs.\n",
-                        "\n",
-                        "header: Selection of the correct strain for bioassay\n",
-                        "\n",
-                        "The current study demonstrated that using the correct mosquito strain when evaluating ITNs is a critical consideration. The benefit of chlorfenapyr was only seen against the resistant strains, and against the susceptible strains Interceptor(r) was more efficacious because it contains a higher dose of alphacypermethrin. Comparing between the products using both a susceptible and a resistant strain revealed the different modes of action of Interceptor(r) and Interceptor(r) G2.\n",
-                        "\n",
-                        "header: Considerations for each bioassay\n",
-                        "\n",
-                        "Both I-ACT and experimental huts use the whole bednet and human host, which represent user conditions; however, there is no risk of disease for human participants in I-ACT because the mosquitoes used are laboratory reared. The WHO tunnel test is a well-established bioassay that has been shown to agree with experimental hut tests in this evaluation as well as others [12, 14]. However, it tests only a sample of net and is therefore only able to accurately measure the chemical durability of an ITN and not the chemical _and_ physical durability. In addition, it requires a high number of mosquitoes (100 per replicate) and more testing days compared to I-ACT. In I-ACT there is possibility of testing more than one species or strain per chamber compared to tunnel test, which makes results more comparable. The I-ACT is a new assay that consistently predicts the results of experimental hut tests, measures with a similar magnitude of difference as a tunnel test and provides high-throughput and precise estimates of whole ITN protective efficacy in this study with chlorfenapyr as well as in previous studies with pyrethroid nets [17] at lower cost than tunnel tests. However, this method is yet to become a WHO/PQ-accepted testing modality despite the observed higher precision vs. current WHO-recommended modalities. The detailed descriptions of cost implications for each bioassay and how to build an I-ACT with the cost of \n",
-                        "establishment are described in Additional file 1: Table S4 and Additional file 2 respectively.\n",
-                        "\n",
-                        "Laboratory and/or semi-field bioassays are not replacements for field evaluation, but they are useful if they can predict the results of field tests because they are substantially cheaper and more standardised. Experimental hut tests remain the gold standard test because they represent field conditions and can be related to public health impact [48]. However, variation in mosquito entrance in huts per night is highly heterogeneous and requires a high level of replication to achieve precision [23]. In addition, variation in hut designs affects outcome measurement [34] and should be considered when interpreting results. However, comparing between products, the same trends were consistently seen: Interceptor(r) G2 was superior to Interceptor(r) against resistant mosquitoes when they were tested in a \"free-flying\" scenario and Interceptor(r) was superior to Interceptor(r) G2 against susceptible strains, while the cone test was suitable for evaluating pyrethroids but not pro-insecticides such as chlorfenapyr.\n",
-                        "\n",
-                        "header: Sample size and power\n",
-                        "\n",
-                        "A sample size calculation for generalized linear mixed effects models (GLMMs) through simulation [23] in R statistical software 3.02 [https://www.r-project.org/](https://www.r-project.org/) was performed for the I-ACT and experimental huts. For the I-ACT, to detect a 10% effect difference between the nets, simulations were performed using an estimated mosquito mortality of 80% for unwashed Interceptor(r) G2, 70% for unwashed Interceptor(r) and 10% for SafiNet(r) (deliberately holed). The design was for five arms replicated in two groups, with each volunteer testing each treatment one time over 20 replicates within its group (i.e. 40 replicates per arm), with an inter-observational variance of 0.42 for the night of observation based on the variance of the random effects observed in a previous study. The evaluation was powered at 81% for 15 mosquitoes of each strain released per chamber using 1000 simulations.\n",
-                        "\n",
-                        "For experimental huts, simulation was performed using a Latin square design with volunteers rotating nightly and accounted for as a fixed effect for 25 nights of data collection in 10 huts. The study had 84% power to detect the difference between Interceptor(r) G2 LN and Interceptor(r) LN on mosquito mortality endpoints, with two huts per treatment arm (i.e. 50 replicates per arm). The study power was calculated based on a previous study of pyrethroid nets conducted in the same area, with the estimation of 20 _An. arabiensis_ mosquitoes per night per hut, 21% mortality in unwashed Interceptor(r) G2, 7% in 20x washed Interceptor(r) G2 and 10% in unwashed Interceptor(r) vs. 4% in 20 times washed and 1% in negative control, and overdispersion parameter for daily variation was set at intermediate 0.44.\n",
-                        "\n",
-                        "To test whether I-ACT measures similarly to the WHO tunnels (H0\\(m\\)2=_m_1) a power calculation using Satterthwaites _t_-test was conducted in STATA 16 software (StataCorp LLC, College Station TX, USA) for two unpaired sample means assuming unequal variance. The power estimated was > 90% based on estimates from previous studies conducted in the same setting: mean mortality of 81.5% for WHO tunnel test with an assumed daily variation of 0.5 and 15 replicates per arm and mean\n",
-                        "mortality estimates of 86.5% and an assumed daily variation of 0.42 with 40 replicates per arm were considered for I-ACT.\n",
-                        "\n",
-                        "header: WHO cone bioassays\n",
-                        "\n",
-                        "WHO cone tests were performed between October 2020 and February 2021 according to standard WHO procedures [9], with two modifications: the test board was set at 60deg tilt [19] and holes were cut in the boards (Fig. 1) to maximise mosquito contact with the test nets during exposures. From each treatment arm, three nets were randomly sampled, and five net swatches of 25 cm x 25 cm size were cut from positions 1 to 5. On each netting sample, four standard WHO cones were positioned over net swatches and secured in place using tape. Five laboratory-bred mosquitoes were introduced into each cone and exposed for 3 min, and four replicates were conducted per net piece (20 mosquitoes were exposed per net piece).\n",
-                        "\n",
-                        "After each exposure, the mosquitoes were removed gently from the cones (by mouth aspiration), placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Knockdown (KD60) was recorded after 60 min and mortality at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Acceptable control mortality was <= 10% after 72 h holding time. Any tests exceeding the specified control cut off were repeated.\n",
-                        "\n",
-                        "header: Test nets\n",
-                        "\n",
-                        "ITNs were supplied by BASF in November 2019 and stored at optimal conditions (25 to 32 degC) before testing and during the experimental phase. Interceptor(r) G2 nets were from two different production batches (two batches were used for experimental hut only and one batch for other biossays) and Interceptor(r) from one production batch. Interceptor(r) G2 is made from 100-denier polyester coated with a mixture of wash-resistant formulation containing 200 mg/m2 chlorfenapyr and 100 mg/m2alpha-cypermethrin. Interceptor(r) LN is made from 100-denier polyester coated with 200 mg/m2 alpha-cypermethrin. SafiNet is an untreated polyester net manufactured by A to Z. Textiles Mills, Ltd., Tanzania, and was used as a control. The nets were washed 20 times according to a protocol adapted from the standard WHO washing procedure [9] using 20 g/l palm soap (Jamaa) and dried flat in a shaded area. The interval of time used between two washes (i.e. regeneration time) was 1 day for both Interceptor(r) G2 and Interceptor(r) TTNs.\n",
-                        "\n",
-                        "header: Cone test results: Use of I-ACT for durability monitoring\n",
-                        "\n",
-                        "I-ACT is designed to be a bridging bioassay that reproduces a more natural interaction between the mosquito and human hosts beneath a bednet [16]. Blood-feeding inhibition and 72-h mortality measured by WHO tunnel and I-ACT were similar, meaning that the use of I-ACT for durability monitoring using WHO thresholds of 80% mortality and 90% feeding inhibition is appropriate. We suggest that I-ACT can be a useful alternative to tunnel tests for bioefficacy monitoring and upstream product evaluation as both the bioassays measure a similar odds ratio and I-ACT gives a precise estimation of bioefficacy because it uses laboratory-reared mosquitoes. This is particularly important when considering the longitudinal evaluation of product performance as occurs in durability bioefficacy evaluation for two reasons. Currently, experimental huts tests are being used to evaluate the insecticidal durability of Interceptor(r) G2 [33]. It was been observed at all experimental hut testing sites that mosquito resistance to insecticides has intensified through time [34]. This needs to be factored into considerations about durability testing. ITN protection may wane more quickly against more resistant mosquito populations as nets age [35], and if older ITNs are tested 3 years after baseline efficacy has been calculated, it is possible that they will be tested against a more resistant mosquito population than that at baseline. Therefore, use of carefully maintained laboratory strains may be helpful to minimise differences in insecticide resistance levels. Second, a previous longitudinal durability trial of ITNs [17] using standard WHO cone and tunnel tests as well as I-ACT shows that ITNs are highly heterogeneous, with up to 100% variance after use (John Bradley personal communication), because of variable use practices (washing, drying, care, sleeping space) [36, 37] that result in differential levels of damage and insecticide content [38]. This fact, coupled with WHO/PQ accepted intra-net insecticidal manufacturing heterogeneities [39], shows the need to expedite testing modalities that improve comprehension of net performances by donor organisations, national malaria control programmes (NMCPs) and manufacturers alike.\n",
-                        "\n",
-                        "header: Table 4: Number and proportion of net pieces passing using each laboratory bioassay based on WHO criteria of 80% control corrected mortality, 95% knockdown in WHO cone test, 90% blood-feeding inhibition or 80% control corrected mortality in tunnel test\n",
-                        "footer: The WHO criteria for tunnel were adopted for I-ACT. Whole nets were used for I-ACT\n",
-                        "columns: ['ITNs type - Unnamed: 0_level_1', 'Number of net pieces passing - Cone (N = 60)', 'Number of net pieces passing - Tunnel (N = 15)', 'Number of net pieces passing - I-ACT (N = 40)']\n",
-                        "\n",
-                        "{\"0\":{\"ITNs type - Unnamed: 0_level_1\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Cone (N = 60)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"Anopheles arabiensis (Kingani strain, resistant)\"},\"1\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":\"n (%)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"n (%)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"n (%)\"},\"2\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"11 (73)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"3\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"22 (37)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"13 (87)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"4\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"5\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"12 (80)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"36 (90)\"},\"6\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"21 (35)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"10 (67)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"7\":{\"ITNs type - Unnamed: 0_level_1\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Cone (N = 60)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"An. gambiae ss. (Kisumu strain, susceptible)\"},\"8\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"9\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"50 (83)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"10\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"2 (3)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"14 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"11\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"12\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"38 (63)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"13\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"10 (17)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"}}\n",
-                        "\n",
-                        "header: Resistant mosquitoes\n",
-                        "\n",
-                        "For pyrethroid-resistant _An. arabiensis_, blood-feeding inhibition was lower with Interceptor(r) G2 than Interceptor(r) in WHO tunnel: 87.8% (95% CI: 82.9- 92.7) vs. 92.9% (95% CI: 89.8-96.0) OR blood-fed = 1.92 [95% CI:14.8-2.48], \\(p\\) < 0.001 and I-ACT: 89.8% (95% CI: 85.8-93.7) vs. 93.9% (95% CI: 91.5-96.3) (OR blood-fed = 1.67 [95% CI: 1.07-2.62], \\(p\\) = 0.024). There was no difference between the two products measured in experimental huts: 95.9% (95% CI: 93.7-98.2) vs. 96.3% (95% CI: 93.0-99.7) (OR blood-fed = 1.06 [95% CI: 0.54-2.10], \\(p\\) = 0.857). A similar trend was observed for the 20x washed nets.\n",
-                        "\n",
-                        "Blood-feeding inhibition was similar for Interceptor(r) G2 compared to Interceptor(r) with _Cx. Quinquefasciatus_ in WHO tunnel: 97.8% (95CI: 96.1-99.5) vs. 97.1% (95% CI: 95.9-98.3) (OR blood fed = 1.36 [95% CI: 0.85-2.17], \\(p\\) = 0.196) and experimental huts: 97.2% (95% CI: 95.9-98.4) vs. 97.2% (95% CI: 95.8-98.7) (OR = 1.04 [95% CI: 0.76-1.42], \\(p\\) = 0.827), whilst in I-ACT the difference was significantly different: 99.0% (95% CI: 98.1-99.9) vs. 91.3% (95% CI: 88.6-93.9) (OR blood fed = 0.11 [95% CI: 0.05-0.26], \\(p\\) < 0.001). A similar trend was observed for the 20x washed nets (Fig. 3a, b, Table 3). The percentage blood-feeding inhibition and mean difference between bioassays for all mosquito species are presented in Additional file 1: Table S2.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The superiority of Interceptor(r) G2 over Interceptor(r) for 72-h mortality endpoints, which challenged metabolically resistant _An. arabiensis_ and _Cx. quinquefasciatus_, was clearly seen in all \"free-flying\" bioassays but not in the WHO cone test where mosquitoes are unable to fly around and be metabolically active. This observation has been seen by several other authors [14, 25-27] leading to a consensus that the overnight tunnel test using a 72-h mortality endpoint is a superior laboratory bioassay for evaluation of chlorfenapyr [2, 28] relative to the cone test, which was designed to test contact insecticides that do not require metabolic conversion into a secondary metabolite (most ITN insecticides are classified as IRAC Group 3 sodium channel modulators). It has been routinely observed in experimental hut bioassays that Interceptor(r) G2 gives greater mortality that Interceptor(r) among pyrethroid-resistant mosquitoes [14, 25-27], while mosquitoes are foraging at night when their metabolic rate is high. Both the tunnel test and the I-ACT consistently predicted the superiority of Interceptor(r) G2 over Interceptor(r) observed in experimental hut tests.\n",
-                        "\n",
-                        "The results of this study's mortality elicited on _Cx. quinqufaciatus_ from chlorfenapyr is consistent with other studies over the past decade [29, 30]. Preliminary investigations for chlorfenapyr intoxication in metabolically resistant _Cx. quinquefasciatus_ in experimental huts by [31] demonstrated relatively high levels of control in both ITNs and for IRS in Benin. Later, [27] observed 3 x mortality with chlorfenapyr + alpha-cypermethrin-treated nets compared to pyrethroid-only nets demonstrating the effect of chlorfenapyr on free-flying exposures that optimize conversion rates of the pro-insecticide. However, it is also known that once ITNs develop holes, a pyrethroid treatment offers little or no protection against pyrethroid-resistant _Cu. quinquefaciatus_[32]. The relatively high level of control sustained in _Cx. quinqufaciatus_ in this study and relatively good mortality are important resistance management attributes that make inclusion of chlorfenapyr into vector control interventions a significant advancement for active ingredient options.\n",
-                        "\n",
-                        "header: Table 1: Outcomes measured in WHO cone bioassays, tunnel, I-ACT and experimental hut tests\n",
-                        "footer: a  Not reported\n",
-                        "columns: ['Variable', 'Cone bioassay', 'WHO tunnel test', 'I-ACT', 'Experimental hut']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Primary outcome\",\"Cone bioassay\":\"72-h mortality\",\"WHO tunnel test\":\"72-h mortality\",\"I-ACT\":\"72-h mortality\",\"Experimental hut\":\"72-h mortality\"},\"1\":{\"Variable\":\"Secondary outcome\",\"Cone bioassay\":null,\"WHO tunnel test\":\"Blood-feeding\",\"I-ACT\":\"Blood-feeding\",\"Experimental hut\":\"Blood-feeding\"},\"2\":{\"Variable\":\"Other outcomes\",\"Cone bioassay\":\"Knockdown at 60 min (KD60)\",\"WHO tunnel test\":null,\"I-ACT\":null,\"Experimental hut\":\"Deterrence a and induced exophily a\"}}\n",
-                        "\n",
-                        "header: Table 2 Logistic regression analysis to compare mosquito mortality in Interceptor® and Interceptor® G2 ITNs after exposure in WHO cone and tunnel tests, Ifakara ambient chamber test and experimental hut, Tanzania\n",
-                        "footer: Odds ratio adjusted for net type, volunteer, hut position/number and day of the experiment\n",
-                        "Laboratory-reared mosquitoes were used for WHO cone, WHO tunnel and I-ACT\n",
-                        "For the experimental hut trial, the ITNs were tested against free-flying wild pyrethroid-resistant Anopheles arabiensis and Culex quinquefasciatus in Lupiro, Ifakara\n",
-                        "*p-value>0.05\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1', 'WHO cone - OR (95% CI)', 'Tunnel - OR (95% CI)', 'I-ACT - OR (95% CI)', 'Hut - OR (95% CI)', 'Huts with netting window - OR (95% CI)']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Anopheles anabiensis (resistant)\",\"WHO cone - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Tunnel - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"I-ACT - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Hut - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Anopheles anabiensis (resistant)\"},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.03, 0.07)\",\"Tunnel - OR (95% CI)\":\"1.33 (1.17, 1.51)\",\"I-ACT - OR (95% CI)\":\"1.55 (1.16, 2.07)\",\"Hut - OR (95% CI)\":\"2.38 (2.09, 2.72)\",\"Huts with netting window - OR (95% CI)\":\"2.43 (1.88, 3.14)\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.05 (0.20, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.42 (1.19, 1.70)\",\"I-ACT - OR (95% CI)\":\"1.61 (1.05, 2.49)\",\"Hut - OR (95% CI)\":\"2.53 (1.96, 3.26)\",\"Huts with netting window - OR (95% CI)\":\"2.55 (1.76, 3.68)\"},\"7\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"8\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"9\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.2, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.23 (1.03, 1.48)\",\"I-ACT - OR (95% CI)\":\"1.50 (1.02, 2.23)\",\"Hut - OR (95% CI)\":\"2.36 (2.02, 2.77)\",\"Huts with netting window - OR (95% CI)\":\"2.32 (1.63, 3.31)\"},\"10\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Culex quinquefasciatus (resistant)\",\"WHO cone - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Tunnel - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"I-ACT - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Hut - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\"},\"11\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"12\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"13\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.05 (0.57, 1.93)*\",\"Tunnel - OR (95% CI)\":\"2.55 (2.26, 2.55)\",\"I-ACT - OR (95% CI)\":\"13.40 (10.75, 16.67)\",\"Hut - OR (95% CI)\":\"1.50 (1.31, 1.73)\",\"Huts with netting window - OR (95% CI)\":\"1.40 (1.19, 1.63)\"},\"14\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"15\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"16\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.74 (0.31, 1.79)*\",\"Tunnel - OR (95% CI)\":\"2.52 (2.13, 3.00)\",\"I-ACT - OR (95% CI)\":\"12.71 (9.43, 17.14)\",\"Hut - OR (95% CI)\":\"1.52 (1.23, 1.88)\",\"Huts with netting window - OR (95% CI)\":\"1.25 (1.00, 1.57)\"},\"17\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"18\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"19\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.46 (0.62, 3.50)*\",\"Tunnel - OR (95% CI)\":\"2.58 (2.17, 3.06)\",\"I-ACT - OR (95% CI)\":\"14.11 (10.42, 19.11)\",\"Hut - OR (95% CI)\":\"1.50 (1.25, 1.80)\",\"Huts with netting window - OR (95% CI)\":\"1.55 (1.24, 1.94)\"},\"20\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"An gambiae s.s. (susceptible)\",\"WHO cone - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Tunnel - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"I-ACT - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Hut - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"An gambiae s.s.\n",
-                        "\n",
-                        "header: Thresholds are not a good idea in field trials\n",
-                        "\n",
-                        "Mortality measured in the experimental hut is lower than in the WHO tunnel tests or I-ACT meaning that tests\n",
-                        "comparing products are appropriate rather than setting a threshold of efficacy. These study data support the WHO Pesticide Evaluation Scheme (WHOPES) efficacy criteria for phase II studies [9]. This is particularly important for field evaluation of public health tools because insecticide resistance involves highly complex mechanisms, genes and gene interactions [6] that are heterogeneous in time and space [40]. Insecticide resistance is modified by selection pressures from constantly changing environmental parameters, such as insecticide/pesticide usage in agriculture and biotic interactions with other organisms that affect both the overall mosquito responses to insecticides and the selection of resistance mechanisms [41]. However, in all comparisons tested in \"free-flying\" bioassays with resistant mosquito strains, it was possible to predict the superiority of Interceptor(r) G2 over Interceptor(r). Therefore, the use of a standard comparator net to provide performance benchmarks when evaluating the performance of new products is critical. The added value of a new product relative to pyrethroid-only nets has proven extremely useful when synthesising evidence for PBO nets [42].\n",
-                        "\n",
-                        "header: Mosquito test systems\n",
-                        "\n",
-                        "IHI laboratory maintains local mosquito strains with resistant mechanisms present in the local population to\n",
-                        "avoid accidental release of new resistance alleles into the wild population. Four types of laboratory-reared mosquitoes with different resistance levels (confirmed at the time of testing) were used in the WHO cone, WHO tunnel and I-ACT tests: _Anopheles arabiensis_ (Kingani strain, upregulation of cytochrome p450s, 14% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is reversed by piperonyl butoxide (PBO) pre-exposure, which acts to block enzymatic detoxification mechanisms by inhibiting their metabolism), _Anopheles gambiae_ s.s. (Kisumu strain, fully susceptible to all insecticide classes at WHO discriminating doses), _Aedes aegypti_ (Bagamoyo strain, fully susceptible to all insecticide classes at WHO discriminating doses) and _Culex quinquefasciatus_ (Bagamoyo strain, 6% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is partially reversed (only moderate susceptibility is restored from inhibition of detoxifying mechanism) by PBO pre-exposure). In I-ACT and tunnel tests, sugar-starved (8 to 9 h) nulliparous female mosquitoes, 5-8 days old, were used. For cone bioassay, 2-5-day-old nulliparous sugar-fed female mosquitoes were challenged. Laboratory colonies were maintained by feeding larvae Tetramin(r) tropical fish food and adults on blood between 3 and 6 days after emergence and 10% sugar solution ad libitum. Temperature and humidity within the insectary are maintained between 27 degC+-5 degC and 40%-100% RH, relatively following MR4 guidelines [18]. For the experimental hut assays, only wild populations of _An. arabiensis_ and _Culex quinquefasciatus_ were collected in sufficient numbers for evaluation.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Interceptor(r) G2 clearly demonstrated superior bio-efficacy against resistant mosquitoes compared to Interceptor(r) when mosquitoes were challenged in free-flying bioassays. The I-ACT measured similar odds ratios as the WHO tunnel, currently used for testing of ITNs with chlorfenapyr. Both free-flying laboratory bioassays (WHO tunnel and I-ACT) predicted the results of the experimental hut test. Experimental hut design has an influence on mosquito mortality; however, using the odds ratio, all free-flying tests gave consistent findings. In this setting, I-ACT was a reliable bioassay for bioefficacy testing of Interceptor(r) G2 and may be a useful additional bioassay for durability monitoring of ITNs treated with pro-insecticides.\n",
-                        "\n",
-                        "header: Study area: Study design\n",
-                        "\n",
-                        "The study was a five-arm comparative efficacy study to determine the performances of Interceptor(r) G2 ITNs and Interceptor(r) against pyrethroid-susceptible and -resistant mosquitoes measured by WHO cone bioassay, WHO tunnel test, Ifakara ambient chamber test (I-ACT) and experimental huts. Study arms were: (i) Interceptor(r) G2, unwashed; (ii) Interceptor(r) G2, washed 20 times; (iii) Interceptor(r), unwashed; (iv) Interceptor(r), washed 20 times; (v) SafNet(r) (negative control). The primary performance metric upon which the study was powered is 72-h mortality (M72), which is measured in all four bioassays. A secondary outcome was blood-feeding, which was measured in the three free-flying bioassays. Additionally, knockdown at 60 min (KD60) was measured in cone tests (Table 1).\n",
-                        "\n",
-                        "header: Susceptible mosquitoes\n",
-                        "\n",
-                        "For unwashed Interceptor(r), blood-feeding inhibition was measured in tunnel and I-ACT and a similar trend was observed in susceptible mosquitoes as was seen in the resistant laboratory strains (Fig. 3c, d, Table 3). For susceptible _An. gambiae_, blood-feeding inhibition was lower with Interceptor(r) G2 than for Interceptor(r) WHO tunnel: 89.7% (95% CI: 83.3-96.1) vs. 94.5% (95% CI: 91.9-97.2), (OR blood fed = 1.95 [95% CI: 1.46, 2.61], \\(p\\) < 0.001) and similar in I-ACT: 97.4% (95% CI: 95.7-99.1) vs. 98.2% (95% CI: 96.8-99.5), (OR blood fed = 1.41 [95% CI: 0.63-3.17], \\(p\\) = 0.407. For susceptible _Ae. Aggyti_ in WHO tunnel, results showed: 93.8% (95% CI:90.6-97.1) vs. 96.3% (95% CI: 94.0-98.7) (OR blood fed = 2.03 [95% CI: 1.40-2.93], \\(p\\) < 0.001) and in I-ACT: 96.8% (95% CI: 94.8-98.9) vs. 98.3% (95% CI: 97.1-99.5) (OR blood fed = 1.92 [95% CI: 0.87-4.24],\n",
-                        "\\(p=0.107\\)). As before, for both species a similar pattern was seen for the 20\\(\\times\\) washed nets.\n",
-                        "\n",
-                        "header: Statistical analysis\n",
-                        "\n",
-                        "Data were entered and validated in Excel using double entry system and exported into STATA 16 software (StataCorp LLC, College Station TX, USA) for further cleaning and analysis. Descriptive statistics were used for data summarization, where arithmetic mean percentage of mosquito control corrected mortality at 72 h and arithmetic mean percentage blood-feeding inhibition for each test and species was presented. Control corrected mortality was calculated by using Abbott's formula: (treatment mortality - control mortality/(1 - control mortality)*100% and blood-feeding inhibition was calculated by taking the total number of unfed mosquitoes divided by total mosquito recapture per hut night [24].\n",
-                        "\n",
-                        "Multivariable mixed-effect logistic regression with binomial error and log link was used to compare the performance of Interceptor(r) G2 to Interceptor(r) on the mortality and blood-feeding endpoints for each species in WHO cone (mortality only), tunnel, I-ACT and experimental huts. Fixed effects were treatment, volunteer, hut number/position (for experimental hut) and night of the experiment. The same regression was used for the comparison of hut design, whereby an interaction term between hut design and ITN type was also included in the model.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"WHO tunnel test\",\"Start_month\":8,\"Start_year\":2020,\"End_month\":11,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Interceptor G2\",\"Insecticide\":\"alpha-cypermethrin, chlorfenapyr\",\"Concentration_initial\":\"100 mg\\/m2, 200 mg\\/m2\",\"Net_washed\":20.0},\"N02\":{\"Net_type\":\"Interceptor\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2\",\"Net_washed\":20.0},\"N03\":{\"Net_type\":\"SafiNet\",\"Insecticide\":null,\"Concentration_initial\":null,\"Net_washed\":20.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: WHO tunnel test\n",
-                        "\n",
-                        "The tunnel tests were carried out from August to November 2020 according to standard WHO procedures [9]. Five 25 cm x 25 cm net pieces per ITN/control were cut adjacent to the swatches cut for cone assays from three nets per treatment arm to make a total of 15 pieces per study arm and to account for possible intra-net variability of insecticide loadings. Five tunnels were run with one sample from each treatment arm with one mosquito strain on a single night. Over 60 nights, all 15 pieces of ITNs per study arm were tested with four mosquito strains.\n",
-                        "\n",
-                        "Non-blood-fed nulliparous females, 5-8 days old, sugar starved for 6-8 h were released in a 60-cm-long glass tunnel. At each end of the tunnel, a 25-cm square mosquito cage covered with polyester netting was fitted. At one third of the length, the netting sample was affixed. The surface of netting \"available\" to mosquitoes is 400 cm2 (20 cm x 20 cm), with 9 x 1-cm-diameter holes: one hole is located at the centre of the square; the other eight were equidistant and located 5 cm from the border. In the shorter section of the tunnel, a small rabbit, its back shaved and restrained in a mesh tunnel, was placed as bait (Fig. 1). In the cage at the end of the longer section of the tunnel, 100 female mosquitoes (one strain \n",
-                        "per replicate) were introduced at 21:00. The following morning at 09:00, the mosquitoes were removed using a mouth aspirator and counted separately from each section of the tunnel, and mortality and blood-feeding rates were recorded. The mosquitoes were placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Mortality was recoded at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Overall mortality was measured by pooling the mortalities of mosquitoes from the two sections of the tunnel. Acceptable feeding success and mortality in controls were 50% and 10%, respectively. Any tests not meeting the specified control cut-off were repeated.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding in WHO tunnel, I-ACT and experimental hut\n",
-                        "\n",
-                        "For all bioassays and strains, blood-feeding inhibition was very high (Fig. 3, Table 3). Both Interceptor(r) G2 and Interceptor(r) gave high levels of blood-feeding inhibition in all \"free-flying\" bioassays.\n",
-                        "\n",
-                        "header: Selection of the correct strain for bioassay\n",
-                        "\n",
-                        "The current study demonstrated that using the correct mosquito strain when evaluating ITNs is a critical consideration. The benefit of chlorfenapyr was only seen against the resistant strains, and against the susceptible strains Interceptor(r) was more efficacious because it contains a higher dose of alphacypermethrin. Comparing between the products using both a susceptible and a resistant strain revealed the different modes of action of Interceptor(r) and Interceptor(r) G2.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "As funding for malaria control falls and mosquito resistance to current public health insecticides increases, new and durable vector control tools that utilise new insecticide classes are needed [1]. For insecticide resistance management, a new Insecticide Treated Net (ITN) Interceptor(r) G2 has been developed, coated with a mixture of the pyrethroid alpha-cypermethrin and the pro-insecticide chlorfenapyr [2]. Chlorfenapyr is an Insecticide Resistance Action Committee (IRAC) Group 13 insecticide, having a pyrrole chemistry that uncouples oxidative phosphorylation via disruption of the proton gradient (as a protonophore) to short circuit mitochondrial respiration through inner mitochondrial membranes of insect cells so that ATP cannot be synthesized, subsequently robbing insects of energy, resulting in death [3, 4]. Metabolic resistance is one of the main mechanisms of resistance observed in malaria vectors [5] where one or several detoxification gene families--cytochrome P450s (P450s), esterases and glutathione S-transferases (GSTs)--are overproduced to detoxify insecticides [6]. While this metabolism is a detoxification process, it can increase the potency of a pro-insecticide and may therefore be exploited as a means to control metabolically resistant insect populations [3]. The unique mode of action of chlorfenapyr on an insect's metabolism is particularly relevant for the control of vectors harboring insecticide resistance mechanisms, as increased metabolic activity increases the conversion of the pro-insecticide into its potent n-dealkylated form and will consequently increase mosquito mortality [7].\n",
-                        "\n",
-                        "For any new ITN to be used in public health, a thorough evaluation of its safety, efficacy and effectiveness is conducted based on World Health Organisation (WHO) standards and criteria [8]. The methods and scope of the laboratory tests and field trials required to attain WHO Prequalification (PQ) listing for ITNs are outlined in a set of WHO guidelines, last updated in 2013 [9]. As part of these guidelines, WHO recommends a set of standardised laboratory tests to ascertain the bioefficacy of pyrethroid ITNs, i.e., the ability of ITN products to kill, incapacitate (knock down) and prevent mosquitoes from blood-feeding. Laboratory bioefficacy tests are also a critical component of ITN durability evaluation used to confirm continued ITN bioefficacy after long-term use in the community [10]. The simplest and most commonly applied ITN bioefficacy test is the WHO cone bioassay where mosquitoes are held close to ITN in plastic cones and the number of mosquitoes knocked down (incapacitated) or killed is counted [9]. For ITNs with feeding inhibition mode of action, the WHO tunnel test is used, where a swatch of ITN with small holes (9 x 1-cm diameter) is made and is placed between mosquitoes and small animal bait overnight [11]. The WHO tunnel test has been shown to agree with experimental but data, using laboratory-reared mosquitoes released into the huts [12]. Thresholds for the mosquito knockdown rate (95%) and mosquito mortality rate (80%) and blood-feeding inhibition (90%) using pyrethroid-susceptible _Anopheles_ mosquitoes serve as performance benchmarks; candidate products are required to fulfill minimum performance standards, which have been established by WHO for qualification to be listed/recommended for their public health values to sustain and protect users from disease transmissions. This includes their physical durability and chemical contents recovered from nets replaced in the field over 3 years or as predicted from wash-resistance testing. For a net to be classified as a long-lasting ITN, i.e., LLIN, > 80% of nets tested should pass WHO cone/tunnel performance benchmarks after 3 years of use [13].\n",
-                        "\n",
-                        "Cone tests are relatively easy to perform, high throughput and sensitive to detecting changes in bioavailable pyrethroids that act through rapid contact neurotoxicity. However, chlorfenapyr requires the mosquito to be metabolically and/or physiologically active (as it would be when encountering the ITN under user conditions) to bioactivate into the potent n-dealkylated form which elicits increased mosquito mortality. As mosquitoes are more metabolically active at night when flying and host-seeking during their typical circadian rhythms, the tunnel test may be more appropriate [14].\n",
-                        "\n",
-                        "The President's Malaria Initiative (PMI) currently uses WHO Tunnel tests for the durability monitoring of chlorfenapyr nets with 4 samples per net evaluated and 48 nets per sampling point [15]. This requires large numbers of mosquitoes and access to small animals for testing, so it cannot be done at all facilities in malaria-endemic areas. To accommodate high-throughput evaluation of whole ITNs for durability evaluation, the Ifakara ambient chamber test (I-ACT) was developed [16]. The Ifakara ambient chamber test (I-ACT) is not currently a recognized method \n",
-                        "approved by the WHO. However, it is currently seeking confirmation by direct comparison to approved methods. This is of particular importance where novel slow-acting chemistries cannot achieve the benchmark standards established for conventional pyrethroid ITN exposures yet may prove to be highly efficacious when tested according to their discrete modes of action. Like the experimental hut, I-ACT makes use of whole nets and human hosts to evaluate bioefficacy of field-used ITNs, but the assay is done under controlled conditions with laboratory-reared mosquitoes. Mosquitoes are released into net chambers within which the test net is hung with a volunteer sleeping beneath, and all mosquitoes are recaptured in the morning. The use of laboratory mosquitoes (rather than conducting experimental hut trials with wild mosquitoes) is done to improve the precision of estimates by releasing mosquito cohorts of a defined number of mosquitoes with high recapture rate (99%) at the conclusion of exposure intervals.\n",
-                        "\n",
-                        "I-ACT bioassay has been used for evaluation of pyrethroid ITNs, was able to discriminate between products [17] and agreed with results of combined WHO cone and tunnel tests [16]. The I-ACT may show suitability for use in evaluation of pro-insecticides because: (i) the assay is run overnight, favouring malarial mosquitoes' circadian rhythms, (ii) the mosquitoes have a large arena to fly in, allowing them to be metabolically active, (iii) the mosquitoes have the opportunity to feed ad libitum and (iv) the I-ACT test eliminates infected mosquitoes that may represent a malaria transmission potential that cannot be completely excluded in WHO experimental huts--a significant safety benefit to volunteers. Therefore, the I-ACT method was evaluated alongside standard methods (i.e., experimental huts, WHO cone and tunnel tests) to provide direct evidence of its comparability. Interceptor(r) (alpha-cypermethrin only) and Interceptor(r) G2 (alpha-cypermethrin and chlorfenapyr) were evaluated to compare the performance of each assay for durability monitoring of pro-insecticidal ITNs.\n",
-                        "\n",
-                        "header: WHO cone bioassays\n",
-                        "\n",
-                        "WHO cone tests were performed between October 2020 and February 2021 according to standard WHO procedures [9], with two modifications: the test board was set at 60deg tilt [19] and holes were cut in the boards (Fig. 1) to maximise mosquito contact with the test nets during exposures. From each treatment arm, three nets were randomly sampled, and five net swatches of 25 cm x 25 cm size were cut from positions 1 to 5. On each netting sample, four standard WHO cones were positioned over net swatches and secured in place using tape. Five laboratory-bred mosquitoes were introduced into each cone and exposed for 3 min, and four replicates were conducted per net piece (20 mosquitoes were exposed per net piece).\n",
-                        "\n",
-                        "After each exposure, the mosquitoes were removed gently from the cones (by mouth aspiration), placed in paper cups and provided with cotton wool moistened with 10% sugar solution. Knockdown (KD60) was recorded after 60 min and mortality at 24, 48 and 72 h. Mosquitoes challenged to untreated nets were used as controls to monitor the quality of the bioassay. The bioassays and holding period were carried out at 27 degC+-2 degC and 60%-100% relative humidity. Acceptable control mortality was <= 10% after 72 h holding time. Any tests exceeding the specified control cut off were repeated.\n",
-                        "\n",
-                        "header: Considerations for each bioassay\n",
-                        "\n",
-                        "Both I-ACT and experimental huts use the whole bednet and human host, which represent user conditions; however, there is no risk of disease for human participants in I-ACT because the mosquitoes used are laboratory reared. The WHO tunnel test is a well-established bioassay that has been shown to agree with experimental hut tests in this evaluation as well as others [12, 14]. However, it tests only a sample of net and is therefore only able to accurately measure the chemical durability of an ITN and not the chemical _and_ physical durability. In addition, it requires a high number of mosquitoes (100 per replicate) and more testing days compared to I-ACT. In I-ACT there is possibility of testing more than one species or strain per chamber compared to tunnel test, which makes results more comparable. The I-ACT is a new assay that consistently predicts the results of experimental hut tests, measures with a similar magnitude of difference as a tunnel test and provides high-throughput and precise estimates of whole ITN protective efficacy in this study with chlorfenapyr as well as in previous studies with pyrethroid nets [17] at lower cost than tunnel tests. However, this method is yet to become a WHO/PQ-accepted testing modality despite the observed higher precision vs. current WHO-recommended modalities. The detailed descriptions of cost implications for each bioassay and how to build an I-ACT with the cost of \n",
-                        "establishment are described in Additional file 1: Table S4 and Additional file 2 respectively.\n",
-                        "\n",
-                        "Laboratory and/or semi-field bioassays are not replacements for field evaluation, but they are useful if they can predict the results of field tests because they are substantially cheaper and more standardised. Experimental hut tests remain the gold standard test because they represent field conditions and can be related to public health impact [48]. However, variation in mosquito entrance in huts per night is highly heterogeneous and requires a high level of replication to achieve precision [23]. In addition, variation in hut designs affects outcome measurement [34] and should be considered when interpreting results. However, comparing between products, the same trends were consistently seen: Interceptor(r) G2 was superior to Interceptor(r) against resistant mosquitoes when they were tested in a \"free-flying\" scenario and Interceptor(r) was superior to Interceptor(r) G2 against susceptible strains, while the cone test was suitable for evaluating pyrethroids but not pro-insecticides such as chlorfenapyr.\n",
-                        "\n",
-                        "header: Sample size and power\n",
-                        "\n",
-                        "A sample size calculation for generalized linear mixed effects models (GLMMs) through simulation [23] in R statistical software 3.02 [https://www.r-project.org/](https://www.r-project.org/) was performed for the I-ACT and experimental huts. For the I-ACT, to detect a 10% effect difference between the nets, simulations were performed using an estimated mosquito mortality of 80% for unwashed Interceptor(r) G2, 70% for unwashed Interceptor(r) and 10% for SafiNet(r) (deliberately holed). The design was for five arms replicated in two groups, with each volunteer testing each treatment one time over 20 replicates within its group (i.e. 40 replicates per arm), with an inter-observational variance of 0.42 for the night of observation based on the variance of the random effects observed in a previous study. The evaluation was powered at 81% for 15 mosquitoes of each strain released per chamber using 1000 simulations.\n",
-                        "\n",
-                        "For experimental huts, simulation was performed using a Latin square design with volunteers rotating nightly and accounted for as a fixed effect for 25 nights of data collection in 10 huts. The study had 84% power to detect the difference between Interceptor(r) G2 LN and Interceptor(r) LN on mosquito mortality endpoints, with two huts per treatment arm (i.e. 50 replicates per arm). The study power was calculated based on a previous study of pyrethroid nets conducted in the same area, with the estimation of 20 _An. arabiensis_ mosquitoes per night per hut, 21% mortality in unwashed Interceptor(r) G2, 7% in 20x washed Interceptor(r) G2 and 10% in unwashed Interceptor(r) vs. 4% in 20 times washed and 1% in negative control, and overdispersion parameter for daily variation was set at intermediate 0.44.\n",
-                        "\n",
-                        "To test whether I-ACT measures similarly to the WHO tunnels (H0\\(m\\)2=_m_1) a power calculation using Satterthwaites _t_-test was conducted in STATA 16 software (StataCorp LLC, College Station TX, USA) for two unpaired sample means assuming unequal variance. The power estimated was > 90% based on estimates from previous studies conducted in the same setting: mean mortality of 81.5% for WHO tunnel test with an assumed daily variation of 0.5 and 15 replicates per arm and mean\n",
-                        "mortality estimates of 86.5% and an assumed daily variation of 0.42 with 40 replicates per arm were considered for I-ACT.\n",
-                        "\n",
-                        "header: Test nets\n",
-                        "\n",
-                        "ITNs were supplied by BASF in November 2019 and stored at optimal conditions (25 to 32 degC) before testing and during the experimental phase. Interceptor(r) G2 nets were from two different production batches (two batches were used for experimental hut only and one batch for other biossays) and Interceptor(r) from one production batch. Interceptor(r) G2 is made from 100-denier polyester coated with a mixture of wash-resistant formulation containing 200 mg/m2 chlorfenapyr and 100 mg/m2alpha-cypermethrin. Interceptor(r) LN is made from 100-denier polyester coated with 200 mg/m2 alpha-cypermethrin. SafiNet is an untreated polyester net manufactured by A to Z. Textiles Mills, Ltd., Tanzania, and was used as a control. The nets were washed 20 times according to a protocol adapted from the standard WHO washing procedure [9] using 20 g/l palm soap (Jamaa) and dried flat in a shaded area. The interval of time used between two washes (i.e. regeneration time) was 1 day for both Interceptor(r) G2 and Interceptor(r) TTNs.\n",
-                        "\n",
-                        "header: Cone test results: Use of I-ACT for durability monitoring\n",
-                        "\n",
-                        "I-ACT is designed to be a bridging bioassay that reproduces a more natural interaction between the mosquito and human hosts beneath a bednet [16]. Blood-feeding inhibition and 72-h mortality measured by WHO tunnel and I-ACT were similar, meaning that the use of I-ACT for durability monitoring using WHO thresholds of 80% mortality and 90% feeding inhibition is appropriate. We suggest that I-ACT can be a useful alternative to tunnel tests for bioefficacy monitoring and upstream product evaluation as both the bioassays measure a similar odds ratio and I-ACT gives a precise estimation of bioefficacy because it uses laboratory-reared mosquitoes. This is particularly important when considering the longitudinal evaluation of product performance as occurs in durability bioefficacy evaluation for two reasons. Currently, experimental huts tests are being used to evaluate the insecticidal durability of Interceptor(r) G2 [33]. It was been observed at all experimental hut testing sites that mosquito resistance to insecticides has intensified through time [34]. This needs to be factored into considerations about durability testing. ITN protection may wane more quickly against more resistant mosquito populations as nets age [35], and if older ITNs are tested 3 years after baseline efficacy has been calculated, it is possible that they will be tested against a more resistant mosquito population than that at baseline. Therefore, use of carefully maintained laboratory strains may be helpful to minimise differences in insecticide resistance levels. Second, a previous longitudinal durability trial of ITNs [17] using standard WHO cone and tunnel tests as well as I-ACT shows that ITNs are highly heterogeneous, with up to 100% variance after use (John Bradley personal communication), because of variable use practices (washing, drying, care, sleeping space) [36, 37] that result in differential levels of damage and insecticide content [38]. This fact, coupled with WHO/PQ accepted intra-net insecticidal manufacturing heterogeneities [39], shows the need to expedite testing modalities that improve comprehension of net performances by donor organisations, national malaria control programmes (NMCPs) and manufacturers alike.\n",
-                        "\n",
-                        "header: Mosquito test systems\n",
-                        "\n",
-                        "IHI laboratory maintains local mosquito strains with resistant mechanisms present in the local population to\n",
-                        "avoid accidental release of new resistance alleles into the wild population. Four types of laboratory-reared mosquitoes with different resistance levels (confirmed at the time of testing) were used in the WHO cone, WHO tunnel and I-ACT tests: _Anopheles arabiensis_ (Kingani strain, upregulation of cytochrome p450s, 14% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is reversed by piperonyl butoxide (PBO) pre-exposure, which acts to block enzymatic detoxification mechanisms by inhibiting their metabolism), _Anopheles gambiae_ s.s. (Kisumu strain, fully susceptible to all insecticide classes at WHO discriminating doses), _Aedes aegypti_ (Bagamoyo strain, fully susceptible to all insecticide classes at WHO discriminating doses) and _Culex quinquefasciatus_ (Bagamoyo strain, 6% mortality upon exposure to WHO discriminating doses of alpha-cypermethrin, which is partially reversed (only moderate susceptibility is restored from inhibition of detoxifying mechanism) by PBO pre-exposure). In I-ACT and tunnel tests, sugar-starved (8 to 9 h) nulliparous female mosquitoes, 5-8 days old, were used. For cone bioassay, 2-5-day-old nulliparous sugar-fed female mosquitoes were challenged. Laboratory colonies were maintained by feeding larvae Tetramin(r) tropical fish food and adults on blood between 3 and 6 days after emergence and 10% sugar solution ad libitum. Temperature and humidity within the insectary are maintained between 27 degC+-5 degC and 40%-100% RH, relatively following MR4 guidelines [18]. For the experimental hut assays, only wild populations of _An. arabiensis_ and _Culex quinquefasciatus_ were collected in sufficient numbers for evaluation.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "The superiority of Interceptor(r) G2 over Interceptor(r) for 72-h mortality endpoints, which challenged metabolically resistant _An. arabiensis_ and _Cx. quinquefasciatus_, was clearly seen in all \"free-flying\" bioassays but not in the WHO cone test where mosquitoes are unable to fly around and be metabolically active. This observation has been seen by several other authors [14, 25-27] leading to a consensus that the overnight tunnel test using a 72-h mortality endpoint is a superior laboratory bioassay for evaluation of chlorfenapyr [2, 28] relative to the cone test, which was designed to test contact insecticides that do not require metabolic conversion into a secondary metabolite (most ITN insecticides are classified as IRAC Group 3 sodium channel modulators). It has been routinely observed in experimental hut bioassays that Interceptor(r) G2 gives greater mortality that Interceptor(r) among pyrethroid-resistant mosquitoes [14, 25-27], while mosquitoes are foraging at night when their metabolic rate is high. Both the tunnel test and the I-ACT consistently predicted the superiority of Interceptor(r) G2 over Interceptor(r) observed in experimental hut tests.\n",
-                        "\n",
-                        "The results of this study's mortality elicited on _Cx. quinqufaciatus_ from chlorfenapyr is consistent with other studies over the past decade [29, 30]. Preliminary investigations for chlorfenapyr intoxication in metabolically resistant _Cx. quinquefasciatus_ in experimental huts by [31] demonstrated relatively high levels of control in both ITNs and for IRS in Benin. Later, [27] observed 3 x mortality with chlorfenapyr + alpha-cypermethrin-treated nets compared to pyrethroid-only nets demonstrating the effect of chlorfenapyr on free-flying exposures that optimize conversion rates of the pro-insecticide. However, it is also known that once ITNs develop holes, a pyrethroid treatment offers little or no protection against pyrethroid-resistant _Cu. quinquefaciatus_[32]. The relatively high level of control sustained in _Cx. quinqufaciatus_ in this study and relatively good mortality are important resistance management attributes that make inclusion of chlorfenapyr into vector control interventions a significant advancement for active ingredient options.\n",
-                        "\n",
-                        "header: Publisher's Note\n",
-                        "\n",
-                        "Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n",
-                        "\n",
-                        "header: Resistant mosquitoes\n",
-                        "\n",
-                        "For pyrethroid-resistant _An. arabiensis_, blood-feeding inhibition was lower with Interceptor(r) G2 than Interceptor(r) in WHO tunnel: 87.8% (95% CI: 82.9- 92.7) vs. 92.9% (95% CI: 89.8-96.0) OR blood-fed = 1.92 [95% CI:14.8-2.48], \\(p\\) < 0.001 and I-ACT: 89.8% (95% CI: 85.8-93.7) vs. 93.9% (95% CI: 91.5-96.3) (OR blood-fed = 1.67 [95% CI: 1.07-2.62], \\(p\\) = 0.024). There was no difference between the two products measured in experimental huts: 95.9% (95% CI: 93.7-98.2) vs. 96.3% (95% CI: 93.0-99.7) (OR blood-fed = 1.06 [95% CI: 0.54-2.10], \\(p\\) = 0.857). A similar trend was observed for the 20x washed nets.\n",
-                        "\n",
-                        "Blood-feeding inhibition was similar for Interceptor(r) G2 compared to Interceptor(r) with _Cx. Quinquefasciatus_ in WHO tunnel: 97.8% (95CI: 96.1-99.5) vs. 97.1% (95% CI: 95.9-98.3) (OR blood fed = 1.36 [95% CI: 0.85-2.17], \\(p\\) = 0.196) and experimental huts: 97.2% (95% CI: 95.9-98.4) vs. 97.2% (95% CI: 95.8-98.7) (OR = 1.04 [95% CI: 0.76-1.42], \\(p\\) = 0.827), whilst in I-ACT the difference was significantly different: 99.0% (95% CI: 98.1-99.9) vs. 91.3% (95% CI: 88.6-93.9) (OR blood fed = 0.11 [95% CI: 0.05-0.26], \\(p\\) < 0.001). A similar trend was observed for the 20x washed nets (Fig. 3a, b, Table 3). The percentage blood-feeding inhibition and mean difference between bioassays for all mosquito species are presented in Additional file 1: Table S2.\n",
-                        "\n",
-                        "header: Table 4: Number and proportion of net pieces passing using each laboratory bioassay based on WHO criteria of 80% control corrected mortality, 95% knockdown in WHO cone test, 90% blood-feeding inhibition or 80% control corrected mortality in tunnel test\n",
-                        "footer: The WHO criteria for tunnel were adopted for I-ACT. Whole nets were used for I-ACT\n",
-                        "columns: ['ITNs type - Unnamed: 0_level_1', 'Number of net pieces passing - Cone (N = 60)', 'Number of net pieces passing - Tunnel (N = 15)', 'Number of net pieces passing - I-ACT (N = 40)']\n",
-                        "\n",
-                        "{\"0\":{\"ITNs type - Unnamed: 0_level_1\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Cone (N = 60)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"Anopheles arabiensis (Kingani strain, resistant)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"Anopheles arabiensis (Kingani strain, resistant)\"},\"1\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":\"n (%)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"n (%)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"n (%)\"},\"2\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"11 (73)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"3\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"22 (37)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"13 (87)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"4\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"5\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"59 (98)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"12 (80)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"36 (90)\"},\"6\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"21 (35)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"10 (67)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"38 (95)\"},\"7\":{\"ITNs type - Unnamed: 0_level_1\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Cone (N = 60)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"An. gambiae ss. (Kisumu strain, susceptible)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"An. gambiae ss. (Kisumu strain, susceptible)\"},\"8\":{\"ITNs type - Unnamed: 0_level_1\":\"Unwashed\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"9\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"50 (83)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"10\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"2 (3)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"14 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"11\":{\"ITNs type - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"Number of net pieces passing - Cone (N = 60)\":null,\"Number of net pieces passing - Tunnel (N = 15)\":null,\"Number of net pieces passing - I-ACT (N = 40)\":null},\"12\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"Number of net pieces passing - Cone (N = 60)\":\"38 (63)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"},\"13\":{\"ITNs type - Unnamed: 0_level_1\":\"Interceptor\\u00ae G2\",\"Number of net pieces passing - Cone (N = 60)\":\"10 (17)\",\"Number of net pieces passing - Tunnel (N = 15)\":\"15 (100)\",\"Number of net pieces passing - I-ACT (N = 40)\":\"40 (100)\"}}\n",
-                        "\n",
-                        "header: Thresholds are not a good idea in field trials\n",
-                        "\n",
-                        "Mortality measured in the experimental hut is lower than in the WHO tunnel tests or I-ACT meaning that tests\n",
-                        "comparing products are appropriate rather than setting a threshold of efficacy. These study data support the WHO Pesticide Evaluation Scheme (WHOPES) efficacy criteria for phase II studies [9]. This is particularly important for field evaluation of public health tools because insecticide resistance involves highly complex mechanisms, genes and gene interactions [6] that are heterogeneous in time and space [40]. Insecticide resistance is modified by selection pressures from constantly changing environmental parameters, such as insecticide/pesticide usage in agriculture and biotic interactions with other organisms that affect both the overall mosquito responses to insecticides and the selection of resistance mechanisms [41]. However, in all comparisons tested in \"free-flying\" bioassays with resistant mosquito strains, it was possible to predict the superiority of Interceptor(r) G2 over Interceptor(r). Therefore, the use of a standard comparator net to provide performance benchmarks when evaluating the performance of new products is critical. The added value of a new product relative to pyrethroid-only nets has proven extremely useful when synthesising evidence for PBO nets [42].\n",
-                        "\n",
-                        "header: Mosquito retention\n",
-                        "\n",
-                        "Immediately after the main experimental hut trial, a retention test was conducted by releasing wild _An. ara-biensis_ mosquitoes that were collected from the area by human landing catch (HLC). Fifteen mosquitoes marked with non-toxic fluorescent powder were released in each of the ten huts with the same treatment arms as the main experiment hut design. Five huts were completely closed while the other five were left with open eaves (with 10-cm baffles to reduce egress) and two window exit traps as per main experimental hut study. This experiment was conducted for 5 nights. Each morning mosquitoes were sorted, scored and held for 72 h to observe delayed mortality for each treatment arm.\n",
-                        "\n",
-                        "header: Susceptible mosquitoes\n",
-                        "\n",
-                        "For unwashed Interceptor(r), blood-feeding inhibition was measured in tunnel and I-ACT and a similar trend was observed in susceptible mosquitoes as was seen in the resistant laboratory strains (Fig. 3c, d, Table 3). For susceptible _An. gambiae_, blood-feeding inhibition was lower with Interceptor(r) G2 than for Interceptor(r) WHO tunnel: 89.7% (95% CI: 83.3-96.1) vs. 94.5% (95% CI: 91.9-97.2), (OR blood fed = 1.95 [95% CI: 1.46, 2.61], \\(p\\) < 0.001) and similar in I-ACT: 97.4% (95% CI: 95.7-99.1) vs. 98.2% (95% CI: 96.8-99.5), (OR blood fed = 1.41 [95% CI: 0.63-3.17], \\(p\\) = 0.407. For susceptible _Ae. Aggyti_ in WHO tunnel, results showed: 93.8% (95% CI:90.6-97.1) vs. 96.3% (95% CI: 94.0-98.7) (OR blood fed = 2.03 [95% CI: 1.40-2.93], \\(p\\) < 0.001) and in I-ACT: 96.8% (95% CI: 94.8-98.9) vs. 98.3% (95% CI: 97.1-99.5) (OR blood fed = 1.92 [95% CI: 0.87-4.24],\n",
-                        "\\(p=0.107\\)). As before, for both species a similar pattern was seen for the 20\\(\\times\\) washed nets.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The laboratory bioassays (I-ACT, WHO cone and WHO tunnel tests) were performed at the Vector Control Product Testing Unit (VCPTU) testing facility located at the Bagamoyo branch of Ifakara Health Institute (IHI), Tanzania (6.446deg S and 38.901deg E). The district experiences average annual rainfall of 800 mm-1000 mm, average temperatures between 24 degC and 29 degC and average annual humidity of 73%. The experimental hut study was conducted in Lupiro village (8.385deg S and 36.673deg E) in Ulanga District, southeastern Tanzania. The village is bordered by irrigated rice fields with average annual rainfall of 1200-1800 mm, average temperatures between 20 and 34 degC and average annual humidity of 69%. The main malaria vector is _Anopheles arabiensis_, constituting > 99.9% of the _An. gambiae_ complex species in the last test conducted in November 2020, and resistance to alphabcypermethrin was recorded (57% mortality at 1 x WHO discriminating concentration) at the time of testing.\n",
-                        "\n",
-                        "header: Table 2 Logistic regression analysis to compare mosquito mortality in Interceptor® and Interceptor® G2 ITNs after exposure in WHO cone and tunnel tests, Ifakara ambient chamber test and experimental hut, Tanzania\n",
-                        "footer: Odds ratio adjusted for net type, volunteer, hut position/number and day of the experiment\n",
-                        "Laboratory-reared mosquitoes were used for WHO cone, WHO tunnel and I-ACT\n",
-                        "For the experimental hut trial, the ITNs were tested against free-flying wild pyrethroid-resistant Anopheles arabiensis and Culex quinquefasciatus in Lupiro, Ifakara\n",
-                        "*p-value>0.05\n",
-                        "columns: ['Unnamed: 0_level_0 - Unnamed: 0_level_1', 'WHO cone - OR (95% CI)', 'Tunnel - OR (95% CI)', 'I-ACT - OR (95% CI)', 'Hut - OR (95% CI)', 'Huts with netting window - OR (95% CI)']\n",
-                        "\n",
-                        "{\"0\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Anopheles anabiensis (resistant)\",\"WHO cone - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Tunnel - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"I-ACT - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Hut - OR (95% CI)\":\"Anopheles anabiensis (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Anopheles anabiensis (resistant)\"},\"1\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"2\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"3\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.03, 0.07)\",\"Tunnel - OR (95% CI)\":\"1.33 (1.17, 1.51)\",\"I-ACT - OR (95% CI)\":\"1.55 (1.16, 2.07)\",\"Hut - OR (95% CI)\":\"2.38 (2.09, 2.72)\",\"Huts with netting window - OR (95% CI)\":\"2.43 (1.88, 3.14)\"},\"4\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"5\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"6\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.05 (0.20, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.42 (1.19, 1.70)\",\"I-ACT - OR (95% CI)\":\"1.61 (1.05, 2.49)\",\"Hut - OR (95% CI)\":\"2.53 (1.96, 3.26)\",\"Huts with netting window - OR (95% CI)\":\"2.55 (1.76, 3.68)\"},\"7\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"8\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"9\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.04 (0.2, 0.09)\",\"Tunnel - OR (95% CI)\":\"1.23 (1.03, 1.48)\",\"I-ACT - OR (95% CI)\":\"1.50 (1.02, 2.23)\",\"Hut - OR (95% CI)\":\"2.36 (2.02, 2.77)\",\"Huts with netting window - OR (95% CI)\":\"2.32 (1.63, 3.31)\"},\"10\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Culex quinquefasciatus (resistant)\",\"WHO cone - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Tunnel - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"I-ACT - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Hut - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\",\"Huts with netting window - OR (95% CI)\":\"Culex quinquefasciatus (resistant)\"},\"11\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Overall\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"12\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"13\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.05 (0.57, 1.93)*\",\"Tunnel - OR (95% CI)\":\"2.55 (2.26, 2.55)\",\"I-ACT - OR (95% CI)\":\"13.40 (10.75, 16.67)\",\"Hut - OR (95% CI)\":\"1.50 (1.31, 1.73)\",\"Huts with netting window - OR (95% CI)\":\"1.40 (1.19, 1.63)\"},\"14\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Unwashed\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"15\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"16\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"0.74 (0.31, 1.79)*\",\"Tunnel - OR (95% CI)\":\"2.52 (2.13, 3.00)\",\"I-ACT - OR (95% CI)\":\"12.71 (9.43, 17.14)\",\"Hut - OR (95% CI)\":\"1.52 (1.23, 1.88)\",\"Huts with netting window - OR (95% CI)\":\"1.25 (1.00, 1.57)\"},\"17\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Washed, 20\\u00d7\",\"WHO cone - OR (95% CI)\":null,\"Tunnel - OR (95% CI)\":null,\"I-ACT - OR (95% CI)\":null,\"Hut - OR (95% CI)\":null,\"Huts with netting window - OR (95% CI)\":null},\"18\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00ae\",\"WHO cone - OR (95% CI)\":\"1\",\"Tunnel - OR (95% CI)\":\"1\",\"I-ACT - OR (95% CI)\":\"1\",\"Hut - OR (95% CI)\":\"1\",\"Huts with netting window - OR (95% CI)\":\"1\"},\"19\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"Interceptor\\u00aeG2\",\"WHO cone - OR (95% CI)\":\"1.46 (0.62, 3.50)*\",\"Tunnel - OR (95% CI)\":\"2.58 (2.17, 3.06)\",\"I-ACT - OR (95% CI)\":\"14.11 (10.42, 19.11)\",\"Hut - OR (95% CI)\":\"1.50 (1.25, 1.80)\",\"Huts with netting window - OR (95% CI)\":\"1.55 (1.24, 1.94)\"},\"20\":{\"Unnamed: 0_level_0 - Unnamed: 0_level_1\":\"An gambiae s.s. (susceptible)\",\"WHO cone - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Tunnel - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"I-ACT - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Hut - OR (95% CI)\":\"An gambiae s.s. (susceptible)\",\"Huts with netting window - OR (95% CI)\":\"An gambiae s.s.\n",
-                        "\n",
-                        "header: Table 1: Outcomes measured in WHO cone bioassays, tunnel, I-ACT and experimental hut tests\n",
-                        "footer: a  Not reported\n",
-                        "columns: ['Variable', 'Cone bioassay', 'WHO tunnel test', 'I-ACT', 'Experimental hut']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Primary outcome\",\"Cone bioassay\":\"72-h mortality\",\"WHO tunnel test\":\"72-h mortality\",\"I-ACT\":\"72-h mortality\",\"Experimental hut\":\"72-h mortality\"},\"1\":{\"Variable\":\"Secondary outcome\",\"Cone bioassay\":null,\"WHO tunnel test\":\"Blood-feeding\",\"I-ACT\":\"Blood-feeding\",\"Experimental hut\":\"Blood-feeding\"},\"2\":{\"Variable\":\"Other outcomes\",\"Cone bioassay\":\"Knockdown at 60 min (KD60)\",\"WHO tunnel test\":null,\"I-ACT\":null,\"Experimental hut\":\"Deterrence a and induced exophily a\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"WHO tunnel test\",\"Start_month\":8,\"Start_year\":2020,\"End_month\":11,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Interceptor G2\",\"Insecticide\":\"alpha-cypermethrin, chlorfenapyr\",\"Concentration_initial\":\"100 mg\\/m2, 200 mg\\/m2\",\"Net_washed\":20.0},\"N02\":{\"Net_type\":\"Interceptor\",\"Insecticide\":\"alpha-cypermethrin\",\"Concentration_initial\":\"200 mg\\/m2\",\"Net_washed\":20.0},\"N03\":{\"Net_type\":\"SafiNet\",\"Insecticide\":null,\"Concentration_initial\":null,\"Net_washed\":20.0}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Notes\n",
-                        "\n",
-                        "_Acknowledgments._ The authors express their sincere thanks to M. Didier Dobri, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire (CSRS) laboratory technician, and Fidele Assamoa for their support in mosquito collection and rearing, the chief and population of the village of Aboude (Agboville), and the entomology fieldworkers of CSRS. They also thank Vestergaard for supplying the long-lasting insecticidal nets tested in this study.\n",
-                        "\n",
-                        "_Author contributions._ A. M., E. C., M. K., T. W., and L. A. M. designed the study. A. M., E. C., C. E., and B. P. led the entomology field activities and participated in data collection. A. M., E. C., C. L. J., T. W., and L. A. M. performed the molecular assays. A. M., E. C., M. K., C. E., C. L. J., B. P., S. R. I, T. W., and L. A. M. were responsible for data analysis and interpretation. L. A. M. drafted the manuscript, which was revised by all coauthors. All authors read and approved the final manuscript.\n",
-                        "\n",
-                        "_Disclaimer._ The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.\n",
-                        "\n",
-                        "_Financial support._ This work was supported by the Sir Halley Stewart Trust (L.A.M.), the Wellcome Trust/Royal Society ([http://www.wellcome.ac.uk](http://www.wellcome.ac.uk) and [https://royalsociety.org](https://royalsociety.org); (101285/Z/13/Z to T.W.) and the US President's Malaria Initiative/Centers for Disease Control and Prevention (S.R.I.).\n",
-                        "\n",
-                        "_Potential conflicts of interest._ All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Study findings raise concerns regarding the operational failure of standard LLINs and support the urgent deployment of vector control interventions incorporating piperonyl butoxide, chlorfenapyr, or clothianidin in areas of high resistance intensity in Cote d'Ivoire.\n",
-                        "\n",
-                        "header: Table 1. Cox Proportional Hazard Model to Determine Impact of Long-Lasting Insecticidal Net/Insecticidal Exposure on Survival of Field-Caught Anopheles gambiae Sensu Lato 72 Hours After Exposure\n",
-                        "footer: Abbreviations: CI, confidence interval; HRR, hazard rate ratio (ratio of hazard rate for control/reference group to hazard rate for treatment group; PBO, piperonyl butoxide.\n",
-                        "a Immediate mortality rates after long-lasting insecticidal net (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. \n",
-                        "b Significance cutoff level defined as α = .05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['Insecticide Exposure', 'No. (No. of Events)', 'HRR (95% CI)', 'P Valueb']\n",
-                        "\n",
-                        "{\"0\":{\"Insecticide Exposure\":\"Untreated netting\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"1\":{\"Insecticide Exposure\":\"PermaNet 2.0 (deltamethrin only)\",\"No. (No. of Events)\":\"1135 (1047)\",\"HRR (95% CI)\":\"1.095 (.968\\u20131.239)\",\"P Valueb\":\".15\"},\"2\":{\"Insecticide Exposure\":\"PermaNet 3.0 side panels (deltamethrin only)\",\"No. (No. of Events)\":\"1157 (1088)\",\"HRR (95% CI)\":\"0.9664 (.9092\\u20131.027)\",\"P Valueb\":\".27\"},\"3\":{\"Insecticide Exposure\":\"PermaNet 3.0 roof panels (PBO + deltamethrin)\",\"No. (No. of Events)\":\"563 (533)\",\"HRR (95% CI)\":\"1.007 (.939\\u20131.079)\",\"P Valueb\":\".85\"},\"4\":{\"Insecticide Exposure\":\"Acetone control\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"5\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 1\\u00d7\",\"No. (No. of Events)\":\"676 (641)\",\"HRR (95% CI)\":\"1.006 (.9696\\u20131.043)\",\"P Valueb\":\".77\"},\"6\":{\"Insecticide Exposure\":\"Deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"683 (645)\",\"HRR (95% CI)\":\"0.9942 (.9539\\u20131.036)\",\"P Valueb\":\".78\"},\"7\":{\"Insecticide Exposure\":\"Permethrin 1\\u00d7\",\"No. (No. of Events)\":\"693 (661)\",\"HRR (95% CI)\":\"1.015 (.9698\\u20131.062)\",\"P Valueb\":\".52\"},\"8\":{\"Insecticide Exposure\":\"Clothianidin 1\\u00d7\",\"No. (No. of Events)\":\"698 (581)\",\"HRR (95% CI)\":\"1.208 (.9227\\u20131.581)\",\"P Valueb\":\".17\"},\"9\":{\"Insecticide Exposure\":\"Chlorfenapyr 1\\u00d7\",\"No. (No. of Events)\":\"708 (580)\",\"HRR (95% CI)\":\"1.692 (1.086\\u20132.637)\",\"P Valueb\":\".02\"},\"10\":{\"Insecticide Exposure\":\"PBO + deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"630 (577)\",\"HRR (95% CI)\":\"0.9662 (.2411\\u20133.873)\",\"P Valueb\":\".96\"},\"11\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"633 (601)\",\"HRR (95% CI)\":\"0.9951 (.9407\\u20131.053)\",\"P Valueb\":\".86\"},\"12\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"652 (610)\",\"HRR (95% CI)\":\"0.9942 (.9393\\u20131.052)\",\"P Valueb\":\".84\"},\"13\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"636 (583)\",\"HRR (95% CI)\":\"0.9931 (.8638\\u20131.142)\",\"P Valueb\":\".92\"},\"14\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"624 (587)\",\"HRR (95% CI)\":\"0.9951 (.917\\u20131.08)\",\"P Valueb\":\".91\"},\"15\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"623 (588)\",\"HRR (95% CI)\":\"0.9943 (.9072\\u20131.09)\",\"P Valueb\":\".90\"},\"16\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"656 (603)\",\"HRR (95% CI)\":\"1.026 (.9509\\u20131.107)\",\"P Valueb\":\".51\"},\"17\":{\"Insecticide Exposure\":\"1\\u00d7 Insecticide dose\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"18\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"117 (92)\",\"HRR (95% CI)\":\"1.016 (.9069\\u20131.138)\",\"P Valueb\":\".78\"},\"19\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"108 (78)\",\"HRR (95% CI)\":\"1.007 (.9403\\u20131.078)\",\"P Valueb\":\".84\"},\"20\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"143 (105)\",\"HRR (95% CI)\":\"1.0 (.9035\\u20131.107)\",\"P Valueb\":\">.99\"},\"21\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"114 (83)\",\"HRR (95% CI)\":\"1.0 (.9363\\u20131.068)\",\"P Valueb\":\">.99\"},\"22\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"137 (94)\",\"HRR (95% CI)\":\"1.022 (.8528\\u20131.225)\",\"P Valueb\":\".81\"},\"23\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"157 (114)\",\"HRR (95% CI)\":\"0.9952 (.9491\\u20131.044)\",\"P Valueb\":\".84\"}}\n",
-                        "\n",
-                        "header: Malaria Prevalence\n",
-                        "\n",
-                        "Of the 912 _A. gambiae_ s.l. mosquitoes assayed, 31 tested positive for _P. falciparum_ (3.4%). For PCR-confirmed _A. coluzzii_, _P. falciparum_ prevalence was 3.50% (28 of 805); the remaining 3 infections were in _A. gambiae_ s.s. (4%; 3 of 75). By resistance phenotype, susceptible _A. coluzzii_ (ie, those that died after pyrethroid exposure) were more likely to be infected with malaria, compared with resistant mosquitoes (kh2 = 4.6987; \\(P\\) =.03); infection rates were 5.94% (13 of 219) and 2.49% (10 of 401), respectively.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Phenotypic resistance was intense: >25% of mosquitoes survived exposure to 10 times the doses of pyrethroids required to kill susceptible populations. Similarly, the 24-hour mortality rate with deltamethrin-only LLINs was very low and not significantly different from that with an untreated net. Sublethal pyrethroid exposure did not induce significant delayed vector mortality effects 72 hours later. In contrast, LLINs containing the synergist piperonyl butoxide, or new insecticides dothianidin and chlorfenapyr, were highly toxic to _A. coluzzii_. Pyrethroid-susceptible _A. coluzzii_ were significantly more likely to be infected with malaria, compared with those that survived insecticidal exposure. Pyrethroid resistance was associated with significant overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_.\n",
-                        "\n",
-                        "header: Resistance Intensity and Synergist Bioassay Testing\n",
-                        "\n",
-                        "Centers for Disease Control and Prevention (CDC) resistance intensity bioassays were performed for 6 public health insecticides (pyrethroids: alpha-cypermethrin, deltamethrin, and permethrin; carbamate: bendiocarb; neonicotinoid: clothianidin; and pyrrole: chlorfenapyr) [22, 23]. The diagnostic doses of all insecticides were evaluated (including clothianidin [90 mg per bottle] [23] and chlorfenapyr [100 mg per bottle]) and 2, 5, and 10 times the diagnostic dose of pyrethroid insecticides were also used. Per test, knockdown was recorded at 15-minute intervals for 30 minutes (pyrethroids and bendiocarb) or 60 minutes (dothianidin and chlorfenapyr) of insecticide exposure. One-hour PBO preexposures were performed using WHO tube assays [24], before deltamethrin CDC bottle bioassay testing [22].\n",
-                        "\n",
-                        "WHO cone and CDC resistance intensity bioassay data were interpreted according to the WHO criteria [21, 22]. Mosquitoes that died after exposure to a LLIN or 1x insecticide dose were stored at -20degC in RNAlater (Thermo Fisher Scientific) and were considered \"susceptible\" for genotypic analysis. Surviving mosquitoes were held and scored for mortality rate after 24, 48 and 72 hours to observe delayed mortality effects. Kaplan-Meier curves were used to visualize survival data, and Cox regression was used to compare postexposure survival. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. Surviving mosquitoes at 72 hours were stored at -20degC in RNAlater and were considered \"resistant\" for genotypic analysis.\n",
-                        "\n",
-                        "header: RESULTS\n",
-                        "\n",
-                        "A total of 4917 female _A. gambiae_ s.l. mosquitoes were collected in Agboville, Cote d'Ivoire. Of those, 912, which were previously tested in either LLIN bioefficacy (n = 384) or resistance intensity (n = 528) bioassays, were selected for molecular species identification. Of the 912 selected, 805 (88.3%) were determined to be _A. coluzzii_, 75 (8.2%) were _A. gambiae_ s.s., and 22 (2.4%) were _A. gambiae_-_A. coluzzii_ hybrids; 10 individuals did not amplify.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Target-Site Mutations\n",
-                        "\n",
-                        "The same cohort of field mosquitoes (n = 912) was tested for the presence of L1014F _kdr_[27] and N1575Y mutations [28]. A subsample of mosquitoes (n = 49) that were exposed to bendiocarb, clothianidin or chlorfenapyr was tested for the presence of the G119S _Ace-1_ mutation [29]. Pearson kh2 and Fisher exact tests (when sample sizes were small) were used to investigate the statistical association between resistance status, allele frequencies, and deviations from Hardy-Weinberg equilibrium.\n",
-                        "\n",
-                        "header: Insecticide Resistance Intensity\n",
-                        "\n",
-                        "A total of 2251 field-caught _A. gambiae_ s.l. were tested in resistance bioassays. Intense pyrethroid resistance was evident with more than 25% of mosquitoes surviving exposure to 10 times the dose of insecticide required to kill a susceptible population (Figure 2A). At the diagnostic dose, mosquito mortality rates did not exceed 25% for any pyrethroid tested, which was consistent with the high survival rates observed during cone bioassays using conventional LLINs (Figure 1). In general, levels of resistance to alpha-cypermethrin, deltamethrin, and permethrin were not significantly different at each insecticide concentration tested (Figure 2A).\n",
-                        "\n",
-                        "By comparison, carbamate tolerance was low, with a mean knockdown of 94.53% (95% CI, 92.11%-96.95%; n = 101) after 30 minutes of exposure to the diagnostic dose of bendiocarb. Similarly, high levels of susceptibility to new insecticides clothianidin and chlorfenapyr were observed, with mean mortality rates of 94.11% (95% CI, 93.43%-94.80%; n = 102) and 95.54% (94.71%-96.36%; n = 112), respectively, 72 hours after exposure to the tentative diagnostic doses. Preexposure to PBO increased the average _A. gambiae_ s.l. mortality rate significantly, from 14.56% (95% CI, 6.24%-22.88%) to 72.73% (64.81%-79.43%) and from 44.66% (34.86%-54.46%) to 94.17% (91.12%-97.22%) after exposure to 1 or 2 times the diagnostic dose of deltamethrin (Figure 2B).\n",
-                        "\n",
-                        "header: Target-Site Resistance Mutations\n",
-                        "\n",
-                        "L1014_F_kdr_ screening revealed that 92.2% (796/863) of _A. gambiae_ s.l. mosquitoes harbored the mutation; 71.5% (617 of 863) were homozygous, 20.7% (179 of 863) were heterozygous, 5.1% (44 of 863) were wild type, and 2.7% (23 of 863) did not amplify. For PCR-confirmed _A. coluzzii_, L1014F_kdr_ prevalence was 87.8% (707 of 805); 66.6% (536 of 805) were homozygous for the mutation, 21.2% (171 of 805) were heterozygous, 5.3% (43 of 805) were wild type, and 2.2% (18 of 805) did not amplify. For _A. coluzzii_, population-level L1014F_kdr_ allele frequency was 0.83, with evidence for significant deviations from Hardy-Weinberg equilibrium (kh2 = 29.124; \\(P\\) <.001). There was no significant association between L1014F_kdr_ frequency and the ability of _A. coluzzii_, to survive pyrethroid exposure, in either LLIN or resistance bioassays (kh2 = 2.0001 [_P_ =.16] and kh2 = 3.6998 [_P_ =.054], respectively). Similarly, there was no significant association between L1014F_kdr_ and the ability of _A. coluzzii_ to survive PBO preexposure and pyrethroid treatment, in either LLIN or resistance bioassays (kh2 = 0.0086; \\(P\\) =.93; Fisher exact test, \\(P\\) =.429, respectively).\n",
-                        "\n",
-                        "For PCR-confirmed _A. gambiae_ s.s., L1014F_kdr_ prevalence was 95.3% (61 of 64); 89.1% (57 of 64) were homozygous for the mutation, 6.3% (4 of 64) were heterozygous, none were wild type, and 4.7% (3 of 64) did not amplify. There was no significant association between L1014F_kdr_ frequency and ability of _A. gambiae_ s.s. to survive pyrethroid or PBO preexposure and pyrethroid treatment (in either LLIN or resistance bioassays), because all tested individuals harbored this mutation (n = 61). For _A. gambiae_ s.s., the population-level L1014F_kdr_ allele frequency was 0.97, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.070; \\(P\\) =.79).\n",
-                        "\n",
-                        "N1575Y screening revealed that 2.3% of _A. gambiae_ s.l. mosquitoes (21 of 912) harbored the mutation; all were\n",
-                        "heterozygotes. N1575Y prevalence was 1.1% (9 of 805) and 16% (12 of 75) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 0.99% (9 of 912) did not amplify. There was no evidence for ongoing N1575Y selection in either species (kh2 = 0.026 [_P_ =.87] and kh2 = 0.62 [_P_ =.43] for _A. coluzzi_ and _A. gambiae_ s.s., respectively). For _A. coluzzi_, there was no significant association between N1575Y frequency and ability of mosquitoes to survive pyrethroid exposure, in LLIN or resistance bioassays (kh2 = 0.0001 [_P_ =.99] and kh2 = 0.3244 [_P_ =.57], respectively).\n",
-                        "\n",
-                        "G119S _Ace-1_ screening revealed that 55.1% of _A. gambiae_ s.l. mosquitoes (27 of 49) harbored the mutation; all were heterozygotes. G119S _Ace-1_ prevalence was 64.9% (24 of 37) and 27.3% (3 of 11) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 1 remaining _A. gambiae-A. coluzzi_ hybrid was wild type. For _A. coluzzi_, population-level G119S _Ace-1_ allele frequency was 0.32, with evidence of significant deviations from Hardy-Weinberg equilibrium (kh2 = 8.525; \\(P\\) =.004). For _A. gambiae_ s.s., population-level G119S _Ace-1_ allele frequency was 0.14, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.274; \\(P\\) =.60). For _A. coluzzi_, there was a significant association between G119S _Ace-1_ frequency and surviving bendiocarb exposure (Fisher exact test, \\(P\\) =.005).\n",
-                        "\n",
-                        "header: Study Area and Mosquito Collections: WHO Cone Bioassay Testing\n",
-                        "\n",
-                        "Two types of LLIN were evaluated in this study. PermaNet 2.0 is a conventional LLIN treated with deltamethrin only (1.4 g/kg +- 25%) and PermaNet 3.0 is a piperonyl butoxide (PBO) synergism LILIN, consisting of a roof containing PBO (25g/kg) and deltamethrin (4 g/kg +- 25%) and side panels containing deltamethrin only (2.8 g/kg +- 25%). WHO cone bioassays were used to test the susceptibility of _A. gambiae_ s.l. exposed to unwashed PermaNet 2.0, PermaNet 3.0 roof panels, and PermaNet 3.0 side panels [21]. To control for potential variation in insecticide/synergist content, each of 5 LLINs per type was cut into 19 pieces, measuring 30 x 30 cm, with each piece tested a maximum of 3 times.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Association of Reduced Long-Lasting Insecticidal Net Efficacy and Pyrethroid Insecticide Resistance With Overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_ in Populations of _Anopheles coluzzii_ From Southeast Cote d'Ivoire\n",
-                        "\n",
-                        "Anne Meiwald,\\({}^{1,2}\\) Emma Clark,\\({}^{1,2}\\) Mejica Kristan,\\({}^{1}\\) Constant Edi,\\({}^{2}\\) Claire L Jeffries,\\({}^{1}\\) Bethanie Pelloquin,\\({}^{1}\\) Seth R. Irish,\\({}^{2}\\) Thomas Walker,\\({}^{1}\\) and Louisa A. Messenger\\({}^{1,0}\\)\n",
-                        "\n",
-                        "\n",
-                        "Resistance to major public health insecticides in Cote d'Ivoire has intensified and now threatens the long-term effectiveness of malaria vector control interventions.\n",
-                        "\n",
-                        "header: Mosquito Survival After Insecticidal Exposure\n",
-                        "\n",
-                        "All _A. gambiae_ s.l. tested in LLIN bioefficacy or resistance intensity bioassays, were held for 72 hours, to assess any impact of insecticide or net exposure on delayed mortality rate. For LLIN bioassays, there was little evidence for any reduction in survival during this holding period (Cox regression \\(P\\) =.15,.27, and.85, respectively, for comparisons between untreated control and PermaNet 2.0, PermaNet 3.0 side panels, and PermaNet 3.0 roof panels) (Table 1 and Figure 3A). Exposure to the diagnostic doses of all insecticides in CDC bottle bioassays did not induce significant delayed mortality effects over 72 hours (Cox regression \\(P\\) >.05 for all insecticides compared with control, with the exception of chlorfenapyr [_P_ =.02]) (Table 1 and Figure 3B). This phenomenon was also observed at increasing pyrethroid doses\n",
-                        "\n",
-                        "Figure 2: _A._ Resistance intensity of field-caught _Anaopheles gambiae_ sensu late (s.l.) after exposure to 1, 2, 5, or 10 times the diagnostic dose of pyrethroid insecticides. Mean knockdown/acute toxicity rates after 30-minute exposure are shown with 95% confidence intervals (Cls). Knockdown or mortality rates at the same dose per insecticide sharing a letter do not differ significantly (_P_ >.05). Mortality rates <00% (_lower and line_) represent confirmed resistance at the diagnostic dose (1a), and rates <00% (_upper and line_) indicate moderate to high-intensity resistance and high-intensity resistance at 5° and 10x, respectively, as defined by the World Health Organization [24]. _B._ Restoration of deltamethrin susceptibility of field-caught _A. gambiae_ s.l. after presopause to piperonyl butoxide (PBO). Mean knockdown/acute toxicity after 30-minute exposure to 1 or 2 times the diagnostic dose of deltamethrin with 95% Cls. Knockdown/mortality rates do not differ significantly between pyrethroid only and synergist plus pyrethroid sharing a letter (_P_ >.05). Red line at 90% mortality rate represents metabolic resistance mechanisms partially involved [24].\n",
-                        "\n",
-                        "\n",
-                        "(Cox regression \\(P\\) >.05 for alpha-cypermethrin, deltamethrin, and permethrin 5x and 10x vs either the control or diagnostic dose) (Table 1; Figure 3C and 3D).\n",
-                        "\n",
-                        "header: Metabolic Resistance Mechanisms\n",
-                        "\n",
-                        "Comparison of metabolic gene expression levels in field populations of _A. coluzzi_ and _A. gambiae s.s._ demonstrated significant up-regulation of _CYP6P4_ (fold change, 5.88 [95% CI, 5.19-44.06] for _A. coluzzi_ and 6.08 [5.43-50.64] for _A. gambiae_ s.s.), _CYP6Z1_ (4.04 [3.69-41.54] and 3.56 [3.24-36.25], respectively),\n",
-                        "\n",
-                        "Figure 3.: The longevity of field-caught _A. gambiae_ sensus into after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (_A_) and \\(x\\) (B, 5x [_O_], and 10× [_D_) the diagnostic dose of pyrethroid insecticides in Centres for Disease Control and Prevention resistance intensity assays. Kaplan-Meier survival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\n",
-                        "\n",
-                        "\n",
-                        "and _CYP6P3_ (12.56 [11.40-12-3.83] and 13.85 [12.53-132.03]), relative to a susceptible laboratory colony, respectively (Figure 4). More modest overexpression of _CYP6P1_ and _GSTE2_ was observed (_CYP6P1_ fold changes, 1.18 [95% CI, 1.08-12.31] and 1.28 [1.17-14.40]; GSTE2, 0.56 [.48-3.32], and 0.67 [.58-4.29], for _A. coluzzi_ and _A. gambiae_ s.s., respectively) (Figure 4). The fold change levels did not differ significantly between the 2 species for any gene nor by malaria infection status in wild _A. coluzzi_. Comparison of metabolic gene expression in phenotyped field populations of _A. coluzzi_ revealed lower fold changes overall, but notably, increased overexpression of _CYP6P3_ in survivors of bendiocarb, deltamethrin, PBO plus deltamethrin, and permethrin (fold change, 3.91 [95% CI, 3.33-22.16], 2.21 [1.88-12.53], 2.64 [2.21-13.69], and 2.21 [1.99-20.03], respectively) (Figure 5).\n",
-                        "\n",
-                        "header: Keywords\n",
-                        "\n",
-                        "_Anopheles coluzzii_; insecticide resistance; _Plasmodium falciparum_; long-lasting insecticidal nets; Cote d'Ivoire; PBO; chlorfenapyr; clothianidin; _CYP6P4_; _CYP6P3_; _CYP6Z1_\n",
-                        "\n",
-                        "In Cote d'Ivoire, malaria is a serious public health problem with the entire population of about 26.2 million people at risk, and disease prevalence reaching as high as 63% in the southwest region [1]. Control of _Anopheles gambiae_ sensu lato (s.l.), the major malaria vector species group, has been through the efforts of the National Malaria Control Programme, which has distributed insecticide-treated nets as the primary vector control intervention. Indoor residual spraying and larviciding in high transmission areas have been recommended as complementary strategies; implementation of the former commenced in late 2020 [2]. Estimates of net coverage across the country remain low, with the proportion of households with at least >=1 insecticide-treated net per 2 persons rising from 31% in 2012 to 47% in 2016, and insecticide-treated net use stagnating at 40% of households reporting sleeping under a net the previous night in both survey years [2]. The most recent universal net campaigns in Cote d'Ivoire in 2017-2018 issued conventional, pyrethroid (deltamethrin) long-lasting insecticidal nets (LLINs), aiming to achieve 90% coverage and 80% use [2]. However, country-wide, multiclass insecticide resistance among populations of _A. gambiae_ s.l. is a growing cause for concern because of potential operational failure of current vector control strategies, both locally and across the sub-Saharan region [2, 3].\n",
-                        "\n",
-                        "Resistance to pyrethroid and carbamate insecticides in _Anopheles_ mosquitoes was first reported from the central \n",
-                        "region of Cote d'Ivoire in the early 1990s [4, 5, 6, 7]. Local resistance to the major insecticide classes recommended by the World Health Organization (WHO) for adult mosquito control--pyrethroids, carbamates, organophosphates, and organochlorines--evolved rapidly [8, 9, 10] and has been increasing in intensity, driven largely by selective pressures imposed by contemporaneous scale-up of public health vector control interventions (including those targeting malaria, trypanosomiasis, and onchocerciasis vectors) and use of agricultural pesticides [11, 12, 13, 14, 7]. This escalation in resistance has now begun to compromise the insecticidal efficacy and community-wide impact of conventional, pyrethroid LLINs in Cote d'Ivoire [14, 15], although some levels of personal protection may still remain [15, 16, 17].\n",
-                        "\n",
-                        "Among vector populations across Cote d'Ivoire, the L1014F _kdr_ mutation is pervasive and has been implicated in some longitudinal trends in decreasing DDT and pyrethroid susceptibility [7, 11]; L1014S _kdr_ and N1575Y resistance mutations have also been detected but at much lower frequencies [18]. Extreme carbamate (bendiocarb) resistance and pyrethroid cross-resistance in some _A. gambiae_ sensu stricto (s.s.) populations are mediated by overexpression of _CYP6P3_ and _CYP6M2_ and duplication of the G119S _Ace-1_ mutation [19]. To support and safeguard future malaria control efforts in Cote d'Ivoire, the current study evaluated the efficacy of conventional and next-generation LLINs for prospective distribution, determined current insecticide resistance profiles of _A. gambiae_ s.l. (principally _Anopheles coluzzi_), and characterized underlying molecular and metabolic resistance mechanisms.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Metabolic Gene Expression\n",
-                        "\n",
-                        "Relative expression of 5 metabolic genes (_CYP6P3_, _CYP6P4_, _CYP6Z1_, _CYP6P1_, and _GSTE2_) was measured in all field collected mosquitoes (n = 912), using multiplex quantitative real-time polymerase chain reaction (PCR) assays, relative to the housekeeping gene ribosomal protein S7 (_RPS7_) [30]. In addition, gene expression levels were measured in susceptible _A. coluzzii_ Nguosso colony mosquitoes (n = 48). All samples were run in technical triplicate. Expression level and fold change of each target gene between resistant and susceptible field samples, relative to the susceptible laboratory strain, were calculated using the 2-\\({}^{\\text{-}\\Delta\\text{ACT}}\\) method incorporating PCR efficiency, normalized relative to the endogenous control gene (_RPS7_).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Cote d'Ivoire has hot spots with some of the highest levels of resistance of _Anopheles_ mosquitoes to public health insecticides worldwide, with potentially severe implications for sustaining gains in malaria control [31]. To safeguard malaria vector control efforts and inform the design of effective resistance management strategies, involving tactical deployment of differing indoor residual spraying and LLIN modalities, there needs to be a clear understanding of contemporary phenotypic and genotypic insecticide resistance.\n",
-                        "\n",
-                        "Our study detected intense pyrethroid resistance in southeast Cote d'Ivoire, as evidenced by high proportions of survivors, after exposure to 10 times the diagnostic doses of pyrethroids, as well as very low knockdown and 24-hour mortality rates for deltamethrin-only LLINs, equivalent to rates for an untreated net. These findings are largely in agreement with historical resistance profiles from this region [7, 10, 11] and indicate that conventional LLINs may no longer be operationally viable in areas of high pyrethroid resistance intensity. Previous phase II studies of pyrethroid-only LLINs in the central region of Cote d'Ivoire have demonstrated similarly poor efficacy with highly resistant _A. gambiae_ s.l. populations but argued for the retention of some degree of personal protection [15-17].\n",
-                        "\n",
-                        "Other observational cohorts have reported higher incidences of malaria among non-net users compared with users in areas of moderate to high pyrethroid resistance [17]. The extent of protective efficacy afforded by pyrethroid LLINs will likely reflect the strength of local vector resistance and levels of both net physical integrity and individual compliance [32, 33]; in Cote d'Ivoire, reported LLIN usage has been low, requiring additional behavioral interventions [2, 34]. Our findings of high mosquito mortality rates after exposure to clothianidin and chlorfenapyr and improved vector susceptibility with PBO treatment (on both LLINs and in resistance bioassays), are consistent with data from other sentinel sites across Cote d'Ivoire [16, 35, 36], and strongly support the deployment of vector control interventions incorporating these new active ingredients.\n",
-                        "\n",
-                        "Study results indicate that _A. coluzzi_ was the predominant local vector species during the rainy season, as observed previously [7], circulating sympatrically with smaller proportions of _A. gambiae_ s.s. These 2 vector species commonly cohabit but can be genetically distinct in terms of resistance mechanisms [37, 38] and can also differ in larval ecology, behavior, migration, and activation [39-41]. In general, resistance mechanisms in _A. coluzzi_ are less well characterized, compared with _A. gambiae_ s.s., in part because these vectors are morphologically\n",
-                        "\n",
-                        "Figure 4: Metabolic gene expression in field _Anopheles coluzzi_ and _Anopheles gambiae_ sensu stricto (s.s.) populations relative to a susceptible colony population. Error bars represent 95% confidence intervals. Statistically significant differences in expression levels relative to the susceptible colony are indicated as follows: *_P_ <.05; **_P_ <.01; ***_P_ <.001.\n",
-                        "\n",
-                        "\n",
-                        "indistinguishable and few studies present data disaggregated by PCR-confirmed species.\n",
-                        "\n",
-                        "We observed several distinct features in our study, including, principally, evidence for ongoing selection of L1014F _kdr_ and G119S _Ace-1 in A. coluzzii_, which was absent in _A. gambiae_ s.s. and higher proportions of N1575Y in _A. gambiae_ s.s.; expression levels of metabolic genes were comparable between species. The lack of association between L1014F _kdr_ genotype and mosquito phenotype, coupled with the identification of 3 CYP450 enzymes (_CYP6P4, CYP6P3,_ and _CYP6Z1_) that were significantly overexpressed in field populations (some of which are known to metabolize pyrrethroids and next-generation LLIN insecticides [42, 43]), indicate a key role for metabolic resistance in this _A. coluzzii_ population. One notable difference in our data set, compared with previous findings in Agboville [7], was the finding of benodiocarb susceptibility. This may be attributable to small-scale spatial and longitudinal heterogeneity in resistance, which can be highly dynamic [37, 44], and/or phenotypic differences between vector species, complicating intervention choice for resistance management.\n",
-                        "\n",
-                        "With the exception of chlorfenapyr, which is known to be a slow-acting insecticide, no delayed mortality effects were detected after insecticidal exposure; the format and dose used for clothianidin testing (another slow-acting insecticide [45]) were instead intended to measure acute toxicity within a 60-minute exposure period. Previous mathematical models using resistant mosquito colonies have suggested that sublethal insecticide treatment may still reduce vector lifespan and inhibit blood-feeding and host-seeking behaviors, thereby interrupting malaria transmission [46, 47]. Our observations are more compatible with reports from Burkina Faso, where different exposure regimens of wild, resistant _A. gambiae_ s.l. populations to deltamethrin LLINs did not induce any delayed mortality effects [47]. Further assessment of sublethal effects are warranted across additional field populations with differing resistance mechanisms, to clarify the impact of insecticidal exposure on the vectorial capacity of resistant mosquitoes.\n",
-                        "\n",
-                        "To date there is a paucity of data regarding the interactions between insecticide resistance and _Plasmodium_ development [48]. In the current study, _A. coluzzii_ that died after pyrethroid exposure were significantly more likely to be infected with malaria. This might be explained by elevated metabolic enzymes and/or prior pyrethroid exposure detrimentally affecting parasite development [49], although it is important to note that we did not detect any significant differences between gene overexpression in malaria-infected versus noninfected _A. coluzzii_. Alternatively, our sampled population may have been physiologically older, as phenotypic resistance is known to decline with age [50]. It is impossible to distinguish between these hypotheses using field-collected vector populations; the experimental design used in this study had other biological and technical limitations, which have been described in detail elsewhere [23, 37]\n",
-                        "\n",
-                        "In conclusion, as new combination and bitreated vector control interventions become available for deployment, contemporary resistance information is crucial for the rationale design of management strategies and to mitigate further selection for particular resistance mechanisms. The results from the current study contribute to growing insecticide resistance data for Cote d'Ivoire, demonstrating a loss of bioefficacy of pyrethroid LLINs and supporting the use of new active ingredients (clothianidin, chlorfenapyr, and PBO). Study findings also highlight the need for expanded insecticide resistance surveillance, including monitoring of metabolic resistance mechanisms, in conjunction with studies to better characterize the impact of sublethal insecticide exposure on vectorial capacity and the interaction between insecticide resistance and _Plasmodium_ parasite development.\n",
-                        "\n",
-                        "Figure 5: Metabolic gene expression in resistant versus susceptible field _Anopheles coluzzi_, which either died or survived after insecticidal exposure. Error bars represent 95% confidence intervals.\n",
-                        "\n",
-                        "header: Mosquito Collections and Species Identification: LIII Efficacy\n",
-                        "\n",
-                        "A total of 2666 field-caught _A. gambiae_ s.l were used to assess the bioefficacy of conventional pyrethroid-treated LLINs (PermaNet 2.0 and PermaNet 3.0 side panels) and next-generation synergisti LLINs (PermaNet 3.0 roof panels), compared with an untreated control (Figure 1). Overall, _A. gambiae_ s.l. knockdown and mortality rates with deltamethrin LLINs were very low and largely equivalent to those for the untreated control net (Figure 1). At 60 minutes, average mosquito knockdown rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels were 1.56% (95% confidence interval [CI], 1.13%-1.99%), 0.54% (.42%-6.5%), and 1.75% (1.49%-2.0%), respectively. By contrast, average mosquito knockdown rates for PBO-containing PermaNet 3.0 roof panels were significantly higher (79.8% [95% CI, 79.07%-80.48%]; kh2 = 705.51, 968.65, and 937.33 [_P_ <.001] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1).\n",
-                        "\n",
-                        "At 24 hours, mortality rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels remained low (6.11% [95% CI, 4.71%-7.51%], 5.44% [4.58%-6.29%], and 3.66% [3.12%-4.19%], respectively), while those with PermaNet 3.0 roof panels increased only marginally but still remained significantly higher (83.81% [95% CI, 83.15%-84.47%];\n",
-                        "\n",
-                        "Figure 1.: Bioefficacy of different unwashed long-lasting insecticidal nets (LLINs) against field-caught _Angabies gambiae_ sensu tatto. Mean knockdown and mortality rates are shown with 95% confidence intervals, at 60 minutes and 24 hours, respectively, after 3-minute exposure to PermaNet 2.0 (deltamethrin only), side panels of PermaNet 3.0 (deltamethrin only, roof panels of PermaNet 3.0 (pieronym) butoxide plus deltamethrin), and an untreated control net. Knockdown or mortality rates in the same time period for each treatment sharing a letter do not differ significantly (_P_ >.05). Green lines at 275% and 295% knockdown represent minimal and optimal effectiveness, respectively, at 60 minutes. Red lines at 250% and >80% mortality represent minimal and optimal LLIN effectiveness at 24 hours, respectively, as defined by the World Health Organization [21].\n",
-                        "\n",
-                        "\n",
-                        "\\(\\chi^{2}\\) = 727.96, 914.61, and 963.09 [\\(P\\) <.001 for all] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1). PermaNet 3.0 roof panels reached minimal effectiveness (knockdown, >=75%) 60 minutes after exposure and optimal effectiveness (mortality rate, >=80%) at 24 hours. Neither of the deltamethrin-only LLINS reached either effectiveness threshold at any time point.\n",
-                        "\n",
-                        "header: Mosquito Processing, Identification of _A. gambiae_ s.l. Species Complex\n",
-                        "\n",
-                        "Members, and _Plasmodium falciparum_ Detection\n",
-                        "\n",
-                        "A subsample of field-caught mosquitoes tested in bioassays was selected for molecular analysis (n = 912). Approximately equal numbers of specimens were chosen to represent phenotypically \"susceptible\" or \"resistant\" mosquitoes for each LLIN type or insecticide dose, selected across different replicates and testing days to capture as much population-level variation as possible. RNA was extracted from individual whole-body mosquitoes according to standard protocols [23]. Field _A. gambiae_ s.l. were identified to species level [25] and were screened for the presence of _Plasmodium falciparum_[26].\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study protocol was approved by the Comite National d'Ethique des Sciences de la Vie et de la Sante (no. 069-19/MSHP/CNESVS-kp) and the London School of Hygiene and Tropical Medicine (nos. 16782 and 16899). Study activities were conducted in the village of Aboude, rural Agboville, Agneby-Tiassa region, southeast Cote d'Ivoire (5'55'N, 4'13'W), selected because of its high mosquito densities and malaria prevalence [1]. Adult mosquitoes were collected using human landing catches, inside and outside households from 6 pm to 6 am, for a total of 190 person/trap/nights between 5 and 26 July 2019. Unfed mosquitoes, morphologically identified as _A. gambiae_ s.l. [20], were tested in bioassays that same day, after a brief recovery period; blood-fed mosquitoes were first held for 2-3 days to allow for blood-meal digestion.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Study findings raise concerns regarding the operational failure of standard LLINs and support the urgent deployment of vector control interventions incorporating piperonyl butoxide, chlorfenapyr, or clothianidin in areas of high resistance intensity in Cote d'Ivoire.\n",
-                        "\n",
-                        "header: Insecticide Resistance Intensity\n",
-                        "\n",
-                        "A total of 2251 field-caught _A. gambiae_ s.l. were tested in resistance bioassays. Intense pyrethroid resistance was evident with more than 25% of mosquitoes surviving exposure to 10 times the dose of insecticide required to kill a susceptible population (Figure 2A). At the diagnostic dose, mosquito mortality rates did not exceed 25% for any pyrethroid tested, which was consistent with the high survival rates observed during cone bioassays using conventional LLINs (Figure 1). In general, levels of resistance to alpha-cypermethrin, deltamethrin, and permethrin were not significantly different at each insecticide concentration tested (Figure 2A).\n",
-                        "\n",
-                        "By comparison, carbamate tolerance was low, with a mean knockdown of 94.53% (95% CI, 92.11%-96.95%; n = 101) after 30 minutes of exposure to the diagnostic dose of bendiocarb. Similarly, high levels of susceptibility to new insecticides clothianidin and chlorfenapyr were observed, with mean mortality rates of 94.11% (95% CI, 93.43%-94.80%; n = 102) and 95.54% (94.71%-96.36%; n = 112), respectively, 72 hours after exposure to the tentative diagnostic doses. Preexposure to PBO increased the average _A. gambiae_ s.l. mortality rate significantly, from 14.56% (95% CI, 6.24%-22.88%) to 72.73% (64.81%-79.43%) and from 44.66% (34.86%-54.46%) to 94.17% (91.12%-97.22%) after exposure to 1 or 2 times the diagnostic dose of deltamethrin (Figure 2B).\n",
-                        "\n",
-                        "header: Malaria Prevalence\n",
-                        "\n",
-                        "Of the 912 _A. gambiae_ s.l. mosquitoes assayed, 31 tested positive for _P. falciparum_ (3.4%). For PCR-confirmed _A. coluzzii_, _P. falciparum_ prevalence was 3.50% (28 of 805); the remaining 3 infections were in _A. gambiae_ s.s. (4%; 3 of 75). By resistance phenotype, susceptible _A. coluzzii_ (ie, those that died after pyrethroid exposure) were more likely to be infected with malaria, compared with resistant mosquitoes (kh2 = 4.6987; \\(P\\) =.03); infection rates were 5.94% (13 of 219) and 2.49% (10 of 401), respectively.\n",
-                        "\n",
-                        "header: Table 1. Cox Proportional Hazard Model to Determine Impact of Long-Lasting Insecticidal Net/Insecticidal Exposure on Survival of Field-Caught Anopheles gambiae Sensu Lato 72 Hours After Exposure\n",
-                        "footer: Abbreviations: CI, confidence interval; HRR, hazard rate ratio (ratio of hazard rate for control/reference group to hazard rate for treatment group; PBO, piperonyl butoxide.\n",
-                        "a Immediate mortality rates after long-lasting insecticidal net (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. \n",
-                        "b Significance cutoff level defined as α = .05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['Insecticide Exposure', 'No. (No. of Events)', 'HRR (95% CI)', 'P Valueb']\n",
-                        "\n",
-                        "{\"0\":{\"Insecticide Exposure\":\"Untreated netting\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"1\":{\"Insecticide Exposure\":\"PermaNet 2.0 (deltamethrin only)\",\"No. (No. of Events)\":\"1135 (1047)\",\"HRR (95% CI)\":\"1.095 (.968\\u20131.239)\",\"P Valueb\":\".15\"},\"2\":{\"Insecticide Exposure\":\"PermaNet 3.0 side panels (deltamethrin only)\",\"No. (No. of Events)\":\"1157 (1088)\",\"HRR (95% CI)\":\"0.9664 (.9092\\u20131.027)\",\"P Valueb\":\".27\"},\"3\":{\"Insecticide Exposure\":\"PermaNet 3.0 roof panels (PBO + deltamethrin)\",\"No. (No. of Events)\":\"563 (533)\",\"HRR (95% CI)\":\"1.007 (.939\\u20131.079)\",\"P Valueb\":\".85\"},\"4\":{\"Insecticide Exposure\":\"Acetone control\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"5\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 1\\u00d7\",\"No. (No. of Events)\":\"676 (641)\",\"HRR (95% CI)\":\"1.006 (.9696\\u20131.043)\",\"P Valueb\":\".77\"},\"6\":{\"Insecticide Exposure\":\"Deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"683 (645)\",\"HRR (95% CI)\":\"0.9942 (.9539\\u20131.036)\",\"P Valueb\":\".78\"},\"7\":{\"Insecticide Exposure\":\"Permethrin 1\\u00d7\",\"No. (No. of Events)\":\"693 (661)\",\"HRR (95% CI)\":\"1.015 (.9698\\u20131.062)\",\"P Valueb\":\".52\"},\"8\":{\"Insecticide Exposure\":\"Clothianidin 1\\u00d7\",\"No. (No. of Events)\":\"698 (581)\",\"HRR (95% CI)\":\"1.208 (.9227\\u20131.581)\",\"P Valueb\":\".17\"},\"9\":{\"Insecticide Exposure\":\"Chlorfenapyr 1\\u00d7\",\"No. (No. of Events)\":\"708 (580)\",\"HRR (95% CI)\":\"1.692 (1.086\\u20132.637)\",\"P Valueb\":\".02\"},\"10\":{\"Insecticide Exposure\":\"PBO + deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"630 (577)\",\"HRR (95% CI)\":\"0.9662 (.2411\\u20133.873)\",\"P Valueb\":\".96\"},\"11\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"633 (601)\",\"HRR (95% CI)\":\"0.9951 (.9407\\u20131.053)\",\"P Valueb\":\".86\"},\"12\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"652 (610)\",\"HRR (95% CI)\":\"0.9942 (.9393\\u20131.052)\",\"P Valueb\":\".84\"},\"13\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"636 (583)\",\"HRR (95% CI)\":\"0.9931 (.8638\\u20131.142)\",\"P Valueb\":\".92\"},\"14\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"624 (587)\",\"HRR (95% CI)\":\"0.9951 (.917\\u20131.08)\",\"P Valueb\":\".91\"},\"15\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"623 (588)\",\"HRR (95% CI)\":\"0.9943 (.9072\\u20131.09)\",\"P Valueb\":\".90\"},\"16\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"656 (603)\",\"HRR (95% CI)\":\"1.026 (.9509\\u20131.107)\",\"P Valueb\":\".51\"},\"17\":{\"Insecticide Exposure\":\"1\\u00d7 Insecticide dose\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"18\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"117 (92)\",\"HRR (95% CI)\":\"1.016 (.9069\\u20131.138)\",\"P Valueb\":\".78\"},\"19\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"108 (78)\",\"HRR (95% CI)\":\"1.007 (.9403\\u20131.078)\",\"P Valueb\":\".84\"},\"20\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"143 (105)\",\"HRR (95% CI)\":\"1.0 (.9035\\u20131.107)\",\"P Valueb\":\">.99\"},\"21\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"114 (83)\",\"HRR (95% CI)\":\"1.0 (.9363\\u20131.068)\",\"P Valueb\":\">.99\"},\"22\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"137 (94)\",\"HRR (95% CI)\":\"1.022 (.8528\\u20131.225)\",\"P Valueb\":\".81\"},\"23\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"157 (114)\",\"HRR (95% CI)\":\"0.9952 (.9491\\u20131.044)\",\"P Valueb\":\".84\"}}\n",
-                        "\n",
-                        "header: Notes\n",
-                        "\n",
-                        "_Acknowledgments._ The authors express their sincere thanks to M. Didier Dobri, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire (CSRS) laboratory technician, and Fidele Assamoa for their support in mosquito collection and rearing, the chief and population of the village of Aboude (Agboville), and the entomology fieldworkers of CSRS. They also thank Vestergaard for supplying the long-lasting insecticidal nets tested in this study.\n",
-                        "\n",
-                        "_Author contributions._ A. M., E. C., M. K., T. W., and L. A. M. designed the study. A. M., E. C., C. E., and B. P. led the entomology field activities and participated in data collection. A. M., E. C., C. L. J., T. W., and L. A. M. performed the molecular assays. A. M., E. C., M. K., C. E., C. L. J., B. P., S. R. I, T. W., and L. A. M. were responsible for data analysis and interpretation. L. A. M. drafted the manuscript, which was revised by all coauthors. All authors read and approved the final manuscript.\n",
-                        "\n",
-                        "_Disclaimer._ The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.\n",
-                        "\n",
-                        "_Financial support._ This work was supported by the Sir Halley Stewart Trust (L.A.M.), the Wellcome Trust/Royal Society ([http://www.wellcome.ac.uk](http://www.wellcome.ac.uk) and [https://royalsociety.org](https://royalsociety.org); (101285/Z/13/Z to T.W.) and the US President's Malaria Initiative/Centers for Disease Control and Prevention (S.R.I.).\n",
-                        "\n",
-                        "_Potential conflicts of interest._ All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.\n",
-                        "\n",
-                        "header: Resistance Intensity and Synergist Bioassay Testing\n",
-                        "\n",
-                        "Centers for Disease Control and Prevention (CDC) resistance intensity bioassays were performed for 6 public health insecticides (pyrethroids: alpha-cypermethrin, deltamethrin, and permethrin; carbamate: bendiocarb; neonicotinoid: clothianidin; and pyrrole: chlorfenapyr) [22, 23]. The diagnostic doses of all insecticides were evaluated (including clothianidin [90 mg per bottle] [23] and chlorfenapyr [100 mg per bottle]) and 2, 5, and 10 times the diagnostic dose of pyrethroid insecticides were also used. Per test, knockdown was recorded at 15-minute intervals for 30 minutes (pyrethroids and bendiocarb) or 60 minutes (dothianidin and chlorfenapyr) of insecticide exposure. One-hour PBO preexposures were performed using WHO tube assays [24], before deltamethrin CDC bottle bioassay testing [22].\n",
-                        "\n",
-                        "WHO cone and CDC resistance intensity bioassay data were interpreted according to the WHO criteria [21, 22]. Mosquitoes that died after exposure to a LLIN or 1x insecticide dose were stored at -20degC in RNAlater (Thermo Fisher Scientific) and were considered \"susceptible\" for genotypic analysis. Surviving mosquitoes were held and scored for mortality rate after 24, 48 and 72 hours to observe delayed mortality effects. Kaplan-Meier curves were used to visualize survival data, and Cox regression was used to compare postexposure survival. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. Surviving mosquitoes at 72 hours were stored at -20degC in RNAlater and were considered \"resistant\" for genotypic analysis.\n",
-                        "\n",
-                        "header: Mosquito Survival After Insecticidal Exposure\n",
-                        "\n",
-                        "All _A. gambiae_ s.l. tested in LLIN bioefficacy or resistance intensity bioassays, were held for 72 hours, to assess any impact of insecticide or net exposure on delayed mortality rate. For LLIN bioassays, there was little evidence for any reduction in survival during this holding period (Cox regression \\(P\\) =.15,.27, and.85, respectively, for comparisons between untreated control and PermaNet 2.0, PermaNet 3.0 side panels, and PermaNet 3.0 roof panels) (Table 1 and Figure 3A). Exposure to the diagnostic doses of all insecticides in CDC bottle bioassays did not induce significant delayed mortality effects over 72 hours (Cox regression \\(P\\) >.05 for all insecticides compared with control, with the exception of chlorfenapyr [_P_ =.02]) (Table 1 and Figure 3B). This phenomenon was also observed at increasing pyrethroid doses\n",
-                        "\n",
-                        "Figure 2: _A._ Resistance intensity of field-caught _Anaopheles gambiae_ sensu late (s.l.) after exposure to 1, 2, 5, or 10 times the diagnostic dose of pyrethroid insecticides. Mean knockdown/acute toxicity rates after 30-minute exposure are shown with 95% confidence intervals (Cls). Knockdown or mortality rates at the same dose per insecticide sharing a letter do not differ significantly (_P_ >.05). Mortality rates <00% (_lower and line_) represent confirmed resistance at the diagnostic dose (1a), and rates <00% (_upper and line_) indicate moderate to high-intensity resistance and high-intensity resistance at 5° and 10x, respectively, as defined by the World Health Organization [24]. _B._ Restoration of deltamethrin susceptibility of field-caught _A. gambiae_ s.l. after presopause to piperonyl butoxide (PBO). Mean knockdown/acute toxicity after 30-minute exposure to 1 or 2 times the diagnostic dose of deltamethrin with 95% Cls. Knockdown/mortality rates do not differ significantly between pyrethroid only and synergist plus pyrethroid sharing a letter (_P_ >.05). Red line at 90% mortality rate represents metabolic resistance mechanisms partially involved [24].\n",
-                        "\n",
-                        "\n",
-                        "(Cox regression \\(P\\) >.05 for alpha-cypermethrin, deltamethrin, and permethrin 5x and 10x vs either the control or diagnostic dose) (Table 1; Figure 3C and 3D).\n",
-                        "\n",
-                        "header: RESULTS\n",
-                        "\n",
-                        "A total of 4917 female _A. gambiae_ s.l. mosquitoes were collected in Agboville, Cote d'Ivoire. Of those, 912, which were previously tested in either LLIN bioefficacy (n = 384) or resistance intensity (n = 528) bioassays, were selected for molecular species identification. Of the 912 selected, 805 (88.3%) were determined to be _A. coluzzii_, 75 (8.2%) were _A. gambiae_ s.s., and 22 (2.4%) were _A. gambiae_-_A. coluzzii_ hybrids; 10 individuals did not amplify.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Phenotypic resistance was intense: >25% of mosquitoes survived exposure to 10 times the doses of pyrethroids required to kill susceptible populations. Similarly, the 24-hour mortality rate with deltamethrin-only LLINs was very low and not significantly different from that with an untreated net. Sublethal pyrethroid exposure did not induce significant delayed vector mortality effects 72 hours later. In contrast, LLINs containing the synergist piperonyl butoxide, or new insecticides dothianidin and chlorfenapyr, were highly toxic to _A. coluzzii_. Pyrethroid-susceptible _A. coluzzii_ were significantly more likely to be infected with malaria, compared with those that survived insecticidal exposure. Pyrethroid resistance was associated with significant overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Target-Site Mutations\n",
-                        "\n",
-                        "The same cohort of field mosquitoes (n = 912) was tested for the presence of L1014F _kdr_[27] and N1575Y mutations [28]. A subsample of mosquitoes (n = 49) that were exposed to bendiocarb, clothianidin or chlorfenapyr was tested for the presence of the G119S _Ace-1_ mutation [29]. Pearson kh2 and Fisher exact tests (when sample sizes were small) were used to investigate the statistical association between resistance status, allele frequencies, and deviations from Hardy-Weinberg equilibrium.\n",
-                        "\n",
-                        "header: Study Area and Mosquito Collections: WHO Cone Bioassay Testing\n",
-                        "\n",
-                        "Two types of LLIN were evaluated in this study. PermaNet 2.0 is a conventional LLIN treated with deltamethrin only (1.4 g/kg +- 25%) and PermaNet 3.0 is a piperonyl butoxide (PBO) synergism LILIN, consisting of a roof containing PBO (25g/kg) and deltamethrin (4 g/kg +- 25%) and side panels containing deltamethrin only (2.8 g/kg +- 25%). WHO cone bioassays were used to test the susceptibility of _A. gambiae_ s.l. exposed to unwashed PermaNet 2.0, PermaNet 3.0 roof panels, and PermaNet 3.0 side panels [21]. To control for potential variation in insecticide/synergist content, each of 5 LLINs per type was cut into 19 pieces, measuring 30 x 30 cm, with each piece tested a maximum of 3 times.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Association of Reduced Long-Lasting Insecticidal Net Efficacy and Pyrethroid Insecticide Resistance With Overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_ in Populations of _Anopheles coluzzii_ From Southeast Cote d'Ivoire\n",
-                        "\n",
-                        "Anne Meiwald,\\({}^{1,2}\\) Emma Clark,\\({}^{1,2}\\) Mejica Kristan,\\({}^{1}\\) Constant Edi,\\({}^{2}\\) Claire L Jeffries,\\({}^{1}\\) Bethanie Pelloquin,\\({}^{1}\\) Seth R. Irish,\\({}^{2}\\) Thomas Walker,\\({}^{1}\\) and Louisa A. Messenger\\({}^{1,0}\\)\n",
-                        "\n",
-                        "\n",
-                        "Resistance to major public health insecticides in Cote d'Ivoire has intensified and now threatens the long-term effectiveness of malaria vector control interventions.\n",
-                        "\n",
-                        "header: Keywords\n",
-                        "\n",
-                        "_Anopheles coluzzii_; insecticide resistance; _Plasmodium falciparum_; long-lasting insecticidal nets; Cote d'Ivoire; PBO; chlorfenapyr; clothianidin; _CYP6P4_; _CYP6P3_; _CYP6Z1_\n",
-                        "\n",
-                        "In Cote d'Ivoire, malaria is a serious public health problem with the entire population of about 26.2 million people at risk, and disease prevalence reaching as high as 63% in the southwest region [1]. Control of _Anopheles gambiae_ sensu lato (s.l.), the major malaria vector species group, has been through the efforts of the National Malaria Control Programme, which has distributed insecticide-treated nets as the primary vector control intervention. Indoor residual spraying and larviciding in high transmission areas have been recommended as complementary strategies; implementation of the former commenced in late 2020 [2]. Estimates of net coverage across the country remain low, with the proportion of households with at least >=1 insecticide-treated net per 2 persons rising from 31% in 2012 to 47% in 2016, and insecticide-treated net use stagnating at 40% of households reporting sleeping under a net the previous night in both survey years [2]. The most recent universal net campaigns in Cote d'Ivoire in 2017-2018 issued conventional, pyrethroid (deltamethrin) long-lasting insecticidal nets (LLINs), aiming to achieve 90% coverage and 80% use [2]. However, country-wide, multiclass insecticide resistance among populations of _A. gambiae_ s.l. is a growing cause for concern because of potential operational failure of current vector control strategies, both locally and across the sub-Saharan region [2, 3].\n",
-                        "\n",
-                        "Resistance to pyrethroid and carbamate insecticides in _Anopheles_ mosquitoes was first reported from the central \n",
-                        "region of Cote d'Ivoire in the early 1990s [4, 5, 6, 7]. Local resistance to the major insecticide classes recommended by the World Health Organization (WHO) for adult mosquito control--pyrethroids, carbamates, organophosphates, and organochlorines--evolved rapidly [8, 9, 10] and has been increasing in intensity, driven largely by selective pressures imposed by contemporaneous scale-up of public health vector control interventions (including those targeting malaria, trypanosomiasis, and onchocerciasis vectors) and use of agricultural pesticides [11, 12, 13, 14, 7]. This escalation in resistance has now begun to compromise the insecticidal efficacy and community-wide impact of conventional, pyrethroid LLINs in Cote d'Ivoire [14, 15], although some levels of personal protection may still remain [15, 16, 17].\n",
-                        "\n",
-                        "Among vector populations across Cote d'Ivoire, the L1014F _kdr_ mutation is pervasive and has been implicated in some longitudinal trends in decreasing DDT and pyrethroid susceptibility [7, 11]; L1014S _kdr_ and N1575Y resistance mutations have also been detected but at much lower frequencies [18]. Extreme carbamate (bendiocarb) resistance and pyrethroid cross-resistance in some _A. gambiae_ sensu stricto (s.s.) populations are mediated by overexpression of _CYP6P3_ and _CYP6M2_ and duplication of the G119S _Ace-1_ mutation [19]. To support and safeguard future malaria control efforts in Cote d'Ivoire, the current study evaluated the efficacy of conventional and next-generation LLINs for prospective distribution, determined current insecticide resistance profiles of _A. gambiae_ s.l. (principally _Anopheles coluzzi_), and characterized underlying molecular and metabolic resistance mechanisms.\n",
-                        "\n",
-                        "header: Metabolic Resistance Mechanisms\n",
-                        "\n",
-                        "Comparison of metabolic gene expression levels in field populations of _A. coluzzi_ and _A. gambiae s.s._ demonstrated significant up-regulation of _CYP6P4_ (fold change, 5.88 [95% CI, 5.19-44.06] for _A. coluzzi_ and 6.08 [5.43-50.64] for _A. gambiae_ s.s.), _CYP6Z1_ (4.04 [3.69-41.54] and 3.56 [3.24-36.25], respectively),\n",
-                        "\n",
-                        "Figure 3.: The longevity of field-caught _A. gambiae_ sensus into after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (_A_) and \\(x\\) (B, 5x [_O_], and 10× [_D_) the diagnostic dose of pyrethroid insecticides in Centres for Disease Control and Prevention resistance intensity assays. Kaplan-Meier survival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\n",
-                        "\n",
-                        "\n",
-                        "and _CYP6P3_ (12.56 [11.40-12-3.83] and 13.85 [12.53-132.03]), relative to a susceptible laboratory colony, respectively (Figure 4). More modest overexpression of _CYP6P1_ and _GSTE2_ was observed (_CYP6P1_ fold changes, 1.18 [95% CI, 1.08-12.31] and 1.28 [1.17-14.40]; GSTE2, 0.56 [.48-3.32], and 0.67 [.58-4.29], for _A. coluzzi_ and _A. gambiae_ s.s., respectively) (Figure 4). The fold change levels did not differ significantly between the 2 species for any gene nor by malaria infection status in wild _A. coluzzi_. Comparison of metabolic gene expression in phenotyped field populations of _A. coluzzi_ revealed lower fold changes overall, but notably, increased overexpression of _CYP6P3_ in survivors of bendiocarb, deltamethrin, PBO plus deltamethrin, and permethrin (fold change, 3.91 [95% CI, 3.33-22.16], 2.21 [1.88-12.53], 2.64 [2.21-13.69], and 2.21 [1.99-20.03], respectively) (Figure 5).\n",
-                        "\n",
-                        "header: Target-Site Resistance Mutations\n",
-                        "\n",
-                        "L1014_F_kdr_ screening revealed that 92.2% (796/863) of _A. gambiae_ s.l. mosquitoes harbored the mutation; 71.5% (617 of 863) were homozygous, 20.7% (179 of 863) were heterozygous, 5.1% (44 of 863) were wild type, and 2.7% (23 of 863) did not amplify. For PCR-confirmed _A. coluzzii_, L1014F_kdr_ prevalence was 87.8% (707 of 805); 66.6% (536 of 805) were homozygous for the mutation, 21.2% (171 of 805) were heterozygous, 5.3% (43 of 805) were wild type, and 2.2% (18 of 805) did not amplify. For _A. coluzzii_, population-level L1014F_kdr_ allele frequency was 0.83, with evidence for significant deviations from Hardy-Weinberg equilibrium (kh2 = 29.124; \\(P\\) <.001). There was no significant association between L1014F_kdr_ frequency and the ability of _A. coluzzii_, to survive pyrethroid exposure, in either LLIN or resistance bioassays (kh2 = 2.0001 [_P_ =.16] and kh2 = 3.6998 [_P_ =.054], respectively). Similarly, there was no significant association between L1014F_kdr_ and the ability of _A. coluzzii_ to survive PBO preexposure and pyrethroid treatment, in either LLIN or resistance bioassays (kh2 = 0.0086; \\(P\\) =.93; Fisher exact test, \\(P\\) =.429, respectively).\n",
-                        "\n",
-                        "For PCR-confirmed _A. gambiae_ s.s., L1014F_kdr_ prevalence was 95.3% (61 of 64); 89.1% (57 of 64) were homozygous for the mutation, 6.3% (4 of 64) were heterozygous, none were wild type, and 4.7% (3 of 64) did not amplify. There was no significant association between L1014F_kdr_ frequency and ability of _A. gambiae_ s.s. to survive pyrethroid or PBO preexposure and pyrethroid treatment (in either LLIN or resistance bioassays), because all tested individuals harbored this mutation (n = 61). For _A. gambiae_ s.s., the population-level L1014F_kdr_ allele frequency was 0.97, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.070; \\(P\\) =.79).\n",
-                        "\n",
-                        "N1575Y screening revealed that 2.3% of _A. gambiae_ s.l. mosquitoes (21 of 912) harbored the mutation; all were\n",
-                        "heterozygotes. N1575Y prevalence was 1.1% (9 of 805) and 16% (12 of 75) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 0.99% (9 of 912) did not amplify. There was no evidence for ongoing N1575Y selection in either species (kh2 = 0.026 [_P_ =.87] and kh2 = 0.62 [_P_ =.43] for _A. coluzzi_ and _A. gambiae_ s.s., respectively). For _A. coluzzi_, there was no significant association between N1575Y frequency and ability of mosquitoes to survive pyrethroid exposure, in LLIN or resistance bioassays (kh2 = 0.0001 [_P_ =.99] and kh2 = 0.3244 [_P_ =.57], respectively).\n",
-                        "\n",
-                        "G119S _Ace-1_ screening revealed that 55.1% of _A. gambiae_ s.l. mosquitoes (27 of 49) harbored the mutation; all were heterozygotes. G119S _Ace-1_ prevalence was 64.9% (24 of 37) and 27.3% (3 of 11) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 1 remaining _A. gambiae-A. coluzzi_ hybrid was wild type. For _A. coluzzi_, population-level G119S _Ace-1_ allele frequency was 0.32, with evidence of significant deviations from Hardy-Weinberg equilibrium (kh2 = 8.525; \\(P\\) =.004). For _A. gambiae_ s.s., population-level G119S _Ace-1_ allele frequency was 0.14, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.274; \\(P\\) =.60). For _A. coluzzi_, there was a significant association between G119S _Ace-1_ frequency and surviving bendiocarb exposure (Fisher exact test, \\(P\\) =.005).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Cote d'Ivoire has hot spots with some of the highest levels of resistance of _Anopheles_ mosquitoes to public health insecticides worldwide, with potentially severe implications for sustaining gains in malaria control [31]. To safeguard malaria vector control efforts and inform the design of effective resistance management strategies, involving tactical deployment of differing indoor residual spraying and LLIN modalities, there needs to be a clear understanding of contemporary phenotypic and genotypic insecticide resistance.\n",
-                        "\n",
-                        "Our study detected intense pyrethroid resistance in southeast Cote d'Ivoire, as evidenced by high proportions of survivors, after exposure to 10 times the diagnostic doses of pyrethroids, as well as very low knockdown and 24-hour mortality rates for deltamethrin-only LLINs, equivalent to rates for an untreated net. These findings are largely in agreement with historical resistance profiles from this region [7, 10, 11] and indicate that conventional LLINs may no longer be operationally viable in areas of high pyrethroid resistance intensity. Previous phase II studies of pyrethroid-only LLINs in the central region of Cote d'Ivoire have demonstrated similarly poor efficacy with highly resistant _A. gambiae_ s.l. populations but argued for the retention of some degree of personal protection [15-17].\n",
-                        "\n",
-                        "Other observational cohorts have reported higher incidences of malaria among non-net users compared with users in areas of moderate to high pyrethroid resistance [17]. The extent of protective efficacy afforded by pyrethroid LLINs will likely reflect the strength of local vector resistance and levels of both net physical integrity and individual compliance [32, 33]; in Cote d'Ivoire, reported LLIN usage has been low, requiring additional behavioral interventions [2, 34]. Our findings of high mosquito mortality rates after exposure to clothianidin and chlorfenapyr and improved vector susceptibility with PBO treatment (on both LLINs and in resistance bioassays), are consistent with data from other sentinel sites across Cote d'Ivoire [16, 35, 36], and strongly support the deployment of vector control interventions incorporating these new active ingredients.\n",
-                        "\n",
-                        "Study results indicate that _A. coluzzi_ was the predominant local vector species during the rainy season, as observed previously [7], circulating sympatrically with smaller proportions of _A. gambiae_ s.s. These 2 vector species commonly cohabit but can be genetically distinct in terms of resistance mechanisms [37, 38] and can also differ in larval ecology, behavior, migration, and activation [39-41]. In general, resistance mechanisms in _A. coluzzi_ are less well characterized, compared with _A. gambiae_ s.s., in part because these vectors are morphologically\n",
-                        "\n",
-                        "Figure 4: Metabolic gene expression in field _Anopheles coluzzi_ and _Anopheles gambiae_ sensu stricto (s.s.) populations relative to a susceptible colony population. Error bars represent 95% confidence intervals. Statistically significant differences in expression levels relative to the susceptible colony are indicated as follows: *_P_ <.05; **_P_ <.01; ***_P_ <.001.\n",
-                        "\n",
-                        "\n",
-                        "indistinguishable and few studies present data disaggregated by PCR-confirmed species.\n",
-                        "\n",
-                        "We observed several distinct features in our study, including, principally, evidence for ongoing selection of L1014F _kdr_ and G119S _Ace-1 in A. coluzzii_, which was absent in _A. gambiae_ s.s. and higher proportions of N1575Y in _A. gambiae_ s.s.; expression levels of metabolic genes were comparable between species. The lack of association between L1014F _kdr_ genotype and mosquito phenotype, coupled with the identification of 3 CYP450 enzymes (_CYP6P4, CYP6P3,_ and _CYP6Z1_) that were significantly overexpressed in field populations (some of which are known to metabolize pyrrethroids and next-generation LLIN insecticides [42, 43]), indicate a key role for metabolic resistance in this _A. coluzzii_ population. One notable difference in our data set, compared with previous findings in Agboville [7], was the finding of benodiocarb susceptibility. This may be attributable to small-scale spatial and longitudinal heterogeneity in resistance, which can be highly dynamic [37, 44], and/or phenotypic differences between vector species, complicating intervention choice for resistance management.\n",
-                        "\n",
-                        "With the exception of chlorfenapyr, which is known to be a slow-acting insecticide, no delayed mortality effects were detected after insecticidal exposure; the format and dose used for clothianidin testing (another slow-acting insecticide [45]) were instead intended to measure acute toxicity within a 60-minute exposure period. Previous mathematical models using resistant mosquito colonies have suggested that sublethal insecticide treatment may still reduce vector lifespan and inhibit blood-feeding and host-seeking behaviors, thereby interrupting malaria transmission [46, 47]. Our observations are more compatible with reports from Burkina Faso, where different exposure regimens of wild, resistant _A. gambiae_ s.l. populations to deltamethrin LLINs did not induce any delayed mortality effects [47]. Further assessment of sublethal effects are warranted across additional field populations with differing resistance mechanisms, to clarify the impact of insecticidal exposure on the vectorial capacity of resistant mosquitoes.\n",
-                        "\n",
-                        "To date there is a paucity of data regarding the interactions between insecticide resistance and _Plasmodium_ development [48]. In the current study, _A. coluzzii_ that died after pyrethroid exposure were significantly more likely to be infected with malaria. This might be explained by elevated metabolic enzymes and/or prior pyrethroid exposure detrimentally affecting parasite development [49], although it is important to note that we did not detect any significant differences between gene overexpression in malaria-infected versus noninfected _A. coluzzii_. Alternatively, our sampled population may have been physiologically older, as phenotypic resistance is known to decline with age [50]. It is impossible to distinguish between these hypotheses using field-collected vector populations; the experimental design used in this study had other biological and technical limitations, which have been described in detail elsewhere [23, 37]\n",
-                        "\n",
-                        "In conclusion, as new combination and bitreated vector control interventions become available for deployment, contemporary resistance information is crucial for the rationale design of management strategies and to mitigate further selection for particular resistance mechanisms. The results from the current study contribute to growing insecticide resistance data for Cote d'Ivoire, demonstrating a loss of bioefficacy of pyrethroid LLINs and supporting the use of new active ingredients (clothianidin, chlorfenapyr, and PBO). Study findings also highlight the need for expanded insecticide resistance surveillance, including monitoring of metabolic resistance mechanisms, in conjunction with studies to better characterize the impact of sublethal insecticide exposure on vectorial capacity and the interaction between insecticide resistance and _Plasmodium_ parasite development.\n",
-                        "\n",
-                        "Figure 5: Metabolic gene expression in resistant versus susceptible field _Anopheles coluzzi_, which either died or survived after insecticidal exposure. Error bars represent 95% confidence intervals.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Metabolic Gene Expression\n",
-                        "\n",
-                        "Relative expression of 5 metabolic genes (_CYP6P3_, _CYP6P4_, _CYP6Z1_, _CYP6P1_, and _GSTE2_) was measured in all field collected mosquitoes (n = 912), using multiplex quantitative real-time polymerase chain reaction (PCR) assays, relative to the housekeeping gene ribosomal protein S7 (_RPS7_) [30]. In addition, gene expression levels were measured in susceptible _A. coluzzii_ Nguosso colony mosquitoes (n = 48). All samples were run in technical triplicate. Expression level and fold change of each target gene between resistant and susceptible field samples, relative to the susceptible laboratory strain, were calculated using the 2-\\({}^{\\text{-}\\Delta\\text{ACT}}\\) method incorporating PCR efficiency, normalized relative to the endogenous control gene (_RPS7_).\n",
-                        "\n",
-                        "header: Mosquito Processing, Identification of _A. gambiae_ s.l. Species Complex\n",
-                        "\n",
-                        "Members, and _Plasmodium falciparum_ Detection\n",
-                        "\n",
-                        "A subsample of field-caught mosquitoes tested in bioassays was selected for molecular analysis (n = 912). Approximately equal numbers of specimens were chosen to represent phenotypically \"susceptible\" or \"resistant\" mosquitoes for each LLIN type or insecticide dose, selected across different replicates and testing days to capture as much population-level variation as possible. RNA was extracted from individual whole-body mosquitoes according to standard protocols [23]. Field _A. gambiae_ s.l. were identified to species level [25] and were screened for the presence of _Plasmodium falciparum_[26].\n",
-                        "\n",
-                        "header: Mosquito Collections and Species Identification: LIII Efficacy\n",
-                        "\n",
-                        "A total of 2666 field-caught _A. gambiae_ s.l were used to assess the bioefficacy of conventional pyrethroid-treated LLINs (PermaNet 2.0 and PermaNet 3.0 side panels) and next-generation synergisti LLINs (PermaNet 3.0 roof panels), compared with an untreated control (Figure 1). Overall, _A. gambiae_ s.l. knockdown and mortality rates with deltamethrin LLINs were very low and largely equivalent to those for the untreated control net (Figure 1). At 60 minutes, average mosquito knockdown rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels were 1.56% (95% confidence interval [CI], 1.13%-1.99%), 0.54% (.42%-6.5%), and 1.75% (1.49%-2.0%), respectively. By contrast, average mosquito knockdown rates for PBO-containing PermaNet 3.0 roof panels were significantly higher (79.8% [95% CI, 79.07%-80.48%]; kh2 = 705.51, 968.65, and 937.33 [_P_ <.001] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1).\n",
-                        "\n",
-                        "At 24 hours, mortality rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels remained low (6.11% [95% CI, 4.71%-7.51%], 5.44% [4.58%-6.29%], and 3.66% [3.12%-4.19%], respectively), while those with PermaNet 3.0 roof panels increased only marginally but still remained significantly higher (83.81% [95% CI, 83.15%-84.47%];\n",
-                        "\n",
-                        "Figure 1.: Bioefficacy of different unwashed long-lasting insecticidal nets (LLINs) against field-caught _Angabies gambiae_ sensu tatto. Mean knockdown and mortality rates are shown with 95% confidence intervals, at 60 minutes and 24 hours, respectively, after 3-minute exposure to PermaNet 2.0 (deltamethrin only), side panels of PermaNet 3.0 (deltamethrin only, roof panels of PermaNet 3.0 (pieronym) butoxide plus deltamethrin), and an untreated control net. Knockdown or mortality rates in the same time period for each treatment sharing a letter do not differ significantly (_P_ >.05). Green lines at 275% and 295% knockdown represent minimal and optimal effectiveness, respectively, at 60 minutes. Red lines at 250% and >80% mortality represent minimal and optimal LLIN effectiveness at 24 hours, respectively, as defined by the World Health Organization [21].\n",
-                        "\n",
-                        "\n",
-                        "\\(\\chi^{2}\\) = 727.96, 914.61, and 963.09 [\\(P\\) <.001 for all] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1). PermaNet 3.0 roof panels reached minimal effectiveness (knockdown, >=75%) 60 minutes after exposure and optimal effectiveness (mortality rate, >=80%) at 24 hours. Neither of the deltamethrin-only LLINS reached either effectiveness threshold at any time point.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study protocol was approved by the Comite National d'Ethique des Sciences de la Vie et de la Sante (no. 069-19/MSHP/CNESVS-kp) and the London School of Hygiene and Tropical Medicine (nos. 16782 and 16899). Study activities were conducted in the village of Aboude, rural Agboville, Agneby-Tiassa region, southeast Cote d'Ivoire (5'55'N, 4'13'W), selected because of its high mosquito densities and malaria prevalence [1]. Adult mosquitoes were collected using human landing catches, inside and outside households from 6 pm to 6 am, for a total of 190 person/trap/nights between 5 and 26 July 2019. Unfed mosquitoes, morphologically identified as _A. gambiae_ s.l. [20], were tested in bioassays that same day, after a brief recovery period; blood-fed mosquitoes were first held for 2-3 days to allow for blood-meal digestion.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Notes\n",
-                        "\n",
-                        "_Acknowledgments._ The authors express their sincere thanks to M. Didier Dobri, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire (CSRS) laboratory technician, and Fidele Assamoa for their support in mosquito collection and rearing, the chief and population of the village of Aboude (Agboville), and the entomology fieldworkers of CSRS. They also thank Vestergaard for supplying the long-lasting insecticidal nets tested in this study.\n",
-                        "\n",
-                        "_Author contributions._ A. M., E. C., M. K., T. W., and L. A. M. designed the study. A. M., E. C., C. E., and B. P. led the entomology field activities and participated in data collection. A. M., E. C., C. L. J., T. W., and L. A. M. performed the molecular assays. A. M., E. C., M. K., C. E., C. L. J., B. P., S. R. I, T. W., and L. A. M. were responsible for data analysis and interpretation. L. A. M. drafted the manuscript, which was revised by all coauthors. All authors read and approved the final manuscript.\n",
-                        "\n",
-                        "_Disclaimer._ The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.\n",
-                        "\n",
-                        "_Financial support._ This work was supported by the Sir Halley Stewart Trust (L.A.M.), the Wellcome Trust/Royal Society ([http://www.wellcome.ac.uk](http://www.wellcome.ac.uk) and [https://royalsociety.org](https://royalsociety.org); (101285/Z/13/Z to T.W.) and the US President's Malaria Initiative/Centers for Disease Control and Prevention (S.R.I.).\n",
-                        "\n",
-                        "_Potential conflicts of interest._ All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.\n",
-                        "\n",
-                        "header: Resistance Intensity and Synergist Bioassay Testing\n",
-                        "\n",
-                        "Centers for Disease Control and Prevention (CDC) resistance intensity bioassays were performed for 6 public health insecticides (pyrethroids: alpha-cypermethrin, deltamethrin, and permethrin; carbamate: bendiocarb; neonicotinoid: clothianidin; and pyrrole: chlorfenapyr) [22, 23]. The diagnostic doses of all insecticides were evaluated (including clothianidin [90 mg per bottle] [23] and chlorfenapyr [100 mg per bottle]) and 2, 5, and 10 times the diagnostic dose of pyrethroid insecticides were also used. Per test, knockdown was recorded at 15-minute intervals for 30 minutes (pyrethroids and bendiocarb) or 60 minutes (dothianidin and chlorfenapyr) of insecticide exposure. One-hour PBO preexposures were performed using WHO tube assays [24], before deltamethrin CDC bottle bioassay testing [22].\n",
-                        "\n",
-                        "WHO cone and CDC resistance intensity bioassay data were interpreted according to the WHO criteria [21, 22]. Mosquitoes that died after exposure to a LLIN or 1x insecticide dose were stored at -20degC in RNAlater (Thermo Fisher Scientific) and were considered \"susceptible\" for genotypic analysis. Surviving mosquitoes were held and scored for mortality rate after 24, 48 and 72 hours to observe delayed mortality effects. Kaplan-Meier curves were used to visualize survival data, and Cox regression was used to compare postexposure survival. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. Surviving mosquitoes at 72 hours were stored at -20degC in RNAlater and were considered \"resistant\" for genotypic analysis.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Study findings raise concerns regarding the operational failure of standard LLINs and support the urgent deployment of vector control interventions incorporating piperonyl butoxide, chlorfenapyr, or clothianidin in areas of high resistance intensity in Cote d'Ivoire.\n",
-                        "\n",
-                        "header: Table 1. Cox Proportional Hazard Model to Determine Impact of Long-Lasting Insecticidal Net/Insecticidal Exposure on Survival of Field-Caught Anopheles gambiae Sensu Lato 72 Hours After Exposure\n",
-                        "footer: Abbreviations: CI, confidence interval; HRR, hazard rate ratio (ratio of hazard rate for control/reference group to hazard rate for treatment group; PBO, piperonyl butoxide.\n",
-                        "a Immediate mortality rates after long-lasting insecticidal net (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. \n",
-                        "b Significance cutoff level defined as α = .05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['Insecticide Exposure', 'No. (No. of Events)', 'HRR (95% CI)', 'P Valueb']\n",
-                        "\n",
-                        "{\"0\":{\"Insecticide Exposure\":\"Untreated netting\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"1\":{\"Insecticide Exposure\":\"PermaNet 2.0 (deltamethrin only)\",\"No. (No. of Events)\":\"1135 (1047)\",\"HRR (95% CI)\":\"1.095 (.968\\u20131.239)\",\"P Valueb\":\".15\"},\"2\":{\"Insecticide Exposure\":\"PermaNet 3.0 side panels (deltamethrin only)\",\"No. (No. of Events)\":\"1157 (1088)\",\"HRR (95% CI)\":\"0.9664 (.9092\\u20131.027)\",\"P Valueb\":\".27\"},\"3\":{\"Insecticide Exposure\":\"PermaNet 3.0 roof panels (PBO + deltamethrin)\",\"No. (No. of Events)\":\"563 (533)\",\"HRR (95% CI)\":\"1.007 (.939\\u20131.079)\",\"P Valueb\":\".85\"},\"4\":{\"Insecticide Exposure\":\"Acetone control\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"5\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 1\\u00d7\",\"No. (No. of Events)\":\"676 (641)\",\"HRR (95% CI)\":\"1.006 (.9696\\u20131.043)\",\"P Valueb\":\".77\"},\"6\":{\"Insecticide Exposure\":\"Deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"683 (645)\",\"HRR (95% CI)\":\"0.9942 (.9539\\u20131.036)\",\"P Valueb\":\".78\"},\"7\":{\"Insecticide Exposure\":\"Permethrin 1\\u00d7\",\"No. (No. of Events)\":\"693 (661)\",\"HRR (95% CI)\":\"1.015 (.9698\\u20131.062)\",\"P Valueb\":\".52\"},\"8\":{\"Insecticide Exposure\":\"Clothianidin 1\\u00d7\",\"No. (No. of Events)\":\"698 (581)\",\"HRR (95% CI)\":\"1.208 (.9227\\u20131.581)\",\"P Valueb\":\".17\"},\"9\":{\"Insecticide Exposure\":\"Chlorfenapyr 1\\u00d7\",\"No. (No. of Events)\":\"708 (580)\",\"HRR (95% CI)\":\"1.692 (1.086\\u20132.637)\",\"P Valueb\":\".02\"},\"10\":{\"Insecticide Exposure\":\"PBO + deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"630 (577)\",\"HRR (95% CI)\":\"0.9662 (.2411\\u20133.873)\",\"P Valueb\":\".96\"},\"11\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"633 (601)\",\"HRR (95% CI)\":\"0.9951 (.9407\\u20131.053)\",\"P Valueb\":\".86\"},\"12\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"652 (610)\",\"HRR (95% CI)\":\"0.9942 (.9393\\u20131.052)\",\"P Valueb\":\".84\"},\"13\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"636 (583)\",\"HRR (95% CI)\":\"0.9931 (.8638\\u20131.142)\",\"P Valueb\":\".92\"},\"14\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"624 (587)\",\"HRR (95% CI)\":\"0.9951 (.917\\u20131.08)\",\"P Valueb\":\".91\"},\"15\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"623 (588)\",\"HRR (95% CI)\":\"0.9943 (.9072\\u20131.09)\",\"P Valueb\":\".90\"},\"16\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"656 (603)\",\"HRR (95% CI)\":\"1.026 (.9509\\u20131.107)\",\"P Valueb\":\".51\"},\"17\":{\"Insecticide Exposure\":\"1\\u00d7 Insecticide dose\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"18\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"117 (92)\",\"HRR (95% CI)\":\"1.016 (.9069\\u20131.138)\",\"P Valueb\":\".78\"},\"19\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"108 (78)\",\"HRR (95% CI)\":\"1.007 (.9403\\u20131.078)\",\"P Valueb\":\".84\"},\"20\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"143 (105)\",\"HRR (95% CI)\":\"1.0 (.9035\\u20131.107)\",\"P Valueb\":\">.99\"},\"21\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"114 (83)\",\"HRR (95% CI)\":\"1.0 (.9363\\u20131.068)\",\"P Valueb\":\">.99\"},\"22\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"137 (94)\",\"HRR (95% CI)\":\"1.022 (.8528\\u20131.225)\",\"P Valueb\":\".81\"},\"23\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"157 (114)\",\"HRR (95% CI)\":\"0.9952 (.9491\\u20131.044)\",\"P Valueb\":\".84\"}}\n",
-                        "\n",
-                        "header: Insecticide Resistance Intensity\n",
-                        "\n",
-                        "A total of 2251 field-caught _A. gambiae_ s.l. were tested in resistance bioassays. Intense pyrethroid resistance was evident with more than 25% of mosquitoes surviving exposure to 10 times the dose of insecticide required to kill a susceptible population (Figure 2A). At the diagnostic dose, mosquito mortality rates did not exceed 25% for any pyrethroid tested, which was consistent with the high survival rates observed during cone bioassays using conventional LLINs (Figure 1). In general, levels of resistance to alpha-cypermethrin, deltamethrin, and permethrin were not significantly different at each insecticide concentration tested (Figure 2A).\n",
-                        "\n",
-                        "By comparison, carbamate tolerance was low, with a mean knockdown of 94.53% (95% CI, 92.11%-96.95%; n = 101) after 30 minutes of exposure to the diagnostic dose of bendiocarb. Similarly, high levels of susceptibility to new insecticides clothianidin and chlorfenapyr were observed, with mean mortality rates of 94.11% (95% CI, 93.43%-94.80%; n = 102) and 95.54% (94.71%-96.36%; n = 112), respectively, 72 hours after exposure to the tentative diagnostic doses. Preexposure to PBO increased the average _A. gambiae_ s.l. mortality rate significantly, from 14.56% (95% CI, 6.24%-22.88%) to 72.73% (64.81%-79.43%) and from 44.66% (34.86%-54.46%) to 94.17% (91.12%-97.22%) after exposure to 1 or 2 times the diagnostic dose of deltamethrin (Figure 2B).\n",
-                        "\n",
-                        "header: Malaria Prevalence\n",
-                        "\n",
-                        "Of the 912 _A. gambiae_ s.l. mosquitoes assayed, 31 tested positive for _P. falciparum_ (3.4%). For PCR-confirmed _A. coluzzii_, _P. falciparum_ prevalence was 3.50% (28 of 805); the remaining 3 infections were in _A. gambiae_ s.s. (4%; 3 of 75). By resistance phenotype, susceptible _A. coluzzii_ (ie, those that died after pyrethroid exposure) were more likely to be infected with malaria, compared with resistant mosquitoes (kh2 = 4.6987; \\(P\\) =.03); infection rates were 5.94% (13 of 219) and 2.49% (10 of 401), respectively.\n",
-                        "\n",
-                        "header: RESULTS\n",
-                        "\n",
-                        "A total of 4917 female _A. gambiae_ s.l. mosquitoes were collected in Agboville, Cote d'Ivoire. Of those, 912, which were previously tested in either LLIN bioefficacy (n = 384) or resistance intensity (n = 528) bioassays, were selected for molecular species identification. Of the 912 selected, 805 (88.3%) were determined to be _A. coluzzii_, 75 (8.2%) were _A. gambiae_ s.s., and 22 (2.4%) were _A. gambiae_-_A. coluzzii_ hybrids; 10 individuals did not amplify.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Phenotypic resistance was intense: >25% of mosquitoes survived exposure to 10 times the doses of pyrethroids required to kill susceptible populations. Similarly, the 24-hour mortality rate with deltamethrin-only LLINs was very low and not significantly different from that with an untreated net. Sublethal pyrethroid exposure did not induce significant delayed vector mortality effects 72 hours later. In contrast, LLINs containing the synergist piperonyl butoxide, or new insecticides dothianidin and chlorfenapyr, were highly toxic to _A. coluzzii_. Pyrethroid-susceptible _A. coluzzii_ were significantly more likely to be infected with malaria, compared with those that survived insecticidal exposure. Pyrethroid resistance was associated with significant overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Association of Reduced Long-Lasting Insecticidal Net Efficacy and Pyrethroid Insecticide Resistance With Overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_ in Populations of _Anopheles coluzzii_ From Southeast Cote d'Ivoire\n",
-                        "\n",
-                        "Anne Meiwald,\\({}^{1,2}\\) Emma Clark,\\({}^{1,2}\\) Mejica Kristan,\\({}^{1}\\) Constant Edi,\\({}^{2}\\) Claire L Jeffries,\\({}^{1}\\) Bethanie Pelloquin,\\({}^{1}\\) Seth R. Irish,\\({}^{2}\\) Thomas Walker,\\({}^{1}\\) and Louisa A. Messenger\\({}^{1,0}\\)\n",
-                        "\n",
-                        "\n",
-                        "Resistance to major public health insecticides in Cote d'Ivoire has intensified and now threatens the long-term effectiveness of malaria vector control interventions.\n",
-                        "\n",
-                        "header: Mosquito Survival After Insecticidal Exposure\n",
-                        "\n",
-                        "All _A. gambiae_ s.l. tested in LLIN bioefficacy or resistance intensity bioassays, were held for 72 hours, to assess any impact of insecticide or net exposure on delayed mortality rate. For LLIN bioassays, there was little evidence for any reduction in survival during this holding period (Cox regression \\(P\\) =.15,.27, and.85, respectively, for comparisons between untreated control and PermaNet 2.0, PermaNet 3.0 side panels, and PermaNet 3.0 roof panels) (Table 1 and Figure 3A). Exposure to the diagnostic doses of all insecticides in CDC bottle bioassays did not induce significant delayed mortality effects over 72 hours (Cox regression \\(P\\) >.05 for all insecticides compared with control, with the exception of chlorfenapyr [_P_ =.02]) (Table 1 and Figure 3B). This phenomenon was also observed at increasing pyrethroid doses\n",
-                        "\n",
-                        "Figure 2: _A._ Resistance intensity of field-caught _Anaopheles gambiae_ sensu late (s.l.) after exposure to 1, 2, 5, or 10 times the diagnostic dose of pyrethroid insecticides. Mean knockdown/acute toxicity rates after 30-minute exposure are shown with 95% confidence intervals (Cls). Knockdown or mortality rates at the same dose per insecticide sharing a letter do not differ significantly (_P_ >.05). Mortality rates <00% (_lower and line_) represent confirmed resistance at the diagnostic dose (1a), and rates <00% (_upper and line_) indicate moderate to high-intensity resistance and high-intensity resistance at 5° and 10x, respectively, as defined by the World Health Organization [24]. _B._ Restoration of deltamethrin susceptibility of field-caught _A. gambiae_ s.l. after presopause to piperonyl butoxide (PBO). Mean knockdown/acute toxicity after 30-minute exposure to 1 or 2 times the diagnostic dose of deltamethrin with 95% Cls. Knockdown/mortality rates do not differ significantly between pyrethroid only and synergist plus pyrethroid sharing a letter (_P_ >.05). Red line at 90% mortality rate represents metabolic resistance mechanisms partially involved [24].\n",
-                        "\n",
-                        "\n",
-                        "(Cox regression \\(P\\) >.05 for alpha-cypermethrin, deltamethrin, and permethrin 5x and 10x vs either the control or diagnostic dose) (Table 1; Figure 3C and 3D).\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Target-Site Mutations\n",
-                        "\n",
-                        "The same cohort of field mosquitoes (n = 912) was tested for the presence of L1014F _kdr_[27] and N1575Y mutations [28]. A subsample of mosquitoes (n = 49) that were exposed to bendiocarb, clothianidin or chlorfenapyr was tested for the presence of the G119S _Ace-1_ mutation [29]. Pearson kh2 and Fisher exact tests (when sample sizes were small) were used to investigate the statistical association between resistance status, allele frequencies, and deviations from Hardy-Weinberg equilibrium.\n",
-                        "\n",
-                        "header: Target-Site Resistance Mutations\n",
-                        "\n",
-                        "L1014_F_kdr_ screening revealed that 92.2% (796/863) of _A. gambiae_ s.l. mosquitoes harbored the mutation; 71.5% (617 of 863) were homozygous, 20.7% (179 of 863) were heterozygous, 5.1% (44 of 863) were wild type, and 2.7% (23 of 863) did not amplify. For PCR-confirmed _A. coluzzii_, L1014F_kdr_ prevalence was 87.8% (707 of 805); 66.6% (536 of 805) were homozygous for the mutation, 21.2% (171 of 805) were heterozygous, 5.3% (43 of 805) were wild type, and 2.2% (18 of 805) did not amplify. For _A. coluzzii_, population-level L1014F_kdr_ allele frequency was 0.83, with evidence for significant deviations from Hardy-Weinberg equilibrium (kh2 = 29.124; \\(P\\) <.001). There was no significant association between L1014F_kdr_ frequency and the ability of _A. coluzzii_, to survive pyrethroid exposure, in either LLIN or resistance bioassays (kh2 = 2.0001 [_P_ =.16] and kh2 = 3.6998 [_P_ =.054], respectively). Similarly, there was no significant association between L1014F_kdr_ and the ability of _A. coluzzii_ to survive PBO preexposure and pyrethroid treatment, in either LLIN or resistance bioassays (kh2 = 0.0086; \\(P\\) =.93; Fisher exact test, \\(P\\) =.429, respectively).\n",
-                        "\n",
-                        "For PCR-confirmed _A. gambiae_ s.s., L1014F_kdr_ prevalence was 95.3% (61 of 64); 89.1% (57 of 64) were homozygous for the mutation, 6.3% (4 of 64) were heterozygous, none were wild type, and 4.7% (3 of 64) did not amplify. There was no significant association between L1014F_kdr_ frequency and ability of _A. gambiae_ s.s. to survive pyrethroid or PBO preexposure and pyrethroid treatment (in either LLIN or resistance bioassays), because all tested individuals harbored this mutation (n = 61). For _A. gambiae_ s.s., the population-level L1014F_kdr_ allele frequency was 0.97, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.070; \\(P\\) =.79).\n",
-                        "\n",
-                        "N1575Y screening revealed that 2.3% of _A. gambiae_ s.l. mosquitoes (21 of 912) harbored the mutation; all were\n",
-                        "heterozygotes. N1575Y prevalence was 1.1% (9 of 805) and 16% (12 of 75) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 0.99% (9 of 912) did not amplify. There was no evidence for ongoing N1575Y selection in either species (kh2 = 0.026 [_P_ =.87] and kh2 = 0.62 [_P_ =.43] for _A. coluzzi_ and _A. gambiae_ s.s., respectively). For _A. coluzzi_, there was no significant association between N1575Y frequency and ability of mosquitoes to survive pyrethroid exposure, in LLIN or resistance bioassays (kh2 = 0.0001 [_P_ =.99] and kh2 = 0.3244 [_P_ =.57], respectively).\n",
-                        "\n",
-                        "G119S _Ace-1_ screening revealed that 55.1% of _A. gambiae_ s.l. mosquitoes (27 of 49) harbored the mutation; all were heterozygotes. G119S _Ace-1_ prevalence was 64.9% (24 of 37) and 27.3% (3 of 11) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 1 remaining _A. gambiae-A. coluzzi_ hybrid was wild type. For _A. coluzzi_, population-level G119S _Ace-1_ allele frequency was 0.32, with evidence of significant deviations from Hardy-Weinberg equilibrium (kh2 = 8.525; \\(P\\) =.004). For _A. gambiae_ s.s., population-level G119S _Ace-1_ allele frequency was 0.14, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.274; \\(P\\) =.60). For _A. coluzzi_, there was a significant association between G119S _Ace-1_ frequency and surviving bendiocarb exposure (Fisher exact test, \\(P\\) =.005).\n",
-                        "\n",
-                        "header: Study Area and Mosquito Collections: WHO Cone Bioassay Testing\n",
-                        "\n",
-                        "Two types of LLIN were evaluated in this study. PermaNet 2.0 is a conventional LLIN treated with deltamethrin only (1.4 g/kg +- 25%) and PermaNet 3.0 is a piperonyl butoxide (PBO) synergism LILIN, consisting of a roof containing PBO (25g/kg) and deltamethrin (4 g/kg +- 25%) and side panels containing deltamethrin only (2.8 g/kg +- 25%). WHO cone bioassays were used to test the susceptibility of _A. gambiae_ s.l. exposed to unwashed PermaNet 2.0, PermaNet 3.0 roof panels, and PermaNet 3.0 side panels [21]. To control for potential variation in insecticide/synergist content, each of 5 LLINs per type was cut into 19 pieces, measuring 30 x 30 cm, with each piece tested a maximum of 3 times.\n",
-                        "\n",
-                        "header: Keywords\n",
-                        "\n",
-                        "_Anopheles coluzzii_; insecticide resistance; _Plasmodium falciparum_; long-lasting insecticidal nets; Cote d'Ivoire; PBO; chlorfenapyr; clothianidin; _CYP6P4_; _CYP6P3_; _CYP6Z1_\n",
-                        "\n",
-                        "In Cote d'Ivoire, malaria is a serious public health problem with the entire population of about 26.2 million people at risk, and disease prevalence reaching as high as 63% in the southwest region [1]. Control of _Anopheles gambiae_ sensu lato (s.l.), the major malaria vector species group, has been through the efforts of the National Malaria Control Programme, which has distributed insecticide-treated nets as the primary vector control intervention. Indoor residual spraying and larviciding in high transmission areas have been recommended as complementary strategies; implementation of the former commenced in late 2020 [2]. Estimates of net coverage across the country remain low, with the proportion of households with at least >=1 insecticide-treated net per 2 persons rising from 31% in 2012 to 47% in 2016, and insecticide-treated net use stagnating at 40% of households reporting sleeping under a net the previous night in both survey years [2]. The most recent universal net campaigns in Cote d'Ivoire in 2017-2018 issued conventional, pyrethroid (deltamethrin) long-lasting insecticidal nets (LLINs), aiming to achieve 90% coverage and 80% use [2]. However, country-wide, multiclass insecticide resistance among populations of _A. gambiae_ s.l. is a growing cause for concern because of potential operational failure of current vector control strategies, both locally and across the sub-Saharan region [2, 3].\n",
-                        "\n",
-                        "Resistance to pyrethroid and carbamate insecticides in _Anopheles_ mosquitoes was first reported from the central \n",
-                        "region of Cote d'Ivoire in the early 1990s [4, 5, 6, 7]. Local resistance to the major insecticide classes recommended by the World Health Organization (WHO) for adult mosquito control--pyrethroids, carbamates, organophosphates, and organochlorines--evolved rapidly [8, 9, 10] and has been increasing in intensity, driven largely by selective pressures imposed by contemporaneous scale-up of public health vector control interventions (including those targeting malaria, trypanosomiasis, and onchocerciasis vectors) and use of agricultural pesticides [11, 12, 13, 14, 7]. This escalation in resistance has now begun to compromise the insecticidal efficacy and community-wide impact of conventional, pyrethroid LLINs in Cote d'Ivoire [14, 15], although some levels of personal protection may still remain [15, 16, 17].\n",
-                        "\n",
-                        "Among vector populations across Cote d'Ivoire, the L1014F _kdr_ mutation is pervasive and has been implicated in some longitudinal trends in decreasing DDT and pyrethroid susceptibility [7, 11]; L1014S _kdr_ and N1575Y resistance mutations have also been detected but at much lower frequencies [18]. Extreme carbamate (bendiocarb) resistance and pyrethroid cross-resistance in some _A. gambiae_ sensu stricto (s.s.) populations are mediated by overexpression of _CYP6P3_ and _CYP6M2_ and duplication of the G119S _Ace-1_ mutation [19]. To support and safeguard future malaria control efforts in Cote d'Ivoire, the current study evaluated the efficacy of conventional and next-generation LLINs for prospective distribution, determined current insecticide resistance profiles of _A. gambiae_ s.l. (principally _Anopheles coluzzi_), and characterized underlying molecular and metabolic resistance mechanisms.\n",
-                        "\n",
-                        "header: Metabolic Resistance Mechanisms\n",
-                        "\n",
-                        "Comparison of metabolic gene expression levels in field populations of _A. coluzzi_ and _A. gambiae s.s._ demonstrated significant up-regulation of _CYP6P4_ (fold change, 5.88 [95% CI, 5.19-44.06] for _A. coluzzi_ and 6.08 [5.43-50.64] for _A. gambiae_ s.s.), _CYP6Z1_ (4.04 [3.69-41.54] and 3.56 [3.24-36.25], respectively),\n",
-                        "\n",
-                        "Figure 3.: The longevity of field-caught _A. gambiae_ sensus into after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (_A_) and \\(x\\) (B, 5x [_O_], and 10× [_D_) the diagnostic dose of pyrethroid insecticides in Centres for Disease Control and Prevention resistance intensity assays. Kaplan-Meier survival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\n",
-                        "\n",
-                        "\n",
-                        "and _CYP6P3_ (12.56 [11.40-12-3.83] and 13.85 [12.53-132.03]), relative to a susceptible laboratory colony, respectively (Figure 4). More modest overexpression of _CYP6P1_ and _GSTE2_ was observed (_CYP6P1_ fold changes, 1.18 [95% CI, 1.08-12.31] and 1.28 [1.17-14.40]; GSTE2, 0.56 [.48-3.32], and 0.67 [.58-4.29], for _A. coluzzi_ and _A. gambiae_ s.s., respectively) (Figure 4). The fold change levels did not differ significantly between the 2 species for any gene nor by malaria infection status in wild _A. coluzzi_. Comparison of metabolic gene expression in phenotyped field populations of _A. coluzzi_ revealed lower fold changes overall, but notably, increased overexpression of _CYP6P3_ in survivors of bendiocarb, deltamethrin, PBO plus deltamethrin, and permethrin (fold change, 3.91 [95% CI, 3.33-22.16], 2.21 [1.88-12.53], 2.64 [2.21-13.69], and 2.21 [1.99-20.03], respectively) (Figure 5).\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Metabolic Gene Expression\n",
-                        "\n",
-                        "Relative expression of 5 metabolic genes (_CYP6P3_, _CYP6P4_, _CYP6Z1_, _CYP6P1_, and _GSTE2_) was measured in all field collected mosquitoes (n = 912), using multiplex quantitative real-time polymerase chain reaction (PCR) assays, relative to the housekeeping gene ribosomal protein S7 (_RPS7_) [30]. In addition, gene expression levels were measured in susceptible _A. coluzzii_ Nguosso colony mosquitoes (n = 48). All samples were run in technical triplicate. Expression level and fold change of each target gene between resistant and susceptible field samples, relative to the susceptible laboratory strain, were calculated using the 2-\\({}^{\\text{-}\\Delta\\text{ACT}}\\) method incorporating PCR efficiency, normalized relative to the endogenous control gene (_RPS7_).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Cote d'Ivoire has hot spots with some of the highest levels of resistance of _Anopheles_ mosquitoes to public health insecticides worldwide, with potentially severe implications for sustaining gains in malaria control [31]. To safeguard malaria vector control efforts and inform the design of effective resistance management strategies, involving tactical deployment of differing indoor residual spraying and LLIN modalities, there needs to be a clear understanding of contemporary phenotypic and genotypic insecticide resistance.\n",
-                        "\n",
-                        "Our study detected intense pyrethroid resistance in southeast Cote d'Ivoire, as evidenced by high proportions of survivors, after exposure to 10 times the diagnostic doses of pyrethroids, as well as very low knockdown and 24-hour mortality rates for deltamethrin-only LLINs, equivalent to rates for an untreated net. These findings are largely in agreement with historical resistance profiles from this region [7, 10, 11] and indicate that conventional LLINs may no longer be operationally viable in areas of high pyrethroid resistance intensity. Previous phase II studies of pyrethroid-only LLINs in the central region of Cote d'Ivoire have demonstrated similarly poor efficacy with highly resistant _A. gambiae_ s.l. populations but argued for the retention of some degree of personal protection [15-17].\n",
-                        "\n",
-                        "Other observational cohorts have reported higher incidences of malaria among non-net users compared with users in areas of moderate to high pyrethroid resistance [17]. The extent of protective efficacy afforded by pyrethroid LLINs will likely reflect the strength of local vector resistance and levels of both net physical integrity and individual compliance [32, 33]; in Cote d'Ivoire, reported LLIN usage has been low, requiring additional behavioral interventions [2, 34]. Our findings of high mosquito mortality rates after exposure to clothianidin and chlorfenapyr and improved vector susceptibility with PBO treatment (on both LLINs and in resistance bioassays), are consistent with data from other sentinel sites across Cote d'Ivoire [16, 35, 36], and strongly support the deployment of vector control interventions incorporating these new active ingredients.\n",
-                        "\n",
-                        "Study results indicate that _A. coluzzi_ was the predominant local vector species during the rainy season, as observed previously [7], circulating sympatrically with smaller proportions of _A. gambiae_ s.s. These 2 vector species commonly cohabit but can be genetically distinct in terms of resistance mechanisms [37, 38] and can also differ in larval ecology, behavior, migration, and activation [39-41]. In general, resistance mechanisms in _A. coluzzi_ are less well characterized, compared with _A. gambiae_ s.s., in part because these vectors are morphologically\n",
-                        "\n",
-                        "Figure 4: Metabolic gene expression in field _Anopheles coluzzi_ and _Anopheles gambiae_ sensu stricto (s.s.) populations relative to a susceptible colony population. Error bars represent 95% confidence intervals. Statistically significant differences in expression levels relative to the susceptible colony are indicated as follows: *_P_ <.05; **_P_ <.01; ***_P_ <.001.\n",
-                        "\n",
-                        "\n",
-                        "indistinguishable and few studies present data disaggregated by PCR-confirmed species.\n",
-                        "\n",
-                        "We observed several distinct features in our study, including, principally, evidence for ongoing selection of L1014F _kdr_ and G119S _Ace-1 in A. coluzzii_, which was absent in _A. gambiae_ s.s. and higher proportions of N1575Y in _A. gambiae_ s.s.; expression levels of metabolic genes were comparable between species. The lack of association between L1014F _kdr_ genotype and mosquito phenotype, coupled with the identification of 3 CYP450 enzymes (_CYP6P4, CYP6P3,_ and _CYP6Z1_) that were significantly overexpressed in field populations (some of which are known to metabolize pyrrethroids and next-generation LLIN insecticides [42, 43]), indicate a key role for metabolic resistance in this _A. coluzzii_ population. One notable difference in our data set, compared with previous findings in Agboville [7], was the finding of benodiocarb susceptibility. This may be attributable to small-scale spatial and longitudinal heterogeneity in resistance, which can be highly dynamic [37, 44], and/or phenotypic differences between vector species, complicating intervention choice for resistance management.\n",
-                        "\n",
-                        "With the exception of chlorfenapyr, which is known to be a slow-acting insecticide, no delayed mortality effects were detected after insecticidal exposure; the format and dose used for clothianidin testing (another slow-acting insecticide [45]) were instead intended to measure acute toxicity within a 60-minute exposure period. Previous mathematical models using resistant mosquito colonies have suggested that sublethal insecticide treatment may still reduce vector lifespan and inhibit blood-feeding and host-seeking behaviors, thereby interrupting malaria transmission [46, 47]. Our observations are more compatible with reports from Burkina Faso, where different exposure regimens of wild, resistant _A. gambiae_ s.l. populations to deltamethrin LLINs did not induce any delayed mortality effects [47]. Further assessment of sublethal effects are warranted across additional field populations with differing resistance mechanisms, to clarify the impact of insecticidal exposure on the vectorial capacity of resistant mosquitoes.\n",
-                        "\n",
-                        "To date there is a paucity of data regarding the interactions between insecticide resistance and _Plasmodium_ development [48]. In the current study, _A. coluzzii_ that died after pyrethroid exposure were significantly more likely to be infected with malaria. This might be explained by elevated metabolic enzymes and/or prior pyrethroid exposure detrimentally affecting parasite development [49], although it is important to note that we did not detect any significant differences between gene overexpression in malaria-infected versus noninfected _A. coluzzii_. Alternatively, our sampled population may have been physiologically older, as phenotypic resistance is known to decline with age [50]. It is impossible to distinguish between these hypotheses using field-collected vector populations; the experimental design used in this study had other biological and technical limitations, which have been described in detail elsewhere [23, 37]\n",
-                        "\n",
-                        "In conclusion, as new combination and bitreated vector control interventions become available for deployment, contemporary resistance information is crucial for the rationale design of management strategies and to mitigate further selection for particular resistance mechanisms. The results from the current study contribute to growing insecticide resistance data for Cote d'Ivoire, demonstrating a loss of bioefficacy of pyrethroid LLINs and supporting the use of new active ingredients (clothianidin, chlorfenapyr, and PBO). Study findings also highlight the need for expanded insecticide resistance surveillance, including monitoring of metabolic resistance mechanisms, in conjunction with studies to better characterize the impact of sublethal insecticide exposure on vectorial capacity and the interaction between insecticide resistance and _Plasmodium_ parasite development.\n",
-                        "\n",
-                        "Figure 5: Metabolic gene expression in resistant versus susceptible field _Anopheles coluzzi_, which either died or survived after insecticidal exposure. Error bars represent 95% confidence intervals.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study protocol was approved by the Comite National d'Ethique des Sciences de la Vie et de la Sante (no. 069-19/MSHP/CNESVS-kp) and the London School of Hygiene and Tropical Medicine (nos. 16782 and 16899). Study activities were conducted in the village of Aboude, rural Agboville, Agneby-Tiassa region, southeast Cote d'Ivoire (5'55'N, 4'13'W), selected because of its high mosquito densities and malaria prevalence [1]. Adult mosquitoes were collected using human landing catches, inside and outside households from 6 pm to 6 am, for a total of 190 person/trap/nights between 5 and 26 July 2019. Unfed mosquitoes, morphologically identified as _A. gambiae_ s.l. [20], were tested in bioassays that same day, after a brief recovery period; blood-fed mosquitoes were first held for 2-3 days to allow for blood-meal digestion.\n",
-                        "\n",
-                        "header: Mosquito Processing, Identification of _A. gambiae_ s.l. Species Complex\n",
-                        "\n",
-                        "Members, and _Plasmodium falciparum_ Detection\n",
-                        "\n",
-                        "A subsample of field-caught mosquitoes tested in bioassays was selected for molecular analysis (n = 912). Approximately equal numbers of specimens were chosen to represent phenotypically \"susceptible\" or \"resistant\" mosquitoes for each LLIN type or insecticide dose, selected across different replicates and testing days to capture as much population-level variation as possible. RNA was extracted from individual whole-body mosquitoes according to standard protocols [23]. Field _A. gambiae_ s.l. were identified to species level [25] and were screened for the presence of _Plasmodium falciparum_[26].\n",
-                        "\n",
-                        "header: Mosquito Collections and Species Identification: LIII Efficacy\n",
-                        "\n",
-                        "A total of 2666 field-caught _A. gambiae_ s.l were used to assess the bioefficacy of conventional pyrethroid-treated LLINs (PermaNet 2.0 and PermaNet 3.0 side panels) and next-generation synergisti LLINs (PermaNet 3.0 roof panels), compared with an untreated control (Figure 1). Overall, _A. gambiae_ s.l. knockdown and mortality rates with deltamethrin LLINs were very low and largely equivalent to those for the untreated control net (Figure 1). At 60 minutes, average mosquito knockdown rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels were 1.56% (95% confidence interval [CI], 1.13%-1.99%), 0.54% (.42%-6.5%), and 1.75% (1.49%-2.0%), respectively. By contrast, average mosquito knockdown rates for PBO-containing PermaNet 3.0 roof panels were significantly higher (79.8% [95% CI, 79.07%-80.48%]; kh2 = 705.51, 968.65, and 937.33 [_P_ <.001] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1).\n",
-                        "\n",
-                        "At 24 hours, mortality rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels remained low (6.11% [95% CI, 4.71%-7.51%], 5.44% [4.58%-6.29%], and 3.66% [3.12%-4.19%], respectively), while those with PermaNet 3.0 roof panels increased only marginally but still remained significantly higher (83.81% [95% CI, 83.15%-84.47%];\n",
-                        "\n",
-                        "Figure 1.: Bioefficacy of different unwashed long-lasting insecticidal nets (LLINs) against field-caught _Angabies gambiae_ sensu tatto. Mean knockdown and mortality rates are shown with 95% confidence intervals, at 60 minutes and 24 hours, respectively, after 3-minute exposure to PermaNet 2.0 (deltamethrin only), side panels of PermaNet 3.0 (deltamethrin only, roof panels of PermaNet 3.0 (pieronym) butoxide plus deltamethrin), and an untreated control net. Knockdown or mortality rates in the same time period for each treatment sharing a letter do not differ significantly (_P_ >.05). Green lines at 275% and 295% knockdown represent minimal and optimal effectiveness, respectively, at 60 minutes. Red lines at 250% and >80% mortality represent minimal and optimal LLIN effectiveness at 24 hours, respectively, as defined by the World Health Organization [21].\n",
-                        "\n",
-                        "\n",
-                        "\\(\\chi^{2}\\) = 727.96, 914.61, and 963.09 [\\(P\\) <.001 for all] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1). PermaNet 3.0 roof panels reached minimal effectiveness (knockdown, >=75%) 60 minutes after exposure and optimal effectiveness (mortality rate, >=80%) at 24 hours. Neither of the deltamethrin-only LLINS reached either effectiveness threshold at any time point.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Cote d'Ivoire\",\"Site\":\"Aboude, Agboville\",\"Start_month\":7,\"Start_year\":2019,\"End_month\":7,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"1.4 g\\/kg\"},\"N02\":{\"Net_type\":\"PermaNet 3.0 roof panels\",\"Insecticide\":\"PBO, deltamethrin\",\"Concentration_initial\":\"25g\\/kg, 4 g\\/kg\"},\"N03\":{\"Net_type\":\"PermaNet 3.0 side panels\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"2.8 g\\/kg\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Notes\n",
-                        "\n",
-                        "_Acknowledgments._ The authors express their sincere thanks to M. Didier Dobri, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire (CSRS) laboratory technician, and Fidele Assamoa for their support in mosquito collection and rearing, the chief and population of the village of Aboude (Agboville), and the entomology fieldworkers of CSRS. They also thank Vestergaard for supplying the long-lasting insecticidal nets tested in this study.\n",
-                        "\n",
-                        "_Author contributions._ A. M., E. C., M. K., T. W., and L. A. M. designed the study. A. M., E. C., C. E., and B. P. led the entomology field activities and participated in data collection. A. M., E. C., C. L. J., T. W., and L. A. M. performed the molecular assays. A. M., E. C., M. K., C. E., C. L. J., B. P., S. R. I, T. W., and L. A. M. were responsible for data analysis and interpretation. L. A. M. drafted the manuscript, which was revised by all coauthors. All authors read and approved the final manuscript.\n",
-                        "\n",
-                        "_Disclaimer._ The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.\n",
-                        "\n",
-                        "_Financial support._ This work was supported by the Sir Halley Stewart Trust (L.A.M.), the Wellcome Trust/Royal Society ([http://www.wellcome.ac.uk](http://www.wellcome.ac.uk) and [https://royalsociety.org](https://royalsociety.org); (101285/Z/13/Z to T.W.) and the US President's Malaria Initiative/Centers for Disease Control and Prevention (S.R.I.).\n",
-                        "\n",
-                        "_Potential conflicts of interest._ All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.\n",
-                        "\n",
-                        "header: Table 1. Cox Proportional Hazard Model to Determine Impact of Long-Lasting Insecticidal Net/Insecticidal Exposure on Survival of Field-Caught Anopheles gambiae Sensu Lato 72 Hours After Exposure\n",
-                        "footer: Abbreviations: CI, confidence interval; HRR, hazard rate ratio (ratio of hazard rate for control/reference group to hazard rate for treatment group; PBO, piperonyl butoxide.\n",
-                        "a Immediate mortality rates after long-lasting insecticidal net (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. \n",
-                        "b Significance cutoff level defined as α = .05.\n",
-                        "\n",
-                        "\n",
-                        "columns: ['Insecticide Exposure', 'No. (No. of Events)', 'HRR (95% CI)', 'P Valueb']\n",
-                        "\n",
-                        "{\"0\":{\"Insecticide Exposure\":\"Untreated netting\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"1\":{\"Insecticide Exposure\":\"PermaNet 2.0 (deltamethrin only)\",\"No. (No. of Events)\":\"1135 (1047)\",\"HRR (95% CI)\":\"1.095 (.968\\u20131.239)\",\"P Valueb\":\".15\"},\"2\":{\"Insecticide Exposure\":\"PermaNet 3.0 side panels (deltamethrin only)\",\"No. (No. of Events)\":\"1157 (1088)\",\"HRR (95% CI)\":\"0.9664 (.9092\\u20131.027)\",\"P Valueb\":\".27\"},\"3\":{\"Insecticide Exposure\":\"PermaNet 3.0 roof panels (PBO + deltamethrin)\",\"No. (No. of Events)\":\"563 (533)\",\"HRR (95% CI)\":\"1.007 (.939\\u20131.079)\",\"P Valueb\":\".85\"},\"4\":{\"Insecticide Exposure\":\"Acetone control\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"5\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 1\\u00d7\",\"No. (No. of Events)\":\"676 (641)\",\"HRR (95% CI)\":\"1.006 (.9696\\u20131.043)\",\"P Valueb\":\".77\"},\"6\":{\"Insecticide Exposure\":\"Deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"683 (645)\",\"HRR (95% CI)\":\"0.9942 (.9539\\u20131.036)\",\"P Valueb\":\".78\"},\"7\":{\"Insecticide Exposure\":\"Permethrin 1\\u00d7\",\"No. (No. of Events)\":\"693 (661)\",\"HRR (95% CI)\":\"1.015 (.9698\\u20131.062)\",\"P Valueb\":\".52\"},\"8\":{\"Insecticide Exposure\":\"Clothianidin 1\\u00d7\",\"No. (No. of Events)\":\"698 (581)\",\"HRR (95% CI)\":\"1.208 (.9227\\u20131.581)\",\"P Valueb\":\".17\"},\"9\":{\"Insecticide Exposure\":\"Chlorfenapyr 1\\u00d7\",\"No. (No. of Events)\":\"708 (580)\",\"HRR (95% CI)\":\"1.692 (1.086\\u20132.637)\",\"P Valueb\":\".02\"},\"10\":{\"Insecticide Exposure\":\"PBO + deltamethrin 1\\u00d7\",\"No. (No. of Events)\":\"630 (577)\",\"HRR (95% CI)\":\"0.9662 (.2411\\u20133.873)\",\"P Valueb\":\".96\"},\"11\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"633 (601)\",\"HRR (95% CI)\":\"0.9951 (.9407\\u20131.053)\",\"P Valueb\":\".86\"},\"12\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"652 (610)\",\"HRR (95% CI)\":\"0.9942 (.9393\\u20131.052)\",\"P Valueb\":\".84\"},\"13\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"636 (583)\",\"HRR (95% CI)\":\"0.9931 (.8638\\u20131.142)\",\"P Valueb\":\".92\"},\"14\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"624 (587)\",\"HRR (95% CI)\":\"0.9951 (.917\\u20131.08)\",\"P Valueb\":\".91\"},\"15\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"623 (588)\",\"HRR (95% CI)\":\"0.9943 (.9072\\u20131.09)\",\"P Valueb\":\".90\"},\"16\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"656 (603)\",\"HRR (95% CI)\":\"1.026 (.9509\\u20131.107)\",\"P Valueb\":\".51\"},\"17\":{\"Insecticide Exposure\":\"1\\u00d7 Insecticide dose\",\"No. (No. of Events)\":null,\"HRR (95% CI)\":\"Reference\",\"P Valueb\":null},\"18\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 5\\u00d7\",\"No. (No. of Events)\":\"117 (92)\",\"HRR (95% CI)\":\"1.016 (.9069\\u20131.138)\",\"P Valueb\":\".78\"},\"19\":{\"Insecticide Exposure\":\"Alpha-cypermethrin 10\\u00d7\",\"No. (No. of Events)\":\"108 (78)\",\"HRR (95% CI)\":\"1.007 (.9403\\u20131.078)\",\"P Valueb\":\".84\"},\"20\":{\"Insecticide Exposure\":\"Deltamethrin 5\\u00d7\",\"No. (No. of Events)\":\"143 (105)\",\"HRR (95% CI)\":\"1.0 (.9035\\u20131.107)\",\"P Valueb\":\">.99\"},\"21\":{\"Insecticide Exposure\":\"Deltamethrin 10\\u00d7\",\"No. (No. of Events)\":\"114 (83)\",\"HRR (95% CI)\":\"1.0 (.9363\\u20131.068)\",\"P Valueb\":\">.99\"},\"22\":{\"Insecticide Exposure\":\"Permethrin 5\\u00d7\",\"No. (No. of Events)\":\"137 (94)\",\"HRR (95% CI)\":\"1.022 (.8528\\u20131.225)\",\"P Valueb\":\".81\"},\"23\":{\"Insecticide Exposure\":\"Permethrin 10\\u00d7\",\"No. (No. of Events)\":\"157 (114)\",\"HRR (95% CI)\":\"0.9952 (.9491\\u20131.044)\",\"P Valueb\":\".84\"}}\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "Study findings raise concerns regarding the operational failure of standard LLINs and support the urgent deployment of vector control interventions incorporating piperonyl butoxide, chlorfenapyr, or clothianidin in areas of high resistance intensity in Cote d'Ivoire.\n",
-                        "\n",
-                        "header: Resistance Intensity and Synergist Bioassay Testing\n",
-                        "\n",
-                        "Centers for Disease Control and Prevention (CDC) resistance intensity bioassays were performed for 6 public health insecticides (pyrethroids: alpha-cypermethrin, deltamethrin, and permethrin; carbamate: bendiocarb; neonicotinoid: clothianidin; and pyrrole: chlorfenapyr) [22, 23]. The diagnostic doses of all insecticides were evaluated (including clothianidin [90 mg per bottle] [23] and chlorfenapyr [100 mg per bottle]) and 2, 5, and 10 times the diagnostic dose of pyrethroid insecticides were also used. Per test, knockdown was recorded at 15-minute intervals for 30 minutes (pyrethroids and bendiocarb) or 60 minutes (dothianidin and chlorfenapyr) of insecticide exposure. One-hour PBO preexposures were performed using WHO tube assays [24], before deltamethrin CDC bottle bioassay testing [22].\n",
-                        "\n",
-                        "WHO cone and CDC resistance intensity bioassay data were interpreted according to the WHO criteria [21, 22]. Mosquitoes that died after exposure to a LLIN or 1x insecticide dose were stored at -20degC in RNAlater (Thermo Fisher Scientific) and were considered \"susceptible\" for genotypic analysis. Surviving mosquitoes were held and scored for mortality rate after 24, 48 and 72 hours to observe delayed mortality effects. Kaplan-Meier curves were used to visualize survival data, and Cox regression was used to compare postexposure survival. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded. Surviving mosquitoes at 72 hours were stored at -20degC in RNAlater and were considered \"resistant\" for genotypic analysis.\n",
-                        "\n",
-                        "header: Insecticide Resistance Intensity\n",
-                        "\n",
-                        "A total of 2251 field-caught _A. gambiae_ s.l. were tested in resistance bioassays. Intense pyrethroid resistance was evident with more than 25% of mosquitoes surviving exposure to 10 times the dose of insecticide required to kill a susceptible population (Figure 2A). At the diagnostic dose, mosquito mortality rates did not exceed 25% for any pyrethroid tested, which was consistent with the high survival rates observed during cone bioassays using conventional LLINs (Figure 1). In general, levels of resistance to alpha-cypermethrin, deltamethrin, and permethrin were not significantly different at each insecticide concentration tested (Figure 2A).\n",
-                        "\n",
-                        "By comparison, carbamate tolerance was low, with a mean knockdown of 94.53% (95% CI, 92.11%-96.95%; n = 101) after 30 minutes of exposure to the diagnostic dose of bendiocarb. Similarly, high levels of susceptibility to new insecticides clothianidin and chlorfenapyr were observed, with mean mortality rates of 94.11% (95% CI, 93.43%-94.80%; n = 102) and 95.54% (94.71%-96.36%; n = 112), respectively, 72 hours after exposure to the tentative diagnostic doses. Preexposure to PBO increased the average _A. gambiae_ s.l. mortality rate significantly, from 14.56% (95% CI, 6.24%-22.88%) to 72.73% (64.81%-79.43%) and from 44.66% (34.86%-54.46%) to 94.17% (91.12%-97.22%) after exposure to 1 or 2 times the diagnostic dose of deltamethrin (Figure 2B).\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Phenotypic resistance was intense: >25% of mosquitoes survived exposure to 10 times the doses of pyrethroids required to kill susceptible populations. Similarly, the 24-hour mortality rate with deltamethrin-only LLINs was very low and not significantly different from that with an untreated net. Sublethal pyrethroid exposure did not induce significant delayed vector mortality effects 72 hours later. In contrast, LLINs containing the synergist piperonyl butoxide, or new insecticides dothianidin and chlorfenapyr, were highly toxic to _A. coluzzii_. Pyrethroid-susceptible _A. coluzzii_ were significantly more likely to be infected with malaria, compared with those that survived insecticidal exposure. Pyrethroid resistance was associated with significant overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_.\n",
-                        "\n",
-                        "header: Malaria Prevalence\n",
-                        "\n",
-                        "Of the 912 _A. gambiae_ s.l. mosquitoes assayed, 31 tested positive for _P. falciparum_ (3.4%). For PCR-confirmed _A. coluzzii_, _P. falciparum_ prevalence was 3.50% (28 of 805); the remaining 3 infections were in _A. gambiae_ s.s. (4%; 3 of 75). By resistance phenotype, susceptible _A. coluzzii_ (ie, those that died after pyrethroid exposure) were more likely to be infected with malaria, compared with resistant mosquitoes (kh2 = 4.6987; \\(P\\) =.03); infection rates were 5.94% (13 of 219) and 2.49% (10 of 401), respectively.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Target-Site Mutations\n",
-                        "\n",
-                        "The same cohort of field mosquitoes (n = 912) was tested for the presence of L1014F _kdr_[27] and N1575Y mutations [28]. A subsample of mosquitoes (n = 49) that were exposed to bendiocarb, clothianidin or chlorfenapyr was tested for the presence of the G119S _Ace-1_ mutation [29]. Pearson kh2 and Fisher exact tests (when sample sizes were small) were used to investigate the statistical association between resistance status, allele frequencies, and deviations from Hardy-Weinberg equilibrium.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Association of Reduced Long-Lasting Insecticidal Net Efficacy and Pyrethroid Insecticide Resistance With Overexpression of _CYP6P4_, _CYP6P3_, and _CYP6Z1_ in Populations of _Anopheles coluzzii_ From Southeast Cote d'Ivoire\n",
-                        "\n",
-                        "Anne Meiwald,\\({}^{1,2}\\) Emma Clark,\\({}^{1,2}\\) Mejica Kristan,\\({}^{1}\\) Constant Edi,\\({}^{2}\\) Claire L Jeffries,\\({}^{1}\\) Bethanie Pelloquin,\\({}^{1}\\) Seth R. Irish,\\({}^{2}\\) Thomas Walker,\\({}^{1}\\) and Louisa A. Messenger\\({}^{1,0}\\)\n",
-                        "\n",
-                        "\n",
-                        "Resistance to major public health insecticides in Cote d'Ivoire has intensified and now threatens the long-term effectiveness of malaria vector control interventions.\n",
-                        "\n",
-                        "header: RESULTS\n",
-                        "\n",
-                        "A total of 4917 female _A. gambiae_ s.l. mosquitoes were collected in Agboville, Cote d'Ivoire. Of those, 912, which were previously tested in either LLIN bioefficacy (n = 384) or resistance intensity (n = 528) bioassays, were selected for molecular species identification. Of the 912 selected, 805 (88.3%) were determined to be _A. coluzzii_, 75 (8.2%) were _A. gambiae_ s.s., and 22 (2.4%) were _A. gambiae_-_A. coluzzii_ hybrids; 10 individuals did not amplify.\n",
-                        "\n",
-                        "header: Mosquito Survival After Insecticidal Exposure\n",
-                        "\n",
-                        "All _A. gambiae_ s.l. tested in LLIN bioefficacy or resistance intensity bioassays, were held for 72 hours, to assess any impact of insecticide or net exposure on delayed mortality rate. For LLIN bioassays, there was little evidence for any reduction in survival during this holding period (Cox regression \\(P\\) =.15,.27, and.85, respectively, for comparisons between untreated control and PermaNet 2.0, PermaNet 3.0 side panels, and PermaNet 3.0 roof panels) (Table 1 and Figure 3A). Exposure to the diagnostic doses of all insecticides in CDC bottle bioassays did not induce significant delayed mortality effects over 72 hours (Cox regression \\(P\\) >.05 for all insecticides compared with control, with the exception of chlorfenapyr [_P_ =.02]) (Table 1 and Figure 3B). This phenomenon was also observed at increasing pyrethroid doses\n",
-                        "\n",
-                        "Figure 2: _A._ Resistance intensity of field-caught _Anaopheles gambiae_ sensu late (s.l.) after exposure to 1, 2, 5, or 10 times the diagnostic dose of pyrethroid insecticides. Mean knockdown/acute toxicity rates after 30-minute exposure are shown with 95% confidence intervals (Cls). Knockdown or mortality rates at the same dose per insecticide sharing a letter do not differ significantly (_P_ >.05). Mortality rates <00% (_lower and line_) represent confirmed resistance at the diagnostic dose (1a), and rates <00% (_upper and line_) indicate moderate to high-intensity resistance and high-intensity resistance at 5° and 10x, respectively, as defined by the World Health Organization [24]. _B._ Restoration of deltamethrin susceptibility of field-caught _A. gambiae_ s.l. after presopause to piperonyl butoxide (PBO). Mean knockdown/acute toxicity after 30-minute exposure to 1 or 2 times the diagnostic dose of deltamethrin with 95% Cls. Knockdown/mortality rates do not differ significantly between pyrethroid only and synergist plus pyrethroid sharing a letter (_P_ >.05). Red line at 90% mortality rate represents metabolic resistance mechanisms partially involved [24].\n",
-                        "\n",
-                        "\n",
-                        "(Cox regression \\(P\\) >.05 for alpha-cypermethrin, deltamethrin, and permethrin 5x and 10x vs either the control or diagnostic dose) (Table 1; Figure 3C and 3D).\n",
-                        "\n",
-                        "header: Metabolic Resistance Mechanisms\n",
-                        "\n",
-                        "Comparison of metabolic gene expression levels in field populations of _A. coluzzi_ and _A. gambiae s.s._ demonstrated significant up-regulation of _CYP6P4_ (fold change, 5.88 [95% CI, 5.19-44.06] for _A. coluzzi_ and 6.08 [5.43-50.64] for _A. gambiae_ s.s.), _CYP6Z1_ (4.04 [3.69-41.54] and 3.56 [3.24-36.25], respectively),\n",
-                        "\n",
-                        "Figure 3.: The longevity of field-caught _A. gambiae_ sensus into after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (_A_) and \\(x\\) (B, 5x [_O_], and 10× [_D_) the diagnostic dose of pyrethroid insecticides in Centres for Disease Control and Prevention resistance intensity assays. Kaplan-Meier survival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\n",
-                        "\n",
-                        "\n",
-                        "and _CYP6P3_ (12.56 [11.40-12-3.83] and 13.85 [12.53-132.03]), relative to a susceptible laboratory colony, respectively (Figure 4). More modest overexpression of _CYP6P1_ and _GSTE2_ was observed (_CYP6P1_ fold changes, 1.18 [95% CI, 1.08-12.31] and 1.28 [1.17-14.40]; GSTE2, 0.56 [.48-3.32], and 0.67 [.58-4.29], for _A. coluzzi_ and _A. gambiae_ s.s., respectively) (Figure 4). The fold change levels did not differ significantly between the 2 species for any gene nor by malaria infection status in wild _A. coluzzi_. Comparison of metabolic gene expression in phenotyped field populations of _A. coluzzi_ revealed lower fold changes overall, but notably, increased overexpression of _CYP6P3_ in survivors of bendiocarb, deltamethrin, PBO plus deltamethrin, and permethrin (fold change, 3.91 [95% CI, 3.33-22.16], 2.21 [1.88-12.53], 2.64 [2.21-13.69], and 2.21 [1.99-20.03], respectively) (Figure 5).\n",
-                        "\n",
-                        "header: Study Area and Mosquito Collections: WHO Cone Bioassay Testing\n",
-                        "\n",
-                        "Two types of LLIN were evaluated in this study. PermaNet 2.0 is a conventional LLIN treated with deltamethrin only (1.4 g/kg +- 25%) and PermaNet 3.0 is a piperonyl butoxide (PBO) synergism LILIN, consisting of a roof containing PBO (25g/kg) and deltamethrin (4 g/kg +- 25%) and side panels containing deltamethrin only (2.8 g/kg +- 25%). WHO cone bioassays were used to test the susceptibility of _A. gambiae_ s.l. exposed to unwashed PermaNet 2.0, PermaNet 3.0 roof panels, and PermaNet 3.0 side panels [21]. To control for potential variation in insecticide/synergist content, each of 5 LLINs per type was cut into 19 pieces, measuring 30 x 30 cm, with each piece tested a maximum of 3 times.\n",
-                        "\n",
-                        "header: Characterization of Insecticide Resistance Mechanisms: Metabolic Gene Expression\n",
-                        "\n",
-                        "Relative expression of 5 metabolic genes (_CYP6P3_, _CYP6P4_, _CYP6Z1_, _CYP6P1_, and _GSTE2_) was measured in all field collected mosquitoes (n = 912), using multiplex quantitative real-time polymerase chain reaction (PCR) assays, relative to the housekeeping gene ribosomal protein S7 (_RPS7_) [30]. In addition, gene expression levels were measured in susceptible _A. coluzzii_ Nguosso colony mosquitoes (n = 48). All samples were run in technical triplicate. Expression level and fold change of each target gene between resistant and susceptible field samples, relative to the susceptible laboratory strain, were calculated using the 2-\\({}^{\\text{-}\\Delta\\text{ACT}}\\) method incorporating PCR efficiency, normalized relative to the endogenous control gene (_RPS7_).\n",
-                        "\n",
-                        "header: Target-Site Resistance Mutations\n",
-                        "\n",
-                        "L1014_F_kdr_ screening revealed that 92.2% (796/863) of _A. gambiae_ s.l. mosquitoes harbored the mutation; 71.5% (617 of 863) were homozygous, 20.7% (179 of 863) were heterozygous, 5.1% (44 of 863) were wild type, and 2.7% (23 of 863) did not amplify. For PCR-confirmed _A. coluzzii_, L1014F_kdr_ prevalence was 87.8% (707 of 805); 66.6% (536 of 805) were homozygous for the mutation, 21.2% (171 of 805) were heterozygous, 5.3% (43 of 805) were wild type, and 2.2% (18 of 805) did not amplify. For _A. coluzzii_, population-level L1014F_kdr_ allele frequency was 0.83, with evidence for significant deviations from Hardy-Weinberg equilibrium (kh2 = 29.124; \\(P\\) <.001). There was no significant association between L1014F_kdr_ frequency and the ability of _A. coluzzii_, to survive pyrethroid exposure, in either LLIN or resistance bioassays (kh2 = 2.0001 [_P_ =.16] and kh2 = 3.6998 [_P_ =.054], respectively). Similarly, there was no significant association between L1014F_kdr_ and the ability of _A. coluzzii_ to survive PBO preexposure and pyrethroid treatment, in either LLIN or resistance bioassays (kh2 = 0.0086; \\(P\\) =.93; Fisher exact test, \\(P\\) =.429, respectively).\n",
-                        "\n",
-                        "For PCR-confirmed _A. gambiae_ s.s., L1014F_kdr_ prevalence was 95.3% (61 of 64); 89.1% (57 of 64) were homozygous for the mutation, 6.3% (4 of 64) were heterozygous, none were wild type, and 4.7% (3 of 64) did not amplify. There was no significant association between L1014F_kdr_ frequency and ability of _A. gambiae_ s.s. to survive pyrethroid or PBO preexposure and pyrethroid treatment (in either LLIN or resistance bioassays), because all tested individuals harbored this mutation (n = 61). For _A. gambiae_ s.s., the population-level L1014F_kdr_ allele frequency was 0.97, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.070; \\(P\\) =.79).\n",
-                        "\n",
-                        "N1575Y screening revealed that 2.3% of _A. gambiae_ s.l. mosquitoes (21 of 912) harbored the mutation; all were\n",
-                        "heterozygotes. N1575Y prevalence was 1.1% (9 of 805) and 16% (12 of 75) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 0.99% (9 of 912) did not amplify. There was no evidence for ongoing N1575Y selection in either species (kh2 = 0.026 [_P_ =.87] and kh2 = 0.62 [_P_ =.43] for _A. coluzzi_ and _A. gambiae_ s.s., respectively). For _A. coluzzi_, there was no significant association between N1575Y frequency and ability of mosquitoes to survive pyrethroid exposure, in LLIN or resistance bioassays (kh2 = 0.0001 [_P_ =.99] and kh2 = 0.3244 [_P_ =.57], respectively).\n",
-                        "\n",
-                        "G119S _Ace-1_ screening revealed that 55.1% of _A. gambiae_ s.l. mosquitoes (27 of 49) harbored the mutation; all were heterozygotes. G119S _Ace-1_ prevalence was 64.9% (24 of 37) and 27.3% (3 of 11) for PCR-confirmed _A. coluzzi_ and _A. gambiae_ s.s., respectively; 1 remaining _A. gambiae-A. coluzzi_ hybrid was wild type. For _A. coluzzi_, population-level G119S _Ace-1_ allele frequency was 0.32, with evidence of significant deviations from Hardy-Weinberg equilibrium (kh2 = 8.525; \\(P\\) =.004). For _A. gambiae_ s.s., population-level G119S _Ace-1_ allele frequency was 0.14, with no significant deviations from Hardy-Weinberg equilibrium (kh2 = 0.274; \\(P\\) =.60). For _A. coluzzi_, there was a significant association between G119S _Ace-1_ frequency and surviving bendiocarb exposure (Fisher exact test, \\(P\\) =.005).\n",
-                        "\n",
-                        "header: Keywords\n",
-                        "\n",
-                        "_Anopheles coluzzii_; insecticide resistance; _Plasmodium falciparum_; long-lasting insecticidal nets; Cote d'Ivoire; PBO; chlorfenapyr; clothianidin; _CYP6P4_; _CYP6P3_; _CYP6Z1_\n",
-                        "\n",
-                        "In Cote d'Ivoire, malaria is a serious public health problem with the entire population of about 26.2 million people at risk, and disease prevalence reaching as high as 63% in the southwest region [1]. Control of _Anopheles gambiae_ sensu lato (s.l.), the major malaria vector species group, has been through the efforts of the National Malaria Control Programme, which has distributed insecticide-treated nets as the primary vector control intervention. Indoor residual spraying and larviciding in high transmission areas have been recommended as complementary strategies; implementation of the former commenced in late 2020 [2]. Estimates of net coverage across the country remain low, with the proportion of households with at least >=1 insecticide-treated net per 2 persons rising from 31% in 2012 to 47% in 2016, and insecticide-treated net use stagnating at 40% of households reporting sleeping under a net the previous night in both survey years [2]. The most recent universal net campaigns in Cote d'Ivoire in 2017-2018 issued conventional, pyrethroid (deltamethrin) long-lasting insecticidal nets (LLINs), aiming to achieve 90% coverage and 80% use [2]. However, country-wide, multiclass insecticide resistance among populations of _A. gambiae_ s.l. is a growing cause for concern because of potential operational failure of current vector control strategies, both locally and across the sub-Saharan region [2, 3].\n",
-                        "\n",
-                        "Resistance to pyrethroid and carbamate insecticides in _Anopheles_ mosquitoes was first reported from the central \n",
-                        "region of Cote d'Ivoire in the early 1990s [4, 5, 6, 7]. Local resistance to the major insecticide classes recommended by the World Health Organization (WHO) for adult mosquito control--pyrethroids, carbamates, organophosphates, and organochlorines--evolved rapidly [8, 9, 10] and has been increasing in intensity, driven largely by selective pressures imposed by contemporaneous scale-up of public health vector control interventions (including those targeting malaria, trypanosomiasis, and onchocerciasis vectors) and use of agricultural pesticides [11, 12, 13, 14, 7]. This escalation in resistance has now begun to compromise the insecticidal efficacy and community-wide impact of conventional, pyrethroid LLINs in Cote d'Ivoire [14, 15], although some levels of personal protection may still remain [15, 16, 17].\n",
-                        "\n",
-                        "Among vector populations across Cote d'Ivoire, the L1014F _kdr_ mutation is pervasive and has been implicated in some longitudinal trends in decreasing DDT and pyrethroid susceptibility [7, 11]; L1014S _kdr_ and N1575Y resistance mutations have also been detected but at much lower frequencies [18]. Extreme carbamate (bendiocarb) resistance and pyrethroid cross-resistance in some _A. gambiae_ sensu stricto (s.s.) populations are mediated by overexpression of _CYP6P3_ and _CYP6M2_ and duplication of the G119S _Ace-1_ mutation [19]. To support and safeguard future malaria control efforts in Cote d'Ivoire, the current study evaluated the efficacy of conventional and next-generation LLINs for prospective distribution, determined current insecticide resistance profiles of _A. gambiae_ s.l. (principally _Anopheles coluzzi_), and characterized underlying molecular and metabolic resistance mechanisms.\n",
-                        "\n",
-                        "header: Mosquito Collections and Species Identification: LIII Efficacy\n",
-                        "\n",
-                        "A total of 2666 field-caught _A. gambiae_ s.l were used to assess the bioefficacy of conventional pyrethroid-treated LLINs (PermaNet 2.0 and PermaNet 3.0 side panels) and next-generation synergisti LLINs (PermaNet 3.0 roof panels), compared with an untreated control (Figure 1). Overall, _A. gambiae_ s.l. knockdown and mortality rates with deltamethrin LLINs were very low and largely equivalent to those for the untreated control net (Figure 1). At 60 minutes, average mosquito knockdown rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels were 1.56% (95% confidence interval [CI], 1.13%-1.99%), 0.54% (.42%-6.5%), and 1.75% (1.49%-2.0%), respectively. By contrast, average mosquito knockdown rates for PBO-containing PermaNet 3.0 roof panels were significantly higher (79.8% [95% CI, 79.07%-80.48%]; kh2 = 705.51, 968.65, and 937.33 [_P_ <.001] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1).\n",
-                        "\n",
-                        "At 24 hours, mortality rates with the untreated control, PermaNet 2.0, and PermaNet 3.0 side panels remained low (6.11% [95% CI, 4.71%-7.51%], 5.44% [4.58%-6.29%], and 3.66% [3.12%-4.19%], respectively), while those with PermaNet 3.0 roof panels increased only marginally but still remained significantly higher (83.81% [95% CI, 83.15%-84.47%];\n",
-                        "\n",
-                        "Figure 1.: Bioefficacy of different unwashed long-lasting insecticidal nets (LLINs) against field-caught _Angabies gambiae_ sensu tatto. Mean knockdown and mortality rates are shown with 95% confidence intervals, at 60 minutes and 24 hours, respectively, after 3-minute exposure to PermaNet 2.0 (deltamethrin only), side panels of PermaNet 3.0 (deltamethrin only, roof panels of PermaNet 3.0 (pieronym) butoxide plus deltamethrin), and an untreated control net. Knockdown or mortality rates in the same time period for each treatment sharing a letter do not differ significantly (_P_ >.05). Green lines at 275% and 295% knockdown represent minimal and optimal effectiveness, respectively, at 60 minutes. Red lines at 250% and >80% mortality represent minimal and optimal LLIN effectiveness at 24 hours, respectively, as defined by the World Health Organization [21].\n",
-                        "\n",
-                        "\n",
-                        "\\(\\chi^{2}\\) = 727.96, 914.61, and 963.09 [\\(P\\) <.001 for all] vs untreated control, PermaNet 2.0, and PermaNet 3.0 side panels, respectively) (Figure 1). PermaNet 3.0 roof panels reached minimal effectiveness (knockdown, >=75%) 60 minutes after exposure and optimal effectiveness (mortality rate, >=80%) at 24 hours. Neither of the deltamethrin-only LLINS reached either effectiveness threshold at any time point.\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study protocol was approved by the Comite National d'Ethique des Sciences de la Vie et de la Sante (no. 069-19/MSHP/CNESVS-kp) and the London School of Hygiene and Tropical Medicine (nos. 16782 and 16899). Study activities were conducted in the village of Aboude, rural Agboville, Agneby-Tiassa region, southeast Cote d'Ivoire (5'55'N, 4'13'W), selected because of its high mosquito densities and malaria prevalence [1]. Adult mosquitoes were collected using human landing catches, inside and outside households from 6 pm to 6 am, for a total of 190 person/trap/nights between 5 and 26 July 2019. Unfed mosquitoes, morphologically identified as _A. gambiae_ s.l. [20], were tested in bioassays that same day, after a brief recovery period; blood-fed mosquitoes were first held for 2-3 days to allow for blood-meal digestion.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Cote d'Ivoire has hot spots with some of the highest levels of resistance of _Anopheles_ mosquitoes to public health insecticides worldwide, with potentially severe implications for sustaining gains in malaria control [31]. To safeguard malaria vector control efforts and inform the design of effective resistance management strategies, involving tactical deployment of differing indoor residual spraying and LLIN modalities, there needs to be a clear understanding of contemporary phenotypic and genotypic insecticide resistance.\n",
-                        "\n",
-                        "Our study detected intense pyrethroid resistance in southeast Cote d'Ivoire, as evidenced by high proportions of survivors, after exposure to 10 times the diagnostic doses of pyrethroids, as well as very low knockdown and 24-hour mortality rates for deltamethrin-only LLINs, equivalent to rates for an untreated net. These findings are largely in agreement with historical resistance profiles from this region [7, 10, 11] and indicate that conventional LLINs may no longer be operationally viable in areas of high pyrethroid resistance intensity. Previous phase II studies of pyrethroid-only LLINs in the central region of Cote d'Ivoire have demonstrated similarly poor efficacy with highly resistant _A. gambiae_ s.l. populations but argued for the retention of some degree of personal protection [15-17].\n",
-                        "\n",
-                        "Other observational cohorts have reported higher incidences of malaria among non-net users compared with users in areas of moderate to high pyrethroid resistance [17]. The extent of protective efficacy afforded by pyrethroid LLINs will likely reflect the strength of local vector resistance and levels of both net physical integrity and individual compliance [32, 33]; in Cote d'Ivoire, reported LLIN usage has been low, requiring additional behavioral interventions [2, 34]. Our findings of high mosquito mortality rates after exposure to clothianidin and chlorfenapyr and improved vector susceptibility with PBO treatment (on both LLINs and in resistance bioassays), are consistent with data from other sentinel sites across Cote d'Ivoire [16, 35, 36], and strongly support the deployment of vector control interventions incorporating these new active ingredients.\n",
-                        "\n",
-                        "Study results indicate that _A. coluzzi_ was the predominant local vector species during the rainy season, as observed previously [7], circulating sympatrically with smaller proportions of _A. gambiae_ s.s. These 2 vector species commonly cohabit but can be genetically distinct in terms of resistance mechanisms [37, 38] and can also differ in larval ecology, behavior, migration, and activation [39-41]. In general, resistance mechanisms in _A. coluzzi_ are less well characterized, compared with _A. gambiae_ s.s., in part because these vectors are morphologically\n",
-                        "\n",
-                        "Figure 4: Metabolic gene expression in field _Anopheles coluzzi_ and _Anopheles gambiae_ sensu stricto (s.s.) populations relative to a susceptible colony population. Error bars represent 95% confidence intervals. Statistically significant differences in expression levels relative to the susceptible colony are indicated as follows: *_P_ <.05; **_P_ <.01; ***_P_ <.001.\n",
-                        "\n",
-                        "\n",
-                        "indistinguishable and few studies present data disaggregated by PCR-confirmed species.\n",
-                        "\n",
-                        "We observed several distinct features in our study, including, principally, evidence for ongoing selection of L1014F _kdr_ and G119S _Ace-1 in A. coluzzii_, which was absent in _A. gambiae_ s.s. and higher proportions of N1575Y in _A. gambiae_ s.s.; expression levels of metabolic genes were comparable between species. The lack of association between L1014F _kdr_ genotype and mosquito phenotype, coupled with the identification of 3 CYP450 enzymes (_CYP6P4, CYP6P3,_ and _CYP6Z1_) that were significantly overexpressed in field populations (some of which are known to metabolize pyrrethroids and next-generation LLIN insecticides [42, 43]), indicate a key role for metabolic resistance in this _A. coluzzii_ population. One notable difference in our data set, compared with previous findings in Agboville [7], was the finding of benodiocarb susceptibility. This may be attributable to small-scale spatial and longitudinal heterogeneity in resistance, which can be highly dynamic [37, 44], and/or phenotypic differences between vector species, complicating intervention choice for resistance management.\n",
-                        "\n",
-                        "With the exception of chlorfenapyr, which is known to be a slow-acting insecticide, no delayed mortality effects were detected after insecticidal exposure; the format and dose used for clothianidin testing (another slow-acting insecticide [45]) were instead intended to measure acute toxicity within a 60-minute exposure period. Previous mathematical models using resistant mosquito colonies have suggested that sublethal insecticide treatment may still reduce vector lifespan and inhibit blood-feeding and host-seeking behaviors, thereby interrupting malaria transmission [46, 47]. Our observations are more compatible with reports from Burkina Faso, where different exposure regimens of wild, resistant _A. gambiae_ s.l. populations to deltamethrin LLINs did not induce any delayed mortality effects [47]. Further assessment of sublethal effects are warranted across additional field populations with differing resistance mechanisms, to clarify the impact of insecticidal exposure on the vectorial capacity of resistant mosquitoes.\n",
-                        "\n",
-                        "To date there is a paucity of data regarding the interactions between insecticide resistance and _Plasmodium_ development [48]. In the current study, _A. coluzzii_ that died after pyrethroid exposure were significantly more likely to be infected with malaria. This might be explained by elevated metabolic enzymes and/or prior pyrethroid exposure detrimentally affecting parasite development [49], although it is important to note that we did not detect any significant differences between gene overexpression in malaria-infected versus noninfected _A. coluzzii_. Alternatively, our sampled population may have been physiologically older, as phenotypic resistance is known to decline with age [50]. It is impossible to distinguish between these hypotheses using field-collected vector populations; the experimental design used in this study had other biological and technical limitations, which have been described in detail elsewhere [23, 37]\n",
-                        "\n",
-                        "In conclusion, as new combination and bitreated vector control interventions become available for deployment, contemporary resistance information is crucial for the rationale design of management strategies and to mitigate further selection for particular resistance mechanisms. The results from the current study contribute to growing insecticide resistance data for Cote d'Ivoire, demonstrating a loss of bioefficacy of pyrethroid LLINs and supporting the use of new active ingredients (clothianidin, chlorfenapyr, and PBO). Study findings also highlight the need for expanded insecticide resistance surveillance, including monitoring of metabolic resistance mechanisms, in conjunction with studies to better characterize the impact of sublethal insecticide exposure on vectorial capacity and the interaction between insecticide resistance and _Plasmodium_ parasite development.\n",
-                        "\n",
-                        "Figure 5: Metabolic gene expression in resistant versus susceptible field _Anopheles coluzzi_, which either died or survived after insecticidal exposure. Error bars represent 95% confidence intervals.\n",
-                        "\n",
-                        "header: Mosquito Processing, Identification of _A. gambiae_ s.l. Species Complex\n",
-                        "\n",
-                        "Members, and _Plasmodium falciparum_ Detection\n",
-                        "\n",
-                        "A subsample of field-caught mosquitoes tested in bioassays was selected for molecular analysis (n = 912). Approximately equal numbers of specimens were chosen to represent phenotypically \"susceptible\" or \"resistant\" mosquitoes for each LLIN type or insecticide dose, selected across different replicates and testing days to capture as much population-level variation as possible. RNA was extracted from individual whole-body mosquitoes according to standard protocols [23]. Field _A. gambiae_ s.l. were identified to species level [25] and were screened for the presence of _Plasmodium falciparum_[26].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Bioassay, cone test\",\"Country\":\"Cote d'Ivoire\",\"Site\":\"Aboude, Agboville\",\"Start_month\":7,\"Start_year\":2019,\"End_month\":7,\"End_year\":2019}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"1.4 g\\/kg\"},\"N02\":{\"Net_type\":\"PermaNet 3.0 roof panels\",\"Insecticide\":\"PBO, deltamethrin\",\"Concentration_initial\":\"25g\\/kg, 4 g\\/kg\"},\"N03\":{\"Net_type\":\"PermaNet 3.0 side panels\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"2.8 g\\/kg\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 2.2.1 Role of CYP6P9a_R Using WHO Tube Assay Samples\n",
-                        "\n",
-                        "Using the hybrid FUMOZ-FANG strains, the role of the _CYP6P9a_R_ allele in the observed pyrethroid resistance was confirmed. The odds ratio of surviving exposure to permethrin when homozygous for the resistant _CYP6P9a_R_ allele (RR) was high at 693 (CI 88-5421; \\(p\\) < 0.0001) compared to the homozygous-susceptible mosquitoes (SS) (Figure 2A). The OR was 131 (CI 27-978; \\(p\\) < 0.0001) when comparing RR to RS, indicating the increasing resistance conferred by _CYP6P9a_.\n",
-                        "\n",
-                        "header: 4.6.2 Genotyping of the CYP6P9b-R Maker Using PCR-RFLP\n",
-                        "\n",
-                        "The PCR was carried out, as described for CYP6P9a. The following primers were used: 6p9brflp_0.5F 5'-CCCCCACAGGTGTAACTCTGAA-3' and 6p9brflp_0.5R 5'-TTATCCGTAAACTCAATAGCGATG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 550 bp. The digestion with the _Tsp_451 restriction enzyme followed 0.2 mL of Tsp451, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product was migrated on the 2% gel. Amplicons from susceptible mosquitoes were cut into two bands with sizes of 400 bp and 150 bp, and the resistant mosquitoes remained undigested with the band at 550 bp.\n",
-                        "\n",
-                        "header: Impact of the Duplicated CYP6P9a and CYP6P9b P450 Genes on the Performance of Bed Nets\n",
-                        "\n",
-                        "The samples collected during the evaluation of Olyset and Olyset Plus in experimental huts were grouped in several categories: dead, alive, blood fed, and unfed; room and veranda. The _CYP6P9a_/b and _6.5 kb SV_ were genotyped in each group using the respective PCR assays as described [28,29,31]. This allowed the relative survival and feeding success of resistant and susceptible insects in the presence of both bed nets to be directly measured.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Experimental Hut Trials Reveal That CYP6P9a/b P450 Alleles Are Reducing the Efficacy of Pyrethroid-Only Olyset Net against the Malaria Vector _Anopheles funestus_ but PBO-Based Olyset Plus Net Remains Effective\n",
-                        "\n",
-                        "Benjamin D. Menze, Leon M. J. Mugenzi, Magellan Tchouakui, Murielle J. Wondji,\n",
-                        "\n",
-                        "1Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; murielle.wondji@ilstmed.ac.uk\n",
-                        "2Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaounde 13591, Cameroon; leon.mugenzi@crid-cam.net (L.M.J.M.); magellan.tchouakui@crid-cam.net (M.T.); micareme.tchouop@crid-cam.net (M.T.)\n",
-                        "* Correspondence: benjamin.menze@crid-cam.net (B.D.M.); charles.wondji@ilstmed.ac.uk (C.S.W.)\n",
-                        "\n",
-                        "\n",
-                        "Malaria remains a major public health concern in Africa. Metabolic resistance in major malaria vectors such as _An. funestus_ is jeopardizing the effectiveness of long-lasting insecticidal nets (LLINs) to control malaria. Here, we used experimental hut trials (EHTs) to investigate the impact of cytochrome P450-based resistance on the efficacy of PBO-based net (Olyset Plus) compared to a permethrin-only net (Olyset), revealing a greater loss of efficacy for the latter. EHT performed with progenies of F5 crossing between the _An. funestus_ pyrethroid-resistant strain FUMOZ and the pyrethroid-susceptible strain FANG revealed that PBO-based nets (Olyset Plus) induced a significantly higher mortality rate (99.1%) than pyrethroid-only nets (Olyset) (56.7%) (\\(p<0.0001\\)). The blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)). Genotyping the _CYP6P9a/b_ and the intergenic _6.5 kb structural variant_ (SV) resistance alleles showed that, for both nets, homozygote-resistant mosquitoes have a greater ability to blood-feed than the susceptible mosquitoes. Homozygote-resistant genotypes significantly survived more with Olyset after cone assays (e.g., _CYP6P9a_ OR = 34.6; \\(p<0.0001\\)) than homozygote-susceptible mosquitoes. A similar but lower correlation was seen with Olyset Plus (OR = 6.4; \\(p<0.001\\)). Genotyping EHT samples confirmed that _CYP6P9a/b_ and _6.5 kb_SV_ homozygote-resistant mosquitoes survive and blood-feed significantly better than homozygote-susceptible mosquitoes when exposed to Olyset. Our findings highlight the negative impact of P450-based resistance on pyrethroid-only nets, further supporting that PBO nets, such as Olyset Plus, are a better solution in areas of P450-mediated resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Table 1: Description of the long-lasting insecticidal nets used.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment Arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment Arm\":\"Untreated\",\"Description\":\"100% polyester with no insecticide\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"4\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"5\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"}}\n",
-                        "\n",
-                        "header: 5 Conclusions\n",
-                        "\n",
-                        "This study reveals that insecticide resistance driven by _CYP6P9a/b_ and _6.5 kb SV_ can reduce the efficiency of Permethrin-based LLINs, such as Olyset, while PBO-based nets remain effective. However, the greater loss of efficacy observed in mosquitoes with multiple and complex resistance supports the need to introduce new products for vector control which is less reliant on pyrethroids. Meantime, PBO-based nets should be preferably deployed in areas of P450-based resistance.\n",
-                        "\n",
-                        "**Supplementary Materials:** The following supporting information can be downloaded at: [https://www.mdpi.com/article/10.3390/pathogens11060638/s1](https://www.mdpi.com/article/10.3390/pathogens11060638/s1), Table S1: Results of the performance of Olyset and Olyset Plus against _An. funestus_ females (crossing FUMOZ-FANG; F5) in experimental hut trial. Table S2: Correlation between _CYP6P9a and CYP6P9b_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S3: Correlation between the _6.5Kv SV_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S4: Correlation between genotypes of _CYP6P9a_, _CYP6P9b_ and _6.5 kb SV_ and mortality and blood-feeding after exposure to Olyset and Olyset Plus in experimental huts. Table S5: _CYP6P9a_ and _CYP6P9b_ acting together further increase the ability of _An. funestus_ to survive and blood feed against Olyset after experimental hut trial.\n",
-                        "\n",
-                        "**Author Contributions:** C.S.W. conceived and designed the study; L.M.J.M. and M.T. (Magellan Tchouakui) generated the lab crosses and performed the validation of the PCR; B.D.M. performed the experimental hut experiments with C.S.W. and genotyped the resistance makers with M.J.W. and M.T. (Micareme Tchoupo); B.D.M. and C.S.W. wrote the paper with assistance from M.T. (Magellan Tchouakui) and L.M.J.M. All authors have read and agreed to the published version of the manuscript.\n",
-                        "\n",
-                        "**Funding:** This research was funded in whole by the Welcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). For the purpose of open access, the authors applied a CC BY public copyright license to any author who accepted a manuscript version following this submission.\n",
-                        "\n",
-                        "**Institutional Review Board Statement:** NA.\n",
-                        "\n",
-                        "**Informed Consent Statement:** NA.\n",
-                        "\n",
-                        "**Data Availability Statement:** NA.\n",
-                        "\n",
-                        "**Acknowledgments:** A big thanks to Charles Wondji for his financial support through the Wellcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). I am also grateful to the Mibellon community.\n",
-                        "\n",
-                        "**Conflicts of Interest:** The authors declare that they have no competing interest.\n",
-                        "\n",
-                        "header: 3 Discussion\n",
-                        "\n",
-                        "The capacity to assess the impact of insecticide resistance on the effectiveness of insecticide-treated control tools is crucial in order to implement suitable insecticide resistance management (IRM) [32]. Evolution in the area of DNA-based resistance markers of P450-mediated resistance [28; 29] currently offers robust tools to screen pyrethroid resistance in field populations of the major malaria vectors such as _An. funestus_ and assess their impact on the effectiveness of LLINs. In this study, we use these markers to evaluate the extent to which pyrethroid resistance is impacting the efficacy of pyrethroid-only nets such as Olyset in comparison to PBO-based net such as Olyset Plus. This study also allowed to assess the interplay between P450 genes in the overall genetic variance to resistance and their combined impact on the efficacy of LLINs.\n",
-                        "\n",
-                        "PBO-Based Nets (Olyset Plus) Exhibit Greater Efficacy than Pyrethroid-Only Nets (Olyset) in the Context of P450-Based Resistance\n",
-                        "\n",
-                        "Olyset presented a moderate mortality compared to Olyset Plus (30.9% vs. 100%; \\(p\\) < 0.0001). The high mortality observed with PBO nets compared to pyrethroid-only nets is similar to what was observed in previous studies [33; 34; 35; 36]. The high mortality observed with PBO-based nets against the hybrid strain FUMOZ/FANG is due to the fact the mechanisms underlying the resistance in this population are driven by _CYP6P9a_ and _CYP6P9b_[37] which are inhibited by PBO [38]. These results demonstrate that PBO-based nets may be a more suitable solution in areas where resistance is mainly mediated by P450 genes.\n",
-                        "\n",
-                        "Figure 7: Combined impact of the triple resistance alleles (_CYP6P9a_/_CYP6P9b_/_6.5 kb SV_) on the efficacy of insecticide-treated nets. (**A**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances of being alive in the presence of Olyset Net. (**B**) Ability to survive exposure to Olyset net for the various combined genotypes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_. (**C**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances to bloodfed in the presence of Olyset net. (**D**) The triple RR/RR/SV+SV+ homozygous mosquitoes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_ exhibit a greater blood-feeding ability than other genotypes. Ns: non-significant; * (0.05); ** (0.01); *** (0.001).\n",
-                        "\n",
-                        "\n",
-                        "The efficacy of nets observed in this study confirms the loss in efficacy of the pyrethroid-only nets against _Anopheles_ mosquitoes, as observed across the continent [39]. This loss of efficacy was observed in Mozambique [7,40], Malawi [41], Congo [42], and Cameroon [6,43]. Overall, similar to this study, it has been noticed that PBO-based nets demonstrate a better performance compared to pyrethroid-only nets [33,34]. The same trends were observed with pyrethroid type II with high mortality observed with PermaNet 3.0 compared to PermaNet 2.0 [28,29,31], suggesting that PBO-based nets with both type I or II can exhibit high performance against resistance sustained by P450s.\n",
-                        "\n",
-                        "P450 Resistance Can Reduce the Efficacy of Pyrethroid-Only Nets Significantly Better Than PBO-Based Nets\n",
-                        "\n",
-                        "When comparing the impact of _CYP6P9a/b_ on pyrethroid-only nets and PBO-based nets using samples from cone assays, we noticed that the impact of _CYP6P9a_ on pyrethroid-only nets was higher than with PBO-based nets (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. Olyset Plus, OR = 6.4; \\(p\\) < 0.001). The same trend was also observed with _CYP6P9b_ and the 6.5 kb SV. This can be explained by the fact that the addition of the PBO inhibits the cytochrome P450 enzymes, which is the main resistance mechanism in the strain used by the mosquitoes [44,45].\n",
-                        "\n",
-                        "On the other hand, Olyset nets have also shown reduced performance against _CYP6P9a_ and _CYP6P9b_-resistant mosquitoes. Strong association was observed between the resistant alleles and the increased ability of the mosquitoes to survive after exposure to these nets. Nevertheless, the impact of _CYP6P9a_ and _CYP6P9b_ seems to be more significant on PermaNet nets impregnated with deltamethrin [29,31], compared to Olyset nets impregnated with permethrin (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. PermaNet 2.0, OR = 239.0; \\(p\\) < 0.001 and Olyset Plus, OR = 6.4; \\(p\\) < 0.001 vs. PermaNet 3.0, OR = 81.0; \\(p\\) < 0.001) [29,31]. This significant impact on PermaNet could be associated with the fact that the hybrid strain is more resistant to type II pyrethroids, as shown by WHO bioassays, where the mosquitoes were more resistant to deltamethrin compared to permentrin (48.5% vs. 80.7%). With regards to the obtained odd ratios, a stronger association was observed between _CYP6P9a/CYP6P9b_ and the loss of efficacy of nets compared to what was observed with GST-based resistance through the L119F-_GSTe2_-resistant allele [43], in line with the greater role of P450 in resistance to pyrethroids in _An. funetus_, compared to that of GST. The 6.5 kb SV was shown to further exacerbate the loss of efficacy in the permethrin-only nets. The design of the simple PCR-based assay to genotype the _6.5 kb SV_ enabled us to assess the impact of such structural variation on the efficacy of insecticide-treated nets, including the pyrethroid-only and the PBO-synergist nets. A greater reduction in the efficacy of _6.5 kb SV_ was present on permethrin-only nets compared to PBO-based nets, which is similar to that observed with _CYP6P9a_ [31] and _CYP6P9b_ [29], in terms of the reduced mortality rate and blood-feeding inhibition. This pattern is also similar to that seen with deltamethrin-based nets (PermaNet 2.0 vs. PermaNet 3.0) [29,31]. Overall, the significantly greater loss of efficacy due to P450s observed with both type I (Olyset and PermaNet 2.0) and PBO-based nets (Olyset Plus and PermaNet 3.0) further supports the deployment of PBO-based nets to control P450-based metabolically resistant mosquito populations. The deployment of PBO-based nets should nevertheless also be monitored to regularly assess their efficacy since they too could be impacted by resistance, as shown by the fact that _CYP6P9a/b_-resistant mosquitoes could blood-feed significant better, even with Olyset Plus. This increased ability of resistant mosquitoes to blood-fed, even with PBO-based net, suggests that PBO-based nets are not immune to the impact of resistance, even if they remain significantly more effective than pyrethoid-only nets. The increased blood-feeding of P450-resistant mosquitoes when exposed to PBO-based nets may also lead to a higher malaria transmission, although this is mitigated by the high mortality observed for Olyset Plus in a experimental hut trial. Overall, evaluating the impact of P450-based resistance on the efficacy of Olyset vs. Olyset Plus further supports the results obtained in a cluster \n",
-                        "randomized controlled trial with both nets in Tanzania showing greater effectiveness of Olyset Plus [46].\n",
-                        "\n",
-                        "header: 1 Introduction\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain an important component of the malaria control strategies used to reduce mortality and morbidity due to this disease in Africa [1, 2]. Mostly insecticides belonging to the pyrethroid group, as well as more and more novel insecticides, are currently recommended for bed net impregnation [3, 4, 5]. In recent years, anopheles mosquitoes have increasingly been reported to show resistance against pyrethroids across Africa. This is the case for major vectors including _Anopheles funestus_[6, 7, 8, 9] and _Anopheles gambiae_[10, 11, 12, 13, 14]. This growing spread of resistance has led to a concern that LLINs may lose their efficacy. However, quantifying such loss of efficacy remains challenging without suitable metrics associated with resistance, highlighting the need to use robust genotype/phenotype analysis for this evaluation. At a time when national malaria control \n",
-                        "programs across Africa are seeking to take evidence-based decisions on their choice of suitable nets to help improve malaria control, it is paramount to determine the extent to which existing resistance to pyrethroid really affects LLIN efficacy. This involves an understanding of the interaction between resistance mechanisms and mosquito responses to exposure to LLINs. Broadly speaking, pyrethroid resistance can be caused by alterations in the target site of the insecticide or enhanced enzymatic activities capable of metabolizing the insecticide before it reaches the target [15-17]. Target site resistance is well studied with mutations in the target of both pyrethroid and DDT insecticides, as well as the voltage-gated sodium channels which lead to knockdown resistance (_kdr_). The development of molecular assays more than twenty years ago made it possible to track this resistance mechanism using a simple PCR [18-21] or high throughput techniques, such as TaqMan assays [22]. It has also been possible to assess the impact of _kdr_ on control tools, notably in _An. gambiae_ where these markers are common [23-25], contrary to _An. funestus_, where it is still not detected [26]. The second best characterized cause of insecticide resistance is metabolic resistance, which is more complex to understand as it can involve the detoxification, sequestration, and transportation of insecticides or their conjugates [16,17,27]. Recent studies have focused on elucidating the genetic factors which cause the increased expression of detoxification genes associated with insecticide resistance in malaria vectors such as _An. funestus_, leading to the development of simple molecular assays used to detect metabolic resistance [28-31].\n",
-                        "\n",
-                        "The recent design of molecular assays for the duplicated _CYP6P9a/b_ and the associated _6.5 kb_ structural variant (SV), refs. [28,29,31] now provides a great opportunity to assess the impact of P450-based pyrethroid resistance on the efficacy of various LLINs, including pyrethroid-only (Olyset) and PBO (Olyset Plus) nets. This will provide evidence-based information on their effectiveness in the presence of a growing and escalading resistance to pyrethroids. Previous assessment of this impact on PermaNet nets made with type II pyrethroid deltamethrin has been performed [28,29,31], but this remains to be carried out for type I pyrethroid nets, such as the common permethrin-based Olyset nets.\n",
-                        "\n",
-                        "Here, using EHT, we show a significantly greater efficacy of the PBO-based net Olyset Plus compared to the pyrethroid-only Olyset net against pyrethroid-resistant _An. funestus_. Using the genotyping of DNA-based markers of P450 resistance, we reveal that _CYP6P9a/b_ P450-based pyrethroid resistance induces a significant loss of efficacy for pyrethroid-only net Olyset against _An. funestus_. A reduced efficacy was also detected for PBO-based net Olyset Plus, but at a lower extent than for Olyset net, revealing that PBO nets are more suitable to tackle such P450-based resistance.\n",
-                        "\n",
-                        "header: 2.3.1 Impact on Mosquito Mortality: 2.3.2 CYP6P9a Impacting the Blood-Feeding\n",
-                        "\n",
-                        "Homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed better than homozygote-susceptible mosquitoes (SS) (OR = 4.5; \\(p\\) < 0.01) when exposed to Olyset. Heterozygote mosquitoes (RS) were significantly worse at blood-feeding with Olyset compared to homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-1.7; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) fed heterozygotes (OR = 5.4; CI = 2.6-10.88; \\(p\\) < 0.001) significantly better (Table S4; Figure 5C). Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.3; CI = 1.3-4.1; \\(p\\) < 0.01) (Table S4). For Olyset Plus, homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed than homozygote-susceptible mosquitoes (RS) (OR = 6.3; \\(p\\) < 0.001) when exposed to Olyset Plus. The significant ability to blood-feed was observed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.0; CI = 1.1-3.5; \\(p\\) = 0.03) (Table S4).\n",
-                        "\n",
-                        "header: 4.6.1 Genotyping of the CYP6P9a-R Marker Using PCR-RFLP\n",
-                        "\n",
-                        "DNA was extracted from various groups of mosquitoes (dead, alive, blood fed, and unfed; room and veranda) using the Livak protocol [49]. The PCR was carried out using 10 mM of each primer and 1 mL of gDNA as the template in 15 mL reaction containing 10x Kapa Taq buffer A, 25 mM of dNTPs, 25 mM of MgCl2, and 1 U of Kapa Taq (Kapa Biosystems, Boston, MA, USA). Amplification was carried out using thermocycler parameters: 95 degC for 5 min, 35 cycles of 94 degC for 30 s, 58 degC for 30 s, 72 degC for 45 s, and a final extension at 72 degC for 10 min. The following primers were used: RFLP6P9aF forward primer 5'-TCCCGAAATACAGCCTTTCAG-3 and RFLP6P9aR reverse primer 5'-ATTGGTCCATCGCTAGAAG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 450 bp. The digestion with Taq1a followed 0.2 mL of Taq1a, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product migrated on the 2% gel. Amplicons from resistant mosquitoes were cut into two bands with sizes of 350 bp and 100 bp, and the susceptible mosquitoes remained undigested with the band at 450 bp.\n",
-                        "\n",
-                        "header: 4.6.3 PCR Assay to Detect the 6.5 kb SV\n",
-                        "\n",
-                        "To easily identify the samples containing the 6.5 kb insertion, the recently designed PCR assay [28] was used to discriminate between mosquitoes with the 8.2 kb (resistant) and 1.7 kb (susceptible) _CYP6P9a_ and _CYP6P9b_ intergenic regions. Briefly, three primers were used: two (FG_5F: CTACCGTCAAAGTCCGGTAT and FG_3R: TTTCGAAAACATCCCTAAA) at regions flanking the insertion point and a third primer (FZ_INS5R: ATATGCCACGAAGGAAAGCAG) in the _6.5 kb_ insertion. One unit of KAPA Taq polymerase (Kapa Biosystems) in 1x buffer A, i.e., 25 mm of MgCl2, 25 mm of dNTPs, and 10 mm of each primer, was used to constitute a 15 mL PCR mix using the following conditions: an initial denaturation step of 3 min at 95 degC, followed by 35 cycles of 30 s at 94 degC, 30 s at 58 degC, and 60 s at 72 degC, with a final extension for 10 min at 72 degC. The amplicon was revealed on a 1.5% agarose gel stained with Midori Green Advance DNA Stain (Nippon genetics Europe GmbH) and revealed on a UV transilluminator.\n",
-                        "\n",
-                        "header: Impact of CYP6P9a on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Genotyping of the _CYP6P9a_ marker allowed us to assess the impact of P450-based metabolic resistance on the loss of efficacy of Olyset, but not for Olyset Plus, since most of the mosquitoes released in Olyset Plus huts died. To avoid confounding effects from blood-feeding, or net entry or exophily status, the distribution of the _CYP6P9a_ genotypes was assessed firstly only among unfed mosquitoes collected in the room. This revealed a highly significant difference in the frequency of the three genotypes between the dead and alive mosquitoes (chi-square = 28.8; \\(p\\) < 0.0001) (Figure 5). Analysis of the correlation between each genotype and mortality revealed that _CYP6P9a_ homozygous-resistant mosquitoes (RR) show an increased ability to survive exposure to the Olyset when compared to homozygote-susceptible mosquitoes (SS) with (OR = 5.0; CI = 2.01-12.4; \\(p\\) = 0.001). The strength of this association was further shown by the significant ability to survive exposure when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.04; CI = 1.1-3.6; \\(p\\) < 0.05). However, heterozygote mosquitoes (RS) did not survive exposure to Olyset better than homozygote-susceptible mosquitoes (SS) (OR = 1.8; CI = 0.8-3.9; \\(p\\) > 0.05) (Figure 5A; Table S4; Figure 5B).\n",
-                        "\n",
-                        "Figure 5: Impact of both _CYP6P9a_ and _CYP6P9b_ on the efficacy of bed nets in the experimental hut trial. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: \\(p\\) < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Association between _CYP6P9a_ and ability to blood-feed when exposed to Olyset. The _CYP6P9a_–R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (**D**) Association between _CYP6P9b_ and ability to survive exposure to Olyset in the experimental hut trial. (**E**) Allelic frequency of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the _CYP6P9b_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9b_S_. (**F**) Association between _CYP6P9b_ gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The _CYP6P9b_-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\n",
-                        "\n",
-                        "header: 2.4.1 The Impact of CYP6P9b on Mortality: 2.4.2 CYP6P9b Impacting the Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _CYP6P9b_ genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could more successfully blood-fed when compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 4.1-24.9; \\(p\\) < 0.001) (Table S4; Figure 5F). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset was not different to that of homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-2.2; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 11.1; CI = 5.3-23.03: \\(p\\) < 0.001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood fed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.7; CI = 2.5-8.1; \\(p\\) < 0.01) (Table S4).\n",
-                        "\n",
-                        "header: 2.1.1. Quality Control and Performance of the Nets against the Hybrid Strain: 2.1.2 Performance of Nets against the Hybrid Strain Using Experimental Hut\n",
-                        "\n",
-                        "A total number of 578 mosquitoes from the hybrid strain were released and recaptured on a period of one week. In total, 141 samples were released in the control hut, 224 in the hut were treated with Olyset, and 213 in the hut were treated with Olyset Plus.\n",
-                        "\n",
-                        "**Mortality**: Analysis of mortality rates revealed very high mortality of the hybrid FUMOZ-FANG strain against the PBO-based Olyset Plus net (99.1%). In contrast, lower mortality was observed for the pyrethroid-only Olyset net (56.7%). Very low mortality was observed in the untreated control net (9.9%) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "**Blood-feeding**: The blood-feeding rate did not significantly differ when comparing the control (7.8%) to Olyset (11.6% \\(p>0.05\\)) and Olyset Plus (5.6%; \\(p>0.05\\)). However, the blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Figure 1: Susceptibility profile of the hybrid FUMOZ-FANG strain to pyrethroids. (**A**) Net quality assessment with recorded mortalities after cone assays with Kisumu, the _An. gambiae_-susceptible lab strain, and the _An. funestus_ FUMOZ-FANG strain. (**B**) Susceptibility profile of the hybrid FUMOZ-FANG strain to type II (deltamethrin) pyrethroids and propoxur with recorded mortalities following 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**C**) Susceptibility profile of the hybrid FUMOZ-FANG strain to deltamethrin (type II pyrethroids) and permethrin (type I pyrethroids) with recorded mortalities following 30 and 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**D**) Proportion of mortality, blood-feeding, and exophily rate for Olyset and Olyset Plus against _An. funestus_ (crossing the FUMOZ-FANG strain (F5)). Ns = \\(p>0.05\\); * = \\(p\\leq 0.05\\); ** = \\(p\\leq 0.01\\); *** = \\(p\\leq 0.001\\).\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "**Exophily**: The exophily rate in the hut with Olyset net (23.7%) was significantly higher than that in the control hut (13.5%) (_p_ = 0.02), as well as than for Olyset Plus (7.5%; \\(p\\) < 0.001). No significant difference was observed between Olyset Plus and the control net (_p_ = 0.3) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Validating the Role of CYP6P9a/b and 6.5 kb SV in Pvrethroid Resistance in the Hybrid FUMOZ-FANG Strains before the Experimental Hut Trials\n",
-                        "\n",
-                        "header: 2.2.2 Validating the Role of CYP6P9b_R_ Using Sample from WHO Tube Assays\n",
-                        "\n",
-                        "To confirm the ability of the _CYP6P9b_R_ allele to predict pyrethroid resistance phenotype in the hybrid strain FUMOZ-FANG, the F5 samples were genotyped. This revealed that those that stayed alive after exposure to permethrin for 90 minutes are mainly homozygote\n",
-                        "\n",
-                        "Figure 2: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (**A**) Tube assay _CYP6P9a_ and mortality. Role of _CYP6P9a_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9a_ genotypes according to resistance phenotypes. (**B**) Tube assay _CYP6P9b_ and mortality. Role of _CYP6P9b_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9b_ genotypes according to resistance phenotypes. (**C**) Cone assay _CYP6P9a_ and mortality. Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: \\(p\\) < 0.001). (**D**) Cone assay _CYP6P9a_ and mortality allele. Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the _CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**E**) Cone assay _CYP6P9b_ and mortality. Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset using the cone test, showing strong association (RR vs. SS: \\(p\\) < 0.001).\n",
-                        "\n",
-                        "\n",
-                        "resistant (20.8%) and heterozygotes (75%), with only two being homozygote-susceptible. Among the 47 mosquitoes found dead after 30 min exposure to permethrin (highly susceptible), 97.87% were found to be homozygote-susceptible, and one remaining was a heterozygote (Figure 2B). A strong association was perceived between permethrin resistance and the _CYP6P9b_ genotypes when comparing RR vs. SS (OR = infinity; \\(p\\) < 0.0001) and RS vs. SS (OR = 715; \\(p\\) < 0.0001).\n",
-                        "\n",
-                        "2.3 Validation of the Role of CYP6P9a and CYP6P9b in Conferring Resistance Using Samples from Cone Assays\n",
-                        "\n",
-                        "Using dead and alive mosquitoes obtained after cone assays, _CYP6P9a_ homozygous-resistant mosquitoes (RR) and heterozygous (RS) could survive significantly better following exposure to Olyset and Olyset Plus than homozygote-susceptible mosquitoes (Table S2). Olyset shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 35.1; \\(p\\) < 0.0001) and RS vs. SS (OR = 34.6; \\(p\\) < 0.0001) for _CYP6P9a_ (Figure 2C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 7.7; \\(p\\) < 0.05) (Table S2; Figure 2D). Concerning _CYP6P9b_, homozygous-resistant mosquitoes (RR) and heterozygous (RS) could also survive significantly better following survive exposure to Olyset net. A strong association was noticed between the gene and the ability to survive when comparing RR to SS (OR = 32.4; \\(p\\) < 0.0001) and RS vs. SS (OR = 97.3; \\(p\\) < 0.0001) (Table S2; Figure 2E).\n",
-                        "\n",
-                        "Olyset Plus shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 6.4; \\(p\\) < 0.001) and RS vs. SS (OR = 7.2; \\(p\\) < 0.001) for _CYP6P9a_ (Table S2; Figure 3A). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.0; \\(p\\) = 0.05) (Table S2; Figure 3B). Concerning the _CYP6P9b_, Olyset Plus equally shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 9.7; \\(p\\) < 0.001) and RS vs. SS (OR = 17.9; \\(p\\) < 0.001) for _CYP6P9b_ (Table S2; Figure 3C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.2; \\(p\\) = 0.01) (Table S2).\n",
-                        "\n",
-                        "Figure 3: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset Plus nets after cone assays. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (_p_ < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset Plus using the cone test, showing the significant ability of the mosquitoes carrying the RR genotype to survive compared to SS (_p_ < 0.001).\n",
-                        "\n",
-                        "header: Study Site: Laboratory Strain: FUMOZ/FANG Crossing\n",
-                        "\n",
-                        "Previous studies revealed that the duplicated P450 _CYP6P9a/b_ and intergenic _6.5 kb sv_ is mainly found in mosquitoes from southern Africa and absent from mosquitoes collected elsewhere in Africa [31,45]. Moreover, these resistance markers have already been selected (close to fixation) in the southern African _An. funestus_ populations, preventing free-flying mosquitoes from being used in this region in order to assess their impact on LLIN efficacy, as all mosquitoes are nearly homozygote-resistant now [7]. Therefore, to evaluate the impact of these cytochrome P450, we opted to use a hybrid strain generated from two _An. funestus_ laboratory colonies: the FANG colony, a completely insecticide-susceptible colony originating from Angola, and the FUMOZ colony derived from southern Mozambique, which is highly resistant to pyrethroids and carbamates [48]. During rearing, the pupae of each strain were kept individually in Falcon tubes (15 mL), locked with a piece of cotton for individual emergence. After the emergence of pupae in the Falcon tube, the males and the females were separated. A reciprocal crossing was performed using 50 males and 50 females from the other strain. After the initial F1 generation obtained from the reciprocal crosses of the 50 males and 50 females of both strains, the hybrid strain was reared to F5 and F6 generation, presenting good segregation in all the three genotypes. The hybrid strain was then used for the release recapture experiment in the huts.\n",
-                        "\n",
-                        "header: Susceptibility Profile of the Hybrid FUMOZ/FANG Strain to Pyrethroids\n",
-                        "\n",
-                        "Before assessing the impact of _CYP6P9a/b_ on the effectiveness of LLINs using the experimental huts, the resistance status of the hybrid FUMOZ-FANG was evaluated in laboratory conditions via WHO tube tests and cone tests, and the role of _CYP6P9a/b_ in terms of resistance was assessed.\n",
-                        "\n",
-                        "**Bioassays:** WHO bioassays were conducted with 0.05% deltamethrin and 0.1% propoxur. To generate highly resistant and highly susceptible mosquitoes, WHO bioassays were conducted with 0.75% permethrin and 0.05% deltamethrin for 30 min and 90 min. For each insecticide, four replicates of 25 mosquitoes from the hybrid strains were used. Alive mosquitoes after 90 min of exposure and dead mosquitoes after 30 min of exposure were then genotyped to establish the association between the _CYP6P9a/b_ and _6.5 kb SV_ resistance alleles and the ability of mosquitoes to survive to these insecticides.\n",
-                        "\n",
-                        "**Cone assays:** Cone test bioassays were conducted with a fragment of Olyset and Olyset Plus using the resistant hybrid strains FUMOZ-FANG. Five batches of 10 unfed females, aged 2-5 days old, were exposed to each bed net for three minutes. They were then transferred into the holding paper cup containers. The knockdown was checked after 60 min and the mortality after 24 h. Alive and dead mosquitoes obtained after exposure were then genotyped. The association between the _CYP6P9a/b_-resistant allele and the ability of mosquitoes to survive to these insecticides was also established.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "All the nets used in the study were first exposed to the susceptible lab strain Kisumu for quality control. The mortality for Olyset and Olyset Plus was 100% (Figure 1A). The _An. funestus_ hybrid strain exposed to bed nets using WHO cone assays exhibited mortality rates of 30.1 \\(\\pm\\) 11.5% for Olyset and 100 \\(\\pm\\) 0% for Olyset Plus, suggesting a greater efficacy of PBO synergist nets compared to pyrethroid-only nets.\n",
-                        "\n",
-                        "**Susceptibility profiles of the FUMOZ-FANG:** The bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to deltamethrin with mortality of 43.7 \\(\\pm\\) 5.9%, and was resistant to propoxur with mortality of 67.3 \\(\\pm\\) 6.6% (Figure 1B). The second set of bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to permethrin with mortality of 43.7 \\(\\pm\\) 1.6% and 39.3 \\(\\pm\\) 1.6%, respectively, for 90 min and 30 min. Resistance was also observed with deltamethrin with a mortality of 86.4 \\(\\pm\\) 3.1% and 42.3 \\(\\pm\\) 2.5%, respectively, for 90 min and 30 min (Figure 1C).\n",
-                        "\n",
-                        "header: Resistance Escalation with Multiple Resistance Alleles Present a Greater Risk of Control Failure\n",
-                        "\n",
-                        "This study confirms the findings by [28], suggesting that the _6.5 kb SV_ acts as an enhancer for nearby duplicated P450 genes _CYP6P9a_ and _CYP6P9b_, leading to their increased overexpression, thus creating greater resistance. This _6.5 kb SV_ is strongly associated with an aggravation of pyrethroid resistance, which reduces the efficacy of pyrethroid-only nets. The fixation of this _6.5 kb SV_, besides the resistant alleles of _CYP6P9a_ and _CYP6P9b_, could explain the resistance escalation currently observed against the _An. funestus_ population from the southern part of Africa reducing the efficacy of bed nets [7,41].\n",
-                        "\n",
-                        "This study revealed that multiple and complex resistance combining elevated expression (_CYP6P9a/b_) [45], as well as the selection of cis-regulatory motifs for transcription binding sites (CnCC/MAF) [31], coupled with structural variations such as the _6.5 kb_, which is known to be enriched with several regulatory elements [28], can lead to a greater reduction in bed net efficacy. The fact that triple homozygote-resistant mosquitoes could survive better against exposure to pyrethroid-only nets compared to all genotypes reveals the greater risk that an unabated increase in resistance can likely lead to the failure of insecticide-based interventions, as predicted by the WHO global plan for insecticide resistance management. This calls for novel nets which do not rely on pyrethroids in the future. However, the impact of these resistance alleles on the efficacy of LLINs in natural populations remains to be established, as we only performed a test with a hybrid strain from two laboratory strains. This work must be urgently carried out, particularly as the molecular tools are increasingly available.\n",
-                        "\n",
-                        "header: 2.5.1 The Impact of 6.5 kb SV on Mortality: 2.5.2 The Impact of 6.5. kb SV on Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _6.5 \\(kb\\)_ SV genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could blood-feed better compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 3.2-31.4; \\(p\\)\\(<\\) 0.0001) (Table S4; Figure 6C). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset differs to that of homozygote-susceptible mosquitoes (SS) but not significantly (OR = 1.7; CI = 0.5-5.4; \\(p\\) = 0.4) (Table S4). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 10.2; CI = 4.9-21.11: \\(p\\)\\(<\\) 0.0001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.2; CI = 2.3-7.9; \\(p\\)\\(<\\) 0.0001) (Table S5; Figure 6D). A similar trend was observed against Olyset Plus nets (Table S5; Figure 6E,F).\n",
-                        "\n",
-                        "Combined Impact of CYP6P9a and CYP6Pb on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "As _CYP6P9b_ genotypes were shown to be independent from those of _CYP6P9a_[28], we also assessed how combinations of genotypes at both genes impact the efficacy of Olyset for mortality and blood-feeding. Analysis of the impact of combined genotypes on mortality with Olyset confirmed the independent segregation of genotypes at both genes with several combinations of genotypes observed including RR/RR, RR/RS, RS/RS, RS/SS and SS/SS (Table S5). A comparison of the distribution of both sets of genotypes revealed that double homozygote-resistant (RR/RR) mosquitoes at both genes had a far greater ability to survive exposure to Olyset than most of the other combinations (Table S4) particularly when compared to double susceptible (SS/SS) (OR = 8.6; CI = 1.8-39.1; \\(p\\)\\(<\\) 0.01) (Table S5). A significantly increased survival is also observed in RR/RR when compared to other combinations although with a lower odds ratio, such as against RR/SS (OR = 2; \\(p\\)\\(<\\) 0.01) (Table S5).\n",
-                        "\n",
-                        "Analysis of the combined genotype distribution for blood-feeding also revealed a significantly increased ability to blood-feed for double homozygote-resistant mosquitoes when exposed to Olyset with the highest significance against double homozygote-susceptible mosquitoes (OR = 9.3; \\(p\\)\\(<\\) 0.001) (Table S5).\n",
-                        "\n",
-                        "Combined Impact of the Triple Resistance Alleles CYP6P9a/CYP6P9b/6.5 kb SV on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Experimental hut trials showed that triple RR/RR/SV\\({}^{+}\\)SV\\({}^{+}\\) homozygotes were more likely to survive exposure to Olyset compared to any other combined genotypes except RR/RS/RS (Figure 7A). RR/RR/SV+SV+ survived more than SS/SS/SV\\(-\\)SV\\(-\\) (OR = 8.5; CI: 2.53-28.46; \\(p\\)\\(=\\) 0.0005) and RS/RS/SV+SV\\(-\\) (OR = 3.57; CI = 1.82- 7.02; \\(p\\)\\(=\\) 0.0002). There was no significant difference between the RR/RR/SV+SV+ and RR/RR/SV+SV\\(-\\) genotypes (OR = 1; CI = 0.16-7.02; \\(p\\)\\(=\\) 1.0), and both had a similar trend in terms of survival with the same odds ratio (Figure 7B). In addition to the ability to survive exposure to the net, RR/RR/SV+SV+ also could blood-feed better than SS/SS/SV\\(-\\) SV\\(-\\) (OR = 7.0; CI: 2.38-20.52; \\(p\\)\\(=\\) 0.0004) and the other combinations in the presence of Olyset (Figure 7C,D).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 2.2.1 Role of CYP6P9a_R Using WHO Tube Assay Samples\n",
-                        "\n",
-                        "Using the hybrid FUMOZ-FANG strains, the role of the _CYP6P9a_R_ allele in the observed pyrethroid resistance was confirmed. The odds ratio of surviving exposure to permethrin when homozygous for the resistant _CYP6P9a_R_ allele (RR) was high at 693 (CI 88-5421; \\(p\\) < 0.0001) compared to the homozygous-susceptible mosquitoes (SS) (Figure 2A). The OR was 131 (CI 27-978; \\(p\\) < 0.0001) when comparing RR to RS, indicating the increasing resistance conferred by _CYP6P9a_.\n",
-                        "\n",
-                        "header: 4.6.2 Genotyping of the CYP6P9b-R Maker Using PCR-RFLP\n",
-                        "\n",
-                        "The PCR was carried out, as described for CYP6P9a. The following primers were used: 6p9brflp_0.5F 5'-CCCCCACAGGTGTAACTCTGAA-3' and 6p9brflp_0.5R 5'-TTATCCGTAAACTCAATAGCGATG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 550 bp. The digestion with the _Tsp_451 restriction enzyme followed 0.2 mL of Tsp451, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product was migrated on the 2% gel. Amplicons from susceptible mosquitoes were cut into two bands with sizes of 400 bp and 150 bp, and the resistant mosquitoes remained undigested with the band at 550 bp.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Experimental Hut Trials Reveal That CYP6P9a/b P450 Alleles Are Reducing the Efficacy of Pyrethroid-Only Olyset Net against the Malaria Vector _Anopheles funestus_ but PBO-Based Olyset Plus Net Remains Effective\n",
-                        "\n",
-                        "Benjamin D. Menze, Leon M. J. Mugenzi, Magellan Tchouakui, Murielle J. Wondji,\n",
-                        "\n",
-                        "1Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; murielle.wondji@ilstmed.ac.uk\n",
-                        "2Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaounde 13591, Cameroon; leon.mugenzi@crid-cam.net (L.M.J.M.); magellan.tchouakui@crid-cam.net (M.T.); micareme.tchouop@crid-cam.net (M.T.)\n",
-                        "* Correspondence: benjamin.menze@crid-cam.net (B.D.M.); charles.wondji@ilstmed.ac.uk (C.S.W.)\n",
-                        "\n",
-                        "\n",
-                        "Malaria remains a major public health concern in Africa. Metabolic resistance in major malaria vectors such as _An. funestus_ is jeopardizing the effectiveness of long-lasting insecticidal nets (LLINs) to control malaria. Here, we used experimental hut trials (EHTs) to investigate the impact of cytochrome P450-based resistance on the efficacy of PBO-based net (Olyset Plus) compared to a permethrin-only net (Olyset), revealing a greater loss of efficacy for the latter. EHT performed with progenies of F5 crossing between the _An. funestus_ pyrethroid-resistant strain FUMOZ and the pyrethroid-susceptible strain FANG revealed that PBO-based nets (Olyset Plus) induced a significantly higher mortality rate (99.1%) than pyrethroid-only nets (Olyset) (56.7%) (\\(p<0.0001\\)). The blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)). Genotyping the _CYP6P9a/b_ and the intergenic _6.5 kb structural variant_ (SV) resistance alleles showed that, for both nets, homozygote-resistant mosquitoes have a greater ability to blood-feed than the susceptible mosquitoes. Homozygote-resistant genotypes significantly survived more with Olyset after cone assays (e.g., _CYP6P9a_ OR = 34.6; \\(p<0.0001\\)) than homozygote-susceptible mosquitoes. A similar but lower correlation was seen with Olyset Plus (OR = 6.4; \\(p<0.001\\)). Genotyping EHT samples confirmed that _CYP6P9a/b_ and _6.5 kb_SV_ homozygote-resistant mosquitoes survive and blood-feed significantly better than homozygote-susceptible mosquitoes when exposed to Olyset. Our findings highlight the negative impact of P450-based resistance on pyrethroid-only nets, further supporting that PBO nets, such as Olyset Plus, are a better solution in areas of P450-mediated resistance to pyrethroids.\n",
-                        "\n",
-                        "header: Impact of the Duplicated CYP6P9a and CYP6P9b P450 Genes on the Performance of Bed Nets\n",
-                        "\n",
-                        "The samples collected during the evaluation of Olyset and Olyset Plus in experimental huts were grouped in several categories: dead, alive, blood fed, and unfed; room and veranda. The _CYP6P9a_/b and _6.5 kb SV_ were genotyped in each group using the respective PCR assays as described [28,29,31]. This allowed the relative survival and feeding success of resistant and susceptible insects in the presence of both bed nets to be directly measured.\n",
-                        "\n",
-                        "header: 4.6.3 PCR Assay to Detect the 6.5 kb SV\n",
-                        "\n",
-                        "To easily identify the samples containing the 6.5 kb insertion, the recently designed PCR assay [28] was used to discriminate between mosquitoes with the 8.2 kb (resistant) and 1.7 kb (susceptible) _CYP6P9a_ and _CYP6P9b_ intergenic regions. Briefly, three primers were used: two (FG_5F: CTACCGTCAAAGTCCGGTAT and FG_3R: TTTCGAAAACATCCCTAAA) at regions flanking the insertion point and a third primer (FZ_INS5R: ATATGCCACGAAGGAAAGCAG) in the _6.5 kb_ insertion. One unit of KAPA Taq polymerase (Kapa Biosystems) in 1x buffer A, i.e., 25 mm of MgCl2, 25 mm of dNTPs, and 10 mm of each primer, was used to constitute a 15 mL PCR mix using the following conditions: an initial denaturation step of 3 min at 95 degC, followed by 35 cycles of 30 s at 94 degC, 30 s at 58 degC, and 60 s at 72 degC, with a final extension for 10 min at 72 degC. The amplicon was revealed on a 1.5% agarose gel stained with Midori Green Advance DNA Stain (Nippon genetics Europe GmbH) and revealed on a UV transilluminator.\n",
-                        "\n",
-                        "header: 3 Discussion\n",
-                        "\n",
-                        "The capacity to assess the impact of insecticide resistance on the effectiveness of insecticide-treated control tools is crucial in order to implement suitable insecticide resistance management (IRM) [32]. Evolution in the area of DNA-based resistance markers of P450-mediated resistance [28; 29] currently offers robust tools to screen pyrethroid resistance in field populations of the major malaria vectors such as _An. funestus_ and assess their impact on the effectiveness of LLINs. In this study, we use these markers to evaluate the extent to which pyrethroid resistance is impacting the efficacy of pyrethroid-only nets such as Olyset in comparison to PBO-based net such as Olyset Plus. This study also allowed to assess the interplay between P450 genes in the overall genetic variance to resistance and their combined impact on the efficacy of LLINs.\n",
-                        "\n",
-                        "PBO-Based Nets (Olyset Plus) Exhibit Greater Efficacy than Pyrethroid-Only Nets (Olyset) in the Context of P450-Based Resistance\n",
-                        "\n",
-                        "Olyset presented a moderate mortality compared to Olyset Plus (30.9% vs. 100%; \\(p\\) < 0.0001). The high mortality observed with PBO nets compared to pyrethroid-only nets is similar to what was observed in previous studies [33; 34; 35; 36]. The high mortality observed with PBO-based nets against the hybrid strain FUMOZ/FANG is due to the fact the mechanisms underlying the resistance in this population are driven by _CYP6P9a_ and _CYP6P9b_[37] which are inhibited by PBO [38]. These results demonstrate that PBO-based nets may be a more suitable solution in areas where resistance is mainly mediated by P450 genes.\n",
-                        "\n",
-                        "Figure 7: Combined impact of the triple resistance alleles (_CYP6P9a_/_CYP6P9b_/_6.5 kb SV_) on the efficacy of insecticide-treated nets. (**A**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances of being alive in the presence of Olyset Net. (**B**) Ability to survive exposure to Olyset net for the various combined genotypes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_. (**C**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances to bloodfed in the presence of Olyset net. (**D**) The triple RR/RR/SV+SV+ homozygous mosquitoes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_ exhibit a greater blood-feeding ability than other genotypes. Ns: non-significant; * (0.05); ** (0.01); *** (0.001).\n",
-                        "\n",
-                        "\n",
-                        "The efficacy of nets observed in this study confirms the loss in efficacy of the pyrethroid-only nets against _Anopheles_ mosquitoes, as observed across the continent [39]. This loss of efficacy was observed in Mozambique [7,40], Malawi [41], Congo [42], and Cameroon [6,43]. Overall, similar to this study, it has been noticed that PBO-based nets demonstrate a better performance compared to pyrethroid-only nets [33,34]. The same trends were observed with pyrethroid type II with high mortality observed with PermaNet 3.0 compared to PermaNet 2.0 [28,29,31], suggesting that PBO-based nets with both type I or II can exhibit high performance against resistance sustained by P450s.\n",
-                        "\n",
-                        "P450 Resistance Can Reduce the Efficacy of Pyrethroid-Only Nets Significantly Better Than PBO-Based Nets\n",
-                        "\n",
-                        "When comparing the impact of _CYP6P9a/b_ on pyrethroid-only nets and PBO-based nets using samples from cone assays, we noticed that the impact of _CYP6P9a_ on pyrethroid-only nets was higher than with PBO-based nets (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. Olyset Plus, OR = 6.4; \\(p\\) < 0.001). The same trend was also observed with _CYP6P9b_ and the 6.5 kb SV. This can be explained by the fact that the addition of the PBO inhibits the cytochrome P450 enzymes, which is the main resistance mechanism in the strain used by the mosquitoes [44,45].\n",
-                        "\n",
-                        "On the other hand, Olyset nets have also shown reduced performance against _CYP6P9a_ and _CYP6P9b_-resistant mosquitoes. Strong association was observed between the resistant alleles and the increased ability of the mosquitoes to survive after exposure to these nets. Nevertheless, the impact of _CYP6P9a_ and _CYP6P9b_ seems to be more significant on PermaNet nets impregnated with deltamethrin [29,31], compared to Olyset nets impregnated with permethrin (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. PermaNet 2.0, OR = 239.0; \\(p\\) < 0.001 and Olyset Plus, OR = 6.4; \\(p\\) < 0.001 vs. PermaNet 3.0, OR = 81.0; \\(p\\) < 0.001) [29,31]. This significant impact on PermaNet could be associated with the fact that the hybrid strain is more resistant to type II pyrethroids, as shown by WHO bioassays, where the mosquitoes were more resistant to deltamethrin compared to permentrin (48.5% vs. 80.7%). With regards to the obtained odd ratios, a stronger association was observed between _CYP6P9a/CYP6P9b_ and the loss of efficacy of nets compared to what was observed with GST-based resistance through the L119F-_GSTe2_-resistant allele [43], in line with the greater role of P450 in resistance to pyrethroids in _An. funetus_, compared to that of GST. The 6.5 kb SV was shown to further exacerbate the loss of efficacy in the permethrin-only nets. The design of the simple PCR-based assay to genotype the _6.5 kb SV_ enabled us to assess the impact of such structural variation on the efficacy of insecticide-treated nets, including the pyrethroid-only and the PBO-synergist nets. A greater reduction in the efficacy of _6.5 kb SV_ was present on permethrin-only nets compared to PBO-based nets, which is similar to that observed with _CYP6P9a_ [31] and _CYP6P9b_ [29], in terms of the reduced mortality rate and blood-feeding inhibition. This pattern is also similar to that seen with deltamethrin-based nets (PermaNet 2.0 vs. PermaNet 3.0) [29,31]. Overall, the significantly greater loss of efficacy due to P450s observed with both type I (Olyset and PermaNet 2.0) and PBO-based nets (Olyset Plus and PermaNet 3.0) further supports the deployment of PBO-based nets to control P450-based metabolically resistant mosquito populations. The deployment of PBO-based nets should nevertheless also be monitored to regularly assess their efficacy since they too could be impacted by resistance, as shown by the fact that _CYP6P9a/b_-resistant mosquitoes could blood-feed significant better, even with Olyset Plus. This increased ability of resistant mosquitoes to blood-fed, even with PBO-based net, suggests that PBO-based nets are not immune to the impact of resistance, even if they remain significantly more effective than pyrethoid-only nets. The increased blood-feeding of P450-resistant mosquitoes when exposed to PBO-based nets may also lead to a higher malaria transmission, although this is mitigated by the high mortality observed for Olyset Plus in a experimental hut trial. Overall, evaluating the impact of P450-based resistance on the efficacy of Olyset vs. Olyset Plus further supports the results obtained in a cluster \n",
-                        "randomized controlled trial with both nets in Tanzania showing greater effectiveness of Olyset Plus [46].\n",
-                        "\n",
-                        "header: 4.6.1 Genotyping of the CYP6P9a-R Marker Using PCR-RFLP\n",
-                        "\n",
-                        "DNA was extracted from various groups of mosquitoes (dead, alive, blood fed, and unfed; room and veranda) using the Livak protocol [49]. The PCR was carried out using 10 mM of each primer and 1 mL of gDNA as the template in 15 mL reaction containing 10x Kapa Taq buffer A, 25 mM of dNTPs, 25 mM of MgCl2, and 1 U of Kapa Taq (Kapa Biosystems, Boston, MA, USA). Amplification was carried out using thermocycler parameters: 95 degC for 5 min, 35 cycles of 94 degC for 30 s, 58 degC for 30 s, 72 degC for 45 s, and a final extension at 72 degC for 10 min. The following primers were used: RFLP6P9aF forward primer 5'-TCCCGAAATACAGCCTTTCAG-3 and RFLP6P9aR reverse primer 5'-ATTGGTCCATCGCTAGAAG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 450 bp. The digestion with Taq1a followed 0.2 mL of Taq1a, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product migrated on the 2% gel. Amplicons from resistant mosquitoes were cut into two bands with sizes of 350 bp and 100 bp, and the susceptible mosquitoes remained undigested with the band at 450 bp.\n",
-                        "\n",
-                        "header: 5 Conclusions\n",
-                        "\n",
-                        "This study reveals that insecticide resistance driven by _CYP6P9a/b_ and _6.5 kb SV_ can reduce the efficiency of Permethrin-based LLINs, such as Olyset, while PBO-based nets remain effective. However, the greater loss of efficacy observed in mosquitoes with multiple and complex resistance supports the need to introduce new products for vector control which is less reliant on pyrethroids. Meantime, PBO-based nets should be preferably deployed in areas of P450-based resistance.\n",
-                        "\n",
-                        "**Supplementary Materials:** The following supporting information can be downloaded at: [https://www.mdpi.com/article/10.3390/pathogens11060638/s1](https://www.mdpi.com/article/10.3390/pathogens11060638/s1), Table S1: Results of the performance of Olyset and Olyset Plus against _An. funestus_ females (crossing FUMOZ-FANG; F5) in experimental hut trial. Table S2: Correlation between _CYP6P9a and CYP6P9b_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S3: Correlation between the _6.5Kv SV_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S4: Correlation between genotypes of _CYP6P9a_, _CYP6P9b_ and _6.5 kb SV_ and mortality and blood-feeding after exposure to Olyset and Olyset Plus in experimental huts. Table S5: _CYP6P9a_ and _CYP6P9b_ acting together further increase the ability of _An. funestus_ to survive and blood feed against Olyset after experimental hut trial.\n",
-                        "\n",
-                        "**Author Contributions:** C.S.W. conceived and designed the study; L.M.J.M. and M.T. (Magellan Tchouakui) generated the lab crosses and performed the validation of the PCR; B.D.M. performed the experimental hut experiments with C.S.W. and genotyped the resistance makers with M.J.W. and M.T. (Micareme Tchoupo); B.D.M. and C.S.W. wrote the paper with assistance from M.T. (Magellan Tchouakui) and L.M.J.M. All authors have read and agreed to the published version of the manuscript.\n",
-                        "\n",
-                        "**Funding:** This research was funded in whole by the Welcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). For the purpose of open access, the authors applied a CC BY public copyright license to any author who accepted a manuscript version following this submission.\n",
-                        "\n",
-                        "**Institutional Review Board Statement:** NA.\n",
-                        "\n",
-                        "**Informed Consent Statement:** NA.\n",
-                        "\n",
-                        "**Data Availability Statement:** NA.\n",
-                        "\n",
-                        "**Acknowledgments:** A big thanks to Charles Wondji for his financial support through the Wellcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). I am also grateful to the Mibellon community.\n",
-                        "\n",
-                        "**Conflicts of Interest:** The authors declare that they have no competing interest.\n",
-                        "\n",
-                        "header: 2.3.1 Impact on Mosquito Mortality: 2.3.2 CYP6P9a Impacting the Blood-Feeding\n",
-                        "\n",
-                        "Homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed better than homozygote-susceptible mosquitoes (SS) (OR = 4.5; \\(p\\) < 0.01) when exposed to Olyset. Heterozygote mosquitoes (RS) were significantly worse at blood-feeding with Olyset compared to homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-1.7; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) fed heterozygotes (OR = 5.4; CI = 2.6-10.88; \\(p\\) < 0.001) significantly better (Table S4; Figure 5C). Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.3; CI = 1.3-4.1; \\(p\\) < 0.01) (Table S4). For Olyset Plus, homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed than homozygote-susceptible mosquitoes (RS) (OR = 6.3; \\(p\\) < 0.001) when exposed to Olyset Plus. The significant ability to blood-feed was observed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.0; CI = 1.1-3.5; \\(p\\) = 0.03) (Table S4).\n",
-                        "\n",
-                        "header: 1 Introduction\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain an important component of the malaria control strategies used to reduce mortality and morbidity due to this disease in Africa [1, 2]. Mostly insecticides belonging to the pyrethroid group, as well as more and more novel insecticides, are currently recommended for bed net impregnation [3, 4, 5]. In recent years, anopheles mosquitoes have increasingly been reported to show resistance against pyrethroids across Africa. This is the case for major vectors including _Anopheles funestus_[6, 7, 8, 9] and _Anopheles gambiae_[10, 11, 12, 13, 14]. This growing spread of resistance has led to a concern that LLINs may lose their efficacy. However, quantifying such loss of efficacy remains challenging without suitable metrics associated with resistance, highlighting the need to use robust genotype/phenotype analysis for this evaluation. At a time when national malaria control \n",
-                        "programs across Africa are seeking to take evidence-based decisions on their choice of suitable nets to help improve malaria control, it is paramount to determine the extent to which existing resistance to pyrethroid really affects LLIN efficacy. This involves an understanding of the interaction between resistance mechanisms and mosquito responses to exposure to LLINs. Broadly speaking, pyrethroid resistance can be caused by alterations in the target site of the insecticide or enhanced enzymatic activities capable of metabolizing the insecticide before it reaches the target [15-17]. Target site resistance is well studied with mutations in the target of both pyrethroid and DDT insecticides, as well as the voltage-gated sodium channels which lead to knockdown resistance (_kdr_). The development of molecular assays more than twenty years ago made it possible to track this resistance mechanism using a simple PCR [18-21] or high throughput techniques, such as TaqMan assays [22]. It has also been possible to assess the impact of _kdr_ on control tools, notably in _An. gambiae_ where these markers are common [23-25], contrary to _An. funestus_, where it is still not detected [26]. The second best characterized cause of insecticide resistance is metabolic resistance, which is more complex to understand as it can involve the detoxification, sequestration, and transportation of insecticides or their conjugates [16,17,27]. Recent studies have focused on elucidating the genetic factors which cause the increased expression of detoxification genes associated with insecticide resistance in malaria vectors such as _An. funestus_, leading to the development of simple molecular assays used to detect metabolic resistance [28-31].\n",
-                        "\n",
-                        "The recent design of molecular assays for the duplicated _CYP6P9a/b_ and the associated _6.5 kb_ structural variant (SV), refs. [28,29,31] now provides a great opportunity to assess the impact of P450-based pyrethroid resistance on the efficacy of various LLINs, including pyrethroid-only (Olyset) and PBO (Olyset Plus) nets. This will provide evidence-based information on their effectiveness in the presence of a growing and escalading resistance to pyrethroids. Previous assessment of this impact on PermaNet nets made with type II pyrethroid deltamethrin has been performed [28,29,31], but this remains to be carried out for type I pyrethroid nets, such as the common permethrin-based Olyset nets.\n",
-                        "\n",
-                        "Here, using EHT, we show a significantly greater efficacy of the PBO-based net Olyset Plus compared to the pyrethroid-only Olyset net against pyrethroid-resistant _An. funestus_. Using the genotyping of DNA-based markers of P450 resistance, we reveal that _CYP6P9a/b_ P450-based pyrethroid resistance induces a significant loss of efficacy for pyrethroid-only net Olyset against _An. funestus_. A reduced efficacy was also detected for PBO-based net Olyset Plus, but at a lower extent than for Olyset net, revealing that PBO nets are more suitable to tackle such P450-based resistance.\n",
-                        "\n",
-                        "header: 2.1.1. Quality Control and Performance of the Nets against the Hybrid Strain: 2.1.2 Performance of Nets against the Hybrid Strain Using Experimental Hut\n",
-                        "\n",
-                        "A total number of 578 mosquitoes from the hybrid strain were released and recaptured on a period of one week. In total, 141 samples were released in the control hut, 224 in the hut were treated with Olyset, and 213 in the hut were treated with Olyset Plus.\n",
-                        "\n",
-                        "**Mortality**: Analysis of mortality rates revealed very high mortality of the hybrid FUMOZ-FANG strain against the PBO-based Olyset Plus net (99.1%). In contrast, lower mortality was observed for the pyrethroid-only Olyset net (56.7%). Very low mortality was observed in the untreated control net (9.9%) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "**Blood-feeding**: The blood-feeding rate did not significantly differ when comparing the control (7.8%) to Olyset (11.6% \\(p>0.05\\)) and Olyset Plus (5.6%; \\(p>0.05\\)). However, the blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Figure 1: Susceptibility profile of the hybrid FUMOZ-FANG strain to pyrethroids. (**A**) Net quality assessment with recorded mortalities after cone assays with Kisumu, the _An. gambiae_-susceptible lab strain, and the _An. funestus_ FUMOZ-FANG strain. (**B**) Susceptibility profile of the hybrid FUMOZ-FANG strain to type II (deltamethrin) pyrethroids and propoxur with recorded mortalities following 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**C**) Susceptibility profile of the hybrid FUMOZ-FANG strain to deltamethrin (type II pyrethroids) and permethrin (type I pyrethroids) with recorded mortalities following 30 and 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**D**) Proportion of mortality, blood-feeding, and exophily rate for Olyset and Olyset Plus against _An. funestus_ (crossing the FUMOZ-FANG strain (F5)). Ns = \\(p>0.05\\); * = \\(p\\leq 0.05\\); ** = \\(p\\leq 0.01\\); *** = \\(p\\leq 0.001\\).\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "**Exophily**: The exophily rate in the hut with Olyset net (23.7%) was significantly higher than that in the control hut (13.5%) (_p_ = 0.02), as well as than for Olyset Plus (7.5%; \\(p\\) < 0.001). No significant difference was observed between Olyset Plus and the control net (_p_ = 0.3) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Validating the Role of CYP6P9a/b and 6.5 kb SV in Pvrethroid Resistance in the Hybrid FUMOZ-FANG Strains before the Experimental Hut Trials\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "All the nets used in the study were first exposed to the susceptible lab strain Kisumu for quality control. The mortality for Olyset and Olyset Plus was 100% (Figure 1A). The _An. funestus_ hybrid strain exposed to bed nets using WHO cone assays exhibited mortality rates of 30.1 \\(\\pm\\) 11.5% for Olyset and 100 \\(\\pm\\) 0% for Olyset Plus, suggesting a greater efficacy of PBO synergist nets compared to pyrethroid-only nets.\n",
-                        "\n",
-                        "**Susceptibility profiles of the FUMOZ-FANG:** The bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to deltamethrin with mortality of 43.7 \\(\\pm\\) 5.9%, and was resistant to propoxur with mortality of 67.3 \\(\\pm\\) 6.6% (Figure 1B). The second set of bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to permethrin with mortality of 43.7 \\(\\pm\\) 1.6% and 39.3 \\(\\pm\\) 1.6%, respectively, for 90 min and 30 min. Resistance was also observed with deltamethrin with a mortality of 86.4 \\(\\pm\\) 3.1% and 42.3 \\(\\pm\\) 2.5%, respectively, for 90 min and 30 min (Figure 1C).\n",
-                        "\n",
-                        "header: Table 1: Description of the long-lasting insecticidal nets used.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment Arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment Arm\":\"Untreated\",\"Description\":\"100% polyester with no insecticide\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"4\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"5\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"}}\n",
-                        "\n",
-                        "header: 2.4.1 The Impact of CYP6P9b on Mortality: 2.4.2 CYP6P9b Impacting the Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _CYP6P9b_ genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could more successfully blood-fed when compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 4.1-24.9; \\(p\\) < 0.001) (Table S4; Figure 5F). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset was not different to that of homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-2.2; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 11.1; CI = 5.3-23.03: \\(p\\) < 0.001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood fed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.7; CI = 2.5-8.1; \\(p\\) < 0.01) (Table S4).\n",
-                        "\n",
-                        "header: 2.2.2 Validating the Role of CYP6P9b_R_ Using Sample from WHO Tube Assays\n",
-                        "\n",
-                        "To confirm the ability of the _CYP6P9b_R_ allele to predict pyrethroid resistance phenotype in the hybrid strain FUMOZ-FANG, the F5 samples were genotyped. This revealed that those that stayed alive after exposure to permethrin for 90 minutes are mainly homozygote\n",
-                        "\n",
-                        "Figure 2: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (**A**) Tube assay _CYP6P9a_ and mortality. Role of _CYP6P9a_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9a_ genotypes according to resistance phenotypes. (**B**) Tube assay _CYP6P9b_ and mortality. Role of _CYP6P9b_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9b_ genotypes according to resistance phenotypes. (**C**) Cone assay _CYP6P9a_ and mortality. Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: \\(p\\) < 0.001). (**D**) Cone assay _CYP6P9a_ and mortality allele. Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the _CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**E**) Cone assay _CYP6P9b_ and mortality. Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset using the cone test, showing strong association (RR vs. SS: \\(p\\) < 0.001).\n",
-                        "\n",
-                        "\n",
-                        "resistant (20.8%) and heterozygotes (75%), with only two being homozygote-susceptible. Among the 47 mosquitoes found dead after 30 min exposure to permethrin (highly susceptible), 97.87% were found to be homozygote-susceptible, and one remaining was a heterozygote (Figure 2B). A strong association was perceived between permethrin resistance and the _CYP6P9b_ genotypes when comparing RR vs. SS (OR = infinity; \\(p\\) < 0.0001) and RS vs. SS (OR = 715; \\(p\\) < 0.0001).\n",
-                        "\n",
-                        "2.3 Validation of the Role of CYP6P9a and CYP6P9b in Conferring Resistance Using Samples from Cone Assays\n",
-                        "\n",
-                        "Using dead and alive mosquitoes obtained after cone assays, _CYP6P9a_ homozygous-resistant mosquitoes (RR) and heterozygous (RS) could survive significantly better following exposure to Olyset and Olyset Plus than homozygote-susceptible mosquitoes (Table S2). Olyset shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 35.1; \\(p\\) < 0.0001) and RS vs. SS (OR = 34.6; \\(p\\) < 0.0001) for _CYP6P9a_ (Figure 2C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 7.7; \\(p\\) < 0.05) (Table S2; Figure 2D). Concerning _CYP6P9b_, homozygous-resistant mosquitoes (RR) and heterozygous (RS) could also survive significantly better following survive exposure to Olyset net. A strong association was noticed between the gene and the ability to survive when comparing RR to SS (OR = 32.4; \\(p\\) < 0.0001) and RS vs. SS (OR = 97.3; \\(p\\) < 0.0001) (Table S2; Figure 2E).\n",
-                        "\n",
-                        "Olyset Plus shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 6.4; \\(p\\) < 0.001) and RS vs. SS (OR = 7.2; \\(p\\) < 0.001) for _CYP6P9a_ (Table S2; Figure 3A). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.0; \\(p\\) = 0.05) (Table S2; Figure 3B). Concerning the _CYP6P9b_, Olyset Plus equally shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 9.7; \\(p\\) < 0.001) and RS vs. SS (OR = 17.9; \\(p\\) < 0.001) for _CYP6P9b_ (Table S2; Figure 3C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.2; \\(p\\) = 0.01) (Table S2).\n",
-                        "\n",
-                        "Figure 3: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset Plus nets after cone assays. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (_p_ < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset Plus using the cone test, showing the significant ability of the mosquitoes carrying the RR genotype to survive compared to SS (_p_ < 0.001).\n",
-                        "\n",
-                        "header: Impact of CYP6P9a on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Genotyping of the _CYP6P9a_ marker allowed us to assess the impact of P450-based metabolic resistance on the loss of efficacy of Olyset, but not for Olyset Plus, since most of the mosquitoes released in Olyset Plus huts died. To avoid confounding effects from blood-feeding, or net entry or exophily status, the distribution of the _CYP6P9a_ genotypes was assessed firstly only among unfed mosquitoes collected in the room. This revealed a highly significant difference in the frequency of the three genotypes between the dead and alive mosquitoes (chi-square = 28.8; \\(p\\) < 0.0001) (Figure 5). Analysis of the correlation between each genotype and mortality revealed that _CYP6P9a_ homozygous-resistant mosquitoes (RR) show an increased ability to survive exposure to the Olyset when compared to homozygote-susceptible mosquitoes (SS) with (OR = 5.0; CI = 2.01-12.4; \\(p\\) = 0.001). The strength of this association was further shown by the significant ability to survive exposure when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.04; CI = 1.1-3.6; \\(p\\) < 0.05). However, heterozygote mosquitoes (RS) did not survive exposure to Olyset better than homozygote-susceptible mosquitoes (SS) (OR = 1.8; CI = 0.8-3.9; \\(p\\) > 0.05) (Figure 5A; Table S4; Figure 5B).\n",
-                        "\n",
-                        "Figure 5: Impact of both _CYP6P9a_ and _CYP6P9b_ on the efficacy of bed nets in the experimental hut trial. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: \\(p\\) < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Association between _CYP6P9a_ and ability to blood-feed when exposed to Olyset. The _CYP6P9a_–R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (**D**) Association between _CYP6P9b_ and ability to survive exposure to Olyset in the experimental hut trial. (**E**) Allelic frequency of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the _CYP6P9b_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9b_S_. (**F**) Association between _CYP6P9b_ gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The _CYP6P9b_-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\n",
-                        "\n",
-                        "header: Study Site: Laboratory Strain: FUMOZ/FANG Crossing\n",
-                        "\n",
-                        "Previous studies revealed that the duplicated P450 _CYP6P9a/b_ and intergenic _6.5 kb sv_ is mainly found in mosquitoes from southern Africa and absent from mosquitoes collected elsewhere in Africa [31,45]. Moreover, these resistance markers have already been selected (close to fixation) in the southern African _An. funestus_ populations, preventing free-flying mosquitoes from being used in this region in order to assess their impact on LLIN efficacy, as all mosquitoes are nearly homozygote-resistant now [7]. Therefore, to evaluate the impact of these cytochrome P450, we opted to use a hybrid strain generated from two _An. funestus_ laboratory colonies: the FANG colony, a completely insecticide-susceptible colony originating from Angola, and the FUMOZ colony derived from southern Mozambique, which is highly resistant to pyrethroids and carbamates [48]. During rearing, the pupae of each strain were kept individually in Falcon tubes (15 mL), locked with a piece of cotton for individual emergence. After the emergence of pupae in the Falcon tube, the males and the females were separated. A reciprocal crossing was performed using 50 males and 50 females from the other strain. After the initial F1 generation obtained from the reciprocal crosses of the 50 males and 50 females of both strains, the hybrid strain was reared to F5 and F6 generation, presenting good segregation in all the three genotypes. The hybrid strain was then used for the release recapture experiment in the huts.\n",
-                        "\n",
-                        "header: Susceptibility Profile of the Hybrid FUMOZ/FANG Strain to Pyrethroids\n",
-                        "\n",
-                        "Before assessing the impact of _CYP6P9a/b_ on the effectiveness of LLINs using the experimental huts, the resistance status of the hybrid FUMOZ-FANG was evaluated in laboratory conditions via WHO tube tests and cone tests, and the role of _CYP6P9a/b_ in terms of resistance was assessed.\n",
-                        "\n",
-                        "**Bioassays:** WHO bioassays were conducted with 0.05% deltamethrin and 0.1% propoxur. To generate highly resistant and highly susceptible mosquitoes, WHO bioassays were conducted with 0.75% permethrin and 0.05% deltamethrin for 30 min and 90 min. For each insecticide, four replicates of 25 mosquitoes from the hybrid strains were used. Alive mosquitoes after 90 min of exposure and dead mosquitoes after 30 min of exposure were then genotyped to establish the association between the _CYP6P9a/b_ and _6.5 kb SV_ resistance alleles and the ability of mosquitoes to survive to these insecticides.\n",
-                        "\n",
-                        "**Cone assays:** Cone test bioassays were conducted with a fragment of Olyset and Olyset Plus using the resistant hybrid strains FUMOZ-FANG. Five batches of 10 unfed females, aged 2-5 days old, were exposed to each bed net for three minutes. They were then transferred into the holding paper cup containers. The knockdown was checked after 60 min and the mortality after 24 h. Alive and dead mosquitoes obtained after exposure were then genotyped. The association between the _CYP6P9a/b_-resistant allele and the ability of mosquitoes to survive to these insecticides was also established.\n",
-                        "\n",
-                        "header: Resistance Escalation with Multiple Resistance Alleles Present a Greater Risk of Control Failure\n",
-                        "\n",
-                        "This study confirms the findings by [28], suggesting that the _6.5 kb SV_ acts as an enhancer for nearby duplicated P450 genes _CYP6P9a_ and _CYP6P9b_, leading to their increased overexpression, thus creating greater resistance. This _6.5 kb SV_ is strongly associated with an aggravation of pyrethroid resistance, which reduces the efficacy of pyrethroid-only nets. The fixation of this _6.5 kb SV_, besides the resistant alleles of _CYP6P9a_ and _CYP6P9b_, could explain the resistance escalation currently observed against the _An. funestus_ population from the southern part of Africa reducing the efficacy of bed nets [7,41].\n",
-                        "\n",
-                        "This study revealed that multiple and complex resistance combining elevated expression (_CYP6P9a/b_) [45], as well as the selection of cis-regulatory motifs for transcription binding sites (CnCC/MAF) [31], coupled with structural variations such as the _6.5 kb_, which is known to be enriched with several regulatory elements [28], can lead to a greater reduction in bed net efficacy. The fact that triple homozygote-resistant mosquitoes could survive better against exposure to pyrethroid-only nets compared to all genotypes reveals the greater risk that an unabated increase in resistance can likely lead to the failure of insecticide-based interventions, as predicted by the WHO global plan for insecticide resistance management. This calls for novel nets which do not rely on pyrethroids in the future. However, the impact of these resistance alleles on the efficacy of LLINs in natural populations remains to be established, as we only performed a test with a hybrid strain from two laboratory strains. This work must be urgently carried out, particularly as the molecular tools are increasingly available.\n",
-                        "\n",
-                        "header: 2.5.1 The Impact of 6.5 kb SV on Mortality: 2.5.2 The Impact of 6.5. kb SV on Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _6.5 \\(kb\\)_ SV genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could blood-feed better compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 3.2-31.4; \\(p\\)\\(<\\) 0.0001) (Table S4; Figure 6C). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset differs to that of homozygote-susceptible mosquitoes (SS) but not significantly (OR = 1.7; CI = 0.5-5.4; \\(p\\) = 0.4) (Table S4). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 10.2; CI = 4.9-21.11: \\(p\\)\\(<\\) 0.0001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.2; CI = 2.3-7.9; \\(p\\)\\(<\\) 0.0001) (Table S5; Figure 6D). A similar trend was observed against Olyset Plus nets (Table S5; Figure 6E,F).\n",
-                        "\n",
-                        "Combined Impact of CYP6P9a and CYP6Pb on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "As _CYP6P9b_ genotypes were shown to be independent from those of _CYP6P9a_[28], we also assessed how combinations of genotypes at both genes impact the efficacy of Olyset for mortality and blood-feeding. Analysis of the impact of combined genotypes on mortality with Olyset confirmed the independent segregation of genotypes at both genes with several combinations of genotypes observed including RR/RR, RR/RS, RS/RS, RS/SS and SS/SS (Table S5). A comparison of the distribution of both sets of genotypes revealed that double homozygote-resistant (RR/RR) mosquitoes at both genes had a far greater ability to survive exposure to Olyset than most of the other combinations (Table S4) particularly when compared to double susceptible (SS/SS) (OR = 8.6; CI = 1.8-39.1; \\(p\\)\\(<\\) 0.01) (Table S5). A significantly increased survival is also observed in RR/RR when compared to other combinations although with a lower odds ratio, such as against RR/SS (OR = 2; \\(p\\)\\(<\\) 0.01) (Table S5).\n",
-                        "\n",
-                        "Analysis of the combined genotype distribution for blood-feeding also revealed a significantly increased ability to blood-feed for double homozygote-resistant mosquitoes when exposed to Olyset with the highest significance against double homozygote-susceptible mosquitoes (OR = 9.3; \\(p\\)\\(<\\) 0.001) (Table S5).\n",
-                        "\n",
-                        "Combined Impact of the Triple Resistance Alleles CYP6P9a/CYP6P9b/6.5 kb SV on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Experimental hut trials showed that triple RR/RR/SV\\({}^{+}\\)SV\\({}^{+}\\) homozygotes were more likely to survive exposure to Olyset compared to any other combined genotypes except RR/RS/RS (Figure 7A). RR/RR/SV+SV+ survived more than SS/SS/SV\\(-\\)SV\\(-\\) (OR = 8.5; CI: 2.53-28.46; \\(p\\)\\(=\\) 0.0005) and RS/RS/SV+SV\\(-\\) (OR = 3.57; CI = 1.82- 7.02; \\(p\\)\\(=\\) 0.0002). There was no significant difference between the RR/RR/SV+SV+ and RR/RR/SV+SV\\(-\\) genotypes (OR = 1; CI = 0.16-7.02; \\(p\\)\\(=\\) 1.0), and both had a similar trend in terms of survival with the same odds ratio (Figure 7B). In addition to the ability to survive exposure to the net, RR/RR/SV+SV+ also could blood-feed better than SS/SS/SV\\(-\\) SV\\(-\\) (OR = 7.0; CI: 2.38-20.52; \\(p\\)\\(=\\) 0.0004) and the other combinations in the presence of Olyset (Figure 7C,D).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 2.2.1 Role of CYP6P9a_R Using WHO Tube Assay Samples\n",
-                        "\n",
-                        "Using the hybrid FUMOZ-FANG strains, the role of the _CYP6P9a_R_ allele in the observed pyrethroid resistance was confirmed. The odds ratio of surviving exposure to permethrin when homozygous for the resistant _CYP6P9a_R_ allele (RR) was high at 693 (CI 88-5421; \\(p\\) < 0.0001) compared to the homozygous-susceptible mosquitoes (SS) (Figure 2A). The OR was 131 (CI 27-978; \\(p\\) < 0.0001) when comparing RR to RS, indicating the increasing resistance conferred by _CYP6P9a_.\n",
-                        "\n",
-                        "header: 4.6.2 Genotyping of the CYP6P9b-R Maker Using PCR-RFLP\n",
-                        "\n",
-                        "The PCR was carried out, as described for CYP6P9a. The following primers were used: 6p9brflp_0.5F 5'-CCCCCACAGGTGTAACTCTGAA-3' and 6p9brflp_0.5R 5'-TTATCCGTAAACTCAATAGCGATG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 550 bp. The digestion with the _Tsp_451 restriction enzyme followed 0.2 mL of Tsp451, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product was migrated on the 2% gel. Amplicons from susceptible mosquitoes were cut into two bands with sizes of 400 bp and 150 bp, and the resistant mosquitoes remained undigested with the band at 550 bp.\n",
-                        "\n",
-                        "header: Impact of the Duplicated CYP6P9a and CYP6P9b P450 Genes on the Performance of Bed Nets\n",
-                        "\n",
-                        "The samples collected during the evaluation of Olyset and Olyset Plus in experimental huts were grouped in several categories: dead, alive, blood fed, and unfed; room and veranda. The _CYP6P9a_/b and _6.5 kb SV_ were genotyped in each group using the respective PCR assays as described [28,29,31]. This allowed the relative survival and feeding success of resistant and susceptible insects in the presence of both bed nets to be directly measured.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Experimental Hut Trials Reveal That CYP6P9a/b P450 Alleles Are Reducing the Efficacy of Pyrethroid-Only Olyset Net against the Malaria Vector _Anopheles funestus_ but PBO-Based Olyset Plus Net Remains Effective\n",
-                        "\n",
-                        "Benjamin D. Menze, Leon M. J. Mugenzi, Magellan Tchouakui, Murielle J. Wondji,\n",
-                        "\n",
-                        "1Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; murielle.wondji@ilstmed.ac.uk\n",
-                        "2Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaounde 13591, Cameroon; leon.mugenzi@crid-cam.net (L.M.J.M.); magellan.tchouakui@crid-cam.net (M.T.); micareme.tchouop@crid-cam.net (M.T.)\n",
-                        "* Correspondence: benjamin.menze@crid-cam.net (B.D.M.); charles.wondji@ilstmed.ac.uk (C.S.W.)\n",
-                        "\n",
-                        "\n",
-                        "Malaria remains a major public health concern in Africa. Metabolic resistance in major malaria vectors such as _An. funestus_ is jeopardizing the effectiveness of long-lasting insecticidal nets (LLINs) to control malaria. Here, we used experimental hut trials (EHTs) to investigate the impact of cytochrome P450-based resistance on the efficacy of PBO-based net (Olyset Plus) compared to a permethrin-only net (Olyset), revealing a greater loss of efficacy for the latter. EHT performed with progenies of F5 crossing between the _An. funestus_ pyrethroid-resistant strain FUMOZ and the pyrethroid-susceptible strain FANG revealed that PBO-based nets (Olyset Plus) induced a significantly higher mortality rate (99.1%) than pyrethroid-only nets (Olyset) (56.7%) (\\(p<0.0001\\)). The blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)). Genotyping the _CYP6P9a/b_ and the intergenic _6.5 kb structural variant_ (SV) resistance alleles showed that, for both nets, homozygote-resistant mosquitoes have a greater ability to blood-feed than the susceptible mosquitoes. Homozygote-resistant genotypes significantly survived more with Olyset after cone assays (e.g., _CYP6P9a_ OR = 34.6; \\(p<0.0001\\)) than homozygote-susceptible mosquitoes. A similar but lower correlation was seen with Olyset Plus (OR = 6.4; \\(p<0.001\\)). Genotyping EHT samples confirmed that _CYP6P9a/b_ and _6.5 kb_SV_ homozygote-resistant mosquitoes survive and blood-feed significantly better than homozygote-susceptible mosquitoes when exposed to Olyset. Our findings highlight the negative impact of P450-based resistance on pyrethroid-only nets, further supporting that PBO nets, such as Olyset Plus, are a better solution in areas of P450-mediated resistance to pyrethroids.\n",
-                        "\n",
-                        "header: 4.6.1 Genotyping of the CYP6P9a-R Marker Using PCR-RFLP\n",
-                        "\n",
-                        "DNA was extracted from various groups of mosquitoes (dead, alive, blood fed, and unfed; room and veranda) using the Livak protocol [49]. The PCR was carried out using 10 mM of each primer and 1 mL of gDNA as the template in 15 mL reaction containing 10x Kapa Taq buffer A, 25 mM of dNTPs, 25 mM of MgCl2, and 1 U of Kapa Taq (Kapa Biosystems, Boston, MA, USA). Amplification was carried out using thermocycler parameters: 95 degC for 5 min, 35 cycles of 94 degC for 30 s, 58 degC for 30 s, 72 degC for 45 s, and a final extension at 72 degC for 10 min. The following primers were used: RFLP6P9aF forward primer 5'-TCCCGAAATACAGCCTTTCAG-3 and RFLP6P9aR reverse primer 5'-ATTGGTCCATCGCTAGAAG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 450 bp. The digestion with Taq1a followed 0.2 mL of Taq1a, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product migrated on the 2% gel. Amplicons from resistant mosquitoes were cut into two bands with sizes of 350 bp and 100 bp, and the susceptible mosquitoes remained undigested with the band at 450 bp.\n",
-                        "\n",
-                        "header: 4.6.3 PCR Assay to Detect the 6.5 kb SV\n",
-                        "\n",
-                        "To easily identify the samples containing the 6.5 kb insertion, the recently designed PCR assay [28] was used to discriminate between mosquitoes with the 8.2 kb (resistant) and 1.7 kb (susceptible) _CYP6P9a_ and _CYP6P9b_ intergenic regions. Briefly, three primers were used: two (FG_5F: CTACCGTCAAAGTCCGGTAT and FG_3R: TTTCGAAAACATCCCTAAA) at regions flanking the insertion point and a third primer (FZ_INS5R: ATATGCCACGAAGGAAAGCAG) in the _6.5 kb_ insertion. One unit of KAPA Taq polymerase (Kapa Biosystems) in 1x buffer A, i.e., 25 mm of MgCl2, 25 mm of dNTPs, and 10 mm of each primer, was used to constitute a 15 mL PCR mix using the following conditions: an initial denaturation step of 3 min at 95 degC, followed by 35 cycles of 30 s at 94 degC, 30 s at 58 degC, and 60 s at 72 degC, with a final extension for 10 min at 72 degC. The amplicon was revealed on a 1.5% agarose gel stained with Midori Green Advance DNA Stain (Nippon genetics Europe GmbH) and revealed on a UV transilluminator.\n",
-                        "\n",
-                        "header: 3 Discussion\n",
-                        "\n",
-                        "The capacity to assess the impact of insecticide resistance on the effectiveness of insecticide-treated control tools is crucial in order to implement suitable insecticide resistance management (IRM) [32]. Evolution in the area of DNA-based resistance markers of P450-mediated resistance [28; 29] currently offers robust tools to screen pyrethroid resistance in field populations of the major malaria vectors such as _An. funestus_ and assess their impact on the effectiveness of LLINs. In this study, we use these markers to evaluate the extent to which pyrethroid resistance is impacting the efficacy of pyrethroid-only nets such as Olyset in comparison to PBO-based net such as Olyset Plus. This study also allowed to assess the interplay between P450 genes in the overall genetic variance to resistance and their combined impact on the efficacy of LLINs.\n",
-                        "\n",
-                        "PBO-Based Nets (Olyset Plus) Exhibit Greater Efficacy than Pyrethroid-Only Nets (Olyset) in the Context of P450-Based Resistance\n",
-                        "\n",
-                        "Olyset presented a moderate mortality compared to Olyset Plus (30.9% vs. 100%; \\(p\\) < 0.0001). The high mortality observed with PBO nets compared to pyrethroid-only nets is similar to what was observed in previous studies [33; 34; 35; 36]. The high mortality observed with PBO-based nets against the hybrid strain FUMOZ/FANG is due to the fact the mechanisms underlying the resistance in this population are driven by _CYP6P9a_ and _CYP6P9b_[37] which are inhibited by PBO [38]. These results demonstrate that PBO-based nets may be a more suitable solution in areas where resistance is mainly mediated by P450 genes.\n",
-                        "\n",
-                        "Figure 7: Combined impact of the triple resistance alleles (_CYP6P9a_/_CYP6P9b_/_6.5 kb SV_) on the efficacy of insecticide-treated nets. (**A**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances of being alive in the presence of Olyset Net. (**B**) Ability to survive exposure to Olyset net for the various combined genotypes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_. (**C**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances to bloodfed in the presence of Olyset net. (**D**) The triple RR/RR/SV+SV+ homozygous mosquitoes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_ exhibit a greater blood-feeding ability than other genotypes. Ns: non-significant; * (0.05); ** (0.01); *** (0.001).\n",
-                        "\n",
-                        "\n",
-                        "The efficacy of nets observed in this study confirms the loss in efficacy of the pyrethroid-only nets against _Anopheles_ mosquitoes, as observed across the continent [39]. This loss of efficacy was observed in Mozambique [7,40], Malawi [41], Congo [42], and Cameroon [6,43]. Overall, similar to this study, it has been noticed that PBO-based nets demonstrate a better performance compared to pyrethroid-only nets [33,34]. The same trends were observed with pyrethroid type II with high mortality observed with PermaNet 3.0 compared to PermaNet 2.0 [28,29,31], suggesting that PBO-based nets with both type I or II can exhibit high performance against resistance sustained by P450s.\n",
-                        "\n",
-                        "P450 Resistance Can Reduce the Efficacy of Pyrethroid-Only Nets Significantly Better Than PBO-Based Nets\n",
-                        "\n",
-                        "When comparing the impact of _CYP6P9a/b_ on pyrethroid-only nets and PBO-based nets using samples from cone assays, we noticed that the impact of _CYP6P9a_ on pyrethroid-only nets was higher than with PBO-based nets (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. Olyset Plus, OR = 6.4; \\(p\\) < 0.001). The same trend was also observed with _CYP6P9b_ and the 6.5 kb SV. This can be explained by the fact that the addition of the PBO inhibits the cytochrome P450 enzymes, which is the main resistance mechanism in the strain used by the mosquitoes [44,45].\n",
-                        "\n",
-                        "On the other hand, Olyset nets have also shown reduced performance against _CYP6P9a_ and _CYP6P9b_-resistant mosquitoes. Strong association was observed between the resistant alleles and the increased ability of the mosquitoes to survive after exposure to these nets. Nevertheless, the impact of _CYP6P9a_ and _CYP6P9b_ seems to be more significant on PermaNet nets impregnated with deltamethrin [29,31], compared to Olyset nets impregnated with permethrin (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. PermaNet 2.0, OR = 239.0; \\(p\\) < 0.001 and Olyset Plus, OR = 6.4; \\(p\\) < 0.001 vs. PermaNet 3.0, OR = 81.0; \\(p\\) < 0.001) [29,31]. This significant impact on PermaNet could be associated with the fact that the hybrid strain is more resistant to type II pyrethroids, as shown by WHO bioassays, where the mosquitoes were more resistant to deltamethrin compared to permentrin (48.5% vs. 80.7%). With regards to the obtained odd ratios, a stronger association was observed between _CYP6P9a/CYP6P9b_ and the loss of efficacy of nets compared to what was observed with GST-based resistance through the L119F-_GSTe2_-resistant allele [43], in line with the greater role of P450 in resistance to pyrethroids in _An. funetus_, compared to that of GST. The 6.5 kb SV was shown to further exacerbate the loss of efficacy in the permethrin-only nets. The design of the simple PCR-based assay to genotype the _6.5 kb SV_ enabled us to assess the impact of such structural variation on the efficacy of insecticide-treated nets, including the pyrethroid-only and the PBO-synergist nets. A greater reduction in the efficacy of _6.5 kb SV_ was present on permethrin-only nets compared to PBO-based nets, which is similar to that observed with _CYP6P9a_ [31] and _CYP6P9b_ [29], in terms of the reduced mortality rate and blood-feeding inhibition. This pattern is also similar to that seen with deltamethrin-based nets (PermaNet 2.0 vs. PermaNet 3.0) [29,31]. Overall, the significantly greater loss of efficacy due to P450s observed with both type I (Olyset and PermaNet 2.0) and PBO-based nets (Olyset Plus and PermaNet 3.0) further supports the deployment of PBO-based nets to control P450-based metabolically resistant mosquito populations. The deployment of PBO-based nets should nevertheless also be monitored to regularly assess their efficacy since they too could be impacted by resistance, as shown by the fact that _CYP6P9a/b_-resistant mosquitoes could blood-feed significant better, even with Olyset Plus. This increased ability of resistant mosquitoes to blood-fed, even with PBO-based net, suggests that PBO-based nets are not immune to the impact of resistance, even if they remain significantly more effective than pyrethoid-only nets. The increased blood-feeding of P450-resistant mosquitoes when exposed to PBO-based nets may also lead to a higher malaria transmission, although this is mitigated by the high mortality observed for Olyset Plus in a experimental hut trial. Overall, evaluating the impact of P450-based resistance on the efficacy of Olyset vs. Olyset Plus further supports the results obtained in a cluster \n",
-                        "randomized controlled trial with both nets in Tanzania showing greater effectiveness of Olyset Plus [46].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "All the nets used in the study were first exposed to the susceptible lab strain Kisumu for quality control. The mortality for Olyset and Olyset Plus was 100% (Figure 1A). The _An. funestus_ hybrid strain exposed to bed nets using WHO cone assays exhibited mortality rates of 30.1 \\(\\pm\\) 11.5% for Olyset and 100 \\(\\pm\\) 0% for Olyset Plus, suggesting a greater efficacy of PBO synergist nets compared to pyrethroid-only nets.\n",
-                        "\n",
-                        "**Susceptibility profiles of the FUMOZ-FANG:** The bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to deltamethrin with mortality of 43.7 \\(\\pm\\) 5.9%, and was resistant to propoxur with mortality of 67.3 \\(\\pm\\) 6.6% (Figure 1B). The second set of bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to permethrin with mortality of 43.7 \\(\\pm\\) 1.6% and 39.3 \\(\\pm\\) 1.6%, respectively, for 90 min and 30 min. Resistance was also observed with deltamethrin with a mortality of 86.4 \\(\\pm\\) 3.1% and 42.3 \\(\\pm\\) 2.5%, respectively, for 90 min and 30 min (Figure 1C).\n",
-                        "\n",
-                        "header: 2.1.1. Quality Control and Performance of the Nets against the Hybrid Strain: 2.1.2 Performance of Nets against the Hybrid Strain Using Experimental Hut\n",
-                        "\n",
-                        "A total number of 578 mosquitoes from the hybrid strain were released and recaptured on a period of one week. In total, 141 samples were released in the control hut, 224 in the hut were treated with Olyset, and 213 in the hut were treated with Olyset Plus.\n",
-                        "\n",
-                        "**Mortality**: Analysis of mortality rates revealed very high mortality of the hybrid FUMOZ-FANG strain against the PBO-based Olyset Plus net (99.1%). In contrast, lower mortality was observed for the pyrethroid-only Olyset net (56.7%). Very low mortality was observed in the untreated control net (9.9%) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "**Blood-feeding**: The blood-feeding rate did not significantly differ when comparing the control (7.8%) to Olyset (11.6% \\(p>0.05\\)) and Olyset Plus (5.6%; \\(p>0.05\\)). However, the blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Figure 1: Susceptibility profile of the hybrid FUMOZ-FANG strain to pyrethroids. (**A**) Net quality assessment with recorded mortalities after cone assays with Kisumu, the _An. gambiae_-susceptible lab strain, and the _An. funestus_ FUMOZ-FANG strain. (**B**) Susceptibility profile of the hybrid FUMOZ-FANG strain to type II (deltamethrin) pyrethroids and propoxur with recorded mortalities following 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**C**) Susceptibility profile of the hybrid FUMOZ-FANG strain to deltamethrin (type II pyrethroids) and permethrin (type I pyrethroids) with recorded mortalities following 30 and 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**D**) Proportion of mortality, blood-feeding, and exophily rate for Olyset and Olyset Plus against _An. funestus_ (crossing the FUMOZ-FANG strain (F5)). Ns = \\(p>0.05\\); * = \\(p\\leq 0.05\\); ** = \\(p\\leq 0.01\\); *** = \\(p\\leq 0.001\\).\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "**Exophily**: The exophily rate in the hut with Olyset net (23.7%) was significantly higher than that in the control hut (13.5%) (_p_ = 0.02), as well as than for Olyset Plus (7.5%; \\(p\\) < 0.001). No significant difference was observed between Olyset Plus and the control net (_p_ = 0.3) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Validating the Role of CYP6P9a/b and 6.5 kb SV in Pvrethroid Resistance in the Hybrid FUMOZ-FANG Strains before the Experimental Hut Trials\n",
-                        "\n",
-                        "header: 5 Conclusions\n",
-                        "\n",
-                        "This study reveals that insecticide resistance driven by _CYP6P9a/b_ and _6.5 kb SV_ can reduce the efficiency of Permethrin-based LLINs, such as Olyset, while PBO-based nets remain effective. However, the greater loss of efficacy observed in mosquitoes with multiple and complex resistance supports the need to introduce new products for vector control which is less reliant on pyrethroids. Meantime, PBO-based nets should be preferably deployed in areas of P450-based resistance.\n",
-                        "\n",
-                        "**Supplementary Materials:** The following supporting information can be downloaded at: [https://www.mdpi.com/article/10.3390/pathogens11060638/s1](https://www.mdpi.com/article/10.3390/pathogens11060638/s1), Table S1: Results of the performance of Olyset and Olyset Plus against _An. funestus_ females (crossing FUMOZ-FANG; F5) in experimental hut trial. Table S2: Correlation between _CYP6P9a and CYP6P9b_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S3: Correlation between the _6.5Kv SV_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S4: Correlation between genotypes of _CYP6P9a_, _CYP6P9b_ and _6.5 kb SV_ and mortality and blood-feeding after exposure to Olyset and Olyset Plus in experimental huts. Table S5: _CYP6P9a_ and _CYP6P9b_ acting together further increase the ability of _An. funestus_ to survive and blood feed against Olyset after experimental hut trial.\n",
-                        "\n",
-                        "**Author Contributions:** C.S.W. conceived and designed the study; L.M.J.M. and M.T. (Magellan Tchouakui) generated the lab crosses and performed the validation of the PCR; B.D.M. performed the experimental hut experiments with C.S.W. and genotyped the resistance makers with M.J.W. and M.T. (Micareme Tchoupo); B.D.M. and C.S.W. wrote the paper with assistance from M.T. (Magellan Tchouakui) and L.M.J.M. All authors have read and agreed to the published version of the manuscript.\n",
-                        "\n",
-                        "**Funding:** This research was funded in whole by the Welcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). For the purpose of open access, the authors applied a CC BY public copyright license to any author who accepted a manuscript version following this submission.\n",
-                        "\n",
-                        "**Institutional Review Board Statement:** NA.\n",
-                        "\n",
-                        "**Informed Consent Statement:** NA.\n",
-                        "\n",
-                        "**Data Availability Statement:** NA.\n",
-                        "\n",
-                        "**Acknowledgments:** A big thanks to Charles Wondji for his financial support through the Wellcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). I am also grateful to the Mibellon community.\n",
-                        "\n",
-                        "**Conflicts of Interest:** The authors declare that they have no competing interest.\n",
-                        "\n",
-                        "header: Study Site: Laboratory Strain: FUMOZ/FANG Crossing\n",
-                        "\n",
-                        "Previous studies revealed that the duplicated P450 _CYP6P9a/b_ and intergenic _6.5 kb sv_ is mainly found in mosquitoes from southern Africa and absent from mosquitoes collected elsewhere in Africa [31,45]. Moreover, these resistance markers have already been selected (close to fixation) in the southern African _An. funestus_ populations, preventing free-flying mosquitoes from being used in this region in order to assess their impact on LLIN efficacy, as all mosquitoes are nearly homozygote-resistant now [7]. Therefore, to evaluate the impact of these cytochrome P450, we opted to use a hybrid strain generated from two _An. funestus_ laboratory colonies: the FANG colony, a completely insecticide-susceptible colony originating from Angola, and the FUMOZ colony derived from southern Mozambique, which is highly resistant to pyrethroids and carbamates [48]. During rearing, the pupae of each strain were kept individually in Falcon tubes (15 mL), locked with a piece of cotton for individual emergence. After the emergence of pupae in the Falcon tube, the males and the females were separated. A reciprocal crossing was performed using 50 males and 50 females from the other strain. After the initial F1 generation obtained from the reciprocal crosses of the 50 males and 50 females of both strains, the hybrid strain was reared to F5 and F6 generation, presenting good segregation in all the three genotypes. The hybrid strain was then used for the release recapture experiment in the huts.\n",
-                        "\n",
-                        "header: 2.3.1 Impact on Mosquito Mortality: 2.3.2 CYP6P9a Impacting the Blood-Feeding\n",
-                        "\n",
-                        "Homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed better than homozygote-susceptible mosquitoes (SS) (OR = 4.5; \\(p\\) < 0.01) when exposed to Olyset. Heterozygote mosquitoes (RS) were significantly worse at blood-feeding with Olyset compared to homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-1.7; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) fed heterozygotes (OR = 5.4; CI = 2.6-10.88; \\(p\\) < 0.001) significantly better (Table S4; Figure 5C). Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.3; CI = 1.3-4.1; \\(p\\) < 0.01) (Table S4). For Olyset Plus, homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed than homozygote-susceptible mosquitoes (RS) (OR = 6.3; \\(p\\) < 0.001) when exposed to Olyset Plus. The significant ability to blood-feed was observed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.0; CI = 1.1-3.5; \\(p\\) = 0.03) (Table S4).\n",
-                        "\n",
-                        "header: 1 Introduction\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain an important component of the malaria control strategies used to reduce mortality and morbidity due to this disease in Africa [1, 2]. Mostly insecticides belonging to the pyrethroid group, as well as more and more novel insecticides, are currently recommended for bed net impregnation [3, 4, 5]. In recent years, anopheles mosquitoes have increasingly been reported to show resistance against pyrethroids across Africa. This is the case for major vectors including _Anopheles funestus_[6, 7, 8, 9] and _Anopheles gambiae_[10, 11, 12, 13, 14]. This growing spread of resistance has led to a concern that LLINs may lose their efficacy. However, quantifying such loss of efficacy remains challenging without suitable metrics associated with resistance, highlighting the need to use robust genotype/phenotype analysis for this evaluation. At a time when national malaria control \n",
-                        "programs across Africa are seeking to take evidence-based decisions on their choice of suitable nets to help improve malaria control, it is paramount to determine the extent to which existing resistance to pyrethroid really affects LLIN efficacy. This involves an understanding of the interaction between resistance mechanisms and mosquito responses to exposure to LLINs. Broadly speaking, pyrethroid resistance can be caused by alterations in the target site of the insecticide or enhanced enzymatic activities capable of metabolizing the insecticide before it reaches the target [15-17]. Target site resistance is well studied with mutations in the target of both pyrethroid and DDT insecticides, as well as the voltage-gated sodium channels which lead to knockdown resistance (_kdr_). The development of molecular assays more than twenty years ago made it possible to track this resistance mechanism using a simple PCR [18-21] or high throughput techniques, such as TaqMan assays [22]. It has also been possible to assess the impact of _kdr_ on control tools, notably in _An. gambiae_ where these markers are common [23-25], contrary to _An. funestus_, where it is still not detected [26]. The second best characterized cause of insecticide resistance is metabolic resistance, which is more complex to understand as it can involve the detoxification, sequestration, and transportation of insecticides or their conjugates [16,17,27]. Recent studies have focused on elucidating the genetic factors which cause the increased expression of detoxification genes associated with insecticide resistance in malaria vectors such as _An. funestus_, leading to the development of simple molecular assays used to detect metabolic resistance [28-31].\n",
-                        "\n",
-                        "The recent design of molecular assays for the duplicated _CYP6P9a/b_ and the associated _6.5 kb_ structural variant (SV), refs. [28,29,31] now provides a great opportunity to assess the impact of P450-based pyrethroid resistance on the efficacy of various LLINs, including pyrethroid-only (Olyset) and PBO (Olyset Plus) nets. This will provide evidence-based information on their effectiveness in the presence of a growing and escalading resistance to pyrethroids. Previous assessment of this impact on PermaNet nets made with type II pyrethroid deltamethrin has been performed [28,29,31], but this remains to be carried out for type I pyrethroid nets, such as the common permethrin-based Olyset nets.\n",
-                        "\n",
-                        "Here, using EHT, we show a significantly greater efficacy of the PBO-based net Olyset Plus compared to the pyrethroid-only Olyset net against pyrethroid-resistant _An. funestus_. Using the genotyping of DNA-based markers of P450 resistance, we reveal that _CYP6P9a/b_ P450-based pyrethroid resistance induces a significant loss of efficacy for pyrethroid-only net Olyset against _An. funestus_. A reduced efficacy was also detected for PBO-based net Olyset Plus, but at a lower extent than for Olyset net, revealing that PBO nets are more suitable to tackle such P450-based resistance.\n",
-                        "\n",
-                        "header: Susceptibility Profile of the Hybrid FUMOZ/FANG Strain to Pyrethroids\n",
-                        "\n",
-                        "Before assessing the impact of _CYP6P9a/b_ on the effectiveness of LLINs using the experimental huts, the resistance status of the hybrid FUMOZ-FANG was evaluated in laboratory conditions via WHO tube tests and cone tests, and the role of _CYP6P9a/b_ in terms of resistance was assessed.\n",
-                        "\n",
-                        "**Bioassays:** WHO bioassays were conducted with 0.05% deltamethrin and 0.1% propoxur. To generate highly resistant and highly susceptible mosquitoes, WHO bioassays were conducted with 0.75% permethrin and 0.05% deltamethrin for 30 min and 90 min. For each insecticide, four replicates of 25 mosquitoes from the hybrid strains were used. Alive mosquitoes after 90 min of exposure and dead mosquitoes after 30 min of exposure were then genotyped to establish the association between the _CYP6P9a/b_ and _6.5 kb SV_ resistance alleles and the ability of mosquitoes to survive to these insecticides.\n",
-                        "\n",
-                        "**Cone assays:** Cone test bioassays were conducted with a fragment of Olyset and Olyset Plus using the resistant hybrid strains FUMOZ-FANG. Five batches of 10 unfed females, aged 2-5 days old, were exposed to each bed net for three minutes. They were then transferred into the holding paper cup containers. The knockdown was checked after 60 min and the mortality after 24 h. Alive and dead mosquitoes obtained after exposure were then genotyped. The association between the _CYP6P9a/b_-resistant allele and the ability of mosquitoes to survive to these insecticides was also established.\n",
-                        "\n",
-                        "header: 2.4.1 The Impact of CYP6P9b on Mortality: 2.4.2 CYP6P9b Impacting the Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _CYP6P9b_ genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could more successfully blood-fed when compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 4.1-24.9; \\(p\\) < 0.001) (Table S4; Figure 5F). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset was not different to that of homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-2.2; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 11.1; CI = 5.3-23.03: \\(p\\) < 0.001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood fed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.7; CI = 2.5-8.1; \\(p\\) < 0.01) (Table S4).\n",
-                        "\n",
-                        "header: Table 1: Description of the long-lasting insecticidal nets used.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment Arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment Arm\":\"Untreated\",\"Description\":\"100% polyester with no insecticide\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"4\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"5\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"}}\n",
-                        "\n",
-                        "header: 2.2.2 Validating the Role of CYP6P9b_R_ Using Sample from WHO Tube Assays\n",
-                        "\n",
-                        "To confirm the ability of the _CYP6P9b_R_ allele to predict pyrethroid resistance phenotype in the hybrid strain FUMOZ-FANG, the F5 samples were genotyped. This revealed that those that stayed alive after exposure to permethrin for 90 minutes are mainly homozygote\n",
-                        "\n",
-                        "Figure 2: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (**A**) Tube assay _CYP6P9a_ and mortality. Role of _CYP6P9a_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9a_ genotypes according to resistance phenotypes. (**B**) Tube assay _CYP6P9b_ and mortality. Role of _CYP6P9b_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9b_ genotypes according to resistance phenotypes. (**C**) Cone assay _CYP6P9a_ and mortality. Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: \\(p\\) < 0.001). (**D**) Cone assay _CYP6P9a_ and mortality allele. Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the _CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**E**) Cone assay _CYP6P9b_ and mortality. Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset using the cone test, showing strong association (RR vs. SS: \\(p\\) < 0.001).\n",
-                        "\n",
-                        "\n",
-                        "resistant (20.8%) and heterozygotes (75%), with only two being homozygote-susceptible. Among the 47 mosquitoes found dead after 30 min exposure to permethrin (highly susceptible), 97.87% were found to be homozygote-susceptible, and one remaining was a heterozygote (Figure 2B). A strong association was perceived between permethrin resistance and the _CYP6P9b_ genotypes when comparing RR vs. SS (OR = infinity; \\(p\\) < 0.0001) and RS vs. SS (OR = 715; \\(p\\) < 0.0001).\n",
-                        "\n",
-                        "2.3 Validation of the Role of CYP6P9a and CYP6P9b in Conferring Resistance Using Samples from Cone Assays\n",
-                        "\n",
-                        "Using dead and alive mosquitoes obtained after cone assays, _CYP6P9a_ homozygous-resistant mosquitoes (RR) and heterozygous (RS) could survive significantly better following exposure to Olyset and Olyset Plus than homozygote-susceptible mosquitoes (Table S2). Olyset shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 35.1; \\(p\\) < 0.0001) and RS vs. SS (OR = 34.6; \\(p\\) < 0.0001) for _CYP6P9a_ (Figure 2C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 7.7; \\(p\\) < 0.05) (Table S2; Figure 2D). Concerning _CYP6P9b_, homozygous-resistant mosquitoes (RR) and heterozygous (RS) could also survive significantly better following survive exposure to Olyset net. A strong association was noticed between the gene and the ability to survive when comparing RR to SS (OR = 32.4; \\(p\\) < 0.0001) and RS vs. SS (OR = 97.3; \\(p\\) < 0.0001) (Table S2; Figure 2E).\n",
-                        "\n",
-                        "Olyset Plus shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 6.4; \\(p\\) < 0.001) and RS vs. SS (OR = 7.2; \\(p\\) < 0.001) for _CYP6P9a_ (Table S2; Figure 3A). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.0; \\(p\\) = 0.05) (Table S2; Figure 3B). Concerning the _CYP6P9b_, Olyset Plus equally shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 9.7; \\(p\\) < 0.001) and RS vs. SS (OR = 17.9; \\(p\\) < 0.001) for _CYP6P9b_ (Table S2; Figure 3C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.2; \\(p\\) = 0.01) (Table S2).\n",
-                        "\n",
-                        "Figure 3: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset Plus nets after cone assays. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (_p_ < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset Plus using the cone test, showing the significant ability of the mosquitoes carrying the RR genotype to survive compared to SS (_p_ < 0.001).\n",
-                        "\n",
-                        "header: Impact of CYP6P9a on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Genotyping of the _CYP6P9a_ marker allowed us to assess the impact of P450-based metabolic resistance on the loss of efficacy of Olyset, but not for Olyset Plus, since most of the mosquitoes released in Olyset Plus huts died. To avoid confounding effects from blood-feeding, or net entry or exophily status, the distribution of the _CYP6P9a_ genotypes was assessed firstly only among unfed mosquitoes collected in the room. This revealed a highly significant difference in the frequency of the three genotypes between the dead and alive mosquitoes (chi-square = 28.8; \\(p\\) < 0.0001) (Figure 5). Analysis of the correlation between each genotype and mortality revealed that _CYP6P9a_ homozygous-resistant mosquitoes (RR) show an increased ability to survive exposure to the Olyset when compared to homozygote-susceptible mosquitoes (SS) with (OR = 5.0; CI = 2.01-12.4; \\(p\\) = 0.001). The strength of this association was further shown by the significant ability to survive exposure when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.04; CI = 1.1-3.6; \\(p\\) < 0.05). However, heterozygote mosquitoes (RS) did not survive exposure to Olyset better than homozygote-susceptible mosquitoes (SS) (OR = 1.8; CI = 0.8-3.9; \\(p\\) > 0.05) (Figure 5A; Table S4; Figure 5B).\n",
-                        "\n",
-                        "Figure 5: Impact of both _CYP6P9a_ and _CYP6P9b_ on the efficacy of bed nets in the experimental hut trial. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: \\(p\\) < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Association between _CYP6P9a_ and ability to blood-feed when exposed to Olyset. The _CYP6P9a_–R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (**D**) Association between _CYP6P9b_ and ability to survive exposure to Olyset in the experimental hut trial. (**E**) Allelic frequency of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the _CYP6P9b_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9b_S_. (**F**) Association between _CYP6P9b_ gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The _CYP6P9b_-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\n",
-                        "\n",
-                        "header: Resistance Escalation with Multiple Resistance Alleles Present a Greater Risk of Control Failure\n",
-                        "\n",
-                        "This study confirms the findings by [28], suggesting that the _6.5 kb SV_ acts as an enhancer for nearby duplicated P450 genes _CYP6P9a_ and _CYP6P9b_, leading to their increased overexpression, thus creating greater resistance. This _6.5 kb SV_ is strongly associated with an aggravation of pyrethroid resistance, which reduces the efficacy of pyrethroid-only nets. The fixation of this _6.5 kb SV_, besides the resistant alleles of _CYP6P9a_ and _CYP6P9b_, could explain the resistance escalation currently observed against the _An. funestus_ population from the southern part of Africa reducing the efficacy of bed nets [7,41].\n",
-                        "\n",
-                        "This study revealed that multiple and complex resistance combining elevated expression (_CYP6P9a/b_) [45], as well as the selection of cis-regulatory motifs for transcription binding sites (CnCC/MAF) [31], coupled with structural variations such as the _6.5 kb_, which is known to be enriched with several regulatory elements [28], can lead to a greater reduction in bed net efficacy. The fact that triple homozygote-resistant mosquitoes could survive better against exposure to pyrethroid-only nets compared to all genotypes reveals the greater risk that an unabated increase in resistance can likely lead to the failure of insecticide-based interventions, as predicted by the WHO global plan for insecticide resistance management. This calls for novel nets which do not rely on pyrethroids in the future. However, the impact of these resistance alleles on the efficacy of LLINs in natural populations remains to be established, as we only performed a test with a hybrid strain from two laboratory strains. This work must be urgently carried out, particularly as the molecular tools are increasingly available.\n",
-                        "\n",
-                        "header: Bed Nets Performance Assessment\n",
-                        "\n",
-                        "The performance of the bed nets was expressed relative to control (untreated nets). This was performed with four parameters in mind.\n",
-                        "\n",
-                        "**(i)**: **Exophily**. The proportion of mosquitoes found exited in the veranda trap Exophily (%) = 100 x (Ev/Et), where Ev is the total number of mosquitoes found in veranda and Et is the total number of mosquitoes in the hut.\n",
-                        "**(ii)**: **Blood-feeding rate (BFR)**. This rate was calculated as follows: blood-feeding rate = (N mosquitoes fed) x 100/total N mosquitoes, where N mosquitoes fed was the number of mosquitoes fed and a total N mosquito was the total number of mosquitoes collected.\n",
-                        "**(iii)**: **Blood-feeding inhibition (BFI)**. The reduction in blood-feeding in comparison with the control hut. Blood-feeding inhibition is an indicator of personal protection (PP). More precisely, the personal protection effect of each bed net is the reduction in the blood-feeding percentage induced by the net when compared to the control. The protective effect of each bed net can be calculated as follows: _personal protection_ (%) = 100 x (_Bu_ - _Bt_)/_Bu_, where _Bu_ is the total number of blood-fed mosquitoes in the huts with untreated nets and _Bt_ is the total number of blood-fed mosquitoes in the huts with treated nets [47].\n",
-                        "**(iv)**: **Immediate and delay mortality**. The proportion of mosquitoes entering the hut that are found dead in the morning (immediate mortality) or after being caught alive and held for 24 h with access to sugar solution (delay mortality) [47]. In this study, we presented the overall mortality calculated as follows: mortality (%) = 100 x (Mt/MT), where Mt is the total number of mosquitoes found dead in the hut and MT is the total number of mosquitoes collected in the hut [28,47].\n",
-                        "**(v)**: As mosquitoes were rather released in the huts, the deterrence, i.e., the reduction in the entry rate of mosquitoes in the treated huts relative to control, could not be determined here.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}, \"Time_elapsed\": {\"title\": \"Time Elapsed\", \"description\": \"For longitudinal studies: how long since the start of the study, in units of months?Report this value only if explicitly stated in the text.\"}, \"Blood_meal_count\": {\"title\": \"Blood Meal Count\", \"description\": \"Count of blood meals taken by mosquitos over observation period. This is a proxy for time, as mosquitoes in the study were fed blood meals once a week and oviposition recorded at each time step.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Cameroon\",\"Site\":\"Yaounde\",\"Start_month\":1,\"Start_year\":2023,\"End_month\":6,\"End_year\":2023,\"Time_elapsed\":5.0,\"Blood_meal_count\":4.0},\"S02\":{\"Study_type\":\"Bioassay, WHO cylinder test\",\"Country\":\"Mozambique\",\"Site\":\"Lab facility\",\"Start_month\":2,\"Start_year\":2023,\"End_month\":5,\"End_year\":2023,\"Time_elapsed\":3.0,\"Blood_meal_count\":null}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Synergist_concentration\": {\"title\": \"Synergist concentration (measured)\", \"description\": \"Enter the measured concentration of the synergist.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated\",\"pHI_category\":\"Good\",\"Insecticide\":null,\"Concentration_initial\":null,\"Synergist\":null,\"Synergist_concentration\":null},\"N02\":{\"Net_type\":\"Olyset\",\"pHI_category\":\"Good\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"8.6 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (2%)\",\"Synergist\":null,\"Synergist_concentration\":null},\"N03\":{\"Net_type\":\"Olyset Plus\",\"pHI_category\":\"Good\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"8.6 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (2%)\",\"Synergist\":\"PBO\",\"Synergist_concentration\":\"4.3 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (1%)\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: 2.2.1 Role of CYP6P9a_R Using WHO Tube Assay Samples\n",
-                        "\n",
-                        "Using the hybrid FUMOZ-FANG strains, the role of the _CYP6P9a_R_ allele in the observed pyrethroid resistance was confirmed. The odds ratio of surviving exposure to permethrin when homozygous for the resistant _CYP6P9a_R_ allele (RR) was high at 693 (CI 88-5421; \\(p\\) < 0.0001) compared to the homozygous-susceptible mosquitoes (SS) (Figure 2A). The OR was 131 (CI 27-978; \\(p\\) < 0.0001) when comparing RR to RS, indicating the increasing resistance conferred by _CYP6P9a_.\n",
-                        "\n",
-                        "header: 4.6.2 Genotyping of the CYP6P9b-R Maker Using PCR-RFLP\n",
-                        "\n",
-                        "The PCR was carried out, as described for CYP6P9a. The following primers were used: 6p9brflp_0.5F 5'-CCCCCACAGGTGTAACTCTGAA-3' and 6p9brflp_0.5R 5'-TTATCCGTAAACTCAATAGCGATG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 550 bp. The digestion with the _Tsp_451 restriction enzyme followed 0.2 mL of Tsp451, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product was migrated on the 2% gel. Amplicons from susceptible mosquitoes were cut into two bands with sizes of 400 bp and 150 bp, and the resistant mosquitoes remained undigested with the band at 550 bp.\n",
-                        "\n",
-                        "header: Impact of the Duplicated CYP6P9a and CYP6P9b P450 Genes on the Performance of Bed Nets\n",
-                        "\n",
-                        "The samples collected during the evaluation of Olyset and Olyset Plus in experimental huts were grouped in several categories: dead, alive, blood fed, and unfed; room and veranda. The _CYP6P9a_/b and _6.5 kb SV_ were genotyped in each group using the respective PCR assays as described [28,29,31]. This allowed the relative survival and feeding success of resistant and susceptible insects in the presence of both bed nets to be directly measured.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Experimental Hut Trials Reveal That CYP6P9a/b P450 Alleles Are Reducing the Efficacy of Pyrethroid-Only Olyset Net against the Malaria Vector _Anopheles funestus_ but PBO-Based Olyset Plus Net Remains Effective\n",
-                        "\n",
-                        "Benjamin D. Menze, Leon M. J. Mugenzi, Magellan Tchouakui, Murielle J. Wondji,\n",
-                        "\n",
-                        "1Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK; murielle.wondji@ilstmed.ac.uk\n",
-                        "2Medical Entomology Department, Centre for Research in Infectious Diseases (CRID), Yaounde 13591, Cameroon; leon.mugenzi@crid-cam.net (L.M.J.M.); magellan.tchouakui@crid-cam.net (M.T.); micareme.tchouop@crid-cam.net (M.T.)\n",
-                        "* Correspondence: benjamin.menze@crid-cam.net (B.D.M.); charles.wondji@ilstmed.ac.uk (C.S.W.)\n",
-                        "\n",
-                        "\n",
-                        "Malaria remains a major public health concern in Africa. Metabolic resistance in major malaria vectors such as _An. funestus_ is jeopardizing the effectiveness of long-lasting insecticidal nets (LLINs) to control malaria. Here, we used experimental hut trials (EHTs) to investigate the impact of cytochrome P450-based resistance on the efficacy of PBO-based net (Olyset Plus) compared to a permethrin-only net (Olyset), revealing a greater loss of efficacy for the latter. EHT performed with progenies of F5 crossing between the _An. funestus_ pyrethroid-resistant strain FUMOZ and the pyrethroid-susceptible strain FANG revealed that PBO-based nets (Olyset Plus) induced a significantly higher mortality rate (99.1%) than pyrethroid-only nets (Olyset) (56.7%) (\\(p<0.0001\\)). The blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)). Genotyping the _CYP6P9a/b_ and the intergenic _6.5 kb structural variant_ (SV) resistance alleles showed that, for both nets, homozygote-resistant mosquitoes have a greater ability to blood-feed than the susceptible mosquitoes. Homozygote-resistant genotypes significantly survived more with Olyset after cone assays (e.g., _CYP6P9a_ OR = 34.6; \\(p<0.0001\\)) than homozygote-susceptible mosquitoes. A similar but lower correlation was seen with Olyset Plus (OR = 6.4; \\(p<0.001\\)). Genotyping EHT samples confirmed that _CYP6P9a/b_ and _6.5 kb_SV_ homozygote-resistant mosquitoes survive and blood-feed significantly better than homozygote-susceptible mosquitoes when exposed to Olyset. Our findings highlight the negative impact of P450-based resistance on pyrethroid-only nets, further supporting that PBO nets, such as Olyset Plus, are a better solution in areas of P450-mediated resistance to pyrethroids.\n",
-                        "\n",
-                        "header: 4.6.3 PCR Assay to Detect the 6.5 kb SV\n",
-                        "\n",
-                        "To easily identify the samples containing the 6.5 kb insertion, the recently designed PCR assay [28] was used to discriminate between mosquitoes with the 8.2 kb (resistant) and 1.7 kb (susceptible) _CYP6P9a_ and _CYP6P9b_ intergenic regions. Briefly, three primers were used: two (FG_5F: CTACCGTCAAAGTCCGGTAT and FG_3R: TTTCGAAAACATCCCTAAA) at regions flanking the insertion point and a third primer (FZ_INS5R: ATATGCCACGAAGGAAAGCAG) in the _6.5 kb_ insertion. One unit of KAPA Taq polymerase (Kapa Biosystems) in 1x buffer A, i.e., 25 mm of MgCl2, 25 mm of dNTPs, and 10 mm of each primer, was used to constitute a 15 mL PCR mix using the following conditions: an initial denaturation step of 3 min at 95 degC, followed by 35 cycles of 30 s at 94 degC, 30 s at 58 degC, and 60 s at 72 degC, with a final extension for 10 min at 72 degC. The amplicon was revealed on a 1.5% agarose gel stained with Midori Green Advance DNA Stain (Nippon genetics Europe GmbH) and revealed on a UV transilluminator.\n",
-                        "\n",
-                        "header: 4.6.1 Genotyping of the CYP6P9a-R Marker Using PCR-RFLP\n",
-                        "\n",
-                        "DNA was extracted from various groups of mosquitoes (dead, alive, blood fed, and unfed; room and veranda) using the Livak protocol [49]. The PCR was carried out using 10 mM of each primer and 1 mL of gDNA as the template in 15 mL reaction containing 10x Kapa Taq buffer A, 25 mM of dNTPs, 25 mM of MgCl2, and 1 U of Kapa Taq (Kapa Biosystems, Boston, MA, USA). Amplification was carried out using thermocycler parameters: 95 degC for 5 min, 35 cycles of 94 degC for 30 s, 58 degC for 30 s, 72 degC for 45 s, and a final extension at 72 degC for 10 min. The following primers were used: RFLP6P9aF forward primer 5'-TCCCGAAATACAGCCTTTCAG-3 and RFLP6P9aR reverse primer 5'-ATTGGTCCATCGCTAGAAG-3'. Then, 3 mL of the PCR product migrated on the 1.5% agarose gel. The expected PCR product was 450 bp. The digestion with Taq1a followed 0.2 mL of Taq1a, 5 mL of PCR product, 1 mL of 10x NEBuffer, and 3.8 mL of distilled water. This mix was incubated at 65 degC for 2 h. Afterwards, 3 mL of the digestion product migrated on the 2% gel. Amplicons from resistant mosquitoes were cut into two bands with sizes of 350 bp and 100 bp, and the susceptible mosquitoes remained undigested with the band at 450 bp.\n",
-                        "\n",
-                        "header: 3 Discussion\n",
-                        "\n",
-                        "The capacity to assess the impact of insecticide resistance on the effectiveness of insecticide-treated control tools is crucial in order to implement suitable insecticide resistance management (IRM) [32]. Evolution in the area of DNA-based resistance markers of P450-mediated resistance [28; 29] currently offers robust tools to screen pyrethroid resistance in field populations of the major malaria vectors such as _An. funestus_ and assess their impact on the effectiveness of LLINs. In this study, we use these markers to evaluate the extent to which pyrethroid resistance is impacting the efficacy of pyrethroid-only nets such as Olyset in comparison to PBO-based net such as Olyset Plus. This study also allowed to assess the interplay between P450 genes in the overall genetic variance to resistance and their combined impact on the efficacy of LLINs.\n",
-                        "\n",
-                        "PBO-Based Nets (Olyset Plus) Exhibit Greater Efficacy than Pyrethroid-Only Nets (Olyset) in the Context of P450-Based Resistance\n",
-                        "\n",
-                        "Olyset presented a moderate mortality compared to Olyset Plus (30.9% vs. 100%; \\(p\\) < 0.0001). The high mortality observed with PBO nets compared to pyrethroid-only nets is similar to what was observed in previous studies [33; 34; 35; 36]. The high mortality observed with PBO-based nets against the hybrid strain FUMOZ/FANG is due to the fact the mechanisms underlying the resistance in this population are driven by _CYP6P9a_ and _CYP6P9b_[37] which are inhibited by PBO [38]. These results demonstrate that PBO-based nets may be a more suitable solution in areas where resistance is mainly mediated by P450 genes.\n",
-                        "\n",
-                        "Figure 7: Combined impact of the triple resistance alleles (_CYP6P9a_/_CYP6P9b_/_6.5 kb SV_) on the efficacy of insecticide-treated nets. (**A**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances of being alive in the presence of Olyset Net. (**B**) Ability to survive exposure to Olyset net for the various combined genotypes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_. (**C**) Distribution of combined genotypes for _CYP6P9a_, _CYP6P9b_ and the _6.5 kb SV_ showing that the three markers combine to increase the chances to bloodfed in the presence of Olyset net. (**D**) The triple RR/RR/SV+SV+ homozygous mosquitoes for the _6.5 kb SV_, _CYP6P9a_ and _CYP6P9b_ exhibit a greater blood-feeding ability than other genotypes. Ns: non-significant; * (0.05); ** (0.01); *** (0.001).\n",
-                        "\n",
-                        "\n",
-                        "The efficacy of nets observed in this study confirms the loss in efficacy of the pyrethroid-only nets against _Anopheles_ mosquitoes, as observed across the continent [39]. This loss of efficacy was observed in Mozambique [7,40], Malawi [41], Congo [42], and Cameroon [6,43]. Overall, similar to this study, it has been noticed that PBO-based nets demonstrate a better performance compared to pyrethroid-only nets [33,34]. The same trends were observed with pyrethroid type II with high mortality observed with PermaNet 3.0 compared to PermaNet 2.0 [28,29,31], suggesting that PBO-based nets with both type I or II can exhibit high performance against resistance sustained by P450s.\n",
-                        "\n",
-                        "P450 Resistance Can Reduce the Efficacy of Pyrethroid-Only Nets Significantly Better Than PBO-Based Nets\n",
-                        "\n",
-                        "When comparing the impact of _CYP6P9a/b_ on pyrethroid-only nets and PBO-based nets using samples from cone assays, we noticed that the impact of _CYP6P9a_ on pyrethroid-only nets was higher than with PBO-based nets (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. Olyset Plus, OR = 6.4; \\(p\\) < 0.001). The same trend was also observed with _CYP6P9b_ and the 6.5 kb SV. This can be explained by the fact that the addition of the PBO inhibits the cytochrome P450 enzymes, which is the main resistance mechanism in the strain used by the mosquitoes [44,45].\n",
-                        "\n",
-                        "On the other hand, Olyset nets have also shown reduced performance against _CYP6P9a_ and _CYP6P9b_-resistant mosquitoes. Strong association was observed between the resistant alleles and the increased ability of the mosquitoes to survive after exposure to these nets. Nevertheless, the impact of _CYP6P9a_ and _CYP6P9b_ seems to be more significant on PermaNet nets impregnated with deltamethrin [29,31], compared to Olyset nets impregnated with permethrin (Olyset, OR = 35.1; \\(p\\) < 0.00001 vs. PermaNet 2.0, OR = 239.0; \\(p\\) < 0.001 and Olyset Plus, OR = 6.4; \\(p\\) < 0.001 vs. PermaNet 3.0, OR = 81.0; \\(p\\) < 0.001) [29,31]. This significant impact on PermaNet could be associated with the fact that the hybrid strain is more resistant to type II pyrethroids, as shown by WHO bioassays, where the mosquitoes were more resistant to deltamethrin compared to permentrin (48.5% vs. 80.7%). With regards to the obtained odd ratios, a stronger association was observed between _CYP6P9a/CYP6P9b_ and the loss of efficacy of nets compared to what was observed with GST-based resistance through the L119F-_GSTe2_-resistant allele [43], in line with the greater role of P450 in resistance to pyrethroids in _An. funetus_, compared to that of GST. The 6.5 kb SV was shown to further exacerbate the loss of efficacy in the permethrin-only nets. The design of the simple PCR-based assay to genotype the _6.5 kb SV_ enabled us to assess the impact of such structural variation on the efficacy of insecticide-treated nets, including the pyrethroid-only and the PBO-synergist nets. A greater reduction in the efficacy of _6.5 kb SV_ was present on permethrin-only nets compared to PBO-based nets, which is similar to that observed with _CYP6P9a_ [31] and _CYP6P9b_ [29], in terms of the reduced mortality rate and blood-feeding inhibition. This pattern is also similar to that seen with deltamethrin-based nets (PermaNet 2.0 vs. PermaNet 3.0) [29,31]. Overall, the significantly greater loss of efficacy due to P450s observed with both type I (Olyset and PermaNet 2.0) and PBO-based nets (Olyset Plus and PermaNet 3.0) further supports the deployment of PBO-based nets to control P450-based metabolically resistant mosquito populations. The deployment of PBO-based nets should nevertheless also be monitored to regularly assess their efficacy since they too could be impacted by resistance, as shown by the fact that _CYP6P9a/b_-resistant mosquitoes could blood-feed significant better, even with Olyset Plus. This increased ability of resistant mosquitoes to blood-fed, even with PBO-based net, suggests that PBO-based nets are not immune to the impact of resistance, even if they remain significantly more effective than pyrethoid-only nets. The increased blood-feeding of P450-resistant mosquitoes when exposed to PBO-based nets may also lead to a higher malaria transmission, although this is mitigated by the high mortality observed for Olyset Plus in a experimental hut trial. Overall, evaluating the impact of P450-based resistance on the efficacy of Olyset vs. Olyset Plus further supports the results obtained in a cluster \n",
-                        "randomized controlled trial with both nets in Tanzania showing greater effectiveness of Olyset Plus [46].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "All the nets used in the study were first exposed to the susceptible lab strain Kisumu for quality control. The mortality for Olyset and Olyset Plus was 100% (Figure 1A). The _An. funestus_ hybrid strain exposed to bed nets using WHO cone assays exhibited mortality rates of 30.1 \\(\\pm\\) 11.5% for Olyset and 100 \\(\\pm\\) 0% for Olyset Plus, suggesting a greater efficacy of PBO synergist nets compared to pyrethroid-only nets.\n",
-                        "\n",
-                        "**Susceptibility profiles of the FUMOZ-FANG:** The bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to deltamethrin with mortality of 43.7 \\(\\pm\\) 5.9%, and was resistant to propoxur with mortality of 67.3 \\(\\pm\\) 6.6% (Figure 1B). The second set of bioassays performed with the reciprocal FUMOZ-FANG strains revealed that the hybrid strain was resistant to permethrin with mortality of 43.7 \\(\\pm\\) 1.6% and 39.3 \\(\\pm\\) 1.6%, respectively, for 90 min and 30 min. Resistance was also observed with deltamethrin with a mortality of 86.4 \\(\\pm\\) 3.1% and 42.3 \\(\\pm\\) 2.5%, respectively, for 90 min and 30 min (Figure 1C).\n",
-                        "\n",
-                        "header: 2.1.1. Quality Control and Performance of the Nets against the Hybrid Strain: 2.1.2 Performance of Nets against the Hybrid Strain Using Experimental Hut\n",
-                        "\n",
-                        "A total number of 578 mosquitoes from the hybrid strain were released and recaptured on a period of one week. In total, 141 samples were released in the control hut, 224 in the hut were treated with Olyset, and 213 in the hut were treated with Olyset Plus.\n",
-                        "\n",
-                        "**Mortality**: Analysis of mortality rates revealed very high mortality of the hybrid FUMOZ-FANG strain against the PBO-based Olyset Plus net (99.1%). In contrast, lower mortality was observed for the pyrethroid-only Olyset net (56.7%). Very low mortality was observed in the untreated control net (9.9%) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "**Blood-feeding**: The blood-feeding rate did not significantly differ when comparing the control (7.8%) to Olyset (11.6% \\(p>0.05\\)) and Olyset Plus (5.6%; \\(p>0.05\\)). However, the blood-feeding rate was higher in Olyset compared to Olyset Plus (11.6% vs. 5.6%; \\(p=0.013\\)) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Figure 1: Susceptibility profile of the hybrid FUMOZ-FANG strain to pyrethroids. (**A**) Net quality assessment with recorded mortalities after cone assays with Kisumu, the _An. gambiae_-susceptible lab strain, and the _An. funestus_ FUMOZ-FANG strain. (**B**) Susceptibility profile of the hybrid FUMOZ-FANG strain to type II (deltamethrin) pyrethroids and propoxur with recorded mortalities following 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**C**) Susceptibility profile of the hybrid FUMOZ-FANG strain to deltamethrin (type II pyrethroids) and permethrin (type I pyrethroids) with recorded mortalities following 30 and 60 min exposure. Data are shown as mean \\(\\pm\\) SEM. (**D**) Proportion of mortality, blood-feeding, and exophily rate for Olyset and Olyset Plus against _An. funestus_ (crossing the FUMOZ-FANG strain (F5)). Ns = \\(p>0.05\\); * = \\(p\\leq 0.05\\); ** = \\(p\\leq 0.01\\); *** = \\(p\\leq 0.001\\).\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "**Exophily**: The exophily rate in the hut with Olyset net (23.7%) was significantly higher than that in the control hut (13.5%) (_p_ = 0.02), as well as than for Olyset Plus (7.5%; \\(p\\) < 0.001). No significant difference was observed between Olyset Plus and the control net (_p_ = 0.3) (Figure 1D; Table S1).\n",
-                        "\n",
-                        "Validating the Role of CYP6P9a/b and 6.5 kb SV in Pvrethroid Resistance in the Hybrid FUMOZ-FANG Strains before the Experimental Hut Trials\n",
-                        "\n",
-                        "header: 2.3.1 Impact on Mosquito Mortality: 2.3.2 CYP6P9a Impacting the Blood-Feeding\n",
-                        "\n",
-                        "Homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed better than homozygote-susceptible mosquitoes (SS) (OR = 4.5; \\(p\\) < 0.01) when exposed to Olyset. Heterozygote mosquitoes (RS) were significantly worse at blood-feeding with Olyset compared to homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-1.7; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) fed heterozygotes (OR = 5.4; CI = 2.6-10.88; \\(p\\) < 0.001) significantly better (Table S4; Figure 5C). Overall, the strength of this association was further shown by the significant ability to blood-feed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.3; CI = 1.3-4.1; \\(p\\) < 0.01) (Table S4). For Olyset Plus, homozygote _CYP6P9a_-resistant mosquitoes (RR) were significantly more likely to blood-feed than homozygote-susceptible mosquitoes (RS) (OR = 6.3; \\(p\\) < 0.001) when exposed to Olyset Plus. The significant ability to blood-feed was observed when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.0; CI = 1.1-3.5; \\(p\\) = 0.03) (Table S4).\n",
-                        "\n",
-                        "header: 5 Conclusions\n",
-                        "\n",
-                        "This study reveals that insecticide resistance driven by _CYP6P9a/b_ and _6.5 kb SV_ can reduce the efficiency of Permethrin-based LLINs, such as Olyset, while PBO-based nets remain effective. However, the greater loss of efficacy observed in mosquitoes with multiple and complex resistance supports the need to introduce new products for vector control which is less reliant on pyrethroids. Meantime, PBO-based nets should be preferably deployed in areas of P450-based resistance.\n",
-                        "\n",
-                        "**Supplementary Materials:** The following supporting information can be downloaded at: [https://www.mdpi.com/article/10.3390/pathogens11060638/s1](https://www.mdpi.com/article/10.3390/pathogens11060638/s1), Table S1: Results of the performance of Olyset and Olyset Plus against _An. funestus_ females (crossing FUMOZ-FANG; F5) in experimental hut trial. Table S2: Correlation between _CYP6P9a and CYP6P9b_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S3: Correlation between the _6.5Kv SV_ genotypes and mosquito mortality against Olyset and Olyset Plus nets after cone assays with the FANG-FUMOZ mosquito strain. Table S4: Correlation between genotypes of _CYP6P9a_, _CYP6P9b_ and _6.5 kb SV_ and mortality and blood-feeding after exposure to Olyset and Olyset Plus in experimental huts. Table S5: _CYP6P9a_ and _CYP6P9b_ acting together further increase the ability of _An. funestus_ to survive and blood feed against Olyset after experimental hut trial.\n",
-                        "\n",
-                        "**Author Contributions:** C.S.W. conceived and designed the study; L.M.J.M. and M.T. (Magellan Tchouakui) generated the lab crosses and performed the validation of the PCR; B.D.M. performed the experimental hut experiments with C.S.W. and genotyped the resistance makers with M.J.W. and M.T. (Micareme Tchoupo); B.D.M. and C.S.W. wrote the paper with assistance from M.T. (Magellan Tchouakui) and L.M.J.M. All authors have read and agreed to the published version of the manuscript.\n",
-                        "\n",
-                        "**Funding:** This research was funded in whole by the Welcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). For the purpose of open access, the authors applied a CC BY public copyright license to any author who accepted a manuscript version following this submission.\n",
-                        "\n",
-                        "**Institutional Review Board Statement:** NA.\n",
-                        "\n",
-                        "**Informed Consent Statement:** NA.\n",
-                        "\n",
-                        "**Data Availability Statement:** NA.\n",
-                        "\n",
-                        "**Acknowledgments:** A big thanks to Charles Wondji for his financial support through the Wellcome Trust (Senior Research Fellowship to CSW 101893/Z/13/Z and 217188/Z/19/Z). I am also grateful to the Mibellon community.\n",
-                        "\n",
-                        "**Conflicts of Interest:** The authors declare that they have no competing interest.\n",
-                        "\n",
-                        "header: Study Site: Laboratory Strain: FUMOZ/FANG Crossing\n",
-                        "\n",
-                        "Previous studies revealed that the duplicated P450 _CYP6P9a/b_ and intergenic _6.5 kb sv_ is mainly found in mosquitoes from southern Africa and absent from mosquitoes collected elsewhere in Africa [31,45]. Moreover, these resistance markers have already been selected (close to fixation) in the southern African _An. funestus_ populations, preventing free-flying mosquitoes from being used in this region in order to assess their impact on LLIN efficacy, as all mosquitoes are nearly homozygote-resistant now [7]. Therefore, to evaluate the impact of these cytochrome P450, we opted to use a hybrid strain generated from two _An. funestus_ laboratory colonies: the FANG colony, a completely insecticide-susceptible colony originating from Angola, and the FUMOZ colony derived from southern Mozambique, which is highly resistant to pyrethroids and carbamates [48]. During rearing, the pupae of each strain were kept individually in Falcon tubes (15 mL), locked with a piece of cotton for individual emergence. After the emergence of pupae in the Falcon tube, the males and the females were separated. A reciprocal crossing was performed using 50 males and 50 females from the other strain. After the initial F1 generation obtained from the reciprocal crosses of the 50 males and 50 females of both strains, the hybrid strain was reared to F5 and F6 generation, presenting good segregation in all the three genotypes. The hybrid strain was then used for the release recapture experiment in the huts.\n",
-                        "\n",
-                        "header: 2.4.1 The Impact of CYP6P9b on Mortality: 2.4.2 CYP6P9b Impacting the Blood-Feeding\n",
-                        "\n",
-                        "A strong association was observed between _CYP6P9b_ genotypes and the ability to blood-feed. Homozygote-resistant mosquitoes (RR) could more successfully blood-fed when compared to homozygote-susceptible mosquitoes (SS) (OR = 10.01; CI = 4.1-24.9; \\(p\\) < 0.001) (Table S4; Figure 5F). The blood-feeding ability of heterozygote mosquitoes (RS) in the presence of Olyset was not different to that of homozygote-susceptible mosquitoes (SS) (OR = 0.8; CI = 0.3-2.2; \\(p\\) = 1). Homozygote-resistant mosquitoes (RR) could blood-feed significantly better than heterozygotes (OR = 11.1; CI = 5.3-23.03: \\(p\\) < 0.001), suggesting an additive effect. Overall, the strength of this association was further shown by the significant ability to blood fed when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 4.7; CI = 2.5-8.1; \\(p\\) < 0.01) (Table S4).\n",
-                        "\n",
-                        "header: Susceptibility Profile of the Hybrid FUMOZ/FANG Strain to Pyrethroids\n",
-                        "\n",
-                        "Before assessing the impact of _CYP6P9a/b_ on the effectiveness of LLINs using the experimental huts, the resistance status of the hybrid FUMOZ-FANG was evaluated in laboratory conditions via WHO tube tests and cone tests, and the role of _CYP6P9a/b_ in terms of resistance was assessed.\n",
-                        "\n",
-                        "**Bioassays:** WHO bioassays were conducted with 0.05% deltamethrin and 0.1% propoxur. To generate highly resistant and highly susceptible mosquitoes, WHO bioassays were conducted with 0.75% permethrin and 0.05% deltamethrin for 30 min and 90 min. For each insecticide, four replicates of 25 mosquitoes from the hybrid strains were used. Alive mosquitoes after 90 min of exposure and dead mosquitoes after 30 min of exposure were then genotyped to establish the association between the _CYP6P9a/b_ and _6.5 kb SV_ resistance alleles and the ability of mosquitoes to survive to these insecticides.\n",
-                        "\n",
-                        "**Cone assays:** Cone test bioassays were conducted with a fragment of Olyset and Olyset Plus using the resistant hybrid strains FUMOZ-FANG. Five batches of 10 unfed females, aged 2-5 days old, were exposed to each bed net for three minutes. They were then transferred into the holding paper cup containers. The knockdown was checked after 60 min and the mortality after 24 h. Alive and dead mosquitoes obtained after exposure were then genotyped. The association between the _CYP6P9a/b_-resistant allele and the ability of mosquitoes to survive to these insecticides was also established.\n",
-                        "\n",
-                        "header: 1 Introduction\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain an important component of the malaria control strategies used to reduce mortality and morbidity due to this disease in Africa [1, 2]. Mostly insecticides belonging to the pyrethroid group, as well as more and more novel insecticides, are currently recommended for bed net impregnation [3, 4, 5]. In recent years, anopheles mosquitoes have increasingly been reported to show resistance against pyrethroids across Africa. This is the case for major vectors including _Anopheles funestus_[6, 7, 8, 9] and _Anopheles gambiae_[10, 11, 12, 13, 14]. This growing spread of resistance has led to a concern that LLINs may lose their efficacy. However, quantifying such loss of efficacy remains challenging without suitable metrics associated with resistance, highlighting the need to use robust genotype/phenotype analysis for this evaluation. At a time when national malaria control \n",
-                        "programs across Africa are seeking to take evidence-based decisions on their choice of suitable nets to help improve malaria control, it is paramount to determine the extent to which existing resistance to pyrethroid really affects LLIN efficacy. This involves an understanding of the interaction between resistance mechanisms and mosquito responses to exposure to LLINs. Broadly speaking, pyrethroid resistance can be caused by alterations in the target site of the insecticide or enhanced enzymatic activities capable of metabolizing the insecticide before it reaches the target [15-17]. Target site resistance is well studied with mutations in the target of both pyrethroid and DDT insecticides, as well as the voltage-gated sodium channels which lead to knockdown resistance (_kdr_). The development of molecular assays more than twenty years ago made it possible to track this resistance mechanism using a simple PCR [18-21] or high throughput techniques, such as TaqMan assays [22]. It has also been possible to assess the impact of _kdr_ on control tools, notably in _An. gambiae_ where these markers are common [23-25], contrary to _An. funestus_, where it is still not detected [26]. The second best characterized cause of insecticide resistance is metabolic resistance, which is more complex to understand as it can involve the detoxification, sequestration, and transportation of insecticides or their conjugates [16,17,27]. Recent studies have focused on elucidating the genetic factors which cause the increased expression of detoxification genes associated with insecticide resistance in malaria vectors such as _An. funestus_, leading to the development of simple molecular assays used to detect metabolic resistance [28-31].\n",
-                        "\n",
-                        "The recent design of molecular assays for the duplicated _CYP6P9a/b_ and the associated _6.5 kb_ structural variant (SV), refs. [28,29,31] now provides a great opportunity to assess the impact of P450-based pyrethroid resistance on the efficacy of various LLINs, including pyrethroid-only (Olyset) and PBO (Olyset Plus) nets. This will provide evidence-based information on their effectiveness in the presence of a growing and escalading resistance to pyrethroids. Previous assessment of this impact on PermaNet nets made with type II pyrethroid deltamethrin has been performed [28,29,31], but this remains to be carried out for type I pyrethroid nets, such as the common permethrin-based Olyset nets.\n",
-                        "\n",
-                        "Here, using EHT, we show a significantly greater efficacy of the PBO-based net Olyset Plus compared to the pyrethroid-only Olyset net against pyrethroid-resistant _An. funestus_. Using the genotyping of DNA-based markers of P450 resistance, we reveal that _CYP6P9a/b_ P450-based pyrethroid resistance induces a significant loss of efficacy for pyrethroid-only net Olyset against _An. funestus_. A reduced efficacy was also detected for PBO-based net Olyset Plus, but at a lower extent than for Olyset net, revealing that PBO nets are more suitable to tackle such P450-based resistance.\n",
-                        "\n",
-                        "header: Table 1: Description of the long-lasting insecticidal nets used.\n",
-                        "footer: None\n",
-                        "columns: ['Treatment Arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment Arm\":\"Untreated\",\"Description\":\"100% polyester with no insecticide\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment Arm\":\"Olyset\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"4\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"5\":{\"Treatment Arm\":\"Olyset Plus\",\"Description\":\"8.6 \\u00d7 10\\u22124 kg\\/m2 (2%) of permethrin and 4.3 \\u00d7 10\\u22124 kg\\/m2 (1%) of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"}}\n",
-                        "\n",
-                        "header: Impact of CYP6P9a on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "Genotyping of the _CYP6P9a_ marker allowed us to assess the impact of P450-based metabolic resistance on the loss of efficacy of Olyset, but not for Olyset Plus, since most of the mosquitoes released in Olyset Plus huts died. To avoid confounding effects from blood-feeding, or net entry or exophily status, the distribution of the _CYP6P9a_ genotypes was assessed firstly only among unfed mosquitoes collected in the room. This revealed a highly significant difference in the frequency of the three genotypes between the dead and alive mosquitoes (chi-square = 28.8; \\(p\\) < 0.0001) (Figure 5). Analysis of the correlation between each genotype and mortality revealed that _CYP6P9a_ homozygous-resistant mosquitoes (RR) show an increased ability to survive exposure to the Olyset when compared to homozygote-susceptible mosquitoes (SS) with (OR = 5.0; CI = 2.01-12.4; \\(p\\) = 0.001). The strength of this association was further shown by the significant ability to survive exposure when possessing a single _CYP6P9a_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.04; CI = 1.1-3.6; \\(p\\) < 0.05). However, heterozygote mosquitoes (RS) did not survive exposure to Olyset better than homozygote-susceptible mosquitoes (SS) (OR = 1.8; CI = 0.8-3.9; \\(p\\) > 0.05) (Figure 5A; Table S4; Figure 5B).\n",
-                        "\n",
-                        "Figure 5: Impact of both _CYP6P9a_ and _CYP6P9b_ on the efficacy of bed nets in the experimental hut trial. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: \\(p\\) < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Association between _CYP6P9a_ and ability to blood-feed when exposed to Olyset. The _CYP6P9a_–R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (**D**) Association between _CYP6P9b_ and ability to survive exposure to Olyset in the experimental hut trial. (**E**) Allelic frequency of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the _CYP6P9b_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9b_S_. (**F**) Association between _CYP6P9b_ gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The _CYP6P9b_-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\n",
-                        "\n",
-                        "header: 2.2.2 Validating the Role of CYP6P9b_R_ Using Sample from WHO Tube Assays\n",
-                        "\n",
-                        "To confirm the ability of the _CYP6P9b_R_ allele to predict pyrethroid resistance phenotype in the hybrid strain FUMOZ-FANG, the F5 samples were genotyped. This revealed that those that stayed alive after exposure to permethrin for 90 minutes are mainly homozygote\n",
-                        "\n",
-                        "Figure 2: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (**A**) Tube assay _CYP6P9a_ and mortality. Role of _CYP6P9a_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9a_ genotypes according to resistance phenotypes. (**B**) Tube assay _CYP6P9b_ and mortality. Role of _CYP6P9b_ in pyrethroid resistance with samples from the WHO tube. Distribution of the _CYP6P9b_ genotypes according to resistance phenotypes. (**C**) Cone assay _CYP6P9a_ and mortality. Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: \\(p\\) < 0.001). (**D**) Cone assay _CYP6P9a_ and mortality allele. Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the _CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**E**) Cone assay _CYP6P9b_ and mortality. Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset using the cone test, showing strong association (RR vs. SS: \\(p\\) < 0.001).\n",
-                        "\n",
-                        "\n",
-                        "resistant (20.8%) and heterozygotes (75%), with only two being homozygote-susceptible. Among the 47 mosquitoes found dead after 30 min exposure to permethrin (highly susceptible), 97.87% were found to be homozygote-susceptible, and one remaining was a heterozygote (Figure 2B). A strong association was perceived between permethrin resistance and the _CYP6P9b_ genotypes when comparing RR vs. SS (OR = infinity; \\(p\\) < 0.0001) and RS vs. SS (OR = 715; \\(p\\) < 0.0001).\n",
-                        "\n",
-                        "2.3 Validation of the Role of CYP6P9a and CYP6P9b in Conferring Resistance Using Samples from Cone Assays\n",
-                        "\n",
-                        "Using dead and alive mosquitoes obtained after cone assays, _CYP6P9a_ homozygous-resistant mosquitoes (RR) and heterozygous (RS) could survive significantly better following exposure to Olyset and Olyset Plus than homozygote-susceptible mosquitoes (Table S2). Olyset shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 35.1; \\(p\\) < 0.0001) and RS vs. SS (OR = 34.6; \\(p\\) < 0.0001) for _CYP6P9a_ (Figure 2C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 7.7; \\(p\\) < 0.05) (Table S2; Figure 2D). Concerning _CYP6P9b_, homozygous-resistant mosquitoes (RR) and heterozygous (RS) could also survive significantly better following survive exposure to Olyset net. A strong association was noticed between the gene and the ability to survive when comparing RR to SS (OR = 32.4; \\(p\\) < 0.0001) and RS vs. SS (OR = 97.3; \\(p\\) < 0.0001) (Table S2; Figure 2E).\n",
-                        "\n",
-                        "Olyset Plus shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 6.4; \\(p\\) < 0.001) and RS vs. SS (OR = 7.2; \\(p\\) < 0.001) for _CYP6P9a_ (Table S2; Figure 3A). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.0; \\(p\\) = 0.05) (Table S2; Figure 3B). Concerning the _CYP6P9b_, Olyset Plus equally shows a strong association between the resistance alleles and the ability to survive when comparing RR vs. SS (OR = 9.7; \\(p\\) < 0.001) and RS vs. SS (OR = 17.9; \\(p\\) < 0.001) for _CYP6P9b_ (Table S2; Figure 3C). The same trend was observed when looking at the allelic frequency R vs. S (OR = 2.2; \\(p\\) = 0.01) (Table S2).\n",
-                        "\n",
-                        "Figure 3: Association between the _CYP6P9a/b_ and the ability to survive exposure to Olyset Plus nets after cone assays. (**A**) Genotype distribution of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (_p_ < 0.001). (**B**) Allelic frequency of _CYP6P9a_ between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the _CYP6P9a_R_–resistant mosquitoes to survive compared to their susceptible counterparts _CYP6P9a_S_. (**C**) Genotype distribution of _CYP6P9b_ between alive and dead mosquitoes after exposure to Olyset Plus using the cone test, showing the significant ability of the mosquitoes carrying the RR genotype to survive compared to SS (_p_ < 0.001).\n",
-                        "\n",
-                        "header: Resistance Escalation with Multiple Resistance Alleles Present a Greater Risk of Control Failure\n",
-                        "\n",
-                        "This study confirms the findings by [28], suggesting that the _6.5 kb SV_ acts as an enhancer for nearby duplicated P450 genes _CYP6P9a_ and _CYP6P9b_, leading to their increased overexpression, thus creating greater resistance. This _6.5 kb SV_ is strongly associated with an aggravation of pyrethroid resistance, which reduces the efficacy of pyrethroid-only nets. The fixation of this _6.5 kb SV_, besides the resistant alleles of _CYP6P9a_ and _CYP6P9b_, could explain the resistance escalation currently observed against the _An. funestus_ population from the southern part of Africa reducing the efficacy of bed nets [7,41].\n",
-                        "\n",
-                        "This study revealed that multiple and complex resistance combining elevated expression (_CYP6P9a/b_) [45], as well as the selection of cis-regulatory motifs for transcription binding sites (CnCC/MAF) [31], coupled with structural variations such as the _6.5 kb_, which is known to be enriched with several regulatory elements [28], can lead to a greater reduction in bed net efficacy. The fact that triple homozygote-resistant mosquitoes could survive better against exposure to pyrethroid-only nets compared to all genotypes reveals the greater risk that an unabated increase in resistance can likely lead to the failure of insecticide-based interventions, as predicted by the WHO global plan for insecticide resistance management. This calls for novel nets which do not rely on pyrethroids in the future. However, the impact of these resistance alleles on the efficacy of LLINs in natural populations remains to be established, as we only performed a test with a hybrid strain from two laboratory strains. This work must be urgently carried out, particularly as the molecular tools are increasingly available.\n",
-                        "\n",
-                        "header: Impact of CYP6P9b on the Efficacy of Olyset and Olyset plus Nets Using Experimental Hut Trial\n",
-                        "\n",
-                        "The impact of _CYP6P9b_ genotypes on the ability of mosquitoes to survive exposure to Olyset was firstly evaluated on the unfed samples collected only in the room. This investigation showed that the three genotypes were significantly different in their distributions (chi-square = 23.4; \\(p\\) < 0.0001) (Figure 5). Analysis of the correlation between each genotype and mortality revealed that _CYP6P9b_ homozygous-resistant mosquitoes (RR) show an increased ability to survive exposure to the Olyset when compared to homozygote-susceptible mosquitoes (SS) (OR = 15.0; CI = 4.5-50.3; \\(p\\) < 0.001) (Figure 5D). Heterozygote mosquitoes (RS) survived exposure to Olyset significantly better than homozygote-susceptible mosquitoes (SS) (OR = 6; CI = 1.9-18.75; \\(p\\) < 0.001). Homozygote-resistant mosquitoes (RR) struggled to survive compared to heterozygotes (OR = 2.4; CI = 1.2-4.8; \\(p\\) = 1) when considering samples from the room only. On the other hand, when considering all the samples, the additive resistance conferred by each allele of _CYP6P9b_ was shown by the fact that homozygote-resistant mosquitoes (RR) could survive significantly better than heterozygotes (OR = 2.5; CI = 1.3-4.8; \\(p\\) < 0.01). Moreover, the strength of this association was further shown by the significant ability to survive exposure when possessing a single _CYP6P9b_-resistant allele (R) compared to the susceptible allele (S) (OR = 2.5; CI = 1.4-4.5; \\(p\\) < 0.01) (Table S4; Figure 5E). The same trend showing a strong association between the _CYP6P9b_ genotypes and the mortality was also observed when analyzing all the samples dead and alive, including the blood-fed mosquitoes and the one collected in the veranda (Table S4).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}, \"Time_elapsed\": {\"title\": \"Time Elapsed\", \"description\": \"For longitudinal studies: how long since the start of the study, in units of months?Report this value only if explicitly stated in the text.\"}, \"Blood_meal_count\": {\"title\": \"Blood Meal Count\", \"description\": \"Count of blood meals taken by mosquitos over observation period. This is a proxy for time, as mosquitoes in the study were fed blood meals once a week and oviposition recorded at each time step.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Cameroon\",\"Site\":\"Yaounde\",\"Start_month\":1,\"Start_year\":2023,\"End_month\":6,\"End_year\":2023,\"Time_elapsed\":5.0,\"Blood_meal_count\":4.0},\"S02\":{\"Study_type\":\"Bioassay, WHO cylinder test\",\"Country\":\"Mozambique\",\"Site\":\"Lab facility\",\"Start_month\":2,\"Start_year\":2023,\"End_month\":5,\"End_year\":2023,\"Time_elapsed\":3.0,\"Blood_meal_count\":null}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Synergist_concentration\": {\"title\": \"Synergist concentration (measured)\", \"description\": \"Enter the measured concentration of the synergist.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated\",\"pHI_category\":\"Good\",\"Insecticide\":null,\"Concentration_initial\":null,\"Synergist\":null,\"Synergist_concentration\":null},\"N02\":{\"Net_type\":\"Olyset\",\"pHI_category\":\"Good\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"8.6 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (2%)\",\"Synergist\":null,\"Synergist_concentration\":null},\"N03\":{\"Net_type\":\"Olyset Plus\",\"pHI_category\":\"Good\",\"Insecticide\":\"Permethrin\",\"Concentration_initial\":\"8.6 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (2%)\",\"Synergist\":\"PBO\",\"Synergist_concentration\":\"4.3 \\u00d7 10\\u207b\\u2074 kg\\/m\\u00b2 (1%)\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Trial 2.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. PermaNet 3.0 (Vestergaard Sarl).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. PermaNet 3.0 + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. PermaNet 3.0 + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 are WHO prequalified pyrethroid-PBO ITNs [3]. Olyset Plus is made of polyethylene filaments coated with 20 g/Kg of permethrin and 10 g/Kg of PBO. PermaNet 3.0 consists of polyester side panels coated with deltamethrin at 2.1 g/kg and a polyethylene roof panel incorporating deltamethrin and PBO at 4.0 g/kg and 25 g/kg respectively.\n",
-                        "\n",
-                        "header: Tunnel tests\n",
-                        "\n",
-                        "The tunnel test consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections using a netting frame fitted into a slot across the tunnel. In one of the sections, a guinea pig was housed unconstrained in a small cage, and in the other section, -100 unfed female mosquitoes aged 5-8 days were released at dusk and left overnight. The net samples were deliberately hoked with nine 1-cm holes to give opportunity for mosquitoes to penetrate the animal bathed chamber for a blood meal an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 oC and 65-85% RH. The next morning, the numbers of mosquitoes found alive or dead, fed or unfed, in each section were scored. Live mosquitoes were provided with 10% glucose solution and delayed mortality was recorded after 24 h. The pyrethroid-PBO ITNs were compared to Olyset Net (a permethrin-only net, Sumitomo chemical) and PermaNet 2.0 (a deltamethrin-only net, Vestergaard Sarl) and an untreated control net. Two to three net pieces were tested per net type.\n",
-                        "\n",
-                        "header: Table 3: Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato exposed to pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination in experimental huts in Cové, southern Benin.\n",
-                        "footer: Results are presented separately for the trials involving Olgest Plus (Trial 1) and PermaNet 3.0 (Trial 2). ‘For each trial, values on this column sharing a superscript letter do not differ significantly, P > 0.05, logistic regression.\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total blood-fed', '% blood-feeding', '95% CIs', '% blood-feeding inhibition', '% personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total blood-fed\":\"Trial 1\",\"% blood-feeding\":\"Trial 1\",\"95% CIs\":\"Trial 1\",\"% blood-feeding inhibition\":\"Trial 1\",\"% personal protection\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total blood-fed\":\"481\",\"% blood-feeding\":\"72a\",\"95% CIs\":\"69\\u201376\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught\":\"511\",\"Total blood-fed\":\"122\",\"% blood-feeding\":\"24b\",\"95% CIs\":\"20\\u201328\",\"% blood-feeding inhibition\":\"67\",\"% personal protection\":\"75\"},\"3\":{\"Treatment\":\"Benedicarb IRS\",\"Total females caught\":\"556\",\"Total blood-fed\":\"528\",\"% blood-feeding\":\"95c\",\"95% CIs\":\"93\\u201397\",\"% blood-feeding inhibition\":\"\\u201331\",\"% personal protection\":\"\\u2013 10\"},\"4\":{\"Treatment\":\"Olyset Plus + bendicarb IRS\",\"Total females caught\":\"288\",\"Total blood-fed\":\"64\",\"% blood-feeding\":\"22b\",\"95% CIs\":\"17\\u201327\",\"% blood-feeding inhibition\":\"69\",\"% personal protection\":\"87\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total blood-fed\":\"420\",\"% blood-feeding\":\"79d\",\"95% CIs\":\"76\\u201383\",\"% blood-feeding inhibition\":\"\\u20139\",\"% personal protection\":\"13\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total blood-fed\":\"47\",\"% blood-feeding\":\"16e\",\"95% CIs\":\"11\\u201320\",\"% blood-feeding inhibition\":\"79\",\"% personal protection\":\"90\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total blood-fed\":\"Trial 2\",\"% blood-feeding\":\"Trial 2\",\"95% CIs\":\"Trial 2\",\"% blood-feeding inhibition\":\"Trial 2\",\"% personal protection\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total blood-fed\":\"391\",\"% blood-feeding\":\"67u\",\"95% CIs\":\"64\\u201371\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total blood-fed\":\"115\",\"% blood-feeding\":\"24v\",\"95% CIs\":\"20\\u201327\",\"% blood-feeding inhibition\":\"65\",\"% personal protection\":\"71\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total blood-fed\":\"519\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 33\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught\":\"233\",\"Total blood-fed\":\"54\",\"% blood-feeding\":\"23v\",\"95% CIs\":\"18\\u201329\",\"% blood-feeding inhibition\":\"66\",\"% personal protection\":\"86\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total blood-fed\":\"406\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 4\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"},\"14\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"}}\n",
-                        "\n",
-                        "header: 3.2.2 Tumed test bioassays.\n",
-                        "\n",
-                        "To further assess the efficacy of the pyrethroid-PBO ITNs and help explain the findings in the experimental huts, tunnel tests were performed using the susceptible _An. gambiae_ Kisum strain and pyrethroid-resistant _An. gambiae_ as from Cove on net samples (30 x 30 cm) obtained from Olyset Plus and Permaket 3.0 nets. The tunnel test is an overnight animal baulesar what simulates host-seeking behaviour of vector mosquitoes under controlled laboratory conditions. Both pyrethroid-PBO ITNs were compared to pyrethroid-only nets which contained similar pyrethroid-insecticles (Olyset Net and Permaket 2.0). Mosquito mortality rates observed in the tunnels are presented in Fig. 3. Mortality in the control tunnels was 11% with the susceptible Kisum strain and 2% with the pyrethroid-resistant Cove strain. All ITN types induced >8% mortality with the susceptible Kisum strain. Olyset Plus killed significantly higher proportions of the Cove mosquitoes compared to Olyset Net (90% vs. 42%). With Permaket 3.0, mortality of Cove mosquitoes exposed to net prices obtained from the PBO treated root of the net (68%) was higher compared to net prices obtained from the sides of the net (34%) and Permaket 2.0 (27%). The results, therefore, showed higher levels of mortality against pyrethroid-resistant mosquitoes from the Cove experimental hut station with the pyrethroid-PBO ITNs relative to pyrethroid-only nets. More detailed results from the tunnel tests are available in the supplementary information (Table S1).\n",
-                        "\n",
-                        "header: Table 2. Entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, P>0.05, negative binomial regression for females caught and logistic regression for exophily.\n",
-                        "columns: ['Treatment', 'Total females caught*', '% deterrence', 'Total exiting', '% exophily*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught*\":\"Trial 1\",\"% deterrence\":\"Trial 1\",\"Total exiting\":\"Trial 1\",\"% exophily*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"664a\",\"% deterrence\":\"-\",\"Total exiting\":\"241\",\"% exophily*\":\"36a\",\"95% CIs\":\"33\\u201340\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught*\":\"511a\",\"% deterrence\":\"23\",\"Total exiting\":\"390\",\"% exophily*\":\"76b\",\"95% CIs\":\"73\\u201380\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"556a\",\"% deterrence\":\"16\",\"Total exiting\":\"282\",\"% exophily*\":\"51c\",\"95% CIs\":\"47\\u201355\"},\"4\":{\"Treatment\":\"Olyset Plus + bendiocarb IRS\",\"Total females caught*\":\"288b\",\"% deterrence\":\"57\",\"Total exiting\":\"228\",\"% exophily*\":\"79b\",\"95% CIs\":\"75\\u201384\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"531a\",\"% deterrence\":\"20\",\"Total exiting\":\"281\",\"% exophily*\":\"53c\",\"95% CIs\":\"49\\u201357\"},\"6\":{\"Treatment\":\"Olyset plus + P-methyl IRS\",\"Total females caught*\":\"304b\",\"% deterrence\":\"54\",\"Total exiting\":\"270\",\"% exophily*\":\"89d\",\"95% CIs\":\"85\\u201392\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught*\":\"Trial 2\",\"% deterrence\":\"Trial 2\",\"Total exiting\":\"Trial 2\",\"% exophily*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"581u\",\"% deterrence\":\"-\",\"Total exiting\":\"226\",\"% exophily*\":\"39u\",\"95% CIs\":\"35\\u201343\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught*\":\"488u\",\"% deterrence\":\"16\",\"Total exiting\":\"369\",\"% exophily*\":\"76v\",\"95% CIs\":\"72\\u201379\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"575u\",\"% deterrence\":\"1\",\"Total exiting\":\"360\",\"% exophily*\":\"63w\",\"95% CIs\":\"59\\u201367\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught*\":\"233v\",\"% deterrence\":\"60\",\"Total exiting\":\"185\",\"% exophily*\":\"79v\",\"95% CIs\":\"74\\u201385\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"450u\",\"% deterrence\":\"23\",\"Total exiting\":\"311\",\"% exophily*\":\"69x\",\"95% CIs\":\"65\\u201373\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught*\":\"223v\",\"% deterrence\":\"62\",\"Total exiting\":\"195\",\"% exophily*\":\"87y\",\"95% CIs\":\"83\\u201392\"}}\n",
-                        "\n",
-                        "header: scientific reports: Abstract\n",
-                        "\n",
-                        "OPEN : Pyrethroid-piperonyl butoxide (PBO) nets reduce the efficacy of indoor residual spraying with pirimiphos-methyl against pyrethroid-resistant malaria vectors\n",
-                        "\n",
-                        "Thomas Syme, Mariai Ghegbo, Dorothy Obuobi, Augustin Fongnikin, Abel Agbevo, Damien Todjinou, & Corine Ngufori\n",
-                        "\n",
-                        "\n",
-                        "Primiphos-methyl is a pro-insecticide requiring activation by mosquito cytochrome P450 enzymes to induce toxicity while PBO blocks activation of these enzymes in pyrethroid-resistant vector mosquitoes. PBO may thus antagonise the toxicity of pirimiphos-methyl IRS when combined with pyrethroid-PBO ITMs. The impact of combining Olyset Plus and PermaNet 3.0 with Actellite 300CS IRS was evaluated against pyrethroid-resistant _Anopheles gambiae_ s.l. in two parallel experimental hut trials in southern Benin. The vector population was resistant to pyrethroids and PBO pre-exposure partially restored deltamethrin toxicity but not permitting. Mosquito mortality in experimental huts was significantly improved in the combinations of bendicocarp IRS with pyrethroid-PBO ITMs (33-3896) compared to bendicocarp IRS alone (14-16%, p < 0.001), demonstrating an additive effect. Conversely, mortality was significantly reduced in the combinations of pirimiphos-methyl IRS with pyrethroid-PBO ITMs (55-59%) compared to pirimiphos-methyl IRS alone (77-78%, p < 0.001), demonstrating evidence of an antagonistic effect when both interventions are applied in the same household. Mosquito mortality in the combination was significantly higher compared to the pyrethroid-PBO ITMs alone (55-59% vs. 22-26% p < 0.001) showing potential of pirimiphos-methyl IRS to enhance vector control when deployed to complement pyrethroid-PBO ITMs in an area where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Our study provides the first evidence of an antagonistic effect when pyrethroid-PBO ITNs are combined with pirimphos-methyl IRS in the same household resulting in lower levels of vector mosquito mortality compared to the IRS alone. In line with WHO recommendations, vector control programmes faced with multiple choice of ITN types to deploy as an additional intervention to improve vector control impact in an area dedicated to IRS with pirimphos-methyl, may consider other types of ITNs like pyrethroid-ITNs which can better complement pirimphos-methyl IRS when deployed together. Nevertheless, the pyrethroid-PBO ITNs performed poorly probably due to the lower levels of restoration of pyrethroid susceptibility with PBO in the vector population. Combining these nets with pirimphos-methyl IRS provided significantly improved vector control compared to the net alone demonstrating the potential for pirimphos-methyl IRS to enhance malaria control when deployed to complement pyrethroid-PBO ITNs in areas where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: Table 4.Mortality results of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs, and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, p>0.05, logistic regression\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total dead', '% mortality*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total dead\":\"Trial 1\",\"% mortality*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total dead\":\"22\",\"% mortality*\":\"3a\",\"95% CIs\":\"2\\u20135\"},\"2\":{\"Treatment\":\"Olyset Plus\",\"Total females caught\":\"511\",\"Total dead\":\"114\",\"% mortality*\":\"22b\",\"95% CIs\":\"19\\u201326\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"556\",\"Total dead\":\"87\",\"% mortality*\":\"16c\",\"95% CIs\":\"13\\u201319\"},\"4\":{\"Treatment\":\"Olyset Plus + Bendiocarb IRS\",\"Total females caught\":\"288\",\"Total dead\":\"95\",\"% mortality*\":\"33d\",\"95% CIs\":\"28\\u201338\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total dead\":\"411\",\"% mortality*\":\"77e\",\"95% CIs\":\"74\\u201381\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total dead\":\"178\",\"% mortality*\":\"59f\",\"95% CIs\":\"53\\u201364\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total dead\":\"Trial 2\",\"% mortality*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total dead\":\"12\",\"% mortality*\":\"2u\",\"95% CIs\":\"1\\u20133\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total dead\":\"127\",\"% mortality*\":\"26v\",\"95% CIs\":\"22\\u201330\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total dead\":\"80\",\"% mortality*\":\"14w\",\"95% CIs\":\"11\\u201317\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + Bendiocarb IRS\",\"Total females caught\":\"233\",\"Total dead\":\"89\",\"% mortality*\":\"38x\",\"95% CIs\":\"32\\u201344\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total dead\":\"350\",\"% mortality*\":\"78y\",\"95% CIs\":\"74\\u201382\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total dead\":\"122\",\"% mortality*\":\"55z\",\"95% CIs\":\"48\\u201361\"}}\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "Adult F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hut station were exposed to filter papers treated with discriminating doses of deltamethrin (0.05%), permethrin (0.75%), benchicaro (0.1%) and pirimphos-methyl (0.25%) in WHO cylinders8. Deltamethrin and perme-thrin were also tested with 60 min pre-exposure to PBO (4%) to assess the involvement of metabolic enzymes in pyrethroid resistance. A comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain. Approximately 100, 3-5-day old mosquitoes of each strain were exposed to each insecticide for 60 min in four batches of 20-25. Similar numbers of mosquitoes were concurrently exposed to untreated filter papers as a control. At the end of exposure, mosquitoes were transferred to appropriately labelled holding tubes, provided access to 10% (w/v) glucose solution, and held at 27 +- 2degC and 75 +- 10% relative humidity. Knockdown was recorded 60 min after exposure and delayed mortality after 24 h for all treatments. The insecticide-treated filter papers were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Study site and experimental hut treatments.: Trial 1.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. Olyset Plus (Sumitomo Chemical).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. Olyset Plus + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. Olyset Plus + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "header: Experimental hut results.\n",
-                        "\n",
-                        "_Mosquito entry and exiting in experimental huts._\n",
-                        "\n",
-                        "A total of 5,404 wild female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trials (Table 2). In both trials, mosquito entry in huts with the pyrethroid-PBO ITN alone and IRS treatments alone did not differ significantly from the controls (p > 0.05) but was significantly reduced with the pyrethroid-PBO ITN plus IRS combinations compared to the single treatments (p < 0.01). Mosquito entry rates did not also differ between the combinations of the pyrethroid-PBO ITN with bendiocarb IRS relative to the combinations with pirimiphos-methyl IRS (p < 0.05). Nevertheless, IRS treatments could not be rotated and thus, treatment-induced deterrence cannot be fully distinguished from differential attractiveness due to hut position.\n",
-                        "\n",
-                        "The proportion of mosquitoes exiting into the veranda of the huts with the untreated net controls was 36% and 39% for Trials 1 and 2 respectively. Exiting rates were higher with the pyrethroid-PBO ITNs alone (76%) relative to the IRS insecticides alone (51-63% with bendiocarb and 53-69% with pirimiphos-methyl IRS, p < 0.005). In both trials, the highest levels of mosquito exiting were achieved with the pyrethroid-PBO ITN plus IRS combinations (79% with bendiocarb and 87-89% with pirimiphos-methyl IRS). Between the combinations, adding\n",
-                        "primiphos-methyl IRS to the pyrethroid-PBO ITN provided significantly higher levels of mosquito exiting relative to adding bendicord IRS (87-89% vs. 79%, P < 0.05).\n",
-                        "\n",
-                        "Blood-feeding rates of wild malaria vector mosquitoes in experimental huts.Blood-feeding rates in huts with the untreated net controls were 72% and 67% for Trials 1 and 2 respectively (Table 3). The IRS treatments did not provide any blood-feeding inhibition relative to the controls. Blood-feeding inhibition rates were high with the pyrethroid-PBO ITN plus IRS combinations in both trials (69-79% with Olyst Plus and 63-66% with PermaNet 3.0) and this was generally similar to what was observed with the pyrethroid-PBO ITNs alone (67% with Olyst Plus and 65% with PermaNet, P > 0.05) showing that the high levels of blood-feeding inhibition in the combinations, was mostly due to the ITNs. Blood-feeding inhibition with the PermaNet 3.0 plus primiphos-methyl IRS combination (66%) was similar to the PermaNet 3.0 plus bendicord IRS combination (63%, P = 0.71) meanwhile the Olyset Plus and primiphos-methyl IRS combination induced higher levels of blood-feeding inhibition compared to the Olyst Plus and bendicord IRS combination (79% vs. 69%, P = 0.036) (Table 3). Personal protection levels showed a similar trend and were also higher with the combinations (86-90%) relative to the IRS alone (- 33-13%).\n",
-                        "\n",
-                        "Mortality rates of wild malaria vector mosquitoes in experimental huts.\n",
-                        "\n",
-                        "Wild vector mosquito mortality in the controls was 3% in Trial 1 and 2% in Trial 2 (Table 4). The pyrethroid-PBO ITNs killed relatively low mosquito proportions (22% with Olgest Plus and 26% with PermaNet 3.0) though this was higher than what was observed with benedicarb IRS alone (14% in Trial 1 and 16% in Trial 2, P < 0.10). The highest mortality was achieved with pirimphos-methyl IRS alone (77% in Trial 1 and 78% in Trial 2). In both trials, mortality in the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations was significantly reduced compared to pirimphos-methyl IRS alone (77% with pirimphos-methyl IRS vs. 59% with Olgest Plus pirimphos-methyl IRS, P < 0.001 and 78% with pirimphos-methyl IRS vs. 55% with PermaNet 3.0 plus pirimphos-methyl IRS, P < 0.001), demonstrating an antagonistic effect. Conversely, mortality was significantly higher in the combinations of benedicarb IRS with Olgest Plus (33%) and PermaNet 3.0 (38%) than benedicarb IRS alone (14-16%, P < 0.001), demonstrating an additive effect (Table 4). Nevertheless, both pyrethroid-PBO plus IRS combinations induced significantly higher\n",
-                        "\n",
-                        "mortality compared to the pyrethroid-PBO ITNs alone (P < 0.001). Between the combinations, mortality was consistently higher with the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations (55-59%) compared to the pyrethroid-PBO ITN plus pedicocarb IRS (33-39%, P < 0.001).\n",
-                        "\n",
-                        "The monthly mortality rates of wild vector mosquitoes which entered the experimental huts during the trials are presented in Fig. 1 for the combinations with pedicocarb IRS and Fig. 2 for the combinations with pirimiphos-methyl IRS. In both trials, mosquito mortality rates in huts with the pyrethroid-PBO ITNs plus pedicocarb IRS declined sharply over time from 65-75% in month 1 to 33-38% in month 3, nevertheless, it was consistently higher than the single treatments alone (Fig. 1). Mosquito mortality in the combination of Olyset Plus and pirimiphos-methyl IRS was similar to the IRS alone in month 1 (> 90%) but declined substantially relative to the IRS in subsequent months. With the PermaNet 3.0 plus pirimiphos-methyl IRS combination,\n",
-                        "\n",
-                        "Figure 1.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pedicocarb IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Oyset Plus (Trial 1) and panel (**b**) presents results from the trial with PermaNet 3.0 (Trial 2). Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from onset of the trial. Mosquito mortality in the control untreated huts did not exceed 5% at any time point.\n",
-                        "\n",
-                        "Figure 2.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pirimiphos-methyl IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Olyset Plus and panel (**b**) presents results from the trial with PermaNet 3.0. Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from the onset of the trial.\n",
-                        "\n",
-                        "\n",
-                        "mosquito mortality was consistently lower than the IRS alone throughout the trial. Through the four months of the trials, the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations consistently induced higher levels of mosquito mortality relative to the pyrethroid-PBO ITNs alone.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Experimental huts are used to assess the capacity of indoor vector control interventions to prevent wild vector mosquito entry and feeding and induce early mosquito exiting and mortality when applied in a human-occupied house, under carefully controlled conditions [3,12]. The hut trials were conducted at the CREC/LSHTM experimental hut station in Cowe, southern Benin (7deg14'N22deg18E), situated in a vast area of rice irrigation, which provides extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzini_ and _An. gambiae_ sensu stricto (s.s.) occur in symparity, with the latter present at lower densities and predominantly in the dry season. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold). Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (> 90%) and overexpression of CYP6P3, an enzyme associated with pyrethroid detoxification [3].\n",
-                        "\n",
-                        "Two experimental hut trials were performed in parallel for 4 months between April and July 2020. Trial 1 assessed the impact of combining Olyset Plus (Sumitomo Chemical), a permethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS while Trial 2 assessed the impact of combining PermaNet 3.0 (Vestergaard Sarl), a deltamethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS.\n",
-                        "\n",
-                        "The following six treatments were tested in each of the experimental hut trials:\n",
-                        "\n",
-                        "header: Data analysis.\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental but treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to the differential attractiveness of the volunteer sleepers and huts. Results from the different experimental hut trials involving Olyset Plus and PermaNet 3.0 were analysed separately. Susceptibility bioassay results were interpreted according to WHO criteria49. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "IRS Indoor residual spraying\n",
-                        "\n",
-                        "WHO World Health Organization\n",
-                        "\n",
-                        "PQ Prenqualification team\n",
-                        "\n",
-                        "PBO Piperonyl butoxide\n",
-                        "\n",
-                        "ITN Insecticide treated net\n",
-                        "\n",
-                        "LLIN Long-lasting insecticidal net\n",
-                        "\n",
-                        "GPIRM Global plan for insecticide resistance management\n",
-                        "\n",
-                        "WHOPES WHO pesticide evaluation scheme\n",
-                        "\n",
-                        "CREC Centre de Recherche Entomologique de Cotonou\n",
-                        "\n",
-                        "LSHTTM London School of Hygiene & Tropical Medicine\n",
-                        "\n",
-                        "Kdr Knockdown resistance\n",
-                        "\n",
-                        "PAMVERC Pan African Malaria Vector Research Consortium\n",
-                        "\n",
-                        "The large-scale implementation of long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS) has resulted in profound reductions in malaria-associated morbidity and mortality across sub-Saharan Africa, over the last two decades1. Unfortunately, resistance to the insecticides applied through these interventions, especially the pyrethroids, is now pervasive in vector populations in malaria-endemic countries', threatening to undermine their impact. In response, a new generation of novel LLINs and IRS based on new active ingredients with the potential to sustain vector control impact in the face of increasing resistance, have been developed3. This includes dual insecticide-treated nets containing a pyrethroid and an alternative effective new compound as well as new IRS insecticides with novel modes of action or improved formulations that have shown potential to provide enhanced control of insecticide-resistant malaria vector populations.\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) combining a pyrethroid and piperonyl butoxido (PBO) were the first novel class of ITNs to be developed for malaria vector control. PBO is a synergism that can enhance the impact of pyrethroids and other insecticides by inhibiting metabolic detoxification enzymes associated with resistance, notably cytochrome P450 monooxygenases3. These nets received a conditional endorsement from the World Health Organisation (WHO) in 2017 based on results from a cluster-randomised controlled trial (CRT) in Tanzania demonstrating a reduction in malaria prevalence in communities allocated to pyrethroid-PBO ITNs relative to pyrethroid-only ITNs4. A full policy recommendation is now expected after results from a second CRT in Uganda also showed that two brands of pyrethroid-PBO ITNs reduced malaria prevalence relative to pyrethroid-only nets4. The WHO endorsement and expanding evidence base for the public health value of pyrethroid-PBO ITNs has prompted mass procurement of these nets by international malaria control agencies4,5. The proportion of pyrethroid-PBO ITNs of all ITNs delivered in sub-Saharan Africa has consequently risen from 3% in 2018 to 35% in 202112; pyrethroid-PBO ITNs are therefore replacing pyrethroid-only nets in many malaria-endemic countries.\n",
-                        "\n",
-                        "The increasing distribution and intensity of pyrethroid resistance has also affected malaria control policy regarding insecticide choice for IRS over the last decade. To improve vector control impact and preserve pyrethroids for ITNs, African IRS programmes partly suspended the use of pyrethroids and organochlorines in favour of carbamates and organophosphates5,12. Whilst both insecticides showed high toxicity against malaria vectors, the short residual duration of the initial formulations approved for IRS proved prohibitive, necessitating the development of longer-lasting formulations13. A new microencapsulated formulation of pirimphos-methyl was later developed (Actellic 300CS) demonstrating prolonged activity against pyrethroid-resistant malaria vector mosquitoes, lasting up to 9 months14,15. This formulation subsequently served as the insecticide of choice for the majority of IRS programmes in sub-Saharan Africa1 providing substantial control of mosquito vectors and malaria across distinct eco-epidemiological settings6,16-22.\n",
-                        "\n",
-                        "[MISSING_PAGE_POST]\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Given the inhibitory effect of PBO on mosquito cytochrome P450 enzymes, the WHO had temporarily recommended against the use of pyrethroid-PBO ITN in areas programmed for IRS with pirimiphos-methyl IRS until further evidence on the potential antagonism between PBO and the organo-thiophosphate pro-insecticide becomes available20. In this study, we found evidence of an antagonistic effect when pyrethroid PBO ITNs were combined with pirimiphos-methyl IRS in the same household in a pyrethroid-resistant area in Southern Benin where resistance was partly conferred by over-expression of mosquito cytochrome P450 enzymes20. Unlike the combination of the pyrethroid-PBO nets with bendicorab IRS which provided improved vector mosquito mortality compared to bendicorab IRS alone, combining these nets with pirimiphos-methyl IRS induced significantly lower levels of vector mosquito mortality rates compared to pirimiphos-methyl IRS alone. This effect\n",
-                        "\n",
-                        "Figure 3.: Mortality (24 h) of susceptible _Anopheles gambiae_ Kisumu and pyrethroid-resistant _An. gambiae_ s.l. Cove strains exposed to Olyset Plus and Permaket 3.0 in tunnel tests. Error bars represent 95% CIs. Both pyrethroid-PBO nets were compared to pyrethroid-only nets (Olyset Net and Permaket 2.0). Permaket 3.0 contains PBO only on the roof of the net. With both strains, 80–120 mosquitoes were exposed overnight to metting pieces cut from whole nets in 2–3 replicate tunnel tests.\n",
-                        "\n",
-                        "\n",
-                        "was remarkably similar between the two brands of pyrethroid-PBO ITNs tested across both hut trials indicating that the negative interaction of the combination on mosquito mortality was less affected by the design and specifications of the pyrethroid-PBO ITN. Wild vector mosquitoes which entered the huts with the combined treatment may have contacted the pirimphos-methyl IRS on the hut wall only after picking up PBO from the ITN while attempting to blood-feed on the sheeper under the net. This pre-exposure to PBO on the ITN could have prevented the metabolic activation of the IRS insecticide in these mosquitoes, resulting in lower mortality rates relative to the IRS alone. This finding supports WHO's recommendation against the deployment of pyrethroid-PBO ITNs in areas that have already been programmed for IRS with pirimphos-methyl IRS. This is mostly important from a programmatic perspective where a vector control programme is faced with multiple choice of ITN types to deploy as an additional intervention to enhance vector control impact in an area that is already dedicated to IRS with pirimphos-methyl. In such a scenario, other types of ITNs like pyrethroid-only nets that were recently shown to complement pirimphos-methyl IRS when applied together23, should be considered.\n",
-                        "\n",
-                        "These findings should however not be interpreted to mean that pirimphos-methyl IRS must not be deployed to complement pyrethroid-PBO ITNs. Though pyrethroid-PBO ITNs have consistently shown improved performance in experimental hut trials against pyrethroid-resistant malaria vectors compared to pyrethroid-only nets34, the margin appears to vary depending on the intensity and mechanisms of pyrethroid-resistance encountered. In our study, the pyrethroid-PBO ITNs when applied alone in a hut induced low vector mosquito mortality (22-26%) compared to what has been observed in hut trials against the same vector population with another type of novel dual ITN (71-76%)35,40. Low levels of mosquito mortality in huts with pyrethroid-PBO ITNs and failure of PBO to fully restore pyrethroid susceptibility in bioassays have also been reported from studies in Burkina Faso37, Cameroon38, Cote d'Ivoire39 and Senegal40. This may indicate the presence of complex resistance mechanisms in the West African region unaffected by PBO. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors which continues to increase over time41, yet the public health value of pyrethroid-PBO ITNs has not been assessed in the region. It is therefore unclear whether these nets will provide the same improved epidemiological impact over pyrethroid-only nets in West Africa as observed in the East African community trials which have been the basis of their endorsement for malaria control. Our results showed a significant improvement in mosquito mortality when pirimphos-methyl IRS was combined with the pyrethroid-PBO ITNs (55-59%) compared to the pyrethroid-PBO ITNs alone (22-26%). Mosquito entry and feeding rates were also significantly lower with the combination compared to the pyrethroid-PBO ITNs alone. This provides some justification for deploying pirimphos-methyl IRS to complement pyrethroid-PBO nets in an area of high and complex pyrethroid-resistance where local vectors are less susceptible to the synergistic effect of PBO. Due to the limited choice of insecticides available for IRS, where a beneficial effect of the combination compared to the pyrethroid-PBO ITN alone has been established, pirimphos-methyl IRS should continue to be used even in the presence of high coverage with pyrethroid-PBO ITNs, preferably as part of an IRS rotational strategy. In line with WHO recommendations for insecticide resistance management42, the sustained rotation of multiple insecticide modes of action for IRS may help preserve efficacy to insecticides approved for IRS.\n",
-                        "\n",
-                        "Footnote 3: [https://doi.org/10.1038/s41598-022-10953-y](https://doi.org/10.1038/s41598-022-10953-y)\n",
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-                        "\n",
-                        "\n",
-                        "following pre-exposure to PBO, restoration of susceptibility to permethrin--the pyrethroid insecticide used on the pyrethroid-PBO ITN tested in the Tanzanian trial (Olvest Plus)--was high in the vector population from the study area (from 18 to 94%)15. Hence, the level of control achieved with pyrethroid-PBO ITN alone in the Tanzanian trial may have been optimal making the addition of pirimphos-methyl IRS unnecessary. This may not be the case in many communities in West Africa considering the afore-mentioned low levels of pyrethroid-PBO synergism reported in susceptibility bioassays and hut trials conducted across the region; in contrast, the addition of pirimphos-methyl IRS to pyrethroid-PBO ITNs in these communities could be more beneficial for control of clinical malaria compared to the ITN alone. As the range of vector control products available to vector control programmes expands, the choice of interventions and product brands must be aligned to local contexts and guided by local evidence. To help guide vector control policy, epidemiological trials and/or other empirical studies investigating the impact and cost-effectiveness of pyrethroid-PBO nets in communities in the West African region as well as their combination with IRS using pro-insectiles like pirimphos-methyl will be necessary.\n",
-                        "\n",
-                        "Although the inhibitory effect of PBO on mosquito cytochrome P450 enzymes formed the basis of our hypothesis for the antagonism observed between pirimphos-methyl IRS and pyrethroid-PBO ITNs in the experimental huts, behavioural interactions could also have contributed. In both trials, mosquito exiting rates into the veranda traps and blood-feeding inhibition were consistently higher in the combinations compared to the IRS insecticides alone. This could be attributed to the excitro-replenent property of the pyrethroid in the ITN stimulating directed movement of mosquitoes away from their source44. This early exiting effect was however significantly higher in the combination with pirimphos-methyl IRS compared to the combination with bendo-carb IRS suggesting a behavioural interaction in the presence of pirimphos-methyl that may have driven more mosquitoes to exit into the veranda, thus reducing mosquitoes' contact with pirimphos-methyl IRS treated walls. This may have compromised the impact of the pirimphos-methyl IRS in the combination compared to when applied alone and to the combination with bendoicarb. Further studies to assess mosquito flight behaviour, in the presence of the combinations would provide useful insight into behavioural interactions between the treatments. Controlled laboratory assays which minimise behavioural responses and assess P450 enzyme activity in exposed mosquitoes, could also elucidate the potential role of P450 enzyme inhibition in the antagonism observed in the experimental huts.\n",
-                        "\n",
-                        "While our trial focused on investigating the impact of combining pyrethroid-PBO ITNs with pirimphos-methyl IRS in the same households, there is a paucity of information on the interactions between these nets and other newly developed vector control products which contain new public health insecticides such as the neonciotohal, clothidinium and the pyrrole chlorfenapxy. Chlorfenapxy, an insecticide used on ITNs13 and being considered for IRS6,46, also requires activation by mosquito P450 enzymes6. Laboratory experiments have indicated the potential of PBO to antagonise the toxicity of chlorfenapxy against mosquitoes in bioassays29,30. Studies investigating the impact of co-deploying pyrethroid-PBO ITNs together with pyrethroid-chlorfenapxy ITNs or chlorfenapxy IRS in the same household will be essential to help inform optimal co-deployment policy.\n",
-                        "\n",
-                        "header: Hut trial outcome measures\n",
-                        "\n",
-                        "The efficacy of the experimental hut treatments was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "1. Deterrence (%)--proportional reduction in the number of mosquitoes collected in the treated hut relative to the number collected in the control.\n",
-                        "2. Exophily (%)--exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda trap.\n",
-                        "3. Blood-feeding inhibition (%)--proportional reduction in blood-feeding in the treated hut relative to the control. Calculated as follows: \\[\\mathit{Blood\\ feeding\\ inhibition}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bfu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "4. Mortality (%)--proportion of dead mosquitoes 24 h after collection.\n",
-                        "5. Personal protection (%)--reduction in the number of blood-fed mosquitoes in the treated hut relative to the untreated net control. Calculated as follows: \\[\\mathit{Personal\\ protection}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "\n",
-                        "header: Susceptibility of wild vector mosquitoes at Cove to insecticides.\n",
-                        "\n",
-                        "WHO susceptibility bioassays were conducted in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove hit site to the constituent insecticides of the experimental hut treatments. Mortality rates of F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hit station following exposure to discriminating doses of deltamethrin and permethrin in WHO cylinder bioassays were low (42% and 11% respectively), confirming a high frequency of pyrethroid resistance in the Cove vector population (Table 1). Pre-exposure to PBO significantly improved mortality with deltamethrin (42% vs. 72%) but not with permethrin (11% vs. 8%). Mortality rates with the discriminating doses of bendiocarb and pirimiphos-methyl were 98% and 99% respectively. This demonstrated susceptibility to pirimiphos-methyl and bendiocarb. All insecticides induced 100% mortality with the laboratory-maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu strain. No mortality was recorded in the control with either strain.\n",
-                        "\n",
-                        "header: Experimental hut trial procedure\n",
-                        "\n",
-                        "During the hut trials, volunteer sleepers were rotated between experimental huts daily to mitigate the impact of individual attractiveness whilst bed nets were rotated weekly between the huts to reduce the impact of hut position on mosquito entry. Three (3) replicate bed nets were used per treatment and rotated within the treatment every 2 days. IRS treatments cannot be rotated and thus remained fixed throughout the trial. Consenting human volunteer sleepers alert in experimental huts between 21:00 and 06:00 to attract free-flying mosquitoes. Each morning, volunteer sleepers collected all live and dead mosquitoes from the different compartments of the hut (under the net, room, veranda) using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were then transferred to the field laboratory for morphological identification using taxonomic keys and scoring of immediate mortality and blood-feeding. All live, female _An. gambiae_ s.l. were provided access to 10% glucose (w/v) solution and held at ambient conditions in the field laboratory. Delayed mortality was recorded after 24 h for all treatments. Mosquito collections were performed 6 nights per week and on the 7th day, huts were cleaned and aired in preparation for the next rotation cycle.\n",
-                        "\n",
-                        "header: Wall cone bioassays\n",
-                        "\n",
-                        "The laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain and wild pyrethroid-resistant _An. gambiae_ s.l. Cove mosquitoes (F1) derived from breeding sites at the hut station were used for this purpose. At each time point, five cones were attached to the walls and ceiling of the IRS-treated huts. Approximately 50, 3-5-day old mosquitoes were transferred into cones in 5 batches of 10 and exposed to the treated surfaces for 30 min. As a control, mosquitoes were exposed in cones attached to the walls and ceiling of an unsprayed hut. At the end of exposure, mosquitoes were transferred to netted, plastic cups. Mosquitoes were provided access to 10% (w/v) glucose solution and delayed mortality after 24 h for all treatments.\n",
-                        "\n",
-                        "\n",
-                        "Ethical considerations.Ethical approval for the conduct of the study was obtained from the ethics review boards of the Beninese Ministry of Health (No. 34) and the London School of Hygiene & Tropical Medicine (Ref: 16969). All human volunteer sleepers gave informed written consent prior to their participation; where necessary, the consent form and information sheet were explained in their local language. They were offered a free course of chemoprophylaxis spanning the duration of the trial and up to 3 weeks following its completion. A stand-by nurse was available for the duration of the trial to assess any cases of fever or adverse reactions to test items. Any confirmed cases of malaria were treated free of charge at a local health facility. Animals used as baits in tunnel test were maintained following institutional standard operating procedures (SOPs) designed to improve care and protect animals used for experimentation. All studies were performed according to relevant national and international guidelines.\n",
-                        "\n",
-                        "header: 3.2.3 Wall cone bioassays.\n",
-                        "\n",
-                        "WHO wall cone bioassays were performed on the IRS treated experimental hut walls 1 week, 2 months and 4 months after application of IRS treatments to assess their residual efficacy with increasing time elapsed from spraying. Mortality rates of the insecticide-susceptible _An. gambiae_ s.s. Kisum strain and pyrethroid-resistant _An. gambiae_ s.l. Cove strain following exposure to IRS-treated surfaces in 30 min wall cone bioassays are presented in Fig. 4. Mortality of mosquitoes exposed to pirimiphos-methyl treated walls was high (>90%) at all time points with both strains, showing no evidence of a decline in residual activity. In contrast, wall cone bioassay mortality on bendicor-treated surfaces declined rapidly, beginning at 67% and 39% in week 1 and falling to lows of 2% and 3% in month 4 with the Kisum and Cove strains respectively. No mortality was recorded in the controls at any time point with either strain.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Trial 2.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. PermaNet 3.0 (Vestergaard Sarl).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. PermaNet 3.0 + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. PermaNet 3.0 + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 are WHO prequalified pyrethroid-PBO ITNs [3]. Olyset Plus is made of polyethylene filaments coated with 20 g/Kg of permethrin and 10 g/Kg of PBO. PermaNet 3.0 consists of polyester side panels coated with deltamethrin at 2.1 g/kg and a polyethylene roof panel incorporating deltamethrin and PBO at 4.0 g/kg and 25 g/kg respectively.\n",
-                        "\n",
-                        "header: Tunnel tests\n",
-                        "\n",
-                        "The tunnel test consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections using a netting frame fitted into a slot across the tunnel. In one of the sections, a guinea pig was housed unconstrained in a small cage, and in the other section, -100 unfed female mosquitoes aged 5-8 days were released at dusk and left overnight. The net samples were deliberately hoked with nine 1-cm holes to give opportunity for mosquitoes to penetrate the animal bathed chamber for a blood meal an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 oC and 65-85% RH. The next morning, the numbers of mosquitoes found alive or dead, fed or unfed, in each section were scored. Live mosquitoes were provided with 10% glucose solution and delayed mortality was recorded after 24 h. The pyrethroid-PBO ITNs were compared to Olyset Net (a permethrin-only net, Sumitomo chemical) and PermaNet 2.0 (a deltamethrin-only net, Vestergaard Sarl) and an untreated control net. Two to three net pieces were tested per net type.\n",
-                        "\n",
-                        "header: Table 3: Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato exposed to pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination in experimental huts in Cové, southern Benin.\n",
-                        "footer: Results are presented separately for the trials involving Olgest Plus (Trial 1) and PermaNet 3.0 (Trial 2). ‘For each trial, values on this column sharing a superscript letter do not differ significantly, P > 0.05, logistic regression.\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total blood-fed', '% blood-feeding', '95% CIs', '% blood-feeding inhibition', '% personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total blood-fed\":\"Trial 1\",\"% blood-feeding\":\"Trial 1\",\"95% CIs\":\"Trial 1\",\"% blood-feeding inhibition\":\"Trial 1\",\"% personal protection\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total blood-fed\":\"481\",\"% blood-feeding\":\"72a\",\"95% CIs\":\"69\\u201376\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught\":\"511\",\"Total blood-fed\":\"122\",\"% blood-feeding\":\"24b\",\"95% CIs\":\"20\\u201328\",\"% blood-feeding inhibition\":\"67\",\"% personal protection\":\"75\"},\"3\":{\"Treatment\":\"Benedicarb IRS\",\"Total females caught\":\"556\",\"Total blood-fed\":\"528\",\"% blood-feeding\":\"95c\",\"95% CIs\":\"93\\u201397\",\"% blood-feeding inhibition\":\"\\u201331\",\"% personal protection\":\"\\u2013 10\"},\"4\":{\"Treatment\":\"Olyset Plus + bendicarb IRS\",\"Total females caught\":\"288\",\"Total blood-fed\":\"64\",\"% blood-feeding\":\"22b\",\"95% CIs\":\"17\\u201327\",\"% blood-feeding inhibition\":\"69\",\"% personal protection\":\"87\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total blood-fed\":\"420\",\"% blood-feeding\":\"79d\",\"95% CIs\":\"76\\u201383\",\"% blood-feeding inhibition\":\"\\u20139\",\"% personal protection\":\"13\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total blood-fed\":\"47\",\"% blood-feeding\":\"16e\",\"95% CIs\":\"11\\u201320\",\"% blood-feeding inhibition\":\"79\",\"% personal protection\":\"90\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total blood-fed\":\"Trial 2\",\"% blood-feeding\":\"Trial 2\",\"95% CIs\":\"Trial 2\",\"% blood-feeding inhibition\":\"Trial 2\",\"% personal protection\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total blood-fed\":\"391\",\"% blood-feeding\":\"67u\",\"95% CIs\":\"64\\u201371\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total blood-fed\":\"115\",\"% blood-feeding\":\"24v\",\"95% CIs\":\"20\\u201327\",\"% blood-feeding inhibition\":\"65\",\"% personal protection\":\"71\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total blood-fed\":\"519\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 33\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught\":\"233\",\"Total blood-fed\":\"54\",\"% blood-feeding\":\"23v\",\"95% CIs\":\"18\\u201329\",\"% blood-feeding inhibition\":\"66\",\"% personal protection\":\"86\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total blood-fed\":\"406\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 4\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"},\"14\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"}}\n",
-                        "\n",
-                        "header: scientific reports: Abstract\n",
-                        "\n",
-                        "OPEN : Pyrethroid-piperonyl butoxide (PBO) nets reduce the efficacy of indoor residual spraying with pirimiphos-methyl against pyrethroid-resistant malaria vectors\n",
-                        "\n",
-                        "Thomas Syme, Mariai Ghegbo, Dorothy Obuobi, Augustin Fongnikin, Abel Agbevo, Damien Todjinou, & Corine Ngufori\n",
-                        "\n",
-                        "\n",
-                        "Primiphos-methyl is a pro-insecticide requiring activation by mosquito cytochrome P450 enzymes to induce toxicity while PBO blocks activation of these enzymes in pyrethroid-resistant vector mosquitoes. PBO may thus antagonise the toxicity of pirimiphos-methyl IRS when combined with pyrethroid-PBO ITMs. The impact of combining Olyset Plus and PermaNet 3.0 with Actellite 300CS IRS was evaluated against pyrethroid-resistant _Anopheles gambiae_ s.l. in two parallel experimental hut trials in southern Benin. The vector population was resistant to pyrethroids and PBO pre-exposure partially restored deltamethrin toxicity but not permitting. Mosquito mortality in experimental huts was significantly improved in the combinations of bendicocarp IRS with pyrethroid-PBO ITMs (33-3896) compared to bendicocarp IRS alone (14-16%, p < 0.001), demonstrating an additive effect. Conversely, mortality was significantly reduced in the combinations of pirimiphos-methyl IRS with pyrethroid-PBO ITMs (55-59%) compared to pirimiphos-methyl IRS alone (77-78%, p < 0.001), demonstrating evidence of an antagonistic effect when both interventions are applied in the same household. Mosquito mortality in the combination was significantly higher compared to the pyrethroid-PBO ITMs alone (55-59% vs. 22-26% p < 0.001) showing potential of pirimiphos-methyl IRS to enhance vector control when deployed to complement pyrethroid-PBO ITMs in an area where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: 3.2.2 Tumed test bioassays.\n",
-                        "\n",
-                        "To further assess the efficacy of the pyrethroid-PBO ITNs and help explain the findings in the experimental huts, tunnel tests were performed using the susceptible _An. gambiae_ Kisum strain and pyrethroid-resistant _An. gambiae_ as from Cove on net samples (30 x 30 cm) obtained from Olyset Plus and Permaket 3.0 nets. The tunnel test is an overnight animal baulesar what simulates host-seeking behaviour of vector mosquitoes under controlled laboratory conditions. Both pyrethroid-PBO ITNs were compared to pyrethroid-only nets which contained similar pyrethroid-insecticles (Olyset Net and Permaket 2.0). Mosquito mortality rates observed in the tunnels are presented in Fig. 3. Mortality in the control tunnels was 11% with the susceptible Kisum strain and 2% with the pyrethroid-resistant Cove strain. All ITN types induced >8% mortality with the susceptible Kisum strain. Olyset Plus killed significantly higher proportions of the Cove mosquitoes compared to Olyset Net (90% vs. 42%). With Permaket 3.0, mortality of Cove mosquitoes exposed to net prices obtained from the PBO treated root of the net (68%) was higher compared to net prices obtained from the sides of the net (34%) and Permaket 2.0 (27%). The results, therefore, showed higher levels of mortality against pyrethroid-resistant mosquitoes from the Cove experimental hut station with the pyrethroid-PBO ITNs relative to pyrethroid-only nets. More detailed results from the tunnel tests are available in the supplementary information (Table S1).\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Our study provides the first evidence of an antagonistic effect when pyrethroid-PBO ITNs are combined with pirimphos-methyl IRS in the same household resulting in lower levels of vector mosquito mortality compared to the IRS alone. In line with WHO recommendations, vector control programmes faced with multiple choice of ITN types to deploy as an additional intervention to improve vector control impact in an area dedicated to IRS with pirimphos-methyl, may consider other types of ITNs like pyrethroid-ITNs which can better complement pirimphos-methyl IRS when deployed together. Nevertheless, the pyrethroid-PBO ITNs performed poorly probably due to the lower levels of restoration of pyrethroid susceptibility with PBO in the vector population. Combining these nets with pirimphos-methyl IRS provided significantly improved vector control compared to the net alone demonstrating the potential for pirimphos-methyl IRS to enhance malaria control when deployed to complement pyrethroid-PBO ITNs in areas where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: Study site and experimental hut treatments.: Trial 1.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. Olyset Plus (Sumitomo Chemical).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. Olyset Plus + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. Olyset Plus + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "header: Table 4.Mortality results of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs, and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, p>0.05, logistic regression\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total dead', '% mortality*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total dead\":\"Trial 1\",\"% mortality*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total dead\":\"22\",\"% mortality*\":\"3a\",\"95% CIs\":\"2\\u20135\"},\"2\":{\"Treatment\":\"Olyset Plus\",\"Total females caught\":\"511\",\"Total dead\":\"114\",\"% mortality*\":\"22b\",\"95% CIs\":\"19\\u201326\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"556\",\"Total dead\":\"87\",\"% mortality*\":\"16c\",\"95% CIs\":\"13\\u201319\"},\"4\":{\"Treatment\":\"Olyset Plus + Bendiocarb IRS\",\"Total females caught\":\"288\",\"Total dead\":\"95\",\"% mortality*\":\"33d\",\"95% CIs\":\"28\\u201338\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total dead\":\"411\",\"% mortality*\":\"77e\",\"95% CIs\":\"74\\u201381\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total dead\":\"178\",\"% mortality*\":\"59f\",\"95% CIs\":\"53\\u201364\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total dead\":\"Trial 2\",\"% mortality*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total dead\":\"12\",\"% mortality*\":\"2u\",\"95% CIs\":\"1\\u20133\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total dead\":\"127\",\"% mortality*\":\"26v\",\"95% CIs\":\"22\\u201330\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total dead\":\"80\",\"% mortality*\":\"14w\",\"95% CIs\":\"11\\u201317\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + Bendiocarb IRS\",\"Total females caught\":\"233\",\"Total dead\":\"89\",\"% mortality*\":\"38x\",\"95% CIs\":\"32\\u201344\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total dead\":\"350\",\"% mortality*\":\"78y\",\"95% CIs\":\"74\\u201382\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total dead\":\"122\",\"% mortality*\":\"55z\",\"95% CIs\":\"48\\u201361\"}}\n",
-                        "\n",
-                        "header: Table 2. Entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, P>0.05, negative binomial regression for females caught and logistic regression for exophily.\n",
-                        "columns: ['Treatment', 'Total females caught*', '% deterrence', 'Total exiting', '% exophily*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught*\":\"Trial 1\",\"% deterrence\":\"Trial 1\",\"Total exiting\":\"Trial 1\",\"% exophily*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"664a\",\"% deterrence\":\"-\",\"Total exiting\":\"241\",\"% exophily*\":\"36a\",\"95% CIs\":\"33\\u201340\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught*\":\"511a\",\"% deterrence\":\"23\",\"Total exiting\":\"390\",\"% exophily*\":\"76b\",\"95% CIs\":\"73\\u201380\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"556a\",\"% deterrence\":\"16\",\"Total exiting\":\"282\",\"% exophily*\":\"51c\",\"95% CIs\":\"47\\u201355\"},\"4\":{\"Treatment\":\"Olyset Plus + bendiocarb IRS\",\"Total females caught*\":\"288b\",\"% deterrence\":\"57\",\"Total exiting\":\"228\",\"% exophily*\":\"79b\",\"95% CIs\":\"75\\u201384\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"531a\",\"% deterrence\":\"20\",\"Total exiting\":\"281\",\"% exophily*\":\"53c\",\"95% CIs\":\"49\\u201357\"},\"6\":{\"Treatment\":\"Olyset plus + P-methyl IRS\",\"Total females caught*\":\"304b\",\"% deterrence\":\"54\",\"Total exiting\":\"270\",\"% exophily*\":\"89d\",\"95% CIs\":\"85\\u201392\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught*\":\"Trial 2\",\"% deterrence\":\"Trial 2\",\"Total exiting\":\"Trial 2\",\"% exophily*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"581u\",\"% deterrence\":\"-\",\"Total exiting\":\"226\",\"% exophily*\":\"39u\",\"95% CIs\":\"35\\u201343\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught*\":\"488u\",\"% deterrence\":\"16\",\"Total exiting\":\"369\",\"% exophily*\":\"76v\",\"95% CIs\":\"72\\u201379\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"575u\",\"% deterrence\":\"1\",\"Total exiting\":\"360\",\"% exophily*\":\"63w\",\"95% CIs\":\"59\\u201367\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught*\":\"233v\",\"% deterrence\":\"60\",\"Total exiting\":\"185\",\"% exophily*\":\"79v\",\"95% CIs\":\"74\\u201385\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"450u\",\"% deterrence\":\"23\",\"Total exiting\":\"311\",\"% exophily*\":\"69x\",\"95% CIs\":\"65\\u201373\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught*\":\"223v\",\"% deterrence\":\"62\",\"Total exiting\":\"195\",\"% exophily*\":\"87y\",\"95% CIs\":\"83\\u201392\"}}\n",
-                        "\n",
-                        "header: Data analysis.\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental but treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to the differential attractiveness of the volunteer sleepers and huts. Results from the different experimental hut trials involving Olyset Plus and PermaNet 3.0 were analysed separately. Susceptibility bioassay results were interpreted according to WHO criteria49. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Experimental hut results.\n",
-                        "\n",
-                        "_Mosquito entry and exiting in experimental huts._\n",
-                        "\n",
-                        "A total of 5,404 wild female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trials (Table 2). In both trials, mosquito entry in huts with the pyrethroid-PBO ITN alone and IRS treatments alone did not differ significantly from the controls (p > 0.05) but was significantly reduced with the pyrethroid-PBO ITN plus IRS combinations compared to the single treatments (p < 0.01). Mosquito entry rates did not also differ between the combinations of the pyrethroid-PBO ITN with bendiocarb IRS relative to the combinations with pirimiphos-methyl IRS (p < 0.05). Nevertheless, IRS treatments could not be rotated and thus, treatment-induced deterrence cannot be fully distinguished from differential attractiveness due to hut position.\n",
-                        "\n",
-                        "The proportion of mosquitoes exiting into the veranda of the huts with the untreated net controls was 36% and 39% for Trials 1 and 2 respectively. Exiting rates were higher with the pyrethroid-PBO ITNs alone (76%) relative to the IRS insecticides alone (51-63% with bendiocarb and 53-69% with pirimiphos-methyl IRS, p < 0.005). In both trials, the highest levels of mosquito exiting were achieved with the pyrethroid-PBO ITN plus IRS combinations (79% with bendiocarb and 87-89% with pirimiphos-methyl IRS). Between the combinations, adding\n",
-                        "primiphos-methyl IRS to the pyrethroid-PBO ITN provided significantly higher levels of mosquito exiting relative to adding bendicord IRS (87-89% vs. 79%, P < 0.05).\n",
-                        "\n",
-                        "Blood-feeding rates of wild malaria vector mosquitoes in experimental huts.Blood-feeding rates in huts with the untreated net controls were 72% and 67% for Trials 1 and 2 respectively (Table 3). The IRS treatments did not provide any blood-feeding inhibition relative to the controls. Blood-feeding inhibition rates were high with the pyrethroid-PBO ITN plus IRS combinations in both trials (69-79% with Olyst Plus and 63-66% with PermaNet 3.0) and this was generally similar to what was observed with the pyrethroid-PBO ITNs alone (67% with Olyst Plus and 65% with PermaNet, P > 0.05) showing that the high levels of blood-feeding inhibition in the combinations, was mostly due to the ITNs. Blood-feeding inhibition with the PermaNet 3.0 plus primiphos-methyl IRS combination (66%) was similar to the PermaNet 3.0 plus bendicord IRS combination (63%, P = 0.71) meanwhile the Olyset Plus and primiphos-methyl IRS combination induced higher levels of blood-feeding inhibition compared to the Olyst Plus and bendicord IRS combination (79% vs. 69%, P = 0.036) (Table 3). Personal protection levels showed a similar trend and were also higher with the combinations (86-90%) relative to the IRS alone (- 33-13%).\n",
-                        "\n",
-                        "Mortality rates of wild malaria vector mosquitoes in experimental huts.\n",
-                        "\n",
-                        "Wild vector mosquito mortality in the controls was 3% in Trial 1 and 2% in Trial 2 (Table 4). The pyrethroid-PBO ITNs killed relatively low mosquito proportions (22% with Olgest Plus and 26% with PermaNet 3.0) though this was higher than what was observed with benedicarb IRS alone (14% in Trial 1 and 16% in Trial 2, P < 0.10). The highest mortality was achieved with pirimphos-methyl IRS alone (77% in Trial 1 and 78% in Trial 2). In both trials, mortality in the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations was significantly reduced compared to pirimphos-methyl IRS alone (77% with pirimphos-methyl IRS vs. 59% with Olgest Plus pirimphos-methyl IRS, P < 0.001 and 78% with pirimphos-methyl IRS vs. 55% with PermaNet 3.0 plus pirimphos-methyl IRS, P < 0.001), demonstrating an antagonistic effect. Conversely, mortality was significantly higher in the combinations of benedicarb IRS with Olgest Plus (33%) and PermaNet 3.0 (38%) than benedicarb IRS alone (14-16%, P < 0.001), demonstrating an additive effect (Table 4). Nevertheless, both pyrethroid-PBO plus IRS combinations induced significantly higher\n",
-                        "\n",
-                        "mortality compared to the pyrethroid-PBO ITNs alone (P < 0.001). Between the combinations, mortality was consistently higher with the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations (55-59%) compared to the pyrethroid-PBO ITN plus pedicocarb IRS (33-39%, P < 0.001).\n",
-                        "\n",
-                        "The monthly mortality rates of wild vector mosquitoes which entered the experimental huts during the trials are presented in Fig. 1 for the combinations with pedicocarb IRS and Fig. 2 for the combinations with pirimiphos-methyl IRS. In both trials, mosquito mortality rates in huts with the pyrethroid-PBO ITNs plus pedicocarb IRS declined sharply over time from 65-75% in month 1 to 33-38% in month 3, nevertheless, it was consistently higher than the single treatments alone (Fig. 1). Mosquito mortality in the combination of Olyset Plus and pirimiphos-methyl IRS was similar to the IRS alone in month 1 (> 90%) but declined substantially relative to the IRS in subsequent months. With the PermaNet 3.0 plus pirimiphos-methyl IRS combination,\n",
-                        "\n",
-                        "Figure 1.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pedicocarb IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Oyset Plus (Trial 1) and panel (**b**) presents results from the trial with PermaNet 3.0 (Trial 2). Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from onset of the trial. Mosquito mortality in the control untreated huts did not exceed 5% at any time point.\n",
-                        "\n",
-                        "Figure 2.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pirimiphos-methyl IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Olyset Plus and panel (**b**) presents results from the trial with PermaNet 3.0. Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from the onset of the trial.\n",
-                        "\n",
-                        "\n",
-                        "mosquito mortality was consistently lower than the IRS alone throughout the trial. Through the four months of the trials, the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations consistently induced higher levels of mosquito mortality relative to the pyrethroid-PBO ITNs alone.\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "Adult F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hut station were exposed to filter papers treated with discriminating doses of deltamethrin (0.05%), permethrin (0.75%), benchicaro (0.1%) and pirimphos-methyl (0.25%) in WHO cylinders8. Deltamethrin and perme-thrin were also tested with 60 min pre-exposure to PBO (4%) to assess the involvement of metabolic enzymes in pyrethroid resistance. A comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain. Approximately 100, 3-5-day old mosquitoes of each strain were exposed to each insecticide for 60 min in four batches of 20-25. Similar numbers of mosquitoes were concurrently exposed to untreated filter papers as a control. At the end of exposure, mosquitoes were transferred to appropriately labelled holding tubes, provided access to 10% (w/v) glucose solution, and held at 27 +- 2degC and 75 +- 10% relative humidity. Knockdown was recorded 60 min after exposure and delayed mortality after 24 h for all treatments. The insecticide-treated filter papers were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Hut trial outcome measures\n",
-                        "\n",
-                        "The efficacy of the experimental hut treatments was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "1. Deterrence (%)--proportional reduction in the number of mosquitoes collected in the treated hut relative to the number collected in the control.\n",
-                        "2. Exophily (%)--exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda trap.\n",
-                        "3. Blood-feeding inhibition (%)--proportional reduction in blood-feeding in the treated hut relative to the control. Calculated as follows: \\[\\mathit{Blood\\ feeding\\ inhibition}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bfu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "4. Mortality (%)--proportion of dead mosquitoes 24 h after collection.\n",
-                        "5. Personal protection (%)--reduction in the number of blood-fed mosquitoes in the treated hut relative to the untreated net control. Calculated as follows: \\[\\mathit{Personal\\ protection}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Experimental huts are used to assess the capacity of indoor vector control interventions to prevent wild vector mosquito entry and feeding and induce early mosquito exiting and mortality when applied in a human-occupied house, under carefully controlled conditions [3,12]. The hut trials were conducted at the CREC/LSHTM experimental hut station in Cowe, southern Benin (7deg14'N22deg18E), situated in a vast area of rice irrigation, which provides extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzini_ and _An. gambiae_ sensu stricto (s.s.) occur in symparity, with the latter present at lower densities and predominantly in the dry season. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold). Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (> 90%) and overexpression of CYP6P3, an enzyme associated with pyrethroid detoxification [3].\n",
-                        "\n",
-                        "Two experimental hut trials were performed in parallel for 4 months between April and July 2020. Trial 1 assessed the impact of combining Olyset Plus (Sumitomo Chemical), a permethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS while Trial 2 assessed the impact of combining PermaNet 3.0 (Vestergaard Sarl), a deltamethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS.\n",
-                        "\n",
-                        "The following six treatments were tested in each of the experimental hut trials:\n",
-                        "\n",
-                        "header: Susceptibility of wild vector mosquitoes at Cove to insecticides.\n",
-                        "\n",
-                        "WHO susceptibility bioassays were conducted in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove hit site to the constituent insecticides of the experimental hut treatments. Mortality rates of F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hit station following exposure to discriminating doses of deltamethrin and permethrin in WHO cylinder bioassays were low (42% and 11% respectively), confirming a high frequency of pyrethroid resistance in the Cove vector population (Table 1). Pre-exposure to PBO significantly improved mortality with deltamethrin (42% vs. 72%) but not with permethrin (11% vs. 8%). Mortality rates with the discriminating doses of bendiocarb and pirimiphos-methyl were 98% and 99% respectively. This demonstrated susceptibility to pirimiphos-methyl and bendiocarb. All insecticides induced 100% mortality with the laboratory-maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu strain. No mortality was recorded in the control with either strain.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Given the inhibitory effect of PBO on mosquito cytochrome P450 enzymes, the WHO had temporarily recommended against the use of pyrethroid-PBO ITN in areas programmed for IRS with pirimiphos-methyl IRS until further evidence on the potential antagonism between PBO and the organo-thiophosphate pro-insecticide becomes available20. In this study, we found evidence of an antagonistic effect when pyrethroid PBO ITNs were combined with pirimiphos-methyl IRS in the same household in a pyrethroid-resistant area in Southern Benin where resistance was partly conferred by over-expression of mosquito cytochrome P450 enzymes20. Unlike the combination of the pyrethroid-PBO nets with bendicorab IRS which provided improved vector mosquito mortality compared to bendicorab IRS alone, combining these nets with pirimiphos-methyl IRS induced significantly lower levels of vector mosquito mortality rates compared to pirimiphos-methyl IRS alone. This effect\n",
-                        "\n",
-                        "Figure 3.: Mortality (24 h) of susceptible _Anopheles gambiae_ Kisumu and pyrethroid-resistant _An. gambiae_ s.l. Cove strains exposed to Olyset Plus and Permaket 3.0 in tunnel tests. Error bars represent 95% CIs. Both pyrethroid-PBO nets were compared to pyrethroid-only nets (Olyset Net and Permaket 2.0). Permaket 3.0 contains PBO only on the roof of the net. With both strains, 80–120 mosquitoes were exposed overnight to metting pieces cut from whole nets in 2–3 replicate tunnel tests.\n",
-                        "\n",
-                        "\n",
-                        "was remarkably similar between the two brands of pyrethroid-PBO ITNs tested across both hut trials indicating that the negative interaction of the combination on mosquito mortality was less affected by the design and specifications of the pyrethroid-PBO ITN. Wild vector mosquitoes which entered the huts with the combined treatment may have contacted the pirimphos-methyl IRS on the hut wall only after picking up PBO from the ITN while attempting to blood-feed on the sheeper under the net. This pre-exposure to PBO on the ITN could have prevented the metabolic activation of the IRS insecticide in these mosquitoes, resulting in lower mortality rates relative to the IRS alone. This finding supports WHO's recommendation against the deployment of pyrethroid-PBO ITNs in areas that have already been programmed for IRS with pirimphos-methyl IRS. This is mostly important from a programmatic perspective where a vector control programme is faced with multiple choice of ITN types to deploy as an additional intervention to enhance vector control impact in an area that is already dedicated to IRS with pirimphos-methyl. In such a scenario, other types of ITNs like pyrethroid-only nets that were recently shown to complement pirimphos-methyl IRS when applied together23, should be considered.\n",
-                        "\n",
-                        "These findings should however not be interpreted to mean that pirimphos-methyl IRS must not be deployed to complement pyrethroid-PBO ITNs. Though pyrethroid-PBO ITNs have consistently shown improved performance in experimental hut trials against pyrethroid-resistant malaria vectors compared to pyrethroid-only nets34, the margin appears to vary depending on the intensity and mechanisms of pyrethroid-resistance encountered. In our study, the pyrethroid-PBO ITNs when applied alone in a hut induced low vector mosquito mortality (22-26%) compared to what has been observed in hut trials against the same vector population with another type of novel dual ITN (71-76%)35,40. Low levels of mosquito mortality in huts with pyrethroid-PBO ITNs and failure of PBO to fully restore pyrethroid susceptibility in bioassays have also been reported from studies in Burkina Faso37, Cameroon38, Cote d'Ivoire39 and Senegal40. This may indicate the presence of complex resistance mechanisms in the West African region unaffected by PBO. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors which continues to increase over time41, yet the public health value of pyrethroid-PBO ITNs has not been assessed in the region. It is therefore unclear whether these nets will provide the same improved epidemiological impact over pyrethroid-only nets in West Africa as observed in the East African community trials which have been the basis of their endorsement for malaria control. Our results showed a significant improvement in mosquito mortality when pirimphos-methyl IRS was combined with the pyrethroid-PBO ITNs (55-59%) compared to the pyrethroid-PBO ITNs alone (22-26%). Mosquito entry and feeding rates were also significantly lower with the combination compared to the pyrethroid-PBO ITNs alone. This provides some justification for deploying pirimphos-methyl IRS to complement pyrethroid-PBO nets in an area of high and complex pyrethroid-resistance where local vectors are less susceptible to the synergistic effect of PBO. Due to the limited choice of insecticides available for IRS, where a beneficial effect of the combination compared to the pyrethroid-PBO ITN alone has been established, pirimphos-methyl IRS should continue to be used even in the presence of high coverage with pyrethroid-PBO ITNs, preferably as part of an IRS rotational strategy. In line with WHO recommendations for insecticide resistance management42, the sustained rotation of multiple insecticide modes of action for IRS may help preserve efficacy to insecticides approved for IRS.\n",
-                        "\n",
-                        "Footnote 3: [https://doi.org/10.1038/s41598-022-10953-y](https://doi.org/10.1038/s41598-022-10953-y)\n",
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-                        "\n",
-                        "\n",
-                        "following pre-exposure to PBO, restoration of susceptibility to permethrin--the pyrethroid insecticide used on the pyrethroid-PBO ITN tested in the Tanzanian trial (Olvest Plus)--was high in the vector population from the study area (from 18 to 94%)15. Hence, the level of control achieved with pyrethroid-PBO ITN alone in the Tanzanian trial may have been optimal making the addition of pirimphos-methyl IRS unnecessary. This may not be the case in many communities in West Africa considering the afore-mentioned low levels of pyrethroid-PBO synergism reported in susceptibility bioassays and hut trials conducted across the region; in contrast, the addition of pirimphos-methyl IRS to pyrethroid-PBO ITNs in these communities could be more beneficial for control of clinical malaria compared to the ITN alone. As the range of vector control products available to vector control programmes expands, the choice of interventions and product brands must be aligned to local contexts and guided by local evidence. To help guide vector control policy, epidemiological trials and/or other empirical studies investigating the impact and cost-effectiveness of pyrethroid-PBO nets in communities in the West African region as well as their combination with IRS using pro-insectiles like pirimphos-methyl will be necessary.\n",
-                        "\n",
-                        "Although the inhibitory effect of PBO on mosquito cytochrome P450 enzymes formed the basis of our hypothesis for the antagonism observed between pirimphos-methyl IRS and pyrethroid-PBO ITNs in the experimental huts, behavioural interactions could also have contributed. In both trials, mosquito exiting rates into the veranda traps and blood-feeding inhibition were consistently higher in the combinations compared to the IRS insecticides alone. This could be attributed to the excitro-replenent property of the pyrethroid in the ITN stimulating directed movement of mosquitoes away from their source44. This early exiting effect was however significantly higher in the combination with pirimphos-methyl IRS compared to the combination with bendo-carb IRS suggesting a behavioural interaction in the presence of pirimphos-methyl that may have driven more mosquitoes to exit into the veranda, thus reducing mosquitoes' contact with pirimphos-methyl IRS treated walls. This may have compromised the impact of the pirimphos-methyl IRS in the combination compared to when applied alone and to the combination with bendoicarb. Further studies to assess mosquito flight behaviour, in the presence of the combinations would provide useful insight into behavioural interactions between the treatments. Controlled laboratory assays which minimise behavioural responses and assess P450 enzyme activity in exposed mosquitoes, could also elucidate the potential role of P450 enzyme inhibition in the antagonism observed in the experimental huts.\n",
-                        "\n",
-                        "While our trial focused on investigating the impact of combining pyrethroid-PBO ITNs with pirimphos-methyl IRS in the same households, there is a paucity of information on the interactions between these nets and other newly developed vector control products which contain new public health insecticides such as the neonciotohal, clothidinium and the pyrrole chlorfenapxy. Chlorfenapxy, an insecticide used on ITNs13 and being considered for IRS6,46, also requires activation by mosquito P450 enzymes6. Laboratory experiments have indicated the potential of PBO to antagonise the toxicity of chlorfenapxy against mosquitoes in bioassays29,30. Studies investigating the impact of co-deploying pyrethroid-PBO ITNs together with pyrethroid-chlorfenapxy ITNs or chlorfenapxy IRS in the same household will be essential to help inform optimal co-deployment policy.\n",
-                        "\n",
-                        "header: 3.2.3 Wall cone bioassays.\n",
-                        "\n",
-                        "WHO wall cone bioassays were performed on the IRS treated experimental hut walls 1 week, 2 months and 4 months after application of IRS treatments to assess their residual efficacy with increasing time elapsed from spraying. Mortality rates of the insecticide-susceptible _An. gambiae_ s.s. Kisum strain and pyrethroid-resistant _An. gambiae_ s.l. Cove strain following exposure to IRS-treated surfaces in 30 min wall cone bioassays are presented in Fig. 4. Mortality of mosquitoes exposed to pirimiphos-methyl treated walls was high (>90%) at all time points with both strains, showing no evidence of a decline in residual activity. In contrast, wall cone bioassay mortality on bendicor-treated surfaces declined rapidly, beginning at 67% and 39% in week 1 and falling to lows of 2% and 3% in month 4 with the Kisum and Cove strains respectively. No mortality was recorded in the controls at any time point with either strain.\n",
-                        "\n",
-                        "header: Experimental hut trial procedure\n",
-                        "\n",
-                        "During the hut trials, volunteer sleepers were rotated between experimental huts daily to mitigate the impact of individual attractiveness whilst bed nets were rotated weekly between the huts to reduce the impact of hut position on mosquito entry. Three (3) replicate bed nets were used per treatment and rotated within the treatment every 2 days. IRS treatments cannot be rotated and thus remained fixed throughout the trial. Consenting human volunteer sleepers alert in experimental huts between 21:00 and 06:00 to attract free-flying mosquitoes. Each morning, volunteer sleepers collected all live and dead mosquitoes from the different compartments of the hut (under the net, room, veranda) using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were then transferred to the field laboratory for morphological identification using taxonomic keys and scoring of immediate mortality and blood-feeding. All live, female _An. gambiae_ s.l. were provided access to 10% glucose (w/v) solution and held at ambient conditions in the field laboratory. Delayed mortality was recorded after 24 h for all treatments. Mosquito collections were performed 6 nights per week and on the 7th day, huts were cleaned and aired in preparation for the next rotation cycle.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "IRS Indoor residual spraying\n",
-                        "\n",
-                        "WHO World Health Organization\n",
-                        "\n",
-                        "PQ Prenqualification team\n",
-                        "\n",
-                        "PBO Piperonyl butoxide\n",
-                        "\n",
-                        "ITN Insecticide treated net\n",
-                        "\n",
-                        "LLIN Long-lasting insecticidal net\n",
-                        "\n",
-                        "GPIRM Global plan for insecticide resistance management\n",
-                        "\n",
-                        "WHOPES WHO pesticide evaluation scheme\n",
-                        "\n",
-                        "CREC Centre de Recherche Entomologique de Cotonou\n",
-                        "\n",
-                        "LSHTTM London School of Hygiene & Tropical Medicine\n",
-                        "\n",
-                        "Kdr Knockdown resistance\n",
-                        "\n",
-                        "PAMVERC Pan African Malaria Vector Research Consortium\n",
-                        "\n",
-                        "The large-scale implementation of long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS) has resulted in profound reductions in malaria-associated morbidity and mortality across sub-Saharan Africa, over the last two decades1. Unfortunately, resistance to the insecticides applied through these interventions, especially the pyrethroids, is now pervasive in vector populations in malaria-endemic countries', threatening to undermine their impact. In response, a new generation of novel LLINs and IRS based on new active ingredients with the potential to sustain vector control impact in the face of increasing resistance, have been developed3. This includes dual insecticide-treated nets containing a pyrethroid and an alternative effective new compound as well as new IRS insecticides with novel modes of action or improved formulations that have shown potential to provide enhanced control of insecticide-resistant malaria vector populations.\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) combining a pyrethroid and piperonyl butoxido (PBO) were the first novel class of ITNs to be developed for malaria vector control. PBO is a synergism that can enhance the impact of pyrethroids and other insecticides by inhibiting metabolic detoxification enzymes associated with resistance, notably cytochrome P450 monooxygenases3. These nets received a conditional endorsement from the World Health Organisation (WHO) in 2017 based on results from a cluster-randomised controlled trial (CRT) in Tanzania demonstrating a reduction in malaria prevalence in communities allocated to pyrethroid-PBO ITNs relative to pyrethroid-only ITNs4. A full policy recommendation is now expected after results from a second CRT in Uganda also showed that two brands of pyrethroid-PBO ITNs reduced malaria prevalence relative to pyrethroid-only nets4. The WHO endorsement and expanding evidence base for the public health value of pyrethroid-PBO ITNs has prompted mass procurement of these nets by international malaria control agencies4,5. The proportion of pyrethroid-PBO ITNs of all ITNs delivered in sub-Saharan Africa has consequently risen from 3% in 2018 to 35% in 202112; pyrethroid-PBO ITNs are therefore replacing pyrethroid-only nets in many malaria-endemic countries.\n",
-                        "\n",
-                        "The increasing distribution and intensity of pyrethroid resistance has also affected malaria control policy regarding insecticide choice for IRS over the last decade. To improve vector control impact and preserve pyrethroids for ITNs, African IRS programmes partly suspended the use of pyrethroids and organochlorines in favour of carbamates and organophosphates5,12. Whilst both insecticides showed high toxicity against malaria vectors, the short residual duration of the initial formulations approved for IRS proved prohibitive, necessitating the development of longer-lasting formulations13. A new microencapsulated formulation of pirimphos-methyl was later developed (Actellic 300CS) demonstrating prolonged activity against pyrethroid-resistant malaria vector mosquitoes, lasting up to 9 months14,15. This formulation subsequently served as the insecticide of choice for the majority of IRS programmes in sub-Saharan Africa1 providing substantial control of mosquito vectors and malaria across distinct eco-epidemiological settings6,16-22.\n",
-                        "\n",
-                        "[MISSING_PAGE_POST]\n",
-                        "\n",
-                        "header: Wall cone bioassays\n",
-                        "\n",
-                        "The laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain and wild pyrethroid-resistant _An. gambiae_ s.l. Cove mosquitoes (F1) derived from breeding sites at the hut station were used for this purpose. At each time point, five cones were attached to the walls and ceiling of the IRS-treated huts. Approximately 50, 3-5-day old mosquitoes were transferred into cones in 5 batches of 10 and exposed to the treated surfaces for 30 min. As a control, mosquitoes were exposed in cones attached to the walls and ceiling of an unsprayed hut. At the end of exposure, mosquitoes were transferred to netted, plastic cups. Mosquitoes were provided access to 10% (w/v) glucose solution and delayed mortality after 24 h for all treatments.\n",
-                        "\n",
-                        "\n",
-                        "Ethical considerations.Ethical approval for the conduct of the study was obtained from the ethics review boards of the Beninese Ministry of Health (No. 34) and the London School of Hygiene & Tropical Medicine (Ref: 16969). All human volunteer sleepers gave informed written consent prior to their participation; where necessary, the consent form and information sheet were explained in their local language. They were offered a free course of chemoprophylaxis spanning the duration of the trial and up to 3 weeks following its completion. A stand-by nurse was available for the duration of the trial to assess any cases of fever or adverse reactions to test items. Any confirmed cases of malaria were treated free of charge at a local health facility. Animals used as baits in tunnel test were maintained following institutional standard operating procedures (SOPs) designed to improve care and protect animals used for experimentation. All studies were performed according to relevant national and international guidelines.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Trial 2.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. PermaNet 3.0 (Vestergaard Sarl).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. PermaNet 3.0 + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. PermaNet 3.0 + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 are WHO prequalified pyrethroid-PBO ITNs [3]. Olyset Plus is made of polyethylene filaments coated with 20 g/Kg of permethrin and 10 g/Kg of PBO. PermaNet 3.0 consists of polyester side panels coated with deltamethrin at 2.1 g/kg and a polyethylene roof panel incorporating deltamethrin and PBO at 4.0 g/kg and 25 g/kg respectively.\n",
-                        "\n",
-                        "header: Tunnel tests\n",
-                        "\n",
-                        "The tunnel test consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections using a netting frame fitted into a slot across the tunnel. In one of the sections, a guinea pig was housed unconstrained in a small cage, and in the other section, -100 unfed female mosquitoes aged 5-8 days were released at dusk and left overnight. The net samples were deliberately hoked with nine 1-cm holes to give opportunity for mosquitoes to penetrate the animal bathed chamber for a blood meal an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 oC and 65-85% RH. The next morning, the numbers of mosquitoes found alive or dead, fed or unfed, in each section were scored. Live mosquitoes were provided with 10% glucose solution and delayed mortality was recorded after 24 h. The pyrethroid-PBO ITNs were compared to Olyset Net (a permethrin-only net, Sumitomo chemical) and PermaNet 2.0 (a deltamethrin-only net, Vestergaard Sarl) and an untreated control net. Two to three net pieces were tested per net type.\n",
-                        "\n",
-                        "header: scientific reports: Abstract\n",
-                        "\n",
-                        "OPEN : Pyrethroid-piperonyl butoxide (PBO) nets reduce the efficacy of indoor residual spraying with pirimiphos-methyl against pyrethroid-resistant malaria vectors\n",
-                        "\n",
-                        "Thomas Syme, Mariai Ghegbo, Dorothy Obuobi, Augustin Fongnikin, Abel Agbevo, Damien Todjinou, & Corine Ngufori\n",
-                        "\n",
-                        "\n",
-                        "Primiphos-methyl is a pro-insecticide requiring activation by mosquito cytochrome P450 enzymes to induce toxicity while PBO blocks activation of these enzymes in pyrethroid-resistant vector mosquitoes. PBO may thus antagonise the toxicity of pirimiphos-methyl IRS when combined with pyrethroid-PBO ITMs. The impact of combining Olyset Plus and PermaNet 3.0 with Actellite 300CS IRS was evaluated against pyrethroid-resistant _Anopheles gambiae_ s.l. in two parallel experimental hut trials in southern Benin. The vector population was resistant to pyrethroids and PBO pre-exposure partially restored deltamethrin toxicity but not permitting. Mosquito mortality in experimental huts was significantly improved in the combinations of bendicocarp IRS with pyrethroid-PBO ITMs (33-3896) compared to bendicocarp IRS alone (14-16%, p < 0.001), demonstrating an additive effect. Conversely, mortality was significantly reduced in the combinations of pirimiphos-methyl IRS with pyrethroid-PBO ITMs (55-59%) compared to pirimiphos-methyl IRS alone (77-78%, p < 0.001), demonstrating evidence of an antagonistic effect when both interventions are applied in the same household. Mosquito mortality in the combination was significantly higher compared to the pyrethroid-PBO ITMs alone (55-59% vs. 22-26% p < 0.001) showing potential of pirimiphos-methyl IRS to enhance vector control when deployed to complement pyrethroid-PBO ITMs in an area where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: 3.2.2 Tumed test bioassays.\n",
-                        "\n",
-                        "To further assess the efficacy of the pyrethroid-PBO ITNs and help explain the findings in the experimental huts, tunnel tests were performed using the susceptible _An. gambiae_ Kisum strain and pyrethroid-resistant _An. gambiae_ as from Cove on net samples (30 x 30 cm) obtained from Olyset Plus and Permaket 3.0 nets. The tunnel test is an overnight animal baulesar what simulates host-seeking behaviour of vector mosquitoes under controlled laboratory conditions. Both pyrethroid-PBO ITNs were compared to pyrethroid-only nets which contained similar pyrethroid-insecticles (Olyset Net and Permaket 2.0). Mosquito mortality rates observed in the tunnels are presented in Fig. 3. Mortality in the control tunnels was 11% with the susceptible Kisum strain and 2% with the pyrethroid-resistant Cove strain. All ITN types induced >8% mortality with the susceptible Kisum strain. Olyset Plus killed significantly higher proportions of the Cove mosquitoes compared to Olyset Net (90% vs. 42%). With Permaket 3.0, mortality of Cove mosquitoes exposed to net prices obtained from the PBO treated root of the net (68%) was higher compared to net prices obtained from the sides of the net (34%) and Permaket 2.0 (27%). The results, therefore, showed higher levels of mortality against pyrethroid-resistant mosquitoes from the Cove experimental hut station with the pyrethroid-PBO ITNs relative to pyrethroid-only nets. More detailed results from the tunnel tests are available in the supplementary information (Table S1).\n",
-                        "\n",
-                        "header: Table 3: Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato exposed to pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination in experimental huts in Cové, southern Benin.\n",
-                        "footer: Results are presented separately for the trials involving Olgest Plus (Trial 1) and PermaNet 3.0 (Trial 2). ‘For each trial, values on this column sharing a superscript letter do not differ significantly, P > 0.05, logistic regression.\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total blood-fed', '% blood-feeding', '95% CIs', '% blood-feeding inhibition', '% personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total blood-fed\":\"Trial 1\",\"% blood-feeding\":\"Trial 1\",\"95% CIs\":\"Trial 1\",\"% blood-feeding inhibition\":\"Trial 1\",\"% personal protection\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total blood-fed\":\"481\",\"% blood-feeding\":\"72a\",\"95% CIs\":\"69\\u201376\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught\":\"511\",\"Total blood-fed\":\"122\",\"% blood-feeding\":\"24b\",\"95% CIs\":\"20\\u201328\",\"% blood-feeding inhibition\":\"67\",\"% personal protection\":\"75\"},\"3\":{\"Treatment\":\"Benedicarb IRS\",\"Total females caught\":\"556\",\"Total blood-fed\":\"528\",\"% blood-feeding\":\"95c\",\"95% CIs\":\"93\\u201397\",\"% blood-feeding inhibition\":\"\\u201331\",\"% personal protection\":\"\\u2013 10\"},\"4\":{\"Treatment\":\"Olyset Plus + bendicarb IRS\",\"Total females caught\":\"288\",\"Total blood-fed\":\"64\",\"% blood-feeding\":\"22b\",\"95% CIs\":\"17\\u201327\",\"% blood-feeding inhibition\":\"69\",\"% personal protection\":\"87\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total blood-fed\":\"420\",\"% blood-feeding\":\"79d\",\"95% CIs\":\"76\\u201383\",\"% blood-feeding inhibition\":\"\\u20139\",\"% personal protection\":\"13\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total blood-fed\":\"47\",\"% blood-feeding\":\"16e\",\"95% CIs\":\"11\\u201320\",\"% blood-feeding inhibition\":\"79\",\"% personal protection\":\"90\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total blood-fed\":\"Trial 2\",\"% blood-feeding\":\"Trial 2\",\"95% CIs\":\"Trial 2\",\"% blood-feeding inhibition\":\"Trial 2\",\"% personal protection\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total blood-fed\":\"391\",\"% blood-feeding\":\"67u\",\"95% CIs\":\"64\\u201371\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total blood-fed\":\"115\",\"% blood-feeding\":\"24v\",\"95% CIs\":\"20\\u201327\",\"% blood-feeding inhibition\":\"65\",\"% personal protection\":\"71\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total blood-fed\":\"519\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 33\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught\":\"233\",\"Total blood-fed\":\"54\",\"% blood-feeding\":\"23v\",\"95% CIs\":\"18\\u201329\",\"% blood-feeding inhibition\":\"66\",\"% personal protection\":\"86\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total blood-fed\":\"406\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 4\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"},\"14\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"}}\n",
-                        "\n",
-                        "header: Study site and experimental hut treatments.: Trial 1.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. Olyset Plus (Sumitomo Chemical).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. Olyset Plus + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. Olyset Plus + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Our study provides the first evidence of an antagonistic effect when pyrethroid-PBO ITNs are combined with pirimphos-methyl IRS in the same household resulting in lower levels of vector mosquito mortality compared to the IRS alone. In line with WHO recommendations, vector control programmes faced with multiple choice of ITN types to deploy as an additional intervention to improve vector control impact in an area dedicated to IRS with pirimphos-methyl, may consider other types of ITNs like pyrethroid-ITNs which can better complement pirimphos-methyl IRS when deployed together. Nevertheless, the pyrethroid-PBO ITNs performed poorly probably due to the lower levels of restoration of pyrethroid susceptibility with PBO in the vector population. Combining these nets with pirimphos-methyl IRS provided significantly improved vector control compared to the net alone demonstrating the potential for pirimphos-methyl IRS to enhance malaria control when deployed to complement pyrethroid-PBO ITNs in areas where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: Hut trial outcome measures\n",
-                        "\n",
-                        "The efficacy of the experimental hut treatments was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "1. Deterrence (%)--proportional reduction in the number of mosquitoes collected in the treated hut relative to the number collected in the control.\n",
-                        "2. Exophily (%)--exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda trap.\n",
-                        "3. Blood-feeding inhibition (%)--proportional reduction in blood-feeding in the treated hut relative to the control. Calculated as follows: \\[\\mathit{Blood\\ feeding\\ inhibition}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bfu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "4. Mortality (%)--proportion of dead mosquitoes 24 h after collection.\n",
-                        "5. Personal protection (%)--reduction in the number of blood-fed mosquitoes in the treated hut relative to the untreated net control. Calculated as follows: \\[\\mathit{Personal\\ protection}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "Adult F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hut station were exposed to filter papers treated with discriminating doses of deltamethrin (0.05%), permethrin (0.75%), benchicaro (0.1%) and pirimphos-methyl (0.25%) in WHO cylinders8. Deltamethrin and perme-thrin were also tested with 60 min pre-exposure to PBO (4%) to assess the involvement of metabolic enzymes in pyrethroid resistance. A comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain. Approximately 100, 3-5-day old mosquitoes of each strain were exposed to each insecticide for 60 min in four batches of 20-25. Similar numbers of mosquitoes were concurrently exposed to untreated filter papers as a control. At the end of exposure, mosquitoes were transferred to appropriately labelled holding tubes, provided access to 10% (w/v) glucose solution, and held at 27 +- 2degC and 75 +- 10% relative humidity. Knockdown was recorded 60 min after exposure and delayed mortality after 24 h for all treatments. The insecticide-treated filter papers were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Table 4.Mortality results of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs, and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, p>0.05, logistic regression\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total dead', '% mortality*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total dead\":\"Trial 1\",\"% mortality*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total dead\":\"22\",\"% mortality*\":\"3a\",\"95% CIs\":\"2\\u20135\"},\"2\":{\"Treatment\":\"Olyset Plus\",\"Total females caught\":\"511\",\"Total dead\":\"114\",\"% mortality*\":\"22b\",\"95% CIs\":\"19\\u201326\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"556\",\"Total dead\":\"87\",\"% mortality*\":\"16c\",\"95% CIs\":\"13\\u201319\"},\"4\":{\"Treatment\":\"Olyset Plus + Bendiocarb IRS\",\"Total females caught\":\"288\",\"Total dead\":\"95\",\"% mortality*\":\"33d\",\"95% CIs\":\"28\\u201338\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total dead\":\"411\",\"% mortality*\":\"77e\",\"95% CIs\":\"74\\u201381\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total dead\":\"178\",\"% mortality*\":\"59f\",\"95% CIs\":\"53\\u201364\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total dead\":\"Trial 2\",\"% mortality*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total dead\":\"12\",\"% mortality*\":\"2u\",\"95% CIs\":\"1\\u20133\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total dead\":\"127\",\"% mortality*\":\"26v\",\"95% CIs\":\"22\\u201330\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total dead\":\"80\",\"% mortality*\":\"14w\",\"95% CIs\":\"11\\u201317\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + Bendiocarb IRS\",\"Total females caught\":\"233\",\"Total dead\":\"89\",\"% mortality*\":\"38x\",\"95% CIs\":\"32\\u201344\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total dead\":\"350\",\"% mortality*\":\"78y\",\"95% CIs\":\"74\\u201382\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total dead\":\"122\",\"% mortality*\":\"55z\",\"95% CIs\":\"48\\u201361\"}}\n",
-                        "\n",
-                        "header: Table 2. Entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, P>0.05, negative binomial regression for females caught and logistic regression for exophily.\n",
-                        "columns: ['Treatment', 'Total females caught*', '% deterrence', 'Total exiting', '% exophily*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught*\":\"Trial 1\",\"% deterrence\":\"Trial 1\",\"Total exiting\":\"Trial 1\",\"% exophily*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"664a\",\"% deterrence\":\"-\",\"Total exiting\":\"241\",\"% exophily*\":\"36a\",\"95% CIs\":\"33\\u201340\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught*\":\"511a\",\"% deterrence\":\"23\",\"Total exiting\":\"390\",\"% exophily*\":\"76b\",\"95% CIs\":\"73\\u201380\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"556a\",\"% deterrence\":\"16\",\"Total exiting\":\"282\",\"% exophily*\":\"51c\",\"95% CIs\":\"47\\u201355\"},\"4\":{\"Treatment\":\"Olyset Plus + bendiocarb IRS\",\"Total females caught*\":\"288b\",\"% deterrence\":\"57\",\"Total exiting\":\"228\",\"% exophily*\":\"79b\",\"95% CIs\":\"75\\u201384\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"531a\",\"% deterrence\":\"20\",\"Total exiting\":\"281\",\"% exophily*\":\"53c\",\"95% CIs\":\"49\\u201357\"},\"6\":{\"Treatment\":\"Olyset plus + P-methyl IRS\",\"Total females caught*\":\"304b\",\"% deterrence\":\"54\",\"Total exiting\":\"270\",\"% exophily*\":\"89d\",\"95% CIs\":\"85\\u201392\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught*\":\"Trial 2\",\"% deterrence\":\"Trial 2\",\"Total exiting\":\"Trial 2\",\"% exophily*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"581u\",\"% deterrence\":\"-\",\"Total exiting\":\"226\",\"% exophily*\":\"39u\",\"95% CIs\":\"35\\u201343\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught*\":\"488u\",\"% deterrence\":\"16\",\"Total exiting\":\"369\",\"% exophily*\":\"76v\",\"95% CIs\":\"72\\u201379\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"575u\",\"% deterrence\":\"1\",\"Total exiting\":\"360\",\"% exophily*\":\"63w\",\"95% CIs\":\"59\\u201367\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught*\":\"233v\",\"% deterrence\":\"60\",\"Total exiting\":\"185\",\"% exophily*\":\"79v\",\"95% CIs\":\"74\\u201385\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"450u\",\"% deterrence\":\"23\",\"Total exiting\":\"311\",\"% exophily*\":\"69x\",\"95% CIs\":\"65\\u201373\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught*\":\"223v\",\"% deterrence\":\"62\",\"Total exiting\":\"195\",\"% exophily*\":\"87y\",\"95% CIs\":\"83\\u201392\"}}\n",
-                        "\n",
-                        "header: Data analysis.\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental but treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to the differential attractiveness of the volunteer sleepers and huts. Results from the different experimental hut trials involving Olyset Plus and PermaNet 3.0 were analysed separately. Susceptibility bioassay results were interpreted according to WHO criteria49. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Experimental hut results.\n",
-                        "\n",
-                        "_Mosquito entry and exiting in experimental huts._\n",
-                        "\n",
-                        "A total of 5,404 wild female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trials (Table 2). In both trials, mosquito entry in huts with the pyrethroid-PBO ITN alone and IRS treatments alone did not differ significantly from the controls (p > 0.05) but was significantly reduced with the pyrethroid-PBO ITN plus IRS combinations compared to the single treatments (p < 0.01). Mosquito entry rates did not also differ between the combinations of the pyrethroid-PBO ITN with bendiocarb IRS relative to the combinations with pirimiphos-methyl IRS (p < 0.05). Nevertheless, IRS treatments could not be rotated and thus, treatment-induced deterrence cannot be fully distinguished from differential attractiveness due to hut position.\n",
-                        "\n",
-                        "The proportion of mosquitoes exiting into the veranda of the huts with the untreated net controls was 36% and 39% for Trials 1 and 2 respectively. Exiting rates were higher with the pyrethroid-PBO ITNs alone (76%) relative to the IRS insecticides alone (51-63% with bendiocarb and 53-69% with pirimiphos-methyl IRS, p < 0.005). In both trials, the highest levels of mosquito exiting were achieved with the pyrethroid-PBO ITN plus IRS combinations (79% with bendiocarb and 87-89% with pirimiphos-methyl IRS). Between the combinations, adding\n",
-                        "primiphos-methyl IRS to the pyrethroid-PBO ITN provided significantly higher levels of mosquito exiting relative to adding bendicord IRS (87-89% vs. 79%, P < 0.05).\n",
-                        "\n",
-                        "Blood-feeding rates of wild malaria vector mosquitoes in experimental huts.Blood-feeding rates in huts with the untreated net controls were 72% and 67% for Trials 1 and 2 respectively (Table 3). The IRS treatments did not provide any blood-feeding inhibition relative to the controls. Blood-feeding inhibition rates were high with the pyrethroid-PBO ITN plus IRS combinations in both trials (69-79% with Olyst Plus and 63-66% with PermaNet 3.0) and this was generally similar to what was observed with the pyrethroid-PBO ITNs alone (67% with Olyst Plus and 65% with PermaNet, P > 0.05) showing that the high levels of blood-feeding inhibition in the combinations, was mostly due to the ITNs. Blood-feeding inhibition with the PermaNet 3.0 plus primiphos-methyl IRS combination (66%) was similar to the PermaNet 3.0 plus bendicord IRS combination (63%, P = 0.71) meanwhile the Olyset Plus and primiphos-methyl IRS combination induced higher levels of blood-feeding inhibition compared to the Olyst Plus and bendicord IRS combination (79% vs. 69%, P = 0.036) (Table 3). Personal protection levels showed a similar trend and were also higher with the combinations (86-90%) relative to the IRS alone (- 33-13%).\n",
-                        "\n",
-                        "Mortality rates of wild malaria vector mosquitoes in experimental huts.\n",
-                        "\n",
-                        "Wild vector mosquito mortality in the controls was 3% in Trial 1 and 2% in Trial 2 (Table 4). The pyrethroid-PBO ITNs killed relatively low mosquito proportions (22% with Olgest Plus and 26% with PermaNet 3.0) though this was higher than what was observed with benedicarb IRS alone (14% in Trial 1 and 16% in Trial 2, P < 0.10). The highest mortality was achieved with pirimphos-methyl IRS alone (77% in Trial 1 and 78% in Trial 2). In both trials, mortality in the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations was significantly reduced compared to pirimphos-methyl IRS alone (77% with pirimphos-methyl IRS vs. 59% with Olgest Plus pirimphos-methyl IRS, P < 0.001 and 78% with pirimphos-methyl IRS vs. 55% with PermaNet 3.0 plus pirimphos-methyl IRS, P < 0.001), demonstrating an antagonistic effect. Conversely, mortality was significantly higher in the combinations of benedicarb IRS with Olgest Plus (33%) and PermaNet 3.0 (38%) than benedicarb IRS alone (14-16%, P < 0.001), demonstrating an additive effect (Table 4). Nevertheless, both pyrethroid-PBO plus IRS combinations induced significantly higher\n",
-                        "\n",
-                        "mortality compared to the pyrethroid-PBO ITNs alone (P < 0.001). Between the combinations, mortality was consistently higher with the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations (55-59%) compared to the pyrethroid-PBO ITN plus pedicocarb IRS (33-39%, P < 0.001).\n",
-                        "\n",
-                        "The monthly mortality rates of wild vector mosquitoes which entered the experimental huts during the trials are presented in Fig. 1 for the combinations with pedicocarb IRS and Fig. 2 for the combinations with pirimiphos-methyl IRS. In both trials, mosquito mortality rates in huts with the pyrethroid-PBO ITNs plus pedicocarb IRS declined sharply over time from 65-75% in month 1 to 33-38% in month 3, nevertheless, it was consistently higher than the single treatments alone (Fig. 1). Mosquito mortality in the combination of Olyset Plus and pirimiphos-methyl IRS was similar to the IRS alone in month 1 (> 90%) but declined substantially relative to the IRS in subsequent months. With the PermaNet 3.0 plus pirimiphos-methyl IRS combination,\n",
-                        "\n",
-                        "Figure 1.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pedicocarb IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Oyset Plus (Trial 1) and panel (**b**) presents results from the trial with PermaNet 3.0 (Trial 2). Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from onset of the trial. Mosquito mortality in the control untreated huts did not exceed 5% at any time point.\n",
-                        "\n",
-                        "Figure 2.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pirimiphos-methyl IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Olyset Plus and panel (**b**) presents results from the trial with PermaNet 3.0. Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from the onset of the trial.\n",
-                        "\n",
-                        "\n",
-                        "mosquito mortality was consistently lower than the IRS alone throughout the trial. Through the four months of the trials, the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations consistently induced higher levels of mosquito mortality relative to the pyrethroid-PBO ITNs alone.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "IRS Indoor residual spraying\n",
-                        "\n",
-                        "WHO World Health Organization\n",
-                        "\n",
-                        "PQ Prenqualification team\n",
-                        "\n",
-                        "PBO Piperonyl butoxide\n",
-                        "\n",
-                        "ITN Insecticide treated net\n",
-                        "\n",
-                        "LLIN Long-lasting insecticidal net\n",
-                        "\n",
-                        "GPIRM Global plan for insecticide resistance management\n",
-                        "\n",
-                        "WHOPES WHO pesticide evaluation scheme\n",
-                        "\n",
-                        "CREC Centre de Recherche Entomologique de Cotonou\n",
-                        "\n",
-                        "LSHTTM London School of Hygiene & Tropical Medicine\n",
-                        "\n",
-                        "Kdr Knockdown resistance\n",
-                        "\n",
-                        "PAMVERC Pan African Malaria Vector Research Consortium\n",
-                        "\n",
-                        "The large-scale implementation of long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS) has resulted in profound reductions in malaria-associated morbidity and mortality across sub-Saharan Africa, over the last two decades1. Unfortunately, resistance to the insecticides applied through these interventions, especially the pyrethroids, is now pervasive in vector populations in malaria-endemic countries', threatening to undermine their impact. In response, a new generation of novel LLINs and IRS based on new active ingredients with the potential to sustain vector control impact in the face of increasing resistance, have been developed3. This includes dual insecticide-treated nets containing a pyrethroid and an alternative effective new compound as well as new IRS insecticides with novel modes of action or improved formulations that have shown potential to provide enhanced control of insecticide-resistant malaria vector populations.\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) combining a pyrethroid and piperonyl butoxido (PBO) were the first novel class of ITNs to be developed for malaria vector control. PBO is a synergism that can enhance the impact of pyrethroids and other insecticides by inhibiting metabolic detoxification enzymes associated with resistance, notably cytochrome P450 monooxygenases3. These nets received a conditional endorsement from the World Health Organisation (WHO) in 2017 based on results from a cluster-randomised controlled trial (CRT) in Tanzania demonstrating a reduction in malaria prevalence in communities allocated to pyrethroid-PBO ITNs relative to pyrethroid-only ITNs4. A full policy recommendation is now expected after results from a second CRT in Uganda also showed that two brands of pyrethroid-PBO ITNs reduced malaria prevalence relative to pyrethroid-only nets4. The WHO endorsement and expanding evidence base for the public health value of pyrethroid-PBO ITNs has prompted mass procurement of these nets by international malaria control agencies4,5. The proportion of pyrethroid-PBO ITNs of all ITNs delivered in sub-Saharan Africa has consequently risen from 3% in 2018 to 35% in 202112; pyrethroid-PBO ITNs are therefore replacing pyrethroid-only nets in many malaria-endemic countries.\n",
-                        "\n",
-                        "The increasing distribution and intensity of pyrethroid resistance has also affected malaria control policy regarding insecticide choice for IRS over the last decade. To improve vector control impact and preserve pyrethroids for ITNs, African IRS programmes partly suspended the use of pyrethroids and organochlorines in favour of carbamates and organophosphates5,12. Whilst both insecticides showed high toxicity against malaria vectors, the short residual duration of the initial formulations approved for IRS proved prohibitive, necessitating the development of longer-lasting formulations13. A new microencapsulated formulation of pirimphos-methyl was later developed (Actellic 300CS) demonstrating prolonged activity against pyrethroid-resistant malaria vector mosquitoes, lasting up to 9 months14,15. This formulation subsequently served as the insecticide of choice for the majority of IRS programmes in sub-Saharan Africa1 providing substantial control of mosquito vectors and malaria across distinct eco-epidemiological settings6,16-22.\n",
-                        "\n",
-                        "[MISSING_PAGE_POST]\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Given the inhibitory effect of PBO on mosquito cytochrome P450 enzymes, the WHO had temporarily recommended against the use of pyrethroid-PBO ITN in areas programmed for IRS with pirimiphos-methyl IRS until further evidence on the potential antagonism between PBO and the organo-thiophosphate pro-insecticide becomes available20. In this study, we found evidence of an antagonistic effect when pyrethroid PBO ITNs were combined with pirimiphos-methyl IRS in the same household in a pyrethroid-resistant area in Southern Benin where resistance was partly conferred by over-expression of mosquito cytochrome P450 enzymes20. Unlike the combination of the pyrethroid-PBO nets with bendicorab IRS which provided improved vector mosquito mortality compared to bendicorab IRS alone, combining these nets with pirimiphos-methyl IRS induced significantly lower levels of vector mosquito mortality rates compared to pirimiphos-methyl IRS alone. This effect\n",
-                        "\n",
-                        "Figure 3.: Mortality (24 h) of susceptible _Anopheles gambiae_ Kisumu and pyrethroid-resistant _An. gambiae_ s.l. Cove strains exposed to Olyset Plus and Permaket 3.0 in tunnel tests. Error bars represent 95% CIs. Both pyrethroid-PBO nets were compared to pyrethroid-only nets (Olyset Net and Permaket 2.0). Permaket 3.0 contains PBO only on the roof of the net. With both strains, 80–120 mosquitoes were exposed overnight to metting pieces cut from whole nets in 2–3 replicate tunnel tests.\n",
-                        "\n",
-                        "\n",
-                        "was remarkably similar between the two brands of pyrethroid-PBO ITNs tested across both hut trials indicating that the negative interaction of the combination on mosquito mortality was less affected by the design and specifications of the pyrethroid-PBO ITN. Wild vector mosquitoes which entered the huts with the combined treatment may have contacted the pirimphos-methyl IRS on the hut wall only after picking up PBO from the ITN while attempting to blood-feed on the sheeper under the net. This pre-exposure to PBO on the ITN could have prevented the metabolic activation of the IRS insecticide in these mosquitoes, resulting in lower mortality rates relative to the IRS alone. This finding supports WHO's recommendation against the deployment of pyrethroid-PBO ITNs in areas that have already been programmed for IRS with pirimphos-methyl IRS. This is mostly important from a programmatic perspective where a vector control programme is faced with multiple choice of ITN types to deploy as an additional intervention to enhance vector control impact in an area that is already dedicated to IRS with pirimphos-methyl. In such a scenario, other types of ITNs like pyrethroid-only nets that were recently shown to complement pirimphos-methyl IRS when applied together23, should be considered.\n",
-                        "\n",
-                        "These findings should however not be interpreted to mean that pirimphos-methyl IRS must not be deployed to complement pyrethroid-PBO ITNs. Though pyrethroid-PBO ITNs have consistently shown improved performance in experimental hut trials against pyrethroid-resistant malaria vectors compared to pyrethroid-only nets34, the margin appears to vary depending on the intensity and mechanisms of pyrethroid-resistance encountered. In our study, the pyrethroid-PBO ITNs when applied alone in a hut induced low vector mosquito mortality (22-26%) compared to what has been observed in hut trials against the same vector population with another type of novel dual ITN (71-76%)35,40. Low levels of mosquito mortality in huts with pyrethroid-PBO ITNs and failure of PBO to fully restore pyrethroid susceptibility in bioassays have also been reported from studies in Burkina Faso37, Cameroon38, Cote d'Ivoire39 and Senegal40. This may indicate the presence of complex resistance mechanisms in the West African region unaffected by PBO. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors which continues to increase over time41, yet the public health value of pyrethroid-PBO ITNs has not been assessed in the region. It is therefore unclear whether these nets will provide the same improved epidemiological impact over pyrethroid-only nets in West Africa as observed in the East African community trials which have been the basis of their endorsement for malaria control. Our results showed a significant improvement in mosquito mortality when pirimphos-methyl IRS was combined with the pyrethroid-PBO ITNs (55-59%) compared to the pyrethroid-PBO ITNs alone (22-26%). Mosquito entry and feeding rates were also significantly lower with the combination compared to the pyrethroid-PBO ITNs alone. This provides some justification for deploying pirimphos-methyl IRS to complement pyrethroid-PBO nets in an area of high and complex pyrethroid-resistance where local vectors are less susceptible to the synergistic effect of PBO. Due to the limited choice of insecticides available for IRS, where a beneficial effect of the combination compared to the pyrethroid-PBO ITN alone has been established, pirimphos-methyl IRS should continue to be used even in the presence of high coverage with pyrethroid-PBO ITNs, preferably as part of an IRS rotational strategy. In line with WHO recommendations for insecticide resistance management42, the sustained rotation of multiple insecticide modes of action for IRS may help preserve efficacy to insecticides approved for IRS.\n",
-                        "\n",
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-                        "Footnote 34: https://doi.org/10.1038/s41598-022-10953-y\n",
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-                        "Footnote 38: https://doi.org/10.1038/s41598-022-10953-y\n",
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-                        "Footnote 39: https://doi.org/10.1038/s41598-022-10953-y\n",
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-                        "Footnote 40: https://doi.org/10.1038/s41598-022-10953-y\n",
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-                        "\n",
-                        "\n",
-                        "following pre-exposure to PBO, restoration of susceptibility to permethrin--the pyrethroid insecticide used on the pyrethroid-PBO ITN tested in the Tanzanian trial (Olvest Plus)--was high in the vector population from the study area (from 18 to 94%)15. Hence, the level of control achieved with pyrethroid-PBO ITN alone in the Tanzanian trial may have been optimal making the addition of pirimphos-methyl IRS unnecessary. This may not be the case in many communities in West Africa considering the afore-mentioned low levels of pyrethroid-PBO synergism reported in susceptibility bioassays and hut trials conducted across the region; in contrast, the addition of pirimphos-methyl IRS to pyrethroid-PBO ITNs in these communities could be more beneficial for control of clinical malaria compared to the ITN alone. As the range of vector control products available to vector control programmes expands, the choice of interventions and product brands must be aligned to local contexts and guided by local evidence. To help guide vector control policy, epidemiological trials and/or other empirical studies investigating the impact and cost-effectiveness of pyrethroid-PBO nets in communities in the West African region as well as their combination with IRS using pro-insectiles like pirimphos-methyl will be necessary.\n",
-                        "\n",
-                        "Although the inhibitory effect of PBO on mosquito cytochrome P450 enzymes formed the basis of our hypothesis for the antagonism observed between pirimphos-methyl IRS and pyrethroid-PBO ITNs in the experimental huts, behavioural interactions could also have contributed. In both trials, mosquito exiting rates into the veranda traps and blood-feeding inhibition were consistently higher in the combinations compared to the IRS insecticides alone. This could be attributed to the excitro-replenent property of the pyrethroid in the ITN stimulating directed movement of mosquitoes away from their source44. This early exiting effect was however significantly higher in the combination with pirimphos-methyl IRS compared to the combination with bendo-carb IRS suggesting a behavioural interaction in the presence of pirimphos-methyl that may have driven more mosquitoes to exit into the veranda, thus reducing mosquitoes' contact with pirimphos-methyl IRS treated walls. This may have compromised the impact of the pirimphos-methyl IRS in the combination compared to when applied alone and to the combination with bendoicarb. Further studies to assess mosquito flight behaviour, in the presence of the combinations would provide useful insight into behavioural interactions between the treatments. Controlled laboratory assays which minimise behavioural responses and assess P450 enzyme activity in exposed mosquitoes, could also elucidate the potential role of P450 enzyme inhibition in the antagonism observed in the experimental huts.\n",
-                        "\n",
-                        "While our trial focused on investigating the impact of combining pyrethroid-PBO ITNs with pirimphos-methyl IRS in the same households, there is a paucity of information on the interactions between these nets and other newly developed vector control products which contain new public health insecticides such as the neonciotohal, clothidinium and the pyrrole chlorfenapxy. Chlorfenapxy, an insecticide used on ITNs13 and being considered for IRS6,46, also requires activation by mosquito P450 enzymes6. Laboratory experiments have indicated the potential of PBO to antagonise the toxicity of chlorfenapxy against mosquitoes in bioassays29,30. Studies investigating the impact of co-deploying pyrethroid-PBO ITNs together with pyrethroid-chlorfenapxy ITNs or chlorfenapxy IRS in the same household will be essential to help inform optimal co-deployment policy.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Experimental huts are used to assess the capacity of indoor vector control interventions to prevent wild vector mosquito entry and feeding and induce early mosquito exiting and mortality when applied in a human-occupied house, under carefully controlled conditions [3,12]. The hut trials were conducted at the CREC/LSHTM experimental hut station in Cowe, southern Benin (7deg14'N22deg18E), situated in a vast area of rice irrigation, which provides extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzini_ and _An. gambiae_ sensu stricto (s.s.) occur in symparity, with the latter present at lower densities and predominantly in the dry season. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold). Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (> 90%) and overexpression of CYP6P3, an enzyme associated with pyrethroid detoxification [3].\n",
-                        "\n",
-                        "Two experimental hut trials were performed in parallel for 4 months between April and July 2020. Trial 1 assessed the impact of combining Olyset Plus (Sumitomo Chemical), a permethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS while Trial 2 assessed the impact of combining PermaNet 3.0 (Vestergaard Sarl), a deltamethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS.\n",
-                        "\n",
-                        "The following six treatments were tested in each of the experimental hut trials:\n",
-                        "\n",
-                        "header: Susceptibility of wild vector mosquitoes at Cove to insecticides.\n",
-                        "\n",
-                        "WHO susceptibility bioassays were conducted in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove hit site to the constituent insecticides of the experimental hut treatments. Mortality rates of F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hit station following exposure to discriminating doses of deltamethrin and permethrin in WHO cylinder bioassays were low (42% and 11% respectively), confirming a high frequency of pyrethroid resistance in the Cove vector population (Table 1). Pre-exposure to PBO significantly improved mortality with deltamethrin (42% vs. 72%) but not with permethrin (11% vs. 8%). Mortality rates with the discriminating doses of bendiocarb and pirimiphos-methyl were 98% and 99% respectively. This demonstrated susceptibility to pirimiphos-methyl and bendiocarb. All insecticides induced 100% mortality with the laboratory-maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu strain. No mortality was recorded in the control with either strain.\n",
-                        "\n",
-                        "header: 3.2.3 Wall cone bioassays.\n",
-                        "\n",
-                        "WHO wall cone bioassays were performed on the IRS treated experimental hut walls 1 week, 2 months and 4 months after application of IRS treatments to assess their residual efficacy with increasing time elapsed from spraying. Mortality rates of the insecticide-susceptible _An. gambiae_ s.s. Kisum strain and pyrethroid-resistant _An. gambiae_ s.l. Cove strain following exposure to IRS-treated surfaces in 30 min wall cone bioassays are presented in Fig. 4. Mortality of mosquitoes exposed to pirimiphos-methyl treated walls was high (>90%) at all time points with both strains, showing no evidence of a decline in residual activity. In contrast, wall cone bioassay mortality on bendicor-treated surfaces declined rapidly, beginning at 67% and 39% in week 1 and falling to lows of 2% and 3% in month 4 with the Kisum and Cove strains respectively. No mortality was recorded in the controls at any time point with either strain.\n",
-                        "\n",
-                        "header: Experimental hut trial procedure\n",
-                        "\n",
-                        "During the hut trials, volunteer sleepers were rotated between experimental huts daily to mitigate the impact of individual attractiveness whilst bed nets were rotated weekly between the huts to reduce the impact of hut position on mosquito entry. Three (3) replicate bed nets were used per treatment and rotated within the treatment every 2 days. IRS treatments cannot be rotated and thus remained fixed throughout the trial. Consenting human volunteer sleepers alert in experimental huts between 21:00 and 06:00 to attract free-flying mosquitoes. Each morning, volunteer sleepers collected all live and dead mosquitoes from the different compartments of the hut (under the net, room, veranda) using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were then transferred to the field laboratory for morphological identification using taxonomic keys and scoring of immediate mortality and blood-feeding. All live, female _An. gambiae_ s.l. were provided access to 10% glucose (w/v) solution and held at ambient conditions in the field laboratory. Delayed mortality was recorded after 24 h for all treatments. Mosquito collections were performed 6 nights per week and on the 7th day, huts were cleaned and aired in preparation for the next rotation cycle.\n",
-                        "\n",
-                        "header: Wall cone bioassays\n",
-                        "\n",
-                        "The laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain and wild pyrethroid-resistant _An. gambiae_ s.l. Cove mosquitoes (F1) derived from breeding sites at the hut station were used for this purpose. At each time point, five cones were attached to the walls and ceiling of the IRS-treated huts. Approximately 50, 3-5-day old mosquitoes were transferred into cones in 5 batches of 10 and exposed to the treated surfaces for 30 min. As a control, mosquitoes were exposed in cones attached to the walls and ceiling of an unsprayed hut. At the end of exposure, mosquitoes were transferred to netted, plastic cups. Mosquitoes were provided access to 10% (w/v) glucose solution and delayed mortality after 24 h for all treatments.\n",
-                        "\n",
-                        "\n",
-                        "Ethical considerations.Ethical approval for the conduct of the study was obtained from the ethics review boards of the Beninese Ministry of Health (No. 34) and the London School of Hygiene & Tropical Medicine (Ref: 16969). All human volunteer sleepers gave informed written consent prior to their participation; where necessary, the consent form and information sheet were explained in their local language. They were offered a free course of chemoprophylaxis spanning the duration of the trial and up to 3 weeks following its completion. A stand-by nurse was available for the duration of the trial to assess any cases of fever or adverse reactions to test items. Any confirmed cases of malaria were treated free of charge at a local health facility. Animals used as baits in tunnel test were maintained following institutional standard operating procedures (SOPs) designed to improve care and protect animals used for experimentation. All studies were performed according to relevant national and international guidelines.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cov\\u00e9\",\"Start_month\":4,\"Start_year\":2020,\"End_month\":7,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated net\",\"Insecticide\":null,\"Concentration_initial\":null,\"Net_washed\":null,\"Net_holed\":null,\"pHI_category\":null},\"N02\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"Deltamethrin, PBO\",\"Concentration_initial\":\"2.1 g\\/kg, 4.0 g\\/kg, 25 g\\/kg\",\"Net_washed\":0.0,\"Net_holed\":9.0,\"pHI_category\":\"Good\"},\"N03\":{\"Net_type\":\"Olyset Plus\",\"Insecticide\":\"Permethrin, PBO\",\"Concentration_initial\":\"20 g\\/kg, 10 g\\/kg\",\"Net_washed\":0.0,\"Net_holed\":9.0,\"pHI_category\":\"Good\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Trial 2.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. PermaNet 3.0 (Vestergaard Sarl).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. PermaNet 3.0 + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. PermaNet 3.0 + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 are WHO prequalified pyrethroid-PBO ITNs [3]. Olyset Plus is made of polyethylene filaments coated with 20 g/Kg of permethrin and 10 g/Kg of PBO. PermaNet 3.0 consists of polyester side panels coated with deltamethrin at 2.1 g/kg and a polyethylene roof panel incorporating deltamethrin and PBO at 4.0 g/kg and 25 g/kg respectively.\n",
-                        "\n",
-                        "header: Tunnel tests\n",
-                        "\n",
-                        "The tunnel test consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections using a netting frame fitted into a slot across the tunnel. In one of the sections, a guinea pig was housed unconstrained in a small cage, and in the other section, -100 unfed female mosquitoes aged 5-8 days were released at dusk and left overnight. The net samples were deliberately hoked with nine 1-cm holes to give opportunity for mosquitoes to penetrate the animal bathed chamber for a blood meal an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 oC and 65-85% RH. The next morning, the numbers of mosquitoes found alive or dead, fed or unfed, in each section were scored. Live mosquitoes were provided with 10% glucose solution and delayed mortality was recorded after 24 h. The pyrethroid-PBO ITNs were compared to Olyset Net (a permethrin-only net, Sumitomo chemical) and PermaNet 2.0 (a deltamethrin-only net, Vestergaard Sarl) and an untreated control net. Two to three net pieces were tested per net type.\n",
-                        "\n",
-                        "header: scientific reports: Abstract\n",
-                        "\n",
-                        "OPEN : Pyrethroid-piperonyl butoxide (PBO) nets reduce the efficacy of indoor residual spraying with pirimiphos-methyl against pyrethroid-resistant malaria vectors\n",
-                        "\n",
-                        "Thomas Syme, Mariai Ghegbo, Dorothy Obuobi, Augustin Fongnikin, Abel Agbevo, Damien Todjinou, & Corine Ngufori\n",
-                        "\n",
-                        "\n",
-                        "Primiphos-methyl is a pro-insecticide requiring activation by mosquito cytochrome P450 enzymes to induce toxicity while PBO blocks activation of these enzymes in pyrethroid-resistant vector mosquitoes. PBO may thus antagonise the toxicity of pirimiphos-methyl IRS when combined with pyrethroid-PBO ITMs. The impact of combining Olyset Plus and PermaNet 3.0 with Actellite 300CS IRS was evaluated against pyrethroid-resistant _Anopheles gambiae_ s.l. in two parallel experimental hut trials in southern Benin. The vector population was resistant to pyrethroids and PBO pre-exposure partially restored deltamethrin toxicity but not permitting. Mosquito mortality in experimental huts was significantly improved in the combinations of bendicocarp IRS with pyrethroid-PBO ITMs (33-3896) compared to bendicocarp IRS alone (14-16%, p < 0.001), demonstrating an additive effect. Conversely, mortality was significantly reduced in the combinations of pirimiphos-methyl IRS with pyrethroid-PBO ITMs (55-59%) compared to pirimiphos-methyl IRS alone (77-78%, p < 0.001), demonstrating evidence of an antagonistic effect when both interventions are applied in the same household. Mosquito mortality in the combination was significantly higher compared to the pyrethroid-PBO ITMs alone (55-59% vs. 22-26% p < 0.001) showing potential of pirimiphos-methyl IRS to enhance vector control when deployed to complement pyrethroid-PBO ITMs in an area where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: 3.2.2 Tumed test bioassays.\n",
-                        "\n",
-                        "To further assess the efficacy of the pyrethroid-PBO ITNs and help explain the findings in the experimental huts, tunnel tests were performed using the susceptible _An. gambiae_ Kisum strain and pyrethroid-resistant _An. gambiae_ as from Cove on net samples (30 x 30 cm) obtained from Olyset Plus and Permaket 3.0 nets. The tunnel test is an overnight animal baulesar what simulates host-seeking behaviour of vector mosquitoes under controlled laboratory conditions. Both pyrethroid-PBO ITNs were compared to pyrethroid-only nets which contained similar pyrethroid-insecticles (Olyset Net and Permaket 2.0). Mosquito mortality rates observed in the tunnels are presented in Fig. 3. Mortality in the control tunnels was 11% with the susceptible Kisum strain and 2% with the pyrethroid-resistant Cove strain. All ITN types induced >8% mortality with the susceptible Kisum strain. Olyset Plus killed significantly higher proportions of the Cove mosquitoes compared to Olyset Net (90% vs. 42%). With Permaket 3.0, mortality of Cove mosquitoes exposed to net prices obtained from the PBO treated root of the net (68%) was higher compared to net prices obtained from the sides of the net (34%) and Permaket 2.0 (27%). The results, therefore, showed higher levels of mortality against pyrethroid-resistant mosquitoes from the Cove experimental hut station with the pyrethroid-PBO ITNs relative to pyrethroid-only nets. More detailed results from the tunnel tests are available in the supplementary information (Table S1).\n",
-                        "\n",
-                        "header: Study site and experimental hut treatments.: Trial 1.\n",
-                        "\n",
-                        "1. Untreated net.\n",
-                        "2. Olyset Plus (Sumitomo Chemical).\n",
-                        "3. Bendiocarb IRS applied at 400 mg/m2 (Ficam 80WP, Bayer).\n",
-                        "4. Olyset Plus + Bendiocarb IRS applied at 400 mg/m2.\n",
-                        "5. Pirimiphos-methyl IRS applied at 1000 mg/m2 (Actellic 300CS, Syngenta).\n",
-                        "6. Olyset Plus + Pirimiphos-methyl IRS applied at 1000 mg/m2.\n",
-                        "\n",
-                        "header: Table 3: Blood-feeding results of wild, pyrethroid-resistant Anopheles gambiae sensu lato exposed to pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination in experimental huts in Cové, southern Benin.\n",
-                        "footer: Results are presented separately for the trials involving Olgest Plus (Trial 1) and PermaNet 3.0 (Trial 2). ‘For each trial, values on this column sharing a superscript letter do not differ significantly, P > 0.05, logistic regression.\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total blood-fed', '% blood-feeding', '95% CIs', '% blood-feeding inhibition', '% personal protection']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total blood-fed\":\"Trial 1\",\"% blood-feeding\":\"Trial 1\",\"95% CIs\":\"Trial 1\",\"% blood-feeding inhibition\":\"Trial 1\",\"% personal protection\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total blood-fed\":\"481\",\"% blood-feeding\":\"72a\",\"95% CIs\":\"69\\u201376\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught\":\"511\",\"Total blood-fed\":\"122\",\"% blood-feeding\":\"24b\",\"95% CIs\":\"20\\u201328\",\"% blood-feeding inhibition\":\"67\",\"% personal protection\":\"75\"},\"3\":{\"Treatment\":\"Benedicarb IRS\",\"Total females caught\":\"556\",\"Total blood-fed\":\"528\",\"% blood-feeding\":\"95c\",\"95% CIs\":\"93\\u201397\",\"% blood-feeding inhibition\":\"\\u201331\",\"% personal protection\":\"\\u2013 10\"},\"4\":{\"Treatment\":\"Olyset Plus + bendicarb IRS\",\"Total females caught\":\"288\",\"Total blood-fed\":\"64\",\"% blood-feeding\":\"22b\",\"95% CIs\":\"17\\u201327\",\"% blood-feeding inhibition\":\"69\",\"% personal protection\":\"87\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total blood-fed\":\"420\",\"% blood-feeding\":\"79d\",\"95% CIs\":\"76\\u201383\",\"% blood-feeding inhibition\":\"\\u20139\",\"% personal protection\":\"13\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total blood-fed\":\"47\",\"% blood-feeding\":\"16e\",\"95% CIs\":\"11\\u201320\",\"% blood-feeding inhibition\":\"79\",\"% personal protection\":\"90\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total blood-fed\":\"Trial 2\",\"% blood-feeding\":\"Trial 2\",\"95% CIs\":\"Trial 2\",\"% blood-feeding inhibition\":\"Trial 2\",\"% personal protection\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total blood-fed\":\"391\",\"% blood-feeding\":\"67u\",\"95% CIs\":\"64\\u201371\",\"% blood-feeding inhibition\":\"\\u2013\",\"% personal protection\":\"\\u2013\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total blood-fed\":\"115\",\"% blood-feeding\":\"24v\",\"95% CIs\":\"20\\u201327\",\"% blood-feeding inhibition\":\"65\",\"% personal protection\":\"71\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total blood-fed\":\"519\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 33\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught\":\"233\",\"Total blood-fed\":\"54\",\"% blood-feeding\":\"23v\",\"95% CIs\":\"18\\u201329\",\"% blood-feeding inhibition\":\"66\",\"% personal protection\":\"86\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total blood-fed\":\"406\",\"% blood-feeding\":\"90w\",\"95% CIs\":\"88\\u201393\",\"% blood-feeding inhibition\":\"\\u201334\",\"% personal protection\":\"\\u2013 4\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"},\"14\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total blood-fed\":\"55\",\"% blood-feeding\":\"25v\",\"95% CIs\":\"19\\u201330\",\"% blood-feeding inhibition\":\"63\",\"% personal protection\":\"86\"}}\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Our study provides the first evidence of an antagonistic effect when pyrethroid-PBO ITNs are combined with pirimphos-methyl IRS in the same household resulting in lower levels of vector mosquito mortality compared to the IRS alone. In line with WHO recommendations, vector control programmes faced with multiple choice of ITN types to deploy as an additional intervention to improve vector control impact in an area dedicated to IRS with pirimphos-methyl, may consider other types of ITNs like pyrethroid-ITNs which can better complement pirimphos-methyl IRS when deployed together. Nevertheless, the pyrethroid-PBO ITNs performed poorly probably due to the lower levels of restoration of pyrethroid susceptibility with PBO in the vector population. Combining these nets with pirimphos-methyl IRS provided significantly improved vector control compared to the net alone demonstrating the potential for pirimphos-methyl IRS to enhance malaria control when deployed to complement pyrethroid-PBO ITNs in areas where PBO fails to fully restore susceptibility to pyrethroids.\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "Adult F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hut station were exposed to filter papers treated with discriminating doses of deltamethrin (0.05%), permethrin (0.75%), benchicaro (0.1%) and pirimphos-methyl (0.25%) in WHO cylinders8. Deltamethrin and perme-thrin were also tested with 60 min pre-exposure to PBO (4%) to assess the involvement of metabolic enzymes in pyrethroid resistance. A comparison was made with the laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain. Approximately 100, 3-5-day old mosquitoes of each strain were exposed to each insecticide for 60 min in four batches of 20-25. Similar numbers of mosquitoes were concurrently exposed to untreated filter papers as a control. At the end of exposure, mosquitoes were transferred to appropriately labelled holding tubes, provided access to 10% (w/v) glucose solution, and held at 27 +- 2degC and 75 +- 10% relative humidity. Knockdown was recorded 60 min after exposure and delayed mortality after 24 h for all treatments. The insecticide-treated filter papers were obtained from Universiti Sains Malaysia.\n",
-                        "\n",
-                        "header: Hut trial outcome measures\n",
-                        "\n",
-                        "The efficacy of the experimental hut treatments was expressed in terms of the following outcome measures:\n",
-                        "\n",
-                        "1. Deterrence (%)--proportional reduction in the number of mosquitoes collected in the treated hut relative to the number collected in the control.\n",
-                        "2. Exophily (%)--exiting rates due to potential irritant effect of the treatment expressed as the proportion of mosquitoes collected in the veranda trap.\n",
-                        "3. Blood-feeding inhibition (%)--proportional reduction in blood-feeding in the treated hut relative to the control. Calculated as follows: \\[\\mathit{Blood\\ feeding\\ inhibition}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bfu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "4. Mortality (%)--proportion of dead mosquitoes 24 h after collection.\n",
-                        "5. Personal protection (%)--reduction in the number of blood-fed mosquitoes in the treated hut relative to the untreated net control. Calculated as follows: \\[\\mathit{Personal\\ protection}(\\%) = \\frac{100(B_{fu} - B_{fu})}{B_{fu}}\\] where _Bu_ is the number of blood-fed mosquitoes in the untreated net control huts and _Bt_ is the number of mosquitoes in the huts with insecticide treatments.\n",
-                        "\n",
-                        "header: Experimental hut results.\n",
-                        "\n",
-                        "_Mosquito entry and exiting in experimental huts._\n",
-                        "\n",
-                        "A total of 5,404 wild female _An. gambiae_ s.l. were collected in the experimental huts over the 4-month trials (Table 2). In both trials, mosquito entry in huts with the pyrethroid-PBO ITN alone and IRS treatments alone did not differ significantly from the controls (p > 0.05) but was significantly reduced with the pyrethroid-PBO ITN plus IRS combinations compared to the single treatments (p < 0.01). Mosquito entry rates did not also differ between the combinations of the pyrethroid-PBO ITN with bendiocarb IRS relative to the combinations with pirimiphos-methyl IRS (p < 0.05). Nevertheless, IRS treatments could not be rotated and thus, treatment-induced deterrence cannot be fully distinguished from differential attractiveness due to hut position.\n",
-                        "\n",
-                        "The proportion of mosquitoes exiting into the veranda of the huts with the untreated net controls was 36% and 39% for Trials 1 and 2 respectively. Exiting rates were higher with the pyrethroid-PBO ITNs alone (76%) relative to the IRS insecticides alone (51-63% with bendiocarb and 53-69% with pirimiphos-methyl IRS, p < 0.005). In both trials, the highest levels of mosquito exiting were achieved with the pyrethroid-PBO ITN plus IRS combinations (79% with bendiocarb and 87-89% with pirimiphos-methyl IRS). Between the combinations, adding\n",
-                        "primiphos-methyl IRS to the pyrethroid-PBO ITN provided significantly higher levels of mosquito exiting relative to adding bendicord IRS (87-89% vs. 79%, P < 0.05).\n",
-                        "\n",
-                        "Blood-feeding rates of wild malaria vector mosquitoes in experimental huts.Blood-feeding rates in huts with the untreated net controls were 72% and 67% for Trials 1 and 2 respectively (Table 3). The IRS treatments did not provide any blood-feeding inhibition relative to the controls. Blood-feeding inhibition rates were high with the pyrethroid-PBO ITN plus IRS combinations in both trials (69-79% with Olyst Plus and 63-66% with PermaNet 3.0) and this was generally similar to what was observed with the pyrethroid-PBO ITNs alone (67% with Olyst Plus and 65% with PermaNet, P > 0.05) showing that the high levels of blood-feeding inhibition in the combinations, was mostly due to the ITNs. Blood-feeding inhibition with the PermaNet 3.0 plus primiphos-methyl IRS combination (66%) was similar to the PermaNet 3.0 plus bendicord IRS combination (63%, P = 0.71) meanwhile the Olyset Plus and primiphos-methyl IRS combination induced higher levels of blood-feeding inhibition compared to the Olyst Plus and bendicord IRS combination (79% vs. 69%, P = 0.036) (Table 3). Personal protection levels showed a similar trend and were also higher with the combinations (86-90%) relative to the IRS alone (- 33-13%).\n",
-                        "\n",
-                        "Mortality rates of wild malaria vector mosquitoes in experimental huts.\n",
-                        "\n",
-                        "Wild vector mosquito mortality in the controls was 3% in Trial 1 and 2% in Trial 2 (Table 4). The pyrethroid-PBO ITNs killed relatively low mosquito proportions (22% with Olgest Plus and 26% with PermaNet 3.0) though this was higher than what was observed with benedicarb IRS alone (14% in Trial 1 and 16% in Trial 2, P < 0.10). The highest mortality was achieved with pirimphos-methyl IRS alone (77% in Trial 1 and 78% in Trial 2). In both trials, mortality in the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations was significantly reduced compared to pirimphos-methyl IRS alone (77% with pirimphos-methyl IRS vs. 59% with Olgest Plus pirimphos-methyl IRS, P < 0.001 and 78% with pirimphos-methyl IRS vs. 55% with PermaNet 3.0 plus pirimphos-methyl IRS, P < 0.001), demonstrating an antagonistic effect. Conversely, mortality was significantly higher in the combinations of benedicarb IRS with Olgest Plus (33%) and PermaNet 3.0 (38%) than benedicarb IRS alone (14-16%, P < 0.001), demonstrating an additive effect (Table 4). Nevertheless, both pyrethroid-PBO plus IRS combinations induced significantly higher\n",
-                        "\n",
-                        "mortality compared to the pyrethroid-PBO ITNs alone (P < 0.001). Between the combinations, mortality was consistently higher with the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations (55-59%) compared to the pyrethroid-PBO ITN plus pedicocarb IRS (33-39%, P < 0.001).\n",
-                        "\n",
-                        "The monthly mortality rates of wild vector mosquitoes which entered the experimental huts during the trials are presented in Fig. 1 for the combinations with pedicocarb IRS and Fig. 2 for the combinations with pirimiphos-methyl IRS. In both trials, mosquito mortality rates in huts with the pyrethroid-PBO ITNs plus pedicocarb IRS declined sharply over time from 65-75% in month 1 to 33-38% in month 3, nevertheless, it was consistently higher than the single treatments alone (Fig. 1). Mosquito mortality in the combination of Olyset Plus and pirimiphos-methyl IRS was similar to the IRS alone in month 1 (> 90%) but declined substantially relative to the IRS in subsequent months. With the PermaNet 3.0 plus pirimiphos-methyl IRS combination,\n",
-                        "\n",
-                        "Figure 1.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pedicocarb IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Oyset Plus (Trial 1) and panel (**b**) presents results from the trial with PermaNet 3.0 (Trial 2). Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from onset of the trial. Mosquito mortality in the control untreated huts did not exceed 5% at any time point.\n",
-                        "\n",
-                        "Figure 2.: Monthly mortality rates of wild, pyrethroid-resistant _Anopheles gambiae_ sensu late entering experimental huts with pyrethroid-PBO ITNs and pirimiphos-methyl IRS, applied alone and in combination in Cové, southern Benin. Panel (**a**) presents results from the trial with Olyset Plus and panel (**b**) presents results from the trial with PermaNet 3.0. Error bars represent 95% CIs. Monthly mortality rates are cumulated with increasing time elapsed from the onset of the trial.\n",
-                        "\n",
-                        "\n",
-                        "mosquito mortality was consistently lower than the IRS alone throughout the trial. Through the four months of the trials, the pyrethroid-PBO ITN plus pirimiphos-methyl IRS combinations consistently induced higher levels of mosquito mortality relative to the pyrethroid-PBO ITNs alone.\n",
-                        "\n",
-                        "header: Table 2. Entry and exiting of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, P>0.05, negative binomial regression for females caught and logistic regression for exophily.\n",
-                        "columns: ['Treatment', 'Total females caught*', '% deterrence', 'Total exiting', '% exophily*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught*\":\"Trial 1\",\"% deterrence\":\"Trial 1\",\"Total exiting\":\"Trial 1\",\"% exophily*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"664a\",\"% deterrence\":\"-\",\"Total exiting\":\"241\",\"% exophily*\":\"36a\",\"95% CIs\":\"33\\u201340\"},\"2\":{\"Treatment\":\"Olyset plus\",\"Total females caught*\":\"511a\",\"% deterrence\":\"23\",\"Total exiting\":\"390\",\"% exophily*\":\"76b\",\"95% CIs\":\"73\\u201380\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"556a\",\"% deterrence\":\"16\",\"Total exiting\":\"282\",\"% exophily*\":\"51c\",\"95% CIs\":\"47\\u201355\"},\"4\":{\"Treatment\":\"Olyset Plus + bendiocarb IRS\",\"Total females caught*\":\"288b\",\"% deterrence\":\"57\",\"Total exiting\":\"228\",\"% exophily*\":\"79b\",\"95% CIs\":\"75\\u201384\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"531a\",\"% deterrence\":\"20\",\"Total exiting\":\"281\",\"% exophily*\":\"53c\",\"95% CIs\":\"49\\u201357\"},\"6\":{\"Treatment\":\"Olyset plus + P-methyl IRS\",\"Total females caught*\":\"304b\",\"% deterrence\":\"54\",\"Total exiting\":\"270\",\"% exophily*\":\"89d\",\"95% CIs\":\"85\\u201392\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught*\":\"Trial 2\",\"% deterrence\":\"Trial 2\",\"Total exiting\":\"Trial 2\",\"% exophily*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught*\":\"581u\",\"% deterrence\":\"-\",\"Total exiting\":\"226\",\"% exophily*\":\"39u\",\"95% CIs\":\"35\\u201343\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught*\":\"488u\",\"% deterrence\":\"16\",\"Total exiting\":\"369\",\"% exophily*\":\"76v\",\"95% CIs\":\"72\\u201379\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught*\":\"575u\",\"% deterrence\":\"1\",\"Total exiting\":\"360\",\"% exophily*\":\"63w\",\"95% CIs\":\"59\\u201367\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + bendiocarb IRS\",\"Total females caught*\":\"233v\",\"% deterrence\":\"60\",\"Total exiting\":\"185\",\"% exophily*\":\"79v\",\"95% CIs\":\"74\\u201385\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught*\":\"450u\",\"% deterrence\":\"23\",\"Total exiting\":\"311\",\"% exophily*\":\"69x\",\"95% CIs\":\"65\\u201373\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught*\":\"223v\",\"% deterrence\":\"62\",\"Total exiting\":\"195\",\"% exophily*\":\"87y\",\"95% CIs\":\"83\\u201392\"}}\n",
-                        "\n",
-                        "header: Table 4.Mortality results of wild, pyrethroid-resistant Anopheles gambiae sensu lato in experimental huts in Covè, southern Benin treated with pyrethroid-PBO ITNs, and pirimiphos-methyl IRS applied alone and in combination.\n",
-                        "footer: Results are presented separately for the trials involving Olyset Plus (Trial 1) and PermaNet 3.0 (Trial 2). *For each trial, values on this column sharing a superscript letter do not di\u001d\n",
-                        "er signi\u001fcantly, p>0.05, logistic regression\n",
-                        "columns: ['Treatment', 'Total females caught', 'Total dead', '% mortality*', '95% CIs']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment\":\"Trial 1\",\"Total females caught\":\"Trial 1\",\"Total dead\":\"Trial 1\",\"% mortality*\":\"Trial 1\",\"95% CIs\":\"Trial 1\"},\"1\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"664\",\"Total dead\":\"22\",\"% mortality*\":\"3a\",\"95% CIs\":\"2\\u20135\"},\"2\":{\"Treatment\":\"Olyset Plus\",\"Total females caught\":\"511\",\"Total dead\":\"114\",\"% mortality*\":\"22b\",\"95% CIs\":\"19\\u201326\"},\"3\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"556\",\"Total dead\":\"87\",\"% mortality*\":\"16c\",\"95% CIs\":\"13\\u201319\"},\"4\":{\"Treatment\":\"Olyset Plus + Bendiocarb IRS\",\"Total females caught\":\"288\",\"Total dead\":\"95\",\"% mortality*\":\"33d\",\"95% CIs\":\"28\\u201338\"},\"5\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"531\",\"Total dead\":\"411\",\"% mortality*\":\"77e\",\"95% CIs\":\"74\\u201381\"},\"6\":{\"Treatment\":\"Olyset Plus + P-methyl IRS\",\"Total females caught\":\"304\",\"Total dead\":\"178\",\"% mortality*\":\"59f\",\"95% CIs\":\"53\\u201364\"},\"7\":{\"Treatment\":\"Trial 2\",\"Total females caught\":\"Trial 2\",\"Total dead\":\"Trial 2\",\"% mortality*\":\"Trial 2\",\"95% CIs\":\"Trial 2\"},\"8\":{\"Treatment\":\"Untreated net\",\"Total females caught\":\"581\",\"Total dead\":\"12\",\"% mortality*\":\"2u\",\"95% CIs\":\"1\\u20133\"},\"9\":{\"Treatment\":\"PermaNet 3.0\",\"Total females caught\":\"488\",\"Total dead\":\"127\",\"% mortality*\":\"26v\",\"95% CIs\":\"22\\u201330\"},\"10\":{\"Treatment\":\"Bendiocarb IRS\",\"Total females caught\":\"575\",\"Total dead\":\"80\",\"% mortality*\":\"14w\",\"95% CIs\":\"11\\u201317\"},\"11\":{\"Treatment\":\"PermaNet 3.0 + Bendiocarb IRS\",\"Total females caught\":\"233\",\"Total dead\":\"89\",\"% mortality*\":\"38x\",\"95% CIs\":\"32\\u201344\"},\"12\":{\"Treatment\":\"P-methyl IRS\",\"Total females caught\":\"450\",\"Total dead\":\"350\",\"% mortality*\":\"78y\",\"95% CIs\":\"74\\u201382\"},\"13\":{\"Treatment\":\"PermaNet 3.0 + P-methyl IRS\",\"Total females caught\":\"223\",\"Total dead\":\"122\",\"% mortality*\":\"55z\",\"95% CIs\":\"48\\u201361\"}}\n",
-                        "\n",
-                        "header: Data analysis.\n",
-                        "\n",
-                        "Differences in proportional outcomes (exophily, blood-feeding, mortality) between experimental but treatments were analysed using a blocked logistic regression model whereas differences in numerical outcomes (mosquito entry) were assessed using a negative binomial regression model. In addition to the fixed effect of the treatment, both models were adjusted to account for variation due to the differential attractiveness of the volunteer sleepers and huts. Results from the different experimental hut trials involving Olyset Plus and PermaNet 3.0 were analysed separately. Susceptibility bioassay results were interpreted according to WHO criteria49. All analyses were performed in Stata version 15.1.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Experimental huts are used to assess the capacity of indoor vector control interventions to prevent wild vector mosquito entry and feeding and induce early mosquito exiting and mortality when applied in a human-occupied house, under carefully controlled conditions [3,12]. The hut trials were conducted at the CREC/LSHTM experimental hut station in Cowe, southern Benin (7deg14'N22deg18E), situated in a vast area of rice irrigation, which provides extensive and permanent breeding sites for mosquitoes. The rainy season extends from March to October and the dry season from November to February. _Anopheles coluzzini_ and _An. gambiae_ sensu stricto (s.s.) occur in symparity, with the latter present at lower densities and predominantly in the dry season. The vector population is susceptible to organophosphates and carbamates but exhibits intense resistance to pyrethroids (200-fold). Molecular genotyping and microarray studies have demonstrated a high frequency of the knockdown resistance L1014F allele (> 90%) and overexpression of CYP6P3, an enzyme associated with pyrethroid detoxification [3].\n",
-                        "\n",
-                        "Two experimental hut trials were performed in parallel for 4 months between April and July 2020. Trial 1 assessed the impact of combining Olyset Plus (Sumitomo Chemical), a permethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS while Trial 2 assessed the impact of combining PermaNet 3.0 (Vestergaard Sarl), a deltamethrin-based pyrethroid-PBO ITN with pirimiphos-methyl IRS.\n",
-                        "\n",
-                        "The following six treatments were tested in each of the experimental hut trials:\n",
-                        "\n",
-                        "header: 3.2.3 Wall cone bioassays.\n",
-                        "\n",
-                        "WHO wall cone bioassays were performed on the IRS treated experimental hut walls 1 week, 2 months and 4 months after application of IRS treatments to assess their residual efficacy with increasing time elapsed from spraying. Mortality rates of the insecticide-susceptible _An. gambiae_ s.s. Kisum strain and pyrethroid-resistant _An. gambiae_ s.l. Cove strain following exposure to IRS-treated surfaces in 30 min wall cone bioassays are presented in Fig. 4. Mortality of mosquitoes exposed to pirimiphos-methyl treated walls was high (>90%) at all time points with both strains, showing no evidence of a decline in residual activity. In contrast, wall cone bioassay mortality on bendicor-treated surfaces declined rapidly, beginning at 67% and 39% in week 1 and falling to lows of 2% and 3% in month 4 with the Kisum and Cove strains respectively. No mortality was recorded in the controls at any time point with either strain.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Given the inhibitory effect of PBO on mosquito cytochrome P450 enzymes, the WHO had temporarily recommended against the use of pyrethroid-PBO ITN in areas programmed for IRS with pirimiphos-methyl IRS until further evidence on the potential antagonism between PBO and the organo-thiophosphate pro-insecticide becomes available20. In this study, we found evidence of an antagonistic effect when pyrethroid PBO ITNs were combined with pirimiphos-methyl IRS in the same household in a pyrethroid-resistant area in Southern Benin where resistance was partly conferred by over-expression of mosquito cytochrome P450 enzymes20. Unlike the combination of the pyrethroid-PBO nets with bendicorab IRS which provided improved vector mosquito mortality compared to bendicorab IRS alone, combining these nets with pirimiphos-methyl IRS induced significantly lower levels of vector mosquito mortality rates compared to pirimiphos-methyl IRS alone. This effect\n",
-                        "\n",
-                        "Figure 3.: Mortality (24 h) of susceptible _Anopheles gambiae_ Kisumu and pyrethroid-resistant _An. gambiae_ s.l. Cove strains exposed to Olyset Plus and Permaket 3.0 in tunnel tests. Error bars represent 95% CIs. Both pyrethroid-PBO nets were compared to pyrethroid-only nets (Olyset Net and Permaket 2.0). Permaket 3.0 contains PBO only on the roof of the net. With both strains, 80–120 mosquitoes were exposed overnight to metting pieces cut from whole nets in 2–3 replicate tunnel tests.\n",
-                        "\n",
-                        "\n",
-                        "was remarkably similar between the two brands of pyrethroid-PBO ITNs tested across both hut trials indicating that the negative interaction of the combination on mosquito mortality was less affected by the design and specifications of the pyrethroid-PBO ITN. Wild vector mosquitoes which entered the huts with the combined treatment may have contacted the pirimphos-methyl IRS on the hut wall only after picking up PBO from the ITN while attempting to blood-feed on the sheeper under the net. This pre-exposure to PBO on the ITN could have prevented the metabolic activation of the IRS insecticide in these mosquitoes, resulting in lower mortality rates relative to the IRS alone. This finding supports WHO's recommendation against the deployment of pyrethroid-PBO ITNs in areas that have already been programmed for IRS with pirimphos-methyl IRS. This is mostly important from a programmatic perspective where a vector control programme is faced with multiple choice of ITN types to deploy as an additional intervention to enhance vector control impact in an area that is already dedicated to IRS with pirimphos-methyl. In such a scenario, other types of ITNs like pyrethroid-only nets that were recently shown to complement pirimphos-methyl IRS when applied together23, should be considered.\n",
-                        "\n",
-                        "These findings should however not be interpreted to mean that pirimphos-methyl IRS must not be deployed to complement pyrethroid-PBO ITNs. Though pyrethroid-PBO ITNs have consistently shown improved performance in experimental hut trials against pyrethroid-resistant malaria vectors compared to pyrethroid-only nets34, the margin appears to vary depending on the intensity and mechanisms of pyrethroid-resistance encountered. In our study, the pyrethroid-PBO ITNs when applied alone in a hut induced low vector mosquito mortality (22-26%) compared to what has been observed in hut trials against the same vector population with another type of novel dual ITN (71-76%)35,40. Low levels of mosquito mortality in huts with pyrethroid-PBO ITNs and failure of PBO to fully restore pyrethroid susceptibility in bioassays have also been reported from studies in Burkina Faso37, Cameroon38, Cote d'Ivoire39 and Senegal40. This may indicate the presence of complex resistance mechanisms in the West African region unaffected by PBO. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors which continues to increase over time41, yet the public health value of pyrethroid-PBO ITNs has not been assessed in the region. It is therefore unclear whether these nets will provide the same improved epidemiological impact over pyrethroid-only nets in West Africa as observed in the East African community trials which have been the basis of their endorsement for malaria control. Our results showed a significant improvement in mosquito mortality when pirimphos-methyl IRS was combined with the pyrethroid-PBO ITNs (55-59%) compared to the pyrethroid-PBO ITNs alone (22-26%). Mosquito entry and feeding rates were also significantly lower with the combination compared to the pyrethroid-PBO ITNs alone. This provides some justification for deploying pirimphos-methyl IRS to complement pyrethroid-PBO nets in an area of high and complex pyrethroid-resistance where local vectors are less susceptible to the synergistic effect of PBO. Due to the limited choice of insecticides available for IRS, where a beneficial effect of the combination compared to the pyrethroid-PBO ITN alone has been established, pirimphos-methyl IRS should continue to be used even in the presence of high coverage with pyrethroid-PBO ITNs, preferably as part of an IRS rotational strategy. In line with WHO recommendations for insecticide resistance management42, the sustained rotation of multiple insecticide modes of action for IRS may help preserve efficacy to insecticides approved for IRS.\n",
-                        "\n",
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-                        "Footnote 40: https://doi.org/10.1038/s41598-022-10953-y\n",
-                        "\n",
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-                        "\n",
-                        "\n",
-                        "following pre-exposure to PBO, restoration of susceptibility to permethrin--the pyrethroid insecticide used on the pyrethroid-PBO ITN tested in the Tanzanian trial (Olvest Plus)--was high in the vector population from the study area (from 18 to 94%)15. Hence, the level of control achieved with pyrethroid-PBO ITN alone in the Tanzanian trial may have been optimal making the addition of pirimphos-methyl IRS unnecessary. This may not be the case in many communities in West Africa considering the afore-mentioned low levels of pyrethroid-PBO synergism reported in susceptibility bioassays and hut trials conducted across the region; in contrast, the addition of pirimphos-methyl IRS to pyrethroid-PBO ITNs in these communities could be more beneficial for control of clinical malaria compared to the ITN alone. As the range of vector control products available to vector control programmes expands, the choice of interventions and product brands must be aligned to local contexts and guided by local evidence. To help guide vector control policy, epidemiological trials and/or other empirical studies investigating the impact and cost-effectiveness of pyrethroid-PBO nets in communities in the West African region as well as their combination with IRS using pro-insectiles like pirimphos-methyl will be necessary.\n",
-                        "\n",
-                        "Although the inhibitory effect of PBO on mosquito cytochrome P450 enzymes formed the basis of our hypothesis for the antagonism observed between pirimphos-methyl IRS and pyrethroid-PBO ITNs in the experimental huts, behavioural interactions could also have contributed. In both trials, mosquito exiting rates into the veranda traps and blood-feeding inhibition were consistently higher in the combinations compared to the IRS insecticides alone. This could be attributed to the excitro-replenent property of the pyrethroid in the ITN stimulating directed movement of mosquitoes away from their source44. This early exiting effect was however significantly higher in the combination with pirimphos-methyl IRS compared to the combination with bendo-carb IRS suggesting a behavioural interaction in the presence of pirimphos-methyl that may have driven more mosquitoes to exit into the veranda, thus reducing mosquitoes' contact with pirimphos-methyl IRS treated walls. This may have compromised the impact of the pirimphos-methyl IRS in the combination compared to when applied alone and to the combination with bendoicarb. Further studies to assess mosquito flight behaviour, in the presence of the combinations would provide useful insight into behavioural interactions between the treatments. Controlled laboratory assays which minimise behavioural responses and assess P450 enzyme activity in exposed mosquitoes, could also elucidate the potential role of P450 enzyme inhibition in the antagonism observed in the experimental huts.\n",
-                        "\n",
-                        "While our trial focused on investigating the impact of combining pyrethroid-PBO ITNs with pirimphos-methyl IRS in the same households, there is a paucity of information on the interactions between these nets and other newly developed vector control products which contain new public health insecticides such as the neonciotohal, clothidinium and the pyrrole chlorfenapxy. Chlorfenapxy, an insecticide used on ITNs13 and being considered for IRS6,46, also requires activation by mosquito P450 enzymes6. Laboratory experiments have indicated the potential of PBO to antagonise the toxicity of chlorfenapxy against mosquitoes in bioassays29,30. Studies investigating the impact of co-deploying pyrethroid-PBO ITNs together with pyrethroid-chlorfenapxy ITNs or chlorfenapxy IRS in the same household will be essential to help inform optimal co-deployment policy.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "IRS Indoor residual spraying\n",
-                        "\n",
-                        "WHO World Health Organization\n",
-                        "\n",
-                        "PQ Prenqualification team\n",
-                        "\n",
-                        "PBO Piperonyl butoxide\n",
-                        "\n",
-                        "ITN Insecticide treated net\n",
-                        "\n",
-                        "LLIN Long-lasting insecticidal net\n",
-                        "\n",
-                        "GPIRM Global plan for insecticide resistance management\n",
-                        "\n",
-                        "WHOPES WHO pesticide evaluation scheme\n",
-                        "\n",
-                        "CREC Centre de Recherche Entomologique de Cotonou\n",
-                        "\n",
-                        "LSHTTM London School of Hygiene & Tropical Medicine\n",
-                        "\n",
-                        "Kdr Knockdown resistance\n",
-                        "\n",
-                        "PAMVERC Pan African Malaria Vector Research Consortium\n",
-                        "\n",
-                        "The large-scale implementation of long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS) has resulted in profound reductions in malaria-associated morbidity and mortality across sub-Saharan Africa, over the last two decades1. Unfortunately, resistance to the insecticides applied through these interventions, especially the pyrethroids, is now pervasive in vector populations in malaria-endemic countries', threatening to undermine their impact. In response, a new generation of novel LLINs and IRS based on new active ingredients with the potential to sustain vector control impact in the face of increasing resistance, have been developed3. This includes dual insecticide-treated nets containing a pyrethroid and an alternative effective new compound as well as new IRS insecticides with novel modes of action or improved formulations that have shown potential to provide enhanced control of insecticide-resistant malaria vector populations.\n",
-                        "\n",
-                        "Insecticide-treated nets (ITNs) combining a pyrethroid and piperonyl butoxido (PBO) were the first novel class of ITNs to be developed for malaria vector control. PBO is a synergism that can enhance the impact of pyrethroids and other insecticides by inhibiting metabolic detoxification enzymes associated with resistance, notably cytochrome P450 monooxygenases3. These nets received a conditional endorsement from the World Health Organisation (WHO) in 2017 based on results from a cluster-randomised controlled trial (CRT) in Tanzania demonstrating a reduction in malaria prevalence in communities allocated to pyrethroid-PBO ITNs relative to pyrethroid-only ITNs4. A full policy recommendation is now expected after results from a second CRT in Uganda also showed that two brands of pyrethroid-PBO ITNs reduced malaria prevalence relative to pyrethroid-only nets4. The WHO endorsement and expanding evidence base for the public health value of pyrethroid-PBO ITNs has prompted mass procurement of these nets by international malaria control agencies4,5. The proportion of pyrethroid-PBO ITNs of all ITNs delivered in sub-Saharan Africa has consequently risen from 3% in 2018 to 35% in 202112; pyrethroid-PBO ITNs are therefore replacing pyrethroid-only nets in many malaria-endemic countries.\n",
-                        "\n",
-                        "The increasing distribution and intensity of pyrethroid resistance has also affected malaria control policy regarding insecticide choice for IRS over the last decade. To improve vector control impact and preserve pyrethroids for ITNs, African IRS programmes partly suspended the use of pyrethroids and organochlorines in favour of carbamates and organophosphates5,12. Whilst both insecticides showed high toxicity against malaria vectors, the short residual duration of the initial formulations approved for IRS proved prohibitive, necessitating the development of longer-lasting formulations13. A new microencapsulated formulation of pirimphos-methyl was later developed (Actellic 300CS) demonstrating prolonged activity against pyrethroid-resistant malaria vector mosquitoes, lasting up to 9 months14,15. This formulation subsequently served as the insecticide of choice for the majority of IRS programmes in sub-Saharan Africa1 providing substantial control of mosquito vectors and malaria across distinct eco-epidemiological settings6,16-22.\n",
-                        "\n",
-                        "[MISSING_PAGE_POST]\n",
-                        "\n",
-                        "header: Susceptibility of wild vector mosquitoes at Cove to insecticides.\n",
-                        "\n",
-                        "WHO susceptibility bioassays were conducted in parallel to the experimental hut trial to determine the susceptibility of the vector population at the Cove hit site to the constituent insecticides of the experimental hut treatments. Mortality rates of F1 progeny of field-collected _An. gambiae_ s.l. from the Cove hit station following exposure to discriminating doses of deltamethrin and permethrin in WHO cylinder bioassays were low (42% and 11% respectively), confirming a high frequency of pyrethroid resistance in the Cove vector population (Table 1). Pre-exposure to PBO significantly improved mortality with deltamethrin (42% vs. 72%) but not with permethrin (11% vs. 8%). Mortality rates with the discriminating doses of bendiocarb and pirimiphos-methyl were 98% and 99% respectively. This demonstrated susceptibility to pirimiphos-methyl and bendiocarb. All insecticides induced 100% mortality with the laboratory-maintained, insecticide-susceptible _An. gambiae_ s.s. Kisumu strain. No mortality was recorded in the control with either strain.\n",
-                        "\n",
-                        "header: Wall cone bioassays\n",
-                        "\n",
-                        "The laboratory-maintained, susceptible _An. gambiae_ s.s. Kisumu strain and wild pyrethroid-resistant _An. gambiae_ s.l. Cove mosquitoes (F1) derived from breeding sites at the hut station were used for this purpose. At each time point, five cones were attached to the walls and ceiling of the IRS-treated huts. Approximately 50, 3-5-day old mosquitoes were transferred into cones in 5 batches of 10 and exposed to the treated surfaces for 30 min. As a control, mosquitoes were exposed in cones attached to the walls and ceiling of an unsprayed hut. At the end of exposure, mosquitoes were transferred to netted, plastic cups. Mosquitoes were provided access to 10% (w/v) glucose solution and delayed mortality after 24 h for all treatments.\n",
-                        "\n",
-                        "\n",
-                        "Ethical considerations.Ethical approval for the conduct of the study was obtained from the ethics review boards of the Beninese Ministry of Health (No. 34) and the London School of Hygiene & Tropical Medicine (Ref: 16969). All human volunteer sleepers gave informed written consent prior to their participation; where necessary, the consent form and information sheet were explained in their local language. They were offered a free course of chemoprophylaxis spanning the duration of the trial and up to 3 weeks following its completion. A stand-by nurse was available for the duration of the trial to assess any cases of fever or adverse reactions to test items. Any confirmed cases of malaria were treated free of charge at a local health facility. Animals used as baits in tunnel test were maintained following institutional standard operating procedures (SOPs) designed to improve care and protect animals used for experimentation. All studies were performed according to relevant national and international guidelines.\n",
-                        "\n",
-                        "header: Experimental hut trial procedure\n",
-                        "\n",
-                        "During the hut trials, volunteer sleepers were rotated between experimental huts daily to mitigate the impact of individual attractiveness whilst bed nets were rotated weekly between the huts to reduce the impact of hut position on mosquito entry. Three (3) replicate bed nets were used per treatment and rotated within the treatment every 2 days. IRS treatments cannot be rotated and thus remained fixed throughout the trial. Consenting human volunteer sleepers alert in experimental huts between 21:00 and 06:00 to attract free-flying mosquitoes. Each morning, volunteer sleepers collected all live and dead mosquitoes from the different compartments of the hut (under the net, room, veranda) using a torch and aspirator and placed them in correspondingly labelled plastic cups. Mosquito collections were then transferred to the field laboratory for morphological identification using taxonomic keys and scoring of immediate mortality and blood-feeding. All live, female _An. gambiae_ s.l. were provided access to 10% glucose (w/v) solution and held at ambient conditions in the field laboratory. Delayed mortality was recorded after 24 h for all treatments. Mosquito collections were performed 6 nights per week and on the 7th day, huts were cleaned and aired in preparation for the next rotation cycle.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cov\\u00e9\",\"Start_month\":4,\"Start_year\":2020,\"End_month\":7,\"End_year\":2020}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated net\",\"Insecticide\":null,\"Concentration_initial\":null,\"Net_washed\":null,\"Net_holed\":null,\"pHI_category\":null},\"N02\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"Deltamethrin, PBO\",\"Concentration_initial\":\"2.1 g\\/kg, 4.0 g\\/kg, 25 g\\/kg\",\"Net_washed\":0.0,\"Net_holed\":9.0,\"pHI_category\":\"Good\"},\"N03\":{\"Net_type\":\"Olyset Plus\",\"Insecticide\":\"Permethrin, PBO\",\"Concentration_initial\":\"20 g\\/kg, 10 g\\/kg\",\"Net_washed\":0.0,\"Net_holed\":9.0,\"pHI_category\":\"Good\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Table 1: Treatment arms and descriptions of longlasting insecticide-treated nets.\n",
-                        "footer: PermaNet is a registered trademark of Vestergaard Frandsen Holding SA. Olyset is a registered trademark of Sumitomo Chemical Co. Ltd. DawaPlusis a registered trademark of Tana Netting Co. Ltd.PBO, piperonyl butoxide.\n",
-                        "columns: ['Treatment arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment arm\":\"Untreated net\",\"Description\":\"Net manufactured manually using netting material from market\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment arm\":\"Olyset\\u00ae Net\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment arm\":\"Olyset\\u00ae Plus\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin and 4.3 x 10-4 kg\\/m2 of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment arm\":\"PermaNet\\u00ae 2.0\",\"Description\":\"5.5 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"4\":{\"Treatment arm\":\"PermaNet\\u00ae 3.0\",\"Description\":\"Combination of 2.8 g\\/kg of deltamethrin coated on polyester with strengthened border (side panels) and deltamethrin (4.0 g\\/kg) and PBO (25 g\\/kg)\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"5\":{\"Treatment arm\":\"Dawa\\u00ae Plus 2.0\",\"Description\":\"8.0 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"TANA Netting\"}}\n",
-                        "\n",
-                        "header: Supporting Information\n",
-                        "\n",
-                        "Additional supporting information may be found online in the Supporting Information section at the end of the article.\n",
-                        "\n",
-                        "Fig. 5: Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hit relative to the control hit. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "header: Entry rate and deterrence: Exit rates and induced exophily\n",
-                        "\n",
-                        "Exit rates refer here to the proportion of mosquitoes present in the veranda trap. As with entrance rates, exit rates varied throughout the study (Fig. 4). On average, the induced exophily rates were between 26.4% (control net) and 48.5% (Olyset Net) (Table S4). The odds of finding a mosquito in the veranda trap were significantly increased in the huts with treated nets, with the exception of the DawaPlus, although even for the DawaPlus a tendency to induce exophily was observed (Fig. 4, Table S4).\n",
-                        "\n",
-                        "header: Molecular analysis\n",
-                        "\n",
-                        "DNA was extracted from mosquito legs by heating at 90*C for 30 min. Species were identified using the SINE200 protocol (Santolamazza _et al._, 2008) and then screened for the voltage gated sodium channel (VGSC) 1014F and 1575Y alleles using Taqman assays (Bass _et al._, 2007; Jones _et al._, 2012).\n",
-                        "\n",
-                        "Total RNA was extracted from six pools of 10 5-day-old non-blood-fed female _An. coluzzii_ from larval collections from Tengrela and VK5 using the RNAqueous(r)4PCR Kit for isolation of DNA-free RNA (Ambion, Inc., Austin, TX, U.S.A.) according to the manufacturer's procedures. The RNA was eluted in 50 mL of elution solution and treated with DNase. The quality and quantity of all the RNA used were assessed \n",
-                        "using a NanoDrop ND1000 (Thermo Fisher Scientific UK Ltd, Renfrew, U.K.).\n",
-                        "\n",
-                        "The expression profiles of five P450 genes (_CYP6M2_, _CYP6Z2_, _CYP6Z3_, _CYP6P3_, _CYP6P4_), previously found to be over-expressed in pyrethroid-resistant field populations from Burkina Faso (Toe _et al._, 2015) and/or known to metabolize pyrethroids (Muller _et al._, 2008; Mitchell _et al._, 2012) were quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). The qPCR analysis was conducted at the LSTM and used the following mosquito populations: Ngousso, an insecticide-susceptible strain originating from Ngousso in Cameroon in 2006 and maintained in the insectary at LSTM; Tengrela specimens reared from larval collections in October-November 2014, and VK5 specimens reared from larval collections in October-November 2014. Approximately 600 ng of RNA was reverse-transcribed to first-strand cDNA using SuperScript(tm) III reverse transcriptase (Invitrogen, Inc., Carlsbad, CA, U.S.A.) according to the manufacturer's procedures. Samples were then purified using the Qiagen Easy Purification Kit (Qiagen Benelux BV, Venlo, the Netherlands) before proceeding to qPCR. Each of the six pool replicates were run in triplicate using 2X SYBR Brilliant III (Agilent Technologies, Inc., Palo Alto, CA, U.S.A.), forward and reverse primers (300 nM) [sequence available in Toe _et al._ (2015)] on the Mx3005p qPCR system (Agilent Technologies, Inc., Palo Alto, California) with the following cycling protocol: 95 degC for 3 min, followed by 40 cycles of 95 degC for 10 s and 60 degC for 10 s. The qPCR data were analysed using the delta Ct values method, taking into account the PCR efficiency (Pfaffi, 2001). The candidate Ct values were normalized against three housekeeping genes, encoding ribosomal protein L40 (ubiquitin) (_AGAP007927_), an elongation factor (_AGAP005128_) and the S7 ribosomal protein (_AGAP010592_). The normalized Ct values of each gene were then compared with the normalized Ct values of the susceptible Ngousso strain.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In VK5, very low levels of mortality were observed after exposure to the discriminating dose of deltamethrin and permethrin, but pre-exposure to PBO significantly increased mortality rates from 2.5% (_n_ = 163) to 45% (_n_ = 158) and from 5% (_n_ = 153) to 26% (_n_ = 156) for deltamethrin and permethrin, respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig. 1). In Tengrela, mortality rates of 34% (_n_ = 85) and 14% (_n_ = 101) were recorded for deltamethrin and permethrin, respectively. When PBO was used, mortality rates increased significantly to 63% (_n_ = 84) and 42% (_n_ = 104), respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig.1). Although there was evidence of synergism, pre-exposure to PBO did not fully restore susceptibility in either site to either pyrethroid.\n",
-                        "\n",
-                        "_Anopheles coluzzi_ was the only species of the _An. gambiae_ complex identified by PCR of a subset of 80 specimens from Tengrela and VK5. High frequencies of the 1014F allele of the VGSC were recorded in Tengrela (0.819, 95% CI 0.750-0.875) and VK5 (0.885, 95% CI 0.824-0.930) with the 1575Y allele present at lower frequencies of 0.169 (95% CI 0.114-0.236) and 0.221 (95% CI 0.158-0.295), respectively. Samples were not genotyped for the 1014S allele as previous extensive surveys have not detected this allele in _An. coluzzi_ from these sites (Toe _et al._, 2015). There was no statistically significant difference in the frequency of either the 1014F or 1575Y allele between the two sites (Table 2).\n",
-                        "\n",
-                        "Several cytochrome P450 genes previously associated with pyrethroid resistance showed elevated expression levels in the Tengrela and the VK5 _An. coluzzi_ populations compared with the susceptible laboratory Ngousso strain (Fig. 2). _CYP6P3_, _CYP6M2_, _CYP6Z3_, _CYP6P4_ and _CYP6Z2_ were found to be\n",
-                        "\n",
-                        "Fig. 1: Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: \\(P\\) < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "overexpressed in both field strains compared with the susceptible laboratory colony (fold change: > 2) with the most highly overexpressed genes in both populations being _CYP6Z2_ and _CYP6Z3_ (Fig. 2).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Very low mortality rates were obtained for both permethrin and deltamethrin in the two study sites following an hour of exposure to WHO diagnostic doses. Southwestern Burkina Faso is known as a hotspot of pyrethroid resistance (Dabire _et al._, 2012; Namountougou _et al._, 2012). Rapid changes in the prevalence of pyrethroid resistance have been observed since the first national LLIN distribution programme in 2010: in 2009, mosquito mortality following deltamethrin exposure was 25% (Dabire _et al._, 2012), in VK has since fallen to just 2.5%. The frequency of the 1014F _kdr_ mutation also increased from 0.28 in 2006 (Dabire _et al._, 2009) to 0.88 in the current study. Similar increases in the prevalence of pyrethroid resistance have been witnessed in Tengrela. Mortality rates of 93% and 46% for deltamethrin and permethrin, respectively, were recorded in 2011 (Namountougou _et al._, 2012; K. H. Toe, unpublished data 2011), but these mortality rates had reduced to 33% and 13% in 2014, the year of the current study (Toe _et al._, 2015). In the present study, pyrethroid mortality was significantly increased by pre-exposure to PBO. This, together with the qPCR data showing elevated expression of multiple P450s in both field populations compared with a susceptible laboratory strain, indicate that oxidases are an important resistance mechanism in _An. coluzzi_ in southwestern Burkina Faso. It is noted that resistance was not fully restored by PBO pre-exposure, indicating that additional resistance mechanisms, such as target site resistance and possibly cuticular modifications (Toe _et al._, 2015), may contribute to the pyrethroid resistance phenotype in these populations.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Do bednets including piperonyl butoxide offer additional protection against populations of _Anopheles gambiae s.l._ that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso\n",
-                        "\n",
-                        "K. H. T oe\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " P. Muller\n",
-                        "\n",
-                        "2H233\n",
-                        "\n",
-                        "33Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Badlo\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Traore\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " N. Sagnon\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " R. K. Dabi Irene\n",
-                        "\n",
-                        "4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " H. Ransons\n",
-                        "\n",
-                        "5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        "\n",
-                        "Malaria control is dependent on the use of longlasting insecticidal nets (LLINs) containing pyrethroids. A new generation of LLINs containing both pyrethroids and the synergist piperonyl butoxide (PBO) has been developed in response to increasing pyrethroid resistance in African malaria vectors, but questions remain about the performance of these nets in areas where levels of pyrethroid resistance are very high. This study was conducted in two settings in southwest Burkina Faso, Vallee du Kou 5 and Tengrela, where _Anopheles gambiae s.l._ (Diptera: Culicidae) mortality rates in World Health Organization (WHO) discriminating dose assays were \\(<\\) 14% for permethrin and \\(<\\) 33% for delatamethrin. When mosquitoes were pre-exposed to PBO in WHO tube assays, mortality rates increased substantially but full susceptibility was not restored. Molecular characterization revealed high levels of _kdr_ alleles and elevated levels of P450s previously implicated in pyrethroid resistance. In cone bioassays and experimental huts, PBO LLINs outperformed the pyrethroid-only equivalents from the same manufacturers. Blood feeding rates were 1.6-2.2-fold lower and mortality rates were 1.69-1.78-fold greater in huts with PBO LLINs vs. non-PBO LLINs. This study indicates that PBO LLINs provide greater personal and community-level protection than standard LLINs against highly pyrethroid-resistant mosquito populations.\n",
-                        "\n",
-                        " insecticide resistance, insecticide resistance management, longlasting insecticidal nets, PBO.\n",
-                        "\n",
-                        "header: Table 2: Frequencies of 1014F and 1575Y kdr alleles in Anopheles coluzzili, in Tengrela and Vallée du Kou 5 (VK5).\n",
-                        "footer: Chi-squared test (http://vassarstats.net/) for the comparison of the frequency of the 1014F and 1575Y mutations in Anopheles coluzzili populations from Tengrela and VK5 (October 2014). No statistical difference was observed in the frequencies of the kdr alleles in the two sites.\n",
-                        "columns: ['1014F mutation - Unnamed: 0_level_1', '1014F mutation - Total n', '1014F mutation - LL', '1014F mutation - LF', '1014F mutation - FF', '1014F mutation - f (1014F)', '1014F mutation - P-value']\n",
-                        "\n",
-                        "{\"0\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"2\",\"1014F mutation - LF\":\"25\",\"1014F mutation - FF\":\"53\",\"1014F mutation - f (1014F)\":\"0.819\",\"1014F mutation - P-value\":\"0.26\"},\"1\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"78\",\"1014F mutation - LL\":\"0\",\"1014F mutation - LF\":\"18\",\"1014F mutation - FF\":\"60\",\"1014F mutation - f (1014F)\":\"0.885\",\"1014F mutation - P-value\":null},\"2\":{\"1014F mutation - Unnamed: 0_level_1\":\"1575Y mutation\",\"1014F mutation - Total n\":\"1575Y mutation\",\"1014F mutation - LL\":\"1575Y mutation\",\"1014F mutation - LF\":\"1575Y mutation\",\"1014F mutation - FF\":\"1575Y mutation\",\"1014F mutation - f (1014F)\":\"1575Y mutation\",\"1014F mutation - P-value\":\"1575Y mutation\"},\"3\":{\"1014F mutation - Unnamed: 0_level_1\":null,\"1014F mutation - Total n\":\"Total n\",\"1014F mutation - LL\":\"NN\",\"1014F mutation - LF\":\"NY\",\"1014F mutation - FF\":\"YY\",\"1014F mutation - f (1014F)\":\"f (A575Y)\",\"1014F mutation - P-value\":\"P-value\"},\"4\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"57\",\"1014F mutation - LF\":\"19\",\"1014F mutation - FF\":\"4\",\"1014F mutation - f (1014F)\":\"0.169\",\"1014F mutation - P-value\":\"0.39\"},\"5\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"77\",\"1014F mutation - LL\":\"49\",\"1014F mutation - LF\":\"22\",\"1014F mutation - FF\":\"6\",\"1014F mutation - f (1014F)\":\"0.221\",\"1014F mutation - P-value\":null}}\n",
-                        "\n",
-                        "header: Performance of PBO LLINs in experimental hut studies\n",
-                        "\n",
-                        "The enhanced performance of PBO-containing LLINs over conventional LLINs was further supported by the experimental hut results. Both blood feeding inhibition and mortality rates were significantly higher in huts containing PBO LLINs than in those containing conventional LLINs from the same manufacturers. An improved performance of the PermaNet 3.0, which contains both higher concentrations of deltamethrin compared with the PermaNet 2.0, plus PBO on the roof of the net, has also been reported in pyrethroid-resistant populations of malaria vectors in Ivory Coast (Koudou _et al._, 2011), Benin (N'Guessan _et al._, 2010) and Burkina Faso (Corbel _et al._, 2010).\n",
-                        "\n",
-                        "Fig. 4: Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the \\(P\\)-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "The magnitude of this effect differs among studies, with previous studies reporting increases in mortality rates of 1.3-1.8-fold, whereas the current study reports an OR of 2.45, which corresponds to a 1.78-fold increase. Only one experimental hit study comparing the Olyset Plus with the Olyset Net has been published to date (Pennetier _et al._, 2013). This study, conducted in Benin in 2013, reported mortality rates 1.9-fold higher in the PBO arm than in the conventional LLIN arm (Pennetier _et al._, 2013), which is in line with the findings of the current study, which found a 1.89-fold (OR 2.1) elevation in mortality rates in the Olyset Plus arm.\n",
-                        "\n",
-                        "In addition to increased mortality rates, the current study, plus two of the previous experimental hit studies, reported significantly higher rates of blood feeding inhibition for PBO vs. conventional LLINs (Corbel _et al._, 2010; N'Guessan _et al._, 2010), indicating that PBO LLINs afford an enhanced level of personal protection in areas where vectors are resistant to pyrethroids. It should be noted that the current study was performed using unwashed nets only. Previous studies have found that the efficacy of PBO LLINS against resistant mosquitoes decreases substantially after the nets have been washed 20 times according to WHO protocols (Corbel _et al._, 2010; Tungu _et al._, 2010; Koudou _et al._, 2011). Thus further studies on the durability of the bio-efficacy of PBO LLINS under field conditions are urgently needed.\n",
-                        "\n",
-                        "header: Blood feeding rates and inhibition\n",
-                        "\n",
-                        "Average blood feeding rates were between 17.0% (PermaNet 3.0) and 56.4% (control net) (Table S4). With the exception of the DawaPlus, all treated nets reduced blood feeding (Fig. 4, Table S4), and the effect was most prominent with the PBO nets PermaNet 3.0 and Olyset Plus (Fig. 4, Table S4), for which the odds ratios (ORs) of blood feeding relative to control nets were 0.19 (95% CI 01.3-0.26) and 0.16 (95% CI 0.11-0.23), respectively.\n",
-                        "\n",
-                        "header: Mortality rates and induced mortality\n",
-                        "\n",
-                        "Average mortality rates ranged from 9.5% in the control arm to 46.1% in the PermaNet 3.0 arm (Table S4). Mortality was statistically higher in all treated net conditions than in the control huts. As with blood feeding inhibition, the PBO nets showed the largest effects in increasing mortality (Fig. 5, Table S4); the ORs for mortality relative to control nets were 5.56 (95% CI 3.92-7.89) and 8.14\n",
-                        "Fig. 2: Expression levels of candidate genes previously associated with pyrethroid resistance in Tengrela and Vallée du Kou 5. The analysis was performed using the 2−\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "(95% CI 5.64-11.75) for the Olyset Plus and PermaNet 3.0, respectively, compared with 2.65 (95% CI 1.87-3.75) and 3.33 (95% CI 2.35-4.74) for the Olyset Net and PermaNet 2.0, respectively.\n",
-                        "\n",
-                        "header: Impact of PBO on LLIN efficacy in pyrethroid-resistant mosquitoes\n",
-                        "\n",
-                        "The performance of nets containing PBO as compared with pyrethroid-only nets from the same manufacturer is shown in Fig. 5. The PermaNet 3.0 deterred more mosquitoes than the PermaNet 2.0 (OR = 0.67, \\(P\\) < 0.01) (Fig. 5, Table S5), but there was no significant difference between the Olyset Plus and Olyset Net (_P_ = 0.412). The Olyset Net induced more exophily than the Olyset Plus (OR = 0.76, \\(P\\) < 0.05), but there was no significant difference in exophily between the PermaNet 2.0 and 3.0 (_P_ = 0.727) (Table S5). The PBO nets from both manufacturers considerably reduced blood feeding compared with non-PBO nets with ORs of 0.34 (_P_ < 0.001) and 0.55 (_P_ < 0.01) for the PermaNet 3.0 and Olyset Plus, respectively (Fig. 5, Table S4). In addition, the PBO nets killed significantly more _An. gambiae s.l._ than the non-PBO nets. The ORs for mortality were 2.45 (_P_ < 0.001) for the PermaNet 3.0 and 2.1 (_P_ < 0.001) for the Olyset Plus (Fig. 5, Table S5).\n",
-                        "\n",
-                        "header: Experimental hut results\n",
-                        "\n",
-                        "In total, 12 915 specimens from four different mosquito genera were collected inside the huts (sleeping rooms and veranda traps) over the 6-week trial (Tengrela, \\(n\\) = 5808; VK5, \\(n\\) = 7107). Most specimens collected from the huts belonged to the _An. gambiae s.l._ species complex, accounting for 75.4% (_n_ = 4379) in Tengrela and 98.8% (_n_ = 7020) in VK5. The second most frequently collected _Anopheles_ species was _An. rhinorensis_, of which 49 and 19 specimens were collected in Tengrela and VK5, respectively. Other _Anopheles_ mosquito species, including _An. funestus_ (_n_ = 3), _An. nili_ (_n_ = 2) and _An. coustani_ (_n_ = 3), were collected in Tengrela. Additional mosquito taxa were also collected, including _Mansonia_ sp. (_n_ = 1330 in Tengrela and \\(n\\) = 45 in VK5), _Culex_ sp. (_n_ = 41 in Tengrela and \\(n\\) = 23 in VK5) and _Aedes_ sp. (_n_ = 1 in Tengrela) (all: Diptera: Culicidae). Because of the low numbers of other genera, only data for _An. gambiae s.l._ were included in the analysis.\n",
-                        "\n",
-                        "Within the total of 11 399 _An. gambiae s.l._ caught at both field sites, there was considerable variation between weeks and treatments in the numbers of mosquitoes entering the huts in both study locations (Fig. 4). The average number of mosquitoes caught per night/per hut was between 13 (PermaNet 3.0) and 20.7 (Olyset Net) mosquitoes (Table S3) and induced deterrence was found only for the Olyset Net, albeit at a low ratio of 1.31 (95% CI 1.02-1.66; \\(P\\) < 0.05) (Fig. 4).\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Use of the longlasting insecticidal net (LLIN) is pivotal in the fight against malaria in Africa. A massive scaling up of the distribution of this commodity has occurred over the past 15 years, with 178 million LLINs delivered for use in sub-Saharan Africa (SSA) in 2015 alone [World Health Organization (WHO), 2016]. Although reliable estimates of LLIN usage are very hard to obtain, the WHO estimates that 53% of the population at risk in SSA slept under an LLIN in 2015 [WHO, 2016]. The results have been dramatic: an estimated 450 million clinical cases of malaria were averted in the last 15 years by the use of LLINs [Bhatt _et al._, 2015].\n",
-                        "\n",
-                        "All LLINs in current use contain pyrethroid insecticides, but there is growing recognition that increases in the prevalence and intensity of pyrethroid resistance, driven at least in part by the scale-up in the use of LLINs, could jeopardize recent gains in malaria control [WHO, 2012]. Direct evidence that pyrethroid\n",
-                        "resistance is reducing either the personal or community-level protection provided by LLINS is challenging to obtain (Kleinschmidt _et al._, 2015; Ranson & Lissenden, 2016), but models of malaria transmission predict that even relatively low levels of resistance can substantially reduce the public health benefits of LILINS (Churcher _et al._, 2016). In countries that rely on the LLIN as the primary malaria prevention tool, the only currently available alternatives to conventional pyrethroid-only LLINS are nets in which the synerigent piperonyl butoxide (PBO) has been included in the fibres making up all, or part, of the net. Piperonyl butoxide inhibits cytochrome P450s, which comprise one of the most important enzyme families involved in pyrethroid resistance, and exposure to PBO has been shown to reduce resistance and sometimes to restore susceptibility to pyrethroids in malaria vectors (Jones _et al._, 2013; Edi _et al._, 2014).\n",
-                        "\n",
-                        "Four brands of LLIN containing PBO have received interim approval from the WHO as conventional LLINS. These are the PermaNet(r) 3.0 (deltamethrin+PBO) (Vestergaard Frandsen Holding SA, Lausanne, Switzerland), the Olvest(r) Plus (permethrin+PBO) (Sumitomo Chemical Asia Pte Ltd, Health and Crop Sciences Sector, Tokyo, Japan), the Veeralin(r) (alpha cypermethrin+PBO) (VKA Polymers Pte Ltd, Karur, India) and the DawaPlus(r) (deltamethrin+PBO) (Tana Netting Co. Ltd, Dubai, U.A.E.). The benefit of the addition of PBO is only expected to manifest in areas in which mosquito populations are resistant to pyrethroids and experimental hut trials of PBO LLINS in areas of resistance have supported this prediction. Increased mosquito mortality was observed in experimental huts containing PBO LLINS compared with conventional LLINS in Ivory Coast, Benin and Burkina Faso (Corbel _et al._, 2010; Ngousso _et al._, 2010; Koudou _et al._, 2011; Pennetier _et al._, 2013) and reductions in blood feeding rates were also reported in trials in the latter two countries. All of these sites reported high levels of pyrethroid resistance.\n",
-                        "\n",
-                        "There has been a dramatic escalation in the strength of pyrethroid resistance in southwestern Burkina Faso since earlier trials of PBO LLINS in 2007. World Health Organization cone bioassays performed in 2012 revealed that none of the conventional LLINS were effective in killing local vector populations and that the performance of the PBO LLIN PermaNet(r) 3.0 was also compromised in these assays (Toe _et al._, 2014). Although the numbers of malaria deaths in Burkina Faso have fallen over the past 10 years, numbers of malaria cases have risen year on year despite countywide LLIN distribution campaigns [National Malaria Control Programme (NMCP), personal communication; K.H.Toe, 2017]. In order to advise the NMCP on whether a switch to PBO LLINS may be warranted to target these highly resistant populations, an experimental hut trial was undertaken in two rice-growing areas in the southwestern region of Burkina Faso.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The results of these experimental hit studies with entomological endpoints suggest that substituting conventional LLINs with PBO LLINs in areas where there is a high prevalence of pyrethroid resistance in local vectors may be an effective strategy to maintain the efficacy of malaria vector control. The public health benefit of this would depend on a wide range of factors, including the level of malaria endemicity, LLIN coverage rates and the predominant mosquito vectors present, but a recent modelling exercise predicts that in some settings, a switch to PBO LLINs could aver up to 0.5 clinical cases per person per year (Churcher _et al._, 2016). These predictions from models are now being evaluated in large-scale field trials. A recent study in Tanzania reported a 33% protective efficacy of PBO LLINs over conventional LLINs after 2 years of use (Protopopoff _et al._, 2018). A larger trial, involving the distribution of over 10.7 million nets in Uganda, is evaluating whether PBO nets reduce malaria prevalence under programmatic conditions ([https://www.againstmalaria.com](https://www.againstmalaria.com), [https://www.pmi.gov](https://www.pmi.gov)).\n",
-                        "\n",
-                        "In light of the results from experimental hit studies, including the current study, and after reviewing data from the first clinical trial of a PBO LLIN, the WHO recently made a policy recommendation that national malaria control programmes should consider deployment of PBO LLINs in areas with pyrethroid-resistant vectors (WHO, 2017). If deployed at scale, PBO LLINs may play an important role in reducing the immediate threat of pyrethroid resistance to malaria control.\n",
-                        "\n",
-                        "header: Experimental hut trials\n",
-                        "\n",
-                        "Each station consisted of six experimental huts built according to the West African style (WHO, 2013). The study had six arms that used, respectively, five different LLINs and one net with no insecticide treatment as a negative control (Table 1). The nets were obtained from the manufacturers and were unpacked and kept in the shade for 24 h, but not washed prior to testing. Two of the nets, the OlysetPlus and PermaNet 3.0, contain PBO, whereas the other three LLINs contain only pyrethroids. The LLINs were holed according to WHO standard procedures (WHO, 2013). A total of six holes (4 cm x 4 cm) per net were cut, two on each of the long sides and one on each of the short sides.\n",
-                        "\n",
-                        "Study participants (male sleepers) spent 6 nights per week under a net in an experimental hut from 20.00 hours to 05.00 hours, followed by 1 day of break. The sleepers were rotated through the six huts so that each sleeper spent 1 night per week under each net type. To complete a full Latin square rotation with all combinations of sleeper, net type and hut, the study ran over 36 days from 8 September to 22 October 2014.\n",
-                        "\n",
-                        "Each morning at 05.00 hours, mosquitoes were collected manually by the sleepers, with supervision, from under the net, inside the hut and on the exit veranda. The collected specimens were morphologically identified to genus and, where possible, to species level (Gillies & Coetzee, 1987), grouped according to their gonotrophic stage (blood-fed, unfed or gravid), and scored as dead or alive. Live mosquitoes were transferred to paper cups, provided with 10% sugar water and kept in the insectary described above for 24 h, after which delayed mortality was recorded. All specimens were stored on silica gel for further molecular analysis.\n",
-                        "\n",
-                        "Data analysis was performed in the open-source statistical software R Version 3.3.2 (R Development Core Team, 2011) using the libraries 'lme4' (Bates _et al._, 2012) and 'glmADMB' (Skaug _et al._, 2012) for generalized linear mixed models (GLMMs). Plots were then generated with the package 'ggplot2' (Wickham, 2009).\n",
-                        "\n",
-                        "In the statistical analysis of hut trial data, in order to increase the number of replicates, give more power and increase confidence in the analysis, data collected from both sites (Tengrela and VK5) were pooled and the following four outcomes for _An. coluzzii_ were compared between the LLINs and the untreated control net, as well as between the PBO and non-PBO nets from the same manufacturer: (a) deterrence (i.e. the reduction in hut entry relative to the control or non-PBO net); (b) induced exophily (i.e. the ratio of the odds of a mosquito being found in the veranda trap compared with the hut); (c) blood feeding inhibition (i.e. the ratio of the odds of blood\n",
-                        "\n",
-                        "fed vs. unfed mosquitoes), and (d) induced mortality [i.e. the ratio of the odds of dead vs. alive mosquitoes] (The original dataset for _An. gambiae s.l._ is supplied in Table S2.) Immediate mortality and mortality at 24 h post-collection were combined for the analysis. Deterrence was analysed as the ratio in total numbers between the treatment arms (or PBO net) vs. the control arm (or non-PBO net). The numbers of mosquitoes in the room and the veranda were combined and analysed using a GLMM with a negative binomial distribution and a log link function using the R function 'glmandmdb()' in the 'glmmADMB' package. In the model, the net type was the fixed effect term and random intercepts were introduced for the sleeper and the hut, and a random slope for the day depending on the location. For proportional outcomes of induced exophily, blood feeding inhibition and induced mortality, the negative binomial model was replaced by a GLMM with a binomial distribution and logit link function using the R function 'glmer()' in the 'lme4' package. In the models, in addition to the terms listed above, a random intercept was introduced for each observation to account for unexplained overdispersion. For statistical testing, the level of significance was set at \\(a\\) = 0.05.\n",
-                        "\n",
-                        "In addition to the ratios described above, averages (i.e. the modes) and 95% confidence intervals (CIs) of the crude values underlying the outcomes were computed by the same models as above but using the individual nets as the intercept. These corresponding crude values were: (a) entry rate (i.e. the number of mosquitoes entering a hut); (b) exit rate (i.e. the number of mosquitoes collected from the veranda trap; (c) blood feeding rate (i.e. the proportion of blood-fed mosquitoes), and (d) mortality rate (i.e. the proportion of dead mosquitoes at 24 h post-collection).\n",
-                        "\n",
-                        "The study participants were recruited from the local communities and gave informed consent. Ethical approval was obtained from the Ethical Committee for Health Research of the Ministry of Health and Ministry of Research in Burkina Faso (Deliberation No. 2013-07-057, 11 July 2013). Malaria chemotherapy was not offered to study participants in line with Ministry of Health recommendations. However, medical supervision was provided throughout the study and any malaria case was treated according to national requirements.\n",
-                        "\n",
-                        "header: Study sites: Characterization of mosquito populations\n",
-                        "\n",
-                        "_Anopheles gambiae s.l._ larvae were collected in Tengrela and VK5 and reared to adults in local insectaries (mean relative humidity: 75+- 10%; mean temperature: 27+- 2*C). To assess susceptibility to pyrethroids, batches of approximately 25 non-blood-fed _An. gambiae s.l._ females aged 3-5 days were exposed to papers treated with 0.75% permethrin or 0.05% deltamethrin. The papers and susceptibility test kits were purchased from the Universiti Sains Malaysia (Penang, Malaysia). In parallel bioassays, mosquitoes were exposed to papers treated with 4% PBO [prepared by the Liverpool School of Tropical Medicine (LSTM)] for 1 h before they were transferred to tubes containing either insecticide-treated papers or insecticide-free papers and exposed for a further 1 h. Knock-down was recorded at the end of exposure and mortality was recorded 24 h later.\n",
-                        "\n",
-                        "The bio-efficacy of the LLINS was tested using non-blood-fed mosquitoes aged 3-5 days from local larval collections, and from the Kisumu susceptible laboratory strain, using the WHO cone bioassay procedure (WHO, 2013). For each LLIN type, two unwashed nets were tested under insectary conditions. Ten mosquitoes per cone were exposed for 3 min to 30 cm x 30 cm net pieces sampled from the top, the short and the long sides of the net. During exposure, the set-up was kept at an angle of 45 degrees as recommended (Owusu & Muller, 2016). Knock-down and mortality were recorded at 60 min and 24 h after exposure, respectively. Conventional LLINs were compared with PBO LLINS using Fisher's exact test (2x2 contingency table, significance level of 0.05) (http://wwwgraphpadcom/quickcales/contingency2) (Roberts & Andre, 1994).\n",
-                        "\n",
-                        "header: Pyrethroid resistance in southwest Burkina Faso: Bio-efficacy of LLINs in cone bioassays\n",
-                        "\n",
-                        "The low levels of mortality observed in cone bioassays in this study are similar to those reported previously in VK7, a neighbouring village to VK5, in the Vallee du Kou rice-growing region. However, whereas the current study found that exposure to the tops of the PermaNet 3.0 nets resulted in mortality levels exceeding the WHO threshold of 80%, equivalent bioassays conducted in the VK7 population in 2012 showed mortality of only 43%. Cone bioassays are not a reliable method of comparing the performance of LLINs containing different pyrethroids because of the inherent differences in extico-repellency within this class, which can affect exposure time (Siegert _et al._, 2009). In particular, cone tests may underestimate the performance of permethrin LLINs; this is indicated by comparison of the cone bioassay results in Fig. 3, in which the Olyset Net was found to induce considerably lower mortality than the PermaNet 2.0, although experimental hut results showed similar levels of mortality in huts with these two net types (Table S3).\n",
-                        "\n",
-                        "Fig. 3: Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (_P_ < 0.001), † (_P_ < 0.0001), n.s. (non-significant, \\(\\frac{1}{2}\\) (_P_ < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wicyclonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "Nevertheless, the results of these cone bioassays provide further evidence that resistance can be at least partially ameliorated by exposure to PBO.\n",
-                        "\n",
-                        "header: _Pyrethroid resistance in Tengrela and VK5 and associated mechanisms_: Efficacy of LLINs under laboratory conditions\n",
-                        "\n",
-                        "All LLINs tested showed good bio-efficacy in cone bioassays against the susceptible laboratory Kisumu strain with 60 min knock-down and 24 h mortality rates of all LLINs above the 98% and 80% WHO thresholds (WHO, 2005) (Table S1). By contrast, in tests using the field-caught mosquitoes, the mortality threshold was met only by the top panels of the PermaNet 3.0 (Fig. 3). Knock-down rates exceeded the 98% threshold for the PermaNet 3.0 in VK5 only (Table S1).\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "The experimental hut studies were carried out at two field stations in southwest Burkina Faso: the first is located in the Vallee du Kou.5 (VK5) near Bobo-Dioulasso (11*39' N, 04*41' W) and belongs to the Institut de Recherche en Science de la Sante (IRSS)/Centre MURAZ, and the second is located at Tengrela (10*40' N, 04*50' W) near Banfora and is maintained by the Centre National de Recherche et de Formation sur le Paludisme (CNRFP). These two sites are separated by approximately 120 km. Previous surveys revealed _Anopheles coluzzii_ (formerly _Anopheles gambiae s.s._ M molecular form) to be the predominant _Anopheles_ species at both sites. High levels of resistance to both DDT and pyrethroids have been reported previously at both sites (Ngufor _et al._, 2014; Toe _et al._, 2015).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Supporting Information\n",
-                        "\n",
-                        "Additional supporting information may be found online in the Supporting Information section at the end of the article.\n",
-                        "\n",
-                        "Fig. 5: Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hit relative to the control hit. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "header: Table 1: Treatment arms and descriptions of longlasting insecticide-treated nets.\n",
-                        "footer: PermaNet is a registered trademark of Vestergaard Frandsen Holding SA. Olyset is a registered trademark of Sumitomo Chemical Co. Ltd. DawaPlusis a registered trademark of Tana Netting Co. Ltd.PBO, piperonyl butoxide.\n",
-                        "columns: ['Treatment arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment arm\":\"Untreated net\",\"Description\":\"Net manufactured manually using netting material from market\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment arm\":\"Olyset\\u00ae Net\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment arm\":\"Olyset\\u00ae Plus\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin and 4.3 x 10-4 kg\\/m2 of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment arm\":\"PermaNet\\u00ae 2.0\",\"Description\":\"5.5 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"4\":{\"Treatment arm\":\"PermaNet\\u00ae 3.0\",\"Description\":\"Combination of 2.8 g\\/kg of deltamethrin coated on polyester with strengthened border (side panels) and deltamethrin (4.0 g\\/kg) and PBO (25 g\\/kg)\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"5\":{\"Treatment arm\":\"Dawa\\u00ae Plus 2.0\",\"Description\":\"8.0 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"TANA Netting\"}}\n",
-                        "\n",
-                        "header: Entry rate and deterrence: Exit rates and induced exophily\n",
-                        "\n",
-                        "Exit rates refer here to the proportion of mosquitoes present in the veranda trap. As with entrance rates, exit rates varied throughout the study (Fig. 4). On average, the induced exophily rates were between 26.4% (control net) and 48.5% (Olyset Net) (Table S4). The odds of finding a mosquito in the veranda trap were significantly increased in the huts with treated nets, with the exception of the DawaPlus, although even for the DawaPlus a tendency to induce exophily was observed (Fig. 4, Table S4).\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Very low mortality rates were obtained for both permethrin and deltamethrin in the two study sites following an hour of exposure to WHO diagnostic doses. Southwestern Burkina Faso is known as a hotspot of pyrethroid resistance (Dabire _et al._, 2012; Namountougou _et al._, 2012). Rapid changes in the prevalence of pyrethroid resistance have been observed since the first national LLIN distribution programme in 2010: in 2009, mosquito mortality following deltamethrin exposure was 25% (Dabire _et al._, 2012), in VK has since fallen to just 2.5%. The frequency of the 1014F _kdr_ mutation also increased from 0.28 in 2006 (Dabire _et al._, 2009) to 0.88 in the current study. Similar increases in the prevalence of pyrethroid resistance have been witnessed in Tengrela. Mortality rates of 93% and 46% for deltamethrin and permethrin, respectively, were recorded in 2011 (Namountougou _et al._, 2012; K. H. Toe, unpublished data 2011), but these mortality rates had reduced to 33% and 13% in 2014, the year of the current study (Toe _et al._, 2015). In the present study, pyrethroid mortality was significantly increased by pre-exposure to PBO. This, together with the qPCR data showing elevated expression of multiple P450s in both field populations compared with a susceptible laboratory strain, indicate that oxidases are an important resistance mechanism in _An. coluzzi_ in southwestern Burkina Faso. It is noted that resistance was not fully restored by PBO pre-exposure, indicating that additional resistance mechanisms, such as target site resistance and possibly cuticular modifications (Toe _et al._, 2015), may contribute to the pyrethroid resistance phenotype in these populations.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Do bednets including piperonyl butoxide offer additional protection against populations of _Anopheles gambiae s.l._ that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso\n",
-                        "\n",
-                        "K. H. T oe\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " P. Muller\n",
-                        "\n",
-                        "2H233\n",
-                        "\n",
-                        "33Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Badlo\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Traore\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " N. Sagnon\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " R. K. Dabi Irene\n",
-                        "\n",
-                        "4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " H. Ransons\n",
-                        "\n",
-                        "5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        "\n",
-                        "Malaria control is dependent on the use of longlasting insecticidal nets (LLINs) containing pyrethroids. A new generation of LLINs containing both pyrethroids and the synergist piperonyl butoxide (PBO) has been developed in response to increasing pyrethroid resistance in African malaria vectors, but questions remain about the performance of these nets in areas where levels of pyrethroid resistance are very high. This study was conducted in two settings in southwest Burkina Faso, Vallee du Kou 5 and Tengrela, where _Anopheles gambiae s.l._ (Diptera: Culicidae) mortality rates in World Health Organization (WHO) discriminating dose assays were \\(<\\) 14% for permethrin and \\(<\\) 33% for delatamethrin. When mosquitoes were pre-exposed to PBO in WHO tube assays, mortality rates increased substantially but full susceptibility was not restored. Molecular characterization revealed high levels of _kdr_ alleles and elevated levels of P450s previously implicated in pyrethroid resistance. In cone bioassays and experimental huts, PBO LLINs outperformed the pyrethroid-only equivalents from the same manufacturers. Blood feeding rates were 1.6-2.2-fold lower and mortality rates were 1.69-1.78-fold greater in huts with PBO LLINs vs. non-PBO LLINs. This study indicates that PBO LLINs provide greater personal and community-level protection than standard LLINs against highly pyrethroid-resistant mosquito populations.\n",
-                        "\n",
-                        " insecticide resistance, insecticide resistance management, longlasting insecticidal nets, PBO.\n",
-                        "\n",
-                        "header: Performance of PBO LLINs in experimental hut studies\n",
-                        "\n",
-                        "The enhanced performance of PBO-containing LLINs over conventional LLINs was further supported by the experimental hut results. Both blood feeding inhibition and mortality rates were significantly higher in huts containing PBO LLINs than in those containing conventional LLINs from the same manufacturers. An improved performance of the PermaNet 3.0, which contains both higher concentrations of deltamethrin compared with the PermaNet 2.0, plus PBO on the roof of the net, has also been reported in pyrethroid-resistant populations of malaria vectors in Ivory Coast (Koudou _et al._, 2011), Benin (N'Guessan _et al._, 2010) and Burkina Faso (Corbel _et al._, 2010).\n",
-                        "\n",
-                        "Fig. 4: Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the \\(P\\)-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "The magnitude of this effect differs among studies, with previous studies reporting increases in mortality rates of 1.3-1.8-fold, whereas the current study reports an OR of 2.45, which corresponds to a 1.78-fold increase. Only one experimental hit study comparing the Olyset Plus with the Olyset Net has been published to date (Pennetier _et al._, 2013). This study, conducted in Benin in 2013, reported mortality rates 1.9-fold higher in the PBO arm than in the conventional LLIN arm (Pennetier _et al._, 2013), which is in line with the findings of the current study, which found a 1.89-fold (OR 2.1) elevation in mortality rates in the Olyset Plus arm.\n",
-                        "\n",
-                        "In addition to increased mortality rates, the current study, plus two of the previous experimental hit studies, reported significantly higher rates of blood feeding inhibition for PBO vs. conventional LLINs (Corbel _et al._, 2010; N'Guessan _et al._, 2010), indicating that PBO LLINs afford an enhanced level of personal protection in areas where vectors are resistant to pyrethroids. It should be noted that the current study was performed using unwashed nets only. Previous studies have found that the efficacy of PBO LLINS against resistant mosquitoes decreases substantially after the nets have been washed 20 times according to WHO protocols (Corbel _et al._, 2010; Tungu _et al._, 2010; Koudou _et al._, 2011). Thus further studies on the durability of the bio-efficacy of PBO LLINS under field conditions are urgently needed.\n",
-                        "\n",
-                        "header: Blood feeding rates and inhibition\n",
-                        "\n",
-                        "Average blood feeding rates were between 17.0% (PermaNet 3.0) and 56.4% (control net) (Table S4). With the exception of the DawaPlus, all treated nets reduced blood feeding (Fig. 4, Table S4), and the effect was most prominent with the PBO nets PermaNet 3.0 and Olyset Plus (Fig. 4, Table S4), for which the odds ratios (ORs) of blood feeding relative to control nets were 0.19 (95% CI 01.3-0.26) and 0.16 (95% CI 0.11-0.23), respectively.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In VK5, very low levels of mortality were observed after exposure to the discriminating dose of deltamethrin and permethrin, but pre-exposure to PBO significantly increased mortality rates from 2.5% (_n_ = 163) to 45% (_n_ = 158) and from 5% (_n_ = 153) to 26% (_n_ = 156) for deltamethrin and permethrin, respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig. 1). In Tengrela, mortality rates of 34% (_n_ = 85) and 14% (_n_ = 101) were recorded for deltamethrin and permethrin, respectively. When PBO was used, mortality rates increased significantly to 63% (_n_ = 84) and 42% (_n_ = 104), respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig.1). Although there was evidence of synergism, pre-exposure to PBO did not fully restore susceptibility in either site to either pyrethroid.\n",
-                        "\n",
-                        "_Anopheles coluzzi_ was the only species of the _An. gambiae_ complex identified by PCR of a subset of 80 specimens from Tengrela and VK5. High frequencies of the 1014F allele of the VGSC were recorded in Tengrela (0.819, 95% CI 0.750-0.875) and VK5 (0.885, 95% CI 0.824-0.930) with the 1575Y allele present at lower frequencies of 0.169 (95% CI 0.114-0.236) and 0.221 (95% CI 0.158-0.295), respectively. Samples were not genotyped for the 1014S allele as previous extensive surveys have not detected this allele in _An. coluzzi_ from these sites (Toe _et al._, 2015). There was no statistically significant difference in the frequency of either the 1014F or 1575Y allele between the two sites (Table 2).\n",
-                        "\n",
-                        "Several cytochrome P450 genes previously associated with pyrethroid resistance showed elevated expression levels in the Tengrela and the VK5 _An. coluzzi_ populations compared with the susceptible laboratory Ngousso strain (Fig. 2). _CYP6P3_, _CYP6M2_, _CYP6Z3_, _CYP6P4_ and _CYP6Z2_ were found to be\n",
-                        "\n",
-                        "Fig. 1: Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: \\(P\\) < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "overexpressed in both field strains compared with the susceptible laboratory colony (fold change: > 2) with the most highly overexpressed genes in both populations being _CYP6Z2_ and _CYP6Z3_ (Fig. 2).\n",
-                        "\n",
-                        "header: Mortality rates and induced mortality\n",
-                        "\n",
-                        "Average mortality rates ranged from 9.5% in the control arm to 46.1% in the PermaNet 3.0 arm (Table S4). Mortality was statistically higher in all treated net conditions than in the control huts. As with blood feeding inhibition, the PBO nets showed the largest effects in increasing mortality (Fig. 5, Table S4); the ORs for mortality relative to control nets were 5.56 (95% CI 3.92-7.89) and 8.14\n",
-                        "Fig. 2: Expression levels of candidate genes previously associated with pyrethroid resistance in Tengrela and Vallée du Kou 5. The analysis was performed using the 2−\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "(95% CI 5.64-11.75) for the Olyset Plus and PermaNet 3.0, respectively, compared with 2.65 (95% CI 1.87-3.75) and 3.33 (95% CI 2.35-4.74) for the Olyset Net and PermaNet 2.0, respectively.\n",
-                        "\n",
-                        "header: Molecular analysis\n",
-                        "\n",
-                        "DNA was extracted from mosquito legs by heating at 90*C for 30 min. Species were identified using the SINE200 protocol (Santolamazza _et al._, 2008) and then screened for the voltage gated sodium channel (VGSC) 1014F and 1575Y alleles using Taqman assays (Bass _et al._, 2007; Jones _et al._, 2012).\n",
-                        "\n",
-                        "Total RNA was extracted from six pools of 10 5-day-old non-blood-fed female _An. coluzzii_ from larval collections from Tengrela and VK5 using the RNAqueous(r)4PCR Kit for isolation of DNA-free RNA (Ambion, Inc., Austin, TX, U.S.A.) according to the manufacturer's procedures. The RNA was eluted in 50 mL of elution solution and treated with DNase. The quality and quantity of all the RNA used were assessed \n",
-                        "using a NanoDrop ND1000 (Thermo Fisher Scientific UK Ltd, Renfrew, U.K.).\n",
-                        "\n",
-                        "The expression profiles of five P450 genes (_CYP6M2_, _CYP6Z2_, _CYP6Z3_, _CYP6P3_, _CYP6P4_), previously found to be over-expressed in pyrethroid-resistant field populations from Burkina Faso (Toe _et al._, 2015) and/or known to metabolize pyrethroids (Muller _et al._, 2008; Mitchell _et al._, 2012) were quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). The qPCR analysis was conducted at the LSTM and used the following mosquito populations: Ngousso, an insecticide-susceptible strain originating from Ngousso in Cameroon in 2006 and maintained in the insectary at LSTM; Tengrela specimens reared from larval collections in October-November 2014, and VK5 specimens reared from larval collections in October-November 2014. Approximately 600 ng of RNA was reverse-transcribed to first-strand cDNA using SuperScript(tm) III reverse transcriptase (Invitrogen, Inc., Carlsbad, CA, U.S.A.) according to the manufacturer's procedures. Samples were then purified using the Qiagen Easy Purification Kit (Qiagen Benelux BV, Venlo, the Netherlands) before proceeding to qPCR. Each of the six pool replicates were run in triplicate using 2X SYBR Brilliant III (Agilent Technologies, Inc., Palo Alto, CA, U.S.A.), forward and reverse primers (300 nM) [sequence available in Toe _et al._ (2015)] on the Mx3005p qPCR system (Agilent Technologies, Inc., Palo Alto, California) with the following cycling protocol: 95 degC for 3 min, followed by 40 cycles of 95 degC for 10 s and 60 degC for 10 s. The qPCR data were analysed using the delta Ct values method, taking into account the PCR efficiency (Pfaffi, 2001). The candidate Ct values were normalized against three housekeeping genes, encoding ribosomal protein L40 (ubiquitin) (_AGAP007927_), an elongation factor (_AGAP005128_) and the S7 ribosomal protein (_AGAP010592_). The normalized Ct values of each gene were then compared with the normalized Ct values of the susceptible Ngousso strain.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The results of these experimental hit studies with entomological endpoints suggest that substituting conventional LLINs with PBO LLINs in areas where there is a high prevalence of pyrethroid resistance in local vectors may be an effective strategy to maintain the efficacy of malaria vector control. The public health benefit of this would depend on a wide range of factors, including the level of malaria endemicity, LLIN coverage rates and the predominant mosquito vectors present, but a recent modelling exercise predicts that in some settings, a switch to PBO LLINs could aver up to 0.5 clinical cases per person per year (Churcher _et al._, 2016). These predictions from models are now being evaluated in large-scale field trials. A recent study in Tanzania reported a 33% protective efficacy of PBO LLINs over conventional LLINs after 2 years of use (Protopopoff _et al._, 2018). A larger trial, involving the distribution of over 10.7 million nets in Uganda, is evaluating whether PBO nets reduce malaria prevalence under programmatic conditions ([https://www.againstmalaria.com](https://www.againstmalaria.com), [https://www.pmi.gov](https://www.pmi.gov)).\n",
-                        "\n",
-                        "In light of the results from experimental hit studies, including the current study, and after reviewing data from the first clinical trial of a PBO LLIN, the WHO recently made a policy recommendation that national malaria control programmes should consider deployment of PBO LLINs in areas with pyrethroid-resistant vectors (WHO, 2017). If deployed at scale, PBO LLINs may play an important role in reducing the immediate threat of pyrethroid resistance to malaria control.\n",
-                        "\n",
-                        "header: Impact of PBO on LLIN efficacy in pyrethroid-resistant mosquitoes\n",
-                        "\n",
-                        "The performance of nets containing PBO as compared with pyrethroid-only nets from the same manufacturer is shown in Fig. 5. The PermaNet 3.0 deterred more mosquitoes than the PermaNet 2.0 (OR = 0.67, \\(P\\) < 0.01) (Fig. 5, Table S5), but there was no significant difference between the Olyset Plus and Olyset Net (_P_ = 0.412). The Olyset Net induced more exophily than the Olyset Plus (OR = 0.76, \\(P\\) < 0.05), but there was no significant difference in exophily between the PermaNet 2.0 and 3.0 (_P_ = 0.727) (Table S5). The PBO nets from both manufacturers considerably reduced blood feeding compared with non-PBO nets with ORs of 0.34 (_P_ < 0.001) and 0.55 (_P_ < 0.01) for the PermaNet 3.0 and Olyset Plus, respectively (Fig. 5, Table S4). In addition, the PBO nets killed significantly more _An. gambiae s.l._ than the non-PBO nets. The ORs for mortality were 2.45 (_P_ < 0.001) for the PermaNet 3.0 and 2.1 (_P_ < 0.001) for the Olyset Plus (Fig. 5, Table S5).\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Use of the longlasting insecticidal net (LLIN) is pivotal in the fight against malaria in Africa. A massive scaling up of the distribution of this commodity has occurred over the past 15 years, with 178 million LLINs delivered for use in sub-Saharan Africa (SSA) in 2015 alone [World Health Organization (WHO), 2016]. Although reliable estimates of LLIN usage are very hard to obtain, the WHO estimates that 53% of the population at risk in SSA slept under an LLIN in 2015 [WHO, 2016]. The results have been dramatic: an estimated 450 million clinical cases of malaria were averted in the last 15 years by the use of LLINs [Bhatt _et al._, 2015].\n",
-                        "\n",
-                        "All LLINs in current use contain pyrethroid insecticides, but there is growing recognition that increases in the prevalence and intensity of pyrethroid resistance, driven at least in part by the scale-up in the use of LLINs, could jeopardize recent gains in malaria control [WHO, 2012]. Direct evidence that pyrethroid\n",
-                        "resistance is reducing either the personal or community-level protection provided by LLINS is challenging to obtain (Kleinschmidt _et al._, 2015; Ranson & Lissenden, 2016), but models of malaria transmission predict that even relatively low levels of resistance can substantially reduce the public health benefits of LILINS (Churcher _et al._, 2016). In countries that rely on the LLIN as the primary malaria prevention tool, the only currently available alternatives to conventional pyrethroid-only LLINS are nets in which the synerigent piperonyl butoxide (PBO) has been included in the fibres making up all, or part, of the net. Piperonyl butoxide inhibits cytochrome P450s, which comprise one of the most important enzyme families involved in pyrethroid resistance, and exposure to PBO has been shown to reduce resistance and sometimes to restore susceptibility to pyrethroids in malaria vectors (Jones _et al._, 2013; Edi _et al._, 2014).\n",
-                        "\n",
-                        "Four brands of LLIN containing PBO have received interim approval from the WHO as conventional LLINS. These are the PermaNet(r) 3.0 (deltamethrin+PBO) (Vestergaard Frandsen Holding SA, Lausanne, Switzerland), the Olvest(r) Plus (permethrin+PBO) (Sumitomo Chemical Asia Pte Ltd, Health and Crop Sciences Sector, Tokyo, Japan), the Veeralin(r) (alpha cypermethrin+PBO) (VKA Polymers Pte Ltd, Karur, India) and the DawaPlus(r) (deltamethrin+PBO) (Tana Netting Co. Ltd, Dubai, U.A.E.). The benefit of the addition of PBO is only expected to manifest in areas in which mosquito populations are resistant to pyrethroids and experimental hut trials of PBO LLINS in areas of resistance have supported this prediction. Increased mosquito mortality was observed in experimental huts containing PBO LLINS compared with conventional LLINS in Ivory Coast, Benin and Burkina Faso (Corbel _et al._, 2010; Ngousso _et al._, 2010; Koudou _et al._, 2011; Pennetier _et al._, 2013) and reductions in blood feeding rates were also reported in trials in the latter two countries. All of these sites reported high levels of pyrethroid resistance.\n",
-                        "\n",
-                        "There has been a dramatic escalation in the strength of pyrethroid resistance in southwestern Burkina Faso since earlier trials of PBO LLINS in 2007. World Health Organization cone bioassays performed in 2012 revealed that none of the conventional LLINS were effective in killing local vector populations and that the performance of the PBO LLIN PermaNet(r) 3.0 was also compromised in these assays (Toe _et al._, 2014). Although the numbers of malaria deaths in Burkina Faso have fallen over the past 10 years, numbers of malaria cases have risen year on year despite countywide LLIN distribution campaigns [National Malaria Control Programme (NMCP), personal communication; K.H.Toe, 2017]. In order to advise the NMCP on whether a switch to PBO LLINS may be warranted to target these highly resistant populations, an experimental hut trial was undertaken in two rice-growing areas in the southwestern region of Burkina Faso.\n",
-                        "\n",
-                        "header: Pyrethroid resistance in southwest Burkina Faso: Bio-efficacy of LLINs in cone bioassays\n",
-                        "\n",
-                        "The low levels of mortality observed in cone bioassays in this study are similar to those reported previously in VK7, a neighbouring village to VK5, in the Vallee du Kou rice-growing region. However, whereas the current study found that exposure to the tops of the PermaNet 3.0 nets resulted in mortality levels exceeding the WHO threshold of 80%, equivalent bioassays conducted in the VK7 population in 2012 showed mortality of only 43%. Cone bioassays are not a reliable method of comparing the performance of LLINs containing different pyrethroids because of the inherent differences in extico-repellency within this class, which can affect exposure time (Siegert _et al._, 2009). In particular, cone tests may underestimate the performance of permethrin LLINs; this is indicated by comparison of the cone bioassay results in Fig. 3, in which the Olyset Net was found to induce considerably lower mortality than the PermaNet 2.0, although experimental hut results showed similar levels of mortality in huts with these two net types (Table S3).\n",
-                        "\n",
-                        "Fig. 3: Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (_P_ < 0.001), † (_P_ < 0.0001), n.s. (non-significant, \\(\\frac{1}{2}\\) (_P_ < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wicyclonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "Nevertheless, the results of these cone bioassays provide further evidence that resistance can be at least partially ameliorated by exposure to PBO.\n",
-                        "\n",
-                        "header: Experimental hut trials\n",
-                        "\n",
-                        "Each station consisted of six experimental huts built according to the West African style (WHO, 2013). The study had six arms that used, respectively, five different LLINs and one net with no insecticide treatment as a negative control (Table 1). The nets were obtained from the manufacturers and were unpacked and kept in the shade for 24 h, but not washed prior to testing. Two of the nets, the OlysetPlus and PermaNet 3.0, contain PBO, whereas the other three LLINs contain only pyrethroids. The LLINs were holed according to WHO standard procedures (WHO, 2013). A total of six holes (4 cm x 4 cm) per net were cut, two on each of the long sides and one on each of the short sides.\n",
-                        "\n",
-                        "Study participants (male sleepers) spent 6 nights per week under a net in an experimental hut from 20.00 hours to 05.00 hours, followed by 1 day of break. The sleepers were rotated through the six huts so that each sleeper spent 1 night per week under each net type. To complete a full Latin square rotation with all combinations of sleeper, net type and hut, the study ran over 36 days from 8 September to 22 October 2014.\n",
-                        "\n",
-                        "Each morning at 05.00 hours, mosquitoes were collected manually by the sleepers, with supervision, from under the net, inside the hut and on the exit veranda. The collected specimens were morphologically identified to genus and, where possible, to species level (Gillies & Coetzee, 1987), grouped according to their gonotrophic stage (blood-fed, unfed or gravid), and scored as dead or alive. Live mosquitoes were transferred to paper cups, provided with 10% sugar water and kept in the insectary described above for 24 h, after which delayed mortality was recorded. All specimens were stored on silica gel for further molecular analysis.\n",
-                        "\n",
-                        "Data analysis was performed in the open-source statistical software R Version 3.3.2 (R Development Core Team, 2011) using the libraries 'lme4' (Bates _et al._, 2012) and 'glmADMB' (Skaug _et al._, 2012) for generalized linear mixed models (GLMMs). Plots were then generated with the package 'ggplot2' (Wickham, 2009).\n",
-                        "\n",
-                        "In the statistical analysis of hut trial data, in order to increase the number of replicates, give more power and increase confidence in the analysis, data collected from both sites (Tengrela and VK5) were pooled and the following four outcomes for _An. coluzzii_ were compared between the LLINs and the untreated control net, as well as between the PBO and non-PBO nets from the same manufacturer: (a) deterrence (i.e. the reduction in hut entry relative to the control or non-PBO net); (b) induced exophily (i.e. the ratio of the odds of a mosquito being found in the veranda trap compared with the hut); (c) blood feeding inhibition (i.e. the ratio of the odds of blood\n",
-                        "\n",
-                        "fed vs. unfed mosquitoes), and (d) induced mortality [i.e. the ratio of the odds of dead vs. alive mosquitoes] (The original dataset for _An. gambiae s.l._ is supplied in Table S2.) Immediate mortality and mortality at 24 h post-collection were combined for the analysis. Deterrence was analysed as the ratio in total numbers between the treatment arms (or PBO net) vs. the control arm (or non-PBO net). The numbers of mosquitoes in the room and the veranda were combined and analysed using a GLMM with a negative binomial distribution and a log link function using the R function 'glmandmdb()' in the 'glmmADMB' package. In the model, the net type was the fixed effect term and random intercepts were introduced for the sleeper and the hut, and a random slope for the day depending on the location. For proportional outcomes of induced exophily, blood feeding inhibition and induced mortality, the negative binomial model was replaced by a GLMM with a binomial distribution and logit link function using the R function 'glmer()' in the 'lme4' package. In the models, in addition to the terms listed above, a random intercept was introduced for each observation to account for unexplained overdispersion. For statistical testing, the level of significance was set at \\(a\\) = 0.05.\n",
-                        "\n",
-                        "In addition to the ratios described above, averages (i.e. the modes) and 95% confidence intervals (CIs) of the crude values underlying the outcomes were computed by the same models as above but using the individual nets as the intercept. These corresponding crude values were: (a) entry rate (i.e. the number of mosquitoes entering a hut); (b) exit rate (i.e. the number of mosquitoes collected from the veranda trap; (c) blood feeding rate (i.e. the proportion of blood-fed mosquitoes), and (d) mortality rate (i.e. the proportion of dead mosquitoes at 24 h post-collection).\n",
-                        "\n",
-                        "The study participants were recruited from the local communities and gave informed consent. Ethical approval was obtained from the Ethical Committee for Health Research of the Ministry of Health and Ministry of Research in Burkina Faso (Deliberation No. 2013-07-057, 11 July 2013). Malaria chemotherapy was not offered to study participants in line with Ministry of Health recommendations. However, medical supervision was provided throughout the study and any malaria case was treated according to national requirements.\n",
-                        "\n",
-                        "header: _Pyrethroid resistance in Tengrela and VK5 and associated mechanisms_: Efficacy of LLINs under laboratory conditions\n",
-                        "\n",
-                        "All LLINs tested showed good bio-efficacy in cone bioassays against the susceptible laboratory Kisumu strain with 60 min knock-down and 24 h mortality rates of all LLINs above the 98% and 80% WHO thresholds (WHO, 2005) (Table S1). By contrast, in tests using the field-caught mosquitoes, the mortality threshold was met only by the top panels of the PermaNet 3.0 (Fig. 3). Knock-down rates exceeded the 98% threshold for the PermaNet 3.0 in VK5 only (Table S1).\n",
-                        "\n",
-                        "header: Experimental hut results\n",
-                        "\n",
-                        "In total, 12 915 specimens from four different mosquito genera were collected inside the huts (sleeping rooms and veranda traps) over the 6-week trial (Tengrela, \\(n\\) = 5808; VK5, \\(n\\) = 7107). Most specimens collected from the huts belonged to the _An. gambiae s.l._ species complex, accounting for 75.4% (_n_ = 4379) in Tengrela and 98.8% (_n_ = 7020) in VK5. The second most frequently collected _Anopheles_ species was _An. rhinorensis_, of which 49 and 19 specimens were collected in Tengrela and VK5, respectively. Other _Anopheles_ mosquito species, including _An. funestus_ (_n_ = 3), _An. nili_ (_n_ = 2) and _An. coustani_ (_n_ = 3), were collected in Tengrela. Additional mosquito taxa were also collected, including _Mansonia_ sp. (_n_ = 1330 in Tengrela and \\(n\\) = 45 in VK5), _Culex_ sp. (_n_ = 41 in Tengrela and \\(n\\) = 23 in VK5) and _Aedes_ sp. (_n_ = 1 in Tengrela) (all: Diptera: Culicidae). Because of the low numbers of other genera, only data for _An. gambiae s.l._ were included in the analysis.\n",
-                        "\n",
-                        "Within the total of 11 399 _An. gambiae s.l._ caught at both field sites, there was considerable variation between weeks and treatments in the numbers of mosquitoes entering the huts in both study locations (Fig. 4). The average number of mosquitoes caught per night/per hut was between 13 (PermaNet 3.0) and 20.7 (Olyset Net) mosquitoes (Table S3) and induced deterrence was found only for the Olyset Net, albeit at a low ratio of 1.31 (95% CI 1.02-1.66; \\(P\\) < 0.05) (Fig. 4).\n",
-                        "\n",
-                        "header: Table 2: Frequencies of 1014F and 1575Y kdr alleles in Anopheles coluzzili, in Tengrela and Vallée du Kou 5 (VK5).\n",
-                        "footer: Chi-squared test (http://vassarstats.net/) for the comparison of the frequency of the 1014F and 1575Y mutations in Anopheles coluzzili populations from Tengrela and VK5 (October 2014). No statistical difference was observed in the frequencies of the kdr alleles in the two sites.\n",
-                        "columns: ['1014F mutation - Unnamed: 0_level_1', '1014F mutation - Total n', '1014F mutation - LL', '1014F mutation - LF', '1014F mutation - FF', '1014F mutation - f (1014F)', '1014F mutation - P-value']\n",
-                        "\n",
-                        "{\"0\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"2\",\"1014F mutation - LF\":\"25\",\"1014F mutation - FF\":\"53\",\"1014F mutation - f (1014F)\":\"0.819\",\"1014F mutation - P-value\":\"0.26\"},\"1\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"78\",\"1014F mutation - LL\":\"0\",\"1014F mutation - LF\":\"18\",\"1014F mutation - FF\":\"60\",\"1014F mutation - f (1014F)\":\"0.885\",\"1014F mutation - P-value\":null},\"2\":{\"1014F mutation - Unnamed: 0_level_1\":\"1575Y mutation\",\"1014F mutation - Total n\":\"1575Y mutation\",\"1014F mutation - LL\":\"1575Y mutation\",\"1014F mutation - LF\":\"1575Y mutation\",\"1014F mutation - FF\":\"1575Y mutation\",\"1014F mutation - f (1014F)\":\"1575Y mutation\",\"1014F mutation - P-value\":\"1575Y mutation\"},\"3\":{\"1014F mutation - Unnamed: 0_level_1\":null,\"1014F mutation - Total n\":\"Total n\",\"1014F mutation - LL\":\"NN\",\"1014F mutation - LF\":\"NY\",\"1014F mutation - FF\":\"YY\",\"1014F mutation - f (1014F)\":\"f (A575Y)\",\"1014F mutation - P-value\":\"P-value\"},\"4\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"57\",\"1014F mutation - LF\":\"19\",\"1014F mutation - FF\":\"4\",\"1014F mutation - f (1014F)\":\"0.169\",\"1014F mutation - P-value\":\"0.39\"},\"5\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"77\",\"1014F mutation - LL\":\"49\",\"1014F mutation - LF\":\"22\",\"1014F mutation - FF\":\"6\",\"1014F mutation - f (1014F)\":\"0.221\",\"1014F mutation - P-value\":null}}\n",
-                        "\n",
-                        "header: Study sites: Characterization of mosquito populations\n",
-                        "\n",
-                        "_Anopheles gambiae s.l._ larvae were collected in Tengrela and VK5 and reared to adults in local insectaries (mean relative humidity: 75+- 10%; mean temperature: 27+- 2*C). To assess susceptibility to pyrethroids, batches of approximately 25 non-blood-fed _An. gambiae s.l._ females aged 3-5 days were exposed to papers treated with 0.75% permethrin or 0.05% deltamethrin. The papers and susceptibility test kits were purchased from the Universiti Sains Malaysia (Penang, Malaysia). In parallel bioassays, mosquitoes were exposed to papers treated with 4% PBO [prepared by the Liverpool School of Tropical Medicine (LSTM)] for 1 h before they were transferred to tubes containing either insecticide-treated papers or insecticide-free papers and exposed for a further 1 h. Knock-down was recorded at the end of exposure and mortality was recorded 24 h later.\n",
-                        "\n",
-                        "The bio-efficacy of the LLINS was tested using non-blood-fed mosquitoes aged 3-5 days from local larval collections, and from the Kisumu susceptible laboratory strain, using the WHO cone bioassay procedure (WHO, 2013). For each LLIN type, two unwashed nets were tested under insectary conditions. Ten mosquitoes per cone were exposed for 3 min to 30 cm x 30 cm net pieces sampled from the top, the short and the long sides of the net. During exposure, the set-up was kept at an angle of 45 degrees as recommended (Owusu & Muller, 2016). Knock-down and mortality were recorded at 60 min and 24 h after exposure, respectively. Conventional LLINs were compared with PBO LLINS using Fisher's exact test (2x2 contingency table, significance level of 0.05) (http://wwwgraphpadcom/quickcales/contingency2) (Roberts & Andre, 1994).\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "The experimental hut studies were carried out at two field stations in southwest Burkina Faso: the first is located in the Vallee du Kou.5 (VK5) near Bobo-Dioulasso (11*39' N, 04*41' W) and belongs to the Institut de Recherche en Science de la Sante (IRSS)/Centre MURAZ, and the second is located at Tengrela (10*40' N, 04*50' W) near Banfora and is maintained by the Centre National de Recherche et de Formation sur le Paludisme (CNRFP). These two sites are separated by approximately 120 km. Previous surveys revealed _Anopheles coluzzii_ (formerly _Anopheles gambiae s.s._ M molecular form) to be the predominant _Anopheles_ species at both sites. High levels of resistance to both DDT and pyrethroids have been reported previously at both sites (Ngufor _et al._, 2014; Toe _et al._, 2015).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Supporting Information\n",
-                        "\n",
-                        "Additional supporting information may be found online in the Supporting Information section at the end of the article.\n",
-                        "\n",
-                        "Fig. 5: Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hit relative to the control hit. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "header: Entry rate and deterrence: Exit rates and induced exophily\n",
-                        "\n",
-                        "Exit rates refer here to the proportion of mosquitoes present in the veranda trap. As with entrance rates, exit rates varied throughout the study (Fig. 4). On average, the induced exophily rates were between 26.4% (control net) and 48.5% (Olyset Net) (Table S4). The odds of finding a mosquito in the veranda trap were significantly increased in the huts with treated nets, with the exception of the DawaPlus, although even for the DawaPlus a tendency to induce exophily was observed (Fig. 4, Table S4).\n",
-                        "\n",
-                        "header: Table 1: Treatment arms and descriptions of longlasting insecticide-treated nets.\n",
-                        "footer: PermaNet is a registered trademark of Vestergaard Frandsen Holding SA. Olyset is a registered trademark of Sumitomo Chemical Co. Ltd. DawaPlusis a registered trademark of Tana Netting Co. Ltd.PBO, piperonyl butoxide.\n",
-                        "columns: ['Treatment arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment arm\":\"Untreated net\",\"Description\":\"Net manufactured manually using netting material from market\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment arm\":\"Olyset\\u00ae Net\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment arm\":\"Olyset\\u00ae Plus\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin and 4.3 x 10-4 kg\\/m2 of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment arm\":\"PermaNet\\u00ae 2.0\",\"Description\":\"5.5 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"4\":{\"Treatment arm\":\"PermaNet\\u00ae 3.0\",\"Description\":\"Combination of 2.8 g\\/kg of deltamethrin coated on polyester with strengthened border (side panels) and deltamethrin (4.0 g\\/kg) and PBO (25 g\\/kg)\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"5\":{\"Treatment arm\":\"Dawa\\u00ae Plus 2.0\",\"Description\":\"8.0 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"TANA Netting\"}}\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Very low mortality rates were obtained for both permethrin and deltamethrin in the two study sites following an hour of exposure to WHO diagnostic doses. Southwestern Burkina Faso is known as a hotspot of pyrethroid resistance (Dabire _et al._, 2012; Namountougou _et al._, 2012). Rapid changes in the prevalence of pyrethroid resistance have been observed since the first national LLIN distribution programme in 2010: in 2009, mosquito mortality following deltamethrin exposure was 25% (Dabire _et al._, 2012), in VK has since fallen to just 2.5%. The frequency of the 1014F _kdr_ mutation also increased from 0.28 in 2006 (Dabire _et al._, 2009) to 0.88 in the current study. Similar increases in the prevalence of pyrethroid resistance have been witnessed in Tengrela. Mortality rates of 93% and 46% for deltamethrin and permethrin, respectively, were recorded in 2011 (Namountougou _et al._, 2012; K. H. Toe, unpublished data 2011), but these mortality rates had reduced to 33% and 13% in 2014, the year of the current study (Toe _et al._, 2015). In the present study, pyrethroid mortality was significantly increased by pre-exposure to PBO. This, together with the qPCR data showing elevated expression of multiple P450s in both field populations compared with a susceptible laboratory strain, indicate that oxidases are an important resistance mechanism in _An. coluzzi_ in southwestern Burkina Faso. It is noted that resistance was not fully restored by PBO pre-exposure, indicating that additional resistance mechanisms, such as target site resistance and possibly cuticular modifications (Toe _et al._, 2015), may contribute to the pyrethroid resistance phenotype in these populations.\n",
-                        "\n",
-                        "header: Molecular analysis\n",
-                        "\n",
-                        "DNA was extracted from mosquito legs by heating at 90*C for 30 min. Species were identified using the SINE200 protocol (Santolamazza _et al._, 2008) and then screened for the voltage gated sodium channel (VGSC) 1014F and 1575Y alleles using Taqman assays (Bass _et al._, 2007; Jones _et al._, 2012).\n",
-                        "\n",
-                        "Total RNA was extracted from six pools of 10 5-day-old non-blood-fed female _An. coluzzii_ from larval collections from Tengrela and VK5 using the RNAqueous(r)4PCR Kit for isolation of DNA-free RNA (Ambion, Inc., Austin, TX, U.S.A.) according to the manufacturer's procedures. The RNA was eluted in 50 mL of elution solution and treated with DNase. The quality and quantity of all the RNA used were assessed \n",
-                        "using a NanoDrop ND1000 (Thermo Fisher Scientific UK Ltd, Renfrew, U.K.).\n",
-                        "\n",
-                        "The expression profiles of five P450 genes (_CYP6M2_, _CYP6Z2_, _CYP6Z3_, _CYP6P3_, _CYP6P4_), previously found to be over-expressed in pyrethroid-resistant field populations from Burkina Faso (Toe _et al._, 2015) and/or known to metabolize pyrethroids (Muller _et al._, 2008; Mitchell _et al._, 2012) were quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). The qPCR analysis was conducted at the LSTM and used the following mosquito populations: Ngousso, an insecticide-susceptible strain originating from Ngousso in Cameroon in 2006 and maintained in the insectary at LSTM; Tengrela specimens reared from larval collections in October-November 2014, and VK5 specimens reared from larval collections in October-November 2014. Approximately 600 ng of RNA was reverse-transcribed to first-strand cDNA using SuperScript(tm) III reverse transcriptase (Invitrogen, Inc., Carlsbad, CA, U.S.A.) according to the manufacturer's procedures. Samples were then purified using the Qiagen Easy Purification Kit (Qiagen Benelux BV, Venlo, the Netherlands) before proceeding to qPCR. Each of the six pool replicates were run in triplicate using 2X SYBR Brilliant III (Agilent Technologies, Inc., Palo Alto, CA, U.S.A.), forward and reverse primers (300 nM) [sequence available in Toe _et al._ (2015)] on the Mx3005p qPCR system (Agilent Technologies, Inc., Palo Alto, California) with the following cycling protocol: 95 degC for 3 min, followed by 40 cycles of 95 degC for 10 s and 60 degC for 10 s. The qPCR data were analysed using the delta Ct values method, taking into account the PCR efficiency (Pfaffi, 2001). The candidate Ct values were normalized against three housekeeping genes, encoding ribosomal protein L40 (ubiquitin) (_AGAP007927_), an elongation factor (_AGAP005128_) and the S7 ribosomal protein (_AGAP010592_). The normalized Ct values of each gene were then compared with the normalized Ct values of the susceptible Ngousso strain.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In VK5, very low levels of mortality were observed after exposure to the discriminating dose of deltamethrin and permethrin, but pre-exposure to PBO significantly increased mortality rates from 2.5% (_n_ = 163) to 45% (_n_ = 158) and from 5% (_n_ = 153) to 26% (_n_ = 156) for deltamethrin and permethrin, respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig. 1). In Tengrela, mortality rates of 34% (_n_ = 85) and 14% (_n_ = 101) were recorded for deltamethrin and permethrin, respectively. When PBO was used, mortality rates increased significantly to 63% (_n_ = 84) and 42% (_n_ = 104), respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig.1). Although there was evidence of synergism, pre-exposure to PBO did not fully restore susceptibility in either site to either pyrethroid.\n",
-                        "\n",
-                        "_Anopheles coluzzi_ was the only species of the _An. gambiae_ complex identified by PCR of a subset of 80 specimens from Tengrela and VK5. High frequencies of the 1014F allele of the VGSC were recorded in Tengrela (0.819, 95% CI 0.750-0.875) and VK5 (0.885, 95% CI 0.824-0.930) with the 1575Y allele present at lower frequencies of 0.169 (95% CI 0.114-0.236) and 0.221 (95% CI 0.158-0.295), respectively. Samples were not genotyped for the 1014S allele as previous extensive surveys have not detected this allele in _An. coluzzi_ from these sites (Toe _et al._, 2015). There was no statistically significant difference in the frequency of either the 1014F or 1575Y allele between the two sites (Table 2).\n",
-                        "\n",
-                        "Several cytochrome P450 genes previously associated with pyrethroid resistance showed elevated expression levels in the Tengrela and the VK5 _An. coluzzi_ populations compared with the susceptible laboratory Ngousso strain (Fig. 2). _CYP6P3_, _CYP6M2_, _CYP6Z3_, _CYP6P4_ and _CYP6Z2_ were found to be\n",
-                        "\n",
-                        "Fig. 1: Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: \\(P\\) < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "overexpressed in both field strains compared with the susceptible laboratory colony (fold change: > 2) with the most highly overexpressed genes in both populations being _CYP6Z2_ and _CYP6Z3_ (Fig. 2).\n",
-                        "\n",
-                        "header: Blood feeding rates and inhibition\n",
-                        "\n",
-                        "Average blood feeding rates were between 17.0% (PermaNet 3.0) and 56.4% (control net) (Table S4). With the exception of the DawaPlus, all treated nets reduced blood feeding (Fig. 4, Table S4), and the effect was most prominent with the PBO nets PermaNet 3.0 and Olyset Plus (Fig. 4, Table S4), for which the odds ratios (ORs) of blood feeding relative to control nets were 0.19 (95% CI 01.3-0.26) and 0.16 (95% CI 0.11-0.23), respectively.\n",
-                        "\n",
-                        "header: Performance of PBO LLINs in experimental hut studies\n",
-                        "\n",
-                        "The enhanced performance of PBO-containing LLINs over conventional LLINs was further supported by the experimental hut results. Both blood feeding inhibition and mortality rates were significantly higher in huts containing PBO LLINs than in those containing conventional LLINs from the same manufacturers. An improved performance of the PermaNet 3.0, which contains both higher concentrations of deltamethrin compared with the PermaNet 2.0, plus PBO on the roof of the net, has also been reported in pyrethroid-resistant populations of malaria vectors in Ivory Coast (Koudou _et al._, 2011), Benin (N'Guessan _et al._, 2010) and Burkina Faso (Corbel _et al._, 2010).\n",
-                        "\n",
-                        "Fig. 4: Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the \\(P\\)-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "The magnitude of this effect differs among studies, with previous studies reporting increases in mortality rates of 1.3-1.8-fold, whereas the current study reports an OR of 2.45, which corresponds to a 1.78-fold increase. Only one experimental hit study comparing the Olyset Plus with the Olyset Net has been published to date (Pennetier _et al._, 2013). This study, conducted in Benin in 2013, reported mortality rates 1.9-fold higher in the PBO arm than in the conventional LLIN arm (Pennetier _et al._, 2013), which is in line with the findings of the current study, which found a 1.89-fold (OR 2.1) elevation in mortality rates in the Olyset Plus arm.\n",
-                        "\n",
-                        "In addition to increased mortality rates, the current study, plus two of the previous experimental hit studies, reported significantly higher rates of blood feeding inhibition for PBO vs. conventional LLINs (Corbel _et al._, 2010; N'Guessan _et al._, 2010), indicating that PBO LLINs afford an enhanced level of personal protection in areas where vectors are resistant to pyrethroids. It should be noted that the current study was performed using unwashed nets only. Previous studies have found that the efficacy of PBO LLINS against resistant mosquitoes decreases substantially after the nets have been washed 20 times according to WHO protocols (Corbel _et al._, 2010; Tungu _et al._, 2010; Koudou _et al._, 2011). Thus further studies on the durability of the bio-efficacy of PBO LLINS under field conditions are urgently needed.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Do bednets including piperonyl butoxide offer additional protection against populations of _Anopheles gambiae s.l._ that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso\n",
-                        "\n",
-                        "K. H. T oe\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " P. Muller\n",
-                        "\n",
-                        "2H233\n",
-                        "\n",
-                        "33Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Badlo\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Traore\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " N. Sagnon\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " R. K. Dabi Irene\n",
-                        "\n",
-                        "4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " H. Ransons\n",
-                        "\n",
-                        "5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        "\n",
-                        "Malaria control is dependent on the use of longlasting insecticidal nets (LLINs) containing pyrethroids. A new generation of LLINs containing both pyrethroids and the synergist piperonyl butoxide (PBO) has been developed in response to increasing pyrethroid resistance in African malaria vectors, but questions remain about the performance of these nets in areas where levels of pyrethroid resistance are very high. This study was conducted in two settings in southwest Burkina Faso, Vallee du Kou 5 and Tengrela, where _Anopheles gambiae s.l._ (Diptera: Culicidae) mortality rates in World Health Organization (WHO) discriminating dose assays were \\(<\\) 14% for permethrin and \\(<\\) 33% for delatamethrin. When mosquitoes were pre-exposed to PBO in WHO tube assays, mortality rates increased substantially but full susceptibility was not restored. Molecular characterization revealed high levels of _kdr_ alleles and elevated levels of P450s previously implicated in pyrethroid resistance. In cone bioassays and experimental huts, PBO LLINs outperformed the pyrethroid-only equivalents from the same manufacturers. Blood feeding rates were 1.6-2.2-fold lower and mortality rates were 1.69-1.78-fold greater in huts with PBO LLINs vs. non-PBO LLINs. This study indicates that PBO LLINs provide greater personal and community-level protection than standard LLINs against highly pyrethroid-resistant mosquito populations.\n",
-                        "\n",
-                        " insecticide resistance, insecticide resistance management, longlasting insecticidal nets, PBO.\n",
-                        "\n",
-                        "header: Mortality rates and induced mortality\n",
-                        "\n",
-                        "Average mortality rates ranged from 9.5% in the control arm to 46.1% in the PermaNet 3.0 arm (Table S4). Mortality was statistically higher in all treated net conditions than in the control huts. As with blood feeding inhibition, the PBO nets showed the largest effects in increasing mortality (Fig. 5, Table S4); the ORs for mortality relative to control nets were 5.56 (95% CI 3.92-7.89) and 8.14\n",
-                        "Fig. 2: Expression levels of candidate genes previously associated with pyrethroid resistance in Tengrela and Vallée du Kou 5. The analysis was performed using the 2−\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "(95% CI 5.64-11.75) for the Olyset Plus and PermaNet 3.0, respectively, compared with 2.65 (95% CI 1.87-3.75) and 3.33 (95% CI 2.35-4.74) for the Olyset Net and PermaNet 2.0, respectively.\n",
-                        "\n",
-                        "header: Table 2: Frequencies of 1014F and 1575Y kdr alleles in Anopheles coluzzili, in Tengrela and Vallée du Kou 5 (VK5).\n",
-                        "footer: Chi-squared test (http://vassarstats.net/) for the comparison of the frequency of the 1014F and 1575Y mutations in Anopheles coluzzili populations from Tengrela and VK5 (October 2014). No statistical difference was observed in the frequencies of the kdr alleles in the two sites.\n",
-                        "columns: ['1014F mutation - Unnamed: 0_level_1', '1014F mutation - Total n', '1014F mutation - LL', '1014F mutation - LF', '1014F mutation - FF', '1014F mutation - f (1014F)', '1014F mutation - P-value']\n",
-                        "\n",
-                        "{\"0\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"2\",\"1014F mutation - LF\":\"25\",\"1014F mutation - FF\":\"53\",\"1014F mutation - f (1014F)\":\"0.819\",\"1014F mutation - P-value\":\"0.26\"},\"1\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"78\",\"1014F mutation - LL\":\"0\",\"1014F mutation - LF\":\"18\",\"1014F mutation - FF\":\"60\",\"1014F mutation - f (1014F)\":\"0.885\",\"1014F mutation - P-value\":null},\"2\":{\"1014F mutation - Unnamed: 0_level_1\":\"1575Y mutation\",\"1014F mutation - Total n\":\"1575Y mutation\",\"1014F mutation - LL\":\"1575Y mutation\",\"1014F mutation - LF\":\"1575Y mutation\",\"1014F mutation - FF\":\"1575Y mutation\",\"1014F mutation - f (1014F)\":\"1575Y mutation\",\"1014F mutation - P-value\":\"1575Y mutation\"},\"3\":{\"1014F mutation - Unnamed: 0_level_1\":null,\"1014F mutation - Total n\":\"Total n\",\"1014F mutation - LL\":\"NN\",\"1014F mutation - LF\":\"NY\",\"1014F mutation - FF\":\"YY\",\"1014F mutation - f (1014F)\":\"f (A575Y)\",\"1014F mutation - P-value\":\"P-value\"},\"4\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"57\",\"1014F mutation - LF\":\"19\",\"1014F mutation - FF\":\"4\",\"1014F mutation - f (1014F)\":\"0.169\",\"1014F mutation - P-value\":\"0.39\"},\"5\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"77\",\"1014F mutation - LL\":\"49\",\"1014F mutation - LF\":\"22\",\"1014F mutation - FF\":\"6\",\"1014F mutation - f (1014F)\":\"0.221\",\"1014F mutation - P-value\":null}}\n",
-                        "\n",
-                        "header: Impact of PBO on LLIN efficacy in pyrethroid-resistant mosquitoes\n",
-                        "\n",
-                        "The performance of nets containing PBO as compared with pyrethroid-only nets from the same manufacturer is shown in Fig. 5. The PermaNet 3.0 deterred more mosquitoes than the PermaNet 2.0 (OR = 0.67, \\(P\\) < 0.01) (Fig. 5, Table S5), but there was no significant difference between the Olyset Plus and Olyset Net (_P_ = 0.412). The Olyset Net induced more exophily than the Olyset Plus (OR = 0.76, \\(P\\) < 0.05), but there was no significant difference in exophily between the PermaNet 2.0 and 3.0 (_P_ = 0.727) (Table S5). The PBO nets from both manufacturers considerably reduced blood feeding compared with non-PBO nets with ORs of 0.34 (_P_ < 0.001) and 0.55 (_P_ < 0.01) for the PermaNet 3.0 and Olyset Plus, respectively (Fig. 5, Table S4). In addition, the PBO nets killed significantly more _An. gambiae s.l._ than the non-PBO nets. The ORs for mortality were 2.45 (_P_ < 0.001) for the PermaNet 3.0 and 2.1 (_P_ < 0.001) for the Olyset Plus (Fig. 5, Table S5).\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Use of the longlasting insecticidal net (LLIN) is pivotal in the fight against malaria in Africa. A massive scaling up of the distribution of this commodity has occurred over the past 15 years, with 178 million LLINs delivered for use in sub-Saharan Africa (SSA) in 2015 alone [World Health Organization (WHO), 2016]. Although reliable estimates of LLIN usage are very hard to obtain, the WHO estimates that 53% of the population at risk in SSA slept under an LLIN in 2015 [WHO, 2016]. The results have been dramatic: an estimated 450 million clinical cases of malaria were averted in the last 15 years by the use of LLINs [Bhatt _et al._, 2015].\n",
-                        "\n",
-                        "All LLINs in current use contain pyrethroid insecticides, but there is growing recognition that increases in the prevalence and intensity of pyrethroid resistance, driven at least in part by the scale-up in the use of LLINs, could jeopardize recent gains in malaria control [WHO, 2012]. Direct evidence that pyrethroid\n",
-                        "resistance is reducing either the personal or community-level protection provided by LLINS is challenging to obtain (Kleinschmidt _et al._, 2015; Ranson & Lissenden, 2016), but models of malaria transmission predict that even relatively low levels of resistance can substantially reduce the public health benefits of LILINS (Churcher _et al._, 2016). In countries that rely on the LLIN as the primary malaria prevention tool, the only currently available alternatives to conventional pyrethroid-only LLINS are nets in which the synerigent piperonyl butoxide (PBO) has been included in the fibres making up all, or part, of the net. Piperonyl butoxide inhibits cytochrome P450s, which comprise one of the most important enzyme families involved in pyrethroid resistance, and exposure to PBO has been shown to reduce resistance and sometimes to restore susceptibility to pyrethroids in malaria vectors (Jones _et al._, 2013; Edi _et al._, 2014).\n",
-                        "\n",
-                        "Four brands of LLIN containing PBO have received interim approval from the WHO as conventional LLINS. These are the PermaNet(r) 3.0 (deltamethrin+PBO) (Vestergaard Frandsen Holding SA, Lausanne, Switzerland), the Olvest(r) Plus (permethrin+PBO) (Sumitomo Chemical Asia Pte Ltd, Health and Crop Sciences Sector, Tokyo, Japan), the Veeralin(r) (alpha cypermethrin+PBO) (VKA Polymers Pte Ltd, Karur, India) and the DawaPlus(r) (deltamethrin+PBO) (Tana Netting Co. Ltd, Dubai, U.A.E.). The benefit of the addition of PBO is only expected to manifest in areas in which mosquito populations are resistant to pyrethroids and experimental hut trials of PBO LLINS in areas of resistance have supported this prediction. Increased mosquito mortality was observed in experimental huts containing PBO LLINS compared with conventional LLINS in Ivory Coast, Benin and Burkina Faso (Corbel _et al._, 2010; Ngousso _et al._, 2010; Koudou _et al._, 2011; Pennetier _et al._, 2013) and reductions in blood feeding rates were also reported in trials in the latter two countries. All of these sites reported high levels of pyrethroid resistance.\n",
-                        "\n",
-                        "There has been a dramatic escalation in the strength of pyrethroid resistance in southwestern Burkina Faso since earlier trials of PBO LLINS in 2007. World Health Organization cone bioassays performed in 2012 revealed that none of the conventional LLINS were effective in killing local vector populations and that the performance of the PBO LLIN PermaNet(r) 3.0 was also compromised in these assays (Toe _et al._, 2014). Although the numbers of malaria deaths in Burkina Faso have fallen over the past 10 years, numbers of malaria cases have risen year on year despite countywide LLIN distribution campaigns [National Malaria Control Programme (NMCP), personal communication; K.H.Toe, 2017]. In order to advise the NMCP on whether a switch to PBO LLINS may be warranted to target these highly resistant populations, an experimental hut trial was undertaken in two rice-growing areas in the southwestern region of Burkina Faso.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The results of these experimental hit studies with entomological endpoints suggest that substituting conventional LLINs with PBO LLINs in areas where there is a high prevalence of pyrethroid resistance in local vectors may be an effective strategy to maintain the efficacy of malaria vector control. The public health benefit of this would depend on a wide range of factors, including the level of malaria endemicity, LLIN coverage rates and the predominant mosquito vectors present, but a recent modelling exercise predicts that in some settings, a switch to PBO LLINs could aver up to 0.5 clinical cases per person per year (Churcher _et al._, 2016). These predictions from models are now being evaluated in large-scale field trials. A recent study in Tanzania reported a 33% protective efficacy of PBO LLINs over conventional LLINs after 2 years of use (Protopopoff _et al._, 2018). A larger trial, involving the distribution of over 10.7 million nets in Uganda, is evaluating whether PBO nets reduce malaria prevalence under programmatic conditions ([https://www.againstmalaria.com](https://www.againstmalaria.com), [https://www.pmi.gov](https://www.pmi.gov)).\n",
-                        "\n",
-                        "In light of the results from experimental hit studies, including the current study, and after reviewing data from the first clinical trial of a PBO LLIN, the WHO recently made a policy recommendation that national malaria control programmes should consider deployment of PBO LLINs in areas with pyrethroid-resistant vectors (WHO, 2017). If deployed at scale, PBO LLINs may play an important role in reducing the immediate threat of pyrethroid resistance to malaria control.\n",
-                        "\n",
-                        "header: _Pyrethroid resistance in Tengrela and VK5 and associated mechanisms_: Efficacy of LLINs under laboratory conditions\n",
-                        "\n",
-                        "All LLINs tested showed good bio-efficacy in cone bioassays against the susceptible laboratory Kisumu strain with 60 min knock-down and 24 h mortality rates of all LLINs above the 98% and 80% WHO thresholds (WHO, 2005) (Table S1). By contrast, in tests using the field-caught mosquitoes, the mortality threshold was met only by the top panels of the PermaNet 3.0 (Fig. 3). Knock-down rates exceeded the 98% threshold for the PermaNet 3.0 in VK5 only (Table S1).\n",
-                        "\n",
-                        "header: Experimental hut results\n",
-                        "\n",
-                        "In total, 12 915 specimens from four different mosquito genera were collected inside the huts (sleeping rooms and veranda traps) over the 6-week trial (Tengrela, \\(n\\) = 5808; VK5, \\(n\\) = 7107). Most specimens collected from the huts belonged to the _An. gambiae s.l._ species complex, accounting for 75.4% (_n_ = 4379) in Tengrela and 98.8% (_n_ = 7020) in VK5. The second most frequently collected _Anopheles_ species was _An. rhinorensis_, of which 49 and 19 specimens were collected in Tengrela and VK5, respectively. Other _Anopheles_ mosquito species, including _An. funestus_ (_n_ = 3), _An. nili_ (_n_ = 2) and _An. coustani_ (_n_ = 3), were collected in Tengrela. Additional mosquito taxa were also collected, including _Mansonia_ sp. (_n_ = 1330 in Tengrela and \\(n\\) = 45 in VK5), _Culex_ sp. (_n_ = 41 in Tengrela and \\(n\\) = 23 in VK5) and _Aedes_ sp. (_n_ = 1 in Tengrela) (all: Diptera: Culicidae). Because of the low numbers of other genera, only data for _An. gambiae s.l._ were included in the analysis.\n",
-                        "\n",
-                        "Within the total of 11 399 _An. gambiae s.l._ caught at both field sites, there was considerable variation between weeks and treatments in the numbers of mosquitoes entering the huts in both study locations (Fig. 4). The average number of mosquitoes caught per night/per hut was between 13 (PermaNet 3.0) and 20.7 (Olyset Net) mosquitoes (Table S3) and induced deterrence was found only for the Olyset Net, albeit at a low ratio of 1.31 (95% CI 1.02-1.66; \\(P\\) < 0.05) (Fig. 4).\n",
-                        "\n",
-                        "header: Pyrethroid resistance in southwest Burkina Faso: Bio-efficacy of LLINs in cone bioassays\n",
-                        "\n",
-                        "The low levels of mortality observed in cone bioassays in this study are similar to those reported previously in VK7, a neighbouring village to VK5, in the Vallee du Kou rice-growing region. However, whereas the current study found that exposure to the tops of the PermaNet 3.0 nets resulted in mortality levels exceeding the WHO threshold of 80%, equivalent bioassays conducted in the VK7 population in 2012 showed mortality of only 43%. Cone bioassays are not a reliable method of comparing the performance of LLINs containing different pyrethroids because of the inherent differences in extico-repellency within this class, which can affect exposure time (Siegert _et al._, 2009). In particular, cone tests may underestimate the performance of permethrin LLINs; this is indicated by comparison of the cone bioassay results in Fig. 3, in which the Olyset Net was found to induce considerably lower mortality than the PermaNet 2.0, although experimental hut results showed similar levels of mortality in huts with these two net types (Table S3).\n",
-                        "\n",
-                        "Fig. 3: Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (_P_ < 0.001), † (_P_ < 0.0001), n.s. (non-significant, \\(\\frac{1}{2}\\) (_P_ < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wicyclonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "Nevertheless, the results of these cone bioassays provide further evidence that resistance can be at least partially ameliorated by exposure to PBO.\n",
-                        "\n",
-                        "header: Experimental hut trials\n",
-                        "\n",
-                        "Each station consisted of six experimental huts built according to the West African style (WHO, 2013). The study had six arms that used, respectively, five different LLINs and one net with no insecticide treatment as a negative control (Table 1). The nets were obtained from the manufacturers and were unpacked and kept in the shade for 24 h, but not washed prior to testing. Two of the nets, the OlysetPlus and PermaNet 3.0, contain PBO, whereas the other three LLINs contain only pyrethroids. The LLINs were holed according to WHO standard procedures (WHO, 2013). A total of six holes (4 cm x 4 cm) per net were cut, two on each of the long sides and one on each of the short sides.\n",
-                        "\n",
-                        "Study participants (male sleepers) spent 6 nights per week under a net in an experimental hut from 20.00 hours to 05.00 hours, followed by 1 day of break. The sleepers were rotated through the six huts so that each sleeper spent 1 night per week under each net type. To complete a full Latin square rotation with all combinations of sleeper, net type and hut, the study ran over 36 days from 8 September to 22 October 2014.\n",
-                        "\n",
-                        "Each morning at 05.00 hours, mosquitoes were collected manually by the sleepers, with supervision, from under the net, inside the hut and on the exit veranda. The collected specimens were morphologically identified to genus and, where possible, to species level (Gillies & Coetzee, 1987), grouped according to their gonotrophic stage (blood-fed, unfed or gravid), and scored as dead or alive. Live mosquitoes were transferred to paper cups, provided with 10% sugar water and kept in the insectary described above for 24 h, after which delayed mortality was recorded. All specimens were stored on silica gel for further molecular analysis.\n",
-                        "\n",
-                        "Data analysis was performed in the open-source statistical software R Version 3.3.2 (R Development Core Team, 2011) using the libraries 'lme4' (Bates _et al._, 2012) and 'glmADMB' (Skaug _et al._, 2012) for generalized linear mixed models (GLMMs). Plots were then generated with the package 'ggplot2' (Wickham, 2009).\n",
-                        "\n",
-                        "In the statistical analysis of hut trial data, in order to increase the number of replicates, give more power and increase confidence in the analysis, data collected from both sites (Tengrela and VK5) were pooled and the following four outcomes for _An. coluzzii_ were compared between the LLINs and the untreated control net, as well as between the PBO and non-PBO nets from the same manufacturer: (a) deterrence (i.e. the reduction in hut entry relative to the control or non-PBO net); (b) induced exophily (i.e. the ratio of the odds of a mosquito being found in the veranda trap compared with the hut); (c) blood feeding inhibition (i.e. the ratio of the odds of blood\n",
-                        "\n",
-                        "fed vs. unfed mosquitoes), and (d) induced mortality [i.e. the ratio of the odds of dead vs. alive mosquitoes] (The original dataset for _An. gambiae s.l._ is supplied in Table S2.) Immediate mortality and mortality at 24 h post-collection were combined for the analysis. Deterrence was analysed as the ratio in total numbers between the treatment arms (or PBO net) vs. the control arm (or non-PBO net). The numbers of mosquitoes in the room and the veranda were combined and analysed using a GLMM with a negative binomial distribution and a log link function using the R function 'glmandmdb()' in the 'glmmADMB' package. In the model, the net type was the fixed effect term and random intercepts were introduced for the sleeper and the hut, and a random slope for the day depending on the location. For proportional outcomes of induced exophily, blood feeding inhibition and induced mortality, the negative binomial model was replaced by a GLMM with a binomial distribution and logit link function using the R function 'glmer()' in the 'lme4' package. In the models, in addition to the terms listed above, a random intercept was introduced for each observation to account for unexplained overdispersion. For statistical testing, the level of significance was set at \\(a\\) = 0.05.\n",
-                        "\n",
-                        "In addition to the ratios described above, averages (i.e. the modes) and 95% confidence intervals (CIs) of the crude values underlying the outcomes were computed by the same models as above but using the individual nets as the intercept. These corresponding crude values were: (a) entry rate (i.e. the number of mosquitoes entering a hut); (b) exit rate (i.e. the number of mosquitoes collected from the veranda trap; (c) blood feeding rate (i.e. the proportion of blood-fed mosquitoes), and (d) mortality rate (i.e. the proportion of dead mosquitoes at 24 h post-collection).\n",
-                        "\n",
-                        "The study participants were recruited from the local communities and gave informed consent. Ethical approval was obtained from the Ethical Committee for Health Research of the Ministry of Health and Ministry of Research in Burkina Faso (Deliberation No. 2013-07-057, 11 July 2013). Malaria chemotherapy was not offered to study participants in line with Ministry of Health recommendations. However, medical supervision was provided throughout the study and any malaria case was treated according to national requirements.\n",
-                        "\n",
-                        "header: Study sites: Characterization of mosquito populations\n",
-                        "\n",
-                        "_Anopheles gambiae s.l._ larvae were collected in Tengrela and VK5 and reared to adults in local insectaries (mean relative humidity: 75+- 10%; mean temperature: 27+- 2*C). To assess susceptibility to pyrethroids, batches of approximately 25 non-blood-fed _An. gambiae s.l._ females aged 3-5 days were exposed to papers treated with 0.75% permethrin or 0.05% deltamethrin. The papers and susceptibility test kits were purchased from the Universiti Sains Malaysia (Penang, Malaysia). In parallel bioassays, mosquitoes were exposed to papers treated with 4% PBO [prepared by the Liverpool School of Tropical Medicine (LSTM)] for 1 h before they were transferred to tubes containing either insecticide-treated papers or insecticide-free papers and exposed for a further 1 h. Knock-down was recorded at the end of exposure and mortality was recorded 24 h later.\n",
-                        "\n",
-                        "The bio-efficacy of the LLINS was tested using non-blood-fed mosquitoes aged 3-5 days from local larval collections, and from the Kisumu susceptible laboratory strain, using the WHO cone bioassay procedure (WHO, 2013). For each LLIN type, two unwashed nets were tested under insectary conditions. Ten mosquitoes per cone were exposed for 3 min to 30 cm x 30 cm net pieces sampled from the top, the short and the long sides of the net. During exposure, the set-up was kept at an angle of 45 degrees as recommended (Owusu & Muller, 2016). Knock-down and mortality were recorded at 60 min and 24 h after exposure, respectively. Conventional LLINs were compared with PBO LLINS using Fisher's exact test (2x2 contingency table, significance level of 0.05) (http://wwwgraphpadcom/quickcales/contingency2) (Roberts & Andre, 1994).\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "The experimental hut studies were carried out at two field stations in southwest Burkina Faso: the first is located in the Vallee du Kou.5 (VK5) near Bobo-Dioulasso (11*39' N, 04*41' W) and belongs to the Institut de Recherche en Science de la Sante (IRSS)/Centre MURAZ, and the second is located at Tengrela (10*40' N, 04*50' W) near Banfora and is maintained by the Centre National de Recherche et de Formation sur le Paludisme (CNRFP). These two sites are separated by approximately 120 km. Previous surveys revealed _Anopheles coluzzii_ (formerly _Anopheles gambiae s.s._ M molecular form) to be the predominant _Anopheles_ species at both sites. High levels of resistance to both DDT and pyrethroids have been reported previously at both sites (Ngufor _et al._, 2014; Toe _et al._, 2015).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Burkina Faso\",\"Site\":\"Vallee du Kou 5\",\"Start_month\":9,\"Start_year\":2014,\"End_month\":10,\"End_year\":2014},\"S02\":{\"Study_type\":\"Hut trial\",\"Country\":\"Burkina Faso\",\"Site\":\"Tengrela\",\"Start_month\":9,\"Start_year\":2014,\"End_month\":10,\"End_year\":2014}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated net\",\"Net_holed\":0.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":null,\"Concentration_initial\":null},\"N02\":{\"Net_type\":\"Olyset Net\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"8.6 x 10-4 kg\\/m2\"},\"N03\":{\"Net_type\":\"Olyset Plus\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"permethrin, PBO\",\"Concentration_initial\":\"8.6 x 10-4 kg\\/m2, 4.3 x 10-4 kg\\/m2\"},\"N04\":{\"Net_type\":\"PermaNet 2.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"5.5 x 10-5 kg\\/m2\"},\"N05\":{\"Net_type\":\"PermaNet 3.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin, PBO\",\"Concentration_initial\":\"2.8 g\\/kg, 4.0 g\\/kg, 25 g\\/kg\"},\"N06\":{\"Net_type\":\"Dawa Plus 2.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"8.0 x 10-5 kg\\/m2\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Supporting Information\n",
-                        "\n",
-                        "Additional supporting information may be found online in the Supporting Information section at the end of the article.\n",
-                        "\n",
-                        "Fig. 5: Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hit relative to the control hit. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "header: Entry rate and deterrence: Exit rates and induced exophily\n",
-                        "\n",
-                        "Exit rates refer here to the proportion of mosquitoes present in the veranda trap. As with entrance rates, exit rates varied throughout the study (Fig. 4). On average, the induced exophily rates were between 26.4% (control net) and 48.5% (Olyset Net) (Table S4). The odds of finding a mosquito in the veranda trap were significantly increased in the huts with treated nets, with the exception of the DawaPlus, although even for the DawaPlus a tendency to induce exophily was observed (Fig. 4, Table S4).\n",
-                        "\n",
-                        "header: Table 1: Treatment arms and descriptions of longlasting insecticide-treated nets.\n",
-                        "footer: PermaNet is a registered trademark of Vestergaard Frandsen Holding SA. Olyset is a registered trademark of Sumitomo Chemical Co. Ltd. DawaPlusis a registered trademark of Tana Netting Co. Ltd.PBO, piperonyl butoxide.\n",
-                        "columns: ['Treatment arm', 'Description', 'Manufacturer']\n",
-                        "\n",
-                        "{\"0\":{\"Treatment arm\":\"Untreated net\",\"Description\":\"Net manufactured manually using netting material from market\",\"Manufacturer\":\"Local market\"},\"1\":{\"Treatment arm\":\"Olyset\\u00ae Net\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"2\":{\"Treatment arm\":\"Olyset\\u00ae Plus\",\"Description\":\"8.6 x 10-4 kg\\/m2 of permethrin and 4.3 x 10-4 kg\\/m2 of PBO incorporated into polyethylene\",\"Manufacturer\":\"Sumitomo Chemical\"},\"3\":{\"Treatment arm\":\"PermaNet\\u00ae 2.0\",\"Description\":\"5.5 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"4\":{\"Treatment arm\":\"PermaNet\\u00ae 3.0\",\"Description\":\"Combination of 2.8 g\\/kg of deltamethrin coated on polyester with strengthened border (side panels) and deltamethrin (4.0 g\\/kg) and PBO (25 g\\/kg)\",\"Manufacturer\":\"Vestergaard Frandsen\"},\"5\":{\"Treatment arm\":\"Dawa\\u00ae Plus 2.0\",\"Description\":\"8.0 x 10-5 kg\\/m2 of deltamethrin coated on polyester\",\"Manufacturer\":\"TANA Netting\"}}\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Very low mortality rates were obtained for both permethrin and deltamethrin in the two study sites following an hour of exposure to WHO diagnostic doses. Southwestern Burkina Faso is known as a hotspot of pyrethroid resistance (Dabire _et al._, 2012; Namountougou _et al._, 2012). Rapid changes in the prevalence of pyrethroid resistance have been observed since the first national LLIN distribution programme in 2010: in 2009, mosquito mortality following deltamethrin exposure was 25% (Dabire _et al._, 2012), in VK has since fallen to just 2.5%. The frequency of the 1014F _kdr_ mutation also increased from 0.28 in 2006 (Dabire _et al._, 2009) to 0.88 in the current study. Similar increases in the prevalence of pyrethroid resistance have been witnessed in Tengrela. Mortality rates of 93% and 46% for deltamethrin and permethrin, respectively, were recorded in 2011 (Namountougou _et al._, 2012; K. H. Toe, unpublished data 2011), but these mortality rates had reduced to 33% and 13% in 2014, the year of the current study (Toe _et al._, 2015). In the present study, pyrethroid mortality was significantly increased by pre-exposure to PBO. This, together with the qPCR data showing elevated expression of multiple P450s in both field populations compared with a susceptible laboratory strain, indicate that oxidases are an important resistance mechanism in _An. coluzzi_ in southwestern Burkina Faso. It is noted that resistance was not fully restored by PBO pre-exposure, indicating that additional resistance mechanisms, such as target site resistance and possibly cuticular modifications (Toe _et al._, 2015), may contribute to the pyrethroid resistance phenotype in these populations.\n",
-                        "\n",
-                        "header: Molecular analysis\n",
-                        "\n",
-                        "DNA was extracted from mosquito legs by heating at 90*C for 30 min. Species were identified using the SINE200 protocol (Santolamazza _et al._, 2008) and then screened for the voltage gated sodium channel (VGSC) 1014F and 1575Y alleles using Taqman assays (Bass _et al._, 2007; Jones _et al._, 2012).\n",
-                        "\n",
-                        "Total RNA was extracted from six pools of 10 5-day-old non-blood-fed female _An. coluzzii_ from larval collections from Tengrela and VK5 using the RNAqueous(r)4PCR Kit for isolation of DNA-free RNA (Ambion, Inc., Austin, TX, U.S.A.) according to the manufacturer's procedures. The RNA was eluted in 50 mL of elution solution and treated with DNase. The quality and quantity of all the RNA used were assessed \n",
-                        "using a NanoDrop ND1000 (Thermo Fisher Scientific UK Ltd, Renfrew, U.K.).\n",
-                        "\n",
-                        "The expression profiles of five P450 genes (_CYP6M2_, _CYP6Z2_, _CYP6Z3_, _CYP6P3_, _CYP6P4_), previously found to be over-expressed in pyrethroid-resistant field populations from Burkina Faso (Toe _et al._, 2015) and/or known to metabolize pyrethroids (Muller _et al._, 2008; Mitchell _et al._, 2012) were quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). The qPCR analysis was conducted at the LSTM and used the following mosquito populations: Ngousso, an insecticide-susceptible strain originating from Ngousso in Cameroon in 2006 and maintained in the insectary at LSTM; Tengrela specimens reared from larval collections in October-November 2014, and VK5 specimens reared from larval collections in October-November 2014. Approximately 600 ng of RNA was reverse-transcribed to first-strand cDNA using SuperScript(tm) III reverse transcriptase (Invitrogen, Inc., Carlsbad, CA, U.S.A.) according to the manufacturer's procedures. Samples were then purified using the Qiagen Easy Purification Kit (Qiagen Benelux BV, Venlo, the Netherlands) before proceeding to qPCR. Each of the six pool replicates were run in triplicate using 2X SYBR Brilliant III (Agilent Technologies, Inc., Palo Alto, CA, U.S.A.), forward and reverse primers (300 nM) [sequence available in Toe _et al._ (2015)] on the Mx3005p qPCR system (Agilent Technologies, Inc., Palo Alto, California) with the following cycling protocol: 95 degC for 3 min, followed by 40 cycles of 95 degC for 10 s and 60 degC for 10 s. The qPCR data were analysed using the delta Ct values method, taking into account the PCR efficiency (Pfaffi, 2001). The candidate Ct values were normalized against three housekeeping genes, encoding ribosomal protein L40 (ubiquitin) (_AGAP007927_), an elongation factor (_AGAP005128_) and the S7 ribosomal protein (_AGAP010592_). The normalized Ct values of each gene were then compared with the normalized Ct values of the susceptible Ngousso strain.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "In VK5, very low levels of mortality were observed after exposure to the discriminating dose of deltamethrin and permethrin, but pre-exposure to PBO significantly increased mortality rates from 2.5% (_n_ = 163) to 45% (_n_ = 158) and from 5% (_n_ = 153) to 26% (_n_ = 156) for deltamethrin and permethrin, respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig. 1). In Tengrela, mortality rates of 34% (_n_ = 85) and 14% (_n_ = 101) were recorded for deltamethrin and permethrin, respectively. When PBO was used, mortality rates increased significantly to 63% (_n_ = 84) and 42% (_n_ = 104), respectively (Fisher's exact test, \\(P\\) < 0.0001) (Fig.1). Although there was evidence of synergism, pre-exposure to PBO did not fully restore susceptibility in either site to either pyrethroid.\n",
-                        "\n",
-                        "_Anopheles coluzzi_ was the only species of the _An. gambiae_ complex identified by PCR of a subset of 80 specimens from Tengrela and VK5. High frequencies of the 1014F allele of the VGSC were recorded in Tengrela (0.819, 95% CI 0.750-0.875) and VK5 (0.885, 95% CI 0.824-0.930) with the 1575Y allele present at lower frequencies of 0.169 (95% CI 0.114-0.236) and 0.221 (95% CI 0.158-0.295), respectively. Samples were not genotyped for the 1014S allele as previous extensive surveys have not detected this allele in _An. coluzzi_ from these sites (Toe _et al._, 2015). There was no statistically significant difference in the frequency of either the 1014F or 1575Y allele between the two sites (Table 2).\n",
-                        "\n",
-                        "Several cytochrome P450 genes previously associated with pyrethroid resistance showed elevated expression levels in the Tengrela and the VK5 _An. coluzzi_ populations compared with the susceptible laboratory Ngousso strain (Fig. 2). _CYP6P3_, _CYP6M2_, _CYP6Z3_, _CYP6P4_ and _CYP6Z2_ were found to be\n",
-                        "\n",
-                        "Fig. 1: Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: \\(P\\) < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "overexpressed in both field strains compared with the susceptible laboratory colony (fold change: > 2) with the most highly overexpressed genes in both populations being _CYP6Z2_ and _CYP6Z3_ (Fig. 2).\n",
-                        "\n",
-                        "header: Blood feeding rates and inhibition\n",
-                        "\n",
-                        "Average blood feeding rates were between 17.0% (PermaNet 3.0) and 56.4% (control net) (Table S4). With the exception of the DawaPlus, all treated nets reduced blood feeding (Fig. 4, Table S4), and the effect was most prominent with the PBO nets PermaNet 3.0 and Olyset Plus (Fig. 4, Table S4), for which the odds ratios (ORs) of blood feeding relative to control nets were 0.19 (95% CI 01.3-0.26) and 0.16 (95% CI 0.11-0.23), respectively.\n",
-                        "\n",
-                        "header: Performance of PBO LLINs in experimental hut studies\n",
-                        "\n",
-                        "The enhanced performance of PBO-containing LLINs over conventional LLINs was further supported by the experimental hut results. Both blood feeding inhibition and mortality rates were significantly higher in huts containing PBO LLINs than in those containing conventional LLINs from the same manufacturers. An improved performance of the PermaNet 3.0, which contains both higher concentrations of deltamethrin compared with the PermaNet 2.0, plus PBO on the roof of the net, has also been reported in pyrethroid-resistant populations of malaria vectors in Ivory Coast (Koudou _et al._, 2011), Benin (N'Guessan _et al._, 2010) and Burkina Faso (Corbel _et al._, 2010).\n",
-                        "\n",
-                        "Fig. 4: Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the \\(P\\)-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "The magnitude of this effect differs among studies, with previous studies reporting increases in mortality rates of 1.3-1.8-fold, whereas the current study reports an OR of 2.45, which corresponds to a 1.78-fold increase. Only one experimental hit study comparing the Olyset Plus with the Olyset Net has been published to date (Pennetier _et al._, 2013). This study, conducted in Benin in 2013, reported mortality rates 1.9-fold higher in the PBO arm than in the conventional LLIN arm (Pennetier _et al._, 2013), which is in line with the findings of the current study, which found a 1.89-fold (OR 2.1) elevation in mortality rates in the Olyset Plus arm.\n",
-                        "\n",
-                        "In addition to increased mortality rates, the current study, plus two of the previous experimental hit studies, reported significantly higher rates of blood feeding inhibition for PBO vs. conventional LLINs (Corbel _et al._, 2010; N'Guessan _et al._, 2010), indicating that PBO LLINs afford an enhanced level of personal protection in areas where vectors are resistant to pyrethroids. It should be noted that the current study was performed using unwashed nets only. Previous studies have found that the efficacy of PBO LLINS against resistant mosquitoes decreases substantially after the nets have been washed 20 times according to WHO protocols (Corbel _et al._, 2010; Tungu _et al._, 2010; Koudou _et al._, 2011). Thus further studies on the durability of the bio-efficacy of PBO LLINS under field conditions are urgently needed.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Do bednets including piperonyl butoxide offer additional protection against populations of _Anopheles gambiae s.l._ that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso\n",
-                        "\n",
-                        "K. H. T oe\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " P. Muller\n",
-                        "\n",
-                        "2H233\n",
-                        "\n",
-                        "33Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Badlo\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " A. Traore\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " N. Sagnon\n",
-                        "\n",
-                        "1Departement des Sciences Biomedicales, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso, 2Epidemiology and Public Health Department, Swiss Tropical and Public Health Institute, Basel, Switzerland, 3University of Basel, Basel, Basel, Switzerland, 4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " R. K. Dabi Irene\n",
-                        "\n",
-                        "4Department of Medical Biology and Public Health, Institut de Recherche en Science de la Sante, Bobo-Dioulasso, Burkina Faso and 5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        " H. Ransons\n",
-                        "\n",
-                        "5Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, U.K.\n",
-                        "\n",
-                        "\n",
-                        "Malaria control is dependent on the use of longlasting insecticidal nets (LLINs) containing pyrethroids. A new generation of LLINs containing both pyrethroids and the synergist piperonyl butoxide (PBO) has been developed in response to increasing pyrethroid resistance in African malaria vectors, but questions remain about the performance of these nets in areas where levels of pyrethroid resistance are very high. This study was conducted in two settings in southwest Burkina Faso, Vallee du Kou 5 and Tengrela, where _Anopheles gambiae s.l._ (Diptera: Culicidae) mortality rates in World Health Organization (WHO) discriminating dose assays were \\(<\\) 14% for permethrin and \\(<\\) 33% for delatamethrin. When mosquitoes were pre-exposed to PBO in WHO tube assays, mortality rates increased substantially but full susceptibility was not restored. Molecular characterization revealed high levels of _kdr_ alleles and elevated levels of P450s previously implicated in pyrethroid resistance. In cone bioassays and experimental huts, PBO LLINs outperformed the pyrethroid-only equivalents from the same manufacturers. Blood feeding rates were 1.6-2.2-fold lower and mortality rates were 1.69-1.78-fold greater in huts with PBO LLINs vs. non-PBO LLINs. This study indicates that PBO LLINs provide greater personal and community-level protection than standard LLINs against highly pyrethroid-resistant mosquito populations.\n",
-                        "\n",
-                        " insecticide resistance, insecticide resistance management, longlasting insecticidal nets, PBO.\n",
-                        "\n",
-                        "header: Mortality rates and induced mortality\n",
-                        "\n",
-                        "Average mortality rates ranged from 9.5% in the control arm to 46.1% in the PermaNet 3.0 arm (Table S4). Mortality was statistically higher in all treated net conditions than in the control huts. As with blood feeding inhibition, the PBO nets showed the largest effects in increasing mortality (Fig. 5, Table S4); the ORs for mortality relative to control nets were 5.56 (95% CI 3.92-7.89) and 8.14\n",
-                        "Fig. 2: Expression levels of candidate genes previously associated with pyrethroid resistance in Tengrela and Vallée du Kou 5. The analysis was performed using the 2−\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "(95% CI 5.64-11.75) for the Olyset Plus and PermaNet 3.0, respectively, compared with 2.65 (95% CI 1.87-3.75) and 3.33 (95% CI 2.35-4.74) for the Olyset Net and PermaNet 2.0, respectively.\n",
-                        "\n",
-                        "header: Impact of PBO on LLIN efficacy in pyrethroid-resistant mosquitoes\n",
-                        "\n",
-                        "The performance of nets containing PBO as compared with pyrethroid-only nets from the same manufacturer is shown in Fig. 5. The PermaNet 3.0 deterred more mosquitoes than the PermaNet 2.0 (OR = 0.67, \\(P\\) < 0.01) (Fig. 5, Table S5), but there was no significant difference between the Olyset Plus and Olyset Net (_P_ = 0.412). The Olyset Net induced more exophily than the Olyset Plus (OR = 0.76, \\(P\\) < 0.05), but there was no significant difference in exophily between the PermaNet 2.0 and 3.0 (_P_ = 0.727) (Table S5). The PBO nets from both manufacturers considerably reduced blood feeding compared with non-PBO nets with ORs of 0.34 (_P_ < 0.001) and 0.55 (_P_ < 0.01) for the PermaNet 3.0 and Olyset Plus, respectively (Fig. 5, Table S4). In addition, the PBO nets killed significantly more _An. gambiae s.l._ than the non-PBO nets. The ORs for mortality were 2.45 (_P_ < 0.001) for the PermaNet 3.0 and 2.1 (_P_ < 0.001) for the Olyset Plus (Fig. 5, Table S5).\n",
-                        "\n",
-                        "header: Table 2: Frequencies of 1014F and 1575Y kdr alleles in Anopheles coluzzili, in Tengrela and Vallée du Kou 5 (VK5).\n",
-                        "footer: Chi-squared test (http://vassarstats.net/) for the comparison of the frequency of the 1014F and 1575Y mutations in Anopheles coluzzili populations from Tengrela and VK5 (October 2014). No statistical difference was observed in the frequencies of the kdr alleles in the two sites.\n",
-                        "columns: ['1014F mutation - Unnamed: 0_level_1', '1014F mutation - Total n', '1014F mutation - LL', '1014F mutation - LF', '1014F mutation - FF', '1014F mutation - f (1014F)', '1014F mutation - P-value']\n",
-                        "\n",
-                        "{\"0\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"2\",\"1014F mutation - LF\":\"25\",\"1014F mutation - FF\":\"53\",\"1014F mutation - f (1014F)\":\"0.819\",\"1014F mutation - P-value\":\"0.26\"},\"1\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"78\",\"1014F mutation - LL\":\"0\",\"1014F mutation - LF\":\"18\",\"1014F mutation - FF\":\"60\",\"1014F mutation - f (1014F)\":\"0.885\",\"1014F mutation - P-value\":null},\"2\":{\"1014F mutation - Unnamed: 0_level_1\":\"1575Y mutation\",\"1014F mutation - Total n\":\"1575Y mutation\",\"1014F mutation - LL\":\"1575Y mutation\",\"1014F mutation - LF\":\"1575Y mutation\",\"1014F mutation - FF\":\"1575Y mutation\",\"1014F mutation - f (1014F)\":\"1575Y mutation\",\"1014F mutation - P-value\":\"1575Y mutation\"},\"3\":{\"1014F mutation - Unnamed: 0_level_1\":null,\"1014F mutation - Total n\":\"Total n\",\"1014F mutation - LL\":\"NN\",\"1014F mutation - LF\":\"NY\",\"1014F mutation - FF\":\"YY\",\"1014F mutation - f (1014F)\":\"f (A575Y)\",\"1014F mutation - P-value\":\"P-value\"},\"4\":{\"1014F mutation - Unnamed: 0_level_1\":\"Tengrela\",\"1014F mutation - Total n\":\"80\",\"1014F mutation - LL\":\"57\",\"1014F mutation - LF\":\"19\",\"1014F mutation - FF\":\"4\",\"1014F mutation - f (1014F)\":\"0.169\",\"1014F mutation - P-value\":\"0.39\"},\"5\":{\"1014F mutation - Unnamed: 0_level_1\":\"VK5\",\"1014F mutation - Total n\":\"77\",\"1014F mutation - LL\":\"49\",\"1014F mutation - LF\":\"22\",\"1014F mutation - FF\":\"6\",\"1014F mutation - f (1014F)\":\"0.221\",\"1014F mutation - P-value\":null}}\n",
-                        "\n",
-                        "header: Introduction\n",
-                        "\n",
-                        "Use of the longlasting insecticidal net (LLIN) is pivotal in the fight against malaria in Africa. A massive scaling up of the distribution of this commodity has occurred over the past 15 years, with 178 million LLINs delivered for use in sub-Saharan Africa (SSA) in 2015 alone [World Health Organization (WHO), 2016]. Although reliable estimates of LLIN usage are very hard to obtain, the WHO estimates that 53% of the population at risk in SSA slept under an LLIN in 2015 [WHO, 2016]. The results have been dramatic: an estimated 450 million clinical cases of malaria were averted in the last 15 years by the use of LLINs [Bhatt _et al._, 2015].\n",
-                        "\n",
-                        "All LLINs in current use contain pyrethroid insecticides, but there is growing recognition that increases in the prevalence and intensity of pyrethroid resistance, driven at least in part by the scale-up in the use of LLINs, could jeopardize recent gains in malaria control [WHO, 2012]. Direct evidence that pyrethroid\n",
-                        "resistance is reducing either the personal or community-level protection provided by LLINS is challenging to obtain (Kleinschmidt _et al._, 2015; Ranson & Lissenden, 2016), but models of malaria transmission predict that even relatively low levels of resistance can substantially reduce the public health benefits of LILINS (Churcher _et al._, 2016). In countries that rely on the LLIN as the primary malaria prevention tool, the only currently available alternatives to conventional pyrethroid-only LLINS are nets in which the synerigent piperonyl butoxide (PBO) has been included in the fibres making up all, or part, of the net. Piperonyl butoxide inhibits cytochrome P450s, which comprise one of the most important enzyme families involved in pyrethroid resistance, and exposure to PBO has been shown to reduce resistance and sometimes to restore susceptibility to pyrethroids in malaria vectors (Jones _et al._, 2013; Edi _et al._, 2014).\n",
-                        "\n",
-                        "Four brands of LLIN containing PBO have received interim approval from the WHO as conventional LLINS. These are the PermaNet(r) 3.0 (deltamethrin+PBO) (Vestergaard Frandsen Holding SA, Lausanne, Switzerland), the Olvest(r) Plus (permethrin+PBO) (Sumitomo Chemical Asia Pte Ltd, Health and Crop Sciences Sector, Tokyo, Japan), the Veeralin(r) (alpha cypermethrin+PBO) (VKA Polymers Pte Ltd, Karur, India) and the DawaPlus(r) (deltamethrin+PBO) (Tana Netting Co. Ltd, Dubai, U.A.E.). The benefit of the addition of PBO is only expected to manifest in areas in which mosquito populations are resistant to pyrethroids and experimental hut trials of PBO LLINS in areas of resistance have supported this prediction. Increased mosquito mortality was observed in experimental huts containing PBO LLINS compared with conventional LLINS in Ivory Coast, Benin and Burkina Faso (Corbel _et al._, 2010; Ngousso _et al._, 2010; Koudou _et al._, 2011; Pennetier _et al._, 2013) and reductions in blood feeding rates were also reported in trials in the latter two countries. All of these sites reported high levels of pyrethroid resistance.\n",
-                        "\n",
-                        "There has been a dramatic escalation in the strength of pyrethroid resistance in southwestern Burkina Faso since earlier trials of PBO LLINS in 2007. World Health Organization cone bioassays performed in 2012 revealed that none of the conventional LLINS were effective in killing local vector populations and that the performance of the PBO LLIN PermaNet(r) 3.0 was also compromised in these assays (Toe _et al._, 2014). Although the numbers of malaria deaths in Burkina Faso have fallen over the past 10 years, numbers of malaria cases have risen year on year despite countywide LLIN distribution campaigns [National Malaria Control Programme (NMCP), personal communication; K.H.Toe, 2017]. In order to advise the NMCP on whether a switch to PBO LLINS may be warranted to target these highly resistant populations, an experimental hut trial was undertaken in two rice-growing areas in the southwestern region of Burkina Faso.\n",
-                        "\n",
-                        "header: Conclusions\n",
-                        "\n",
-                        "The results of these experimental hit studies with entomological endpoints suggest that substituting conventional LLINs with PBO LLINs in areas where there is a high prevalence of pyrethroid resistance in local vectors may be an effective strategy to maintain the efficacy of malaria vector control. The public health benefit of this would depend on a wide range of factors, including the level of malaria endemicity, LLIN coverage rates and the predominant mosquito vectors present, but a recent modelling exercise predicts that in some settings, a switch to PBO LLINs could aver up to 0.5 clinical cases per person per year (Churcher _et al._, 2016). These predictions from models are now being evaluated in large-scale field trials. A recent study in Tanzania reported a 33% protective efficacy of PBO LLINs over conventional LLINs after 2 years of use (Protopopoff _et al._, 2018). A larger trial, involving the distribution of over 10.7 million nets in Uganda, is evaluating whether PBO nets reduce malaria prevalence under programmatic conditions ([https://www.againstmalaria.com](https://www.againstmalaria.com), [https://www.pmi.gov](https://www.pmi.gov)).\n",
-                        "\n",
-                        "In light of the results from experimental hit studies, including the current study, and after reviewing data from the first clinical trial of a PBO LLIN, the WHO recently made a policy recommendation that national malaria control programmes should consider deployment of PBO LLINs in areas with pyrethroid-resistant vectors (WHO, 2017). If deployed at scale, PBO LLINs may play an important role in reducing the immediate threat of pyrethroid resistance to malaria control.\n",
-                        "\n",
-                        "header: Experimental hut results\n",
-                        "\n",
-                        "In total, 12 915 specimens from four different mosquito genera were collected inside the huts (sleeping rooms and veranda traps) over the 6-week trial (Tengrela, \\(n\\) = 5808; VK5, \\(n\\) = 7107). Most specimens collected from the huts belonged to the _An. gambiae s.l._ species complex, accounting for 75.4% (_n_ = 4379) in Tengrela and 98.8% (_n_ = 7020) in VK5. The second most frequently collected _Anopheles_ species was _An. rhinorensis_, of which 49 and 19 specimens were collected in Tengrela and VK5, respectively. Other _Anopheles_ mosquito species, including _An. funestus_ (_n_ = 3), _An. nili_ (_n_ = 2) and _An. coustani_ (_n_ = 3), were collected in Tengrela. Additional mosquito taxa were also collected, including _Mansonia_ sp. (_n_ = 1330 in Tengrela and \\(n\\) = 45 in VK5), _Culex_ sp. (_n_ = 41 in Tengrela and \\(n\\) = 23 in VK5) and _Aedes_ sp. (_n_ = 1 in Tengrela) (all: Diptera: Culicidae). Because of the low numbers of other genera, only data for _An. gambiae s.l._ were included in the analysis.\n",
-                        "\n",
-                        "Within the total of 11 399 _An. gambiae s.l._ caught at both field sites, there was considerable variation between weeks and treatments in the numbers of mosquitoes entering the huts in both study locations (Fig. 4). The average number of mosquitoes caught per night/per hut was between 13 (PermaNet 3.0) and 20.7 (Olyset Net) mosquitoes (Table S3) and induced deterrence was found only for the Olyset Net, albeit at a low ratio of 1.31 (95% CI 1.02-1.66; \\(P\\) < 0.05) (Fig. 4).\n",
-                        "\n",
-                        "header: _Pyrethroid resistance in Tengrela and VK5 and associated mechanisms_: Efficacy of LLINs under laboratory conditions\n",
-                        "\n",
-                        "All LLINs tested showed good bio-efficacy in cone bioassays against the susceptible laboratory Kisumu strain with 60 min knock-down and 24 h mortality rates of all LLINs above the 98% and 80% WHO thresholds (WHO, 2005) (Table S1). By contrast, in tests using the field-caught mosquitoes, the mortality threshold was met only by the top panels of the PermaNet 3.0 (Fig. 3). Knock-down rates exceeded the 98% threshold for the PermaNet 3.0 in VK5 only (Table S1).\n",
-                        "\n",
-                        "header: Pyrethroid resistance in southwest Burkina Faso: Bio-efficacy of LLINs in cone bioassays\n",
-                        "\n",
-                        "The low levels of mortality observed in cone bioassays in this study are similar to those reported previously in VK7, a neighbouring village to VK5, in the Vallee du Kou rice-growing region. However, whereas the current study found that exposure to the tops of the PermaNet 3.0 nets resulted in mortality levels exceeding the WHO threshold of 80%, equivalent bioassays conducted in the VK7 population in 2012 showed mortality of only 43%. Cone bioassays are not a reliable method of comparing the performance of LLINs containing different pyrethroids because of the inherent differences in extico-repellency within this class, which can affect exposure time (Siegert _et al._, 2009). In particular, cone tests may underestimate the performance of permethrin LLINs; this is indicated by comparison of the cone bioassay results in Fig. 3, in which the Olyset Net was found to induce considerably lower mortality than the PermaNet 2.0, although experimental hut results showed similar levels of mortality in huts with these two net types (Table S3).\n",
-                        "\n",
-                        "Fig. 3: Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (_P_ < 0.001), † (_P_ < 0.0001), n.s. (non-significant, \\(\\frac{1}{2}\\) (_P_ < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wicyclonlinelibrary.com].\n",
-                        "\n",
-                        "\n",
-                        "Nevertheless, the results of these cone bioassays provide further evidence that resistance can be at least partially ameliorated by exposure to PBO.\n",
-                        "\n",
-                        "header: Experimental hut trials\n",
-                        "\n",
-                        "Each station consisted of six experimental huts built according to the West African style (WHO, 2013). The study had six arms that used, respectively, five different LLINs and one net with no insecticide treatment as a negative control (Table 1). The nets were obtained from the manufacturers and were unpacked and kept in the shade for 24 h, but not washed prior to testing. Two of the nets, the OlysetPlus and PermaNet 3.0, contain PBO, whereas the other three LLINs contain only pyrethroids. The LLINs were holed according to WHO standard procedures (WHO, 2013). A total of six holes (4 cm x 4 cm) per net were cut, two on each of the long sides and one on each of the short sides.\n",
-                        "\n",
-                        "Study participants (male sleepers) spent 6 nights per week under a net in an experimental hut from 20.00 hours to 05.00 hours, followed by 1 day of break. The sleepers were rotated through the six huts so that each sleeper spent 1 night per week under each net type. To complete a full Latin square rotation with all combinations of sleeper, net type and hut, the study ran over 36 days from 8 September to 22 October 2014.\n",
-                        "\n",
-                        "Each morning at 05.00 hours, mosquitoes were collected manually by the sleepers, with supervision, from under the net, inside the hut and on the exit veranda. The collected specimens were morphologically identified to genus and, where possible, to species level (Gillies & Coetzee, 1987), grouped according to their gonotrophic stage (blood-fed, unfed or gravid), and scored as dead or alive. Live mosquitoes were transferred to paper cups, provided with 10% sugar water and kept in the insectary described above for 24 h, after which delayed mortality was recorded. All specimens were stored on silica gel for further molecular analysis.\n",
-                        "\n",
-                        "Data analysis was performed in the open-source statistical software R Version 3.3.2 (R Development Core Team, 2011) using the libraries 'lme4' (Bates _et al._, 2012) and 'glmADMB' (Skaug _et al._, 2012) for generalized linear mixed models (GLMMs). Plots were then generated with the package 'ggplot2' (Wickham, 2009).\n",
-                        "\n",
-                        "In the statistical analysis of hut trial data, in order to increase the number of replicates, give more power and increase confidence in the analysis, data collected from both sites (Tengrela and VK5) were pooled and the following four outcomes for _An. coluzzii_ were compared between the LLINs and the untreated control net, as well as between the PBO and non-PBO nets from the same manufacturer: (a) deterrence (i.e. the reduction in hut entry relative to the control or non-PBO net); (b) induced exophily (i.e. the ratio of the odds of a mosquito being found in the veranda trap compared with the hut); (c) blood feeding inhibition (i.e. the ratio of the odds of blood\n",
-                        "\n",
-                        "fed vs. unfed mosquitoes), and (d) induced mortality [i.e. the ratio of the odds of dead vs. alive mosquitoes] (The original dataset for _An. gambiae s.l._ is supplied in Table S2.) Immediate mortality and mortality at 24 h post-collection were combined for the analysis. Deterrence was analysed as the ratio in total numbers between the treatment arms (or PBO net) vs. the control arm (or non-PBO net). The numbers of mosquitoes in the room and the veranda were combined and analysed using a GLMM with a negative binomial distribution and a log link function using the R function 'glmandmdb()' in the 'glmmADMB' package. In the model, the net type was the fixed effect term and random intercepts were introduced for the sleeper and the hut, and a random slope for the day depending on the location. For proportional outcomes of induced exophily, blood feeding inhibition and induced mortality, the negative binomial model was replaced by a GLMM with a binomial distribution and logit link function using the R function 'glmer()' in the 'lme4' package. In the models, in addition to the terms listed above, a random intercept was introduced for each observation to account for unexplained overdispersion. For statistical testing, the level of significance was set at \\(a\\) = 0.05.\n",
-                        "\n",
-                        "In addition to the ratios described above, averages (i.e. the modes) and 95% confidence intervals (CIs) of the crude values underlying the outcomes were computed by the same models as above but using the individual nets as the intercept. These corresponding crude values were: (a) entry rate (i.e. the number of mosquitoes entering a hut); (b) exit rate (i.e. the number of mosquitoes collected from the veranda trap; (c) blood feeding rate (i.e. the proportion of blood-fed mosquitoes), and (d) mortality rate (i.e. the proportion of dead mosquitoes at 24 h post-collection).\n",
-                        "\n",
-                        "The study participants were recruited from the local communities and gave informed consent. Ethical approval was obtained from the Ethical Committee for Health Research of the Ministry of Health and Ministry of Research in Burkina Faso (Deliberation No. 2013-07-057, 11 July 2013). Malaria chemotherapy was not offered to study participants in line with Ministry of Health recommendations. However, medical supervision was provided throughout the study and any malaria case was treated according to national requirements.\n",
-                        "\n",
-                        "header: Materials and methods\n",
-                        "\n",
-                        "The experimental hut studies were carried out at two field stations in southwest Burkina Faso: the first is located in the Vallee du Kou.5 (VK5) near Bobo-Dioulasso (11*39' N, 04*41' W) and belongs to the Institut de Recherche en Science de la Sante (IRSS)/Centre MURAZ, and the second is located at Tengrela (10*40' N, 04*50' W) near Banfora and is maintained by the Centre National de Recherche et de Formation sur le Paludisme (CNRFP). These two sites are separated by approximately 120 km. Previous surveys revealed _Anopheles coluzzii_ (formerly _Anopheles gambiae s.s._ M molecular form) to be the predominant _Anopheles_ species at both sites. High levels of resistance to both DDT and pyrethroids have been reported previously at both sites (Ngufor _et al._, 2014; Toe _et al._, 2015).\n",
-                        "\n",
-                        "header: Study sites: Characterization of mosquito populations\n",
-                        "\n",
-                        "_Anopheles gambiae s.l._ larvae were collected in Tengrela and VK5 and reared to adults in local insectaries (mean relative humidity: 75+- 10%; mean temperature: 27+- 2*C). To assess susceptibility to pyrethroids, batches of approximately 25 non-blood-fed _An. gambiae s.l._ females aged 3-5 days were exposed to papers treated with 0.75% permethrin or 0.05% deltamethrin. The papers and susceptibility test kits were purchased from the Universiti Sains Malaysia (Penang, Malaysia). In parallel bioassays, mosquitoes were exposed to papers treated with 4% PBO [prepared by the Liverpool School of Tropical Medicine (LSTM)] for 1 h before they were transferred to tubes containing either insecticide-treated papers or insecticide-free papers and exposed for a further 1 h. Knock-down was recorded at the end of exposure and mortality was recorded 24 h later.\n",
-                        "\n",
-                        "The bio-efficacy of the LLINS was tested using non-blood-fed mosquitoes aged 3-5 days from local larval collections, and from the Kisumu susceptible laboratory strain, using the WHO cone bioassay procedure (WHO, 2013). For each LLIN type, two unwashed nets were tested under insectary conditions. Ten mosquitoes per cone were exposed for 3 min to 30 cm x 30 cm net pieces sampled from the top, the short and the long sides of the net. During exposure, the set-up was kept at an angle of 45 degrees as recommended (Owusu & Muller, 2016). Knock-down and mortality were recorded at 60 min and 24 h after exposure, respectively. Conventional LLINs were compared with PBO LLINS using Fisher's exact test (2x2 contingency table, significance level of 0.05) (http://wwwgraphpadcom/quickcales/contingency2) (Roberts & Andre, 1994).\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}, \"Start_month\": {\"title\": \"Start Month\", \"description\": \"Numerical start month of the reporting period (some studies may report multiple time periods)\"}, \"Start_year\": {\"title\": \"Start Year\", \"description\": \"Reporting period start year (some studies may report on multiple time periods)\"}, \"End_month\": {\"title\": \"End Month\", \"description\": \"Numerical end month of the reporting period\"}, \"End_year\": {\"title\": \"End Year\", \"description\": \"Reporting period end year\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Burkina Faso\",\"Site\":\"Vallee du Kou 5\",\"Start_month\":9,\"Start_year\":2014,\"End_month\":10,\"End_year\":2014},\"S02\":{\"Study_type\":\"Hut trial\",\"Country\":\"Burkina Faso\",\"Site\":\"Tengrela\",\"Start_month\":9,\"Start_year\":2014,\"End_month\":10,\"End_year\":2014}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Concentration_initial\": {\"title\": \"Insecticide concentration (initial)\", \"description\": \"Enter the initial concentration of the insecticide(s) for net type at the time of distribution.\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"Net_age\": {\"title\": \"Net Age\", \"description\": \"Age of the net in months - if reported (some studies return to villages with nets given 1-2 years prior). When a range of net age's are given, use the midpoint in the range or the median in a distribution.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"Untreated net\",\"Net_holed\":0.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":null,\"Concentration_initial\":null},\"N02\":{\"Net_type\":\"Olyset Net\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"permethrin\",\"Concentration_initial\":\"8.6 x 10-4 kg\\/m2\"},\"N03\":{\"Net_type\":\"Olyset Plus\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"permethrin, PBO\",\"Concentration_initial\":\"8.6 x 10-4 kg\\/m2, 4.3 x 10-4 kg\\/m2\"},\"N04\":{\"Net_type\":\"PermaNet 2.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"5.5 x 10-5 kg\\/m2\"},\"N05\":{\"Net_type\":\"PermaNet 3.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin, PBO\",\"Concentration_initial\":\"2.8 g\\/kg, 4.0 g\\/kg, 25 g\\/kg\"},\"N06\":{\"Net_type\":\"Dawa Plus 2.0\",\"Net_holed\":6.0,\"Net_age\":\"NA\",\"pHI_category\":\"Good\",\"Insecticide\":\"deltamethrin\",\"Concentration_initial\":\"8.0 x 10-5 kg\\/m2\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Disclaimer\n",
-                        "\n",
-                        "The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the CDC. Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the US Department of Health and Human Services.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "Data supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichroo-diphenyl-trichloroethane; ELISA: Enzyme-linked immunosorbent Assay; IRS: Indoor residual spray; ITS: Insecticide-treated net; PBO: Piperonyl butoxide; PCR Polymerase chain reaction; WHO: World Health Organization; WHORES: World Health Organization Pesticide Evaluation Scheme.\n",
-                        "\n",
-                        "header: Use of nets for the study\n",
-                        "\n",
-                        "During the study, two types of nets (PermaNet(r) 2.0 and PermaNet(r) 3.0) with and without IRS were assessed. Moreover, an untreated net was used with IRS and untreated net without IRS was used as a negative control. Both PermaNet(r) 2.0 and PermaNet(r) 3.0 nets are treated with the pyrethroid deltamethrin, but PermaNet 3.0 has also PBO synergist. During the study, each ITN in each hut was replaced by a new replicate net every 3 weeks since the trial had no replicate huts. Eighteen holes (4 cm x 4 cm) were cut in each net purposefully to simulate a torn net [11].\n",
-                        "\n",
-                        "header: Indoor residual spraying (IRS)\n",
-                        "\n",
-                        "Actcllic(r) 300CS, a pirimiphos-methyl-based IRS insecticide, was applied to the three IRS treatment huts 1 week before the start of the trial. Pirimiphos-methyl is an organophosphate insecticide being used for IRS by the malaria elimination program of Ethiopia. During the spraying, filter papers were affixed to the wall surfaces at three heights (high, middle and low) to ensure the application was properly and uniformly applied and determine the concentration of the applied insecticide. The spray operators were trained on the spraying techniques and the wall surfaces of huts were lined with chalk to properly maintain the swath and avoid overlap following the WHO protocol.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "An experimental hut study evaluating the impact of pyrethroid-only and PBO nets alone and in combination with pirrimiphos-methyl-based IRS in Ethiopia\n",
-                        "\n",
-                        "Delenasaw Yevhalaw, Meshesha Balkew, Endalew Zermene, Sheleme Chibsa, Peter Mumba, Cecilia Flatley, Akiliu Seyoum, Melissa Yoshimizu, Sarah Zohdy, Dereje Dengela\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid resistance observed in populations of malaria vectors is widespread in Ethiopia and could potentially compromise the effectiveness of insecticide-based malaria vector control interventions. In this study, the impact of combining indoor residual spraying (IRS) and insecticide-treated nets (ITNs) on mosquito behaviour and mortality was evaluated using experimental huts.\n",
-                        "\n",
-                        "header: Wall bioassay\n",
-                        "\n",
-                        "Cone bioassays were conducted twice during the trial period to assess the bio-efficacy of Actclic 300CS. At each time point, three cones were fixed to the wall surfaces at three heights from the ground (high 160 cm, medium 100 cm and low 40 cm) of each hut and ten mosquitoes were used per cone with a total of 30 mosquitoes per hut, for each of the three sprayed huts and a control hut (120 mosquitoes in total). Mosquitoes were exposed for 30 min after which immediate knockdown was recorded and mortality was recorded after a 24 h holding period. Non-blood-fed, 3-5 day old adult female _Anopheles gambiae_ sensu lato (s.l.) were used for bioassays.\n",
-                        "\n",
-                        "The first cone bioassay was conducted 3 weeks after spray using a confirmed susceptible _An. arabiensis_ strain from Sekoru Insectary without pre-exposure to PBO synergist. The second bioassay was conducted at the end of the trial (10 weeks after spray). In the second bioassay, both susceptible _An. arabiensis_ laboratory strain from Sekoru Insectary and wild _An. gambiae_ s.l. were used. The assay was carried out with and without PBO pre-exposure for both strains. Wild populations of _An. gambiae_ s.l. used for the bioassays were raised from larvae and pupae collected from semi-permanent and permanent breeding habitats around Wolfkite area, central Ethiopia. They were reared to adults at Asendabo Field Insectary. Mosquitoes were exposed to papers treated with PBO (2%) in a WHO cylinder for 1 h.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Malaria is an important public health and socio-economic problem in Ethiopia. The main malaria vector in Ethiopia is _Anopheles arabiensis,_ while _Anopheles pharoetiss, Anopheles funestus_ and _Anopheles nili_ play a secondary role in malaria transmission. _Anopheles stephensi,_ a common and efficient urban malaria vector in Southeast Asia and the Mediterranean Region, has also recently been reported in Ethiopia and neighboring countries including Djibouti and Sudan [1]. There is widespread resistance to dichloro-diphenyl-trichloroethane (DDT) and pyrethroids in _An. arabiensis_ and resistance has also been detected to organophosphates in some populations. _Anopheles pharoensis_ was also reported to be highly resistant to DDT [2]. The _kdr_ mutation (L1014F) has been detected in _An. arabiensis_ from many sites, while _ace-1R_ and _kdr_ (L1014S) were not detected in all populations [3, 4, 5]. Bottle bioassay tests with _An. arabiensis_ populations from all surveyed areas of Ethiopia found resistance to both permethrin and deltamethrin. However, susceptibility to permethrin and deltamethrin was restored following pre-exposure of mosquitoes to the synergist piperonyl butoxide (PBO), indicating involvement of P450-based metabolic resistance mechanisms in the Ethiopian populations of _An. arabiensis_[6].\n",
-                        "\n",
-                        "Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main vector control interventions in Ethiopia. Ethiopia has a long history of IRS. It began spraying DDT in the late 1950s during the Malaria Eradication Programme and malathion was also used for IRS in a few areas until 2010. Deltamethrin, a pyrethroid insecticide, was used for IRS from 2009 to 2013, and since then, bendiocarb and propoxur (carbamate insecticides) and pirimihos-methyl (organophosphate insecticide) have been used for IRS in Ethiopia. The PMI VectorLink Ethiopia Project piloted the clothianidin (neonicotinoid)-based insecticides, SumiShield(r) 50WG and Fludora Fusion WP-SB, in 2020 in Menge District in the Benishangul Gumuz Region. Insecticide-treated nets have been widely distributed to households in malaria risk areas every 3 years mainly through the health extension programme [7].\n",
-                        "\n",
-                        "Recent studies in Tanzania showed that the impact of PBO nets could be comparable to the impact of pyrethroid-only nets when combined with IRS, and that there was no significant difference in malaria prevalence between the pyrethroid-only net + IRS and PBO net + IRS study arms [8]. This is of interest for malaria control operations in Ethiopia, as such data can inform future decisions on ITN procurements and strategic\n",
-                        "distribution, as well as insecticide choices for IRS, which may reduce overall malaria vector control costs. In addition, it is also important to understand possible antagonistic effects of PBO and pirimiphos-methyl, as suggested by the World Health Organization (WHO), before recommending the combined use of PBO nets and pirimiphos methyl IRS for malaria control in Ethiopia.\n",
-                        "\n",
-                        "This study aimed at assessing the impact of the combination of currently available vector control tools (IRS, pyrethroid-only nets, and PBO nets) on mosquito behaviour, mortality and the possible antagonistic effect of PBO and pirimiphos-methyl using experimental hurts in Asendabo, Ethiopia. The hypothesis for the Ethiopia context was that the use of PBO nets in combination with IRS would better control malaria vectors than PBO nets alone and that the use of IRS in combination with pyrethroid-only nets would better control malaria vectors than pyrethroid-only nets alone.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "We declare that we have no competing interests.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The personal protection rate of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 33.3% and 50%, respectively. The mean killing effect of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 2% and 49%, respectively. Huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone demonstrated significantly higher excito-repellency than the control hut. However, mosquito mortality in the hut with IRS + untreated net, hut with IRS + PermaNet(r) 2.0 and hut with IRS + PermaNet(r) 3.0 were not significantly different from each other (p > 0.05). Additionally, pre-exposure of both the susceptible _Anopheles arabiensis_ laboratory strain and wild _Anopheles gambiae_ sensu late to PBO in the cone bioassay tests of Actellite 300CS sprayed surfaces did not reduce mosquito mortality when compared to mortality without pre-exposure to PBO.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Mosquito mortality rates from the huts with IRS alone were similar to mosquito mortality rates from the huts with the combination of vector control intervention tools (IRS + ITNs) and mosquito mortality rates from huts with PBO nets alone were significantly higher than huts with pyrethroid-only nets. The findings of this study help inform studies to be conducted under field condition for decision-making for future selection of cost-effective vector control intervention tools.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A Latin Square Design was employed using six experimental huts to collect entomological data. Human volunteers slept in huts with different types of nets (pyrethroid-only net, PBO net, and untreated net) either with or without IRS (Actallic 300CS). The hut with no IRS and an untreated net served as a negative control. The study was conducted for a total of 54 nights. Both alive and dead mosquitoes were collected from inside nets, in the central rooms and verandah the following morning. Data were analysed using Stata/SE 14.0 software package (College Station, TX, USA).\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "In this study, significantly higher mosquito mortality rates were recorded from treatment huts with IRS than huts with ITNs alone, regardless of net type. Interestingly, the hut with IRS + untreated net had similar mosquito mortality rates as huts with combinations of vector control tools (IRS + pyrethroid-only nets and IRS + PBO nets). Moreover, immediate mortality rate of mosquitoes in IRS + untreated net was significantly higher than PermaNet 2.0 and PermaNet 3.0 nets. These suggest that IRS alone provides the greatest vector control impact. Indoor residual spraying alone or in combination with ITNs offered significantly higher personal protection against mosquito bites than the control hut. Moreover, the proportion of mosquito mortality was significantly higher in huts with PBO nets than huts with pyrethroid-only nets, but remained significantly lower than the huts that received IRS. In general, the findings of this study revealed that IRS alone was as efficacious as combinations of vector tools (IRS + ITNs) for malaria vector control at least over the 9-week study period. Field studies to further assess the impact of combinations of vector tools on malaria vector control in different eco-epidemiological settings are also warranted. This study \n",
-                        "provides additional data for considering future selection of the most appropriate vector control tools for improved malaria vector control and management of insecticide resistance. This study also indicated that there was no antagonistic effect between PBO and IRS with pirimib-phos-methyl. However, further studies may be needed to determine if there are antagonistic effects in large scale IRS applications to evaluate the efficacy of PBO vs. IRS against different resistant vector populations in Ethiopia for decision-making to deploy either PBO nets or pirimib-phos-based IRS intervention.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Overall, a total of 386 mosquitoes, comprising 235 _Anopheles_ spp. (60.9%) and 151 _Culex_ spp. (39.1%), were collected during the study. Of the _Anopheles_ mosquitoes, _An. gambiae_ s.l, _An. pharoensis_ and _Anopheles__custani_ accounted for 228 (97.0%), 6 (2.6%) and 1 (0.4%), respectively. The proportion of the total _Anopheles_ mosquitoes collected from each hut is presented in Fig. 1. There were no significant differences in mean percentage of _Anopheles_ mosquitoes between the different treatment and control huts (p > 0.05).\n",
-                        "\n",
-                        "An immediate mosquito mortality of 100% was recorded from all three huts with IRS throughout the study period. The mean immediate mosquito mortality rates from the control, PermaNet(r) 2.0 and PermaNet(r) 3.0 huts were 13.3% (95% CI 3-23%), 20.0% (95% CI 6-33%) and 70.0% (95% CI 55-84%), respectively (Fig. 2). The immediate mosquito mortality rate from the hut with IRS + untreated net was significantly higher than the PermaNet(r) 3.0 hut (p < 0.001). Similarly, the immediate mosquito mortality rate from the hut with PermaNet(r) 3.0 was significantly higher than that from the control hut (p < 0.001) as well as the PermaNet(r) 2.0 (p < 0.001) hut. The mosquito mortality rate from the PermaNet(r) 2.0 hut was not significantly different from that of the control hut (p = 0.4). On average the killing effect of PermaNet(r) 2.0 and PermaNet(r) 3.0 compared to the untreated net were 2.2% and 48.9%, respectively. The killing effects in the huts with PermaNet(r) 2.0 and PermaNet(r) 3.0 increased to 60.0% and 75.5% when combined with IRS. Delayed mosquito mortality rates of 14.3% and 41.7% were recorded from huts with PermaNet(r) 2.0 and PermaNet(r) 3.0, respectively.\n",
-                        "\n",
-                        "Of the 50 sub-samples of _An. gambiae_ s.l. analysed using species-specific PCR, 46 (92%) were identified as _An. arabiensis_ and four mosquito samples failed to amplify. The blood-feeding rate of the _Anopheles_\n",
-                        "\n",
-                        "Fig. 1: Proportion of mosquitoes collected that were _Anopheles_ spp. in each experimental hut, Asendabo, Ethiopia. Control is no IRS and untreated net \n",
-                        "collected from each of the huts is presented in Fig. 3. Overall, 26 of the _Anopheles_ (11.1%) were blood-fed upon collection. Of the fed mosquitoes, human, bovine and mixed human/bovine blood were detected in 14 (53.9%), 2 (7.7%), 5 (19.2%) of the specimens, respectively. Blood meals from the remaining 5 (19.2%) of the specimens were unidentified. The overall proportion of human blood-fed _Anopheles_ was 73.1% (n = 19). The personal protection rates of PermaNet(r) 2.0 and PermaNet(r) 3.0 nets were 33.3% and 50%, respectively. When combined with IRS, the personal protection rate of PermaNet(r) 2.0 increased to 83.3% while the personal protection rate of PermaNet(r) 2.0 decreased to 83.3%.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "The personal protection rate of PermaNet(r)\n",
-                        "\n",
-                        "\n",
-                        "PernaNet(r) 3.0 remained the same (50%). The proportions of human blood-fed _Anopheles_ collected from the control, PernaNet(r) 2.0, PernaNet(r) 3.0 and combined IRS-treated huts were 13.3% (95% CI 3.4-23.3), 11.4% (95% CI 0.9-22.0), 7.5% (95% CI 0.0-15.7) and 5.2% (95% CI 1.2-9.3), respectively.\n",
-                        "\n",
-                        "Overall, the majority of _Anopheles_ were collected from the rooms of the huts. The proportions of _Anopheles_ collected from the verandah of the IRS-treated and the control huts were 16.5% and 13.3%, respectively (Fig. 4). The proportion of _Anopheles_ collected from verandah of a hut with PernaNet(r) 2.0 was significantly higher than the proportion of _Anopheles_ collected from the control hut (p = 0.02).\n",
-                        "\n",
-                        "The mean knockdown rates of the susceptible _An. arabiensis_ laboratory strain in the bioassays conducted 3 weeks post-spray were 20.2% and 2.9% in the IRS-treated and control huts, respectively. The mean 24 h mortality rates from IRS-treated and control huts were 100% and 2.9%, respectively (Fig. 5).\n",
-                        "\n",
-                        "The combined mean 30 min knockdown rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. exposed to IRS-treated huts 10 weeks post-spray, with and without pre-exposure to PBO, was 1.1% (Fig. 6). The 24 h mortality rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. at 10 weeks post-spray which were exposed to IRS-treated hut wall surfaces were 100%, irrespective of pre-exposure of mosquitoes to PBO.\n",
-                        "\n",
-                        "header: Blood meal host source analysis and mosquito identification\n",
-                        "\n",
-                        "All the fed mosquitoes collected from the different huts were assayed to determine the blood meal host source using direct ELISA [13]. Moreover, a sub-sample of _An. gambiae_ s.l. was also molecularly identified to species using species-specific PCR following established protocol [14].\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data from the five treatment huts were compared with data from the control hut to determine the insecticidal effects on the variables listed above.\n",
-                        "\n",
-                        "Personal protection, killing effect, and exophily were estimated using the following formulas [9, 11].\n",
-                        "\n",
-                        "\\[\\text{Personal protection }(\\%) = 100 \\times (B_{u} - B_{t} )/B_{u} ;\\]\n",
-                        "\n",
-                        "where \\(B_{t}\\) is the total number of freshly blood-fed mosquitoes in the huts with treated nets. \\(B_{u}\\) is the total number of freshly blood-fed mosquitoes in the huts with untreated nets.\n",
-                        "\n",
-                        "\\[\\text{Killing effect }(\\%) = 100 \\times (K_{t} - K_{u} )/T_{u} ;\\]\n",
-                        "\n",
-                        "where \\(K_{t}\\) is number of mosquitoes killed (immediate) in the huts with treated nets. \\(K_{u}\\) is number of mosquitoes killed (immediate) in the huts with untreated nets. \\(T_{u}\\) is the total number of mosquitoes collected from the huts with untreated nets.\n",
-                        "\n",
-                        "\n",
-                        "Exophily (%) = (E_v/Et) x 100;\n",
-                        "\n",
-                        "where Ev is the number of mosquitoes found in verandah. Et is the total number of mosquitoes found inside the hut and verandah\n",
-                        "\n",
-                        "For all three parameters, mosquito entry was estimated by adding the number of dead and alive mosquitoes observed inside the hut and the verandah. Estimates of personal protection and killing effect were determined using mosquitoes from all collection nights.\n",
-                        "\n",
-                        "To test differences in mean mosquito density in each of the treatment huts, ANOVA was employed after log-transformation of non-normal distributed mosquito count data. T-tests were employed to determine the differences in mean proportions of blood-fed mosquitoes and mosquitoes exiting early from the different test huts and control hut.\n",
-                        "\n",
-                        "In all analyses, the hut with the untreated net and without IRS was used as a negative control, but comparisons were made between all treatment groups. In all analyses, individual huts and sleepers were included in the models as random effects.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Malaria vector control interventions have been intensified over the last decade in Ethiopia resulting in remarkable progress, with a reduction in children under-five mortality rates from 123 deaths per 1000 live births in 2005 to 55 deaths per 1000 live births in 2019 [15]. The vector control activities including mass-distribution/replacement of ITNs every 3 years in accordance with WHO recommendations and deployment of IRS in targeted areas appear to have played vital roles in the successes obtained in malaria control in Ethiopia [16]. However, malaria still remains a public health challenge in the country, where an estimated 52% of the population lives in areas at risk of malaria. With the successes gained in malaria control, a national malaria elimination goal is set for 2030 [17]. This study evaluated the impact of combining currently available vector control tools on mosquito behaviour and mortality.\n",
-                        "\n",
-                        "Compared to PernaNet(r) 2.0, PernaNet(r) 3.0 is impregnated with a higher concentration of deltamethrin and the roof panel is treated with both deltamethrin and PBO. The higher killing effect of PernaNet(r) 3.0 compared to PernaNet(r) 2.0 in this study could be attributed to the presence of the PBO synergist on the roof panel of the net and/or higher dosage of deltamethrin in the net. The synergist PBO works to inhibit P450 detoxification of pyrethroid insecticides and can result in partial or complete restoration of susceptibility to certain pyrethroids, such as deltamethrin, hence enhancing efficacy of the insecticides [18]. The role of PBO in affecting susceptibility in both wild and laboratory strain mosquitoes exposed to Actellite(r) 300CS\n",
-                        "\n",
-                        "Fig. 4: Proportion of _Anopheles_ collected from inside nets, rooms and verandahs of different experimental huts, Asendabo, Ethiopia \n",
-                        "sprayed walls could not be determined as mortality rates in all IRS treatment huts were 100%. PBO nets have been shown to provide high levels of protection against _An. gambiae_ in a recent experimental hut trial in Cote d'Ivoire [19]. Moreover, the improved protective role of PBO nets on malaria prevalence was also documented in Uganda [20]. Personal protective efficacy of PBO nets may also last longer compared to the standard nets [21]. PBO nets are particularly important as a malaria control tool in areas where the protection against PBO is not a viable alternative to the standard nets.\n",
-                        "\n",
-                        "Fig. 5: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain following wall bioassays 3 weeks after ActeIlic 300CS spraying, Asendabo, Ethiopia\n",
-                        "\n",
-                        "Fig. 6: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain and wild _An. gambiae_ s.l. (with and without pre-exposure to PBO) following wall bioassays 10 weeks after IRS using ActeIlic 300CS, Asendabo, Ethiopia \n",
-                        "pyrethroid resistance is high in the vector populations [22] and is, therefore, under consideration for more widespread distribution in Ethiopia. Despite these added advantages of PBO nets, the cost is currently higher than pyrethroid nets which may limit their wider use in malaria vector control in malaria endemic areas. Additionally, this study provides an example of a setting with high levels of pyrethroid resistance where PBO nets may not be sufficient.\n",
-                        "\n",
-                        "The proportion of _Anopheles_ retrieved from the verandah of huts with both PermaNet 2.0 and PermaNet 3.0 was higher than those from the verandah of the control hut. Huts with PermaNet 2.0 induced significantly higher exito-repellency compared to control huts. Insecticides often induce toxicity to mosquitoes. However, the non-toxic 'exito-repellent' effects of insecticides are often less understood. Pyrethroid and organophosphate insecticides are also known to have these non-killing effects. The impact of the non-killing effects of the insecticides on the risk of malaria is not clearly understood. Though exito-repellency appears to reduce indoor human-vector contact, it may on the contrary enhance risk of outdoor mosquito biting [23].\n",
-                        "\n",
-                        "The role of IRS as a key malaria vector control intervention tool is evident. Immediate mortality was observed in mosquitoes collected from the three huts sprayed with Actellic(r) 300CS up to the end of the study at 10 weeks post-spray. The proportion of blood-fed _Anopheles_ was significantly lower in the IRS-treated huts compared to the control hut. This could be due to the fast-acting nature of Actellic(r) 300CS insecticide with both contact and fumigant effects. This shows the potential of Actellic(r) 300CS as an insecticide of choice for IRS in areas where the local vector population is susceptible to the insecticide. _Anopheles arabiensis_ is susceptible to pirimibos-methyl in most parts of Ethiopia [10, 24]. Some earlier studies conducted in Ethiopia showed the effectiveness of combining IRS with ITNs compared to ITNs alone in suppressing vector populations [25]. Added benefits of combining IRS and ITNs in malaria prevention was also reported from other areas in sub-Saharan Africa [26-28]. In contrast, limited or no added benefit of combining IRS with ITNs as compared to ITNs alone has been reported elsewhere [29, 30]. This inconsistency could be attributed to differences in the intensity of malaria transmission and insecticide resistance of the local vector populations. The impact of the combined use of available vector control interventions appears to be more pronounced when applied in high transmission areas, and where the local vector populations are susceptible to the insecticide used for the IRS component [25].\n",
-                        "\n",
-                        "The importance of combining IRS and ITNs for malaria vector control is believed partly to come from the low or inconsistent utilization of ITNs by users and short residual efficacy of IRS insecticides [31, 32]. In Ethiopia, household surveys have shown approximately 50% of children under five and pregnant women use their nets with geographic variation [33]. The gap often created as a result of low utilization of ITNs may be complemented with IRS. ITN utilization and care depends on personal decisions and behavior [34]. However, once properly implemented, IRS rapidly suppresses local vector populations with no additional behaviors required from the residents, hence reducing malaria incidence [35]. However, effectiveness of IRS for malaria vector control is highly affected by both physiological and behavioral resistance of the local vector population to the insecticide [36], apart from operational factors and higher costs.\n",
-                        "\n",
-                        "As the study was conducted during the dry season of the year, the mosquito population density was not high and, therefore, limits the generalizability of the results. However, the findings of this study could provide preliminary information for vector control programs for the rational choice of future vector control intervention tools. In addition, combining indoor residual spraying using pirimibos-methyl with PBO nets has been reported to be antagonistic in some studies, but a negative interaction or antagonism between PBO and pirimibos-methyl was not detected in this study.\n",
-                        "\n",
-                        "header: Variables\n",
-                        "\n",
-                        "Four study outcomes measured were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in hut entry by mosquitoes in treatment huts relative to the control hut (hut with untreated net and no IRS).\n",
-                        "2. Exophily--the proportion of mosquitoes found resting on the walls of the verandah.\n",
-                        "3. Blood-feeding inhibition--the reduction in blood-feeding in treatment huts in comparison with control hut (hut with untreated net and no IRS).\n",
-                        "4. Immediate and delayed mortality--the proportion of mosquitoes entering the hut that were dead in the morning (immediate mortality) or after being caught alive and held for 24 h with access to a sugar solution (delayed mortality).\n",
-                        "\n",
-                        "header: Methods\n",
-                        "\n",
-                        "The study was conducted using six experimental huts located at Asendabo, Jimma Zone, Oromia Regional State, Ethiopia, located between latitudes 7\" 42' 50\" N and 07\" 53' 50\" N and longitudes 37\" 11' 22\" E and 37\" 20' 36\" E, at altitudes ranging from 1672 to 1864 m above sea level. The huts were used previously for evaluation of vector control tools [9], but the roofs, walls, floors, ceilings and verandahs of each hut were completely renovated for use in this study. The huts are located approximately 0.3 km from the reservoir shore of Gilgel Gibe hydroelectric power dam in Nada District, Oromia Regional State, Ethiopia. Earlier work showed that _An. arabiensis_ population from the study area was resistant to DDT, pyrethroids and malathion, while they were susceptible to primiphos-methyl and some of the carbamates used in malaria vector control [10].\n",
-                        "\n",
-                        "The six treatments in this study included:\n",
-                        "\n",
-                        "1. Control hut (no IRS, untreated net only).\n",
-                        "2. Hut with PermaNet(r) 2.0 (deltamethrin) only (no IRS).\n",
-                        "3. Hut with PermaNet(r) 3.0 (deltamethrin with PBO) only (no IRS).\n",
-                        "4. Hut sprayed with Actcllic 300CS (1 g/m2) and untreated net.\n",
-                        "5. Hut sprayed with Actcllic 300CS (1 g/m2) and PermaNet(r) 2.0.\n",
-                        "6. Hut sprayed with Actcllic 300CS (1 g/m2) and PermaNet(r) 3.0.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Disclaimer\n",
-                        "\n",
-                        "The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the CDC. Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the US Department of Health and Human Services.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "Data supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Indoor residual spraying (IRS)\n",
-                        "\n",
-                        "Actcllic(r) 300CS, a pirimiphos-methyl-based IRS insecticide, was applied to the three IRS treatment huts 1 week before the start of the trial. Pirimiphos-methyl is an organophosphate insecticide being used for IRS by the malaria elimination program of Ethiopia. During the spraying, filter papers were affixed to the wall surfaces at three heights (high, middle and low) to ensure the application was properly and uniformly applied and determine the concentration of the applied insecticide. The spray operators were trained on the spraying techniques and the wall surfaces of huts were lined with chalk to properly maintain the swath and avoid overlap following the WHO protocol.\n",
-                        "\n",
-                        "header: Wall bioassay\n",
-                        "\n",
-                        "Cone bioassays were conducted twice during the trial period to assess the bio-efficacy of Actclic 300CS. At each time point, three cones were fixed to the wall surfaces at three heights from the ground (high 160 cm, medium 100 cm and low 40 cm) of each hut and ten mosquitoes were used per cone with a total of 30 mosquitoes per hut, for each of the three sprayed huts and a control hut (120 mosquitoes in total). Mosquitoes were exposed for 30 min after which immediate knockdown was recorded and mortality was recorded after a 24 h holding period. Non-blood-fed, 3-5 day old adult female _Anopheles gambiae_ sensu lato (s.l.) were used for bioassays.\n",
-                        "\n",
-                        "The first cone bioassay was conducted 3 weeks after spray using a confirmed susceptible _An. arabiensis_ strain from Sekoru Insectary without pre-exposure to PBO synergist. The second bioassay was conducted at the end of the trial (10 weeks after spray). In the second bioassay, both susceptible _An. arabiensis_ laboratory strain from Sekoru Insectary and wild _An. gambiae_ s.l. were used. The assay was carried out with and without PBO pre-exposure for both strains. Wild populations of _An. gambiae_ s.l. used for the bioassays were raised from larvae and pupae collected from semi-permanent and permanent breeding habitats around Wolfkite area, central Ethiopia. They were reared to adults at Asendabo Field Insectary. Mosquitoes were exposed to papers treated with PBO (2%) in a WHO cylinder for 1 h.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichroo-diphenyl-trichloroethane; ELISA: Enzyme-linked immunosorbent Assay; IRS: Indoor residual spray; ITS: Insecticide-treated net; PBO: Piperonyl butoxide; PCR Polymerase chain reaction; WHO: World Health Organization; WHORES: World Health Organization Pesticide Evaluation Scheme.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Malaria is an important public health and socio-economic problem in Ethiopia. The main malaria vector in Ethiopia is _Anopheles arabiensis,_ while _Anopheles pharoetiss, Anopheles funestus_ and _Anopheles nili_ play a secondary role in malaria transmission. _Anopheles stephensi,_ a common and efficient urban malaria vector in Southeast Asia and the Mediterranean Region, has also recently been reported in Ethiopia and neighboring countries including Djibouti and Sudan [1]. There is widespread resistance to dichloro-diphenyl-trichloroethane (DDT) and pyrethroids in _An. arabiensis_ and resistance has also been detected to organophosphates in some populations. _Anopheles pharoensis_ was also reported to be highly resistant to DDT [2]. The _kdr_ mutation (L1014F) has been detected in _An. arabiensis_ from many sites, while _ace-1R_ and _kdr_ (L1014S) were not detected in all populations [3, 4, 5]. Bottle bioassay tests with _An. arabiensis_ populations from all surveyed areas of Ethiopia found resistance to both permethrin and deltamethrin. However, susceptibility to permethrin and deltamethrin was restored following pre-exposure of mosquitoes to the synergist piperonyl butoxide (PBO), indicating involvement of P450-based metabolic resistance mechanisms in the Ethiopian populations of _An. arabiensis_[6].\n",
-                        "\n",
-                        "Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main vector control interventions in Ethiopia. Ethiopia has a long history of IRS. It began spraying DDT in the late 1950s during the Malaria Eradication Programme and malathion was also used for IRS in a few areas until 2010. Deltamethrin, a pyrethroid insecticide, was used for IRS from 2009 to 2013, and since then, bendiocarb and propoxur (carbamate insecticides) and pirimihos-methyl (organophosphate insecticide) have been used for IRS in Ethiopia. The PMI VectorLink Ethiopia Project piloted the clothianidin (neonicotinoid)-based insecticides, SumiShield(r) 50WG and Fludora Fusion WP-SB, in 2020 in Menge District in the Benishangul Gumuz Region. Insecticide-treated nets have been widely distributed to households in malaria risk areas every 3 years mainly through the health extension programme [7].\n",
-                        "\n",
-                        "Recent studies in Tanzania showed that the impact of PBO nets could be comparable to the impact of pyrethroid-only nets when combined with IRS, and that there was no significant difference in malaria prevalence between the pyrethroid-only net + IRS and PBO net + IRS study arms [8]. This is of interest for malaria control operations in Ethiopia, as such data can inform future decisions on ITN procurements and strategic\n",
-                        "distribution, as well as insecticide choices for IRS, which may reduce overall malaria vector control costs. In addition, it is also important to understand possible antagonistic effects of PBO and pirimiphos-methyl, as suggested by the World Health Organization (WHO), before recommending the combined use of PBO nets and pirimiphos methyl IRS for malaria control in Ethiopia.\n",
-                        "\n",
-                        "This study aimed at assessing the impact of the combination of currently available vector control tools (IRS, pyrethroid-only nets, and PBO nets) on mosquito behaviour, mortality and the possible antagonistic effect of PBO and pirimiphos-methyl using experimental hurts in Asendabo, Ethiopia. The hypothesis for the Ethiopia context was that the use of PBO nets in combination with IRS would better control malaria vectors than PBO nets alone and that the use of IRS in combination with pyrethroid-only nets would better control malaria vectors than pyrethroid-only nets alone.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "An experimental hut study evaluating the impact of pyrethroid-only and PBO nets alone and in combination with pirrimiphos-methyl-based IRS in Ethiopia\n",
-                        "\n",
-                        "Delenasaw Yevhalaw, Meshesha Balkew, Endalew Zermene, Sheleme Chibsa, Peter Mumba, Cecilia Flatley, Akiliu Seyoum, Melissa Yoshimizu, Sarah Zohdy, Dereje Dengela\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid resistance observed in populations of malaria vectors is widespread in Ethiopia and could potentially compromise the effectiveness of insecticide-based malaria vector control interventions. In this study, the impact of combining indoor residual spraying (IRS) and insecticide-treated nets (ITNs) on mosquito behaviour and mortality was evaluated using experimental huts.\n",
-                        "\n",
-                        "header: Use of nets for the study\n",
-                        "\n",
-                        "During the study, two types of nets (PermaNet(r) 2.0 and PermaNet(r) 3.0) with and without IRS were assessed. Moreover, an untreated net was used with IRS and untreated net without IRS was used as a negative control. Both PermaNet(r) 2.0 and PermaNet(r) 3.0 nets are treated with the pyrethroid deltamethrin, but PermaNet 3.0 has also PBO synergist. During the study, each ITN in each hut was replaced by a new replicate net every 3 weeks since the trial had no replicate huts. Eighteen holes (4 cm x 4 cm) were cut in each net purposefully to simulate a torn net [11].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The personal protection rate of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 33.3% and 50%, respectively. The mean killing effect of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 2% and 49%, respectively. Huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone demonstrated significantly higher excito-repellency than the control hut. However, mosquito mortality in the hut with IRS + untreated net, hut with IRS + PermaNet(r) 2.0 and hut with IRS + PermaNet(r) 3.0 were not significantly different from each other (p > 0.05). Additionally, pre-exposure of both the susceptible _Anopheles arabiensis_ laboratory strain and wild _Anopheles gambiae_ sensu late to PBO in the cone bioassay tests of Actellite 300CS sprayed surfaces did not reduce mosquito mortality when compared to mortality without pre-exposure to PBO.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "In this study, significantly higher mosquito mortality rates were recorded from treatment huts with IRS than huts with ITNs alone, regardless of net type. Interestingly, the hut with IRS + untreated net had similar mosquito mortality rates as huts with combinations of vector control tools (IRS + pyrethroid-only nets and IRS + PBO nets). Moreover, immediate mortality rate of mosquitoes in IRS + untreated net was significantly higher than PermaNet 2.0 and PermaNet 3.0 nets. These suggest that IRS alone provides the greatest vector control impact. Indoor residual spraying alone or in combination with ITNs offered significantly higher personal protection against mosquito bites than the control hut. Moreover, the proportion of mosquito mortality was significantly higher in huts with PBO nets than huts with pyrethroid-only nets, but remained significantly lower than the huts that received IRS. In general, the findings of this study revealed that IRS alone was as efficacious as combinations of vector tools (IRS + ITNs) for malaria vector control at least over the 9-week study period. Field studies to further assess the impact of combinations of vector tools on malaria vector control in different eco-epidemiological settings are also warranted. This study \n",
-                        "provides additional data for considering future selection of the most appropriate vector control tools for improved malaria vector control and management of insecticide resistance. This study also indicated that there was no antagonistic effect between PBO and IRS with pirimib-phos-methyl. However, further studies may be needed to determine if there are antagonistic effects in large scale IRS applications to evaluate the efficacy of PBO vs. IRS against different resistant vector populations in Ethiopia for decision-making to deploy either PBO nets or pirimib-phos-based IRS intervention.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Mosquito mortality rates from the huts with IRS alone were similar to mosquito mortality rates from the huts with the combination of vector control intervention tools (IRS + ITNs) and mosquito mortality rates from huts with PBO nets alone were significantly higher than huts with pyrethroid-only nets. The findings of this study help inform studies to be conducted under field condition for decision-making for future selection of cost-effective vector control intervention tools.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "We declare that we have no competing interests.\n",
-                        "\n",
-                        "header: Blood meal host source analysis and mosquito identification\n",
-                        "\n",
-                        "All the fed mosquitoes collected from the different huts were assayed to determine the blood meal host source using direct ELISA [13]. Moreover, a sub-sample of _An. gambiae_ s.l. was also molecularly identified to species using species-specific PCR following established protocol [14].\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Overall, a total of 386 mosquitoes, comprising 235 _Anopheles_ spp. (60.9%) and 151 _Culex_ spp. (39.1%), were collected during the study. Of the _Anopheles_ mosquitoes, _An. gambiae_ s.l, _An. pharoensis_ and _Anopheles__custani_ accounted for 228 (97.0%), 6 (2.6%) and 1 (0.4%), respectively. The proportion of the total _Anopheles_ mosquitoes collected from each hut is presented in Fig. 1. There were no significant differences in mean percentage of _Anopheles_ mosquitoes between the different treatment and control huts (p > 0.05).\n",
-                        "\n",
-                        "An immediate mosquito mortality of 100% was recorded from all three huts with IRS throughout the study period. The mean immediate mosquito mortality rates from the control, PermaNet(r) 2.0 and PermaNet(r) 3.0 huts were 13.3% (95% CI 3-23%), 20.0% (95% CI 6-33%) and 70.0% (95% CI 55-84%), respectively (Fig. 2). The immediate mosquito mortality rate from the hut with IRS + untreated net was significantly higher than the PermaNet(r) 3.0 hut (p < 0.001). Similarly, the immediate mosquito mortality rate from the hut with PermaNet(r) 3.0 was significantly higher than that from the control hut (p < 0.001) as well as the PermaNet(r) 2.0 (p < 0.001) hut. The mosquito mortality rate from the PermaNet(r) 2.0 hut was not significantly different from that of the control hut (p = 0.4). On average the killing effect of PermaNet(r) 2.0 and PermaNet(r) 3.0 compared to the untreated net were 2.2% and 48.9%, respectively. The killing effects in the huts with PermaNet(r) 2.0 and PermaNet(r) 3.0 increased to 60.0% and 75.5% when combined with IRS. Delayed mosquito mortality rates of 14.3% and 41.7% were recorded from huts with PermaNet(r) 2.0 and PermaNet(r) 3.0, respectively.\n",
-                        "\n",
-                        "Of the 50 sub-samples of _An. gambiae_ s.l. analysed using species-specific PCR, 46 (92%) were identified as _An. arabiensis_ and four mosquito samples failed to amplify. The blood-feeding rate of the _Anopheles_\n",
-                        "\n",
-                        "Fig. 1: Proportion of mosquitoes collected that were _Anopheles_ spp. in each experimental hut, Asendabo, Ethiopia. Control is no IRS and untreated net \n",
-                        "collected from each of the huts is presented in Fig. 3. Overall, 26 of the _Anopheles_ (11.1%) were blood-fed upon collection. Of the fed mosquitoes, human, bovine and mixed human/bovine blood were detected in 14 (53.9%), 2 (7.7%), 5 (19.2%) of the specimens, respectively. Blood meals from the remaining 5 (19.2%) of the specimens were unidentified. The overall proportion of human blood-fed _Anopheles_ was 73.1% (n = 19). The personal protection rates of PermaNet(r) 2.0 and PermaNet(r) 3.0 nets were 33.3% and 50%, respectively. When combined with IRS, the personal protection rate of PermaNet(r) 2.0 increased to 83.3% while the personal protection rate of PermaNet(r) 2.0 decreased to 83.3%.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "The personal protection rate of PermaNet(r)\n",
-                        "\n",
-                        "\n",
-                        "PernaNet(r) 3.0 remained the same (50%). The proportions of human blood-fed _Anopheles_ collected from the control, PernaNet(r) 2.0, PernaNet(r) 3.0 and combined IRS-treated huts were 13.3% (95% CI 3.4-23.3), 11.4% (95% CI 0.9-22.0), 7.5% (95% CI 0.0-15.7) and 5.2% (95% CI 1.2-9.3), respectively.\n",
-                        "\n",
-                        "Overall, the majority of _Anopheles_ were collected from the rooms of the huts. The proportions of _Anopheles_ collected from the verandah of the IRS-treated and the control huts were 16.5% and 13.3%, respectively (Fig. 4). The proportion of _Anopheles_ collected from verandah of a hut with PernaNet(r) 2.0 was significantly higher than the proportion of _Anopheles_ collected from the control hut (p = 0.02).\n",
-                        "\n",
-                        "The mean knockdown rates of the susceptible _An. arabiensis_ laboratory strain in the bioassays conducted 3 weeks post-spray were 20.2% and 2.9% in the IRS-treated and control huts, respectively. The mean 24 h mortality rates from IRS-treated and control huts were 100% and 2.9%, respectively (Fig. 5).\n",
-                        "\n",
-                        "The combined mean 30 min knockdown rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. exposed to IRS-treated huts 10 weeks post-spray, with and without pre-exposure to PBO, was 1.1% (Fig. 6). The 24 h mortality rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. at 10 weeks post-spray which were exposed to IRS-treated hut wall surfaces were 100%, irrespective of pre-exposure of mosquitoes to PBO.\n",
-                        "\n",
-                        "header: Variables\n",
-                        "\n",
-                        "Four study outcomes measured were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in hut entry by mosquitoes in treatment huts relative to the control hut (hut with untreated net and no IRS).\n",
-                        "2. Exophily--the proportion of mosquitoes found resting on the walls of the verandah.\n",
-                        "3. Blood-feeding inhibition--the reduction in blood-feeding in treatment huts in comparison with control hut (hut with untreated net and no IRS).\n",
-                        "4. Immediate and delayed mortality--the proportion of mosquitoes entering the hut that were dead in the morning (immediate mortality) or after being caught alive and held for 24 h with access to a sugar solution (delayed mortality).\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A Latin Square Design was employed using six experimental huts to collect entomological data. Human volunteers slept in huts with different types of nets (pyrethroid-only net, PBO net, and untreated net) either with or without IRS (Actallic 300CS). The hut with no IRS and an untreated net served as a negative control. The study was conducted for a total of 54 nights. Both alive and dead mosquitoes were collected from inside nets, in the central rooms and verandah the following morning. Data were analysed using Stata/SE 14.0 software package (College Station, TX, USA).\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data from the five treatment huts were compared with data from the control hut to determine the insecticidal effects on the variables listed above.\n",
-                        "\n",
-                        "Personal protection, killing effect, and exophily were estimated using the following formulas [9, 11].\n",
-                        "\n",
-                        "\\[\\text{Personal protection }(\\%) = 100 \\times (B_{u} - B_{t} )/B_{u} ;\\]\n",
-                        "\n",
-                        "where \\(B_{t}\\) is the total number of freshly blood-fed mosquitoes in the huts with treated nets. \\(B_{u}\\) is the total number of freshly blood-fed mosquitoes in the huts with untreated nets.\n",
-                        "\n",
-                        "\\[\\text{Killing effect }(\\%) = 100 \\times (K_{t} - K_{u} )/T_{u} ;\\]\n",
-                        "\n",
-                        "where \\(K_{t}\\) is number of mosquitoes killed (immediate) in the huts with treated nets. \\(K_{u}\\) is number of mosquitoes killed (immediate) in the huts with untreated nets. \\(T_{u}\\) is the total number of mosquitoes collected from the huts with untreated nets.\n",
-                        "\n",
-                        "\n",
-                        "Exophily (%) = (E_v/Et) x 100;\n",
-                        "\n",
-                        "where Ev is the number of mosquitoes found in verandah. Et is the total number of mosquitoes found inside the hut and verandah\n",
-                        "\n",
-                        "For all three parameters, mosquito entry was estimated by adding the number of dead and alive mosquitoes observed inside the hut and the verandah. Estimates of personal protection and killing effect were determined using mosquitoes from all collection nights.\n",
-                        "\n",
-                        "To test differences in mean mosquito density in each of the treatment huts, ANOVA was employed after log-transformation of non-normal distributed mosquito count data. T-tests were employed to determine the differences in mean proportions of blood-fed mosquitoes and mosquitoes exiting early from the different test huts and control hut.\n",
-                        "\n",
-                        "In all analyses, the hut with the untreated net and without IRS was used as a negative control, but comparisons were made between all treatment groups. In all analyses, individual huts and sleepers were included in the models as random effects.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Malaria vector control interventions have been intensified over the last decade in Ethiopia resulting in remarkable progress, with a reduction in children under-five mortality rates from 123 deaths per 1000 live births in 2005 to 55 deaths per 1000 live births in 2019 [15]. The vector control activities including mass-distribution/replacement of ITNs every 3 years in accordance with WHO recommendations and deployment of IRS in targeted areas appear to have played vital roles in the successes obtained in malaria control in Ethiopia [16]. However, malaria still remains a public health challenge in the country, where an estimated 52% of the population lives in areas at risk of malaria. With the successes gained in malaria control, a national malaria elimination goal is set for 2030 [17]. This study evaluated the impact of combining currently available vector control tools on mosquito behaviour and mortality.\n",
-                        "\n",
-                        "Compared to PernaNet(r) 2.0, PernaNet(r) 3.0 is impregnated with a higher concentration of deltamethrin and the roof panel is treated with both deltamethrin and PBO. The higher killing effect of PernaNet(r) 3.0 compared to PernaNet(r) 2.0 in this study could be attributed to the presence of the PBO synergist on the roof panel of the net and/or higher dosage of deltamethrin in the net. The synergist PBO works to inhibit P450 detoxification of pyrethroid insecticides and can result in partial or complete restoration of susceptibility to certain pyrethroids, such as deltamethrin, hence enhancing efficacy of the insecticides [18]. The role of PBO in affecting susceptibility in both wild and laboratory strain mosquitoes exposed to Actellite(r) 300CS\n",
-                        "\n",
-                        "Fig. 4: Proportion of _Anopheles_ collected from inside nets, rooms and verandahs of different experimental huts, Asendabo, Ethiopia \n",
-                        "sprayed walls could not be determined as mortality rates in all IRS treatment huts were 100%. PBO nets have been shown to provide high levels of protection against _An. gambiae_ in a recent experimental hut trial in Cote d'Ivoire [19]. Moreover, the improved protective role of PBO nets on malaria prevalence was also documented in Uganda [20]. Personal protective efficacy of PBO nets may also last longer compared to the standard nets [21]. PBO nets are particularly important as a malaria control tool in areas where the protection against PBO is not a viable alternative to the standard nets.\n",
-                        "\n",
-                        "Fig. 5: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain following wall bioassays 3 weeks after ActeIlic 300CS spraying, Asendabo, Ethiopia\n",
-                        "\n",
-                        "Fig. 6: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain and wild _An. gambiae_ s.l. (with and without pre-exposure to PBO) following wall bioassays 10 weeks after IRS using ActeIlic 300CS, Asendabo, Ethiopia \n",
-                        "pyrethroid resistance is high in the vector populations [22] and is, therefore, under consideration for more widespread distribution in Ethiopia. Despite these added advantages of PBO nets, the cost is currently higher than pyrethroid nets which may limit their wider use in malaria vector control in malaria endemic areas. Additionally, this study provides an example of a setting with high levels of pyrethroid resistance where PBO nets may not be sufficient.\n",
-                        "\n",
-                        "The proportion of _Anopheles_ retrieved from the verandah of huts with both PermaNet 2.0 and PermaNet 3.0 was higher than those from the verandah of the control hut. Huts with PermaNet 2.0 induced significantly higher exito-repellency compared to control huts. Insecticides often induce toxicity to mosquitoes. However, the non-toxic 'exito-repellent' effects of insecticides are often less understood. Pyrethroid and organophosphate insecticides are also known to have these non-killing effects. The impact of the non-killing effects of the insecticides on the risk of malaria is not clearly understood. Though exito-repellency appears to reduce indoor human-vector contact, it may on the contrary enhance risk of outdoor mosquito biting [23].\n",
-                        "\n",
-                        "The role of IRS as a key malaria vector control intervention tool is evident. Immediate mortality was observed in mosquitoes collected from the three huts sprayed with Actellic(r) 300CS up to the end of the study at 10 weeks post-spray. The proportion of blood-fed _Anopheles_ was significantly lower in the IRS-treated huts compared to the control hut. This could be due to the fast-acting nature of Actellic(r) 300CS insecticide with both contact and fumigant effects. This shows the potential of Actellic(r) 300CS as an insecticide of choice for IRS in areas where the local vector population is susceptible to the insecticide. _Anopheles arabiensis_ is susceptible to pirimibos-methyl in most parts of Ethiopia [10, 24]. Some earlier studies conducted in Ethiopia showed the effectiveness of combining IRS with ITNs compared to ITNs alone in suppressing vector populations [25]. Added benefits of combining IRS and ITNs in malaria prevention was also reported from other areas in sub-Saharan Africa [26-28]. In contrast, limited or no added benefit of combining IRS with ITNs as compared to ITNs alone has been reported elsewhere [29, 30]. This inconsistency could be attributed to differences in the intensity of malaria transmission and insecticide resistance of the local vector populations. The impact of the combined use of available vector control interventions appears to be more pronounced when applied in high transmission areas, and where the local vector populations are susceptible to the insecticide used for the IRS component [25].\n",
-                        "\n",
-                        "The importance of combining IRS and ITNs for malaria vector control is believed partly to come from the low or inconsistent utilization of ITNs by users and short residual efficacy of IRS insecticides [31, 32]. In Ethiopia, household surveys have shown approximately 50% of children under five and pregnant women use their nets with geographic variation [33]. The gap often created as a result of low utilization of ITNs may be complemented with IRS. ITN utilization and care depends on personal decisions and behavior [34]. However, once properly implemented, IRS rapidly suppresses local vector populations with no additional behaviors required from the residents, hence reducing malaria incidence [35]. However, effectiveness of IRS for malaria vector control is highly affected by both physiological and behavioral resistance of the local vector population to the insecticide [36], apart from operational factors and higher costs.\n",
-                        "\n",
-                        "As the study was conducted during the dry season of the year, the mosquito population density was not high and, therefore, limits the generalizability of the results. However, the findings of this study could provide preliminary information for vector control programs for the rational choice of future vector control intervention tools. In addition, combining indoor residual spraying using pirimibos-methyl with PBO nets has been reported to be antagonistic in some studies, but a negative interaction or antagonism between PBO and pirimibos-methyl was not detected in this study.\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This work was funded by the US President's Malaria initiative.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichroo-diphenyl-trichloroethane; ELISA: Enzyme-linked immunosorbent Assay; IRS: Indoor residual spray; ITS: Insecticide-treated net; PBO: Piperonyl butoxide; PCR Polymerase chain reaction; WHO: World Health Organization; WHORES: World Health Organization Pesticide Evaluation Scheme.\n",
-                        "\n",
-                        "header: Disclaimer\n",
-                        "\n",
-                        "The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the CDC. Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the US Department of Health and Human Services.\n",
-                        "\n",
-                        "header: Indoor residual spraying (IRS)\n",
-                        "\n",
-                        "Actcllic(r) 300CS, a pirimiphos-methyl-based IRS insecticide, was applied to the three IRS treatment huts 1 week before the start of the trial. Pirimiphos-methyl is an organophosphate insecticide being used for IRS by the malaria elimination program of Ethiopia. During the spraying, filter papers were affixed to the wall surfaces at three heights (high, middle and low) to ensure the application was properly and uniformly applied and determine the concentration of the applied insecticide. The spray operators were trained on the spraying techniques and the wall surfaces of huts were lined with chalk to properly maintain the swath and avoid overlap following the WHO protocol.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "Data supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "An experimental hut study evaluating the impact of pyrethroid-only and PBO nets alone and in combination with pirrimiphos-methyl-based IRS in Ethiopia\n",
-                        "\n",
-                        "Delenasaw Yevhalaw, Meshesha Balkew, Endalew Zermene, Sheleme Chibsa, Peter Mumba, Cecilia Flatley, Akiliu Seyoum, Melissa Yoshimizu, Sarah Zohdy, Dereje Dengela\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid resistance observed in populations of malaria vectors is widespread in Ethiopia and could potentially compromise the effectiveness of insecticide-based malaria vector control interventions. In this study, the impact of combining indoor residual spraying (IRS) and insecticide-treated nets (ITNs) on mosquito behaviour and mortality was evaluated using experimental huts.\n",
-                        "\n",
-                        "header: Wall bioassay\n",
-                        "\n",
-                        "Cone bioassays were conducted twice during the trial period to assess the bio-efficacy of Actclic 300CS. At each time point, three cones were fixed to the wall surfaces at three heights from the ground (high 160 cm, medium 100 cm and low 40 cm) of each hut and ten mosquitoes were used per cone with a total of 30 mosquitoes per hut, for each of the three sprayed huts and a control hut (120 mosquitoes in total). Mosquitoes were exposed for 30 min after which immediate knockdown was recorded and mortality was recorded after a 24 h holding period. Non-blood-fed, 3-5 day old adult female _Anopheles gambiae_ sensu lato (s.l.) were used for bioassays.\n",
-                        "\n",
-                        "The first cone bioassay was conducted 3 weeks after spray using a confirmed susceptible _An. arabiensis_ strain from Sekoru Insectary without pre-exposure to PBO synergist. The second bioassay was conducted at the end of the trial (10 weeks after spray). In the second bioassay, both susceptible _An. arabiensis_ laboratory strain from Sekoru Insectary and wild _An. gambiae_ s.l. were used. The assay was carried out with and without PBO pre-exposure for both strains. Wild populations of _An. gambiae_ s.l. used for the bioassays were raised from larvae and pupae collected from semi-permanent and permanent breeding habitats around Wolfkite area, central Ethiopia. They were reared to adults at Asendabo Field Insectary. Mosquitoes were exposed to papers treated with PBO (2%) in a WHO cylinder for 1 h.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Malaria is an important public health and socio-economic problem in Ethiopia. The main malaria vector in Ethiopia is _Anopheles arabiensis,_ while _Anopheles pharoetiss, Anopheles funestus_ and _Anopheles nili_ play a secondary role in malaria transmission. _Anopheles stephensi,_ a common and efficient urban malaria vector in Southeast Asia and the Mediterranean Region, has also recently been reported in Ethiopia and neighboring countries including Djibouti and Sudan [1]. There is widespread resistance to dichloro-diphenyl-trichloroethane (DDT) and pyrethroids in _An. arabiensis_ and resistance has also been detected to organophosphates in some populations. _Anopheles pharoensis_ was also reported to be highly resistant to DDT [2]. The _kdr_ mutation (L1014F) has been detected in _An. arabiensis_ from many sites, while _ace-1R_ and _kdr_ (L1014S) were not detected in all populations [3, 4, 5]. Bottle bioassay tests with _An. arabiensis_ populations from all surveyed areas of Ethiopia found resistance to both permethrin and deltamethrin. However, susceptibility to permethrin and deltamethrin was restored following pre-exposure of mosquitoes to the synergist piperonyl butoxide (PBO), indicating involvement of P450-based metabolic resistance mechanisms in the Ethiopian populations of _An. arabiensis_[6].\n",
-                        "\n",
-                        "Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main vector control interventions in Ethiopia. Ethiopia has a long history of IRS. It began spraying DDT in the late 1950s during the Malaria Eradication Programme and malathion was also used for IRS in a few areas until 2010. Deltamethrin, a pyrethroid insecticide, was used for IRS from 2009 to 2013, and since then, bendiocarb and propoxur (carbamate insecticides) and pirimihos-methyl (organophosphate insecticide) have been used for IRS in Ethiopia. The PMI VectorLink Ethiopia Project piloted the clothianidin (neonicotinoid)-based insecticides, SumiShield(r) 50WG and Fludora Fusion WP-SB, in 2020 in Menge District in the Benishangul Gumuz Region. Insecticide-treated nets have been widely distributed to households in malaria risk areas every 3 years mainly through the health extension programme [7].\n",
-                        "\n",
-                        "Recent studies in Tanzania showed that the impact of PBO nets could be comparable to the impact of pyrethroid-only nets when combined with IRS, and that there was no significant difference in malaria prevalence between the pyrethroid-only net + IRS and PBO net + IRS study arms [8]. This is of interest for malaria control operations in Ethiopia, as such data can inform future decisions on ITN procurements and strategic\n",
-                        "distribution, as well as insecticide choices for IRS, which may reduce overall malaria vector control costs. In addition, it is also important to understand possible antagonistic effects of PBO and pirimiphos-methyl, as suggested by the World Health Organization (WHO), before recommending the combined use of PBO nets and pirimiphos methyl IRS for malaria control in Ethiopia.\n",
-                        "\n",
-                        "This study aimed at assessing the impact of the combination of currently available vector control tools (IRS, pyrethroid-only nets, and PBO nets) on mosquito behaviour, mortality and the possible antagonistic effect of PBO and pirimiphos-methyl using experimental hurts in Asendabo, Ethiopia. The hypothesis for the Ethiopia context was that the use of PBO nets in combination with IRS would better control malaria vectors than PBO nets alone and that the use of IRS in combination with pyrethroid-only nets would better control malaria vectors than pyrethroid-only nets alone.\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "We declare that we have no competing interests.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The personal protection rate of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 33.3% and 50%, respectively. The mean killing effect of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 2% and 49%, respectively. Huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone demonstrated significantly higher excito-repellency than the control hut. However, mosquito mortality in the hut with IRS + untreated net, hut with IRS + PermaNet(r) 2.0 and hut with IRS + PermaNet(r) 3.0 were not significantly different from each other (p > 0.05). Additionally, pre-exposure of both the susceptible _Anopheles arabiensis_ laboratory strain and wild _Anopheles gambiae_ sensu late to PBO in the cone bioassay tests of Actellite 300CS sprayed surfaces did not reduce mosquito mortality when compared to mortality without pre-exposure to PBO.\n",
-                        "\n",
-                        "header: Use of nets for the study\n",
-                        "\n",
-                        "During the study, two types of nets (PermaNet(r) 2.0 and PermaNet(r) 3.0) with and without IRS were assessed. Moreover, an untreated net was used with IRS and untreated net without IRS was used as a negative control. Both PermaNet(r) 2.0 and PermaNet(r) 3.0 nets are treated with the pyrethroid deltamethrin, but PermaNet 3.0 has also PBO synergist. During the study, each ITN in each hut was replaced by a new replicate net every 3 weeks since the trial had no replicate huts. Eighteen holes (4 cm x 4 cm) were cut in each net purposefully to simulate a torn net [11].\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Mosquito mortality rates from the huts with IRS alone were similar to mosquito mortality rates from the huts with the combination of vector control intervention tools (IRS + ITNs) and mosquito mortality rates from huts with PBO nets alone were significantly higher than huts with pyrethroid-only nets. The findings of this study help inform studies to be conducted under field condition for decision-making for future selection of cost-effective vector control intervention tools.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "In this study, significantly higher mosquito mortality rates were recorded from treatment huts with IRS than huts with ITNs alone, regardless of net type. Interestingly, the hut with IRS + untreated net had similar mosquito mortality rates as huts with combinations of vector control tools (IRS + pyrethroid-only nets and IRS + PBO nets). Moreover, immediate mortality rate of mosquitoes in IRS + untreated net was significantly higher than PermaNet 2.0 and PermaNet 3.0 nets. These suggest that IRS alone provides the greatest vector control impact. Indoor residual spraying alone or in combination with ITNs offered significantly higher personal protection against mosquito bites than the control hut. Moreover, the proportion of mosquito mortality was significantly higher in huts with PBO nets than huts with pyrethroid-only nets, but remained significantly lower than the huts that received IRS. In general, the findings of this study revealed that IRS alone was as efficacious as combinations of vector tools (IRS + ITNs) for malaria vector control at least over the 9-week study period. Field studies to further assess the impact of combinations of vector tools on malaria vector control in different eco-epidemiological settings are also warranted. This study \n",
-                        "provides additional data for considering future selection of the most appropriate vector control tools for improved malaria vector control and management of insecticide resistance. This study also indicated that there was no antagonistic effect between PBO and IRS with pirimib-phos-methyl. However, further studies may be needed to determine if there are antagonistic effects in large scale IRS applications to evaluate the efficacy of PBO vs. IRS against different resistant vector populations in Ethiopia for decision-making to deploy either PBO nets or pirimib-phos-based IRS intervention.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Overall, a total of 386 mosquitoes, comprising 235 _Anopheles_ spp. (60.9%) and 151 _Culex_ spp. (39.1%), were collected during the study. Of the _Anopheles_ mosquitoes, _An. gambiae_ s.l, _An. pharoensis_ and _Anopheles__custani_ accounted for 228 (97.0%), 6 (2.6%) and 1 (0.4%), respectively. The proportion of the total _Anopheles_ mosquitoes collected from each hut is presented in Fig. 1. There were no significant differences in mean percentage of _Anopheles_ mosquitoes between the different treatment and control huts (p > 0.05).\n",
-                        "\n",
-                        "An immediate mosquito mortality of 100% was recorded from all three huts with IRS throughout the study period. The mean immediate mosquito mortality rates from the control, PermaNet(r) 2.0 and PermaNet(r) 3.0 huts were 13.3% (95% CI 3-23%), 20.0% (95% CI 6-33%) and 70.0% (95% CI 55-84%), respectively (Fig. 2). The immediate mosquito mortality rate from the hut with IRS + untreated net was significantly higher than the PermaNet(r) 3.0 hut (p < 0.001). Similarly, the immediate mosquito mortality rate from the hut with PermaNet(r) 3.0 was significantly higher than that from the control hut (p < 0.001) as well as the PermaNet(r) 2.0 (p < 0.001) hut. The mosquito mortality rate from the PermaNet(r) 2.0 hut was not significantly different from that of the control hut (p = 0.4). On average the killing effect of PermaNet(r) 2.0 and PermaNet(r) 3.0 compared to the untreated net were 2.2% and 48.9%, respectively. The killing effects in the huts with PermaNet(r) 2.0 and PermaNet(r) 3.0 increased to 60.0% and 75.5% when combined with IRS. Delayed mosquito mortality rates of 14.3% and 41.7% were recorded from huts with PermaNet(r) 2.0 and PermaNet(r) 3.0, respectively.\n",
-                        "\n",
-                        "Of the 50 sub-samples of _An. gambiae_ s.l. analysed using species-specific PCR, 46 (92%) were identified as _An. arabiensis_ and four mosquito samples failed to amplify. The blood-feeding rate of the _Anopheles_\n",
-                        "\n",
-                        "Fig. 1: Proportion of mosquitoes collected that were _Anopheles_ spp. in each experimental hut, Asendabo, Ethiopia. Control is no IRS and untreated net \n",
-                        "collected from each of the huts is presented in Fig. 3. Overall, 26 of the _Anopheles_ (11.1%) were blood-fed upon collection. Of the fed mosquitoes, human, bovine and mixed human/bovine blood were detected in 14 (53.9%), 2 (7.7%), 5 (19.2%) of the specimens, respectively. Blood meals from the remaining 5 (19.2%) of the specimens were unidentified. The overall proportion of human blood-fed _Anopheles_ was 73.1% (n = 19). The personal protection rates of PermaNet(r) 2.0 and PermaNet(r) 3.0 nets were 33.3% and 50%, respectively. When combined with IRS, the personal protection rate of PermaNet(r) 2.0 increased to 83.3% while the personal protection rate of PermaNet(r) 2.0 decreased to 83.3%.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "The personal protection rate of PermaNet(r)\n",
-                        "\n",
-                        "\n",
-                        "PernaNet(r) 3.0 remained the same (50%). The proportions of human blood-fed _Anopheles_ collected from the control, PernaNet(r) 2.0, PernaNet(r) 3.0 and combined IRS-treated huts were 13.3% (95% CI 3.4-23.3), 11.4% (95% CI 0.9-22.0), 7.5% (95% CI 0.0-15.7) and 5.2% (95% CI 1.2-9.3), respectively.\n",
-                        "\n",
-                        "Overall, the majority of _Anopheles_ were collected from the rooms of the huts. The proportions of _Anopheles_ collected from the verandah of the IRS-treated and the control huts were 16.5% and 13.3%, respectively (Fig. 4). The proportion of _Anopheles_ collected from verandah of a hut with PernaNet(r) 2.0 was significantly higher than the proportion of _Anopheles_ collected from the control hut (p = 0.02).\n",
-                        "\n",
-                        "The mean knockdown rates of the susceptible _An. arabiensis_ laboratory strain in the bioassays conducted 3 weeks post-spray were 20.2% and 2.9% in the IRS-treated and control huts, respectively. The mean 24 h mortality rates from IRS-treated and control huts were 100% and 2.9%, respectively (Fig. 5).\n",
-                        "\n",
-                        "The combined mean 30 min knockdown rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. exposed to IRS-treated huts 10 weeks post-spray, with and without pre-exposure to PBO, was 1.1% (Fig. 6). The 24 h mortality rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. at 10 weeks post-spray which were exposed to IRS-treated hut wall surfaces were 100%, irrespective of pre-exposure of mosquitoes to PBO.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data from the five treatment huts were compared with data from the control hut to determine the insecticidal effects on the variables listed above.\n",
-                        "\n",
-                        "Personal protection, killing effect, and exophily were estimated using the following formulas [9, 11].\n",
-                        "\n",
-                        "\\[\\text{Personal protection }(\\%) = 100 \\times (B_{u} - B_{t} )/B_{u} ;\\]\n",
-                        "\n",
-                        "where \\(B_{t}\\) is the total number of freshly blood-fed mosquitoes in the huts with treated nets. \\(B_{u}\\) is the total number of freshly blood-fed mosquitoes in the huts with untreated nets.\n",
-                        "\n",
-                        "\\[\\text{Killing effect }(\\%) = 100 \\times (K_{t} - K_{u} )/T_{u} ;\\]\n",
-                        "\n",
-                        "where \\(K_{t}\\) is number of mosquitoes killed (immediate) in the huts with treated nets. \\(K_{u}\\) is number of mosquitoes killed (immediate) in the huts with untreated nets. \\(T_{u}\\) is the total number of mosquitoes collected from the huts with untreated nets.\n",
-                        "\n",
-                        "\n",
-                        "Exophily (%) = (E_v/Et) x 100;\n",
-                        "\n",
-                        "where Ev is the number of mosquitoes found in verandah. Et is the total number of mosquitoes found inside the hut and verandah\n",
-                        "\n",
-                        "For all three parameters, mosquito entry was estimated by adding the number of dead and alive mosquitoes observed inside the hut and the verandah. Estimates of personal protection and killing effect were determined using mosquitoes from all collection nights.\n",
-                        "\n",
-                        "To test differences in mean mosquito density in each of the treatment huts, ANOVA was employed after log-transformation of non-normal distributed mosquito count data. T-tests were employed to determine the differences in mean proportions of blood-fed mosquitoes and mosquitoes exiting early from the different test huts and control hut.\n",
-                        "\n",
-                        "In all analyses, the hut with the untreated net and without IRS was used as a negative control, but comparisons were made between all treatment groups. In all analyses, individual huts and sleepers were included in the models as random effects.\n",
-                        "\n",
-                        "header: Author details\n",
-                        "\n",
-                        "1School of Medical Laboratory Sciences, Faculty of Health Sciences, Jimma University, Jimma, Ethiopia. 2Tropical and Infectious Diseases Research Center, Jimma University, Jimma, Ethiopia. 3HAR Associates, PMI VectorLine Project Ethiopia, Addis Ababa, Ethiopia. 4US President's Malaria Initiative, USAID, Washington, DC, USA. 7US President's Malaria Initiative, Center for Disease Control and Prevention, Atlanta, GA, USA.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A Latin Square Design was employed using six experimental huts to collect entomological data. Human volunteers slept in huts with different types of nets (pyrethroid-only net, PBO net, and untreated net) either with or without IRS (Actallic 300CS). The hut with no IRS and an untreated net served as a negative control. The study was conducted for a total of 54 nights. Both alive and dead mosquitoes were collected from inside nets, in the central rooms and verandah the following morning. Data were analysed using Stata/SE 14.0 software package (College Station, TX, USA).\n",
-                        "\n",
-                        "header: Variables\n",
-                        "\n",
-                        "Four study outcomes measured were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in hut entry by mosquitoes in treatment huts relative to the control hut (hut with untreated net and no IRS).\n",
-                        "2. Exophily--the proportion of mosquitoes found resting on the walls of the verandah.\n",
-                        "3. Blood-feeding inhibition--the reduction in blood-feeding in treatment huts in comparison with control hut (hut with untreated net and no IRS).\n",
-                        "4. Immediate and delayed mortality--the proportion of mosquitoes entering the hut that were dead in the morning (immediate mortality) or after being caught alive and held for 24 h with access to a sugar solution (delayed mortality).\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This work was funded by the US President's Malaria initiative.\n",
-                        "\n",
-                        "header: Blood meal host source analysis and mosquito identification\n",
-                        "\n",
-                        "All the fed mosquitoes collected from the different huts were assayed to determine the blood meal host source using direct ELISA [13]. Moreover, a sub-sample of _An. gambiae_ s.l. was also molecularly identified to species using species-specific PCR following established protocol [14].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Ethiopia\",\"Site\":\"Asendabo\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_holed\":18.0,\"pHI_category\":\"Torn\",\"Synergist\":null},\"N02\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"deltamethrin\",\"Net_holed\":18.0,\"pHI_category\":\"Torn\",\"Synergist\":\"PBO\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Abbreviations\n",
-                        "\n",
-                        "DDT: Dichroo-diphenyl-trichloroethane; ELISA: Enzyme-linked immunosorbent Assay; IRS: Indoor residual spray; ITS: Insecticide-treated net; PBO: Piperonyl butoxide; PCR Polymerase chain reaction; WHO: World Health Organization; WHORES: World Health Organization Pesticide Evaluation Scheme.\n",
-                        "\n",
-                        "header: Disclaimer\n",
-                        "\n",
-                        "The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the CDC. Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the US Department of Health and Human Services.\n",
-                        "\n",
-                        "header: Indoor residual spraying (IRS)\n",
-                        "\n",
-                        "Actcllic(r) 300CS, a pirimiphos-methyl-based IRS insecticide, was applied to the three IRS treatment huts 1 week before the start of the trial. Pirimiphos-methyl is an organophosphate insecticide being used for IRS by the malaria elimination program of Ethiopia. During the spraying, filter papers were affixed to the wall surfaces at three heights (high, middle and low) to ensure the application was properly and uniformly applied and determine the concentration of the applied insecticide. The spray operators were trained on the spraying techniques and the wall surfaces of huts were lined with chalk to properly maintain the swath and avoid overlap following the WHO protocol.\n",
-                        "\n",
-                        "header: Availability of data and materials\n",
-                        "\n",
-                        "Data supporting the conclusions of this article are included within the article.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "An experimental hut study evaluating the impact of pyrethroid-only and PBO nets alone and in combination with pirrimiphos-methyl-based IRS in Ethiopia\n",
-                        "\n",
-                        "Delenasaw Yevhalaw, Meshesha Balkew, Endalew Zermene, Sheleme Chibsa, Peter Mumba, Cecilia Flatley, Akiliu Seyoum, Melissa Yoshimizu, Sarah Zohdy, Dereje Dengela\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid resistance observed in populations of malaria vectors is widespread in Ethiopia and could potentially compromise the effectiveness of insecticide-based malaria vector control interventions. In this study, the impact of combining indoor residual spraying (IRS) and insecticide-treated nets (ITNs) on mosquito behaviour and mortality was evaluated using experimental huts.\n",
-                        "\n",
-                        "header: Ethics approval and consent to participate: Consent for publication\n",
-                        "\n",
-                        "Not applicable.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Malaria is an important public health and socio-economic problem in Ethiopia. The main malaria vector in Ethiopia is _Anopheles arabiensis,_ while _Anopheles pharoetiss, Anopheles funestus_ and _Anopheles nili_ play a secondary role in malaria transmission. _Anopheles stephensi,_ a common and efficient urban malaria vector in Southeast Asia and the Mediterranean Region, has also recently been reported in Ethiopia and neighboring countries including Djibouti and Sudan [1]. There is widespread resistance to dichloro-diphenyl-trichloroethane (DDT) and pyrethroids in _An. arabiensis_ and resistance has also been detected to organophosphates in some populations. _Anopheles pharoensis_ was also reported to be highly resistant to DDT [2]. The _kdr_ mutation (L1014F) has been detected in _An. arabiensis_ from many sites, while _ace-1R_ and _kdr_ (L1014S) were not detected in all populations [3, 4, 5]. Bottle bioassay tests with _An. arabiensis_ populations from all surveyed areas of Ethiopia found resistance to both permethrin and deltamethrin. However, susceptibility to permethrin and deltamethrin was restored following pre-exposure of mosquitoes to the synergist piperonyl butoxide (PBO), indicating involvement of P450-based metabolic resistance mechanisms in the Ethiopian populations of _An. arabiensis_[6].\n",
-                        "\n",
-                        "Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main vector control interventions in Ethiopia. Ethiopia has a long history of IRS. It began spraying DDT in the late 1950s during the Malaria Eradication Programme and malathion was also used for IRS in a few areas until 2010. Deltamethrin, a pyrethroid insecticide, was used for IRS from 2009 to 2013, and since then, bendiocarb and propoxur (carbamate insecticides) and pirimihos-methyl (organophosphate insecticide) have been used for IRS in Ethiopia. The PMI VectorLink Ethiopia Project piloted the clothianidin (neonicotinoid)-based insecticides, SumiShield(r) 50WG and Fludora Fusion WP-SB, in 2020 in Menge District in the Benishangul Gumuz Region. Insecticide-treated nets have been widely distributed to households in malaria risk areas every 3 years mainly through the health extension programme [7].\n",
-                        "\n",
-                        "Recent studies in Tanzania showed that the impact of PBO nets could be comparable to the impact of pyrethroid-only nets when combined with IRS, and that there was no significant difference in malaria prevalence between the pyrethroid-only net + IRS and PBO net + IRS study arms [8]. This is of interest for malaria control operations in Ethiopia, as such data can inform future decisions on ITN procurements and strategic\n",
-                        "distribution, as well as insecticide choices for IRS, which may reduce overall malaria vector control costs. In addition, it is also important to understand possible antagonistic effects of PBO and pirimiphos-methyl, as suggested by the World Health Organization (WHO), before recommending the combined use of PBO nets and pirimiphos methyl IRS for malaria control in Ethiopia.\n",
-                        "\n",
-                        "This study aimed at assessing the impact of the combination of currently available vector control tools (IRS, pyrethroid-only nets, and PBO nets) on mosquito behaviour, mortality and the possible antagonistic effect of PBO and pirimiphos-methyl using experimental hurts in Asendabo, Ethiopia. The hypothesis for the Ethiopia context was that the use of PBO nets in combination with IRS would better control malaria vectors than PBO nets alone and that the use of IRS in combination with pyrethroid-only nets would better control malaria vectors than pyrethroid-only nets alone.\n",
-                        "\n",
-                        "header: Wall bioassay\n",
-                        "\n",
-                        "Cone bioassays were conducted twice during the trial period to assess the bio-efficacy of Actclic 300CS. At each time point, three cones were fixed to the wall surfaces at three heights from the ground (high 160 cm, medium 100 cm and low 40 cm) of each hut and ten mosquitoes were used per cone with a total of 30 mosquitoes per hut, for each of the three sprayed huts and a control hut (120 mosquitoes in total). Mosquitoes were exposed for 30 min after which immediate knockdown was recorded and mortality was recorded after a 24 h holding period. Non-blood-fed, 3-5 day old adult female _Anopheles gambiae_ sensu lato (s.l.) were used for bioassays.\n",
-                        "\n",
-                        "The first cone bioassay was conducted 3 weeks after spray using a confirmed susceptible _An. arabiensis_ strain from Sekoru Insectary without pre-exposure to PBO synergist. The second bioassay was conducted at the end of the trial (10 weeks after spray). In the second bioassay, both susceptible _An. arabiensis_ laboratory strain from Sekoru Insectary and wild _An. gambiae_ s.l. were used. The assay was carried out with and without PBO pre-exposure for both strains. Wild populations of _An. gambiae_ s.l. used for the bioassays were raised from larvae and pupae collected from semi-permanent and permanent breeding habitats around Wolfkite area, central Ethiopia. They were reared to adults at Asendabo Field Insectary. Mosquitoes were exposed to papers treated with PBO (2%) in a WHO cylinder for 1 h.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "The personal protection rate of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 33.3% and 50%, respectively. The mean killing effect of huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone was 2% and 49%, respectively. Huts with PermaNet(r) 2.0 alone and PermaNet(r) 3.0 alone demonstrated significantly higher excito-repellency than the control hut. However, mosquito mortality in the hut with IRS + untreated net, hut with IRS + PermaNet(r) 2.0 and hut with IRS + PermaNet(r) 3.0 were not significantly different from each other (p > 0.05). Additionally, pre-exposure of both the susceptible _Anopheles arabiensis_ laboratory strain and wild _Anopheles gambiae_ sensu late to PBO in the cone bioassay tests of Actellite 300CS sprayed surfaces did not reduce mosquito mortality when compared to mortality without pre-exposure to PBO.\n",
-                        "\n",
-                        "header: Use of nets for the study\n",
-                        "\n",
-                        "During the study, two types of nets (PermaNet(r) 2.0 and PermaNet(r) 3.0) with and without IRS were assessed. Moreover, an untreated net was used with IRS and untreated net without IRS was used as a negative control. Both PermaNet(r) 2.0 and PermaNet(r) 3.0 nets are treated with the pyrethroid deltamethrin, but PermaNet 3.0 has also PBO synergist. During the study, each ITN in each hut was replaced by a new replicate net every 3 weeks since the trial had no replicate huts. Eighteen holes (4 cm x 4 cm) were cut in each net purposefully to simulate a torn net [11].\n",
-                        "\n",
-                        "header: Competing interests\n",
-                        "\n",
-                        "We declare that we have no competing interests.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "In this study, significantly higher mosquito mortality rates were recorded from treatment huts with IRS than huts with ITNs alone, regardless of net type. Interestingly, the hut with IRS + untreated net had similar mosquito mortality rates as huts with combinations of vector control tools (IRS + pyrethroid-only nets and IRS + PBO nets). Moreover, immediate mortality rate of mosquitoes in IRS + untreated net was significantly higher than PermaNet 2.0 and PermaNet 3.0 nets. These suggest that IRS alone provides the greatest vector control impact. Indoor residual spraying alone or in combination with ITNs offered significantly higher personal protection against mosquito bites than the control hut. Moreover, the proportion of mosquito mortality was significantly higher in huts with PBO nets than huts with pyrethroid-only nets, but remained significantly lower than the huts that received IRS. In general, the findings of this study revealed that IRS alone was as efficacious as combinations of vector tools (IRS + ITNs) for malaria vector control at least over the 9-week study period. Field studies to further assess the impact of combinations of vector tools on malaria vector control in different eco-epidemiological settings are also warranted. This study \n",
-                        "provides additional data for considering future selection of the most appropriate vector control tools for improved malaria vector control and management of insecticide resistance. This study also indicated that there was no antagonistic effect between PBO and IRS with pirimib-phos-methyl. However, further studies may be needed to determine if there are antagonistic effects in large scale IRS applications to evaluate the efficacy of PBO vs. IRS against different resistant vector populations in Ethiopia for decision-making to deploy either PBO nets or pirimib-phos-based IRS intervention.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Mosquito mortality rates from the huts with IRS alone were similar to mosquito mortality rates from the huts with the combination of vector control intervention tools (IRS + ITNs) and mosquito mortality rates from huts with PBO nets alone were significantly higher than huts with pyrethroid-only nets. The findings of this study help inform studies to be conducted under field condition for decision-making for future selection of cost-effective vector control intervention tools.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Overall, a total of 386 mosquitoes, comprising 235 _Anopheles_ spp. (60.9%) and 151 _Culex_ spp. (39.1%), were collected during the study. Of the _Anopheles_ mosquitoes, _An. gambiae_ s.l, _An. pharoensis_ and _Anopheles__custani_ accounted for 228 (97.0%), 6 (2.6%) and 1 (0.4%), respectively. The proportion of the total _Anopheles_ mosquitoes collected from each hut is presented in Fig. 1. There were no significant differences in mean percentage of _Anopheles_ mosquitoes between the different treatment and control huts (p > 0.05).\n",
-                        "\n",
-                        "An immediate mosquito mortality of 100% was recorded from all three huts with IRS throughout the study period. The mean immediate mosquito mortality rates from the control, PermaNet(r) 2.0 and PermaNet(r) 3.0 huts were 13.3% (95% CI 3-23%), 20.0% (95% CI 6-33%) and 70.0% (95% CI 55-84%), respectively (Fig. 2). The immediate mosquito mortality rate from the hut with IRS + untreated net was significantly higher than the PermaNet(r) 3.0 hut (p < 0.001). Similarly, the immediate mosquito mortality rate from the hut with PermaNet(r) 3.0 was significantly higher than that from the control hut (p < 0.001) as well as the PermaNet(r) 2.0 (p < 0.001) hut. The mosquito mortality rate from the PermaNet(r) 2.0 hut was not significantly different from that of the control hut (p = 0.4). On average the killing effect of PermaNet(r) 2.0 and PermaNet(r) 3.0 compared to the untreated net were 2.2% and 48.9%, respectively. The killing effects in the huts with PermaNet(r) 2.0 and PermaNet(r) 3.0 increased to 60.0% and 75.5% when combined with IRS. Delayed mosquito mortality rates of 14.3% and 41.7% were recorded from huts with PermaNet(r) 2.0 and PermaNet(r) 3.0, respectively.\n",
-                        "\n",
-                        "Of the 50 sub-samples of _An. gambiae_ s.l. analysed using species-specific PCR, 46 (92%) were identified as _An. arabiensis_ and four mosquito samples failed to amplify. The blood-feeding rate of the _Anopheles_\n",
-                        "\n",
-                        "Fig. 1: Proportion of mosquitoes collected that were _Anopheles_ spp. in each experimental hut, Asendabo, Ethiopia. Control is no IRS and untreated net \n",
-                        "collected from each of the huts is presented in Fig. 3. Overall, 26 of the _Anopheles_ (11.1%) were blood-fed upon collection. Of the fed mosquitoes, human, bovine and mixed human/bovine blood were detected in 14 (53.9%), 2 (7.7%), 5 (19.2%) of the specimens, respectively. Blood meals from the remaining 5 (19.2%) of the specimens were unidentified. The overall proportion of human blood-fed _Anopheles_ was 73.1% (n = 19). The personal protection rates of PermaNet(r) 2.0 and PermaNet(r) 3.0 nets were 33.3% and 50%, respectively. When combined with IRS, the personal protection rate of PermaNet(r) 2.0 increased to 83.3% while the personal protection rate of PermaNet(r) 2.0 decreased to 83.3%.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "The personal protection rate of PermaNet(r)\n",
-                        "\n",
-                        "\n",
-                        "PernaNet(r) 3.0 remained the same (50%). The proportions of human blood-fed _Anopheles_ collected from the control, PernaNet(r) 2.0, PernaNet(r) 3.0 and combined IRS-treated huts were 13.3% (95% CI 3.4-23.3), 11.4% (95% CI 0.9-22.0), 7.5% (95% CI 0.0-15.7) and 5.2% (95% CI 1.2-9.3), respectively.\n",
-                        "\n",
-                        "Overall, the majority of _Anopheles_ were collected from the rooms of the huts. The proportions of _Anopheles_ collected from the verandah of the IRS-treated and the control huts were 16.5% and 13.3%, respectively (Fig. 4). The proportion of _Anopheles_ collected from verandah of a hut with PernaNet(r) 2.0 was significantly higher than the proportion of _Anopheles_ collected from the control hut (p = 0.02).\n",
-                        "\n",
-                        "The mean knockdown rates of the susceptible _An. arabiensis_ laboratory strain in the bioassays conducted 3 weeks post-spray were 20.2% and 2.9% in the IRS-treated and control huts, respectively. The mean 24 h mortality rates from IRS-treated and control huts were 100% and 2.9%, respectively (Fig. 5).\n",
-                        "\n",
-                        "The combined mean 30 min knockdown rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. exposed to IRS-treated huts 10 weeks post-spray, with and without pre-exposure to PBO, was 1.1% (Fig. 6). The 24 h mortality rate of the _An. arabiensis_ laboratory strain and wild population of _An. gambiae_ s.l. at 10 weeks post-spray which were exposed to IRS-treated hut wall surfaces were 100%, irrespective of pre-exposure of mosquitoes to PBO.\n",
-                        "\n",
-                        "header: Data analysis\n",
-                        "\n",
-                        "Data from the five treatment huts were compared with data from the control hut to determine the insecticidal effects on the variables listed above.\n",
-                        "\n",
-                        "Personal protection, killing effect, and exophily were estimated using the following formulas [9, 11].\n",
-                        "\n",
-                        "\\[\\text{Personal protection }(\\%) = 100 \\times (B_{u} - B_{t} )/B_{u} ;\\]\n",
-                        "\n",
-                        "where \\(B_{t}\\) is the total number of freshly blood-fed mosquitoes in the huts with treated nets. \\(B_{u}\\) is the total number of freshly blood-fed mosquitoes in the huts with untreated nets.\n",
-                        "\n",
-                        "\\[\\text{Killing effect }(\\%) = 100 \\times (K_{t} - K_{u} )/T_{u} ;\\]\n",
-                        "\n",
-                        "where \\(K_{t}\\) is number of mosquitoes killed (immediate) in the huts with treated nets. \\(K_{u}\\) is number of mosquitoes killed (immediate) in the huts with untreated nets. \\(T_{u}\\) is the total number of mosquitoes collected from the huts with untreated nets.\n",
-                        "\n",
-                        "\n",
-                        "Exophily (%) = (E_v/Et) x 100;\n",
-                        "\n",
-                        "where Ev is the number of mosquitoes found in verandah. Et is the total number of mosquitoes found inside the hut and verandah\n",
-                        "\n",
-                        "For all three parameters, mosquito entry was estimated by adding the number of dead and alive mosquitoes observed inside the hut and the verandah. Estimates of personal protection and killing effect were determined using mosquitoes from all collection nights.\n",
-                        "\n",
-                        "To test differences in mean mosquito density in each of the treatment huts, ANOVA was employed after log-transformation of non-normal distributed mosquito count data. T-tests were employed to determine the differences in mean proportions of blood-fed mosquitoes and mosquitoes exiting early from the different test huts and control hut.\n",
-                        "\n",
-                        "In all analyses, the hut with the untreated net and without IRS was used as a negative control, but comparisons were made between all treatment groups. In all analyses, individual huts and sleepers were included in the models as random effects.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "A Latin Square Design was employed using six experimental huts to collect entomological data. Human volunteers slept in huts with different types of nets (pyrethroid-only net, PBO net, and untreated net) either with or without IRS (Actallic 300CS). The hut with no IRS and an untreated net served as a negative control. The study was conducted for a total of 54 nights. Both alive and dead mosquitoes were collected from inside nets, in the central rooms and verandah the following morning. Data were analysed using Stata/SE 14.0 software package (College Station, TX, USA).\n",
-                        "\n",
-                        "header: Variables\n",
-                        "\n",
-                        "Four study outcomes measured were:\n",
-                        "\n",
-                        "1. Deterrence--the reduction in hut entry by mosquitoes in treatment huts relative to the control hut (hut with untreated net and no IRS).\n",
-                        "2. Exophily--the proportion of mosquitoes found resting on the walls of the verandah.\n",
-                        "3. Blood-feeding inhibition--the reduction in blood-feeding in treatment huts in comparison with control hut (hut with untreated net and no IRS).\n",
-                        "4. Immediate and delayed mortality--the proportion of mosquitoes entering the hut that were dead in the morning (immediate mortality) or after being caught alive and held for 24 h with access to a sugar solution (delayed mortality).\n",
-                        "\n",
-                        "header: Funding\n",
-                        "\n",
-                        "This work was funded by the US President's Malaria initiative.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Malaria vector control interventions have been intensified over the last decade in Ethiopia resulting in remarkable progress, with a reduction in children under-five mortality rates from 123 deaths per 1000 live births in 2005 to 55 deaths per 1000 live births in 2019 [15]. The vector control activities including mass-distribution/replacement of ITNs every 3 years in accordance with WHO recommendations and deployment of IRS in targeted areas appear to have played vital roles in the successes obtained in malaria control in Ethiopia [16]. However, malaria still remains a public health challenge in the country, where an estimated 52% of the population lives in areas at risk of malaria. With the successes gained in malaria control, a national malaria elimination goal is set for 2030 [17]. This study evaluated the impact of combining currently available vector control tools on mosquito behaviour and mortality.\n",
-                        "\n",
-                        "Compared to PernaNet(r) 2.0, PernaNet(r) 3.0 is impregnated with a higher concentration of deltamethrin and the roof panel is treated with both deltamethrin and PBO. The higher killing effect of PernaNet(r) 3.0 compared to PernaNet(r) 2.0 in this study could be attributed to the presence of the PBO synergist on the roof panel of the net and/or higher dosage of deltamethrin in the net. The synergist PBO works to inhibit P450 detoxification of pyrethroid insecticides and can result in partial or complete restoration of susceptibility to certain pyrethroids, such as deltamethrin, hence enhancing efficacy of the insecticides [18]. The role of PBO in affecting susceptibility in both wild and laboratory strain mosquitoes exposed to Actellite(r) 300CS\n",
-                        "\n",
-                        "Fig. 4: Proportion of _Anopheles_ collected from inside nets, rooms and verandahs of different experimental huts, Asendabo, Ethiopia \n",
-                        "sprayed walls could not be determined as mortality rates in all IRS treatment huts were 100%. PBO nets have been shown to provide high levels of protection against _An. gambiae_ in a recent experimental hut trial in Cote d'Ivoire [19]. Moreover, the improved protective role of PBO nets on malaria prevalence was also documented in Uganda [20]. Personal protective efficacy of PBO nets may also last longer compared to the standard nets [21]. PBO nets are particularly important as a malaria control tool in areas where the protection against PBO is not a viable alternative to the standard nets.\n",
-                        "\n",
-                        "Fig. 5: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain following wall bioassays 3 weeks after ActeIlic 300CS spraying, Asendabo, Ethiopia\n",
-                        "\n",
-                        "Fig. 6: Mean 30 min knockdown and 24 h mortality rates of the susceptible _An. arabiensis_ laboratory strain and wild _An. gambiae_ s.l. (with and without pre-exposure to PBO) following wall bioassays 10 weeks after IRS using ActeIlic 300CS, Asendabo, Ethiopia \n",
-                        "pyrethroid resistance is high in the vector populations [22] and is, therefore, under consideration for more widespread distribution in Ethiopia. Despite these added advantages of PBO nets, the cost is currently higher than pyrethroid nets which may limit their wider use in malaria vector control in malaria endemic areas. Additionally, this study provides an example of a setting with high levels of pyrethroid resistance where PBO nets may not be sufficient.\n",
-                        "\n",
-                        "The proportion of _Anopheles_ retrieved from the verandah of huts with both PermaNet 2.0 and PermaNet 3.0 was higher than those from the verandah of the control hut. Huts with PermaNet 2.0 induced significantly higher exito-repellency compared to control huts. Insecticides often induce toxicity to mosquitoes. However, the non-toxic 'exito-repellent' effects of insecticides are often less understood. Pyrethroid and organophosphate insecticides are also known to have these non-killing effects. The impact of the non-killing effects of the insecticides on the risk of malaria is not clearly understood. Though exito-repellency appears to reduce indoor human-vector contact, it may on the contrary enhance risk of outdoor mosquito biting [23].\n",
-                        "\n",
-                        "The role of IRS as a key malaria vector control intervention tool is evident. Immediate mortality was observed in mosquitoes collected from the three huts sprayed with Actellic(r) 300CS up to the end of the study at 10 weeks post-spray. The proportion of blood-fed _Anopheles_ was significantly lower in the IRS-treated huts compared to the control hut. This could be due to the fast-acting nature of Actellic(r) 300CS insecticide with both contact and fumigant effects. This shows the potential of Actellic(r) 300CS as an insecticide of choice for IRS in areas where the local vector population is susceptible to the insecticide. _Anopheles arabiensis_ is susceptible to pirimibos-methyl in most parts of Ethiopia [10, 24]. Some earlier studies conducted in Ethiopia showed the effectiveness of combining IRS with ITNs compared to ITNs alone in suppressing vector populations [25]. Added benefits of combining IRS and ITNs in malaria prevention was also reported from other areas in sub-Saharan Africa [26-28]. In contrast, limited or no added benefit of combining IRS with ITNs as compared to ITNs alone has been reported elsewhere [29, 30]. This inconsistency could be attributed to differences in the intensity of malaria transmission and insecticide resistance of the local vector populations. The impact of the combined use of available vector control interventions appears to be more pronounced when applied in high transmission areas, and where the local vector populations are susceptible to the insecticide used for the IRS component [25].\n",
-                        "\n",
-                        "The importance of combining IRS and ITNs for malaria vector control is believed partly to come from the low or inconsistent utilization of ITNs by users and short residual efficacy of IRS insecticides [31, 32]. In Ethiopia, household surveys have shown approximately 50% of children under five and pregnant women use their nets with geographic variation [33]. The gap often created as a result of low utilization of ITNs may be complemented with IRS. ITN utilization and care depends on personal decisions and behavior [34]. However, once properly implemented, IRS rapidly suppresses local vector populations with no additional behaviors required from the residents, hence reducing malaria incidence [35]. However, effectiveness of IRS for malaria vector control is highly affected by both physiological and behavioral resistance of the local vector population to the insecticide [36], apart from operational factors and higher costs.\n",
-                        "\n",
-                        "As the study was conducted during the dry season of the year, the mosquito population density was not high and, therefore, limits the generalizability of the results. However, the findings of this study could provide preliminary information for vector control programs for the rational choice of future vector control intervention tools. In addition, combining indoor residual spraying using pirimibos-methyl with PBO nets has been reported to be antagonistic in some studies, but a negative interaction or antagonism between PBO and pirimibos-methyl was not detected in this study.\n",
-                        "\n",
-                        "header: Blood meal host source analysis and mosquito identification\n",
-                        "\n",
-                        "All the fed mosquitoes collected from the different huts were assayed to determine the blood meal host source using direct ELISA [13]. Moreover, a sub-sample of _An. gambiae_ s.l. was also molecularly identified to species using species-specific PCR following established protocol [14].\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Ethiopia\",\"Site\":\"Asendabo\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Net_holed\": {\"title\": \"Net Holed\", \"description\": \"Numerical count of the number of holes a net has - 0 if new and NA if not reported\\nNote - for tunnel tests there are holes that allow the mosquitoes the option of passing through to feed on the guinea pig.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 2.0\",\"Insecticide\":\"deltamethrin\",\"Net_holed\":18.0,\"pHI_category\":\"Torn\",\"Synergist\":null},\"N02\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"deltamethrin\",\"Net_holed\":18.0,\"pHI_category\":\"Torn\",\"Synergist\":\"PBO\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Chemical analysis results\n",
-                        "\n",
-                        "For PermaNet 3.0, PBO was only recorded from the roof panel of the nets; for Olyset Plus, PBO was recorded on all 5 panels (Table 6). Compared to the pyrethroid component, much less PBO was retained in both types of pyrethroid-PBO ITNs after 20 washes (58.7% vs. 25% with Olyset Plus and 80.9% vs. 68.8% with PermaNet 3.0, P < 0.05). The decrease in PBO content after washing was more evident in Olyset Plus than in PermaNet 3.0 netting, hence the wash retention index of PBO was higher with PermaNet 3.0 compared to Olyset Plus (98.1% vs. 93.3%).\n",
-                        "\n",
-                        "Figure 4: Tunnel test mortality (%) of susceptible and resistant strains of _Anopheles gambiae_ s.l. exposed to Olyset Plus vs. Olyset Net\n",
-                        "\n",
-                        "Figure 3: Mortality (%) of susceptible and resistant strains of _An. gambiae_ s.l. in cone bioassays with Olyset Plus and Olyset Net. S = susceptible, R = resistant\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays\n",
-                        "\n",
-                        "To help further explain the results obtained in the experimental huts, WHO cone bioassays and tunnel tests were performed on samples of netting (\\(30\\times 30\\) cm) obtained from Olyset Net and Olyset Plus when unwashed and after 10 and 20 washes. Washing was performed in the laboratory following WHO guidelines [22]. PermaNet 3.0 was not tested in the laboratory bioassays owing to the restricted application of PBO to the roof of the net preventing a realistic direct comparison with Olyset Plus in bioassays especially tunnel tests. Net samples from each ITN type and each wash point were tested against the following strains:\n",
-                        "\n",
-                        "1. _An. gambiae_ sensu lato (s.l.) strains from Cove, Benin (Cove strain) which is highly pyrethroid resistant. It originates from the experimental hut station in Cove and has shown > 200-fold resistance compared to the susceptible Kisumu strain in susceptibility bioassays. Resistance is mediated by elevated levels of P450s and high frequencies of _kdr_ [20].\n",
-                        "2. _An. gambiae_ VKPer strain, which originated from the Kou Valley in Burkina Faso. VKPer has moderate levels of pyrethroid resistance mediated only by high frequencies of _kdr_.\n",
-                        "3. _An. gambiae_ s.s. Kisumu strain, a reference susceptible strain which originated from Kisumu Kenya.\n",
-                        "\n",
-                        "Approximately two hundred 2-5 days old mosquitoes of each strain were exposed for 3 min in cone bioassays to four net samples of each net type in cohorts of 5 mosquitoes per cone. Knock down in cone bioassays was recorded after 1 h and mortality after 24 h.\n",
-                        "\n",
-                        "Two to three hundred 5-8 days old mosquitoes of each strain were also exposed to each net type in tunnel tests in replicates of 50 mosquitoes per net sample. The tunnel test is a laboratory assay designed to simulate natural host-seeking behaviour of mosquitoes at night in the presence of a net. It consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections by means of a netting frame fitted into a slot across the tunnel. An anesthetized guinea pig was housed unconstrained in a small cage in one section, and mosquitoes were released in the other section at dusk and left overnight. The net samples were holed with nine 1-cm diameter holes to allow host-seeking mosquitoes to penetrate the baited chamber; an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 degC and 75-85% RH. The next morning, the numbers found alive or dead, fed, or unfed, in each section were recorded. Live mosquitoes were provided with sugar solution and delayed mortality recorded after 24 h. The guinea pigs used in this study were kept in accordance with institutional guidelines for animal care.\n",
-                        "\n",
-                        "header: Experimental hut treatments\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 were compared in the experimental huts when unwashed and after 20 standardized washes. A WHO-recommended pyrethroid-only long-lasting net (Olyset Net) was included to demonstrate the added effect of PBO on the insecticide-resistant local vector species. Nets were washed using savon de Marseilles and rinsed twice following WHO procedures for washing nets for experimental hut studies [22].\n",
-                        "\n",
-                        "The following seven (7) treatments were thus tested in seven experimental huts:\n",
-                        "\n",
-                        "1. Untreated polyethylene net\n",
-                        "2. Olyset Net unwashed (permethrin only)\n",
-                        "3. Olyset Net washed 20 times.\n",
-                        "4. PermaNet 3.0 unwashed (Roof: deltamethrin plus PBO; sides: deltamethrin only)\n",
-                        "5. PermaNet 3.0 washed 20 times.\n",
-                        "6. Olyset Plus unwashed (permethrin plus PBO on all panels)\n",
-                        "7. Olyset Plus washed 20 times.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "An experimental hut trial was performed in Cove, Benin to compare PermaNet 3.0 (deltamethrin plus PBO on roof panel only) to Olyset Plus (permethrin plus PBO on all panels) against wild pyrethroid-resistant _Anopheles gambiae_ sensu lato (s.l) following World Health Organization (WHO) guidelines. Both nets were tested unwashed and after 20 standardized washes compared to Olyset Net. Laboratory bioassays were also performed to help explain findings in the experimental huts.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding rates in experimental huts\n",
-                        "\n",
-                        "The blood-feeding rates of wild pyrethroid-resistant _An. gambiae s.l_ that entered the experimental huts are presented in Fig. 2 with further details on mosquito feeding provided in Table 4. The percentage blood-feeding was lower in huts with the unwashed pyrethroid-PBO ITNs and was lowest of all with unwashed Olyset Plus as compared to unwashed PermaNet 3.0 (8% vs 19%, P < 0.001). For all net types, the data showed an overall increase in blood feeding with washed nets compared to unwashed nets and a decrease in blood-feeding inhibition relative to the untreated net. Percentage blood-feeding when washed 20 times did not differ significantly between the pyrethroid-PBO types (38% with PermaNet 3.0 vs. 35% with Olyset Plus, P = 0.708). The proportions of mosquitoes collected resting in the nets were lowest with the\n",
-                        "\n",
-                        "Fig. 1: Mortality of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "unwashed pyrethroid-PBO nets (2-6%), which is consistent with the higher toxicity observed with this type of net. Personal protection with both types of pyrethroid-PBO ITMs were > 80% when unwashed but this declined after 20 washes to 53% with PermaNet 3.0 and 11% with Olyset Plus.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Comparative efficacy of two pyrethroid-piperonyl butoxide nets (Olyset Plus and PermaNet 3.0) against pyrethroid resistant malaria vectors: a non-inferiority assessment\n",
-                        "\n",
-                        "Corine Ngufor, Josias Fagbohoun, Abel Agbevo, Hanafy Ismail, Joseph D. Challenger, Thomas S. Churcher, Mark Rowland\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid-PBO nets were conditionally recommended for control of malaria transmitted by mosquitoes with oxidase-based pyrethroid-resistance based on epidemiological evidence of additional protective effect with Olyset Plus compared to a pyrethroid-only net (Olyset Net). Entomological studies can be used to assess the comparative performance of other brands of pyrethroid-PBO ITNs to Olyset Plus.\n",
-                        "\n",
-                        "header: Table 4: Blood-feeding rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not significantly different (P > 0.05) * Value set to zero as more blood fed mosquitoes were caught in washed Olyset Net huts than control hut\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total blood fed\",\"Control\":\"496\",\"Olyset Net\":\"324\",\"Olyset Net.1\":\"807\",\"PermaNet 3.0\":\"95\",\"PermaNet 3.0.1\":\"231\",\"Olyset Plus\":\"56\",\"Olyset Plus.1\":\"442\"},\"3\":{\"Net type\":\"% Blood fed\",\"Control\":\"52a\",\"Olyset Net\":\"29b\",\"Olyset Net.1\":\"52a\",\"PermaNet 3.0\":\"19c\",\"PermaNet 3.0.1\":\"38d\",\"Olyset Plus\":\"8e\",\"Olyset Plus.1\":\"35d\"},\"4\":{\"Net type\":\"95% conf intervals\",\"Control\":\"48.4\\u201354.7\",\"Olyset Net\":\"25.9\\u201331.2\",\"Olyset Net.1\":\"49.7\\u201354.7\",\"PermaNet 3.0\":\"15.9\\u201322.9\",\"PermaNet 3.0.1\":\"33.7\\u201341.3\",\"Olyset Plus\":\"6.1\\u201310.2\",\"Olyset Plus.1\":\"32.1\\u201337.3\"},\"5\":{\"Net type\":\"% Blood-feeding inhibition\",\"Control\":\"-\",\"Olyset Net\":\"44\",\"Olyset Net.1\":\"0\",\"PermaNet 3.0\":\"63\",\"PermaNet 3.0.1\":\"27\",\"Olyset Plus\":\"85\",\"Olyset Plus.1\":\"33\"},\"6\":{\"Net type\":\"inside net\",\"Control\":\"294\",\"Olyset Net\":\"184\",\"Olyset Net.1\":\"542\",\"PermaNet 3.0\":\"28\",\"PermaNet 3.0.1\":\"130\",\"Olyset Plus\":\"17\",\"Olyset Plus.1\":\"241\"},\"7\":{\"Net type\":\"inside net (%)\",\"Control\":\"31a\",\"Olyset Net\":\"16bc\",\"Olyset Net.1\":\"35a\",\"PermaNet 3.0\":\"6d\",\"PermaNet 3.0.1\":\"21b\",\"Olyset Plus\":\"2e\",\"Olyset Plus.1\":\"19c\"},\"8\":{\"Net type\":\"Personal protection (%)\",\"Control\":\"-\",\"Olyset Net\":\"35\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"81\",\"PermaNet 3.0.1\":\"53\",\"Olyset Plus\":\"89\",\"Olyset Plus.1\":\"11\"}}\n",
-                        "\n",
-                        "header: Mosquito entry and exiting rates in experimental huts: Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts\n",
-                        "\n",
-                        "Mortality rates of wild pyrethroid resistant mosquitoes that entered the experimental huts are presented in Fig. 1 with further details provided in Table 3. The lowest mortality was achieved with Olyset Net (18% before washing and 12% after 20 washes). Percentage mortality with unwashed pyrethroid-PBO nets was highest with PermaNet 3.0 (41%) but this declined significantly after 20 washes (17%, P < 0.001). Mortality with unwashed Olyset Plus was 28% and while this value was significantly lower than the mortality shown by unwashed PermaNet 3.0 (P < 0.001), it did not decrease significantly after 20 washes (28% vs. 24%, P = 0.433) whereas for PermaNet 3.0 it declined. Hence, Olyset Plus induced higher mortality rates than PermaNet 3.0 after 20 washes (24% vs 17%, P < 0.001). With respect to the pyrethroid-only ITN, both pyrethroid-PBO nets induced significantly higher mortality rates than Olyset Net with nets washed 20 times (P < 0.001) though the difference was higher with Olyset Plus (24% vs. 12%), than with PermaNet 3.0 (17% vs. 12%). After 20 washes, mortality with PermaNet 3.0 declined to the same level as the unwashed Olyset Net, (17% vs. 18%, P = 0.061) but remained significantly higher with Olyset Plus (24% vs. 17%, P = 0.036).\n",
-                        "\n",
-                        "header: Cone bioassay results: Tunnel test results\n",
-                        "\n",
-                        "The results from the tunnel tests comparing Olyset Plus and Olyset Net unwashed and after 10 and 20 washes against all three strains are presented in Figs. 4 and 5 for mortality and blood-feeding inhibition respectively. Mortality rates were generally higher in the tunnels tests compared to the cone bioassays and decreased as the strain become more pyrethroid-resistant (Fig. 4). Mortality rates with the Kisumu strain were very high with Olyset Net and Olyset Plus (> 95%). With the VKPer strain, mortality remained > 80% after 20 washes with both ITN types. With the Cove strain, mortality was significantly higher with Olyset Plus compared to Olyset Net at 0 and 10 washes but about the same after 20 washes. With unwashed nets, blood-feeding inhibition in the tunnel tests was consistently higher with Olyset Plus (> 90%) compared to Olyset Net (27-75%) for all three strains tested (Fig. 5). After 10 and 20 washes, blood-feeding inhibition of the Kisumu and VKPer strain remained > 80% with Olyset Plus and Olyset Net. With Cove strain, blood-feeding inhibition was also higher with Olyset Plus at 0 and 10 washes but declined to about the same level as Olyset Net after 20 washes (56%).\n",
-                        "Mortality in the untreated control tunnel was < 10% with all three strains.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality with permethrin and alpha-cypermethrin treated papers was 100% with the laboratory-maintained pyrethroid-susceptible _An. gambiae_ Kisumu strain. With wild pyrethroid resistant _An. gambiae_ s.l. from Cove, mortality rates were <50% with all three pyrethroid insecticides tested (Table 1) thus confirming the high levels of pyrethroid resistance in this vector population. Mortality however increased from 19.2 to 52.5% with permethrin, 41.7% to 69.7% with deltamethrin and 0% to 82.4% with alphacypermethrin after pre-exposure of the Cove strain to PBO synergist (Table 1). This result demonstrated that mixed function oxidases are overexpressed in the wild Cove vector population and their effect can be effectively inhibited by the PBO synergist.\n",
-                        "\n",
-                        "header: Table 2 Entry and exiting rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not signi\u001d\n",
-                        "cantly different (P>0.05)* Value set to zero as more mosquitoes caught in Olyset Net huts than control huts\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"N collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"N females\\/night\",\"Control\":\"23a\",\"Olyset Net\":\"27ab\",\"Olyset Net.1\":\"37b\",\"PermaNet 3.0\":\"12c\",\"PermaNet 3.0.1\":\"15d\",\"Olyset Plus\":\"16d\",\"Olyset Plus.1\":\"30ab\"},\"3\":{\"Net type\":\"% deterrence\",\"Control\":null,\"Olyset Net\":\"0*\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"49\",\"PermaNet 3.0.1\":\"36\",\"Olyset Plus\":\"28\",\"Olyset Plus.1\":\"0\"},\"4\":{\"Net type\":\"N exiting\",\"Control\":\"429\",\"Olyset Net\":\"639\",\"Olyset Net.1\":\"669\",\"PermaNet 3.0\":\"319\",\"PermaNet 3.0.1\":\"370\",\"Olyset Plus\":\"468\",\"Olyset Plus.1\":\"712\"},\"5\":{\"Net type\":\"% exiting\",\"Control\":\"45a\",\"Olyset Net\":\"56b\",\"Olyset Net.1\":\"43a\",\"PermaNet 3.0\":\"65cd\",\"PermaNet 3.0.1\":\"60ce\",\"Olyset Plus\":\"68d\",\"Olyset Plus.1\":\"56be\"},\"6\":{\"Net type\":\"95% conf. limits\",\"Control\":\"41.5\\u201347.7\",\"Olyset Net\":\"53.4\\u201359.2\",\"Olyset Net.1\":\"40.8\\u201345.7\",\"PermaNet 3.0\":\"61.0\\u201369.5\",\"PermaNet 3.0.1\":\"56.2\\u201363.9\",\"Olyset Plus\":\"64.4\\u201371.4\",\"Olyset Plus.1\":\"53.1\\u201358.6\"}}\n",
-                        "\n",
-                        "header: Experimental hut site: Insecticide resistance bioassays\n",
-                        "\n",
-                        "To assess the frequency of pyrethroid resistance and presence of mixed function oxidases in the Cove vector population during the trial, adult mosquitoes that emerged from larvae collected from breeding sites close to experimental huts were tested in WHO cylinder bioassays with and without pre-exposure to PBO. A total of ~100 mosquitoes of the pyrethroid resistant _An. gambiae_ s.l. Cove strain and the pyrethroid susceptible _An. gambiae_ Kisumu strain were exposed to treated filter papers in WHO cylinder bioassays in batches of 25. Tests were performed with papers treated with permethrin 0.75%, alpha-cypermethrin 0.05% and deltamethrin 0.05%. To assess presence of MFO, some mosquitoes were also pre-exposed to papers treated with 4% PBO prior to exposure to insecticide-treated papers. Exposure to PBO and to insecticides lasted 1 h, knockdown was recorded after 60 min and mortality after 24 h.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain one of the most powerful tools to reduce malaria transmission in a community and provide personal protection to the user [1, 2]. They have contributed significantly to recent reductions in malaria burden [3]. Their efficacy is however threatened by increasing resistance to pyrethroids [4, 5]; the insecticide of choice used on bed-nets owing to its safety, low cost and rapid activity on vector mosquitoes [6]. To maintain the effectiveness of insecticide treated nets for malaria control, new types of LLINs treated with alternative insecticides and compounds which can either replace or complement pyrethroids on bed-nets are urgently needed.\n",
-                        "\n",
-                        "A new class of insecticide treated nets (ITNs) combining pyrethroids and piperonyl butoxide (PBO) (pyrethroid-PBO ITNs) have been developed [7]. PBO is a synergist that inhibits specific metabolic enzymes such as mixed-function oxidases within mosquitoes that detoxify or sequester insecticides before they can have a toxic effect on the mosquito. Pyrethroid-PBO nets can therefore induce increased mortality of pyrethroid-resistant malaria vectors that express mixed function oxidase based pyrethroid resistance mechanisms that are inhibited by the PBO in the net. These nets were given an interim endorsement as a new WHO class of vector control products in 2017 based on epidemiological data from a cluster randomized controlled trial in North Eastern Tanzania [8], that demonstrated additional malaria control with one prototype pyrethroid-PBO net (Olyset Plus) compared to a pyrethroid-only net (Olyset Net), against pyrethroid resistant malaria vectors of moderate intensity, partly conferred by monooxygenase-based resistance mechanism. Pyrethroid-PBO ITNs are conditionally recommended for malaria vector control instead of pyrethroid-only ITNs in areas of confirmed intermediate levels of resistance mediated by monooxygenase-based resistance mechanism [9]. This endorsement has been followed by an increasing uptake of pyrethroid-PBO nets worldwide [10]; in sub-Saharan Africa for example, the proportion of pyrethroid-PBO nets of all nets delivered increased from 3% in 2018 to 35% in 2021.\n",
-                        "\n",
-                        "While Olyset Plus was the first in class pyrethroid-PBO net to demonstrate public health value as observed in the Tanzanian trial [8], there are currently five additional types of pyrethroid-PBO ITNs on the World Health Organization (WHO) list of prequalified vector control products: PermaNet 3.0, Veeralin, Tsara Boost, Tsara Plus and very recently DuraNet Plus [7]. These nets have all demonstrated superiority over pyrethroid-only nets in terms of mosquito mortality and blood-feeding inhibition in multiple experimental hut trials across Africa [11, 12, 13, 14, 15, 16, 17]. However, they differ from Olyset Plus in their design and specifications; typically, the location of PBO on the net (i.e., all panels vs. roof panel only), the type and dose of pyrethroid used, bioavailability and retention of PBO after washing and could, therefore, differ in entomological and epidemiological impact. A recent large cluster-randomized trial in Uganda, evaluating the efficacy of Olyset Plus and PermaNet 3.0 compared to pyrethroid-only nets in a setting of high pyrethroid resistance, also demonstrated better protection against malaria with pyrethroid-PBO nets compared to pyrethroid-only nets for up to 18 months confirming the findings of the Tanzanian trial [18]. While the Ugandan trial was not powered to directly compare between the different ITN brands tested, the results showed that the additional protective effect of the pyrethroid-PBO net compared to the pyrethroid-only net was initially large with PermaNet 3.0 at 6 months post-distribution but lasted only up to 12 months whereas additional protective effect with Olyset Plus appeared to have a delayed onset which was not observed at 6 months but became evident at 12 months and lasted up to 18 months. There are major differences in design between Olyset Plus and PermaNet 3.0, which could have implications on their epidemiological impact; Olyset Plus is a polyethylene net incorporated with permethrin and PBO on all panels while PermaNet 3.0 is a polyester net coated with deltamethrin with PBO restricted to the roof of the net.\n",
-                        "\n",
-                        "To generate assurance of comparative performance of new candidate products within an established WHO vector control product class, without the need for epidemiological evidence for each new product, the WHO has developed new experimental hut study guidelines for assessing their non-inferiority to a first in class product for which evidence of public health value has already been generated [19]. These provisional guidelines are to be piloted with pyrethroid-PBO nets by comparing other WHO/PQ-listed pyrethroid-PBO nets with the first in class product, Olyset Plus. This study compared the efficacy and wash resistance of Olyset Plus and PermaNet\n",
-                        "3.0 and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against wild free-flying pyrethroid resistant malaria vectors in Southern Benin.\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays results\n",
-                        "\n",
-                        "The 3-min cone bioassay mortality results for all 3 mosquito strains and wash points tested are presented in Fig. 3. Unwashed Olyset Net induced very low mortality rates against the susceptible Kisumu strain (17-20%) and even lower mortality rates against the pyrethroid resistant strains (< 5%). Cone bioassay mortality rates with unwashed Olyset Plus were higher across all 3 strains compared to Olyset Net though mortality decreased as the strain tested became more pyrethroid-resistant. Cone bioassay mortality however dropped significantly with washed net samples. Mortality with the untreated net samples did not exceed 5% with any strain tested.\n",
-                        "\n",
-                        "header: Insecticide resistance in malaria vectors in Cove: Experimental hut trial results\n",
-                        "\n",
-                        "A total of 6711 pyrethroid resistant female _An. gambiae_ s.l. were collected during the experimental hut trial. The entry and exiting rates of wild pyrethroid resistant _An. gambiae_ s.l. from the experimental huts with the different ITN types tested in the trial are presented in Table 2. Compared to the control, Olyset Net did not deter mosquitoes from entering the experimental huts (0% when unwashed and after 20 washes). Mosquito deterrence was significantly higher with PermaNet 3.0 compared to Olyset Plus both unwashed (49% vs. 28%, P < 0.001) and after 20 washes (36% vs. 0%, P < 0.001). Nevertheless, early exiting of mosquitoes from the experimental huts into the veranda trap did not differ significantly between both pyrethroid-PBO net types both when unwashed (65% with PermaNet 3.0 vs 68% with Olyset Plus, P = 0.232) and after 20 washes (60% with PermaNet 3.0 vs. 56% with Olyset Plus, P = 0.053).\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{l c c c c c c}\n",
-                        "**Net type** & **Control** & **Olyset Net** & & **PermNet 3.0** & **Olyset Plus** \\\\ Number of washes & 0 & 0 & 20 & 0 & 20 & 0 & 20 \\\\ N collected & 962 & 1135 & 1546 & 489 & 616 & 688 & 1275 \\\\ N females/night & 233 & 27\\({}^{\\text{hb}}\\) & 37\\({}^{\\text{b}}\\) & 12\\({}^{\\text{c}}\\) & 15\\({}^{\\text{d}}\\) & 16\\({}^{\\text{d}}\\) & 30\\({}^{\\text{hb}}\\) \\\\ \\% deterrence & – & 0\\({}^{\\text{a}}\\) & 0\\({}^{\\text{a}}\\) & 49 & 36 & 28 & 0 \\\\ N eating & 429 & 639 & 669 & 319 & 370 &\n",
-                        "\n",
-                        "header: Table 1: Mortality of pyrethroid resistant Anopheles gambiae s.l. from Cove in WHO cylinder bioassays with and without pre-exposure to PBO\n",
-                        "footer: None\n",
-                        "columns: ['Variable', 'N exposed', 'N dead', '% Dead']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Control\",\"N exposed\":101,\"N dead\":0,\"% Dead\":0.0},\"1\":{\"Variable\":\"PBO 4% only\",\"N exposed\":103,\"N dead\":3,\"% Dead\":2.9},\"2\":{\"Variable\":\"Permethrin 0.75%\",\"N exposed\":104,\"N dead\":20,\"% Dead\":19.2},\"3\":{\"Variable\":\"Deltamethrin 0.05%\",\"N exposed\":91,\"N dead\":38,\"% Dead\":41.7},\"4\":{\"Variable\":\"Alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":0,\"% Dead\":0.0},\"5\":{\"Variable\":\"PBO 4% then permethrin 0.75%\",\"N exposed\":99,\"N dead\":52,\"% Dead\":52.5},\"6\":{\"Variable\":\"PBO 4% then deltamethrin 0.05%\",\"N exposed\":99,\"N dead\":69,\"% Dead\":69.7},\"7\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4},\"8\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4}}\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "At the end of the experimental hut trial, five pieces of netting (\\(25\\times 25\\) cm) obtained from the panels of replicate nets of each net type (before and after washing) used in the huts were assessed for deltamethrin, permethrin and PBO content using HPLC. Insecticide was extracted from each net piece with an area of 48 sq cm collected from the five net samples (\\(25\\times 25\\) cm) obtained from each whole net. The insecticide content of each sample was determined by injecting ten ul aliquots of the extract on a reverse-phase Hypersil GOLD C18 column (75 A, \\(250\\times 4.6\\) mm, 5-um particle size; Thermo Scientific) at room temperature. A mobile phase of 70% acetonitrile in water was used at a flow rate of 1 ml min\\({}^{-1}\\) to separate the target analyte. Chromatographic peaks of the insecticides and internal standard were detected at a wavelength of 232 nm with the Ultimate 3000 UV detector and analysed with Dionex Chromeleon(tm) 6.8 Chromatography Data System software. Quantities of insecticide were calculated from standard curves established by known concentrations of the insecticide authenticated standards and corrected by internal standard readings in each sample relative to control.\n",
-                        "\n",
-                        "Data from chemical analysis was used to calculate the percentage retention of each active ingredient after 20 washes relative to the unwashed net and the wash retention index. Wash-resistance index was calculated according to WHO guidelines [22] as indicated below:\n",
-                        "\n",
-                        "\\[\\text{Wash resistance index} = 100\\times\\text{n}\\sqrt{(\\text{tn}/\\text{t0})}\\] \\[\\left(\\text{free migration stage behaviour}\\right)\\]\n",
-                        "\n",
-                        "where \\(\\text{tn}=\\text{total active ingredient content after n washing cycles, t0=total active ingredient content before washing, n=number of washes.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "With unwashed nets, mosquito mortality was higher in huts with PermaNet 3.0 compared to Olyset Plus (41% vs. 28%, P < 0.001). After 20 washes, mortality declined significantly with PermaNet 3.0 (41% unwashed vs. 17% after washing P < 0.001), but not with Olyset Plus (28% unwashed vs. 24% after washing P = 0.433); Olyset Plus induced significantly higher mortality than PermaNet 3.0 and Olyset Net after 20 washes. PermaNet 3.0 showed a higher wash retention of PBO compared to Olyset Plus. A non-inferiority analysis performed with data from unwashed and washed nets together using a margin recommended by the WHO, showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality (25% with Olyset Plus vs. 27% with PermaNet 3.0, OR = 1.528, 95%Cl = 1.02-2.29) but not in reducing mosquito feeding (25% with Olyset Plus vs. 30% with PermaNet 3.0, OR = 1.192, 95%Cl = 0.77-1.84). Both pyrethroid-PBO nets were superior to Olyset Net.\n",
-                        "\n",
-                        "header: Table 3 Mortality of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not signi\u001d\n",
-                        "cantly di\u001c\n",
-                        "erent (P>0.05)\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total dead\",\"Control\":\"12\",\"Olyset Net\":\"209\",\"Olyset Net.1\":\"284\",\"PermaNet 3.0\":\"198\",\"PermaNet 3.0.1\":\"103\",\"Olyset Plus\":\"190\",\"Olyset Plus.1\":\"297\"},\"3\":{\"Net type\":\"Mortality (%)\",\"Control\":\"1a\",\"Olyset Net\":\"18b\",\"Olyset Net.1\":\"12c\",\"PermaNet 3.0\":\"41d\",\"PermaNet 3.0.1\":\"17b\",\"Olyset Plus\":\"28e\",\"Olyset Plus.1\":\"24e\"},\"4\":{\"Net type\":\"95% conf. limits\",\"Control\":\"0.6\\u20132.0\",\"Olyset Net\":\"16.2\\u201320.1\",\"Olyset Net.1\":\"10.3\\u201313.5\",\"PermaNet 3.0\":\"36.1\\u201344.8\",\"PermaNet 3.0.1\":\"13.8\\u201319.8\",\"Olyset Plus\":\"24.4\\u201331.1\",\"Olyset Plus.1\":\"21.0\\u201325.6\"},\"5\":{\"Net type\":\"Corrected for control (%)\",\"Control\":\"-\",\"Olyset Net\":\"17\",\"Olyset Net.1\":\"11\",\"PermaNet 3.0\":\"41\",\"PermaNet 3.0.1\":\"17\",\"Olyset Plus\":\"27\",\"Olyset Plus.1\":\"22\"}}\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Following the interim endorsement of pyrethroid-PBO nets by the WHO [23], malaria vector control programmes are faced with additional choice of different brands of prequalified pyrethroid-PBO nets [7]. Considering the wide variations in design of the available brands of these nets, studies generating the required assurance of comparative performance to Olyset Plus (the first in class pyrethroid-PBO net to demonstrate empirical evidence of entomological and epidemiological impact) are necessary. This study compared the efficacy and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against pyrethroid resistant malaria vectors in a highly endemic country of West Africa, following WHO guidelines [19, 22].\n",
-                        "\n",
-                        "The WHO susceptibility bioassays confirmed the high levels of pyrethroid resistance in the vector population at the experimental hut site during the trial, corroborating previous findings [20]. The increased mortality achieved in bioassays with pre-exposure to PBO showed that pyrethroid-resistance was indeed at least partly mediated by increased mono-oxygenase activity. The experimental hut trial demonstrated improved levels of mortality and blood-feeding inhibition with both pyrethroid-PBO ITN types compared to a standard pyrethroid-only LLIN against a vector population that was very resistant to pyrethroids; this was supported by results from the laboratory assays which compared Olyset Plus to Olyset Net against pyrethroid-resistant mosquito strains. These observations are partly attributable to the synergistic effect of the PBO on pyrethroid resistance and are consistent with other experimental hut trials across Africa and epidemiological trials performed in Tanzania [8] and Uganda [18].\n",
-                        "\n",
-                        "Twenty washes in experimental hut studies are indicated by WHO as a proxy for the ability of an ITN to withstand multiple washes under operational use over a 3-year life span [19, 22]. With unwashed nets, the levels of improved mortality relative to the standard pyrethroid-only net were higher with PermaNet 3.0 than with Olyset Plus. However, unlike with Olyset Plus, this effect was lost with PermaNet 3.0 after twenty washes;\n",
-                        "Figure 5: Blood-feeding inhibition (%) of susceptible and resistant strains of _An. gambiae s.l._ in tunnel tests with Olyset Plus and Olyset Net. Blood-feeding inhibition was calculated relative to the control tunnel. 5 = susceptible, R = Resistant\n",
-                        "PermanNet 3.0 killed significantly lower proportions of mosquitoes than Olyset Plus and same proportions as an unwashed pyrethroid-only ITN. Though the RCT in Uganda was not powered to assess differences between the pyrethroid-PBO ITN brands tested (PermanNet 3.0 vs. Olyset Plus) [18], the results from our trial appear consistent with some of the differences in epidemiological effect observed between both brands: (1) The high experimental hut mortality with the unwashed PermaNet 3.0 supports the higher initial protective effect observed with the net in the Ugandan trial at the 6 months epidemiological survey which was not seen with Olyset Plus. (2) The higher rate of decline in experimental hut mortality after washing with PermaNet 3.0 compared to Olyset Plus is consistent with the shorter-lived epidemiological effect in the Ugandan trial with PermaNet 3.0 (up to 12 months) compared to Olyset Plus which remained more protective than the pyrethroid-only net at 18 months. However, care should be taken to not over-interpret the comparisons between results from our hut trial and Ugandan RCT considering the different geographical settings and the lack of sufficient power to differentiate between the epidemiological impact of both pyrethroid-PBO net type in the Ugandan trial.\n",
-                        "\n",
-                        "The difference in hut performance between both pyrethroid-PBO ITNs can be attributed to differences in the retention and movement of PBO across the polymer fibre in Olyset Plus compared to PermaNet 3.0 and/or differences in design and specification. Retention of biofeficacy of pyrethroid-PBO nets is a fine balance between migration and replenishment of PBO from the core to the surface of fibres and the maintenance of an internal reservoir sufficient to last the lifespan of the LLIN, which is typically set at 3 years of use [24]. The chemical analysis results showed a faster release of PBO in the Olyset Plus netting after 20 washes compared to PermaNet 3.0 though it is unclear whether this may have increased the bioavailability of the PBO on the surface of Olyset Plus after washing. Whether sufficient PBO would remain within the fibres of both pyrethroid-PBO nets after 3 years of household use is presently unknown and is the subject of ongoing WHO durability trials of Olyset Plus and PermaNet 3.0 which are not yet completed [18, 25]. Another factor which may contribute to the discrepancies in efficacy of the two pyrethroid-PBO ITNs are differences in design and specification: Olyset Plus is treated with the pyrethroid permethrin while PermaNet 3.0 is treated with deltamethrin, and Olyset Plus contains PBO on every panel whereas in PermaNet 3.0, PBO is available only on the top panel of the net. It is not clear whether the restricted application of PBO to the roof of the net would affect bioefficacy. This would require comparative trials of the PermaNet 3.0 with pyrethroid-PBO restricted to the upper panel and pyrethroid to side panels versus an ITN with all 5 panels treated with pyrethroid-PBO. Behavioural studies of mosquitoes around nets indicate that mosquitoes may first make multiple contacts with the roof panel in response to odour plumes [26]; however, experimental hut trials comparing restricted versus full PBO coverage on nets are too few to be definitive on the question of efficacy.\n",
-                        "\n",
-                        "Despite the differences in performance observed between both pyrethroid-PBO net types with regards to their impact after 20 standardized washes, the non-inferiority analysis performed in accordance with recent WHO guidelines [19] showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality but not with blood-feeding inhibition. The higher blood-feeding inhibition observed with Olyset Plus could be due to the high excito-repellency of permethrin in Olyset Plus compared to deltamethrin in PermaNet 3.0 [27]. Alternatively, the study may not have had sufficient power to demonstrate non-inferiority of PermaNet 3.0 to Olyset Plus for both endpoints; further studies are on-going to help guide power calculations for ITN non-inferiority studies. The non-inferiority margin used for the analysis was defined by WHO as an odds ratio of 0.7 in mosquito mortality and feeding between a candidate net and the first in class net considered acceptable for both products to be in the same policy class. According to these guidelines, if non-inferiority is demonstrated in two independent experimental hut trials in different geographical locations representative of where the products will be deployed, then PermaNet 3.0 will be placed in the same WHO vector control product class as Olyset Plus [19]. It is however not clear whether non-inferiority must be demonstrated for both endpoints (mortality and blood-feeding) for a second-in class product to become part of an intervention class. While the guidelines were developed more like a compromise between the risk of accepting an inferior product and the feasibility of conducting epidemiological trials, the findings from our trial show that non-inferiority experimental hut trials are complex, and results must be interpreted with care. Comparative performance between products may also depend on other location-specific factors, such as the intensity of insecticide resistance and behaviour of the target vector population which should be taken into consideration when choosing between products of the same class.\n",
-                        "\n",
-                        "While the present hut trial in Benin and earlier hut trials in Benin, Tanzania, Cameroon, Burkina Faso, Cote d'Ivoire and Vietnam where the vectors were also resistant have shown some additional effect of pyrethroid-PBO nets over a standard pyrethroid net [12, 13, 15-17], the margin appears to vary depending on the level of \n",
-                        "pyrethroid-resistance encountered [28]. The absolute increase in hut trial mortality with Olyset Plus compared to Olyset Net in the present study (24-28% vs. 12-18%) conducted in an area of intense pyrethroid-resistance [20] is lower than what has been previously reported with Olyset Plus in another area in Northern Benin where pyrethroid resistance was less prevalent (67-81% vs. 36-42%) [16]. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors [29] mediated by complex and multiple insecticide resistance mechanisms which may not be effectively tackled by the synergistic effects of PBO in pyrethroid-PBO ITNs [4, 23]. It is therefore not clear whether a diminishment in experimental but mortality with pyrethroid-PBO nets due to increasing intensity of pyrethroid-resistance would translate to a reduced epidemiological effect of pyrethroid-PBO ITNs in West Africa compared to East Africa which has been the site of the only epidemiological trials so far.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ITNCondition` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Chemical analysis results\n",
-                        "\n",
-                        "For PermaNet 3.0, PBO was only recorded from the roof panel of the nets; for Olyset Plus, PBO was recorded on all 5 panels (Table 6). Compared to the pyrethroid component, much less PBO was retained in both types of pyrethroid-PBO ITNs after 20 washes (58.7% vs. 25% with Olyset Plus and 80.9% vs. 68.8% with PermaNet 3.0, P < 0.05). The decrease in PBO content after washing was more evident in Olyset Plus than in PermaNet 3.0 netting, hence the wash retention index of PBO was higher with PermaNet 3.0 compared to Olyset Plus (98.1% vs. 93.3%).\n",
-                        "\n",
-                        "Figure 4: Tunnel test mortality (%) of susceptible and resistant strains of _Anopheles gambiae_ s.l. exposed to Olyset Plus vs. Olyset Net\n",
-                        "\n",
-                        "Figure 3: Mortality (%) of susceptible and resistant strains of _An. gambiae_ s.l. in cone bioassays with Olyset Plus and Olyset Net. S = susceptible, R = resistant\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Comparative efficacy of two pyrethroid-piperonyl butoxide nets (Olyset Plus and PermaNet 3.0) against pyrethroid resistant malaria vectors: a non-inferiority assessment\n",
-                        "\n",
-                        "Corine Ngufor, Josias Fagbohoun, Abel Agbevo, Hanafy Ismail, Joseph D. Challenger, Thomas S. Churcher, Mark Rowland\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid-PBO nets were conditionally recommended for control of malaria transmitted by mosquitoes with oxidase-based pyrethroid-resistance based on epidemiological evidence of additional protective effect with Olyset Plus compared to a pyrethroid-only net (Olyset Net). Entomological studies can be used to assess the comparative performance of other brands of pyrethroid-PBO ITNs to Olyset Plus.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality with permethrin and alpha-cypermethrin treated papers was 100% with the laboratory-maintained pyrethroid-susceptible _An. gambiae_ Kisumu strain. With wild pyrethroid resistant _An. gambiae_ s.l. from Cove, mortality rates were <50% with all three pyrethroid insecticides tested (Table 1) thus confirming the high levels of pyrethroid resistance in this vector population. Mortality however increased from 19.2 to 52.5% with permethrin, 41.7% to 69.7% with deltamethrin and 0% to 82.4% with alphacypermethrin after pre-exposure of the Cove strain to PBO synergist (Table 1). This result demonstrated that mixed function oxidases are overexpressed in the wild Cove vector population and their effect can be effectively inhibited by the PBO synergist.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "An experimental hut trial was performed in Cove, Benin to compare PermaNet 3.0 (deltamethrin plus PBO on roof panel only) to Olyset Plus (permethrin plus PBO on all panels) against wild pyrethroid-resistant _Anopheles gambiae_ sensu lato (s.l) following World Health Organization (WHO) guidelines. Both nets were tested unwashed and after 20 standardized washes compared to Olyset Net. Laboratory bioassays were also performed to help explain findings in the experimental huts.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding rates in experimental huts\n",
-                        "\n",
-                        "The blood-feeding rates of wild pyrethroid-resistant _An. gambiae s.l_ that entered the experimental huts are presented in Fig. 2 with further details on mosquito feeding provided in Table 4. The percentage blood-feeding was lower in huts with the unwashed pyrethroid-PBO ITNs and was lowest of all with unwashed Olyset Plus as compared to unwashed PermaNet 3.0 (8% vs 19%, P < 0.001). For all net types, the data showed an overall increase in blood feeding with washed nets compared to unwashed nets and a decrease in blood-feeding inhibition relative to the untreated net. Percentage blood-feeding when washed 20 times did not differ significantly between the pyrethroid-PBO types (38% with PermaNet 3.0 vs. 35% with Olyset Plus, P = 0.708). The proportions of mosquitoes collected resting in the nets were lowest with the\n",
-                        "\n",
-                        "Fig. 1: Mortality of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "unwashed pyrethroid-PBO nets (2-6%), which is consistent with the higher toxicity observed with this type of net. Personal protection with both types of pyrethroid-PBO ITMs were > 80% when unwashed but this declined after 20 washes to 53% with PermaNet 3.0 and 11% with Olyset Plus.\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays\n",
-                        "\n",
-                        "To help further explain the results obtained in the experimental huts, WHO cone bioassays and tunnel tests were performed on samples of netting (\\(30\\times 30\\) cm) obtained from Olyset Net and Olyset Plus when unwashed and after 10 and 20 washes. Washing was performed in the laboratory following WHO guidelines [22]. PermaNet 3.0 was not tested in the laboratory bioassays owing to the restricted application of PBO to the roof of the net preventing a realistic direct comparison with Olyset Plus in bioassays especially tunnel tests. Net samples from each ITN type and each wash point were tested against the following strains:\n",
-                        "\n",
-                        "1. _An. gambiae_ sensu lato (s.l.) strains from Cove, Benin (Cove strain) which is highly pyrethroid resistant. It originates from the experimental hut station in Cove and has shown > 200-fold resistance compared to the susceptible Kisumu strain in susceptibility bioassays. Resistance is mediated by elevated levels of P450s and high frequencies of _kdr_ [20].\n",
-                        "2. _An. gambiae_ VKPer strain, which originated from the Kou Valley in Burkina Faso. VKPer has moderate levels of pyrethroid resistance mediated only by high frequencies of _kdr_.\n",
-                        "3. _An. gambiae_ s.s. Kisumu strain, a reference susceptible strain which originated from Kisumu Kenya.\n",
-                        "\n",
-                        "Approximately two hundred 2-5 days old mosquitoes of each strain were exposed for 3 min in cone bioassays to four net samples of each net type in cohorts of 5 mosquitoes per cone. Knock down in cone bioassays was recorded after 1 h and mortality after 24 h.\n",
-                        "\n",
-                        "Two to three hundred 5-8 days old mosquitoes of each strain were also exposed to each net type in tunnel tests in replicates of 50 mosquitoes per net sample. The tunnel test is a laboratory assay designed to simulate natural host-seeking behaviour of mosquitoes at night in the presence of a net. It consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections by means of a netting frame fitted into a slot across the tunnel. An anesthetized guinea pig was housed unconstrained in a small cage in one section, and mosquitoes were released in the other section at dusk and left overnight. The net samples were holed with nine 1-cm diameter holes to allow host-seeking mosquitoes to penetrate the baited chamber; an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 degC and 75-85% RH. The next morning, the numbers found alive or dead, fed, or unfed, in each section were recorded. Live mosquitoes were provided with sugar solution and delayed mortality recorded after 24 h. The guinea pigs used in this study were kept in accordance with institutional guidelines for animal care.\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays results\n",
-                        "\n",
-                        "The 3-min cone bioassay mortality results for all 3 mosquito strains and wash points tested are presented in Fig. 3. Unwashed Olyset Net induced very low mortality rates against the susceptible Kisumu strain (17-20%) and even lower mortality rates against the pyrethroid resistant strains (< 5%). Cone bioassay mortality rates with unwashed Olyset Plus were higher across all 3 strains compared to Olyset Net though mortality decreased as the strain tested became more pyrethroid-resistant. Cone bioassay mortality however dropped significantly with washed net samples. Mortality with the untreated net samples did not exceed 5% with any strain tested.\n",
-                        "\n",
-                        "header: Experimental hut treatments\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 were compared in the experimental huts when unwashed and after 20 standardized washes. A WHO-recommended pyrethroid-only long-lasting net (Olyset Net) was included to demonstrate the added effect of PBO on the insecticide-resistant local vector species. Nets were washed using savon de Marseilles and rinsed twice following WHO procedures for washing nets for experimental hut studies [22].\n",
-                        "\n",
-                        "The following seven (7) treatments were thus tested in seven experimental huts:\n",
-                        "\n",
-                        "1. Untreated polyethylene net\n",
-                        "2. Olyset Net unwashed (permethrin only)\n",
-                        "3. Olyset Net washed 20 times.\n",
-                        "4. PermaNet 3.0 unwashed (Roof: deltamethrin plus PBO; sides: deltamethrin only)\n",
-                        "5. PermaNet 3.0 washed 20 times.\n",
-                        "6. Olyset Plus unwashed (permethrin plus PBO on all panels)\n",
-                        "7. Olyset Plus washed 20 times.\n",
-                        "\n",
-                        "header: Experimental hut site: Insecticide resistance bioassays\n",
-                        "\n",
-                        "To assess the frequency of pyrethroid resistance and presence of mixed function oxidases in the Cove vector population during the trial, adult mosquitoes that emerged from larvae collected from breeding sites close to experimental huts were tested in WHO cylinder bioassays with and without pre-exposure to PBO. A total of ~100 mosquitoes of the pyrethroid resistant _An. gambiae_ s.l. Cove strain and the pyrethroid susceptible _An. gambiae_ Kisumu strain were exposed to treated filter papers in WHO cylinder bioassays in batches of 25. Tests were performed with papers treated with permethrin 0.75%, alpha-cypermethrin 0.05% and deltamethrin 0.05%. To assess presence of MFO, some mosquitoes were also pre-exposed to papers treated with 4% PBO prior to exposure to insecticide-treated papers. Exposure to PBO and to insecticides lasted 1 h, knockdown was recorded after 60 min and mortality after 24 h.\n",
-                        "\n",
-                        "header: Table 1: Mortality of pyrethroid resistant Anopheles gambiae s.l. from Cove in WHO cylinder bioassays with and without pre-exposure to PBO\n",
-                        "footer: None\n",
-                        "columns: ['Variable', 'N exposed', 'N dead', '% Dead']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Control\",\"N exposed\":101,\"N dead\":0,\"% Dead\":0.0},\"1\":{\"Variable\":\"PBO 4% only\",\"N exposed\":103,\"N dead\":3,\"% Dead\":2.9},\"2\":{\"Variable\":\"Permethrin 0.75%\",\"N exposed\":104,\"N dead\":20,\"% Dead\":19.2},\"3\":{\"Variable\":\"Deltamethrin 0.05%\",\"N exposed\":91,\"N dead\":38,\"% Dead\":41.7},\"4\":{\"Variable\":\"Alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":0,\"% Dead\":0.0},\"5\":{\"Variable\":\"PBO 4% then permethrin 0.75%\",\"N exposed\":99,\"N dead\":52,\"% Dead\":52.5},\"6\":{\"Variable\":\"PBO 4% then deltamethrin 0.05%\",\"N exposed\":99,\"N dead\":69,\"% Dead\":69.7},\"7\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4},\"8\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4}}\n",
-                        "\n",
-                        "header: Table 4: Blood-feeding rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not significantly different (P > 0.05) * Value set to zero as more blood fed mosquitoes were caught in washed Olyset Net huts than control hut\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total blood fed\",\"Control\":\"496\",\"Olyset Net\":\"324\",\"Olyset Net.1\":\"807\",\"PermaNet 3.0\":\"95\",\"PermaNet 3.0.1\":\"231\",\"Olyset Plus\":\"56\",\"Olyset Plus.1\":\"442\"},\"3\":{\"Net type\":\"% Blood fed\",\"Control\":\"52a\",\"Olyset Net\":\"29b\",\"Olyset Net.1\":\"52a\",\"PermaNet 3.0\":\"19c\",\"PermaNet 3.0.1\":\"38d\",\"Olyset Plus\":\"8e\",\"Olyset Plus.1\":\"35d\"},\"4\":{\"Net type\":\"95% conf intervals\",\"Control\":\"48.4\\u201354.7\",\"Olyset Net\":\"25.9\\u201331.2\",\"Olyset Net.1\":\"49.7\\u201354.7\",\"PermaNet 3.0\":\"15.9\\u201322.9\",\"PermaNet 3.0.1\":\"33.7\\u201341.3\",\"Olyset Plus\":\"6.1\\u201310.2\",\"Olyset Plus.1\":\"32.1\\u201337.3\"},\"5\":{\"Net type\":\"% Blood-feeding inhibition\",\"Control\":\"-\",\"Olyset Net\":\"44\",\"Olyset Net.1\":\"0\",\"PermaNet 3.0\":\"63\",\"PermaNet 3.0.1\":\"27\",\"Olyset Plus\":\"85\",\"Olyset Plus.1\":\"33\"},\"6\":{\"Net type\":\"inside net\",\"Control\":\"294\",\"Olyset Net\":\"184\",\"Olyset Net.1\":\"542\",\"PermaNet 3.0\":\"28\",\"PermaNet 3.0.1\":\"130\",\"Olyset Plus\":\"17\",\"Olyset Plus.1\":\"241\"},\"7\":{\"Net type\":\"inside net (%)\",\"Control\":\"31a\",\"Olyset Net\":\"16bc\",\"Olyset Net.1\":\"35a\",\"PermaNet 3.0\":\"6d\",\"PermaNet 3.0.1\":\"21b\",\"Olyset Plus\":\"2e\",\"Olyset Plus.1\":\"19c\"},\"8\":{\"Net type\":\"Personal protection (%)\",\"Control\":\"-\",\"Olyset Net\":\"35\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"81\",\"PermaNet 3.0.1\":\"53\",\"Olyset Plus\":\"89\",\"Olyset Plus.1\":\"11\"}}\n",
-                        "\n",
-                        "header: Mosquito entry and exiting rates in experimental huts: Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts\n",
-                        "\n",
-                        "Mortality rates of wild pyrethroid resistant mosquitoes that entered the experimental huts are presented in Fig. 1 with further details provided in Table 3. The lowest mortality was achieved with Olyset Net (18% before washing and 12% after 20 washes). Percentage mortality with unwashed pyrethroid-PBO nets was highest with PermaNet 3.0 (41%) but this declined significantly after 20 washes (17%, P < 0.001). Mortality with unwashed Olyset Plus was 28% and while this value was significantly lower than the mortality shown by unwashed PermaNet 3.0 (P < 0.001), it did not decrease significantly after 20 washes (28% vs. 24%, P = 0.433) whereas for PermaNet 3.0 it declined. Hence, Olyset Plus induced higher mortality rates than PermaNet 3.0 after 20 washes (24% vs 17%, P < 0.001). With respect to the pyrethroid-only ITN, both pyrethroid-PBO nets induced significantly higher mortality rates than Olyset Net with nets washed 20 times (P < 0.001) though the difference was higher with Olyset Plus (24% vs. 12%), than with PermaNet 3.0 (17% vs. 12%). After 20 washes, mortality with PermaNet 3.0 declined to the same level as the unwashed Olyset Net, (17% vs. 18%, P = 0.061) but remained significantly higher with Olyset Plus (24% vs. 17%, P = 0.036).\n",
-                        "\n",
-                        "header: Cone bioassay results: Tunnel test results\n",
-                        "\n",
-                        "The results from the tunnel tests comparing Olyset Plus and Olyset Net unwashed and after 10 and 20 washes against all three strains are presented in Figs. 4 and 5 for mortality and blood-feeding inhibition respectively. Mortality rates were generally higher in the tunnels tests compared to the cone bioassays and decreased as the strain become more pyrethroid-resistant (Fig. 4). Mortality rates with the Kisumu strain were very high with Olyset Net and Olyset Plus (> 95%). With the VKPer strain, mortality remained > 80% after 20 washes with both ITN types. With the Cove strain, mortality was significantly higher with Olyset Plus compared to Olyset Net at 0 and 10 washes but about the same after 20 washes. With unwashed nets, blood-feeding inhibition in the tunnel tests was consistently higher with Olyset Plus (> 90%) compared to Olyset Net (27-75%) for all three strains tested (Fig. 5). After 10 and 20 washes, blood-feeding inhibition of the Kisumu and VKPer strain remained > 80% with Olyset Plus and Olyset Net. With Cove strain, blood-feeding inhibition was also higher with Olyset Plus at 0 and 10 washes but declined to about the same level as Olyset Net after 20 washes (56%).\n",
-                        "Mortality in the untreated control tunnel was < 10% with all three strains.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain one of the most powerful tools to reduce malaria transmission in a community and provide personal protection to the user [1, 2]. They have contributed significantly to recent reductions in malaria burden [3]. Their efficacy is however threatened by increasing resistance to pyrethroids [4, 5]; the insecticide of choice used on bed-nets owing to its safety, low cost and rapid activity on vector mosquitoes [6]. To maintain the effectiveness of insecticide treated nets for malaria control, new types of LLINs treated with alternative insecticides and compounds which can either replace or complement pyrethroids on bed-nets are urgently needed.\n",
-                        "\n",
-                        "A new class of insecticide treated nets (ITNs) combining pyrethroids and piperonyl butoxide (PBO) (pyrethroid-PBO ITNs) have been developed [7]. PBO is a synergist that inhibits specific metabolic enzymes such as mixed-function oxidases within mosquitoes that detoxify or sequester insecticides before they can have a toxic effect on the mosquito. Pyrethroid-PBO nets can therefore induce increased mortality of pyrethroid-resistant malaria vectors that express mixed function oxidase based pyrethroid resistance mechanisms that are inhibited by the PBO in the net. These nets were given an interim endorsement as a new WHO class of vector control products in 2017 based on epidemiological data from a cluster randomized controlled trial in North Eastern Tanzania [8], that demonstrated additional malaria control with one prototype pyrethroid-PBO net (Olyset Plus) compared to a pyrethroid-only net (Olyset Net), against pyrethroid resistant malaria vectors of moderate intensity, partly conferred by monooxygenase-based resistance mechanism. Pyrethroid-PBO ITNs are conditionally recommended for malaria vector control instead of pyrethroid-only ITNs in areas of confirmed intermediate levels of resistance mediated by monooxygenase-based resistance mechanism [9]. This endorsement has been followed by an increasing uptake of pyrethroid-PBO nets worldwide [10]; in sub-Saharan Africa for example, the proportion of pyrethroid-PBO nets of all nets delivered increased from 3% in 2018 to 35% in 2021.\n",
-                        "\n",
-                        "While Olyset Plus was the first in class pyrethroid-PBO net to demonstrate public health value as observed in the Tanzanian trial [8], there are currently five additional types of pyrethroid-PBO ITNs on the World Health Organization (WHO) list of prequalified vector control products: PermaNet 3.0, Veeralin, Tsara Boost, Tsara Plus and very recently DuraNet Plus [7]. These nets have all demonstrated superiority over pyrethroid-only nets in terms of mosquito mortality and blood-feeding inhibition in multiple experimental hut trials across Africa [11, 12, 13, 14, 15, 16, 17]. However, they differ from Olyset Plus in their design and specifications; typically, the location of PBO on the net (i.e., all panels vs. roof panel only), the type and dose of pyrethroid used, bioavailability and retention of PBO after washing and could, therefore, differ in entomological and epidemiological impact. A recent large cluster-randomized trial in Uganda, evaluating the efficacy of Olyset Plus and PermaNet 3.0 compared to pyrethroid-only nets in a setting of high pyrethroid resistance, also demonstrated better protection against malaria with pyrethroid-PBO nets compared to pyrethroid-only nets for up to 18 months confirming the findings of the Tanzanian trial [18]. While the Ugandan trial was not powered to directly compare between the different ITN brands tested, the results showed that the additional protective effect of the pyrethroid-PBO net compared to the pyrethroid-only net was initially large with PermaNet 3.0 at 6 months post-distribution but lasted only up to 12 months whereas additional protective effect with Olyset Plus appeared to have a delayed onset which was not observed at 6 months but became evident at 12 months and lasted up to 18 months. There are major differences in design between Olyset Plus and PermaNet 3.0, which could have implications on their epidemiological impact; Olyset Plus is a polyethylene net incorporated with permethrin and PBO on all panels while PermaNet 3.0 is a polyester net coated with deltamethrin with PBO restricted to the roof of the net.\n",
-                        "\n",
-                        "To generate assurance of comparative performance of new candidate products within an established WHO vector control product class, without the need for epidemiological evidence for each new product, the WHO has developed new experimental hut study guidelines for assessing their non-inferiority to a first in class product for which evidence of public health value has already been generated [19]. These provisional guidelines are to be piloted with pyrethroid-PBO nets by comparing other WHO/PQ-listed pyrethroid-PBO nets with the first in class product, Olyset Plus. This study compared the efficacy and wash resistance of Olyset Plus and PermaNet\n",
-                        "3.0 and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against wild free-flying pyrethroid resistant malaria vectors in Southern Benin.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Olyset Plus outperformed PermaNet 3.0 in terms of its ability to cause greater margins of improved mosquito mortality compared to a standard pyrethroid net, after multiple standardized washes. However, using a margin of non-inferiority defined by the WHO, PermaNet 3.0 was non-inferior to Olyset Plus in inducing mosquito mortality. Considering the low levels of mortality observed and increasing pyrethroid-resistance in West Africa, it is unclear whether either of these nets would demonstrate the same epidemiological impact observed in community trials in East Africa.\n",
-                        "\n",
-                        "header: Discussion\n",
-                        "\n",
-                        "Following the interim endorsement of pyrethroid-PBO nets by the WHO [23], malaria vector control programmes are faced with additional choice of different brands of prequalified pyrethroid-PBO nets [7]. Considering the wide variations in design of the available brands of these nets, studies generating the required assurance of comparative performance to Olyset Plus (the first in class pyrethroid-PBO net to demonstrate empirical evidence of entomological and epidemiological impact) are necessary. This study compared the efficacy and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against pyrethroid resistant malaria vectors in a highly endemic country of West Africa, following WHO guidelines [19, 22].\n",
-                        "\n",
-                        "The WHO susceptibility bioassays confirmed the high levels of pyrethroid resistance in the vector population at the experimental hut site during the trial, corroborating previous findings [20]. The increased mortality achieved in bioassays with pre-exposure to PBO showed that pyrethroid-resistance was indeed at least partly mediated by increased mono-oxygenase activity. The experimental hut trial demonstrated improved levels of mortality and blood-feeding inhibition with both pyrethroid-PBO ITN types compared to a standard pyrethroid-only LLIN against a vector population that was very resistant to pyrethroids; this was supported by results from the laboratory assays which compared Olyset Plus to Olyset Net against pyrethroid-resistant mosquito strains. These observations are partly attributable to the synergistic effect of the PBO on pyrethroid resistance and are consistent with other experimental hut trials across Africa and epidemiological trials performed in Tanzania [8] and Uganda [18].\n",
-                        "\n",
-                        "Twenty washes in experimental hut studies are indicated by WHO as a proxy for the ability of an ITN to withstand multiple washes under operational use over a 3-year life span [19, 22]. With unwashed nets, the levels of improved mortality relative to the standard pyrethroid-only net were higher with PermaNet 3.0 than with Olyset Plus. However, unlike with Olyset Plus, this effect was lost with PermaNet 3.0 after twenty washes;\n",
-                        "Figure 5: Blood-feeding inhibition (%) of susceptible and resistant strains of _An. gambiae s.l._ in tunnel tests with Olyset Plus and Olyset Net. Blood-feeding inhibition was calculated relative to the control tunnel. 5 = susceptible, R = Resistant\n",
-                        "PermanNet 3.0 killed significantly lower proportions of mosquitoes than Olyset Plus and same proportions as an unwashed pyrethroid-only ITN. Though the RCT in Uganda was not powered to assess differences between the pyrethroid-PBO ITN brands tested (PermanNet 3.0 vs. Olyset Plus) [18], the results from our trial appear consistent with some of the differences in epidemiological effect observed between both brands: (1) The high experimental hut mortality with the unwashed PermaNet 3.0 supports the higher initial protective effect observed with the net in the Ugandan trial at the 6 months epidemiological survey which was not seen with Olyset Plus. (2) The higher rate of decline in experimental hut mortality after washing with PermaNet 3.0 compared to Olyset Plus is consistent with the shorter-lived epidemiological effect in the Ugandan trial with PermaNet 3.0 (up to 12 months) compared to Olyset Plus which remained more protective than the pyrethroid-only net at 18 months. However, care should be taken to not over-interpret the comparisons between results from our hut trial and Ugandan RCT considering the different geographical settings and the lack of sufficient power to differentiate between the epidemiological impact of both pyrethroid-PBO net type in the Ugandan trial.\n",
-                        "\n",
-                        "The difference in hut performance between both pyrethroid-PBO ITNs can be attributed to differences in the retention and movement of PBO across the polymer fibre in Olyset Plus compared to PermaNet 3.0 and/or differences in design and specification. Retention of biofeficacy of pyrethroid-PBO nets is a fine balance between migration and replenishment of PBO from the core to the surface of fibres and the maintenance of an internal reservoir sufficient to last the lifespan of the LLIN, which is typically set at 3 years of use [24]. The chemical analysis results showed a faster release of PBO in the Olyset Plus netting after 20 washes compared to PermaNet 3.0 though it is unclear whether this may have increased the bioavailability of the PBO on the surface of Olyset Plus after washing. Whether sufficient PBO would remain within the fibres of both pyrethroid-PBO nets after 3 years of household use is presently unknown and is the subject of ongoing WHO durability trials of Olyset Plus and PermaNet 3.0 which are not yet completed [18, 25]. Another factor which may contribute to the discrepancies in efficacy of the two pyrethroid-PBO ITNs are differences in design and specification: Olyset Plus is treated with the pyrethroid permethrin while PermaNet 3.0 is treated with deltamethrin, and Olyset Plus contains PBO on every panel whereas in PermaNet 3.0, PBO is available only on the top panel of the net. It is not clear whether the restricted application of PBO to the roof of the net would affect bioefficacy. This would require comparative trials of the PermaNet 3.0 with pyrethroid-PBO restricted to the upper panel and pyrethroid to side panels versus an ITN with all 5 panels treated with pyrethroid-PBO. Behavioural studies of mosquitoes around nets indicate that mosquitoes may first make multiple contacts with the roof panel in response to odour plumes [26]; however, experimental hut trials comparing restricted versus full PBO coverage on nets are too few to be definitive on the question of efficacy.\n",
-                        "\n",
-                        "Despite the differences in performance observed between both pyrethroid-PBO net types with regards to their impact after 20 standardized washes, the non-inferiority analysis performed in accordance with recent WHO guidelines [19] showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality but not with blood-feeding inhibition. The higher blood-feeding inhibition observed with Olyset Plus could be due to the high excito-repellency of permethrin in Olyset Plus compared to deltamethrin in PermaNet 3.0 [27]. Alternatively, the study may not have had sufficient power to demonstrate non-inferiority of PermaNet 3.0 to Olyset Plus for both endpoints; further studies are on-going to help guide power calculations for ITN non-inferiority studies. The non-inferiority margin used for the analysis was defined by WHO as an odds ratio of 0.7 in mosquito mortality and feeding between a candidate net and the first in class net considered acceptable for both products to be in the same policy class. According to these guidelines, if non-inferiority is demonstrated in two independent experimental hut trials in different geographical locations representative of where the products will be deployed, then PermaNet 3.0 will be placed in the same WHO vector control product class as Olyset Plus [19]. It is however not clear whether non-inferiority must be demonstrated for both endpoints (mortality and blood-feeding) for a second-in class product to become part of an intervention class. While the guidelines were developed more like a compromise between the risk of accepting an inferior product and the feasibility of conducting epidemiological trials, the findings from our trial show that non-inferiority experimental hut trials are complex, and results must be interpreted with care. Comparative performance between products may also depend on other location-specific factors, such as the intensity of insecticide resistance and behaviour of the target vector population which should be taken into consideration when choosing between products of the same class.\n",
-                        "\n",
-                        "While the present hut trial in Benin and earlier hut trials in Benin, Tanzania, Cameroon, Burkina Faso, Cote d'Ivoire and Vietnam where the vectors were also resistant have shown some additional effect of pyrethroid-PBO nets over a standard pyrethroid net [12, 13, 15-17], the margin appears to vary depending on the level of \n",
-                        "pyrethroid-resistance encountered [28]. The absolute increase in hut trial mortality with Olyset Plus compared to Olyset Net in the present study (24-28% vs. 12-18%) conducted in an area of intense pyrethroid-resistance [20] is lower than what has been previously reported with Olyset Plus in another area in Northern Benin where pyrethroid resistance was less prevalent (67-81% vs. 36-42%) [16]. Compared to East Africa, West Africa has shown historically higher intensity of pyrethroid resistance in malaria vectors [29] mediated by complex and multiple insecticide resistance mechanisms which may not be effectively tackled by the synergistic effects of PBO in pyrethroid-PBO ITNs [4, 23]. It is therefore not clear whether a diminishment in experimental but mortality with pyrethroid-PBO nets due to increasing intensity of pyrethroid-resistance would translate to a reduced epidemiological effect of pyrethroid-PBO ITNs in West Africa compared to East Africa which has been the site of the only epidemiological trials so far.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "With unwashed nets, mosquito mortality was higher in huts with PermaNet 3.0 compared to Olyset Plus (41% vs. 28%, P < 0.001). After 20 washes, mortality declined significantly with PermaNet 3.0 (41% unwashed vs. 17% after washing P < 0.001), but not with Olyset Plus (28% unwashed vs. 24% after washing P = 0.433); Olyset Plus induced significantly higher mortality than PermaNet 3.0 and Olyset Net after 20 washes. PermaNet 3.0 showed a higher wash retention of PBO compared to Olyset Plus. A non-inferiority analysis performed with data from unwashed and washed nets together using a margin recommended by the WHO, showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality (25% with Olyset Plus vs. 27% with PermaNet 3.0, OR = 1.528, 95%Cl = 1.02-2.29) but not in reducing mosquito feeding (25% with Olyset Plus vs. 30% with PermaNet 3.0, OR = 1.192, 95%Cl = 0.77-1.84). Both pyrethroid-PBO nets were superior to Olyset Net.\n",
-                        "\n",
-                        "header: Insecticide resistance in malaria vectors in Cove: Experimental hut trial results\n",
-                        "\n",
-                        "A total of 6711 pyrethroid resistant female _An. gambiae_ s.l. were collected during the experimental hut trial. The entry and exiting rates of wild pyrethroid resistant _An. gambiae_ s.l. from the experimental huts with the different ITN types tested in the trial are presented in Table 2. Compared to the control, Olyset Net did not deter mosquitoes from entering the experimental huts (0% when unwashed and after 20 washes). Mosquito deterrence was significantly higher with PermaNet 3.0 compared to Olyset Plus both unwashed (49% vs. 28%, P < 0.001) and after 20 washes (36% vs. 0%, P < 0.001). Nevertheless, early exiting of mosquitoes from the experimental huts into the veranda trap did not differ significantly between both pyrethroid-PBO net types both when unwashed (65% with PermaNet 3.0 vs 68% with Olyset Plus, P = 0.232) and after 20 washes (60% with PermaNet 3.0 vs. 56% with Olyset Plus, P = 0.053).\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{l c c c c c c}\n",
-                        "**Net type** & **Control** & **Olyset Net** & & **PermNet 3.0** & **Olyset Plus** \\\\ Number of washes & 0 & 0 & 20 & 0 & 20 & 0 & 20 \\\\ N collected & 962 & 1135 & 1546 & 489 & 616 & 688 & 1275 \\\\ N females/night & 233 & 27\\({}^{\\text{hb}}\\) & 37\\({}^{\\text{b}}\\) & 12\\({}^{\\text{c}}\\) & 15\\({}^{\\text{d}}\\) & 16\\({}^{\\text{d}}\\) & 30\\({}^{\\text{hb}}\\) \\\\ \\% deterrence & – & 0\\({}^{\\text{a}}\\) & 0\\({}^{\\text{a}}\\) & 49 & 36 & 28 & 0 \\\\ N eating & 429 & 639 & 669 & 319 & 370 &\n",
-                        "\n",
-                        "header: Non-inferiority assessment\n",
-                        "\n",
-                        "According to recent provisional WHO guidelines [19], for a candidate pyrethroid-PBO ITN product to be included in this new WHO intervention class without the need for epidemiological evidence, it must demonstrate non-inferiority to the first in class product which has already demonstrated public health value (Olyset Plus) and superiority to a pyrethroid-only LLIN in experimental hut trials [19]. Briefly, the candidate pyrethroid-PBO product is deemed non-inferior if: (1) The lower 95% confidence interval estimate of the odds ratio describing the difference in mosquito mortality between the candidate and active comparator product is greater than 0.7. and (2) The upper 95% confidence interval estimate of the odds ratio describing the difference in mosquito blood-feeding between the candidate and active comparator product is less than 1.43. Following the WHO guidelines, both unwashed and washed data of each product were analysed together to generate single estimates of efficacy representative of the overall performance over the lifetime of the product in the field.\n",
-                        "\n",
-                        "Each primary endpoint for non-inferiority (mortality and blood-feeding rate for unwashed and washed nets combined), was assessed using binomial generalized linear mixed models (GLMMs) with a logit link function fitted using the 'lme4' package of R version 3.5.3 for Windows as described earlier. Results from the non-inferiority assessment of PermaNet 3.0 to Olyset Plus are presented in Table 5 below. The odds ratio for\n",
-                        "Fig. 2: Blood-feeding rates of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "the difference in mosquito mortality between PermaNet 3.0 and Olyset Plus was 1.528 (95% confidence interval: 1.021-2.289) while the odds ratio for the difference in mosquito blood feeding was 1.192 (95% confidence interval: 0.772-1.841). Following the WHO criteria described above, PermaNet 3.0 was non-inferior to Olyset Plus in terms of its ability to kill wild pyrethroid- resistant _An. gambiae_ s.l. in the experimental hut trial in Cove Benin. In contrast PermaNet 3.0 was not non-inferior to Olyset Plus in terms of proportions of mosquitoes that blood-fed and, therefore, fails to demonstrate non-inferiority for blood feeding inhibition in this trial. The results also showed superiority of both pyrethroid-PBO net types to Olyset Net both in terms of mosquito mortality (25-27% vs. 15%, P < 0.001) and reducing blood-feeding (25%-30% vs. 42%, P < 0.001).\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "At the end of the experimental hut trial, five pieces of netting (\\(25\\times 25\\) cm) obtained from the panels of replicate nets of each net type (before and after washing) used in the huts were assessed for deltamethrin, permethrin and PBO content using HPLC. Insecticide was extracted from each net piece with an area of 48 sq cm collected from the five net samples (\\(25\\times 25\\) cm) obtained from each whole net. The insecticide content of each sample was determined by injecting ten ul aliquots of the extract on a reverse-phase Hypersil GOLD C18 column (75 A, \\(250\\times 4.6\\) mm, 5-um particle size; Thermo Scientific) at room temperature. A mobile phase of 70% acetonitrile in water was used at a flow rate of 1 ml min\\({}^{-1}\\) to separate the target analyte. Chromatographic peaks of the insecticides and internal standard were detected at a wavelength of 232 nm with the Ultimate 3000 UV detector and analysed with Dionex Chromeleon(tm) 6.8 Chromatography Data System software. Quantities of insecticide were calculated from standard curves established by known concentrations of the insecticide authenticated standards and corrected by internal standard readings in each sample relative to control.\n",
-                        "\n",
-                        "Data from chemical analysis was used to calculate the percentage retention of each active ingredient after 20 washes relative to the unwashed net and the wash retention index. Wash-resistance index was calculated according to WHO guidelines [22] as indicated below:\n",
-                        "\n",
-                        "\\[\\text{Wash resistance index} = 100\\times\\text{n}\\sqrt{(\\text{tn}/\\text{t0})}\\] \\[\\left(\\text{free migration stage behaviour}\\right)\\]\n",
-                        "\n",
-                        "where \\(\\text{tn}=\\text{total active ingredient content after n washing cycles, t0=total active ingredient content before washing, n=number of washes.\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `Observation` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. \n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Chemical analysis results\n",
-                        "\n",
-                        "For PermaNet 3.0, PBO was only recorded from the roof panel of the nets; for Olyset Plus, PBO was recorded on all 5 panels (Table 6). Compared to the pyrethroid component, much less PBO was retained in both types of pyrethroid-PBO ITNs after 20 washes (58.7% vs. 25% with Olyset Plus and 80.9% vs. 68.8% with PermaNet 3.0, P < 0.05). The decrease in PBO content after washing was more evident in Olyset Plus than in PermaNet 3.0 netting, hence the wash retention index of PBO was higher with PermaNet 3.0 compared to Olyset Plus (98.1% vs. 93.3%).\n",
-                        "\n",
-                        "Figure 4: Tunnel test mortality (%) of susceptible and resistant strains of _Anopheles gambiae_ s.l. exposed to Olyset Plus vs. Olyset Net\n",
-                        "\n",
-                        "Figure 3: Mortality (%) of susceptible and resistant strains of _An. gambiae_ s.l. in cone bioassays with Olyset Plus and Olyset Net. S = susceptible, R = resistant\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays results\n",
-                        "\n",
-                        "The 3-min cone bioassay mortality results for all 3 mosquito strains and wash points tested are presented in Fig. 3. Unwashed Olyset Net induced very low mortality rates against the susceptible Kisumu strain (17-20%) and even lower mortality rates against the pyrethroid resistant strains (< 5%). Cone bioassay mortality rates with unwashed Olyset Plus were higher across all 3 strains compared to Olyset Net though mortality decreased as the strain tested became more pyrethroid-resistant. Cone bioassay mortality however dropped significantly with washed net samples. Mortality with the untreated net samples did not exceed 5% with any strain tested.\n",
-                        "\n",
-                        "header: Mosquito entry and exiting rates in experimental huts: Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts\n",
-                        "\n",
-                        "Mortality rates of wild pyrethroid resistant mosquitoes that entered the experimental huts are presented in Fig. 1 with further details provided in Table 3. The lowest mortality was achieved with Olyset Net (18% before washing and 12% after 20 washes). Percentage mortality with unwashed pyrethroid-PBO nets was highest with PermaNet 3.0 (41%) but this declined significantly after 20 washes (17%, P < 0.001). Mortality with unwashed Olyset Plus was 28% and while this value was significantly lower than the mortality shown by unwashed PermaNet 3.0 (P < 0.001), it did not decrease significantly after 20 washes (28% vs. 24%, P = 0.433) whereas for PermaNet 3.0 it declined. Hence, Olyset Plus induced higher mortality rates than PermaNet 3.0 after 20 washes (24% vs 17%, P < 0.001). With respect to the pyrethroid-only ITN, both pyrethroid-PBO nets induced significantly higher mortality rates than Olyset Net with nets washed 20 times (P < 0.001) though the difference was higher with Olyset Plus (24% vs. 12%), than with PermaNet 3.0 (17% vs. 12%). After 20 washes, mortality with PermaNet 3.0 declined to the same level as the unwashed Olyset Net, (17% vs. 18%, P = 0.061) but remained significantly higher with Olyset Plus (24% vs. 17%, P = 0.036).\n",
-                        "\n",
-                        "header: Experimental hut treatments\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 were compared in the experimental huts when unwashed and after 20 standardized washes. A WHO-recommended pyrethroid-only long-lasting net (Olyset Net) was included to demonstrate the added effect of PBO on the insecticide-resistant local vector species. Nets were washed using savon de Marseilles and rinsed twice following WHO procedures for washing nets for experimental hut studies [22].\n",
-                        "\n",
-                        "The following seven (7) treatments were thus tested in seven experimental huts:\n",
-                        "\n",
-                        "1. Untreated polyethylene net\n",
-                        "2. Olyset Net unwashed (permethrin only)\n",
-                        "3. Olyset Net washed 20 times.\n",
-                        "4. PermaNet 3.0 unwashed (Roof: deltamethrin plus PBO; sides: deltamethrin only)\n",
-                        "5. PermaNet 3.0 washed 20 times.\n",
-                        "6. Olyset Plus unwashed (permethrin plus PBO on all panels)\n",
-                        "7. Olyset Plus washed 20 times.\n",
-                        "\n",
-                        "header: Mosquito blood-feeding rates in experimental huts\n",
-                        "\n",
-                        "The blood-feeding rates of wild pyrethroid-resistant _An. gambiae s.l_ that entered the experimental huts are presented in Fig. 2 with further details on mosquito feeding provided in Table 4. The percentage blood-feeding was lower in huts with the unwashed pyrethroid-PBO ITNs and was lowest of all with unwashed Olyset Plus as compared to unwashed PermaNet 3.0 (8% vs 19%, P < 0.001). For all net types, the data showed an overall increase in blood feeding with washed nets compared to unwashed nets and a decrease in blood-feeding inhibition relative to the untreated net. Percentage blood-feeding when washed 20 times did not differ significantly between the pyrethroid-PBO types (38% with PermaNet 3.0 vs. 35% with Olyset Plus, P = 0.708). The proportions of mosquitoes collected resting in the nets were lowest with the\n",
-                        "\n",
-                        "Fig. 1: Mortality of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "unwashed pyrethroid-PBO nets (2-6%), which is consistent with the higher toxicity observed with this type of net. Personal protection with both types of pyrethroid-PBO ITMs were > 80% when unwashed but this declined after 20 washes to 53% with PermaNet 3.0 and 11% with Olyset Plus.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "An experimental hut trial was performed in Cove, Benin to compare PermaNet 3.0 (deltamethrin plus PBO on roof panel only) to Olyset Plus (permethrin plus PBO on all panels) against wild pyrethroid-resistant _Anopheles gambiae_ sensu lato (s.l) following World Health Organization (WHO) guidelines. Both nets were tested unwashed and after 20 standardized washes compared to Olyset Net. Laboratory bioassays were also performed to help explain findings in the experimental huts.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality with permethrin and alpha-cypermethrin treated papers was 100% with the laboratory-maintained pyrethroid-susceptible _An. gambiae_ Kisumu strain. With wild pyrethroid resistant _An. gambiae_ s.l. from Cove, mortality rates were <50% with all three pyrethroid insecticides tested (Table 1) thus confirming the high levels of pyrethroid resistance in this vector population. Mortality however increased from 19.2 to 52.5% with permethrin, 41.7% to 69.7% with deltamethrin and 0% to 82.4% with alphacypermethrin after pre-exposure of the Cove strain to PBO synergist (Table 1). This result demonstrated that mixed function oxidases are overexpressed in the wild Cove vector population and their effect can be effectively inhibited by the PBO synergist.\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays\n",
-                        "\n",
-                        "To help further explain the results obtained in the experimental huts, WHO cone bioassays and tunnel tests were performed on samples of netting (\\(30\\times 30\\) cm) obtained from Olyset Net and Olyset Plus when unwashed and after 10 and 20 washes. Washing was performed in the laboratory following WHO guidelines [22]. PermaNet 3.0 was not tested in the laboratory bioassays owing to the restricted application of PBO to the roof of the net preventing a realistic direct comparison with Olyset Plus in bioassays especially tunnel tests. Net samples from each ITN type and each wash point were tested against the following strains:\n",
-                        "\n",
-                        "1. _An. gambiae_ sensu lato (s.l.) strains from Cove, Benin (Cove strain) which is highly pyrethroid resistant. It originates from the experimental hut station in Cove and has shown > 200-fold resistance compared to the susceptible Kisumu strain in susceptibility bioassays. Resistance is mediated by elevated levels of P450s and high frequencies of _kdr_ [20].\n",
-                        "2. _An. gambiae_ VKPer strain, which originated from the Kou Valley in Burkina Faso. VKPer has moderate levels of pyrethroid resistance mediated only by high frequencies of _kdr_.\n",
-                        "3. _An. gambiae_ s.s. Kisumu strain, a reference susceptible strain which originated from Kisumu Kenya.\n",
-                        "\n",
-                        "Approximately two hundred 2-5 days old mosquitoes of each strain were exposed for 3 min in cone bioassays to four net samples of each net type in cohorts of 5 mosquitoes per cone. Knock down in cone bioassays was recorded after 1 h and mortality after 24 h.\n",
-                        "\n",
-                        "Two to three hundred 5-8 days old mosquitoes of each strain were also exposed to each net type in tunnel tests in replicates of 50 mosquitoes per net sample. The tunnel test is a laboratory assay designed to simulate natural host-seeking behaviour of mosquitoes at night in the presence of a net. It consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections by means of a netting frame fitted into a slot across the tunnel. An anesthetized guinea pig was housed unconstrained in a small cage in one section, and mosquitoes were released in the other section at dusk and left overnight. The net samples were holed with nine 1-cm diameter holes to allow host-seeking mosquitoes to penetrate the baited chamber; an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 degC and 75-85% RH. The next morning, the numbers found alive or dead, fed, or unfed, in each section were recorded. Live mosquitoes were provided with sugar solution and delayed mortality recorded after 24 h. The guinea pigs used in this study were kept in accordance with institutional guidelines for animal care.\n",
-                        "\n",
-                        "header: Cone bioassay results: Tunnel test results\n",
-                        "\n",
-                        "The results from the tunnel tests comparing Olyset Plus and Olyset Net unwashed and after 10 and 20 washes against all three strains are presented in Figs. 4 and 5 for mortality and blood-feeding inhibition respectively. Mortality rates were generally higher in the tunnels tests compared to the cone bioassays and decreased as the strain become more pyrethroid-resistant (Fig. 4). Mortality rates with the Kisumu strain were very high with Olyset Net and Olyset Plus (> 95%). With the VKPer strain, mortality remained > 80% after 20 washes with both ITN types. With the Cove strain, mortality was significantly higher with Olyset Plus compared to Olyset Net at 0 and 10 washes but about the same after 20 washes. With unwashed nets, blood-feeding inhibition in the tunnel tests was consistently higher with Olyset Plus (> 90%) compared to Olyset Net (27-75%) for all three strains tested (Fig. 5). After 10 and 20 washes, blood-feeding inhibition of the Kisumu and VKPer strain remained > 80% with Olyset Plus and Olyset Net. With Cove strain, blood-feeding inhibition was also higher with Olyset Plus at 0 and 10 washes but declined to about the same level as Olyset Net after 20 washes (56%).\n",
-                        "Mortality in the untreated control tunnel was < 10% with all three strains.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Comparative efficacy of two pyrethroid-piperonyl butoxide nets (Olyset Plus and PermaNet 3.0) against pyrethroid resistant malaria vectors: a non-inferiority assessment\n",
-                        "\n",
-                        "Corine Ngufor, Josias Fagbohoun, Abel Agbevo, Hanafy Ismail, Joseph D. Challenger, Thomas S. Churcher, Mark Rowland\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid-PBO nets were conditionally recommended for control of malaria transmitted by mosquitoes with oxidase-based pyrethroid-resistance based on epidemiological evidence of additional protective effect with Olyset Plus compared to a pyrethroid-only net (Olyset Net). Entomological studies can be used to assess the comparative performance of other brands of pyrethroid-PBO ITNs to Olyset Plus.\n",
-                        "\n",
-                        "header: Table 4: Blood-feeding rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not significantly different (P > 0.05) * Value set to zero as more blood fed mosquitoes were caught in washed Olyset Net huts than control hut\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total blood fed\",\"Control\":\"496\",\"Olyset Net\":\"324\",\"Olyset Net.1\":\"807\",\"PermaNet 3.0\":\"95\",\"PermaNet 3.0.1\":\"231\",\"Olyset Plus\":\"56\",\"Olyset Plus.1\":\"442\"},\"3\":{\"Net type\":\"% Blood fed\",\"Control\":\"52a\",\"Olyset Net\":\"29b\",\"Olyset Net.1\":\"52a\",\"PermaNet 3.0\":\"19c\",\"PermaNet 3.0.1\":\"38d\",\"Olyset Plus\":\"8e\",\"Olyset Plus.1\":\"35d\"},\"4\":{\"Net type\":\"95% conf intervals\",\"Control\":\"48.4\\u201354.7\",\"Olyset Net\":\"25.9\\u201331.2\",\"Olyset Net.1\":\"49.7\\u201354.7\",\"PermaNet 3.0\":\"15.9\\u201322.9\",\"PermaNet 3.0.1\":\"33.7\\u201341.3\",\"Olyset Plus\":\"6.1\\u201310.2\",\"Olyset Plus.1\":\"32.1\\u201337.3\"},\"5\":{\"Net type\":\"% Blood-feeding inhibition\",\"Control\":\"-\",\"Olyset Net\":\"44\",\"Olyset Net.1\":\"0\",\"PermaNet 3.0\":\"63\",\"PermaNet 3.0.1\":\"27\",\"Olyset Plus\":\"85\",\"Olyset Plus.1\":\"33\"},\"6\":{\"Net type\":\"inside net\",\"Control\":\"294\",\"Olyset Net\":\"184\",\"Olyset Net.1\":\"542\",\"PermaNet 3.0\":\"28\",\"PermaNet 3.0.1\":\"130\",\"Olyset Plus\":\"17\",\"Olyset Plus.1\":\"241\"},\"7\":{\"Net type\":\"inside net (%)\",\"Control\":\"31a\",\"Olyset Net\":\"16bc\",\"Olyset Net.1\":\"35a\",\"PermaNet 3.0\":\"6d\",\"PermaNet 3.0.1\":\"21b\",\"Olyset Plus\":\"2e\",\"Olyset Plus.1\":\"19c\"},\"8\":{\"Net type\":\"Personal protection (%)\",\"Control\":\"-\",\"Olyset Net\":\"35\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"81\",\"PermaNet 3.0.1\":\"53\",\"Olyset Plus\":\"89\",\"Olyset Plus.1\":\"11\"}}\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "With unwashed nets, mosquito mortality was higher in huts with PermaNet 3.0 compared to Olyset Plus (41% vs. 28%, P < 0.001). After 20 washes, mortality declined significantly with PermaNet 3.0 (41% unwashed vs. 17% after washing P < 0.001), but not with Olyset Plus (28% unwashed vs. 24% after washing P = 0.433); Olyset Plus induced significantly higher mortality than PermaNet 3.0 and Olyset Net after 20 washes. PermaNet 3.0 showed a higher wash retention of PBO compared to Olyset Plus. A non-inferiority analysis performed with data from unwashed and washed nets together using a margin recommended by the WHO, showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality (25% with Olyset Plus vs. 27% with PermaNet 3.0, OR = 1.528, 95%Cl = 1.02-2.29) but not in reducing mosquito feeding (25% with Olyset Plus vs. 30% with PermaNet 3.0, OR = 1.192, 95%Cl = 0.77-1.84). Both pyrethroid-PBO nets were superior to Olyset Net.\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "At the end of the experimental hut trial, five pieces of netting (\\(25\\times 25\\) cm) obtained from the panels of replicate nets of each net type (before and after washing) used in the huts were assessed for deltamethrin, permethrin and PBO content using HPLC. Insecticide was extracted from each net piece with an area of 48 sq cm collected from the five net samples (\\(25\\times 25\\) cm) obtained from each whole net. The insecticide content of each sample was determined by injecting ten ul aliquots of the extract on a reverse-phase Hypersil GOLD C18 column (75 A, \\(250\\times 4.6\\) mm, 5-um particle size; Thermo Scientific) at room temperature. A mobile phase of 70% acetonitrile in water was used at a flow rate of 1 ml min\\({}^{-1}\\) to separate the target analyte. Chromatographic peaks of the insecticides and internal standard were detected at a wavelength of 232 nm with the Ultimate 3000 UV detector and analysed with Dionex Chromeleon(tm) 6.8 Chromatography Data System software. Quantities of insecticide were calculated from standard curves established by known concentrations of the insecticide authenticated standards and corrected by internal standard readings in each sample relative to control.\n",
-                        "\n",
-                        "Data from chemical analysis was used to calculate the percentage retention of each active ingredient after 20 washes relative to the unwashed net and the wash retention index. Wash-resistance index was calculated according to WHO guidelines [22] as indicated below:\n",
-                        "\n",
-                        "\\[\\text{Wash resistance index} = 100\\times\\text{n}\\sqrt{(\\text{tn}/\\text{t0})}\\] \\[\\left(\\text{free migration stage behaviour}\\right)\\]\n",
-                        "\n",
-                        "where \\(\\text{tn}=\\text{total active ingredient content after n washing cycles, t0=total active ingredient content before washing, n=number of washes.\n",
-                        "\n",
-                        "header: Table 1: Mortality of pyrethroid resistant Anopheles gambiae s.l. from Cove in WHO cylinder bioassays with and without pre-exposure to PBO\n",
-                        "footer: None\n",
-                        "columns: ['Variable', 'N exposed', 'N dead', '% Dead']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Control\",\"N exposed\":101,\"N dead\":0,\"% Dead\":0.0},\"1\":{\"Variable\":\"PBO 4% only\",\"N exposed\":103,\"N dead\":3,\"% Dead\":2.9},\"2\":{\"Variable\":\"Permethrin 0.75%\",\"N exposed\":104,\"N dead\":20,\"% Dead\":19.2},\"3\":{\"Variable\":\"Deltamethrin 0.05%\",\"N exposed\":91,\"N dead\":38,\"% Dead\":41.7},\"4\":{\"Variable\":\"Alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":0,\"% Dead\":0.0},\"5\":{\"Variable\":\"PBO 4% then permethrin 0.75%\",\"N exposed\":99,\"N dead\":52,\"% Dead\":52.5},\"6\":{\"Variable\":\"PBO 4% then deltamethrin 0.05%\",\"N exposed\":99,\"N dead\":69,\"% Dead\":69.7},\"7\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4},\"8\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4}}\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain one of the most powerful tools to reduce malaria transmission in a community and provide personal protection to the user [1, 2]. They have contributed significantly to recent reductions in malaria burden [3]. Their efficacy is however threatened by increasing resistance to pyrethroids [4, 5]; the insecticide of choice used on bed-nets owing to its safety, low cost and rapid activity on vector mosquitoes [6]. To maintain the effectiveness of insecticide treated nets for malaria control, new types of LLINs treated with alternative insecticides and compounds which can either replace or complement pyrethroids on bed-nets are urgently needed.\n",
-                        "\n",
-                        "A new class of insecticide treated nets (ITNs) combining pyrethroids and piperonyl butoxide (PBO) (pyrethroid-PBO ITNs) have been developed [7]. PBO is a synergist that inhibits specific metabolic enzymes such as mixed-function oxidases within mosquitoes that detoxify or sequester insecticides before they can have a toxic effect on the mosquito. Pyrethroid-PBO nets can therefore induce increased mortality of pyrethroid-resistant malaria vectors that express mixed function oxidase based pyrethroid resistance mechanisms that are inhibited by the PBO in the net. These nets were given an interim endorsement as a new WHO class of vector control products in 2017 based on epidemiological data from a cluster randomized controlled trial in North Eastern Tanzania [8], that demonstrated additional malaria control with one prototype pyrethroid-PBO net (Olyset Plus) compared to a pyrethroid-only net (Olyset Net), against pyrethroid resistant malaria vectors of moderate intensity, partly conferred by monooxygenase-based resistance mechanism. Pyrethroid-PBO ITNs are conditionally recommended for malaria vector control instead of pyrethroid-only ITNs in areas of confirmed intermediate levels of resistance mediated by monooxygenase-based resistance mechanism [9]. This endorsement has been followed by an increasing uptake of pyrethroid-PBO nets worldwide [10]; in sub-Saharan Africa for example, the proportion of pyrethroid-PBO nets of all nets delivered increased from 3% in 2018 to 35% in 2021.\n",
-                        "\n",
-                        "While Olyset Plus was the first in class pyrethroid-PBO net to demonstrate public health value as observed in the Tanzanian trial [8], there are currently five additional types of pyrethroid-PBO ITNs on the World Health Organization (WHO) list of prequalified vector control products: PermaNet 3.0, Veeralin, Tsara Boost, Tsara Plus and very recently DuraNet Plus [7]. These nets have all demonstrated superiority over pyrethroid-only nets in terms of mosquito mortality and blood-feeding inhibition in multiple experimental hut trials across Africa [11, 12, 13, 14, 15, 16, 17]. However, they differ from Olyset Plus in their design and specifications; typically, the location of PBO on the net (i.e., all panels vs. roof panel only), the type and dose of pyrethroid used, bioavailability and retention of PBO after washing and could, therefore, differ in entomological and epidemiological impact. A recent large cluster-randomized trial in Uganda, evaluating the efficacy of Olyset Plus and PermaNet 3.0 compared to pyrethroid-only nets in a setting of high pyrethroid resistance, also demonstrated better protection against malaria with pyrethroid-PBO nets compared to pyrethroid-only nets for up to 18 months confirming the findings of the Tanzanian trial [18]. While the Ugandan trial was not powered to directly compare between the different ITN brands tested, the results showed that the additional protective effect of the pyrethroid-PBO net compared to the pyrethroid-only net was initially large with PermaNet 3.0 at 6 months post-distribution but lasted only up to 12 months whereas additional protective effect with Olyset Plus appeared to have a delayed onset which was not observed at 6 months but became evident at 12 months and lasted up to 18 months. There are major differences in design between Olyset Plus and PermaNet 3.0, which could have implications on their epidemiological impact; Olyset Plus is a polyethylene net incorporated with permethrin and PBO on all panels while PermaNet 3.0 is a polyester net coated with deltamethrin with PBO restricted to the roof of the net.\n",
-                        "\n",
-                        "To generate assurance of comparative performance of new candidate products within an established WHO vector control product class, without the need for epidemiological evidence for each new product, the WHO has developed new experimental hut study guidelines for assessing their non-inferiority to a first in class product for which evidence of public health value has already been generated [19]. These provisional guidelines are to be piloted with pyrethroid-PBO nets by comparing other WHO/PQ-listed pyrethroid-PBO nets with the first in class product, Olyset Plus. This study compared the efficacy and wash resistance of Olyset Plus and PermaNet\n",
-                        "3.0 and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against wild free-flying pyrethroid resistant malaria vectors in Southern Benin.\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Olyset Plus outperformed PermaNet 3.0 in terms of its ability to cause greater margins of improved mosquito mortality compared to a standard pyrethroid net, after multiple standardized washes. However, using a margin of non-inferiority defined by the WHO, PermaNet 3.0 was non-inferior to Olyset Plus in inducing mosquito mortality. Considering the low levels of mortality observed and increasing pyrethroid-resistance in West Africa, it is unclear whether either of these nets would demonstrate the same epidemiological impact observed in community trials in East Africa.\n",
-                        "\n",
-                        "header: Experimental hut site: Insecticide resistance bioassays\n",
-                        "\n",
-                        "To assess the frequency of pyrethroid resistance and presence of mixed function oxidases in the Cove vector population during the trial, adult mosquitoes that emerged from larvae collected from breeding sites close to experimental huts were tested in WHO cylinder bioassays with and without pre-exposure to PBO. A total of ~100 mosquitoes of the pyrethroid resistant _An. gambiae_ s.l. Cove strain and the pyrethroid susceptible _An. gambiae_ Kisumu strain were exposed to treated filter papers in WHO cylinder bioassays in batches of 25. Tests were performed with papers treated with permethrin 0.75%, alpha-cypermethrin 0.05% and deltamethrin 0.05%. To assess presence of MFO, some mosquitoes were also pre-exposed to papers treated with 4% PBO prior to exposure to insecticide-treated papers. Exposure to PBO and to insecticides lasted 1 h, knockdown was recorded after 60 min and mortality after 24 h.\n",
-                        "\n",
-                        "header: Non-inferiority assessment\n",
-                        "\n",
-                        "According to recent provisional WHO guidelines [19], for a candidate pyrethroid-PBO ITN product to be included in this new WHO intervention class without the need for epidemiological evidence, it must demonstrate non-inferiority to the first in class product which has already demonstrated public health value (Olyset Plus) and superiority to a pyrethroid-only LLIN in experimental hut trials [19]. Briefly, the candidate pyrethroid-PBO product is deemed non-inferior if: (1) The lower 95% confidence interval estimate of the odds ratio describing the difference in mosquito mortality between the candidate and active comparator product is greater than 0.7. and (2) The upper 95% confidence interval estimate of the odds ratio describing the difference in mosquito blood-feeding between the candidate and active comparator product is less than 1.43. Following the WHO guidelines, both unwashed and washed data of each product were analysed together to generate single estimates of efficacy representative of the overall performance over the lifetime of the product in the field.\n",
-                        "\n",
-                        "Each primary endpoint for non-inferiority (mortality and blood-feeding rate for unwashed and washed nets combined), was assessed using binomial generalized linear mixed models (GLMMs) with a logit link function fitted using the 'lme4' package of R version 3.5.3 for Windows as described earlier. Results from the non-inferiority assessment of PermaNet 3.0 to Olyset Plus are presented in Table 5 below. The odds ratio for\n",
-                        "Fig. 2: Blood-feeding rates of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "the difference in mosquito mortality between PermaNet 3.0 and Olyset Plus was 1.528 (95% confidence interval: 1.021-2.289) while the odds ratio for the difference in mosquito blood feeding was 1.192 (95% confidence interval: 0.772-1.841). Following the WHO criteria described above, PermaNet 3.0 was non-inferior to Olyset Plus in terms of its ability to kill wild pyrethroid- resistant _An. gambiae_ s.l. in the experimental hut trial in Cove Benin. In contrast PermaNet 3.0 was not non-inferior to Olyset Plus in terms of proportions of mosquitoes that blood-fed and, therefore, fails to demonstrate non-inferiority for blood feeding inhibition in this trial. The results also showed superiority of both pyrethroid-PBO net types to Olyset Net both in terms of mosquito mortality (25-27% vs. 15%, P < 0.001) and reducing blood-feeding (25%-30% vs. 42%, P < 0.001).\n",
-                        "\n",
-                        "header: Table 2 Entry and exiting rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not signi\u001d\n",
-                        "cantly different (P>0.05)* Value set to zero as more mosquitoes caught in Olyset Net huts than control huts\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"N collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"N females\\/night\",\"Control\":\"23a\",\"Olyset Net\":\"27ab\",\"Olyset Net.1\":\"37b\",\"PermaNet 3.0\":\"12c\",\"PermaNet 3.0.1\":\"15d\",\"Olyset Plus\":\"16d\",\"Olyset Plus.1\":\"30ab\"},\"3\":{\"Net type\":\"% deterrence\",\"Control\":null,\"Olyset Net\":\"0*\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"49\",\"PermaNet 3.0.1\":\"36\",\"Olyset Plus\":\"28\",\"Olyset Plus.1\":\"0\"},\"4\":{\"Net type\":\"N exiting\",\"Control\":\"429\",\"Olyset Net\":\"639\",\"Olyset Net.1\":\"669\",\"PermaNet 3.0\":\"319\",\"PermaNet 3.0.1\":\"370\",\"Olyset Plus\":\"468\",\"Olyset Plus.1\":\"712\"},\"5\":{\"Net type\":\"% exiting\",\"Control\":\"45a\",\"Olyset Net\":\"56b\",\"Olyset Net.1\":\"43a\",\"PermaNet 3.0\":\"65cd\",\"PermaNet 3.0.1\":\"60ce\",\"Olyset Plus\":\"68d\",\"Olyset Plus.1\":\"56be\"},\"6\":{\"Net type\":\"95% conf. limits\",\"Control\":\"41.5\\u201347.7\",\"Olyset Net\":\"53.4\\u201359.2\",\"Olyset Net.1\":\"40.8\\u201345.7\",\"PermaNet 3.0\":\"61.0\\u201369.5\",\"PermaNet 3.0.1\":\"56.2\\u201363.9\",\"Olyset Plus\":\"64.4\\u201371.4\",\"Olyset Plus.1\":\"53.1\\u201358.6\"}}\n",
-                        "\n",
-                        "header: Table 3 Mortality of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not signi\u001d\n",
-                        "cantly di\u001c\n",
-                        "erent (P>0.05)\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total dead\",\"Control\":\"12\",\"Olyset Net\":\"209\",\"Olyset Net.1\":\"284\",\"PermaNet 3.0\":\"198\",\"PermaNet 3.0.1\":\"103\",\"Olyset Plus\":\"190\",\"Olyset Plus.1\":\"297\"},\"3\":{\"Net type\":\"Mortality (%)\",\"Control\":\"1a\",\"Olyset Net\":\"18b\",\"Olyset Net.1\":\"12c\",\"PermaNet 3.0\":\"41d\",\"PermaNet 3.0.1\":\"17b\",\"Olyset Plus\":\"28e\",\"Olyset Plus.1\":\"24e\"},\"4\":{\"Net type\":\"95% conf. limits\",\"Control\":\"0.6\\u20132.0\",\"Olyset Net\":\"16.2\\u201320.1\",\"Olyset Net.1\":\"10.3\\u201313.5\",\"PermaNet 3.0\":\"36.1\\u201344.8\",\"PermaNet 3.0.1\":\"13.8\\u201319.8\",\"Olyset Plus\":\"24.4\\u201331.1\",\"Olyset Plus.1\":\"21.0\\u201325.6\"},\"5\":{\"Net type\":\"Corrected for control (%)\",\"Control\":\"-\",\"Olyset Net\":\"17\",\"Olyset Net.1\":\"11\",\"PermaNet 3.0\":\"41\",\"PermaNet 3.0.1\":\"17\",\"Olyset Plus\":\"27\",\"Olyset Plus.1\":\"22\"}}\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `ClinicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `ClinicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `ClinicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cove\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"deltamethrin\",\"Synergist\":\"PBO\",\"Net_washed\":20.0,\"pHI_category\":\"All\"},\"N02\":{\"Net_type\":\"Olyset Plus\",\"Insecticide\":\"permethrin\",\"Synergist\":\"PBO\",\"Net_washed\":20.0,\"pHI_category\":\"All\"},\"N03\":{\"Net_type\":\"Olyset Net\",\"Insecticide\":\"permethrin\",\"Synergist\":null,\"Net_washed\":20.0,\"pHI_category\":\"All\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n",
-                        "** Messages: **\n",
-                        "user: Your ability to extract and summarize this context accurately is essential for effective analysis. Pay close attention to the context's language, structure, and any cross-references to ensure a comprehensive and precise extraction of information. Do not use prior knowledge or information from outside the context to answer the questions. Only use the information provided in the context to answer the questions.\n",
-                        "Context information is below.\n",
-                        "---------------------\n",
-                        "header: Chemical analysis results\n",
-                        "\n",
-                        "For PermaNet 3.0, PBO was only recorded from the roof panel of the nets; for Olyset Plus, PBO was recorded on all 5 panels (Table 6). Compared to the pyrethroid component, much less PBO was retained in both types of pyrethroid-PBO ITNs after 20 washes (58.7% vs. 25% with Olyset Plus and 80.9% vs. 68.8% with PermaNet 3.0, P < 0.05). The decrease in PBO content after washing was more evident in Olyset Plus than in PermaNet 3.0 netting, hence the wash retention index of PBO was higher with PermaNet 3.0 compared to Olyset Plus (98.1% vs. 93.3%).\n",
-                        "\n",
-                        "Figure 4: Tunnel test mortality (%) of susceptible and resistant strains of _Anopheles gambiae_ s.l. exposed to Olyset Plus vs. Olyset Net\n",
-                        "\n",
-                        "Figure 3: Mortality (%) of susceptible and resistant strains of _An. gambiae_ s.l. in cone bioassays with Olyset Plus and Olyset Net. S = susceptible, R = resistant\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays results\n",
-                        "\n",
-                        "The 3-min cone bioassay mortality results for all 3 mosquito strains and wash points tested are presented in Fig. 3. Unwashed Olyset Net induced very low mortality rates against the susceptible Kisumu strain (17-20%) and even lower mortality rates against the pyrethroid resistant strains (< 5%). Cone bioassay mortality rates with unwashed Olyset Plus were higher across all 3 strains compared to Olyset Net though mortality decreased as the strain tested became more pyrethroid-resistant. Cone bioassay mortality however dropped significantly with washed net samples. Mortality with the untreated net samples did not exceed 5% with any strain tested.\n",
-                        "\n",
-                        "header: Experimental hut treatments\n",
-                        "\n",
-                        "Olyset Plus and PermaNet 3.0 were compared in the experimental huts when unwashed and after 20 standardized washes. A WHO-recommended pyrethroid-only long-lasting net (Olyset Net) was included to demonstrate the added effect of PBO on the insecticide-resistant local vector species. Nets were washed using savon de Marseilles and rinsed twice following WHO procedures for washing nets for experimental hut studies [22].\n",
-                        "\n",
-                        "The following seven (7) treatments were thus tested in seven experimental huts:\n",
-                        "\n",
-                        "1. Untreated polyethylene net\n",
-                        "2. Olyset Net unwashed (permethrin only)\n",
-                        "3. Olyset Net washed 20 times.\n",
-                        "4. PermaNet 3.0 unwashed (Roof: deltamethrin plus PBO; sides: deltamethrin only)\n",
-                        "5. PermaNet 3.0 washed 20 times.\n",
-                        "6. Olyset Plus unwashed (permethrin plus PBO on all panels)\n",
-                        "7. Olyset Plus washed 20 times.\n",
-                        "\n",
-                        "header: Background: Methods\n",
-                        "\n",
-                        "An experimental hut trial was performed in Cove, Benin to compare PermaNet 3.0 (deltamethrin plus PBO on roof panel only) to Olyset Plus (permethrin plus PBO on all panels) against wild pyrethroid-resistant _Anopheles gambiae_ sensu lato (s.l) following World Health Organization (WHO) guidelines. Both nets were tested unwashed and after 20 standardized washes compared to Olyset Net. Laboratory bioassays were also performed to help explain findings in the experimental huts.\n",
-                        "\n",
-                        "header: Abstract\n",
-                        "\n",
-                        "Comparative efficacy of two pyrethroid-piperonyl butoxide nets (Olyset Plus and PermaNet 3.0) against pyrethroid resistant malaria vectors: a non-inferiority assessment\n",
-                        "\n",
-                        "Corine Ngufor, Josias Fagbohoun, Abel Agbevo, Hanafy Ismail, Joseph D. Challenger, Thomas S. Churcher, Mark Rowland\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "Pyrethroid-PBO nets were conditionally recommended for control of malaria transmitted by mosquitoes with oxidase-based pyrethroid-resistance based on epidemiological evidence of additional protective effect with Olyset Plus compared to a pyrethroid-only net (Olyset Net). Entomological studies can be used to assess the comparative performance of other brands of pyrethroid-PBO ITNs to Olyset Plus.\n",
-                        "\n",
-                        "header: Mosquito entry and exiting rates in experimental huts: Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts\n",
-                        "\n",
-                        "Mortality rates of wild pyrethroid resistant mosquitoes that entered the experimental huts are presented in Fig. 1 with further details provided in Table 3. The lowest mortality was achieved with Olyset Net (18% before washing and 12% after 20 washes). Percentage mortality with unwashed pyrethroid-PBO nets was highest with PermaNet 3.0 (41%) but this declined significantly after 20 washes (17%, P < 0.001). Mortality with unwashed Olyset Plus was 28% and while this value was significantly lower than the mortality shown by unwashed PermaNet 3.0 (P < 0.001), it did not decrease significantly after 20 washes (28% vs. 24%, P = 0.433) whereas for PermaNet 3.0 it declined. Hence, Olyset Plus induced higher mortality rates than PermaNet 3.0 after 20 washes (24% vs 17%, P < 0.001). With respect to the pyrethroid-only ITN, both pyrethroid-PBO nets induced significantly higher mortality rates than Olyset Net with nets washed 20 times (P < 0.001) though the difference was higher with Olyset Plus (24% vs. 12%), than with PermaNet 3.0 (17% vs. 12%). After 20 washes, mortality with PermaNet 3.0 declined to the same level as the unwashed Olyset Net, (17% vs. 18%, P = 0.061) but remained significantly higher with Olyset Plus (24% vs. 17%, P = 0.036).\n",
-                        "\n",
-                        "header: Mosquito blood-feeding rates in experimental huts\n",
-                        "\n",
-                        "The blood-feeding rates of wild pyrethroid-resistant _An. gambiae s.l_ that entered the experimental huts are presented in Fig. 2 with further details on mosquito feeding provided in Table 4. The percentage blood-feeding was lower in huts with the unwashed pyrethroid-PBO ITNs and was lowest of all with unwashed Olyset Plus as compared to unwashed PermaNet 3.0 (8% vs 19%, P < 0.001). For all net types, the data showed an overall increase in blood feeding with washed nets compared to unwashed nets and a decrease in blood-feeding inhibition relative to the untreated net. Percentage blood-feeding when washed 20 times did not differ significantly between the pyrethroid-PBO types (38% with PermaNet 3.0 vs. 35% with Olyset Plus, P = 0.708). The proportions of mosquitoes collected resting in the nets were lowest with the\n",
-                        "\n",
-                        "Fig. 1: Mortality of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "unwashed pyrethroid-PBO nets (2-6%), which is consistent with the higher toxicity observed with this type of net. Personal protection with both types of pyrethroid-PBO ITMs were > 80% when unwashed but this declined after 20 washes to 53% with PermaNet 3.0 and 11% with Olyset Plus.\n",
-                        "\n",
-                        "header: Cone bioassay results: Tunnel test results\n",
-                        "\n",
-                        "The results from the tunnel tests comparing Olyset Plus and Olyset Net unwashed and after 10 and 20 washes against all three strains are presented in Figs. 4 and 5 for mortality and blood-feeding inhibition respectively. Mortality rates were generally higher in the tunnels tests compared to the cone bioassays and decreased as the strain become more pyrethroid-resistant (Fig. 4). Mortality rates with the Kisumu strain were very high with Olyset Net and Olyset Plus (> 95%). With the VKPer strain, mortality remained > 80% after 20 washes with both ITN types. With the Cove strain, mortality was significantly higher with Olyset Plus compared to Olyset Net at 0 and 10 washes but about the same after 20 washes. With unwashed nets, blood-feeding inhibition in the tunnel tests was consistently higher with Olyset Plus (> 90%) compared to Olyset Net (27-75%) for all three strains tested (Fig. 5). After 10 and 20 washes, blood-feeding inhibition of the Kisumu and VKPer strain remained > 80% with Olyset Plus and Olyset Net. With Cove strain, blood-feeding inhibition was also higher with Olyset Plus at 0 and 10 washes but declined to about the same level as Olyset Net after 20 washes (56%).\n",
-                        "Mortality in the untreated control tunnel was < 10% with all three strains.\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "Mortality with permethrin and alpha-cypermethrin treated papers was 100% with the laboratory-maintained pyrethroid-susceptible _An. gambiae_ Kisumu strain. With wild pyrethroid resistant _An. gambiae_ s.l. from Cove, mortality rates were <50% with all three pyrethroid insecticides tested (Table 1) thus confirming the high levels of pyrethroid resistance in this vector population. Mortality however increased from 19.2 to 52.5% with permethrin, 41.7% to 69.7% with deltamethrin and 0% to 82.4% with alphacypermethrin after pre-exposure of the Cove strain to PBO synergist (Table 1). This result demonstrated that mixed function oxidases are overexpressed in the wild Cove vector population and their effect can be effectively inhibited by the PBO synergist.\n",
-                        "\n",
-                        "header: Supplementary laboratory bioassays\n",
-                        "\n",
-                        "To help further explain the results obtained in the experimental huts, WHO cone bioassays and tunnel tests were performed on samples of netting (\\(30\\times 30\\) cm) obtained from Olyset Net and Olyset Plus when unwashed and after 10 and 20 washes. Washing was performed in the laboratory following WHO guidelines [22]. PermaNet 3.0 was not tested in the laboratory bioassays owing to the restricted application of PBO to the roof of the net preventing a realistic direct comparison with Olyset Plus in bioassays especially tunnel tests. Net samples from each ITN type and each wash point were tested against the following strains:\n",
-                        "\n",
-                        "1. _An. gambiae_ sensu lato (s.l.) strains from Cove, Benin (Cove strain) which is highly pyrethroid resistant. It originates from the experimental hut station in Cove and has shown > 200-fold resistance compared to the susceptible Kisumu strain in susceptibility bioassays. Resistance is mediated by elevated levels of P450s and high frequencies of _kdr_ [20].\n",
-                        "2. _An. gambiae_ VKPer strain, which originated from the Kou Valley in Burkina Faso. VKPer has moderate levels of pyrethroid resistance mediated only by high frequencies of _kdr_.\n",
-                        "3. _An. gambiae_ s.s. Kisumu strain, a reference susceptible strain which originated from Kisumu Kenya.\n",
-                        "\n",
-                        "Approximately two hundred 2-5 days old mosquitoes of each strain were exposed for 3 min in cone bioassays to four net samples of each net type in cohorts of 5 mosquitoes per cone. Knock down in cone bioassays was recorded after 1 h and mortality after 24 h.\n",
-                        "\n",
-                        "Two to three hundred 5-8 days old mosquitoes of each strain were also exposed to each net type in tunnel tests in replicates of 50 mosquitoes per net sample. The tunnel test is a laboratory assay designed to simulate natural host-seeking behaviour of mosquitoes at night in the presence of a net. It consists of a square glass cylinder (25 cm high, 25 cm wide, 60 cm in length) divided into two sections by means of a netting frame fitted into a slot across the tunnel. An anesthetized guinea pig was housed unconstrained in a small cage in one section, and mosquitoes were released in the other section at dusk and left overnight. The net samples were holed with nine 1-cm diameter holes to allow host-seeking mosquitoes to penetrate the baited chamber; an untreated net sample served as the control. The tunnels were kept overnight in a dark room at 25-29 degC and 75-85% RH. The next morning, the numbers found alive or dead, fed, or unfed, in each section were recorded. Live mosquitoes were provided with sugar solution and delayed mortality recorded after 24 h. The guinea pigs used in this study were kept in accordance with institutional guidelines for animal care.\n",
-                        "\n",
-                        "header: Table 4: Blood-feeding rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not significantly different (P > 0.05) * Value set to zero as more blood fed mosquitoes were caught in washed Olyset Net huts than control hut\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"Total collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"Total blood fed\",\"Control\":\"496\",\"Olyset Net\":\"324\",\"Olyset Net.1\":\"807\",\"PermaNet 3.0\":\"95\",\"PermaNet 3.0.1\":\"231\",\"Olyset Plus\":\"56\",\"Olyset Plus.1\":\"442\"},\"3\":{\"Net type\":\"% Blood fed\",\"Control\":\"52a\",\"Olyset Net\":\"29b\",\"Olyset Net.1\":\"52a\",\"PermaNet 3.0\":\"19c\",\"PermaNet 3.0.1\":\"38d\",\"Olyset Plus\":\"8e\",\"Olyset Plus.1\":\"35d\"},\"4\":{\"Net type\":\"95% conf intervals\",\"Control\":\"48.4\\u201354.7\",\"Olyset Net\":\"25.9\\u201331.2\",\"Olyset Net.1\":\"49.7\\u201354.7\",\"PermaNet 3.0\":\"15.9\\u201322.9\",\"PermaNet 3.0.1\":\"33.7\\u201341.3\",\"Olyset Plus\":\"6.1\\u201310.2\",\"Olyset Plus.1\":\"32.1\\u201337.3\"},\"5\":{\"Net type\":\"% Blood-feeding inhibition\",\"Control\":\"-\",\"Olyset Net\":\"44\",\"Olyset Net.1\":\"0\",\"PermaNet 3.0\":\"63\",\"PermaNet 3.0.1\":\"27\",\"Olyset Plus\":\"85\",\"Olyset Plus.1\":\"33\"},\"6\":{\"Net type\":\"inside net\",\"Control\":\"294\",\"Olyset Net\":\"184\",\"Olyset Net.1\":\"542\",\"PermaNet 3.0\":\"28\",\"PermaNet 3.0.1\":\"130\",\"Olyset Plus\":\"17\",\"Olyset Plus.1\":\"241\"},\"7\":{\"Net type\":\"inside net (%)\",\"Control\":\"31a\",\"Olyset Net\":\"16bc\",\"Olyset Net.1\":\"35a\",\"PermaNet 3.0\":\"6d\",\"PermaNet 3.0.1\":\"21b\",\"Olyset Plus\":\"2e\",\"Olyset Plus.1\":\"19c\"},\"8\":{\"Net type\":\"Personal protection (%)\",\"Control\":\"-\",\"Olyset Net\":\"35\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"81\",\"PermaNet 3.0.1\":\"53\",\"Olyset Plus\":\"89\",\"Olyset Plus.1\":\"11\"}}\n",
-                        "\n",
-                        "header: Results\n",
-                        "\n",
-                        "With unwashed nets, mosquito mortality was higher in huts with PermaNet 3.0 compared to Olyset Plus (41% vs. 28%, P < 0.001). After 20 washes, mortality declined significantly with PermaNet 3.0 (41% unwashed vs. 17% after washing P < 0.001), but not with Olyset Plus (28% unwashed vs. 24% after washing P = 0.433); Olyset Plus induced significantly higher mortality than PermaNet 3.0 and Olyset Net after 20 washes. PermaNet 3.0 showed a higher wash retention of PBO compared to Olyset Plus. A non-inferiority analysis performed with data from unwashed and washed nets together using a margin recommended by the WHO, showed that PermaNet 3.0 was non-inferior to Olyset Plus in terms of mosquito mortality (25% with Olyset Plus vs. 27% with PermaNet 3.0, OR = 1.528, 95%Cl = 1.02-2.29) but not in reducing mosquito feeding (25% with Olyset Plus vs. 30% with PermaNet 3.0, OR = 1.192, 95%Cl = 0.77-1.84). Both pyrethroid-PBO nets were superior to Olyset Net.\n",
-                        "\n",
-                        "header: Background\n",
-                        "\n",
-                        "Long-lasting insecticidal nets (LLINs) remain one of the most powerful tools to reduce malaria transmission in a community and provide personal protection to the user [1, 2]. They have contributed significantly to recent reductions in malaria burden [3]. Their efficacy is however threatened by increasing resistance to pyrethroids [4, 5]; the insecticide of choice used on bed-nets owing to its safety, low cost and rapid activity on vector mosquitoes [6]. To maintain the effectiveness of insecticide treated nets for malaria control, new types of LLINs treated with alternative insecticides and compounds which can either replace or complement pyrethroids on bed-nets are urgently needed.\n",
-                        "\n",
-                        "A new class of insecticide treated nets (ITNs) combining pyrethroids and piperonyl butoxide (PBO) (pyrethroid-PBO ITNs) have been developed [7]. PBO is a synergist that inhibits specific metabolic enzymes such as mixed-function oxidases within mosquitoes that detoxify or sequester insecticides before they can have a toxic effect on the mosquito. Pyrethroid-PBO nets can therefore induce increased mortality of pyrethroid-resistant malaria vectors that express mixed function oxidase based pyrethroid resistance mechanisms that are inhibited by the PBO in the net. These nets were given an interim endorsement as a new WHO class of vector control products in 2017 based on epidemiological data from a cluster randomized controlled trial in North Eastern Tanzania [8], that demonstrated additional malaria control with one prototype pyrethroid-PBO net (Olyset Plus) compared to a pyrethroid-only net (Olyset Net), against pyrethroid resistant malaria vectors of moderate intensity, partly conferred by monooxygenase-based resistance mechanism. Pyrethroid-PBO ITNs are conditionally recommended for malaria vector control instead of pyrethroid-only ITNs in areas of confirmed intermediate levels of resistance mediated by monooxygenase-based resistance mechanism [9]. This endorsement has been followed by an increasing uptake of pyrethroid-PBO nets worldwide [10]; in sub-Saharan Africa for example, the proportion of pyrethroid-PBO nets of all nets delivered increased from 3% in 2018 to 35% in 2021.\n",
-                        "\n",
-                        "While Olyset Plus was the first in class pyrethroid-PBO net to demonstrate public health value as observed in the Tanzanian trial [8], there are currently five additional types of pyrethroid-PBO ITNs on the World Health Organization (WHO) list of prequalified vector control products: PermaNet 3.0, Veeralin, Tsara Boost, Tsara Plus and very recently DuraNet Plus [7]. These nets have all demonstrated superiority over pyrethroid-only nets in terms of mosquito mortality and blood-feeding inhibition in multiple experimental hut trials across Africa [11, 12, 13, 14, 15, 16, 17]. However, they differ from Olyset Plus in their design and specifications; typically, the location of PBO on the net (i.e., all panels vs. roof panel only), the type and dose of pyrethroid used, bioavailability and retention of PBO after washing and could, therefore, differ in entomological and epidemiological impact. A recent large cluster-randomized trial in Uganda, evaluating the efficacy of Olyset Plus and PermaNet 3.0 compared to pyrethroid-only nets in a setting of high pyrethroid resistance, also demonstrated better protection against malaria with pyrethroid-PBO nets compared to pyrethroid-only nets for up to 18 months confirming the findings of the Tanzanian trial [18]. While the Ugandan trial was not powered to directly compare between the different ITN brands tested, the results showed that the additional protective effect of the pyrethroid-PBO net compared to the pyrethroid-only net was initially large with PermaNet 3.0 at 6 months post-distribution but lasted only up to 12 months whereas additional protective effect with Olyset Plus appeared to have a delayed onset which was not observed at 6 months but became evident at 12 months and lasted up to 18 months. There are major differences in design between Olyset Plus and PermaNet 3.0, which could have implications on their epidemiological impact; Olyset Plus is a polyethylene net incorporated with permethrin and PBO on all panels while PermaNet 3.0 is a polyester net coated with deltamethrin with PBO restricted to the roof of the net.\n",
-                        "\n",
-                        "To generate assurance of comparative performance of new candidate products within an established WHO vector control product class, without the need for epidemiological evidence for each new product, the WHO has developed new experimental hut study guidelines for assessing their non-inferiority to a first in class product for which evidence of public health value has already been generated [19]. These provisional guidelines are to be piloted with pyrethroid-PBO nets by comparing other WHO/PQ-listed pyrethroid-PBO nets with the first in class product, Olyset Plus. This study compared the efficacy and wash resistance of Olyset Plus and PermaNet\n",
-                        "3.0 and assessed the non-inferiority of PermaNet 3.0 to Olyset Plus in experimental huts against wild free-flying pyrethroid resistant malaria vectors in Southern Benin.\n",
-                        "\n",
-                        "header: Table 1: Mortality of pyrethroid resistant Anopheles gambiae s.l. from Cove in WHO cylinder bioassays with and without pre-exposure to PBO\n",
-                        "footer: None\n",
-                        "columns: ['Variable', 'N exposed', 'N dead', '% Dead']\n",
-                        "\n",
-                        "{\"0\":{\"Variable\":\"Control\",\"N exposed\":101,\"N dead\":0,\"% Dead\":0.0},\"1\":{\"Variable\":\"PBO 4% only\",\"N exposed\":103,\"N dead\":3,\"% Dead\":2.9},\"2\":{\"Variable\":\"Permethrin 0.75%\",\"N exposed\":104,\"N dead\":20,\"% Dead\":19.2},\"3\":{\"Variable\":\"Deltamethrin 0.05%\",\"N exposed\":91,\"N dead\":38,\"% Dead\":41.7},\"4\":{\"Variable\":\"Alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":0,\"% Dead\":0.0},\"5\":{\"Variable\":\"PBO 4% then permethrin 0.75%\",\"N exposed\":99,\"N dead\":52,\"% Dead\":52.5},\"6\":{\"Variable\":\"PBO 4% then deltamethrin 0.05%\",\"N exposed\":99,\"N dead\":69,\"% Dead\":69.7},\"7\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4},\"8\":{\"Variable\":\"PBO 4% then alpha-cypermethrin 0.05%\",\"N exposed\":102,\"N dead\":84,\"% Dead\":82.4}}\n",
-                        "\n",
-                        "header: Conclusion\n",
-                        "\n",
-                        "Olyset Plus outperformed PermaNet 3.0 in terms of its ability to cause greater margins of improved mosquito mortality compared to a standard pyrethroid net, after multiple standardized washes. However, using a margin of non-inferiority defined by the WHO, PermaNet 3.0 was non-inferior to Olyset Plus in inducing mosquito mortality. Considering the low levels of mortality observed and increasing pyrethroid-resistance in West Africa, it is unclear whether either of these nets would demonstrate the same epidemiological impact observed in community trials in East Africa.\n",
-                        "\n",
-                        "header: Experimental hut site: Insecticide resistance bioassays\n",
-                        "\n",
-                        "To assess the frequency of pyrethroid resistance and presence of mixed function oxidases in the Cove vector population during the trial, adult mosquitoes that emerged from larvae collected from breeding sites close to experimental huts were tested in WHO cylinder bioassays with and without pre-exposure to PBO. A total of ~100 mosquitoes of the pyrethroid resistant _An. gambiae_ s.l. Cove strain and the pyrethroid susceptible _An. gambiae_ Kisumu strain were exposed to treated filter papers in WHO cylinder bioassays in batches of 25. Tests were performed with papers treated with permethrin 0.75%, alpha-cypermethrin 0.05% and deltamethrin 0.05%. To assess presence of MFO, some mosquitoes were also pre-exposed to papers treated with 4% PBO prior to exposure to insecticide-treated papers. Exposure to PBO and to insecticides lasted 1 h, knockdown was recorded after 60 min and mortality after 24 h.\n",
-                        "\n",
-                        "header: Chemical analysis\n",
-                        "\n",
-                        "At the end of the experimental hut trial, five pieces of netting (\\(25\\times 25\\) cm) obtained from the panels of replicate nets of each net type (before and after washing) used in the huts were assessed for deltamethrin, permethrin and PBO content using HPLC. Insecticide was extracted from each net piece with an area of 48 sq cm collected from the five net samples (\\(25\\times 25\\) cm) obtained from each whole net. The insecticide content of each sample was determined by injecting ten ul aliquots of the extract on a reverse-phase Hypersil GOLD C18 column (75 A, \\(250\\times 4.6\\) mm, 5-um particle size; Thermo Scientific) at room temperature. A mobile phase of 70% acetonitrile in water was used at a flow rate of 1 ml min\\({}^{-1}\\) to separate the target analyte. Chromatographic peaks of the insecticides and internal standard were detected at a wavelength of 232 nm with the Ultimate 3000 UV detector and analysed with Dionex Chromeleon(tm) 6.8 Chromatography Data System software. Quantities of insecticide were calculated from standard curves established by known concentrations of the insecticide authenticated standards and corrected by internal standard readings in each sample relative to control.\n",
-                        "\n",
-                        "Data from chemical analysis was used to calculate the percentage retention of each active ingredient after 20 washes relative to the unwashed net and the wash retention index. Wash-resistance index was calculated according to WHO guidelines [22] as indicated below:\n",
-                        "\n",
-                        "\\[\\text{Wash resistance index} = 100\\times\\text{n}\\sqrt{(\\text{tn}/\\text{t0})}\\] \\[\\left(\\text{free migration stage behaviour}\\right)\\]\n",
-                        "\n",
-                        "where \\(\\text{tn}=\\text{total active ingredient content after n washing cycles, t0=total active ingredient content before washing, n=number of washes.\n",
-                        "\n",
-                        "header: Table 2 Entry and exiting rates of wild pyrethroid resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin\n",
-                        "footer: Values along a row bearing the same letter label are not signi\u001d\n",
-                        "cantly different (P>0.05)* Value set to zero as more mosquitoes caught in Olyset Net huts than control huts\n",
-                        "columns: ['Net type', 'Control', 'Olyset Net', 'Olyset Net.1', 'PermaNet 3.0', 'PermaNet 3.0.1', 'Olyset Plus', 'Olyset Plus.1']\n",
-                        "\n",
-                        "{\"0\":{\"Net type\":\"Number of washes\",\"Control\":\"0\",\"Olyset Net\":\"0\",\"Olyset Net.1\":\"20\",\"PermaNet 3.0\":\"0\",\"PermaNet 3.0.1\":\"20\",\"Olyset Plus\":\"0\",\"Olyset Plus.1\":\"20\"},\"1\":{\"Net type\":\"N collected\",\"Control\":\"962\",\"Olyset Net\":\"1135\",\"Olyset Net.1\":\"1546\",\"PermaNet 3.0\":\"489\",\"PermaNet 3.0.1\":\"616\",\"Olyset Plus\":\"688\",\"Olyset Plus.1\":\"1275\"},\"2\":{\"Net type\":\"N females\\/night\",\"Control\":\"23a\",\"Olyset Net\":\"27ab\",\"Olyset Net.1\":\"37b\",\"PermaNet 3.0\":\"12c\",\"PermaNet 3.0.1\":\"15d\",\"Olyset Plus\":\"16d\",\"Olyset Plus.1\":\"30ab\"},\"3\":{\"Net type\":\"% deterrence\",\"Control\":null,\"Olyset Net\":\"0*\",\"Olyset Net.1\":\"0*\",\"PermaNet 3.0\":\"49\",\"PermaNet 3.0.1\":\"36\",\"Olyset Plus\":\"28\",\"Olyset Plus.1\":\"0\"},\"4\":{\"Net type\":\"N exiting\",\"Control\":\"429\",\"Olyset Net\":\"639\",\"Olyset Net.1\":\"669\",\"PermaNet 3.0\":\"319\",\"PermaNet 3.0.1\":\"370\",\"Olyset Plus\":\"468\",\"Olyset Plus.1\":\"712\"},\"5\":{\"Net type\":\"% exiting\",\"Control\":\"45a\",\"Olyset Net\":\"56b\",\"Olyset Net.1\":\"43a\",\"PermaNet 3.0\":\"65cd\",\"PermaNet 3.0.1\":\"60ce\",\"Olyset Plus\":\"68d\",\"Olyset Plus.1\":\"56be\"},\"6\":{\"Net type\":\"95% conf. limits\",\"Control\":\"41.5\\u201347.7\",\"Olyset Net\":\"53.4\\u201359.2\",\"Olyset Net.1\":\"40.8\\u201345.7\",\"PermaNet 3.0\":\"61.0\\u201369.5\",\"PermaNet 3.0.1\":\"56.2\\u201363.9\",\"Olyset Plus\":\"64.4\\u201371.4\",\"Olyset Plus.1\":\"53.1\\u201358.6\"}}\n",
-                        "\n",
-                        "header: Non-inferiority assessment\n",
-                        "\n",
-                        "According to recent provisional WHO guidelines [19], for a candidate pyrethroid-PBO ITN product to be included in this new WHO intervention class without the need for epidemiological evidence, it must demonstrate non-inferiority to the first in class product which has already demonstrated public health value (Olyset Plus) and superiority to a pyrethroid-only LLIN in experimental hut trials [19]. Briefly, the candidate pyrethroid-PBO product is deemed non-inferior if: (1) The lower 95% confidence interval estimate of the odds ratio describing the difference in mosquito mortality between the candidate and active comparator product is greater than 0.7. and (2) The upper 95% confidence interval estimate of the odds ratio describing the difference in mosquito blood-feeding between the candidate and active comparator product is less than 1.43. Following the WHO guidelines, both unwashed and washed data of each product were analysed together to generate single estimates of efficacy representative of the overall performance over the lifetime of the product in the field.\n",
-                        "\n",
-                        "Each primary endpoint for non-inferiority (mortality and blood-feeding rate for unwashed and washed nets combined), was assessed using binomial generalized linear mixed models (GLMMs) with a logit link function fitted using the 'lme4' package of R version 3.5.3 for Windows as described earlier. Results from the non-inferiority assessment of PermaNet 3.0 to Olyset Plus are presented in Table 5 below. The odds ratio for\n",
-                        "Fig. 2: Blood-feeding rates of wild pyrethroid-resistant _Anopheles gambiae_ s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\n",
-                        "the difference in mosquito mortality between PermaNet 3.0 and Olyset Plus was 1.528 (95% confidence interval: 1.021-2.289) while the odds ratio for the difference in mosquito blood feeding was 1.192 (95% confidence interval: 0.772-1.841). Following the WHO criteria described above, PermaNet 3.0 was non-inferior to Olyset Plus in terms of its ability to kill wild pyrethroid- resistant _An. gambiae_ s.l. in the experimental hut trial in Cove Benin. In contrast PermaNet 3.0 was not non-inferior to Olyset Plus in terms of proportions of mosquitoes that blood-fed and, therefore, fails to demonstrate non-inferiority for blood feeding inhibition in this trial. The results also showed superiority of both pyrethroid-PBO net types to Olyset Net both in terms of mosquito mortality (25-27% vs. 15%, P < 0.001) and reducing blood-feeding (25%-30% vs. 42%, P < 0.001).\n",
-                        "\n",
-                        "header: Insecticide resistance in malaria vectors in Cove: Experimental hut trial results\n",
-                        "\n",
-                        "A total of 6711 pyrethroid resistant female _An. gambiae_ s.l. were collected during the experimental hut trial. The entry and exiting rates of wild pyrethroid resistant _An. gambiae_ s.l. from the experimental huts with the different ITN types tested in the trial are presented in Table 2. Compared to the control, Olyset Net did not deter mosquitoes from entering the experimental huts (0% when unwashed and after 20 washes). Mosquito deterrence was significantly higher with PermaNet 3.0 compared to Olyset Plus both unwashed (49% vs. 28%, P < 0.001) and after 20 washes (36% vs. 0%, P < 0.001). Nevertheless, early exiting of mosquitoes from the experimental huts into the veranda trap did not differ significantly between both pyrethroid-PBO net types both when unwashed (65% with PermaNet 3.0 vs 68% with Olyset Plus, P = 0.232) and after 20 washes (60% with PermaNet 3.0 vs. 56% with Olyset Plus, P = 0.053).\n",
-                        "\\begin{table}\n",
-                        "\\begin{tabular}{l c c c c c c}\n",
-                        "**Net type** & **Control** & **Olyset Net** & & **PermNet 3.0** & **Olyset Plus** \\\\ Number of washes & 0 & 0 & 20 & 0 & 20 & 0 & 20 \\\\ N collected & 962 & 1135 & 1546 & 489 & 616 & 688 & 1275 \\\\ N females/night & 233 & 27\\({}^{\\text{hb}}\\) & 37\\({}^{\\text{b}}\\) & 12\\({}^{\\text{c}}\\) & 15\\({}^{\\text{d}}\\) & 16\\({}^{\\text{d}}\\) & 30\\({}^{\\text{hb}}\\) \\\\ \\% deterrence & – & 0\\({}^{\\text{a}}\\) & 0\\({}^{\\text{a}}\\) & 49 & 36 & 28 & 0 \\\\ N eating & 429 & 639 & 669 & 319 & 370 &\n",
-                        "---------------------\n",
-                        "Query: Your task is to extract data from a research paper.\n",
-                        "The `EntomologicalOutcome` details can be split across the provided context. Respond with details by looking at the whole context always.\n",
-                        "If you don't find the information in the given context or you are not sure, omit the key-value in your JSON response. The `EntomologicalOutcome` data you're extracting is dependent on the provided `Observation, ITNCondition` tables containing entities which you need to reference. There can be multiple `EntomologicalOutcome` data entries for each unique combination of these references.Here are the data already extracted from the paper:\n",
-                        "\n",
-                        "###Observation###\n",
-                        "Schema:\n",
-                        "{\"title\": \"Observation\", \"description\": \"A collection of observations from a single publication.     Each Observation entity extracted should have unique `Site`, `Start_month`, `End_month`, and `Time_elapsed`     (if reported). Country and Site are typically located in Africa.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `observation_ref`\\nSpecifications:\\nstr_startswith: \\\"S\\\"\"}, \"Study_type\": {\"title\": \"Study Type\", \"description\": \"The different study types from a single publication\"}, \"Country\": {\"title\": \"Country\", \"description\": \"Country where the study was conducted\"}, \"Site\": {\"title\": \"Site\", \"description\": \"Specific geographic information (such as district name) where the study was conducted and where the mosquitoes was collected. If mosquitoes were collected from a lab, enter 'Lab facility' here.\"}}}\n",
-                        "Data:\n",
-                        "{\"S01\":{\"Study_type\":\"Hut trial\",\"Country\":\"Benin\",\"Site\":\"Cove\"}}\n",
-                        "\n",
-                        "###ITNCondition###\n",
-                        "Schema:\n",
-                        "{\"title\": \"ITNCondition\", \"description\": \"The paper contains a collection of nets including untreated nets, no nets, and different brands of treated nets, which is used to compare effectiveness in malaria prevention and control.\", \"type\": \"object\", \"properties\": {\"reference\": {\"title\": \"Reference\", \"description\": \"Unique identifier where other tables can reference entries with the key `itncondition_ref`\\nSpecifications:\\nstr_startswith: \\\"N\\\"\"}, \"Net_type\": {\"title\": \"Net Type\", \"description\": \"Name of net brand or type\"}, \"Insecticide\": {\"title\": \"Insecticide\", \"description\": \"Enter the active ingredient insecticide or insecticides combination (in order of concentration, delimited by comma) used in the net type here.\"}, \"Synergist\": {\"title\": \"Synergist\", \"description\": \"Enter the synergist or synergists combination (delimited by comma).\"}, \"Net_washed\": {\"title\": \"Net Washed\", \"description\": \"Numerical count of number of net washes - 0 if none and NA if not reported.\"}, \"pHI_category\": {\"title\": \"Phi Category\", \"description\": \"Classification of the nets holed surface area, as one of \\\"Good\\\", \\\"Damaged\\\", \\\"Torn\\\", \\\"Serviceable\\\", or \\\"All\\\", as proposed by WHO\"}}}\n",
-                        "Data:\n",
-                        "{\"N01\":{\"Net_type\":\"PermaNet 3.0\",\"Insecticide\":\"deltamethrin\",\"Synergist\":\"PBO\",\"Net_washed\":20.0,\"pHI_category\":\"All\"},\"N02\":{\"Net_type\":\"Olyset Plus\",\"Insecticide\":\"permethrin\",\"Synergist\":\"PBO\",\"Net_washed\":20.0,\"pHI_category\":\"All\"},\"N03\":{\"Net_type\":\"Olyset Net\",\"Insecticide\":\"permethrin\",\"Synergist\":null,\"Net_washed\":20.0,\"pHI_category\":\"All\"}}\n",
-                        "\n",
-                        "\n",
-                        "Answer: \n",
-                        "**************************************************\n",
-                        "** Response: **\n",
-                        "assistant: None\n",
-                        "**************************************************\n",
-                        "\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for ref, paper in tqdm(papers.loc[references].iterrows(), total=len(references)):\n",
-                "    if ref in pred_extractions and pred_extractions[ref]: continue\n",
-                "        \n",
-                "    indexes[paper.name] = load_index(\n",
-                "        paper, llm_model=llm_model, \n",
-                "        embed_model=embed_model, \n",
-                "        weaviate_client=weaviate_client,\n",
-                "        index_name='LlamaIndexDocumentSections',\n",
-                "    )\n",
-                "\n",
-                "    ### comment out langfuse code \n",
-                "    # global_handler.set_trace_params(\n",
-                "    #     name=f\"extract-{ref}\",\n",
-                "    #     session_id=ref,\n",
-                "    #     user_id='jonnytr',\n",
-                "    #     tags=['itn-recalibration', ref, 'complete-extraction']\n",
-                "    # )\n",
-                "\n",
-                "    pred_extractions[ref], responses[ref] = extract_paper(\n",
-                "        paper, schema_structure=ss,\n",
-                "        index=indexes[paper.name], \n",
-                "        llm_models=[llm_model, 'gpt-4-turbo'], embed_model=embed_model, \n",
-                "        verbose=2,\n",
-                "    )"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "38e7de0c-a1d9-460c-a649-6e3da0a7780b",
-            "metadata": {},
-            "source": [
-                "### View extraction results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "44975cd7-5132-4624-8d22-82de44851fb7",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>accrombessi2023efficacy</th>\n",
-                            "      <th>mieguim2021insights</th>\n",
-                            "      <th>gebremariam2021evaluation</th>\n",
-                            "      <th>gichuki2021bioefficacy</th>\n",
-                            "      <th>syme2021which</th>\n",
-                            "      <th>zahouli2023small</th>\n",
-                            "      <th>diouf2022evaluation</th>\n",
-                            "      <th>ibrahim2020exploring</th>\n",
-                            "      <th>kibondo2022influence</th>\n",
-                            "      <th>meiwald2022association</th>\n",
-                            "      <th>menze2022experimental</th>\n",
-                            "      <th>syme2022pyrethroid</th>\n",
-                            "      <th>toe2018do</th>\n",
-                            "      <th>yewhalaw2022experimental</th>\n",
-                            "      <th>ngufor2022comparative</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>ITNCondition</th>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(9, 5)</td>\n",
-                            "      <td>(4, 7)</td>\n",
-                            "      <td>(2, 11)</td>\n",
-                            "      <td>(1, 4)</td>\n",
-                            "      <td>(6, 3)</td>\n",
-                            "      <td>(5, 5)</td>\n",
-                            "      <td>(5, 5)</td>\n",
-                            "      <td>(3, 4)</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(3, 6)</td>\n",
-                            "      <td>(3, 6)</td>\n",
-                            "      <td>(6, 6)</td>\n",
-                            "      <td>(2, 5)</td>\n",
-                            "      <td>(3, 5)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Observation</th>\n",
-                            "      <td>(3, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 6)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 6)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 5)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 9)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 7)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ClinicalOutcome</th>\n",
-                            "      <td>(3, 12)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(2, 7)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 1)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>(4, 4)</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(2, 1)</td>\n",
-                            "      <td>(2, 3)</td>\n",
-                            "      <td>(2, 3)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>EntomologicalOutcome</th>\n",
-                            "      <td>(1, 6)</td>\n",
-                            "      <td>(9, 6)</td>\n",
-                            "      <td>(2, 6)</td>\n",
-                            "      <td>(1, 9)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(6, 8)</td>\n",
-                            "      <td>(1, 1)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(4, 7)</td>\n",
-                            "      <td>(3, 5)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(3, 9)</td>\n",
-                            "      <td>(1, 2)</td>\n",
-                            "      <td>(2, 8)</td>\n",
-                            "      <td>(1, 10)</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                     accrombessi2023efficacy mieguim2021insights  \\\n",
-                            "ITNCondition                          (3, 3)              (9, 5)   \n",
-                            "Observation                           (3, 7)              (1, 7)   \n",
-                            "ClinicalOutcome                      (3, 12)              (1, 3)   \n",
-                            "EntomologicalOutcome                  (1, 6)              (9, 6)   \n",
-                            "\n",
-                            "                     gebremariam2021evaluation gichuki2021bioefficacy  \\\n",
-                            "ITNCondition                            (4, 7)                (2, 11)   \n",
-                            "Observation                             (1, 7)                 (1, 6)   \n",
-                            "ClinicalOutcome                         (1, 3)                 (2, 7)   \n",
-                            "EntomologicalOutcome                    (2, 6)                 (1, 9)   \n",
-                            "\n",
-                            "                     syme2021which zahouli2023small diouf2022evaluation  \\\n",
-                            "ITNCondition                (1, 4)           (6, 3)              (5, 5)   \n",
-                            "Observation                 (1, 7)           (1, 7)              (2, 6)   \n",
-                            "ClinicalOutcome             (1, 3)           (1, 1)              (1, 7)   \n",
-                            "EntomologicalOutcome        (1, 3)           (6, 8)              (1, 1)   \n",
-                            "\n",
-                            "                     ibrahim2020exploring kibondo2022influence  \\\n",
-                            "ITNCondition                       (5, 5)               (3, 4)   \n",
-                            "Observation                        (1, 3)               (1, 5)   \n",
-                            "ClinicalOutcome                       NaN               (4, 4)   \n",
-                            "EntomologicalOutcome               (1, 7)               (4, 7)   \n",
-                            "\n",
-                            "                     meiwald2022association menze2022experimental  \\\n",
-                            "ITNCondition                         (3, 3)                (3, 6)   \n",
-                            "Observation                          (1, 7)                (2, 9)   \n",
-                            "ClinicalOutcome                         NaN                (3, 3)   \n",
-                            "EntomologicalOutcome                 (3, 5)                (1, 7)   \n",
-                            "\n",
-                            "                     syme2022pyrethroid toe2018do yewhalaw2022experimental  \\\n",
-                            "ITNCondition                     (3, 6)    (6, 6)                   (2, 5)   \n",
-                            "Observation                      (1, 7)    (2, 7)                   (1, 3)   \n",
-                            "ClinicalOutcome                  (3, 3)    (2, 1)                   (2, 3)   \n",
-                            "EntomologicalOutcome             (3, 9)    (1, 2)                   (2, 8)   \n",
-                            "\n",
-                            "                     ngufor2022comparative  \n",
-                            "ITNCondition                        (3, 5)  \n",
-                            "Observation                         (1, 3)  \n",
-                            "ClinicalOutcome                     (2, 3)  \n",
-                            "EntomologicalOutcome               (1, 10)  "
-                        ]
-                    },
-                    "execution_count": 22,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pd.DataFrame({ref: {k: v.dropna(axis=1, how='all').shape \\\n",
-                "                    for k,v in ext.items() if v.size} \\\n",
-                "              for ref, ext in pred_extractions.items()})"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "42d68ac2-0b76-4e4f-9b17-068fe62dc7c1",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>accrombessi2023efficacy</th>\n",
-                            "      <th>mieguim2021insights</th>\n",
-                            "      <th>gebremariam2021evaluation</th>\n",
-                            "      <th>gichuki2021bioefficacy</th>\n",
-                            "      <th>syme2021which</th>\n",
-                            "      <th>zahouli2023small</th>\n",
-                            "      <th>diouf2022evaluation</th>\n",
-                            "      <th>ibrahim2020exploring</th>\n",
-                            "      <th>kibondo2022influence</th>\n",
-                            "      <th>meiwald2022association</th>\n",
-                            "      <th>menze2022experimental</th>\n",
-                            "      <th>syme2022pyrethroid</th>\n",
-                            "      <th>toe2018do</th>\n",
-                            "      <th>yewhalaw2022experimental</th>\n",
-                            "      <th>ngufor2022comparative</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>ITNCondition</th>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(9, 5)</td>\n",
-                            "      <td>(4, 7)</td>\n",
-                            "      <td>(2, 11)</td>\n",
-                            "      <td>(1, 4)</td>\n",
-                            "      <td>(6, 3)</td>\n",
-                            "      <td>(5, 5)</td>\n",
-                            "      <td>(5, 5)</td>\n",
-                            "      <td>(3, 4)</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(3, 6)</td>\n",
-                            "      <td>(3, 6)</td>\n",
-                            "      <td>(6, 6)</td>\n",
-                            "      <td>(2, 5)</td>\n",
-                            "      <td>(3, 5)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Observation</th>\n",
-                            "      <td>(3, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 6)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 6)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 5)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 9)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(2, 7)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ClinicalOutcome</th>\n",
-                            "      <td>(3, 12)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(2, 7)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(1, 1)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>(4, 4)</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(3, 3)</td>\n",
-                            "      <td>(2, 1)</td>\n",
-                            "      <td>(2, 3)</td>\n",
-                            "      <td>(2, 3)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>EntomologicalOutcome</th>\n",
-                            "      <td>(1, 6)</td>\n",
-                            "      <td>(9, 6)</td>\n",
-                            "      <td>(2, 6)</td>\n",
-                            "      <td>(1, 9)</td>\n",
-                            "      <td>(1, 3)</td>\n",
-                            "      <td>(6, 8)</td>\n",
-                            "      <td>(1, 1)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(4, 7)</td>\n",
-                            "      <td>(3, 5)</td>\n",
-                            "      <td>(1, 7)</td>\n",
-                            "      <td>(3, 9)</td>\n",
-                            "      <td>(1, 2)</td>\n",
-                            "      <td>(2, 8)</td>\n",
-                            "      <td>(1, 10)</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                     accrombessi2023efficacy mieguim2021insights  \\\n",
-                            "ITNCondition                          (3, 3)              (9, 5)   \n",
-                            "Observation                           (3, 7)              (1, 7)   \n",
-                            "ClinicalOutcome                      (3, 12)              (1, 3)   \n",
-                            "EntomologicalOutcome                  (1, 6)              (9, 6)   \n",
-                            "\n",
-                            "                     gebremariam2021evaluation gichuki2021bioefficacy  \\\n",
-                            "ITNCondition                            (4, 7)                (2, 11)   \n",
-                            "Observation                             (1, 7)                 (1, 6)   \n",
-                            "ClinicalOutcome                         (1, 3)                 (2, 7)   \n",
-                            "EntomologicalOutcome                    (2, 6)                 (1, 9)   \n",
-                            "\n",
-                            "                     syme2021which zahouli2023small diouf2022evaluation  \\\n",
-                            "ITNCondition                (1, 4)           (6, 3)              (5, 5)   \n",
-                            "Observation                 (1, 7)           (1, 7)              (2, 6)   \n",
-                            "ClinicalOutcome             (1, 3)           (1, 1)              (1, 7)   \n",
-                            "EntomologicalOutcome        (1, 3)           (6, 8)              (1, 1)   \n",
-                            "\n",
-                            "                     ibrahim2020exploring kibondo2022influence  \\\n",
-                            "ITNCondition                       (5, 5)               (3, 4)   \n",
-                            "Observation                        (1, 3)               (1, 5)   \n",
-                            "ClinicalOutcome                       NaN               (4, 4)   \n",
-                            "EntomologicalOutcome               (1, 7)               (4, 7)   \n",
-                            "\n",
-                            "                     meiwald2022association menze2022experimental  \\\n",
-                            "ITNCondition                         (3, 3)                (3, 6)   \n",
-                            "Observation                          (1, 7)                (2, 9)   \n",
-                            "ClinicalOutcome                         NaN                (3, 3)   \n",
-                            "EntomologicalOutcome                 (3, 5)                (1, 7)   \n",
-                            "\n",
-                            "                     syme2022pyrethroid toe2018do yewhalaw2022experimental  \\\n",
-                            "ITNCondition                     (3, 6)    (6, 6)                   (2, 5)   \n",
-                            "Observation                      (1, 7)    (2, 7)                   (1, 3)   \n",
-                            "ClinicalOutcome                  (3, 3)    (2, 1)                   (2, 3)   \n",
-                            "EntomologicalOutcome             (3, 9)    (1, 2)                   (2, 8)   \n",
-                            "\n",
-                            "                     ngufor2022comparative  \n",
-                            "ITNCondition                        (3, 5)  \n",
-                            "Observation                         (1, 3)  \n",
-                            "ClinicalOutcome                     (2, 3)  \n",
-                            "EntomologicalOutcome               (1, 10)  "
-                        ]
-                    },
-                    "execution_count": 23,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pd.DataFrame({ref: {k: v.dropna(axis=1, how='all').shape \\\n",
-                "                    for k,v in ext.items() if v.size} \\\n",
-                "              for ref, ext in pred_extractions.items()})"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c7c36b44-4dd9-4e9e-b38c-1ceb2f37cd61",
-            "metadata": {
-                "jp-MarkdownHeadingCollapsed": true
-            },
-            "source": [
-                "# Evaluations"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f29f23f7-13d1-4f94-a2f0-edd00c099b7a",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[UserModel(id=UUID('6d8ee721-49b7-4d1f-bf4c-d6b0eb187c62'), first_name='Jonny', last_name='Tran', full_name='Jonny Tran', username='jonnytr', role=<UserRole.admin: 'admin'>, workspaces=['jonnytr', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing'], api_key='a14427ea-9197-11ec-b909-0242ac120002', inserted_at=datetime.datetime(2024, 1, 11, 1, 48, 44, 477722), updated_at=datetime.datetime(2024, 3, 22, 6, 56, 39, 809768)),\n",
-                            " UserModel(id=UUID('e96c9d89-36f4-4240-a592-9734087371d3'), first_name='Amelia', last_name='Bertozzi-Villa', full_name='Amelia Bertozzi-Villa', username='ameliabv', role=<UserRole.admin: 'admin'>, workspaces=['ameliabv', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing'], api_key='9df5c40e-b5ce-4d19-ac07-50381a5c79aa', inserted_at=datetime.datetime(2024, 1, 11, 1, 48, 44, 549157), updated_at=datetime.datetime(2024, 3, 22, 6, 56, 39, 815293)),\n",
-                            " UserModel(id=UUID('91b12ed4-2aff-46d6-9eb3-c15d44e00e2a'), first_name='Kate', last_name='Battle', full_name='Kate Battle', username='kateb', role=<UserRole.admin: 'admin'>, workspaces=['kateb', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing'], api_key='27fc2244-e9a0-4e24-adb2-6c85b25b05c2', inserted_at=datetime.datetime(2024, 1, 11, 1, 48, 44, 553978), updated_at=datetime.datetime(2024, 3, 22, 6, 56, 39, 820769))]"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "users = ex.Workspace.from_name('itn-recalibration').users\n",
-                "username_to_user = {u.username: u for u in users}\n",
-                "users"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "2b7c2daa-a13a-40f8-99d1-c7555c7e1ea2",
-            "metadata": {},
-            "source": [
-                "### LLM v.s. Gold-standard"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "609d3528-89a9-4e97-96bb-cb8e9b39187b",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "c4e2940893be4da38738edd2833e1235",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "0it [00:00, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "true_extractions = {}\n",
-                "for ref, paper in tqdm(papers.loc[references].iterrows()):\n",
-                "    true_extractions[paper.name] = get_paper_extractions(\n",
-                "        paper, extraction_dataset, field='extraction', answer='extraction-correction', schemas=ss, users=username_to_user['jonnytr'])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "ee696003-5b24-4a0b-9d75-3e861bdde993",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n"
-                    ]
-                },
-                {
-                    "data": {
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Net_age has different dtypes: float64 and object                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                    \u001b[0m         Column Net_age has different dtypes: float64 and object                    \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n"
-                    ]
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Start_month has different dtypes: int64 and float64                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                    \u001b[0m         Column Start_month has different dtypes: int64 and float64                 \u001b[2m           \u001b[0m\n"
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column End_month has different dtypes: int64 and float64                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=168465;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=668759;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#97\u001b\\\u001b[2m97\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column End_month has different dtypes: int64 and float64                   \u001b[2m           \u001b[0m\n"
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column End_year has different dtypes: int64 and float64                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                    \u001b[0m         Column End_year has different dtypes: int64 and float64                    \u001b[2m           \u001b[0m\n"
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Start_month has different dtypes: int64 and float64                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                    \u001b[0m         Column Start_month has different dtypes: int64 and float64                 \u001b[2m           \u001b[0m\n"
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column End_month has different dtypes: int64 and float64                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
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-                            "\u001b[2;36m                    \u001b[0m         Column End_month has different dtypes: int64 and float64                   \u001b[2m           \u001b[0m\n"
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-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column End_year has different dtypes: int64 and float64                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
-                            "</pre>\n"
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-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=349467;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=494108;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#97\u001b\\\u001b[2m97\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column End_year has different dtypes: int64 and float64                    \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
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-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "  return true_df, pred_df\n"
-                    ]
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-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row3_col2, #T_5d316_row7_col2, #T_5d316_row11_col2, #T_5d316_row12_col1, #T_5d316_row15_col2, #T_5d316_row19_col2, #T_5d316_row23_col2, #T_5d316_row27_col2, #T_5d316_row28_col1, #T_5d316_row31_col2, #T_5d316_row35_col2, #T_5d316_row39_col0, #T_5d316_row39_col1, #T_5d316_row39_col2, #T_5d316_row43_col0, #T_5d316_row43_col1, #T_5d316_row43_col2, #T_5d316_row44_col1, #T_5d316_row47_col2, #T_5d316_row48_col2, #T_5d316_row49_col2, #T_5d316_row50_col2, #T_5d316_row51_col2, #T_5d316_row52_col1, #T_5d316_row55_col2, #T_5d316_row59_col2, #T_5d316_row67_col2, #T_5d316_row68_col1, #T_5d316_row71_col2, #T_5d316_row72_col1, #T_5d316_row75_col2, #T_5d316_row76_col0, #T_5d316_row76_col1, #T_5d316_row76_col2, #T_5d316_row79_col2, #T_5d316_row80_col1, #T_5d316_row83_col2, #T_5d316_row87_col2, #T_5d316_row91_col2, #T_5d316_row94_col1, #T_5d316_row95_col2 {\n",
-                            "  background-color: #006837;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row4_col0 {\n",
-                            "  background-color: #c3e67d;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row4_col1 {\n",
-                            "  background-color: #98d368;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row4_col2, #T_5d316_row8_col0, #T_5d316_row10_col1 {\n",
-                            "  background-color: #e2f397;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row5_col0 {\n",
-                            "  background-color: #ddf191;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row5_col1 {\n",
-                            "  background-color: #a0d669;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row5_col2, #T_5d316_row12_col2, #T_5d316_row93_col0 {\n",
-                            "  background-color: #feffbe;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row6_col0 {\n",
-                            "  background-color: #c82227;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row6_col1, #T_5d316_row28_col2, #T_5d316_row94_col2 {\n",
-                            "  background-color: #e3f399;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row6_col2 {\n",
-                            "  background-color: #b71126;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row8_col1 {\n",
-                            "  background-color: #33a456;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row8_col2 {\n",
-                            "  background-color: #feea9b;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row9_col0 {\n",
-                            "  background-color: #f67f4b;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row9_col1, #T_5d316_row68_col0, #T_5d316_row80_col0 {\n",
-                            "  background-color: #66bd63;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row9_col2, #T_5d316_row61_col2, #T_5d316_row63_col0 {\n",
-                            "  background-color: #e14430;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row10_col2 {\n",
-                            "  background-color: #fba05b;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row12_col0, #T_5d316_row53_col1, #T_5d316_row68_col2, #T_5d316_row80_col2, #T_5d316_row88_col1 {\n",
-                            "  background-color: #b7e075;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row13_col0, #T_5d316_row46_col2, #T_5d316_row58_col0 {\n",
-                            "  background-color: #d22b27;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row13_col1 {\n",
-                            "  background-color: #fdc171;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row13_col2, #T_5d316_row58_col2 {\n",
-                            "  background-color: #be1827;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row14_col0, #T_5d316_row74_col2 {\n",
-                            "  background-color: #f67a49;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row14_col1, #T_5d316_row72_col2, #T_5d316_row78_col1 {\n",
-                            "  background-color: #e9f6a1;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row14_col2, #T_5d316_row32_col2 {\n",
-                            "  background-color: #e24731;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row16_col0, #T_5d316_row74_col1 {\n",
-                            "  background-color: #dcf08f;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row16_col1 {\n",
-                            "  background-color: #04703b;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row16_col2 {\n",
-                            "  background-color: #fee695;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row17_col0 {\n",
-                            "  background-color: #fa9b58;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row17_col1, #T_5d316_row40_col1 {\n",
-                            "  background-color: #0d8044;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row17_col2, #T_5d316_row41_col0 {\n",
-                            "  background-color: #e75337;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row18_col0, #T_5d316_row21_col2 {\n",
-                            "  background-color: #f57245;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row18_col1 {\n",
-                            "  background-color: #fed884;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row18_col2 {\n",
-                            "  background-color: #e34933;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row20_col0 {\n",
-                            "  background-color: #feeda1;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row20_col1 {\n",
-                            "  background-color: #fdfebc;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row20_col2 {\n",
-                            "  background-color: #fedc88;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row21_col0, #T_5d316_row29_col0 {\n",
-                            "  background-color: #fdc372;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row21_col1, #T_5d316_row84_col1 {\n",
-                            "  background-color: #128a49;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row22_col1, #T_5d316_row37_col1, #T_5d316_row73_col0 {\n",
-                            "  background-color: #fffbb8;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row22_col2, #T_5d316_row26_col2 {\n",
-                            "  background-color: #b10b26;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row24_col0 {\n",
-                            "  background-color: #dc3b2c;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row24_col1 {\n",
-                            "  background-color: #6ec064;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row24_col2, #T_5d316_row86_col0 {\n",
-                            "  background-color: #c41e27;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row25_col0, #T_5d316_row33_col0, #T_5d316_row56_col2 {\n",
-                            "  background-color: #fff6b0;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row25_col1 {\n",
-                            "  background-color: #f2faae;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row25_col2 {\n",
-                            "  background-color: #fee797;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row26_col1, #T_5d316_row30_col1 {\n",
-                            "  background-color: #9bd469;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row28_col0, #T_5d316_row92_col2, #T_5d316_row94_col0 {\n",
-                            "  background-color: #93d168;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row29_col1 {\n",
-                            "  background-color: #daf08d;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row29_col2, #T_5d316_row32_col0, #T_5d316_row38_col0 {\n",
-                            "  background-color: #f8864f;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row30_col0 {\n",
-                            "  background-color: #fdb163;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row30_col2, #T_5d316_row85_col0 {\n",
-                            "  background-color: #f36b42;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row32_col1, #T_5d316_row57_col1, #T_5d316_row81_col1 {\n",
-                            "  background-color: #0a7b41;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row33_col1 {\n",
-                            "  background-color: #fee491;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row33_col2 {\n",
-                            "  background-color: #ecf7a6;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row34_col0 {\n",
-                            "  background-color: #fee18d;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row34_col1 {\n",
-                            "  background-color: #fffab6;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row34_col2, #T_5d316_row93_col2 {\n",
-                            "  background-color: #fdc574;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row36_col0 {\n",
-                            "  background-color: #feec9f;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row36_col1, #T_5d316_row92_col1 {\n",
-                            "  background-color: #fdc776;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row36_col2, #T_5d316_row60_col2 {\n",
-                            "  background-color: #dff293;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row37_col0 {\n",
-                            "  background-color: #fdbb6c;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row37_col2 {\n",
-                            "  background-color: #f88950;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row38_col2, #T_5d316_row41_col1, #T_5d316_row42_col0 {\n",
-                            "  background-color: #ec5c3b;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row40_col0 {\n",
-                            "  background-color: #eff8aa;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row41_col2 {\n",
-                            "  background-color: #e44c34;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row42_col1 {\n",
-                            "  background-color: #fff0a6;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row42_col2 {\n",
-                            "  background-color: #d93429;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row44_col0 {\n",
-                            "  background-color: #87cb67;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row44_col2, #T_5d316_row64_col0, #T_5d316_row69_col0 {\n",
-                            "  background-color: #d9ef8b;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row45_col0 {\n",
-                            "  background-color: #54b45f;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row45_col1 {\n",
-                            "  background-color: #279f53;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row45_col2 {\n",
-                            "  background-color: #78c565;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row46_col0 {\n",
-                            "  background-color: #e65036;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row46_col1, #T_5d316_row69_col2 {\n",
-                            "  background-color: #f1f9ac;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row52_col0, #T_5d316_row84_col0 {\n",
-                            "  background-color: #39a758;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row52_col2 {\n",
-                            "  background-color: #84ca66;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row53_col0 {\n",
-                            "  background-color: #f16640;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row53_col2 {\n",
-                            "  background-color: #da362a;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row54_col0 {\n",
-                            "  background-color: #d02927;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row54_col1, #T_5d316_row77_col0 {\n",
-                            "  background-color: #fec877;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row56_col0 {\n",
-                            "  background-color: #c9e881;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row56_col1 {\n",
-                            "  background-color: #0e8245;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row57_col0, #T_5d316_row86_col1 {\n",
-                            "  background-color: #bde379;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row57_col2 {\n",
-                            "  background-color: #fffebe;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row58_col1 {\n",
-                            "  background-color: #fff2aa;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row60_col0 {\n",
-                            "  background-color: #a5d86a;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row60_col1 {\n",
-                            "  background-color: #2aa054;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row61_col0 {\n",
-                            "  background-color: #f57748;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row61_col1 {\n",
-                            "  background-color: #e8f59f;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row63_col1 {\n",
-                            "  background-color: #d42d27;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row63_col2, #T_5d316_row82_col0 {\n",
-                            "  background-color: #f67c4a;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row64_col1 {\n",
-                            "  background-color: #a7d96b;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row64_col2 {\n",
-                            "  background-color: #f5fbb2;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row65_col0 {\n",
-                            "  background-color: #d3ec87;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row65_col1 {\n",
-                            "  background-color: #5ab760;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row65_col2 {\n",
-                            "  background-color: #fffcba;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row66_col0 {\n",
-                            "  background-color: #d62f27;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row66_col1, #T_5d316_row73_col2 {\n",
-                            "  background-color: #feda86;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row66_col2 {\n",
-                            "  background-color: #c01a27;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row69_col1 {\n",
-                            "  background-color: #b3df72;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row70_col0 {\n",
-                            "  background-color: #eef8a8;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row70_col1 {\n",
-                            "  background-color: #73c264;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row70_col2 {\n",
-                            "  background-color: #fee593;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row72_col0 {\n",
-                            "  background-color: #9dd569;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row73_col1 {\n",
-                            "  background-color: #c1e57b;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row74_col0 {\n",
-                            "  background-color: #fdb96a;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row77_col1 {\n",
-                            "  background-color: #f88c51;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row77_col2 {\n",
-                            "  background-color: #cfeb85;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row78_col0 {\n",
-                            "  background-color: #fafdb8;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row78_col2, #T_5d316_row92_col0 {\n",
-                            "  background-color: #fff7b2;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row81_col0, #T_5d316_row90_col1 {\n",
-                            "  background-color: #7ac665;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row81_col2 {\n",
-                            "  background-color: #c5e67e;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row82_col1 {\n",
-                            "  background-color: #fca55d;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row82_col2 {\n",
-                            "  background-color: #ef633f;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row84_col2 {\n",
-                            "  background-color: #69be63;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row85_col1 {\n",
-                            "  background-color: #7fc866;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row85_col2 {\n",
-                            "  background-color: #db382b;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row86_col2 {\n",
-                            "  background-color: #b50f26;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "#T_5d316_row88_col0 {\n",
-                            "  background-color: #8ccd67;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row89_col0 {\n",
-                            "  background-color: #c7e77f;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row89_col1 {\n",
-                            "  background-color: #6bbf64;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row89_col2 {\n",
-                            "  background-color: #f4fab0;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row90_col0 {\n",
-                            "  background-color: #d1ec86;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row90_col2 {\n",
-                            "  background-color: #fbfdba;\n",
-                            "  color: #000000;\n",
-                            "}\n",
-                            "#T_5d316_row93_col1 {\n",
-                            "  background-color: #15904c;\n",
-                            "  color: #f1f1f1;\n",
-                            "}\n",
-                            "</style>\n",
-                            "<table id=\"T_5d316\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank\" >&nbsp;</th>\n",
-                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
-                            "      <th id=\"T_5d316_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">grits</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank\" >&nbsp;</th>\n",
-                            "      <th class=\"blank level1\" >&nbsp;</th>\n",
-                            "      <th id=\"T_5d316_level1_col0\" class=\"col_heading level1 col0\" colspan=\"3\">con</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank\" >&nbsp;</th>\n",
-                            "      <th class=\"blank level2\" >&nbsp;</th>\n",
-                            "      <th id=\"T_5d316_level2_col0\" class=\"col_heading level2 col0\" >f1</th>\n",
-                            "      <th id=\"T_5d316_level2_col1\" class=\"col_heading level2 col1\" >precision</th>\n",
-                            "      <th id=\"T_5d316_level2_col2\" class=\"col_heading level2 col2\" >recall</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"index_name level0\" >Reference</th>\n",
-                            "      <th class=\"index_name level1\" >Schema</th>\n",
-                            "      <th class=\"blank col0\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col1\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col2\" >&nbsp;</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"4\">padonou2012decreased</th>\n",
-                            "      <th id=\"T_5d316_level1_row0\" class=\"row_heading level1 row0\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row0_col0\" class=\"data row0 col0\" >0.756303</td>\n",
-                            "      <td id=\"T_5d316_row0_col1\" class=\"data row0 col1\" >0.692308</td>\n",
-                            "      <td id=\"T_5d316_row0_col2\" class=\"data row0 col2\" >0.833333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row1\" class=\"row_heading level1 row1\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row1_col0\" class=\"data row1 col0\" >0.083380</td>\n",
-                            "      <td id=\"T_5d316_row1_col1\" class=\"data row1 col1\" >0.046901</td>\n",
-                            "      <td id=\"T_5d316_row1_col2\" class=\"data row1 col2\" >0.375210</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row2\" class=\"row_heading level1 row2\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row2_col0\" class=\"data row2 col0\" >0.396072</td>\n",
-                            "      <td id=\"T_5d316_row2_col1\" class=\"data row2 col1\" >0.435679</td>\n",
-                            "      <td id=\"T_5d316_row2_col2\" class=\"data row2 col2\" >0.363066</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row3\" class=\"row_heading level1 row3\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row3_col1\" class=\"data row3 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row4\" class=\"row_heading level0 row4\" rowspan=\"4\">randriamaherijaona2015do</th>\n",
-                            "      <th id=\"T_5d316_level1_row4\" class=\"row_heading level1 row4\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row4_col0\" class=\"data row4 col0\" >0.642210</td>\n",
-                            "      <td id=\"T_5d316_row4_col1\" class=\"data row4 col1\" >0.722487</td>\n",
-                            "      <td id=\"T_5d316_row4_col2\" class=\"data row4 col2\" >0.577989</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row5\" class=\"row_heading level1 row5\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row5_col0\" class=\"data row5 col0\" >0.588205</td>\n",
-                            "      <td id=\"T_5d316_row5_col1\" class=\"data row5 col1\" >0.710748</td>\n",
-                            "      <td id=\"T_5d316_row5_col2\" class=\"data row5 col2\" >0.501704</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row6\" class=\"row_heading level1 row6\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row6_col0\" class=\"data row6 col0\" >0.071835</td>\n",
-                            "      <td id=\"T_5d316_row6_col1\" class=\"data row6 col1\" >0.570687</td>\n",
-                            "      <td id=\"T_5d316_row6_col2\" class=\"data row6 col2\" >0.038330</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row7\" class=\"row_heading level1 row7\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row7_col2\" class=\"data row7 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row8\" class=\"row_heading level0 row8\" rowspan=\"4\">quinones1998permethrin</th>\n",
-                            "      <th id=\"T_5d316_level1_row8\" class=\"row_heading level1 row8\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row8_col0\" class=\"data row8 col0\" >0.577778</td>\n",
-                            "      <td id=\"T_5d316_row8_col1\" class=\"data row8 col1\" >0.866667</td>\n",
-                            "      <td id=\"T_5d316_row8_col2\" class=\"data row8 col2\" >0.433333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row9\" class=\"row_heading level1 row9\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row9_col0\" class=\"data row9 col0\" >0.228571</td>\n",
-                            "      <td id=\"T_5d316_row9_col1\" class=\"data row9 col1\" >0.800000</td>\n",
-                            "      <td id=\"T_5d316_row9_col2\" class=\"data row9 col2\" >0.133333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row10\" class=\"row_heading level1 row10\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row10_col0\" class=\"data row10 col0\" >0.377521</td>\n",
-                            "      <td id=\"T_5d316_row10_col1\" class=\"data row10 col1\" >0.577068</td>\n",
-                            "      <td id=\"T_5d316_row10_col2\" class=\"data row10 col2\" >0.280519</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row11\" class=\"row_heading level1 row11\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row11_col0\" class=\"data row11 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row11_col1\" class=\"data row11 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row11_col2\" class=\"data row11 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"4\">kilian2008long</th>\n",
-                            "      <th id=\"T_5d316_level1_row12\" class=\"row_heading level1 row12\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row12_col0\" class=\"data row12 col0\" >0.666667</td>\n",
-                            "      <td id=\"T_5d316_row12_col1\" class=\"data row12 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row12_col2\" class=\"data row12 col2\" >0.500000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row13\" class=\"row_heading level1 row13\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row13_col0\" class=\"data row13 col0\" >0.093128</td>\n",
-                            "      <td id=\"T_5d316_row13_col1\" class=\"data row13 col1\" >0.336296</td>\n",
-                            "      <td id=\"T_5d316_row13_col2\" class=\"data row13 col2\" >0.054048</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row14\" class=\"row_heading level1 row14\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row14_col0\" class=\"data row14 col0\" >0.222399</td>\n",
-                            "      <td id=\"T_5d316_row14_col1\" class=\"data row14 col1\" >0.555997</td>\n",
-                            "      <td id=\"T_5d316_row14_col2\" class=\"data row14 col2\" >0.138999</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row15\" class=\"row_heading level1 row15\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row15_col0\" class=\"data row15 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row15_col1\" class=\"data row15 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row15_col2\" class=\"data row15 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row16\" class=\"row_heading level0 row16\" rowspan=\"4\">mosqueira2015pilot</th>\n",
-                            "      <th id=\"T_5d316_level1_row16\" class=\"row_heading level1 row16\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row16_col0\" class=\"data row16 col0\" >0.590000</td>\n",
-                            "      <td id=\"T_5d316_row16_col1\" class=\"data row16 col1\" >0.983333</td>\n",
-                            "      <td id=\"T_5d316_row16_col2\" class=\"data row16 col2\" >0.421429</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row17\" class=\"row_heading level1 row17\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row17_col0\" class=\"data row17 col0\" >0.270542</td>\n",
-                            "      <td id=\"T_5d316_row17_col1\" class=\"data row17 col1\" >0.946895</td>\n",
-                            "      <td id=\"T_5d316_row17_col2\" class=\"data row17 col2\" >0.157816</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row18\" class=\"row_heading level1 row18\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row18_col0\" class=\"data row18 col0\" >0.208057</td>\n",
-                            "      <td id=\"T_5d316_row18_col1\" class=\"data row18 col1\" >0.384905</td>\n",
-                            "      <td id=\"T_5d316_row18_col2\" class=\"data row18 col2\" >0.142557</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row19\" class=\"row_heading level1 row19\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row19_col0\" class=\"data row19 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row19_col1\" class=\"data row19 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row19_col2\" class=\"data row19 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row20\" class=\"row_heading level0 row20\" rowspan=\"4\">azizi2022implementing</th>\n",
-                            "      <th id=\"T_5d316_level1_row20\" class=\"row_heading level1 row20\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row20_col0\" class=\"data row20 col0\" >0.443452</td>\n",
-                            "      <td id=\"T_5d316_row20_col1\" class=\"data row20 col1\" >0.506803</td>\n",
-                            "      <td id=\"T_5d316_row20_col2\" class=\"data row20 col2\" >0.394180</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row21\" class=\"row_heading level1 row21\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row21_col0\" class=\"data row21 col0\" >0.340708</td>\n",
-                            "      <td id=\"T_5d316_row21_col1\" class=\"data row21 col1\" >0.927483</td>\n",
-                            "      <td id=\"T_5d316_row21_col2\" class=\"data row21 col2\" >0.208684</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row22\" class=\"row_heading level1 row22\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row22_col0\" class=\"data row22 col0\" >0.048724</td>\n",
-                            "      <td id=\"T_5d316_row22_col1\" class=\"data row22 col1\" >0.487241</td>\n",
-                            "      <td id=\"T_5d316_row22_col2\" class=\"data row22 col2\" >0.025644</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row23\" class=\"row_heading level1 row23\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row23_col0\" class=\"data row23 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row23_col1\" class=\"data row23 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row23_col2\" class=\"data row23 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row24\" class=\"row_heading level0 row24\" rowspan=\"4\">magesa1991trial</th>\n",
-                            "      <th id=\"T_5d316_level1_row24\" class=\"row_heading level1 row24\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row24_col0\" class=\"data row24 col0\" >0.117396</td>\n",
-                            "      <td id=\"T_5d316_row24_col1\" class=\"data row24 col1\" >0.786553</td>\n",
-                            "      <td id=\"T_5d316_row24_col2\" class=\"data row24 col2\" >0.063432</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row25\" class=\"row_heading level1 row25\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row25_col0\" class=\"data row25 col0\" >0.472308</td>\n",
-                            "      <td id=\"T_5d316_row25_col1\" class=\"data row25 col1\" >0.531347</td>\n",
-                            "      <td id=\"T_5d316_row25_col2\" class=\"data row25 col2\" >0.425077</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row26\" class=\"row_heading level1 row26\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row26_col0\" class=\"data row26 col0\" >0.047690</td>\n",
-                            "      <td id=\"T_5d316_row26_col1\" class=\"data row26 col1\" >0.715353</td>\n",
-                            "      <td id=\"T_5d316_row26_col2\" class=\"data row26 col2\" >0.024667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row27\" class=\"row_heading level1 row27\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row27_col0\" class=\"data row27 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row27_col1\" class=\"data row27 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row27_col2\" class=\"data row27 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row28\" class=\"row_heading level0 row28\" rowspan=\"4\">clegban2021evaluation</th>\n",
-                            "      <th id=\"T_5d316_level1_row28\" class=\"row_heading level1 row28\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row28_col0\" class=\"data row28 col0\" >0.727273</td>\n",
-                            "      <td id=\"T_5d316_row28_col1\" class=\"data row28 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row28_col2\" class=\"data row28 col2\" >0.571429</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row29\" class=\"row_heading level1 row29\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row29_col0\" class=\"data row29 col0\" >0.340793</td>\n",
-                            "      <td id=\"T_5d316_row29_col1\" class=\"data row29 col1\" >0.596387</td>\n",
-                            "      <td id=\"T_5d316_row29_col2\" class=\"data row29 col2\" >0.238555</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row30\" class=\"row_heading level1 row30\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row30_col0\" class=\"data row30 col0\" >0.307667</td>\n",
-                            "      <td id=\"T_5d316_row30_col1\" class=\"data row30 col1\" >0.717891</td>\n",
-                            "      <td id=\"T_5d316_row30_col2\" class=\"data row30 col2\" >0.195788</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row31\" class=\"row_heading level1 row31\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row31_col0\" class=\"data row31 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row31_col1\" class=\"data row31 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row31_col2\" class=\"data row31 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row32\" class=\"row_heading level0 row32\" rowspan=\"4\">robert1991influence</th>\n",
-                            "      <th id=\"T_5d316_level1_row32\" class=\"row_heading level1 row32\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row32_col0\" class=\"data row32 col0\" >0.239583</td>\n",
-                            "      <td id=\"T_5d316_row32_col1\" class=\"data row32 col1\" >0.958333</td>\n",
-                            "      <td id=\"T_5d316_row32_col2\" class=\"data row32 col2\" >0.136905</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row33\" class=\"row_heading level1 row33\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row33_col0\" class=\"data row33 col0\" >0.469655</td>\n",
-                            "      <td id=\"T_5d316_row33_col1\" class=\"data row33 col1\" >0.410948</td>\n",
-                            "      <td id=\"T_5d316_row33_col2\" class=\"data row33 col2\" >0.547930</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row34\" class=\"row_heading level1 row34\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row34_col0\" class=\"data row34 col0\" >0.403457</td>\n",
-                            "      <td id=\"T_5d316_row34_col1\" class=\"data row34 col1\" >0.482707</td>\n",
-                            "      <td id=\"T_5d316_row34_col2\" class=\"data row34 col2\" >0.346559</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row35\" class=\"row_heading level1 row35\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row35_col0\" class=\"data row35 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row35_col1\" class=\"data row35 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row35_col2\" class=\"data row35 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row36\" class=\"row_heading level0 row36\" rowspan=\"4\">pwalia2019high</th>\n",
-                            "      <th id=\"T_5d316_level1_row36\" class=\"row_heading level1 row36\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row36_col0\" class=\"data row36 col0\" >0.437500</td>\n",
-                            "      <td id=\"T_5d316_row36_col1\" class=\"data row36 col1\" >0.350000</td>\n",
-                            "      <td id=\"T_5d316_row36_col2\" class=\"data row36 col2\" >0.583333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row37\" class=\"row_heading level1 row37\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row37_col0\" class=\"data row37 col0\" >0.325086</td>\n",
-                            "      <td id=\"T_5d316_row37_col1\" class=\"data row37 col1\" >0.487629</td>\n",
-                            "      <td id=\"T_5d316_row37_col2\" class=\"data row37 col2\" >0.243815</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row38\" class=\"row_heading level1 row38\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row38_col0\" class=\"data row38 col0\" >0.241466</td>\n",
-                            "      <td id=\"T_5d316_row38_col1\" class=\"data row38 col1\" >0.397412</td>\n",
-                            "      <td id=\"T_5d316_row38_col2\" class=\"data row38 col2\" >0.173416</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row39\" class=\"row_heading level1 row39\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row39_col0\" class=\"data row39 col0\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row39_col1\" class=\"data row39 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row39_col2\" class=\"data row39 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row40\" class=\"row_heading level0 row40\" rowspan=\"4\">mnzava2001malaria</th>\n",
-                            "      <th id=\"T_5d316_level1_row40\" class=\"row_heading level1 row40\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row40_col0\" class=\"data row40 col0\" >0.541171</td>\n",
-                            "      <td id=\"T_5d316_row40_col1\" class=\"data row40 col1\" >0.947049</td>\n",
-                            "      <td id=\"T_5d316_row40_col2\" class=\"data row40 col2\" >0.378819</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row41\" class=\"row_heading level1 row41\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row41_col0\" class=\"data row41 col0\" >0.158601</td>\n",
-                            "      <td id=\"T_5d316_row41_col1\" class=\"data row41 col1\" >0.174461</td>\n",
-                            "      <td id=\"T_5d316_row41_col2\" class=\"data row41 col2\" >0.145384</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row42\" class=\"row_heading level1 row42\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row42_col0\" class=\"data row42 col0\" >0.172444</td>\n",
-                            "      <td id=\"T_5d316_row42_col1\" class=\"data row42 col1\" >0.452667</td>\n",
-                            "      <td id=\"T_5d316_row42_col2\" class=\"data row42 col2\" >0.106510</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row43\" class=\"row_heading level1 row43\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row43_col0\" class=\"data row43 col0\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row43_col1\" class=\"data row43 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row43_col2\" class=\"data row43 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row44\" class=\"row_heading level0 row44\" rowspan=\"4\">kitau2012species</th>\n",
-                            "      <th id=\"T_5d316_level1_row44\" class=\"row_heading level1 row44\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row44_col0\" class=\"data row44 col0\" >0.750000</td>\n",
-                            "      <td id=\"T_5d316_row44_col1\" class=\"data row44 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row44_col2\" class=\"data row44 col2\" >0.600000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row45\" class=\"row_heading level1 row45\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row45_col0\" class=\"data row45 col0\" >0.823427</td>\n",
-                            "      <td id=\"T_5d316_row45_col1\" class=\"data row45 col1\" >0.882243</td>\n",
-                            "      <td id=\"T_5d316_row45_col2\" class=\"data row45 col2\" >0.771963</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row46\" class=\"row_heading level1 row46\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row46_col0\" class=\"data row46 col0\" >0.154216</td>\n",
-                            "      <td id=\"T_5d316_row46_col1\" class=\"data row46 col1\" >0.536790</td>\n",
-                            "      <td id=\"T_5d316_row46_col2\" class=\"data row46 col2\" >0.090042</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row47\" class=\"row_heading level1 row47\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row47_col0\" class=\"data row47 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row47_col1\" class=\"data row47 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row47_col2\" class=\"data row47 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row48\" class=\"row_heading level0 row48\" rowspan=\"4\">apetogbo2022insecticide</th>\n",
-                            "      <th id=\"T_5d316_level1_row48\" class=\"row_heading level1 row48\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row48_col0\" class=\"data row48 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row48_col1\" class=\"data row48 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row48_col2\" class=\"data row48 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row49\" class=\"row_heading level1 row49\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row49_col0\" class=\"data row49 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row49_col1\" class=\"data row49 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row49_col2\" class=\"data row49 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row50\" class=\"row_heading level1 row50\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row50_col0\" class=\"data row50 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row50_col1\" class=\"data row50 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row50_col2\" class=\"data row50 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row51\" class=\"row_heading level1 row51\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row51_col0\" class=\"data row51 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row51_col1\" class=\"data row51 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row51_col2\" class=\"data row51 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row52\" class=\"row_heading level0 row52\" rowspan=\"4\">hougard2003efficacy</th>\n",
-                            "      <th id=\"T_5d316_level1_row52\" class=\"row_heading level1 row52\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row52_col0\" class=\"data row52 col0\" >0.857143</td>\n",
-                            "      <td id=\"T_5d316_row52_col1\" class=\"data row52 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row52_col2\" class=\"data row52 col2\" >0.750000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row53\" class=\"row_heading level1 row53\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row53_col0\" class=\"data row53 col0\" >0.190358</td>\n",
-                            "      <td id=\"T_5d316_row53_col1\" class=\"data row53 col1\" >0.666255</td>\n",
-                            "      <td id=\"T_5d316_row53_col2\" class=\"data row53 col2\" >0.111042</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row54\" class=\"row_heading level1 row54\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row54_col0\" class=\"data row54 col0\" >0.088665</td>\n",
-                            "      <td id=\"T_5d316_row54_col1\" class=\"data row54 col1\" >0.354659</td>\n",
-                            "      <td id=\"T_5d316_row54_col2\" class=\"data row54 col2\" >0.050666</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row55\" class=\"row_heading level1 row55\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row55_col0\" class=\"data row55 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row55_col1\" class=\"data row55 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row55_col2\" class=\"data row55 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row56\" class=\"row_heading level0 row56\" rowspan=\"4\">ketoh2018efficacy</th>\n",
-                            "      <th id=\"T_5d316_level1_row56\" class=\"row_heading level1 row56\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row56_col0\" class=\"data row56 col0\" >0.629630</td>\n",
-                            "      <td id=\"T_5d316_row56_col1\" class=\"data row56 col1\" >0.944444</td>\n",
-                            "      <td id=\"T_5d316_row56_col2\" class=\"data row56 col2\" >0.472222</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row57\" class=\"row_heading level1 row57\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row57_col0\" class=\"data row57 col0\" >0.656152</td>\n",
-                            "      <td id=\"T_5d316_row57_col1\" class=\"data row57 col1\" >0.960794</td>\n",
-                            "      <td id=\"T_5d316_row57_col2\" class=\"data row57 col2\" >0.498189</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row58\" class=\"row_heading level1 row58\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row58_col0\" class=\"data row58 col0\" >0.092387</td>\n",
-                            "      <td id=\"T_5d316_row58_col1\" class=\"data row58 col1\" >0.458636</td>\n",
-                            "      <td id=\"T_5d316_row58_col2\" class=\"data row58 col2\" >0.051367</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row59\" class=\"row_heading level1 row59\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row59_col0\" class=\"data row59 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row59_col1\" class=\"data row59 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row59_col2\" class=\"data row59 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row60\" class=\"row_heading level0 row60\" rowspan=\"4\">kilian2011evidence</th>\n",
-                            "      <th id=\"T_5d316_level1_row60\" class=\"row_heading level1 row60\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row60_col0\" class=\"data row60 col0\" >0.700000</td>\n",
-                            "      <td id=\"T_5d316_row60_col1\" class=\"data row60 col1\" >0.875000</td>\n",
-                            "      <td id=\"T_5d316_row60_col2\" class=\"data row60 col2\" >0.583333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row61\" class=\"row_heading level1 row61\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row61_col0\" class=\"data row61 col0\" >0.215940</td>\n",
-                            "      <td id=\"T_5d316_row61_col1\" class=\"data row61 col1\" >0.561445</td>\n",
-                            "      <td id=\"T_5d316_row61_col2\" class=\"data row61 col2\" >0.133677</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row62\" class=\"row_heading level1 row62\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row62_col0\" class=\"data row62 col0\" >0.835558</td>\n",
-                            "      <td id=\"T_5d316_row62_col1\" class=\"data row62 col1\" >0.835558</td>\n",
-                            "      <td id=\"T_5d316_row62_col2\" class=\"data row62 col2\" >0.835558</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row63\" class=\"row_heading level1 row63\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row63_col0\" class=\"data row63 col0\" >0.135656</td>\n",
-                            "      <td id=\"T_5d316_row63_col1\" class=\"data row63 col1\" >0.096897</td>\n",
-                            "      <td id=\"T_5d316_row63_col2\" class=\"data row63 col2\" >0.226093</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row64\" class=\"row_heading level0 row64\" rowspan=\"4\">menze2020experimental</th>\n",
-                            "      <th id=\"T_5d316_level1_row64\" class=\"row_heading level1 row64\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row64_col0\" class=\"data row64 col0\" >0.598639</td>\n",
-                            "      <td id=\"T_5d316_row64_col1\" class=\"data row64 col1\" >0.698413</td>\n",
-                            "      <td id=\"T_5d316_row64_col2\" class=\"data row64 col2\" >0.523810</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row65\" class=\"row_heading level1 row65\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row65_col0\" class=\"data row65 col0\" >0.610860</td>\n",
-                            "      <td id=\"T_5d316_row65_col1\" class=\"data row65 col1\" >0.814480</td>\n",
-                            "      <td id=\"T_5d316_row65_col2\" class=\"data row65 col2\" >0.488688</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row66\" class=\"row_heading level1 row66\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row66_col0\" class=\"data row66 col0\" >0.098766</td>\n",
-                            "      <td id=\"T_5d316_row66_col1\" class=\"data row66 col1\" >0.389185</td>\n",
-                            "      <td id=\"T_5d316_row66_col2\" class=\"data row66 col2\" >0.056560</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row67\" class=\"row_heading level1 row67\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row67_col0\" class=\"data row67 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row67_col1\" class=\"data row67 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row67_col2\" class=\"data row67 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row68\" class=\"row_heading level0 row68\" rowspan=\"4\">kawada2014small</th>\n",
-                            "      <th id=\"T_5d316_level1_row68\" class=\"row_heading level1 row68\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row68_col0\" class=\"data row68 col0\" >0.800000</td>\n",
-                            "      <td id=\"T_5d316_row68_col1\" class=\"data row68 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row68_col2\" class=\"data row68 col2\" >0.666667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row69\" class=\"row_heading level1 row69\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row69_col0\" class=\"data row69 col0\" >0.598516</td>\n",
-                            "      <td id=\"T_5d316_row69_col1\" class=\"data row69 col1\" >0.673330</td>\n",
-                            "      <td id=\"T_5d316_row69_col2\" class=\"data row69 col2\" >0.538664</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row70\" class=\"row_heading level1 row70\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row70_col0\" class=\"data row70 col0\" >0.543992</td>\n",
-                            "      <td id=\"T_5d316_row70_col1\" class=\"data row70 col1\" >0.779722</td>\n",
-                            "      <td id=\"T_5d316_row70_col2\" class=\"data row70 col2\" >0.417708</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row71\" class=\"row_heading level1 row71\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row71_col0\" class=\"data row71 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row71_col1\" class=\"data row71 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row71_col2\" class=\"data row71 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row72\" class=\"row_heading level0 row72\" rowspan=\"4\">mbogo1996impact</th>\n",
-                            "      <th id=\"T_5d316_level1_row72\" class=\"row_heading level1 row72\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row72_col0\" class=\"data row72 col0\" >0.714286</td>\n",
-                            "      <td id=\"T_5d316_row72_col1\" class=\"data row72 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row72_col2\" class=\"data row72 col2\" >0.555556</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row73\" class=\"row_heading level1 row73\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row73_col0\" class=\"data row73 col0\" >0.484551</td>\n",
-                            "      <td id=\"T_5d316_row73_col1\" class=\"data row73 col1\" >0.646067</td>\n",
-                            "      <td id=\"T_5d316_row73_col2\" class=\"data row73 col2\" >0.387640</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row74\" class=\"row_heading level1 row74\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row74_col0\" class=\"data row74 col0\" >0.321995</td>\n",
-                            "      <td id=\"T_5d316_row74_col1\" class=\"data row74 col1\" >0.590324</td>\n",
-                            "      <td id=\"T_5d316_row74_col2\" class=\"data row74 col2\" >0.221372</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row75\" class=\"row_heading level1 row75\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row75_col0\" class=\"data row75 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row75_col1\" class=\"data row75 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row75_col2\" class=\"data row75 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row76\" class=\"row_heading level0 row76\" rowspan=\"4\">darriet2005pyrethoid</th>\n",
-                            "      <th id=\"T_5d316_level1_row76\" class=\"row_heading level1 row76\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row76_col0\" class=\"data row76 col0\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row76_col1\" class=\"data row76 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row76_col2\" class=\"data row76 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row77\" class=\"row_heading level1 row77\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row77_col0\" class=\"data row77 col0\" >0.354495</td>\n",
-                            "      <td id=\"T_5d316_row77_col1\" class=\"data row77 col1\" >0.248146</td>\n",
-                            "      <td id=\"T_5d316_row77_col2\" class=\"data row77 col2\" >0.620366</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row78\" class=\"row_heading level1 row78\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row78_col0\" class=\"data row78 col0\" >0.512755</td>\n",
-                            "      <td id=\"T_5d316_row78_col1\" class=\"data row78 col1\" >0.555485</td>\n",
-                            "      <td id=\"T_5d316_row78_col2\" class=\"data row78 col2\" >0.476130</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row79\" class=\"row_heading level1 row79\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row79_col0\" class=\"data row79 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row79_col1\" class=\"data row79 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row79_col2\" class=\"data row79 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row80\" class=\"row_heading level0 row80\" rowspan=\"4\">bamou2021increased</th>\n",
-                            "      <th id=\"T_5d316_level1_row80\" class=\"row_heading level1 row80\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row80_col0\" class=\"data row80 col0\" >0.800000</td>\n",
-                            "      <td id=\"T_5d316_row80_col1\" class=\"data row80 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row80_col2\" class=\"data row80 col2\" >0.666667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row81\" class=\"row_heading level1 row81\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row81_col0\" class=\"data row81 col0\" >0.768137</td>\n",
-                            "      <td id=\"T_5d316_row81_col1\" class=\"data row81 col1\" >0.960172</td>\n",
-                            "      <td id=\"T_5d316_row81_col2\" class=\"data row81 col2\" >0.640114</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row82\" class=\"row_heading level1 row82\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row82_col0\" class=\"data row82 col0\" >0.224733</td>\n",
-                            "      <td id=\"T_5d316_row82_col1\" class=\"data row82 col1\" >0.285732</td>\n",
-                            "      <td id=\"T_5d316_row82_col2\" class=\"data row82 col2\" >0.185197</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row83\" class=\"row_heading level1 row83\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row83_col0\" class=\"data row83 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row83_col1\" class=\"data row83 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row83_col2\" class=\"data row83 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row84\" class=\"row_heading level0 row84\" rowspan=\"4\">pennetier2013efficacy</th>\n",
-                            "      <th id=\"T_5d316_level1_row84\" class=\"row_heading level1 row84\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row84_col0\" class=\"data row84 col0\" >0.857143</td>\n",
-                            "      <td id=\"T_5d316_row84_col1\" class=\"data row84 col1\" >0.928571</td>\n",
-                            "      <td id=\"T_5d316_row84_col2\" class=\"data row84 col2\" >0.795918</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row85\" class=\"row_heading level1 row85\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row85_col0\" class=\"data row85 col0\" >0.197799</td>\n",
-                            "      <td id=\"T_5d316_row85_col1\" class=\"data row85 col1\" >0.758230</td>\n",
-                            "      <td id=\"T_5d316_row85_col2\" class=\"data row85 col2\" >0.113734</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row86\" class=\"row_heading level1 row86\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row86_col0\" class=\"data row86 col0\" >0.063679</td>\n",
-                            "      <td id=\"T_5d316_row86_col1\" class=\"data row86 col1\" >0.654074</td>\n",
-                            "      <td id=\"T_5d316_row86_col2\" class=\"data row86 col2\" >0.033469</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row87\" class=\"row_heading level1 row87\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row87_col0\" class=\"data row87 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row87_col1\" class=\"data row87 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row87_col2\" class=\"data row87 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row88\" class=\"row_heading level0 row88\" rowspan=\"4\">darriet2002experimental</th>\n",
-                            "      <th id=\"T_5d316_level1_row88\" class=\"row_heading level1 row88\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row88_col0\" class=\"data row88 col0\" >0.740741</td>\n",
-                            "      <td id=\"T_5d316_row88_col1\" class=\"data row88 col1\" >0.666667</td>\n",
-                            "      <td id=\"T_5d316_row88_col2\" class=\"data row88 col2\" >0.833333</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row89\" class=\"row_heading level1 row89\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row89_col0\" class=\"data row89 col0\" >0.633679</td>\n",
-                            "      <td id=\"T_5d316_row89_col1\" class=\"data row89 col1\" >0.792098</td>\n",
-                            "      <td id=\"T_5d316_row89_col2\" class=\"data row89 col2\" >0.528066</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row90\" class=\"row_heading level1 row90\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row90_col0\" class=\"data row90 col0\" >0.613992</td>\n",
-                            "      <td id=\"T_5d316_row90_col1\" class=\"data row90 col1\" >0.767490</td>\n",
-                            "      <td id=\"T_5d316_row90_col2\" class=\"data row90 col2\" >0.511660</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row91\" class=\"row_heading level1 row91\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row91_col0\" class=\"data row91 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row91_col1\" class=\"data row91 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row91_col2\" class=\"data row91 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level0_row92\" class=\"row_heading level0 row92\" rowspan=\"4\">kawada2014preventive</th>\n",
-                            "      <th id=\"T_5d316_level1_row92\" class=\"row_heading level1 row92\" >Observation</th>\n",
-                            "      <td id=\"T_5d316_row92_col0\" class=\"data row92 col0\" >0.472973</td>\n",
-                            "      <td id=\"T_5d316_row92_col1\" class=\"data row92 col1\" >0.350000</td>\n",
-                            "      <td id=\"T_5d316_row92_col2\" class=\"data row92 col2\" >0.729167</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row93\" class=\"row_heading level1 row93\" >ITNCondition</th>\n",
-                            "      <td id=\"T_5d316_row93_col0\" class=\"data row93 col0\" >0.500000</td>\n",
-                            "      <td id=\"T_5d316_row93_col1\" class=\"data row93 col1\" >0.916667</td>\n",
-                            "      <td id=\"T_5d316_row93_col2\" class=\"data row93 col2\" >0.343750</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row94\" class=\"row_heading level1 row94\" >EntomologicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row94_col0\" class=\"data row94 col0\" >0.727273</td>\n",
-                            "      <td id=\"T_5d316_row94_col1\" class=\"data row94 col1\" >1.000000</td>\n",
-                            "      <td id=\"T_5d316_row94_col2\" class=\"data row94 col2\" >0.571429</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_5d316_level1_row95\" class=\"row_heading level1 row95\" >ClinicalOutcome</th>\n",
-                            "      <td id=\"T_5d316_row95_col0\" class=\"data row95 col0\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row95_col1\" class=\"data row95 col1\" >0.000000</td>\n",
-                            "      <td id=\"T_5d316_row95_col2\" class=\"data row95 col2\" >1.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n"
-                        ],
-                        "text/plain": [
-                            "<pandas.io.formats.style.Styler at 0x34d8e21a0>"
-                        ]
-                    },
-                    "execution_count": 83,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "llm_metrics = grits_from_batch(true_extractions, pred_extractions, exclude_columns=['Site'])\n",
-                "llm_metrics.style.background_gradient(axis=1, vmin=0, vmax=1, cmap='RdYlGn')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "a60800ce-9a96-4508-ad8f-349c241540e6",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<style type=\"text/css\">\n",
-                            "</style>\n",
-                            "<table id=\"T_b0c83\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
-                            "      <th id=\"T_b0c83_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">grits</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level1\" >&nbsp;</th>\n",
-                            "      <th id=\"T_b0c83_level1_col0\" class=\"col_heading level1 col0\" colspan=\"3\">con</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level2\" >&nbsp;</th>\n",
-                            "      <th id=\"T_b0c83_level2_col0\" class=\"col_heading level2 col0\" >f1</th>\n",
-                            "      <th id=\"T_b0c83_level2_col1\" class=\"col_heading level2 col1\" >precision</th>\n",
-                            "      <th id=\"T_b0c83_level2_col2\" class=\"col_heading level2 col2\" >recall</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"index_name level0\" >Schema</th>\n",
-                            "      <th class=\"blank col0\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col1\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col2\" >&nbsp;</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_b0c83_level0_row0\" class=\"row_heading level0 row0\" >Easy</th>\n",
-                            "      <td id=\"T_b0c83_row0_col0\" class=\"data row0 col0\" >50.1%</td>\n",
-                            "      <td id=\"T_b0c83_row0_col1\" class=\"data row0 col1\" >71.1%</td>\n",
-                            "      <td id=\"T_b0c83_row0_col2\" class=\"data row0 col2\" >48.5%</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_b0c83_level0_row1\" class=\"row_heading level0 row1\" >Hard</th>\n",
-                            "      <td id=\"T_b0c83_row1_col0\" class=\"data row1 col0\" >28.2%</td>\n",
-                            "      <td id=\"T_b0c83_row1_col1\" class=\"data row1 col1\" >54.1%</td>\n",
-                            "      <td id=\"T_b0c83_row1_col2\" class=\"data row1 col2\" >26.4%</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n"
-                        ],
-                        "text/plain": [
-                            "<pandas.io.formats.style.Styler at 0x34049e6b0>"
-                        ]
-                    },
-                    "execution_count": 84,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "df = llm_metrics.query(\"Schema != 'ClinicalOutcome'\").groupby('Schema').mean()\n",
-                "df.groupby({'Observation': 'Easy', 'ITNCondition': 'Easy', 'EntomologicalOutcome': 'Hard', 'ClinicalOutcome': 'Hard'})\\\n",
-                "    .mean(0).map(lambda n: '{:.1%}'.format(n)).style.background_gradient(axis=1, vmin=0, vmax=1, cmap='RdYlGn')"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "71dfe132-d82e-46f6-b4b7-bdeb3e1bd23e",
-            "metadata": {},
-            "source": [
-                "## Precision of partial extractions"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "5e07edc2-586c-448d-bc31-c21f61edec67",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(3, 3)"
-                        ]
-                    },
-                    "execution_count": 27,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "### comment out langfuse code \n",
-                "# true_tables = defaultdict(list)\n",
-                "# pred_tables = defaultdict(list)\n",
-                "# latencies = defaultdict(list)\n",
-                "# pred_sizes = defaultdict(list)\n",
-                "# costs = defaultdict(list)\n",
-                "# \n",
-                "# traces_iter = get_langfuse_traces(global_handler.langfuse, references=references, user_id='jonnytr', \n",
-                "#                                   input_match='Please complete the following')\n",
-                "# for trace in traces_iter:\n",
-                "#     reference = list(trace.metadata.get('metadata', {}).values())[0]['reference']\n",
-                "#     schema = next((schema for schema in ss.schemas \\\n",
-                "#                if set(schema.columns).intersection(trace.output['items'][0].keys()).difference({'observation_ref', 'reference', 'itncondition_ref'})), None)\n",
-                "#     if not schema:\n",
-                "#         continue\n",
-                "#         \n",
-                "#     pred_df = json_to_df(trace.output['items'], schema=schema).reset_index()\n",
-                "# \n",
-                "#     pred_tables[schema.name].append(pred_df)\n",
-                "#     true_tables[schema.name].append(true_extractions[reference][schema])\n",
-                "#     latencies[schema.name].append((trace.end_time - trace.start_time).total_seconds())\n",
-                "#     pred_sizes[schema.name].append(pred_df.size)\n",
-                "#     costs[schema.name].append(trace.calculated_total_cost)\n",
-                "# \n",
-                "# pred_sizes = pd.concat([pd.Series(values, name=schema) for schema, values in pred_sizes.items()], axis=1).stack()\n",
-                "# pred_sizes.index.names = ['index', 'Schema']\n",
-                "# pred_sizes.name = 'N values'\n",
-                "# pred_sizes.index = pred_sizes.index.swaplevel()\n",
-                "# \n",
-                "# latencies = pd.concat([pd.Series(values, name=schema) for schema, values in latencies.items()], axis=1).stack()\n",
-                "# latencies.index.names = ['index', 'Schema']\n",
-                "# latencies.name = 'latencies'\n",
-                "# latencies.index = latencies.index.swaplevel()\n",
-                "# \n",
-                "# costs = pd.concat([pd.Series(values, name=schema) for schema, values in costs.items()], axis=1).stack()\n",
-                "# costs.index.names = ['index', 'Schema']\n",
-                "# costs.name = 'costs'\n",
-                "# costs.index = costs.index.swaplevel()\n",
-                "# \n",
-                "# len(pred_tables), len(true_tables)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f9f4c823-a3bc-4684-b4f8-d61902b3dfb6",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[06/13/24 00:51:34] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> WARNING:extralit.metrics.utils:                                            <a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">99</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Mortality_rate has different dtypes: float64 and object             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m[06/13/24 00:51:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=440171;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=944946;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\u001b\\\u001b[2m99\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column Mortality_rate has different dtypes: float64 and object             \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> WARNING:extralit.metrics.utils:                                            <a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">99</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Mortality_rate has different dtypes: float64 and object             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=5376;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=224478;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\u001b\\\u001b[2m99\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column Mortality_rate has different dtypes: float64 and object             \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n",
-                        "/Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py:43: FutureWarning:\n",
-                        "\n",
-                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[06/13/24 00:51:38] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> WARNING:extralit.metrics.utils:                                            <a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">99</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Net_holed has different dtypes: float64 and object                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m[06/13/24 00:51:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=807084;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=132183;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\u001b\\\u001b[2m99\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column Net_holed has different dtypes: float64 and object                  \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> WARNING:extralit.metrics.utils:                                            <a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">99</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Column Net_holed has different dtypes: float64 and object                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:extralit.metrics.utils:                                            \u001b]8;id=853636;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=282247;file:///Users/jonnytr/Projects/extralit/src/extralit/metrics/utils.py#99\u001b\\\u001b[2m99\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         Column Net_holed has different dtypes: float64 and object                  \u001b[2m           \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/var/folders/yg/6jm_z4c91d30k5l7hm3s7h580000gp/T/ipykernel_49560/670417324.py:8: PerformanceWarning:\n",
-                        "\n",
-                        "indexing past lexsort depth may impact performance.\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Schema</th>\n",
-                            "      <th>N values</th>\n",
-                            "      <th>latencies</th>\n",
-                            "      <th>f1</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>Schema (counts)</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>index</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>EntomologicalOutcome</td>\n",
-                            "      <td>18.0</td>\n",
-                            "      <td>3.445</td>\n",
-                            "      <td>0.600000</td>\n",
-                            "      <td>0.428571</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>EntomologicalOutcome (n=212)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>EntomologicalOutcome</td>\n",
-                            "      <td>18.0</td>\n",
-                            "      <td>3.651</td>\n",
-                            "      <td>0.600000</td>\n",
-                            "      <td>0.428571</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>EntomologicalOutcome (n=212)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>EntomologicalOutcome</td>\n",
-                            "      <td>36.0</td>\n",
-                            "      <td>5.231</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>EntomologicalOutcome (n=212)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>EntomologicalOutcome</td>\n",
-                            "      <td>36.0</td>\n",
-                            "      <td>5.489</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>EntomologicalOutcome (n=212)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>EntomologicalOutcome</td>\n",
-                            "      <td>72.0</td>\n",
-                            "      <td>8.477</td>\n",
-                            "      <td>0.634146</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>0.464286</td>\n",
-                            "      <td>EntomologicalOutcome (n=212)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>21</th>\n",
-                            "      <td>Observation</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>0.922</td>\n",
-                            "      <td>0.733333</td>\n",
-                            "      <td>0.855556</td>\n",
-                            "      <td>0.641667</td>\n",
-                            "      <td>Observation (n=26)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>22</th>\n",
-                            "      <td>Observation</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>0.582</td>\n",
-                            "      <td>0.609524</td>\n",
-                            "      <td>0.711111</td>\n",
-                            "      <td>0.533333</td>\n",
-                            "      <td>Observation (n=26)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>23</th>\n",
-                            "      <td>Observation</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>1.004</td>\n",
-                            "      <td>0.609524</td>\n",
-                            "      <td>0.711111</td>\n",
-                            "      <td>0.533333</td>\n",
-                            "      <td>Observation (n=26)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>24</th>\n",
-                            "      <td>Observation</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>0.587</td>\n",
-                            "      <td>0.612245</td>\n",
-                            "      <td>0.714286</td>\n",
-                            "      <td>0.535714</td>\n",
-                            "      <td>Observation (n=26)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>25</th>\n",
-                            "      <td>Observation</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>0.734</td>\n",
-                            "      <td>0.612245</td>\n",
-                            "      <td>0.714286</td>\n",
-                            "      <td>0.535714</td>\n",
-                            "      <td>Observation (n=26)</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>282 rows × 7 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                     Schema  N values  latencies        f1  precision  \\\n",
-                            "index                                                                   \n",
-                            "0      EntomologicalOutcome      18.0      3.445  0.600000   0.428571   \n",
-                            "1      EntomologicalOutcome      18.0      3.651  0.600000   0.428571   \n",
-                            "2      EntomologicalOutcome      36.0      5.231  1.000000   1.000000   \n",
-                            "3      EntomologicalOutcome      36.0      5.489  1.000000   1.000000   \n",
-                            "4      EntomologicalOutcome      72.0      8.477  0.634146   1.000000   \n",
-                            "...                     ...       ...        ...       ...        ...   \n",
-                            "21              Observation       6.0      0.922  0.733333   0.855556   \n",
-                            "22              Observation       4.0      0.582  0.609524   0.711111   \n",
-                            "23              Observation       4.0      1.004  0.609524   0.711111   \n",
-                            "24              Observation       4.0      0.587  0.612245   0.714286   \n",
-                            "25              Observation       4.0      0.734  0.612245   0.714286   \n",
-                            "\n",
-                            "         recall               Schema (counts)  \n",
-                            "index                                          \n",
-                            "0      1.000000  EntomologicalOutcome (n=212)  \n",
-                            "1      1.000000  EntomologicalOutcome (n=212)  \n",
-                            "2      1.000000  EntomologicalOutcome (n=212)  \n",
-                            "3      1.000000  EntomologicalOutcome (n=212)  \n",
-                            "4      0.464286  EntomologicalOutcome (n=212)  \n",
-                            "...         ...                           ...  \n",
-                            "21     0.641667            Observation (n=26)  \n",
-                            "22     0.533333            Observation (n=26)  \n",
-                            "23     0.533333            Observation (n=26)  \n",
-                            "24     0.535714            Observation (n=26)  \n",
-                            "25     0.535714            Observation (n=26)  \n",
-                            "\n",
-                            "[282 rows x 7 columns]"
-                        ]
-                    },
-                    "execution_count": 28,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "### comment out langfuse code \n",
-                "# results = grits_from_batch(true_tables, pred_tables, \n",
-                "#                            index_columns=['reference', 'observation_ref', 'itncondition_ref'], \n",
-                "#                            index_names=['Schema', 'index'])\n",
-                "# \n",
-                "# results[pred_sizes.name] = results.index.map(pred_sizes)\n",
-                "# results[latencies.name] = results.index.map(latencies)\n",
-                "# \n",
-                "# results = pd.concat(([results[[pred_sizes.name, latencies.name]].droplevel(level=[1,2], axis=1), results[('grits', 'con')]]), axis=1)\n",
-                "# results = results.reset_index(level=['Schema'])\n",
-                "# results['Schema (counts)'] = results['Schema'].map({k:f'{k} (n={v})' for k,v in results.reset_index()['Schema'].value_counts().items()})\n",
-                "# results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "264c86fc-a707-4c7e-b529-012ed390d793",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Schema\n",
-                            "EntomologicalOutcome    0.936392\n",
-                            "ITNCondition            0.761387\n",
-                            "Observation             0.899817\n",
-                            "Name: precision, dtype: float64"
-                        ]
-                    },
-                    "execution_count": 29,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "### comment out langfuse code \n",
-                "# results.groupby('Schema')['precision'].mean()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "5bcde074-55c7-4911-984f-87edc8d29d35",
-            "metadata": {},
-            "outputs": [
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f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&u||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),v=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",v.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),y=a(\"hoverlabel.bordercolor\",v.bordercolor||i.contrast(g)),m=n.extendFlat({},v.font);m.color||(m.color=y),n.coerceFont(a,\"hoverlabel.font\",m)}a(\"captureevents\",!!d)}},26828:function(t,e,r){\"use strict\";var n=r(38248),i=r(36896);t.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,u,c=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<c.length;h++)l=c[h],u=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(u+t,s)}}},45216:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460),a=r(51272),o=r(87192),s=r(13916);function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),u=a(\"clicktoshow\");if(l||u){o(t,e,r,a);for(var c=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var v=f[d],g=i.coerceRef(t,e,p,v,\"\",\"paper\");if(\"paper\"!==g&&i.getFromId(p,g)._annIndices.push(e._index),i.coercePosition(e,p,a,g,v,.5),c){var y=\"a\"+v,m=i.coerceRef(t,e,p,y,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==m&&m!==g&&(m=e[y]=\"pixel\");var x=\"pixel\"===m?h[d]:.4;i.coercePosition(e,p,a,m,y,x)}a(v+\"anchor\"),a(v+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),c&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),u){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 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L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,O=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),I=O.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=I.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",W(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color,fontWeight:n.weight,fontStyle:n.style,fontVariant:n.variant},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(u.stroke,e.bordercolor).call(u.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var q=e.font,H=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(H);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(Y).on(\"edit\",(function(r){e.text=r,this.call(Y),S(\"text\",r),y&&y.autorange&&M(y._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(Y)}else n.selectAll(\"#\"+L).remove();function W(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function Y(r){return r.call(c.font,q).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),v=c.bBox((f?n:G).node()),b=v.width,_=v.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),q=Math.round(D+2*N);function H(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var Y=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var K,J,$,Q,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:y,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ut=e[et+\"anchor\"],ct=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(Y=!0):Y=!0),K=it._offset+it.r2p(e[et]),Q=.5}else{var dt=\"domain\"===ht;\"x\"===et?($=e[et],K=dt?it._offset+it._length*$:K=T.l+T.w*$):($=1-e[et],K=dt?it._offset+it._length*$:K=T.t+T.h*$),Q=e.showarrow?.5:$}if(e.showarrow){ft.head=K;var vt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(vt=1-vt),ft.tail=it._offset+it._length*vt):\"paper\"===gt?\"y\"===et?(vt=1-vt,ft.tail=T.t+T.h*vt):ft.tail=T.l+T.w*vt:ft.tail=it._offset+it.r2p(vt),J=tt}else ft.tail=K+vt,J=tt+vt;ft.text=ft.tail+tt;var yt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,yt-1)),\"pixel\"===nt){var mt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-yt;mt>0?(ft.tail+=mt,ft.text+=mt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ct,ft.head+=ct}else J=tt=lt*H(Q,ut),ft.text=K+tt;ft.text+=ct,tt+=ct,J+=ct,e[\"_\"+et+\"padplus\"]=lt/2+J,e[\"_\"+et+\"padminus\"]=lt/2-J,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(Y)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(c.setClipUrl,U?L:null,t);else{var wt=N+_t-v.top,Tt=N+bt-v.left;G.call(h.positionText,Tt,wt).call(c.setClipUrl,U?L:null,t)}V.select(\"rect\").call(c.setRect,N,N,A,D),j.call(c.setRect,F/2,F/2,B-F,q-F),R.call(c.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-q/2)),I.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){O.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,v=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,v,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=v-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=O.append(\"g\").style({opacity:u.opacity(q)}).classed(\"annotation-arrow-g\",!0),W=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(u.stroke,u.rgb(q));if(g(W,H,e),k.annotationPosition&&W.node().parentNode&&!a){var Y=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));Y+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var K,J,$=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-Y)+\",\"+(p-X),transform:s(Y,X)}).style(\"stroke-width\",V+6+\"px\").call(u.stroke,\"rgba(0,0,0,0)\").call(u.fill,\"rgba(0,0,0,0)\");d.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(R);K=t.x,J=t.y,y&&y.autorange&&M(y._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(K,J),i=n[0]+t,a=n[1]+r;R.call(c.setTranslate,i,a),S(\"x\",m(y,t,\"x\",T,e)),S(\"y\",m(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",m(y,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",m(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),I.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};e.showarrow&&At(0,0),z&&d.init({element:R.node(),gd:t,prepFn:function(){kt=I.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",m(y,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",m(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(y)i=m(y,t,\"x\",T,e);else{var l=e._xsize/T.w,u=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(u+t/T.w,l,0,1,e.xanchor)}if(x)o=m(x,r,\"y\",T,e);else{var c=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-c/2;o=d.align(f-r/T.h,c,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),y&&x||(n=d.getCursor(y?.5:i,x?.5:o,e.xanchor,e.yanchor))}I.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",W(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}t.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&y(t,r);return a.previousPromises(t)},drawOne:y,drawRaw:x}},33652:function(t,e,r){\"use strict\";var n=r(33428),i=r(76308),a=r(72196),o=r(3400),s=o.strScale,l=o.strRotate,u=o.strTranslate;t.exports=function(t,e,r){var o,c,f,h,p=t.node(),d=a[r.arrowhead||0],v=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),y=(r.arrowwidth||1)*(r.startarrowsize||1),m=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=v.backoff*y+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},c={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-c.x,T=o.y-c.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);c.x+=k,c.y+=A,t.attr({x2:c.x,y2:c.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var O=p.getPointAtLength(E),I=p.getPointAtLength(E-.1);h=Math.atan2(O.y-I.y,O.x-I.x),c=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+\"px,\"+E+\"px\",t.style(\"stroke-dasharray\",L)}function z(){t.style(\"stroke-dasharray\",\"0px,100px\")}function D(e,a,o,c){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append(\"path\").attr({class:t.attr(\"class\"),d:e.path,transform:u(a.x,a.y)+l(180*o/Math.PI)+s(c)}).style({fill:i.rgb(r.arrowcolor),\"stroke-width\":0}))}m&&D(v,o,f,y),x&&D(d,c,h,g)}},79180:function(t,e,r){\"use strict\";var n=r(23816),i=r(42300);t.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:r(13916),supplyLayoutDefaults:r(45216),includeBasePlot:r(36632)(\"annotations\"),calcAutorange:r(90272),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:r(26828)}},45899:function(t,e,r){\"use strict\";var n=r(13916),i=r(67824).overrideAll,a=r(31780).templatedArray;t.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},42456:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460);function a(t,e){var r=e.fullSceneLayout.domain,a=e.fullLayout._size,o={pdata:null,type:\"linear\",autorange:!1,range:[-1/0,1/0]};t._xa={},n.extendFlat(t._xa,o),i.setConvert(t._xa),t._xa._offset=a.l+r.x[0]*a.w,t._xa.l2p=function(){return.5*(1+t._pdata[0]/t._pdata[3])*a.w*(r.x[1]-r.x[0])},t._ya={},n.extendFlat(t._ya,o),i.setConvert(t._ya),t._ya._offset=a.t+(1-r.y[1])*a.h,t._ya.l2p=function(){return.5*(1-t._pdata[1]/t._pdata[3])*a.h*(r.y[1]-r.y[0])}}t.exports=function(t){for(var e=t.fullSceneLayout.annotations,r=0;r<e.length;r++)a(e[r],t);t.fullLayout._infolayer.selectAll(\".annotation-\"+t.id).remove()}},52808:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460),a=r(51272),o=r(87192),s=r(45899);function l(t,e,r,a){function l(r,i){return n.coerce(t,e,s,r,i)}function u(t){var n=t+\"axis\",a={_fullLayout:{}};return 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0!==t.zLabelVal&&(a.z=t.zLabelVal)),e.appendArrayPointValue(a,r,i),a},e.appendArrayPointValue=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],u=a(l);if(void 0===t[u]){var c=o(n.nestedProperty(e,l).get(),r);void 0!==c&&(t[u]=c)}}},e.appendArrayMultiPointValues=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],u=a(l);if(void 0===t[u]){for(var c=n.nestedProperty(e,l).get(),f=new Array(r.length),h=0;h<r.length;h++)f[h]=o(c,r[h]);t[u]=f}}};var i={ids:\"id\",locations:\"location\",labels:\"label\",values:\"value\",\"marker.colors\":\"color\",parents:\"parent\"};function a(t){return i[t]||t}function o(t,e){return Array.isArray(e)?Array.isArray(t)&&Array.isArray(t[e[0]])?t[e[0]][e[1]]:void 0:t[e]}var s={x:!0,y:!0},l={\"x unified\":!0,\"y unified\":!0};e.isUnifiedHover=function(t){return\"string\"==typeof t&&!!l[t]},e.isXYhover=function(t){return\"string\"==typeof t&&!!s[t]}},83292:function(t,e,r){\"use strict\";var n=r(33428),i=r(38248),a=r(49760),o=r(3400),s=o.pushUnique,l=o.strTranslate,u=o.strRotate,c=r(95924),f=r(72736),h=r(72213),p=r(43616),d=r(76308),v=r(86476),g=r(54460),y=r(24040),m=r(10624),x=r(92456),b=r(77864),_=r(31140),w=x.YANGLE,T=Math.PI*w/180,k=1/Math.sin(T),A=Math.cos(T),M=Math.sin(T),S=x.HOVERARROWSIZE,E=x.HOVERTEXTPAD,L={box:!0,ohlc:!0,violin:!0,candlestick:!0},C={scatter:!0,scattergl:!0,splom:!0};function P(t,e){return t.distance-e.distance}function O(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa?t.xa._id:\"\",t.ya?t.ya._id:\"\"].join(\",\")}e.hover=function(t,e,r,a){t=o.getGraphDiv(t);var l=e.target;o.throttle(t._fullLayout._uid+x.HOVERID,x.HOVERMINTIME,(function(){!function(t,e,r,a,l){r||(r=\"xy\");var u,f,p,x=Array.isArray(r)?r:[r],b=t._fullLayout,_=b.hoversubplots,w=b._plots||[],T=w[r],A=b._has(\"cartesian\"),M=e.hovermode||b.hovermode,E=\"x\"===(M||\"\").charAt(0),I=\"y\"===(M||\"\").charAt(0);if(A&&(E||I)&&\"axis\"===_)for(var D=x.length,U=0;U<D;U++)if(w[u=x[U]]){f=g.getFromId(t,u,\"x\"),p=g.getFromId(t,u,\"y\");var G=(E?f:p)._subplotsWith;if(G&&G.length)for(var W=0;W<G.length;W++)s(x,G[W])}if(T&&\"single\"!==_){var Y=T.overlays.map((function(t){return t.id}));x=x.concat(Y)}for(var X=x.length,Z=new Array(X),K=new Array(X),J=!1,$=0;$<X;$++)if(w[u=x[$]])J=!0,Z[$]=w[u].xaxis,K[$]=w[u].yaxis;else{if(!b[u]||!b[u]._subplot)return void o.warn(\"Unrecognized subplot: \"+u);var Q=b[u]._subplot;Z[$]=Q.xaxis,K[$]=Q.yaxis}if(M&&!J&&(M=\"closest\"),-1===[\"x\",\"y\",\"closest\",\"x unified\",\"y unified\"].indexOf(M)||!t.calcdata||t.querySelector(\".zoombox\")||t._dragging)return v.unhoverRaw(t,e);var tt=b.hoverdistance;-1===tt&&(tt=1/0);var et=b.spikedistance;-1===et&&(et=1/0);var rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=[],gt=[],yt={hLinePoint:null,vLinePoint:null},mt=!1;if(Array.isArray(e))for(M=\"array\",it=0;it<e.length;it++)(ot=t.calcdata[e[it].curveNumber||0])&&(st=ot[0].trace,\"skip\"!==ot[0].trace.hoverinfo&&(gt.push(ot),\"h\"===st.orientation&&(mt=!0)));else{var xt,bt,_t=t.calcdata.slice();for(_t.sort((function(t,e){return(t[0].trace.zorder||0)-(e[0].trace.zorder||0)})),at=0;at<_t.length;at++)ot=_t[at],\"skip\"!==(st=ot[0].trace).hoverinfo&&m.isTraceInSubplots(st,x)&&(gt.push(ot),\"h\"===st.orientation&&(mt=!0));if(l){if(!1===c.triggerHandler(t,\"plotly_beforehover\",e))return;var wt=l.getBoundingClientRect();xt=e.clientX-wt.left,bt=e.clientY-wt.top,b._calcInverseTransform(t);var Tt=o.apply3DTransform(b._invTransform)(xt,bt);if(xt=Tt[0],bt=Tt[1],xt<0||xt>Z[0]._length||bt<0||bt>K[0]._length)return v.unhoverRaw(t,e)}else xt=\"xpx\"in e?e.xpx:Z[0]._length/2,bt=\"ypx\"in e?e.ypx:K[0]._length/2;if(e.pointerX=xt+Z[0]._offset,e.pointerY=bt+K[0]._offset,rt=\"xval\"in e?m.flat(x,e.xval):m.p2c(Z,xt),nt=\"yval\"in e?m.flat(x,e.yval):m.p2c(K,bt),!i(rt[0])||!i(nt[0]))return o.warn(\"Fx.hover failed\",e,t),v.unhoverRaw(t,e)}var kt=1/0;function At(r,n){for(at=0;at<gt.length;at++)if((ot=gt[at])&&ot[0]&&ot[0].trace&&!0===(st=ot[0].trace).visible&&0!==st._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(st._module.name)){if(ct=M,m.isUnifiedHover(ct)&&(ct=ct.charAt(0)),\"splom\"===st.type?lt=x[ut=0]:(lt=m.getSubplot(st),ut=x.indexOf(lt)),pt={cd:ot,trace:st,xa:Z[ut],ya:K[ut],maxHoverDistance:tt,maxSpikeDistance:et,index:!1,distance:Math.min(kt,tt),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:d.defaultLine,name:st.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},b[lt]&&(pt.subplot=b[lt]._subplot),b._splomScenes&&b._splomScenes[st.uid]&&(pt.scene=b._splomScenes[st.uid]),\"array\"===ct){var a=e[at];\"pointNumber\"in a?(pt.index=a.pointNumber,ct=\"closest\"):(ct=\"\",\"xval\"in a&&(ft=a.xval,ct=\"x\"),\"yval\"in a&&(ht=a.yval,ct=ct?\"closest\":\"y\"))}else void 0!==r&&void 0!==n?(ft=r,ht=n):(ft=rt[ut],ht=nt[ut]);if(dt=vt.length,0!==tt)if(st._module&&st._module.hoverPoints){var s=st._module.hoverPoints(pt,ft,ht,ct,{finiteRange:!0,hoverLayer:b._hoverlayer,hoversubplots:_,gd:t});if(s)for(var l,u=0;u<s.length;u++)l=s[u],i(l.x0)&&i(l.y0)&&vt.push(B(l,M))}else o.log(\"Unrecognized trace type in hover:\",st);if(\"closest\"===M&&vt.length>dt&&(vt.splice(0,dt),kt=vt[0].distance),A&&0!==et&&0===vt.length){pt.distance=et,pt.index=!1;var c=st._module.hoverPoints(pt,ft,ht,\"closest\",{hoverLayer:b._hoverlayer});if(c&&(c=c.filter((function(t){return t.spikeDistance<=et}))),c&&c.length){var f,h=c.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(h.length){var p=h[0];i(p.x0)&&i(p.y0)&&(f=St(p),(!yt.vLinePoint||yt.vLinePoint.spikeDistance>f.spikeDistance)&&(yt.vLinePoint=f))}var v=c.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(v.length){var g=v[0];i(g.x0)&&i(g.y0)&&(f=St(g),(!yt.hLinePoint||yt.hLinePoint.spikeDistance>f.spikeDistance)&&(yt.hLinePoint=f))}}}}}function Mt(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)f&&f._id!==t[o].xa._id||p&&p._id!==t[o].ya._id||(n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n));return i}function St(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}At();var Et={fullLayout:b,container:b._hoverlayer,event:e},Lt=t._spikepoints,Ct={vLinePoint:yt.vLinePoint,hLinePoint:yt.hLinePoint};t._spikepoints=Ct;var Pt=function(){var t=vt.filter((function(t){return f&&f._id===t.xa._id&&p&&p._id===t.ya._id})),e=vt.filter((function(t){return!(f&&f._id===t.xa._id&&p&&p._id===t.ya._id)}));t.sort(P),e.sort(P),vt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];y.traceIs(s.trace,\"bar-like\")||y.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(vt=t.concat(e),M)};Pt();var Ot=M.charAt(0),It=(\"x\"===Ot||\"y\"===Ot)&&vt[0]&&C[vt[0].trace.type];if(A&&0!==et&&0!==vt.length){var zt=Mt(vt.filter((function(t){return t.ya.showspikes})),et,It);yt.hLinePoint=St(zt);var Dt=Mt(vt.filter((function(t){return t.xa.showspikes})),et,It);yt.vLinePoint=St(Dt)}if(0===vt.length){var Rt=v.unhoverRaw(t,e);return!A||null===yt.hLinePoint&&null===yt.vLinePoint||j(Lt)&&N(t,yt,Et),Rt}if(A&&j(Lt)&&N(t,yt,Et),m.isXYhover(ct)&&0!==vt[0].length&&\"splom\"!==vt[0].trace.type){var Ft=vt[0],Bt=(vt=L[Ft.trace.type]?vt.filter((function(t){return t.trace.index===Ft.trace.index})):[Ft]).length;At(V(\"x\",Ft,b),V(\"y\",Ft,b));var Nt,jt=[],Ut={},Vt=0,qt=function(t){var e=L[t.trace.type]?O(t):t.trace.index;if(Ut[e]){var r=Ut[e]-1,n=jt[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(jt[r]=t)}else Vt++,Ut[e]=Vt,jt.push(t)};for(Nt=0;Nt<Bt;Nt++)qt(vt[Nt]);for(Nt=vt.length-1;Nt>Bt-1;Nt--)qt(vt[Nt]);vt=jt,Pt()}var Ht=t._hoverdata,Gt=[],Wt=q(t),Yt=H(t);for(it=0;it<vt.length;it++){var Xt=vt[it],Zt=m.makeEventData(Xt,Xt.trace,Xt.cd);if(!1!==Xt.hovertemplate){var Kt=!1;Xt.cd[Xt.index]&&Xt.cd[Xt.index].ht&&(Kt=Xt.cd[Xt.index].ht),Xt.hovertemplate=Kt||Xt.trace.hovertemplate||!1}if(Xt.xa&&Xt.ya){var Jt=Xt.x0+Xt.xa._offset,$t=Xt.x1+Xt.xa._offset,Qt=Xt.y0+Xt.ya._offset,te=Xt.y1+Xt.ya._offset,ee=Math.min(Jt,$t),re=Math.max(Jt,$t),ne=Math.min(Qt,te),ie=Math.max(Qt,te);Zt.bbox={x0:ee+Yt,x1:re+Yt,y0:ne+Wt,y1:ie+Wt}}Xt.eventData=[Zt],Gt.push(Zt)}t._hoverdata=Gt;var ae=\"y\"===M&&(gt.length>1||vt.length>1)||\"closest\"===M&&mt&&vt.length>1,oe=d.combine(b.plot_bgcolor||d.background,b.paper_bgcolor),se=z(vt,{gd:t,hovermode:M,rotateLabels:ae,bgColor:oe,container:b._hoverlayer,outerContainer:b._paper.node(),commonLabelOpts:b.hoverlabel,hoverdistance:b.hoverdistance}),le=se.hoverLabels;if(m.isUnifiedHover(M)||(function(t,e,r,n){var i,a,o,s,l,u,c,f=e?\"xa\":\"ya\",h=e?\"ya\":\"xa\",p=0,d=1,v=t.size(),g=new Array(v),y=0,m=n.minX,x=n.maxX,b=n.minY,_=n.maxY,w=function(t){return t*r._invScaleX},T=function(t){return t*r._invScaleY};function A(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;s<t.length;s++)(u=t[s]).pos+u.dp+u.size>e.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos>e.pmax-1&&(u.del=!0,n--);for(s=0;s<t.length&&!(n<=0);s++)if((u=t[s]).pos<e.pmin+1)for(u.del=!0,n--,o=2*u.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos+u.dp+u.size>e.pmax&&(u.del=!0,n--)}}}for(t.each((function(t){var n=t[f],i=t[h],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var u,c,p=R(t,e),v=t.anchor,A=\"end\"===v?-1:1;if(\"middle\"===v)c=(u=t.crossPos+(a?T(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?T(t.by):w(t.bx));else if(a)c=(u=t.crossPos+T(S+p.y)-T(t.by/2-S))+T(t.by);else{var M=w(A*S+p.x),E=M+w(A*t.bx);u=t.crossPos+Math.min(M,E),c=t.crossPos+Math.max(M,E)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 0!==x&&Math.min(c,x)-Math.max(u,m)>1&&(\"top\"===i.side?(s=i._mainLinePosition,l=r.height):l=i._mainLinePosition)}g[y++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?k:1)/2,pmin:s,pmax:l}]})),g.sort((function(t,e){return t[0].posref-e[0].posref||d*(e[0].traceIndex-t[0].traceIndex)}));!i&&p<=v;){for(p++,i=!0,s=0;s<g.length-1;){var M=g[s],E=g[s+1],L=M[M.length-1],C=E[0];if((a=L.pos+L.dp+L.size-C.pos-C.dp+C.size)>.01&&L.pmin===C.pmin&&L.pmax===C.pmax){for(l=E.length-1;l>=0;l--)E[l].dp+=a;for(M.push.apply(M,E),g.splice(s+1,1),c=0,l=M.length-1;l>=0;l--)c+=M[l].dp;for(o=c/M.length,l=M.length-1;l>=0;l--)M[l].dp-=o;i=!1}else s++}g.forEach(A)}for(s=g.length-1;s>=0;s--){var P=g[s];for(l=P.length-1;l>=0;l--){var O=P[l],I=O.datum;I.offset=O.dp,I.del=O.del}}}(le,ae,b,se.commonLabelBoundingBox),F(le,ae,b._invScaleX,b._invScaleY)),l&&l.tagName){var ue=y.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,Gt);h(n.select(l),ue?\"pointer\":\"\")}l&&!a&&function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Ht)&&(Ht&&t.emit(\"plotly_unhover\",{event:e,points:Ht}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:Z,yaxes:K,xvals:rt,yvals:nt}))}(t,e,r,a,l)}))},e.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=q(i),o=H(i),s=z(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,u=t.eventData;if(u){var c=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),p=Math.max(s,l),v=t.trace;if(y.traceIs(v,\"gl3d\")){var g=i._fullLayout[v.scene]._scene.container,m=g.offsetLeft,x=g.offsetTop;c+=m,f+=m,h+=x,p+=x}u.bbox={x0:c+o,x1:f+o,y0:h+a,y1:p+a},e.inOut_bbox&&e.inOut_bbox.push(u.bbox)}else u=!1;return{color:t.color||d.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,fontWeight:t.fontWeight,fontStyle:t.fontStyle,fontVariant:t.fontVariant,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:u}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||d.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}).hoverLabels,l=0,u=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(u=t.offset)})).each((function(t){t.offset-=u})),F(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var I=/<extra>([\\s\\S]*)<\\/extra>/;function z(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,s=e.rotateLabels,c=e.bgColor,h=e.container,v=e.outerContainer,g=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||x.HOVERFONT,k=e.fontSize||x.HOVERFONTSIZE,A=e.fontWeight||i.font.weight,M=e.fontStyle||i.font.style,L=e.fontVariant||i.font.variant,C=t[0],P=C.xa,I=C.ya,z=a.charAt(0),R=z+\"Label\",F=C[R];if(void 0===F&&\"multicategory\"===P.type)for(var B=0;B<t.length&&void 0===(F=t[B][R]);B++);var N=G(r,v),j=N.top,U=N.width,V=N.height,q=void 0!==F&&C.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(q){var H,W,Y=!0;for(H=0;H<t.length;H++)if(Y&&void 0===t[H].zLabel&&(Y=!1),W=t[H].hoverinfo||t[H].trace.hoverinfo){var 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R,B=x.height/2,N=j-x.top-B,U=\"clip\"+i._uid+\"commonlabel\"+I._id;if(y<x.width+2*E+S){R=\"M-\"+(S+E)+\"-\"+B+\"h-\"+(x.width-E)+\"V\"+B+\"h\"+(x.width-E)+\"Z\";var V=x.width-y+E;f.positionText(s,V,N),\"end\"===O&&s.selectAll(\"tspan\").each((function(){var t=n.select(this),e=p.tester.append(\"text\").text(t.text()).call(p.font,v),i=G(r,e.node());Math.round(i.width)<Math.round(x.width)&&t.attr(\"x\",V-i.width),e.remove()}))}else f.positionText(s,z*(E+S),N),R=null;var q=i._topclips.selectAll(\"#\"+U).data(R?[0]:[]);q.enter().append(\"clipPath\").attr(\"id\",U).append(\"path\"),q.exit().remove(),q.select(\"path\").attr(\"d\",R),p.setClipUrl(s,R?U:null,r)}t.attr(\"transform\",l(y,m))})),m.isUnifiedHover(a)){h.selectAll(\"g.hovertext\").remove();var J=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===J.length)return[];var 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ut,ct,ft,ht,pt=h.select(\"g.legend\"),dt=G(r,pt.node()),vt=dt.width+2*E,gt=dt.height+2*E,yt=J[0],mt=(yt.x0+yt.x1)/2,xt=(yt.y0+yt.y1)/2,bt=!(y.traceIs(yt.trace,\"bar-like\")||y.traceIs(yt.trace,\"box-violin\"));\"y\"===z?bt?(ct=xt-E,ut=xt+E):(ct=Math.min.apply(null,J.map((function(t){return Math.min(t.y0,t.y1)}))),ut=Math.max.apply(null,J.map((function(t){return Math.max(t.y0,t.y1)})))):ct=ut=o.mean(J.map((function(t){return(t.y0+t.y1)/2})))-gt/2,\"x\"===z?bt?(ft=mt+E,ht=mt-E):(ft=Math.max.apply(null,J.map((function(t){return Math.max(t.x0,t.x1)}))),ht=Math.min.apply(null,J.map((function(t){return Math.min(t.x0,t.x1)})))):ft=ht=o.mean(J.map((function(t){return(t.x0+t.x1)/2})))-vt/2;var _t,wt,Tt=P._offset,kt=I._offset;return ht+=Tt-vt,ct+=kt-gt,_t=(ft+=Tt)+vt<U&&ft>=0?ft:ht+vt<U&&ht>=0?ht:Tt+vt<U?Tt:ft-mt<mt-ht+vt?U-vt:0,_t+=E,wt=(ut+=kt)+gt<V&&ut>=0?ut:ct+gt<V&&ct>=0?ct:kt+gt<V?kt:ut-xt<xt-ct+gt?V-gt:0,wt+=E,pt.attr(\"transform\",l(_t-1,wt-1)),pt}var At=h.selectAll(\"g.hovertext\").data(t,(function(t){return O(t)}));return At.enter().append(\"g\").classed(\"hovertext\",!0).each((function(){var t=n.select(this);t.append(\"rect\").call(d.fill,d.addOpacity(c,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(p.font,{weight:A,style:M,variant:L,family:T,size:k})})),At.exit().remove(),At.each((function(t){var e=n.select(this).attr(\"transform\",\"\"),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var h=t.bgcolor||o,v=d.combine(d.opacity(h)?h:d.defaultLine,c),g=d.combine(d.opacity(o)?o:d.defaultLine,c),y=t.borderColor||d.contrast(v),m=D(t,q,a,i,F,e),x=m[0],b=m[1],_=e.select(\"text.nums\").call(p.font,{family:t.fontFamily||T,size:t.fontSize||k,color:t.fontColor||y,weight:t.fontWeight||A,style:t.fontStyle||M,variant:t.fontVariant||L}).text(x).attr(\"data-notex\",1).call(f.positionText,0,0).call(f.convertToTspans,r),C=e.select(\"text.name\"),P=0,O=0;if(b&&b!==x){C.call(p.font,{family:t.fontFamily||T,size:t.fontSize||k,color:g,weight:t.fontWeight||A,style:t.fontStyle||M,variant:t.fontVariant||L}).text(b).attr(\"data-notex\",1).call(f.positionText,0,0).call(f.convertToTspans,r);var I=G(r,C.node());P=I.width+2*E,O=I.height+2*E}else C.remove(),e.select(\"rect\").remove();e.select(\"path\").style({fill:v,stroke:y});var z=t.xa._offset+(t.x0+t.x1)/2,R=t.ya._offset+(t.y0+t.y1)/2,B=Math.abs(t.x1-t.x0),N=Math.abs(t.y1-t.y0),H=G(r,_.node()),W=H.width/i._invScaleX,Y=H.height/i._invScaleY;t.ty0=(j-H.top)/i._invScaleY,t.bx=W+2*E,t.by=Math.max(Y+2*E,O),t.anchor=\"start\",t.txwidth=W,t.tx2width=P,t.offset=0;var X,Z,K=(W+S+E+P)*i._invScaleX;if(s)t.pos=z,X=R+N/2+K<=V,Z=R-N/2-K>=0,\"top\"!==t.idealAlign&&X||!Z?X?(R+=N/2,t.anchor=\"start\"):t.anchor=\"middle\":(R-=N/2,t.anchor=\"end\"),t.crossPos=R;else{if(t.pos=R,X=z+B/2+K<=U,Z=z-B/2-K>=0,\"left\"!==t.idealAlign&&X||!Z)if(X)z+=B/2,t.anchor=\"start\";else{t.anchor=\"middle\";var J=K/2,$=z+J-U,Q=z-J;$>0&&(z-=$),Q<0&&(z+=-Q)}else z-=B/2,t.anchor=\"end\";t.crossPos=z}_.attr(\"text-anchor\",t.anchor),P&&C.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",l(z,R)+(s?u(w):\"\"))})),{hoverLabels:At,commonLabelBoundingBox:K}}function D(t,e,r,n,i,a){var s=\"\",l=\"\";void 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h=t.hovertemplateLabels||t;t[u+\"Label\"]!==i&&(h[u+\"other\"]=h[u+\"Val\"],h[u+\"otherLabel\"]=h[u+\"Label\"]),l=(l=o.hovertemplateString(f,h,n._d3locale,t.eventData[0]||{},t.trace._meta)).replace(I,(function(e,r){return s=U(r,t.nameLength),\"\"}))}return[l,s]}function R(t,e){var r=0,n=t.offset;return e&&(n*=-M,r=t.offset*A),{x:r,y:n}}function F(t,e,r,i){var a=function(t){return t*r},o=function(t){return t*i};t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var 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b=y+v.textShiftX,_=m+t.ty0-t.by/2+E,w=t.textAlign||\"auto\";\"auto\"!==w&&(\"left\"===w&&\"start\"!==h?(c.attr(\"text-anchor\",\"start\"),b=x?-t.bx/2-t.tx2width/2+E:-t.bx-E):\"right\"===w&&\"end\"!==h&&(c.attr(\"text-anchor\",\"end\"),b=x?t.bx/2-t.tx2width/2-E:t.bx+E)),c.call(f.positionText,a(b),o(_)),t.tx2width&&(r.select(\"text.name\").call(f.positionText,a(v.text2ShiftX+v.alignShift*E+y),o(m+t.ty0-t.by/2+E)),r.select(\"rect\").call(p.setRect,a(v.text2ShiftX+(v.alignShift-1)*t.tx2width/2+y),o(m-t.by/2-1),a(t.tx2width),o(t.by+2)))}))}function B(t,e){var r=t.index,n=t.trace||{},a=t.cd[0],s=t.cd[r]||{};function l(t){return t||i(t)&&0===t}var u=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(s,n,t,e)};function c(e,r,n){var i=u(r,n);l(i)&&(t[e]=i)}if(c(\"hoverinfo\",\"hi\",\"hoverinfo\"),c(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),c(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),c(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),c(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),c(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),c(\"fontWeight\",\"htw\",\"hoverlabel.font.weight\"),c(\"fontStyle\",\"hty\",\"hoverlabel.font.style\"),c(\"fontVariant\",\"htv\",\"hoverlabel.font.variant\"),c(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),c(\"textAlign\",\"hta\",\"hoverlabel.align\"),t.posref=\"y\"===e||\"closest\"===e&&\"h\"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:g.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:g.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||\"log\"===t.xa.type&&t.xerr<=0)){var f=g.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+f+\" / -\"+g.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" ± \"+f,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||\"log\"===t.ya.type&&t.yerr<=0)){var h=g.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+h+\" / -\"+g.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" ± \"+h,\"y\"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&\"all\"!==p&&(-1===(p=Array.isArray(p)?p:p.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===p.indexOf(\"y\")&&(t.yLabel=void 0),-1===p.indexOf(\"z\")&&(t.zLabel=void 0),-1===p.indexOf(\"text\")&&(t.text=void 0),-1===p.indexOf(\"name\")&&(t.name=void 0)),t}function N(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,u=r.event,c=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),f||c){var h=d.combine(s.plot_bgcolor,s.paper_bgcolor);if(c){var v,y,m=e.hLinePoint;n=m&&m.xa,\"cursor\"===(i=m&&m.ya).spikesnap?(v=u.pointerX,y=u.pointerY):(v=n._offset+m.x,y=i._offset+m.y);var x,b,_=a.readability(m.color,h)<1.5?d.contrast(h):m.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=g.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=A,b=v),-1!==w.indexOf(\"across\")){var 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n=r(33816),i=r(31780).templatedArray;r(36208),t.exports=i(\"image\",{visible:{valType:\"boolean\",dflt:!0,editType:\"arraydraw\"},source:{valType:\"string\",editType:\"arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},sizex:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizey:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},x:{valType:\"any\",dflt:0,editType:\"arraydraw\"},y:{valType:\"any\",dflt:0,editType:\"arraydraw\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"arraydraw\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\",editType:\"arraydraw\"},xref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.x.toString()],dflt:\"paper\",editType:\"arraydraw\"},yref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.y.toString()],dflt:\"paper\",editType:\"arraydraw\"},editType:\"arraydraw\"})},63556:function(t,e,r){\"use 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b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll(\"image\").data(c[e]||[]);w.enter().append(\"image\"),w.exit().remove(),w.each((function(t){g.bind(this)(t),y.bind(this)(t)}))}}}},7402:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"images\",layoutAttributes:r(65760),supplyLayoutDefaults:r(25024),includeBasePlot:r(36632)(\"images\"),draw:r(60963),convertCoords:r(63556)}},3800:function(t,e,r){\"use strict\";var n=r(25376),i=r(22548);t.exports={_isSubplotObj:!0,visible:{valType:\"boolean\",dflt:!0,editType:\"legend\"},bgcolor:{valType:\"color\",editType:\"legend\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"legend\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"legend\"},font:n({editType:\"legend\"}),grouptitlefont:n({editType:\"legend\"}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"v\",editType:\"legend\"},traceorder:{valType:\"flaglist\",flags:[\"reversed\",\"grouped\"],extras:[\"normal\"],editType:\"legend\"},tracegroupgap:{valType:\"number\",min:0,dflt:10,editType:\"legend\"},entrywidth:{valType:\"number\",min:0,editType:\"legend\"},entrywidthmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"pixels\",editType:\"legend\"},indentation:{valType:\"number\",min:-15,dflt:0,editType:\"legend\"},itemsizing:{valType:\"enumerated\",values:[\"trace\",\"constant\"],dflt:\"trace\",editType:\"legend\"},itemwidth:{valType:\"number\",min:30,dflt:30,editType:\"legend\"},itemclick:{valType:\"enumerated\",values:[\"toggle\",\"toggleothers\",!1],dflt:\"toggle\",editType:\"legend\"},itemdoubleclick:{valType:\"enumerated\",values:[\"toggle\",\"toggleothers\",!1],dflt:\"toggleothers\",editType:\"legend\"},groupclick:{valType:\"enumerated\",values:[\"toggleitem\",\"togglegroup\"],dflt:\"togglegroup\",editType:\"legend\"},x:{valType:\"number\",editType:\"legend\"},xref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"legend\"},y:{valType:\"number\",editType:\"legend\"},yref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"legend\"},uirevision:{valType:\"any\",editType:\"none\"},valign:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"middle\",editType:\"legend\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"legend\"},font:n({editType:\"legend\"}),side:{valType:\"enumerated\",values:[\"top\",\"left\",\"top left\",\"top center\",\"top right\"],editType:\"legend\"},editType:\"legend\"},editType:\"legend\"}},65196:function(t){\"use strict\";t.exports={scrollBarWidth:6,scrollBarMinHeight:20,scrollBarColor:\"#808BA4\",scrollBarMargin:4,scrollBarEnterAttrs:{rx:20,ry:3,width:0,height:0},titlePad:2,itemGap:5}},77864:function(t,e,r){\"use strict\";var n=r(24040),i=r(3400),a=r(31780),o=r(45464),s=r(3800),l=r(64859),u=r(42451);function c(t,e,r,c){var f=e[t]||{},h=a.newContainer(r,t);function p(t,e){return i.coerce(f,h,s,t,e)}var d=i.coerceFont(p,\"font\",r.font);if(p(\"bgcolor\",r.paper_bgcolor),p(\"bordercolor\"),p(\"visible\")){for(var v,g=function(t,e){var r=v._input,n=v;return i.coerce(r,n,o,t,e)},y=r.font||{},m=i.coerceFont(p,\"grouptitlefont\",i.extendFlat({},y,{size:Math.round(1.1*y.size)})),x=0,b=!1,_=\"normal\",w=(r.shapes||[]).filter((function(t){return t.showlegend})),T=c.concat(w).filter((function(e){return t===(e.legend||\"legend\")})),k=0;k<T.length;k++)if((v=T[k]).visible){var A=v._isShape;(v.showlegend||v._dfltShowLegend&&!(v._module&&v._module.attributes&&v._module.attributes.showlegend&&!1===v._module.attributes.showlegend.dflt))&&(x++,v.showlegend&&(b=!0,(!A&&n.traceIs(v,\"pie-like\")||!0===v._input.showlegend)&&x++),i.coerceFont(g,\"legendgrouptitle.font\",m)),(!A&&n.traceIs(v,\"bar\")&&\"stack\"===r.barmode||-1!==[\"tonextx\",\"tonexty\"].indexOf(v.fill))&&(_=u.isGrouped({traceorder:_})?\"grouped+reversed\":\"reversed\"),void 0!==v.legendgroup&&\"\"!==v.legendgroup&&(_=u.isReversed({traceorder:_})?\"reversed+grouped\":\"grouped\")}var M=i.coerce(e,r,l,\"showlegend\",b&&x>(\"legend\"===t?1:0));if(!1===M&&(r[t]=void 0),(!1!==M||f.uirevision)&&(p(\"uirevision\",r.uirevision),!1!==M)){p(\"borderwidth\");var S,E,L,C=\"h\"===p(\"orientation\"),P=\"paper\"===p(\"yref\"),O=\"paper\"===p(\"xref\"),I=\"left\";if(C?(S=0,n.getComponentMethod(\"rangeslider\",\"isVisible\")(e.xaxis)?P?(E=1.1,L=\"bottom\"):(E=1,L=\"top\"):P?(E=-.1,L=\"top\"):(E=0,L=\"bottom\")):(E=1,L=\"auto\",O?S=1.02:(S=1,I=\"right\")),i.coerce(f,h,{x:{valType:\"number\",editType:\"legend\",min:O?-2:0,max:O?3:1,dflt:S}},\"x\"),i.coerce(f,h,{y:{valType:\"number\",editType:\"legend\",min:P?-2:0,max:P?3:1,dflt:E}},\"y\"),p(\"traceorder\",_),u.isGrouped(r[t])&&p(\"tracegroupgap\"),p(\"entrywidth\"),p(\"entrywidthmode\"),p(\"indentation\"),p(\"itemsizing\"),p(\"itemwidth\"),p(\"itemclick\"),p(\"itemdoubleclick\"),p(\"groupclick\"),p(\"xanchor\",I),p(\"yanchor\",L),p(\"valign\"),i.noneOrAll(f,h,[\"x\",\"y\"]),p(\"title.text\")){p(\"title.side\",C?\"left\":\"top\");var z=i.extendFlat({},d,{size:i.bigFont(d.size)});i.coerceFont(p,\"title.font\",z)}}}}t.exports=function(t,e,r){var n,a=r.slice(),o=e.shapes;if(o)for(n=0;n<o.length;n++){var 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n,a,s=O(r),l=t.data()[0][0],c=l.trace,h=o.traceIs(c,\"pie-like\"),d=!r._inHover&&e._context.edits.legendText&&!h,v=r._maxNameLength;l.groupTitle?(n=l.groupTitle.text,a=l.groupTitle.font):(a=r.font,r.entries?n=l.text:(n=h?l.label:c.name,c._meta&&(n=i.templateString(n,c._meta))));var g=i.ensureSingle(t,\"text\",s+\"text\");g.attr(\"text-anchor\",\"start\").call(u.font,a).text(d?S(n,v):n);var y=r.indentation+r.itemwidth+2*p.itemGap;f.positionText(g,y,0),d?g.call(f.makeEditable,{gd:e,text:n}).call(L,t,e,r).on(\"edit\",(function(n){this.text(S(n,v)).call(L,t,e,r);var a=l.trace._fullInput||{},s={};if(o.hasTransform(a,\"groupby\")){var u=o.getTransformIndices(a,\"groupby\"),f=u[u.length-1],h=i.keyedContainer(a,\"transforms[\"+f+\"].styles\",\"target\",\"value.name\");h.set(l.trace._group,n),s=h.constructUpdate()}else s.name=n;return a._isShape?o.call(\"_guiRelayout\",e,\"shapes[\"+c.index+\"].name\",s.name):o.call(\"_guiRestyle\",e,s,c.index)})):L(g,t,e,r)}function S(t,e){var 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t.showlegend})),y=v.concat(g),m=d.trace;m._isShape&&(m=m._fullInput);var x,b,_,w,T,k=m.legendgroup,A={},M=[],S=[],E=[],L=(s.shapes||[]).map((function(t){return t._input})),C=!1,P=m.legend,O=m._fullInput;if(O&&O._isShape||!n.traceIs(m,\"pie-like\")){var I,z=k&&k.length,D=[];if(z)for(x=0;x<y.length;x++)(I=y[x]).visible&&I.legendgroup===k&&D.push(x);if(\"toggle\"===l){var R;switch(m.visible){case!0:R=\"legendonly\";break;case!1:R=!1;break;case\"legendonly\":R=!0}if(z)if(h)for(x=0;x<y.length;x++){var F=y[x];!1!==F.visible&&F.legendgroup===k&&tt(F,R)}else tt(m,R);else tt(m,R)}else if(\"toggleothers\"===l){var 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h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=c([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function c(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(c(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(c(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),v=e._has(\"cartesian\"),g=e._has(\"gl3d\"),y=e._has(\"geo\"),m=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,u=0;u<d.length;u++){var c=d[u].toLowerCase();if(c===a||c===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\"),L(C);var P=[],O=[],I=[],z=[];(v||b||m||x||_)+y+g+w+T+k>1?(O=[\"toggleHover\"],I=[\"resetViews\"]):y?(P=[\"zoomInGeo\",\"zoomOutGeo\"],O=[\"hoverClosestGeo\"],I=[\"resetGeo\"]):g?(O=[\"hoverClosest3d\"],I=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],O=[\"toggleHover\"],I=[\"resetViewMapbox\"]):b?O=[\"hoverClosestGl2d\"]:m?O=[\"hoverClosestPie\"]:A?(O=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],I=[\"resetViewSankey\"]):O=[\"toggleHover\"],v&&(O=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(O=[]),!v&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==I[0]&&(I=[\"resetScale2d\"])),g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(v||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||y?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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L(z),L(P.concat(I)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},66400:function(t,e,r){\"use strict\";var n=r(33428),i=r(38248),a=r(3400),o=r(9224),s=r(25788).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover 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n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},c.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button config\");r.addEventListener(\"click\",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute(\"data-toggle\",t.toggle||!1),t.toggle&&n.select(r).classed(\"active\",!0);var s=t.icon;return\"function\"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute(\"data-gravity\",t.gravity||\"n\"),r},c.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n=\"http://www.w3.org/2000/svg\";if(t.path){(e=document.createElementNS(n,\"svg\")).setAttribute(\"viewBox\",[0,0,t.width,r].join(\" \")),e.setAttribute(\"class\",\"icon\");var a=document.createElementNS(n,\"path\");a.setAttribute(\"d\",t.path),t.transform?a.setAttribute(\"transform\",t.transform):void 0!==t.ascent&&a.setAttribute(\"transform\",\"matrix(1 0 0 -1 0 \"+t.ascent+\")\"),e.appendChild(a)}return t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]),e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement(\"a\");return e.href=\"https://plotly.com/\",e.target=\"_blank\",e.setAttribute(\"data-title\",a._(this.graphInfo,\"Produced with Plotly.js\")+\" (v\"+s+\")\"),e.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",e.appendChild(this.createIcon(o.newplotlylogo)),t.appendChild(e),t},c.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1},c.destroy=function(){a.removeElement(this.container.querySelector(\".modebar\")),a.deleteRelatedStyleRule(this._uid)},t.exports=function(t,e){var r=t._fullLayout,i=new u({graphInfo:t,container:r._modebardiv.node(),buttons:e});return r._privateplot&&n.select(i.element).append(\"span\").classed(\"badge-private float--left\",!0).text(\"PRIVATE\"),i}},26680:function(t,e,r){\"use strict\";var n=r(25376),i=r(22548),a=(0,r(31780).templatedArray)(\"button\",{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},step:{valType:\"enumerated\",values:[\"month\",\"year\",\"day\",\"hour\",\"minute\",\"second\",\"all\"],dflt:\"month\",editType:\"plot\"},stepmode:{valType:\"enumerated\",values:[\"backward\",\"todate\"],dflt:\"backward\",editType:\"plot\"},count:{valType:\"number\",min:0,dflt:1,editType:\"plot\"},label:{valType:\"string\",editType:\"plot\"},editType:\"plot\"});t.exports={visible:{valType:\"boolean\",editType:\"plot\"},buttons:a,x:{valType:\"number\",min:-2,max:3,editType:\"plot\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"plot\"},y:{valType:\"number\",min:-2,max:3,editType:\"plot\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"bottom\",editType:\"plot\"},font:n({editType:\"plot\"}),bgcolor:{valType:\"color\",dflt:i.lightLine,editType:\"plot\"},activecolor:{valType:\"color\",editType:\"plot\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"plot\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"}},85984:function(t){\"use 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g=d(\"bgcolor\");d(\"activecolor\",i.contrast(g,l.lightAmount,l.darkAmount)),d(\"bordercolor\"),d(\"borderwidth\")}}},50216:function(t,e,r){\"use strict\";var n=r(33428),i=r(24040),a=r(7316),o=r(76308),s=r(43616),l=r(3400),u=l.strTranslate,c=r(72736),f=r(79811),h=r(84284),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR,g=r(85984),y=r(48040);function m(t){return t._id}function x(t,e,r){var n=l.ensureSingle(t,\"rect\",\"selector-rect\",(function(t){t.attr(\"shape-rendering\",\"crispEdges\")}));n.attr({rx:g.rx,ry:g.ry}),n.call(o.stroke,e.bordercolor).call(o.fill,function(t,e){return e._isActive||e._isHovered?t.activecolor:t.bgcolor}(e,r)).style(\"stroke-width\",e.borderwidth+\"px\")}function b(t,e,r,n){var i,a;l.ensureSingle(t,\"text\",\"selector-text\",(function(t){t.attr(\"text-anchor\",\"middle\")})).call(s.font,e.font).text((i=r,a=n._fullLayout._meta,i.label?a?l.templateString(i.label,a):i.label:\"all\"===i.step?\"all\":i.count+i.step.charAt(0))).call((function(t){c.convertToTspans(t,n)}))}t.exports=function(t){var e=t._fullLayout._infolayer.selectAll(\".rangeselector\").data(function(t){for(var e=f.list(t,\"x\",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append(\"g\").classed(\"rangeselector\",!0),e.exit().remove(),e.style({cursor:\"pointer\",\"pointer-events\":\"all\"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll(\"g.button\").data(l.filterVisible(f.buttons));h.enter().append(\"g\").classed(\"button\",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if(\"all\"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on(\"click\",(function(){t._dragged||i.call(\"_guiRelayout\",t,a)})),r.on(\"mouseover\",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on(\"mouseout\",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(\".selector-text\"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(\".selector-rect\"),i=t.select(\".selector-text\"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr(\"transform\",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_=\"left\";l.isRightAnchor(r)&&(x-=f,_=\"right\"),l.isCenterAnchor(r)&&(x-=f/2,_=\"center\");var w=\"top\";l.isBottomAnchor(r)&&(b-=h,w=\"bottom\"),l.isMiddleAnchor(r)&&(b-=h/2,w=\"middle\"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+\"-range-selector\",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr(\"transform\",u(x,b))}(t,h,f,o._name,r)}))}},48040:function(t,e,r){\"use strict\";var n=r(73220),i=r(3400).titleCase;t.exports=function(t,e){var r=t._name,a={};if(\"all\"===e.step)a[r+\".autorange\"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n[\"utc\"+i(s)],u=e.count;switch(e.stepmode){case\"backward\":r=t.l2r(+l.offset(o,-u));break;case\"todate\":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+\".range[0]\"]=o[0],a[r+\".range[1]\"]=o[1]}return a}},41152:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"rangeselector\",schema:{subplots:{xaxis:{rangeselector:r(26680)}}},layoutAttributes:r(26680),handleDefaults:r(22148),draw:r(50216)}},11200:function(t,e,r){\"use 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n=r(3400),i=r(31780),a=r(79811),o=r(11200),s=r(10936);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,\"rangeslider\");if(_(\"visible\")){_(\"bgcolor\",e.plot_bgcolor),_(\"bordercolor\"),_(\"borderwidth\"),_(\"thickness\"),_(\"autorange\",!u.isValidRange(h.range)),_(\"range\");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf(\"y\"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf(\"y\"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,\"yaxis\");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x=\"fixed\"),\"match\"!==w(\"rangemode\",x)&&w(\"range\",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},20060:function(t,e,r){\"use strict\";var 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h=u(n.d2p(r._rl[0])),p=u(n.d2p(r._rl[1]));if(t.select(\"rect.\"+v.slideBoxClassName).attr(\"x\",h).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMinClassName).attr(\"width\",h),t.select(\"rect.\"+v.maskMaxClassName).attr(\"x\",p).attr(\"width\",n._width-p),\"match\"!==a.rangemode){var d=n._height-c(n.d2pOppAxis(i._rl[1])),g=n._height-c(n.d2pOppAxis(i._rl[0]));t.select(\"rect.\"+v.maskMinOppAxisClassName).attr(\"x\",h).attr(\"height\",d).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMaxOppAxisClassName).attr(\"x\",h).attr(\"y\",g).attr(\"height\",n._height-g).attr(\"width\",p-h),t.select(\"rect.\"+v.slideBoxClassName).attr(\"y\",d).attr(\"height\",g-d)}var y=.5,m=Math.round(f(h-l))-y,x=Math.round(f(p-l))+y;t.select(\"g.\"+v.grabberMinClassName).attr(\"transform\",s(m,y)),t.select(\"g.\"+v.grabberMaxClassName).attr(\"transform\",s(x,y))}(a,0,r,l,u,f),\"bottom\"===r.side&&c.draw(t,r._id+\"title\",{propContainer:r,propName:r._name+\".title\",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:L+l._height+l._offsetShift+10+1.5*r.title.font.size,\"text-anchor\":\"middle\"}})})))}},97944:function(t,e,r){\"use strict\";var n=r(79811),i=r(72736),a=r(74636),o=r(84284).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var e=n.list({_fullLayout:t},\"x\",!0),r=t.margin,i=[];if(!t._has(\"gl2d\"))for(var a=0;a<e.length;a++){var o=e[a];if(l(o)){i.push(o);var u=o[s];u._id=s+o._id,u._height=(t.height-r.b-r.t)*u.thickness,u._offsetShift=Math.floor(u.borderwidth/2)}}t._rangeSliderData=i},e.autoMarginOpts=function(t,e){var r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return\"bottom\"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},49692:function(t,e,r){\"use strict\";var n=r(3400),i=r(11200),a=r(10936),o=r(97944);t.exports={moduleType:\"component\",name:\"rangeslider\",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(11200),handleDefaults:r(94040),calcAutorange:r(26652),draw:r(20060),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},10936:function(t){\"use 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w=h(t)&&t._fullLayout._activeSelectionIndex===e;w&&(m=t._fullLayout.activeselection.fillcolor,y=t._fullLayout.activeselection.opacity);for(var T=[],k=1;k>=0;k--){var A=r.append(\"path\").attr(g).style(\"opacity\",k?.1:y).call(o.stroke,x).call(o.fill,m).call(s.dashLine,k?\"solid\":_,k?4+b:b);if(d(A,t,a),w){var M=l(t.layout,\"selections\",a);A.style({cursor:\"move\"});var S={element:A.node(),plotinfo:p,gd:t,editHelpers:M,isActiveSelection:!0},E=n(u,t);i(E,A,S)}else A.style(\"pointer-events\",k?\"all\":\"none\");T[k]=A}var L=T[0];T[1].node().addEventListener(\"click\",(function(){return function(t,e){if(h(t)){var r=+e.node().getAttribute(\"data-index\");if(r>=0){if(r===t._fullLayout._activeSelectionIndex)return void v(t);t._fullLayout._activeSelectionIndex=r,t._fullLayout._deactivateSelection=v,f(t)}}}(t,L)}))}(t._fullLayout._selectionLayer)}function d(t,e,r){var n=r.xref+r.yref;s.setClipUrl(t,\"clip\"+e._fullLayout._uid+n,e)}function v(t){h(t)&&t._fullLayout._activeSelectionIndex>=0&&(a(t),delete t._fullLayout._activeSelectionIndex,f(t))}t.exports={draw:f,drawOne:p,activateLastSelection:function(t){if(h(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=v,f(t)}}}},34200:function(t,e,r){\"use strict\";var n=r(98192).u,i=r(92880).extendFlat;t.exports={newselection:{mode:{valType:\"enumerated\",values:[\"immediate\",\"gradual\"],dflt:\"immediate\",editType:\"none\"},line:{color:{valType:\"color\",editType:\"none\"},width:{valType:\"number\",min:1,dflt:1,editType:\"none\"},dash:i({},n,{dflt:\"dot\",editType:\"none\"}),editType:\"none\"},editType:\"none\"},activeselection:{fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"none\"},opacity:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"none\"},editType:\"none\"}}},81004:function(t){\"use strict\";t.exports=function(t,e,r){r(\"newselection.mode\"),r(\"newselection.line.width\")&&(r(\"newselection.line.color\"),r(\"newselection.line.dash\")),r(\"activeselection.fillcolor\"),r(\"activeselection.opacity\")}},5968:function(t,e,r){\"use strict\";var n=r(72760).selectMode,i=r(1936).clearOutline,a=r(9856),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var a=r.getAttribute(\"d\"),u=e.gd,c=u._fullLayout.newselection,f=e.plotinfo,h=f.xaxis,p=f.yaxis,d=e.isActiveSelection,v=e.dragmode,g=(u.layout||{}).selections||[];if(!n(v)&&void 0!==d){var y=u._fullLayout._activeSelectionIndex;if(y<g.length)switch(u._fullLayout.selections[y].type){case\"rect\":v=\"select\";break;case\"path\":v=\"lasso\"}}var m,x=o(a,u,f,d),b={xref:h._id,yref:p._id,opacity:c.opacity,line:{color:c.line.color,width:c.line.width,dash:c.line.dash}};1===x.length&&(m=x[0]),m&&5===m.length&&\"select\"===v?(b.type=\"rect\",b.x0=m[0][1],b.y0=m[0][2],b.x1=m[2][1],b.y1=m[2][2]):(b.type=\"path\",h&&p&&l(x,h,p),b.path=s(x),m=null),i(u);for(var _=e.editHelpers,w=(_||{}).modifyItem,T=[],k=0;k<g.length;k++){var A=u._fullLayout.selections[k];if(A){if(T[k]=A._input,void 0!==d&&k===u._fullLayout._activeSelectionIndex){var M=b;switch(A.type){case\"rect\":w(\"x0\",M.x0),w(\"x1\",M.x1),w(\"y0\",M.y0),w(\"y1\",M.y1);break;case\"path\":w(\"path\",M.path)}}}else T[k]=A}return void 0===d?(T.push(b),T):_?_.getUpdateObj():{}}}}},5840:function(t,e,r){\"use strict\";var n=r(3400).strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}t.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},22676:function(t,e,r){\"use strict\";var n=r(23640),i=r(43156);t.exports={moduleType:\"component\",name:\"selections\",layoutAttributes:r(93956),supplyLayoutDefaults:r(74224),supplyDrawNewSelectionDefaults:r(81004),includeBasePlot:r(36632)(\"selections\"),draw:n.draw,drawOne:n.drawOne,reselect:i.reselect,prepSelect:i.prepSelect,clearOutline:i.clearOutline,clearSelectionsCache:i.clearSelectionsCache,selectOnClick:i.selectOnClick}},43156:function(t,e,r){\"use strict\";var n=r(14756),i=r(61456),a=r(24040),o=r(43616).dashStyle,s=r(76308),l=r(93024),u=r(10624).makeEventData,c=r(72760),f=c.freeMode,h=c.rectMode,p=c.drawMode,d=c.openMode,v=c.selectMode,g=r(65152),y=r(85448),m=r(55496),x=r(1936).clearOutline,b=r(9856),_=b.handleEllipse,w=b.readPaths,T=r(93940).newShapes,k=r(5968),A=r(23640).activateLastSelection,M=r(3400),S=M.sorterAsc,E=r(92065),L=r(91200),C=r(79811).getFromId,P=r(73696),O=r(39172).redrawReglTraces,I=r(83280),z=I.MINSELECT,D=E.filter,R=E.tester,F=r(5840),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,u,c,f,h,p,v,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){W(t,e,a);var _=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 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0!==h?h:J(_)),u=function(t,e,r){return{pointNumber:t,searchInfo:e,subtract:!!r}}(_.pointNumber,_.searchInfo,p),c=G(a.selectionDefs.concat([u])),g=0;g<s.length;g++)if(f=tt(s[g]._module.selectPoints(s[g],c),s[g]),b.length)for(var w=0;w<f.length;w++)b.push(f[w]);else b=f;if($(e,s,v={points:b}),u&&a&&a.selectionDefs.push(u),o){var T=a.mergedPolygons,k=d(a.dragmode);m(et(T,k),o,a)}x&&ft(e,v)}}}function q(t){return\"pointNumber\"in t&&\"searchInfo\"in t}function H(t){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(e,r,n,i){var a=t.searchInfo.cd[0].trace._expandedIndex;return i.cd[0].trace._expandedIndex===a&&n===t.pointNumber},isRect:!1,degenerate:!1,subtract:!!t.subtract}}function G(t){if(t.length){for(var e=[],r=q(t[0])?0:t[0][0][0],n=r,i=q(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(q(t[o]))e.push(H(t[o]));else{var s=R(t[o]);s.subtract=!!t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!e[o].subtract);return a},isRect:!1,degenerate:!1}}}function W(t,e,r){var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(p(a)&&d(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||Y(r),o||(x(e),n._lastSelectedSubplot=i.id)}function Y(t,e){var r=t.dragmode,n=t.plotinfo,i=t.gd;(function(t){return t._fullLayout._activeShapeIndex>=0})(i)&&i._fullLayout._deactivateShape(i),function(t){return t._fullLayout._activeSelectionIndex>=0}(i)&&i._fullLayout._deactivateSelection(i);var o=i._fullLayout._zoomlayer,s=p(r),l=v(r);if(s||l){var u,c,f=o.selectAll(\".select-outline-\"+n.id);f&&i._fullLayout._outlining&&(s&&(u=T(f,t)),u&&a.call(\"_guiRelayout\",i,{shapes:u}),l&&!U(t)&&(c=k(f,t)),c&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",i,{selections:c}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function X(t){return t._id}function Z(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(X),u=r.map(X);for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!U({subplot:n})||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type){if(a._xaxes[l[0]]&&a._yaxes[u[0]]){var c=K(a._module,i,e[0],r[0]);c.scene=t._fullLayout._splomScenes[a.uid],s.push(c)}}else if(\"sankey\"===a.type){var 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c=l[u],f=c.data,h=c.fullData,p=c.pointIndex,d=c.pointIndices;d?([].push.apply(f.selectedpoints,d),i._fullInput!==i&&[].push.apply(h.selectedpoints,d)):(f.selectedpoints.push(p),i._fullInput!==i&&h.selectedpoints.push(p))}}else for(n=0;n<e.length;n++)delete(i=e[n].cd[0].trace).selectedpoints,delete i._input.selectedpoints,i._fullInput!==i&&delete i._fullInput.selectedpoints;!function(t,e){for(var r=!1,n=0;n<e.length;n++){var i=e[n],o=i.cd;a.traceIs(o[0].trace,\"regl\")&&(r=!0);var s=i._module,l=s.styleOnSelect||s.style;l&&(l(t,o,o[0].node3),o[0].nodeRangePlot3&&l(t,o,o[0].nodeRangePlot3))}r&&(P(t),O(t))}(t,e)}function Q(t,e,r){for(var i=(r?n.difference:n.union)({regions:t},{regions:[e]}).regions.reverse(),a=0;a<i.length;a++){var o=i[a];o.subtract=st(o,i.slice(0,a))}return i}function tt(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=u(t[i],n,r);return t}function et(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var 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n=e[r][0].trace,i=t._fullLayout._splomScenes;if(i){var a=i[n.uid];a&&(a.selectBatch=[])}}}(t);for(var O={},I=0;I<P.length;I++){var z=P[I],D=z.indexOf(\"y\"),R=z.slice(0,D),F=z.slice(D),B=o&&s?r:void 0;if(B=at(f,R,F,B)){var N=n;if(!l){var j=C(t,R,\"x\"),U=C(t,F,\"y\");N=Z(t,[j],[U],z);for(var V=0;V<N.length;V++){var q=N[V],H=q.cd[0],G=H.trace;if(\"scattergl\"===q._module.name&&!H.t.xpx){var W=G.x,Y=G.y,X=G._length;H.t.xpx=[],H.t.ypx=[];for(var K=0;K<X;K++)H.t.xpx[K]=j.c2p(W[K]),H.t.ypx[K]=U.c2p(Y[K])}\"splom\"===q._module.name&&(O[G.uid]||(O[G.uid]=!0))}}var J=rt(B,N);u=u.concat(J),c=c.concat(N)}}var Q={points:u};$(t,c,Q);var tt=h.clickmode.indexOf(\"event\")>-1&&e;if(!a&&e){var et=ot(t,!0);if(et.length){var nt=et[0].xref,pt=et[0].yref;if(nt&&pt){var dt=ut(et);ct([C(t,nt,\"x\"),C(t,pt,\"y\")])(Q,dt)}}t._fullLayout._noEmitSelectedAtStart?t._fullLayout._noEmitSelectedAtStart=!1:tt&&ft(t,Q),h._reselect=!1}if(!a&&h._deselect){var vt=h._deselect;(function(t,e,r){for(var 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v=\"pixel\"===h.xsizemode?h.xanchor:h.x0,g=\"pixel\"===h.xsizemode?h.xanchor:h.x1;(f=c(u=i.getFromId(t,h.xref),v,g,h.path,a.paramIsX))&&(h._extremes[u._id]=i.findExtremes(u,f,s(h)))}if(\"paper\"!==h.yref&&\"domain\"!==d){var y=\"pixel\"===h.ysizemode?h.yanchor:h.y0,m=\"pixel\"===h.ysizemode?h.yanchor:h.y1;(f=c(u=i.getFromId(t,h.yref),y,m,h.path,a.paramIsY))&&(h._extremes[u._id]=i.findExtremes(u,f,l(h)))}}}},85448:function(t){\"use strict\";t.exports={segmentRE:/[MLHVQCTSZ][^MLHVQCTSZ]*/g,paramRE:/[^\\s,]+/g,paramIsX:{M:{0:!0,drawn:0},L:{0:!0,drawn:0},H:{0:!0,drawn:0},V:{},Q:{0:!0,2:!0,drawn:2},C:{0:!0,2:!0,4:!0,drawn:4},T:{0:!0,drawn:0},S:{0:!0,2:!0,drawn:2},Z:{}},paramIsY:{M:{1:!0,drawn:1},L:{1:!0,drawn:1},H:{},V:{0:!0,drawn:0},Q:{1:!0,3:!0,drawn:3},C:{1:!0,3:!0,5:!0,drawn:5},T:{1:!0,drawn:1},S:{1:!0,3:!0,drawn:5},Z:{}},numParams:{M:2,L:2,H:1,V:1,Q:4,C:6,T:2,S:4,Z:0}}},43712:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460),a=r(51272),o=r(46056),s=r(65152);function 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T=m+\"0\",k=m+\"1\",A=t[T],M=t[k];t[T]=g(t[T],!0),t[k]=g(t[k],!0),\"pixel\"===b?(a(T,0),a(k,10)):(i.coercePosition(e,_,a,w,T,.25),i.coercePosition(e,_,a,w,k,.75)),e[T]=y(e[T]),e[k]=y(e[k]),t[T]=A,t[k]=M}if(\"pixel\"===b){var S=t[x];t[x]=g(t[x],!0),i.coercePosition(e,_,a,w,x,.25),e[x]=y(e[x]),t[x]=S}}c&&n.noneOrAll(t,e,[\"x0\",\"x1\",\"y0\",\"y1\"]);var E,L,C=\"line\"===u;if(c&&(E=a(\"label.texttemplate\")),E||(L=a(\"label.text\")),L||E){a(\"label.textangle\");var P=a(\"label.textposition\",C?\"middle\":\"middle center\");a(\"label.xanchor\"),a(\"label.yanchor\",function(t,e){return t?\"bottom\":-1!==e.indexOf(\"top\")?\"top\":-1!==e.indexOf(\"bottom\")?\"bottom\":\"middle\"}(C,P)),a(\"label.padding\"),n.coerceFont(a,\"label.font\",r.font)}}}t.exports=function(t,e){a(t,e,{name:\"shapes\",handleItemDefaults:l})}},60728:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460),a=r(72736),o=r(43616),s=r(9856).readPaths,l=r(65152),u=l.getPathString,c=r(97728),f=r(84284).FROM_TL;t.exports=function(t,e,r,h){if(h.selectAll(\".shape-label\").remove(),r.label.text||r.label.texttemplate){var p;if(r.label.texttemplate){var d={};if(\"path\"!==r.type){var v=i.getFromId(t,r.xref),g=i.getFromId(t,r.yref);for(var y in c){var m=c[y](r,v,g);void 0!==m&&(d[y]=m)}}p=n.texttemplateStringForShapes(r.label.texttemplate,{},t._fullLayout._d3locale,d)}else p=r.label.text;var x,b,_,w,T={\"data-index\":e},k=r.label.font,A=h.append(\"g\").attr(T).classed(\"shape-label\",!0).append(\"text\").attr({\"data-notex\":1}).classed(\"shape-label-text\",!0).text(p);if(r.path){var M=u(t,r),S=s(M,t);x=1/0,_=1/0,b=-1/0,w=-1/0;for(var E=0;E<S.length;E++)for(var L=0;L<S[E].length;L++)for(var C=S[E][L],P=1;P<C.length;P+=2){var O=C[P],I=C[P+1];x=Math.min(x,O),b=Math.max(b,O),_=Math.min(_,I),w=Math.max(w,I)}}else{var 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n,i=t[0][0],o=e.gd,p=i.getAttribute(\"d\"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?\"paper\":T._id,yref:M?\"paper\":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&\"drawrect\"===r)L.type=\"rect\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&\"drawline\"===r)L.type=\"line\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&\"drawcircle\"===r){L.type=\"circle\";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],z=n[s][2],D=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&(\"date\"===y.xaxis.type||\"log\"===y.xaxis.type),N=y.yaxis&&(\"date\"===y.yaxis.type||\"log\"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(z=v(y.yaxis,z),D=v(y.yaxis,D),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(z+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-D+R-z)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type=\"path\",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case\"rect\":a=\"drawrect\";break;case\"circle\":a=\"drawcircle\";break;case\"line\":a=\"drawline\";break;case\"path\":var l=o[s].path||\"\";a=\"Z\"===l[l.length-1]?\"drawclosedpath\":\"drawopenpath\"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case\"line\":case\"rect\":case\"circle\":f(\"x0\",v.x0),f(\"x1\",v.x1),f(\"y0\",v.y0),f(\"y1\",v.y1);break;case\"path\":f(\"path\",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},1936:function(t){\"use strict\";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._outlining=!1}}},65152:function(t,e,r){\"use strict\";var n=r(85448),i=r(3400),a=r(54460);e.rangeToShapePosition=function(t){return\"log\"===t.type?t.r2d:function(t){return 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n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var o,s,l,u,c,f,h,p,d=r.type,v=a.getRefType(r.xref),g=a.getRefType(r.yref),y=a.getFromId(t,r.xref),m=a.getFromId(t,r.yref),x=t._fullLayout._size;if(y?\"domain\"===v?s=function(t){return y._offset+y._length*t}:(o=e.shapePositionToRange(y),s=function(t){return y._offset+y.r2p(o(t,!0))}):s=function(t){return x.l+x.w*t},m?\"domain\"===g?u=function(t){return m._offset+m._length*(1-t)}:(l=e.shapePositionToRange(m),u=function(t){return m._offset+m.r2p(l(t,!0))}):u=function(t){return x.t+x.h*(1-t)},\"path\"===d)return y&&\"date\"===y.type&&(s=e.decodeDate(s)),m&&\"date\"===m.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t=\"pixel\"===o?e(l)+Number(t):e(t):h[a]&&(t=\"pixel\"===s?r(u)-Number(t):r(t)),++a>p&&(t=\"X\"),t}));return a>p&&(d=d.replace(/[\\s,]*X.*/,\"\"),i.log(\"Ignoring extra params in segment \"+t)),c+d}))}(r,s,u);if(\"pixel\"===r.xsizemode){var b=s(r.xanchor);c=b+r.x0,f=b+r.x1}else c=s(r.x0),f=s(r.x1);if(\"pixel\"===r.ysizemode){var _=u(r.yanchor);h=_-r.y0,p=_-r.y1}else h=u(r.y0),p=u(r.y1);if(\"line\"===d)return\"M\"+c+\",\"+h+\"L\"+f+\",\"+p;if(\"rect\"===d)return\"M\"+c+\",\"+h+\"H\"+f+\"V\"+p+\"H\"+c+\"Z\";var w=(c+f)/2,T=(h+p)/2,k=Math.abs(w-c),A=Math.abs(T-h),M=\"A\"+k+\",\"+A,S=w+k+\",\"+T;return\"M\"+S+M+\" 0 1,1 \"+w+\",\"+(T-A)+M+\" 0 0,1 \"+S+\"Z\"}},41592:function(t,e,r){\"use strict\";var n=r(4016);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(46056),supplyLayoutDefaults:r(43712),supplyDrawNewShapeDefaults:r(65144),includeBasePlot:r(36632)(\"shapes\"),calcAutorange:r(96084),draw:n.draw,drawOne:n.drawOne}},97728:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t,r){return e(t.x1,r)-e(t.x0,r)}function i(t,r,n){return e(t.y1,n)-e(t.y0,n)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:i(t,0,r)/n(t,e)},dx:n,dy:i,width:function(t,e){return Math.abs(n(t,e))},height:function(t,e,r){return Math.abs(i(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 0:Math.sqrt(Math.pow(n(t,e),2)+Math.pow(i(t,0,r),2))},xcenter:function(t,n){return r((e(t.x1,n)+e(t.x0,n))/2,n)},ycenter:function(t,n,i){return r((e(t.y1,i)+e(t.y0,i))/2,i)}}},89861:function(t,e,r){\"use 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n=r(25376),i=r(66741),a=r(92880).extendDeepAll,o=r(67824).overrideAll,s=r(85656),l=r(31780).templatedArray,u=r(60876),c=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:c,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:u.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:u.railBgColor},bordercolor:{valType:\"color\",dflt:u.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:u.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:u.tickLength},tickcolor:{valType:\"color\",dflt:u.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:u.minorTickLength}}),\"arraydraw\",\"from-root\")},60876:function(t){\"use strict\";t.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},8132:function(t,e,r){\"use 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u=n.select(this);u.call(m,o,s,t).call(M,o,b),u.on(\"click\",(function(){n.event.defaultPrevented||(s.execute&&(s.args2&&o.active===l?(v(t,o,0,e,r,a,-1),i.executeAPICommand(t,s.method,s.args2)):(v(t,o,0,e,r,a,l),i.executeAPICommand(t,s.method,s.args))),t.emit(\"plotly_buttonclicked\",{menu:o,button:s,active:o.active}))})),u.on(\"mouseover\",(function(){u.call(w)})),u.on(\"mouseout\",(function(){u.call(T,o),c.call(_,o)}))})),c.call(_,o),x?(k.w=Math.max(y.openWidth,y.headerWidth),k.h=b.y-k.t):(k.w=b.x-k.l,k.h=Math.max(y.openHeight,y.headerHeight)),k.direction=o.direction,a&&(c.size()?function(t,e,r,n,i,a){var o,s,l,u=i.direction,c=\"up\"===u||\"down\"===u,h=i._dims,p=i.active;if(c)for(s=0,l=0;l<p;l++)s+=h.heights[l]+f.gapButton;else for(o=0,l=0;l<p;l++)o+=h.widths[l]+f.gapButton;n.enable(a,o,s),n.hbar&&n.hbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),n.vbar&&n.vbar.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\")}(0,0,0,a,o,k):function(t){var e=!!t.hbar,r=!!t.vbar;e&&t.hbar.transition().attr(\"opacity\",\"0\").each(\"end\",(function(){e=!1,r||t.disable()})),r&&t.vbar.transition().attr(\"opacity\",\"0\").each(\"end\",(function(){r=!1,e||t.disable()}))}(a))}function m(t,e,r,n){t.call(x,e).call(b,e,r,n)}function x(t,e){s.ensureSingle(t,\"rect\",f.itemRectClassName,(function(t){t.attr({rx:f.rx,ry:f.ry,\"shape-rendering\":\"crispEdges\"})})).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\")}function b(t,e,r,n){var i=s.ensureSingle(t,\"text\",f.itemTextClassName,(function(t){t.attr({\"text-anchor\":\"start\",\"data-notex\":1})})),a=r.label,u=n._fullLayout._meta;u&&(a=s.templateString(a,u)),i.call(o.font,e.font).text(a).call(l.convertToTspans,n)}function _(t,e){var r=e.active;t.each((function(t,i){var o=n.select(this);i===r&&e.showactive&&o.select(\"rect.\"+f.itemRectClassName).call(a.fill,f.activeColor)}))}function w(t){t.select(\"rect.\"+f.itemRectClassName).call(a.fill,f.hoverColor)}function T(t,e){t.select(\"rect.\"+f.itemRectClassName).call(a.fill,e.bgcolor)}function k(t,e){var r=e._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},a=o.tester.selectAll(\"g.\"+f.dropdownButtonClassName).data(s.filterVisible(e.buttons));a.enter().append(\"g\").classed(f.dropdownButtonClassName,!0);var u=-1!==[\"up\",\"down\"].indexOf(e.direction);a.each((function(i,a){var s=n.select(this);s.call(m,e,i,t);var h=s.select(\".\"+f.itemTextClassName),p=h.node()&&o.bBox(h.node()).width,d=Math.max(p+f.textPadX,f.minWidth),v=e.font.size*c,g=l.lineCount(h),y=Math.max(v*g,f.minHeight)+f.textOffsetY;y=Math.ceil(y),d=Math.ceil(d),r.widths[a]=d,r.heights[a]=y,r.height1=Math.max(r.height1,y),r.width1=Math.max(r.width1,d),u?(r.totalWidth=Math.max(r.totalWidth,d),r.openWidth=r.totalWidth,r.totalHeight+=y+f.gapButton,r.openHeight+=y+f.gapButton):(r.totalWidth+=d+f.gapButton,r.openWidth+=d+f.gapButton,r.totalHeight=Math.max(r.totalHeight,y),r.openHeight=r.totalHeight)})),u?r.totalHeight-=f.gapButton:r.totalWidth-=f.gapButton,r.headerWidth=r.width1+f.arrowPadX,r.headerHeight=r.height1,\"dropdown\"===e.type&&(u?(r.width1+=f.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=f.arrowPadX),a.remove();var h=r.totalWidth+e.pad.l+e.pad.r,p=r.totalHeight+e.pad.t+e.pad.b,d=t._fullLayout._size;r.lx=d.l+d.w*e.x,r.ly=d.t+d.h*(1-e.y);var 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor=\"#808BA4\",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,u=o.height;this.position=t;var c,f,h,p,d=this.position.l,v=this.position.w,g=this.position.t,y=this.position.h,m=this.position.direction,x=\"down\"===m,b=\"left\"===m,_=\"up\"===m,w=v,T=y;x||b||\"right\"===m||_||(this.position.direction=\"down\",x=!0),x||_?(f=(c=d)+w,x?(h=g,T=(p=Math.min(h+T,u))-h):T=(p=g+T)-(h=Math.max(p-T,0))):(p=(h=g)+T,b?w=(f=d+w)-(c=Math.max(f-w,0)):(c=d,w=(f=Math.min(c+w,l))-c)),this._box={l:c,t:h,w:w,h:T};var k=v>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>u&&(E=u-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=y>T,P=s.barWidth+2*s.barPad,O=s.barLength+2*s.barPad,I=d+v,z=g;I+P>l&&(I=l-P);var D=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);D.exit().on(\".drag\",null).remove(),D.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:I,y:z,width:P,height:O}),this._vbarYMin=z+O/2,this._vbarTranslateMax=T-O):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=c-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll(\"#\"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),k||C?(this._clipRect=U.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:v,height:y})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var q=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(q),C&&this.vbar.on(\".drag\",null).call(q)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},84284:function(t){\"use strict\";t.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:\"right\",right:\"left\",top:\"bottom\",bottom:\"top\"}}},36208:function(t){\"use strict\";t.exports={axisRefDescription:function(t,e,r){return[\"If set to a\",t,\"axis id (e.g. *\"+t+\"* or\",\"*\"+t+\"2*), the `\"+t+\"` position refers to a\",t,\"coordinate. 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L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var it=\"y\"===t._id.charAt(0),st=1;\"auto\"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=Dt(C[r].value,t);var ut=t.c2p(C[r].value);(it?lt>ut-st:lt<ut+st)?C.splice(f?r+1:r,1):lt=ut}}zt(t)&&360===Math.abs(c[1]-c[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead=\"\",t._inCalcTicks=!0;var ct,ft,ht=function(e){e.text=\"\",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var pt=C[r].minor,dt=C[r].value;pt?L.push({x:dt,minor:!0}):(n=t._prevDateHead,ct=H.tickText(t,dt,!1,C[r].simpleLabel),void 0!==(ft=C[r].periodX)&&(ct.periodX=ft,(ft>p||ft<h)&&(ft>p&&(ct.periodX=p),ft<h&&(ct.periodX=h),ht(ct))),C[r].skipLabel&&ht(ct),v.push(ct))}return v=v.concat(L),t._inCalcTicks=!1,u&&v.length&&(v[0].noTick=!0),v};var st=[2,5,10],lt=[1,2,3,6,12],ut=[1,2,5,10,15,30],ct=[1,2,3,7,14],ft=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],ht=[-.301,0,.301,.699,1],pt=[15,30,45,90,180];function dt(t,e,r){return e*s.roundUp(t/e,r)}function vt(t){var e=t.dtick;if(t._tickexponent=0,i(e)||\"string\"==typeof e||(e=1),\"category\"!==t.type&&\"multicategory\"!==t.type||(t._tickround=null),\"date\"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,\"\"),a=n.length;if(\"M\"===String(e).charAt(0))a>10||\"01-01\"!==n.substr(5)?t._tickround=\"d\":t._tickround=+e.substr(1)%12==0?\"y\":\"m\";else if(e>=M&&a<=10||e>=15*M)t._tickround=\"d\";else if(e>=L&&a<=16||e>=E)t._tickround=\"M\";else if(e>=C&&a<=19||e>=L)t._tickround=\"S\";else{var o=t.l2r(r+e).replace(/^-/,\"\").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||\"L\"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var 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r=!1,n=e._id,i=t._fullLayout._axisConstraintGroups,a=0;a<i.length;a++)if(i[a][n]){r=!0;break}r&&\"domain\"===e.constrain||(e._input.domain=e.domain=e._inputDomain,delete e._inputDomain)}}},51184:function(t,e,r){\"use strict\";var n=r(33428),i=r(3400),a=i.numberFormat,o=r(49760),s=r(89184),l=r(24040),u=i.strTranslate,c=r(72736),f=r(76308),h=r(43616),p=r(93024),d=r(54460),v=r(93972),g=r(86476),y=r(72760),m=y.selectingOrDrawing,x=y.freeMode,b=r(84284).FROM_TL,_=r(73696),w=r(39172).redrawReglTraces,T=r(7316),k=r(79811).getFromId,A=r(22676).prepSelect,M=r(22676).clearOutline,S=r(22676).selectOnClick,E=r(21160),L=r(33816),C=L.MINDRAG,P=L.MINZOOM,O=!0;function I(t,e,r,n){var a=i.ensureSingle(t.draglayer,e,r,(function(e){e.classed(\"drag\",!0).style({fill:\"transparent\",\"stroke-width\":0}).attr(\"data-subplot\",t.id)}));return a.call(v,n),a.node()}function z(t,e,r,i,a,o,s){var l=I(t,\"rect\",e,r);return n.select(l).call(h.setRect,i,a,o,s),l}function D(t,e){for(var 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t.append(\"path\").attr(\"class\",\"zoombox\").style({fill:e>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"transform\",u(r,n)).attr(\"d\",i+\"Z\")}function j(t,e,r){return t.append(\"path\").attr(\"class\",\"zoombox-corners\").style({fill:f.background,stroke:f.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"transform\",u(e,r)).attr(\"d\",\"M0,0Z\")}function U(t,e,r,n,i,a){t.attr(\"d\",n+\"M\"+r.l+\",\"+r.t+\"v\"+r.h+\"h\"+r.w+\"v-\"+r.h+\"h-\"+r.w+\"Z\"),V(t,e,i,a)}function V(t,e,r,n){r||(t.transition().style(\"fill\",n>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),e.transition().style(\"opacity\",1).duration(200))}function q(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}function H(t){O&&t.data&&t._context.showTips&&(i.notifier(i._(t,\"Double-click to zoom back out\"),\"long\"),O=!1)}function G(t){var 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0!==t.onwheel?\"wheel\":\"mousewheel\";t._onwheel&&t.removeEventListener(r,t._onwheel),t._onwheel=e,t.addEventListener(r,e,{passive:!1})}else void 0!==t.onwheel?t.onwheel=e:void 0!==t.onmousewheel?t.onmousewheel=e:t.isAddedWheelEvent||(t.isAddedWheelEvent=!0,t.addEventListener(\"wheel\",e,{passive:!1}))}function X(t){var e=[];for(var r in t)e.push(t[r]);return e}t.exports={makeDragBox:function(t,e,r,s,u,f,v,y){var O,I,V,Z,K,J,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y===\"nsew\",yt=1===(v+y).length;function mt(){if(O=e.xaxis,I=e.yaxis,tt=O._length,et=I._length,$=O._offset,Q=I._offset,(V={})[O._id]=O,(Z={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;Z[a._id]=a}K=X(V),J=X(Z),it=D(K,y),at=D(J,v),ot=!at&&!it,nt=W(t,t._fullLayout._axisMatchGroups,V,Z);var o=(rt=W(t,t._fullLayout._axisConstraintGroups,V,Z,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has(\"scattergl\"),ct=s._has(\"splom\"),ft=s._has(\"svg\")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?\"nsew\"===t?r?\"\":\"pan\"===e?\"move\":\"crosshair\":t.toLowerCase()+\"-resize\":\"pointer\"}(at+it,t._fullLayout.dragmode,gt),bt=z(e,v+y+\"drag\",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents=\"none\",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(q(i),2!==t||yt||Ht(),gt)o.indexOf(\"select\")>-1&&S(r,i,K,J,e.id,Pt),o.indexOf(\"event\")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u=\"s\"===v||\"w\"===y?0:1,f=s._name+\".range[\"+u+\"]\",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return\"date\"===t.type?n:\"log\"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a(\".\"+r+\"g\")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a(\".\"+String(r)+\"g\")(n))}(s,u),d=\"left\",g=\"middle\";if(s.fixedrange)return;v?(g=\"n\"===v?\"top\":\"bottom\",\"right\"===s.side&&(d=\"right\")):\"e\"===y&&(d=\"right\"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:\"#444\",horizontalAlign:d,verticalAlign:g}).on(\"edit\",(function(t){var e=s.d2r(t);void 0!==e&&l.call(\"_guiRelayout\",i,f,e)}))}}}function zt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St=\"\",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr(\"d\",\"M0,0Z\")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>P||o>P?(St=\"xy\",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr(\"d\",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>P||o>P){St=\"xy\";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr(\"d\",G(Tt))}else s();else!at||o<Math.min(Math.max(.6*a,C),P)?a<C||!it?s():(Tt.t=0,Tt.b=et,St=\"x\",Lt.attr(\"d\",function(t,e){return\"M\"+(t.l-.5)+\",\"+(e-P-.5)+\"h-3v\"+(2*P+1)+\"h3ZM\"+(t.r+.5)+\",\"+(e-P-.5)+\"h3v\"+(2*P+1)+\"h-3Z\"}(Tt,wt))):!it||a<Math.min(.6*o,P)?(Tt.l=0,Tt.r=tt,St=\"y\",Lt.attr(\"d\",function(t,e){return\"M\"+(e-P-.5)+\",\"+(t.t-.5)+\"v-3h\"+(2*P+1)+\"v3ZM\"+(e-P-.5)+\",\"+(t.b+.5)+\"v3h\"+(2*P+1)+\"v-3Z\"}(Tt,_t))):(St=\"xy\",Lt.attr(\"d\",G(Tt)));Tt.w=Tt.r-Tt.l,Tt.h=Tt.b-Tt.t,St&&(Ct=!0),t._dragged=Ct,U(Et,Lt,Tt,At,Mt,kt),Dt(),t.emit(\"plotly_relayouting\",ht),Mt=!0}function Dt(){ht={},\"xy\"!==St&&\"x\"!==St||(R(K,Tt.l/tt,Tt.r/tt,ht,rt.xaxes),Vt(\"x\",ht)),\"xy\"!==St&&\"y\"!==St||(R(J,(et-Tt.b)/et,(et-Tt.t)/et,ht,rt.yaxes),Vt(\"y\",ht))}function Rt(){Dt(),q(t),Gt(),H(t)}Pt.prepFn=function(e,r,n){var a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":m(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,m(s)?(Pt.xaxes=K,Pt.yaxes=J,A(e,r,n,Pt,s)):(Pt.clickFn=It,m(a)&&Ot(),ot||(\"zoom\"===s?(Pt.moveFn=zt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(vt,kt,$,Q,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(vt,$,Q)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Wt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,\"ew\"===it||\"ns\"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(K,e),Vt(\"x\")),at&&(F(J,r),Vt(\"y\")),Wt([n,i,tt,et]),qt(),void t.emit(\"plotly_relayouting\",ht)}var o,s,l=\"w\"===it==(\"n\"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if(\"w\"===it?e=p(K,0,e):\"e\"===it?e=p(K,1,-e):it||(e=0),\"n\"===at?r=p(J,1,r):\"s\"===at?r=p(J,0,-r):at||(r=0),o=\"w\"===it?e:0,s=\"n\"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt(\"x\"),nt.isSubplotConstrained&&it||Vt(\"y\");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Wt([o,s,f,h]),qt(),t.emit(\"plotly_relayouting\",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:J,y:K}[t]:nt[t+\"axes\"],n=nt.isSubplotConstrained?{x:K,y:J}[t]:[],i=0;i<r.length;i++){var a=r[i],o=a._id,s=nt.xLinks[o]||nt.yLinks[o],l=n[0]||V[s]||Z[s];l&&(e?(e[a._name+\".range[0]\"]=e[l._name+\".range[0]\"],e[a._name+\".range[1]\"]=e[l._name+\".range[1]\"]):a.range=l.range.slice())}}function qt(){var r,n=[];function i(t){for(r=0;r<t.length;r++)t[r].fixedrange||n.push(t[r]._id)}function a(t,e){for(r=0;r<t.length;r++){var i=t[r],a=i[e];i.fixedrange||\"sync\"!==a.tickmode||n.push(a._id)}}for(st&&(i(K),i(rt.xaxes),i(nt.xaxes),a(e.overlays,\"xaxis\")),lt&&(i(J),i(rt.yaxes),i(nt.yaxes),a(e.overlays,\"yaxis\")),ht={},r=0;r<n.length;r++){var o=n[r],s=k(t,o);d.drawOne(t,s,{skipTitle:!0}),ht[s._name+\".range[0]\"]=s.range[0],ht[s._name+\".range[1]\"]=s.range[1]}d.redrawComponents(t,n)}function Ht(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];it&&(r=r.concat(K)),at&&(r=r.concat(J)),nt.xaxes&&(r=r.concat(nt.xaxes)),nt.yaxes&&(r=r.concat(nt.yaxes));var n,i,a={};if(\"reset+autosize\"===e)for(e=\"autosize\",i=0;i<r.length;i++){var 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r,i,a,o,s,h,p,d,v,g=c(0,e);function y(t){return e.invert(t)}function m(r){var n=e.rotate(),i=e.invert(t.midPt);r(\"projection.rotation.lon\",-n[0]),r(\"center.lon\",i[0]),r(\"center.lat\",i[1])}return g.on(\"zoomstart\",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=y(r)})).on(\"zoom\",(function(){if(h=n.mouse(this),function(t){var r=y(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?y(h)&&(d=y(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=y(r=h),v=!0,t.render(!0);var l=e.rotate(),u=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":u[0],\"geo.center.lat\":u[1],\"geo.projection.rotation.lon\":-l[0]})})).on(\"zoomend\",(function(){n.select(this).style(u),v&&f(t,e,m)})),g}function d(t,e){var r,i={r:e.rotate(),k:e.scale()},a=c(0,e),h=function(t){for(var e=0,r=arguments.length,i=[];++e<r;)i.push(arguments[e]);var a=n.dispatch.apply(null,i);return a.of=function(e,r){return function(i){var o;try{o=i.sourceEvent=n.event,i.target=t,n.event=i,a[i.type].apply(e,r)}finally{n.event=o}}},a}(a,\"zoomstart\",\"zoom\",\"zoomend\"),p=0,d=a.on;function y(t){var r=e.rotate();t(\"projection.rotation.lon\",-r[0]),t(\"projection.rotation.lat\",-r[1])}return a.on(\"zoomstart\",(function(){n.select(this).style(l);var t,u,c,f,y,b,_,w,T,k,A,M=n.mouse(this),S=e.rotate(),E=S,L=e.translate(),C=(u=.5*(t=S)[0]*o,c=.5*t[1]*o,f=.5*t[2]*o,y=Math.sin(u),b=Math.cos(u),_=Math.sin(c),w=Math.cos(c),T=Math.sin(f),[b*w*(k=Math.cos(f))+y*_*T,y*w*k-b*_*T,b*_*k+y*w*T,b*w*T-y*_*k]);r=v(e,M),d.call(a,\"zoom\",(function(){var t,a,o,l,u,c,f,p,d,y,b=n.mouse(this);if(e.scale(i.k=n.event.scale),r){if(v(e,b)){e.rotate(S).translate(L);var _=v(e,b),w=function(t,e){if(t&&e){var 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c=r.match(i);o=\"x\"+c[1],s=\"y\"+c[2]}for(var f=0;f<t.length;f++)a=t[f],\"gl2d\"===e&&n.traceIs(a,\"gl2d\")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},2428:function(t,e,r){\"use strict\";var n=r(62644),i=r(97264),a=r(29128),o=r(33816),s=r(89184);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var 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a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var 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e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},39952:function(t,e,r){\"use strict\";var n=r(67824).overrideAll,i=r(17188),a=r(64859),o=r(9616),s=r(33816),l=r(57952),u=r(65460),c=r(84888).op;e.name=\"gl2d\",e.attr=[\"xaxis\",\"yaxis\"],e.idRoot=[\"x\",\"y\"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(26720),e.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,\"gl2d\",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},17188:function(t,e,r){\"use strict\";var n,i,a=r(24040),o=r(54460),s=r(93024),l=r(67792).gl_plot2d,u=r(67792).gl_spikes2d,c=r(67792).gl_select_box,f=r(5408),h=r(92568),p=r(2428),d=r(16576),v=r(71888),g=v.enforce,y=v.clean,m=r(19280).doAutoRange,x=r(72760),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=r(33816).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=c.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+\"px\",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+\"px\",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&\"all\"!==m){var x=m.split(\"+\");-1===x.indexOf(\"x\")&&(v.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(v.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(v.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(v.textLabel=void 0),-1===x.indexOf(\"name\")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",v.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,\"bgcolor\")||v.color,borderColor:s.castHoverOption(g,y,\"bordercolor\"),fontFamily:s.castHoverOption(g,y,\"font.family\"),fontSize:s.castHoverOption(g,y,\"font.size\"),fontColor:s.castHoverOption(g,y,\"font.color\"),nameLength:s.castHoverOption(g,y,\"namelength\"),textAlign:s.castHoverOption(g,y,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},12536:function(t,e,r){\"use strict\";var n=r(67824).overrideAll,i=r(65460),a=r(98432),o=r(84888).op,s=r(3400),l=r(9616),u=\"gl3d\",c=\"scene\";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex(\"scene\"),e.attributes=r(6636),e.layoutAttributes=r(346),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),e.supplyLayoutDefaults=r(5208),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},6636:function(t){\"use strict\";t.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},86140:function(t,e,r){\"use strict\";var n=r(76308),i=r(94724),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:\"plot\"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},64380:function(t,e,r){\"use strict\";var n=r(49760).mix,i=r(3400),a=r(31780),o=r(86140),s=r(14944),l=r(28336),u=[\"xaxis\",\"yaxis\",\"zaxis\"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noAutotickangles:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},44728:function(t,e,r){\"use strict\";var n=r(43080),i=r(3400),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,\"font\"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size),s.title.font.weight&&(r.labelFontWeight[o]=s.title.font.weight),s.title.font.style&&(r.labelFontStyle[o]=s.title.font.style),s.title.font.variant&&(r.labelFontVariant[o]=s.title.font.variant)),\"showline\"in s&&(r.lineEnable[o]=s.showline),\"linecolor\"in s&&(r.lineColor[o]=n(s.linecolor)),\"linewidth\"in s&&(r.lineWidth[o]=s.linewidth),\"showgrid\"in s&&(r.gridEnable[o]=s.showgrid),\"gridcolor\"in s&&(r.gridColor[o]=n(s.gridcolor)),\"gridwidth\"in s&&(r.gridWidth[o]=s.gridwidth),\"log\"===s.type?r.zeroEnable[o]=!1:\"zeroline\"in s&&(r.zeroEnable[o]=s.zeroline),\"zerolinecolor\"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),\"zerolinewidth\"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),\"ticks\"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,\"ticklen\"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),\"tickcolor\"in s&&(r.lineTickColor[o]=n(s.tickcolor)),\"tickwidth\"in s&&(r.lineTickWidth[o]=s.tickwidth),\"tickangle\"in s&&(r.tickAngle[o]=\"auto\"===s.tickangle?-3600:Math.PI*-s.tickangle/180),\"showticklabels\"in s&&(r.tickEnable[o]=s.showticklabels),\"tickfont\"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size),s.tickfont.weight&&(r.tickFontWeight[o]=s.tickfont.weight),s.tickfont.style&&(r.tickFontStyle[o]=s.tickfont.style),s.tickfont.variant&&(r.tickFontVariant[o]=s.tickfont.variant)),\"mirror\"in s?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,\"showbackground\"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},5208:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(24040),o=r(168),s=r(64380),l=r(346),u=r(84888).op,c=\"gl3d\";function f(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),f=[\"up\",\"center\",\"eye\"],h=0;h<f.length;h++)r(\"camera.\"+f[h]+\".x\"),r(\"camera.\"+f[h]+\".y\"),r(\"camera.\"+f[h]+\".z\");r(\"camera.projection.type\");var p=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),d=r(\"aspectmode\",p?\"manual\":\"auto\");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},346:function(t,e,r){\"use strict\";var n=r(86140),i=r(86968).u,a=r(92880).extendFlat,o=r(3400).counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},9020:function(t,e,r){\"use strict\";var n=r(43080),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},87152:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],\"date\"===c.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(54460),i=r(3400),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},94424:function(t){\"use strict\";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},98432:function(t,e,r){\"use strict\";var n,i,a=r(67792).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(5408),u=r(89184),c=r(24040),f=r(3400),h=f.preserveDrawingBuffer(),p=r(54460),d=r(93024),v=r(43080),g=r(16576),y=r(94424),m=r(44728),x=r(9020),b=r(87152),_=r(19280).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=c.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r=\"orthographic\"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"touchstart\",(function(){w=!0})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)\"skip\"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;_.hovertemplate||S||(-1===M.indexOf(\"x\")&&(k.xLabel=void 0),-1===M.indexOf(\"y\")&&(k.yLabel=void 0),-1===M.indexOf(\"z\")&&(k.zLabel=void 0),-1===M.indexOf(\"text\")&&(h.textLabel=void 0),-1===M.indexOf(\"name\")&&(c.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:c.name,color:d.castHoverOption(_,T,\"bgcolor\")||c.color,borderColor:d.castHoverOption(_,T,\"bordercolor\"),fontFamily:d.castHoverOption(_,T,\"font.family\"),fontSize:d.castHoverOption(_,T,\"font.size\"),fontColor:d.castHoverOption(_,T,\"font.color\"),nameLength:d.castHoverOption(_,T,\"namelength\"),textAlign:d.castHoverOption(_,T,\"align\"),hovertemplate:f.castOption(_,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||w)?r.emit(\"plotly_click\",C):r.emit(\"plotly_hover\",C),this.oldEventData=C}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var M=[\"xaxis\",\"yaxis\",\"zaxis\"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],c=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var 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r={};if(i.isPlainObject(t))r.id=t.id,r.style=t;else if(\"string\"==typeof t)if(r.id=t,-1!==y.styleValuesMapbox.indexOf(t))r.style=T(t);else if(y.stylesNonMapbox[t]){r.style=y.stylesNonMapbox[t];var n=r.style.sources[\"plotly-\"+t],a=n?n.tiles:void 0;a&&a[0]&&\"?api_key=\"===a[0].slice(-9)&&(a[0]+=e._mapboxAccessToken)}else r.style=t;else r.id=y.styleValueDflt,r.style=T(y.styleValueDflt);return r.transition={duration:0,delay:0},r}function T(t){return y.styleUrlPrefix+t+\"-\"+y.styleUrlSuffix}function k(t){return[t.lon,t.lat]}b.updateData=function(t){var e,r,n,i,a=this.traceHash,o=t.slice().sort((function(t,e){return _[t[0].trace.type]-_[e[0].trace.type]}));for(n=0;n<o.length;n++){var s=o[n],l=!1;(e=a[(r=s[0].trace).uid])&&(e.type===r.type?(e.update(s),l=!0):e.dispose()),!l&&r._module&&(a[r.uid]=r._module.plot(this,s))}var u=Object.keys(a);t:for(n=0;n<u.length;n++){var c=u[n];for(i=0;i<t.length;i++)if(c===(r=t[i][0].trace).uid)continue t;(e=a[c]).dispose(),delete 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e=r.div.getBoundingClientRect(),a=[t.originalEvent.offsetX,t.originalEvent.offsetY];t.target.getBoundingClientRect=function(){return e},r.xaxis.p2c=function(){return i.unproject(a).lng},r.yaxis.p2c=function(){return i.unproject(a).lat},n._fullLayout._rehover=function(){n._fullLayout._hoversubplot===r.id&&n._fullLayout[r.id]&&u.hover(n,t,r.id)},u.hover(n,t,r.id),n._fullLayout._hoversubplot=r.id})),i.on(\"dragstart\",(function(){r.dragging=!0,a()})),i.on(\"zoomstart\",a),i.on(\"mouseout\",(function(){n._fullLayout._hoversubplot=null})),i.on(\"drag\",s),i.on(\"zoom\",s),i.on(\"dblclick\",(function(){var t=n._fullLayout[r.id];o.call(\"_storeDirectGUIEdit\",n.layout,n._fullLayout._preGUI,r.getViewEdits(t));var e=r.viewInitial;i.setCenter(k(e.center)),i.setZoom(e.zoom),i.setBearing(e.bearing),i.setPitch(e.pitch);var a=r.getView();t._input.center=t.center=a.center,t._input.zoom=t.zoom=a.zoom,t._input.bearing=t.bearing=a.bearing,t._input.pitch=t.pitch=a.pitch,n.emit(\"plotly_doubleclick\",null),n.emit(\"plotly_relayout\",r.getViewEditsWithDerived(a))})),r.clearOutline=function(){v(r.dragOptions),d(r.dragOptions.gd)},r.onClickInPanFn=function(t){return function(e){var i=n._fullLayout.clickmode;i.indexOf(\"select\")>-1&&g(e.originalEvent,n,[r.xaxis],[r.yaxis],r.id,t),i.indexOf(\"event\")>-1&&u.click(n,e.originalEvent)}}},b.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var a,o=t.dragmode;a=function(t,r){r.isRect?(t.range={})[e.id]=[u([r.xmin,r.ymin]),u([r.xmax,r.ymax])]:(t.lassoPoints={})[e.id]=r.map(u)};var 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e,r,s=this,u=s.isSmith,c=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,v=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,O=E.clampTiny,I=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){m.hover(c,t,s.id),c._fullLayout._lasthover=F,c._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){c._dragging||y.unhover(c,t)};var B,N,j,U,V,q,H,G,W,Y={element:F,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function K(t,e){return Math.atan2(M-e,t-A)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function $(t,e){if(0===t)return s.pathSector(2*D);var 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I,z,D,B=g.makeRectDragger(c,T,\"crosshair\",-x,-x,m,m),N={element:B,gd:u};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),v.visible&&h<f,{transform:l(b,_)}),N.prepFn=function(){I=null,z=null,D=null,N.moveFn=j,N.doneFn=q,w(u)},N.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<S.MINDRAG&&(t=0,e=0),[t,e]},y.init(N)}function j(t,e){if(I)I(t,e);else{var n=[t,-e],a=[Math.cos(M),Math.sin(M)],s=Math.abs(o.dot(n,a)/Math.sqrt(o.dot(n,n)));isNaN(s)||(I=s<.5?H:G)}var l={};!function(t){null!==z?t[i.id+\".radialaxis.angle\"]=z:null!==D&&(t[i.id+\".radialaxis.range[\"+r+\"]\"]=D)}(l),u.emit(\"plotly_relayouting\",l)}function q(){null!==z?a.call(\"_guiRelayout\",u,i.id+\".radialaxis.angle\",z):null!==D&&a.call(\"_guiRelayout\",u,i.id+\".radialaxis.range[\"+r+\"]\",D)}function H(t,e){if(0!==r){var n=b+t,a=_+e;z=Math.atan2(d-a,n-p),i.vangles&&(z=U(z,i.vangles)),z=F(z);var o=l(p,d)+s(-z);c[\"radial-axis\"].attr(\"transform\",o),c[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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i=t[0].trace,o=\"h\"===i.orientation,s=\"waterfall\"===i.type,l=\"funnel\"===i.type;function u(t){return c(o?r:n,+t,!0).text}var f,h,p=i.textinfo,d=t[e],v=p.split(\"+\"),g=[],y=function(t){return-1!==v.indexOf(t)};if(y(\"label\")&&g.push((h=t[e].p,c(o?n:r,h,!0).text)),y(\"text\")&&(0===(f=a.castOption(i,d.i,\"text\"))||f)&&g.push(f),s){var m=+d.rawS||d.s,x=d.v,b=x-m;y(\"initial\")&&g.push(u(b)),y(\"delta\")&&g.push(u(m)),y(\"final\")&&g.push(u(x))}if(l){y(\"value\")&&g.push(u(d.s));var _=0;y(\"percent initial\")&&_++,y(\"percent previous\")&&_++,y(\"percent total\")&&_++;var w=_>1;y(\"percent initial\")&&(f=a.formatPercent(d.begR),w&&(f+=\" of initial\"),g.push(f)),y(\"percent previous\")&&(f=a.formatPercent(d.difR),w&&(f+=\" of previous\"),g.push(f)),y(\"percent total\")&&(f=a.formatPercent(d.sumR),w&&(f+=\" of total\"),g.push(f))}return g.join(\"<br>\")}(e,r,n,i):v.getValue(s.text,r),v.coerceString(m,o)}(I,n,i,S,O);k=function(t,e){var r=v.getValue(t.textposition,e);return v.coerceEnumerated(x,r)}(D,i);var B=\"stack\"===w.mode||\"relative\"===w.mode,N=n[i],j=!B||N._outmost,U=N.hasB,V=g&&g-y>_;if(F&&\"none\"!==k&&(!N.isBlank&&s!==u&&f!==p||\"auto\"!==k&&\"inside\"!==k)){var q=I.font,H=d.getBarColor(n[i],D),G=d.getInsideTextFont(D,i,q,H),W=d.getOutsideTextFont(D,i,q),Y=D.insidetextanchor||\"end\",X=r.datum();R?\"log\"===S.type&&X.s0<=0&&(s=S.range[0]<S.range[1]?0:S._length):\"log\"===O.type&&X.s0<=0&&(f=O.range[0]<O.range[1]?O._length:0);var Z,K,J,$,Q,tt=Math.abs(u-s),et=Math.abs(p-f),rt=tt-2*_,nt=et-2*_;if(\"outside\"===k&&(j||N.hasB||(k=\"inside\")),\"auto\"===k)if(j){k=\"inside\",Z=z(r,F,Q=a.ensureUniformFontSize(t,G)),J=(K=l.bBox(Z.node())).width,$=K.height;var it,at=J>0&&$>0;it=V?U?E(rt-2*g,nt,J,$,R)||E(rt,nt-2*g,J,$,R):R?E(rt-(g-y),nt,J,$,R)||E(rt,nt-2*(g-y),J,$,R):E(rt,nt-(g-y),J,$,R)||E(rt-2*(g-y),nt,J,$,R):E(rt,nt,J,$,R),at&&it?k=\"inside\":(k=\"outside\",Z.remove(),Z=null)}else k=\"inside\";if(!Z){var ot=(Z=z(r,F,Q=a.ensureUniformFontSize(t,\"outside\"===k?W:G))).attr(\"transform\");if(Z.attr(\"transform\",\"\"),J=(K=l.bBox(Z.node())).width,$=K.height,Z.attr(\"transform\",ot),J<=0||$<=0)return void Z.remove()}var st,lt=D.textangle;st=\"outside\"===k?function(t,e,r,n,i,a){var o,s=!!a.isHorizontal,l=!!a.constrained,u=a.angle||0,c=i.width,f=i.height,h=Math.abs(e-t),p=Math.abs(n-r);o=s?p>2*_?_:0:h>2*_?_:0;var d=1;l&&(d=s?Math.min(1,p/f):Math.min(1,h/c));var v=L(u),g=C(i,v),y=(s?g.x:g.y)/2,m=(i.left+i.right)/2,x=(i.top+i.bottom)/2,b=(t+e)/2,w=(r+n)/2,T=0,k=0,M=s?A(e,t):A(r,n);return s?(b=e-M*o,T=M*y):(w=n+M*o,k=-M*y),{textX:m,textY:x,targetX:b,targetY:w,anchorX:T,anchorY:k,scale:d,rotate:v}}(s,u,f,p,K,{isHorizontal:R,constrained:\"both\"===D.constraintext||\"outside\"===D.constraintext,angle:lt}):P(s,u,f,p,K,{isHorizontal:R,constrained:\"both\"===D.constraintext||\"inside\"===D.constraintext,angle:lt,anchor:Y,hasB:U,r:g,overhead:y}),st.fontSize=Q.size,h(\"histogram\"===D.type?\"bar\":D.type,st,I),N.transform=st;var ut=M(Z,I,w,T);a.setTransormAndDisplay(ut,st)}else r.select(\"text\").remove()}(t,e,R,r,T,H,G,W,Y,rt,it,g,y),e.layerClipId&&l.hideOutsideRangePoint(u,R.select(\"text\"),w,O,f.xcalendar,f.ycalendar)}));var W=!1===f.cliponaxis;l.setClipUrl(u,W?null:e.layerClipId,t)}));u.getComponentMethod(\"errorbars\",\"plot\")(t,D,e,g)},toMoveInsideBar:P}},45784:function(t){\"use strict\";function e(t,e,r,n,i){var a=e.c2p(n?t.s0:t.p0,!0),o=e.c2p(n?t.s1:t.p1,!0),s=r.c2p(n?t.p0:t.s0,!0),l=r.c2p(n?t.p1:t.s1,!0);return i?[(a+o)/2,(s+l)/2]:n?[o,(s+l)/2]:[(a+o)/2,l]}t.exports=function(t,r){var n,i=t.cd,a=t.xaxis,o=t.yaxis,s=i[0].trace,l=\"funnel\"===s.type,u=\"h\"===s.orientation,c=[];if(!1===r)for(n=0;n<i.length;n++)i[n].selected=0;else for(n=0;n<i.length;n++){var f=i[n],h=\"ct\"in f?f.ct:e(f,a,o,u,l);r.contains(h,!1,n,t)?(c.push({pointNumber:n,x:a.c2d(f.x),y:o.c2d(f.y)}),f.selected=1):f.selected=0}return c}},72592:function(t,e,r){\"use strict\";t.exports=i;var n=r(3400).distinctVals;function i(t,e){this.traces=t,this.sepNegVal=e.sepNegVal,this.overlapNoMerge=e.overlapNoMerge;for(var r=1/0,i=e.posAxis._id.charAt(0),a=[],o=0;o<t.length;o++){for(var s=t[o],l=0;l<s.length;l++){var u=s[l],c=u.p;void 0===c&&(c=u[i]),void 0!==c&&a.push(c)}s[0]&&s[0].width1&&(r=Math.min(s[0].width1,r))}this.positions=a;var f=n(a);this.distinctPositions=f.vals,1===f.vals.length&&r!==1/0?this.minDiff=r:this.minDiff=Math.min(f.minDiff,r);var 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i,a=t.cd[0].trace.hoveron,o=[];return-1!==a.indexOf(\"boxes\")&&(o=o.concat(l(t,e,r,n))),-1!==a.indexOf(\"points\")&&(i=u(t,e,r)),\"closest\"===n?i?[i]:o:i?(o.push(i),o):o},hoverOnBoxes:l,hoverOnPoints:u}},67244:function(t,e,r){\"use strict\";t.exports={attributes:r(63188),layoutAttributes:r(16560),supplyDefaults:r(90624).supplyDefaults,crossTraceDefaults:r(90624).crossTraceDefaults,supplyLayoutDefaults:r(68832).supplyLayoutDefaults,calc:r(62555),crossTraceCalc:r(96404).crossTraceCalc,plot:r(18728).plot,style:r(25776).style,styleOnSelect:r(25776).styleOnSelect,hoverPoints:r(27576).hoverPoints,eventData:r(10392),selectPoints:r(8264),moduleType:\"trace\",name:\"box\",basePlotModule:r(57952),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"boxLayout\",\"zoomScale\"],meta:{}}},16560:function(t){\"use 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b=t.selectAll(\"path.box\").data(\"violin\"!==r.type||r.box.visible?i.identity:[]);b.enter().append(\"path\").style(\"vector-effect\",o?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),b.exit().remove(),b.each((function(t){if(t.empty)return n.select(this).attr(\"d\",\"M0,0Z\");var e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,b=h?(a+o)/2:f.l2p(e)+v,_=r.whiskerwidth,w=h?a*_+(1-_)*b:f.l2p(e-d)+v,T=h?o*_+(1-_)*b:f.l2p(e+d)+v,k=f.l2p(e-s*x)+v,A=f.l2p(e+l*x)+v,M=\"sd\"===r.sizemode,S=c.c2p(M?t.mean-t.sd:t.q1,!0),E=M?c.c2p(t.mean+t.sd,!0):c.c2p(t.q3,!0),L=i.constrain(M?c.c2p(t.mean,!0):c.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),C=void 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n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"scaled\",\"absolute\",\"raw\"],editType:\"calc\",dflt:\"scaled\"},sizeref:{valType:\"number\",editType:\"calc\",min:0},anchor:{valType:\"enumerated\",editType:\"calc\",values:[\"tip\",\"tail\",\"cm\",\"center\"],dflt:\"cm\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"norm\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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O=C.selectAll(\"text\").data(P);function I(e,r,n,i){if(!e.match(\"s\")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace(\"s\",\"f\").replace(/\\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}O.enter().append(\"text\"),O.attr(\"text-anchor\",(function(){return A})).attr(\"class\",(function(t){return t})).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),O.exit().remove();var z,D=v.mode+v.align;if(v._hasDelta&&(z=function(){var e=k(t,{tickformat:v.delta.valueformat},v._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=v.delta.suffix,s=v.delta.prefix,l=function(t){return v.delta.relative?t.relativeDelta:t.delta},u=function(t,e){return 0===t||\"number\"!=typeof t||isNaN(t)?\"-\":(t>0?v.delta.increasing.symbol:v.delta.decreasing.symbol)+s+e(t)+o},h=function(t){return 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U,V,q,H=j.select(\"path\");w(M)?(H.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(U=N,V=L(r[0].lastY),q=L(r[0].y),function(){var t=i(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):H.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\"),H.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var W=_.selectAll(\"g.threshold-arc\").data(F);W.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove();var Y=_.selectAll(\"g.gauge-outline\").data([x]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(P).call(T),Y.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:W,layer:Y,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=I.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=I.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,u,c=r[0].trace,f=n.gauge,p=n.layer,v=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=c.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(y.l,y.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=y.h,M=c.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(c._hasNumber||c._hasDelta?1-h.bulletNumberDomainSize:1);function L(t){t.attr(\"width\",(function(t){return 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O=f.selectAll(\"g.value-bullet\").data([c.gauge.bar]);O.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),O.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?O.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y)))):O.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y))):0),O.exit().remove();var I=r.filter((function(){return 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r=\"slices.\"+t;a(r+\".show\")&&(a(r+\".fill\"),a(r+\".locations\"))})),a(\"spaceframe.show\")&&a(\"spaceframe.fill\"),a(\"surface.show\")&&(a(\"surface.count\"),a(\"surface.fill\"),a(\"surface.pattern\")),a(\"contour.show\")&&(a(\"contour.color\"),a(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach((function(t){a(t)})),o(t,e,n,a,{prefix:\"\",cLetter:\"c\"}),e._length=null):e.visible=!1}t.exports={supplyDefaults:function(t,e,r,i){s(t,e,0,i,(function(r,i){return n.coerce(t,e,a,r,i)}))},supplyIsoDefaults:s}},6296:function(t,e,r){\"use 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n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(16716),s=r(45464),l=r(92880).extendFlat;t.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=r(67792).gl_mesh3d,i=r(67792).delaunay_triangulate,a=r(67792).alpha_shape,o=r(67792).convex_hull,s=r(33040).parseColorScale,l=r(3400).isArrayOrTypedArray,u=r(43080),c=r(8932).extractOpts,f=r(52094);function h(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var p=h.prototype;function d(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=u(t[n]);return e}function v(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function g(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function y(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}p.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return l(r)&&void 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p={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:u(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var m=c(t);this.color=\"#fff\";var x=t.intensitymode;p[x+\"Intensity\"]=t.intensity,p[x+\"IntensityBounds\"]=[m.min,m.max],p.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],p.vertexColors=d(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],p.cellColors=d(t.facecolor)):(this.color=t.color,p.meshColor=u(t.color));this.mesh.update(p)},p.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},t.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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a,s=t._fullData,l=[];for(i=1/0,a=0;a<s.length;a++){var u=s[a];if(\"ohlc\"===u.type&&!0===u.visible&&u.xaxis===e._id){l.push(u);var c=e.makeCalcdata(u,\"x\");u._origX=c;var f=o(r,e,\"x\",c).vals;u._xcalc=f;var h=n.distinctVals(f).minDiff;h&&isFinite(h)&&(i=Math.min(i,h))}}for(i===1/0&&(i=1),a=0;a<l.length;a++)l[a]._minDiff=i}return i*r.tickwidth}(t,r,e),c=e._minDiff;e._minDiff=null;var f=e._origX;e._origX=null;var h=e._xcalc;e._xcalc=null;var p=u(t,e,f,h,i,l);return e._extremes[r._id]=a.findExtremes(r,h,{vpad:c/2}),p.length?(n.extendFlat(p[0].t,{wHover:c/2,tickLen:s}),p):[{t:{empty:!0}}]},calcCommon:u}},23860:function(t,e,r){\"use strict\";var n=r(3400),i=r(52744),a=r(31147),o=r(20279);function s(t,e,r,n){r(n+\".line.color\"),r(n+\".line.width\",e.line.width),r(n+\".line.dash\",e.line.dash)}t.exports=function(t,e,r,l){function u(r,i){return 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T=a.getDistanceFunction(n,b,_,w);if(a.getClosest(l,T,t),!1===t.index)return null;var k=l[t.index];if(k.empty)return null;var A=c[k.dir],M=A.line.color;return o.opacity(M)&&A.line.width?t.color=M:t.color=A.fillcolor,t.x0=u.c2p(k.pos+v-y,!0),t.x1=u.c2p(k.pos+v+y,!0),t.xLabelVal=void 0!==k.orig_p?k.orig_p:k.pos,t.spikeDistance=w(k)*s/i,t.xSpike=u.c2p(k.pos,!0),t}function f(t,e,r,a){var o=t.cd,s=t.ya,l=o[0].trace,u=o[0].t,f=[],h=c(t,e,r,a);if(!h)return[];var p=o[h.index].hi||l.hoverinfo,d=p.split(\"+\");if(\"all\"!==p&&-1===d.indexOf(\"y\"))return[];for(var v=[\"high\",\"open\",\"close\",\"low\"],g={},y=0;y<v.length;y++){var m,x=v[y],b=l[x][h.index],_=s.c2p(b,!0);b in g?(m=g[b]).yLabel+=\"<br>\"+u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat):((m=i.extendFlat({},h)).y0=m.y1=_,m.yLabelVal=b,m.yLabel=u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat),m.name=\"\",f.push(m),g[b]=m)}return f}function h(t,e,r,i){var a=t.cd,o=t.ya,l=a[0].trace,f=a[0].t,h=c(t,e,r,i);if(!h)return[];var 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n=r(71688).wrap,i=r(94288).hasColorscale,a=r(47128),o=r(68944),s=r(43616),l=r(3400),u=r(38248);function c(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}t.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,v,g=r.map((function(t){var e;if(\"trace\"===t.categoryorder)e=null;else if(\"array\"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!u(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return 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n=r(33428),i=r(67756).Gz,a=r(36424),o=r(93024),s=r(3400),l=s.strTranslate,u=r(43616),c=r(49760),f=r(72736);function h(t,e,r,i){var a=e._context.staticPlot,o=t.map(F.bind(0,e,r)),c=i.selectAll(\"g.parcatslayer\").data([null]);c.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",a?\"none\":\"all\");var h=c.selectAll(\"g.trace.parcats\").data(o,p),m=h.enter().append(\"g\").attr(\"class\",\"trace parcats\");h.attr(\"transform\",(function(t){return l(t.x,t.y)})),m.append(\"g\").attr(\"class\",\"paths\");var x=h.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),p);x.attr(\"fill\",(function(t){return t.model.color}));var w=x.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);_(w),x.attr(\"d\",(function(t){return 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z=E.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);E.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(z),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var D=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(D)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){u.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",O).on(\"dragend\",I)),h.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function 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y(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(v),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||m()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,u=o.svgBrush;if(u.wasDragged=!1,u.grabbingBar=\"ns\"===s.region,u.grabbingBar){var c=l.map(e.unitToPaddedPx);u.grabPoint=r-c[0]-n.verticalPadding,u.barLength=c[1]-c[0]}u.clickableOrdinalRange=s.clickableOrdinalRange,u.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(u.stayingIntervals=u.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),u.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,u.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,m(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var u=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(u?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),u?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}t.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);i.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(\".background\").data(o);a.enter().append(\"rect\").classed(\"background\",!0).call(d).call(v).style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",l(0,n.verticalPadding)),a.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(\".highlight-shadow\").data(o);s.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),s.attr(\"y1\",(function(t){return t.height})).call(x);var u=t.selectAll(\".highlight\").data(o);u.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),u.attr(\"y1\",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},61664:function(t,e,r){\"use strict\";t.exports={attributes:r(82296),supplyDefaults:r(60664),calc:r(95044),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:r(19976),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},19976:function(t,e,r){\"use strict\";var n=r(33428),i=r(84888)._M,a=r(24196),o=r(9616);e.name=\"parcoords\",e.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this,r=t.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":r,preserveAspectRatio:\"none\",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},95044:function(t,e,r){\"use strict\";var n=r(3400).isArrayOrTypedArray,i=r(8932),a=r(71688).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},30140:function(t){\"use strict\";t.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},60664:function(t,e,r){\"use strict\";var n=r(3400),i=r(94288).hasColorscale,a=r(27260),o=r(86968).Q,s=r(51272),l=r(54460),u=r(82296),c=r(71864),f=r(30140).maxDimensionCount,h=r(26284);function p(t,e,r,i){function a(r,i){return n.coerce(t,e,u.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=c.cleanRanges(h,e))}}t.exports=function(t,e,r,l){function c(r,i){return n.coerce(t,e,u,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var v=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,\"values\",g);var y={weight:l.font.weight,style:l.font.style,variant:l.font.variant,family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(c,\"labelfont\",y),n.coerceFont(c,\"tickfont\",y),n.coerceFont(c,\"rangefont\",y),c(\"labelangle\"),c(\"labelside\"),c(\"unselected.line.color\"),c(\"unselected.line.opacity\")}},95724:function(t,e,r){\"use strict\";var n=r(3400).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},29928:function(t,e,r){\"use strict\";var n=r(61664);n.plot=r(24196),t.exports=n},51352:function(t,e,r){\"use strict\";var n=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\"               p17_20, p21_24, p25_28, p29_32,\",\"               p33_36, p37_40, p41_44, p45_48,\",\"               p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\"             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick    = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\"    return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\"    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\"    return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\"    return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\"    return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\"    return (\",\"        fOutside(p[0], lo[0], hi[0]) ||\",\"        fOutside(p[1], lo[1], hi[1]) ||\",\"        fOutside(p[2], lo[2], hi[2]) ||\",\"        fOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\"    return (\",\"        vOutside(p[0], lo[0], hi[0]) ||\",\"        vOutside(p[1], lo[1], hi[1]) ||\",\"        vOutside(p[2], lo[2], hi[2]) ||\",\"        vOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    return mOutside(A, loA, hiA) ||\",\"           mOutside(B, loB, hiB) ||\",\"           mOutside(C, loC, hiC) ||\",\"           mOutside(D, loD, hiD);\",\"}\",\"\",\"bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    mat4 pnts[4];\",\"    pnts[0] = A;\",\"    pnts[1] = B;\",\"    pnts[2] = C;\",\"    pnts[3] = D;\",\"\",\"    for(int i = 0; i < 4; ++i) {\",\"        for(int j = 0; j < 4; ++j) {\",\"            for(int k = 0; k < 4; ++k) {\",\"                if(0 == iMod(\",\"                    int(255.0 * texture2D(maskTexture,\",\"                        vec2(\",\"                            (float(i * 2 + j / 2) + 0.5) / 8.0,\",\"                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\",\"                        ))[3]\",\"                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\",\"                    2\",\"                )) return true;\",\"            }\",\"        }\",\"    }\",\"    return false;\",\"}\",\"\",\"vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float x = 0.5 * sign(v) + 0.5;\",\"    float y = axisY(x, A, B, C, D);\",\"    float z = 1.0 - abs(v);\",\"\",\"    z += isContext ? 0.0 : 2.0 * float(\",\"        outsideBoundingBox(A, B, C, D) ||\",\"        outsideRasterMask(A, B, C, D)\",\"    );\",\"\",\"    return vec4(\",\"        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\",\"        z,\",\"        1.0\",\"    );\",\"}\",\"\",\"void main() {\",\"    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\",\"    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\",\"    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\",\"    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\",\"\",\"    float v = colors[3];\",\"\",\"    gl_Position = position(isContext, v, A, B, C, D);\",\"\",\"    fragColor =\",\"        isContext ? vec4(contextColor) :\",\"        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\",\"}\"].join(\"\\n\"),i=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"void main() {\",\"    gl_FragColor = fragColor;\",\"}\"].join(\"\\n\"),a=r(30140).maxDimensionCount,o=r(3400),s=1e-6,l=new Uint8Array(4),u=new Uint8Array(4),c={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var u=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*u,a.offset=2*l*n,e(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function v(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,u=r[64*a+l];63===l&&0===o&&(u*=-1),n[i++]=u}return n}function g(t){var e=\"0\"+t;return e.substr(e.length-2)}function y(t){return t<a?\"p\"+g(t+1)+\"_\"+g(t+4):\"colors\"}function m(t,e,r,n,i,a,s,l,u,c,f,h,p,d){for(var v=[[],[]],g=0;g<64;g++)v[0][g]=g===i?1:0,v[1][g]=g===a?1:0;s*=d,l*=d,u*=d,c*=d;var y=t.lines.canvasOverdrag*d,m=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return o.extendFlat({key:f,resolution:[x,b],viewBoxPos:[s+y,l],viewBoxSize:[u,c],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,\"auto\"!==M?A[3]*M:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:s+y)+(_-y)+k*m.x[0],scissorWidth:(n===r?x-s+y:u+.5)+(n===e?s+y:0),scissorY:l+w+T*m.y[0],scissorHeight:c,viewportX:_-y+k*m.x[0],viewportY:w+T*m.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=2047,r=Math.max(0,Math.floor(t[0]*e),0),n=Math.min(e,Math.ceil(t[1]*e),e);return[Math.min(r,n),Math.max(r,n)]}t.exports=function(t,e){var r,l,g,b,_,w=e.context,T=e.pick,k=e.regl,A=k._gl,M=A.getParameter(A.ALIASED_LINE_WIDTH_RANGE),S=Math.max(M[0],Math.min(M[1],e.viewModel.plotGlPixelRatio)),E={currentRafs:{},drawCompleted:!0,clearOnly:!1},L=function(t){for(var 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a=u[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",l=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===l[s]){var h=a.constraintrange;l[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[f[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,u[e].filter(a));l[e].sort(n),u[e].filter((function(t){return!a(t)})).sort((function(t){return u[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(u[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[l[e]]},[f[e]]])}})}}).reglPrecompiled=o},74996:function(t,e,r){\"use strict\";var n=r(45464),i=r(86968).u,a=r(25376),o=r(22548),s=r(21776).Ks,l=r(21776).Gw,u=r(92880).extendFlat,c=r(98192).c,f=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:c,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},f,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},f,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(7316);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},45768:function(t,e,r){\"use strict\";var n=r(38248),i=r(49760),a=r(76308),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o[\"_\"+e.type+\"colormap\"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},74174:function(t,e,r){\"use strict\";var n=r(38248),i=r(3400),a=r(74996),o=r(86968).Q,s=r(31508).handleText,l=r(3400).coercePattern;function u(t,e){var r=i.isArrayOrTypedArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l(\"labels\"),l(\"values\")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),h){e._length=h,c(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),v=l(\"texttemplate\");if(v||(p=l(\"textinfo\",i.isArrayOrTypedArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),v||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}else\"none\"===p&&l(\"textposition\",\"none\");o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var m=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==m||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},53644:function(t,e,r){\"use strict\";var n=r(10624).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},21552:function(t,e,r){\"use strict\";var n=r(43616),i=r(76308);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},69656:function(t,e,r){\"use strict\";var n=r(3400);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r<e.length;r++){var i=t[e[r]];if(i||0===i||\"\"===i)return i}},e.castOption=function(t,r){return n.isArrayOrTypedArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},75792:function(t,e,r){\"use strict\";t.exports={attributes:r(74996),supplyDefaults:r(74174).supplyDefaults,supplyLayoutDefaults:r(90248),layoutAttributes:r(85204),calc:r(45768).calc,crossTraceCalc:r(45768).crossTraceCalc,plot:r(37820).plot,style:r(22152),styleOne:r(10528),moduleType:\"trace\",name:\"pie\",basePlotModule:r(80036),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},85204:function(t){\"use strict\";t.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},90248:function(t,e,r){\"use strict\";var n=r(3400),i=r(85204);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},37820:function(t,e,r){\"use strict\";var n=r(33428),i=r(7316),a=r(93024),o=r(76308),s=r(43616),l=r(3400),u=l.strScale,c=l.strTranslate,f=r(72736),h=r(82744),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(78048).TEXTPAD,g=r(69656),y=r(53644),m=r(3400).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c=\"funnelarea\"===u.type;\"_hasHoverLabel\"in u||(u._hasHoverLabel=!1),\"_hasHoverEvent\"in u||(u._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,s=g.castOption(t.insidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,l=g.castOption(t.insidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,u=g.castOption(t.insidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant;return{color:n||o.contrast(e.color),family:i,size:a,weight:s,style:l,variant:u}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var 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m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;\"funnelarea\"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,u=o.split(\"+\"),c=function(t){return-1!==u.indexOf(t)},f=c(\"label\"),h=c(\"text\"),p=c(\"value\"),d=c(\"percent\"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u=\"px0\",c=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u=\"px1\",c=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,n=z(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var m=n.select(this).selectAll(\"g.slice\").data(e);m.enter().append(\"g\").classed(\"slice\",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A=\"M\"+(u+i.px0[0])+\",\"+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(u+k*i.px0[0])+\",\"+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(u+k*i.px1[0])+\",\"+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+u+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,weight:g.castOption(t.outsidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,style:g.castOption(t.outsidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,variant:g.castOption(t.outsidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if(\"outside\"===E)d=L(m,i);else if(d=w(m,i,v),\"auto\"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:z(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*v.r+\",\"+n*v.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var k=n.select(this).selectAll(\"g.titletext\").data(y.title.text?[0]:[]);if(k.enter().append(\"g\").classed(\"titletext\",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,y.title.font).call(f.convertToTspans,t),e=\"middle center\"===y.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(v):C(v,h),r.attr(\"transform\",c(e.x,e.y)+u(Math.min(1,e.scale))+c(e.tx,e.ty))})),T&&function(t,e){var r,n,i,a,o,s,u,c,f,h,p,d,v;function y(t,e){return t.pxmid[1]-e.pxmid[1]}function m(t,e){return e.pxmid[1]-t.pxmid[1]}function x(t,r){r||(r={});var i,c,f,p,d=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),v=n?t.yLabelMin:t.yLabelMax,y=n?t.yLabelMax:t.yLabelMin,m=t.cyFinal+o(t.px0[1],t.px1[1]),x=d-v;if(x*u>0&&(t.labelExtraY=x),l.isArrayOrTypedArray(e.pull))for(c=0;c<h.length;c++)(f=h[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,f.pts)||0)||((t.pxmid[1]-f.pxmid[1])*u>0?(x=f.cyFinal+o(f.px0[1],f.px1[1])-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x):(y+t.labelExtraY-m)*u>0&&(i=3*s*Math.abs(c-h.indexOf(t)),(p=f.cxFinal+a(f.px0[0],f.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=p)))}for(n=0;n<2;n++)for(i=n?y:m,o=n?Math.max:Math.min,u=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),f=t[1-n][r],h=f.concat(c),d=[],p=0;p<c.length;p++)void 0!==c[p].yLabelMid&&d.push(c[p]);for(v=!1,p=0;n&&p<f.length;p++)if(void 0!==f[p].yLabelMid){v=f[p];break}for(p=0;p<d.length;p++){var b=p&&d[p-1];v&&!p&&(b=v),x(d[p],b)}}}(_,y),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,l.setTransormAndDisplay(i,t.transform);var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),u=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var c=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(c)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+u):s+=\"l\"+t.labelExtraX+\",\"+c+\"v\"+(f-c)+\"h\"+u}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+u;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(m,y),T&&y.automargin){var A=s.bBox(d.node()),M=y.domain,S=h.w*(M.x[1]-M.x[0]),E=h.h*(M.y[1]-M.y[0]),P=(.5*S-v.r)/h.w,O=(.5*E-v.r)/h.h;i.autoMargin(t,\"pie.\"+y.uid+\".automargin\",{xl:M.x[0]-P,xr:M.x[1]+P,yb:M.y[0]-O,yt:M.y[1]+O,l:Math.max(v.cx-v.r-A.left,0),r:Math.max(A.right-(v.cx+v.r),0),b:Math.max(A.bottom-(v.cy+v.r),0),t:Math.max(v.cy-v.r-A.top,0),pad:5})}}))}));setTimeout((function(){v.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:I,attachFxHandlers:x,computeTransform:R}},22152:function(t,e,r){\"use strict\";var n=r(33428),i=r(10528),a=r(82744).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(e){var r=e[0].trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll(\"path.surface\").each((function(e){n.select(this).call(i,e,r,t)}))}))}},10528:function(t,e,r){\"use strict\";var n=r(76308),i=r(69656).castOption,a=r(21552);t.exports=function(t,e,r,o){var s=r.marker.line,l=i(s.color,e.pts)||n.defaultLine,u=i(s.width,e.pts)||0;t.call(a,e,r,o).style(\"stroke-width\",u).call(n.stroke,l)}},35484:function(t,e,r){\"use strict\";var n=r(52904);t.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},11072:function(t,e,r){\"use strict\";var n=r(67792).gl_pointcloud2d,i=r(3400).isArrayOrTypedArray,a=r(43080),o=r(19280).findExtremes,s=r(44928);function l(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var u=l.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:i(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,s,l,u=this.xData=this.pickXData=t.x,c=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)i=n[2*l],s=n[2*l+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=u.length,n=new Float32Array(2*e),r=new 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a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(m,r,i),s.click(t,{target:!0})}}})}},83248:function(t,e,r){\"use strict\";var n=r(49812),i=r(67756).Gz,a=r(33428),o=r(26800),s=r(48932),l=r(11820),u=r(49760),c=r(76308),f=r(43616),h=r(3400),p=h.strTranslate,d=h.strRotate,v=r(71688),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(72736),b=r(24040),_=r(84284),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=m(e),a=i.trace,c=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,v={justify:o.sankeyJustify,left:o.sankeyLeft,right:o.sankeyRight,center:o.sankeyCenter}[a.node.align],g=t.width*(c.x[1]-c.x[0]),y=t.height*(c.y[1]-c.y[0]),x=i._nodes,b=i._links,_=i.circular;(n=_?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[g,y]:[y,g]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodeAlign(v).nodes(x).links(b);var w,T,k,A=n();for(var M in n.nodePadding()<p&&h.warn(\"node.pad was reduced to 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l=0;for(T=0;T<r.sourceLinks.length;T++)l+=r.sourceLinks[T].value;for(T=0;T<r.sourceLinks.length;T++)(e=r.sourceLinks[T]).concentrationOut=e.value/l;var c=0;for(T=0;T<r.targetLinks.length;T++)c+=r.targetLinks[T].value;for(T=0;T<r.targetLinks.length;T++)(e=r.targetLinks[T]).concenrationIn=e.value/c}}(),a.node.x.length&&a.node.y.length){for(w=0;w<Math.min(a.node.x.length,a.node.y.length,A.nodes.length);w++)if(a.node.x[w]&&a.node.y[w]){var C=[a.node.x[w]*g,a.node.y[w]*y];A.nodes[w].x0=C[0]-d/2,A.nodes[w].x1=C[0]+d/2;var P=A.nodes[w].y1-A.nodes[w].y0;A.nodes[w].y0=C[1]-P/2,A.nodes[w].y1=C[1]+P/2}\"snap\"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(w=0;w<n.length;w++){var s=t[n[w].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(x=A.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:c.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return\"top\"===t.circularLinkType?\"M \"+(n.targetX-e)+\" \"+(n.targetY+r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 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\"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY+r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\"}(t.link,e);var r=Math.abs((t.link.target.x0-t.link.source.x1)/2);e>r&&(e=r);var n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p=\"M\"+n+\",\"+u,d=\"C\"+s+\",\"+u+\" \"+l+\",\"+f+\" \"+a+\",\"+f,v=\"C\"+l+\",\"+h+\" \"+s+\",\"+c+\" \"+n+\",\"+c,g=e>0?\"L\"+(a+e)+\",\"+(f+t.link.width/2):\"\";return p+d+(g+=\"L\"+a+\",\"+h)+v+\"Z\"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr(\"d\",M())}function P(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function O(t){return t.link.width>1||t.linkLineWidth>0}function I(t){return p(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function z(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(a){if(\"fixed\"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),\"snap\"===a.arrangement)){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force(\"collide\",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force(\"constrain\",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var 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a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}t.exports=function(t,e,r,n,i){var o=t._context.staticPlot,s=!1;h.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){s=!0}));var v=t._fullLayout._dragCover,b=r.filter((function(t){return m(t).trace.visible})).map(k.bind(null,n)),_=e.selectAll(\".\"+l.cn.sankey).data(b,g);_.exit().remove(),_.enter().append(\"g\").classed(l.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",o?\"none\":\"auto\").attr(\"transform\",I),_.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",o?\"none\":\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),_.transition().ease(l.ease).duration(l.duration).attr(\"transform\",I);var L=_.selectAll(\".\"+l.cn.sankeyLinks).data(y,g);L.enter().append(\"g\").classed(l.cn.sankeyLinks,!0).style(\"fill\",\"none\");var 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n=r(21776).Ks,i=r(21776).Gw,a=r(98304),o=r(52904),s=r(45464),l=r(49084),u=r(98192).u,c=r(92880).extendFlat,f=r(67824).overrideAll,h=o.marker,p=o.line,d=h.line;t.exports=f({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n,i,a=r.length,o=a/2;if(h.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var u=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){u=!0;break}var c=u||n.length>v.TOO_MANY_POINTS||h.hasMarkers(e)?\"rect\":\"round\";if(u&&e.connectgaps){var f=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=f,n[i+1]=p):(f=n[i],p=n[i+1])}return{join:c,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=c.getFromId(t,e.xaxis,\"x\"),u=c.getFromId(t,e.yaxis,\"y\"),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type||\"log\"===i.type)){for(var l=s(o),u={x:0,y:1}[a],c={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,v=-1/0,g=0,y=0;g<f;g++,y+=4){var m=t[g];if(n(m)){var x=r[2*g+u],b=l(m,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=m-_,k=m+w;p[y+c[0]]=x-i.c2l(T),p[y+c[1]]=i.c2l(k)-x,p[y+c[2]]=0,p[y+c[3]]=0,d=Math.min(d,m-_),v=Math.max(v,m+w)}}}h[a]={positions:r,errors:p,_bnds:[d,v]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(h.hasMarkers(e)){var s=r.font,u=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var f=n.sizes?n.sizes[i]:n.size,p=l(s)?s[i].size:s.size,d=l(u)?u.length>1?u[i]:u[0]:u,v=l(c)?c.length>1?c[i]:c[0]:c,g=y[d],m=y[v],x=f?f/.8+1:0,b=-m*x-.5*m;o.offset[i]=[g*x/p,b/p]}}return o}}},80220:function(t,e,r){\"use strict\";var n=r(3400),i=r(24040),a=r(80088),o=r(2876),s=r(88200),l=r(43028),u=r(43980),c=r(31147),f=r(74428),h=r(66828),p=r(70840),d=r(124);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g(\"marker.line.width\",y||m?1:0)),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,v,g),g(\"line.shape\")),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,v,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},26768:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(13448).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=n.isArrayOrTypedArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},99396:function(t,e,r){\"use strict\";var n=r(76688);t.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},80088:function(t,e,r){\"use strict\";var n=r(67072);e.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},41272:function(t,e,r){\"use strict\";var n=r(24040),i=r(3400),a=r(44928);function o(t,e,r,o){var s=t.xa,l=t.ya,u=t.distance,c=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=i.isArrayOrTypedArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=i.isArrayOrTypedArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family,h.tw=Array.isArray(p.weight)?p.weight[f]:p.weight,h.ty=Array.isArray(p.style)?p.style[f]:p.style,h.tv=Array.isArray(p.variant)?p.variant[f]:p.variant);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.ma=i.isArrayOrTypedArray(d.angle)?d.angle[f]:d.angle,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var v=d&&d.line;v&&(h.mlc=Array.isArray(v.color)?v.color[f]:v.color,h.mlw=i.isArrayOrTypedArray(v.width)?v.width[f]:v.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var y=s.c2p(h.x,!0),m=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:y-x,x1:y+x,xLabelVal:k?k[f]:h.x,y0:m-x,y1:m+x,yLabelVal:A?A[f]:h.y,cd:T,distance:u,spikeDistance:c,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,u,c,f,h,p,d,v=t.cd,g=v[0].t,y=v[0].trace,m=t.xa,x=t.ya,b=g.x,_=g.y,w=m.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=m.p2c(w-k),M=m.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!y.xperiodalignment,P=!!y.yperiodalignment;for(c=0;c<i.length;c++){if(l=b[a=i[c]],f=Math.abs(m.c2p(l)-w),C){var O=m.c2p(y._xStarts[a]),I=m.c2p(y._xEnds[a]);f=w>=Math.min(O,I)&&w<=Math.max(O,I)?0:1/0}if(f<L){if(L=f,u=_[a],h=x.c2p(u)-T,P){var z=x.c2p(y._yStarts[a]),D=x.c2p(y._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[c]}}}else for(c=i.length-1;c>-1;c--)l=b[a=i[c]],u=_[a],f=m.c2p(l)-w,h=x.c2p(u)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,y)]},calcHover:o}},38983:function(t,e,r){\"use strict\";var n=r(64628);n.plot=r(89876),t.exports=n},89876:function(t,e,r){\"use strict\";var n=r(38540),i=r(13472),a=r(24544),o=r(23352),s=r(3400),l=r(72760).selectMode,u=r(5048),c=r(43028),f=r(14328),h=r(26768).styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,u=i.r*n,c=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-u-(1-e.domain[1])*f,o-c-(1-r.domain[1])*h]}(t.exports=function(t,e,r){if(r.length){var v,g,y=t._fullLayout,m=e._scene,x=e.xaxis,b=e.yaxis;if(m)if(u(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"],p)){var _=m.count,w=y._glcanvas.data()[0].regl;if(f(t,e,r),m.dirty){if(!m.line2d&&!m.error2d||m.scatter2d||m.fill2d||m.glText||w.clear({}),!0===m.error2d&&(m.error2d=a(w)),!0===m.line2d&&(m.line2d=i(w)),!0===m.scatter2d&&(m.scatter2d=n(w)),!0===m.fill2d&&(m.fill2d=i(w)),!0===m.glText)for(m.glText=new Array(_),v=0;v<_;v++)m.glText[v]=new o(w);if(m.glText){if(_>m.glText.length){var T=_-m.glText.length;for(v=0;v<T;v++)m.glText.push(new o(w))}else if(_<m.glText.length){var k=m.glText.length-_;m.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(v=0;v<_;v++)m.glText[v].update(m.textOptions[v])}if(m.line2d&&(m.line2d.update(m.lineOptions),m.lineOptions=m.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),m.line2d.update(m.lineOptions)),m.error2d){var A=(m.errorXOptions||[]).concat(m.errorYOptions||[]);m.error2d.update(A)}m.scatter2d&&m.scatter2d.update(m.markerOptions),m.fillOrder=s.repeat(null,_),m.fill2d&&(m.fillOptions=m.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,u=m.lineOptions[e],c=[];s._ownfill&&c.push(e),s._nexttrace&&c.push(e+1),c.length&&(m.fillOrder[e]=c);var f,h,p=[],d=u&&u.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var v=s._nexttrace;if(v){var g=m.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=v.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=m.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),m.fill2d.update(m.fillOptions))}var M=y.dragmode,S=l(M),E=y.clickmode.indexOf(\"select\")>-1;for(v=0;v<_;v++){var L=r[v][0],C=L.trace,P=L.t,O=P.index,I=C._length,z=P.x,D=P.y;if(C.selectedpoints||S||E){if(S||(S=!0),C.selectedpoints){var R=m.selectBatch[O]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<I;g++)F[g]||B.push(g);m.unselectBatch[O]=B}var N=P.xpx=new Array(I),j=P.ypx=new Array(I);for(g=0;g<I;g++)N[g]=x.c2p(z[g]),j[g]=b.c2p(D[g])}else P.xpx=P.ypx=null}if(S){if(m.select2d||(m.select2d=n(y._glcanvas.data()[1].regl)),m.scatter2d){var U=new Array(_);for(v=0;v<_;v++)U[v]=m.selectBatch[v].length||m.unselectBatch[v].length?m.markerUnselectedOptions[v]:{};m.scatter2d.update(U)}m.select2d&&(m.select2d.update(m.markerOptions),m.select2d.update(m.markerSelectedOptions)),m.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};c.hasText(e)&&h(t)}))}else m.scatter2d&&m.scatter2d.update(m.markerOptions);var V={viewport:d(y,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},q=s.repeat(V,m.count);m.fill2d&&m.fill2d.update(q),m.line2d&&m.line2d.update(q),m.error2d&&m.error2d.update(q.concat(q)),m.scatter2d&&m.scatter2d.update(q),m.select2d&&m.select2d.update(q),m.glText&&m.glText.forEach((function(t){t.update(V)}))}else m.init()}}).reglPrecompiled=p},74588:function(t,e,r){\"use strict\";var n=r(3400);t.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,u=r.selectBatch,c=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(c[f].length){var h=n.repeat([],r.count);h[f]=c[f],o.draw(h)}else 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n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},starts:{x:{valType:\"data_array\",editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"},maxdisplayed:{valType:\"integer\",min:0,dflt:1e3,editType:\"calc\"},sizeref:{valType:\"number\",editType:\"calc\",min:0,dflt:1},text:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"tubex\",\"tubey\",\"tubez\",\"tubeu\",\"tubev\",\"tubew\",\"norm\",\"divergence\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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L=function(){m=0,M=[],S=[],E=[]};(!m||m<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?v:\"y\"===t?g:y},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},O=function(t){return t[m-1]<t[0]?-1:1},I=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=O(C(A[1])),V=O(C(A[3])),q=O(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var H=j(e,r,i),G=j(e,r,i+1),W=j(e,r+1,i),Y=j(e+1,r,i);if(I[H]*U<I[G]*U&&z[H]*V<z[W]*V&&D[H]*q<D[Y]*q||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn(\"Encountered arbitrary coordinates! 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y=n[c];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var m=o[c];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var x=l[c];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=u,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},25668:function(t,e,r){\"use strict\";var n=r(67792).gl_streamtube3d,i=n.createTubeMesh,a=r(3400),o=r(33040).parseColorScale,s=r(8932).extractOpts,l=r(52094),u={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=c.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,f={};function d(t,e){var n=r[e],o=i[u[e]];return a.simpleMap(t,(function(t){return 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n=r(45464),i=r(21776).Ks,a=r(21776).Gw,o=r(49084),s=r(86968).u,l=r(74996),u=r(27328),c=r(92880).extendFlat,f=r(98192).c;t.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:c({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:c({},l.marker.line.color,{dflt:null}),width:c({},l.marker.line.width,{dflt:1}),editType:\"calc\"},pattern:f,editType:\"calc\"},o(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:l.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current 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u=n.select(this),f=s.ensureSingle(u,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",h?\"none\":\"all\")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),_?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(Z){var i=(t.x1>Z?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return W(e(t))}})):f.attr(\"d\",W),u.call(m,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,A,t);var p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=X(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(Z){var i=t.x1>Z?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:X(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split(\"+\"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y(\"label\")&&c.label&&m.push(c.label),c.hasOwnProperty(\"v\")&&y(\"value\")&&m.push(b.formatValue(c.v,l)),!h){y(\"current path\")&&m.push(b.getPath(t.data));var x=0;y(\"percent parent\")&&x++,y(\"percent entry\")&&x++,y(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=\" of \"+t),m.push(v)};y(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),y(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),y(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return y(\"text\")&&(v=s.castOption(r,c.i,\"text\"),s.isValidTextValue(v)&&m.push(v)),m.join(\"<br>\")}var k=s.castOption(r,c.i,\"texttemplate\");if(!k)return\"\";var A={};c.label&&(A.label=c.label),c.hasOwnProperty(\"v\")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty(\"color\")&&(A.color=c.color);var M=s.castOption(r,c.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},85676:function(t,e,r){\"use strict\";var n=r(33428),i=r(76308),a=r(3400),o=r(82744).resizeText,s=r(60404);function l(t,e,r,n){var o=e.data.data,l=!e.children,u=o.i,c=a.castOption(r,u,\"marker.line.color\")||i.defaultLine,f=a.castOption(r,u,\"marker.line.width\")||0;t.call(s,e,r,n).style(\"stroke-width\",f).call(i.stroke,c).style(\"opacity\",l?r.leaf.opacity:null)}t.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(e){var r=n.select(this),i=e[0].trace;r.style(\"opacity\",i.opacity),r.selectAll(\"path.surface\").each((function(e){n.select(this).call(l,e,i,t)}))}))},styleOne:l}},16716:function(t,e,r){\"use strict\";var n=r(76308),i=r(49084),a=r(29736).axisHoverFormat,o=r(21776).Ks,s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:c(),y:c(),z:c()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},56576:function(t,e,r){\"use strict\";var n=r(47128);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},79164:function(t,e,r){\"use strict\";var n=r(67792).gl_surface3d,i=r(67792).ndarray,a=r(67792).ndarray_linear_interpolate.d2,o=r(70448),s=r(11240),l=r(3400).isArrayOrTypedArray,u=r(33040).parseColorScale,c=r(43080),f=r(8932).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function v(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(v(t,n))}return e}function y(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function m(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++)r(i,[l,u],n),t.set(l,u,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=m(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=y(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,u=1+a+1,c=i(new Float32Array(l*u),[l,u]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(c,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,c,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=u(t),v=l.dataScale,g=t.z[0].length,y=t._ylength,m=l.contourLevels;this.data=t;var 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A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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m=\"contours.\"+y[c],x=h(m+\".show\"),b=h(m+\".highlight\");if(x||b)for(f=0;f<3;++f)h(m+\".project.\"+y[f]);x&&(h(m+\".color\"),h(m+\".width\"),h(m+\".usecolormap\")),b&&(h(m+\".highlightcolor\"),h(m+\".highlightwidth\")),h(m+\".start\"),h(m+\".end\"),h(m+\".size\")}g||(l(t,\"zmin\",\"cmin\"),l(t,\"zmax\",\"cmax\"),l(t,\"zauto\",\"cauto\")),a(t,e,u,h,{prefix:\"\",cLetter:\"c\"}),s(0,e,0,h),e._length=null}},opacityscaleDefaults:s}},91304:function(t,e,r){\"use strict\";t.exports={attributes:r(16716),supplyDefaults:r(60192).supplyDefaults,colorbar:{min:\"cmin\",max:\"cmax\"},calc:r(56576),plot:r(79164),moduleType:\"trace\",name:\"surface\",basePlotModule:r(12536),categories:[\"gl3d\",\"2dMap\",\"showLegend\"],meta:{}}},60520:function(t,e,r){\"use strict\";var n=r(13916),i=r(92880).extendFlat,a=r(67824).overrideAll,o=r(25376),s=r(86968).u,l=r(29736).descriptionOnlyNumbers;(t.exports=a({domain:s({name:\"table\",trace:!0}),columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell value\")},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0,dflt:1},color:{valType:\"color\",arrayOk:!0,dflt:\"grey\"}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell 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\",cn:{table:\"table\",tableControlView:\"table-control-view\",scrollBackground:\"scroll-background\",yColumn:\"y-column\",columnBlock:\"column-block\",scrollAreaClip:\"scroll-area-clip\",scrollAreaClipRect:\"scroll-area-clip-rect\",columnBoundary:\"column-boundary\",columnBoundaryClippath:\"column-boundary-clippath\",columnBoundaryRect:\"column-boundary-rect\",columnCells:\"column-cells\",columnCell:\"column-cell\",cellRect:\"cell-rect\",cellText:\"cell-text\",cellTextHolder:\"cell-text-holder\",scrollbarKit:\"scrollbar-kit\",scrollbar:\"scrollbar\",scrollbarSlider:\"scrollbar-slider\",scrollbarGlyph:\"scrollbar-glyph\",scrollbarCaptureZone:\"scrollbar-capture-zone\"}}},55992:function(t,e,r){\"use strict\";var n=r(23536),i=r(92880).extendFlat,a=r(38248),o=r(38116).isTypedArray,s=r(38116).isArrayOrTypedArray;function l(t){if(s(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,l(t[r]));return e}return t}function u(t,e){return t+e}function c(t){var e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)o(r[e])?r[e]=Array.from(r[e]):s(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(f(a)))}return r}function f(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=\"\";return e}function h(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function p(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function d(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,u=0;u<t.length;u++)r=t[u],o.rows.push({rowIndex:u,rowHeight:r}),((a+=r)>=e||u===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=u,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=u+1,a=0);return n}t.exports=function(t,e){var r=c(e.cells.values),o=function(t){return 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,W=0|s[2],Y=8191&W,X=W>>>13,Z=0|s[3],K=8191&Z,J=Z>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,X)|0;var xt=(u+(n=n+Math.imul(f,K)|0)|0)+((8191&(i=(i=i+Math.imul(f,J)|0)+Math.imul(h,K)|0))<<13)|0;u=((a=a+Math.imul(h,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,X)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(d,K)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(v,K)|0,a=a+Math.imul(v,J)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(y,K)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(m,K)|0,a=a+Math.imul(m,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,K)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,K)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,K)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,H)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,K)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,K)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(L,K)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(O,K)|0,i=(i=i+Math.imul(O,J)|0)+Math.imul(I,K)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(D,K)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,K)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,K),i=(i=Math.imul(B,J))+Math.imul(N,K)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var zt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=zt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g<d);var y=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(y),f=y.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var f=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4==++s||0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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if(h.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).right===p?v.right=h:v.left=h);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var v;h.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=h,s.left=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).right===p?v.right=s:v.left=s);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}}return l[0]._color=1,new a(o,l[0])},o.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return s(t,this.root);case 2:return l(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return u(e,r,this._compare,t,this.root)}},Object.defineProperty(o,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new c(this,t)}}),Object.defineProperty(o,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new c(this,t)}}),o.at=function(t){if(t<0)return new c(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new c(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new c(this,[])},o.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new c(this,n);r=i<=0?r.left:r.right}return new c(this,[])},o.remove=function(t){var e=this.find(t);return e?e.remove():this},o.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var f=c.prototype;function h(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(f,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(f,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),f.clone=function(){return new c(this.tree,this._stack.slice())},f.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var o=new Array(t.length),s=t[t.length-1];o[o.length-1]=new e(s._color,s.key,s.value,s.left,s.right,s._count);for(var l=t.length-2;l>=0;--l)(s=t[l]).left===t[l+1]?o[l]=new e(s._color,s.key,s.value,o[l+1],s.right,s._count):o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);if((s=o[o.length-1]).left&&s.right){var u=o.length;for(s=s.left;s.right;)o.push(s),s=s.right;var c=o[u-1];for(o.push(new e(s._color,c.key,c.value,s.left,s.right,s._count)),o[u-1].key=s.key,o[u-1].value=s.value,l=o.length-2;l>=u;--l)s=o[l],o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);o[u-1].left=o[u]}if(0===(s=o[o.length-1])._color){var f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?h(s,s.left):s.right&&h(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(o),p.left===s?p.left=null:p.right=null,new a(this.tree._compare,o[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},3837:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=r(4935),i=r(501),a=r(5304),o=r(6429),s=r(6444),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\"],this.tickFontWeight=[\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\"],this.labelFontWeight=[\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(c=!0),l(\"tickFontStyle\")&&(c=!0),l(\"tickFontWeight\")&&(c=!0),l(\"tickFontVariant\")&&(c=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),u(\"tickColor\");var v=l(\"labels\");l(\"labelFont\")&&(v=!0),l(\"labelFontStyle\")&&(v=!0),l(\"labelFontWeight\")&&(v=!0),l(\"labelFontVariant\")&&(v=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\");var g=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],y=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,g,this.ticks,y):this._text=n(this.gl,this.bounds,this.labels,g,this.ticks,y),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,z=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,z,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,z=(A+1+(1^O))%3,this.zeroEnable[z]&&Math.min(a[0][z],a[1][z])<=0&&Math.max(a[0][z],a[1][z])>=0&&this._lines.drawZero(I,z,this.bounds,P,this.zeroLineColor[z],this.zeroLineWidth[z]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var D=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];D[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,D,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(q[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)q[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),O=0;O<3;++O)q[O]+=k*U[O]*this.labelPad[O]/r[5*O];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);o(u[I],u[P],u[O],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);var I,z=d([l[I],l[P],l[O],l[I+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||u[R][1]>u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<<x)!==w&&N!==D&&(F<0&&(F=N),(S=u[N])[0]<u[F][0]&&(F=N));var B=-1;for(x=0;x<3;++x){var N;(N=R^1<<x)!==w&&N!==D&&N!==F&&(B<0&&(B=N),(S=u[N])[0]>u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1<<A?-1:1;return y};var n=r(8828),i=r(6760),a=r(5202),o=r(3250),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},501:function(t,e,r){\"use strict\";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(2762),i=r(8116),a=r(1879).n,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1879:function(t,e,r){\"use strict\";var n=r(3236),i=r(9405),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.n=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.Q=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},4935:function(t,e,r){\"use strict\";t.exports=function(t,e,r,n,o,l){var u=i(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,n,o,l),p};var i=r(2762),a=r(8116),o=r(4359),s=r(1879).Q,l=window||n.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=[r.style,r.weight,r.variant,r.family].join(\"_\"),c=u[l];c||(c=u[l]={});var f=c[e];f||(f=c[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r.family,fontStyle:r.style,fontWeight:r.weight,fontVariant:r.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var h=(n||12)/12,p=f.positions,d=f.cells,v=0,g=d.length;v<g;++v)for(var y=d[v],m=2;m>=0;--m){var x=p[y[m]];a.push(h*x[0],-h*x[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v<n[d].length;++v)if(n[d][v].text){var g={family:n[d][v].font||i[d].family,style:i[d].fontStyle||i[d].style,weight:i[d].fontWeight||i[d].weight,variant:i[d].fontVariant||i[d].variant};s(n[d][v].x,n[d][v].text,g,n[d][v].fontSize||12,1.25,p)}c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},6444:function(t,e){\"use strict\";function r(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length<i;)f=\"0\"+f;return c+\".\"+f}return c}e.create=function(t,e){for(var n=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:r(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},5445:function(t,e,r){\"use strict\";t.exports=function(t,e,r,l,f){var h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=m?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var O=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}}return _};var n=r(5033),i=r(5202),a=r(6429),o=r(6760),s=r(5665),l=r(5352),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},2762:function(t,e,r){\"use strict\";var n=r(1888),i=r(5298),a=r(9618),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\"),r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){\"use strict\";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=\"raw\"===t.coneSizemode,x=0;x<r.length;x++){var b=r[x];s=Math.min(b[0],s),l=Math.max(b[0],l),u=Math.min(b[1],u),c=Math.max(b[1],c),f=Math.min(b[2],f),h=Math.max(b[2],h);var _=i[x];if(n.length(_)>o&&(o=n.length(_)),x&&!m){var w=2*n.distance(p,b)/(n.length(d)+n.length(_));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=b,d=_),v.push(_)}var T=[s,u,f],k=[l,c,h];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(m?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x<r.length;x++)for(var E=(b=r[x])[0],L=b[1],C=b[2],P=v[x],O=n.length(P)*A,I=0;I<8;I++){a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vertexIntensity.push(O,O,O),a.vertexIntensity.push(O,O,O);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return a};var i=r(614);t.exports.createMesh=r(9060),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:\"cone\"})}},9060:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(6760),l=r(7608),u=r(9618),c=r(6729),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return m.update(e),m}},614:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},737:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},5171:function(t,e,r){var 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worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},2260:function(t,e,r){\"use strict\";var n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new 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c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},2992:function(t,e,r){var n=r(3387).sprintf,i=r(5171),a=r(1848),o=r(1085);t.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=n(\"Error compiling %s shader %s:\\n\",l,s),c=n(\"%s%s\",u,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var v=parseInt(d.split(\":\")[2]);if(isNaN(v))throw new Error(n(\"Could not parse error: %s\",d));h[v]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+\"\\n\",h[p+1])){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=n(\"^^^ %s\\n\\n\",y)}return{long:u.trim(),short:c.trim()}}},2510:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(2478),i=r(7762),a=r(1888),o=r(9405),s=r(2762),l=r(6768);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,O;l?(P=x*(o[C]-c),O=x*(o[C+1]-c)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),O=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var z,D;l?(z=m*(r[I]-u),D=m*(r[I+1]-u)):(z=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),D=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(C+V)*b+(I+U):C*b+I],H=n.le(d,q);if(H<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(H===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),W=1-G,Y=4*H,X=4*(H+1);F=W*v[Y]+G*v[X],B=W*v[Y+1]+G*v[X+1],N=W*v[Y+2]+G*v[X+2],j=W*v[Y+3]+G*v[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*O,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},6768:function(t,e,r){\"use strict\";var n=r(3236);t.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},7319:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),u=r(9618),c=r(7319),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var 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r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return(r=Math.sqrt(r))+(a=Math.sqrt(a))<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),y=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},840:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},7201:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),u=r(7608),c=r(9618),f=r(6729),h=r(7765),p=r(1888),d=r(840),v=r(7626),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(I)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],z=Math.min(z,F),D=Math.max(D,F);else for(R=0;R<n.length;++R)F=n[R][2],z=Math.min(z,F),D=Math.max(D,F);this.intensity=P||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?m.push(X[0],X[1],X[2],this.opacity):(m.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[Y]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],v.push(Z[0],Z[1]),g.push(R);q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t;for(U=0;U<3;++U){var Y,X,Z,K;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(X=S?S[Y]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],u.push(Z[0],Z[1]),K=w?w[Y]:T[R],l.push(K[0],K[1],K[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=i(t),I=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,P,O,R,I,z,D,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},8120:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1913:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(2762),i=r(9405),a=r(2478),o=r(3603);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*O,D=6*Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},4747:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3603:function(t,e,r){\"use strict\";var n=r(3236),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);t.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},2142:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(2762),i=r(9405),a=r(529),o=r(2478),s=r(3603);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||\"sans-serif\",y=p.fontStyle||\"normal\",m=p.fontWeight||\"normal\",x=p.fontVariant||\"normal\";i=p.fontSize||12;for(var b=1/(u[o+2]-u[o]),_=u[o],w=v.split(\"\\n\"),T=0;T<w.length;T++)for(n=a(g,w[T],{fontStyle:y,fontWeight:m,fontVariant:x}).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-T*i*1.2,(d-_)*b);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{fontStyle:t.labelFontStyle[o],fontWeight:t.labelFontWeight[o],fontVariant:t.labelFontVariant[o],textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title,{fontStyle:t.titleFontStyle,fontWeight:t.titleFontWeight,fontVariant:t.titleFontVariant}).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1850:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(3589),i=r(1913),a=r(2142),o=r(4747),s=r(8120);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},u.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},u.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},u.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=c(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=c(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=c(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=c(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],labelFontStyle:t.labelFontStyle||[\"normal\",\"normal\"],labelFontWeight:t.labelFontWeight||[\"normal\",\"normal\"],labelFontVariant:t.labelFontVariant||[\"normal\",\"normal\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\",titleFontStyle:t.titleFontStyle||\"normal\",titleFontWeight:t.titleFontWeight||\"normal\",titleFontVariant:t.titleFontVariant||\"normal\"}),this.static=!!t.static,this.setDirty()},u.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4437:function(t,e,r){\"use strict\";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];\"distanceLimits\"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(r[0]=e.zoomMin),\"zoomMax\"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},4100:function(t,e,r){\"use strict\";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),u=r(7169),c=r(351),f=r(4772),h=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,z={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},D=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:z,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,D);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],q=[V[0].slice(),V[1].slice()];function H(){if(!U()){N();var t=F.camera.tick();z.view=F.camera.matrix,O=O||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,O=O||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||q[0][p]!==d[0][p]||q[1][p]!==d[1][p],q[0][p]=d[0][p],q[1][p]=d[1][p];if(I=I||y,O=O||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(D[0]=T,D[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(z))}i.end()}}}()),F.axesPixels=a(F.axes,z,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(z)),S.axes=A,x.object&&S.draw(z),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(z),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=D,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(z),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(z);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(z),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(z)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(O=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(H(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(O=!0,H())},F},createCamera:n}},6640:function(t,e,r){var n=r(3236);e.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),e.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},4696:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},5964:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},9366:function(t,e,r){\"use strict\";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join(\"_\"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var u,c,f=n(t,s);if(r&&1!==r){for(u=0;u<l.positions.length;++u)for(c=0;c<l.positions[u].length;++c)l.positions[u][c]/=r;for(u=0;u<f.positions.length;++u)for(c=0;c<f.positions[u].length;++c)f.positions[u][c]/=r}var h=[[1/0,1/0],[-1/0,-1/0]],p=f.positions.length;for(u=0;u<p;++u){var d=f.positions[u];for(c=0;c<2;++c)h[0][c]=Math.min(h[0][c],d[c]),h[1][c]=Math.max(h[1][c],d[c])}return o[t]=[l,f,h]};var i={}},1283:function(t,e,r){var n=r(9405),i=r(3236),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){\"use strict\";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),u=r(9366),c=r(5964),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var z=p(0,0,0,S(_,O)),D=p(0,0,0,S(w,I));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=O,O=I,I=R;var F=C;C=P,P=F}z[0]<0&&(O[C]=-1),D[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var 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float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting 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* cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},7815:function(t,e,r){\"use strict\";var n=r(2931),i=r(9970),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],P=(l-M)/(S-M),O=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(q,D,N,P),n.lerp(H,R,j,P),n.lerp(G,F,U,P);var W=n.create(),Y=n.create();n.lerp(W,V,H,O),n.lerp(Y,q,G,O);var X=n.create();return n.lerp(X,W,Y,I),X}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],I=d(C),z=C;O.push(I);var D=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:O,divergences:D});for(var B=0;B<100*u&&P.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(z,N)-A>-1e-4*A&&(P.push(N),z=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),D.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(6740),c=r(6405).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:\"streamtube\"})}},990:function(t,e,r){var n=r(9405),i=r(3236),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color — in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},9499:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),u=r(5298),c=r(9994),f=r(9618),h=r(3711),p=r(6760),d=r(7608),v=r(2478),g=r(6199),y=r(990),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},z=T.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=z;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],\"mirror\");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,y,0,E*P),b.set(o,y,1,L*P),b.set(o,y,2,C*P)}s.free(x.data);var O=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+y+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var W=o+k[H][0],Y=y+k[H][1],X=this._field[0].get(W+1,Y+1),Z=this._field[1].get(W+1,Y+1);G=this._field[2].get(W+1,Y+1),E=b.get(W+1,Y+1,0),L=b.get(W+1,Y+1,1),C=b.get(W+1,Y+1,2),t.intensity&&(K=t.intensity.get(W,Y));var K=t.intensity?t.intensity.get(W,Y):G+this.objectOffset[2];F[N++]=W,F[N++]=Y,F[N++]=X,F[N++]=Z,F[N++]=G,F[N++]=0,F[N++]=K,F[N++]=E,F[N++]=L,F[N++]=C,O[0]=Math.min(O[0],X+this.objectOffset[0]),O[1]=Math.min(O[1],Z+this.objectOffset[1]),O[2]=Math.min(O[2],G+this.objectOffset[2]),z=Math.min(z,K),I[0]=Math.max(I[0],X+this.objectOffset[0]),I[1]=Math.max(I[1],Z+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),D=Math.max(D,K),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[O,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;o<3;++o)J[o]=J[o].slice(),J[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<J[o].length;++y)J[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(W=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var yt=q?ht:1-ht;if(Y=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[vt].get(W,Y):(this.intensity.get(W,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,O=T*L,I=T*E,z=C*f.get(_,M)+P*f.get(_,S)+O*f.get(w,M)+I*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+I*p.get(w,S);if(isNaN(z)||isNaN(D)){m&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},7766:function(t,e,r){\"use strict\";var n=r(9618),i=r(5298),a=r(1888);t.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),\"number\"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);\"float32\"!==r&&\"float64\"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return f(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return f(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,f(this,this._shape[0],t),t}}}),p.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},p.dispose=function(){this.gl.deleteTexture(this.handle)},p.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},p.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var l=u(t)?t:t.raw;if(l)this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l);else{if(!(t.shape&&t.stride&&t.data))throw new Error(\"gl-texture2d: Unsupported data type\");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");!function(t,e,r,o,s,l,u,f){var h=f.dtype,p=f.shape.slice();if(p.length<2||p.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var v=0,g=0,y=d(p,f.stride.slice());if(\"float32\"===h?v=t.FLOAT:\"float64\"===h?(v=t.FLOAT,y=!1,h=\"float32\"):\"uint8\"===h?v=t.UNSIGNED_BYTE:(v=t.UNSIGNED_BYTE,y=!1,h=\"uint8\"),2===p.length)g=t.LUMINANCE,p=[p[0],p[1],1],f=n(f.data,p,[f.stride[0],f.stride[1],1],f.offset);else{if(3!==p.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===p[2])g=t.ALPHA;else if(2===p[2])g=t.LUMINANCE_ALPHA;else if(3===p[2])g=t.RGB;else{if(4!==p[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}p[2]}if(g!==t.LUMINANCE&&g!==t.ALPHA||s!==t.LUMINANCE&&s!==t.ALPHA||(g=s),g!==s)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var m=f.size,x=u.indexOf(o)<0;if(x&&u.push(o),v===l&&y)0===f.offset&&f.data.length===m?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,f.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,f.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,f.data.subarray(f.offset,f.offset+m)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,f.data.subarray(f.offset,f.offset+m));else{var b;b=l===t.FLOAT?a.mallocFloat32(m):a.mallocUint8(m);var 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i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=u(a,o,s);a/=l,o/=l,s/=l;var c=i[0],f=i[4],h=i[8],p=c*a+f*o+h*s,d=u(c-=a*p,f-=o*p,h-=s*p),v=(c/=d)*e+a*r,g=(f/=d)*e+o*r,y=(h/=d)*e+s*r;this.center.move(t,v,g,y);var m=Math.exp(this.computedRadius[0]);m=Math.max(1e-4,m+n),this.radius.set(t,Math.log(m))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),v=Math.max(h,p,d);h===v?(s=s<0?-1:1,l=f=0):d===v?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=u(s,l,f);s/=g,l/=g,f/=g}var y,m,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=u(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=u(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,H,G,W),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var 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Sr=function(t,e){this.type=Xt,this.needle=t,this.haystack=e};Sr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Jt),n=e.parse(t[2],2,Jt);return r&&n?ae(r.type,[Xt,Yt,Wt,Gt,Jt])?new Sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+re(r.type)+\" instead\"):null},Sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!oe(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new xe(\"Expected first argument to be of type boolean, string, number or null, but found \"+re(ge(e))+\" instead.\");if(!oe(r,[\"string\",\"array\"]))throw new xe(\"Expected second argument to be of type array or string, but found \"+re(ge(r))+\" instead.\");return 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e={color:Zt,string:Yt,number:Wt,enum:Yt,boolean:Xt,formatted:Qt,resolvedImage:te};return\"array\"===t.type?ee(e[t.value]||Jt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&\"string\"===e.type?{typeAnnotation:\"coerce\"}:void 0);return n?Xr(new un(n,e)):Zr(r.errors)}un.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)},un.prototype.evaluate=function(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{var o=this.expression.evaluate(this._evaluator);if(null==o||\"number\"==typeof o&&o!=o)return this._defaultValue;if(this._enumValues&&!(o in this._enumValues))throw new xe(\"Expected value to be one of \"+Object.keys(this._enumValues).map((function(t){return JSON.stringify(t)})).join(\", \")+\", but found \"+JSON.stringify(o)+\" instead.\");return o}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}};var hn=function(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!Xe(e.expression)};hn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},hn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)};var pn=function(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!Xe(e.expression),this.interpolationType=n};function dn(t,e){if(\"error\"===(t=fn(t,e)).result)return t;var r=t.value.expression,n=Ye(r);if(!n&&!Kr(e))return Zr([new qt(\"\",\"data expressions not supported\")]);var i=Ze(r,[\"zoom\"]);if(!i&&!Jr(e))return Zr([new qt(\"\",\"zoom expressions not supported\")]);var a=gn(r);if(!a&&!i)return Zr([new qt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')]);if(a instanceof qt)return Zr([a]);if(a instanceof wr&&!$r(e))return Zr([new qt(\"\",'\"interpolate\" expressions cannot be used with this property')]);if(!a)return Xr(new hn(n?\"constant\":\"source\",t.value));var o=a instanceof wr?a.interpolation:void 0;return Xr(new pn(n?\"camera\":\"composite\",t.value,a.labels,o))}pn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},pn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},pn.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?wr.interpolationFactor(this.interpolationType,t,e,r):0};var vn=function(t,e){this._parameters=t,this._specification=e,jt(this,rn(this._parameters,this._specification))};function gn(t){var e=null;if(t instanceof Ar)e=gn(t.result);else if(t instanceof kr)for(var r=0,n=t.args;r<n.length;r+=1){var i=n[r];if(e=gn(i))break}else(t instanceof tr||t instanceof wr)&&t.input instanceof Ee&&\"zoom\"===t.input.name&&(e=t);return e instanceof qt||t.eachChild((function(t){var r=gn(t);r instanceof qt?e=r:!e&&r?e=new qt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):e&&r&&e!==r&&(e=new qt(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))})),e}function yn(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Qr(r);if(\"object\"!==l)return[new Bt(e,r,\"object expected, \"+l+\" found\")];for(var u in r){var c=u.split(\".\")[0],f=n[c]||n[\"*\"],h=void 0;if(i[c])h=i[c];else if(n[c])h=Hn;else if(i[\"*\"])h=i[\"*\"];else{if(!n[\"*\"]){s.push(new Bt(e,r[u],'unknown property \"'+u+'\"'));continue}h=Hn}s=s.concat(h({key:(e?e+\".\":e)+u,value:r[u],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:u},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Bt(e,r,'missing required property \"'+p+'\"'));return s}function mn(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||Hn;if(\"array\"!==Qr(e))return[new Bt(a,e,\"array expected, \"+Qr(e)+\" found\")];if(r.length&&e.length!==r.length)return[new Bt(a,e,\"array length \"+r.length+\" expected, length \"+e.length+\" found\")];if(r[\"min-length\"]&&e.length<r[\"min-length\"])return[new Bt(a,e,\"array length at least \"+r[\"min-length\"]+\" expected, length \"+e.length+\" found\")];var s={type:r.value,values:r.values};i.$version<7&&(s.function=r.function),\"object\"===Qr(r.value)&&(s=r.value);for(var l=[],u=0;u<e.length;u++)l=l.concat(o({array:e,arrayIndex:u,value:e[u],valueSpec:s,style:n,styleSpec:i,key:a+\"[\"+u+\"]\"}));return l}function xn(t){var e=t.key,r=t.value,n=t.valueSpec,i=Qr(r);return\"number\"===i&&r!=r&&(i=\"NaN\"),\"number\"!==i?[new Bt(e,r,\"number expected, \"+i+\" found\")]:\"minimum\"in n&&r<n.minimum?[new Bt(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Bt(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function bn(t){var e,r,n,i=t.valueSpec,a=Ut(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,u=\"array\"===Qr(t.value.stops)&&\"array\"===Qr(t.value.stops[0])&&\"object\"===Qr(t.value.stops[0][0]),c=yn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Bt(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;return e=e.concat(mn({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Qr(r)&&0===r.length&&e.push(new Bt(t.key,r,\"array must have at least one stop\")),e},default:function(t){return Hn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&c.push(new Bt(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||c.push(new Bt(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!$r(t.valueSpec)&&c.push(new Bt(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!Kr(t.valueSpec)?c.push(new Bt(t.key,t.value,\"property functions not supported\")):s&&!Jr(t.valueSpec)&&c.push(new Bt(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!u||void 0!==t.value.property||c.push(new Bt(t.key,t.value,'\"property\" property is required')),c;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Qr(a))return[new Bt(s,a,\"array expected, \"+Qr(a)+\" found\")];if(2!==a.length)return[new Bt(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(u){if(\"object\"!==Qr(a[0]))return[new Bt(s,a,\"object expected, \"+Qr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Bt(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Bt(s,a,\"object stop key must have value\")];if(n&&n>Ut(a[0].zoom))return[new Bt(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ut(a[0].zoom)!==n&&(n=Ut(a[0].zoom),r=void 0,o={}),e=e.concat(yn({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:xn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return cn(Vt(a[1]))?e.concat([new Bt(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(Hn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Qr(t.value),l=Ut(t.value),u=null!==t.value?t.value:n;if(e){if(s!==e)return[new Bt(t.key,u,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Bt(t.key,u,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var c=\"number expected, \"+s+\" found\";return Kr(i)&&void 0===a&&(c+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Bt(t.key,u,c)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Bt(t.key,u,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Bt(t.key,u,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Bt(t.key,u,\"integer expected, found \"+l)]}}function _n(t){var e=(\"property\"===t.expressionContext?dn:fn)(Vt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Bt(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Bt(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Xe(r))return[new Bt(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Xe(r))return[new Bt(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ze(r,[\"zoom\",\"feature-state\"]))return[new Bt(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Ye(r))return[new Bt(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function wn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ut(r))&&i.push(new Bt(e,r,\"expected one of [\"+n.values.join(\", \")+\"], \"+JSON.stringify(r)+\" found\")):-1===Object.keys(n.values).indexOf(Ut(r))&&i.push(new Bt(e,r,\"expected one of [\"+Object.keys(n.values).join(\", \")+\"], \"+JSON.stringify(r)+\" found\")),i}function Tn(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case\"has\":return t.length>=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!Tn(n)&&\"boolean\"!=typeof n)return!1}return!0;default:return!0}}vn.deserialize=function(t){return new vn(t._parameters,t._specification)},vn.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var kn={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function An(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};Tn(t)||(t=En(t));var e=fn(t,kn);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:Sn(t)}}function Mn(t,e){return t<e?-1:t>e?1:0}function Sn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0])return!0;for(var e=1;e<t.length;e++)if(Sn(t[e]))return!0;return!1}function En(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?Ln(t[1],t[2],\"==\"):\"!=\"===r?On(Ln(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?Ln(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(En))):\"all\"===r?[\"all\"].concat(t.slice(1).map(En)):\"none\"===r?[\"all\"].concat(t.slice(1).map(En).map(On)):\"in\"===r?Cn(t[1],t.slice(2)):\"!in\"===r?On(Cn(t[1],t.slice(2))):\"has\"===r?Pn(t[1]):\"!has\"===r?On(Pn(t[1])):\"within\"!==r||t}function Ln(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Cn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Mn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function Pn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function On(t){return[\"!\",t]}function In(t){return Tn(Vt(t.value))?_n(jt({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):zn(t)}function zn(t){var e=t.value,r=t.key;if(\"array\"!==Qr(e))return[new Bt(r,e,\"array expected, \"+Qr(e)+\" found\")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,\"filter array must have at least 1 element\")];switch(a=a.concat(wn({key:r+\"[0]\",value:e[0],valueSpec:i.filter_operator,style:t.style,styleSpec:t.styleSpec})),Ut(e[0])){case\"<\":case\"<=\":case\">\":case\">=\":e.length>=2&&\"$type\"===Ut(e[1])&&a.push(new Bt(r,e,'\"$type\" cannot be use with operator \"'+e[0]+'\"'));case\"==\":case\"!=\":3!==e.length&&a.push(new Bt(r,e,'filter array for operator \"'+e[0]+'\" must have 3 elements'));case\"in\":case\"!in\":e.length>=2&&\"string\"!==(n=Qr(e[1]))&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));for(var o=2;o<e.length;o++)n=Qr(e[o]),\"$type\"===Ut(e[1])?a=a.concat(wn({key:r+\"[\"+o+\"]\",value:e[o],valueSpec:i.geometry_type,style:t.style,styleSpec:t.styleSpec})):\"string\"!==n&&\"number\"!==n&&\"boolean\"!==n&&a.push(new Bt(r+\"[\"+o+\"]\",e[o],\"string, number, or boolean expected, \"+n+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var s=1;s<e.length;s++)a=a.concat(zn({key:r+\"[\"+s+\"]\",value:e[s],style:t.style,styleSpec:t.styleSpec}));break;case\"has\":case\"!has\":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for \"'+e[0]+'\" operator must have 2 elements')):\"string\"!==n&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));break;case\"within\":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for \"'+e[0]+'\" operator must have 2 elements')):\"object\"!==n&&a.push(new Bt(r+\"[1]\",e[1],\"object expected, \"+n+\" found\"))}return a}function Dn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return Hn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new Bt(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Qr(a)&&Kr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new Bt(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(u[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Bt(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&tn(Vt(a))&&\"identity\"===Ut(a.type)&&f.push(new Bt(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Ut(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new Bt(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new Bt(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new Bt(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new Bt(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new Bt(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new Bt(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new Bt(n,r,'missing required property \"source\"'));return 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Arrows\"](t)&&(t>=11026&&t<=11055||t>=11088&&t<=11097||t>=11192&&t<=11243)||pi[\"CJK Symbols and Punctuation\"](t)||pi.Katakana(t)||pi[\"Private Use Area\"](t)||pi[\"CJK Compatibility Forms\"](t)||pi[\"Small Form Variants\"](t)||pi[\"Halfwidth and Fullwidth Forms\"](t)||8734===t||8756===t||8757===t||t>=9984&&t<=10087||t>=10102&&t<=10131||65532===t||65533===t)}(t))}function yi(t){return pi.Arabic(t)||pi[\"Arabic Supplement\"](t)||pi[\"Arabic Extended-A\"](t)||pi[\"Arabic Presentation Forms-A\"](t)||pi[\"Arabic Presentation Forms-B\"](t)}function mi(t){return t>=1424&&t<=2303||pi[\"Arabic Presentation Forms-A\"](t)||pi[\"Arabic Presentation Forms-B\"](t)}function xi(t,e){return!(!e&&mi(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||pi.Khmer(t))}function bi(t){for(var e=0,r=t;e<r.length;e+=1)if(mi(r[e].charCodeAt(0)))return!0;return!1}var _i=\"deferred\",wi=\"loading\",Ti=\"loaded\",ki=\"error\",Ai=null,Mi=\"unavailable\",Si=null,Ei=function(t){t&&\"string\"==typeof t&&t.indexOf(\"NetworkError\")>-1&&(Mi=ki),Ai&&Ai(t)};function Li(){Ci.fire(new zt(\"pluginStateChange\",{pluginStatus:Mi,pluginURL:Si}))}var Ci=new Rt,Pi=function(){return Mi},Oi=function(){if(Mi!==_i||!Si)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");Mi=wi,Li(),Si&&Mt({url:Si},(function(t){t?Ei(t):(Mi=Ti,Li())}))},Ii={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:function(){return Mi===Ti||null!=Ii.applyArabicShaping},isLoading:function(){return Mi===wi},setState:function(t){Mi=t.pluginStatus,Si=t.pluginURL},isParsed:function(){return null!=Ii.applyArabicShaping&&null!=Ii.processBidirectionalText&&null!=Ii.processStyledBidirectionalText},getPluginURL:function(){return Si}},zi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new hi,this.transition={})};zi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1)if(!xi(n[r].charCodeAt(0),e))return!1;return!0}(t,Ii.isLoaded())},zi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},zi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var Di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new vn(t,e);if(cn(t)){var r=dn(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=ue.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};Di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},Di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new Di(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Fi.prototype.getValue=function(t){return w(this._values[t].value.value)},Fi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].value=new Di(this._values[t].property,null===e?void 0:w(e))},Fi.prototype.getTransition=function(t){return w(this._values[t].transition)},Fi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].transition=w(e)||void 0},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Fi.prototype.transitioned=function(t,e){for(var r=new Ni(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},Fi.prototype.untransitioned=function(){for(var t=new Ni(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var Bi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};Bi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ni.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var ji=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ji.prototype.getValue=function(t){return w(this._values[t].value)},ji.prototype.setValue=function(t,e){this._values[t]=new Di(this._values[t].property,null===e?void 0:w(e))},ji.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ji.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Ui=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Ui.prototype.isConstant=function(){return\"constant\"===this.value.kind},Ui.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},Ui.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Vi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Vi.prototype.get=function(t){return this._values[t]};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},qi.prototype.interpolate=function(t,e,r){var n=rr[this.specification.type];return n?n(t,e,r):t};var Hi=function(t,e){this.specification=t,this.overrides=e};Hi.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Ui(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Ui(this,t.expression,e)},Hi.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ui(this,{kind:\"constant\",value:void 0},t.parameters);var n=rr[this.specification.type];return n?new Ui(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Hi.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new Ui(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Ui(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Ui(this,{kind:\"constant\",value:s},e)}return new Ui(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Hi),Wi=function(t){this.specification=t};Wi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return 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Vi(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate($n,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Qn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Ft,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof Ui&&Kr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Rt),Ji={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},$i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Qi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function ta(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Ji[i].BYTES_PER_ELEMENT),o=r=ea(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:ea(r,Math.max(n,e)),alignment:e}}function ea(t,e){return Math.ceil(t/e)*e}Qi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Qi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Qi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi(\"StructArrayLayout2i4\",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ia.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i4i12\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Qi);aa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2i4ub8\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return 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this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}($i);Ma.prototype.size=48;var Sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ma(this,t)},e}(ga);oi(\"PlacedSymbolArray\",Sa);var Ea=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var 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r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},so.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},so.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=f(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},so.prototype.getBinding=function(t,e,r){return new Ka(t,e)};var lo=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};lo.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},lo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},lo.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},lo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Fa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Fa.members,this.expression.isStateDependent))},lo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var uo=function(t,e,r){this.binders={},this._buffers=[];var n=[];for(var i in t.paint._values)if(r(i)){var a=t.paint.get(i);if(a instanceof Ui&&Kr(a.property.specification)){var o=fo(i,t.type),s=a.value,l=a.property.specification.type,u=a.property.useIntegerZoom,c=a.property.specification[\"property-type\"],f=\"cross-faded\"===c||\"cross-faded-data-driven\"===c;if(\"constant\"===s.kind)this.binders[i]=f?new ao(s.value,o):new io(s.value,o,l),n.push(\"/u_\"+i);else if(\"source\"===s.kind||f){var h=ho(i,l,\"source\");this.binders[i]=f?new lo(s,l,u,e,h,t.id):new oo(s,o,l,h),n.push(\"/a_\"+i)}else{var p=ho(i,l,\"composite\");this.binders[i]=new so(s,o,l,u,e,p),n.push(\"/z_\"+i)}}}this.cacheKey=n.sort().join(\"\")};uo.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof oo||e instanceof so?e.maxValue:0},uo.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof oo||o instanceof so||o instanceof lo)&&o.populatePaintArray(t,e,r,n,i)}},uo.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ao&&n.setConstantPatternPositions(t,e)}},uo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof oo||h instanceof so||h instanceof lo)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},uo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof io||r instanceof ao)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},uo.prototype.getBinderAttributes=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof oo||r instanceof so)for(var n=0;n<r.paintVertexAttributes.length;n++)t.push(r.paintVertexAttributes[n].name);else if(r instanceof lo)for(var i=0;i<Fa.members.length;i++)t.push(Fa.members[i].name)}return t},uo.prototype.getBinderUniforms=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof io||r instanceof ao||r instanceof so)for(var n=0,i=r.uniformNames;n<i.length;n+=1){var a=i[n];t.push(a)}}return t},uo.prototype.getPaintVertexBuffers=function(){return this._buffers},uo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof io||i instanceof ao||i instanceof so)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},uo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},uo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof lo){var 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u=t.readVarint();n=7&u,i=u>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Ys.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,u=-1/0;t.pos<e;){if(n<=0){var c=t.readVarint();r=7&c,n=c>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>u&&(u=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,u]},Ys.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),u=Ys.types[this.type];function c(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];c(l=f);break;case 2:for(n=0;n<l.length;n++)c(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=Zs(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)c(l[n][i])}1===l.length?l=l[0]:u=\"Multi\"+u;var h={type:\"Feature\",geometry:{type:u,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var Ks=Js;function Js(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields($s,this,e),this.length=this._features.length}function $s(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new Ks(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Js.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Ws(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Ws,VectorTileLayer:Ks},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ia,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new Da,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function al(t,e){return t.x===e.x&&(t.x<0||t.x>po)||t.y===e.y&&(t.y<0||t.y>po)}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f=mo(o,c);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:yo(o),properties:o.properties,type:o.type,patterns:{}};this.hasPattern?this.features.push(js(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},il.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((O=d).every((function(t){return t.x<0}))||O.every((function(t){return t.x>po}))||O.every((function(t){return t.y<0}))||O.every((function(t){return t.y>po}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!al(y,m)){f.vertexLength+4>Da.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>Da.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===el[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];nl(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=as(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}var O;this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},oi(\"FillExtrusionBucket\",il,{omit:[\"layers\",\"features\"]});var ol={paint:new Xi({\"fill-extrusion-opacity\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Gi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},sl=function(t){function e(e){t.call(this,e,ol)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return Io(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var u=zo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),c=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];qo(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,P=A+h,O=M+p,I=S+d,z=new a((k+s)/L,(A+l)/L);z.z=E/L,y.push(z);var D=new a(C/I,P/I);D.z=O/I,m.push(D)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;To(r,e)&&(n=ul(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],u=a[s+1],c=o[s],f=[l,u,o[s+1],c,l];_o(r,f)&&(n=Math.min(n,ul(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ki);function ll(t,e){return t.x*e.x+t.y*e.y}function ul(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),u=o.sub(i),c=ll(s,s),f=ll(s,l),h=ll(l,l),p=ll(u,s),d=ll(u,l),v=c*h-f*f,g=(h*p-f*d)/v,y=(c*d-f*p)/v,m=1-g-y,x=i.z*m+r.z*g+a.z*y;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var cl=ta([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,fl=ta([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]).members,hl=tl.VectorTileFeature.types,pl=Math.cos(Math.PI/180*37.5),dl=Math.pow(2,14)/.5,vl=function(t){var e=this;this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((function(t){e.gradients[t.id]={}})),this.layoutVertexArray=new aa,this.layoutVertexArray2=new oa,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new Da,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};vl.prototype.populate=function(t,e,r){this.hasPattern=Ns(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),p,r)){var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js(\"line\",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},vl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},vl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},vl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},vl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},vl.prototype.upload=function(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,fl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,cl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},vl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},vl.prototype.lineFeatureClips=function(t){if(t.properties&&t.properties.hasOwnProperty(\"mapbox_clip_start\")&&t.properties.hasOwnProperty(\"mapbox_clip_end\"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}},vl.prototype.addFeature=function(t,e,r,n,i){var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),u=a.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(t);for(var c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},vl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var s=\"Polygon\"===hl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var c,f=this.overscaling<=16?15*po/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<pl&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,c),\"miter\"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if(\"flipbevel\"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,O=k?0:C;if(p&&this.addCurrentVertex(c,v,P,O,h),\"fakeround\"===E)for(var I=Math.round(180*w/Math.PI/20),z=1;z<I;z++){var D=z/I;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(D)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-P,-O,h)}else if(\"butt\"===E)this.addCurrentVertex(c,m,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},vl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var 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e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==Il.Bytes)return t.push(this.readFixed64());var e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==Il.Bytes)return t.push(this.readSFixed64());var e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===Il.Varint)for(;this.buf[this.pos++]>127;);else if(e===Il.Bytes)this.pos=this.readVarint()+this.pos;else if(e===Il.Fixed32)this.pos+=4;else{if(e!==Il.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),Kl(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),Kl(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),Kl(this.buf,-1&t,this.pos),Kl(this.buf,Math.floor(t*Dl),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),Kl(this.buf,-1&t,this.pos),Kl(this.buf,Math.floor(t*Dl),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error(\"Given varint doesn't fit into 10 bytes\");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t)))))}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Nl(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),Pl(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),Pl(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Nl(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Il.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,jl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ul,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,Hl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Vl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,ql,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,Gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,Wl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,Yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,Xl,e)},writeBytesField:function(t,e){this.writeTag(t,Il.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Il.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Il.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var $l=3;function Ql(t,e,r){1===t&&r.readMessage(tu,e)}function tu(t,e,r){if(3===t){var n=r.readMessage(eu,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,u=n.top,c=n.advance;e.push({id:i,bitmap:new Jo({width:o+2*$l,height:s+2*$l},a),metrics:{width:o,height:s,left:l,top:u,advance:c}})}}function eu(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}var ru=$l;function nu(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,u=0,c=t;u<c.length;u+=1)for(var f=c[u],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else f.h===p.h?(p.x+=f.w,p.w-=f.w):f.w===p.w?(p.y+=f.h,p.h-=f.h):(o.push({x:p.x+f.w,y:p.y,w:p.w-f.w,h:f.h}),p.y+=f.h,p.h-=f.h);break}}return{w:s,h:l,fill:e/(s*l)||0}}var iu=1,au=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},ou={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};ou.tl.get=function(){return[this.paddedRect.x+iu,this.paddedRect.y+iu]},ou.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-iu,this.paddedRect.y+this.paddedRect.h-iu]},ou.tlbr.get=function(){return this.tl.concat(this.br)},ou.displaySize.get=function(){return[(this.paddedRect.w-2*iu)/this.pixelRatio,(this.paddedRect.h-2*iu)/this.pixelRatio]},Object.defineProperties(au.prototype,ou);var su=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=nu(i),o=a.w,s=a.h,l=new $o({width:o||1,height:s||1});for(var u in t){var c=t[u],f=r[u].paddedRect;$o.copy(c.data,l,{x:0,y:0},{x:f.x+iu,y:f.y+iu},c.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,v=d.x+iu,g=d.y+iu,y=p.data.width,m=p.data.height;$o.copy(p.data,l,{x:0,y:0},{x:v,y:g},p.data),$o.copy(p.data,l,{x:0,y:m-1},{x:v,y:g-1},{width:y,height:1}),$o.copy(p.data,l,{x:0,y:0},{x:v,y:g+m},{width:y,height:1}),$o.copy(p.data,l,{x:y-1,y:0},{x:v-1,y:g},{width:1,height:m}),$o.copy(p.data,l,{x:0,y:0},{x:v+y,y:g},{width:1,height:m})}this.image=l,this.iconPositions=r,this.patternPositions=n};su.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2*iu,h:i.data.height+2*iu};r.push(a),e[n]=new au(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},su.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},su.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},oi(\"ImagePosition\",au),oi(\"ImageAtlas\",su);var lu={horizontal:1,vertical:2,horizontalOnly:3},uu=-17;var cu=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};cu.forText=function(t,e){var r=new cu;return r.scale=t||1,r.fontStack=e,r},cu.forImage=function(t){var e=new cu;return e.imageName=t,e};var fu=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function hu(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=fu.fromFeature(t,i);f===lu.vertical&&y.verticalizePunctuation();var m=Ii.processBidirectionalText,x=Ii.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),bu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new fu;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,bu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new fu;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,bu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=uu,d=0,v=0,g=\"right\"===s?1:\"left\"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*Ll,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,O=null,I=null,z=Ll,D=!(l===lu.horizontal||!c&&!vi(L)||c&&(pu[L]||yi(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,O=R.paddedRect;var F=R.displaySize;S.scale=S.scale*Ll/f,P={width:F[0],height:F[1],left:iu,top:-ru,advance:D?F[1]:F[0]},C=w+(Ll-F[1]*S.scale),z=P.advance;var B=D?F[0]*S.scale-Ll*_:F[1]*S.scale-Ll*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)O=j.rect,P=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;P=V.metrics}C=(_-S.scale)*Ll}D?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=z*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=P.advance*S.scale+u)}if(0!==k.length){var q=h-u;d=Math.max(q,d),wu(k,0,k.length-1,g,A)}h=0;var H=a*_+A;T.lineOffset=Math.max(A,w),p+=H,v=Math.max(H,v),++y}else p+=a,++y}var G=p-uu,W=_u(o),Y=W.horizontalAlign,X=W.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-uu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,X,d,v,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}fu.fromFeature=function(t,e){for(var r=new fu,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},fu.prototype.length=function(){return this.text.length},fu.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},fu.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},fu.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},fu.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},fu.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&pu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&pu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},fu.prototype.substring=function(t,e){var r=new fu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},fu.prototype.toString=function(){return this.text},fu.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return 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wu(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Tu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return\"height\"===r||\"both\"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}du[10]=!0,du[32]=!0,du[38]=!0,du[40]=!0,du[41]=!0,du[43]=!0,du[45]=!0,du[47]=!0,du[173]=!0,du[183]=!0,du[8203]=!0,du[8208]=!0,du[8211]=!0,du[8231]=!0;var ku=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new 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i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?f(wr.interpolationFactor(i,e,a,o),0,1):0;\"camera\"===t.kind?n=er(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Lu=Object.freeze({__proto__:null,getSizeData:Mu,evaluateSizeForFeature:Su,evaluateSizeForZoom:Eu,SIZE_PACK_FACTOR:Au});function Cu(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=c.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),u+=p;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=f.dist(h)}return!0}function Pu(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Ou(t,e,r){return t?.6*e*r:0}function Iu(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function zu(t,e,r,n,i,a){for(var 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a(0,0);c.push({tl:z,tr:D,bl:R,br:F,tex:y,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:Y,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return c}(0,r,l,i,o,s,n,t.allowVerticalPlacement),y=t.textSizeData,m=null;\"source\"===y.kind?(m=[Au*i.layout.get(\"text-size\").evaluate(s,{})])[0]>ec&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tc+'. Reduce your \"text-size\".'):\"composite\"===y.kind&&((m=[Au*d.compositeTextSizes[0].evaluate(s,{},v),Au*d.compositeTextSizes[1].evaluate(s,{},v)])[0]>ec||m[1]>ec)&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tc+'. Reduce your \"text-size\".'),t.addSymbols(t.text,g,m,l,o,s,c,e,u.lineStartIndex,u.lineLength,p,v);for(var x=0,b=f;x<b.length;x+=1)h[b[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function nc(t){for(var e in t)return t[e];return null}function ic(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var ac=tl.VectorTileFeature.types,oc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function sc(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=s?Math.min(ec,Math.round(s[0])):0,d=s?Math.min(ec,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*u,16*c,256*f,256*h)}function lc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function uc(t){for(var e=0,r=t.sections;e<r.length;e+=1)if(bi(r[e].text))return!0;return!1}var cc=function(t){this.layoutVertexArray=new la,this.indexArray=new va,this.programConfigurations=t,this.segments=new Da,this.dynamicLayoutVertexArray=new ua,this.opacityVertexArray=new ca,this.placedSymbolArray=new Sa};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,oc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},oi(\"SymbolBuffers\",cc);var fc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Da,this.collisionVertexArray=new da};fc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,kl.members,!0)},fc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},oi(\"CollisionBuffers\",fc);var hc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Mu(this.zoom,e[\"text-size\"]),this.iconSizeData=Mu(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.canOverlap=r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&this.canOverlap,\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return lu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};hc.prototype.createArrays=function(){this.text=new cc(new co(this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new co(this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Ca,this.lineVertexArray=new Pa,this.symbolInstances=new La},hc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=El[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},hc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof he&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),u=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get(\"symbol-sort-key\");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new zi(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T=mo(m,w);if(n._featureFilter.filter(d,T,r)){w||(T.geometry=yo(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=he.factory(A);uc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===Pi()||this.hasRTLText&&Ii.isParsed())&&(k=Sl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof pe?E:pe.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:T.geometry,properties:m.properties,type:ac[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),O=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(lu.vertical)>=0;for(var I=0,z=k.sections;I<z.length;I+=1){var D=z[I];if(D.image)f[D.image.name]=!0;else{var R=di(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,O,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},hc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},hc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},hc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},hc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},hc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},hc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},hc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},hc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,this.canOverlap?a.sortKey:void 0),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===lu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,O=b.sectionIndex,I=d.vertexLength,z=C[1];sc(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),sc(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),sc(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),sc(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),lc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&O===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[O])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},hc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},hc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},hc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},hc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new fc(ha,Al.members,_a),this.iconCollisionBox=new fc(ha,Al.members,_a);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},hc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},hc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},hc.prototype.hasTextData=function(){return this.text.segments.get().length>0},hc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},hc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},hc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},hc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},hc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},hc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},hc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},hc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi(\"SymbolBucket\",hc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),hc.MAX_GLYPHS=65535,hc.addDynamicAttributes=lc;var pc=new Xi({\"symbol-placement\":new qi(Ft.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new qi(Ft.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new qi(Ft.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Hi(Ft.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new qi(Ft.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new qi(Ft.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new qi(Ft.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new qi(Ft.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new qi(Ft.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Hi(Ft.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new qi(Ft.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new qi(Ft.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Hi(Ft.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Hi(Ft.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new qi(Ft.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new qi(Ft.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Hi(Ft.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Hi(Ft.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new qi(Ft.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new qi(Ft.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new qi(Ft.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Hi(Ft.layout_symbol[\"text-field\"]),\"text-font\":new Hi(Ft.layout_symbol[\"text-font\"]),\"text-size\":new Hi(Ft.layout_symbol[\"text-size\"]),\"text-max-width\":new Hi(Ft.layout_symbol[\"text-max-width\"]),\"text-line-height\":new qi(Ft.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Hi(Ft.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Hi(Ft.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Hi(Ft.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new qi(Ft.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Hi(Ft.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new qi(Ft.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new qi(Ft.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Hi(Ft.layout_symbol[\"text-rotate\"]),\"text-padding\":new qi(Ft.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new qi(Ft.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Hi(Ft.layout_symbol[\"text-transform\"]),\"text-offset\":new Hi(Ft.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new qi(Ft.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new qi(Ft.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new qi(Ft.layout_symbol[\"text-optional\"])}),dc={paint:new Xi({\"icon-opacity\":new Hi(Ft.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Hi(Ft.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Hi(Ft.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Hi(Ft.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Hi(Ft.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new qi(Ft.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new qi(Ft.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Hi(Ft.paint_symbol[\"text-opacity\"]),\"text-color\":new Hi(Ft.paint_symbol[\"text-color\"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Hi(Ft.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Hi(Ft.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Hi(Ft.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new qi(Ft.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new qi(Ft.paint_symbol[\"text-translate-anchor\"])}),layout:pc},vc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};vc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},vc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},vc.prototype.outputDefined=function(){return!1},vc.prototype.serialize=function(){return null},oi(\"FormatSectionOverride\",vc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||cn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new hc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=dc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new vc(a),s=new un(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new hn(\"source\",s):new pn(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Ui(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=dc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof he)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof me&&ge(t.value)===Qt){var e=t.value;a(e.sections)}else t instanceof we?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ki),yc={paint:new Xi({\"background-color\":new qi(Ft.paint_background[\"background-color\"]),\"background-pattern\":new Wi(Ft.paint_background[\"background-pattern\"]),\"background-opacity\":new qi(Ft.paint_background[\"background-opacity\"])})},mc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki),xc={paint:new Xi({\"raster-opacity\":new qi(Ft.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new qi(Ft.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new qi(Ft.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new qi(Ft.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new qi(Ft.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new qi(Ft.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new qi(Ft.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new qi(Ft.paint_raster[\"raster-fade-duration\"])})},bc=function(t){function e(e){t.call(this,e,xc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki);var _c=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ki),wc={circle:Go,heatmap:es,hillshade:ns,fill:Hs,\"fill-extrusion\":sl,line:bl,symbol:gc,background:mc,raster:bc};var Tc=s.HTMLImageElement,kc=s.HTMLCanvasElement,Ac=s.HTMLVideoElement,Mc=s.ImageData,Sc=s.ImageBitmap,Ec=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Ec.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Ec.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Ec.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Ec.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Lc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Lc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Lc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Lc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=S()?t:s};function Pc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=C(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:ci(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(fi(e.error)):n(null,fi(e.data)))}else{var i=!1,a=C(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?ci(e):null,data:ci(n,a)},a)}:function(t){i=!0},s=null,l=fi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Oc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Oc.prototype.setNorthEast=function(t){return this._ne=t instanceof zc?new zc(t.lng,t.lat):zc.convert(t),this},Oc.prototype.setSouthWest=function(t){return this._sw=t instanceof zc?new zc(t.lng,t.lat):zc.convert(t),this},Oc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof zc)e=t,r=t;else{if(!(t instanceof Oc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Oc.convert(a))}var o=t;return this.extend(zc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new zc(e.lng,e.lat),this._ne=new zc(r.lng,r.lat)),this},Oc.prototype.getCenter=function(){return new zc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Oc.prototype.getSouthWest=function(){return this._sw},Oc.prototype.getNorthEast=function(){return this._ne},Oc.prototype.getNorthWest=function(){return new zc(this.getWest(),this.getNorth())},Oc.prototype.getSouthEast=function(){return new zc(this.getEast(),this.getSouth())},Oc.prototype.getWest=function(){return this._sw.lng},Oc.prototype.getSouth=function(){return this._sw.lat},Oc.prototype.getEast=function(){return this._ne.lng},Oc.prototype.getNorth=function(){return this._ne.lat},Oc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Oc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Oc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Oc.prototype.contains=function(t){var e=zc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Oc.convert=function(t){return!t||t instanceof Oc?t:new Oc(t)};var Ic=6371008.8,zc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};zc.prototype.wrap=function(){return new zc(h(this.lng,-180,180),this.lat)},zc.prototype.toArray=function(){return[this.lng,this.lat]},zc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},zc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ic*Math.acos(Math.min(i,1))},zc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Oc(new zc(this.lng-r,this.lat-e),new zc(this.lng+r,this.lat+e))},zc.convert=function(t){if(t instanceof zc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new zc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new zc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Dc=2*Math.PI*Ic;function Rc(t){return Dc*Math.cos(t*Math.PI/180)}function Fc(t){return(180+t)/360}function Bc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nc(t,e){return t/Rc(e)}function jc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=zc.convert(t);return new Uc(Fc(r.lng),Bc(r.lat),Nc(e,r.lat))},Uc.prototype.toLngLat=function(){return new zc(360*this.x-180,jc(this.y))},Uc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Rc(jc(e));var t,e},Uc.prototype.meterInMercatorCoordinateUnits=function(){return 1/Dc*(t=jc(this.y),1/Math.cos(t*Math.PI/180));var t};var Vc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gc(0,t,t,e,r)};Vc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Pc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Pc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},Vc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*po,(t.y*e-this.y)*po)},Vc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var qc=function(t,e){this.wrap=t,this.canonical=e,this.key=Gc(t,e.z,e.z,e.x,e.y)},Hc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Vc(r,+n,+i),this.key=Gc(e,t,r,n,i)};function Gc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Hc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Hc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Hc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Hc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hc(e,this.wrap,e,r,n),new Hc(e,this.wrap,e,r+1,n),new Hc(e,this.wrap,e,r,n+1),new Hc(e,this.wrap,e,r+1,n+1)]},Hc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Hc.prototype.wrapped=function(){return new Hc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.unwrapTo=function(t){return new Hc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Hc.prototype.toUnwrapped=function(){return new qc(this.wrap,this.canonical)},Hc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Hc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Uc(t.x-this.wrap,t.y))},oi(\"CanonicalTileID\",Vc),oi(\"OverscaledTileID\",Hc,{omit:[\"posMatrix\"]});var Wc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return k('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Wc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Wc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Wc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Wc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Wc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Wc.prototype.getPixels=function(){return new $o({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Wc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},oi(\"DEMData\",Wc);var Yc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Yc.prototype.encode=function(t){return this._stringToNumber[t]},Yc.prototype.decode=function(t){return this._numberToString[t]};var Xc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Zc={geometry:{configurable:!0}};Zc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Zc.geometry.set=function(t){this._geometry=t},Xc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Xc.prototype,Zc);var Kc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Kc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},p(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Kc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Kc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=p({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Kc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Kc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),p(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,f=Object.keys(this.deletedStates[o][u]);c<f.length;c+=1){var h=f[c];delete this.state[o][u][h]}s[u]=this.state[o][u]}r[o]=r[o]||{},p(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Jc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ti(po,16,0),this.grid3D=new ti(po,16,0),this.featureIndexArray=new Ia,this.promoteId=e};function $c(t,e,r,n,i){return b(t,(function(t,a){var o=e instanceof Vi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Qc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function tf(t,e){return e-t}Jc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<po&&c[1]<po&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Jc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Ol(this.rawTileData)).layers,this.sourceLayerCoder=new Yc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Jc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Qc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Qc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Co(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(Po(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(tf);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=yo(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Jc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),d=this.vtLayers[h].feature(n);if(i.needGeometry){var v=mo(d,!0);if(!i.filter(new zi(this.tileID.overscaledZ),v,this.tileID.canonical))return}else if(!i.filter(new zi(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,h),y=0;y<f.length;y++){var m=f[y];if(!(a&&a.indexOf(m)<0)){var x=s[m];if(x){var b={};void 0!==g&&u&&(b=u.getState(x.sourceLayer||\"_geojsonTileLayer\",g));var _=p({},l[m]);_.paint=$c(_.paint,x.paint,d,b,o),_.layout=$c(_.layout,x.layout,d,b,o);var w=!c||c(d,x,b);if(w){var T=new Xc(d,this.z,this.x,this.y,g);T.layer=_;var k=t[m];void 0===k&&(k=t[m]=[]),k.push({featureIndex:n,feature:T,intersectionZ:w})}}}}}},Jc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=An(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Jc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Jc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},oi(\"FeatureIndex\",Jc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var ef=function(t,e){this.tileID=t,this.uid=v(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};ef.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<N.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},ef.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},ef.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof hc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof hc&&o.hasRTLText){this.hasRTLText=!0,Ii.isLoading()||Ii.isLoaded()||\"deferred\"!==Pi()||Oi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Aa},ef.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},ef.prototype.getBucket=function(t){return this.buckets[t.id]},ef.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Ec(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Ec(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},ef.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},ef.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},ef.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=An(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.needGeometry){var d=mo(p,!0);if(!o.filter(new zi(this.tileID.overscaledZ),d,this.tileID.canonical))continue}else if(!o.filter(new zi(this.tileID.overscaledZ),p))continue;var v=r.getId(p,i),g=new Xc(p,l,u,c,v);g.tile=f,t.push(g)}}},ef.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},ef.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},ef.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=E(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},ef.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},ef.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},ef.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},ef.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<N.now()},ef.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},ef.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=N.now()+t},ef.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},ef.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var rf=s.performance,nf=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},rf.mark(this._marks.start)};nf.prototype.finish=function(){rf.mark(this._marks.end);var t=rf.getEntriesByName(this._marks.measure);return 0===t.length&&(rf.measure(this._marks.measure,this._marks.start,this._marks.end),t=rf.getEntriesByName(this._marks.measure),rf.clearMarks(this._marks.start),rf.clearMarks(this._marks.end),rf.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=Jo,t.CanonicalTileID=Vc,t.CollisionBoxArray=Aa,t.Color=ue,t.DEMData=Wc,t.DataConstantProperty=qi,t.DictionaryCoder=Yc,t.EXTENT=po,t.ErrorEvent=Dt,t.EvaluationParameters=zi,t.Event=zt,t.Evented=Rt,t.FeatureIndex=Jc,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=su,t.ImagePosition=au,t.LineBucket=vl,t.LngLat=zc,t.LngLatBounds=Oc,t.MercatorCoordinate=Uc,t.ONE_EM=Ll,t.OverscaledTileID=Hc,t.Point=a,t.Point$1=a,t.Properties=Xi,t.Protobuf=Ol,t.RGBAImage=$o,t.RequestManager=W,t.RequestPerformance=nf,t.ResourceType=_t,t.SegmentVector=Da,t.SourceFeatureState=Kc,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=ra,t.StructArrayLayout3ui6=va,t.StructArrayLayout4i8=na,t.SymbolBucket=hc,t.Texture=Ec,t.Tile=ef,t.Transitionable=Fi,t.Uniform1f=Ka,t.Uniform1i=Za,t.Uniform2f=Ja,t.Uniform3f=$a,t.Uniform4f=Qa,t.UniformColor=to,t.UniformMatrix4f=ro,t.UnwrappedTileID=qc,t.ValidationError=Bt,t.WritingMode=lu,t.ZoomHistory=hi,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=lc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=u,t.bindAll=m,t.browser=N,t.cacheEntryPossiblyAdded=function(t){++xt>ht&&(t.getActor().send(\"enforceCacheSizeLimit\",ft),xt=0)},t.clamp=f,t.clearTileCache=function(t){var e=s.caches.delete(ct);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fu,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return 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generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else\"Feature\"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new j(s,K,J,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,K,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",u,s.length,+Date.now()-c)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),X(a),Y(i),X(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?W(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(+t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=n.minPoints,l=i/(a*Math.pow(2,e)),u=0;u<t.length;u++){var c=t[u];if(!(c.zoom<=e)){c.zoom=e;for(var f=this.trees[e+1],h=f.within(c.x,c.y,l),p=c.numPoints||1,d=p,v=0,g=h;v<g.length;v+=1){var y=g[v],m=f.points[y];m.zoom>e&&(d+=m.numPoints||1)}if(d>=s){for(var x=c.x*p,b=c.y*p,_=o&&p>1?this._map(c,!0):null,w=(u<<5)+(e+1)+this.points.length,T=0,k=h;T<k.length;T+=1){var A=k[T],M=f.points[A];if(!(M.zoom<=e)){M.zoom=e;var S=M.numPoints||1;x+=M.x*S,b+=M.y*S,M.parentId=w,o&&(_||(_=this._map(c,!0)),o(_,this._map(M)))}}c.parentId=w,r.push(q(x/d,b/d,w,d,_))}else if(r.push(c),d>1)for(var E=0,L=h;E<L.length;E+=1){var C=L[E],P=f.points[C];P.zoom<=e||(P.zoom=e,r.push(P))}}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?Z({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?Z({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time(\"creation\"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time(\"clipping\");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling 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i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var u={};if(i){var c=i.finish();c&&(u.resourceTiming={},u.resourceTiming[n.source]=JSON.parse(JSON.stringify(c)))}r(null,u)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already 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s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var u=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",u,u),t.window.removeEventListener(\"test\",u,u)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var c=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",c,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",c,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",c,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,u=l.x+1,c=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:u,y:c-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:u-1,y:c},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u+h,y:c},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,v=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(m(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(m(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function m(t,e,r,n,i){r[0]=0,n[0]=-v,n[1]=+v;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+v}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?v:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?v:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(y(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),y(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=\"-transition\",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setSourceProperty=function(t){this._tileJSONRequest&&this._tileJSONRequest.cancel(),t(),this.map.style.sourceCaches[this.id].clearTiles(),this.load()},r.prototype.setTiles=function(t){var e=this;return this.setSourceProperty((function(){e._options.tiles=t})),this},r.prototype.setUrl=function(t){var e=this;return this.setSourceProperty((function(){e.url=t,e._options.url=t})),this},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(L),P=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,n.clusterMinPoints||2),extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties,filter:n.filter},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var z=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),D=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,\"raster-dem\":C,geojson:P,video:z,image:I,canvas:D};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var 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Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Pt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new G(this),this.clearDepth=new W(this),this.clearStencil=new Y(this),this.colorMask=new X(this),this.depthMask=new Z(this),this.stencilMask=new K(this),this.stencilFunc=new J(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\"),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE)};Pt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Pt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Pt.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Pt.prototype.createVertexBuffer=function(t,e,r){return new q(this,t,e,r)},Pt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Pt.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Pt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Pt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Pt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Pt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Pt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Pt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ot=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(zt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(zt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function zt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Dt(){return new t.window.Worker(oa.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new Dt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var qt={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Ht(t,e,r){r.push({command:qt.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:qt.removeSource,args:[t]}),r[t]=!0}function Wt(t,e,r,n){Gt(t,r,n),Ht(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Xt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Zt(t){return t.id}function Kt(t,e){return t[e.id]=e,t}function Jt(e,r){if(!e)return[{command:qt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:qt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:qt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:qt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:qt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:qt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:qt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:qt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:qt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:qt.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Yt(e,r,a)?n.push({command:qt.setGeoJSONSourceData,args:[a,r[a].data]}):Wt(a,r,n,i)):Ht(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:qt.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Zt),h=r.map(Zt),p=e.reduce(Kt,{}),d=r.reduce(Kt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:qt.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(c in Xt(s.layout,l.layout,n,o,null,qt.setLayoutProperty),Xt(s.paint,l.paint,n,o,null,qt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:qt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:qt.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:qt.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:qt.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:qt.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),P=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var z=0,D=v;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var O=1;O<L.path.length;O++)C.push(L.path[O]);var I=2.5*k;if(l){var z=C.map((function(t){return re(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),W=G.x+de,Y=G.y+de;d.push(W,Y,k,0);var X=W-k,Z=Y-k,K=W+k,J=Y+k;if(T=T&&this.isOffscreen(X,Z,K,J),w=w||this.isInsideGrid(X,Z,K,J),!e&&this.grid.hitTestCircle(W,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,z=0,D=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),W=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,W,l,u,c,n,_,v.predicate,X,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=Z(a.verticalIconBox)).box.length>0:(O=Z(a.iconBox)).box.length>0,S=S&&O.offscreen}var K=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if(K||J?J?K||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<P.circles.length;tt+=4)Q.circles.push(P.circles[tt+0]),Q.circles.push(P.circles[tt+1]),Q.circles.push(P.circles[tt+2]),Q.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),u=a.get(\"text-variable-anchor\"),c=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=ze(y.text),k=_?De:T;d(e.text,s,k);var A=w?De:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=ze(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:De;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?De:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var z=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var D=!0;if(u){var R=i.variableOffsets[v];R?(z=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||_,z.x,z.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?z.x:0,h?z.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function ze(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var De=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var qe=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},He=t.pick(qt,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ge=t.pick(qt,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),We=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(We,!1)},r.prototype._load=function(e,r){if(!r||!qe(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}var l={};for(var u in this.sourceCaches){var c=this.sourceCaches[u];l[u]=c.used,c.used=!1}for(var f=0,h=this._order;f<h.length;f+=1){var p=h[f],d=this._layers[p];d.recalculate(e,this._availableImages),!d.isHidden(e.zoom)&&d.source&&(this.sourceCaches[d.source].used=!0)}for(var v in l){var g=this.sourceCaches[v];l[v]!==g.used&&g.fire(new t.Event(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:v}))}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),qe(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Jt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in He)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&qe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var Xe=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ze=_r(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Ke=_r(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Je=_r(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),$e=_r(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Qe=_r(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),tr=_r(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),er=_r(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),rr=_r(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=_r(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=_r(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Ze,background:Ke,backgroundPattern:Je,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r<t.length;r++)if(null!==t[r]){var n=t[r].split(\" \");e.push(n.pop())}return e}Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var Ar=function(t,e,r,n,i,a){var o=t.gl;this.program=o.createProgram();for(var s=kr(r.staticAttributes),l=n?n.getBinderAttributes():[],u=s.concat(l),c=r.staticUniforms?kr(r.staticUniforms):[],f=n?n.getBinderUniforms():[],h=[],p=0,d=c.concat(f);p<d.length;p+=1){var v=d[p];h.indexOf(v)<0&&h.push(v)}var g=n?n.defines():[];a&&g.push(\"#define OVERDRAW_INSPECTOR;\");var y=g.concat(Ze.fragmentSource,r.fragmentSource).join(\"\\n\"),m=g.concat(Ze.vertexSource,r.vertexSource).join(\"\\n\"),x=o.createShader(o.FRAGMENT_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(x,y),o.compileShader(x),o.attachShader(this.program,x);var b=o.createShader(o.VERTEX_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(b,m),o.compileShader(b),o.attachShader(this.program,b),this.attributes={};var _={};this.numAttributes=u.length;for(var w=0;w<this.numAttributes;w++)u[w]&&(o.bindAttribLocation(this.program,w,u[w]),this.attributes[u[w]]=w);o.linkProgram(this.program),o.deleteShader(b),o.deleteShader(x);for(var T=0;T<h.length;T++){var k=h[T];if(k&&!_[k]){var A=o.getUniformLocation(this.program,k);A&&(_[k]=A)}}this.fixedUniforms=i(t,_),this.binderUniforms=n?n.getUniforms(t,_):[]}}};function Mr(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Lr=function(t){return{u_matrix:t}},Cr=function(e,r,n,i){return t.extend(Lr(e),Mr(n,r,i))},Pr=function(t,e){return{u_matrix:t,u_world:e}},Or=function(e,r,n,i,a){return t.extend(Cr(e,r,n,i),{u_world:a})},Ir=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},zr=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},jr=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Yr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Wr(r,a);return{u_matrix:Yr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Wr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Wr(t,e){return 1/ge(t,1,e.tileZoom)}function Yr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Xr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Zr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Zr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Kr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},$r=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Qr=function(e,r,n,i,a,o,s,l,u,c){return t.extend($r(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,zr(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],O=E.circleArray[C+1],I=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,O,I,z,0),k.emplace(A++,P,O,I,z,1),k.emplace(A++,P,O,I,z,2),k.emplace(A++,P,O,I,z,3)}(!Kr||Kr.length<2*h)&&(Kr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Kr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Dr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var an=t.identity(new Float32Array(16));function on(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function sn(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=on(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var O=C.get(P);if(O.hidden)he(O.numGlyphs,d);else{var I=v[P];if(I)for(var z=0;z<O.numGlyphs;z++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(O.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function ln(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function un(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g=\"map\"===l,y=\"map\"===u,m=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!y&&!m,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=!1,w=e.depthModeForSublayer(0,Mt.ReadOnly),T=n.layout.get(\"text-variable-anchor\"),k=[],A=0,M=i;A<M.length;A+=1){var S=M[A],E=r.getTile(S),L=E.getBucket(n);if(L){var C=a?L.text:L.icon;if(C&&C.segments.get().length){var P=C.programConfigurations.get(n.id),O=a||L.sdfIcons,I=a?L.textSizeData:L.iconSizeData,z=y||0!==v.pitch,D=e.useProgram(ln(O,a,L),P),R=t.evaluateSizeForZoom(I,v.zoom),F=void 0,B=[0,0],N=void 0,j=void 0,U=null,V=void 0;if(a){if(N=E.glyphAtlasTexture,j=d.LINEAR,F=E.glyphAtlasTexture.size,L.iconsInText){B=E.imageAtlasTexture.size,U=E.imageAtlasTexture;var q=\"composite\"===I.kind||\"camera\"===I.kind;V=z||e.options.rotating||e.options.zooming||q?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||L.iconsNeedLinear;N=E.imageAtlasTexture,j=O||e.options.rotating||e.options.zooming||H||z?d.LINEAR:d.NEAREST,F=E.imageAtlasTexture.size}var G=ge(E,1,e.transform.zoom),W=te(S.posMatrix,y,g,e.transform,G),Y=ee(S.posMatrix,y,g,e.transform,G),X=T&&L.hasTextData(),Z=\"none\"!==n.layout.get(\"icon-text-fit\")&&X&&L.hasIconData();m&&ae(L,S.posMatrix,e,a,W,Y,y,c);var K=e.translatePosMatrix(S.posMatrix,E,o,s),J=m||a&&T||Z?an:W,$=e.translatePosMatrix(Y,E,o,s,!0),Q=O&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),tt={program:D,buffers:C,uniformValues:O?L.iconsInText?Qr(I.kind,R,x,y,e,K,J,$,F,B):$r(I.kind,R,x,y,e,K,J,$,a,F,!0):Jr(I.kind,R,x,y,e,K,J,$,a,F),atlasTexture:N,atlasTextureIcon:U,atlasInterpolation:j,atlasInterpolationIcon:V,isSDF:O,hasHalo:Q};if(b&&L.canOverlap){_=!0;for(var et=0,rt=C.segments.get();et<rt.length;et+=1){var nt=rt[et];k.push({segments:new t.SegmentVector([nt]),sortKey:nt.sortKey,state:tt})}}else k.push({segments:C.segments,sortKey:0,state:tt})}}}_&&k.sort((function(t,e){return t.sortKey-e.sortKey}));for(var it=0,at=k;it<at.length;it+=1){var ot=at[it],st=ot.state;if(p.activeTexture.set(d.TEXTURE0),st.atlasTexture.bind(st.atlasInterpolation,d.CLAMP_TO_EDGE),st.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),st.atlasTextureIcon&&st.atlasTextureIcon.bind(st.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),st.isSDF){var lt=st.uniformValues;st.hasHalo&&(lt.u_is_halo=1,cn(st.buffers,ot.segments,n,e,st.program,w,f,h,lt)),lt.u_is_halo=0}cn(st.buffers,ot.segments,n,e,st.program,w,f,h,st.uniformValues)}}function cn(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function fn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u=\"fillOutlinePattern\"===l&&d?Or(S,t,v,x,E):Pr(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Cr(S,t,v,x):Lr(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function hn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get(\"fill-extrusion-pattern\"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=c?Er(k,t,A,h,v,f,g):Sr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function pn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=jr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function dn(e,r,n,i,a,o){var s=e.context,l=s.gl,u=r.dem;if(u&&u.data){var c=u.dim,f=u.stride,h=u.getPixels();if(s.activeTexture.set(l.TEXTURE1),s.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(f),r.demTexture){var p=r.demTexture;p.update(h,{premultiply:!1}),p.bind(l.NEAREST,l.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(s,h,l.RGBA,{premultiply:!1}),r.demTexture.bind(l.NEAREST,l.CLAMP_TO_EDGE);s.activeTexture.set(l.TEXTURE0);var d=r.fbo;if(!d){var v=new t.Texture(s,{width:c,height:c,data:null},l.RGBA);v.bind(l.LINEAR,l.CLAMP_TO_EDGE),(d=r.fbo=s.createFramebuffer(c,c,!0)).colorAttachment.set(v.texture)}s.bindFramebuffer.set(d.framebuffer),s.viewport.set([0,0,c,c]),e.useProgram(\"hillshadePrepare\").draw(s,l.TRIANGLES,i,a,o,Ct.disabled,Ur(r.tileID,u),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function vn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),mn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),bn=new t.Color(0,1,1,1);function _n(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,mn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,bn)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,Rr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,Rr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);sn(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&un(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&un(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(nn(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),nn(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Ir(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,O,n.id,C,P,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Br(c.posMatrix,f,v,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Nr(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1))for(var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get(\"line-dasharray\"),c=n.paint.get(\"line-pattern\"),f=c.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":u?\"lineSDF\":h?\"lineGradient\":\"line\",v=e.context,g=v.gl,y=!0,m=0,x=i;m<x.length;m+=1){var b=x[m],_=r.getTile(b);if(!f||_.patternsLoaded()){var w=_.getBucket(n);if(w){var T=w.programConfigurations.get(n.id),k=e.context.program.get(),A=e.useProgram(d,T),M=y||A.program!==k,S=c.constantOr(null);if(S&&_.imageAtlas){var E=_.imageAtlas,L=E.patternPositions[S.to.toString()],C=E.patternPositions[S.from.toString()];L&&C&&T.setConstantPatternPositions(L,C)}var P=f?Hr(e,_,n,p):u?Gr(e,_,n,u,p):h?qr(e,_,n,w.lineClipsArray.length):Vr(e,_,n);if(f)v.activeTexture.set(g.TEXTURE0),_.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),T.updatePaintBuffers(p);else if(u&&(M||e.lineAtlas.dirty))v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v);else if(h){var O=w.gradients[n.id],I=O.texture;if(n.gradientVersion!==O.version){var z=256;if(n.stepInterpolant){var D=r.getSource().maxzoom,R=b.canonical.z===D?Math.ceil(1<<e.transform.maxZoom-b.canonical.z):1,F=w.maxLineLength/t.EXTENT*1024*R;z=t.clamp(t.nextPowerOfTwo(F),256,v.maxTextureSize)}O.gradient=t.renderColorRamp({expression:n.gradientExpression(),evaluationKey:\"lineProgress\",resolution:z,image:O.gradient||void 0,clips:w.lineClipsArray}),O.texture?O.texture.update(O.gradient):O.texture=new t.Texture(v,O.gradient,g.RGBA),O.version=n.gradientVersion,I=O.texture}v.activeTexture.set(g.TEXTURE0),I.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}A.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(b),l,Ct.disabled,P,n.id,w.layoutVertexBuffer,w.indexBuffer,w.segments,n.paint,e.transform.zoom,T,w.layoutVertexBuffer2),y=!1}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===u){var c=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);fn(e,r,n,i,c,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);fn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))hn(t,e,r,n,a,Et.disabled,Lt.disabled),hn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();hn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=t.depthModeForSublayer(0,Mt.ReadOnly),o=t.colorModeForRenderPass(),s=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],l=s[0],u=0,c=s[1];u<c.length;u+=1){var f=c[u],h=e.getTile(f);h.needsHillshadePrepare&&\"offscreen\"===t.renderPass?dn(t,h,r,a,Et.disabled,o):\"translucent\"===t.renderPass&&pn(t,h,r,a,l[f.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=vn(m,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Xr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,\"opaque\"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?\"backgroundPattern\":\"background\"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?en(b,i,t,u,{tileID:x,tileSize:l},g):tn(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)An(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Sn=function(t,e){this.context=new Pt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ot.maxUnderzooming+Ot.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Sn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Sn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Xe.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Xe.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,O.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Xe.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Sn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Fr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Sn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Fr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Sn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Sn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?Lt.unblended:Lt.alphaBlended},Sn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Sn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Sn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass=\"offscreen\";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&Mn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&_n(this),this.context.setDefault()},Sn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),Mn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Sn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Sn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Sn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Sn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Sn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Sn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Sn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Ln=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Ln.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Ln(n,i)},Ln.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Ln.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Ln.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Cn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Cn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Cn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Cn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Cn.prototype.clone=function(){return new Cn(this.top,this.bottom,this.left,this.right)},Cn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Pn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Cn,this._posMatrixCache={},this._alignedPosMatrixCache={}},On={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Pn.prototype.clone=function(){var t=new Pn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},On.minZoom.get=function(){return this._minZoom},On.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},On.maxZoom.get=function(){return this._maxZoom},On.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},On.minPitch.get=function(){return this._minPitch},On.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},On.maxPitch.get=function(){return this._maxPitch},On.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},On.renderWorldCopies.get=function(){return this._renderWorldCopies},On.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},On.worldSize.get=function(){return this.tileSize*this.scale},On.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},On.size.get=function(){return new t.Point(this.width,this.height)},On.bearing.get=function(){return-this.angle/Math.PI*180},On.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},On.pitch.get=function(){return this._pitch/Math.PI*180},On.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},On.fov.get=function(){return this._fov/Math.PI*180},On.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},On.zoom.get=function(){return this._zoom},On.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},On.center.get=function(){return this._center},On.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},On.padding.get=function(){return this._edgeInsets.toJSON()},On.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},On.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Pn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Pn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Pn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Pn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Pn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new Ln([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Pn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},On.unmodified.get=function(){return this._unmodified},Pn.prototype.zoomScale=function(t){return Math.pow(2,t)},Pn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Pn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Pn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},On.point.get=function(){return this.project(this.center)},Pn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Pn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Pn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Pn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Pn.prototype.coordinateLocation=function(t){return t.toLngLat()},Pn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Pn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Pn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Pn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Pn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Pn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Pn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Pn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Pn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Pn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Pn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Pn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Pn.prototype,On);var In=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};In.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},In.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},In.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c=\"\";if(c+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||u)&&(c+=\"/\"+Math.round(10*l)/10),u&&(c+=\"/\"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+c):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+c),\"#\"+p.join(\"&\")}return\"#\"+c},In.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},In.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},In.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var zn={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},zn),Rn=t.extend({deceleration:20,maxSpeed:1400},zn),Fn=t.extend({deceleration:1e3,maxSpeed:360},zn),Bn=t.extend({deceleration:1e3,maxSpeed:90},zn),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Un(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Nn.prototype.clear=function(){this._inertiaBuffer=[]},Nn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Nn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Un(r.pan.mag(),o,t.extend({},Dn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,jn(s,l)}if(r.zoom){var u=Un(r.zoom,o,Rn);s.zoom=this._map.transform.zoom+u.amount,jn(s,u)}if(r.bearing){var c=Un(r.bearing,o,Fn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),jn(s,c)}if(r.pitch){var f=Un(r.pitch,o,Bn);s.pitch=this._map.transform.pitch+f.amount,jn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Vn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),qn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Gn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Gn.prototype.reset=function(){delete this._mousedownPos},Gn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},Gn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseup=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Wn=function(t){this._map=t};Wn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Wn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Wn.prototype.mousedown=function(){this._delayContextMenu=!0},Wn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Wn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Wn.prototype.isEnabled=function(){return!0},Wn.prototype.isActive=function(){return!1},Wn.prototype.enable=function(){},Wn.prototype.disable=function(){};var Yn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Xn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Yn.prototype.isEnabled=function(){return!!this._enabled},Yn.prototype.isActive=function(){return!!this._active},Yn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Yn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Yn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Yn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Yn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Yn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Yn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Yn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Zn=function(t){this.reset(),this.numTouches=t.numTouches};Zn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Zn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Xn(n,r)))},Zn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Xn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Zn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Kn=function(t){this.singleTap=new Zn(t),this.numTaps=t.numTaps,this.reset()};Kn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Kn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Kn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Kn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Kn({numTouches:1,numTaps:2}),this._zoomOut=new Kn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var $n={};$n[0]=1,$n[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=$n[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)<this._clickTolerance))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Qn.prototype.mouseupWindow=function(t){this._lastPoint&&r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Qn.prototype.enable=function(){this._enabled=!0},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Qn),ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ni=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ni.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ni.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ni.prototype.touchmove=function(t,e,r){if(this._active&&!(r.length<this._minTouches))return t.preventDefault(),this._calculateTransform(t,e,r)},ni.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ni.prototype.touchcancel=function(){this.reset()},ni.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Xn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ii=function(){this.reset()};function ai(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ii.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ii.prototype._start=function(t){},ii.prototype._move=function(t,e,r){return{}},ii.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ii.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ai(r,e,i),s=ai(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ii.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ai(n,e,a),l=ai(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ii.prototype.touchcancel=function(){this.reset()},ii.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},ii.prototype.disable=function(){this._enabled=!1,this.reset()},ii.prototype.isEnabled=function(){return this._enabled},ii.prototype.isActive=function(){return this._active};function oi(t,e){return Math.log(t/e)/Math.LN2}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}},e}(ii);function li(t,e){return 180*t.angleWith(e)/Math.PI}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e},e}(ii);function ci(t){return Math.abs(t.y)>Math.abs(t.x)}var fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ci(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ci(t)&&ci(e)&&a}},e}(ii),hi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=hi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var vi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onTimeout\"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%vi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>vi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var mi=function(){this.reset()};mi.prototype.reset=function(){this._active=!1},mi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},mi.prototype.enable=function(){this._enabled=!0},mi.prototype.disable=function(){this._enabled=!1,this.reset()},mi.prototype.isEnabled=function(){return this._enabled},mi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new Kn({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var bi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};bi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},bi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},bi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},bi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var _i=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};_i.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},_i.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},_i.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},_i.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!0}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};Mi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},Mi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Gn(e,t));var n=e.boxZoom=new Yn(e,t);this._add(\"boxZoom\",n);var i=new Jn,a=new mi;e.doubleClickZoom=new yi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new xi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new fi;this._add(\"touchPitch\",s);var l=new ei(t),u=new ri(t);e.dragRotate=new _i(t,l,u),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",u,[\"mouseRotate\"]);var c=new ti(t),f=new ni(t);e.dragPan=new bi(r,c,f),this._add(\"mousePan\",c),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ui,p=new si;e.touchZoomRotate=new wi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new gi(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var v=e.keyboard=new pi;this._add(\"keyboard\",v),this._add(\"blockableMapEvent\",new Wn(e));for(var g=0,y=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},Mi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},Mi.prototype.stop=function(t){if(!this._updatingCamera){for(var e=0,r=this._handlers;e<r.length;e+=1)r[e].handler.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}},Mi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},Mi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Mi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Mi.prototype.isMoving=function(){return Boolean(Ti(this._eventsInProgress))||this.isZooming()},Mi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},Mi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},Mi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},Mi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ai(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ai(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],m(this._map))}else this.stop(!0)},Mi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},Mi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},Mi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ai(t))return this._fireEvents(e,r,!0);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)},Mi.prototype._fireEvents=function(e,r,n){var i=this,a=Ti(this._eventsInProgress),o=Ti(e),s={};for(var l in e){var u=e[l].originalEvent;this._eventsInProgress[l]||(s[l+\"start\"]=u),this._eventsInProgress[l]=e[l]}for(var c in!a&&o&&this._fireEvent(\"movestart\",o.originalEvent),s)this._fireEvent(c,s[c]);for(var f in o&&this._fireEvent(\"move\",o.originalEvent),e){var h=e[f].originalEvent;this._fireEvent(f,h)}var p,d={};for(var v in this._eventsInProgress){var g=this._eventsInProgress[v],y=g.handlerName,m=g.originalEvent;this._handlersById[y].isActive()||(delete this._eventsInProgress[v],p=r[y]||m,d[v+\"end\"]=p)}for(var x in d)this._fireEvent(x,d[x]);var b=Ti(this._eventsInProgress);if(n&&(a||o)&&!b){this._updatingCamera=!0;var _=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),w=function(t){return 0!==t&&-i._bearingSnap<t&&t<i._bearingSnap};_?(w(_.bearing||this._map.getBearing())&&(_.bearing=0),this._map.easeTo(_,{originalEvent:p})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:p})),w(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Mi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Mi.prototype._requestFrame=function(){var t=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add((function(e){delete t._frameId,t.handleEvent(new ki(\"renderFrame\",{timeStamp:e})),t._applyChanges()}))},Mi.prototype._triggerRenderFrame=function(){void 0===this._frameId&&(this._frameId=this._requestFrame())};var Si=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){e=t.LngLatBounds.convert(e);var n=r&&r.bearing||0;return this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),n,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x=\"number\"==typeof i.offset.x?new t.Point(i.offset.x,i.offset.y):t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(b,_).rotate(n*Math.PI/180),T=x.add(w).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(T)),zoom:m,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,I=function(){return 0},O=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ei.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=r.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString(\"AttributionControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\",this._setElementTitle(e,\"MapFeedback\")}},Ei.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"visibility\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ei.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Li=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Li.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Li.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Li.prototype.getDefaultPosition=function(){return\"bottom-left\"},Li.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Li.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Li.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Ci=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ci.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ci.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ci.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ci.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Pi={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Oi=t.window.HTMLImageElement,Ii=t.window.HTMLElement,zi=t.window.ImageBitmap,Di=60,Ri={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Di,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Fi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Ri,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Di)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Pn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ci,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Pi,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ii))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1),t.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new In(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Li,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?Di:t)>Di)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Oi||zi&&r instanceof zi){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof Oi||zi&&r instanceof zi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof Oi||zi&&r instanceof zi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new Sn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype._onMapScroll=function(t){if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}var p=this._sourcesDirty||this._styleDirty||this._placementDirty;return p||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event(\"idle\")),!this._loaded||this._fullyLoaded||p||(this._fullyLoaded=!0),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"orientationchange\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext&&n.loseContext(),Bi(this._canvasContainer),Bi(this._controlContainer),Bi(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Si);function Bi(t){t.parentNode&&t.parentNode.removeChild(t)}var Ni={showCompass:!0,showZoom:!0,visualizePitch:!1},ji=function(e){var n=this;this.options=t.extend({},Ni,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};ji.prototype._updateZoomButtons=function(){var t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute(\"aria-disabled\",e.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",r.toString())},ji.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},ji.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ui(this._map,this._compass,this.options.visualizePitch)),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},ji.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},ji.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ui=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new ei({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ri({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Vi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ui.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var qi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Hi(t,e,r){var n=t.classList;for(var i in qi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Gi,Wi=function(e){function n(n,i){if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._scale=n&&n.scale||1,this._draggable=n&&n.draggable||!1,this._clickTolerance=n&&n.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var a=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");a.setAttributeNS(null,\"display\",\"block\"),a.setAttributeNS(null,\"height\",\"41px\"),a.setAttributeNS(null,\"width\",\"27px\"),a.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");o.setAttributeNS(null,\"stroke\",\"none\"),o.setAttributeNS(null,\"stroke-width\",\"1\"),o.setAttributeNS(null,\"fill\",\"none\"),o.setAttributeNS(null,\"fill-rule\",\"evenodd\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"fill-rule\",\"nonzero\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),l.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,c=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<c.length;u+=1){var f=c[u],h=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");h.setAttributeNS(null,\"opacity\",\"0.04\"),h.setAttributeNS(null,\"cx\",\"10.5\"),h.setAttributeNS(null,\"cy\",\"5.80029008\"),h.setAttributeNS(null,\"rx\",f.rx),h.setAttributeNS(null,\"ry\",f.ry),l.appendChild(h)}var p=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");p.setAttributeNS(null,\"fill\",this._color);var d=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");d.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),p.appendChild(d);var v=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");v.setAttributeNS(null,\"opacity\",\"0.25\"),v.setAttributeNS(null,\"fill\",\"#000000\");var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),v.appendChild(g);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");x.setAttributeNS(null,\"fill\",\"#000000\"),x.setAttributeNS(null,\"opacity\",\"0.25\"),x.setAttributeNS(null,\"cx\",\"5.5\"),x.setAttributeNS(null,\"cy\",\"5.5\"),x.setAttributeNS(null,\"r\",\"5.4999962\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#FFFFFF\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\"),m.appendChild(x),m.appendChild(b),s.appendChild(l),s.appendChild(p),s.appendChild(v),s.appendChild(y),s.appendChild(m),a.appendChild(s),a.setAttributeNS(null,\"height\",41*this._scale+\"px\"),a.setAttributeNS(null,\"width\",27*this._scale+\"px\"),this._element.appendChild(a),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),Hi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[r,-1*(24.6+r)],\"bottom-right\":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,qi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){if(!this._isDragging){var r=this._clickTolerance||this._map._clickTolerance;this._isDragging=e.point.dist(this._pointerdownPos)>=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\")))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Yi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Xi=0,Zi=!1,Ki=function(e){function n(r){e.call(this),this.options=t.extend({},Yi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Gi=\"denied\"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Xi=0,Zi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Zi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Wi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Wi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Xi--,Zi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Xi>1?(e={maximumAge:6e5,timeout:0},Zi=!0):(e=this.options.positionOptions,Zi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:\"metric\"},$i=function(e){this.options=t.extend({},Ji,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):ta(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ta(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ta(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ta(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}$i.prototype.getDefaultPosition=function(){return\"bottom-left\"},$i.prototype._onMove=function(){Qi(this._map,this._container,this.options)},$i.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},$i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},$i.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},na=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.y<u?[\"top\"]:i.y>this._map.transform.height-u?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,qi[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Hi(this._container,a,\"popup\")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var t=this._container.querySelector(na);t&&t.focus()}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function aa(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return aa(new t.Point(0,0))}var oa={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Fi,NavigationControl:ji,GeolocateControl:Ki,AttributionControl:Ei,ScaleControl:$i,FullscreenControl:ea,Popup:ia,Marker:Wi,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Ft.workerCount},set workerCount(t){Ft.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return oa})),r}()},3108:function(t,e,r){\"use strict\";t.exports=r(26099)},26099:function(t,e,r){\"use strict\";var n=r(64928),i=r(32420),a=r(51160),o=r(76752),s=r(55616),l=r(31264),u=r(47520),c=r(18400),f=r(72512),h=r(76244);function p(t,e){for(var r=e[0],n=e[1],a=1/(e[2]-r),o=1/(e[3]-n),s=new Array(t.length),l=0,u=t.length/2;l<u;l++)s[2*l]=i((t[2*l]-r)*a,0,1),s[2*l+1]=i((t[2*l+1]-n)*o,0,1);return 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t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 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return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign 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t.offset&&(t.offset=[t.offset,0]),this.positionOffset=m(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else{var i=t.style,s=t.weight,l=t.stretch,u=t.variant;t=n.parse(n.stringify(t)),i&&(t.style=i),s&&(t.weight=s),l&&(t.stretch=l),u&&(t.variant=u)}var c=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),f=p(t.size),h=Math.round(f[0]*d(f[1]));if(h!==e.fontSize[r]&&(o=!0,e.fontSize[r]=h),!(e.font[r]&&c==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[c],e.font[r]))){var v=t.family.join(\", \"),g=[t.style];t.style!=t.variant&&g.push(t.variant),t.variant!=t.weight&&g.push(t.weight),_&&t.weight!=t.stretch&&g.push(t.stretch),e.font[r]={baseString:c,family:v,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:y(v,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:g.join(\" \")})},T.fonts[c]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,v(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=c.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),c.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=c.mallocUint8(this.count),P=c.mallocFloat(2*this.count);this.textWidth=[];for(var O=0,I=0;O<this.counts.length;O++){for(var z=this.counts[O],D=this.font[O]||this.font[0],R=this.fontAtlas[O]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(I),N=this.text.charAt(I-1);if(C[I]=R.ids[B],P[2*I]=D.width[B],F){var j=P[2*I-2],U=P[2*I],V=P[2*I-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}P[2*I+1]=V}else P[2*I+1]=.5*P[2*I];I++}this.textWidth.push(P.length?.5*P[2*I-2]+P[2*I-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),c.freeUint8(C),c.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),c=x(s*i);n.width=l,n.height=c,n.rows=s,n.cols=o,n.em&&n.texture({data:u({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,c],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,-1*(i+=\"number\"==typeof t?t-n.baseline:-n[t])}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=c.mallocUint8(G);for(var W=(t.color.subarray||t.color.slice).bind(t.color),Y=0;Y<G;Y+=4)H.set(l(W(Y,Y+4),\"uint8\"),Y)}else{var X=t.color.length;H=c.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var K=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(K);for(var J=0;J<this.batch.length;J++)this.batch[J]={count:this.counts.length>1?this.counts[J]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[J]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*J,4*J+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[J]:this.opacity,baseline:null!=this.baselineOffset[J]?this.baselineOffset[J]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[J]?this.alignOffset[J]:this.alignOffset[0]:0,atlas:this.fontAtlas[J]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*J,2*J+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},t.exports=T},55212:function(t,e,r){\"use strict\";var n=r(55616);function 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n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error(\"PolyBool: Cannot convert GeoJSON object to PolyBool polygon\")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++)if(n(e,(s=t.children[r]).region))return void o(s,e);var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function u(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var c=[];function f(t){var e=[u(t.region,!1)];c.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:\"Polygon\",coordinates:[]}:1==c.length?{type:\"Polygon\",coordinates:c[0]}:{type:\"MultiPolygon\",coordinates:c}}};t.exports=e},72200:function(t,e,r){var n=r(48088);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var 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y,m,x=g();if(x)t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(x.seg.myFill.above=!x.seg.myFill.above):x.seg.otherFill=h.seg.myFill,r&&r.segmentUpdate(x.seg),h.other.remove(),h.remove();if(a.getHead()!==h){r&&r.rewind(h.seg);continue}t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=v?v.seg.myFill.above:i,h.seg.myFill.above=m?!h.seg.myFill.below:h.seg.myFill.below):null===h.seg.otherFill&&(y=v?h.primary===v.primary?v.seg.otherFill.above:v.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:y,below:y}),r&&r.status(h.seg,!!d&&d.seg,!!v&&v.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&c(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var 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r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},11403:function(t){t.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},u=s,c=0;c<n.length;c++){var f=(g=n[c])[0],h=(g[1],g[g.length-1]);if(g[g.length-2],e.pointsSame(f,a)){if(k(c,!0,!0))break}else if(e.pointsSame(f,o)){if(k(c,!0,!1))break}else if(e.pointsSame(h,a)){if(k(c,!1,!0))break}else if(e.pointsSame(h,o)&&k(c,!1,!1))break}if(u===s)return 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},82368:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},9696:function(t,e,r){\"use 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this.big_endian?16777216*r[t]+65536*r[t+1]+256*r[t+2]+r[t+3]:r[t]+256*r[t+1]+65536*r[t+2]+16777216*r[t+3]},n.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},n.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},n.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return this.read_uint32(e);case 9:return 0|this.read_uint32(e);default:return null}},n.prototype.scan_ifd=function(t,n,i){var a=this.read_uint16(n);n+=2;for(var o=0;o<a;o++){var s=this.read_uint16(n),l=this.read_uint16(n+2),u=this.read_uint32(n+4),c=this.exif_format_length(l),f=u*c,h=f<=4?n+8:this.read_uint32(n+8),p=!1;if(h+f>this.input.length)throw e(\"unexpected EOF\",\"EBADDATA\");for(var d=[],v=h,g=0;g<u;g++,v+=c){var y=this.exif_format_read(l,v);if(null===y){d=null;break}d.push(y)}if(Array.isArray(d)&&2===l&&(d=r(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===i({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:u,entry_offset:n+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);n+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(n)})},t.exports.ExifParser=n,t.exports.get_orientation=function(t){var e=0;try{return new n(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},44600:function(t,e,r){\"use strict\";var n=r(9696).eW,i=r(9696).eI;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return 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t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a<r;a++)t[a]=n}if(t.length<r)throw Error(\"Not enough colors\");for(var o=new Uint8Array(4*r),s=0;s<r;s++){var l=i(t[s],\"uint8\");o.set(l,4*s)}return o},range:function(t,e,r){var n=e.bounds;return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},13472:function(t,e,r){\"use strict\";var n=r(72160),i=r(76752),a=r(50896),o=r(55616),s=r(47520),l=r(28912),u=r(71152),c=r(37816),f=c.float32,h=c.fract32,p=r(60463),d=r(51160),v=r(10272);function g(t,e){if(!(this instanceof g))return new g(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=g.shaders.has(t)?g.shaders.get(t):g.shaders.set(t,g.createShaders(t)).get(t),this.update(e)}t.exports=g,g.dashMult=2,g.maxPatternLength=256,g.precisionThreshold=3e6,g.maxPoints=1e4,g.maxLines=2048,g.shaders=new p,g.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:\"\\nprecision highp float;\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:\"\\nprecision highp float;\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:\"\\nprecision highp float;\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*g.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}y.dashLength=tt,y.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=y.count,ut=t.color;ut||(ut=\"transparent\");var ct=new Uint8Array(4*lt+4);if(Array.isArray(ut)&&\"number\"!=typeof ut[0]){for(var ft=0;ft<lt;ft++){var ht=n(ut[ft],\"uint8\");ct.set(ht,4*ft)}ct.set(n(ut[0],\"uint8\"),4*lt)}else for(var pt=n(ut,\"uint8\"),dt=0;dt<lt+1;dt++)ct.set(pt,4*dt);y.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ct})}}else e.passes[p]=null})),t.length<this.passes.length){for(var p=t.length;p<this.passes.length;p++){var y=this.passes[p];y&&(y.colorBuffer.destroy(),y.positionBuffer.destroy(),y.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&m.push(this.passes[x]);return this.passes=m,this}},g.prototype.destroy=function(){return 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Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),c(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return 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vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\\n\"]),this.drawMarker=t(s);var l=c({},o);l.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\n// `invariant` effectively turns off optimizations for the position.\\n// We need this because -fast-math on M1 Macs is re-ordering\\n// floating point operations in a way that causes floating point\\n// precision limits to put points in the wrong locations.\\ninvariant gl_Position;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),v&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}x.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if(\"number\"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if(\"undefined\"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t[\"@@iterator\"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)return r?[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var f=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(u),v=0;v<r.length;v++){var g=r[v];d[g]=p?p[g]:1}l.subdata(d)}for(var y=h.length;y--;){var m=n(h[y],2),x=m[0],b=m[1];f.push(c({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return f},x.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,l=this.maxSize,f=this.maxColors,v=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=h(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=c({},x.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,b=0;if(p(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=x.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=x.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=x.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=x.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,l=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=d(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?s(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=g.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:g.fract32(t,p),usage:\"dynamic\"}),l({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var u=t.addMarker(e[s]);a[u]||(a[u]=new Uint8Array(r.count)),a[u][s]=1}for(var c=0;c<a.length;c++)if(a[c]){var f={data:a[c],type:\"uint8\",usage:\"static\"};i[c]?i[c](f):i[c]=o.buffer(f),i[c].data=a[c]}}else i[t.addMarker(e)]=!0;return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g.fract(e.scale),e.translateFract=g.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/l),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/l);A({data:M,usage:\"dynamic\"})}if(b){var E,L=n,C=L.count,P=L.color,O=L.borderColor,I=L.colorBuffer;if(t.tooManyColors){if(P.length||O.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=v[4*D],E[8*z+1]=v[4*D+1],E[8*z+2]=v[4*D+2],E[8*z+3]=v[4*D+3];var R=O[z];E[8*z+4]=v[4*R],E[8*z+5]=v[4*R+1],E[8*z+6]=v[4*R+2],E[8*z+7]=v[4*R+3]}}}else if(P.length||O.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%f,E[4*F+1]=Math.floor(P[F]/f)),null!=O[F]&&(E[4*F+2]=O[F]%f,E[4*F+3]=Math.floor(O[F]/f))}I({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},x.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},x.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var s=0;s<t.length;s+=4)a.push(t.slice(s,s+4));else for(var u=0;u<t.length;u+=4)a.push(t.subarray(u,u+4));t=a}for(var c=0;c<t.length;c++){var f=t[c];f=o(f,\"uint8\");var h=l(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[c]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},x.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},x.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var b=r(50896);t.exports=function(t,e){var r=new m(t,e),n=r.render.bind(r);return b(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},55795:function(t,e,r){\"use strict\";var n=r(38540),i=r(55616),a=r(76752),o=r(3951),s=r(67752),l=r(51160),u=r(47520);function c(t,e){if(!(this instanceof c))return new c(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x,s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y,l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}t.exports=c,c.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},c.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,u=0;u<l.length;u++)i.push(this.passes[l[u]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},c.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values 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Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(u(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var c=0;c<s.columns;c++)s.bounds[c]=a(o.data[c],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var v=s.columns,g=s.count,y=s.viewport.width,m=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=y/v,w=m/v;s.passes=[];for(var T=0;T<v;T++)for(var k=0;k<v;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:v},y:{buffer:s.buffer,offset:T,count:g,stride:v}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],O=E[3];M.viewport=[x+L*y+S[0],b+C*m+S[1],x+P*y-S[2],b+O*m-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},c.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var u=e[i],c=this.traces[i],f=c.passes,h=c.passOffset;f=f.map((function(t,e){n[h+e]=u}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},c.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},28624:function(t){t.exports=function(){function t(t,e){this.id=W++,this.type=t,this.data=e}function e(t){if(0===t.length)return[];var r=t.charAt(0),n=t.charAt(t.length-1);if(1<t.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+t.substr(1,t.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(t))return e(t.substr(0,r.index)).concat(e(r[1])).concat(e(t.substr(r.index+r[0].length)));if(1===(r=t.split(\".\")).length)return['\"'+t.replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];for(t=[],n=0;n<r.length;++n)t=t.concat(e(r[n]));return t}function r(t){return\"[\"+e(t).join(\"][\")+\"]\"}function n(t){return\"string\"==typeof t?t.split():t}function i(t){return\"string\"==typeof t?document.querySelector(t):t}function a(t){var e,r,a,o,s=t||{};t={};var 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},65168:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},2084:function(t,e,r){var n=r(38700),i=r(50896);function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},24747:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},65616:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return 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o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},73040:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},4592:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},45348:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n=r(50896);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day(),\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(t){return this.newDate(t.getFullYear(),t.getMonth()+1,t.getDate())}});var u=t.exports=new i;u.cdate=a,u.baseCalendar=s,u.calendars.gregorian=l},15168:function(t,e,r){var n=r(50896),i=r(38700);n(i.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"}),i.local=i.regionalOptions[\"\"],n(i.cdate.prototype,{formatDate:function(t,e){return\"string\"!=typeof 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-                            "            Plotly.purge(gd);\n",
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-                            "// Listen for the removal of the full notebook cells\n",
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-                "### comment out langfuse code \n",
-                "# px.box(\n",
-                "#     results, \n",
-                "#     y='precision',\n",
-                "#     x='Schema',\n",
-                "#     color='Schema (counts)',\n",
-                "#     title='Precision of LLM Partial Extraction Completions', \n",
-                "#     boxmode='overlay',\n",
-                "#     width=700,\n",
-                "# ).update_layout(\n",
-                "#     xaxis_title='Schemas',\n",
-                "#     yaxis_dtick=0.1,\n",
-                "#     # showlegend=False,\n",
-                "# )"
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-        },
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-                            "            Plotly.purge(gd);\n",
-                            "            observer.disconnect();\n",
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-                            "\n",
-                            "// Listen for the removal of the full notebook cells\n",
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-                            "\n",
-                            "// Listen for the clearing of the current output cell\n",
-                            "var outputEl = gd.closest('.output');\n",
-                            "if (outputEl) {{\n",
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-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "### comment out langfuse code \n",
-                "# px.scatter(\n",
-                "#     results, \n",
-                "#     y='latencies',\n",
-                "#     x='N values',\n",
-                "#     color='Schema',\n",
-                "#     title='Latency v.s. number of values', \n",
-                "#     # boxmode='overlay',\n",
-                "#     width=700,\n",
-                "# ).update_layout(\n",
-                "#     # xaxis_title='Schemas',\n",
-                "#     yaxis_title='Latency (s)',\n",
-                "#     # yaxis_dtick=0.1,\n",
-                "#     # showlegend=False,\n",
-                "# )"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f23f3bb1-f77f-4aff-955c-6caf868d1290",
-            "metadata": {},
-            "source": [
-                "## Manual extraction v.s. corrected concensus review"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "df79f495-1bb9-44e5-8201-b29f1cd2147a",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
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-                        "text/plain": [
-                            "0it [00:00, ?it/s]"
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-                            "{'ngufor2016efficacy': {'Observation': (3, 3),\n",
-                            "  'ITNCondition': (21, 6),\n",
-                            "  'EntomologicalOutcome': (12, 7)},\n",
-                            " 'okia2013bioefficacy': {'Observation': (4, 7),\n",
-                            "  'ITNCondition': (5, 6),\n",
-                            "  'EntomologicalOutcome': (18, 6),\n",
-                            "  'ClinicalOutcome': (8, 3)},\n",
-                            " 'mosha2008comparative': {'Observation': (1, 7),\n",
-                            "  'ITNCondition': (2, 5),\n",
-                            "  'EntomologicalOutcome': (3, 11),\n",
-                            "  'ClinicalOutcome': (2, 15)},\n",
-                            " 'quinones1997anopheles': {'Observation': (4, 7),\n",
-                            "  'ITNCondition': (3, 3),\n",
-                            "  'EntomologicalOutcome': (8, 3),\n",
-                            "  'ClinicalOutcome': (1, 1)},\n",
-                            " 'ahoua2012status': {'Observation': (2, 7),\n",
-                            "  'ITNCondition': (2, 5),\n",
-                            "  'EntomologicalOutcome': (6, 8),\n",
-                            "  'ClinicalOutcome': (2, 9)},\n",
-                            " 'sreehari2009wash': {'Observation': (1, 5),\n",
-                            "  'ITNCondition': (4, 6),\n",
-                            "  'EntomologicalOutcome': (8, 10),\n",
-                            "  'ClinicalOutcome': (13, 5)},\n",
-                            " 'abdulla2005spatial': {'Observation': (1, 7),\n",
-                            "  'ITNCondition': (3, 1),\n",
-                            "  'EntomologicalOutcome': (3, 1),\n",
-                            "  'ClinicalOutcome': (2, 3)},\n",
-                            " 'tan2016longitudinal': {'Observation': (1, 5),\n",
-                            "  'ITNCondition': (2, 8),\n",
-                            "  'EntomologicalOutcome': (2, 5),\n",
-                            "  'ClinicalOutcome': (1, 3)},\n",
-                            " 'wills2013physical': {'Observation': (19, 7),\n",
-                            "  'ITNCondition': (7, 4),\n",
-                            "  'EntomologicalOutcome': (2, 5),\n",
-                            "  'ClinicalOutcome': (14, 4)},\n",
-                            " 'marchant2002socially': {'Observation': (1, 7),\n",
-                            "  'ITNCondition': (2, 5),\n",
-                            "  'EntomologicalOutcome': (2, 5),\n",
-                            "  'ClinicalOutcome': (30, 3)},\n",
-                            " 'nevill1996insecticide': {'Observation': (2, 7),\n",
-                            "  'ITNCondition': (1, 10),\n",
-                            "  'EntomologicalOutcome': (1, 2),\n",
-                            "  'ClinicalOutcome': (4, 7)},\n",
-                            " 'msuya1991trial': {'Observation': (10, 7),\n",
-                            "  'ITNCondition': (3, 3),\n",
-                            "  'EntomologicalOutcome': (5, 4),\n",
-                            "  'ClinicalOutcome': (1, 2)},\n",
-                            " 'tungu2021efficacy': {'Observation': (1, 5),\n",
-                            "  'ITNCondition': (3, 7),\n",
-                            "  'EntomologicalOutcome': (12, 10),\n",
-                            "  'ClinicalOutcome': (3, 4)}}"
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "true_extractions = {}\n",
-                "for ref, paper in tqdm(papers.loc[references].iterrows()):\n",
-                "    true_extractions[paper.name] = get_paper_extractions(\n",
-                "        paper, extraction_dataset, schemas=ss, field='extraction', answer='extraction-correction')\n",
-                "{ref: {k: v.shape for k,v in ext.items() if v.size} for ref, ext in true_extractions.items()}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f8dcabcb-642b-4e73-a0d8-21bd066cf875",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/json": {
-                            "ascii": false,
-                            "bar_format": null,
-                            "colour": null,
-                            "elapsed": 0.003042936325073242,
-                            "initial": 0,
-                            "n": 0,
-                            "ncols": null,
-                            "nrows": 56,
-                            "postfix": null,
-                            "prefix": "",
-                            "rate": null,
-                            "total": null,
-                            "unit": "it",
-                            "unit_divisor": 1000,
-                            "unit_scale": false
-                        },
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "14190dd7b01443b79e2f2b9f9399c093",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "0it [00:00, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'ngufor2016efficacy': {},\n",
-                            " 'okia2013bioefficacy': {},\n",
-                            " 'mosha2008comparative': {},\n",
-                            " 'quinones1997anopheles': {},\n",
-                            " 'ahoua2012status': {},\n",
-                            " 'sreehari2009wash': {},\n",
-                            " 'abdulla2005spatial': {},\n",
-                            " 'tan2016longitudinal': {},\n",
-                            " 'wills2013physical': {},\n",
-                            " 'marchant2002socially': {},\n",
-                            " 'nevill1996insecticide': {},\n",
-                            " 'msuya1991trial': {},\n",
-                            " 'tungu2021efficacy': {}}"
-                        ]
-                    },
-                    "execution_count": 18,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pred_extractions = {}\n",
-                "for ref, paper in tqdm(papers.loc[references].iterrows()):\n",
-                "    pred_extractions[paper.name] = get_paper_extractions(\n",
-                "        paper, extraction_dataset, schemas=ss, field='extraction', \n",
-                "        answer='extraction-correction', suggestion='extraction-correction', \n",
-                "        user=username_to_user['jonnytr'])\n",
-                "{ref: {k: v.shape for k,v in ext.items() if v.size} for ref, ext in pred_extractions.items()}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "9c5d61b8-aa13-4eae-b68c-0723ac5aa5e0",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "pred_extractions = {ref: paper_extraction for ref, paper_extraction in pred_extractions.items() \\\n",
-                "                    if not all([df.empty for df in paper_extraction.extractions.values()])}\n",
-                "true_extractions = {ref: paper_extraction for ref, paper_extraction in true_extractions.items() \\\n",
-                "                    if ref in pred_extractions}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "4c4f48f5-c405-4a37-9aa1-202fcd20ac55",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "0"
-                        ]
-                    },
-                    "execution_count": 20,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "len(true_extractions)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "08d27b5e-c786-44fa-bd9c-5aa07547d8ac",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1 suggestion_metrics = grits_from_batch(true_extractions, pred_extractions, exclude_column     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2 </span>                                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3 </span>suggestion_metrics.style.background_gradient(axis=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>, vmin=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>, vmax=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>, cmap=<span style=\"color: #808000; text-decoration-color: #808000\">'RdYlGn'</span>)          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">4 </span>                                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/Projects/extralit/src/extralit/metrics/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">extraction.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">51</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">grits_from_batch</span>       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 48 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">f\"Invalid type for true_extractions: {</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">type</span>(true_extract   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 49 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 50 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 51 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>metrics_df = pd.concat(metrics, axis=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>)                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 52 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>metrics_df.index.names = index_names[:metrics_df.index.nlevels]                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 53 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 54 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> compute_mean:                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">concat.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">382</span> in      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">concat</span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">379 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">elif</span> copy <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">and</span> using_copy_on_write():                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">380 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>copy = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">False</span>                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">381 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>382 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>op = _Concatenator(                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">383 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>objs,                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">384 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>axis=axis,                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">385 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>ignore_index=ignore_index,                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">concat.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">445</span> in      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__init__</span>                                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">442 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.verify_integrity = verify_integrity                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">443 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.copy = copy                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">444 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>445 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>objs, keys = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._clean_keys_and_objs(objs, keys)                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">446 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">447 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># figure out what our result ndim is going to be</span>                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">448 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>ndims = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._get_ndims(objs)                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">concat.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">507</span> in      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_clean_keys_and_objs</span>                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">504 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>objs_list = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">list</span>(objs)                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">505 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">506 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">len</span>(objs_list) == <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>:                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>507 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">\"No objects to concatenate\"</span>)                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">508 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">509 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> keys <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">510 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>objs_list = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">list</span>(com.not_none(*objs_list))                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
-                            "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">ValueError: </span>No objects to concatenate\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m in \u001b[92m<module>\u001b[0m:\u001b[94m1\u001b[0m                                                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1 suggestion_metrics = grits_from_batch(true_extractions, pred_extractions, exclude_column     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m2 \u001b[0m                                                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m3 \u001b[0msuggestion_metrics.style.background_gradient(axis=\u001b[94m1\u001b[0m, vmin=\u001b[94m0\u001b[0m, vmax=\u001b[94m1\u001b[0m, cmap=\u001b[33m'\u001b[0m\u001b[33mRdYlGn\u001b[0m\u001b[33m'\u001b[0m)          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m4 \u001b[0m                                                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/Projects/extralit/src/extralit/metrics/\u001b[0m\u001b[1;33mextraction.py\u001b[0m:\u001b[94m51\u001b[0m in \u001b[92mgrits_from_batch\u001b[0m       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 48 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mInvalid type for true_extractions: \u001b[0m\u001b[33m{\u001b[0m\u001b[96mtype\u001b[0m(true_extract   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 49 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 50 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 51 \u001b[2m│   \u001b[0mmetrics_df = pd.concat(metrics, axis=\u001b[94m0\u001b[0m)                                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 52 \u001b[0m\u001b[2m│   \u001b[0mmetrics_df.index.names = index_names[:metrics_df.index.nlevels]                        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 53 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 54 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m compute_mean:                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/\u001b[0m\u001b[1;33mconcat.py\u001b[0m:\u001b[94m382\u001b[0m in      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mconcat\u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m379 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94melif\u001b[0m copy \u001b[95mand\u001b[0m using_copy_on_write():                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m380 \u001b[0m\u001b[2m│   │   \u001b[0mcopy = \u001b[94mFalse\u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m381 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m382 \u001b[2m│   \u001b[0mop = _Concatenator(                                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m383 \u001b[0m\u001b[2m│   │   \u001b[0mobjs,                                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m384 \u001b[0m\u001b[2m│   │   \u001b[0maxis=axis,                                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m385 \u001b[0m\u001b[2m│   │   \u001b[0mignore_index=ignore_index,                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/\u001b[0m\u001b[1;33mconcat.py\u001b[0m:\u001b[94m445\u001b[0m in      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m__init__\u001b[0m                                                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m442 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.verify_integrity = verify_integrity                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m443 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.copy = copy                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m444 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m445 \u001b[2m│   │   \u001b[0mobjs, keys = \u001b[96mself\u001b[0m._clean_keys_and_objs(objs, keys)                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m446 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m447 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# figure out what our result ndim is going to be\u001b[0m                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m448 \u001b[0m\u001b[2m│   │   \u001b[0mndims = \u001b[96mself\u001b[0m._get_ndims(objs)                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/pandas/core/reshape/\u001b[0m\u001b[1;33mconcat.py\u001b[0m:\u001b[94m507\u001b[0m in      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_clean_keys_and_objs\u001b[0m                                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m504 \u001b[0m\u001b[2m│   │   │   \u001b[0mobjs_list = \u001b[96mlist\u001b[0m(objs)                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m505 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m506 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mlen\u001b[0m(objs_list) == \u001b[94m0\u001b[0m:                                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m507 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mNo objects to concatenate\u001b[0m\u001b[33m\"\u001b[0m)                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m508 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m509 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m keys \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m510 \u001b[0m\u001b[2m│   │   │   \u001b[0mobjs_list = \u001b[96mlist\u001b[0m(com.not_none(*objs_list))                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
-                            "\u001b[1;91mValueError: \u001b[0mNo objects to concatenate\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "suggestion_metrics = grits_from_batch(true_extractions, pred_extractions, exclude_columns=['Site'])\n",
-                "\n",
-                "suggestion_metrics.style.background_gradient(axis=1, vmin=0, vmax=1, cmap='RdYlGn')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "d0d7a119-fde2-47dd-be75-85b00b88779d",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<style type=\"text/css\">\n",
-                            "</style>\n",
-                            "<table id=\"T_d0087\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
-                            "      <th id=\"T_d0087_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">grits</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level1\" >&nbsp;</th>\n",
-                            "      <th id=\"T_d0087_level1_col0\" class=\"col_heading level1 col0\" colspan=\"3\">con</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"blank level2\" >&nbsp;</th>\n",
-                            "      <th id=\"T_d0087_level2_col0\" class=\"col_heading level2 col0\" >f1</th>\n",
-                            "      <th id=\"T_d0087_level2_col1\" class=\"col_heading level2 col1\" >precision</th>\n",
-                            "      <th id=\"T_d0087_level2_col2\" class=\"col_heading level2 col2\" >recall</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th class=\"index_name level0\" >Schema</th>\n",
-                            "      <th class=\"blank col0\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col1\" >&nbsp;</th>\n",
-                            "      <th class=\"blank col2\" >&nbsp;</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_d0087_level0_row0\" class=\"row_heading level0 row0\" >Easy</th>\n",
-                            "      <td id=\"T_d0087_row0_col0\" class=\"data row0 col0\" >97.4%</td>\n",
-                            "      <td id=\"T_d0087_row0_col1\" class=\"data row0 col1\" >97.4%</td>\n",
-                            "      <td id=\"T_d0087_row0_col2\" class=\"data row0 col2\" >97.9%</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th id=\"T_d0087_level0_row1\" class=\"row_heading level0 row1\" >Hard</th>\n",
-                            "      <td id=\"T_d0087_row1_col0\" class=\"data row1 col0\" >89.2%</td>\n",
-                            "      <td id=\"T_d0087_row1_col1\" class=\"data row1 col1\" >88.3%</td>\n",
-                            "      <td id=\"T_d0087_row1_col2\" class=\"data row1 col2\" >95.7%</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n"
-                        ],
-                        "text/plain": [
-                            "<pandas.io.formats.style.Styler at 0x34b50ada0>"
-                        ]
-                    },
-                    "execution_count": 100,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "df = suggestion_metrics.query(\"Schema != 'ClinicalOutcome'\").groupby('Schema').mean()\n",
-                "df.groupby({'Observation': 'Easy', 'ITNCondition': 'Easy', 'EntomologicalOutcome': 'Hard', 'ClinicalOutcome': 'Hard'})\\\n",
-                "    .mean(0).map(lambda n: '{:.1%}'.format(n)).style.background_gradient(axis=1, vmin=0, vmax=1, cmap='RdYlGn')"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "da89f632-e7d0-4b56-95ba-fce00365e23d",
-            "metadata": {},
-            "source": [
-                "# Push new extraction records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "3ba02a63-d4a0-4fff-bcf9-325d9cbf8302",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[10/30/24 12:40:53] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> WARNING:patch_pipeline_export_record:No             <a href=\"file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">patch_pipeline_export_record.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py#80\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">80</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         ClinicalOutcome extraction for                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         ibrahim2020exploring, generating an empty table.    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m[10/30/24 12:40:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m WARNING:patch_pipeline_export_record:No             \u001b]8;id=75945;file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py\u001b\\\u001b[2mpatch_pipeline_export_record.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=349027;file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py#80\u001b\\\u001b[2m80\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         ClinicalOutcome extraction for                      \u001b[2m                                  \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         ibrahim2020exploring, generating an empty table.    \u001b[2m                                  \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> WARNING:patch_pipeline_export_record:No             <a href=\"file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">patch_pipeline_export_record.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py#80\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">80</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         ClinicalOutcome extraction for                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         meiwald2022association, generating an empty table.  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m WARNING:patch_pipeline_export_record:No             \u001b]8;id=862372;file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py\u001b\\\u001b[2mpatch_pipeline_export_record.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=400002;file:///Users/bertozzivill/repos/ITN-recal-data-extraction/patch_pipeline_export_record.py#80\u001b\\\u001b[2m80\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         ClinicalOutcome extraction for                      \u001b[2m                                  \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         meiwald2022association, generating an empty table.  \u001b[2m                                  \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "60"
-                        ]
-                    },
-                    "execution_count": 24,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "records = create_extraction_records(pred_extractions, responses=responses, papers=papers, dataset=extraction_dataset)\n",
-                "len(records)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "d682b346-0558-4dcd-907f-0a18e3a12ba1",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None),\n",
-                            " FeedbackRecord(fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None)]"
-                        ]
-                    },
-                    "execution_count": 30,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "3848fe73-6a8b-407a-853f-ef59ff375c49",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "5bed2c016bc34fc0aeb28917490a5c19",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "Output()"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-                        ],
-                        "text/plain": []
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "extraction_dataset.add_records(records)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "1a820435-53f2-4c15-a88a-afc4854309bf",
-            "metadata": {},
-            "source": [
-                "## Update existing records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "c5857206-5713-416a-a24e-11b9470acc89",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "record_updates = []\n",
-                "\n",
-                "for record in tqdm(extraction_dataset.records):\n",
-                "    reference = record.metadata['reference']\n",
-                "    schema_name = record.metadata['type']\n",
-                "\n",
-                "    update_record = next((r for r in records if reference == r.metadata['reference'] and schema_name == r.metadata['type']), None)\n",
-                "    if update_record:\n",
-                "        record.fields = update_record.fields\n",
-                "        record_updates.append(record)\n",
-                "        \n",
-                "len(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "4879c50f-b033-424d-a131-caefd2ecc5d7",
-            "metadata": {
-                "scrolled": true
-            },
-            "source": [
-                "## Delete records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "ca9c0dec-66a0-4435-8a8e-c17a177c6d59",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "120"
-                        ]
-                    },
-                    "execution_count": 33,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "delete_records = [r for r in extraction_dataset.records if r.metadata['reference'] in references]\n",
-                "len(delete_records)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "6f4e31d4-aa51-48fe-9ec4-959b81cb0c67",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[RemoteFeedbackRecord(id=UUID('5a126d29-a23e-4d99-a397-1377a42f1fc3'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'pmid': '35987650', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'type': 'Observation'}, vectors={}, responses={'ameliabv': []}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886419), updated_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886419)),\n",
-                            " RemoteFeedbackRecord(id=UUID('3d52cd67-668a-4576-bbc5-ad4fd26d0687'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'pmid': '35987650', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886422), updated_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886422)),\n",
-                            " RemoteFeedbackRecord(id=UUID('bd1e7479-a11e-48f4-9716-8d2bfd3d578b'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'pmid': '35987650', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886426), updated_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886426)),\n",
-                            " RemoteFeedbackRecord(id=UUID('071dfaca-7719-43f6-a102-341f905626ab'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'pmid': '35987650', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886429), updated_at=datetime.datetime(2024, 6, 9, 20, 40, 0, 886429)),\n",
-                            " RemoteFeedbackRecord(id=UUID('1e89a75b-6181-4099-b776-199cbd5d2658'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323669), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323669)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f6baf956-bf8d-4bde-9620-bbf4a13f1f9c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323672), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323672)),\n",
-                            " RemoteFeedbackRecord(id=UUID('73772b4f-fd49-4eb4-a7ec-941ed0f44ab4'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323676), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323676)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f1150462-13d5-4156-8038-1e43268be3ed'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323679), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323679)),\n",
-                            " RemoteFeedbackRecord(id=UUID('22f6a703-064e-499b-8e6a-3e58dea2e3cb'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ITNCondition'}, vectors={}, responses={'ameliabv': []}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323697), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323697)),\n",
-                            " RemoteFeedbackRecord(id=UUID('1ee9ac1c-073d-4419-a3b2-61e45a4b4cd8'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323700), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323700)),\n",
-                            " RemoteFeedbackRecord(id=UUID('52fbcf88-2b38-45f7-8009-95840f3f7a7c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323703), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323703)),\n",
-                            " RemoteFeedbackRecord(id=UUID('11325543-96e0-4834-866c-5021f4d88997'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323707), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323707)),\n",
-                            " RemoteFeedbackRecord(id=UUID('fe766984-57eb-44e6-a129-321768753d8d'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ITNCondition'}, vectors={}, responses={'ameliabv': []}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323710), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323710)),\n",
-                            " RemoteFeedbackRecord(id=UUID('8dc5c719-b0ef-4f24-8b47-e7b24a070883'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323714), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323714)),\n",
-                            " RemoteFeedbackRecord(id=UUID('96e69e8d-0a60-4162-a14b-482529091255'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323717), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323717)),\n",
-                            " RemoteFeedbackRecord(id=UUID('29e87dfc-19b7-4539-9ed8-9c4fd2895f65'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323721), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323721)),\n",
-                            " RemoteFeedbackRecord(id=UUID('22d763c5-b616-486b-ac39-841da4fbad9f'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ITNCondition'}, vectors={}, responses={'ameliabv': []}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323724), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323724)),\n",
-                            " RemoteFeedbackRecord(id=UUID('4eb3a1b3-41cb-4fe2-909b-b348d18caffa'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323727), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323727)),\n",
-                            " RemoteFeedbackRecord(id=UUID('898f9522-3ae5-40c7-9b08-c9bc10ad318a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323731), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323731)),\n",
-                            " RemoteFeedbackRecord(id=UUID('78771d04-3ebc-44b8-a5c1-e86dcb264bca'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323734), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323734)),\n",
-                            " RemoteFeedbackRecord(id=UUID('cbc3d3d5-93fb-4f43-816d-6a3b6d4fb99c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323737), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323737)),\n",
-                            " RemoteFeedbackRecord(id=UUID('c5932a80-5e62-4676-bd2d-ee72b3c2986f'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323741), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323741)),\n",
-                            " RemoteFeedbackRecord(id=UUID('aa210166-345f-42a9-8b20-eeb111b95e41'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323744), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323744)),\n",
-                            " RemoteFeedbackRecord(id=UUID('44418262-04e4-49f9-b1a8-d39ab3ba0db9'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323747), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 323747)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f566c9a9-4f99-464f-b939-b82a0c77e35d'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852517), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852517)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f6f42420-1cb5-4110-9722-5062077e7e41'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852528), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852528)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f417165b-e9fb-44ad-a5a1-1ccd3f3e9a9b'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852531), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852531)),\n",
-                            " RemoteFeedbackRecord(id=UUID('76155a5f-9295-426d-87e4-25bba9eccd7e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852535), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852535)),\n",
-                            " RemoteFeedbackRecord(id=UUID('597ab1b7-637f-46cd-a3c9-adc1ff5636bf'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852539), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852539)),\n",
-                            " RemoteFeedbackRecord(id=UUID('8ffe47bc-f2f8-4b67-8176-602b1e6e9a67'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852543), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852543)),\n",
-                            " RemoteFeedbackRecord(id=UUID('647e504d-dd6f-42a8-a14b-40f1dda20f6a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852546), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852546)),\n",
-                            " RemoteFeedbackRecord(id=UUID('be3ab3df-313b-4429-9f7e-1d75995f12eb'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852550), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852550)),\n",
-                            " RemoteFeedbackRecord(id=UUID('aedb3853-a8fe-4a1b-8f17-6652365ebcf8'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '36726160', 'doi': '10.1111/mve.12316', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852553), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852553)),\n",
-                            " RemoteFeedbackRecord(id=UUID('03511a36-bc9e-47cd-8e88-d50fd9caec55'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '36726160', 'doi': '10.1111/mve.12316', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852556), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852556)),\n",
-                            " RemoteFeedbackRecord(id=UUID('2bf35187-765b-4fb6-8935-4d046584b76e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '36726160', 'doi': '10.1111/mve.12316', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852560), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852560)),\n",
-                            " RemoteFeedbackRecord(id=UUID('59cedb58-c565-4865-b41b-8f3364396d32'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '36726160', 'doi': '10.1111/mve.12316', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852563), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852563)),\n",
-                            " RemoteFeedbackRecord(id=UUID('0c8b02a4-a557-43fc-b1e1-b1d4da6b1f16'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852580), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852580)),\n",
-                            " RemoteFeedbackRecord(id=UUID('042e6118-77b6-442f-b4a8-d950bb6ce975'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852584), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852584)),\n",
-                            " RemoteFeedbackRecord(id=UUID('a0fc80f6-9539-4e45-a5e9-36566f9bfb65'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852587), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852587)),\n",
-                            " RemoteFeedbackRecord(id=UUID('39467d2b-3c6f-441a-95e0-e677ad6c9b25'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852591), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852591)),\n",
-                            " RemoteFeedbackRecord(id=UUID('7f243860-1f22-4ec8-8f5b-baaec90375be'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852595), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852595)),\n",
-                            " RemoteFeedbackRecord(id=UUID('7be3b6ef-0382-43bb-8dd3-206159fe6cb6'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852598), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852598)),\n",
-                            " RemoteFeedbackRecord(id=UUID('5e415fa8-70ca-4527-8072-427aa689d100'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852602), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852602)),\n",
-                            " RemoteFeedbackRecord(id=UUID('f27980e4-aed5-4b23-8714-ac8103dcdf45'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852605), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852605)),\n",
-                            " RemoteFeedbackRecord(id=UUID('365017c6-68b0-4ad3-b8cd-9d3dd270a08c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ITNCondition'}, vectors={}, responses={'ameliabv': []}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852608), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852608)),\n",
-                            " RemoteFeedbackRecord(id=UUID('eeb02640-b086-439b-8e93-85ae70ff147a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852629), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852629)),\n",
-                            " RemoteFeedbackRecord(id=UUID('48dbf463-8539-4edb-a21b-6e7ba62f47ac'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852632), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852632)),\n",
-                            " RemoteFeedbackRecord(id=UUID('b6eba409-42a7-4a5b-8e33-d6a451c50408'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852636), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852636)),\n",
-                            " RemoteFeedbackRecord(id=UUID('3b4ec76a-d392-45cf-9d2d-746bc676a5ee'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852639), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852639)),\n",
-                            " RemoteFeedbackRecord(id=UUID('085c5c49-2f38-4bb2-bfee-1213520d7920'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852643), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852643)),\n",
-                            " RemoteFeedbackRecord(id=UUID('90fcbcb8-5a61-4ab7-8736-a91a1ac316be'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852646), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852646)),\n",
-                            " RemoteFeedbackRecord(id=UUID('cbca3a2b-03dd-49e1-a93e-91b0f6128c6e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852649), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 7, 852649)),\n",
-                            " RemoteFeedbackRecord(id=UUID('5d687132-1687-4bf4-a068-075c0304f7bd'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342445), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342445)),\n",
-                            " RemoteFeedbackRecord(id=UUID('28a85c80-e8d5-4db0-afcf-9a5f7b0e27ac'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342449), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342449)),\n",
-                            " RemoteFeedbackRecord(id=UUID('7b1f689d-c4f0-4033-ad59-76c513708e0c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342453), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342453)),\n",
-                            " RemoteFeedbackRecord(id=UUID('addeba0c-2ad3-4f24-8147-bca72952bcfa'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342457), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342457)),\n",
-                            " RemoteFeedbackRecord(id=UUID('05492554-ba85-496e-89a5-295ac46ec347'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342461), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342461)),\n",
-                            " RemoteFeedbackRecord(id=UUID('59f59129-05b7-42e8-994b-1a0d8e82acba'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342465), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342465)),\n",
-                            " RemoteFeedbackRecord(id=UUID('c4f4731e-a606-4b11-b282-396ab1b8774c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342469), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342469)),\n",
-                            " RemoteFeedbackRecord(id=UUID('077f388e-52ff-4dea-9060-1e5e09667376'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342473), updated_at=datetime.datetime(2024, 6, 21, 7, 25, 8, 342473)),\n",
-                            " RemoteFeedbackRecord(id=UUID('1b8d85b2-0bd8-4481-adc0-6cbe037990ee'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501715), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501715)),\n",
-                            " RemoteFeedbackRecord(id=UUID('9fd5cc7c-0e65-4dfe-b289-2c992b7eac41'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501727), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501727)),\n",
-                            " RemoteFeedbackRecord(id=UUID('6d3d3c20-b243-4421-ba13-a9b66a64caca'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501732), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501732)),\n",
-                            " RemoteFeedbackRecord(id=UUID('69e0d688-189c-4670-8153-88b68e93557a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'accrombessi2023efficacy', 'doc_id': 'e5386539-bd6f-3c8b-ae43-13bceeba7522', 'pmid': '36706778', 'doi': '10.1016/S0140-6736(22)02319-4', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501736), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501736)),\n",
-                            " RemoteFeedbackRecord(id=UUID('c0e71e9e-bf19-4579-9309-f4ee2ba23e55'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501740), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501740)),\n",
-                            " RemoteFeedbackRecord(id=UUID('418e9ec8-da8e-4429-ab38-04b490a7f5e1'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501743), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501743)),\n",
-                            " RemoteFeedbackRecord(id=UUID('fd82e8c8-c5c9-45d3-bc99-8fd6d8094838'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501747), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501747)),\n",
-                            " RemoteFeedbackRecord(id=UUID('18daebe4-cac7-4d7c-acc5-38c0be929d25'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'mieguim2021insights', 'doc_id': '1713f6db-c0fc-31cf-8b0b-7f2484f81187', 'pmid': '33388082', 'doi': '10.1186/s13071-020-04525-0', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501750), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501750)),\n",
-                            " RemoteFeedbackRecord(id=UUID('ff4279bc-6ad7-483c-b641-cbd4c5c3db83'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501754), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501754)),\n",
-                            " RemoteFeedbackRecord(id=UUID('12ee4fcb-873f-40b1-ba03-823cc182b779'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501758), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501758)),\n",
-                            " RemoteFeedbackRecord(id=UUID('736b4754-f8a0-4f4f-8708-efe10c6c66be'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501761), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501761)),\n",
-                            " RemoteFeedbackRecord(id=UUID('3f8b9c27-7dcc-4e54-8591-017bd3559a59'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gebremariam2021evaluation', 'doc_id': 'fdda2d65-54ee-3345-8502-941bccb1dba7', 'pmid': '33488090', 'doi': '10.1177/1178630220974730', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501765), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501765)),\n",
-                            " RemoteFeedbackRecord(id=UUID('8a0a0d89-94a3-4747-a8ae-730c5d8e63bd'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501768), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501768)),\n",
-                            " RemoteFeedbackRecord(id=UUID('7b39984f-0014-4b59-bb1c-d9020d327112'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501772), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501772)),\n",
-                            " RemoteFeedbackRecord(id=UUID('a129a260-aa69-44c2-b451-565a35f8129a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501776), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501776)),\n",
-                            " RemoteFeedbackRecord(id=UUID('324f0810-c815-452c-8a38-266e9e1f0e61'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'gichuki2021bioefficacy', 'doc_id': '4aad902e-7bc0-3d0d-b8d8-622a46a98a20', 'pmid': '34930459', 'doi': '10.1186/s40249-021-00916-2', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501779), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501779)),\n",
-                            " RemoteFeedbackRecord(id=UUID('ef08a6c4-c044-49fb-a48b-a6f41960f8c1'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501786), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501786)),\n",
-                            " RemoteFeedbackRecord(id=UUID('2704a3fd-af5d-4a7f-b060-c888dcbfb75b'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501790), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501790)),\n",
-                            " RemoteFeedbackRecord(id=UUID('a0b538af-387c-4677-8597-989370bb203f'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501793), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501793)),\n",
-                            " RemoteFeedbackRecord(id=UUID('91a3a881-7351-43b9-b85e-479b43a94ff2'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2021which', 'doc_id': '5f74e356-d54f-343e-a1b7-2cf0c3b0be7c', 'pmid': '33507978', 'doi': '10.1371/journal.pone.0245804', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501797), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501797)),\n",
-                            " RemoteFeedbackRecord(id=UUID('a9dd46ba-c7e9-43f4-8083-1b75a968abc9'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501800), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501800)),\n",
-                            " RemoteFeedbackRecord(id=UUID('a0e2cc05-8355-4f1f-b496-bcfc379a0110'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501804), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501804)),\n",
-                            " RemoteFeedbackRecord(id=UUID('985e51fe-baf8-48e2-a121-1125ce697131'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501807), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501807)),\n",
-                            " RemoteFeedbackRecord(id=UUID('ee5808b3-afef-4177-a2fe-6e8f8a36c954'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'zahouli2023small', 'doc_id': '5210dc37-a785-375a-b6bb-47b45bc0b993', 'pmid': '36726160', 'doi': '10.1186/s12936-023-04455-z', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501810), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501810)),\n",
-                            " RemoteFeedbackRecord(id=UUID('d3e4f4cc-89c7-49f9-908f-abe2b8d585e2'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501814), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501814)),\n",
-                            " RemoteFeedbackRecord(id=UUID('bdd777b5-5eaf-4171-9b2e-3f3e32a1f686'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501817), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501817)),\n",
-                            " RemoteFeedbackRecord(id=UUID('db871e36-a76a-4bc5-a7be-73c6901d33f0'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501821), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501821)),\n",
-                            " RemoteFeedbackRecord(id=UUID('e7446fd7-f255-4dc6-b5fa-5bddc4e9227e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'diouf2022evaluation', 'doc_id': 'eeb51aa0-4e1a-3f3b-9ffa-acdb2f073b7d', 'pmid': '35780153', 'doi': '10.1186/s12936-022-04230-6', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501824), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501824)),\n",
-                            " RemoteFeedbackRecord(id=UUID('711830d8-8c0f-4ef5-be68-97586669f58d'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501828), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501828)),\n",
-                            " RemoteFeedbackRecord(id=UUID('4824d0a8-bfd3-41ab-9339-6db1755c2d9c'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501831), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501831)),\n",
-                            " RemoteFeedbackRecord(id=UUID('0a16518f-ca1a-4842-bdca-e4ab0a5c5201'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501835), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501835)),\n",
-                            " RemoteFeedbackRecord(id=UUID('1c0835b1-ed4c-4fba-90c2-12403f7c788a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ibrahim2020exploring', 'doc_id': '986c31ab-a1d8-3384-8982-6d711a821b29', 'pmid': '32331386', 'doi': '10.3390/genes11040454', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501838), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 501838)),\n",
-                            " RemoteFeedbackRecord(id=UUID('986418bc-e5ed-47cd-b592-a11d5452038e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864066), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864066)),\n",
-                            " RemoteFeedbackRecord(id=UUID('cde8d347-7113-42cf-9e68-9013ee3c884a'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864077), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864077)),\n",
-                            " RemoteFeedbackRecord(id=UUID('4c627a19-6c2c-42f1-84d0-da75cf31fda7'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864081), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864081)),\n",
-                            " RemoteFeedbackRecord(id=UUID('b2704587-c488-4c9b-910b-3f199ba03969'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'kibondo2022influence', 'doc_id': '420b8983-dd49-3fa8-b774-e19e6b68671a', 'pmid': '35410250', 'doi': '10.1186/s13071-022-05207-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864085), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864085)),\n",
-                            " RemoteFeedbackRecord(id=UUID('5d2063df-2b7e-4a35-bc77-0e8cf6ac152e'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864089), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864089)),\n",
-                            " RemoteFeedbackRecord(id=UUID('086ce38e-0565-4a53-8667-1cb8cd937779'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864092), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864092)),\n",
-                            " RemoteFeedbackRecord(id=UUID('5b478043-e828-4f9d-9a7b-53352d491df5'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864096), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864096)),\n",
-                            " RemoteFeedbackRecord(id=UUID('c53276fc-b746-473e-9125-091c8cb38685'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'meiwald2022association', 'doc_id': 'bfb47409-3c77-3c03-b9ec-1e89186f3484', 'pmid': '33175129', 'doi': '10.1093/infdis/jiaa699', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864100), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864100)),\n",
-                            " RemoteFeedbackRecord(id=UUID('99870ad8-e779-46b0-b135-94014a14416f'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864103), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864103)),\n",
-                            " RemoteFeedbackRecord(id=UUID('56cc8d09-e590-4d19-a097-79324db974f6'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864107), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864107)),\n",
-                            " RemoteFeedbackRecord(id=UUID('b7cbea5b-551e-4192-9958-cbe614132ad3'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864110), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864110)),\n",
-                            " RemoteFeedbackRecord(id=UUID('239ce31e-8cdd-4535-9def-b8e5c0c57738'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'menze2022experimental', 'doc_id': 'ce46d818-978e-3f40-bc7f-b71a4355c2e7', 'pmid': '35745492', 'doi': '10.3390/pathogens11060638', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864114), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864114)),\n",
-                            " RemoteFeedbackRecord(id=UUID('02cdef32-e8e7-4a11-97e6-5a108d5eda13'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864117), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864117)),\n",
-                            " RemoteFeedbackRecord(id=UUID('e6a7f887-ec67-487b-9f89-e64cedca12d4'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864121), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864121)),\n",
-                            " RemoteFeedbackRecord(id=UUID('5cf2e42e-5494-45db-a1e7-b0431e1b33c1'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864124), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864124)),\n",
-                            " RemoteFeedbackRecord(id=UUID('42e3d90e-cf19-4932-9393-f22753b7d7dc'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'syme2022pyrethroid', 'doc_id': '075adb8e-9994-3cee-af44-523e580e2cea', 'pmid': '35478216', 'doi': '10.1038/s41598-022-10953-y', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864128), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864128)),\n",
-                            " RemoteFeedbackRecord(id=UUID('e05cd5d7-d397-4b77-9f35-198bd56e54c7'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864131), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864131)),\n",
-                            " RemoteFeedbackRecord(id=UUID('d646bbd0-323e-4b20-a568-3be63217e946'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864135), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864135)),\n",
-                            " RemoteFeedbackRecord(id=UUID('0caf1b77-fdb2-48d6-b7dc-47c5a1204174'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864138), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864138)),\n",
-                            " RemoteFeedbackRecord(id=UUID('cc9d7253-6a35-47c0-b316-629149ba2fbc'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'toe2018do', 'doc_id': 'b3de4026-d18f-362f-875c-1c7a1fb6d3b4', 'pmid': '35478216', 'doi': '10.1111/mve.12316', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864142), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864142)),\n",
-                            " RemoteFeedbackRecord(id=UUID('2636f941-01cd-4909-8063-ac2e44408c59'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864145), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864145)),\n",
-                            " RemoteFeedbackRecord(id=UUID('13ec59d6-e6d2-4954-82b8-9270b694ab43'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864148), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864148)),\n",
-                            " RemoteFeedbackRecord(id=UUID('130d0928-c50e-4635-b8cc-b4f19e04fef4'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864152), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864152)),\n",
-                            " RemoteFeedbackRecord(id=UUID('83d29bb0-b450-4c8e-8779-4f74936c7151'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'yewhalaw2022experimental', 'doc_id': 'fbbd582e-2e1e-33d1-bee7-c21641f2027f', 'pmid': '35987650', 'doi': '10.1186/s12936-022-04263-x', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864156), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864156)),\n",
-                            " RemoteFeedbackRecord(id=UUID('0775684e-d548-4b7b-9217-1c4a19d33550'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ITNCondition'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864159), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864159)),\n",
-                            " RemoteFeedbackRecord(id=UUID('6ea06848-c457-4472-b22c-1bf0658c8838'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'Observation'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864163), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864163)),\n",
-                            " RemoteFeedbackRecord(id=UUID('8980230f-e210-452f-bf87-98439adb90ac'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'ClinicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864167), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864167)),\n",
-                            " RemoteFeedbackRecord(id=UUID('b705f5e5-8009-42f8-8e82-a98ce8405eff'), client=<httpx.Client object at 0x316a2f9d0>, fields=['extraction', 'metadata', 'context'], metadata={'reference': 'ngufor2022comparative', 'doc_id': '5f8d4493-ba86-3757-9bce-0ae59d1831f4', 'pmid': '35016676', 'doi': '10.1186/s12936-022-04041-9', 'type': 'EntomologicalOutcome'}, vectors={}, responses={}, suggestions=[], external_id=None, inserted_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864170), updated_at=datetime.datetime(2024, 10, 30, 19, 41, 58, 864170))]"
-                        ]
-                    },
-                    "execution_count": 34,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "delete_records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "74d445d4-19e5-425d-90cb-a34f81483bfc",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "extraction_dataset.records.delete(delete_records)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "66856b02-ff96-4596-8525-b12be976da2e",
-            "metadata": {},
-            "source": [
-                "# Updating existing records"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "3fbbf92a-2d4f-44b2-b007-e60695e73268",
-            "metadata": {},
-            "source": [
-                "### Delete records by reference"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "0783d2e8-9c4d-4f2e-b4f4-94430234d204",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# record_updates = []\n",
-                "# for record in preprocessing_dataset.filter_by(metadata_filters=ex.TermsMetadataFilter(name='reference', values=['whopes2008report'])).records:\n",
-                "#     record_updates.append(record)\n",
-                "# len(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "adfe3ee6-7b95-4c98-8933-bb03293e43b8",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# preprocessing_dataset.records.delete(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "258bb5f0",
-            "metadata": {},
-            "source": [
-                "### Update record metadata for some references"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "c2c048a3",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
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-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Mendeley Reference Key</th>\n",
-                            "      <th>Measured_outcome</th>\n",
-                            "      <th>Approx_num_rows</th>\n",
-                            "      <th>Tables_count</th>\n",
-                            "      <th>Notes_on_approx_num_rows</th>\n",
-                            "      <th>Check_out_by</th>\n",
-                            "      <th>Check_in_by</th>\n",
-                            "      <th>Needs_digitization</th>\n",
-                            "      <th>Notes_on_extraction</th>\n",
-                            "      <th>title</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>issn</th>\n",
-                            "      <th>pmid</th>\n",
-                            "      <th>doi</th>\n",
-                            "      <th>collections</th>\n",
-                            "      <th>file_id</th>\n",
-                            "      <th>mime_type</th>\n",
-                            "      <th>file_name</th>\n",
-                            "      <th>size</th>\n",
-                            "      <th>filehash</th>\n",
-                            "      <th>file_path</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>reference</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>terlouw2010impact</th>\n",
-                            "      <td>Terlouw2010</td>\n",
-                            "      <td>Incidence</td>\n",
-                            "      <td>6</td>\n",
-                            "      <td>4</td>\n",
-                            "      <td>Also a good calibration comparison</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Impact of mass distribution of free long-lasti...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>14752875</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>10.1186/1475-2875-9-199</td>\n",
-                            "      <td>[Human Health Outcomes]</td>\n",
-                            "      <td>6fb4e3d6-9255-4851-569e-a1e5541788a8</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>terlouw2010impact.pdf</td>\n",
-                            "      <td>867692</td>\n",
-                            "      <td>87ee2c18418661d5fe7a1493ce25d9650d7fa45d</td>\n",
-                            "      <td>data/pdf/terlouw2010impact.pdf</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tokponnon2014impact</th>\n",
-                            "      <td>Tokponnon2014</td>\n",
-                            "      <td>Incidence and Prevalence</td>\n",
-                            "      <td>4</td>\n",
-                            "      <td>6</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Impact of long-lasting, insecticidal nets on a...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>14752875</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>10.1186/1475-2875-13-76</td>\n",
-                            "      <td>[Entomological Outcomes, Human Health Outcomes]</td>\n",
-                            "      <td>4f4bb455-b9b2-e3d3-43d2-127e3fd6edf8</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Tokponnon_et_al_2014_Mal_J.pdf</td>\n",
-                            "      <td>1092191</td>\n",
-                            "      <td>58ee5a6fa350ff703f5cf58aeb4f4b5c2398cb85</td>\n",
-                            "      <td>data/pdf/Tokponnon_et_al_2014_Mal_J.pdf</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>pinder2015efficacy</th>\n",
-                            "      <td>Pinder2015</td>\n",
-                            "      <td>Incidence &amp; Prevalence</td>\n",
-                            "      <td>~10</td>\n",
-                            "      <td>2,4</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Josh: this study is comparing ITN to ITN+IRS. ...</td>\n",
-                            "      <td>Efficacy of indoor residual spraying with dich...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1474-547X (Electronic)</td>\n",
-                            "      <td>25498847</td>\n",
-                            "      <td>10.1016/S0140-6736(14)61007-2</td>\n",
-                            "      <td>[Human Health Outcomes]</td>\n",
-                            "      <td>75f3a17c-758e-e557-1418-91e3c2272c14</td>\n",
-                            "      <td>application/pdf</td>\n",
-                            "      <td>Pinder_et_al_2015_Lancet.pdf</td>\n",
-                            "      <td>597394</td>\n",
-                            "      <td>6ef33d0382608d35470c5d4ac5e3ffc7e6e7f242</td>\n",
-                            "      <td>data/pdf/Pinder_et_al_2015_Lancet.pdf</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>3 rows × 32 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                    Mendeley Reference Key          Measured_outcome  \\\n",
-                            "reference                                                              \n",
-                            "terlouw2010impact              Terlouw2010                 Incidence   \n",
-                            "tokponnon2014impact          Tokponnon2014  Incidence and Prevalence   \n",
-                            "pinder2015efficacy              Pinder2015    Incidence & Prevalence   \n",
-                            "\n",
-                            "                    Approx_num_rows Tables_count  \\\n",
-                            "reference                                          \n",
-                            "terlouw2010impact                 6            4   \n",
-                            "tokponnon2014impact               4            6   \n",
-                            "pinder2015efficacy              ~10          2,4   \n",
-                            "\n",
-                            "                               Notes_on_approx_num_rows Check_out_by  \\\n",
-                            "reference                                                              \n",
-                            "terlouw2010impact    Also a good calibration comparison         None   \n",
-                            "tokponnon2014impact                                None         None   \n",
-                            "pinder2015efficacy                                 None         None   \n",
-                            "\n",
-                            "                    Check_in_by Needs_digitization  \\\n",
-                            "reference                                            \n",
-                            "terlouw2010impact          None               None   \n",
-                            "tokponnon2014impact        None               None   \n",
-                            "pinder2015efficacy         None               None   \n",
-                            "\n",
-                            "                                                   Notes_on_extraction  \\\n",
-                            "reference                                                                \n",
-                            "terlouw2010impact                                                 None   \n",
-                            "tokponnon2014impact                                               None   \n",
-                            "pinder2015efficacy   Josh: this study is comparing ITN to ITN+IRS. ...   \n",
-                            "\n",
-                            "                                                                 title  ...  \\\n",
-                            "reference                                                               ...   \n",
-                            "terlouw2010impact    Impact of mass distribution of free long-lasti...  ...   \n",
-                            "tokponnon2014impact  Impact of long-lasting, insecticidal nets on a...  ...   \n",
-                            "pinder2015efficacy   Efficacy of indoor residual spraying with dich...  ...   \n",
-                            "\n",
-                            "                                       issn      pmid  \\\n",
-                            "reference                                               \n",
-                            "terlouw2010impact                  14752875      None   \n",
-                            "tokponnon2014impact                14752875      None   \n",
-                            "pinder2015efficacy   1474-547X (Electronic)  25498847   \n",
-                            "\n",
-                            "                                               doi  \\\n",
-                            "reference                                            \n",
-                            "terlouw2010impact          10.1186/1475-2875-9-199   \n",
-                            "tokponnon2014impact        10.1186/1475-2875-13-76   \n",
-                            "pinder2015efficacy   10.1016/S0140-6736(14)61007-2   \n",
-                            "\n",
-                            "                                                         collections  \\\n",
-                            "reference                                                              \n",
-                            "terlouw2010impact                            [Human Health Outcomes]   \n",
-                            "tokponnon2014impact  [Entomological Outcomes, Human Health Outcomes]   \n",
-                            "pinder2015efficacy                           [Human Health Outcomes]   \n",
-                            "\n",
-                            "                                                  file_id        mime_type  \\\n",
-                            "reference                                                                    \n",
-                            "terlouw2010impact    6fb4e3d6-9255-4851-569e-a1e5541788a8  application/pdf   \n",
-                            "tokponnon2014impact  4f4bb455-b9b2-e3d3-43d2-127e3fd6edf8  application/pdf   \n",
-                            "pinder2015efficacy   75f3a17c-758e-e557-1418-91e3c2272c14  application/pdf   \n",
-                            "\n",
-                            "                                          file_name     size  \\\n",
-                            "reference                                                      \n",
-                            "terlouw2010impact             terlouw2010impact.pdf   867692   \n",
-                            "tokponnon2014impact  Tokponnon_et_al_2014_Mal_J.pdf  1092191   \n",
-                            "pinder2015efficacy     Pinder_et_al_2015_Lancet.pdf   597394   \n",
-                            "\n",
-                            "                                                     filehash  \\\n",
-                            "reference                                                       \n",
-                            "terlouw2010impact    87ee2c18418661d5fe7a1493ce25d9650d7fa45d   \n",
-                            "tokponnon2014impact  58ee5a6fa350ff703f5cf58aeb4f4b5c2398cb85   \n",
-                            "pinder2015efficacy   6ef33d0382608d35470c5d4ac5e3ffc7e6e7f242   \n",
-                            "\n",
-                            "                                                   file_path  \n",
-                            "reference                                                     \n",
-                            "terlouw2010impact             data/pdf/terlouw2010impact.pdf  \n",
-                            "tokponnon2014impact  data/pdf/Tokponnon_et_al_2014_Mal_J.pdf  \n",
-                            "pinder2015efficacy     data/pdf/Pinder_et_al_2015_Lancet.pdf  \n",
-                            "\n",
-                            "[3 rows x 32 columns]"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers.loc[references]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "5c2f9768",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "references = ['terlouw2010impact', 'tokponnon2014impact', 'pinder2015efficacy']\n",
-                "\n",
-                "record_updates = []\n",
-                "for record in tqdm(extraction_dataset.filter_by(\n",
-                "        metadata_filters=ex.TermsMetadataFilter(name='reference', values=references)).records):\n",
-                "    reference = record.metadata['reference']\n",
-                "    schema_name = record.metadata['type']\n",
-                "\n",
-                "    if record.metadata['pmid'] != papers.loc[reference].get('pmid'):\n",
-                "        record.metadata['pmid'] = papers.loc[reference].get('pmid')\n",
-                "        print(\"Check it's correct: \", record.metadata)\n",
-                "        record_updates.append(record)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "447421bf",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "extraction_dataset.records.update(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "370e79a8-9ed3-498e-96a0-d40ef64c3149",
-            "metadata": {},
-            "source": [
-                "### Update Document pmid, doi, and doc_id references"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "797f547a-9615-4949-a3e7-f5f026dc8108",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "record_updates = []\n",
-                "for record in papers_dataset.records:\n",
-                "    if len(record_updates) == 1: \n",
-                "        print(record.metadata)\n",
-                "    reference = record.metadata['reference']\n",
-                "\n",
-                "    doc_id = papers.loc[reference, 'id']\n",
-                "    if 'doc_id' not in record.metadata or record.metadata['doc_id'] != doc_id:\n",
-                "        record.metadata['doc_id'] = doc_id\n",
-                "    \n",
-                "    record_updates.append(record)\n",
-                "len(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "bac96980-eea4-4f28-a87a-f0450b6bb4c7",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "papers_dataset.records.update(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "eadd7331-49b2-401c-8a11-1f8484ceb6cc",
-            "metadata": {
-                "scrolled": true
-            },
-            "source": [
-                "# Create the `2-Data-Extractions` dataset, if it isn't already created"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c4a8f662-9adc-4286-8ba9-6197d0dc1e83",
-            "metadata": {},
-            "source": [
-                "### Extraction dataset"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "45caae42-8efa-4ef3-a9f5-5600a470667b",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[06/20/24 16:29:19] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> INFO:argilla.client.feedback.dataset.local.mixins:✓ Dataset succesfully  <a href=\"file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">mixins.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py#306\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">306</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         pushed to Extralit                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m[06/20/24 16:29:19]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m INFO:argilla.client.feedback.dataset.local.mixins:✓ Dataset succesfully  \u001b]8;id=762461;file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py\u001b\\\u001b[2mmixins.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=533519;file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py#306\u001b\\\u001b[2m306\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m         pushed to Extralit                                                        \u001b[2m             \u001b[0m\n"
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-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> INFO:argilla.client.feedback.dataset.local.mixins:<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteFeedbackDataset</span><span style=\"font-weight: bold\">(</span> <a href=\"file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">mixins.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py#307\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">307</span></a>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #ffff00; text-decoration-color: #ffff00\">8c51f5e3</span><span style=\"color: #ffff00; text-decoration-color: #ffff00\">-25bb-4141-b716-04ce92b208e2</span>                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>-Data-Extractions                                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">workspace</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Workspace</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #ffff00; text-decoration-color: #ffff00\">45eab76f</span><span style=\"color: #ffff00; text-decoration-color: #ffff00\">-4d5a-4fb9-a9d2-6e7323bdf08a</span>,          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #800080; text-decoration-color: #800080\">itn</span>-recalibration-scratch, <span style=\"color: #808000; text-decoration-color: #808000\">inserted_at</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">06</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">23:21:44</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">734369</span>,  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">updated_at</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">06</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">23:21:44</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">734369</span><span style=\"font-weight: bold\">)</span>                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">url</span>=<span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">http://10.24.49.60/dataset/8c51f5e3-25bb-4141-b716-04ce92b208e2/a</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">nnotation-mode</span>                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">fields</span>=<span style=\"font-weight: bold\">[</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTextField</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'7e322bdd-4a02-476d-994b-f9e22b879e8f</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">client</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'metadata'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">title</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Extraction steps:'</span>,             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">required</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">use_markdown</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">use_table</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"font-weight: bold\">)</span>,        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTextField</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'62cc925a-c5c6-4446-8dd8-8c75dcfb4256'</span><span style=\"font-weight: bold\">)</span>,         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">client</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'extraction'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">title</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Extracted data:'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">required</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">use_markdown</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">use_table</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>,                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTextField</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'3ac56bd6-a5b3-4b53-9882-92db06c58faa'</span><span style=\"font-weight: bold\">)</span>,         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">client</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'context'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">title</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Relevant attributions (predicted by </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">RAG):'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">required</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">use_markdown</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">use_table</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"font-weight: bold\">)]</span>                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">questions</span>=<span style=\"font-weight: bold\">[</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteMultiLabelQuestion</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'ca859c1e-113f-4afc-a116-</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">ebb867138f39'</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">client</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'context-relevant'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">title</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Which of </span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">the document section(s) attributed to this data extraction table?'</span>,      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">description</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Please identify which section in the source PDF the data </span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">extract came from, and select the matching section header(s) in this </span>    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">multi-selection list.'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">required</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'multi_label_selection'</span>,     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">labels</span>=<span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'Not listed'</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #808000; text-decoration-color: #808000\">visible_labels</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">labels_order</span>=<span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">LabelsOrder.natural:</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'natural'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteMultiLabelQuestion</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'c709ffd5-bcba-4366-a46d-40bf31c81da1'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'extraction-source'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Where did the extracted </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">data primarily came from?'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">description</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">required</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'multi_label_selection'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">labels</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'Text'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #008000; text-decoration-color: #008000\">'Table'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #008000; text-decoration-color: #008000\">'Figure'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">]</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">visible_labels</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">labels_order</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;LabelsOrder.natural: </span><span style=\"color: #008000; text-decoration-color: #008000\">'natural'</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTextQuestion</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'3e53c996-6d44-4e7c-ae6e-4cb96c029ad2'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'extraction-correction'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Provide a correction </span>  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">to the extracted data:'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">description</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">required</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'text'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">use_markdown</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">use_table</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTextQuestion</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'b1f3e200-a437-42b5-9db1-45857dac1473'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'notes'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Mention any notes here to record any </span>  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">special case or to justify decisions made in the extraction.'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">description</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">required</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'text'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">use_markdown</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">use_table</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)]</span>                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #000000; text-decoration-color: #000000\">   </span><span style=\"color: #808000; text-decoration-color: #808000\">documents</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #000000; text-decoration-color: #000000\">   </span><span style=\"color: #808000; text-decoration-color: #808000\">guidelines</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>                                                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #000000; text-decoration-color: #000000\">   </span><span style=\"color: #808000; text-decoration-color: #808000\">metadata_properties</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">[</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTermsMetadataProperty</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'b442012c-57</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">63-4615-b759-094487f245e3'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;httpx.Client object at </span>             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x31d2272b0</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'reference'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Reference'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">visible_for_annotators</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'terms'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">values</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTermsMetadataProperty</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'ddcf9932-2602-46cf-bc59-0d9924d7adf</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">0'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;httpx.Client object at </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x31d2272b0</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'type'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Question Type'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">visible_for_annotators</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'terms'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">values</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTermsMetadataProperty</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'46931ecd-1853-44a0-8b34-a15f332531c</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">3'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;httpx.Client object at </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x31d2272b0</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'pmid'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Document Pubmed ID'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">visible_for_annotators</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'terms'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">values</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTermsMetadataProperty</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'a2e6215a-ea19-42bc-a0f4-60ec86b4a88</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">b'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;httpx.Client object at </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x31d2272b0</span><span style=\"color: #000000; text-decoration-color: #000000\">&gt;, </span><span style=\"color: #808000; text-decoration-color: #808000\">name</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'doc_id'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">title</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Document ID'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">visible_for_annotators</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'terms'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">values</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span>                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RemoteTermsMetadataProperty</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">id</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UUID</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'5499c947-0e23-4c70-8884-9ae73e8811e</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">8'</span><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">)</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">client</span><span style=\"color: #000000; text-decoration-color: #000000\">=&lt;httpx.Client object at </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x31d2272b0</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'annotators'</span>,     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">title</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Annotators'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">visible_for_annotators</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'terms'</span>,           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #808000; text-decoration-color: #808000\">values</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)]</span>                                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>            <span style=\"color: #808000; text-decoration-color: #808000\">vectors_settings</span>=<span style=\"font-weight: bold\">[]</span>                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"font-weight: bold\">)</span>                                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m INFO:argilla.client.feedback.dataset.local.mixins:\u001b[1;35mRemoteFeedbackDataset\u001b[0m\u001b[1m(\u001b[0m \u001b]8;id=698971;file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py\u001b\\\u001b[2mmixins.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=347992;file:///Users/jonnytr/Projects/argilla-data-extraction/argilla/src/argilla/client/feedback/dataset/local/mixins.py#307\u001b\\\u001b[2m307\u001b[0m\u001b]8;;\u001b\\\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mid\u001b[0m=\u001b[93m8c51f5e3\u001b[0m\u001b[93m-25bb-4141-b716-04ce92b208e2\u001b[0m                               \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mname\u001b[0m=\u001b[1;36m2\u001b[0m-Data-Extractions                                               \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mworkspace\u001b[0m=\u001b[1;35mWorkspace\u001b[0m\u001b[1m(\u001b[0m\u001b[33mid\u001b[0m=\u001b[93m45eab76f\u001b[0m\u001b[93m-4d5a-4fb9-a9d2-6e7323bdf08a\u001b[0m,          \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mname\u001b[0m=\u001b[35mitn\u001b[0m-recalibration-scratch, \u001b[33minserted_at\u001b[0m=\u001b[1;36m2024\u001b[0m-\u001b[1;36m06\u001b[0m-\u001b[1;36m20\u001b[0m \u001b[1;92m23:21:44\u001b[0m.\u001b[1;36m734369\u001b[0m,  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mupdated_at\u001b[0m=\u001b[1;36m2024\u001b[0m-\u001b[1;36m06\u001b[0m-\u001b[1;36m20\u001b[0m \u001b[1;92m23:21:44\u001b[0m.\u001b[1;36m734369\u001b[0m\u001b[1m)\u001b[0m                                   \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33murl\u001b[0m=\u001b[4;94mhttp\u001b[0m\u001b[4;94m://10.24.49.60/dataset/8c51f5e3-25bb-4141-b716-04ce92b208e2/a\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[4;94mnnotation-mode\u001b[0m                                                           \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mfields\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mRemoteTextField\u001b[0m\u001b[1m(\u001b[0m\u001b[33mid\u001b[0m=\u001b[1;35mUUID\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'7e322bdd-4a02-476d-994b-f9e22b879e8f\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32m'\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mclient\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'metadata'\u001b[0m, \u001b[33mtitle\u001b[0m=\u001b[32m'Extraction steps:'\u001b[0m,             \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mrequired\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'text'\u001b[0m, \u001b[33muse_markdown\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33muse_table\u001b[0m=\u001b[3;91mFalse\u001b[0m\u001b[1m)\u001b[0m,        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTextField\u001b[0m\u001b[1m(\u001b[0m\u001b[33mid\u001b[0m=\u001b[1;35mUUID\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'62cc925a-c5c6-4446-8dd8-8c75dcfb4256'\u001b[0m\u001b[1m)\u001b[0m,         \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mclient\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'extraction'\u001b[0m, \u001b[33mtitle\u001b[0m=\u001b[32m'Extracted data:'\u001b[0m, \u001b[33mrequired\u001b[0m=\u001b[3;92mTrue\u001b[0m,  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtype\u001b[0m=\u001b[32m'text'\u001b[0m, \u001b[33muse_markdown\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33muse_table\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,                        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTextField\u001b[0m\u001b[1m(\u001b[0m\u001b[33mid\u001b[0m=\u001b[1;35mUUID\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'3ac56bd6-a5b3-4b53-9882-92db06c58faa'\u001b[0m\u001b[1m)\u001b[0m,         \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mclient\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'context'\u001b[0m, \u001b[33mtitle\u001b[0m=\u001b[32m'Relevant attributions \u001b[0m\u001b[32m(\u001b[0m\u001b[32mpredicted by \u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mRAG\u001b[0m\u001b[32m)\u001b[0m\u001b[32m:'\u001b[0m, \u001b[33mrequired\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'text'\u001b[0m, \u001b[33muse_markdown\u001b[0m=\u001b[3;92mTrue\u001b[0m,                  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33muse_table\u001b[0m=\u001b[3;91mFalse\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m                                                        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mquestions\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mRemoteMultiLabelQuestion\u001b[0m\u001b[1m(\u001b[0m\u001b[33mid\u001b[0m=\u001b[1;35mUUID\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'ca859c1e-113f-4afc-a116-\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mebb867138f39'\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mclient\u001b[0m=\u001b[3;35mNone\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'context-relevant'\u001b[0m, \u001b[33mtitle\u001b[0m=\u001b[32m'Which of \u001b[0m   \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mthe document section\u001b[0m\u001b[32m(\u001b[0m\u001b[32ms\u001b[0m\u001b[32m)\u001b[0m\u001b[32m attributed to this data extraction table?'\u001b[0m,      \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mdescription\u001b[0m=\u001b[32m'Please identify which section in the source PDF the data \u001b[0m   \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mextract came from, and select the matching section header\u001b[0m\u001b[32m(\u001b[0m\u001b[32ms\u001b[0m\u001b[32m)\u001b[0m\u001b[32m in this \u001b[0m    \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mmulti-selection list.'\u001b[0m, \u001b[33mrequired\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'multi_label_selection'\u001b[0m,     \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mlabels\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'Not listed'\u001b[0m\u001b[1m]\u001b[0m, \u001b[33mvisible_labels\u001b[0m=\u001b[3;35mNone\u001b[0m,                              \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mlabels_order\u001b[0m=\u001b[1m<\u001b[0m\u001b[1;95mLabelsOrder.natural:\u001b[0m\u001b[39m \u001b[0m\u001b[32m'natural'\u001b[0m\u001b[39m>\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                         \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteMultiLabelQuestion\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'c709ffd5-bcba-4366-a46d-40bf31c81da1'\u001b[0m\u001b[1;39m)\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'extraction-source'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Where did the extracted \u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mdata primarily came from?'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mdescription\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mrequired\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m, \u001b[0m            \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'multi_label_selection'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mlabels\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m[\u001b[0m\u001b[32m'Text'\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'Table'\u001b[0m\u001b[39m, \u001b[0m\u001b[32m'Figure'\u001b[0m\u001b[1;39m]\u001b[0m\u001b[39m, \u001b[0m       \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvisible_labels\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mlabels_order\u001b[0m\u001b[39m=<LabelsOrder.natural: \u001b[0m\u001b[32m'natural'\u001b[0m\u001b[39m>\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m    \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTextQuestion\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'3e53c996-6d44-4e7c-ae6e-4cb96c029ad2'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m     \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mclient\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'extraction-correction'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Provide a correction \u001b[0m  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mto the extracted data:'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mdescription\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mrequired\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'text'\u001b[0m\u001b[39m, \u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33muse_markdown\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m, \u001b[0m\u001b[33muse_table\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                                    \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTextQuestion\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'b1f3e200-a437-42b5-9db1-45857dac1473'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m     \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mclient\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'notes'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Mention any notes here to record any \u001b[0m  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mspecial case or to justify decisions made in the extraction.'\u001b[0m\u001b[39m, \u001b[0m          \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mdescription\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[39m, \u001b[0m\u001b[33mrequired\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'text'\u001b[0m\u001b[39m, \u001b[0m\u001b[33muse_markdown\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m, \u001b[0m       \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33muse_table\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1;39m]\u001b[0m                                                        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[39m   \u001b[0m\u001b[33mdocuments\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m                                                        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[39m   \u001b[0m\u001b[33mguidelines\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m                                                       \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[39m   \u001b[0m\u001b[33mmetadata_properties\u001b[0m\u001b[39m=\u001b[0m\u001b[1;39m[\u001b[0m\u001b[1;35mRemoteTermsMetadataProperty\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'b442012c-57\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32m63-4615-b759-094487f245e3'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=<httpx.Client object at \u001b[0m             \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;36m0x31d2272b0\u001b[0m\u001b[39m>, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'reference'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Reference'\u001b[0m\u001b[39m, \u001b[0m                      \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvisible_for_annotators\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'terms'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mvalues\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTermsMetadataProperty\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'ddcf9932-2602-46cf-bc59-0d9924d7adf\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32m0'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=<httpx.Client object at \u001b[0m\u001b[1;36m0x31d2272b0\u001b[0m\u001b[39m>, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'type'\u001b[0m\u001b[39m, \u001b[0m          \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Question Type'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mvisible_for_annotators\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'terms'\u001b[0m\u001b[39m, \u001b[0m       \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvalues\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                                                           \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTermsMetadataProperty\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'46931ecd-1853-44a0-8b34-a15f332531c\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32m3'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=<httpx.Client object at \u001b[0m\u001b[1;36m0x31d2272b0\u001b[0m\u001b[39m>, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'pmid'\u001b[0m\u001b[39m, \u001b[0m          \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Document Pubmed ID'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mvisible_for_annotators\u001b[0m\u001b[39m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'terms'\u001b[0m\u001b[39m, \u001b[0m  \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvalues\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                                                           \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTermsMetadataProperty\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'a2e6215a-ea19-42bc-a0f4-60ec86b4a88\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32mb'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=<httpx.Client object at \u001b[0m\u001b[1;36m0x31d2272b0\u001b[0m\u001b[39m>, \u001b[0m\u001b[33mname\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'doc_id'\u001b[0m\u001b[39m, \u001b[0m        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtitle\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Document ID'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mvisible_for_annotators\u001b[0m\u001b[39m=\u001b[0m\u001b[3;91mFalse\u001b[0m\u001b[39m, \u001b[0m\u001b[33mtype\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'terms'\u001b[0m\u001b[39m, \u001b[0m        \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvalues\u001b[0m\u001b[39m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m                                                           \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1;35mRemoteTermsMetadataProperty\u001b[0m\u001b[1;39m(\u001b[0m\u001b[33mid\u001b[0m\u001b[39m=\u001b[0m\u001b[1;35mUUID\u001b[0m\u001b[1;39m(\u001b[0m\u001b[32m'5499c947-0e23-4c70-8884-9ae73e8811e\u001b[0m \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[32m8'\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m, \u001b[0m\u001b[33mclient\u001b[0m\u001b[39m=<httpx.Client object at \u001b[0m\u001b[1;36m0x31d2272b0\u001b[0m\u001b[1m>\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m'annotators'\u001b[0m,     \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mtitle\u001b[0m=\u001b[32m'Annotators'\u001b[0m, \u001b[33mvisible_for_annotators\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtype\u001b[0m=\u001b[32m'terms'\u001b[0m,           \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[33mvalues\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m                                                            \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m            \u001b[33mvectors_settings\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m                                                   \u001b[2m             \u001b[0m\n",
-                            "\u001b[2;36m                    \u001b[0m         \u001b[1m)\u001b[0m                                                                        \u001b[2m             \u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# ex.Workspace.create('llm_extraction')\n",
-                "\n",
-                "llm_extraction_dataset = ex.FeedbackDataset(\n",
-                "    fields=[\n",
-                "        ex.TextField(name=\"metadata\", title=\"Extraction steps:\", required=False, use_markdown=True),\n",
-                "        ex.TextField(name=\"extraction\", title=\"Extracted data:\", required=True, use_table=True),\n",
-                "        ex.TextField(name=\"context\", title=\"Relevant attributions (predicted by RAG):\", required=False, use_markdown=True),\n",
-                "    ],\n",
-                "    questions =[\n",
-                "        ex.MultiLabelQuestion(\n",
-                "            name=\"context-relevant\",\n",
-                "            title=\"Which of the document section(s) attributed to this data extraction table?\",\n",
-                "            description=\"Please identify which section in the source PDF the data extract came from, and select the matching section header(s) in this multi-selection list.\",\n",
-                "            type=\"dynamic_multi_label_selection\",\n",
-                "            labels=['Not listed'],\n",
-                "            required=True,\n",
-                "        ),\n",
-                "        ex.MultiLabelQuestion(\n",
-                "            name=\"extraction-source\",\n",
-                "            title=\"Where did the extracted data primarily came from?\",\n",
-                "            labels=[\"Text\", \"Table\", \"Figure\"],\n",
-                "            required=True,\n",
-                "        ),\n",
-                "        ex.TextQuestion(\n",
-                "            name=\"extraction-correction\",\n",
-                "            title=\"Provide a correction to the extracted data:\",\n",
-                "            required=False,\n",
-                "            use_table=True,\n",
-                "        ),\n",
-                "        ex.TextQuestion(\n",
-                "            name=\"notes\",\n",
-                "            title=\"Mention any notes here to record any special case or to justify decisions made in the extraction.\",\n",
-                "            required=False,\n",
-                "            use_markdown=True,\n",
-                "        ),\n",
-                "    ],\n",
-                "    metadata_properties=[\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"reference\",\n",
-                "            title=\"Reference\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"type\",\n",
-                "            title=\"Question Type\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"pmid\",\n",
-                "            title=\"Document Pubmed ID\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"doc_id\",\n",
-                "            title=\"Document ID\",\n",
-                "            visible_for_annotators=False),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"annotators\",\n",
-                "            title=\"Annotators\",\n",
-                "            visible_for_annotators=True),\n",
-                "    ],\n",
-                ")\n",
-                "\n",
-                "llm_extraction_dataset = llm_extraction_dataset.push_to_argilla(\n",
-                "    name=\"2-Data-Extractions\", \n",
-                "    workspace=\"itn-recalibration-scratch\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "041844f4-8e44-4319-8f34-5aad09437bb2",
-            "metadata": {},
-            "source": [
-                "### Review dataset"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "5cccb19c-3af5-401f-852a-0e25b8d33514",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "review_dataset = ex.FeedbackDataset(\n",
-                "    fields=[\n",
-                "        ex.TextField(name=\"metadata\", title=\"Metadata:\", required=False, use_markdown=True),\n",
-                "        ex.TextField(name=\"extractions\", title=\"Extractions:\", required=True, use_markdown=True),\n",
-                "        ex.TextField(name=\"metrics\", title=\"Evaluation metrics:\", required=False, use_markdown=True),\n",
-                "        ex.TextField(name=\"alignment\", title=\"GriTS table alignment between gold-standard and LLM+Manual extractions, showing the longest common sequence (LCS) between matched data cells that were used to calculate the Precision and Recall\", \n",
-                "                     required=False, use_markdown=True),\n",
-                "    ],\n",
-                "    questions =[\n",
-                "        ex.LabelQuestion(\n",
-                "            name=\"context-relevant\",\n",
-                "            title=\"Did the evaluation metrics correspond qualitatively with the concordance between the gold-standard and putative extractions?\",\n",
-                "            labels=['Yes', 'No'],\n",
-                "            required=True,\n",
-                "        ),\n",
-                "        ex.TextQuestion(\n",
-                "            name=\"notes\",\n",
-                "            title=\"Mention any notes:\",\n",
-                "            required=False,\n",
-                "            use_markdown=True,\n",
-                "        ),\n",
-                "    ],\n",
-                "    metadata_properties=[\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"reference\",\n",
-                "            title=\"Reference\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"type\",\n",
-                "            title=\"Question Type\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"pmid\",\n",
-                "            title=\"Document Pubmed ID\",\n",
-                "            visible_for_annotators=True),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"doc_id\",\n",
-                "            title=\"Document ID\",\n",
-                "            visible_for_annotators=False),\n",
-                "        ex.TermsMetadataProperty(\n",
-                "            name=\"annotators\",\n",
-                "            title=\"Annotators\",\n",
-                "            visible_for_annotators=True),\n",
-                "    ],\n",
-                ")\n",
-                "\n",
-                "review_dataset = review_dataset.push_to_argilla(\n",
-                "    name=\"Review-Evaluations\", \n",
-                "    workspace=\"auto-extraction\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f4b95e87-74c2-493c-8fb7-59b86e334571",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "test_dataset = ex.FeedbackDataset(\n",
-                "    fields=[\n",
-                "        ex.TextField(name=\"alignment\",\n",
-                "                     required=True, use_markdown=True),\n",
-                "    ],\n",
-                "    questions =[\n",
-                "        ex.LabelQuestion(\n",
-                "            name=\"context-relevant\",\n",
-                "            title=\"Did the evaluation metrics correspond qualitatively with the concordance between the gold-standard and putative extractions?\",\n",
-                "            labels=['Yes', 'No'],\n",
-                "            type='dynamic_label_selection', visible_labels=3,\n",
-                "            required=True,\n",
-                "        ),\n",
-                "        ex.MultiLabelQuestion(\n",
-                "            name=\"segment\",\n",
-                "            title=\"Test\",\n",
-                "            type='dynamic_multi_label_selection', visible_labels=3,\n",
-                "            labels=[],\n",
-                "            required=True,\n",
-                "        ),\n",
-                "        ex.TextQuestion(\n",
-                "            name=\"notes\",\n",
-                "            title=\"Mention any notes:\",\n",
-                "            required=False,\n",
-                "            use_markdown=True,\n",
-                "        ),\n",
-                "    ],\n",
-                ")\n",
-                "\n",
-                "test_dataset = test_dataset.push_to_argilla(\n",
-                "    name=\"Test\", \n",
-                "    workspace=\"auto-extraction\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "86da8a0d-b2b6-4355-b8cd-dac7f33a79db",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/examples/document_extraction/PDF_preprocessing.ipynb b/examples/document_extraction/PDF_preprocessing.ipynb
deleted file mode 100644
index 7420b41c3..000000000
--- a/examples/document_extraction/PDF_preprocessing.ipynb
+++ /dev/null
@@ -1,2908 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "f089250a-b770-4cd2-a7ac-9dcc4a42775b",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "%load_ext autoreload\n",
-                "%autoreload 2\n",
-                "# %load_ext heat"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "e21edbd0-c479-48db-b53d-becdf0cd060b",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "# %%heat\n",
-                "from collections import defaultdict, Counter\n",
-                "import requests, json, rich, re, os, sys, unicodedata, io, hashlib, glob, collections\n",
-                "from IPython.display import IFrame, HTML, Image, JSON\n",
-                "from functools import partial\n",
-                "from tqdm.auto import tqdm\n",
-                "from pathlib import Path\n",
-                "from dotenv import load_dotenv\n",
-                "load_dotenv()\n",
-                "# sys.path.insert(0, 'src')\n",
-                "\n",
-                "import plotly.express as px\n",
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "\n",
-                "import extralit as ex\n",
-                "from extralit.client.feedback.schemas.enums import ResponseStatusFilter\n",
-                "from extralit.client.feedback.integrations.textdescriptives import TextDescriptivesExtractor\n",
-                "from extralit.client.feedback.utils import create_token_highlights, audio_to_html, image_to_html, video_to_html\n",
-                "from extralit.client.feedback.utils import assign_records, assign_workspaces\n",
-                "\n",
-                "from pdf2image import convert_from_path\n",
-                "\n",
-                "import deepdoctection as dd\n",
-                "from unstructured.staging.argilla import stage_for_argilla\n",
-                "from llmsherpa.readers import LayoutPDFReader\n",
-                "\n",
-                "from extralit.extraction.models.paper import PaperExtraction, SchemaStructure\n",
-                "from extralit.preprocessing.methods import unstructured as unstructured \n",
-                "from extralit.preprocessing.methods import llmsherpa as llmsherpa\n",
-                "from extralit.preprocessing.methods import nougat as nougat\n",
-                "from extralit.preprocessing.segment import Segments, FigureSegment\n",
-                "from extralit.preprocessing.merge_segments import merge_extractions\n",
-                "from extralit.preprocessing.document import *\n",
-                "from extralit.convert.json_table import json_to_df, df_to_json\n",
-                "from extralit.convert.html_table import html_table_to_json\n",
-                "from extralit.convert.text import remove_markdown_from_string\n",
-                "from extralit.pipeline.ingest.record import get_record_data\n",
-                "from extralit.metrics.extraction import grits_from_pandas, grits_multi_tables\n",
-                "\n",
-                "from extralit.server.context.vectordb import get_weaviate_client\n",
-                "\n",
-                "os.environ[\"PYTORCH_MPS_HIGH_WATERMARK_RATIO\"] = \"0.0\"\n",
-                "pd.set_option('display.max_rows', 100)\n",
-                "pd.set_option('display.width', 1500)\n",
-                "pd.set_option('max_colwidth', 100)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "cb9cbd73-436b-4a54-8d20-4fa8bc6abc22",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/var/folders/yg/6jm_z4c91d30k5l7hm3s7h580000gp/T/ipykernel_75182/2468869230.py:9: ResourceWarning: unclosed <socket.socket fd=78, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('172.16.0.2', 64655), raddr=('10.24.49.73', 80)>\n",
-                        "  weaviate_client = get_weaviate_client()\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import weaviate\n",
-                "from extralit.extraction.vector_store import WeaviateVectorStore\n",
-                "from llama_index.core.vector_stores import (\n",
-                "    MetadataFilter,\n",
-                "    MetadataFilters,\n",
-                "    FilterOperator, ExactMatchFilter,\n",
-                ")\n",
-                "\n",
-                "weaviate_client = get_weaviate_client()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "9797f734-e7cc-4dd7-87ef-7c66172d31e9",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from llama_index.core import set_global_handler, Settings, callbacks, PromptTemplate\n",
-                "### comment out langfuse code \n",
-                "# import langfuse\n",
-                "# from langfuse.llama_index import LlamaIndexCallbackHandler\n",
-                "\n",
-                "# langfuse_callback_handler = LlamaIndexCallbackHandler(\n",
-                "#     public_key=os.environ['LANGFUSE_PUBLIC_KEY'],\n",
-                "#     secret_key=os.environ['LANGFUSE_SECRET_KEY'],\n",
-                "#     host=os.environ['LANGFUSE_HOST'],\n",
-                "# )\n",
-                "# if not Settings.callback_manager.handlers:\n",
-                "#     Settings.callback_manager.add_handler(langfuse_callback_handler)\n",
-                "# set_global_handler(\"langfuse\")\n",
-                "\n",
-                "# Ensure the global handler is NOT set to langfuse\n",
-                "set_global_handler(\"simple\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "id": "7a4383b3-540a-4080-88ba-216cbccd9282",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "client = ex.init(\n",
-                "    api_url='http://10.24.49.60/',\n",
-                "    api_key=\"386a5806-0182-41f6-8076-f4b557818f0b\",\n",
-                "    workspace='preprocessing',\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "id": "bcd39e63-cf3d-400a-a450-2701c4e15832",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[UserModel(id=6d8ee721-49b7-4d1f-bf4c-d6b0eb187c62, first_name=Jonny, last_name=Tran, full_name=Jonny Tran, username=jonnytr, role=admin, workspaces=['jonnytr', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing', 'itn-recalibration-scratch'], inserted_at=2024-01-11 01:48:44.477722, updated_at=2024-03-22 06:56:39.809768),\n",
-                            " UserModel(id=e96c9d89-36f4-4240-a592-9734087371d3, first_name=Amelia, last_name=Bertozzi-Villa, full_name=Amelia Bertozzi-Villa, username=ameliabv, role=admin, workspaces=['ameliabv', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing', 'itn-recalibration-scratch'], inserted_at=2024-01-11 01:48:44.549157, updated_at=2024-03-22 06:56:39.815293),\n",
-                            " UserModel(id=91b12ed4-2aff-46d6-9eb3-c15d44e00e2a, first_name=Kate, last_name=Battle, full_name=Kate Battle, username=kateb, role=admin, workspaces=['kateb', 'itn-recalibration', 'gold-standard', 'test-extraction', 'preprocessing', 'itn-recalibration-scratch'], inserted_at=2024-01-11 01:48:44.553978, updated_at=2024-03-22 06:56:39.820769)]"
-                        ]
-                    },
-                    "execution_count": 20,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "remote_dataset = ex.FeedbackDataset.from_argilla(name=\"Table-Preprocessing\", workspace=\"itn-recalibration\")\n",
-                "\n",
-                "users = ex.Workspace.from_name('itn-recalibration').users\n",
-                "users_id_to_username = {u.id: u.username for u in users}\n",
-                "users"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "17374647-df33-4aab-9827-0162e5b522ae",
-            "metadata": {},
-            "source": [
-                "# Load papers"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "id": "219e6dd1-cae3-461c-bdad-78a50ca38cac",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(197, 32)"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers = pd.read_parquet('config/extraction_queue_197papers_2024-04-03.parquet')\n",
-                "extractions = pd.read_csv('data/processed/extractions_preJonny_cleaned.csv', index_col='reference')\n",
-                "\n",
-                "papers = papers.assign(collections=papers['collections'].map(', '.join))\n",
-                "\n",
-                "papers.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "id": "3155d4ed-b741-4c93-9c35-eeb428f189b3",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "# Upload PDFs if not already exist in the Extralit database\n",
-                "# for ref, paper in tqdm(papers.iterrows()):\n",
-                "#     doc = remote_dataset.add_document(ex.Document.from_file(\n",
-                "#         paper.file_path, reference=ref, pmid=paper.pmid, doi=paper.doi, id=paper.get('id')))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "id": "bda4d8fc-8814-4ae0-9145-7dc490ffe11c",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "bc52eb13e2f54ec1978013affd468883",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/670 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "111"
-                        ]
-                    },
-                    "execution_count": 19,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers_uploaded = {r.metadata['reference'] \\\n",
-                "                   for r in tqdm(remote_dataset.filter_by(\n",
-                "                       metadata_filters=ex.TermsMetadataFilter(name='type', values=['table'])).records)}\n",
-                "len(papers_uploaded)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "76af3e87-93ee-42ef-a2c8-1caee119b151",
-            "metadata": {},
-            "source": [
-                "# Push to Extralit"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "a456dffb-e5f9-4b9b-adfa-8cbb1e847556",
-            "metadata": {
-                "jp-MarkdownHeadingCollapsed": true
-            },
-            "source": [
-                "## Create dataset"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "cfd9e1be-2268-4df3-adb6-062aad21bcb4",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "FeedbackDataset(\n",
-                            "   fields=[TextField(name='metadata', title='Metadata:', required=True, type='text', use_markdown=True, use_table=False), TextField(name='header', title='Title:', required=True, type='text', use_markdown=True, use_table=False), TextField(name='image', title='Image:', required=False, type='text', use_markdown=True, use_table=False), TextField(name='text-1', title='Method 1:', required=False, type='text', use_markdown=True, use_table=False), TextField(name='text-2', title='Method 2:', required=False, type='text', use_markdown=True, use_table=False), TextField(name='text-3', title='Method 3:', required=False, type='text', use_markdown=True, use_table=False), TextField(name='text-4', title='Method 4:', required=False, type='text', use_markdown=True, use_table=False), TextField(name='text-5', title='Method 5:', required=False, type='text', use_markdown=True, use_table=False)]\n",
-                            "   questions=[LabelQuestion(name='ranking', title='Which method extracted the most complete and accurate information?', description=None, required=True, type=<QuestionTypes.dynamic_label_selection: 'dynamic_label_selection'>, labels={'text-1': 'Method 1', 'text-2': 'Method 2', 'text-3': 'Method 3', 'text-4': 'Method 4', 'text-5': 'Method 5', 'none': 'None'}, visible_labels=None), MultiLabelQuestion(name='mismatched', title='Which of the method(s) extracted the wrong table/figure? (if any)', description='This indication helps in evaluating these models accuracy', required=False, type=<QuestionTypes.dynamic_multi_label_selection: 'dynamic_multi_label_selection'>, labels={'text-1': 'Method 1', 'text-2': 'Method 2', 'text-3': 'Method 3', 'text-4': 'Method 4', 'text-5': 'Method 5'}, visible_labels=None, labels_order=<LabelsOrder.natural: 'natural'>), TextQuestion(name='header-correction', title='Correct the table or figure title:', description=None, required=False, type='text', use_markdown=False, use_table=False), TextQuestion(name='text-correction', title='Correct the extracted data:', description=None, required=False, type='text', use_markdown=True, use_table=True), TextQuestion(name='notes', title='Mention any notes here', description=None, required=False, type='text', use_markdown=False, use_table=False)]\n",
-                            "   guidelines=None)\n",
-                            "   metadata_properties=[TermsMetadataProperty(name='reference', title='Reference', visible_for_annotators=True, type='terms', values=None), TermsMetadataProperty(name='page_number', title='Page Number', visible_for_annotators=True, type='terms', values=None), TermsMetadataProperty(name='number', title='Table/Figure Number', visible_for_annotators=True, type='terms', values=None), TermsMetadataProperty(name='type', title='Element type', visible_for_annotators=True, type='terms', values=None), TermsMetadataProperty(name='pmid', title='Document Pubmed ID', visible_for_annotators=True, type='terms', values=None), TermsMetadataProperty(name='doc_id', title='Document ID', visible_for_annotators=False, type='terms', values=None), FloatMetadataProperty(name='probability', title='Detection probability', visible_for_annotators=True, type='float', min=None, max=None), TermsMetadataProperty(name='annotators', title='annotators', visible_for_annotators=True, type='terms', values=None)])\n",
-                            "   vectors_settings=[])\n",
-                            ")"
-                        ]
-                    },
-                    "execution_count": 6,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from extralit.pipeline.export.dataset import create_preprocessing_dataset\n",
-                "\n",
-                "dataset = create_preprocessing_dataset()\n",
-                "\n",
-                "remote_dataset = dataset.push_to_argilla(name=\"Table-Preprocessing\", workspace=\"itn-recalibration-scratch\")\n",
-                "\n",
-                "# Initialize the TextDescriptivesExtractor\n",
-                "# tde = TextDescriptivesExtractor(\n",
-                "#     model=\"en\",\n",
-                "#     metrics=['descriptive_stats'],\n",
-                "#     fields=[\"text-1\"],\n",
-                "#     visible_for_annotators=True,\n",
-                "#     show_progress=True,\n",
-                "# )\n",
-                "\n",
-                "# pdf_reader = LayoutPDFReader(\"https://readers.llmsherpa.com/api/document/developer/parseDocument?renderFormat=all\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "049797df-0875-4c79-9cf3-71ceaa05a2ba",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": []
-        },
-        {
-            "cell_type": "markdown",
-            "id": "ac0ecee1-12ed-4e00-8f6f-b08e4716ccc5",
-            "metadata": {},
-            "source": [
-                "# Update dataset"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7fa5953a-be37-44b5-bcb4-3050c93c0f3f",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "## Choose papers queue"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "id": "79eb1486-bc8a-4de8-82c0-ff72bfa155e1",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "'verma2022laboratory.accrombessi2021assessing.ngufor2022comparative.gleave2021piperonyl.diouf2022evaluation.syme2021which.meiwald2022association.gichuki2021bioefficacy.kibondo2022influence.zahouli2023small.toe2018do.nash2021systematic.accrombessi2023efficacy.syme2022pyrethroid.menze2022experimental.gebremariam2021evaluation.briet2020physical.ibrahim2020exploring.mieguim2021insights'"
-                        ]
-                    },
-                    "execution_count": 21,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# papers_subset = ['Atieli2010effectgambiaeMalaria', Dabire2006personalpyrethroidsMalaria', 'Ochomo2017insecticidekenyaEmerging', 'Vatandoost2013washtestArthropod']\n",
-                "# papers_subset = ['Canning2023unwantedcountriesStudies', 'Tiruneh2020prevalencemetaanalysisPlos', 'Daley2018addressingintervention', 'Goodson2011changingafricaInfectious']\n",
-                "# papers_subset = ['ketoh2018efficacy', 'darriet2005pyrethoid', 'magesa1991trial', 'whopes2008report', 'quinones1998permethrin', 'kawada2014small', 'kawada2014preventive', 'randriamaherijaona2015do', 'kilian2011evidence',]\n",
-                "# papers_subset = ['mbogo1996impact', 'menze2020experimental', 'kitau2012species', 'kilian2008long', 'mnzava2001malaria', 'pmi2018senegal', 'hougard2003efficacy', 'pennetier2013efficacy',]\n",
-                "# papers_subset = ['whopes2007report', 'ngufor2016efficacy', 'okia2013bioefficacy', 'mosha2008comparative', 'quinones1997anopheles', 'ahoua2012status', 'sreehari2009wash', 'abdulla2005spatial', 'pmi2019nigeria', 'tan2016longitudinal',]\n",
-                "# papers_subset = ['pmi2019myanmar', 'wills2013physical', 'tokponnon2014impact', 'marchant2002socially', 'pinder2015efficacy', 'nevill1996insecticide', 'muller2006effects', 'terlouw2010impact', 'msuya1991trial', 'tungu2021efficacy',]\n",
-                "papers_subset = ['hamel2011combination', 'ter2003impact', 'moiroux2014human', 'schellenberg2001effect', 'tamari2020protective', 'yewhalaw2022experimental', 'tungu2021field', 'sanou2021insecticide', 'kouassi2020susceptibility', 'mulatier2019prior', \n",
-                "                  'ngongang2022reduced', 'lorenz2020comparative', 'hien2021evidence', 'ngufor2020efficacy', 'sovi2022physical', 'adageba2022bio', 'githinji2020impact', 'grisales2021pyriproxyfen', 'tungu2021bio', 'tungu2021effectiveness',]\n",
-                "papers_subset = ['verma2022laboratory', 'accrombessi2021assessing', 'ngufor2022comparative', 'gleave2021piperonyl', 'diouf2022evaluation', 'syme2021which', 'meiwald2022association', 'gichuki2021bioefficacy', 'kibondo2022influence', 'zahouli2023small', 'toe2018do', 'nash2021systematic', 'accrombessi2023efficacy', 'syme2022pyrethroid', 'menze2022experimental', 'gebremariam2021evaluation', 'briet2020physical', 'ibrahim2020exploring', 'mieguim2021insights',]\n",
-                "'.'.join(papers_subset)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "id": "decef88a-4506-4d65-8629-c5fc6e1921e2",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Index(['verma2022laboratory', 'accrombessi2021assessing', 'ngufor2022comparative', 'gleave2021piperonyl', 'diouf2022evaluation', 'syme2021which', 'meiwald2022association', 'gichuki2021bioefficacy', 'kibondo2022influence', 'zahouli2023small', 'toe2018do', 'nash2021systematic', 'accrombessi2023efficacy', 'syme2022pyrethroid', 'menze2022experimental', 'gebremariam2021evaluation', 'briet2020physical', 'ibrahim2020exploring', 'mieguim2021insights'], dtype='object', name='reference')"
-                        ]
-                    },
-                    "execution_count": 22,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers_subset = papers.loc[papers_subset]\n",
-                "papers_subset.index"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "id": "443939fc-8d9c-4c67-9ce2-79246b467b44",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(120, 32)"
-                        ]
-                    },
-                    "execution_count": 18,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers_subset = papers.loc[papers.index.difference(papers_uploaded)]\n",
-                "papers_subset.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "id": "85aee0d5-70a3-4892-9947-3655a49506d6",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['abdella2009does',\n",
-                            " 'abdulla2001impact',\n",
-                            " 'abilio2015bio',\n",
-                            " 'accrombessi2021assessing',\n",
-                            " 'accrombessi2023efficacy',\n",
-                            " 'agossa2014laboratory',\n",
-                            " 'agossa2015impact',\n",
-                            " 'akoton2018experimental',\n",
-                            " 'allossogbe2017who',\n",
-                            " 'alonso1993malaria',\n",
-                            " 'anshebo2014estimation',\n",
-                            " 'asidi2004experimental',\n",
-                            " 'asidi2005experimental',\n",
-                            " 'asidi2012loss',\n",
-                            " 'awolola2014impact',\n",
-                            " 'badolo2012experimental',\n",
-                            " 'bayili2017evaluation',\n",
-                            " 'bayili2019experimental',\n",
-                            " 'birhanu2019bio',\n",
-                            " 'bobanga2013field',\n",
-                            " 'bogh1998permethrin',\n",
-                            " 'boussougou2017physical',\n",
-                            " 'briet2020physical',\n",
-                            " 'camara2018efficacy',\n",
-                            " 'chandre2010field',\n",
-                            " 'chouaibou2006efficacy',\n",
-                            " 'corbel2010field',\n",
-                            " 'curtis2003insecticide',\n",
-                            " 'dalessandro1995mortality',\n",
-                            " 'darriet2011combining',\n",
-                            " 'diouf2022evaluation',\n",
-                            " 'djenontin2009managing',\n",
-                            " 'djenontin2010indoor',\n",
-                            " 'etang2004reduced',\n",
-                            " 'etang2007preliminary',\n",
-                            " 'etang2013evaluation',\n",
-                            " 'etang2016when',\n",
-                            " 'fadel2019combination',\n",
-                            " 'fane2012anopheles',\n",
-                            " 'gebremariam2021evaluation',\n",
-                            " 'gichuki2021bioefficacy',\n",
-                            " 'gleave2021piperonyl',\n",
-                            " 'guillet2001combined',\n",
-                            " 'hassan2008retention',\n",
-                            " 'hauser2019ability',\n",
-                            " 'hawley2003community',\n",
-                            " 'henry2005protective',\n",
-                            " 'hougard2002useful',\n",
-                            " 'howard2000evidence',\n",
-                            " 'hughes2020anopheles',\n",
-                            " 'ibrahim2019high',\n",
-                            " 'ibrahim2020exploring',\n",
-                            " 'jones2012aging',\n",
-                            " 'kawada2014small',\n",
-                            " 'kibondo2022influence',\n",
-                            " 'kilian2015field',\n",
-                            " 'kolaczinski2000comparison',\n",
-                            " 'kouassi2020susceptibility',\n",
-                            " 'koudou2011efficacy',\n",
-                            " 'kulkarni2007efficacy',\n",
-                            " 'kweka2011durability',\n",
-                            " 'kweka2017efficacy',\n",
-                            " 'kweka2019bio',\n",
-                            " 'lindblade2005evaluation',\n",
-                            " 'lines1987experimental',\n",
-                            " 'magbity1997effects',\n",
-                            " 'mahande2018bio',\n",
-                            " 'malima2009behavioural',\n",
-                            " 'malima2013evaluation',\n",
-                            " 'masalu2018potential',\n",
-                            " 'massue2016durability',\n",
-                            " 'maxwell2002effect',\n",
-                            " 'maxwell2006tests',\n",
-                            " 'meiwald2022association',\n",
-                            " 'menze2022experimental',\n",
-                            " 'mieguim2021insights',\n",
-                            " 'mosha2008experimental',\n",
-                            " 'msangi2008effects',\n",
-                            " 'msuya1991trial',\n",
-                            " 'mulatier2019prior',\n",
-                            " 'murray2020barrier',\n",
-                            " 'musa2020long',\n",
-                            " 'nash2021systematic',\n",
-                            " 'nguessan2016chlorfenapyr',\n",
-                            " 'ngufor2014combining',\n",
-                            " 'ngufor2014olyset',\n",
-                            " 'ngufor2017which',\n",
-                            " 'ngufor2022comparative',\n",
-                            " 'obi2020monitoring',\n",
-                            " 'ohashi2012efficacy',\n",
-                            " 'okoyo2015comparing',\n",
-                            " 'okumu2012implications',\n",
-                            " 'omondi2017quantifying',\n",
-                            " 'oumbouke2019evaluation',\n",
-                            " 'oxborough2013itn',\n",
-                            " 'oxborough2015new',\n",
-                            " 'pmi2017madagascar',\n",
-                            " 'pmi2018mozambique',\n",
-                            " 'pmi2018senegal',\n",
-                            " 'pmi2019myanmar',\n",
-                            " 'pmi2019nigeria',\n",
-                            " 'pmi2019tanzania',\n",
-                            " 'randriamaherijaona2017durability',\n",
-                            " 'riveron2018high',\n",
-                            " 'solomon2018bed',\n",
-                            " 'spitzen2017effect',\n",
-                            " 'syme2021which',\n",
-                            " 'syme2022pyrethroid',\n",
-                            " 'tami2004evaluation',\n",
-                            " 'tiono2018efficacy',\n",
-                            " 'toe2018do',\n",
-                            " 'tungu2015evaluation',\n",
-                            " 'vatandoost2006comparative',\n",
-                            " 'verma2022laboratory',\n",
-                            " 'whopes2007report',\n",
-                            " 'whopes2008report',\n",
-                            " 'winkler2012efficacy',\n",
-                            " 'yewhalaw2022experimental',\n",
-                            " 'zahouli2023small',\n",
-                            " 'zhou2016insecticide']"
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "papers_subset.index.tolist()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "075b961c-2d35-4317-b48a-f5afb638c236",
-            "metadata": {
-                "scrolled": true
-            },
-            "source": [
-                "## Extract segments"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "id": "8389d02d-7321-4308-be10-ea17d91b37b1",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# nougat_model = nougat.NougatOCR()\n",
-                "# nougat_model.batch_size = 1\n",
-                "nougat_model = None"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "id": "57cd8e2e-4e76-4581-a813-33e83057fe72",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "b708665ba6fc4a37814360a3aac592ab",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/19 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "### verma2022laboratory: data/pdf/LLINPhaseIVV2022.pdf ###\n",
-                        "### accrombessi2021assessing: data/pdf/Assessing_the_efficacy_of_two_dual_active_ingredients_long_lasting_insecticidal_.pdf ###\n",
-                        "### ngufor2022comparative: data/pdf/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_.pdf ###\n",
-                        "### gleave2021piperonyl: data/pdf/CD012776.pdf ###\n",
-                        "### diouf2022evaluation: data/pdf/Evaluation_of_the_residual_efficacy_and_physical_durability_of_five_long_lasting.pdf ###\n",
-                        "### syme2021which: data/pdf/pone_0245804_pdf.pdf ###\n",
-                        "### meiwald2022association: data/pdf/jiaa699.pdf ###\n",
-                        "### gichuki2021bioefficacy: data/pdf/Bioefficacy_and_durability_of_Olyset_Plus_a_permethrin_and_piperonyl__butoxi.pdf ###\n",
-                        "### kibondo2022influence: data/pdf/Influence_of_testing_modality_on_bioefficacy_for_the_evaluation_of_Interceptor.pdf ###\n",
-                        "### zahouli2023small: data/pdf/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with.pdf ###\n",
-                        "### toe2018do: data/pdf/Toe_et_al_2018_Medical_and_Veterinary_Entomology.pdf ###\n",
-                        "### nash2021systematic: data/pdf/1_s20_S2667114X21000418_main.pdf ###\n",
-                        "### accrombessi2023efficacy: data/pdf/PIIS0140673622023194.pdf ###\n",
-                        "### syme2022pyrethroid: data/pdf/s41598_022_10953_y.pdf ###\n",
-                        "### menze2022experimental: data/pdf/pathogens_11_00638_v2.pdf ###\n",
-                        "### gebremariam2021evaluation: data/pdf/gebremariam_et_al_2021_evaluation_of_long_lasting_insecticidal_nets_duranetr_under_laboratory_and_semi_field.pdf ###\n",
-                        "### briet2020physical: data/pdf/Attrition_physical_integrity_and_insecticidal_activity_of_long_lasting__insecti.pdf ###\n",
-                        "### ibrahim2020exploring: data/pdf/genes_11_00454_pdf.pdf ###\n",
-                        "### mieguim2021insights: data/pdf/Insights_into_factors_sustaining_persistence_of_high_malaria_transmission_in__fo.pdf ###\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "19"
-                        ]
-                    },
-                    "execution_count": 25,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "outputs = defaultdict(lambda: {})\n",
-                "texts = defaultdict(lambda: {})\n",
-                "tables = defaultdict(lambda: {})\n",
-                "figures = defaultdict(lambda: {})\n",
-                "\n",
-                "for id, paper in tqdm(papers_subset.iterrows(), total=len(papers_subset)):\n",
-                "    print(f'### {id}: {papers.loc[id, \"file_path\"]} ###', flush=True)\n",
-                "    \n",
-                "    ## Unstructured.io ##\n",
-                "    text_segs, table_segs, figure_segs = create_or_load_unstructured_segments(paper, load_only=True, redo=False)\n",
-                "    texts[id]['unstructured'] = text_segs\n",
-                "    tables[id]['unstructured'] = table_segs\n",
-                "    figures[id]['unstructured'] = figure_segs\n",
-                "\n",
-                "    # llmsherpa ##\n",
-                "    text_segs, table_segs, _ = create_or_load_llmsherpa_segments(paper, load_only=True, redo=False)\n",
-                "    texts[id]['llmsherpa'] = text_segs\n",
-                "    tables[id]['llmsherpa'] = table_segs\n",
-                "\n",
-                "    ## Nougat ##\n",
-                "    text_segs, table_segs, _ = create_or_load_nougat_segments(paper, nougat_model=nougat_model, load_only=True)\n",
-                "    texts[id]['nougat'] = text_segs\n",
-                "    tables[id]['nougat'] = table_segs\n",
-                "\n",
-                "    ## pdffigures2 ##\n",
-                "    _, table_segs, figure_segs = create_or_load_pdffigures2_segments(paper, load_only=True, redo=False,\n",
-                "                                                                    jar_path='bin/pdffigures2.jar',)\n",
-                "    tables[id]['pdffigures2'] = table_segs\n",
-                "    figures[id]['pdffigures2'] = figure_segs\n",
-                "\n",
-                "    ## deepdoctection ##\n",
-                "    _, table_segs, _ = create_or_load_deepdoctection_segments(paper, load_only=True, redo=False)\n",
-                "    tables[id]['deepdoctection'] = table_segs\n",
-                "\n",
-                "len(tables)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "e8f92a66-fda2-4f94-b54e-d5cb3c6d3a0e",
-            "metadata": {
-                "jp-MarkdownHeadingCollapsed": true
-            },
-            "source": [
-                "### Figure digitalization (experiments)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "id": "86319d94-44fe-42f8-a788-fd8de3e58255",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "data/pdf/tan2016longitudinal.pdf\n",
-                        " None\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<table><thead><th><th colspan=\"1\"></th><th colspan=\"1\">PermaNet® (n) %, 95 % CI</th><th colspan=\"1\">Olyset® (n) %, 95 % CI</th><th colspan=\"1\">All nets (n) %, 95 % CI</th></th></thead><tbody><tr><td colspan=\"1\">LLIN age 12 months</td><td colspan=\"1\">(14) 77.8, 58.6–96.7†</td><td colspan=\"1\">(6) 33.3, 11.6–55.1</td><td colspan=\"1\">(20) 55.6, 39.3–71.8</td></tr><tr><td colspan=\"1\">24 months</td><td colspan=\"1\">(7) 38.9, 16.4–61.4</td><td colspan=\"1\">(6) 30.0, 9.9–50.1</td><td colspan=\"1\">(13) 34.2, 19.1–51.4</td></tr><tr><td colspan=\"1\">Minimal effectiveness 12 months</td><td colspan=\"1\">(18) 100.0, 100.0–100.0†</td><td colspan=\"1\">(14) 77.8, 58.6–97.0</td><td colspan=\"1\">(32) 88.9, 78.6–96.9</td></tr><tr><td colspan=\"1\">24 months</td><td colspan=\"1\">(11) 61.1, 38.6–83.6</td><td colspan=\"1\">(14) 70.0, 49.9–88.1</td><td colspan=\"1\">(25) 65.8, 50.7–80.9*</td></tr></tbody></table>"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "execution_count": 21,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "id = 'tan2016longitudinal'\n",
-                "i = -1\n",
-                "table = tables[id]['llmsherpa'].items[i]\n",
-                "\n",
-                "print(papers.loc[id, 'file_path'])\n",
-                "print(table.header, table.number)\n",
-                "HTML(table.html)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "id": "d422ee41-a29e-436c-bf51-cb664f2ceaf3",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "FigureSegment(header='Fig. 2 Correlation of chemical and bioassay results (95 % confidence intervals shown in shaded area)', page_number=10, number=2)"
-                        ]
-                    },
-                    "execution_count": 23,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "fig = figures['tan2016longitudinal']['pdffigures2'][0]\n",
-                "fig"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "id": "e6e8c343-39c6-4c77-bd14-111a8a2084c8",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<img 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\">"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "execution_count": 24,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "HTML(image_to_html(fig.image))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "id": "fc490766-1f29-4591-be82-1180f2521de5",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:416: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/tan2016longitudinal/tan2016longitudinal-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0520 13:54.02 @segment.py:246]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Correlation of chemical and bioassay results (95 % confidence intervals shown in shaded area)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "FigureExtractionResponse(summary='The figure shows the correlation between chemical concentrations (Deltamethrin and Permethrin) and bioassay results (Knock down and Mortality) in mosquitoes. The shaded areas represent 95% confidence intervals.', html='<table>\\n<thead>\\n<tr>\\n<th></th>\\n<th>Deltamethrin</th>\\n<th>Permethrin</th>\\n</tr>\\n</thead>\\n<tbody>\\n<tr>\\n<td>Knock down (% mosquitoes)</td>\\n<td></td>\\n<td></td>\\n</tr>\\n<tr>\\n<td>Mortality (% mosquitoes)</td>\\n<td></td>\\n<td></td>\\n</tr>\\n<tr>\\n<td>Mean insecticide concentration in mg/m²</td>\\n<td></td>\\n<td></td>\\n</tr>\\n</tbody>\\n</table>')"
-                        ]
-                    },
-                    "execution_count": 26,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_figure_extraction_response = fig.extract_html_table()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 27,
-            "id": "6b5c2d46-8a0d-473d-b605-dc2bbac57f6b",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<table>\n",
-                            "<thead>\n",
-                            "<tr>\n",
-                            "<th></th>\n",
-                            "<th>Deltamethrin</th>\n",
-                            "<th>Permethrin</th>\n",
-                            "</tr>\n",
-                            "</thead>\n",
-                            "<tbody>\n",
-                            "<tr>\n",
-                            "<td>Knock down (% mosquitoes)</td>\n",
-                            "<td></td>\n",
-                            "<td></td>\n",
-                            "</tr>\n",
-                            "<tr>\n",
-                            "<td>Mortality (% mosquitoes)</td>\n",
-                            "<td></td>\n",
-                            "<td></td>\n",
-                            "</tr>\n",
-                            "<tr>\n",
-                            "<td>Mean insecticide concentration in mg/m²</td>\n",
-                            "<td></td>\n",
-                            "<td></td>\n",
-                            "</tr>\n",
-                            "</tbody>\n",
-                            "</table>"
-                        ],
-                        "text/plain": [
-                            "<IPython.core.display.HTML object>"
-                        ]
-                    },
-                    "execution_count": 27,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "HTML(fig.html)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "97efd4ef-c6d3-4cd5-a9dd-044c6ee08339",
-            "metadata": {
-                "jp-MarkdownHeadingCollapsed": true
-            },
-            "source": [
-                "### deepdoctection (experiments)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "e92c5fe3-486e-4296-b636-c144879b1ebf",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "analyzer =dd.get_dd_analyzer(config_overwrite=[\n",
-                "    \"PT.LAYOUT.WEIGHTS=microsoft/table-transformer-detection/pytorch_model.bin\",   # TATR table detection model\n",
-                "    \"PT.ITEM.WEIGHTS=microsoft/table-transformer-structure-recognition/pytorch_model.bin\",  # TATR table segmentation model\n",
-                "    \"PT.ITEM.FILTER=['table']\",\n",
-                "    \"OCR.USE_DOCTR=True\",  # we disable Tesseract and use DocTr as OCR engine\n",
-                "    \"OCR.USE_TESSERACT=False\",\n",
-                "])\n",
-                "\n",
-                "df = analyzer.analyze(path='data/pdf/Etang_et_al_2007_Trans_RSTMH.pdf')\n",
-                "df.reset_state()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "9577cb2e-d4cb-4d01-903d-526dc3d4f8b1",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# doc = iter(df)\n",
-                "page = next(doc)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f3f98599-3c68-4087-bef5-220bc5cc5ae7",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "page.annotations[1]._category_name"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "74b33bd9-ae32-411e-b51c-2c25ac792be6",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "page.annotations[0]._annotation_id"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "ae277688-0848-4c83-9b59-aa7e7605022f",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "table.annotation_id"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f898123d-ea83-4ed4-b065-8e5228ee67cc",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "table = page.tables[0]\n",
-                "print(table.html)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "9fe8d5d9-ebd8-4b55-bf98-4a8b6f431270",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "table.cells"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f9181c56-6802-40bd-9cdd-bac79bbde616",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "page.viz(ignore_default_token_class=True, interactive=True)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d7235e69-eddd-45f9-bdd8-ec34cbaf479a",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "#### Evaluate tables extracted"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "4e940570-fac7-4f80-a329-da823d51e778",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "df = tables['Etang2007']['unstructured'][0].to_df()\n",
-                "df = df.dropna(axis=0, how='all').dropna(axis=1, how='all')\n",
-                "df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "fa7e5bea-93dd-4ac6-bb93-a892ebc9d538",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "# for id, paper in tqdm(papers_subset.iterrows(), total=len(papers_subset)):\n",
-                "#     text_segs, table_segs, _ = create_or_load_nougat_segments(paper, nougat_model=nougat_model, load_only=False, save=True)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "203095af-75db-4d00-ab94-3df87c157fb0",
-            "metadata": {},
-            "source": [
-                "## Create records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "id": "1817cbdf-b1cb-4be7-ae95-9b5ede7bcf78",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "95396ae97c1841ce9e59eea7e67699ba",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "0it [00:00, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/verma2022laboratory/LLINPhaseIVV2022-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:04.51 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Schematic flow of work plan\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/verma2022laboratory/LLINPhaseIVV2022-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:04.58 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Graph showing % KnockDown (%KD) and % Mortality in cone bioassays to determine the regeneration time of HILNet\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/verma2022laboratory/LLINPhaseIVV2022-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.02 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Graph showing % KnockDown and % Mortality in wash resistance studies\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/accrombessi2021assessing/figures/figure-4-4.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.07 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Study area. Map showing Cove, Zagnanado and Ouinhi Districts, located in Zou department, central Benin, West Africa (Panel a); the 60 study clusters identified with core and buffer area and intervention allocation (Panel b); a minimum of 1000 m area was created between households in adjacent clusters (Panel c). Source of map: Own from the study investigators (CT, JC, MA, ED).\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "verma2022laboratory 3 tables 3 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/accrombessi2021assessing/Assessing_the_efficacy_of_two_dual_active_ingredients_long_lasting_insecticidal_-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.14 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Study area. Map showing Cove, Zagnanado and Ouinhi Districts, located in Zou department, central Benin, West Africa (Panel a); the 60 study clusters identified with core and buffer area and intervention allocation (Panel b); a minimum of 1000m area was created between households in adjacent clusters (Panel c). Source of map: Own from the study investigators (CT, JC, MA, ED).\u001b[0m\n",
-                        "\u001b[32m[0621 00:05.22 @staging.py:69]\u001b[0m  \u001b[5m\u001b[35mWRN\u001b[0m  \u001b[97mAttempting to fix broken JSON: ... 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60</td>\\n    </tr>\\n  </tbody>\\n</table>\"}\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "accrombessi2021assessing 2 tables 1 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ngufor2022comparative/figures/figure-6-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.23 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ngufor2022comparative/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.28 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Mortality of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ngufor2022comparative/figures/figure-7-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.33 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Blood-feeding rates of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ngufor2022comparative/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.39 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Blood-feeding rates of wild pyrethroid-resistant Anopheles gambiae s.l. in experimental huts in Cove, Benin evaluating different net types. Vertical lines indicate 95% confidence interval estimates whilst bars with the same letter label are not significantly different (P > 0.05)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ngufor2022comparative/figures/figure-9-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.44 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mortality (%) of susceptible and resistant strains of An. gambiae s.l. in cone bioassays with Olyset Plus and Olyset Net. S R\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ngufor2022comparative/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.49 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mortality (%) of susceptible and resistant strains of An. gambiae s.l. in cone bioassays with Olyset Plus and Olyset Net. S = susceptible, R = resistant\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ngufor2022comparative/figures/figure-9-10.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:05.57 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Tunnel test mortality (%) of susceptible and resistant strains of Anopheles gambiae s.l. exposed to Olyset Plus vs. Olyset Net\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ngufor2022comparative/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.01 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Tunnel test mortality (%) of susceptible and resistant strains of Anopheles gambiae s.l. exposed to Olyset Plus vs. Olyset Net\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ngufor2022comparative/figures/figure-10-11.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.08 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Blood-feeding inhibition (%) of susceptible and resistant strains of An. gambiae s.l. in tunnel tests with Olyset Plus and Olyset Net. Blood-feeding inhibition was calculated relative to the control tunnel. S\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ngufor2022comparative/Comparative_efficacy_of_two_pyrethroid_piperonyl_butoxide_nets_Olyset_Plus_and_-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.16 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Blood-feeding inhibition (%) of susceptible and resistant strains of An. gambiae s.l. in tunnel tests with Olyset Plus and Olyset Net. Blood-feeding inhibition was calculated relative to the control tunnel. S = susceptible, R = Resistant\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "ngufor2022comparative 9 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/gleave2021piperonyl/CD012776-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.27 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1.   Study flow diagram.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/gleave2021piperonyl/CD012776-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.34 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2.   ‘Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "gleave2021piperonyl 133 tables 2 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/diouf2022evaluation/figures/figure-4-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.44 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Localities where LLINs were distributed and durability monitored\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/diouf2022evaluation/figures/figure-8-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:06.47 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/diouf2022evaluation/Evaluation_of_the_residual_efficacy_and_physical_durability_of_five_long_lasting-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.06 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Retained proportions not removed (red), retention after sampling (blue) and cumulative number of nets removed by type and semester after 3 years\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/diouf2022evaluation/figures/figure-10-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.11 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Categories of holes by type and semester after 3 years. Size 1, size 2, size 3\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/diouf2022evaluation/Evaluation_of_the_residual_efficacy_and_physical_durability_of_five_long_lasting-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.17 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Categories of holes by type and semester after 3 years. Size 1, size 2, size 3\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/diouf2022evaluation/Evaluation_of_the_residual_efficacy_and_physical_durability_of_five_long_lasting-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.25 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Insecticide concentration in mg/m2 according to the type LLINs and semester\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/diouf2022evaluation/Evaluation_of_the_residual_efficacy_and_physical_durability_of_five_long_lasting-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.32 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Correlation of chemical and bioassay results by type of LLIN and semester\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "diouf2022evaluation 8 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/syme2021which/figures/figure-9-2.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.41 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 1. Monthly mortality rates of wild, free-flying pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts with pirimiphos-methyl CS IRS applied alone and in combination with DuraNet1 in Covè, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/syme2021which/pone_0245804_pdf-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.47 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 1. Monthly mortality rates of wild, free-flying pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts with pirimiphos-methyl CS IRS applied alone and in combination with DuraNet1 in Covè, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/syme2021which/pone_0245804_pdf-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:07.58 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 2. Monthly mortality rates of wild, free-flying pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts with chlorfenapyr IRS applied alone and in combination with DuraNet1 in Covè, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/syme2021which/figures/figure-10-4.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.03 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 3. Monthly mortality rates of wild, free-flying pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts with bendiocarb IRS applied alone and in combination with DuraNet1 in Covè, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/syme2021which/pone_0245804_pdf-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.09 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 3. Monthly mortality rates of wild, free-flying pyrethroid-resistant Anopheles gambiae sensu lato entering experimental huts with bendiocarb IRS applied alone and in combination with DuraNet1 in Covè, southern Benin. Mortality was cumulated over successive months. Error bars represent 95% CI.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/syme2021which/pone_0245804_pdf-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.14 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig 4. Mortality (72 h) of laboratory maintained, insecticide-susceptible Anopheles gambiae sensu stricto Kisumu colony exposed to IRS-treated hut walls in monthly, 30 mins wall cone bioassays in experimental huts in Covè, southern Benin. Error bars represent 95% CI. Approximately fifty (50) 2–3 days old mosquitoes were exposed for 30 mins to each IRS treated hut in batches of 10 per cone and mortality recorded after 72 h.\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "syme2021which 5 tables 4 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/meiwald2022association/figures/figure-4-3.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.19 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. A, Resistance intensity of field-caught Anopheles gambiae sensu lato (s.l.) after exposure to 1, 2, 5, or 10 times the diagnostic dose of pyrethroid insecticides. Mean knockdown/acute toxicity rates after 30-minute exposure are shown with 95% confidence intervals (CIs). Knockdown or mortality rates at the same dose per insecticide sharing a letter do not differ significantly (P > .05). Mortality rates <90% (lower red line) represent confirmed resistance at the diagnostic dose (1×), and rates <98% (upper red line) indicate moderate to high-intensity resistance or high-intensity resistance at 5× and 10×, respectively, as defined by the World Health Organization [24]. B, Restoration of deltamethrin susceptibility of field-caught A. gambiae s.l. after preexposure to piperonyl butoxide (PBO). Mean knockdown/acute toxicity after 30-minute exposure to 1 or 2 times the diagnostic dose of deltamethrin with 95% CIs. Knockdown/mortality rates do not differ significantly between pyrethroid only and synergist plus pyrethroid sharing a letter (P > .05). Red line at 98% mortality rate represents metabolic resistance mechanisms partially involved [24].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/meiwald2022association/figures/figure-6-20.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.25 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. The longevity of field-caught Anopheles gambiae sensu lato after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (A) and 1× (B), 5× (C), and 10× (D) the diagnostic dose of pyrethroid insecticides in Centers for Disease Control and Prevention resistance intensity assays. Kaplan-Meier sur- vival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/meiwald2022association/jiaa699-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.32 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. The longevity of field-caught Anopheles gambiae sensu lato after exposure to long-lasting insecticidal nets (LLINs) in World Health Organization cone assays (A) and 1× (B), 5× (C), and 10× (D) the diagnostic dose of pyrethroid insecticides in Centers for Disease Control and Prevention resistance intensity assays. Kaplan-Meier survival curves indicate the proportion alive each day after exposure. Immediate mortality rates after LLIN (60 minutes and 24 hours) or insecticidal exposure (30 or 60 minutes, depending on insecticide) were excluded.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/gichuki2021bioefficacy/figures/figure-4-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:08.46 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 A rectangular net and its individual panels showing positions for cutting netting pieces (positions 1–9 for bioassays; HP1–HP5 for chemical assays)\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "meiwald2022association 3 tables 2 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "gichuki2021bioefficacy 7 tables 1 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/kibondo2022influence/figures/figure-7-6.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.00 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Mosquito mortality after exposure to Interceptor® and Interceptor® G2 ITNs in Ifakara ambient chamber test (IACT), WHO tunnel test, cone and experimental hut. a An. arabiensis, b Cx. quinquefasciatus, c An. gambiae s.s., d Ae. aegypti\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/kibondo2022influence/Influence_of_testing_modality_on_bioefficacy_for_the_evaluation_of_Interceptor-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.09 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Mosquito mortality after exposure to Interceptor® and Interceptor® G2 ITNs in Ifakara ambient chamber test (IACT), WHO tunnel test, cone and experimental hut. a An. arabiensis, b Cx. quinquefasciatus, c An. gambiae s.s., d Ae. aegypti\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/kibondo2022influence/figures/figure-10-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.16 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mosquito blood-feeding inhibition after exposure to Interceptor and Interceptor® G2 ITNs in Ifakara ambient chamber test (IACT), WHO tunnel test and experimental hut. a An. arabiensis, b Cx. quinquefasciatus, c An. gambiae s.s., d Ae. aegypti\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/kibondo2022influence/Influence_of_testing_modality_on_bioefficacy_for_the_evaluation_of_Interceptor-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.21 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mosquito blood-feeding inhibition after exposure to Interceptor and Interceptor® G2 ITNs in Ifakara ambient chamber test (IACT), WHO tunnel test and experimental hut. a An. arabiensis, b Cx. quinquefasciatus, c An. gambiae s.s., d Ae. aegypti\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/kibondo2022influence/figures/figure-12-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.26 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Comparison of mosquito mortality and blood-feeding inhibition of An. arabiensis in four different experimental hut designs in Ifakara, Tanzania\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/kibondo2022influence/Influence_of_testing_modality_on_bioefficacy_for_the_evaluation_of_Interceptor-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.32 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Comparison of mosquito mortality and blood-feeding inhibition of An. arabiensis in four different experimental hut designs in Ifakara, Tanzania\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/kibondo2022influence/Influence_of_testing_modality_on_bioefficacy_for_the_evaluation_of_Interceptor-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.39 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Factors determining bioassay results beyond product characteristics\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/zahouli2023small/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.50 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Effects of treatments on blood-feeding (A) and immediate and extended mortality (B) rates in multiple insecticide-resistant Anopheles gambiae from experimental huts in Tiassalé, Côte d’Ivoire. Error bars show the standard error of the mean. Letters indicate the results of the\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "kibondo2022influence 5 tables 4 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/zahouli2023small/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:09.58 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Mean knock-down (A) and corrected mortality (B) rates in the susceptible Anopheles gambiae s.s. Kisumu strain exposed to net samples using cone bioassays before and after the experimental hut trial in Tiassalé, Côte d’Ivoire. KD60: Knock-down at 60 min. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/zahouli2023small/figures/figure-11-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.04 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mean knock-down (A) and corrected mortality (B) rates in the pyrethroid-resistant Anopheles gambiae s.l. Tiassalé strain exposed to net samples using cone bioassays before and after the experimental hut trial in Tiassalé, Côte d’Ivoire. KD60: Knock-down at 60 min. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/zahouli2023small/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.14 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Mean knock-down (A) and corrected mortality (B) rates in the pyrethroid-resistant Anopheles gambiae s.l. Tiassalé strain exposed to net samples using cone bioassays before and after the experimental hut trial in Tiassalé, Côte d’Ivoire. KD60: Knock-down at 60 min. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/zahouli2023small/figures/figure-13-10.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.27 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Mean blood-feeding inhibition (A) and corrected mortality (B) rates in the susceptible Anopheles gambiae s.s. Kisumu strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassalé, Côte d’Ivoire. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/zahouli2023small/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.33 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Mean blood-feeding inhibition (A) and corrected mortality (B) rates in the susceptible Anopheles gambiae s.s. Kisumu strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassalé, Côte d’Ivoire. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/zahouli2023small/figures/figure-14-11.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.42 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Mean of blood-feeding inhibition (A) and corrected mortality (B) rates in the pyrethroid-resistant Anopheles gambiae s.l. Tiassalé strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassalé, Côte d’Ivoire. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/zahouli2023small/Small_scale_field_evaluation_of_PermaNet_Dual_a_long_lasting_net_coated_with-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.49 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Mean of blood-feeding inhibition (A) and corrected mortality (B) rates in the pyrethroid-resistant Anopheles gambiae s.l. Tiassalé strain exposed to net samples using tunnel tests before and after experimental hut trial in Tiassalé, Côte d’Ivoire. Error bars show the standard error of the mean\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/toe2018do/figures/figure-4-2.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:10.58 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1. Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: P < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "zahouli2023small 4 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/toe2018do/Toe_et_al_2018_Medical_and_Veterinary_Entomology-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.02 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1. Mortality rates (with binomial confidence intervals) after exposure to deltamethrin and permethrin in World Health Organization discriminating dose assays with or without pre-exposure to piperonyl butoxide (PBO) in (A) Vallée du Kou 5 and (B) Tengrela (October 2014). *Significant differences in mortality: P < 0.0001. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/toe2018do/Toe_et_al_2018_Medical_and_Veterinary_Entomology-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.07 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2. Expression levels of candidate genes previously associated with pyrethroid resistance in Tengrela and Vallée du Kou 5. The analysis was performed using the 2-ΔΔCt method with data normalized against three control genes and presented as a ratio of expression levels in the Ngousso susceptible laboratory strain. Relative gene expressions were transformed to log scale before plotting to minimize large differences in gene expression. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/toe2018do/figures/figure-6-6.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.13 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3. Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (P < 0.001), † (P < 0.0001), n.s. (non-significant), ‡ (P < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/toe2018do/Toe_et_al_2018_Medical_and_Veterinary_Entomology-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.19 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3. Mean mortality rates with binomial confidence intervals for mosquitoes collected from (A) Tengrela and (B) Vallée du Kou 5 after exposure to longlasting insecticidal nets (LLINs) for 3 min. Significant differences in mortality between pyrethroid-only and pyrethroid plus piperonyl butoxide-treated LLINs are indicated by * (P < 0.001), † (P < 0.0001), n.s. (non-significant), ‡ (P < 0.0001) for the comparison between the sides and top of the PermaNet 3.0. Numbers in brackets are the total numbers of mosquitoes tested. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/toe2018do/figures/figure-7-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.23 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4. Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the P-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/toe2018do/Toe_et_al_2018_Medical_and_Veterinary_Entomology-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.30 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4. Primary outcomes measured in the experimental hut trials in Tengrela and Vallée du Kou 5 (VK5). (A–D) Crude data for the measured outcomes. (A) Entry, number of mosquitoes per night entering a hut. (B) Exit, proportion of mosquitoes collected from the veranda trap. (C) Blood feeding, proportion of mosquitoes found blood-fed. (D) Mortality, proportion of mosquitoes dead at 24 h post-collection. (E–H) These outcomes measured relative to the control (i.e. untreated mosquito net) for both sites combined. (E) Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. (F) Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. (G) Blood feeding inhibition, the OR for a mosquito being blood-fed. (H) Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). Symbols show the average and whiskers show the limits of the 95% confidence intervals around the average. The dashed line shows the ratio or OR of 1, indicating no association of the outcome with the treatment. The data used to produce the plot together with the P-values are provided in Table S3. [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/toe2018do/figures/figure-8-10.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.36 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5. Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/toe2018do/Toe_et_al_2018_Medical_and_Veterinary_Entomology-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.41 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5. Outcome measures comparing piperonyl butoxide (PBO)-treated and non-PBO-treated nets from the same manufacturers. Deterrence, the ratio of mosquitoes entering a hut relative to the control hut. Exophily, the odds ratio (OR) for a mosquito being found in the veranda trap. Blood feeding inhibition, the OR for a mosquito being blood-fed. Mortality, the OR for a mosquito being dead in the morning (immediate mortality) or after being caught alive and held for 24 h (delayed mortality). [Colour figure can be viewed at wileyonlinelibrary.com].\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/nash2021systematic/figures/figure-4-4.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.47 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1. Maps of EHT sites where ITNs and/or IRS have been investigated. Panels show sites across sub-Saharan Africa (A); Eastern Africa (54 trials), including Ethiopia, Tanzania and Madagascar (B); West (80 trials) and Central Africa (1 trial), including The Gambia, Mali, Ivory Coast, Burkina Faso, Togo, Benin, Nigeria and Cameroon (C). Point size indicates the number of studies at each site, whilst point colour denotes the design of hut used, be it East African (red), Ifakara (green) or West African (blue).\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "toe2018do 4 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/nash2021systematic/figures/figure-6-5.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:11.52 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2. Characteristics of EHT data. A The number of EHTs investigating IRS or ITNs that collected data on each hut design and mosquito species (as reported in the study). Bar colours indicate the design of the hut used in the trial as shown in the legend, be it East African (red), West African (blue) or Ifakara (green). B Summary of the reported mortality observed in hut trials evaluating unwashed pyrethroid-only ITNs and how these change over time. Points are coloured according to the hut design (see A). C Histograms showing the average number of mosquitoes collected per night in the control huts of EHTs. The dotted line represents the mean number collected in trials of each hut design. Note: Some trials collected multiple mosquito species, therefore single trials may be counted more than once in this figure.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/nash2021systematic/1_s20_S2667114X21000418_main-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.01 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2. Characteristics of EHT data. A The number of EHTs investigating IRS or ITNs that collected data on each hut design and mosquito species (as reported in the study). Bar colours indicate the design of the hut used in the trial as shown in the legend, be it East African (red), West African (blue) or Ifakara (green). B Summary of the reported mortality observed in hut trials evaluating unwashed pyrethroid-only ITNs and how these change over time. Points are coloured according to the hut design (see A). C Histograms showing the average number of mosquitoes collected per night in the control huts of EHTs. The dotted line represents the mean number collected in trials of each hut design. Note: Some trials collected multiple mosquito species, therefore single trials may be counted more than once in this figure.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/nash2021systematic/figures/figure-7-6.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.07 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3. Entomological outcomes as predicted by bioassay survival. A Comparison of the level of pyrethroid resistance as measured using a discriminating dose bioassay and the percentage of mosquitoes which enter a hut with a pyrethroid-only ITN and survive 24 h after being collected. Solid lines show the fitted relationship using either the logistic (green) or log-logistic (yellow) models. Vertical and horizontal lines show 95% confidence interval estimates for each data point. B-C The rela- tionship between EHT survival (24 h post-collection, unless the ITN incorporated the insecticide chlorfenapyr, in which case 72-h survival was used) and the probability of being caught inside a control hut relative to a hut with any type of ITN (where anywhere above the grey dashed-line indicates more mosquitoes were caught in the control hut) (B), mosquitoes successfully feeding and surviving (C), or exiting the hut without feeding (D). The point size in B-D is proportional to the total number of mosquitoes collected in the trial and coloured according to hut design: East (red) or West (blue). Solid lines in A-D show the best-fit relationship whilst the lighter shaded area indicates 95% credible intervals for the best-fit curves. E-F The models from A-D were combined to summarise how the average probability that blood-feeding mosquitoes will be killed, exit without feeding, deterred from entering or successfully blood-feed, varies with bioassay survival. The relationship be- tween bioassay and EHT survival is highly uncertain, so both the logistic model (E) and the log-logistic model (F) are presented.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/nash2021systematic/1_s20_S2667114X21000418_main-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.17 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3. Entomological outcomes as predicted by bioassay survival. A Comparison of the level of pyrethroid resistance as measured using a discriminating dose bioassay and the percentage of mosquitoes which enter a hut with a pyrethroid-only ITN and survive 24 h after being collected. Solid lines show the fitted relationship using either the logistic (green) or log-logistic (yellow) models. Vertical and horizontal lines show 95% confidence interval estimates for each data point. B-C The relationship between EHT survival (24 h post-collection, unless the ITN incorporated the insecticide chlorfenapyr, in which case 72-h survival was used) and the probability of being caught inside a control hut relative to a hut with any type of ITN (where anywhere above the grey dashed-line indicates more mosquitoes were caught in the control hut) (B), mosquitoes successfully feeding and surviving (C), or exiting the hut without feeding (D). The point size in B-D is proportional to the total number of mosquitoes collected in the trial and coloured according to hut design: East (red) or West (blue). Solid lines in A-D show the best-fit relationship whilst the lighter shaded area indicates 95% credible intervals for the best-fit curves. E-F The models from A-D were combined to summarise how the average probability that blood-feeding mosquitoes will be killed, exit without feeding, deterred from entering or successfully blood-feed, varies with bioassay survival. The relationship between bioassay and EHT survival is highly uncertain, so both the logistic model (E) and the log-logistic model (F) are presented.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/nash2021systematic/figures/figure-8-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.31 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4. Entomological outcomes as predicted by bioassay survival according to East and West African hut designs. A Comparison of the level of pyrethroid resistance as measured using a discriminating dose bioassay and the percentage of mosquitoes which enter a hut and survive 24 h. Solid lines show the fitted relationship for the East (red) and West (blue) African hut design assuming the log-logistic function. Comparable plots showing the logistic function are provided in Supplementary file 1: Fig. S7A. B-C The relationship between EHT survival (24 h post-collection, unless the ITN incorporated the insecticide chlorfenapyr, in which case 72-h survival was used) and the probability of being caught inside a control hut relative to a hut with any type of ITN (where anywhere above the grey dashed-line indicates more mosquitoes were caught in the control hut) (B), mosquitoes successfully feeding and surviving (C), or exiting the hut without feeding (D). The point size in B-D is proportional to the total number of mosquitoes collected in the trial and coloured according to hut design. Solid lines in A-D show the best-fit relationship whilst the lighter shaded area indicates 95% credible intervals for the best-fit curves. E-F The models from A-D were combined to summarise how the average probability that blood-feeding mosquitoes will be killed, exit without feeding, deterred from entering or successfully blood-feed, varies with bioassay survival for either East (E) or West (F) African hut design. Both (E-F) show the log-logistic model, see Supplementary file 1: Figs. S7B–C for models using the logistic function.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/nash2021systematic/1_s20_S2667114X21000418_main-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.41 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4. Entomological outcomes as predicted by bioassay survival according to East and West African hut designs. A Comparison of the level of pyrethroid resistance as measured using a discriminating dose bioassay and the percentage of mosquitoes which enter a hut and survive 24 h. Solid lines show the fitted relationship for the East (red) and West (blue) African hut design assuming the log-logistic function. Comparable plots showing the logistic function are provided in Supplementary file 1: Fig. S7A. B-C The relationship between EHT survival (24 h post-collection, unless the ITN incorporated the insecticide chlorfenapyr, in which case 72-h survival was used) and the probability of being caught inside a control hut relative to a hut with any type of ITN (where anywhere above the grey dashed-line indicates more mosquitoes were caught in the control hut) (B), mosquitoes successfully feeding and surviving (C), or exiting the hut without feeding (D). The point size in B-D is proportional to the total number of mosquitoes collected in the trial and coloured according to hut design. Solid lines in A-D show the best-fit relationship whilst the lighter shaded area indicates 95% credible intervals for the best-fit curves. E-F The models from A-D were combined to summarise how the average probability that blood-feeding mosquitoes will be killed, exit without feeding, deterred from entering or successfully blood-feed, varies with bioassay survival for either East (E) or West (F) African hut design. Both (E-F) show the log-logistic model, see Supplementary file 1: Figs. S7B–C for models using the logistic function.\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "nash2021systematic 6 tables 4 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/menze2022experimental/figures/figure-4-5.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:12.53 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Association between the CYP6P9a/b and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (A) Tube assay CYP6P9a and mortality. Role of CYP6P9a in pyrethroid resistance with samples from the WHO tube. Distribution of the CYP6P9a genotypes according to resistance phenotypes. (B) Tube assay CYP6P9b and mortality. Role of CYP6P9b in pyrethroid resistance with samples from the WHO tube. Distribution of the CYP6P9b genotypes according to resistance phenotypes. (C) Cone assay CYP6P9a and mortality. Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: p < 0.001). (D) Cone assay CYP6P9a and mortality allele. Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (E) Cone assay CYP6P9b and mortality. Genotype distribution of CYP6P9b between alive and dead mosquitoes after exposure to Olyset using the cone test, showing strong association (RR vs. SS: p < 0.001).\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "accrombessi2023efficacy 8 tables 0 figures\n",
-                        "syme2022pyrethroid 8 tables 0 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/menze2022experimental/pathogens_11_00638_v2-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.04 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Association between the CYP6P9a/b and the ability to survive exposure to Olyset nets after WHO tubes cone assays. (A) Tube assay CYP6P9a and mortality. Role of CYP6P9a in pyrethroid resistance with samples from the WHO tube. Distribution of the CYP6P9a genotypes according to resistance phenotypes. (B) Tube assay CYP6P9b and mortality. Role of CYP6P9b in pyrethroid resistance with samples from the WHO tube. Distribution of the CYP6P9b genotypes according to resistance phenotypes. (C) Cone assay CYP6P9a and mortality. Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of mosquitoes carrying the RR genotype in order to survive compared to SS (RR vs. SS: p < 0.001). (D) Cone assay CYP6P9a and mortality allele. Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (E) Cone assay CYP6P9b and mortality. Genotype distribution of CYP6P9b between\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/menze2022experimental/figures/figure-5-6.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.09 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. Association between the CYP6P9a/b and the ability to survive exposure to Olyset Plus nets after cone assays. (A) Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (p < 0.001). (B) Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (C) Genotype distribution of CYP6P9b between alive and dead mosquitoes after exposure to Olyset Plus using the cone test, showing the significant ability of the mosquitoes carrying the RR genotype to survive compared to SS (p < 0.001).\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/menze2022experimental/pathogens_11_00638_v2-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.13 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. Association between the CYP6P9a/b and the ability to survive exposure to Olyset Plus nets after cone assays. (A) Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the mosquitoes to carry the RR genotype to survive compared to SS (p < 0.001). (B) Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (C) Genotype distribution of CYP6P9b between alive and dead mosquitoes after\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/menze2022experimental/pathogens_11_00638_v2-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.23 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 4. Association between 6.5 kb SV and the ability to survive against exposure to Olyset and Olyset Plus nets using cone assays. (A) Distribution of 6.5 kb SV genotypes between dead and alive mosquitoes after exposure to Olyset, showing that 6.5 kb SV_R allows mosquitoes to survive significantly better against exposure to this net. (B) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset with the cone test, showing the significant ability of the 6.5 kb SV _R_-resistant mosquitoes to survive compared to their susceptible counterparts 6.5 kb SV _S. (C) Distribution of 6.5 kb SV genotypes between dead and alive mosquitoes after exposure to the Olyset Plus net. (D) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset Plus with the cone test, showing the significant ability of the 6.5 kb SV _R_-resistant mosquitoes to survive compared to their susceptible counterparts 6.5 kb SV _S.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/menze2022experimental/figures/figure-7-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.30 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 5. Impact of both CYP6P9a and CYP6P9b on the efficacy of bed nets in the experimental hut trial. (A) Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: p < 0.001). (B) Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (C) Association between CYP6P9a and ability to blood-feed when exposed to Olyset. The CYP6P9a-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (D) Association between CYP6P9b and ability to survive exposure to Olyset in the experimental hut trial. (E) Allelic frequency of CYP6P9b between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the CYP6P9b_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9b_S. (F) Association between duplicated CYP6P9b gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The CYP6P9b-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/menze2022experimental/pathogens_11_00638_v2-Figure5-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.36 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 5. Impact of both CYP6P9a and CYP6P9b on the efficacy of bed nets in the experimental hut trial. (A) Genotype distribution of CYP6P9a between alive and dead mosquitoes after exposure to Olyset, showing strong association (RR vs. SS: p < 0.001). (B) Allelic frequency of CYP6P9a between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the CYP6P9a_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9a_S. (C) Association between CYP6P9a and ability to blood-feed when exposed to Olyset. The CYP6P9a-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset net. (D) Association between CYP6P9b and ability to survive exposure to Olyset in the experimental hut trial. (E) Allelic frequency of CYP6P9b between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the significant ability of the CYP6P9b_R_-resistant mosquitoes to survive compared to their susceptible counterparts CYP6P9b_S. (F) Association between duplicated CYP6P9b gene and ability to blood-feed when exposed to Olyset in the experimental hut trial. The CYP6P9b-R allele increases the strength of resistant mosquitoes by taking a blood meal in contrast to the susceptible ones when exposed to Olyset.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/menze2022experimental/figures/figure-9-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.44 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 6. Impact of the 6.5 kb SV-based metabolic resistance on bed nets’ efficacy in experimental hut trials. (A) Distribution of 6.5 kb SV genotypes between dead and alive mosquitoes after expo- sure to Olyset net, showing that 6.5 kb SV _R allows mosquitoes to survive significant better after exposure to this insecticide-treated net. (B) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the 6.5 kb SV _R_-resistant mosquitoes to survive compared to their susceptible counterparts 6.5 kb SV _S. (C) Distribution of 6.5 kb SV genotypes between the blood fed (BF) and unfed (UF) mosquitoes after exposure to Olyset (OL), showing that 6.5 kb SV_R significantly allows mosquitoes to blood-feed in the presence of this insecticide-treated net. (D) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the ability of the 6.5 kb SV _R_-resistant mosquitoes to blood-feed significantly better compared to their susceptible counterparts\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/menze2022experimental/pathogens_11_00638_v2-Figure6-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:13.50 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 6. Impact of the 6.5 kb SV-based metabolic resistance on bed nets’ efficacy in experimental hut trials. (A) Distribution of 6.5 kb SV genotypes between dead and alive mosquitoes after exposure to Olyset net, showing that 6.5 kb SV _R allows mosquitoes to survive significant better after exposure to this insecticide-treated net. (B) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset with experimental hut trial, showing the significant ability of the 6.5 kb SV _R_-resistant mosquitoes to survive compared to their susceptible counterparts 6.5 kb SV _S. (C) Distribution of 6.5 kb SV genotypes between the blood fed (BF) and unfed (UF) mosquitoes after exposure to Olyset (OL), showing that 6.5 kb SV_R significantly allows mosquitoes to bloodfeed in the presence of this insecticide-treated net. (D) Allelic frequency of 6.5 kb SV between alive and dead mosquitoes after exposure to Olyset with the experimental hut trial, showing the ability of the 6.5 kb SV _R_-resistant mosquitoes to blood-feed significantly better compared to their susceptible counterparts 6.5 kb SV _S. (E) Distribution of 6.5 kb SV genotypes between blood-fed (BF) and unfed (UF) mosquitoes after exposure to Olyset Plus, showing that 6.5 kb SV_R significantly allows mosquitoes to blood-feed in the presence of this insecticide-treated net. (F) Allelic frequency\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/gebremariam2021evaluation/figures/figure-3-3.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.00 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1. Experimental hut design of the study.\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "menze2022experimental 1 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/gebremariam2021evaluation/gebremariam_et_al_2021_evaluation_of_long_lasting_insecticidal_nets_duranetr_under_laboratory_and_semi_field-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.03 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1. Experimental hut design of the study.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/gebremariam2021evaluation/figures/figure-4-4.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.06 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Mean percent knockdown and mortality of An. arabiensis exposed in 3 minutes cone bioassay to DuraNet®, Jimma, and southwestern Ethiopia.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/gebremariam2021evaluation/gebremariam_et_al_2021_evaluation_of_long_lasting_insecticidal_nets_duranetr_under_laboratory_and_semi_field-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.17 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Mean percent knockdown and mortality of An. arabiensis exposed in 3 minutes cone bioassay to DuraNet®, Jimma, and\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "gebremariam2021evaluation 3 tables 2 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/briet2020physical/figures/figure-5-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.22 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1 Causal diagram for factors determining the effects of LLINs on malaria transmission. Solid lines indicate the main causal relationships between the measured quantities; dashed lines indicate which factors impact malaria transmission (via relationships estimated from experimental hut data and captured in the mathematical model)\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/briet2020physical/figures/figure-7-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.27 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Survival (1‑attrition) (a), use of nets currently in the household (b), proportion of original cohort of nets in use (c), mean of the natural log of estimated hole area (d), reduction in vectorial capacity of pyrethroid susceptible mosquitoes (e), and reduction in vectorial capacity of pyrethroid resistant mosquitoes (f), by country\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/briet2020physical/Attrition_physical_integrity_and_insecticidal_activity_of_long_lasting__insecti-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.40 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Survival (1‑attrition) (a), use of nets currently in the household (b), proportion of original cohort of nets in use (c), mean of the natural log of estimated hole area (d), reduction in vectorial capacity of pyrethroid susceptible mosquitoes (e), and reduction in vectorial capacity of pyrethroid resistant mosquitoes (f), by country\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/briet2020physical/figures/figure-8-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:14.51 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Persistence of active ingredients by LLIN brand\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/briet2020physical/Attrition_physical_integrity_and_insecticidal_activity_of_long_lasting__insecti-Figure4-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.00 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 4 Lethality in cone tests and calibration of active ingredient content. The lethality and calibration curves are shown only for four specific LLIN brands. The lines for the other LLIN brands are very close to that for PermaNet 2.0\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/briet2020physical/figures/figure-11-11.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.05 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 5 Predicted entomological effects of holed and intact LLINs. a Pyrethroid resistant mosquitoes. b Pyrethroid sensitive mosquitoes. The vertical black line corresponds to the target active agent content for PermaNet 2.0. The continuous lines correspond to intact LLINs and the dashed lines to LLINs with a holed area of 50 cm2. The effect size, on the vertical axis is the proportion by which availability of humans to mosquitoes is reduced, or killing of mosquitoes increased, when the LLIN is in use\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ibrahim2020exploring/figures/figure-3-3.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.10 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1. A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "briet2020physical 10 tables 5 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ibrahim2020exploring/genes_11_00454_pdf-Figure1-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.12 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1. ap sho ing the sa pling locality ( ajerar i a) in the Sahel of northern igeria.\u001b[0m\n",
-                        "\u001b[32m[0621 00:15.17 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 1. A map showing the sampling locality (Gajerar Giwa) in the Sahel of northern Nigeria.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/ibrahim2020exploring/figures/figure-8-4.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.20 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Resistance profiles of F1 An. funestus females. (a). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (b). Results of the cone bioassays with PermaNet 3.0 (side and roof), PermaNet Net. Results are the average of percentage Plus and Olyset mortalities ± SEM of five replicates; (c). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at P < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (d). Time-course bioassay for LT50 estimation/test for strength of permethrin resistance.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ibrahim2020exploring/genes_11_00454_pdf-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.27 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Resistance profiles of F1 An. funestus females. (a). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (b). Results of the cone bioassays with PermaNet®3.0 (side and roof), PermaNet®2.0, Olyset®Plus and Olyset®Net. Results are the average of percentage mortalities ± SEM of five replicates; (c). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl aleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the verage of percent g mortaliti s from four replicate each ± SEM. **** = statistically significant at P < 0 0001, in a two-tailed Chi-square test between results fr m syn rgised b o ssay and conventional bioassays; (d). Time-course bioassay for LT50 estimation/test for strength of permethrin resistance.\u001b[0m\n",
-                        "\u001b[32m[0621 00:15.33 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 2. Resistance profiles of F1 An. funestus females. (a). Results of WHO susceptibility bioassays with various insecticides. Results are the average of percentage mortalities from four replicates each ± standard error of mean (SEM); (b). Results of the cone bioassays with PermaNet® 3.0 (side and roof), PermaNet® 2.0, Olyset® Plus and Olyset® Net. Results are the average of percentage mortalities ± SEM of five replicates; (c). Effect of pre-exposure of synergist piperonylbutoxide (PBO) against permethrin and diethyl maleate (DEM) against DDT (dichlorodiphenyltrichloroethane). Results are the average of percentage mortalities from four replicates each ± SEM. **** = statistically significant at P < 0.0001, in a two-tailed Chi-square test between results from synergised bioassay and conventional bioassays; (d). Time-course bioassay for LT50 estimation/test for strength of permethrin resistance.\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/ibrahim2020exploring/genes_11_00454_pdf-Figure3-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.40 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. enotyping of L119F STe2 utation. (a): distribution of the 119F utation a ong the 44 rando ly selected F0 fe ales; (b): the 119F utation genotype for 12 each of T-alive and -dead F1 fe ales, o ozygote resistant, RS = heterozygote resistant, and SS = susceptible; (c): agarose-gel s i t e 119 e t e istri ti fr allele-s ecific ( - ), t a el: 12 -ali e 1 f l s ( , , 6, and 8–12 are R , 1, 4, 5 and 7 are RS), bott m panel: 12 DDT-dea F1 females (13, 4, 5, 7, 9, 21– 4 are RS, 16 and 18 are SS, and 20 failed).\u001b[0m\n",
-                        "\u001b[32m[0621 00:15.46 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Figure 3. Genotyping of L119F GSTe2 mutation. (a): distribution of the 119F mutation among the 44 randomly selected F0 females; (b): the 119F mutation genotype for 12 each of DDT-alive and -dead F1 females, RR = homozygote resistant, RS = heterozygote resistant, and SS = susceptible; c: agarose-gel showing the L119F genotype distribution from allele-specific PCR (AS-PCR), top panel: 12 DDT-alive F1 females (2, 3, 6, and 8–12 are RR, 1, 4, 5 and 7 are RS), bottom panel: 12 DDT-dead F1 females (13, 14, 15, 17, 19, 21–24 are RS, 16 and 18 are SS, and 20 failed).\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "ibrahim2020exploring 0 tables 6 figures\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/mieguim2021insights/figures/figure-4-7.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.52 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 1. Seasonal relative abundance of Anopheles mosquito species in Mvoua. S1 short dry season, S2 long rainy season, S3 long dry season, S4 short rainy season\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1311: PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1135: PydanticDeprecatedSince20: The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, otherwise load the data then use `model_validate` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  warnings.warn(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/pydantic/main.py:1143: PydanticDeprecatedSince20: `load_str_bytes` is deprecated. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/\n",
-                        "  obj = parse.load_str_bytes(\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/mieguim2021insights/figures/figure-5-8.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:15.57 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Seasonal variation in human biting behavior of malaria vectors in Mvoua from August 2018 to April 2019. In Inside (homes), Out outside, S1–S4 as defined in Fig. 1\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/pdffigure2/mieguim2021insights/Insights_into_factors_sustaining_persistence_of_high_malaria_transmission_in__fo-Figure2-1.png'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:16.05 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 2 Seasonal variation in human biting behavior of malaria vectors in Mvoua from August 2018 to April 2019. In Inside (homes), Out outside, S1–S4 as defined in Fig. 1\u001b[0m\n",
-                        "/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/core/readers/file/base.py:535: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/jonnytr/Projects/ITN-recal-data-extraction/data/preprocessing/unstructured/mieguim2021insights/figures/figure-6-9.jpg'>\n",
-                        "  docs = reader.load_data(input_file, **kwargs)\n",
-                        "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
-                        "\u001b[32m[0621 00:16.12 @segment.py:251]\u001b[0m  \u001b[32mINF\u001b[0m  \u001b[97mExtracting figure table: Fig. 3 Anopheles funestus and An. gambiae (s.l.) night‑biting cycle in Mvoua from August 2018 to April 2019\u001b[0m\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "mieguim2021insights 4 tables 3 figures\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "285"
-                        ]
-                    },
-                    "execution_count": 26,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from llama_index.core import global_handler\n",
-                "records = []\n",
-                "\n",
-                "for ref, paper in tqdm(papers_subset.iterrows()):\n",
-                "    doc = remote_dataset.add_document(ex.Document.from_file(\n",
-                "        paper.file_path, reference=ref, pmid=paper.pmid, doi=paper.doi, id=paper.get('id')))\n",
-                "    \n",
-                "    metadata = {\"reference\": paper.name}\n",
-                "    if isinstance(doc.pmid, (str, int)) and doc.pmid != 'nan':\n",
-                "        metadata['pmid'] = doc.pmid\n",
-                "    elif doc.id:\n",
-                "        metadata['doc_id'] = str(doc.id)\n",
-                "\n",
-                "    # Table segments\n",
-                "    table_alignments = merge_extractions(\n",
-                "        deepdoctection=tables[ref]['deepdoctection'],\n",
-                "        unstructured=tables[ref]['unstructured'],\n",
-                "        llmsherpa=tables[ref]['llmsherpa'],\n",
-                "        nougat=tables[ref]['nougat'],\n",
-                "        pdffigures2=tables[ref]['pdffigures2'],\n",
-                "    )\n",
-                "    table_records = table_alignments.to_records(dataset=remote_dataset, metadata=metadata)\n",
-                "    records.extend(table_records)\n",
-                "\n",
-                "    # Figure segments\n",
-                "    ### comment out langfuse code \n",
-                "    # global_handler.set_trace_params(\n",
-                "    #     name=f\"preprocess-{ref}\",\n",
-                "    #     session_id=ref,\n",
-                "    #     user_id='jonnytr',\n",
-                "    # )\n",
-                "    figure_segments = merge_extractions(\n",
-                "        unstructured=figures[ref]['unstructured'],\n",
-                "        pdffigures2=figures[ref]['pdffigures2'],\n",
-                "    )\n",
-                "    for alignment in figure_segments.items:\n",
-                "        for source, segment in alignment.extractions.items():\n",
-                "            if os.path.exists(segment.image) and segment.header:\n",
-                "                _figure_extraction_response = segment.extract_html_table()\n",
-                "        \n",
-                "    figure_records = figure_segments.to_records(dataset=remote_dataset, metadata=metadata)\n",
-                "    records.extend(figure_records)\n",
-                "\n",
-                "    print(paper.name, len(table_records), 'tables', len(figure_records), 'figures')\n",
-                "\n",
-                "records = [r for r in records if r is not None]\n",
-                "len(records)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 29,
-            "id": "638c5a8a-8815-4d2d-aed7-1056ea1a284d",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Pushing records to Extralit... <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\"> 90%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:04</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "Pushing records to Extralit... \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━\u001b[0m \u001b[35m 90%\u001b[0m \u001b[36m0:00:04\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-                        ],
-                        "text/plain": []
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "remote_dataset.add_records(records)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f922936f-3ac0-4111-9049-d7c57945964b",
-            "metadata": {},
-            "source": [
-                "## Update Records"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 121,
-            "id": "43444fd9-a88c-40ec-8c2d-8fe2abd21bf2",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "24b6e0660e2842d0982fcf84bc77a4f8",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/840 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "record.external_id False 1 Table 1 Characteristics of monitoring sentinel sites\n",
-                        "record.external_id False 1 Table 2 Resistance intensity of An. gambiae s.l. 30 min after exposure to deltamethrin\n",
-                        "\n",
-                        "record.external_id False 1 Table 3 Resistance intensity of An. gambiae s.l. 30 min after exposure to permethrin\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4 Resistance intensity of An. gambiae s.l. 30 min after exposure to alphacypermethrin\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 \n",
-                        "Table 5 Allelic frequencies of kdr L1014F and L1014S in the An. gambiae complex\n",
-                        "\n",
-                        "record.external_id False 1 Table 1: Characteristics for study LLINs\n",
-                        "record.external_id False 1 Table 2: The experimental hut trial treatment arms\n",
-                        "record.external_id False 1 Table 3 Risk assessment for the study\n",
-                        "record.external_id False 1 Table 4 Deterrence, exophily, blood feeding and mortality in the 7 treatment groups\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5 The mean knockdown and 24 h mortality induced by net pieces, cut from nets ‘held back’\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 6 The mean knockdown and 24 h mortality by hut-used and held back LLINs\n",
-                        "record.external_id False 1 Table 7 Results for the test systems’ species and Kdr identification\n",
-                        "record.external_id False 1 Table 1: Mortality rate of Anopheles gambiae s.l. from Yaoundé exposed to certain insecticides.\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2. Gene expression analysis of field collected Anopheles coluzzii from Yaoundé compared to the susceptible Ngousso (NG) and Kisumu (KIS) strains.\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3. Knockdown and mortality of different Anopheles gambiae s.l. colonies used for cone bioassays.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4. Evaluation of behavioural response of resistant Anopheles gambiae s.|. population exposed to PermaNet® 2.0 nets.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5. Personal and community protection confered by Perma- Net® 2.0 nets when exposed to a resistant Anopheles coluzzii population.\n",
-                        "\n",
-                        "record.external_id False 1 Table 1 Summary results obtained against wild An. gambiae s.l. in experimental huts\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Results of non-inferiority statistical analysis of the performance of Panda® Net and Yahe® LN nets versus standard reference PermaNet® 2.0\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3: Results of chemical analysis of LLINs used in the experimental trial\n",
-                        "\n",
-                        "record.external_id False 1 Table 1 Reduction of entry rate of Anopheles gambiae s.l. obs Adjohoun, Dangbo, Misserete and Seme\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Endophily rate of Anopheles gambiae s.l. observed before and after two interventions in districts of Adjohoun, Dangbo, Misserete and Seme\n",
-                        "record.external_id False 1 \n",
-                        "\n",
-                        "record.external_id False 1 Table 4 Mortality rate of Anopheles gambiae s.l. observed before and after IRS and LLITN interventions in districts of Adjohoun, Dangbo, Misserete and Seme\n",
-                        "\n",
-                        "record.external_id False 1 Table 1: Numbers of the principal mosquito species caught and dissected during the 3 years of the study\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Daily biting rate (ma), parity rate (PR), sporozolte rate (s), daily Inoculation rate (dh), yearly Inoculation rate (yh), and percent modification relative to the first year of the study in the northern and southern halves of the village\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 2 Table 1. Data on four spraying or insecticide-treated net projects in lowland areas of the United Republic of Tanzania\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 2 Table 2. Effects of insecticide-treated materials and house spraying in Kenya and West Africa\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3. Comparison of treatment efficacies in areas outside tropical Africa\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1 Percentage mortality of An. gambiae (s.l.) from Opeibea using WHO insecticide susceptibility intensity assays in 2017\n",
-                        "\n",
-                        "record.external_id False 1 Table 2 Percentage mortality of An. gambiae (s.l.) from Opeibea recorded using CDC bottle intensity assays in 2017\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1: Evaluation of fipronil in the house spraying of experimental huts and of bednets impregnated with lambda-cyhalothrin at the experimental station of M’bé (Côted’Ivoire) against the natural populations ofAnopheles gambiae.\n",
-                        "record.external_id False 1 Table 1: Mortality rates on wild populations of Anopheles: coluzzi at VK1 using EMCs. Averages taken for each configuration, 2 houses per configuration. \n",
-                        "\n",
-                        "record.external_id False 1 Table 2 Residual efficacy tests on (A) Anopheles gambiae “Kisumu” and (B) Anopheles coluzzii VK1 using WHO test cones. Averages taken for each configuration, 2 houses per configuration. \n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3 Distribution of the frequency of L1014F and L1014S kdr mutations in Anopheles coluzzii in VK1. \n",
-                        "\n",
-                        "record.external_id False 1 Table 4: Allelic frequency and genotype of the Ace–1R mutation in Anopheles coluzzi at VKI. \n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5: Analysis of the blood source of bloodford Anopheles coluzzi collected using EMCs at VKI. \n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1. Knockdown (60 min) and mortality (24 hours) of wild adult An. gambiae s.l. mosquitoes of Kolokopé using WHO tube test.\n",
-                        "\n",
-                        "record.external_id False 1 Table 2.: Characterization of the An. gambiae s.l. mosquito populations from Kolokopé.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3: WHO cone bioassay against susceptible An. gambiae s.s. Kissum of nets before and after washing, and after the hut trial.\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4. WHO cone bioassay against wild resistant An. gambiae s.l. Kolokope of all treatment arms before and after the hut trial.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5. Summary of trial results obtained for free flying An. gambiae s.l. in experimental huts (98 nights) in Kolokope, Togo.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 6. Active ingredient and synergist contents of each net sample before and after the experimental hut trial.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1. Efficacy on tunnel test of mosquito nets impregnated with bifenthrin and chlorpyrifos-methyl, alone and in mixture, against a pyrethroid-resistant strain of Anopheles gambiae (VKPR)\n",
-                        "record.external_id False 1 Table 1. Comparison of the sporozoite rate (in % with No. tested in parentheses) as determined by dissection and by ELISA, in An. gambiae and An. funestus and in villages with or without vector control. Presence of permethrin impregnated nets is indicated by solid boxes, lambdacyhalothrin nets by boxes with broken lines and DDT spraying by a double box.\n",
-                        "record.external_id False 1 \n",
-                        "record.external_id False 1 Table 2. In the six months before introduction of impregnated nets into Mng'aza the inoculation rate was as high as 7 infective bites per person per night, which gives some idea of the scale of the problem which has to be overcome in such villages if malaria is to be controlled.Estimated numbers of bites by the two vector species per person Per night based on the light trap catches and their relationship to human biting catches. The sporozoite rates are based on the results of dissections and ELISA tests weighted for the numbers biting at the season concerned. Boxes indicate vector control methods as in Table 1.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3: Blood meal identification on anopheles caught in pit traps before or after introduction of vector control with impregnated nets in Mlingano and Mng’aza and DDT spraying in Mindu. The blood meals were identified as from human (H), bovine (B) or goat (G) or were unidentifiable (-ve). Data for An. funestus and the closely related An. rivulorum are pooled as, on some occasions, these may not have been correctly separated.\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4. Numbers of An. gambiae caught in human biting catches over the four quarters of the night inside houses, and in the first quarter outside. Data are derived from pooling several complete nights' collections before or after net introduction in Mng'aza and Mlingano; for each village the second line of data is from collections terminated at 21.30 h. N indicates the number of collections on which the data are based; n indicates the numbers caught. The percentages are based on the total inside collection. The mean biting times are derived from hour-by-hour data before pooling into the four quarters shown in the table. (The standard errors of the mean biting times are shown in minutes.)\n",
-                        "record.external_id False 1 Table 1: Numbers of female Anopheles gambiae s.l. collected in human-landing collections and number per room resting indoors obtained using pyrethrum spray catches in pairs of villages with insecticide-treated or untreated bed nets\n",
-                        "\n",
-                        "record.external_id False 1 Table 2. Indoor resting collections of female Anopheles gumbiae s.1. classified by gonotrophic stage in pairs of villages with insecticide-treated or untreated bed nets.\n",
-                        "record.external_id False 1 Table 3. Parous rates of Anopheles gambiae s.1. from human-landing collections in pairs of villages with insecticide-treated or untreated bed nets\n",
-                        "record.external_id False 1 Table 4. Parous rates of Anopheles gambiae s.l. from exit trap collections in pairs of villages with insecticide-treated or untreated bed nets\n",
-                        "\n",
-                        "record.external_id False 1 Table. 5. Sporozoite rates ofAnopheles gambiae s.1. in the cross-over study, October 1993\n",
-                        "record.external_id False 1 Table 1 Information on the houses used for all-night trapping collection bottle rotator in May to June 2011\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2 Information on the houses used for all-night trapping of An. arabiensis and An. funestus s.s. by CDC traps equipped with a collection bottle rotator\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1: Washing of study nets\n",
-                        "\n",
-                        "record.external_id False 1 Table 2 Physical condition of nets during net surveys\n",
-                        "record.external_id False 1 Table 3 Alpha-cypermethrin content at baseline (10 nets for each type, 3 samples per net), RSD = Relative Standard Deviation; IQR = Inter-Quartile RangeTable 4 Alpha-cypermethrin content during follow-up; IQR = Inter-Quartile Range (single sample per net)\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4 Alpha-cypermethrin content during follow-up; IQR = Inter-Quartile Range (single sample per net)\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5 Bio assay results for Anopheles gambiae s.s. expressed as geometric mean Net type Time of follow-up\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 6: Optimal and minimal effectiveness of tested nets based on bio-assay results\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1 Experiment arms tested for each mosquito species\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2 Results for Anopheles gambiae KISUMU\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4 Results for Culex quinquefasciatus\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5 Results from regression analysis\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 6 Extrapolated holed surface area in cm2 at  which the proportion feeding is equal to the feeding in the no-net experiment arm\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 1: Description of the long-lasting insecticidal nets used.\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Results of the performance of the five brands of LLINs against wild An. funestus females in experimental huts.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3. Results of the performance of the four brands of LLINs against wild Mansonia spp. in experimental huts.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4: Impact of L119F-GSTe2 mutation on the ability of various insecticide-treated nets to kill mosquitoes.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 5. Impact of L119F-GSTe2 mutation on the efficacy of various bed nets to prevent blood feeding.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 6: Impact of L119F-GSTe2 mutation on the efficacy of various bed nets in repellency.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 7. Impact of GSTe2 on the ability of field populationAn. funestusto survive using samples fromcone assays.\n",
-                        "record.external_id False 1 Table 1. Mortality of mosquitoes freely entering into the huts.\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 2. Cone bioassay tests on treated nets using insectaryreared mosquitoes.\n",
-                        "\n",
-                        "record.external_id False 1 Table 3. Blood feeding rates and insecticide-induced feeding inhibition of free flying Anopheline mosquitoes.\n",
-                        "\n",
-                        "record.external_id False 1 Table 4. Exophily and insecticide-induced exiting of free flying Anopheline mosquitoes\n",
-                        "record.external_id False 1 Table 1: Washes of net samples. PN1 = Permanet 1st generation, PN2 = Permanet 2nd generation, conv = conventionally treated.\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Results from chemical residue analysis. A: Average net chemical residue in mg/m2 at different times of follow-up\n",
-                        "\n",
-                        "record.external_id False 1 Table 2: Results from chemical residue analysis. B: Proportion of nets with deltamethrin ≥ 4 mg/m2 at different times of follow-up\n",
-                        "record.external_id False 1 Table 3: Bioassay data with Anopheles gambiae s.s (Kisum strain). Three minute exposure using the cone test. A: Average net functional mortality in % at different times of follow-up\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3: Bioassay data with Anopheles gambiae s.s (Kisum strain). Three minute exposure using the cone test. B: Average net knockdown rate within 60 minutes in % at different times of follow-up\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 4: Net effectiveness based on WHO recommended criteria. Optimal effectiveness: KD60 ≥ 95% or mortality ≥ 80%, minimal effectiveness: KD60 ≥ 75% or mortality ≥ 50%.\n",
-                        "A. Proportion of nets with optimal effectiveness at different times of follow-up\n",
-                        "\n",
-                        "record.external_id False 1 Table 4: Net effectiveness based on WHO recommended criteria. Optimal effectiveness: KD60 ≥ 95% or mortality ≥ 80%, minimal effectiveness: KD60 ≥ 75% or mortality ≥ 50%.\n",
-                        "B. Proportion of nets with minimal effectiveness at different times of follow-up\n",
-                        "\n",
-                        "record.external_id False 1 Table 5: Performance of stored nets: Chemical residue for PermaNet 1st generation baseline samples unused, unwashed but exposed to air and light.Correlation between results from two analytical protocols for the determination of deltamethrin based on either gas chromatography (GC-ECD) or HPLC (CIPAC). The dotted (black) line represents equality between results and the continuous (red) line the linear regression.\n",
-                        "record.external_id False 1 Table I. Colour and shape preferences for bednets in communities of Ingwavuma magisterial district, KwaZulu-Natal\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table IV. Difference in malaria incidence (cases per 1 000 person- years) in seven pairs of blocks between 1996 and 1998\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table ll. Number of mosquitoes collected in window traps (February - May 1997) and their physiological status\n",
-                        "\n",
-                        "\n",
-                        "\n",
-                        "record.external_id False 1 Table 3. Bioassay tests (% mortality) on walls and nets between March and August 1997, and in February 1998\n",
-                        "record.external_id False 1 Table V. Malaria incidence rate ratios (within pair) in the seven pairs of blocks and overall in 1997 and 1998\n",
-                        "\n",
-                        "\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">30</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">27 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>]                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">28 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>)                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">29 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>30 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>nodes = vector_store.get_nodes(filters=filters)                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">31 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> nodes:                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">32 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># record.external_id = nodes[0].id_</span>                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">33 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">print</span>(<span style=\"color: #808000; text-decoration-color: #808000\">'record.external_id'</span>, nodes[<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>].id_ == record.id, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">len</span>(nodes), header)      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/Projects/extralit/src/extralit/extraction/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">vector_store.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">107</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">get_nodes</span>        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">104 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">get_nodes</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, node_ids: Optional[List[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>]] = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>, filters: Optional[Metadata   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">105 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>-&gt; List[BaseNode]:                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">106 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>collection = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._client.collections.get(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.index_name)                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>107 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>all_properties = get_all_properties(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._client, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.index_name)                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">108 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">109 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> filters <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">110 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>filters = _to_weaviate_filter(filters)                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/vector_stores/weaviate/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">utils.</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">96</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">get_all_properties</span>                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 93 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> client.collections.exists(class_name):                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 94 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">f\"{</span>class_name<span style=\"color: #808000; text-decoration-color: #808000\">} schema does not exist.\"</span>)                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 95 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 96 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>properties = client.collections.get(class_name).config.get().properties                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 97 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> [p.name <span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> p <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> properties]                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 98 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 99 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/collections/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">config.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">81</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">get</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 78 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">If Weaviate reports a non-OK status.</span>                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 79 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 80 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>_validate_input([_ValidateArgument(expected=[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">bool</span>], name=<span style=\"color: #808000; text-decoration-color: #808000\">\"simple\"</span>, value=simple)   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 81 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>schema = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.__get()                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 82 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> simple:                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 83 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> _collection_config_simple_from_json(schema)                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 84 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> _collection_config_from_json(schema)                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/collections/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">config.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">49</span> in      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__get</span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 46 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.__tenant = tenant                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 47 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 48 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__get</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>) -&gt; Dict[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>, Any]:                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 49 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>response = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.__connection.get(                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 50 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>path=<span style=\"color: #808000; text-decoration-color: #808000\">f\"/schema/{</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._name<span style=\"color: #808000; text-decoration-color: #808000\">}\"</span>,                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 51 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>error_msg=<span style=\"color: #808000; text-decoration-color: #808000\">\"Collection configuration could not be retrieved.\"</span>,                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 52 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>status_codes=_ExpectedStatusCodes(ok_in=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">200</span>, error=<span style=\"color: #808000; text-decoration-color: #808000\">\"Get collection configura</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/connect/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">v4.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">561</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">get</span>         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">558 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">559 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>request_url = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.url + <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._api_version_path + path                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">560 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>561 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.__send(                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">562 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"GET\"</span>, url=request_url, params=params, error_msg=error_msg, status_codes=sta   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">563 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>)                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">564 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/connect/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">v4.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">449</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__send</span>      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">446 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>params=params,                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">447 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>headers=<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.__get_latest_headers(),                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">448 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>)                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>449 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>res = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._client.send(req)                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">450 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> status_codes <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">and</span> res.status_code <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> status_codes.ok:        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">451 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> UnexpectedStatusCodeError(error_msg, response=res)                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">452 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> cast(Response, res)                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_client.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">914</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">send</span>              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 911 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 912 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>auth = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._build_request_auth(request, auth)                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 913 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 914 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>response = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._send_handling_auth(                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 915 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>request,                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 916 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>auth=auth,                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 917 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>follow_redirects=follow_redirects,                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_client.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">942</span> in                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_send_handling_auth</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 939 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>request = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">next</span>(auth_flow)                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 940 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 941 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">while</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>:                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 942 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>response = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._send_handling_redirects(                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 943 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>request,                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 944 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>follow_redirects=follow_redirects,                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 945 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>history=history,                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_client.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">979</span> in                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_send_handling_redirects</span>                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 976 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> hook <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._event_hooks[<span style=\"color: #808000; text-decoration-color: #808000\">\"request\"</span>]:                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 977 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>hook(request)                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 978 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 979 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>response = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._send_single_request(request)                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 980 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 981 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> hook <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._event_hooks[<span style=\"color: #808000; text-decoration-color: #808000\">\"response\"</span>]:                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 982 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>hook(response)                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_client.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1015</span> in                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_send_single_request</span>                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1012 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>)                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1013 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> request_context(request=request):                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1015 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>response = transport.handle_request(request)                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1016 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1017 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">assert</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(response.stream, SyncByteStream)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1018 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/_transports/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">default.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">233</span> in       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">230 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>extensions=request.extensions,                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">231 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>)                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">232 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> map_httpcore_exceptions():                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>233 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>resp = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._pool.handle_request(req)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">234 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">235 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">assert</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(resp.stream, typing.Iterable)                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">236 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">connection_pool.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">216</span> in  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">213 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>closing = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._assign_requests_to_connections()                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">214 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">215 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._close_connections(closing)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>216 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> exc <span style=\"color: #0000ff; text-decoration-color: #0000ff\">from</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">217 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">218 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Return the response. Note that in this case we still have to manage</span>              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">219 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># the point at which the response is closed.</span>                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">connection_pool.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">196</span> in  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">193 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">194 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">195 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Send the request on the assigned connection.</span>                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>196 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>response = connection.handle_request(                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">197 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   </span>pool_request.request                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">198 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>)                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">199 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> ConnectionNotAvailable:                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">connection.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">101</span> in       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 98 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._connect_failed = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 99 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> exc                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">100 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>101 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._connection.handle_request(request)                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">102 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">103 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_connect</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, request: Request) -&gt; NetworkStream:                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">104 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>timeouts = request.extensions.get(<span style=\"color: #808000; text-decoration-color: #808000\">\"timeout\"</span>, {})                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">http11.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">143</span> in           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">140 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> ShieldCancellation():                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">141 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> Trace(<span style=\"color: #808000; text-decoration-color: #808000\">\"response_closed\"</span>, logger, request) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> trace:                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">142 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._response_closed()                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>143 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> exc                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">145 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Sending the request...</span>                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">146 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">http11.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">113</span> in           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">handle_request</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">110 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>reason_phrase,                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">111 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>headers,                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">112 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>trailing_data,                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>113 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>) = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._receive_response_headers(**kwargs)                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">114 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>trace.return_value = (                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">115 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>http_version,                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">116 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>status,                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">http11.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">186</span> in           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_receive_response_headers</span>                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">183 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>timeout = timeouts.get(<span style=\"color: #808000; text-decoration-color: #808000\">\"read\"</span>, <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">184 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">185 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">while</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>:                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>186 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>event = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._receive_event(timeout=timeout)                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">187 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(event, h11.Response):                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">break</span>                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">189 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> (                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">http11.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">224</span> in           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_receive_event</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">221 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>event = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._h11_state.next_event()                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">222 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">223 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> event <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> h11.NEED_DATA:                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>224 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>data = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._network_stream.read(                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">225 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.READ_NUM_BYTES, timeout=timeout                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">226 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>)                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">227 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_backends/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">sync.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">126</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">read</span>    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">123 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>exc_map: ExceptionMapping = {socket.timeout: ReadTimeout, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">OSError</span>: ReadError}      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">124 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> map_exceptions(exc_map):                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">125 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._sock.settimeout(timeout)                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>126 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._sock.recv(max_bytes)                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">127 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">128 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">write</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, buffer: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">bytes</span>, timeout: typing.Optional[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">float</span>] = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>) -&gt; <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">129 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> buffer:                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
-                            "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
-                            "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">KeyboardInterrupt</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m in \u001b[92m<module>\u001b[0m:\u001b[94m30\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m27 \u001b[0m\u001b[2m│   │   │   \u001b[0m]                                                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m28 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m29 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m30 \u001b[2m│   │   \u001b[0mnodes = vector_store.get_nodes(filters=filters)                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m31 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m nodes:                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m32 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# record.external_id = nodes[0].id_\u001b[0m                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m33 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mprint\u001b[0m(\u001b[33m'\u001b[0m\u001b[33mrecord.external_id\u001b[0m\u001b[33m'\u001b[0m, nodes[\u001b[94m0\u001b[0m].id_ == record.id, \u001b[96mlen\u001b[0m(nodes), header)      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/Projects/extralit/src/extralit/extraction/\u001b[0m\u001b[1;33mvector_store.py\u001b[0m:\u001b[94m107\u001b[0m in \u001b[92mget_nodes\u001b[0m        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m104 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mget_nodes\u001b[0m(\u001b[96mself\u001b[0m, node_ids: Optional[List[\u001b[96mstr\u001b[0m]] = \u001b[94mNone\u001b[0m, filters: Optional[Metadata   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m105 \u001b[0m\u001b[2m│   │   │   \u001b[0m-> List[BaseNode]:                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m106 \u001b[0m\u001b[2m│   │   \u001b[0mcollection = \u001b[96mself\u001b[0m._client.collections.get(\u001b[96mself\u001b[0m.index_name)                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m107 \u001b[2m│   │   \u001b[0mall_properties = get_all_properties(\u001b[96mself\u001b[0m._client, \u001b[96mself\u001b[0m.index_name)                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m108 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m109 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m filters \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m:                                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m110 \u001b[0m\u001b[2m│   │   │   \u001b[0mfilters = _to_weaviate_filter(filters)                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/llama_index/vector_stores/weaviate/\u001b[0m\u001b[1;33mutils.\u001b[0m \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[1;33mpy\u001b[0m:\u001b[94m96\u001b[0m in \u001b[92mget_all_properties\u001b[0m                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 93 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m client.collections.exists(class_name):                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 94 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m{\u001b[0mclass_name\u001b[33m}\u001b[0m\u001b[33m schema does not exist.\u001b[0m\u001b[33m\"\u001b[0m)                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 95 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 96 \u001b[2m│   \u001b[0mproperties = client.collections.get(class_name).config.get().properties                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 97 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m [p.name \u001b[94mfor\u001b[0m p \u001b[95min\u001b[0m properties]                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 98 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 99 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/collections/\u001b[0m\u001b[1;33mconfig.py\u001b[0m:\u001b[94m81\u001b[0m in \u001b[92mget\u001b[0m  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 78 \u001b[0m\u001b[2;33m│   │   │   │   \u001b[0m\u001b[33mIf Weaviate reports a non-OK status.\u001b[0m                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 79 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 80 \u001b[0m\u001b[2m│   │   \u001b[0m_validate_input([_ValidateArgument(expected=[\u001b[96mbool\u001b[0m], name=\u001b[33m\"\u001b[0m\u001b[33msimple\u001b[0m\u001b[33m\"\u001b[0m, value=simple)   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 81 \u001b[2m│   │   \u001b[0mschema = \u001b[96mself\u001b[0m.__get()                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 82 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m simple:                                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 83 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m _collection_config_simple_from_json(schema)                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 84 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m _collection_config_from_json(schema)                                        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/collections/\u001b[0m\u001b[1;33mconfig.py\u001b[0m:\u001b[94m49\u001b[0m in      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m__get\u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 46 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m.__tenant = tenant                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 47 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 48 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m__get\u001b[0m(\u001b[96mself\u001b[0m) -> Dict[\u001b[96mstr\u001b[0m, Any]:                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 49 \u001b[2m│   │   \u001b[0mresponse = \u001b[96mself\u001b[0m.__connection.get(                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 50 \u001b[0m\u001b[2m│   │   │   \u001b[0mpath=\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m/schema/\u001b[0m\u001b[33m{\u001b[0m\u001b[96mself\u001b[0m._name\u001b[33m}\u001b[0m\u001b[33m\"\u001b[0m,                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 51 \u001b[0m\u001b[2m│   │   │   \u001b[0merror_msg=\u001b[33m\"\u001b[0m\u001b[33mCollection configuration could not be retrieved.\u001b[0m\u001b[33m\"\u001b[0m,                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 52 \u001b[0m\u001b[2m│   │   │   \u001b[0mstatus_codes=_ExpectedStatusCodes(ok_in=\u001b[94m200\u001b[0m, error=\u001b[33m\"\u001b[0m\u001b[33mGet collection configura\u001b[0m   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/connect/\u001b[0m\u001b[1;33mv4.py\u001b[0m:\u001b[94m561\u001b[0m in \u001b[92mget\u001b[0m         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m558 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m559 \u001b[0m\u001b[2m│   │   \u001b[0mrequest_url = \u001b[96mself\u001b[0m.url + \u001b[96mself\u001b[0m._api_version_path + path                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m560 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m561 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.__send(                                                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m562 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mGET\u001b[0m\u001b[33m\"\u001b[0m, url=request_url, params=params, error_msg=error_msg, status_codes=sta   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m563 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m564 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/weaviate/connect/\u001b[0m\u001b[1;33mv4.py\u001b[0m:\u001b[94m449\u001b[0m in \u001b[92m__send\u001b[0m      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m446 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mparams=params,                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m447 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mheaders=\u001b[96mself\u001b[0m.__get_latest_headers(),                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m448 \u001b[0m\u001b[2m│   │   │   \u001b[0m)                                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m449 \u001b[2m│   │   │   \u001b[0mres = \u001b[96mself\u001b[0m._client.send(req)                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m450 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m status_codes \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m res.status_code \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m status_codes.ok:        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m451 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mraise\u001b[0m UnexpectedStatusCodeError(error_msg, response=res)                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m452 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m cast(Response, res)                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/\u001b[0m\u001b[1;33m_client.py\u001b[0m:\u001b[94m914\u001b[0m in \u001b[92msend\u001b[0m              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 911 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 912 \u001b[0m\u001b[2m│   │   \u001b[0mauth = \u001b[96mself\u001b[0m._build_request_auth(request, auth)                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 913 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 914 \u001b[2m│   │   \u001b[0mresponse = \u001b[96mself\u001b[0m._send_handling_auth(                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 915 \u001b[0m\u001b[2m│   │   │   \u001b[0mrequest,                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 916 \u001b[0m\u001b[2m│   │   │   \u001b[0mauth=auth,                                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 917 \u001b[0m\u001b[2m│   │   │   \u001b[0mfollow_redirects=follow_redirects,                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/\u001b[0m\u001b[1;33m_client.py\u001b[0m:\u001b[94m942\u001b[0m in                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_send_handling_auth\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 939 \u001b[0m\u001b[2m│   │   │   \u001b[0mrequest = \u001b[96mnext\u001b[0m(auth_flow)                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 940 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 941 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwhile\u001b[0m \u001b[94mTrue\u001b[0m:                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 942 \u001b[2m│   │   │   │   \u001b[0mresponse = \u001b[96mself\u001b[0m._send_handling_redirects(                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 943 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mrequest,                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 944 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mfollow_redirects=follow_redirects,                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 945 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mhistory=history,                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/\u001b[0m\u001b[1;33m_client.py\u001b[0m:\u001b[94m979\u001b[0m in                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_send_handling_redirects\u001b[0m                                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 976 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mfor\u001b[0m hook \u001b[95min\u001b[0m \u001b[96mself\u001b[0m._event_hooks[\u001b[33m\"\u001b[0m\u001b[33mrequest\u001b[0m\u001b[33m\"\u001b[0m]:                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 977 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mhook(request)                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 978 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 979 \u001b[2m│   │   │   \u001b[0mresponse = \u001b[96mself\u001b[0m._send_single_request(request)                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 980 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 981 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mfor\u001b[0m hook \u001b[95min\u001b[0m \u001b[96mself\u001b[0m._event_hooks[\u001b[33m\"\u001b[0m\u001b[33mresponse\u001b[0m\u001b[33m\"\u001b[0m]:                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 982 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mhook(response)                                                        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/\u001b[0m\u001b[1;33m_client.py\u001b[0m:\u001b[94m1015\u001b[0m in                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_send_single_request\u001b[0m                                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1012 \u001b[0m\u001b[2m│   │   │   \u001b[0m)                                                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1013 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1014 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwith\u001b[0m request_context(request=request):                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1015 \u001b[2m│   │   │   \u001b[0mresponse = transport.handle_request(request)                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1016 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1017 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94massert\u001b[0m \u001b[96misinstance\u001b[0m(response.stream, SyncByteStream)                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m1018 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpx/_transports/\u001b[0m\u001b[1;33mdefault.py\u001b[0m:\u001b[94m233\u001b[0m in       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m230 \u001b[0m\u001b[2m│   │   │   \u001b[0mextensions=request.extensions,                                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m231 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m232 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwith\u001b[0m map_httpcore_exceptions():                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m233 \u001b[2m│   │   │   \u001b[0mresp = \u001b[96mself\u001b[0m._pool.handle_request(req)                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m234 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m235 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94massert\u001b[0m \u001b[96misinstance\u001b[0m(resp.stream, typing.Iterable)                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m236 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mconnection_pool.py\u001b[0m:\u001b[94m216\u001b[0m in  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m213 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mclosing = \u001b[96mself\u001b[0m._assign_requests_to_connections()                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m214 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m215 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._close_connections(closing)                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m216 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m exc \u001b[94mfrom\u001b[0m \u001b[94mNone\u001b[0m                                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m217 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m218 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Return the response. Note that in this case we still have to manage\u001b[0m              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m219 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# the point at which the response is closed.\u001b[0m                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mconnection_pool.py\u001b[0m:\u001b[94m196\u001b[0m in  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m193 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m194 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m195 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[2m# Send the request on the assigned connection.\u001b[0m                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m196 \u001b[2m│   │   │   │   │   \u001b[0mresponse = connection.handle_request(                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m197 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0mpool_request.request                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m198 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m)                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m199 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mexcept\u001b[0m ConnectionNotAvailable:                                             \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mconnection.py\u001b[0m:\u001b[94m101\u001b[0m in       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 98 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._connect_failed = \u001b[94mTrue\u001b[0m                                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m 99 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m exc                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m100 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m101 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m._connection.handle_request(request)                                    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m102 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m103 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_connect\u001b[0m(\u001b[96mself\u001b[0m, request: Request) -> NetworkStream:                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m104 \u001b[0m\u001b[2m│   │   \u001b[0mtimeouts = request.extensions.get(\u001b[33m\"\u001b[0m\u001b[33mtimeout\u001b[0m\u001b[33m\"\u001b[0m, {})                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mhttp11.py\u001b[0m:\u001b[94m143\u001b[0m in           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m140 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mwith\u001b[0m ShieldCancellation():                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m141 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mwith\u001b[0m Trace(\u001b[33m\"\u001b[0m\u001b[33mresponse_closed\u001b[0m\u001b[33m\"\u001b[0m, logger, request) \u001b[94mas\u001b[0m trace:                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m142 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m._response_closed()                                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m143 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m exc                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m144 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m145 \u001b[0m\u001b[2m│   \u001b[0m\u001b[2m# Sending the request...\u001b[0m                                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m146 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mhttp11.py\u001b[0m:\u001b[94m113\u001b[0m in           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92mhandle_request\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m110 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mreason_phrase,                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m111 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mheaders,                                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m112 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mtrailing_data,                                                         \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m113 \u001b[2m│   │   │   │   \u001b[0m) = \u001b[96mself\u001b[0m._receive_response_headers(**kwargs)                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m114 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mtrace.return_value = (                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m115 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mhttp_version,                                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m116 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mstatus,                                                                \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mhttp11.py\u001b[0m:\u001b[94m186\u001b[0m in           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_receive_response_headers\u001b[0m                                                                        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m183 \u001b[0m\u001b[2m│   │   \u001b[0mtimeout = timeouts.get(\u001b[33m\"\u001b[0m\u001b[33mread\u001b[0m\u001b[33m\"\u001b[0m, \u001b[94mNone\u001b[0m)                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m184 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m185 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwhile\u001b[0m \u001b[94mTrue\u001b[0m:                                                                        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m186 \u001b[2m│   │   │   \u001b[0mevent = \u001b[96mself\u001b[0m._receive_event(timeout=timeout)                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m187 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(event, h11.Response):                                            \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m188 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mbreak\u001b[0m                                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m189 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m (                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_sync/\u001b[0m\u001b[1;33mhttp11.py\u001b[0m:\u001b[94m224\u001b[0m in           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[92m_receive_event\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m221 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mevent = \u001b[96mself\u001b[0m._h11_state.next_event()                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m222 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                               \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m223 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m event \u001b[95mis\u001b[0m h11.NEED_DATA:                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m224 \u001b[2m│   │   │   │   \u001b[0mdata = \u001b[96mself\u001b[0m._network_stream.read(                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m225 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.READ_NUM_BYTES, timeout=timeout                                   \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m226 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m)                                                                          \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m227 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[2;33m/Users/jonnytr/micromamba/lib/python3.10/site-packages/httpcore/_backends/\u001b[0m\u001b[1;33msync.py\u001b[0m:\u001b[94m126\u001b[0m in \u001b[92mread\u001b[0m    \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m123 \u001b[0m\u001b[2m│   │   \u001b[0mexc_map: ExceptionMapping = {socket.timeout: ReadTimeout, \u001b[96mOSError\u001b[0m: ReadError}      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m124 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwith\u001b[0m map_exceptions(exc_map):                                                      \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m125 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m._sock.settimeout(timeout)                                                 \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m126 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m._sock.recv(max_bytes)                                              \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m127 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m128 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mwrite\u001b[0m(\u001b[96mself\u001b[0m, buffer: \u001b[96mbytes\u001b[0m, timeout: typing.Optional[\u001b[96mfloat\u001b[0m] = \u001b[94mNone\u001b[0m) -> \u001b[94mNone\u001b[0m:        \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m│\u001b[0m   \u001b[2m129 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m buffer:                                                                     \u001b[31m│\u001b[0m\n",
-                            "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
-                            "\u001b[1;91mKeyboardInterrupt\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "record_updates = []\n",
-                "\n",
-                "for record in tqdm(remote_dataset.records):\n",
-                "    metadata = record.metadata\n",
-                "    data = get_record_data(record, fields='header', answers='header-correction')\n",
-                "    header = data.get('header-correction', data.get('header'))\n",
-                "    \n",
-                "    if 'number' in metadata:\n",
-                "        metadata['number'] = metadata['number'].replace('FigureSegment', 'Figure').replace('TableSegment', 'Table')\n",
-                "        \n",
-                "    if 'type' in metadata:\n",
-                "        metadata['type'] = metadata['type'].replace('FigureSegment', 'figure').replace('TableSegment', 'table').lower()\n",
-                "    elif 'table' in metadata.get('number', '').lower() or 'figure' in metadata.get('number', '').lower():\n",
-                "        metadata['type'] = metadata['number'].split(' ')[0].lower()\n",
-                "    else:\n",
-                "        metadata['type'] = 'table'\n",
-                "    \n",
-                "    record.metadata = metadata            \n",
-                "    \n",
-                "    record_updates.append(record)\n",
-                "len(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 117,
-            "id": "fa69261e-9783-4070-a2bc-67025b5b9596",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "d075cf03ad3d4af39a473665d5ef9bd1",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "Output()"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-                        ],
-                        "text/plain": []
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "remote_dataset.records.update(record_updates)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "03b51eb4-cf6f-41ba-a623-a4190728ed69",
-            "metadata": {
-                "jp-MarkdownHeadingCollapsed": true
-            },
-            "source": [
-                "# Evaluate OCR Accuracy"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "0af04a2f-91a0-4ee1-855e-454cf9b3a666",
-            "metadata": {
-                "editable": true,
-                "scrolled": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "results = {}\n",
-                "count = 0\n",
-                "for reference in tqdm(papers_uploaded):\n",
-                "    pred_tables = get_paper_tables(papers.loc[reference], remote_dataset, select='ranking')\n",
-                "    true_tables = get_paper_tables(papers.loc[reference], remote_dataset, select='text-correction')\n",
-                "    count += len(pred_tables)\n",
-                "\n",
-                "    if len(pred_tables) != len(true_tables): \n",
-                "        print(len(pred_tables), len(true_tables))\n",
-                "\n",
-                "    pred_tables = [seg.html for seg in pred_tables.items]\n",
-                "    true_tables = [seg.html for seg in true_tables.items]\n",
-                "    results[reference] = grits_multi_tables(true_tables, pred_tables).mean().to_frame().T\n",
-                "\n",
-                "results = pd.concat(results).droplevel(level=1, axis=0)\n",
-                "metrics.mean(0).to_frame().T.map('{:.1%}'.format)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "991f60a8-2b46-49cd-a04b-ac59a1eb613b",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "pred_tables"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "8a74cf1d-92d7-426c-96b2-aec678811bd6",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "count/sum(table_counts.values()), count, sum(table_counts.values())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "0fc26c0e-7a9b-4757-b480-4db5ab2d025f",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "table_counts = {}\n",
-                "figure_counts = {}\n",
-                "for reference in papers_uploaded:\n",
-                "    tables = get_paper_tables(papers.loc[reference], remote_dataset, select='text-correction', skip_status=None)\n",
-                "    if not tables: continue\n",
-                "    table_counts[reference] = max([table.number for table in tables.items if table.number and 'table' in table.header.lower()], default=0)\n",
-                "    figure_counts[reference] = max([table.number for table in tables.items if table.number and table.header.lower().strip().startswith('fig')], default=0)\n",
-                "\n",
-                "sum(figure_counts.values()), sum(table_counts.values())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7d96f35f-0496-4feb-b1a2-9a49745efe40",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "figure_counts"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "1f485ca6-d8cf-4cee-9388-2ff398a851b6",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "results = {}\n",
-                "durations = {}\n",
-                "count = 0\n",
-                "for reference in tqdm(papers_uploaded):\n",
-                "    pred_tables = get_paper_tables(papers.loc[reference], remote_dataset, select='ranking')\n",
-                "    true_tables = get_paper_tables(papers.loc[reference], remote_dataset, select='text-correction')\n",
-                "    count += len(pred_tables)\n",
-                "\n",
-                "    if len(pred_tables) != len(true_tables): \n",
-                "        print(len(pred_tables), len(true_tables))\n",
-                "\n",
-                "    for i, (pred_table, true_table) in enumerate(zip(pred_tables.items, true_tables.items)):\n",
-                "        results[(reference, i)] = grits_multi_tables([true_table.html], [pred_table.html])\n",
-                "        durations[(reference, i)] = pred_table.duration\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "66e63bc4-10dc-42e9-9ad5-ac18afa93b1f",
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "metrics = pd.concat(results).droplevel(level=-1, axis=0)\n",
-                "durations = pd.Series(durations, name='duration')\n",
-                "metrics.loc[durations.index, 'duration (s)'] = durations.values\n",
-                "metrics.columns = metrics.columns.map(lambda x: '_'.join(x).strip('_'))\n",
-                "metrics"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7fdf6c68-0694-426d-905e-740212962f9c",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "durations_m = durations.groupby(level=0).sum() / 60\n",
-                "px.box(durations_m, orientation='h', width=500, height=220, \n",
-                "       title='Manual table correction time per paper')\\\n",
-                "    .update_xaxes(title='duration (minutes)', dtick=5)\\\n",
-                "    .update_yaxes(title='')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "00f379d8-bc18-4673-88f2-856cdcbc9ec3",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "px.scatter(metrics.assign(grits_top_f1=metrics['grits_top_f1']*100,\n",
-                "                          grits_con_f1=metrics['grits_con_f1']*100),\n",
-                "           title='Manual correction time per table depends <br>on OCR quality',\n",
-                "           x='grits_top_f1',\n",
-                "           y='duration (s)',\n",
-                "           trendline='ols',\n",
-                "           range_x=[100, 60],\n",
-                "           range_y=[0, 800],\n",
-                "           width=500,\n",
-                "           height=400)"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/examples/document_extraction/setup_workspace.ipynb b/examples/document_extraction/setup_workspace.ipynb
index c59dd9376..e0d03ce1f 100644
--- a/examples/document_extraction/setup_workspace.ipynb
+++ b/examples/document_extraction/setup_workspace.ipynb
@@ -1,580 +1,551 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "f9d7358b",
-            "metadata": {},
-            "source": [
-                "# Setting Up Workspaces in Extralit\n",
-                "\n",
-                "In this tutorial, we will learn how to set up and manage workspaces in Extralit using the default credentials on a fresh installation. It will walk you through the following steps:\n",
-                "\n",
-                "1. Connecting to Extralit with default credentials 🔑\n",
-                "2. Creating your first workspace 🏗️\n",
-                "3. Listing available workspaces 📋\n",
-                "4. Adding PDF documents to the workspace 📄\n",
-                "5. Creating and uploading a schema 📊\n",
-                "6. Running PDF preprocessing 🔍\n",
-                "7. Running LLM extractions 🤖\n",
-                "\n",
-                "![Extralit Workspace Management](https://raw.githubusercontent.com/argilla-io/argilla/main/docs/assets/argilla_workspace_management.png)\n",
-                "\n",
-                "## Introduction\n",
-                "\n",
-                "A **workspace** is a space inside your Extralit instance where authorized users can collaborate on datasets. Workspaces are accessible through both the Python SDK and the UI. When you first install Extralit, you'll need to create workspaces to organize your data and user access.\n",
-                "\n",
-                "For more details on workspace management, refer to the [Extralit documentation](https://docs.extralit.ai/latest/admin_guide/workspace/).\n",
-                "\n",
-                "Let's get started!\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "5601c172",
-            "metadata": {},
-            "source": [
-                "## 1. Connecting to Extralit\n",
-                "\n",
-                "First, we need to import the Extralit library and connect to our instance using the default credentials."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "ce009aae",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/jonny/micromamba/envs/extralit/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "ename": "ConnectError",
-                    "evalue": "[Errno 61] Connection refused",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mConnectError\u001b[0m                              Traceback (most recent call last)",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_transports/default.py:67\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[0;34m()\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 67\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_transports/default.py:231\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m--> 231\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_sync/connection_pool.py:256\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    255\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_connections(closing)\n\u001b[0;32m--> 256\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_sync/connection_pool.py:236\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[0;32m--> 236\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[1;32m    241\u001b[0m     \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m    243\u001b[0m     \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_sync/connection.py:101\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connect_failed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 101\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_sync/connection.py:78\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 78\u001b[0m     stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m     ssl_object \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mget_extra_info(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mssl_object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_sync/connection.py:124\u001b[0m, in \u001b[0;36mHTTPConnection._connect\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconnect_tcp\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[0;32m--> 124\u001b[0m     stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_network_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect_tcp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m     trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m stream\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_backends/sync.py:215\u001b[0m, in \u001b[0;36mSyncBackend.connect_tcp\u001b[0;34m(self, host, port, timeout, local_address, socket_options)\u001b[0m\n\u001b[1;32m    214\u001b[0m         sock\u001b[38;5;241m.\u001b[39msetsockopt(\u001b[38;5;241m*\u001b[39moption)  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m--> 215\u001b[0m     sock\u001b[38;5;241m.\u001b[39msetsockopt(socket\u001b[38;5;241m.\u001b[39mIPPROTO_TCP, socket\u001b[38;5;241m.\u001b[39mTCP_NODELAY, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m SyncStream(sock)\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[1;32m    140\u001b[0m     \u001b[38;5;66;03m# was passed to throw().  This prevents a StopIteration\u001b[39;00m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpcore/_exceptions.py:14\u001b[0m, in \u001b[0;36mmap_exceptions\u001b[0;34m(map)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, from_exc):\n\u001b[0;32m---> 14\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m to_exc(exc) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n",
-                        "\u001b[0;31mConnectError\u001b[0m: [Errno 61] Connection refused",
-                        "\nThe above exception was the direct cause of the following exception:\n",
-                        "\u001b[0;31mConnectError\u001b[0m                              Traceback (most recent call last)",
-                        "Cell \u001b[0;32mIn[1], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpathlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Connect to Extralit using default credentials\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m client \u001b[38;5;241m=\u001b[39m \u001b[43mrg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mExtralit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mapi_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttp://localhost:6900/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mextralit.apikey\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSuccessfully connected to Extralit at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclient\u001b[38;5;241m.\u001b[39mapi_url\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla/src/argilla/client.py:73\u001b[0m, in \u001b[0;36mExtralit.__init__\u001b[0;34m(self, api_url, api_key, timeout, retries, **http_client_args)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     53\u001b[0m     api_url: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m DEFAULT_HTTP_CONFIG\u001b[38;5;241m.\u001b[39mapi_url,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhttp_client_args,\n\u001b[1;32m     58\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Inits the `Extralit` client.\u001b[39;00m\n\u001b[1;32m     60\u001b[0m \n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m            before raising an exception. Defaults to `5`.\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mapi_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhttp_client_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_default(\u001b[38;5;28mself\u001b[39m)\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla/src/argilla/_api/_client.py:146\u001b[0m, in \u001b[0;36mAPIClient.__init__\u001b[0;34m(self, api_url, api_key, timeout, retries, **http_client_args)\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi \u001b[38;5;241m=\u001b[39m ExtralitAPI(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhttp_client)\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m UnauthorizedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ExtralitCredentialsError() \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla/src/argilla/_api/_client.py:160\u001b[0m, in \u001b[0;36mAPIClient._validate_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_validate_connection\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m     user \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43musers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_me\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLogged in as \u001b[39m\u001b[38;5;132;01m{\u001b[39;00muser\u001b[38;5;241m.\u001b[39musername\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with the role \u001b[39m\u001b[38;5;132;01m{\u001b[39;00muser\u001b[38;5;241m.\u001b[39mrole\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog(message\u001b[38;5;241m=\u001b[39mmessage, level\u001b[38;5;241m=\u001b[39mlogging\u001b[38;5;241m.\u001b[39mINFO)\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla/src/argilla/_exceptions/_api.py:91\u001b[0m, in \u001b[0;36mapi_error_handler.<locals>._handler_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_handler_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     90\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 91\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     93\u001b[0m         _error_switch(status_code\u001b[38;5;241m=\u001b[39me\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mstatus_code, error_detail\u001b[38;5;241m=\u001b[39me\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mtext)\n",
-                        "File \u001b[0;32m~/Projects/extralit/argilla/src/argilla/_api/_users.py:90\u001b[0m, in \u001b[0;36mUsersAPI.get_me\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;129m@api_error_handler\u001b[39m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_me\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m UserModel:\n\u001b[0;32m---> 90\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttp_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/api/v1/me\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     91\u001b[0m     response\u001b[38;5;241m.\u001b[39mraise_for_status()\n\u001b[1;32m     92\u001b[0m     response_json \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mjson()\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:1055\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, url, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget\u001b[39m(\n\u001b[1;32m   1039\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1040\u001b[0m     url: URLTypes,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1048\u001b[0m     extensions: typing\u001b[38;5;241m.\u001b[39mOptional[RequestExtensions] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1049\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Response:\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1051\u001b[0m \u001b[38;5;124;03m    Send a `GET` request.\u001b[39;00m\n\u001b[1;32m   1052\u001b[0m \n\u001b[1;32m   1053\u001b[0m \u001b[38;5;124;03m    **Parameters**: See `httpx.request`.\u001b[39;00m\n\u001b[1;32m   1054\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1055\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1056\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1057\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1058\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1059\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1060\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1061\u001b[0m \u001b[43m        \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1062\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1063\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1064\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:828\u001b[0m, in \u001b[0;36mClient.request\u001b[0;34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[1;32m    813\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m    815\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_request(\n\u001b[1;32m    816\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m    817\u001b[0m     url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    826\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mextensions,\n\u001b[1;32m    827\u001b[0m )\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m)\u001b[49m\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:915\u001b[0m, in \u001b[0;36mClient.send\u001b[0;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[1;32m    907\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    908\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[1;32m    909\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[1;32m    910\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[1;32m    911\u001b[0m )\n\u001b[1;32m    913\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[0;32m--> 915\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    916\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    918\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    919\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    920\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    921\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    922\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:943\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m    940\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[1;32m    942\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 943\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    944\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    945\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    946\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    947\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    948\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:980\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[0;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[1;32m    977\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    978\u001b[0m     hook(request)\n\u001b[0;32m--> 980\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_client.py:1016\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m   1011\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m   1012\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1013\u001b[0m     )\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[0;32m-> 1016\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1018\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[1;32m   1020\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_transports/default.py:231\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    218\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[1;32m    219\u001b[0m     method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    220\u001b[0m     url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    228\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    229\u001b[0m )\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m--> 231\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool\u001b[38;5;241m.\u001b[39mhandle_request(req)\n\u001b[1;32m    233\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m    236\u001b[0m     status_code\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mstatus,\n\u001b[1;32m    237\u001b[0m     headers\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m    238\u001b[0m     stream\u001b[38;5;241m=\u001b[39mResponseStream(resp\u001b[38;5;241m.\u001b[39mstream),\n\u001b[1;32m    239\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    240\u001b[0m )\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m    135\u001b[0m     value \u001b[38;5;241m=\u001b[39m typ()\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[1;32m    140\u001b[0m     \u001b[38;5;66;03m# was passed to throw().  This prevents a StopIteration\u001b[39;00m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n\u001b[1;32m    142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value\n",
-                        "File \u001b[0;32m~/micromamba/envs/extralit/lib/python3.9/site-packages/httpx/_transports/default.py:84\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[0;34m()\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m     83\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(exc)\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m mapped_exc(message) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mexc\u001b[39;00m\n",
-                        "\u001b[0;31mConnectError\u001b[0m: [Errno 61] Connection refused"
-                    ]
-                }
-            ],
-            "source": [
-                "import extralit as ex\n",
-                "import extralit as ex\n",
-                "import pandas as pd\n",
-                "import pandera as pa\n",
-                "from pandera.typing import Index, Series\n",
-                "import os\n",
-                "import tempfile\n",
-                "from pathlib import Path\n",
-                "\n",
-                "# Connect to Extralit using default credentials\n",
-                "client = ex.Extralit(api_url=\"http://localhost:6900/\", api_key='extralit.apikey')\n",
-                "\n",
-                "print(f\"Successfully connected to Extralit at {client.api_url}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "fcdd3afe",
-            "metadata": {},
-            "source": [
-                "## 2. Creating Your First Workspace\n",
-                "\n",
-                "After connecting to Extralit, let's create our first workspace. We'll define a new `Workspace` object and call the `create()` method."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "64b5f09b",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Workspace 'test-workspace' created successfully with ID: df91e2b9-c712-4474-af82-891478f23d40\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Define a new workspace\n",
-                "workspace_name = \"test_workspace\"\n",
-                "new_workspace = ex.Workspace(name=workspace_name, client=client)\n",
-                "\n",
-                "# Create the workspace\n",
-                "created_workspace = new_workspace.create()\n",
-                "\n",
-                "print(f\"Workspace '{workspace_name}' created successfully with ID: {created_workspace.id}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c937e5c7",
-            "metadata": {},
-            "source": [
-                "## 3. Listing Available Workspaces\n",
-                "\n",
-                "Now, let's check all the workspaces available in our Extralit instance."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "d4df67d6",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Total number of workspaces: 1\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<h3>Workspaces</h3><table><tr><th>name</th><th>id</th><th>updated_at</th></tr><tr><td>test-workspace</td><td>df91e2b9-c712-4474-af82-891478f23d40</td><td>2025-04-16T01:27:58.174591</td></tr></table>"
-                        ],
-                        "text/plain": [
-                            "<argilla.client.Workspaces at 0x77c0709f9ff0>"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# List all workspaces\n",
-                "workspaces = client.workspaces\n",
-                "\n",
-                "# Display workspace information\n",
-                "print(f\"Total number of workspaces: {len(workspaces)}\\n\")\n",
-                "\n",
-                "# In a notebook, this will display a table with workspace information\n",
-                "workspaces"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "8ca5b2b8",
-            "metadata": {},
-            "source": [
-                "## 4. Adding PDF Documents to the Workspace\n",
-                "\n",
-                "Let's add two PDF documents to our workspace. For this tutorial, we'll create temporary PDF files. In a real-world scenario, you'd use actual scientific papers."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "59084a6d",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Created reference CSV at /tmp/references.csv\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>reference</th>\n",
-                            "      <th>file_path</th>\n",
-                            "      <th>title</th>\n",
-                            "      <th>authors</th>\n",
-                            "      <th>year</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>smith2023first</td>\n",
-                            "      <td>/tmp/sample1.pdf</td>\n",
-                            "      <td>Study on Sample Data</td>\n",
-                            "      <td>Smith, J.</td>\n",
-                            "      <td>2023</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>johnson2022analysis</td>\n",
-                            "      <td>/tmp/sample2.pdf</td>\n",
-                            "      <td>Analysis of Experimental Results</td>\n",
-                            "      <td>Johnson, A.</td>\n",
-                            "      <td>2022</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "             reference         file_path                             title  \\\n",
-                            "0       smith2023first  /tmp/sample1.pdf              Study on Sample Data   \n",
-                            "1  johnson2022analysis  /tmp/sample2.pdf  Analysis of Experimental Results   \n",
-                            "\n",
-                            "       authors  year  \n",
-                            "0    Smith, J.  2023  \n",
-                            "1  Johnson, A.  2022  "
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# In a real-world scenario, you would use actual PDFs. Here we'll create temp files\n",
-                "# Define the paths for our temporary PDF files\n",
-                "temp_dir = tempfile.gettempdir()\n",
-                "pdf_file1 = Path(temp_dir) / \"sample1.pdf\"\n",
-                "pdf_file2 = Path(temp_dir) / \"sample2.pdf\"\n",
-                "\n",
-                "# Create empty PDF files - in reality, these would be your actual PDFs\n",
-                "with open(pdf_file1, \"wb\") as f:\n",
-                "    f.write(b\"%PDF-1.5\\n%Example Document 1\")\n",
-                "    \n",
-                "with open(pdf_file2, \"wb\") as f:\n",
-                "    f.write(b\"%PDF-1.5\\n%Example Document 2\")\n",
-                "\n",
-                "# Create a reference dataframe with metadata for the PDFs\n",
-                "references_df = pd.DataFrame({\n",
-                "    \"reference\": [\"smith2023first\", \"johnson2022analysis\"],\n",
-                "    \"file_path\": [str(pdf_file1), str(pdf_file2)],\n",
-                "    \"title\": [\"Study on Sample Data\", \"Analysis of Experimental Results\"],\n",
-                "    \"authors\": [\"Smith, J.\", \"Johnson, A.\"],\n",
-                "    \"year\": [2023, 2022]\n",
-                "})\n",
-                "\n",
-                "# Save the dataframe to a temporary CSV file\n",
-                "references_csv = Path(temp_dir) / \"references.csv\"\n",
-                "references_df.to_csv(references_csv, index=False)\n",
-                "\n",
-                "print(f\"Created reference CSV at {references_csv}\")\n",
-                "references_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "abc8562f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Workspace(id=UUID('df91e2b9-c712-4474-af82-891478f23d40') inserted_at=datetime.datetime(2025, 4, 16, 1, 27, 58, 174591) updated_at=datetime.datetime(2025, 4, 16, 1, 27, 58, 174591) name='test-workspace')"
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "new_workspace"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "a147c6c2",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Import the documents into the workspace\n",
-                "# For demonstration purposes, we'll use the extralit client directly\n",
-                "# Initialize the extralit client with the same credentials\n",
-                "extralit_client = ex.Extralit(api_url=\"http://localhost:6900/\", api_key='extralit.apikey')\n",
-                "\n",
-                "# Import the documents\n",
-                "result = extralit_client.import_documents(\n",
-                "    workspace=workspace_name,\n",
-                "    papers=str(references_csv),\n",
-                "    metadatas=[\"title\", \"authors\", \"year\"]\n",
-                ")\n",
-                "\n",
-                "print(f\"Imported {len(result)} documents into workspace '{workspace_name}'\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "98a2025d",
-            "metadata": {},
-            "source": [
-                "## 5. Creating and Uploading a Schema\n",
-                "\n",
-                "Now, let's create a simple schema to define the structure of the data we want to extract from our documents."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f15f4b2f",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Define a simple schema using Pandera\n",
-                "class Publication(pa.DataFrameModel):\n",
-                "    \"\"\"\n",
-                "    General information about the publication, extracted once per paper.\n",
-                "    \"\"\"\n",
-                "    reference: Index[str] = pa.Field(unique=True, check_name=True)\n",
-                "    title: Series[str] = pa.Field()\n",
-                "    authors: Series[str] = pa.Field()\n",
-                "    publication_year: Series[int] = pa.Field(ge=1900, le=2100)\n",
-                "    doi: Series[str] = pa.Field(nullable=True)\n",
-                "    \n",
-                "    class Config:\n",
-                "        singleton = {'enabled': True}  # Indicates this is a document-level schema\n",
-                "\n",
-                "# Define a second schema for experimental data\n",
-                "class ExperimentalData(pa.DataFrameModel):\n",
-                "    \"\"\"\n",
-                "    Experimental data extracted from the paper, may appear multiple times.\n",
-                "    \"\"\"\n",
-                "    experiment_id: Series[str] = pa.Field()\n",
-                "    sample_size: Series[int] = pa.Field(gt=0)\n",
-                "    study_type: Series[str] = pa.Field()\n",
-                "    result_value: Series[float] = pa.Field()\n",
-                "    significance: Series[float] = pa.Field(le=1.0, ge=0.0)\n",
-                "\n",
-                "# Create a schema structure object\n",
-                "from extralit.extraction.models.schema import SchemaStructure\n",
-                "\n",
-                "# Save schemas to a temporary JSON file\n",
-                "schema_file = Path(temp_dir) / \"schemas.json\"\n",
-                "schema_structure = SchemaStructure(schemas={\"Publication\": Publication, \"ExperimentalData\": ExperimentalData})\n",
-                "schema_structure.to_json(schema_file)\n",
-                "\n",
-                "print(f\"Created schema file at {schema_file}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "dc61a1ab",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Upload the schema to the workspace\n",
-                "result = extralit_client.upload_schemas(\n",
-                "    workspace=workspace_name,\n",
-                "    schemas=str(schema_file)\n",
-                ")\n",
-                "\n",
-                "print(f\"Uploaded schemas to workspace '{workspace_name}'\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7a92c20d",
-            "metadata": {},
-            "source": [
-                "## 6. Running PDF Preprocessing\n",
-                "\n",
-                "Next, let's run the PDF preprocessing step to extract text and table content from our documents."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "768e7176",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Run PDF preprocessing\n",
-                "from extralit.preprocessing.pdf import process_pdfs\n",
-                "\n",
-                "# Get the references from our dataframe\n",
-                "references = references_df[\"reference\"].tolist()\n",
-                "\n",
-                "# Run the preprocessing step\n",
-                "preprocessing_result = process_pdfs(\n",
-                "    workspace=workspace_name,\n",
-                "    references=references,\n",
-                "    text_ocr=[\"default\"],  # Using the default text OCR model\n",
-                "    table_ocr=[\"default\"],  # Using the default table OCR model\n",
-                "    output_dataset=\"PDF_Preprocessing_Results\"\n",
-                ")\n",
-                "\n",
-                "print(f\"Preprocessing completed for {len(preprocessing_result)} documents\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "44aa776d",
-            "metadata": {},
-            "source": [
-                "## 7. Running LLM Extractions\n",
-                "\n",
-                "Finally, let's run the LLM extraction step to extract structured data according to our schema."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "19221abe",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Run LLM extractions\n",
-                "from extralit.extraction.llm import extract_data\n",
-                "\n",
-                "# Run the extraction step\n",
-                "extraction_result = extract_data(\n",
-                "    workspace=workspace_name,\n",
-                "    references=references,\n",
-                "    output_dataset=\"Data_Extraction_Results\"\n",
-                ")\n",
-                "\n",
-                "print(f\"LLM extractions completed for {len(extraction_result)} documents\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c1b6ec61",
-            "metadata": {},
-            "source": [
-                "## 8. Checking Extraction Results\n",
-                "\n",
-                "Let's check the results of our extractions by listing the datasets created and viewing the extracted data."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "4dbb695d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# List datasets in the workspace\n",
-                "datasets = extralit_client.list_datasets(workspace=workspace_name)\n",
-                "print(f\"Datasets in workspace '{workspace_name}':\\n\")\n",
-                "for dataset in datasets:\n",
-                "    print(f\"- {dataset.name} ({dataset.id})\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "6eee019d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Export the extracted data\n",
-                "extracted_data = extralit_client.export_data(\n",
-                "    workspace=workspace_name,\n",
-                "    output=\"temp_output.csv\"  # This will save the data to a CSV file\n",
-                ")\n",
-                "\n",
-                "# Display the extracted data\n",
-                "if isinstance(extracted_data, dict):\n",
-                "    for schema_name, data_df in extracted_data.items():\n",
-                "        print(f\"\\nExtracted data for schema '{schema_name}':\\n\")\n",
-                "        display(data_df)\n",
-                "else:\n",
-                "    print(\"\\nExtracted data:\")\n",
-                "    display(extracted_data)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7a30a5ea",
-            "metadata": {},
-            "source": [
-                "## Conclusion\n",
-                "\n",
-                "Congratulations! You've successfully tested the primary functionalities of Extralit with default credentials on a fresh install. You have:\n",
-                "\n",
-                "1. Connected to Extralit with default credentials\n",
-                "2. Created a workspace\n",
-                "3. Added PDF documents\n",
-                "4. Created and uploaded a schema\n",
-                "5. Run PDF preprocessing\n",
-                "6. Run LLM extractions\n",
-                "7. Checked the extraction results\n",
-                "\n",
-                "This workflow demonstrates the basic process of using Extralit for data extraction from scientific papers. In a real-world scenario, you would upload actual scientific papers and create more complex schemas tailored to your specific data extraction needs.\n",
-                "\n",
-                "For more detailed information, refer to the [Extralit documentation](https://docs.extralit.ai/latest/)."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.13"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f9d7358b",
+   "metadata": {},
+   "source": [
+    "# Setting Up Workspaces in Extralit\n",
+    "\n",
+    "In this tutorial, we will learn how to set up and manage workspaces in Extralit using the default credentials on a fresh installation. It will walk you through the following steps:\n",
+    "\n",
+    "1. Connecting to Extralit with default credentials 🔑\n",
+    "2. Creating your first workspace 🏗️\n",
+    "3. Listing available workspaces 📋\n",
+    "4. Adding PDF documents to the workspace 📄\n",
+    "5. Creating and uploading a schema 📊\n",
+    "6. Running PDF preprocessing 🔍\n",
+    "7. Running LLM extractions 🤖\n",
+    "\n",
+    "![Extralit Workspace Management](https://raw.githubusercontent.com/argilla-io/argilla/main/docs/assets/argilla_workspace_management.png)\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "A **workspace** is a space inside your Extralit instance where authorized users can collaborate on datasets. Workspaces are accessible through both the Python SDK and the UI. When you first install Extralit, you'll need to create workspaces to organize your data and user access.\n",
+    "\n",
+    "For more details on workspace management, refer to the [Extralit documentation](https://docs.extralit.ai/latest/admin_guide/workspace/).\n",
+    "\n",
+    "Let's get started!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5601c172",
+   "metadata": {},
+   "source": [
+    "## 1. Connecting to Extralit\n",
+    "\n",
+    "First, we need to import the Extralit library and connect to our instance using the default credentials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ce009aae",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "`np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mextralit\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpa\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Index, Series\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtempfile\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/__init__.py:5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"A flexible and expressive pandas validation library.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplatform\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/__init__.py:4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Pandera backends.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# ensure that base builtin checks and hypothesis are registered\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpandas\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/base/builtin_checks.py:14\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mre\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Iterable, Optional, TypeVar, Union\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mchecks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Check\n\u001b[32m     16\u001b[39m T = TypeVar(\u001b[33m\"\u001b[39m\u001b[33mT\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     19\u001b[39m \u001b[38;5;129m@Check\u001b[39m.register_builtin_check_fn\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mequal_to\u001b[39m(data: Any, value: Any) -> Any:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/api/checks.py:17\u001b[39m\n\u001b[32m     15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m errors\n\u001b[32m     16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mchecks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseCheck, CheckResult\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SearchStrategy\n\u001b[32m     19\u001b[39m T = TypeVar(\u001b[33m\"\u001b[39m\u001b[33mT\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     22\u001b[39m \u001b[38;5;66;03m# pylint: disable=too-many-public-methods\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/strategies/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Data synthesis strategies for pandera, powered by the hypothesis package.\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpandas_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/strategies/pandas_strategies.py:42\u001b[39m\n\u001b[32m     32\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     35\u001b[39m     DataType,\n\u001b[32m     36\u001b[39m     is_category,\n\u001b[32m   (...)\u001b[39m\u001b[32m     40\u001b[39m     is_timedelta,\n\u001b[32m     41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mengines\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numpy_engine, pandas_engine\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01merrors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseStrategyOnlyError, SchemaDefinitionError\n\u001b[32m     44\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     45\u001b[39m     HAS_HYPOTHESIS,\n\u001b[32m     46\u001b[39m     STRATEGY_DISPATCHER,\n\u001b[32m     47\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/engines/pandas_engine.py:757\u001b[39m\n\u001b[32m    738\u001b[39m             is_python_string = data_container.map(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m))  \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n\u001b[32m    739\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m is_python_string.astype(\u001b[38;5;28mbool\u001b[39m) | data_container.isna()\n\u001b[32m    742\u001b[39m Engine.register_dtype(\n\u001b[32m    743\u001b[39m     numpy_engine.Object,\n\u001b[32m    744\u001b[39m     equivalents=[\n\u001b[32m    745\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    746\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject_\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    747\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject0\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    748\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mO\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    749\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mbytes\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    750\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmixed-integer\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    751\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmixed\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    752\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mbytes\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    753\u001b[39m         \u001b[38;5;28mbytes\u001b[39m,\n\u001b[32m    754\u001b[39m         \u001b[38;5;28mobject\u001b[39m,\n\u001b[32m    755\u001b[39m         np.object_,\n\u001b[32m    756\u001b[39m         np.bytes_,\n\u001b[32m--> \u001b[39m\u001b[32m757\u001b[39m         \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstring_\u001b[49m,\n\u001b[32m    758\u001b[39m     ],\n\u001b[32m    759\u001b[39m )\n\u001b[32m    761\u001b[39m \u001b[38;5;66;03m# ###############################################################################\u001b[39;00m\n\u001b[32m    762\u001b[39m \u001b[38;5;66;03m# # time\u001b[39;00m\n\u001b[32m    763\u001b[39m \u001b[38;5;66;03m# ###############################################################################\u001b[39;00m\n\u001b[32m    766\u001b[39m _PandasDatetime = Union[np.datetime64, pd.DatetimeTZDtype]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/numpy/__init__.py:400\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(attr)\u001b[39m\n\u001b[32m    397\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr], name=\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __expired_attributes__:\n\u001b[32m--> \u001b[39m\u001b[32m400\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m    401\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m` was removed in the NumPy 2.0 release. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    402\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__expired_attributes__[attr]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m    403\u001b[39m         name=\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    404\u001b[39m     )\n\u001b[32m    406\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr == \u001b[33m\"\u001b[39m\u001b[33mchararray\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m    407\u001b[39m     warnings.warn(\n\u001b[32m    408\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m`np.chararray` is deprecated and will be removed from \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    409\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mthe main namespace in the future. Use an array with a string \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    410\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mor bytes dtype instead.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m)\n",
+      "\u001b[31mAttributeError\u001b[39m: `np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead."
+     ]
+    }
+   ],
+   "source": [
+    "import extralit as ex\n",
+    "import pandas as pd\n",
+    "import pandera as pa\n",
+    "from pandera.typing import Index, Series\n",
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Connect to Extralit using default credentials\n",
+    "client = ex.Extralit(api_url=\"http://localhost:6900/\", api_key='extralit.apikey')\n",
+    "\n",
+    "print(f\"Successfully connected to Extralit at {client.api_url}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcdd3afe",
+   "metadata": {},
+   "source": [
+    "## 2. Creating Your First Workspace\n",
+    "\n",
+    "After connecting to Extralit, let's create our first workspace. We'll define a new `Workspace` object and call the `create()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64b5f09b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Workspace 'test-workspace' created successfully with ID: df91e2b9-c712-4474-af82-891478f23d40\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a new workspace\n",
+    "workspace_name = \"test_workspace\"\n",
+    "new_workspace = ex.Workspace(name=workspace_name, client=client)\n",
+    "\n",
+    "# Create the workspace\n",
+    "created_workspace = new_workspace.create()\n",
+    "\n",
+    "print(f\"Workspace '{workspace_name}' created successfully with ID: {created_workspace.id}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c937e5c7",
+   "metadata": {},
+   "source": [
+    "## 3. Listing Available Workspaces\n",
+    "\n",
+    "Now, let's check all the workspaces available in our Extralit instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4df67d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of workspaces: 1\n",
+      "\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
\ No newline at end of file
+    {
+     "data": {
+      "text/html": [
+       "<h3>Workspaces</h3><table><tr><th>name</th><th>id</th><th>updated_at</th></tr><tr><td>test-workspace</td><td>df91e2b9-c712-4474-af82-891478f23d40</td><td>2025-04-16T01:27:58.174591</td></tr></table>"
+      ],
+      "text/plain": [
+       "<argilla.client.Workspaces at 0x77c0709f9ff0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# List all workspaces\n",
+    "workspaces = client.workspaces\n",
+    "\n",
+    "# Display workspace information\n",
+    "print(f\"Total number of workspaces: {len(workspaces)}\\n\")\n",
+    "\n",
+    "# In a notebook, this will display a table with workspace information\n",
+    "workspaces"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ca5b2b8",
+   "metadata": {},
+   "source": [
+    "## 4. Adding PDF Documents to the Workspace\n",
+    "\n",
+    "Let's add two PDF documents to our workspace. For this tutorial, we'll create temporary PDF files. In a real-world scenario, you'd use actual scientific papers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "59084a6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created reference CSV at /tmp/references.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>reference</th>\n",
+       "      <th>file_path</th>\n",
+       "      <th>title</th>\n",
+       "      <th>authors</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>smith2023first</td>\n",
+       "      <td>/tmp/sample1.pdf</td>\n",
+       "      <td>Study on Sample Data</td>\n",
+       "      <td>Smith, J.</td>\n",
+       "      <td>2023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>johnson2022analysis</td>\n",
+       "      <td>/tmp/sample2.pdf</td>\n",
+       "      <td>Analysis of Experimental Results</td>\n",
+       "      <td>Johnson, A.</td>\n",
+       "      <td>2022</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             reference         file_path                             title  \\\n",
+       "0       smith2023first  /tmp/sample1.pdf              Study on Sample Data   \n",
+       "1  johnson2022analysis  /tmp/sample2.pdf  Analysis of Experimental Results   \n",
+       "\n",
+       "       authors  year  \n",
+       "0    Smith, J.  2023  \n",
+       "1  Johnson, A.  2022  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# In a real-world scenario, you would use actual PDFs. Here we'll create temp files\n",
+    "# Define the paths for our temporary PDF files\n",
+    "temp_dir = tempfile.gettempdir()\n",
+    "pdf_file1 = Path(temp_dir) / \"sample1.pdf\"\n",
+    "pdf_file2 = Path(temp_dir) / \"sample2.pdf\"\n",
+    "\n",
+    "# Create empty PDF files - in reality, these would be your actual PDFs\n",
+    "with open(pdf_file1, \"wb\") as f:\n",
+    "    f.write(b\"%PDF-1.5\\n%Example Document 1\")\n",
+    "    \n",
+    "with open(pdf_file2, \"wb\") as f:\n",
+    "    f.write(b\"%PDF-1.5\\n%Example Document 2\")\n",
+    "\n",
+    "# Create a reference dataframe with metadata for the PDFs\n",
+    "references_df = pd.DataFrame({\n",
+    "    \"reference\": [\"smith2023first\", \"johnson2022analysis\"],\n",
+    "    \"file_path\": [str(pdf_file1), str(pdf_file2)],\n",
+    "    \"title\": [\"Study on Sample Data\", \"Analysis of Experimental Results\"],\n",
+    "    \"authors\": [\"Smith, J.\", \"Johnson, A.\"],\n",
+    "    \"year\": [2023, 2022]\n",
+    "})\n",
+    "\n",
+    "# Save the dataframe to a temporary CSV file\n",
+    "references_csv = Path(temp_dir) / \"references.csv\"\n",
+    "references_df.to_csv(references_csv, index=False)\n",
+    "\n",
+    "print(f\"Created reference CSV at {references_csv}\")\n",
+    "references_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abc8562f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Workspace(id=UUID('df91e2b9-c712-4474-af82-891478f23d40') inserted_at=datetime.datetime(2025, 4, 16, 1, 27, 58, 174591) updated_at=datetime.datetime(2025, 4, 16, 1, 27, 58, 174591) name='test-workspace')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a147c6c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the documents into the workspace\n",
+    "# For demonstration purposes, we'll use the extralit client directly\n",
+    "# Initialize the extralit client with the same credentials\n",
+    "extralit_client = ex.Extralit(api_url=\"http://localhost:6900/\", api_key='extralit.apikey')\n",
+    "\n",
+    "# Import the documents\n",
+    "result = extralit_client.import_documents(\n",
+    "    workspace=workspace_name,\n",
+    "    papers=str(references_csv),\n",
+    "    metadatas=[\"title\", \"authors\", \"year\"]\n",
+    ")\n",
+    "\n",
+    "print(f\"Imported {len(result)} documents into workspace '{workspace_name}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98a2025d",
+   "metadata": {},
+   "source": [
+    "## 5. Creating and Uploading a Schema\n",
+    "\n",
+    "Now, let's create a simple schema to define the structure of the data we want to extract from our documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f15f4b2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a simple schema using Pandera\n",
+    "class Publication(pa.DataFrameModel):\n",
+    "    \"\"\"\n",
+    "    General information about the publication, extracted once per paper.\n",
+    "    \"\"\"\n",
+    "    reference: Index[str] = pa.Field(unique=True, check_name=True)\n",
+    "    title: Series[str] = pa.Field()\n",
+    "    authors: Series[str] = pa.Field()\n",
+    "    publication_year: Series[int] = pa.Field(ge=1900, le=2100)\n",
+    "    doi: Series[str] = pa.Field(nullable=True)\n",
+    "    \n",
+    "    class Config:\n",
+    "        singleton = {'enabled': True}  # Indicates this is a document-level schema\n",
+    "\n",
+    "# Define a second schema for experimental data\n",
+    "class ExperimentalData(pa.DataFrameModel):\n",
+    "    \"\"\"\n",
+    "    Experimental data extracted from the paper, may appear multiple times.\n",
+    "    \"\"\"\n",
+    "    experiment_id: Series[str] = pa.Field()\n",
+    "    sample_size: Series[int] = pa.Field(gt=0)\n",
+    "    study_type: Series[str] = pa.Field()\n",
+    "    result_value: Series[float] = pa.Field()\n",
+    "    significance: Series[float] = pa.Field(le=1.0, ge=0.0)\n",
+    "\n",
+    "# Create a schema structure object\n",
+    "from extralit.extraction.models.schema import SchemaStructure\n",
+    "\n",
+    "# Save schemas to a temporary JSON file\n",
+    "schema_file = Path(temp_dir) / \"schemas.json\"\n",
+    "schema_structure = SchemaStructure(schemas={\"Publication\": Publication, \"ExperimentalData\": ExperimentalData})\n",
+    "schema_structure.to_json(schema_file)\n",
+    "\n",
+    "print(f\"Created schema file at {schema_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc61a1ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Upload the schema to the workspace\n",
+    "result = extralit_client.upload_schemas(\n",
+    "    workspace=workspace_name,\n",
+    "    schemas=str(schema_file)\n",
+    ")\n",
+    "\n",
+    "print(f\"Uploaded schemas to workspace '{workspace_name}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a92c20d",
+   "metadata": {},
+   "source": [
+    "## 6. Running PDF Preprocessing\n",
+    "\n",
+    "Next, let's run the PDF preprocessing step to extract text and table content from our documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "768e7176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run PDF preprocessing\n",
+    "from extralit.preprocessing.pdf import process_pdfs\n",
+    "\n",
+    "# Get the references from our dataframe\n",
+    "references = references_df[\"reference\"].tolist()\n",
+    "\n",
+    "# Run the preprocessing step\n",
+    "preprocessing_result = process_pdfs(\n",
+    "    workspace=workspace_name,\n",
+    "    references=references,\n",
+    "    text_ocr=[\"default\"],  # Using the default text OCR model\n",
+    "    table_ocr=[\"default\"],  # Using the default table OCR model\n",
+    "    output_dataset=\"PDF_Preprocessing_Results\"\n",
+    ")\n",
+    "\n",
+    "print(f\"Preprocessing completed for {len(preprocessing_result)} documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44aa776d",
+   "metadata": {},
+   "source": [
+    "## 7. Running LLM Extractions\n",
+    "\n",
+    "Finally, let's run the LLM extraction step to extract structured data according to our schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19221abe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run LLM extractions\n",
+    "from extralit.extraction.llm import extract_data\n",
+    "\n",
+    "# Run the extraction step\n",
+    "extraction_result = extract_data(\n",
+    "    workspace=workspace_name,\n",
+    "    references=references,\n",
+    "    output_dataset=\"Data_Extraction_Results\"\n",
+    ")\n",
+    "\n",
+    "print(f\"LLM extractions completed for {len(extraction_result)} documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1b6ec61",
+   "metadata": {},
+   "source": [
+    "## 8. Checking Extraction Results\n",
+    "\n",
+    "Let's check the results of our extractions by listing the datasets created and viewing the extracted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4dbb695d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List datasets in the workspace\n",
+    "datasets = extralit_client.list_datasets(workspace=workspace_name)\n",
+    "print(f\"Datasets in workspace '{workspace_name}':\\n\")\n",
+    "for dataset in datasets:\n",
+    "    print(f\"- {dataset.name} ({dataset.id})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eee019d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export the extracted data\n",
+    "extracted_data = extralit_client.export_data(\n",
+    "    workspace=workspace_name,\n",
+    "    output=\"temp_output.csv\"  # This will save the data to a CSV file\n",
+    ")\n",
+    "\n",
+    "# Display the extracted data\n",
+    "if isinstance(extracted_data, dict):\n",
+    "    for schema_name, data_df in extracted_data.items():\n",
+    "        print(f\"\\nExtracted data for schema '{schema_name}':\\n\")\n",
+    "        display(data_df)\n",
+    "else:\n",
+    "    print(\"\\nExtracted data:\")\n",
+    "    display(extracted_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a30a5ea",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Congratulations! You've successfully tested the primary functionalities of Extralit with default credentials on a fresh install. You have:\n",
+    "\n",
+    "1. Connected to Extralit with default credentials\n",
+    "2. Created a workspace\n",
+    "3. Added PDF documents\n",
+    "4. Created and uploaded a schema\n",
+    "5. Run PDF preprocessing\n",
+    "6. Run LLM extractions\n",
+    "7. Checked the extraction results\n",
+    "\n",
+    "This workflow demonstrates the basic process of using Extralit for data extraction from scientific papers. In a real-world scenario, you would upload actual scientific papers and create more complex schemas tailored to your specific data extraction needs.\n",
+    "\n",
+    "For more detailed information, refer to the [Extralit documentation](https://docs.extralit.ai/latest/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/extralit-frontend/components/base/base-breadcrumbs/BaseBreadcrumbs.vue b/extralit-frontend/components/base/base-breadcrumbs/BaseBreadcrumbs.vue
index 681e017c1..a606a8379 100644
--- a/extralit-frontend/components/base/base-breadcrumbs/BaseBreadcrumbs.vue
+++ b/extralit-frontend/components/base/base-breadcrumbs/BaseBreadcrumbs.vue
@@ -33,7 +33,7 @@
         </span>
       </li>
     </ul>
-    <!-- <base-action-tooltip :tooltip="$t('copied')">
+    <BaseActionTooltip :tooltip="$t('copied')">
       <a
         v-if="copyButton"
         class="breadcrumbs__copy"
@@ -45,7 +45,7 @@
       >
         <svgicon name="copy" width="16" height="16" />
       </a>
-    </base-action-tooltip> -->
+    </BaseActionTooltip>
   </div>
 </template>
 
diff --git a/extralit-frontend/components/base/base-code/BaseCode.vue b/extralit-frontend/components/base/base-code/BaseCode.vue
index cdda30036..59d42df95 100644
--- a/extralit-frontend/components/base/base-code/BaseCode.vue
+++ b/extralit-frontend/components/base/base-code/BaseCode.vue
@@ -1,9 +1,9 @@
 <template>
-  <base-action-tooltip class="button" :tooltip="t('copied')">
+  <BaseActionTooltip class="button" :tooltip="t('copied')">
     <a href="#" @click.prevent="copy(code)">
       <svgicon name="copy" width="16" height="16" />
     </a>
-  </base-action-tooltip>
+  </BaseActionTooltip>
 </template>
 
 <script>
diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index e6d52f96f..935eb9781 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -1,5 +1,5 @@
 <template>
-  <VerticalResizable class="wrapper" :id="`${recordCriteria.datasetId}-r-v-rz`">
+  <VerticalResizable :id="`${recordCriteria.datasetId}-r-v-rz`" class="wrapper">
     <template #left>
       <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" collapsable>
         <template #up>
@@ -74,10 +74,10 @@
       @toggle-expand="expandedGuidelines = !expandedGuidelines"
     >
       <template #panelHeader>
-        <p v-text="$t('document')" />
+        <p v-text="$t('guidelines')" />
       </template>
       <template #panelContent>
-        <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
+        <AnnotationGuidelines />
       </template>
     </BaseCollapsablePanel>
   </VerticalResizable>
diff --git a/extralit-frontend/components/features/annotation/settings/SettingsInfo.vue b/extralit-frontend/components/features/annotation/settings/SettingsInfo.vue
index 0dea5cd22..8ece0e8be 100644
--- a/extralit-frontend/components/features/annotation/settings/SettingsInfo.vue
+++ b/extralit-frontend/components/features/annotation/settings/SettingsInfo.vue
@@ -7,7 +7,7 @@
           <div class="settings__item">
             <p class="settings__dataset-name --body1" v-html="settings.dataset.name" />
           </div>
-          <base-action-tooltip :tooltip="$t('copied')">
+          <BaseActionTooltip :tooltip="$t('copied')">
             <base-button
               :title="$t('button.tooltip.copyToClipboard')"
               class="secondary small"
@@ -15,7 +15,7 @@
             >
               {{ $t("copyLink") }}
             </base-button>
-          </base-action-tooltip>
+          </BaseActionTooltip>
         </div>
       </div>
       <div class="settings__area">
diff --git a/extralit-frontend/components/features/annotation/settings/SettingsInfoReadOnly.vue b/extralit-frontend/components/features/annotation/settings/SettingsInfoReadOnly.vue
index 7ba1511f9..03d021faf 100644
--- a/extralit-frontend/components/features/annotation/settings/SettingsInfoReadOnly.vue
+++ b/extralit-frontend/components/features/annotation/settings/SettingsInfoReadOnly.vue
@@ -6,7 +6,7 @@
         <div class="settings__item">
           <p class="setting__dataset-name --body1" v-html="settings.dataset.name" />
         </div>
-        <base-action-tooltip :tooltip="$t('copied')">
+        <BaseActionTooltip :tooltip="$t('copied')">
           <base-button
             :title="$t('button.tooltip.copyToClipboard')"
             class="secondary small"
@@ -14,7 +14,7 @@
           >
             {{ $t("copyLink") }}
           </base-button>
-        </base-action-tooltip>
+        </BaseActionTooltip>
       </div>
     </div>
     <div class="settings__area">
diff --git a/extralit-frontend/components/features/user-settings/user-token/UserToken.component.vue b/extralit-frontend/components/features/user-settings/user-token/UserToken.component.vue
index 9f34e88c5..fc76b3c53 100644
--- a/extralit-frontend/components/features/user-settings/user-token/UserToken.component.vue
+++ b/extralit-frontend/components/features/user-settings/user-token/UserToken.component.vue
@@ -6,7 +6,7 @@
     <span class="user-token-value --body1" v-text="'**** **** **** ****'" />
 
     <div class="user-token-button">
-      <base-action-tooltip :tooltip="$t('copied')">
+      <BaseActionTooltip :tooltip="$t('copied')">
         <BaseButton
           :title="$t('button.tooltip.copyToClipboard')"
           class="secondary small"
@@ -14,7 +14,7 @@
         >
           {{ $t("userSettings.copyKey") }}
         </BaseButton>
-      </base-action-tooltip>
+      </BaseActionTooltip>
     </div>
   </div>
 </template>

From 55bd46b84d46c2da0b6fd3866938106b5160864f Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 13:28:12 -0700
Subject: [PATCH 03/20] revert

---
 .../document_extraction/setup_workspace.ipynb     |  4 ++--
 .../annotation/container/mode/FocusAnnotation.vue | 15 +++++++--------
 .../container/mode/useDocumentViewModel.ts        |  5 -----
 3 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/examples/document_extraction/setup_workspace.ipynb b/examples/document_extraction/setup_workspace.ipynb
index e0d03ce1f..24d4198d7 100644
--- a/examples/document_extraction/setup_workspace.ipynb
+++ b/examples/document_extraction/setup_workspace.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "ce009aae",
    "metadata": {},
    "outputs": [
@@ -51,7 +51,7 @@
      "traceback": [
       "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
       "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mextralit\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpa\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Index, Series\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtempfile\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mextralit\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpa\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Index, Series\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtempfile\u001b[39;00m\n",
       "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/__init__.py:5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"A flexible and expressive pandas validation library.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplatform\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n",
       "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/__init__.py:4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Pandera backends.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# ensure that base builtin checks and hypothesis are registered\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpandas\u001b[39;00m\n",
       "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/base/builtin_checks.py:14\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mre\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Iterable, Optional, TypeVar, Union\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mchecks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Check\n\u001b[32m     16\u001b[39m T = TypeVar(\u001b[33m\"\u001b[39m\u001b[33mT\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     19\u001b[39m \u001b[38;5;129m@Check\u001b[39m.register_builtin_check_fn\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mequal_to\u001b[39m(data: Any, value: Any) -> Any:\n",
diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index 5ddec913a..f164a9abb 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -1,7 +1,7 @@
 <template>
-  <VerticalResizable :id="`${recordCriteria.datasetId}-r-v-rz`" class="wrapper">
+  <VerticalResizable class="wrapper" :id="`${recordCriteria.datasetId}-r-v-rz`">
     <template #left>
-      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" :collapsable="shouldShowDocumentPanel">
+      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" collapsable>
         <template #up>
           <section class="wrapper__records" aria-label="Focus Annotation View">
             <DatasetFilters :recordCriteria="recordCriteria">
@@ -25,13 +25,13 @@
             <Record v-else :datasetVectors="datasetVectors" :recordCriteria="recordCriteria" :record="record" />
           </section>
         </template>
-        <template #downHeader v-if="shouldShowDocumentPanel">
+        <template #downHeader>
           <p v-text="$t('document')" />
         </template>
-        <template #downHeaderExpanded v-if="shouldShowDocumentPanel">
+        <template #downHeaderExpanded>
           <p v-text="$t('document')" />
         </template>
-        <template #downContent v-if="shouldShowDocumentPanel">
+        <template #downContent>
           <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
         </template>
       </HorizontalResizable>
@@ -69,16 +69,15 @@
       </HorizontalResizable>
     </template>
     <BaseCollapsablePanel
-      v-if="shouldShowDocumentPanel"
       hideOnDesktop
       :isExpanded="expandedGuidelines"
       @toggle-expand="expandedGuidelines = !expandedGuidelines"
     >
       <template #panelHeader>
-        <p v-text="$t('guidelines')" />
+        <p v-text="$t('document')" />
       </template>
       <template #panelContent>
-        <AnnotationGuidelines />
+        <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
       </template>
     </BaseCollapsablePanel>
   </VerticalResizable>
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
index 8ca022fe7..5f64464b1 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
@@ -20,10 +20,6 @@ export const useDocumentViewModel = (props: { record: any }) => {
     return document.id !== null;
   });
 
-  const shouldShowDocumentPanel = computed(() => {
-    return hasDocumentLoaded.value && document.url !== null;
-  });
-
   const fetchDocument = async (metadata: any) => {
     try {
       await waitForAsyncValue(() => workspaces.selectedWorkspace?.id);
@@ -118,7 +114,6 @@ export const useDocumentViewModel = (props: { record: any }) => {
     fetchDocumentSegments,
     focusDocumentPageNumber,
     clearDocument,
-    shouldShowDocumentPanel,
   };
 };
 

From aa2e301596bdd601bf2669ad632db1f2b101e43d Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 13:45:14 -0700
Subject: [PATCH 04/20] fix deps

---
 .../document_extraction/setup_workspace.ipynb | 55 ++++++++++---------
 .../base-resizable/HorizontalResizable.vue    |  2 +-
 .../base/base-resizable/VerticalResizable.vue |  4 +-
 extralit/pyproject.toml                       |  4 +-
 4 files changed, 33 insertions(+), 32 deletions(-)

diff --git a/examples/document_extraction/setup_workspace.ipynb b/examples/document_extraction/setup_workspace.ipynb
index 24d4198d7..c57cce6c7 100644
--- a/examples/document_extraction/setup_workspace.ipynb
+++ b/examples/document_extraction/setup_workspace.ipynb
@@ -40,27 +40,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "ce009aae",
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "`np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mextralit\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mex\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpa\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Index, Series\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtempfile\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/__init__.py:5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"A flexible and expressive pandas validation library.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplatform\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/__init__.py:4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Pandera backends.\"\"\"\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# ensure that base builtin checks and hypothesis are registered\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_checks\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbuiltin_hypotheses\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbackends\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpandas\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/backends/base/builtin_checks.py:14\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mre\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Any, Iterable, Optional, TypeVar, Union\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mchecks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Check\n\u001b[32m     16\u001b[39m T = TypeVar(\u001b[33m\"\u001b[39m\u001b[33mT\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     19\u001b[39m \u001b[38;5;129m@Check\u001b[39m.register_builtin_check_fn\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mequal_to\u001b[39m(data: Any, value: Any) -> Any:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/api/checks.py:17\u001b[39m\n\u001b[32m     15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m errors\n\u001b[32m     16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mchecks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseCheck, CheckResult\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SearchStrategy\n\u001b[32m     19\u001b[39m T = TypeVar(\u001b[33m\"\u001b[39m\u001b[33mT\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     22\u001b[39m \u001b[38;5;66;03m# pylint: disable=too-many-public-methods\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/strategies/__init__.py:3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Data synthesis strategies for pandera, powered by the hypothesis package.\"\"\"\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpandas_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/strategies/pandas_strategies.py:42\u001b[39m\n\u001b[32m     32\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     35\u001b[39m     DataType,\n\u001b[32m     36\u001b[39m     is_category,\n\u001b[32m   (...)\u001b[39m\u001b[32m     40\u001b[39m     is_timedelta,\n\u001b[32m     41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mengines\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m numpy_engine, pandas_engine\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01merrors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseStrategyOnlyError, SchemaDefinitionError\n\u001b[32m     44\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandera\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstrategies\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase_strategies\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     45\u001b[39m     HAS_HYPOTHESIS,\n\u001b[32m     46\u001b[39m     STRATEGY_DISPATCHER,\n\u001b[32m     47\u001b[39m )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/pandera/engines/pandas_engine.py:757\u001b[39m\n\u001b[32m    738\u001b[39m             is_python_string = data_container.map(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m))  \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n\u001b[32m    739\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m is_python_string.astype(\u001b[38;5;28mbool\u001b[39m) | data_container.isna()\n\u001b[32m    742\u001b[39m Engine.register_dtype(\n\u001b[32m    743\u001b[39m     numpy_engine.Object,\n\u001b[32m    744\u001b[39m     equivalents=[\n\u001b[32m    745\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    746\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject_\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    747\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mobject0\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    748\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mO\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    749\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mbytes\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    750\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmixed-integer\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    751\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmixed\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    752\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mbytes\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    753\u001b[39m         \u001b[38;5;28mbytes\u001b[39m,\n\u001b[32m    754\u001b[39m         \u001b[38;5;28mobject\u001b[39m,\n\u001b[32m    755\u001b[39m         np.object_,\n\u001b[32m    756\u001b[39m         np.bytes_,\n\u001b[32m--> \u001b[39m\u001b[32m757\u001b[39m         \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstring_\u001b[49m,\n\u001b[32m    758\u001b[39m     ],\n\u001b[32m    759\u001b[39m )\n\u001b[32m    761\u001b[39m \u001b[38;5;66;03m# ###############################################################################\u001b[39;00m\n\u001b[32m    762\u001b[39m \u001b[38;5;66;03m# # time\u001b[39;00m\n\u001b[32m    763\u001b[39m \u001b[38;5;66;03m# ###############################################################################\u001b[39;00m\n\u001b[32m    766\u001b[39m _PandasDatetime = Union[np.datetime64, pd.DatetimeTZDtype]\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/numpy/__init__.py:400\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(attr)\u001b[39m\n\u001b[32m    397\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr], name=\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __expired_attributes__:\n\u001b[32m--> \u001b[39m\u001b[32m400\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m    401\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m` was removed in the NumPy 2.0 release. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    402\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__expired_attributes__[attr]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m    403\u001b[39m         name=\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    404\u001b[39m     )\n\u001b[32m    406\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr == \u001b[33m\"\u001b[39m\u001b[33mchararray\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m    407\u001b[39m     warnings.warn(\n\u001b[32m    408\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m`np.chararray` is deprecated and will be removed from \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    409\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mthe main namespace in the future. Use an array with a string \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    410\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mor bytes dtype instead.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m)\n",
-      "\u001b[31mAttributeError\u001b[39m: `np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully connected to Extralit at http://localhost:6900/\n"
      ]
     }
    ],
@@ -90,21 +78,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "64b5f09b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Workspace 'test-workspace' created successfully with ID: df91e2b9-c712-4474-af82-891478f23d40\n"
+     "ename": "ConflictError",
+     "evalue": "Extralit SDK error: ConflictError: Conflict with the server. Resource or entity already exists. Details: {\"detail\":\"Workspace name `extralit` is not unique\"}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mHTTPStatusError\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:91\u001b[39m, in \u001b[36mapi_error_handler.<locals>._handler_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     90\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m91\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     92\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_api/_workspaces.py:52\u001b[39m, in \u001b[36mWorkspacesAPI.create\u001b[39m\u001b[34m(self, workspace)\u001b[39m\n\u001b[32m     51\u001b[39m response = \u001b[38;5;28mself\u001b[39m.http_client.post(url=\u001b[33m\"\u001b[39m\u001b[33m/api/v1/workspaces\u001b[39m\u001b[33m\"\u001b[39m, json=workspace.model_dump())\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     53\u001b[39m response_json = response.json()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/httpx/_models.py:763\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    762\u001b[39m message = message.format(\u001b[38;5;28mself\u001b[39m, error_type=error_type)\n\u001b[32m--> \u001b[39m\u001b[32m763\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPStatusError(message, request=request, response=\u001b[38;5;28mself\u001b[39m)\n",
+      "\u001b[31mHTTPStatusError\u001b[39m: Client error '409 Conflict' for url 'http://localhost:6900/api/v1/workspaces'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/409",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mConflictError\u001b[39m                             Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      3\u001b[39m new_workspace = ex.Workspace(name=workspace_name, client=client)\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Create the workspace\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m created_workspace = \u001b[43mnew_workspace\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mWorkspace \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mworkspace_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m created successfully with ID: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcreated_workspace.id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_resource.py:94\u001b[39m, in \u001b[36mResource.create\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     93\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[33m\"\u001b[39m\u001b[33mResource\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m     response_model = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapi_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     95\u001b[39m     \u001b[38;5;28mself\u001b[39m._model = response_model\n\u001b[32m     96\u001b[39m     \u001b[38;5;28mself\u001b[39m._update_last_api_call()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:93\u001b[39m, in \u001b[36mapi_error_handler.<locals>._handler_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     91\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m func(*args, **kwargs)\n\u001b[32m     92\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m---> \u001b[39m\u001b[32m93\u001b[39m     \u001b[43m_error_switch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus_code\u001b[49m\u001b[43m=\u001b[49m\u001b[43me\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstatus_code\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_detail\u001b[49m\u001b[43m=\u001b[49m\u001b[43me\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:87\u001b[39m, in \u001b[36mapi_error_handler.<locals>._error_switch\u001b[39m\u001b[34m(status_code, error_detail)\u001b[39m\n\u001b[32m     77\u001b[39m switch = {\n\u001b[32m     78\u001b[39m     \u001b[32m400\u001b[39m: BadRequestError,\n\u001b[32m     79\u001b[39m     \u001b[32m401\u001b[39m: UnauthorizedError,\n\u001b[32m   (...)\u001b[39m\u001b[32m     84\u001b[39m     \u001b[32m500\u001b[39m: InternalServerError,\n\u001b[32m     85\u001b[39m }\n\u001b[32m     86\u001b[39m exception_class = switch.get(status_code, ExtralitAPIError)\n\u001b[32m---> \u001b[39m\u001b[32m87\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exception_class(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexception_class.message\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. Details: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror_detail\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, status_code=status_code)\n",
+      "\u001b[31mConflictError\u001b[39m: Extralit SDK error: ConflictError: Conflict with the server. Resource or entity already exists. Details: {\"detail\":\"Workspace name `extralit` is not unique\"}"
      ]
     }
    ],
    "source": [
     "# Define a new workspace\n",
-    "workspace_name = \"test_workspace\"\n",
+    "workspace_name = \"extralit\"\n",
     "new_workspace = ex.Workspace(name=workspace_name, client=client)\n",
     "\n",
     "# Create the workspace\n",
@@ -125,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "d4df67d6",
    "metadata": {},
    "outputs": [
@@ -140,13 +141,13 @@
     {
      "data": {
       "text/html": [
-       "<h3>Workspaces</h3><table><tr><th>name</th><th>id</th><th>updated_at</th></tr><tr><td>test-workspace</td><td>df91e2b9-c712-4474-af82-891478f23d40</td><td>2025-04-16T01:27:58.174591</td></tr></table>"
+       "<h3>Workspaces</h3><table><tr><th>name</th><th>id</th><th>updated_at</th></tr><tr><td>extralit</td><td>e8154298-bf70-42ab-aca1-68cbd3b1b650</td><td>2025-08-13T00:41:32.506179</td></tr></table>"
       ],
       "text/plain": [
-       "<argilla.client.Workspaces at 0x77c0709f9ff0>"
+       "<extralit.client.resources.Workspaces at 0xffff66483c90>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue b/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
index f07c38158..ed4a2d063 100644
--- a/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
+++ b/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
@@ -125,7 +125,7 @@ export default {
     }
   },
   destroyed() {
-    this.resizer.removeEventListener(EVENT.MOUSE_DOWN, this.mouseDownHandler);
+    this.resizer?.removeEventListener(EVENT.MOUSE_DOWN, this.mouseDownHandler);
   },
   methods: {
     savePosition() {},
diff --git a/extralit-frontend/components/base/base-resizable/VerticalResizable.vue b/extralit-frontend/components/base/base-resizable/VerticalResizable.vue
index 1c1f95cb9..dba430f5c 100644
--- a/extralit-frontend/components/base/base-resizable/VerticalResizable.vue
+++ b/extralit-frontend/components/base/base-resizable/VerticalResizable.vue
@@ -2,7 +2,7 @@
   <div class="resizable-v" :class="resizing ? '--v-resizing' : ''">
     <div class="resizable-v__left"><slot name="left" /></div>
 
-    <div class="resizable-v__bar" ref="resizableBar">
+    <div ref="resizableBar" class="resizable-v__bar">
       <div class="resizable-v__bar__inner" />
     </div>
 
@@ -64,7 +64,7 @@ export default {
     }
   },
   destroyed() {
-    this.resizer.removeEventListener(EVENT.MOUSE_DOWN, this.mouseDownHandler);
+    this.resizer?.removeEventListener(EVENT.MOUSE_DOWN, this.mouseDownHandler);
   },
   methods: {
     limitElementWidth(element) {
diff --git a/extralit/pyproject.toml b/extralit/pyproject.toml
index e2865f785..399898bf0 100644
--- a/extralit/pyproject.toml
+++ b/extralit/pyproject.toml
@@ -35,8 +35,8 @@ dependencies = [
     "pypandoc ~= 1.13",
     "beautifulsoup4 ~= 4.12.2",
     "pandas ~= 2.2.2",
-    "pandera[io] ~= 0.19.3",
-    "numpy >= 1.26.4,<2.0.0",
+    "pandera[io] >= 0.21.0, < 0.27.0",
+    "numpy >= 1.26.4",
     "spacy ~= 3.7.2; python_version < '3.13'",
     "spacy >= 3.8.0; python_version >= '3.13' and python_version < '3.13.3'",
     # Install spaCy with wheels only for Python 3.13.3+

From e53eaba6a44e985cc8de424ab7fd9e3908ea3b7a Mon Sep 17 00:00:00 2001
From: JTran-IDM <nhat.tran@mavs.uta.edu>
Date: Wed, 13 Aug 2025 15:03:07 -0700
Subject: [PATCH 05/20] Improve document panel behavior and handling of missing
 documents

- Collapse document panel when no document is available
- Show message when document is missing instead of PDF viewer
- Add createParams method to GetDocumentByIdUseCase
- Refactor useDocumentViewModel to use createParams and expand/collapse panel
- Update tests for new document panel logic
---
 .../container/mode/FocusAnnotation.vue        | 46 ++++++++++--
 .../mode/useDocumentViewModel.spec.js         | 70 +++++++------------
 .../container/mode/useDocumentViewModel.ts    | 53 ++++++++------
 .../usecases/get-document-by-id-use-case.ts   | 33 ++++++++-
 4 files changed, 130 insertions(+), 72 deletions(-)

diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index f164a9abb..d84e02fd9 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -1,7 +1,12 @@
 <template>
-  <VerticalResizable class="wrapper" :id="`${recordCriteria.datasetId}-r-v-rz`">
+  <VerticalResizable :id="`${recordCriteria.datasetId}-r-v-rz`" class="wrapper">
     <template #left>
-      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" collapsable>
+      <HorizontalResizable
+        :id="`${recordCriteria.datasetId}-r-h-rz`"
+        ref="documentPanel"
+        class="wrapper__left"
+        collapsable
+      >
         <template #up>
           <section class="wrapper__records" aria-label="Focus Annotation View">
             <DatasetFilters :recordCriteria="recordCriteria">
@@ -32,7 +37,15 @@
           <p v-text="$t('document')" />
         </template>
         <template #downContent>
-          <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
+          <PDFViewer
+            v-if="document.url"
+            :url="document.url"
+            :file-name="document.file_name"
+            :pageNumber="document.page_number"
+          />
+          <div v-else class="no-document-message">
+            <p>{{ $t("no_document_available") }}</p>
+          </div>
         </template>
       </HorizontalResizable>
     </template>
@@ -117,6 +130,23 @@ export default {
       expandedGuidelines: false,
     };
   },
+  watch: {
+    isDocumentPanelExpanded(newValue) {
+      if (this.$refs.documentPanel && this.$refs.documentPanel.isExpanded !== newValue) {
+        this.$refs.documentPanel.isExpanded = newValue;
+      }
+    },
+    "document.url"(newUrl, oldUrl) {
+      // Collapse panel when document is cleared
+      if (!newUrl && oldUrl) {
+        this.isDocumentPanelExpanded = false;
+      }
+      // Expand panel when document is loaded
+      else if (newUrl && !oldUrl) {
+        this.isDocumentPanelExpanded = true;
+      }
+    },
+  },
   methods: {
     async onSubmit() {
       await this.submit(this.record);
@@ -146,7 +176,6 @@ export default {
         this.isDraftSaving = false;
       }
     },
-
   },
   setup(props) {
     return {
@@ -231,4 +260,13 @@ export default {
     display: block;
   }
 }
+
+.no-document-message {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  color: var(--fg-secondary);
+  font-style: italic;
+}
 </style>
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js
index 8841c692f..72cb0ed87 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.spec.js
@@ -85,6 +85,7 @@ describe("useDocumentViewModel", () => {
     mockGetDocumentUseCase = {
       setDocument: jest.fn(),
       setSegments: jest.fn(),
+      createParams: jest.fn(),
     };
 
     // Setup Vue composition API mocks
@@ -102,69 +103,52 @@ describe("useDocumentViewModel", () => {
     jest.restoreAllMocks();
   });
 
-  describe("shouldShowDocumentPanel computed property", () => {
+  describe("hasDocumentLoaded computed property", () => {
     it("should return false when document has no id", () => {
       mockDocument.id = null;
-      mockDocument.url = "http://example.com/doc.pdf";
 
-      const viewModel = useDocumentViewModel({ record: {} });
+      useDocumentViewModel({ record: {} });
 
       // Get the computed function that was passed to mockComputed
       const computedCalls = mockComputed.mock.calls;
-      const shouldShowDocumentPanelCall = computedCalls.find(
-        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
+      const hasDocumentLoadedCall = computedCalls.find(
+        (call) => call[0].toString().includes("document.id")
       );
 
-      expect(shouldShowDocumentPanelCall).toBeDefined();
-      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
-      expect(shouldShowDocumentPanel).toBe(false);
+      expect(hasDocumentLoadedCall).toBeDefined();
+      const hasDocumentLoaded = hasDocumentLoadedCall[0]();
+      expect(hasDocumentLoaded).toBe(false);
     });
 
-    it("should return false when document has no url", () => {
+    it("should return true when document has an id", () => {
       mockDocument.id = "doc-123";
-      mockDocument.url = null;
 
-      const viewModel = useDocumentViewModel({ record: {} });
+      useDocumentViewModel({ record: {} });
 
       // Get the computed function that was passed to mockComputed
       const computedCalls = mockComputed.mock.calls;
-      const shouldShowDocumentPanelCall = computedCalls.find(
-        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
+      const hasDocumentLoadedCall = computedCalls.find(
+        (call) => call[0].toString().includes("document.id")
       );
 
-      expect(shouldShowDocumentPanelCall).toBeDefined();
-      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
-      expect(shouldShowDocumentPanel).toBe(false);
-    });
-
-    it("should return true when document has both id and url", () => {
-      mockDocument.id = "doc-123";
-      mockDocument.url = "http://example.com/doc.pdf";
-
-      const viewModel = useDocumentViewModel({ record: {} });
-
-      // Get the computed function that was passed to mockComputed
-      const computedCalls = mockComputed.mock.calls;
-      const shouldShowDocumentPanelCall = computedCalls.find(
-        (call) => call[0].toString().includes("hasDocumentLoaded") && call[0].toString().includes("document.url")
-      );
-
-      expect(shouldShowDocumentPanelCall).toBeDefined();
-      const shouldShowDocumentPanel = shouldShowDocumentPanelCall[0]();
-      expect(shouldShowDocumentPanel).toBe(true);
+      expect(hasDocumentLoadedCall).toBeDefined();
+      const hasDocumentLoaded = hasDocumentLoadedCall[0]();
+      expect(hasDocumentLoaded).toBe(true);
     });
   });
 
-  describe("clearDocument behavior", () => {
-    it("should call clearDocument when no document identifiers are present", () => {
-      mockDocument.id = "doc-123";
+  describe("createParams method", () => {
+    it("should call createParams from use case", () => {
+      const metadata = { pmid: "12345" };
+      const workspaceId = "workspace-123";
 
-      const viewModel = useDocumentViewModel({
-        record: {
-          metadata: {}, // No pmid, doi, doc_id, or reference
-        },
+      mockGetDocumentUseCase.createParams.mockReturnValue({
+        workspace_id: workspaceId,
+        pmid: "12345"
       });
 
+      useDocumentViewModel({ record: { metadata } });
+
       // Verify that watch was called for props.record?.metadata
       expect(mockWatch).toHaveBeenCalled();
 
@@ -176,10 +160,6 @@ describe("useDocumentViewModel", () => {
 
       expect(metadataWatchCall).toBeDefined();
 
-      // Simulate the watch callback being called with empty metadata
-      const watchCallback = metadataWatchCall[1];
-      watchCallback({}, null);
-
       // Since we can't easily test the async updateDocument function,
       // we'll verify that the watch was set up correctly
       expect(mockWatch).toHaveBeenCalledWith(expect.any(Function), expect.any(Function), { immediate: true });
@@ -194,7 +174,7 @@ describe("useDocumentViewModel", () => {
       expect(viewModel).toHaveProperty("fetchDocumentSegments");
       expect(viewModel).toHaveProperty("focusDocumentPageNumber");
       expect(viewModel).toHaveProperty("clearDocument");
-      expect(viewModel).toHaveProperty("shouldShowDocumentPanel");
+      expect(viewModel).toHaveProperty("isDocumentPanelExpanded");
     });
 
     it("should return functions for methods", () => {
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
index 5f64464b1..1201d31ec 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
@@ -15,6 +15,7 @@ export const useDocumentViewModel = (props: { record: any }) => {
   const { state: dataset } = useDataset();
   const { state: document, set: setDocument, clear: clearDocument } = useDocument();
   const isLoading = ref(false);
+  const isDocumentPanelExpanded = ref(false);
 
   const hasDocumentLoaded = computed(() => {
     return document.id !== null;
@@ -24,25 +25,28 @@ export const useDocumentViewModel = (props: { record: any }) => {
     try {
       await waitForAsyncValue(() => workspaces.selectedWorkspace?.id);
 
-      const params: {
-        workspace_id: string;
-        doc_id?: string;
-        pmid?: string;
-        doi?: string;
-        reference?: string;
-      } = { workspace_id: workspaces.selectedWorkspace!.id };
-
-      if (metadata?.reference) params.reference = metadata.reference;
-      if (metadata?.doc_id) params.doc_id = metadata.doc_id;
-      if (metadata?.pmid) params.pmid = metadata.pmid;
-      if (metadata?.doi) params.doi = metadata.doi;
-
-      // Ensure at least one identifier is provided
-      if (Object.keys(params).length === 0) {
-        throw new Error("No valid document identifier found in metadata");
+      const params = getDocument.createParams(metadata, workspaces.selectedWorkspace!.id);
+
+      // Check if we have any valid identifiers
+      const hasValidIdentifier = params.reference || params.doc_id || params.pmid || params.doi;
+
+      if (!hasValidIdentifier) {
+        // No valid identifiers, clear the document
+        clearDocument();
+        return;
       }
 
-      await getDocument.setDocument(params);
+      const result = await getDocument.setDocument(params);
+
+      // If no documents were found, the use case will have already cleared the storage
+      if (!result) {
+        console.log("No documents found for the provided criteria");
+        // Collapse the document panel when no document is found
+        isDocumentPanelExpanded.value = false;
+      } else {
+        // Expand the document panel when a document is found
+        isDocumentPanelExpanded.value = true;
+      }
     } catch (e) {
       const identifier = metadata?.pmid || metadata?.doi || metadata?.doc_id || metadata?.reference || "unknown";
       console.error(`Error fetching document with identifier "${identifier}":`, e);
@@ -55,14 +59,20 @@ export const useDocumentViewModel = (props: { record: any }) => {
   };
 
   const updateDocument = async (metadata: any) => {
+    const hasValidIdentifier = metadata?.pmid || metadata?.doi || metadata?.doc_id || metadata?.reference;
+
     if (metadata?.pmid != null && document.pmid !== metadata.pmid) {
-      fetchDocument(metadata);
+      await fetchDocument(metadata);
     } else if (metadata?.doi != null && document.doi !== metadata.doi) {
-      fetchDocument(metadata);
+      await fetchDocument(metadata);
     } else if (metadata?.doc_id != null && document.id !== metadata.doc_id) {
-      fetchDocument(metadata);
-    } else if (!metadata?.pmid && !metadata?.doi && !metadata?.doc_id && hasDocumentLoaded.value) {
+      await fetchDocument(metadata);
+    } else if (metadata?.reference != null && document.reference !== metadata.reference) {
+      await fetchDocument(metadata);
+    } else if (!hasValidIdentifier && hasDocumentLoaded.value) {
       clearDocument();
+      // Collapse the document panel when no document is available
+      isDocumentPanelExpanded.value = false;
     }
 
     if (metadata?.page_number != null) {
@@ -114,6 +124,7 @@ export const useDocumentViewModel = (props: { record: any }) => {
     fetchDocumentSegments,
     focusDocumentPageNumber,
     clearDocument,
+    isDocumentPanelExpanded,
   };
 };
 
diff --git a/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts b/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts
index c26ac8292..e4d9bc90a 100644
--- a/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts
+++ b/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts
@@ -6,7 +6,33 @@ export class GetDocumentByIdUseCase {
   constructor(
     private readonly documentRepository: DocumentRepository,
     private readonly documentStorage: IDocumentStorage
-  ) { }
+  ) {}
+
+  createParams(
+    metadata: any,
+    workspaceId: string
+  ): {
+    workspace_id: string;
+    doc_id?: string;
+    pmid?: string;
+    doi?: string;
+    reference?: string;
+  } {
+    const params: {
+      workspace_id: string;
+      doc_id?: string;
+      pmid?: string;
+      doi?: string;
+      reference?: string;
+    } = { workspace_id: workspaceId };
+
+    if (metadata?.reference) params.reference = metadata.reference;
+    if (metadata?.doc_id) params.doc_id = metadata.doc_id;
+    if (metadata?.pmid) params.pmid = metadata.pmid;
+    if (metadata?.doi) params.doi = metadata.doi;
+
+    return params;
+  }
 
   async setDocument(params: {
     workspace_id: string;
@@ -18,7 +44,9 @@ export class GetDocumentByIdUseCase {
     const documents = await this.documentRepository.getDocuments(params);
 
     if (documents.length === 0) {
-      throw new Error("No documents found with the provided criteria");
+      // Clear the document storage when no documents are found
+      this.documentStorage.clear();
+      return null;
     }
 
     // For now, we'll use the first document found
@@ -28,6 +56,7 @@ export class GetDocumentByIdUseCase {
     }
 
     this.documentStorage.set(documents[0]);
+    return documents[0];
   }
 
   async setSegments(workspace: string, reference: string): Promise<Segment[]> {

From 60e93aea55cabec09eac03548a5ea71e3136b5e0 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 15:17:04 -0700
Subject: [PATCH 06/20] Enhance DiskImportExportMixin path to expand user
 directory in the provided path to support '~'.

---
 .../document_extraction/setup_workspace.ipynb | 110 ++++++++++--------
 extralit/src/extralit/datasets/_io/_disk.py   |  12 +-
 2 files changed, 71 insertions(+), 51 deletions(-)

diff --git a/examples/document_extraction/setup_workspace.ipynb b/examples/document_extraction/setup_workspace.ipynb
index c57cce6c7..6aaf7212e 100644
--- a/examples/document_extraction/setup_workspace.ipynb
+++ b/examples/document_extraction/setup_workspace.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "ce009aae",
    "metadata": {},
    "outputs": [
@@ -78,40 +78,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "64b5f09b",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ConflictError",
-     "evalue": "Extralit SDK error: ConflictError: Conflict with the server. Resource or entity already exists. Details: {\"detail\":\"Workspace name `extralit` is not unique\"}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mHTTPStatusError\u001b[39m                           Traceback (most recent call last)",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:91\u001b[39m, in \u001b[36mapi_error_handler.<locals>._handler_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     90\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m91\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     92\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_api/_workspaces.py:52\u001b[39m, in \u001b[36mWorkspacesAPI.create\u001b[39m\u001b[34m(self, workspace)\u001b[39m\n\u001b[32m     51\u001b[39m response = \u001b[38;5;28mself\u001b[39m.http_client.post(url=\u001b[33m\"\u001b[39m\u001b[33m/api/v1/workspaces\u001b[39m\u001b[33m\"\u001b[39m, json=workspace.model_dump())\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     53\u001b[39m response_json = response.json()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/extralit/lib/python3.11/site-packages/httpx/_models.py:763\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    762\u001b[39m message = message.format(\u001b[38;5;28mself\u001b[39m, error_type=error_type)\n\u001b[32m--> \u001b[39m\u001b[32m763\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPStatusError(message, request=request, response=\u001b[38;5;28mself\u001b[39m)\n",
-      "\u001b[31mHTTPStatusError\u001b[39m: Client error '409 Conflict' for url 'http://localhost:6900/api/v1/workspaces'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/409",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[31mConflictError\u001b[39m                             Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      3\u001b[39m new_workspace = ex.Workspace(name=workspace_name, client=client)\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Create the workspace\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m created_workspace = \u001b[43mnew_workspace\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mWorkspace \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mworkspace_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m created successfully with ID: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcreated_workspace.id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_resource.py:94\u001b[39m, in \u001b[36mResource.create\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     93\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[33m\"\u001b[39m\u001b[33mResource\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m     response_model = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapi_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     95\u001b[39m     \u001b[38;5;28mself\u001b[39m._model = response_model\n\u001b[32m     96\u001b[39m     \u001b[38;5;28mself\u001b[39m._update_last_api_call()\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:93\u001b[39m, in \u001b[36mapi_error_handler.<locals>._handler_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     91\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m func(*args, **kwargs)\n\u001b[32m     92\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m---> \u001b[39m\u001b[32m93\u001b[39m     \u001b[43m_error_switch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus_code\u001b[49m\u001b[43m=\u001b[49m\u001b[43me\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstatus_code\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_detail\u001b[49m\u001b[43m=\u001b[49m\u001b[43me\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/_exceptions/_api.py:87\u001b[39m, in \u001b[36mapi_error_handler.<locals>._error_switch\u001b[39m\u001b[34m(status_code, error_detail)\u001b[39m\n\u001b[32m     77\u001b[39m switch = {\n\u001b[32m     78\u001b[39m     \u001b[32m400\u001b[39m: BadRequestError,\n\u001b[32m     79\u001b[39m     \u001b[32m401\u001b[39m: UnauthorizedError,\n\u001b[32m   (...)\u001b[39m\u001b[32m     84\u001b[39m     \u001b[32m500\u001b[39m: InternalServerError,\n\u001b[32m     85\u001b[39m }\n\u001b[32m     86\u001b[39m exception_class = switch.get(status_code, ExtralitAPIError)\n\u001b[32m---> \u001b[39m\u001b[32m87\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exception_class(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexception_class.message\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. Details: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror_detail\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, status_code=status_code)\n",
-      "\u001b[31mConflictError\u001b[39m: Extralit SDK error: ConflictError: Conflict with the server. Resource or entity already exists. Details: {\"detail\":\"Workspace name `extralit` is not unique\"}"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Workspace 'extralit' already exists. Using the existing workspace.\n"
      ]
     }
    ],
    "source": [
     "# Define a new workspace\n",
     "workspace_name = \"extralit\"\n",
-    "new_workspace = ex.Workspace(name=workspace_name, client=client)\n",
+    "try:\n",
+    "    new_workspace = ex.Workspace(name=workspace_name, client=client)\n",
     "\n",
-    "# Create the workspace\n",
-    "created_workspace = new_workspace.create()\n",
+    "    # Create the workspace\n",
+    "    workspace = new_workspace.create()\n",
     "\n",
-    "print(f\"Workspace '{workspace_name}' created successfully with ID: {created_workspace.id}\")"
+    "    print(f\"Workspace '{workspace_name}' created successfully with ID: {created_workspace.id}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Workspace '{workspace_name}' already exists. Using the existing workspace.\")\n",
+    "    workspace = client.workspaces(workspace_name)"
    ]
   },
   {
@@ -119,48 +110,73 @@
    "id": "c937e5c7",
    "metadata": {},
    "source": [
-    "## 3. Listing Available Workspaces\n",
+    "## 3. Listing Available Datsets\n",
     "\n",
-    "Now, let's check all the workspaces available in our Extralit instance."
+    "Now, let's check all the Datatsts available in the workspace\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "id": "d4df67d6",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List all workspaces\n",
+    "dataset= client.datasets(\"papers-ocr-benchmarks_dataset\")\n",
+    "dataset.settings.to_json(\"./datasets/settings.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "77a5690b",
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of workspaces: 1\n",
-      "\n"
-     ]
-    },
     {
      "data": {
-      "text/html": [
-       "<h3>Workspaces</h3><table><tr><th>name</th><th>id</th><th>updated_at</th></tr><tr><td>extralit</td><td>e8154298-bf70-42ab-aca1-68cbd3b1b650</td><td>2025-08-13T00:41:32.506179</td></tr></table>"
-      ],
       "text/plain": [
-       "<extralit.client.resources.Workspaces at 0xffff66483c90>"
+       "Settings(guidelines=None, allow_extra_metadata=False, distribution=OverlapTaskDistribution(min_submitted=1), fields=[], questions=[], vectors=[], metadata=[])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# List all workspaces\n",
-    "workspaces = client.workspaces\n",
-    "\n",
-    "# Display workspace information\n",
-    "print(f\"Total number of workspaces: {len(workspaces)}\\n\")\n",
-    "\n",
-    "# In a notebook, this will display a table with workspace information\n",
-    "workspaces"
+    "dataset.settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cc75d87",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'reference'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_disk\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m./extralit-dataset/\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/datasets/_io/_disk.py:59\u001b[39m, in \u001b[36mDiskImportExportMixin.to_disk\u001b[39m\u001b[34m(self, path, with_records)\u001b[39m\n\u001b[32m     57\u001b[39m \u001b[38;5;28mself\u001b[39m.settings.to_json(path=settings_path)\n\u001b[32m     58\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m with_records:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrecords\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrecords_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     61\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m path\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_dataset_records.py:391\u001b[39m, in \u001b[36mDatasetRecords.to_json\u001b[39m\u001b[34m(self, path)\u001b[39m\n\u001b[32m    380\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mto_json\u001b[39m(\u001b[38;5;28mself\u001b[39m, path: Union[Path, \u001b[38;5;28mstr\u001b[39m]) -> Path:\n\u001b[32m    381\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    382\u001b[39m \u001b[33;03m    Export the records to a file on disk.\u001b[39;00m\n\u001b[32m    383\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    389\u001b[39m \n\u001b[32m    390\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m391\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_dataset_records.py:159\u001b[39m, in \u001b[36mDatasetRecordsIterator.to_json\u001b[39m\u001b[34m(self, path)\u001b[39m\n\u001b[32m    158\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mto_json\u001b[39m(\u001b[38;5;28mself\u001b[39m, path: Union[Path, \u001b[38;5;28mstr\u001b[39m]) -> Path:\n\u001b[32m--> \u001b[39m\u001b[32m159\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mJsonIO\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrecords\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_io/_json.py:41\u001b[39m, in \u001b[36mJsonIO.to_json\u001b[39m\u001b[34m(records, path)\u001b[39m\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m path.exists():\n\u001b[32m     39\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m already exists.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m record_dicts = \u001b[43mGenericIO\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrecords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflatten\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m     42\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path, \u001b[33m\"\u001b[39m\u001b[33mw\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m     43\u001b[39m     json.dump(record_dicts, f)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_io/_generic.py:41\u001b[39m, in \u001b[36mGenericIO.to_list\u001b[39m\u001b[34m(records, flatten)\u001b[39m\n\u001b[32m     39\u001b[39m dataset_records: \u001b[38;5;28mlist\u001b[39m = []\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m record \u001b[38;5;129;01min\u001b[39;00m records:\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     record_dict = \u001b[43mGenericIO\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_record_to_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflatten\u001b[49m\u001b[43m=\u001b[49m\u001b[43mflatten\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     42\u001b[39m     records_schema.update([k \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m record_dict])\n\u001b[32m     43\u001b[39m     dataset_records.append(record_dict)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_io/_generic.py:104\u001b[39m, in \u001b[36mGenericIO._record_to_dict\u001b[39m\u001b[34m(record, flatten)\u001b[39m\n\u001b[32m    101\u001b[39m     record_dict[\u001b[33m\"\u001b[39m\u001b[33mscore\u001b[39m\u001b[33m\"\u001b[39m] = score\n\u001b[32m    102\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m record_dict\n\u001b[32m--> \u001b[39m\u001b[32m104\u001b[39m record_dict = \u001b[43mrecord\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m flatten:\n\u001b[32m    106\u001b[39m     record_dict.update(\n\u001b[32m    107\u001b[39m         **record_dict.pop(\u001b[33m\"\u001b[39m\u001b[33mfields\u001b[39m\u001b[33m\"\u001b[39m, {}),\n\u001b[32m    108\u001b[39m         **record_dict.pop(\u001b[33m\"\u001b[39m\u001b[33mmetadata\u001b[39m\u001b[33m\"\u001b[39m, {}),\n\u001b[32m    109\u001b[39m         **record_dict.pop(\u001b[33m\"\u001b[39m\u001b[33mvectors\u001b[39m\u001b[33m\"\u001b[39m, {}),\n\u001b[32m    110\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_resource.py:199\u001b[39m, in \u001b[36mRecord.to_dict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    197\u001b[39m server_id = \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m._model.id) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._model.id \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    198\u001b[39m status = \u001b[38;5;28mself\u001b[39m.status\n\u001b[32m--> \u001b[39m\u001b[32m199\u001b[39m fields = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfields\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    200\u001b[39m metadata = \u001b[38;5;28mself\u001b[39m.metadata.to_dict()\n\u001b[32m    201\u001b[39m suggestions = \u001b[38;5;28mself\u001b[39m.suggestions.to_dict()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_resource.py:307\u001b[39m, in \u001b[36mRecordFields.to_dict\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    305\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    306\u001b[39m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m307\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_is_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m    308\u001b[39m     fields[key] = cast_image(value)\n\u001b[32m    309\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._is_chat(key):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/extralit/extralit/src/extralit/records/_resource.py:322\u001b[39m, in \u001b[36mRecordFields._is_image\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m    320\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.record.dataset:\n\u001b[32m    321\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m322\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m.\u001b[49m\u001b[43mschema\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m.type == \u001b[33m\"\u001b[39m\u001b[33mimage\u001b[39m\u001b[33m\"\u001b[39m\n",
+      "\u001b[31mKeyError\u001b[39m: 'reference'"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.to_disk(\"./datasets/\")"
    ]
   },
   {
diff --git a/extralit/src/extralit/datasets/_io/_disk.py b/extralit/src/extralit/datasets/_io/_disk.py
index 3068c5b18..862867fae 100644
--- a/extralit/src/extralit/datasets/_io/_disk.py
+++ b/extralit/src/extralit/datasets/_io/_disk.py
@@ -159,11 +159,15 @@ def _load_dataset_model(cls, path: Path):
 
     @classmethod
     def _define_child_paths(cls, path: Union[Path, str]) -> Tuple[Path, Path, Path]:
-        path = Path(path)
-        if not path.is_dir():
-            raise NotADirectoryError(f"Path {path} is not a directory")
+        path = Path(os.path.expanduser(str(path)))
+
+        # Resolve ~ in the path if present
         main_path = path / cls._DEFAULT_CONFIG_REPO_DIR
-        main_path.mkdir(exist_ok=True)
+        main_path.mkdir(parents=True, exist_ok=True)
+
+        if not main_path.is_dir():
+            raise NotADirectoryError(f"Path {path} is not a directory")
+
         dataset_path = path / cls._DEFAULT_DATASET_PATH
         settings_path = path / cls._DEFAULT_SETTINGS_PATH
         records_path = path / cls._DEFAULT_RECORDS_PATH

From ba0faae9214b94bbd48ed1dd9f6e9229dfc09962 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 15:25:08 -0700
Subject: [PATCH 07/20] Refactor document handling to use
 GetDocumentByRecordMetadataUseCase

- Update useDocumentViewModel to utilize new use case for fetching documents by record metadata.
- Simplify document panel behavior by removing unnecessary state management for panel expansion.
- Adjust HorizontalResizable component to use optional chaining for safer DOM element access.
- Clean up FocusAnnotation.vue template for improved readability.
---
 .../base-resizable/HorizontalResizable.vue    |  8 +++---
 .../container/mode/FocusAnnotation.vue        | 28 ++-----------------
 .../container/mode/useDocumentViewModel.ts    | 22 ++-------------
 extralit-frontend/v1/di/di.ts                 |  4 +--
 ...t-document-by-record-metadata-use-case.ts} |  9 ++----
 5 files changed, 15 insertions(+), 56 deletions(-)
 rename extralit-frontend/v1/domain/usecases/{get-document-by-id-use-case.ts => get-document-by-record-metadata-use-case.ts} (90%)

diff --git a/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue b/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
index ed4a2d063..51ce03d1c 100644
--- a/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
+++ b/extralit-frontend/components/base/base-resizable/HorizontalResizable.vue
@@ -4,10 +4,10 @@
       <slot name="up" />
     </div>
 
-    <div class="resizable-h__bar" ref="resizableBar">
+    <div ref="resizableBar" class="resizable-h__bar">
       <div class="resizable-h__bar__inner" />
     </div>
-    <div class="resizable-h__down" v-if="!collapsable">
+    <div v-if="!collapsable" class="resizable-h__down">
       <slot name="down" />
     </div>
     <BaseCollapsablePanel
@@ -97,8 +97,8 @@ export default {
 
   mounted() {
     this.resizer = this.$refs.resizableBar;
-    this.upSide = this.resizer.previousElementSibling;
-    this.downSide = this.resizer.nextElementSibling;
+    this.upSide = this.resizer?.previousElementSibling;
+    this.downSide = this.resizer?.nextElementSibling;
 
     this.limitElementHeight(this.upSide);
     if (!this.collapsable) {
diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index d84e02fd9..a50b9d136 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -1,12 +1,7 @@
 <template>
-  <VerticalResizable :id="`${recordCriteria.datasetId}-r-v-rz`" class="wrapper">
+  <VerticalResizable class="wrapper" :id="`${recordCriteria.datasetId}-r-v-rz`">
     <template #left>
-      <HorizontalResizable
-        :id="`${recordCriteria.datasetId}-r-h-rz`"
-        ref="documentPanel"
-        class="wrapper__left"
-        collapsable
-      >
+      <HorizontalResizable :id="`${recordCriteria.datasetId}-r-h-rz`" class="wrapper__left" collapsable>
         <template #up>
           <section class="wrapper__records" aria-label="Focus Annotation View">
             <DatasetFilters :recordCriteria="recordCriteria">
@@ -96,7 +91,7 @@
   </VerticalResizable>
 </template>
 
-<script>
+<script lang="ts">
 import { useFocusAnnotationViewModel } from "./useFocusAnnotationViewModel";
 import { useDocumentViewModel } from "./useDocumentViewModel";
 
@@ -130,23 +125,6 @@ export default {
       expandedGuidelines: false,
     };
   },
-  watch: {
-    isDocumentPanelExpanded(newValue) {
-      if (this.$refs.documentPanel && this.$refs.documentPanel.isExpanded !== newValue) {
-        this.$refs.documentPanel.isExpanded = newValue;
-      }
-    },
-    "document.url"(newUrl, oldUrl) {
-      // Collapse panel when document is cleared
-      if (!newUrl && oldUrl) {
-        this.isDocumentPanelExpanded = false;
-      }
-      // Expand panel when document is loaded
-      else if (newUrl && !oldUrl) {
-        this.isDocumentPanelExpanded = true;
-      }
-    },
-  },
   methods: {
     async onSubmit() {
       await this.submit(this.record);
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
index 1201d31ec..9895cf939 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
@@ -1,6 +1,6 @@
 import { ref, watch, computed } from "vue-demi";
 import { useResolve } from "ts-injecty";
-import { GetDocumentByIdUseCase } from "@/v1/domain/usecases/get-document-by-id-use-case";
+import { GetDocumentByRecordMetadataUseCase } from "~/v1/domain/usecases/get-document-by-record-metadata-use-case";
 import { useDocument } from "@/v1/infrastructure/storage/DocumentStorage";
 import { Segment } from "@/v1/domain/entities/document/Document";
 import { useDataset } from "@/v1/infrastructure/storage/DatasetStorage";
@@ -10,12 +10,11 @@ import { useWorkspaces } from "~/v1/infrastructure/storage/WorkspaceStorage";
 
 export const useDocumentViewModel = (props: { record: any }) => {
   const notification = useNotifications();
-  const getDocument = useResolve(GetDocumentByIdUseCase);
+  const getDocument = useResolve(GetDocumentByRecordMetadataUseCase);
   const { state: workspaces } = useWorkspaces();
   const { state: dataset } = useDataset();
   const { state: document, set: setDocument, clear: clearDocument } = useDocument();
   const isLoading = ref(false);
-  const isDocumentPanelExpanded = ref(false);
 
   const hasDocumentLoaded = computed(() => {
     return document.id !== null;
@@ -27,26 +26,14 @@ export const useDocumentViewModel = (props: { record: any }) => {
 
       const params = getDocument.createParams(metadata, workspaces.selectedWorkspace!.id);
 
-      // Check if we have any valid identifiers
       const hasValidIdentifier = params.reference || params.doc_id || params.pmid || params.doi;
 
       if (!hasValidIdentifier) {
-        // No valid identifiers, clear the document
         clearDocument();
         return;
       }
 
-      const result = await getDocument.setDocument(params);
-
-      // If no documents were found, the use case will have already cleared the storage
-      if (!result) {
-        console.log("No documents found for the provided criteria");
-        // Collapse the document panel when no document is found
-        isDocumentPanelExpanded.value = false;
-      } else {
-        // Expand the document panel when a document is found
-        isDocumentPanelExpanded.value = true;
-      }
+      await getDocument.setDocument(params);
     } catch (e) {
       const identifier = metadata?.pmid || metadata?.doi || metadata?.doc_id || metadata?.reference || "unknown";
       console.error(`Error fetching document with identifier "${identifier}":`, e);
@@ -71,8 +58,6 @@ export const useDocumentViewModel = (props: { record: any }) => {
       await fetchDocument(metadata);
     } else if (!hasValidIdentifier && hasDocumentLoaded.value) {
       clearDocument();
-      // Collapse the document panel when no document is available
-      isDocumentPanelExpanded.value = false;
     }
 
     if (metadata?.page_number != null) {
@@ -124,7 +109,6 @@ export const useDocumentViewModel = (props: { record: any }) => {
     fetchDocumentSegments,
     focusDocumentPageNumber,
     clearDocument,
-    isDocumentPanelExpanded,
   };
 };
 
diff --git a/extralit-frontend/v1/di/di.ts b/extralit-frontend/v1/di/di.ts
index e7b40a862..96c46d206 100644
--- a/extralit-frontend/v1/di/di.ts
+++ b/extralit-frontend/v1/di/di.ts
@@ -39,7 +39,7 @@ import { useWorkspaces } from "@/v1/infrastructure/storage/WorkspaceStorage";
 import { GetHfDatasetCreationUseCase } from "~/v1/domain/usecases/get-hf-dataset-creation-use-case";
 import { GetDatasetsUseCase } from "@/v1/domain/usecases/get-datasets-use-case";
 import { GetDatasetByIdUseCase } from "@/v1/domain/usecases/get-dataset-by-id-use-case";
-import { GetDocumentByIdUseCase } from "@/v1/domain/usecases/get-document-by-id-use-case";
+import { GetDocumentByRecordMetadataUseCase } from "~/v1/domain/usecases/get-document-by-record-metadata-use-case";
 import { GetDocumentsByWorkspaceUseCase } from "@/v1/domain/usecases/get-documents-by-workspace-use-case";
 import { GetLLMExtractionUseCase } from "@/v1/domain/usecases/get-extraction-completion-use-case";
 import { GetExtractionSchemaUseCase } from "@/v1/domain/usecases/get-extraction-schema-use-case";
@@ -115,7 +115,7 @@ export const loadDependencyContainer = (context: Context) => {
 
     register(GetDatasetsUseCase).withDependencies(DatasetRepository, useDatasets).build(),
 
-    register(GetDocumentByIdUseCase).withDependencies(DocumentRepository, useDocument).build(),
+    register(GetDocumentByRecordMetadataUseCase).withDependencies(DocumentRepository, useDocument).build(),
 
     register(GetDocumentsByWorkspaceUseCase).withDependency(DocumentRepository).build(),
 
diff --git a/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts b/extralit-frontend/v1/domain/usecases/get-document-by-record-metadata-use-case.ts
similarity index 90%
rename from extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts
rename to extralit-frontend/v1/domain/usecases/get-document-by-record-metadata-use-case.ts
index e4d9bc90a..3c38dd346 100644
--- a/extralit-frontend/v1/domain/usecases/get-document-by-id-use-case.ts
+++ b/extralit-frontend/v1/domain/usecases/get-document-by-record-metadata-use-case.ts
@@ -2,14 +2,14 @@ import { Segment } from "../entities/document/Document";
 import { IDocumentStorage } from "../services/IDocumentStorage";
 import { DocumentRepository } from "@/v1/infrastructure/repositories/DocumentRepository";
 
-export class GetDocumentByIdUseCase {
+export class GetDocumentByRecordMetadataUseCase {
   constructor(
     private readonly documentRepository: DocumentRepository,
     private readonly documentStorage: IDocumentStorage
-  ) {}
+  ) { }
 
   createParams(
-    metadata: any,
+    metadata: Record<string, any> | null,
     workspaceId: string
   ): {
     workspace_id: string;
@@ -44,8 +44,6 @@ export class GetDocumentByIdUseCase {
     const documents = await this.documentRepository.getDocuments(params);
 
     if (documents.length === 0) {
-      // Clear the document storage when no documents are found
-      this.documentStorage.clear();
       return null;
     }
 
@@ -56,7 +54,6 @@ export class GetDocumentByIdUseCase {
     }
 
     this.documentStorage.set(documents[0]);
-    return documents[0];
   }
 
   async setSegments(workspace: string, reference: string): Promise<Segment[]> {

From b8d97df4e1f60448926b5a053f562987a71473ec Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 15:34:48 -0700
Subject: [PATCH 08/20] refactor

---
 .../features/documents/DocumentsList.vue      |   2 +-
 .../documents/useDocumentsListViewModel.ts    | 170 ++++++++++++------
 .../import/analysis/ImportAnalysisTable.vue   |   7 +-
 3 files changed, 122 insertions(+), 57 deletions(-)

diff --git a/extralit-frontend/components/features/documents/DocumentsList.vue b/extralit-frontend/components/features/documents/DocumentsList.vue
index 702c47071..bbc74c081 100644
--- a/extralit-frontend/components/features/documents/DocumentsList.vue
+++ b/extralit-frontend/components/features/documents/DocumentsList.vue
@@ -121,7 +121,7 @@ export default {
     },
   },
   setup(props) {
-    return useDocumentsListViewModel();
+    return useDocumentsListViewModel(props);
   },
 };
 </script>
diff --git a/extralit-frontend/components/features/documents/useDocumentsListViewModel.ts b/extralit-frontend/components/features/documents/useDocumentsListViewModel.ts
index 0bc7c2473..a9586ac8d 100644
--- a/extralit-frontend/components/features/documents/useDocumentsListViewModel.ts
+++ b/extralit-frontend/components/features/documents/useDocumentsListViewModel.ts
@@ -3,6 +3,7 @@
  * Handles document loading and grouping logic
  */
 
+import { ref, computed, watch, onMounted } from "@nuxtjs/composition-api";
 import { useResolve } from "ts-injecty";
 import { GetDocumentsByWorkspaceUseCase } from "~/v1/domain/usecases/get-documents-by-workspace-use-case";
 import { Document } from "~/v1/domain/entities/document/Document";
@@ -13,65 +14,132 @@ export interface DocumentGroup {
   metadata: Record<string, any> | null;
 }
 
-export function useDocumentsListViewModel() {
+export const useDocumentsListViewModel = (props: { workspaceId: string }) => {
   const getDocumentsByWorkspaceUseCase = useResolve(GetDocumentsByWorkspaceUseCase);
 
-  return {
-    // Use cases
-    getDocumentsByWorkspaceUseCase,
+  // Reactive state
+  const documents = ref<Document[]>([]);
+  const isLoading = ref(false);
+  const error = ref<string | null>(null);
 
-    // Document loading
-    async loadDocuments(workspaceId: string): Promise<Document[]> {
-      return await getDocumentsByWorkspaceUseCase.execute(workspaceId);
-    },
+  // Computed properties
+  const groupedDocuments = computed(() => {
+    return groupDocumentsByReference(documents.value);
+  });
 
-    // Document grouping logic
-    groupDocumentsByReference(documents: Document[]): DocumentGroup[] {
-      const groups = new Map<string, DocumentGroup>();
-
-      documents.forEach((document) => {
-        const key = document.reference || "no-reference";
-
-        if (!groups.has(key)) {
-          groups.set(key, {
-            reference: document.reference,
-            documents: [],
-            metadata: document.metadata,
-          });
-        }
-
-        groups.get(key)!.documents.push(document);
-      });
-
-      return Array.from(groups.values()).sort((a, b) => {
-        // Sort by reference, with 'no-reference' at the end
-        if (!a.reference && b.reference) return 1;
-        if (a.reference && !b.reference) return -1;
-        if (!a.reference && !b.reference) return 0;
-        return a.reference.localeCompare(b.reference);
-      });
-    },
+  const totalFiles = computed(() => {
+    return documents.value.length;
+  });
 
-    // Statistics calculation
-    calculateStats(documents: Document[]) {
-      const groupedDocuments = this.groupDocumentsByReference(documents);
-      return {
-        totalReferences: groupedDocuments.length,
-        totalFiles: documents.length,
-      };
-    },
+  const stats = computed(() => {
+    const grouped = groupDocumentsByReference(documents.value);
+    return {
+      totalReferences: grouped.length,
+      totalFiles: documents.value.length,
+    };
+  });
+
+  // Document loading
+  const loadDocuments = async () => {
+    if (!props.workspaceId) {
+      documents.value = [];
+      return;
+    }
+
+    isLoading.value = true;
+    error.value = null;
+
+    try {
+      const response = await getDocumentsByWorkspaceUseCase.execute(props.workspaceId);
+      documents.value = response;
+    } catch (err) {
+      // eslint-disable-next-line no-console
+      console.error("Failed to load documents:", err);
+      error.value = "Failed to load documents. Please try again.";
+      documents.value = [];
+    } finally {
+      isLoading.value = false;
+    }
+  };
+
+  // Document grouping logic
+  const groupDocumentsByReference = (docs: Document[]): DocumentGroup[] => {
+    const groups = new Map<string, DocumentGroup>();
+
+    docs.forEach((document) => {
+      const key = document.reference || "no-reference";
+
+      if (!groups.has(key)) {
+        groups.set(key, {
+          reference: document.reference,
+          documents: [],
+          metadata: document.metadata,
+        });
+      }
+
+      groups.get(key)!.documents.push(document);
+    });
 
-    // Document actions
-    openDocument(document: Document) {
-      if (document.url) {
-        window.open(document.url, "_blank");
+    return Array.from(groups.values()).sort((a, b) => {
+      // Sort by reference, with 'no-reference' at the end
+      if (!a.reference && b.reference) return 1;
+      if (a.reference && !b.reference) return -1;
+      if (!a.reference && !b.reference) return 0;
+      return a.reference.localeCompare(b.reference);
+    });
+  };
+
+  // Document actions
+  const openDocument = (document: Document) => {
+    if (document.url) {
+      window.open(document.url, "_blank");
+    }
+  };
+
+  // Watch for workspace changes and reload data
+  watch(
+    () => props.workspaceId,
+    async (newWorkspaceId, oldWorkspaceId) => {
+      // Load data when workspace changes, including from null to a value
+      if (newWorkspaceId && newWorkspaceId !== oldWorkspaceId) {
+        await loadDocuments();
       }
     },
+    { immediate: false }
+  );
+
+  // Load data on component mount only if workspace is available
+  onMounted(async () => {
+    if (props.workspaceId) {
+      await loadDocuments();
+    }
+  });
+
+  // Retry mechanism for error recovery
+  const retryLoad = async () => {
+    await loadDocuments();
   };
-}
 
-// Backward compatibility export
-export const getDocumentsByWorkspace = async (workspaceId: string): Promise<Document[]> => {
-  const viewModel = useDocumentsListViewModel();
-  return await viewModel.loadDocuments(workspaceId);
+  // Public method to refresh the documents list
+  const refresh = async () => {
+    await loadDocuments();
+  };
+
+  return {
+    // Reactive state
+    documents,
+    isLoading,
+    error,
+
+    // Computed properties
+    groupedDocuments,
+    totalFiles,
+    stats,
+
+    // Methods
+    loadDocuments,
+    openDocument,
+    retryLoad,
+    refresh,
+  };
 };
diff --git a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
index cf23c7264..bc8610cd2 100644
--- a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
+++ b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
@@ -613,11 +613,8 @@ export default {
   },
 
   setup(props) {
-    const viewModel = useImportAnalysisTableViewModel(props);
-    return {
-      ...viewModel,
-    };
-  }
+    return useImportAnalysisTableViewModel(props);
+  },
 };
 </script>
 

From eaa369738032eaf87ea0e5c755534237e7881e49 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 17:49:43 -0700
Subject: [PATCH 09/20] Updated FocusAnnotation component to display a message
 when a document is not found, enhancing user feedback

---
 .../document_extraction/setup_workspace.ipynb  |  6 +++---
 .../container/mode/FocusAnnotation.vue         | 18 +++---------------
 extralit-frontend/translation/en.js            |  3 +++
 extralit/src/extralit/settings/_resource.py    |  3 +--
 4 files changed, 10 insertions(+), 20 deletions(-)

diff --git a/examples/document_extraction/setup_workspace.ipynb b/examples/document_extraction/setup_workspace.ipynb
index 6aaf7212e..0d630cf95 100644
--- a/examples/document_extraction/setup_workspace.ipynb
+++ b/examples/document_extraction/setup_workspace.ipynb
@@ -117,14 +117,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "d4df67d6",
    "metadata": {},
    "outputs": [],
    "source": [
     "# List all workspaces\n",
-    "dataset= client.datasets(\"papers-ocr-benchmarks_dataset\")\n",
-    "dataset.settings.to_json(\"./datasets/settings.json\")"
+    "dataset = client.datasets(\"imdb\")\n",
+    "dataset.settings.to_json(\"./datasets/imdb/settings.json\")"
    ]
   },
   {
diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index a50b9d136..ccc0e7776 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -26,10 +26,10 @@
           </section>
         </template>
         <template #downHeader>
-          <p v-text="$t('document')" />
+          <p v-text="hasDocumentLoaded ? $t('document') : $t('document.notFound')" />
         </template>
         <template #downHeaderExpanded>
-          <p v-text="$t('document')" />
+          <p v-text="hasDocumentLoaded ? $t('document') : $t('document.notFound')" />
         </template>
         <template #downContent>
           <PDFViewer
@@ -39,7 +39,7 @@
             :pageNumber="document.page_number"
           />
           <div v-else class="no-document-message">
-            <p>{{ $t("no_document_available") }}</p>
+            <p>{{ $t("document.notFound") }}</p>
           </div>
         </template>
       </HorizontalResizable>
@@ -76,18 +76,6 @@
         </template>
       </HorizontalResizable>
     </template>
-    <BaseCollapsablePanel
-      hideOnDesktop
-      :isExpanded="expandedGuidelines"
-      @toggle-expand="expandedGuidelines = !expandedGuidelines"
-    >
-      <template #panelHeader>
-        <p v-text="$t('document')" />
-      </template>
-      <template #panelContent>
-        <PDFViewer :url="document.url" :file-name="document.file_name" :pageNumber="document.page_number" />
-      </template>
-    </BaseCollapsablePanel>
   </VerticalResizable>
 </template>
 
diff --git a/extralit-frontend/translation/en.js b/extralit-frontend/translation/en.js
index da4167871..26b125b2c 100644
--- a/extralit-frontend/translation/en.js
+++ b/extralit-frontend/translation/en.js
@@ -297,6 +297,9 @@ export default {
     datasets: "Datasets",
     documents: "Documents",
   },
+  document: {
+    notFound: "Document not found",
+  },
   import: {
     title: "Import Documents to {workspaceName} Workspace",
     historyTitle: "Import History",
diff --git a/extralit/src/extralit/settings/_resource.py b/extralit/src/extralit/settings/_resource.py
index ce2d24d67..ea3f8b659 100644
--- a/extralit/src/extralit/settings/_resource.py
+++ b/extralit/src/extralit/settings/_resource.py
@@ -251,8 +251,7 @@ def to_json(self, path: Union[Path, str]) -> None:
         """
         if not isinstance(path, Path):
             path = Path(path)
-        if path.exists():
-            raise FileExistsError(f"File {path} already exists")
+        path.parent.mkdir(parents=True, exist_ok=True)
         with open(path, "w") as file:
             json.dump(self.serialize(), file)
 

From 3ab6b4c2d21f826ec0c4dc2634eaa2de14bdd45d Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 17:52:55 -0700
Subject: [PATCH 10/20] fix

---
 .../features/import/analysis/ImportAnalysisTable.vue       | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
index bc8610cd2..cf23c7264 100644
--- a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
+++ b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
@@ -613,8 +613,11 @@ export default {
   },
 
   setup(props) {
-    return useImportAnalysisTableViewModel(props);
-  },
+    const viewModel = useImportAnalysisTableViewModel(props);
+    return {
+      ...viewModel,
+    };
+  }
 };
 </script>
 

From bd4d1647dda7a79b8b3493085d6e04edf0405101 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 17:58:06 -0700
Subject: [PATCH 11/20] latest

---
 .../features/import/history/ImportHistoryList.vue          | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/extralit-frontend/components/features/import/history/ImportHistoryList.vue b/extralit-frontend/components/features/import/history/ImportHistoryList.vue
index 09765c098..04c2be05f 100644
--- a/extralit-frontend/components/features/import/history/ImportHistoryList.vue
+++ b/extralit-frontend/components/features/import/history/ImportHistoryList.vue
@@ -1,12 +1,5 @@
 <template>
   <div class="import-history-list">
-    <!-- Header -->
-    <div class="history-header">
-      <h3>Import History</h3>
-      <p class="history-subtitle">
-        View and manage all your document import operations
-      </p>
-    </div>
 
     <!-- Loading State -->
     <div v-if="isLoading" class="loading-container">

From f8bed41a044ebe6bbb02358a2b445e5b0710f0dd Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 18:01:28 -0700
Subject: [PATCH 12/20] latest

---
 .../import/analysis/ImportAnalysisTable.vue   | 30 ++++++++--------
 .../useImportAnalysisTableViewModel.ts        | 34 ++++++++++++++++---
 2 files changed, 45 insertions(+), 19 deletions(-)

diff --git a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
index cf23c7264..81f23e14a 100644
--- a/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
+++ b/extralit-frontend/components/features/import/analysis/ImportAnalysisTable.vue
@@ -82,7 +82,7 @@ export default {
       default: null,
     },
     pdfData: {
-      type: Object,
+      type: Object as () => { matchedFiles: any[] } | null,
       default: () => ({ matchedFiles: [] }),
     },
     workspace: {
@@ -401,8 +401,6 @@ export default {
       return files.join(", ");
     },
 
-
-
     getStatusText(status: string) {
       const statusMap = {
         add: "Add",
@@ -449,9 +447,6 @@ export default {
       }
     },
 
-
-
-
     formatColumnTitle(fieldName: string) {
       // Convert field names to readable titles
       return fieldName
@@ -460,9 +455,6 @@ export default {
         .trim();
     },
 
-
-
-
     emitUpdate() {
       const confirmedDocuments: Record<string, any> = {};
       const documentActions = { ...this.documentActions, ...this.localDocumentActions };
@@ -562,6 +554,19 @@ export default {
       this.localDocumentActions = {};
     },
 
+    // Add missing methods
+    retryAnalysis() {
+      if (this.$refs.viewModel && this.$refs.viewModel.retryAnalysis) {
+        this.$refs.viewModel.retryAnalysis();
+      }
+    },
+
+    reset() {
+      this.resetLocalState();
+      if (this.$refs.viewModel && this.$refs.viewModel.reset) {
+        this.$refs.viewModel.reset();
+      }
+    },
 
     // Helper method to prepare data for ImportHistoryCreate payload
     getImportHistoryData() {
@@ -613,11 +618,8 @@ export default {
   },
 
   setup(props) {
-    const viewModel = useImportAnalysisTableViewModel(props);
-    return {
-      ...viewModel,
-    };
-  }
+    return useImportAnalysisTableViewModel(props);
+  },
 };
 </script>
 
diff --git a/extralit-frontend/components/features/import/analysis/useImportAnalysisTableViewModel.ts b/extralit-frontend/components/features/import/analysis/useImportAnalysisTableViewModel.ts
index db4a20985..99eb2e8eb 100644
--- a/extralit-frontend/components/features/import/analysis/useImportAnalysisTableViewModel.ts
+++ b/extralit-frontend/components/features/import/analysis/useImportAnalysisTableViewModel.ts
@@ -5,7 +5,16 @@ import type { ImportAnalysisResponse, ImportStatus } from "~/v1/domain/entities/
 import { Workspace } from "~/v1/domain/entities/workspace/Workspace";
 import { TableData } from "~/v1/domain/entities/table/TableData";
 
-export function useImportAnalysisTableViewModel(props: any) {
+export const useImportAnalysisTableViewModel = (props: {
+  workspace: Workspace | null;
+  dataframeData: TableData | null;
+  pdfData: {
+    matchedFiles: any[];
+  } | null;
+}) => {
+  const importAnalysisUseCase = useResolve(GetImportAnalysisUseCase);
+
+  // Reactive state
   const isAnalyzing = ref(false);
   const hasError = ref(false);
   const errorMessage = ref("");
@@ -13,8 +22,17 @@ export function useImportAnalysisTableViewModel(props: any) {
   const documentActions = ref<Record<string, ImportStatus>>({});
   const lastAnalysisKey = ref<string>(""); // Track last analysis to prevent duplicates
 
-  const importAnalysisUseCase = useResolve(GetImportAnalysisUseCase);
+  // Computed properties
+  const hasRequiredData = () => {
+    return (
+      props.workspace &&
+      props.dataframeData &&
+      props.dataframeData.data.length > 0 &&
+      props.pdfData?.matchedFiles?.length > 0
+    );
+  };
 
+  // Methods
   const reset = () => {
     isAnalyzing.value = false;
     hasError.value = false;
@@ -58,7 +76,7 @@ export function useImportAnalysisTableViewModel(props: any) {
         actions[reference] = docInfo.status;
       });
       documentActions.value = actions;
-    } catch (error) {
+    } catch (error: any) {
       hasError.value = true;
       errorMessage.value = error.message || "Failed to analyze import";
     } finally {
@@ -91,7 +109,7 @@ export function useImportAnalysisTableViewModel(props: any) {
           newVal.dataframeLength !== oldVal.dataframeLength ||
           newVal.matchedFilesLength !== oldVal.matchedFilesLength
         ) {
-          analyzeImport(props.workspace, props.dataframeData, props.pdfData.matchedFiles);
+          analyzeImport(props.workspace!, props.dataframeData!, props.pdfData!.matchedFiles);
         }
       }
     },
@@ -99,13 +117,19 @@ export function useImportAnalysisTableViewModel(props: any) {
   );
 
   return {
+    // Reactive state
     isAnalyzing,
     hasError,
     errorMessage,
     analysisResult,
     documentActions,
+
+    // Computed properties
+    hasRequiredData,
+
+    // Methods
     reset,
     analyzeImport,
     retryAnalysis,
   };
-}
+};

From b3c471f5ddfe40b4977e15fc17796f6b08779dcc Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 18:59:06 -0700
Subject: [PATCH 13/20] Refactor confirmation messages in BaseFlowModal and
 update FocusAnnotation component to use new translation keys

- Changed confirmation messages in BaseFlowModal.vue to use the new translation key for cancel confirmation.
- Updated FocusAnnotation.vue to display a consistent message for document loading status using the new translation key.
- Enhanced useImportBatchProgressViewModel.ts to include import history tracking and improved error handling during import history creation.
---
 .../base/base-flow-modal/BaseFlowModal.vue    |  4 +-
 .../container/mode/FocusAnnotation.vue        |  4 +-
 .../import/useImportBatchProgressViewModel.ts | 99 ++++++++++++++++++-
 extralit-frontend/translation/en.js           |  4 +-
 4 files changed, 102 insertions(+), 9 deletions(-)

diff --git a/extralit-frontend/components/base/base-flow-modal/BaseFlowModal.vue b/extralit-frontend/components/base/base-flow-modal/BaseFlowModal.vue
index 184810fda..3094a332c 100644
--- a/extralit-frontend/components/base/base-flow-modal/BaseFlowModal.vue
+++ b/extralit-frontend/components/base/base-flow-modal/BaseFlowModal.vue
@@ -240,13 +240,13 @@ export default {
     },
 
     showCancelConfirmation() {
-      if (confirm(this.$t("common.confirmCancel"))) {
+      if (confirm(this.$t("button.confirmCancel"))) {
         this.$emit("cancel");
       }
     },
 
     showCloseConfirmation() {
-      if (confirm(this.$t("common.confirmCancel"))) {
+      if (confirm(this.$t("button.confirmCancel"))) {
         this.$emit("close");
       }
     },
diff --git a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
index ccc0e7776..0471b5994 100644
--- a/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
+++ b/extralit-frontend/components/features/annotation/container/mode/FocusAnnotation.vue
@@ -26,10 +26,10 @@
           </section>
         </template>
         <template #downHeader>
-          <p v-text="hasDocumentLoaded ? $t('document') : $t('document.notFound')" />
+          <p v-text="$t('home.documents')" />
         </template>
         <template #downHeaderExpanded>
-          <p v-text="hasDocumentLoaded ? $t('document') : $t('document.notFound')" />
+          <p v-text="$t('home.documents')" />
         </template>
         <template #downContent>
           <PDFViewer
diff --git a/extralit-frontend/components/features/import/useImportBatchProgressViewModel.ts b/extralit-frontend/components/features/import/useImportBatchProgressViewModel.ts
index ed62cffa3..627184368 100644
--- a/extralit-frontend/components/features/import/useImportBatchProgressViewModel.ts
+++ b/extralit-frontend/components/features/import/useImportBatchProgressViewModel.ts
@@ -10,6 +10,7 @@ import type { DocumentMetadata } from "~/v1/domain/entities/import/ImportAnalysi
 import { BulkUploadDocumentsUseCase } from "~/v1/domain/usecases/bulk-upload-documents-use-case";
 import { GetJobStatusUseCase, type JobStatus } from "~/v1/domain/usecases/get-job-status-use-case";
 import { CreateImportHistoryUseCase } from "~/v1/domain/usecases/create-import-history-use-case";
+import { TableData } from "~/v1/domain/entities/table/TableData";
 
 interface BatchInfo {
   batchIndex: number;
@@ -36,7 +37,7 @@ export const useImportBatchProgressViewModel = (
       failedJobs: number;
     };
     workspace?: any;
-    dataframeData?: any;
+    dataframeData?: TableData;
     bibFileName?: string;
     pdfFiles?: File[];
   },
@@ -44,7 +45,7 @@ export const useImportBatchProgressViewModel = (
 ) => {
   const bulkUploadUseCase = useResolve(BulkUploadDocumentsUseCase);
   const jobStatusUseCase = useResolve(GetJobStatusUseCase);
-  const importHistoryUseCase = useResolve(CreateImportHistoryUseCase);
+  const createImportHistoryUseCase = useResolve(CreateImportHistoryUseCase);
 
   // Reactive state
   const isUploading = ref(false);
@@ -54,6 +55,9 @@ export const useImportBatchProgressViewModel = (
   const hasError = ref(false);
   const errorMessage = ref("");
 
+  // Import history tracking
+  const importHistoryId = ref<string | null>(null);
+
   // Batch processing
   const batches = ref<BatchInfo[]>([]);
   const currentBatchIndex = ref(0);
@@ -202,6 +206,7 @@ export const useImportBatchProgressViewModel = (
     completedReferences.value = 0;
     totalReferences.value = 0;
     errors.value = [];
+    importHistoryId.value = null; // Reset import history ID
     stopStatusPolling();
   };
 
@@ -414,9 +419,98 @@ export const useImportBatchProgressViewModel = (
 
     resetState();
     createBatchesInternal();
+
+    await createImportHistory();
+
     await startBatchUpload();
   };
 
+  const createImportHistory = async () => {
+    if (!props.workspace?.id || !props.bibFileName || !props.dataframeData) {
+      console.warn("Cannot create import history: missing workspace ID, filename, or dataframe data");
+      return;
+    }
+
+    try {
+      const addReferences = Object.entries(props.uploadData.documentActions)
+        .filter(([_, action]) => action === "add")
+        .map(([reference]) => reference);
+
+      // Create filtered dataframe data with only "add" references
+      const filteredDataframeData = {
+        schema: props.dataframeData.schema,
+        data: props.dataframeData.data.filter((row: any) => {
+          const possibleReferenceFields = ["reference"];
+          const referenceField = possibleReferenceFields.find((field) => row[field]);
+
+          if (!referenceField) {
+            return false;
+          }
+          return addReferences.includes(row[referenceField]);
+        }),
+      };
+
+      // Validate that we have data to send
+      if (filteredDataframeData.data.length === 0) {
+        console.warn("No rows with 'add' status found in dataframe data");
+      }
+
+      // Prepare metadata with document actions and associated files for all references
+      const metadata: Record<string, any> = {};
+
+      // Build metadata in the format expected by backend: {"reference": {"status": "add|update|skip|failed", "associated_files": [...]}}
+      Object.entries(props.uploadData.confirmedDocuments).forEach(([reference, docMetadata]) => {
+        metadata[reference] = {
+          status: props.uploadData.documentActions[reference] || "add",
+          associated_files: docMetadata.associated_files.map((fileInfo) =>
+            typeof fileInfo === "string" ? fileInfo : fileInfo.filename
+          ),
+          document_create: docMetadata.document_create,
+        };
+      });
+
+      metadata.summary = {
+        total_documents: Object.keys(props.uploadData.confirmedDocuments).length,
+        add_count: Object.values(props.uploadData.documentActions).filter((action) => action === "add").length,
+        update_count: Object.values(props.uploadData.documentActions).filter((action) => action === "update").length,
+        skip_count: Object.values(props.uploadData.documentActions).filter((action) => action === "skip").length,
+        failed_count: 0,
+      };
+
+      const importHistoryData = {
+        workspace_id: props.workspace.id,
+        filename: props.bibFileName,
+        data: filteredDataframeData,
+        metadata,
+      };
+
+      console.log("Creating import history with data:", importHistoryData);
+
+      const response = await createImportHistoryUseCase.execute(importHistoryData);
+
+      // Store the import history ID for potential future use
+      importHistoryId.value = response.id;
+    } catch (error) {
+      console.error("Failed to create import history:", error);
+
+      // Provide more specific error information
+      if (error instanceof Error) {
+        if (error.message.includes("Dataframe data must contain a reference field")) {
+          console.error("Data validation failed: missing reference field in dataframe");
+        } else if (error.message.includes("workspace")) {
+          console.error("Workspace validation failed");
+        } else if (error.message.includes("filename")) {
+          console.error("Filename validation failed");
+        } else {
+          console.error("Unknown error during import history creation:", error.message);
+        }
+      }
+
+      // Don't fail the import if history creation fails
+      // Just log the error and continue
+    }
+  };
+
   const cancelUpload = async () => {
     if (isCancelling.value) return;
 
@@ -601,6 +695,7 @@ export const useImportBatchProgressViewModel = (
     errors,
     statusPollingInterval,
     pollingIntervalMs,
+    importHistoryId,
 
     // Computed properties
     totalBatches,
diff --git a/extralit-frontend/translation/en.js b/extralit-frontend/translation/en.js
index 26b125b2c..156a781ac 100644
--- a/extralit-frontend/translation/en.js
+++ b/extralit-frontend/translation/en.js
@@ -130,6 +130,7 @@ export default {
     "hf-login": "Sign in with Hugging Face",
     sign_in_with_username: "Sign in with username",
     cancel: "Cancel",
+    confirmCancel: "Are you sure you want to cancel? Any unsaved changes will be lost.",
     continue: "Continue",
     delete: "Delete",
     exportToHub: "Push to Hub",
@@ -141,9 +142,6 @@ export default {
       datasetSettings: "Dataset settings",
     },
   },
-  common: {
-    confirmCancel: "Are you sure you want to cancel? Any unsaved changes will be lost.",
-  },
   to_submit_complete_required: "To submit complete \nrequired responses",
   some_records_failed_to_annotate: "Some records failed to annotate",
   changes_no_submit: "You didn't submit your changes",

From 0f704b09c25798ed6d8eebbdbc2acd53b58590a4 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 19:34:02 -0700
Subject: [PATCH 14/20] Refactor PdfMatchingService to improve file matching
 logic

- Updated matchFiles method to accept TableData type for dataframeData.
- Enhanced matching phases with clearer comments and improved handling of entries with and without file attributes.
- Replaced webkit path matching with exact matching for entries lacking file attributes.
- Introduced new methods for checking reference and title matches as substrings.
- Removed deprecated methods and streamlined the matching process for better performance and clarity.
---
 .../v1/domain/services/FileMatchingService.ts | 377 ++++++------------
 1 file changed, 126 insertions(+), 251 deletions(-)

diff --git a/extralit-frontend/v1/domain/services/FileMatchingService.ts b/extralit-frontend/v1/domain/services/FileMatchingService.ts
index 90732da5e..e36a9c59d 100644
--- a/extralit-frontend/v1/domain/services/FileMatchingService.ts
+++ b/extralit-frontend/v1/domain/services/FileMatchingService.ts
@@ -1,3 +1,4 @@
+import { TableData } from "../entities/table/TableData";
 import type { IFileMatchingService, FileMatchResult, FileMatchingResult } from "./IFileMatchingService";
 import type { ParsedEntry } from "./IFileParsingService";
 
@@ -14,7 +15,8 @@ interface PrefixMatchResult {
 }
 
 export class PdfMatchingService implements IFileMatchingService {
-  matchFiles(files: File[], dataframeData): FileMatchingResult {
+  matchFiles(files: File[], dataframeData: TableData): FileMatchingResult {
+    // @ts-ignore
     const entries: ParsedEntry[] = dataframeData?.data || [];
 
     if (!entries || entries.length === 0 || files.length === 0) {
@@ -31,7 +33,7 @@ export class PdfMatchingService implements IFileMatchingService {
     const referenceFileCount = new Map<string, number>();
     const processedFiles = new Set<File>();
 
-    // Phase 1: Maximum prefix path matching (highest priority)
+    // Phase 1: Maximum prefix path matching for entries with file attributes
     const prefixMatches = this.findMaximumPrefixMatches(files, entries);
 
     for (const match of prefixMatches) {
@@ -50,45 +52,28 @@ export class PdfMatchingService implements IFileMatchingService {
       }
     }
 
-    // Phase 2: Webkit path matching for remaining files
+    // Phase 2: Exact matching for entries without file attributes
     const remainingFiles = files.filter((file) => !processedFiles.has(file));
 
     for (const file of remainingFiles) {
-      const webkitMatch = this.findWebkitPathMatch(file, entries);
-      if (webkitMatch) {
+      const exactMatch = this.findExactMatch(file, entries);
+      if (exactMatch) {
         matchedFiles.push({
           file,
-          bibEntry: webkitMatch.entry,
-          matchType: webkitMatch.type,
-          confidence: webkitMatch.confidence,
+          bibEntry: exactMatch.entry,
+          matchType: exactMatch.type,
+          confidence: exactMatch.confidence,
         });
 
-        const currentCount = referenceFileCount.get(webkitMatch.entry.reference) || 0;
-        referenceFileCount.set(webkitMatch.entry.reference, currentCount + 1);
+        const currentCount = referenceFileCount.get(exactMatch.entry.reference) || 0;
+        referenceFileCount.set(exactMatch.entry.reference, currentCount + 1);
         processedFiles.add(file);
       }
     }
 
-    // Phase 3: Other matching methods for remaining files
+    // Add remaining unmatched files
     const finalRemainingFiles = files.filter((file) => !processedFiles.has(file));
-
-    for (const file of finalRemainingFiles) {
-      const match = this.findBestNonWebkitMatch(file, entries);
-      if (match) {
-        matchedFiles.push({
-          file,
-          bibEntry: match.entry,
-          matchType: match.type,
-          confidence: match.confidence,
-        });
-
-        const currentCount = referenceFileCount.get(match.entry.reference) || 0;
-        referenceFileCount.set(match.entry.reference, currentCount + 1);
-        processedFiles.add(file);
-      } else {
-        unmatchedFiles.push(file);
-      }
-    }
+    unmatchedFiles.push(...finalRemainingFiles);
 
     // Sort matched files by confidence (highest first), then by reference for grouping
     matchedFiles.sort((a, b) => {
@@ -105,8 +90,7 @@ export class PdfMatchingService implements IFileMatchingService {
   }
 
   /**
-   * Find maximum prefix path matches for all files against all entries
-   * This method implements the core maximum prefix matching algorithm
+   * Find maximum prefix path matches for entries with file attributes
    */
   private findMaximumPrefixMatches(
     files: File[],
@@ -124,6 +108,13 @@ export class PdfMatchingService implements IFileMatchingService {
       confidence: number;
     }> = [];
 
+    // Only process entries that have file attributes
+    const entriesWithFiles = entries.filter(entry => entry.filePaths && entry.filePaths.length > 0);
+
+    if (entriesWithFiles.length === 0) {
+      return matches;
+    }
+
     // Create a map of all possible file-entry combinations with their prefix match scores
     const allCombinations: Array<{
       file: File;
@@ -135,9 +126,7 @@ export class PdfMatchingService implements IFileMatchingService {
       const filePath: string = (file as any).webkitRelativePath || file.name;
       const importFilePath = this.normalizePath(filePath);
 
-      for (const entry of entries) {
-        if (!entry.filePaths || entry.filePaths.length === 0) continue;
-
+      for (const entry of entriesWithFiles) {
         for (const bibFilePath of entry.filePaths) {
           const importBibPath = this.normalizePath(bibFilePath);
           const prefixResult = this.calculateMaximumPrefixMatch(importFilePath, importBibPath);
@@ -194,127 +183,24 @@ export class PdfMatchingService implements IFileMatchingService {
   }
 
   /**
-   * Calculate maximum prefix match between two import paths
+   * Find exact matches for entries without file attributes
    */
-  private calculateMaximumPrefixMatch(filePath: string, bibPath: string): PrefixMatchResult {
-    if (!filePath || !bibPath) {
-      return { prefixLength: 0, confidence: 0, type: "no_match" };
-    }
-
-    // Split paths into components
-    const filePathParts = filePath.split("/").filter((part) => part.length > 0);
-    const bibPathParts = bibPath.split("/").filter((part) => part.length > 0);
-
-    // Find maximum prefix match from the end (most specific parts first)
-    let maxPrefixLength = 0;
-    let matchType = "no_match";
-
-    // Check for exact path match first
-    if (filePath === bibPath) {
-      return {
-        prefixLength: Math.max(filePathParts.length, bibPathParts.length),
-        confidence: 1.0,
-        type: "exact_path_match",
-      };
-    }
-
-    // Check suffix matching (from the end of the path)
-    const minLength = Math.min(filePathParts.length, bibPathParts.length);
-
-    for (let i = 1; i <= minLength; i++) {
-      const filePathSuffix = filePathParts.slice(-i);
-      const bibPathSuffix = bibPathParts.slice(-i);
-
-      let allMatch = true;
-      for (let j = 0; j < i; j++) {
-        if (filePathSuffix[j] !== bibPathSuffix[j]) {
-          allMatch = false;
-          break;
-        }
-      }
+  private findExactMatch(file: File, entries: ParsedEntry[]): MatchCandidate | null {
+    // Only process entries that don't have file attributes
+    const entriesWithoutFiles = entries.filter(entry => !entry.filePaths || entry.filePaths.length === 0);
 
-      if (allMatch) {
-        maxPrefixLength = i;
-        matchType = i === minLength ? "full_suffix_match" : "partial_suffix_match";
-      } else {
-        break; // Stop at first non-match since we're looking for continuous suffix
-      }
-    }
-
-    // If no exact suffix match, try filename similarity
-    if (maxPrefixLength === 0) {
-      const fileName = filePathParts[filePathParts.length - 1];
-      const bibFileName = bibPathParts[bibPathParts.length - 1];
-
-      if (fileName && bibFileName) {
-        // Remove .pdf extension for comparison
-        const cleanFileName = fileName.replace(/\.pdf$/i, "");
-        const cleanBibFileName = bibFileName.replace(/\.pdf$/i, "");
-
-        const similarity = this.calculateStringSimilarity(cleanFileName, cleanBibFileName);
-        if (similarity >= 0.8) {
-          return {
-            prefixLength: 1,
-            confidence: similarity * 0.7, // Lower confidence for filename-only matches
-            type: "filename_similarity",
-          };
-        }
-      }
+    if (entriesWithoutFiles.length === 0) {
+      return null;
     }
 
-    // Calculate confidence based on prefix length and path lengths
-    if (maxPrefixLength > 0) {
-      const maxPathLength = Math.max(filePathParts.length, bibPathParts.length);
-      const minPathLength = Math.min(filePathParts.length, bibPathParts.length);
-
-      // Base confidence on how much of the shorter path is matched
-      let confidence = maxPrefixLength / minPathLength;
-
-      // Bonus for matching more of the longer path
-      const longerPathBonus = (maxPrefixLength / maxPathLength) * 0.2;
-      confidence = Math.min(1.0, confidence + longerPathBonus);
-
-      // Penalty for very different path lengths
-      const lengthDifference = Math.abs(filePathParts.length - bibPathParts.length);
-      const lengthPenalty = lengthDifference * 0.1;
-      confidence = Math.max(0.1, confidence - lengthPenalty);
-
-      return {
-        prefixLength: maxPrefixLength,
-        confidence,
-        type: matchType,
-      };
-    }
-
-    return { prefixLength: 0, confidence: 0, type: "no_match" };
-  }
-
-  private findWebkitPathMatch(file: File, entries: ParsedEntry[]): MatchCandidate | null {
-    const filePath: string = (file as any).webkitRelativePath || file.name;
-
-    for (const entry of entries) {
-      const webkitMatch = this.checkWebkitPathMatch(filePath, entry.filePaths);
-      if (webkitMatch) {
-        return {
-          entry,
-          type: webkitMatch.type,
-          confidence: webkitMatch.confidence,
-        };
-      }
-    }
-    return null;
-  }
-
-  private findBestNonWebkitMatch(file: File, entries: ParsedEntry[]): MatchCandidate | null {
     const fileName: string = file.name.toLowerCase().replace(/\.pdf$/, "");
     let bestMatch: MatchCandidate | null = null;
     let bestConfidence = 0;
 
-    for (const entry of entries) {
+    for (const entry of entriesWithoutFiles) {
       const matches = [
-        this.checkFileFieldMatch(fileName, entry.filePaths),
-        this.checkExactMatch(fileName, entry.reference),
-        this.checkTitleMatch(fileName, entry.title),
+        this.checkReferenceSubstringMatch(fileName, entry.reference),
+        this.checkTitleSubstringMatch(fileName, entry.title),
       ].filter(Boolean);
 
       if (matches.length > 0) {
@@ -333,146 +219,145 @@ export class PdfMatchingService implements IFileMatchingService {
       }
     }
 
-    return bestConfidence >= 0.6 ? bestMatch : null;
+    return bestConfidence >= 0.8 ? bestMatch : null;
   }
 
-  private checkWebkitPathMatch(
-    filePath: string,
-    parsedFilePaths?: string[]
-  ): { type: string; confidence: number } | null {
-    if (!parsedFilePaths || parsedFilePaths.length === 0) return null;
-
-    const importFilePath = this.normalizePath(filePath);
-
-    for (const bibFilePath of parsedFilePaths) {
-      const importBibPath = this.normalizePath(bibFilePath);
-
-      // Use the same maximum prefix matching logic for consistency
-      const prefixResult = this.calculateMaximumPrefixMatch(importFilePath, importBibPath);
-
-      if (prefixResult.confidence >= 0.8) {
-        return {
-          type: `webkit_${prefixResult.type}`,
-          confidence: prefixResult.confidence * 0.95, // Slightly lower than maximum prefix matches
-        };
-      }
+  /**
+   * Check if filename contains the reference as a substring
+   */
+  private checkReferenceSubstringMatch(fileName: string, reference?: string): { type: string; confidence: number } | null {
+    if (!reference) return null;
 
-      // Fallback to filename similarity for webkit paths
-      const filePathName = this.extractFileNameFromPath(importFilePath);
-      const bibPathName = this.extractFileNameFromPath(importBibPath);
+    const refKey = reference.toLowerCase();
 
-      if (filePathName && bibPathName) {
-        const similarity = this.calculateStringSimilarity(filePathName, bibPathName);
-        if (similarity >= 0.9) {
-          return { type: "webkit_path_filename", confidence: similarity * 0.85 };
-        }
-      }
+    // Check for exact match first
+    if (fileName === refKey) {
+      return { type: "exact_reference", confidence: 1.0 };
     }
 
-    return null;
-  }
-
-  private checkFileFieldMatch(
-    fileName: string,
-    parsedFilePaths?: string[]
-  ): { type: string; confidence: number } | null {
-    if (!parsedFilePaths || parsedFilePaths.length === 0) return null;
-
-    for (const filePath of parsedFilePaths) {
-      const pathFileName = this.extractFileNameFromPath(filePath);
-      if (pathFileName) {
-        const cleanPathFileName = pathFileName.toLowerCase().replace(/\.pdf$/, "");
-
-        if (fileName === cleanPathFileName) {
-          return { type: "file_field_parsed", confidence: 0.95 };
-        }
-
-        const similarity = this.calculateStringSimilarity(fileName, cleanPathFileName);
-        if (similarity >= 0.8) {
-          return { type: "file_field_parsed", confidence: similarity * 0.9 };
-        }
-      }
+    // Check if filename contains reference as substring
+    if (fileName.includes(refKey)) {
+      return { type: "reference_substring", confidence: 0.9 };
     }
 
     return null;
   }
 
-  private checkExactMatch(fileName: string, reference?: string): { type: string; confidence: number } | null {
-    if (!reference) return null;
-
-    const refKey = reference.toLowerCase();
-    if (fileName === refKey) {
-      return { type: "exact", confidence: 1.0 };
-    }
-    return null;
-  }
-
-  private checkTitleMatch(fileName: string, title?: string): { type: string; confidence: number } | null {
+  /**
+   * Check if filename contains normalized title words as substrings
+   */
+  private checkTitleSubstringMatch(fileName: string, title?: string): { type: string; confidence: number } | null {
     if (!title) return null;
 
     const cleanTitle = title
       .toLowerCase()
-      .replace(/[^\w\s]/g, " ")
-      .replace(/\s+/g, " ")
+      .replace(/[_\-\s]+/g, " ")
       .trim();
 
     const cleanFileName = fileName
-      .replace(/[^\w\s]/g, " ")
-      .replace(/\s+/g, " ")
+      .replace(/[_\-\s]+/g, " ")
       .trim();
 
-    const titleWords = cleanTitle.split(" ").filter((word) => word.length > 3);
-    const fileWords = cleanFileName.split(" ");
+    // Check for exact match first
+    if (cleanFileName === cleanTitle) {
+      return { type: "exact_title", confidence: 1.0 };
+    }
+
+    // Split title into words and check if filename contains each word
+    const titleWords = cleanTitle.split(" ").filter((word) => word.length > 2);
+
+    if (titleWords.length === 0) return null;
 
     let matchedWords = 0;
     for (const titleWord of titleWords) {
-      if (fileWords.some((fileWord) => fileWord.includes(titleWord) || titleWord.includes(fileWord))) {
+      if (cleanFileName.includes(titleWord)) {
         matchedWords++;
       }
     }
 
-    if (titleWords.length > 0) {
+    if (matchedWords > 0) {
       const confidence = matchedWords / titleWords.length;
       if (confidence >= 0.6) {
-        return { type: "title", confidence: confidence * 0.8 };
+        return { type: "title_substring", confidence: confidence * 0.8 };
       }
     }
 
     return null;
   }
 
-  private calculateStringSimilarity(str1: string, str2: string): number {
-    const longer = str1.length > str2.length ? str1 : str2;
-    const shorter = str1.length > str2.length ? str2 : str1;
-
-    if (longer.length === 0) return 1.0;
+  /**
+   * Calculate maximum prefix match between two import paths
+   */
+  private calculateMaximumPrefixMatch(filePath: string, bibPath: string): PrefixMatchResult {
+    if (!filePath || !bibPath) {
+      return { prefixLength: 0, confidence: 0, type: "no_match" };
+    }
 
-    const distance = this.levenshteinDistance(longer, shorter);
-    return (longer.length - distance) / longer.length;
-  }
+    // Split paths into components
+    const filePathParts = filePath.split("/").filter((part) => part.length > 0);
+    const bibPathParts = bibPath.split("/").filter((part) => part.length > 0);
 
-  private levenshteinDistance(str1: string, str2: string): number {
-    const matrix: number[][] = [];
+    // Find maximum prefix match from the end (most specific parts first)
+    let maxPrefixLength = 0;
+    let matchType = "no_match";
 
-    for (let i = 0; i <= str2.length; i++) {
-      matrix[i] = [i];
+    // Check for exact path match first
+    if (filePath === bibPath) {
+      return {
+        prefixLength: Math.max(filePathParts.length, bibPathParts.length),
+        confidence: 1.0,
+        type: "exact_path_match",
+      };
     }
 
-    for (let j = 0; j <= str1.length; j++) {
-      matrix[0][j] = j;
-    }
+    // Check suffix matching (from the end of the path)
+    const minLength = Math.min(filePathParts.length, bibPathParts.length);
 
-    for (let i = 1; i <= str2.length; i++) {
-      for (let j = 1; j <= str1.length; j++) {
-        if (str2.charAt(i - 1) === str1.charAt(j - 1)) {
-          matrix[i][j] = matrix[i - 1][j - 1];
-        } else {
-          matrix[i][j] = Math.min(matrix[i - 1][j - 1] + 1, matrix[i][j - 1] + 1, matrix[i - 1][j] + 1);
+    for (let i = 1; i <= minLength; i++) {
+      const filePathSuffix = filePathParts.slice(-i);
+      const bibPathSuffix = bibPathParts.slice(-i);
+
+      let allMatch = true;
+      for (let j = 0; j < i; j++) {
+        if (filePathSuffix[j] !== bibPathSuffix[j]) {
+          allMatch = false;
+          break;
         }
       }
+
+      if (allMatch) {
+        maxPrefixLength = i;
+        matchType = i === minLength ? "full_suffix_match" : "partial_suffix_match";
+      } else {
+        break; // Stop at first non-match since we're looking for continuous suffix
+      }
     }
 
-    return matrix[str2.length][str1.length];
+    // Calculate confidence based on prefix length and path lengths
+    if (maxPrefixLength > 0) {
+      const maxPathLength = Math.max(filePathParts.length, bibPathParts.length);
+      const minPathLength = Math.min(filePathParts.length, bibPathParts.length);
+
+      // Base confidence on how much of the shorter path is matched
+      let confidence = maxPrefixLength / minPathLength;
+
+      // Bonus for matching more of the longer path
+      const longerPathBonus = (maxPrefixLength / maxPathLength) * 0.2;
+      confidence = Math.min(1.0, confidence + longerPathBonus);
+
+      // Penalty for very different path lengths
+      const lengthDifference = Math.abs(filePathParts.length - bibPathParts.length);
+      const lengthPenalty = lengthDifference * 0.1;
+      confidence = Math.max(0.1, confidence - lengthPenalty);
+
+      return {
+        prefixLength: maxPrefixLength,
+        confidence,
+        type: matchType,
+      };
+    }
+
+    return { prefixLength: 0, confidence: 0, type: "no_match" };
   }
 
   private normalizePath(path: string): string {
@@ -480,14 +365,4 @@ export class PdfMatchingService implements IFileMatchingService {
 
     return path.toLowerCase().replace(/\\/g, "/").replace(/\/+/g, "/").replace(/^\/*/, "").replace(/\/*$/, "").trim();
   }
-
-  private extractFileNameFromPath(path: string): string | null {
-    if (!path) return null;
-
-    const importPath = this.normalizePath(path);
-    const parts = importPath.split("/");
-    const fileName = parts[parts.length - 1];
-
-    return fileName ? fileName.replace(/\.pdf$/, "") : null;
-  }
 }

From 40469e3d6057891b5d2f767e49e4b0c013bc01a8 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 19:40:25 -0700
Subject: [PATCH 15/20] changelogs

---
 extralit-frontend/CHANGELOG.md |  3 ++-
 extralit-server/CHANGELOG.md   |  2 +-
 extralit/CHANGELOG.md          | 11 ++++++++++-
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/extralit-frontend/CHANGELOG.md b/extralit-frontend/CHANGELOG.md
index e8a4fa3ba..9644bf474 100644
--- a/extralit-frontend/CHANGELOG.md
+++ b/extralit-frontend/CHANGELOG.md
@@ -14,7 +14,8 @@ These are the section headers that we use:
 * "Security" in case of vulnerabilities.
 -->
 
-## [Extralit] [0.6.1](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
+
+## [Extralit] [Unreleased](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
 ### Changed
 - Refactored the frontend to use a single fetchDocument method that queries documents by any identifier and workspace, replacing the previous fetchDocumentByID and fetchDocumentByPubmedID methods. The view model and use case now expect and handle the new API response format
 
diff --git a/extralit-server/CHANGELOG.md b/extralit-server/CHANGELOG.md
index dcbe2f64b..9dedca1d2 100644
--- a/extralit-server/CHANGELOG.md
+++ b/extralit-server/CHANGELOG.md
@@ -14,7 +14,7 @@ These are the section headers that we use:
 * "Security" in case of vulnerabilities.
 -->
 
-## [Extralit] [0.6.1](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
+## [Extralit] [Unreleased](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
 
 ### Added
 - Introduced presigned URL generation for document access in `get_document` function, ensuring valid file URLs.
diff --git a/extralit/CHANGELOG.md b/extralit/CHANGELOG.md
index 940c72271..2281581dc 100644
--- a/extralit/CHANGELOG.md
+++ b/extralit/CHANGELOG.md
@@ -14,9 +14,18 @@ These are the section headers that we use:
 * "Security" in case of vulnerabilities.
 -->
 
-## [Extralit] [0.6.1](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
+
+## [Extralit] [Unreleased](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.1)
 ### Changed
 - Updated backend document fetching logic to use the new unified endpoint, including improved input validation and error messages
+- Refactored document handling to use `GetDocumentByRecordMetadataUseCase` for improved document fetching
+
+### Fixed
+- Fixed document panel behavior and handling of missing documents
+- Improved error handling and validation for document operations
+- Fixed import analysis table display issues
+- Enhanced component state persistence across import modal steps
+
 
 ## [Extralit] [0.6.0](https://github.com/extralit/extralit/compare/v0.5.0...v0.6.0)
 

From e63d7cb502032cef7205c9531af004afa25cc91b Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 19:48:37 -0700
Subject: [PATCH 16/20] fix tests

---
 codecov.yml                                   |  1 +
 .../container/mode/useDocumentViewModel.ts    |  1 +
 .../v1/domain/services/FileMatchingService.ts | 75 ++++++++++++-------
 3 files changed, 52 insertions(+), 25 deletions(-)

diff --git a/codecov.yml b/codecov.yml
index 7a2d72942..6cd801cd1 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -6,6 +6,7 @@ coverage:
     patch:
       default:
         enabled: false
+comment: false
 
 flags:
   frontend:
diff --git a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
index 9895cf939..fe52361cf 100644
--- a/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
+++ b/extralit-frontend/components/features/annotation/container/mode/useDocumentViewModel.ts
@@ -109,6 +109,7 @@ export const useDocumentViewModel = (props: { record: any }) => {
     fetchDocumentSegments,
     focusDocumentPageNumber,
     clearDocument,
+    isDocumentPanelExpanded: computed(() => false), // Default to collapsed
   };
 };
 
diff --git a/extralit-frontend/v1/domain/services/FileMatchingService.ts b/extralit-frontend/v1/domain/services/FileMatchingService.ts
index e36a9c59d..407d82fb3 100644
--- a/extralit-frontend/v1/domain/services/FileMatchingService.ts
+++ b/extralit-frontend/v1/domain/services/FileMatchingService.ts
@@ -31,13 +31,14 @@ export class PdfMatchingService implements IFileMatchingService {
 
     // Track which references have been matched to support multiple files per reference
     const referenceFileCount = new Map<string, number>();
-    const processedFiles = new Set<File>();
+    // Track which files have been used to ensure one-to-one file-to-bib-entry matching
+    const usedFiles = new Set<File>();
 
     // Phase 1: Maximum prefix path matching for entries with file attributes
-    const prefixMatches = this.findMaximumPrefixMatches(files, entries);
+    const prefixMatches = this.findMaximumPrefixMatches(files, entries, usedFiles);
 
     for (const match of prefixMatches) {
-      if (!processedFiles.has(match.file)) {
+      if (!usedFiles.has(match.file)) {
         matchedFiles.push({
           file: match.file,
           bibEntry: match.entry,
@@ -48,15 +49,15 @@ export class PdfMatchingService implements IFileMatchingService {
         // Track multiple files per reference
         const currentCount = referenceFileCount.get(match.entry.reference) || 0;
         referenceFileCount.set(match.entry.reference, currentCount + 1);
-        processedFiles.add(match.file);
+        usedFiles.add(match.file); // Mark file as used
       }
     }
 
-    // Phase 2: Exact matching for entries without file attributes
-    const remainingFiles = files.filter((file) => !processedFiles.has(file));
+    // Phase 2: Exact matching for remaining files against all entries
+    const remainingFiles = files.filter((file) => !usedFiles.has(file));
 
     for (const file of remainingFiles) {
-      const exactMatch = this.findExactMatch(file, entries);
+      const exactMatch = this.findExactMatch(file, entries, usedFiles);
       if (exactMatch) {
         matchedFiles.push({
           file,
@@ -67,12 +68,12 @@ export class PdfMatchingService implements IFileMatchingService {
 
         const currentCount = referenceFileCount.get(exactMatch.entry.reference) || 0;
         referenceFileCount.set(exactMatch.entry.reference, currentCount + 1);
-        processedFiles.add(file);
+        usedFiles.add(file); // Mark file as used
       }
     }
 
     // Add remaining unmatched files
-    const finalRemainingFiles = files.filter((file) => !processedFiles.has(file));
+    const finalRemainingFiles = files.filter((file) => !usedFiles.has(file));
     unmatchedFiles.push(...finalRemainingFiles);
 
     // Sort matched files by confidence (highest first), then by reference for grouping
@@ -94,7 +95,8 @@ export class PdfMatchingService implements IFileMatchingService {
    */
   private findMaximumPrefixMatches(
     files: File[],
-    entries: ParsedEntry[]
+    entries: ParsedEntry[],
+    usedFiles: Set<File> = new Set()
   ): Array<{
     file: File;
     entry: ParsedEntry;
@@ -109,7 +111,7 @@ export class PdfMatchingService implements IFileMatchingService {
     }> = [];
 
     // Only process entries that have file attributes
-    const entriesWithFiles = entries.filter(entry => entry.filePaths && entry.filePaths.length > 0);
+    const entriesWithFiles = entries.filter((entry) => entry.filePaths && entry.filePaths.length > 0);
 
     if (entriesWithFiles.length === 0) {
       return matches;
@@ -123,6 +125,9 @@ export class PdfMatchingService implements IFileMatchingService {
     }> = [];
 
     for (const file of files) {
+      // Skip files that are already matched
+      if (usedFiles.has(file)) continue;
+
       const filePath: string = (file as any).webkitRelativePath || file.name;
       const importFilePath = this.normalizePath(filePath);
 
@@ -151,14 +156,14 @@ export class PdfMatchingService implements IFileMatchingService {
     });
 
     // Progressive file addition with deduplication
-    const usedFiles = new Set<File>();
+    const localUsedFiles = new Set<File>();
     const referenceFileCounts = new Map<string, number>();
 
     for (const combination of allCombinations) {
       const { file, entry, prefixResult } = combination;
 
       // Skip if file is already matched with higher confidence
-      if (usedFiles.has(file)) continue;
+      if (localUsedFiles.has(file)) continue;
 
       // Allow multiple files per reference, but ensure quality matches
       const currentFileCount = referenceFileCounts.get(entry.reference) || 0;
@@ -174,7 +179,7 @@ export class PdfMatchingService implements IFileMatchingService {
           confidence: prefixResult.confidence,
         });
 
-        usedFiles.add(file);
+        localUsedFiles.add(file);
         referenceFileCounts.set(entry.reference, currentFileCount + 1);
       }
     }
@@ -183,13 +188,11 @@ export class PdfMatchingService implements IFileMatchingService {
   }
 
   /**
-   * Find exact matches for entries without file attributes
+   * Find exact matches for remaining files against all entries
    */
-  private findExactMatch(file: File, entries: ParsedEntry[]): MatchCandidate | null {
-    // Only process entries that don't have file attributes
-    const entriesWithoutFiles = entries.filter(entry => !entry.filePaths || entry.filePaths.length === 0);
-
-    if (entriesWithoutFiles.length === 0) {
+  private findExactMatch(file: File, entries: ParsedEntry[], usedFiles: Set<File> = new Set()): MatchCandidate | null {
+    // Skip if file is already matched
+    if (usedFiles.has(file)) {
       return null;
     }
 
@@ -197,7 +200,7 @@ export class PdfMatchingService implements IFileMatchingService {
     let bestMatch: MatchCandidate | null = null;
     let bestConfidence = 0;
 
-    for (const entry of entriesWithoutFiles) {
+    for (const entry of entries) {
       const matches = [
         this.checkReferenceSubstringMatch(fileName, entry.reference),
         this.checkTitleSubstringMatch(fileName, entry.title),
@@ -225,7 +228,10 @@ export class PdfMatchingService implements IFileMatchingService {
   /**
    * Check if filename contains the reference as a substring
    */
-  private checkReferenceSubstringMatch(fileName: string, reference?: string): { type: string; confidence: number } | null {
+  private checkReferenceSubstringMatch(
+    fileName: string,
+    reference?: string
+  ): { type: string; confidence: number } | null {
     if (!reference) return null;
 
     const refKey = reference.toLowerCase();
@@ -254,9 +260,7 @@ export class PdfMatchingService implements IFileMatchingService {
       .replace(/[_\-\s]+/g, " ")
       .trim();
 
-    const cleanFileName = fileName
-      .replace(/[_\-\s]+/g, " ")
-      .trim();
+    const cleanFileName = fileName.replace(/[_\-\s]+/g, " ").trim();
 
     // Check for exact match first
     if (cleanFileName === cleanTitle) {
@@ -333,6 +337,27 @@ export class PdfMatchingService implements IFileMatchingService {
       }
     }
 
+    // If no exact suffix match, try exact filename match
+    if (maxPrefixLength === 0) {
+      const fileName = filePathParts[filePathParts.length - 1];
+      const bibFileName = bibPathParts[bibPathParts.length - 1];
+
+      if (fileName && bibFileName) {
+        // Remove .pdf extension for comparison
+        const cleanFileName = fileName.replace(/\.pdf$/i, "");
+        const cleanBibFileName = bibFileName.replace(/\.pdf$/i, "");
+
+        // Only match if filenames are exactly the same (no fuzzy matching)
+        if (cleanFileName === cleanBibFileName) {
+          return {
+            prefixLength: 1,
+            confidence: 0.8, // Lower confidence for filename-only matches
+            type: "filename_similarity",
+          };
+        }
+      }
+    }
+
     // Calculate confidence based on prefix length and path lengths
     if (maxPrefixLength > 0) {
       const maxPathLength = Math.max(filePathParts.length, bibPathParts.length);

From 45f7908ef1d6dd86c8f585c08a65375ff0c72fcb Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Wed, 13 Aug 2025 19:56:22 -0700
Subject: [PATCH 17/20] fix tests

---
 .../v1/domain/services/FileMatchingService.ts | 29 ++++++++++++++++---
 1 file changed, 25 insertions(+), 4 deletions(-)

diff --git a/extralit-frontend/v1/domain/services/FileMatchingService.ts b/extralit-frontend/v1/domain/services/FileMatchingService.ts
index 407d82fb3..d483622b4 100644
--- a/extralit-frontend/v1/domain/services/FileMatchingService.ts
+++ b/extralit-frontend/v1/domain/services/FileMatchingService.ts
@@ -222,7 +222,7 @@ export class PdfMatchingService implements IFileMatchingService {
       }
     }
 
-    return bestConfidence >= 0.8 ? bestMatch : null;
+    return bestConfidence >= 0.9 ? bestMatch : null;
   }
 
   /**
@@ -241,9 +241,21 @@ export class PdfMatchingService implements IFileMatchingService {
       return { type: "exact_reference", confidence: 1.0 };
     }
 
-    // Check if filename contains reference as substring
+    // Check if filename contains reference as substring, but only if it's a significant match
     if (fileName.includes(refKey)) {
-      return { type: "reference_substring", confidence: 0.9 };
+      // Calculate how much of the filename the reference represents
+      const referenceRatio = refKey.length / fileName.length;
+
+      // Only accept if reference represents at least 60% of the filename
+      // This prevents short references from matching long filenames inappropriately
+      if (referenceRatio >= 0.6) {
+        return { type: "reference_substring", confidence: 0.9 };
+      }
+
+      // For less significant matches, reduce confidence
+      if (referenceRatio >= 0.4) {
+        return { type: "reference_substring", confidence: 0.7 };
+      }
     }
 
     return null;
@@ -347,7 +359,7 @@ export class PdfMatchingService implements IFileMatchingService {
         const cleanFileName = fileName.replace(/\.pdf$/i, "");
         const cleanBibFileName = bibFileName.replace(/\.pdf$/i, "");
 
-        // Only match if filenames are exactly the same (no fuzzy matching)
+        // Check for exact filename match
         if (cleanFileName === cleanBibFileName) {
           return {
             prefixLength: 1,
@@ -355,6 +367,15 @@ export class PdfMatchingService implements IFileMatchingService {
             type: "filename_similarity",
           };
         }
+
+        // Check for similar filenames (e.g., with version suffixes)
+        if (cleanFileName.includes(cleanBibFileName) || cleanBibFileName.includes(cleanFileName)) {
+          return {
+            prefixLength: 1,
+            confidence: 0.7, // Even lower confidence for similar filenames
+            type: "filename_similarity",
+          };
+        }
       }
     }
 

From 0d50fb0f7122277885e9825b887709f8a9441733 Mon Sep 17 00:00:00 2001
From: JTran-IDM <nhat.tran@mavs.uta.edu>
Date: Wed, 13 Aug 2025 19:59:47 -0700
Subject: [PATCH 18/20] fix tests

---
 extralit/pdm.lock                         | 30 +++++++----------------
 extralit/src/extralit/_api/_workspaces.py | 13 +++++++++-
 2 files changed, 21 insertions(+), 22 deletions(-)

diff --git a/extralit/pdm.lock b/extralit/pdm.lock
index fef71b678..6957d1cd0 100644
--- a/extralit/pdm.lock
+++ b/extralit/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "dev"]
 strategy = []
 lock_version = "4.5.0"
-content_hash = "sha256:4eed977ec0ae819f547f71e5131d350c7686fa2a8399a998cb62994db78d442c"
+content_hash = "sha256:835ce32222428fd6dcc0dd37ce61240d8bfdb2b32bc0d64bdf350073b75d3b60"
 
 [[metadata.targets]]
 requires_python = ">=3.9.2,<3.14"
@@ -3006,15 +3006,6 @@ files = [
     {file = "multidict-6.4.4.tar.gz", hash = "sha256:69ee9e6ba214b5245031b76233dd95408a0fd57fdb019ddcc1ead4790932a8e8"},
 ]
 
-[[package]]
-name = "multimethod"
-version = "1.10"
-summary = ""
-files = [
-    {file = "multimethod-1.10-py3-none-any.whl", hash = "sha256:afd84da9c3d0445c84f827e4d63ad42d17c6d29b122427c6dee9032ac2d2a0d4"},
-    {file = "multimethod-1.10.tar.gz", hash = "sha256:daa45af3fe257f73abb69673fd54ddeaf31df0eb7363ad6e1251b7c9b192d8c5"},
-]
-
 [[package]]
 name = "multiprocess"
 version = "0.70.16"
@@ -3350,37 +3341,34 @@ files = [
 
 [[package]]
 name = "pandera"
-version = "0.19.3"
+version = "0.26.0"
 summary = ""
 dependencies = [
-    "multimethod",
-    "numpy",
     "packaging",
-    "pandas",
     "pydantic",
     "typeguard",
+    "typing-extensions",
     "typing-inspect",
-    "wrapt",
 ]
 files = [
-    {file = "pandera-0.19.3-py3-none-any.whl", hash = "sha256:4ff2f0446f4b8dd7c2fa2aac547044911f4957d137456bfe2b281ccd02cc5ff5"},
-    {file = "pandera-0.19.3.tar.gz", hash = "sha256:1bf9dc8a30525cb5bc77edb7d6a044cc59d59c3ef405517825cf6b04c6160c07"},
+    {file = "pandera-0.26.0-py3-none-any.whl", hash = "sha256:85e4ab058e8e6c1f13f364450e080fcb52c8320bb40ca952342ac8709d1a7a4e"},
+    {file = "pandera-0.26.0.tar.gz", hash = "sha256:37dbca1e53d12f70e576c45d886a31e454b48990c40d92c3466c26d28141b07b"},
 ]
 
 [[package]]
 name = "pandera"
-version = "0.19.3"
+version = "0.26.0"
 extras = ["io"]
 summary = ""
 dependencies = [
     "black",
     "frictionless",
-    "pandera==0.19.3",
+    "pandera==0.26.0",
     "pyyaml",
 ]
 files = [
-    {file = "pandera-0.19.3-py3-none-any.whl", hash = "sha256:4ff2f0446f4b8dd7c2fa2aac547044911f4957d137456bfe2b281ccd02cc5ff5"},
-    {file = "pandera-0.19.3.tar.gz", hash = "sha256:1bf9dc8a30525cb5bc77edb7d6a044cc59d59c3ef405517825cf6b04c6160c07"},
+    {file = "pandera-0.26.0-py3-none-any.whl", hash = "sha256:85e4ab058e8e6c1f13f364450e080fcb52c8320bb40ca952342ac8709d1a7a4e"},
+    {file = "pandera-0.26.0.tar.gz", hash = "sha256:37dbca1e53d12f70e576c45d886a31e454b48990c40d92c3466c26d28141b07b"},
 ]
 
 [[package]]
diff --git a/extralit/src/extralit/_api/_workspaces.py b/extralit/src/extralit/_api/_workspaces.py
index 3b671d73e..5bcdf72a9 100644
--- a/extralit/src/extralit/_api/_workspaces.py
+++ b/extralit/src/extralit/_api/_workspaces.py
@@ -510,7 +510,18 @@ def list_schemas(
                     continue
 
             logger.info(f"Successfully loaded {len(schemas)} schemas from workspace '{workspace_name}'")
-            return SchemaStructure(schemas=list(schemas.values()))
+
+            # Ensure all schemas are proper DataFrameSchema instances
+            schema_list = []
+            for schema in schemas.values():
+                if isinstance(schema, pa.DataFrameSchema):
+                    schema_list.append(schema)
+                else:
+                    logger.warning(
+                        f"Schema {getattr(schema, 'name', 'unknown')} is not a DataFrameSchema instance, skipping"
+                    )
+
+            return SchemaStructure(schemas=schema_list)
 
         except Exception as e:
             logger.error(f"Unexpected error loading schemas from workspace '{workspace_name}': {str(e)}")

From 1880fd24ed44d614873ee594591dcb7d16caea9d Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Thu, 14 Aug 2025 08:36:24 -0700
Subject: [PATCH 19/20] fix tests

---
 extralit/src/extralit/_api/_workspaces.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/extralit/src/extralit/_api/_workspaces.py b/extralit/src/extralit/_api/_workspaces.py
index 5bcdf72a9..6bd1d1785 100644
--- a/extralit/src/extralit/_api/_workspaces.py
+++ b/extralit/src/extralit/_api/_workspaces.py
@@ -487,6 +487,8 @@ def list_schemas(
             schemas = {}
             for obj in schema_versions.values():
                 try:
+                    # The object_name includes the full path (e.g., "schemas/test_schema_name")
+                    # We need to use the full path for get_file since that's how it was stored
                     file_response = self.get_file(workspace_name, obj.object_name)
 
                     # Skip empty content

From 8c856952d3659bf1f014f5e201dc9baab7d1c1f7 Mon Sep 17 00:00:00 2001
From: JonnyTran <nhat.c.tran@gmail.com>
Date: Thu, 14 Aug 2025 08:50:14 -0700
Subject: [PATCH 20/20] fix tests

---
 .github/workflows/extralit.yml            |  6 +++++-
 .kiro/steering/structure.md               |  8 ++++----
 extralit/src/extralit/_api/_workspaces.py | 15 +++++++++------
 3 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/extralit.yml b/.github/workflows/extralit.yml
index 8bf89dae0..460bb2295 100644
--- a/.github/workflows/extralit.yml
+++ b/.github/workflows/extralit.yml
@@ -96,6 +96,10 @@ jobs:
         run: |
             pdm run test-cov tests/unit
 
+      - name: Check test status
+        if: steps.tests-unit.outcome == 'failure'
+        run: exit 1
+
       - name: Run integration tests
         id: tests-integration
         continue-on-error: true
@@ -119,7 +123,7 @@ jobs:
           fail_ci_if_error: false
 
       - name: Check test status
-        if: steps.tests-unit.outcome == 'failure' || steps.tests-integration.outcome == 'failure'
+        if: steps.tests-integration.outcome == 'failure'
         run: exit 1
 
       - name: Build package
diff --git a/.kiro/steering/structure.md b/.kiro/steering/structure.md
index 6a2a1dd49..3b1319c1a 100644
--- a/.kiro/steering/structure.md
+++ b/.kiro/steering/structure.md
@@ -78,15 +78,15 @@ extralit-frontend/
   ```typescript
   export const useMyViewModel = (props) => {
     const dependency = useResolve(MyUseCase);
-    
+
     const methodOne = () => {
       // implementation
     };
-    
+
     const methodTwo = async (param) => {
       // implementation
     };
-    
+
     return {
       dependency,
       methodOne,
@@ -222,4 +222,4 @@ examples/
 - **Vue/TypeScript**: PascalCase for components, camelCase for utilities
 - **API endpoints**: kebab-case in URLs, snake_case in Python
 - **Database**: snake_case for tables and columns
-- **CSS classes**: kebab-case with BEM methodology where applicable
\ No newline at end of file
+- **CSS classes**: kebab-case with BEM methodology where applicable
diff --git a/extralit/src/extralit/_api/_workspaces.py b/extralit/src/extralit/_api/_workspaces.py
index 6bd1d1785..debe41064 100644
--- a/extralit/src/extralit/_api/_workspaces.py
+++ b/extralit/src/extralit/_api/_workspaces.py
@@ -462,7 +462,7 @@ def list_schemas(
 
             if not schema_files.objects:
                 logger.info(f"No valid schemas found in workspace '{workspace_name}' with prefix '{prefix}'")
-                return SchemaStructure(schemas=[])
+                return SchemaStructure(schemas=[], singleton_schema=None)
 
             # Filter by exclude list
             if exclude:
@@ -475,7 +475,9 @@ def list_schemas(
             # Group by schema name to get the latest version
             schema_versions = {}
             for obj in schema_files.objects:
-                schema_name = obj.object_name
+                # Extract the actual schema name from the object path
+                # object_name is like "schemas/test_schema_name", we want just "test_schema_name"
+                schema_name = os.path.basename(obj.object_name)
                 if schema_name not in schema_versions or (
                     obj.last_modified
                     and schema_versions[schema_name].last_modified
@@ -500,9 +502,10 @@ def list_schemas(
                     schema_json = file_response.content.decode("utf-8")
                     schema = pa.DataFrameSchema.from_json(schema_json)
 
-                    # Add to dictionary
-                    schemas[schema.name] = schema
-                    logger.debug(f"Successfully loaded schema '{schema.name}' from '{obj.object_name}'")
+                    # Add to dictionary using the actual schema name (without prefix)
+                    schema_name = os.path.basename(obj.object_name)
+                    schemas[schema_name] = schema
+                    logger.debug(f"Successfully loaded schema '{schema_name}' from '{obj.object_name}'")
 
                 except FileNotFoundError:
                     logger.debug(f"Schema file '{obj.object_name}' not found in workspace '{workspace_name}'")
@@ -523,7 +526,7 @@ def list_schemas(
                         f"Schema {getattr(schema, 'name', 'unknown')} is not a DataFrameSchema instance, skipping"
                     )
 
-            return SchemaStructure(schemas=schema_list)
+            return SchemaStructure(schemas=schema_list, singleton_schema=None)
 
         except Exception as e:
             logger.error(f"Unexpected error loading schemas from workspace '{workspace_name}': {str(e)}")