diff --git a/Getting_started.ipynb b/Getting_started.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "flexible-fountain",
+   "metadata": {},
+   "source": [
+    "# FoNS Datathon 2021 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "naked-correspondence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import *\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "active-armor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_descriptors = pd.read_csv(\"train_descriptors.csv\")\n",
+    "train_mord3d = pd.read_csv(\"train_mord3d.csv\")\n",
+    "train_morgan = pd.read_csv(\"train_morgan.csv\")\n",
+    "train_rdk = pd.read_csv(\"train_rdk.csv\")\n",
+    "\n",
+    "train_crystals = pd.read_csv(\"train_crystals.csv\")\n",
+    "train_distances = pd.read_csv(\"train_distances.csv\")\n",
+    "train_centroid_distances = pd.read_csv(\"train_centroid_distances.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ongoing-cornell",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_descriptors = pd.read_csv(\"test_descriptors.csv\")\n",
+    "test_mord3d = pd.read_csv(\"test_mord3d.csv\")\n",
+    "test_morgan = pd.read_csv(\"test_morgan.csv\")\n",
+    "test_rdk = pd.read_csv(\"test_rdk.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "spread-knowing",
+   "metadata": {},
+   "source": [
+    "### Data pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "explicit-repair",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13449, 984)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how=\"any\")\n",
+    "train_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "systematic-johns",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3363, 984)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_descriptors_full = test_descriptors[train_descriptors_full.columns]\n",
+    "test_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "intelligent-phone",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(13449, 124) (3363, 124)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_PCA = decomposition.PCA(n_components=.95)\n",
+    "scaler_for_PCA = preprocessing.StandardScaler()\n",
+    "train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))\n",
+    "test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))\n",
+    "print(train_descriptors_PCA.shape, test_descriptors_PCA.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "arbitrary-consistency",
+   "metadata": {},
+   "source": [
+    "### A (very!!) simple and bad model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "hollow-flexibility",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"a\"\n",
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(train_descriptors_PCA, train_crystals[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "sublime-audio",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IHMdpgKwY0DIKsAi4KVoeA64GdorW7wR2zTn3OGAlsHJiYqKx4NYGmJkxGx2dG0s7Otp4rG+9WOGlS+fWsXRp8zIvXpweA5yVkKrXk7Yl7108piKrtHLfHKdqkBPn30nl/7+AByKlfyewkWD3/51612nHIK9Y4WQpmfHxYucXGRhSq/hbUWRZij9PoZc1YKQdTE9ny9fLLyzH6QfylH/HZvIysxuBZ8Xrku4ElliBaJ+ySctgWcu6dcXPrzfr1eWXp18na3set9ySvz/NyTwxkW5G6bZPYnYWzjgjf/BZLb3mRHecfqVtNn9J5wM/AfaWdLek97WrrkaYnYWjj85X/PXoRN6NZh20CxZsva2X85c0ovih+y8sxxkU2tbyN7N319m/qF11ZxG32IuECo6PZ+9rd2hnXs+iGeLeyCmnBBknJnojtj/vfg0NbZ1uohdeWI4zKFRqhG9aiz2NoSF417uyW971QjtrW+2LF6cfv3RpcTnjnkXWtWKyzFW9mK456z5K8K//CjMzPr+A47SNLGdAL5WyHL5FHYvz5+dP7ZY29Vt87fFxs2222frcWkdtnrO3noM2z+kr9UZWwiJk3ccimVMdx6kPOQ7fSrX8i9qLN2zIt+kn825AaJnGtut16+Dpp7c+99FH56rpyy5rXM54+803h1axtPUxZiXn/K5DK4PH0vKXnHde/2VMdZy+JOut0EulrJb/zEyxln9eqW1V18uTXySssjZsdHq62KTSzdRVJoM++bXj9Dt4yz8wNZXvyC1C7cjYok7erNZ82ujbc88NEUn17N1xz6NoXWXTkdmGHMdpC5VS/gCnnZad2rgItcqtiKLNi1LJUqCXXlrfQdvtEM5+SWjnODH9lOOq3VRO+QNsv31r5yeVW5oCHh0NPYwiUSqtKNBOzk2cRj8ktHOcmH7KcdURsuxBvVTKtPnX2qibKbUpBlqZA7SX8u40+j3c5u/0E730X+sUlJXbB9gF2LeRc8ooZSn/os7ZWmWWp9xanfy5VxRos3L04uTXjpNGL+e4ahctKX/gSmBHYAEhEds1wOfqnVdm6XScf7JMT2crt7IUdy8o0Hqtol6Q0XFawVv+jSv/a6PPY4FPRss31DuvzNLtln9ycFdSAY6PD87DlNcq6pXeieO0QhWf4zzlX8ThOyJpN+BdwCWlORu6wLJl6QOj8oije9KcRVmpFJLO2l6NLqiVKy0hHATnrYd0OoNAtwMkeo6st0JcgHcCNwCnR+vPB75e77wyS5n5/Jtx8MYt/UYdwlktjTxTUidIk2t0ND0txcxMNW2ljjMI0K3JXMoqZSr/PLtfM2ahPDNR1vVqlWkrXc9mbPFZco2Pp1+rirZSxxkE8pR/XbOPpL0kXS7ppmh9X0kfb1tXpM3kDYxatizE6LdCcgxBVqy+2dz1Zk0ozcYtZ8m1bl36wLJuDyYrm141xTlOR8l6K8QFuAp4JZHjN9p2U73zyixlT+OY1Vqemdna9NFK6z/LIVyWCaXZFnlejySr55B2z7oZAdRs3VV0+jnVhRajfX4efV6b2HZdvfPKLO2YwzeNMsw+SRNKIy+SenMGp9GsLT7Pjl/UlNNNJdpK3W7CcqpEnvIvEu2zVtILAAOQ9A7g3pI7ID1BmTlp0lI7l8nsbDBbpFEvvcLU1Namp5ii9yArAujII9tvSmkl+sjzETlOoIjy/wBwJvAiSfcAJwHT7RSqW2SFO3aC9euL26LzpqMsaotvNSNonrJsd86UVhS45yNynIisLkFtAeYBOxQ9vsxSttlnetpseDh094eHt8wc1YiNvogNv1EzUdFUElnXGB7unO27iImsXaaUVkw3gzQq23HqQYs2/79OK/XOK7OUqfynp8tT8GWVsbH6o4WLJKVr1GncigIrch/bNQ6gVQU+KPmYHKcerSr/DyfKKcBPgLMKnHcW8ACJyCDgH4HbCIPGvgHsXO86VrLyj1v8vVSKDKRqpaXdjlZqN1v+Zt1tebvT2OkXWlL+W50A2wLfK3DcQcD+Ncr/d4GRaPnTwKeL1NntEb7tLLHCqKdQ6pmRslqeWT2G8fHWwjWLmLUGdSJ2H/Hs9At5yr+ZyVzGCCke6vkSfgSsr9n2fTPbGK3+FNijifpbYni40zXms2FDcIwuWwbbbDN339BQ2D80lB3ZA/k5StIiYyBEI/3Jn8AxxzQ3uUURB+mll9Y/phu0OsjLncbOQJD1VogLcCPBTHMDcDPBlPNn9c6Lzl1ExoAw4FvAkTnnHgesBFZOTEyU9ibsRZv/6KjZ0qWNn5dnZ67nHM4rWWkeaq9fZGKcXnOGlmGvd5u/0y/Qos1/MlGeQ2S2KVKylD/Bd/ANQEWuU4bZJ2nemDev+wq/1RKbbWKSEUxDQ+X6NvJMSq2+pDpNWfZ6j/Zx+oGmlD9h8pbMknVezTW2Uv7A0QSn8ViRa1gJyr+s6Rt7qSSVVSd6M1nKsWiPpR3O0MWLt66nnp/B7fVOlchT/iM5FqFrAAPSMuAbBez+tUg6DPgocLCZpVii20OW3bufiQc0zc7C6ad3rr5aLrsMDj0ULr+8ufObZZ994JZbtt4e34vly9PPm5gIvo207Y5TJTLdiGb2PDN7fvRZW+oqfknnE1r4e0u6W9L7gC8COwA/kHSdpDNK+yY5DOLQ/YmJLSN9O1VfFpddVn+SnKLKtagzNk3xx6xYkb1v0DKUOk7TZHUJkoUwcfsrCeGbBwEHFTmvrNKq2afMkbu9UEZHW3Po1ivNzDdQT5YiNvFGHKn1vkO9etxe71QBWnT4HkuI+HkQ+CHwOHBFvfPKLK0o/5mZoCy7rbDLKvPnb5kJrN6xrTi2Y+U4Ph5KPUWZN1Bt3rxiv1Ujztg82YeHm3pUHGfgyFP+ReL8TwReAawys9cBLwPWlNv/aB+nnAJPPdVtKcpjwwb40pfS7dZJpqfhzDObq2N8PEzmcvzxYTzAunVBra5aFcYGpJlipqbC8Wk8+iiccEL9ehtJ2LZ4cfZ1OmUKc5x+pojyf8LMngCQtK2Z3Qbs3V6xymMQ7f0bN9Y/5sADm1eCDz8clHWaI/mpp+DEE9PPW748vDjSOOOM+oOpGhk8dfPN6S+A6elsZ6/jOFsoovzvlrQz8E2Co/Y/gN+0U6gyqWIUx+RkaxFOTz+dH0G0bl22Y3b9+vRzzOrn22/UGXvzzVsbfVzxO04x8kI9ATCzP4gWT5X0Q2An4LttlapEli0LLeBBC/XMo55JqAyS9zROCwHZoZRQvxcWp6c48cTwgoG5cyI7jlMeRSZwP03SawDM7Cozu9jM+saKPjUFRx9dPxTRaYysmbSWLcu+13F4ar1Qzscf37K8bl17J4ZxnKpSxOzzC+Djkv5H0j9KWtJuocrmgguCScBpL6tX5zt+V62Co46am0juqKPCyyJ+EbQyRaPTXVpNmOd0lrrK38zONbPDCXH+vwI+Len2tktWErOzW0wITnsxC4o8z19Q+xKO12PTUbMmozJol/KqglKMBxw2kyHW6RJZMaC1haD8Pwv8P+BbRc8ro7QS59+ugVBe2lOyEtK1e6KUdmXqrEoGUJ/gpjchJ85fYX82kj4NvC1S+l8DvmFmD7XzhVTLkiVLbOXKlU2dOzS0dWvTKQepM/d2bCx7voKyWLQovdcxPg7z54eex8RE8Gk0IkfWdScnw1iKQSHrfybB5s2dl8cJSLrGzFJN9XWjfYA7gAPMbG25YnWGvOgTp3mGh8Mfu8iYg1YYH4fTTmuv4odss1I8yA3mRjUVlaeRgWv9jCfM6z+K2PzP6FfFD7Dnnt2WYPAYHQ2tuXYrfgit7nYrfiiupBp1Pldl1i9PmNd/NDONY1/xwx92W4LBYt688NkpU1ptC7kZ52nWOcntGzZsPY1mFo30JKuiFKemgmlucjL0CPOmFnV6hCxnQC+VVhy+3XZgemm9xEnlpqcbd55mOVzTrjU6OjeJXVY2WKnxaR/LyiJadkZSz3A62NCtmbzKKq78vcRKN217XkRJVhRKkaiivEyl3YhiKTtyqCqRSFUmT/lnRvtIugOemclrgpDSWcDOwGoze147eyRJWon28ZG9g09aREk8YKxRZ3/ttbKen25EsZQdOVSVSKQqkxftU3cmL+B7wBFmtquZjQNvAi5qj6jl4gNMqsGCBeEztuFLW0YSZzE8nL691hE7OVnsuE5QduRQVSKRnHSKOHxfYWaXxitm9h3g4PaJVB6eEqA6JEeYQjBiZDE2Fo4t4ojtJYdt2ZFDVYlEctIpovzXSvq4pEWSJiWdAvRFwgRvwVSD9euLp7COo1CWLy8WndJsFEs7UjqU/SLqpReb0wWynAFxITh4TwOuJSR5+wJ94vAdtLl7q16GhtK3Zzlva0unnLTtdKSmRee0ErHj0T6DDa3M4fvMgTC/6LFlF1f+XrLCM+OSFZVTtvItQl6EUdlK1iN2nDzylH+RfP6vkXQLcEu0/lJJdedLknSWpAck3ZTYtkDSDyTdHn3u0nBXpQGyZpVy+otaU02as9Ys/xqxE7gTWTWzzI2bNgU5G8l4ecIJMDIS5B8Z2XouZE+B7TRLEZv/54HfI7Lzm9n1wEEFzjsHOKxm28eAy83shcDl0XrbqLVnOv3J4YfPtbFv2tT4NR59dIviTZuEPhkpFCvbZl8URRymRRR0PI9y/H03bQrryReAR+w4TZPVJYgLcHX0eW1i2/X1zouOWwTclFj/JbBbtLwb8Msi12nW7JNlI/bSf2V6Ot3E0WwZH9/ynORdd3S0cRNKUTml/Otk+TKGh7cc46mUnTxoxewD3BVN42iSRiWdDNza5Lvm2WZ2b/TSuRd4VtaBko6TtFLSyjVr1jRVmaeSHRxOPz3M7VvWXMzJCX7yIoWeeirU2wi1EUJFxxTUktXDSW73iB2nWYoo/+OBDwDPAe4G9gNOyDuhDMxshZktMbMlCxcubHd1Th/QrhnZ6plImql3aiqMkt28Gc49tzkFnfXSSG73hGpOsxRR/nub2ZSZPdvMnmVmRwIvbrK++yXtBhB9PtDkdRynJcbHtyy3e1BTswo6njug3vbki+bOO13xO8Uoovz/ueC2IlwMHB0tHw38R5PXcZym2WabMEFMTJrppJZWI4SaUdDLl8P09JaW/vBwWF9eN9ZusKnCnMgdIcsZABwAfBi4C/g/iXIqBRy+wPnAvcDTBHPR+4BxQpTP7dFnocFizTp8u+2k9NKZMj6e72AdH5875mN8fGsn7sxM/riQ2myf9a7ntAcf19AYNJnV82DgEILN/4zErkcIE7jfXuI7KJdms3puu21w2DmDz8wMHHlk9v7R0a2fhdpW9Oxs9jXiLJ6zs3DMMfD001tf/6yz3OTSbjwTaWPkZfVMfSMkCzBZ75h2F2/5D26ZN6/+6Nx6RQqhoM2cG7cYZ2ZCWGfWcXHLPyu0MnlMLXkpFDy9QmNkPSv1wmarCq2kdwB+AOycWN8F+F6988oszSj/mZnuKzYv9YtktnRp69cpmt+nthRR6vHLJe+Y+Li05zDLTOEmjMbxcQ2Nkaf8M80+iW7DtWb2snrb2kkzZp+s7qHTW4yPw+OPlxe/3wxSUCF5jI3VlzHN9JBnpgA3YTRKnLo7+VuMjXl4axZNTeaSYLOkZ4LhJE0Cdf4q3ceHt/cH69aVo/iHijzJGdRT/MPD9WUcHU2P289Lv+CpGRrHxzWUx0iBY04B/kvSVdH6QUBGBHLvMDHhLf9BYbvt4NnPDkpxwQJ45JG5ztvRUdi4sT11b7PN1s7dWsbHQ+homgLKeg7jsQV5+5x0pqZc2ZdB3faSmX0X2B/4GnAB8HIz+167BWsVH94+ODzxBGzYEJbnz4f3vW/uIK2NG9NTebTSG4BQx9lnZ0/lODkZeg1r12Yro7z0C56awekqWc4A4EXR5/5pJeu8dhSP9vFSW0ZGih3XaCRRmuOwVcesR/s43YIm4/y/ZGbvl/TD9HeGvb4dL6M0mnH45sVsO06ctjlp0slzHM7OhgRwq1cHs8yyZW56cHqfPIdv3WifXqBR5Z8WEeA4SbbZBo49Fi691BW6M7jkKf9Mh6+kt+Vd1MwualWwdlF0Mm+nujz9dFD8zYZUek/A6Xfyon2OiD6fBbwGuCJafx1wJdCzyt9D5ZwiNPuc1PYs42kZwV8ATv+QGQ9hZseY2TGAAYvN7O1m9nZgn45J1yQeKucUodnnpNF5cz0LpdOLFAmGW2TR7FsR9wN7tUmeUvBQOWdyMiRui8M0pbn7WwmpbGRwVtxLWLUqxAk1Mnm747STIsr/Sknfk/ReSUcD3wbSIoB6hqmpEA/uVJM4PcLy5eHTDM47r7xRoVk9hrTtjfYSHKdTFBnk9WeElM4vJUzhuMLMPthmuVpm2227LYHTDaT0Fn2Zs101MjjLUzg4vUrRMZC/AL5tZn8OfE/SDm2UqRTWr++2BE43OP74oNjbaWdvJL9MI70Ex+koWaO/4gK8H/g58P+i9RcCl9c7r8zSzAjfeul3vTRfmk2f3O76x8fDb99LqZJ7SRanepAzwrdIy/8DwIHAw9HL4nZC+GdP407f9rFpU3fqHRoKLe2ddw7J3JKMjW2Zl7eX7OyehdLpVYoo/yfN7JkcipJGoPdTOvufq/2MjmYnPWsHmzeHtvO6deFzfDxdofaanb1Mf4PjlEWRlM5XSforYHtJbwBOAL7VXrGcfuCpp8pLmz05CXfdlZ6dM42nnw4RXWvXbr2vXhplx3GKtfw/CqwBbgT+FLgU+Hg7hXKqxfj4lpZxI2S15D1VsuPUJ7flL2kIuMHMXgJ8qaxKJf05cCzBfHQjcIyZPVHW9cEH0fQTDz0Ufq/h4cb8CVkt+dis4rl3HCeb3Ja/mW0Grk9O49gqkp4DfAhYEr1UhoE/Kuv6MT6Ipn/YtCn8XsdlzA+3dGnjLflW7eyeksEZdIqYfXYDbpZ0uaSL49JivSMEH8IIMAb8psXrbYUPoukvVq8OI3Knp0MPAMLn9DRcdllnI2ZOOAGOOspTMjiDTd18/pIOTttuZlelbS9UqXQisAx4HPi+mW31N5Z0HNFcwRMTEy9f1aBncf58ePTRZiV0ymZyMt85HKdk6Dazs0Hxp/0tekVGxylKXj7/zJa/pO0knQS8E3gR8GMzuyouLQizC/AW4HnA7sA8SVvNuWVmK8xsiZktWbhwYcP1PP54sxI6ZRMrzZmZrePzIUysUoYztgxTzSmnpCt+8N6kM1jkmX3OBZYQHLK/D3y2pDoPBe4wszVm9jRhXoDXlHTtZ2g0csRpD0nFPjUFZ501d/L1eJL0Vk04ZWXPzFPwHirqDBJ5yn+xmR1pZmcC7wBeW1Kdq4FXSxqTJGApcGtJ136G2G7sdJZ6in1qKsTmx8kO1q4tx3Zf1qjeLAWflTDOcfqVPOX/zNTWZraxrArN7GrgQkKyuBsjGVaUdf2YNPOC0ziN3sf584N5p0zFXoSyRvWmjRGQtiSMc5xBIU/5v1TSw1F5BNg3Xpb0cCuVmtknzOxFZvYSMzvKzJ5s5XppuM2/HJ58MkTc1E6GkkVsbjnhhM6GSpaVPTMtF89554VIJMcZJOpG+/QCS5YssZUrVzZ0TlFlVWXie5T1CNRGtyQnLR8bCy/YLN+KNPe6Y2PtDc+snVe3E3U6Tq/TVLSPM9jMmwcjI9mKP20QVXLg1IYNYXBW1ku29rrtzqrp2TMdpzEGVvnvvnu3JehN4hb5rruG5GhpjI8XV5yNmFXaHSrp2TMdpzgDq/zvuafbEvQmsbLOU8SN+EuyHKR5dTuO030GVvnvs0+3Jeg9kqacPEXciIkmzdxy/PGeVdNxep2BVf633NJtCXqLWlNOWos9SSMmmlpzy/Llbn93nF6nyGQuzgBw2mlbD7YCOPro9DTKrZpopqZc2TtOLzOwLX9nLmlmnKkpOPdcN9E4ThVx5T9A5M2nm2XG8RBJx6kmbvYZAJIx9YsW+fy1juPUZ2Bb/lWJ808mUoPG569tJBumz27lOIPDwCr/qsT5P/LI3Dw6p5wSnLhFzThFs2GWlTLZcZzeYGBz+0C2CWTQaCWPztBQeooHaW7enqx76bNbOU7vUtncPlWJWGklj07RbJhlpUx2HKc3GGjl308RK2VnIa1Vyln2+qI+grJSJjuO0xsMtPLvBcbHw+QmtY7ZWjZvbm72sSJ5dPLs9UVDPRt1JDuO09sMtM0fwsxSjz5askANMDwMG6N50HbdFdatSz9uchL23BMuvzz/eqOjsMMOsH59UPCHHx4GauXlsS/LXp/M5z8xERR/P/WuHKdq5Nn8B1755yncXmNsLCjq2rxEw8OhZ5ClcOsp5aJOXcdxBos85T/wg7zWr++2BMV57DG4tWYq+yKRO/Xy6ExM+MAvx3HmMvA2/35TcO2YAcvt9Y7j1NIV5S9pZ0kXSrpN0q2SDmhXXYcf3q4rd45Wwyk9f4/jOLV0y+xzGvBdM3uHpFEgJ7N8a1x6abuuDNtv39isV81SRu/FUyw7jpOk4y1/STsCBwFfATCzp8zsoXbV165BSDvv3Fxs/uho9r5589w84zhOZ+iG2ef5wBrgbEnXSvqypHm1B0k6TtJKSSvXrFnTdGXtsPmPj8NOO22dEydmbCzE9i9dOnf70qXw5JNhX5qSP/NMN884jtMZuqH8R4D9gdPN7GXAo8DHag8ysxVmtsTMlixcuLDpyupNV9gM69bl9yhihX3ZZcGBOzMTFPkVV4RQzviYNCVfOyWiK37HcdpBN5T/3cDdZnZ1tH4h4WXQFpLOTigvjULWdSYn5yrsrNG14ErecZzu0XHlb2b3AXdJ2jvatBRo63TrcWt6cjJ9sFMzpA2OSrPPF02Z7DiO00m6Fe3zQWA2ivT5NXBMJyptR3rneqNvPRum4zi9SFeUv5ldB6QOOW4nw8OwaVO519y8OT9Fgo+udRynFxn4Eb6wJZ1x2YofYMGC/P0+utZxnF5k4JV/0uHaDh55JH8qw9rRtePjYXDYUUf5PLiO43SPgVf+aQ7XMnnqqfrO29jhfN55YUTwunWtzYPrE6k7jtMqA5/SOSudMWzxAbTqCyiaGrmMvPpxTyYvf7/jOA5UeA5fyHasTk6GSVbMwmc8DqDMOmopI/LHQ0cdxymDgVf+RR2uzYZeNuK8LWMeXA8ddRynDAZe+RdNZ9yIAo7n2m00904ZkT8+kbrjOGUw8MofiuXLWbYsP/XD8DBMT28xE5k1npahjLz6HjrqOE4ZVEL5F2FqCo4/fusXQJyhc+NGWL68nHpayenjE7M4jlMGAx/t0yj1JkN3HMfpFyo9gXuj+IxXjuNUATf7OI7jVBBX/jX46FnHcaqAm30S1I6eTU684qYgx3EGCW/5J/DRs47jVAVX/gl89KzjOFWhsso/zbbvo2cdx6kKlVT+WZOqH364j551HKcaVFL5Z9n2L73UR886jlMNKjnCNyvHf9G8/I7jOP1ApfP5p+G2fcdxqk7XlL+kYUnXSrqk03V7ZkzHcapON1v+JwK3dqNiz4zpOE7V6Yryl7QH8Ebgy92oH1pPrew4jtPPdKvl/wXgI0Cme1XScZJWSlq5Zs2ajgnmOI5TBTqu/CW9CXjAzK7JO87MVpjZEjNbsnDhwqbr80RtjuM4W9ONxG4HAm+WdDiwHbCjpBkzO7LsijxRm+M4TjpdjfOXdAhwspm9Ke+4ZuP8Fy0KCr+Wyclg53ccxxlkKhvn74naHMdx0umq8jezK+u1+lvBB3M5juOkM9Atfx/M5TiOk85AK38fzOU4jpPOwE/jODXlyt5xHKeWgW75O47jOOm48nccx6kgrvwdx3EqiCt/x3GcCuLK33Ecp4L0xTSOktYAKYka+oJdgbXdFqJJXPbu4LJ3h0GUfdLMUjNj9oXy72ckrczKrdHruOzdwWXvDlWT3c0+juM4FcSVv+M4TgVx5d9+VnRbgBZw2buDy94dKiW72/wdx3EqiLf8HcdxKogrf8dxnAriyr8kJJ0l6QFJNyW2LZD0A0m3R5+7dFPGLCQ9V9IPJd0q6WZJJ0bbe15+SdtJ+pmk6yPZPxlt73nZYyQNS7pW0iXRel/ILulOSTdKuk7SymhbX8gOIGlnSRdKui169g/oB/kl7R3d87g8LOmkRmV35V8e5wCH1Wz7GHC5mb0QuDxa70U2Ah82sxcDrwY+IGkx/SH/k8DrzeylwH7AYZJeTX/IHnMicGtivZ9kf52Z7ZeIMe8n2U8DvmtmLwJeSvgNel5+M/tldM/3A14OPAZ8g0ZlNzMvJRVgEXBTYv2XwG7R8m7AL7stY8Hv8R/AG/pNfmAM+AXwqn6RHdgj+qO+Hrikn54b4E5g15pt/SL7jsAdREEv/SZ/Qt7fBX7cjOze8m8vzzazewGiz2d1WZ66SFoEvAy4mj6RPzKbXAc8APzAzPpGduALwEeAzYlt/SK7Ad+XdI2k46Jt/SL784E1wNmRye3LkubRP/LH/BFwfrTckOyu/J1nkDQf+Dpwkpk93G15imJmmyx0gfcAXinpJV0WqRCS3gQ8YGbXdFuWJjnQzPYHfp9gKjyo2wI1wAiwP3C6mb0MeJQeNPHkIWkUeDPw782c78q/vdwvaTeA6POBLsuTiaRtCIp/1swuijb3jfwAZvYQcCXB99IPsh8IvFnSncC/Aa+XNEN/yI6Z/Sb6fIBgc34lfSI7cDdwd9RLBLiQ8DLoF/khvHR/YWb3R+sNye7Kv71cDBwdLR9NsKX3HJIEfAW41cw+l9jV8/JLWihp52h5e+BQ4Db6QHYz+0sz28PMFhG671eY2ZH0geyS5knaIV4m2J5vog9kBzCz+4C7JO0dbVoK3EKfyB/xbraYfKBB2X2Eb0lIOh84hJBa9X7gE8A3gQuACWA18E4zW98lETOR9L+B/wRuZIvt+a8Idv+ell/SvsC5wDChMXOBmf2NpHF6XPYkkg4BTjazN/WD7JKeT2jtQzChfNXMlvWD7DGS9gO+DIwCvwaOIXqG6HH5JY0BdwHPN7PfRtsauveu/B3HcSqIm30cx3EqiCt/x3GcCuLK33Ecp4K48nccx6kgrvwdx3EqiCt/Z6CQNJ7IdnifpHsS66NdkulKSX05MbgzuIx0WwDHKRMzW0fI7omkU4ENZvaZeL+kETPb2B3pHKd38Ja/M/BIOkfS5yT9EPi0pFMlnZzYf1OU0A5JR0bzA1wn6UxJwzXX+n1JFyTWD5H0rWj5dEkrk/MKpMiyIbH8DknnRMsLJX1d0s+jcmC0/eBEz+XaeFSt47SKK3+nKuwFHGpmH846QNKLgT8kJCzbD9gETNUc9gPg1VFKA6LjvxYtn2Ihr/2+wMHR6OOinAZ83sxeAbydMPIU4GTgA5E8rwUeb+CajpOJm32cqvDvZrapzjFLCZNj/DykO2J7apJjmdlGSd8FjpB0IfBGQkpmgHdFqY1HCPnUFwM3FJTvUGBxVC/AjlEr/8fA5yTNAheZ2d0Fr+c4ubjyd6rCo4nljczt9W4XfQo418z+ss61vgZ8AFgP/NzMHpH0PEIr/RVm9mBkztku5dxkPpXk/iHgADOrbdl/StK3gcOBn0o61MxuqyOf49TFzT5OFbmTkL4XSfsDz4u2Xw68Q9Kzon0LJE2mnH9ldP772WLy2ZHwgvmtpGcT0u2mcb+kF0saAv4gsf37wJ/FK1HSMSS9wMxuNLNPAyuBFzX0TR0nA1f+ThX5OrAgmv1rGvgVgJndAnycMDvVDQT7/m61J0fmo0sICv6SaNv1wLXAzcBZBHNNGh+LzrkCuDex/UPAEkk3SLoFOD7aflLkkL6eYO//TpPf2XHm4Fk9HcdxKoi3/B3HcSqIK3/HcZwK4srfcRyngrjydxzHqSCu/B3HcSqIK3/HcZwK4srfcRyngvx/sj+RgCOjhUEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predictions = model.predict(train_descriptors_PCA)\n",
+    "plt.plot(train_crystals[target], predictions, \"bo\")\n",
+    "plt.xlabel(\"True values\")\n",
+    "plt.ylabel(\"Predicted values\")\n",
+    "plt.title(\"Predicted z-value against true values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informal-award",
+   "metadata": {},
+   "source": [
+    "### Cross-validation to evaluate the model's performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "musical-flash",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average negative MAE: -3.258\n",
+      "Standard deviation: 0.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "cv_scores = model_selection.cross_val_score(\n",
+    "    model, \n",
+    "    train_descriptors_PCA, \n",
+    "    train_crystals[target], \n",
+    "    scoring=\"neg_mean_absolute_error\")\n",
+    "\n",
+    "print(\"Average negative MAE:\", round(np.mean(cv_scores), 3))\n",
+    "print(\"Standard deviation:\", round(np.std(cv_scores), 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "based-invitation",
+   "metadata": {},
+   "source": [
+    "### Outputting predictions for automated checking \n",
+    "Link: https://github.com/stevenkbennett/fons_datathon_testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "legislative-region",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(test_descriptors_PCA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "original-smell",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"task_x_predictions.csv\", predictions)\n",
+    "# Alternatively, if using pandas, you can use the following:\n",
+    "pd.DataFrame(predictions).to_csv(\"task_x_predictions.csv\", header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "distant-museum",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "trained-moses",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/LICENSE b/LICENSE
index 3cbef7b..9688b06 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Andrew Tarzia
+Copyright (c) 2021 Imperial-CMTH
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/Milan/cell_volume.ipynb b/Milan/cell_volume.ipynb
new file mode 100644
index 0000000..b0ab0c7
--- /dev/null
+++ b/Milan/cell_volume.ipynb
@@ -0,0 +1,862 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd \n",
+    "import sklearn as sk \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_crystals = pd.read_csv('data/train_crystals.csv')\n",
+    "df_distances = pd.read_csv('data/train_distances.csv')\n",
+    "df_cent_distances = pd.read_csv('data/train_centroid_distances.csv') \n",
+    "\n",
+    "df_inter = df_crystals.join(df_distances.set_index('identifiers'), on='identifiers')\n",
+    "df = df_inter.join(df_cent_distances.set_index('identifiers'),\n",
+    "                        on='identifiers', rsuffix='_centroid') \n",
+    "\n",
+    "df.head() \n",
+    "df['mean_centroid']\n",
+    "\n",
+    "train = pd.read_csv('data/train_descriptors.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>identifiers</th>\n",
+       "      <th>n_heavy_atoms</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>gamma</th>\n",
+       "      <th>z_value</th>\n",
+       "      <th>...</th>\n",
+       "      <th>percentile_25</th>\n",
+       "      <th>percentile_75</th>\n",
+       "      <th>min_centroid</th>\n",
+       "      <th>max_centroid</th>\n",
+       "      <th>mean_centroid</th>\n",
+       "      <th>mode_centroid</th>\n",
+       "      <th>median_centroid</th>\n",
+       "      <th>std_centroid</th>\n",
+       "      <th>percentile_25_centroid</th>\n",
+       "      <th>percentile_75_centroid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>COXSUE</td>\n",
+       "      <td>12</td>\n",
+       "      <td>CC(=O)NN=C(C)C1=NCCS1</td>\n",
+       "      <td>4.7362</td>\n",
+       "      <td>17.0760</td>\n",
+       "      <td>11.4150</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>94.432</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.939994</td>\n",
+       "      <td>10.352198</td>\n",
+       "      <td>4.736200</td>\n",
+       "      <td>9.147929</td>\n",
+       "      <td>7.426769</td>\n",
+       "      <td>4.736200</td>\n",
+       "      <td>8.088933</td>\n",
+       "      <td>1.563737</td>\n",
+       "      <td>6.512305</td>\n",
+       "      <td>8.652986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TUWFAU</td>\n",
+       "      <td>15</td>\n",
+       "      <td>CC(C)N(C)c1ccc(cc1C#N)C#N</td>\n",
+       "      <td>8.9832</td>\n",
+       "      <td>12.6212</td>\n",
+       "      <td>9.6863</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>92.414</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.603302</td>\n",
+       "      <td>10.985316</td>\n",
+       "      <td>4.751947</td>\n",
+       "      <td>11.683526</td>\n",
+       "      <td>8.171218</td>\n",
+       "      <td>7.056717</td>\n",
+       "      <td>8.596524</td>\n",
+       "      <td>1.894844</td>\n",
+       "      <td>7.056717</td>\n",
+       "      <td>9.235177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DUTHEG</td>\n",
+       "      <td>14</td>\n",
+       "      <td>CC1=CC(=O)N2C(=NC(=NC2=N1)C)N</td>\n",
+       "      <td>11.1369</td>\n",
+       "      <td>18.9130</td>\n",
+       "      <td>4.0311</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.790356</td>\n",
+       "      <td>10.413188</td>\n",
+       "      <td>4.031100</td>\n",
+       "      <td>10.543597</td>\n",
+       "      <td>7.449690</td>\n",
+       "      <td>4.031100</td>\n",
+       "      <td>7.034673</td>\n",
+       "      <td>2.328665</td>\n",
+       "      <td>5.765142</td>\n",
+       "      <td>9.435290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AXUDED08</td>\n",
+       "      <td>10</td>\n",
+       "      <td>COC(=O)c1ccccc1</td>\n",
+       "      <td>13.1840</td>\n",
+       "      <td>5.9231</td>\n",
+       "      <td>16.9510</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.337268</td>\n",
+       "      <td>9.426187</td>\n",
+       "      <td>4.032894</td>\n",
+       "      <td>9.448610</td>\n",
+       "      <td>6.808171</td>\n",
+       "      <td>4.423993</td>\n",
+       "      <td>6.079166</td>\n",
+       "      <td>1.978778</td>\n",
+       "      <td>5.173547</td>\n",
+       "      <td>8.653620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ARAGUV</td>\n",
+       "      <td>10</td>\n",
+       "      <td>CC(=O)NC1=NNC(=C1)C</td>\n",
+       "      <td>18.6000</td>\n",
+       "      <td>19.9320</td>\n",
+       "      <td>7.7992</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.557215</td>\n",
+       "      <td>9.660297</td>\n",
+       "      <td>5.266146</td>\n",
+       "      <td>10.701777</td>\n",
+       "      <td>7.114870</td>\n",
+       "      <td>5.266146</td>\n",
+       "      <td>6.403891</td>\n",
+       "      <td>1.997464</td>\n",
+       "      <td>5.539808</td>\n",
+       "      <td>8.313428</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  identifiers  n_heavy_atoms                         smiles        a        b  \\\n",
+       "0      COXSUE             12          CC(=O)NN=C(C)C1=NCCS1   4.7362  17.0760   \n",
+       "1      TUWFAU             15      CC(C)N(C)c1ccc(cc1C#N)C#N   8.9832  12.6212   \n",
+       "2      DUTHEG             14  CC1=CC(=O)N2C(=NC(=NC2=N1)C)N  11.1369  18.9130   \n",
+       "3    AXUDED08             10                COC(=O)c1ccccc1  13.1840   5.9231   \n",
+       "4      ARAGUV             10            CC(=O)NC1=NNC(=C1)C  18.6000  19.9320   \n",
+       "\n",
+       "         c  alpha    beta  gamma  z_value  ...  percentile_25 percentile_75  \\\n",
+       "0  11.4150   90.0  94.432   90.0      4.0  ...       5.939994     10.352198   \n",
+       "1   9.6863   90.0  92.414   90.0      4.0  ...       6.603302     10.985316   \n",
+       "2   4.0311   90.0  90.000   90.0      4.0  ...       5.790356     10.413188   \n",
+       "3  16.9510   90.0  90.000   90.0      8.0  ...       5.337268      9.426187   \n",
+       "4   7.7992   90.0  90.000   90.0     16.0  ...       5.557215      9.660297   \n",
+       "\n",
+       "   min_centroid  max_centroid  mean_centroid  mode_centroid  median_centroid  \\\n",
+       "0      4.736200      9.147929       7.426769       4.736200         8.088933   \n",
+       "1      4.751947     11.683526       8.171218       7.056717         8.596524   \n",
+       "2      4.031100     10.543597       7.449690       4.031100         7.034673   \n",
+       "3      4.032894      9.448610       6.808171       4.423993         6.079166   \n",
+       "4      5.266146     10.701777       7.114870       5.266146         6.403891   \n",
+       "\n",
+       "   std_centroid  percentile_25_centroid  percentile_75_centroid  \n",
+       "0      1.563737                6.512305                8.652986  \n",
+       "1      1.894844                7.056717                9.235177  \n",
+       "2      2.328665                5.765142                9.435290  \n",
+       "3      1.978778                5.173547                8.653620  \n",
+       "4      1.997464                5.539808                8.313428  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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>ABC</th>\n",
+       "      <th>ABCGG</th>\n",
+       "      <th>nAcid</th>\n",
+       "      <th>nBase</th>\n",
+       "      <th>SpAbs_A</th>\n",
+       "      <th>SpMax_A</th>\n",
+       "      <th>SpDiam_A</th>\n",
+       "      <th>SpAD_A</th>\n",
+       "      <th>SpMAD_A</th>\n",
+       "      <th>LogEE_A</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SRW10</th>\n",
+       "      <th>TSRW10</th>\n",
+       "      <th>MW</th>\n",
+       "      <th>AMW</th>\n",
+       "      <th>WPath</th>\n",
+       "      <th>WPol</th>\n",
+       "      <th>Zagreb1</th>\n",
+       "      <th>Zagreb2</th>\n",
+       "      <th>mZagreb1</th>\n",
+       "      <th>mZagreb2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.773011</td>\n",
+       "      <td>8.632564</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.599164</td>\n",
+       "      <td>2.244739</td>\n",
+       "      <td>4.388162</td>\n",
+       "      <td>14.599164</td>\n",
+       "      <td>1.216597</td>\n",
+       "      <td>3.370933</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.764834</td>\n",
+       "      <td>54.650723</td>\n",
+       "      <td>185.062283</td>\n",
+       "      <td>8.046186</td>\n",
+       "      <td>219</td>\n",
+       "      <td>11</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4.833333</td>\n",
+       "      <td>2.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.813451</td>\n",
+       "      <td>10.346393</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.488707</td>\n",
+       "      <td>2.357869</td>\n",
+       "      <td>4.715737</td>\n",
+       "      <td>18.488707</td>\n",
+       "      <td>1.232580</td>\n",
+       "      <td>3.592641</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.421492</td>\n",
+       "      <td>46.551655</td>\n",
+       "      <td>199.110947</td>\n",
+       "      <td>7.111105</td>\n",
+       "      <td>364</td>\n",
+       "      <td>22</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>6.805556</td>\n",
+       "      <td>3.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.922841</td>\n",
+       "      <td>9.584172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.685332</td>\n",
+       "      <td>2.436274</td>\n",
+       "      <td>4.872549</td>\n",
+       "      <td>16.685332</td>\n",
+       "      <td>1.191809</td>\n",
+       "      <td>3.577079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.693445</td>\n",
+       "      <td>46.214439</td>\n",
+       "      <td>191.080710</td>\n",
+       "      <td>8.307857</td>\n",
+       "      <td>271</td>\n",
+       "      <td>22</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>5.666667</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.140017</td>\n",
+       "      <td>6.873356</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.857279</td>\n",
+       "      <td>2.214320</td>\n",
+       "      <td>4.428639</td>\n",
+       "      <td>12.857279</td>\n",
+       "      <td>1.285728</td>\n",
+       "      <td>3.188884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.703009</td>\n",
+       "      <td>38.792641</td>\n",
+       "      <td>136.052429</td>\n",
+       "      <td>7.558468</td>\n",
+       "      <td>121</td>\n",
+       "      <td>11</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3.722222</td>\n",
+       "      <td>2.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.399237</td>\n",
+       "      <td>7.308033</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.638727</td>\n",
+       "      <td>2.249778</td>\n",
+       "      <td>4.311276</td>\n",
+       "      <td>11.638727</td>\n",
+       "      <td>1.163873</td>\n",
+       "      <td>3.200300</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.616314</td>\n",
+       "      <td>52.270726</td>\n",
+       "      <td>139.074562</td>\n",
+       "      <td>7.319714</td>\n",
+       "      <td>125</td>\n",
+       "      <td>8</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>4.333333</td>\n",
+       "      <td>2.250000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1613 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         ABC      ABCGG  nAcid  nBase    SpAbs_A   SpMax_A  SpDiam_A  \\\n",
+       "0   8.773011   8.632564      0      0  14.599164  2.244739  4.388162   \n",
+       "1  10.813451  10.346393      0      0  18.488707  2.357869  4.715737   \n",
+       "2  10.922841   9.584172      0      0  16.685332  2.436274  4.872549   \n",
+       "3   7.140017   6.873356      0      0  12.857279  2.214320  4.428639   \n",
+       "4   7.399237   7.308033      0      0  11.638727  2.249778  4.311276   \n",
+       "\n",
+       "      SpAD_A   SpMAD_A   LogEE_A  ...     SRW10     TSRW10          MW  \\\n",
+       "0  14.599164  1.216597  3.370933  ...  8.764834  54.650723  185.062283   \n",
+       "1  18.488707  1.232580  3.592641  ...  9.421492  46.551655  199.110947   \n",
+       "2  16.685332  1.191809  3.577079  ...  9.693445  46.214439  191.080710   \n",
+       "3  12.857279  1.285728  3.188884  ...  8.703009  38.792641  136.052429   \n",
+       "4  11.638727  1.163873  3.200300  ...  8.616314  52.270726  139.074562   \n",
+       "\n",
+       "        AMW  WPath  WPol  Zagreb1  Zagreb2  mZagreb1  mZagreb2  \n",
+       "0  8.046186    219    11     54.0     58.0  4.833333  2.777778  \n",
+       "1  7.111105    364    22     70.0     80.0  6.805556  3.583333  \n",
+       "2  8.307857    271    22     74.0     87.0  5.666667  3.000000  \n",
+       "3  7.558468    121    11     44.0     48.0  3.722222  2.444444  \n",
+       "4  7.319714    125     8     46.0     49.0  4.333333  2.250000  \n",
+       "\n",
+       "[5 rows x 1613 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = train.drop(['identifiers', 'name', 'InchiKey', 'SMILES', 'Unnamed: 0'], axis=1) \n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import IterativeImputer, SimpleImputer\n",
+    "\n",
+    "imp = SimpleImputer(strategy=\"most_frequent\")\n",
+    "Y = df['cell_volume'].values\n",
+    "imp.fit(X,Y)\n",
+    "X = imp.transform(X) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import RidgeCV\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# X = X[:10]\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= .3, random_state=69) \n",
+    "\n",
+    "\n",
+    "plt.hist(df['z_prime'], bins=20)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.feature_selection import SequentialFeatureSelector\n",
+    "# from sklearn.neighbors import KNeighborsClassifier\n",
+    "# from sklearn.datasets import load_iris\n",
+    "\n",
+    "# X, y = load_iris(return_X_y=True)\n",
+    "# knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "# sfs = SequentialFeatureSelector(knn, n_features_to_select=3)\n",
+    "# sfs.fit(X, y)\n",
+    "\n",
+    "\n",
+    "# sfs.get_support()\n",
+    "\n",
+    "# sfs.transform(X).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RidgeCV(alphas=array([ 0.1,  1. , 10. ]), cv=5, fit_intercept=True,\n",
+       "        gcv_mode=None, normalize=False, scoring=None, store_cv_values=False)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Train the model\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, Y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-deep')\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "def evaluation(model, X_test, y_test):\n",
+    "    prediction = model.predict(X_test)\n",
+    "    print(prediction.shape)\n",
+    "    mae = mean_absolute_error(y_test, prediction)\n",
+    "    mse = mean_squared_error(y_test, prediction)\n",
+    "    \n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "    plt.scatter(prediction, y_test, c='red', label=\"should be a line\", s=5, alpha=0.8)\n",
+    "#     plt.scatter(y_test, 'blue', label=\"actual\", s=5, alpha=0.6)\n",
+    "    plt.legend()\n",
+    "    plt.ylabel('cell_volume')\n",
+    "    plt.title(\"MAE {}, MSE {}\".format(round(mae,4), round(mse,4)))\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print('MAE score:', round(mae/(np.mean(y_test)), 4))\n",
+    "    print('MSE score:', round(mse,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4035,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE score: 0.353\n",
+      "MSE score: 241842.569\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluate results\n",
+    "evaluation(ridge, X_test, Y_test)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D1qZ6GuunFUnT+RCpcgsmfU9Jr/awLPxy4N3AN8P2h4D3hbe3hzHh/ddZahB5O/CIu4+7+1HgMHBFUY5CFmBsfEsriYRxydpW0MnIrJ0ZTDI2/uZfSWcGk2XeI5HCZDWwa2Y1ZvZz4DSwD/g1MOju6QHOXmBteHstcAIgvH8IWDm9fY6fmf67bjOz/Wa2v6+vL/cjklmGzo7zq2MDBIFzqGdAV9rm4NXXRyLjXE1OBvScHCIIgoIeRyRfWZ3Idfcp4G1m1gZ8G9gy12bh97m6kR7Rnvm79gB7ALq6urS2XxG0NNZRX1fDWHKS+roaWhrryr1LVeOClQ2RcS4mJwM+uPO7OqEuZZXTK87dB4GngSuBNjNLf2isA14Nb/cCFwOE97cC/dPb5/gZWUTDYxMzTuQOj02UeY+qx7FXRiLjXBw/NTzjeTiuC72kDLKZvbMq7OFjZsuB3wW6gaeAPwg32wE8Ft7eG8aE9//AUwVf9gLvD2f3bAQ2AT8p1oHI/JoalpGuupCwVCzZ+ZdbVkfGuWhtqouMRUohm57+GuApM3sBeA7Y5+7fAT4DfNrMDpMas38g3P4BYGXY/mngDgB3PwA8ChwEvgfcHg4bySI7cXqEIBwoCzwVzyebwmxxMpIxLz8zzkVbUwMNdam3XENdgjZN/5QyWHBM391fAN4+R/sR5ph94+5J4OZ5Huse4J7cd1MKkW0PU/X0Zwsy6udnxrkYGEmSnEidwE1OBAyMJLmgrbGg/RPJlc4ixUBbU8OMefrz9TBVT3+24XPjkXEuRjLOpWTGIqWgpB8DQ6PjnBtPjaSdG59iaHTuxKXCbLOtWL4sMs7FhotaaGx480KvDRe1FLRvIvlQ7Z0YmMoYksiM01RPf7ZXTo3Oii/qyG8hlUQiwcN33Uhv3wjrVzeTSKjPJaWnV10MjCYnIuPpVE9/piBjrkFmnKva2gSda1qV8KVs9MqLgbUXNEXGMr/Mv4k0p0mqnZJ+DLx65mxkLPOrq6mNjEWqjZJ+DFzU3hgZy/x+69ILImORaqOkHwMv9Q5GxjK/qSlIn94wS8Ui1UxJPwY2rW2NjGV+v/z166QvTnZPxSLVTEk/Bn51fCAylvlZwiNjkWqjpB8DjQ01kbHMb2vnyshYpNoo6cfAG4FHxjK/k/1jkbFItVHSj4GeV4YiY5nf+tXNM0onaH1hqXaadBwDrSuWRcYyv2KXTggCV5kLKSv19GPg4JG+yFiiJRJGaxGSdLp09S27nuDO+58h0DCblIF6+jHw696ByFjmV8w1BuYqXd3erIVUpLTU04+B1wfHImOZXzHXGFDpaqkE6unHwPgb0bHMr62pns2dHXT39LO5wESt0tVSCdTTj4GWhuhY5ucO7j7tq7DHU+lqKTcl/RgYTUbHMr/BkSQHj/bjDgeP9jM4ov88qW5K+jEwcC46lgiZHXJ10KXKKemLRGhdUU9DXept0lCXoHWFTr5KdVPSL6IgcAZGknihA79SMQZHx0lOBAAkJwIG51lUXqRaaPZOkRRzPvf0x9RMj/LK/G/X0yDVTj39IinmfG7Q1ZuVor25gW2XrCSRMLZdslIXU0nVWzDpm9nFZvaUmXWb2QEz+2TY3mFm+8zs5fB7e9huZvZlMztsZi+Y2eXTHmtHuP3LZrZj8Q4rO8Ucjin2hTfF/hCR/JgZn//oVfzVp/8V937sqqKUYtAQoJRTNsM7k8B/c/efmlkz8LyZ7QM+Ajzp7veZ2R3AHcBngBuBTeHXO4DdwDvMrAPYCXQBHj7OXncvS02AYg/HFPvCm/SHSHr/dPVmeQSB88d//ebr5N6PX5P362QxhgBFcrVg0nf3k8DJ8PaImXUDa4HtwLXhZg8BT5NK+tuBr3qqK/MjM2szszXhtvvcvR8g/OC4Afh6EY8na4tRByV94U0x6OrNyjAwkuTA0X4ADhztZ2AkycrW5Xk9lmrvSCXIaUzfzDqBtwM/BlaHHwjpD4YLw83WAiem/Vhv2DZfe+bvuM3M9pvZ/r6+xasGWQ11UHT1ZgXIHIUpYFSmGl5zsvRlPXvHzJqAbwGfcvfhiEQ01x0e0T6zwX0PsAegq6tr0QY+F6MnvRRm2yyFYyiu4mV9/fUmlSCrpG9my0gl/Ifd/R/C5lNmtsbdT4bDN6fD9l7g4mk/vg54NWy/NqP96fx3vXDFHI5ZClM2NeY8m1t0nKtivuZE8pHN7B0DHgC63f2L0+7aC6Rn4OwAHpvW/uFwFs+VwFA4/PN94Hozaw9n+lwfti0JizFl887dz/CRXU/w2RJN2dSModkSGR+2mbFItclmTP9q4EPAu83s5+HXTcB9wO+Z2cvA74UxwOPAEeAw8LfAxwHCE7h3A8+FX7vSJ3WXgmKP1w6OJDlw5AxB4Bw4cqYkhb405jyb5unLUpPN7J0fMn+Zqevm2N6B2+d5rAeBB3PZwWpR9PHaMhT60pjzbGbGvfo/kSVEV+RWqPbmBrZt7CBhsG1jR8l6mJoxNJv+T2QpUe2dIlmMi73u/fg16mFWAM1okqVESb9IdOHN0qQZTbLUaHinSIp9ElQF1yrDYszKUu0dKSf19IvEzLj7o1fR2zfC+tXNBQ8D6C+HylDMGkj6q0EqgZJ+kQSBc+f9/0z3sUG2bGjjvk+8q6A3dFtTPZdtaOfQsQE2d7Zr+mSZFHNGkz7IpRJoeKdI+obG6D42CED3sUH6hsYKerypKefoq0MEgXPklSGmpjQcUC6TkwEnTo0QBEFBj6PrIKQSqKdfJL2nR2bFq9tX5P94fSOcG58C4Nz4FL19I3SuaS1oHyV3ExNT3HzndwgcEgZ/f+97qauryeuxij0EKJIP9fSLpHn5ssg4V+tWNZEeHUpYKpbSO3S8n/Q59MBTcb6CwPmTv3mWT33xH/nc7md1cl7KQkm/SCangsg4VyPn3jjfEzQzRs69UdDjSX7SF8gB5y+Uy5dqG0kl0PBOkbx0on9WvHXjqrwf7/yJ3J4BNm/Qidxyqamp4e/vfS+HjvezbWMHNTX5De0AtDTWsazWmJpwltUaLY11RdxTkeyop18kw8PJyDhXb7wRcPBoP4E7B47288Ybhf3lIPmrq6vhrZeuKijhA/SPJElOpJ7H5ERAfwmK6IlkUtIvkqt+6y2Rca4OHjsTGUv1GR2biIxFSkFJv0ieffG1yDhXay9YERlL9dlwUQuNDakR1caGWjZc1FLmPZI40ph+FrIpuLWi0SLjXCUsERlL9UkkEjx8143np2wmEnpOpfT0qltAtjVw9j17NDLOVWaOL1XOV22YxVVbm6BzTasSvpSNevoLyPbS+bGJIDLOVeuKepbX13BufIrl9TW0rlj82TvpJRrTtWHuVW0YkSVH3Y0FZHvp/MDZ6DhXQ6PjM67IHRpd/Dnd5ViiUURKSz39BZRrCcEgY3glM14MmUM6GuIRWXqU9LOQXi6vlCxjUdzMeFF+Z8ZQTmYsItVPwzsVK7OXvfi97vbmBrZu7MAMtpZwXV4RKR0l/QpVzF53Q010nOYOxptfGt0RWXqU9CtU8/K68wM6Fsb5Sk5Fx2lDZ8c5dGwgVU3y2IAKgoksQUr6FeqV10fPD+h4GOcrs8jzfEWftciHyNKnE7kVKl1PP714RyH19DM79vN09Ms2U0lESmfBnr6ZPWhmp83sl9PaOsxsn5m9HH5vD9vNzL5sZofN7AUzu3zaz+wIt3/ZzHYszuEsjnJcpTp8dmLG4h3DZ/MvzrWmPTqeLj1TSQlfZGnKZnjn/wA3ZLTdATzp7puAJ8MY4EZgU/h1G7AbUh8SwE7gHcAVwM70B0Wly7YMQ9Fl5twCcnBDXV1kLCLxsWDSd/d/AjLXiNsOPBTefgh437T2r3rKj4A2M1sDvAfY5+797j4A7GP2B0lFGjo7zsGjZ5gKnINHz5Ts5GZ7cwNbO9sxYGtne0HTJy9a2RQZi0h85Hsid7W7nwQIv18Ytq8FTkzbrjdsm699FjO7zcz2m9n+vr6+PHcvO5OTAT0nhwiC+evktDTW0VCfOvXRUF9bstWO3MESCSxhWCJR0PTJ+vpEZCwi8VHsE7lzDUJ4RPvsRvc9wB6Arq6uRRtLmZwM+I9/+vj5gmZf23UTtbWzk+Hw2ATjE6lTn+MTUwyPTZTkoqWhs+N0Hz1D4NAd/oWR7+8dy1hfNzMWkfjIt8t3Khy2Ifx+OmzvBS6ett064NWI9rI59trQjIJmx14bmnO7ck1jbGpYFhnn4sjJkchYROIj357+XmAHcF/4/bFp7Z8ws0dInbQdcveTZvZ94N5pJ2+vBz6b/24XbjJjSCczTjMz7v7oVecXvijVrJZXXh+dMXvnlddH6VzTmtdjXbquhdNDgzNiEYmnBZO+mX0duBa4wMx6Sc3CuQ941MxuBY4DN4ebPw7cBBwGxoBbANy938zuBp4Lt9vl7pknh0tqbGwyMk4LAueP/+bZkteYX7+6mcaGWsaSkzQ21LJ+dXPej/X2yy7i2QODM2IRiacFk767f2Ceu66bY1sHbp/ncR4EHsxp7xbRxRc1RcZp6RrzwPka8x2tyxd9/4q5tN7ro+ciYxGJj9hO42hd0RAZn1fE+fK5SiSM1iJcGfvci32RsYjER2yT/tGME7eZcVpmobNCCp/lopgXhb1ja0dkLCLxEdukPzSajIzTjp0ejowXSzEvCpuYqouMJVo213OIVIvYFlybnAoi4zQLouPFkr4obCw5WfBFYVs3tvGtp2fGkp3JyYAP7vzu+RPqD99145zXc4hUiyX76l2od9Z95PXIOK2tpT4yXixzXRSWr7UXtkbGMr9jrw0xlkzN7BpLTs57PYdItViSST/dO/ujv3iaD/zJd5mcnJ349//yRGSc1tbUQENd6r+poS5BW1NplhBsaayjvi61xFV9XU1BPf0XjpyKjGV+mefQVXxUqt2STPrHTw3P6J0dPzV7HL6vP4iM04bHJkhOpO5LTgQF9bhzMTg6PuMYBkfzH9P/xaEzkbHMr3NNK8vrUx++y+tr8r5ATqRSLMkx/damusgYIOnRcVpTw7IZi5kUUg4hF5mzdQqZvXPZ+haeefHUjFiyk0gk+Nqum4pyvYRIJViSr+COluVs6UxVfNjS2U5HS/4XUx1/bWRGOYTjr5Wmbs3ouYnIOBdbL7kwMpZotbUJOte0KuHLkrAkX8XuqZo5iUTqq5CyxE4QGS+WDRe10NiQ+kOssaGWDRfl3ztftqwmMhaR+FiSwzuDI0kOHk2V9jlwpL+g0gntLQ2R8WIpZhmGt2QsmpIZi0h8LMmefjFLJzQ11EXGi6lYZRhe6h2MjEUkPpZk0i/mUoPdx/sj48USBM6du5/hI7ue4LMFlmHYtrGDdGHQhKViEYmnJZn03VPLcp3/KmBM/+JVTZHxYklX9wwCP1/dM19mCS7bkDqxfdmGdsyW5NMuIllYku/+M8Pn6O4ZAKC7Z4Azw/mXEu5oTc0EMoOtG9tLUlYZKOoQ1dDZcV46nhrSeen4YMkWdxeRyrMkT+QOjYzPile1Neb1WGbGfbf/DkNnx2krwvh6ttqbG9i2seP84i2FDFG1NdWzeUM73T39bN7QXrIlH0Wk8izJpD85ORUZ5yqRsJIshj6dmXHvx68pyoeNO6n6AeFXakpr8fZVRKrHkhzeOfbaYGRcLdIfNoX+dTF0dpxDPf0EgXOop1/DOyIxtiST/vPdr0XGuTrY28ctu75HX191rjjV1lTPls4OahLGls4ODe+IxNiSHN75fwfORMa5ONjbx2e+9CwA//m+Z/nCf72KretWFbR/pWZm3POxq0t+XkJEKs+S7OkX0xe+8lxknHbFbzRFxuVWrKEiEaluSvoLuPna9ZFx2qWdF0bGIiKVQEl/ATdcszkyTnv7ZasjYxGRSrAkx/Q3r63j0CsTM+J81dbW8vf3/D4/fekUV2xdTW3t3P9lF7Y3R8YiIpVgSfb0/911WyNjgP9wdVNkPF1DQy1XvXXtvAkfUtU3t27sIJEwtl3SUbJqnCIiuSh5T9/MbgD+CqgBvuLu9xX7d/zGhlWRMcAHtl/LN575zoy4EGbGnxfpYioRkcVS0qRvZjXA/wJ+D+gFnjOzve5+sJi/J10v59CxgdTKWXPUy6mpqWPdsr0AAAR8SURBVOFbf/5eDh3vZ9vGDmpqCl9YpBxX7oqI5KLUPf0rgMPufgTAzB4BtgNFTfrZ1supq6vhrZdW15x7EZFClHpMfy1wYlrcG7adZ2a3mdl+M9tfyBWwmpcuIjJbqZP+XBl4RrV7d9/j7l3u3rVqlXrhIiLFVOqk3wtcPC1eB7xa4n0QEYmtUif954BNZrbRzOqA9wN7S7wPIiKxVdITue4+aWafAL5Pasrmg+5+oJT7ICISZyWfp+/ujwOPl/r3iojIEr0iV0RE5mbuvvBWZWJmfcCxOe66AHi9xLtTbDqGyqBjqAw6huLa4O5zTn+s6KQ/HzPb7+5d5d6PQugYKoOOoTLoGEpHwzsiIjGipC8iEiPVmvT3lHsHikDHUBl0DJVBx1AiVTmmLyIi+anWnr6IiORBSV9EJEaqKumb2Q1m9iszO2xmd5R7f/JhZj1m9qKZ/dzM9pd7f7JlZg+a2Wkz++W0tg4z22dmL4ff28u5jwuZ5xj+zMxeCZ+Pn5vZTeXcx4WY2cVm9pSZdZvZATP7ZNheNc9FxDFUzXNhZg1m9hMz+0V4DHeF7RvN7Mfh8/CNsMZYRamaMf1w1a2XmLbqFvCBYq+6tdjMrAfocvdKuYgjK2b2LmAU+Kq7/2bY9j+Afne/L/wQbnf3z5RzP6PMcwx/Boy6+1+Uc9+yZWZrgDXu/lMzawaeB94HfIQqeS4ijuHfUyXPhaUW6ljh7qNmtgz4IfBJ4NPAP7j7I2b218Av3H13Ofc1UzX19M+vuuXuE0B61S0pAXf/J6A/o3k78FB4+yFSb9yKNc8xVBV3P+nuPw1vjwDdpBYiqprnIuIYqoanjIbhsvDLgXcD3wzbK/J5qKakv+CqW1XCgSfM7Hkzu63cO1Og1e5+ElJvZODCMu9Pvj5hZi+Ewz8VOyySycw6gbcDP6ZKn4uMY4Aqei7MrMbMfg6cBvYBvwYG3X0y3KQic1Q1Jf0FV92qEle7++XAjcDt4ZCDlM9u4F8AbwNOAv+zvLuTHTNrAr4FfMrdh8u9P/mY4xiq6rlw9yl3fxupxaCuALbMtVlp92ph1ZT0l8SqW+7+avj9NPBtUi+WanUqHJ9Nj9OeLvP+5MzdT4Vv3gD4W6rg+QjHkL8FPOzu/xA2V9VzMdcxVONzAeDug8DTwJVAm5mlS9ZXZI6qpqRf9atumdmK8MQVZrYCuB74ZfRPVbS9wI7w9g7gsTLuS17SiTL0b6nw5yM8gfgA0O3uX5x2V9U8F/MdQzU9F2a2yszawtvLgd8ldW7iKeAPws0q8nmomtk7AOEUrr/kzVW37inzLuXEzC4h1buH1AI2X6uWYzCzrwPXkiofewrYCfxf4FFgPXAcuNndK/ZE6TzHcC2p4QQHeoCPpsfGK5GZXQP8M/AiEITNd5IaE6+K5yLiGD5AlTwXZvZWUidqa0h1nh91913he/wRoAP4GfCf3H28fHs6W1UlfRERKUw1De+IiEiBlPRFRGJESV9EJEaU9EVEYkRJX0QkRpT0RURiRElfRCRG/j83XkgJg6p0sQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plt.hist(df['cell_volume'].values, bins=200)\n",
+    "plt.scatter(df['z_value'].values, df['cell_volume'].values, s=5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating two different dataframes which handle small and large values respectively. We then train two separate models and combine them at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  identifiers  n_heavy_atoms        smiles       a       b       c   alpha  \\\n",
+      "0    OXAMID04            6.0  NC(=O)C(N)=O  3.5592  4.8685  5.2934  90.842   \n",
+      "\n",
+      "      beta    gamma  z_value  ...  percentile_25 percentile_75  min_centroid  \\\n",
+      "0  103.472  106.075      1.0  ...       4.271916      6.961343        3.5592   \n",
+      "\n",
+      "   max_centroid  mean_centroid  mode_centroid  median_centroid  std_centroid  \\\n",
+      "0      7.244294       5.298531         3.5592         5.174372      1.138927   \n",
+      "\n",
+      "   percentile_25_centroid  percentile_75_centroid  \n",
+      "0                  4.8685                5.648854  \n",
+      "\n",
+      "[1 rows x 39 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_small = pd.DataFrame().reindex_like(df).dropna()\n",
+    "df_small = pd.DataFrame().reindex(columns=df.columns)\n",
+    "df_large = pd.DataFrame().reindex_like(df)\n",
+    "\n",
+    "for i in range(len(df)):\n",
+    "    if df['cell_volume'].values[i] <= 90:\n",
+    "        df_small.loc[-1] = df.iloc[i]  # adding a row\n",
+    "        df_small.index = df_small.index + 1  # shifting index\n",
+    "\n",
+    "\n",
+    "df_small = df_small.sort_index()  # sorting by index\n",
+    "print(df_small)\n",
+    "\n",
+    "\n",
+    "#         df_small.append( pd.DataFrame(df.iloc[i]) )\n",
+    "#         print(pd.DataFrame(df.iloc[i]))\n",
+    "#         print(df.iloc[i].transpose())\n",
+    "#         df_small.append( df.iloc[i].transpose() )\n",
+    "\n",
+    "        \n",
+    "# df_small.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9414, 1553)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 2 2 ... 1 2 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "import numpy as np\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=4, random_state=69).fit(X_train)\n",
+    "\n",
+    "kmeans.labels_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "(9414, 1553)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(kmeans.labels_[1000])\n",
+    "print(X_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "can't assign to function call (<ipython-input-40-93953f933f5e>, line 5)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-40-93953f933f5e>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    X_train_arrays[kmeans.labels_[i]].append() = X_train[i]\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m can't assign to function call\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train_arrays = np.zeros((4, 9414, 1553))\n",
+    "print(X_train_arrays.shape)\n",
+    "\n",
+    "for i in range(len(X_train)):\n",
+    "    X_train_arrays[kmeans.labels_[i]] = X_train[i] \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9414, 1553)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train_arrays[1].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 1, ..., 1, 1, 2])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Train the model\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, Y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "base"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Milan/centroid_distances_mean.ipynb b/Milan/centroid_distances_mean.ipynb
new file mode 100644
index 0000000..c977ce9
--- /dev/null
+++ b/Milan/centroid_distances_mean.ipynb
@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd \n",
+    "import sklearn as sk "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_crystals = pd.read_csv('data/train_crystals.csv')\n",
+    "df_distances = pd.read_csv('data/train_distances.csv')\n",
+    "df_cent_distances = pd.read_csv('data/train_centroid_distances.csv') \n",
+    "\n",
+    "df_inter = df_crystals.join(df_distances.set_index('identifiers'), on='identifiers')\n",
+    "df = df_inter.join(df_cent_distances.set_index('identifiers'),\n",
+    "                        on='identifiers', rsuffix='_centroid') \n",
+    "\n",
+    "df.head() \n",
+    "df['mean_centroid']\n",
+    "\n",
+    "train = pd.read_csv('data/train_descriptors.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>identifiers</th>\n",
+       "      <th>n_heavy_atoms</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>gamma</th>\n",
+       "      <th>z_value</th>\n",
+       "      <th>...</th>\n",
+       "      <th>percentile_25</th>\n",
+       "      <th>percentile_75</th>\n",
+       "      <th>min_centroid</th>\n",
+       "      <th>max_centroid</th>\n",
+       "      <th>mean_centroid</th>\n",
+       "      <th>mode_centroid</th>\n",
+       "      <th>median_centroid</th>\n",
+       "      <th>std_centroid</th>\n",
+       "      <th>percentile_25_centroid</th>\n",
+       "      <th>percentile_75_centroid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>COXSUE</td>\n",
+       "      <td>12</td>\n",
+       "      <td>CC(=O)NN=C(C)C1=NCCS1</td>\n",
+       "      <td>4.7362</td>\n",
+       "      <td>17.0760</td>\n",
+       "      <td>11.4150</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>94.432</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.939994</td>\n",
+       "      <td>10.352198</td>\n",
+       "      <td>4.736200</td>\n",
+       "      <td>9.147929</td>\n",
+       "      <td>7.426769</td>\n",
+       "      <td>4.736200</td>\n",
+       "      <td>8.088933</td>\n",
+       "      <td>1.563737</td>\n",
+       "      <td>6.512305</td>\n",
+       "      <td>8.652986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TUWFAU</td>\n",
+       "      <td>15</td>\n",
+       "      <td>CC(C)N(C)c1ccc(cc1C#N)C#N</td>\n",
+       "      <td>8.9832</td>\n",
+       "      <td>12.6212</td>\n",
+       "      <td>9.6863</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>92.414</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.603302</td>\n",
+       "      <td>10.985316</td>\n",
+       "      <td>4.751947</td>\n",
+       "      <td>11.683526</td>\n",
+       "      <td>8.171218</td>\n",
+       "      <td>7.056717</td>\n",
+       "      <td>8.596524</td>\n",
+       "      <td>1.894844</td>\n",
+       "      <td>7.056717</td>\n",
+       "      <td>9.235177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DUTHEG</td>\n",
+       "      <td>14</td>\n",
+       "      <td>CC1=CC(=O)N2C(=NC(=NC2=N1)C)N</td>\n",
+       "      <td>11.1369</td>\n",
+       "      <td>18.9130</td>\n",
+       "      <td>4.0311</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.790356</td>\n",
+       "      <td>10.413188</td>\n",
+       "      <td>4.031100</td>\n",
+       "      <td>10.543597</td>\n",
+       "      <td>7.449690</td>\n",
+       "      <td>4.031100</td>\n",
+       "      <td>7.034673</td>\n",
+       "      <td>2.328665</td>\n",
+       "      <td>5.765142</td>\n",
+       "      <td>9.435290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AXUDED08</td>\n",
+       "      <td>10</td>\n",
+       "      <td>COC(=O)c1ccccc1</td>\n",
+       "      <td>13.1840</td>\n",
+       "      <td>5.9231</td>\n",
+       "      <td>16.9510</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.337268</td>\n",
+       "      <td>9.426187</td>\n",
+       "      <td>4.032894</td>\n",
+       "      <td>9.448610</td>\n",
+       "      <td>6.808171</td>\n",
+       "      <td>4.423993</td>\n",
+       "      <td>6.079166</td>\n",
+       "      <td>1.978778</td>\n",
+       "      <td>5.173547</td>\n",
+       "      <td>8.653620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ARAGUV</td>\n",
+       "      <td>10</td>\n",
+       "      <td>CC(=O)NC1=NNC(=C1)C</td>\n",
+       "      <td>18.6000</td>\n",
+       "      <td>19.9320</td>\n",
+       "      <td>7.7992</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>90.000</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.557215</td>\n",
+       "      <td>9.660297</td>\n",
+       "      <td>5.266146</td>\n",
+       "      <td>10.701777</td>\n",
+       "      <td>7.114870</td>\n",
+       "      <td>5.266146</td>\n",
+       "      <td>6.403891</td>\n",
+       "      <td>1.997464</td>\n",
+       "      <td>5.539808</td>\n",
+       "      <td>8.313428</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  identifiers  n_heavy_atoms                         smiles        a        b  \\\n",
+       "0      COXSUE             12          CC(=O)NN=C(C)C1=NCCS1   4.7362  17.0760   \n",
+       "1      TUWFAU             15      CC(C)N(C)c1ccc(cc1C#N)C#N   8.9832  12.6212   \n",
+       "2      DUTHEG             14  CC1=CC(=O)N2C(=NC(=NC2=N1)C)N  11.1369  18.9130   \n",
+       "3    AXUDED08             10                COC(=O)c1ccccc1  13.1840   5.9231   \n",
+       "4      ARAGUV             10            CC(=O)NC1=NNC(=C1)C  18.6000  19.9320   \n",
+       "\n",
+       "         c  alpha    beta  gamma  z_value  ...  percentile_25 percentile_75  \\\n",
+       "0  11.4150   90.0  94.432   90.0      4.0  ...       5.939994     10.352198   \n",
+       "1   9.6863   90.0  92.414   90.0      4.0  ...       6.603302     10.985316   \n",
+       "2   4.0311   90.0  90.000   90.0      4.0  ...       5.790356     10.413188   \n",
+       "3  16.9510   90.0  90.000   90.0      8.0  ...       5.337268      9.426187   \n",
+       "4   7.7992   90.0  90.000   90.0     16.0  ...       5.557215      9.660297   \n",
+       "\n",
+       "   min_centroid  max_centroid  mean_centroid  mode_centroid  median_centroid  \\\n",
+       "0      4.736200      9.147929       7.426769       4.736200         8.088933   \n",
+       "1      4.751947     11.683526       8.171218       7.056717         8.596524   \n",
+       "2      4.031100     10.543597       7.449690       4.031100         7.034673   \n",
+       "3      4.032894      9.448610       6.808171       4.423993         6.079166   \n",
+       "4      5.266146     10.701777       7.114870       5.266146         6.403891   \n",
+       "\n",
+       "   std_centroid  percentile_25_centroid  percentile_75_centroid  \n",
+       "0      1.563737                6.512305                8.652986  \n",
+       "1      1.894844                7.056717                9.235177  \n",
+       "2      2.328665                5.765142                9.435290  \n",
+       "3      1.978778                5.173547                8.653620  \n",
+       "4      1.997464                5.539808                8.313428  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th>nAcid</th>\n",
+       "      <th>nBase</th>\n",
+       "      <th>SpAbs_A</th>\n",
+       "      <th>SpMax_A</th>\n",
+       "      <th>SpDiam_A</th>\n",
+       "      <th>SpAD_A</th>\n",
+       "      <th>SpMAD_A</th>\n",
+       "      <th>LogEE_A</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SRW10</th>\n",
+       "      <th>TSRW10</th>\n",
+       "      <th>MW</th>\n",
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+       "      <th>mZagreb1</th>\n",
+       "      <th>mZagreb2</th>\n",
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+       "      <td>11</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.813451</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>18.488707</td>\n",
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+       "      <td>16.685332</td>\n",
+       "      <td>1.191809</td>\n",
+       "      <td>3.577079</td>\n",
+       "      <td>...</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "      <td>11</td>\n",
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+       "      <td>3.722222</td>\n",
+       "      <td>2.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.399237</td>\n",
+       "      <td>7.308033</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>4.311276</td>\n",
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+       "      <td>1.163873</td>\n",
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+       "      <td>8</td>\n",
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+       "      <td>49.0</td>\n",
+       "      <td>4.333333</td>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1613 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         ABC      ABCGG  nAcid  nBase    SpAbs_A   SpMax_A  SpDiam_A  \\\n",
+       "0   8.773011   8.632564      0      0  14.599164  2.244739  4.388162   \n",
+       "1  10.813451  10.346393      0      0  18.488707  2.357869  4.715737   \n",
+       "2  10.922841   9.584172      0      0  16.685332  2.436274  4.872549   \n",
+       "3   7.140017   6.873356      0      0  12.857279  2.214320  4.428639   \n",
+       "4   7.399237   7.308033      0      0  11.638727  2.249778  4.311276   \n",
+       "\n",
+       "      SpAD_A   SpMAD_A   LogEE_A  ...     SRW10     TSRW10          MW  \\\n",
+       "0  14.599164  1.216597  3.370933  ...  8.764834  54.650723  185.062283   \n",
+       "1  18.488707  1.232580  3.592641  ...  9.421492  46.551655  199.110947   \n",
+       "2  16.685332  1.191809  3.577079  ...  9.693445  46.214439  191.080710   \n",
+       "3  12.857279  1.285728  3.188884  ...  8.703009  38.792641  136.052429   \n",
+       "4  11.638727  1.163873  3.200300  ...  8.616314  52.270726  139.074562   \n",
+       "\n",
+       "        AMW  WPath  WPol  Zagreb1  Zagreb2  mZagreb1  mZagreb2  \n",
+       "0  8.046186    219    11     54.0     58.0  4.833333  2.777778  \n",
+       "1  7.111105    364    22     70.0     80.0  6.805556  3.583333  \n",
+       "2  8.307857    271    22     74.0     87.0  5.666667  3.000000  \n",
+       "3  7.558468    121    11     44.0     48.0  3.722222  2.444444  \n",
+       "4  7.319714    125     8     46.0     49.0  4.333333  2.250000  \n",
+       "\n",
+       "[5 rows x 1613 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = train.drop(['identifiers', 'name', 'InchiKey', 'SMILES', 'Unnamed: 0'], axis=1) \n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import IterativeImputer, SimpleImputer\n",
+    "\n",
+    "imp = SimpleImputer(strategy=\"most_frequent\")\n",
+    "Y = df['mean_centroid'].values\n",
+    "imp.fit(X,Y)\n",
+    "X = imp.transform(X) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import RidgeCV\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "# X = X[:10]\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= .3, random_state=69) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RidgeCV(alphas=array([ 0.1,  1. , 10. ]), cv=10, fit_intercept=True,\n",
+       "        gcv_mode=None, normalize=False, scoring=None, store_cv_values=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Train the model\n",
+    "ridge = RidgeCV(cv=10)\n",
+    "ridge.fit(X_train, Y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-deep')\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "def evaluation(model, X_test, y_test):\n",
+    "    prediction = model.predict(X_test)\n",
+    "    print(prediction.shape)\n",
+    "    mae = mean_absolute_error(y_test, prediction)\n",
+    "    mse = mean_squared_error(y_test, prediction)\n",
+    "    \n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "    plt.scatter(prediction, y_test, c='red', label=\"should be a line\", s=5, alpha=0.8)\n",
+    "#     plt.scatter(y_test, 'blue', label=\"actual\", s=5, alpha=0.6)\n",
+    "    plt.legend()\n",
+    "    plt.ylabel('mean_centroid')\n",
+    "    plt.title(\"MAE {}, MSE {}\".format(round(mae,4), round(mae,4)))\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print('MAE score:', round(mae, 4))\n",
+    "    print('MSE score:', round(mse,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4035,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE score: 0.2716\n",
+      "MSE score: 0.1408\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluate results\n",
+    "evaluation(ridge, X_test, Y_test)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test = pd.read_csv('data/test_descriptors.csv')\n",
+    "df_test = df_test.drop(['identifiers', 'name', 'InchiKey', 'SMILES', 'Unnamed: 0'], axis=1) \n",
+    "X_test = imp.transform(df_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.head()\n",
+    "test_set = X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3363,)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction = ridge.predict(test_set)\n",
+    "prediction.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt('predictions_task3_milan.csv', prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "base"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Milan/predictions_task3_milan.csv b/Milan/predictions_task3_milan.csv
new file mode 100644
index 0000000..66058b5
--- /dev/null
+++ b/Milan/predictions_task3_milan.csv
@@ -0,0 +1,3363 @@
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diff --git a/Peru/t1.py b/Peru/t1.py
new file mode 100644
index 0000000..bed68a1
--- /dev/null
+++ b/Peru/t1.py
@@ -0,0 +1,21 @@
+import pandas as pd
+import sklearn as sk
+import numpy as np
+from rdkit import Chem
+from pathlib import Path
+import glob
+
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer, SimpleImputer
+
+test_csvs = glob.glob("../data/test_*.csv")
+tests = {Path(t).stem: pd.read_csv(t) for t in test_csvs}
+print(tests.keys())
+
+train_csvs = glob.glob("../data/train_*.csv")
+train = {Path(t).stem: pd.read_csv(t) for t in train_csvs}
+print(train.keys())
+
+X = train['train_descriptors']
+X = X.drop(['identifiers', 'name', 'InchiKey', 'SMILES'], axis = 1)
+
diff --git a/Peru/tom_code.py b/Peru/tom_code.py
new file mode 100644
index 0000000..5ac7a49
--- /dev/null
+++ b/Peru/tom_code.py
@@ -0,0 +1,21 @@
+import pandas as pd
+import sklearn as sk
+import numpy as np
+from rdkit import Chem
+from pathlib import Path
+import glob
+
+test_csvs = glob.glob("../data/test_*.csv")
+tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}
+tests.keys()
+
+
+train_csvs = glob.glob("../data/train_*.csv")
+train = {Path(t).stem : pd.read_csv(t) for t in train_csvs}
+train.keys()
+
+
+train['train_crystals'][]
+
+
+
diff --git a/README.md b/README.md
index ad70fec..361e7ef 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1 @@
-# FONS Datathon
-Automated checking repository
-
-This is a repository for automated testing of your predictions against the test data.
-To score your predictions, simply navigate to your team's branch and upload your predictions as a `.csv` file using the names `task_x_predictions.csv` or `bonus_x_predictions.csv` according to the number of task you wish to be scored. Then, if not already done so, create a pull request from your branch into the main branch. Once you have submitted your pull request, wait a few minutes and your results will appear in the comments! If you have already created a pull request, all you have to do is navigate to it and again, wait a few minutes for your scores. 
-
-You do not have to submit all the tasks at once! Feel free to submit as many of tasks or bonus tasks as you wish! Note that this auto-checking service can only check the 4 main tasks as well as the first 3 bonus tasks. *For any other bonus tasks, please contact a demonstrator for manual scoring.*
-
-
-These `.csv` files should be one prediction per line, with a total line length of 3,363 to match the test dataset size.
-If you do not submit this, the scoring will fail.
-The script used to score your models is `test_scoring.py` so check that file if you're confused as to why you're scoring doesn't work.
-
-
-If you are confused how to format your `.csv`, see `example_predictions.csv`, the `Getting_started.ipynb` notebook or ask a demonstrator.
-If you are new to GitHub and haven't submitted a pull request before, please ask a demonstrator.
-An example pull request can be seen by selecting the `pull requests` tab on the top and looking in `Example Pull Request`.
+# digifab-datathon
\ No newline at end of file
diff --git a/Tom/RDkit_tests.ipynb b/Tom/RDkit_tests.ipynb
new file mode 100644
index 0000000..b240946
--- /dev/null
+++ b/Tom/RDkit_tests.ipynb
@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdkit import Chem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "222 µs ± 2.95 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<rdkit.Chem.rdchem.Mol at 0x7fde0d24d350>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%timeit m = Chem.MolFromSmiles('C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O')\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Atom' object has no attribute 'GetNumAtoms'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-14cfe34e5e46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0matom\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetAtoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetNumAtoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Atom' object has no attribute 'GetNumAtoms'"
+     ]
+    }
+   ],
+   "source": [
+    "for atom in m.GetAtoms():\n",
+    "    print(atom.GetNumAtoms())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.GetAtomWithIdx(0).IsInRing()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['H', 'O', 'H', 'C', 'O', 'H', 'N', 'C', 'O', 'H', 'O', 'N'],\n",
+       "      dtype='<U1')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.choice(['C','N','O','H'], size = 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "511 µs ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def c():\n",
+    "    m = Chem.MolFromSmiles('C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O')\n",
+    "    m = Chem.MolFromSmiles(\"\".join(np.random.choice(['C','N','O'], size = 12)))\n",
+    "    m = Chem.rdmolops.AddHs(m)\n",
+    "    ssr = Chem.GetSymmSSSR(m)\n",
+    "    ri = m.GetRingInfo()\n",
+    "    m2 = Chem.AddHs(m)\n",
+    "    return [len(i) for i in ssr]\n",
+    "\n",
+    "\n",
+    "%timeit c()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<rdkit.Chem.rdchem.Mol at 0x7fde0d24d350>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = Chem.MolFromSmiles('C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O')\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(24, 24)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Chem.rdmolops.GetAdjacencyMatrix(m).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  1.,  1.,  2.,  3.,  4.,  5.,  6.,  4.,  5.,  6.,  5.,  4.,\n",
+       "         3.,  6.,  7.,  8.,  9.,  8.,  7.,  7.,  5.,  6.,  6.],\n",
+       "       [ 1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  4.,  5.,  6.,  5.,  4.,\n",
+       "         3.,  6.,  7.,  8.,  9.,  8.,  7.,  7.,  5.,  6.,  6.],\n",
+       "       [ 1.,  1.,  0.,  1.,  2.,  3.,  4.,  5.,  3.,  4.,  5.,  4.,  3.,\n",
+       "         2.,  5.,  6.,  7.,  8.,  7.,  6.,  6.,  4.,  5.,  5.],\n",
+       "       [ 2.,  2.,  1.,  0.,  1.,  2.,  3.,  4.,  2.,  3.,  4.,  3.,  2.,\n",
+       "         1.,  4.,  5.,  6.,  7.,  6.,  5.,  5.,  3.,  4.,  4.],\n",
+       "       [ 3.,  3.,  2.,  1.,  0.,  1.,  2.,  3.,  3.,  4.,  5.,  4.,  3.,\n",
+       "         2.,  5.,  6.,  7.,  8.,  7.,  6.,  6.,  2.,  3.,  3.],\n",
+       "       [ 4.,  4.,  3.,  2.,  1.,  0.,  1.,  2.,  2.,  3.,  4.,  5.,  4.,\n",
+       "         3.,  6.,  7.,  8.,  9.,  8.,  7.,  5.,  1.,  2.,  2.],\n",
+       "       [ 5.,  5.,  4.,  3.,  2.,  1.,  0.,  1.,  1.,  2.,  3.,  4.,  3.,\n",
+       "         2.,  5.,  6.,  7.,  8.,  7.,  6.,  4.,  2.,  3.,  3.],\n",
+       "       [ 6.,  6.,  5.,  4.,  3.,  2.,  1.,  0.,  2.,  3.,  4.,  5.,  4.,\n",
+       "         3.,  6.,  7.,  8.,  9.,  8.,  7.,  5.,  3.,  4.,  4.],\n",
+       "       [ 4.,  4.,  3.,  2.,  3.,  2.,  1.,  2.,  0.,  1.,  2.,  3.,  2.,\n",
+       "         1.,  4.,  5.,  6.,  7.,  6.,  5.,  3.,  3.,  4.,  4.],\n",
+       "       [ 5.,  5.,  4.,  3.,  4.,  3.,  2.,  3.,  1.,  0.,  1.,  2.,  3.,\n",
+       "         2.,  3.,  4.,  5.,  6.,  5.,  4.,  2.,  4.,  5.,  5.],\n",
+       "       [ 6.,  6.,  5.,  4.,  5.,  4.,  3.,  4.,  2.,  1.,  0.,  1.,  2.,\n",
+       "         3.,  2.,  3.,  4.,  5.,  4.,  3.,  1.,  5.,  6.,  6.],\n",
+       "       [ 5.,  5.,  4.,  3.,  4.,  5.,  4.,  5.,  3.,  2.,  1.,  0.,  1.,\n",
+       "         2.,  1.,  2.,  3.,  4.,  3.,  2.,  2.,  6.,  7.,  7.],\n",
+       "       [ 4.,  4.,  3.,  2.,  3.,  4.,  3.,  4.,  2.,  3.,  2.,  1.,  0.,\n",
+       "         1.,  2.,  3.,  4.,  5.,  4.,  3.,  3.,  5.,  6.,  6.],\n",
+       "       [ 3.,  3.,  2.,  1.,  2.,  3.,  2.,  3.,  1.,  2.,  3.,  2.,  1.,\n",
+       "         0.,  3.,  4.,  5.,  6.,  5.,  4.,  4.,  4.,  5.,  5.],\n",
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+       "         3.,  0.,  1.,  2.,  3.,  2.,  1.,  3.,  7.,  8.,  8.],\n",
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+       "       [ 8.,  8.,  7.,  6.,  7.,  8.,  7.,  8.,  6.,  5.,  4.,  3.,  4.,\n",
+       "         5.,  2.,  1.,  0.,  1.,  2.,  3.,  5.,  9., 10., 10.],\n",
+       "       [ 9.,  9.,  8.,  7.,  8.,  9.,  8.,  9.,  7.,  6.,  5.,  4.,  5.,\n",
+       "         6.,  3.,  2.,  1.,  0.,  1.,  2.,  6., 10., 11., 11.],\n",
+       "       [ 8.,  8.,  7.,  6.,  7.,  8.,  7.,  8.,  6.,  5.,  4.,  3.,  4.,\n",
+       "         5.,  2.,  3.,  2.,  1.,  0.,  1.,  5.,  9., 10., 10.],\n",
+       "       [ 7.,  7.,  6.,  5.,  6.,  7.,  6.,  7.,  5.,  4.,  3.,  2.,  3.,\n",
+       "         4.,  1.,  2.,  3.,  2.,  1.,  0.,  4.,  8.,  9.,  9.],\n",
+       "       [ 7.,  7.,  6.,  5.,  6.,  5.,  4.,  5.,  3.,  2.,  1.,  2.,  3.,\n",
+       "         4.,  3.,  4.,  5.,  6.,  5.,  4.,  0.,  6.,  7.,  7.],\n",
+       "       [ 5.,  5.,  4.,  3.,  2.,  1.,  2.,  3.,  3.,  4.,  5.,  6.,  5.,\n",
+       "         4.,  7.,  8.,  9., 10.,  9.,  8.,  6.,  0.,  1.,  1.],\n",
+       "       [ 6.,  6.,  5.,  4.,  3.,  2.,  3.,  4.,  4.,  5.,  6.,  7.,  6.,\n",
+       "         5.,  8.,  9., 10., 11., 10.,  9.,  7.,  1.,  0.,  2.],\n",
+       "       [ 6.,  6.,  5.,  4.,  3.,  2.,  3.,  4.,  4.,  5.,  6.,  7.,  6.,\n",
+       "         5.,  8.,  9., 10., 11., 10.,  9.,  7.,  1.,  2.,  0.]])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Chem.rdmolops.GetDistanceMatrix(m)"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Tom/Untitled.ipynb b/Tom/Untitled.ipynb
new file mode 100644
index 0000000..12405ed
--- /dev/null
+++ b/Tom/Untitled.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "higher-zimbabwe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:root] *",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tom/centrosymmetric.ipynb b/Tom/centrosymmetric.ipynb
new file mode 100644
index 0000000..04586cb
--- /dev/null
+++ b/Tom/centrosymmetric.ipynb
@@ -0,0 +1,1303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "decimal-taiwan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sklearn as sk\n",
+    "import numpy as np\n",
+    "from rdkit import Chem\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "automatic-departure",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('task_4_predictions', (3362, 1)),\n",
+       " ('bonus_3_predictions', (3362, 1)),\n",
+       " ('task_3_predictions', (3362, 1)),\n",
+       " ('example_predictions', (3362, 1)),\n",
+       " ('task_2_predictions', (3362, 1)),\n",
+       " ('bonus_2_predictions', (3362, 1)),\n",
+       " ('bonus_1_predictions', (3362, 1)),\n",
+       " ('task_1_predictions', (3362, 1))]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "test_csvs = glob.glob(\"../../fons_datathon_testing/*.csv\")\n",
+    "tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}\n",
+    "[(k, v.shape) for k,v in tests.items()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "further-transaction",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['test_mord3d', 'test_morgan', 'test_rdk', 'test_descriptors'])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "test_csvs = glob.glob(\"../data/test_*.csv\")\n",
+    "tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}\n",
+    "tests.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "authorized-cherry",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['train_mord3d', 'train_centroid_distances', 'train_morgan', 'train_descriptors', 'train_crystals', 'train_distances', 'train_rdk'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_csvs = glob.glob(\"../data/train_*.csv\")\n",
+    "train = {Path(t).stem : pd.read_csv(t) for t in train_csvs}\n",
+    "train.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "basic-monitoring",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>Unnamed: 0</th>\n",
+       "      <th>PNSA1</th>\n",
+       "      <th>PNSA2</th>\n",
+       "      <th>PNSA3</th>\n",
+       "      <th>PNSA4</th>\n",
+       "      <th>PNSA5</th>\n",
+       "      <th>PPSA1</th>\n",
+       "      <th>PPSA2</th>\n",
+       "      <th>PPSA3</th>\n",
+       "      <th>PPSA4</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Mor30p</th>\n",
+       "      <th>Mor31p</th>\n",
+       "      <th>Mor32p</th>\n",
+       "      <th>MOMI-X</th>\n",
+       "      <th>MOMI-Y</th>\n",
+       "      <th>MOMI-Z</th>\n",
+       "      <th>PBF</th>\n",
+       "      <th>InchiKey</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11365</td>\n",
+       "      <td>139.014118</td>\n",
+       "      <td>-151.840319</td>\n",
+       "      <td>-26.831052</td>\n",
+       "      <td>-6.601753</td>\n",
+       "      <td>-25.306720</td>\n",
+       "      <td>246.931665</td>\n",
+       "      <td>269.714928</td>\n",
+       "      <td>12.363132</td>\n",
+       "      <td>11.726736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.102422</td>\n",
+       "      <td>0.229437</td>\n",
+       "      <td>0.071489</td>\n",
+       "      <td>1415.287854</td>\n",
+       "      <td>1322.721151</td>\n",
+       "      <td>281.235078</td>\n",
+       "      <td>0.515382</td>\n",
+       "      <td>UYSCRWBEPUAZRA-UHFFFAOYSA-N</td>\n",
+       "      <td>CC(=O)NN=C(C)C1=NCCS1</td>\n",
+       "      <td>Optimised_Molecules_V3.3460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>58476</td>\n",
+       "      <td>166.984155</td>\n",
+       "      <td>-158.378249</td>\n",
+       "      <td>-22.890526</td>\n",
+       "      <td>-5.656366</td>\n",
+       "      <td>-19.797281</td>\n",
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+       "      <td>232.610117</td>\n",
+       "      <td>11.769867</td>\n",
+       "      <td>8.307504</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.190373</td>\n",
+       "      <td>0.009479</td>\n",
+       "      <td>-0.023172</td>\n",
+       "      <td>1632.533280</td>\n",
+       "      <td>1362.201373</td>\n",
+       "      <td>461.559327</td>\n",
+       "      <td>0.760204</td>\n",
+       "      <td>RYPSYCXCJSHDJX-UHFFFAOYSA-N</td>\n",
+       "      <td>CC(C)N(C)c1ccc(cc1C#N)C#N</td>\n",
+       "      <td>Optimised_Molecules_V3.19496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31879</td>\n",
+       "      <td>138.070489</td>\n",
+       "      <td>-205.785002</td>\n",
+       "      <td>-26.822085</td>\n",
+       "      <td>-8.947174</td>\n",
+       "      <td>-25.723125</td>\n",
+       "      <td>227.011313</td>\n",
+       "      <td>338.345463</td>\n",
+       "      <td>18.257982</td>\n",
+       "      <td>14.710672</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.074258</td>\n",
+       "      <td>0.201520</td>\n",
+       "      <td>-0.012468</td>\n",
+       "      <td>1237.088719</td>\n",
+       "      <td>806.448222</td>\n",
+       "      <td>438.826218</td>\n",
+       "      <td>0.196809</td>\n",
+       "      <td>PVRUUABKIQNQFL-UHFFFAOYSA-N</td>\n",
+       "      <td>CC1=CC(=O)N2C(=NC(=NC2=N1)C)N</td>\n",
+       "      <td>Optimised_Molecules_V3.5074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2885</td>\n",
+       "      <td>123.516982</td>\n",
+       "      <td>-122.535380</td>\n",
+       "      <td>-17.941428</td>\n",
+       "      <td>-6.807521</td>\n",
+       "      <td>-17.505054</td>\n",
+       "      <td>191.418986</td>\n",
+       "      <td>189.897760</td>\n",
+       "      <td>14.965059</td>\n",
+       "      <td>10.549876</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.042547</td>\n",
+       "      <td>0.057453</td>\n",
+       "      <td>-0.132380</td>\n",
+       "      <td>739.692109</td>\n",
+       "      <td>597.300035</td>\n",
+       "      <td>145.664769</td>\n",
+       "      <td>0.100125</td>\n",
+       "      <td>QPJVMBTYPHYUOC-UHFFFAOYSA-N</td>\n",
+       "      <td>COC(=O)c1ccccc1</td>\n",
+       "      <td>Optimised_Molecules_V3.988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2660</td>\n",
+       "      <td>95.811124</td>\n",
+       "      <td>-100.166153</td>\n",
+       "      <td>-19.007021</td>\n",
+       "      <td>-5.271903</td>\n",
+       "      <td>-20.033231</td>\n",
+       "      <td>225.502882</td>\n",
+       "      <td>235.752960</td>\n",
+       "      <td>14.949062</td>\n",
+       "      <td>12.408051</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.083721</td>\n",
+       "      <td>0.066667</td>\n",
+       "      <td>-0.018557</td>\n",
+       "      <td>820.747989</td>\n",
+       "      <td>688.428301</td>\n",
+       "      <td>138.730178</td>\n",
+       "      <td>0.199216</td>\n",
+       "      <td>LUFRABHJXNJTNZ-UHFFFAOYSA-N</td>\n",
+       "      <td>CC(=O)NC1=NNC(=C1)C</td>\n",
+       "      <td>Optimised_Molecules_V3.795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <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>13444</th>\n",
+       "      <td>34720</td>\n",
+       "      <td>250.640231</td>\n",
+       "      <td>-565.944945</td>\n",
+       "      <td>-74.976844</td>\n",
+       "      <td>-33.290879</td>\n",
+       "      <td>-56.594495</td>\n",
+       "      <td>101.770837</td>\n",
+       "      <td>178.912849</td>\n",
+       "      <td>19.023500</td>\n",
+       "      <td>10.524285</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.004609</td>\n",
+       "      <td>-0.040854</td>\n",
+       "      <td>-0.025187</td>\n",
+       "      <td>1443.614193</td>\n",
+       "      <td>952.861633</td>\n",
+       "      <td>568.943494</td>\n",
+       "      <td>0.259317</td>\n",
+       "      <td>AMGTUMYRSHDKFL-UHFFFAOYSA-N</td>\n",
+       "      <td>NC1=NC(=NO1)C1=[N+]([O-])ON=C1N(=O)=O</td>\n",
+       "      <td>Optimised_Molecules_V3.7209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13445</th>\n",
+       "      <td>58781</td>\n",
+       "      <td>59.052753</td>\n",
+       "      <td>-42.012192</td>\n",
+       "      <td>-10.468332</td>\n",
+       "      <td>-1.312881</td>\n",
+       "      <td>-4.668021</td>\n",
+       "      <td>299.020058</td>\n",
+       "      <td>212.733318</td>\n",
+       "      <td>8.507001</td>\n",
+       "      <td>6.647916</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.127214</td>\n",
+       "      <td>0.782229</td>\n",
+       "      <td>-0.252107</td>\n",
+       "      <td>893.533400</td>\n",
+       "      <td>569.096869</td>\n",
+       "      <td>489.173836</td>\n",
+       "      <td>0.810754</td>\n",
+       "      <td>UPOSSYJVWXLPTA-UHFFFAOYSA-N</td>\n",
+       "      <td>O=C1CCCCCCCCCC1</td>\n",
+       "      <td>Optimised_Molecules_V3.19870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13446</th>\n",
+       "      <td>38371</td>\n",
+       "      <td>148.274341</td>\n",
+       "      <td>-208.307535</td>\n",
+       "      <td>-39.210266</td>\n",
+       "      <td>-11.572641</td>\n",
+       "      <td>-34.717922</td>\n",
+       "      <td>164.042120</td>\n",
+       "      <td>230.459359</td>\n",
+       "      <td>12.801458</td>\n",
+       "      <td>12.803298</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.007986</td>\n",
+       "      <td>0.111940</td>\n",
+       "      <td>-0.013753</td>\n",
+       "      <td>760.080102</td>\n",
+       "      <td>605.320181</td>\n",
+       "      <td>227.957235</td>\n",
+       "      <td>0.471642</td>\n",
+       "      <td>VMONVJKJUDZYIE-UHFFFAOYSA-N</td>\n",
+       "      <td>OC(=O)CN1C=CC=CC1=O</td>\n",
+       "      <td>Optimised_Molecules_V3.9058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13447</th>\n",
+       "      <td>45201</td>\n",
+       "      <td>164.015880</td>\n",
+       "      <td>-195.186588</td>\n",
+       "      <td>-24.968183</td>\n",
+       "      <td>-8.132775</td>\n",
+       "      <td>-19.518659</td>\n",
+       "      <td>208.223630</td>\n",
+       "      <td>247.795884</td>\n",
+       "      <td>17.212000</td>\n",
+       "      <td>10.324829</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.140341</td>\n",
+       "      <td>-0.029752</td>\n",
+       "      <td>-0.242994</td>\n",
+       "      <td>1382.006709</td>\n",
+       "      <td>998.934558</td>\n",
+       "      <td>386.301897</td>\n",
+       "      <td>0.074602</td>\n",
+       "      <td>AMOPWISLTWLTGM-UHFFFAOYSA-N</td>\n",
+       "      <td>Cc1cc(nc2ccccc12)C(N)=O</td>\n",
+       "      <td>Optimised_Molecules_V3.12909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13448</th>\n",
+       "      <td>7655</td>\n",
+       "      <td>195.605337</td>\n",
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+       "      <td>12.588751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.119138</td>\n",
+       "      <td>-0.137471</td>\n",
+       "      <td>-0.059327</td>\n",
+       "      <td>2357.256880</td>\n",
+       "      <td>2086.645415</td>\n",
+       "      <td>273.917686</td>\n",
+       "      <td>0.082341</td>\n",
+       "      <td>WBZIRDVHGBWLDE-UHFFFAOYSA-N</td>\n",
+       "      <td>COC(=S)Nc1ccc(cc1)N(=O)=O</td>\n",
+       "      <td>Optimised_Molecules_V3.2645</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13449 rows × 217 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0       PNSA1       PNSA2      PNSA3      PNSA4      PNSA5  \\\n",
+       "0           11365  139.014118 -151.840319 -26.831052  -6.601753 -25.306720   \n",
+       "1           58476  166.984155 -158.378249 -22.890526  -5.656366 -19.797281   \n",
+       "2           31879  138.070489 -205.785002 -26.822085  -8.947174 -25.723125   \n",
+       "3            2885  123.516982 -122.535380 -17.941428  -6.807521 -17.505054   \n",
+       "4            2660   95.811124 -100.166153 -19.007021  -5.271903 -20.033231   \n",
+       "...           ...         ...         ...        ...        ...        ...   \n",
+       "13444       34720  250.640231 -565.944945 -74.976844 -33.290879 -56.594495   \n",
+       "13445       58781   59.052753  -42.012192 -10.468332  -1.312881  -4.668021   \n",
+       "13446       38371  148.274341 -208.307535 -39.210266 -11.572641 -34.717922   \n",
+       "13447       45201  164.015880 -195.186588 -24.968183  -8.132775 -19.518659   \n",
+       "13448        7655  195.605337 -271.839613 -30.268230 -12.356346 -38.834230   \n",
+       "\n",
+       "            PPSA1       PPSA2      PPSA3      PPSA4  ...    Mor30p    Mor31p  \\\n",
+       "0      246.931665  269.714928  12.363132  11.726736  ...  0.102422  0.229437   \n",
+       "1      245.249610  232.610117  11.769867   8.307504  ...  0.190373  0.009479   \n",
+       "2      227.011313  338.345463  18.257982  14.710672  ...  0.074258  0.201520   \n",
+       "3      191.418986  189.897760  14.965059  10.549876  ...  0.042547  0.057453   \n",
+       "4      225.502882  235.752960  14.949062  12.408051  ...  0.083721  0.066667   \n",
+       "...           ...         ...        ...        ...  ...       ...       ...   \n",
+       "13444  101.770837  178.912849  19.023500  10.524285  ... -0.004609 -0.040854   \n",
+       "13445  299.020058  212.733318   8.507001   6.647916  ...  0.127214  0.782229   \n",
+       "13446  164.042120  230.459359  12.801458  12.803298  ...  0.007986  0.111940   \n",
+       "13447  208.223630  247.795884  17.212000  10.324829  ...  0.140341 -0.029752   \n",
+       "13448  199.284386  276.952516  16.245253  12.588751  ...  0.119138 -0.137471   \n",
+       "\n",
+       "         Mor32p       MOMI-X       MOMI-Y      MOMI-Z       PBF  \\\n",
+       "0      0.071489  1415.287854  1322.721151  281.235078  0.515382   \n",
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+       "...         ...          ...          ...         ...       ...   \n",
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+       "13445 -0.252107   893.533400   569.096869  489.173836  0.810754   \n",
+       "13446 -0.013753   760.080102   605.320181  227.957235  0.471642   \n",
+       "13447 -0.242994  1382.006709   998.934558  386.301897  0.074602   \n",
+       "13448 -0.059327  2357.256880  2086.645415  273.917686  0.082341   \n",
+       "\n",
+       "                          InchiKey                                 smiles  \\\n",
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+       "4      LUFRABHJXNJTNZ-UHFFFAOYSA-N                    CC(=O)NC1=NNC(=C1)C   \n",
+       "...                            ...                                    ...   \n",
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+       "13445  UPOSSYJVWXLPTA-UHFFFAOYSA-N                        O=C1CCCCCCCCCC1   \n",
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+       "13447  AMOPWISLTWLTGM-UHFFFAOYSA-N                Cc1cc(nc2ccccc12)C(N)=O   \n",
+       "13448  WBZIRDVHGBWLDE-UHFFFAOYSA-N              COC(=S)Nc1ccc(cc1)N(=O)=O   \n",
+       "\n",
+       "                               name  \n",
+       "0       Optimised_Molecules_V3.3460  \n",
+       "1      Optimised_Molecules_V3.19496  \n",
+       "2       Optimised_Molecules_V3.5074  \n",
+       "3        Optimised_Molecules_V3.988  \n",
+       "4        Optimised_Molecules_V3.795  \n",
+       "...                             ...  \n",
+       "13444   Optimised_Molecules_V3.7209  \n",
+       "13445  Optimised_Molecules_V3.19870  \n",
+       "13446   Optimised_Molecules_V3.9058  \n",
+       "13447  Optimised_Molecules_V3.12909  \n",
+       "13448   Optimised_Molecules_V3.2645  \n",
+       "\n",
+       "[13449 rows x 217 columns]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['train_mord3d'].drop('identifiers', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "oriented-ethiopia",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>identifiers</th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>name</th>\n",
+       "      <th>ABC</th>\n",
+       "      <th>ABCGG</th>\n",
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+       "      <td>31879</td>\n",
+       "      <td>Optimised_Molecules_V3.5074</td>\n",
+       "      <td>10.922841</td>\n",
+       "      <td>9.584172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.685332</td>\n",
+       "      <td>2.436274</td>\n",
+       "      <td>4.872549</td>\n",
+       "      <td>...</td>\n",
+       "      <td>191.080710</td>\n",
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+       "      <td>271</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AXUDED08</td>\n",
+       "      <td>2885</td>\n",
+       "      <td>Optimised_Molecules_V3.988</td>\n",
+       "      <td>7.140017</td>\n",
+       "      <td>6.873356</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>136.052429</td>\n",
+       "      <td>7.558468</td>\n",
+       "      <td>121</td>\n",
+       "      <td>11</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3.722222</td>\n",
+       "      <td>2.444444</td>\n",
+       "      <td>QPJVMBTYPHYUOC-UHFFFAOYSA-N</td>\n",
+       "      <td>COC(=O)c1ccccc1</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ARAGUV</td>\n",
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+       "      <td>Optimised_Molecules_V3.795</td>\n",
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+       "      <td>CC(=O)Nc1cc(C)[nH]n1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1618 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  identifiers  Unnamed: 0                          name        ABC      ABCGG  \\\n",
+       "0      COXSUE       11365   Optimised_Molecules_V3.3460   8.773011   8.632564   \n",
+       "1      TUWFAU       58476  Optimised_Molecules_V3.19496  10.813451  10.346393   \n",
+       "2      DUTHEG       31879   Optimised_Molecules_V3.5074  10.922841   9.584172   \n",
+       "3    AXUDED08        2885    Optimised_Molecules_V3.988   7.140017   6.873356   \n",
+       "4      ARAGUV        2660    Optimised_Molecules_V3.795   7.399237   7.308033   \n",
+       "\n",
+       "   nAcid  nBase    SpAbs_A   SpMax_A  SpDiam_A  ...          MW       AMW  \\\n",
+       "0      0      0  14.599164  2.244739  4.388162  ...  185.062283  8.046186   \n",
+       "1      0      0  18.488707  2.357869  4.715737  ...  199.110947  7.111105   \n",
+       "2      0      0  16.685332  2.436274  4.872549  ...  191.080710  8.307857   \n",
+       "3      0      0  12.857279  2.214320  4.428639  ...  136.052429  7.558468   \n",
+       "4      0      0  11.638727  2.249778  4.311276  ...  139.074562  7.319714   \n",
+       "\n",
+       "   WPath  WPol  Zagreb1  Zagreb2  mZagreb1  mZagreb2  \\\n",
+       "0    219    11     54.0     58.0  4.833333  2.777778   \n",
+       "1    364    22     70.0     80.0  6.805556  3.583333   \n",
+       "2    271    22     74.0     87.0  5.666667  3.000000   \n",
+       "3    121    11     44.0     48.0  3.722222  2.444444   \n",
+       "4    125     8     46.0     49.0  4.333333  2.250000   \n",
+       "\n",
+       "                      InchiKey                     SMILES  \n",
+       "0  UYSCRWBEPUAZRA-UHFFFAOYSA-N      CC(=O)NN=C(C)C1=NCCS1  \n",
+       "1  RYPSYCXCJSHDJX-UHFFFAOYSA-N  CC(C)N(C)c1ccc(C#N)cc1C#N  \n",
+       "2  PVRUUABKIQNQFL-UHFFFAOYSA-N  Cc1cc(=O)n2c(N)nc(C)nc2n1  \n",
+       "3  QPJVMBTYPHYUOC-UHFFFAOYSA-N            COC(=O)c1ccccc1  \n",
+       "4  LUFRABHJXNJTNZ-UHFFFAOYSA-N       CC(=O)Nc1cc(C)[nH]n1  \n",
+       "\n",
+       "[5 rows x 1618 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['train_descriptors'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "relative-coaching",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>1.216597</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.764834</td>\n",
+       "      <td>54.650723</td>\n",
+       "      <td>185.062283</td>\n",
+       "      <td>8.046186</td>\n",
+       "      <td>219</td>\n",
+       "      <td>11</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4.833333</td>\n",
+       "      <td>2.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>58476</td>\n",
+       "      <td>10.813451</td>\n",
+       "      <td>10.346393</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.488707</td>\n",
+       "      <td>2.357869</td>\n",
+       "      <td>4.715737</td>\n",
+       "      <td>18.488707</td>\n",
+       "      <td>1.232580</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.421492</td>\n",
+       "      <td>46.551655</td>\n",
+       "      <td>199.110947</td>\n",
+       "      <td>7.111105</td>\n",
+       "      <td>364</td>\n",
+       "      <td>22</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>6.805556</td>\n",
+       "      <td>3.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31879</td>\n",
+       "      <td>10.922841</td>\n",
+       "      <td>9.584172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.685332</td>\n",
+       "      <td>2.436274</td>\n",
+       "      <td>4.872549</td>\n",
+       "      <td>16.685332</td>\n",
+       "      <td>1.191809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.693445</td>\n",
+       "      <td>46.214439</td>\n",
+       "      <td>191.080710</td>\n",
+       "      <td>8.307857</td>\n",
+       "      <td>271</td>\n",
+       "      <td>22</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>5.666667</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2885</td>\n",
+       "      <td>7.140017</td>\n",
+       "      <td>6.873356</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.857279</td>\n",
+       "      <td>2.214320</td>\n",
+       "      <td>4.428639</td>\n",
+       "      <td>12.857279</td>\n",
+       "      <td>1.285728</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.703009</td>\n",
+       "      <td>38.792641</td>\n",
+       "      <td>136.052429</td>\n",
+       "      <td>7.558468</td>\n",
+       "      <td>121</td>\n",
+       "      <td>11</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3.722222</td>\n",
+       "      <td>2.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2660</td>\n",
+       "      <td>7.399237</td>\n",
+       "      <td>7.308033</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.638727</td>\n",
+       "      <td>2.249778</td>\n",
+       "      <td>4.311276</td>\n",
+       "      <td>11.638727</td>\n",
+       "      <td>1.163873</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.616314</td>\n",
+       "      <td>52.270726</td>\n",
+       "      <td>139.074562</td>\n",
+       "      <td>7.319714</td>\n",
+       "      <td>125</td>\n",
+       "      <td>8</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>4.333333</td>\n",
+       "      <td>2.250000</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>13444</th>\n",
+       "      <td>34720</td>\n",
+       "      <td>11.589507</td>\n",
+       "      <td>11.335446</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.593111</td>\n",
+       "      <td>2.444247</td>\n",
+       "      <td>4.716186</td>\n",
+       "      <td>18.593111</td>\n",
+       "      <td>1.239541</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.554852</td>\n",
+       "      <td>62.912651</td>\n",
+       "      <td>214.008667</td>\n",
+       "      <td>12.588745</td>\n",
+       "      <td>344</td>\n",
+       "      <td>20</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>5.916667</td>\n",
+       "      <td>3.277778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13445</th>\n",
+       "      <td>58781</td>\n",
+       "      <td>8.594671</td>\n",
+       "      <td>6.996374</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15.070075</td>\n",
+       "      <td>2.071027</td>\n",
+       "      <td>4.107492</td>\n",
+       "      <td>15.070075</td>\n",
+       "      <td>1.255840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.272571</td>\n",
+       "      <td>40.358042</td>\n",
+       "      <td>168.151415</td>\n",
+       "      <td>5.254732</td>\n",
+       "      <td>206</td>\n",
+       "      <td>13</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>3.611111</td>\n",
+       "      <td>2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13446</th>\n",
+       "      <td>38371</td>\n",
+       "      <td>8.065904</td>\n",
+       "      <td>7.608752</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13.246202</td>\n",
+       "      <td>2.248763</td>\n",
+       "      <td>4.497527</td>\n",
+       "      <td>13.246202</td>\n",
+       "      <td>1.204200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.879472</td>\n",
+       "      <td>40.495691</td>\n",
+       "      <td>153.042593</td>\n",
+       "      <td>8.502366</td>\n",
+       "      <td>158</td>\n",
+       "      <td>12</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>4.583333</td>\n",
+       "      <td>2.527778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13447</th>\n",
+       "      <td>45201</td>\n",
+       "      <td>10.813451</td>\n",
+       "      <td>9.387164</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.683997</td>\n",
+       "      <td>2.402187</td>\n",
+       "      <td>4.804373</td>\n",
+       "      <td>17.683997</td>\n",
+       "      <td>1.263143</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.576441</td>\n",
+       "      <td>45.924017</td>\n",
+       "      <td>186.079313</td>\n",
+       "      <td>7.753305</td>\n",
+       "      <td>283</td>\n",
+       "      <td>21</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>5.055556</td>\n",
+       "      <td>3.083333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13448</th>\n",
+       "      <td>7655</td>\n",
+       "      <td>10.187224</td>\n",
+       "      <td>9.217325</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.004191</td>\n",
+       "      <td>2.270288</td>\n",
+       "      <td>4.540576</td>\n",
+       "      <td>17.004191</td>\n",
+       "      <td>1.214585</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.121400</td>\n",
+       "      <td>44.722734</td>\n",
+       "      <td>212.025563</td>\n",
+       "      <td>9.637526</td>\n",
+       "      <td>338</td>\n",
+       "      <td>17</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>5.944444</td>\n",
+       "      <td>3.277778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13449 rows × 1614 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0        ABC      ABCGG  nAcid  nBase    SpAbs_A   SpMax_A  \\\n",
+       "0           11365   8.773011   8.632564      0      0  14.599164  2.244739   \n",
+       "1           58476  10.813451  10.346393      0      0  18.488707  2.357869   \n",
+       "2           31879  10.922841   9.584172      0      0  16.685332  2.436274   \n",
+       "3            2885   7.140017   6.873356      0      0  12.857279  2.214320   \n",
+       "4            2660   7.399237   7.308033      0      0  11.638727  2.249778   \n",
+       "...           ...        ...        ...    ...    ...        ...       ...   \n",
+       "13444       34720  11.589507  11.335446      0      0  18.593111  2.444247   \n",
+       "13445       58781   8.594671   6.996374      0      0  15.070075  2.071027   \n",
+       "13446       38371   8.065904   7.608752      1      0  13.246202  2.248763   \n",
+       "13447       45201  10.813451   9.387164      0      0  17.683997  2.402187   \n",
+       "13448        7655  10.187224   9.217325      0      0  17.004191  2.270288   \n",
+       "\n",
+       "       SpDiam_A     SpAD_A   SpMAD_A  ...     SRW10     TSRW10          MW  \\\n",
+       "0      4.388162  14.599164  1.216597  ...  8.764834  54.650723  185.062283   \n",
+       "1      4.715737  18.488707  1.232580  ...  9.421492  46.551655  199.110947   \n",
+       "2      4.872549  16.685332  1.191809  ...  9.693445  46.214439  191.080710   \n",
+       "3      4.428639  12.857279  1.285728  ...  8.703009  38.792641  136.052429   \n",
+       "4      4.311276  11.638727  1.163873  ...  8.616314  52.270726  139.074562   \n",
+       "...         ...        ...       ...  ...       ...        ...         ...   \n",
+       "13444  4.716186  18.593111  1.239541  ...  9.554852  62.912651  214.008667   \n",
+       "13445  4.107492  15.070075  1.255840  ...  8.272571  40.358042  168.151415   \n",
+       "13446  4.497527  13.246202  1.204200  ...  8.879472  40.495691  153.042593   \n",
+       "13447  4.804373  17.683997  1.263143  ...  9.576441  45.924017  186.079313   \n",
+       "13448  4.540576  17.004191  1.214585  ...  9.121400  44.722734  212.025563   \n",
+       "\n",
+       "             AMW  WPath  WPol  Zagreb1  Zagreb2  mZagreb1  mZagreb2  \n",
+       "0       8.046186    219    11     54.0     58.0  4.833333  2.777778  \n",
+       "1       7.111105    364    22     70.0     80.0  6.805556  3.583333  \n",
+       "2       8.307857    271    22     74.0     87.0  5.666667  3.000000  \n",
+       "3       7.558468    121    11     44.0     48.0  3.722222  2.444444  \n",
+       "4       7.319714    125     8     46.0     49.0  4.333333  2.250000  \n",
+       "...          ...    ...   ...      ...      ...       ...       ...  \n",
+       "13444  12.588745    344    20     78.0     92.0  5.916667  3.277778  \n",
+       "13445   5.254732    206    13     50.0     51.0  3.611111  2.916667  \n",
+       "13446   8.502366    158    12     50.0     54.0  4.583333  2.527778  \n",
+       "13447   7.753305    283    21     72.0     84.0  5.055556  3.083333  \n",
+       "13448   9.637526    338    17     64.0     70.0  5.944444  3.277778  \n",
+       "\n",
+       "[13449 rows x 1614 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = train['train_descriptors']\n",
+    "X = X.drop(['identifiers','name', 'InchiKey', 'SMILES'], axis = 1)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "composed-fifth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "x[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "instant-girlfriend",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import IterativeImputer, SimpleImputer\n",
+    "imp = IterativeImputer(max_iter=10, random_state=0)\n",
+    "imp = SimpleImputer(strategy=\"most_frequent\")\n",
+    "\n",
+    "imp.fit(X)\n",
+    "\n",
+    "X = imp.transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "satisfied-stamp",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9 µs, sys: 4 µs, total: 13 µs\n",
+      "Wall time: 963 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(random_state=0)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "clf = RandomForestClassifier(random_state=0)\n",
+    "Y = train['train_crystals']['is_centrosymmetric']\n",
+    "\n",
+    "clf.fit(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "capable-aluminum",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "f1_score(clf.predict(X), Y, average = 'macro')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "operating-fence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% Imports + Data loading\n",
+    "from traceback import clear_frames\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn as sk\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.experimental import enable_iterative_imputer\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.feature_selection import f_regression, chi2\n",
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "from sklearn.kernel_ridge import KernelRidge\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "widespread-penalty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_data(path):\n",
+    "    feature_headers = [*pd.read_csv(path, nrows=1)]\n",
+    "    return pd.read_csv(path,\n",
+    "        usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]\n",
+    "    )\n",
+    "\n",
+    "features = read_data('../data/train_descriptors.csv')\n",
+    "    \n",
+    "data = pd.read_csv('../data/train_crystals.csv')\n",
+    "# %% Train / test splitting\n",
+    "target = data['is_centrosymmetric']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    features, target, test_size=0.33, random_state=42)\n",
+    "\n",
+    "y_train = y_train.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "looking-retail",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tom/miniconda3/lib/python3.7/site-packages/sklearn/impute/_base.py:438: UserWarning: Deleting features without observed values: [1049 1050 1051 1052 1053 1054 1092 1095 1103 1104 1105 1106 1107 1108\n",
+      " 1109 1110 1111 1112 1113 1114 1115 1116 1117 1119 1120 1121 1122 1124\n",
+      " 1125 1126 1127 1128 1129 1130 1131 1132 1133 1171 1174 1182 1183 1184\n",
+      " 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1198 1199\n",
+      " 1200 1201 1203 1204 1205 1206]\n",
+      "  \"observed values: %s\" % missing)\n",
+      "/Users/tom/miniconda3/lib/python3.7/site-packages/sklearn/impute/_base.py:438: UserWarning: Deleting features without observed values: [1049 1050 1051 1052 1053 1054 1092 1095 1103 1104 1105 1106 1107 1108\n",
+      " 1109 1110 1111 1112 1113 1114 1115 1116 1117 1119 1120 1121 1122 1124\n",
+      " 1125 1126 1127 1128 1129 1130 1131 1132 1133 1171 1174 1182 1183 1184\n",
+      " 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1198 1199\n",
+      " 1200 1201 1203 1204 1205 1206]\n",
+      "  \"observed values: %s\" % missing)\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'f1_' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-64-34305c7b601e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# %% Prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;31m# %% plotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'f1_' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# %% Full model defn as pipeline\n",
+    "pclf = Pipeline([\n",
+    "    ('imputer', SimpleImputer(strategy='mean', verbose=1)),\n",
+    "    ('scaler', MinMaxScaler()),\n",
+    "    ('feature_sel', SelectKBest(chi2, k = 10)),\n",
+    "    ('fitting', RandomForestClassifier(random_state=0))\n",
+    "])\n",
+    "# %% Fitting\n",
+    "pclf.fit(X_train, y_train)\n",
+    "\n",
+    "# %% Prediction\n",
+    "y_pred = pclf.predict(X_test)\n",
+    "print(f1_score(y_test, y_pred))\n",
+    "# %% plotting\n",
+    "plt.plot(y_test, y_pred, 'r.')\n",
+    "plt.show()\n",
+    "# %% saving\n",
+    "#np.savetxt('./out/task_1_predictions.csv', y_pred)\n",
+    "# %%"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:root] *",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tom/gemini_tests.ipynb b/Tom/gemini_tests.ipynb
new file mode 100644
index 0000000..50b526d
--- /dev/null
+++ b/Tom/gemini_tests.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install gemini "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[read the docs for gemmi](https://gemmi.readthedocs.io/en/latest/cif.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gemmi.cif.Document with 1 blocks (b)>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gemmi import cif\n",
+    "path = \"./example_data/1520367.cif\"\n",
+    "\n",
+    "doc = cif.read_file(path)  # copy all the data from mmCIF file\n",
+    "doc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gemmi.cif.Block b>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "block = doc.sole_block()  # mmCIF has exactly one block\n",
+    "block"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['add_item',\n",
+       " 'as_string',\n",
+       " 'find',\n",
+       " 'find_frame',\n",
+       " 'find_loop',\n",
+       " 'find_loop_item',\n",
+       " 'find_mmcif_category',\n",
+       " 'find_or_add',\n",
+       " 'find_pair',\n",
+       " 'find_pair_item',\n",
+       " 'find_value',\n",
+       " 'find_values',\n",
+       " 'get_index',\n",
+       " 'get_mmcif_category',\n",
+       " 'get_mmcif_category_names',\n",
+       " 'init_loop',\n",
+       " 'init_mmcif_loop',\n",
+       " 'item_as_table',\n",
+       " 'move_item',\n",
+       " 'name',\n",
+       " 'set_mmcif_category',\n",
+       " 'set_pair',\n",
+       " 'write_file']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[s for s in dir(block) if not s.startswith(\"_\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'S O O O O N N N H N H H N N C H C H C H C C C H C H C C H C H C C H H C H H C H H C H H H C C H H C H H C H H C H H H'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "greeted = []\n",
+    "\" \".join([e for e in block.find_loop(\"_atom_site_type_symbol\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = block.find(\"_atom_site_\", [\"type_symbol\", \"fract_x\", \"fract_y\", \"fract_z\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['S1', 'S', '0.64362(5)', '0.89209(4)', '0.21229(5)'],\n",
+       " ['O1', 'O', '0.63025(14)', '0.85278(11)', '0.11014(12)'],\n",
+       " ['O2', 'O', '0.60005(14)', '0.80440(11)', '0.34253(13)'],\n",
+       " ['O3', 'O', '0.7369(3)', '0.5374(2)', '0.0105(2)'],\n",
+       " ['O4', 'O', '0.01675(15)', '0.29690(13)', '0.46768(13)'],\n",
+       " ['N1', 'N', '1.03693(16)', '1.02888(13)', '0.12921(15)'],\n",
+       " ['N2', 'N', '0.82009(17)', '0.98932(14)', '0.33051(15)'],\n",
+       " ['N3', 'N', '0.82334(16)', '0.93179(13)', '0.15283(15)'],\n",
+       " ['H3A', 'H', '0.8778', '0.9181', '0.0800'],\n",
+       " ['N4', 'N', '0.29922(18)', '1.34606(14)', '0.23173(16)'],\n",
+       " ['H4A', 'H', '0.2984', '1.3991', '0.1597'],\n",
+       " ['H4B', 'H', '0.2458', '1.3572', '0.3061'],\n",
+       " ['N5', 'N', '0.7780(2)', '0.39414(15)', '0.17797(18)'],\n",
+       " ['N6', 'N', '-0.12590(17)', '0.29101(14)', '0.67914(16)'],\n",
+       " ['C1', 'C', '0.8966(2)', '1.04214(17)', '0.37812(19)'],\n",
+       " ['H1C', 'H', '0.8490', '1.0476', '0.4629'],\n",
+       " ['C2', 'C', '1.0423(2)', '1.08856(17)', '0.30739(19)'],\n",
+       " ['H2C', 'H', '1.0938', '1.1242', '0.3427'],\n",
+       " ['C3', 'C', '1.1083(2)', '1.08001(17)', '0.1823(2)'],\n",
+       " ['H3B', 'H', '1.2068', '1.1110', '0.1322'],\n",
+       " ['C4', 'C', '0.89499(19)', '0.98506(14)', '0.20906(18)'],\n",
+       " ['C5', 'C', '0.54982(19)', '1.02960(16)', '0.21984(17)'],\n",
+       " ['C6', 'C', '0.5527(2)', '1.11900(17)', '0.10271(17)'],\n",
+       " ['H6A', 'H', '0.6085', '1.1065', '0.0221'],\n",
+       " ['C7', 'C', '0.4728(2)', '1.22533(17)', '0.10697(18)'],\n",
+       " ['H7A', 'H', '0.4770', '1.2854', '0.0287'],\n",
+       " ['C8', 'C', '0.3850(2)', '1.24455(16)', '0.22805(18)'],\n",
+       " ['C9', 'C', '0.3870(2)', '1.15564(17)', '0.34438(18)'],\n",
+       " ['H9A', 'H', '0.3334', '1.1684', '0.4252'],\n",
+       " ['C10', 'C', '0.4680(2)', '1.04931(17)', '0.33939(17)'],\n",
+       " ['H10A', 'H', '0.4676', '0.9905', '0.4171'],\n",
+       " ['C11', 'C', '0.7042(3)', '0.4872(2)', '0.1286(3)'],\n",
+       " ['C12', 'C', '0.5803(3)', '0.5149(2)', '0.2385(3)'],\n",
+       " ['H12A', 'H', '0.4848', '0.4937', '0.2397'],\n",
+       " ['H12B', 'H', '0.5855', '0.6039', '0.2308'],\n",
+       " ['C13', 'C', '0.6009(3)', '0.4326(3)', '0.3624(3)'],\n",
+       " ['H13A', 'H', '0.6383', '0.4836', '0.3993'],\n",
+       " ['H13B', 'H', '0.5069', '0.3893', '0.4283'],\n",
+       " ['C14', 'C', '0.7125(3)', '0.3383(2)', '0.3199(2)'],\n",
+       " ['H14A', 'H', '0.7872', '0.3297', '0.3577'],\n",
+       " ['H14B', 'H', '0.6636', '0.2558', '0.3450'],\n",
+       " ['C15', 'C', '0.8977(4)', '0.3359(2)', '0.0991(3)'],\n",
+       " ['H15A', 'H', '0.9264', '0.3842', '0.0077'],\n",
+       " ['H15B', 'H', '0.8653', '0.2512', '0.1129'],\n",
+       " ['H15C', 'H', '0.9814', '0.3332', '0.1239'],\n",
+       " ['C16', 'C', '0.0001(2)', '0.31690(16)', '0.57052(19)'],\n",
+       " ['C17', 'C', '0.1164(2)', '0.37323(17)', '0.59727(19)'],\n",
+       " ['H17A', 'H', '0.1601', '0.4527', '0.5290'],\n",
+       " ['H17B', 'H', '0.1952', '0.3156', '0.6007'],\n",
+       " ['C18', 'C', '0.0347(2)', '0.39435(19)', '0.7295(2)'],\n",
+       " ['H18A', 'H', '0.0185', '0.4835', '0.7182'],\n",
+       " ['H18B', 'H', '0.0912', '0.3653', '0.7851'],\n",
+       " ['C19', 'C', '-0.1138(2)', '0.31722(18)', '0.79009(19)'],\n",
+       " ['H19A', 'H', '-0.1131', '0.2393', '0.8576'],\n",
+       " ['H19B', 'H', '-0.1959', '0.3655', '0.8285'],\n",
+       " ['C20', 'C', '-0.2600(2)', '0.2317(2)', '0.6906(2)'],\n",
+       " ['H20A', 'H', '-0.2461', '0.2219', '0.6074'],\n",
+       " ['H20B', 'H', '-0.3421', '0.2839', '0.7151'],\n",
+       " ['H20C', 'H', '-0.2807', '0.1501', '0.7567']]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[list(a) for a in b]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:root] *",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Tom/scikit_tests.ipynb b/Tom/scikit_tests.ipynb
new file mode 100644
index 0000000..7962099
--- /dev/null
+++ b/Tom/scikit_tests.ipynb
@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install\n",
+    "\n",
+    "`conda install -c intel scikit-learn` or `pip install -U scikit-learn`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(random_state=0)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "clf = RandomForestClassifier(random_state=0)\n",
+    "X = [[ 1,  2,  3],  # 2 samples, 3 features\n",
+    "         [11, 12, 13]]\n",
+    "y = [0, 1]  # classes of each sample\n",
+    "clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(X)  # predict classes of the training data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Using the sklearn pipeline approach seems like a good idea:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9736842105263158"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# create a pipeline object\n",
+    "pipe = make_pipeline(\n",
+    "     StandardScaler(),\n",
+    "     LogisticRegression()\n",
+    " )\n",
+    "\n",
+    "# load the iris dataset and split it into train and test sets\n",
+    "X, y = load_iris(return_X_y=True)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
+    "\n",
+    "# fit the whole pipeline\n",
+    "pipe.fit(X_train, y_train)\n",
+    "\n",
+    "# we can now use it like any other estimator\n",
+    "accuracy_score(pipe.predict(X_test), y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_regression\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "\n",
+    "X, y = make_regression(n_samples=1000, random_state=0)\n",
+    "lr = LinearRegression()\n",
+    "\n",
+    "result = cross_validate(lr, X, y)  # defaults to 5-fold CV\n",
+    "result['test_score']  # r_squared score is high because dataset is easy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {
+    "eed28f5d-d8f3-4f82-896d-ba67a7d031c6.png": {
+     "image/png": 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jgAACCCDQLgLMe/UQ9RAaoAEaoAEaSEIDzPt2JZF6iwACCMyaAPNegpJEgsJMS9dMExuxoQEamJcGmPezTvu0jwACCCCAAAJDBJj36iHqITRAAzRAAzSQhAaY90MZmqcIIIAAAgvMewlKEgnKvIwh52FC0gAN0EC6GmDey0wRQAABBBBAYI4EmPfqIeohNEADNEADNJCEBpj3c8wAnQoBBBBoAQHmvQQliQSFmZaumSY2YkMDNDAvDTDvW5A56iICCCCAAALdIcC8Vw9RD6EBGqABGqCBJDTAvO9OgmkkCCCAQBMEmPcSlCQSlHkZQ87DhKQBGqCBdDXAvG8itdMGAggggAACCFQkwLxXD1EPoQEaoAEaoIEkNMC8r5i9eRsCCCDQEwLMewlKEgkKMy1dM01sxIYGaGBeGmDe9yT7NEwEEEAAAQTSIMC8Vw9RD6EBGqABGqCBJDTAvE8jOdQLBBBAIBUCzHsJShIJyryMIedhQtIADdBAuhpg3qeSHuoHAggggAACvSDAvFcPUQ+hARqgARqggSQ0wLzvRe5pkAgggEBlAsx7CUoSCQozLV0zTWzEhgZoYF4aYN5Xzt+8EQEEEEAAAQSmJ8C8Vw9RD6EBGqABGqCBJDTAvJ8+sdMCAggg0CUCzHsJShIJyryMIedhQtIADdBAuhpg3ncpxTQWBBBAAAEEkifAvFcPUQ+hARqgARqggSQ0wLxPPm/UQQQQQGCuBJj3EpQkEhRmWrpmmtiIDQ3QwLw0wLyfaw7oZAgggAACCPSdAPNePUQ9hAZogAZogAaS0ADzvu9pqfEjgAACOxJg3ktQkkhQ5mUMOQ8TkgZogAbS1QDzfsckzU8IIIAAAgggMFMCzHv1EPUQGqABGqABGkhCA8z7meZ8GkcAAQRaR4B5L0FJIkFhpqVrpomN2NAADcxLA8z71uWROowAAggggECbCTDv1UPUQ2iABmiABmggCQ0w79ucUuo7Aggg0DwB5r0EJYkEZV7GkPMwIWmABmggXQ0w75tP9LSIAAIIIIAAAqUEmPfqIeohNEADNEADNJCEBpj3pfmaFxBAAIFeEmDeS1CSSFCYaemaaWIjNjRAA/PSAPO+l7moQSOAAAIIILBcBJj36iHqITRAAzRAAzSQhAaY98uVDjovAgggkCYB5r0EJYkEZV7GkPMwIWmABmggXQ0w79NMFvUKAQQQQACBjhJg3quHqIfQAA3QAA3QQBIaYN53NNs0LAQQQGBCAsx7CUoSCQozLV0zTWzEhgZoYF4aYN5PmM35GAIIIIAAAghMQoB5rx6iHkIDNEADNEADSWiAeT9JKuczCCCAQHcJMO8lKEkkKPMyhpyHCUkDNEAD6WqAed/dhNPIEEAAAQQQSJAA8149RD2EBmiABmiABpLQAPM+wUxRlxBAAIFlJMC8l6AkkaAw09I108RGbGiABualAeb9MmaETo0AAggggED/CDDv1UPUQ2iABmiABmggCQ0w7/uXiBoxAgggMI4A816CkkSCMi9jyHmYkDRAAzSQrgaY9+NSNq8hgAACCCCAQMMEmPfqIeohNEADNEADNJCEBpj3DWd5mkMAAQRaToB5L0FJIkFhpqVrpomN2NAADcxLA8z7lmeVuo8AAggggEC7CDDv1UPUQ2iABmiABmggCQ0w79uVROotAgggMGsCzHsJShIJyryMIedhQtIADdBAuhpg3s867dM+AggggAACCAwRYN6rh6iH0AAN0AAN0EASGmDeD2VoniKAAAIILDDvJShJJCjMtHTNNLERGxqggXlpgHkvM0UAAQQQQACBORJg3quHqIfQAA3QAA3QQBIaYN7PMQN0KgQQQKAFBJj3EpQkEpR5GUPOw4SkARqggXQ1wLxvQeaoiwgggAACCHSHAPNePUQ9hAZogAZogAaS0ADzvjsJppEggAACTRBg3ktQkkhQmGnpmmliIzY0QAPz0gDzvonUThsIIIAAAgggUJEA8149RD2EBmiABmiABpLQAPO+YvbmbQgggEBPCDDvJShJJCjzMoachwlJAzRAA+lqgHnfk+zTMBFAAAEEEEiDAPNePUQ9hAZogAZogAaS0ADzPo3kUC8QQACBVAgw7yUoSSQozLR0zTSxERsaoIF5aYB5n0p6qB8IIIAAAgj0ggDzXj1EPYQGaIAGaIAGktAA874XuadBIoAAApUJMO8lKEkkKPMyhpyHCUkDNEAD6WqAeV85f/NGBBBAAAEEEJieAPNePUQ9hAZogAZogAaS0ADzfvrETgsIIIBAlwgw7yUoSSQozLR0zTSxERsaoIF5aYB536UU01gQQAABBBBIngDzXj1EPYQGaIAGaIAGktAA8z75vFEHEUAAgbkSYN5LUJJIUOZlDDkPE5IGaIAG0tUA836uOaCTIYAAAggg0HcCzHv1EPUQGqABGqABGkhCA8z7vqelxo8AAgjsSIB5P+cE5alnvpHd/+CHE/1/5LGvZk8/+7vZSy9/L9v61l9ln3z28ySSC0ZYukaY2IgNDdBAmzTAvN8xSfMTAggggAACCMyUAPN+zvWQNuWlTfT1sSd+Y6Laz6OPfy37yhP/I3viqa9nL2z5TvbmO3+dffzpv6v/0CsN0AANdFgDzPuZ5nwaRwABBFpHgHk/5y/9E790wVjmCwsLlV/fbbc9sqOPOTO79PJ7sk2PfJZ9tP1nkrg5x7OJCb02mKs0QAM08AsNMO9bl0fqMAIIIIAAAm0mwLw3f55pDeXQw06oXN+pUgvae+8DsmOOPTM774Kbs1tue2lg7G//3KIOcynzaRqggS5ogHnf5pRS3xFAAIHmCYydSMSVvl348ktpDE2a96OTuz333Cdbf9VD2bvbfiJuihA0kIAGIvG+4soHFv3f8sofi08C8Unpu0FfmPfNp3haRAABBBBAAIElCDDv5eQznZc1bd6P1oDi5z323Ds786wN2YObPs4+/OTfZjqelOcssTNl0dz7yvUP9pZJyvFKqW+vb/2LQu3cevsrtOM7Yq4aYN4vkbV5GQEEEOgZAeb9nBORWZr3+URu5cpV2b33b5trgpFS4q0vrrhNRQNfOumiwr+xDz38qd/POf/tTUUT+jH+75OV9z3LQg0XAQQQQACB5SXAvJeTz3ReNg/zPq8DxePue6zMLr70y9nWt38003GlOKf5+NOfFc69d9ppp96xSDE+Kffpyad/u1A7a9eeRju+I+aqAeb98iaFzo4AAgikRqAwQcmTfyvvx5sMkySf8zDv8/jFVcfbP//PuSYakzDxmeZ1hmkaTJn3acTB70N74sC8Ty1N1B8EEEAAAQQ6TYB5z5iZab1k3uZ9XgtasWL3bMPGR7OPtv90puNLaZ7FvG/PnC8l3URfmPe0k4ommfedzjkNDgEEEKhNgHk/58nqPM37mLhduf6h3kzWUkm29EPin2uAeU8LuRY8VtMC8752HucDCCCAAAIIIDA5Aeb9nOshfcuJl8u8z038A9ccmb245bu9qAkx76vNt/r2O1hlvMx72qmik3m8h3k/eULnkwgggEAXCTDv5zxZLTPvDzjgsOy9D/5h7P+tb/3VYOL1yGNfzdZv2JQdfMixY+MXE7bYImzTI5/1YrI2j0TKOST1dTTAvKeXOnrx3v/KmPddTDWNCQEEEEAAgWQJMO/nXA/pW75bZt4ftfa0sbWfqA3Ffbhf2/rn2eYXv51FDeiOL7+RXXrZ3dlxx6/LYmV9btAv9bhixR7Z/Q9+2PmaEPPe3HvSvy/Me9qZVDtNf455n2y+qGMIIIDAshAYm/DbNr/5BKbMvD/wwCNqT6ZiS/xHH/9aFsb/uAnbfvsd3Kvt0ppOnrTX/O9BX5gy72mnL1pvapzM+2XJBZ0UAQQQQACBvhJg3jPva9dh6uS9Zeb90cecOdV5P9r+s+yxJ34jW3fO9dluu+0xth4UtaKdd945u++BbVOds864l+O9zHtz70l1x7ynnUm10/TnmPd9TUeNGwEEECgmMDbJZ943n8A0ad7nScL7H/xjduxxZ4+N5e13vtbpiVrOwmPzmsV0cqbM+8nZ0V0/2THvi5M1RxFAAAEEEEBgJgSY98z7mdZJZmXeD8+V3nn/77LLr7w/22WXFWNrQjvvvMvg3t7Dn+3Sc+Z9P+ePTWiYeU87TeioiTaY9zPJ9TSKAAIItJbA2OSeed98AjML8z4ShHe3/X22evXhpfE86qhTZzopbSJJ0UbzesN0eZky75eXP/23jz/zvrX5pI4jgAACCCDQRgLMe+b9TOsk8zDv8znPSy9/Lzvk0ONKa0KxAn/ffQ/M3n7vb2c65rw/835k3rdv7jdvjZSdj3lPO2XamPdx5n0bU0l9RgABBGZHYGxiz7xvPoGZlXkfCcUDD31UGs+ddtqps5O0eSdTztf870VXmTLvaaWr2p7VuJj3s0v4tIwAAggggAACiwgw75n3MzWy52neR36+7aN/zk444bzSulAY+Gevu3amY57VPGGpdpn35t5LaaTsdeY97ZRpY97HmfeL8jQHEEAAgV4TGJvUM++bT2Bmad5v//zn2f77H1Ia002PfNrIJO3jT/89i//zTmKmPd8nn/08i3vDTdtO0ec//ORfZ9Ju0bmaOhYsPvnsP2bW74+2/3RmbU/LIIoa8fsybTtLfb4N5n1od5Y6WIpR3ddn8bv2wcf/km3//D9nroe6Y+3j+5n3vc5JDR4BBBBAAIF5E2DeM+9nOgeYt3kf84eYL8XOi2HUF/2PhR3Pbv7WTMcd/QgzfZ7zzJTN+9RraB98HDWB2ddH6s5vg9uHn/zbzLXKvG++9l031t7/ixgw7+edBjofAgggkDaBwmQ+T/CZ980nMLM07yPZOe/8m0pjetMtL1ROesPMevTxr2XXXPdkdtbZ12SHHX5itnLlqmzXXX91H7Xddtsz22+/g7OYkJ551obsxps3Zy+/+v3K56ianMUk7PkX/nDR/xe3fLf0XC+/9ifZDTc+m518yiWD2wkM3/8t+n3IIcdmr7z2p6WfH+1bTGQ2v/jt7JbbtmQXXHhbduxxZw+2nNt9972+4B1s9tnngOygg9ZmEef1GzZlTzz19Zlc6BCci5hE3Eb7Hj+/+c5fZ7fe/nJ2yqmX/ZLHrl/0O3gceeTJ2SWX3Z0998IfFH6+qM3hY89t/lZ2xZUPZMcety7be+/9sygIxN+RFSt2H1xQEib27Xe+NvfdH0IHofszztww2EJwOF7Rv1133W0Qr9NOv3Lwvjfe/OFE4w8WMbEcjcnao0//gnP+dzUeb7zp+UXvHf3s8M+vb/2LifuVx+nNt3+U3XHn69m6c67PDj/iS9lee+27Q9922WXXgTZOOvmi7Nrrn8xi28X8s008vvrGnxWO+b0P/qHwPKHl2E0k/qaFobvnnvt80d+4X2P87dl47eOFny3qb8Tnkce+Ovi9jDHuf8ChO7QZcdlpp50Hx9asOSo74cTzs/MuuHnwOx/6js8XtetYs9+TzPu0k0a9QwABBBBAoGMEmPfM+5nm+Mth3sf8ZOtbf5XtsefeX8yfhuei8fzkUy5tZNxxUfwjj/1ads21T2RnnnV1dtjhJ2R77bVj3Sjm4DF3i5rSWWdvzG6+5cXsldd/MPX533n/73aYXz7z/O8VjjdqE8Nz6yrPy+ao4+Z+O9bQNo6toQWn4HVT1NBe+5OpWYzrV/5aLF54/MnfzK7e+Ngv60JHDOa+MQce1kfMu1evPiI77vhzsnPPu3HQx6ef+2Y2y8UZccHJg5s+GdSkjjn2rGzVqjWDuflwv/bZd3V29DFnZpddfm/22ONfm+rCkNff/OEOmog6yfC58udxG4oqehl+j7pBs/WBXL99eWTedyzLNBwEEEBgSgKFCUqeqDDvm086Zm3eh0Gbx2/08aoND4+dFIQhHIl8XKUd5tjo56v+HJOyu+55u7GVzTHxLDp3GOWjCVxMKo4/4dzC94+2sZRRHRO2O+/aOrgAYNzEd7Td0YALLMAAACAASURBVJ/DzL5y/UPZu9t+sqi/o/2v+vORR51SOMannv2dHc7x1jt/k51z7g3Z8MULo/0b/TkuTHjplf+5Qztl/QozNCaeo22U/RwXOFx62d2D1QBlbU57PCZLd93zVq1+5f2NiX3o54mnfqvS+If7GhdI5O00/Xj2umtq9yf6FqvJH37088FFFaOT8ip9jN/l+x/8sJFV6aeedkUhn/se2LbD2N7/8J+yK9c/OLbYlPe9Cpe4p+Mll96VrVy5X+H587aWelyxYo8sxnDv/dvmcvX/sLb69Jx5P2VW5+MIIIAAAgggUIcA8555v8NcpOm8e7nM+xjHbXe8Wjr/2XnnnbNJLxCPWsFVVz8yWAAwTd0oFhDce//7E9eNxtW+lprbLfV6XEReRQtbXv1+tmHjo1PX0OLi+rvvfWdiFuP6GvPr9Vc9lO27ak2pHpbiEa9HTSkWZXz57reybR/+UyU+4/oVrwW/uEBgt932qN23vfc+YHDRSFxAstR5Rl+PW0dUGfMk74lFE6Pn83PztfWuMmXe10nhvBcBBBDoPoGxCQvzvvkEY9bm/X0PfFAa01gxXpTgxKrSsknlJMlq/pkwdauawEX9yo9VMe9jdf6FF93+xYrvvA/jHsu2igvzN1bs1zG8x50nfy0uAIiLGvJxTfNYxbx/4KGPKxmgef+GH2NF+qipOtzfmCBNM+GJK5jjSvnhNpt4HjsdxKrp4bFM+jx2k3h3299X7mNq5v2LL38vW7v2tEZYxNXvr23988osimJZxbx/7InfGOxoUTVmsStI0bnyY7Hbw/CK/artLvW+J5/+xtjz5uf3WP87lHnf/cTTCBFAAAEEEEiIAPOeeT/TvL6szhIriGc9V4i6xurVh5fOB6++5iu1+hALBWIHw6XmSnVfDxO/zq6IObflNO+fff73s0MPPb5xFnHx/JZX/rhWXHIeRY9xIX4seqkbk6XeH3XHovNVPfb+B/+YXXjRbYtW1y913qLX46KEhx/9aq3+TFPLKurD8DHmff0aQFXd9OF9zPuEMkRdQQABBBIgMDaJY943n3TM2ryP+9oPJ47Dz8859/rChDau8B1+X5PPY7VqrM6eJslayryP+3PFdu11+x0TrqJ+xT216rZV5/0XXXzn1FdUL2Xex0R2kpXWw+OIVehFk7J33v/JRKvah9uO5zHZrWOOF8UqPxa3NYjbFOTb9Y+ea9Kfo9hR9VYQKZn3YVoP3+Ji0vEPfy622Z9kR4I8RkuZ97FbQt2VG7HVYN7+6GPcxmG4/00+Z943/92Yx495n0BmqAsIIIAAAgj0hwDznnlfOp/I89NpHpfTvI9+x60Ey+ZBkXfXGdu4lfxl56h6PLbWjy3d6/RnOc37WH1edWx13xer0ONWBHVYFL03bmvZdH0kH0tRnaioD0XH4naUcQu7vK0mHmOcG2tcjMK8n918vijmjlXnzbzvTwJqpAgggEAVAmMTJuZ99S/YqsnIrM37cV/0l11xX+EEYJbmfSTisYL9yad/u/DcVbiNM+/DtI1V8pMk/HFPtKLzz9q8j75eevk9hecu6k/RsXHmfVxdPa1xn/OMe9YPX/kdF0qUFSDyz9R5POPMq6biEGw++ew/slglX+e8dd4bV6pXuQ9dKuZ9bGFYZ3x13hsX48TuBkWaXOrYOPM+LvCpa9xHvyPuRee97oanazOoc7ED877578Y8jsz7Kqmb9yCAAAIIIIBAQwSY98z7wvlEnptO+1g2d57Hyvvo+9vv/bi0NhCGZ9xirOoYZ2nex9wudv8rW2BR1MeumvfBImpoTz0z+W5vcWF8nXn+L/ivqPyZSc37qAtOskV+1bHE7QGKtDJ6jHk/u/n8KGs/12M9rqb/y9+D/24o/9EMAggggEALCIxNzpj39b5kqyQlszbvN4wx7sLQKupjkXkfk4W493mYzLHVe0yiXn3jz7JYdR0GbkwCY2uz2Jr98ivuy/bf/5CxWor7vsdni86/1LFx5n3ZleR77bUqiy2142rjGN+mRz4b3Kv6mmufyE488fzBZOjp575Z2J8y8/7gg48Z3I/r5ltfGlyMEPfninvKB4/3PviHwbbiYWxef8MzWWwzvtRVzg9u+qTw/EvxiNfLzPuI1W677bkoFlE0CFM3OMRFC7Gl+uNPfn1wJf5RR5266P3Dk6PY1SDOGfdPD7N9+LV4HobuKademt12xyvZY49/LXtxy3ezuCVB7AJx8aVfziL2o58Z/vnRr/z6xByiX+vOuX5s+1EEOOPMDYP+bX7xj7Ktb/8oi23/33r3x4O+RhHipJMvGhuvAw88YhDjcbH54ON/yeJ3bPh/2Rb+6865bof3DX+m6Hnct37cufPX4rPDbEefx70N4z518XvxzHPfzN5484cDFnELg7hI45773s1iNfu4W0bENvSTbGtYZt7ffMuLWazqL+prbPsff9NiJ4GHHv508Pcm4nXeBTdnq1atGcQ1H3v+GH+n4qKT0fbyn/fb/5AsLmSKrfVefeMHWdxyI/9sbC357rafDHQR91+8cv2D2dqjT8+CW/75eGTeN//dmMeAed+CzFEXEUAAAQQQ6A4B5j3z/ou5QJ6PNvm43OZ9jOXwI760w1xmeF5TZ5fEIvM+LoA+7vh1g/nV3fe+O6gDxO3WYk6V143iQvhYYHDZ5fdmq/Y7qLQv0a/YAj3u0V4lBrGCe3jufM11Txa2HXWZ4fdVeb7UxftFK+9/UUNbN6ihRQ0qaiIxNx1lkdfQYl46HIvR57GIYJKdCmNuH/Ww0fbyn+Ne8Rdfcudgfh3j/Gj7T7/gHQsj4pxx+8uIWdSQIr677LLrDu1NYt4/98IfDGpHeT+KHuP2imHAP/r41wY1h2AXtZvgGMcuuezubJ99V+/Ql9F27rxr6xfjKdNR1FeGdXDBhbcWthkxGn5flefR37LzOj67OkJX2DLvu5NgGgkCCCDQBIHCBCVPfpj3zScWszbv4wruPH6jjzHBKEpocvM+VmvHKvaYdMU9qIreW3Zs++c/zx56ePvYydi5599Uq838XGXmfZjG8X94nDEZvOe+97JYkZ9/vugxVkmXXWU+bN4ffMixg2T9ldd/MLa9onPEdutxocBw/4af77vvgdmHn/xr7XbjXGXm/cqV++1wvoMOWjuY5BT1b/hYTL6KTP+8v2H05zrJj4Vezjv/poEJPtzW6PPQ0rgrmw87/ISJGMR5brjpuR3Gm/ctHmMld1xYEheajPap6OfnX/jDUq7R3lL3Vy9qM4zy4T7lz8OILnr/NMdiAjpux4W4YCDM6irniIJLXJCR93f0MQzWpX7HRs9TZt6vXLljYSGM8vMvuGVJXcX54yKU0fOEJkf7Gz9HMSUuFAiDfvQzS/0cBZCYROXGMvO++e/GPAY546IY/vLYW00kP9pAAAEEEEAAAQQWFhaY98z72nODPG+t8piCeR8XLpfl1huvfbzy+HPzPuZrp552eRYXO2+raLTnrGION7gP+xgDNozl/P11HuOi7KJxhnlfp50q783N+5h/x7w5LoKfpIYWCzriovSifsexmBdX6c/we67a8HBhexG3q6/5Shb1ruH3V3keY4t4xwKf6Fdd8z5qMlH/KhvnYYefOFgIUqUvcVFIjKNs5764kL9u/S52BCjqWywmqNIn75ldfaBvbJn3clMEEEAAgWEChQlKnrQw75tPQGZp3ofhVpbARkxjRXRR4nPfA9uyMNaXurq46LOjx+Iq6bL7z8fVuq+/+cPCPoy2M/xzmXmf6zR//NJJF05shg+fLyYzsdo2zNBYbT78Wt3n8flrr3+q9Pfspps3T9R+mXmfs4jHE048f3C1e9U+h0Ffpp/jTzh3hxX0Ecu4SKJq23FxR6zoHu7f8PMiE3aptmOleNlW53GV+iS3aoirzk87/crSftbdMn5e5n1cHR9jHmaaP49t6WKivRTP0ddDu5dfeX9hm9F2bFM4+plxP5eZ93k/43H3PVZOtao9ikGjF7Dk7dctMJSN5alnf2ewY0HZ645P973JvB9O0TxHAAEEEEAAgRkTYN4z72vNaerm+imY92Es53Oi0ceYo1cdU2zFHiuUYxV01c+UvS/mr1FzGe1P/Bw758Vii7LPlh2fp3kf8+u4aLyuSVzU9zDGY+fGIhZRd3njrb+sxSIWcBS1FTtIFp2/7rHY8WD41opVPn/a6VcU9in6eeFFt2Ufbf/VbnhV2ov3PPLYr5UuQIm6a9V24n3M++nm8HVYe+941hXM+/93YWHhf/U/WQbHzDhv1TwCCPSMQGkCFUkU8378l+okSccszfuz111TGs/YGisM1En6XPczsX142SQ1tt2q214V8z64Dm9/Xfccs37/lesfKozNQQcfXZtH9HUp8z4mwpNMgGLiVDTRGz0WuzPUZRYT9KLt0aPtWCFft724oGC0X/FzGMCTXAyQnz+2iisrJBx99Bm1+jkv8/6ii+8sZBFX18f28PnYJnk859zi2xLEloZ1NLaUeR+7aMTWgpP0Mf9MFBKKNBHxzN/jsfnvtSaZMu97loUaLgIIIIAAAstLgHnPvJ/pPKGsLjKve95Hnl42R4p50zz7MTpniIUfUQ8pmr/FAojR9y/18zzN+6X6Uvf12Go9towvYnH1xscqs4jbOha1sXr1EVMvTKk7pvz9jz3xG4V9in5G/Sl/3ySPD276uPT2h2W3ySw6D/M+7RpBUcy6eqyCeV/6+1T0u+/Ywrx5nbS8aa2zI4BA1wiM/SPGvG8+gZmVeX/Hna+PjeWFF90+VVJcNzGKRLnonu9x3/i6bS1l3ochXHVr9Lrnbur9YXKuXn14YYy2vPr92kzGmfdhXr++9S9qtxljjau6i+I2nPBNsn18zjHuIT7cVv48xpO/p8pj2eQq2ovbN1RpY9x7QnNltxF4Yct3Krc/D/M++lq2A8G11z9Zua9lPGJbugMOOKwwbvfev61y+0uZ93ELhLI+VD0e92zMNTX8GNvqVW3D+5r/3qvDlHnftTTTeBBAAAEEEEiaAPOeeT/TeUIK5n3USobnRsPP99//kJmOf6l5wONP/mZh32JOsNRnR19vs3kfY4kd3opqMWHqj4617OfNL/5RIc9pTfKy81U5Xla7ioURsXCiShvj3nPeBTcXjvmsszdWbrusvmTb/OWtDYyLe1dfY97P3Wwv/Psx/D1Z8znzPum0X+cQaB+BsX+kmPfNJypNm/exTXRciRsrbMu+UOI+XE1siV83OTrqqFMX9Sn6EldY12lrKfP+1ttfqdVenXM3+d4bb3p+EY+IWSRndc9TNgGK9tZv2FS7veHzH3nkyYX9jLZjW/2tb/9o4vbjvvJFOo1V16Hl4X6Me376GesL2znu+HMqtzGu/XjtksvuLjxH3DNwqc/mr8/DvI/dLIqY7n/AobVWxud9Lnq8/c7XCs9x0skXVWYxzryPi3rqxL+oj3EstnIsYnHLbVsq97Osbceb/z4sYsq8b18iqccIIIAAAgi0mADznnk/03lCCuZ97MBYVi/aY8+9Zzr+onx/9FjRivPYLr7uvdnbbt4Hl6JaTMQuVuaPciv6+dHHv1Y4H75qw8OVPl/U5jTHnnvhDwr7ExcpxGvTtJ1/NuqFoZfROkDcfiEWIuTvG/fIvJ/PXH9cDLz2ixgw75n3Lc6pdR0BBGZAYFGCM5zwMO+bT2CaMu/fevfHWay2L7uf1XAcL7hwuq2oJk2iNl7zlUJ9xbZZddocZ97H6uiqE5k655zFe19+9fuFPM6/4JZaPKJvZeZ9TFrenMJcj7bLTOvQVJjm07CJwkEY9cP6zJ9X3YEg7gm3yy4rCtuISdc0/Rv+bNzLL+/b8GOdWx3Mw7zfb7+DC/t52x2vNsbio+0/HdyOYJhDPI9YxmvD3MqejzPvb7p5c6U2ytrOj8d97Uf7GD/XueAib8tj899/VZgy72eQ6WkSAQQQQGAeBN5eWFj4e/9bxeDQhYUF5j3zvpF5SFmem4J5H30r21UudnAr6/u8jpftzvf0s79bq29dMO9jYU7RfPbxJ79eicWTT3+j8PNxi815xXP4PGW394s6yfD7pn1+2ulXFo774Uc/r3Qe5v3yzP2njXsXP8+8Z97PY9LiHAgg0B4ChQlOniwy75tPYMrM+5hMhTE67v+pp12eHXPsWaVbWOdxG35cs+aobFvNle5NJUCxpfZwX/LndbfyHmfen73u2krJeFNjmqadjz/99yx2Hsg55I+HHX5i7TGUmfdHrT2tdlujYxp3C4ZJ7nU/2n6MNx/78GNsmTf63qKfywzaffZdncXFAUWfmfRY0XbxcZX4ex/8Q6XzzNq8f3HLdwtZxg4J77z/d5X6WJVNrLIfjlf+/JnnvlnpPGXmfawkeOf9n1RqY6m+PvHUbxX2cd9Va1pzkc9SY+z668z79iSQeooAAgggsAOBH+W5kcfWFF6PZ943X+/oeq5ad3ypmPcrV64qnCfF36um59B1Gd1519bCvt10ywu15ohdMO/vue+9QhbX3/BMJRZlC0bi1oqxAKhubKZ9f9yWoeg7MXbMm7bt4c/HwoWi88SFIcPvK3vOvPddUKaNeR9n3rcmhyz8m7OwsGDb/B2mR35AAIFpCZT9sRkcZ943n8CUmfdFiea0x1btd1AWq4fnnWzk54urg4vGECu78/dUeRxn3sd23lXaSOU9e+21eNK8atWa2mMoM++bWGFcdt/wiOVrW/+8dl9H2Z98yqWFunhw0yeV2r7gwlsLP3/u+TdV+vxof8b9HFeoF2k4TOJxn8tfm7V5HyvWi/q39ujTK/Uv72eVx2uue7LwXFVvW1Fm3tfZyWCpfsYuHHHhQhGTE048v/JFF0udx+vNfzfmTJn306Z1Po8AAgggsEwEmPcLrSu4Mu+tum98zpTntPljKuZ9UR0i5kxxYfpym/exOrpo/rb+qodqxacL5n3UYItYXHb5vZVYxK3o9txzn8I2Ykv+t975m0rt5Pqd5jFqR0VjiWPvbmvm4v28fy+9/L3Cc33ppAsrjZd5P7v5fR4jj9UYM+9bl0uO/u1h3i/TRMxpEegqgdE/Mjv8zLyv9uVaJwmZl3kf95sP07tO34reGxOgF176dhYrruM+WbHF+1lnX5Odcupl2fEnnDv2/xFHnLSDnvLEfd0519fq1zjzPu6hXtTvWR57fetfZJse+SyLHQRiG7Bzzr1+sGPCUjzi9aLt3mPXhbr9LTPvv3z39Fcwl221tmLF7rX7WTSus87eWKiLqn0vug9caKuprdeH+3z5lfcX9vXe+9+vxGLW5v26c64r7F9c4DA8jiaeR3zy3+Hhx6uufqTSucrM+6Z3zzju+HWF/Yw+R9Fqw8ZHszfe/GGlPjfBTRv1vkeZ911NN40LAQQQ6DwB5j3zXn7pYoBFGkjFvN9tt+Jb18V9wZuYr3y0/WfZ5he/nd11z9vZ+g2bflk32pidcuqlY2tGUSM57PATCudvF15U7/aPqZj35TW0pVkcfsSXClmce96NleNUVm+J+fDuu++VXX7FfdnLr/1J5fYm1ccDD31UOJbYMXHSNss+9+62vy8816GHHl/pXMz7enP2sjg4Pj1H5j3zvvMzJgNEAIFaBAoTnNyYYd5P/8U7mrzM2ryP7dDCxPzks/+olKSO9i9+jm32Y9IVV6nGPdRzPTT1GBO4ovOWHRtn3s/jfvdxJXqstA5DdNx2c9PwiS31y8ZfdLzMvH/o4U9rtVPUdlwQUTSWpiZZ551/U2H7t97+cqW+x4SzqH+h++h7k/8vvvTLhee65bYtlfo6a/O+7AKZSy69q1EOwbRslf9FF99RiUWZeb/x2scrfb5Iq0XHHnviNwpjNqqZgw5amwWn2Gnig4//tdE+FPXLsWrfp8z7WjmcNyOAAAIIpEOAec+8l08y7xdpIAXzPmpDRbfvi/lRrNKedJ7y/gf/mIXRFDucle1+NjoHq/PzmWddXatvy2nez7qGFnPpqnGKW+vFjgpLsV69+vAsLpB46OHt2fszuNXmxmu+UtiHuFijyZpNtPXcC39QqPH99ju4EjfmfbW5elUNet/kPJn3zPt0pjZ6ggACKRAoTKbyJI95P/kXblmyMgvzPq6WPva4dVncq/zDTyY3ocLAiq3JYiV4roFZPMbK2DI+RcfLzPuYgG7//D9rtVXU/rhj9z/4YXbggUfMlEcwjiuFx/Vj9LUy877qdu6j7Q3/vPnFPyocb5idw++b9Hns3lCkqyqGeBQIij4772MxEa0y/lmb9/vsc8Cy86haVCkz72++9aVKLKvwzt9zzrk31OISxaa4ECIu1ojbNzS9jV/eL49Lf6cy71NIDfUBAQQQQGACAsx75n3jOa3ccencMXVGKZj3b77z16Vzo9Wrj6it21hAEbfrW7GieDV/U3Pzk06+uFbflsO8jxpa3Fe9bGeDpliccMJ5tVhEn+qce+edd84OOfS47IILb8vuvX9b9vZ7P651vqLfw7K6T51+TfveqrtcMu/b/7e2SINtPMa8Z95PMAfxEQQQ6DCBsQkd8775BKbMvI+Jz1LbrscVzWecedVgC7LYzjvM+tiarO6q7aIEJq7OjYnbtMlxlc8fe9zZtSYCZeb9HnvuXaudonGXHYtJWLCuMp4m3vPO+/Xu+VVm3j/17O9MzaTMvD/kkGOnbjt4l03iqpj3r7z2p3OLybi4Xnr5PZVYzNq8L1tBMa7vTb9W9T5yZeb9XfdMf6uH0d/j2LYxbu0x6ViDa5jIsdvAW+9OX7gY7Z+fy79bmfcdzjgNDQEEEOg2AeY9877S/EAeWJ4HdpFNCub9c5u/VTovqlubiVXO++9/SGl7k86/ij4Xc+k6mpi3ef/Clu9ksXq9qO9NHzvu+HNqsYjdI88t2fGwat/CzI9bRb7x1l/WOnces2nm41X7WOV9VeqlzPt+/V3ONZriI/Oeed/t6ZLRIYBAXQJjE03mffMJTJl5H6u7lytxiC2mdt9j5VgtjEuK4yrj2G5t9H/Z9uZ1J4hl5n2sOp4FszDujzrq1Il5xH3tR1nkP5eZrcz7hayKeR8XmYzT4rxeu+Syuytpb5bmfRjU8xrvuPOceOL5lViUmff3PbCt0ufr/q7Hrhw33vR8tscUf9ti3LEqf90512evbf3zmfSz7ri6/n7mfd00zvsRQAABBBIhwLxn3ssVbZu/SAMpmPfjzKCz1127qM9l842nn/3dqVaYxyrovC4y/Fi282PK5v2zm781uH/8uHnyuNfq1tDqmvd5DCP2e++9/1R1g6hhnXb6FdlLL3+vslbi/FEnGMdgXq9F3STnUfbIvG++9l3G2vHxrMf9vZ7X74zzTHUBwUmJzEt0AwEEOkJgbDLFvB//pTpJ0pGaeR+mcdzLfNyXc1xZve6c67Lb7ng1i/tJb3n1+9l7H/zDklvWP/XMNwrbTd28P/OsDYX9zhnFRQknnXxRds11T2abHvl0sPtBbCtWZVJQts05876aeR8XmuRxWM7HFMz7Dz7+lyRYpGre53+fYwv8q65+JFu130FT8dpll12z6294Zsm/e/l5PU72/cm870h2aRgIIIBA/wgw75n3SxpE8sPJ8sM2c0vBvI9bg5XNnWN+U4XvW+/8TbZy5arSdqL9Aw44LDvn3Ouz2+98LYta4su/rBst1f6jj3+tsN1Uzfu33/vbbO+9x9++btIaWpmJPKl5H+yjbnDt9U9NfTvIMPGvuPKB7JPP/qOSZuJ2mWW6m+fxKnW6Mu5r155WaaxLadzr/fvbP2nMK5j3/8/CwsLP/J8Lg/9tgr9VzPv+zQGNGIGZEhibTDHvm08wUjPvzz3vxlINxD2gH/3Kr09sVj3x1NcL207ZvH/ksV8r7HN8YcfV4XF/7rjH26SJ2F57FU94mffVzPu42rssedqw8dHsuhuensv/x5/8zUoamOXK+5g0l7G48KLb5sIheN/3wAeVWMx75f3o72hsHfjEU7+Vrb/qocHOGrGivozfuONnnnV1Fm2Ntu/nZr4vmfczzfk0jgACCCAwOwLMe+a9/NDK+0UaSMG8P/iQY0vnPVVrfjEHKpsjhcEZ7cTOZ5PMiR5+9KuFbadq3seubGUsjjzy5CwuRpiURdQZitqexrwfjskzz/9edvXGx7Jjjj0z23XXFYXnKjr/8LETTjgvi1sUDLdb9LysFhL1wHnVbeI8n3y29Nyded/MXL5IB47VY1vBvP/v2aWyWh4h8Ozw376Kz5n3IxD9iAAC0xEYm6xVTeR9GVf/Mk7JvH/z7R8NtoQu+gKKyVmVK1THxf7BTR8X6itl8/7oo88o7PP+BxyavfrGny05QRnHIyZwO++8c2H7zPtq5v3rb/6wkF9oeMsrfzxVfMbFbtLXyiasDz38aSN93XXX3Qp5PPDQx420P+m4iz633Ob9aJ/i9hhRHNlw9SPZUWtPK/1bWPT38cr1DyXHd3R8bf2ZeT9dUufTCCCAAALLRoB5z7yXHzLvF2lguc37uF/5TjvtVDhnjIuZ3//gHxf1eXQeEXWQstv/nXPuDVmVe4qPtjn88933vlvYvxTN++BZVtM56+xrKpnaw2Mfff7AQx8VsmjKvB8+30fbf5rFbpkbr308ixpd3P6xaO5bdCziPtxW0fPTz1hf2F7s6ln0/uU8xryvXtNezjj14dzM+2XL44tOzLwvouIYAgjMlUBhMpUnZ8z75hOYlMz7G258tjD+Bx54RPbhJ/86dUJ9x5ffKGw/VfP+9a1/UTixjYlq3N9t2kQxJsb579boI/O+mnkfV3iXFQ7iiv1pY9T052dt3q9efUShpuJ3u+mxTNteaub96Hje//Cfsrio4sKLbl9yS8jYQn/ai3lGz+/nX3zfMu/nmgM6GQIIIIBAcwSY98z75PJv+WXz9Zy6TJfbvN94zVcK54tRj4jV11XGc3VJGwcffMzUCz7i/Dff8mJhH1M072MlIsCECgAAIABJREFU92gtJ35es+ao7MNP/q0Sz3HM45YDRe3Pwrwf7Udc3B47UV562d3ZvqvWFPYj71tcEPLs878/drzRTv7+4cequhvt3yx/Zt4v/9/KWca3TW0z75tLzBtoiXnfAERNIIDAdAQKk6k8sWLeN5/ApGTelxmLMXlqIrmJ+2HlWhp+TNW8L0uSjj6m2qR2KWbPv/CHhTyCDfO+mnkfjMvuXZ6iYV32O9bUyvvYsm74dyt/fu75NzXyO7yUpuu8nrp5PzyW2HUkCidlt7kIzhddfGdyjIfH0NbnzPvpkjqfRgABBBBYNgJ1zfv/b2Fh4X/3f1kZrF1YWPi9PH8uejzt9Cvke1bTT6WB5TTvY2X1OBO26vw56jdFvx933Pn6VGzy+crFl365sP0UzfsTTzy/sK+33PZSIywuv+K+wvbnYd7n8YjHuEXfvfe/P1Y/sbJ++DOjz4NJkW723nv/iW8rMHqOpn5m3jdf+24qNn1rp6wuPfS7ZNv8+aX6zPv5sXYmBBAoIVCYTOVfCsz75hOYlMz7NQetLYz/a1v/fGwSXjV5iitqcy0NP6Zq3q/fsKmwv3F1ddUxj3vfzbcWT16CDfO+unl/yqmXFsbp+BPObSRO42JY97VZm/dlF8isWrUmuQlxm8z7PM6vvP6DbOXK/Qr1FjuU5O/z2Nx3JfO+JFtzGAEEEEAgdQJ1zfv/I/UB9aR/zHvm/Exz+uU072+86fnCeUzUH2KL9Lfe/XGlse+338GF7bz1zt9U+vxSc6UjjjipsP0UzfuYAw7XtvLncXu/pcZZ5fW1R59e2P68zfu8r2++89dZ3EIyH+fw4x577j0w+fP3jj4+/dw3Cz8XbcTCltH3L+fPzPvm5vPLGccunJt5n1T2ybxPKhw6g0A/CZQmU5FQMe+bT2BSMu/32mvfRfGP7a+mvdd9JEyx7f6KFXssaj90lap5f94FNxf295773m1kYhErN4YnO8PPmffVzfvrb3imkGMUILZ99M+NxKqppP/kUy4p7OuDmz5ppJ+bHvm0sP3Q1gsvfbuRczTFoo3mfYz91ttfLmW87cN/SopxU7FaznaY9/1MRo0aAQQQ6AAB5n07g8i8Z97PNJ9fLvP+1Td+kO22W3E9JuaKcX/2qjn/rrvutmg+FLcR2/75zyu3UXauuHVZtDVcG8mf1zXvP/ns54W3QYz2muhrjGGPPVYu6mvU0D7+9N+nZhHb1hexjv4vl3kfY35w08eLxpzH6JXX/rR03HEbgRUrdi/8bNyKoUwTy3E8bpOZj2n48cijTkmqn8vBxjmb9wXGMWXeJ5VMMu+TCofOINBPAoUJSp6sMO+b/5JOxbwvm9iEAToukaj6Wtm9ukJbqZr3ZeZiE1ucx1XtwTb/3Rp9ZN5XN+9ffu1PSjneePPmRvRbVedLve+sszcW9vWe+95rpJ/jLpKJi1GW6t88Xy/7/brvgW1J9XOUSfzujv6+5j+7733z35HM+34mo0aNAAIIdIAA876dQWTeM+9nOhdZDvM+LjA+9NDjS+cwO++8S7bllT+uNO4PPv6XwnZ232Nlpc+Pzq1Gf77p5s2F7cd8q655H22XLSCJefPouev+HAZ9Pg8cftx112ZqaLfe/kph+3Gu5TTvY3FPjHF4zPnzML3HcSzbNTF2c2hi0dC4c9d57cUt3y0cX/we1WnHe5uvD/SNKfM+qWSSeZ9UOHQGgX4SKExQ8kSMed984pGKeR8JUNlVsO9/8I9TJahxVfPBhxxbqq1Uzfuz111b2Ocm7uV21dWPFLad/64x76ub96Hdw4/4UiHPvfc+IIsr1lNJ8C+48NbCfsbEvKk+xqqJXEfDj7GCoalbYDTR17aa9zH2stUgW179fmNxbIJxF9pg3vczGTVqBBBAoAMEmPftDCLznnk/03x+3ub9u9v+Pjv6mOLbF+ZzxYsuvqPymH+x6GPnRfPNX+zY+NPK7RTNU+K+6qtXF29DH32dxLzfZ58DFvU12mpqi/8yE3vaHdmihnZQyW0to//Lad5H7Fbtd1Ah1yee+vpYDdz3wAeFn4sx3XLblrGfLdLMrI69vvUvCvu53/6HJNPHWY1du83X/adhyrxPKplk3icVDp1BoJ8EChOUSKTiP/O++S/xlMz7snuXPfLYV6dKUMfdWy10lap5f+nl9xT+Ppxz7g1T8YiV4mUXSuS/a8z7epO3u+55qzBWwfPCi26bKl7TJNqjn12/YVNhPy+7/N7G+vjM879XeI5gcfwJ5469D91of2f5c1vN+yjE5L+no4+xzeMsmfWxbeZ9P5NRo0YAAQQ6QIB5384gMu+Z9zPN5+dp3j+7+VvZgWuOLJ27xFxm9erDs7pzmJUrVxW2+cRTvzUVu43XPl7Ybj7nmsS8P/jgYwrbfOrZ35mqr/m8bN9Vawrbf/Qrvz5V+9ff+GxhuzmL5TTvt3/+n6W3YIhaV86m6DF2K1hVwmyvvVZlYZoXfW7ex2LOHxek5Lzzx5133jn7+NOfJdHHeTNxvub9gCpMmfdJJZPM+6TCoTMI9JPAouQkT1LikXnf/Jd1SuZ9GHvD8c6fh8lWJakoes8LW75Tep+uvP1Uzfvb7ni1kMcee+6dxRXsReNd6thH23+aHXnkyYXt5jzikXlfz7yPq/THXZkesVwqNpO8HvGMe7dV/WxsCT8c5/x50/dOO/mUSwrPE+e75NK7Kve36rjifbE6oM4Kg3mY9+998A+Nj/WBhz4qZLvnnvs0fq46/Lv6XuZ9P5NRo0YAAQQ6QIB5384gMu+Z9zPN6edh3m99668GF7CH0ZjPN4seY0HB5hf/qPZ41649rbDds9ddU7utfA7z3OZvjb2tYPR/EvP+zLM2FPb12uufmriveZ/j8djj1hW2f/oZ6ydu/4WXvl26LX0ex6rm/Szmw3HhQ96P4ce4/UKVXQ/v+PIbhZ+Ptg459Lgsbs0wzLip53VZxCr74fHlz598+rdn0r+mxqmd5uv2y8mUeZ9UMsm8TyocOoNAPwkUJid5ksK8bz4JSMm8L7u6N6443fTIp7UT1Odf+MOs7KrsXFPxmKp5X7ZVVvR53TnXZXHFcZ0kLiYhZZO7YR7xnHlfz7yPOMTfp6Kro4PnTjvtnG3Y+OjAYK4Ts7L3xsTvmmufyGJb/qr3B4y2Xn71+4V/Y6N/zz7/+7X0VNa3OP7K6z8ovRo+eJxz7vW1LjoYd664eOG2O14ZbHH44KaPK49hHuZ9FGu+dNKF2VLb940b3/Br8Tu8pmT7wnXnXF957MNtej7+e5V5389k1KgRQACBDhBg3rcziMx75v1Mc/pZmfdxEXXMxU497fIlTfCYD8Z275OuDr9642Olc9rHnviN2vyeee6bWVwIHf0a938S8/66G54ubDO2548L8aedi8VFAEV9jrrEw49+Xrv9uIghVqAXtTl8rKp5f8lld2fHHHvmoC9161dFbGLl/FElF2+ceOL5lcYbt14Yt6AlDPyoZxSdf5JjUSc64cTzs7rz9VNOvbQwDqedPvnipkn67zPj6wVd58O8TyqZZN4nFQ6dQaCfBAqTkzxJZN43nzSkZN6/8tqfDkzOPN7DjytW7JHFitMqiVEk47fe/nK2+x4rd9DTLrus2OHnvP1UzfsYa9nkOvp+4UW3V57wPf3cNwdXEedjjseY0JXdO5t5X9+8j3iV3eog537MsWdlk14pHdujPfzoV7O4bcJuu+35hZbrmPcxYT7ggMO++Gzer3iMC13iKvS62xaW/U6Ou6I9znfwIccOCjyxYr6sjbLj8ZmnnvlGdvGlX8723nv/L8aTmnl/xpm/Wmlx2OEnZjfevDnb+vaPao83OLz+5g+zcUay78fmvx+D+zjmv/z9eauf6ZpRI4AAAggkToB5n3iASrrHvGfeTzRXKJs3jR4vqy9Ezvva1j8f+//VN36QxQKJmIfFvcNjbnPe+Tdlhx/xpWypVfbD884wyh99/GsTjzN2Vyy7aD5qQJse+axS22EE33Tz5kUXnZfVjSYx76Ovw2Mffh4m9ONP/uZUt5Xb8ur3S2tou+22x2C+PaqBop9jJ8Fbbnsp2333vXbobxmLOuZ9PubYqfDa65/MXn3jzyrFZ7Sfb7/348Ft+PL2Rh/vvvfdyu2GlkfHOtxe6Oj6G56ptbPfcH+j/bhwIy4EyNuta97HAoX8s6OPZ529MYsdEobP6fls6gF958q8L8nWlucw8355uDsrAggMEShNTiJZYU40n4ykZN5HUnTW2deM1UCsYg0Dc9tH/7woUY1JQNzf/pBDjl3URkwmY6IwmvTGzymb9w89vL2wz/k4DjzwiOyW27ZksTXdaFIZV7/HNumxwrhocnvpZXdn++xzQGH7zPvJzPuY9I7bMj6PW1zpHffUe/LpbxRqOdp5852/HhQ14mr6006/MovbJeSfH36sY96HRmLF/vDnR5+HVsLgjwJO3Mqi6H/Vbf6Wupghzr1mzVHZ+qseGqy8KLodRFxw8PZ7fzu46CEKRGevuzbbZ9/VhWNI2bzPOQfftUefnq3fsGlQTHnjzR8u+t3Nf5fj9zB2HYmJflnhJNo95dTLStvI2/I42fcn834oQ/MUAQQQQKBNBJj3bYrWr/rKvGfezzSvLzPv87nKrB+POOKkwQUC085NYoX/uL6efMqlg/ll0Tbqsao6dn0suu1dLG4omy9PYt7HOJeaT8QuBHFhe9wOoGjuHcfigokyZmVb8+d8Tjr5ouyRx75auB18XkOL8+fvzx/H1dAmMe/zduMxLvi4/Mr7B/WqMLrLVuXHjoNxoccFF96WxYKe4TaGn8f8OhbxlDEqOh67NIybY0f7e+yxMjv/gluye+57N4t5e9HCg6hNbn7x29mdd23NLrr4ztIFOHXN+1hUERdgDI9z9HlcZBC/07G7QZl26tb2ilg5NlktoQvcmPe/StASeMa8TyAIuoBA3wmMTUyY980nDKmZ97FVfJUty2Iisd9+B2eHHnr8wGgcl8hHghtXvcaEZzTZjZ9TNu9jEnPCCecV9nt0LLEC+aCDjx7832uvfcd+JiY3H23/GfP+q/81mIyNsoyf46KISZLt2MY9igVFbZYdiwn7vvsemK3a76CxV4AXfb6ueR8GeZynqK2qx6reTzD0G6vjq7Yb74t71cXtAOIebzFZrvPZNpj3ReOJIlH8/saFDFHIWL368Gyp3+G8nX1XrcliFcIkWvWZpb9Tlyq2LSwsWHnf98zV+BFAAIE0CTDv04zLUr1i3jPvZ5rXL5d5H3Ob2B2xrsFaNl95+bU/qTRvjrnloG502AnZ/gccmq1YsfvY+eXNt740MIvzudbw46Tm/WOPf610dfxw++Oej9uFMnZMKLvQf7jNxTW08SziAofYNXC4jfz5tOZ93k7+GH2LnQBjccov5sNHZCtX7ld47vwz+WPUDyfd5j4ulF+qlpifJ3+M88Vih+hv6Cs/vtRjXfM+tH/1NV+p3H7Z+d+ccOe/st89x5euIXSJEfN+qbRtrq8z7+eK28kQQKCIwNjEhHnffJKQmnkfSU5sHVa2nXtZQjru+GWX3zuYALfRvA8ecbVxmHrjxljntZiw5yucrbxv3ryPmEVR4vIr7pt6kl4lrnXN++jfU8/+zpJXcY87d1XzPs4V/2MLvl133a0xDZf1ra3mfdl4ljoexY3YTj/n7LH570jmfVGq5hgCCCCAQAsIMO9bEKSCLjLvmfczze3nbd6HcX7DTc9NvP34uPlN7MgYpu9Sc6aqr1919SMD9rHSu+gzk5r3MYYNGx8tbLPoPEXHxpn30X7USusYyUXnGD4WtYxod17m/fC56zyPBRAvvvy9qX5nnt38rcGFHXXOO8l7JzHvo65UVrOt2gfmffM1gnF/l7r2GvO+IFNbvkPM++Vj78wIIPBLAmMTWuZ980lHWSIYptByJh2PfuXXB1eyVk1Ii94Xq5mHV0+31byPOETCffTRZ4z9/ShiMHrstNOv2GG7NOb9bMz7/Hfniad+q/A2DqNxmeTnMMPPOPOq7N1tP5nod/WV1/40i/vsTXLuuuZ98Hjp5e8NdrmY5HxLfSaKNvG3LFZg5OyXeozbSRS1G7eaWOqzVV9fagvDovNXORZb78ctRt55/+8a62vVMfXtfcx7+SkCCCCAQEsJMO/bGTjmPfN+pvn9PMz7VavWDLY5f+Kprze20r5sDhIrp6vs3DhujhWr8e/88htfcJ+FeR/9DwM+dpgb15ey15Yy76P9Rx77tWyvvVZN1H5+3qgx3Hr7K1+wmNa8j1s15m03/Ri3K3zjrb/8oq9lGqlyPG45GVveN7mIaHi8sUPmfQ98MFFfw8CPWy4utWvE8PmGnzPvm6+jV9FUV97DvE8qmWTeJxUOnUGgnwTGJnbM++aTjlTN+0h0tr79o+yMMzfUvpp6p512zuK+XqNGXpvN++AR90GPpL3qdtrDCfuBa44cTBZHE0jm/WzN+1/E7efZPfe9lx111Klj/74Nx6vseWzpdsKJ52e33/la9v4H/zjR5G9UA5tf/KPsksvuzo486pRst932rNTHScz7/LxRDIm/O/F7WjbOKsdjZUHc/uGGG5/N3nznr2uzmId5H7emiHvpBd8mds+IMceKj6ef/d3a4835e6z3Pcq872cyatQIIIBABwgw79sZROY9836meX6T5n2YiatXH5GddPLF2WVX3Jfdfe87y7IrWNx6MeZ2deeX8f5TTr0si/u+D8+RZmXexzk+/vTfs4ce3p6ddfbG7KCD1lZeLV/FvI/2w8w+/Yz1E7GIOMYF/sMspjXvo4YVbVxx5QPZIYcel8VF6FXm+mXviZgde9y6LBb7DPezqeex/f6FF91e6ZYMZX3Mj8f8/5JL78qee+EPGunr+x/+06AOFBct7L//IZU5Mu/rzf+b0lJX2mHeJ5VMMu+TCofOINBPAmMTEOZ980nH5he/PdhiK9gO/3/6uW82kmA2kbC8+sYPsvUbNg1Wnu+yy4pCjcTV1nHv+muuezKL9xedN8zO4THmz59/4Q8L31/URhz7aPtPC9t58ulv1GqnrP2ljn/w8b8MroaOiWbZldWx60BMjuJ+43Ebgrj3eFG70eecw/BjTCqL3l927LnN3ypspwmDedtH/1zY9jPP/16tPpb1fcur3y9sPy4eKfvMpMfjfnQ33fJCdtrpVw62Zhu3zV9c9R4xjMl3bLMXq/jDDJ703FU+t/3znw8m/C+98j+zZ5///UIuoZNYRV+lvXHveevdH2d33Pl6dva6a7M1B63Nyn63Y/IbV8DH5Dc0f+X6B7PYIjF+D8a1v9RrL275buH4Znn/+Lff+9ts0yOfDf6exaQ7Cjbxu5pP8Ecfw6w/+JBjs7hYInYRic8vNS6vN/s9ybzvZzJq1AgggEAHCDDv2xlE5j3zfqb5/rg53nA9oOh51E3if9Rbpp2LzWLO8vKr3x/MFdeuPa10BXUshIj7tV93w9NZmP5F/Yid7YrG/8JL3y58f1EbVY9FbSlqBNF23Nqu6LxxrO48MEzo9Vc9NLjYvWyeHSzCCL/2+vIaWtzCsahPsQCg6hiH3xfthfF+9cbHslNPu3ywS+G4FeVh1kcdIBb13Hjz5mzrW3810XmH+1Dl+Yef/Ntg3n7xJXcOFmHsvvtepXP2mMPHbhOhqzD+77rnrcFCpCrnmeY9UWuLC0/idzJqRUVximOzriFNMwafbbZ2MQuezPukkknmfVLh0BkE+klgbEIUiccsvoy0mX7CkMcortwNcy3u8x1JakxK+r519C+S9h8MeITpGhOa2ForZ+YxXX2HniNeEbfQc5jicQ/zmNSGkd6n2MV4YwV9XEwRLF7Y8p1BISNY1L2YpE3cYtzvvP+TL4o2MfYo4MSxjz+d7cUabeK0XH1l3vczGTVqBBBAoAMEmPftDCLznnnfqzngrHL8mD/GxeL5PDsuOJj0dnOz6uO82i2uoU12671Z9TliM7iIYct3BrWAMKWjzpeS8fzutr8fXLgS8/X4H32Miyo+/ORf/c76u91ZDTDvk0ommfdJhUNnEOgnAea9pKezSc+sJjraTdecFxuxoYF2a4B5389k1KgRQACBDhBg3rcziMx79RD1EBqgARqgARpIQgPM+6SSSeZ9UuHQGQT6SYB5L0FJIkFhuLXbcBM/8aMBGmhCA8z7fiajRo0AAgh0gADzvp1BZN6rh6iH0AAN0AAN0EASGmDeJ5VMMu+TCofOINBPAsx7CUoSCUoTpo82mIc0QAM00G4NMO/7mYwaNQIIINABAsz7dgaRea8eoh5CAzRAAzRAA0logHmfVDLJvE8qHDqDQD8JMO8lKEkkKAy3dhtu4id+NEADTWiAed/PZNSoEUAAgQ4QYN63M4jMe/UQ9RAaoAEaoAEaSEIDzPukkknmfVLh0BkE+kmAeS9BSSJBacL00QbzkAZogAbarQHmfT+TUaNGAAEEOkCAed/OIDLv1UPUQ2iABmiABmggCQ0w75NKJpn3SYVDZxDoJwHmvQQliQSF4dZuw038xI8GaKAJDTDv+5mMGjUCCCDQAQLM+3YGkXmvHqIeQgM0QAM0QANJaIB5n1QyybxPKhw6g0A/CTDvJShJJChNmD7aYB7SAA3QQLs1wLzvZzJq1AgggEAHCDDv2xlE5r16iHoIDdAADdAADSShAeZ9Uskk8z6pcOgMAv0kwLyXoCSRoDDc2m24iZ/40QANNKEB5n0/k1GjRgABBDpAgHnfziAy79VD1ENogAZogAZoIAkNMO+TSiaZ90mFQ2cQ6CcB5r0EJYkEpQnTRxvMQxqgARpotwaY9/1MRo0aAQQQ6AAB5n07g8i8Vw9RD6EBGqABGqCBJDTAvE8qmWTeJxUOnUGgnwSY9xKUJBIUhlu7DTfxEz8aoIEmNMC872cyatQIIIBABwgw79sZROa9eoh6CA3QAA3QAA0koQHmfVLJJPM+qXDoDAL9JMC8l6AkkaA0Yfpog3lIAzRAA+3WAPO+n8moUSOAAAIdIMC8b2cQmffqIeohNEADNEADNJCEBpj3SSWTzPukwqEzCPSTAPNegpJEgsJwa7fhJn7iRwM00IQGmPf9TEaNGgEEEOgAAeZ9O4PIvFcPUQ+hARqgARqggSQ0wLxPKplk3icVDp1BoJ8EmPcSlCQSlCZMH20wD2mABmig3Rpg3vczGTVqBBBAoAMEmPftDCLzXj1EPYQGaIAGaIAGktAA8z6pZJJ5n1Q4dAaBfhJg3ktQkkhQGG7tNtzET/xogAaa0ADzvp/JqFEjgAACHSDAvG9nEJn36iHqITRAAzRAAzSQhAaY90klk8z7pMKhMwj0kwDzXoKSRILShOmjDeYhDdAADbRbA8z7fiajRo0AAgh0gADzvp1BZN6rh6iH0AAN0AAN0EASGmDeJ5VMMu+TCofOINBPAsx7CUoSCQrDrd2Gm/iJHw3QQBMaYN73Mxk1agQQQKADBJj37Qwi8149RD2EBmiABmiABpLQAPM+qWSSeZ9UOHQGgX4SYN5LUJJIUJowfbTBPKQBGqCBdmuAed/PZNSoEUAAgQ4QYN63M4jMe/UQ9RAaoAEaoAEaSEIDzPukkknmfVLh0BkE+kmAeS9BSSJBYbi123ATP/GjARpoQgPM+34mo0aNAAIIdIAA876dQWTeq4eoh9AADdAADdBAEhpg3ieVTDLvkwqHziDQTwLMewlKEglKE6aPNpiHNEADNNBuDTDv+5mMGjUCCCDQAQLM+3YGkXmvHqIeQgM0QAM0QANJaIB5n1QyybxPKhw6g0A/CTDvJShJJCgMt3YbbuInfjRAA01ogHnfz2TUqBFAAIEOEGDetzOIzHv1EPUQGqABGqABGkhCA8z7pJJJ5n1S4dAZBPpJgHkvQUkiQWnC9NEG85AGaIAG2q0B5n0/k1GjRgABBDpAgHnfziAy79VD1ENogAZogAZoIAkNMO+TSiaZ90mFQ2cQ6CcB5r0EJYkEheHWbsNN/MSPBmigCQ0w7/uZjBo1Aggg0AECzPt2BpF5rx6iHkIDNEADNEADSWiAeZ9UMsm8TyocOoNAPwkw7yUoSSQoTZg+2mAe0gAN0EC7NcC872cyatQIIIBABwgw79sZROa9eoh6CA3QAA3QAA0koQHmfVLJJPM+qXDoDAL9JMC8l6AkkaAw3NptuImf+NEADTShAeZ9P5NRo0YAAQQ6QIB5384gMu/VQ9RDaIAGaIAGaCAJDTDvk0ommfdJhUNnEOgnAea9BCWJBKUJ00cbzEMaoAEaaLcGmPf9TEaNGgEEEOgAAeZ9O4PIvFcPUQ+hARqgARqggSQ0wLxPKplk3icVDp1BoJ8EmPcSlCQSFIZbuw038RM/GqCBJjTAvO9nMmrUCCCAQAcIMO/bGUTmvXqIeggN0AAN0AANJKEB5n1SySTzPqlw6AwC/STAvJegJJGgNGH6aIN5SAM0QAPt1gDzvp/JqFEjgAACHSDAvG9nEJn36iHqITRAAzRAAzSQhAaY90klk8z7pMKhMwj0kwDzXoKSRILCcGu34SZ+4kcDNNCEBpj3/UxGjRoBBBDoAAHmfTuDyLxXD1EPoQEaoAEaoIEkNMC8TyqZZN4nFQ6dQaCfBJj3EpQkEpQmTB9tMA9pgAZooN0aYN73Mxk1agQQQKADBJj37Qwi8149RD2EBmiABmiABpLQAPM+qWSSeZ9UOHQGgX4SYN5LUJJIUBhu7TbcxE/8aIAGmtAA876fyahRI4AAAh0gwLxvZxCZ9+oh6iE0QAM0QAM0kIQGmPdJJZPM+6TCoTMI9JMA816CkkSC0oTpow3mIQ3QAA20WwPM+34mo0aNAAIIdIAA876dQWTeq4eoh9AADdAADdBAEhpg3ieVTDLvkwqHziDQTwLMewlKEgkKw63dhpv4iR8N0EATGmDe9zMZNWoEEECgAwSY9+0MIvNePUQ9hAZogAZogAaxuqLJAAAgAElEQVSS0ADzPqlkknmfVDh0BoF+EmDeS1CSSFCaMH20wTykARqggXZrgHnfz2TUqBFAAIEOEGDetzOIzHv1EPUQGqABGqABGkhCA8z7pJJJ5n1S4dAZBPpJgHkvQUkiQWG4tdtwEz/xowEaaEIDzPt+JqNGjQACCHSAAPO+nUFk3quHqIfQAA3QAA3QQBIaYN4nlUwy75MKh84g0E8CzHsJShIJShOmjzaYhzRAAzTQbg0w7/uZjBo1Aggg0AECzPt2BpF5rx6iHkIDNEADNEADSWiAeZ9UMsm8TyocOoNAPwkw7yUoSSQoDLd2G27iJ340QANNaIB5389k1KgRQACBDhBg3rcziMx79RD1EBqgARqgARpIQgPM+6SSSeZ9UuHQGQT6SYB5L0FJIkFpwvTRBvOQBmiABtqtAeZ9P5NRo0YAAQQ6QIB5384gMu/VQ9RDaIAGaIAGaCAJDTDvk0ommfdJhUNnEOgnAea9BCWJBIXh1m7DTfzEjwZooAkNMO/7mYwaNQIIINABAsz7dgaRea8eoh5CAzRAAzRAA0logHmfVDLJvE8qHDqDQD8JMO8lKEkkKE2YPtpgHtIADdBAuzXAvO9nMmrUCCCAQAcIMO/bGUTmvXqIeggN0AAN0AANJKEB5n1SySTzPqlw6AwC/STAvJegJJGgMNzabbiJn/jRAA00oQHmfT+TUaNGAAEEOkCAed/OIDLv1UPUQ2iABmiABmggCQ0w75NKJpn3SYVDZxDoJwHmvQQliQSlCdNHG8xDGqABGmi3Bpj3/UxGjRoBBBDoAAHmfTuDyLxXD1EPoQEaoAEaoIEkNMC8TyqZZN4nFQ6dQaCfBJj3EpQkEhSGW7sNN/ETPxqggSY0wLzvZzJq1AgggEAHCDDv2xlE5r16iHoIDdAADdAADSShAeZ9Uskk8z6pcOgMAv0kwLyXoCSRoDRh+miDeUgDNEAD7dYA876fyahRI4AAAh0gwLxvZxCZ9+oh6iE0QAM0QAM0kIQGmPdJJZPM+6TCoTMI9JMA816CkkSCwnBrt+EmfuJHAzTQhAaY9/1MRo0aAQQQ6AAB5n07g8i8Vw9RD6EBGqABGqCBJDTAvE8qmWTeJxUOnUGgnwSY9xKUJBKUJkwfbTAPaYAGaKDdGmDe9zMZNWoEEECgAwSY9+0MIvNePUQ9hAZogAZogAaS0ADzPqlkknmfVDh0BoF+EmDeS1CSSFAYbu023MRP/GiABprQAPO+n8moUSOAAAIdIMC8b2cQmffqIeohNEADNEADNJCEBpj3SSWTzPukwqEzCPSTAPNegpJEgtKE6aMN5iEN0AANtFsDzPt+JqNGjQACCHSAAPO+nUFk3quHqIfQAA3QAA3QQBIaYN4nlUwy75MKh84g0E8CzHsJShIJCsOt3Yab+IkfDdBAExpg3vczGTVqBBBAoAMEmPftDCLzXj1EPYQGaIAGaIAGktAA8z6pZJJ5n1Q4dAaBfhJg3ktQkkhQmjB9tME8pAEaoIF2a4B5389k1KgRQACBDhBg3rcziMx79RD1EBqgARqgARpIQgPM+6SSSeZ9UuHQGQT6SYB5L0FJIkFhuLXbcBM/8aMBGmhCA8z7fiajRo0AAgh0gADzvp1BZN6rh6iH0AAN0AAN0EASGmDeJ5VMMu+TCofOINBPAsx7CUoSCUoTpo82mIc0QAM00G4NMO/7mYwaNQIIINABAsz7dgaRea8eoh5CAzRAAzRAA0logHmfVDLJvE8qHDqDQD8JMO8lKEkkKAy3dhtu4id+NEADTWiAed/PZNSoEUAAgQ4QYN63M4jMe/UQ9RAaoAEaoAEaSEIDzPukkknmfVLh0BkE+kmAeS9BSSJBacL00QbzkAZogAbarQHmfT+TUaNGAAEEOkCAed/OIDLv1UPUQ2iABmiABmggCQ0w75NKJpn3SYVDZxDoJwHmvQQliQSF4dZuw038xI8GaKAJDTDv+5mMGjUCCCDQAQLM+3YGkXmvHqIeQgM0QAM0QANJaIB5n1QyybxPKhw6g0A/CTDvJShJJChNmD7aYB7SAA3QQLs1wLzvZzJq1AgggEAHCDDv2xlE5r16iHoIDdAADdAADSShAeZ9Uskk8z6pcOgMAv0kwLyXoCSRoDDc2m24iZ/40QANNKEB5n0/k1GjRgABBDpAgHnfziAy79VD1ENogAZogAZoIAkNMO+TSiaZ90mFQ2cQ6CcB5r0EJYkEpQnTRxvMQxqgARpotwaY9/1MRo0aAQQQ6AAB5n07g8i8Vw9RD6EBGqABGqCBJDTAvE8qmWTeJxUOnUGgnwSY9xKUJBIUhlu7DTfxEz8aoIEmNMC872cyatQIIIBABwgw79sZROa9eoh6CA3QAA3QAA0koQHmfVLJJPM+qXDoDAL9JMC8l6AkkaA0Yfpog3lIAzRAA+3WAPO+n8moUSOAAAIdIMC8b2cQmffqIeohNEADNEADNJCEBpj3SSWTzPukwqEzCPSTAPNegpJEgsJwa7fhJn7iRwM00IQGmPf9TEaNGgEEEOgAAeZ9O4PIvFcPUQ+hARqgARqggSQ0wLxPKplk3icVDp1BoJ8EmPcSlCQSlCZMH20wD2mABmig3Rpg3vczGTVqBBBAoAMEmPftDCLzXj1EPYQGaIAGaIAGktAA8z6pZJJ5n1Q4dAaBfhJg3ktQkkhQGG7tNtzET/xogAaa0ADzvp/JqFEjgAACHSDAvG9nEJn36iHqITRAAzRAAzSQhAaY90klk8z7pMKhMwj0kwDzXoKSRILShOmjDeYhDdAADbRbA8z7fiajRo0AAgh0gADzvp1BZN6rh6iH0AAN0AAN0EASGmDeJ5VMMu+TCofOINBPAsx7CUoSCQrDrd2Gm/iJHw3QQBMaYN73Mxk1agQQQKADBJj37Qwi8149RD2EBmiABmiABpLQAPM+qWSSeZ9UOHQGgX4SYN5LUJJIUJowfbTBPKQBGqCBdmuAed/PZNSoEUAAgQ4QYN63M4jMe/UQ9RAaoAEaoAEaSEIDzPukkknmfVLh0BkE+kmAeS9BSSJBYbi123ATP/GjARpoQgPM+34mo0aNAAIIdIAA876dQWTeq4eoh9AADdAADdBAEhpg3ieVTDLvkwqHziDQTwLMewlKEglKE6aPNpiHNEADNNBuDTDv+5mMGjUCCCDQAQLM+3YGkXmvHqIeQgM0QAM0QANJaIB5n1QyybxPKhw6g0A/CTDvJShJJCgMt3YbbuInfjRAA01ogHnfz2TUqBFAAIEOEGDetzOIzHv1EPUQGqABGqABGkhCA8z7pJJJ5n1S4dAZBPpJgHkvQUkiQWnC9NEG85AGaIAG2q0B5n0/k1GjRgABBDpAgHnfziAy79VD1ENogAZogAZoIAkNMO+TSiaZ90mFQ2cQ6CcB5r0EJYkEheHWbsNN/MSPBmigCQ0w7/uZjBo1Aggg0AECzPt2BpF5rx6iHkIDNEADNEADSWiAeZ9UMsm8TyocOoNAPwkw7yUoSSQoTZg+2mAe0gAN0EC7NcC872cyatQIIIBABwgw79sZROa9eoh6CA3QAA3QAA0koQHmfVLJJPM+qXDoDAL9JMC8l6AkkaAw3NptuImf+NEADTShAeZ9P5NRo0YAAQQ6QIB5384gMu/VQ9RDaIAGaIAGaCAJDTDvk0ommfdJhUNnEOgnAea9BCWJBKUJ00cbzEMaoAEaaLcGmPf9TEaNGgEEEOgAAeZ9O4PIvFcPUQ+hARqgARqggSQ0wLxPKplk3icVDp1BoJ8EmPcSlCQSFIZbuw038RM/GqCBJjTAvO9nMmrUCCCAQAcIMO/bGUTmvXqIeggN0AAN0AANJKEB5n1SySTzPqlw6AwC/STAvJegJJGgNGH6aIN5SAM0QAPt1gDzvp/JqFEjgAACHSDAvG9nEJn36iHqITRAAzRAAzSQhAaY90klk8z7pMKhMwj0k8BY8/6KKx/I7r73Hf8xoAEaoAEaoAEamLkGDjjgsLF5ycLCwlv9TNeMGgEEEEAgcQLM+8QDVNK9seZ9XFSoHqIeRAM0QAM0QAM0MA8NnHfBzUvVQ/67JJ9xuHkCzPvmmWoRAQRqEljqS8HrCwsYYEADNEADNEADaWiAeV8z0fN2BBBAAIG5EGDezwVz4ycZa94vpJH7yEHFgQZogAZogAZoIDTAvG88FSxtkHlfisYLCCAwLwK+/H350wAN0AAN0AANtEUDzPt5ZYjOgwACCCBQhwDzvg6tdN7LvJcDtyUH1k9apQEaoAEaYN7PL4dk3s+PtTMhgEAJAV/8vvhpgAZogAZogAbaogHmfUlC5zACCCCAwLISYN4vK/6JT868lwO3JQfWT1qlARqgARpg3k+c8tX+IPO+NjIfQACBpgn44vfFTwM0QAM0QAM00BYNMO+bzgS1hwACCCDQBAHmfRMU598G814O3JYcWD9plQZogAZogHk/v1yReT8/1s6EAAIlBHzx++KnARqgARqgARpoiwaY9yUJncMIIIAAAstKgHm/rPgnPjnzXg7clhxYP2mVBmiABmiAeT9xylf7g8z72sh8AAEEmibgi98XPw3QAA3QAA3QQFs0wLxvOhPUHgIIIIBAEwSY901QnH8bzHs5cFtyYP2kVRqgARqgAeb9/HJF5v38WDsTAgiUEPDF74ufBmiABmiABmigLRpg3pckdA4jgAACCCwrAeb9suKf+OTMezlwW3Jg/aRVGqABGqAB5v3EKV/tDzLvayPzAQQQaJrA/7mwsOA/BjRAA5No4P9aqJ84/9/+5vibSwM0MIUGXmg6EdIeAggggAACDRBg3jcAcRma+HyKnGSS+ZPPmHfTAA3QAA3QAA1MqoH/ZRlypb6eknnf18gbNwIIIIAAAh0gsPcE5v32DozbEBBAAAEEEEAAAQQQGCbAvB+m4TkCCCCAAAIIIIAAAu0lwLxvb+z0HAEEEEAAgd4TYN73XgIAIIAAAggggAACCCwsLDDvyQABBBBAAAEEEEAAgW4QYN53I45GgQACCCCAQC8JMO97GXaDRgABBBBAAAEEEBghwLwfAeJHBBBAAAEEEEAAAQRaSoB539LA6TYCCCCAAAIILCww76kAAQQQQAABBBBAAAEr72kAAQQQQAABBBBAAIGuEGDedyWSxoEAAggggEAPCTDvexh0Q0YAAQQQQAABBBBYRMDK+0VIHEAAAQQQQAABBBBAoJUEmPetDJtOI4AAAggggEAQYN7TAQIIIIAAAggggAACVt7TAAIIIIAAAggggAACXSHAvO9KJI0DAQQQQACBHhJg3vcw6IaMAAIIIIAAAgggsIiAlfeLkDiAAAIIIIAAAggggEArCTDvWxk2nUYAAQQQQACBIMC8pwMEEEAAAQQQQAABBKy8pwEEEEAAAQQQQAABBLpCgHnflUgaBwIIIIAAAj0kwLzvYdANGQEEEEAAAQQQQGARASvvFyFxAAEEEEAAAQQQQACBVhJg3rcybDqNAAIIIIAAAkGAeU8HCCCAAAIIIIAAAghYeU8DCCCAAAIIIIAAAgh0hQDzviuRNA4EEEAAAQR6SIB538OgGzICCCCAAAIIIIDAIgJW3i9C4gACCCCAAAIIIIAAAq0kwLxvZdh0GgEEEEAAAQSCAPOeDhBAAAEEEEAAAQQQsPKeBhBAAAEEEEAAAQQQ6AoB5n1XImkcCCCAAAII9JAA876HQTdkBBBAAAEEEEAAgUUErLxfhMQBBBBAAAEEEEAAAQRaSYB538qw6TQCCCCAAAIIBAHmPR0ggAACCCCAAAIIIGDlPQ0ggAACCCCAAAIIINAVAsz7rkTSOBBAAAEEEOghAeZ9D4NuyAgggAACCCCAAAKLCFh5vwiJAwgggAACCCCAAAIItJIA876VYdNpBBBAAAEEEAgCzHs6QAABBBBAAAEEEEDAynsaQAABBBBAAAEEEECgKwSY912JpHEggAACCCDQQwLM+x4G3ZARQAABBBBAAAEEFhGw8n4REgcQQAABBBBAAAEEEGglAeZ9K8Om0wgggAACCCAQBJj3dIAAAggggAACCCCAgJX3NIAAAggggAACCCCAQFcIMO+7EknjQAABBBBAoIcEmPc9DLohI4AAAggggAACCCwiYOX9IiQOIIAAAggggAACCCDQSgLM+1aGTacRQAABBBBAIAgw7+kAAQQQQAABBBBAAAEr72kAAQQQQAABBBBAAIGuEGDedyWSxoEAAggggEAPCTDvexh0Q0YAAQQQQAABBBBYRMDK+0VIHEAAAQQQQAABBBBAoJUEmPetDJtOI4AAAggggEAQYN7TAQIIIIAAAggggAACVt7TAAIIIIAAAggggAACXSHAvO9KJI0DAQQQQACBHhJg3vcw6IaMAAIIIIAAAgggsIiAlfeLkDiAAAIIIIAAAggggEArCTDvWxk2nUYAAQQQQACBIMC8pwMEEEAAAQQQQAABBKy8pwEEEEAAAQQQQAABBLpCgHnflUgaBwIIIIAAAj0kwLzvYdANGQEEEEAAAQQQQGARASvvFyFxAAEEEEAAAQQQQACBVhJg3rcybDqNAAIIIIAAAkGAeU8HCCCAAAIIIIAAAghYeU8DCCCAAAIIIIAAAgh0hQDzviuRNA4EEEAAAQR6SIB538OgGzICCCCAAAIIIIDAIgJW3i9C4gACCCCAAAIIIIAAAq0kwLxvZdh0GgEEEEAAAQSCAPOeDhBAAAEEEEAAAQQQsPKeBhBAAAEEEEAAAQQQ6AoB5n1XImkcCCCAAAII9JAA876HQTdkBBBAAAEEEEAAgUUErLxfhMQBBBBAAAEEEEAAAQRaSYB538qw6TQCCCCAAAIIBAHmPR0ggAACCCCAAAIIIGDlPQ0ggAACCCCAAAIIINAVAsz7rkTSOBBAAAEEEOghAeZ9D4NuyAgggAACCCCAAAKLCFh5vwiJAwgggAACCCCAAAIItJIA876VYdNpBBBAAAEEEAgCzHs6QAABBBBAAAEEEEDAynsaQAABBBBAAAEEEECgKwSY912JpHEggAACCCDQQwLM+x4G3ZARQAABBBBAAAEEFhGw8n4REgcQQAABBBBAAAEEEGglAeZ9K8Om0wgggAACCCAQBJj3dIAAAggggAACCCCAgJX3NIAAAggggAACCCCAQFcIMO+7EknjQAABBBBAoIcEmPc9DLohI4AAAggggAACCCwiYOX9IiQOIIAAAggggAACCCDQSgLM+1aGTacRQAABBBBAIAgw7+kAAQQQQAABBBBAAAEr72kAAQQQQAABBBBAAIGuEGDedyWSxoEAAggggEAPCTDvexh0Q0YAAQQQQAABBBBYRMDK+0VIHEAAAQQQQAABBBBAoJUEmPetDJtOI4AAAggggEAQYN7TAQIIIIAAAggggAACVt7TAAIIIIAAAggggAACXSHAvO9KJI0DAQQQQACBHhJg3vcw6IaMAAIIIIAAAgggsIiAlfeLkDiAAAIIIIAAAggggEArCTDvWxk2nUYAAQQQQACBIMC8pwMEEEAAAQQQQAABBKy8pwEEEEAAAQQQQAABBLpCgHnflUgaBwIIIIAAAj0kwLzvYdANGQEEEEAAAQQQQGARASvvFyFxAAEEEEAAAQQQQACBVhJg3rcybDqNAAIIIIAAAkGAeU8HCCCAAAIIIIAAAghYeU8DCCCAAAIIIIAAAgh0hQDzviuRNA4EEEAAAQR6SIB538OgGzICCCCAAAIIIIDAIgJW3i9C4gACCCCAAAIIIIAAAq0kwLxvZdh0GgEEEEAAAQSCAPOeDhBAAAEEEEAAAQQQsPKeBhBAAAEEEEAAAQQQ6AoB5n1XImkcCCCAAAII9JAA876HQTdkBBBAAAEEEEAAgUUErLxfhMQBBBBAAAEEEEAAAQRaSYB538qw6TQCCCCAAAIIBAHmPR0ggAACCCCAAAIIIGDlPQ0ggAACCCCAAAIIINAVAsz7rkTSOBBAAAEEEOghAeZ9D4NuyAgggAACCCCAAAKLCFh5vwiJAwgggAACCCCAAAIItJIA876VYdNpBBBAAAEEEAgCzHs6QAABBBBAAAEEEEDAynsaQAABBBBAAAEEEECgKwSY912JpHEggAACCCDQQwLM+x4G3ZARQAABBBBAAAEEFhGw8n4REgcQQAABBBBAAAEEEGglAeZ9K8Om0wgggAACCCAQBJj3dIAAAggggAACCCCAgJX3NIAAAggggAACCCCAQFcIMO+7EknjQAABBBBAoIcEmPc9DLohI4AAAggggAACCCwiYOX9IiT/P3vvAT3Hcd95FkEwgwEkwQhmghFMIAiCOYCZYAQJZhJgAAMI5gCCGQSDkm0l07aCJa1sWZKlU7IkW5ZlWXd7b9/63d3e+fbuvLe39gX7vOe8a6/WCn3vO1RzGzNV3dXd1TNV3R+8hzfz7+nprvrUd6p//ft2VbEBAhCAAAQgAAEIQAACSRLAvE+y2Sg0BCAAAQhAAAIigHmPDiAAAQhAAAIQgAAEIMDIezQAAQhAAAIQgAAEIACBvhDAvO9LS1IPCEAAAhCAwAAJYN4PsNGpMgQgAAEIQAACEIDABAFG3k8gYQMEIAABCEAAAhCAAASSJIB5n2SzUWgIQAACEIAABEQA8x4dQAACEIAABCAAAQhAgJH3aAACEIAABCAAAQhAAAJ9IYB535eWpB4QgAAEIACBARLAvB9go1NlCEAAAhCAAAQgAIEJAoy8n0DCBghAAAIQgAAEIAABCCRJAPM+yWaj0BCAAAQgAAEIiADmPTqAAAQgAAEIQAACEIAAI+/RAAQgAAEIQAACEIAABPpCAPO+Ly1JPSAAAQhAAAIDJIB5P8BGp8oQgAAEIAABCEAAAhMEGHk/gYQNEIAABCAAAQhAAAIQSJIA5n2SzUahIQABCEAAAhAQAcx7dAABCEAAAhCAAAQgAAFG3qMBCEAAAhCAAAQgAAEI9IUA5n1fWpJ6QAACEIAABAZIAPN+gI1OlSEAAQhAAAIQgAAEJggw8n4CCRsgAAEIQAACEIAABCCQJAHM+ySbjUJDAAIQgAAEICACmPfoAAIQgAAEIAABCEAAAoy8RwMQgAAEIAABCEAAAhDoCwHM+760JPWAAAQgAAEIDJAA5v0AG50qQwACEIAABCAAAQhMEGDk/QQSNkAAAhCAAAQgAAEIQCBJApj3STYbhYYABCAAAQhAQAQw79EBBCAAAQhAAAIQgAAEGHmPBiAAAQhAAAIQgAAEINAXApj3fWlJ6gEBCEAAAhAYIAHM+wE2OlWGAAQgAAEIQAACEJggwMj7CSRsgAAEIAABCEAAAhCAQJIEMO+TbDYKDQEIQAACEICACGDeowMIQAACEIAABCAAAQgw8h4NQAACEIAABCAAAQhAoC8EMO/70pLUAwIQgAAEIDBAApj3A2x0qgwBCEAAAhCAAAQgMEGAkfcTSNgAAQhAAAIQgAAEIACBJAlg3ifZbBQaAhCAAAQgAAERwLxHBxCAAAQgAAEIQAACEGDkPRqAAAQgAAEIQAACEIBAXwhg3velJakHBCAAAQhAYIAEMO8H2OhUGQIQgAAEIAABCEBgggAj7yeQsAECEIAABCAAAQhAAAJJEsC8T7LZKDQEIAABCEAAAiKAeY8OIAABCEAAAhCAAAQgwMh7NAABCEAAAhCAAAQgAIG+EMC870tLUg8IQAACEIDAAAlg3g+w0akyBCAAAQhAAAIQgMAEAUbeTyBhAwQgAAEIQAACEIAABJIkgHmfZLNRaAhAAAIQgAAERADzHh1AAAIQgAAEIAABCECAkfdoAAIQgAAEIAABCEAAAn0hgHnfl5akHhCAAAQgAIEBEsC8H2CjU2UIQAACEIAABCAAgQkCjLyfQMIGCEAAAhCAAAQgAAEIJEkA8z7JZqPQEIAABCAAAQiIAOY9OoAABCAAAQhAAAIQgAAj79EABCAAAQhAAAIQgAAE+kIA874vLUk9IAABCEAAAgMkgHk/wEanyhCAAAQgAAEIQAACEwQYeT+BhA0QgAAEIAABCEAAAhBIkgDmfZLNRqEhAAEIQAACEBABzHt0AAEIQAACEIAABCAAAUbeowEIQAACEIAABCAAAQj0hQDmfV9aknpAAAIQgAAEBkgA836AjU6VIQABCEAAAhCAAAQmCDDyfgIJGyAAAQhAAAIQgAAEIJAkAcz7JJuNQkMAAhCAAAQgIAKY9+gAAhCAAAQgAAEIQAACjLxHAxCAAAQgAAEIQAACEOgLAcz7vrQk9YAABCAAAQgMkADm/QAbnSpDAAIQgAAEIAABCEwQYOT9BBI2QAACEIAABCAAAQhAIEkCmPdJNhuFhgAEIAABCEBABDDv0QEEIAABCEAAAhCAAAQYeY8GIAABCEAAAhCAAAQg0BcCmPd9aUnqAQEIQAACEBggAcz7ATY6VYYABCAAAQhAAAIQmCDAyPsJJGyAAAQgAAEIQAACEIBAkgQw75NsNgoNAQhAAAIQgIAIYN6jAwhAAAIQgAAEIAABCDDyHg1AAAIQgAAEIAABCECgLwQw7/vSktQDAhCAAAQgMEACmPcDbHSqDAEIQAACEIAABCAwQYCR9xNI2AABCEAAAhCAAAQgAIEkCWDeJ9lsFBoCEIAABCAAARHAvEcHEIAABCAAAQhAAAIQYOQ9GoAABCAAAQhAAAIQgEBfCGDe96UlqQcEIAABCEBggAQw7wfY6FQZAhCAAAQgAAEIQGCCACPvJ5CwAQIQgAAEIAABCEAAAkkSwLxPstkoNAQgAAEIQAACIoB5jw4gAAEIQAACEIAABCDAyHs0AAEIQAACEIAABCAAgb4QwLzvS0tSDwhAAAIQgMAACWDeD7DRqTIEIAABCEAAAhCAwAQBRt5PIGEDBCAAAQhAAAIQgAAEkiSAeZ9ks1FoCEAAAhCAAAREAPMeHUAAAhCAAAQgAAEIQICR92gAAhCAAAQgAAEIQAACfSGAed+XluX2270AACAASURBVKQeEIAABCAAgQESwLwfYKNTZQhAAAIQgAAEIACBCQKMvJ9AwgYIQAACEIAABCAAAQgkSQDzPslmo9AQgAAEIAABCIgA5j06gAAEIAABCEAAAhCAACPv0QAEIAABCEAAAhCAAAT6QgDzvi8tST0gAAEIQAACAySAeT/ARqfKEIAABCAAAQhAAAITBBh5P4GEDRCAAAQgAAEIQAACEEiSAOZ9ks1GoSEAAQhAAAIQEAHMe3QAAQhAAAIQgAAEIAABRt6jAQhAAAIQgAAEIAABCPSFAOZ9X1qSekAAAhCAAAQGSADzfoCNTpUhAAEIQAACEIAABCYIMPJ+AgkbIAABCEAAAhCAAAQgkCQBzPskm41CQwACEIAABCAgApj36AACEIAABCAAAQhAAAKMvEcDEIAABCAAAQhAAAIQ6AsBzPu+tCT1gAAEIAABCAyQAOb9ABudKkMAAhCAAAQgAAEITBBg5P0EEjZAAAIQgAAEIAABCEAgSQKY90k2G4WGAAQgAAEIQEAEMO/RAQQgAAEIQAACEIAABBh5jwYgAAEIQAACEIAABCDQFwKY931pSeoBAQhAAAIQGCABzPsBNjpVhgAEIAABCEAAAhCYIMDI+wkkbIAABCAAAQhAAAIQgECSBDDvk2w2Cg0BCEAAAhCAgAhg3qMDCEAAAhCAAAQgAAEIMPIeDUAAAhCAAAQgAAEIQKAvBDDv+9KS1AMCEIAABCAwQAKY9wNsdKoMAQhAAAIQgAAEIDBBgJH3E0jYAAEIQAACEIAABCAAgSQJYN4n2WwUGgIQgAAEIAABEcC8RwcQgAAEIAABCEAAAhBg5D0agAAEIAABCEAAAhCAQF8IYN73pSWpBwQgAAEIQGCABDDvB9joVBkCEIAABCAAAQhAYIIAI+8nkLABAhCAAAQgAAEIQAACSRLAvE+y2Sg0BCAAAQhAAAIigHmPDiAAAQhAAAIQgAAEIMDIezQAAQhAAAIQgAAEIACBvhDAvO9LS1IPCEAAAhCAwAAJYN4PsNGpMgQgAAEIQAACEIDABAFG3k8gYQMEIAABCEAAAhCAAASSJIB5n2SzUWgIQAACEIAABEQA8x4dQAACEIAABCAAAQhAgJH3aAACEIAABCAAAQhAAAJ9IYB535eWpB4QgAAEIACBARLAvB9go1NlCEAAAhCAAAQgAIEJAoy8n0DCBghAAAIQgAAEIAABCCRJAPM+yWaj0BCAAAQgAAEIiADmPTqAAAQgAAEIQAACEIAAI+/RAAQgAAEIQAACEIAABPpCAPO+Ly1JPSAAAQhAAAIDJIB5P8BGp8oQgAAEIAABCEAAAhMEGHk/gYQNEIAABCAAAQhAAAIQSJIA5n2SzUahIQABCEAAAmkQWGyMuajD/1cZY7Ka/3+9w/Lkdd0xjeahlBCAAAQgAAEIQAACPSGAed+ThqQaEIAABCAAAQhAAAKDJ4B5P3gJAAACEIAABCDQHYEVNY31ukZ8jPv/wBizTXdIOTIEIAABCEAAAhCAAAQmCGDeTyBhAwQgAAEIQAACEIAABJIkgHmfZLNRaAhAAAIQgEA6BL4+IAP/x8aYk9NpGkoKAQhAAAIQgAAEINATApj3PWlIqgEBCEAAAhCAAAQgMHgCmPeDlwAAIAABCEAAAt0SOMIY88OBGPhvd4uSo0MAAhCAAAQgAAEIQMBKAPPeioWNEIAABCAAAQhAAAIQCEpAM67O7/j/Uw1y6cs6LtNuQSlyMAhAAAIQgAAEZk7gfQ0CjhinxC8r098YYxbMnDQFgAAEIAABCEAAAhAYIgHM+yG2OnWGAAQgAAEIQAACEJgFge8NINc9ngd/aRagOScEIAABCEAAAt0R2NUY82c9D2oe6w4fR4YABCAAAQhAAAIQgEApAcz7Ujx8CAEIQAACEIAABCAAgWAEtGyqlk8dN7j7+vefGmN2CkaPA0EAAhCAAAQgEA2Be3oc0PwvxpjtoiFNQSAAAQhAAAIQgAAEhkYA835oLU59IQABCEAAAhCAAARmSeCXepzrHn8IYfUsQXNuCEAAAhCAAAS6IzDHGPOHPQ1qLu8OG0eGAAQgAAEIQAACEIBAJQHM+0pE7AABCEAAAhCAAAQgAIFgBLR8qpZRHTe6+/b3HxhjtglGjQNBAAIQgAAEIBAdgTOMMT/tWVDzjegoUyAIQAACEIAABCAAgZgIHPCzJaT+2hjT1f9/qhlj/6TDsuR1XBFTI1AWCEAAAhCAAAQgAAEIBCbweM0YPDVjX/cMSwMz43AQgAAEIAABCERI4HM9CmqUJD06QsYUCQIQgAAEIAABCEAgLgJbehQD+yQdvx0XfkoDAQhAAAIQgAAEIACB4ATmGmP+qMdx/i8HJ8YBIQABCEAAAhCIksBCY8x/7ElQ8/4oCVMoCEAAAhCAAAQgAIHYCOxsjPnTnsTAVeb9j4wxi2NrAMoDAQhAAAIQgAAEIACBDghoOdWq+DjFz//WGLNPB7w4JAQgAAEIQAACkRJ4pQdBzV8YY/aIlC/FggAEIAABCEAAAhCIj8DtPYiBfRKPH4wPPSWCAAQgAAEIQAACEIBAZwS0rKpPnJzSPk90RosDQwACEIAABCAQJYGdjDF/knhQc1+UZCkUBCAAAQhAAAIQgECsBLYxxvwg8Ri4KuH4V8aYvWJtAMoFAQhAAAIQgAAEIACBDggcaYz5YY/i/H9jjNmhA04cEgIQgAAEIACByAncnHBA898bY7aNnC/FgwAEIAABCEAAAhCIj8ASY8xPEo6Dq8z7B+JDTokgAAEIQAACEIAABCDQOQEtr1oVK6fy+RWd0+IEEIAABCAAAQhES+D7iQY150VLlIJBAAIQgAAEIAABCMRO4FOJxsBVycZ/xQOusUuP8kEAAhCAAAQgAAEIdERgV2PMn/cgzv+djvhwWAhAAAIQgAAEEiFwijHmx4kFNb+RCFuKCQEIQAACEIAABCAQJ4F9jTF/l1gMXGXc6/OL48RNqSAAAQhAAAIQgAAEIDAVAlpm1SdujnWfHxljjp8KKU4CAQhAAAIQgEDUBD6WUFDzj8aYQ6OmSeEgAAEIQAACEIAABFIgsDGhGNgnufibKUCnjBCAAAQgAAEIQAACEOiQwBxjzL9MOM7/QIdsODQEIAABCEAAAgkR2McY87eJBDWvJMSVokIAAhCAAAQgAAEIxEtge2PMHycSA1eZ9z80xhwZL2pKBgEIQAACEIAABCAAgakRONMY89ME4/x/b4zZY2qUOBEEIAABCEAAAtETeCqBgOb/MsbsEj1JCggBCEAAAhCAAAQgkAqB6xKIgauMe32+JRXglBMCEIAABCAAAQhAAAJTIKBlV33i6Jj2WTcFLpwCAhCAAAQgAIGECGjk0f8aeVBzS0I8KSoEIAABCEAAAhCAQBoEfjvyGLgqofjnxpjd0kBNKSEAAQhAAAIQgAAEIDAVAguNMf+QUJz/Pxhjtp0KGU4CAQhAAAIQgEBSBFZGHND8c2PMNknRpLAQgAAEIAABCEAAAikQOM4Y86OI4+Aq8/6OFCBTRghAAAIQgAAEIAABCEyZwKsJxfjnT5kNp4MABCAAAQhAICEC34wwqPmJMea0hBhSVAhAAAIQgAAEIACBtAh8JMIYuMq01+f/LQ+4piU0SgsBCEAAAhCAAAQgMDUCOxlj/iSBOP8LUyPCiSAAAQhAAAIQSJLAMcaYf4osqPl4kiQpNAQgAAEIQAACEIBAKgTmG2P+MrIYuMq8/6kx5vRUAFNOCEAAAhCAAAQgAAEIzIDArZHH+P/JGHPoDLhwSghAAAIQgAAEEiPw8xEFNX9vjNk/MX4UFwIQgAAEIAABCEAgPQIbIoqBq4x7ff6p9BBTYghAAAIQgAAEIAABCEyVgJZh/YOI4/zNU6XBySAAAQhAAAIQSJbAHsaYfx9JUPN0shQpOAQgAAEIQAACEIBASgS2Ncb8j5HEwFXm/X8wxhyQElzKCgEIQAACEIAABCAAgRkROMUYo2VZq2LsaX/+fxtjdpkRE04LAQhAAAIQgECCBB6IIKD5340xOyTIjiJDAAIQgAAEIAABCKRJ4MIIYmCfpOFzaeKl1BCAAAQgAAEIQAACEJgJgU9EGOffNhMSnBQCEIAABCAAgWQJzDHG/OGMg5qrkqVHwSEAAQhAAAIQgAAEUiXwlRnHwFXm/b81xuyYKlzKDQEIQAACEIAABCAAgRkQ2McY87cRxfn/3BijKf35BwEIQAACEIAABGoRONsY89MZBTW/W6uk7AwBCEAAAhCAAAQgAIEwBA43xvxwRjFwlXGvz68LU02OAgEIQAACEIAABCAAgUEReCaSGF9T+C8bFHkqCwEIQAACEIBAUAJfnEFQ8yNjzOKgteBgEIAABCAAAQhAAAIQ8Cfw1gxiYB/j/rv+VWBPCEAAAhCAAAQgAAEIQKBAYHtjzP8WQZz/yUKZeAsBCEAAAhCAAARqEzjIGPMPUw5qfqF2KfkCBCAAAQhAAAIQgAAEwhHY1RjzZ1OOgavM+x8bY04MV0WOBAEIQAACEIAABCAAgcERuHrGMf7fG2P2Hxx1KgwBCEAAAhCAQHACr00xqPkrY8xewWvAASEAAQhAAAIQgAAEIFCPwNopxsBVxr0+/0i94rM3BCAAAQhAAAIQgAAEIGAh8K0ZxvnPWsrDJghAAAIQgAAEIFCbwM7GmD+dUlDzYO3S8QUIQAACEIAABCAAAQiEJzDHGPMvphQDV5n3f22M2Tt8FTkiBCAAAQhAAAIQgAAEBkfgWGPMP80gzv+3xpgdBkebCkMAAhCAAAQg0BmB26cQ0PyRMWZuZzXgwBCAAAQgAAEIQAACEKhH4AxjzE+nEAdXmfeP1Cs2e0MAAhCAAAQgAAEIQAACJQQ+OIMY/5qS8vARBCAAAQhAAAIQqE1gG2PMDzoOai6uXSq+AAEIQAACEIAABCAAgW4JfLbjGLjKuP/Xxpjtuq0iR4cABCAAAQhAAAIQgMCgCMw3xvx/U4zzvzsoulQWAhCAAAQgAIGpEVhijPlJR0HNb06tFpwIAhCAAAQgAAEIQAAC/gQONMb8x45i4CrjXp9f5l9U9oQABCAAAQhAAAIQgAAEPAk8NKUY/8fGmBM8y8RuEIAABCAAAQhAoDaBX+0gqPmhMebI2iXhCxCAAAQgAAEIQAACEJgOgRc7iIF9jPuvTqd6nAUCEIAABCAAAQhAAAKDI7CtMeZfTSHO/9DgyFJhCEAAAhCAAASmSmBfY8zfBQ5qtky1BpwMAhCAAAQgAAEIQAAC9QjsaIz5d4Fj4Crz/j8bY46qV0z2hgAEIAABCEAAAhCAAARqELig4xj/r40xe9UoD7tCAAIQgAAEIACBRgQ2Bgxq/twYs1ujUvAlCEAAAhCAAAQgAAEITI/AjQFj4CrjXp+/Z3pV40wQgAAEIAABCEAAAhAYLIEvdRjnrx8sVSoOAQhAAAIQgMBUCWxvjPnjQEHNHVMtOSeDAAQgAAEIQAACEIBAcwLfCxQDV5n3f2GM2b15MfkmBCAAAQhAAAIQgAAEIOBJ4DBjzH/qIM7/n40xcz3LwG4QgAAEIAABCECgNYFrAwQ0f2iMmdO6JBwAAhCAAAQgAAEIQAAC0yFwkjHmxwHi4Crz/u7pVIezQAACEIAABCAAAQhAAALGmNc7iPEvgSwEIAABCEAAAhCYNoHfbRHU/NQYs3zaBeZ8EIAABCAAAQhAAAIQaEngl1rEwFWmvT7/73jAtWUL8XUIQAACEIAABCAAAQjUIzDPGPP/BIzzv1zv9OwNAQhAAAIQgAAEwhA4zhjzo4ZBzafDFIGjQAACEIAABCAAAQhAYKoEFhhj/qZhDFxl3usB13OmWhtOBgEIQAACEIAABCAAAQiIwJ2BYvz/bIxZBFIIQAACEIAABCAwKwIfaRDU/AdjzAGzKjDnhQAEIAABCEAAAhCAQEsCTzSIgauMe33+ay3LxdchAAEIQAACEIAABCAAgWYEtjHG/NcB4vw3mp2eb0EAAhCAAAQgAIEwBOYbY/6yZlCzKcypOQoEIAABCEAAAhCAAARmQmCuMeaPasbAVeb9PxpjDplJbTgpBCAAAQhAAAIQgAAEICACpxpjftIizv9/jTG7gRICEIAABCAAAQjMmsCGGgHN/2GM2XHWBeb8EIAABCAAAQhAAAIQaEng4hoxcJVxr89falkevg4BCEAAAhCAAAQgAAEItCfwmRZx/pr2p+cIEIAABCAAAQhAoD2BOiOPrm9/Oo4AAQhAAAIQgAAEIACBKAj8VovEXtHQ/z+NMTtHUSMKAQEIQAACEIAABCAAgWET2NcY83cN4vw/NMbMGTY6ag8BCEAAAhCAQEwELvQIaH4vpgJTFghAAAIQgAAEIAABCLQkcKQx5ocecXDRqLe9X92yHHwdAhCAAAQgAAEIQAACEAhHQMu+2uJ217afGmPODnd6jgQBCEAAAhCAAATCEPhKSVDzY2PMiWFOw1EgAAEIQAACEIAABCAQDYEPlMTAruRecft/Y4zZJpraUBAIQAACEIAABCAAAQhAYHtjzB/XiPM11T7/IAABCEAAAhCAQHQEDi8ZefTR6EpLgSAAAQhAAAIQgAAEINCewO7GmL+okdgrGvc/McYsaV8EjgABCEAAAhCAAAQgAAEIBCag5V+Lsbvr/T8YYw4OfG4OBwEIQAACEIAABIIReMsS1Py1MWbvYGfgQBCAAAQgAAEIQAACEIiLwDpLDOxK7hW3/0pc1aA0EIAABCAAAQhAAAIQgECBwHc94vwXCvvzFgIQgAAEIAABCERHYFdjzJ+NBTWPRFdKCgQBCEAAAhCAAAQgAIFwBOYYY/7lWAxcNOlt7//eGLNfuCJwJAhAAAIQgAAEIAABCEAgMAEtA6vlYG3xvLb9O2PMToHPyeEgAAEIQAACEIBAcAJrCwHNvzbGbBf8DBwQAhCAAAQgAAEIQAACcRE4yxjz00Ic7Erw5dufjKv4lAYCEIAABCAAAQhAAAIQsBD4xZIYf5VlfzZBAAIQgAAEIACB6Aho5NG/+FlQc1l0paNAEIAABCAAAQhAAAIQ6IbA50sSe7lpr9d/Y4zZoZsicFQIQAACEIAABCAAAQhAICCBPY0xf2mJ839gjNkm4Hk4FAQgAAEIQAACEOiUwBnGmK90egYODgEIQAACEIAABCAAgbgIHGKM+UdLYq9o3Ov9yriKTWkgAAEIQAACEIAABCAAgRICj47F+JpK/6SS/fkIAhCAQGsC9xlj+A8DNIAGQmtgA30LfSsaQAMdaODk1pEPB4AABCAAAQh0R2DzWGJv3Lj/Vnen5sgQgAAEIAABCEAAAhCAQAcE5hpj/qdCnP92B+fgkBCAAAS2IjCeTOBvY2AAAzSABtAAGkADcWpg01ZRDH9AAAIQgAAE4iKwkzHmTwqJvWI88SNjzPFxFZfSQAACEIAABCAAAQhAAAIeBFb8LMb/G2PMAo/92QUCEIBAKwLFZALv4zQqaBfaBQ2gATSABtDAOxrAvG8V9vFlCEAAAhCYAoFbHeb9z03h3JwCAhCAAAQgAAEIQAACEOiGwNeMMY91c2iOCgEIQGBrApgBGEJoAA2gATSABtBAKhrAvN86juMvCEAAAhCIj8A2xpg/GDPw/8oYs2d8RaVEEIAABCAAAQhAAAIQgIAngYOMMdt57stuEIAABFoRSCVZTzkxltAAGkADaAANoAHM+1ZhH1/uiMARxpgP8x8GaAANFDTwG8aYnxYM/O8VPqO/oM9EA2hg1hpQ7MI/CAyJwCVch4nT0AAaQANoAA2ggYQ0wPruhYQKphCmEBpAA2gADaCBuDWAeT+kFFs6dT2HeJJ7CjSABtAAGkADaCAhDSh24R8EhkRA95Hc68MADaABNIAG0AAaSEUDBC4Eb2gADaABNIAG0EAyGsC8H1KKLZ26Yt5z85fKzR/lRKtoAA2gATQgDWDepxNnUtIwBDDv6fu4/qEBNIAG0AAaSEkDySTrU4JKWekE0AAaQANoAA10owHM+zDJK44SlgDmfTe/d/pRuKIBNIAG0AAa6EYDmPdhY0GOFj8BzPtu+hL6aLiiATSABtAAGuhGA+Xm/a677pnNn78f/2GABtAAGkADaAANdK6Bbbfdrirgw7yPPzE2xBJi3ndzo1LVH/A53NEAGkADaAANNNMA5v0QI9Zh1xnzvllfQR8LNzSABtAAGkADs9FAuXl/z30/n7353h/wHwZoAA2gATSABtBA5xpYeNCxVQEh5v2wk26x1h7zfjY3MlX9BZ/TLmgADaABNIAG7BrAvI81qqRcXRHAvLf3BfSRcEEDaAANoAE0EKcGMO95OIGHM9AAGkADaAANxKEBzPuuclUct2MCmPdx3uhwA0q7oAE0gAbQABqwawDzvuPgkMNHRwDz3t4X0EfCBQ2gATSABtBAnBrAvMewicOwoR1oBzSABtAAGsC8jy7JRYH8CGDex3mjww0o7YIG0AAaQANowK4BzHu/GI+9+kMA897eF9BHwgUNoAE0gAbQQJwawLzHLMIsQgNoAA2gATQQhwYw7/uTHRtYTSrN+2OOPTM79riz+Q8DNIAG0AAaQANooFMNKOYw1QlIzPuBBatU15Sa9zvuNK/T3yX3AdwHoQE0gAbQABpAA7kGDjlksU+8jnmPYROHYUM70A5oAA2gATSAeU9aLVECleb9a2/8XkYfRx+HBtAAGkADaAANdK0BxRyY94lGlBS7SwKl5r3uQ7v+bXJ8+n80gAbQABpAA2hAGrjnvp/3idcx7/nB8INBA2gADaABNBCHBjDvu8xXcewOCWDevzeOPoS+nHZAA2gADaCBoWsA877DiI9Dp0wA8554nQc00AAaQANoAA1EoQHMe4QYhRCHfuNM/UkeoQE0gAb8NYB5n3I+bNBlx7wn7ibuRgNoAA2gATQQhQYw7wcdk1J5NwHMe/roKPpo8kP++SFYwQoNoIG+agDznqCEoAQNoAE0gAbQQFIawLx3Z5v4JGoCmPf0tUn1tX29AaZeJHfQABpAAz/IMO+jjhkp3OwIYN4TrxOvowE0gAbQABqIQgOY9wgxCiFy80wCBQ2gATSABnw1gHk/u2wWZ25FAPOeuJu4Gw2gATSABtBAFBrAvG8V0/Hl/hLAvKePjqKP9s2NsB95NDSABtBAfzWAeU9QQlCCBtAAGkADaCApDWDe9zdb1vOaYd7T1ybV15IE6G8SgLalbdEAGsC873nUSfWaEsC8J14nXkcDaAANoAE0EIUGMO8RYhRCJHlA8gANoAE0gAZ8NYB53zQXxfdmTADznribuBsNoAE0gAbQQBQawLyfcVTI6WMlgHlPHx1FH+2bG2E/8mhoAA2ggf5qAPOeoISgBA2gATSABtBAUhrAvI8110W5Kghg3tPXJtXXkgTobxKAtqVt0QAawLyviNr4eKgEMO+J14nX0QAaQANoAA1EoQHMe4QYhRBJHpA8QANoAA2gAV8NYN4PNZeWfL0x74m7ibvRABpAA2gADUShAcz75ONKKtANAcx7+ugo+mjf3Aj7kUdDA2gADfRXA5j3BCUEJWgADaABNIAGktIA5n03mSqO2jkBzHv62qT6WpIA/U0C0La0LRpAA5j3ncd9nCBNApj3xOvE62gADaABNIAGotAA5j1CjEKIJA9IHqABNIAG0ICvBjDv08yEUWqDeU/cTdyNBtAAGkADaCAKDWDeE5lCwEoA854+Ooo+2jc3wn7k0dAAGkAD/dUA5j1BCUEJGkADaAANoIGkNIB5b000sTF+Apj39LVJ9bUkAfqbBKBtaVs0gAYw7+MPHCnhTAhg3hOvE6+jATSABtAAGohCA5j3CDEKIZI8IHmABtAAGkADvhrAvJ9JIouTtieAeU/cTdyNBtAAGkADaCAKDWDetw/sOEIvCWDe00dH0Uf75kbYjzwaGkADaKC/GsC8JyghKEEDaAANoAE0kJQGMO97mSgbQqUw7+lrk+prSQL0NwlA29K2aAANYN4PIfSkjg0IYN4TrxOvowE0gAbQABqIQgOY9wgxCiGSPCB5gAbQABpAA74awLxvkIbiKzEQwLwn7ibuRgNoAA2gATQQhQYw72MIDSlDhAQw7+mjo+ijfXMj7EceDQ2gATTQXw1g3hOUEJSgATSABtAAGkhKA5j3Eaa5KJIPAcx7+tqk+lqSAP1NAtC2tC0aQAOY9z6hG/sMkADmPfE68ToaQANoAA2ggSg0gHmPEKMQIskDkgdoAA2gATTgqwHM+wGm0fpRZcx74m7ibjSABtAAGkADUWgA874fwSW1CE4A854+Ooo+2jc3wn7k0dAAGkAD/dUA5j1BCUEJGkADaAANoIGkNIB5HzxJxQGnQwDznr42qb6WJEB/kwC0LW2LBtAA5v10gj/OkhwBzHvideJ1NIAG0AAaQANRaADzHiFGIUSSByQP0AAaQANowFcDmPfJJcEo8DsEMO+Ju4m70QAaQANoAA1EoQHMe8JTCFgJYN7TR0fRR/vmRtiPPBoaQANooL8awLwnKCEoQQNoAA2gATSQlAYw762JJjbGTwDznr42qb6WJEB/kwC0LW2LBtAA5n38gSMlnAkBzHvideJ1NIAG0AAaQANRaADzHiFGIUSSByQP0AAaQANowFcDmPczSWRx0vYEMO+Ju4m70QAaQANoAA1EoQHM+/aBHUfoJQHMe/roKPpo39wI+5FHQwNoAA30VwOY9wQlBCVoAA2gATSABpLSAOZ9LxNlQ6gU5j19bVJ9LUmA/iYBaFvaFg2gAcz7IYSe1LEBAcx74nXidTSABtAAGkADUWgA8x4hRiFEkgckD9AAGkADaMBXA5j3DdJQfCUGApj3xN3E3WgADaABNIAGotAA5n0MoSFliJAA5j19dBR9tG9uhP3Io6EBNIAG+qsBzHuCEoISNIAG0AAaQANJaQDzPsI0F0XyIYB5T1+bVF9LEqC/sKoi3gAAIABJREFUSQDalrZFA2gA894ndGOfARLAvCdeJ15HA2gADaABNBCFBjDvEWIUQiR5QPIADaABNIAGfDWAeT/ANFo/qox5T9xN3I0G0AAaQANoIAoNYN73I7ikFsEJYN7TR0fRR/vmRtiPPBoaQANooL8awLwnKCEoQQNoAA2gATSQlAYw74MnqTjgdAhg3tPXJtXXkgTobxKAtqVt0QAawLyfTvDHWZIjgHlPvE68jgbQABpAA2ggCg1g3iPEKIRI8oDkARpAA2gADfhqAPM+uSQYBX6HAOY9cTdxNxpAA2gADaCBKDSAeU94CgErAcx7+ugo+mjf3Aj7kUdDA2gADfRXA5j3BCUEJWgADaABNIAGktIA5r010cTG+Alg3tPXJtXXkgTobxKAtqVt0QAawLyPP3CkhDMhgHlPvE68jgbQABpAA2ggCg1g3iPEKIRI8oDkARpAA2gADfhqAPN+JoksTtqeAOY9cTdxNxpAA2gADaCBKDSAed8+sOMIvSSAeU8fHUUf7ZsbYT/yaGgADaCB/moA856ghKAEDaABNIAG0EBSGsC872WibAiVwrynr02qryUJ0N8kAG1L26IBNIB5P4TQkzo2IIB5T7xOvI4G0AAaQANoIAoNYN4jxCiESPKA5AEaQANoAA34agDzvkEaiq/EQADznribuBsNoAE0gAbQQBQawLyPITSkDBESwLynj46ij/bNjbAfeTQ0gAbQQH81gHlPUEJQggbQABpAA2ggKQ1g3keY5qJIPgQw7+lrk+prSQL0NwlA29K2aAANYN77hG7sM0ACmPfE68TraAANoAE0gAai0ADmPUKMQogkD0geoAE0gAbQgK8GMO8HmEbrR5Ux74m7ibvRABpAA2gADUShAcz7fgSX1CI4Acx7+ugo+mjf3Aj7kUdDA2gADfRXA5j3BCUEJWgADaABNIAGktIA5n3wJBUHnA4BzHv62qT6WpIA/U0C0La0LRpAA5j30wn+OEtyBDDvideJ19EAGkADaAANRKEBzHuEGIUQSR6QPEADaAANoAFfDWDeJ5cEo8DvEMC8J+4m7kYDaAANoAE0EIUGMO8JTyFgJYB5Tx8dRR/tmxthP/JoaAANoIH+agDznqCEoAQNoAE0gAbQQFIawLy3JprYGD8BzHv62qT6WpIA/U0C0La0LRpAA5j38QeOlHAmBDDvideJ19EAGkADaAANRKEBzHuEGIUQSR6QPEADaAANoAFfDWDezySRxUnbE8C8J+4m7kYDaAANoAE0EIUGMO/bB3YcoZcEMO/po6Poo31zI+xHHg0NoAE00F8NYN4TlBCUoAE0gAbQABpISgOY971MlA2hUpj39LVJ9bUkAfqbBKBtaVs0gAYw74cQelLHBgQw74nXidfRABpAA2gADUShAcx7hBiFEEkekDxAA2gADaABXw1g3jdIQ/GVGAhg3hN3E3ejATSABtAAGohCA5j3MYSGlCFCApj39NFR9NG+uRH2I4+GBtAAGuivBjDvCUoIStAAGkADaAANJKUBzPsI01wUyYcA5j19bVJ9LUmA/iYBaFvaFg2gAcx7n9CNfQZIAPOeeJ14HQ2gATSABtBAFBrAvEeIUQiR5AHJAzSABtAAGvDVAOb9ANNo/agy5j1xN3E3GkADaAANoIEoNIB534/gkloEJ4B5Tx8dRR/tmxthP/JoaAANoIH+agDznqCEoAQNoAE0gAbQQFIawLwPnqTigNMhgHlPX5tUX0sSoL9JANqWtkUDaADzfjrBH2dJjgDmPfE68ToaQANoAA2ggSg0gHmPEKMQIskDkgdoAA2gATTgqwHM++SSYBT4HQKY98TdxN1oAA2gATSABqLQAOY94SkErAQw7+mjo+ijfXMj7EceDQ2gATTQXw1g3hOUEJSgATSABtAAGkhKA5j31kQTG+MngHlPX5tUX0sSoL9JANqWtkUDaADzPv7AkRLOhADmPfE68ToaQANoAA2ggSg0gHmPEKMQIskDkgdoAA2gATTgqwHM+5kksjhpewKY98TdxN1oAA2gATSABqLQAOZ9+8COI/SSAOY9fXQUfbRvboT9yKOhATSABvqrAcx7ghKCEjSABtAAGkADSWkA876XibIhVArznr42qb6WJEB/kwC0LW2LBtAA5v0QQk/q2IAA5j3xOvE6GkADaAANoIEoNIB5jxCjECLJA5IHaAANoAE04KsBzPsGaSi+EgMBzHvibuJuNIAG0AAaQANRaADzPobQkDJESADznj46ij7aNzfCfuTR0AAaQAP91QDmPUEJQQkaQANoAA2ggaQ0gHkfYZqLIvkQwLynr02qryUJ0N8kAG1L26IBNIB57xO6sc8ACWDeE68Tr6MBNIAG0AAaiEIDmPcIMQohkjwgeYAG0AAaQAO+GsC8H2AarR9Vxrwn7ibuRgNoAA2gATQQhQYw7/sRXFKL4AQw7+mjo+ijfXMj7EceDQ2gATTQXw1g3hOUEJSgATSABtAAGkhKA5j3wZNUHHA6BDDv6WuT6mtJAvQ3CUDb0rZoAA1g3k8n+OMsyRHAvCdeJ15HA2gADaABNBCFBjDvEWIUQiR5QPIADaABNIAGfDWAeZ9cEowCv0MA8564m7gbDaABNIAG0EAUGsC8JzyFgJUA5j19dBR9tG9uhP3Io6EBNIAG+qsBzHuCEoISNIAG0AAaQANJaQDz3ppoYmP8BDDv6WuT6mtJAvQ3CUDb0rZoAA1g3scfOFLCmRDAvCdeJ15HA2gADaABNBCFBjDvEWIUQiR5QPIADaABNIAGfDWAeT+TRBYnbU8A8564m7gbDaABNIAG0EAUGsC8bx/YcYReEsC8p4+Ooo/2zY2wH3k0NIAG0EB/NYB5T1BCUIIG0AAaQANoICkNYN73MlE2hEph3tPXJtXXkgTobxKAtqVt0QAawLwfQuhJHRsQwLwnXideRwNoAA2gATQQhQYw7xFiFEIkeUDyAA2gATSABnw1gHnfIA3FV2IggHlP3E3cjQbQABpAA2ggCg1g3scQGlKGCAlg3tNHR9FH++ZG2I88GhpAA2igvxrAvCcoIShBA2gADaABNJCUBjDvI0xzUSQfApj39LVJ9bUkAfqbBKBtaVs0gAYw731CN/YZIAHMe+J14nU0gAbQABpAA1FoAPMeIUYhRJIHJA/QABpAA2jAVwOY9wNMo/Wjypj3xN3E3WgADaABNIAGotAA5n0/gktqEZwA5j19dBR9tG9uhP3Io6EBNIAG+qsBzHuCEoISNIAG0AAaQANJaQDzPniSigNOhwDmPX1tUn0tSYD+JgFoW9oWDaABzPvpBH+cJTkCmPfE68TraAANoAE0gAai0ADmPUKMQogkD0geoAE0gAbQgK8GMO+TS4JR4HcIYN4TdxN3owE0gAbQABqIQgOY94SnELASwLynj46ij/bNjbAfeTQ0gAbQQH81gHlPUEJQggbQABpAA2ggKQ1g3lsTTWyMnwDmPX1tUn0tSYD+JgFoW9oWDaABzPv4A0dKOBMCmPfE68TraAANoAE0gAai0ADmPUKMQogkD0geoAE0gAbQgK8GMO9nksjipO0JYN4TdxN3owE0gAbQABqIQgOY9+0DO47QSwKY9/TRUfTRvrkR9iOPhgbQABrorwYw7wlKCErQABpAA2gADSSlAcz7XibKhlApzHv62qT6WpIA/U0C0La0LRpAA5j3Qwg9qWMDApj3xOvE62gADaABNIAGotAA5j1CjEKIJA9IHqABNIAG0ICvBjDvG6Sh+EoMBDDvibuJu9EAGkADaAANRKEBzPsYQkPKECEBzHv66Cj6aN/cCPuRR0MDaAAN9FcDmPcEJQQlaAANoAE0gAaS0gDmfYRpLorkQwDznr42qb6WJEB/kwC0LW2LBtAA5r1P6MY+AySAeU+8TryOBtAAGkADaCAKDWDeI8QohEjygOQBGkADaAAN+GoA836AabR+VBnznribuBsNoAE0gAbQQBQawLzvR3BJLYITwLynj46ij/bNjbAfeTQ0gAbQQH81gHlPUEJQggbQABpAA2ggKQ1g3gdPUnHA6RDAvKevTaqvJQnQ3yQAbUvbogE0gHk/neCPsyRHAPOeeJ14HQ2gATSABtBAFBrAvEeIUQiR5AHJAzSABtAAGvDVAOZ9ckkwCvwOAcx74m7ibjSABtAAGkADUWgA857wFAJWApj39NFR9NG+uRH2I4+GBtAAGuivBjDvCUoIStAAGkADaAANJKUBzHtroomN8RPAvKevTaqvJQnQ3yQAbUvbogE0gHkff+BICWdCAPOeeJ14HQ2gATSABtBAFBrAvEeIUQiR5AHJAzSABtAAGvDVAOb9TBJZnLQ9Acx74u4k4u4H17+dXbfq6Yn/jz35mSTK73stKe53x11vTNRXDF54+eu9rXOx/rwPG4M9/MjHrXra8Ogn0RPXgWg0gHnfPrDjCL0kgHlPPx1NP018FjY+gyc80QAaSE0DmPcEJQQlaAANoAE0gAaS0gDmfS8TZUOoFOY9fW0Sfe1Z56zOjDET/6++9rEkyt/khvzgg4+fqK8YrH/kY72tcxNOfMcv4bXi4rVWPV1y6X3oietANBrAvB9C6EkdGxDAvKefjqafJu7yi7vgBCc0gAb6qgHMe4ISghI0gAbQABpAA0lpAPO+QRqKr8RAYPDm/cZNX8puvX1z1P/vu/9DSfWHXdykYt7/lwcXMO9JBDX5jWHeo5smupn2dzDvYwgNKUOEBDDvyY0M/l5g2tcjzkfchAbQABqwawDznqCEoAQNoAE0gAbQQFIawLyPMM1FkXwIDN68v/nWV6yjUW2jvGe17fAjTkmqP+ziJhfzHvO+C10N6ZiY9/bk05A0kEJdMe99Qjf2GSABzHtyI4O/F0jhGkYZibXQABoYggYw7wlKCErQQG818OqW72RPb/z8xP/Nr3+nVp2f2fiFiWPEsgbqljd/b6JsqvPLm79dq45DuODZ6gi/NIM9zPsBptH6UWXMe8z7JK7NmPeY97aYiW3+MdOQzPsH17+dnXf+bRP/b79zSxL93ZB1jXnfj+CSWgQngHlPjpTrFxpAA2gADaCBKDSAeY8QoxDikG+aqbt/IqwuqzvuesM6wu/ONW/V0v2cOdtOHGfJqZfXOkbdsvvu/+gTn54om0Yrrrz6kSjK51uPWe33+JP/zMrvipXr4Rfx9RHzPniSigNOhwDmPeZ9EtcWzHvM+1nFZX0575DM+6uvfdwaSy87/eok+ru+aK5JPTDvpxP8cZbkCGDeR5wHaNLX8Z3ucq6whS0aQANooFsNDMK8f2Xzt7M1d78vW3HRmuy448/JDjhwUbbLLntk2223w+hGc9tt52bz5s3PFuxzSHbwIYuzU5dekV2/6plMptgb7/k+N50EbmggUQ1g3nd7AenDBRrzPk2NYN4nlwSjwO8QwLzHvE8ipsS8x7zvQ4w3yzpg3psM8z7+GBvznvAUAlYCmPeJ5v9med3n3PFf82gj2ggNoIEUNdBb8/71t34/u+W2V7Njjzsrmzt3O+vT4D5rae6407zs9OXXZA89/MtJJNtSFCFlpvPsSgOY92irSluY92lqBPPemmhiY/wEMO8x75O4n8C8x7yvip/4vDx+wrzHvE/hN4J5H3/gSAlnQgDzHvM+iXg9hesMZSyPF2fF59Sll2c77bTrxP+71r4H7dP/oYHINNA7814j5a9b9XQ2f/5+jQ17l6m/z76HZqtu3Mho/MhEPKuLHeeNMwgptgvmffxtVGwv2/trrntiYg3Ny698KFgwgXmfpkYw72eSyOKk7QkM3rx/8ZXfyh5+5OOt/q+95/3OGP+0069qdWyV7cmnfz3YNcZ2XUth28ZNX7JyfP6lr/WWzRNPfdZa582vf6e3dU5Bi6mWEfMe8z4F7WLetw/sOEIvCWDek/Ml9kMDvdbACSdeYL2fvv3OLb2udwqxGWVMM0fdZbv1yrzXNPcHLjza2gG5DPkm23WO+x/6KB0aF3M0ELkGMO/Tv+gdfPDxE336XnsdGOy3h3mfpkYw73uZKBtCpQZv3oe4qXnuhf9q4rqQx/MyzEKcg2OkeW2g3Wi3WDSAeY95H4sWy8qBeT+E0JM6NiCAeR95nq+sXwv52Zlnrcr23POAqP/fsHoT9z7otbYGMO+5ZwrZV3KsbvXUG/NeF6x8Dfs8gdfl6zbbbJPdeNPztTtIBN2toOEL36IGMO/T1wPmffptWPxNhnqPed8gDcVXYiCAeR8guYJ5z3Uh1LWE46ClrjSAeY9535W2Qh4X8z6G0JAyREgA8z5AvB6yr5rVsU48aYXzgeEu/YY6x1559SP4Eha9albml1795sT/lzd/G17v/UE2JPNes6jZtPDaG99FC5bfzqz6W87rvi/vhXl/6eXrvC6o8/fcPzt9+bXZDaufyx546BezZzf95rs/4E0vfjXTCMw717yVXXLpfdlRRy2rfBjgipXr+aHzQ0cDEWsA897d+adyYcS8T78Nu9Aa5n2EaS6K5EMA8z5AzIB5z3Whi+sKx0RXITWAeY95H1JPXR0L894ndGOfARLAvA8Qr3fVb03zuJj36caGzz73RatPtNtue5PDH5h5f8aZq6xauPKqDWiBvj4JDSRv3l92xQPWH2H+pNo228zJFp9wfvbg+rezN97zB7Ua5ZXN385uvGmTcyp+zPt0L+TTDPg41+x0Esq8j7kNtVxI3t8VX/vyBG7X5n3MbUvZ3H0H5v0A02j9qDLmfYAbRMx7d9/IdQM2aCAODWDeY96n8FvEvO9HcEktghPAvA8Qr6fQB1aVEfM+jpiqqp1sn2Pel7fdkEbeY96Xa8H2+2FbXMySNu9vvX1zpunri4ZV8f2++x6WPfjwL9Uy7F0CXXvvBzIdr3h8zPu4xOxqO7YPt50w79Nve8z79Nuwiz4Y8z54kooDTocA5n2AZGCs5v2WN38v2/z67wa57yj2m5r28dUtvxP8uMVzxP7+ldd+e1D1f/2t3880xWNM7aIy6cH2mMo0zbLoN7jlze9513+W5v202+nqax/fKkeS50uWnX61N6+u2nLaLLqqR1fHxbyfTvDHWZIjgHkfIF7vqt+a5nEx79PNRWHel7cd5r3JGHlfrpFp9rWcq7wtkjXvn974+Wz77Xey3ijqhvH4xedlr24Jm/RQ0kJT9G+77dzReTHvy8XFjw8+s9YA5n36GsS8T78Nu+gHMO+TS4JR4HcIYN4HSAaGNu9luj/8yMcn/j/6+KecxpOW2tLsX0cfc0amZbnmzJnz7j3JdtvtmO273+HZU89+zvn98X7x9be+Pzr/Vdc8lp2+/JrssMNPznbddc9s++13fPe4uv+YN29+tmDBwaPlvc6/4I7s3nUfrGUojp/X9ffGTV+a4CFGz7/0Ne866dgvv/ot63HK2Dz08C9n551/W3boYSdlO++8W6H+22Wa6vKgg4/LLlhxZ/bAaFa179cqj6u+2v7EU5+1lrWuif7c81+2HmfTC1+xllX3l2vuft9oabcDFx6dST+5+an3e+yxb7Zo0WnZFVc+lD3+1GetxyirV5PP9MDI2nven51x5vXZAQcetZUOVTbp8PAjTsmkQdeD8prxzva72vDoJ6dShyb1zr+jdTGvvf6pUT5hzz0PyDSTX94m2267XbbPvodmJ528Irth9abREnz594qvXZv3+g1pKcCzz70pO+ro07P58/fLdtxxl3fLqQEO+v3stdeB2WGHn5Rp1NFtd7wW5AEM6bDYtvq95nyKr8cvPner/Yrfsb1/5LFm2lBeSLMlnnPuzdnRxywf9ck77jRvqzLttNOuIxbqV5afcV12y22vjvqnYpsN8T3mPeEpBKwEMO8DxOt96FNXXrVhtDa4jM6m/xVHFa+NxfeK75oeN//emrvfG31cNQstYN6X5xGln6IW8/e337mld3pi5H25Fmbx++Sc9dokSfNeyQAlDPLOZfx18QnnZUqEdCWGhzb8Srbb7gsyzPt6YuuqPTgu7eDSAOZ9+trAvE+/DV2/zzbbMe+tiSY2xk8A8z5AMjC0eV8nuXP/Qx/NjjhyifMepHhPsuHRT5Tei7z4ym9l19/wbHbMsWdkOxRMt+IxfN7LSJWJquO16VeL3z3rnNXWOl597WO1znHHXW9aj6OHHorn0/t71/3CyJj3qXO+zz77HJLduebNiWONH9vnb1u8ofOsf+RjtY6vtsjLV3y96JJ7tjqO7mdX3fDsyGgs7lf1Xvp75PFf3epYPvXz2UemvQxpPZBSVY7i53roQA8gFM+hUerFffL32223w1b7Fb8z6/f6DZ119o1bPUCRl9v1qsEEZ5+zesIIDm3ey2TV7+mUJZeM8hCu8lRtnzt3+2zJqZeNHlZpyvuAAxdZ27bq3FWfy2D3KZMeuLpr7XuyJadenu2++4LGZZk7d7vs5FMuyR578jNe5/UpW2r7YN7HHzhSwpkQwLwPEK+n1h92Vd5rrnvCeZ1q+tBaV2Xt03Hr3N/1qd6+dcG8Z+S9r1bYb/Y5+STNez0J5Lr52/+ARZ1MWTkuVo2q0FT649v5e/aipg1og1wDmPfpa8GWTNcooryNeU2/jZu0Ieb9TBJZnLQ9Acz7AMnAWZj3Mjg0Ir5sua7xexOX6SvjSYZ9PpPX+Pea/q2Rpjfe9HyQ6+M0zXuxPe30q2qxHWek6bnrTGluu+7Y4g2dx9WOtmNom495r1H4ml1hvB6+f0s7Sga7ytBku+5tyx6O9ynbqUsvz9SeOn9q5v3d9/1cNm/eno3bZNfd9sruu/9D77ZJSPP+ggvvyMZHkvu0R9k+c+Zsm5173i2Z+qO6epmleX/RxXdnMvnL6lb3M82coj7vtTe+W5tFXXax7Y953z6w4wi9JIB5HyBej62/m1V5MO9nk6/CvC/njnmPeT+rPpHzlv82bXySM+81IkBT1dluyjSN3bSmE7TBDLXtmY1fyG6+9ZVMN6fLz7g2W3ralaPp3S659L7aCSSVSetvrnvgI5nWo9PUekpwKbmi1/MvuD27btXTo+N2OVtBzkajTDTNn6btU32UNCiWR+VTskOjPu67/4OZa4rJ/HhNXrWcgqZ4vPb6J7MLV9yVnXn2DdmpS68YleP05deOpuq8cuXD2W13bBlN9TfEG/kmXG3fUeLuiad/Lbv9ztdH08tqWkW1tzStkS0ahaRRXPc/+JFOEiapmPeaLlea1HS5mtIn16NG8lxz3ZPZg+vfzrSPjfGjT3za2h+uvPoR6/62Y5Rte3nztzNNYaupOVdctGY0umjpspXZactWZueef2t26eX3Z6tvfmHUzmXHafqZLZmeonmvaXfXPfDhkd7VrsV+T4n+61c9M+pvXO3clF/d7ymJq+Sz+kD1jfqtatpc9cvqM/V7rnvMLvbHvO9lomwIlcK8D5AMnLZ5r7WuNe207d6jbNv6Db9i7S8Vh5Z9r+1nZ561KtP9Upu+d1rmvdaj1jTWbeus72sa8zb1tsUbOm5o817XUU2FH6LOodZq1DIQbUYwF+uihxIU86Rk3q+6ceNWS18U61PnvXIRun/Uby+keb9w4TFB9GKri35/Wt6iTn8xS/P+0ENP7IyF+oCQM5jUYTqrfTHvhxB6UscGBDDvA8Trs+rXYjsv5n19oypEG2Lel3PHvMe8D/E74xjlv7NQfJIz7+++9wPOGzYZv6HAhDqOTPgDFx498f+xJz69VVmV5JC5vu++hznrpxtumT6+ZROr4xefl2l6PNvN+vg2PdEvo6aLtQhlMKrsWr9z/LxVfyvBdeJJF2arb34xk5HoW//ifkog3Xzry6O1STXSoOqcxc81uuWgg44draMng1UPIBSP3fV7Pchg05ASTaHPrWPazqUy+J5LyeqLL713tD5nca3WIlPbe+lUyXEZhEps+56vbL9Q5r3af5yLknJl5/b5TMmyCy9a4zXSZ5dddh89WPL8S1/d6ryhzXv1RUo86iECrdlbZ5Shft+afrI48siHQ3EfrdtZZG3TkKa5LO5T9V7rWhbPUXyvh6Vs31d/U9yvyXv1FVqD7Ljjz8lUZpvux7dpNJFGIIaaElfnHq+fphkdr88zz31xZNIX10sdL1v+t9Z/lbnfZLTW+Hmb/o153yANxVdiIIB5/972NzjTNO/1QJWmeM/7vzqvWmbL1sd1bd6rjFr72XZu323TMO/1cGzbkd7j7aEHHn3rOL7fNMz7jZu+FMy4V901Ytj1kMh4/Vx/P7vpN7PddtvbW+M+M0ZorXOXKRjbtPm6V68T61bdR+p+5oH1bydj3ktHhxx6wrszJrh0UtzeV/NeLHTPp4EPxfr2+b3rdzrWtyp24R8EhkQA8z5AvN7nvrNO3TDv29/71eGd74t5X84d8x7zPv+t8Fr+W4mBT3Lm/eITzrcmF3Sj3MUo7baNdNXVj1rLq5Gs+bFlss+fv591v7Ebp9GUbvn3XK9aN+egg4/zOt748fW3Ehgy/Tc+/+V3y+g6V9V2Pb3+zlSYcxqXp1hGGWBPb/x8rXLJSNSamMXjtHmvhFRVvUN+/tSzv2FNKu2//5HBy6FjjrORHlSGsjq98PI3Mk3pKFNv/PtN/pYJfONN/g8MuMoWyry3JepsBqirHLbtt96+Odt5591r85K5WnxwI5R5r3V8Tzr5osxmljdpw8OPWNJolHadBLJvucrW6NVoM9txrli5vlTztjYtbnv4kY9nbUZK6XengFomVfG4dd/vtdfCifrp+pAfRw8YXH7lQ5mS6TYOZds0A0LdEYn5edu+Yt4PKX/Wq7pi3gdIBk7TvL/sigesfeMuu+wxWjd65VUbsptueXG03vctt72SXXr5ukwPoSle1QOftr7OZd7vt9/ho5hZD/NqxiyN0N704ldHRtJLr34z08NmimnVZ2t0c5XhqFmPbOf32TYN8375GddNsBW3RUctyzSiXA+/6RqjOEezcYm1Youy69IOO+zpa7HGAAAgAElEQVScyYz2qeP4Pl2b93pY03Z/poen9ZCyZuDR1O2PPv6p0YPUa+95/8gA1oOUZXWWbpo+TCfj7oADjyo9/t4LDhqVQ3GN4n1x0/fEWTOpnXjSCusDimovW7ljMu91z1z2kLt+Y9Kj7kl0L5Sbus+/9LVMD+doRjHbjAV7zN9v9NC6rf6aeW5ce1V/2+JJnVezTaiPUi5B67br3v2V13579F8Piuj3owdapK+qh0j1m68qR/65HurWefP/xx1/trWtZYTn+/i8+swaZht5r3sH6VCzgel3M8Hi+S9nGx79xOgBcd3rlLW52kwzH+Z17fsr5n2vYkwqE44A5n2AeL3v/adv/TDvZ2OMYd6Xc8e8x7z37cPYr/y3NA0+SZn3umHebrsdrTeHunmdBrC656gy7/W5Rk3Ybu5t2zTVeFkZFBj4jIiwHXt8m0aArr33A6XnKyuLkozz99zfu27j53f9XWdpBJl2VclN13lc24848tTGTMp4lX3mmq5VSaey79X5TMey1VnnrjrOvet+wfpd2/HqbDtlyaWt1jCN0byXWaoHHepwsO2rmSx0rFDmvVjbztNmmx4EuP3Od6YPrdJQ/nkfzHslH20PfDRhqQc89PvK+dR9LTPvlexXsrNJufLv7LDjLiOTqW652u6PeR8uO8WRpkoA8z5AMnBa5r3uOcYfbJIpp9lcqqZnVxlfePnr1r67aN7roUmZ8VUPSdr6zCee+mx21FHLnH24rqe52Wj7ftm2rs17PfxQjM/1fsmpl3kZ7/eu+2Dp6HU9FFBWN9dnXZv3qnN+7dSr9CVDX0sHuMqk7dLaqhuendBi8VhavqjsGK7PZD4Xj1N8L+3LcPVZVk0Pmowvf+C6Z4/FvJdpWfZwtx6s10MjLnb5dv3GFJNvs83W9/Ou+rcx73fdba/RklUaBKB7gLwMPq968EK/6/Fy5m2u7U3vK/XAUX6c4quWiPIpW519cvN+3rw9s3POu2W0tFhVfzx+/Bdf+cbou64cjPoj18wp48dK/W/M+6nGgJwsHQKY9wHi9dT7x1Dln4V5r9k0tTSp4kfFmvnyuXrV8ryKG5VHrHv9bMNE8ZKurVq2Vw/bKQZVzK4ZP/Ve5dIyoneueWv0IGvTe5i8jH037zWDmeJUMRO/05dfM2pnLXWqWaV0n5izsL1Ow7xXrKoZPvVgpR7q1bK5it20XLGWDtU98PU3PDt6CFUPxtrKGWKbNFaMT/P3oZYf8ymjWEiT8tiULxYLLSOtdtNy1jkLfT4+267P8dln9gZ7l22QlHmvERj5j2z8VSZdl6CaHrvMvNca0uP1qPpb61i6yuJaW6/qmGWfy4DSCGHXOV3blbDcfY99atevrCz5Z77mvS4C+XdCvs7CvHdppc4oCVdb5dtdiVqdO9/H9dqVea9202wbTYPKGM17XZhD6VEX/JjNe9VTibE6/XPq5r2C0FDtmx9HD2TVYVj8nbrMe/2mNBIrP0ebV80G8VyAmVqK5a56j3mfTvaLkm5FAPM+QDJwWub9eL94zLFnNjbDi32azHs9GLnm7vfVNuCKx9F7JQI02n+8rPnfPiNZx4+pv10xYdlMNrbj3HHXm86y5WXUOuF1l6rRLFyueEEPHyuhZStP2bauzfu8vnqdN29+7enuddPuekBbyw+U1c322ZPPfC4T+2K58vcy2OsugaQlJjRzW34M12ss5r2SZK4yahm7umufawaO4gMprmM3Me/1oKWS3TJabW1ZZ5tmd3A9WKD2q3OsfN9pmvd62FhGSNukvsoujW+//U5WHRxz7BmNWORMUnnFvN8qRuMPCOQEMO8DxOup9INdl3Na5r0eBtVsO5qdyxUvjscmO++8W7Z02craMakvM80cdu31T42W56k7uEV1OHDh0SOD/66176mMgTQr0JGLlr77P3/Yb7zOmomouJ/Pe83o41vnrvfTTEKnLLnEGcsV66uZcS++5B5rTNuFea8c44Pr335nJrqjlmWaEa1Ynqr3mk1MD5vI5G7DUUveFdvV5U1pdrHiflXvzzz7Bu9yiYUettWD0EcfszzToKeq+hc/173IBSvuHM2814YF3+2HqZ+UeX/u+bdaxa6LQNWohVkJ1mXea9pr2/R1unhqOjc9daaLvAxoJRdOPuXiTGte64khW110QSz+0Mffj9ZRXrYy0xSaetrtqWc/lz3w0C+OkmWLTzivdPo4XTQ1faftvK5tpy693FkeJdv0hJ2mF9WFRx2zLup6ukiJJE0zeuNNz4+eQlJyc/wi72PeK7Fb1jnquOpE9VRTPi2pyqApIB974tOZfhh6CuvUpVdkGmVV5DkL815TINqmM1fyb8ub36vVNrY20zF0rGI99V7n1Llt3ylus5n3Ss4dceSS0dN/erJTI6XU3kq4qg3VzkrW6oEU25STxbJcfsWDlWUolid/H5t5r/oW6zX+fu+9F45++3pKVpz0m9Da53pyUmuXjydZlSR09TF1E/a2kfdqFz08oUTjnWveHPUZ+n2obCpXPo3tO32IPQH8jo528l7u4pLL1o2eHNXTo/pvS85ritn8c59XBZC5JsZfQ06br6dex9u0+Lf6dz1ZOdkPvzBqX9s1If++2r6sHuP1yv92mfe6xuTHzl/VZypxK+3cdseWUT8o40V94aJFpzlHa+n7xx53tpNxXpaQr5j3eW6J18QIYN4HSAbOwryXkdN0avKQfZ/rWLoW5n158VU3/q7vlG2fpnmvqdfLyuL6TNfSYl2L7zU7gut7ru3TMu+VyKoaDeMqo6udFQ/WTXSV3as1fWBQRqArWZq3TwzmvR6gUUyWl6n4qriz6dJxV5Q8EJCfo4l579JD0+0aEGF70ED33Fqqo+5xp2ne1y1b1f6657fNRqBtWnqg6vupf455n1gUSXGnRQDzPkC8nnr/GKr8XZv3ephNo601oCKPNZq8yvBsMhOYjZMe6FROqSwnX7eMql/ZwEKZvnWP6bu/vBNbPae57eXN3860dKstfquqh/KnWp6rOGtTSPNeJrXuHW2526qy2T5XPKrZm/LluupyrloeyXZOn22HHLK4UgdaskozXFR5HD7n0z4aDKcHbLqcmaAuX/af/gMBSZn3egrGJnCtAxereFzGmoz4Yl3emYbzlUwXOVddlDS0PfElE81l+qhjl1H+6pbfcR5X59OoST0NVCxT8b3WINcUc66yFbfrgm+7oOghAD2IUHfUgEY+yPzNk0E+5r2mLyyWP3+v6RGbrNEsxnrqSVNezsK8F19NJ5rXo/iqaYWK/Ju81zGKx8zf65w+x8vNe7WxRocoEavEmM93tY90r0SrpoPMz118VaKvyRqmMZn3Sqq6njxUcKE11st+/+Ik7Wv9yCIb20Md+rypeb/b7gtG0/bod1IM7qraUrNt2KYNzcuqm4GqY9g+tyXTtda6bd8m20KZ91oLVvrP61t8VQJQS55o+rKyMkrjMsmL3y2+V9soaC87xvhnNvNeU/EXpwp9d+reigd1pAkZQcUyFd+71nceL1OIvzHvp5W74jyBCWDeB0gGTtu8V8we+w2zRpprdEWxT87f6yHVuv3utMz7plPc5/WxrQWuetcZGZEfyxZv6Fh17xuqZuBpOsW9yqmH1V2jpm+65SXvdlbMNv5AaK4XPbSZM2nyqrjKFQ/pHDGY92WJdD0w26Te+o7i5oMOPs76O8z5xmDeq6xacjAvU/FVI/zr1j9l8151dT3Ioody67JIbX/M+8BRHofrCwHM+wDxemr9YVflLYs5mi5Xk5dV+RdX/F+8tvu+V4xZJ57My1F8ffnVb41G2vues85+ZYO6+mze66HfEO2seCcf/BfSvHflpOu0rW1fPQzwQMlArKLuiu9nad67Hg621a/ONnlydQfVFpnwfvqGe0jmSZn3tpHBErtGR4eEEvJYLvO++COVaV5l6rjKpCTBwYcstt58yzCqOw2lnhAqlq34/rRlK704u6Yh1Ho7rnr4blcCreohAjEZHy2veujJp6rvVpVDydF1D3y4dT2qzmP7XKOci+2Rv9eoZ9v+dbbpGPnxiq8+az3qPLqgKlHaxGAvllMPaoyb03l5miR4YzLvXVOUK7CocxFWsFX2oE3Oq655ryn49RttO7Lwnek4d7Dqqcn6kbZkemzmvaZEchkI6ofrjABU/+Vak0lte8aZ19f6vdvM+1wjeq07da8S/gv2OcTavr7XiOJvvul7zPu+5MYGVw/M+wDJwGmb95rdqmlfNc3vuUb93njTptrln4Z5r+mqNetVG0aKdYrXtPy9Hvite1xbvKHjhTTvD1x4dO1yjdfDZbpqDcnxfV1/a1rVnFXxVSNd2sbyOmdZHBODee9q60MOPaHWg6s2vop1i0zH38di3rvK2SSvkrp5/+jjn7K2mWY+tLVxn7Zh3g8uDqXCfgQw7wPE633qK9vUpSvzXvG960HM8dij7t8aWNSkzjLuDzjwKOs1tW4ZbPsP0bzXQ7EaRGjj0WSbptxXzjEF81710wMlmsW3jh77aN6Lheq19p7312JRhxv7xmvwJ2PeayoY22huCVhrPcYqsirz/vAjltQeiV6sq2vEtLg0ueCqE9eNqr4//l8jNTXdfvH8tvfHHnfWxHc1HbiObds/9DaNcB4vu/6ua2aGLlfb48kgnD82hb/qpYCtzUMJ+q4t6NO5dM625a77fSVybfXUNEl11zeMxbzXVPOu/kvLR9RlpFkN9j9gkVXnufZnqffb79xiLdtpp19Vu662BGts5r2mmM+5j782GbWj351Gvo0fS39rNFudqXHLzHsl0JWwrKs/Letg07Nmlmj78IdvWTDv/TJP7BUdAcz7AMnAaZr36teqZq/y7be63s81k8zpy6+t3c9Pw7yv+zCajZ/LdNODAXXvO2zxhq67Ic37ug9V2+qskdG2+KDOzGDHHX+29RhHHX16ba3YyqgZy2xl1LZZm/eaFt4Ww6hsbUeb5SxcDyLrHLGY94o1bQMjNEtdXg/f19TNe9XTNr2pRrn5Mkh1P8z76OJEChQHAcz7APF6qv1i6HJ3Yd7fsPo565IvxdhLg0j22/+I0VTrmiFTg3UU3+shUNeMp8XvNxl8p3xf8Rjj7+UpaNCLRoBrLXaZ8XpIWkvaauYq3bPoAVxN8z7+Xf1dZt5r+U0tF5z/dy0jIC75Pr6vmhY+tC58jqeZ32y58SIbxbQa0KnlaMXgypUPj9pZM5/a8vz6rryiaZn3mj1Ug8+WnnblaKZW6VCzGus+UEtv+mhR7VRnSQeNUi+2rWumat2TFPeren/UUcsqdVA28l511b2WHpQd/SYvvvtnLFaNllEVq2Lb2t5rKYoms+r56I19MO8rBV4lEpnGNuFq27XXP9n6+FXnb/p5mXmvxNIzz32xVdkPP+IUKxd13nWTVnkdZZ66nuzySbTZTBVdnPPjd/2qC6tNK3WTb12Xs8nxtZaRrW5KmjQ5nr7jSrjoXE2P2fZ7Gqlsq+faez9Qq0yxmPeuIFZr6DZlpSmyXMlHsZulea86KSgZb0MFK3Xra0umx2bea+2j8brq78MOP6lxP6z1nVyBn2ZI8eVYZt43ebAgP+9hh59srXPbqd/y41e92q4zY22wKY7cD6WAwFYEMO8DJAOnad5PM36t6veqPtfDU7a4oMnyYtMw77XkUlWdqj5XnV2JqVcqloIZP7Yt3tB1pe79g2vafCUsNe39+Hnr/q34b+x6N/pby9r4HktJLdsx2kzpP37u/fY73HqOWZv3d9z1prVcmvIz1IM6Zff/sZj3ai/b7H1Katfl4LqX1Hql47qI9W8NqLD9JjQrXKxlDlEuzPutYjT+gEBOQPeR1j5B23UfGuL3xzHiNUpCtk1o814jMDVTkkuje+yx7ygXWDbAS17Bg+vfHi0J6zqO4mstDenLQvGyYgjb8bQEmQzjOtdULRmqWX0PPeykd+9vysz78XK6BvVpKvbxfWP8W22kfLGNp7bpnk955mc2fsFZH/HWgxGKvYvH0Qhu12AwDcSqy6M4bb6W6NT9s+JtzdrpcywNdtOsXWUj5uV9NfW4XDOCXXnVBq/y+dQh36eYw9XDAFqKWL7E8y991etcehhfS666lklTOyr/PIuBlnkdeZ3+tSuZkffrS6agu/nWl71+BLMQWNnNu542alMmTWvoujjWTTSNl0NPvxU79/y9OuKqkZU2s6jJlOfjZfL9WwZvXt7iqzpB32PEup+eNrMmZlvcQNhMMJ2jzpNtoXm9/tbvj56AK7af3uspvTrnisG81+9FF+3xuoix1i6qU5/xfV1Tp+pcszbvb7vjtYk6q1x1p2O1JdNjMu9dv0m1b5NR7cU2lrk+rhv9PW/enpl+I8V9Xe9t/bGOoRFG+XpXru+WbXddI1bduNGrXGXH9vnM1m+NscK8z9NPvMZEAPM+MfP++gBLPvn0aaH2scUbSuLVPX7X5r2SjppFqG65bPurfmP9/+jvjc9/udbxbfGGjlv3nspl3h9w4KJa5bHVVdtcD6/ouu76TnG7K5mputaZ2ad4TNt7V6Js1ub9BRfeYdWLRijZ6tFk29MbP289hxjHZN5rxJPtt1OWCLbx6IN571rerO29mo1XTNsw72MKESlLRAQw7wPE6zH1dbMsS0jzXmaoa6Cd8k+K36vy9eMsrl/1jPNhAI3c9807aRlVW0yxYMHB2cZNX2oVY2mksUbl1xm05op3UzHvb7rlRStPMdZA0DrTpyuW2XvBQc7jFdutqXl/0MHHjUxqX72M61B/q5wHLjzaWc671r6nkY5c9yRdmfd6cF6zs7bJtz75zOcyMS22TfG9jm9jyLbpG+vTYJ6Mea81oYtCLb5fc/f7ohWty7zXhVU39m0a2bVeoTq7NsfVd5VQ0wWhyDl/X7Xmu62zVUKsbZl8v+8akaKLn+8xYt5Po3nztii+Nnk4wTXFqs4xawbHHX/ORD013U6dcsVg3t//0Ecn6qF209Nydepi21fryxc1UHw/a/NeM3gUy5O/r7P+u+psS6bHZN6Lc1634qtGM9narM42jdAbf0o2P4f6OZ9jucz7iy+91+v7rnNohH1eluJrnVkBXMf22Y55H1F6i6LUIYB5HyAZ6DIv1RetuHht7b7NldzR8TY8+snax/Ppw1z7aESB7g8Uv8jkW37GtaMn9o9ffF525KKllf9to9A1vZ7rfK7tXZv3Wk7Lde662/d1jPB+7MnP1DqHLd6QBkKZ96csubRWeVwcNCq6eN3N32uJB9d3ittdSxvpwY/ifm3fa7rVvGzF11mb94sWnWYtl0z9tnXOv6/fsWu61tDmvZYBUEJH06RqylRNS6qpUBcdtayyv7BNm6+2qtvvxWLea3pZzeix8qoNmUyEOixcs1H4xtt526f2inlfJ4Rj3wERwLwPEK+n1h92Vd6Q5v3Jp1xijWHkL9ywelPjOOb2O1+3DhJTTOB73D0sS7y+E1N8onG52rSJ6/4uBfNeD2C4Hk7Wvd7dNWekFUfN+qy6F2Ny2/sm5r1y3m3aqvhd5UBdy09p9H1xX9/30zTvQ7LQPZ9rltcQ+WZffuw3+wcCkjHvy0yqkD+O0KJ0mfchDHbXGiW6YQ1RD03vYevMqxKjSjDavtfkItCkHq6k7oJ9DgkyXWWTMoX8jtY3svE99/xba7e7vmM7ls4RssxNjqUlGsbLpoCwzrFiMO+VTBuvh/5uM2V5zuD1t7RepX3q01mb9yqjbaqfussx2JLpMZn3rnVjdZOWt1ObV9fsClrPyue4LvO+bTJSyT5NATyu7WlNMY15P6D0Wb+qinkfIBnoivPUH1XFqLZ+05Xc0fFCjQ63nTffpmu5DKfTlq10Lpcy3tfW+VsJPZ0jP5/Pa9fmvZIyPuXw2cc1KkGztvl8P9/HFm+IcyjzXmZifq42rzKG1abjGtA12ee4roe/tcaoz/d993nIMWverM17rWM+zk5/33LbK0Hrr2lebecJYd4/+sSnR2vDuupiO2+dbffd/8FaLGZp3ushnQtX3JW54t069bbtW2dkm+9vI6b9MO/7FWRSm2AEMO8DxOsx9XWzLEso816DJ1wz72r9+LZ1dOWGlUevmq78tTe+a415QvgeTevlur9LwbzXAxO2mETbtJRAUyYPrH/beg9RPNe0fJuyOmi2VlsuWfpvMovDNM37sno1+WzTC1/J9IB2sY30XveCs5wtuUld+E7zhwCSMe812ntcrPnfTZ46mpZoXOb96cuvadzh5mXXqJWcQfE11JrDrguGptjLy2B7vfpa+1TPekJMhtM0EqGamqfIJH+v9Rfvu/9DpeW31SmmbVrDs7imTF43BSF1krPa1/bknY5dd51QXz4aja0LsdYv0vpJZf81WiKvW/5ad/RYDOa9y3zd8GiYJ1BtMxSIVxfmvRI8WjtLowLVz5S1nz6zTelVN3ltS6bHZN67njBuMhOG7Xd03aqnJ34Hat/FJ5zv1Y/Zkplz525Xezo1W9l23GneRNmOX3yuV7lsx6uzDfM+WHKKA02XAOZ9gGTgtMx7Te1ep19qsq+W/urKgMtjJ72+/Oq3atWla/Neo4Kb8LJ9x7Zut+os89i2v2ubLd7QcUKZ93UfXnSVU9tt5r0SWmXfyT+79PJ1E9du1VMPI+b7hHhVQknHHf8/a/O+uBZlsWxKaIaod36Mk0+5eKLuOl8b814PpLhmYCvWpe17rVOa18PndRbmve6jjjjSvk592/oXv193xjAfXjHtg3k/3SCQsyVDAPM+QLweU183y7KEMu9POvkia1yx//5HBln/WteDXXfby3qOqgGTLqNcZZ4Ve1eZUjDvXQ8my+eouyzCOH/NBFaMc8bfx2Deq8znnneLtZyrb64/o3LK5r1YXHjRGisLzXI23r783dwgj5ldMub9Qw//slWs6mhuvX1ztIJ1mfdaL7iNMGS82p66k0HetjPPy/XI479qZa4LRr6P7VXr8NjM5fyioGkEly5bOWq3F1/5rdJj2Y7vs01Po+Xns71q7R49nShd1TG8fc49jX1csyLUGR2gfW1sdOwQddAUL0pK60EVTfUi4912vjrb3hk95rfWt+oQg3lve5Bk223nZgqOQ3DWKEMbwxDmvRKv+i1pHch99jnEuRaW7fyubXWnjbUl02Mx7/WEsS2BrqdEQ/UrStTaWOpBJB/92Mx733Vxq45vm8pLidSq74X4HPM+meQXBd2aAOZ9gGTgtMx7mXwh+ivbMRQjuWaqsvX5bbfVjbe7Nu9Dri+einl/UYBRUbmWbLGHr3nvGll1ypJLgupd94M23c7SvNfIMdusQSqnRrPnfEO8am1WW/2bmPcqt+5b9UCR7Ziht2n63DoMpm3eX3bFA1NjcfOtYWdkqMN1Gvti3m8dpPEXBH5GAPM+QLw+jT4shXOEMO8Vx8+du701BqiTA67ipeurLaY457xbSuOCJ5/+dev3Fp9wXun3qsrT5vNUzXtNb2+L89UuIZYCLju+zhGLee+awUtGfF1dpG7eP/r4p6y/r9NOv6o2i7rs2D+OhwGSMe9da3Orc7l+1TPRCtZl3rftEDX61XZR3Xffw4Kx2PLm96wXDY2mrfoBK8FgK9/4NiVQ9FTZeefflukp/xde/kblsavOrc81ut93JJPWeFRQoeUGdIF4/S1/c9hWFtVBP6w2/ze//p1SDprOcJyl/tYob1uZbNtcI8LrTpU4fuwnnv61TE9Y2qa5sZW57jZpf/ycrr9jMO933nn3ibbae8FB3nVw1S3frmk+bQybmvdKEOqhC9c6Q7Zz1dlWNXNHXq/8NWbzXlMY2ep+wIGLgrWv+gLbOXbbfYHXOWzmvR5eyvm2ebUd+7DDTw5y7KpyYd6TXEuUAOZ9gGTgtMz7rkZmyLhvc41VfKXY1fbflezBvK++8bbFG7r+hhp5H4t5v/wMu6kss7nq2lvn882v/641fpmlea91NG0xlbYpyVunflX7uh6SaGLeu9rMVZfidj3Yb+srtE2fFffN38ds3p919o3WMudlL3ttwgLzfjR7hmIX/kFgSAQw7wPE61XXyaF8HsK8v/Gm563XPg3KqJrSvg7np579nPU8Vfktlz8xb978YIML69RD+6Zq3t94k33KfA2Q1D1kXQ62/ctmcWrrVdnO12TbG+/5vjVOXbjwmNoMUjfvxc82db7vgK4m/PlOde5gmoySMe/LbravaLHmR9ewXea91rZsc27XVISh1yu0jZbWU/9VAYI6WhniZTfQts80akTT/mjEj6YvrDpPGUONoHBNjWg7d75NnaJGBKntNr341drtdOeat2rXOz93/qqgqaxu4jt//n4T51FC7OXN3y79ro6rfbRvfr78VcfUscvO7fpMMz7oIQxXIig/R9tXGaauMoxvn7V5L/3aRsnIeBwva9O/1977gYl2FOMm5v0TT3228+k4jzr69Fp1tyXTYxl5r+UfbHoOPfrc9pS1zBsfzdgMdj0w5fPdqn1sx8a8H1Jei7o2IIB5HyAZmLp5f8KJF1ivHfn1RLH3MceemWl6c8WUmgnr+Ze+5hWfKUmWH6f4inlffQNuizfEsG/m/Tnn3mzVyJJTLw8SG+SxgzRX1GD+fpbmvZaPyMsx/qplvfKyh3g9/4Lbreeqa967RrXn5ddsXocedlKm8912x2vZgw//UqY+0mcmPi3BlB+n+BqreX/9Dc9ay5uXXfdcyoXowQnNzPjg+rdHLHxmO9PME/lxiq+Y95j3DWI9vpI+Acz7APF6iGtpH44Rwrx3DbyquySlD089EFC8Duq9crxl11LlkG2DlvTds8+9KWh85VMH7ZOqeb/8jOsm+Iuj7h996161X5kmYzHvVQebFjWTc1X9xj/vg3lvy70qTzxeV/6uvudPkVEy5r3g6kc6fhHR3+rcYoXvMu91Y92mzEok2ViEnIZS5bN1ljqvz5rouoBrbQ4lFWxl9dmm0UA1g2gAACAASURBVPNXrnw40+iNJrye2fiFVqObNDOAmD7w0C96n38a5r1YaO1MG0OtkV3FyrWOdtP1OBXIKdFsK0/obSmZ93oy0lb/I448tbKNqtow/1x9ie0cdc17rWFvW6Peduw22/pk3usBIxuL444/J1j7qp1dZkzZDVSuD1uQh3mffkaJGiRLAPM+QDIwZfP+rrXvtV43dC3RA6cy6tqMqtBoWtt1CfO++kZ+KOa9a3Y0LeOQxw4hXjUtp02LszTvdW9qW3ZO5Xzsyc8Erb8r+VrHvNdD5K6l6HSPqmlsNz7/5cblPu74s61tFKN5r5ntbIMK1HZqUz343+YBjBNPWmFlgXmPeZ9sxEnB2xDAvA8Qr4eIJfpwjDKjVDk4nzq6ZpXVjJk+36+zz5GLllqvh5phtew4ZYP39NmTz5QPUCs7dpPPUjXvXTNMyhdpwsH2nQ2PfsLaxoqpujLvlbvUvYHibc147DNT8R6WAYuK+eoOOIzNvNcDttKnZhj3ZbH33gutbaalXG1tzLbqe/+UGCVl3rvWNJzWSL8mDesy77XWepPj5d+5/6GPWn+4oRMvNuNHHbpGAOVlqXrVKHKto24b6a1j+fzX1KUyxavOZftco591462pfnzO5drn2OPO8qr3tMx7zb5gmx5V68vbOBS3aZ/xeupYOmZxP5/34nv84nMnjlc8/o47zRs9BHHBhXdk11z3ZHbX2veMLtYPP/LxzPV/6bKV1mOmZN4r0VTkkL8P+ZDNhkc/aT1HHfP+6Y2fr5ylQsHCqUsvz5R01LRdd9/7gez+Bz/ibD+1q8107pN5f++69stX+PzGbGvLS0saQVb1fVsfjnnfJpfDdyHQigDmfYBkYMrmvS3+Un+uGWX0wGlVn172eZkxiXlffQM/FPP+qmses8aNoWcNUkI6j3uLr7M07/X70T1JsTz5+7YP1o//Nl0jueuY9+dfcIe1rBppoyTO+Dnr/u2aNjVG8/6ii++2stAggTV3v7c1i0VHLbMeH/Me875V1MeXUyWAeR8gXq97Terr/m3Ne81CbMv7Kn7xNf/rsNUSqHlsVHzVbDZlx1F+sLj/+HvVQbGmTOjHnvh06bHKzuP7WYrmvXLrrpls287eXOQmI901yDKUeS/+V6xcn5140oXZPvseap2Rdlwjvn+/9Oo3a+ln1ub9xk1fyq68akOmB0W11LWLvW/9i/s1mS26qAXeV+cIYmCUlHl/xpnXWy8GeiLdZ2q4WQDvyrzXlPDFH2z+ftGi02p1YlVMbOabzuUz4nP82Jqq/YbVz43WZZ83b09r+fN62F51sb/40ntb1U/cVly8djSlXpMOU+tM6+mo8boV/56Wea9z2pIu4lT2VKM+swV/OlaxHr7vb7rlRWdb6sGatfe8v/aTcTq3K2GVknmvfsmmZU1v6cu3ar91D3zEeo465r3r6VoFjmefszqTuV9VDtvnu+46+Tvvk3mvBxRs7XvMsWc04mVjqG22RLPvE6eY96nmjCh3Twlg3gdIBqZq3ruWvFJ/XpUQc10fittd05TrOoV5X31jPhTz/qZbXrLGLrrHKeqp7XuNBrPFSLM2723LjqmcdQ3rKj6aZctW/zrm/e577GM9xuWBlgzcd7/Drcevy8I1tf+y068OpilbPCu+bXMDeTtqDVVbe2HeY973NB6lWuUEMO8DxOt5/zr017bmvSv3r2uWcrjK5YX8r3jQdj3U7GFVbenKK9qOp/NoySYtiaPR2DKuq45f5/MUzXt5JjZW2qb73zr1r9p37wUHWc/Vxrx//a3vZzfetCnTw+o2z8FVt7rb6z7wPgvzXg/Vr775xZH3pHv9unX03V9L31a1NZ9X5wFiZ5SUee9KNkjU6x74cJSC7cq81xR5th9zqBGVuXBtay3rYYn886avujBrvWhdqE8+5eKsjpl/w+pNrc+vcr+65TvZffd/MLvokntGQY+vma+kj0ZUu+qukRttgyffC7MehrDp4MIVdznLp89s39GxXHUq266n6GzH03qDdaezKZ7n9OXXWo+bknmv+mht8nE++x+wqBHrIp/8vWYxGD++/vY17zVNj+37Mow1LXx+niavtv6jT+a9HmqwsQv5cIb6Sk2NOn4eTY3s0ya2ZGeo64Tt2NOaCcc1nViBk5Iu/INAbAQw7wMkA1M1713rNYfqN13Xc/WLmPfVN+1DMe+VIC1cK7d6/8LLX/eKLXziD90H2M4za/Pe9uCzyqmR3T718t1nl112t9bf17zXw9Y2froP3/z6d1qX9fW3fj/bfvudrOeIzbzXdPg2FrrP0IhE3zZx7adYW8uW2M6BeY95H1sgSXmmQgDzPkC87upzh7a9rXmvwVC269O0t62++YXK663iSNcU/1Xl1RKeWtf92uufykLEoyma964yi12I2K/423Pd9zQ17zULxH77HzEVrdY1rKdt3uuBm7YzP1f9XvLPH3n8Vyt/l8V25311TiBGRkmZ9y++8g2riSHRalR+jIC7Mu9da2lreuVQHFwjeHYPPDJD5dVN86OPf2o0hY4rqZJ3TjvvvHumJ9JC1TM/jpjqacKzzr4xs40Yzs+vV62rl39vlq+vvPbb1rUQpQObca5ttim4lQjSserWReseFbnk7zXaRG1a93jF/TW9Tn684mtq5r2WfCiWX++lr2Jd27y/ftUzE8fXOXzN+/POv836fT0l2KZcCi7H662/+2Teu/pImdpt2BW/q2mQbBx1U1Tcz/XeZrBj3k8l6cNJIGAjgHkfIBmYqnnvmlEo1ChaXfdt1wttw7yvvlF3JbHWP/Ixr+ttfh12tbMeFs73aftqG82iUR0+x9W9gGvtcD0Q6nMMn31c93OzNu/POfdm6+8k5NJzzzkestdv0de8X+uYfvaYY88M0kauZbdUxtjM+/vu/5C1zULNOKgBBa6+E/Me894WzLGt9wQw7wPE6z6xwhD2aWve33r7Zuc1ynXt6mL7daue9oo/ZLxrevw2ZdDAOs1muebu93md06YjlxGu/Kxt/xi2yYi1cZszZ9vgZXYtF9TEvFfcqPjeVvYutsVs3uteSv5KF/W2HRPzvvoeP4bfdtsyJGXeq7IanWITrAxdjaRuCyT097sy71XOHXbY2cqiboLOVWfXes4a8ej6TqjtelLp8CNOsdZP7S+uoc5lO86WN783mhXA9RS+nvSPRW9LTr3MykntN143V5vqGOP7+vytNWxsv0c9Herz/bJ9DjjwKOuxUzPvXcng51/6WmtG4ud6itDXvLdNmamA1vbwR1l7jX/mCjz7ZN6LkS1IVQK9ycMw4wz1t+tJ60MOPcFLP5j3vU8uUcG0CGDeB0gGpmreL1220hrXhDKHjl98rvX4itPq3hvoIVVbfHf1tY95XXvy69kdd71pPc7Rx4RbXubgQxZbz6GZCPJy+Ly64rW+mfdi4ZrSXbOh+bCq2kcjpV1TRM7avL/ltletetE9X6hl+Fwzo+k35Wvea4Sb7Teomcmq+Pt8vvKqDdbj65x1zXuNkrOVVX2eT1mq9nG12alLrwhy/Guvf9JaftUpVP9cVcdZfa6lEG1tN7ZNsQv/IDAkApj3AeL1WfVrsZ23rXnvigfG+mmfvrzVPr7mvfgrT6blk/ba68BW51QdNQW7lqus264pmvf3P/RRKy89dFu3/lX7u+4b65r3esBSy61W6XHHHXfJlMM86eSLsnPPuyW77IoHRv+vue7JTNqy/bcNhNN5YjXvNXOtbfbZcTZqT+n6pJNXZOd4srANwtRxMe8x7wv6Gj1x6/wx6gmAqo4h1OdlFy7dhIY6T6jjdGneu5JV9677hSAcrrjyIWubLz3tyiDHr2Ks6fz0tF1BiO++D72mtKssmg3ANu25yrTm7uo1f1zHDbm9jiFfx+j3KePpy695t03ydtJTgXr4wef7rn00C4KOkx+z+JqaeX/m2TdY63H3fT/XilHO7tBDT7Qe39e8twUXWnsqP37TV1cirE/mvdgcuPBoK38F3k3ZFb93yWXrrMdffoZf8hbzfkh5JuqaAAHM+wDJwFTN++OOP8fan9+55q3W1wsxKVv+CfO++sZ+SOa9K7YYzcQVYHYzrUNejN2L72dt3j/z3BedZQs180DZmq++5v2VDnNdMwcU48Qm7zU72r77HubkUNe8dy1tePIpl7Quq+rnMj70AHOT+o9/x/XAuHSLec/I+wRiS4oYngDmfYB4fbyvHerfrmuYrjGaaryKi+saq4cktZTitP5rLfOqso5/LhNf/oTy0PPmzXfGHcU40fZ+7tztMuUXx49f9neK5r30YKt/FyPvjzpqmfVcdcx7DVjSzMy2MmvbbrsvGD20qsGZTQaHuZbojdG81+BOLbHsYrHrbnuNlmyW2f76W9+vpWXp3BWrYt5X3+OX9ROpfJbcyPvNr/+us9PX6Puytchn0Shdmvea3t3WMZx5VpgbWT0JZDt+nSfu2jLXmtK2qSHVKbY9tu/3XdMr6uEG32N0uZ8ugraLhBJwMsHzc+u9bfoWfbfJhVTHtT2tp4tSfs6mr6513KXH1Mz7m2550fo70u+3KZ/8e+rvXMl6H/Ney0/YfuMXXHhH67ItPuE867HrmvdaP368jHsE/P0//uQ/mzi+zqdZJXLOZa/Lz7jO+n09QVn2Pd/PFi48xnp835snzPvw2RyOCIEWBDDvAyQDUzXvZWSNX8/096obnm19vdB123bsfBvmffWN/ZDM+6ee/Q3r/ZX00tYc1sxSO+40z6nHWZv3ir9ss06p7scdf3br3+JTz37OucyfzuFr3ruWxTplyaWty3i3Y0r+vL+oa97rAaT8u8XXUA/b33jT89bja4k133jatd+6Bz5iPXZeD8x7zPsWMR9fTZcA5n2AeN3V7w5te1vzXtfk/JpUfO1iNHaXbTNaKveJT49m0dXa9sobF+tT9V6+gG8OTPVI0bwve8BUXljI9nENBq1j3pc9rKs8adsZrXbfYx+rRmI077UMnkvDpy1bmb32xndbtd/eey+0Hh/zvvoeP+TvZlbHSs68F6iyad50EZgGTE0xplHZVefq0rzX+uy2zkFPtGnUelXZyj5X4sFmmut8MtTLvhv6M9tUO9tvv9PUyuCatvrc82+dWhmqmK64aI1VCzes/i9PR+q9TS/6btXxXZ/b1jLSk3eu/X23H33McmtZVf7UzHslSG3cFaw2eeKuyLDsRsDHvN+46UvWsl3YQhMq38bnv+x8qKCuea+pdcf5qY8rcmjzvq1577qZ0lOmTR+KyevjWoNTfbPWU833K3vFvE83a0TJe0kA8z5AMjBV8941FX3bGa10HbPNolO8dmLeV9/YD8m8V9zgirX1UKjPPa4r9pC5XNTe+PsYzPtLL7/fWcZ1D3zYK75y1V8PAIzXufi3r3l/x11vWI+juM51bp/tm1//TrZgwcHWY+flrGveP7j+bevxFuxzSKuy5vXRmrd52YqvSurm+zR5VT7H9SBHfh7Me8z7XkajVKqKAOZ9gHi9Sb/cx++U5ex8Rt67plLXderFV77R6jo4a96PPfHpkb+jh/2U48+vva5XjcCXwe1T7hTN+5df/ZaTQZ08uA8fxWg2zr7mvXKdLnM91LJJtsGHKnNs5r0eTLH5VirrKUvCzEKlwcq29sK8r77H9/k9xL5Pkua9nlhx/TAk5ksvX+fVmTdtnBde/np22OEneY3M7NK81w2npsix/YD1tH7T+ul7SiTajjuN9e7Hy33AgYsmyqKOa3y/rv52JSTaGpwhy+saQXP4Eae8y0nvx9tUJqC+27QsttHVWu9G2mx6TAWwrnUyVf46QYsr8VV3ilrbFP51ppbf/4BJDasumgKrKSctTeAKuHRsH/NeszGMa0J/n7q03bT5LoNCx65r3p940oqJMoZYmiHn3ta81xOweurZxlEjhfLzNHlVoGc7rpZK8D0e5n1VbobPITBVApj3AZKBqZr3WtPP1qcrln/p1W969+vF/l/XINcMLcVzYd5X39gPzbxXMtj1oLYeQGzysPaKi9daNV7UYgzmvZLdrmXR9tvv8K1mLiv+3qreu+47ivX3Ne9d8amO1eYBAy27VCyP7X1d8/6Vzd+2akn3c5qJoYpb1eeuB6FVds0iUPV91+dl9ys5F8x7zPupRomcLBYCmPcB4nVX3zu07W3Ne9eAG12n1j/yscbXwNjaQflNxVG2AWL5NVmvvg89p2jeyxB3zayqteVDtZlGxLvWqfc17zc8+glrPKmHMBQXti2rpqEvtnvxfWzm/WNPfsZaVj1cX/ce3MZN7TVnzhzrOTDvq+/xbUxT25akeS/IumktM/j0RH0XjaF1qvPpXXymVe7SvFf9NEV+sRPL3++yyx6NE4EPbfgVJ9tpTpmv+mlk8s477zZRx/32P6KT9rVpZvXNL0ycX5ynzcJWtuI2PVCSt3/+qqScnkzUf1uCTt8pHqPue1cC6P9n716A7jnr+7A/EgghIRB3kCUQEnchkLhJAnQBgbgKEAKEuAgJAeJ+McggECDETZjEcd06xrFT6jhuaLFDQuoSN6R2nTTjSadJZzpp2unYzaRxxu2kudRx3Dg2nM7vxSsdnXfP7jl79vI8ux/NaN73/77vObv72e+772/3ey7xagX73lf8fJT+sW+r9a/7WGJ5v+04EI9UjGfAdLGKcr7Op/raLuV9LLfu4mW8ekLXVwV47/t/ZutgEeu2b3l/+RVvrt3OeFZ6F7fN22y7OLrL8b26r4sveW3tOsbfiq6Da9NF9X1eLkx5n8s1IOtB4EhAed/DxcBSy/umAmqfBwRWf3vifQbPOffC2r8/1SxQfdz3wsG2Uus113x0r7+9N9x4V+36xavqVNtx6MdtL/sY5zP73PfSyvuwueDC4w+QrDLz4Ac/ahXnvbsYxoNB4/3Hq9vGx23n6TmU97FN8Qpq6+u7/nm8D+i+L/UZ82/dTL1+v/H5ruV9rOO29xGNBxjE7/8u+6b6mXhW0KWXXbd1m9fXc9/yPpax7YkVh7zCW7XuTff/yEeevYq3AVv/2V0+f+GL3raThfJeeW9+XaSA8r6HeX2XY/ESfubQ8j4K3ftvebLIq179ob3//pVgHk9y2lYux1sz7XKtssTyPvZN3ZMXY0bb9fruLvs3Ct/1uW/9813L+21v7xRPvtplHdp+Jjq/9fVa/zy38n7b2zud97TLerF43wd/dquF8l55v/a7cTS0bw1LPAKg7RdviO+3vb9jXJDo62Vk4kJhPAtyvQDdpdzZVtq9/4N/oRezT376V7b+UXviE5+79zOg4+Wu4/2k13b+3Z/HBYS2Z1THqxJcc+3HV329H8tb3vaFu5e/vk5NFznjQmFfvnGho+4Z67Eu+14UHOJ3YP0+33Ddp2qtXvLSd67i/3W/6vO4zfp97Pv5tj9S8czgsNvn/mIAi/1ardu2jyWW93FRc9vL3Fxw4Uv2fnn1yPe2lxCq3HYd7rY9srXLe/DGs7TiYm+1DnUf9y3v3/Tmz9beX7yn0D752vazfZT3sd3bHiEbBUU8injb8uu+Hica2y7YPuQhj97r/pT3i7zgZKPzFVDe93AxsNTyPo73Z5zxhNq/afH3Mt4bcNf3w4sHeEWBt/53NsrSbX+LlPftJ/ZLLO8/c8evrh70oIffK0f3ztQJq/Of/sJVvGJV3TngJ2771irmsbpzx20PMM+lvI8HVzZt++POuWCnVyeL8524gHnyyafey7HuVbvCdp/yfpth3E88ADuOhXVz5ObXYq6sezn/bRfGu5T3mw/eqHIUzxS69PI37WS5ud7r/75sy4N5Yzlnn33+Kp6ZuP7z2z6Pn9v2ynHVOq9/VN4r7/MdKa3ZgALK+x7m9W3H4aV9/dDyPrwe/4Rn32vGqP5OPeGJz9npb1+J5k1PVtrlFQeiK6mc1j/G7Jezx7YnBl34zH5efj22fVvxHk67lvfb3u++r1fCjvtZ32/rn+dW3m97v/t4RbI+svaqqz+41UJ5336O38c+mPo+in3mfcDFI9Cedv4VW0Mcv9wPeMDpq1e+6v17Pzq92jHx7M54dnHde0nmUN7Her7wRTdsNXjqeZfu/Az8j//YX1k98lGP23pf17/ljtYDT/XotnhP6qte+s6d35e58l7/GAeheAWB9YN09XnTM7urZ+rGRY0o8vd95kS1DnExZtsDROLZBfuW09X9DvUxnoFRV+jGutY9GyJ+dt9nbWyu+6c/+52tz6y57PLrdzaK9YiLg9X+bfpYYnkfbq9+7Ue3bt9FF7+m9qLopnf8Ox51t+2BAOtuu5b3r3jl+2rXK/IRy6pbh7qvffDDf7HxImi1bvuW9x//xDdr1y9ekSOWWbcu+3ytj/I+lhd5r7Zx82NcJNz1WUHxN+fhj3jM1vvadZiuDJT3A17WcdcE9hdQ3vdwMbDk8j5Ksc2/Eev/jmN2PLu9roiK90GMWTwexb/+YOLq9lFuxfxd/Xv9o/K+/cR+ieV9zAo/fMb4ybW5Wc9QvM9oPIAwitJ44Mi2c7S4TbxdVJTj67evPs+lvI9tj5dcr/tdqtY1ZuEo0GMe3jzvi7kuHgQdD1iufr76GDNqzPbVv9c/7lPef+r2b6/Ca/3265/HW27EdYCPfOwvHXsgcJz/xisnxHWMulcEiFesiGPJ+v1Vn3cp75uexVXdb5y//MiZTzp64EEUDpv/Nz1D6fbPfaf2PLe673hWYryawkd+9BdqLH5jFRe94gEGdefK8dYjz7jgyloL5b3yfv9Rzy1mIKC872Fer65HLP1jH+X9tmt28SC8uCY7R+N4QmDd3+z4ux8vr9+2zfEA1WpGWP845lvwtq1j3fdf/8bbatc73qpz1wd5193v+tdi/lo3Wf981+uN2x5g2uVJYOvrVn1+5llP3rqO+5b3215t6+WvfG9rjqr1afq47QGm8bvfdLtdvxfnXuv7aP1z5X37Of6uzjn/XNHlfcDGswCioF4Pb93ncdCPk7I4EMb7UdQ9eyDuL/7wxYn8lS++8ejCQ919VV/LpbyPP2qbz76p1jE+xks3x8ssx7N/68IY2/zSl9/SeHEgHiRRd9vNr1XlfbX8eBZQXNSI4jJKqXjAxeZtNv8dFyrCf9sf6ngZmS9/9b/bej9VeV+tQ1zYiPfwjmeNxB/wzeVt/jueAR7vJ7Pt0Y1xv9dce2vr/Wze7xj/ftazX976u1C5xM/2sU5NL7n55KdccnQRZdtyorSPP+6R0Wq94uNRbs45/jYA8b1Sy/vIbNMf3Uc9+tyji2zbXgIqyooYOjbf62bbULNreR+vlrH5bKFqX8SF2rjI2FQ8x8sAx4XBzfV61KPOOXrwVHVf1cd9y/vIzraXxI2cxCsHxINs4m0s4uW14tU6Nv+/9ZPf3Jr1vsr7eE+meOnOajs3P8Yz6eMtOOLn6n4fItfxChl1DxSr7it+1+pu2/Q15f0MLh/ZhDkJKO97uBhYcnkfBWC8MlZ1XG/6GOVo/F2J/2OWbfrZeJZwnNso7+95tbh9XyFrqeV9zBA3vuPHG88Dm7K3+b14Fn68klu86tDm9+LfkeumuWXs78WFu7r13PxavKpFvN1VzOtND6KN2TQufMazbTbvI/69T3kfFvFg+Lr72fxavMdozHxxTeCBD3zo1gdXx+3i+/GWatseON2lvI913VaAb67rtn/Hca5p/297ZtPm/cU1hCOLMx7fahF5jPOEeIXFzfuJfyvvlfdzGkJty84Cyvse5vWm4/mSvtdHeR9/p7Y92PAFl76x8W9nydbx4Lq6v83R67RtV5wX1d02pweR1m1D7Ou69Y6vvfWGL7Zud919rn8tzqM3r92uL2/X8v7iS66pXc949en15XX5/L0f+HrtfVfruW95v+1VAvp6a6f4HazWbf1jPJG4y/av3yaesLZ+n5ufK++V92uZuOdCyNoX7w7QVC+bXwU6Lg489+JX370+deu4+bX4wxcnrg996I8cPcsxLjTUPSp983br/45CulqHbR+Hftn8arnx7NRtF+yqdY4/UnHBMIrseGTQM5/1stXZj3t644E7bhvvQb7r+zZvlvfVsquPcWEhyvwojuMA95Krbl7FgTRKySimYlnbhpK4j3hkYTyivtruuo+b5X217Opj7OunnveCo8zEcl/2ivccPVjg+Ze+4ej9uJueSRL3EYabz76oW48pvvauW35q59+D+Nk+1jFesaGpcAyzuJAUv6NXvuSmo30eZW84Rjlc7Zf1j1HGxgM41r9WfV5qeR/WcaGs7fc0XsYpXko/fkejzI2XTYpXkYiLgZVB9TEK4W3vBbRreR/rFb+H1X3WfYyLYPHozHivzPh9jfeJjLc4iAuYdT8fx9IYIh50+iOOfb9Leb/t7TPqll33tab36O2rvA/HGCKbLujGuoXNE5900b2Pw2ef33ocjgctbXsAVtPvsfJ+54s0fpDAGALK+x4uBpZc3sfx+nN3fnf1iEc89tjfx7q/X7t8LZ7JGs/Kj/veNmN45n37if2Sy/vITlwoi3J6l8xt+5nIYvx+xv1FJut+LuaS+H5O/297pbW69W/7WvXg/r7K+zjnfPozXlRr2bYudd+PVyX88Ee/ceTfd3kfD/ZtehW/uvVZ/1pbeR8W20r29fvZ9fNY3gc+9HNHFtvuV3mvvB9jOLSM7ASU95n9nc5pZth3Xfoo72OZ254tHddi4wk1+65XCT//mMeeVzv/7Fowb7ve3NfbKw9lGPN03SwTr2y17cleu65LvE1b3X1XX9vVdtv1+nhG/q7rUvdzMetty3q1jvuW99t6uec891UHrWu1/tseHBCvwlX9TNeP8Tas1XbXfVTe53VO2XU/t92u+Gfer29gPKvx/qec1hjsurDv+7XTTnvo0bOFd3kW+baDRF/vyb6+/fGSeZvPYN532zZ/PgqjeGbu+nKaPm8r7zfvf59/R+l1081fa12XtvJ+n2VuQAvDjAAAIABJREFU/mw88KC6QNrkMNX3IpPxkpab67357/iZXfK763bE+5I3Pehic/lN/4738ol12zYMlFzeh2dcJGp7Bl2TT/W9eLZ8XHyL3/vqa+sf9ynv41UBnvSki2rvZ/0+d/k8npl04zt++HvaV3kfbvu8qsTmeo5V3sd6fugj31jF34jNdTjk32c95qmrricYyvvsLgZZoWULKO97uBhYenkffyviVabiAbSH/G2I28YzXddf0UV5f88Dzj3zfv+LGfHAkuc9/9pVzHL7ZDNeyjOeIR4PqI98x/+fuO1btfdx5llPvvtnqp/N4WO8glPTS9S3ecTF8/VngvVV3odNvEzqtpfhb1uv9e/Hg+R/7Lb/8m7/vsv7WNc4HsUDj0888T61+399fTY/byvv4/7j7QDi5e83b7vvv+MVwuLJD1X2lPf3HDtrLGN28R+BJQko73uY16vj69I/9lXex1vh1Byfj74WT5bq8kSPnPdN/L3fdt101z5l2ytjxhOgct72eKXfbfs63q++67rHteO2+WzX8n5bruMJsoc8wGDb/a577Fvex/Xp9dtXnz/mMU/tbLm+D+LVhKv7XP8YT7ZretXo9fuo+3zb/a4vQ3m///lunXXuX5tVeR/Yt3/ub6zi0TNtB6T1sO/6eTxrPJ6pveuz0GN9xizvY3lxQfPcxz+z9sCx63ZWPxfPrP3Cl+tf4nlbsIcq7+M9oHd9/+0hyvsopiNX8RYF27Y9l6/HS79U+3Dbx75eHmZ9m19zzY/ufbFvc/0uvfxNd/+hn2t5H2YxND3sYWe27qdNn+rf8aoh1fu991HexzrFs2UOLfDjPT7f/Z57XtGhz/I+Lgi/6MVvb32GemW0/nHM8j4s472K694DdX2ddvn86Lhz0dUHHXeU90u6zmRbCxBQ3vdwMXAO5X38rYiT+Xikfvzt3OVvwvrPPOKRZx+9NHfcz/r/yvt7Cijl/b2zsZ6Tts/jwSXx8u7xagTbivw4L45X0YoZq+7tlba9zGO8LVnb8qf6/sd+7D9fPeWpzc9wWf89rD6PZwjF28Otr3ef5X11v29+651HrxhYLXfXj/Fs+3ilufUHV8R9DlHeV+saD7aOl7mPV5uK85Zd1nWX8r66/3hp/20X5ZuWFa+QFcfdKAWq+4qPyvt7jp01fsr7AgZMq9irgPJ+Y75cP176fL/5qqmMrF4JZ1fTphklruX1/USzuL6/y8vyv/2mu1bXXf/ZY3PGrttV93Px1r81f4+O3l531wcqxCua1t3HLttUt05jfS16h22zU8zf+57jxHrHkzKjV6nzWP/aruX9tuvQcV/XvuGT95qxdnV7x7t+YqcH0u5b3sd5zfo2Vp/Hq9t+8tO/0mld17cpXo24us/Nj9GTrP/srp/HdfVtbyW9vgzl/X7H4139c/u52ZX3FXD8kbns8ut7eSZ6XLiIR7zE+3NX97/rx7HL+1iveJmReBWChz/8rK0HkPVf9s3P4/2lu76kelyIjANuvAR9XWm0uay2f0f5Fyf+myfYTf5xsSCePXHe0y5dxR+2tmU0fb96T+1dHzjQtF5jfS9eMqnpWfDxvaFeVimc4qV8mkzrvhcXX+KRpOtGcy7vYzvjQUDxkkLbLorWOUUeYwBdfzWMbUPTPs+8r9zjFQ/ivT/3fQWTeM+keMWET3/2O/fah32W99U6xrE93k4gXkpq28tgbdqNXd7HusZx+A3XfarzgzTiGZl9PCK47jgcLz9WeR7yse6+zzn3wl7uu2294tUINvfzxr/joov/COQmoLzv4WJgvIdhnMDU/b/+rNK240j1/XhWa919ved9Pz3K8SwuQL32dR9fPe38y1dRsm0cy47+Hc/qjfkq3r4mTubjb0y1/usfb3nvT9duy2Zpt36bus/jIkSdSTw4re7nt30t5pW6+9n3YuW2+4+vxysa1S1jnwdb93k/8V6VdesTz0Rv2o59vld3/++65T/q7f7X1yWyExfJ3veBr69iGVHKxyy2LYPVbd9wXf1F13jbtupncv340Y/9paO3r4pnqtc9ISDmz5hDr3jhW+9+CfrNbYljUd1+OjQHMatHcR1vgxfPbqo7XsQ6x4wWP/Pmt37+6Jn7m+sX/463oqtbx3gyRN3PH/K1OBbE70bkJ45hdcvdd+4NiygM4gH2dTNp2MQ5SjxgOl7B6/q33LH1AbGxz+vW6fbP3fvc5hCDHG+77T2BN3KlvM9tmrQ+Qwso73uY13M85k2xTn2W97fd/u3GB/3G37suxe66S8x3MfPF9b3qWuX69+s+f9WrP3Q0jzz4wY9averqD64+c8evHjRHxPLjVZ02/hYd/Tuuh9atQ93X4ppo3X3EHBe9Rd1tcvlaFOB16x5fi/PFXV99ILYncnPmWU/een/ry9m1vI+cxFu+rt+2+jxeNTnejmsfyze9+XM7X9/dt7yP9ai7Nh3rGw8q7uNBLw956Bm1FnEOv+98G28bG7erPJs+Ku+V92v5aHwk7tGJzj6/lGP+bJzUvef9f/6o7In3iohf2Lr3jq42Nk5240Q9Xu4tLjrESeYh6xsvxxa/eJv/7/vek13WIV6qJC6yxPuLx3tTx8lrtZ3rH+N95OPlQuJVBapn83ZZXt1t4iJfbHs8ozoOivF+803+UbZHaRUvhxfrHvuv7n53/VpccIrh5dWv/ejRI+rPOusprcVkrGMU/6945ft6eRTWruva58/Fy8JsZq76d/WS5n0ub/2+4o/4O975Z48ukoTletaqzyOL8X6vF19yzVFpX3cBMC6iVOu8/jEusq8vr+nzeITd+m2rz6v342y67fr33nrDF4/dT/Ueies/1+Xz+B2JZ+fERcC639HqmBQXCOMZQZvLiGc7Vdu1/nH9pSA3b9P277jgHQN4PFtm2wNg4lGAjzvngqNnr3ziU798bL1iGTe+48ePrVufZUg8WCie6RRlRbylxvr2r3/edByPbV3/2erzKC7anHb5fhyHb37XTxxlPd4HtG4fx+9FnEDEA8Ve+KK3Hb30/i73vcvP3HTznzm2fX1d4K+773e/5z/uxa1t25T3Q1+zcv8DCSjvXQxsPUbG36Uo/mImj4sTccHl0Hm47Zjq+8s48R9zP8eMX8396x/jGeBjrsehy4oHkMec+6GP/KerODeJZ+gc8nKgh67P5u3jVfLiXCIeEBPrF+u6z4PeN++v5H/HM9XieBkXMo8sbvvWYi123Y/K+4GmPXdbuoDy3rze26zSZ3kfx/a4BlP3wMJq1oonaz3pyRcfXYOKY/wufw/i3CMeWBfPSD/9wY88Nr+13UdV3lfrEOsX1xLjSZDxd7nt9tX34zpt9BLbnqQT19L2eXBCPACvegBCtW7rH88++/yjV/eMt4GNB0bWPYgvvtZHuVtt464f49yv6VWVwziuETc9UCLmoniZ/c0nZ4VJXJtct6g+37W8j+2Imb663ebH2IcvffktrU+CjbcdrXsF2MhhPBhk837j313K+xe+6Iba+4r7u//9H3DUX1z96g8dXTuN37G6LDR1AJu/A+vrHd7xBLS6Vypbz0M8qLbu1TUe+KCHbX3ArvJ+Gefwkcf1TG35vNzyfv0Xofo8TmjjGQPxi/GBD//80cl4nOjGH6zqZ+b4MYrseLR/XAystnuKC4KV/49+/C8frUesTzwTPB6RP9bFyXjwRFwYjT8UYRHPXo5M7PryO3PMx1DbFO+fGUVoOFf7eqkXldqMY7iOQeQeq29OftEpCvJ4NYu4CBbrFcNBPCiibVt8v36IqDsOh+dYx7657BflfenXxBa7/sp7FwP9/ZSB2WcgZpptzwqPiw9zmUVsR/2sy6UcF+X9YudRG94soLw3q/U2q/Rd3sff2De+6fatTwpZL3Xi2bvnnHvB6pLnvW515UtuWkU5+cqrP3D0ebwK6NOf8aKjV65peuXWuL+2v+tNxWXc/rTTHrp68lMuOXp15Je94pbVNdd+fHX9Wz5/9Iq5sT7x5Ml4QmHTE/3ifqKsbluXze9f+Myrdim9Gn/mve//mb2Xu7keXf4dfc22l8+v9nMUw/FE1Xhr3Hglt3j1zyiK4+2Atr16QTx5LPZ9dR/rH/cp7+/80vda1y/WIV5dLvZ7PJgj3g4hSv/I5LZzhXiQRjwp6FGPOqd2HbuU99H/bHtQyPr2N30eD/bYth/jSYZ1D3xZv7+TTz51dd7TLjt6a7J4ZYX4PY5Xvo0nr257Ban4nYgng5255ZUTlPflzNzbsrPL1xdZ3u8C42eW8QtgP9vPMiADMlBeBpT3zVecfDdbAeW9i4FbT/r9LSrvb5F9Vr/P4pWX1i9WVZ/Hq73FM8W51btx4TJ2BpT32c6LVmxaAeW9eb23WWWI8j7+Vtxw41d2ek/sagY75GPb36a28v6QZVe3fcYFV3Z6YtNnP/+rq9NPf0TtXFrdd9vHqcr7cI8nwMUzw9vWcdfvx6sMx4Ns+yjvY/3ilVVjvt91+W0/Fw8kiWI77rvP8j7uL95OtW35Td9vKu/j/uNthHd9ufum5ax/L44fcd/K+2WfIyjvDSW9DSVxQPE/AxmQARmQgaEzoLyf9oqWpXcWUN6bFc3KMjDrDMQrNsXbAK1feKo+f8pTnz/rbR969nH/5uu+M6C87zzPueG8BZT3ZrXe5pWhyvv4exBvq7nDdZHamayazdo+RjHb9rdnyPI+ytznX/qGg16pMl71N94euW1bt31/yvI+7OMVg7e9v/y2da77ehT31YNo+yrvY/32eb/6uvWqvhZZe/0bb7s7b32X9/F2vfH2yF0fbNBW3odFvOVuHwV+vKLCNdfeerfFmZ55f7dF2/Fojt9X3htKFv0LMMdfatvkwo4MyMDcM7DDSWpcdPEfgdwElPfmbnO3DMw6A1e++MatF0ff8rYvzHrb5z572b75nV8o73MbE61PJgLKe7Nab/PKkOV9/F2OZ1HHS6Vvewnyqhjd92Ncb4lnKje9p3o1F8Szw+OZ8Zvvrb7vMjd//syznrx63we+3su+iJc1j+35kTOftHVO3Vx+9e+py/twjrcZftr5V+y97rENR+8//7J3r7781d+827LP8j7WL95i9ZAMnnHGE1bv/9DP3b1+cZ99l/dVXuNtky+6+DV7v6LBLuV9LOPDH/3G6uEPP6vTvor99ahHn3ss9/G7UOVx/aOXzZ/fbF7ldP2j8t5Qcq+D43o4fL6Mg4D9bD/LgAyUlgHlfSaXtqzGvgLKe3O3uVsGssrAJz/9K6t3vOsnelmneDbLtvcrjQt66xcNS5s7rK9ZeY4ZUN7vO8b5+YUIKO/Nar3MRfF34+03fXX1hCc+p/b/Wz/5X/S2nCjxY1nxXuennvqg2qJvvfTb/PwBD3jwUQH/utf/2CreH7zL37x49aV4GfV4z/WnPPV5qwc84PS91+PUU09fPffiVx/dTzxTust6tN3mk5/65dUNN9519N7jF1/y2tWFz3zpKp6Vvm0/ffTjvzjIerStZ933473gn3reC1bxvvCb+3Dz35GDeD/1uv151UvfWbu973n/n++8rTFTxEveP/KRZ7euW6zriSfeZ3Xu45+1evNbP7+K7Gxu73Muurp2HW/79F899rObt93l3/GAjg986OeO1vnSy9+0eu5FVx+9ncCTnnRR7XLjFSB2ud/4mS/d9etHD6qJIn5zv9T9O/bnOedecPQqBl+66zeOLeeS511Tu051+3bXdfRz5ZzbKO8NJccOCn6By/kFtq/sKxmQgSVmQHm/kEtn89tM5b2529wtA1llIJ7lEheR4plI8bKXd35p//ekv/WT31yd//QXNl6c8qx78+oS59Xct1l5P79B0xb1IqC8N6tlNavt+7ckivyP3fpLq+vfcsfqpS+/ZXXJ8163evZzXnFUTEYh+oLLrlvFKyXFy3JHGXz7574z2PZGUf6uW35qFQ8KeNGL37563vOvPVqXeKZ+PPv58ivefFSiv+3tX6otmffd9qX8/Ofu/O7qbW//8ipK+HCMZ9I/+zmvXD3/Ba9fxas9xKsFTPmg2Xhw8HXXf2Z15UtuOsrfMy548dH6xf5/1dUfXN10859Z3fGFXxssdznl4Lbbv7267vrPbli84uh34ZVXf2B14zu+trrjzr+5CIuc9ktJ66K8N5Q4QMiADMiADMhAURlQ3vdyYcqdjC+gvHesLepYW9JJrXXtVg5X5X31LJCTTjr5qIiPC7rxbJQo9+pso7CPl2mN0r7t2T/xMp919+Fr3fYZN259ZUB5P/4gaIlFCCjvzevmFhmQARmQARnIIgPKe0HMIoh9nYC6HxczZEAGZGD+GVDeF3Hhy0oeF1Dem7vN3TKQVQY2y/uqxF//eMopDzx6H8t4GczTTnvo6j73uW/js+zXbxvvYfn5L/43WW2zOXH+c6J9vNs+Vt4fH9R8hUBKSXlvVjO3yIAMyIAMyEAWGVDeC2IWQXSCvdsJNidOMiADMvB3V8p7F9YKFVDem7vN3TKQVQZ2Ke/Xy/h9Pn/s2eevPvv5/zqr7TVDmaNl4J4MKO8LnSat9tACynuzmtlFBmRABmRABrLIgPJeELMIopPoe06iWbCQARmQgeYMKO+Hvmbl/gcSUN6bu83dMpBVBoYo7+9zn5NWL3zR21Zfuus3stpWs1XzbMVneT7K+4GmPXdbuoDy3qxmfpEBGZABGZCBLDKgvBfELILoYsHyLhbY5/a5DMhA1wwo70u/JrbY9Vfem7vN3TKQVQbiJe1f/ZqPrM59/LNa37u+7Vn3J510/9VFF79mdesnv5nVNnadNdzOnDr3DCjvFzuP2vBmAeW9Wc0cIwMyIAMyIANZZEB5L4hZBHHuJ8a2z8UfGZABGegvA8r75itOvputgPLe3G3uloFsM3DHF35tdfO7fmJ11cvetTr/6VeszjzryatTTz196/vbP/CBD12d/binr55/6RtWN9z4ldWdX/petttmButvBmM5H0vlfbbzohWbVkB5b1Yzz8iADMiADMhAFhlQ3gtiFkF0EWA+FwHsS/tSBmRg6Awo76e9omXpnQWU9+Zuc7cMFJeBL3/1N1efu/O7q0995q+tPnHbt44+j9Jv6L/17t88KQPDZkB533mec8N5CyjvzWpmHBmQARmQARnIIgPKe0HMIohOzIc9MefLVwZkYE4ZUN7P+4rZjLdOeW/uNnfLgAzIgAzIQBYZUN7PeOK0aYcIKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeH3I9ym0nFFDeO9YWdax1gt3tBJsbNxmQgRIyoLyfcCK06JwFlPfmdfO6DMiADMiADGSRAeW9IGYRxBJObq2jizAyIAMykEcGlPc5X++ybg0Cyntzt7lbBmRABmRABrLIgPK+YWLzrSULKO8do7M4Rrv2lMe1J/vBfpABGZgyA8p7Q4mhRAZkQAZkQAaKyoDyfsnX04reduW9Y21Rx9opT1It20USGZABGRg2A8r7omdKKz+cgPLevG5elwEZkAEZkIEsMqC8F8QsgujEfNgTc758ZUAG5pQB5f1wV6vc86ACyntzt7lbBmRABmRABrLIgPJ+0JnPnZcroLx3jM7iGD2n6ze2xfVIGZABGeiWAeW9ocRQIgMyIAMyIANFZUB5X+7VsIWvufLesbaoY60T7G4n2Ny4yYAMlJAB5f3Cp1Kbv01AeW9eN6/LgAzIgAzIQBYZUN4LYhZBLOHk1jq6CCMDMiADeWRAeb/tWpOvZy6gvDd3m7tlQAZkQAZkIIsMKO8znxqt3lQCynvH6CyO0a495XHtyX6wH2RABqbMgPLeUGIokQEZkAEZkIGiMqC8n+paluUeKKC8d6wt6lg75UmqZbtIIgMyIAPDZkB5f+BU5+ZzFVDem9fN6zIgAzIgAzKQRQaU94KYRRCdmA97Ys6XrwzIwJwyoLyf67Wy2W+X8t7cbe6WARmQARmQgSwyoLyf/dxpA7sJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDed7sS5VaTCyjvHWuLOtY6we52gs2NmwzIQAkZUN5PPhdagTwFlPfmdfO6DMiADMiADGSRAeW9IGYRxBJObq2jizAyIAMykEcGlPd5XumyVq0Cyntzt7lbBmRABmRABrLIgPK+dW7zA8sUUN47RmdxjHbtKY9rT/aD/SADMjBlBpT3hhJDiQzIgAzIgAwUlQHl/TKvpM1gq5X3jrVFHWunPEm1bBdJZEAGZGDYDCjvZzBZ2oQhBJT35nXzugzIgAzIgAxkkQHlvSBmEUQn5sOemPPlKwMyMKcMKO+HuE7lPkcQUN6bu83dMiADMiADMpBFBpT3I0x+FlGigPLeMTqLY/Scrt/YFtcjZUAGZKBbBpT3hhJDiQzIgAzIgAwUlQHlfYnXwaxzSkl571hb1LHWCXa3E2xu3GRABkrIgPLebEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLDF3jObAAAgAElEQVRzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBpT3gphFEJ2YD3tizpevDMjAnDKgvJ/lhbIlbJTy3txt7pYBGZABGZCBLDKgvF/C6GkbOwgo7x2jszhGz+n6jW1xPVIGZEAGumVAeW8oMZTIgAzIgAzIQFEZUN53uAzlJjkIKO8da4s61jrB7naCzY2bDMhACRlQ3ucwGlqHDAWU9+Z187oMyIAMyIAMZJEB5b0gZhHEEk5uraOLMDIgAzKQRwaU9xle5rJKuwgo783d5m4ZkAEZkAEZyCIDyvtdRjc/s0AB5b1jdBbHaNee8rj2ZD/YDzIgA1NmQHlvKDGUyIAMyIAMyEBRGVDeL/Ay2jw2WXnvWFvUsXbKk1TLdpFEBmRABobNgPJ+HsOlrehdQHlvXjevy4AMyIAMyEAWGVDeC2IWQXRiPuyJOV++MiADc8qA8r73i1TucBwB5b2529wtAzIgAzIgA1lkQHk/zvBnKcUJKO8do7M4Rs/p+o1tcT1SBmRABrplQHlvKDGUyIAMyIAMyEBRGVDeF3cRzAr/UEB571hb1LHWCXa3E2xu3GRABkrIgPLeeEqgVkB5b143r8uADMiADMhAFhlQ3gtiFkEs4eTWOroIIwMyIAN5ZEB5X3uhyRfzF1Dem7vN3TIgAzIgAzKQRQaU9/kPjtZwEgHlvWN0Fsdo157yuPZkP9gPMiADU2ZAeW8oMZTIgAzIgAzIQFEZUN5PciHLQg8XUN471hZ1rJ3yJNWyXSSRARmQgWEzoLw/fLBzD7MUUN6b183rMiADMiADMpBFBnop7x99xuNX55x7gf8ZyIAMyIAMyIAMDJ6Bk08+dZVSavo/Lrr4j0BuAsp7J4BZnAAqxIYtxPjylQEZKCEDyvvcxkTrk4mA8t68bl6XARmQARmQgSwy0Et533IBveniuu81lw98+MiADMiADMjA/hlQ3mdy9ctq3EtAee8EMIsTwBJKJeuo/JQBGZCBYTOgvL/XjOYfBCoB5b153bwuAzIgAzIgA1lkQHm/fyGgRGEmAzIgAzIgA3lnQHlfXX7yMScB5b0TwCxOABViwxZifPnKgAyUkAHlfU4jonXJSEB5b143r8uADMiADMhAFhlQ3uddPiiH7B8ZkAEZkAEZ2D8DyvuMroBZlbsFlPdOALM4ASyhVLKOyk8ZkAEZGDYDyvu75zOfEFgXUN6b183rMiADMiADMpBFBpT3+xcCShRmMiADMiADMpB3BpT365egfJ6LQGt5f/9TTlv5n4EMyIAMyIAMyMAYGdjhLTBjdvEfgSUJNJb3J554olnd+YoMyIAMyIAMyMAoGbjf/U7ZpX9Iu/yQn8m7yLB/7B8ZkAEZkIGlZEB5v6RLbOVsa2t5v8NF9KX8DttOf69kQAZkQAZkYPoMKO/LmTOtaT8CjeW9WV0/IAMyIAMyIAMykFkG7JDMdoiT2OlPYu0D+0AGZEAG8s2A8r6fi1fupV8B5X2+xwzHc/tGBmRABmRABo5nQHnf7yzo3vIXUN4fPw44NjKRARmQARmQgXwzoLxX3suADMiADMiADBSTAeV9/hfGlriGyvt8T3aciNo3MiADMiADMnA8A8r7JU6sy95m5f3x44BjIxMZkAEZkAEZyDcD6Ysp+Z+BDMhApwz8eIfC879n3cnasdrfKhn4YQYuX/Y1J1ufqYDyPt+THSei9o0MyIAMyIAMHM+A8j7TodJqDSagvD9+HHBsZCIDMiADMiAD+WZgsKHIHRMgMH+B0zqU91+ZP4stJECAAIGFCSjv8z3ZcSJq38iADMiADMjA8Qwo7xc2rNrcpLw/fhxwbGQiAzIgAzIgA/lmwPhGgACBzgLK+850bkiAAAECMxJQ3ud7suNE1L6RARmQARmQgeMZUN7PaBC1KTsJKO+PHwccG5nIgAzIgAzIQL4Z2GnA8UMECBCoE1De16n4GgECBAgsTUB5n+/JjhNR+0YGZEAGZEAGjmdAeb+0adX2Ku+PHwccG5nIgAzIgAzIQL4ZML0RIECgs4DyvjOdGxIgQIDAjAQenlJ6o/8ZyIAMtGTgt/Z8y6l/3XJ/jjuOvTIgA10zELOL/wgsSeAp/qaa02RABg7IwLf3nOOjEL3hgOV1/fvudmZDGZhPBpY0p9lWAgR6FlDe9wzq7ggQIECAAAECBGYr8Mt7XvT7vdlK2DACBAgQIECAAAEC5Qh8Zc85Psr7uG7uPwIECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDA6ALK+9HJLZAAAQIECBAgQKBQAeV9oTvOahMgQIAAAQIECCxaQHm/6N1v4wkQIECAQFkCyvuy9pe1JUCAAAECBAgQmE5AeT+dvSUTIECAAAECBAgQ6CqgvO8q53YECBAgQIDAMYF3p5R+Z8D//0lKabXn//96wPWJbf2tlNLJxyR8gQABAgQIECBAgMC0Asr7af0tnQABAgQIECBAgEAXAeV9FzW3IUCAAAECBGoFTkkp/dM9y/V9y/jcfv5dtRK+SIAAAQIECBAgQGBaAeX9tP6WToAAAQIECBAgQKCLgPK+i5rbECBAgAABAlsF3rSg8v4fppTus1XCNwgQIECAAAECBAhMJ6C8n87ekgkQIECAAAECBAh0FVDed5VzOwIECBAgQKBW4ISU0t9ZQIH/g5TSZbUCvkiAAAECBAgQIEBgegHl/fT7wBoQIECAAAECBAgQ2FdAeb+vmJ8nQIAAAQIEWgUuTCn9ycwL/G+2KvgBAgQIECBAgAABAtMJKO+ns7dkAgQIECBAgAABAl0FlPdd5dyOAAECBAgQaBT4uRmX93+YUjq7cet9kwABAgQIECBAgMB2gTNTSn9h4P9/Z895/N8NvD6xvVdsJ/EdAgQIECBAgAABAgRSSsp7MSBAgAABAgQGEXhkSunf7HnBcFXIz39+EDF3SoAAAQIECBAgsCSB7xYy+/Y1o/9fKaUHLWkH21YCBAgQIECAAAECHQSU9x3Q3IQAAQIECBDYTeDjM7wg+c9SSg/YbfP9FAECBAgQIECAAIGtAk9JKf2HGc7L28r+G7dK+AYBAgQIECBAgAABApWA8r6S8JEAAQIECMtUGF4AACAASURBVBDoXeCklNL/NrMLktf3ruQOCRAgQIAAAQIElirw52Y2K28r7v9+SunEpe5k202AAAECBAgQIEBgDwHl/R5YfpQAAQIECBDYX+BVM7og+fdSSifsT+AWBAgQIECAAAECBGoF4mXkf29G83Jdef+DlNKltVvviwQIECBAgAABAgQIbAoo7zdF/JsAAQIECBDoXWAO7+f5/ZTSc3uXcYcECBAgQIAAAQJLF3jPzMv7X1z6Drb9BAgQIECAAAECBPYQUN7vgeVHCRAgQIAAgW4Cc3g/z5/vtuluRYAAAQIECBAgQKBRIF5O/n+caYH/71JKj23cet8kQIAAAQIECBAgQGBdQHm/ruFzAgQIECBAYDCBkt/P8/dTSmcMJuOOCRAgQIAAAQIEli5wxUzL+9uXvmNtPwECBAgQIECAAIE9BZT3e4L5cQIECBAgQKCbQMnv53lrt012KwIECBAgQIAAAQI7C3xrZgX+/5FSuv/OW+8HCRAgQIAAAQIECBAIAeW9HBAgQIAAAQKjCZT4fp6/nVI6eTQhCyJAgAABAgQIEFiqwGNSSvEy86uZ/P/6pe5I202AAAECBAgQIEDgAAHl/QF4bkqAAAECBAjsJ1Di+3levd8m+mkCBAgQIECAAAECnQW+MJPi/tc7C7ghAQIECBAgQIAAgWULKO+Xvf9tPQECBAgQGF3gBSmlHxRyUfJ7o+tYIAECBAgQIECAwJIFTkkp/dNCZuVtrxDwJymlC5a8E207AQIECBAgQIAAgQMElPcH4LkpAQIECBAg0E2ghPfz/OOU0vndNs+tCBAgQIAAAQIECHQWeGvh5f3PdN5yNyRAgAABAgQIECBAQHkvAwQIECBAgMDoAiW8n+dPjq5igQQIECBAgAABAgRSOiGl9HcKLfD/VUrp4XYiAQIECBAgQIAAAQKdBZT3nenckAABAgQIEDhEIOf38/yXKaWHHbJxbkuAAAECBAgQIEDgAIFnpZS+X2CB/5EDttlNCRAgQIAAAQIECBBISXkvBQQIECBAgMAkAjm/n+d7JxGxUAIECBAgQIAAAQL3CHyjsPL+H6eUTrpn9X1GgAABAgQIECBAgEAHAeV9BzQ3IUCAAAECBPoRyPH9PP9RSum+/WyeeyFAgAABAgQIECDQWeBRKaV/U1CB//LOW+qGBAgQIECAAAECBAhUAsr7SsJHAgQIECBAYHSBHN/P86rRFSyQAAECBAgQIECAQL3AJwsp7/9G/er7KgECBAgQIECAAAECewoo7/cE8+MECBAgQIBAvwI5vZ/nr/S7ae6NAAECBAgQIECAwEEC90sp/e+ZF/h/lFJ60kFb6cYECBAgQIAAAQIECFQCyvtKwkcCBAgQIEBgMoH/LIMLkv8+pfSEyQQsmAABAgQIECBAgEC9wDUZzMqrhnX4Wv1q+yoBAgQIECBAgAABAh0ElPcd0NyEAAECBAgQ6Fcg3s/z/224INh0sbCv732p301ybwQIECBAgAABAgR6E/i1iWflbTP3/51SOr23rXRHBAgQIECAAAECBAgo72WAAAECBAgQyELgtgkvSP5uSum0LBSsBAECBAgQIECAAIHjAuellP7DhPPytvL+ncdX1VcIECBAgAABAgQIEDhAQHl/AJ6bEiBAgAABAv0JTPl+njf0txnuiQABAgQIECBAgMAgAv9JZuX9P0wpnTjIlrpTAgQIECBAgAABAssVUN4vd9/bcgIECBAgkJ3AFO/n+VsppROyk7BCBAgQIECAAAECBO4t8JCU0r/IqMC//N6r518ECBAgQIAAAQIECPQgoLzvAdFdECBAgAABAv0JjPl+nj9IKV3c36q7JwIECBAgQIAAAQKDCnwwk/L+rwy6le6cAAECBAgQIECAwHIFlPfL3fe2nAABAgQIZCkw5vt5/kKWAlaKAAECBAgQIECAQL3AfVJK//PEBf4fppTOrl89XyVAgAABAgQIECBA4EAB5f2BgG5OgAABAgQI9C8wxvt5/tuU0o/0v+rukQABAgQIECBAgMCgAldOXN5/ftCtc+cECBAgQIAAAQIEli2gvF/2/rf1BAgQIEAgS4Ex3s/zU1luuZUiQIAAAQIECBAg0C7w1yYq8P9ZSukB7avnJwgQIECAAAECBAgQ6CigvO8I52YECBAgQIDAsAJDvp/n76SU7j/s6rt3AgQIECBAgAABAoMJnJtS+v8mKPCvH2yL3DEBAgQIECBAgAABAiGgvJcDAgQIECBAIEuBId/P83VZbrGVIkCAAAECBAgQILC7wF0jl/d/L6V0wu6r5ycJECBAgAABAgQIEOggoLzvgOYmBAgQIECAwDgCQ7yf5387zqpbCgECBAgQIECAAIFBBU5LKf3zkQr876eUnjvo1rhzAgQIECBAgAABAgRCQHkvBwQIECBAgEDWAn2+n+efpJSekfXWWjkCBAgQIECAAAECuwvcNFJ5//O7r5KfJECAAAECBAgQIEDgAAHl/QF4bkqAAAECBAgML9Dn+3n+9PCrawkECBAgQIAAAQIERhOIl7H/+wMX+L+fUjpjtC2yIAIECBAgQIAAAQLLFlDeL3v/23oCBAgQIFCEQB/v5/mvUkoPL2JrrSQBAgQIECBAgACB3QUuSSn9YMAC/9bdV8VPEiBAgAABAgQIECBwoIDy/kBANydAgAABAgSGF+jj/Tw/NPxqWgIBAgQIECBAgACBSQR+aaDy/rdTSidPskUWSoAAAQIECBAgQGCZAsr7Ze53W02AAAECBIoTOOT9PP+XlNJ9i9tiK0yAAAECBAgQIEBgN4EzU0p/MECBf/Vui/dTBAgQIECAAAECBAj0JKC87wnS3RAgQIAAAQLDChzyfp4vG3bV3DsBAgQIECBAgACByQU+23N5/73Jt8gKECBAgAABAgQIEFiegPJ+efvcFhMgQIAAgWIFuryf518vdmutOAECBAgQIECAAIHdBe6fUvonPRX4f5xSOn/3RftJAgQIECBAgAABAgR6ElDe9wTpbggQIECAAIFxBP7yHhck/yil9KRxVstSCBAgQIAAAQIECEwu8MY9ZuVVw8/+5ORbYgUIECBAgAABAgQILFNAeb/M/W6rCRAgQIBAsQLxfp7/tuFC4/pFyK8Wu5VWnAABAgQIECBAgEA3gd/YcVZen5vXP/+XKaWHdVu0WxEgQIAAAQIECBAgcKCA8v5AQDcnQIAAAQIExhf4zA4XJH8vpfSg8VfNEgkQIECAAAECBAhMKnBBSulPdpiX1wv79c/fN+naWzgBAgQIECBAgACBZQso75e9/209AQIECBAoUuCUHd7P8+Yit8xKEyBAgAABAgQIEDhc4Gc7lvf/KKV038MX7x4IECBAgAABAgQIEOgooLzvCOdmBAgQIECAwLQCb2i4IPkPUkonTrt6lk6AAAECBAgQIEBgMoGHppTi5e/Xn1G/y+dXTbbGFkyAAAECBAgQIECAQAgo7+WAAAECBAgQKFbgb9VckPxBSumyYrfIihMgQIAAAQIECBDoR+BHa2blpgL/l/tZrHshQIAAAQIECBAgQOAAAeX9AXhuSoAAAQIECEwr8LSU0h9vXJT8pWlXydIJECBAgAABAgQIZCEQL38fL4PfVNhX3/v3KaUnZLHWVoIAAQIECBAgQIDAsgWU98ve/7aeAAECBAgUL/D1tQuSf5hSemzxW2QDCBAgQIAAAQIECPQj8Iq1Wbkq6us+frmfxbkXAgQIECBAgAABAgQOFFDeHwjo5gQIECBAgMC0AvF+nv/Pn16U/Oy0q2LpBAgQIECAAAECBLIT+NWWAv93U0qnZbfWVogAAQIECBAgQIDAMgWU98vc77aaAAECBAjMSuCjKaX/M6V06qy2ysYQIECAAAECBAgQOFzgySmlP2oo8G84fBHugQABAgQIECBAgACBngSU9z1BuhsCBAgQIEBgOoGTUkqXT7d4SyZAgAABAgQIECCQtcCf3VLe/1ZK6YSs19zKESBAgAABAgQIEFiWgPJ+Wfvb1hIgQIAAAQIECBAgQIAAAQIECCxM4IEppd/bKPB/kFK6aGEONpcAAQIECBAgQIBA7gLK+9z3kPUjQIAAAQIECBAgQIAAAQIECBAgcKDAuzfK+1848P7cnAABAgQIECBAgACB/gWU9/2bukcCBFoE/oeUkv8ZyIAMyIAMyIAMlJCBm1rmGt8mUIrAZ8zgzkFkQAZSSn/wpwX+91NK/5NMyIQMyECmGfirpQxY1pMAAQIECAwgoLwfANVdEiDQLLDaeLS/f6fEgIEMyIAMyIAM5JmBTzePNb5LoBiBb5jBzdwyIAMyIAMyIAOFZOC3i5mwrCgBAgQIEOhfQHnfv6l7JECgRUA5kWc5Yb/YLzIgAzIgAzJwPAPK+5bBzreLEVDeH//9dsxjIgMyIAMyIAN5ZkB5X8yIaUUJECBAYAAB5f0AqO6SAIFmASdGeZ4Y2S/2iwzIgAzIgAwcz4Dyvnmu891yBJT3x3+/HfOYyIAMyIAMyECeGVDelzNjWlMCBAgQ6F9Aed+/qXskQKBFwIlRnidG9ov9IgMyIAMyIAPHM6C8bxnsfLsYAeX98d9vxzwmMiADMiADMpBnBpT3xYyYVpQAAQIEBhBQ3g+A6i4JEGgWcGKU54mR/WK/yIAMyIAMyMDxDCjvm+c63y1HQHl//PfbMY+JDMiADMiADOSZAeV9OTOmNSVAgACB/gWU9/2bukcCBFoEnBjleWJkv9gvMiADMiADMnA8A8r7lsHOt4sRUN4f//12zGMiAzIgAzIgA3lmQHlfzIhpRQkQIEBgAAHl/QCo7pIAgWYBJ0Z5nhjZL/aLDMiADMiADBzPgPK+ea7z3XIElPfHf78d85jIgAzIgAzIQJ4ZUN6XM2NaUwIECBDoX0B537+peyRAoEWg8cToOc991eqlL7/F/wxkQAZkQAZkQAYGz8Dppz+icS5JKSnvWwY73y5GoLG8P+OMJwz++2bGd44jAzIgAzIgAzIQGTjvaZe2zeDK+2JGTCtKgAABAgMIKO8HQHWXBAg0CzQO6O9890+u7vra3/U/AxmQARmQARmQgcEzcNZjnto4lyjvm4c63y1KoLG8v+DClwz++2bGd44jAzIgAzIgAzIQGbjm2o+3zeDK+6LGTCtLgAABAj0LKO97BnV3BAi0CzQO6Mp7J3JO5mVABmRABmRgrAwo79sHNz8xGwHlvQdEeYCGDMiADMiADGSRAeX9bOZLG0KAAAECwwgo74dxda8ECDQIKO+dLGZxsjhWMWQ5SkgZkAEZyDcDyvuGic235iagvDeDm8FlQAZkQAZkIIsMKO/nNmbaHgIECBDoWUB53zOouyNAoF1Aee9kMYuTRWVavmWafWPfyIAMjJUB5X374OYnZiOgvDeDm8FlQAZkQAZkIIsMKO9nM1/aEAIECBAYRkB5P4yreyVAoEFAee9kMYuTxbGKIctRQsqADMhAvhlQ3jdMbL41NwHlvRncDC4DMiADMiADWWRAeT+3MdP2ECBAgEDPAsr7nkHdHQEC7QLKeyeLWZwsKtPyLdPsG/tGBmRgrAwo79sHNz8xGwHlvRncDC4DMiADMiADWWRAeT+b+dKGECBAgMAwAsr7YVzdKwECDQLKeyeLWZwsjlUMWY4SUgZkQAbyzYDyvmFi8625CSjvzeBmcBmQARmQARnIIgPK+7mNmbaHAAECBHoWUN73DOruCBBoF1DeO1nM4mRRmZZvmWbf2DcyIANjZUB53z64+YnZCCjvzeBmcBmQARmQARnIIgPK+9nMlzaEAAECBIYRUN4P4+peCRBoEFDeO1nM4mRxrGLIcpSQMiADMpBvBpT3DRObb81NQHlvBjeDy4AMyIAMyEAWGVDez23MtD0ECBAg0LOA8r5nUHdHgEC7gPLeyWIWJ4vKtHzLNPvGvpEBGRgrA8r79sHNT8xGQHlvBjeDy4AMyIAMyEAWGVDez2a+tCEECBAgMIyA8n4YV/dKgECDgPLeyWIWJ4tjFUOWo4SUARmQgXwzoLxvmNh8a24CynszuBlcBmRABmRABrLIgPJ+bmOm7SFAgACBngWU9z2DujsCBNoFlPdOFrM4WVSm5Vum2Tf2jQzIwFgZUN63D25+YjYCynszuBlcBmRABmRABrLIgPJ+NvOlDSFAgACBYQSU98O4ulcCBBoElPdOFrM4WRyrGLIcJaQMyIAM5JsB5X3DxOZbcxNQ3pvBzeAyIAMyIAMykEUGlPdzGzNtDwECBAj0LKC87xnU3REg0C6gvHeymMXJojIt3zLNvrFvZEAGxsqA8r59cPMTsxFQ3pvBzeAyIAMyIAMykEUGlPezmS9tCAECBAgMI6C8H8bVvRIg0CCgvHeymMXJ4ljFkOUoIWVABmQg3wwo7xsmNt+am4Dy3gxuBpcBGZABGZCBLDKgvJ/bmGl7CBAgQKBnAeV9z6DujgCBdgHlvZPFLE4WlWn5lmn2jX0jAzIwVgaU9+2Dm5+YjYDy3gxuBpcBGZABGZCBLDKgvJ/NfGlDCBAgQGAYAeX9MK7ulQCBBgHlvZPFLE4WxyqGLEcJKQMyIAP5ZkB53zCx+dbcBJT3ZnAzuAzIgAzIgAxkkQHl/dzGTNtDgAABAj0LKO97BnV3BAi0CyjvnSxmcbKoTMu3TLNv7BsZkIGxMqC8bx/c/MRsBJT3ZnAzuAzIgAzIgAxkkQHl/WzmSxtCgAABAsMIKO+HcXWvBAg0CCjvnSxmcbI4VjFkOUpIGZABGcg3A8r7honNt+YmoLw3g5vBZUAGZEAGZCCLDCjv5zZm2h4CBAgQ6FlAed8zqLsjQKBdQHnvZDGLk0VlWr5lmn1j38iADIyVAeV9++DmJ2YjoLw3g5vBZUAGZEAGZCCLDCjvZzNf2hACBAgQGEZAeT+Mq3slQKBBQHnvZDGLk8WxiiHLUULKgAzIQL4ZUN43TGy+NTcB5b0Z3AwuAzIgAzIgA1lkQHk/tzHT9hAgQIBAzwLK+55B3R0BAu0Cynsni1mcLCrT8i3T7Bv7RgZkYKwMKO/bBzc/MRsB5b0Z3AwuAzIgAzIgA1lkQHk/m/nShhAgQIDAMALK+2Fc3SsBAg0Cynsni1mcLI5VDFmOElIGZEAG8s2A8r5hYvOtuQko783gZnAZkAEZkAEZyCIDyvu5jZm2hwABAgR6FlDe9wzq7ggQaBdQ3jtZzOJkUZmWb5lm39g3MiADY2VAed8+uPmJ2Qgo783gZnAZkAEZkAEZyCIDyvvZzJc2hAABAgSGEVDeD+PqXgkQaBBQ3jtZzOJkcaxiyHKUkDIgAzKQbwaU9w0Tm2/NTUB5bwY3g8uADMiADMhAFhlQ3s9tzLQ9BAgQINCzgPK+Z1B3R4BAu4Dy3sliFieLyrR8yzT7xr6RARkYKwPK+/bBzU/MRkB5bwY3g8uADMiADMhAFhlQ3s9mvrQhBAgQIDCMgPJ+GFf3SoBAg4Dy3sliFieLYxVDlqOElAEZkIF8M6C8b5jYfGtuAsp7M7gZXAZkQAZkQAayyIDyfm5jpu0hQIAAgZ4FlPc9g7o7AgTaBZT3ThazOFlUpuVbptk39o0MyMBYGVDetw9ufmI2Asp7M7gZXAZkQAZkQAayyIDyfjbzpQ0hQIAAgWEElPfDuLpXAgQaBJT3ThazOFkcqxiyHCWkDMiADOSbAeV9w8TmW3MTUN6bwc3gMiADMiADMpBFBpT3cxszbQ8BAgQI9CygvO8Z1N0RINAuoLx3spjFyaIyLd8yzb6xb2RABsbKgPK+fXDzE7MRUN6bwc3gMiADMiADMpBFBpT3s5kvbQgBAgQIDCOgvB/G1b0SINAgoLx3spjFyeJYxZDlKCFlQAZkIN8MKO8bJjbfmpuA8t4MbgaXARmQARmQgSwyoLyf25hpewgQIECgZwHlfc+g7o4AgXYB5b2TxSxOFpVp+ZZp9o19IwMyMFYGlPftg5ufmI2A8t4MbgaXARmQARmQgSwyoLyfzXxpQwgQIEBgGAHl/TCu7pUAgQYB5b2TxSxOFscqhixHCSkDMiAD+WZAed8wsfnW3ASU92ZwM7gMyIAMyIAMZJEB5f3cxkzbQ4AAAQI9CyjvewZ1dwQItAso750sZnGyqEzLt0yzb+wbGZCBsTKgvG8f3PzEbASU92ZwM7gMyIAMyIAMZJEB5f1s5ksbQoAAAQLDCCjvh3F1rwQINAgo750sZnGyOFYxZDlKSBmQARnINwPK+4aJzbfmJqC8N4ObwWVABmRABmQgiwwo7+c2ZtoeAgQIEOhZQHnfM6i7I0CgXUB572Qxi5NFZVq+ZZp9Y9/IgAyMlQHlffvg5idmI6C8N4ObwWVABmRABmQgiwwo72czX9oQAgQIEBhGQHk/jKt7JUCgQUB572Qxi5PFsYohy1FCyoAMyEC+GVDeN0xsvjU3AeW9GdwMLgMyIAMyIANZZEB5P7cx0/YQIECAQM8CyvueQd0dAQLtAsp7J4tZnCwq0/It0+wb+0YGZGCsDCjv2wc3PzEbAeW9GdwMLgMyIAMyIANZZEB5P5v50oYQIECAwDACyvthXN0rAQINAsp7J4tZnCyOVQxZjhJSBmRABvLNgPK+YWLzrbkJKO/N4GZwGZABGZABGcgiA8r7uY2ZtocAAQIEehZQ3vcM6u4IEGgXUN47WcziZFGZlm+ZZt/YNzIgA2NlQHnfPrj5idkIKO/N4GZwGZABGZABGcgiA8r72cyXNoQAAQIEhhFQ3g/j6l4JEGgQUN47WcziZHGsYshylJAyIAMykG8GlPcNE5tvzU1AeW8GN4PLgAzIgAzIQBYZUN7Pbcy0PQQIECDQs4DyvmdQd0eAQLuA8t7JYhYni8q0fMs0+8a+kQEZGCsDyvv2wc1PzEZAeW8GN4PLgAzIgAzIQBYZUN7PZr60IQQIECAwjIDyfhhX90qAQIOA8t7JYhYni2MVQ5ajhJQBGZCBfDOgvG+Y2HxrbgLKezO4GVwGZEAGZEAGssiA8n5uY6btIUCAAIGeBZT3PYO6OwIE2gWU904WszhZVKblW6bZN/aNDMjAWBlQ3rcPbn5iNgLKezO4GVwGZEAGZEAGssiA8n4286UNIUCAAIFhBJT3w7i6VwIEGgSU904WszhZHKsYshwlpAzIgAzkmwHlfcPE5ltzE1Dem8HN4DIgAzIgAzKQRQaU93MbM20PAQIECPQsoLzvGdTdESDQLqC8d7KYxcmiMi3fMs2+sW9kQAbGyoDyvn1w8xOzEVDem8HN4DIgAzIgAzKQRQaU97OZL20IAQIECAwjoLwfxtW9EiDQIKC8d7KYxcniWMWQ5SghZUAGZCDfDCjvGyY235qbgPLeDG4GlwEZkAEZkIEsMqC8n9uYaXsIECBAoGcB5X3PoO6OAIF2AeW9k8UsThaVafmWafaNfSMDMjBWBpT37YObn5iNgPLeDG4GlwEZkAEZkIEsMqC8n818aUMIECBAYBgB5f0wru6VAIEGAeW9k8UsThbHKoYsRwkpAzIgA/lmQHnfMLH51twElPdmcDO4DMiADMiADGSRAeX93MZM20OAAAECPQso73sGdXcECLQLKO+dLGZxsqhMy7dMs2/sGxmQgbEyoLxvH9z8xGwElPdmcDO4DMiADMiADGSRAeX9bOZLG0KAAAECwwgo74dxda8ECDQIKO+dLGZxsjhWMWQ5SkgZkAEZyDcDyvuGic235iagvDeDm8FlQAZkQAZkIIsMKO/nNmbaHgIECBDoWUB53zOouyNAoF1Aee9kMYuTRWVavmWafWPfyIAMjJUB5X374OYnZiOgvDeDm8FlQAZkQAZkIIsMKO9nM1/aEAIECBAYRkB5P4yreyVAoEFAee9kMYuTxbGKIctRQsqADMhAvhlQ3jdMbL41NwHlvRncDC4DMiADMiADWWRAeT+3MdP2ECBAgEDPAsr7nkHdHQEC7QLKeyeLWZwsKtPyLdPsG/tGBmRgrAwo79sHNz8xGwHlvRncDC4DMiADMiADWWRAeT+b+dKGECBAgMAwAsr7YVzdKwECDQLKeyeLWZwsjlUMWY4SUgZkQAbyzYDyvmFi8625CSjvzeBmcBmQARmQARnIIgPK+7mNmbaHAAECBHoWUN73DOruCBBoF1DeO1nM4mRRmZZvmWbf2DcyIANjZUB53z64+YnZCCjvzeBmcBmQARmQARnIIgPK+9nMlzaEAAECBIYRUN4P4+peCRBoEFDeO1nM4mRxrGLIcpSQMiADMpBvBpT3DRObb81NQHlvBjeDy4AMyIAMyEAWGVDez23MtD0ECBAg0LOA8r5nUHdHgEC7gPLeyWIWJ4vKtHzLNPvGvpEBGRgrA8r79sHNT8xGQHlvBjeDy4AMyIAMyEAWGVDez2a+tCEECBAgMIyA8n4YV/dKgECDgPLeyWIWJ4tjFUOWo4SUARmQgXwzoLxvmNh8a24CynszuBlcBmRABmRABrLIgPJ+bmOm7SFAgACBngWU9z2DujsCBNoFlPdOFrM4WVSm5Vum2Tf2jQzIwFgZUN63D25+YjYCynszuBlcBmRABmRABrLIgPJ+NvOlDSFAgACBYQSU98O4ulcCBBoElPdOFrM4WRyrGLIcJaQMyIAM5JsB5X3DxOZbcxNQ3pvBzeAyIAMyIAMykEUGlPdzGzNtDwECBAj0LKC87xnU3REg0C6gvHeymMXJojIt3zLNvrFvZEAGxsqA8r59cPMTsxFQ3pvBzeAyIAMyIAMykEUGlPezmS9tCAECBAgMI6C8H8bVvRIg0CCgvHeymMXJ4ljFkOUoIWVABmQg3wwo7xsmNt+am4Dy3gxuBpcBGZABGZCBLDKgvJ/bmGl7CBAgQKBnAeV9z6DujgCBdgHlvZPFLE4WlWn5lmn2jX0jAzIwVgaU9+2D28J+4qKU0lkz3WblvRncDC4DMiADMiADWWRAeT/TadNmESBAgEBfAsr7viTdNWMCCwAAIABJREFUDwECOwso750sZnGyOFYxZDlKSBmQARnINwPK+53nt6X84IdTSn+YUvpqSulBM9to5b0Z3AwuAzIgAzIgA1lkQHk/synT5hAgQIBA3wLK+75F3R8BAq0Cynsni1mcLCrT8i3T7Bv7RgZkYKwMKO9b57al/cD7U0rVrPovUkofSSnddyYIynszuBlcBmRABmRABrLIgPJ+JtOlzSBAgACBoQSU90PJul8CBLYKVBdEaz++890/mcWJxFilgeUoqGRABmRABmRgugwo77fOa0v9xi1r5X01q/6vKaU3zgBEea+wcZ4lAzIgAzIgA1lkQHk/g8nSJhAgQIDAkALK+yF13TcBArUC1YXQ2o/K++kKDOURexmQARmQgaVlQHlfO6st+Ys315T31cz6t1NKFxaMo7xX2GRR2Czt74ztNVvJgAzIwPEMKO8LniitOgECBAiMIaC8H0PZMggQuJdAdQG09qPy/vhJjRM9JjIgAzIgAzIwTAaU9/ea0fwjpbc3lPcxu34/pfStlNLZBWIp75X3ynsZkAEZkAEZyCIDyvsCJ0mrTIAAAQJjCijvx9S2LAIEjgRqS/vqQqnyfphyQunDVQZkQAZkQAaOZ0B5bzrdEHhzNZO2fPyDlNLnU0oP2Lh9zv9U3itssihs/C06/reICRMZkIGlZUB5n/PIaN0IECBAIAMB5X0GO8EqEFiagPLehUMXDmVABmRABmQgiwwo75c2hrZu73Utpf3mHPvPU0q3pJTu03rP0/+A8t5xN4vj7tIKKturlJUBGZCB4xlQ3k8/GFoDAgQIEMhaQHmf9e6xcgTmKbB50fNe/z7kmffXXf+Z1UUXv6bz/5c875rVFS986+plr3jP6pprb1299wNfX935pb/lIpcLnTIgAzIgAzIw0wwo7+c5bB6wVdfuWd5Xc+w/Tim96oDljnFT5f1Mj2NKoeOlEBMmMiADMpB3BpT3Y4x+lkGAAAECBQso7wveeVadQKkC1UXO2o+HlPfPvfjVtffZ8SLs0X2dcMKJqzPOeMLqFa983+q227+tvHHRUwZkQAZkQAZmlAHlfanj5GDr/epD5saU0ndTSk8bbO0Ou2Pl/YyOXUqpvEsp+8f+kQEZkIHmDCjvDxvq3JoAAQIEZi+gvJ/9LraBBPITaCzYcyvv1y/gnnjiiatLnve61R13/k3FzUAXP2//3HdWp5zywGP/X/mSm5gPZO6iQvNFBT7z93n7TXcdO+bEceimm7/muLOA447yPr9BceI1euX67Nfx8++nlH4xpfToibdlc/HK+wUc08wt859b7GP7WAZkYA4ZUN5vjmn+TYAAAQIE7iWgvL8Xh38QIDCGQLHlfXUB97TTHrJ67/t/RqkzwAXQT3/mr9fm47Ir3sx7AO85nPTbBhevDs3AW2/4Yu1x54Ybv+K4s4DjjvJ+jNGvqGVcVc17PXz8/ZTS7SmlUzMRUN4v4Jh26N9EtzdXyYAMyIAMjJEB5X0m06HVIECAAIFcBZT3ue4Z60VgxgK1JUl1gTTnZ95X6xgf73e/U1bvfs9PKXZ6vgiqvHehYIwLBZYhZ+sZUN4vOw/K+xlPnN027cr1ea+nz383pXRLSuk+3Vapt1sp73ueW9f/lvh82X9L7H/7XwZkQAb2y4Dyvrf5zh0RIECAwDwFlPfz3K+2ikDWAqOX94973DNWL3/le1v/f8lVN6/iGd7nPe2y1YMf8ujG9YyLuaeeevrqM3f8Vwr8Hi+EKu/3O+F1gYCXDByeAeX94YYl51B5n/XMOMXKXd5TYV83R/6DlNKLptioP12m8r7HmbXk496S1z3eEueKF7712P9f+PLfdk5X8O/H9W/5/LF9euWLb7RPC96nJRynvvzV3zyWuzi+zOXVu973wZ+t3b5P3f7tXn63lPcTToQWTYAAAQIlCCjvS9hL1pHAzATqLmbe/bUhnnl/6WXXdTq5+NBHvrF65rNetjrhhBPuXr/NC7rPfs4rOt13CSejU6yj8n7ZJdoUmbNMmVPeLzsDyvuZTZmHb87zN2e9Af79vZTS0w9f1b3vQXmvyFr8eUs8ULvud/pzd3538TYlz8QXPvOqY/v1pJNOtk8d8wbNwJfu+vVjuYvjyyXPu2bQ5Y71u/ra132sdvs++OG/2Mv2Ke/3nuPcgAABAgSWJaC8X9b+trUEshCoPQGoLqLkVN5XJ0Vvv+mu1X3ve9L/z967Bu9R3XeeDeImBJK4CnGRQCDQDQTiIiSEkBB3JCEkkLhJiJsAgbgJEAiBJECAk9gbEseOncQZexw7thN7nDjOhdhxslNbW7NT+2KrprZ2K1NbO9mdfbWprZ0XWzVTu731fewjjvo5p/v09X+6/58X/+p++uk+fc7nfJ/+/05/z8WZ7+OPn5K+e+gvGmm8mPtN5i3m/eQ20Saz9in7xGkf837i2Mege8z7KGLDmDJxg4lJW97+lyRJvpYkyawOC9+Zeb/ypi3pDcs3lv5Th9t1t+1M77l3d/rA1rfT3S9+LWVE9OR+Rjf9fwLzfph6wrwfZr02/ftvOj3M+3q6w7zvMALkVhCAAAQg0EcCmPd9rDXyDIGeE3Ca4OYFaYzmvRp5Gze96s33g9v2Y9431Ksf875eA7jpFxKkR31MBg1g3k9unWPe9zyqbD77y0xM2tH2PyVJcihJkqnNF2Usxc7M+5NOOsUbN5flqo6ys86bl2p5q31v/4CYu6GYezL8f3eVEfN+mP/zMe+HWa+u33BMxzDv6+kO834sTuMABCAAAQhAwCaAeW/TYB8CEOiEQO7LvFjNe61nNmPmuc68X798Ay8SG3qRiHlfrwEc08sM8kJd9kUDmPeTW6uY953Efn26yVVlzeWGzv+nJEl2JUlyfIuwemne23yPO+74dMHClelrr397kLH3C3u+nk6devrY35YH9g2yvBMRJ2DeD/N/Pub9MOt1Ip4RZe6JeV9Pd5j3LUZ8JA0BCEAAAkMggHk/hFqkDBDoGQGnAW5ezMVq3qsRt/zGTc68L1x0Uycv1NSB4IOPftbJvYoarR994R/SQx/8deN5mYzm/ftHPks/+sLfN86yqA5d33/48d+l+nN9F9sxabDpqXSPfPKLVC9hYiurKz96FrShG5VfzxrXPSfq2OEPP2s1PzGb922XvYk61TOsiXRMGofe/6tG0zPp+raY9z2LItvP7iITk07Q9r9LkuSWlorZe/Pe1MkJJ5yUDtHQfv6FrzrbGpoBzPcM43g58wjzvhyvvugL836Y9Rq7/jDv6+kO876laI9kIQABCEBgKAQw74dSk5QDAj0i4HwpZV7GxWzea/1Nk097e9GcRY2/UHvhpd9Lb7/z6fSKBSvSs866MJ0y5cSj9z7ppKnpWWddkF588VWp1gZ98pkvtWa2ffDRz9Ptj3+c6kXXZfOvS6fPOCc9+eRTj+ZFHE444cT0tNPOTGWALLlyTXrrusfTHTs/Sd95788KuciYevGl3z/698TTXzwmbcP5mmV3HD3HPt+336UB++rr3x7L2yt7v+Usu6Z7vWf9C+kVC25MZ86clR533HFHy3vKKdPSOXOXpGvWPpbuefkbzuubeIEhg/r5F393lI/rblg/uudpp52RnnjiyUfzIo0pfxdeuCBdsXJL+vCjh9N33vtxo3l6de+/HOMmlq4yHjz8l+mD295Jr77mjvS82Zcek1e9wD931sXpW+/80HltNj0Z0/p9rd/4UqpZM/Q7kn5PPPHzKX413a9m2jj/gstHnXa2Pfxe+vaBHwWln72f7/Nrb/zRWPlfee2bznu8e+gn6f1b3kgXL1mdnnnm+enxxx9/tK5OPXV6Ou/Sa9Lb73gqfWv/nzqv9+Xh8Id/kz70yKER13PPnZuKpX5zGtl4+ulnpnq2KV3VlS+NJo/vf/fH6abNr6fXLLsznT37svTkU6YdLaeegXr+zD5//uj7LQ++lb53+KeV8iUt28+Ou+95/uh9zDNHW/1W7fPy9l/21F0on7ff+WF63/17j2rcfs7qGauyn3/B/HTZtXePfgsHGzK41WHKVS5XRzF1Fnl612+mq1ZvS+defGU6bdrMo9ykST0zVC8hZVYnocef+LX0ljWPpvMvv2H0e7PrW/xV53o2XXDhFeniJbeka9ZuH/0/Up5D7hF6DuZ9jyLIbrJ6uf0cmMD9z5IkWdxwkQdj3pt6Wb9hT6PPg9DnRlvnYd7XM4JC6gXzvn3GIfXQ9DmY98Os16Z10nR6mPf1dId533CUR3IQgAAEIDA0Apj3Q6tRygOBHhA4+rLfvHiztzGb977GxSXzljby4lBGuYyrc86Zk8vI5mX2Z8w4J9ULzKZM63cP/UV606oHUxmD5h5ltzKmZf7JAPON0t/94tcqp5+Xn1Ajt4kGvAzebF5khNppy/iV8ax1W7Pn+j7Pu3RZYya+zO8Htr496jQgY953z7zjMucWLV41MvrsslXdnzXrkrF8yJy005NBefPqh8Y6jLjyKTPcvtbel/62PvRuunDRqmMMYVc6vmMytC+/Ynn63Atf9d7HvmfR/ty5S8bKL2Pevk7mul4yh64dLH3pd+v7vZm09ay58+5ng3/f+i1fd/296YGDPzkmfya9uttnn/9yumDhimM6JfjqwT4uU/v6G9an+/b/Sal86Zlkp9PE/hlnnFcqD4bZ07s+TS+//IZRh4ky+VBHixtXbEpl+pu0qmzVOcN1X/v3pFku9Ps5+5yLnOfa12tN6rx8aFT96jWPHGP829eH7uu5Kw3r2ZZ3v5DvMO97EDl2m8V5oTrs4Lz/nCTJ15IkObchBBNu3qtD4F33POf9W3fbzvSmm7eO/l+rE1kRY/1vVqeikN96H87BvK9nBIXUsWbbuWH5xrG/pmeRCckL5zRX35j3zbFEl+EsMe/DWbl05Xu/Zv3v/8eG4h+SgQAEIAABCPSRAOZ9H2uNPEOg5wRyX8TFbN5rJLzVkDi6r9GwrsZImWPPPPtbQcaI6/72MY1AfumVeqO29VLLHlFpp191f8/Lf+BkNBnM+8ef+EKqUfVV2MmM1ajgMlrKnqsR5jI5q9zfdY2M3JU3bam9hEOReS+TfPr0s4Pz7Ru1r5HA9qwCrjKVPaYZC+pOZ15k3msU/hlnzg4uv10GzU7g68Cy943vjEav2+eH7quT0Ov7/riWHm19agT8lVetrVRGO8/q3KBR63baefsxmPcy3dUZxi5HlX2NVg8d7e5iUmTeyxxXp5XQvK27/QlvPWik/enTzwpOK+Se+t/pKleZY5j3PY8qm8/+nBDtdXzOPydJsi9JklNqFnfCzXut6V7m97nrud8edTzM460Zqvqy5E9R2THv6xlBRXz5frh8Me+HW7cx/24x7+vpDvO+ZlTH5RCAAAQgMHQCmPdDr2HKB4EICeS+uI/ZvF+0+GZn3jVKqE6jUqMH7Wmw815Qhnyn6b9lGFfJ0+Ytb5YeARqSp8lq3mu6+SbqViNsNfq1Sp1qJHZIHZU955J5VxeO8M7Lb555/8RTv3F0CvfQfPmmdtfvMzSNMudddNHCVNPZ55Ux77s8817TmJ8y9bRa+VZHnuzU6i+/+i9G05CXKWf2XE2N7usYkFfe7HdauqFM54xsPlyfNZJOU7tn75X9PNHm/a7nvly7HrLl1++8yjMiz7yXvl2/0+y97c++/4cPPXKw1Mwjdpp5+5j3EUZ5/c/S+Xmam+Dv/kOSJDuSJDmuIubemffm+b3hvleOWWooWw+aHcSc2+ct5n09I6jPdU/e69U95n09fuivGj/M+2rcjN4w7ytGc1wGAQhAAAKThQDm/WSpacoJgYgI5BpSsZr3Wr/dN3V1nTz71l20X0pOmXJCes65c0drDGvdeU0tXjSdvdYKLpuv53Z/JffFqPI0derpo7XtNcW1RswuufKW1OQpu16xXYbJaN5rdJlZQ9xmoX1NP33pZctG/LTmutazzp6T/XzruscrvZguMu+13vus8+aN1n5XXWotaxl20l02D9nPVyxYEWSWmga6vXWZgtK2Rpzb631n76nPrpkEXtn7LSefIvNes0woLyq3yq/6OO+8eaP1tl33to9pyYyqo/185r2Mcd9UwWbdd40yDDH3tVSDYa50Vdd2/s2+Opjonko3ZNYNLT9g0q2y1UjKkOUb9LvQqOiRLucuSTU1vcmzb6uOLkV5mkjz/smnvxg0E8SMmeem6iCiss+Zu2S0nryvzOa4lpgoKnv2e595r9+TfgsmbddWusk+J1zPqZde/cNC416zk2htez1Tfvm/Zc2o7DqWN3MJ5n1E0d1wsqIp6nO1H8H3/yZJkpsrIO+tea9nl+95pfpQB9vs862PnzHv6xlBfaxz8txMnWPeN8MRPZbjiHlfjldWX5j3FSI5LoEABCAAgclEAPN+MtU2ZYVAJARyX4iWNZztBsD1yzc4015189ZaL/Q0mnHp1eucac8847zK5uU99+52pqmXkDJFdM8nn/mSc3pujS7VyNVrr7vbO1JeBv+bb/8gqOxKT4al64W0jNQ1ax9LZcDkjezUd2++9f1Uo82vWXZneuqpM46m5zPvtfb2G2997+ifyuTKg9a1ts8r2j/yyS+Cym3rp+q+a817rV2ukc92WWQ4r711e/r6vu+O5U3s9MLW9eLJpKHp6nc++Wtj1xblO2vez559WaqpraUtdUrxXf/+kb9NNUJYa53nTTu/YePL3jR8aeu4y7yXBmXWmTKbrTo63Hf/a6mmxtc68CZdrXX+8KOH0gULV6ah5r3qZe26HalG9+8/8K+OpmXSNFtpU1P3L79xU67RLDPBXFNm6zLvNU2+zFpTbm1lcmuNcC0xkR1VrinwZZb66keaeXb376T6PVw0Z9Ex6aojwI0r7k+15nq2A4KMfhnceR0Eduz8pFK5NUNCnhkrfT6w9W3vWu773v5BqmfntGmfP19sXtq/f8sbuXnTjAT2M2TjplePYWPS27T59WPOs6/J7kuLRfWvJU18ncB0T2l/60MHvLrUPTVTizpRmTxmt2VHn/rMMP1vyaYtfeq3o9+Fnh3m/4F4PvPsp6n+17qW+VDng2xa+iwWq1ZvS/X/IavtLEvxfWzHh6Pn0WmnnXE0Pcz7SCK7YWXjTJdeIz3250mSXFoCf6/Ne/2vUnzlqgv9X+ky9ss+o5r6jHlfzwhqqh5Ip3/14GpDKT6mLvtXl32qM8z7evrCvC8RwXEqBCAAAQhMRgKY95Ox1ikzBCaYgPOlm3kRF5t5L1Ni2bXjJobJb1mjxDRGZaj5plPX6OPX3vij4JcNL7z0e96R2/PnXx+UjvJjymRvzz137siQN/kus9VLVBmrc+YsHpkzIdfKTLXvb/Y1Q0HI9RNxjsu8N/k2WxnGPnM5m+cdOz9OfbMYaL3oQx/8dSkWMu/VAWP1LQ9XXq9c65xrZLopj72VwZudnj1bJtdnl3kvs9lOWyPB1cnAdX3oMY2811ISK1c9MDL/Q6+zz3tr/5+Omeomn0pba7fb54fsu8z7bPm11vjbB35UmLYMcdvQNHnTVuuq37v+xWO4ykx1dSLJ5lvrsqsO7PTMvjpUZM8v+qwOIeecM8eZnjS67eF3jxrCRWlpLXbNAGDyY2/V4UEmf1Ea5vtHt3/gTGf74x8Fp2HS8m3V6UTmt51Ps6/fkPLguzZ7/N1Df5FefvkN3rTyOqVk0/KZ97YW1dHjrnueS9WhJXt90Wct1WDKaW9lwJX5P2ffR0b/YzuOpJr5AvN+wiK6qUmSnGH9aar5eZm/xUmSXGv9rUqS5Dbrb0OSJA9af5oOflfm7+VfrfWu9d71dyhJkk+sv0+TJPma9fetJEm+n/mTwf2Z9fevkyT5t9bfv0uS5N9bf/+LrdUe7P/nX5X/nAA19Nq813PgttufdD5TVE97Xv5G6WeU/WwJ2TedlkLOrXJOX8z7Nji0kWaVOujqmhjLG1ueyuRniOZ9mfKX0W1b6ZbJg31uUQdO+9yu9kMZTbR5H5rPqtzUed4VB2mJt6pp2tdh3gdEbpwCAQhAAAKTmQDm/WSufcoOgQki4GwAmEZBDOa9TBaNLJTZ6TPElF97Smq7EVK0r0aepk03Zba3Mto++OhnpRtDGpU4wzP1+s4nf70wPdf0/ccdd/xoCvOi8oR8H9ooH6J5rzXCZYKGcDLn6Hcgw8zWhtnXyFdzXshWI7bfO/zTUte40pVxN99jGGoktOuavGMu896UUVtN5b//3fKmePaeerlw4OCfl85fNh11Rlm85BZnndx6287S6bvMe7v8mjo8OyI+myf7s2ZJsK83++okZE9Rr5H9hz/8LDi/Gt3v0qKeDyGjze08rlm73ZlHTeevWRXsc0P2j3zy997ZKpZde1dwel2Y99kZMEz9aGkAdY4JKa99jrSxeMlqJ0/NFmGfm7fvM+9N/rT0x+NPlJ/xw9zztjuecuZRzyVzTp1t6P+WvHtoaQZTXs92vyNeGrJ5/R+TJPnnzN//62FTxI7vu5uC///8VQeHkx16NYd6b95r2RWfFqvMTuR7NqhTojqULb/xvtHMNfZyNnouKuZW5021BTTby4GDP6n0TFNnLMUp5u+Brfud5btlzaNHzzHn+ra+2a5MWdXhL3utjpnv7a06jD70yKHRTF8qrz3zizqanjf70uBYQM9rdejSbDf6PzXv0mWploixZ+PRvpbI0WxBK1ZuGc3m1UQsaJdJswtly6/Piins8/L2NfuMKw1ffKX/eZpFScvR2MsTiac6NSrm2/LgW7kzYuXlp+x3Yfqeb+m7fhxdlEfFlOooqLaGvZzY8cdPGX1W/HrHnc9448UYzXvFtlmd+Dpz61mw+YF96ZIr16Rnn33h0aXXFG/rfYS4aHajMh00xVyaNLPiqW1lliZTbC/OF1+ydMR975vjM8MV1VnZ76U7zTB13Q3rR7NNZZcBVGdWDWLQwAnNwiUmZe9R9nyZ35o9auVNW0Z5Mnz0f0YdtNUBX7MhPrhtf3ro/b8ay09X5r1mN9Rsd1ctXTd67tod/dXOO+usC0bLbOm5qc7HVTrcuthh3pvwiS0EIAABCEBgQghg3k8Idm4KgclNwPlSyryIa8O8lzGk6YiL/jTCVC/n7BGHJl/2Vt+vWLm58vSc992/18ngwgsXpO8fCTfVsg2sX47mnzKW9iXzrh5raGavXbjoprHrNNI6e17bn4dm3uuFi2ZGqMItO1raaFAvNsqOvq9yf9c1Gu1sv3Q0edJU567z847lmfd6IaHR7nnXT8R3+n2qM4Ypt9lqRHXZ/OSZ95rxosqzwDcS3eRz+vSz03cPlTcY9LwzadjbjZteCS63ZhBwTe8vE2T3nq8Hp5PlrJeS2SUqlEd1WgjVUNvmvV64uTpA6GWbptLPlin0s14iumZG0L3ylsSw0y8y74uWILDTcu27XqbL7HGdO1HHAsx7p/7t3wL70a8RP5nq8H9OkmSzp6nRe/Nez1Pf7+3Bbe/UfrbIXJYhr6WOfPdxHZ8y5YT0mmV3pOrwVuZZJmPclV6dY8pLXh70DM6mr2WL7GvUkVlLPdnGevYa89lnRio9xTKahWvJlbekWaPOXF+0VSy9YOGK0XJhdh6r7rs6LCsPZTq6rt/40hhDpZGNZ57b/ZU0L97Lll1xkjrpthXnv/zaN0c6VeyVvXfeZxno+l1UnTEnr64Uqylt34x02XypHX7FghVjJr4r3pjoafPVts/mX501bB76rWnAQN6ySnYa+n2rM09RO0HmrTpDhP7u9DvTclpq69n5a2Jfs31de909Rzsk2OXJ21c8K21U6eAbkm91+FKHhrw82N/peagO25pJzKTfpnmvjgXbH/941Omn6P2UnU/tq941+5yrw4HJe8gW894TTXEYAhCAAAQg0A0BzPtuOHMXCEDAIpDbQGrDvM82Zqp+Ni+PNMI1pLHjOkejTlzrdSptrSnvuqbMMY02zZZPjb2ikZ0u80JrHpe5dxPnDs2818iCqlw0slYmbrY+9Xnzljcrp1s1P+Y6jbLP5kkaK/PSU2nlmffq4GLuF9tW7LPl1+cy07SrTHkvc/Wyt0q5fSa0ya9G3lRJVyPGTBr2Vi/iQtNbs/YxZxoajR+ahu88vXyz82X2NULLd4193MetqWnztWSDyZO9vf3Op4PyZ+c1u//o9g+dad+7YU9Q2nnmvTp+1Z2OU8sr2GXWvkycbDkm8rPr/182z3zGnO+hBv7bJElusuJv7fbevFes4asLjUqs+iyRWSrjSvG4L/2Q4zL977x7V/CzM0bz/oU9X3d2VPSV/5XXvunkrs4UoYakL237uGLN629YX2r2IJceujDv1d6TwVpVTxp5XXZ2I1dZzTHpWx0xq+bH1IP0fcdd4fo29/dtFcOEdBAx97e3yovdXuijea/OFL4lleyyuvbVMcA3K4U6Wcw+f36lZ5ny05T29DtQrFu2M1S2vOqwoE4tmgXNp6UyxzXLodro2fuEftYMiq/9asawtsx71aHi8NA8+c7T7ApPPv3Fytww7zNRFB8hAAEIQAAC3RLAvO+WN3eDAAR8DQtzPFbzXqP2n3jqN0pNqehqRD6961NnI2zp1bdVblTZ99FLAMPS3m7a/Hpu+q512zVtn512F/tDMu/1klEjxOpw04tPux7NvqaOrJNunWvVEcTkw94+UfLFgM+8nzZtRmNT/dUpp+9ajWh2vfwsa4z7zPuLLlpYuW41ukK6s+vF7GvGgKovvWTg2lPlmjT1YtDHyT6uqWjtKYfN9XphW3dEiO6j/Ln0NO/Sa4Ly16Z5r5FPrlFPmsGiaNSUzdC3rxejrtH3GpXmu8Y+nmfe15ku39zD9eJRU7+a72PYYt5jzJtn0gC3/1+SJN9PkmTer1ogvTfv33z7B87/caq7LQ/sq/RskUlyjqezZFVNqJOSTJ2iZ1xs5v3Tu35zNFV0mXKr7eEq5223P+mtqzLpZ89VnFRnOu31+PZ3AAAgAElEQVS2zXvFPFctvbV22RW3NWGiajYIX2fgLNvQz1pGKkTfLl2YY5q9yRVPh+bBnGc6QvbNvNfMbJpNzZSjylZxuEbuG6baajCAa5a0MukrrnznvXpLlynG1YwZZe5bdO6ll11bu90gXlqyo+heRd8rttdMLW2Y9+rUYk/fX5SXou81q8WG+8JnS7P1hHnP+1sIQAACEIDAhBLAvJ9Q/NwcApOTQG5jKVbz3jSKtA6j1mqssi69GkJaT82kZW9D1qW3G1J5+zPPOG/sHtcsu/OYhn32etda5jLrtDZd9tw2Pw/JvJc5W5eVRspovT1bK9rXSKaqRmzdPOl61ygZmYBl0naZrSqbRimXSWciznVNnb9q9bZS+faZ91VfrhgOWj82qxd91pSc5pwqW1d+pYOQtJ574avOPJVZl77oPhrhli23fjshL5fbNO/1Py2bL30usy59UdldI/v1QljGftG1PvNeSyyUWf/Xdx8ZWNnyq14OHGx//VxfnrLHMe8x77MaHeDn/5QkybVDGHmvJaJ89VNlthSNGtfyWr40zXGZYZpaXp3C5sxZnLpibXOu2er5V/Qcjsm810hSV3xnyuPb+mYOKzLvZX5pBKueweqUqnXuNaW4pmj33cscv2Te0lQzVGWf5yGf2zbvl994nzf/mq5+xoxzRn8hU9fX7eymJQ3a0rcM/CJ9++rjoUcOejubmjpWO1Ta0JJQmsVHHWxcZr86rSq9Ppn36pThqxeVUR1e9YxxtQENH7O1Z3nTEgSnnXaGV396jmkGwBBjWB1QfPVXdFwdV/WbNnn0bfW8UXtQz1VtQ54/ao9oyayiPLi+V1wb0qFAjC6+ZGm6aPHNo5nSfHWlNtfb7/zQWc4bV2yqlMcHt+0PWkJCbVENvhBnLV9X1GFDv5MqS2Fh3k/OF7aUGgIQgAAEoiGAeR9NVZARCEweAs4GjmnUxW7em3yqgel7WeVqLJpjrpE9Wvc4xGAyaRRtteamyafZFq3JLWPPnGtv9cLEXtet6N51vx+SeX/3vbsrNdqzDH3rmFfRXzbtqp81naetE+3fsHxjqfL6zPsdOz8ulU7VMtS5zrWGZdmXXC4zXBxffOn3a5VfL5uydaPPVUwNm5HWnMymqxeMIS9ub7vjqbFrldZjO47UKqudP40Sz+ZPnzXlv32ea79N897VqUD5KjtThSvf5pgv/77RkOY6bX3mfVOzwfjS18vG7GgxO19d7mPeY967nh0DOvYPSZJc9atmRu9H3mvKbl/dFC0RlX2uvH3gRyMT1ZeeYmetbW6mR85e/9Y7PxyNZlRnJ18a+v+Xvc7+rOfgG2997+ifbykUpWOfl7dfNOuTa837c2ddPOqUkC3HxRdfNWKgTg5mtLtmu9E9tj387mjtcV88mjXvNZp39ZpH0h07P8kdUa52h5Yo0xT5edPul+00ari3ad67OtMpZtQMaNnllRQ/aYkCXaNpwbPszWdxNnkvs1WbSh0FTDrZ7UjfG/bU0/ftT5bOm35Peaa0RkUrpnN1ztBvTuu4Z01eGf0ygLNljHHNe7WhsksKSec3r34oVWfXbMdJxXJqp2v992z59FmxuHQkXtl4RtfIRNb7ley7Bv2G9TzNM/KffOZLpetXGs3rwCITWW2KZ579dKwNod+EfvvLrr07t3OHOmqU+S2Yc33tEXEUK3F+9VfT4ZtrtNUzTx3Hll69bqwOZPC76qWKea9ZHjVK3pWejmk2q4cfPZRqFjg7fyaPe17+Rrrq5q3eZQr0nCm7NBvm/eR5SUtJIQABCEAgSgKY91FWC5mCwLAJeBskapS0Yd6rgSdDLORv13O/PXphcM/6F1KZF9mXA3ZjSi8ENL1ktvHk+6xR1K4RA2po+66pcnztrdvHGKshmGeyqQFtl83e18udTZv31p6mLqQsQzLvy2gjj0325aepmybWvT94+C9HLyNUv5p2UkajXrjIiM/7c00DXta89pn3GsGQx6PJ7/SbfP6Fr45GImh99F+Wf1Nu2cXFNQV82dFRLvNeL7RcLyvLlFnTpRuN2Nuil/lF99BIcTs9sx8y7b2vA4rMh6L7hn7vWzJEoxqL0vCZ33U7POi+vpFHvnVKi/Lq+n73nq876yakI4zPXNf/QNe9yh7TtLBGK9mt1uHUGtVaw7psuk2en33Znc0nnzH3e6qB/yNJkh1JkhxnNSt6bd7L1HJ1glX96P+yDJbQZ4NiYl9nNxkc927YEzzDkUaBymxy6URpvfTKN4LzpZjElY5m/QotW9F5LvNe8Yd9X800VnfpFMWvGl2u+CGkM5kr3zK8NVW2nTezr3ZYlf+lbZr3Nkd1zpbxHqJLdYDwjZjW0m0uNnnHpG/NTmBY2duRvte/WErf117n1rdmSZBhmJcX+zux0MwVdn7MvtKSzkN4aYS57/dr0tM2RvPe1ojyeMWCG0ejt21Orn1xdrW/lMZVS9el6jRul33O3CXp6/u+W1g3WorEN5NI6BJMdn53PunuTKu8nT79rOB3PXo3oTjRLpO9HxLf2/nSM8g3q4dmAPF1QrLT0L5i67z3QyaPZc17tX99yyiog5iWXszmxfdZbT7Xc155U+eRMgMzMO+tCIpdCEAAAhCAQPcEMO+7Z84dITDpCXgbYWpQtGHeqweyr3FTdFyNG00x5mtMqad86EjZPS//gbPsixavGo2mUaMxpINB0Tkqr2k42tsig6TIwNDLHvX4Vi99GTJ5nQGKuPq+H5J5f+DgTyrrzuaz/fGPnfWpl4/2eaH7enmhDh6u0fO2Xsruz59/fan8uMx7vegMLUfV8zTiZ93tT6QaZZZ9gVa2zPb5Zdeqd5n3ehlUtVzmuiuvWuvUS90lMDQiyC6v2ddv1tzbt9VLMXO+2ep5svfN745GuBQ900K+10h2k7a91UsnX77M8TbNe9eoN/3fUMeFV/f+y0ae+b5nxANb9xeW3Wfe1zVtDFtti9YW1cvUuRdfmWpE1PMv/u7YqDM7rTb2i/732XpiHyO/Bxr4L0mS/FdJksxwtDh6bd5rWRkf/7Kxvs+Q0PNZIyzLPmtkOLpGXSu/ZeKjiTLvba6aur6JjpQaDVzFXM+y1zJRmqLdzqPZX7tuR+m6atO8N/maNm1GcPvQlFdtK9+o21Bj0aR13/17nbxG+n7+y6WZSd83rXrQmaY6KZr7Fm0ffvSwMw3F45sf2Becju6jDriumbBMHWgbo3lv50+me5lOu75YVzG16takrWeO1pwvqg/zvc/YVnxW5jes3+pZZ11wNB8mP9qq44Fm8DD3DNmqzabfkp2O2VdHgDImtDpJmGvtrToIaBmDkPyYc5559re8o9tN2mXNe19HZy3X4hppb/Li26rd54tv713/YnB5ff8r1Tbz3bvMcQ0gMMw82390xDIcggAEIAABCEwWApj3k6WmKScEIiKQG6DHZt6bxod6+Gv9M1ejQibgBx/9rLABo6nnXNd3daxoOlGNDMqbxjCbz5NPmTZ6ISnzRy86s9PxGXZltkMx7zXSqEy58871dfrQdKJ512W/e+2NP0ovv/yG1jSotUqz98z77DLvZXTmXVPnO41C8E1tmNV2lc8aDVgmfy7zXiNfyqThOlcvArP5/+X09uEjEl3p+pbW0LTDrvPtY74Xb9l8tvFZMyrYeXHtt2nel3mmNl1+jRx1ldc+5jPvq5hXdrr2vp49IaOUTPnViUdTyqqTjWbDCfn/at+v7L7v5abJD1sM+x5pwJ4i3xX699a81zIrMqhcdXHCCSemRTGu/VzQSHnXGsb6XxkyY4mdlr0v40odkVx5DDU6Jtq8lwFYd6Yem0lT+zLpXKODq8RNbZv3MqJ3Pvnrhf9/XWxk+Ln0U7T8gp1Wnr7V2c8+t8y+9K1lFFz5U8eDkLR812t2hpDrs+eo3aj2qCtPOhazea/3B2XMZ1N2zfTlK6+Oqy1llrgw14Rsr7thvTPdMuuka712X96qTsGvONDX2VqzN4WUTTMQuNLQMT1zQ9LInqNZInxl1fEy5r1iblda6qCgd1DZe4d+1rPcNQBF6er3HJIO5r0rlOIYBCAAAQhAoDMCmPedoeZGEICAIeBsnJgGS6zmvRo3e9/4jreXdYhJojXKTDknYisTuKiRph79rkZeSH71wk9LDWjEZmiDMJufoZj3mvoyW7aqn/XCwcV/yZW3BN9j46ZXvGslutKucqzsyHOXea8XWVU55V2nEch6gVelXKHXaKRaXh6y37nMe3UQyp5X9rPLvNfImbLpZM+vat5rhg7fSLJQtnXOC5mhoi3zXqZznbzXvXbdbTsL691n3led4jirG/NZa5hOmzazEg/9dpdcuSaV6dBEJzGTJ7PFvMecr/tbi+B61xT5Ju62t70z7zVDizoruowXw73s6Guf6XLNsjsKn5nmueHbamprky97e+OK+4PSnmjzfn1Apy9f2ds+vuWBfU62ZTsbtG3eX3vdPUF17eKlEfa2bsy+Zmlzne865jPbtM646/wyx9TR2/VbDDEqtZa4KY+91Xr17x6qPlvZPZnp4u20Yzbvq5rGWorBLmN2/7EdH1aqZ5nk2bT0uUzHCk3V70pDbZMyOsueu+zau5zpnhvYcVrLornypXSz9wr9rGVcfFPT614hvwlzr4WLbnLmT3Vtzqm6vfPuZ51p79j5SVDavudJaIe0onwz8t4O0diHAAQgAAEIjBHAvB9DwgEIQKBtAs4GhGlQxWzeq/Gx8qYtzvzLdCuaRt43haEpe9vb0FERms5ZL4nq5Oessy5Mtz50oJBJtkE3FPNeUwZmy1b1s6bKc9WF1gANSTO7BqIrLXNML7nOOHN2qin6tMamRpBpOszsn0bGmmvMtgnz/vwL5geVKaTc5pxNm19PNZrO5DNvqxkTtM7rLFP+uUvGyi4Wrg4umPf/tbPuDh7+yyD2efVS5ztN92+04Nu2Zd5rVoI6ea977a01zHuNlvfxqnpco4f0Atf14j+0rBplqal1q3YQc+Ud8x7zPlR/EZ6nKfI/9UyR74rnJ9y8l5HwyGPve//0+75n/QvpipWbR3FI0fNCUyGXfR6cf8HlY89mdTLT+uquZ0TZY4oTslrR8jEh6Uykea/YpspI4JByNXGOlqNydQYsu/Z1m+a99KolceqU1zXDwIyZ5wanqfg9qz9x09rmdfJlrtWU7Nn0tRSX+d639XUCLdv5Jpv+Bx/93LsWfKzmvUzubDlCP+t3kOVvPqsNJ1M5NC37PF1nT71v0gydWU36cj2vdUwd0e17ld1X/Ohry6nDVF56ej/jmmlF5VPnsLxri77zLQOhtEPNe7UV1Mna8DZbtcWL3i0V5U/fa/p8V71qmZeQ6zHvXaEUxyAAAQhAAAKdEcC87ww1N4IABAyBscaJaaRoG7t5r/V47fza+0U9kLW+mH1+1/uh5r1pyGmtyuU33uc0KkPzrhc8ZdZpG4p5LwPYcKy79THRFPhFaT/+xBdyNae1qGXuP7f7K6leBhWlZ77XC4WsBmI073851aLfuFfnhDvv3pVqVHAZnbqm/cS8d5v3E21gT6R575s1I/vbaetzbOa9eX5oZOGKlVvSU091r2MawuPiS5Y2siaz8oR533vz/v9KkuSfrb9/SpLk31t//2OSJP/W+vs3SZJ8lvn70yRJvm/9/d8hOpzgc/4+SZIrTXAduJ1w875JZldfc3tps1lrG7vyoHjIPKPqbtdvfGnsHjKwQuKMiTTvQ82munzqXK/4Olt/N928tVTdtWnel42FXSxcHailn5BOKjI5s3z0ed6l15Ri5MqXOaapyl33eOe9H+feQ2xc172y91u515n75m1vWL7RmXas5r0M0bzyFH3nM6PX3rq9VroXXrhgjKNmTSrKj77fvOXNsWtV32prhVxfdI46rbv0c9c9z+Wmr840rusU+xXds+h7dXbyLdsQ+jzVsgSu/N19z/O182fyr05u2XuoE5v5Pm+LeR8YXXEaBCAAAQhAoB0CmPftcCVVCEAgh8BY48FuTMRu3uvFiW8N46J113yNH710kvHf9t/7Rz4LaqRlG3Affvx3ozXttd6i1iF29d626zC7L1MzdO09n1EdMvV1Nt9dfXaN4JIp1dT9NQI2y1Sfi6YgVL1pRgjXtbPPn5++/Oq/qJxHzayQTbfsC0vXtPlNjrzXaAXfdIZan373nq9XLr9rVBPmvdu8f+/wT8e0Iu1oiYS2n3lK/613flhYz62NvH/nh86y65nRRdnVcaLoOeSbNr+NkffZvOj/qTrE3XHXrlRruJ588qlOXtlnjfksE2f/u/lmQfaers8DMu//H8vAlpn9Hy0DW2b2v7MMbGNm/+uMif3nloEtM/tbSZJ8zfrTKO9PMn+HkiTZZ/29nCTJLutvR5IkD1p/G5IkuS3ztypJkmutv8VJksyz/s5PkuQM6++UnDizia/+J6OzCLeqVzE9rkJBB2Hea3SplqL66Av/UPiMy/7mfVOvb7jvldJpZdM2n31T5z+240jhPSbSvH90e7Xptk25u9hqFHD2N1lmGSnlsU3zPqTDYBEnpZEtoz6HtKW2PPiW89qiNmpRnuzvNXW+K39507VrdPwJJ5w4dl3Z2NnOh72vZd9ceYrVvFcnRjv/ZfddvwOVX0vXlU3LPv/Kq9aOcZwy5cSgNJdde/fYtcqTOjPZ96i67+scoCnn89LU0mkubajzdt51od+5livT/ULNexdzXV93VgA7/yprloFm4zj0wV8XMvC9v1I7xr5H1X2mza8QyXEJBCAAAQhMJgKY95OptikrBCIhMNZ4sBsTsZv3api4Rn2oDBpJmNdweeiRg86y37Tqwdzr8tKciO9GhssLX01l5mv045QpJzjLZdfrgoUrg8o4FPNeTKq8VHbVp0xmm6XZL1qD8ImnfsN5nYz7qh05TP5OPXX6WNqxmfe+F+CauvbQ+38VpEdT3uxWxoGpB7Mt+wJysqx5r+eFa5pbjRrKcp2oz22Z94c//GxMJ9KL9DNRZc3edyLN+2xeNGWrnncy8zUK1vWi3/zezPaSeVfXZhlg3muk9m9nDOuPLbPaGNe7LcNa5vV2y7CWeX1/xrCWgW0b1tq/wjKsZV5nTevpkcRyQ8+GOjo4f78TeNxMkV9HA70279XBR7F01Wmh9cyRoeKqQ83Ak30mVf2s9btd99AMXEVp+mKXjZteLby2KG3zva9jY1PTqpv75G0Vh2pGMJnNGjm7Zu32VNM4awR13t/p088aYxu6jJTJT5vm/daH6q9R7fu//OZb3y/UwI0r7h/jIy1qJipT/rpbdSJw6VvLXfjS9rVlll59m/caX1qu4wcO/rkzTzGa95oivc4zTOV3Lc2hOgnpsOriZ45dd/29To4ffPSzwnpydWZXnp574auF15r7523V4cGlu5kzZ+Wm73vmq8NH3v1Cv/MtTxdq3rs62mugRFPvEVSObQ+/52QXssQH5v3Qw13KBwEIQAACkRPAvI+8gsgeBIZIwNl4MI2xPpj3s2df5ixD0QsITU1uymlvFy5a1UjjMbSR2fR5hz/8m1RrvmlKRrtc2f1nd/9OYTmHYt6r7E2tneprcBdNjeh7WVF2+YSsXjSi37XuYGzm/Zq1jzn1+PSu3yzUYbbM9me9THGNEMa8d4+8Fzu9WMs+DzQFrMxtm+1E7bdl3qs806aNTw3/yxe3v4ii7D6ToIuR90X1LXNn++MfpVoiJKsf+7M6KhWllfd9gHm/f4jBGGXKJfA/2BqLYL/KFPmuAvbavFc9nHLKtFSj5/N+03nfaQYpV33ufvFr6Rtvfa/U38uvfdM5i4riLFecFLK290SZ9yeccFJlpnm87e9e3/fH6R13PpOqHeXi46qXkGOaDcm+T9F+m+b9zifrjXxW3tdv2OPUqPRWVDbf1OKa5aZJfbs6Zea1S9SpwVWXd979bGGZispsvp8+/eyxe8Ro3jcxO5traQXx1TTuhkeV7cqbtowxVLrqkFSUnqbXz9axfud6T1B0bcj3an+6OnVKi3lrw/s6OjQxc5Py/aRn1ocQ814zUqg9lOV27rlzS/9e9fvWrC+umb3U+St7D30OeTeDee8KpTgGAQhAAAIQ6IwA5n1nqLkRBCBgCDgbD6ZB0Qfz3jfyXtOe5TU+1fB1NdBOP/3M3EZnXpqxfacpN/WixNSnvdVokKL8Dsm837Hz48LyFvHQ96vXPOLkqdFneddranibv/b1MiDvmpDvXvDMBBCbee+aUnLGjHNq/9b2vvGdMa5ii3nvN+995mvdjhQheg05p03zXrOTZH+H+iyDJiRvbZ8Ts3lvl33nk7/uXVf0mmV31mKJeW/CM7YWgf/e9budgGN1psi3inN0d8LNe8UKMlN8f1o+x9VBLst+1eptlX73vg642fTb+Bxi5kyUea+2iP3MbXJf67BftfRW5//CJjhrRqUy+W3TvG/if7umGXdxCVnuSrNrua7t4ljejGA+81AdlMvUXd65rrg/RvP+rLMuqF3mxUtWj9WzjO08PiHfaTZAl1YOHCw2710z8Z122hm182TnW+xc+Tt4+C+997nwwgVj1yivdrp19l97/dtj6SuPIc9733sPVxnbOBbyrgLz/mj8xA4EIAABCEBgIghg3k8Ede4JgUlOwNnAMQ2S2M17TXPnW/O+aNp8NQxda4Wr7HXW367T4GzjWt96iyHGsXrBGy3Y2ybWkGyjrErTN01gyAirkDxpWmibhdlXD/u860+ZetrYdUWzQ+SlZ77TupkmD/Y2NvN+5hnnjeVzwcIVucxMGfO2D25zr52Iee8372+9bedYXUg7q27eWrs+8uoq9Dt1OrK1bPY16js0Dd95vrVrm3o++O4berwv5r3K4+tkoU45oeV1nYd5P8mjUnfxtVSC87nQ0fEmpsh3lWzCzXt1AHT9Du1jmuFGIwhvunlrqhHhPuYaxW1fF7I/Y+a53vR892nq+NXX3F6Y34ky78sa4CGsdY46mmoK6KYYutJR2yo0PzqvTfO+ifZcHfPeNdORi1kbx/LaGPqtuu65Y+cnpeour57nO2bpidG8L9tecJXZZd7r/YTr3DLHqpr3Gl3vqt+yv82ivKpzl+s+eUtKuN69NDH7gcnr2wd+5MxTiHn/yt5vOa91lbGNYw9ue6dQM5j3rlCKYxCAAAQgAIHOCGDed4aaG0EAAoZAbiMldvNe02r6Gk+33/l0YQNIBr/r+lhMLNMQrbPVtHZTp54+Vs6TT5lWyEdrkbv4hHSMqJPnOtf6zPsmXoTqhYBrakpNxZ2XZ03D5+Ko9UTzrgv5bu7FVzrTjs28d73wX3bt3bXLf8WCFc7yl30ZN1nWvJemtN6lS496yaznRYju2jxHo7pd+Xv40UO186b/aa609Xyou+ZpE0z6ZN7L0Js+45wxnkVTphZxwrw34Rlbi8B/4/rddnTsF0mSLLHy0uRuL8x7+ze7983vpq6ZhFQX+u2XXau+bSM5TyNLr15X+D9losz7886bV5g3u15C9h/Yut8Zw7oYaSSsOlZoZK2mwff9uWZlKGsQDtm8P+mkqWP/I1282zh21VK/vn3Mdz33243pbsmVa8bKjnnv71jr+g1XNe/feueHY+ylMbWRXfepesy3RN+el//Aex/X8lWaRbFqHrLX+TouhJj3zz7/ZSe3Nn6frjT1jM6WJ/sZ877JEIy0IAABCEAAAqUJYN6XRsYFEIBAXQK5jZTYzXuNAnI1fnTs8Se+UNgA0ksK1/XqLd/U2mvZRtdEfJ4zZ/FYObVkQN6adMqnvnctLXDtdfVN17Y4+Mx71XPdETh337t7jKPSzXtBpnJ+8NHPnNddd/29hRrN46SXIy796lgfzPslV95Sq/wyEXzrtGLe+18Q6net0dEu7dy/5Y1adZKn19DvtOajK2911lU291bnBNc6oLrf1ocOTHjZ+2Tei6lrZJ1YHnz/ryqzxLyvG9YN8vp/cD0TWj7W9BT5rorpnXmv3/2Bg3+ennHmbOdzWobve4d/Gvz79/0Pb7luR3mfTOa9Ou25Op8aznru3nbHU6mWz1FH1aL2gfmf6pqNCvP+8/hrIvWd1za5Zc2jzt/v07s+Df7tGg34tu7R6Cc3lr7vvnnHXVO2l20vuNJ3l3XiRt77pn/XMg6u/Fc95luK6qVX/9B7n9NOO3NMe+o8XDUP2esUf5rnmr0NMe+fePqLzmvtdNrcx7x3hUkcgwAEIAABCERFAPM+quogMxCYHARyGykxm/eaks01mleNKpnv7x/528KGoEYOzjpvnpPBsmvvKrw+22CM9bPL0C4aLW7KohH62YbqvEuXRcvGVVaTf63paspVdqv1+3ym3/bHPy5M1zU6SS9Ly+bDnC/tXnrZsrG6MWWNzbx3mcV1Z0NYtPhmb/nLvoybTCPvpSHflKkaSS1jxuhsIravvPZNZ71quv8m8rP21u3O9DXyp4zp1EResmn0zbx3jbrSOq96PmXLFvoZ835yBJ8lS/lz87+tg+1/TpLk0yRJppfMY5XTe2ne67es9b71W3fVx/XLNwT//l1LXynWevGl32/9L29qZ/O8GsLIez2PNXLeVVcy2p/b/ZXg+jJczFaxZjZdzPvPzXvXzBJqf3Wh77zlvO68+9mxelM9hrRnTN0XbS+97NqxezDy/nNtFPHT91VH3vs6jZ955vmVf+uu/M6efdlYHUtHb+3/U+991D7LPjO0tJwr/SrHdO9s+vocYt5r5hjXtQsXrerkN/vuoZ8UcmDkfZVQi2sgAAEIQAACjRHAvG8MJQlBAAKhBJyNFNNwidW8P3DwJ6nWbDf5zG41VV9og+/hRw9709lw3yvB6YTer2hqZo12qWN8ZPMh01kvS7KMtE5d9lzX54vmLBq79tRTp6dHPvlF0PWuNNs8lmfei4HWaa5y/xUrN49xUHoafVZUp7qfq5OIRuTsfeM7lfKzfsMeZ35MPcdm3qCglxQAACAASURBVPum99cLzCr18cDWt3PLj3mf/4JQJrXWmDR6sbcaSdfG9PkhvxNpQS8djz9+yljerlhwYyWtZPX1znt/lro6JYmBRpKH5jObbt7n0DTbNO/1fyV0NGVeWcx36iCnF662drSvaZbNOVW2mPeh4dukOu9vsjpr6XObU+S7Kqy35r1+21qeylUPGuGttYNDfv/Tp589loau//DjnwddH3KPOucMwbx/4aXfG2OsepORV7fDmoz6rAYw7z+Pv1xLyyj213JadXRZ99pNm/eO1ZvqMWTN7dB7u0a5Y95/ro0QjlXNe6XtGuDQ5NryusfMM85z6khT1/vK53qvoGd+U+8+fOvWh5j36pSWfZ7pc53O/z4OVY9j3rtCKY5BAAIQgAAEOiOAed8Zam4EAQgYAs5Gimm4xGbeyzB+6JFD6emnj0+5ZvKslyJ7Xv6Gt9GYbSypseia9lHpqTF5x127GmlQahq3u+55Li0abS1jadasS1JNndbEy8tVq7c563jN2u1BjJbfeJ/zenV6yLKM4XORea9RMM+/+Lul8q5OHEZf2e3mLW8GpeVb4qGKWSjjWtrM5sX+HJt5v+72J5z5lalfVuePPPZ+OmWKe8SfYYB5X/yC0PfyVgznz7++kRH4Mot37Pw4vfjiq1KtJRn6jHB1dtEoz7yRZKFp67x717/o1KPKvmDhivTdQ38RnFfffVX2R7d/mOpFZWgnlTbNe3WKOPvsC1MtjaB9X75Dj992+5NOhuroFJqG6zzMexOesbUI/NQ821va/u9JkuxIkuQ4655d7PbavFcnLz1TXHWi56jr95095lrWSelpWZzsuRPxeQjmvWatcdXRE0/9Rm3G7g5cF5ZK17f+epmOBes3vuQsY93lsqQ5X9oy+oo0OWfuEme+qnbcLbpf6PcPP3rIma91Dc1wpHxMnXr62D0w74tjc7sO65j3p08/a4y/ngNNxLfK4/tHPnO2RadMOSH3d3H5Fcud+QqZCcVm49vX7BGu512Iea8BD65r63ZK9eW1ynHM+y5CM+4BAQhAAAIQ8BLAvPei4QsIQKAtAs5Gimm4tGHea51vGTAhfy+98o2R4SODdPmNm1JfQ9TkV9vVtzyc22h0NZT27f+TVKPJ7XTsffW4Lmv46j7qbCCGNyzfeHSkp6YPd+XBHJN5b+6tERtr1+1I89aOM9dltzJENT20a816HdPU1NlrXJ8f2/Hh0fyYfGmrkasbN71Se9SO6551jrnM+6zRrZeNWx96t7D8GhmjTg6+NSs1DWnoiNpdz/22k6NYLr36tjRvlILhcej9v0pvXv3QWJ3qRYldN9qPzbxXhxqXFpVXTUeoFyamnL6tXhTduu7xsZdFrvJj3he/IFTHpYWLbhrTjtGSnj/qKFK2c4Xq77U3/mg0Nf851gwpZcx7X6cjjejb/vhHtUetyVjXc92UNbvVaCI9I6rMQKBRP1o72B6RGIt5b8qpNUdXr3kk1YhM3+/Nd1z/17Tsgu+5KLPLd23Iccz7tsK9Xqf7Z0a7DW+7nCLfVQG9Nu/1e/aZgKqnZ54tXj/72uvucT6Htz50oNZzJORZE3LOEMx7dZ7L/m7Unqo7E4vacdl09ZmR95/HX2rzuhg1OcI9RMfZc3yjkxctXtXI707tale5Me8/10a2Tlyf65j3rt+96uTpXcXPZVdessd2v/g1Zx0r7s+ea3/2ddZRbG+fV3Xf17E0xLzXPV3vmhTvvvPejxvJX9Vymesw712hFMcgAAEIQAACnRHAvO8MNTeCAAQMAWfDyzS42zDvTdptbBcsXFl5OncZS64p5ux8qiEs00LnqhFnTx0vY1UvK5585kujEfZXLb3VOS11GfPevrfWB1+xcsvITJOZf/jDz8YakRrdrxeNGuU8c+Ysb92GNmDVUJR5JaPHzou9L0NWa0XLyJ536bKRISZTzP7TMgem0dn21mXea4mFKxasGCuD8qsXaPYa33qZqd7/96x/IbWNR7vM2tc6ra++/u1S5XKtD23SnTHz3PTue3eP0rSnDtQIWb0gWbP2MeeME1dfc3t63nnzxsoWm3mvetdLQVPe7Fazaei3pU4l9gtldaCQ8amRY2KUvU6/edeU/Jj3YS8I9dzSEhpZrvZnPbP0AlEdefa9/YPRaBvzO5Y+97/745EJrBHd6mTlGjWv9MqY9/pt2XnI7qvDhn6fmpbVftaY/auvuaPwt6nRR+fOujj3PjLxV928dbQOrJ7vmirelF37+w/8q1Qj+zZtfn3UScv3zIjNvLd5aupk/U/Y/MC+0aw1ro5Ehz7461E59RvV+fb19n4Id8PPt8W8N+EZW4vAD22dNbT/d0mSLLbuMRG7vTfvFa/4/odcfMnSo89L3+/dNwPM0qvXFV7rS7PJ4+q469Lbxk2vNpa/82ZfOnYPxXVNlUOjRrNluPzyG2qnv+3hd8fS1X0w7z+PvxQbZNnr81VLJ1bfirNPPvnUsbyp06bdBqmqQS1R5io35v3n2ghhW8e8v3HF/c460IyCIfcuOudez/JtRc9uzZ7m0kbojIBF+XK193W/0HcfvraqZkQsuncX32PeT0Soxj0hAAEIQAACRwlg3h9FwQ4EINAVAWcDyjSq+mTea3RDlRGidkNL5fWthWyYZLc63zcCMXuuPlc1711paepwGetaM/SUU6bl1qW5Xi8EXeaMzSG7v+XBt4LSNvfIbt9654edNXh95v3bB36UutaeNHlVxw31tnettW3OMVvVt4zMLKeiz5pJQqa/Sce31fTgyotrOlL7Gr3wlfnaF/Ne04SG6FS6lpmfNxuGOGi6XnUMcY0uwbwPf0GoziuuNShtrWX39QK2qLNT9poy5r1+S74Ra9l0XZ/Voajo96jvZb77jCdXujr2y7LnL9uQvTZm8z6bV31W54jTTjtj9Mwseg6Z6/V7bGI6Vsz7rsK/Xt3ne0ZnDWz/KUmSrZGUvvfmvZ6jGjHpq5eidoQ6S7quVaygDqkhz/E2z/GtgaylV5q6b9vmvcukbaKjlc/kwrz/PP568+0fOPWttuNE69vXofi53V+pre0rr1rrLDfm/efaCHl+1DHvfbOiqINtyL2LzlGnfdezWx2y8q5VJ2DXdYoh864L+U4xqG9ZtVDz3tehTNP9h+Sh7XMw7yOJ3sgGBCAAAQhMVgKY95O15ik3BCaQgLMBZRpVRS/d8hoo1y/fkJu2uUfd7Rlnzk6fePqLjTWoNJWgb+Ro3bzq+ibN+7L5mTt3SeVp7lfetKVyfcZg3kurmrFg2rSZlcsh3jL3Q6bc9/02tAZ2mc4evjqeNeuSkfGo+/TFvFden3z6i0EdJHzlNsf1ctisj4h5nxzVtDqp+LSXd1xLElx3w/qj6RjOTW7LmveaXcRVtyF5CjXvxUSjyq9ZdkerZe+beR/C2D5Hnaa05EuexkK/w7yfwIgw3lt/x9ZbxX0zRf7pERVzEOa9RurOPt89g4tmxin67ftMIE1/XHRt29/7OhdoaZSm7t22eX/SSeOdRhctvrlW/sUluySV+V1i3h9r0GqGIMPG3mqWtKY0VCWdO+/e5cyXZlCqkp655r3DPx11crTLavYx74/VhmHm29Yx79899JNRR0zD3t6GxqS+fPk6NWk2PnVY8V1njquDtZ0fs6+Z5sw5VbY+413ph5r36tTr68hfN39VypS9BvM+ogiOrEAAAhCAwGQkgHk/GWudMkNgggk4G0+mERWreS/z89LLrk01ZaOmbc42bOp+VpqaVq7sKHzDzbXVS64rFtyYyrzNy59GYujFlyuNqsf04u72O5+uzUp5r9KxIRbzXtz3vvnddPbsyyrxVccLrV2fV38h38nArtOJQEsyaMS9uVefzHvl+dndv5M7C0KRzjVVvl5KmfK7DF5G3pd7QWhYPvHUb6TqGFJUB2W+17IVeklcZZTZkU/+Pr3z7mdHs4uUuWcZ896UfcfOT1LfC8Uy97bP1fNSy2GEznZy+x1POdm/9sYfHdW7yW/ZrZZA8U3rb+e5zL5mX1i7bscxSymUzVf2fMz7CY4K47z9t8ro0nHuzyOYIt9FdhDmvX7Dj+044nx2qS60nFT2d25/9s3upNH3IUaQnVbT+xrJ6dDTqLNbU/dq27zX8i/ZMmgq/Tr5942s1n0w74+Nvx7Y+vYYf3Ea6fut79eqhzp16DMpNfvX6/v+uHK+Vq3e5iyvyox5f6w2iuqvjnmvtBcuuslZF5detqxy/SpdLVGVfaboc8hSKbr+nnt3O69XukVMfN+rw6/a6a586Vioea/0fc83dUZTLO3LQxfHMe9doRTHIAABCEAAAp0RwLzvDDU3ggAEDAFvI0cNnTrmvRqcU6eeXutP08HL+JkzZ/Fovex1t+0crT1cdXRp2UbVwcN/mWpqzLLTKpuGo8q/eMktqRpaeklS5v4ymddv2DNqeGv6YpNmma1ezolZ0+a5euyr4X3NsjtH022ffc5FI3PNV99d1Zf4+qbNt9mr4a21+mbOnBXEddq0GaM110MNOPtevn2tE37z6oeca0666lijGfRSQ8Z/Nk29TMiyV+eW7Hl5n+fMXTKWxiXzri6VRl762e9kvq+9dXvh0gA2i0vmLU137Px4LE/qFJMtvwzA7D3zPmv912wa+t3nXRPynaZ+z6arjhsh1+ado991Nl19lq7yrgv5TmuhPv7EF0bPnrJT46u+NGJFmlQeX9jz9dr5UZ5l4muGFY3E1DNVIzW19rqejS4O6qATUtbsObqPpoDWmpl6gW3rL2Rf03Xq5aXyueflb5TOg56rrvK8vu+7pdPKls18fuOt76Var1lTHmt5jpByZc+RGaTfr5n9wqTdxBbz3oRnbC0CuSZ3Vp/W5/+QJMkDVjqx7eaWa+nVtzX2u3eNvhanpp7RuaPv5y7JLccHH/189Dy36u3oc0nGdpOxl3lG6f+c2S/aanmqbN4000DRdaHft23eK3bK5r9O3fvWujf3wLw/1qCVvtUeM3zsbZWlzEJ0FarvxUtWO/Ml01e/6ZB72ee8/No3vaO9VW7M+2O1YbNz7dc179VxytabvV91FjldZ6dj7z+6/YMgzWi5Lt/09kWDHFycdGzN2se8+VIey5j3mqnPN7PIjSvuDyqjL5+u4/qthf7eMO9jC+PIDwQgAAEITDICmPeTrMIpLgRiIJDb0Klj3rsaJ30+JgP84UcPp7eseTRdevW6VGsFykDSn16MLVi4ItUakppKU+dpSjkZQU2VWYaLGrR33/N8esPyjaP7mTxoSkYZu0uuvCVddfPW9MFt76R6gRLaEGwqjzGkE2Lem3yqfp574aujWQmuWrpuVI+qz4suWjgyLlff8nC688lfrz1jgbmfa6sR9DJKb77l4VTTmMr4Ux60xIE0ZeqzbOcP171iPKaREmKsly56iajfksqvzgQyUFeueiDVqKW39v9pY7+lGDnEmifVz9O7fnM0+l0dEbTmo8zVUR3NWZzOn399qpkg1BFl85Y3U02Nr2noYy1PmXzJMFJnGc3Ccu1196Tq4HG07HOXpPMvv2FUdj0ntjywL9U6seJV5h4xnLtv/5+MOiyo48DyG+9LNbPFvEuXjepY9az/LeowoZfID2zdP1p+pM3/LZj3MYSG0eXh67ZBELAf4xT5LqiDMe/1LMsdfe/oeGg//7Y//rG3PXLRnEWpnlP2+VX3dz335dEzTnF6aBp69mc1pw6VdaeeNvdv27xXuySbf31WnPnhxz8P5qD8qp6KOrZh3o8btF3pWzGY/odrvXOjr7ytzndpQ8fULsm7NvudfqMzZp7rTU9pYt6PayPL0f5c17xXWq7ZyVQX+h2Xfc+j9oDv969Os6GdRpQv31Jd6mhWdnp6tRP1TPZpWcfLmPfKn5aP8KWntNQpx66rKvt6/j64bf9oVkMNGAlJA/PeFUpxDAIQgAAEINAZAcz7zlBzIwhAwBDwNkzUYCnbqAtpdHBOuYY7vMrxKmPew7YcW3jBCw2ggbY1gHlvwjO2FoGv+F6iO45rivxF1rUx7w7KvFenHt+yROqQV/TsuP6G9d42yamnzkjvu39v+v6Rvy1MJ3sfdb7dsPHlUcdMo5eHHgkzN5WWb4rn6TPOSZVO3eW72jbvNYrUZ2yp02jIkjYqo5Z1ya4FPWXKCWN1hnnvjhOuX75hjJXR4y/1/VqD+j4Y/DtRB0yTj+xWeQ7pkKlOMVquyL5eS9zZn7WPee/WRvaZZT43Yd5rMIHrd/rL+jglXb/xpULTXaa8nqEnnnjKWJ0qHdX18y98NVhzKp86pWvpiKxG9FkzUD30SLGGj3zyi7Hnkkt3SrOsea/OuHlLTWl2Ni01VqUz656X/yBds3b7MdP8Y97HHKqRNwhAAAIQgMBRApj3R1GwAwEIdEXA2WgyDSnM+3KNbNPYZjtx3DDvJ449uoc9GkADdTWAed9V+Ner+/yWiUtztv9rkiRbelWqJBmUea/f/mM7PvS2K7T0Sd7zQSMZNetHTh2nmsJ+5U1b0kcee380Gt82zmUwaY16mVWa2lkzF2nZLZdxXca8f+e9P/NO8ay8ytCWyaN7aaaS7J9mrMkrd9vmve4tk97HVUuU3XXPc+lrr3/7GCNKs1NpFq/b73w6PePM2WPXa/YZ15T8mPfuOED61lrjvnrQcS0FtGJloL5Xb8vRd7HxaTR54OBPRkuf+fKlJcakgb1vfOcYk/f9I5+NZg7TbHRZw1TLBGhpnmyamPdubZi6yG6bMO+Vppbhy9aF/VmdrrScnOrYjCjXqPDX9/3xyNwvWj5Qs3tk8x7yedvD7+XmS89SnfPe4Z8ek/6oQ9Z9r6RadsIuh/ZXrd42dkzHy5r3yr+WrCpaulDstEyYZiXQ/wp1KDBlF8t33vvxqGODRsyrg5qW/MrmWZ8x73sWvZFdCEAAAhCYrAQw7ydrzVNuCEwgAWcDwjQqMO/LNbJNY43txHHDvJ849uge9mgADdTVAOb9BEaE8d76SyYudWz7MkW+i+7gzPvc0fdzFh81NXzPCY3y1XIsjnr2HtNo0JM9Izh96ZQx75XXtbdu997fdw9zXKNefeXV8S7Me5lyvlGuJp/aaspqTX1++ulnjhmy9nky8/e/++P0knlXj3HBvPfHASN9O5ZhsNlm96vpO9y8lwZ37/m6d1S1nR9pecaMc0Yjo+3j9v4JJ5yUPv/i76ZXX3P7mDYw7/3acD0jmjLvlbY6hdj1lLd/ytTTgs/VslJlpsvPllPLM+TlxXx30klT05lnnJdKX+ZYdqsOVPqNZY/rcxXzXnl95bVvHjNC3pV29pietdJ69njeZ8x7V4jEMQhAAAIQgEB0BDDvo6sSMgSB4RPIbVhg3pdrZGcbpHzunh/mfffM0TnM0QAaaEoDmPfDDzwrlPDXPC+9f9ajKfJdxR6cea/nQN7o+51P/nquka3rP/z470ajJ10j5j06yG3LuK4pa95rNKXWEnelVXQsBvNeXDXzQXba+6K8u77X9Oh73/zuqB4x78v/75e+b179kHNGCBfvKsdCphzP/s9+eten6cknn1pJ4yaPU6acmD66/cORNjDvTyl81mXrIPu5SfNeHatuXfd4o7qTjusY9yqvrtdsKkZDVbfnzrp4NPJdMwa40qhq3iuPbx/4UeGsMK57ljmGee8KkTgGAQhAAAIQiI4A5n10VUKGIDB8As4GjmlsYN6XfymTbXjzuVuGmPfd8kbf8EYDaKBJDWDeDz/wrFDCbCP5f0uSZEeFdGK7ZJDmvUwi12hytS0umrMo2NB69vkvpxdeuCC3nWLaK6FbjSpfs/ax0XrLZZ9bKtfGTa+mmkY89H46LxbzXuXV2uSaJr9M/u1zNVW+poY27DDvq///f3b376QB/+9K1ZVGxVfVt+r0lb3fSmfNuqTUPY0+pKtnnv2to9rAvI/LvDe/Wa3Trg44pt6qbKfPOCfd/vhHR+vapF1ne9/9e0uPVjd5X7BwxdGp9dsw71UudTK4f8sbo1lJzH2b2OoZsOG+V46Zbj+Po6bfd933xZd+v5H62LR5rzN9657/GFsgRX4gAAEIQAACHRLIvpco+r+p70/rMH/cCgIQGCCB3AcN5n31lzJ5DS++a48r5n17bNEtbNEAGmhbAwFmxv4BxmIUKZ/A+796cWymyB9KA7gz815my9Spp4/97Xn5G4288M8+Fx7bcWTsXub+zzz7afA9ZZg/+cyX0iVX3hI0rbdlMIzaNxq9rzWJNUJUI4vrjhJVOZWG1jfWOs/Kl9LXOsannjrDWWatmZzlY3+++OKrxq6be/GVudfY15fd1xrnMnhDptE3PDUd9aPbPxjLk2YjMPVqtorDy+Tp1tt2jqWhtEJHoupemza/7kxjz8t/UCovrnz70pbR7Tq/zDHpW23tuvrWOt9N6Vvmp9ZIV0cXU/95W+lo9S0Pj9XX9cs3jNXJ6dPPqs2sDN/sufMuXTaWJ8Uc2fPKfr5m2R1j6aozQ9l0sufrd2p+V/b23UM/qZX2Bx/9LJUJPMuxZnxeXZ9zzpyRNt4/8lmt+2fLaT7ve/sH6bJr70pPOOHEIO2pk1h2lgnp12Zl9vU/wNynzvb9I3+bbn5gXzp37pLcpUV8HDXt/6WXLUvvvHtX+urr3y6dpy0P7HOW76VX/7B0Wi4OmPf5wSjfQgACEIDApCeAeT/pJQAACHRPILdxhHmPUeNq2MV8DPMezcasT/KGPtFAvgYw77sPBHtwx4NJkmiK/IU9yGuZLHZm3g/huSPTRAblXfc8l2qd5fnzr08vuPCK0Sh/bWWILFq8Kr1xxf2jkYwy/e1R4kNg0GQZDn/4N+njT3xhZLwuXLQqVYcBdUQQy8uvWD5aI1ojTd9463uNmEJN5n2IaUnf6tgSrO+nv9iqvo98ok4qn6Zrb90++l1pFgzp46KLFqbSyy1rHk0ff+LXUuV7iPUxWcokA1mG7fIb70s1s4b9TL1s/nWpOmFs3PTKaO33rpioc8LWhw6M8qQOF+dfMD+dff78VB2d1FHi7nt3p02Z1XXKpP8v6qi2dt2OVDNNXHzJ0qP89DsRv6uW3pquXvNIKtP9uRe+Gv3vBfO+TAjHuRCAAAQgMAkJYN5PwkqnyBCYaAKY97+WbyTUadRxbfdsMe+7Z47OYY4G0EBTGsC8n+iwMMr7nx1lrupnCvOeGBzjEw2gATSABtBAFBrAvK8f2JECBCAAAQgMmgDm/aCrl8JBIE4CmPc0FqNoLDZl/GDeYyI2pSXSQUtooHsNYN7HGSySq1YIYN4Tgw8qBud/Zvf/M2EOczSABprSAOZ9K7EeiUIAAhCAwHAIYN4Ppy4pCQR6QwDznheHg3pxiHnPC4ymXmCQDlpCA91rAPO+N/EjGa1PAPOeGHxQMTj/M7v/nwlzmKMBNNCUBjDv6wd2pAABCEAAAoMmgHk/6OqlcBCIkwDmPS8OB/XiEPOeFxhNvcAgHbSEBrrXAOZ9nMEiuWqFAOY9MfigYnD+Z3b/PxPmMEcDaKApDWDetxLrkSgEIAABCAyHAOb9cOqSkkCgNwQw73lxOKgXh5fMuzqdOvX0Y/4umrNoUGVsqoFOOrzsQQNoIDYNYN73Jn4ko/UJYN4TgxOfogE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODEZuExCQAAIABJREFU4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QwLynsRhFYxGTrHuTDOYwRwNoIDYNYN73Jn4ko/UJYN4TgxODowE0gAbQABqIQgOY9/UDO1KAAAQgAIFBE8C8H3T1UjgIxEkA857GYhSNxdgMJPKDqYkG0AAa6F4DmPdxBovkqhUCmPfE4MTgaAANoAE0gAai0ADmfSuxHolCAAIQgMBwCGDeD6cuKQkEekMA857GYhSNRUyy7k0ymMMcDaCB2DSAed+b+JGM1ieAeU8MTgyOBtAAGkADaCAKDWDe1w/sSAECEIAABAZNAPN+0NVL4SAQJwHMexqLUTQWYzOQyA+mJhpAA2igew1g3scZLJKrVghg3hODE4OjATSABtAAGohCA5j3rcR6JAoBCEAAAsMhgHk/nLqkJBDoDQHMexqLUTQWMcm6N8lgDnM0gAZi0wDmfW/iRzJanwDmPTE4MTgaQANoAA2ggSg0gHlfP7AjBQhAAAIQGDQBzPtBVy+Fg0CcBDDvaSxG0ViMzUAiP5iaaAANoIHuNYB5H2ewSK5aIYB5TwxODI4G0AAaQANoIAoNYN63EuuRKAQgAAEIDIcA5v1w6pKSQKA3BDDvaSxG0VjEJOveJIM5zNEAGohNA5j3vYkfyWh9Apj3xODE4GgADaABNIAGotAA5n39wI4UIAABCEBg0AQw7wddvRQOAnESwLynsRhFYzE2A4n8YGqiATSABrrXAOZ9nMEiuWqFAOY9MTgxOBpAA2gADaCBKDSAed9KrEeiEIAABCAwHAKY98OpS0oCgd4QyDXvH9y2P937xnf4gwEaQANoAA2gATTQugbOO29eblySJMn+3kRYZBQC+QRyzfsFC1e2/nsjxqeNgwbQABpAA2gADUgDt93xVFEM/o/5YQ3fQgACEIAABAZNAPN+0NVL4SAQJ4GiAJ3vkwQGMEADaAANoAE0EIcGMO/jjCfJVXkCueZ9Esfvjece9YAG0AAaQANoAA1IA5j35WM9roAABCAAgeEQwLwfTl1SEgj0hgANMRpiaAANoAE0gAbQQF80gHnfmxCTjBYQwLznuduX5y75RKtoAA2gATSAeV8Q2PE1BCAAAQgMmgDm/aCrl8JBIE4CNMJohKEBNIAG0AAaQAN90QDmfZzxJLkqTwDznuduX5675BOtogE0gAbQAOZ9+ViPKyAAAQhAYDgEMO+HU5eUBAK9IUAjjEYYGkADaAANoAE00BcNYN73JsQkowUEMO957vbluUs+0SoaQANoAA1g3hcEdnwNAQhAAAKDJoB5P+jqpXAQiJMAjTAaYWgADaABNIAG0EBfNIB5H2c8Sa7KE8C857nbl+cu+USraAANoAE0gHlfPtbjCghAAAIQGA4BzPvh1CUlgUBvCNAIoxGGBtAAGkADaAAN9EUDmPe9CTHJaAEBzHueu3157pJPtIoG0AAaQAOY9wWBHV9DAAIQgMCgCWDeD7p6KRwE4iRAI4xGGBpAA2gADaABNNAXDWDexxlPkqvyBDDvee725blLPtEqGkADaAANYN6Xj/W4AgIQgAAEhkMA8344dUlJINAbAjTCaIShATSABtAAGkADfdEA5n1vQkwyWkAA857nbl+eu+QTraIBNIAG0ADmfUFgx9cQgAAEIDBoApj3g65eCgeBOAnMSZKEPxigATSABtAAGkADfdDA9DjDKXIFgdIEziQGpw2CBiatBg4l5Y3Am9HLpNVLH+Iz8jj8dsTs0pEOF0AAAhCAAASGQwDzfjh1SUkgAAEIQAACEIAABCAAAQhAAAIQgAAEIHAMgZcrmPeXHpMCHyAAAQhAAAIQgAAEIACBrghg3ndFmvtAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKBjApj3HQPndhCAAAQgAAEIQAACEKhBAPO+BjwuhQAEIAABCEAAAhCAAAQgAAEIQAACEIBAzAQw72OuHfIGAQhAAAIQgAAEIACBYwlg3h/Lg08QgAAEIAABCEAAAhCAAAQgAAEIQAACEBgMAcz7wVQlBYEABCAAAQhAAAIQmAQEMO8nQSVTRAhAAAIQgAAEIAABCEAAAhCAAAQgAIHJSQDzfnLWO6WGAAQgAAEIQAACEOgnAcz7ftYbuYYABCAAAQhAAAIQgAAEIAABCEAAAhCAQCEBzPtCRJwAAQhAAAIQgAAEIACBaAhg3kdTFWQEAhCAAAQgAAEIQAACEIAABCAAAQhAAALNEsC8b5YnqUEAAhCAAAQgAAEIQKBNApj3bdIlbQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITCABzPsJhM+tIQABCEAAAhCAAAQgUJIA5n1JYJwOAQhAAAIQgAAEIAABCEAAAhCAAAQgAIG+EMC870tNkU8IQAACEIAABCAAAQgkCeY9KoAABCAAAQhAAAIQgAAEIAABCEAAAhCAwEAJYN4PtGIpFgQgAAEIQAACEIDAIAlg3g+yWikUBCAAAQhAAAIQgAAEIAABCEAAAhCAAASSBPMeFUAAAhCAAAQgAAEIQKA/BDDv+1NX5BQCEIAABCAAAQhAAAIQgAAEIAABCEAAAqUIYN6XwsXJEIAABCAAAQhAAAIQmFACmPcTip+bQwACEIAABCAAAQhAAAIQgAAEIAABCECgPQKY9+2xJWUIQAACEIAABCAAAQg0TQDzvmmipAcBCEAAAhCAAAQgAAEIQAACEIAABCAAgUgIYN5HUhFkAwIQgAAEIAABCEAAAgEEMO8DIHEKBCAAAQhAAAIQgAAEIAABCEAAAhCAAAT6SADzvo+1Rp4hAAEIQAACEIAABCYrAcz7yVrzlBsCEIAABCAAAQhAAAIQgAAEIAABCEBg8AQw7wdfxRQQAhCAAAQgAAEIQGBABDDvB1SZFAUCEIAABCAAAQhAAAIQgAAEIAABCEAAAjYBzHubBvsQgAAEIAABCEAAAhCImwDmfdz1Q+4gAAEIQAACEIAABCAAAQhAAAIQgAAEIFCZAOZ9ZXRcCAEIQAACEIAABCAAgc4JYN53jpwbQgACEIAABCAAAQhAAAIQgAAEIAABCECgGwKY991w5i4QgAAEIAABCEAAAhBoggDmfRMUSQMCEIAABCAAAQhAAAIQgAAEIAABCEAAAhESwLyPsFLIEgQgAAEIQAACEIAABDwEMO89YDgMAQhAAAIQgAAEIAABCEAAAhCAAAQgAIG+E8C873sNkn8IQAACEIAABCAAgclEAPN+MtU2ZYUABCAAAQhAAAIQgAAEIAABCEAAAhCYVAQw7ydVdVNYCEAAAhCAAAQgAIGeE8C873kFkn0IQAACEIAABCAAAQhAAAIQgAAEIAABCPgIYN77yHAcAhCAAAQgAAEIQAAC8RHAvI+vTsgRBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQaIYB53whGEoEABCAAAQhAAAIQgEAnBDDvO8HMTSAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0D0BzPvumXNHCEAAAhCAAAQgAAEIVCWAeV+VHNdBAAIQgAAEIAABCEAAAhCAAAQgAAEIQCByApj3kVcQ2YMABCAAAQhAAAIQgIBFAPPegsEuBCAAAQhAAAIQgAAEIAABCEAAAhCAAASGRADzfki1SVkgAAEIQAACEIAABIZOAPN+6DVM+SAAAQhAAAIQgAAEIAABCEAAAhCAAAQmLQHM+0lb9RQcAhCAAAQgAAEIQKCHBDDve1hpZBkCEIAABCAAAQhAAAIQgAAEIAABCEAAAiEEMO9DKHEOBCAAAQhAAAIQgAAE4iCAeR9HPZALCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0TgDzvnGkJAgBCEAAAhCAAAQgAIHWCGDet4aWhCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgMLEEMO8nlj93hwAEIAABCEAAAhCAQBkCmPdlaHEuBCAAAQhAAAIQgAAEIAABCEAAAhCAAAR6RADzvkeVRVYhAAEIQAACEIAABCY9Acz7SS8BAEAAAhCAAAQgAAEIQAACEIAABCAAAQgMlQDm/VBrlnJBAAIQgAAEIAABCAyRAOb9EGuVMkEAAhCAAAQgAAEIQAACEIAABCAAAQhAIEkSzHtkAAEIQAACEIAABCAAgf4QwLzvT12RUwhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlCKAeV8KFydDAAIQgAAEIAABCEBgQglg3k8ofm4OAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH2CGDet8eWlCEAAQhAAAIQgAAEINA0Acz7pomSHgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIiGAeR9JRZANCEAAAhCAAAQgAAEIBBDAvA+AxCkQgAAEIAABCEAAAhCAAAQgAAEIQAACEOgjAcz7PtYaeYYABCAAAQhAAAIQmKwEMO8na81TbghAAAIQgAAEIAABCEAAAhCAAAQgAIHBE8C8H3wVU0AIQAACEIAABCAAgQERwLwfUGVSFAhAAAIQgAAEIAABCEAAAhCAAAQgAAEI2AQw720a7EMAAhCAAAQgAAEIQCBuApj3cdcPuYMABCAAAQhAAAIQgAAEIAABCEAAAhCAQGUCmPeV0XEhBCAAAQhAAAIQgAAEOieAed85cm4IAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFuCGDed8OZu0AAAhCAAAQgAAEIQKAJApj3TVAkDQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIREgA8z7CSiFLEIAABCAAAQhAAAIQ8BDAvPeA4TAEIAABCEAAAhCAAAQgAAEIQAACEIAABPpOAPO+7zVI/iEAAQhAAAIQgAAEJhMBzPvJVNuUFQIQgAAEIAABCEAAAhCAAAQgAAEIQGBSEcC8n1TVTWEhAAEIQAACEIAABHpOAPO+5xVI9iEAAQhAAAIQgAAEIAABCEAAAhCAAAQg4COAee8jw3EIQAACEIAABCAAAQjERwDzPr46IUcQgAAEIAABCEAAAhCAAAQgAAEIQAACEGiEAOZ9IxhJBAIQgAAEIAABCEAAAp0QwLzvBDM3gQAEIAABCEAAAhCAAAQgAAEIQAACEIBA9wQw77tnzh0hAAEIQAACEIAABCBQlQDmfVVyXAcBCEAAAhCAAAQgAAEIQAACEIAABCAAgcgJYN5HXkFkDwIQgAAEIAABCEAAAhYBzHsLBrsQgAAEIAABCEAAAhCAAAQgAAEIQAACEBgSAcz7IdUmZYEABCAAAQhAAAIQGDoBzPuh1zDlgwAEIAABCEAAAhCAAAQgAAEIQAACEJi0BDDvJ23VU3AIQAACEIAABCAAgR4SwLzvYaWRZQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIhBDAvA+hxDkQgAAEIAABCEAAAhCIgwDmfRz1QC4gAAEIQAACEIAABCAAAQhAAAIQgAAEINA4Acz7xpGSIAQgAAEIQAACEIAABFojgHnfGloShgAEIAABCEAAAhCAAAQgAAEIQAACEIDAxBLAvJ9Y/twdAhCAAAQgAAEIQAACZQhg3pehxbkQgAAEIAABCEAAAhCAAAQgAAEIQAACEOgRAcz7HlUWWYUABCAAAQhAAAIQmPQEMO8nvQQAAAEIQAACEIAABCAAAQhAAAIQgAAEIDBUApj3Q61ZygUBCEAAAhCAAAQgMEQCmPdDrFXKBAEIQAACEIAABCAAAQhAAAIQgAAEIACBJEkw75EBBCAAAQhAAAIQgAAE+kMA874/dUVOIQABCEAAAhCAAAQgAAEIQAACEIAABCBQigDmfSlcnAwBCEAAAhCAAAQgAIEJJYB5P6H4uTkEIAABCEAAAhCAAAQgAAEIQAACEIAABNojgHnfHltShgAEIAABCEAAAhCAQNMEMO+bJkp6EIAABCAAAQhAAAIQgAAEIAABCEAAAhCIhADmfSQVQTYgAAEIQAACEIAABCAQQADzPgASp0AAAhCAAAQgAAEIQAACEIAABCAAAQhAoI8EMO/7WGvkGQIQgAAEIAABCEBgshLAvJ+sNU+5IQABCEAAAhCAAAQgAAEIQAACEIAABAZPAPN+8FVMASEAAQhAAAIQgAAEBkQA835AlUlRIAABCEAAAhCAAAQgAAEIQAACEIAABCBgE8C8t2mwDwEIQAACEIAABCAAgbgJYN7HXT/kDgIQgAAEIAABCEAAAhCAAAQgAAEIQAAClQlg3ldGx4UQgAAEIAABCEAAAhDonADmfefIuSEEIAABCEAAAhCAAAQgAAEPNQQdAAAgAElEQVQIQAACEIAABLohgHnfDWfuAgEIQAACEIAABCAAgSYIYN43QZE0IAABCEAAAhCAAAQgAAEIQAACEIAABCAQIQHM+wgrhSxBAAIQgAAEIAABCEDAQwDz3gOGwxCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6DsBzPu+1yD5hwAEIAABCEAAAhCYTAQw7ydTbVNWCEAAAhCAAAQgAAEIQAACEIAABCAAgUlFAPN+UlU3hYUABCAAAQhAAAIQ6DkBzPueVyDZhwAEIAABCEAAAhCAAAQgAAEIQAACEICAjwDmvY8MxyEAAQhAAAIQgAAEIBAfAcz7+OqEHEEAAhCAAAQgAAEIQAACEIAABCAAAQhAoBECmPeNYCQRCEAAAhCAAAQgAAEIdEIA874TzNwEAhCAAAQgAAEIQAACEIAABCAAAQhAAALdE8C87545d4QABCAAAQhA4P9v706A5SgPPMF/kpAQkkAIEIhD6AAMAnHfN5jLmBtx3+IQN0IIEAgBQkgCPOP20O0Z2909vtrjsw+77Xbbxk37iImJmNidjYmd6JiJ3YnoWfdER8z2zu7MdvT2dLc7N75n0l31lFmVWS8rKyvr5whFXVl5/L7/e6Tfv74sAgQIDCqgvB9UzvsIECBAgAABAgQIECBAgAABAgQINFxAed/wAbJ7BAgQIECAAAECBDoElPcdGO4SIECAAAECBAgQIECAAAECBAgQaJOA8r5No+lYCBAgQIAAAQIE2i6gvG/7CDs+AgQIECBAgAABAgQIECBAgACBiRVQ3k/s0DtwAgQIECBAgACBMRRQ3o/hoNllAgQIECBAgAABAgQIECBAgAABAkUElPdFlCxDgAABAgQIECBAoBkCyvtmjIO9IECAAAECBAgQIECAAAECBAgQIFC5gPK+clIrJECAAAECBAgQIDA0AeX90GitmAABAgQIECBAgAABAgQIECBAgMBoBZT3o/W3dQIECBAgQIAAAQJlBJT3ZbQsS4AAAQIECBAgQIAAAQIECBAgQGCMBJT3YzRYdpUAAQIECBAgQGDiBZT3Ex8BAAQIECBAgAABAgQIECBAgAABAm0VUN63dWQdFwECBAgQIECAQBsFlPdtHFXHRIAAAQIECBAgQIAAAQIECBAgQCCEoLwXAwIECBAgQIAAAQLjI6C8H5+xsqcECBAgQIAAAQIECBAgQIAAAQIESgko70txWZgAAQIECBAgQIDASAWU9yPlt3ECBAgQIECAAAECBAgQIECAAAECwxNQ3g/P1poJECBAgAABAgQIVC2gvK9a1PoIECBAgAABAgQIECBAgAABAgQINERAed+QgbAbBAgQIECAAAECBAoIKO8LIFmEAAECBAgQIECAAAECBAgQIECAwDgKKO/HcdTsMwECBAgQIECAwKQKKO8ndeQdNwECBAgQIECAAAECBAgQIECAQOsFlPetH2IHSIAAAQIECBAg0CIB5X2LBtOhECBAgAABAgQIECBAgAABAgQIEOgUUN53arhPgAABAgQIECBAoNkCyvtmj4+9I0CAAAECBAgQIECAAAECBAgQIDCwgPJ+YDpvJECAAAECBAgQIFC7gPK+dnIbJECAAAECBAgQIECAAAECBAgQIFCPgPK+HmdbIUCAAAECBAgQIFCFgPK+CkXrIECAAAECBAgQIECAAAECBAgQINBAAeV9AwfFLhEgQIAAAQIECBDIEVDe58B4mgABAgQIECBAgAABAgQIECBAgMC4Cyjvx30E7T8BAgQIECBAgMAkCSjvJ2m0HSsBAgQIECBAgAABAgQIECBAgMBECSjvJ2q4HSwBAgQIECBAgMCYCyjvx3wA7T4BAgQIECBAgAABAgQIECBAgACBPAHlfZ6M5wkQIECAAAECBAg0T0B537wxsUcECBAgQIAAAQIECBAgQIAAAQIEKhFQ3lfCaCUECBAgQIAAAQIEahFQ3tfCbCMECBAgQIAAAQIECBAgQIAAAQIE6hdQ3tdvbosECBAgQIAAAQIEBhVQ3g8q530ECBAgQIAAAQIECBAgQIAAAQIEGi6gvG/4ANk9AgQIECBAgAABAh0CyvsODHcJECBAgAABAgQIECBAgAABAgQItElAed+m0XQsBAgQIECAAAECbRdQ3rd9hB0fAQIECBAgQIAAAQIECBAgQIDAxAoo7yd26B04AQIECBAgQIDAGAoo78dw0OwyAQIECBAgQIAAAQIECBAgQIAAgSICyvsiSpYhQIAAAQIECBAg0AwB5X0zxsFeECBAgAABAgQIECBAgAABAgQIEKhcQHlfOakVEiBAgAABAgQIEBiagPJ+aLRWTIAAAQIECBAgQIAAAQIECBAgQGC0Asr70frbOgECBAgQIECAAIEyAsr7MlqWJUCAAAECBAgQIECAAAECBAgQIDBGAsr7MRosu0qAAAECBAgQIDDxAsr7iY8AAAIECBAgQIAAAQIECBAgQIAAgbYKKO/bOrKOiwABAgQIECBAoI0Cyvs2jqpjIkCAAAECBAgQIECAAAECBAgQIBBCUN6LAQECBAgQIECAAIHxEVDej89Y2VMCBAgQIECAAAECBAgQIECAAAECpQSU96W4LEyAAAECBAgQIEBgpALK+5Hy2zgBAgQIECBAgAABAgQIECBAgACB4Qko74dna80ECBAgQIAAAQIEqhZQ3lctan0ECBAgQIAAAQIECBAgQIAAAQIEGiKgvG/IQNgNAgQIECBAgAABAgUElPcFkCxCgAABAgQIECBAgAABAgQIECBAYBwFlPfjOGr2mQABAgQIECBAYFIFlPeTOvKOmwABAgQIECBAgAABAgQIECBAoPUCyvvWD7EDJECAAAECBAgQaJGA8r5Fg+lQCBAgQIAAAQIECBAgQIAAAQIECHQKKO87NdwnQIAAAQIECBAg0GwB5X2zx8feESBAgAABAgQIECBAgAABAgQIEBhYQHk/MJ03EiBAgAABAgQIEKhdQHlfO7kNEiBAgAABAgQIECBAgAABAgQIEKhHQHlfj7OtECBAgAABAgQIEKhCQHlfhaJ1ECBAgAABAgQIECBAgAABAgQIEGiggPK+gYNilwgQIECAAAECBAjkCCjvc2A8TYAAAQIECBAgQIAAAQIECBAgQGDcBZT34z6C9p8AAQIECBAgQGCSBJT3kzTajpUAAQIECBAgQIAAAQIECBAgQGCiBJT3EzXcDpYAAQIECBAgQGDMBZT3Yz6Adp8AAQIECBAgQIAAAQIECBAgQIBAnoDyPk/G8wQIECBAgAABAgSaJ6C8b96Y2CMCBAgQIECAAAECBAgQIECAAAEClQgo7ythtBICBAgQIECAAAECtQgo72ththECBAgQIECAAAECBAgQIECAAAEC9Qso7+s3t0UCBAgQIECAAAECgwoo7weV8z4CBAgQIECAAAECBAgQIECAAAECDRdQ3jd8gOweAQIECBAgQIAAgQ4B5X0HhrsECBAgQIAAAQIECBAgQIAAAQIE2iSgvG/TaDoWAgQIECBAgACBtgso79s+wo6PAAECBAgQIECAAAECBAgQIEBgYgWU9xM79A6cAAECBAgQIEBgDAWU92M4aHaZAAECBAgQIECAAAECBAgQIECAQBEB5X0RJcsQIECAAAECBAgQaIaA8r4Z42AvCBAgQIAAAQIECBAgQIAAAQIECFQuoLyvnNQKCRAgQIAAAQIECAxNQHk/NForJkCAAAECBAgQIECAAAECBAgQIDBaAeX9aP1tnQABAgQIECBAgEAZAeV9GS3LEiBAgAABAgQIECBAgAABAgQIEBgjAeX9GA2WXSVAgAABAgQIEJh4AeX9xEcAAAECBAgQIECAAAECBAgQIECAQFsFlPdtHVnHRYAAAQIECBAg0EYB5X0bR9UxESBAgAABAgQIECBAgAABAgQIEAghKO/FgAABAgQIECBAgMD4CCjvx2es7CkBAgQIECBAgAABAgQIECBAgACBUgLK+1JcFiZAgAABAgQIECAwUgHl/Uj5bZwAAQIECBAgQIAAAQIECBAgQIDA8ASU98OztWYCBAgQIECAAAECVQso76sWtT4CBAgQIECAAAECBAgQIECAAAECDRFQ3jdkIOwGAQIECBAgQIAAgQICyvsCSBYhQIAAAQIECBAgQIAAAQIECBAgMI4CyvtxHDX7TIAAAQIECBAgMKkCyvtJHXnHTYAAAQIECBAgQIAAAQIECBAg0HoB5X3rh9gBEiBAgAABAgQItEhAed+iwXQoBAgQIECAAAECBAgQIECAAAECBDoFlPedGu4TIECAAAECBAgQaLaA8r7Z42PvCBAgQIAAAQIECBAgQIAAAQIECAwsoLwfmM4bCRAgQIAAAQIECNQuoLyvndwGCRAgQIAAAQIECBAgQIAAAQIECNQjoLyvx9lWCBAgQIAAAQIECFQhoLyvQtE6CBAgQIAAAQIECBAgQIAAAQIECDRQQHnfwEGxSwQIECBAgAABAgRyBJT3OTCeJkCAAAECBAgQIECAAAECBAgQIDDuAsr7cR9B+0+AAAECBAgQIDBJAsr7SRptx0qAAAECBAgQIECAAAECBAgQIDBRAsr7iRpuB0uAAAECBAgQIDDmAsr7MR9Au0+AAAECBAgQIECAAAECBAgQIEAgT0B5nyfjeQIECBAgQIAAAQLNE1DeN29M7BEBAgQIECBAgAABAgQIECBAgACBSgSU95UwWgkBAgQIECBAgACBWgSU97Uw2wgBAgQIECBAgAABAgQIECBAgACB+gWU9/Wb2yIBAgQIECBAgACBQQWU94PKeR8BAgQIECBAgAABAgQIECBAgACBhgso7xs+QHaPAAECBAgQIECAQIeA8r4Dw10CBAgQIECAAAECBAgQIECAAAECbRJQ3rdpNB0LAQIECBAgQIBA2wWU920fYcdHgAABAgQIECBAgAABAgQIECAwsQLK+4kdegdOgAABAgQIECAwhgLK+zEcNLtMgAABAgQIECBAgAABAgQIECBAoIiA8r6IkmUIECBAgAABAgQINENAed+McbAXBAgQIECAAAECBAgQIECAAAECBCoXUN5XTmqFBAgQIECAAAECBIYmoLwfGq0VEyBAgAABAgQIECBAgAABAgQIEBitgPJ+tP62ToAAAQIECBAgQKCMgPK+jJZlCRAgQIAAAQIECBAgQIAAAQIECIyRgPJ+jAbLrhIgQIAAAQIECEy8gPJ+4iMAgAABAgQIECBAgAABAgQIECBAoK0Cyvu2jqzjIkCAAAECBAgQaKOA8r6No+qYCBAgQIAAAQIECBAgQIAAAQIECIQQlPdiQIAAAQIECBAgQGB8BJT34zNW9pQAAQIECBAgQIAAAQIECBAgQIBAKQHlfSkuCxMgQIAAAQIECBAYqYDyfqT8Nk6AAAECBAgQIECAAAECBAgQIEBgeALK++HZWjMBAgQIECBAgACBqgWU91WLWh8BAgQIECBAgAABAgQIECBAgACBhggo7xsyEHaDAAECBAgQIECAQAEB5X0BJIsQIECAAAECBAgQIECAAAECBAgQGEcB5f04jpp9JkCAAAECBAgQmFQB5f2kjrzjJkCAAAECBAgQIECAAAECBAgQaL2A8r71Q+wACRAgQIAAAQIEWiSgvG/RYDoUAgQIECBAgAABAgQIECBAgAABAp0CyvtODfcJECBAgAABAgQINFtAed/s8bF3BAgQIECAAAECBAgQIECAAAECBAYWUN4PTOeNBAgQIECAAAECBGoXUN7XTm6DBAgQIECAAAECBAgQIECAAAECBOoRUN7X42wrBAgQIECAAAECBKoQUN5XoWgdBAgQIECAAAECBAgQIECAAAECBBoooLxv4KDYJQIECBAgQIAAAQI5Asr7HBhPEyBAgAABAgQIECBAgAABAgQIEBh3AeX9uI+g/SdAgAABAgQIEJgkAeX9JI22YyVAgAABAgQIECBAgAABAgQIEJgoAeX9RA23gyVAgAABAgQIEBhzAeX9mA+g3SdAgAABAgQIECBAgAABAgQIECCQJ6C8z5PxPAECBAgQIECAAIHmCSjvmzcm9ogAAQIECBAgQIAAAQIECBAgQIBAJQLK+0oYrYQAAQIECBAgQIBALQLK+1qYbYQAAQIECBAgQIAAAQIECBAgQIBA/QLK+/rNbZEAAQIECBAgQIDAoALK+0HlvI8AAQIECBAgQIAAAQIECBAgQIBAwwWU9w0fILtHgAABAgQIECBAoENAed+B4S4BAgQIECBAgAABAgQIECBAgACBNgko79s0mo6FAAECBAgQIECg7QLK+7aPsOMjQIAAAQIECBAgQIAAAQIECBCYWAHl/cQOvQMnQIAAAQIECBAYQwHl/RgOml0mQIAAAQIECBAgQIAAAQIECBAgUERAeV9EqZ5l3gkh/Ll/DGRABmRABmRABsYgA/9TPadHtpIhoLzPQPEUAQIECBAgQIAAAQIECBAgQIAAgTYIKO+bM4qfDiEk/jGQARmQARmQARkYgwz8rDmnUBO3J8r7iRtyB0yAAAECBAgQIECAAAECBAgQIDApAsr75oy08t6HF3x4QwZkQAZkQAbGJQPK+9GdQyrvR2dvywQIECBAgAABAgQIECBAgAABAgSGKqC8HypvqZUr7xU241LY2E9ZlQEZkAEZUN6XOs2rdGHlfaWcVkaAAAECBAgQIECAAAECBAgQIECgOQLK++aMhfJeEaIMkwEZkAEZkIFxyYDyfnTnkMr70dnbMgECBAgQIECAAAECBAgQIECAAIGhCijvh8pbauXKe4XNuBQ29lNWZUAGZEAGlPelTvMqXVh5XymnlREgQIAAAQIECBAgQIAAAQIECBBojoDyvjljobxXhCjDZEAGZEAGZGBcMqC8H905pPJ+dPa2TIAAAQIECBAgQIAAAQIECBAgQGCoAsr7ofKWWrnyXmEzLoWN/ZRVGZABGZAB5X2p07xKF1beV8ppZQQIECBAgAABAgQIECBAgAABAgSaI6C8b85Y9Czvj1i+Jrntjm3+MZABGZABGZABGRh6Bs48+7p+H9BQ3o/uHFJ5Pzp7WyZAgAABAgQIECBAgAABAgQIECAwVAHl/VB5S628Z3m/9sSLk7c+9lP/GMiADMiADMiADAw9A7fc9rLyvtRpXK0LK+9r5bYxAgQIECBAgAABAgQIECBAgAABAvUJKO/rs+63JeW9MmboZYwPgPgAjAzIgAzIQJEMKO/7nbaN9HXl/Uj5bZwAAQIECBAgQIAAAQIECBAgQIDA8ASU98OzLbtm5b3yXnkvAzIgAzIgA43IgPK+7Glcrcsr72vltjECBAgQIECAAAECBAgQIECAAAEC9Qko7+uz7rcl5b3CphGFTZEZmZYxc1cGZEAG2p0B5X2/07aRvq68Hym/jRMgQIAAAQIECBAgQIAAAQIECBAYnoDyfni2ZdesvFfeK+9lQAZkQAZkoBEZUN6XPY2rdXnlfa3cNkaAAAECBAgQIECAAAECBAgQIECgPgHlfX3W/bakvFfYNKKwMZu23bNpja/xlQEZKJIB5X2/07aRvq68Hym/jRMgQIAAAQIECBAgQIAAAQIECBAYnoDyfni2ZdesvFfeK+9lQAZkQAZkoBEZUN6XPY2rdXnlfa3cNkaAAAECBAgQIECAAAECBAgQIECgPgHlfX3W/bakvFfYNKKwKTIj0zJm7sqADMhAuzOgvO932jbS15X3I+W3cQIECBAgQIAAAQIECBAgQIAAAQLDE1DeD8+27JqV98p75b0MyIAMyIAMNCIDyvuyp3G1Lq+8r5XbxggQIECAAAECBAgQIECAAAECBAjUJ6C8r8+635aU9wqbRhQ2ZtO2ezat8TW+MiADRTKgvO932jbS15X3I+W3cQIECBAgQIAAAQIECBAgQIAAAQLDE1DeD8+27JqV98p75b0MyIAMyIAMNCIDyvuyp3G1Lq+8r5XbxggQIECAAAECBAgQIECAAAECBAjUJ6C8r8+635aU9wqbRhQ2RWZkWsbMXRmQARlodwaU9/1O20b6uvJ+pPw2ToAAAQIECBAgQIAAAQIECBAgQGB4Asr74dmWXbPyXnmvvJcBGZABGZCBRmRAeV/2NK7W5ZX3tXLbGAECBAgQIECAAAECBAgQIECAAIH6BJT39Vn325LyXmHTiMLGbNp2z6Y1vsZXBmSgSAaU9/1O20b6uvJ+pPw2ToAAAQIECBAgQIAAAQIECBAgQGB4Asr74dmWXbPyXnmvvJcBGZABGZCBRmRAeV/2NK7W5ZX3tXLbGAECBAgQIECAAAECBAgQIECAAIH6BJT39Vn325LyXmHTiMKmyIxMy5i5KwMyIAPtzoDyvt9p20hfV96PlN/GCRAgQIAAAQIECBAgQIAAAQIECAxPQHk/PNuya1beK++V9zIgAzIgAzLQiAwo78uextW6vPK+Vm4bI0CAAAECBAgQIECAAAECBAgQIFCfgPK+Put+W1LeK2waUdiYTdvu2bTG1/jKgAwUyYDyvt9p20hfV96PlN/GCRAgQIAAAQIECBAgQIAAAQIECAxPQHk/PNuya1beK++V9zIgAzIgAzLQiAwo78uextW6vPK+Vm4bI0CAAAECBAgQIECAAAECBAgQIFCfgPK+Put+W1LeK2waUdgUmZFpGTN3ZUAGZKDdGVDe9zttG+nryvuR8ts4AQIECBAgQIAAAQIECBAgQIAAgeEJKO+HZ1t2zcp75b3yXgZkQAZkQAYakQHlfdnTuFqXV97Xym1jBAgQIECAAAECBAgQIECAAAECBOoTUN7XZ91vS8p7hU0jChuzads9m9b4Gl8ZkIEiGVDe9zttG+nryvuR8ts4AQIECBAgQIAAAQIECBAgQIAAgeEJKO+HZ1t2zcp75b3yXgZkQAZkQAYakQHlfdnTuFqXV97Xym1jBAgQIECAAAECBAgQIECAAAECBOoTUN7XZ91vS8p7hU0jCpsiMzItY+auDMiADLQ7A8r7fqdtI31deT9SfhsnQIAAAQIECBAgQIAAAQIECBAgMDwB5f3wbMuuWXmvvFfey4AMyIAMyEAjMqC8L3saV+vyyvtauW2MAAECBAgQIECAAAECBAgQIECAQH0Cyvv6rPttSXmvsGlEYWM2bbtn0xpf4ysDMlAkA8r7fqdtI31deT9SfhsnQIAAAQIECBAgQIAAAQIECBAgMDwB5f3wbMuuWXmvvFfey4AMyIAMyEAjMqC8L3saV+vyyvtauW2MAAECBAgQIECAAAECBAgQIECAQH0Cyvv6rPttSXmvsGlEYVNkRqZlzNyVARmQgXZnQHnf77RtpK8r70fKb+MECBAgQIAAAQIECBAgQIAAAQIEhiegvB+ebdk1K++V98p7GZABGZABGWhEBpT3ZU/jal1eeV8rt40RIECAAAECBAgQIECAAAECBAgQqE9AeV+fdb8tKe8VNo0obMymbfdsWuNrfGVABopkQHnf77RtpK8r70fKb+MECBAgQIAAAQIECBAgQIAAAQIEhiegvB+ebdk1K++V98p7GZABGZABGWhEBpT3ZU/jal1eeV8rt40RIECAAAECBAgQIECAAAECBAgQqE9AeV+fdb8tKe8VNo0obIrMyLSMmbsyIAMy0O4MKO/7nbaN9HXl/Uj5bZwAAQIECBAgQIAAAQIECBAgQIDA8ASU98OzLbtm5b3yXnkvAzIgAzIgA43IgPK+7Glcrcsr72vltjECBAgQIECAAAECBAgQIECAAAEC9Qko7+uz7rcl5b3CphGFjdm07Z5Na3yNrwzIQJEMKO/7nbaN9HXl/Uj5bZwAAQIECBAgQIAAAQIECBAgQIDA8ASU98OzLbtm5b3yXnkvAzIgAzIgA43IgPK+7Glcrcsr72vltjECBAgQIECAAAECBAgQIECAAAEC9Qko7+uz7rcl5b3CphGFTZEZmZYxc1cGZEAG2p0B5X2/07aRvq68Hym/jRMgQIAAAQIECBAgQIAAAQIECBAYnoDyfni2ZdesvFfeK+9lQAZkQAZkoBEZUN6XPY2rdXnlfa3cNkaAAAECBAgQIECAAAECBAgQIECgPgHlfX3W/bakvFfYNKKwMZu23bNpja/xlQEZKJIB5X2/07aRvq68Hym/jRMgQIAAAQIECBAgQIAAAQIECBAYnoDyfni2ZdesvFfeK+9lQAZkQAZkoBEZUN6XPY2rdXnlfa3cNkaAAAECBAgQIECAAAECBAgQIECgPgHlfX3W/bakvFfYNKKwKTIj0zJm7sqADMhAuzOgvO932jbS15X3I+W3cQIECBAgQIAAAQIECBAgQIAAAQLDE1DeD8+27JqV98p75b0MyIAMyIAMNCIDyvuyp3G1Lq+8r5XbxggQIECAAAECBAgQIECAAAECBAjUJ6C8r8+635aU9wqbRhQ2ZtO2ezat8TW+MiADRTKgvO932jbS15X3I+W3cQIECBAgQIAAAQIECBAgQIAAAQLDE1DeD8+27JqV98p75b0MyIAMyIAMNCIDyvuyp3G1Lq+8r5XbxggQIECAAAECBAgQIECAAAECBAjUJ6C8r8+635aU9wqbRhQ2RWZkWsbMXRmQARlodwaU9/1O20b6uvJ+pPw2ToAAAQIECBAgQIAAAQIECBAgQGB4Asr74dmWXbPyXnmvvJcBGZABGZCBRmRAeV/2NK7W5ZX3tXLbGAECBAgQIECAAAECBAgQIECAAIH6BJT39Vn325LyXmHTiMLGbNp2z6Y1vsZXBmSgSAaU9/1O20b6uvJ+pPw2ToAAAQIECBAgQIAAAQIECBAgQGB4Asr74dmWXbPyXnmvvJcBGZABGZCBRmRAeV/2NK7W5ZX3tXLbGAECBAgQIECAAAECBAgQIECAAIH6BJT39Vn325LyXmHTiMKmyIxMy5i5KwMyIAPtzoDyvt9p20hfV96PlN/GCRAgQIAAAQIECBAgQIAAAQIECAxPQOiNVcQAACAASURBVHk/PNuya1beK++V9zIgAzIgAzLQiAwo78uextW6vPK+Vm4bI0CAAAECBAgQIECAAAECBAgQIFCfgPK+Put+W1LeK2waUdiYTdvu2bTG1/jKgAwUyYDyvt9p20hfV96PlN/GCRAgQIAAAQIECBAgQIAAAQIECAxPQHk/PNuya1beK++V9zIgAzIgAzLQiAwo78uextW6vPK+Vm4bI0CAAAECBAgQIECAAAECBAgQIFCfgPK+Put+W1LeK2waUdgUmZFpGTN3ZUAGZKDdGVDe9zttG+nryvuR8ts4AQIECBAgQIAAAQIECBAgQIAAgeEJKO+HZ1t2zcp75b3yXgZkQAZkQAYakQHlfdnTuFqXV97Xym1jBAgQIECAAAECBAgQIECAAAECBOoTUN7XZ91vS8p7hU0jChuzads9m9b4Gl8ZkIEiGVDe9zttG+nryvuR8ts4AQIECBAgQIAAAQIECBAgQIAAgeEJKO+HZ1t2zcp75b3yXgZkQAZkQAYakQHlfdnTuFqXV97Xym1jBAgQIECAAAECBAgQIECAAAECBOoTUN7XZ91vS8p7hU0jCpsiMzItY+auDMiADLQ7A8r7fqdtI31deT9SfhsnQIAAAQIECBAgQIAAAQIECBAgMDwB5f3wbMuuWXmvvFfey4AMyIAMyEAjMqC8L3saV+vyyvtauW2MAAECBAgQIECAAAECBAgQIECAQH0Cyvv6rPttSXmvsGlEYWM2bbtn0xpf4ysDMlAkA8r7fqdtI31deT9SfhsnQIAAAQIECBAgQIAAAQIECBAgMDwB5f3wbMuuWXmvvFfey4AMyIAMyEAjMqC8L3saV+vyyvtauW2MAAECBAgQIECAAAECBAgQIECAQH0Cyvv6rPttSXmvsGlEYVNkRqZlzNyVARmQgXZnQHnf77RtpK8r70fKb+MECBAgQIAAAQIECBAgQIAAAQIEhiegvB+ebdk1K++V98p7GZABGZABGWhEBpT3ZU/jal1eeV8rt40RIECAAAECBAgQIECAAAECBAgQqE9AeV+fdb8tKe8VNo0obMymbfdsWuNrfGVABopkQHnf77RtpK8r70fKb+MECBAgQIAAAQIECBAgQIAAAQIEhiegvB+ebdk1K++V98p7GZABGZABGWhEBpT3ZU/jal1eeV8rt40RIECAAAECBAgQIECAAAECBAgQqE+gTeX9fSGEq+qjq3xLynuFTSMKmyIzMi1j5q4MyIAMtDsDyvvKz/OqXKHyvkpN6yJAgAABAgQIECBAgAABAgQIECDQIIE2lfc7QghJCOG9EMLxDTIuuivKe+W98l4GZEAGZEAGGpEB5X3R07eRLKe8Hwm7jRIgQIAAAQIECBAgQIAAAQIECBAYvkAby/tY4P9NCOHdEMLi4RNWtgXlvcKmEYWN2bTtnk1rfI2vDMhAkQwo7ys7vxvGipT3w1C1TgIECBAgQIAAAQIECBAgQIAAAQINEGhreR8L/PjvL0II8RjnNMC63y4o75X3ynsZkAEZkAEZaEQGlPf9TttG+rryfqT8Nk6AAAECBAgQIECAAAECBAgQIEBgeAJtL+/TEv/fhBAuHB5jJWtW3itsGlHYFJmRaRkzd2VABmSg3RlQ3ldybjeslSjvhyVrvQQIECBAgAABAgQIECBAgAABAgRGLDAp5X0s8f8+hPCVEMLyEZvnbV55r7xX3suADMiADMhAIzKgvM87XWvE88r7RgyDnSBAgAABAgQIECBAgAABAgQIECBQvcAklffpLPy/CiG8HUJYVD3njNaovFfYNKKwMZu23bNpja/xlQEZKJKBlpf3K0MI82Z01jbaNyvvR+tv6wQIECBAgAABAgQIECBAgAABAgSGJjCJ5X1a4v9ZCOG+EMKsoemWW7HyXnmvvJcBGZABGZCBRmSgpeX9ghDC9hBC/CDn4nKnaY1aWnnfqOGwMwQIECBAgAABAgQIECBAgAABAgSqE5jk8j4t8X8SQji1OtKB16S8V9g0orApMiPTMmbuyoAMyEC7M9Cy8j5+UPPWEMKfhhDS8z/l/cCnrN5IgAABAgQIECBAgAABAgQIECBAgMCwBJT3v/gj7s9DCF8MIRwyLOgC61XeK++V9zIgAzIgAzLQiAy0qLw/PYTw047SXnlf4KTUIgQIECBAgAABAgQIECBAgAABAgQIjEZAef8PM7DiH3P/8oPLqe49guFQ3itsGlHYmE3b7tm0xtf4yoAMFMlAC8r7Q0MInwkh/F1GcR/P+cy8H8HJrk0SIECAAAECBAgQIECAAAECBAgQINBbQHnfXd6ns7H+Qwjhmt50lb/a6vJ+46bPJw898ok9/m17/dsK6xF9aGH3Oz/eYzziGD3y6LvGZERjUqRQa9MyMjjZJfLrO76X+TvoyWd+w++gBvwOGuPyfm4IIZ7f/rec0j4911PeV34qa4UECBAgQIAAAQIECBAgQIAAAQIECMxUQHmfXd6nf9j9YQhh7UyRC76/1eX9CWsvTk27bu+9/y0lzYhKmjd3/1HXWKQlx7x5843JiMakTcV8kWORwcku7x9/6tOZv4NWrjzJ76AG/A4a0/L+hhDC/57+96zPrfK+4AmqxQgQIECAAAECBAgQIECAAAECBAgQqE9Aed+7vI/Fwt9+cNnVg4Y8LEMr75965jeTuv49s+lzmaWL8r55JZ3itHljUqTwbtMyMjjZGVTeN3v8x6y8PzaE8Ad9yvrpHxZR3g/5xNbqCRAgQIAAAQIECBAgQIAAAQIECBAoL6C871/ep3/s/b8+uAzrnPLMhd4xtPK+5B+z0+Md6Hbhwv2V9w2YMVmk4FWcNrs4KzKG476MDE52BpX3zR7/MSnvl4QQ3g4h/I8BznWU94VOTy1EgAABAgQIECBAgAABAgQIECBAgECdAsr74uV9Wmb/SQjhqiEMkvJ+TErvcS9M0/1XnDa7OEvHqc23MtjMDD698bPJTete2OPfE099JvPDWYNmVHnfzPFPx7Ph5f1eIYQnQwh/MUBpn57PKe+HcDJrlQQIECBAgAABAgQIECBAgAABAgQIzExAeV++vE//6PvtEMKqmfF3vVt5r7yvtBhLC5i8W8Vps4uzvHFr0/My2MwMXnnVI+l/57puL7rkrkp/Rynvmzn+6e+YBpf3l4YQ/u0MSvs018r7rtNQDwgQIECAAAECBAgQIECAAAECBAgQaIKA8n7w8j7+8TdepvXdEMK+FQym8l55X2kxlhYwebeK02YXZ3nj1qbnZbCZGVTeN3Nc6v7Zb2B5vzyE8MUKSnvlfQUnrVZBgAABAgQIECBAgAABAgQIECBAgMBwBJT3Myvv0z8A/+cQwoYQwuwZDJPyXnmvvJeBWjNQdxk4fXvK+2aWxMr7Zo7L9J+fYT9uUHm/KITwVgjhryss7uP5m5n3Mzhp9VYCBAgQIECAAAECBAgQIECAAAECBIYjoLyvprxPS/x/HUI4b8Chqr28/9CxZyd33bOj0n/3PfBWZvn42JOfytzOy9t+L3P5YZcS1v/TRHGqoBv1z4EMNjODdZX3r27/TuZ/Fx559F3/XWjAB5kaUN7PCiHcGkL4TxWX9uk5m/J+wBNWbyNAgAABAgQIECBAgAABAgQIECBAYHgCyvtqy/v4B+G/DyF8PYQQL+9a5n+1l/fnnb9OQdKAgmRUBaritJnF6ajyMIrtymAzM1hXeT+KzNlm8cyNuLw/I4TwL4dU2ivvy5ydWpYAAQIECBAgQIAAAQIECBAgQIAAgVoFlPfVl/fpH4X/MoSwPYQwv+CIKu8nuEgfRaGkOC1eYo1ifCZhmzLYzAwq75s5LnX/ThhReX9YCOEzIYSfD7m4j+dqZt4XPEG1GAECBAgQIECAAAECBAgQIECAAAEC9Qko74dX3qclfrzc630FhlR5r7yv9UoIilMFXd1l4PTtyWAzM6i8b+a4TP/5Gfbjmsv7eSGEeE7632so7dPzM+V9gZNTixAgQIAAAQIECBAgQIAAAQIECBAgUK+A8n745X36R+I/CiGc1GN4lffKe+W9DNSagWGXf/3Wr7xvZkmsvG/muPT7ear69RrL++tCCP+xxtI+PS9T3vc4KfUSAQIECBAgQIAAAQIECBAgQIAAAQKjERikvI9/ZD21gf9+fQR/+E3/AFz09m8/uBzs0ozhVt4rbmstbhWnCrqqy76y65PBZmZQed/McSn78zXT5Wso79eEEL43wnM35X3GyainCBAgQIAAAQIECBAgQIAAAQIECBAYrcAg5X3Rotpy+bP6/+sHl4fdq2P4W13ev7HzB8lrb3x3j3873/rjUoX1m7vf32Mdcb07dr2Xu54XX/p6cu31zyQnn3JZsuzQo5L9lyxLFi7cPzn44BXJylUnJ6ed/pHk9jtfS7a9/p3cdcy0BEnfv/udHyfPbPpcsu7Wl5KLLr4zOW7NucnhRxybHHjg4cmiRQckixcvndrHVatPSdaeeHFy2RXrk/UP/eMklpzpOqq6bUNxuuvtHyUbHvu1JJaNJ5384eSI5WuSAw44LFm4cHGyaNGS5MADj0hWrT45OfOsa5NYRL287fdm5PjGzvcy8xfzXdW4TF/Pjl0/zNzm9h3fG2ibbczgrrd/XKnR9DGIj/PGfudb7w80DtO3ETP00COfSK697unkrLOvn8pt/H0V87zPPvtO/Y444ojjkmOOOTM5/YyPTv1Oi79Lpq9nkMev7/hel9+HL7s/87/f511wS9dyWb/TO5+L6+21P/Hnt3P59P72N7/f83291pn32qvbv5Pccdf2JB7DMR86K1m69Mhkv8VLp2yXLFmWLFu2Ojn+hAuSeOzrH/54Ev9bk7euQZ4f5Fjjf5Pi760zzrwmiWMfs7BgwX7JQUuXJytWrE3WnnhJcuPNm5MXX/5GpfuaHt8Qy/sDQgjvhhD+boTFfcy48r7jJNRdAgQIECBAgAABAgQIECBAgAABAgSaIaC8zy/YM8uLiv/Q/O9DCFd/EIVWl/cnrL040/Pe+98qVTpcd8Ozmes5+5wb91jPE0//enLscecms2bNynzP9LGcPXt2ctLJlyXPb/nqHutKy4xBbre++q3k+hufTdYcf/5UUTR9u0Uez5s3f6qw27jp85Xt2ziX9y+89LXkvPPXTZX0RfzSZWbNmp0cfcwZyYOP/MpAjnEc03V13u677wFJLOcGyUe/9xx73DmZ24wfOun33vT1tmfw2c1fzDSK5XdqMNPb6N055un92+98deBtPPnMbySXXHpPcuSRJySzZ8/JXH+6nbzbJQccmlz5kQ1Jv6K81/HHdeStfybPx/X22u7jT306c7srV57U83291jn9tQ2PfTJZc/wFyZw5e2VuK+/45u+zKDnn3JuS+OGv6esc5PFjT/yzzO0vX75mj/XHbcYPaMyZMzfzPVn7HD+kFLcxyL7lvWcI5X38wOKGEML/mXUMI3hOed+M/y9iLwgQIECAAAECBAgQIECAAAECBAgQ6BBQ3o+2vE//MP/tEMKXe/3hOs7CzvsDe7/n89Yby89+763q9TrL+zib//wLb0tiUZt37L2enzt37+SW27bO2CaWRkcdfVoSPxTQa3tlXovHdObZ1yVVzEwdx/I+FpTnnnfzwEVnp3X8YMdLW3+n1DjH2bt77ZVdqN2//mOl1lXkZ2vrtm/mHusjj77bd3uTksFxK+8/8tHHpq4K0ZnHmd6PV5u49fZX+mYiK3dtLO+3bP3tqSubzNQ1lv4XX3J3Eq+AkWVX9Lmi5f0NNz2XxA9rDbLf8b8PcV/j1TWK7lev5Sou7y8LIfyvgxzXEN+jvO/4PwTuEiBAgAABAgQIECBAgAABAgQIECDQDAHlfTPK+/iH+p6Xj1Xe/+I7iPvNvI+XEo+FeRV/7I+X2u9VbPR77bLLH6hkP7KOJV7yeaaXSh638v7Jp3892X//Qyo1jV+f8Ojjnyw1zvFnMWtM4mX7+2Wi7OtXX/NE5rbiZb6LFHSTksFxK+/j1zlkZaiK5+JXRMSvESiTtbaV9/HKGvHy8lV4pus47PBjkviBgDKuncv2K+/jz/M5595YyT6ffsbVye53fjLwvqb7XVF5f3QI4eupY8NulffN+P8i9oIAAQIECBAgQIAAAQIECBAgQIAAgQ4B5X1zyvuef7RX3vcv72MZvfqoaor7WDDEWYyPPfmpgQuQYRancf9ikR0vh54WLWVvx6m8v3/9O7kz3mdaBs2dOz95eEP/Weypb9yXrG3utde8qe/vTper4vaQQ1Zlbitmq8j6JyWDyvvQlZNY3hbJR7pMm8r7+FUsZS+Rn/XznPXc4v0PTuJXdqRuZW77lffxiiJZ2xz0uRtu2jzQfnYe0wzL+4UhhO0hhL8e9BhqeJ/yvuP/ELhLgAABAgQIECBAgAABAgQIECBAgEAzBJT3yvsZ/4G/84/9effruGx+nHGa9cf+Qw89Orno4juTG29+Prnnvl3JvffvTm5e92Jy1tnXJ/vtd1Dme9L1HHzwiuTN3e8PZJRVnM6aNSs5+JCVySmnXpFc/dHHp/YlXv786Y2fTZ57/ktJ/A7oBx78WHLV1RuSNcef3/c7j+P3HBeZgZ01LuNS3j/0yCf6Ohx44OHJ2efckNx2x7bkkUd/Ndm46fPJM5s+l6x/+OPJNdc+lRx19Ok9v75g/vyFU/5ZTtOfi99tH7/jPs1I5+2NN8+8MEu3F78PvXPd6f2YoaIF4kwyGL9HfVwy2IbyPn6IZMXKE6e+Zz3+foq/B2LhG48t5jleIeLOu7cnF150RxJngad5yLstU95e+uF7p34fxt+J8d8Ry9dkrj/+Lk2XKXIb15vmOeu26u+8f/zJT/X9kE+82kb8Lvs7734jefSJf5o8+9wXpj68E4vqE9Ze1Pf98aoJr73x3Z7HlXWsvcr7OFZZ4xivsBFn419/46apsY8fHIr7eeHFdyZLD16R+Z50PXvvvWBGVwqIxzBgeT8rhHBfCOHP031p8K3yvhn/X8ReECBAgAABAgQIECBAgAABAgQIECDQIaC8V96XLiGyiol+zw27vD/ggMP2KDLWHH/BVPHVa992vvV+8tFrnuhZ2MTipNc68l7rLE5Xrjo5iZfhL3vZ5Vde+/3kw5fd33Mm6U3rXhho/8ahvN/84leSffbZd4+xTcugww7/UBJn2hb5AEMs6Y455szcdS079Khk51t/XMgyFqjpPnTeHnnkCYXen5eZzufPPW9d5jZWrT6l8DaqyuCll93X6AyOa3kfv9v8lFOvTO6+d2eyY9d7hcc15iR+jUSvPMfyduu2b5ZaZ5q/K696JDN7F11y10DrS9c7/bbK8j4W6vstXpq53/FnNF4ZI35gqt/P+LbXv52cfMplueuJ64r/bZl+LP0e55X3++53YDJ37t5d2zviiOOShzf8k57biJfFjx/mWLQo+4NEcT/POPOanuvot88DlPdnhRD+VefvxIbf/0kI4f0x/fenA9gu6jj/d5cAAQIECBAgQIAAAQIECBAgQIAAgYYKKO+V9zP6436/P/6nrw+7vO/8I/bs2XOSdbe+VOq4HnjwH+XO7o4FcXocZW4vv+LB5MSTLk2e3vjPB3p/57aeefazuVcJiJdyjh9C6Fy+yP2ml/exkF+xYm1XqZWOc5x9fv4Ft5Y+7rjOCy68LXOdcd2x3CtiFz8IkO7L9Nt4BYUi6+i1TBzPhQsXZ24jFmq93tv5WtUZjEXj9OONj0edwXEr75cfeXxy2RXrk1e3f6fwWHaOa3o/FrjXXvd0En8essYlZj1dtsztOJb38UoAWQbxuTjbfuNzXyhlcfMtW3Jd4zrvumdHqfXllffT9zmOWZEPI6XjGX/f5F0JZN68fZI3dv6g1H6m6423Jcr7w0MIXwwh/P304/G4+ystRuyhvG/o/xmzWwQIECBAgAABAgQIECBAgAABAgQ6BZT3E1zef+jYs6cKiFhCzPRfvDx55x/9p9+vq7yPJVacxTp9+0UexxnueX9Yj5dgL7KOzmX6zfDsXLbI/U3PfymZv8+izH28577yx9z08j5eOjxvPOKM8iJmecvES+xnrXvBgv2S7Tu+V2jdcXZs1jouubT3pcLz9qnz+TieWeuOM7W3v/n9QvsX11d5Bjf/VhK/YiBr30aZwXEr76sel49e+2TmmMTfF/HnvDNbRe6PW3m/+YUv534tRiyw41UKihz39GXyXGP+4+Xzd73948LrLVLexytcTN+HIo/jV8Fk/UzG58p82Gf6tgqU938WQtgSQvh/87bveeV95//pcJ8AAQIECBAgQIAAAQIECBAgQIAAgf4CyvtmlPf/WwjhB73+yL32xIsH+qN+/GN8r/VW9Vr8Hvfpf/jvfFxXeR9nY3dut8z9Hbt+mMTyNsvkuus3DrzeMvvQb9k4yzZr/+Jlnvu9d/rrTS7v39z9/tRs7qxjXXviJUmccTz9eMo8jsd+yCGrMi1vuOm5Quu+/sZnM9+/ePHSUjNns/Y7XpY769hPO/0jhfYta51VPXdNAzM4buV9VWORrif+PMTZ/FmZue+Bt0tnZtzK+zPPujbz2KPHoF97ktoee9y5ueu+467thW37lferjzq11IcB0v1Lb1esPDFzP087/erC+5iuK70tUN7/XVbmPNeowr4zF2be9///RZYgQIAAAQIECBAgQIAAAQIECBAgMHIB5f1oy/u/DCFsDyHsHUL4dK8/eCvvfzpVQFx3Q3ZhGu0WLFhc+nuj05Iivc279PJMCpB03VXcxsupZ8183n//Q0oXNE0u72MplvXzEL8b+qWtv1P6WLPs4/dFZ20jlqBZy09/Ll7yfK+95mau48FHfqXQOqavMz5+5bXfz/1++UcefXfg9WZta5DnmpjBSS/v4zjec9+uzCxecuk9pTMzTuX9jl3vJXvvvSDz2ONXnpSZHZ/187D5xa/k/pwfc8yZhW17lffxijGbNv9W4XVl7edN617INFi2bPXA6y1Q3mduM+v3qucaUegr70f+f7vsAAECBAgQIECAAAECBAgQIECAAIH+Asr70ZT38Xth4/fDLusYIuX9x35R0GcVE+lzvcr7QS85nK473uZdrnzZoUcNXIB0rr+K+yeedGlmYbJ12zdL7WOTy/tjPnRW5jGed/66UsfYyzuWevG72qeXSrNmzU62vf7tQtuJH6qZ/v74+JRTryz0/qz9u/b6ZzLXuWTJshnP6M/a3iDPNS2DyvufTn23edaHSeKM7rJjPE7lfd6HFuLPYfyATtljz1r+1NOuyvyZ/MXviu8U2kav8v7Y484ptI6sfUuf2/LyNzL3cfbsOQN9dUJcr/K+EYV75rhm/XenwHPK+46TbncJECBAgAABAgQIECBAgAABAgQINFXg+BDCQy359+0Cf7is8o+gg67rX4cQzskIhPJ+huX9xue+MOMC5PktX80c1zirPy1JRn172RXrM/fx3vvfKrWPTS3v43e6z5mTPaP98Sc/VeoY+43V6Wd8NNMyFoL93htfv3/9O5nvnzt3frJ9x/cKrWP6dg47/JjMdV52+QMDrW/6+qt43LQMKu9/8cGng5Yu3yM78Tvfy37NxDiV9+ece+MexxzPBeIVSuJXoVSR93gljbzzizvvfqPQNnqV97fevrXQOnodSxzj+fssytzPF1/+xkDrV94r7zPOVT1FgAABAgQIECBAgAABAgQIECBAgACBwgI78v643pDn/yyEcF8IYVbOESnvZ1De77PPvpXMSt79zo+TeAnj6ZmJsxd7FSd1vnbDTZv32L+4v/H5MvvR1PI+rxBftOiA0iVkP4+8cqpoUb7r7R8l++57QOZ4rLtlS6nxiPu6cdPnM9cVM/nCS18rvb5+xz/o603LoPL+F+X9ypUnZebn9ZIfJBmn8v7Qw7I/7HLKqVdU9vMSr9IRP8A1/b8L8fH5F9xaaDu9yvv4obFBfxY735f3wZ9nNn1uoPXn/X7McvDcWBT9Zt7nnIB7mgABAgQIECBAgAABAgQIECBAgACB4Qg0tbz/qxDC2yGEffscdu3l/UknX5Y89cxvVvIvlmedJcL0+yeszb68eNnZ4nmXzV+1+pSe25++P70e531/8hs736tsG3H78bvNH3rkE8n1N25Krrr60eTiS+5Ozj3v5uSss6/v+S8ea1ZRctXVG0rtX1PL+8uvfCjz+I4+5oxSx9drjNPX8mbUxhn56TL9bi+86I7M/V256uTC60i3ccFFt2eua9Xq8utK19nrti0ZbFN5H4vizS98Obn73jeTa659Kok/DxddclfP3wnp74z9Fi/NzE+8pHqvHEx/bVzK+/jhmayvCoi/H6+57ulSxzzdYPrj+P32Wb93Vx91WqHt5JX3e89fWOj90/cn63Hefxs2PPbJgbahvB+LQj4zl1lZDSEo7/uciHuZAAECBAgQIECAAAECBAgQIECAAIFqBZpY3sdL+a8qeJi1l/dVfn94VpHQ+dywy/u4/s7tzeT+vvsdmPnH8Fde+9aMtrHzrfeT+9d/LDn1tCuTRYuWZG4j5w/uhZaNBV+Z425qeX/SyR/OPN4zzro2ee2N71b679HHP5m5reNPuLCw5bPPfSFzHXG2fJkZtbGIjFcXyMpALNHKjG3esm3N4LiX9/FDFPFDPPFDGnPn7p2ZgaxcFH2u7MzrcSnvX972e7lWjzz6q5X8zKQ/S/H3a5b3gQceXmg7eeX9kiXLCr0/3Y9et8etOTdzHx948GMDbaNAef/zLBPPNbb0V94XPCG3GAECBAgQIECAAAECBAgQIECAAAEC1Qg0qbz/NyGEi0oelvJ+BpfNP+PMawYqJ7KKkMU5s1e3vjpYeR+/d/nKj2xIFi7MvuxyVUXH2efcWMqgqeX9kSvWZhZQVTkVWU/Zme6HH3Fs5j4Xvfx+zGHe1wXMmzc/2f7m90uN7fRctz2D41reb37xK0m8Asrs2bMz81Mkq0WWKTvzICw8UQAAIABJREFUelzK+02bfyvX7aWtvzOjn5npP0M3r3sxc1sLFuxXaDt55X287P/0bQ36eM3xF2TuY/zQ2CDrLFDe/+cQwrshhL8tkkPLjLzUV96XPDG3OAECBAgQIECAAAECBAgQIECAAAECMxNoQnn/FyGEjSGEOQMcivJ+BuV92eK6V5FRZXkfv8N86cErMguVqouMM8++rlRB09Ty/oADDqvFq5f/smWrS1lef+Ozmfu85IBDk93v/KTQutaeeEnmOk47/SOF3p+X6UnI4DiW9zfevHkos+yzcv3Io++WytC4lPd5hXg0eP2NPyx1zHk/P+nzd979RubPZ/zgxe53ftx3W3n7unz5mr7vTfeh3+0IyvuffXCuc1wI4Q+zsue5kRf2nblV3g9wcu4tBAgQIECAAAECBAgQIECAAAECBAgMLjDK8v5vPph9tnjw3Q/K+5aV9489+alk770XdP7hfKj321Le77tv9qXj6yyBDj54RalC7dXt38n97u0Nj/1a33W9uv0Pct9ftnjtLPhiYTgJGRy38v6SS+8d6u+C6T8rZTM0LuV9nFE+/Vjj4/iVFbve7l+od/6s9Lu//uGPZ24rbq/IBwVaXt6npz7XhRD+Y9aYNPS5fx9C+JMJ+rcgHSi3BAgQIECAAAECBAgQIECAAAECBAgQqENgVOX9eyGEEyo4QOV9i8r7l1753WTBgt6Xyd97/sLkQ8eencRLq996+9bk4Q3/JImXgX7hpa8lW7b+du53u+fN8m5LeR8vQz3qoqdseR+Lv7UnXpy536ef8dG+5f0NNz2X+d74fdhFZvVmFY9FMji/JRkcp/J+3S1bMse6M/Pxig2nnnZlcs21TyVxxnf8INDzW74y9bshfn3Ha298N/PfqtWnZK67reX9Pfftyjze2bPn9P2Zy/qZ6fVcNOwco877r7z2+323NyHlfTwVmvfBFYj+e6dRQ+/P5AOXFZz2WQUBAgQIECBAgAABAgQIECBAgAABAgTaLVB3ef8fQgjXVkiqvG9ReX/8CdnfPRwLjEMPPTq58+7tya63f9S38Mkqk/K+e7kt5f2++x2YWZIdfcwZyVlnX1/Lv8uuWF96bPK+sz7OfH9j53s91xcvnZ1VbsUPdmRloMhzk5TBcSnvX3ntW8m8eftkjnWcLX7qaVclzzz72YHHfPVRp2Wuu63lfa9Cfceu3j9zRX6GOpe59/7dmbbx53bnW+/3HbMJKu/T06LDQgifCSH8POt3W0OeU96no+WWAAECBAgQIECAAAECBAgQIECAAAECQxCoq7z/v0MIW0IIe1d8DMr7lpT3sXzLKybi95e/ubt/0dNZGk2/f/U1T2Suvy3l/YEHHp55fNfd8Gzfgmy6VZ2P44cx8i75f9sd23L3fdPzX8o83ljmPr/lq7nv63Vsk5bBOsr7OBM+6+f69jtfLTxGF11yV+Y65syZm9x1z47C68kb+0MPOyZz/W0t75/emP+7Nl6hIM9pkOdvvf2VTNt58+YX2s4ElvfpKdIZIYR/mfWz04DnlPfpKLklQIAAAQIECBAgQIAAAQIECBAgQIDAEASGXd7H2WNfDCEcMoR9j6tU3rekvD//wtsyS57DDj+m0AzNfsXSJZfek7n+tpT3q1afnHl8F1x4W6GSrJ/fMF+/8KI7Mvf9qKNPz933iy+5O/M90WHQfZ20DOaW98tWD2w43X7N8ednjlPR8j5+/UHehzvid8xP394gj/ff/5DMfWxref/iy9/IPN5YCm/c9PlKTNNxuPqjj2dua/HipYW2M8HlfTy/mRVCuDWE8J8aUNh3jqPyfkgntFZLgAABAgQIECBAgAABAgQIECBAgACBKDDM8v5HIYRThsysvG9JeX/IstWd5cAv799975uFSp60LMq7PfmUy3+5zs4ipC3lfbw6QedxpfePW3NuJX55rlU8/+xzX8jc91mzZidbtv72Hvu/6+0fJ/stXpr5nltue3mP5Yvu46RlMH4ffJqTztuDDjpiYMPp1itWnpi5jaLlfSyTO/ctvb/3/IXJm7v/aMb7Ga/osddeczO30dby/o2dP0jiFSpSy87bXle7mD62RR6fcuoVmds57PAPFRq7CS/v09OnhSGE7SGE/69zrEZ4X3mfjoxbAgQIECBAgAABAgQIECBAgAABAgQIDEFgGOX9z0II930wa2wIu9y1SuV9C8r73e/8JImXwJ5eRsyZs1dS1XcwL1mybI/1x+21pby/9rqnM48vfnf8TL9yoEhJN9NlDj/i2Mz9z5pdvf7hj2cuGy/Fvf3N7xcqBafv7yRmcNvr3850jDPdp/sM+njf/Q7M3EbR8j5+eGf674X4+MSTLq1kHx978lOZ64/baGt5H8dy/5zfh1VfqePgg1dk+p562lWFxk9533W+s/yDKxllmmb9nAzpOeV917B4QIAAAQIECBAgQIAAAQIECBAgQIAAgWoFqizv/zKE8GoIYZ9qd7Hn2pT3LSjvX3/jDzPLiDi7etDSsPN9L73yu5nrj8VGW8r7x5/6dO4xPvjIr1Ti2Gla9f3rb3w2c/8PWro8icV65/byrqIQrz7QuVyZ+5OYwfihjqxyL36QZudbfzywZer+/JavZq4/brNoeX/DTc9lruOii++c8f7F/bz6micy1x/3sWx5f9XVj2au68KK9jV1zftZX7nypMImx59wYea+LqvwKxO2vPyNJF49Iytj117/TKF9Vd5nnv9cGkL4t1muNT2nvM8cFk8SIECAAAECBAgQIECAAAECBAgQIECgGoGqyvtvhxBWVLNLpdaivG9Beb/5hS9nFjwHH7KyUMGTllp5t5df+VDm+mPR0ZbyPn43+KJFB2Qe50knX1aJY55vFc+/uv07mVdfiGMUZ0en23h9x/eSuXP3zjzOsmVrus54O6kZjFcryCr8Nj73hV+adzqVuX/DTZsz1x23V7S8v/yKBzPXkXVFhjL7FpeNHwrJmxke97FsnvI+gHLueetmbNl5bFWU93n7Go+7qu+9v/IjGzLHLm4jflVG5zHl3Vfe554Pzf7gCkf/JXrW/E95nzssXiBAgAABAgQIECBAgAABAgQIECBAgMDMBWZa3v/PIYQLZr4bA69Bed+C8j5+r3lW+VDF5bt3vvV+knfp7rjNtpT3sfyKJWGWY5z9WkUZm1ewVfX82hMvztz/s86+/pdF383rXsxcJn4tQvwAw6D7MqkZPHLF2kzPm9a9MLBlHIM4FktzLpkeM1q0vM+bzV7F5d0fzPn6hfRnqGx5f+vtr2RazuSKEFl5rqK8f/Glr+d+7/1558/8wwbxyg0HHnh4pscBBxy2x9U0so4zPqe873tutCSE8HYI4X+kua3hVnnfd1gsQIAAAQIECBAgQIAAAQIECBAgQIAAgcEFBi3v/zyEsD6EEGd/jfJ/yvsWlPc7dv0ws+SJJcRLW39nRiXiZZc/kLvuuP42lffPPf+l3EJu1epTKrkUel7JVsXz969/J3Os5u+zKIkZidtYsfLEzGXiOM9kHyY1g+eed3Om51FHnzYjz9vueDVzvWmxWLS8X3fLlsz1rFx18oz2783df5QccsiqzHWn+1i2vL/vgbcz1zfTfZ2e6yrK+7jOuF/psXbezpmzV7Lp+S/NyPejPb6OIF5NYfox5T1W3hc+vTo2hPAHneM4xPvK+8LDYkECBAgQIECAAAECBAgQIECAAAECBAiUFyhb3v9NCOHdEMJ+5Tc1lHco71tQ3sfiJs6yzyobrrp6Q+GiZ3oB9MRTn0lmz56Tud50W20q7+Px530ffDzeWNRON6rq8a63fzTjdcd15F36/467tiebX/xK5ocTZs2alcTvV5/psUxiBm+7Y1vmz0e8WsOTz/zGQKZxVvf8+Qsz15v+3BUt7x/e8G7meuLP9UzG/MKL7shcb7p/8bZsef/CS1/LXOfeey9I3tz9/kCWWZmuqry/+96dmfsbj331UacO/GGf57d8Jdk7Z/zjV15sffVbhS2U96XPmy4PIfxJZ46HcF95X3pYvIEAAQIECBAgQIAAAQIECBAgQIAAAQLFBcqU9++FENYUX3UtSyrvW1Len3ralZlFUpx1HcvArBKr13NPPv3ryYIFizPX2VlmtK28j5d/j2Vh5zF23j/7nBsrLRIffeKfJmuOvyC57vqNpccoa/zyStUPHXt28uHL7s88rlWrZzYLO92PSczg9h3fS+bOzf7e+8OPODbZseu9UuMaC/WDli7PHKfOHBYt72Ppnbd/x605d6CvSrjiqof77l/c17Ll/e53fpL7oYV4Sf00ZzO9raq8j19tcOihR+danHLqFaV9X37ld5MlBxyau86yX3egvB/oPGpuCGFjCOG/df7MVXhfeT/QsHgTAQIECBAgQIAAAQIECBAgQIAAAQIEigkUKe//XQjhimKrq30p5X1Lyvu77tmRW/jEMnDzC18uVH7FAu2W217eo8COM4mzyou2lfexGMybTZ0efyxlyxaTnYXjK699K7nx5s1J5/elX3vd04XGp3M9Wfc3PveFzHGaPXt27tUZ4nhnravsc9Vl8MdjlcHTTr860zzmZfVRpyXbXv9OX9/4c3fn3W8kixYt6VpX3pUvipb3cQyPP+HCrnWmOY63Z5x5TRIvgV9krF/d/gdTy3e+P97P+90wyM9I3r7GmejxKwDSr38osr95y1RV3sf1P/bkp3KPP9qsOf78ZOu2bxby3fDYr/Us7vdbvDSJHxbJO66s55X3MzqnOjSE8LkQws+nZ36Gj5X3MxoWbyZAgAABAgQIECBAgAABAgQIECBAgEBvgV7l/X8NIWwJIczrvYqRvqq8b0l5v+vtHycHH7wit6SbN29+csml9yYvvvyNzPLn1e3fSeLs1uVHHr/HOubMmZucd/66PZ6PBUYby/tYgl148Z2Zx9tZ2ixfvmZqxvwzz3625wzbOJs2fh/9FVc+lKxafUoSi/TO9cT7VZX3cd/jhwumrz/vcczF9je/n5mJrDKw13OVZXD5mj32v8kZfGbT53p+vcQ+++w7NfYbN32+yzl6xe9Gj2N/xBHH7XHMyw49KjnxpEv3eD6OZZnyPl6+P341Ql4G4u+NW2/fmryx8wdd+5eO9bObv5jE2fZZV+KI+x0znbXuQcr7+HOSta70uZjX+HMXXeKHJs46+/o9/l364XszjyM9nirL+7jOD1/+QM99juN/6WX3Jc8+94U99it+1cX6hz+enHLqlT0/BBA/xDGIp/K+kvOr00IIP00zWMGt8r6SYbESAgQIECBAgAABAgQIECBAgAABAgQIZAtklfdxltYXQwhLs9/SqGeV9y0p72OJdP/6j/Us6dLSYfH+B08VbvFy7StXnZwsXXpkZqGcLn/dDc8m6259KbOgamt5H2dCx++4Tw363cZLky9ZsmyqOI/fd33oYcdMPe51Cf7OdVZZ3l9/47OF9/u00z+yR6GYlpyD3A4rg/GYmpzBWBh3jmfe/VjC7rvfgcnChfv3XD4W5c+98C+SOD5Z6ypT3sdxzPtKg851x307ZNnq5JhjzpyaMR5L8rivnct03l+wYL9k84tfSY4+5ozMZQYpm+MHGrI+yNC53X7342Xne2W36vI+Xj4/70MW0/c1mh1yyKpk5cqTkoMOOiKJ32E/fZnpj+MHL+KVOnodU95ryvvKzrdmhRBuDSH86fTxGeCx8r6yYbEiAgQIECBAgAABAgQIECBAgAABAgQI7Ckwvbx/P4Rw0p6LNfYZ5X2LyvtY4MTZ3QOUCbnvueTSe6ZKoyYXp3nFVRXPX3PtU0mc9V2lada6qizv41UUiu7zwxveHagU7GVbfQZ/MZO6yRmMl56faemc5iJeIj4WzNG4qvJ+x673Sl2RId2XvNt58/aZumR83Mcqy/u4vue3fDWJs9Xztt3v+brL+7jPO9/646nZ8/32rezr8QMVN978/MA/o8r7ys+9FoYQ3gwh/FXZsexYXnlf+bBYIQECBAgQIECAAAECBAgQIECAAAECBP5BIC3v/48Qwn3/8PTY3FPet6y8jzPGL7v8gZ6XYO4oEXILsjlz9kribOdYTMV/TS5O030c1u1Tz/xmZcVslv3+S5YlDz7yK7+0ruI41p54ce7YpvsQrxQQZw1Xsb3OdVSbwU2/3L+mZzB+aGLFirV93VP/rNuYhY0dl1evqryP4/PKa9+autJG1nbLPBdz0/kVAFWX93Fft7z8jYH3dRTlfZr/+CGceBWOMp55y8YsDHL1gnRf4q3yfmjnYkd8cIWlv88bvx7PK++HNixWTIAAAQIECBAgQIAAAQIECBAgQIAAgRBeCiFsDyHMH1MM5X3Lyvu0uHnw4Y9PXQ6/R4GQWzDF77DuLBDjOptenKbHPazbWEjfeff2ZPmRx+e6lbFetGhJcvoZVyePPPqrQynQ73vg7b77GT/kMSyvuN5JzOCbu9+f+n7zvfYqd7WGOMP67HNuSLbv+F7XmFRZ3scxid+xHse96Nc5dGZ6r73mJedfeFuy/c3vd+3jMMr7NJexfD7zrGuTAw44rNBXgsT9HWV5H/f7hZe+NvU1BXFMO/2K3p+/z6KpMXpj5w+6nFOTMrfK+6GfmV0cQvhfio7tB8sp74c+LDZAgAABAgQIECBAgAABAgQIECBAgMAkC8TvQR3n/w2tvL9p3QtJ1r9YJpQpH2aybJzNnLUPcVZnmfU+u/mLmet59PFPllpPr23G77DO2td4uete7+v1WpxVfc99O5MT1l7c8zu243cqx+9hvuDC25KnN/7zzO3F79/O2r8Nj5UziN9pnbWe+OGAXsfSpNdiHq78yIapy4XHoq1fcROL3Phd4mtPvCSJl+F/ZtPnkvhhgGEeUyxps5w7n9u67ZtD3Yd4fJOawZe3/V5y1dWPJqtWn5z0KnEPPmRlcsml9yYv5vxOijOvO8csvf/8lq/MaOxee+O7U/sXv3u911csxO9kP+ro06dyu+31b2du8/71H8vcx2hQZcbjhwaeeOozUx8KueueHcm6W7ZkbveOu17vud1XXvv9zPfd98BbPd9X9lhefuV3p9yi37x5vWfjL1p0wNTvh/jfgSpK+3Rft776rcxjvff+3ZUd6/qHP565jS1bf3ugbdxy28v9fqf+rGEnXXNCCI+GEP5Lv/8WKO8bNnJ2hwABAgQIECBAgAABAgQIECBAgAABAg0UGFp5n5YHbn9x2flRO8SyOM4IffzJTyX3r38nuee+XclDj3xiqkjeseuHA5Usoz6mpmw/FmSxVIyed9/7ZnLv/W8l8bvk4+X2X3zp60n8wEJT9nWU+zGpGYyl86bnv5TED/vED9M88OA/mvo5fH3aLPtRjU38+X/2uS8kGx77tan9i2V8/NBAzO4wvlZhVMc5yu3G3wHxAxrROP7+jR8+WP/QP57KQfw6g1HuW9O2PYblfXpqt38I4e0Qwl/3KfHNvE/F3BIgQIAAAQIECBAgQIAAAQIECBAgQIDAHgLK+wKXzW9auWF/mvGBCONgHGRABmSg2gyMcXmfnmB9KITw7R4FvvI+lXJLgAABAgQIECBAgAABAgQIECBAgAABAnsIKO+V92Z9yoAMyIAMyEAjMtCC8j490bo8hPDvMkp85X0q5JYAAQIECBAgQIAAAQIECBAgQIAAAQIE9hBQ3itsGlHYmL1a7exVnjxlQAbGMQMtKu/jCdfcEMLGEML/01HiK+/3OBX1BAECBAgQIECAAAECBAgQIECAAAECBAikAsp75b3yXgZkQAZkQAYakYGWlffpudaBIYR3Qwh/F0JQ3qcqbgkQIECAAAECBAgQIECAAAECBAgQIEBgDwHlvcKmEYXNOM4Qtc9mNsuADMhAtRloaXmfnnydFkLYO33glgABAgQIECBAgAABAgQIECBAgAABAgQITBdQ3ivvlfcyIAMyIAMy0IgMtLy8n34O5jEBAgQIECBAgAABAgQIECBAgAABAgQIEOgSUN4rbBpR2Ji9Wu3sVZ48ZUAGxjEDyvuuczQPCBAgQIAAAQIECBAgQIAAAQIECBAgQGDCBJT3ynvlvQzIgAzIgAw0IgPK+wk7C3W4BAgQIECAAAECBAgQIECAAAECBAgQINAloLxX2DSisBnHGaL22cxmGZABGag2A8r7rnM0DwgQIECAAAECBAgQIECAAAECBAgQIEBgwgSU98p75b0MyIAMyIAMNCIDyvsJOwt1uAQIECBAgAABAgQIECBAgAABAgQIECDQJaC8V9g0orAxe7Xa2as8ecqADIxjBpT3XedoHhAgQIAAAQIECBAgQIAAAQIECBAgQIDAhAko75X3ynsZkAEZkAEZaEQGlPcTdhbqcAkQIECAAAECBAgQIECAAAECBAgQIECgS0B5r7BpRGEzjjNE7bOZzTIgAzJQbQaU913naB4QIECAAAECBAgQIECAAAECBAgQIECAwIQJKO+V98p7GZABGZABGWhEBpT3E3YW6nAJECBAgAABAgQIECBAgAABAgQIECBAoEtAea+waURhY/ZqtbNXefKUARkYxwwo77vO0TwgQIAAAQIECBAgQIAAAQIECBAgQIAAgQkTUN4r75X3MiADMiADMtCIDCjvJ+ws1OESIECAAAECBAgQIECAAAECBAgQIECAQJeA8l5h04jCZhxniNpnM5tlQAZkoNoMKO+7ztE8IECAAAECBAgQIECAAAECBAgQIECAAIEJE1DeK++V9zIgAzIgAzLQiAwo7yfsLNThEiBAgAABAgQIECBAgAABAgQIECBAgECXgPJeYdOIwsbs1Wpnr/LkKQMyMI4ZUN53naN5QIAAAQIECBAgQIAAAQIECBAgQIAAAQITJqC8V94r72VABmRABmSgERlQ3k/YWajDJUCAAAECBAgQIECAAAECBAgQIECAAIEuAeW9wqYRhc04zhC1z2Y2y4AMyEC1GVDed52jeUCAAAECBAgQIECAAAECBAgQIECAAAECEyagvFfeK+9lQAZkQAZkoBEZUN5P2FmowyVAgAABAgQIECBAgAABAgQIECBAgACBLgHlvcKmEYWN2avVzl7lyVMGZGAcM6C87zpH84AAAQIECBAgQIAAAQIECBAgQIAAAQIEJkxAea+8V97LgAzIgAzIQCMyoLyfsLNQh0uAAAECBAgQIECAAAECBAgQIECAAAECXQLKe4VNIwqbcZwhap/NbJYBGZCBajOgvO86R/OAAAECBAgQIECAAAECBAgQIECAAAECBCZMQHmvvFfey4AMyIAMyEAjMqC8n7CzUIdLgAABAgQIECBAgAABAgQIECBAgAABAl0CynuFTSMKG7NXq529ypOnDMjAOGZAed91juYBAQIECBAgQIAAAQIECBAgQIAAAQIECEyYgPJeea+8lwEZkAEZkIFGZEB5P2FnoQ6XAAECBAgQIECAAAECBAgQIECAAAECBLoElPcKm0YUNuM4Q9Q+m9ksAzIgA9VmQHnfdY7mAQECBAgQIECAAAECBAgQIECAAAECBAhMmIDyXnmvvJcBGZABGZCBRmRAeT9hZ6EOlwABAgQIECBAgAABAgQIECBAgAABAgS6BJT3CptGFDZmr1Y7e5UnTxmQgXHMgPK+6xzNAwIECBAgQIAAAQIECBAgQIAAAQIECBCYMAHlvfJeeS8DMiADMiADjciA8n7CzkIdLgECBAgQIECAAAECBAgQIECAAAECBAh0CSjvFTaNKGzGcYaofTazWQZkQAaqzYDyvusczQMCBAgQIECAAAECBAgQIECAAAECBAgQmDAB5b3yXnkvAzIgAzIgA43IgPJ+ws5CHS4BAgQIECBAgAABAgQIECBAgAABAgQIdAko7xU2jShszF6tdvYqT54yIAPjmAHlfdc5mgcECBAgQIAAAQIECBAgQIAAAQIECBAgMGECynvlvfJeBmRABmRABhqRAeX9hJ2FOlwCBAgQIECAAAECBAgQIECAAAECBAgQ6BJQ3itsGlHYjOMMUftsZrMMyIAMVJsB5X3XOZoHBAgQIECAAAECBAgQIECAAAECBAgQIDBhAsp75b3yXgZkQAZkQAYakQHl/YSdhTpcAgQIECBAgAABAgQIECBAgAABAgQIEOgSUN4rbBpR2Ji9Wu3sVZ48ZUAGxjEDyvuuczQPCBAgQIAAAQIECBAgQIAAAQIECBAgQGDCBJT3ynvlvQzIgAzIgAw0IgPK+wk7C3W4BAgQIECAAAECBAgQIECAAAECBAgQINAloLxX2DSisBnHGaL22cxmGZABGag2A8r7rnM0DwgQIECAAAECBAgQIECAAAECBAgQIEBgwgSU98p75b0MyIAMyIAMNCIDyvsJOwt1uAQIECBAgAABAgQIECBAgAABAgQIECDQJaC8V9g0orAxe7Xa2as8ecqADIxjBpT3XedoHhAgQIAAAQIECBAgQIAAAQIECBAgQIDAhAko75X3ynsZkAEZkAEZaEQGlPcTdhbqcAkQIECAAAECBAgQIECAAAECBAgQIECgS0B5r7BpRGEzjjNE7bOZzTIgAzJQbQaU913naB4QIECAAAECBAgQIECAAAECBAgQIECAwIQJKO+V98p7GZABGZABGWhEBpT3E3YW6nAJECBAgAABAgQIECBAgAABAgQIECBAoEtAea+waURhY/ZqtbNXefKUARkYxwwo77vO0TwgQIAAAQIECBAgQIAAAQIECBAgQIAAgQkTUN4r75X3MiADMiADMtCIDCjvJ+ws1OESIECAAAECBAgQIECAAAECBAgQIECAQJeA8l5h04jCZhxniNpnM5tlQAZkoNoMKO+7ztE8IECAAAECBAgQIECAAAECBAgQIECAAIEJE1DeK++V9zIgAzIgAzLQiAwo7yfsLNThEiBAgAABAgQIECBAgAABAgQIECBAgECXgPJeYdOIwsbs1Wpnr/LkKQMyMI4ZUN53naN5QIAAAQIECBAgQIAAAQKLMcO1AAAGSUlEQVQECBAgQIAAAQITJqC8V94r72VABmRABmSgERlQ3k/YWajDJUCAAAECBAgQIECAAAECBAgQIECAAIEuAeW9wqYRhc04zhC1z2Y2y4AMyEC1GVDed52jeUCAAAECBAgQIECAAAECBAgQIECAAAECEyagvFfeK+9lQAZkQAZkoBEZUN5P2FmowyVAgAABAgQIECBAgAABAgQIECBAgACBLgHlvcKmEYWN2avVzl7lyVMGZGAcM6C87zpH84AAAQIECBAgQIAAAQIECBAgQIAAAQIEJkxAea+8V97LgAzIgAzIQCMyoLyfsLNQh0uAAAECBAgQIECAAAECBAgQIECAAAECXQLKe4VNIwqbcZwhap/NbJYBGZCBajOgvO86R/OAAAECBAgQIECAAAECBAgQIECAAAECBCZMQHmvvFfey4AMyIAMyEAjMqC8n7CzUIdLgAABAgQIECBAgAABAgQIECBAgAABAl0CynuFTSMKG7NXq529ypOnDMjAOGZAed91juYBAQIECBAgQIAAAQIECBAgQIAAAQIECEyYgPJeea+8lwEZkAEZkIFGZEB5P2FnoQ6XAAECBAgQIECAAAECBAgQIECAAAECBLoElPcKm0YUNuM4Q9Q+m9ksAzIgA9VmQHnfdY7mAQECBAgQIECAAAECBAgQIECAAAECBAhMmIDyXnmvvJcBGZABGZCBRmRAeT9hZ6EOlwABAgQIECBAgAABAgQIECBAgAABAgS6BJT3CptGFDZmr1Y7e5UnTxmQgXHMgPK+6xzNAwIECBAgQIAAAQIECBAgQIAAAQIECBCYMAHlvfJeeS8DMiADMiADjciA8n7CzkIdLgECBAgQIECAAAECBAgQIECAAAECBAh0CSjvFTaNKGzGcYaofTazWQZkQAaqzYDyvusczQMCBAgQIECAAAECBAgQIECAAAECBAgQmDAB5b3yXnkvAzIgAzIgA43IgPJ+ws5CHS4BAgQIECBAgAABAgQIECBAgAABAgQIdAko7xU2jShszF6tdvYqT54yIAPjmAHlfdc5mgcECBAgQIAAAQIECBAgQIAAAQIECBAgMGECynvlvfJeBmRABmRABhqRAeX9hJ2FOlwCBAgQIECAAAECBAgQIECAAAECBAgQ6BJQ3itsGlHYjOMMUftsZrMMyIAMVJsB5X3XOZoHBAgQIECAAAECBAgQIECAAAECBAgQIDBhAsp75b3yXgZkQAZkQAYakQHl/YSdhTpcAgQIECBAgAABAgQIECBAgAABAgQIEOgSUN4rbBpR2Ji9Wu3sVZ48ZUAGxjEDyvuuczQPCBAgQIAAAQIECBAgQIAAAQIECBAgQGDCBJT3ynvlvQzIgAzIgAw0IgPK+wk7C3W4BAgQIECAAAECBAgQIECAAAECBAgQINAloLxX2DSisBnHGaL22cxmGZABGag2A8r7rnM0DwgQIECAAAECBAgQIECAAAECBAgQIEBgwgSU98p75b0MyIAMyIAMNCIDyvsJOwt1uAQIECBAgAABAgQIECBAgAABAgQIECDQJdCzvF+56uTkoUc+4R8DGZABGZABGZCBoWfgwovvTEIIvf79rOssxgMCBAgQIECAAAECBAgQIECAAAECBAgQINAigZ7lfZ8/oPf647rXepcPfPjIgAzIgAzIQPkMKO9bdBLqUAgQIECAAAECBAgQIECAAAECBAgQIECgW0B5X744ULYwkwEZkAEZkIHRZEB5330e5xEBAgQIECBAgAABAgQIECBAgAABAgQItEhAeT+a8kHpw10GZEAGZEAGymdAed+ik1CHQoAAAQIECBAgQIAAAQIECBAgQIAAAQLdAsr78sWBsoWZDMiADMiADIwmA8r77vM4jwgQIECAAAECBAgQIECAAAECBAgQIECgRQLK+9GUD0of7jIgAzIgAzJQPgPK+xadhDoUAgQIECBAgAABAgQIECBAgAABAgQIEOgWUN6XLw6ULcxkQAZkQAZkYDQZUN53n8d5RIAAAQIECBAgQIAAAQIECBAgQIAAAQItElDej6Z8UPpwlwEZkAEZkIHyGVDet+gk1KEQIECAAAECBAgQIECAAAECBAgQIECAQLeA8r58caBsYSYDMiADMiADo8mA8r77PM4jAgQItFbg/wf0TJeYujC6lwAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's definitely steal this flowchart:\n",
+    "\n",
+    "<img src=\"attachment:eed28f5d-d8f3-4f82-896d-ba67a7d031c6.png\" alt=\"drawing\" width=\"500\"/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gemmi\n",
+      "  Downloading gemmi-0.4.5-cp37-cp37m-macosx_10_9_x86_64.whl (2.0 MB)\n",
+      "\u001b[K     |████████████████████████████████| 2.0 MB 3.6 MB/s eta 0:00:01\n",
+      "\u001b[?25hInstalling collected packages: gemmi\n",
+      "Successfully installed gemmi-0.4.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gemmi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:root] *",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Tom/tutorial-ml-in-chemistry-research-rdkit-mol2vec.ipynb b/Tom/tutorial-ml-in-chemistry-research-rdkit-mol2vec.ipynb
new file mode 100644
index 0000000..333c243
--- /dev/null
+++ b/Tom/tutorial-ml-in-chemistry-research-rdkit-mol2vec.ipynb
@@ -0,0 +1,1718 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIMPLE ML IN CHEMISTRY RESEARCH: RDKIT AND MOL2VEC "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preface\n",
+    "\n",
+    "While considering which topic to pick to write a tutorial I suddenly understood that there's no better way to show an interesting thing than to describe something you're truly aware of. Alright, I got a higher education in chemistry, then actually went crazy on a thing called data science hoping that one day I'll be able to put two of them together. That's why I've actually entered mlcourse.ai. And I suppose the time has come. In this tutorial I'll try to show some insights on how can some of Python packages and machine learning be easily and efficiently applied in chemistry without going deep to the strange and contradictory world of deep neural networks for chemistry and drug development.\n",
+    "\n",
+    "<img src=\"https://as1.ftcdn.net/jpg/01/62/02/60/500_F_162026016_NbhvWZvqo7SLke6dhEdNw6yPyuedR8Ae.jpg\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "As far as I've already found out, data science approaches can actually be implemented everywhere. The only limitation is data that you have (or you don't). Since chemistry is known to be a rather exact science (because we do have laws and numbers in it, still we don't have enough principles that are mathematically strict) it must have data to be analyzed. You might understand that in real-world chemistry we can find many examples of the things that are seeking to have a good data scientist's look at.\n",
+    "I'll tell the simplest ever example: two main general tasks that many chemistry researchers face almost everyday are predicting physical, chemical, biochemical (or some other desirable) properties of a molecule (or a substance when we talk about physical properties) by it's general descriptors - chemical formula and 3D structure - and predicting chemical formula and 3D structure for an unknown yet molecule or substance with desirable properties respectively. They are called QSPR (quantitative structure-property relationship) and QSAR (quantitative structure-activity relationship) problems. From the history of the problem we can see many different approaches based on some chemical and mathematical logic, some quantum mechanics approaches and etc. The main disadvantage of all these methods is that they're really computationally hard (and also not always accurate). And now doesn't the problem sound familiar? What if don't need to wait for weeks to find values of some properties that we need? Let's find it out.\n",
+    "\n",
+    "# Representation of chemical data\n",
+    "Let's consider a case where we want to implement some machine learning in chemistry research. The first issue we're goind to face is how to represent the data in the way that keeps much information.\n",
+    "When it comes to chemical formulas, in chemoinformatics there're several general ways of representation.\n",
+    "\n",
+    "**SMILES (Simplified Molecular Input Line Entry System)**\n",
+    "\n",
+    "\n",
+    "This is the simplest way to reflect a molecule. The idea behind is to use simple line notations for chemical formulas that are based on some rules. Atoms of chemical elements are represented by chemical symbols in capital letter, hydrogen is usually ignored. Single bonds are not displayed; for double, triple and quadruple bonds we shall use *'='*, *'#'*, *'$'* respectively. Atoms that are bonded must stand nearby. Ring structures are written by breaking each ring at an arbitrary point (although some choices will lead to a more legible SMILES than others) to make a 'straight non-ring' structure (as if it wasn't a ring) and adding numerical ring closure labels to show connectivity between non-adjacent atoms. Aromacity is commonly illustrated by writing the constituent B, C, N, O, P and S atoms in lower-case forms b, c, n, o, p and s, respectively. To represent side chains of atomic gpoups branches are used. \n",
+    "\n",
+    "A simple example:\n",
+    "\n",
+    "<img src=\"http://www.daylight.com/dayhtml/doc/theory/theory4.gif\"/>\n",
+    "\n",
+    "A fact that usually you cand find SMILES representation of a molecule in any database of chemical information makes the thing simple. But also you can write it by yourself, if needed, which is definetely a huge advantage.\n",
+    "\n",
+    "Pros:\n",
+    "- Easy to find or to write by yourelf.\n",
+    "- Illustrates some main chemical concepts such as atomic map, bond structure, can represent stereochemical properties (isomerism).\n",
+    "- Can be further processed as a string data, if needed.\n",
+    "\n",
+    "Cons:\n",
+    "- Doesn't give information upon space structure of the molecule (2D, but mostly 3D).\n",
+    "- Different formulas may be written for the same molecule.\n",
+    "\n",
+    "**MDL Molfile**\n",
+    "\n",
+    "\n",
+    "It's a file format that keeps information about the atoms, bonds, connectivity and coordinates of a molecule.\n",
+    "\n",
+    "Typical MOL consists of some header information, the Connection Table containing atom info, then bond connections and types, followed by sections for more complex information. MOL representations can either hold information about 2D or 3D structure of the molecule. This format is so widely spread that most cheminformatics software systems are able to read it.\n",
+    "\n",
+    "An example of how it looks like:\n",
+    "\n",
+    "<img src=\"https://chem.libretexts.org/@api/deki/files/112699/MOL_FILE_1.png?revision=1&size=bestfit&width=777&height=601\"/>\n",
+    "\n",
+    "Pros:\n",
+    "- Acceptable by most chemoinformatics software.\n",
+    "- Represents space structure (2D or 3D)\n",
+    "- Invariant for particular molecule.\n",
+    "\n",
+    "Cons:\n",
+    "- Huge size.\n",
+    "- Can't be perfomed by yourself.\n",
+    "\n",
+    "**Coloumb matrices and other quantum descriptors**\n",
+    "\n",
+    "\n",
+    "The Coulomb matrix as well as some custom quntum approaches comes from idea that molecular properties can be calculated from the Schrödinger equation, which takes the Hamiltonian operator as its input. The Hamiltonian operator for an isolated molecule can be specified by the atom coordinates and nuclear charges. \n",
+    "In fact, there're many custom ways to represent a molecule with the help of quantum chemistry. The only limitation is the presense of such data.\n",
+    "\n",
+    "Pros:\n",
+    "- Mathemathicaly and chemically strict representations.\n",
+    "\n",
+    "Cons:\n",
+    "- Hard to compute.\n",
+    "- Seeks relative data.\n",
+    "\n",
+    "**Custom set of descriptors**\n",
+    "\n",
+    "One can actually represent the inforation by creating suitable descriptors that are belivied to be helpful - bond count, atom count, presence of particular groups of atoms an etc.\n",
+    "Such descriptors may be exctracted from SMILES strings, MOL representations or performed manually.\n",
+    "\n",
+    "Pros:\n",
+    "- Extracting suitable information based on chemical logic.\n",
+    "\n",
+    "Cons:\n",
+    "- Hard to produce manually.\n",
+    "- Needs some other representation for computer processing.\n",
+    "- May not represent a molecule correctly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RDkit for working with SMILES and MOL representations\n",
+    "\n",
+    "RDKit is a collection of cheminformatics and machine learning tools written in C++ and Python.\n",
+    "Which is more important, it allows to work with many representations of chemical data and has a power to extract almost each chemical descriptor from the data you have. Check the docs for deeper understanding.\n",
+    "\n",
+    "In this tutorial we'll try to implement ML to a propety prediction task. RDkit is the one to help at the first step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_kg_hide-input": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): done\n",
+      "Solving environment: done\n",
+      "\n",
+      "## Package Plan ##\n",
+      "\n",
+      "  environment location: /Users/tom/miniconda3\n",
+      "\n",
+      "  added / updated specs:\n",
+      "    - rdkit\n",
+      "\n",
+      "\n",
+      "The following packages will be downloaded:\n",
+      "\n",
+      "    package                    |            build\n",
+      "    ---------------------------|-----------------\n",
+      "    openssl-1.1.1j             |       h9ed2024_0         2.2 MB\n",
+      "    ------------------------------------------------------------\n",
+      "                                           Total:         2.2 MB\n",
+      "\n",
+      "The following packages will be UPDATED:\n",
+      "\n",
+      "  ca-certificates    conda-forge::ca-certificates-2020.12.~ --> pkgs/main::ca-certificates-2021.1.19-hecd8cb5_1\n",
+      "\n",
+      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
+      "\n",
+      "  openssl            conda-forge::openssl-1.1.1j-hbcf498f_0 --> pkgs/main::openssl-1.1.1j-h9ed2024_0\n",
+      "\n",
+      "\n",
+      "\n",
+      "Downloading and Extracting Packages\n",
+      "openssl-1.1.1j       | 2.2 MB    | 2                                     |   1% ^C\n",
+      "openssl-1.1.1j       | 2.2 MB    | ##8                                   |   8% \n",
+      "\n",
+      "CondaSignalInterrupt: Signal interrupt SIGINT\n",
+      "\n",
+      "Collecting pandas==0.23.0\n",
+      "  Downloading pandas-0.23.0.tar.gz (13.1 MB)\n",
+      "\u001b[K     |████████████████████████████████| 13.1 MB 1.1 MB/s eta 0:00:01\n",
+      "^C\n",
+      "\u001b[31mERROR: Operation cancelled by user\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "#Here we install the package. For me it's been a nightmare to install rdkit into Kaggle's environment. \n",
+    "#But wonderful Kaggle's technical support helped me to find the way.\n",
+    "\n",
+    "#!conda install -y -c rdkit rdkit;\n",
+    "#!pip install pandas==0.23.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll work with the data on lipophilicity of small organic molecules. \n",
+    "\n",
+    "**Lipophilicity** is a physical property of a substance that refers to an ability of a chemical compound to dissolve in lipids, oils and generally in non-polar solvents. That's a fundamental property that plays great part in biochemical and technological behaviour of a substance. It's usually evaluated by a distribution coefficent P -  the ratio of concentrations of a particular compound in a mixture of two immiscible phases at equilibrium (in our case water and octanol). The greater P values refer to greater lipophilicity. Usually lipophilicity is presented as log10P like in our dataset. \n",
+    "\n",
+    "A thing important to mention is that it takes much time to perform an experiment to measure the corresponding P value, because it must be an exact and reproducible procedure repeated at least 3-5 times and each time you have to wait till the system's equilibrium is reached.\n",
+    "\n",
+    "*Chemical insights: from theory P value must turn great when a molecule is large and doesn't have many polar atoms or atomic groups (such containing O, N, P, S, Br, Cl, F an etc.) in it.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>smiles</th>\n",
+       "      <th>logP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C[C@H]([C@@H](C)Cl)Cl</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C(C=CBr)N</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CCC(CO)Br</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[13CH3][13CH2][13CH2][13CH2][13CH2][13CH2]O</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CCCOCCP</td>\n",
+       "      <td>0.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        smiles  logP\n",
+       "0                        C[C@H]([C@@H](C)Cl)Cl   2.3\n",
+       "1                                    C(C=CBr)N   0.3\n",
+       "2                                    CCC(CO)Br   1.3\n",
+       "3  [13CH3][13CH2][13CH2][13CH2][13CH2][13CH2]O   2.0\n",
+       "4                                      CCCOCCP   0.6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Let's load the data and look at them\n",
+    "df= pd.read_csv('./logP_dataset.csv', names=['smiles', 'logP'])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you see the only information we have here is SMILES representation of molecular formulas. But RDkit is able to work with MOL\n",
+    "representations. And it's actually nice to know RDkit still provides an opportunity to transform SMILES to MOL. Let's check what we can do.   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'rdkit.Chem.rdchem.Mol'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Importing Chem module\n",
+    "from rdkit import Chem \n",
+    "\n",
+    "#Method transforms smiles strings to mol rdkit object\n",
+    "df['mol'] = df['smiles'].apply(lambda x: Chem.MolFromSmiles(x)) \n",
+    "\n",
+    "#Now let's see what we've got\n",
+    "print(type(df['mol'][0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that MOLs are represented as special rdkit.Chem items of a corresponding C++ class.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Visualization of molecules**\n",
+    "\n",
+    "RDkit provides visualization of MOLs with ***rdkit.Chem.Draw*** module. To visualize a set of molecules you should pass a set of corresponging MOLs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<path d=\"M 926.277 782.64 L 929.989 788.64 Q 930.357 789.232, 930.949 790.304 Q 931.541 791.376, 931.573 791.44 L 931.573 782.64 L 933.077 782.64 L 933.077 793.968 L 931.525 793.968 L 927.541 787.408 Q 927.077 786.64, 926.581 785.76 Q 926.101 784.88, 925.957 784.608 L 925.957 793.968 L 924.485 793.968 L 924.485 782.64 L 926.277 782.64 \" fill=\"#000000\"/>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from rdkit.Chem import Draw\n",
+    "mols = df['mol'][:20]\n",
+    "\n",
+    "#MolsToGridImage allows to paint a number of molecules at a time\n",
+    "Draw.MolsToGridImage(mols, molsPerRow=5, useSVG=True, legends=list(df['smiles'][:20].values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Numbers of atoms**\n",
+    "\n",
+    "Since size of a molecule can be approximated by a number of atoms in it, let's extract corresponding values from MOL.\n",
+    "RDkit provides ***GetNumAtoms()*** and ***GetNumHeavyAtoms()*** methods for that task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AddHs function adds H atoms to a MOL (as Hs in SMILES are usualy ignored)\n",
+    "# GetNumAtoms() method returns a general nubmer of all atoms in a molecule\n",
+    "# GetNumHeavyAtoms() method returns a nubmer of all atoms in a molecule with molecular weight > 1\n",
+    "\n",
+    "\n",
+    "df['mol'] = df['mol'].apply(lambda x: Chem.AddHs(x))\n",
+    "df['num_of_atoms'] = df['mol'].apply(lambda x: x.GetNumAtoms())\n",
+    "df['num_of_heavy_atoms'] = df['mol'].apply(lambda x: x.GetNumHeavyAtoms())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take look at how number of atoms is connected with target variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.jointplot(df.num_of_atoms, df.logP)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No clear dependence is observed so we need some more features to be exctracted.\n",
+    "The next obvious step is to count numbers of the most common atoms.\n",
+    "RDkit supports subpattern search represented by ***GetSubstructMatches()*** method. It takes a MOL of a substructure pattern as an argument. So you can futher extract occurance of each pattern you'd like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "((0,), (1,), (2,), (3,))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First we need to settle the pattern.\n",
+    "c_patt = Chem.MolFromSmiles('C')\n",
+    "\n",
+    "# Now let's implement GetSubstructMatches() method\n",
+    "print(df['mol'][0].GetSubstructMatches(c_patt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The method returns a tuple of tuples of positions of corresponding patterns. To extract the number of matches we need to take the length of a corresponding tuple of tuples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We're going to settle the function that searches patterns and use it for a list of most common atoms only\n",
+    "def number_of_atoms(atom_list, df):\n",
+    "    for i in atom_list:\n",
+    "        df['num_of_{}_atoms'.format(i)] = df['mol'].apply(lambda x: len(x.GetSubstructMatches(Chem.MolFromSmiles(i))))\n",
+    "\n",
+    "number_of_atoms(['C','O', 'N', 'Cl'], df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df[['num_of_atoms','num_of_C_atoms','num_of_N_atoms', 'num_of_O_atoms', 'logP']], diag_kind='kde', kind='reg', markers='+')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the bottom plots we notice some linear dependence of logP on numbers of particular atoms. We also see normal distribution of the target variable which values lie approximately from -5 to 5. Let's build our first model.\n",
+    "We'll count on ridge regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['num_of_atoms', 'num_of_heavy_atoms', 'num_of_C_atoms',\n",
+      "       'num_of_O_atoms', 'num_of_N_atoms', 'num_of_Cl_atoms'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import RidgeCV\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#Leave only features columns\n",
+    "train_df = df.drop(columns=['smiles', 'mol', 'logP'])\n",
+    "y = df['logP'].values\n",
+    "\n",
+    "print(train_df.columns)\n",
+    "\n",
+    "#Perform a train-test split. We'll use 10% of the data to evaluate the model while training on 90%\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(train_df, y, test_size=.1, random_state=1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model and evaluate results. As an evaluation metric we can choose MAE or MSE. Evaluation function plots a number of first predictions (by default - 300)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "def evaluation(model, X_test, y_test):\n",
+    "    prediction = model.predict(X_test)\n",
+    "    mae = mean_absolute_error(y_test, prediction)\n",
+    "    mse = mean_squared_error(y_test, prediction)\n",
+    "    \n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "    plt.plot(prediction[:300], \"red\", label=\"prediction\", linewidth=1.0)\n",
+    "    plt.plot(y_test[:300], 'green', label=\"actual\", linewidth=1.0)\n",
+    "    plt.legend()\n",
+    "    plt.ylabel('logP')\n",
+    "    plt.title(\"MAE {}, MSE {}\".format(round(mae, 4), round(mse, 4)))\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print('MAE score:', round(mae, 4))\n",
+    "    print('MSE score:', round(mse,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE score: 0.5694\n",
+      "MSE score: 0.5317\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train the model\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, y_train)\n",
+    "#Evaluate results\n",
+    "evaluation(ridge, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Offtop: particular atoms, bonds and rings**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that you can also extract some ring information or iterate through each molecule's atoms and bonds with RDkit. Methods ***GetRingInfo()***, ***GetAtoms()*** and ***GetBonds()*** yield corresponding generators over rings and atoms in molecule. \n",
+    "\n",
+    "Some toy examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rings with 1 backbone: 1\n",
+      "Number of rings with 2 backbones: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "atp = Chem.MolFromSmiles('C1=NC2=C(C(=N1)N)N=CN2[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O')\n",
+    "\n",
+    "# Getting number of rings with specified number of backbones\n",
+    "print('Number of rings with 1 backbone:', atp.GetRingInfo().NumAtomRings(1))\n",
+    "print('Number of rings with 2 backbones:', atp.GetRingInfo().NumAtomRings(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Atom: C Valence: 3\n",
+      "Atom: O Valence: 2\n",
+      "Atom: C Valence: 4\n",
+      "Atom: N Valence: 2\n",
+      "Atom: C Valence: 2\n",
+      "Atom: Cl Valence: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "m = Chem.MolFromSmiles('C(=O)C(=N)CCl')\n",
+    "#Iterating through atoms to get atom symbols and explicit valencies \n",
+    "for atom in m.GetAtoms():\n",
+    "    print('Atom:', atom.GetSymbol(), 'Valence:', atom.GetExplicitValence())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Descriptors**\n",
+    "\n",
+    "***rdkit.Chem.Descriptors*** provides a number of general molecular descriptors that can also be used to featurize a molecule. Most of the descriptors are straightforward to use from Python.\n",
+    "\n",
+    "Using this package we can add some useful features to our model:\n",
+    "- rdkit.Chem.Descriptors.TPSA() - the surface sum over all polar atoms or molecules also including their attached hydrogen atoms;\n",
+    "- rdkit.Chem.Descriptors.ExactMolWt() - exact molecural weight;\n",
+    "- rdkit.Chem.Descriptors.NumValenceElectrons() - number of valence electrons (may illustrate general electronic density)\n",
+    "- rdkit.Chem.Descriptors.NumHeteroatoms() - general number of non-carbon atoms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdkit.Chem import Descriptors\n",
+    "df['tpsa'] = df['mol'].apply(lambda x: Descriptors.TPSA(x))\n",
+    "df['mol_w'] = df['mol'].apply(lambda x: Descriptors.ExactMolWt(x))\n",
+    "df['num_valence_electrons'] = df['mol'].apply(lambda x: Descriptors.NumValenceElectrons(x))\n",
+    "df['num_heteroatoms'] = df['mol'].apply(lambda x: Descriptors.NumHeteroatoms(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check the improvement of the model perfomance with new features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['num_of_atoms', 'num_of_heavy_atoms', 'num_of_C_atoms',\n",
+      "       'num_of_O_atoms', 'num_of_N_atoms', 'num_of_Cl_atoms', 'tpsa', 'mol_w',\n",
+      "       'num_valence_electrons', 'num_heteroatoms'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df = df.drop(columns=['smiles', 'mol', 'logP'])\n",
+    "y = df['logP'].values\n",
+    "\n",
+    "print(train_df.columns)\n",
+    "\n",
+    "#Perform a train-test split. We'll use 10% of the data to evaluate the model while training on 90%\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(train_df, y, test_size=.1, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tOOOMM3DgwAGccMIJ+M3f/E0sLCzgd3/3dwEAH//4x3s+XrVaxQ033IANGzZg3759uPjii/H6178eQohp/lojYXBHA7Esk4iIiIhGJd6ffQBkf98OvP8jH/kIbrjhBgDAzp07ce211+JlL3sZzjjjDADACSecMNrzWYv3vve9uPXWW+E4Dp544gk89dRTOOWUU9b2C0wBgzsaiGWZRERERDSq1QKxrG3duhXf/va3cfvtt2Nubg6veMUrsGXLFjz00EOr/qzneTAmPt+11iKKIgDA9ddfj7179+Kuu+6C7/vYvHkzms3mRH+PcbHnjgZiWSYRERERFd3hw4dx/PHHY25uDg8++CDuuOMOhGGIW265BY899hgA4MCBAwCAxcVFHD16tPWzmzdvxl13xUt/3XjjjZBSth7zGc94Bnzfx80334zHH398yr/V6Bjc0UDM3BERERFR0b361a+GUgrnn38+3ve+9+Hiiy/GSSedhGuvvRZveMMbsGXLFlxxxRUAgNe97nW44YYbWgNVrrrqKtxyyy14yUtegu9973uYn58HAPzKr/wK7rzzTlx00UW4/vrrcdZZZ+X5Kw6FZZk0EHvuiIiIiKjoKpUKbrqp99rMr3nNazq+ft7znod7772347Y77rij9f9/9Ed/BADYtGkTbr/99p6PubS0NM7mTgwzdzQQM3dERERERLOBwR0NxJ47IiIiIqLZwOCOBmJZJhERERENw9rpTsgsu7W8ngzuaCCWZa60bcc2SC3z3gwiIiKiwqhWq9i/fz8DvIxYa7F//35Uq9WRfo4DVWgglmWudPU/X42Pv/7jeNEzX5T3phAREREVwmmnnYZdu3Zh7969eW9KaVSrVZx22mkj/QyDOxqIZZkraasR6SjvzSAiIiIqDN/3ccYZZ+S9GeseyzJpIJZlrmSsgTQsyyQiIiKiYmFwRwOxLHMlbZi5IyIiIqLiYXBHA7EscyVjDQeqEBEREVHhMLijgViWuZKxhpk7IiIiIiocBnc0EDN3K2mr2XNHRERERIXD4I4GYs/dSszcEREREVERMbijgViWuZI2mj13RERERFQ4DO5oIJZlrsTMHREREREVEYM7GohlmStxnTsiIiIiKiIGdzQQyzJX0pbr3BERERFR8TC4o4FYlrkS17kjIiIioiJicEcDMXO3EnvuiIiIiKiIGNzRQOy5W0kbrnNHRERERMXD4I4GYlnmSszcEREREVERMbijgViWuRJ77oiIiIioiHIP7oQQrhDibiHEV/PeFlqJZZkrcVomERERERVR7sEdgN8G8EDeG0G9sSxzJa5zR0RERERFlGtwJ4Q4DcDlAD6W53ZQfyzLXIk9d0RERERURHln7v4cwDUA+kYPQojfEELcKYS4c+/evdPbMgLAssxu1lpm7oiIiIiokHIL7oQQrwXwtLX2rkHfZ6291lp7kbX2opNOOmlKW0cplmV2srAAwMwdERERERVOnpm7SwC8XgixHcD/AvBKIcRnctwe6oFlmZ3S14LTMomIiIioaHIL7qy1/7e19jRr7WYAbwLwHWvtW/LaHuqNmbtO2mgAzNwRERERUfHk3XNHBWasgYVlcNemlbljzx0RERERFYyX9wYAgLV2K4CtOW8GdUmzVAzulmk7ncydtRYWFo7g9RciIhqPsQYCAkKIvDeFiCaMZ47UVxrIMLhbNq2euxsfuhHv+No7JvocRES0Pvzi538Rt++6Pe/NIKIpYHBHfSmjADC4a5e+FpPO3B1oHMCh5qGJPgcREa0P++r7cLBxMO/NIKIpYHBHfbEsc6X0NZl0z53UshVcExERjaOpmjymEK0TDO6or7QsM/0vTS9zp4zigZiIiDIRqpCDwIjWCQZ31BfLMleaVs+dNMzcERFRNpi5I1o/GNxRXyzLXGla0zJZlklERFkJdTjxi5JEVAwM7qgvTstcaVrr3DFzR0REWWHmrryUUbjxwRvz3gwqEAZ31BczdysZa+A7PnvuiIhoZjRVkz13JbXz8E5cfdPVeW8GFQiDO+qLPXcraaNR9aqT77ljWSYREWUkVCGPKSWljGpdjCcCGNzRANpqCAgGd22MNah61cn33LEsk4iIMmCtZc9diWmreb5AHRjcUV/aaPiuz+CuTRrcTbq8RRnFJSiIiGhsoQ4BgAFASWmjeb5AHRjcUV/KKPgOg7t22mpUvAoiHcFaO7HnYVkmERFlIVRxcMeeu3Jijz51Y3BHfWnLzF03Yw08x4Mr3IleKWNZJhERZaGpmgCYuSsrbTV77qgDgzvqSxuNwA0Y3LXRRsMRDgI3mGjfHa/EERFRFtKyTPbclZM27LmjTgzuqC9tNcsyuxhr4AoXvutP9EDJzB0REWWBmbtyY48+dWNwR30po1iW2cVYM5XMHXvuiIgoC2lwx567cuK0TOrG4I76YlnmStrGZZm+40/0QMnMHRERZSEdqMJjSjml/XY8V6MUgzvqS1sGd92MNXAdlz13REQ0E1qZO/bclVJakslzBkoxuKO+uBTCSmlZ5sR77liWSUREGeA6d+WW/l05MZNSDO6oL5ZlrqSNhismn7ljWSYREWWBPXfllgZ1PGegFIM76ovr3K3UytxNuOeOZZlERJQFTssst7QskxMzKcXgjvpiWeZKnJZJRESzhANVyi39u/LvSykGd9SXNszcddNWw3W4zh0REc2Gpmpizp9jWWZJpWWZ7LmjFIM76ovTMleadubOWjux5yAiovJrqiYWggVeMCwpTsukbgzuqC9tNMsyu0yz5y59PiIiorUKdYiFYIFLIZQUyzKpG4M76ksZxbLMLtOclglwZ01ERONh5q7cWmWZHKhCCQZ31Je2zNx1m+Y6dwCDOyIiGk+okswde+5KiWWZ1I3BHfWljYbneBAQ7P1KtPfcTaMskztrIiIaBzN35caBKtSNwR31pYyCK1w4wmH2LtGalun4LMskIqLCS4M79tyVEy8GUzcGd9SXtnHmjsHdso7MHcsyiYio4EIdYjFY5PGkpLiIOXVjcEd9aRNnqRjcLdNGt6ZlTjJzp4yC53g8GBMR0VhamTv23JVSWo7J8wVKMbijvrTVLMvsYqxpTcuc5IFSGomqV+XOmoiIxpIuhcDjSTmlf1f23FGKwR31pYxi5q5L+7TMSS9iXvNqPBgTlcRStJT3Jkzdevydi4g9d+XGaZnUjcEd9ZVOy2Rwt6x9oMpEe+6MRM1ncEdUBkvREp7/V8/PezOm7vQ/Px2hCvPejHWP0zLLjevcUTcGd9SXthruQw/DUZrBXaJ9oMqke+5YlklUDnVZx6Hmobw3Y6qUUTjQOICGauS9Kese17krN2buqBuDO+pLGQX3qb1wjGVwl+hYxHxCB0pjDYw1CNyAV+KISiDS0brrh0kzdg3J4C5vzNyVG3vuqBuDO+pLGw1PWzgWDO4S2ujWQJVJZe6klvAdH77j82BMVAKRjtbdhZqmagIAM3cFwJ67cuO0TOrG4I760lbDNRYOBIO7RCtzN8GeO2UUfNfnUghEJbEuM3eambui4Dp35cZ17qgbgzvqSxkFl5m7DtPouZNGwnM8BndEJRHpCBYW1tq8N2VqmLkrDq5zV27peQLPFyjF4I760kbDM2Dmrk269t8ke+7SskwGd7Phh3t+iO8/8f28N4MKLO0/W0/70VZwx8xd7tKBKjyelBPLMqmbl/cGUHFpq+Fqw8xdm2ll7liWOTtufOhGNGQDL37Wi/PeFCqodF+hrYYLN+etmY7WQBVm7nLHnrtya5VlrrPSb+qPmTvqSxsdl2Uyc9fS0XM3ocydMoplmTNEasleBxqoFdyto5MvZu6Kg9Myy41lmdSNwR31FffcMXPXTpt4EfNpTMtkcDcbIh3x80EDtWfu1ovWQBVm7nIX6hBz/hyA9XWBYb3gIubUjcEd9aUtl0Lo1rHO3YRKXFiWOVukkTxhooGYuaO8aKPj/nnHg+9yeZ0y4iLm1I3BHfXVKstkcNeirZ54z50yipm7GcLMHa0mzWKtp/cJp2UWQ6hDVL0qhBDwHI8TM0uolblbRxePaDAOVKG+lFFwFcsy2xlr4mmZE+y5k5pLIcwSqSXWyYwMWqN1WZapuM5dETRVE1WvCgDwHWbuykhZxb8tdWDmjvqKyzIZ3LXjtEzqFhlm7miwPMsy//J7f4mP/eBjU3/esmbu/vyOP8d191yX92YMramaqHgVAIgzd5yYWTraaFS8yrq6eESDMXNHfWmrmbnroo2efM8dB6rMFKklAifIezPK45FH4v8+97n5bkeG8szcPXboMRxXPW7qz1vWnrudh3diKVrKezOGFqpwOXPHnrtS0laj4lb4t6UWZu6oL07LXMlYM/FpmVwKYbZEOuIV0yx96lPxvxLJM3PXkI1c9t8TmZZ5+DAQhtk93hpoqye275+Epmqi4rZl7thzVzrKqDhzx547SjC4o7600fAkg7t201jnjmWZs0Uayc9HlqQEdLlOUtL+szwuAjRUPsFdUzUx589lm7l797uBz38+u8dbA21mL7hjz125acPMHXVicEd9aauTzJ3lyWtCWw335lsQHD7Kde4IADN3mYsiwJRrf5PuK/LYjzZUI5cr+qEKcXz1+Gwzd/U60Mi3zFP/4E5E99yZ6zaMIp2WCbDnrqy0Zc8ddWLPHfWljYarNBzDzF3KWAPnxz+Gv2PXxA6Syihm7maI1MzcZUpKwC3X+NH1WJbZVE0cVz0u2+CuAFld3TiGyNhct2EU7QNV2HNXTszcUTdm7jJ0287b8t6ETHEphJWMNXC0RXAsnOi0TPbczY5IR+x1yFIBTuCzludAlTzLMo+vHZ9tWaZS8b8caaNnKvvVPlCFPXflxJ476sbgLkOv/OQrZ2qK1mq01fCUhmNYlpmKF3Y38JcaE13njmWZs4M9dxmTsrRlmespcxfqCZRlKpV74K+tQTRDAVL7QBX23JUTp2VSNwZ3GZJGthrny0AbDVdqZu7axJk7g2CpMdFpmSzLnB3sucsYM3eZaqhGLs/bKsvMMnNXgPeGthqRna3gjj135aaNRuAGPA5RC4O7jBhrYKxpjX8ug1ZZprHcaSTS4M5fqk9unTuWZc4U9txlrAAn8FlLjwvM3I2pCGWZ1iCaof1y+0AV9tyVU1qWyb8tpRjcZST9UM3SiOTVaKvhScWyzDbpBNHgaJ3TMgkAe+4yV+KyzLymZZam564AgX+cuZud/TLXuSs/lmVSt9yCOyFEVQjxb0KIHwohfiSEeH9e25KFNIsTHj2Y85ZkJy7L5FII7VqZuyNLk13njsHdzGDPXcaiKPcT+KzlWpZZpmmZRcjcwcxUcNc+UIU9d+WkjeZAFeqQZ+YuBPBKa+0WABcAeLUQ4uIct2csKgnuoijfNXiypK2GKxWXQmhjrIGjDLwjS1BGwdrsR2Iro+A5Hlzh8kA8A9hzl7ECZGeylutAlbzXuStd5s5AzlBwx5678lNGIXADni9QS27BnY2loyX95N/sLB7TRcomACAM6zlvSXaUUfAkp2W2S6dliiNH4Tt+K3v3h7f8IR7Y+0AmzyG15ECVGcKeu4yVuCyTmbsxZTwt85EDj+APtv7BSD+jrUaE2bn4wHXuyi8ty+RFRkrl2nMnhHCFEPcAeBrAt6y13+vxPb8hhLhTCHHn3r17p7+RQ1JhfAALo/IEd3FZJnvu2sWZOw0cOYLADVonbV/796/h0YOPZvIc7WWZLLMoPvbcZawA2Zms5TVQRRudW9lwqMPJ9NxlWJa5/dB23Lz95pF+RsPMVHDXPlCFPXflxEXMqVuuwZ21VltrLwBwGoCXCCHO7fE911prL7LWXnTSSSdNfyOHlAZ3UdTMeUuyE5dlMnPXTlsNRxvg8GH4rt8qcdlX35dZuQuXQpgt0kheMc1SCYO7vDJ3adYsr8zdhsoGKKOyC2ozztxpo0feNm0NIszOfpnr3JWftknPHY9DlCjEtExr7SEAWwG8OudNWTOZ9NqFWV6lzJnSCq62DO7aGGvgKrMic7evvi+zK6JScymEWRLpiJ+PLJU0uPMdf+rvkzRrltc6d1Wvippfy640M+OBKsaakfexceZudj7v7LkrP2UUM3fUIc9pmScJIY5L/r8G4KcBPLXsjYIAACAASURBVJjX9oyrlbmTJcrcGQXPgMFdG2MUHAvgyJFWz53UEofDw5kdNDktc7ZILVmWmaWS9tzV/NrU3yd5Zu7SKY01r5ZdaWbGgf+agjtrIWcouOuYlsmeu1LitEzq5uX43KcC+KQQwkUcZH7eWvvVHLdnLDIpxyxT5k4bDddx4WjN4C6htYKbBHeBexIiHWF/Yz8AZJq586tJcDdDU9nWI2MNtOXnI1MlXQqh5tWmX5Yp8y3LrLiV7DN3GQd3o/5NNAwiMTvvz6ZeHqjiCfbclZG2mtMyqUNuwZ219l4AL8zr+bOm0uCuREshKCPh+hU4tg7DK0IAAKM1HM8H6vU4c6cl9sskuMuw5249lWXuWdqDk+dPhhAi700ZWfo3Z69DdpZsCNgQC3lvSIbWY+auVZaZdeYuw7JMbfXayjLF7FzMYeau/NKyTB6HKFWInrsySHvuIlWmskwNz/XgQMBoHhAAwBgNx/WAhQUEwkOkI+yr7wOQYeZunZVl/uxnfhYP7MtmGYlpS3sumbnLzl+cuR9/tumRvDcjU6EKc83c5XHSF+oQFa/4mbvRgzuLSMzOqk3tA1XYc1dOaVnmejhfoOHkWZZZKiqKR12Hpeq503BdPw7uVJT35hSCNhKucIENG+DDgTRyObjLqudOS/g7dsJ7/G6o55R/Z30sOoYj4ZG8N2NN0oCevQ7ZOSYUhCjX+z7SETZWN05/oEpOmTtrLUIVxmWZBe+5G3laJgwiZ3Yu5rQGqnzrW/Dl6JlKKr7WOnc8DlGCmbuMLGfuwpy3JDvaariOB0cImAxLYWaZ0RqO4wAbNiCwzkQyd8oo+Lt2w3vgoXVxII50hLos8PqQUQR89KO972LmLnMKpnSvZ6vnbtplmTn13EkTT/x1HbeE0zIt5Axl7kIdotpUwC/+IrzdT7HnroSUUczcUQcGdxlRMsnclSi4U0YlZZkOjOIBAUjKMp0kc2cEpJ5A5s5IeM0IXqjWxc461GGxg7vdu4EPfrDnXey5y56Ehi5jcOfnUJapGhAQUw/ummp5iEeRM3farKXnziJyZie4a6omKv/0FeDoUfga6+KYst6ki5jzOEQpBncZaU3LLFFw1yrLFAzuUtoouI4XZ+6MaE3LrHm1bHvumhG8SK6LA3HhM3dKxSeVPTBzlz0JAz1Do+aHEekIc/5cLpm7+WB+6s/bvrZa0TN3a5mWqZ3ZKcUOZQPVz30B2LIFnrbsuSuhdBHz9XC+QMNhcJcRlQR1pSvL5ECVDh2ZO21bPXenLp6a2Y5Vagm/EcEL109wl9mV/UnQum9wx5677ClbvsxdqHMaqKIaWAgWpn7xIe23AzLM3BkT/8t7oEpSkjkr5Y3N/U+jes4W4MIL4avRf18qPm00gk9dz78ttTC4y4hMyzJ19sGdNhp/ctufZP64q1G2LXPHq30AAGNMK7gLNFo9d6cunJrtUgiNcF0Fd7OcufMdn5m7DElRrsxdGkDkschwQzYw78/nUpbZytx5GWXu0oxd3sEd4uAuzdoXmlJoHt6Hyv/x34BqFZ6yqwal//If/4LvP/H9KW0gZUFpicpN3+JFRmphcJeRtOduEjv8I+ERvP+W92f+uKvRVsNzfbjC4UCVRHtZpq9Mq+fu1MVTsy3LPNZcF8FdenI1q8Gd1BJVr8peh6xoDemgVMGd1MnSJsLLZVrmfDD94C7UYWdZZhaZu/QYlPE6dyNPyxQzFNxt24bQE6i+5D8BtdpQmbuvPvxV3Lz95iltIGVBW4NKPSz9+QINj8FdRmSyVMBEMndW55IZ0NbA9XwOVGnTXpYZSDuRzJ3UEn69Ca8RlX5nnb5mhQ/ulALsyiEKkY7i4I5XTLMhZRzc9XitZ1WkIwRuANdxc1nnbt6fn/rzrhiokkXmLr3AknfmLi3LnIVqlkYDTQ/x36JahSf1qtu9luUhKF/aalSWGrzISC0M7jKS9tyFE7iap83oVxfHZa1Neu58OI7DnruENhqOm/TcSQ1pJPY39uOUhVMyXQrBqzfhNcsf3KUXQwof3LX/t400EhWvwrLMrEgJVbLMXSu4E+70yzJz6rlbMVCloJm7tZVlxmYic6c1mo6J/xbV6lCZO201g4QZYqyBhYWvLNQsXHCgqWBwlxGpIjgGiCbQZJ1H5s5YAwEBxw/Yc9fGJGv/YcMGBJHGUrSEuqxj09ymTJdC8Jca6yK4S0+QCh3cpZmCHqWZrcwdT4ayISWkW77gruJV8svc5VGWqcLse+4mlLkbeVqmsKgoIJLNzLZjUqzWCIWJh9vUanHmbpVzlDwuJtPaaaPhCheeATQrrCjB4C4jSkWYl5MpyzR2+ov6aqvhCgfwvCS4K3eQMaz2gSp+pLBnaQ9OrJ2IwA2y67nTEv6xBrxm+WvoZyK4SzMFPYK7tOeOmbuMpGWZKE9ZZqjDXDN3eQ1UaU3LzDpzN4F17uwIZcBaWNQkEIUF3mclpArhQsB13DhzF62+rt9aAl7Kj7YaLhx4BszcUQuDu4xIFWE+AqIJnIxrM/0yCW3iHQZ8n8FdG20VXNcDNm5EECo8efRJbJrbBN/xMy3L9Jfq66LnrhXcqQKfKA0I7thzl7EoKm1ZpiOcXJZCmPenv85dx0CVrDN3GZdltv93GFoANQVE4bHMtmNSQh2hatz4i2oVXqRWz9ytYcgM5SfO3DlwTTzwjQhgcJcZpSMsREBoJtBzl5wQjHJ1cVzKKHjCTTJ3LqdlJuLMXTItsymxe2l3HNy5fqZlmd7ROjypoUoeVM985s4wc5eppCzTlChz1z5QJY8MWl49d62BKlktYj6hpRAAjBR0a2FRVYAMC7w2Z6KpmqjY5DSvVhsqc8eeu9mSnqvNUubuJX//Ejx59Mm8N6PUGNxlRKZlmXYCPXfJVbRpHqDTVD/LMjsZa1oDVYKmxO6ju3Hi3ImZZu6kiuBLA8+vzMzOeq1CNQMDVdhzNz0lLMvMdaBKTj13K9a5K2hZZvq6jFIhoQVQ0w6iaAaCOx2iatszd5LTMksmPVdzLWbm77ZnaQ+OhEfy3oxSY3CXEWXU5MoykxPHTE8gP/Yx4NFH+z+n0XAh4rJMx4Fhuh9A8rqkmbtGFGfuapvg/+AeyEY2ZTpShfBr8/CCKtQEBvQUSZq5y+Tkb1LYczc9UkJ6opRlmbkMVMmp5y5UIapu27TMgpZlpn+PkYM748xEz12oml3BnVo9c5dDGwitXXqu5pnR3sd5inQ0M4HorGJwl5GZy9zdcANw331971ZGtQ1UYVlmKs7cJdMyGyGeWnoqLsu87lOQB/Zl8hxKS3hzC/CCyrooy3SFW+zMHXvupkdKKFeUKnMXqhAVt5Jr5i73de6yyty57mTKMkf4u2gHqBl3NjJ3JkIlDe5qNfghe+7Kpr0sc1aCcmnkzASis4rBXUaUkXHmzk4uc5dpcGdMz5PV9uf00oEqjgPDnT2AJLjzfGBxEX4jgoXFpurx8Hbsgsxo3SOpIvi1BbhBtfQ7wEhHOK563MwGd+y5y1grc1ee4K4IA1VyX+cuq8xdpTKRgSpD72eNiTN31p2JpRBCFaKKtszdEMvrcFrmbInLMgVcA6gZOU+TmsHdpDG4y4jUEvPamZ3MnbWDgzvT1nPnuOy5S8QLu3uA6yJwfADApqM6viKa5Tp3cwvwKrXS7wAjHeH42vEzG9xFOkLVZc9dZqSEdAW0KF9w5zrrp+duxbTMrDJ31Wq+A1WUasvcFXiflWiaEFXrxV9Uq/DD1XvuuM7dbEnP1WYtczcr2zqrGNxlRBmFeeMh6nrD3vTvN429o2z13GW5wx0ic+dawaUQurTKMgH4QXzycuKeI/A1IDOalKqMgj+/CC8of9AwE5m7AQNVpJao+TVm7rISRaUry2wfqDLt90lh1rnLKnOXcXCXHlOHvoimNbQDVK0LKbNf0zZrTR2iguWyTC9cPWMys9Myd+7MNKs7K5RRcebOAmpG/m7M3E0eg7uMSKMwDx8hOt+wV33lKmw/tH2sx55I5m6V4E4ZFZdlppk7fhABdAZ3QVADAGx6/Gn4BpAZBcDSKHjzG+LM3YzsrNcq1GHxgzv23E1PshRCaTN30y7LTHvupr3OnZpg5i7Hskwj4wt4VcxGWaY0Cn56mletwm9Eq/bczey0zLe9Ddi2Le+tmLq0hcYzs7E+aFr2y+BushjcZUQZ2TO4q9cPQ415EMil565jWqYLk+HV0lmmrYHrxuWYfmUOALDp4Sfgn/zM7JZCsAr+woZkWma5d4CRjrCxshF1WZ/qOo4jGaLnbiavdBdRSZdCqHgjDFS58cbMApiGapRnnbsJZO5GHaiioxCuAQJ4iKLiB3dGJ4PRgKF77mZ2WmajAYTFz6ZmTZu4yso1gJqBCpK0LHgmLyDMEAZ3GZFGYV5UEKHzDVsPl6B27Rjrsdey0OqqrB14AtEqy0ynZbIsE0BX5i4N7u7/D/hnnwuZ0TAdaTX8hQ1wqzVomOIGPRmIdISaX0PgBgh1QQ/Mq2Tu0vKzMv+dpiYN7kqUuQt1iMAZIXN39dXA44+P/bzW2jhzl0dZpp7QOnc5Z+60iuBaIBAuIjUDwZ01cNC2iHkjWr3nblbLMqVcl2WZ6UAVxwJG2MIfh9KL4GW/cJ03BncZUVZhQVQQtgV32miEHsauzc+tLDMN7jgts8WgreeuOg8fLhYf3g7/rLMzCe6stdAw8DYcB1GtwcX0J+xNU6QjBE6AOX+uuKWZq/Tc+a6fyyTEUpISyrGlCu46pmUOsx9VauC+eZTn9RwPvuvns85d27TMpmqOf9I5gYEqo65zp2UE1wC+8BCpgl2MMmZF5kobDac9c9doDjctcxaP91G0LoO79FxNeB5cKwp/HEovLjC4mywGdxmRRqPmVaHaMi1pKYoaN7jLvSzTY3CXaC/LDGrz2GSqEKdvhr/heMgMdqrKKLhWQCxuiA/Gwi31TjAtWav5teIGd6tk7vIallFKSebOlKwsM32PDHXipVR8ojqmhmqg5tdyufDQGqiydy8c4cB3/fEz85Msyxzy9YkzdwKB8Io3UOULXwDe+c6Om4xR8dRrAKhUhlvnblbLMtdr5i4py8TGjfCsKPz5Qvr+m8n32AxhcJcRZRV8vwofDqJkvbP0ZHXs4M7kNS0TXAqhS7zOXZK5m1vEiccMcM458KvzmQw/kUbGDfCLi3FwB6fwO+txhCpE4MaZu0xKtyZhlZ473/GHz8rQYFJCingtsbJoH6gy1AWArII72UDNi4O7XJZC2HY7cMopwK//ejalmUrlvs6dlmlZptc6zhfG7t3A0lLHTcYaOCL5MAkBzwughinLnMV92TrN3KVlmdi4ES5E4f926eemzOc1RcDgLiPSavh+BRXrtq5QpsGd7CrfeMfX3oHHDj429GNPJHM3zDp3tm2gSsF3GNOisZy521g7Hqc93YyDu7kFyAwmVbXKYddJcJee+E61LPM3fgN4+unhv3+YzN2wJ+40WBRBCTvyQJWr//lqPHLgkQlt1Hg6MndD7Ed/+VVHcfDYvrGfN83c5ZFVbj65A9VrPwHcdhsAoHZwCY17vj/eg04iczfiUghahnAhEDh+8coyDx2KL9q20Votl2UC8P0qpBoclM7sIubrNHMXV/sgztyZGcjcsSxzKhjcZURBw/OrCGyvzF3nzvR7T3wPTxx9YujHzr3nzmVwlzKwcLw4uPtPi2fjhs9Z4Nxz4deyCe6klvBNW3A3A2UW48gluLv1VuCJ4T9/q2bu2HOXGZsGdyP23N2x6w7sPLxzQls1nlCF8bTMIQeq3Hqaxr4sgru8Mnc334zmIw+h8uH/F/ipnwI+9jHUaoto/K/PjPe4k+i52x3vB4aelqlkXJZZxODu4MEVr03HQBUAXlAZblrmLB7voyiTXtVZo42Oz9U2bIBri1/u2CrLnMX32AxhcJcRaTX8oIqKdRAmO/1GdAwAoLoOAsqoVSdWtcul585quAbM3HUx1rbKMsXGjagqLGfuRAbBnZHwDZi5myStgeYIk+4GDFRhz1220hL2UcsyIx0Vr0wuMWrPnRaAjMYvUe7ouZvm/vtrX0P4zGeg+oLzWjdVver4yyGkmbssyzKTASSjTsv0nQKWZR46tDK4M3p5KQQAflDjtMySic/Vkp47U/yMGDN308HgLiPKGniVKgIjljN3jSPxfV1lEFLLkd7YrZ67LHe4xgxeCsF09dwxuAOQlGUm6zdhwwbA84Azz8wuuNMSvsa6ytxV3Eqxg7tBmTvNnrsstYI7B3Hp+JBCHRZ2KY2RpmVaC+0AURbBXV6Zu3odTce0lggBgJpTQWPcpQMmkLkza5mWmWbuihjcdZVlxj137Zm71ddO5bTM2dI6V9u4Ea4pfkaMPXfTweAuIxIafjCHinGWe+6aR+P7uoI7tWM75H/8+9CPnUfPXass0/fhuJyWmYrLMuPMHTZsAJ73PCAI4M8tQmYwvl0ZBU/bdRXc5ZK5a4xw8syeu6mRMg4AtMCKE9VBQhUW72Q7EekIQaTh/uEHV3+PKJVk7sZfQ63Vczft92ajgabQraUQAKDmVtDQY/5OUgJBEB+7RnhvDGLMiNMytYx77txg1amTU9cjc6dNV89dUOW0zJKJz9WQZO5s4c8XOC1zOhjcZUTBwKvW4uBOdQZ3KzJ3VkMePjD0Y+fRc9cqy2TmrkN7zx1e+lLgT/4EAODML0DY8a+aSSPhaxsHjtUqPFvuK1yhDotflsmeu6mR7Zm7EU7gowN7Ee4af+HvSYh0hGDXbrjbH1/9PaJUKTJ3IVT2wZ1ScaWE52WWvVvTOnc2Du6iEVorpqJPz117WaZXmVt1qjOnZU7BTTcB//iPmTxU61xt40Z4uvhBE8syp4PBXUYkDPxKrbMss09wp4Qd6cps+mHNcof7eKWJUPbfBm00XGOX17kr+A5jWuKyzCD+4oQTgNe8Jv7/Wg2+wdhXc6WW8FVb5m4Gpl+No5W58woc3GmNf98k2HM3BelkYe1gpBP4UIcI9+6e0FaNJ9QhKjuegKuGKHdLM3cD9s3Dym2du3odTShUvLayTLeKxrhls0oBvg+4bmbB3cjTMlUEF04c3JmCZYp7lWW2L2IOwK/UIK0auKC8ObAfev/eiW3mRGiNxzaY4q092MeBf9uKvf/4qUweq1WWuWEDXGMKf76QniMVfTtnHYO7jChh4FXmUDGirSwzXnOmuyxTCgM1wsF7Epm7d215At/yd/S9v3NaptcqX1nvOjJ37Wo1+EaMNCinF6UlPGWAhQWgVuvfIL1zZykmg6XB0VQXMR8xuHtCH8IrrsTAnjvXGW7MPQ223HMnRjqBj4TNJNs1CZGOEDy2A64aotwtzdxlEdzllblrNBDarsydV0PDZLCIuefFwV1GGZrWIubDTstslWVWEBXt5LRXWWbXtEynWoOAGPh+0I069NLRiW3mREiJX389sE0WczmUbn+j/w0fxrZMHksZBS/N3Clb+ONQeo5U9O2cdQzuMiJh4FfnEOjlhtF6FAd33YuGxpm74Q906Y44ywN0JCzCAVce41S/5VIIXQYGd9qOn7k7dgS+FfEJTLXaP7i76ipg69axnqsIZqHnLtQhjgUYmLljWWY2pAzhwRm95861CKMpvX9GFOkIwSPb42EHepWAQMokczd+BqKh4uBu6lnleh1NE3UOVPGqaIyb6UozdxmWZY48UEXFwZ3v+YhsgS6uKQUcPdp7WqbjLt9Qq8EX7sDfV1sDncGyPlMlJZoeCtt3261pIzwpD8alpGNqlWXWanAtoAp6kSvFgSrTweAuI0pYeNU5VLRY7rkLk6UQunY40rEjjbqexEAVAws14EQjLssEyzK7aFi4fYI7z2DszJ1cOgxfJANbBgV3YZjJgSFvkY5Q8eJpmWOPSh+WMSNl7qSWaHi9BxClPXcsy8yGVCGq8GFGKMs01kA6Bc7chXUE+w8m0zJXOaFJM3cZrKHWkMtlmdN8b5r6MUirELhB67aaX0PDjrm/mkDmLs0edLdO9P1+lfTceVXIIp2cHoknc682LTNdXmfQRUgDAz1r+7IognKG/zvmTRqF3fMWePjhsR+r/VzNgwNdPzb+Bk4QyzKng8FdRqSw8GsLqGgsl2Um69ytmJYpRtsJ6TCZIJdhgGWEhRxw5bEjc+exLDMVZ+6ClXf4PnwNyHC87IFaOgpfJFdaq1V4us/0K6Vmp3l8gFlY505picgFdI9sykhj7mlVSktUhBdn7oYM7tILKmFRg7uD+xCc/hNwXX/VCggrJbSz3Hs4jjRzN+33ZhgeQ9WtQIjlxQpr/hwa42a6JpK5S8oyh+yBj8syHQRegMgWaP978GD83xXTMvWK4M7HKpk72NmrQpAS0sXAC9ZTceutwO7Ve38jI7F7EcD994/9lMqo1rmaCweqvjT2Y05Sqyxz1t5jM4bBXUaUMPBq851lmTLJ3HXtSKVjR2qY12F80jLNzF08kh/LmTueuMJaCyvQuyxTCPhWQNbH61WQx47Aa8/clTy4m4Vpmenr3+zxmW3vuWPmbnxShagKf6RpmenFtHDcddQmJDpyEJXnnhX3Za5y8mlkfOyY5cxdM2p0lGQCSXCHMYO79sxdhsGdYwAVDndhQCsJV8SZu0IFd4cOxf/tMS2zI7ir1eLM3YAKEz3Lmbu8l6f4yEeGapeQVmHPosgkuNNWt87VPOEWP7hj5m4qGNxlRAoLfy7J3CUH5oZswNM9eu6cldm8QXTy81keoLWwA8tKWtMy0567WdvZT4CxBsICwu8R3AHwrQM55o5VLh2B766f4G4Weu7SE4ZeZaORjhDcez+cRpNXIjMgtUTF8UfK3KUX06KCTsoLjx1G8Pyz4Qp31bLMNIM0yvGhn1bP3ZQvPIRRHdXu4C7IILhLl0LIeKBKoEcI7rSKM3d+AYO7Wq1HWaaGK9p67qpV+NYZeGJtYGey5046BcjcaT3UfisyCocrFo0f/XD8p2ybbO4KB7pRzN7jFJdCmA4GdxlRDuLMnbJtmbs6NoaAbOu500bDitHKbrTKfrqQweAPV6ss0/fhuD6DOyRXQRFPEO0lztyNGdzVj8J3kuBxQHD318/eg+3NPWM9VxHMQllmejDqtU6XNBL+rf8Kd6nOz0gGpIpGDu7Si2mFzdzVlxCcfd5QmTudBHXRuMsGYDlzJyBgYQeOv89SU9ZR9Wsdt81VFnAMo53M/d2df4dHDzy6fMMEyjK10Qj0CGWZKi7L9L0KIhToYs6hQ8CJJ66euatW4UEM7LmLyzJnbF+WZO66f6+P/eBjuPyzl+Pyz16O3/n670x+O4YM7mRyYWD3Y/eO/5RtLTSe8KCaxe65S8+P2cYwWQzusqA1pAP41XlUVFvPnapjQ9h5NSk9UVcy38ydgW3tYHqJyzJt21II/CBqq+HaAcEdXMjGeMGdqi/BSwcRVKvwVO91a7546kE80Oi/lMWsiHQ8Va/IwV36+jd6BA+RjhA0IjgZLGBPcS9y1amsqSwzi1LGzB0+jMhECH7iTDhDLJehk+NClpk7IcSq4+8zYwyaOkKlbRkEAHju8T+JhxdG+/vc8OANuP/ptrK1CZVl+gZQ4bA9d8k6d34F0YjB6kSlwV3XZ0ZbDcfpLMv0V1k7VWM2p2VKd+UF6288+g2c94zz8KZz3oR/+NE/TH47hs3cJRcGdi/tAY6NF4y1ztX8ZEmeomfuWJY5FQzusiAllAt4fgUVZZenZapmHNy1XU1K39hyhJG9aeYuywDLiFUyd0bD1WnmzmNWAmnmDgOCOwdyzKtmsrEE31sO7lzdO7iLhFlR7juLOjJ3zaPAAw9M9gmtjf+tJbjrkU2RWsI/1oRrs734sl5JI1F1AmhhRy7LHLS0S27uvhvRXAVBZS5ZC3GVzF3ac5fBSPd0EXMA0+u7azYRzgWo+p3B3QtPuQA/ODEaKXuojOp8Hdozd1mXZQ49UEXFPXdBDbJIn/eDB/tk7uyKskzPDl6PVc9iWWar567zfaGMwk8966fwqp981XSCiREzd3ued+rYx7zWuZrnwXM8qEaxM3csy5wOBndZiKI4c+dXO8syVWNl5k6vIbhLpwtleDK/Wuaue1rmzO3sJ8BYA8ciPsHowRfu+GWZjWMdwV2/zJ0sSXAXqnigSk0J1B+8D3jXuyb7hOmBd4Seu7Q3tdcizNJIBPUwztyx525sUkeouMHayjKLuMbVXXchqvoI3ADuEIOpWj13GQyGSBcxBzC9vrt6Hc356oqBKqcdvxlaWOxeWn2SYEpb3RncTShzV1G9J+H23CYl4cKNe+5EgT7vhw4BJ5zQc507x+nquVslc2dgoadUwpuZVs9d13wDo+A5cdAzlcqKETJ3J7sbsfuMk8YeqqLt8oV413Ghm8WcGpySRkJA8Hg5YQzusiBl3HMXVFGRdrksUzexsdl5oJbJwXuUE/O0T8Oo7E7mtbBQAz5cyih4qi1zx6UQ4itkg8oyhQfZHHMphOYxeH5yYlStwpP9Mnd29QWRZ0CkIwRWYO7d70VdKGBpwpO+0vfxWjJ3PYK7SEfw6024dkplbyWntELVHa0sMy3HLOQCxvfdh9B3UHErSeZuleCu1XOXQXCXR+au0UA4X0G1qyxTVCq48CmBu3ffPfRDKaNax9L4hiRzl+U6d2lZ5kiZOweBX0NUpAue/coy0WNaphl88aBMmTupJTzHiz970wgmtB7qvSmtxnOCTdh96sLYwV17Wabn+FBjLsc0aVJLVL0qM3cTxuAuC1EE6QJ+UEUgzXLmTidlmW0ZMpWsxbSmaZkZnszHmbv+O7s41W+We+544rqcuesb3Lnjl2U26/C9tuBO6Z6lXJFTjsxdpCME7/8g5qyL+knHAfUJH5jSq6prCu46P7PW2lZZJnvusiGNbC1+PezFrDDp1QzHXUdtEnbsQOTYVuZOrzJhMS3Bzzpz5whnOie31wQqWgAAIABJREFU9Tqac8GK4A5BgBc+Cfxg9w+Gfiht+mTuMl7nLtCAGjZzZ+JpmX6lVrzMXZ+BKu6KzN0qLRliBoO7tOfOrizLTDN3RSrLjKDxnOAZ2HOcN37mru1czXW9wmfuIh2h5tcY3E0Yg7ssSAkl2jJ3ac+dCZOeu+UPu1xD2Y1JdhaZBncCAzN3HWWZrg8zazv7CVg9uPPGXsRcNuvwg+TEyPPgGdHzxCNybCGDO6nlSDvtSEcIbv1XzL3vA2jYaOzm8lWtIbhLy5e7gzttNYQQcOsNuIY9d1mQRsF3A7hmOYu1mjAZYx8V8WRhxw5E0MvB3ZADVaIsgrs8Mnf1Opo1HxWvsywTvo8LnzC4e89ombuePXcZl2UGevl1X43WMs7cBTVEokCf91GmZRoM7LmLl0KYsbLMVuau8/dXRsF3/WQZkuKUZUponF45GburMtOyTM/1h17WYxKaQ0wslkai5tVyuRhqrGmdn5cdg7sMmLAJ4wCuX0FFmuWyTBPGSyG0HahVK7gb/kSk1XOXYVmmQbyD6fucRsNVJi7L9LgUAjDEtEzHH3rqWj8yrMMPlseIe8LtubOOhF11OEMe3n/L+/H3d/390N8f6QiVeoS5hRNQ183pBXejrHOXXARp2M4TQKmTLFO9DneWe+4OHABe97q8twJAPHzKd/24zHXYgSpRHb4GwnHXUcuaMcCuXYisQuAGce/yqsFdfOzINHP36U/DVWZ6ZZm1Hpk7IfDCfd5ombtBPXdZDVRBmrkbtixzObiTokABUDpQZcW0TNPZc1erwe+3dmr6M7MY3KU9d12ZO2mWyzKLlbkzeE7tZOxWh+P979Gja3/Ktsyd5/pDL+uRtaZq4ic/8pOrfp/UMrfM3Tce+Qbe/uW3T/1588DgLgMqbMAz8eLWgVw+IDVM1DdzN8obOz2JzzJzN1TPXftSCAzuVs/cueNn7lTYhBcsnxjFwV2PEfzu4AN0Xo6ER3A0Gv5AlS4lUJvfiIZqwtaLl7lrlWV2lf1FOorXJDx2DI61s1uWeeAA8P3v570VAOKLXp7rx8Hy0Jm7OhZDICpacL13L+ziQmsibFyWOWTPXQYLZLcyd7fdBsfYKWbuvJXBHYDnLgXYX9+PA40DQz1Uz8xdxmWZOi3LHPK9pnW8KHhQmRstc5dRMNpXv4Eq1sDpnpap7eCeuxksy7RhCOX2zty1BqpYPfm1HkfI3D2ndko8YOj444HDh9f8lPF8hPhCvOv5UDmVZTZVE/vq+1b9vjRz1x2IT8Oh5iEcbq79tZ4lDO4yIMMGfBNndCpRW+bORit77uRaMndJcJfhybwRFnLADlxbDVfpJHPnwcza9KwJWDW4c3zIMUsiZNSAX5lrfe05bqtPs+P7hO2YwloUyqihTyKtjYcP+fUmnNocKl4FzfBYvFTBpIxTltmVGWr1h9Xrs12WKeVIr8cktcoyIYaeDhxFDSxGQFikRaUBYMcO6NOfA0c4cB0X7hCDqdIgY1A/9LBambu9e6fXE1qvo1lxV0zLBAAnqOCCTecOPVRFGdVZQjWBgSrGGvhrKMt0gyqMGOE1ffnLgR/+cIwtXcWhQ8CmTcP13KnBFwYNbLwUyQxpZbztyuDOd/xWaerE99HDZu6EwWlzp2J/Yz/04vxYg8RaZZmeB88NcsvcRToa6vMgtUT1iady6Q2URhZz8NYEMLjLgIqa8KwAfB9BFA9UUUZBQmNeiY4lB9bSc5dm7rIuyxzYc9e2dorjsecOSKdlYkDmLoDsEYiNQsom/Gp7cOf3nOQWuShkWeYowZ0yCq5w4TYjoJIsZF5xgGiCO99xyjLRI3Pn+kC9DsfMcFmmlCO9HpMkrYLvVeBaMfQ02DCKl5wJizTgAgB27kR4+rNaA2KGy9zF77FMM3d798KZ1iLm9TrCSu/MHYIALzzxnKH77qY6UGXYzF2yzxJBgMCI4Y/jhw4Bd945xpYO8fg9yjJNj7JMT5vB69w5mLmyzPQY2X1Ok2buALSydxM17LRMYVAL5nBC7QQ8fUJ1vODOLF+Id73e5wvTEOloqNc30hHm9h3OZT2+SEcM7mh4MmrAtw7gOPEi5rKJhmxgDj58r9JRKpAOxxgUWHVrZe4yHqgiB5SVKKPgySRz57LnDhgic+cFkEP2bvSjZAivI7jz+gZ3RSzL1EYPfTU7LVdDGC4Hd4u1yU7M1BqoVkcry0ze+w2sHLO93HM3pbK3SZAyDqgzOmEeh7IKnufHmaahp2U24rLMAmbuotNOXQ7u3CGCO5lm7jII7tLM3b59cKcV3DUafTN3CAJceNwLhu67G3qgytatwB13rGlzDSwqGlBDnvBpo+EKBwgC+GaE5TekBO69d03buKooiv8tLq74DK9YCqFahS/7Z+7S98isZe5a7S7dPXfJUggApjNUZYTMXeBXcerCqdh9vDdWcNdelun5Fegon4Eh6Wdhtf2MNBI1mc9AOKmZuaMRqLABDwIQAoF1EOkQdVnHHHx4XgDVdtLRytyNEtxNqudutbLMdCkEz4OZsSt5k2CsgZNMEO0lztyNOVBFhl2ZOw+qa2dtjSlscKfs8Jm7SEfxVD3XBVw3Du421CY7VMUYYH5+xOAuKcsUna93q+cuzdzNas9deqU5zH+KmLQ6ztyNUpYpm1jULkKnYMH1iuDOX32gSnLiMW6gqo2GMvEgl7gsc4qZu8Dpn7nb8LzhM3fDDlT52teAb35zTZurrYZvnaEvJMTr3LlxlY4RAzNgHZSaXHB36BBw3HE9p4gaa+E6bcerahWeMn0zjq3gbsaO94Myd77rA4cOwY3U5I+Zw/bcCQvfr+CUhVOwe6ObQVlmshSCFww9HChr6WdhtX2cNBLVKJ8e9UhHmQyrmgUM7jIgZTPO3AGowEUo24I7vytzl/QQDJpU2a1VlpnhlQ6DeAfTr7+pc1pmsOqJwY7DO/B73/o9XPOta/Def3lvKZtW42mZGJC5q0COOWZXagm/Ot/62nO9FTtrrSWsKG5Z5rClL5GOEDg+UImv8te8GuqLlckGd1oDc3NxIDNkb1/6WW10lf1JI+PtVwrutAZWdPn0Dz89/vPKZL9SgNLMOLgLkrLMETJ3xkfkFOOENFQhPnvfZ4GdOxE98+RWcOe4HvQqf6vWOndjlo+lJZnCWmD/fjiYbNmwNhrv3/p+XHPoC/habefKpRAAIAhwdu05ePzQ41iKVj+Z7ZW5++7h+/DYXFeWOYqGujCx8/BOfOex73TcZqxFIDyoYYM7o+IeNt9HoEfI3KXB3ST6iduDu+6yTBg4Tuci5r7UfYMcbTSEncHMnUyDu87fv1WWeeAAPGUy/Qx86YEvYX99f+eNo2buFk/FnkVkVpbpBfln7lZ7jaWWqEUGKocgiz13NBIVNePMHZLgTjXj4M568Ql/e+ZOriFz11rnLrsdkxGActB3R6Rtkupv9dwN3tlv27EN33j0G9g0twmfufczeGDfA5lta1HEmTv0De48L4AccjHcnrSOe47aB6q4/op17tKhLYXM3I3QcxfqEIHw4zJJIM7czVcmX5bp+/G/ITNVyhosOLXembtkEp1jbC49d+/453fgqaWnxnuQNLgrwFAV1ZG5G+79HckQiwgQFiS4e3j/w7j6n6+OM3ennNRRlrnaZ6PVczdm/2Cr5PnAAcCYiffcHWwexB/f9sfYpAL8vDgbbzr3TSu/KQjgK4uTF07G08eeXvUxtdGITGfm7trHv4R/Oe5A53FLyqE+y9989Ju49q5rO24zMAgcb+gSsbTnDkEwWnAnZTwR8cknh/v+UaTBneOsLMvs7rlLM3d9fl9tNQIjYAQmP1kyQ612F6y8AOc5HhCGcG12x8ylaAlX/tOVuPeprmzsSJm7pCxzzoxflimTzJ0ftBII09YK7obI3MXB3fTPX9ZTz13vs1QaiZIhfBvvQAN4iFTYCu68oApll9q+N35jde+EBmmVZWY9LdPBcqlLF6VVvKab4wxVlim1xPknn49rLrkGX3n4K6X8ABlr4Np4sdBe/KAyXnBXr0MF8Vo1qV7BXRTFwU/32OciUEaN1nPneEBykT8O7oLJZ+5cd7nvrtqjfKyLshqL3hwaTud2SS0RJLvQvDJ32mgcbB7EqYunrv1B0hK3gmTuvDRzN2zPnWpiAyoIXSz/fXN0JDyCg82DOLxnO8KTN6HyYPwGd10fepX9fitzN+YAq7g3zAX27o2fe8JlmcooLAaLuObIhcDGjcCJz1v5TUEARFH8OZerX8DpNS0zhIJyRWdZ5pDB3cHmwRUXYIy1CJxg6CzxWJm7c86Js3fPetZwPzOsQ4ficfo9M3cryzIHZe6MNfCsQIh4f+aKfD9Lw1qeZdAncxeG8HQGpYDbtwNbt+LzWwyWoqWVF/SGzdw5FkFQwykLp+ChqhyzLFO1qqw8v9o6x5y2YTN3kYpQy6kskz13NBIZNeElL2VFeAjT4M64cXDXdkBXKkJNiZEO3umHIMvMnRaID5KyzxU8FY98BpCUZQ4O7tqnUgVuMHwvwgzRRg/M3Pl+dbyyzGPHICt+3COQ6BncJZm7Ik5n1EaP1HMXCK8jc9eY9yefuXNdoFYbOlMlYbDBX0Cjq6cr0hH85HOfV8+dthqHmofGe5ACZe4kNHy/OlLPXaia/z97bxpkWXqXdz7vds7dMiuzlq6q1lrQopHQ1j32jAQOGzvw2CYmCJuIiQkbzAdo4bHliJlhIvjgYBzYYc9ABME4DDYxgAYDRooYJIgRGBnEYoMNRt3TjZBES2qps9fKqq7KujeXu5x3nQ/vOeee9d5z7r2ZWa2uf4RCXbncPHnzLO/z/p7/88eWE5AM98XvcBQdAQD27D3I3e1CoMoSchf/znJdceeMFyGxuKPudGPg0/v/ZOJtz1XVUtxV9dxFVvnn1grkbjQbla5RA4uAisYUIRXNQkAY107cPf448LnPNfv6NjUcLui5K5A7Ifycu5q+LGMNmAXY6yz9N7VlFjZPklEICblb+3f6zGeAf/yP8ZGnP4IO75SvqaZpmdRBBDG5C6L1xJ0xYPAb8SwIG88H3XQ1Jnc6Qledj/PojUTuHoi7DZRWUSruAsIhjcRUT9GzDEKEOSGndISupQuTKotlrPY++A16lC0BlKD14s5kxd3yUQjKKn8ThRd3X4sX0NJAFdGBWuf3PjmBCnn6PgLe6lmM6ZZRQu7uQ1vm+Bjm5KjR16biLu6564keJh1+tuSuQWkYbAVlcaesQgAGEAJm7LmQO201htPhei9yv/TcOT97U4jYltnw/JY6wsAJSA6409wYaFipuPu6XUiYfKBKQ3HX5vlQ+ToFckdBTnXzIRV30+nGxF1Vz13klG8nWIXcDW/CDPM9UhYOAQ9a2DJj0RwECHRLW+bjj59OqMoiWyYK4o4QCFKdwAx48UOdJ72vp4CodD5kYZ2SpmVKCW428MwcjfDs9GU8f/AcvuUt31J+j9qQu7Dne+7YbK1nXnatxoNO47Eem67GPXdaoqvPZ/2iZuN0c/xrvc5N3BFC3kII+T1CyLOEkC8QQv6n8zqWdUupKO29CcERJWmZlsa2zOwoBImuZa3InbUGwpxCzx0ntbtM2khw4kVMk567Irn7WhR3xvldzVpxF3TWI5bjMbRg6fsIAJwFJZuFjGfptRmncVal974K++yfNfpaaSRC5MndpHuG4q6hmNGw2Aq3MWUV5M4RYGvrXHruXDx+4WuG3BkDzRDbMmnjdOBIR+hAQBhATY5P+SCXVyru3jyY974BYDxoFKjScQxyXXHnjL+P3LnjrfVnQO4YZZsld8U5d4m4K5K7hoEqo6/+GcxXnst9zDqLgIeNNxK8LZN7W6Z2zZP3EnJ3muKuypbpXF7cAeCEQUXV779vPcBmKNcZlkp77uptmcyh8bD62jo8xM89BnwP/68Q8rCa3C1Zp1lnYSjARcenZZKT9XrutASP158s6Gx0HnKbSq6FpeTOROjo8zm/5PPPQY2bbT6/3us8yZ0G8L86594J4AMAPkwIedc5Hs/KpVXBlhmLu66h4GE3d8NRJkLX8YVhJsUyzkBYL/I2VZYAii0id7pA7rAw6UsZldoJv1bF3VJyF3TXJ3cBy9syq8idvH9tmb7nriFxqSR37PRtmZS2t2V2tjFl+fNfGYXAUmB7+1x67pK//9ri7n7puVMKSjAIKjy5a7hIkTpCQDkCSxCNzz+l9yg6QkgE9h4SOXFHOYdZsqlnjEIH3CcZr1Gp2LpzB7hy5dR77ow1c1tmt1v9ReuSu8SWWXx2NiV30aj0/ntyFzbuX06JKGO+5042vGa0Bt73PuC557wY3WRle+6KtkxYfx5kSlABHS2wZb6OyV2VuBMstmVarD3gW40O8AuPUXzvFwI/N2+FnjtlFIQBiBDelmmP4E5W35RKx3MAfr15n5M7qeW52TKVitbeOHu91LmJO+fcvnPu6fi/jwE8C2DDncZnU0pHEDHlCoiANMqTO0Mhwm7OYqOVRM8xKEYap/UZaxCYDY5CcG55z53RaSM2pQx2iRhdRu7+4W/8Q/zhy3+4meM/p7JG+yHmNYENXtytR+6SxW1SXJTF3Tz2+f57+GrXvOcu0hECR/PkLiCnP+eutS3TYqtzwYu7zAaHNBLCAtjeBt1Es37LSn7ecLaeLfNgdg//7d9Fvbj7+MeBj31srZ/RqJSC4gSCxeKu4fsZGYmQCISWIroPdmWPoiN8k7uCvS1PnpKB3oyJpdeGNgpdrD/WIWfLvHYNFGfUc9fQljlVy0WRcQaRKQSqWOmDwFawZY7UccmBYmAR8E5p+PWiY2LUW7EDS5qJO+fw33+nwZ58DXj724EvfrHRz2pcMbn7zec/jR/6Vpu7R3lyl9+M5JRD1Rz33JZ5f24e1lUTcsctYNYJPAPw++Nn8XZxBY9++hlQQlcid8oq/9zgHP2gjw4NcDC9t/IxGaNSAc+48GMsatZ1p1mNe+5MYsvc4Pn10Y8C/+pfLf0yqR+IuzMtQsjbATwG4I8rPvf9hJCnCCFP3Yn7B+630kqCx5QrpGJuy9QEPOzlyZ2W6DoOxVuIO7dhW6ZzntzxenGnjUpRPyUUltZbOIF8z52gomRX+dLBl/D0/tObOf5zKqOVn3NHSOXnRae73oDM2JaZJ3chdEEoy/tY3BlnllrPkpJG+p61LLkLT1ncrdRzF4s7gdyDW1mFwBLgwgUwc/a2zGTnc11yN4wO8R/fBrg6cfe5zwGf/exaP6NRKQXNKTjlYKDNA1VMhJAKhI5C3ge2zMPoEO+bDLAXTPwGRtaWucTeboxGlwioJPlzxcoFqly9CnrKi/U2gSpd3l2d3BkJXXxvmpI7Oy7dmyz8MOmmGwnGGjDmxZJwNO1/Xlha49krwPOjPeC97928NTMOVHnh8EXs7SAn7gyxJVumoLwU0pWUddYHqrhTCoj6/Of9eI4NV/KM1JmFu3P+nswIA6T0v9OaM+D2o7v4+t2v87NNp7OVeu6kkQgMUgfQe3o38Kfu1srHlF2rcSqgA3EuLoxWc+7Uhu9HTz4J/KN/BNy9u+RnSyjqXldjPlatcxd3hJABgE8A+J+dc6VtV+fcTzvn/pxz7s9duXLl7A+wQeXIHRWQVmXEXRc680DXRnlbZkty522ZG8LYsbjTySiEqp+Z2Q1Kxd2C3aBl5E4aib3h3maO/5zKagWKamEHACLsQa3zNzo5gYoXt0lxUW72T22Z96FtxpO7FqMQLEnJXZd3MeE4m7TMNj13xGE7EXeZa0AaCWHgxZ09+0CV5OG4bqCK1hKSA6PxQfUXGHM2O8FKQXEKQYUPAGk6585IBEQgcBTRdPXelU3VUXSE973G8IK752c5puJONLBlanRJAMmxln0vJXd373pydwajEDZpy0yOtdRzZyU0QX6jsWnPHWZlW6bzkfRNN8pSsQAgcCQdS7OwtB/fcHdy14u7TSdmxuRuoiYwhX5E61wqRpPiXNSTu8SWeVppmf/sn52KC0CltszMWis+JwkhKblb15Y5ig6x290FvvVbQe/cXSktM7FlJuLusQuP4mm+OrhI+0DhqawJ+Ok+Q2uqNbnb5Pk1HPpn+o/8yMIvS45x6Sb8bAb8+q9v6ujOpc5V3BFCBLyw+yXn3K+c57GsU1pJ8FgIhTRAZKUXd4qAd3s5W6YyEbrgfme2BbkLzAYX89Z6crdI3CWpYIjFHVlC7pb03EkjsTfagLh78smFvX+nWY3EXUN7T2WNx96WlrNlhmVxl8Y+33/2Au0sjG1O7kJXIHcCZ0Pu2vTcEYutzjamHLnrRRmFwDjgwoVztWWOovXIXWL73Z/WDJbWevN9QlUlZcGW2TBQxUqELEAIBnmfiLs33TxGl3fxytErLcWdQocIb9tfR9xlA1WuXQN7nY1CSKh0+hxxbn1yR2WJnFo4hG3EXYbcBY5BNRELSsFQeHH37nd7erXJinvuJmoCU2ifsCgHqgga1JI74/y4n1Mjd1L6vsMNV/KM1KQs7gCkPXfr2jKH+hg7vUvAt34r2Gt3V+q5K5K7x6+8B890V+8VzrbQMMqgz1vcNSR3G+25G42AH/5h4Od+DnjllQU/2x/j0kyIn/kZ4MMf3tzxnUOdZ1omAfARAM865378vI5jE6WMTMkdj2cZncgT9LRf8OduOEajC+GFVcPFZWrL3NTFYH1ak2ZY2HPHY5HBkp67BeKuEbnbhLj7ju8AXn55/ddZoYyWfp5MTYlOD2qd3aiTk3RxmxQXHegCwbifbZkaLefcZchdT/QwYfb+smVaC03g0zKryJ12PlDlHEYhJA/HdcldMpuxVtwZczbiLkPuvC2zIbmzCgEVCMAQTc/flnkUHWH75gFu7NzAl+5+KRV3hPvretF5kpI7hrXe81ygyrVrZzfnbkOjEErizlqAUsz0zJO7orhbci3P9AwRcyVxbWAhwl5jSuXtrom4o5BNxJ3W0DQmdxcurJWMWFkZcmcpySVmGjjQQo84ZyLtUStWmpZ5WuTulMSd0hIcLN8CY1Ve3DnUitqmNTJj7G5dAf7yXwa9/Vp5A6pRoIrMk7trj+Pp7TXSMo1K4UJK7k7zGVpTSd7Ask0BaVWclrnB+9FwCLzrXcD3fz/wT/5J/c9ORs0ssvxHEfCjP3r+AWNr1nmSu28B8HcB/BVCyJ/E//v2czyelUtrmd5ECBcIqMBoNkJPAbzTy/nAlZbznorGtky72bTMpOdugWDTtp0tc9mcu43ZMqfTc7volpK7oLuQhi6t8RiakbwtMwihCxYCGS/GN3pz3FBpWBg0O08jEyFwJE/umD07W2YTcWcMNCde3HHAZRbcysbkbnsb1Ngz77nbVFpmMmpjP6qxZWp9ZrbM5PxnoM3JXRxaEoIjis5+UVOso8kQ25LgxuVH8KWDL6WBKuDJ71V/nhir0aHx5t+mbJlJz91ZzLnbkC0zOdZkIwtKAdwnUWvqWgeqJBsgVeQuKPTFLzyurLgDbRaoojUMBQ6mB+l7sNGKe+48uSvYMmHT401K8AXkzhow606P3Cl1auTOXzc15E7KjQSqDDHDzoWrwCOPeBp+q9Ar14TcqZknd9Qvv9/1pvfhxZ7CWK5278raMhk5R3KXrkuWB8r01CnYMnd3gR/8QeDf/tvaNWJix1xI7n7+54EbN87lPdxknWda5n9yzhHn3Hudc++P//cb53U865QyEiK5iXCOkMTiTjrwXr/gA1foEuF3H9sGqmzYlrmw587q1H7SxJaZvZEKKirF3WF0uP7A5en03OZxLRV3VEAFdPWbwngMRZG3ZQbdkn0hIS26oYg6yzLOwrawZQYmL+6m1Nxfc+60hmIEHd4Bc4DMiAdpJISyaaDKuZG7NdMyE1vmLVX9OjftIV6xa45baFJKQTF4WyZpYct0CiEVCAiHnN0H4u7kANuX3+TJ3cGc3IHzpSMJjNXo0nBtcpfO5AxDoNcrkbs74zt4YfTCyq9frEa2zDBsTe6iWUw0tAaEQKSj8iiEBj13o/i5U0zLzIm7BnZ/Y+3clgkG2SD1E8qPb7g7ubt5cWetDyi5fLlG3DnQUs9dUEvuUlvmaZK7vb2lfWltSxmJLg1Ltsz0WZqOQliT3JEIu7vXAUJAty7AHGTcDs7hS7sWR27x+kTJmZ+PGpfY3sW7Dig+e3u10KpsCw2nHEac8jihmkrCrJb23Fl9Oj13Ozte4AlRe43JJeLu868+g+Mf+9+Bf/7P/Xv4Og5eOfdAla+F8lg8volwjoBwL+4iB9Hdyu0mKaPQQ+A/1ljc2XgUwqZ77uojc40p9NwtsWUWe+6K2DvSEbbD7fWsmdb6i/acxJ23ZdZfMoIJaM5WJ4snJ+niNikWhCVxJ2tin++H8uSujbhDzpY5Jvr0RyG0mXOnNXRsle1qgulsbp9RRiFQdk7uzqHnbhAM1iZ3SR/Cvql+nZ8NPo+f2Go2mH69A1FQjKxmy+ShTyq+H8Td7BDbb/sG3Ni9gVsnt/LiDmThotkYjQ6Nnw/rkjtjgStXAEpLovJjn/8YfuwPf2zl1y+WthqcxLbMReQuiry400vIXRzJL5N7n9ZwnHlyR1zrnrvh8Ca4qSF3QRemGNJSd1zIpGWCpffihRWTu1TcbZKCHxx4qyfnmOqpf04XbZnFnjselBKYk/Jpmc6Lu9PqudMaePHFjb6stv660QTpglyZvC2TW/8MX7mMwVBo7OxcBwAwQvIJ5tbif/srwG9sLU6+lHKKIPuI7Pfx+KsWz9xcLU3cb8TPW2g0PydxFyfHLiV3Tntb5ibXLwm5A7zdteZaTjIR6sTdD330Cfyf30yAv/gX0/vV67UeiLsNlNIZcicEQsIxnA3RlRa82y/03Cl0aVtxt9m0TGcMHImbj+tGISQPa2TI3ZppmY9eenQ9a2ayGD83W6ZeTu7EGuJuPIYmKNgyO2Vxp2cINu1Z31Bp0pyw8jO9AAAgAElEQVRgpeIuJnf9oI8x5P1ly4z7ZTjl6BqK6Wze0+XJnQEGA2/ROeOhrMYZXOxexFF0tBY11Km4q27q185giDPYUFnVlum0t2US0Xyo9CnWoTrBhXe8Gzd2bgBAWdwts2WyAJoCbo2FhXEGTMfijjFQl78utdXrzeQslH9eUD8mRojqL2ox505bjZ6hkElAlVJQgb8v6hXm3I3uvoLLk/KC0hCHgApoThvdt42d2xwD0pDcaX165O6114CHHgKAenJXEHdchPXkznriy+wpORGkBLa3N27NVEahyzo+RyA+N0qBKm5NW+bREUY9it3eJQAAJSx/jzIGmmJpYrZSMwibWUcwhsfuCjzz6lMrHZY2Ot9zd17kLk6OXTbCRlntA1U2Re6S638w8P/mvNYaK5eIO7X/Kn7uHWN/7vd6r2tr5gNxt4HSRoGzObkLicBwOvS2zG7f7ybFpaxClwReWDUkUNZZ8A323CWvoxaIu+xukBd3aDznrlbcXX50PXKXvF/nZcs0aim5U4KsR+6oy9syw3KSm9SRtzXcp+SujbgLM+SuL/oYO3l/BaoYA8VjcWdpntxZhUD6hwAlFEaf7eBYbTUCFmAQDHAUrT68W2sFboFbqG7q185gRM5gB1Mpb0tmAozQ5vZepxHwEAELEDWJpj/FMtZgAon+o+/Bjd2CuBMxkVxC7hjhEJZANZmhVlPaanBtgMuXY3GX31E31mw0rU5bDe5IvSUTaN1z19NkLu60RtTxC3W1Crk7eAWXpl7MZcvCIWCBH03U4H5gXCYtEzyN4F9YSsGQsxJ3yJE7C5c+x5MSvJzAnJSx2g8xP01b5jd908bFnbYKXd6Bzszj1VbPXTBSelumWuO9H40w7BLsdHYAAAwU1uTFnSXLRYuUEwQ2v454/Li/8hzgHLkj50nu4hFNS95j6TZsy0wsmcn8YcYWkLt47VszCkE7g5f1AX5373e9A+GBuHtjlzJzYeNtmSy2ZVqI/lapyTdkASwB7KyZCDCwGx2FkNyQ9CJbZuYh1sSWuZTcySm+sfOWzZC7c7NlKtCaAeZATO5Ysx3gyhqPoYjNp2WGHWhbFHcSXUM2a2vYUGni2g0x1y5P7uzsvuu509RbBbuWYlrsuZMa6PfLD/ozKGN93P1uZ3etXlZlFN40Fdin1e+7sQZDekajEBjmtsw2PXc8REiDdIFxXnUiTzDQFPSd78LbLrwNBCRH7pYFmyTDx4Ula/UPGmvAlMmQO5TInV5nbEuhtNXgtpm4azLEXFuN0BBYxCNGlEIUeDqhSSFQpUnP3eg2LkWs2pbJAj8frgm5Q5bc8Xngy8JfRkMTdzqBKgVxZ5uQu6CTWrGLZbWK0zJPabSLUj7VcNPkzuoSuSumZa4dqHJ4iFHo/Jw7AIzQErkzDcSdklGu5w4A3jO7gC8On1se0V9Rfq3m1wyccmhBgeOzTw1W8bWwSNw55xNrN2rLjEeBpLWE3HFTT+4MLP673f8GH3nmIw/I3YOKH2xZcgeOY3mM3syA97e8jSQuT7g4hKNQs2YnjnF2o4EqiX1MFR+S2Z9pDXguUAXL0zLj90CwikAVNcOjf/Tl9chd8vA910CV+kuGUw7F1yB34zEUbIncFXdQlZHoaXpfkjsDB9vwuCIdIdA2T+7M7GxsmW167lhC7himmUQzZRREpD25o2y9fo4VSlsNRhh2Ojtr9d1pI/EW2cU+qz5vtTMYsbMZhaAz5K5pz13kNEIeIuABoiY2uVOso8kQ21MLPPooQh7i4a2HEfJsWubynjvGOAJHodYQqsYZMKnnPXc2L+7MzVegX93cSBlP7rA5cucMuHF+3ICROXJX2XNn7cLNx+Hxa7jkOhXkDi3JnU0X0gHhaULgwop77qZqignZ8MzIIrkrjIkwpByoIipmp6ZfbxSYI3HP3Qri/8kngQ99qP7zCbn7ylfav/aC0lajU0HuSqMQ1rhHm+EBjoXFdrgNIF4XZc9DY/yIqSVBZ1LNELj8OqLX3cbX9d6EL7z2hdbHlf09GWUw2wPgT/+09eusW4klfpGAVlaBg0EYPxN3I5WQu6QWkTtY9BVqr1sNg++5/tfwqec+hXsXgtf1OIQH4m4DlRU24BwB/E5Zb6rBe4OcuNPWQLDAi7uGthvj4lEIG7oYjFYgDj7Fc0HPHaNZW2bztMyABTns7ZyDJBaP/tofvq577oyWYIvIHRNe3K0qTk5OoGHzPXdhr2zLNBF6luZSWO+X0rQluVMFcqen95ctU2sohrm4K5C7IFJArxeTu7MfhcAow253d63ETG0UrrguZsRU9kIZZzBkZzMKQVHMe+4a2nYkDALeQchCRE1IyinW0fPPYtuIVOTc2L3RKi3T30cFhCNpQMEqZawBk6qW3Jm9r0K98tLKr18sT+5QH6YCtJ5zx6zziZRGlsldUdwBC+ndaHyAy6RfSss0xCHgoe9VayrueJIK3VDcKQVNHC73LuPAnpx+z13JlllIyxSd2n5LoxUo4iHmqwihV19dLCxOyZaprEZXlHvusmmZ6waqHB3sY8twUOIXdFXkzhIsnXWr1AyiIO4wGOCxwSN45tYzrY/L2AK5290B/st/af0661ZCsRe9x8ooCFAvtDeV9p0NUwEWBqpIYjCQqCXu2lk81L2Cv/GOv4GPfv3kAbl7o5e2xZ47fzPtzQxo3zd5JhaHxCrAQaCbkrvUlrkZG411FqEBFLGLbZm8QO6WpWXW9Nxpq8EccOO2xAvD5+Eaxst+6JMfyi+Ezojcffqrn8YvfPYXSh+3Ri8kd96WibXInYQp2DK7JUIntUTXslpbw0RN8OF/9+HVjgHAD376B/HS4WoLP01caQFVV17c5cndRE/gxienF0G8UqCKF+5dVyB3VkHMEnJHlzaSL6uf/MxP4pn95g/3xJbZhty9cvQKfuh3fyj3MW0UBA9wTQa4dVJOetPOYCTOwHKqVNpzSosLpwUVwSAUHYS8A6nPWdx95QvY5nN69Y6L70BPxP+Oxd1SWybjCECh1giH8eQuL+6yP9dYcwq2TGy0544bhzBL7upsmUp5UblA3A2nQ1wS25W2TMGEJ3eNbZkJuSs7VCorvodcHVzFXXV0yrZMLB2FIMLyeJ3064325M6Rpb1TVfWlo+fxfzz81drP/9Q7Rnjy0gx4+eWNpoZqp9Hh3VzYToncWazVFz0c7WPXhem/KWFlckeW98JLXSZ3GAzwWPA2/MmtP2l9XNkWGkZicvf888DR6n3Yq5RsYMtUVkGAeaG9qc3porhjrNaWqWDRl6i1vGtYcB7ge9//vfjFN997IO7e6KWshkisN0IgcHNyh27XI2ibbfINIMAa2248uSONAwaWlTU6TWuyNXNf/MKxEKiyYlpmkoo4+N7/EVuKVi4gi+Wcw88+87OIsjujZ9Rz99TNp/DRz3209HFrdLprV1WCCSi6ui3TnhzjnjzEpe6l9GO8Ux6wK41E1zFoUn0+vHr0Kj72+Y+tdAwA8Gtf/jX89P/30yt9rybNUzylkQiVTckdowyCCszE4mTWtSpry2zydzIGOiF3jmOaWZRKIxHMYnJH2NqBKr/3wu/hi3e/2PjrE1tmm567z7z6GfzGc/lxospqcB7g2oxj/2S/9D3GWYy4abwps3Il4q6lLVPCIBRdBOI+IHcvfBHbnQvpv3/8r/04vvu93+3/wbknIotsmfHMKuHYWsmf2mrwSM/FXSH90DizOVtU8vOM25i400aBGYfAkgy58/feHLlzsftkMFhM7uQhLnUu1gSqhF4UtCR3AW0WqGLjxe5D/YdwVx+esS0TFT13YW2ghFESDMTbh1fYrPryyUv4xMPVqbsA8B+uzfDMyVeAhx8GXnih9evXlSd3PaiMuMuNQoiHmK9jyxwd3sYOmZPpyp47iqWbJkpJiOLSezDAVdP1oTstq9RzBws89hjwmc+0fq11Kh1ivuA9lsb/7sz6jZKNPFNakTvryV2NK0ITCy5CfOPlb8QrneiBuHujV7nnLiPuwjB3U1HO97IJsMY7swYWAhR2Q+lC1vhELA6aNsEWSzsDxlumZbJqcieVj+7H3//7uHFHYW9/+cys5OETmbMXdxM1wdP7T5duPEYvScukwofnrCjuDuwJtoLBvEcH9eKu53itLXM4G+bft5alrca/+ZN/s1JDvabN7cPSSgTSpOQOiK2ZF3qbs2b+nb+T/3skc+5WGYUAjmkmjdGTOxmLO7r2KARtdauEOuPak7u94V5p196TuxDXJwz7xxXiDhaG+rCQUy2l0lEgTW2Z1vm5ipwHCEV3pUCCTdbhq1/F9tbl9N87nR10eHx+NyF3NkvuVr/PGWvAIpmKu2LPnbYmnfm0iUrF3YZsmUbOwC3m4i5L7pDprzPGp+QtI3f6BJf6V7z4yZQlQMhD//Em4g6ZnjtaDg7DbAY8+qgPeYhLyxmYA670ruAgGvlj3pSFe5W0zKCe3BmjQLE6uZNygv2OrnVeKFgc2SnwyCMbtWZqZ9AJYnIXbwyWyB3ja9kyhyd3sUv76b8pZfkchMSWuSK525Kr3WO1NeB8PufOWAN88IPAH/1R69eCaz6iq1ipuFtE7oxCAAYCgC7pP25cbcgdcegr1N5bDRyYCPw6hNkH4u6NXsplyB3nCB0DIwwiUoAQ4BapkEvIHScUuim5g4MgbGOBKiYWdwIUum7eTWY3qIm4W0juRncRWAK86U24IR7C3m9/fOkxJt+fe3iekS1zoia4M7mDm8c3cx9vRu6wmrhzDrfoBNcG13IfrhR3VqELUdqFTmo0G621yFVGwcHhN7/6m+2+MQ4OaDXEXM7JHRCHqlzobk7c/eqv5u0pq/TcxX1gXfBcT5o0EsFUAv3+RkYh6Bf3YF56ofnXW+177jrNe+72RnulXXttNbgIcX1MKsld0vO5Tl9fo1IKivpAIUZYo80FaaRfLAQBAtFZa1NjE3V06yVc2L1e/UnOQbGE3MX3XYH1yJ1xBmwWpYEqpZ670yB3eoPkbjoGs0BgMuJOeGWWI3fKP2MRhguv55Gb4vKFayUrmIGDiOcKNrZlppuYFbbMj38c+PKXc/ccoyS4o7jUvYS7SWLmppwJGXE31dMSuauyZfKwW9sXltoyQVYSQlLNcHsAmMPqzSYNiyM9Bt7xjo2KO+UMuqJXsmWmLQ5RBMYE9Br36NH4ADtiK/13fVrm4utK6QgCeZqKfh+DyK0k7rIhP5xyL9w/+MHV+u4+/Wnge76n/fcBaQLrouegsirtN+Sgm0lkHY3ygSqLyB314k7WjMzRcOCi49chVD8Qd2/0yu6cgPuks67ogigNBAGE9bt3QELuBAThjXdmDSwC8NUvhG//duCLc7uXtQYUJCZ3deLO5kchLJiJBwD6cATxq/8vAE+wsg+96N4dhPEFfeMb/mvsPfXppYdcKe7OKFAlWXgUm5uXirt1yF0UYX+b4vrWw7kP807PL2YyO6HKKPQg6snddAht9coBPMoqPPHYEz4OuEW5KIKhaNxzF+momtxtdTZzUzXGnzOFvoj2oxB8H1gXAlM9/x5lFMQ0JneUr0/u7tyGfaV5guEqPXd7o71SmIKyyou7Y1dpmU5stuskcjYqKaFIxpbZ4H4X6cjvgguBMOghOk9y5xyODm5i+6E3V38+sWUuHYXgQ7nqXBVNylgDNo0yc+5cfs6dM5sLNEA7W6ZgAoSQhUPUzWzqyZ2J3RtKIRLUh3XBVou7ReSORLh08c3+OZYpSxwC3nzurLdlxqnQtELc/et/PT+uuLSOwBzB5d7lzc+6u30buHoVyih/zy+MQqhMywx79eROq7VsmVLNYChw91ZF351zUMTNxd0GEzM1DLphP9ePWRqFwPhafdHD2RC74dxyTSnLW8cbp2VGCCpsmYOZxbFsP8LAZFxWjDB/nSfirq3t8eAAOKy31S4qaSQ6qkGgivOknTu6mVmbbcgddeg7Ubtx5m2ZAQIWwAGQ47PtW9xkPRB3G6gyuaO+iT5+8HBHUnGnnYHgAQRhUA0H7hpYT+5W3Wl96SV/AcRl44ATAVbrQdfOgHOf8tbIljk+Av/jpwCU0zLl6CBNEL3xyJ/Hnry99JCTB/+5kDvtCVpxqKgxCmwZuSMrovzxGPuXAlzfyu/6cxH3g2QWA9JK9Ei8IKmoZBG+Kr3TVuO73/vd+J3nfwevjV9r/H0mDghqRe4iVSZ3g3Az5C75O+j8A7jVKARjoFNyJ/LkTkcIlAGCwJO7NefcaWdbbeCkPXfd3fVsmdZABB1cO7KVtsxksbLOLL1GFacKpuSugWUnMpHfOBICYdCFrOklOpN67TUcdYDtC1erP885mF2clpnYMgXh65G7aApmne9Fq+q5swZq0+JO20a2TABL6Z2ejcEcECYzqbRGFBD0RT9vy1TKv+4ScTdiCpevvK3SlhnwDkzDTTmTsTkGLMifb888A7zyirccZu7XRkbgOAVxN53633l7O30vDUHBllnRcxd2a//2eVtm+2sp2ZC4dbtC3GkNzYAjeXw65C7o1weqSAnGxFruilF0iJ3uXESw4pqsoS2zktwNBtia6NVsmTEwADLk7to1YHvbU+Q2NZ2uTJWlVejqhuSu2wUDOR1xt4jcMWCAoF7cwYEHHRBC0IfAeHLKG5qnWA/E3QYqK4QSctfjPb+IJATcIbU/KvjwgnbkzkEQvrq4m07zN3xjQF0TcpexZQKLbZnOQBz7G1PZlnmAIE4QvXHp67HXW26dWkjuNiDuFjUuT9QEf+Gtf2E1ckdWJHcnJ9jfFbjWL9gyKS/NYJJWoUvDhbZMAPkwmhaljMLF7kX8zW/8m/jFz/5i4+9L0l9tw91CL+4qyN0g2Ay5SwRiQdzd5XI1WyYpkDs185s6hPgH/briDqZVIm4yCmGns9PIMumcwwujF6ptmWEX14e6OlAFZ0TulIKKR4GULE81JY1EaL24C8IeolMQdweTg2Zf+OyzOLq6mwtUyZUQywNVYltmQFjtvblJ6WgKTrjvR6vouTsdW6ZpRO4ALB1krhNyp10aqDLjBP2g74Okkp15KT2563RqxZ11FkfCYvfa23P3TOcciAM4j10QbXvuWABpM8+nn/op4O/9PS9wc+ROpuTuYJO2zDt3vCWTkIy4y4+JsMSBJWuTuESnXztoO+krX92W6f8G+3dfKH9SKShOcRgdbpbcOQcNi06B3JVGIfAAeg2yP9LH2O3P+2l9QnKFLXNpz51M10NpDQYYjFcTd54m+78xpxmH1wc+0LrvTo+PMXSrPXulUeguIXfSyDm5K/RVH0wOVgtYaUjukr9Vl4aQNWtvTby4A4A+AoynD8jdG7qUMxBinpYZOooe7/qHDgBhybznzlnfc0d5bb9bsSwcBF3DljmZ5MSd350DBGHQNQLAYH7DaGLLVFaDH9aIu8N7COKb7Jsvfz1udpot2gCU0zIvXFhb3B1MDvCen3pP7ecnaoJvecu3lMidNRqULiF3sKuJu/EYt7ZpmdzViTtWT+6Shf6q/UdJOM53vvM78dt7v934+5Ie0jbkLpxVkLue2Ay5S16jYMv8tt1fw5eim80DVWKrYJcGmJrM30HNEAj/IKCMrU3uFGyr10hsmbudZuTu9vg2pJFlW6bT4EEX1++pGlumQ0edkbgjNrVlNkkHTm2ZnCPs9E+F3H3z//3NePmwgV32i1/E0eWtdMhxqRrZMr14EISvNdbBGBXHFqCS3Glrli5C21RK7hqKu2XkzkQTcMIQKJvruRsEA3/cLWyZR5MhBhIILl/NkTvjjA8WY6K5LRMutcB5h0p8vR4eAr/8y8D3fZ8/nsyz0mgJDopLvUubJXeFMBUAFaMQ/L0pW0GnjxlZ0HMH4gONVqBcyTm7P6wYpSMlNCM4io6AS5dybqK1SmsoRhDyDgwFXPze59IyowiMB2vdo4dmgp3BXNx5clcYhcCWiztlZCW5G5xIHEcr2DIzGw6MsjkNWyFU5bfGn8WHHv1S62MAAOkUOsvInVE+f6HX8462zAbed/4/34mnbj7V/gc3JHdycozA+HWvrJjnCnhxx+K1fJ+GGM8eiLs3dGkYcJGxZVqKHu+k4s7bMmNy5/zXCsob78yaWNytnJZZJHfOxmmZrDbK2Tibov5GgSpOQxz6G1NJ3B0NU3HX3drFjLmlXvDaQJXd3bV77kazEe5N79V+fqImePdD78ZwOszt2hvjbXB1JajwdpcVxd3+ALg+qBB3hZhuaTW6tHNqtsxkZmFP9Bb2xRQrJXdt5txFOi/ugj7GPb5ZcVcgd1NicEiiVj13ntwFmGYEs1IRRCzu2AYCjzRsq2s8sWXudHYaWSb3hnt4+87bK2yZGjzo4NphNbnTMLg8PaNAFdhWtkxP7khM7vqIsHlxN1ETjFWD8/HZZ3F0obNY3Nkm5I77mP01yF1O3MWBKrk5d84stY+1KW01uDKbs2VGU08wE3GnFCJeY8tcIu6Gt1/AjqSgQSdH7qyzvvechz5opYktk8w3PQMezjcTfvmXgb/6V70driDutIrATsOWWRB3AQtKtsyqnrvtwSUc0yVpmVhtbmeSmHirwt4NKaF4LO6ajqJpUkpBCwrBBLglMPF1U0rLFAH0Gj13I0yxe2HurKG0EPpkDCylvjVjQUkdISiuIwYDDI4jnMiT1vQq6xzjlM/vLyuEqhxFxxjXCP9lpayObZmFczsz8kJZBWFRacs8kSc5Z0zjakju1OQYwgIBqb+3miy5ox2MZ+3F9v1SD8TdBsqTu3ncdWAJeqzjb+KIxV1yw0Hcc0dF44Z5Q/yCZ2VbZoHcJYlYgrBaeqhhwESG3MEtFHfKGfBY3IlCipg8HiFkfgHf6Qww41gat1try9zZWZvcncgTSCNr/d4TNcEgGOD9196fGyraKC1zVXJ3coL9vm1M7nqsU2vLTMndirbM5KHYdIGdlIniIaZNA1VM5NMms7ZM0ce4y07VlmmIxZQ126mH1lBxPH+XhnlypyMEoV/MlprrVyiNdj13ySiEpj13e6M9fMOlbyjbMuM+4Ku6g7uTu6VjMHC4PAZGZ9Fzl9gyafOeuyAWd2Gnj2hD42Kypa1u1hvy4os47JKF4q4osoqVBHYIItKF8ipljJ6Pbamcc2dr52Su9POc8eJuU+RuNgPnXtxFOvLkLrFlFsndkp670WsvY1dzMC5y5C7Z5GSMQxMLN2vYc5eQOx5CJuMknnsOePxx/99C5HvutAIHPXVxNwgG5bRMgtIohO0LD+GI1/Xc6bktcxVxZyR6CtifVPRqSwlNY3GX2OI3MedMSihOIajPN1BV4k5K8HXJHYmwszsXd6UNPWNgKGlA7hREhS1TnEzAKcesJbE3sOlajZEMuXvf+4Bnn124bitWJKcrE33ptLdlFs+b978/TY9VRvkZy70euMuHZimjVnOnDYeN0jLl5BiBJQgoTweuFytny2QdjKNTHv9zivVA3G2gNGyB3BH0aJbczefJqXgmE2eisS3TwCGgovFw6PzBaf8AzIq7JC2TsDS+tvQzM6lgac/dorRMZ3xy4Gzm7SqZCzw6HiKIA2dCFiLiZCmZWUju1hR3SSLVWFYfw0RN0BM9PH798Zw101qzlNxpGLjJCtRpPMatjiqPQqggd8pp9Hg806ei1iF3Lk7VSxfYbQI+ViF3VbbMkG6G3J3EN+bsLp61MAAmVDcPVIlDProszJM7LSECL+5YcebRCuXFXfMHcTIKYaezg+H4APjxH1/49XvDWNxV2TKZAA+7uBjulkJ0NCwuT4Bh096zFcuoyPcvUtZ4FEKkI4QmJnedPiQ2N7stKW11M4IdRThy0VJyt2iTTsOAsyAekL2uuJvbMllR3MHUpu2uUtpqsA2KOx1NwUSIQJoMufP3B+0y5C7puVtE7u6+jB0XgvHAO1Diss6COYByAQIC21LcCR7Mxd3+PnD9+vz3LPTcnYW42wq2/O+XPOudgyUokbutnYdwLFwlIbJGrWnLlHjbJMB+VHGvkBKKwYs7SpeOr2hcSkFzCk55PryuOApBhKuTO+cwYgq7F9+UfohlKRmQSctc7kqqInc4OcEgGLTuu/MtNPNAlfS+GQTAxYv+PGlYkZqunKIrYaoDVSaTdLNWWQVh4MVdgdxJI9vPvTPGP+cvZPqc68jddAxhCQIi6sUdxVzc8S7Gpz3b9RTrgbjbQCnYPLkzBD0WznvucuTOQvAwJnctbJlMrLZ4TChSrucunnNHeG1apoEDjwVZ2nO3jNxZAKNR2ZZ5coggFr8d3sGMu6VkJqELJXK3AXGX3DzrrFZTNUVP9PDYtcdyoSrGqIXkjhD/UExETruDOsG+kNW2TOLyaZlOo8s70ASVO5+JRW+VnrskPprEISFtbrZatu+5K5G7YIPirpbcOUyg/GJw2c5x3HOXkrtMgII0EYLQL2a9RWdNckdcO3IXbzZ0eRfWGcyeeXLh1y8id5wHQLeLa93Lpb47E4u70eR0yZ2SM4j4kdTalsk5wu4WImI3QwMypVVUuwmWK6VwZKdLeu7cEltmTO7ouuTO0yIAZ0LutNXgUm/MlmnkDJwJhDqeSaW1F3dV5G6JuBsN97FLemAiyPfcWR8sBkp9uMN0+X07l5bJO9XirthzpyQY4gRtAJMO27i4m+optsKtfKCKtTCkIi1z6wI6uvr5Z4wGBfWBRisIIWUU3moG2DcVTgKl5j13gN8I2IRDIyF3THjBEF83yiofKgT4QJUgXP0ePZ1i2CXY2X4o/RCltDzEnJKl15UyspLc4eQEW+FWe3Hn5qE5uZ47AHj4YX9uNqxIz1be9JEwMbnL/HwXZzXE61Bp5NyWWei5k9MTmIYhg2kdHvpU0GwWQh25mx4jcBTBgnurpj5NFgD6vI/xknmc93M9EHcbqBy5EwKhJeiSYE7uMsPCVdyfJ5hotmCAjzcWLFjNlpncPHM9d1lyVw5wLggAACAASURBVH0D16ggd4t67mD8jkyVuJscI4gJR8hDRAxwJ4tvYGmgSlagbKjnLhV3bcmd0aUHZbEE5VAriLuT4wMY4koLw5TcFcRdj3dhKHJ/16RGsxG2w+2VbJnZhDHacNZY+r2xLbMNuQunsoLc4XRtmbCY6Jm/PpfYg70t01sFuyzE1GbInVEQYULuNjDnDrbVBk5CWAkh2HEhRtHi+UR7oz08cvERWGfz4RrOQsTi7np4udR3p+E8uVvQp7qJ0ioCj4MGKKWNnAqRiRAYzEchcLK5GWJxmfEJ9MsvLv9CpXBkJ7gQ1qRlcg5mmtkyAypq+6GblDaqZMvMz7mzPtl3Q5WKu02SO8YREAY5HXtxx/z9QTndTtwd3sYO73tyR5ESq8SWCcb8plzUpOfOpRa4QIT+WADg5s1acaeNTIX25d5l3O1v6BxdZsu0Fpai7DbpdnEhAo6mZQHm+8rXIHdW4m3sIm6h4vkuJRT1z19jzeb67pSC5iQmdxRa+fe23HMXrm7LHI0w6hLsdjKjECjP36NiW+ay68qTu2pxNwgGrWfd6dgNBhR67gAv7m7ebPxakZqtvOkjYdCxBCa7rk3OxfjvrIzy9+s4UCV7rPLeHZgvPtvuhxb77YDF5G6RuLMWmiIVyv2gj7F+IO7e0KWIhQjn5C40QI+GuUAVlSV3IoTgQeOHtyEOgq8o7irInTXGz7mjvDYa2Pu4M+QOS9IyF5G78VzcUUL9+7FkOORp9twliVR15C4Rd++88k68fPRyKgKX2TIBT0NV1P6GsH+8j+uuD0Lyg5hScpd576XT6LIwN9MnW8PpPVwdqpVsmdmEMUbbzVacp2U2FHdaItDO77TF1Q/6GHOkwuxwdoiPPN1umPpTN5/CH7z4B7VpmRZxulyTWXfZtEzewdRl5w0qBGEfQJyWua4tsyW5S2yZALCrBUYqvyh46fAlfPRzH03/vTfcw42dG/NZSMnrJOSu08F1cbE0686QmNydclqm0hFEfH01tWVKIxFqeFsmCxAFdHNBDXFp6hcG2fr5P/n5ciiTUjjSk/XIXZx8J5hYK/kz13NHKWjBDqrdKaRlthR3i8ITjIrAKfcWqtmJt2UyVxmo8gdXJniqP6q3ZZ7cwW5wAYRzEDd/H3ygCgDGwAlr5LjwtsxE3HUgk7/l/r5fSAOVPXfJfNTLvcu428PZ2DLjuWsltwml2JYER6PyvFlr1+y5swpv7VzDPpuUbZ9Spq0EJ/Jk8+SOCu+S0vOeO8GEfz+09rbMVa+p0QjDjsNOZ97bRYtW/NSWuYTcWTUf0ZBUQu6CFchdZq2W7bmzzuIH3vkinnj2R/HEJ5/A77/4+0tfK9LR6j13xKILkRfQyXmeiDurILSLh5gXyB2x7ccJxeLuD178AzzxySfwxCefwP/y5s/DqPL1JWdjBPDnSeXaSCmYuL8eAPrBAGOz2WfJWdYDcbeB8uRuLu7+h8nX4Qe//nvmtkzQ1P6oiKd8nInaMQTFMsT5JKwmC+3iDbWK3FntyR3lUDW7c9negiaBKhrW4/bhsHTxyOlxal8DgI6lmB0vtniddqAKsJzcccpxqXvJzyZCbFlZMAoBiPvuGvRuFOvW5DVcI1ulj1faMqHRZR3/oCzsUDnnMJod4upQrmzLTPoUWgeqxBsYzcldhID5OXFJ9UQPY2FTYfa51z6HH/3PP9r4GADg33/l3+Pjf/bxxbZMNWk0687G1wclNBZ38+tFWQXR8ec1o3z9QBWyeOFfLGNNajvakRRDnV8UfObVz+Af/Lt/gKmaQluNV49fxVsvvNVbwjMLNwXjLdjdLrZIWNr0MAm5O+W0TKXnEeGNA1V0hMDCkzsWQvJTEHfEQc3y78lPPvmT+LM7f5b7mJURTswUg2BQ/UKcg9qG5I4FjZ0dla9j5oKisufO2dq03VVq8+RuBkZ9aqicZchdEM9oy8y5++S1Q/xm92Y9uZvcw053Z54aGp9X1lkwP+UbnDCYpuQuCVQRHUgYfw8Zj31/E1BB7lRKpC/3LuNu121G3N2+XUHuCrZMWiHuAGxrhsPDci+WiUPDvC2z/f1MWo1L21dBHMoEKu65IyB+1t0myR2LyR3m5C7dqJQSCHzOwaobcLN7/r3qirntuHSPSm2ZS8idVWl6eFor9tw553LnZHbjbjgd4mf6X8YHxru4N72HTz33qaWvF5lo5fuCIhZdGuSJb3IdZMidMC62ZSIn7hRx7WlxHKbyiWc/gYma4ANv/gD+r4t7OKnYuFezMQQYgjrXnNbelpmIu3CAsV3dGn/e9UDcrVvGQFH4YcYAwDkekgKPhNfnaZkZW6aG8z13PKwVVqUfEX+PXbaj8slPAlev+pt+UsnNMyMCfM8d8T13Fbtzzrnc8FMCsrznDnlyl+3riWZjhJ35gid0FNF4sYXsNANVFvXcKaNgnU131rIU0lrTzJbZMAU1W/vRHVynZTtX8mC2UTZQxcxtmYW/yViNEVCOQeRWIndZW2brQJVo6nfGm6Zl6ijtxUyqL/oYM5tuSgynw4UD56tKGokjeVRL7gxsY3GXhCEAfvByVtxJqxF0/XntbZnrkTtFbDtSmiV3U4eRzZ/PUzXFYXSIX3n2V/Dq0at4qP8QQu4t4UVyJ2Jyx4wrj0qIxd0y2+e6pbXMk7uGtswcuRPkdMhdgcYro0qW9rGL0GOd9G9SKs7B7DJy5++7ggWQa8S2G6tz4q7UcwcLRTcs7iLVuOdu2RBzoyJw5u2p0TQmd9TO59xlyJ1iBJrR2mt5OBv54dPxMPdEtCRz7sAYGGlgy4x72JKeOyFCL+729/0IhGSTqhCoYrTMk7uOPRtbpjGwhFSLO8NxdHyn9HET23kZWS39VzqFYPcSrk9YyQEAKaGJw2531/fdLSJ3+/vASxWz8ip/qIRiJO65m2+kp7bMKALC0KeirmidHx68ih2TF2SU8vwszniI+bLrSlkNQStsmeOxt2W2mHVnnQVxAA1icpd5Zg9nQzzEt/HEzWv44Js/WOq1rqrIyNV77ohDlxb6GovizsbiLrFlZp6Zktr2tDgmd/sn+/iOR78DTzz+BPqWI6pIHPXkjpWcZUlZGcFmNkP6ne0H4u4NXcmuUZLKxLk/oZMUL3hxlySfKWLBg463ZTa0CBiCxbZMa4F/+k+BD3/Y//tuZjFc2XPn7Sic8sodDP/QIyCxOCWEgIDAVaDupDRs2nOXLG6SC1fOxukiGAA6jmN2snihmCycTiNQZVFa5lT7MJXEHhnyMO1dM3bxnDsgeU/bL8r2o3u4Hlysfk1HcgsPCS/udMXswdFshB3aRyjtSj13OVtm60CVGUJDakc0FEsamaaoJtUP+hhTkwqz0WyE4WzY6qEsjfSLh6Svs9Rz5+a2zCVCQKkI3Plzocu7mGbmqCmnIeLzmrJ271VVabKY6hTLuLlNeGdsMLT5hdJUT3GldwUfeeYj2Bt5SyYQn6OZe0/aM9ztghtbeq8NcbisBYbydAe6Kh2Bk3bkLrVlcu6vVU42k8AXl3UWjqC08FdWle7fR4iwLWqoHZCKu0UC3iffBQh4AGXXIXe6FKiSn3Nn/f1jQ6WtBp/JxeQuGaZtzHJyp2Z+zh0LIKM4UCWxZWbJnVKQHNCc1JM7dYydwRWAUjCH1PqV9txRn7S4VNzFtjsW3x+DoAdJTN6SCZTJnVbpeX2pewkHmxB3zgF37gBXrgDI2DLh5s96a/0ohIrNhm0rcHRSTrS0xsTibsVAFWcgdi7j+rErBTNBKSjqcLF7cfmsu498BPiX/7LhDy2Qu4y4E1Sk4s6Tu9XE3Wh0C7su/6yqIneGohm5IwVyFwSAtdji/VbkzjjjU3FFmdyNZiPsBBeAmzcbr0siK1cid845SObQZeFCcieN9LbMXg/cFmyZ1LXfUBiNvLg73k+TxgPQyp46NZtAkHpxZ6IZuEG69ut3tzF2D8TdG7eUguJkHrmbJPUkjd4ABNj8hkMchOiA82YDNZ1zsDEZrN3J/qVfAj7xCeDJJ4G3vz2fNFjVc2eNn3PHRKUH3dj4hpHphaIgqUWt8m2ABd/a9hcbMsTLOS/uehlxB45oskLP3aYDVSrI3URNctaLkIWpvbERuWsRlJOtfTPCtfBS5ec4aBrvDMSpVLzaljmcDrFLugg1EMn2/Qw5W2ZbcidnCCxpbsu0CoHI7/T3RR9jotNzOLECLho6X3rdRNwtClRpTO6iNMGxK/LiTsKk5/UmyN1Ktsx4obl7KDEi+d9lqqb4W9/4t/D51z6P33n+d3Bj14u7ki0ztoqj0wGvCPzQxOECOpiZaKWNi6altEx3tBuTOx35vs3UlomNkrvkvVBFcVdB7g6JxHadJRMAhFgcqOJcnMbIIVi4FrnTpZ67MrnTbcnd3bu17o2U3C0Sd0BK75anZUbgXCBMxJ1SiIjz5K4g7lJyV9dzZ8bY3fELP2aRDlnOBqpwytM5nfUH5QMzErHkbZk2n5QJlHrutCn03AVmfXE3Gvn3Ok4anqhJOS2zrucOwLYLcHRSvqcaG9syVx1i7jSC3Uu4NtLYP3q18EkJTbzAXUrujo+bJybHdk9BRX4j3WZsmWEItoYtc3h4Czsk/6yijOc3auL3e5k4yj5j0yLEDzInYTtxV1irZTdkh9MhdvuXgJs3/bqkCbmzaiWir60GswAXncXkzigEypZsmdb5MJNVyd2tk1tp0ngIVinuZDRBQDiCmnurllPwzK/e7+1gjNN73p12PRB365aU6a4RAH9jL4g7nuu5c+BBCCE6jRZKCXbnIqzf8b13D/hLf8lbQ/r9ObEAanruzLznrmKnxDjjaYWY34AWibvE980vP+QvNmTE3XQKKSiCYP7QDwnHrKG4y9Gn2czPM1lz+OlxdAxOWCW5S/rtkmptyyzM+Gtat+wRrncfqvxc1tYLJOKu65PfCiE3o9kIOzZAYPzNrG0po/JpmW3InYoQONqI3BlrYJ0FDyrIHVHpeZuEeLSxZiqj8uKuNOeuec9dzpYpephm5qgpZzLkjq8fqEJXDFQxBjtHEYYk/0Cb6ikudC7gu97zXfgXf/wvUnJXfND7tMwF5A5+sOsOH5xqqIoPVEnCfPhyGzpicqfs3JbJsFFxl7wXSjYgd0RiOyj3zaa1zJaZkCEmEPAQao301aItk5m8uNOwUG2f/h/6EPBbv1X5qZTcLbJlAo3FnVYyJnchpJx6ckftvOeuYMtUi8gdpti56Mkac/MFZDLnDpT6CPllMexa+79PTOGCoAtFasRdwZaZkDsv7tTCcLJGlbFkAvVpmbU9d6SDo0m1uGOENg40Kpbf8NrC9Uhg/+4LhU9KKOpwqXdpObk7Pm4etiIlNEMluUttmUEAzsXKtszR8R3s0n7uY5Xkjjh/XS1Yn0iryz13gBd3EK3SMv1ajdaTu60rwP5+aUOvriKn/KZxRQr3opJGIjAAK46bqLJlpuRu3v+aHNs6tszrW/4aDByrJHMqmkIQjoAHkBWuCKMkmJvbGfr9Hb8WeZ3WA3G3bsUPlzT9qILccVIgd0EXQoSNdlKS3UUqltgyk6CP2LudVoW487NsSKn3JilttT/Ji+SuxpaZfD258lCZ3A2HkIMuAhakX98hArPp4htYbaDKYOB/1wX9f8vq5PAOrh7ZWnKXFXclW2bRK1+olckdGZdm3CXFQNIZcgAgYRHw0O9CF2YlDmdD7BqB0ABR1H5WXHbwKyPt0jJNLO6afIeKm8pJp4LcOTknd/HMvoMWA7SXkTsL55P6Gog7lZkX1g16mBL/Ws45aGIhen4xzyhfLc02U5osHnBdrGQUAkYj7M4IRjR/3k3VFF3exfc9/n04kSc5W2au5y5L7nS1LfMsxJ0uJLU2ClQxGXLHQ0jqNmrLTN4LXaA6VeTOi7uapEwgHoWwQMBrDcO80PADstftuYs3ohgDtbZE7kxmLECjGo/T+3vp0JvYMoHm4k5H4DxAwGNxF/fc9UUfxhm4ZFMyjtfXDPXkjkrsXn4LgFjcxc8xP+cOMbkTy8VdkdyFPUhi82MQkt8xa8vM/C0u9y7jLlfrk7sKcVeyZcYkqaqV4ALtVo5CsCYRdyuSOxgEnT6u2x727xXGh0gJDduc3DUVd0r53AMm8mutUs+dWNk6P5zcw47Ib9zQqlEIBJWOmtzhOl0mdwAwGGDLBa3IXXGtVuy5271wDbh3DwK0GblLxF3LzYe5uOvkrZVVgSralNIy0w30FdIyT7Y7MNZgK95YC+rInZwgoNznXVSte+UsbcEAYnFHzcbnpp5VPRB361bG7w0gL+4ygSrJgl9R5+fciU6ji804P7aAclE/HDor7orkrsqWmcy5o9WoPkX9JXLnL4hf/Owv4of/ww+nn9NWQzjq/f/xwz9NzByNEPVChGxOaEIaIJo1m3NXtGX++f/4XTjYCddawJ2Mh7g6ru65W0ruWANyt0Lc8j6b4Pr2w5Wf445Cywy5IwaB6HhbQ0HcjWYj7CgWk7v2BCO1smAVW2aEALTRKIRIR37WT1hB7lw077mL2pM7aTPiLgzrbZkNeu60lhlbZg9T4t+PE3mCrmUg/XgUAl+9nyP9WbRdz126aLx7Fxc6Oxix/M+f6im6oot3P/Ru/PVH/jree/W9ABbYMrtdsApxp4kDCzvY5YNTTcz01Dgr7prZMkPlx2l4cuc2S+7i96kYklRJ7qjChU7NjDsgTstcQO60hmEEjDAEorMmuTOlQJXinDv//y0Wu0rVWuXWFXff9gvfhi8ffHl+fEr61FDRQaRickcsQh7mqZJSUAzQtJ7cHQqL7e3LAODTMvWc3KXijnEYuaS/pkDumAhBHBDtv7Kw586Yec/dxe5F3ONy4+Kuboh5rS2T9XA0K7tnjDWVaZk/+/TP4kf+048sPSxJDILOANfoNm4d5m2ZLoqa99ydnLQjd7Sm546JnLjTK7orRrMhdsOd3MdYlS0TDpqRheLOp2UG5U8MBhgY1t6W6VBP7rq7wJUrEMeTZuIOpjRXt0kpNYvFXQC9QNxJIyGkBXo9MJtxRtiE3LUXd/tbwPWt62mvXABWmRSu5AyCeldE1ZiZkrgLtzDu1Ac13e/1QNytW4nfu9hzlwlUEYRBxw8UTQAR9sBF6O0lS3YFjDVg1uNuW/e11s6b1RuQu3zPXfliikyE0BbIHaGw8ULn9vh2rllaWeUviitXcrZMZZUnd70wT+5YgNkScZdc7EVy9/z4FdzbCdZawB1PR7h25DCuuIlOlQ9USf4u+Z4726znboVF2b6IcG33LZWf4yTfc6dicscdSftHkhpOh9idYvWeu4wts3WgiooQgvlk1SWVDnKNe0aS6os+xnaWS8u82L3YTtwZicPZob8Otrcr0jLb2jL93zwMetCwMNbgT2//Kb4pupAuZtcld845aNZusW2s8RTh4AD9rUuY+CjA9PMJuQOAT33Xp/DY9ccAVNgy4a3i6HYXkLsudmjvdG2ZRmaSWnkjcZe1ZYYsRETsZsVdYqcv2jKryB3T2O4sIXd2MblLNgqFCCHdOuJOp+ctGAM1xZ47f422sqktEndG+VEIhc2aUgUBEEUlcfeFO1/I0XmtJTgPfV+bmgFaY0YMQhaCEzY/f5WCpM4vSGvEnaIOYTyPkmXumdlAFUZ5c1tm/AwgQYC3RiFeGu416LmLaR8LPF0+BXI3CAb+75rpuTN14o73cVgRkJTMR/QCen5u3D65jdsn5bl4xZKwCLoDXOe7pbRMqySoI9jp7JwCuXO+546w+YaMyffccR6sTu6iQz9OI1OlEK3G5C5OJy5Wv4+Bpq3SMv3mfz5QJddz190FHn4Y4vC4mS0TejVyNzlGYOP79jJbpjJzW2YSuhev81axZd7q2pzrKSAcsmKGtJRTBFTE8ykbkDvR9+JuE7MYz6EeiLt1S6ncbIw0LbNoy8ySuyD0hIfTpRdRSu7EglEIxrQjd0nPXY0QkUYitLRE7kxmfkxWdHlyR/zDpsqW2QsK4i5EtKQfTBoJTnmJ3EVGYtoL1iN30bEndxU7lxM1Qe/2PR/c8m3fhvDLX01FUiNbZpKC2gLlK6MwEhpXdt9U+flSzx2xCETHp03JCnI3dQiNv5m1rZwtsy25UxECcN/3saRScVdF7swsl5b5jovvaC3uxmoMMz7xPZoZcueMhmsh7lSm544EATqOYaZneObWM3h8vOWvN6w/xNym8eztRiFwyoG7d9HrX8Ckw3LCJiF3xRI0s6lj/E6t4LEtU5nSAkgTeHJH+6lN9jRKZzcWKKt3KmQqMhECaU/NlpnYnlWBkFeSO6ax3ckvAHPFOZhZMO5CKU/uKPM9d2uIO53tuSPEp0Rm0zLj91YtEzSF46sVd1p6+kuWXPwZcpcMMTfW4LXxa7n302gFLkKEoustVkql5M7TiQy5o/C0pEbc6bjPHUApLZNZxOROlFwQpUpsmYnNMQhwYxJi7+SVxT13Rqfrg42Ju9HIz3yNKxF3jgCuQO4q0zLFAEcVvV3W+gReRvOjEJRVlX1MudLab3QHHVzvXMH+LD9qQckpOAi2w22/+bbJnrt4DSYwF3fFnjvGV7dljvSJH6eRKb+hl3nOJz13y8gddG49lNZggC1FcaLa2TJ51paZGWI+mo380PWHH4YYHS8nd84hItZfS23F3fgIgaXLxV0mUIVnyN1c3K1A7oIoTcoEgBC8sqdOqQiCCQQ1G2clcRf0MQ42P1rnrOqBuFu3Ys//sp67xJapqSd3ggmogNU+kJJKyB0NgsXkLivuGpA7Cm8h1BUnuR8MXNFzZ+YXYvZGr0yG3BXF3WgE2RG5m1nIO5gt6QeTRmIQDPJ4fTbDzETrizs9wdUTYDwrP9wmaoJe5IC//beBH/gBBM89D/ma34FsHKiy5OZerNvj27gyY2CD6l1/Tli6yHTOQRILIULfi1dlyzzRCAwQrSDucrbMtuQutlo2ScuURiKsEneij7Ge5NIyH+m9ubW4A4Dj6MiTu4y4Sx4ejcmdVelgbQiBrqWY6ime3n8ajx125+SOi7XIXUKGbBtyl4xCODhAb7CLSWGXcarn5C5buVjsOO2XU76Q3LGwix3SPV1ylzn3aENbZpbcCSr8rMBJ+17TukrnkxbFXRW54wbbS2yZzDSzZQrRgVxjtEau544Q+IyE+etp4t9b3eYeIeVicVdFI4pVYct8bfwarLO591NrCSYEgiAWd1ojypC7NOU5DunQ1NWLOwrwwF8HWXKXzrmj1MfkLxN3Wvs5d3R+P7gxCbCn7yzpuVPp9/hzdAPibjLJWWCTdgLq5s/phWmZwTaOdPlvaawGpWVyp0x5M6NUsxkkJwhYgGv9q9iX+cAWLWcQYNgOt5eTuza2zITcsTy5K41C4IF3S61QQzPGzqAg7oo9fFrHtkwsJ3c14m4g0Y7cFWyZJJ5raJ31PXedmNwND5eTOykxE2QlW6acHEO4BuLOKgjpyV02LTMVd21dT6MR9umkQO6qA1WkmiGgAQLRrRR3RhcCVUQf4wAPyN0btorkriIt0w8L1+nOPhWB330M+NLFpXGxuBNBs567oi2zgtyZRuSu0HNHaJqWqazKiS5tNYStEXfDIWSYF3cd3sGsot+teAxbwdb8InUOOprCOINpt/C+/cRP+MTQhnVspri2SNxZ6pNHv/3bEQY9RGNP+Kw1y8kdFVAhb7XztX+8j+sn8H+7iuJg6eLSOAMC3+/BM7HeSQ1nQ+we+dlfkVpB3BXTMtvMXdMKAWlB7sBKtsyABV7AzmJyN9zHI7/+n3EwbR6okjzEjlRM7jIP2py4a9Rzp8CTxZEQ6BqKqZp6cndHpAssyvjKu8IA0hlbbchdOgrh7l30ti9hUthlnKoacpe1ZWasgJ7c6XLPHQV42MUu6Z5yz12WGvNm5E5HCKQBhAAhBIGjUNMNiruYbBXJnbaF98k539vV261/sTi1snYBo3Ua2BEEXSisI+4yPXcAaCHa3sQbhcWgmIW1jNxVhUQUq0Lc7Z/4zbMcuTMK/z97bxpr2XbXB/7WuPcZ7lSv3nOVbWw/GxzmYJqARUMbE6xgoiSdD1GajhQ+RKKlfOgoSIlopZMPKIOiSB0GgUQr0J1uvhA6okMspLTDHJMBBydOMMbGPGO/51uv6lXde8895+xhTf1h7WGtPZyzz731bCPXX7Ke69ate8+091q/9Zu4SCGTgLmDRsITzzy7kLlzvrNvF7hLQnAXeO6s8+8LFx4U7EoJ7HjuIATedknwUpI1fXP113vMXfVvBPMHEK8buEOwQa5K1wfBXXqClelvWutDgW6J+STmLs9RcgrJJO6e3Me1y6O0a8/cUQ/uyqeclkmc99yFssz6sKj23HFx0P01nEtkTZ1GPZSy+CCzYu40xc7AN+UM5NBByHKJo8Id3nPnEB3E1+xdw9zdvw/x5HK/BHu7RSGIPyi5iSzTUbCugqUL7nQBYeHTSwMf8I3B3cUFHuC6ScoEKlnmwEGE0gUElxByTJZZgKPD3Ak8A3dfslMzd0M9d3WgSpXgpK32Rd/1KbOYyNw5n0I0yojsClQZ89yBQIycZBVmgLkjbRVCV5bpPXdowZ3zp2gtuOMxcydmKPbIgUpTYiEX7e9RCoX0zzGbixjc/fiPA7/3ezt/XjhrFF6WOXBCtlVbzDVtGCXJJMqt/z7jpgSqVODugMX7wfoB7q1cI/HrDictuCtNCWkpwGPzeD2X+SVOL3Of5KYOZze7PXeHsFFaFUiomO65c6zH3BFCKjkEgLLERXaBL39obsTcrfSmJ8u09kDmzrQx5jVzd1Vc4eOvfRxf95AEnrvbMXc1iDiIKa2rEB4/xvz4rgd3E5i7SJZZBUIJKnygijI90GIqWeapS15n5q5sTrQnB6qY3AeqVNelBEexJ4n3kGk8d8Em1Tm/KYmYDK2xSslu5o4QMJC45Df+ZY3sT4gU5a3Bs7xKzAAAIABJREFUXXuvooTGsszqGu16CXfOPs/dAeBuJmYNuKv92xFzZxSYkP4+ZkrP3KFi7rqyTLLDc2d9fxYX/j5DMRKoQjl0Inavx5WnqmHupMSLn73GS29I2vUXGPDcaXDm19LXm7ljoB3mzg2Cu5PZKVauf++z1aFAN612EnNXFCi5XzPpnefwgpvh1U3r0/PMHZ3G3B3quSOB566blln33N2GuSMFTjvgrgcWKz+32uO5K6HHmbvcHpyWyTsH8Zz6Wp6L/KKVZT6+3P/+ZRkKQaEIDgd32RoSFIyKYeauWmdLlUMQ5q85EwSq1PUkByieYC1wdYVzddH33I0xdzyBFDOogYNDrXJfK1HNQiyw4e4ZuPuSHaWgqeunZQaBKpzyxqPBrf8ewSaCO6M8c7dL9nWDQBUKAs4l1BC40wUS05Vlxsxdz3Nn4cFJkgCbjQ9UMcqnZUqKhLeb+FTOkevdm4paltn8njxHvvAsT5by+MRvtZp8ARprkBOD53elZRrSMEoJT5vNog9UmcDcicPA3fnqFdxf2dGOqB64qzT2zPVlmRf5Bc4eb5EsjlDcANw1UhbcRJZZQlIxibkrjE/W7DJ3QHVTPZlDf+bT2BKNF1/TNwN3NuuBu/pUcbLnLki6gxCYaYIPf+7D+PI7X47ZOm+Zu1ucCgMhuLtBFcJrr2F+9gKyToH3GHPXlWVGzF3ZYaSq7jUuEpy59HWuQmg3wVOZu1IVSBxtvF4J2I0qQEYfUy3L1PFhFhCDESiF1cz7iXYN3QXulGqZu+T2zB0PwB0DiTZO9Wu7N0Sk8/huDe6SpM/cXQ8wd1qByxQyWaAwZcTcRVUeSqEk1rNhQ2up9gER9WNjjsQ9d9Z5WSblMOmeFOYB5u7Fz2V46bnODa/L3FkNVvU3vt7MXchMNp67gSqE48WdQXBnbuO5y3OUzMsycXaG+yqNQlWUKsDBcJKc7E7LdO7wtMyauaOsuS6jtEwpwXepn/bMJdc4e+7N0ddop4vTaQ03JVAFI4EqyyWWW31Yz50dYO5oy9w1gSqvXeyXZWYZCk5gKWAPYfQBqHwDCQbW7XtVCh98O+Ay/16qMvddppxHaZmlrta/Q5i79RpIU5xvX408d5JwFEPMnfGHh1LOUGKAuVNF468HKuaO22fg7kt26o6VHZ47L8tU0KqipBmrQADdD+5U4QNV2I7Nzr4qhMUiutlYZ0FBK+ZuRJZZMYz1+LTMHZ67+vTo9BS4vIxlmYLGskw5j8vJB0YZFYO7LEOx8AAxS1i8CB8A7jZqg7mmWGoy2nM302gYpUQkKDL/elpnwdiEnjvJDpNlPvkM7hdiNIwgpQJZdfNTRgXMXT8t8zK/xOnjDZL5SXPDPGSahDHcIFBFl5BMTvbcSUcH0/UWcoHNUYqrf/qTOLYCL7yWHwzujpNjrEgJHB3FssyDmbuO584QfOgzH8K77r0r2mCx28oyq032QUxp7at6/BjzOy9g25GQjDJ3HVmmYoHnrtQ9H4mmXgZ8auXrK8t07Yk223W/C6ZQmZf3ViMJbyS9T2PaEKn4fgfEYARKYStJVKMyNDv7wwLwIGTqO9RuOA2rWw0F6QSqVLLMpwXurL6x566RZYbMnVVgIoFM515i1WXu6s9oWXrmjgx77ky1Sa3ZK+ZaBYqXZcLLMgmDTsVumbbW3n8aeu4ugZcWnTV0oOcuClSBuT24y7IRWSZpN8jGX0GDsszFHX9/7IyxJvDcdZi7feAgz32gChXA2RlOSxodBmlVQJAJnrui8HuaQ5m72nNn2+szlmVK6JscwCnlJddnXeYuTvSt90f7wF0JA8kHUmWXSxxt9OGyTOv6zJ0zuMgC5u7R42nMXbW9MYfcFwCU2QaS8EFw99//D8Br6sr/sa5AYszLMuu0zPpw85BAlfUaODrytpZIlikGA1VKXUCKtAJ3A8xdEJ4GADM+Q0kszObpKUE+n/MM3N12glMjAMNpmdVJiiozz9yh6iPh+zs0jPImT8Z2BFV00zK7zN1y2fHcaVAHcDbC3JkC0qBfhTAiywzlpjg9BS4udoK7JJkj3wM8eoEqeY68AXfB62aMf74TE43W5RpHJbA4fQHrsRJz1TJ3UsyazaKZEqhCBZQ8kLm7/Czuqz6DVc+SplibtiemlsxyR6A7xfIX2yc4Qwop070AemiUVRD/6aPASy8dztwZBckkzIS7SiMvHWPujhNc/rP/C6fzO3juSXYwuLub3sHVcdIetlRjrMGSptM9d1ZHzN1cE/zmy7+Jb7z/jf7aqqS0jMvbyTJrT+UBJ8tNFcJrr2F+942+CmGK5y7suas8w4KJirkzw8ydTHBmZLtZe/ll4CMfOfyJ7hhldXOi7asQJhwS6MIH81STUIHiKYI7XV1DagJzV4dJ7BoWKCAGflnguZsPSoemThO2U433z4bF9TeUZa6HN57aqBuDu0aWGXnuNLhMkcyW3h+jFAqnWubOtcydItY/n4G1VBftmgsAPuui6tSyQaAK5dDJnqCuWpYZpGW+YQ1seEdKN8TchbJMPCXmLlB6tLLMNlDFGQNLh8Hd0dFzWFHVK7GP0jJDWeZk5g4NcyfLWLqsVA4egrux++/1tU+rdhO9X2UJTWzF3PGmZ61bYs5FcjPmbrXCKiU46STheuauff3qz5XaF6gCCyEG1vvlEstNeXDPXVeWyYhnL6O0zIevTWPuqo/2QYc+AMp8Awle3bdjcKcpcKk8QFK69F2mnIMb1zJ3ZZucO3nWa2C5xIP1g0iWmVCBcoABVLqEEIn38ZL+7zGqBAsO2AkhmDuOzfr1O9B8PecZuLvtBKdGAIbTMisZlC5yz9wB02WZuqyYOzHNczcUqLJYdGSZFgwUQiTQA9Kf0vhAjjHmTlkVG6VruekQc3d52d7wq0nTJXK7+3mXti/LLOb+Z2QyAHerqs5g4infdb7CMrdYvPAmn8rYma3aYl66gLmbNTIv6wzoFOZO0AM9d+e4Z8dP/Jds1gF3AIQAA20qNuq5zC9xmpz6zq8bgDttNcT5Q+ADHziYuTO6RMKSSctnaUokjowzd8sEF8Lg7M4bceoSrMv1pJ4ewH8e7yZnWB1Jfz12AlUSIkAJhUrENFlmEJY00wQfe/SxHnNH+bRetrG5rSxz9vx9D+4mMneR564OhJrNwEsVg7uIuRNtFcK/+BfAj/7o4U90x+gOczda/RJMofKIuUuIuFEFyOhjqqsQwtL3EeZOTQF3U5m7ZHYr5q4B/tXQ0IsFz9ylCr3DoZ2zj7k7xHPHvefOOYfz9TnmYt5h7jSYTCHTCtxpjcJpz9wx3h5K1uAOw8ydLrNmzQVqWWZVhVAdcoKQSpa5pz91IC2TAHgbOcVLFy+139f13NXBR6jWh6cF7qp7j3Ou6bRkaMvHndEgDk25czhieQJpSdQ1CLSHAt3Uw6Hqj97kOUrqGnAnCh2/p6qcxtxVjAwWi2nrulIeMFEBTnjD3A157m4E7i4vsUpcT3LNuIh+XsPcEexh7uwwc7dYYHFdYKu2kw8KtdWeuQsO4jnluC6vwShDylPg7l2I1SY6oBqcLEPBbhC0BKAsNhCU+9fEDoC7KplV6cIzu4zFskxVg7vDmDt1tMBFfoG78zbJVNLhQJXSlpBi5n28A/dWXcmGw1k4js329bMivJ7zDNzddoJTIwCDaZm1R0CVWdOjIaiA4uPdPPVYrUBBKlnmDQNVOsydtQaU1J67/oe80CPMXQDu+sxdJQ04OwMuLyFoEKjSAXdJuvQ+ih3TS8vMMuSzCtwltF2Ea3A3lbl7/DksNcVifur71DqT6QxzhZa5S2YogyTDvbJMegNZ5vpV3MdwUiYAHLF5BO5EwNyF/h1tNbY6w9HyDhKW7D9pHRhVlxF/8IP+tP9Q5o4nT4e5Wwhcvv+9OJ2dgS6WuJOc4kk2LRG1NCXu8mOslqLH3Nmq/2su5tgmZFoVQui5q17ub3jh6/21Wz1+tuvwZcI04O6AzUcoy5y98CZsqYELwd0uz11QAq0YGs8dG/LcEYAJiTPFW+Yuy566F0E507A/3nO3//UsdI4E7TUpmdzboXnI1OBHh+BujLmrg2l2DAPd47lD5blbeG/WDafL3LGBQJXE+BCByfMUZZmCCbAqsvz8+hxvOXlLzNxZDZ6kkPOjGNzVzF19nVQbez3iudOq8MxGNQyB585oVHtZ71Pad9hTyzKD+wEAvJjcw0uXHXDXZe5oyNyZgwMrehOAO2UVKKH+NQ1kmdZU4HVoFgsca+ZBVjBNFQLtyzL3ridF0YK7O3cgCx3bNyrmbimXWJdr2DQZZ+6Wy92BK+EE6ikR+DGb5Oe6507I9nNzwORPHoKARLkBQMXcBfun+nOlqdudlgnje0W7M5+DZnlz8DFl6jT1iLmjDK9tX/M1CP6BQpzd3c/SV+COupswd1tIKnrMnS0LWApcWH/fKANwx4NamEaWeSC4e/WOxPPz56ODLEkESjfkuVMQIoEYA3cdWSYALCCxyfp9yH8U5hm4u+XYsohNywNpmXU6nS5zvzFHIMvc67mbIMvc1XM3xNw5CwoyytwVpkCiXZ+5G0vLNLuZu4JaJCwIVJkdId9zCliaEguxiJi7fOYXyEzgxszd+uHLOELSlmV3xvfc2TZQJVmgqG601lnQPafTTQrqIbLM7CHu0/GUvSWf47oyv5embIC3T8tsPz9X+RWO2Rz09Mxvcs0NZZmFBn71V8H0jsLlganBnSPoyX260xwgjDF33/YtuPzu7/AL1HKJu8nZZGlmaUrcZUdYLXwqV1eWyQirwB3dL8s0GpyEzJ3DO87egZPM+rCW6lT8toEqSh3uufOhGRR48gTs7vOQjqLYtgvRrrTMBpjUgRM1c1f0mTtDAC5TnCrWeu6ybHSjf9NRrg0amOq5K3WJhLTXZEIlivIpgrtalmn3MHdlCcX2M3d0L3NXp2UmKPd4d3aNcR3mbgjcaTy9EnOrm0TKnVOBOwBNkfn5ugJ39eviHLQznrmbL1HANAyql94FzHNZooTxm/YhcFfExcQ0rEIwGhTBerwP3A2kZQLAi8dvjZm7rufOtaymYMKzjk+RuaslmUDcR2uC59ebxQLHivTAnXUWjPK+585O89xFzF1WRteINgqCcDDq77/rsQ6x62vP3E0Fd0p5wMREFLbTl2Wmkw6MurN6/Dkcm/6hLuscuBujfcjZPuaOWMghWWb1fGvwO2V8D3KfuXtt+5qXZFYj7r7QrDGjs92iIBYLww479IGXd0sqesxd3R156eoqBB+61pVlNuvfriqSz3wm/vN6jfNTHvntAECOyDJLqyClZ+7UQKK31mVbe1TNgkhssqvxx/RFPM/A3S2nbrVvpA9DaZmsYu5Uu9AIJnzZ9SRw5wNVdoK7MC1zD3NnrL/pc5EM+jp8MbAbZe66gSraaogaDAaeO2WVZ+6IjWWZ8yMUAycr4QwGqtTMXQjurq7a5zlhrl/7HJYsxSxZonCqJztswF0ty0yXTW2DB3dPV5bpnMOD8gnu8dPR71nyOdau3WTWnjvWqUK4yC9wRufA2RkSkQ5KE/aNUjmEMsA73gH6H34L1tm9QK0ebRS4SEDcfpDSgNQx5u5PfScuTlO/QC0WuCuODwN3ZInVnPVlmdVnfyZm2Ir9zJ1yCjyQYc1KeL/dw4fACy8038e4mCQjHJu2x3DHz3jwIP43ToPlpb++hcAcAtttuxDt6rnryjLrKgRexNeE09r7dkSCU8VwmV/6z8PrwdxZ3Zxo+xPgaVUIksbM3U0qQMbG6D6QG/fcBZU4I7NXlkn85kzyxCs7bsjw6E5apg/aiANVUoNe2u7O2VVifkNwtyk3eLB+gC87/rL2M1kUMMwXi8v5MUpiUNgSKQ3W0y5zNwbuytzX9FTDgr4/U6ligCqEIpkYqNJl7u5+xR7mzjSKD8mkZyLD9cEY4Ld+a/fr1p0gUGWrts11zkAD5k41zGRv5nOcFH1w16RldgKipjB3NtvC1PkDJyeQhYquRaWLRuF0nBxjxc3w613LMieCO1cW0Ag8dyPgjsnkRszd6uIBjl3/0IYOyDITMCjqRsFdfQ0yMXAINJ8DWYalXE4uMvdVCOh57l7bvuaTMqsRL9zrdXV2x243UNRh7vhh9wUAZVExdyw+5Kx/zgXqQDjP2ntZZuC5U7UtYYS5+/Sngfe8J/7aZoMHxyRKygQ8uBvaXyqrIeQMIplDUdfb13jmriPLJAk2+TPm7ktyVJlDBN0Yw547n06nywKiK8vcF6iiS7BKlnlT5u5/fuen8Ie63RhbV3nueOKN6J0PuWdVBpi7EVmm99y5PnNXZkCW+XSoKFBlgZxjJwAarEJI/YWXcfSZu6myzCcPcMQXoOkMMyJ68oet2mKem1aWOVu0Mb3OTJNlcjp5U/Yke4IFSZAuxpm7I7FswF2XuQvTpS7zS5wiBU5PIXmyV/o6NDrb+pvv+94H8ku/5N/3qfp/4zd4zO4Hd4UpfCLrEHMnFtiUGx/lnJ4BiwWeo8vDwB1mWKVkMFClYe5mHHiyW+qpTVsNASFwljt885u+GXj0KAJ3t69CGA5U+fH/8ON454+9E+/84Xfgv/tf3xT9nbEGbLMFnnsOACJw55xDrvNB5o6TjiyTtrJMnpcRc2dUAWYBIiWk8oc0G7V5XZg7HcoymWi62HZNaVTM3PEEhXqKnruKuQtfk1HPHcWkQJVdPXe6kmUKKlBycmOGp3uv8sxd8L4SIDUx879zrPX/G3nPTfDe7ZwOuDtfn4NTjtP0tH09swxa+ARLuThGSSwKUyKhlRKGVfK6KnBD7WLuypi5CyshGs8dquJnyXeuI3XUfRNQUoO7N31NH9x1PXdBoIqGgSuDx/rRjwJ/6S/tf+3CCQJVQuaOEdoACGsMqNvB3OVuENxRWnvuOszdnsNClW0gHPUH3Yz5g85N+/O1qsI0AF+HwPQ4c3eALNOo0ltXCO0FqjRVCE2gyg2Yu9UjHKO/TnWl+MZoJOAw1H9WhqZJieYD+4jq+R4lR9OZOzeRuXv+3l7PXZmtIR2FcBSqOOweWqocksmq+6/93NTg7pLU3uWqy5QxcB2kZdbgbszjX3oVWDTrNc4XLgpTAYIDlO6PcAoymYEkCYQlvc+z1mWvNmTBUmzy6QE3X0zzDNzdcnSZxzrdgbRML8s0lefOf+9BzN0+z12YljnQc/erx0/wimkvjKbnjlUAs3Mj2sfcDaZl1sxd5bmTTKJcXwEnJ/6GFjJ3PPVAbceNu0nLrDcfeY4i8a9nxt3NZZlXj7BMlkCaej11JzGzAXc1czc7ah7DFOaOUw59AHN3vj7HfXYyWmAOAEu5wDX8z/Pgzg2WmF9kFzgzEjg99UEwA3HA+0Zla79Je9/7gA9+8KDETG09c0cnMHcPNw/xvBLDzJ1cYKM2bQnrcom7ZIHH2eNpz8Eo3LUpVgn6skxnWs/dV70D+PCHdx6w+NPf9qT+h/7tDH/tW/6aZ+6ef775PsanVUCM/h5dg7v4Z/zOo9/B937t9+IXzv4qPvQm23sufJMBd72ZfE4ktrk/8S2MPyVnA+mugnXTMt2oLFNXgU71przpqHrazJ1zUMRC1GXTjE303FXR2tUkPEF5gyChsdENcxeHS4T/9X9QKOsY+B3DCIuCTfADP9Ce8ivVMEOSSZ+6dxtwFzCa3nMXnKgTh8TR6YEqSvn1IMsiFUjz86wBl+OJv80E4G4mZvjUk0/h/vJ+LBXOc2jhi8WTxYkHd1YhqZk7yqEZ8Y+jAnfKav/nDmOiVREVE0eeO63BIlnm7kAVo0vQMKCEMeBP/km8+NY/3g9UiWSZBqxidAkhXnERMiPbbX/jum8myTLV+AZvscBxZodlmUNpmROYuzL34KAeyVOU162SQJkyZu6oGvfc1czdhAMkvwfz9znOBJTrVCGUpffcyWTSgVF3VqtHOKb9QzLKu7JMBQYCZtGrKKpHWeUP+HeAu6VcTu66M9aAdQ7ie547APzuC3vBebG9RuIYOPr9ufumVBkEk+PMHasrZRQE78sy94I7Y/znIiQi1mucz0wP3CV0GNwpayCSGSAlpEHv82yGZJlshs0B6aVfTPMM3N1ylMrbHixgmLljAtppaN0yd36BQh/cZRnwT/5J80eflkkr2dcE5i5J/O+uN4HbLbbMxibX2nNHBTTrs0yFKSDVbuYu9HMpo8DNAHO3vgLOznrgLmEJCsl33rhLU+Io6QSqJBVzR127KFxded/TVFnm6jUs05MK3IlekflWbTHfqoC5W6I008GdB+0HgLvrc9zH0R5wt8SaBOBOw8syCW02n0DF3GneyjLHJA47RmUb73v6tm8DPvrRKkJ9GrgzNXPnMA4If+InAKXwYP0A93Kxn7mbeebuLuaHMXda4kq6AVlm6LljwNd8DfCbvzn6s5SNPXdpXp0Gd2SZXYnOoVMvgl1QbKzB/aP7+Mp/+0k4Ati83Qxpq8GuNy1zF4C7MUkmULHLQc+dJm0VAusyd9p7futNeZN097SZO62hOGnTMieWmBemaBgdAJBUorhF+Xf/YVWbks5GN/yv/4OCqv1GO8bLMqvX1zngR37Eb1r8L2sCVQQTKNltmDu713OXWrrfh1NP7SEfia/XzgxLzbrTYe7+4OIPcP/ofty9mGUw3IM7uTxGSR0K277PjV/d+GCS0lUhQEnSW0/DEDOgA+5sy2xxymH2MHe1/70ZQoB//a/x4t2vwKcvP93KvDqeO+NMlCQqKI8lctstcHHRU9DsnBFwF8sy93jutgZXRewnqr2a3WAMbfV+cFdso+RaIWcxc1cHnABtD+mutMyJzJ3SZRN65WWZpnnMkedOpoNKpX2z2jzBMe+vz13mzloNBgruCNTIAdMU5u4Qz522ut17VTPI3KVzH+SzY4p8jRQcHOxwcFfmkHwHc8f89VAGzB0zdqDEfAe4Mx0Z73qN86Tse+7GmDtoyGQBSAlhSe/zrI2KpOwAsODz3h7xj8o8A3e3nG6rfZSWWRmuORNQVkMFLJ+gAoqiD+4+9Sng7/295o8HyzIJaaWZlTdmS03st6iZO8oHfR2FLpAoGzN3lO1h7mzkuRNUoNysgLMzf7reZe4k3cvc9QJVJEXKU2TMxszd889Pl2VuLnC0OANmMyycGGbuMt0yd4tjFLVHw1kwPiVQZbpX5nz1Cu69ugbe/ObR7zlKjrFGcHOMZJnt77nIL3BaEC/LFOleX+PQ6HzrfU+zGfDud+8Gat1/63TL3I3dpP/W3wJeeskzljmfxtwtFrhrksPAnRLe09Fh7nyPE217tiqGcudzCpNw6/e1I8vcefgyYWoGpQtomrqDX/s1cAvobbvoG2vA19uWuWMpsmohGgtTARD5Uuoql4a5y4uOLLP0m+MhcPc0mbuyhK429EAly5zwepYBowNUzN0NDjXGpgF32MPclSXKui9wxzDC2mtWV2xTfe+qo/afGnPX9dzFsszEscOYOyH6sv9qtDM38tx96sIzd3VdEICKufOJjXJxgpIChVFIWAfcVd525cwouPPrcwDuXAuuPbPl/47R/bLMmpnpzml6CkZZm+Y7yNy1a6kgPN78Z5l/LlOvpwrU1mtULMskgSxTx2A0HClxXHjgEv1oZ0AZ89ffgYEqZR6DOylTvweof4buMHcubwvLwzlQlqlV0WzKOWs7ELXVbVpmklRSbxwcUrTaXuBY9tOsuwd6dYCNcOPMlzLKh+qJgfvEbOZlmfLpyDJD5k4k+3szi2yNhFSKoEPBnS4geVrdt0Pmzl/rF9LLupXVkCxpmLt6b1FLRkf3GvV7dh0wmus1Hoii77ljAuUAkFXOQCRzz9xZ9D7PPlAlBt0LMe/tEf+ozDNwd8tRKm+j0oHBQBXBBLQz0LpsPXesAnddSdhmEy1QRvsFhfIdHpQwUAVoQ1WUAijFlujoRm0raZpgwi+SHSDiZZl2VJbZDVRRVoHrPnOnzl8G3vEO//OC6N+Upygk3Xnyr+xAoErCcJaeIaMmBnf37k1n7rJLLJfPeebO8kHmbhYyd8sTFK6sXrenz9w9+Ln/A/evAfyNvzH6PcvkCNe0fe0bWSahUUT7ZX6JsxyeuZPzmzF3xbbdpH3Xdx2UmFl77qjzoGBwyhJ45RXPWG7oNM/dconntJwE7ow1sM7iLIMHd0OeuyqtbRK4s6bdsIebto4skwp5Iz9H83vqxa3zM4wzYNdr4PwcwgIqAHeeuVtH4G5bg7tdzB3osCyz8txFJ6+68LLM6rm/bszdeg01k83JPuMTqxBsrAqQVKDA0wN3Tbx5cA3chrmjNGDu6ntYfe8KetQEFShvAe60M9FGhRIG62LmLgGb7rkLwd1AkbnGzcHdveW9mE0OmTs5Q8GBPF/HzJ1gPr1SlXBwe8Bdu82hJKxCMM3fcML3g7sucxfMi6cvtr67jufOM3fBZ5RJlKFsr37/p0oz6zCVSh6aqSyQZbbrdG2/GJtjK7Fax1J3ay0Y4VWgSvyZ38/cbSKJtOBJxAzrmrVBBe7Ka7/OdvdAB6ZlKl0EzJ1omLumo7QGd4R5Oe+BIUVX2SWO5XHv6565a8cYX7PDHYkUNeH4CqAJssyJgSqDsswqUCVi7mQ6iblLiIAghzN3ShWQIqmYuwDcVfeXy7nvJlZWeWVQVYXQyjILcLPjULgGd6tARrxe45xuBj13xSBzZyDTxagsU5sgPK2ahVxiY56eh/vzOc/A3S2n140xKss00UIzKsvsgbsSDHS3pydk7oD2dDXLgNkMW6hYlrmPuTMFpLKxLDNk7qz35dSbfq1LXxTLWOy5+53/AvyFv9CXZfIEuSB7ZZm9QBVBcJqeIiMmTss8ANyti2ssT5+vwB0bZu62ZcvcLU+aWF0LN425mxqE8BM/gfOX/gvu/4//0yDIqWeZHGFNW9ZUalelZbII3F1kFzjdGM/cyfRGm1zXpjikAAAgAElEQVRVbCHqmOZv+AYw6ybLMrUzYFz6QJWxU94a3K3PcX+zo8RcbfzzqZm7gk0Cd8oqSCZxkjnv6RhKywyZu3e/G/jEJ4DHw36+Ueaul5Z5e88dswPMnTVgn/h94Nu/3Rvdt+2i3wC/SpY54wG4G2Pu/s2/gfjZ/ycKVNHEeVAlJbgy0Weqy9ydpCe4yq9a5u5AidPoXF15cFcBaUb5tECVAebu6coyFVJD/al39VxHPXfUTfLcNQxafQ+rAUXXc0dx47TMbidn13NnCJCC7S82rmcvc2fB5fBhQjRdcFd77ljHc8epr4RgAooBeX7dHBD6dcvLMlW1PuwCd6LnuWtli6Hnbq8ss1LRDM2LZy+2vrsec2ejsBlBO8xdvXZdXIz+7miCMBVgQJZZrfVGq/GeOwDHkIPMnZdldjx3m9XenjRVZJFFRfLEh6rVf29UI09tDomGZL4HyjJD5k4wCe06sszKc8cph2E4+Jpaldc4mZ/1vs5EXIpuralkmXRUljnFc3cQc1fLMoeYuzAtU872M3fFBgkVPS//lCl1ASlSXxQfgbuKuZsTIMsqcJd4WaaOZZkzvYO5q+9dHebuHOthWWZ3DTAGijgImXpwpwfAnVb9QJVkOViZ9UdhnoG7W476hq+HmAV67FoGFoI7LqGcgVIFRCjLJK4P7rbbYeaOcdiRQ7hrm+ORCxbcOlRlu4VazqCIjSQ51jlQ0FHPXakLH6jChj0b3ZNrVSeSEdJWIeQlyldfAd7//uFAFY5pgSq1ty/LUAiKs9kZMqLjEvN796bLMtUGR2f3vCzT0GHP3bpse+6WZw1IMlN67qrNyN4FxDngr/91nP+pb8X9N7xj57cezU5xzQLmTrfMnekyd9faB6okcxQHFJDXo4vc3wABb0I/RJZpNbiQoMBwIqCryl1fecV77q4xXoWgOp67gk0KVKk/a8db4z0dg4EqDHPuO7YgJfDt3w788i8P/jzlNHi9Ya+vB2O8LLPH3N2m5873/nUBorYa7OO/B7znPf5EONtEf8evAuaOz7CtkiIzlWFWWOD7vz/+Rf/qX0Fss2ZRdRW4Y5QBhIDzJAJ3epfnzg4nFN5orq6g04C5myjLLKzyMp9qEpagJE8R3JkSM8e81Dqv47xHmDsywXMXHJINM3eu9dxRd0vPXTctM2BkGZBCTN/EKYVXTinK5WwY3MGAy8OZu5dXL3vPXZe5Y/7wkRIKboF1uWm6UpvQKq0bcDqVuWNoU0NDTxqjzLOBe2WZw1umt528LWbuOp67SJZJRQNK6+cLYDpzF/jtgIG0zIC5GwOjAHBCUqy2MaA0zvq0zE4whrq6iIDa0Pg4/A5zV4bMnWoOb06S6pBoCMAdKMtUVX8eUMsyhz13jDIYejhzt1JrHA+Au+6ezBh/cCgcGb2uvPJmBNxVeQlLMZ/uuasDr4LD/TpQJWTuWDKDIzs8bQDyYoOESvAbMHelKSDFbFSWeZmiAnfah2ZxDm5sm5apS6R6RxXQAHPn1td41a56ssyEp31wl+coJYXkScXcuX6gSh3AE8wiPcLGPgN3X5Kjv/Xd4MuAsqfU/y/PY+YOJurR8LLMAXA3KMukO9My/+/5J/EDq3/WfqGWzmQZsqXfPEeeO6dBScDcddIyC5V70y9p71xd5g5AA7x0AFobWeZ//TjKd7wNmM8HwV3BcVigSp4jF/CyTOibyzJthuVz9z1zp2mPuct0hvmmaBglufRR3LAWFhNkmbRKQd23Kav+/hzXvZtTd5azE6xZdROMwB2L5B/X5TWOrgsvy0wWg7rzfaPKDKI+gRcCzJLpgSrOgIu0Yu4GWMNqUd2+8mmUpsTpWo0zd2UnLTMj05i7agOx3JTYoIRldDBQZSZmbQ3GDmlmJMsE2o3bAHN3W1lmYsiwLPNjHwfe8x4IR6ACcGesAbu6bgNV5AJb7Z9TpjPMSgv8zM/Esqdf+RXw0jTAxKgC1JEm3p3JJPpM9Tx3MgB3wNPz3a1WUKlsPXcTPYyFbb1YQCXJuQXI7o7WCjNL/SFYDe6sQsKSAebOTvPcdZm7riyzYe5uA+7itExKg6CNagMlCT8I3P3Vb73E//fmYgTc2YPTMud8Dgc3zNwx2ngGE0NwjSJi7upAlTKsqkiSnsSvq6xhQX2MsToKVNkL7nbIMr/s5MvwyuqV9jmGzB0seGBLiJ4rcDPmrgvueJCW2Xju1G5ZJp1jlceA0laMb6/nzpm9zE9ZZpBBLYkUSdxzZ/UwczcE7g5h7oL+vFol5X+fiqsQKIe+ARu+slscH93tfb17zzd1oApiu0Q43nOHYXBHCDCfY0mSNi3zU58CfuqnRh9brewKh1OOx9njyHM3Fv8fTlFmSFgF7g5l7kwJIdMem6l1iTMyx0Xq8x9KV4E7xsBD5s4UFbib7rlbb68gCEfK4/vOIHOX51CcNgoVqfvgTpshcHeM7Q1Sx78Y5hm4u+U0pt1wOPc3pcZz56UCSk9g7jab1miP9gbNuIQdkSmVTuO31WfaL9TSme0W26VfVGJZZtVzx8Tgza7UORIXfzQojasQgJbWVqqNIsbJCbBeQ/z2f0L5FW9vPFAh3Z2wBDlze5m7uZi38s88R868eT1zqi/LnMLc5TnW1GB59gYP7hSJmLv6eYlCN2E4iZihEBRYr2HdBFlmzdzt25TlOZCmeLB+0NOMd+dofoo195+H0pQ+ybROywwY2cIUSFZbL8tM5ihuwGCoMgdPqpsl594/N5m5874bCjIcBV0D2kd/gHvLeyBFuZe5a2SZazsJ3NUHCWyTYU6kB8Vd5i703AEtuBuQGGq0HVUAWnDXDVQRtwxUMQqJJX1ZZr4Ff/QYeNe7vCwzD8CdM+BX1y1zJxbYVhKSTGWYGS+Fwa//uv8H6zXw7/89RKmbRV6XeeMDBuAT5ULmrg6RGPLcAU/Pd3d1BZWIg2SZ9aFD6PVKePrUmbvUMShBm+esrcZczKMNuivLVt66Yxhlfc9dGKgC13rubgPu0EnLRNtXaawBs5W88QBwlwlgPe+nHFtnYeFAb+C5AzDsuauYOwCQjuJaomHuBKsUJ1pPkmX2wF0QOEJJIMsUfOeaVKtohuYkOcGqrFiFnucuDuLy4O4peO7qfx4xd6x5fmaf547NPXsWTH0o0E2rVU7vPSwsVQ4ZfP6FSKNUUF37rRCAu6q4O5r1+nDmrgF3A7LMwHN3I+bOZjg+fr73dcp5tCezgedu7LrynjsMgzsAmM9xhKRl7v7jfwR+7udGH1tdlRUOIwzW2Yi5Q5L49WNHKE5RbpGw5IbgTkHKWd9zZ0rcJQtcSgtXMXdSpFVaZuu5U7rETE1g7gJwV2TXUVJyPZIno+BOMglwDmHahM72sQ6Au9kxNngG7r4kRw18ICCEv2HVaZnCyzK1apk7TrmP5e2aieubWbU4GK3ACPU3kpHHYGHxcXXeblbrQJUsQ7aowV2gDa+qEDjlnmXqeu5UjgTxc6KENSeCNair/6sD0ApKgeUS8g9fhnrzG5swFRKwgClPkVO3l7mTTPoTbOP7cApeMXeuvBlz9+gR1guOo+TYyzIVIuauOQGVsmEtJZMe3F1fw8CCTvHcTTFtV+Du/Pq8pxnvznJ2gmvRgjuhbcXc8UiWWZoS8nrbMnfkcAZDqdwnSgGeuXO7pRzNONdIs5gD7JAsswZ3ly97QJvno8zda9vXQIlPR8VyiZO1wlZt95v6a5Z4s8EJW+CKFHFaZnXQEIG7r/5q/9g+9an+6+FMK8usXhPkuT9lrxgzAKDiZh1K9WhdInG0z9w9egj2x76ySUdVHVkmu7xqmbtk2YI7nWGmq+TcX/xF/w8+9CHgne+ECLrsuswGFwmMjZm7UVmmEIcxd//gH/jNytBcXUElPAhU2S/LLEzVcRd4gyWXKG7wuR8bYzRSVykcAlnmTMziYvMqcj+8zw1NuPlugEj9GgaeO0b9ls0cWCZcj3Y2it9nhDWeO5+wV61BexIQmylLKAZsZ31wZ6wBdxRk4KCmNwPg7v7RfYjrdbvxzDJoRpoDQekorhMP3IGYuVNGYcZS/16kaR/cdbqrIs+dNQ0Tx8jtZJnNdQH0PXcw4MG6IVmCMmRQ6t95C89dHZ7kS8wDT+FYWiaAY77woSbBNLLMziZdweyV9ZVlBhl85qScRRtoZXXjPfSBKk+LuWtTODkXTW1J13PHKLsZc0dKHJ+8off1IebOp2XS0aAiZVWTdj048zmWTrTgbr3eKX3vZT4AzWsReu4gpVd+7GLuVI6EJ+CUxVUdE6a0JWQy770mWpeY0wTSUmzWT6BgvKeferm1aZi7crfnbkCWWW6vo89b81SZ7O998hwlJ/7wkBBIS3pF7doEnbbVLOan2OBmvucv9DwDd7ccbXVfitNh7jiT0LBQpk11EkxAkQHPSr1wVl83RleyTDHK3FnnYOHw0Vc/6r8QMXf+ZhqnZVqvDafCh7p0wV2ZRalXQCXLtIHcgYqAuSvifpDTU8iv+waUMD1JJlCFHlC788atjGrAXWnKirlzFXN3Q3D38CGuZwxLWZWYK0TMnQd3s4hNSliCkhPg+tqfTk/x3FHsP3EvCmwW/jU8SU52futycYY1d3DOeeOygb85EhZtMEtVQGYlsFyCJTNYTA9DqUerogV3nHugNiUt0xhoSsCYAHVk2HNXvSYPNq96KWpRjDJ3L69ebmUliwXIZos7szt4vN3tu2s+b+u137yQMpZlVv1fEbgjxPfdDYA77Uxz2gzAX9Pn5z44KPCk+kCVm4/Wvv+oy/6ZJ4/BvvKr/a8Gg867ssxVC+7SZSMhyVQly/zzf74Fd7/yK8Cf/tPghW59YyqPwZ1MoQNJrTHK//0QuLt79zDm7jd+A/j93x/+u6sraMkjWeY+sFyaEgnh0UYp4emNGOux0VZ5zx1rmTtlFWZ8Fm2UVJlDun5hfHcoDaoQhpi7upYCgHAUZXEz2atn7mJZZp2WaawBt/4gSu85LGlGKQ/uUtZ7z7XVvm5AHtZzNxMzCCrw3Ow5iP/tR6AePfDfk+c95m6VAEnFDHLCoTnx4M4qzGqJ7FDPnS5aVQlQ9XaGnrsg4IzTCeBuGCztAncGFiwMVKkPLOvZboHj46fiuYtkmXZHzx2AY7nESsXeLotaltkBdy4+2B0apXLIgEkRMo0YLG21D9PAHubu0LTMoD/P77VacBdWIXDKYaaszeE4H8x1fNa3TlAuYs9dVbOzS5bZeO6GqhAAD+4sa2WZe8CdZ+7ibXzN2PeYO0ui/UJ3ijJDwlN/fe27L6zXwC/8QvNHZXUA7oLgJu0TKM80x+X1I19HUAW2+depJQpmasdeY4C5K/NN5LeuR4q0H6qV51CcNHtR6Ujv3uqZu/h9WSzOsCHPwN2X5KgBEyY4b0+2AQjuwV14iiio8Br2veCuZe7MyH26vgl/5Pwj/gtBoMp2JqrvGem5G2CZSpVF1QVAv+duIdsOOq3KuMj97l3Id/+3TWVCF9ylPEVOzGTmrjSlLzFn1geqmCIuMZ8qy3z1VawlcJQceXBX2AHmLo3YpIQn3h+4WvlFek9Rr+8vnCCnynM8OOW4f3R/72m/nC1BnX9NlC68HxL+lDZcRMpiiyRdAoSApCkSS6Oy+SmjdAGeBsydnSjLLApoQcFpBQiHPHdlCbzwAs71Be4v7+1k7lbFql2cKg/p3fndvaEqIXN3LJYe3HWqECilMbgD/GZiKOK9U0AMIYBXXokkmQBAxX4wsmu0UUgc67FV2iiwytMrCIPK28esrQa/3nopNIB5eoSt8+9347l773v98/rkJ31ozPvfD1EoKFtfu0Uf3AUbAF0nBFY+ouPk2BcfZ5kHlYcwd9vt+HWxWkFJFsgy97+ehS58r1bE3CU3YqzHRhuNGapk44C568oyS5W16oUdw2jA3A147nQgp5SO9k6Xp043LZMGjKG2GsxVLMcBskxFgW3Sr7DRVoM7ejC4m4u5l2cTAnGxaguvs8wfFNWvA5iXZYbMHSNNoMqcz3fIMsvo4DEMHDEB+OGUw/A9zF2lohma4+S4lTj2PHcuTsvkMmZQtlvgjW+8neculGU2aZn7wN0xVrrDwlZBPD1ZZvX/dzE/pSqig26ZzPuVSVM8d7Usc7GYxtyZmLmra0sGqxAIDmPu1mtczQiOF3d6f8WEjO5RtvJz+7TJHZ67TrplNLMZjgyPmbuuuisYo8veZ5JTDgKC4yTIgpASwvW73cIpdF6BOzb6+AF4y9D3fR/wl/9y86XSKQ/uOpVAvhic49QKXGxe88xd5c0N9y8tczdy767TMkPmLt/09qnADuaOoTkEkAMHZ74mqSPLXJ5hQ5/eYeHnc56Bu1vOqOcuAHe8AndKlxCV3JFTDj0E7uqbWQ3uqkAVJpIdzJ3BMZ3ht89/238hCFTZzmtwZ6Pvb3ruBmQKhcp8qlAwDXPnHJRVWIgFikp6oHQRSV/wgQ9AftO37AR3xQ5w56rfIajwskhTAHmOglTMnS3g8k5a5lTmTtiWuctdn7mjacQmSSZ931TN3E3x3E2RfhQFzo/pXr+dfxASy9IHppR12A38CXaYglqWGeR82fybxJG9MsbuKF226a+ce3A3hf0ry6abioJEctHwe3B0hPPnEtwnRzuZOyCQlVSHFXfnd/f67urPDTYbHMsjrBDLMn1aJu+Du1rK3P15GAF3z8ceDCaSW3ruvLerC2jCpD1BGFSwIBlnwPKikWjNZ8fYVhKSTGWY5cYzjO9/P/CzPwv87u8C3/qtEGBQ5UAYEnyqWlRirpXfuFc+olsxd1k2Du6urqAEa+6luw6z6ilM4Zm7ANx55u5pgjuFFNUhWMDczcU8Zu5UERU4jw2jvE2tHPPc1eqO2zJ3bIS5q2WZTEyXZTbgrl9h48HdcK1JbwbAHZyDuLpuJcd5DkNbeVlCuJdlVtLDKFDFKsxriewQuNMdcBelZZrGc8co82zg05Jl9jx3HebOmdbjm2Ue3D2ttMyGudvjuZudYtXp8DJVl2tPlkksuN3N3JU6j9Z6kcwib6F2xodp4HVg7uqCex4zdyG4a5i7Q8Dd1RVWKY2BUjWUxQdQpvJwcpDYUxmMZ+6wW5apaNtzt4+5q5UVwTDCcJKeNCFZADxzZ/bIMnWBRM72M/p//+979YpS/nAdPvdBpot+oEoFsM9sgsvtE5TONIFtPAg3Kq1CaulBnrsy33j/XmcSMev7rvMciiFg7hjKvHsfU7G/HhW44/bpVf58HucZuLvlDHruuuBOJFAw0FoFfSzCl0pOZe7YDs+dc/hv0rfjIw8q5i6UZab+90WBKs6CwrMsQ0CkVEWzkNbDKIOlBPjkJ6FMXDCuddlEEQMA7t2DqBi3IXBXs5Z2O7wxrDfohBAvi6xlmdRgKZegoD5iWSm/iJ6e+otfj0sOAAAPH/pAFbn0nrvc9Jk7lsTMHUtQMBeAuwlpmR3mrtAFfujXfij+xjzH+THZ67fzP1RgWfqOvgjc0Y4ss8wg59UilCSQhjQAfOpoXULMls3vZc5NZ+54wNwNvRdlCQiB8+dT3C/kTuYOQI+5e272HB5tHu18GBFzl554cLdPlgmMgjvtbLNxAOCv6c99boC5k3vByK7RWiFBn7kzaL1TnDDooj3FNXVvVBWDPZsd+7AhVMxdpvy18T3fA/yjfwR88zf7RZ6JxhOiup67Xcxd1XO3yq/8SerJyeHM3djG6uoKWgTMHZsoy0QH3InUB5E8pWnBHfYwd/kBzN1IWqZSTaAKAEjQvd1iY9OVZXpQU3nutAJzlSxzh0wrmlqWOdBP2oC7A5m7pVzijUdvBLZbiEK3BxdZBk3Reu4Ix7UE0gDcKRbIMrkHdy6RfXBnVKQqiTxpVbph/TN1IL0dmvqgdWh64E5rvyF0Dpp0mDsmoCRrr4VDmbssw2+crvDzv/vz/o86KDEfeX6Dj3l2ipXL4IKNa/256UbaK1jM9e5AjlKX0aGwTBaRt1A506SGNn2ZT8NzZ9pNueCJPzRHYJkJPHcmYO6ss/jbv/y3d//wy0usEv94u+MTfdvxaZnMy+fH0jLtHuZuPsdSkemeO1UMpmWGSZkAPHNn9zB3xu/7OOXjzN0HPgD85E8C//yfA299K/DZzwKAB23pAqzjPa/9kKdIcbF9Ak1sw9zx4HUq68PNsV1uF9w5h6Lc9kgIoEpp7a4dNXNXrS8CtFft4Q8DOrLM5AibhOxkT79Y5xm4u+Xs9NxVi119w1GmbFKdBK2Mv90PzRC4AwXlYlyWCYt3zd6Ojz36mL94g0CV7WwE3BHiF3eKfhVCRc+HQwmFfd93Ae97H8oyw/Iqaz13uuO5Axo5ZWGKJuWsHkIIEnAUmxWGpjRl85qGssyCWKQ8xYynvqNstfJehSpCeJ80s3z1c7DE+ceTpoPgbkZkj7kriK0CVVx0Ajs03ksZg7vz9Tn+8b/7x/E35jnOFw73FrtrEAAAlOKoBNbbS5S6aDYrjLLIH1WoDHLZgrvE3oC5Mwpi3oI7egBzV4O7UeZOKUBKPDjhuHdddd4NbAhnfAYCEoO7zQZn6ZmXBO56GCG4m50OM3eHgDvEBcRjskwmxtNsp4yyChK877lzFqxekAiPmDutFVgQPz9fnGBbSUgylWG2VZ65+67v8vej7/xOAFXvZtU/FdazAABPZ3FtSs1WhJ67elM2Umg9Ovtkmbz1WU0Bd40sM9goydrP+5TGWI0ZqvvkLuauzCGx++AHqFJAR5g7pxUsCZg7sL3dYqOPGy5inCllbVqmLg8PVKmZO4HedXJTcPfn/tifww9/9w8Djx9DGLQS1A5zJ4nwzJ3sMHdaQzkNyaRfnwbBXRmFJPgqhD6z5WWZe5g7q0dlmSfpSQvuCGn7NY2BYSQC2pJJlJK318INmLv/9/gV/OAv/SCccz3mrn6f9zF3cnEMDoo8CD1pqhB4B9wRi4Xaw9yZAjJITBVpfACi644zeHXGulz31+2y8kgnyXTmzuoOc2cb9U9flukacFfoAn/3N/5uvA505+oKK2GHmbvOgZ6d6rnT2J2WqTAZ3Bmje8XbjLLYbwd4cGd2y2oLUyJJZrvvCz/90565u38feMtbgD/8Q/+8YCBny36gSkV+nGKGR/ljMEdAk74sU1Xe5r3grpZl5jnKRIyAu3RQlqloh7kbAnedvfxCLLCR5OlV/nwe5xm4u+UMeu5qQ3XD3ElouEp/vIe568oyq4vXVyEMPwbrLI7YDG87fRs+9uhjMXOX+Lc4pLtNxNy53ml6oQvIJGbuKKGw/8sPwn360zAUmG/KDnPXB3fKqkHmDgASKlDkw0WddZhK/XMa5g4aCUswE7MY3AHDJ4CdWV+8iiVJvcctTbHY6r4sk8ie564kFm61mibLrJm74DVdFav+olgUeDC305g7AEtNcL299MxdfdpM4i6iUheQy+qEMUkgDQ733FkFXjN3tSxzCnNXgTtGGJgj4547KXE+N7j/MItSScMhhGAu5u3pYyXLjE7Hxx5GmJY5v4OVzftpmdTLMjMd3NyXy0jyUY+eKMukMrkdc2cUEtKP//dhDBW4ozzyYBmjwZIA3M1OsGUWMMYzd+vCM3cnJ94f8Wf+jP85PGnS0LQqomuXy1kTJV7/Dg7ar0Kowd0hi94+WSYjnbTM3eOZu9hzl8jZU5ZlaqSkklrvYu503rsHDs0u5s5qBYI2cVOCRSXQBz3uniwz9GL54mNO+c7NXjRVSfuW2RHmDgeDu4Vc4G2nbwNee82zCvVmK8ugSQDuqBjw3KFJyxTc9yPqIXDXkWWGzJYJS8wJg6Z7wN0O5m4hFsh01gL32nenFHQQDgNUa38SgLsbeO5eEht84vEn8KHPfmi8CsHs9txhscCxk9GBmUFdYt726jrnYIjDTO/x3OkSMjgUlukCpas+6855iXt1ULaQvu6mt26v1561qw9sJzN3sQXGOguCqr8zlGUG4K5+LrtCuoonj2AJegfUgD+AsgSNZM84A1qBuzFZ5l7P3XyOZYHpgSojnrsoKROoZJluN3NnSyRy4Yvgx97nogDuVP7Dt7wF+MxnAOdacCdjH2Ijy6RzPCyeeHVDnSIfdH6WVmHm+G5ZppTtGr1eo1zOBveWgwd8eY6StlU1Q/dWbU0f3MkFNhLPwN2X4ox67oBYlkkq5o4EnjvXXyibP9dVCE2gihgFd/UN+V333+V9dzW4yzJsZQ3uwp673Z670pQ9WSatTgSVVeBgESukdAneSdeUO2SZAJBSiTzrb6br398Dd1mGHNozd2LuQ1VWqyZMYspCsNZbLFm1+MxmWGxVX5YJETF3lFAwUOjrq+meO2KjTewguMtznM/MNM8dgKWmWG8voUzZeHs45bEs05RIjqqbupRIzA2YO6sgFkfVkxFg1k1LyyyKZhNDgXHPnZQ4FwXuf+bJoN+unoVc9GSZh4K748UdrFzekWX6BXjGZxM9d7YP7gZkmd4Tu/OhxfMP/2Fz6gmMgzvtWnkdpzyS6WmjouLouVz4w5ws88zdOvfgDvBFuF//9f4pcNnIMrubX57OoV0gyzSxLDMCd/P54czdDlmmCmQzjEvvj9kxhanChaJAlacsy7QKM1Kx8UGJeY+50+Ukzx0Nmbt601YBim6PmgBDqW4hywyZOxLLMrnzseCHMXcOGetX2Girwe0BnrvuZvXxY4j5so1fzzJoEshTmcAqbcGdX7dIy9zxqqA6EQPgTkVrUyzL7DB3jO7uuTPjzB0hBEfyqN2U1747raF5Gw4DVAeAksXg7k1vOoi5e4lf4y9+zV/ET33kp+K0zM7zG0v3BODBnRHRPbU+TApTD5VV4JZAqn7xczjKlJEHSsyWUPW9xBhoiiYtM2EJtNXQ8zQG1LUkE5jO3Lk+c9f47YCWuetUIdTPZVdI1/Xlqzi2YjD0jFLm71GmTaFldLcs0zN3djdzV7jJgSrd+zGoMIYAACAASURBVDfgAf6NmDtbIk0X4FSMyzKLoj3EeetbPbhTCoqRqucuiZm7ivw4ZQs8LC8gguAlz3AG4I6I8b2GtV6FUjN36zXKRTocqCJm/TUgy6CIbfeVpH9vNVZHB2JAxdzx3Z3MX6zzBQV3hJCfJoQ8JIT81y/k47jNjHrugDYtU6TQxEY9Gl6WqaP0HwB+4SSkx9ztAne28hF9471v9L67eqO63WJbXYe9njtSMXdkgLkzBWQyj77WgDujIAiLWCEdlIjWMwXcjTF3XXBX6CpQBRoJr5g7DuDx45a5myDLXJsMR6wCrWmKxUZFzF2mMw/uOpsUSRjKq8cwBNGGaWiacvoOuGvK2OspCpyn5WTm7kgxrLMrz86FsswQ3NkS8rgCd0mCRLvDPXdWt+COczA7sU6hLGFY7bkjo547nXA8IRle+P0HOzeDC7GIqhCmMnfKKF+kWxQ4Xt7Bld3Gskw4MDY9UEXDNlIiAONpmTLZC0ai+Zf/EvjEJ9rfYzUS0u92i5k7AR2cNhqrwdL2EGYu5h7cbbfI1BazTdleH8FwkTQpicp0wF0ya9LmgIC5q8DdkTzCSq3hZunhzN0eWWbIcDC2v8S80AUS12HuRIriqYI7jRmRno2vZZlGYf7gcYe5K2Lf8cgwFrDteQW+q9fQdPwzkjAoddMScxdtVFggy9SqqJg7AT21KqUCd1vaD8K6KXPXzOPHEC9+eVucnOdRJYSkEtcJaTZyXpbpPXdlVVUyCu6simWZncCRGqw1bOANZZnASB2C1jCU9pm7IVnmIcwdLvF33vN38PO/+/N4dfNqxNw1skyj41CN7iwWODY8uqda5wJZpr+OlFEQjkBqt7P/rDRldK+Us2XL3CkFJVjzOhBC/MY5ZX3mblkpR6Yyd1Y1PYJcJNCoAtnqtbo6VOwydzW42xXStbp8FccYXqcYZX5P1oA7v1fjhEZ2iXCUVRB6N3OXbr3iSVs9UZY5wXO3j7lzDoXTSBLP3I2CwLJs1+2aucsylMLXDLCOgkUbL3U840d4pFcQgXzb71+q98J6hcRot6kx/l4ZMneLdFgVJodlmSVxrd0HHGWXuesmY6NaU7mDO+QQ84tkvtDM3f8J4Lu/wI/hVjPquQM6aZmu8txVX6N+kXfXnc3qtoo27wSqMC785tH2TzY8WPPM3UcefCSWZVYPLWLuEPTcDcgyS6uQpIvoayFzJwmH1O3NUQVRxPXsA3cJT5AXwxdM+G8SHgSqOFV57mbIFhJ4+PAgWea12WLJKtCaplisiz5z53iPUUqIQHH5GJb607pd4+W2tifLrJ9XM3mOc1n6tLgJs7QM19llBO447aRlWh2BO6nd4bJMZ8BD5s4cEKjSMHfjaZkP58Bz/BjspU/vBnchc5emgNa+t24Ccyecl/QcJydY2WzAczcA7saqEIY8dw8f9mWZVUWGm5qqpXW0aGujIVn/ACfcpAsqoFQI7gy47IA76aVl2fYKM542YSvhCJFCNcxdJ3AiSZu0Of/3leyn2pQLJiCpwHaZHMbcad0GIA3N1RUUbaOqmdjfG1gaX/zelWU+XebObzo0bMzc/ca/i94LpcteN+jQ+LTM6pnluT+Njpi79v0ShD9F5i7w3BkF5sjNZJlEjzB3uB24e+uL/rBTqZa5qw4dEp54zx0LwB31B6CKEwjmN+4q4f2eu87hK0Pb12pDWWbN6twwLRMYAXdKQTNEvqhB5u4Nb/Cv675QMACX+SUMcfiqu1+F9774Xnzi8Scw42GJecsk7ZVlatpj7mgnUEVZBWEJhPXF0WNTOg0ZKH48c1fdS8oyCkwCKsnbjO1m7ibcX5Q1DSPIuYQiAXNnvUwdQjRA11ZJwVPA3dXqIY7pbPDvGKmYO10zpdWhOdjodVWaElK5nT13JMuwlEvP3q3X/nMysq74eo54T8IpH2butBu/3osChaRVoMqOoKWQuas9d1mGUnglWLceovaxncojPDQrSEubdZ+DNf7X0imkRO723J2exszdPBmWZQ4xd3k+wNx1ZZk6Xuvh7wuJpcjWEw9evojmCwrunHO/DuDJF/Ix3HZGe+6Aln6WKRRx1Q3HX4iEEK/zX/eZO/XcWczcUQZCKBwB3MDN38CBUop33XsX/vOD/wy7mLeBKrzWgwen8da2PXdDzJ1VSJJhcFdWoTDStBINbVTv1FowX9Bd6GKYueMp8pGY7zIAwVGgSg3uxAzZIgFefRU4OfGnUVOYO5tjyStwlyTDnjvHB5g7jmL1BJaS3SehqKRzA7LM+nk1UxR4wPPpskzDsc5X3tcQSXvbjXjhFOTpc9WDlkg0DpNlVr6ImLk7IFClAnfMtUW63e85n1vcX7wBeOml3bJMsWh9A4T4jQjkNFmmo8By6TdbNrtVWqbqJN01i3KHuQPnPnxmChAG+uDOKiRUDnru6t8vmIhOzrVVYLMRcJddY5YsB3+1EEkjG/JpZgFzJ2JZjakT94Ji5mM2x+pIHsbc1dfmLlkmsaOyTOts73PYyDLDQBWR+nTbpzTaGqRU+vtkyNxtSqhIEl301AtD02Pu7txpmTsT96hJwncyJbvGdLrVKKWt5055z51gB6RlliUUsdgSNQzuDG4H7k6f82zWq6965g4Bc8clVhIxc1dVUyjp6zM8c8d7EjbdBXeh586a5n7uY/InVCHsYe56XXdaN4qG5iVgEmUX3C0W/qDyandgFAC8pB7iRf48CCH4K+/6KwDQyjJpGydv3QRwV5IOuHNgTPSZOwtIA6hd4M4qyOCwSc6PWgalLJse1ObXiwU2aSyFVVcXB8syw005Fwl0s9fiLRipZJXcEWi1G9yF7NZq/RjHLFYx1UMJHWTuBNmRlmkUxB5ZJrZbHMmjFtwBo4diYY5DPYywYeZO23HmLstQJBwJT3xX4Nj3VSwoAKgve2PL3FUF4YxXITMVGPXgjuNMHOOR20TMHQ+YO2U1ZnQCuAuYu2IuR8BdX5rvKllmva/0B2ede0XXX1/NwjJs1n/0YMoXmrnbO4SQ7yeEfJgQ8uFHj3bHoH8hZqrnThNPiYff22zWAsD2GVzhm//sg2bjZ6sFiBAC4gA3QPfXssyz2RmOk2N8ll43zF3GWrNv9P215464flqmVZCzeGMYyTKpQBLo75Xp94NIJqGMlxcM6aITnqIYSaka9NzlOXJT+kAVPkM2F34jcHyM7/in34Hffc7uZ+5cjiNegVZCsCBJH9xZ1mfumES5uoChpHdK1nteLEEBvRfcuSzDI5rj+cXzvZ8xNEeW+yoE08q/GOWRrKqEgTyqTuySBFLZw2SZ2y00IxD16SvnoNZF7ODoVMwdowwUI4EqSuF8pnH/5M2ND2Js/sQb/wS+4s5XtF9YLnFsxbS0TEv9xiU5xsrEp+FhoMpkWSbvyDKBQXDHHJrenr3TA3cGCevXKdSbLaDqJSs7zF0gn64lJNhuPbhLx8Bd2pFlBsEbFQPZSPjq+pZgU35MZ1gtxOTNF4B20zy0SdHaszVoDe+M8miT8DMf/Rn8zQ/+zeif+fea9Dx3BcNT6yUyzm86FEzM3F3nLSuBqueuuw4MDGOdtMwecxd47ghHeaCsuh7dZe6itMyKuTsE3CkFBYutK0eYO3dwz10zjx+Dn97xbNb5ecXcBWXuPMW1dB3mDhW44xBMeBWKHJBldsEdZS34sRo0rEKo10IzfEBjjNl5/48SM0PPHUP0GAQVUCIAd1nmr6Wzs0m+u5fMY7yYvAEA8N1f/t1479vei+fm/lDPyzJbZnIX0+j9XWg71eBVPV6W2aYeeuYOEGYPc2dVFMQmFkf+ugH84QCn0f7Hh1WQ6NDkzR/+XrhltUanqX8/B9RKzRgDRV3ruRNJ47kTlTw//FwyR2CGmLtPfxr45CcBAG//0bc3gG+1ucCxGL6PNtUK1friVSFVWubIdVWaAlI7n6Y6NJUC6SQ9wUV20a5JI9JMY1Tk5wSAr3r+q/C1L3xt/I1CgBs3GvTSgDuW7L4vVK/ny6uX8cd/8c8CDx4A19coWQXu6i7B6jXRVoNRgdP0FA/JBiJg+BlpbSWl05jRZJos0znP3M3EILgTcgbFYhWNzrfgjrZhVVQ06pXme6zpMXcAsLAcm2fM3dMf59z/7pz7JufcNz3//LSN8OdzBj139Waj9tzJND5Nqr+NCqiToyil74nb4kniep47AKDOn7p2x8KBVj/3zuwOrrhpA1WYxYIkOzx3tneaXkLvlGUKyiF1h7nrbGz2eu7EDPlIzLeyA2mZWYbcli1zNxONLPOV1St4sththgeAtSuxFO3zWrC0L8s0tLdJSViC4voSlmAvc3eanuLKZtFrWp/mhuBO5Rsw0P5nZ2SWTuA698EskgSyzCD8oiQWybw69UwSJMoextyt11CVvAIAQGkFWCZIt8qy2cTskmWezzTu33mLf413MHc/9j0/hq97w9e1X1gscNIx/w8+DFNCWrTgTm9j5q7avMyED1RpFoBDqhAYa4NK6iEEzPrEw0nTA3c+CXZnWiaX0YKknQFLB8BdliEr1pjN+n47oFIS1Mxd5aENnx8HaRbdhrnj3L+O1lbgjh1WhVBfm0PgrpJihSoIxngUVvAke9ID9tpqiA6440xE/+62UzN3hji4zD8HpQqk2xK2SuUDKrXBBFkmpTuYu044gqS3YO6Iaz43QCUHDaoQuCNewuYOAHfEYWvHwB1ux9ydPecBz+c+12fuhPezhsxd3TtYSgbJdnnudPS+xIEjtgHTzUZzNhtl74zdz9wNyTIN7QSqMAElaNxzN5v5e8oE391LuMCLszc2r8Uvf98vN1H9UVqmbUvaB2exgCxNtEaYKjTMMzCVLNMoCANIR6CyYZ88/n/23jzWluQ+D/tq7dPnnLu9bebNjMR5lERLsob0EGFoOYhJSo5hQ3CMiIocO3KshDYcQYpMLbbsRIIBr4Ds5B8Bhi2HsgQIlhEoiiM7ViRoge0YVmyYtBYr1kK+oTic92bmLffes3V3VXXlj6ruruquPqfvm6HFEfkDBuS767nn9Omqr77NWhduEzJ3i+Oubyyoyml/vVi4DjF//a+qFV7TF9gc+59BqVsj9ilylIIWrOu5Cw7SOeUR0wQADARadam3gE/L/MhHgL/9t7FVW7x8+TJ+49FvAAAui3OcJGoQgCFz52SZzPWRjjF3unIAJyGXB9AemL3t5G146fwltyZJORqq0ii7wvnW934r/vCX/uH4CxlzibRqJJxlt0ORUWQ8g2ByHNz55/PTl5/GJy9/0x1yfvzjbUE4a0Jm/LXdyDLP8jO8Tgt3v47SMhtZpkbOskEVUDt17a4FxtxzsV6jmslkiinh3B1EBNd1VW6idU7S4cFZWDsUzgICm+1hRv2zbT7rwd1n+4wyd03XDfxpEu2ZfOFv8seLCNzt1A4lRwfu6u7NO7Z5dMWj7muOs2MH7ppAFWpwRGcR3R157lKyTKsh86PoY33mLmSFUtLUg+BOzlHq9I1mEKiinG+qNKULVOE5djPuwN3JCR4Xj1Hk4rAs05Y4kt0p3ILNog1+C+56oEMyiWp1jrq3SKfmdHaKx3p9kLkry62LcZ84SyuwrlYuMMT3abkTNr+w2NqV5TagnHNk2nXfTZ7VCqq3ADMQ1GNSunCqCoZ6WeYYc1dVuJ8pPL287QIEppz0N7NY4FjRaeDOOBnnyewEl6YXqOL9qZxycMq712RMlkkteD9Q5ebN5OLsDl8mbsZ74E7VChnLBqIUHXQrCp5F4M5YEwWq5Dx3UfVNoMp8WLwLACLLW+9Fn9mAlOC2O3k2jeyHkFZqdoIZLnN2NeYuKOoezMWFk1cH90dG/Km4P7UvdTnYcLRBHiIEMexNBnfaX9MUuvDgbreGMC7NstkgtkE+B2YgywyYO93zdAkqnpi5CxlfIGbutK48cxfLuveNrSpoUmNblyOyTPsGwd0NKE475s52LFnj44qYO+aKhZUIZJmCpZm74HkIwU/dl2Vasx/cJTrFwjmW6UAVTck4c6eUe28JMZ25o5e4s3wu+TlKu64wlwa6P1BFKBP5sAzqYaBKw9wxgWo7Au6UQsVJpHIQs4ULIjLGHQ5wOvTc8U7u3CREXhwH19Ghe4xSrTQXGJFlBusMB20PyFvmbvfAqYBWK9xb3QMA3H18F4Bbu4/z3kGen/YeFTJ3HtypMeZOFZB2z7rv/947p3dw9/En3Hvt2rW9zF0/qXxsBFhUpRPNbodSMsfc8QPgLsvwYPsAW7XF9vlngX//71E14I4wJ28OmDvOBU7n15wsMjgE4j3mbsay/bJMxpxkd7Xy4C7N3IFzyF7Hryq2UZqxTNxbtR1h7iCx2X0e3H3OzajnLjxNljkUbW44wccphz456kyixmBnK6cXbqsQOnBHAdSJE6EmUAXwp4dUdcwd0TgisyipMeq564V/WN9Zko3JMj2rJgNWSJthqMzBQBU5RzFyihQFqrAMVbEBZjMUugiYO+fPMEdLXJaXKGfisCyTVFgG4I7N5pBUtH1nW7XFXA8jvTMxQ7m5gKGHmbu5mMOgRhF0qF1WQ3BXlZuWgZsyR1ZiVa3dc9NK11i7OVNGQdYEpPFgEQJpKcoiwax89KPAP/2nw4+v106WGVyjDHQaYClLaBowdykGq6pwT5YuIfSZZ/Yyd4NZLieBO1X7HqGWuVv30jK7/q9ImjnK3NlhWmZfkumH2c6sf3D6zJ01yPgszdy1HU6ilVM238PzjonORY6C1rDbratCWKY3JULm7aI6CEMSwp1u+8/rMI3NS82OIXGZk6sxd/tkmRcXwPFxJFunhEYgrU2PC6b1egX+ldY79WaBO2vAqXAbowbcFU5eJEgXnFDV1TBYKzGRLLMs3Ya+9dzF4EFSMS6j2jPWWlgCUNZjrMJAFRAIlk1Oy2x8SluzGwd3b0CWKa7fdGzcvXtD5s7XfUSeu0aWKWgH7uQwUKXx/DQTyhaN1RG4O8zc7ZdlRsxd6LmjvUAVJhyQrSr32s89+z6VuZMb3Dl+W/JzjASBMbUBO8DcicpEPiyXltkwd4Hnzjjp4yhzVxSoMh6t9ZJnqJpi+DHmTnQx8w24u1wE76ND4M4HtTQ/l/MMmnpFAkvIMoP1LJJlvvaaA3drD+7OPbhTaxzPe/41P5RQ1KkqBMJHwZHSpet6GxufHXDn7A7uPvj1rsx9BNzphCxzbISlUZVONNstSuESaTkT44c+3sPYyFYf3nkK+NVf7cBdI1VtmDufQHm2uAHA+TbjtEwP7mAwE/l+WSZjzpd6eenAXe96a4exQd5AVW4jBl9SOQR3MOAiAe5uPoPNlzyfflyfxfNbXYXwIwD+JYDfQQh5mRDyod/Kx/MkM5qWGYA7J8ts+liCj1MBdTTvmLvdDrulK81umTtrwPxFuV+W6V7K4+wYl8RLZzYbB+5oPpBltp67HrjTtQYDAV2kwV1lKogeuFO1GhhRBRUduKNp5q4gJpkONvDcFRvYWeaizxvPXUaBV1/FxdI9N0XGDssyicIyCxjJ2QwLnre+u63aItdkyNyJGcrtCjU5HKhCCMEpX+LcdsC1WfBD/5tj7g5vBptZEom12jhfA+mYu4YFKE3p/EfBY88sQ5UKrfmJnwD+3t8bfny1ckXS4Uk3CIyesMGsqhbcMdDxQBVeuhCZZ5+9OnNX2GnMne7A3YVaJwJV3PMXdd2NlZiTEeYuMRTp92dyBuBOIxOzQZ2CIbZdcATPurj45m8JZJmUUGSWodhcYKeLUXDH5axj7npScQgBboPEvdCw33Td1QKXs+klwwD2M3e+rzJ8LO0mwXTXdz/prZUDipiZ0W82c8e4A3Itc7d5A8xd0OXU99z1AjuelLkz1oBZAhKA3shz16RlchF1Gu4bpQoQC2z1zr2WdXhYaN44c3f9liuKD5k7v2nNpLvGG+bOpTyjC1Rhe5i73oEqo0EVgumYu3ajeYi5G5PTISHL9Mxcv8RcMolKkCG4m8rcZW7zn5rYUziBuStVzNwRC8q4Sz30H9O1htCAbA5aU1OWrUS2GcGEA+y+AkX1Dg4XcoENq9vnu/H+XS4CsDKFuRMBc+e7e1W1G2XuwkCV09mpAyqvvgqs17i/vg8gYO70FsfL68lf3eQg1P6eXzcl5oSNgrtK+RCosQmZu4cfd+tSlo0zd/V+NjkcQQ4wd4J2zN3YfcHLMptuwIfPXnPgzheE9xNEm7Cb5eIM1DrfZpuWGXR+VlYj5/l4/U2CuSsztoe569lgql2UZiz9/jQcZ8EY7kkWTz2HzRdOSzX/bJrf6rTMP2qtvW2tFdba56y1H/mtfDxPMqM9d8FCx+XMnSZ5/XEzggmoo0XH3G022C0yVDA9WaZfgGxa7mZg2w3rSXaCS7V2b6BHj7CFwhHLY1mm99w1C5sJ2IY2gS4fKTH3J2JZ2Wn1UwC3Ye4aKWV/Mp6hnMvkjdsByCAts9xCzWdglIFR5sEdA157Ded+ISgydliWSTSOZoF+fjbDgna+u63aYq6QYO5yVAyoeyewY3Mmj3FuA+YuJcustsgmyikA4IjMHLgzVQTumhO2NlwiAHcSDGWqbmKzcWmV/VmvoXobEQoyDbD0mbsRWeY9vnPM3bPPXo25WyxwVNRYV+u9peouarp2Xy+PsNbbTspsre8qdK/hXMxb1rYpSu8HcSiaSMscZe4I6ilAGEgGqkie7Q1UEUE/nftcDT6PvbFzy7HdXmBXl8iP05sSMZu3sqFBR2XPc6fDbq8G3BmBC2nfPM/dmCwzqH5JyTKVUQNw92Yzd8afPAvCof1BiSq3jrmzNGDuVBvqsG+cLDMAd5HnLo41d6FUV2fudK3BbGcLAPqeO3eA5zx3E5k7XWEB1zlaz7LoXqtrDa4ngjsh3LUfhmQ04I7ULbgz1nTM3S2XKDxg7ooCStDOczcG7vbIMllYYl6bvWDi0EZ6tOeOYFhi3jB3u1231k5g7qy1eGle4c71L05+PmJop3juCj3w3DEunSyTBLJM4xQMaiThGkWBSsSbbckkqgDc9UHuQiywYWbI3OVXAHd95s5LdrUqRz13oSzz9vJ2zNyt7uGLr31xy9xd2B2Oj26M/npq0R5+NknMnDIoO+a5K2OPc38acHd2B3cvXurA3T7PHZu2jxCgez13JffM3b77ggfLDXP34KkjJ8v0NQNp5k6Czhc4VdypahKyTIUa+VTmrpFlyhFwx1hU1QUAVbmL1jnB5RDc2TrN3IlFFLz3VpnPyzLf4Ix67sLTZOlO5CsbLzSccuhlwNxtNtjNXU+L9W9mJ9XpmLs6xdzZOmbuyku38Xr9dWxtlWTuGmAnwKCD0+HKVMhq2p0m+okCVbiErLpFQVsN3nuTSSahajXuueMzFAuZ3Bwq0wtUKTcoljPMuAMDucix8ylbjz0+KCcwdyumsQzN0XnuwJ1/4+70DnNlh2mZ2RwlA8yEQBUAOBXHOEfM3FFC45tNVUw66W9mSSRW2jF3baIgE23hdGUqZKbH3IGhSskwtts0uFutXPJYX5Y5lbnz3VQMBHXq5FIp3Kdb1+13VeZuuQTbFsgDpjX5MBrmbrl0BwFshjXxC21dO98kScgyhXD/hYtoXTvAynrgboy5C5LYDk5KlinzdM9dw9yJWbTZ1z3mDgDm8ODOVshPRsBdNu/kvD1mwzF3vUCVZjPiN6zHhuFS2qunZRIyWZbZ986NyTKFiTujGGWujPrNlGUy4TZs/tRbl4Vn7mjM3E0Cd11/WMvcjVQhNHUyVx1TGzCLCNxRylCjY+64df1wUz13ShWQhGHGZ9idxKDeyTLraeAu8G66b3a9eeLsegfuiuKw584zdxXvZJkqAe6UTYC7Jk0yWAenyTIPg7s29Cf03PXTMpmAYk/G3L26eRULRbA8Sd+DXFl9CF73AIk8h6h0dGBUw3ZpmQ240xWEti5ifi+4ozFzR4VjYxvmjseqkIVYYEP0wHN3GS6/h+pWel6+JmynBXcJWWajflC1wjNHz7hAlcZzt76Hr3zuKztZJkocnzw1+uuZ7TzpTYeqk2Wm31dhT21yQuZu9SmXHNqkhiZmoLzYM4LwcVnmzmU9dJ67kfuCB8stuLueA+fnqKgNPHfomLvGx5bnOFXUMXehLNN2ssxczseZu7p2Pvejo06WOQbuOIc0iDp+VbWL9lupgzMDkw5UkYsoeO+tMp8Hd29w3v/8+/FVd74q/mCzUfRDhAA3QGHj091Wltkwd9uti/gHUFV+wa87eQq1SDJ3YVpmC+6WS8fc1eWAuTMIFjVCo5t7qb28bx9zx7MoZUvVGiJR/gi4gJgxcFfOxpm7GNztUM67ZKSc59hJ9/jPM/d3FYIc3GjuiMYs7O+bzbAgMmbuqiG4k2KGasYn9dwBwFl2gse0u7FcFBe4nl8fMndXAXc0x1pvnSyTdgtZ87q6IBFEC1kGjrJKPCebDfDpTw832us1NEVPlkmnMXeBLHMfc3dBKpxkJ8Dv//3ABz94+Oc245m16HQ89TBMBVkZ9/UAcj5DAf9YjIHhXSjOwToErZ0HkcXgZ6/n7ollmQaZnA+YO026QBUe9NNZa1ETCzaP5dNzSGyLS+ysQn6a3gDy2bxNSdT9GhMpe+DODGWZiuFSmKszdycnk2WZbeGwZ11LMxKokpBlhiEHb3QacCeogPI1FKraQdSInqfKqkmeOzoWqGLtIPlO8uyJ0jKN9YxUBO46RkfryjF3QjpZ/oRRuoKA64fcHecDcMd0Pf2wJpRmPnoEnJ66sCBr0sxd478e89y1skx6kLmjgWzR1J3nblpaptnrb4qqEELPHUmUmPMEuJvA3N19fBd3Lsjg8LWZSJZpDzB31AHjkI0zxKdlBn2XqtxC+MOARpo8mIBFbUYy6fIDttukPHUhF9hQHaVlAsBluF2YxNzRmLmjBEoVySoETljE3J3OTmFhsa02Lbh777PvxcuXL8MUDpjJdwAAIABJREFUO1wKg+PjacxdXdeglO2VZSrj3kej4//es/wMFASPzmaHZZlTwR0Y1NjhYwPuPHOnkQB3Pi0ZnOPB9gGeWjyFB0cMFmg7Sh1zZ2PmzoO7s5JCBAw/p6KTZcIgl4vxtMxUoIqXkQ6GsaiHGXBBNiFzJ1mGqpdfodGzYPj5ui/7OrzrqXelH9dn8UzXhX1+kvO+5983/GCPuQNj4DWws2ooy1zksSwzdy9JVe6QwZm+GXUXHB3xPzlZZheo8onHn2g3t9u6cOBuH3On+szdcPEIPXdSZJCFbk9GdG0GzB3gbu7rap0OVGGZS7hMbA5bn96f+3OQ/xlDWV6imGcRc3cu3KL1WHhPjqAHZZkKNWTQC9aCu8BzNy+HwQAZy1AucxhSTDIvn2YnOKdxWuaN+Y3BzeYqzN0RzbE2O9QEXaBKX5ZpYmAqCUeZOmltPDOf+hTwRV/UfXy1giJ95m4k+bI/Zen8YQc8d4oadz288IL7b+p4IHGyOMFFeYFn8Wzyy5RREJVur38ntwrAHevkyANwd3TkwF3DzGndAtZ2vvEbHUhJjJOwvgHmrvHcWduW7oaR9kJ0XjljDaglIL336ZxKbHcr7IhBfpYGoSJfQG2N/706lhMKAWb7ssw+uCO4K/dL2AbTgLuJskzApQMbrUCxL1DFJgJV8CYydy7QRlAO7Zk7pQoISyEsaV8PVWvIhPy8P4zLWJa5WLi1oiwHgSoiy9PM+4FxzF0f3PV67kDBG0A1YZRy/ZpSzLE9nsXgTlcOZI91d/UnBHcPHgDXr7u1sFawr70KGIPadgC/WT+a+7/rpIMDd5x0gSo8JcuMi4kZZTA6jK6/QlrmAXA36rmjQ+auYhjKMicwd3fP7+LOYzsK7ihJg9exEVygCMFdU2Iuus5NVe4gLIXkcvx69MxduHY078V6swatKiiG2HMnFriwqyFzJ4IDhymeu6A/z8ky9zB3hLZKpaaD94Y8w4PTh/jC1Qr31/fx/OnzuDm/iZdf+f9wOec4ztL3e8Cvjzpk7pxEdDQt8xBz53vuAOCOfAovXWO4vt0H7sx0WSZhbQ3EYHY7lNR1SXKRpRn9RuJKCB5sH+B33PgdeJBbd31bt672D9c0vNQxz3FawKlqQlmmNT7pu0Ym88HhZveHJgJVOB1n7nqyTFXGJIPkElXfw406Ce6+5h1fM/KgPrvn88zdZ2L64I7SFtyFDJegAmqZx7LMBtyFzJ1fNJ2nJ8XcuRMjwC8w1WUH7nSB5cBzZ1v5DweN4tVLUzoGaIy5qxUEyyB13Zr9lR0yd8B+cDfjMxQzPs7caQv8jb+B7BOfRKV2KHPRgTueY+ef3nPm3qBTmLuK1FHJKvLcxdyGzF1pklUI5WI2qecOAM5mZ3icAHehTKBURfrUaWSWPMfK7FDVGjJIT4zAnUYsy6QcVepm3mzM+tLM9RqK1HEVArmKLNMzd4SOMncK9SSGYzDLJbDZTGPuSuOAGpoTeb9QeXDXbKCnMHd9DyLe8x7gHe9I/u5QonNw+uAOBpLPYAlgwwCYINBFyLyVAZrGLzQAdxkuy0sYYiHP0ifOIptDNSyOSQWqkJZdMkaD0x64Kwkumb56WuYhcNfry2S2SwceZe5Sssw3E9yhkWWK1q+iVAlxfOrAnX98lU0EayWGcdGdTpele7/6hDwnywyYu9liXEa1Z1ygCoaeO/9727RMX/g8ZRqv0FzMsV1mMbirCnDQ9kDi4ITg7uFD4Pr11gNurp3BZE7e1RQON4xdLMu0ji3ykjzBBDRPMHfWDGSZrWzRmm4dfJNkmVM8d5LJJ2fuHn0Cdx7Wo37l0FtZ1/X+QBUAgsU+utrLwCNZZrmFAHXsajUCDsoSFSfRWk8IgagJ1OYyUnY0s5ALbFANPHcXLLiHTmHueJ+5A7SqOnAX5h+AtUnOlakgqMB1tsSDL36m9dzdPrrtPG+v/Aou5wwns3FwN/TccXcQNBJIovq9ov0J/t47/Abuntr9zF3//r1nxL7ezO0WJbOu524fuPNA+eHuIb70+pfiYb1BdXrUAtao587aric2z3G2tRC6DmSZropFGQVhKVg2wXMXMnccaXBHKTKDaO9T6SI6xJQ8mwzu3qqz951PCPkSQsj/SQj5ZULIjxBC0sfln594eoEqABy4QxUxXJxy6HkeyzKzBtx5z11tWtp9tAoBNgJ3F8WF26jCpZsdsXmCuXNfLwiHVrEsM9MY99w1gSrg7SneWD+IoAJrNcLc8QzljI0yd2Ljfrb8N7+AqipQzGW7yOcix467m8Bj720rmD0I7hRqiKBkFbMZFpb3mLuhvCjjGapFNhncneZnOOedrG2nd7iWX4tlmbpANsGj08yS5ljXBSobyDKZbEF7qUsHiCPmTqR77rZb4LnnhuButYIm9slkmWUJ7YHhmOfOVqV7Da7AWLZzFVlmoVpwx2nQ5WVMVCg8TZaJyY93jFlPTr/nztaQInPpa0GNhCHoZJkya0+EjTXgdiifnrMZHlbnyA0BuXYt+atFvmw39X1PEoQAr9Exd3bouTsp4RJ5pXR/xxQJ5D5Zpvfc9YOZmCXRRmwKuOOURymbb3SMrZ0sk4kWaCldgp9ddxvXhrmzGpIfDghiTMTMXZa1p/VhpyngQPiTMHeu/6/H3LEgUMVoz9yNyK8S48Ad3w/ups71687jBLTgDvCHnc88Db3IB+mSQK/EnDTgjnSBKklwp6PutThNsgM/k9IyD0jgxsCdpjZm7qhPkWzA3VWYu4e/gTtrMQqk3d8XViHsf10kd7aH9m/0FRpMyDa5V5U7CFBnT9jH3PXAHeBDhzYrr9rA0HNXF+55qGusyhXmmrqDo2YmMHc68NyJJi1TFe5jASAB4sPKxv5xw+Z4+LabwHaLe+t7uL287Txvr/0aLjO0BfGpCUPu2rTMcM3pjWPu9oCxENzhDHeP9N5AFV1fIVCF7AF3feYudegTAOUH2wf40htfige7B6je9lwLWFs5vapaWwOnwjF329qBuyYt00vUVa0gLQWTWRSiFU2CuSvZCLgjBLIH7pQq46T6z3VwB+AHAPxjAB8E8FEA3/cZf0S/HabP3AEQNVBYHXVmCSag5lnM3GXuJWkuzLo2LXCjNt0fFqZlRoEqUmKndzji84TnzqeEkZi5q0yFTNshc4eQuXPlkc2NXo2Au4a5SzFUMz5DIdPgTtUKcr0Dft/vg/z1T6C6eIRixmPmjrlN3blZYy7mKJs+nbGpa2f67YO7msfM3U4PA1VYhnKeOYnclLTM+TU85u51WlUrLOUSMz6LZZm6nBTA0MxSzLGxJcq6C25gjLc34TYlMmLuBKrUzXyzAX7n7xyAO7teQcFEGxHHwk0rMddeHkyRZu6asJGpvTzRTGXu6gqyqDpwx0RPlkmmM3fGDAqI940rfJ8I7owZMHecZ16KOEyvA3wQSiiXtBgcwuRshod6hVzBMQGJ4fOFC6/wPydi3fueO5OQZe5qB+4IORx40Mx26x5Pirm7vIQ9Pm5LgJthNdprb7TEXPeYuze7CqHx3DHRboyUriDObjhw1zB3Cd9xalw5dADuIuau13OXL8eZkj0zJss0YaBKI8u8iueOenDXC8LSVdH5MqfMCy8Av/RL7v+H4I4JqGeegs6zAdMFjASqsFCWSQebYHfw2A9UGcoW27TMfeDOpyGOTbLnTqmh544JKEo6WeZVmbti/BAhep2tOSzLFLOIHW5k4ExkgSxzCw7q0jLHZH1F0XadhSNBUW1XSXmqC6rYts/5ulrj2S3DJYI1awJzpzgZMnc6YO4icMfaQJUW3JkMD24uoBc5Hu0e4ebiZlsifintfnAXpG8a6zpU+Z5AFWWqw8ydv/7u1Ee4m5d7A1WaEJcpI6iI9nrR7HYoSY0Zn42DOy/LrG2NR7tHeMf1d+DB9gGqL3gmkpqy2q/1SsE0rGqe42ytIVTH3Dm23GU3SEsdWzx27w4DVRrmbgzcAa7EPLiuK11GsnnJM1S9RFNNuvCy3w5zCNwdWWv/rrX2V621fwPA8/8BHtNbfxLgjluCHelVIVAP7kLPnXR31AbchW/eMf9TDQvqF/LW1L1cOskngBnLIrp7wNyFgSqmhFRDTX/E3FHhekL8Y9TWPJEss5TphMvKVJCXG+CFFyC/4p2ofuWXUM54F6giPLg7Psbj4hxPL59GweoJ2nwS+4tmMyxqFjN3OzNg7iSTqHI5nbmbX8M5dzeoy/ISx9lxWw3RTKlLZBNO+pthcoYMHBf1tv0b3Mm7B3fVDlmv0Dmjcpy5+4qvGIA7s7oERRwawwhLF5L3Z8DcDW/Qqir2+w32jZcATmLutlXLXIdF7zAGNbsac6d6SXf7ZjJzZ21ClunkK6zXZalp4LmTMwfK6rrbwPeZO57joVkhV3YU3Il82W7qm5Lu7pN95s4MZZm7GhdNGuxU390BWaY6XoBT3krxACfLNEGgiuob4BOeu34/3hsd97oIcCahm0AVoyCu34SoETN3YgJzx3vM3WwWMXcRw5MvnrznrueBc547twZoL8sc3cQlxm1KPbibx15prcqrMXcvvAD84i+6/99n7p6+CTM/zNxpYp3nzgceOXBH0rLMYG1itAu0eaK0zKswd5VnL8gB5u4KaZl3L17CHbUY/TwLvJV1PQXcxVLLOuW5UwUEmGPuxq7HooBiCeYODGq77pi7nuduU23a53xVrfDMClcDd0pBs57njox77niwnjUJtzcKigfHAq/dWuB6dgZOuZNlrn4Tl9zsBXdOTdWoKTpZphqRZbo8gT1KkEANcada4K7cHgxU6XcMj42g/AC4My5QRWRtCnc0/rm8KC4wF3PcPnI1EtU7vzwCTqzxnntWlVO3Hz7dWReoIuLMgAG4SylBUrJMapM1W4ADd+HeR+kylmWK2cBvbGDB5RXqmT7L59AdeUYIeZEQ8m5CyLsB5L1/f35S00vLBBpwZyLmjlMOnfeYOx8U0lyYoWF2LC3T9GSZDXO3PZphLuZuwxN67hB47giLImErUyFT9f5AFSadD63xoHjWoT8HA1UkiTfTwWOQF2vg+echf89/ivLhq0Pmjhrg+BjnHtyV1O5n7qpqKBvJc9w0M3zXT38Xrn/vdZjaIN9WaeYuF9M9d4sbeCz3g7vKVJMCGNqREkuSoYZt/wYehDNUxWZQjiqpiHx+7Ywwd2p9CdHr3mOEJqXAAPAXf+4v4mc+8TP+D6rczbHx3CVkmQc7fvZNI8uU+8GdMgpiVwayzM6XiLqGoZ3nLioxB4ZF5j5QZapHkNmJnYCN7CQKVHHG89DDAXhZpvSeOybbuPc2KbIP7sQcj9SlY+56n2uGz5fQ1MJa61n3GNyx2sZVCM3G1LMRxxuNy9pvCKf67g7IMtXRfCB/ZQFYTskyVa1cBH9PlqmpfVPBHWMCgst2Y6RqBXHtJoRBx9whfcDVH9qvQgiYO91Py5wt3CZxxMf5HT/5Hfj5l39+8PFUFYLz3HWMFQOFkDMHkiaM8n2IczHHNudDcHeV9/U73znO3D19CzqXg74/ICgxZ6IDd7zH3A3AXR1ZIVxqaOi5816hJuFwb8/d/kCVpVxiozaOAQxlmcTGclsmXIqkUsOeu/PzQddmM5Wp8Mr2VXxhfTT6GBjl7UFubeuDskwhZhEb18jAmchiWSZhzvO7B9xVzA6ZO8JQ7Vbjnju1aZ/zdbXGs+c1LutgHT8E7spylLlr0zLDnjtKh8zd1uLBkuDezRluZ86n/Pzp8/j17cuoSI2cp++jAMBsZ1twlVRsryxT1WqwxkZDSHs/uLOb4S5f7QV3yupkfH9qBDvA3MF0skxih9dhUINwY34DN+Y38HD7EOrbPwy57HyJbh3sgTtC8Fwhca3svLlN2ndlKue54wI1IUlw98Cs8F/Q/y0OVKHD660ZaWlrbQJcD6kM9t5SzFCFANwYV1lyBTXVZ/sc2qneA/C/APif/X/3g3//zc/sQ3sLT1KWSbCj8SmiYAIqz2LPnf+25oTM2ADcjRRK16iTsswW3BGOQYk5DZi7YPNe6tLJ+/YFqlAvy9QBc5fQKh8MVMl48qSyMhXk+SVw5w6yL/0KVMcLlJLFPXfUACcneFw8dswdPcDcJXp2MJvhe/R/gk9926fwa9/ya3jtz74GXqokc1fO+MAYPzany+s4l+75vigucJKdDJk7UyKbcNLfjhA4sl6ex4KTr4a5K9aQNn5sGZPpk9YR5k5vV0lwN8bc/bvX/x1eOn/J/aOqHMtBuQOEKebOJ+490XhZZhQ3nhjH3AXgrimNtraVZY6mZaY8d/20zD0TJqftnWbx6jN3IotCRADAUICJgLEQrI2KZzWGgSpy4Tx3lo/6cmg+dyDSGh8VH6dl8hptRHVUheDZCAfu/AZsKnN3QJa5XWSYi/hvYbar1BiXZcbgrk1re1OZO+nAXSPLtBrixi0IYzvmDmY6c9cHd5HnLkxVlKjCw7/e/MqDX8HHH3188PFUoAplLAhU6Tx3k2WZxskyc5Fjm7PoffJEsswx5u7WDZh8NmDuCHob+MZzx9Ayd4piCO5gomJix9wFnjt6hbTMAxI4SiiWcumCQRpw52WZ/b9HUTtk7mYzJz3z1Rj42Mein/8rr/8Kvmj+HLJsnLmjkadwAnMn88iHZYgFFQKUCxfuVNdQVeGSUrN8nLkrS1RseBAmwKF2G8/c2aHnLmDu1rsLPHNpcamDw6JD95fLS2jJ4547ap0vtikxj5g7PgxUuVB4MLO4d03itjgDANw5vYNfUi/jGDJSE/QnzEEwaErM93jujII8tJ74v/n5NccncY46k6PgbltXWLJx8BmOk2WOHD7udiihHXPXVHX0D5X8c/lw97AFdw+2DwZdxu0hZwjuAPzxTyzxN/95d49snifH3JG9zN3r9Qr/Cp/umLvNpi1OT01Wk3Z/CnhQHTJ3Mo/BnVJXsmC8FWbvX2Kt/cB/qAfy22pGZJkFqWPPHRVQmYiZOx8U0oG7GqyJvgcZCVTBkLlbLrFdZpgL5k3W3SlM5LmjLJI7ldXWee56gTD9QBUZAAfVJCL1RjAxztzxDGVGXc9RbypTYfHwwjF3ZIPqHW9HUS6Qcbdo5TzHjnTM3dtP347Hm08eBHcD5m42A9tscH0elD03G67eY60ygZrY6czdDIAxEXNXhqmkdYVMTLspA3DMHQQYaBewwUV78l7u1gPJo2RZ9Dvb2WyAt7/dHSpsNm2yqtqsoi4YwG+WR5i70pTYab8RKssW3I157ppQhieaIFDl3ure6JdVpoLcFJ0skzAn16rrFtyJQJYZAcWE5071whD2DSUTZZkpcEcacEdg/KJa2xrEArSRZVIBJRlQFDDcgtdDb+xcLvByvUG+79ae561fbCCpTsgyW8O+l2UerStcmg2stSBTmbsDsszNnGMh400rDWSZ1foCuohBjq61M+h/pgNVuITgmTvx92ynuPU0xMf7zN20KoQa1l2Pzcaz8dz1mCHJJNTMrw+JcBxd6+RBh2N1h4EqJgpUIeByBk2mgjsFTjnmfI5tdj5k7q7io/3CL3Tf/+DBgLnTt66D9Tx3GcuQ8azdZEfgjqILVGFDWaZbm8JAlSBYJvCkRbLMy/Th0SHmDujW35MeczeQZVIL7Nz1FB3QNOzdP/yHwLd+q3uO/Hz03kfx7uUXA/Oh2qX7+4LKC3s4LdNJLbufV3vmjlAK6v2/ShWeuZuPg4OiSDIpknJUuzWQV4Pnoc/cre69hGee/pL4mp7P999fLi6gMj4A/qOeO9qtZy1z96jEQ6Fw/4ThaeokmM8dP4fKGtwk40Aa8IFjbRWCO2TnzI5WjDjm7gDT5p+P+brEydkc92caz4wEqqxsiSVP12L0xzF36dfPbjeoYOJwoqb6oBnPgjbM3VzMYWFxXpz3QtgSzB0Aks+ButuHNrJMZRRkTUC5GL13l6bCDtqBu0ePAK2j/IH+SEvjtMw6DrySYoYqDJNK9DC+1WeSUJ4Q8rWJ/76aEJIuUvpcn1RaJgh2LF5oBBNu8Q49d6wH7uqYuUvKMkkny1yIBXZ6BzPPsV1IL8tMMHeN546KyHNX7dbILBuc+veZu4xn7WPUMBCJU+s2UCWx6ZnxGQpBkwbySpeQDx87cMckqi/5IhRf/7Uxc0e089ztHHNXEnNQlqm8+b6dPB+mUPUWg+bvKCVFDUwKVDmdneI8B6BUC+4ylsWyzFpNOunvHoTEshYOwHmPEeddWmZVbiF7jy3j8e9sZ7t1QOZtbwM++cn2w2q7jgutsd9zV+gCRXM65pk7RhgYSXvu+kWiV5oxz92v/Rpw/373OxpwF6ZlCuYAVZOWeYUqBN3ziewbBvrGmDueeZ+ZN+jXnp3zr7dgwskyi8IFqqSYu2yJh7REvm8DkecQHsA5SXUvUMUgkGUacBKDu2xTghHmXvurMHd7ZJmbjGIh4o2US8t0z0V5+Rh6dRF9PhmoQtmbLsvkXIIzD6y3W5f4eu0mhA6ZuzoOaxoZx9zZDtgRMs7cUYEqXB96o4xKgruULJOGVQi1BiNPIssU7j2T0V7PXdldI1OGkE6a2Wfu3vUCzNd97YDpCkO5Qs9d5dNsx8Cdtn3mrpeW2ZSYT0nLtIc7xdr7U1P3oPUgiEsw4dI++z13gPPdvfIK8Of//MCy8LF7H8OL2fOjHXfu7+jW+noCGBUia69hIO7VZNYFYzhwxyFnc1T1HnBHhuBOUO6Kz5VyRdd7PHfrB/fw7Lvfh4syeJ8fur9cXEAL3v5cRlwUvxoBd5xwaNOTZb6+xgOyw70j4DY6r/YXmiWO2X7gFHruGkWUoGI0hdYxd9PAHdZr3JG3cDfbjjJ3a5TTmTsmB77l9nEVG3DQLu2TjTB3AbgDgBvzG3hl9UrM3MEH//UBU57Hr4Vn0StTQdbE3RtHmLuqVijg9ny4dw9YLl142migCo3C5FQ/zFD2wJ1P9nyisLfP0pnqgv4QgP8VwH/t//u7AL4dwL8ghPzxz9Bje+vOiCyz5Bh67kLmbrvFjrrTthbcoQ48d+OBKs3XEEJwJI9wueDYziVynnvPnY2/3i82znMXMHe7FWRioR4wd0H6oxqJkD0YqCJIkrlTm0v3PUdHDtzVCqWpukAVnmPHAXzVV+G8OMfto9sorD7M3PXTlWaz4UKeYu5YhkpyB6Kn9tzN3O8cDVSpFTI57cQNACAljqyACMAdCwNVyg2yHluTsQxlfzH2IAdSAs8/H0kz9XYFQYeMbbKzDj1wV5Y+fMMzdylZpi4PS1LGJkzLrPyG9tEj4AMfAH7wB9svq0zlklYDcGc47cDdvkCVpsS8Ga3b7r4pMyabHkwS3FlwmUWJuG0ipt+kc8qhfCKgsQbMJJi72REezoGc7mGR8tz5xWo1rDERAjzw3GkbRG03bMRuh2Ox7FJ5p3ruUrJMa4HLS2xmdMDcMZAuLdOqQfhHK8sMA1UIQ00AO6WeYcJo4pk7JqBmErh/H0pQiPkRhKlj5m6SLFO6e3FTgwAEzJ0eMncZHwV3Y8xdKlCF0ViWyUE9czcR3PmU5LmYu9CviLmrribLBDpp5sOHwA23UeSUQ924Bv1H/6uB5y48IIwCVbwnVlCvYjAmAva6pyphLAA/geduelrmRHAXyDIbRUMzggbgLpRlAu498t3fDbz3ve7zwd/y0fsfxYv8C/aCuzAt0x3i7u8e7G/4DemUAqxl7koIKlw/5lhyclGgokOZnKQSVbHxPahD5m6r/N//+utOlvn+PzRk7g6AOyUDdogQsBoo1M6xSQPPHYPx9wblmZ8b9y7xoF7j3rzGbdM9t3fKHMdiuff5Y7arVmj2apzxdCCJ/50HDwubIvP1GnfyZ3CXr/eAuwpHBx5jM+61Tq9PZblBRjvbRxLceaD8YPsA13N3IJMEd5a0aZma0RjchZ2DjEPDdOCuCfFJ3LtL68CdXS7dYe5y6fIhRrqCJWgUqFLZWDYvZQ7VY+7MFSwYb4WZCu5qAF9mrf2gtfaDAL4cQAngvQC+6zP14N6yk5Jl+qeaBxeYk2XymLkjBidi2QKBUOdPQZIbbddz112Ux9kxLnOK7Zx3zB0JmTvbeg0EE5HnrtoNQQKQCFThneRPo4ZIpAxJJrGpNuOBKsymZZnnD9sCZskkSlOi0EXM3LEa9sMfbj13JQ6Du37PDmazScxdxjOUgkwOVDmZneAyA+qyGA9UsQryKslMUmJp2IC5037DVpW7ASiXIkPZv5k3mwlCgDt3InCndptBKEQoY+lPqcsW3NmqbD0HdMxzp6vDkpSx6ffcWQt80ze51++iO+mtTAW52sZpmZy1m766z9zpQ8zddFkmw3j4TDQJcKeI62AMPXftJr1h7qho497bpMg+c5cf42EO5PuSWPMcvIaXZcZScQgBZoJAlTD+vWEjdjucNK/DVdMy+xuG7RaQEpu6TDN3/n5XWZ0Gd8pE91pCiJNzjvU5XXG0r6IQ1IO7e/ecdzdfQKgnYO6EZ9vDQ6SWuYuZIcEEqj3gTtUqZjn8tMxdAHop65g7bSowwsBF5kDGhFGmasHdVmAoy5zYtdVOirljAqpWrsPxAHOn/HOoqO2YO+uTjoMDBI06At007IGzseduEnN3VXDXMHf9QBVSD3vuAMfc/dzPAd/7vVEsvqkNfvHVX8SLePogc9ekopqpaZmBl7UmnceX+feR8ooLOVsMusHaKQqoJHMnXEl6VUGRhOdOeebuR38Uq6XAM1/w5ViVK9jGRjKFueMs7jCzFIXeOTa533NHGXQdyDIJw43XVnhQnePeTOG26r72zprjJBsvMAdiNVUL7qiIgUMwVT3Rc7fbOXC3fA532eUouFuhwlJMOyTmfJy5K8stsjBxlNPJzN291b0ec+cBr1LQnIyCO8oFaliX0F6TLsQnydxpWFiXAK9UC+72yjIbS4q1UL3AKynnqML15HNVlglFkOXCAAAgAElEQVTgeWvtq8G/XwPwDmvtIwATdjOfYzOSluk+1fPcNSckSnlwp3EijlrGxXWnhJ67tCwz3BQcZ8e4PM6wPc6DtMyQuYsjoENZRlmsk7KBQaCKmLU3ekXsKHNnkU40mvGZC0FJyTIvH0NeuwkArZyxNGWclql2KHQBAoLT2SmKuuqM6KlJMXcpWWaCuZNMojpZou4t0mPDKcdcE6y35+PMndV7jfGDEQJHhg9lmaQBd9vBopGJfLgYBx67FLjrxyozQlGPsCCFLrDzp2N1VYF6WQcbS8v0iXtPNH1Z5t//+8Av/zI+9V3fhAeXnSxT6dL9DhEsVH1Z5p4qBLW+xC++6sMejHHX9mTP3cTC96TnzoILGRV3m9q4REzPwAjmje5NJ1rKc5cf41GOvQlvmM1a5k6RehioYmy7ATZ1UJUQgLvj7MQBi6umZfaZu4sL4OQEG7V5cuaud691oPDNWZYa5q5VWdy75+TdswWEDpg7kj7g6g8V/j3r7zO/cP8Xep67XvCGfHOYu8hz18oy8yswdxqCOpn/ltueLLO6miwTcMzdL/yCO9wLZZlGOcaaHmLuas/c2a4KoR4WPjde1mbCQJUQ/LRpmYeYu6myzNBzBzsAq6oBd2HPHQA8+yzwnd/p7s2B3+w3Hv0Gbs5v4rQc1p+EEzOTnf1ibIayTLSe7qaWpek4FPkiAoLRlKVjr3t7B8kkKg/u+gxmLnIUukA9z4Ef+zGsZxRnszNkPOvuy1OYu8DXBfh8A1WkZZlUxJ67XYXrs2t4uHuIe6LE7aJ7/HceA8d5uk6mmVaCiOawwIW7jKZlWn2YuQtlmSfP4y45H2fuiMJyKnPHJdSIrLastsiaFG7KoZuqjnCaQJXtwxbcXc+v4966D+6CQJW+LDMAd4RxMFDs1A6i9n7kUebOPZbdzP+sq4A7pVAJChkcEMlsjor0PHefo8zdPyeE/GNCyJ8ghPwJAD8O4J8RQhYA9hezfC7OPuZODtOC2gSg7RY7aJxkR+2m3NgajO/33PWZu5PZCS6/8kVsv+lPOnDHeATuTN9zFzJ3wQlOOANZZgDu9MjGprmJjcoySZ1m7lbnkNdvtd9bmQqFLuKeO73D4+IxTmenjgU0Xn4xchNEWbanvN2DSMgyU8wdy1DeuuYqJyYwdwBwWlE8Xr/uwB2bQ/7Mz3Xgrq5RkhrZhJP+dqTE0lBI0E6WKWS7OatUAdljxSTPUPbBXSgDCsFdVTn2aMDc8SRQA2JZZpiYN8rc+dP/Jxpfln0sj3C5fQx8+MPAD/8w/hL/F/hB/Nv2yypVQM46kDCQZdIuLfPtZ2/Hx+59rA0gwHKJH5G/im/4sW8AANgrSjUcczdBDqi123iHDAOx4GIGBgQG/ZiBCWWZuti4AyMaX4/5/ASaAfm+09w8hzAW2geqhJ4k57kLZJkwSebuuEktvYrn7vjY/e3hAczFBXB8jE21STJ3zcFCBTNIdkwxd4Db3E3qZpwwBjaQZQrglVdcQmO+gFCmY+7IFT13RQE7y/Cev/sebObcMXc9T1fGMhSCXNlz19ZkjKZlGpeWmV0F3CkI7pk7Vg/B3VWZu6/4Cgfusqzd8LXMXR0zd3fO7uCb3/PN7b855U6xsNu1IR4tuOsVPmvEB49j4KdNywzYsv6412f//eskO+k8d0rBVpU7FAw9d1S4azkly/y+7wP+yl9x/z94b3303kfx7tvvHn59b/qyzENVCFJ0qYHWWtQ0kGX6SHulSrfm50dxwmA4RZFMLxRcupL0qnJe1WD9pYRixmfYzQVsWWCNCku5jH3VU9IyOY1+LgfBTgeyzEGgir+nmApitcX8+tMgIPg4u8TTu+51+prfoPgvv+gP7X3+nG2hScu0XpYpBgdRzbhgjwPBSwG4e+b0C3Afm+EhNNzruyNmcCg2NoJno+C8rHbt/srVSYzIMqXEg90hz13A3O0Bd+AcHBRbtYU0B2SZ/nEXzHusl0uX7D6WlmlZBO6UjNldB+6Ce98Vk7HfCjMV3H0zgL8H4HcBeBHADwH4Zmvt5vOJmolJBKoI/1RHzB1zJ5VhvOvOVjjNTlGF0ix/UVKkT6TroOcO8KeHaoMtMelAFXQMFKdx4WZZbPaDuyZQReY95i4tywz/N5yMZ84gm2LuNpcQN59uv7cBdw1zl7EMyig83D7EWX7mgKIpO616aqpqmOaVkmWOMHcNiCHY72Fo5qxiON88xEV5gZMSkD/9c13nXFmikiw6STo4UmKpaI+560qIy2o7YFwzmaOyE5m79Rp6mega2yfLNJ0sU+uy3Ugwf630p4mefqLxByYnNsPl+WvA7/29wIsv4hN4hEvVSSkrVUbgzqVlellmXcOwLhTnXU+/C2f5GX727s+6L14u8ZGzu3hcuGvSqBKsxt4o7HBcWuZEWeZiMWTu5Mx57pr3vlaOgWkk1FS45M+igNltwRLX4nzhZET5PlaYMSfLLLYD2VrD3EWBKs2iKPxmqapwnJ9ezXPXMBR+09vO5eV+5s4/jhJ6kOyY8twBblOqp6SWThjtVQmthP7ePSfvni8hlGfu6toFe0xJyxQZas/cFXPHmhQzEXjuemmCAldn7mrjWN3Qc8fEIFCFy5mrD5gwqtYQzDN3tAfujOrY3alzcuJYqutdUnHE3JF4Pfvw7/5w9HWauHoTRQJZZoq56/nBGenK3OtAchzJMsd67iZ67i7Ki7Y2xOgK1JLoHuICVUZkmU3IDhC9tz52/2N48ekXD4I7FshvXRroAeZOztoNv4UFsQBpDg/9IYlTQwjH3I0wUigKVFYPPXc8Q1Xt3Eaf1IPN80IssJkLFL/v/eCe9boSuLu4gGLoMXcUhQ6Yu8jnJaADcCfXO+DWLRfrT7a4HQTyvuvuFl/7zj+y9/lz9TeBLJM6cDcmy7wqc5cvT114XOLQelNtkFsGKtIApz9inyyz2LTs+JMEqoR/EyN+DevXC/QCVcCYY+70DtKgS8tMyTL9PqbQhTskPMTcIQB3VeX3W0ENWZY7aXQzn6vMnXUC6P8HwM8C+GkA/8zaMf3b5wfXr7cm8WZSzJ2g7qSyLWbcbLCzCiezkxY41QhlmWmJXFKWWV5ip3cBuIsDVRr2QjDZ3uwADxISbxhXTG065s6DO2stDLHg2fDUul8+G86Mz1BY5UBtL9lObdeQt263P6MyFUrdyTIJIZjxGe6v7+N0dup+VpPcN5aYmejZGYA764tle8A84xl2agdK6OSN/qnmeLx95Ji7WiDbVt3NpihQZiyZIjo6UuJIU1dU3oA7kXWyzKpoZRXt4xY5yv5J6xhzt15DLecDZo0Stj9QxTTMXdneGClhI7LMKxa392e5xLFmbvP0wQ8CAO6q17EOfHOVKSFnnUzF+QdIUpYJAB968UP4yMc+AgD4dfIIvzhf4/HOgTulylZOPWWchPWNgbuw7kTrEiy4ywrmZdxFAbPbgGO4cZvnHtzN9kt1hKVQu/Vg89sHdzro0ISU7j6VZTieXdFz11x3ftPbTiPLHGHuWlkmTOsvbUbVapy5e7Nkmf754ZRDS+6YO1K7QJWGuVMKipNJ17YLVAFQFFgv3eMucxEwdz1PEq9He+5UPZKWmZJlhoEqtQb3pdSaYlzKHv4uG4I7nWDunuDQ5oUXYnA34rnrj2Pu3MZsIMvsM3fEBRU1M2TurpKWeXVZptFqcAgTMXd9WWY4KeZu39e3f18HXg/33M3aw902nddfN63nzlQQTELmS7chTiXRFkUbpR/9fC6hqsKtv9YM1peFXGDz3hex/u8/hKV096wI3HnFxuhcXECzuL/WMXdFsucuVKJUpoK83ABPPYUb8xs4ITnylX/t69pVUpyd7X3+XO1PIMtk3O2pxpi7BAAeTAjujs5cBUAC3K2rNY7qoUpsbISYRQf57dQ1ykevtTYRTjlUSpY5EqgylGXSQJaJceaOMXAvy5TGHXwZYkdkmZ650z4Je4oss1FKVRWUYBEAlbMFKhqDO0OnpaG/VWZqFcLXA/hXAL4OwNcD+H8JIV/3mXxgb+n5hm8A/vpfjz7EW+YuAHd95m67xa4ucZKfticVxt8wAMcMpGWZaL8GAI6luzlu1TYpywxv+pzFnruq2ibB2CBQRebOB+Mj2Ykcvsn2Mncsc0zW0dGgyLwqNpC3n2u/t5VlBpunXOS4t77XavQPxrJX1TCqub+QN6d8PQCXsQw7vZssyQSAM81xvnvswJ1mkMZJXpvf0z9JOjhCYKkIhKXtzZxxAe0faqWL4alplg9lNJtNtzlourM++UlgtYJa5oNNVShj6U+py9ZzZ3TVdl2Nlpj7g4EnnsUCRw8usaIa9mu+BrrW+M3yNaxNkIplKsh5DO4MT1chAMAfe+GP4Sd+/SfwaPcIP/DaT+K/+8QxCl04BkGV7vmeOOFCv3dS4I66g58wRMSoHrijwjEtRQG92yYlV3PPfuX58d6HIEChNysoinhjLgSYqYNAlV7P3cUFkOftPeZKnruGueuDu+Njx9z1wZ33s9S2dt1VCeZOVHroucObzdzJiLnTsBD5EqIyLn3Od2hOubZZ47krS6zn7nktZ16WWcfgYSEX2DDzpjB3lPH2IKhhxjiXDtxNqI1Qvg8xFzm20HGJuame7MT7ne8cZe4mgzsv9eOUu7W0x9z1VSUhuDPWtIqXaWmZV6hC8ODO1UTE71PBhLsvp2SZ4fj1zFrrmLvbh5k7etUqBJm3bJwxCtSiVQo0ZdRKO3AnmIQSNPn81MUOBkNmToqZ6xtLeO4Af4DxB74aq/f97jS4m8Lc9UKvuKUojGMbcXnZJicD7vVv7m2qVpCXa+DWLVyfX3cF5s1BymrlroUDwIlFgSqHZZnukOQKzN3RdeygkuBuVa2wNGxwGD02/fCcdu7fR3m8aG0iTpaJSczd9fw6HmwfJGSZI1UIo7JMf28cuR81+5id3kXM3dgBuSQcZVDT5Dx3IXM3h6bognuUulIy9lthpu5c/icA77HW/glr7X8D4D8G8D2fuYf122+EP2UfeO5q3TJ3drNGYUqc5GftSYVB3fbOUBCYRFpVDRuDu6wH7ihrF3b3MzsZp2CyvdkBQFkVyRPoQaBKNkdljdtgWZK8Ce4Ddy3bdu1aLM2sa1TVDvLpZ9vvLXUZBaoALjDildUrLXNX6nLvwmzLEjrluQuZu4TfrnkMhS6uBO5OjcDjnWfuFI3BXVGgFHQ0xjc5UmJZYZy5U0Nwl4m5SxENZ7vtZJmEAN/2bcC3fAuwWkEv8rQsc4rnTnVdVw3L2x8H7q4AaPuzWID/6I9hZhk2OcPLly/DWIOVDTZzRkHMg8WcMpf8FaZlBhuea/k1/MEv+YP4oX/7Q/ihT/44/uQvS9dTWJy7TetEGS7gQO1kWWaeu/+ta6CuHbhrSsx1IMsMmMPW6L7bwew2aXDnvXb5fH/Km7AUan0BzUn8mggBruv29TO27sBfw9zlebcBm8LcWdv1eTVBE800ssxqTJbporKJxcAfNua5YyOpwk8ymlgwnvmOQY76lU/DErcR4ZZAqcIxd4xMOqxx0iMny1zl7josG1mmNRHQXogFNkSPe+4eP8TlZuhZHgtU6Zg742SZzQl9qnuw/7ushuCZY+6IisGdVk/G3H3gA8B73tP+M/Tc7QMlEbgjCc9dFKhiozWX0jSzNSktM1DRjE3Uc9cyd/H7VDLpZHu+VmQ0IMUfnPzmxW8iYxmeXj49lHH2JkzLDIPTxkYEkfC1qqLDpCbQSJkKgkt30Cpo8jBHlVtIwgfKFiFmULqErUrXqdkHd9J13a2rNY4yd99upa3AtLTMXhcpB8XOeCXJa68Bt7o6Zs545LmTj1etLPNpeb0Dd48fH2TtAH/Pj9IyxX7PnR2ym4OZz9sD73x+jJ1Ng7t1tcZS073XQziCZ2nm7qWXUD77VM9zh6Tnrs4kHu8e41ruDoYbkBeBOxJ47ii693KCuWMg2KothLGtvLROhJJdmbkDi5k7TiNQTbKsDRUDcOVk7LfCTN2tUmvta8G/H17hez8/CJi7lCzTM3dlsYGgArNs4QoWrYWxNpZlJk41DEHkBWhujh24G8oyW6+ByKCC2PBK7ZAlYtQHgSrZHBW0k0Y9IbgrdeluoGGoyv37jtVaOOahKeIOA1UAz9ytPHPHDjN3qtqB97wPA3CX8Ns1j2Gndlei7M9qifPiwoG7ijhwV/lNQ1milPTqssxyCO7aQBVdDkC5zOYo+9r/kLkDgL/wFxxz9/3fD7WYDU4VGeVJttha2/PcdaEKY547Zd8guFsugR/+4bZj7e5jJyldo1cOH4C7oSxzKL340Isfwvf83Pfg+ZO34cteLltwd1VZJiVsehWCEB2LFej9Q1+tURV4X5bZMHfFNtkt1oK75f6UN04o1OoCupc0BynBdZ2WZQrRMXfZFZi7snTfy1iauWs8dyOyzMpUmNcsPmnF/kCVN4u5MwSd504yqNfutQd1gnJo3+dUcTLJT+qkR3CyzAbcSeY8rxQRMzQXc2ygxpk7VeKiSjN3/KDnjnWbuAmdgI658+DOVpFUVBsVdyVOna/+auCv/tX2n0/C3FXEdD13Y7LMfqBKU2JubXttT0/LnAjuvPxY62ognxZUOLZsInPXsnbAJM9dc+Bn6jry4qdGBmXORlXuUKD5WQ1zVyvP3AnH3CXW2KpyRefDn5+j0qVPUyYDsNnUIayrdZq5azyQKemw78hUPUaQg6CoPbh7/fUI3DEqoG0QqHJ+6WSZ+Q3cnt+6MrijTf2NtXDnKY3nbrj+WWuhe5H8yZnPHShdLl0yuK2SgSrrao0jtT89NRwnwU2w9C+9hPKZp2LPHSXJtMxzWeMoO2r3CUlw1yhY+p672Swty9Q7SA2nGqnHwF3guZsA7rIQ3LVpmXFwmKxJ9DWfq8zd/00I+UlCyDcSQr4RwP8F4J985h7Wb79pNmKhN62VZXrmbldtkYsckmXuhEypmLkj6TQ+x9z1AlU8c5fz3N/ww0CVIC1zvoTedgt1qQpkiXCUJHMH78Gr0+BuX1pmK6XsM3cvvYRqnkXf2w9UATxztw6YO1PuB3fldiix61ch7GHutmp7NeauzvC49FUIBWJwVxSoOL2aLFNKnJYEeR2mZWZucwYnkeyDu8y/RtGEzJ3/ufjIR4Af+iGo+Swhy+RJFq65KUbgjjbXKUt/T60PL2z7ZrEAPv1pHB/fcODu/C6+4PgLsGZdcXFlNeSyY6045TCs67kzia7Cr7rzVbi1uIUPvftDwHqNs/wMj4vH0Ppqskx3YjkxLbMJXSpLz/y490sk89FVxNyFskxTbJPsRgvubj2z9yEIMKj1JRTvdfswBm6c1A5wLBDnAXPnwd3J7AQXxcU05i70CY3JMhPMXZNEV+oSs5q6hT84NNBGuVTIXmIomyqPnTCaOs+W89wxqMtziEb1QLhLAvSv35T3MwuqENYz97jLjAGXlzCCRQcPC7nAxpbjzB1qFFZFFSsAWqn8uOfOMXfN9VNXhzsBtTUQwoO7OgHu3ojc2k/D3PUDVfrjUqaHssxBoErAiDcTBo7U1kSeO2MNbCpB2U/TY7Zvhp67CqzPZrEY3D0gO3z3z3539DU7tcMf+KJ/ifd9/HvwHT/1HXj30+92nzgE7rjoZJmoDx5IimzeMndGV9GG0MmiK7fGC8/ccdK+3//1p/81PvJR51euql2yQklIx9wpXSbBX8PcrcpVB+5kAO6EcO/vFLu82QBZ5q7NsOcODEUjFX7tNeDmzfZznIvYc/foArh1C08tn8JzR88+OXNX1zDUrX2Cpz13lanAQUH4gfdKnjtQuly6ZPC6SssyyxWW1RXAnZilKxpeegnlUzfa/ZWgAprapCzzQWZaQAccYO6qKmbDkrJM4jx32gLcH24mDuaafUwTqGIXC5SN9DYxErznuesdvknp9mQhuPtcZO6stX8WwPcDeCeAdwH4fmvt58vLrzDtaW8Ql90uSN53tqtL5CJ3bJVkQOmLof2GmIIkvUyGJErMI1nmeKAKb05z/IJY6gLyELhjAjSfg1t36sJr7GXuUgxV8ybS105j5u7uXaiZiFi/fs8dEDB3+ZmTuRiFOh9fmKtq52oEwukv5GPMnWcGr+S5sxnOK8/c7QwyDZSqA3elIFeTZQqBD7y+wA+ev7/XcwcnZdUlZI9xzbIFSnKAuQOcNOq7vgv6+tlQlsnSVQgNqAvBHWs9dwx1YhFRtUpeW5NnsQDe/34cz89a5u6Fp15w8ja/Ae6DO5eW2WPueqCIEoqf/5M/j//2P/pTAKU4lcdOlqmqlnGfMoww1GM9UOE04C7L3KIdxDDHnrtqIMtU1AEDUxbJbrEW3J3dGnwuHEEY9HY1LG4lBBwU2p+eJgNV8hzX8mt4VDyKurhGJ5SSjckyxzx3tWlLbrlFJCF3KY3D54DbK8oy/8k/AX7iJ5KfatMymYASzCVl+soRQbkLi1AKFZvqucvctq8osJq517YU1IE73gN3YoFNXcJeDovK3WOrwUCxKuPAFWMNmBn33LlAFX8PqQFdjoRQBRMydztTuMMSD9JNrZ+MuetNw9xNCVRpAIlCPR6o4gMdQrUAoyLw3HXMFiWuo7OeZW+IuWurEFrPnRph7nQbqHK3fBX/4Jf/QfQ1r25exb/NHuMvzf4gfuA//wF85+/5TveJA4EqIYiv666kfWwcuPNg0OhYlumZO22cJLft5fXg7t/c+zf4R7/2jwA4xU8K3Ek5R6WrqConnJC5O5JOcXHS1Kw0M5+nDzg8669q1WPuKHZ1CVHDPcfHnf+YMdEePFamgnx4ATz1FP7Me/8M/sff9T88AXPn92TGwDAn+edcJitGXlm9gtvk+HAASp+5q8txWWY57DodG5Hl6RTPu3dR3roWyzIJkrLMB0K1YSoAcH3edVQ2wwhzh5z9BMpUWqYl3nPnwF1zzfWnsZcUugBOT6GPl2CEjcq3JYnB3eAwXUqIOgZ3TgH3ORaoAgDW2v/dWvvt1tpvs9b+H5/JB/XbcThhIBagQXJXlJb56qvYHefIee4ATQvubMDcpTctNYnlPM3NMQpUCQ4PDerYc3e0AB4+BOAu9lT/Wj9QBVkG6c2w4gC4G6XOeYby2vGQucv4ANwNAlUCzx0hzu9SLmZ7ZZnC9t64Ez13GXeBKld5459ihofVBTZqg6OtcadEqmh/T8WneXTakRK8VLhdZVHvWROIUNUKWa9IXs7mcdwvMGTumvlrfw3qT31omJY54rlrah12OgxU8dcpHfHcvVHm7uZN4I/8kXYDdff8Lt55651YZwQ4P4epjZMcH3WLufOpxT13qdPsW4tbDrwvlzjjR3i8ewxlrgbu6FU8dyG4CxbBsBRX95k7JtymwcsyU9djLtx7d2+JOQBOONRmBc3oUIpLKLSXakcb2oC5a4z0B9PsgJht6DN36zWwXKarEAJZZmYAXpMo/GkM3DHQlnmcND/1U+6/xDRBN4I6SZpiaJ8vQUXX4TWRuaNCoqaA3e3cdQuglBRYrWAYjV5TwQQYoSg3I8wdsbiGfBCqkgpUIX59sNZCB542bgnUFHAH3TF3atuFgMHLY99s5u6A5854CKN8cXaSuevLwpBIywx+DyMMWjC3qU3ZH4K1eGxav5j33GmjBqBGMunSsIsCKApsqcaqigH6qlzhBuZ4n34W73v+fTiZ+QOrQ547JtqDXIMJJeazuatlQFP90t1vWs9drSB41jF3/jBnU23cPQBOlpm6/oXIoBiB2m3SzJ3oPHdJWSbgam9+/MeHD96DO13rgeeuqCvwbeHWjIA55UxA+7VJGQX54BFw6xaOsiOcXn+285JeiblTnZ/bBxWp/roL4O75Xdwh1wbVLYMJwZ3IUdQVbDkiyyxt8kA6NS4tc4S5u3Hak2XapCzzAa8i5m4u5u2etZnIc0ewn7nzBIFUHtyNMXf+ce/UDvjLfxnVH/v6vbYWSRjKkLnrH75JCakDcKe1O8j7XGHuCCErQshl4r8VISS94nx+ksMJhTCIQFCUlnn/PnbLmZdlyiRzx0CTzEBNbFs8Ckxh7roTCk451NEceOBu0qUuIeXwZDDy3FEBzGaQlmJTbQ4yd2PU+YzPUJwdxczdJz+JSrAkuBswdz4ts/lZ5VyObjSrqnAdceH0ZZkjzJ1ksq1CmDpnJMenitewEAvQjUuDiqoQWJrRHJ1mU9wAA/iwEAZAawe6Rbzos9kcBCRiO5LMnR9F6hHmbrggRMydtVGowmhaptVRz+OV5+/8HeBDH2qv77vnnrnL4FLTagVhKcgy9twZRtqeu/rQ6dxyiVM6d8ydviJzR69QYt4HdyThudNVFKMuqO/H2u1gyl2yc6t5j+RiP7gThEFtV1B95g5uc9QssBp1J8sMPHc35jfcxm4Kc7dPllkUQJ6nqxC85KnUJTJDHMs0hbkDjQ/Bdjvgb/2t8cf38KHbTCVGE1df4LybnrkL7mtKec9dP6xpZIgQoLVLF1z7Ly99UbnhdHDwsBBzbHZjzJ3FNcyG4M4mwB3noJagtjWM1W0Yj6gBrYYbx/4o1OPgzqi4TuMJp2Xu6v3MHSHESW+pe1yjgSr9KHbE4Mf1w3bv7wY0DtYFP1eWZfqeu37wkZNlasfIZhm2usC6Wkdfs67WWNLZ8L11Bc+dA6/TmTujVUKWqaBq3bHXFO0au1EduFPbVRLcSSZR5QL68rxNUw5nIR1zt6pW4+DuT/9pd+/vT8Pc9e4DnDAH7ja7yG8HeNlq4LmTrz/qvia4piczd8TnIBgDwxz766oQhszd3cd3cQenVwJ3lFBwwlGpkbTMor4ic5cIennpJZTXTycxdw+5isAd4KSZMbjzCdt9qeN73uNClNovZOA+UKWRZU5i7m7dQjWTew/TJPn/2XvXGEuyvD7wd14RN+4rMyuzuqr6XUzPCEPP4G4NzIMBDx8GwXht2XzpYUAAACAASURBVBbCwrItL0IyslZC2v2EHxhkeT+Ad7+xRqvdL9YiFr7sykKWZSFsIXaNYXeaGR4zeDDVU93VVd2dWfm4j4g4j4j9cOJxIuLEvXEzsweazb/UmsnKvDdv3huP8zu/F6/qxCxzhw5zF5i86v3LpbSVYv9/qULI83yW5/nc898sz/PNWds30xhOuAVBzg23wdyV4K7YBUkF7TJ38C8eDUG3xLzD3LWqEErmjgroqcPcZRJhuAHcFbJMhCGCjGKlVh3QWk7AbIR4XzfciI+Q7rfA3cOHjRORFt6QpVw2A1V4hEQn2B/Z4IiQh0jGQa+kRskEon3iClExOgD6mbuiCmGXE3+fRPiGfB/zcA6sVk19d5oi5f7+v97xgDtOC0a2YO46kscwRJARG1xTTh9zB3RukkCzF8idRCeYiIm92GoNLWgVqEIJq3bHqzHGFg5fJVAlCABK7e54cm5lmc98HEuRW3BnlA2ccaOvSZGWuSFQpTHTKQ4wqsDdTlUIpL/wvTEtcJcXi1BGWdNzp2Qj0MWWHxfMXRp7F8CUUEQ82srcCcqh18vO4hewi6OKuYOfuavA3VDmrk+WGcfAaNRTYk5hsmLjQucdcKeM8kpTO1UIb70F/LN/1v/6Tk6sx6U1eZ4jo1ZKKZiA4gVzV2y2CSactMxhzB2EAM0Bk8ZYBPaanHJSyzJpG9xNsEq6PXd5nkMx4JYJvcwdb8kywWxmY5ZnjTRKnhNouQXclWXhfANzd12yzAGeO6A4RikgoftlmY7cuRza6oFzN0i2FZm7G61906lC8DB3pQTUXFjP6lqtsVbrxibaUi4xo57XMaAKIWv8fVsCVaIJJC2YvpZSwCb3ympTLmABJMtrcOcyd8tz78adrRARUKsL7+aHy9yVsswOuPuBHwCePAHeeKP5YJe5c3vuCEOcKwgfuHOYO6mKZPDy2jSZWFBvzA7MHauYu7JD1coye5i7oeDu6VMbIAYg4qNKIePOUi4xjc3VZJlZBrz1FtK9SQPcKZL5PXc0GQDunEAVl7n77GdtTVj1g1aWGesYQmc1c+e5h6au5w7YGKYCtMBdkWbcOP6EsMxdsTaytUNkcI/xh2FuEi+/SSMIs/JF0vTQVJ67x48RT8OauXPBHasDVbyyTKDhBeiCO9EEd67njnKoaVQzd0ZtBncN5o4UzF3uD1RhYuMJGLIQyd6kKct8+BCSNvvoAhbgIr3oMHcAcBA5zF20gblTcZe5I6S507uBudOZ3o25oxM80k8tuFsuEcAp1UwSyKGLwepFWJlPG9yVaXdpJhG05bRBgNBNhAI2MnftmyRQBBB4WLhU21TJRBcdRmFQXcQZZd3HKAUZsKuBu2Lm4Rzvrt7F0/gpPnb4MSyZQX56ai/4OWmAOyvLbJaYb/wcp1Ps52ERqCI7HVWbhhHmTbPtTAvcZTIFzYqNDId1MqZZgFylBBaeuz7/z1iMtzJ3nAqoeAVNuymPnLAKHBnkzSoEpawsc3yIk/UJ8iga5rnbxNyNRn7mrvCzpCZFqNEoVwc2yzIb10mt7UKwb3rAXVkpQIRNY1ScQDFUQMaCO4e5GyJN5NyWQycxltwuAFMOK8vkXcnwJJxilS46aYFZnoFmwL7mHXCnM9UJVAGloDmByY39u0gN7pTcIsssStoFCxAym15sZlMH3JmtcsUhUypZtnnuAKuE0bTwApY9d5nqyDJVC9y54WImb8oWtxWZu/fivpkGUyzlEhm38k5j/EBVUAGVrCtwB1gmrJyFXGDKPWFFg5g7FEnbQ2SZEyia240MrZvgrugrU5mGKKXJNK9lmWqF0+QURknbTeu55gQsgBxx6OWF33MX+NMyqyoE+0cBP/ZjXfZuk+cul+DLdSNMBSh86oXET8oY4vad+puEWIC3XO7uudPakWUWm3CteXD2APez+TBwB1SbsBEfITY9JeYrvUNaZtSVi77zDnBwgJTmTVkm6QlUoXEH3B2ODz3MnSp8bBukjpyD50WgiqrBXeaxNkgYRDSswF1q0s1rSyJanruWDYYxBJntdQZgPaE7JGN/GOYG3H2ThtOCuXOm9BhUzN04qD13JbgjjizTJ3fLc2Q9zF2s4xrcOTKBZs+dgJ5EFXOX5grBqMvsdJi70QihKTtK0MvcbToBR3yEZD6umbs8t+COZI2FUsACLNJFJy0TQMXcjfgISVEG7Bul/GldDXC3wXNXvgdDZ59NYJDV4G7/CGm5k5SmSGm2myyzkPlAqVqWWexeQ2vITCNog/IwRGhI5Y8DsJm5K5JQ3XEN6O4kOsHeaM9q4NMUOuTVIoYSVsWN10+uoHYtbu+ZeTjH7733e3hh7wWEPESQM8Rn71twl5FqxxNoyTKLtMxtsswDE9gqhB1lmdZruLss072xsIYsU3XTMktwJ5OKKW1P6YPYNIJxqHhpwUqHuaMVuNNOWm91jkdR1Z+5CsnV0jKLnq9e5q6QZfqYO99mRPn6O+Aujnv73JLT95E87coydabtNZtzCx44gdqf1547FkDptAIRg45tzsEyy9wtmT2vUga7GG957gBgEkyxmnQ3rZSR4Bmwp1lzIQxYQNHaSGwydzWjIzJi6xw2jVJVTxQhxIaq7I1rcJdr8F2uZT3TYO62ME4crAZ3mwJV2uCO15UQGZqeu41F5nlelVRvGkaZrbBgpmDupBfUiLJjMIoqcOdKM5dyianwSJ63BKqwUs2RZZ2/zzc0HIFmdiPDm5ZpVMHcjey6hOQNWWaWZzh7/y3I2RiBh9UUTEAFAmq99J6rJXPXSMtsM3cA8KM/CvzyLzdSWns9d4QhyRX4xapHllnUaKgEwdHd5u8pGenTU5vkvWUYdZg7ZjcOBQ+9gSoPTh/gvt4B3JXMnRjbUJXWLNILTNd6uOduNO6CzjffBF5+uVE1VYG7tucuTXGMuBGoAljmzv1sbaBKydxtAHeMgZeBKgW4o47nvPGricGcjSsGcxBzV3b6SVnI6ZvHn8gIZGKPZdtHeQPubuYSwymzlQHOlB4DzGbA8THiibBpmSy0xuWSuRO1RLED7soI3ha4a/TctQJVMpI3PXfjsGLuZKYQjqZojzdQJSNYqZWVAPWAu00AJuQh0mlUM3enpwDnUJn2MnftQBUAlecuZCGSiPenZaoEgQ/cuTfyDWmZwG5JSgfMLlRLWWZ463YtE0gSyKGLwXL6ZJkluMt1l3EtPqOhzF2vLNNjwk5Nir1wD6lJkScJdChq5s53nEoJJdggX9K2mYdzfOXdr+D+/n0AwBQCy/MC3Jkmc2d9iQ5zt01XP51iX3PL3BlVpdwOGUa5d9exM21wp5Kqz46iBohay0YBsmDCymqSBHoDc/fFj34Rz8+f3/gSBA2gk3U3LRPFpkHF3GXg5XFamuGLneLD6BDHWF9NlrmJuSO1LDNUmQV3jvekrw/NBqo4v6Osp+hh7/6Hlx/jf/yWdzvsWFW1wJjdBGMEan9WHcOCB1BFcID0+FW9UzB3WRJjwSyDlhYRhYZ5PHfBBKu9blqgTmOIDJhL0pVlGs9CpZBAZXlmwViZlgnaeE+9oxSUqPsQIxFh7YK7wpN11SkB2jbPHWAX8JLVcf/9gSotcNcuMW957nqZuywr0hC3By7MghkWsFHwfcydlTiiwdy54G6RLjATUz9zt4GpoYQio8SCuwGyTIQhRE4sY9qWZZbMXW4ggqiQhWfVaypf9/G7DyD3Z/2eu5B1PodyXObOLTHvgLvnngP+0l8CfumX6n/b5LmDBr9Yepk7jQzIc3u/uL0B3A3suTNlWmYpyxQ94O7sAe6rySXAXdF115plcoGZZk2GfsOI0MPcvfkmcP++9TUPYO7OkFRqqXI+/9Ln8e23v736mpUhbAPAHXPBnRDFMeevQthjk4Ysc5OtJaCi6bljeef4DHOKNLWbJ1olO23kfhjmz9df82d4BOGNQmLAuZkUUb1xJGrmjtsSyQx1WIo3LdMTEjENplirNRbpwu+5c2SZggroaFQzd9AIon5w15BlGqu7F7of3G1l7qajmrl7+BB46aXOrkzAAizkwivLnIfz6rnSbcyd7yIzgLkTjix26IzFGBy0Zu6O7tQ7SUlimbtr8Nzp0nOX9zF3GOy5a++AAgDl/YEqYzFGyEKkT9+DmU2rizilHs+dlFABGyZd2zLzcI6vHX+tAnczMnLAXd6VZdLNVQiNmU5xoJj13Hn8MpuGEn9KaGda4E7JpNr46fTctWSZKjc2UEX1g7tf+K9+oYqo7hvBrCxMe3Y0OeV1YqebENgCd0fjIxznK7sQ9pUMl7NJlrmNucu0leDorBPb35fSyNFiUDeBO61xSlOcRaQLoFRaMWBlDLzam3WZOymhaHfx4B1q+/pMvMaSatyKbiEpZYLcw9yJCVZ7Uee1qdUCPAPmqQfc+XahGQPNC+bOWfRbWeYWz13J3BXXhrEYYz0bOcyduZ5AFVaXmA/x3MUcCIhlEwXzlJh7EvBsD1wdqOKCtY1F5lpXUffbZsRHSGhWe+48jxEssOEk45qNcCstlnKJaTjbPVCFskqpkOX5VuYOYQhhrGrDViE44K5IPVQwEEHB3CFreO4A4Pj4G1B7U39aJhVQAStSZrvfL6sQNgaqlPNDPwT82q/VX/d47gTh1nN3sewyd6WSQ2tbzXP3uebvKMHd06fDPHeU2et1Ky2zDe5iFeM0PsWzOtoO7krw7jJ3NKuvY8Us4nNMyfANYjGa9DJ3qUmr9YhgwgJgT6BKQnRjHQYAf/+Tfx9f+MgXqq8HM3elLFM3ZZl+5i7DHp8M99xRUSulCmVFe20zyijSpAR36Q24u5nLDae8y9yVssxiMRqPeO254yhkmbmTlunxMhXgzgUelFBMgyneW72HSER+z53L3EVBzdzlBuEmcOcGqhhgJZcb0zK3gbt0EjbB3Ysvdk7c8qLTDlTZC/eqm+2Ij5AErJ+50z3M3QDPHSV2YbMLuCNBiAPY14jlEsHtu3WheJoiJTvKMj2eO7tLC+RKQcID7oq434YscxNzV362zvQxd2U1xYiPkLz3DvTBXgPcZZkH3Al6LczdXmhv6C/vvwwAmNIRFssT+/p1F9y5ssyMbAlUmc2wn5JLVSFYr+ElZZnF72HE9dzpxu8XVECXzJ1MrhRiwbmAStfeHfWG545kG8HdSXJq/92TLlhNW5bZYu7yMOzx3Nn3QuoUocwhctIFdz7mjvQwd2dn3dd2eor1JLBMVMt3Z7SsNuTKpEC1N62ZOxFCGQkjE+QYzuyzHDCrBRZU4nB8iDSTQBjCMNr5eybBBCsHSFV/0noBYYB5knuYOw3eDgJizEqessJzR13mbiC4Yw64m4bXD+4KWeYgzx3lWAtUG3abmLuGZMy5F5o8A3Vklry81vWBu54alfaM+AhpAe6MUf2eu22yzHDW3KzMsipdtm8YYVbNYUyj8qh3wtB6j4z0p2UaXTN3VECTDPnKvs6VWmEaTHH89JGVZfYxd2ILc9cKVKm6Atvz8svA22/XX/d57krm7nzRYe6qgC2l7IZoH7jbIS2zZu6KtYIIOyDqzbM38eLei6DaDOu5A5rM3STodN0tkwtMyfDzToQeWeaDBxbcOcwdIwyG5Mjb3XpSIoXZuim9C3PHc8sAC53bja8e5i4lBnt8uhO4k7nD3Hk230YZRVyEVd2Au5u59HDCO4bNSpZZMncjVqdlluAOGCTLbN9A5uEcqUl7eu5anrtRUDN3xCAcd4NQ+5i7dbq0QTGeyGVBBwSqjEUty3z4EPmLL3S8X+VztJm70m8HFBLPkPUzd1r6gcUAWWb5GnaKyRUC+xjVaZl3nq3BXZJAErObLLP03DngjhBiWQCVQiJDELYYuTBEqPOmLHNLWmbHc8f9nrtU21L5ER8hPn4MvT9rBKp0AKGUkOL6mDsAuH9QyDL5GMvlaZWq6HruGKnrIgZ77mJUzF0nYXXDUMpqZv2rXwX+7b/1/+BGz13NOvmYO50b5EkMI+WVQiwEC6HSGIp2b76c8KonziCvQaTjuQPQTMzcFKrSlmW2mDsZsiJCvNu3ZzKDNF0hzCk4KFRan999KY1sF+bu5MSCu2nYAXdaJpVPmlMOvTeF+vR31cwdD6GMgpKJTWkdODQHzHqJJWyseKpTYDyGZp5AFTHBair6mbvYdANVjOwyd5TWzJ2TlinygbJMTprM3SRogrvgepm7IbLMtUDlo/ZVIWSFl7Cx8emmZToKlsZzjMddcOeUVG+bkIc1c9fztwhmexN7ZZlygVm017yfLRb22rah3uBSskxjr/1Wzls/t91cUVDEQIQRCCH2HFwX4E6u8NLeSzg5fww5m3jvr/bvbFaIuOOWmJfM3Syc4SK9QN5WAzz/vE2+Lef8HPl83nmPOWXISA5+dtFh7jjlMJzZ+xEyiHst+fpstlOgirUg6G6JOW2+9gdnD+z9yrl/904b3PEIcSS64E4uMCPD/HYAICIbntMYD3NHCCkqCVrXhTRFAr11U5pRpwoB2UbmjpWBKqAAIb3MnSQZ9sTUevwxENyV94Aez90oZ0jSG+buZq44golOrHqHuQtrcCcZauZuE7gzBhnpJgCWC+CIR4XJusncMWfHU41EnZYJs1GWKY2sA1V0jlW66E0ZGiTLpLmVdMUx8PAh1AvPgVPeiKT1gjseNbTflrmjG8Fd4AMWA2SZgL1h78LcIQhwUIK75dKCu1LvXjJ3V5RlAkWUuUqREoMg8oO7hixzC3PX8V8xgawdjgJUBuwRHyE5eQ96b14tJMpjpTFSQu1a3N4zFbgrZZnBFIv4zF7wVdYvy8wyWxuyLS1znRWeO72TLLNRG/Fv/g3wi7/o/8GO5y6tziGKWnpt5XX1ayWEWFYtTWBUeiXmTvAAWsZ+5o4y6FKWSZyEQJ8ss+y62+S725KWuWJZR5IJFO9FppHGSwSw5nudOMxdbvyyTF+gCtAP7sYC67HogjvncxFUQM2nUD/0NxzmbgRllE3d2wHcsZxYWSYkDqNDy6xHkRc8TMQEq3HQlYzGK+u5W3fBXXuRbn9p7blrM3dqCLhzSoDHYox43AJ37RqWS0zF3GVmmCzTx9w5skxfAh7jotrozFrM3ca0zFKWOZC5S4ipPXdeWaatCHDBnVtkvpRLTMf7zU2TszNgb2/j72aUwdBCljmgxNyCu9zKMlsBTlaWqWzHYaEKEYRBxTVz9+Leizhevgc5izYwd8ReZ3yBKgVz58oyy97CkqWp5tlngXffbZzPZm9WVUuUU17PxGkX3DFqmTuTJgBysOc84O7iwl4r9vexbRhhFXOXkVKWGVq7hDMPTh/Y+9VlwJ2IEEe8A+4W6RJTNiwpEwB4OIZiaIJmj+cOKBj9NoMmJVLoAcwdr5m7TeCOMfDMSlaD3B6nDNTrW0+JwZ6YIjFFWqbekpbJgkagiqRZ5+ejnCEppMVae66ZH/L58/XX/BkeTrvMXcPAzRjigNhAFR5CUthUPFLXHFBCYLLWge+RZQJ2ASyogGCikKI4D3HKWwUV0IGoe+5IhnDavYG4sswqUEXllrnrWdgERXR234Q8RGqk3SE7PS3A3bOdk9AtNC+nw9yxEAkn/bJMk/qZuwGyzPL5dwV3+1lQM3d3n4Mkmb2wJgkkdmTuGLMgOE0bNweWA1qnkDAIo1nzMUGAYAfmzluFwEVvoMqIjxCJCMnTd6HnU4e5413PXcu3c5XpMHfhDMvkAlKnCJTZKMs0dECgylLbtMyepLu+oW4FxOPH/VJFH7hzZJmltFN7gjE44dDp2vrBxBVkmSKEkik06XoRGOWVrNEgryV3vkCV9bFdhCy6XWzVuLJMT6DKimcdSSZQM3cyXSMEswsOxx/W57ljYI1Uza3gLuJYj1inyFzLFLyQ0pcbca50uZRlKpXYneeBw3IgWy2xyJMGc9cbqDLmXeZuvQTPCfaW2ivL9HruUHvuuAPutjJ3xcZMg7mLeA3ukH3TmTtBBWJeH7s+WaYLzsthzE3LzBvAq5GW2d6sWK1gWLeH0DchC5GSkrnzdzHaigBRyTIFFR1Z5mx80HwdAwBHQ5a5i+dO+2SZlgFXyCCKmp2ACMgWc3e8Poac+sGdrRBp9kO64zJ3ZaAK0OO7CwLg8NB23hXvh55PO9evKizo9LwbqEI5DKeQ6cqGb9271/wd06mtB4ii7fJJFNd8oxuyTC6CDkP24GwHcCeEvd+7zN2oC+6WamXrMgYOG0UgOer7uTFW5vriiw3mDiik+W0vrpRI82tk7sq0TL2uaqrKDb32WOZu1gxU2fA6rCzTYe5I3vXc5RxJWqZl7nav/zDMDbj7Jg2nHKL1dleyTGIT/uKANJm71aoIgNjQH1bKMlsX8Xk4x1jYE98bqOIydwGrmTuaIRgqy9QZVumyl84ewtzFOraRw0+f2hqE5+56wV3IwgabNxbjKimzfK40ZL2JeL3MXRQNYu4CFuyUlgkhcGCCirmjzz5ngVimkaUJ1K7gjhB7c4vjDnNnlIQkWZdxDUOEKh/uufPJMpno1hrAMnelLDM5PYaZNwNV/MxdV3p3mSmP7dtje+OejvawTBe2a8mQGoSg2KktEkWHyjKDxRoBC3CWrXZn7krG6PHj3o0GP7hzZZn2HPcxMIIJKGmZu22FyptG8ABKJX5ZJuVVHYMmWwJV1seWTbjweGTKcWWZnkCVFTVe5q7cFU/TlWXuQKGdTjaV9wSqENYry/z1B7+Or77/1fp7JydYhxTrkPYwd/V7ooxqnCMiGEFlGjLdkbkDgYlXWGapBXfbmLuIeZi7JQRhmC+VvwrBw9zRYmGnHeZO+Hbo21OUtDc8dxFzmLsMPLg+5m5QFQK1sszyml713DnMnZKxh7kLas8dsuo+WD5HL3P3zjswUTicuYMuPHfGz9xRARXyirl7ZvJMR5Y5nd7ambmjhFrmLssa3vr+B1CIDFBy3ZVllswdySBGJXPHoZK65+6l/ZdwnJ5CTTYwd3w7c+fKMgFP1105L7xQ++7Oz6FmY6/yAIDt3520E3gZDKNQaWzDt9rgbjaz3v8Bkkz7fNSCJSctUwSRvec4s5MskxB7j3aZuxHrbBYu9QozNhzcuRJcAMCjR8DRERCGFty1mLsOo5+mFtxdF3NX1MJIIytwVx5z7UlphrnYwXPHAqQorvtlmnFblgmOuGLubmSZN3PJ4Ux0bjSlthwAMJ8j5qQOVCnKQssLBlDKMluL5g3MXQXueJO5M6TluePU3swK4+k25q4KVFEZVrKfufvMC5/Bz33h53rfk9vj23hv9V4T3D17xw/uWrs0X/zoF/HTn//p6uuqM+/RI+/vkkZCUM/FYDQa5Lm7jCzzH68/ib/2rX/Navjv3kVgbC2BTFYI0JSeDhoh7ELZI8uUNO+COyEQ6BypchYqW3ru2hdiyjYEqhSyzPj8uMXc9YG7bmH2Zeal/Zfwqz/yq9X7NxvvY6GWUKuLjkeuKnp3e+62MHdYLnEwOsCxWfq7EXuGtZm7oeBOyyoAoxOo0nqtglpwp7W8UoiFECNoLb2Gd+4yd64s0+O5O4lP7IJzU0l4nyyzYKJXRPmZu4I5kGmMkIgC3LlVCMbLBjBC/czd2Rn+1Zf/Ff7df/l39fdOTrAWwFrAG6jCHFmmznSTuQtGls1TSbU4GTIsJzDrFZaZ7YyqmTuP5y6YYBXSLnMXr8Apx/wi9csy29cWNy0TTlomYcM8d6zF3IW0xdwNl4f1zW4l5ryQZW5g7qSHueN1oEqW56BOjPzGtMy334aJRoPTMlNS9Nz1bEBUssyCuXtm8kw3LXN2uDtzRxmy4nqXIdvaywfA1uUka5uW6TC+ZaCRInkF7gIqIEtwV3ru1AXkOPQzd6xImd2Wlun03AEbEjNd3935OfR04k37BQAx3292PaKWZcrjdxEYNFQeAHYGd5QwZGVYV5F9UKabu/fASpbp9NRuHAfcjdjIy9wt9BpTz6ZY7whhgXyp5CkkmUDtoS/Hhmp5AlVy1UnLbM9OzF3xFpVhd6XPsz0pybEXzHfw3DVlmackbZABADCiAZIqLfOGubuZS45gosPcVYsjwDJ3PK8DVUhWM3duOXSbsu4Dd4HL3HXTMhueu0xZucPJCVIGhNPuDYQSigwt5k5lWMlV747HWIzxqec/1fue3N+/jz85/RN7IX3vPeD99yGPDrp9JEUqozu3olv4xJ1P1D/DQqTzsZVUtAEwLHDxlawO9dwFLNgZ3L2azHFb7NkbzK1bCIyVSMp07U/uHPCcbXDHXHA3bt2oCEGYUcgyhKL0NvakrfllmYGXuXMDVZLFKfRs0qxC8IA7eU2eO0oovu/+91VfT6e3sNQx5PICAfHE+jtVCG0pVmcKcLc/2sf7+XI3WSZzPHdPngwGd8rp2HF77nx9ZZxyKJXAKAl2FXAXjKCoZeY6iyMmKnCnSb8ss2Lu5vPN4K5PlpkkQBhipeMe5s4JVCG8K8uE8QeqbPDcXaQXWCmHDTk5wZrnWPN8s+eO1axSBSiCyDJ3OtmtDzEn0MkaK5PgVnSrZu5oD3MXwO+5owLz86QbqKJVNy2TUsdzVy/6OWi92OubNnPHx1gHpAXurpm52+a5o9zKMovriddz5+muok64WJvZ2piW+ejRYOYu5KFl7jZ57qgNGull7tIFZrOjLrjb5rkjDIZY5s4g3+65gw3VUckKxmhQF9wRVgSq5BAje34KZlN2szxDalI8P38ex/kSMvKrdEoVkg44uAdojsUYS7nEWq0bGzy94K7N3E0j7+YUAPC9bgl5KdOXb3/DvyGzK3NXhocVzB0ltAItygEpOzF3QJe5C2kD3CmjoHODUTtEbdNQapm7orgbb74JvPQSAHRlmT65dpoizdQAWWZxHg2SZdr/W27K9jF3kmbYC+fDmTseQMIgz3Okco0FkZ16oEiMkcT2GNu19ujDMDfg7ps0nIrOjaZcHOV5ekxj4AAAIABJREFUbpk7ljvMne2TcSWXzMfc9bARXVlm/b0MNRgsd6RxeIj88WNIjmqXzp1OoEoYIkyN9Qtc8jC6f3AfD84eWObu934PuHvXJli12J2ABVt3i6wUxtibX8s7AwDSKO/O4S6eu13TMiGlZe0mEyv304DUKVIVIxxQhtsZD7jjuU2XSn3gDkVRZ7E7hTi2f19P2lpvWmbbP4e6CiHikQV303H1/jDSw9w5oQzXObPZIZYmhlxdIGi/fuKRZQ5h7qIDvJ+vtjIIjd9FeX1+7ijLFHDP8UKWqbsx6oIJaJXam9EVfE5cjKAYoPqYuxJgoq5iuXZZ5oYCc6B4L3KDVMUIiAAHq2L78zy38kIPuOOk33N3kV5U/VwALLhjGdZEdz13jheylNAr43ruRlC5hlLpTswdBcGFXiFi1rOami2eO553PI0qKZi7OMNF0mLuMg3W3ojq9NwV7AZh0GoAuHN6oiIRWabTBXfhNYA7VgeqDKlCiEUNOKv7mHM910pulGVmqN+H8jl7mbtHj2DCYdL8ER8hyRVgTOEf7GPumrLMTqDKpAAY5fkyWJaJoucuazCTfWPB3bpI523JMjMNRfMqUCVgAWSywlqtEfEItye3cYwYMgr8aZlF5YMKuff7k2CCpVzayibnvd3K3OU5cHEBPR13PXcluDvogjubnkwh33rTr8oowd2t7mN9U4XcuR2qjEEYVNegs+QMOtM4jA7ttWiAlw8/8zPAxz4GoPDcBU1wt1IrTOkIJNpBlglY5q5cDxwfV4Ez7UAVQbg/UCXbXB4OFOtNo5EV15XeTfFClgk4zB1h6HQ5o+i5c8BdajYHqjAmrH8vN3iiTvEMmXZexyicIFkX4E7L7jXzQz5/vv6aP8PDeTctcyzGICA2LWs2Q8yy2nNHCnDnLEYpGdZzBwB7o70Wc+c8BLUcpfSS4OgI6uEDCAOvTr8TqMI5gozYnrtLnhT39+/jwekDu0v2u78LvPhi/fzObAtmAYrdUp3YnT03LrkYZSQC346TeyO/ZuYOSllwN51aiWQGyPUCqYwR7lA+2njONrgrtPGSwwvuAjDI0qe0pQDX23PHA29aZhmoMuIjJKtz6EntfaCsp8ScdcM7rmOm430sghzy/GnHV2mZOziyzHzz5zibAWdn2B/t4xhrf/F9z9ByFzeO7UJsF1mm23NXnOM683juuC3ONkqCXYEtEcEIumDufAmptSwTNXPXkmUejg9rcHcZWeaGAnOg9txJmSBkwgKRQpaZ5RkoCKgHVDCf545z4Pwc5+l5h7mLiUZM9Ebmrlz4uzUtIoygcwOp0p0qMxgIzonETEys4kCXzF3XDzoRBbhrM3dJDEE55mw8OC2T5rA9d3ADVdgwz50TJT4WY6w5anBHrkmWSZ0S862eO1547lrMXStQpb3x6FoUTIu525iW+egRTCiGMXesCArj3KbaehirgAWQAWvIMjs9d8G0mUQ7WJZJHJXC9uuXKCpGMmOassxioa1IDhGVzF0AlcZ2QyaY2A0enkKO/LVHgglImkML7gXsnHIELGhIMoENXXclc7deA8J2T7aft2LW9w87D6+Yu8dv+8FBcf0fztxxJy2zWKsVzF0J7kpJJiFkOHP3d/5OdZ2NRIQ4IA1wt0gXtuNuQ+ehb0ROaubu7Kw6nryBKn3gbiBz597X/D/okWUSiswjy5TUyjIbzJ3PZlP9AdyufYzEE32Oe7SbIzEaTZAUya9G3zB3N3PJEVR4D55q9/snfxLxwcymZbLQgru2547Srv+pCFTxee4iYU98xgO7wC2m47nLNHB0hPStNxG0itbLKcs6XVlSAIaVXO0kSXLn5f2X8fD8IbKD/Qrc+ej2ocxdatJecCcz5fXnYDyud8W3eO52ClQpF7GrVSWvCDOK9OIppEo6DNPg5+ykZRIkcg1uADrqXuhDMKRFl8umMBXAMnedhT4fEKiyvoBx5DGM8h7mrts1cx0zDWZYTgPIp+91jp3Kc1cxd1tkmZ/4BPDgAQ5MgPex9ibd9Q1j3CZdPnli0852AXfEBXe15669myiogBoJmDS+oiwzKmSZXaacO/JSTfsDVQ6jQ5zEJ8hns8vLMrcwdybTlrmjATihUAVzpzMNDuY9X3kZT16OtsqEXuYOCutcdsGdkR1ZZiNQJRxD5QZKpzvJrFlOcD4CpmJSJAYXzJ2nJHsSTLBipuu5S1bghGMixkhM0mAqtzF3Oje1LJMwqLa3pj2lLNP13LHMAXc5uOfas+tUzN0Qzx0rSsz5JllmNySB8QAZqdMyOyXmmfH33D16BBOI4cydTgAhoGXqBViCChtkNh4j1rE3UGUWzqzqwwV3g2SZKAJVBpSYowR3MYxRLVlm4bljqGSZAQ8hZWw3ZMQEB6MDnHODOPBL7gMWQJEcKujvOJ2ISVVgXs5W5q54L3xWghJMi4OjzsMZtYEqG8EdMNxzR13mrinLLM/JN8/exMv7L9sHDAV3zkQ8QixII1BlKZeYIexdr/SNBXfF9c8Fd+0qBOoBd2mK1Axn7rRKNwMmzjvgjoL6mTuaYx7ObQAftqdlgrEK3D3OL3CPdTdFRtEccWrPOW3kTvf6D8PcgLtv0nDe9dwBdvf7JD4BPv95xNTUzB0MsFo1WDm2gbnbmJbJu8ydz3MnHz1EaPrBnTQSjLAqyCIgHCu1tousS0wkbFfdO/vMSiE2gLttu0XVDbWPucu1n7n7+MctsAQ299ztWoXQlmWiYNEWZ1aWeRnvWcmcNJg7gnh9bs3hHrllAI609NxtCFMBAJ3rzkKfbpJlshAjIhDnCjoQjueOdwFhK5ThOmcWzrAYM8inxx0AzyiznUNlzx22VCFEEfDDP4z9P3kH75P1TrJM67nLrCTzxRd3kGXWZm735uYDd5xy6FFoZZlXkMLxsJBlejwRvNzwQcHclQxhC9xFIrIR7nujy8kyXebOC+6sd1OqBCELGuEfOtMWEHsWN8wnyzw6As7O/J67LMXaJBbcOR1QRtUMWCXLdANVwggKxnrudmXuQmAaTFvMXY/njqguc5da5o6MJ5iLaSOMw5uWSSlY5gaqFAtgJzynd5Sy0jyXuaPGkWXm4OE1M3eX9dy1qxDa4G6L524jcxfw4WmZOgGCwKbw9QWqBLUs887kTiXLzPKs9qCNx3ViprMY7xtXltlmJvsmAINM153jhhEGIxPL2hYbSYIHULJm7hhl2EsJ3suXvVUIkma9zB1gNzDazN1Wz935OTCfe0PAShksv+Vn7jQjUE8e+dcDO4K7yl/mJjEzZuWPRW1VVYMAXA7ciQLcucydXGCai0swdxQ6LY7tHZk7o1Jk+QYPXTEN5m7T+cIYmLHXW5e581UhpCzH3mhvsOcOnCPIKVKd4nF2gXu8+3lGkz0k0q6NtNFVyuqfl7kBd9+kuSNu4XnVZU0q5g5ArOPac1eAu2agiqfEvIw8bi0CX9p7CS/vvQygkGU6385ILb2svApHR0jfeYiwJ/mSEorUpI1dsoBwrE2808KmPff37+PBpLiI9IC7MpVx01SLpOefrw3XzshMQ/gu5p/5DPAf/6Nd1G1g7i4ty3SYuxLcSZVcLlikXFw7mn2eU6xXZwi6+AsAMMk5FknBqhTM3b/8nX+Jn/2/frbzs66fqJw+5q7quTMEyd7YFhm7aZlo9vyUSazXEajSnmkwxTJiUGcnCFq7ij5Z5tbd9x/9URz87h/hfRrvJMtkjCPLDfD4Mf6PT07xk5888/9gG9wZWW2QWL9iWYXgSctkAioK7ELiKrLMcIyUAblH0l2BuyyDpqjTMhmzGwjOguJofITjCe0wdz/0Kz+Er7z7FftFnyzTZe42BaoUGwmc8Ca4y/3gjpfy2HLazJ0D7vTTYyutNBIZZ3YzpnqYrCoqyk2wBnM3Kpk7uRtzB8vczUbzJnPHSGfhNAkmWEN3mTsZW9AwHmPOJo3YeG10Vw7V9twVbCwnrAZ3v/ZrwN/6W90XrFSjD3EsxtajuFgAeQ5Nrwnc7eC5E0xYz90m5s4jC2MiaIA75lzvbo9v4/v/t+8HefvH8F23/8/6QXluwZ3YoedOp4AQVj7tkWXaQBXqlWXGKsaIF8mcbVnmgBLzjKDw3DX/vr4RYFBpjKxdhUALcMdq6WMgRpAqqTdksgxH6xzvyJMNzF1mmbuejb2JmDQ67gDgxb0X8Y9+/R+B/AzB5L+f4Ly8h5VF5k+fVsydb3MKAPhhs8AcKKsQCOR7TxD4pMQ7M3d1oEqZlglKLXNXnFcPzx/ipX0bXHJ55i5vgLulXGKa80uBO1Vs9v4K+UOQN/8eyM8Q/NHxH+FWVPsMOeXQrWCTkt3blvLNmLAqr20hJY4ss7zP9nnuJM2xN9ofDu4c5u4JlrgbdD/PUQPc3TB3N3PJ+b75J/Dzb3+i8+8NcKfiOi0TBvlq2ejlsv1hw2SZX/jIF/Dzf/nnARQLdBfcOTt6lefu8BDpk0cINoC7RCeNC3RAOVYmuZJW+f7BfTwIipvXSy9dSZa5lbkTnue4X8QTv/32ZuaO7xioUi5iXeaOcMjlub1IbpE29D4n0JRlgmC9Pu+V0377eoLfO/2a/aJg7v7DN/4D3jr3vEeeXVDGA5g2UIMjy5QZkvmkcZOltMdz5/h2rnOmwRTLkECenXQ+Y045NMmdtMwtgSoA8J3fif08wBmVl2DuDPDkCR4+N8Fb4+5NCkCPLNNJCysDVTwMhpVlBtCCeRmBoSPCMRJub/btm7UFd63d6OqBogvuorwD7r56/FWbhAv0yzIHMXcaqU4Q8LDB3KlM2YV7L3PXBXf64gxrta5lmXmO+OIEYz62lR73jhqhKm73USlfbzB3owkUySC13Kkyg4Ja5i6cN5g7TbvH5kRMsMrTHs+dACaTju9ukyzT5KbB3FlZZgG2//iPgV/6JeA//afmY6XsMHdxZsEL4riQZe4W7OCb3Tx3BbhzmDtlVIO5UzrtqEoocz13zcCRX/wbv4jspzKkr/4yfj88r4+TszPrn9sWxlRMQ5aJrJ+5GwXAfG6Zu+mdCtwtpFMLMJk0mbshskyK4T13KMCdjG1aJmnKMlUaI3f+bsFDqExjFV/YDZnFAkcJxTurJ95ru2ACEpm9Xu3A3P34J38c2U9lyH4qw93pXVuZBNhj7ugI+NrXgL09bwhY+X6LIw+4K3pP5flTCN+GxM7MHatlmeVGGSEW3BXXqrPkrAZOl2XuGDrgbmYuA+4IVMHcPTRP8d8+/8P2mP/HKZ6bP1f9HCe8maJrDFKWD1q3MOYwd5vun5zbLkKgSrm2UuAmqDSZ9TNOw9nOzJ00Eo/JEvfCLos7mtZgURu1m+3mQzA34O6bNUHgBQ5HUZe5Y9Tams16ZZkGUoM740nL3LZgLS/wVQofqb0GrudOvvsOwtz/PJRYits9oUIisMp3CxNoz/39+3hAi4Vhwdy1bxJDAlVGfITE9IM7metKWtIYQmr27jqZO1eWWTJ3hEOuLzr69sHjAXccFOv4AkHmf22vrffwRgnuCubujcdvVBc1d1w/ZTlMhBvTMkepQTKLGlIqy2D5wF3+wcgygxkWASAXp53dWJuWWYO7QcwdITj49OcBdM36m8Z6DS1zt9qfIGV5DWTcccGdlC1wV/vF+mSZahQMloj1DR9FiAW8hnfGA3tNKBLgGu/BX/2rjQXm4fgQx2HXE3aWnFXXtUFpmR7mjlILdKW2Pg+7m2wfqzMNAeJd3FhQ2AV3JYNdMXerFdYhQyQiy0bdudXw3bn9g420TJe5Q2bDmnY4rhkIziK7WKmCoMbjLpBG4bnL0m5apsvc0ZEH3LWODcZA87yQZdaMju00LD6Pp0+t8uGnfqr5WFXE4bvMnVrbCozFApoAPLwGcLeL545yxCHtBqq0PXfEw9wV/2TBT/17CCEghCCYzPFt60nNPD96BDz3HEzuLyRvT+X/FhZI+tizgAWQP/b3oL73cwCAg9FBJa1tFHrvGKhCCbXg1RgYMjAtkzBbr5J1ZZmJiiEMaisGDyDHIVarU7shc3qKIx3gncU7m5m7cbiRuWuDO6D+PA5GBzhLHBXE888Df/AH25k7D7irAlUYEIy6v/NyzF3W2QjjGaCK2paL9ALzsAj0GNpz50zEI8Q86waqGLY7uAOrmLsLvcLe9LB6n91xr7UArN9uHA5at9TM3ZYESleWWRw7VUeeM9JIhAYYiQixim29gd6clgnGEJaeO7bGvbDrvxzNbyE29jMypnscfdjnBtx9s+YHfxD4F/+i889H4yOcrG3XXcncARY4yWTVqkLwM3eZh7lrDCFgGar+kAzweu7Sk3d7I70ZZV1ZJhXIkF+Jzr6/fx8PTNH19+KLUMaflrlVlsmLHfBe5s4gED0Xwk9/2oK76/Tc+WSZVCBdXthdp8uAO6/nzoK7Pjntt6o9vJ28ZxcO6zUuZgG+/vTrlTHZnT5ZZkdiCafnLtaIp2FDSkUp77J9rd3/65xpMMWSZ5AXZ17mrlzsVD6UAZ/j/ud/AMBuTCPlhdfw8WOspgHSoCdUpQR3RUCODbJxQpPKMJOspwohCmA2eFiGjAjHiLkf3HEeVFKjDpv0K7/SOEeOxkc4EarD3J3Gp01w58oy3UCVTcwd7MIpNSkCETaKdbfKMtvM3XSKi0IDVDEyJydY397HWIwtYLl90AV3xftTyTJ9zJ2Ru8l3QXE+ZpiFsyJZ0UnL9DF3Ju4ydyq1ksTxGHO0wZ3xMnel585lk4RTe4GnT4Ef/3HgP/9n4Dd/s36sUlA0a3ruioRnnJ1BUydR9QrjMnfbA1UE1gGtXlMD3JWeOy07zJ0bLtYOVKkmivD6+Rhfevwl+3UJ7jIzvOeu9NxR+Jk7KqAmY6xzibEY22tYydylizpg5CqyTPiTr7uvhUPJxKZlOscNowW4c1Qhggqo+QSr03etp//0FEd5hCfLJ/2eOwbo/+YfbGTu2oEq7uyP9nGanNb/4IA7X8JzBe5u3+k8l2U2C3AXecBdca/exXPXqEIoGc6cVNeq8/S8BneXZe5o3g1U0fQS4I7WoDOLMZ91QQ9Qbvo4G5NSIh2JHZi7zPZtbjqP3bRMWjJ3zIaSOZOaFIEh4MKG2qlMWcC36bW4zB2LcTe63fmR0fwWkqzYLPSEyX3Y5wbcfbNmPAZefrnzz1WcOGrmDihYnnjZrEKg3Z67rNjl2KqDzlDR3RnJQWmr5+7oCDLXCHtuXiVz15Rl2ov5lZi7g/t4IN+17818fnVZ5rPP2rRC0wTBEhqiz6P0mc8Av/Vb23vuLpOW6cgyQxpAxgtrXvZJRIc8J9CUZRKKdbrsldPyYIRXxy/jy+9+GVit8OV9e8PxMXduzHv1/JuYOxZiFEskk7Ajy/Qyd+QDYu7CGZbMQKbrjtSmLcs0JBu0QNu/87J9/HTzYsqdcscSjx9jNRZIA7oZ3DmeO+Ewd+U57lukCyqgQgEdDPP/9I0QI8QBvDUmnAnoXANad5m71hxFRzimSQPcpTpFrONq06ojy3QDVUaj/iqEAqRJIxGKyL4uh7njOfyyTMrt6y+n6JY6P5pixEY1c3dygvXRngPu9hrgzvYMtmSZLnMXTWwS4LZY7vbrIwTnY2oDVcpNqc99DubOM37mTq3sxpPDAjeYOwRd5s4TqFJ57uCUmBNnEff0KXDnDvBP/gnwT/9p/dg+5m42A05OLIC5hoVRCaCHgCgejBDvT+rYe1+gisfzQwuvYV7IFn1+OEQRXnsa4I0nb9ivL8HclbJM43pWnSkL29dqjbEY22tYAe4azN1VZJl5NshzFxAOqRKYTIG2mLtUJxBOV2DAAsh7d7B6/LBi7g6pleX3MndGbew47WPuyjmIWszdCy8MYu7EM3c7z1WmJ6soQOBjm6PI+oovwdy54Xc8I9W1qsHcDe25c18SjxCzrOu5U2T3tMzCXwkAF3mC+X6X3QTK610L3EViGHPHhZVlGrV5459z8JK5K85jSrppmalOERoAjFXn1iDPXW5zIp7wBPfGXaAf7d+2fZTAoA2lD9vcgLs/5TkaH+E4bnruAHuwy2TVkJFV5l1nMqOrIshNw/Imc0dd5q703DHLGPrGG6jiSGIuO/f37+PB4i3gT6w/pzctc1vPHat3S3F4aAGeMwqZ30ANAN/5ncBXvmJ3x6+r584ny2QB5HoJmSkEW8Cq/0X4ZZlxuuoFdwhDvB7dt7vQ6zW+NFvihfkLvbLM9mdp0zI9zF0ZqLJKkUSi8VjGPMydUh9ooMqC2i6uIGyCBJuWmTeYuyELtIORvbnzVz42+HXQsgLi8WOsQoJUDAR3ur4Jlj4zoAxU6RaMq1DAiKvJMgUTiAPqrTHhPKg8d9rTvebO0fgIx6TJLJULseP42L7vStXH7i4l5sX1Ls0UQj6yLJN2bsaZf3HjDVThHBe3Jrg3Omoyd7dmNbi7NWt67ozqlpg7vlQRTaFIfjnmbkQwCxzm7lOfgrl3p/OZhsxunOi9WUOaaZm70IK7TDTAnfYE8TR77pqyTOWCu1u3bMfW179uF9CABXdOqmrEowa427YBMHRKAD2IuQtGiL/r9ZpFLe0FnNsAFK29aX2EUtBio9P0MXezGV5/O7s8c1d+pkJY4Oup4BHMHk9lGXj5OSujrlGWOdBzR4WVZWrdZe5M2gB3ggmoe89g9e7bdkPm9BRH3ALO3p47Izd+pj7PnTv74T5O4xZz9+hRr+euCtnx9L4yWjB3RwcIfNVIhNjnv91lenxThWi1JP88B1ThueuAu0sxd6ablin9svRNI8CgVAwYg3OmMJ/7wV0nUCVNd2DuLLgz29iwhiyzYO4o66RllrJMMIaIR8PAHecIMpsT8Z6QuDvpgrvR/hESFCqZG+buZq57fGmZgN1NS9N1XYyJQnLRYkSyzDS6afrGZe6s567bcycZelPfvIEqxcVxl4VNe17YewFPlk8gi12iy6ZlVj4HwCvNlDD9zN1kAnzsY7aOYUPP3ZVlmTyEjJe2K+Yypb9ecMewVqteOS2CAK8FL9pd6NUKb0Rn+MwLn/Ezdz5ZZhDAkA2BKosEyYg3mTvGkPkCVUj2gcgyx2KMGBqxAIJREyRUsswqUGVLFUIx+6P96vFDp9yxxOPHWAVAKkgvuPtt8xAxhwV3LjCmrUCVVr2FYAI65BsDCoYMpxxxQLw1JlyE0LmxzN2WEImj8RGOzaLB3FXgbn1cs3alsqAvUKWvxDzPkGYSQRCBU1F1sllwh55AFd4NVOEcF/sR7gW3mszdwbQGd/vTrizTYe6qtMwSUERTKJp7peSbpgR302Bqr1vF3+RjhgghVpp5a9YA0Eql4CIAJhPsmSa4M7k/UIVleYe5E05hPU5PLbgTAvjoR22lh/1ljXPXx9xdRxhBCaCHMGSccsQ67jJ3hDSCinyqEpYDRslGanRjPvpRfPzr5/ja8dcgjbRA4vnnL8fcEfSnZTrMHSGkkmZWHXdAzdxJaf/b0FMKtGWZPeC181o4lEqRZaaVlsmR6BTC2TgMWAB59zZWJ49rz12wX32vPQELOimz7fH13LnjZe6A7Z47z/XRerABeWuvX0XyR3+0YxWC47lryDJr5m4vLBjXy6ZlEtMNVEnz3cEdYVDSBjRdjDnmkX+zwK3DAQBIiWTEh3vu8mw7YHICVcrPzPWcl2NlmWgyd9kw5u6dxTuYa+ZlaUezA8Qsr+/BH8Da5E9zbsDdn/KUnjudaWR5XSgcsACJWoPkteSSki64M0aBdtfenWG5K8uspSJH4yM8Xj5GPp8jFQRhj7zIG6hS7OJcdZH57OxZPDx/CADFLnjzJPv+j3w//uarf3Pj81Q3VKAL7vLcMnd9njvASjOBjczdldMyeQiZrJDmlwR3pZyDutIZirWK+6PYwxCvsedr5k4c47PPf9bvufPJMjekZYY8xGgRIw5oI+GOUQ/bJyUUuoXZ1zGUUExIiNMRELRS+6wsM2v23A1h7iJ7c98FjDIurP/i5AQrmiHhaPgkqtEa/935r+A311+tqxCquhMnUCUzYK3P1coyOQy/oiyTCqwF8SbdMi4suCuYu03n9+H4ECfyHMhq2dBpcgpKqAV3rt8O2I25KzzGMlMIgwic1RLCjeCuj7mbh7hH97BWa+R5bsHd3rgCd/HeuAnujKrAb8lwuecIi+zflWTda9amoYTiLMxqWWaxKdXHDE2CCVYHkwa4azB3mtVR8X3Pwxho1XPXClRxPXe3ilS//X0rAwQq5q78G2fhzHayzWbI3n8PJN/i+R44JYAexNxRjrVadwNVgCpURTteVndoDhgt+8EP5xh/8jP4Fn4bf/DeH9gk5R2YuwqwBwE0I95FY8ACSCMR67jqoy2lmV7mrmTtttkvCLObcZndXvPKTltjmbsUJmulZVJmj+285bl75hCr03fthszZGY7GR9X3fM+9jbn7kVd/BF/86Bd7X5/XcwcAe3u4SC86rF/ptfP9vrIaR97a6wcHO0gdqZOW2ZBl5htkmZdh7ojuyjIvDe5i2/k5pvXrak3Hc5emSEd86yY74MoydVUo7/9BBq5zCMJBuMvctSw1JXNHqU01VvH2QBXOEWYE3zj7Bu6mot4Yd2YkIiQBBc7Oio3UG+buZq5xSuaulGRWqVRUYE0zMOfCSln3wM+0bvxM31hwZ29+bi/eM5NnMBZjvHnxEOnBrDf1zSvLLHZxrrpgv39wHw9OHwCwAKN90n78zsfxvS9978bnqLwrQBfcaQ3JiT8ts5wS3G3w3F1Klukyd2IEGS8gKS4XqBIE9sbg3IA5KNYmRoh+cPdx3MHXT76Os9UJ/pic4pPPfrKXuetUIYjQy9xVgSrnKyQBaSTc0Z6euw+KuQOAKYvwNOqa5Blh0GjJMgcs0KbBFJTQ3asQkhi4dQsrvUbK+5m7JNdYFjds7SR1MWcDR+fdSHhbfixgrsjcCSZsFYLnvbCBKhmiCP3lAAAgAElEQVRypbw9eO5UsvK9vQp8nCVneHHvReu5c5Mygf4S8z7PXZ4hzXXF3DVlmfAubjjlFpyWU4K7qcBBFkBQYY//kxPE86hm7majLrgrd+Kp47krj+HRCMIAq1z6JV49wwjFeZDXgSobmDugCFXZHzfYUaVTG2IyHmOuqCdQZXNaJuc1uCvLlvvAXS4lDMmr421/tG+ZlNkM+umx9T5ew+xSYi6owFqtu8wdUIWq9Hl+bLiYBXdl319nvud78NpiYhUPO3ruqkAVIWA49f4triyzBHfTYIqFXFhWph2oMiBMBWjKMoczd5YRb6esMsKQ5LIh3Q5YAHl0gNXFSe25m96uvteeynPnCT4p53te+h58x93v6H19nbRMh7l7snyCe9N7jZ/n80JS72PuyiqE/dm1WASsBSHzyDIplEqgM41Up9VnfGnmDrqxUbiQC0xjcwlwZ1lanJ3hYkT6wR3vMndpyIbJMotuXO2pVmr+IAPLMrsxzZ2OXI/nLtDoeO42sogFc/fw/CHuxcwL7iIRIREEOD0tmLsbcHcz1ziHkQ1UcSWZgAUTsbCx2eUw2o2YzzI96ENkOak9dwSNi/7r917Hlx5/CXJv1nvyegNVipPrqnT2/f37eHBmwd1WLXXPNJi7dpG5lFCCbn7eT3/a/u+GnrtLyTJd5i4YIV2eIw05wst67lo3Bk4Y1ibZyNyNVI5Xbr2C/918GR9lt7HvlIG6ozPdlWWK0JuWWQaqRGdLJLzp16tueO58gMwdAEz52IK7ls+iLcs0yAZ9jpRQ7I/2d5RlBlYSde8eVmplqxB6wF0KjQXSmmGoZCm0Yp1sFUJzYcgphwoYdM+icehwyhHz3MvccR5Cw8BoCZZtDmuqZOXzeQU+TuNTvHLrlaYssxxXljnQcydzjTCcWKnQYObOOf5KcDfh2FOsDik5OcF6Gtbgbho2PHfGNANVqrRM6oC7DFjnsupbGzIMFOfCYBpMwYtrusnMZuZuf1IzabCSURGMLLiTBBeyBe5acl6UXrPcQJMMrJTUs8DP3O3tVZ+n0im404e4F1rGJJtNC3C3fXNxyOxchaDiblom0Agq8i7wcyDTqvCk9fyez30Or399ZRUPO3ruGj133L9B1JZlAqhlmamn525AmArgyDKzbPPf50zJImat6hXGOJJcNWSZggqoW/tYrc4wKdMy53er5/G9HsDeLy67GdVh7p591m5w7u3h8fIx7s1a4I5aP7LvumWZOwJ5ML8WcFeFh7VlmUWgyiK1EtvqtVymCkFEiKG6ssw42zlQhRe1Fzg7w0WQ13LR9s8V52I1JbgbKstEVjB3G+73RaBKQFilSqI9ssywAHeR2MVzR/CN82/g3toP7kZ8ZNU1Fbi7kWXezDVO2Wt3vD6uwlQA62dLuL0RleNLITRmF+ZOIc9z5AQgzg7ka3dfwxtP3kC6N0HYc8J4mbsi8fHKzN1+zdxdFtxVgSpAl7mT0jJ3m17nK68A//yfb5ZlXjYts2DuwmAMuV5AjvjlbiwecMcIxTpLEfTdOIu4/dfvvY7/Vfw+Xg9fbgJhZ7yyzCCsSn/dKQNVRqcLJCxvee64l7mTMB9IoAoAzIIpnkbWB+UOpxwamQPuhskyAbuo2EluxwogefcuVnKFlKEf3OUaS0jHc1f7aitw51nkCiqgA24ZgavKMlnmB3cigEYGrdLG9cc3FbhrMXf3VwIXyTn0//I/98syh3ruoBGOmuBOGWWT1ryBKsIvyxwzzGXhYZMFuJsEGIuxDQkZi15ZJiUUBMT6jstrYBha5g67XbMYYZA0xzSYghBSSTM3Mnd7UQPcKS1r5i7Jm4EqWTeIp+m5c2SZpbcmjm0QSckEOMydUkmD4WWUYRpMcTELoJ8eD7r/DJndSsybnrtyw8ZkpmLulO6mZQL1RmcG9DNbn/oUXvvyu3jj7d+x78Mzzwxn7pxAFdMH7jzM3SwYIMvcMpUss/QXD2HueMncNcEroxxJriFIy3MXMKwihkmsLbg7eK76nm8CFjRY1l2n47kTArh7twZ3beaO9tfEMMJgnn8W6rOfvibmTvjTMkGgtWrWIACXZ+7yLribrvXuzB3l1rd8doYLnvUzdyxo+pbTFGnAhzN3yAvmbsNnXsgyA7jMHfcHquh897TMEtwtSS+4i1lWgDtzA+5u5vrnaHyEt87fajB3AQsQczTCUnzgLjN6B8+d9fXRDCBOuWnF3M0nvXJBSmin5LoEd1els+8fXA9z1xuoIiUU38LcEQL8w3/Y62m4FllmOLaBKsEweYP3OT3MXZxJBD0ppxiPgcUCr919Df9PeIzXJq9UuvX2+AJVKsDSqpZIdIIRCzE6uUBMTIu583julIKC+eBkmaO5Ze4mzZuVTcvMHFnmsCoEwMqBrsTc0Wwjc1eCO+XcBCllMG4VQluWSQWUYL2MwNARTCBhuTcMiQcj6DyzzN2WhfthdIiT9QnyPYe5S05x+Bv/Lw6MwNN8DfzET9QP2Cktk9u0TBgE4dhKhRzmTvSAO0YZtIe5Ow9zzJO8ydyNRc3cRcyCu9weu1bW57AWrCkFBOeWucvSKqFvyJTsSCm9K6WZblekO5NggtVs1GTujIIIImAy6YA7Lwgp0jKz3DI6pRyRMwGVGxumcnBQX/9ccCcTiNZS4WB0gNMJ+2CYu573wZ02c1f+m1uH0CcLY6XnzgkW60wU4S8+8wl85cmXYe7dAYqgo52YuyCwDLsHEJZetI4sM13YQJXACVT5oGWZLIAysvDctdIyiW7IMktQutofY3J8DpyeYv/wOVDSf38tz5srMXduWiYA/OzPAt/2bXi88DN3ffeZMj1Zsn4wuss0mTtXlkmgjWz67YBLVSEEzG62mbS+lyzSBaYrdQlwV/grT59iTY13Uw1w6nDKkRJpQIcxdwU7qmWyGTAxBmYye3w5sswOc6ebgSqxjocxdwZ48+xN3F3B+56HLISkObKnJ9D5DXN3Mx/AHI2P8NbFWy3mrpBl5k1w165CMJkemJZJ7A0tz+yH7oCYirl7/h7CuT8lqrzoNwJVSubuihfJa5dl9jF3Vzh5LxWo0pZlluAuHJY65X1OD3O3IqrXK4nPfhb49V/Ha/deAwC8vvetvcydz/Re9Sbp5m5aohOE0mBkCJJMNnwyfczdBynLnEV7XnDHy0J1Ny1zB+ZuZ88dhQV3coWUbpBl5gqLLKmYO1HJMm2ICFCCO08VgqAw7GqyzMqr5PMk8Zq527ZwD3mIkIdYOJ6ws+QM+ycrHB2+gOOf/Ang7/5d5xfvkJZJGQwySJiCuQuq7iWd6X7mjgkYeJi7IMc8zprM3Ygi4tZ3tybFY4ro+bZnS1Bh2SIXUOQEqyzZyUNbgruSnWkwdz5ZpphgNQ2azJ1R4IUsc2+dDfPceZi7Ki3ZlWQCTXCnU4jWcbI/2sfZmEKfPQUfcP8ZMrt47krmzr1XNIrMW15Wd6qNTqCSp/pm/zPfh2fWFF//mA0M2SUtM9UFc8f8mzBliqRPltlh7kpZ5hDmrvQ7ZxlMXxpoawQPobSyaZkNcMeRkKzruTMSq2mIyZNT4PQU7NYRDkYH25m7S95/O547APjbfxsYj/Fk+QR3p80+u03MHS82jHzBbZeZUoLY9nOLnELpHnC3I3NHCEFEgsaG7FIuMVvKSzN3y7N3MYXo3bC2dTjOPT9NB29KM8JgOIVWcrgs02Xu8hZzpwtZJiE7M3dLucS9i9zL3BFCMMoZ0rNje828AXc3c93jZe54iLVAJ5q4HTG/a6BKlmegOWkkLr649yKkkfjG93wcwbf4e72o4z2pXmNgb0pX3fF45dYr+N0nv4tX/6dX8fO/8/O1+XiHaQSq3LuHn/7WJ/j9R0UJrZRQnFxpp24ezhufz9bxyDKD0QQyXUEGW1jETc/ZYe441tBVT0xnvvAF4Ld/G38xuo+xofiOw2+vdOvt8coyi14gt0C59AeJp+eIZgdIdNLYbWd94C7vevqua6bjA2jmr0JoyjKHM3d3pnd6dzZ9wxhvMnfEbJZlOuCuZO6qaG30B6roKLyWKgQA4KEHHBXF9UarrbJMwF6/3j8IK1nm6foEB2cJDqfP1EXm5bSYOx1acOFbNFSBKsQgGE0hRL3g0JkG17sxdxfcYL7SNXN3fIx1QGvmTq1rpgTdvriKLXLOEZER67nbgbmjbXBXMHd9csRJMMFqIlrMnYQII2A8xmytm2mZfcxdlttzlziBKjywO/RtcNfw3MlOH+JBdIDTUW7BXV/H5o6zq+cOaFoCKqC6hbmjud3o3NoD97nP4bU3Y7zxkv1sB/fcOYEqmpPBgSqzYFYFqnhlmUM8d4TVzB3Jq9L2TSN4AJXJjseXMV6ELjU3OFSmsBpzTN55v2J8n50929tVV4bfXJvnzhmfLFMwsVGWqTN96U3k9lDWU2IO2s/c7QjuACBiIWK5rr5eyAWmi3R3cMcElJY4P38Pc/T79QRvyTKltGnqQ8AdZTCM2qTlLcwd15mtcdrE3KkYQQaAkKrnLjUD0jILP8m9i8wL7gBgBIHk9Bg6N94+yg/z/PmKh/mQzmF02GHuQhEh5s1AFSsBaK62bM/d9mEgtrg1N1bG6YA7Qgheu/safuvt38J3Pfdd3sdX4M6VZQYjwOwWF++bO9M7+MN/8IdVPP8rt17Z+TkabBTn+NW/wPCJ//LbePW514Z57rbMX/8Lfx0/+NEfHP4AxqwUcLGombtogiXJIQOG/cvIMnsCVWJq+i+6kwnwvd+L+b//v/H2v38dsx84gin8iXmeN0zn3p47D3OXmhQhD0FOTjCa30KiYytrcDx37YRNI9PBHXOXmWnRC9VmUGxaZnHzLVK4NgWEuPMLf/kXdgL0lDIYRpDfvYv1V9fISIZ8ve7yGlojzRQWZt3p2Gl47jzMnaAC6ns+C/NmejXPXem52r/V+R4PQisDUnLQwv3u9C7e3WP4SMncnT3BfjDH0eR21eFZTQvcrQLrgfN9JrQAd5JkCKMpeMF2ACVzl3kXNy5Atj9cgDuqMV9ITMRezdwJ4ECMkSPHk+UT+3wFIDeOF7J8z9oMhMiJ9dztEJBUngNll1nF3PUFqogJVlFiF9IAYAwUMkyCEZCPMV5ZeV85XgaQUrCiCkGTvGKsbHCCGcDcNZ9vf7SPM5JDn5+CX9MyokwkHeq5A+CXZTrMnWAe8J/Deu4INoOf7/5uvP5zwJe+W+NHsBtzV4E7c4lAFV/P3cBAFSvLLD13w6oQAh5CGiuHpW3mjgN7tMncLeTCnrd/8rgCd7/xX/9G1Q3aef4reu7KdNb2/UpnGk/jp3hm0izi5pT3/i5OOUxmmbtNxelDhzFRyzIdVQgHhS6Yu0ZoyWXBHQ0b9UVLucRscRnmzoK7i8Ux5gf9j602fcpJU6RioCyT2PvgVnDHOVgb3LFuaGCq4gqoNdIyN62hGLOAEMDdM9MP7ohAfF6Auxvm7maue47GR3j74u0mcydGnbTMcrHjzmBZZk5gii491gJ3gPXd/c47v7MxLRNoMXdhydxdfcfjI7c+glefeRWvPvPqoC6V9pQ30HJn/8mMYP3eI/tNKaGuqLHnlO92MyCFiff0tA5UiWZIGSC3JXf2jU+WSRnWPN/8fH/lrwD/+l/j4EICkwkYtayPNLLxY8qz012lrznMXVmDgONjjPYPEeu447lrM3dK2UTPocBq1ykXQ+2buivLNK2S3iHPuZPnjjBklCJ55hY45QhyBhkvuz+oNZJMYqnXQJY10jLdc9zkpuNnFUxAMQJDcS2yTN8iiAcRNMmhtRzE3N2d3sXjSV577hbv4WB6VIetNH5xU5a5CkkvO8qoTV1NSYYwmhVSIQt8LXPnT4vjXDTTWktwRyTm54llwuJzYLXCmpomc1cyJbCyTPc9bsfvA1Z+tYbeXLPS/ruK53SZu0QnmwNVRrRm7pIEOuBWDj8eI1omjYWf17NGqfXcZboZqMIDaGwDd7IjyzwYHeCUWyDGr2kZQYhluYYkK/qYuwa42+S5A0Fm9HZP2q1beC18CW+MLSM9mLkrA1WCwMqnfZ47VoO7/4+9d42xLbvrA3/rsV/nVNWtuq+u6m67X2638aPxbRlmSDDEDBibOAY0EQkwYSAm0cAMIl9mFMhoIgKjSPkwiA8jISRbyockBCnSBA1jDWEIw5eIjN2NZUyC8bvbXbd9b3e9z9mPtfaaD2uvffb7cc4+j6pbP8lq37rnnLvrnH32Xr/1e/zNpm6tLbNPoYq5XgsBSdGpLdOyXERxhFhVFKoUlTuTuWMxxl99TZ8je3u1xM48ZxHlzuEOLGpptT2DN87fwC3vVun9bSxUSTbPqkYuzQOaWMfTzB2ZZe6GsmUCgMcdTJPNa6UUzsNzjE+nvdsyTb7y9PxN7PD69Ywmd3GaP55LubNohzl3MWw1I3e0as5d5MOJZ+RuGnXP3AHAwVFUT+6YDf/0rSup3F2Tuw1AZebOchPlLltNbKV5HIO4V1umsWWiVBxyb/9ebihsEZXKXULu+liSlgljzYxVjDecCJPjpNY8DBGyxRXG3rBtrQIYW+ZoGyGDvkjOk7mrKVSZWC1z8z72MeDTn9YLhKS5sCp3VyzMATLKXYbc+cLPkLs78IVfasssFqpU5XaGhFkMFc/frC1TxqIXuesLSigkI7i4vYOxNYZDGAK/TO6EjBAjxnl4DjhOkk3IzPlJyV1cmbkz6sYgtsyqQhUrUe5k1GnhfrB1gMORnLVlTo6wu7tfTe6Kyh1XlWUqgH4vQiUhKcC9sT6unC2zmtwx85kbGHKnfNw4nmqy9I0vAc88g0kyRLqS3BXeY4uVM3cWKCYsTsuluoBRBqpIer03163GUQg2mZG7yQSRndhyRyN450EujyNqSCKFvl9IgrRQxeK2ngnYZsssnCe77i6OeaR35wf8TllMzyBsnXOXfAaVmTszCqGmAY8ltsyYoDVn89KP/Dxepm9AKTWfcsdIrXJXLFTZdrZxFiw2544Rpq+9UZSzCTZB2zJFqcAptWVmjj/N3JEI4y98SX+faxbO2ecskrkDKhozAT3jrlCmArRn7oa0ZTJuaTeVKbAx6yRQCBnlyZ1Kst91JT4N8JiHqdT360AGusCGWr1fKyV302Ps2Nu1j+NUW4pTx04YIuDd1i2pcmfVfw76gZrcWaCFzF3RlunDjvX72itzJ4CRNcL2RVRbYuNxD/7Z0TW5GxqEkI8QQv6CEPIlQsg/XOexrBN1mbupTTraMjsqdzKakbsK5Q5A7Ze3slDFGSZzNxTMF//h5CEEUZhcJDmUMEREh2nH6gVDxpKbn+2MEHKi7Q2D2TI5pryF3D3xBPDcc8DXv55aRKtyd5VtmYTqzF3Glmlm3OHBA3g3HyuRu6rMXRQFlc2MQ6GO3DHKtDIhZZIpWd4lj1EG+Z5vw8XdXYztMRxYCCqUuyDW5OYsPNPkTgSp+p29uenFZIUtM7FXD2LLrFI2bEcrd1HQaePoYOsA9+1wptwFJ9i7+QRuj27jzWlF5i6r3FmqUbmbEgFHEhDXzVmFRCzAI1lfqFJly4wn2HlrosndF/8M+OAH08X1yBpp9StjyxQFWyanvKzcgWJi6VKKrmCEYQt2qmIbpadRubPUjNxNp1q5oxYwHsM708qdSu4Nda9DoUcA5NoyDbk7OqpX7mSNckeC4ckdtTCNpt2VuzpbplHuasldMueuRUV57L//n+C6W/jGyTd6Ze4CEUBZHJJVZ+7McO9pNO0+566DcpfaMqMIMSWdrhFauRNlW2ZK7jLnu8nciYmec7dXXcCWe/0FM3dAdWNmVd4O6DAKIbFlDrHZS+uGmCfk7sTPjEKQUq+7ijMoO8DjbkruzsNzbFtbvS2ZQJJLlRFO/RPc8Oo3CzjlEBabXauDYA7lriUXzjmYjGErmpIvxnhZuRM+nOQ+ZDJ3XZW7g60DPfar5j13uQv//FhfMzvkUy8T1kbuCCEMwP8O4KMA3g3gxwkh717X8awTt7xbuIgu8sodszF1WU5poIyVhkP3smXK5AIOUjrZn7/1PMbWuKdypxdlfRY2y4RZJB2eHQIAJtOkQS4METK1tKbGWth2qtoBgM0chK6FcN5ylxpb5oXdQu4A4OMf1/9tUO5qC1WSxYKBmXGHhw/h3LqLQASIZJTe2NLxCdnXluXXHhJmp7tKuTO2zDheLrmjhCJ+97fhAlGi3HEEwUXpcUGSG8spdzybuTOFKhKssDNr7FyLKnepLbNigcMtFxJKD/HucG3Z39rHIZvO2jLlBXbvvh23vFvVtsycchfXK3dMkztbAnDdRLnTN/5IBLpQpUI1YIxrQm9gRiFE59h580wrYV/5IvDBD2KaKHce96qVu0KZRClzB4YLK8kfdwS1LGyR2fe1k3LH4xy5yyp31sUUBCTNI+rMXVVLJEEcJ8OWExsp57YehfDWW/mF+s6OLoOSUpO7KuUOviZ3A+ZojXI3ry0ziqO0UCWqGUxs8ucxaZhzl4EZFdR1Q4USmrTasnrlztgyRWHOXdRgy+w6xLyncmfbHiIldFsmLdsyeYVyN4kmGL/9HZ3I3aKZO6C6MfPwrJ7cNY5CGFK5Y5YuuSu0ZXIwREXlLqpXkNrgWSNMY10Ydx6eY4uP5iN33NHHFZ5hx6v/7Mz5m16rwxABq9/8z2Km3LWQOzPnTrUpd0FJuQtES6EKY7Clwv7obqOy7Noj+BcnECq+Vu4GxHcC+JJS6itKqRDAbwP44TUez9pwe6SrlovkbuLQgi2zHDbtZcs0mbsYJXJHCcX799/fL3OXNBNuypfCfPHvn98HAEz9M/0XiXK3FltmjtzZCB2OgHe7SFa+XlG5oxwTazaWohYV5C6b0wEaRiEkGQ4DX/j6+B8+BL1zN7Wrpcodr8jciTUpd4RpZcIod0u85Jld4YtQV/s71KomdypP7iIZpsodzZSBaFtmOUMYJeUHC2XuGpQ7brsQREGKqNMMs4PtAxySc+D0FLGKcQIfuwfPlGyZb5y/gZAqvWBQekzEBYvrlTvCUuUuJXfJjV8EUz1cuSLDyWg5cycZwVT6GD84wdga4eK1rwDf/d055W4STSqUu4Its9iWmdiirR7FO+zF92Pr5mPpnzspd1TOClUmEwiL6c8wWfx7lpdaM/V5U63cxVJAUJVes63EgluyZVIKbG8Dp6eVGzN73h6O4ovlKHdiOkyhipKVQ5SpKVQBQDvcE+7t38PLhy/re2fH75zLXfgW0cpdzZy7Yltmbs5dsVClx5w7RQAVhjpz18WWabkIlSgV8TBmlZU7ZuE0OIVFLbB3PN9NuVswcwfMSlWyqBqDAHQfhTDYEHM02DLDDLkLAr3xMAc8y8PUOD6CM2wxb05yp0upTsUFdrZu1T6OUw7BaV654+ih3HUgd4SAK8COSaZQxSr1SgSikLlL5tw1rqE4hyOAA6+F3DljTCenEJDgGyJSDIV1krsnAGSGkeG15Gc5EEL+PiHkM4SQzzx48GBlB7dKpOQus0BwmIOpQ3NKA6Vlu1tnWyZIY+YOAH7m/T+D9++/v/L5lcqdpxfUfcoElglD7g7PE+XO5J2CACFpKR1ZBiwrtUECmngEDpt/gGrdEHMLsNsWly++CHzqU+mFrqjcxSqGUuUZcFq5Q3WhyptvArduweUuzoKzxrbMUIaw6fLe/7RQhZXJ0CxzJ/sNou8Jk5e7iPRQbofaCIJJ6XGB0vPTzoLElinzyp0ZdyIrGrxMo+CitszGzJ3rQdCkUKVr5k6eACcnOA/P4cUU/Im3lcjdT/0fP4V/+YXfnjXJ+j4uqGhR7iScZORBjtyFfuWMPkDbMkX2OikEzkiELXsLlHGMjy40WXrHO8rkLqPcyVjk2gaNMlRS7ux+yh2zHGxnShbMXLRG5Q6RzjTGsVburJlyh4uL1K4EaHJXOQIgIXc6c2faMu1qcgekuTtN7iqUO3Guyd0C52ERiyp3OVumEun3KotUuaPdlbvPHn4WBKRzIZTDHQQ2haAtyl2B3B37xwhlONvoNZsNR0edbJlAMuoh9BGTakto6VhsF5FKro9Vmbvshi6zcewf6w2Zd7yj0zENlbkrjkM4PC8PMAeAp3efxo+88COVr5O1ZQ5SqMJ5mrnLtWWSiszdIuTOHmOabAoe+8e4wce9y1SAmXJ3Ek+ws3279nHalpkhd2GIgKruyh0lulCl5Xv8bUccPxg9lW/LLNkyAziqf+bupSMXf+Pt399K7vzpGQQUuLUZIsVQWCe5q7pKlrrZlFK/pZT6gFLqA3fu3FnBYa0edcrd1M4rdzRTtmAgpeg2CkEVyF2FB/kTL30C3/W276p8fqVyl5C7TVHujL3p8OwQ28TFxLRrhSEiun5bpsMdhDbT9oaBMndm8dm6uCQE+JmfSf+YXQwC1Xk7IGmArFDuiuTuPDzPkbuYFJW7YTIOdWgqVJEqU6iyZFumVBnljtkIwipyJ3DL2Z3ZMkWUWpsZKyh3VW2ZQ9oyq5QNy0FMEutjh42jg+0D3I+OgJMTHE2PsBdQ4OAgR+6UUvjM65/BZ1//7KxUZTrFBZX1yh3jmBCpbZmOA8tyc8pdHalgJgdjIIRuynR2gN1djP/Tl3Hx9n2AkBblTpZsmcX3zEqUc6sPuaMs17ybG2Jep9yJZAbf2ZlW7jjTx+E4gBDwuJcq8aIiqwlocidECJUZAcAtB1FVWyaQ5u5ERT5pz93DkX8MMfYGtWWaWYJdhpgD+e+72fjIF6pU2HZBIEQEogDSobnw3sE9fOb1z/TaTHG5C5+T2sxdXaHKGxdv5EeDMKa/L2+80Um5M79fHIWavHZR7hwPkZKl849xCzHVJRzZ4z7yj/SGzHd+J/C+97W/PrU6zS5swq5TVu7qMnd3x3fxKx/6lcrXMRsAQ7Vl5oeYxzlb5rDkboSp0srd4fkhDqyb8yl3VqLcIcTOTv162nOSBrIAACAASURBVGIWBM/YMoMAAVOdmsxzyl1Lidq7jzh+YfLefFsmysqdrfT72pnccY7vfjjCTz37o41WWM/b1p0BjFSq/JcZ65xz9xqAt2X+/CSA19d0LGvFrZGWx3OFKsyGbxHQzE5hVQth3LHenUGPQqiac9cFVYUqzPFA1GYqd885B5iYBrkwREji1St3tl1S7kKLImDddsAqX6/Clqn/qt+Fvqjc1ZGFbEDfIC1UeetbwM2b8LiXI3f6hpeHzu0sPleoDo2FKsaWueS2TEYKyh1z4EflIeaBErjp7OLB6VegHK2cmA2S7Iy2SnJnClUWtGUyykBQrSoQ2waLgbCjcnd3fBcPw2PIMxfH/jF2JzHw+OO4NbLTQpVXT1/FW9O38Mr9V2bkzvfTfGL1MXI9wzFSgOOA264mImhX7mRBuTtFoBdZNwTGn/tzXHy7JjKTSFsalVLlzJ0q2zKz/wV0TXzEANsetb5P6e9FCuSOzTJ3VZ9HOnTdlJxMp4h48tkRohszmZOxZcrK+WYMFJEIQeMZqbEsVyt3xUIVIP33tHKXv74Ym5zY8loXcH1gbJmDFKooCadKuVMUkQj0fbBD2+BTN57Ss/d6fN80uUOibJZ/F1OoEsowp9wdnh3OLJkG47Em313JnSKQUQjZtS3T9hBB6rVEYRQCkCd3OeXuYx/T/2uBuSYvlLnz9sqFKmfVyl0TzCiE4YaYJ24qKRGzrC2TJfbHzJw7359LbQMAz5kpd/fP7+OA7wKe3/KsMizuIPInmGxx7LgthSo8r9z5TtzNlmmUO96u3IFzTXobbJlZ5c6zPFxEF+0Wacb0hnQYNit3lgd/24Pg/kJOmE3EOpW7/w/A84SQZwghNoC/DeB313g8a4PLXYytcVm5s0g5c1fY1ejXllk/564NVbZM4nlwBDbGq2yyK/fP7+PZrScxkTNyFxG1+sydZZUzdxZFSOe0iA5M7rL16VVlKkByM6wYYu5yVy84MsqdudhSbpWVuxplcCg0FaoIJbVy1yMzMw8oofnMHXcQROUbcKAERtyDwxxMRpZeAHKTuZvtXAqUFZihRiGY16p8DcsCj/WOaZdCFU45brp7+FZ0jKPzB9i7kMCdO9h1d3EWnCGSEV4+fBl/5W1/BZ9743OQNteLBt/HBcJGcjchQg+j5VwPVzdlM0E9AWDcKo1COEWgF1k3bmD8hb/E5JbeTW+yZerh8nlbJlBQ7hJi01e5yy7gc5m7uiHm4YXONyXkTnA6+z6NRrkhx/WZO73JwjKkhtdl7oCZLbOiddLY5MR4NHihSlxz/Fk02jKNcqdkjpgYMBBEMuq8yUkIwb2De70Wfw5zEHBA1jRW1tkyz8Kz8jzVUVKe0TJywICCQEYBYkq6F6og1hu/BeXOHGv2uI+mR7Xf2So05Xu7ok/mrgmMzApVhnDyMGZBEkCJCAqzdZK2ZYrhlDtnC1MiAaVweHaIfbozp3LnIgomOB1b+fl7BVSRu4DG3WyZ1JC7lswdoK9Dvp+zZZYKVUS+UOU0OIXN7GaLNOfa+t9G7piL6Y0RJOtARC8Z1kbulFICwP8A4P8G8J8A/I5S6gvrOp514/bodkm5m1rIZ+6YVRqFoG2ZXTN3UWPmrglVtkw4Dmy5ocrdjWcwSdql1qrcFckdJ/oiOY8t8+AAePbZ3I/M4t9xut9sgbJyV0e+zOceh0H6s5wt8+bNki2TcavclhlHlQutodBoy0wyd3pneoWZO+4iqFLuIOBwB9vONs49rsld8h1imVytVHGpntksChfN3JnXqlzg2Da4Ijr71PEWcbDzOO6TCxy//hXsKhdgDJRQ3PRuasXu8BV86OkP4c7oDr50m+mFznSKCxU22zKphJPc2LntpqRNRPW5LFZ0OAiBU+Unyt0NjKmDi5F+bqMts1DIUdUwaqWbK92VO0ootqyCLVMEWh2qsmVmlbujIz3njmdU19EIHrVnyh3K8xEBgIIiFAF4jAy5sxEh1s2YO4XFnlHuYlG6NqTK3dgbdFFk3uOhlLuqyAAF0SQ38z604aX9l/ordxapVe6qClXMBtV2cf7YeNxZtQMSW6aZ49dlFII7QghZ2hQwMwCzbdg2s3ESnNR+Z6uQKneLZO7cfOZOKaUVrApbZhM45cNm7ghFTAlkGIBmMpmcMIi4MAphoUKVEaYOBcJQ2zKxPSe5cxCFPk5HdKYoVqCyUIWqHsoduil3htyZUQi8olBF5jN3J/5JO8nsqtxxF/6W2+1YLxnWOudOKfV/KaXeqZR6Tin1v67zWNaN26PbOeXO4Q4mFnI3lKpRCNqW2VG5i5szd02oUu7gurClXnRtAlJyd3aIZ28/j0niUZeBj5iopao2laiyZTLMX+7y0kvAJz+Z+5FRFmyn34W+OOeuTrkDknMnQ+4CEcAhifqytZU2WM0ydxbiwikZxlH7uIYF0NSWKZWEkiZzt2TlzmTuDLkTQelxmty5eq6Vx3LKXS5zV7FIN3OmFrVlmtdqV+66XSf2t/ZxeNvF0atfxB6bnfMmd/fy/Zfx0sFLuHdwDy/vx7oBkDFcyGmtCkApw5TG6Y09R+7CoHa+Jmc2BMkrdydqOiN373yvzrChWbkr2mJTW2ahPRDQLXRdUWnLlA2FKka5y9gyBaOz4xiN4MHKK3cVtkxKCCJRVO5cCBlq8lC8J+TIXf7387iHWMU437KHJXfJ+9k6xLwi/5hT7hrIHYN+H/rcB3srd9xBwABZR+4yyp3Z1DWEqVK561imAiQbuX1smc4IEYmTUQizY2UV57Z5v3spdx0JexOKyt2RfwSXu7kN8S4YfBRCki+LAz+3yc5JxSiERdsyXb0pdnh+iAM1no/c2Z4+Lpe0K3eM5EchENlLuZOctn9nqmyZKNoyQziYZe6Mctf6ulK2jp/wLO+a3F1jubgzvpPu4AGJcsdUTmnQM1UGasucl9yxPLlzRX9L4LLgcGem3B28GxOirYRROIWtWOems8FQsGU6zEEwsvUQ84GIjlEW7AWVuyabH1NALML0z77w4UqibVyEpDfY7BDzYltmFEe9FsB9se1sgxJaWhQSQkBBZzMel6ncZTN39hiO7dWQO32T3LK3cOYmu/tWhtxhRu6Ki1MzCmEltkwZdlfutg9weNvRyp09WzSYQeYvH76Me/v38NL+S3jljtTNj56H0+C0QbmzEDKVhun1iAaj3NWTO8YrMndxQu5u3sT4/d+Bi/ACkYyglC5aMhko4doZ5S4/J61qkWp+1mehaLG8JSqdc1dXqFKVuWPIK3fgqXInEFeWA7BEucsqVpbtQhCULZmAJnzHx4hUWdUnhGDX3cXD7Q7Wqx7oq9xl33fz3TCjECLIVBHPgoEg7JG5A3RjZp/f0+UufKYgaDVRrSpUsZkNm9nV5K6Xckch+xSquJrcSRVX2zILyh2A+ZS7RTN3GeXu/vn93nk7YDYKIZLDFKpQQjWRCYPcRpgFhkCGmIrp7L1ahNxxD1NHk7v75/dxILz52jItFxEDTm20kruIk7xyB7k85S5D7oqlgYEMYCfkzuNeN3LXR7kb2bXzKC8zrtZvc4nxGx/5DTy582T6Z0PudnLKXXkUQteSCAadB5KxBIvVIIUqcBz8n/8SeNvHHu/1WsuCy108nDwEAcFjd57BhEogjhGFPqx17GNUKXe72wjCi/lsmRVI2zLdnuSO5efcNWXidEA/b8t0IpUuCE2DVqrccauw9wa9+79EcudyF5/9+5+tJPCcMkgZraYtM8nc7W/tw7VHCGQduXP10GKXIgpntkxKM+ROqUpb5hCjEMxr1ZE7FisEIuj8fh1sHeBwz0L44FXsjWZE4dboFr7wrS9gEk3w9O7TuHdwD//brVDPxHMdfPpLn8Yn7n2i8jXNuT1T7rxUuYsiv16543Y1uRvtAL/2DzH2X8PF7/5BurA258zIGmHqWdhOM3f5URRVVsAqNa8NP/eBn8udp52VO5O5m0wgGJkdx3gMT6FduauwI3LbhaCoJne7u8A3v1mp3AHaKvdwPOyiKFXuFp1z5/sQPK4ZhaBr6vtscr5w6wX8/n/z+x1/C2PLBGTNotFsJkyjac6xs21vVxeq9AAFQSz0qIdOoxCcESKo5JqSUe4MucsQZPN+r1u5Ozw77J23A2brGF/4wyh3hCW2zLzLgROGI6HzkynBXlS5czQROjw7xP7ImVO5cxFR4NSS7cqdY81ma/ZW7gDBO1wbGMspdzTjXDEIZQgn05Z5Epw0Hrv+BTpm7riL6ciCmHQbG3KZcK3cbQjeeeudFcpdXMjcVbRlqh6FKnHznLsm1Nkyv/0N3a63CXCZi68dfw37W/sYuduY2gQ4O0MYTtOdn5WiKnMnw8EsIUBGuetL7oqZuwZbpg7oZ+bcyQBuGJfInVmQMW7rEhYDKRExLDVzB6B2RqMO0ScbG0tsxMpl7oxyJ8PS4/RNMrFlOolylxmFYHK1TbbMIZQ7i1rVhN6ywCUQxAF4x+/N/tY+7t+gODq+j93tWcX2be82/t1X/h3u7d8DIQQvHbyEV/Z8qJMT/OFzOvfx0sFLla9pfnc7KS3hjh6uDmhbZu1mBLPSx+kHZ5S7u3cx3r2Li/Aip5oACblz2Uy5Q57cVdsy9Tnd5/t8sH2QW5i2KXfGQh3v3pgpd1TllTvFOmTudJEIU0gXU9xyEDWRu+NjRErm1Jv0r91dPHzfc+B3+y+y69A7c9dUqIK4suyLgZaKZdpgSlW6wmEOgu+4B/Hs07WFKhfRBSihufN4y97K5TEBzGnLDBB3tGXa3hYiqkpFNlXkLlXuepC7wTJ3mbbMujEIXcAIW3jungElVNsyo7Bky3xTnuVJyMLKHUU0PceRf4Q7AZ+L3HFbK3cnTLSTu/EIeO01/YMw7KncEYguJSWclwtViqMQZAgb5UKV5oPoodx5Vu0mzGXGNbnbUNjMxpTm2/1YpXLXw5Y5ROauUKiif7gZ80Ec7uCrx1/FwfaBztDYVC9OIh/WOnZlqtoyZagDwkPZMpPPw/G2Wx6ZRzFz12jLRFm5cwMJ3NIjPCqVOwLAlP+EISKLrb6tNAFPchbFqu+hkWbuTKGKPUIQ15A7S5O7M1vXpZu2xezNTSBOh03Pfhc+yCgEoEG54xw8Bvy+yt1Y4XjyJnZ3Z4v926Pb+MOv/iHu7euF8f7WPuyY4htHX8Un3zXFJ+59otYubTI/Jm/BuaMthFJCiKByhpl+nJ23BQuBUzlJFzNjS9scp2JaIncTh2ZGIcSVtsycWlTRKNgXDtN28rrPlBIKz/IwuTHSO+nTqT5nspm7mM6UO5SLeMzrpIUqyfXfsr1m5e74GJGqVt33vD08vDMGv7E39+9eRNfMXbdClbjWlhnJfpm7vkjn3NnV3zGLWjgNTnPnH5CQu4FsmZ0zd94YIVVlWyYrk7s0c9fDlrmMtszDs/nJHaccUzEdLHOXKneZ99oiHG+J8zyBWmQUguVhahO8cfI67ozugPnBfMqd4yFgwAWi8nmWfRy1IMYu8Oqr+gdBkBaBtYFRBkl62DKzmbuipR6mUEX/vctdiFh0z9y1kDuPe/AddiVtmdfkbkNhMxsTKguFKmVyF8t+c+5iFYNmbu5dUancbRi5c7lW7g62EnLHFXB8jDDyYa/DgVxlyxxYuUttmV6/GXJd2zKBZLFQyNw5vkgXhMZWlM/cAYiTHbgoQujw1beVJuCEa1smljsKgREGGUtMoolW7twxAhXNSC4AKAWfxHAsV7dlJuQubcvMzGiTUJVDzJc+CoEQcAUE0bRzocrB9gEOPYkjNcXerZm9/PboNk6Ck5w699L5Fv6fo8/i049f4Cdf/Mna1zQExSh3FrN0DiQMIURYWZZhnlcsVDkVk7QdbmyPa5W7LLmTSpZGIVBCcwtmK9nJXuTcbhtiDiSEdMdNbZlRtqhjNIInSEa5K583gMma+dqWaVr9mmyZZhSCkpWNyLvuLh5MHqy3LbNQblNS7iqOm4IijJLM3ZJg8t918/HMyIciudt2amyZPZS71JbZtS3T0lY9GYvceWM2lixrRkgWUu4WyNxtO9uYRBP9+WL+zB2g35NJNBkwcwfIKD8TlFOOt+R5vpFyUeXOIjg8e13/3tPp3IUqRx4wInbjucEphxhlyF0YIlCiX+auC2EqKXfJ/S9zzwzjKN3gM/n+1uPgXCt3LYUqLnfh2/Sa3F1jdXCYA5/K3A5sJbmLZW7QeR0YKKSUegEx0BBzUKoJzIaQO4c5+OrRV0vkbm3K3S/9EvAjP5L+MVXuRDBY5s4oB8uacwfM2tcMAhHAnYb1mTtCtXJnyF0YIrL5IHOF5oEZZL5sWyYlVNsys22ZdrIzaRDHCCyibZnWFs4tXbpgGmcpZeksy6pClbQtc4jMHa0ZhQCAK4IgnHaeYXawdYD7dohjF9h97Kn057dGWt3NWtruTXbwv1z8Hj76cA83vQpSkSAd80FmC37BiF64N5E7ZiE3KUkInMqLdBfdZjYUFE78kzK5s5ApVIlT9QKofr8sa/GFq8ncNQ3mHdtjXGw5s7bM7NzO0QieJKlyJxBX5hEpobO2zAScWYgYOih35evVnruHh5OHS8nczVuoklPuEFeSUt2WuVxy5zI3HW9Rp9wB6Kbc9R2FQChkQu46KXfMRsSAWETVmbvMvSXN3PVR7gbI3FFCsePs4MQ/AaBtmfNk7sxxDJ25i8Mgtw7jlOHN+Hw4W6blYWoB98+S2X6+P7dy96YH3GDNo1s45RCuM7NlBoEmd52VO9WNMBVHIVAOyYhW3RIEMkg3+Mxao5Mts2vmziIQ9JrcXWNFMCcve3y2C055uSZWqh5z7lLlTg0z5w7QF6sNIXcud3HkH2F/ax8WtSCJQnT8JsIogL0OYvGe9wC3b6d/dLiDSTRBrOLBLiRp5q6nzbOvchdHBeVuEtbaMhlhmtyZC3QYIrLXacvkaQnJUtsyKZvZMu2xXrS7PCULAAAhEFgUbpK5O7PivHLHC8pd1Zy7ZdsykZA74Xe2Ze5v7eOQTXHkAnuPz2YxmhEvL9x6If3ZS9M9fBOn+MSDJxpfMy1UoQVyF4YQIkobRkvHXmHLPInyC62xNcaDyYNclXpK7owtEzJn/bRYOaNoSM+iyt00moJk5mQVMbby5C4imWvIeAxPIJ+5qyJ3oIhEAKZm/4bFLK3c7eWtlTKWnTJ3Dy6Wo9wNUqhCamyZhEIUSO7QMNfXuk0Yc76UlDt7uzzn7h/8A+AT1aVDVTBOi862zITgSylAWYUt0x5IuVvw+p9tzFw0c9fJ2tfltZLyEBkV2jIJx1vxxbCZO5vi8PhV/XtPp/O1ZTojvDkCdnjz56fJnV1Q7qLuyh3pqNwVbZmJzRVCpA/Ryl1PcmeUuy6ZOw4I1k3lvky4JncbipTcZW0SFfPD4jju1ZY56Jw7QF9gNojcAdoiRgjBSHFMjx4gEsF6lLsCjHJnM3uwsQypda3njcrjPTN3GVtmIAO4F0G5UCV5j41VpUTu1qTcmfrrIQhRE9JRCKly5yBwWJnc2RQOS4aYcwnBkJKInC2T1GTuBipUqbVlQhcwBSLorNyN7TEswvD1XWD3YEbunr/5PH74XT+cu3H+l8EdfE9wgO+bNi/OZrbMDLmj0OROhpULd/08O1+oEkU4LZI7e4wHFw/Kyh2LM4Uq+bbFSuUuIT2LLFxd7mISTRo/z5E10oPXTVsmUfnMXZRpy6yxZebm3CVICXNGuVNK4cXffBGfF9/U5K5GAVuncldVblMuVFE1mTuaKHfLG41jrLZ131NGGQhIidy97+778NzN5/IPfuopYL+7SjWzZapuhSrM1rZMWVDurLItc12ZO2CWu5OxxF88/As8vfv0XK9TpfrOC+1SSUYhkKwtU88kHlS58zgOv/XlGbmbS7kb4cIGdoobCAXotkyulTultHIXR/3aMlmHz7ww504X1NAcudOFKrO2TGA45c6zPPjbHuTjB9fK3TVWA3PyZi/OlPG83Q3GltkzczdUoQoAvPvdvfIAy4S58JgdvREsTI4fIBRrUu4KYETf0IcqUwFmpKDvjcrlLnzZpy2zMOfubFqfuaMVyp21PnKXtmUOYGVsQjoKIavc2VXKHUnn3J2zOGcJyc4IFFXKHR1wFEJdWyYADgq/h3IHAAf0Bi5sYG9rpla/cPsF/Kv/+l/lHvc4tvH/fv1DoG7z4iRV7pJzO0fuRFTZhAjomYEyu24XokzuEuWuRO64yih3eVssp7ys3FkdFxsNcJiDi+ii8fP0LA++Z6WFKhEyyt1oBC9UrcodA0UoQ3CVtY/xUlvmH3/9j/HnD/4cr+NMZ+5INbnbdXdxEV0sR7mbp1CF6I0PuK7OJTJUt2US/T4sc/GTKncNG0oWs0pDuH/lQ7+CDz/34YX+bW3LDHXmrssoBGohZEAsRe68STN3zuwY15W5A2aNmb//5d/HEztPlElwR5jv2RD3I9MMGYsotw7jiY1w2LZMhsPj1xbL3CWf5Y7bbPPllCMiSh/vm29q5S4O+yl3XayOxTl3RA+Fz5G7OIJjGpOTDclBlTsaQ+zfvSZ311gNUuUuc8MvKSLQof9utkzd5CelyAXqu6JWufujP0rteeuG2dUxXvwRcTA5eaiVuw0gd4SQdFDtUDDKQl/CWMrctRWqyNkoBF/4cM4m9W2ZFcpdaNH1FaokO/pxHC+V3KWjENqUO0srd1v2Fs5ohIhn3rtMrlYSVVqkG1vmIKMQ2myZMuildO5be2Bxh4WfbQMnJ62LE/O72xXkLmpS7pgFQQttmR2UO8/ycsqdVHlyZ7EK5S6xrC2UuUvs2k3vtansTjN3iPOZu0B2UO6SIeaZt8aiVqlQ5VN/+ikwwnAsLgDHQcRJ9Zw7T1s5h1TDLWqBEtrqbGjM3DkOcHZWawvTyl243EIVNitUqfuOWdQqKXdDQF+vRWdbprkmRnGUb8s0yp0zO8Z1Ze6AmXL3yVc+WTsbswsYYaCEDnIvoIQirihUsWgFuVu0LdMC7k++pdc385K75Puy4zVvyKffpbe9DXjtNcRhAKFkp3v4LHPX4TOvsGUWyV2YIXeAvha2rneMctelUKXle3pZcU3uNhQpucuOQkjCu1lyF0vZaXfdXPBjJReac7euBXoXmF0l06I1Yg4mZ28hFOFGKHeAfv+GKlMBygvgrihm7hptmYTmC1VkAPd00lioAgCqqNytK3PHLL2xoVY4CiFV7qi+qRtklLttexvnVBSUu1muVlbYyowts6l8oyss2kDuQOHLsLMtEwAOtvaxFzvtlmPbBk5PWxc61IxCqFLu4iiXBcqC8QrlLjyrVu54RrnjI0yIyCl3pUKVItnefwIc7WSkCQ5LyF2Tcsc9+BbRx3Z2hkjJvHIXxIVClfL1gBIz3y2v3GXJ3Yl/gn/7n/8tPv7Cx3W+aXcXEaeV5HXX3U1fYyhYzOp0XmfzvdmfpZm78/NackcJQRRHufdhaLjcbZxdCOjfdRnkjhK9Gde1UAUArJjAhyjFQIBZ2RMwc5/0Ve4YYQtHEfbcPXzxzS/iD77yB/jx9/743K/TSfnpCFP7L6P8RpgpNBpUuaMxDuMT7Uyat1DFHNf27cbH5cjdq69q91PHOEkv5a5gyzRKaK5QJQ7TQhVAf7daPz9KtZ3U99sLVcT0mtxdY3UwOxNF5S4mBXLXWbkjOnMXy7k+9Fpb5gbB5S445bg90heuEfMwvThGJMONOW6b2cPaMp98e/q6fVCcc9fcllmh3B2f15I7AKAxZjm9KEJkVS8QV4G0LVNVD3ce7N+pyNz5Fi0rd5zMlDsSQTAym++VLVQh1bbMQASN5Rtd0ZS544ogjMNeu9sH77iH3btva3+gZWly16rcGVumkx7vLHMnapU7zm39uKROOxYRLqKLXMX82K6xZRbIXba0hdNy46u1d6uybKQPOit3cQjs7AD370NA5jN3vsA0mkIpBUW0AlxEOsQcBVumw4FndU7yt//st/H9z34/nr/5vJ4rduOG/u5WXD/33L30NYZC0zlZfJxFrdx3IKfcRVGDcse0cjfYUZfRRRGwmb0k5Y4gFgIxVOfvr6UIfMhqW2bm/CZEX6v6Zu6GOEd23V385md/Ex9/4eO40WIrbAKjbDByp5U7Y8vMDjEf2JZpeZgiwiH3cWCUu3kKVZJrxs5Ws9uqSO4CGaSbbG1glEEiaYHuotwBzcqdEmljMtCR3BGiX3s6bZ9zd63cXWOVqFLuKm2ZXTN3hEIqgVjKuewotbbMDYLLXTw2fiw91pE1wuTiGKEMNkZxdLgzrC3T0zfZRZW7RlsmoYijGbkLRAD36Cy1ZZrcSI7cKb2oBqCVO169QFwFOLU0uUMMuqShxYD+jvjCR6ziVKENLFJN7kzmDmHuJsjoLHOn2zILoxCYBV/4g9yIqmyGBoxQBHHUT7nbPkgX/I3oqNylhSqmtIRaiKhKlDsB7lSTw3TOopSAUjhnEmNrnLtOjq3qQpWpCnWmOYogiMpltirbMqm18Pe5i3KX2qh3d4GHD/PK3XgMbyowFVNt01cEhFeQGkIRFhQrTjkE4nQBZCxve57ON61cuaNWJ0JSmX/MzrlDfVsfIxSRjFZSqNKYuaNWTjkeCoyw3sqdHRP4tDjEvJxrBPS9pq9yN8S1f8/bw2unry1kyQQGVu6IUe7CgnKn37sh59xNxBRvjBX2z7GALdMqH1fN40QsgCefBF57Db7sPr5JvycdyZ25TmUKVYptmUEc5ZxXHvdg0w6fH+et5C7XarsBpXtD4prcbSjqMnclW6bq15Yp5Xx2lMug3Dncyc2+GTljTCanCXHZDHI3uC0zscn0vTAZO4JBo3KXLBYM/HAC1xfpgPa0LTO7MFAZ5S4MEVlkfW2ZjOu66lgsV7mjDGfBGcb2GIRoAldJ7izM2jIL5E5n7pKHEgVWUKcsamEqpoPkRVptmSrq7Ina+wAAIABJREFUl7nb2k8X/I0w5K5NuUuIrZNRhgXBjNzV2DI55bNNsDjGiVvYQYdW7h5OHpaVu2gKjEZpro0XbZlF5a6C8PVFZ+VO+Gl5lVAyn7mbRphGU00mQGY74hlQQrUdsaDcSSWhlMLn3/g8Ds8P8eHnPpzmm7C7W6/cecMrd11VnioVNVUbOAe4Vnmrjpsl70MXx8u86KLcLc2WCQIpIsiObZkAYIHC58g5Bcz5WDrnaU/lruE60we77i7ecfMd+J6nvmeh12FkuHKvNHMnwlw8JlXIBlTufOFjFDO4X//m/OSu6rgqUFLuOjZlAjP1Tbh2d+XOzLkztsyE3Cml9AgG9LRlmtfuSO6uonJ3tX6bK4TKzF3FDJB+bZmJLfOKKnff8fh34Fc/9Kvpn0fOFib+1xBbi++uD4XBC1Uoh8M75JwKKCp3k2iStl4WQZP2NQM/uICzdSPNbbrcBQHJN7uCIM4WqnCK7TV9BowwCM4gRbT0tsyz8Ax3RncAJMOpeQW5YzPl7kz5eeWOWzPljqBkyxxyAO8v/he/iHfeemfl33EwBKofGf7B534QT2w3z64DoG/kJyftyl2y+21bWXKnWskdI0zbMpPzb+LyUivhiI8qbZnH/rFeNE2npcxjpVo0oHLXtNBPR5ck5C6Ko3zm7iLEVBC9A63qyV0YR7m2TEL0xpBUEp985ZP46W//aTDKdDOhf6RtmbR6Y8bs/g+u3HXYULg9uo3f+hu/lftZuiAFAMeBYGG9chdHS51zZwpVZFZhLWBphSqEIu6buVMJucvaMk2rZOGc/82P/WavGXM2swdZN/z15/863nPnPQvb0Ye0ZZrMXakts6pQZQFyR4kuJDsIXODLX15YuetP7sJ0k60NjDDIkQfxxNML2zJNYWA2C9qpUMW85mTSOEbEOCKEc03urrEi1NkycxXzSGyZPdoy446PL+IyFKrseXv46PMfTf88cncwCc7B2BYsvhnHPXjmbk6LSXHO3dH0KN2JL0IPxc3YMqMp3K3ZY03WMfeconLH1qf6csohLIa4MMdpaJjhuGZX2+EOAoYKWybSzN15HEDlyJ2dIXflQhWLWQhlWEvE++B7n/7e2r/jhOJcheA9yPBjW4/hsa3H2h9o2/rm3arcJYUq9kwZFlRBBbq5jTstyp0QgFIIbVZSy8f2WO+El5S7iVbuJhOIwpzByrbMBmtrVzjcQSjDbsrd3h4UCgVIoxG8SYhpFOufN5C7KI5gF67/nHKch+f4F5//F/iTn/0TAMgod89BTGjtrLYdZ2ctyh0lFD/2nh/L/SxH7lwXgtaQOzBEsVi6cmfm3NVtKC0tc0cYhNTvQx/lLuDI2zJrlLvi+9762gNl7p7afQpP7T618OsMacukhGpbpsi7HGrJ3ZxtmYC+Zx/Ye4uRu77K3ZNPAl/7GgLWvZGbUb1Z1EkNK9gyi8pdIAJdppK5nl0rd91wbcvcUJjFSNsohFh1bMtMZnAtSu422ZZZxGh0A5NwkgwOH45QLYLBbZlz7kIWlbtj/7g2L8VooVAlmsLZaSZ3VOm5SQCSzN0abZmUQ1oMUtQvtIaA+Y6YPIrDHARMVSh3mLVlxgXljhWVu0LmzswBW+LvAQCcMIRYko3V3GxbM3eJLTNR3Sih+rwKpprc2TWZO5pR7oTQ5K6wMDGfUVbRG1kjTMRkZsskKleoUtmWOZByZ467DqmNencX0ub6vTDX/dEI3nmgM3cNtkwGk7nL3y8sZuHf/Pm/wYuPvYhn93Sxyp63N2vLzBT+FLHr7q4lc1eFonIX1VSxU6rfh2Vm7rJz7lZuyzS20x7KpAXWWbnri6Eyd0OBkQGVO8K0LTMWOXJnNpNLoxDmVO4Afa3a39rX5G7Rtsw+5O7VVxG4Vr/MXdyR3FUpdxlyF8pQl6nMQ+6MctdC7kIZIpLRNbm7xmpQVfXMCCu1ZUoVd7RlmsydmMuOQhJCuMm2zCK88Q1MxFRn7jZEuXPYwIUqc+5CFufcHflHtXkpRlheuRMB3J3ZXCyPexXKHcm3ZTYsEJcNRhkEp3q485IzdwAKyl0FuUuUO8/y4MchAqpmmTs+y9xJotJZUwbmccu+ETFCEZC4V6FKZ5i5Qx3JnZ0hcVxRiMBPlLsacpeUHGTJXfE7Yj6j0py7aKIXTZNJQu4KhSpLytyZ465DNnMntkb5z9/z4F0EOnOnpL6+Vyl3lCJSMpe5A/S59Fsv/1auqCKXuWvYmNlz99ai3FWBU47IbEK5bm2hAyNs6Zk7h8/m3DUWqizJlhnFohd5tQ2565C564uhMndDYRnKXUzymzN84MwdkCh3t54GvvKVxdsyO5C7KI70RtfeHgKXL0e5K5I7wnICRiAD2ITlrmee5Q2m3JnZw22FVpcR1+RuQ2FOunKhCiqUu/aTkhFjy+x30c8eD8H6FujzYORsYeIxhMFFmttZNzbFllmp3NXZMgmbqXAAfBnA3Z3NyXG5W7owFjN3EVuvcie4vgnTFSp3LncRkLhCuVNwuANKKEaWhwmPK22ZgqJE7tKRCUtu9uJg8IlcrnLXdRSCPVsAc1CI0EcECe5VL4xzhSpCILRpmdxZZXJXtmWuLnMHNCt36eiS3V1EYzf/XXLdJHOXKHe1tkyGSIlKcvfFN7+IH33Xj6Y/23OTtswbNxot1ctQ7hYhd3lbZh25oxCxxDK/Qdk5d03KXTELOgRYzefcBItocpcdoTGocrdBm8JDZ+5igtLAeNOWuWVvzR68KLmzPBw8/s6FbJkmY9tZuQOAt70Ngbck5a6lLTMQgVbuMi3XvZS7FnJnXu8sPNuoDYghcE3uNhg2s/MlFckuUY7cxXFuvkodTGg+juO5P3RK1jerbB6MrBEmWw6i6cXCs6iGwtCFKoyU80RdYMidSmaBNSl3Ziiuga8iOAVyV5m5k7NRCCFbX14ztWVSLD1zB2SUO+YgoGVy5zOVfmbbyU02tVtyKx3ALQlKbZnGjrfsGxEnLKcoDoqetsyccgcCEU4hVAzLqW7sY5TpVs2E3AUdlbuU3NUUqtS2ZS54TTTH1vReZzN3YuS2KHcNhSpKgBfuABa18BPv/Ykc0TDKnbpxo3FjZs8bXrmbd+OiVKjSpNyp5WfuUuWuboj5kpS7wWyZQyl3A2XuhgIjbLBNalP7L2l+w81iNrZUwWI8hHJ38E7g4kITl4rveBdYzOpH7p58EoHTX7lr2tiYPbjKlomcLdNGXrlzudttzWOUO6v5szbWzE06R4fANbnbYNjMrm7LzNkyJWiPUQjzZu4AXZgwpOq0bIysEaZjRxOLTVLuBszc7Tg7tYpbExhl4JQjlNo62Zy5Y5AyW00s4OzNyN0N9wZuejdzz6GK5Ofc1dSSrwKMMAimN0ZYxXDnwf4dY8u0MrbMKuWOqnR8hNnZNc9NlTuVLBgq7MSL5JK6glMGn8XLeb/MzbbrKAR3RuIsMEShDwEJ7lYvjE0ORgmhlTtrTuWOIkfudt3d0vDkXXe32/iHBqQujRZbpsncRWMv/11yXbhn01l2pMaWyQhDCFkanbPr7uJnX/rZ3M8sZsHlLs53nMbv7uNbj7cuFPtgUOWO1JO7UM3nYOkKhzlpoUrd77Pn7eH26Hbl3y0CRmhv8mpRXrZlDqTcDfEdGRJD2zLjClvmFvPwhCxsPi1I7m6PbuOZvWeBZ5+dS7UzuDu+W7pfF2FRK6/cuXy5yl12FALBTLmTAZxCocot71a386lD5g6ontN7FXC1fpsrhkpbZoVyx3jXIeZSB3/nvKl97Re/drlsmdYIE4/rxYm1GaR0aHL3/K3n8cc//cdzPdfsLjvcwbF/3Jy5S1Q4EQtQAPz23fTvb49u489+7s/yzwHJKXcRVeu1ZVo0uQEv75JXWagCWWvLBDS5y5ZjUMb1dzyOIQlyc9ayv8/SbZmEI2BLKm7pqNwZi5jjzqxNxpYpENeSO0IIaKzbWrkiCC1a2ZYJNCt3xTmDH3nHR/ADz/1A7nXu7d/D7/3E77X8wu2osjYX/z7N3HlOfiFiWSAyhsMdXEQXjcqdztzlvwN/+t/9aeVid9fdxfGL70T0n5+s/e7+xkd/o3MjYxdYbP6Ni9yC1HEgapRnSikizL/J2QUud/W5hPrGyt/5m7+zlAWlVibL2comWERbmXO2zIGUu3fdfhf+/X/77xd6jSFhNjaHei1ZYcu8a+3i828WWkUXJHe/++O/qz+L554DHjyY+3W+9Atfal3HlWyZXyqXUtVhocxdhXLnIE/u/tkP/LNu15wOmTtgNqf3qpG7a+Vug+Ewp3oUQnbOXddCFdOWKee3o1wmYgfMyF3I8taudWJoWyYw/+eSzd01jkKgDDLO7KTFFLiZ3/krHkOuLTMI1j4KQTLdNMvmtLJ0QcmWyR0EEJXK3cyWuZ27qWjlTj9O20jLx7sKmxOjDAGbFQMMiq7KXZI3tDPKHQeFiAIIqNrMHYDZKI6eyt3UDDGvyNwRQkrvOyHD5JCL1/oi0tElL7yA6Ds/kF9wE6KtmdzDeXiuC1V4PakpKnd116M9bw9Hdoxod7v2d+SUD0vuFlXuVFa5q95QYqS6WGZIONzBRXjR+LtYzFp4ZlsVDInvVaiSsYUbDKXcDfUaQ2Fw5Q6qfK1mDJYsPNj3FxqFYDNbny/PPrvQ63T5LErkrmKcTB0WasusGoVQsGV2vub0yNyZf/sq4ZrcbTCqlLti5k6qrkPMk0IVFc81xPwyYmSNMHGoJhYbYst0uLMx1lZT0qCUalbuKE9tmb7w4VaQu9JzQNLnwPcR0vU1raZtmVSXSiwL1cpdFbmLc8pdjtxZmtzFUQhVM4R4JbZMwhHwNSt3Zs5dltwRBhEkYwrc6swdAHBFIFJyR/pl7swohIItc5lwuNNNuXv+eYj/+ZfLiybXhcccTe5i1Ct3JC6RuzqY3F0ko5V9dwfN3DXYMleh3F1EF2tZMBrlrq8tE1hO5m7TMPQohKq2THCeW6cBWFi5S/HccwvZMrsg9116+mkEIzslQW2ghEJBIZJR+/lfnHNHK2yZYLlClc7okbkDrp5yd7V+myuGUuaOsLItU8U92jLjhTJ3lw0ja4SJTRAyYG9TbJl0eOVuXpgczySawGL1rX+MMMRiRu4coYBbtxpfW7dlJjeH6RTRtlproYrgFJLRpS62KkchKAE1ncy+cUIgIPFMubMLyl3S9CgDXy/SK8ApX36hCtXkrsoWujA6tmWCELAYsLO2TMIgJucQDOAN4030KI4IUAQh75m5S0YhSAJwezWbQm3KXXZ0SRRH5d1314VHrUS5q5lzRxhCxKVClTqYxszKf29JGDJzF5G4mtxRTX68Je5tu9yFiAWcNdx3GNUbmv3InQXI5Sl3mwRO+WCEVbupVGLLzCt3WYcVgMtL7v7qX0UQfAKO/1qn5xJCQAlFIIPeyl3RnVZVqNL9l+iYueP6vbwehXCNlaFT5q6zLTNpMJL9KpIvM0bWCBMLiCwGa0MI1dCZu0Vg1IAm1Q7Q4wNSW6YI4EaqXblTBXK3xkKV1JbJ6EqHmFNCde160F25M99x6U9qyd0i6kZXmCrvpRaqdLAWsRhwRtuz4yIM4vwUgpUtklnwjC0zaFDuzI3d/H+j3KnJBQQrt5UuC52VO6Da7uR58KiDs+CstlCFUoaQSLCONsq1KXcLDDHPzbkj1Zk7RjgiEi91kzNtv13DgpESioiiX+YuOV76KCh3A49CkDW2zKUpd3/trwG//uuLv04DGGUQsdBt2oQgGPVzHDHCEIgO5M4od4kyp22ZajbnTgRw1JzkrkfmLpt7vyq4Wr/NFUNRuaOE5gY8Aj2GmJNMW+YSW8I2CSNrhAlXlZmbdWHoOXeLwCwYj/yj2qZMIN+W6Qsfbhh3Uu7SwefTKaKa/MsqwAiD4AQxI8tV7gqZOwBwqA0/uEj/HEchJGbvRcmWaQL6/lQv0iuwiqHA6dy9NY5CAHR2zvEKyt35CaIWcscUgYgCbcvkFeTOGpfm1tnMhlQSkWsjnk5AY4C0WHqGQmvmzsy5A6rJluvCo3arLTMm6GzL3HP3cORfTuUuduxaWzOjDBHipW5yEkIqR8SsAozw/sqdmZ+Zmat5VZW7IW2Z+bbMzGe9THLnusD3fu/ir9MAQ3akypCsHpvSjLLuyh3nOjeMsi1TF6osoNwBncjdVbNkAte2zI1GcTdXD8xUFUPM2y++WrmLEPf04l9meNzDhEpE1ubM5/vwcx/GtrPd/sAVwJQ0iFg0KneMccikqCCYnMIR0LmkBjAQxHFynhpyt0blTqxAuTPlCEa5AwCHWgjCKcwnHkQ+bEXTx27b27lzU+9cAjKc1rbaLqJudIVZqKzVlgngf/wThu1fmNXFc8IgLs4hWpTg1JYJTe6KC5M9bw+/9N2/lPsZIQQ3nBs4cQm2JxfgDubLesyBQZQ7ImeFKjXKHYCNVu7efefd+Fvv+VtzPTdL7oRrgytSWVhCCUWE5Sp3gCbsayF3lCGi/cidITs5WyZh+Ecf/EdXTtEYslCFkUS5K24kFMmdlEAcVxYdbSrM94lTrrNvy1DuDLnLPK+YubMVnV+5AzqRu6tWpgJck7uNRpVyFwNz2jKTQpVHLXNHqtvy1oWPPv/RdR9CCpPjaSV32UKVo2/BxWynrQ40OwphOkVI4vUOMWcEcsnKHaBvTjnljjkIwkn65yCa6p3IBFW2TKPcsRrlbiWjEOhm2DJ/5S+fBLZns+UsyhFNzmoHVBtwJIUqEAhZWbnjlOOffOiflJ63v7WP+zSEd34CfgOt5/lQ6JS5Ey2ZO4SNyp15/c7KnbeHrx59daXK3RM7T+Dnv+Pn53pujtw5Friq/j0ZTWyZS/5sjd1r1aBEZ+562TKT70fWlkkIwa99368NfnzrxpC2TH29TmyZrEG5CwJ9vVvR9WQIZEeL+MLHDedGyzNm6KzccZ4rPNHKncq1ZTpqzkKVwgy9Onjcu5LK3dXakrli6NaWGYN2OPG1chdDxsutgN4kjKwRphCI+DB15VcN2cxd0yB0RjlkosL5bz2A02EXv1SoQuL12jIZgWTL99VTQvPKHXMQhLPMXSB8fbNKUGXLjAkgAr9euVuFLTP5vixNuaO09aYLAPjyl3MqMadcF6q0kDumks0FIRDy+rr/Ig62D3DIfcizU/CazOMysLBy57rwFG+2Zc6j3AWrVe4WgcWsvHJXs7xhlCEkMeiSlz9rs2XOodxZvKzcXVUMWajCKNOjEIq2TM7zhSq+P4wlc4XIbpYEYnXKXalQZQXK3TW5u8ZKYTM7txgtnvhAn7ZMBolHULlTQWXm5hr5zN2u06TcsdRiGbz1rU6VyAx0NgphOkUEuV5bJk+Uu6XbGQvKHXcQRH7650AEOeWuNOeOMMQUkGEDuVuBLdOQOrYsctd1F7twU+eUQ0wv2pU7lYziEAIB607u9rf2ccgmEBdntZnHZaDNwpfOuUNN5s7z4IHjLDwDi1UNudP3Et5R9V1HW+YiKCp3VgO5E0QtfZOzjbAvC4yw/pk7Q+4uwee8KIbO3EkoxGS2eaL/kQrl7jKTO9k/c6dQXWiUf2Ce3KUCRnYUwryFKo945u6a3G0wKm2ZpcxdH1tm/Ehl7kbWCJM4QPT02y/FzvOqYUoa2pQ7yjLK3ZtvwLWb83ZAotxlM3dYn3LHKYekBJIu35ZZUu4sF4HIkrtm5Y4QAqKAKJjUkouVjEJYpnJnWXNXeafKXU0TogFDZs5dD3J3sHWA++QC4vy0llwvAw5vtmU63ElnUtYqdzHFeXiuFcfKQpXNz9wtghy5G7kNyh1HRNSVVe5ootx1td8CgJWoMnQNZHTVGHrOXd0Q8ytF7uZQ7sxrNP8jPK/cJe2juUKVa+VuLlyTuw1G5SgEYL62TMqSOXfLD5JvCsxu88VTB9fKXQVcpnM8R9Oj9kIV470/egDHqR8enT6nkLmL1PqUO0a1LTOmK1Duipm7ErkL4GSizkVyBwBUAaE/abRlLj07uArlbg5wakH4k1ZyxxWd2TIZOi9MDrYOcKhOtXK3Slsma1Z5OOWghELEoj5zF7PmzF1PW+aet/q2zEWQI3d/+8fAt3YqH8coR0TV0jN3bTnKZYFRBtG3UMVy0+dedQxZqJKdc9fYlnnZyd0cyp15jeYHVhWq5Ech2DVzO1vRUbnzLO9KnvfX5G6DMeKj3BfKhHdLQ8w7nJjpnLtYrHRHet0YWSOc+CeXYnGyaqSZu+C4ZRTCjNwFx2/CzVTT10Erd4kt0/cRqmi9Q8xXVKjicAfb9nbmzx4CJXI3K5fMbma77m6JeDAFhMGk1ja2UlvmMjI4b3878I//8VxP5YwjJFrSbNrU0spd1Fu529/ax6E4TmyZK1TuOhABU6pS25YpSdKWWWfLNOSu27lzGZW7KNYbSpHn1A65p5QhpFdXuWN0jlEIyTzHq9gaWITDHXjWMEPAjdNC0KtpyzRzI+dpyzSv0fyPVCh32UIVGcCJF5hzB7Rmu6+qcnf1fqMrhF/9vl/NWbxM2UJOuUNPW2Ysl75juUkYWSMc+8fXyl0F0sxdq3JnQSotY/inb8HZr94Rzz2nmLlTcv22TEZgLZkU/cef/Y+4NZrNAHQtF8HI1oH68Ri+9HPK3Qce/wD+9d/817nXYIogDOuVu5XYMpOF8VJsmY4D/L2/N9dTObPgc9Ra7gxS5bivLXP7AIfREeTkYqXkzuUuJmLS+BiTu6udcycpzsMT7EkMUqiy5+7hzcmbUFCXog4/p9xVEeAELLUtLr8tc11DzH3WvPlRRGrLvASf86L45Q/+ci8Vqg2MUEQsbm/LvITkLmfLnEO5az3/q5S7gi3zhiKLtWU+orbMq/cbXSHcHd/N/VnbMquUu/aP0bRlxurRsWUCiXIXnFyKnedVo2vmLmvLDM6O4W69rfW1dVtmJnMXi/XZMglDwHTmbtmLl2f2nsn92WEOAs8CplNgPNY3yYxyRwnF07tP549XAWEwrVfuVmDL5HyJyt0C4MzC1AKsmpr79HGgEIbcUdUvcxe+CeFfgKvV3R7bMnfAbDOmNnMnCM7CM9weqFBly95CKENY1KqcF7dp6EPuBF0+kXH4uubccYie5NV6hGyZt0e32x/UAxQEIevQljmnFX1dKNkyeyp3ZhB68wNZaRRCtjQwEAGceIHMHW1/7lUld1d/m+YKodKW2VW5owwS8SPVlglcK3dNMHPujvxm5Y4yjtgodxfHcLfriaABIzQlhJrcrc/axZPFzipsmUU43EHg2ZrcAQjiEA5pfh+oIgijae2cLoutYhSCnfvvpoAzC9NOyt0scxf0IXfbBzicPoAgy1d2smjL3AEzcleZgfM8eJFqLFQxi8+u3wFCCHbd3Utjae9O7jjiFZC7dQ1Hno1C6KHc2Qm5ewRsmUODger3m10tW2Z2tMg8yl2ne1TBlmnaR3OFKvECmbsW1Q64JnfX2ADoZiZUzLnrmrl7tObcAZrciTWqRpuM3Jy7psxdti1zcgbnxs3W16agui1TKcTTCaSSa7uA5toyV7wz7TAHgWvNyJ1sJ3dMAWHYotwtuxhmQ5U7i9mdbJnc2IJ7Knc3nBsI4wgnLlabueuo3E2jmsyd68ITSApVhsncATp3d1lcD9mhy43kLrHPLXuTs228xbJAKUPI0Ct+Ybs6/vEo2DKHBgXRQ+Oz35MrQO6Kyl2XEUgGjHQkd5W2zDiXubPnJXeMdSJ310PMr7F2pKMQ5plzZ5Q7tfzhrZsEj+vg9LVyV0bnzB1PMneTCQIIuOP6x6bPQTJnLAwROXyt1i7dlgndlrkO5W7kABcXABJy13IjYYogjPzaKnNO+QpsmZs51JjzhNy1LEJzoxCo6rzrTAjBwdYBXttBrXK6DHQpVDE26to5d6FW7phsIXc9Ngb2vL1LszFWVO7qjnumYK5AuVvHnLvUltlDudu9lTz3WrnrC6PcsSum3C00CqGrclckd5WFKnPaMjlvLVMB1qewLxuPzir/CsAsjmMRpT+LodIsRRPSzN0jaMsEcGl2n1cJj3s4j84xiSbYdrZrH6cLVSRw/z78nRGcDjt4lFDdljmdIhp7a10gcsohCNan3I1s4OwMgLFlNm80MBhyV9+WuXRbZtKet4m2TE3umj9HhsQWLARCGvfa3DnYPsBruxR8lbbMDgOvWzN3QYxQhg3KXT9bJnC5lLuutkyaKndXtS1zjiHmph33Ci5ylw1KCCI+myMJ4OqRu76jELoqd8W2zIpCFVtivkKVjsrdVbVlXr3f6IqDKYJYivS21NmWSRmkNG2Zjw6nN+TuWrkrw+Uu3jh/AzvOTnOtPE/I3eEhgu1RJ3uGXlxLTe5GLiwaDnnovcAph2QAoatfvOhClQK5a1ksU4VmckctKFUz4XwgpMpdh53PVSJV7tD8OeYKVUh3Wyagyd2rN/lKlbt333l36jKoQy5zV9WWGepcLJMqt2Ay6Dx7KoOrmrkDVlCosqY5dzNbZg/lLjmfHqW1wVBgoIgYzX/WV4DcbdlbOPaPAaxYuUOcGx207MzdM3vP4Lue/K7+r7/huCZ3lwy6hTBjy4TqMecuhlTykZtzB+DSLFBWCZe7ODw/bGzKBJI5dwTA66/D33I7kTtKiM7cTaeIxi4stlwy0gRGkna8FbRlFuFwB77HM+QuaiV3DAShCGoJDKd8+eTO2tBCFUPuWq55jGQzd/2Uu/3xPl7do625viHxkXd8pPUxrXPufH1f4G22zB6EY8/du1TKnZnLFcVRe+ZuyTbxtSl3LLFl9iF3Rrm7tmX2BiUUoVVwhVyBtsz33X0fPvfG5/DR5z/aW7mjhM5dqBJnlLupmMKTC7RldiB3z+49i1//yK/3f/0Nx/U2zSVDidypOG8HqAGjfJa5uwS11kPhWrmrh8tdHJ4dNubtAG03iRkFXn0V/sjutIOXzrmbThGNnLX2IAn1AAAgAElEQVQuENduy3StArlrsWUqgigK1lyoYpS7zfrecMtJbJnNCweesWUGpL8t89VttXHFU7k5d8XNKteF5+vd7lpbZkJq+pw7V1K5M+/DklW1dY5C6NuWab4f17bM/tDKXWHj8Aoody8dvIRX7r8CYA7lrk+hSnYUAmGIoaCS6NE0msITy1Xuriquyd0lAzXDeRNI9LBlpqMQHp2P/TpzVw/P8nASnDQ2ZQLJucMp8I1vIPCsjspd0pbp+whHzlrJtW7LBOQ6bJncQeCwGblTEZyW94KBIJBB7c67xVYw587MveKbdXPk3MbU6qDcgUIY5Q6yH7nbOsBrW3Klyl0XtGbuEuWOxajMqMxVqHLJlLtebZlXtFCFmsxdj03ca1vm/NCZO3LlbJn39u/h5cOXAcyRuZvTlkkIAQFJeyW0crdAW+aGxQpWietv8iUDm9eWSXVQ9Vq5u4aBIWltyh0jDNIodw7rlrkjFLFKbJmuvdbdfzO0OF6XcmcXyF2LckdBEIqwtu3OoissVLE2a0HCLRtT3r5ZwwmbFaqQuNeu88H2AQ5dsdHkrrItc6IXREwBqLjGz6NYXUXlzhTLLLtYbJ22zKjnHD/zGV+Tu/5ghCHkhXvLFSB3L9x+Aa+fvY7T4HR5yt3Wlv5f9rkgkFnlTpL5ClWulbtrXCaUbZmquy0zmXP3SLZlXpIFyirRmdwZ5e7VVxHYrNMOHiWZQhXP3gBbplqfcmfTDLkTre8fUwShDJpHISyZpBpSt2mjECzmwLfaS0EYyRaq9Mzcbe1D0vZZequGmXNXmSdzXXjThNzVHLe5T7AehGPPu4rKXUJk5lkw9sC6ClUY5XruWp9RCNQCJXRt42ouM/Scu4Its5i5u4TkjlOO9919H145fEXPm+txDe2s3P3gDwKf+lT+uaCpO20qFrBldszcXVVs1t3rGq2gmV0NILFldjjxs3Pu+lz0LztG1giMsOsdyQoYctdqy8wqdxb62TI3QLnjSSGMJKsvDHCYg8CieeWuxerIQBDJaL22THtDlTvK4dsUvOV8YoTpzYU5bZlA+7iFVcNk7ipnuHke3ES5qxu+bu4TbZbWLC6Tcme+2yIWLW2ZCcltaVxdFGu1Zc6h3F3fI+cDI9oGm7smb2+n13wAl5LcATp39yff/JOU/HdFZ+WOUmA0KjyX5pW768zdXLhuy7xkYKDzt2UiRqzkI2fLvCyLk1XDVK93Vu7u34dPH+teqGLm3LnrVe50W6YC67ngGQIOdxBw5JQ7lzWTYwaKUIa1ytHfefHvpArFsmCKVDYuc0c5fIu0krtsoUpfcnd3fBdU9VM+VoF0FIKsUe4uAgCoLYKZFap0v+1/8O0fxD/9r/7pfAe8Brx///34o6/9UbV1NYFRo5et3P3Q8z+EFx97can/RhUYs5LMXb9ClesylfnAiC5Uya3DbtzQ13wpNTEJgkvXlgno3N2nv/TpXpZMQK8Z5j2fKJKNYRjlDteZuzmwWXeva7SiaMuU6NOWqRDH8SNVqOJx7zpvV4NUuWsZhUAJRUwpQAgCKnsodzEwnSJ0rbUXqqzNlskcBAwzcgfReqOkIAjjqPZYn9l7Bs/fen7oQ82B25r480tK7hhhmgALgQCit6XornTBN0zJaMzcuS68c0Puqo87zdyx7uRubI/x3W//7vkOeA34u/f+Lj75yie72TKXfB+8M76Dewf3lvpvVMFk7nqNQlhBA+9VBSW0TKYp1QTvWM+Jg+9fWuXuP7z2H3qVqQA9lLvK5xaUuwjXyt0c2Ky71zVaQQmFFLOB0LFSvdoypXr0MneXJTOyavQrVCHAnTvwRdCxUIVBKqPcWRthy4zJGgpVuIOAqZTc+eiQuUvJ3fouz8aWuWm7+Zxy+LyDckcYpBRQIuqt3AHAfjzeOFummXNXmbnzPFgTH4yw+swd7a/cXTb85Pt+Ep/+y0/jjfM32tsyl6zcrQuM8d7K3bUtc35oWyYpXyv39oCjI/3/L6kt871334uHk4dzKXcLkTsZIVYxQhnClbjO3M2B62/zJQMDTWtiAWPLbP8Spcqdite6aFw1RtboWrmrgblgdxqFwChwcNC5EpnSmXIXOXy9tkzKtHJHVk9WXO4ioCqj3Ek4VgdbphLrJXeOzkFs2m6+ztyR1iwgI1Rnr4S2t/ZduB5gq3aI/LrgWQ2ZO9cFfB8ed+ttmXOMQrhs2PP28EPP/xD++ef+eX1b5orm3K0LlM7RlkmXn+O9qqCEImQV36u9PeCtt/T/v6TkzuEO3nPnPZ02dLNYTLnTG3O+8OFwB0TG122Zc+DRWeVfEVBCEIuCLbNzoYoZhfDofOzXmbt6cMrBKe+u3O3vwxd+N+Uum7lz+NqVO4HElrmOQhUSz8gdkXBa3j/WYstcBbg31v/dMJWHU47pk/vg4+3Gx2nlWCKUIeyWgedVOKA3NtaWWdeWCd+Hx9zarKYhNfyKOxk+ce8T+Py3Pt9uy9ywz3coGOWuz/XDYte2zHnBKK0m0zdvXnrlDgDuHdzrb8scQLmbRlPdC2Byi71f6Fq5u8YlAgVFnLVl9lLu4kcuc3et3DXD415r5i6r3JndtDbklDt7vcqdtmWuR7lzuIOAyLxy10LuaKLcrdMWaN6nTdvN55RjKv3WzQJOdeYulCHsOXrD9vmNjfvdGzN3yTBgjzkNmTsr+e9mEfah8aFnPoSnd5+uJ3f8ESB3PZU7m9lX9v1YNmhVWyZwJWyZAPDS/kv9bZkDKHdTMYVnLUDuOH+kC1Wu9lX+CoISClloy+yUuWMzW+ajlLm7O76LJ3eeXPdhbCyeu/kc7o7vNj5mlrk7QNArcyeBwEe4xdZKsBlhECSGpGr1bZnMQQCRV+5abZkksWWuUbmjPPffTYHFLPjC7zDnjkFKiWBO5e5d9hMg6qz9gSuEydwBNZ+L68Ijdq2dd562zMsISih+7gP/f3v3HyvLWd93/PN9ntmZ3fvD2MaObbi+xPwwiEBkhwsKJW2TxhBaRSWBUBEiICqK04hI0EhRQpGa0KpS0rT9q1ITR01FFZIINUGkcUoAiQpV/RFMYggEktKUNCQOOArY91yfsz9mnv4xu3vOvT5nd2Z2z5l5Zt8vyfK55+ze+5yZnd3nO9/v831+RE9Nnzr252e1z11bXIM1dzdlN+nyMy6f4qj6y1u59UQfyzIl6VWXX6UP/58P13rOJpk7Z2XTwMmmmbtXvlK6vLuv6X6/y/eQN6diepi5y61GcGdBech3as3dc25+jj7+to+3PYzO+v0f/v21jym7ZZrCHXdo8vVJxU3MvYow3+du4LtRltnGPndJpvG8sYyKQmMr1gd35jTRVOfaXHO3bL7RrexV4pJKwV1ytCyzwSTjLc96rfS1u5sO81Qs9rkbuBMaFA2HGtlgRUOVeTa255k7Sfrxv/HjJ27IvcjcdS0zuy1NGqrcPLxZn3rwU6c4qv5y5nTgj7lxeLQs8+Agyq0QpLJj5sNvfrjWczbP3E0PM3dF0Sy4e9nLyv92VP/f5XvG2Q373IWaZZk7tuYOm/POK3emyZ23a3B1cOKk6brnmCs3kd7f13TgOlGWWbRRlukzHcwOyo1a9/bmmbvRyud4OU2U62KLgdUyuOvYBDhxSbkH4LrMnUvKssxiqswavPbe+lYphIajPB2LssxhMjz+9x+NNNJg5zN3kla+Rx2uuevWa3tbluW3Pf39uuZw64ljMndf+Ur5dcSZuyY2W3PnlRez69fc9TTLfpo4YpFxR/YAkaSiaubuSLdMgjvU4c0rf949OnjF/ZW7ZjnnVITiMHPXdrfMxCnP0nYyd/lYunhRunp1nrlbHdw5M02UU5Z5jKrj8u5o5q7Ba8+scxOKlWvupDJzF/yJDVUWGat120j0nTujTczbspgP9PX36xp3/oKmd91xfFlmD9bcNbFR5s555bPp5mvudlwrV7+ZvdHMPmdmhZldaWMMsfI3ZO5yhWrdMudlmbu25g6b884r/8bLOrj91spds7wlypfBnWu/LPPCOeXPubudTcxn8+DuySc1doWytFrmrs3AajFR6dqNoGVwt2YdXWJesyLXpJg2C+46aJgMtT/d1zQ/plumVGbuCr8+c5f043g0dbjmrp8TxkVmtmvXbl+dWAZ76629WHPXxGbBXaJ8crB5t8wd19bV/1lJr5f0iZb+/WiVZZk37HNXYQ3FInO3a2vusLmyTCLXOK/WTEVadMssyzInibXeUCUvcuVFfvYNVW7M3LlQKbibWtF65s6br1SCe5ZqZ+56FNyt3OdOKjN3uTt5zd0OlWWu4nu+z11XO932lTOn6XFb1+xy5m6TskyXKJ+MydxtqJV3+RDC56XVdfE4njN/fVmmglylNXf+MHNHcIcanDnlIa+8x510pFvm/r6m3lpfczcrZuWNjTb2uZsdDe4KZem5lc/x5jS2otVmJolLOtdMRaoe3CUu0STkGoeiN8Hdyn3upHlwNzvxvO1SQ5VVfFLeaOpr2eLi853P+bPhzWuSTyjLPGKTzJ1zXsV0cpi5a9pQZcd1/l3ezB6U9KAkXd7htqYL3vnryzLrdMtcrrnr/GlHh3jnVYSi8h530vVr7g58qL1PzjYtg7vi7NexNcncOXOatBzcnRuc088+8LOt/fsnqZ65SzQLuSaFlLl+7HO5ds3daKQ355d1+Wv7xz5/2SVyx9fcHe5z188Jo2fN3ZnyzmuaT9eXZUbaLbOJjTJ3PlE+nZSZOxqqNHZqs3wz+5ikO4/50XtCCB+q+veEEB6S9JAkXblypVvty1rwtG6ZFTcxdy5RMCkPu7WJOTa3LMusuMdd+ZzFmruJ9myqZ6QXT3mUK8YyL9ErwtkHTIsPuNnF80quXtXYBw2z8yuf4+U08e1uQ+DM6V3f+q7W/v2TLIKa9ZuYJ8qLXJNQKPUXzmJop26xz92Ja+6GQ7322l3S/vG/r9uRTczXOczc9TS4W5Zl7vZ5PiuVyjIPDsjcVX2uT5RPx2XmjrLMxk7t6g8hPHBaf/cuc+7G4E5yyfrTaN7LBWkaZqy5Qy2L4OhgdlC5oYpzbtlQZU8TXcraC+6uK8ts4W595jONbzon/+STmjoprVCWOXVS0pNywm2qs+ZuplyTkLe63nObjpZlnrTmTteunTgRWqy5S5J+HI+mFp+XfV2TtizLJNtxJryVmbun3Yy7cKHM2E0mu1eW6byS0DS4Gzw9c0dwVxu3diLj5hs8LlQty5Rz8sE0CTNq8VFLs4YqXtNQSAcHuhoOdCFtL3tyXVlmC3frsyTT+MJI/urXNCgkG6wO2rz51jN3XVWnLDMPhSZh1pvgLnGJnDntT/dP7Ja5MrhbBDVk7iT1OHO37HTbz9+vaxaZu6fNq8zK7N3jj5dlhTsUoGzcLTPk2p9cI3O3gba2QvheM/uypFdKetjMfqeNccSoXHOXL/9ctVumnJOXaVoQ3KGeo5m7Jg1V9op2g7tlt8xw9t0ypXnm7sJI472vK5tJWpNpd+Y0TuhqeJw6DVVmIe9VcCeV2bu9yd6J+9xpb+/EidByc+udX3PX8+DOWHN3lhbB9LGZ4Ftukf7yL3cqaydt2i3TKx+m2j/Yo6HKBlq5+kMIHwwhXAohZCGEO0II39XGOGLk7LiGKhWDu2CatjTBRbycuQYNVfyyocpefqCLLa65a7OhirTI3A013ntCWa61wZ23xZo7grsb1c3cjdWfskypDO6uTq6euOauSlnmzgd3g93olsn7x9k4PN4nBHePPbZ7wd0m3TLNqchS7Y/3DjN3Pb1WTxNHLDLOeeXFDWvuqtyB9F4+SFOxzx3qadRQZT651nisq7Nr3SjLbGErBKnsPPnEOafxtScqZe68eU0dZZnHqZy58wPlKssys4qv2RiMkpGemj51/Jq70Whl5s4tumUO+hPsNrHMYPY0+Dksy+Rz/iwsM6XHHe9bb93J4O7c4FyZdWvAO688S7U/vsaauw30892tx27cCqGwUO1O7HLN3TG14cAK15Vl+upr7orZVEpT7U322i3LPNots4XM3QP3PKDf+JPP6S1PXVN2XtUzdzu+Nuo4lTN33pdlmcqVtrgNx7Ytbq6szNydP74b67Khyo5n7hY3Q/uauTssy2RCfBaWmbtVZZk7tA2CJL3zW9/Z+LnevPJ0UAZ3rLlrrJ/vbj3mnFdRHK65y+3wQ3vNE4+UZXKhoLqjDVWqlmV655VPJ9JopKuTq7rYhW6ZLTVUefu3vF3/4eontL//ZJm5W/NB5eYNVeiW+XS1yjJVzIO7/mSqFsHdSfvcrSrLXKw1W+zztqsO94Hr582TlWWC2LrlmjvKMpfODc7p3GB1V+iTlJm7gfanT5G52wDBXWSc88pv3OeucnCneXBnpzhC9E2Thipl5q4M7lrP3Fm5/m9WzFrJ3N135326PbtFDw//rAzu1mQMvHOaJGTujrMoRzw2uDki8QPNwiK4689d87WZu1VlmYs1dz0KdptYbhXQ0wqWw+CVCfFZWPl6uvXWnWyosglv8+Bu8hSZuw30892tx7xLbijLrJ+56+v+Pjgdi8xdnX3uvEuUz6bL4K7NhipmJmdOk3zS2oTu7fe8QT//7MeUFetvrCw3Ie5pZmETdTN3Y+tXWeZoUK5jabLPnSU0VFnwRcXPzQgdrgHjc/4sHG4aT7fMbXDmyrLMReaObpmNENxF5ullmUGuygt/nrmbiK0QUM+iW2adhirOl2vuZueGmuSTys87LYlLNM7HrZUqvflFb9RXhrN6wV1PJ5+bqNdQJWhiRa+Cu5WZuzUNVeS9XEFZplQGd329ebIsy2RCfCbWdsskuKvFO69ikGh/dkC3zA1wxCLjvFdxQ7fM6g1V5mWZlGughkb73DmvfDbV3oVUF9ILspZLgROXaJJPWsta3/zMZ+sNnzdl+frjcDg5YxJ+o+oNVeb73KlQNmjWta2L1pZlzmYrgzsfpGTHyzIlyYceN1RhE/MzRbfM7Vo2VMkPWHO3gX6+u/VYuRXCkU3M6+5zp1xOrLlDdU0aqrh5+fDehbTVkswFb75cc9fWjY2bbtI//u9Bf+fP10+sF2Pc9a6Gx2mUuUv7tebOmTt+IrnoyHfSRMg5/eCj6lWw25QL/W2osgw2yNCeibWbmB8c7Fy3zE1455WnSRncseauMYK7yHifPL1b5prW6pKuy9zRRQt1NMvcJcqddPV80mozlYVFMNDaetMs08seT/STn14f6FKWebLqmbuBZirK4G7Qn4nVKBmd3ExmNA/aVmTuHvrP7HMnST5YbytYlg0+7riz5ZHshrVlmRKZuxrKzF2i/XxM5m4DzB4i49z1wV1hNcoyC2kitkJAPU0aqjjvVZi0N+pYcNfWhM5Munhx7R530pE7wWTunqZOWWauQhNXKO1RpmqYDE/+3ddl7hbfr3IzsOd86O/Nk8PW/P38/bpmbVmmRHBXQ5m5G2i/GJeZOxqqNMLVH5llWWYIkln9bpnKe7vWAKdjkbkb59UbqnifKDdpb+Rb3eNuOR634gP4rFysdhwWGQXPJPxpqpdlpsotaOyC0kF/JlYrg7sKmbuVP98hfc7crQw2sHVrNzGXCO5qcOaUJ177YXqYuWPOWhtHLDLeJyqclXczQqi+ifl8MX2hwJs+all0y6y1z52VmburQ9etzF2bWeuqmbvlVghk7m60OI/HbgVwhE/m+9z5fq0xGybDk3/3qpk7grt5Q5V+Hoe+7+PXNSs3MR+NysCO4K6ysizTaz9MNEqGZSKD4K42jlhknDkViZOmUymEsiyzakOVovySfe5Qx7Khyqx6QxXvB8qdtJepEw1VFkFB65m7SmWZ8+wUXQ2fZtFMZG3mLhmUZZmJKfX9OY6jZLS+LPOk19higkRwJy/r7z53q4INbN3KzJ1UlmYS3FXmzWs8cHIyJcHK962Wu23HiOAuMs6c8oEvW14XhXJXccI6b6hSfsmbPqpr0lBlueYutU5k7rx5OXPtbslQd80d3e6OlbikYkOVvHfB3TAZbtRQZeXPd4gL1ts1aZRlnq21x/uWW+iWWYN3XntJ0EgJzVQ2wNUfGW9ehfdl5q4oyjV3dYM73vRRQ7OGKuWau6tp6ERwl7ik/Yx1xeDO0VBlparBXR5CL4O7jRuqMFmSV8XlDBGiLPNsreyWKZXBHZm7yrx57SWFRiGhmcoGuPojU5ZlXh/cVSq/OFKWyZs+6mjUUMUlZeZuEDpTltl6mVLNskwyd8erEtwlSaqZ9a8sc6M1d2blf0yW+t1QZdW+a9i6tcebssxavPPa87lGwdNMZQMctcg4cyq8OyzLrJO5W6y56+mHGk6HM7fM3FUvyyyDu6s+70Tmzjvf/k0NtkLYisqZOwWNE/UquBsNVqy5W0wgVwVv3hPcaR7c9TRzR1nm2VqbKSVzV4szp2s+16jwlGVuoJ/vbj22aBOr6VTK85plmWH+JRcLqvPmVYSiZkOVchPzPZ/reR0I7mIqy1zsv5Ww2fSxqmbuchWaeFe5lDgGK9fcmZXZu1WToVe/mommJD8cyT/ztraHcSooyzxbh92NT7juvu3bpLvvPsMRxc2b1zU30yh3BHcbILiLjDdflmXOM3eFVSy/uK4sk4sF1TVrqDIvy/SzTuxzF1NZJmvuVquUuUsGmpkk36/M3co1d9L64O63f3v7g4qQf9YluTvvansYp6ITe3rukLXdMn/oh85wNPHzzmvPphoVBHebILiLzLIsc77mrla3TNbcoYEmDVW8mzdUsWk3yjLNx5O5W665609Qsk2VM3dOKnoY3K3c4280YjJUwWJLjT5am0nCVhFMb5c3rz1NNZoZDVU2wKsxMmVZZrnmLuS5AmvucMqaNFRZZu40paHKQt01d5RlHusl3/AS3Tq6deVjfDLQzEkT16/g7tJNl3TvM+89+QHrMneQJL3othfptnOUZWJza7tlohbvvPY01mgmGqpsgMxdZLzzy8xdKHJZULW9u1hzh4aOZu4qd8tMyk3M9zXuROauE2vubrqJbplb8PCbH177GJ+kyk2a+tCr4O7Ft79Y7/ue9538ADJ3lXzgjR9oewinhkzS2VpmStv+fOkJb157YaznTEVZ5gYI7iJztFtmPp3IhapPpCwTzThzCgrKi3xtOdzyOYtumcVBJ4K7TnTLfM1rpHtXZF3mFjdfkgGNL5pKBplyJ01cqNwEqBfI3O08go2zRTC9Xc6c9ooDDReZO97PGuHVGBlnTvk8c1cUs+XG5Gt5fxjckblDDWYmk2mYDKtliTVvRW/SXnFAQ5WF8+elb/qmtQ9bdMukoUpzPknnZZn9ytytRXC38xbv0QQbZ4OyzO3yzutafqDRJBDcbYCrPzLlJuZl5q7IZzUzd+WDeRNCXd75yiWZkuS8L9fc5fudyNx1oiyzokVQx5q75pJBWZa5c8EdZZlQmbUjuDsbZEq3y5vXXr6v0aSgocoGuPoj482rcGXmLs9ncqFaJuW6skyCO9Tkzdcqb/O+7FZ4dfZUJ4I7bz6amxrLNXeUZTbmk1QTr+pbxfQFmTuovBkXy/td7Ghgs13eeY2LiUbjgszdBng1RuboVghFXqMs80jmjjch1FU/c5doPylfa13InMSUuXOefe425ZNU+wMpLaxyKXEvENxB/d7qoWuW3Y0Jprdi8Tk9Gud0y9wARy0yiUs0TVSWZRa5nMjc4fR585X3uJPKTo9PDKUL6flTHFV1iUuimewsyzK5ThtLBpmCSWmI45xvDWWZEGWZZ4nM3XYtPvdGBzmZuw3waoxMlmSaeCvLMmc1u2XmizV3NElFPc5c7czdE5k6scedFFeZ0qKhStXOpHi6xTYS2a4Fd/ffLz33uW2PAi3rRHfgHUEgvV2LYzk6mBHcbYDZQ2RSn2q8yNzluXytNXfsc4dm6pZl+iTV1Uy6uwOdMqW4yjKXmbtIxttFi2O3c5m7H/uxtkeADvDmef84I84cx3qLlmWZ+1OCuw3s2Cdf/DKfaew1b6gyrX4CWXOHDdRtqOJ8omDqRDMVSUqsA1shVORYw7ExM5MrpDRwDLF7WHN3dmKqConBsixzKmkyIbhriKs/MmVZZjhcc1cnc5eTuUMztRuqzCcWXdjjTpp/AEdyd3VRUhjLeLvKBynlIw47iLLMs0MgvV3LzJ0NpKeeoqFKQxy1yKQ+1diFht0y5196qnFRT+2GKvMbCJ3J3HVhE/OKaKiyHQmZO+yomLZ+iR0lsNu1zNy5VLp2jcxdQwR3kcl8pomTNJspL2bVu2WayQeVmT7uhKCm2g1V5ncyuxTcxXJ3lYYq20HmDruKbNLZceYIpLdo2VDFZQR3G+Dqj0yZuSuWmbvKJ3AR3M2/Buqo3VBlfiezM90yI7q76obnJFGWuamkMGX0DMMOoizz7HCst2tZlklwtxFekZHJkkzjxZq7Ot0yJflgZO7QSN2yzC5m7mK5u+pvu738fyTj7aoyc8cxxO6J6WZW7DjW27Usy/RZueaO4K4RZvmRyXymiZWZu1plmZK8TF4Ed6ivdubOdStzF9VWCPNxxjLerkqCEdxhJ1GWeXYoy9yuZeYuGdFQZQMctcjcWJZZK3MnKzc952JBTbW3QuhY5i6mdtWLcTI524wPUmqUZWL3UCp4djjW27XM3CVDyjI3wCsyMlmSaWJHtkKok7kLrnw8a+5QU9M1d10J7mLK3C02xTWu0434YAR32EneCDjOCpuYb9cyczc4R3C3Aa7+yKQ+1djyI5uYk7nD6WvaLbMr+9xF1S2TNuZbkQRTRnCHHUSp4Nnh/Xq7lt0ykxHB3QbimO1gKfOZxsqXmbu6DVU8DVXQQOz73MX0ARzThutdRuYOu+oHXvoDuvyMy20PYyewvnG7Fp/Tw+wcDVU2wCdfZMqyzPmau7plmWTu0FDdssyurbmLqSwzpkC0y5JgSm3Q9jCAM/fuv/nutoewM/RzSW0AAA7zSURBVLgZt13eyrmGDWmosgmOWmRSny4zd3lev1sm+9yhiboNVbq2z11MWyGwhmM7vEwpG8EDOEWUwG6Xd74syRzSUGUTBHeRyXymifJl5s7XCu4cmTs0EnvmLqa7q955JQQlG/Nk7gCcMprXbJc3r9FgJGVsYr4JXpGRKTN3szJz12SfO4I7NLAolaj8+MU+dx1qqBLL3VXKMrcjkSl1BHcATg+VFttF5m47mOVHJksyjcNsvs9d3TV3ZO7QjDNXq6FK1zJ3UXXLjCjL2GVeThnBHYBTFNMeqjFw5srM3XBIQ5UNxDHbwVJZljk77JbZpKEKa+5QU+pTnRucq/x4b2Vp4fnB+VMcVXWpT5W6tO1hVDJwA6U+jrF2WRKM4wjgVA3cQANuIm1N6tNy3kBwtxEWdkRmcYdoNh03KMt0coXI3KG2X/juX9Clmy5Vfrx3Xp/9kc925o7m9734+/Tq57667WFUcummS/rIWz7S9jCi5+UoywRwqu6/6369//Xvb3sYvfHyZ71cv/z6X5Ye+k9lWSbz1UYI7iKU2UCT2Xi+FUL1F76XY80dGrnnlntqP+eFt73wFEbSzIX0QmdKRNcxM937zHvbHkb0EpG5A3C6nDm94JkvaHsYveGd1/NvfT4NVTbELD9CqSUaFxMVoWFZJsEdgJ7zcgR3ABCj4VCaTAjuGmKWH6HMpRrnk/r73Nm8LJM1dwB6zsuUEdwBQHyG8+7cBHeNENxFKHMDTfLJvCyzbrfMQOYOQO8lckprdHgFAHQEwd1GmOVHKHWDsiyzKORrtHf3cvI0VAGwA7yc0oTMHQBEZxHcMV9thKMWocylmhRT5aH+JuauIHMHoP/u379Z9wzvansYAIC6snnVBZm7RuiWGaHUDzQupmXmrk63THPscwdgJ7z3zjdJL/rOtocBAKiLssyNENxFKPPZPLjL5WqWZZK5A7AT3vvetkcAAGiC4G4jzPIjlPl5WWbdTczNyRPcAQAAoKsI7jbSyizfzH7OzL5gZp8xsw+a2c1tjCNWqU811qx+WabmWyEQ3AEAAKCLaKiykbaO2kclvSSE8M2S/ljSu1saR5Qyn2lSTFWEmmWZ5sutEFhzBwAAgC6iocpGWgnuQggfCSHM5n/8n5IutTGOWKVJpnGYKS9yuRqB2nITc+6EAAAAoIsoy9xIF2b5/1DSfznph2b2oJk9YmaPPP7442c4rO7Kkqwsywz1yjKvXL2of/ZxEdwBAACgmwjuNnJq3TLN7GOS7jzmR+8JIXxo/pj3SJpJev9Jf08I4SFJD0nSlStXwikMNTpZMtREM4Wa3TIvhIG+40siuAMAAEA3Edxt5NSCuxDCA6t+bmZvk/Tdkr4zhEDQVkM6GGqsXEmR1+qWuQzqWHMHAACALmLN3UZa2efOzF4r6Sck/e0QwlNtjCFmZeYulwu5vNV44S+COzJ3AAAA6KIkKQM75quNtHXU/q2ki5I+amaPmtnPtzSOKKWDTGMrlId6DVUI7gAAANB5wyGZu4ZaydyFEJ7fxr/bF9lgpLFynSuKWmvuCO4AAADQeQR3jTHLj1CWjjSxvHa3TNbcAQAAoPMI7hojuItQmmQaeynPZ2TuAAAA0C9ZRnDXELP8CGXJUJOBUzGbEtwBAACgX4ZD5qsNcdQilPpU44GpyKd0ywQAAEC/UJbZGLP8CGU+03jglM+m9bplLi4S1twBAACgqwjuGiO4i1CWZJokpiKfkbkDAABAvxDcNcYsP0KHZZk0VAEAAEDP0FClMWb5Ecp8mbnLcxqqAAAAoGde/nLp8uW2RxGlVjYxx2ZSn2qcmIpZw7JM1twBAACgq9773rZHEC1SOBHKkkzjRMqLmVydLByZOwAAAKC3mOVHKPOZJl6suQMAAACwxCw/QqlPNfZSUdAtEwAAAECJWX6EsqTM3OVNM3esuQMAAAB6h+AuQmXmLlCWCQAAAGCJWX6EMp9p7IKKIpd3lGUCAAAAILiLUlmWGZQXuRxr7gAAAACI4C5KqU+XmbtaWyH4eSDImjsAAACgdwjuIpT5TBObl2WSuQMAAAAggrsolZm7otzEnIYqAAAAAERwF6UsyTSxQnnI5Zo0VKEsEwAAAOgdgrsIOXPyMk1U0C0TAAAAgCSCu2ilwWnfh2aZO4I7AAAAoHeY5UcqC177A9FQBQAAAIAkgrtopfLaT8SaOwAAAACSCO6ilanM3DXqlklwBwAAAPQOwV2kMiXaTyTvk+pPcq4M7AjuAAAAgN4huItUuszc1SzLZL0dAAAA0EvM9COVWdJszR1ZOwAAAKCXCO4ilSopu2XWCe68J3MHAAAA9BQz/UhlrmHmjuAOAAAA6CVm+pHKbFCuuSO4AwAAACCCu2ilNtDUS9416JYJAAAAoHcI7iKVuYEkMncAAAAASsz0I5US3AEAAAA4gpl+pBaZu1rdMgnuAAAAgN5iph+pzKWS2OcOAAAAQIngLlKpnwd3vmZDFTJ3AAAAQC8x049UNg/uPMEdAAAAABHcRWuZuWPNHQAAAAAR3EUr85mkmsGd96y5AwAAAHqK4C5SWTKU1GATczJ3AAAAQC8x049UmlCWCQAAAOAQM/1ILTN3yaD6kwjuAAAAgN5iph+pNGmw5o597gAAAIDeIriLVDYoM3eONXcAAAAARHAXrWVZJvvcAQAAABDBXbTSZeaOhioAAAAACO6ilQ1GkiRXt6EKa+4AAACAXiK4i1Sass8dAAAAgEPM9CO1zNx5yjIBAAAAENxFK8vOSaJbJgAAAIASM/1IpWkZ3NXqluk9a+4AAACAniK4i1SWLsoyydwBAAAAILiLVrooy0wI7gAAAAAQ3EVrsebO+5pbIRDcAQAAAL3ETD9SWXZeUoOGKqy5AwAAAHqJ4C5Sy7JM1twBAAAAUEvBnZn9czP7jJk9amYfMbNntTGOmC0yd7W6ZRLcAQAAAL3V1kz/50II3xxCuE/Sb0n6py2NI1qDJJUkuYQ1dwAAAABaCu5CCE8e+eN5SaGNccTMmdOwcPLZqMaTWHMHAAAA9FWNmr7tMrN/Iemtkp6Q9B0rHvegpAcl6fLly2czuEh88h2f1mh0sfoTXvpS6R3vOL0BAQAAAGiNhXA6STMz+5ikO4/50XtCCB868rh3SxqGEH5q3d955cqV8Mgjj2xxlAAAAAAQDzP7VAjhynE/O7XMXQjhgYoP/RVJD0taG9wBAAAAAI7XVrfMFxz549+X9IU2xgEAAAAAfdHWmrufMbMXSiok/amkf9TSOAAAAACgF1oJ7kIIb2jj3wUAAACAvmLTMwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAQshtD2GyszscUl/2vY4jnGbpL9qexBoBed+d3Hudxvnf3dx7ncX5353de3cPyeEcPtxP4gquOsqM3skhHCl7XHg7HHudxfnfrdx/ncX5353ce53V0znnrJMAAAAAOgBgjsAAAAA6AGCu+14qO0BoDWc+93Fud9tnP/dxbnfXZz73RXNuWfNHQAAAAD0AJk7AAAAAOgBgjsAAAAA6AGCuw2Y2WvN7I/M7Itm9pNtjwenz8y+ZGZ/YGaPmtkj8+/damYfNbP/Pf//LW2PE5szs18ys6+a2WePfO/Ec21m756/F/yRmX1XO6PGNpxw7n/azP58fu0/amZ/78jPOPc9YWZ3m9nHzezzZvY5M3vn/Ptc+z234txz7e8AMxua2e+a2afn5/+98+9Hd+2z5q4hM/OS/ljSqyV9WdInJX1/COEPWx0YTpWZfUnSlRDCXx353r+U9NchhJ+ZB/m3hBB+oq0xYjvM7G9J2pP0H0MIL5l/79hzbWYvlvSrkl4h6VmSPibp3hBC3tLwsYETzv1PS9oLIfyrGx7Lue8RM7tL0l0hhN8zs4uSPiXpeyT9oLj2e23Fuf8H4trvPTMzSedDCHtmNpD03yS9U9LrFdm1T+auuVdI+mII4U9CCBNJvybpdS2PCe14naT3zb9+n8oPA0QuhPAJSX99w7dPOtevk/RrIYRxCOH/SvqiyvcIROiEc38Szn2PhBAeCyH83vzrq5I+L+nZ4trvvRXn/iSc+x4Jpb35Hwfz/4IivPYJ7pp7tqQ/O/LnL2v1mwD6IUj6iJl9yswenH/vjhDCY1L54SDpG1obHU7bSeea94Pd8KNm9pl52eaiNIdz31Nm9o2S7pf0v8S1v1NuOPcS1/5OMDNvZo9K+qqkj4YQorz2Ce6as2O+R41r/70qhPAtkv6upHfMy7cA3g/6799Jep6k+yQ9Julfz7/Pue8hM7sg6dclvSuE8OSqhx7zPc5/xI4591z7OyKEkIcQ7pN0SdIrzOwlKx7e2fNPcNfclyXdfeTPlyT9RUtjwRkJIfzF/P9flfRBlSn4r8xr9Rc1+19tb4Q4ZSeda94Pei6E8JX5B38h6Rd1WH7Due+Z+XqbX5f0/hDCb8y/zbW/A44791z7uyeE8HVJ/1XSaxXhtU9w19wnJb3AzO4xs1TSmyT9Zstjwikys/PzRdYys/OSXiPpsyrP+9vmD3ubpA+1M0KcgZPO9W9KepOZZWZ2j6QXSPrdFsaHU7L4cJ/7XpXXvsS575V5U4V/L+nzIYR/c+RHXPs9d9K559rfDWZ2u5ndPP96JOkBSV9QhNd+0vYAYhVCmJnZj0r6HUle0i+FED7X8rBwuu6Q9MHy/V+JpF8JIXzYzD4p6QNm9nZJ/0/SG1scI7bEzH5V0rdLus3MvizppyT9jI451yGEz5nZByT9oaSZpHd0oWMWmjnh3H+7md2nsuzmS5J+WOLc99CrJL1F0h/M195I0j8R1/4uOOncfz/X/k64S9L75t3wnaQPhBB+y8z+hyK79tkKAQAAAAB6gLJMAAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOgBgjsAAAAA6AGCOwAAAADoAYI7AAAAAOiB/w+1VErPCeT9lQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE score: 0.4624\n",
+      "MSE score: 0.3519\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train the model\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, y_train)\n",
+    "#Evaluate results and plot predictions\n",
+    "evaluation(ridge, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Summimg up**\n",
+    "\n",
+    "RDkit is a wonderful tool to work with chemical data, especially represented as SMILES strings or in MOL format. We've seen some basic general abilities of the package but some other powerful tools are yet to be found in the docs.\n",
+    "\n",
+    "Trying to improve our model perfomance at the next step we'll take a look at a copletely different way to featurize molecules. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# mol2vec - learning vector representations of molecular subctructures\n",
+    "\n",
+    "From package description 'Mol2vec is an unsupervised machine learning approach to obtain high dimensional embeddings of chemical substructures. It is based on word2vec algorithm and if word2vec learns such embeddings that vectors representing similar words group together, so should Mol2vec learn substructure embeddings where vectors of chemically related substructures end up close in vector space'. \n",
+    "\n",
+    "In other words mol2vec takes inspiration in one of the most widespread NLP technique.\n",
+    "Without going deep in detaials we can describe the process as following: \n",
+    "\n",
+    "**1.** A molecule is divided into substructures of a fixed radius (usually encoding distance to other atoms in bonds, i.e. radius = 1 represents a single heavy atom or a group of closest atoms around a heavy atom), substructures are encoded via [Morgan fingerprints](https://docs.chemaxon.com/display/docs/Extended+Connectivity+Fingerprint+ECFP) with the help of RDkit. Substructures reflect words in 'chemical sentences'.\n",
+    "\n",
+    "**2.** These encoded substructures are fed to Word2vec yielding vector representations of substructures in a number of dimensions according to their occurance together.\n",
+    "\n",
+    "**3.** Summing up substructure vectors we get vector representations of whole molecules.\n",
+    "\n",
+    "\n",
+    "The main idea behind is that we can use vector representations of molecules in different dimensions as features in supervised learning, supposing similar molecules will have close vector representations and will have close target values. \n",
+    "\n",
+    "Another wonderful point is that mol2vec provides a pre-trained model which has learned 20M different compounds with radius=1 for Morgan fingerprints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_kg_hide-input": false
+   },
+   "outputs": [],
+   "source": [
+    "#Installing a package\n",
+    "!pip install git+https://github.com/samoturk/mol2vec;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start from scratch with initial dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#Load the dataset and extract target values\n",
+    "mdf= pd.read_csv('./logP_dataset.csv', names=['smiles', \n",
+    "                                           'target'])\n",
+    "target = mdf['target']\n",
+    "mdf.drop(columns='target',inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once again we need to transform SMILES representations to MOL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Transforming SMILES to MOL\n",
+    "mdf['mol'] = mdf['smiles'].apply(lambda x: Chem.MolFromSmiles(x))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll load a pre-trained mol2vec model. It's trained with radius=1 for Morgan fingerprints to yield 300 dimensional embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Loading pre-trained model via word2vec\n",
+    "from gensim.models import word2vec\n",
+    "model = word2vec.Word2Vec.load('../example_data/model_300dim.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importing necessary functions. \n",
+    "\n",
+    "***mol2alt_sentence()*** constructs a so-called 'molecular sentence' with desired Morgan fingerprints' radius (uses RDkit backend) where 'words' are unique substructure identifiers; ***MolSentence()*** is an internal wrapper function; ***sentences2vec()*** generates molecular embeddings with the help of the trained model; ***DfVec()*** is an internal wrapper for embeddings' generator (attribute .vec yields aggregated vectors).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mol2vec.features import mol2alt_sentence, mol2sentence, MolSentence, DfVec, sentences2vec\n",
+    "from gensim.models import word2vec\n",
+    "print('Molecular sentence:', mol2alt_sentence(mdf['mol'][1], radius=1))\n",
+    "print('\\nMolSentence object:', MolSentence(mol2alt_sentence(mdf['mol'][1], radius=1)))\n",
+    "print('\\nDfVec object:',DfVec(sentences2vec(MolSentence(mol2alt_sentence(mdf['mol'][1], radius=1)), model, unseen='UNK')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's implement those functions above and construct a feature matrix of vector representations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Constructing sentences\n",
+    "mdf['sentence'] = mdf.apply(lambda x: MolSentence(mol2alt_sentence(x['mol'], 1)), axis=1)\n",
+    "\n",
+    "#Extracting embeddings to a numpy.array\n",
+    "#Note that we always should mark unseen='UNK' in sentence2vec() so that model is taught how to handle unknown substructures\n",
+    "mdf['mol2vec'] = [DfVec(x) for x in sentences2vec(mdf['sentence'], model, unseen='UNK')]\n",
+    "X = np.array([x.vec for x in mdf['mol2vec']])\n",
+    "y = target.values\n",
+    "\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the perfomance of the model with vector representations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.1, random_state=1)\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, y_train)\n",
+    "evaluation(ridge, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wow! Vector representations alone absolutely outscored features built with chemical logic. mol2vec is a really powerful tool!\n",
+    "\n",
+    "Now let's put embeddings together with already constructed features to see the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "mdf = pd.DataFrame(X)\n",
+    "new_df = pd.concat((mdf, train_df), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(new_df, y, test_size=.1, random_state=1)\n",
+    "ridge = RidgeCV(cv=5)\n",
+    "ridge.fit(X_train, y_train)\n",
+    "evaluation(ridge, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we got the best among our results. This one is actually a rather good result even for computional chemistry."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classification task\n",
+    "We'll take another task to prove the skills. This time it's going to be a classification problem. We'll use a dataset from [MoleculeNet](http://moleculenet.ai/datasets-1) database - a benchmark for computional chemistry. There's a number of datasets for classification, so I've picked the one that is not so large - HIV dataset. From description: \"The HIV dataset was introduced by the Drug Therapeutics Program (DTP) AIDS Antiviral Screen, which tested the ability to inhibit HIV replication for over 40,000 compounds. Screening results were evaluated and placed into three categories: confirmed inactive (CI),confirmed active (CA) and confirmed moderately active (CM). We further combine the latter two labels, making it a classification task between inactive (CI) and active (CA and CM)\". \n",
+    "\n",
+    "In the dataset once again we can find SMILES representations and two target columns, but only 'HIV_active' is used as a benchmark.\n",
+    "A benchmark for Logistic Regression for this task [is ROC AUC = 0.782](http://moleculenet.ai/latest-results).\n",
+    "Actually I haven't dealed to find any information about how was this result achieved. The only information I could find is that 10 % of the data were used to validate and 10 % were used as test data. To make it fair we'll use 20 % of the data for the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "#Read the data\n",
+    "hiv = pd.read_csv('../input/mlchem/HIV.csv')\n",
+    "hiv.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Let's look at the target values count\n",
+    "sns.countplot(data = hiv, x='HIV_active', orient='v')\n",
+    "plt.ylabel('HIM active')\n",
+    "plt.xlabel('Count of values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We notice great class disbalance. Now let's repeat the same approaches.\n",
+    "At the first step - descriptors from RDkit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Transform SMILES to MOL\n",
+    "hiv['mol'] = hiv['smiles'].apply(lambda x: Chem.MolFromSmiles(x)) \n",
+    "\n",
+    "#Extract descriptors\n",
+    "hiv['tpsa'] = hiv['mol'].apply(lambda x: Descriptors.TPSA(x))\n",
+    "hiv['mol_w'] = hiv['mol'].apply(lambda x: Descriptors.ExactMolWt(x))\n",
+    "hiv['num_valence_electrons'] = hiv['mol'].apply(lambda x: Descriptors.NumValenceElectrons(x))\n",
+    "hiv['num_heteroatoms'] = hiv['mol'].apply(lambda x: Descriptors.NumHeteroatoms(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train and evaluate the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = hiv.HIV_active.values\n",
+    "X = hiv.drop(columns=['smiles', 'activity','HIV_active', 'mol'])\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import auc, roc_curve\n",
+    "def evaluation_class(model, X_test, y_test):\n",
+    "    prediction = model.predict_proba(X_test)\n",
+    "    preds = model.predict_proba(X_test)[:,1]\n",
+    "    fpr, tpr, threshold = roc_curve(y_test, preds)\n",
+    "    roc_auc = auc(fpr, tpr)\n",
+    "    \n",
+    "    plt.title('ROC Curve')\n",
+    "    plt.plot(fpr, tpr, 'g', label = 'AUC = %0.3f' % roc_auc)\n",
+    "    plt.legend(loc = 'lower right')\n",
+    "    plt.plot([0, 1], [0, 1],'r--')\n",
+    "    plt.xlim([0, 1])\n",
+    "    plt.ylim([0, 1])\n",
+    "    plt.ylabel('True Positive Rate')\n",
+    "    plt.xlabel('False Positive Rate')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print('ROC AUC score:', round(roc_auc, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_test = StandardScaler().fit_transform(X_test)\n",
+    "\n",
+    "lr = LogisticRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "\n",
+    "evaluation_class(lr, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally let's add molecular embeddings with mol2vec."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Constructing sentences\n",
+    "hiv['sentence'] = hiv.apply(lambda x: MolSentence(mol2alt_sentence(x['mol'], 1)), axis=1)\n",
+    "\n",
+    "#Extracting embeddings to a numpy.array\n",
+    "#Note that we always should mark unseen='UNK' in sentence2vec() so that model is taught how to handle unknown substructures\n",
+    "hiv['mol2vec'] = [DfVec(x) for x in sentences2vec(hiv['sentence'], model, unseen='UNK')]\n",
+    "X_mol = np.array([x.vec for x in hiv['mol2vec']])\n",
+    "X_mol = pd.DataFrame(X_mol)\n",
+    "\n",
+    "#Concatenating matrices of features\n",
+    "new_hiv = pd.concat((X, X_mol), axis=1)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(new_hiv, y, test_size=.20, random_state=1)\n",
+    "\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_test = StandardScaler().fit_transform(X_test)\n",
+    "\n",
+    "lr = LogisticRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "evaluation_class(lr, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's an absolutely outstanding result when comparing to best results on MoleculeNet. Even though we were unable to reproduce the split, still results show that we are very close to or even better than best results published on MoleculeNet. Incredible!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "We've tried a number of approaches to handle chemical data and built rather good predictive models for physical and biological properties of chemical matter with wonderful ***RDkit*** and ***mol2vec*** packages. I suppose, further feature construction can bring an even better result, but still it's shown that we are able to predict important general properties rather fast and easy, without constructing great neural networks or spending weeks on quantum calculations, which can actually be implemented in a lab if an approximated evaluation of the target is needed. Another important outcome is the fact that these approaches are suitable for both regression and classification tasks (in classification task we dealed to beat a MoleculeNet benchmark). Also I'd like to mention that ***RDkit*** allows to do many useful things that are not shown in the kernel, thus, are yet to research, if needed. The docs can be found [here](https://www.rdkit.org/docs/) and [here](https://mol2vec.readthedocs.io/en/latest/). \n",
+    "\n",
+    "Will ***mol2vec*** approach be useful in a QSAR problem solving is a question to research. But still it's a good thing to start with when it comes to ML in chemistry.\n",
+    "\n",
+    "Thanks for your attention!\n",
+    "\n",
+    "**References**:\n",
+    " - Kaggle Datasets: LogP of Chemical Structures. https://www.kaggle.com/matthewmasters/chemical-structure-and-logp\n",
+    " - Jaeger, S., Fulle, S., & Turk, S. (2018). Mol2vec: Unsupervised machine learning approach with chemical intuition. Journal of chemical information and modeling, 58(1), 27-35. URL = {http://dx.doi.org/10.1021/acs.jcim.7b00616}, eprint = {http://dx.doi.org/10.1021/acs.jcim.7b00616}\n",
+    " - AIDS Antiviral Screen Data. https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data"
+   ]
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/bonus_1_predictions.csv b/bonus_1_predictions.csv
new file mode 100644
index 0000000..7b8a1ad
--- /dev/null
+++ b/bonus_1_predictions.csv
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diff --git a/bonus_2_predictions.csv b/bonus_2_predictions.csv
new file mode 100644
index 0000000..31485d8
--- /dev/null
+++ b/bonus_2_predictions.csv
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diff --git a/bonus_3_predictions.csv b/bonus_3_predictions.csv
new file mode 100644
index 0000000..f958e8c
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\ No newline at end of file
diff --git a/chris/task3.py b/chris/task3.py
new file mode 100644
index 0000000..404c604
--- /dev/null
+++ b/chris/task3.py
@@ -0,0 +1,57 @@
+# Literally just Gino's task1 code, with 2 strings changed...
+
+from traceback import clear_frames
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error
+from sklearn.svm import SVR
+from sklearn.linear_model import RidgeCV
+
+feature_headers = headers = [*pd.read_csv('../data/train_descriptors.csv', 
+                             nrows=1)]
+features = pd.read_csv(
+    '../data/train_descriptors.csv',
+    usecols=[c for c in headers if not c in [
+        'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+)
+data = pd.read_csv('../data/train_centroid_distances.csv')
+#%% Train / test splitting
+target = data['mean']
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.3, random_state=42)
+y_train = y_train.to_numpy()
+# # %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(f_regression, k = 1551)), # using it all seems better..!?   
+    ('fitting', KernelRidge())
+])
+#%% Fitting
+pclf.fit(X_train, y_train)
+#%% Prediction
+y_pred = pclf.predict(X_test)
+print("MAE::::::", mean_absolute_error(y_test, y_pred))
+#%% plotting
+plt.plot(y_test, y_pred, 'r.')
+plt.show()
+#%% saving
+test_data = pd.read_csv(
+    '../data/test_descriptors.csv',
+    usecols=[c for c in headers if not c in [
+        'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+).to_numpy()
+test_pred = pclf.predict(test_data)
+np.savetxt('task_3_predictions.csv', test_pred)
diff --git a/chris/task_3_predictions.csv b/chris/task_3_predictions.csv
new file mode 100644
index 0000000..90639b8
--- /dev/null
+++ b/chris/task_3_predictions.csv
@@ -0,0 +1,3363 @@
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diff --git a/chris/task_cellvol_predictions.csv b/chris/task_cellvol_predictions.csv
new file mode 100644
index 0000000..31485d8
--- /dev/null
+++ b/chris/task_cellvol_predictions.csv
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diff --git a/chris/taskcellvol.py b/chris/taskcellvol.py
new file mode 100644
index 0000000..9a679d1
--- /dev/null
+++ b/chris/taskcellvol.py
@@ -0,0 +1,65 @@
+# Literally just Gino's task1 code, with 2 strings changed...
+
+from traceback import clear_frames
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression, chi2
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error
+from sklearn.svm import SVR
+from sklearn.linear_model import RidgeCV
+from sklearn.cluster import KMeans
+# feature_headers = headers = [*pd.read_csv('../data/train_descriptors.csv', 
+#                              nrows=1)]
+# features = pd.read_csv(
+#     '../data/train_descriptors.csv',
+#     usecols=[c for c in headers if not c in [
+#         'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+# )
+
+feature_headers = headers = [*pd.read_csv('../data/train_mord3d.csv', 
+                             nrows=1)]
+features = pd.read_csv(
+    '../data/train_mord3d.csv',
+    usecols=[c for c in headers if not c in [
+        'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles']]
+)
+data = pd.read_csv('../data/train_crystals.csv')
+#%% Train / test splitting
+target = data['cell_volume']
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.3, random_state=42)
+y_train = y_train.to_numpy()
+# # %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='most_frequent', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(f_regression, k = 10)), # using it all seems better..!?
+    ('fitting', KernelRidge())
+])
+#%% Fitting
+pclf.fit(X_train, y_train)
+#%% Prediction
+y_pred = pclf.predict(X_test)
+print("MAE::::::", mean_absolute_error(y_test, y_pred))
+#%% plotting
+plt.plot(y_test, y_pred, 'r.')
+plt.show()
+#%% saving
+# test_data = pd.read_csv(
+#     '../data/test_descriptors.csv',
+#     usecols=[c for c in headers if not c in [
+#         'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+# ).to_numpy()
+# test_pred = pclf.predict(test_data)
+# # np.savetxt('task_cellvol_predictions.csv', test_pred)
diff --git a/fons_pres.pdf b/fons_pres.pdf
new file mode 100644
index 0000000..9182022
Binary files /dev/null and b/fons_pres.pdf differ
diff --git a/gen_mol2vec.py b/gen_mol2vec.py
new file mode 100644
index 0000000..4554d33
--- /dev/null
+++ b/gen_mol2vec.py
@@ -0,0 +1,26 @@
+import pandas as pd
+import numpy as np
+from rdkit import Chem
+from pathlib import Path
+from gensim.models import word2vec
+from mol2vec.features import mol2alt_sentence, mol2sentence, MolSentence, DfVec, sentences2vec
+from gensim.models import word2vec
+
+model = word2vec.Word2Vec.load('./example_data/model_300dim.pkl')
+
+def compute_vecs(descriptors_path):
+    mdf = pd.read_csv(descriptors_path)
+    mdf['mol'] = mdf['SMILES'].apply(lambda x: Chem.MolFromSmiles(x))
+    mdf['sentence'] = mdf.apply(lambda x: MolSentence(mol2alt_sentence(x['mol'], 1)), axis=1)
+    mdf['mol2vec'] = [DfVec(x) for x in sentences2vec(mdf['sentence'], model, unseen='UNK')]
+    return np.array([x.vec for x in mdf['mol2vec']])
+
+print('doing train')
+vecs = compute_vecs('./data/train_descriptors.csv')
+header = " ,".join(str(i) for i in range(300))
+np.savetxt('./data/train_mol2vec.csv', vecs, delimiter = ',', header = header)
+
+print('doing test')
+vecs = compute_vecs('./data/test_descriptors.csv')
+header = " ,".join(str(i) for i in range(300))
+np.savetxt('./data/test_mol2vec.csv', vecs, delimiter = ',', header = header)
\ No newline at end of file
diff --git a/out/bonus_3_predictions.csv b/out/bonus_3_predictions.csv
new file mode 100644
index 0000000..f958e8c
--- /dev/null
+++ b/out/bonus_3_predictions.csv
@@ -0,0 +1,3363 @@
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\ No newline at end of file
diff --git a/out/task_1_predictions copy.csv b/out/task_1_predictions copy.csv
new file mode 100644
index 0000000..a8ec05e
--- /dev/null
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diff --git a/out/task_1_predictions.csv b/out/task_1_predictions.csv
new file mode 100644
index 0000000..eab2291
--- /dev/null
+++ b/out/task_1_predictions.csv
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diff --git a/out/task_4_predictions.csv b/out/task_4_predictions.csv
new file mode 100644
index 0000000..70f3292
--- /dev/null
+++ b/out/task_4_predictions.csv
@@ -0,0 +1,3363 @@
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diff --git a/task1_bonus_tom.py b/task1_bonus_tom.py
new file mode 100644
index 0000000..198519e
--- /dev/null
+++ b/task1_bonus_tom.py
@@ -0,0 +1,77 @@
+# %% Imports + Data loading
+from traceback import clear_frames
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+from pathlib import Path
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression, chi2
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error, f1_score
+from sklearn.svm import SVR
+
+from sklearn.ensemble import RandomForestClassifier
+
+import glob
+train_csvs = glob.glob("./data/train_*.csv")
+train = {Path(t).stem : pd.read_csv(t) for t in train_csvs}
+print(train.keys())
+
+def read_descriptors(path):
+        headers = [*pd.read_csv(path, nrows=1)]
+        return pd.read_csv(path, usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']])
+
+features = pd.concat([
+    read_descriptors('./data/train_descriptors.csv'),
+    train['train_rdk'].drop('0', axis = 1),
+    train['train_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    train['train_mol2vec'],
+    ], axis = 1)
+
+data = pd.read_csv('./data/train_crystals.csv')
+# %% Train / test splitting
+target = data['packing_coefficient']
+
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.33, random_state=42)
+
+y_train = y_train.to_numpy()
+
+# %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(f_regression, k = 300)),
+    ('fitting', KernelRidge())
+])
+# %% Fitting
+pclf.fit(X_train, y_train)
+
+# %% Prediction
+y_pred = pclf.predict(X_test)
+print('mean_absolute_error: ', mean_absolute_error(y_test, y_pred))
+# %% testing
+
+test_csvs = glob.glob("./data/test_*.csv")
+tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}
+
+test_data = pd.concat([
+    read_descriptors('./data/test_descriptors.csv'),
+    tests['test_rdk'].drop('0', axis = 1),
+    tests['test_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    tests['test_mol2vec'],
+    ], axis = 1)
+
+pclf.fit(features, target)
+test_pred = pclf.predict(test_data)
+# %% saving
+np.savetxt('./out/bonus_1_predictions.csv', test_pred)
+# %%
diff --git a/task1_gino.py b/task1_gino.py
new file mode 100644
index 0000000..0b0fc38
--- /dev/null
+++ b/task1_gino.py
@@ -0,0 +1,55 @@
+#%% Imports + Data loading
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error
+from sklearn.svm import SVR
+
+headers = [*pd.read_csv('./data/train_descriptors.csv', nrows=1)]
+features = pd.read_csv(
+    './data/train_descriptors.csv',
+    usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+)
+data = pd.read_csv('./data/train_crystals.csv')
+#%% Train / test splitting
+target = data['calculated_density']
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.33, random_state=42)
+y_train = y_train.to_numpy()
+# %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(f_regression, k = 300)),
+    ('fitting', KernelRidge())
+])
+#%% Fitting
+pclf.fit(X_train, y_train)
+#%% Prediction
+y_pred = pclf.predict(X_test)
+print(mean_absolute_error(y_test, y_pred))
+#%% plotting
+plt.plot(y_test, y_pred, 'r.')
+plt.show()
+# %%
+test_data = pd.read_csv(
+    './data/test_descriptors.csv',
+    usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+).to_numpy()
+
+pclf.fit(features, target)
+test_pred = pclf.predict(test_data)
+#%% saving
+np.savetxt('./out/task_1_predictions.csv', test_pred)
+# %%
diff --git a/task2_tom.py b/task2_tom.py
new file mode 100644
index 0000000..1c3e822
--- /dev/null
+++ b/task2_tom.py
@@ -0,0 +1,78 @@
+# %% Imports + Data loading
+from traceback import clear_frames
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+from pathlib import Path
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression, chi2
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error, f1_score
+from sklearn.svm import SVR
+
+from sklearn.ensemble import RandomForestClassifier
+
+import glob
+train_csvs = glob.glob("./data/train_*.csv")
+train = {Path(t).stem : pd.read_csv(t) for t in train_csvs}
+print(train.keys())
+
+def read_descriptors(path):
+        headers = [*pd.read_csv(path, nrows=1)]
+        return pd.read_csv(path, usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']])
+
+features = pd.concat([
+    read_descriptors('./data/train_descriptors.csv'),
+    #train['train_rdk'].drop('0', axis = 1),
+    train['train_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    #train['train_mol2vec'],
+    ], axis = 1)
+
+data = pd.read_csv('./data/train_crystals.csv')
+# %% Train / test splitting
+target = data['is_centrosymmetric']
+
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.33, random_state=42)
+
+y_train = y_train.to_numpy()
+
+# %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(chi2, k = 50)),
+    ('fitting', RandomForestClassifier(random_state=0))
+])
+# %% Fitting
+pclf.fit(X_train, y_train)
+
+# %% Prediction
+y_pred = pclf.predict(X_test)
+print('f1 score: ', f1_score(y_test, y_pred, average = 'macro'))
+# %% testing     
+
+test_csvs = glob.glob("./data/test_*.csv")
+tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}
+
+test_data = pd.concat([
+    read_descriptors('./data/test_descriptors.csv'),
+    #tests['test_rdk'].drop('0', axis = 1),
+    tests['test_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    #tests['test_mol2vec'],
+    ], axis = 1)
+
+pclf.fit(features, target)
+test_pred = pclf.predict(test_data)
+#%% saving
+with open('./out/task_2_predictions.csv', 'w') as f:
+    f.write("\n".join('True' if i else 'False' for i in test_pred))
+# %%
diff --git a/task3_bonus_tom.py b/task3_bonus_tom.py
new file mode 100644
index 0000000..292866c
--- /dev/null
+++ b/task3_bonus_tom.py
@@ -0,0 +1,83 @@
+# %% Imports + Data loading
+from traceback import clear_frames
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+from pathlib import Path
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression, chi2
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error, f1_score
+from sklearn.svm import SVR
+
+from sklearn.ensemble import RandomForestClassifier
+
+import glob
+train_csvs = glob.glob("./data/train_*.csv")
+train = {Path(t).stem : pd.read_csv(t) for t in train_csvs}
+print(train.keys())
+
+def read_descriptors(path):
+        headers = [*pd.read_csv(path, nrows=1)]
+        return pd.read_csv(path, usecols=[c for c in headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']])
+
+features = pd.concat([
+    read_descriptors('./data/train_descriptors.csv'),
+    train['train_rdk'].drop('0', axis = 1),
+    train['train_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    train['train_mol2vec'],
+    ], axis = 1)
+
+data = pd.read_csv('./data/train_crystals.csv')
+# %% Train / test splitting
+target = data['spacegroup_symbol']
+
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.33, random_state=42)
+
+y_train = y_train.to_numpy()
+
+from sklearn.neighbors import RadiusNeighborsClassifier
+RadiusNeighborsClassifier()
+
+# %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    ('feature_sel', SelectKBest(chi2, k = 100)),
+    #('fitting', RandomForestClassifier(random_state=0)),
+    ('fitting', RadiusNeighborsClassifier(radius = 50))
+])
+# %% Fitting
+pclf.fit(X_train, y_train)
+
+# %% Prediction
+y_pred = pclf.predict(X_test)
+print('f1 score: ', f1_score(y_test, y_pred, average = 'macro'))
+# %% testing
+
+test_csvs = glob.glob("./data/test_*.csv")
+tests = {Path(t).stem : pd.read_csv(t) for t in test_csvs}
+
+test_data = pd.concat([
+    read_descriptors('./data/test_descriptors.csv'),
+    tests['test_rdk'].drop('0', axis = 1),
+    tests['test_mord3d'].drop(['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles'], axis = 1),
+    tests['test_mol2vec'],
+    ], axis = 1)
+
+
+pclf.fit(features, target)
+test_pred = pclf.predict(test_data)
+# %% saving
+print(test_pred)
+open('./out/bonus_3_predictions.csv','w').write("\n".join(test_pred))
+# %%
diff --git a/task4_gino.py b/task4_gino.py
new file mode 100644
index 0000000..6b8a42a
--- /dev/null
+++ b/task4_gino.py
@@ -0,0 +1,77 @@
+#%% Imports + Data loading
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+import pandas as pd
+
+from sklearn.pipeline import Pipeline
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import f_regression
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import SequentialFeatureSelector
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.kernel_ridge import KernelRidge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error
+from sklearn.linear_model import RidgeCV
+from sklearn.neural_network import MLPRegressor
+from sklearn.decomposition import PCA, TruncatedSVD
+from sklearn.cluster import KMeans
+from sklearn.metrics import f1_score
+
+feature_descriptors_headers = [*pd.read_csv('./data/train_descriptors.csv', nrows=1)]
+features_descriptors = pd.read_csv(
+    './data/train_descriptors.csv',
+    usecols=[c for c in feature_descriptors_headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+)
+feature_mord3d_headers = [*pd.read_csv('./data/train_mord3d.csv', nrows=1)]
+features_mord3d = pd.read_csv(
+    './data/train_mord3d.csv',
+    usecols=[c for c in feature_mord3d_headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles']]
+)
+features = pd.concat((features_descriptors, features_mord3d), axis=1)
+#features = pd.read_csv('./data/train_mol2vec.csv')
+#headers = [*pd.read_csv('./data/train_rdk.csv', nrows=1)]
+#features = pd.read_csv('./data/train_rdk.csv', usecols=[c for c in headers if not c in ['0']])
+data = pd.read_csv('./data/train_crystals.csv')
+#%% Train / test splitting
+target = data['is_centrosymmetric'].to_numpy().astype(np.uint32)
+X_train, X_test, y_train, y_test = train_test_split(
+    features, target, test_size=0.33, random_state=42)
+y_train = y_train
+# %% Full model defn as pipeline
+pclf = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean', verbose=1)),
+    ('scaler', MinMaxScaler()),
+    #('feature_sel', SelectKBest(f_regression, k = 100)),
+    ('fitting', TruncatedSVD(2))
+])
+#%% Fitting
+pclf.fit(X_train, y_train)
+#%% Prediction
+y_pred = pclf.predict(X_test)
+print(f1_score(y_test, y_pred, average='macro'))
+#%% plotting
+#plt.plot(np.linspace(np.min(y_test), np.max(y_test)), np.linspace(np.min(y_test), np.max(y_test)), 'k-')
+#plt.plot(y_test, y_pred, 'r.')
+#plt.xlim([0, 50])
+#plt.show()
+# %%
+
+test_descriptors_headers = [*pd.read_csv('./data/test_descriptors.csv', nrows=1)]
+test_descriptors = pd.read_csv(
+    './data/test_descriptors.csv',
+    usecols=[c for c in test_descriptors_headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
+)
+test_mord3d_headers = [*pd.read_csv('./data/test_mord3d.csv', nrows=1)]
+test_mord3d = pd.read_csv(
+    './data/test_mord3d.csv',
+    usecols=[c for c in test_mord3d_headers if not c in ['identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'smiles']]
+)
+test_features = pd.concat((test_descriptors, test_mord3d), axis=1)
+test_pred = np.round(pclf.predict(test_features))
+#%% saving
+np.savetxt('./out/task_4_predictions.csv', test_pred)
+# %%
diff --git a/task_1_predictions.csv b/task_1_predictions.csv
new file mode 100644
index 0000000..902f2f6
--- /dev/null
+++ b/task_1_predictions.csv
@@ -0,0 +1,3363 @@
+1.519287296180124258e+00
+1.595668238440623554e+00
+1.761665516099711226e+00
+1.616008280898476812e+00
+1.346486401111206987e+00
+1.756734981712256172e+00
+1.850615103194002131e+00
+1.194324032913819167e+00
+1.259515970382778960e+00
+1.415963559309076913e+00
+1.224706778853580147e+00
+1.351782439193193852e+00
+1.545554475551341511e+00
+1.965095791039772166e+00
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new file mode 100644
index 0000000..ef17673
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diff --git a/task_3_predictions.csv b/task_3_predictions.csv
new file mode 100644
index 0000000..90639b8
--- /dev/null
+++ b/task_3_predictions.csv
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diff --git a/task_4_predictions.csv b/task_4_predictions.csv
new file mode 100644
index 0000000..2923ee8
--- /dev/null
+++ b/task_4_predictions.csv
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