[38ee8c]: / homeworks / hw2 / day2_hw_lgg_gbm.ipynb

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "118afe79-328a-478e-b304-46892f7eec69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import flexynesis\n",
    "import torch\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import random\n",
    "import lightning as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41940d7-9466-4804-855e-84d7753fa533",
   "metadata": {},
   "source": [
    "# Finding Survival Markers in Lower Grade Glioma (LGG) and Glioblastoma Multiforme (GBM)seed"
   ]
  },
  {
   "attachments": {
    "36cf8c63-6090-43a4-ad49-7444e27cddcb.png": {
     "image/png": 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Xi90RMMbERLv+2uehuX66B8c5pF/WYLqfk5MDpWCMtH/r1q1btmyhC127do1mXDn/3oT5d2y5ZGMj5DD2Ftx9aQYGY9t3Nxnk3GK6GXlQRd8qNwCoiwe5ySMhPz+f10J6+k6h0MFDNPqGBVt6n33uVGJiIPD5v/Whv9yIjh07ugaEe8ScwX8rUVpaWlxct3cqd5k/kniO3GszLy+Pl08EmF7svlduc/XIDULfW4gBScOSksJuiNHkBri3LmS4j8EvxpCeG3yZhyylTBEQAREQAREQARFoAoGGZ8vROi2r2PrPZ5tROCPO69xx3XqlhZU5lmeXuraxiT7rFt3PkAlOkDyoX6qFgLEKkt30Z69HW6vbLGzx5tK/f7r+nlP7B5fdXFgX3WaXDhmwkxCA2IR89s6CXLu6dCtHVjpNL9jZTjmXjO86ukf779dtcgV6pMXHx+y03LVHanxCbJuqWhEQJYuzOL0uiBqL5nQCb5HgNHocMHOKq3JKAj1dtLleKAw2/l/Meeyxx7zNZlpy6KGH7rXXXt5MX3rlypVr166dMGGCLz/cT2Y7zz77bElJCTMZZh2YUcV+++0Xzp78Dz/8cMSIET169IhgE+Wl6dOnf//99zfddJPJgrT8pZde6tmz53nnnWcevvjii+++++4Xv/iFbyLEVWYs8LngggvQ+xqsburUqfgZMmTIySef7Ixfe+21zZs3u5+ICBgcccQRTLHeffddyFxzzTXuaoOJ1atXv/7668Fm3LUbbrghOJ8J81dffcWdiix1eQtOnjyZXl900UXeTG/66aefHj169CGHHOLN9KWXL1+OHyZ49Peyyy6zq7RkxowZBx988AEHHGA5L774IgYXX3yxrzg/P/jgg7KysvPPP993idkgd+Tcc8/t1auX79IP/NP34FA7WqH1hQfkP//5T/BNIZ/b511of8wxx0R+1sJ16tFHHwUdg8dN1NesWcNgu/rqq33qVTgPzZ7/8ccfMz550LzP0RNPPEF7EHYjD6roG+MGAG53ZSTMnz//yy+/PProoxnMrnbeDDytMHTyrrvUhETwsz9mzBjGf4R/n2jCAxtNw3iUHnroISy9wyPcIxaNQ59NM76u8cxbgpe2twr+4Jg0aVL//oG/gTzzzDOR3z/B2N0r1+szQvrbb79F+B45cmQ4GzcIfW8hBiSleKjDFQyZH81Nd2/dTz75JPKj5G18gy/zkO1RpgiIgAiIgAiIgAg0gUADelP0Hqs3bC1ftCp6+8iWxEmdNqbLScN3Uql8RTgL0uX07FAfBeMyfQmC0Y4e2oEVji6fLcY4H5OoLstBPvt4Sd6Rg+uCd5yZdzN+Mvt08P/TembtLmNmz8JJn73zYwkCcpITYzI7JF40PmN410BYkNsujTTrTH07xSXHt/GKaQh5HZPq9cT60DJfNVH8ZDuzWeuL2KNt7sZimh2yRCyK4x7xGTVqFHIGgSH8i/2KFSuYUxEaM3HixHCd27Bhw+zZs6PXztARmC5eddVVTKGZJzAv+vzzzzMzM4m7CVcF/lEimkU769evH9rZxo0b+/TpQ3XIOnzzExWPKRlpJvzdunXzTvjDtSpCPt7mzJnDjGvx4sXHHnusd+5NrMeZZ55JWWawBN9NmTKFMIrI2lO4ivr27cvs164yGR42bJj5QUYJWYSon7lz5+6///7Ra2djx451gXIhfUaTOXPmTAYVSBETiZIwtgwtyvJt2hm1EDzldLRo3GJDR9A7vOEzURbcHWb24DjP7i7Qa0YC4TMMLca5GSxZsuSNN96gCKox0VLcGoYlzxpmkXVk59+XQINDfj3jjDN8+f+tn4iAiIPc30GDBlkb0KHoHUOU8K5dH1S+fu3iSGDgLVy4EL2P94NF8RApmZWVhfARHC3oqzr6n+7ZJ1yIdw5qHUMCgdXePMF+mvDABjsJzpk3b57VyJA77LDDgg12MacZX9euJfai408lhCpuE4qYybJHHnmkL1rQFXEJh71pr1wGBmMggnbmKmqWRPPedG/jm+Vl3ix9lBMREAEREAEREIE9nkDo6WgTul29Nqsqr6DVjrVaTfDgiiByXXZAj+OGRBLOMI4+vMt59iXYQf/qA7s57YyrhI8Fa2e+UrYi0pvpllh6M116bK/kbinxbMOPDpWWFNs3nY35Ewh/c/Fitleas4+c2PVem3+0snunbvxyZX7kVZkgitye/5WrzEItVgVtq3fv3qyO+eabb/bdd1+mlIggX3/9NRNgdAECwY466ihm+/ztnHn7Aw88gEiEmhNs49bXGAH+nZx/n7dFhVSEKoeYgqTCVSSnzz77jBiQyspKqj7++OOpi+gPLn366adLly5FdVq0aBF6EzMo2oD0wNypUWAtNICoHKed0RFqRAHkEvWi4BAMEtwSF9FDM958800aQBcOP/xwC3/wtWHZsmUwIZbtqaeewjmTFmdAsw0v3wiC0FuwYEHTtDMkSHNlzmmh/Xz//feZGyMqWT6hGfQRyLAlh5+058ADD9y0aZMJoyg7FouBDZIBeiKhHAAfOnQo9wUJlT6SwBgpkH5hj1TqjdDBbbj7Aqh169YxVPCDioRkAGdWw8HZyJuahoyIEyoiIBENiHhABEcaQFCejR9uDVFayDE0EjNEDbqPH+LX0DqZFTNOuB3oEYTvweGUU07JyMjAZ8gBidTIyEFJxOHPf/5zyNBxBB0Y0rWmzZPdg0Ol7kN3Vq1axUgmnpEYENPOGF3vvPMO0Y48MmbJ5JzoS0Y7OvLw4cOboNfABKEKSW7w4MGudksEI+XWA/PUU08FHY15+OGHKc49wp4HnNF74YUXhruhPufhfuKQQYhM47QzHgSMuadUYYMKUf7tt99mJJDPqCBCE4YhRy8hWpFHoBsJjLdXX33V2yrKohej3L333nssrWVsjB8/3hffxxgjlInwPR5tAkvRL4jo4QZhFvIxYeRwNy+99FKriIeFek888US6wzpZmoosBV5727jGeJ99Ylc7d+5MrCVYeI/xRiJci39LwJhLNIa74Htgg23A5ZxHn6DlPO80xv7Nw72fQz5iuA1JgLvGE3fOOeeg/GLDW5HXMtKt73UdfauwhCHPBa9i0qA77rjj3DJke7ORz1uXSyxzRkfjJ68U/s2AJ51hTAMYtLwKUNMYzNw+DPh4sfteudxHXiDcbipCs2aoYO+9iZRF6GTw8AccMbP8jPwarK3Q/0UtPAg8mPxJysNIwxgbxBoHj//gmx7y9eWvgI1rQ72f//3vfzOiXON5P9hzR/GQHY++nU0beMHNVo4IiIAIiIAIiMCeSmCn5YG70smyhSt3pTjRTUnxMV1TE/brm/rg2UNOHZHGBvb2cX8J9vlPTawX/rbuvKzSZxnhJ6qWVwtbnVu/Z78rVRu443612rDNb5NbXL/3P3YxOwuIB2SmXH9w95sO63HDod0v3Cfj4H6pxKk54Qz7tvH1d6EcbWznCLCqmlbejA5tm0HMwuFz3275dNk2E87YCm2f3u1/NqHHX0/ue//p/Q/sW78BU2pDK2HrufxPpWzqy9/yCZFgcsK8DiGAqRfzLnJQoJi3MPyY/iGuhbTxdRd9jUngk08+yfSSHWGQQpDPLKaM2SNzoXHjxqFfIKY899xzyDR4xgNRaQMHDmTi8dZbbxFHcNJJJyFw4MEm5L4qIvxEkWFySzuxQapgasF8iYmTBaCZgkPsSXBLnE/mPxigvPC4EUPHzMRdcglmp4CizUzzSLt8X8LCT/Dmy9/Fn6iTNgk3P+gFVMR80irihqKYYICuB086wuQT5Qiw2DN9RbciJAoViS5ghhRFPktQTQREIGC2DB9ENNfOCPcFzwDnYzqCkUdOYsyYYoiyhh/yyYEYrWKujgrGfWcwIGFYLcyWiQMiTAY5A8ER6Yd8JszMe9G8aAwJJr30DumTNAKcuQ0etOTTLwyoFHv0Ju4pw4muMbRQWJhhWqWN+qZSGuk+tI3ihB/SL54XhhlzVyb2ZHKDaLBPviHfhnrIEdVgS3gSuY9ID1aFs0diCEaKakNrbcxDm/6C1Ipwl5EnItxQ5zlygucasQBpklm9WaIR8+zzRLtBxePDM4iAiG7IMCCeCMuQo5f8yCPQjQRm9WC0D2OYbnJP6SAvHMRWBGXUE+QPRApf+3lAaAbaLm8VBhJXecmEe0zgY8+FOSFNs0mTQOyYNm0az06DAqjJ9wwYhL+XX36Z9wlvQkYvbyHkRd8DG9LG14VoftrtRj3nw4PDmHSlaEnwIxaOgD1x7iXAGwZL3+vaeY4mwUBlSS/C2UEHHcQ/1fBnhFcDtceKq8Qp80yhzCJj4ZY/QXhlkeBx5nXBg8YLjX7xZ4dpsr6qva9chjpCLaOdUcEL6qOPPuIPNey9NxGpl06xGyZPKzVGHoS+utxPRgv3FFnf5DlesLYKNXj8+246L6iQry/n2SVCNox3mrfx7rkL1/Ho2+nqVUIEREAEREAEREAEQhKol59CXvZmDhk6hJ9MHuyfRr2XSHeYdES7/UYF61wYx/Xr0Sct6ZUL7vJqQK54WmJyu/g2Nx+ZWVVZwWGR6e3iWteKRfx1MCc7h+IDBg7gb43O3iW6pcZzcqX9XLyllE27khsfJJVXUlW1Y59+XIVcomjHDrh6p68pPG3nIwXmZdWdSokN7ffqYq5UhIT3iM9NhZWV1TVxMfUCWW4JOfXkeqYl+vZfi+DZXfIJckVl1VNXBGIB7HPKqPTzx3VxGmJG7amgdmkPWbG5o6fu/7aJPlNQcpgQ8g/mDGwUB/7+zRyDCRhTFD5MeDBgAhxsw4TZeSNBdAnBNcx/bKrMXJcFU+hlzKL5d2+mMRbQhB92dWFGimfmosxhyGcGhYcBAwbwkw9CmM2gvP4bTCMhoWfxvJiCg5BEDnNIdBnmKjgkxiG4JUx+mIrjnGm5hXQRH3TvvfcyyyXEyVsprDBGiCETPkSy0GyLgSIHJeuVV14hwWNrKgl+vMV3Uxp1ki6jXxB0w3TRonsuueQSiyCApEUDUTvzYbcDHXfZ2oMiA39b4YUGwZop9AhUCbtq09eQ9wXtDG+YUQrJhikx6gCqDcEXiAt8IE80FuSJNCQft1deeaUFs1CEW4+QarUQDGVxKIRsMFdkbCBL2SX7xrMteGQsmWSJTbgBidrCBmG4YkMlijM3pnnMyZlCo1N43UaZRn5yChRFrrvuOlqLEMNAZVAxbBj2jCt0AdNZbG2g17nl2FVvfpRpYm0I/0EY8g5IRKKQSJEyEbYgxkyeMQAxzKCBGMH9inBDo2wMZiAlDohaUL0Z6lQBBG9xauHWM/7505CHi7m996ovHXIEBj/+MLR3ER1hSDNokfCQeukj++IxYGgD9XKJVvmqYPDQYHsvoegxOCM8Jr6y7ifdZH/ACCvQnSUJpA1eF9xxRJMTTjjB3hIMEhrve2ABGGzjdRVlmueCYWn/VgErfvLYurLBjxiggBbyReFKuQSeva9rlx9NAgkb7cbtXch4QNF22hyymteJb3E371IePf4QIUwVM5443sy8OlA/+Rnulcs7BMns7LPPxoZRgcRMjv3p472J/KHGSCBQF7MoByGWwR9i9NBtyeeJQ6MkETz+fTcdLS/c68vnP2TDCLfk8XeNd0UidBybaNrpXCkhAiIgAiIgAiIgAiEJNEI7YyJqLpiskmBiUFBWzfmSREqxCDE7pdXmNvWKj7eyge3j2pfXjN0c+mpsWkxNWqvMVKaLto9YnU6EXoaKYXV5vbn0iG5Ji3fsap9fWvXFygKO2nRXaWNlbYiEywlOEOT12tzsuvpqLw/rGmK1SEb7OA4iQGUzD6/OyT5qSAe3mDGroIJdw5zz8X3au3SUid4dOAygtS3GzMovZ/ex/TLrI7/mZZXQTnOFtkhjmqCdrcsrRz5zJ2+WVtY4n3ju3SHRCWdIkCs8hzAEdyG7uH6bueCr/ys5zCJoKpMiplhMdPm3ff7ejwISsv1MPBq0QRAhvIIPihLzasQF9jBmjoSGhU+kFvQd5xwRyi29IZO5JaoK/xRPcAcyB4E2Fs3k7KNJoOUhlzAXZV5B6AePJ11jvopigpqGgmMKXXBLUAzxT6VWCx1BdCOOw1epBS8QwEIVFnQDNLc6D2OTZpj2EyXBukVvB32udt9Pms2szIQzaqFTTJKt4/zcZ599fFXTfoI+WHhIQXQu39Vw94X7S2cdMcibakk+8zqcQB7tDLGGwEamr0Ajk8gv88+UGIULHYGfVMEgtHwcMv2jtT6FwgJ5sEEctLCvCAOS6pCKMEZboV+PP/44VTComPybcmd1Rf9Nq9DFnD2Tf0Yv02MUAdb0kc8dp/vYmB7NdNcZW4L+knDd9F1t8CdCDLeJmCmQOuNwSNEXUAkZn8zkiU9ByyaBBwpymxjbu/6g8SjxRyHyKyoV30gwPrmKu8Pj/89//pMaebh8gojrgiUij0CfMcsPWQ6JtksUEpeAQM4LL7xgZmj94eRR/m2AFay8ixgeGEd+THyV2k/kGN+wDGlmmbSK242kwvpuxCNeFIwZ/jR3qrQrG42NMw6XoDrEQf5twA1I3uSMOhuQIR+xcARQZMLV0rR83paMEPc+37v241whR9o/N9JaVlmy8PyKK65wlFDHsHT/CMEw5o8hWu6KB79yLVCRaFBnQ3H+JAIROeFuYqMGofNMgq6ZcEaat5M96Q2O/wivL69z0tE3LHLHm9ZOX2P0UwREQAREQAREQAQaoZ15YbFN/3MzN3+/roDIr7iYNn84rs8riz98YNpOG7I4+yfO/O1hVRmrz/rV9u11GpC7RCLt6APS773ZmxNl+pD+qa/NybGIKoSnJ6ZvYjXlxMFpKYkxa/MqJi/NG9IlaYLnTEyUNA7W5DsuhsC21qtzyr5clb9sS/1hna1btzpnbGAvId+HNZhnjen00LS6v7Mioj30Zda1B3VrlxCzpajygS+z3D77SGCnje5UXhmimz6f3p+DOrd1J2ni6p7PNlx38HaOBK2qrvl8RcErs7e6VZwDOidymIC3bIS0k8OwQeD7YFHefpnt6S+7s/VMS6Cz7vPCd1vJ4azSJZtLnp65BfHOXQpOzNtYvDavvKSiunNyfHq7Jo6fYLc/cA5zOWpkusU/7DMtZ0bNRJf4IydweNvToA2THGY+FGfqxVSNDzINm08hXRH1gytmcV5BhzmM1z9pNp1h+sc8k9k4KhUSTGM3CzOtCgEIpczKmuaC4oCCw2zKgh3CtYSwO9ck0jbXcjkkEN2YgbgQJNI0lZggK0iPgs+L9Bb/YdL00SbMVh1aCQnrC3qHt49mQAAO9wh0REYgDXATfe0MeV/QApiRmnPskRpRLbl3CLKoVOTwDS5bPEgaezJdQBYJVAwTdLxNAilmweSDo5AiDEhmsDjhw9gmAI17hJaKgMiHbfUY5HY1+m+GtJseWynrDnN4m8bTbIKM0HFsVCNbmBqLMf/+AXZ71uxBiL5eryX6HTx5TtGmLd8GczBSqkY7I6aGDwE7hD7xOICaW2z3K+QN9dbVYBqxg7uPKodChyCORum9iRRH2EI8ZRM9BgCjAkE8womuDY5Ab3tYgMwYY+cyGypAoGovBMv3FrE0o4JL7j5GeEy8ZU2otRyGk/dShDQvQwpSI+owZzKCHW2R9w96YnCpaGyCS/lyeHNSI6It22+5S9wgUy29d8f48IhFSSDCP925iiInkLeCn19XxCtH8gpCbyXE0oJMsbFB7m0/riyTqyFfuXbLvDVa2kqFu4mNGoSu8SRCjrcGx3+E15fXOenoGxa5401rp68x+ikCIiACIiACIiACjdY+qmtazdlU9rdP1uSV1MUfpbYNOOFvmTWhpLE6xIHLNWzk4ye+fXtsr7AHEfqNd/6N3DOkS9uFm0osm0MziQjjP2eVU1zl1c6qt29Hh+I/Z+BNICUdPjBtWNfA+rXgz/HD099flLdmx25oU5Zt47+UxNiCHYtGrcj4zPZd28evyfVHXgQ79OawzPPssZ2f/GYzLSSfaL67P17XLn4jsiABYs4SYe7YoR29ipi7FDLRy3MeKI7vm7rxvqkBwwP6ptwysRcLUbfs2CRuQ375T1+rj+ngaE3aYYKdLRd1AWsUX5lTdtVLy0iwfRvtCXj8X/swy0IlYc7JfJ4NZfi27bGZ6ofUzlBGItswP2H+xuSHqAKDwYQH0YHYHBMUmDpacApVsMbNN4dBg2A6wSI+wrWY5LCoE/mssdoZbWBuzASV2ZopOMgW1G47c6PvWMPCtQStwdQ3zAjFcrEPVgpJjkk764BcDBThPAS80GvabDa7+xukLqaJxkAyuEaaTUeYR9lkiUZiAwTm8xbf4S2CGcIZKifbkJHvDWsys5D3BUVm5cqVrPZyrowbYYNoNMaN0CSuWg6yrI0BppQWEUYoCvVaHBayJhKMTY/BTikfeVeLNxFhQLpZIiMKSoSD8SFM7MEHH6TSJmhn3npJgx1ViBWybs0pSgRzfgQ11vPi35RfekcQCvIxi8KQ7Rjw0fTLV5f3J86JnHL7xIVDylNARcTB8RxxX1gvyZMOEyDgLeQNbeyDhh+it9Dy8Axh3/5uDFF2gOI9QMgMlqyU5JFk3WjI0dvgCPQSQINDjDvrrLNcbBQQ+HPVllFjycMYeX2o8waikI8J4wRu7vFhcLpoQRuizkOEBMOeqwQAMiRwde211/IiIgdiwaVQuxq0CS7ly+F2888V3keSbeCo3bSzkI9YOALm2YXvuZBVX43R/6QiHhnEZbtrPB0Ex8Ek2IMFokLDXaIsae4Uf/qQ4F4jQ1va2fgSjH9oo1+7GFvegYx/+5ebkDeRGiO/Bn1VRP4Zbvx7S0V4fXnNGtWwyB33urV0uHY6JTq4iHJEQAREQAREQAREoE1jEUxdVfTH91c64ayxxX32reNiE8YM9mVG+ZNtxc7du7NX1vEVRFYrqaj/m6jvqu/nAf1SLt8/rIqHbnXF/t18dfmEM7Ytu2R8V4SnJnxOHZU+YWCqt2hxRbVXOKPqC/bJaNSC0IP6pnBiaXBjZm8oIvvi8V3oVPBVck4c0QlZ0C5tKggoFD1TA5Mf32d9XgjxwmfTcn4yg2LmSQwOf3HnoC4mvcRM0TwmJKSJT0FeYbsrcpjoksnfxREFmHjwl+xwNq53BFkwj2XSyISN4nyYV7MIlNWLqAYEF/Dv57hiURXaHKFA2FOWKQ1yiU3PmBijqSE3MPtlruWmx66KaBIIZEzSmCY5bQ4RzeVEaAnOmfCjiVA7vaD9SGzeGpmdMiVzwhmXbOUa+V6z3ZpGhAIpdxCM7Prv6rIJIeFFNB6JhHkvAUrcUBQupBbuS+TZJlNWJAMmpeyohU/67jyTCL4vxLMwp2Wu7sxoALcYzk6gJNYGA5eDjsC9Zo9wxhjxWWwVR6UMMDwgVXA+AD2iAQRMcd9tka9zHjLR4ICkFB2h++gpDG+blrtREdJnlJmMXixt6Z8VobN0nMHDuLWd41goyk+6xlZWjGry2fQqWLuMskYzY07LLmZO1IiAlIcOGZTRYiOWmwsKFwoXfEMb1Qwz5nazdpVn3PdQcBX9FJGIx5wlilSNek4msZDhRi9XI49Aq5EXBQ8mnKFNmg9jCRmXWshntPNy40QFhpnZR/4O95jYe4kdtXDIjfM9C+F80kd7tfJoIFrxJCKtIroxSimCUowfFiPTTSwZFd4HNpxNuLqC83HLu92nYPKTukw6D/mIhSNg0gl3Frc8O/TI1eh9XbvMBhMMVGx4xrlfvLvgQEyixZySTw7o+DAs7ZwQwhidT8YMIw2tDQNebvwzD52i5c4gZALdlvHPH3Ng588jesH2lMGW3AV77dgtiGYQBjsJzgk3/n03PeSfucHeyAnZMF/jrWCUHTfjcO0M2QZlioAIiIAIiIAIiIARaFzc2eKt5USc+Tae3xWUsRkd44b1bbKHvXu1v/P4Po98vYmIMF+rODdgQOe2hE0lxkaaThA0l5Ecd/zwjkcPqd8rLS62NUdPWquc/LR3r+R/ntrvgWlZbAfm3bwfM846GNk9+aeHdrcQPJZDUtwJU21cKnw/qeW6g7uzCHTayoLc4srAnGPHh0ud28WdNDL9pBEdrTHMj/BfXdvAWN+hnjtK8X92RjtrTGcC8bxbm+EhJSGWpaYju7X7+WE9WKHJ7mmGjmZ2aR9/1tjOLIZdvLm4tCJwkByWdPaIQWlfrS5wcWrkY4y656mtpSdRUvjQSqQldA12XzZJhQk5kxZCWrjEvIVM9nJm6kUEDTMoQqvYmDmcDXMb121ispgwo1bwIZOJFvKTbSc/adIkVDnblogixIkwAcaGSDSUCGIECENgkSbhOUx4yGdazv5EznP0CdqMEws6s1LM0+wATfsZsiUmarCTDpqOmbFoyG3mRQ6CBZMxF1JnNnwTcUZMDZM6PNgEzF1qxoTTXJiQo8iYvkkfkZnsEgINaYQnBB2CfTjxgP19sKQNzDwjkOQe0VPOhrv//vsxZkcw7jiD4Wc/+5m1n+LB94WjA7H0dRDyxDRB2+WTRhW1HOZpDA88s4QNA3Zks2MZSNNyZv6PPvooabStM844g4TrskuQ6f2EG5BeG4QVtAM7ZpR8KnVrwbxmjU1zx5EO+XgLQp7pPbN0HhxCOOmpG0vcHabo6DsETDnJwFs2ctpLgAFAgAxqEUUiIEU74z6azks7kbeQruBMqZA3NHIDwl2ly4yc4KBLGsyZBshYCPSURSFlTDLVDzl6w43Ayy+/3Op13WftJznuJUaavrAUlEWssEV5wRWDjZtuBUN+O29svxXyMcEDwXrQ48No5CnjXw5CunKZ+EQitLMjQQ1wTk3hFmCAZENY2fPPP08aCZtXCrGQ5LDi1T2whCWGtPFp96664ISFs3nfV9igWPFCYKySDvmIhSPAuKJJqIePPPII944umBP8eF/Xwc0Il8PYO/3003kMefa5R7wo7A8Fuxf2NrOyyHannXYaz4vXFTn8O4HhpTh/bJlAT/Fwr1zaj8SG4sYHV9C2Gr1uSYOIoEgO7rzxxhtDvgbdIPQVdD9pj0tbglbxCTn+vW/paF5fOMR/yIbxfvY23rUhXMcb1U7nTQkREAEREAEREAERCCbQmn/WC84NmVNUUXPbu2tQVXxXEYzuPrHvy4v+c/+0//gu2c+nzrr9sMrOK477iW/NJn/563r7le0vOsErFTkP/NP32jVrsdln332IXHD5wQk2IGMh4aJNJQg9XE2Ob9O/c9u+6YndUuLZR4wVlKyCzCmuJEyMMLSyqkBtyFudk2O7piQQLIYylRS/098CV2SXcdilVdQpOc57ziYrQ9dvK1+QVcKeXxigYfXrlDg4oy0LSKnLiqBVrc4td3+17ZoSzx7/dinyNy3bmF+xMruUY0OpCOP0pFh2bevbMZF2uh3KCsuqNxZUmH8q5agB09SCndOS5dllM9cWcqQDIlvndrEsSu2eGug13ggL2FpUuT6/fH1eOVpYt9QEznywS5wtYNJYQlyb3oHN0VqzrnPmmkJ6TSPZ5mxoRhKEgRNc6f9cDovmCPhiksMUl3UiTDzcSiXXl2hsMGagMm75yzozTxPInAcuEQGBLuZyfAmqYBbKhI2W+C41789wLSHOjr4zheN2N2+NzeiNYC7mt8zSI/uEJGa+WxCyCDQQCJhg02vSPFYU9Fr67gsBYsgWFjXmNYsmTUWMjeDG0ymaais6o/GDTfQDknuKXuzrVJS1NNmMscSfLCgp9AtxEH0H3Taa29HYGsMhjeDHd0MjWDb5EqOIEci3jSvnJ+TobXAEuuLhEkQY+SoKZ+nLD/mY8DJklNoqP599Y39CAG/cdxt+dJ92+pxEY+Mr0oSf4R6xkAS4I/yDQXBTm1CvK0JFPOBNe2/wNNGe4D+VnPPghI1A/igJVo6CjcnZ9UHodWu18x1uWEb5+mpCwxrV8Qbb6e2U0iIgAiIgAiIgAiLQCO1s4ZbyX7+1omLHmY+OXRO1MwKgjjmw6/238Lco58qbiF4785ZSWgREQAREQAREQAREQAREQAREQAREQAREQASai8BO8VaRnc5YWxQsnEUuEu5q4B8kD9un0++uCiechSuofBEQAREQAREQAREQAREQAREQAREQAREQARH4wQhEtZbQWvPVym3N0Kw2reN7dk0795i0q06TcNYMPOVCBERABERABERABERABERABERABERABERgtxFohHbWMSku5L5d7RJiE+Ni+nTstl+fESHb2bFt+5i4tu0PHB3ft0fSwaNjhmTGdE2XcBaSlTJFQAREQAREQAREQAREQAREQAREQAREQARaDoFG7HcWtNHZjl60bsWhlM2+u3jk/c6Q3jDY0YJW7IlrO22zdTSb8nICOhtys+u5bXjMElF2U2anduzZt9h2hXdlSWDAlsZsJ4wNO/JS0I5U5xL5fLvt2zHgYEF2fHdbKfOTjeHNGxv64oTaaQy75PLNFry0B/9mQCYNo4g1hp2DyQnezZftvWkwOwQ7PxSnv05wpAgnNuIEM1prOxBjT2vT0tIwpkaaQWNoAE6wsd2a8UARq9eaZC2kR5jRfjbLd9BoOR9y6D7thAnFaTzNwxsV7Y7Nv61V+hYBERABERABERABERABERABERABERCBlkCgEXFnP3t9ZcgWJyfEXH9I9x4pP+iRi8g3U6dO5eBzE7k4+r179+4oR9OnT0cY6tSpEwoRKg9nzGdkZKD+cNI8AhBH6eXl5WE5YsROIXIrVqxYsmQJpVCISPA9fvx4OkvBL774Ai3JnfKOlvT555936dKFA9GNxrfffnv44YfjmSZ99dVXyEn4Wb16NTLTIYccsmXLFqp2B88PGzYM7YwiJuHRGFq17777OqkOn1lZWXPmzOGIOi4tX74clYrGYPD111+T5oMN3+PGjaOPn332GX0fM2YMmXibPXv2UUcdRXrz5s2zZs1CMkNiW7VqFZIix73z0xqJTMY58XbeH31ftGjRqFGjBgwYQNUY0Eg8GBOOFMRswYIFvXr1GjJkyLx583Jycrp160a/qLdPnz5Y6iMCIiACIiACIiACIiACIiACIiACIiACeyqBRmhny7eWhKTAOZvNdYZASP/hMom9QjNC5zIDtCqkK3QrE5IIvEISIufoo49G/UEFQy8jugoVacqUKV7tjBz0MkqhqeEqMzPTBYKhEBGWRXFkKUQoVxGqHIIU4hE5eOabEDA0tc6dO++9997oaOQgbOGHZqDlOaHNeRg4cGDv3r0JJfv000+xdM7R5r755pvhw4ejZOGHBIKgKWtUhHpljTQ/5nzNmjVoeZYPBGsMIhoeqMV+otYhpdEMiuCHCDUqhRXpjRs3YkO+fVt3cnNz0cuQ2/BMPq7oCyiQ5EaOHEldtIR8fURABERABERABERABERABERABERABERgzybQiHM2f2AQtqoxQqXIPQhAyE98MOYb4cn0LEohPKEcYUOclDlB8EICI5KLqCuv23Xr1iFdOU3KVi+aAZcIwurRo8d3331n6pLlDx48GCkKb84PtSNymeBlmQR5WQI9a1PtB+HJOSG8iyIbNmzAxi3/JE1rCUnr16+fCXDkEH1mfvimRnNFWctkhSktXLhwIcKWM6Mi5DYXFIZ4hzLI+k2WZJoNlwgrI41whiVddmUtsXLlSgLfkALtJy0k0s1i5dAZaQPNcwqjr6x+ioAIiIAIiIAIiIAIiIAIiIAIiIAIiMAeQ6ARcWc/cJ/LSsuokZgvi6UKrh29DM3IYrKI9kI8Qs3B3lmi9fBxxefPn2/7oBFO5WxIoCjZQkhvJmkcolWZ6mRrFQnUMhtWMqLELV682LkikotLXhXMeaOdWPLTFpNagwlbW7ZsGTmsBvVqeRjz02wQsJYuXUr7J06caC1Ea9u6dSuuULVsXzPSCG0obkharE61Somko+NebYviqF0WU4YNa0jRzpAa+UYZJHLNCrpv+g5Jrwcu8ZPlpUTzzZ07F2+k3UZvrqASIiACIiACIiACIiACIiACIiACIiACIrAnEWih2hkSEnFSgE5NS3Xil4878hDyDYFX5CProDdhiR7koqhIowE5ZQqdi4As1CiXYw4RoQhG8znnJ5aEkrGrmgWLoVs57Yyre+21F5ec6mTRYYSD2SJHrzd0qwkTJnhzSA8dOpQibIXmwtPMAPXNAsQoxaJOouGoxZVlqamLj3OZ5o192Zw0RhgdjYch2pyZIQKidvHTND4WuiJ7IYER/kYMmuuF88lVWHk92CUENTqO2oiChvbHnmuuiBIiIAIiIAIiIAIiIAIiIAIiIAIiIAIisOcRaHFrNpF4ELwIuULlQRFz0VUh0ZtkhmpGKUQ0xCY0HSLCCLxCgbId990+/Zixlxn6ERv5e72hRlEXkWUoZahCrNO0CDXkJ3bQ57QBgtrYWZ+Fik6cojjeWBPKWk4EJn6iRlE7cXDUjhNqZ78wU/2Q3vhpHy5Z1bTcYseoyy3k5BLyHB2h5Qh/eObjvWpHZ5orbz6UWC5KEJm1kOWWtAcnWIKCqLSZM2eywb8Fr1ELtQ8aNIgFmwSdeSP1rG18442mIikiruFh/fr1dvoB9wWfVI0HJ8y5UkqIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwB5GoNnizhBZUHZ2kU5VVXVJcTF+TDPK6JIRchUktSCW2TpEb42jR49m6eK0adNQdlCREMUI70LiQcMy2Yg0oWoIXv379ydszcpizEGWaGdEeJFGFGOHfqKu0LCIyXIKEQFiSEgEZBG2Ru2UxT9inO16Rg4SG2szqR0PtJ9LZJLGktM/rS62Y0OWojGWT3rGjBk9e/Z0EW0oWezojwZnjcEPEpsZUwoxC/XKXLHYk9Zav8hhCzai3lDc7CqNwcmXX35pTaUj1IW9K8ICT1ae0kjsqdSq4Nuw4Hb//feHCdVRhEyOCEAyY4M51EnMCO5DVbS69C0CIiACIiACIiACIiACIiACIiACIiACeyqB1pyoGGXfjn1ofkhLztm8+8S+CaU52dnZIQ2akIk60zkjo1u3ruEWbEbwicRDzBRyj+lBESx9lxCq+CAbmd7kuxrlT6sdNQrJKcoiIc3oONFwu+hn153QBpx4V7nyE13SKY8hG69MERABERABERABERABERABERABERABEdgzCDRb3Flz4UAt6tCRDbs6oM4g0zTBLcqXV+uJ3gMhZi7KLPpSPssm1+7zg/DXWO3P54Gfu+6Eu8DH63nXfXq9KS0CIiACIiACIiACIiACIiACIiACIiACLZlAI+LOWnI31DYREAEREAEREAEREAEREAEREAEREAEREAERaHYCu7SusNlbI4ciIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0HIISDtrOfdCLREBERABERABERABERABERABERABERABEWhZBKSdtaz7odaIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0HALSzlrOvVBLREAEREAEREAEREAEREAEREAEREAEREAEWhYBaWct636oNSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi2HgLSzlnMv1BIREAEREAEREAEREAEREAEREAEREAEREIGWRUDaWcu6H2qNCIiACIiACIiACIiACIiACIiACIiACIhAyyEg7azl3Au1RAREQAREQAREQAREQAREQAREQAREQAREoGURkHbWsu6HWiMCIiACIiACIiACIiACIiACIiACIiACItByCMTm5eW1nNaoJSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLQcgi0Li4ubjmtUUtEQAREQAREQAREQAREQAREQAREQAREQAREoOUQ0JrNlnMv1BIREAEREAEREAEREAEREAEREAEREAEREIGWRUDaWcu6H2qNCIiACIiACIiACIiACIiACIiACIiACIhAyyEg7azl3Au1RAREQAREQAREQAREQAREQAREQAREQAREoGURkHbWsu6HWiMCIiACIiACIiACIiACIiACIiACIiACItByCEg7azn3Qi0RAREQAREQAREQAREQAREQAREQAREQARFoWQSknbWs+6HWiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAItBwC0s5azr1QS0RABERABERABERABERABERABERABERABFoWAWlnLet+qDUiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIth4C0s5ZzL9QSERABERABERABERABERABERABERABERCBlkUgtmU1R60RAREQAREQAREQAREQAREQAREQAREQARGImkBlZWVxcXF5efn27dujLiTDRhBoDd9GmMtUBERABERABERABERABERABERABERABESgZRBAOMvNzU1PT09OTm7TRosLd8td+dHFnWUXVWYVlHdLSUhPjmu9W5C2UKdVNdtX5ZTSuB6pCUnxMSFbGY1NyIK7L/O/1aRtpVUrs0v7pid2SIqLpneV1duxj4tp3T01ITEu9Nsqr6Ry/bbyjPbxGcnxrZt78PGPC2tzy0orqnukJbRP/N97rudsKEyMjRncJSka2rIRAREQAREQAREQAREQAREQAREwAkREIZylpKQIyO4j0Lg59vEPzCqtrKE1/3fqwHG9628MmVyyVj578Qjkg93X4gierXmvXD4qvZ1f70BZeHZG1nMzNlVUB9rPJy0p9rpDeh0xuKP9jPD9u3dXTl2ed9MRfU4Y0SmCWTSXvl1bUFBWtV9majj1KpyTkorqm15dunRLCQZPXji8d4dEZ/neguy/fbIGRebFS0e6TG9ia1HF799buTCrPsBw3z4pNxzW23uborHx+myW9K1vLf96Vf6vj+575JAQdyFykzYXVpzz+DxfMwZ2TjpoQNppozMM764M1/s+W/vp0rz+nduu2BoQHIM/Q7u2u/+sIeRv2Fb+u3dXrMiuN5swsMP1E3p19Ihur8/Z8ui0DfbsUKRtXJvLD+xx6l4ZPrcPfbH+5e83k/nIecP6d2rruxruJ27/8tHqaSu3VdfURecOykj62eF9GtSh/vrJmvcXZF99cM8zx3Y567F5AH/0vGH9gupFRvzH5DWz1heWVdb06Zh42QE99u+bao3ZmF9Ov75ZnR8X02Z8Zuo1B/fksXLtJFj4p68uKa+seeicoS6TBMQe/WoDzwIOR/dsf9MRvdGyH5y6nuHtG9veUuHSb8zZcu9n67h6w4RepwQhDVdK+SIgAiIgAiIgAiIgAiIgAiKwZxBgqWbXrl33jL602F6EDpAJ19wdulOrDxfmeG2+WrnN+/O/lXbNC27AL19f9sTXGxHOMtPbIm0grm0rqfrTB6s+2LkjwQXJqWm+BcNoHHe+v2pTQUXIiiJkvjVvK8pCfEwbJMukuJ2ixnYIJqFLL99acsGTCxDOrCyqR0yb1jPWFFz1wqLckkorE41NaO+7lkusFg5CrsdusEnupnArOyfH8x/9Wra1hLvMbbV2ufHQ2OFKcCLCWc8OiemwbtPa/jOf7mda24BIRLTCZp+lAABAAElEQVTUxc8sQDhDDts3M9UE5c+W5V374mKnlN314ar7PlvHTyLCDhvUgW/S5DwzI8t82jcs3l2Qbem35m71XoqQRtg678l5aLvYDOvW7qD+aYBgqFz/8mILM4xQ1mlt2DiePnvu0cVPL/hyReABRw0ktg7FE9mLn+i5N76yhM6SRgX7eHHOTa8traq9p+Qgq/327eXzNhR5VUXytxRWXP3ios+X5SXGBl4+360tuOaFxbg6c+8u/Hzg84AK1qhPflm12btEo4rLWAREQAREQAREQAREQAREQAT+pwkwp9ZSzd19B+uDRBpVExPmW47KREewUh8v2klKa5SrH8CY1jJFp6I/nTTAQmZQw+54dwWKwD+nrD16WHpzr59r/j6tyyvH6Vl7d7lk/+6N8v7Xj9egGBKn9sDZQywaC8ns0mcWEv72wrebiLzDWzQ2jap0142jb9ITFwxPTgiIidzTe6aseWd+NsqgrwGNHa7PzQyoWkRjuUjDnOLKMx6dS+bLl410qzhRu+7+cDUKFKLVP04bhGiFAfrdVc8vQiF6Z97WM8Z2mb2+8JPFueTzvBw1NJ0En+dmbnrsqw3IfKeP6YLoZpmz1xUWl1fzE2UNsY8oKvd8mUHI70embUAFbpcQ8+9zhxK9hU1RefUlzyygwURyER8aslT0mah4DBXGz6PnD4tt0/r+qetenbXlyekbUQnfmLOVWhCjHztvWGVNzdmPz1udUwrqiUM6Hnnf915hzlvdY19tpJvHDu9088Q+jMwzH52Hf8IPUbT/HreGe4fP4LhRrwelRUAEREAEREAEREAEREAEREAEROCHJNAU7YwpPRPjmWsK9qtdukXICTNey/Q2fd7Govs/X0fUSbv4GBQrFgmiCxDR84vXlg7vnjyyezLL4rB/99oxTJ5ZdThjdUDyOKBfKqvGkBvO37cbk3ByQvqxil6dvQWZA+2AlWKX7t/DMoO/WQpH5il7dXZrzdht6meH96bG6u3bmfD3TQ+sj0N2YcXc+rwylIhR3ZNZpOmdw28rqSR4jZVrXVPijx6afva4rkgJlGKqz4q2uRuLUASIVDpjTMaJIzuT7+sp0WFd2sdjzKXfvLV8WNd2tx/Xj7T3E87V1S8sWp1ThuWbc7cS4seCPm+pCOm5G4qQcjC46+QBbpUo0UNXHtTjnilrEWu4FI1NcBXE6z3zTRY7x3EJyeaKg3qgfZDm5hIJBbp/TVlL1YhKk/bKOHzHwliEpH9MWQthZKbjRqSzl1mwZ3Ka1iTu6ZheKdzE+JidtNAoh6trCRocTvgZciWpMyMxdVkeGhmJP504wIQz0qwbZaS9PS+bcYt2ZmPv0IEdnHCGzXn7dEWTQsTkFuzfr279I6GFtZe6kcDt9NX5B/ZLIyfCh5HPoksMEOZMOCONknj9ob3u/GCVrfAlh3HF8ky73SN7JP/0sN7EvkVw671EYB0/6ZGNdnqBdra+Vsm1YLeLxneDPN0/Z++uD36xnkweW54RAtZ4M/Boe71xx6csDSiJVxwYeFop9ddTB7IH3IDObblNRw/rxALMKUty4eYtpbQIiIAIiIAIiIAIiIAIiIAIiIAI/BcJNEU7Y4+wjxblfLgox7Szr1YF1nMhS9nCLusMq7pQmkgzS88trsR4flbRE+cPJ9JkbV7Zhvxym/OjprHIi1VvqABMnplyE7fCfxTcVKvLhPMTG9P6le83M1fHEoULCQwBy6r2fTOHN4HjhBEBSct9iB764Pox7qfbagoNAq2BQJjznpj/3CUjnHz2+NcbmeqzUo/dmkj36dj24AFpWGJGp7hEQVQhNCm0ADZ+8vU0IbZNQXmdjlBYVpVXspOmQDMiuCosq6aD2BRXVLer2GnBpmt/yMSCrCLy2TnLu7UZOccN78R/ViQaG59zbtD/fbyaTCTLlIRYbugf3lvZ9awhQ7q2W7SpmLAplvK1T4zh5rJWdGHWqr17p6S2jWUZJlu2UQpWndvHEbXkc+t+NqpJMDEFbuO2cosXmzAoILm6TzTD1RmT2FRYjjTMfeeWefOD0wtqt5AjAoveea/eMKE3/1mOrVg8qVZO9dq8dNlOm9MhM9njg/BUXlXDcs4352xtUDtbWiuMwvOAnVU2pDr+s+p4smyIcrNiWrcmAPOyZxf++7yh3i3zvA3zpQkrg8aI7smWb7vmdUoO7Cdoei7yqF0a3i1gY9vDPXPRCNJIpXbHzYDvrPwAW1pCM5AIyypqxvRqf/F+3e10BdviDVE+Su2M8Lo1uQFN2T5PTd/If+zIRijijjz9XwREQAREQAREQAREQAREQAREQAR2lcBOc/4onRGPg3bGVB/Zi+n4R4sCgSRoMV7tjDV3ZBJaRTgSWsB1Ly1mp6T3F2Yjo5DP/JkQrTtP7N89JeGNuVuY3iNV2OI7onVQXlxLwvlBCPv3tA2YXbRfdyJfSLCVGAqdK+gS6/LqZte9PPvru6uWYAMy26P9z6cMZB995LYbXlmyZHMxgtqtx/Q1G1r41IXDid6697O16D7vzt+Kdvbwl+vRyAZ3aXfvGYM5Y9GUPkQ9tw28t6ewYt0fGtl9Zw4J3pE9giskPNYGsp/UlQf2iFJWsDbbjlfIfPYT/e212Vssbd+TRmdEY+M7FPK92rAsYpFMIbIt5+k72pm5RUj9/Qn9WXTN4lCUNaIITxuT8cDUgNBJ3NM/Jg1CJyW8iNgob2NcOpomOWPfiQGcmUBEobtKIprh6rVfUxvi1yUl3psZMk3EIvm9O9af2+AzI7KMAUCmG3vIgos3BSIB7TOka5JJTp8uzcUSPYv202C0M3iisUY+MXNNbQNQnHcKtNvh3P7/yFcbGKI8nr+Y2IccG71s8P+HE/rvbBj616WeNcJsYcY6UOyOrz00A7eknW5oCScQh3RHMCb5PO9uTzqCE5Fin7t4BEOiW+0ZIy5cLqQHb+ZPD+/96zeXIzu6TDQ4Mt1PJURABERABERABERABERABERABERg1wk0RTtjkkywCREoLCvbp08KISREvpBwrSHOizP7CDtCCFi4KXC8I3ExaGefLMk17Yyc3x/fzyJfvl0bWBTGEkjbtYq1nJxgSPgSmZH9oDVQ7/n71B0nwRF7IbUz/Ngngr5AgAw2dArhjAQq2I0Tel370mJCYOoKt2p18qjOtuzx4P4d0M5W1Sosts70JxN6UQRLQpCI0Jm/sWjWuoK9era3sq6nzlXIRARXbA4VskhjMwleY78qbyk65f1JOqQNqp/XDNkFTYhd9BG5iDOasTqfqzvCvwKG547rSoHWrVtz39d+t2llrcSDFsml6w+p28brsEEd7/4osFlYoEDET8gmuRLs0G+LNInOY4EhY+bSZxc+cf4wtBizaXC4OleWQOwj0TMtrCLm7K3pPmHRXQ2ZQCdizaO7hHZp2hmrcclk6z2+kZV5cOgIqjSaozMOTjTMrlWrabXb/BPCZk8iZ7wyegmrDPYWOYcit7+zgvs1tld7ho2rus2O/repjdKriXhDTW6jIrQ8jtfcUlh5xfML6SlnxbLSGRGQSwQSRm6Ju7pXj/Z3nzzAyWcIZ/wk0xkoIQIiIAIiIAIiIAIiIAIiIAIi4AhUV1fn5OQkJycnJSW5TCWiIdAU7Qy/TH3Rztj0ig2MmE4fPqSjkyq4yqJFvlm7d/1Li72NyN+xUBFj22KMq9lFgR2jXGAOaRYYmnYWwQ8hMFhmpKCe1Ukkg7okkQ7WYpxnNBFftNfv3l3JRB3la/22gFwyKKN+9PTrHIjVYrcmpxGkJQXWqfFxG4dxybZzsrVmdnVA5yS0M1pu2pm3p2YQ8juyq5BFosk0yGtyA+FRfGjML4/MBBGhOk5Ei8bGirvvzYUVd39Yv52Wy3eJ1KS6cWV6KPkc48h4ING3U10QHJLLiG7Jtp2WK2iJRjXpt8f0dbWw1dqkf89h5SyrKUf1qFtmiM/Iw9VXuy0Wdmt1fVe9P2knwrHFqXnzv16Z/9qcLUO6JF12QA8bkwQ/2jrHY4Z1yqwNA2RjL3cAJeKRBVuxFvj5bzfhir3z+CYkM7J2xoJKzIiaZPx41U1uEBsIoucS2mlD9Na3l3tbyBgIt9mc18yliRn8z6zN/ETGvenw3qaXWdc4msCOHCWBQXJCpFdKamLdVR46nCCWsUvao19tYIc7tDM7hCH4EXbNCE44+YxLEs6C+ShHBERABERABERABERABERABCBQUVHx/PPPL1y40Gj07NnzoosuSk1Nvffee8eMGXPwwQf/8pe/vOmmm7p2rQtO8kKLcMlr1tLSS5cuffrpp++8884GG7ZixYrHHnvsrrvuimAZaaIboRgrMZmcf7M6v6wyMGE+dnggXsZ9bPVWSmLs747faTt89uA3G9veyNJsXb+yVSnygStukhk/I/hh5yYMinbsIEaaKXfIWTcKQufkeOLg2ACeswtdLUhpttk5M38TSmxBmRnkFgf2I6tX5lwxT4IWYEAoDUvz3E7tbO6GiQklJLw99RT1J6Nx5S8TxW8i+LBCo2FTf1pILcfURjZZnJ05iMbGVxU72cHzoP5pxB8h3zw/c9Prc+pjqXzG9hOhxKSW/NLA+lzLLK0dPMH2TWiSOeFWWsTW9+sKvNpZ5OHqawBHW5DjHQw+A/czsNXXrFYsrkSfYrS7/Lfnb0VToyXk9Etvy7JE9vZiYy9+IrOa0spZAc7+3do1sPxkVFfWqrU2rthZj/282MDLWfoSA2sVXkbg9JX57swBbDhQggYw7B12TpglDtRbnNvh/RkhzcplhDOadNtxfb1bsKF80UK2sSPckuLLtwTWovp21vO5Ta/dKI2q3V5yKbVbxbHFHJYmo3tJ+orbT1b73vXhatRYftIH1qKyCpg0UZA3/WdpILdVKzr+m6Mz3SEVtXn6EgEREAEREAEREAEREAEREIEfIwH2U7r//vv5Rh3r0qXL5s2bH3/88YceeuhXv/oVmUbk7LPP7tChbtfsPYOR61qzdKd+wt8od8ReMWFGNfh+XSFzcrc+0Zz0rD3FD0EB+cDm0sy9X/puM0Erpt1462IDLPy8MHPTxMEdUZoWbyq2xX3YRPDDqkAM2Dhp3oYiPJC2wwe8nl36qoN6sLUWkT4sLLWjNpEb2B8NA9bHMVcfnBHQmFiVhgqGlkeaPcv47l+rTZAI9xmQ0Zb4u1e+32KqHMWnrQycnOANYQsuG1Lja5qrYOfenNE926PUoJ3d+tYKdmSz+Cx0Cg67dGbR2DhjEoSPIZyRuP3YfraWc3l2/QZeXktfOrNjIi1hcaJtoYV2GW5nq8Y2yVU0a11gzSY/OyUHdCv3iTxcnZklOtZKexvyA6GIkT+HDuhgsiwHp/590iDTgxjM02tXRB7cP3BK5tUH9/z5a0s/X5bHFoHuqE3Sdv6p+UdrI3HdIb28UWZs8I/iicRGiBayL489y5kt0My1ikAtjnxlqfJdH616+Jyh9qxBgO3MsEHc5Nuw55VU7l+78peVm3e8s4IVqfecHtXaxi+Wb2MrQJ7xx84fbmsqXe3oaFx6Z162aWdvzcsOVDogUGm4D7iMGIjspJHJi3MxNj2RkwRI9+zQwBmg67aVm3CGMa95RPzxtQf+frMqv+6tXxvniFm4ZihfBERABERABERABERABERABH48BBYvXpyVlfWb3/wmLS0wXyO47PLLL//qq69KS+vWqJH53nvv9erVq3PnzvPnz3/zzTfz8/PT09PPO+88ItQcqE8++eS777678sorvSrbnDlz3nnnncLCQuLX1q9ff/755yPPTZ48+fPPP6+pqSFz0qRJlZWVf/7znw899FDyiYCbMGHCMcccg1sa9uqrr1I2MzPzggsuaNcuoMzYhwYTNcYK0/j4+OOOO27kyJH33HPPFVdc0a1bNwxoIZtEtW3blrIbaj94wD/eioqKhg0bhjfMaMAzzzyzYMGCxMTEI4888sADDyRz0aJFr7/+ekFBAR0888wz+/TpYzXy7av0gAMOcJeaqJ1R/pjh6cgEJCYOSfdFsCAinD6mC3rZFc8tPGlU562FlVOWBmbI4zMD8Sm+D/uUPTdjEzLBGY/N5egAr6YQwQ8qG0FPCDE3vbaUQwzLqmoiaGeEn7w9L5vlgbe+tZxN1tjcnTVitpbt9mP70h4CiGyfMva2Z88pommI3CEfOcPXWt/Paw/udf3Li1HlECw4UfTDhTnoYnhjEytbVeqzJ6aJswI4pPKQgR0u2Ddwy90ngitnEyFBA46873uvAUrNzRP7sEiTjdvY1f70R+YSikUU3ndrC922U2YfjY3zjIxiUVE/f30pm2cR34R8ydWvV25Dx3FmwYmL9+9+29srnp2RxYGYxB+9M68+8CrYOPomcdiiFS+pqLZloYQ3Hhqk4EQYrr7aOfmBnM0F9YGQPgP3k9jHXx+defPry9BPT35ozuhe7WkDK3YxYBc204YINyPqjW3O/vzRak4AQKVlCa1brYklpwcgAZM4YshOx4PS4Ce+3vjBwuzrD+1lZ8hyeKtPO6PUlQf1QPNlMF/09ILBXZJYMskdYRCiCF9+YA8Mrjio5y1vLENgQpWOjWnz4cJsKPHQcSmaD4cYYMZmdj95ebGzRxO//6whPNqvzt5C8ObP/rOUBxDJm7GBAu7MQiZOH5PB8bi3vbPisEEd1uWVU4owNM6dwJjjd/m2Fbshy4bMRC8zsZKrnKka0kaZIiACIiACIiACIiACIiACIvCjJYAkxPJME84MQkZGximnnOIFgpZku6GhWCFCDR069MMPP3z00Ud/97vfmRlaGNrZVVdd5RXOtmzZ8txzz02cOHHQoEFvv/02EW1VVVXff/89Gtm5554bGxv77LPPUu9BBx2EpDV9+vSLL76YpZRTpkxBlqI6wt/Qxfr27fvGG2889dRT1157rWvSa6+9RsELL7yQdaZc3XvvvVHK8Hz88ccTWfLNN9+g6y1ZsgSfJ5544rHHHktTV69effrpp1Mpq1PXrQts9U5j6NfVV1+9cuVK5DZEQ3w+8cQT1I7Dzz777MEHH7zjjjsiVJqQUBfbsdMyLlcgcoL1UBgQ1WLLvo6tXQboK8KUnn3QmaUTboZwxqT6tmP7oSjVFt3Jtn1CzBMXDmPfIlarse8Y2x5NrBUREBGxC+eHSwhbRLEhE7BgEOGM4Je02m22gqvA+B+nD7pwfDdEH8KdOA8UrYE1jA+cNYTtyaw1fz55wCEDOpBPxBnCGfrLn04cEFiUF/SxPdEtGwPMMKYIBSkOlj+fPJCrIZtx/r7dgIZ0QrSOz3EEV87SyLuflnDCJSi8/9ly2oEZSRxiyCpI9DK0TnQWEoQywdn5icbGGZNAMKIXSGaPTNswf2OxHSpKp+ZuKGyz80pALwTClG48rDcFGQ+wIhaMXefxZjfa6590g01qvWODL7RI+4/BxgBAkSECy3s8ZTTD1Vs7+5TRSHwihHnzQ6YJkXv8/GHsowdVzkww4ezMsV3+6DnFktNmrzu0F2MPTZa+Awp7SuEQPu8tyCHBDbJdw1wtJkTSqe/WBRTqcB9Cz56/ZASjjluPhEcbSPAsPHb+MB46SnH8xa+OyqR2gtcQeXF41t5dzqs9YcNGjveOee+X1TizVh/Hp+NMgo5wlTA3thiDOao0EhiC2j9PH2zrrMO1lnwOij23tnZOX6UUGt9fTx1IMBqX7ARSW7EbwUOvtISQDwJFJi/Jtf8+XZqHWQQnuiQCIiACIiACIiACIiACIiACPxICBGcReBVNZ2fOnEk0FvpUv379EKf2339/QsYoSJDau+++S7QaOpfXz7fffosMd9RRR2VmZiKW2aVp06bts88+RLERI0aIGfKW5SNs4RZjfubl5c2YMYMINQLT0LPQv1C+iosDBwzaJyYmZtu2beXl5Ycddtill16KbrDffvvNmjWLq6tWreIbdY9v2sNmbQMGDOjevfvYsWPHjRs3evRoQtWQ6swP27rRtsMPP7x///60hEqpDt2Q5tHgNm3azJ071yz5Dq7UXWrtbZzLba4EU242bCLWiWk2ekRIt/M2FnGcJYscbYkZNpMemUMkDlobOogVieCnsnr76tzSjOT4Bift5opt1NkMvk/Htj6pwq7ijW3dmc+7Dcssv8FvtsdCOONcAjob2Zi+cEABylFsGCDRu4pcke9qVfV2tqaKi23DIr5w9yIaG3NbXlWDCkkYXWAbtdat0FNoNivvfCdy+trAT6KE1uaWEVHoWwAYbGk50TcpnIcm5BMYyDkYxHkh9ERZHCDgbRsXw/Zk4fCCiKWg4cZehIrAe8ajc+85fVCEQyRZxrg6p4xTIDiNwVQzn0NCO0sqalgHbWtLfVd35SeHFTDs3TZ20bgKHgaIeic9NJsd396+ZnSDD1E0VchGBERABERABERABERABERABH4MBDZt2oQmFaGnX3zxBUFk3l3zWThJYBcyFksakZzcWQHEi7HO0RY8OoecFUAamYllj0cccYTLJ0HQGd+obHwTDsYGajfeeOMjjzxSUlK/sxMFf//73992221u0egtt9xCLBgSG+s9vd4o26NHYPkUH0LYCB8jYo7iKGIs/CwrKyMI7uc//zlCHooeyy3pAstOrXY2dENNQyOj7O233872bQhh9M71moWliG4dO3YkGA1BrbaSVv/6178GDhw4ePBgOysguFJqN8umr9m08pG/ERF8R1sG2yM1sY4PyysO7IH+RTQKwhk/9+5dvx9TBD9MswfuiB0Ldh6c06V9PP8F51sO3hpscMiyaG1Rym30BW0upBPLjN5VBCfBl1C1htQeHRB8yeVEY2PG6C9e7OgmUUon3O4Im9+7lrhE9E1yRXY9cc64rmhnbOYVvXYGEMIqI1fd5Dt79QuLiMwa1aP+iQiuiFCsyEPXHWcRXHYXc6KUQb21BA8DVqciK1+0fzcJZ15QSouACIiACIiACIiACIiACIjALhLo3bs3YhmBXURgmatly5ax/hFByuc5KSkJucoyCd1ikSZrKvnJ2km2HiP0DKHNu2aTHcpsdSQ2ubmBDX/4tG/fnnAz21wMEY1Vk5bvW3OWkpJCvNgll1zCVQQ79krznvJJznXXXYfONXv2bNZsEl/GKkt0LkLPUNyslLmN8E2v8cAqTmxoBq1NTk5es2aNK8K2brTW/QxZqV2tk9Cc6Q+fYKMxVDPq5Tg/9u9ntyZUmL9NGhhZYPrh26kafzwEiB/kXAuivRiNLaHXLPVlDSZ60x78YXE3svJpY6IN9NuDUahrIiACIiACIiACIiACIiACItCMBNgOH1nqySefRJ8iOoxNythTnygt1jb6ahkyZAgSG/FZBHa9//77yExm06lTJ2LTCNp68cUXvUVGjBiBdsZm/GhkKFx2icwvv/ySiDDqeumll77++mtvEZcePnz48uXLN27ciLZFZByeXZAXNmyF9tFHH7HHGWodBmyORiZrSKdOnUrCu8G/cxgywRkIKGIIgqzN5AwBzhwAAg3GmPg19EEyXcGQldrVSAFQrvzuThDmw55ZK3NKWXFGDIstA9zdlcq/CEQg8PMjel93SM9mX94YocYIlzgWI8LVPeMSG8AROscZIHtGd9QLERABERABERABERABERABEWg5BNjjnwWM9957rzWJgK+zzjrL1zziwhDUiBdjB30usRkZO5SZDZcQtlgded99982bNw8FyvnhoAA8o09ZJlobO5ShT9liSbY8Y1sxX8SZlWWdKStAWWvJT44yYJWl14wtyVgQigaHZ/YmGzVqFGY0j59spmYe+PYWcZmWb23mVE2ckLPXXnvtu+++9IKVqpxLQA7p0047jf3diD6zsiErtUu7d78zq0PfIiACIiACIiACIiACIiACIiACIiACIiACzU6gwf3OXI0sYGTpJYsugyPOnA0J9Ck2F2P9pjczZJoQNo6wZBd/4sJII7r94Q9/MEsi18hs8IwC6iJCjdWUwf65xCkHNJXoM7tKdBtbnt18882dOzcivoTVmvTFVm6aHwLi8MxqzWDpLbhSK9Ii4s6CGSlHBERABERABERABERABERABERABERABESguQigHxFN1qA3ArKiEc7wExcXx1JNYrsQszjCEhHNOecSH/czXIK6Qgpn2HOJeDRXkCWWLO0kWq1RwhnF2VjNObEEkllwpl3yVeoKKu7MoVBCBERABERABERABERABERABERABERABP6XCEQfd7Y7erVt2za2EsMziyK9Ulez17V48WJ6On78eBeG1uxVRHAo7SwCHF0SAREQAREQAREQAREQAREQAREQAREQgZZL4L+rnbVcLs3aMu3M3aw45UwEREAEREAEREAEREAEREAEREAEREAERGAPIhBz6623RtmdyZMnl5eXu5WlHFXA2aXuZFDOFmV1K0eWfvLJJ4MGDQrecS24Fo47nTNnTmZmZvAl5TRIYO3atbNnz252emyMN2vWLO41nhMSEnzNWLJkCWMg3MJgjH0GbA24tPbDsmr24XPeaPz8+fNxxZEWLtMSbF747rvvEuqZnJzsu8TP77//ngFG8xh7GRkZXhv2Mpw2bdqHH37ICbhbt27t2bNnNIurg6sImcMRthwUwhEhvqvhKrVeWN/5prXkcKwvxbOzs2k/uyFySLDXG0ftEunKA+XdwtBr8AOkP/74Yw4YbvZBFbnlHDzM+nbvrYxs77vKcGW3SwaDLz/6n1XV21+etXl+VpH9l1Nc2T0tIaZN63Aevli+La+ksmuK/+mYvCR3xpr8zPS28bH1/yaxMb/83QXZZVU13VP99iH9f7goh5dnx6SG9wUIWVyZIiACIiACIiACIiACIiACPzYCRUVFbP//Y+v1D9zf+jlegxUz4eeIUDNDCPjNb37z29/+tqKiwnJeeeUVRI2srKy//vWvnFnQoDcMONmUo0yjsZRNMAF0lt1BD+Hppz/96YYNG9yddVVzbis3HYXU5fgSPgMe4Ouvvx5vjzzyCMffvvDCC2b/97///fzzz3/iiScuu+wyHKKvef1QL0No1apV3kzS5P/pT3/C28KFCzlog9F4xhln0Foz40QP6rr77rtRpnBIjeecc87GjRt9Tpr889NPP/3ggw98xSNUar147bXXKEhTn3766SuvvPLll1/GA7ohHfzjH/+ITOkc5uXl8TSRz7khLpMEEhuH5nI7vJm7KQ23//f//t+uiFBNa9g//vGP4Nsdvau3337bjhyOvojPEmHr319ueHXWlk8W5763IOf37608/oHZCGQ+M/fzlVmbP12a5366xJPTN+Ln48U5LofE099kkfn+gmxvZoT0w1+un72uMIKBLomACIiACIiACIiACIiACIiACPzABBpxzua4ceP+/e9/M+Hn3AE2abOGcp4CG8KRnj59+nnnnfcDt/7HXN3RRx99xBFHNDsBgqQ4GuNXv/qV1zNhXKgq33zzjU/ZcTYhDVCOUJcQjIhTe+utt/72t78df/zxRGC9+eabDz/88NChQ1esWHHJJZcwcg488EDnKlzi9ddfJ6bsoYceGjZsGDbos2guv//975FOiC9Dj0OuQp4zuf2qq676yU9+QpvvuuuucA53Pb/BStHL9t9/f6voX//6F9LzmWeeaT8hRtDlmDFj7Odnn31mCd83jxuWdM2Xvzt+zpw5k3A/w7s7/O8mn7/85S+jiXJtsPZrDul5+KCOmJVW1lzzwqKXvt908IC0BksFG7w+Z+upe9UFwVVWbycYLdhGOSIgAiIgAiIgAiIgAiIgAiLQXASYEJlQ01wO5SeYQCO0MzQypBOWdLGsjKVzCDf8JEE+4WYEziAE2CSfKJsXX3yRqyeddNLPfvazmJgYIpLuv/9+xBdacMABB1x77bXeFXwEvCArIKkUFxcfdthhN954Y2JioretBQUF6CAsamOJ33HHHYdIR9QVWgzOqRRx4de//jWhTBMmTECOoaJvv/22e/fuJ5xwAsFH1I7Gwfq47777jvxRo0bhH+2G+CnWpt1yyy2IBZEbQKfwiXBDj6ju5ptvZj1gyCJYEvHEGjRonHLKKezYd/LJJyMMoRDRQpay0ik0IFb/oeyE9EBTcY6KQfN69epFqREjRlAKnwhD9K5///4IQ8hbnM86ZcoUxKNwRd5//310K27NwQcfzLO07777Tpw40UuVkCiwI5bRMFpIO8FC8+gmPx977DFEUrNn8SbF+Tz++ONeDy4d0mDZsmVHHXWULfCkLMabN2+mC1SHcMZP+sISYDKdn3AJXgTEqV100UVO2aFHF154IaoZ6/VoMAFoSGkuTpVzNyBMOJLvDcIJIPfcc4+JgIwEFiyzDDMcQFQ5hjEDnsZTkXfE0s6VK1dGqDS4Ixzx6z0NBOmTQDaGk1miLZLDGPMWJEKNAUAO34xk2kCopsmalZWVRO3deeedJOj44Ycf/vzzzxMNSibj6tlnn+VRwiFDnZsY8ukjRo9+gYLlqBZP+s4770yaNInquPunn346I4GxgWJObCCrTXGOW9oMQ8YkUX48PmjoqJlnn302I4dvcDHsOaKYUcdgo1X33Xcfdxwnt99+e1paWkj+3i5b2pblYs8QZTknj/zFF1/Mgxyy+FNPPYUlgY3eHsGE24osC/ZDDjnkpptu8r1Sgit1OW3j2uzXN/XV2VvIOePRuXcc129E98Dy4WdnZK3JLbv1mL6kV+WUXvrswtU5pent4i7ar/sJIwKrcfns3Tvlu7UFmPXpGHiDfb4sEJ42sHNS4FqrVlU12wkre29+dkX19n37pOAqKT6mvKrmH5PXYllRXXPIgA7IbWasbxEQAREQAREQAREQAREQARGIhgDzcVZ9RdhbKRonsolMoBFrNvv27ctElG2q8MiUm4k3Co6t4COTS0ghVhk/mTPfcccdKAIYECLE9ButgTV3LFVjrs4lb7OYk7/33nu33XYbugYzbebA3qsU55LN/6+77joWKuIW4WP48OFoB8hquGXyzAyZqTUG6GvoLAhMuLV1gqgzzO0JekIPYk0f+gKNx4bd2UwMitwANoFC1GAqziydCTxdo3khixCXh8iF0vHoo4/m5uYydad5GNMpNsayTtERlIVwHmgqOh26Ax6Qdf75z39iiZRGTxEiaS1qC4E29KKwsNCW8oUsgqwDRpb74Y0HCckDe2uAfSMjcheoiE4deuihtBldBrUFoW3kyJFoLt5YHh5C6PEJt6AvpAHK1KWXXmrVffHFFyR69Ohx4oknIoNaJsoLuueAAQPsZ4RvzBDIGG9mgzzEh9vH7e7SpQtDi/yxY8d6r6LvXH311U7+s0sPPvggG/OhunL3uTWvvvoq+SEBoruxgpJloU8++SSu2EnNPLjvaCpl8LN3G8OVwUNdjElXHNSImyhc5CAyMkLQGd1VSyAsIlGRRiVkxLqRY1eJ4GNQ8eHGoQv/5S9/IaiNriE8sfyTQYL4xfrWcE8fg4cOsuWctYoRhTZtkYw0hpF8zTXXMNTZlPA///kPNTJOkMZQvRlR6FmwZUSxpBT1HD/HHnssGjoFeeJQtHnM6TVPPS1HXCMfDjgJyd+64/3GM8CRBRkqN9xwA+vBTd0LWZzbh1RNcW+PkLBhi4gPEF5W6PJe/xHSLDdfmFX80eIctC3M2PuMFZ1mn1dSxeZllp6/sWi/zNQHzx565JD0f0xeM2N1vuX36pDYr1Pbt+ZutZ9vzN0ycXDHuNi6rdMe+mL9Bwtzbj+u398mDVyRXXrrWysw+/vkNQhntxydee8Zg7OLK4rLd1rCbH70LQIiIAIiIAIiIAIiIAIiIALhCDBBI6aEGS6xI+FslL+LBBoRd4YMMX78eAK+kFqYrhNIgpyB2sVEl0yiyZxOQZQHYV8DBw5kIk3sFSoD9qgVJq4ROMO0nNgr13TMLr/8cts6nfAxZv54YPmYGaARsB04M3AGRL9+/U499VREAYQk9B1UsAsuuAAzBA5qJ0oFnYi4MPbDoi6qZraPQwyIozFdgHayztSWzhEXgyuuRm4APukp83/W36HioTKELIJkQJwXmoVJPKhRiBFYhvuErBRjJKpjjjmGBMqXBRkREITIRcfJBB3xaAQBed0GF0GqQM4g8g4zQKGneO1JE1TFfSRWiDSqKDcIsQO1ixuH6DB69GiffRN+WqQPTUX4QLXktiKw4sf2wgcOCyq5j8igDTpHiMTGDQlGC7fDSqEA8pog4syGH+8L67VdRbFyp1uQwx0nQImAKeRL4sgQW80sGCBiLsyJosKAUYfUaJbuO3KljFUskbRsJy/+EYAh5B3zCJSgRlLkXqPMci/Qf51zSxCnZnAI0ws+psBrzJiHAKoxGjEhWhbO9n//93/IbRGePp4R4hbND5rv/2fvPMCrKNo2DOm9kpDQey8KiqIgYkHFgg17ryj2Xj57x+5nwe+3N1QUFVSaKGJHkd57D0mAhPTKfx8GluW0nCQnISTPXrkOs7PvtHvnbNgn7zvDhLF86xAl+/bty6Xjjz/edBsBC5UNhZpM5jZk6LzpFRqZMeYS4prpM7MUH1IOMplOBrUn/tg4HUwVBsVXD2mVLzJedchwFRa3RoRqSSkWvEPRe/31150qd3v6+MTV/JhLreLDLu6X6tbMZHZKjrhmQHPSnZtG/Lt+x7g56f3a7H5eDeuVhEZ2w6CW6G7IcDcc1fLVGetNqW/mZVx4aEqLOIdL2uX9mz0zZc3W/BIWWRsxsMXRHePJfGpYx2Gj5xhjfYqACIiACIiACIiACIiACIiALwQIUcKzhLdO3lJxnvCliGwqS6AS2hlV84aM+wlKGS/G+PtwV3hj5xTHKOLprLa5ZNKoJ/gHsegVpwgWJtMoaHiLmFOkN2SFV3YdJodPbrkllPAOTA5+LtZVI8FQOdIPPi9oTCZYj4ZoBeHMWPLWjUMKnjWcGgMSOI7h/WQMSJOosAPoGog1vL3jw4LecfXVVyPzufaZ4D5qszYeRT2xhmyas396ahQbSyWxosyI3UPHMcX5VhgRzV6baxF8cOxFiAO125PGwOggJp84SgRKJ5vqn6IcPfroo3gncXstPY6xG28gdBbc0HxpxeiqCF5mILjUGfWQ+8IkZPjcaO4IE4PtGrlT1InDFBKPk+6OGkVP8HfDgQ6J0OJmJSzmtGXEVtO9Ll26GBdCq7e+NIrQaa13hn8WGx0wl0wNuPUxdlRRWuETJdqquQoJ82Ux89n69pl6vHz7Dj30UGMDJaOcWk0b4JwChKtG+0NDNAZkorfyFTbc7OqnpQDyNbSoMmlNQU/8rXatBPUjnJlT2jVf5AqLWyNCVWdiMPeYFXQP5zWrZk+Ji/qlHtbG4WuWFBWSHL37GeLJuHNThzZqDtLIZ3vOGh3XJfHFH9f9tSZ7aXo+EZ1dUnZbZheUlpXvZOsAfizjNZkOd1Rc1UxOdGhgXETlnslWVUqIgAiIgAiIgAiIgAiIgAg0WAK8c1nvYg0WQo0OvHLvaThxED2HyxLeW3SL938SuDjh32Gt3GTy7Z02uhVv2ghP5BstDFnUhC4idpBJxJlRGYhBwy8Mbc6qgbWWSOMHZCzpgPH84nWa8DEEIHyLcEjBkYeG8JHhVd+4IFE/Yor1Bm5V6JSosAP0nPpxeyFBqCauOggNVOLU586dO5PJ6OgJCSLOjGxhmkNDNAlkQRJeGrUHS5oieE4xapOmWuLp7K5V5LstYnk5gdRKm0r4pE7D3+QgpRnOlkH1E0h+iEcoRKiNlqDJknAIGdwpSPreIvZIrmw+YMQRo+wggaGMIJGYRdBQoHAn5NYbfY314BgCV62BcAvwCkR5xCGL/hAPiyRkrroCRFxz4mP5ZJkiPjZqtW4UW5woza0nH3ETNzH8uUgTklnhXpN00vI3ZPJbNXtPePr2Uco4x5GYM2cOuptZWc9tbUatZm7zBMAAknzl+QobY0sac1vWyvTC37KxEtSPvVED+R7RAdwVPd0+q5Q1Ir6q6Kq33347/oncaMRW/F4tM7eJNolh3VMdzyKno3TPAmQZuXt3bFi3bXcINsYEciZE7hYHOWW5tAHt476el7EsPe/MPZsGkB8T7njYPnBSu8GdHC5m+cVl8zbm9ty1ktrGrKJDWpHXiCXPcgp3Pygc5zpEQAREQAREQAREQAREQAREQATqAIFKrHdGb1u1asWrOEsRsfi36TxaBitDIYpZjiqug8L/Cy2MfQZ54Uf2wvEHHcTyz0IRYEF9YvqQb/BIYkkjAjBNWJ+pCq8TiiOTcZW3d+IKWcAITeTpp5+mZlxLUGcefvhhrrLaPRoTnnGoRawhRZ1OGpNr38ipsAMsyU/sJE2jNxnFhFd61z6jIBD6R6OIIPg0sWAZyo5pkYLExJGJj55ZeqnCRu1dHTBgALIRfmGMkfhHqrI8euxm9jS6DJomt4Zug87qiWWDuxMG3ErELDZ5nDZtmuWnZtlUmKBmL3GpoAAXehaRg9wXDmRNFvlCk0LU4A6aTOMY6NQW3QajdXBDCYmltywchqrCKeGQzARTCg2OcELWxiICl9hAKDE0a1U1q2ZGCgfQcfuIUWUNOOqxi2uWJQn4oBGzbBw2TACatl8l7Uuj1ihwOmP+U8rMH1MVDmJ8jwhGJv7RPuHtDRnxCJdGfPcIyWT+MLGR/JhddjMvae/fPlMQ2ZFdApiTnupBkSS6FtEWKQogtM532Qjonoq45leKP/Wzuh9fGfwHWeKQ0NFKFecxgmQGNHw/cWEzGJG8zbpprn3zlIMQNmF+Rk5R2T/rdvy6cneEL8ZzN+awchk7ck5fvp3NAVj1zF4DYZusgJaVX3pSd4fubw7goqkRzslOAlkFpc/+sJY0S6H1bxv71u8bl6fn08qL09bhm7anhP4VAREQAREQAREQAREQAREQARGoEwQq53fG2zUOMizPZNxPGIFxN7MC04zDl9PI0AXw+2C1e/QmLuGWRdpuec011xDEx06CpkJC2+yv8bz3Yo9GhjKFASsoEarJazC6BkuGUQ+hf6zKz8prBL7hC4amZnxMWL+J0C2KUJvVHLVZlVsJ7x1AVkCzMEuk4f+CiIMLktsi9IQOmPBVIsWMyw8dIB9vNQQdRDRGgZZEptsa7F3FxhwUQT1h2TJOocfyUpYbFzluiyCEIdzADbUIGYiQQyfnIDzp0LCIajRN0EN2CTBp3z8/++wztCd77Ke9LBInrRNRa2Vyv+bOnUumudcmnz5wpywbc1O4m1YOCVQbeCKIgNda951F8aw4U7ZexUMVM1MQzki0bKJqr4SIP9plpnGgxiK50n+0P08A8Zsz681RGxtT2KsyaS+Nuo6CW4B8huJGtRQ3BtxZVlJDGHKt3OQQHck3jqXi2HWBac/dN1P6ggsuMAbWxLbqtHKMx6WXb5+xRG1kOTwzu6xuWJVYOQRNc+PYgpMcgCBi8unqz2gVdP2ieeJvNWFPUDmCI6oZmWjHfFm8FDcwsbRaZ5dVFsIz84qqzOYk3Gtc2Lhr9oYcpXZphgHupMPrjmrJVgCsQYaIhsiF5oU9hi3iw176cd2oqWs4PbVn0tDuu7Uzoz72aRUTGRpIJCYxmxhYx+3Htn7wu5WXf7iQHJZUu/eEttjfdXyb+yesuHbMYjKpNiSwcn/PsCpXQgREQAREQAREQAREQAREQAREoIYINMazo4aqdq2WtnjLNfFfrlfxPyIg0YpoczXA3QbNyC4budqYHJxWqMdJLfJkbOV77wCdx40FgcbuIuS2CI45xNbRAcI8EWvMCzw5+ENZapqPjVpmJAhhow8+7jtL3Cu48AqkXQ48mxDv2OrBXiFpHMGQThiUJTo4GXg/xYmMyhGhvJv59yoYWXoM9zG3fcaXjTnmJRoUhvTZLBCGiAlPS3lx7SfMacv1rjlZVtiok311Ttk2gcjECiORXZvw9O1juqIo4VLqWsQ1h28o9TBhXC/5mOMLf/wHca5EKuWLjGRmLUJHE74UNz1B1eX+8klvvdziCrtNzCaSWZOofVQwShENnJ5bjDoWZLS3CivaY1BYUk5sZkzYPn+3wIWtxCVzTwn9KwIiIAIiIAIiIAIiIAIiIAIisD8J7PP+VtMdsVYjctsQcoB3RcCLrOZUYYVih5O9OfXeATrv2n+3Reyv+lZDKG5ue+W2BquUPYEjj4/CGaVQxPCZwluHdbtYKg75oE+fPvbaTBr5yVq1yvVqhTm4+91xxx0VmvnXAKc/Dk914mTk6ZLJt99Eaz8KT0Vg7vauOdlX2KiTfXVOq3y/7AO3d4Dp6qNwRin06OoIZ9Rg70aF/F3h+14cvayaXTWUggIbuwpnXML7rGlFWwqYGpw+w4ID+HHKxK+NH6dMnYqACIiACIiACIiACIiACIiACNQFArXqd1YXBlybffjiiy9Yf51gvdps1GqLzU9//vln4uxQRlggv0KdwiqohAjsXwILFy5ctmyZ636y+7dXal0EREAEREAEREAEREAEREAERKBhEpB21jDvu0YtAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJQMQFFCVXMSBYiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAINk4C0s4Z53zVqERABERABERABERABERABERABERABERCBiglIO6uYkSxEQAREQAREQAREQAREQAREQAREQAREQAQaJoFK77NZWFiYl5dXWlq6c+fOholMoxaB+k2AHSrZFpYdLd3uGFu/x67RiYAIiIAIiIAIiIAIiIAIiIAIiIATgcppZzk5OUVFRSEhIeHh4bxgO9WlUxEQgXpAAFkccTw3N7ekpCQ6OroejEhDEAEREAEREAEREAEREAEREAEREIEqE6iEdobHGcJZRESEVLMq41ZBEaj7BPiCBwcH43qWn59PQt5ndf+WqYciIAIiIAIiIAIiIAIiIAIiIAI1R6AS650RqonHmYSzmrsZqlkE6g4Bvul83/nW150uqSciIAIiIAIiIAIiIAIiIAIiIAIiUPsEKqGdEcaFK0rtd1EtioAI7BcCfN/51u+XptWoCIiACIiACIiACIiACIiACIiACNQRApXQwlgFSU5ndeS2qRsiUAsE+L77siVIWlpadnZ2eXl5LXTpgG4iICAgNjY2JSXFX6Ngv5Zt+SWRIYFhwZX4K4i/Wlc9IiACIiACIiACIiACIiACItBACFRCO/NEZOqK0m8Wly5KLy8p82SifBHYSyA4sFG35IBhXYOO7+CH6be33opSmqhuCVXzdiCcIQn17NkzMDDQaG369ESgrKxsw4YNEKuOfJZfXDZl8bbfVmWl7SjasqO4tNyx33FceFBKbGin5IjTeia1axLu9kYrUwREQAREQAREQAREQAREQAREoGoEqitevPZn8afzFNVVNfgNtBQa69zN5XM3Fy/LLB95eEjtUNBE9cS5mrcDj7MePXoQ3emLh5qnPjSQfCg1b958wYIFVdPO0nOKx/yTNnXJNuQzJ2JZBaX8LEnLGz8vo2ezqOF9mg5oH+dko1MREAEREAEREAEREAEREAEREIGqEaiWdoYjj4SzqnFXKQgweTo1CagF7zNNVF/mW9VuB6GaeJyZgE0jn+nTCwGLlS93xG7z15rsJyevySms+A8V8zfl8jO0e5ObBrcMCVQsp52i0iIgAiIgAiIgAiIgAiIgAiJQFQLVerMiVLMqbaqMCOwhUDtTqHZa2TOmA/hfgaqDN4+YzHf+2HTfNyt8Ec6s/n+/MPOGz5Zu3lFk5SghAiIgAiIgAiIgAiIgAiIgAiJQNQLV8jtjjbOqtapSImAI1M4Uqp1W6sE9rRooRWvW6K3/eObmj2ZurkITKzLy7/5q+ejzu0aEBFahuIqIgAiIgAiIgAiIgAiIgAiIgAgYAtXyO9PmAJpGi+5tWR0ItTOFaqeV6nDwV9kD4nb4a7ANoZ7Z63Pe/XNTlUe6Iavo2R/WVrm4CoqACIiACIiACIiACIiACIiACECgWn5nIigCIiAC8juroTmQlV/62KRVOx0bae5z9GkV2zklatKC9O35JftccHfy8/LtX89NP713sruLyhMBERABERABERABERABERABEaiYQLX8ziquXhYiIAINg4BdQVPa3HNPHHycEd/MS0c+sxtHhwW9dclBr1/Y6+Zj242/4bAz+6Tar3pKfzQzrbTcRYHzZK18ERABERABERABERABERABERCBfQnUoHYWFtz46VMT+rQI3bfFSp91aRo84siYC/pGRYQ0rnThulfgsaEJPZuF1E6/Hjoxvvr8vXe121PrvRhww1onBNX9vf5uPCqWuZoUtc+yUL2ahZB5Xp8oLwP0/VLz2CBqiwqtwW8cndkvtwOFyIhEJuEpzV6cW7ZsyczMJOHJpiHk+zhnULu+XZBpN+bb9PK5PXo0j/7y3803fbpga17xnSd0OPPgiuWzbfklM1Zst1eltAiIgAiIgAiIgAiIgAiIgAiIgO8EavBNPjo04LQekf3bVEs7Q/0Zd0UK2tn9Q+L/vLU5OprvY6ublmf1jjysVVjt9O2cg6Oqyb/K/ezdPOSF0xN/vaX5xGtT/7y1xWtnNzm6Q3iVa/Ox4IYNGyZOnLho0SIf7S2zSw6NZq6OHBhj5ZB44IR4Ms892KN2lhgZOP/uloe19jbDZ9zU7P7j46mtbWIQtcWG1eA3zt55p3SN3g5L8KJRt2kyP//88+OOO+70008/5ZRTLrzwwgULFpC5cOHCoUOHeipVqXwq7N+//9tvv22Vuv3227/99ltaIX/dunVWPjlnn302mdZx1llnWT355JNPHn30UWxWrFgxdepUeyk/pqmqwuPXlVlb8/YJyTysXXy3ZtFj/9n07OQVM1dvv/7jeek7iu48sUPzuIqfJ9/MzaiwRRmIgAiIgAiIgAiIgAiIgAiIgAi4JbB/3uTddsU1s2l0IMrFT8sLDhq14ZhXHQtm47Tlaqac/UjgxTMSXVvv2zL07fOST+waER/umGCRIY0Hdwz/79lNhnaLcDX2Yw6q2UsvvfT7779Xrc6hXfd2DyfHbikVuAcGBTTCpS4yxNuXiKuxuyD8sbrw+Nc3b8zeJwSvav30Umq/3A6UJuNK5unzww8//Pjjj5999tkZM2ZMmDChd+/eN9988/bt2ynIWDyVqlQ+mmmbNm3Gjx9fWlpq+mOKmyacelhSUnLbbbehrJnjrbfeatGixX/+8x+KWKWWLl363XffmVP/fnq5ffZLs9btsJ+S7poazefUxbtVsJzC0ru/XIQz2mkHpThZup7O35RbVKptkV3BKEcEREAEREAEREAEREAEREAEKiawH/YKaJMQ9Oa5SYSw0bt120uv+SxjQ5ZDUHh0aMKp3SNCghpvzCrllXrGysJ5m4rIf3yKI9poS07Z+u1lyftG1ZF/SveIB0+IJxSurLzRL6sKrh/riHIa0jn8kZMS0CyIe/prbdHIsZnFZTu/vKJpSGBj4vJiwgKKSneOnZP75NSst85Lat8kePAuYY6C316Tsim7jC6Rto7HhyZ0Tw0JaNyoQ1IwL/vfL8q/a/xWmhg1LLHvsxvKdq0jNPm61F9XFT42efvsO1tMXVpwfOdwBrJ6a8nYOXm3DIoNDWqcXVB+4Yfpq7Y6HElO6RGBixOZOYXlD07cPnlJPpnHdgp/4uQE+pZfvPPdv3a89usO3JTuOz7uvZk51x4R8838vIcn7Y66mnJd6qYdZZd9nE4pXJ9+uD71oYnbF2wufvv8pOToQN6lc4vKR3ye+e8GBz1z0Kv/fLftu0WOhp4/PbFtQvCZ76TRgTeGNzm0VVhAQKO120ov+nDLtvxyPLDoMPG2rI/0++pCJxR76tv77wld0Ju27j1v1KhDk+A3z0lCe1qWUYLvYWpM4I7C8lVbSw9qHvLMaYlw+G11od2+jqRRtZiTOJExYejSVYc7fNC2F+yWG5gYEL5nwjYyz+gVxPsTBAAAQABJREFU+Z8h8TePy8SZjtOXzkyctLiAKcE8xKkwONCB7peVBdeNzZx1R4vw4MYohoR/3v/dNm7QCW9sZibfeUzc+X2igFxYspP7+8qMbOrhHjG1TuoaQT6ULh+TvmRLST24HQUFBW+88cbTTz990EEHMcyEhISbbropKSlp69a906aoqOi1115D/2rWrNnFF1+MhxqWn332GaIbElvnzp0feuih1q1b33///fipjR07tkePHldddRU25igsLJw0adLLL7+MJDd79uy+ffvuueLx36ioqPh4hz+gOfAy++CDDw4//HBz+sMPP9AfmsZFDtUvPT0d4W/OnDn05JZbbunQoQPuaeHh4UuWLCEK9dZbb2V08+bNS05OHjFixAknnLCn1mr9uzl77/fXVDRx/parB7Z+8JRO1308r6Rs54Qb+q3dWsClNBdLtw2n7ShunVCxh5rbssoUAREQAREQAREQAREQAREQgYZMwJvLTE1wQYEae3lKUmTgI5O2IYo1iw0ce1lTGrp+QMzZvSMnLs6/ZZxD52oZF5QYETBxUQGq1uYdZRggwbBy1orMfYKYkL1GnZaYnlt261dbxy/IIyrw1qNj2yUGv3hmk5yictSNj//JPaJt2GvDHRoHMhP2czcW3/hl5sy1RRcdEn1h36jvF+fj3WbC7lrEBVF22jLH66j9SIwM6JwcjMxB3xBQUOtaxQdFhQWgxHEYSxyszGpZCFIndg1/9sesl6ZnU9tdx8Z9MisX3SQ6LOCe4+KMccekYDpGn+nkC2ckItlQ4StnNWF0CC6/rioYOTAW9S0uPAApbeSAWMSm8Qscspc55m0qPrRVKA1xenm/aES6KUvzXx/eJCEi4JGJ2xk1OfcP2d2WKYIxHTDphIjAJlGO9OhzmhzSKvSZaVn3f7sNCF9cnsLdocNrt5de/kn6J7NyBrQLQ78zpXz/xN0M4QzV6cIPtlzwwZb5m4ov+yT94o+2LE0vQdgb1rPiCpctW3bvvfeiQXCMHDny77//pvWHH36YNI5CyBmE/r3wwgvFxcXkp6Wl4TFkLIm8872fTpYbs8tQrEYcGWvyUcEWbi4uNcpoo0bxQIvcvRoagiCK2OItJW/+7vAMGj8//9N/c5lCrIyG7oba+OPygkEdwtGImQaot0vTi1/+OZsiTJiggMZXHB59+WHRf64tHPmFQ98kHtmEhXKPgPPfX7KfmLIdLfiRExPqyO1wAuV6ileXl2Pjxo0UwdfMsgkJCbn00kvbtWtHDpf4fPPNN7np//3vfy+44AJkMny+OF555RW0MOSzmJgY9DLM1q9fzzRo27btwIEDrdpI/Prrr+hWXbt2Pemkk3AWM5dMzaRNh+325CCEIdWZgymEeEdcpzHmE22OGdWpUyd0sbKysjvuuCM1NRUFkHy0M4wR/l5//fVt27YNHz589OjRtI6ahupHvCdaob0t17TpT4WfSF1ONmk7it6YvrpFfPgbF/ZKjAzZlFXUITmyrHzn1EUZPAtIM2G8HBT3clWXREAEREAEREAEREAEREAEREAEPBGobb8zvKuI4EOEmrLUIVEhk+HV1b9NGPIBPmj3fevw6/ljTdFftzUnge6Akw4JhKRnhyUWl+40BuSYA8UNH5+z3tmCHxneW1kF5biw4dLF+zKZKFO4gCFhDGy/e6Wtbfll137u8ClDIPv15maX9IvGDwh3oWuOiPlrbQaftIg/GppR6/jdZH7YJaXh1IYMhJCyKK1k6vWpXZt6i+bDkwi9jFZuGBgzf3MxAgrpGwfGxkfsVq+mLy947idH5h9rCv+4tTnqCbpJeXmjF37KZhF1iqP30ZkJu/SyF6ZnvftXDsbW8eov2bgyQeyDv3NO6haxOK0YXW/cvLyVmSU/rygkXrKkrBGim2XvKdGvdRgcFmx2vFF/9E/O1f1jkPDoADqaQ8ubsQORjvF6Km7yUQCdDMwKawh/p3SP/Gx27rnvb8FgYPswFvwiwVVe8HfrGU4ld50Sc3fXXXchQKBEBAYGvvfee0888cS4cePWrFmzefNm5JV+/frNnDlz8uTJyDFHH300vkgsc9anTx9cmXA+clelr3nfLsxH/0Lga5MYjBKKhsUqe54Kb80r+3Ju3g0DY1HKkMBYiQ899OkftjeJCmSaUQopFk0NF7OVmaWIwkwqU9XFh0Sj9hoHSeKRf7ul+ZWHRwOKq8w9c6+RLFNiApnb+/12eBq+7/k7djgUxoiIvfGwTmWJiMTF7LnnnuMOcuB0Nm3atGHDhj322GODBw/OycmJjY3NynJ8XziGDBmC25pJW5/ff//9ySefzCllr7vuOgQv3Mqsq24T//zzz8qVK82l5s2bM9PsZk2bNsW5bPXq1biw4VyGJX5nQUFBKGWot//++y/GiYmJuJsFBAQw6/A+w/fttNNOQ9ez11OddGaum6/eh39uoM6Rg9t+fFUfEsyQwIDGT53ZNTk6pHViBA5oo2esmbTA4ZHqerit0NVMOSIgAiIgAiIgAiIgAiIgAiIgAk4Eals7w+WKHkxfsTtqDxWJU3QHtJ6l6bvdIgg5LCrZra7gp/N/5zU5rHUY8WtXfZpOUKF9ALinEQ+IcGYyjUp1Vu+ovKJyhDOTOWt9MU5ARktas23valOkOyc7JLApSwrwFMNl47hO4cRd8jqK3oGHmimOzEEir7jceCBlFzqqZaErLwdRn+YqVRELadJle/xfOJ29YbdHCZ0vKN6JUBUW1JiVsz66ONmq1opOJerTyjQJeo6kOPygyAkL8nDHe2GXDEdVhHziXEYwF/5wTkVcT3F2w4gh82NdZdR3j9+GzxoRr4+c1AiPv6s/dUiNXg4TcGo3wNuOUyq/aVAsmhEdQ42665g4vK7Ix/svJjwADy97EXsabzJce8LCwvDxWb58OZfQ0fD0MTYPPPDAgAEDWBKetefxQlq8eDHCGTLHk08+iQ8gismXX35pr61S6dd/zb7wkKgLDolGf2RSIe960c6cat6QVUZg5py7WhIAS9St01X7KdsFzNm41wOIwF4zD7FZnrFbLskv2Wnu4X6/Hfaee0obdy1PV5s0cXh9ZmRkIEhZNmPGjMFNDAc0cvDe4hPfLuvqMcccQ2gnXoS4nuXl5UVGRjIZTCsEfjo1R0DlX7uOd99919Qwffp0I6Vxaoz5NAljQPrKK69EhrNaNPtL7LJy3DuTMJ8otuSceeaZljF+alxCVmPKkbj66qtffPHFK664An0QM6urln3VEnERQek5zq5nVIV89tuKbacfnLp+e8EPizJGnd3t0DZxPGrGzNx4fLekh07tHBYU8PWcNNdGqdA1UzkiIAIiIAIiIAIiIAIiIAIiIAIVEqjtt6n1u5Y269ksZNZ6h3zQs5ljj0IEJsQC/HRMd3FZCg3erf58dWXTNgnBrDNFSKbrYPD9IZrSyn/pjMSVW0sz88q6pziEGqN2odbxNoyCg1mzmL3jxbsKkY7MF6dnEYZ541GxtGtWnhr21j5vnmf2irSasBJGCgsJalS66/WWNaqsSxUmOu3pM8ogBekwziMdS3b2eW6DKcvK+riSGZ3RdNKpzi/m5uFzd1X/GJQyVjFjtGwKOW9jMQGDiIZfX5WCc59TEdbhMjlJuwI2N+9wiHq4sL3+q8MtiHjPk7tH4miGY+CJo9MwxQPrlqNjR52WcPa7DscxTwfrnTnJZzhhmf5fOSYd7HBG6bvq04xPL21KmhXfvAhntMIyUkTPEXlnuRrZm27fvj2nrOzOJyobigwJVqEywbPmqt2+UmniTOkekbz4fP3oErrLPTK1odi6Vvv40HgWxbt+bAbr9DEnv7oyxdXG5KDDptrmIWmWvTOXUEDsB5GbrNm3f2+HvT9VSyOZISr9/PPP55xzjqkhNzeXgEeWJzOnhGSSwMGwZcuWJNDC8PD66quvfvvtN7Qz7jVeaVY0rqv/GrthIsMRz2tqIwyT7Qgs7cxkVueTZdFolF0IzBxjcjKi999/H0XPVIvfHK6R+EuySQW+cl26dBk0aFB1WjRlU2NC3WpnXF2Vmf/CVIfTHDMyIcKhPzI3Cckc8dE8wjnvPqnjxqzCv9fs9tQztfFJhVZaCREQAREQAREQAREQAREQAREQAd8JeHWg8r0az5a4MqGkWD9/ry1C7nn5zETycTd7+pQEHHyQG6YsySe48uZBsawoj/uVUSmO6RiOGWtFoWj0SA3hBzcre1NISEhCSGZE2LFG2JAuEaxR9f7MHHy43rkgmTXIWHn95O4RS7bsdt9AE7ljcBwaGWGSOF5NXepYRAzvKjy5iJFEw8K7zV6/lzTuQlwl3pNWWH0fFcyLsdMlesUPchUrjlGOWL8P/85BRGPtNmojXo9F6NmXwKmU/ZTNBDi9tF80jnIkcDejeXRJYBLLieiGmmbvETGnLE6PdIUiZvzCkGmgyor4LNSFavnuBck3DHAocYzomVMTSsp3TlqcT5BshVvzuW7s+Pua3U6FdIxQzUkjUs16Xqb/f6ze63Jlcpw+WYiK1dlxVmL3QxQ0p6vBwbuc2vY41uGdhAHhnMaM0Dkn+8qefjAzFzdA6L36i4OwdWTllzMzmbG4pA0/eLdoAi4MDmoRgjMdoZrEV+I4BkzmA/ksi8YnGisV8mNVNWlJASv3odVigIcjU3r8Qje6MPbcwf1+O6xue0nYvbRc0whhhN8SbIsWhsCEcDZq1Chksu7du2NMtRj0798fgYx0fn7+7bffvmrVKlRRVDNWGcMrDTmMnTGNsVP96FaoWihlvfYcZ5xxBkLb2rVrqc0YWwmrrGuOVTmXzFWUMqJN8Xlk1TNypkyZQj8J3kSks7wgTYWPPPIIXpDoa3hEIrSxPprVkNvErhYq/kiJcYhi3o+uzaKbx4d98Mf60T+v+WX51g3bC9hGoLCk7NxDm7sW9KVC11LKEQEREAEREAEREAEREAEREAER2PtK73cWxomGleP5sSrHg4wl0l8+s8mEqx2OOXnFO0d8nsEqY49P3s4SYwhY7CmJmFVQsrOwdOeQLo5wwtsGs3z77hXcWRPtxNGOECpzsFzX57Nzhx8chWpGJaw8xQ+XXvsl+zqW2L/F8QK5aytPx+abHDR32WHRLNZOGveoUdMc+xtyvP3njseGJrhGR5qrDMQWcGnyGs3ZWIzQhs7FD45Udu8wVi4zB6WsgnsTjt0by4y8gnLw1p876An2//tjB0oWHnA09/e6ItQ09ljcXZHLP0QFLkorxr3ujd8cEg/+d2YTA4oj6JhLtx0dxyWHONGo0Zh/c9gb4acbmiGiIRRGOdYca3TtZ5nIlKhmpHF9uvfbbSwYN+bfXPS1f+9wOHaB6+FJjni6Sh2sGjb8oKhOScGfXdoUXy0EoIdOjL+nNI413QiA/fCffdZuc62ZdaNMJsrFTz/9ZNLoEa6W5OBwFBcXh1aCNMMWjXgDuTXzJdPcoC/m5v7nhLiteQ4HNHup53/KYjOHcVekwBN9FhmXq0QQQ4y71jM1dPRvO94YnjRtZDPyF6YVw/npUxMIWSU8k9Xrvri86W1fOxaGQ197eur2tglB1x0Zww8501cUsK0ECQ5r5phTvPb2++0wPanm5/nnn4/4dc8995h68MzCUYuATePJRSYrlKFAHXvssShQJ554IlIat5VMdtXkKtIYewUQl0naKmKqItZy06ZNbB1gTvns2LEjM4GNMu3GTqUsYythNzBpAoFxMSOuc8aMGfSWTQCQ/FiO7cYbb2SlM6sgCVZY4yqCL8LfkUceaW3WabepQrpHs6jJix1zxsuxfEteVkHJMV2a4HGWmev4C0F6TlFuYRnLnzmVatckPCJknz88OBnoVAREQAREQAREQAREQAREQAREwBOBxiwn5OmaUz6r/LBotz1z4P8cfltVO/CBQiTK2LWaGDWc3jOSfQwRknC/ighu/MvNzXE3+9+ufQwrrB91pm1iMCvlO1nis0ZkorX41PQbm7GTwKUfpXdKDkEZsVZJoxROQHii9X1uA5GSTpV4P8VpKKBx443ZjvjHSh34HLHHKG5KRmG0ytLnNVuNP5OV52sCpGzmiDRGAZz4EA3tlQMWVztXSqwER4Cn2czUtITIxbr+OYU7fRkXfmeu2wXgUoffH/KZvesIZ5d9nG66Z8//5Zq90ir5OPXcd999CxYsII1+kZmZSfDm//73P7ZfZOUpdjNEucAJ6Pnnnz/rrLOuueYalnInUA4z7NFc/vjjD2IDWc3K3kR1JqpVD66FBGOyW+g+WK3LjlXwGndICmKAzCKo4hGJpmm7vk+SCNp2TYJXZVZ8r/fv7cjOzk5Jccjcng6WnDMxs/hYoTp5+eQSzxDWpIuOjnZriUsa69yxbL+5imCKfoonF01ziRhJFub3Ur/bOv1oz64FdN5tH0xXUQPpZIUtsoUomq8nnlZ+UWn58Lfm5RZ5nELGcvghzW4/vv3arfnXfzw/t6j02bO79Wsb/9TE5d/su+TZrce0OrVnklW5EiIgAiIgAiIgAiIgAiIgAiIgAr4T2G/amVMXWb2LLQt/W12IvEU8Y0RI4yNe2mR35nKyr8Kp0c4u+nCfTehYUgr1h7jFv9YWVbgufhUabbBFCCM9tmM4Lle9m4UszSj5fXUhToKWVGrH4qSdcQn1gUg9NBScj+yWXtJoZyybZdaedzXzi3bmWu0BlFPl2+GjdnYAodjvXfVRO6Ofr81Y/+XsfZ5Xbjt/2REtRwxqk11QUlBSnhITOnlh+iMTluHhaBnjcTb2ql7hwTUeoW+1qIQIiIAIiIAIiIAIiIAIiIAI1CcCNRizWSlMrFhfXNro9F4RhLMt3lLyyKRt/hXO6MzUpW60G3zWiBVlL86bx+2O66xUt2XsiQBBoF/Pz+PHk4GXfByInMLivBibS2YzxwrNGqxBdW5HhdAsTytjafy/lPbEoUKelsGZvZO/nZ+JA5qV4zbx3u/r12wtOPfQZjFhQc9OXv/V7DS7cEaRMw9KlnDmFp0yRUAEREAEREAEREAEREAERMAXAnVFO6OvBGzy40unq2bzxJTtrgVZUmrIG3sXUHM1UI53AovubdntqfXebXS11gjsr9uBfGaNUWmDwhMHC1SFidTY0NuObf3U5NUVWk5fmsmPWzPWTbv0sFS3l5QpAiIgAiIgAiIgAiIgAiIgAiLgC4E6pJ350l3ZiIAI1DUCdpGorvXtQO/P8V0SFmzKnTA/o2oDiYsIevCkdizKWLXiKiUCIiACIiACIiACIiACIiACIgABrYCjaSACIiACdZfADYNaDu7k2DChskdiZPATp3ZoErXPlh2VrUT2IiACIiACIiACIiACIiACIiAC1fI7Cw5sVFLBLnAiXM8JVDNgkylUC0fDmaj75XbI76xG5zAbtj5wUruezdJf/2VDadne2FjvjR7cMvqBE9vhd+bdTFdFQAREQAREQAREQAREQAREQAQqJFAtv7NuydUqXmHnZFDvCdTOFKqdVurBzRKoOnsTT++d/MrZnQ9pFVNh+GVSVMi1A1o8e0ZHCWd19m6qYyIgAiIgAiIgAiIgAiIgAgcWgWp5JQzrGjR3c/GBNWD1tk4RYArVQn80UX2EXIXbERAQUFJSEhQUhPeZ2VlSn14IlJaWQszH2+Fk1iUlctQZHTdsL/xmXsbvq7K35BTb99OMDgvqlBxxas8mR7aL0wJnTuh0KgIiIAIiIAIiIAIiIAIiIALVIVAt5eL4DkHLMss/nVdanR6obIMlcF6vIKZQLQxfE9UXyFW7HbGxsdu3b09ISKiyJORL3+qHTVlZGawgVp3htIgPGzmoJT9l5TvTc4vTc4qjQoNSY0IiQmol/rk6XVdZERABERABERABERABERABETgwCTTOy8vzsedpaWlu3/qmrij9ZnHpovRyrX3mI8kGbsbqY8QG4uJUO8KZRVsT1UJhT3i/HdnZ2SkpKXZ71zRPBszKy8tdLynHTgB5kUdohTztRZQWAREQAREQAREQAREQAREQARHY7wT8oJ3t9zGoAyIgAjVEwBftrIaaVrUiIAIiIAIiIAIiIAIiIAIiIAIiUBcIVGLlHbOIT13otPogAiJQCwTMEma10JCaEAEREAEREAEREAEREAEREAEREIE6S6AS2llQUBALXdfZkahjIiAC/iXA951vvX/rVG0iIAIiIAIiIAIiIAIiIAIiIAIicGARqIR2FhkZWVxcjCvKgTVC9VYERKAKBPim833nW1+FsioiAiIgAiIgAiIgAiIgAiIgAiIgAvWGQCWcSsLCwkpKSvLz80NCQvBGIYSz3lDQQERABCwCqGZ4nCGchYaG8q238pUQAREQAREQAREQAREQAREQAREQgQZIoBJ7BRg6hYWFbM3Jq7Uc0BrgdNGQGwIBZHHEcTzOJJw1hNutMYqACIiACIiACIiACIiACIiACHgnUGntzHt1uioCIiACIiACIiACIiACIiACIiACIiACIiAC9YZAJdY7qzdj1kBEQAREQAREQAREQAREQAREQAREQAREQAREwBcC0s58oSQbERABERABERABERABERABERABERABERCBhkhA2llDvOsaswiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgC8EpJ35Qkk2IiACIiACIiACIiACIiACIiACIiACIiACDZGAtLOGeNc1ZhEQAREQAREQAREQAREQAREQAREQAREQAV8ISDvzhZJsREAEREAEREAEREAEREAEREAEREAEREAEGiIBaWcN8a5rzCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAr4QkHbmCyXZiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAINEQC0s4a4l3XmEVABERABERABERABERABERABERABERABHwhIO3MF0qyEQEREAEREAEREAEREAEREAEREAEREAERaIgEpJ01xLuuMYuACIiACIiACIiACIiACIiACIiACIiACPhCQNqZL5RkIwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0BAJSDtriHddYxYBERABERABERABERABERABERABERABEfCFgLQzXyjJRgREQAREQAREQAREQAREQAREQAREQAREoCESkHbWEO+6xiwCIiACIiACIiACIiACIiACIiACIiACIuALAWlnvlCSjQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQEMkIO2sId51jVkEREAEREAEREAEREAEREAEREAEREAERMAXAtLOfKEkGxEQAREQAREQAREQAREQAREQAREQAREQgYZIQNpZQ7zrGrMIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAvBKSd+UJJNiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAg2RgLSzhnjXNWYREAEREAEREAEREAEREAEREAEREAEREAFfCAT5YmTZLEvP/+CvzbPW7SgqLbcyG1oiNCigb6uYSw5L7ZQc0dDGrvGKgAiIgAiIgAiIgAiIgAiIgAiIgAiIQIMi0DgvL8/HAecVlZ791vyGrJrZQaGgfXFVz8jQyomP9hqUFgEREAEREAEREAEREAEREAEREAEREAERqOMEKhGzWVyYL+HMup2gAIh1qoQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiED9I1AJv7MtW7YszYtsHh/Ws3lM/QNRqRHN37hj4/bCzpF5TZs2rVRBGYuACIiACIiACIiACIiACIiACIiACIiACBxABCqhnaWlpbVr1+4AGltNd3XVqlUpKSk13YrqFwEREAEREAEREAEREAEREAEREAER8C+BnTt3Nm7c2L91HtC1CYiX26flurzA0SUREAEREAEREAEREAEREAEREAEREIH6RiAjI6OwsDA3N/eAG1hoaGhRUVFNdDsqKorKk5OTK6z8qQkLpyzYVNKQ9pCUdlbhrJCBCIiACIiACIiACIiACIiACIiACIhAPSGAcFZaWtq+ffugoJqSRGbNmtW3b9+a4EXNffr0MTXjN4ezmL/SMFm7di1wkpKSvPQc4Wz6sm2Nw+NCGldiAX0vFR4Ql2pqohwQg1cnRUAEREAEREAEREAEREAEREAEREAEGg4BxCY8zliQKjAw0BKeamL4NVq56bC9ieqnURJbt27N4lTl5eUBAR51MTzOEM4aNSThDNrSzmriO6I6RUAEREAEREAEREAEREAEREAEREAE6hwBfLUI1aw5j7NaGLBdJvNvc+iJwPEinNEcoZoNyuPMEJZ25t+ZptpEQAREQAREQAREQAREQAREQAREQATqNAFcq0zAYw19MngULg6/11+nsdbfzkk7q7/3ViMTAREQAREQAREQAREQAREQAREQARGoXwSQ5OrXgA6A0Ug7OwBukrooAiIgAiIgAiIgAiIgAiIgAiIgAiLgLwJGfqq5T/pJ5TVRv78IqJ5KEZB2VilcMhYBERABERABERABERABERABERABETjgCRhhywzDVeSq+/kH/A04oAYg7eyAul3qrAiIgAiIgAiIgAiIgAiIgAiIgAjsIpCZmRkVFRUWFiYelSVgCWdWwqkGe35WVtbatWvZgzIuLs6eby/imm9yXPNNKX/l2/vgmqbbmzZtSkxM7NSpk+tV5fhOQNqZ76xkKQIiIAIiIAIiIAIiIAIiIAL1h0BhYeHixYu3b98eHx/ftWvXCiWYjRs3MvjU1FTv2/D5C1BOTs6OHTsiIiLoXtXq3Lp1a2xsbG3uqFhUVISeFRISkpSUVLU+V6rU008/fcoppxx99NG+l6p9Jr73zVjW8jTz3r1JkyZ98MEHkZGRCGfoUHl5eZdccsmJJ57ovZQvV/neTZs2zVVBa9KkyeDBg73X4PtGBy+99NIff/zRsmXLzz//nLZ82bjAe9OVuhrQuFGX1OiikvLVmXnl+2+JtoTIkHZNIjZlF27KKqxU/+3G0s7sNGo7XVBSvnRL3qbsomaxoT2bRQUysxo1KivfuSHLkRMc2HhjVlFMeFB0aKDbnhnL5rGhQYGOgr4ftBgVGhgTts/d35JTXFhSbipJiAz21KjvrdSmZXZBaVZBaeuEff7esmF7YVRYUFz4PsOszV6pLREQAREQAREQAREQARGoswRmzJjx5JNP5ufnmx4iUd1+++3HH3+8pw6XlZWdf/75XP3iiy+Sk5M9mbnNLy0tffvtt7l08cUX05BbG9fM8ePHv/nmmyeffPLdd9/tepWcOXPm3HTTTU6Xrrjiissuu4zRPf/888gTXB00aNA999yD/GFZLlmy5JZbbmHs3333XXR0tJVftQT93Lx583HHHde+fXtqvvHGG5s1a/bpp59WtraqUXJqpY4wceqV76fVmWa+t4Klq2jlVByDBx54IDAw8PXXX0eBLSgoCA8Pz87OfuGFF3755ZfHHnsMHcqpiNOp9ya+/vrr//3vf05FzOnUqVPt09WtjafK3eaT6Tafmj3lu23U90y0jSfO6Hp05yaGUUlZ+ajJKybM3eJjDRQ8uGXMiz+s8tHek1lwYMAHVxzctkkEwh092ZpXfPHbs7flFbvan9yr6f1DOx3x9C+ul0xOjcsK/Clj4cKFuDgmJCR069YtNDTUU1caWv6Yf9L+7zfH323MERka+OjJ7Q9uGb01r+TyDxe+eHan3s2jR3625NSeTa48ovkeq33+tVvuc6Gik+s/dVPtgxNWLs/Y/YuTCkICA4b1Trr88GZhwQGe6tu8o+ijmWnXDmjuJMN5sq+5/LGzt3zyd9qPN/e1N3HtmMVHto+774S29kxf0i9MW3tCt8TuqVG+GMtGBERABERABERABERABA44AoRx/ec//6Hb55xzTvPmzZctW4aKhBzQoUOHtm0r/f/nCoePKvTxxx9jNnz4cN+1swqrxVGue/fulhkvnib9119/mdGdccYZqFo///wzvmCvvPJKcHAwBviy3X///UY09ItwMHny5Pnz53fp0gXtDFXx8ssvr1D4sPpsT/iFUh1hYh/XAZoeO3YsEZp33HHHypUrH3rooX/++eeQQw5BGH3kkUeee+45rvLdqc7QUAk9FfdyyRRh3hoPMk7NHLY+3eZbpSplb0pV7fOFc3oc2ibuhSkrv56T1iI+7Kkzu943tNO8DTlrt+7VHLzUfHi7+MGdE6uvnd03tGOrhPAr35+zcFPOEe0TnhvefdRZ3a76YI6Xpj1dqlntjGcWLoLceMRaPnlU3XvvvT169PDUm4aT//qMDV/NTb9mQPMTuzaJCQ9csiX/zV833D5u2ceX9wi0qdc3DW7ZKn4fXyo7IhSr245t7cXAbuxLmjk96vSOOxvt3J5fujgt770/N81at+PN87sahzjXGjJzSyYuzLzw0JT9rp259q06Od8uyOyYHCHtrDoMVVYEREAEREAEREAERKAuE1i6dKnpHu//xomMT3xqOMgnMO3//u///vzzT3xtDj300Ouvvx5PCKfhrFu3bvTo0chVuOQceeSRV155pQmNTE9Pf+211/B+wr5Xr15XX301b/VPPPGEKX7XXXedeuqpw4YN81T877//fu+991ArDj744KZNm1qNolYUFxefdtpp9tdJ5Ko33njD2CxYsIB+knPeeeeNGjWKzJEjR5577rm0fumll9JPaj7iiCMIdqMzqFRWzU6Jp556asOGDSeddBJuQSR69+5NPa1atcJs0aJFb731Fp/obshkF1xwAW56N9xwA8IZV+kJxM466yxEFsLuTLXkfPTRRytWrMCe4EqqJd80gYyI/kJZFqK688470bycKA0dOhS3O1yQ8J4jApSx47VXYbRsTTDhnqI8Mi46T8QuPE2UqFsgDPbFF1/s2LEjwtOECRMQAeh2SkoK6FavXs3tQ43i9Kuvvvrhhx+OOuqoWbNmzZs3z/BkIhlu1qenqYiv32effbZ+/XrieQ8//HDuQmX9B43YZDXklOAWjxs37t1338WM78LMmTMx4JM0t48pgUKKR6R3Idh7E04t2k8pWGFZY+Dlk37yNUQ1pua0tDS+6WB/+eWXOfVSyt6NKqeJkUT8eu/39WNnbaKS1Zn5V38w98kzurZJDB8xqHVUaNCNYxxfmRuPaYvZhW/9e+1RrS/u3zIooDGRcE9+v6xn85jTeqfgufbLXUcOHPUbp6PO7hYXEVxaVv717LTnp66MDQ/+9sZ+05duHdzF4df21q/rTuie1DoxorRs59MTl383f693W7O4sPKdO9duLaC531due2bS8hbx4aSjw4JGX9SrXVJkSWk56t4LU1eSaR2Pn97FeMwt2pRz7UfzqOESumdd9nsiNzcXL9nDDjtsxIgRTKk1a9bwzef+ES2MlOb35g6gCtdvL/xi9paL+qWe1zfFdLtbSuTjp3Z4cdra9dsKmU/WWMbNST+uc0KHpIjLPlh41sHJU5dsQ9LCEeyWwa2O7ZyQV1z24V+bOydHxDum0c5Xpq+bsSKLzJbxYbcf28pIPzSEa9iOwlL8yIZ0Tbj1mNY2ac5qZ3ciNLBxSkwIJ6kxoXSpXWI4ct7kxVuHdm/y7/qcF39cm7bD4dzYNSXysVPa/74q681fHX5zN3y+ZFiv5EsPS3W1ia0z8ZKM4t0/NuGpB8wbB7Xslupw2HbtMIGfd321nEujf9nwy4qskYNacorEiYzI2HukRt17QpsnJq9etNlxF64f2OKk7o5fiq71MPBXf16fnuPANXPNjuCgxoe3ib3juNahQQH5xWWPT1r999odXEqNDb1vSJsuKXu9x8nUIQIiIAIiIAIiIAIiIAI1TcBaOPzss89GsOjXr9+QIUNwQKNdnB5uvvlmPNFQJVi9a8qUKchSTi9xiCkXXXQRxqyjxIseB6to3XfffbwDshoUuoMpi8MXsoglCWGP9MN7u6fiyC7EjWKGVDR79mzqIW0OtBIS9NOune252IhoJ1zJOCUKFREKzzLSLDrGJ2445JDA1Y5PlCyWf0JcQ8Xj1PVAtmPsSFpmwTKM586d++WXX6Lc8WKLPc5HyDQ//fQTbnpEVhkZwqqHPlOWmE1yfv31V5iQgBKZHKSRz6wmTCmae/TRR6nNnPJpKI0ZM4Y1qqgKyY/mCHpFpjz99NMtM+8JPzKB6r///osSChOW6HrwwQcRlZBT3QIxBMxg0QE4feaZZ+iqSSM/Ia0y2C1btuxCMp983PRI42dDdCTr7lnj8jQVCYxFS8UMHRZ5aOLEiQwWdzCroC8Jbpxx0XL7SRPIpoTNIbYa4czUSZoc8rmKTZ8+fTzVY+w9XXWaNk4dNlc9lcXYe3FTG+IssaWQ4RSxmC/drbfe6ktBp85U4bRnc0cc9Ddz0qyyOYWlRi+78LAWESG75aDEyBCkDCSzy45s9duKbV/M2nT78e3vOakjlr1axKCFvTljDVdfv7AnxR/4esnAjgnDD2m2PD1vxvKtBGOiuz06YelFh7dEelu/reCRCUtvOqbtdUe3sWtnn/+9kdDRybccvnhz7s/LMr+anZZb5NDNX7ugZ2ps2MPjl9LQOYc0m7fB8cQwx4hBbY7pkvTG9NVFpeU3H9vusWGdP/170/VHt/EYjrenYNX/5ctAYZx++SYwHXH9xb+RIHCUYxb/u+6669A+Te24p6ETk+aJgDMk4jR/nbjwwgt5QPP3EB7cxNXzNwQeMT7aYIbLMX/6QFulHqKIzRShFR7BfM3IJHTZHl3Mg4nvqulPTX+a0EjEL3tDrC/24NB2/drE2jM3bC9C7iFnY3bRiz+uaxodcsOgljGhQU9PWcNaaax3lpFbnF/icPV8cvJqvKWGdE0kgpKJdcsXy3KLyvAaw8ENnejuIW0Gd4rHYNb6vXPC3pDbNAGk5K/MKEDuufvr5dzEm45uSRTngk25n/yT1joh/LBdvR3YPh41za2N22prLnNFRr79p7hs92qE05dvf2bKmr6tYm49phWB1jd/sdTBzd2gWAbupO6J9LBz08iBHeKKy8qxhPYJXRMRB+duzDnvnflgh0NiRPCzP6wtLd/pth5q2Jxd9OvKrKVb8kcMbH5y9ybTlm57YIJjAr/xywaEsxEDWtCZguKye75ZUXNAVLMIiIAIiIAIiIAIiIAIuCWATGa9/vz22284CvHOhaMD79gIXqg5CCWsa8arGSobqtP06dPt9aBAcYp8RiQmZqRZUp23vG+++QatBNcnMlG7kFrwmdq2bRteS6Y4a9uj/ngqjkSFWf/+/XHIwu2rTZs2phSfPXv2pFrEIyvHnkA9oSE8s4zDV+fOnbmKXxLqxvfff88np6wjhOcU3k8EcuKpZC/umh4wYAB9oD9IV4wIDylGZxRGHESQacz2BSwejxJE36iBN1ynddleffVV8p999lkoGWmM1lFeTHMME13S5OM/hZOdEyXTbTQalmZ7/PHHcdar1KYHfmSCcEafTzjhBF6i0V/oiXmpdwvEjI7Pd955h/ljJEj8y5ghRt9ETrVsuApnZotZHZ+0dYmEp6m4fLnD3YHpcdVVV3E76I/xnbSXrTCNRGBUApNw+qQJZj6ZvAXbV+4nTQ75XOVrYpWiOSttEibHNd/pqtt+OlXFqb0et0VcM3HlY0aZrww6Mt9uHJtczWoiJyXWoVazuJjvlRMDx9sxqtkr01YRX7nYoXGVjZm58YQeychkuK39sDjjofFL07KLLjjMIfFzPP7dsimLMj6ZuYH0rZ8vnLQg/aelWy1hbpdJo2lLMq/5cO78jTs6JEeOHNx26m398SALaNy4U9OoD//csGxLLoJd2o6i8/vtrpNSp/VuOm9D9q8rtv29JuvnZVuP7JB4af+WvPXXoN8ZUxkJmccEUwqlnL8P8IDGs5He8EcJjpIShyrEwZ8FEEFNgn1eeDTz1w8S3377LV94yuKtio6G1IVijXGFNojW77//PhowzyP+SMKTjlnCQ4dWeH7RLs9KtvLlUUV/TNA7MequDqK7euf/jy27vLeSohzB9r4fLH92/4ltsW8RF3rnV8s3ZBXG7lnsf3t+CfIQsZNmZbSezaL/M2HFysz8zTuKUbUeObk92w4c1SEe3yuWzz+kVYzvjbIKG+IRyh3L8N97Qtv2TRw+cRMXZVIPklzZzp1Tl2w9t29TdjZAEHS18b0hv1he88lit/XgRDagfdydx7Xm6uBOCWe8OXfC/EykMdcOJ0YGX3Z4sw/+2ozUeGrPJKNyjjyq5em9Hbvk/Lhs+6asov8O79K4caMmUSH3j19B1GpOkWOPAic4VjdeP68LdXKKU+jbv29klwY0tciQQNZTQ6fDx/CbuRkIcKjpVhElREAEREAEREAEREAERKAWCOAAxRvQ77//jmsVTgy0yMsXmwmasE2WCSOQkMyMjAw+ecM69thjrV5xSpqCRO1ZmcgNvPpxyuubecn68MMPefnirRAHIsuMhKfiRhMhFA7HK5zFiIgkRM4URKKy1+CUNmIcGorJJ3CSd0neK6+55hrLki6ZRdAGDhxorYxG/5HkiC60zEzC9AEvEAQjfKxWrVpF5QABFzoj/Te7EBhRw6msOWWTUDpA+pNPPsFRw7z8AtMUJJ/3XMbIS6uxR6Fzijo86KCD8FzjpnDg7EaYJJ0xxr58+pEJ8wSBFRGQu8CbNbtMIq3SBy9A0BZxo8EGBYdRo7KZNJ8WAdLHHHOMGTW6J5PQCiXmEoc5dZ2KJvQVb0duChoWQqd1601B3z+5g2hhrvaJiYlmu0++DnjecKfw4WLmMLXIiYmJ4f4a70JT1lM9XvJdG/WS41QPp16M2SjDfG2Z8+xsgOKJOydCipG5+T4agF5qqOalVRl51NAxORIVzKrq+eHd/1rt2LvD6eB1+PWfVl9zVOs3L+5N2u6thmXbxAicVzbu2R9zRUaecWrjEqIEn0TgwWLDdkdUJkGdfNqPTk0j120rGPGRQ65tnxT5/DndcUz7a5WjG3ir8WOM7TMgOiz4oJYhY67uay5ReWpc6ObswhrUzgjMxBWWBw2B5Rw0jKsqO54YVd70w+0nKj6zkG8RDyYUN7zVMOO5g+ps2Xu3weWN+GrmB3OF7zYF+b6hnVGcpzaB6DwEiV3nGcRfHtDX8A3mUeVlTxmrXb8kEE2oJ7uw1EkTxcWMmD5PTfRt5fAC4yDQj0/2eW3kEHMdx9ptjkkzqOPunZs7N40Ye5XjWYbc1qdlNAurLUnLI67QYVrJg3hjRKKOSREvnd15xortX81Jn7cpl90/XRdZ88Wmko1X2vyN87raNagbP3cs4sA3jdhJvk+4m5ka+XLN35h7Rf9mFQ7K2PdqHmUS3LiDWkabR6u5iawN52XgSI1GOKP4MZ3j0c5w4kOSe+T7VWf8b27v5lGHt4298WhHXLepX58iIAIiIAIiIAIiIAIiUDsEcBBDD0KdQQfhQARhJ0ocgghJ462NPuCAZryceLHi1FrAy3SPddBI8G5lbIwyQiAbggL5vJwbM3IwMItfmxzz6am4kdhMrCWWRoCzF3Sb5lURpYBGkWmMAeGE+F7g6MRrICoPvhSIUDjg8NKHwW233WbVQ+QmghrxqlaOSdBzk2BrRRIURMchOoo0rfCWijOdXQMyxvZPM0ZykGaAwGEoWUutGVcpi5W9rEkTc8cCVcRIEqfFeysHTiROrm2upUyOf5kQHMa7M6/VOKChcHHgQYavjBcghhudMcoUwyftulibCaflkrG3oJlRmPvlOhUJlcWxhi7hDolcy8G9xmunUmtDIT8ZBcrtJ66LhLLx1cAfk8hlBFZuH/zxtWRuIIYSK4oPmvEidFsDQ/B+1ZQyI+VG4+hj+SRyyXtZU8rTJ2KZVRW6Jx6muA3hA2jZo43YhT8r31+JRZtzkJzw8Lr7y93aekpM6JEdEv5Zm0W+pVXG7/IyCQ8JZD0y1jXrmhp1x5AOZ/VJHffvZqsnuPKw/DorJiFNkJkcHZJXtI8Qb1m6TbxxYe9/12Xd+YWjGysz8l79cTVrmbGkEqf3jFv8z5osEp1ToohOa5ng+KZzFJaUTVu87bkpjqCxpOiQNokR5x7ajFXSalA7oyUeB3y3kdiZzXzVjUsq7qZeHoJMd3MXjQ0rRDq6b3v+kq7QhshQFDq+P4hi+J3tqmD3B5IcwhknLVq0YJlDFFm0M0R0dGW+/HbLmks3j3OIXgs35bGsmNUKCuvwt+axqNnwg5tamfYE08V+ak+zxhmnYS66G3GCT0xa3Twu9NDWsSMGthg1dY29VIXpNVsLEJ6axYYg6F7+0SI6MKBd3LCeSV/PczgJOh2+2DgV8fspoqG9zsBdwHbJ0I3CgwOIeDVXSbRNDPe9w9Z3m+KBLjKXl3qCTA92tRoc4OhNYWn58V0S3rmoGz6Af61xRNSyCtuHl/VIiKicE6IZiD5FQAREQAREQAREQAREoGoEeFFnsXbe0XA1QL4hKAfJDO2MtFkXn1OzpBQuWkgG9u0saZEFeVBneLfCN4JTxBTiilDZeKWiTtbJMWoUa+ZQHH8l48eAJauGeSlO0yzUg35nYipZaAxjc9AEcgCiCS9xe/J2/2t833DjsqQZVBXiisgh2hGR6+GHH8YU1y27RsbLKZnIH8gWuyuy/YMygj0ZjIVPXlFN1CoOOybWlRdJm7kjicJiz7HURlbW562Wl2Jc4dBETAyj3dI1bSghBhFniisJUgiLnXGKc4mrsdscPzIhPBOHGAQjtgEkzQpuMEFEw+2Lpr0Acdsxp0zqueKKK9DXjCsiL+l2A09TkXvBaz6OkNxfmHBHmI0sCWUW7LPXUOU0d5xeEWJsbivxsyaElgrJIZ+r/mqOLw6Rp6AggNfJQ9NT//kuGFHSyYAJRr4lnHEVrYODBL3FR48E4gwGRrkz9k6VVP8UeQvfrkGdEq8a2PqDP9azGjg+ZSVlOyfM3cL6Yt1SE5OjQwnM6ts6bkdBCUuesRUAWhXhkx/9uYEdORHL6F9IUEBcePDURRm3HNeO/TFvGjN/YMfEjk2jWMLM9x4u3LRjQMfEE3skE9HJHgWX9G9RvrPRgo05hO7dMLjNxe/MTooKefm8Hu/9tm7zjt3f3znrs4/p0uT9P9Zn5ha/eE6PbXnFRIbShxrUzthShIfdtddeiwrGo5YD3102pEDytwcMM2z7/LCedwaH06nbTFcbmiCyl4cgf0hBGLb2XqG4PRAaFZk/R/AUw76mvRZNz81nz2ZRSDkfzNx8dKd4y+cInYurfVrG2C19TDff5Ym2MC2PIGGK4IZ27SeLnxzW4fNZW9o1Cf+/C7sh+LA+WqW0M/RglB1qI8iRLQsQ0T67oqdxlPtmvsNt2+mYuGhrhTZORWrnlE0SiDzt0yr6pqMdm+NwfDhzc4u4MH912Es9qzMLeEAQMEujCzfn8tkyLpSA0C5NI64d0OLaAY1+W5XFImh/rc42ew44OqdDBERABERABERABERABGqeAG9kBFSyzNaZZ56JfIaUZsIY0SOIIkSpQUfDJwsFgd0G6Q5rV9k7RSleyMnEsYv3KRy7iNEj7I6tJIkLw2GHl3NixKgTLy0cdqyXeZaZZ+9LT8V5KUMjowbEI3yOzDu/aRfXJxIUd9XOjLcE0aZWD+k2SgQHAzQCHH1DlzExm8bMaGfEKhF/ZxW0Erwn4kOHdEVcKplgwf+LBLoDqhk1G6cz8yZrfKbYGY/lz4BpKuEtlW0TqAflDt8OlBfkoUGDBjF2qxWnhOViYijhGAhYhkDn0YYwxvvJqYinUz8yYXSoftwOBot2yW2l0Xbt2hmnMLdAPPXKNR8miHHcL7PSGYqB3QZubqcit4ZdFHiLR21gw1ZTxL4rq70ST2kjHnm6Sj7OmOiebg2Y2CwSVWENFRpQOXsjIC4TuMpXkgnAbOELRUFfymKDUmb/NL0FhVl93t55vqoI3OTgikRzppQxsNdA2l6qyuk7xi5Ek7pqQCt+qIQX43vGLWINs3d/W4cENv6GfjSzOasQB51NWYW/Lt92x5D2tw9pz5vz3A07WIYM6e2Mg1Mn3XL44U/98uLUlazZ//s9A6mHIND//rgaFYw0+ppr95zy7vpy0duXHvTwqZ0fOrUzlReXlj/4zRJK4RD38rk9frr9CKogwpSdOk/uuduH6fHvlr9z2UEmZjO7oOTBMUsJCGWbghrUzpjQPF94elpyrNnYmK+c8aVkExYzVB4xrmOuTg7uiEjUTEFmEvtK2LUz4yxqKmfBQhR0fhkQsewk51Wn9QrLIqZccUTz135ef/PYpaxQhl/YP2t3vPTTOla/YjdGnBIrrMHJgIJUwsaOOLIFBTam5uiwQFb6ZzJlF5SywBYKzn+nO54p6Tl8D51K7z1ll8k/VmWXN9qZlV86fl4Gq32xlhlRh6bIiowCduGcMD8DTytW7GId/eBdwYasfM+mAZ5skK72NrCfUqf3Svr477ROyZG9W0RNX7YdV6//XdCVHUvpjuug6DA698LNed1Sd4dqeu+1p4FTCkQPf7fy2oEtuAts9YCk3Tklks1J0SKfHtaB2Fuzb2lTm/uh97Z0VQREQAREQAREQAREQAT8QoC1qFi3ixcltA/kKurE4wwlwixqhscZK4vjDEE+4hfuCNjbPR4OPfRQAoz++9//4uGFDXIbThLEWiKp4JLDKtUmnxcuyvJSxgvgxRdfjFqHhERsEA25LY7wxNaNo0ePNrtq8o5G3+yuElTlOnyjjtn9uXBzY8e5l19+mdBU7NGecFByLeglB9UGvzwMwIK+xtslL7YoWbjFIcDhXgcW0mb7OxbbQqsibpSliiztjLIMGR8ltixAGKIe6vTeDSdKeJwZ8c7odzgNeS9uH44fmcDfzAcWbjNNMBA2fEBEcwvEafE4++2z99Ckce5jBhqRFFEV7xa7DYKs26mI2yPweZE3iipmuJ7Z3/TtlVQ5zYxiKvJFsC/qR20IiDRnn29VawLnShRYvgvMDVMDC7ex7BXirNPKd671G4XL0ydTzklTc1LHzFWq9VSDa4uVzSGubuQn81mVn+XJcOCyFixbtiXvqFG/tkyIYGdMS/y644uFRG4Sn8dWAGYfTBbp7//UL6bRz//ZxA9Rk+k5RYhfZGYVlKCpmavsIcCPSb/4wyp+TNp8Eul54Vv/4uPWuWkUpVakO0QADjbWHPz877i/7SgsMdGg7M5pNuhELzvrjb+R53B8w+nM2DOWGtTO+Bq899576KYs0IgXIlIat5CGmSIESJJgrrNU/+rVq42Eb/rkl0/+rEFz/PEEyRZ1jDpRWM20sNdP8CZPeTZPoXt83+yXajp91kHJyE/oWXeMW2baYhfIe4a04XeBu18HbrrDLLRbPnVah/9MWHnLriW92CPyvhPa8lvliv7NH/xu5UXvOaJWWQ0NH7RPZ6Wd3KOJm+p2RR2z4Nr9ExzbPuIW1zI+7KGh7cwaasN6J01anGkqR6c7rkvCD0u2TVy4lW09HS6OP63DbeqSw1Ld2gzrleS2Of9mQsNLhZcc1iwjt8S43eGDdnn/Zh2SIvj+eOrwke3ipizeOnt9Dr57Xqo1lzzB4SpwVmYWXPbBQtLImtRGL+88vvWd45Zf/5lD7eZg+TOzn6k51acIiIAIiIAIiIAIiIAI1A6Bvn37su0j6gzvSqwFhqODJXOwnhcRgogjvFhZUgjKjj1QkdX08RLKzMzkPd9atYqeEymJYkU+b1vWolfkszYW0Xk4lJnlzNwW5w3nggsuOOeccyiONkGLrHNtaNibduJj3vicMlnTHckMPYvAI7eqipcKqYq1sPGHohsUN1joD9ofFeIcZJxCrBZZQp7dPHHa4BKHVTNxjkiKqIGUMsMxRcBulXWiaqfEJRZ658WWGwFM60ZYZb0k/MuE+cDOAwg9rEfGi7PRerwAsQjQQ7Qnq59Iq/ZL5CMIEq7Imzujs9aYs9t4morIiIBl6jKdkBesqWu1VWHCVR9wLULkMpsNIiKj+eIAyCluYsY/kkEAAEAASURBVMijNOdLce82LKnGQaN2M8J7OZwyXTt2oOSgjuFH5tRboibXbnWsPGg/2GTTErbs+Vba7AZgnVYqkVNYylJrrkUQ41wzTY5Dwtv3Yg1qZ3ypkIHZiQP/L9M8j1RUVbNXADtp4ovLjOfhwnYhhKNj48uM98XmvPPOQ582mwwg1fFXAh7f5k8oTmh4JuJwa497dzKoudMTuyXyg1y1Lb+EEELkKtMWgsuPN/c16a+vdexvwDH1xj4mwSebWloGVoJozfcu6c7OqRhYWxD0bxc78fqDqZ8QYpzdiKnEsww/Mqtaq04So8/vaj+1p1kg7MurelMPWxmYNfKvPrI5ewigBH12pWM/ZnO4tdlzsWb/ZeF/fpza+O56x0OHg7GjS7LPJrRZXNBkehoUVx8+uZ2x4dMiTPqV4Y5HmzkIvLUuuR34zDXZSdHBr57Thb04Q4MaI9WZgty+jy/vwY1A3k6IDKoLfnl7xqR/RUAEREAEREAEREAEGhwBNAsOt8N2G8xot0TqQkCx51hpa7UvK4cEYpBdTfNUHKnL7Rpk9qp8SfOmSTCgL5ZubVy7wauop8BALlneQ061MepKDceJEtIkh1OdVT6tMhNulpNiSB+8APG9h9TsiapVidupiLxghbhZln5PMEbkYA6/11ydCpHbjOJWE5/V6Vg9LtuY1f58HB7+qOjEPhrbzYiIRLBn+UkerMw86xLCPK0bHzQr018JNuPgTyj8/YSnA97FSPVuPcumTp1KlD7+cZbI7XsHiD+v1EPQ95plWQ8I3D9+BVupop3Vg7FoCCIgAiIgAiIgAiIgAiLQQAgQLsr2AmyDYBaqr+OjZk06gkmJja3j/XTtHvGtrBrGqujE/7perekctr8wC+fXXEPEotZQEzVXs6FB/bjXeSEz4LHJITHuo9m8lDrQL9Wg35mFBgGLwzq1EujEHNapfxP8lcD6WwfKvatwhpw3a9YsNpflQVMF4cy/vVVt9Y8AEbLsz1D/xqURiYAIiIAIiIAIiIAIiEA9JkCk5wE0OpbHOoB6a+8qktl+Uc2sPuCxhcubOa2JtNUQCb/Xb9zN7E0oXdMEakM7q+kxVK1+QqMRztiNhd1PqlaDSomAFwLsAuHlqi6JgAiIgAiIgAiIgAiIgAiIgAjsLwJGfjKqVk2kGRfVcqDQ8WlO/ZLeX8QaeLsNVztjWT5CNRv47dfwRUAEREAEREAEREAEREAEREAERKABErAkLcZ+IKYb4C3bj0NuuNrZfoSupkVABERABERABERABERABERABERABPYXASOW0brlC+bUk+rnmyaqX4/pmKd6nLqt0xoiIO2shsCqWhEQAREQAREQAREQAREQAREQAREQgbpFAEmLnQzLy8vNToaWiObUy7qc76lvTkOowilY7HDc1hAcFNBoZ3mjxnv3gXRrVs8yG9Zo69nN03BEQAREQAREQAREQAREQAREQAREQAR8J4ADF5sWpqenl5WVIULV0EF/arRmU7/1aYZPi1aOSVcqH+EsIyMDOEZVNGVdP4f0aBZcVuiQzxrS0TgvL8/H8aalpbVr185H44ZgtmrVqpSUlIYwUo1RBERABERABERABERABERABERABOoNAbSzoqKi3NzcA25EoaGh9Lwmuo3HGcJZUlJShZU/NWHhlAWbSkobkHwm7azCWeHRQNqZRzS6IAIiIAIiIAIiIAIiIAIiIAIiIAJ1mACeWfig1eEO1nbXBMQLccVseoGjSyIgAiIgAiIgAiIgAiIgAiIgAiIgAvWQgIQzp5sqIE5A7KfSzuw0lBYBERABERABERABERABERABERABERABERCBvQSkne1loZQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI2AlIO7PTUFoEREAEREAEREAEREAEREAEREAEREAEREAE9hKQdraXhVIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiYCcg7cxOQ2kREAEREAEREAEREAEREAEREAEREAEREAER2EsgKC0tbe+Z11R8fLzX6w3uIkB8p9fg6GjAIiACIiACIiACIiACIiACIiACIiACInDgE2i8c+fOA38UGoEIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI+J+AYjb9z1Q1ioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI1A8C0s7qx33UKERABERABERABERABERABERABERABERABPxPQNqZ/5mqRhEQAREQAREQAREQAREQAREQAREQAREQgfpBQNpZ/biPGoUIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiID/CUg78z9T1SgCIiACIiACIiACIiACIiACIiACIiACIlA/CEg7qx/3UaMQAREQAREQAREQAREQAREQAREQAREQARHwPwFpZ/5nqhpFQAREQAREQAREQAREQAREQAREQAREQATqBwFpZ/XjPmoUIiACIiACIiACIiACIiACIiACIiACIiAC/icg7cz/TFWjCIiACIiACIiACIiACIiACIiACIiACIhA/SAg7ax+3EeNQgREQAREQAREQAREQAREQAREQAREQAREwP8EpJ35n6lqFAEREAEREAEREAEREAEREAEREAEREAERqB8EpJ3Vj/uoUYiACIiACIiACIiACIiACIiACIiACIiACPifgLQz/zNVjSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAvWDgLSz+nEfNQoREAEREAEREAEREAEREAEREAEREAEREAH/E5B25n+mqlEEREAEREAEREAEREAEREAEREAEREAERKB+EJB2Vj/uo0YhAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLgfwLSzvzPVDWKgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjUDwLSzurHfdQoREAEREAEREAEREAEREAEREAEREAEREAE/E9A2pn/mapGERABERABERABERABERABERABERABERCB+kFA2ln9uI8ahQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgP8JSDvzP1PVKAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUD8ISDurH/dRoxABERABERABERABERABERABERABERABEfA/AWln/meqGkVABERABERABERABERABERABERABERABOoHAWln9eM+ahQisB8IlJeX74dWvTZZB7vktb+6KAIiIAIiIAIiIAIiIAIiIAIiUNcJBFWngwUFBRkZGbm5uXpfrQ5GlRWBA5FA48aNd+7cWVRU5JfOh4aG+qWq8PDwyMjI1q1bu/ZKzytXJsrxhUBAQEBUVFRSUhKzy9Ve88qViXJ8IaB55Qsl2YiACNQFAnpe1YW7oD6IgAj4QsD788qXGrzYON5+vVz2cokXhtWrVzdr1iwsLEzamRdQuiQC9Y9Aenp6aWkpElVQULX0d4vMv//+26dPH+u0ygl6tXbt2uDgYCf5TM+rKiNVQX4HFxYWbtq0qW3btk7ymeaVpkeVCWheVRmdCoqACNQyAT2vahm4mhMBEagyAS/PqyrXaRWseswmHmcIZyEhIRLOLJpKiEBDIGDczfwonPkRGloeHcvLy3N6Lul55UfIDa0q5hK/6fh9xyxyGrvmlRMQnfpOQPPKd1ayFAER2L8E9Lzav/zVugiIgO8EvDyvfK/Ek2XVtTNCNfE481Sv8kVABOorAaI1+foHBgYiovnrgJW/qqJjeAPxNwc7fz2v7DSUrgIBft8xi5wKal45AdFpZQloXlWWmOxFQAT2FwE9r/YXebUrAiJQWQJun1eVrcTVvurxVkh6HK41KkcERKCBEEDtMiM1a59VM23VVhP09LyqCaoNqk63U8htZoPCosFWk4DbKeQ2s5oNqbgIiIAIVJOA20eT28xqNqTiIiACIlBNAjX0aKq6dlbN8ai4CIjAAU2AR5LVf7vsZaXXbitYviUvPCSwS0pkYmSIlU8pt2l7plWzEiIgAiIgAiIgAiIgAiIgAiIgAiKwfwlIO9u//D22ztLU27dvT05OJgDNo1G9vpCfnw+BuLg4tk3cLwNFymFFfJYGj4mJ2S8dqPuNgsh4nFmfy7bkvvTjmhuPbjN6xrpZ67KtIZzSM/nGY9pEBAdYlk5lsXTK8WTpS77VrhJuCWRlZbEkHBtHsoyXWwNlioATAX4lbdu2jV9JldoepKSkhKdoREREfHy8U4U6FYF6SaDW/vO2detW2mrevHm9xFibg9q4cSOhPYmJibXZqNoSAbcEysrK0tLSmI3MSbcGlc2kwi1btiQkJPirQk8d4H+VrADbsmXLBvve6olMZfMzMzOLior0bK8st1qzl3ZWa6h9bYhn3DPPPPPPP/9QgFeOG2644ZRTTvG1cOXtHn300R9++GH69OlO60PZa/r7779vv/12K+fggw++9dZb27RpY+V4SowbN+7999/v2bNnu3bt3nvvvc8+++z//u//KmwOh6bXXntt7NixptohQ4bcddddfnnJX7du3UUXXXT11VdffPHFnvpMPiLOhAkTXn/9dfQ7Thnvvffem5KS4qVIhZeokEfh2WefXaHlgWJg/M5gRYetz9yi0nkbcq7+aH7jRo2GdG3Sr21sQXH5j0u3fjs/fUVG3hvndw8M4MpeeyttElY9Vj45LF72xBNP9O/f/+STTyafV/Gnn3763HPP7devn1t7bHR4IrBjx46HH37YPF6wue66684//3xPxspvOAR4CedJa42XXz3nnHPOFVdcQY7Tr6Rrr732jDPOcLLH7KSTTuLXhP1BPXHixJdfftk8Rbt06cLvGp6i3p+EzMz58+fT9P76k4lFQIkDggBPsIULF6LMfvXVV/wfht9KQ4cOZcr16tXr1VdfdR0CYu5HH33UqVOnI488srJX7faLFy/mi8B/hPguWPlO35Qa/c8bA7nmmmsYZk5ODr8TrT7wm5Ev8ltvvfXNN9/wp0doXHLJJeYqr9A8/Dds2PDuu+9a9lVLwPnZZ5/96aef6MO0adN41f/yyy9PPPFEvuYvvfSS9zqPOuqowYMHP/LII97NqnnVx//smVb43+nSpUv5zyp/matmuyouAlUmwG/V0aNH895kajjttNNuvvlmdo03T7nHH3+c7w6XRo4cyW/JGTNmGDO+4GvWrLn77rvN/5DtrTtVeNZZZ/FQqjlhi+/+zJkzTz/9dJ4MVjd4RjEKt08kbD788MOpU6fyXOIl0SpShUR1nkigmzt37qRJk6rQbqWK+PiEpE4w8prDTGjSpEmlmpBx7RDYZznt2mlSrXghwP+HeMrw/sDTZ8SIEbw/jBo1avbs2V6KVPMST2eEIV/+x9CnTx/6xlsN/XnwwQcrbBfJgycpf4JAruL/qbTC/+QqLIUBvxIQzo477jjGzueUKVO+/vprXwr6y4Zn6HPPPceffW688cYzzzyT8dL5alb+/fffjxkzppqVHEDFWyaE3XJsG+SzYb2TXzqn61kHpyxJy/tsVloVhvDCCy/8+OOPTz75JC8D/FGL/zf88ccfyKk4T1WhtgZe5J133uHxwlvfU089xQvkG2+8sWTJkgbORMOHgJGh0SB4yJtfPbxPrl692v4r6ZZbbuEZ/uKLL/7+++92e/5n3759e5Sy7777zoLJH06ZY6mpqfwpiD9XMM3Q0bjq/Uk4Z84cXuyRA6x6lBABLwTQg7iKi/qCBQtIoKMZrdZTEeyZYExgtwber7otYmXavym18J83hsBvQ6Qx3IdxT+DglZhXaIbwxRdf8J+NY489lj/78crKF9N0Em3o559/XrlypdXnKid41eTLjlx++OGH8x88XsirXFVdKIjaCDrrT0p1oUvqQwMkwO9W5BK+U4jy/G14/PjxfH/hYJ5yzz//PI5dTljQfJm6ZH777bdOlzi1KuRdBg0LgRutytXMLzn8xp88eTKuHvwnwTyR8HSjb/w29/RE4huHOwU2xcXF1exDPXsiDRw4ECC1/OZbzVvQoIrL76xu3W7U+k2bNvGXQwQCesZ/fT755BPzR8V58+bxd1TeQMjkz/utWrXiL37/z959wNlVVH8A3xRIgNAh9N6kq3QERRQBEbEhTSnyVxQUBUERUBAEGwiiWFBEEaSJUgQUVKpKkYAC0nvvCSGQhJT/d3PCcLn37cvL7tu3b3fnfvLZzJs7d+bMuTNnzvmdM3NXWGEF0eZuicqB3NtgCGmiIT366KOKUWhWWWWVn//85wwSto2Ar5VXXploYxTBHcQUAMJuuOGGf/zjH1tvvbUcUJefI0aM4ErdY489SoCa8DFeC1TxuHJ63HPPPdwgW2yxBa/ju971LhFVTCOPw/s0oWbkKXzfffdBOghTbuH1118/sZs/xKrA4WAH0A477ED9SoFv0V+bg9Zcc8011lhDuzrrQXKE3KcvUhb/7//+j96mg4rZSTRmzBiVQOipjAg47LDDIH0IWHrppbkfOVo322wzq1FqXcKyhLd6TUixCYthDrFc/ehHPyL6lZx33nlp5LRz5mKxj7w9mH/44Ycjnu6FDzh/xBFH0Cmpp0suuaSE6Lyjjz7ayqEGVXnEUsGzRJ2l4iPykEMO6WFEW7FTrUzjhssgKf6db8SwZRYcOW369KnTOv5+13PbrzM67u6z2VJX3Pnc1fc8v8v6ixfLpzTKOY5KtcXdwE9ZJrBUtr3XrbAX6r0oEGVKf1vJh/7VlsgIBEM0aGYGHtEhwkhOaTrcfPPNZgFg3ewwRKFsPYeP+xejBie1pFYIeeosF4IgstKSRIZbJmJ9waJU3ojiUCnC2TRpBeaff36LlOXJEsB/TvtPkhCMS90vinTliUp/CWTwLoyjJFr5Y9RAupIDbu24444K5ytzAAfEzjMOr7vuuiI36AC0CHAwHeDQQw/deeedzz77bAUs1lOmTKFFcJLRT+QYokTc/vvvn+76WZKKlATTgUJlnG+66abFhqRLM6WovFFpzBpaCnXFwOZKPOmkk0pKwnLLLVfV8QRp0tboPBQJtBU1qOgpFWKDDTbQuhXQrCHVxZ4ceOCBJshBBx0kE8RmIlOWmKmq4ss0eYuUP/zww1//+texDh/whLNQVD5dUXTeVlttdeaZZwoQGzduXFGrpLzRNlXCzal1Hbd7VChZqrbakQceeECcGiy+WCzKwwVotiWNqLeVvRJjsRExeh3MTB3JicyBlnFA8KZZQNcScMQa2n777c0yMysRwAaxJpZwanCVAqwkRhljJ2yleKRUISFgDpq87nZbInFRdLUEs/7UTDNfffXVIxo33G9gO0ZQVSLREFhPrZFI1f5C9Ah/NK+22mp2YwTH0t+SfCB4YXNMQmYvhZmMZaEz5epY1iS8wiIwiEdvR2hb8dVUJWS1RRCqTItUoion2ocDOe6sfd5FJyV0C39DLWNj0HLYrhAcUobEdPAWfY71K+iGwgFHI2rXXXddChO9BwRGrsGzTFEiSTHKE4BGQlW0KACZGUuHs0uLJUNfYYq4G14LMQImOcVObawa/skSa8hlgoY2SUaTg+56kHCk8wnUFxbkce5WuhH5zijaddddlSF0hBnTNRWmmaU6PXjuuefuueeecLcA0dItXSaeAFskFImjp/oIpRJ/RMyJlKHCwrD0CDBH3UEw1UdQ0vXXXy/AgaJJqKkNf9zlfYXrgRRRlZrALnKTCku3tvYUN3e8/PLLaqBKBnDmkb333lvTulzqIweRdUi/QvKSxdJyrFhC1XCeFSpHDB3OMzJ1Cnu9C84fSJ9lDIVpa2qirb8k8B+ppb/LLzzXb/dc+8y91jlzr7U/sPai6e6cw4euvOjc9z/3Klit+lTqcipfLEOdpUwwvN0N4GzLLbeEUQbQViyZ0qnCnChxIKL66WTmxSmnnMKWIy6q04ECRNpY/sHQbLzddtutVE/+OSA5QDrR1UAM7G1Dhfe4uCTpstFCGPLuRPeppJwiJDlLWw5RnNjCWwO2gBfwoxg/fEIbbbRRURI++eSTJZG+1lprKaMGuJjhVxWtvA6e8pdaSZElqFNzOTFoOWBhNdLEM1IwrPiBgwQ3rLA0HGlxDYYTDGivvfbyk0+Oh4/iQfRRmYBrEhSY4t2qVLSyW3d4KDkvjcMSw4szpai88QmB7YSH8N6Rt1Q4ykxVSajqeFQISgL/nwEPxSaNiy3+73//K54rBA2kdWhC9Bn3JzOb1W25lNBxaTqhqV1kTtTmKAkF8M1dNt6pp54KbqPSyKRHUecobyWtkmoU+7vxkDvW1AbAJdqqHeH3As/hDys6FUsJSmlJI+ptZY+SVmIspmFmvMFEWE5kDrSSAzH8qF4RRiCqwLwzbYMGUo4X2VILkUlUEWvkHvuLkSLTLE63JIoVmqTmNZPH1khysnsSiS1WZwm+9957NbrYYosFDWYxQ3LfffcV01CVSKrSO3JYlEaRZummS6SaEphwI/CpOlQOKm6Rhqp8cJdrkCx1mhD7lNXGGVDfsibhSTZvjRnOZLZwhHWjqqqErCnqaV9adKtIW063CQeGtwkdmYzgQMwuoICfUCrQmAQHJo+BhIMeoUWjRo0iPUNOsU+gRWam3dHUo5CVFEERJfQVm2VobFEz6A0qxM6x4xpwY2LzKFIl4y4QDfDEWOIu0BYLRxxB3Ep/CRqXn8QBTSjyFaYLmt5EPD+Jraa6IJrs2muvjX2dbC1Epp35qTZdg0bxBpNrMkF1Go27mrZC0AWRRH+lhpL76Of51FM1M9i0GLyybADpyOsw0qh6sDAFoirIXRznhAB+WnE0kc9HLaFp8l13BIgB6eJWVFs8EYAe6QLYYXKxj2gG6sVT1b/Ua0o2pZCBxybUWW/E0hXvSHycV2CvE9du9dl+kROMapzU5ydMXmjuOWaCbbUeq1Oh0BUOHByL58wI6kWd8rWqz3mdHKCZgTnsgaVImVwuGgzY3a3SdIgwDaKGPWYWZ/YNBg4I6SWRaORkOKkIYI1ZFksSDvhJZlKFLUN+Em68IDQ8aaExLom4gN3gLYLXGCO0LTeGGRmeJKHxVhLplEUWrMfJbSL39Zre+B/0QB+FpIsS4kRB7Rv3cmoQcwCUY3eSA1UNSAH1lIGumGHkuEXHgBPBfazItAuahkwKUihacZc3UWZRKlp35PC3qcTYiyA1OXEVZ0pReeM2I0UpKv6aFApHcxJFJSE2DBZ1POiVyQIR4+TggAy4amZjHR0cqwmqplqYa5QfM0gB8ZiaEEGWCgvZpsCwEmPHFjVS39NdCWi1voOkTVjn28Y0pNrJDE9kUaukX+mOp5i+pmSxHulqR/hcUUtPo2ESLwRL8RF+XD+LGhEmlCRD8LZZyh5rtspYL52CVyQspzMHWs+BtNRq2kQztWNS+ynWidpmLqc9OtZWphBPf0wiAQTCEdLdID4qtG7y/UdOyKJuSCRWZ50lGCSk/qBEgtQyy/i5pasSSYA5Ka0vYckC3ykPxU8ZNFEi8XNUJTBtRIgfdiGPgEqWsp+kdFU+cCrYjeHitFAmQVr1LWshwGrTO3BbCiGsSsiaoj7UG8SzoLWYr7biQI47a6vX0RHHJUY0vuCyFCoVu8FNQt54sDcngw1rSLfe+0sr8peSF4FdoZ8xb2SmUC8/Q6SGeCp9Ky3qj1uKeYpbwOPFC+LG6oZqcXSwbeJWzOooHO2S1CqPCouPl9J8Cy7dIaR0R6dSAboaex6UBvuLUzPjkA4OCuFsykNSUmHIl3RAXUF/EfYKJijgFnwwPRXkxTc0gWsISP3FWDKLwhorgUdokNS+iHEo9TGWJW4KxYr7lfzUNYWRF2pfahrx2Ej/ti4Kfu7tE3NTu72R0LW4VP56sjOsbMKkKeMnTpGT8sc88tIjL0xcY4l5apZPJaN86S9GsVIScOaucBXGSf2nFMtXiQNEhMFsTtEYaDZhHFK/ak6HdLJGkfOlCvPPAcYBmrGYMpu2IkKEBV5cknSWYCQMY1e7n+J96Ot8Kp6i/4VPIngiE/zKqyF0URlQhQIRBBQFOHtrivS4W1O0Gr0EZjTK8eD0gCic/w5yDoT+YDclPnAPFLkRq3NyExZvAWH5/Cz0b33rW4v5ka4pFd0K3Sk0geJTxZlSVN5Yv1GMSsBepWxESZlFJSGaU63M0PHsdz799NMBPXBqM47Qjr5EbVSLVDPLEH4NB4xbWgGl2Wvve0eUGUoasIwCw88aXkzwYqyeUd7f+MRHaFBBicxQtOpolenxYqLakVDSgm9F9SyeqmpEXUmGZil72q0yNqnKxb7kdOZAyzgQnid+pjBGQPl2zIjbSgTwH1v1THbhtJHJFpNglAUGRAhYoFP5YoWQd5tvWE/uJrkxuxKJcVRnCQ7pQcnUhNlEAbCDKuRkVSLZxqQYORnQvK+BkVGJcokmSqSa/UVhkuEpEQSQUSX5YPkgt4lTBagfRTrrW9ZFyzTJ1aqErCnqYxiUBHWx6ZzuQw5k7KwPmV+jaceH8VIShVAVjrh0JD/DQ2kiYL311qMDgcyJ0erz/A8yefjJVpVIO++sWqyaY+qSs2LBSF6AHYlWPa2ZP0H4iYi2olsj0kQq/cw2H48DNVCYPKLVtiKHVIXcq5D/U7shcOMWoclmY9iz6kWfyVQmBCv3hT2ewBSZjcgUjHKJssEQrIv6/Y2dCySmBO8HdKCo0pGSynC0egUi7AS74T8fRbWPgWCyGC14IjVS/dUERmELVNQCxqPClcQLRBdMvojqI22e45V5BXEV05Nfm3rwH+/97Fl33vHEy1OnTpsyddrV97549GUP2La518ZLVMvHszrrVqRLf61Y4hMV4Fc3Krw1JW25tSGlVDLV0Oas6yvyDEKTDnbGh2aKBUZv4leng2XbERXGp0gEs8CxDn1Fc263lRwgWjmExe+IJtMuMVVckmQG1l+KliU8iWXlQzIHwdR0Y4z/gw5NPJrChpNlK0nCcDiXRHosKOnEgJJoBeohj/0Av9NKTUAkWs9/BxUHbAsKISYijDKT+g45EiZgOAkAT5kSogzEsxNrpB8AN3xslo8oE3erUjGspqJ+VayzOFOKyptJZC5wRVDPmJcUJCZr8cFIV3U81p04d0Yme5VCRXQX8R3nVyQkOr6TkIw68xf2DavSQVqHrQaWSzLfFd/pkwhsOpEB3VYJi1FOqicm4+xqldWOIBUHnN2DHmdxpEYjQXEqaUS9rezhSZWxlMDAGkrk5Z+ZA63hgEgIEQOgMZtgHMvo+ELtClkotm7cwqEiB+jPRDLdhGC7eKrkx/FnUaBYobgnBSJCvNsSyfJdZwmOYNU4ViUW97QvpyqRHP08QyCdCtBHLR9GKRK2iRKpZn9JVEsAc4yFWAo4JQpK8kHwF9YRns7hKQZhBJ/r/CXuRPxFzEfazVqVkDVFfWhTNS39Oi3mW63hQN6z2Ro+N9oK2Mh5ZAJZCQ7PWMvjvAmuOXiWfG5D6iCPYmD8UW9Sgzge+R9YOAwMygq7BeCVUKFUrEhNussosiUbYMTCoU0m/SkVrvl4ugvmO+qoozwuB80OuOE2SXcjoYbUHHcK2EiLUZ56lwqzi0gNmlZYUFQ3u8aocTrOnlcMW2g/DKcE+XdFG2mOaaSeR4R6Bd6vsK9TWaKghC6gGDdsal3CPgUwZSxIfhKysaOz2keRazrLOBRbAWUoncJbrJPYhcFZvWxY8Jqi41g98E6CHD5syLtXXfDn1z3+hfPunnvOYVOnTZ80ZdqoEcMO3Xr5JefvjIWc3YtiLVKaJWN9NX5gjhJMbiN8dqvK5Y1A+KMdMcEKm2hs7TG5StOBESVKCJ9t8xFSZN7Z7GO4ZgYObA6Qq3GsksnlvdvkbsYVlyRjwEok7jg0yLCu8SQ+eGLMJP6I5aH9C28MWUd6SwC+kyRkHlRFOg8Hr49TRZxoWRWtNq1wUDtlSSvqCXQjtZgTg5kDAiu4wRwJVGSCtcNeTg6wFJNFcRKYxm1gfXdChRN5yMDQdugJxbtGYEkqGq5GoMFJlwhEqah4dKW8GfMGrcEfyhXjM6L1i3RKV3U8J8nSxMjq8INSFWKjdDyoEq5KkWhodo61TOTFrfgYuuB9P2likO5EZyyaVZcqOC8iXEw6nATwRVX+1tQq091iIvSxakdsjHDCJgRQ/DiyS+uIILuSRiQuryoZmqjsqbzEWDijrbvpTI9ip3I6c6BlHLCJ22Th3Qw0B1JDXBRbt+AefPDBvPsmUQQWWA1DfDmhn0wTwWDZTVOsWKFMajMQzWjvnkQiauoswSY+UjnJRK+DpaTThu6aEimwpAhAE0Wh8mJPmyiRakpgmB2MnpDEFuZh0e1HmSnJBwKTzsMqd6XFIhmzSboW6Y80cJMKpAkNKd+VhKwp6h3vQ1JlM6fK1XbI6fxGXvfo4KRKkefdqyE/VYcDVCKRX0kZipKibABGs5xLMGyHxShWZ0p31bT6xVIli6irYl3la5cGWRKCXRWWD18jx2uWxwEeDD7k2JGqsFgY2i000CN16ky3GFe0ZDKrK6aB0uwbjdix9FRKmBocO6ReIiBuVfuIKiBazV6k2iTQ79WQntK80IxP7yjJ32LJ9k/bLcWZnKSHkVZK3/3MK1fdO/bOpyaMGD50tdFz77DOIouMmrNUJroZz7JSgJiRrv4FZVr/Ur63ho1qSzmltNqKYYYayvKqOKjIFlOPR8tsTfn1p0MqNpgTDoh1dmGRA4NnXBHIBKklabZWB0KPFDXMyG2zNViXJGFNkW6ZMxRNcOWrojUeIW9LYrn4UvpdejCPq95+WZZaI6q0yltQjC6KBElo7SgFgqW7aKtKRXfNhVKFxV7UVN4UMJjpM/WnT1XHs9hROVAYmkNqiOHH58efV4ILUwGHSJgj1s2UUzNhrYTKQbUgRzpbhOeK5WdXq6x2pJpTrL+kEdWUDMXypXQ3lL0iY21NgO7Z7ZG/s1libM2fWV7VZEuzMg1+Tixf5CiqZz2pPCq0dpfMje5JpK6WYNKDDCGObDOvSW3fSiQkVfuLJJK8xJYgvigf5FgmPM4enKWhF4/zUvPQCHbRBOldFftVeVhsEZZnx67ovIEXXRH8aeXfqrzqeesNYRA9bybXMLscMD+rU9T0myVwpiEaYfpG5Oy2W9zvMLvPKl/SQWdZQ1eKmgd13/bVYg0EnK0ZxZxZpnGsDtOsTHUWJ7p1CbuM5qp9bOSleBb9rqiEUuuaJf3tXMBaksirpuFl/qUCEtUycbeaHznFv2EApBxvLaVTzcWcYrs5XeUAdcFVyp8xG96A0kp3889BzoEZK9KbPMONMITES1sVUvkZgrBTEkpURTqJnda+qmit+UiqOScyB0ocqLnOJkSpKgY9nu5KV6Wiu8UCpeb87GqmVAdz9dmqugIyq6mH2OIkFMKeo66wM2h1tf5qTiDa/lZ7Wiw8u1pltSPVnGL9MxSiNzSibkxz9ddpotq7ImPtG7UVruRvK5KX05kDLeOAwR9x3M1qsasKuyeRupqbppg4X6c52zBeAvqjI30rkdBQ7W8dkorywbMk5GyZ1WRR/V5XhVWxxTg6tvi9l6gt/20TDrwRLTK7BA0ef/vsciaXzxwY8BwQd1bd99GTXjsfgTEQerx6AGE9SfPJl/TgLK968nbys8GBqv8qj6s8NnrOgTyues7DXEPmQOZAaziQ5VVr+JxbyRzIHOg5B6ryqud19uhbAeDnnlOQa8gcyBzopxwohnr1PI0JKomrh+ma/MzyqiZbcmaDHOhq/HSV32C1udgg50BX46er/EHOrtz9zIHMgT7kQFdyqav8PiQ1N505kDkwyDnQS3KpR3s2Ba7b+TzIX0zufubAYOMAeMtmWwegiDqW1v2e/02V9JyZzhEQ/OxvipqOOrO86jlvB3MNXW3UyuNqMI+Knvc9j6ue8zDXkDmQOdAaDmR51Ro+51YyBzIHes6BruRVD2vuUdwZ09TJDr2E6vWwY/nxzIHMgV7igN2UJr7DLAM6LwJn0WIxJ9KN5DeFWpCZY1Y1WgLOVJ7lVVM4PAgrscYZ8NURFazI42oQDommdDmPq6awMVeSOZA50AIOZHnVAibnJjIHMgeawoH68qqHTfQo7kzbzIaahwL2kKz8eOZA5kA7c8A3lX2L+vnnn/edsqbQCZu4//77e16Vs5+dOuxTaDWryvKqJltyZg85kMdVDxmYH6/JgTyuarIlZ2YOZA60IQeyvGrDl5JJyhzIHGg6B3qKnTWdoFxh5kDmQL/gwNJLL13dF9nnlAdJPgvd55RkAjIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBwYGB3q0Z3NgsCD3InMgc6B7HOBm7N6DvfdUG5LUe53NNWcOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDrSAA21n+ragz7mJzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgUY4kLGzRriUy2QOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQODEYOZOxsML713OfMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgUY4kLGzRriUy2QOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQODEYOZOxsML713OfMgcyBzIESB3yitJSTf2YO9JwD0ye+2vNKcg2ZA5kDmQOZA5kDmQOZA5kDmQN9y4Hhvdf8Sy+99Le//U39I0aMeP/73x8Nvfrqq3/+85+lhw0b9sEPfrCr1m+//fZ77733bW972/LLL99Vmcbze1LbpZdeOmnSpGhr+PDh6FlzzTVn61t+LFIEPPTQQ+9973v/+te/Tp069UMf+tBVV101duxYOfPOO2/jHckl240DEydOvOeee1ZZZZVnn33Wm11hhRXajcKe01OcAlHbYosttummm/a85lxDiznw2muvkWauyZMnS0+ZcRm306dPJ6YemjjfV64YgqQNlx0RhG2QEsuNTJktpjk31/4cmPr8M5MfuHPyI/dOefyh155+bOpzT08d+9y08eOmjn0e8fdt9cUDRu4lkYZQHlc9f6f0K1O4Zj30kwUWWGDIkM65XLpuuukm2sgzzzyz7LLLbrPNNgsuuGCpQP7ZJhyor1p4j4899hhS3/rWtyatY8yYMQ8//LDMtdZai07SJh2ZXTL+97//LbroogyEJ554Qkdm9/H2LJ+soSJ5TIlVV121mDOA01lepZd7//3333zzzQ8++OAiiyyy2WabrbbaaulW9xKXX375hAkTyPO55pqrezWUnnrllVf+8pe/zD///FtuuWXpVlN+0j+vvfbau+66iy5KUm211VYjR45Uc4i15ZZb7u1vf3s09M9//vPpp5/eZJNNFl988ciBDFjC5pxzzu22264pxPRGJc8///yTTz5JfIU0I9B6o5VeqjMLq0YY24vY2QsvvHDMMccEEWuvvfYyyywj/a9//Stl1sHOrrzyyt/85jdf//rXm4Kd9aS273znO+RIkZWm8QknnABHK2bWSX/uc58jKBUgDo466iiJHXbY4dRTTyU45GTsrA7r2v+Wl/h///d/559//nHHHWel+da3vtX+NM8uhTWnQMbOZpeNfVWe+KJX+ctvAStrhIwbH5npLUiJjuteigf322w+if02n7+RenKZAcyBSXf959Vb/znxthsm/W/MlKcfb6SnaTilRB5XjfCtZpn77rtv3LhxNW+xKzbaaKOSigKLoX6wstIjxx9/PD1k3XXXTTk50T4cqK9aXHjhheGE/sAHPnDkkUcG2dSPRx55RPrLX/5yHezs05/+9OOPP37GGWcstNBC7dPfoMQKtfvuu3/jG9/gWv7lL395zTXXtBuF3aOnaA2lGvbff//Bg51leRXv/fTTTz/ppJPSGPjpT3/62c9+lhGRcooJLvk99tgDlqRYMb+UPvnkk83od7zjHc3Czp577jl2OhnSG9gZ0P/ggw8GgaVe6N1pp53GJZ/EGof96NGjFTjrrLOABjiWsDNpOW6ZO3WkXKq8TxI6cvbZZ1922WWk2WGHHfbhD3+4T8joXqNZWDXCt0YBoEbqqlPm73//OxGggHirOsXa9pZFbsUVV4R///CHPzRvQfINYt5cZ4AzUoB0MPkJCH2crbC1tuVJJgwHvF9/l1hiCa6kOljwAODVrrvuuvTSS0dHLHJN7FEoyqUKKRPrrLNOKTP/bJAD4sj4jsbPuISVNfhUI8VOngGixd+MozXCsQFWZsI1l0y47i+vXv+3Kc891cSu5XE1u8wkHm+99VZTvPQg4EwsUgk4U+a3v/1tAGdf/epXCfDf//73XPpsGBBMVkhKPGyHnw2qFn/605++9rWv2dsh4iyAs1kSL+rQ1dx1YZaNNliAjq3kkksuKbRkpZVWavCpflTsK1/5SqK2uVF1ba5HZXnlvf/73/8O4Iw5zL1xyy23/OIXv/jZz34mLb4kDYyUsBvAVJ1jjjlSzgBIfPOb3wScCXwGGgo3+/GPf/zAAw+A6oqQ4g9+8AM++2pnyYcAztyycrUtdgYfhHiCPtG51FJLVTvSL3IGrbBq5O20CDu75JJLCAuBmuDkElmiNMmO//znP0bY+973vr333juiN1Mxoa0QK+HoAlzt/fzUpz5F1SNTBPucd955Zh1Ua88993Tr+uuvNw/f85737LVX5w6RL3zhCy+++OIpp5ySqpIAe/36178OzFvsDN1RQBw3nTn5yU9+UkP2UR544IHFR6TJffqoxKOPPspfZ5vewgsvrC2FuQftxKSVQk9KHbnzzjuPPvpoTxF/Yui40UgE+yx4Hor11+xgsUBOtycHfve734XvlwPfK2aKvOtd71p99dXbk9oeUsUBFVMg6oEC25G94447moaGNO+QGGx6gBDlNdZYw3wMh9VTTz1lCbzuuutMUpOFG9k8BcMViXnLW95iXhRz8DDtQynm5/QsOfDyyy/z2AtIAZ/NsnAPCyS8A4iWI9F6yMw2f/zVMde9fPnvX/7bBdMmlMGaplNeHFcb5M3CXfAXOkYgl+CzAM7mmWee0kOCzn7+85/LtFRZpCQciPGlL33JlhlaPjOmphISepEjJn71q18RLAT4IYccogm4vEgH4fzqEexwwAEHzDdfZzhqvprFgdlSLei93unVV19dbF3sAI8vkNQbl+/tW6Cpu9///veFqMjZb7/9KNtbb7118am+TdOgqBNoOOecc+644w4xaPRqRkHfUtXc1j/2sY8VoeqSHsW4OPPMMy+44AIwqA0uZmgAiBzwbBPK1QYbbAAvYA0xUtK+tqCwzfWoLK+8JnG+/lKAGacSG264oT0B7EcgWtiGjErb7QHH1GYu+diCbcJ+4hOfYEuCY5LZaxioR9hp2ptv2DC02bwyzX12tAFDUN9www3aMjz23XdfNm8Mue233155hvB6660ncFVb1ggbqoRKjRo1atttt/VIb1wWLJ1VM4kUOxmNcJGwemoxSi3iCWlQ3d0SBhcTQ9eY/zZ1Vb1EqZI+SVC8w+RBWMhe0gzBuNon9PSk0UErrBphWq9jZxF4CeFy4Fd40kh/eFkQZwIY/dKMcIulWQ3Dio2NUUDsKNDNhiMFzDrgFHfZPvvsw2v6ve99T+Uf/ehHzXYuF9qbYpCs5My5++67KRDFbUruip+ce+654anEEyCPZDHQhRMTT3Gr5iZK6ADgDzH8BgiD4lEf1ebyExlqIJhKHQHz2RBhFRQbbyEk42677bboV/rbVQdTgZxoWw4YEka1lxv+Xns2uzqApm270Dhhf/jDH9K03WWXXcxTgz+gYWOb+s6CAn+bpzA18+snP/nJ+uuvb1JgjilPiQ8Um7pQajSgtASfAc7MyprTsPRg/pk4YMEm66hNFKCU2bIEsMM/p1k5xyqDaC1jeysamjZ13B9/Pf7i3066p7xytaD1ThAtj6uuGV0yR7sCzlTA5xfVbL755hK0Fzu4jz32WGnLVldKSOhFFCSH8vB/XHTRRUS6U3WIes5OCYDaxRdfTMKHTRhN5L8950CDqgX10ou44oorYGcRVJi0a3qvBTqFJdJv/eRTgXV6X1Rlr1J4V89JbWINaI6BKoyOMxI6gM4m1t8OVfGgB9jBYNlpp51KehRAAU7ttbrMLC/XpLOmM3kQD+/gjISrSsNcSt1pfz1qkMsrShoj11sjOdO7g4G6/GQLA4wA3N6yGW22brHFFtAltjOL1bSlEtsGSE9mbxo5ygjgUmfa7wJ4hZrZ2nXuuecaXZBxgV1m0Gc+8xmcp5DzfLgbQ0799HZiASxrTIrtUDMlnx9l4403BkslCpubEHeiwne+853pCDBWQymkht/dTjU70NFTbF1nI+eggw6C95Fj7A5rU7FMn6fNbmsrbZz4CujTRlqxPn1OWDcIGMzCapbs6nXsDAXmtndgMjg8z0+QdjLCY4oeccQR5AXU2ZGBZhFnS6LbymHyg8+IGwFlZIHYFvu5VKgMceBANHoDNJo6SECkB2sm+GPpiyB22yeNb2UCzovC1h5KYULxizUEwBc5BJkNmwGiSYu4MflJMXerHSHjLIGCaIo1FGvuqoOOSi0Wy+k25IDoKqudmEQrgSF9+OGHQ4jakM6mkFRc3tKnP2h4HGXWhhjeXNmGOo2cj9Qyz/FlebOEwML4Wk0Wi3pNYpLaF8BZjmKoyaWamVZlpq+jSft8D45DrPyDd+QwtJpvqn9lTnvl5XHn/HTceb+I8/77kPg8ruown85D5P73v/9VhuCtRpzFs1B1CW6eiHnhg0yi+NBDDxWpWlPLimc5KRkzP/rRjxxBy1ekLSa9aAVamQJsIT8ZflzrUT7/7TkHGlQtRI1hPu2Xm5YxLLTEapu065pkWKmV97ptzkjma82Src+kR+kOBI0bG5EoHGBBZ1ia9sGYjKyDYHLoUXR+gTbUJPGetumBzOAj9C5AqmK77bYb88Ryv/POO8NTar6d9tejBrO8YuGad15cTf0W7MWagICzLoWDKPaRj3yEM56g9kWXULBtmZIPYLVZilltSMDLEnbGyWH8eMoIYVfaR8UkoYQLTyYcPKj15Fs19kShiu0SoSb6TEMBS7GsjUzwWSB6nmruBVRSIYdNnWpZ+tYp3Ci5ZCw9wD7QofgYvWZ6iLNrN+xMv4QOmLawS4z10vvvKdiDXFjVGaJuzQJsqv9wg3eF+puQDs+jwFkYTMv0INNa2pLvL1+TLd/iS02PVICuJk1pc6VMwLkZqKr4kgB54XI3PG+pWBITKceMtdHAgpRyignn+dUEzpShKRIoKKSngvmk40GLPeBMuquOzHKnes0O0mXtCS3SltPtxgF+0Thz16sn5ZFneSv5SdqN5p7QAx1O21GtXlGV9T5OJ7VhWQ4zLDVhYPOR+glbDIPN7E4GWyqWEtQ+CgElsqZikYrlRJEDzlNwtZtTK8LQMoJWfFP9K/3i6SeMPeOkaS+/1FZk53FV/3V0pb14KiKMaE1cjAQsfYkOY7WyirnblRISzcVn4GwX8pMZH6qOaAXRx1HAX6I+Y2eJGz1MNK5aUGhp19SP+FwAn1asuVUC2m2NqFIoh0IV+7nsJvHT95fo29DAmoX7aSZfe/jFi97x0KNiZgE4nC+eescaiq0zPsopkypFR+oKO1OgH+lRg01egbFIXULYi05nBwNZnGTCwGR+Mmkd5gXPUubd7363wkzdNBLYs/L9jM/uxbwwEkRjRZnQz1deeWU/yRBhyO4S8vFUqicSMZzCegWccb7K9zNs26ik9EhTfgbxYS9EhaJYbDXTi+LpWuBjYs1GtKAnSjrbUcJ0gPrRe6XZ8oLp6iNx8WzL/ors423SnL9emensKDfOgJYR0MSGsrCqw8xWYGfmodOOQtxbFE3pRFAY4eLRCAKru0M33EqWuXR8XIOHE8Tup00EtDcFSBmDEhgHkqfGCXKxGSF2FEcoGVmgQGooEoaCyQaxBnOYe8UlSoGufLZuKV887ClVm/Ywd9WR8Pem8tVEzQ7a8V4tmXPaigNeve+linyEpfK686fx87QVhc0lxrJqFpfqTFPGMLZC294PILO2CUbAn9DXnacTTxVjPEv1xM/YT1TzVs4scWDsJbeMu+TWKV9+Z9saRRnpKL2yfvFz/GXnvHjqd1974uG2pTaPq268GtgZAc4O58UUvcJXz2Ep+iyqqq+ElPx/oeow4J2c5XFgDcmfgbNuvJSuHpkt1cKGDEYmDURt4jWclhXVBjTjjYtHlo4CxRbbcOEQMuOgFUPLoSi2awi+G3guZKc7hSux+C5Cj9LryBQ0BGcBc9OdTFuHYMhPIQUBsRUfL6UHgB41UOWVbZjALOFUzEk2rC3VJ554IvlpqzULwoEnYkTizCJmRem1GhJh9gqtYCEGSm54JAjSaGEOB9wGcgJDk/bkvOghUF0xZkXNmvM3PRsQlTFmL7DR2BUEXyKpGz/DjiaObESLM5H/+Mc/ssp1rWj2CokFiomkS8Afc15JLQKkiqG1pF9A7d0gpjceAff78gPxJWzWFwKl44DR3mirt+vMwqoOh4fWudfEW/wqUVspBjuOJDRDnF/GdQYsB8AXsTNeNQ8aiM41JFYcnwSkN+2dXytfnIvN/2SNaDJzMlRAsL2d20UMO3UkzgggL8gIkLb8ZikQs+xIoqGUqNnBokuqVD7/bBMOkPVxdBeFz4rFjdO/vkPcXDbGSs8Y8y2O7373u7ZdW8J9QxqXRJI6gpROX/8z282lZ2DXFsCZPs79yMtt3tNOpOPacW1OZCYPByY/ePeTB+/6zNH7tjNwlt5UHleJFY1oC8x1X2P0CH2JpWFzffHMndlSQkAbNDR762wgEgjgGA3fR0tOxERVTnSbA7OlWji0KBry8YfikRHJEHWSkeOAi4BLDBhGuxOEu01kbzxoCyoLGb4QMTVCqIpRJ73RYlvV6b3HTOSDhAiYsPQoLyv0TAdfmLxMG7ECbUX27BIzmOWVc+u8ZcKTAWuzpOAy2BBxGp/sCCtSjjIxr8NvwVylQrOO2Rq47UPJALgIZSpCbI4Mkh+bP0yiOJVIeaccps2PXZ3sIaIlxh7D2ecIem+bIbBPeCmqNGRX48c//nFD2s/Pf/7zJUxZZ4suGfFcipEMEIC4wsa3oU1++1wCtAUZeIN2zpJmgLM23FXac3YNEmFVh1G9iJ0FpB2TP6alhbAUvWXPowngxFnwmeBVg8ykTVg4upnfbkGaBfgwvw3EOA1NIBgVEPxs4nmL5IgzDsWsBjZnjYGdm6WlnkM30GCyOTEtUDMqRQp5LbZberD6M+Z5eqSrjqQC1Roip6sOdlU+57cPByKQiovMnhfDr30IawElMbDTamedsyLa3k/tMysFitIGGFS2XZieZq61PzZWt4C2AdwE1OzhfU8TcRZ9nHzemJGvzozYb9tegznW+PajGUFr2xeEsLFn/+TR3TZ95R9/aWciS7TlcRUMgZKEqpM0mRKj4qe4A2fFUuvpPPwZxDJIJW7VV0JKOoxN+o6zZMPQsqBmzs2wJ6W4maBm6zlztjjQoGrh1ThIRPy7ytMhpNLgCRcbW9pJJg7nLdpvjDr5Yj18CF6ifS4HdyLGYNb9OMilfWjrISWlSZRqK+lRYm0E4/hkqg1fDG+nM1MsnXoBZ/EIY8et/s6ZwSyvxFE6t4QmDCAT5OGdiktyfln4HuzQCtQYshaGM3M1TFoqtEc4Knz6EHhqYPjunI9vFvdOQZrkA+YEoIgyUTM83UlnICqfIAgY2nar0pBLQ9FGSOUtDd/+9retCPLJkHS3iQnfLgDwoQeptqNZiTQt5i41kSiE+0cmQ8PRZtJFrBA+KEcIW+xXi5Lt8BfWKWIr9pzFaQntQFXjNGRh1QivhtTXt+pU4TuV6YuWdYo1cguMZUo7hiPOTqo+gkjLql3NJRXN/k2YeunEUyJGDSEpqlXB3eH3qjJjq3d7mDPLjnRVf1cd7Kp8zs8caEMOmFzmqfkYpNyZAABAAElEQVSYMDVEyjS7ZVr8hIha7EMR7FX6q9KpmtOrBPRG5SncrFj5sA2WHbvBYsWcbqcfmjjfV64Y0u3HZ/ngADgBrTqKqjmz5ENbFZj63FPPHnfwhGsu7T2q7tvqiweM3Kv36s/jCm8dZ8Ff6DtIs+QzVyW1HkJRlNKeml0lxJ4yT+XjKWfJ8D4sYKeFfWHi0UrmkEXZIdZ2gZXGQB+SmptOHBAr4K2V7BqZZpxM4UXiDDgj+3BvZnXVq+ak7tRMDHJ5xXR1VJFdurFdF4ugSHgidgRmCimLUyaDdc4jcxBKso79DDU79l0W2etZV9r8GyIdBNaVOVx8NtIMajKhNZ+5Zxfoi+1iWQpVX0R/yWl/YYWTsyudGmF+K847myUdZk4x2rxa3sJfWkiiDCitml9fTJAjjeiXVRoayZllR7qqpKsOdlU+52cOtCEHTK7qRJYZm6nbkOD+QtLEe556+sTOTy9Vr6k3PTLP6otOGNWLEcTVRruX07nP7rqXfr3b6A2XHdG9GvJTzeXAK//66zPH7j/1+TcOJG5u/a2pzaC66ZFJ+24+/2AeV42f7izGIcIcSm9ndpWQjJqVGNiGP1nmyTgvkmdR7g3ncbGJnO42B2CartLjNTNLZfrRz0Eur5iu8fWV9MqEhvmWsZ+2MRaBMzli0CIMLQpLd2XDmtTFeT27Il39rTx3v2q8Rwfz337EgZpyqWZmP+pUI6S2BXbWCKG5TOZA5kC/5sD6669vJ8Jg29za81dWM9zsTdVecHvHJ9Z5U04b/9jzzGcGQKBQGzO4UdLGnv3T5086vNHS7V3uxkcm3ZjHVXu/o0xd5kDmQM85sNNOO9mtlj563vMKcw3twAEf+rD/nXpcwtTagbZMQ+ZA9zgwgIVVxs66NyTyU5kDmQOzxwE7//vj5v/Z62SzS88aOPPd+pcmzTdu2kvz94PQs2CPQCGJ/Tafv9ncyvU1yoHnfnjYuHNmfmax0WfavlweV23/ijKBmQOZAz3igM9SuXpURX64/TjgU5iu9qMrU5Q50H0ODGBh1W/Mre6/vfxk5kDmQOZAP+RAI8BZdGv62bdUD79o5x6DOfLXA/rqBT195D4DDzgLZuZx1VeDKrebOZA5kDmQOZA5kDmQOTDgOZCxswH/inMHMwcyB/ofBxoHzjr7NnXaqGcm9a9OZpijT97XU1/9xMuX/75Pmm5No3lctYbPuZXMgcyBzIHMgcyBzIHMgcHGgYydDbY3nvubOZA50O4cmD3gbEZvpp49pnrAcJv3M8McLX5BTx68y4Rra390osWU9GpzeVz1Kntz5ZkDmQOZA5kDmQOZA5kDg5MDPTrv7L///e/g5FrudeZA5kA7c6Bff/S6G8BZvIt5Hp0wcdFh7fxeqrSBOWTms8+qnGl6zlOH7vnKPy5verXtWeEAGFdZv2rPoZWpyhwY5ByoqV9leTXIR0XufuZAe3KgprzqIak9ws56g6Ae9ic/njmQOZA50H850G3gTJcnnzdm5F7rT5xrSP/q/gCAOdqf4c9++4sTrrq4/elsIoX9fVyts06/+XhuE99aripzIHOg9Rx4+umnF1pooUbaHT9+/BNPPFEtmeVVlSc5J3Mgc6A3ONBzedVDqnqEnfWw7fx45kDmQOZA5kDiwMR7nhp3ya3pZzcSI+94duL6o7vxYN8+0t9hjr7l3ixbf+EX337p4jNmWWzgFcjjauC909yjzIHMgd7mwIsvvggge+WVVzQ099xz+0L6ggsu2NuN5vozBzIHMge6wYF77733H//4xzPPPOPZ0aNHv+Md71hllVW6UU/jj+TzzhrnVS6ZOZA5kDnQWxwAnD19Yk/Popp648PzvDytt0jszXrBHDc+0s8+d9Cb/Gha3eMvPevF045rWnX9raI8rvrbG8v0Zg5kDvQlB1544YW77rrrpZdemjLjkvBTZl/SlNvOHMgcyByoxYG777777LPPfvTRRyfNuCT8lFmrbNPyMnbWNFbmijIHMgcyB7rNgZ4DZzObvuD2btPQtw/ueWan1yhfTeTApLtufeaYLzSxwv5YlXGVYdn++OIyzZkDmQOt50DNLZk1M1tPW24xcyBzIHOgyAERZ8Wfka6ZWS3W7ZyMnXWbdfnBzIHMgcyB5nCgacBZR8fQlybNO65fhp5hZYbPmjOeXq/l2e8e2DF9+uu/Bu//P7l23ODtfO555kDmQOZAwxx49dVXq2VrZlaL5ZzMgcyBzIFWcuC5556rNlczs1qs2zkZO+s26/KDmQOZA5kDTeCA7wPYsNmEil6vYvo5twwf3i/PshQfdHKGOV5/jz38/7kTDpl09396WMnAeDyPq4HxHnMvMgcyB3qbA3ZqVpuomVktlnMyBzIHMgdayQE7NavN1cysFut2Ti/aV1OnTr3uuuvWXHPNRRZZpNv0eXDs2LGPPPLIyJEjV1xxxX5qEBa7jydrrLFGg1+0KT7YjfQ///nPVVddtYf870a7g+ERXrjJkyfX7+mcc84511xzVcuIfn/ttdeK+UsssYTCtJOHHnpowoQJyyyzTHWEPPDAA8osvfTSxQf7MO2T5I5jTB2cPn064h2Nsfjiiy+22GKJsKdmXPPPP//yyy8/ZEj5E5BdPfXYY489//zzpvy8886bqvr73/++1FJLrbbaailnACR6/n2AKhOGTJk26plJYxcaVr3V/jkOqNpguZEbLjui/UltZwonXHvZuPN+0c4Utpi2gTeuLrjggvXWW89iceWVV1KT8HPYsGHE78orr5yWj9tuu+3ZZ5/dcsstE7etI3fcccf222+fckhoZdZee+2UkxOt54CXQstdaaWVomnLnxNb5FAX/Y1MWocXSk+wCC688MKtJ7JXWzQO//jHP9IiumqFCvHhD3/YCO+qgPySgv3wjIsO/Ja3vGXo0JmxAkISHOBFdcHbpMDUqbOXbrFrUjCXmWvapoYamZKlnt53332eWmuttRZYYIFUz+9//3tK1Nvf/vaU07cJ/b322msxn3mI+eTSHHPMgSSjGv0SVEQD24sm1oLUqgT729/+RknedtttVcUkfOc735k69Z///MetTTfdVI4PHdx55500bQ3NN998qYzE5Zdfrt13v/vdxczWpGnI5jW9d/XVVy8qtzVbr9MFXfN4TXNAE//73/9UWO14zVZyZhM50EMhZiF27D2hZCLH1EDbk08+aXZ43dZogiKoNZWMAUYoGz8t903sSLUqc9bQtTBZpHw8JBUIyROKR8pMiWnTpsVpicvOuFJ+v5BXidpqohexMzLrtNNOO+CAA3qC3UB/TjnlFO/JQZWGzmGHHVZcGKr9af+cX/7ylwcddFBrxvrpp5/+uc99rif8b39+9hWFZFaSYl3RoExNzew73/mO5a341LHHHutjRt/73veMcyOc/P3MZz4TGkAU80Xeo4466iMf+UjNxbJYVWvSDLAf/OAHRx99dKg4pOpxxx1HlPvppMaPfvSjYZux7lyR6RPmX/ziF4tM6+ops57Op/ypp56KM6NGjdKpf/3rX3/605+02JoOtqyVcZfe0httTT17zMgDN584cWJvVN7bddpht+Fu/e9rob3Nltmof9q053/8jdkoPziKDqRxRSX91re+RTySrhQtcMAKK6xgxaHdsrj23XffT33qU97qNddcQ3IWsbNbb731xBNPTNgZIXz88cczWa0+g2MUtGMvLZp77LEHbS2wMyvg/vvvz1Hkc4eWv1//+te+HQYF+vSnPx3U0xO8xM0226wdO9NdmgBnb33rWzGhqwqMZGXqFPjrX/96yCGHMBlCwZZw8fAxR2lTJ5xwAvUD8vLVr36VHWfKACXxtmgHdtV0b+R/9rOfffnllwMYpfWde+650UojU7LU00svvfTHP/6x8UBLPO+887gqVXXZZZf96le/cmx2bxDfjToN8i9/+ctQfl/BGzdu3JlnnunVeEEsO2KKENtoo42INePc9/K22Wabr33ta/PMM09JgummV0b5hCzoHbPcgwn393K5Xb1rYvALX/hCBFtgMhG3wQYbBM3IOPTQQ6UhaK0xxBKvxowZ86UvfckXSwMz1RfAR7pbStTpAh7i5J577vnxj3+89BTUzLjSBNHh1k9/+lPxK6UyffgTUb//PYS0Y++9O/+67r2346qrOgTYfPjDM+m6/fbOnDvv7OBHIOE23HBmvpzrrutMr7hix3veMzMT4nrllZ1pWOs223T8/e8d998/8xZU1rcW4ca1Ahhmlmn6fz0RYmbxN77xDWA3A9AbhBUsuuiiF154IatHJpuL1DL4AcHCL/7v//6PE4WB+fjjjxtIG2+8cdP7Uqxw/PjxlqT777/fqkScSu++++5mK0zmiiuuCBn7sY99jPgtPoVC0/Cmm26KAkkt6RfyqtiRaroXsbNqY7ObQ74QrDvvvDMxKm1U/fnPf/ZzduvJ5TMHWskBQWTWrTqwxUknnZTo+c1vfkMqeQTQSbH74Q9/OGLEiEsuucRP3sLQq+hSJkJyPqdn+yQBIKOTCTortn7DDTfI/+53vyvizBmNv/jFL6hHyAac0XSpRBYDUvXf//63dHqw5lO0GWEUu+yyC13HCsEb7xHQIUYdeOCBdKn0+ABICDpr7m7NIk/meXTCxEVnOqmK+e2fjh12+23eaQDkqxsceP5nR7/26APdeHBgPzKAxxUdiTobry+0bVDaLAMrvv3tb19//fWU7yK4NrDHQBv2zkJJueU8C9rEpEBF+Z9gB2BQFjKMwOoJBOHbl6YnuPWzn/1sgGFnIs7q4GKYs8kmm9AounqDAJci/mtU05qAy+973/vAMfgJWJGmfe22226c+jS0D37wg7x0aeJ0VXNv5NP6EPyXv/ylFD/YyJQs9RR5559/Pu3ove9974MPPkit0k0Ikap0thRy1Rt9abBOo5r+BhEIkmABoJ8zzjgjXrrAwJNPPjmqEmu23377UQV1qli57lCMS0jBEUccYWrYk1EsCVkGqBkPvAJHHnkkrO2ss86KAtRX4bo0UgkjofhUb6e/+c1vEtSQO5jCV77ylR/NuLpqtGYXvHoQMB2bZKj5IB5uscUWugzXIDp++9vfctXXLNknmU8/3fGZz3S2vNdeoqQ7Ezff3JkD2AzsjAfnq1/tzE/XAQd0fP/7nYWdCB/Pjh4tFKsjokh/9KOOMKd22KETOzv11I7f/S492pkAtMkpmB1vutv0H90WYuIlrQKg1U984hPQXiMTuAwGBZx9/etf32GHHYSkibUE+IKoRBXAx6HGrEUFzHSLftP7UqwQIs8Ku/jii7X7hz/8gVxFElEDOAMXcuAxCbnr3v/+9wt6SA+iFnAWBVi1pup2223HSOwX8ir1omaiD847s2aQnpYrFz3AiAnKRCAbIsaKBU8BKh3p5lZE5IJXBaAaPaVuGKneIhlByBKFcdey8f3vf1/mwQcfLFaFaiKf6CR2mffyNeStyzQUkrz2k33uBUcl6a/AYPKdDEIbmhn2boXvgpYTxRDMJyatZgWiIxBigIK0FtGj41GY4zcyCTX1RCZvSdBmJgg8lmkVlGNEKswPGcX8xZ+kQFj+3VVS5TqCQm1hSIlRytAeogYKxO9mSBdsueiii/DNU15EbA9GsPGtEj2tsiLRkBNdcYC7wFht0JPptXrXe++9twWel4nrgChUs8XPm02vDEgveLBNAu/pPeuvv/4nP/nJIgcMG5mxVTMcIFwTLnjfhjPcRpR+LmVOwlk+pYDBHNoVZyzQnJ7BTrBs1PHRFavtR+kmfiKg2uvJ540Z+Wp/PSfeDrtqj3JOIxyYfN8dY894A51v5JHBU8a4gqAN7P5SaqEqQIFZdnPdddel7xYDnGf5SC7QdA5QOznPLPpRM48RC5mB5CdA7UMf+lDoln4KQxBKQ1uw1KYdiPHUIP8LKQBMUGVTJBHNCvfASTiz3HLLsSNuZqbPuEI9o5zArfqKjZR25NEV7dwULppe3yynZLWnnuWsjY5TFBneNv2IB6GkUbpSzX2bEA+F/8yNhOUJpWGq1NyBC/baZ5992Cl0vyCbqQUzYsExf4ohNiAG8YNAhFLvWOy290KZvV+QK100CrB6CMYPfOADYFMGfLLgSo/3xk/BYvBcTavcLI557U3BT7FFwKAhCnqg64bFWrMLBq3uCzoTiFqTSOF4RIdea8K+v+LQqlm+rTLZ1gGc+fuXv3Qcc0wndSecwHh/E5nPPNNx/fWdOdOmddSMqtxuu44f/7jj4IM7MOmBBzo++MGO12GGN9XTVj/C3LN2o0qsMbvJaBFf/La3vQ0ULtPUFvMVLxQoYXgbDNYCr1tJwFavdoftxgSLgFYTSlvwmdtvv10acOYnyExwmUFbJEO0BLdcFIAauxXgRvvLq2IvaqZbjZ0RVVwHACPQzOc//3lbXn/yk58EQ8FP1jkSnwMBjgZTs+CJOA13HJT9lltusQ2+2A0b3+DrxhO8iSQ655xzSGeG9zHHHOMdy+TWgJ0FpmabrvOSCGXeD+9bzdAKeJyXHcNOi2Cvqn1uKRIgDdRA2+abb07RkWMEU3GS5KXrBDSma5AOi5beXXXVVcKIyGjQlXyxx0G83pkhyAMNIFW7KkSVkWctQZtFQnnoCUgCAEcPLlLlWATxdyFejV0wmUhOffeI3aCqDYKLjHIrnZznrsXGXdCYGWhvIAeIApBs3cEWbcHsrEmQZgt8sZ6crs8BwJlLGWOjfkl3vXeqgAAr+pOfxGVEWUvH4TXkprRXANovYVXy++oy3azxJY83UgM4QxV9hW6qLy6ZhHuQKh09SpTXfMpdTfBJmmLkA6MCUkwPoHMob1Kkx/t7wicCersL847rdBv00yt/c7N7L+6F077fvQcHyVOD4ZubHC3JXCQ2OcnSxXWXXjRTjSri5J2UkxMt5gBMQdSMXYSpXZqqtIUvcqyGTCPpHXfcUTwOBU+UAU8+nCg9khOUVXgBezKxgk5OMU7QmMMuQs+3h4ibWcQPAMLOQYZDeqSVCRoOo4Yq5SAO4DWrIVqf5ZSs9tSDtvqK0KfkM01ZSQAmGMpee+2ly10FKLWys9oKUwKaU2wXKFB8ZcVbEb3ipCeZjB02kTAc767kQoZ+itax0SGJu6iEtRUWPqiR+ZY2bIKhyUM49VZbbQW1DEu+2G7vpcOSTWENYYX5axCiFkrIBGbBQQwZfcio2QXII4ntSvp2iWDRaljHqmVZG1SCmEoF2vnnt77VSZ1AM6Fy8CI7a3/wg86cI47o/Fu8wjF0440dcLTq5by7/fbrEMJme4wDk5U577xqqfbKEXcJMbB/GVnAhKuvvtq6zJIytlmIXiUYwS3j1pA2htPpPeEJiFWj97oE2wVnR/0RPwQRQ621CT3yDWA0RMBTIoP8CeBMDsMQXB7jv/3lVepCV4lW79kUXUXGWf6DoSJupPGXmGCNv+td70Ko2S7oTMKyF7ACZAemA5UIJ1LqDAFBlCjvrahQYZDZjTfeCH2zbFg8ZJJNsLMA+7l0rEweN+wAZ5Aje4aNPI84/gM2x60HmSKv03FUVl/luXQoLlAACZATUCDRUE3YVRpQF48K8DiktokR4JryVmtBOhJcvjBE7XI+EKzWUU2AmdEjKBe1yqAQwFdsRYUwl3vuucfUwgHLgO7b1wbW1RdDU1x0aqv4YCltNlq2dVk+/lAmADQgQqPf46Ka4ZKlePJSDfknDsD+A9CRCODM8GgE4gGAGk4xEtRDf7Jk8i1YFMXEygEeUYZ+/vOfmyZx5lfbMpz0TEoqIk096k4pE/4VQjb1olQgnnKXOmXhN21NHxKZ6mMoCtKUNjcF86fjLVJV/S7RG58IqDJh4h9umXv39V4Z1WofSZWSbuSID/IvfzRgtlj36k1XT7jy4tl6ZLAVHgzjii6U1iAgQgrJ964b8esMtiHRV/0VVX344Ycz/sOfH2RY5iQodfEzJajB3in9mc8VIAKMyAGDwSI4FDhMPFFy1MnvSrtwZA/u+cv+5Eg2HZIVGrW15i/yGCP0f6+eeWwM0PwTYNoVDTV7qjAg1TZem2OY1sYPV7o0W5dlYWsO3anPhwqtmLpbfEFd9THyqdMS6PfXmGeiw8I4+LfeeutSqBqr0OZ0UYfCGlKdAVRxQus7K8mDccvWNoAdm9/FsPKzZaF5zFJNi0ugG1PsgVxIipgG1IrMYHyxHKm4OkLn76oLqY91EsxbgRcKBF5cp2Rf3XIM2fAZ8EMCv2ziiqClXXd9g6idduqwbVfs2LhxMzPf8Y7OzZtnntm5kTM2cPsQjmPOal4+WmZX7s9+1jHj2wk1i7RLJgMqrDyj3WAQV7iTzr9+GcMmsrWAgW8eyU4GVywQsWq8Xrz5/8d81AorVWgkKM34NO/Yp6YYFEKUEolqRSu2Tcql9Uu+KRB0tr+8KvaiZrrV2BnmoiOAUolYtKCV1LsUiMvGTpg61nsloARCx4EFhEuxG3AxsWnp3QRKBYpSPzs8SoLPAGER6Khwelw6hIvFBooEPLKLU3gh4c5WN1KjpF2fEugJoR8EoCrVU00QypGJBlBIpHUqlUxLtZHEqagXyMMZPodUJlVSjDiLu27pKQpht2i2ZshHGLcMdcq8omAFIpZqqyYEPSnGxeFKdzlAhMvhtiA4mfC4/uW1SB1pWaIzzGzBBcMaiVFNRXDNkgDLp2gy2GVSJgDHxlWgxpQqW4zhwoIFvFmXiFnVEpcU6MCdZ9lEKwsY6jHFolEKgZxQERIZkZl+StR8Sr4JIgBTAoZrFaFGwHONWEGaMGWzdQBgZ730iYAieyM95MLbO3Zbp5rfL3IG0uHurWH42N/9uDUN9etWBvy4YmqmIH1+O67H9L6s72G2pZyc6CsOiDRhgbgcIs4oosryiVoW0WO5DM9xwkDBQ7xKDupy1zkyTk1ydgwcra+Ib592AU88xxzzLuwCHIBXqNyJdUhl1NG3qdmAKqdfcVTLBGQIaEonYbWyR3Q/V7QI5HJqisCxWWJnNXtK82QLxKcGaIlMbsUEVVGfhDRy9sPR+hw70zXMh1oa7YnPtuCw10STpZyUACFJhyXoEbsy1WCXBoVQVF0y+qK8rTPMw6ItI18UDz7QFTEhlHOmVoTMmD7xoKhP+yWTtRWZvfcXUA7FE/qgX0xLthvzQXNeHyU/2pXGKNNfTrULDdLGcHNBBgUrvec97zHyG3ywZcXefGByZ7NQlzjWqCjSZsBEnXfTcU2+fOAQJtE1gaC55Qj0rrAzd2cEh7zxeGdd7XoxAMkByNSuu+7qEMCQ/0Gsk5QkjGergPP+pI2QuBVSrgWvmL/BADaFTcAI/xRpJMrBvPOXRIWllKJtrGVFIezZoLP95VXwts7fNwCdOoWaeAscoDaiIcCdiDOUSYIkgJwRntIOGrPFwLhx8G2VDA9GBGDcgnnBqmRC4mw/DFSCuISYxgsrxhPKD7UDQgTmJ8HFw7PSVSX8ypWasy8ypUsJYz3Q30bgkvRsotnj0gQ3amF5gYIpRvG18OOSdBF0SzU4iN0EE3dGRfAgkII2QERaBgxWTDNGU+FIaCsSsR8wFjB6WES3mYdwN9yz4lrJ0EP/MEW9l/xxhhIniz8DjLcwB9zub4MjARZGpgTaGxXyKHqV0Fs/DU6Ar/XV0s7zZq2VCaGL5oQiFmloh7SRk05nM5ZMNDKUhwHNpnPMPgVKs7jmU6k7BiE9g1cQlwQwA9TMaN6YBjmc6mnDRK9+IqDU36HjJs07btr4+XPoWYkxA/Dnqzdf+8oNXXhgB2B3u9+lgR16Zu0AkFU/wdZ9fuUne4cD1krrI7+96ikAoZXF0el+hn0iYasLHU/CtziDkLhFUczYGYZQwpkJLmmqL6gI7kA3xjEcDlSCQuuDg1yPyrDcgo2i/m2BTGp8ZLbmL90GrBOnwTbeYs2eJgyOymQfj4NlhGLZ1aubylOckj3VeENNLxm7ZwTAArlS5fz9NQewNyKK0CPxbSi7f+KV2a3s635M95gjqR7mZEBRdMVAHAJcFh0TO42ipNbFFthhEz+NDVt/woedqurVhOOfdD+wQsSY7wHbmcgmeACCXpZ8g7ZmF+qTx5x0KFX68GjskGUOp3CN+o+38q7DVyKy1oFlYW3DVB1PJgzN2XQrrzyTlnvumZmYsQFsZnr33TuxM6Emjz3Wsf32nU/VueKMZV8MaP/Lpy3swGPupa1mMA1j2BANIcZaF+fFDhJz40Cb2KgXu6G7Ov+uWb1mvtnMR9T4VmbC6YxbkyhccYQPX076EnS0yzQmeCPNOgYvFING21lezZJvvW5QQQHwN10Y5wLuUO+AOESnIBr8FXRm0HC+QdN4gQKq5ERyQhnpJqoZkOQqQULWHla64aW8hVO1rHGRz0pC3Ckl3jclMkYYXtgLSXjJdwC/Y9pjuxw5BTENJLWmHK/JxCgpCkZtTt9PcFjNwqVMHyhAmDHHI4QP5oNLDpeI2swErpX6AEHIRCpCnC8LXsEBfbdSCvjkzFG5cZnatWDor7HLqxmOF9NPvCXmkNQievisnColU7wPfqoHzGFBKrl3UoU5ERzgHAvULBbs8JU1whzxtwEZp8JGgg9KGAbxOuBo5oVT57yRuHgO6QFtCJzpQnwHQNAcVydlVI5ghzhGx4GsMs0UI9M4d4szzcjv6in5cZn7ZjcO+EkCkAaGNEkSrsjXS/XL/yfe2+lTbdk1/dxbakLwLSOgJw0NhtOpesKf4rPjzp+5M6WYmdM1OTDAxhWFAVJAxhKbwlh4WYr+v5ocyJl9zgFbDahecVncQWP8xKwgqycbyfLHeUat5RalidFs6cZMEWpwBKyVfFF93p2+IoC1+ToXz6Xr2jbBxgv1XpgGQ4O+4WQrx8JAIREp9IzeC1PAWzsBqbutp5xlBDOl71HdIyzU++2KDIp6bEis2dP0FNWLYhn7V5gD4kSAUAyKOHkmFeuThKgxbnjgl70UNri4xKrYPSOULOgxqkkwiIBMx/8xWHxzsEQqTAHwBHZkypVu2TnkVcaGITiUMeAoZ2PAW3YxCWmP8DiH86z2+uVcsDCCSlX13k/ncQsEM/Zoswahg4Bi7CHPfKf5e1kij+BfNbtQhzD4BcnPY0164I9hYNhrwnRgR9R5sK9u2YiV/iUa4uxBnwh4+eXOPBt2jzyyM7H11rb7dSbiilMNqxs8X7//xv8+Hxgnnb3nPW9ktmfKbDU+RZaZuTFo2U2GqnkBnTBCvFOASQSQAscJBBEYTE4jh2HlRfdqvwTrwFsExBlXQR7ZAtETf2MnECJFn8k3oZCR5BVoxZj3NQxPRbicw6ASne0srxKRXSV6Pe4MvlNsm2bwxS9+0VCwz1++0/FtEiQ+SD1riXwLiUAwOgFjz2BS5rjjjks1WPyK2AE0nXy0nFgCAT2kcODrXifxER+j9Eg6jVJzCgMpVOgI/xT1A1yD3EUUd2orJSBK1cWVkLKNFMDv4s4q7qxMhSU8m+qJBKXWZgp88FPaSmCqSGOIjcTmA0jFgWiUp9hSWno8fmIO1MyBWeG2Ug9p61kXaDJuGbLpWVHclmd9NMF00ykPbvk8hxbjkFrNCfE1E6jdKhHkrAB2CcJMleRETQ4QXtQC2Fns3KxZpprpqbSnJu6Cn8wCuoUpAFCOCVJ8sOZwKhbowzQthO5CvKLBaER8OAzNblo+0SnfqDZTJIxMEDkfRVdPKcM/THSIxAx/C7DYaLeuULkIEAX69TXukk4PecuuIa9NG/XMpLELDWtZi01sSIhQE2sbwFVNfvieCVflk84afcMDI/QsKRscci6dhw4ISnKeSByhVdVAGslplIm5XPM4UFzfxRpAQCypqqedBgxKaXSFM0ncAWs54tSaR8IAqSlGOF1XHMeRRx4ZW/lYCqEtM+Gw11EwesuQswupT7otcgqGEkFY3ibdqbjdqTRJObwVTlGHieBiMbi5/rKuw48rHIFZZLTQtY4//vj0SCMJ9kX1+KSeu9/YZawbBh3+I8ML4i2mBErriJiUCJXFDfYUmJgBEreKNJsLYDVvDVpavKsGJ74x62SqKhAHFmU8qy3t8rwWw9DcooUWj8qJwr33l5Hl4BGNagL8kYJ0AF5QP6iZfPPdkb41u5C2mlYp9K7hGrat2IFET46v9GKgILu0RlSfarcc5w/5pKbNmAJDfcuRCSuyzBVfDEjULr54x7bbdsDFXHhZ3bApkNfZd2PHzvySgAPUfD3ABWwC9BhC4AIj3HcLTZNAo0TbwKeEVnWW64tLQI9mWUypcTgvgW/AuFjl8iFT9pZJoBM8YjhJW/FN/PRULyVEP5hTxekjspWhatIB7ww5Uww/I6goySu8RapgIFRZrQgiEiAobKK8YiSKFip1PCzHUmYTfw4pRifNVr11djI2Uo9oHcKOmR2FORy8GBYyuN2YZh43fiK4LgBo0ytJrcPIvK0k8S2ZAHg4GvNboGwxokrMmqWLh6qYmeqpk0Cz1gMmqFOsekukGCcD7ba4+CmGZrSVMquP18zBUqyLGGAMce5gsR6M1WKVSyLdPFVUwpT0uL2f3ehXTcIGZKZRVBwtNrdWQwVxMgyYxjngEdhZP+W8gQSWLY1qE6Q644oMqfkUDwZpWDrZzbA0UItsL9ZTSkdsZsokr0o56VaLEzZsPn3ijGW/tQ2/duDmhladNh+aON9Xrihj/XXKt+zWfpvNt9/m87esuToNVUdRNafO47166/mTjxx75szdKL3a0OxWft9WXzxgZJ+ppHWo/fVuo9vkSxTVUVTNqdORfGtAcoCvngEQUEjqIC2OzhbHJKXMgZHgx3UAWf2+NFKmWIPgCCYxiy4ZAnGXtmbnUZ8rWiwItsDsaonFDqa0UCYmQOBNKVM36fyNqEywOdZvPEj40LVSJZFgWYQG5RY/cUmbalxeeSM8x5gvaKBooZSaG8A/S8YvEND+TUEYBir9NhnFPeFABIhUzb2e1NmUZ23ge8tbOmvyQZTYs3nOOZ0Hlm2wgW1hnfmPPOLDFzNxMT833rjjl7/smOFz70x8+tOdd53eecYZnTs9gWI+GnDhhRzzvjAm3KnzywCFE7w7H//YxzrEL0ZbsQ0Wviyey1uwu1mwSCCYMCloFAStk4geXI0IqEbKFElgQxkbhFVpbDRRehSb60aanKmzJBFxbEDipQjjNlFeAZ3B4iWyQfD2LMqsKa9Khbvxs9fjzrqiqaQQ6J4zO4Hl+Ovzl8R0bPjq6vFiPvlbU0bUzFS4uDGT3S6exWlKXHyNLDDFdqWLkFPpVv2fkKkiGalwTZrT3fqJIksDQSuW17ualVvDXKWSNWkrlsnpEgeqwFmpQIM/vaY+1+caJLVajHpaHTmlGdfgUzV371dHdbW29s9p2VcCSqyY+7EJExfpl6FnN+XQs9K7rPVz/J87/fD5apwDA/6LAY2zIpdsQw5UF1NEUg/6r4ZQn8ki8f/1r3+Jvu+qmLulaP2uSqZ81lps7Eg5kahj6ZVK9urPblsQVapqnmnVvW4KMkinFKWGmrX1zxth1qZqB2GiaKkVu5+wy2Jm99I1bb3uVdXcp1ZbraNwmFBn3T4mWfieZIdzzS691EH4HQ891LHMMkzsN9p3znM66lnE1Yygq867O+zwRp2gNP+6uuwVc8Vdb8EJTqmkeL2U7kmiN4QYG6oYlJrIa6L0SHV2L1FfzoA7qkK4ifLK+e8RhVokXmbxZ9PTfYadlXpiv67QWUMZGGn/oNDTkpuoVL4bP219r+ocfCBcKPZ1+kxPN+rMjwxaDgAcuX/rd78EStYvnO8OBg608isBJX6+du6YkZ9af+LIdowsK5Fa+jkwtteVOtXcnxOuvXTq8083t84BX1seVwP+FecO9iMO2BRiH1CctVKTbHZpOoClZoGc2RQOAHFYYSwjgS0qZKIDzuqbx01pd3BWIkwEODI4+95Vr0eM6ICy9ccrC7HWvzVRhM5StKvUdiWtC7wAnJUicJtOVbtgZzomBrgUBtzc3trYXK0QuuEchGp+zskcqM8BXgtX/TL5buZAiQN9FXQWZMw7burEkW0k80vMqfMzhwjVYY5bE67MJ53V51Dtu3lc1eZLzs0caDkHnNVb+n5iy0nIDc7kAKQsg2WtGQ2OYHe1pq3cSm9zIAux3uZwzfohZb0NlpXa7YPvy5QoyD8zBzIHMgcyB1rAgYnn3zL3hGktaCg30WIOTLjm0ha3ODCay1+iGBjvMfcicyBzIHMgcyBzIHMgc6AFHMjYWQuYnJvIHMgcyBzo6MMNm4n7Qy68I6X7USK21/UjgltJ6ivX/33aKzM+6t7KVgdKWxk+GyhvMvcjcyBzIHMgcyBzIHMgc6B3OZCxs97lb649cyBzIHOgfTgwdOzEeV/ql6Fntte1DxvbipJXb7qyrejpX8Tc9HC9j8/2r75kajMHMgcyBzIHMgcyBzIHMgd6jwMZO+s93uaaMwcyBzIH3uBA3x52luiYfu6t3fimcHo8J9qNA6/efG27kdSP6Mlfce1HLyuTmjmQOZA5kDmQOZA5kDnQhxzol+dG9yG/ctOZA5kDmQPd44A9m917sLlPDZk8dd7nJo9dcFhzq+3t2vLeuq44/L7Vfvuhde55x6u3LvTQTUs8+u+pL73YVcmcX+VAHldVnuSczIHMgcyBzIHMgcyBzIHMgSoHeoSdzTvvvNUac07mQOZA5kDfcmD8+PF9S0C19TYBzoKwqWeNGXHAZpMmTarS2c45YI4Nlx3RzhS2nraAfi6YtOoFQ1ftWPHjHSt2fHCu+zeb+J8FH7xpqcf+PfXFZ1tPUr9rsT3H1eTJk/sdJzPBmQOZA4OTA1leDc73nnudOTAIOdAj7GwQ8it3OXMgcyBzYABwYJ7HX5m0SD8LPRsAbG96F6rHdV306koXdazUsfxHOpbv2H6ehwJHW+bxf095ri3CHpvOgZ5XiIcZk+05G3MNmQOZA5kDmQOZA5kDmQMDmwMZOxvY7zf3LnMgc6AtONAmh50lXrx27pgRn1p/0sghKaf9Ez4XsOFuo9ufzvah8OIJy18MQlt2h45lO5Ydf/++C90pHm3ZJ26e8vRj7UNkn1OSjzzr81eQCcgcyBzIHMgcyBzIHMgcaH8OZOys/d9RpjBzIHMgc6D5HJhv3NRnR+YloPmMbWWNjeM+j8y70iGvrdSx9Ac6lu5Y6uWHP79wJ462/JM3v/bEQ60kOLeVOZA5kDmQOZA5kDmQOZA5kDnQHzmQDaf++NYyzZkDmQOZAz3lwMTzb5l7j/VemafffG05H+tefeXd48njo5b72qTlOpbcpmPJjsVXf2z/GTjaCk/d/Nqj91ebGPA53ePhgGdL7mDmQOZA5kDmQOZA5kDmQOZAkQMZOytyI6czBzIHMgd6hQNt9a2A1MMhF97Rseva6WdODEIOPDX30oe+unTH4lt1LN6x6GpPfmmRzni0lZ4eM/mhuwchN3KXMwcyBzIHMgcyBzIHMgcyBzIHanKgF7Gz6dOn//Wvf51rrrk222yzYtv33HPPww8/vPbaay+++OLF/O6ln3jiiUceeWTjjTfu3uP5qcyB7nFgwoQJL7/88rRp07p6fOjQoaNGjZpnnnmqBR577LHXXnutmL/UUkvNOeecU6ZMuf/++1W7/PLLL7zwwsUC0vfee++IESOWXXbZUn5f/RwzZszqq69uggcB5jvix40bt+SSSy6xxBKJKjPUteCCC6644opDhpRP1+rqKSLiueeeW2WVVeabb75U1V/+8pdllllmjTXWSDk50UMODB07cd6Xpo2frz+FnuVj3dNLb3rA1LNzLXHYhCU6Rm/ZMbpj4VWeOWDRuzq/1/n4v4c9cmdqdEAmcLJ/jauLLrqo+iJWXXXVqVOnksNxa9iwYW9729sI5PhJor7wwgvpqeWWW86C4ud999335JNPEtorr7xyupsTLeNATf4//fTTDzzwABXCeuc9Fol59dVX//Wvf22xxRZ0jGJ+f08/88wzF1988UMPPdRVR+hF22+//ejRNY68pFDdeeedvq+90korlSwLnDTy11xzzVRtHd6mMq1M/POf/6RK0ZGiURaNa5FFFjGdq6/YBGdDmcgrrLBCmtoeNOv1y2hZYIEFEvF/+MMf6F1vfetbU07fJgzdf/zjH+jXi7e85S3G8BxzzIGkO+64I0ktqi/RtPTSSwepbj377LNKJsqvvPJK6vfWW2+tKlNj8803T7f++9//urXJJpvIeeWVV+666y5KtYZCjcSfG264IRWee+65savIw3Sr9xKGqO7Te1FldtdvqNqFVF7X5p13XoZDykkJTZgLfhpUyqT8nGgBB3oixJBHUjH0YmTG1KiZ+eCDD5ZMyLSa914fu5I8JHbIK0O6Kq/Q8+KLLxqupFnRqu0X8qoOM3sROyOzfvazn2mbQlZczGQyjA8++OBiZpFEsvLXv/7197///WJmMT1x4sT999//qKOOUsP//vc/FnXGzor8yekWcADCBQkqKbWldpWpiZ194xvfgDEVC//whz9U8sgjj3z++eepUMCmL37xi+985ztTmaeeeuqQQw7ZZZdd2gQ7I9+POeaYH/zgB0Q2IknVo48++rbbbvPT7N51110/+tGPyj/33HPPOeecyHz729+uC0WOdfXUNddcc8YZZyh/8skn40ws/zLPP/98LSae9KNEewadBQOnn3vrsH028C76ET8zqS3gwPMjRx8+fnTHIu/sWKRjgbc8/+XRM3G04Q/d3oLWcxP1OfD3v/89Ctx4440WhdCmYGG06tNPP33DDTd0l5OGlN5oo41OOOEEP3/xi1/AJpKJThPjivjWt7516aWXqoH6+773ve+II46oejjqU5Lv9oQDNflv3TzxxBNpzjQBq+dJJ51UVCQsizCRa6+9tifttuGzBieU53Of+1xXtEEMldl7771LBYAF++23H14BI9iujIsPf/jDUca6hpOG9LHHHhs59XlbqrkFP03kww8//Cc/+UlMzFNnXF49RBUG9L3vfa+oMlEpDzjgAHMc4qOAXu+2226IZAT99Kc/Vd58/93vfjf//PNHJlHw29/+tgW9aKQJ4uirX/3q2LFjN910UwrwWWedpZs67q0ZzCG1vK9HH33USySLvEfD3i2AV8LOdFOPvvvd70IWfvOb39x9993EWgJGr7rqKq3gA3wKowJ9wDTl11tvPXz7zne+Q7EcPny4hmDTrPqf//znIjkaob/nZW699daDDjoIuAlDVBvlFge6qrZmF6IwHtKlP/nJT4aaXawBSPH5z39eE8rIJzrgg8UCfZt+8cWOP/xhKB/6XntNC0/6ffcNufrqIYss0rHDDjMDEe64Y8g11wy9664hK644fdNNp22wwfSgWc4//tHpfV9xxY53v3tm4fvvH3LVVZ2ZyyzT8b73TbvqqqGve446oLIrrzz9bW+b/rp/vxVd77YQQ5xZ/M1vfhMmDuT1Bi3QAPSamV6xoVvsj1nPtVDMaW66K8nzox/9KCYycWRmEbYmV7Fpppw5Tl6Rz4QVkeVuv5BXxV5U02/qZPV2U3LIvh133DGqglAyretXO2nSJF7QOmVg9iCGyZMnK/Oud72rFNdW58F8K3OgWRwQcVbUaYrVMmOsWxDerqLSfvWrX6Xyp5xyCjWC+4gGoEKKE/vnj3/8o/wNNtggoros81bZFOGVnu2TBIBMyIOgs2LrHIDy6fT6Tn0hT01MZAPOaDBmKLn5hS984frrr3/HO96RHqz5VKwWe+65JwWLGvSf//zH46BDDDnssMNm6alLledEgxwYMnnqqOcmj1vwTaENDT6bi/UtB256eGJrCBg7YuGvj3tHx0L+dcy72tiDF+vE0ZZ+4ubh99/aGgJyKyUOUFIjh5zkq/jgBz8YPyncq622WroLbvjyl79Mr2WkMcYgCMkKVV6YBuDszDPPpK9zToQ9JnKn1Fb+2UscqMl/QQdeHxBTZA0D6ROf+AR9wN+gQYzSn//8516ip2+rZR3UAc7QBhO54IILqkTiD3OAWoJ1wAJ6yIc+9CF4GQc82IXukcY8y6IOb6s193YOkOj4449PrSCVBigs4L3vfS8se+eddxZjJZ0K/P73vxeExYkIHcMKyNoHPvABaRwQT7Dlllvi4U033eQRupPuq7x9Io/gVl4QARUkQbLY0mz+z3zmMzpYlFriJziPgYn+pr5LMMKZ4t5g+AbiFq8t3M2+jWJJKuhaa61F3BkGCtCflYkC3/72t4MA4R2f+tSnDJuWYWeA8q222gqAqOlDDz3UQA2vRpHylK7ZBW/fCCfVhaSlksUE9nK6Q2NZH5/+9KexV6PFAn2bfuaZIfvu2wk77LHH5IimHTOmM2f99acHdnb88cMOPbSoiw7bf/+p3/nOVIX/+c+Zzy666PRHHpk2dEbQ7cknDz355M7y228/DXZ22mlDzz77TdG4K6ww/fTTp2y44UwArre7320hJl4ScMZKEiEBqGIBwaSMz2qmMpdccknqiGlumYgghpTZ9ERNySP2DZGAabGfdIzdd9+dJ49CklqH5Jqz8O51112XzQjyI9NEn/ULeZV6UTPxpkFWs0QPM5nQV1xxBbQr6mFXF2PEDjzwQNh/3KIQnHbaaRQ4kx8qT2h6GeY/WWkAwdeNmFtuueXxxx//yle+4hGCGHh58803E0B+MtTPO++8fffdl5JBVlo5ZMLp2NuKyVeDmhWTf9lll5HXcrSiBjn5yhxoCge4C0aOHBlRALOs8Oqrr/73v/9NoFjgqQsEUGyisb6aAnSLqIGSZJ9CUV2YZc29V0AHTWGrcrEJwJnM6HWE0BOaLnhfgGXcDuuvv74+zvIpBV566SWtSFCe8IGeQcPYYYcdhKAXHx/g6SFD5tlopYV333zE8ou+0dOhQ+bfZt1F9t5invVW6Ai33Yx7wxeaZ6GdN5n33WsMGdYdkT7trDEx8N5oqF1TLUOL2pUBfU/X+DkX+MaLG39hgS98eI1f77zNdWP2/OWD79pnyqrr9T1lmYIKB0hdebfffjvNR4yGnVBUI6p5FOS2sQkOcOZnSNfSTpBKfTmjmRyoyX96rzboAP5aAbfZZhs+p2iVT05gEYizmUT0/7poCB//+MfhMroiygasEHo+TGSPPfaAuKUu1uFtKtOyBOsGuMMSiYgz7cJS9QIEJi0UlNeQyVOkB/wtICvCysKSEqKigIGx0EILSXA9MsLNYtgrSJ3JWny8D9O0Qcbzl770pYTlETuxeahKlTgy0YU8r95s3GVCwpIYg8y9oiYMaLDNLeFiqSpmHY2RN9omMoyK957uRkKADLWnZZqPdwQbff/73691TfN2AHa9KQYyk9ZIgHjCfDnRY/TW7AJqdZ/dWnPnctQs6FKvNYGNEWJS6njb/rz66qEBnB100NRLLply1FGd1vpJJw0755w3qbXPPjvkhhs6Y80cmXPuuW+6FV3bdttpP/zh1AMPnAple/DBIR/5yPAJE9q20zMJE24pZTn2V4iAhdtoqZlZ7AnYRAgClIMJWcxveroryaOhxRZbzF+Sx98SGYJqQUCkkIksKg1EE1K6/eXVLBnYCQD36mXdAhDYWWkaY58T0EjPpArAtkSZBQHirjlhCBTetvBFMMVBaRDKr33ta2xvwKe9nDBOiCxB87GPfYxjwWIjJkUN3sqf/vQnMIRjO5SkXkDKVE5kwzJE/wLdWOBieRT45S9/yUvD0UFsqUoUd1cxRL3KnFz5AOMAxCdAnxiT9XvHgLFMQnUjoIxKIZoyHolwXDqBn1Z9TgZ+M8hy/Qpbc9c2H5eZhfjUIsodqxE/TSUWmi6Y7+ZaEqbSFoP0iETNp+Sr355QgRKMvW233ZYKRQ/4yEc+giEWFdO5WEm/SE+8t14gbbULQ0fOsfT3dgWETX154qiNV578+ItPHnOBdWnpY3caNt9c016ZDDubf9sXn/hWpxN+/q3XWWCH9WQOGTF8oY9u8OhXz542YaZQrdbcVc48j7/SMarz8KOeXG9fesRH1pnn8Etnnqk057AhO7191JqLz3HZna9efV/nJoW41ltmxEfXnefa+ydedmdt3+nrBfP/7c6BCXOMOuL59Tvm869j5MqvfnjYbRu/fOsyT948x11vHCvT7n2YQR9Mtn+dd9YIV+lUtpAoydwKbyKjKxaXnXbaiQrEz+GiyFK0LrzwQra6Q0kaqTmXaQoHavI/Ikq8pjgdhlYc706LtF9++/yOSsznXJfD1wiMsKGPl53CIAfs6K9YM8EgEq7Y+lqTt1GglX+Fj9GOmDxi6qNdUUWUn3RmED1K9FmRJMd9JFOFYeVWnAsGh2Jkgb95McUECF6zXVEYCGTNUV/tsGUhPMGl/YPFkLpiN6XXWWcdf0ORhiUJtqIH0pYd4FgsCXYUwCXMSmhhcf8jSzNAMQAlA96GzfSUWAo6JKesPQ00UrZhutWriUANQqvXUCTowFzF1113HbOXxWqmCxPz7hi5NbvgbQa8UvO8S9Ued9xx/sLdeOUBK3XOPurVznav8mOP7QTCBJodc0wnaibg0nbLgw8edtRRw3bddeYmzajZxs9NNpl6001D4GjVtjbZZPpnP9tZwwEHTFtjjTmUOf/8obvv/qYaqk/1bQ6pfvnll8fGGrZhbNermZnotL573cZ/cUd/utvcRE3JY0hTJBBA0oLwqBnFiYYAoVEwtc9+9rOAGlNVWLE4WfntL69myb1ex84IKUJB4DHsLPZ5lWRfiUTqAraS9cq7JRCR/4QYpd55TwSNCkP+EpRF6B1wJgA4XDEQNF4L0YOxrnDsQEZVSzukiERwI1FubwJvldNAiNe0IJXoyT8zB+pzwIC0PVMZiQDOjNXIqf+gxc9oT05R8Ly100AFGYuD9SyXEYgKamaLeHLW1a+zr+6aQUnhQ0N896CUSZ0Nf1oislQgnnKXeWCvgdkqZg0AJ0rU3gSyWxoCbgtSfRmS6u+/iQU+tD7g7NGDz4SIzbflmgt+bMM5l1pwrrWXAZyNvejmcX/+74iVFlv8y++f731rv3T5bfNttfZrT4174qg/DJ1rzmWO223BHdZ7/nf/nN2+v3bumEW246MuxLjNZhUj5xjyi50XHTF8SGBnft5wwFJOthg/adoH15rnnFte/uafX1TlpfsssfxCw194ZeoOa89z7AcW2viExydNaVE4/Wx2KBefPQ5MHDbXWR0bnjVqw45VOuZYafJH4WgTbl32qRzWPXts7HlpLoe0b4LWtNdee3E9ivml6uyzzz7UcWqufUOiciLCRaAK3YxyRbtNDuGek5FraJADJf7Tb2m2QglgH1y/f/vb38Lq5kKjvjKtLYIN1jyoisHIxFfCxVw0jZoqfVe8bT2jGCBO2hIlkJyLaEB2UY+qqkyUTMUoQiBCzkvGUYwN+wC+/vWv23wNjRLKJHrAwSDsIGkBCsZMWEat72ZqkWAhXoqdTbdqJgL5Qr+7/MdALuJL8IR4zIhzSU8RYpRn8ZhCIlJmsEWjwAVAEtU63TKhAlqNo4Hgla0Bo+m33g56vGWK/Y9//GMkSfiLWtEeusyqRZXwOthZnS6kvnSVECVEdLhLYnRVpm/zN9pojjgXKxFoc9rNN3diZzvv/AbIteOO02BnYsfSAdGbbjrd5s2zzhpqI+dFF3WW32KLzmPOanZn9Ojpu+wy7ZRTht55Zw2IreYjfZVp4gdwZrSb15YAR13VzEwUkh7mQtqQnvJ7I1FT8rBzgftmWRwdqF2IMGUjEUAUO2QAuGbnqXknUMkI90mB9pdXqQtdJXodO9MwuYZ3wCwImnjjmktaV/SBDJjQLGdyJAZWzZKUD26EtOOXDOK9IRMDO4tgQg/KZ677aa8o5IJLFki33XbbFf0VNevPmZkDNTkwI85sgXCOxY5FcsRVs3Ax0/LpoBkB50mZsKwanLwNioncZMfSDQAAQABJREFUpiWIzBdNSWa5WD6+rESS8qD26pGQRSIbT3OUFYPDidQAwooYYmQW66z5lAJ2K0AMJXjkhKlyNtKcuGKcc2HTN4Ew4LGzKc+NH3f5fwFnmDDpgU7tZ+QaS49YcXTH9I6Xrri9M/P+p6dPnjJy1SVgZ0OGzVQLpk8DQk0fNn/n1pVuXI9Mnq8bT6VHfvbxReeaY0gnCTOub2y94PBhQzY78fEXX512xidHCzSDnQlMA5wdcvELF90+YYn5hv1tvyU/t9l8J171pu9mpApzov9y4LWhc549fb2z516vY8W9h05/QxXuvz3qR5RTXimpCLZecBmGxsUZmc7WEe4kxMzB1YGdOW3TZ2rIZ+ddAGgcQd2POjsASK3y38Y0oUMMfmYG16/AGcgmdMBB4xz4sU3Pbr5GPtU3APjTYBfgxS5QEbXKCK95iBVVqsrbButvbjHbFcFAYFAXAAXYwQymEdVXmdDATDVbBSfqSFKERP0DztylfIohYHDZ7SUsBcTG9ehWn2Nn1GOGNDOtGATHriaFKHhV3oZSHeERrD+heWqAJoMVdLxkRfKncrgKTCvWI5YWk21OwgSbHtIt5mRyRZtlRCVLkKhMBXovASCj2Hsd+uUgYDM63O2kdGCFmhbSgVGGhJyuujBLCk0Bl37BLN797nfTxmf5SIsL3HZbGczy+YTx4zupKHwn1qb1mQrlc8/NLL/GGtMdvGTPZiBoyn/8411iZ+4uskhnDelx6ba9GIA+fwETF8xF7MdMqZmpCwB05qFirRm6WqxKHnG+jt6zNZDsMmIhYmJphf0mDusCyDtizWwhh7oIh7Rstb+8Sl3oKtEK7Ayoj7MkuECw6mfyjIwgjryoUsl5YpFw1Bxh5yURmtUycgKhB5bFXcAE9SKkkpwET8RdZ0yQwqLfWeMcsIQy7Mxezrib/2YONM4BqpjChlNoPP42Apx5BBZGjUhBZ3J4GwgUQZrSnEVGqfVeBD5MKkL6hXOHMOVOVKatrkUXXZRgDZLIUFAXhJpwRzP6Y+XmaC2B1DWfSv1ywoX5bts/hdjeaoCaiYzVrIhUpr8kRq6yxLiOWxun9qW/dgJkcS20yyYgs1fGPNgxffpcay0zfNF5RZnZ1DlkjuFzLDqvMi9eePNCO268zPG7DZlz+PQp08ZePOb1R2fv/yPv6v6eTdCYLW83PDxpg2VnVnL6TeP/ds+rgDNEzD3H0CEdnarPqBFD/nznK4Az6afHT50ybfr6y3S/0dnrXi7dRxyYNqQVZkkfda4dm6UsVSMp4C/WJjvfg2I6EtWLPsZ0j9PKrWX2fJX2iLVj9wYQTTX5TzEQOWgrWSz3DvalGHAPM7BZJnov7S8kVKgRO2QA8aObXYGb2PnINvM8DzpzgOJREzurydtuttqzx1i8YFCXatg4QkioSQwlalLgJvIBaqUzXk1P1rKTvASQVgERKhMkCCwOE2emGjZaEUCQLKOekdyjp6MjdhGmT6CqzgaLZKYVayeaGOQeic1oIiH0RQEnWQvCAHhhQrE8bTOgKFvGYtbY8GinAqAtNOpi4WIacxielG0xccX8XkoL7+UXD6zQ1lEDNVA8CjOBHIAg01U+4KzBLhRJFfVjIgBfYttW/PX2ofPFYu2Qfumlmd8KcGDZXnt1AhGOK3Q8mf2Vvry50kozIbN7750JmS2zjJyZ6d12m3rDDcMPOWT4448P2W67aaNH1+vQ3Xd3PuWLAfUKtcc9y4Hdu4Z3cdbXzEQv3wm5ARhtDe01JQ9HBRTYhQYj1leS5RTpIY2LwscIT4CPYu0sr4q9qJluhVLL4uVggbUTfyk0LKixZ82mfevZnXfeSZREJv7K4XYgTcg1R2ASJX5aDBSQGTgrcZNcNKkJb87CQyKThnFKbrXblEVHM2qRXA7hEhVWS+aczIH6HDAsYxAGiBa+svqPxN37779fWFkc7xo5DBs+IggUSJejzGSxrjvIQwRWXOI3rbttCJyh31yDBpL7IsUEnMsxs3j8JCiFMsWLiSGPzfCAMIpRV0/Jj4uaZY5Tlfyk2ZDdRC1vatGF+HrZAfv/6P22mnOZhcddduuU518ef9WdVpvFv7L9Inu8c8lvfqxTkRje+YGhUZuuKj11/ESRaEOGD52z+G2BhhkzednO8z67dy06atgR2yz4x/9OuOeZzkC5uO56+jXYmd2a1+y/5FsWm+O0G1+Sf839Ew+8YCb0+fnN/5+98wCTonja+N4RJUgOIiCCIKIYQURFVEyICCKggoqgoogYPjFnTAhmxfA3ICpBQEAMiBGUoCQVUJRgAFGCSM4Xvt9eHU0zM7u3d7dhdq/m4Tl6e3p6qt/u6al+u6qmQvH0tA9/0pBneyDT/xWBWCKAuxDf4MMNCqWLnUjW2MzSJJiQ0bjY2sSrAvU3liJo3fsg4Ik/ugTGKWyYYTfELj16L+4abB2hQstB7CdqIT66EmeCJrGox40bx2ISZQz1gyWlKPb7YJ3zwxNbd7E45LBC3tOfw1FvMCVDuxMFCV9OtEGsDWDW2DhEGGztRa2CFkGtwiyFAuywcthrUUBATcJUn0tYW6FMchYyDk0yDi0Kfwt2PfFBI/wWlq2szjhoJt6UhM2RC5mCEBXFmEwclpmX3LounALEE08H85jjdsTgx8KFa8lnkQjjjOqIXZ6ghNOGKY/rBjfiwBGKh4iw1/EhzhCAIYopHOwYi1DiF7OTIWtP1rk4nMKJIxuWR/B94Ztg2mISfEgBHZsFBUQGNbMQZoRgekLT/Kkw47Bp/plWdOgQZLgGDiwmH7PBYfehh4Iq7plnZtmWcx07BovNmRMkxWwHT346jsmT04l0RuZpp/nd+J2nlecXi1GeXBm0rAQ9M6WN7K/QudgfOJoco5+eMw9zEaISLx5RibeIK5XoD2a+gtpjVYh5KSYUPPg8dLYBrJ/nqzxhjLndmUwNWFAz9bdp08YhECa47J6hsTFcOIuuQAH4NbZfiPHELAOJjsKHqsdmAvsV7MMwL1x11VX0ELMw201m1sPRgBcnzpjUQG3sOfBXZkyH3RnzL9USXVKE4b6qLzr6RX9GjgB8GWoBOlnkxBmVsy+KbbZ9F+gnvi3NuEWH4BEgHo19ljTD2DGSHQUS+JNPqKAHiE8QjyrCi4c1m+eEdZB41ShJEv+VT4WwP8bjHOoqGsK7H2WCZ5/dDH7yhLKEwDIfhaCIfGWMeGcH3Hl+iVqVNkyct/GTHwEhOyPz3zemEvuszNF1d/7xb3bVcpkbtpc4oGLJ2pU3fbZg/fg5lDnw4a6VLmi2ZdqvpPN1/LF/5UBBXSeHda++cXvWvR//d3ubio6bViqTvm5rVpWyxdo1KfvitE07dufu/vU4vvy1J+0//+9do+ZtcVyiPyNHoPlBpQPTgqSkHoVEIIhk0h72e8FO2w1Ci0VH4lsBZLIZiaaEFsvSWqKrSEn0Mb7NYl+l6ZgiwKayG396kDcdBJmYzGBiIwSKkUT3eg0UkiAENSG9WDLwkxUBCweoRlPGfiLyxNZcFeeECInwGI7RllGjRiEA7I84xECLsEbFpRpGCWYQls2Ix8JVqDEGEgak8EpieEXcbmwICJuFnT5eiqZ8AhN8nIRQ92iJ+CoiBo197LHHxDyQ5sP64W1KPi7nDRs2hNUSNwW7+ziLdSEKJFQjzCM/zbNAMUwi+E4CmeywghLsJAc/ObgXS3cpjMmeZPKX/QMM1szPWCfwo4TUE8s7bAPlGxfcFMILlhzWjDSLZWKrh2pCKAnpa7aWCX2FQxXjnxtREgBB20AU6lr/5N93Xyaf1MQZs3HjEieckD1vXhqWZYg3eHAw8L85atTIPvvsLHgxcvikpjvY2eDBxd5+O53YOfIlAfg1vh5AYfgmbHQYQuIW8+KLL/J8ifcPixQYTN6A5i5xThCrkTsK8S23JpyUPB2OTJhlCkCzxnPvxHPmYQWHh6bYdiAS9rDQ+iTMfMWqlqeeVS3PI1oHzKCxiPf/fCW9EOpvGs9bqHPh8+lp4zQevmT4s/DrUGaehrtyIdukFGDuY3KEmIekcPi62/VTGOqBCdrO9EzDg8KVYnomi3PPMpqpCIRCgIie9jhkAEPrOAoztvO758MlDOAwof0ct/DVT5w05Vm21R1mGJDhkbThssX2vAoiEgNSh5kq9ms8sKHqsetEDLF6M5nMV44ccyoOiR2LV61+ZlLkN0orXqzW/Z2KVy63bvj0LTMWy4XQZHxzc/2EuVifwaTWfe7yrd8t2/n7mirdT1o16MOdfwQ91qv3OWO/I2r/2ffNyO8lJYceccLojeXyexXlYcGgzGb8vuPvTZnH1i5Zv0qJsT9uHfLNRlwyTW1tDyvzZMcqj3624Z05wd2Re8+udMmx5XDw7DkiGMotX8eb3asn8HuI7lHkzslXcwpZeNbynVcMzzeGhbxpSl7uw3FltMwoAo6CxCyKeYI9RTMDM0WjYkUytUZRGK1KEAiFf+Tvu9RAEhdUVv7h28K2HI57nmXYO2eFJisFzwJ2pp+xhdFmGY9fgoS0t8UOk8aUCdXRERaD55plWiTPtbj4hKnfnEK5wiHAoU3xHoxwvqJ1aHc4nGLcYM9Cpv6UT9BNNJyVrLQUR2x8LLDroQvgPU1+YXBgeHOLqKzQCyOG+9rFi9OaNi1B/rZtuT6bY8akX3pp8WbNsqdPD34XYsWKtL59iwkvxs8WLbJffjmDGGek33gjvU+f4r17Zz3/fMaIEUFPT0ixYcMyiFbXuXPxDh2yRo/O6NGj+KhRe93puLxTp6x+/fCHpYKAuMHCLzNW6QXsZjDEEQYT4hUjLz6yESxXiKOQk1gh7pywS3misVJiviJco6cQrAEZ246ZObHzlaec+cqMud1ZntIwrYchzric/jCubeL9HqZOCnOEKWBOMXebak2mJhSBCBFwbOa4iTPqcZSJpGYehyQlzmgdqh7zo6OZvMLdmXYZz6sw5bPLSDoSTtx9VTLmVL3ilOJVykGNIXzQJTMQ2LFkFbHM+KRmWumSGybMqdrzlLT0NOzRsndnEg2tyuWt4OZKN6xZ+rADd/zyTwGaXDDijBvxfYAtO7OOrFXyyFoBPrJJzjmN9xv7w5bb2lTcv3T61aOCjB72ZfxtXD04Mz/Rocq5TcrgqnnbxFznTTL1UAQUgTgggHbEktVxI2Zg9uEdmfozbgiEwr/ovO8Eaj6ChHWDHQTW0QWcDfOhJEiHyHkHP2OL3uh+SB1QuH96xrQKv7ZyVxKHHFpnf4kvDnf02y3EMNAtVXg92V0+TI5vh3ejRtk7d+6N7EET+JJmly57c4hrNnFiBl8f/fPPtNq1swmCZo5evbJ69cot2a1bVrduuen27bNMnVBpOdZ75qJ9EnyQjUOy6AV8Cc3paNlmFnISM/IkUYInOrz+wBrQTbYky3wVqiMSz52FkkzzFQE/IwDDhd0ijHsoIZlQkpcFC9UozS8YAqUbeVCBYara78igy0nZFg34J8U2ffnT+rGztsxYUv6khuVbHUpos3UjZ/I5Ts7+N2pm5Yta1B54MemsLTvWjdirEMi1ef5dV73gQRNenr6Jf3KLO8+o2L1Z+eOfWsnPxWt239i6Qvfjyk3/fcfTFwRX5uMXbMUADeLst3W7v/tzB58XIPOP/zLmrtgpl0fyN4FGZ5GIF+cyika0AFcko4Wk1qMIFAyB9u3b4zk1YcKEUJezLqVMqLOarwgkIwJ43kEuJKPksZOZSC2wbLGrP3Y16yQWO2x9VbNyZ77qDhUmaRDABDJPK8ikaYwK6jMElt8QjHzhPta9M23diOnFq5TPWLs3ytXmb37hHx6dWdt3ZW4oSOj9xU0bBxa775bvnCxL23l15qaT65e+88xKfGKTfLw44cgmXBXkEPHrfOjcylL7krW7O7y2Kt930gsUAUVAEVAEUggBoj7Jp4FSqE3aFEUgDwT4rgVHHoX0dJIgoJNYknRUYcVU7qywCOr1ioAioAjkiQCmZ0Q9y7NY3gWysm3izJTf/Y8z3J45lWdiwOLgBxkKfzz+xQb+ST3wZZe9s4YvaTasVuLXNbuEVutYOJpMjYPcfQQmRD1z52tO5AjouIocKy2pCCgCioAioAgoAopAkUVgb1C9IguBNlwRUAQUgVgjUKph/tw2Yy2PqX9X3RomHfVERlb2otW5xFnhK29eNzocX+El0RpSCQEdV6nUm9oWRUARUAQUAUVAEVAEYoSAcmcxAlarVQQUAUUgCRD4Y/9c98kkkFVFdCFwXasKrjzNUAQUAUVAEVAEFAFFQBFQBBSBKCOg3FmUAdXqFAFFQBFwI1C64QHuTD/kTA8kDfnS/KDcz7r7ATeVIWUQ0HGVMl2pDVEEFAFFQBFQBBQBRSB2CCh3FjtstWZFQBFQBHIRyO+nNuMG3OiN5eJ2r0LeSONSuQFUTNyY5DdHMcwvYlpeEVAEFAFFQBFQBBSBIohAob4VcNBBBxVByLTJioAi4HMEFi5c6EMJo/a5gOi1bV31atGrLLY1KcERCl+Q0c8FhAInz3zfjquSJUvmKbwWUAQUAUXADwjofOWHXlAZFAFFIA4IqN1ZHEDWWygCioAiEPDh5wIWN22cLB2jAd2TpaeSS04dV8nVXyqtIqAIKAKKgCKgCCgCiUJAubNEIa/3VQQUgaKFgA9Dnk1YsC1Z+kCDUoXqqaT4XEB6WuDy5uWf6FDlrEP3Mw0h8/wjyj56XuWmtfaxsWrTaL/H21fp2aJ8qeJppnCMEjquYgSsVqsIKAKKgCKgCCgCikCKIVAon80Uw0KbowgoAopA7BDAZ9NXbpu76taYn10pdu2Nbs2+9a2LbjMLUBvI+N9tc3KfA2pVKL5ua+a5TcosWbu7w2urypVKn35jreLF0tZvy+zYtOyva3Zf8Poqmj+wfWUINUq2O7zMTa0rNH9y5a7M7ALAEuElOq4iBEqLKQKKgCKgCCgCioAiUMQRULuzIj4AtPmKgCIQPwR85ba5eUdW/FpeuDv1PXn/wlWgVycSgW7HlTuwQvH+E9ad8tzfD36yvmG1EkccUPLmUytAnLV8euXJz/498PMNh1Yvwb8a5Yu1P6Lsxz9va/Xc36e98DcFbmwdw+/A6rhK5LDQeysCioAioAgoAoqAIpBUCKjdWVJ1lwqrCCgCyYxAxXbHbPzoB5+0YGL1BoGNPpFFxSgUArhtzhq+plBVxPLirseU+3dr5qRF2xpULTFhwdZpv+1YtSmDQGOvzty0KYfA/WHlTu5/Uv3SVcoUw0vztonr4Nq27co6/YW/N2yPIcOrDpux7HatWxFQBBQBRUARUAQUgZRCIIbcWUZGxgcffGDQqlGjRrNmzaL1KZY///xz2bJlp59+uqk/hRObNm364osvzj///GLFikW3mb/nHFGHUQRu165dtLo7uq2OSm2rVq0aP378H3/8Eaq2evXqXXDBBTVr1nQX+O2333bv3m3nU7hUqVJkLlq0CPQaNWpUvXp1uwDpn376qXTp0g0aNHDkJ+rnjBkzjjrqqLJly4oAWVlZv/zyy3///Vc35zBS8ahyVK1atXHjxunpTkPXUFctXboUhI844oiKFSuaqsaOHVu/fv1jjz3W5CRjwj9um6M3lksWAPu2iqHxUbKAEEZOnzseVimTnpYW+PG22iWCzFhg5LwtD01eP/S7zaZF951dCbfMyYu233lmxYys7Kn9alUpG3zZLVq9q+vQ1aZY1BM+xy2S9s6bN48Jtn379sWLF//666/XrVvnuKpKlSq8UGbOnGnyyWnevPl+++33zz//fPvttyhmderUkbN///33d999d/LJJ1erljRf4DXtStIEWsTy5cvRkA899FC7CTt37qQvTjnlFJOJbvDzzz/zs0mTJvvvn2qmuIXRqTIzM3/99VcGPwpSrVq1DGI52sc+6se2bdtWrFhhCgC7rWOY/DgkPGVeu3YtSpS5u2iG5icJVCZUQcpw6qCDDjKnkkJl2r59+zfffIOiSNsZwyw9SpQoQRMWLFiA/CTS0tKYnWiamZE4BSb2IoXV0NatW9u2bUtVTHr2A/Ljjz9y6sQTT6QqOhp1GqXa/bB8+umn3Pe0004z6MUtwSPMQM3Ozj7ssMPKly8f/r5hmkDTuLx27druGlJ7lnC311c5hZnEaAhDfcmSJbyaWfvIoxEqM/697DlfIR4sAtMsCgND2r3Ek95xD9ekmK9EeM+/MeTOdu3a9cgjjzRs2LBy5cqkv//++zJlygwaNOiEE07wFIVponPnzq+88oqZNB3F7ALUxkLank8dhVPp5+rVq0HynHPO4YmKbrt40wiMc+fOffrpp995553C1H/LLbecffbZZ511Fu91BG7Tpk0Kc2cQZ0cffXSfPn1CIcZahTKeBa699lpbPaKGMWPG8CK87rrrmHmhmdCkBwwYcO6555rK0fZ69OhBbT7hztDebrjhhpEjR/KAIyREeb9+/WbPns1Ppn4a0qtXL/L/l3NIJgoNY8zmf0Nd9fHHH7/wwgus3wABZCpUCPImkyZNeuONN0aNGmUwSdIEbps7FgfjOiX2WFc9adbG6lgXyVABpSHTNkVSMv5lypZML10iDU/MEXO33Ht2pUuOLffJom2zlwdtzThe6VqtSc2SL03ftHJjRtWyxYqnp23ckXXT+HUEQbvwqLJ3nVUJok1KRvdvCowrFtL33HPPmjVr0LJat24N1SLbOSw4wapp06b8PeSQQ1hh8kY+7rjjSJDDFM0LCGWMOZY5fOLEia+//jpaL7Xde++9FOjQoQN/9YgDAo899th7773HxhKEAj1Ip5hXJPlPPfXUnDlzRAxYMzSHSpUqrV8ffBxeeumlww8/PA4Sxu0WBdapNm/ejDbCbvqBBx7I2CZ9+eWXI7an+jFlypT77ruP50XaddNNN9mKVtwaG0rml19++f333zfivfbaa+xFGql27NjRt29f2iha4lVXXcWQ4GxSqEwosawRNmzYcNJJJ23cuHH48OFohvQRqi+k/9ChQ1u0aMEUxDqcCY31zp133snWLKfQpc1aD83wzTff5LmAWUAh5KnhQpnowAFa7a+//kLVhJ9CI5XpbsuWLU8++SS7BQIjYtx1112kYdAMznIq1n/Z52C88QjDIXIv2sKuRqibhmkCGILkFVdc0bVrV8flPp8lmLrGjoUhDVx5ZfAvx5IlgSlTAlWrBi64ILcpCxcGcxYtCmAncPLJgeOPz80nZ9q0YLp+/UCbNrmZMK5ffRVMs/tzzjmBL78MLFuWewpWltUJW+3RXjfn1u/5X4EnMWrjKWZqwj6AZSCDhGcfQsozM/69HGq+Yk0nDzKTEgrGkCFD5KGzwXEP16SYr+wmuNMx5M7kZkz06GekmfRvvPFGtIFQ3BlMPDMmLJtbSsmxC/C2g6MJVVLz84sAO5zwNfm9ylEeypwHjExe9l9++WXq7Yva7WWJ4smLmTItW7acMGGC+WkneGebn6jOqMLssw0cOBClefLkyTCkw4YN4+epp54K3UxJ+H6mVEmbCxOVgCB7++23MTqzBaBF5PPagPj+6KOP7r//fqwOocZQjB599FEeVfShCy+8EM3Gfmw9r2IfmFni//7v/8444ww2NFgNcgnqDkA999xzKTCofOK2ubhp48Biuw/9m1ajs0j6BpR8y51tzwgG++//ftAk6roxa7/oW6tdkzJwZ9BkY3vVaFStxLNTN74yI0j88d0A/vYZ/e+KDRlzV+xse1iZE+uVJicWRwqMK4gVWWcy98K83HrrrQLUbbfdxsbVww8/LD9luh48eLDMn7xQ4MhYgrIifeihh7p3786U26VLF3Ym2E6jqlC7x7HohaJcJ3vAIM/rHhaMtccll1wyffp07GhGjx7NXpEQoAYfliWoBA888AD8Astm3sIoCeZsCiQKrFOxwcYGM24ubLONGzcOlQPyF8bEU/2AYrvooovMk5Io3DxlRn4YE/hTQxU5xJs2bRpPKGODRTWDhJLdunXjoU4KlYnpCC4MRkBmIdwvoH7YsBdFGvqYES7tZeSzcoTYQg+0EUADfOutt6Cc7FUk2iYTl2Of/plnnoFQAx8M2Xhk4NrYJJCqQI9FPvQECaY+u/5Ypx988EE4QZg7dGOm6OdzjlA39WwCsz1sBbMEpiSeF/p8lli9OtC7d1Dwnj0D4kY1d24wB2JTuLNBgwK3375Py26+OTB4cLDw9Om51+KT888/AXFief75wHPPBcuz3QN39vrrgREj9rkcoo2cFi32yYzdjwJPYthLssqDWr300kuZuxiZcFJXX321O5My8e9lz/kKugYhIabRPeT9hRm7ED6CcKjhmhTzVfhB4nShCl+6MGdxN2O+YyVMJSyD0c+ktoULF7JHxIC77LLLyGGuBFaUA+ZH1sx4E3Tq1AnNz1GAVwg2KfBxjDPSUtX8+fP5SXeyp8F0iQrCteglctb+C+uJmkLl/DUswOeff87lZPKXvQ4pj2xIIoWvueaaxYuDy02ERHGRApgEU2bWrFl2/aRpI6OKrQ9pArKRyQYvqqq5kDcH7WLMXXnlle+++64Qgrz7eSmSRv7HH38cKKQ8DZFM9pnhcSUTikpkY6tNyC/QY0NSCrMDI8X464bUnOIVwluNuRh54LPJz7NaAARbmkYf8TLjEkDgWswGeT3gCcKGGHMB+bSOtyAl0WbYIEJrJ9MTHJYBgj91otZTLOUPYMTsnHc/L3jUaGNaiLMn3cGWmiBAbx5wwAHozX4ABIt6LApv3/f9Rt+h7YnFKK1ATjQ8Dvg+KDB+4lzAeMa80W6C51UUgEyU/UA2V3mL8IjdfffdzA8Y+tmXJ2+6QrvEN2TCAm/dy2+opoBxUNwg9a0H4n9bM3fv+63M9duzShZLm3RtTb4bcO/H/wlxBlDL12fwF7szAY2t6W27c9PRhTE1xhUaBd6avIvRgqAPIoSITRo839E92I/ETxDFBk0DHYYVJhN7KMP/CCvXYpEjgHYEZSDmY9jgEKiBJT2Xs5fWsWNHaBG7Kgxt8MyA1mRjH4+eMNvM9lVFIY2mhAef2KezZ0mTgTGU+oEOjyUmOqrbuzmeWHnKjHpMPg8gm4VoPp7yoFNhk8IpOx6I/1UmVgeofyydzPYn9jWwnHYrTHuhvZiURowYAcckmcxUcEasNeBDbeIMiuHff/9lZWGulQRbuWjRTHQ8LwwJHjTJB2Fcbc477zxi4LBWolrHhbH7yapt5cqV3Jpb8AjzgGNPh3LLfjmwsJ5FQ2aJh+GhrJI8myDraIzO3EFdRPKkniWwIJOFBX8nTw488kiwTU8/HdhDe0oTA2vWBL79NphmcezpiNKuHTZ9ATaSYNl++y1w/vmBnMVo7uX+/A/zAgQTi+9y5codf/zxjBbPTIrFv5c95ytBUh5hFmv8dOy6hRqu/p+vpGlh/saJO4O4YTsFjubMM89EGt5bYCdiwX8xqzIRMH2QA4vETEdJ2BNIImY3tAQIeEcBfH0hhugY/L0//PBDqYo5iEgHjCoYfeYp3BBuvvlm/sJGSQH5CzMKlcvEytTM/gOsEwZTKI533HEHLofwVrAACMOrl/LIRg7sL4XZ6oES4tas4eHOhNVigqOMsRk2N6KNzPXwCOyrYEnOBginkIp4IlKGWZuNF5rPwb0QgGbyJuB1QkOYSWk+XC/copSfOnUqlBMbFeybCXOBATAtZQcS3o2Bi2yoU/hisMHyySefYMsto1kud0Mq+fwFBHQyEiAAnnlWi8AAKO+enj17shdEu0CMZtLeiy++mPcBTeMFQC/TobAtiASNyBtOiDY3OKDBbgzAQqr2798fvhJUjYQpmWAgsW8MbhIyDM3PLH7QBmiy/AQHdtUcXFUCAUHjYX4XDcCIgahmxYW+QhmeKfYcsEA0kylDyzRQLvS8ilP4peITii0kzwWrCLQlHmpGGuVD7bYZSZIigelZYuXcVbfG/FJB/VuPVEKALwb4szmvztxcvnT63WdV4jOaT18Q1LE+WLjt8fMr80GADxYGt1jwzeRf3UrFIdFYzQztVo14Z7eeXnG/EmmfLAq6t0T9SAGjM167vOhhDXhvstmAwW94lJg/4RTQQNi0QJ9hDmfPhktQIWBwcLTHrQnr4PCV6NkoIgDp+eqrr0qFvOh5Y8Kg8ZOFEy9ZCdtkbod+eOSRR6Jwvvjii+y6sdFoThXxBNoyVIuA8FWOBxfaSCj1A6UCzRzGGW0fiyc22hOCnqfMUGYIw2YzyxP2SuGyHeQOQ4IRgi7NQoAN7969ewsV5X+VCR8CmsZqzkabbXJaaueYNEOdNCsF/rKUoMvEFsER7pbFBYsRHiLDjkkNWD8Ii8oyDasI47AJXcUSCWxZimJtIKs8c9OYJsRxBMVY7iJKPn8ZgUgLS4hFHmsfZmYxHfBsAt3NzMCBf4antEk9S4idNIZmGNTiV4Zn7VNPBVt5//3OtuL4yYHJCjya+yDeXd++AdbcmKwQU44yY8a4S/krh1cw7uQSAg//Ldb7hA/zzETu+Pey53wFJcIGDywERjNMSvhcYyVjwxpquPp/vrJb4Zku7pkbxUzMC01tcDQQK+annWBaOeaY4HqSLTj4L4YO+gEKBFtDULAofI4C5lr0P27Bihoe7bPPPoNvgrVhf4PZhwvZX8KWCgYKHcVcgicdFA/55OBGikUM0xb8GpSZ2L6x8v/hhx+g7QiFThneT5wigZsDEy5jGn80CCOmXWTmpqib+NlhZgxnRDHIAjGQgV2iJDmYiNs4kOM+sKxB/YXt4qUIVSdDkLcj+rGEOaNpsj+JGBh58ZalXbx75B2D+ssWNLQgNYMGQ1neqeZGQOGA1JyCl+QRBWF5LeVZLbM/BB/2RNxI7sLsT5NhfwCTfWw2tKVyXlr0C5JjUM0rH60dJhEShLMOcFDiaSYsO3QbLzaoSc/9KCNzCiSAgjbK0KI5LIFAFf4RtQ/LQXIgjwiLAHooB7Kn6ttWo6BAmRnx6G6eKTINccYp+C/ZTzPFPK/iLLQvoS4YAzxu1AM1TBrlmOeLoQXh6FhUmAqTKIHpWQI/uLkZu55SSYAWxkEpwHHEDWjszvg3a08csbjdN88bTVy49eT6pbsfV45/WdmBwV9u+G3d7tMbBsN3nn9EWf5JDcNmbX78iw0PTlpPTLRvbqgFiTZ16Xa+xZln/fktkBpGZxKvB7WKaRYrBuwpUAPsKdcBCybqJoeXPoqN/GRmJuwOG8sE1Q5zublWE9FCAPMTDmqD8cH8HDVV1OAw9bMJTdxlCjg2osJckvKnUDJpI6oCaj9mOyzz0GY91Q9UVlakaOzYzqNtomlQPiEbk54ys/CB8YHFRovGkpSNVdR7sdyXTkT3RkNGj2J1AweEAQE72VTlf5UJsVngCFkfyYAUfGRJBS+GDQ7IYAkB4+lYGoAPUf/xd8E/w9TMACDN6gl1EbqZC+UUseRYNMFQcDDd8ZOVC5o2m7VSgBUNayVTTxQTaMXcGgtfuo87Qn9QOQn+Ii0LQD4XxjwMXUhDsDkI1YRIRPL/LEEYspyZby/5hQng7NnBxtnmthddhJtX0HbMUNwnnRR03hw+POjIKUFxWJ0T5szzgGDEK/fllzGC8Tzvo0zevDzUCMRoZzDwgoY38Mw0Qsezlz3nKyYfVmQ82hj0MB0hGFyw/akWI6oj4f/5yiGw+2fMuTMsv4QGYt6EpHRL4JkDU8BqGQ6ewRSeNcB8lymG1wxDjY7kJwZc1IkSaWqWOcj85JUpbmXkoDUS5oMELyH7qyu8X9mblUvMTMrUBl3K1MZeB6t6tnwpRpAF2SXAGZOZmktQhtgVJGG+gSLDTmoL9Ve+9SOKFA567mIwepIpCcSAPkPftYNnyS4N6peDOOPCyCHNs1qq4r5M/ViVyzapW1rJgfQ8+OCDTSQCXgw8YPK2cIODaR6ckcSY5x2T8IAUoRoVlXz0PKzJ2PY0axUWP2h7mBxSP9wo+2w8MuxEMXo5MAZEbYKfwuMgTHjRqMhWgEp4jnj6zIVoqHQ6eo+dCQ9oRoKU9LyKUzxlGHuSYFLm/QF1yA4haiWIYVvBzJAC3Flio55NrN4gkJjtdjNGIkoocRYRTFYhTM9mDffairXKJCR528R1d334X4OqxX9dE9xh4jhq0F+ScPwd/cMW/jWoWuKvDRk7cwKlOQroT0EAsoz3KZu9BhCUFnYczU9HAttzVoxksol1/fXXwxpgac5PttzYeEMpYs2GFuSwDXFUoj+jiwDm5/gTEMCI7dVQNjj2HTE342DNzx5qan+OyW51nmkWb+wyonhgpS57wOjSbvUDVYp9ZakNRZplAg9RnpXHqIBbZvxX2DuX2+HBx4HiZ3Nn7OizJoIJQndizYLSyFmK+V9lYlHDZEUH0QUGTxZuxCrBYsXkmISsZcS6ikvwWaEG2H9oBbrY3qnlEixf4EOx2DKXk8CKB9URSNEbZT3Pwl7MEs2iCWNPCEoWbjITchXfMYDFtuuJYpohSt/hfEO7WODA94n7Ld3H6lJuRFpWSeS4mxChMP6fJXKCGO3TGj6fkBMuO1Cx4t78PZ/04AOUuZl8H4XAP1iJCINGLgY5obgzzgrrYC7fW7X/UiwA+fwF72WMuVgbitGMZ6bIHudeds9XWHES84pFGeOZdT3TKYYOcD55Quv/+SrPJsScOxNDJLccxgKFacJ9luBz8DIS+5P44kwx7jKSwwsSf0OMv5hYMWVicS4MHVOkqIlYbkM62JdTwGzZsbwn6h4WsFBv7GyYYvBH5MhPFE1JMIgpI5MdWg7UHi9pvNJE15TVvqnBM8GuC6SJnIIX8CwTKpN3ibBmIjxiMF7Rm82LBzs4TJZ4uUrDHfVEDmme1WK4h58sKgi8GG8jQ0Q67shPSEaQBDdhiECSt6B5Sdjl8TaFWxHPVnEapZsieQjtSpIoTRtRI9B9jcy8xbH+E+dZgOK5oDchgkFGQgJjUClqR+xe7UaY/CZQTdgDkavoR6gu9gZ5uJCZKVV6nAJiOGkq97zKnMVbAT66VatWaId48jLbMorcjp+mfNIlEmh6NnpjcIPL50dqGAfFGWTfmp6BQ0ZWtiHO8oRl2b+5FFueJfNbIDWMGQnmwOyKVxfva0GAGK+8msNwZ8zDso2HIoFFvASfhbthhYk+Q8wEdBKWpqi/kez25Rd2Le9GgJc79tRskqG2hd9aRonFMh0GQcKDiM6Jcmv2IN2VF50ctsPxZsBqD4stsz+HBuJWP9h6hCYmXJQYQKGNm5VInOHylBlHDZ5B2c5HHsRz+GyyfEURgjjjLF3P6kPCHIvwflaZMPJCSOh72yscN0wxGhD5zV+WDHCaXCLxTGgmreYsLDP7ynBn8nkBU55nR6gofDOFcYAdY0LDApEIYqYYd+dZM+wkqilb9SweGTmGTTOFY5Fgb4Pmy5INYdDnZbHGupJxKIQg6ztZJXk2IbxUSTRLsNVeLMdThYBlOaHOMb4LhifDv5LYdMbyLyfAeLDR1sdmsS0Icmf9+2PyEmjfPnhVmEOiRu+xPAlTMPGnWP/iqAu1Kg+LCOTOTEgve85XGJ3BkwhVgnaB1Q7FIsfRz/NVnq2IU7wzhxxoe0SjZwQwZWBZI2eFXgF6OAUYGWZD5k22VnBtowAzi13ArhCVAqYMrZEE+ZiJMdVitoNSCN8Ew2W+BiBX4RIIz4VfJwUwGcNGDEc5LIF5pzJw0WZg65i2yJHySMiyH0aAwjAa7EuQjzEd7BXffTfvOSkc/i8umWyzUBtGNOKXF768fZYWgRg7JLiL0kDUXyTEvxIWhu01eEZmZMdujH25J6SmANgCOzwXb+s8q8WRlvcTbQFAidYh+3vcHdLHjh/BhhjqHQBSgFZDqLNRZm5qJ3iKiNdGc5CEiYMeERM8uww7NvSR5LBhxbJBiEhuQZr67cI+T+N2wVtcJh0RlaGF5odBH6jydodHg5pk9DJW5UAJ6NGjhw+JM+Sno3mgMJpjfBKrjhw8icTgFG6aTLYm4HblCyz4R4t1vedVpuMYDEzNIEAOgw2tEY2KqBkMflMmqROJinq2rnqk9r+JhVeNzgqG/5vdw+qSBas0ha5KjXHFG593BFMo7l1ywH+ht0S4J8deI1bh9CqvTl61BK+ATYA4QyWTCTyFOty/TaETARybF/Qf1BgOscp3S8w2JNoCKhDvRN6n6F3oD5F4x7irSr0cFEsoRYw1QEZgRFXwVD9wyMAKidUpej6+USwucHFICCCeMiOJfLWDfUcWNaxc2Dsk06hMOBgyZjAUFZ0f4kwCy0gT/KwyMeEQsQfyCx2eNQIHMWTwpjSO5CxA4DqXLl1KJl60tN0d6IalAcscngLxLrI7jug9OHVyLZmsFiUYHGNAxgPaNY8YfBxx7vbMl4fiD4uZrSMcttSJHs7EyIKLn6wySJtVJCafUNj2rSNPoyRjLgo7xsKWR5jFo6xqEQ9bBL6uhn6L5RErWc8mhLkRbkCsqZNolsBh0/wz7ZLVIZ8IkO9kEAPpgQeCJ1mLlyxpSuV+kdPt4Lm3xJ7UpEm5kc4sK4U953z2Pwscxif7WCx2ZNDCEnhmJqSXPecrniBEJZo5ouL1jEmN2Pya+So8xn6er8JLztmY253J1OCQgzmUfQMmCPh1DG2YMSkAHcPch/EtG3HYIjJ7wmpRADtAQolhNsUpU8Deo8NtkAOdQ8haGJwnnngCLVAiSeFcSW22AFiZwdBJbFFePOzWslVFMZQYzHelJKokFJuk0VDlS8YIAwcs+grtYgMBGstQbPYtSFOAw5FJndxRamP/RM66i5mgAPb2L56SQg7CmvGxeZqJPQ6KlEQtIZOGiH+rudy+uyekxvsPAKGrUL6ZwfOsFhKH97e4uFItKh3RqXhaeM3zOuSLolgmc2vEYGeMOZ3NH3aKyOG9hZ8ziRxs9gEH0hojI96sUIQUACV3aDxeugiMdSEFGBJYaON4QhoujzSPMS9FfibFwTuScWWLCq9E/9Kb6BB0B2PYPksa0Dx71lEsIT9xomTjl4eOu/OY8LSK1zDPC8NSbOlpmthEoBSiPcADhrqKStBruZZtSdlFxBcbNZcHh76m8oS0MRY3TYjp2eKmjQPBzwX7+lCjs8J0D+gNmRb9MGGFEckn16bMuMJSideujSovbuZMzCgklqutV0jazkFBZ8GGPS/6Lm9tibSCboOWRSW83Nn8sCvXdCwQYCeV1z16l6mcvTHUJPlp9xdpFvCsrMQyHQ2BDTa7gKmhCCbweAVG28KIgY3Zvqf6gRU/GqkYH2FwRGCphCDmKTOOCKx32ENFJPQoHkYJT2FUJlYNrFNE76UMqySxQyTtf5UJPxLUQjRb8EdgyF8WMujtpNFs4QFl4mIlgp7PgpxBLqf4aw5iUkNjscRgR5lMoxKTYD6U1QFVMR5YdMi6g2Lci/sCnT1IyMf8lt1ZoJN7mbtQA1orZ1lpwmOSxkBM+oKA1zAaqLumcOQJlkvYW0gUbKZrAlvLtayhYP1gzfjZunVrPhbh2YQwxnGox3DHrHmTepbAwYxPauKMiZUh38udOzdoWcYhXwwQrPhbsybhobFhDGYQUdztsMmnBoh9Rwgl+ZIAAdT4egAHlCisAkOIboUSZf3OykK6FUqUF2LBujVYdaEPFs7UYR5t0rwI5OlwZPKOiH8ve85XKAkQ3AjDgcBMp0KFm/kqDCr+n6/CCM+pNMj48CVCncVloDBxMdgXwhIKBS7U6599IbYmYFgpAKnJEtptiBRKNsnnKhgZY8LtKAyvj1uokE3mFFKxbYtBmZGKjQu2YSFTsQZCGDNTcwmsEDu34k9naogkwfofVRXZIilsl2FfArEdscyQGaCQ2S7pmc4XpOGr5Syt4KZ0CqwlUvGm97ypZDIr8e4RU/MwxaiHkiDjaGOYSxJ1Cq2FHcLwd4+kjKMGEKCX/d98h9jyEyWDoYiaYh4f8hkq9CmW+aGeX8+r2JfD0tOh0PBsgkwY40ojlXt2cueYwolNrH5m0o7FQQOQuB23Vz/O5x/ZxPHQn8ZT7lHkzolbP4a/0RXD1/jwowHhZY7D2Z/vrBOHu+T3Fu5R5M7Jb51aPiURQBGlXUmqIYTvkUj0pUjK2HfxVD88M+2rEpsWjcix3LBFsjV5k19IlYntTDQ3U1uYBAsr/Escq7/I5yvAxx+IdRmrP3s9FeaOKXYKzZaGG8MISED8N7FlQ09mGWXyC9Nq384SeAeJzQCxi8Rn8913gwHLmjcPfjSTY/lyvhWWy4vx84QTAnimEeOMgwRkI2f5lBreaHh6Qorx0YD33w9gsMYGxLhxwS8D2FHvuLxz5wAfLJR7iRssW/KYWdALWHtgQyMMJnYzUKIwaME7FeKIZIKKpEwkIvikl3miYQPgUkKRLZ5tSex85SlSvjJjbncWShpW17bDmrsY3WCMyzwDeLkvceSEv4oVuIM443Kk4o3lqIefMtGbfAzlsI+F6cd42GRGnvC8RSSXi/O/oyQyR0KccVW+IA1fLWdN70RCAkb4VqZTTLWOZvrtZ7169QiUiCFkKME4S5lQZ0Plg0DyqsVwo+hDjqYxVNyZdhnPqzzDuEQ4zu3K/Z+ucO4xOxbn7KDFRdZddWvMz86bZ4+LLCFvQsD7kOf0RGQI+PajAZGJH5NSKWN0FhN0tNJkQCB51YM80Y2FTuWpfnhm5ile3Ap4akT23W1N3uQni8oE+FiWGbGLYEJ8KdwNj3CV5L7QnePbWQLXIIe5Dl/S5J85iMjCl/b4+ugffwTq1AkGQTPHVVcF+CcHvmTGnaxDh711QqXxL9SBg7bx0aYXzPcAKS+WU6EujDw/FpNYqLv7pJd5oguwbE+W+SoU+AnjzkIJ5Ld8fAxx1HdIBc+KSS2Gx3asAUcZ/ZnaCOBngV8AUfZCNZM51PbFCFVM84s4AqUb1Yyn5+bmHVmBfBu8xrWLIDiwO4vrLVPxZmConpt2x4JGakQ6sxulaUUgZRBQnSplulIbEjkChx12WNG0vwsDEU5ZyROAZ5926CS2Dxyp+0O5szz6lgD27hJ8jkA89t2nNKeIIMBXnBzf+ikiDddmRh0B+WjAxo9+iHrN7gonVm8Q2OjO9ktOkPFRo7Mo9QZIzl6+Uz03gVOJsyiNKa1GEYgVAqpTxQpZrdfHCBCxzgSt87GYKlpECOgkFhFMyV9on3jtyd8cbYEioAgoAsmHQNy+uTl6Yzk/o6PemtHtHX+GjYtuGyOpTQnZSFDSMoqAIqAIKAKKgCKgCCgCYRBQ7iwMOHpKEVAEFIE4IYDnZqzvtK56tVjfojD1q7dmYdALda3SZ4yrUOBoviKgCCgCioAioAgoAoqAIhAhAsqdRQiUFlMEFAFFIIYIYHoWa/pscdPGMWxA4apWr7rC4Rfyagl8FvJ0qp/QcZXqPaztUwQUAUVAEVAEFAFFIE4IKHcWJ6D1NoqAIqAIhEcg1vTZhAXbwguQqLNKcMQUeTwWi6btlY6rmI4rrVwRUAQUAUVAEVAEFIEihYByZ0Wqu7WxioAi4GsEYkef8UHP+aUq+bDxSnDEoVOKIH2m4yoO40pvoQgoAoqAIqAIKAKKQNFBQL+zWXT6WluqCCgCSYCAfDcg6p/dXLkhM1DKd81XgiNuXSLx8odM2xS3OybwRikwrnbt2pVAAPXWioAioAhEjoDOV5FjpSUVAUUgqRFQu7Ok7j4VXhFQBFIQgVhYn/kw2FkKEBzJNfiKiPVZMMRbqwrJ1TUqrSKgCCgCioAioAgoAoqAzxFQuzOfd5CKpwgoAkURgahbnw1Y7C+rMyXOEjKsU976TMdVQsaV3lQRUAQUAUVAEVAEFIGUR0DtzlK+i7WBioAikJQIRNH6bF31ar6CQAmOBHZHCluf6bhK4LjSWysCioAioAgoAoqAIpDaCCh3ltr9q61TBBSBJEYA+qzGTW0L3wBfOWwqwVH4Di1kDSlJn+m4KuSo0MsVAUVAEVAEFAFFQBFQBMIgoNxZGHD0lCKgCCgCCUaA72NCn/G3MHJMWLCtMJdH61oCUb3ZvbrGoooWnoWph16gLwpTg3+u1XHln75QSRQBRUARUAQUAUVAEUhVBDTeWar2rLZLEVAEUgQBiLPSjdpu+Oj7gn18k8vnb6+UcCzULCjhXeAQAMrp5zvrDPlmY1J/fLNojquJEyc6epOfJUqU2L17tzv/7LPPnjx5cqNGjRo3bmzOfvnll3Xr1j3kkENMjiYShcDq1av//fffww8/3AjAzyVLlpQpU6ZJkyZ0q8n/66+//v7776ZNm+63334mMzUSa9as+eCDD/74449QzalXr1779u2rVw/J+P/yyy/ly5c/8MADTQ3Lc46qVasy+NPT0zMyMn777TdzlgSZiX0E3DIvXbr0n3/+OeCAAzwFW7Fixfbt26UJxYoVa9CggaSXLVvGKGK0VKxY0TRw3Lhx9evXP/roo01OohJZWVkffvghg/bMM8+0Zfjpp5+Q/Ljjjvv999//++8/+xTpypUrn3zyybT3s88+o6XmAaEfP/74Y1O4WrVqzZo1sx8Tc8pvic2bNy9evDg7O5upuFy5cuHF27ZtG8ODxlJ4//33twu7h405yy0WLVrEz8MOO4zHweRrIg4IFHISCzXt79y5c9asWa1atTJN4KFgDPC6b9iwYaVK8VDvMzMzGbo8pAcffHCtWrVsSRhvyEC+yXQnZsyYwYA0ovp8vnLL78hR7swBiP5UBBQBRcCPCOC/WbrhAaufmZRf4VZuyAwk+jsBRZPgyG9PJaS8mAEmKX2G6RwMYEJwS+xNYb5EAFRqKLCaNYN2qay3WXWTEIbl+OOPlzKnnHLKwIEDUVtHjRplVlOvv/76+eef77k+l6v0b3wQYE3yzDPPpKWlPfroo3JHiM4HH3yQpQhsCFTIyy+/DPvDEvruu+/+5ptvWLdAn1H41FNPjY+E8bkLxBksT58+fULdbubMmZS58sorPQts2LDhjjvuuOyyyy688EIpwAjnYITDRrVs2XLQoEHgdsUVV9iXw05+/vnndk48026ZH374YVghnmhIv7POOuv+++9nYNgi9evXb8uWLaVKBSc9xsbw4cNJMGBeeukl2vjII4+MGDGiQoXgV4bJfOutt95++2378kSlGb1MQdyd9XPt2rWNGI8//ji9w2D+/vvv//zzT/IXLlzI3yOOOIK/8GVwZ1OmTOFa+pHmyIW7du2SHMg1aIUff/yRfqQSM+NJMb/9/eGHH/r370+vCfv57LPPhpl+4SluvvlmIQTpcYCCYZQWuYeNaSl8yvXXX88tKEPmc889B51qziY8sX59YNy4dEZ0z55ZMq6XLk2bOjWtatVAhw5ZIt5PP6V9/XX6L7+k1a+ffeKJWc2bZ0s+OdOnB5+F+vUDp52WW3jZsrQpU4KZdeoEzjora8qU9GXLpDg7SYFDDsk+5pjseO4yFGYS85z2pTHjx4+nK6Gf5CdveeZJiDPIaOY0Xh+xHvmMQEYjBDdvHx7Yvn37du/eHWF4bEmznwFpyKP9v//9D0I/twOs/1BX7rnnnhdffFG4M//PV5bs3knlzrxx0VxFQBFQBPyGgPhvbvz4+x2LV0UuWzDY2eLIi0e5JNTGda0qFE2CI8pQxqy6YPizVhWSywCNEZUyPqcF6FjUZbnqxBNP7NatGyyYXcl7773HWtqUwRKBs+vXr3/hhRfuvPNOu6SmE4vA4MGDv/vuO9Y/hgjbunUrxBkUySWXXMKKBa5n5MiR/Bw7diy0wkcffcTy442cw1yS2CZE6+5YnIUhzrgL3NCECRPct1u7di0LS5g17HTMWSCFOBswYMAZZ5wBD3XxxRd/9dVXpM36c8eOHb179+7QoYO5JJ4JT5nnz58PcQYdBm369ddfCxVoLMsQj/Eghi1VqlSxpWVpfcMNN3OcoLwAAEAASURBVJx++ulgOHv2bJoJe87QevLJJw1XbpdPYPrTTz/t1auXCMAinEPSrMwlcdddd5UsWfKBBx6Qn/x9//33zzvvPMzWMG9hfW7yr732WmY/ftKVcFKAEGsGwdy6YAmIUczubr/9dphEmjlkyJCnn346VFXPP/88BCKEIOTpQw89BNEGdeg5bOwa4NnZKYGnwNDv6quvhkjlpnaBxKbXrEm77rog7dCjxy7hWObNC+Y0a5Yt3NmTTxa76y6bfCl2ww2ZAwdmUnjGjNxrq1XLXr48Kz0n4tSQIelDhgTLt2+fBXc2dGj6qFE5J/a08+CDs996K+P443MJuD3Zsfq/wJNYqGmfV/knn3yCeaYt8ZtvvokdIkQVHPpjjz3GrgBvB7tA1NPUz9hDGHh5JmHuyCMJc3frrbded911l1566aZNmzp16sTsahvHiRhMWUxEtkhJNF/ZYtvpfQaZfULTioAioAgoAn5DQOizfEVAG7A4MYY5wm4UWcsgv42cPOUJMmgn7+MYkuclCSlgxlVC7p68N4UpYFecpXXyNiH1JMf7skePHrBCpmk45ZHGOZG/eHXhjAYNRJq9+p49e0KcsfDGuopFNctjc1VRTrCAhDS58cYbbXdOeChskaCTQAYzLsyXMJGwUXrqqacOOuggY6Rmn4pD2lPmjRs30u/i+iQkkcMFG7sPGsXBILFPYWSEBRZiY5/IIpxTGKxBqR911FFxaEvktzjnnHNwNjfjdtKkSXnyv5ARdOXll19+7LHHQhx73qt06dIMAJ/PbPQRD/K5555LE4oXL85uB6Q5PYU76m233cbjDOPZsWPHV199FUNUysydOxdiFyse3IpPOOEEIRk9h42NCTVfcMEFXEICF1cM9OyzPk9PnZouxFn//pkffZQxYEAQh+eeK/buu/swFWvXpn33XdDWjPlv9Oh9TkkD27bNevbZzP/7v0xYtt9/T+vUqfjWrT5veiDUtM/cxVCB+rcbMG3aNMYGwx5eFcaKcYWFsl0g6mmsIDGDFYNWRiP1c8c5c+aQYI+HJ5pXFVbtvM4ct+YUYxvdQyzO5GyyzFeOttg/PYadfVrTioAioAgoAn5DIHIGbV31agkRHspMWbOEIF+Ym0KfEQHNtwyaYc1IFKaZRfPa5s2bo2fj1WXiJRVNHHzVatgE6JJDDz3USEVkLmxzJBYSdkY4aXKWAEm//vorS47OnTtjV4IxGjwLK2RzVVFOYIIBhhzEvTI4YCVRp04dAxF+gvbyEjoGOya8jUz5OCc8ZcZkA7NQehmCCaMz+D4Ggy0Y3Bm2dW3atLnoootat24NnSpnodt+/vlnvBcXLFgAIYjBHb5+8E002VcP+2mnnYYRCq6LiA1thEFZu3bt7Aa60xjiHXnkkXRf27ZtCd/mbg6Lcxb2QAEs7sv9kwPjiTAEtBKRJLFu3ToMdqBCQAOTsZtuuundd98VN1tciVu0aEFhGojLmzhseg4bu41PPPEE/AW82yuvvIK9EryGfdbn6UcfDU5oGJo98kjmGWdk3X575uDBQfpswADbEi3YCBw/+Tt7dho8WvD3vkfLltnXXpv52GOZ8+ZlEPCNMu+95/ep0nPap1m8tZnZhK6SVjIeMCE3EcckXIM7VuC+kBT213333YcZo9QydepUEjySOFnjdIxGwUwFs4bBbNmyZR13wlQNgs9hF58s85WjLfZPv48nW1ZNKwKKgCKgCBgEImHQgg6bcTyE3YB/UXYjjqhH+VY+ZNAYTjB6ysYWsqclSAqmDYWsRy+PHQLQPUKcYWlyzTXXYEvVpUsX8UZkYx9XL9xn8OPDVsXNI8ROqqSrGeMdQ5whPGY4Ys4jDcEDDvpJlp1+axqGY/PmzYNVYZEMj2aLRw7f/cB6lOUr5or49q5aFYzewLIWmyyYcQg1WBhGCOOEuFf8ZeH97bff2pUkMA1/hFUU5mbIIFIJPRRKJNoCXwZrRgEoY/4S+8wUxk8Tn03W7VDJWLXwmJhTPkzgiIplGdwWpBidhfs8QkJ38hdYoEqhIbDCI5zfmDFjJBOjMwYAtAVcGGaVkTcKMpHAZ5SHqYz8qniWbNGixAknBP/dcUcuL5adjaldkJG4+OK95rRdugTT2I5t3Jgr3YknBr0vR45Mxzhv4sRg+VNP3Vs+t9Ce/6pXz8Yoil+LFnlQbHtK+eJ/z2nfUzL2Ucg3YcWY2fiJMbJn4WhlYuMGHc9dhg4dyuSJWSuDlsEp5r0QZHjHQ/vijGnfEa4fDpf9AEfQxmSZr+y2ONJB0PVQBBQBRUARSFIEYND4CicR0DzjoE1YsC1QKh5GOrAbGtcsSYeQp9hBF86cIGicTeCXBHRcefZOwTLZFiYUzi233OJzG42CtS5lroIiGTZsGBQn/A7eLoSVkaURHAGeazSThTScCOSa20cmZUAoZENY6REGy1RCGvJCfvKJNyJnQT6as75K8J1Qon0hMFGEIFnwzzXi4dPHIT+vuuoqBgnmZjCAfExTPhrAapYY3lBmuIAR5RALJogqTtl2K6a2hCQwtIQRxsAKuz94NFn5h5KE8EmwxoTJ55AymKoJlcZPggCKNRb+qjirhqrEP/kQZESqojsgxInvhsEOQf0RDzMcnDFFTmhxrIrg1Mgh5h2UBFHP6Ee+AxN5QzA344BjhV3F1s+M/MhriHXJBQucZBYfj80Jy8kXMPbevFKl3Dhl//6bW75Jk2xYM3w2CX8Gg0bRrl2DnwjYe82+qapVgzWYy/c96a9f7mnfUz7ZEhDWlQIyy8Whi/lCMfMSzyNx+o455hhuDaHGX15GTLaMT2yiCTrZtWtXIzajlwB/K3MOBIbSrVGjBiWTaL4ybXEklDtzAKI/FQFFQBFIPgQMg4bohkQjc/72mH++WtmN5BsuEUsMfUbZ5geVnv3njjgzaDquIu6lfBQktBYxdwg+Heud6nzIpEX3RYBQXLhx4XlnIqNDMeCkY4gGMToQA4R9L9VfuQiwSMPqQTgIsli2GTDxgANM20/WJ6jR7ywzJa4261IE5isHtmwwLwSxwo3LzjRpxgOhhbDDIsobXp8sUFlm41qF+6opk/AETBDgY6iCM7L5bmYoqWgFzYEllAIwnpBodCudSw7eqQ6f1lD1+CQfc0KoT2KrIw9OpljuyJcciFeFUaQ81LjZkg9xhnkaFmcY+GBpGKH8+IHiHAfnziDhEvlL78PGRlhD3Ipt2pT7rQAClvXsGSQicGklPBn+lXx5s0GDXMpsyZJcyqxOHXJy0927Z373XfE77ii+cmVau3ZZ1auHk/rXX4NX8cWAcIX8cc497XvKhQ0XTxAbABKVn4CAFLNDPXpeVchMJiJ2cQiyBvFteDqc4qlWuDwSvJ4cryROYZjGwVlIN75cwbWG/U+K+SoUbiHJ2lAXaL4ioAgoAoqAPxHIYdBq8iUB+ZjA/JKxIs7gNfiHDx3umepJ58/BEEWp6Gtx5KSvYxoNTcdVFHstVFXYa7DKkgj0ocpofqIQYHsfPzVMh/iWIi5XHEQ9QxjMbfDZhDjAVZPlMU5qyUUcxBlPsUjCYwhTLGyvWL/JNxkRA1stsZuIs0h53g6yA8MNgsfTxbNmzfriiy+OPvporsLY6p133iHBYwuzxjoWYxO+skeO/TUAhg2nMOkin8HDQMKShQEDfUaOTw5W/qzAie0Fd4mXYhipYJFAA9s0xrkcBEeDVxKXzzAX+vYUX6vAEod2wZDi+4aPrfAOGJq99tpr+OrCg2BLCFkGlTZw4ED8NxmoMgmED2jFRznxZsX+Dg6FmglExbDnY7zMEvkyWIsbdDgamn/mph06BBmugQOL5Ux4RMQLPPRQ0KPzzDOz9tiMBst27BgsNmdOkBSzHTyD5/Y9Jk9Ol0hnp50W0q9z3ysS9ivUtO8pEOQ4EwIRMHHZZuQQENCOxO95SSEzeePAemMHzbiSAcncIp+1hdBnOoLXhuk+6aSTuJGZr9j+wcpSDiTEbM0QZxRLivkqFG5BulcPRUARUAQUgVRCQMzQzmSdgMPdN8FYEbOX75y1PBhco8AHvAbX4pjJX0kXuCq9MEkREG4LHo2xhCUardBxlfCudAQTccvjKMDXsvDchJ1xl9SchCBgd9DChQuRQRgQEQbK4O677ya+FSslPPLI5ONrgwYNEpeZhAjs/5uyVMMI6+GHH4ZwRFpoCGOuhQ2aSfuqIfg34YeLqZFIRbB/8U+EFmG9igsnRh8k5KN72J4QMwuOTApzIbQa61g8fMlhWTty5EicfImbxlDxSTNlnBNW/KWXXsLpOJRUUowvZsCUyfpcSmLYgl0VS27xY7WfmlBV+SofP0oYMdhApIL+6NWrl4gH4cUnC2HN+Ikx0bXXXovLLXY6UA8cUobxHOozoxSgr7HiIVwadnnQc9yITKjJp59+2pgFST1+/nvffZl8UhNnzMaNCYWWPW9eGpZlCCxfDDCS16iRffbZWfBi5PBJTbfD5uDBxd5+O51QgfIlAfg1vh5AYcgmzP34/iwevphdv/jii8wD8o1j/FthMHnizF3inAg17YsYjqGOnHBtfHaZszj8MtHFWlpYbAakzXyNHj0aUp4piLvLKCXgoBQw85VDKrsVSTFfOeS3f6Y5TOzsc+HT9DQ0ZPgyelYRUAQUgfgj4J6d3Dnxl8oPdzSUB8LAeohIDk7N5sWa1y2Fvx7F7Ew/NCQhMrhHkTsnIYIl/KbuceUYVEhoDyEdV3aXuUcROZha2GU0rQiAANHTMUric3uph8a9995rwlqFat3tt9+Ou3Gos+58jCNYEkMWG3dXdxm/5bCqx7gDrsSEA3dIyADYtWsXjbLzMWUi32HJRT14BYaqx74clLBasnNCpQmjhsmqY/Wn81UouNz5dBMkgiG+cV/lg5jY9dAF8J4m331h5Dl8u5NbiENo5FfFoeTixWlNm5bgRtu25fpsjhmTfumlxZs1y54+fTf5K1ak9e1bTHgxfrZokf3yyxnEOCP9xhvpffoU79076/nnM0aMCHp6QooNG5bxwQfpnTsX79Aha/TojB49io8atdedjss7dcrq1w9/WCoIiBss/DLvVnqBoJ+4AwuDSfRD2Cg+shEsV4gjFpNYKHE854FQhWOaH/k8Y8RI7HxlxChwQu3OCgydXqgIKAKKQJIhIHZDSSa0iut7BHRc+b6LVMBUQICozByp0BJXG+rVq4d1g5iBuE4GMzhLGc9ToTIxujGWWaHK+C0fmi985HsYFg6H2J4xrSQUvaOk/kwsAu6+E3lgS6MlmG+59UaNsnfu3GU3ky9pdumyN4e4ZhMnZvD10T//TKtdO5sgaObo1SurV6/ckt26ZXXrlptu3z7L1AmVlmO9Zy7aJ8F3Tjkki17gSxTmdLRsM2MxiRkhHYngLOCaBxxl4vOzAPNMss9Xyp3FZ2jpXRQBRUARUAQUAUVAEVAEFAFFwIkATnx4Tk2YMMF5Ys9v1qVhHP32lNL/FYFkQqBx48a2L1syiR4zWfnoKCxbzKqPYcU6icUQXD9VrdyZn3pDZVEEFAFFQBFQBBQBRUARUASKEgJEfSIqWVFqsbZVEQh+DZNDgUgNBHQSS41+zLMVex2D8yyqBRQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgSKFgHJnRaq7tbGKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgC+UBAubN8gKVFFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBIoUAsqdFanu1sYqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI5AMB5c7yAZYWVQQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEShSCOh3NotUd2tjFQFFQBFQBBQBRcDvCJQsWdLvIqp8ioAioAjkIKDzlQ4ERUARKCIIqN1ZEelobaYioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAvhFQ7izfkOkFioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAkUEAeXOikhHazMVAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAE8o2Acmf5hkwvUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAESgiCCh3VkQ6WpupCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAikG8E9Dub+YZML1AEFAFFQBFQBBQBRSBRCGRkZHzwwQfm7jVq1GjWrBmfuvvpp58WL15s8k2iXbt2H330kfkpicMPP7xcuXLffffdSSedVL16dcncuXPnxx9/fOqpp1aqVMlRXn/GCIFVq1atXbu2adOmpv5169b9+uuvpUuXbtKkCX/Jz87Otnu2fPnytWrVMuVTIAEI48eP/+OPP0K1pV69ehdccEHNmjVDFVi0aBGw1K5d2xT4M+eoWrVq48aN09P3sRX4O+fgqTGFE5Jwy7xkyRJEO+CAAxo1ahRKJPdVS5cuBcAjjjiiYsWK5qqxY8fWr1//2GOPNTmJSjjmKxGD+UfauGPHjhkzZjB37d69G4HPOuusMmXKUIYWzZw505aZKY6p7KuvvtqwYYPkFytWjFZzlV3Mn+nffvuNBtqyMaRLlSpl57jTIHPUUUeVLVvWPuUeAObstm3bOMuNmDr2339/k6+JOCBQ+EmM9y9v5FNOOQVpeWqWLVtmi80k1rBhQ3K2b9++YMECChx66KFVqlSxy8QonZmZySuJF1ODBg3sVw8z9vLly9FAkCTMradNm8aArFy5spTx+XwVpiFySrmzPCHSAoqAIqAIKAKKgCKgCPgFgV27dj3yyCOo0SijpL///ntWm4MGDYKC+fzzz5Hyr7/+WrlyZYsWLUTiM844w5Q3bWA9xlXkn3jiic8995zks/QiB65BuTMDVEwTrEmefPLJtLQ0uk9uxGr5hhtuOPDAA9evXw+5+eabb8Js/vPPP927dzdrj9NPP/2OO+6IqWBxrhzi7Oijj+7Tp0+o+0KjUCZUAciUW2655YorrujatavU8L+cg2cENooR/vTTT8OzyCkeGQrDLCSWO3PL/MADD3z44Yd169ZlOXrOOec89NBDDAwHIO6rILtfeOGFk08+ecCAAWPGjKlQoQKXTJo06Y033hg1apTj8oT8tOcrIwDzD9zZ6tWr6QvaC1/PaH8x5xg6dCjjH7KJuei4444rXjx3rcp8BXfG2X///ffggw+mKuYrWs189dZbbznoUXMjnySuvfba//77zxaGzpJW2Jl2Gj6RqWDkyJHCmMgp9wAwl8Bu9OvXT+DasmULE0vz5s3NWR8lJk3iFRWgB884I1eqRYsC06YF09CgbdrkZi5dGvjqq2C6Tp3AOecETBk5XaNG4PjjA6HJ9NxK4vhfIScxJH3vvfeeeuqpOXPmkOYNzpxvi8/4//rrr9kRuPrqqyWfEfXMM8/w7NvFop7evHkz4xAij6eS6ZT05Zdfzl0ee+wxBObp++WXX1q3bs0rzMyxtgzoJLytmI/l/eX/+coW3jOt3JknLJqpCCgCioAioAgoAoqAfxHo27evKM0Ybtx4441osYMHD27fvj0Sjx49muX0kCFDRHpWmCRMedMkdoNJQ9agzp577rkmXxPxQYC1x7fffssaCS5M7oh92cMPP3zhhRfeeeed9BpkEAwICw94BCiVcePGxUew+N8F+4VQvJgI07JlywkTJrgFW7NmDbzY9OnTZZBLASBlqfboo49ixMRSEzy/+OIL0nL2lVdeoUACrXI8Zf7xxx8hznhysaKaMmVK//796f1DDjnENNnzKs7y4P/f//0f/Pjvv/+O0QrNXLFiBUMLQjyBbTRim4R7/uEUcsKs0bOyrr7++ut79+4NfQDvIxcyp7lbAbHIAl4KsJi/5JJLeI5gSM29fJj49NNPjVS0GmYcuzOT40jMnj377bffZma280MNAFMGGgXzVSgMKFd4WGCEdzNn/ZLgZXTRRYHNmwPlywfWrQuUKBEUbPr0QO/ewQQW0P/8ExAr0eefD8imTocOQe7MlAmW23N8+GGgXbs9PxL8f4EnMeTmwYfvxprMtOGggw4SEo0cXvE9e/bE8JY0hDjmt7wXIKp4Tbz88sux5s4geeG4MXWHl+cdxLzaoUMHXknMPMOGDcOAVJ5BJmGxmDNNIMGgNdtCkp8s85XdCkd6Hxtmxzn9qQgoAoqAIqAIKAKKgCLgZwRw6zvhhBNYNhdMSDiLgQMHOmwiClaVXpUvBPDG6tWrl73mx1+PxUbnzp2pByuDjh07itsae/74ptFHFMjKysrXXVK7sAx+bJeM3zHthYcCPegk0ixBWdHNnTtXcJg3bx52EBgBJRAWT5k3btzIilTcD/FvQjxIJVtIz6soAAsjxBPeqVgb4a939913X3bZZdjx2Zf7MI3rFvQ9LKExqKTX4AHr1KkT+SDHJourcJfzYQM9RWKj4ptvvrn//vthuCZPnkx7MZ1jiLJ7AQ+CISpX4YjXpk2b22+/3a4h1AAwZWDcoFegVDDBg2uG0TCnfJSYODFInHHw1+ITcyVcsybw7bfBNLOcp9XkkUcG4MvYRejSJVisV6/g3+Q/IFKZ7bt16+bZFOgnChij2mrVqmFdyPjBWTIO5paYlbVt21YMWhlXSMjjxujC4gzijJ88g0y/7meQp/jBBx9ksjUPOIWTd74yXaN2ZwYKTSgCioAioAgoAoqAIpBMCKCeosWyZX3mmWeGlxsWxibIhFngEvwvYBOw78AaInwNeja6CIitH3QYFkNSMwFlSBDuSn7iI4OFFGm6GG8dVt2kyXz88cdZt0iZIv4XoyQoJ0CwrdLgHzHTM6tKgqDhFUgZnI/uuecexjnGaAnEzVNm2BMOPPIwOsO4g7Sjiz2vohVEIMK5j0UsjGGXLl0wuCtRogRWKpiKEAAOXimBLbVv7Z5/JJyT8ZwV2ghCmcP03SeffAJnJPVQUmItbd26lYU6RprY42DPhcmhT50T7fbnpDdt2sRGxX333SchzCBMea6ZB1577TUc7TEWo++uvPJKKFQOwlrxpJs6Qg0AU4CqJIAaLwWmdJ9i8uabQYFxJp09OzBsmIfV2NixAUwIZ83CZsk0bW+iatXcS1q3DowZEyzDnBmXmF97ZYhB6nj8TwPQht+OGDHCUT3P9cSJE02EU55x2CgcuqHPeO8znBzlo/4Tbtc8j0QbpH7YbaYdMXLn588//8yUa3sWiwwYrHEhfC6ksJEqWeYrI7A7odyZGxPNUQQUAUVAEVAEFAFFwNcI3HTTTUY+ogJdfPHF5qdnAqsE/CzMKWPuxGqNtRyGKnhCHcmuvh6JQ4Dwz9zcRI0xCRiQSy+9FJaTYNKwP3fddVcK+28WHn64A7PYozZGuPAyEMQMcqiZxHJnYRqI4RiOWsTzwlIDHs221wh1FQtpXLdw8YM9Z/y8++67pK+55hoM0HAiY2ltnvRQNcQn3z3/EJ+RBkpPQSoZz2XkEZqYxGeffWbinWFCKNwZbl8cRuyrrrqKxbz56efEO++8g8Od3VIebewE4byI/kZYQ5zg4M4K1gThSRk2dDpov/766wWrJ4ZXQWFPnhysH4aIsPeQXzbzddJJQcfM4cMDgwfDhQeL4cz+5ZfBhDmwR8Mek39mAOQE+DPnUy+B/zL2aGZDBY9sKGN2U/gwCKwx+y6xfsCFvGZuIfgmLBhUmow0eTBh0zCi7NSp0zHHHGODj7JB1Ai+WOII2pgs85XdFkdauTMHIPpTEVAEFAFFQBFQBBQBvyNw8803i8kG60+cOPIUlwhBoQKjHHbYYQRXIjL3q6++mmc9WiB2CMDyUDkE2X777UeCNZLcC7JMEixj8LGFRIN3iKTT5aqi9pdFnYGOtmO/wycaJa4fUcBw22RdB1lDgvWeY2mXWKwwkSPgHcJfdNFFGJvwVOYpD3YcBEuiGM5QXAUPjpEdFnbEReLruvBosV5a5ymhFHDPPxBhWMKy/mcpzocChA6G78ONEfZTroI4wN7KcQuohOuuu45M3FrhCglgh20L/muOYn77CftAp/D82sQu3We+tkkaQHj8TU5+m4DRIgOAqGfcyP4eYn7riVV5eDEOyEEC+WEo/dlnQfrMOFDjAIjLKj6bwqBRkg0hB3c2ZUqglPVx0gEDAns+JRErmRNaL37NWHWZ+R9ZIKQY7bz9SWN0ySk+oGF/YDcW8mIiyp4NjypmrebTvUyh2PAS5oxtPERy3JdxyEYF1pQcTGh8yALWmDGZLPOVozn2T413ZqOhaUVAEVAEFAFFQBFQBJIAAaww+DA8R1Q4FGw34GsIu54ELU9dEWX9b8wDSeC6xcKDJQo2KdJuMaFKXQyi0DIWaUAHByF1sWzD0RWTLsAknjpWOdA0GG6QMBxNFO5auCoIaWTi38OQEvIsX8ZxODDixtWqVSt8sTFbY4EKQQMTh+dm4eSK4dVQPNQuzrZIi5stByttMmlOmBvDjQIRB7SahIgS988wl/jhFFwt7AOBzGxhcMo2TzSdBY1YYOIMJoXgcXxaBHrFj8QZfOhrrwXbPnNmoGPHwLJlwbRjtybnA46B/v2DH+Lkuzd8OsBx8IUBvqXLP8ijDz4I3Huv43yK/cT3lrnLuG8zVJjZMFGUZgqNJX79sWs4ExE+4Hy2Gx9MQ5xBW2PcCn2GBShGZ+4dCJ7oH374gTmWg2EPiY9HqhEyGecrI7zanRkoNKEIKAKKgCKgCOyDAKYKRCAyYR32Oac/FIGCIpCQcUVcLUPKILhjp5qFKGF9YdAK2ia9LgoIEHGZZRJ2NPQFFih4eHXv3p2uwQOL0EiYGGC6gtcMxEpUCNMoSOzLKsQeEwaBTzFgFkHAIJZ5RBQyn9rEpAubLHD2j/h8CwLzMYLHw3/Nnz8fX0U8oRAPmg8jsh49eoQXlWUtFluYjlKMjwbAJUEL4s8FGxX+wriddc8/jOF+/frxOUi4MBxO8VDG/4uf+RKJwGHwTdjc5euqhBRetGgRNsL2Ry0Qg8eccUj/EjGKjycSlD2/skEH42vfunVrLBb5SimDn6qoBFQj8fnN7+0KXp7PhkrQgBUrAvyTY968wMKFe+vEfAmLQkKhcXgGzidQ2rvv7i2f6ilCGRq6irbyjGAqy+dT+QIvfsqEyWPwH3zwwTGFgTcONDckNe7AciNmGOKvwdnB+MOCyXjjYwLQvma+euutt4xUTLwMTrsh/p+vjPDuhHJnbkw0RxFQBBQBRUARCCLw/vvvE5ZYuTMdDdFFICrjynb8iUQ8x6fiWWWxXLcv5MN8+HzhA2VnajrWCDh27Ikmg/HIeeedx31ZDxOHjgQhwwmKJGGSiG3HTn6spUrq+qEMGO1EXpfA2wx1CcVtGgXm+X18zLUxSuDfhH8WvSz1Y+iBKxZp6CTMkcJzZ9jQ0d7hw4eLqy9f3RXWlY0ffB5jJHB+q3XPP/QL7apUqdILL7wgZ+GVcAqTSI7SQZ7d5HhkMMzhK8MCV36limd5qExjQGTuS5NhxmHNyOF579u3rzkVYYK+hteAQBGXOgCUC3kKsESLsJJ4FJOvBNx6a2DQoNzbdeoUGD8+GOCsQYPcnJo1A7CHkyYFfzL+HQ6buYVS9j/3aMdm1jF3QalzsKcCCox8mFPos5giwvYDQ0u+bCM3Gj9+/Pfff0+m7aqJVKjKoeYr+5lNivkqDKRp4c1iw1y5cOFCh9YVprCeUgQUAUUgbgi4Zyd3TtyE0RslNQJ8JB7uDM2eVrhHkTsnqRurwscNAR1XcYM6SW/EDj97+EKFmCZ4ZpqzSZ3gibA/KejZlkjK2BdieIVRD/aVGODY+X5OY1eICRWsh/lMRCTS4udITDfHd+6oB5fGSOqBm4vQQAkLOMzHHKu/qLwHMV1hdV3UrCmxNORTiUOHDmWgwoBgYRpJdydrmbS0oOTYlDVrltsEgp3hfYlj5sMPB3r3DgY+e+mlwDvvBNgwwOgMTu3994PenRijjRsX9Pe8+urg1wO++MKfCEQyQUVSJpLWoZfiNQnvHElhv5VJ7HxVeDSS5nVS+KZqDYqAIqAIKAKKgCKgCCgCioDPEXB41Iq0npk+b0iE4tWrV2/mzJktW7YMVZ6zlAl11jMfIw58izxP+TYTmq8A/BFxzdwtSq51tcOT0d2c1M6JkLtMbhDcMey6dCGyXW6j4MXkuPTSAP/k6NBhbwHiCfg7pEAsJrFcHFz/4arM4cpOjoxkn6+UO0uOcaZSKgKKgCKgCMQHAWJR4VIn92JzLz431bukPAI6rlK+i7WBBUYA3x/8gCRsvGclrEtt/yDPMpqpCCQXAnzg2PZlSy7hVVoHAjqJOQBJ1Z/KnaVqz2q7FAFFYC8C7D/jvuEOJbC3hKYUgT0IECPGTZl5jh8dV3sw0//zRiDycZV3XVpCEUgtBPgyZp8+fVKrTdoaRSAPBAjBzpFHIT2dJAjoJJYkHVVYMZU7KyyCer0ioAj4H4Fy5coRKaZIWMX7vzN8LyHhTjkcYjJ+GEWOTB1XDkD0ZxgEIh9XYSrRU4qAIqAIKAKKgCKgCCgCCUEgPSF31ZsqAoqAIhBPBAggsmrVKqKxYj0Uz/vqvVIAAQk4zfhxh6HRcZUC/ZuoJoQZV4kSSe+rCCgCioAioAgoAoqAIhAKAbU7C4WM5isCikDqIMDXyviA99q1a2FAlD5LnX6NS0twzMS+jPHj+OYdN9dxFZceSM2bhBlXqdlgbZUioAgoAoqAIqAIKALJjIByZ8nceyq7IqAIRIwANEfdunUjLq4FFYGIENBxFRFMWkgRUAQUAUVAEVAEFAFFQBFIZgTUZzOZe09lVwQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEYglAmp3Fkt0tW5FQBFQBBQBRUARUATyicCuXbvyeYUWVwQUAUUgMQjofJUY3PWuioAiEHcE1O4s7pDrDRUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUASSBAHlzpKko1RMRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgbgjoD6bcYdcb6gIKAKJQGD79u18Z3PLli36nc1EwJ/E95TvIVarVs39nU1apeMqibs2oaKHH1cJFU1vrggoAoqAIqAIKAKKgCLgREC5Myci+lsRUARSDwEIjt9//71WrVrVq1dX7iz1+jemLYLj2LFjB+Pn4IMPdtBnOq5iinxqVx5mXKV2w7V1ioAioAgoAoqAIqAIJCMC6rOZjL2mMisCikD+EMDiDOKsZMmSSpzlDzgtHQgwZhg5jB9GkQMPHVcOQPRn5AiEGVeRV6IlFQFFQBFQBBQBRUARUATig4DancUHZ72LIqAIJBIBXDXV4iyRHZD89y5duvTff//taIeOKwcg+jO/CHiOqzwrycjI+Pjjjx3FmjRpcsghh5CJmeS33377888/7969G2PJM844o0yZMuSvXr36u+++s68qUaJE27Ztp06dunHjRsnHGu7www/nKruYpuOMAD3122+/lStXjj4tVqyYffeZM2ceeeSRZcuWtTNTIL1mzZoPPvjgjz/+CNWWevXqtW/fnve4ZwFMgBcuXMhz0ahRoypVqkiZFStWkC9pYGzQoIHntQnM/OWXX8qXL3/ggQcaGZbnHFWrVqUhPIwmnwSjwjynkg8aFStWJL1s2TLOMlrkp5wdN25c/fr1jz76aPmZ2L9sFUyfPn3+/Pk7d+6kK88++2wZwz/99BPCO2Q7//zzycnOzp49ezYF1q9ff9BBB5155pn777+/KUkm6NndLafA4d9//2USMyVJzJ07l+HBXtcBBxzAfFijRg37bHzSmK4zIdv3olGlSpWyc9xpz+fdPWzMhdu2beMsD0Ljxo1tuEwBTcQOgUJOYgjG0zFr1qxWrVo5hPwn5zj22GPtfM+xYReIetoeeJs3b0Yo+xa8sNhjtnN46hctWvTff/8x1OvWrWtO+X++MqJ6JpQ784RFMxUBRSClEGAG50ipJmlj4ouA5xDyzIyvXHq35EagYENo165dAwcOhCmrXLmyaT8EGTksHe+44w5Yg1NOOQVd9pWc49VXX0WpZfHGVejfxYvn6n74IMOdvfXWW+vWrWNBS1Vbt2599NFHDz300Ndff92xdDc30kRMERg9evQzzzxDV0LWs+R47rnnDFMGH3rLLbfQX0KSxlSMOFcOcQbL06dPn1D3ZaFImSuvvNJdALrJXAil8sQTT5x44okU69evH9sbQk9AKg0fPtx9bQJzNmzYwKN62WWXXXjhhSIGDx0Hnbt06dKWLVsOGjTIZk55ih2MOZdDM02ePPmll16i/COPPDJixIgKFSpQG5mMk7fffjuBDTS3hgW77777IPShzJhhXnzxReR84403IHe++eYb5Dz++ONNYRI0CtLzoYcemjJlyqmnnsrMRgM5Xn75ZRn577zzDpUwp/GMdO/evW/fvnJ5ZmYmz05aWhqTmOTIVPnJJ5+cfPLJtWvX/vzzz4cMGWJGiH3TWKevv/56Bqd9F0CQWdfOtNOez7t72JhLFi9efPPNN7MjQg4j//HHHz/uuOPMWf8k0idPDvz1V+Dgg7NOP12kSvvll7Tp04Pp+vWzTjstN3PZsrQpU4LpOnWyzjprbxk5XaNGdvPm2YmgQeX+7r+FmcSktvHjxzPhz5gxw64cyvX222/nYbG5M8+xYV8V9bRj4H399ddMOPZdzjrrrAceeMDksId30003MZWxE8AU3bNnz6uvvpqz/p+vTBNCJZQ7C4WM5isCioAioAgoAoqAIuBTBK699lrhCGz5Bg8ezHIR/kVoNTiF66677tlnn2UdJcUee+wxTF3sS0ij9VJMMlF2L7/8cna/TzjhBEcx/RlrBNjJZ/F///33wzKw0r700ktZTfEXwxlW2vBHsRYgUfVjcWb4L08Z4IYmTJjgeYrRXrNmTVhimKZ7770XjonnAu5AzECMGZrntQnJxACKFTK9iZWQEQAaCOJswIABGEax1Lz44ou/+uor0qbAPTmH/IQDgkg6LYdlYITccMMNp59+OhhiqMUlf/31F/PAk08+6X7STW3xTEDlf/nll5BWxxxzDPeV1tGETp068ROmnjHvkGfUqFFMQXB/Yi3Yv39/ngImMToX4xeIM6i0o446at68eXBSYEUv02TsaoERus3U9t5770GccZVYosHiQZxB5E2aNEk4JlMy1omPPvrI3AJRebphxk2OI+H5vHsOG/vC559//ogjjoA3hD2EeWTah5e0C/givW1b8e7dA5s3B8qX34XhUg7TlzZjRvGcF1B2tWpZy5cHciwu04cMKTZkCDJntW8f5M72lLFbkTFhQlbbtnZOAtOFmcRkoGJl6Zaf0cuoNlaEnmPDfVUUczwHXrucQ+7CE92rV6+LLrrIvikMIHamEydOhDujdUxHPKfMSP6fr+xWeKb3sQf2LKGZioAioAgoAgVGAF0NSxBbRS5wVZFfiDELywYOtmEjvyrqJdmnWrlyJSv5qNesFSYcAZ+MsYTj4CsBcIVAYWWz19ijYVl24403YnBBf0UoKpYdGHqsWrUqwvJaLIoIQFxSG+5p/K1UqdI555yDtQ5pOhRGAAYhivdKpapYnmFNCWWAO57YS2JlyTDmgLhx+MolvOGYwmFpxYNpu6CyzkRaKDDEw78JO6nvv//eU1SW6LA/2JAKNcZ7Vp53QMCqi8bCvXbr1g1qyfPy+Gfi3sVNsUORW9M6KMIwLrToLfAFV1xxhSmDbzuXwObTOmi41q1b0zo8E7HEGTNmDLhRc9OmTXv06AHHahrIpAcdicGLceFkhFAtq378Ok2xOCfg8nBfvfvuuxHms88+u+2222C7YDw7duxIq0Vn83zePYeNLTysSocOHWCQeQTASiYTu4Af0ukffhgkzjg2b07//HOHSGlr16ZJYIGsrPTRox1n+ZndtCl8Wcbo0Vk51prFckyZ3MWSLoeHAnNL2CWH5EwCDHix2JJTnmPDcVV0f4YfePiZMobZezvssMMc9+XBFC9ye6Lz/3zlaIX7p9qduTHRHEVAEVAEooAArBkm3GyQCnGGYnfXXXfZkU0Kcw/0SLZt8Wg499xzCWxhb9uyK4vhNPtU1M/qi73q5s2b5/deH374IQ4jL7zwArF18rzWXXjTpk3IMGfOHLkWg4JLLrkErRdXCwKUnHTSSZ51Ahev4c6dO3ue1Uz/IOA5xuBJP/30U1wFzYInlMDvvvsuNghDhw7Ns2SoGjQfBDCyIJKIgYJVN0Gy+Gn8dGQZxszDYRww8ZhgISpXUZIAQKRZckPxM2WxvmViYcpq1qyZqVkTcUNA3DOxSRFrKfY/sCHi7kSg48CRDauZuAmTLDfC5xHjIwwqoc8Y3ljcIDncGcO4TZs20gox5fNJizAhIXYbwmCUYUTCuKNOnTrmOYXvxprDnLUTvO6x2DJ+uwwMHLhYuC5YsAAoYIuwqMJ0lCeaGzm+DW3XE7c0L30UCdyNsQdkYsE5l8nKtJTRjgphhKHh1apV46djCiKaGwf5zHKwhFjdwjai4aBdnHfeeeRDNPMXy024RRIcQMoYMMa5hpbCTM/cXUrG7S80Io8wqqA86WhK06ZNw8gUK0Ke9Icffpi+g93Ledydz7vnsLElZ+oWD2VIQwgX8yKwyyQ8nZ5jCpfdrFnanDnpb7/tthpLHzcus2XLtNmz4dHc0mZXqSKXZLVqVfK994Jl1q0L7Alx6C6fLDmipWNricWlkRnj2QcffBBt354KPMeGuSQWifADD3WOm0LaOm4Ne8schRMxUxNGl7jbC9fv//nK0RD3T+XO3JhojiKgCCgCUUCA3UWUJLRhjJlZAuFXws4M78VY67KsHNjYYamApvjUU0+hsOL6YYdNiaRtaLp33nknwkdS2F2GUCYQZ9dccw3hPKBIcK/AWUPSrBlCcWcEcwEo5c7cePotx3OMQdfS16x8lBGLT3/xiAlZJrfD4AJjChaTsixkVSaLSTlr/N2++OILE++MvW7hznCj4DBiY6lBP5qfmogbArADbNFjkwL3AUNNZ4lNTdwESMYbQTqw6YKVDTYOcCVQJzwL0AdQEnBqLPx4H7EExVIJ107fNpD3tU3o8JAK1+MQmNU1B1tiJh+bFDxVieaG7RIbVGPHjqW9NJz0n3/+CR2TcOdr+CD24SCJcFNFPPxVmXkQTOg/XvownqY5SCuunaFGPlwb1rXQT/QpDwhGW9TDDqKpwSQI40iaASA5TGvGFAs+wvaHNZfEOoHnNaaRtlcpzSRuHZwXrYD4Y38R7qxgYghiqH9ontigwccVrJ7YXZW2YkX6Z59Rf8Zbb5Vo0iT9vfds5iv7xBNxzEwfOTJz4MD0HFo569RT0yXkmZEJA2pcGXbtSjcvrJwAf+Z8KiVQ4AmngDZuc2e+aiCDjWHGvoV7iYE2wlmMQ4m7yjPL3io7c+zb+X++yhNh5c7yhEgLKAKKgCJQEARee+01LiMChfhT8P5Ao0VHJMQmKjLKDXtKsGlswGKnTWGs91GX2b0hrgf7h6hQeO6gEKMgolER+INNVPZjr7rqKsJ7hxKIBQMuKqyfMXAjjAhaI9eyrsCNC7cFnERYZqB0ckfuZW4BmbVkyRLe02znQrph3YA528iRI/lKFyyJQ1r8HRBeIu+wRIFicwvDjjeZLMvxTGG5It4ovXv3JhNll7cprXBUwm65xHpg0Yi/CSom2i10GwZK2NcQtgOrtDfffBMQyGTf2LEp7ZZBc2KEgOcYY53PWog7skbCiAka1DHe4HEY/yycWN9KnBcGKmMbLrVLly50PbFs6Heb7omR/ClTLRGyjUmFNIonDiUV+oBFFBOObAizfsalApsyKYM9qTsKEhOCPJ6st/GBYjrCfyQhX6NLmd4pWENYWjDjMRmyIIEOoFOwHylYVUXnKrDivcmXAWgyr078GXlF4gHHISDwuhk2bBjGWX7mziCYjFcjYpMuWbKkuxMxVeMzfLypzSle0/IZBFaqBM7n/c7Uin0TOgNRvTiVcO6MVTShGyCMhDPCJBA1g/e7RONCUUFNMs0hIZwXbuNQbCYf+gCdhA05dh9btGghtma8MgCEXQRP7ky6GxVIdgGZ+pjfqBASzcyHpv44JNB8mF15qG2SFDMcMRZDALadmMBR2ExOfqVCvQRYop7R+7Ivkt8aYloeXoz6s8C/QYOsNm3Sv/gC+iwrRzMkPxu7wsxMfDaFQQuW7NrVwZ2lf/11SSteZ+b99wf2fPomppLHv3J0bDbgCROG2yaPDJthJLDZxNU3/sKEuiOvJ5QNhx4ihdmNw7iSyZmZDeIMNY/4jMxd/p+vQjXW5Gu8MwOFJhQBRUARiBoC8GKwPOwXCXFGvfJ2ISARbw72jVEEUfjYIUfngxLCKo3NRqJ4CInGa5JTBL/gZQNVAauFfon1Fq8i4r+GUfvQyfCORP1CP2P9gMUZMvBug9oQPgsVlpr51L19C2QgE4oKHZdLUOCQnxx0OLe0SMhLHaIEJo4E5d2owa+RidsmLjP/+9//sKRACUZhJRPDCrRedyXYCPDZLERFORbZJFAam9JIwkKC8LrYwvAmZkXBxrX7ppoTHwQ8xxiLE1mpQvgSg8Y93vAvZqXEmoeQND/++COiQu8yDsUeirP0MnphfJqQqndhyUTTxBGMbqJTOMTpL8ykwSUw6bA2HNBqEvHXtmhLVbh82C72LX799ddbb72VhwUSBPqAGdiHcvpHJIyzWFiKBRNSickSNrC8VQmc7x8585QEqpqG8M6VknwzkRei4yreyyxWJRye4xQPOBZYREljNw7uiWHDDMAbEz3EUTL+PyE02SZEk5FbwxZBzWMCFmpSQmui7eyW2aKiabC/yHYgWy+sxs0pDF5MzSZTEtTDwGDDRn5CpTEfsrXD7kKoSxw1RPcn3Ae3li88mJoZq8bAECUNFajAxBk4Y8LG7IFXrw+JswAhzN54g4anfftt8c6d03IiDBTLyTFoZPIZAd5Hd9yRtnJlFmpk9ermVG6ifPmszp35l3nDDRnjx2fedZezQKr8ZqDypLAZyTbz+++/z/NCIiHjNgyisPPsd9rPoynMbMYsJKdQ9lgCiCoiBfw8X5kmhEoodxYKGc1XBBQBRaDgCLiVQtksIl+2XokATbxY9CRiUvD6wVKM94q8F6dOnSo3xqSLT1NDN7BnztYxX3OHSEL3clduC0r4D6glNnkw8KFmGKswYb/lFhJ7BZcK6DOqgsIzFbqlxYQN4gyFD3kohjZvCpsEGjxGbTirwsrBr3EXtkMliC8qHdGX3JWw7AcEyEGM4FD6TVWS4AXMWVRPmDgiarGB7CigP+OJgHuMMSyFuIHktY0FjFSyjiXYOZQu9C759DLWIphIQNOwIIRx87NViGmIfxLYU6CemoPYZ1ijsEbl8eezVhihYHvC+jO/RDPPIPMSl/unpUVHEuhLSB9mOfqOzRVoUHx2ik7zC9BSqBOcMdl8gmxiFsJtnNHLmpN9IEw22KNi3wU8qdk/sfM9mynRqfhaKI8eL26MtWW/jWUzL1O5hIedhDsmN5njxo2jsbxYSRMsj0kVdYLyfnC+RodBdWELkE4RqcR6TpQi+GIziUmCJuBziok69mhoL/QgrBDIoMzw1oB7QlfBbZPdNeIocUkYwzpiVlCYjTc0FurhmYJdov6EHNDiqDESzc0IwE4nW4kQJeweoemxiWhORZgAJTafIOD4fARRpaCPQZvDjoYZYVUxLZY2c2ZazgBO++svrMnScj7XkPb992nWxyWzc2xFCYWGJFmuwPnBzOOOyxg+nH+ZgwdnnXtuTAVObOX4FGM0KgfPNQoSabdrZAKFZMjhMWA+xCGSmPmKbXseTwwFeO5IMDuRY6T183xlhAyVUJ/NUMhoviKgCCgCBUcA8w00JNRflEVxr4ApozpMujC8Qn9CF+RdghYIKyRbzRKVA9UHKy0UKQqzAOAvpmpkQkJhw0WOODaGkgz1i68TwC7hwIL2SRq/LcSgPC8w/mLpY18rt2DBxt4R9uEs2BAb+sOEBXFLC3HGDnDXrl2xEvp/9s4Czqrii+Nvg1i6S2kFQUVBOhQFCZFSCQkVVAQRFERAUREERfgriiIYSHeDgBiEEgoCSkh3SecC2//vZdbh8oq3uy/u2z33w2eZO3fizG/mzrvzm3POcEK8uTQV5uMYfz3sovN7jzIdDYc7g0HDflMn5iPPTSHqk1rBosw3+GJAEQNWkV9iPjSx6ERfCbF1gRLwGwKuxhij2iyD3XhjVPBUbUKqv9zydQhTwKII5QhlM2guQcKuEFAvCNSAOQGzBBcW35Dm6Kiqp0wm2ACiiUBKRUmrvOaMjmGUPuA63diGO2aRGK8gQO+w2ofuVEtoXgqn5jBeqSvVFILzL3wLKC9RDF2IEsz60F+GPlDn1hGJ+oY6fsGyrWb+RHGMn1flLJx3WbkPhxahIbzXSM43A38dVYr4veZ9Zy5V3lT5qcW8EUD49bTCPhOfGYjHxYJZ4c/+HF8FhBnwNApTUxWv/tJkNu1Ag66kISqSd0FtuuCuAacNWOZClcKTop7JWQQ6u938BmGK/j5FaX+O7N+wCafT+zPATycfcnY1MkXD6MGaEY9FG2cg2CW45S0QsZ+KLwsAwdybS2VhRMFZ3DK73xKETZxIXXE9e8Z9+KGqNByTzPnzMeRM+E+7NiF/fvwUhl73f8eZAHYGm34TNVAV2Y1eLQbxrh7pNP4PsHtHpXZ7pXq+4qWGsFZsPsl47zTLZvH56pZICnd2S4gkgSAgCAgCyUGAb18YIgwbMU9AFR/DAT5lWBGxgsWbGDtIFMoHIn9ZHUGl8QuENSJbiO3bt1f1qbWuorHQI0Ndi911HrnRO4P5QqMNsow0GCzg4YX0lEw8YbgnrB5U4eYqCMPiwZ2RkW9084+0o7R//fUXX2lY3qmzsRyFIQvfcHwlQyDyY8muL+WTRdXI1iv0n9NCyAiHQnpcNZEYlRn2nBXxRwC2josPbkgZGDRch6gC5a+fEXA1xpQGIssA+FbH8caWI8oUfNYz5pVRIWIzMhkh8KqEWTb4uSHBWx0rZNQuXMmPTTQXrxIvsiLumV5IjFqf01x6raULVH6I9K0E/IkAq316BLt1FADttAzc97s/hbRUXRgEYcvPhhPerCCOlWz8+kAbodOEdlJ2q3oTh9YxI8ncyL4XikgIjBm1ejR8+HCdBmcIyh+CjlEBBgbvrDZcZXMCrhD9NUCwG0J2Gf12i9Y5rr7QhGJ/jklJ/cRTOxQnl1MxQIOLLHQic5e5IVCimJYDFN9U5s8VysETuV1p/MTwuUW9bBzCovKZwSmldmn8c2s+4UHXyG8lw4C20Il224Gu3ne7YaNndR3QhVsqEDp2LPJgbqmlim/VyuDOxo2LGzDAiAw1DA5QN4M7M5TOtF+z6/E6VyoOQJc77UTHF9/V2PApOHYDD51WR2n1fIXpMQMeX6v8lrEY4b3Tsll/vtKiOg0Id+YUFokUBAQBQSClCKC1wac8CiDofVAW3AHGAmyTEmbTlY85wqhhc4uZFfvDeM8lDIeFCpjeIyUGUw6+rthXJwwDxZKYT0D9Hcnvk/k3ieUWri5YM7BhS3qqYIeWrU4YMX7SOI3L1RmX7OWSGM6Lr1Uyqu9R9ddO2ubNm+P0nd9yPklJyTef2s83f8KyFczJO2orlTQk5muVD3psOaHGwMSxEJKxbNiyZQt50SnDLwlcHpae1ALXxqczbB0FKvYQ38/645uMcvkTAVdjjKUaKxMIMtQiHMcbpmfQamhBcsECsyGpBgxsMv3Ly6JeDX82JHXXxaSRuhuYulunDwdM3c3UreOnDQ5d2fXrSHOAp3oDxhyvw8xLOqwDLNK49K31A/yaJ0M/DvbQsWmaRnR8FJAYJnyaltTWsep2Ki2luXrkND00Ipd22rJlAABAAElEQVTTR1aIhAS0ghg+lSH6P19+upb4J5/UkdHPP6/i49u0iW7TJjHcuLFOEN+xY3THjjqvBQMpn8Qs2KgUioRCgNrAM5cTFPOVWWC7cIijyoBdCle3W7duVc5NXCWQeEFAEBAEAoKA4+xETKA8LjPHYrYJ0XPLL3jUdpTHbkfQcCsARcWnsN6LdkxjjqFS0qOZxXeqZtnYlsduziufj2zhUsUtV3cko1HIYD4vDD6Rr14a4rQQWorYEIKIyoaz3W4VVpxsYVFvsp3pmlFKahgPMna/egEcV0kV3uvpnY4xakErkO5mBeh0vBHJgNS769jt/vjjj1j1YqSWZg8KcDquzFZIXu87KVAQsBoCmCWiaIxRmyvBWJfilxMDN1cJJD7ZCPC14CEPxa822x6Ov4MyXyUPfPaN0MQPlB5c8mSWXK4QkEnMFTLejU/hfJVyYUTvLOUYSgmCgCAgCLhEAJ7IQwUQN4pU0A2OWzcuq7yuNeb4Kex0W95NIW4eeUjAGfu8Dju92teV4yNqpKVcBCBfHJsMZeYhmG6El0deQcDVtr/mxZyON7tItAjx9IyRcpolzrzSF1KIIBDsCECK4eUg2Fsh8gsCSUIAtXquJGWRxJZFQCYxy3aNdwUT7sy7eEppgoAgIAgIAoKAIOARAljvepROEgkCgoAgIAgIAoKAICAICAIBReCG57aAiiGVCwKCgCDgWwSUNpNv65DSUy8CrsaPq/jUi4S0zJsIyPjxJppSliAgCAgCgoAgIAgIAj5DQLgzn0ErBQsCgoCVENCmglYSSmQJGgRcjR9X8UHTMBE0oAjI+Ako/FK5ICAICAKCgCAgCAgCniIg3JmnSEk6QUAQCGoE8J+FJybR8gjqTgyI8IwZRg7jx2ntMq6cwiKRt0TA/bi6ZXZJIAgIAoKAICAICAKCgCDgTwTE35k/0Za6BAFBIJAIQHME5HzGQLZZ6vY9AjKufI+x1CAICAKCgCAgCAgCgoAgIAgEEgHhzgKJvtQtCAgCgoAgIAgIAoKAHQLp06e3i5FbQUAQEASsiYDMV9bsF5FKEBAEvI6AcyMUr1cjBQoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAkGHgHBnQddlIrAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAnxAQ7sxPQEs1goAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAkGHgHBnQddlIrAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAnxAQ7sxPQEs1goAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAkGHgJyzGXRdJgILAoKAIOB9BOLj40NDZTfF+8BKiYKAjxC4evXqb7/9tmPHjri4uLJlyz7yyCPp0qWjrp07dx48eLBevXqq3pMnT65du5bIrFmzVqpUqWLFij6SR4r1FgKnT5+mWyMiIuhW/pqLpdNXr15NX6ey6frff/+dO3fugQMHzI01h4sVK9a8efMCBQqYI1V43759MTEx5ngSZ8iQgR+1bdu2nT17ltuiRYuaE1g2vGfPHqCgmXfccYejkK5apHLdc889OXLk0LlmzZpVokSJChUq6JhABWJjYxcuXKhrz5gxIxNRnjx5iImOjl60aFHp0qUZ6jrBzz//TH8VL1587969OpIAY/7OO+8kcOLEiaVLlx49epT21qhRo1y5ckSqWqpUqVKoUCFzLhWOior6448/HnzwQXWbkJCwa9cunYy50Zxr+/btxNx+++0koDuYP3VKRC1TpkxISIiO8Txw8eJF5mGqpgTKd5/xypUriMHABpls2bKZE5vFM8cTpop//vmHgGMuu5Ry63UEUjKJKWHsRimRx48f5+1mtNx7771hYWHEuB+6Xm+UuUDzwGOkIZv5qd1LxCPmK37ImIGLXL90YovPV1pOVwHhzlwhI/GCgCAgCKQqBPgIu3btGp+qXIT50ORi1c0vMb9we+Mvd90wjwZXK1BCN5twtYLGrTlSP5WAIKAQuLpxNYGrm1bx99omI6wu4vfUfbVHRAd1W7lIBgKVrv81AkUzqhj1VP4mCYHDhw+//vrr58+fZ+l44cKFyZMns6r8+uuv+X5ds2bNvHnzFHdGuG/fvizFWUKvW7duzJgxTZs2ffvtt5O39kuShJI4eQj88ssvffr0Ya0BgwbLMG7cOPOqfsSIETNnzoQFSF7hls0FcXb//fd36dLFlYTwF6RxmqBz584sz8wZgahgwYJdu3bdvXs3HM2hQ4deeOEFkpnTWC3Mr3C/fv1++uknXmTEfuqpp3hzzULy8+20RYsXL/7iiy9q1qw5cOBAGp49e3ZyLVmy5Lvvvps2bZq5hECF+eQYPHgw7cqVKxcysOR+55132rZt26NHDxrFI+Jh+jRDxDzWrFmz9OnTk8Ysc6ZMmX799Ve4oWeeeaZ8+fIwUPDIzGm9e/du2bKlquXTTz81vy86++zZsz/55JM///xTxSADhSt5iIGM1mgzqTK1Pvfcc5TJI5hZJHzggQfCw8OZaeEC8uXLN378+Lx58+rCPQls3Ljxtddey5kzJ/Q36emyUqVKucoIxdatWzdqJMHly5c//vhj2EaV2E48cwkgwyCninPnzhE/atSou+++25wgsGGEmjXLBuv4/PPGX67du20rVtggUZs3TxRt61YjZvt2W8mStpo1bZUrJ8YTs8r4vrCVKGGrUycxcs8e2/LlRrhwYVuDBrZly2yaa2UXCZYV3vjmrYfEjD76LyWTmBLJbpTOnz///fffhwFnEPKLwG8B74iboeujdqli7QbeihUrmHDMNTZo0GDQoEE6hvUFY3j9+vVqQnv55Zc7duzIU+vPV7oJrgLGaymXICAICAKCQKpEgK3LyMhI/vK5xi+ZJ21c++8+ncwI/5V4p+iznuXrci9UmoYobQY0WQZTpsKe4LDuUBTJ1F8j/aqLKlfXmsamulBpCg0P//KRmjlz5m+//VYtOPm2ZqU3adIkM7nAzjALwkaNGr3xxhtKTWnBggV87z766KNVq1b1sCJJ5mcEYMc0rdCkSRM4he7duysZVq1ahZKOn+XxT3VonJmHrmOl1apVgxF2jCfmxx9/1PEffvghxAGKZlCQf//9NxQSHMeMGTOGDh3apk0bzc7o9NYJoGwFccbyu3Dhwps3b2ad+dhjjymNKiUkve+0Ray3e/bsWbdu3f3790OqQppDrIMDA8lS7YX4g+BTbYHj++ijj2ijIvGhPj/77DMINXN3oHqmqS4otg4dOqB4SAIIQYikL7/8UuV977334KGgGs15zWF6n2GwZcsWc6QiI+bMmWOOREV3+PDh8HF8MpnjCQ8bNkyBCaMNR7B8+XLFrNklc3M7YMAAMr711lt8iUH2fX79cpUeBhA9IwYtbaSBsH5Tp051I54qZ+TIkbVr1yY9PCzc38SJE4cMGeKqCv/Hnzhh69TJqLZDB9t1DSrbhg1GDKyg4s6GDrX16XOTXD16gLyRePXqxLz58sG9ooFoJPv8c9uIEUagaVODOxszxjZlinGrL4g2YqpU0RG+DaRkEnMcpYx5iDNeCra7Tp061bBhQyY6xrnToevThjkdePw2cal60XNv3749E6xZDKSFOFMTGj9b/fv351Mkf/78wTJfmdtiFxbuzA4QuRUEBAFBILgRQI+MNfOl6xdqZd5qjOLUWiz5mgI1jyYkmrfgDYpyoMnOffeR52SZh40aqUi0638Vj9a1lqE6IZcrBNAv2LBhw9ixY/XamK3pDz74AKLcnOX777/nlr1fbd/3+OOPs3usb82JJWwdBJTiDEpnuXPn1p0FJQTvic4gy2/riGopSdBowIoZXkaxKqgpoYODhE4tPS0lOcJs3boVfhDijDCUGcoarDzN3BnxTlvEwFD6U2jYoaOEUjn6ayxl0eOzWhu1PGiNET5z5oyy3EQnBS6sfv36lbWikU56PQCLBB+q6CpmMFTMYKCUiXqnTp2wT3SzNUhGtNigoqaYmBWsQTFxhbODoWB4qLeMN45NBa6vvvrq5vpv3CEwZqGbNm1KEneGzFiYMv1SENpkyAPdSU8tW7YM41MsT2FOs2TJAhnx4osvYppH10OfKRs9RgUDm4y3FA9A4FZoCxetg/K4IbflQ2iQKeKMv488YkNBsF8/2/Dhhu5Yu3Y3pKdNv/9uq14de0BY1BvxOtSoka1hQ9vBg7bx49EZtEHv8DdzZv3cogHHUcrg5DVR+uOMuttuu41hj/ROh65PW+V+4GFnigIpGzxms2vkgfhGnVNNaLDGcGdQ/zQnGOcrO3iFO7MDRG4FAUFAEAhWBPhu5hMNswLoM5+2wY5HQxlNSDSfAh7YwhVZ5gvWzLFdikfjLySaaKI54qNiUDAhwOrInEB9ZJtjsPzCuxnrbR3JmgpzJ30rAQsiANcJR8a6Fy0GJnOW2UpIuFE29nG3ZEGZrSASO0Zo2bz77rvoYyJP9erVoZ8AkxUdzrZgWDTRbAVpHWXA2hp6BY0hXlKYIHgl3CeZk7lqUcmSJXHrhnUeS9MWLVpg8AiHgpYWTsEo0/z6m0vzfxgugBbxccJXCgqzWD7CF/DRgiTokSE5ClNoWToKTLtQmNUe00jJWh1lHFTt7rvvPqwpFY3lij5TfNzvv/9u5s6YGzH/VJwUrARKcHfddRcjhGKRx5WGI+QFppco99lZq90STNUotIdUSpTXCPCXFxwxUDAEkCNHjoAAfff8888TicM+0jAeoNWUweYtxYNuIwu8GxdNU7eqRuv/VdZ+KJopVTncdWJu2bOnrX//m7gzGoLhJ9zZunU2p9wgj7p2NZr7+uuG2SZpZs60PfecEWPly3GUsoPyzTffIDNdie8FAiiM89fp0PVp09wPPPVaKZ1QsxjMP7xTKgYWmO09Nf6DZb4yt8UuLNyZHSByKwgIAoJAkCHAxyhfpWzmsIPqf9Hh0VBGgzsTBs3/4Pu6Rh8pmnkitkGirbqIQzT8o4kamh1iaudWKdfYPTLfkszifIFZWgkrBFArwGSMv8zqMAvHjh3DZzncAUsRuCEW2AKUUwQwWEaBCE0H9ZR1GuMfzWvUeUAS3Ph9RIHCaV4rRD788MOoO9HFmN2hMYrMyjGWls1Vi3Bx9eabb2Kgx9Ia/mj69OmEX3rpJdSaoF8pENJNFxLAgB3fhEkpLKGW55VXXsHMdvTo0Shk6UgVwNsXrDEO7NRtrVq1MATDvhWGSK3bn332WbLb5XJ/C5nVrl07NhLQmlG6nHb2m3bZ9bgiHpwfeughuwTub/Hdxt4GDB0DkhoVq0WAXEiCniBMGe7PcGWFJzW4M8W1QTLSfTQTn27uyzc/xVcaPt2JYcYwx1snjCpZ+HX6QZNf7PauX28IaDb7a9XK4M7QGrtwIVH2GjUM483Jkw1DTmXAzeuOmzOnV/78NtzljR5tu352gtMkwRHJAOBFhn5iBsACPalD16eNZIhiKYxFs9KRNNcF7WuO5BVQ7HawzFfmttiFhTuzA0RuBQFBQBAIGgT4cWLrkg1qL9pmJq/xikEjb8/76yqfaMkrR3JZBIEAsmZmBHCOxj+lhiYMmkaGZSRLaxgWtcRS8bhDwpIIRRudjI1rtYjSMQSgGPAm3rhxY3OkhC2CAN2KrgHujdSBgCy2ccCEOyd4B1zXoYCjdJGw2PXkqD6LNMoPYrAqww0WHtM0HYNXHVgzSAcUeSDOUN9DTUkfs+gHkZJaBQdrYrcIGcRf5ISgwWLXXIirFqHHgbMkUsIVtmrVCs07zkbAZwOA4GYIHs0i3Bl2l0oSGGHcNTK2cYWuG4i2IFpXWH4p5Rodz5F8mKhrO2U+ddC6hU3GVxoXfCicF+8LBqoooOlctwzoAqFTGTaQaIqYcJURUg8lPp5ig2aedV2ld4yHoWM0TpgwAZdPOKDE/ZkyKKb7lIoZWQgzA8CpEYMvdroSU1P60enpB45VqBg4QS6wxVVWnTp14CxcpQxU/ObN9jVzfMKlS0ak6ZxY2/WDJYzI/9T1bJx8gBsSbDYVg8aj1q1dcmc8Vcc56OxGWUF4Ye2I1AwGWCqupA5dn7YYpUheB+3H0FwXE695O5/PFTUUg2W+MrfFLnyD8rd7ILeCgCAgCAgCVkaATz02GDHtCThxZkbpk79+Rg1NGXWa4yUcLAjAmh17pQn/lKmmRcSGPiv74eGRv/23AW0RsQIkhnIsggNsc/0ctYmOkjmG9SQesll86khWrZh0KbcpOlIC1kEAL+8Iw+pCiYTJLRY6WCNi8AI/ghIKPBqP/ve//6mU1pE8sJJgScfaDKZAi4HiHjCyfiMGqoVT6mCU9FMLBjDYVBpJsNs4vYInxaTRLKf7FqF+jmdxdLKgCNlRo+3QiDTcOspHrJwhqrhw3oSVJe21888Is4ZHMJgCpZCl2s7iHJtKbfxFZOvWrXETpp5SmvJQniR9TJb0nKWAkpcqxJMvKJTCMJfmSh5xRkW4f3ryySfpXNTocHlGOYqMAwctAJ1FPMQZftZ79eoFXQ5X4iFxxgch04U+EkFZ9FvT5RkGEjExxr+JE1UPoHxn4xAALk7e1NeuXYnBIkV0nE25HOjVy4YCLhtAKteNxzeHduww7jkxIBgvRj4cun4XME/mtyAZQ9enbYedx+JYTbN2FTFuWaGoSFRomX7NfietP1/ZNcd8K9yZGQ0JCwKCgCAQBAhcXDP54NyP+MzSn1yWElrpoAmDZqlO8UQYa7JmZsmFQVNooHfGApLlH6Z8eMzhQlcFy46nn37aDBdUAi6fUOXgEd/cLNLQfWC1aafZYc4i4cAiAEeGAKieMb2z7mWljafw4sWLQ5ypa9SoUSTg0D07x8yBFTvgtaNfib98XGhpSSCO8ZCFlytGPgFWbiw+9VMLBmBMcOOFbyNIQLTP6H2sOJETvSrs+Ai4bxEnJGDYBeFCSsgpiDbU0lHRgjS0YGPVKhpFOTvZQADij5lKx+PKrQI2fv9dWIHBr2HzuG7dOoxSFVY8VIcPEGD/gIMI9UXv/5f1xv/MgUyJnOwJK42eFzplvE1Yw91I4YMQuqIogvFeQ/PxguO1TelIIgC2utCIdNZ3333HcYp816GXh4c+uDCGARdp3EgEGpCJaCky/lFtYxhgSUcVvBEe8m5uCvfFIww29T9dvvLrOHiw7boHPINZe+8942H9+jaz5pw6kdPRwFOXowNsLeHpjMvEqOuHQRCAqGV4s9dF79On7I1BLgdk6LoCi4EKV4tqpDmBnq8g/hjzHIXBaFRbPmbN0OCar8wNJCw2m3aAyK0gIAgIAtZFIPrY9lMz+kb+ZRyfF/FGvasJxqa6NS/tB21mw07WlFCk0ghYxEJTy+M+YLhCs9nSuAkni0x8maF/pLwIsUzC54jdqXysM1mVQbFhlKQgZYmIIoM4QXM/wAL4FFUUZaGpzkhlsQHdaZZH2ySaIyUM72BWTQIQrPCgYLQbrD59+tit8awGGoQXbst4YTHO4nXGHRjHLyLk8uXLIVxw6eWmRSikYBHJ6joC/+o2GydFwrq2bdsWfw5oOVmkpeahSwORCm1B5UdMu27EnBzLzd69e2uZUV2xO3yTpzSKozlVGoriGA16H6KBGHDQeQlgI6lYZnPtxGMxipcxVTtvGRqd5lzmsMpol92cwMMwdpSDBw9uxDGQNhu6gagWqowQXhB5sGbc4kata9eu8LyKNIE3UWloI5poKuz4l75G+Y62YLvK4OGoTdKwa/L555+nXGzH6nwUM2AAWwKGMSZKt9Wq2TZsMDTLuD755KYKCxQwjtFUKtdg+Z8C4o009CRU8/nziScJ4EBNufvbsWMHxDTnz0KSYuLN+8VrooyIsW+Fn+J4jRulBChk7i+sgxkwXIo6h0lXrgA9H7q+boTScy9atKi5Ij1fgS2Q8pbxFG1K3ln94REU85W5UXbhELTm7KI8vOU0ZbsznjzMKMkEAUFAEPApAo6zk2OMTwXwUeHnln56aurruvB0NZ6PrNZd36YwsDf+ctcN81JYiKvs0Gep4CBOx1HkGOMKASvHK3UzH0m4p+6rPSJ89UnKWZypgEFzHEWOMa56B9USLI8whmLhrdefjolR0OAzF/c6+uPVMY3EWAoBVHLoVnVkpKUE85EwcFusCd0X7kkauxIwT0YrE5Uc86LULo3Vbul65QnLqWBOW4QqE1ZR0CXmLJTD+252121+ag7DzSkyyxzpNIwbNWYSu9Wf5/OV0zKTF4lCGWLzjsA9edJGp7WgEYO6nyIcnSbweiTdxESN9pAqGWVSTO/Hjh0LdwO/oONTUi/KdGS34FSPAZ86dzE21hYWZjRx+nTDYVmlSsahmVwYVXfunMiLcVu1qu3bbw0fZ1wEIBt5it7tpEm29u2NUwU4NACPeSisoYw2Z45xMsCUKUZidZEdFvG11xLrgnzEUxj8Mvpc9ALWzZynoRhMOCn0NM3e9/4rI2n/ezJBeZLGXKs6CowfArux4f+ha5bK8zDfHszATC/mGTiw85XnwrtKKXpnrpCReEFAEBAErIJA7PljJ8d3vbxpgVmgmNVjMj/QNjL9Te6EzQmsE8Z+U84QsE53mCU5O+ajc2Nu2qg3P7V4WBTQ+B7FGdAtuwl3JHabw7fMIgkCi4Ab9iSwgvmodnx7rV27FgNVV+Xz1M7/l6uU5ni4FV/b4pmr80rYfdc7bRF+zRyrdl+OY/rgioFKSPmcpjT7/NlwVzydh9ylJ6JakDVTYpcubbNT1+EkTf7pC/PixYttnD564ABnMhhO0PT1wgs2/qmrXTsb/9TVtOmNMqHS+Ofq4pxTLvWUXsD9nE6Jvp4OpyTgi0kMptXuzBAlof+HbvKQ4duDjT27vME+Xwl3ZtehcisICAKCgLUQiPx78YmxnWLPJ/q1NQuXsOhtW3PD/Y31L84QwIqTIzhTgQKa9dH2RMLgstN01SLoM/6Na5uvcpEMrtJIvCAgCFgcgebNm8+dO3fePJe6z6xLSWPxVoh4gkCSEOC0XDf6wkkqKtUkzpDBBssWjJdMYsHYa8mQWbizZIAmWQQBQUAQ8BMC55YOPzXV8P7r9IrduybruV2XcpZy+tRqkcoDmiigWaFffGqn6f8GPjf5ZOqw3/Q/dFKjIGAFBHAer73yWUEekUEQ8AMC+OCzuBs+P4CQaqqQSSzVdKX7hsg5m+7xkaeCgCAgCAQMgVNTerohzpRY0ZOeCw8LppkcBbRPNv0cMEylYpsNO81jrzRJZUgYCmi/XUhljZLmCAKCgCAgCAgCgoAgIAhYBIFgWnFZBDIRQxAQBAQBPyDw71ftz/342S0rSoiKjNi37JbJLJVA6LMAdkdQOzhzj5vQZ+7xkaeCgCAgCAgCgoAgIAgIAslGQLizZEMnGQUBQUAQ8BUCxz5rfnGt6cQgt/VEzXw1Y2ic2ySWeyj0WUC6JBUTZwpPoc8CMq6kUkFAEBAEBAFBQBAQBFI9AsKdpfoulgYKAoJAkCFwdHhjuyM1b9mA8D8n3DKN1RJAn3H+ptWkSsXypHriTPUd9Bnuz1JxP0rTBAFBQBAQBAQBQUAQEAT8j4BwZ/7HXGoUBAQBQcAlAsdGtuRgTZePXTyIXvZpprhLLh5aN5rTA8T3mX+6J40QZwrMdYeixPeZf8aV1CIICAKCgCAgCAgCgkAaQUDO2UwjHS3NFAQEgSBA4MTYTpfXz06eoCE/DrY1HJK8vAHMhfYZtfcsXzeAMqT6qtMUcaZ6E+0zAl1rZQ/Szo2Ojg5SyUVsQUAQSGsIyHyV1npc2isIpFkERO8szXa9NFwQEASshcCZuf0vrByTbJliti3JcvlgsrMHMKP4PvMp+GmQOFN4iu8zn44rKVwQEAQEAUFAEBAEBIE0hYBwZ2mqu6WxgoAgYFEELq6acGb+oBQKFzutS1hoUM7qQp+lsOtdZb+6cfW5MUNdPU318dBn2G+m+mZKAwUBQUAQEAQEAUFAEBAEfI1AUK6yfA2KlC8ICAKCgD8RuHZgw7/fdkh5jfHnj2Y6sjbl5QSkBOgz3J8FpOpUXOmxV5qk4tZ50jTODRD6zBOgJI0gIAgIAoKAICAICAKCgBsEhDtzA448EgQEAUHAHwicHNfFW9VETemUIcxbhfm7HDk3wLuIY63p3QKDtLQvf7sQpJKL2IKAICAICAKCgCAgCAgCFkFAuDOLdISIIQgIAmkUgZOTX0XvzIuNT7d5phdL82dRcuymF9FOs27OHDGUYzcdMZEYQUAQEAQEAUFAEBAEBIEkISDnbCYJLkksCAgCgoA3Ebi8acH5n77wZok2W/SSQZnKPn4lJMK7xfqnNCw3qxUsUa1ACf9Ul1prSeNuzhy7FcdnlYpmrFwkg+OjYIyJj4///vvvIyIiHn30UbP827Zt27t37wMPPHDbbbf9/PPPt99++1133aUTbNq06erVq9WrV1cxJ0+e/OOPP3bv3p0lS5aKFStWqFBBpyRw4sSJ06dP33333ToyLi6OxFFRUffcc09YWNBqt+r2WDIQGxu7b99NpuuhoaF33HGHFnbt2rXlypXLnDmzjkkdAUbjwoULDxw44Ko5xYoVa9y4cb58+VwlOHLkyLFjx+69917eC9IwgC9cuEnhlLw5cuRwld0i8SBw6NChPHny8ObS9a6kWrNmTZkyZXLmzKkS8NbT3rJly5obOGfOnBIlStx///2uCvFbvCfzFS3KlCkT0tIQ5iUtW65cuZjQVJ8SeenSpe3btxOg+VmzZtXJCOzYsYMYpj7CvEeLFy+uVKlSwYIFdZpFixaRC0xUDKAxJQI4MFarVg30dEofBRB+165dCQkJdC6zrvtarly5QotoCImzZctmTmxuqTmesBt87FLKrdcRSPkkxs/runXratWqpWXjV5ifXV4Nxme6dOmIZ/wQoxMwkAoVKqRvfRqwG3jIdvbsWV1j0aJFM2S48YnlZga2+HylW+QqINyZK2QkXhAQBAQBHyOQEH9yUndf1BGyYrjt4bd8UbIfysRyc2bDTn6oKBVXIW7OHDsXy83KbV0uvB3TWzmGBdWQIUOQkKUgBJkW9aOPPtqzZ88HH3zAAvLbb79t0KAB6y799Mcff/z3338VdwYF88477+TPn798+fLr168fN24cxETfvn1DQkJID0326aefEqYolZ2v5M6dO58/f56PY77vx48fr9aounAJeAUB2J/nnnvOXBSrJmhQFfPPP/+8/vrrEyZMMLNp5sTBG4Y4gzfp0qWLqyYwYknz/PPPOybgdejXr99vv/3GGhIAGbS1a9f+5ptvYE/MiRneTZpY2v/j559/PnXqVDqXtxgum3cwPNzJMm3ZsmVvv/32l19+qbizpUuXjho1CvZn8ODBU6ZMyZ49O60mknEyceJEMwKBCnsyX82YMYMphTGwf/9+Jjear9oOTXDu3DnVpyzdX3nlFfhBJiLaMmLECE14EUP/tm/f/sknn+RRdHQ0hfzvf/8zc2fDhw9/+eWXFXc2a9asTz75BBqaEiDjxowZQ143wy/l0P3111+9evVCeDYwKO2zzz5z8xZDsfXo0UNxJZcvX2Zih0BUMti11CyYG3zMyQIVPnfONmdOKL8wHTrEX/+dse3ZE7JyZUiePLamTeOVVNu2hfz6a+iOHSElSiRUrx5fqVKCiidm9Wrjtwnm8+GHExPv3RuyYoURWbiwrV69+BUrQvfuVcltsEx33JFQvnzCdSI9MdLX/6VkElOyzZ07l1ENj6xueYsHDBhQvHhxeChGzujRo2HV+RHnN0Lz5g8//DDjytdNo3zHgcccS5O1JIhXmJ7473I1A1t/vvqvBS7/dzIpu0wrDwQBQUAQEAS8h8DpWf1izxz2Xnk3SorZMD1z1Y6REQVuRAVPSFlu9ixfN3hEtpak4ubMaX8oy82utYyFZaq5oMM6duyomsN6m8uTpqGbAHEGs9azZ0+l24IWG6vTOnXqVK5cediwYeh9wEFAQOjShg4dyprzww8/JAb6BpWWbt266acS8BYCRYoU0Quna9euderUqWnTphS+YcMGaBH4I29VZLVyUP9xz1zADc2bN8+p2PAgW7duRauIVdx31y+GLuwSl0oP+ci6jkWm0+wWiYSehjiDJUHrhBf5mWeeQQNFa4lqIdFt+fjjj/UtAdbb3bt3f+SRR8AQHrxu3bqo4PEWk8xOM8ucKyBhz+crpholPDw+9AEkIH1KJz744IN0K4psL774Im/EoEGDTp06Bd3Aq4GiloeNgoOGOHvjjTeaN2+usjBC3n33XSbAUqVKeVhIUpMhKmrCffr0gUl86623Ro4cCZfnqhBYVNR7mZPZwHj//fch2mBCb9lSp/i4qsL/8SdPhrz8skE7PPtstFJc3rjRiKlYMUFxZx9/HPbWW2EmwcK6d48bMiSOxGvWJObNmzfh0KH40OsamSNHho4caaRv3Dge7mzs2NBp064/+K+I4sUTJkyIrVw5kYD7L9pX/6dkEps9e/YPP/yA5rgWLjIykpHP7+zTTz8NfwpfxvzALeQyPxPTpk3TKX0dcDXwYHgVqe1UgOsTsJMZOIjmK6ftIvKmQeYqkcQLAoKAICAIeBeBqMNbzi4a6t0yzaXFz+2pVEjMkcESxnIzWES1oJznxvhwXFmwvZ6LhOVmajpzE/JrwYIFLCMVAkuWLDGzXW5ggWXgadeuXbVR2GOPPcatmjGwenv22WehKnQJFy9eXLVq1QsvvKBMNVkEtmzZUj+VgI8QYHmPFYzSo8Fyjc71j36Bj5rju2JRZOjQoQPEGawE2kMs5/RLQaUsaKFF0EKyGpHkFBBUQYlHu4S/jr/gtIvWwahqXQ+SoQ/C8FC5WG/HxMT079+/TZs29913H5HWuZI3XzHnoIx28OBBTNXQRIPtYtYigDk5ymW0Dk1YGP9XX33VjT2vHQiQbmTXxBlPYc3atWtnZ+Rrlyslt/QRuxFMsxSC8Og/sj9BT/3000+9e/emT2E8mzVrhqoOXCFp4MohzWk7ja1ataraFLllS53ikxKx/Zl35cpQRZz16hW3aFHswIEGDiNGhE2ffhNTcepUyB9/GLpm/O7NmHHTIyVtw4bxn30W17NnHCzb/v0hTzwRHhnpz3Yksy7oMEZF69atdf7Dh42ddfTB+av8KjCECGPUj745ypjcmic6ndHrAacDj4G6c+dO1N5h6qH23FRqNwMHy3zlpkVOhp2b1PJIEBAEBAFBwCsInF0wyCvluCok7ti2LKf+dvXU+vFy5mby+kiUztzjlprO3ESPBiUUTIFoMsuw+fPnN2rUyH3z1VO8jWASpV0IEckKrW3btrgHIswSl+/10qVL66L4OCbMYo8FHkox7733nvg70+D4KLB582aUASE0VfmY7dApDRs29FF1wVsslApLONZjTz31FEpJaGfAgGhSmHZh+fjEE0+4sY+zSNvhy1q1aoVGEtbW2OvBB2kzPS0hyikQanaWp4wNFKmwpN6yZQtkK+aH2PqhtoadlzIP1NkDG0jSfIXwXLADGzduxJwT1omGY4MJsw+v9NVXX6Gkgz4OLcIXGK8GV968ee0ayJTFS6QvrZiGlqLeZoB9gAXg70svveQIuF2Byb7F8pq8qBaqElTgzJkzaluC2RuVsddee2369OnKzBY9uCpVqpAYwTDRVYK5aakq1ik+6pH1/37wgcFIoGg2eHBc3brxffrEDRtm0GcDB5o10Yx2YPjJ3/XrQ+DRjPubr2rVEjp3jvvww7iNG2NxiEea2bODgOvgx5cxDE+qW4MKJHqayi8e5BQ26epHGSKVML/1zHhsYjH76Sw+CjgdeOqrAM4aGerVq4dqJFOxUwHsZuBgma+ctkVFBsF4ciO9PBIEBAFBIBgRuPLPskvrZ/la8qhx7dOHOfm28HW9XilfVM+SB6MonbnHDb2zVKN6xnoM1QnUzWjy77//zl+13HKPAE/ZslZf5LdMqRIodYzVq1ejFoFntOPHj2MR5mFeSZY8BFiKwKQUKBCUdvfJa3LycilCBAsmWCeMN0uWLIkij+aMMHvkglBLXuH+zIWJLqcEQAKiV8IbykunqRYlBrZacEZvvvmmnT4a1otoksIPPvTQQ7AwgAAU+AXjL6txNTP4syGu6krSfAX3x+QGO0BDQEZbplM4lmI49iLAzoGrulT8n3/+iU6ivlQkK3wy6kMVsJ1kP0Bddsaw7gtP0tP06dOz8QC3BSlGZ33xhXFCFHQnf4EFT21wu9B5uPObOXOmimR/gsGAyiRcIQyF59V5jo/nZXo3ZZUq6apWNf717ZvIi8G6bNhgMBKtWyeqURNu0cIIozumz/yoXt1gZ6ZODUU5b8ECI33t2jfSc2u+8uVLePpp4+n27UH5GcwGgPqZhiyD2EWtskWLFjSHTS9YYxhVfvrxL4aSqbnVfgtD5/G9wSuDTjqW5tC+y5cvd6zdcQYOlvnKsS06RrgzDYUEBAFBQBDwEwLnltzkr8R3tWbYsch3hfu6ZFE9SyrConTmCWKpSfUMHTFWYhhqoVvBUhObHTMCmkFQkfoWl+rKHsScGF8qypbTHKnCGTNmJIAOFDvGbIZjvMm+t7ItckwsMSlHAMVA/JfTuSkvKtWXgD0RbYQdQ5WSgQ3LAPGkbNyIx6gZWkSZQFocCpSkcNo1efJkLPhYiPJ+oWVmlnngwIE4zDp69CinQ0K7QJHAYpMAR4Tk4uXFcBWdUCgz3m58GsJ0o7/GI3MhgQ27n6/MstFxsANcSn508fRTiAM8hdHReINSZpv6kV2AKQvHYfpSyl8wj9jHKa0Z0mPkC9pcEAE+tYCDIENXiOb8+uuv9AtVK/6OSVWNYWJgfhm9ilMjGdQhDAWaaEnSmvQcHzu4/Ha7ZUvIpk3Gv6NHE1ktjk+4dMmo33wQbs6ciXpMp08nJitbNqFKlQRUyXB/BoNG+pYtXXJnPM2TxyhBZyccXBcDcuzYsfDIaKFyNog6XhmXeXCs/ChzKgicGoS7HcnunzZi9cxryGFEcHzMsTVr1lQq8Ha1O87AQTRf2bVF3wp3pqGQgCAgCAgC/kDgyvYVkVt+8EdNbGzOfzMiJMY/dXm9FlE9SxKkVzeuFqUzTxBLTapn+JOGL2CNDZlVv359c/NxRPLLL7/oGFxBsT9MYmI4XQ6fxBA0+ikrNEy9XC1EVS516BtZ7Bg6XYgEvIUAlAGYm81mvVVy6iuH0QhWekwqa2JlPcSoBkn4pqBoNS69UC3hQlrIFOXkyyw5a1QsedFd4kLbDqdd0G06AU2GdGMFy6EBGAOyQCU9kwB+vnWagAfczFd2soEA7AAX1BKMG1QyBAEnJ2hn6izdyZK81tWoUQP1LrWXAIGF8k7u3LnhIl0ZndnJlrxbjE/xaAYVgpIOwxUiTzngwy5V70Ngpko8bUc9Da6N0wywdzMfFeqmai/i46YWrzy6eDE6MtL4N3ZsrCoQk1bckxHm5E1dxe7dieHChRNJNB61bWsYcvbtGw7v1qhR/PXXReewD+zcaZTAiQH2D4LkHpeXuGLgpxmmWPlYQAcTpW+O2lQt0CPH/w1iauXIIF0vkjhSz25m4KCYr3Tr7ALCndkBIreCgCAgCPgWgfO/fOnbCm4uPWzN6JsjgulOVM88762rm1Z5njiNp0w1qmfoUOBSGl858Cx2ugn4fmJVhr4G+9LooYwaNYr1NotGuh7HQyTmuEysgfgWJxnqKizYcJjtdGDgvxy6jaKwIWKFhtkm+8zi8swpVl6JxH1V+fLlvVJUWigEN3DYbGLSCBuCshV+9NVpicTQfPxqBwUI8ErYEuLbiwUnA2Dx4sXQZ0jO4nnSpEkEWEKjtaQu2sg7Cxejm8Y6ljcdn1nEwARh+MlSFgSgz3SagAfczFduZMNyGbKA8xAgFuGeIBmZiDhPFRA85JXsCucsFKY7DieFL2PDAKxQ1kPhyy6Zd2/RFkRzEHYMzUFagY0t5CZVUC9TK7rDuFQfP348WyDQEBxtgW4RkwBDguvs2bNuhEGxbsWKFV7Ex01dXnmEerT+pwts2tRguIYMCVN+52NibO+/b1h0PvpofPr0OpWtWTMj2Z9/GqSY2cDzRor/QkuXhipPZw8/7E437b/klvufYclLzcjkdVbDgJkBNplfbWx+8ZTHqGC6Y34LlF4tJD4H+2InvnLlSnRmIe4BUc9XhN3MwEExX7kaEzep97tKJPGCgCAgCAgCXkEg+vjOy3/eZIjhlWLdFBK9+tvMD7SNTG+cwxV0F6pnPcvXDTqxAyKwKJ15DrtSPatcxLD2Ct5LuT3CTS+8GI6N7BpCDEoZrLdZZKpH7F2juEEY2gsvP0OHDuXAe/WI728cbOESWBeiCte377///jvvvKPOicN+BJdS+pEEvI4AS3p1boPXS06VBUKFwK1w2AWtwxyPgc0KkzBcEn+TR6/4HyhsTnkfeQ25qB1OHId3BKBFWDlzCqSdSOY3FBtVHA9BaivlFN5QTLCxY0UBDTTsMgbq1v18hVQqgWKUzK2DO4BggnJCYwv6SR0RAPuPyZhK7Ngild1ciDkNEx0sFfwUEKl4DN6h0mCvzMm8G0ZsaqQiikU3UHtwgxDELxvyEM+eROfOndnqYJ+DqZtLyQBL6MqgngT0NVo8uEvzHB9VrKX+vvtuHEdqYox51124QkvYuDHRolOdGKBFzZ8/oX79eHgxYjhSc8UKex2gYcPCJk4MPX/eOCWANPBrnB5AAJ/6eL7j/FmYJrSwMYFkjlXHSS9cuJABhmmkriVQAfOI5UQLxFBsuJIHm99+/fpxzjU+zpRFP5MGtwGRlh047J35qKB2lCV79uypDjowz1euZuCgmK/coBqSbA1VOpVNEjdFyyNBQBAQBAKCgOPs5BgTEMGo9PSMvmcX+9vNdljJGleb30LZbW/85a4b5gUKFjf19ry/rkXoM8dR5BjjpiE+fYSnM2tyZ3vqvtojooNP2568wrvWzNa1Vvbk5fVuLsdRRIxSnEl5RSwSWHhTDrpjjppi7BjjMomFmbIeumV1KEdQiCImbplYEggCniMAM8vK3316HP3A4bpKw2BG78zM/7pKaeV4lMVQJ8GTkTaR9kRaeCXabqd5inIW77XjW+9YIMQBWkuO8Y4xuFFDTdVu9efF+cqxRscYNG6gGDycshyzm2NQOoN1Ra8NX/7meN+F6SaE11Mo5vYcGIr2EF0A76njUyKAF/FJiRiOeXftCrn33nTEX7kSHWaolNlmzgxt1y68YsWE1asN1yKHD4d07RqmeDFucW02enQsPs4If/ddaJcu4Z06xX/+eeyUKaEdOoRDio0fH7twYehTT4U3bRo/Y0bss8+GT5t2g0oj+xNPxHfrFqfqwgyWgxfgl/ltpRdQssZxp2Iw2QpCyYtDNqgoJVfKJzHPa+ftRndSceWe5/J6SmZdxhuzh5n1u2UtgZ2vbineLROI3tktIZIEgoAgIAh4DYGLayZ5rSyPC4rbuzrr+V2XcpTyOIeFEorqmSedcW3Tak+SSRqNwMhVFy3CnWmRfBFQ3qBclcz6HD0dV08d45WvYsd4iREEUohAsWLFsPpRaiBOi+IpaZw+UpEMZi43CYLiEYpUybDAuu222xxbp4+SdHwU1DFepEehzJI0AaYcN1dkBxsYKS9cleBFfLwlkiqnVKmEqKhoc5mcpNmixY0Y/JotWBDL6aMHD4bcfnsCTtD01bFjfMeOiSnbtIlv0yYx3LhxvC4TKu269p7OdFOAc065VBS9sGbNGv3YW7qZKZ/EtEi3DFjk7WbKRS30ltLaJQj2+Uq4M7sOlVtBQBAQBHyFwOVNC2LPGwdj+f+Knvhc+Gu/x8YFpd+Htf/uq1aghP9BC6IaOSggiKS1iKhYbga72aZFkBQxBIEUIoCJMZZT8+a51H1mXepomJzCSiW7IBBYBDimMEkKO4GV1j+1c3AuLJt/6vJuLTKJeRdPy5Ym3Jllu0YEEwQEgdSGwOX1fvV0ZoYvISoyYv+yS0VqmyODJbz2uHBn7voKg013j+WZCwQ4MaByW+NUO7kEAUEgsAjg9Qnn6IGVQWoXBPyMAKeFqgND/VyvVOcLBGQS8wWqFizzhmGwBYUTkQQBQUAQSE0IXFo/M4DNiZrxasbQoNQ7w2wzgLhZv2prejqzPm7onVlfSJFQEBAEBAFBQBAQBAQBQcAKCAh3ZoVeEBkEAUEg9SMQuWVpQkyA1+rhfxpnOQXjhdlmMIrtB5nFWjMlIAt9lhL0JK8gIAgIAoKAICAICAJpBwHhztJOX0tLBQFBIJAIXNn2UyCrv1539LJPM8VdCrgYyRAAs81k5EoLWa5uWpUWmumjNq4/eM1HJUuxgoAgIAgIAoKAICAICAKpCQHhzlJTb0pbBAFBwLoIXNkaeO4MdEJ++sC6GLmWTPTOXGMjT5KPwHox20w+eJJTEBAEBAFBQBAQBASBNISAcGdpqLOlqYKAIBAoBOIun406sjVQtZvrjdm6OMvlQ+aYoAgLd+aqm5Ls7Cw0NGuDlvneHpmhbAVzmRnLVSEyc80G5sjw/Lfn7TUse8uXQsLTmeNTTVhsNlNNV0pDBAFBQBAQBAQBQUAQ8CkCcs6mT+GVwgUBQUAQMBC4tu8P6wARM71L2ItL4uKD7NwA6LNqBUpYB8ZglCQ0c9Zii3dBhMWdP5P1sdbRe7YdfuZBGlLoiwURFWrEnT2VtWGr2BNHDj5xP5E52r+Wu8s78ZfOh0Rkzt1t4MHHy8RdOBuMrXYvM/RZ5SIZ3Kfx/9P06dP7v1KpURAQBASBZCAg81UyQJMsgoAgEIwIiN5ZMPaayCwICAJBhsC1AxusI3HCuSOZjv5uHXk8lERcnjkCdXbMR46RbmJydXkH4mx/gzsONCp9+rN+6e+4m3/heQtCnF2Y9e2Bx+868d5L4QUKR1SoSSE523WLObh7f/2SBx4rFRIalqvz225KDt5H4vIsePtOJBcEBAFBQBAQBAQBQcBvCAh35jeopSJBQBBIuwhEHdpsqcZHTX4xQ5ilJBJh/IFA7NGD5yZ+hioZlUVtXc/fTFUesYWE6LoTrl0hHF6wsBGj7TTj4mwJCeF5CuhkqSkgLs9SU29KWwQBQUAQEAQEAUFAEPARAmKz6SNgpVhBQBAQBG4gEH3snxs31gil2zIzqmwLa8jikRTi8swRpmubVjtGuok5P3Wkfpqn1/9gxC4vmx978hh/sz/1QpZHmoblzBP77+HLS2eR7OyogXl6DCn+476QjJkSYmPOfv2hzisBQUAQEAQEAUFAEBAEBAFBIE0hIHpnaaq7pbGCgCAQGASij20PTMWua41ePChTwlXXzy33RLgzxy65ujFp3JkuoeDH0zOULndu3Mexxw+F5ciTqVpdW3x83NmTcGSh2XKmL1GGlNmatEclLe7saZTRsPS0O1tAFxXsATkuINh7UOQXBAQBQUAQEAQEAUHADwgId+YHkKUKQUAQSNMIxJ45ZM32h6z81JqCiVS+QwAWrPDE3yDLzn49+Ow3hipZ9tZdQiMyH+lYh3MD9tcvEZoxU+5XBqQvXjr9Hfecn/z5odaV99crEXvqWO6u/X0nlZQsCAgCgoAgIAgIAoKAICAIWBkBsdm0cu+IbIKAIJAaEIg5d9SazYj5c1qWKh0uR6ROP1bWxDywUoWky1Bk2u+cBnDqw1cvLpykhMlQ6t6EuNioXYZLvoRrVzlMkwQZ763CbeSKhSpN1O6tmavXU+HU99eaR226wTk2NnbhwsSuIVn+/PkrVqyojrrbuXPnwYMH69Wr9++//65du7ZRo0b6CLzNmzfv3bu3Ro0a+fLlu3jx4j//GIbkZcuWzZYtm67r5MmT5KKQrFmzVqpUiWL1IwIbN26k8MaNG4eHy9ejGRgvh+Pi4nbt2hUVFXXvvfeGhd3wTHn48OGjR4/ed999ERERXq4y0MUxXOfOnXvgwAFXghQrVqx58+YFCrj8tXIEBxgZyWfOnClZsmShQoVclWyReF7J48ePm4XhHbQTOz4+ftu2bWfPngWNokWL6sR79uwBwHvuuSdHjhw6ctasWSVKlKhQoYKOCWBg3rx5DzzwQOHChZcvX37+vOFwU1+M54wZM/7xxx9qalLxDP7FixfXrl07Z86cjlmY8apXr65LCJYAXcyATEhIKFOmDJ3rXuwrV65s3749JibGboomF/Fkv/322x1LcDWxO6aUGK8jkPJJjGHPi/Dgg8bp5/pyjEzSQNLlpDxgN/BOnTrFXKSLZVLKkCGDviXAfLVjxw7SFLl+6UfWn6+0qE4D8vXjFBaJFAQEAUHAawjEXTiR5LJCQrNVa5OpbJ3zy0Zd27dOZ89SoWmWik9GHfrr/C9fJsRcU/EZCpfLWe/VmDOHiIy7dEon9iQQN7dXSNvJfMx5kjjgaTDbrFagRMDFsIgAyTDYzNd/VHjBIpd+mEETsjVux9+rm1bj7CxT1Tr5+n3OyZvZW76EyzOO74xcvTRvQkK+t0ce7dqYYzc5UuDqn79apOEiRnR09ODBg++8885cuXIR3rRpU6ZMmYYOHVq1atU1a9awTIU727dvH2nq1KmjuLNff/21Z8+ezz33HMQZrFnnzp1ZlJ47dw4wR40adffddxMgb9++faEnWG+vW7duzJgxTZs2ffvtt0OunybBdzBhyDUqfeihh6QXfIQAC5IXXniBroFQuHbt2pQpU1gkw5b26dNn5cqVt912G/QZff3II4/4SICAFAtxdv/993fp0sVV7VC6pHGawCk4ly5d6t69O2QxiO3evZvwM88846pwK8SvWLFi4MCBZkkaNGgwaNAgHcNg6Nq1K23JkyfPoUOHGCS8xTyFY/riiy9q1qxJ9pkzZ2bPnp3IJUuWfPfdd9OmTdPZAxhg6qAhH3zwAdzZ2LFjT58+Xbx4cS0PzWGGYbKCDhsxYoSKhzki5q677mKacsxCfNBxZ2w8vPbaazTn6lXDVwZdVqpUKQ2CXQCKrVu3bmqL4vLlyx9//DE7GSoNzOPrr7/OTN6yZUu7XK4mdrtkgbrl12bWLONoouefTzygaPdu24oVtjx5bM2bJwq1dasRs327rWRJW82atsqVE+OJWbXKCJcoYatTJzFyzx7b8uVGuHBhW4MGtmXLbHv3Jj5Kl8525502eGN/7jKkZBJTcs+ePfuTTz75888/E5tx/T+7yCQNJHM5KQw7DrzRo0fPnz+f7wFV8rfffgtFpmthWmYMr1+/ng8VZq2XX365Y8eOPLX+fKWb4Cog3JkrZCReEBAEBAHvIBAXeWNnxpMSQyOylfz8REhYOoiwbDWfiTq8+eA75clYoNP4bNXbxV08ka3a03meGrync46E2Kh87b/IUadL3KXTYVly5Xqs1/6+ZWLPHvGkFpUm7tiWzKc2X85zr+dZAphy7XHhzlIEf+ZaDcmftUFL/qmCzk8bdWbE25keqJW1URv+ERm1fdPFOd8ROPW/N/L0HFLs+x2E486fOTW0p8oify2CAAtpFswIw6L61Vdf5Qsb7sypbCzLe/XqxUqb9TYJRo4ciULHe++9x5qWNdjEiROHDBnCVjbEGXpqb7zxRmhoKMkWLFjAavzRRx9VxfJBD3HGep4VgnBnTnH2SuSHH36IntSwYcMoDcYHNqRHjx4zZszYsmXLjz/+yELlm+tXKuPO0DhzyotpSKtVqwYprG/NAafggNuJEydQz4RLmjNnDsQNRLDilcx5rRNucv1S8qDd2b59+zZtjAlZX6tWrfr7778hxfLmzUuT4U9JgNIoLz60eN26dffv34/SCrw5KniMIngos0qpLifgAeYQBrZZDJrGLdw96+rHHnvM/EiFHbM4prF4zIABA2jFW2+9BafQu3fvz69frmT+9NNP0Tmli2EVmajhU6ZOncr0O3z48NWrV0MsOs3o7avO3AAAQABJREFUdGJ3mjIgkSdO2Dp1Mmru0MGmtGk3bDBiYAUVdzZ0qK1Pn5tE69HDxkRI4tWrE/Pmy2dDO/P6D5Tt889timtt2tTgzsaMsU2ZclN2iDZiqhg69P64UjiJ8WozyZsF5TV3jEzSQDKXluywq4EHw+tmF4dfK4gzvhZgzBctWtS/f3++LtAYDbr5yhE34/NILkFAEBAEBAHfIRB/9WKSCs/z1AcQZ3tfybu3e8FTU3qiVsa/8Fy3Z6vW9tIf0/d2L7SvRxES5HnyfVi27LVfjPzr+73d8u9/486Q9JlyPfZGkuoicfS4dunCQpKaS9IHIwL7Hiq4t3pu8z+IMxpyYkDnfQ8VOtLxkf2PFjvyfF3VtItzx+6rlf9w2+oHm95z4LFSMccOBmOTPZF5/cFEFU5PElswDQpK0Fssm53KtmzZMogzyDVFnJEmXbp0Tz31FAQZeg3YeaG5RuT333/PXzaKFXFG+PHHH2eJq2+h0rDWfOKJJ1jowkqQQC6vIwCDiYbgSy+9pEw14TSffvppaoFToPsgzlh4P/vss6xY4D29XnuQFugUHGyFGjZsqMgyeDeahkVVUDQQEy3YUl49jPXsBEa9FMUlIs22q6goKtUPFLjQUcLKr1+/flBv6PHZZbf4LeQpA95sBWZxgT0XD50d1EWZUcnCrNusWTP0KOmppUuXwnuyRYGZHqQhijwYGpMG0gELZSYBpl9GL2o7RKp5HqUzdIedVu10Ynea0oKRaJAp4oy/S5faBg82ZBw+3DZ16k3Cnjxp+/13I4b5z6lWZaNG6PTZ3njDBkj79tmaNLFFRt5UgjVvMHhkVNjR5Y6RrgaSTxvldOAxUJlj4cVg6pl2HAVgs40NHhLwCNaYv1D//E0F81W4Y2slRhAQBAQBQcCLCCREGyr6nl8xp/adXfRRXKRhTnV1r/GZkOmeeuHZ86Pmfnx0u3R5isVHXd73ejF0zXh0YkzHK/8sIxCa2fiktoUmZ1bPuHOxrUQtI7tcwYPA1U3GXr23roSYqKgdxpeN3RW9f6ddjNxaCgE4FFZW7E6jIOYo2M8//4zGDasv1tL6KUoNhFmeccE7qFsKwbsZi3OdjGWbNnPjk/2HH3748ssvWZCzUGcbWdlf6MQS8AoCrEMoh3X1iy++iHYJeoXwINjUs0qpVasWmlOswHFi9c4776g1iVcqDepCXIEDH6FpXxxm0cZgQQwrXaSFOrHrF6wUsX6C3YZTQ5+uU6dOSq0MLUWcoGF2zdK0RYsWX3/9NRxKhw4dILjximV+o+0KDNQtUw1EvKo9S5YsWoOS2Yb5Co1LlObsZDNn4RGzkNk6zC6xBW9VL2COrWTDapUAfy9cuABXDmuGvduRI0dQMaPvnn/+eSKV6yimdzBRBpt0NzMAGV0pYDqd2C2IhlORlHUyimZDhhjP69UzzC179rT1729rZ7iXuHFh+Imzu3XrbPBojhePunY1ol9/3TDbJM3MmbbnnnNMaK2YytfNU3///Xf1+ivhHCNdDaSCBQv6rj1OB576qUL5XZHdsH4w/sq9g5KE+QfbahWGBeZnS43/oJuvHIFNzirLsRSJEQQEAUFAEHCFQEJ8jKtHTuPP/fCJjs//zBe2hITL62flbTM8IS6m5GdHwrLl52nUwU0HB1SxxcddXDOZ28Jv/5axeCUU3M4u/EDn9TwQNa9vxAuzPU8vKQUBQSDgCOA9R8uAH+7WrVvrWx2AOMOYi9XXb7/9Bvmi4wlgcIHrXwJKiYzdYDcWXspakFrgI9gexw05xp6amzAXK+GUIMBamux01rhx41BLgSNDEwcaiMjJkydjusUaCcssdE9wNJP6TgxIBnTKfs0VOKjpgSTqPGBoQRbJsb2Q1PQv/JH5jAiVjJUnLynqHvBNrFehWjDWRiUEW+w333wTy2vYc9o7ffp0wqguMn4wImP8WM01GDMPcqpG4ctPc2fQRu+++y4sP1oq5cqVM4NjzkI8ilfBxZ3hdBJb2o8++ojuQ69QkVwEaAvDEn4cpgz3ZxwWMX78eLgzNVYZDHQfOxy4njSj4T5sN7G7TxyQp7ghC79OP2jyC4+769cbspjNlFu1MrgzdMeuT4rG0xo1DOPNyZMNQ05lwI3XR9ycOb3y57e1bWsbPdp2/VAcp0mCL9LNQPJzY3AoyZ4cXsxKly6NKjrqk7yzfGxoMaB9zZMYkqu3PujmK90iHRDuTEMhAUFAEBAEfINAch3x39ZzUYaiFc4sGBRz+gB6Z4YHtMtnj33RCido2R/smK/tZycnvpIocXx8QlQkJpxZq7QyU2+et2f5gXWeJ3aasnL+Yi3vrNhr1Sz1NDQkpGPZGuXzFl50YMviA1t1lgZF7n6s2D3bzh4bt31tVFziB7R+KoHUhEBM2eo9IjqkphZZqi1s86pzMFEEwwWSU9nwt12/fn2c7LA8g/AyG/u0u35BwUDQcKQAJ/opKs1czqRJk7B6w1STvCzXq5g8x2CRoXbFzeklnEIEoEIoAQNbdukJwIBwRIByGc6KWnU3qxTIBTRx7PiFFFYdpNmVeo5TcDgogNMtINdQxbLIcZO3BBmaG95E+TG0S4yfIFgzaBQ4JogzKGxWqtj6oceBXyQSw6y1atUK+omTBFjZclAA+qHwaFbjzpQxuF3r1C0HUELKM2vh08+cwE0WczIrhxmK9N2ECRNw+YRPSbxWKfNbuk+fTkiYaRZOjRicVNKVeD2jH+2OW3XfTLuJXZ0V4z6Ln59u3mxfIccnXLpkRJrOibX954Pe9p+6no0jbTBpxWZTMWikZ8PIFXfGU/WrqLMbFQT/5Wog+blljExc9qlKmYW4OMTAzJ0xTUHua6mYh9VQDLr5SjdBB8TfmYZCAoKAICAI+AQBOK+klkuWooP+ylyuwenZ75yZ05/scZfP8Pfo8CZXd/124rsX469dznTPjR2ewx88tOfl3LEXTuRu0i+pdZH+wH1NJ8akyHtORHi6SfU6trzjAV376qd696/cqHqBEl8/3O6XZj1U/GcPtvy2Tvtahe7sV/GxHe0GpA+T/RsNWJIDEeVrJjmPHzNAnD1R8ks/VpjmqipatChbvlyuiDMQUZ6eOASAVTf0GVoPaK9AwWifxPg7IxnOgDGDIpLD4zWOODGBdMAb2tatWzm1E6UntHvUhe2YK7shnV0CyUCAQyHJpcgyHeCWeJYiqkD1NFgOR04GCEnK4goc3O1jtwjby6EBwUKc0XDYLiz4dF+boYAKZNmpHqGuheIVHJlOwHh4//330S1l+YoxIClRCyVZ0LkmxK8fCpU4xddNSx0BNhuefPJJdiM4NJNBC0OKRS1NwwqbaVm1kc4iHuIMPV+cVDJvo4ToIXHmamK3IHowKjExxr+JExOlw1WA8uF23bFbYuSuXYkB0+GNNnVYbq9etiNHbI0bJ+Zy1cYdO4wn17chXCUJvnhXA8nPLYHlZ2rVlaJTZveTxLhFBVIl4GxZJiuzl8agnq+EO9P9LgFBQBAQBHyCQEj6iCSVGxKeodhHOzLcdg8cmbbBjD5hrGnjrxhO0IwrJCTh2uUMRcuXGH4oolQihxJ75iCqZ+q5538xAu2Wpajn6Z2mnPBoh0zh6fWj58pUK5wlZ9cVU8tPG/zm2rmlc+Yvl+f2gpmzP1Gy/Pz9f5efNqjijA/CQ0N7V6ins0ggNSGwp+6rQpxZp0MxxsSF0KZNm1B8yJ07N9pnBLDnwiYI/Q7U1vjMRfUMRgwP5ZgIsV3Mio79bdSgMATDuRKOS9hYVlQdf3HGxOoOPRfrtDF1SIJOyn333QdlCbYshukmjjTF8oUTylh1w2CyCBk1ahRdRi+kjianvBVOwcFOE80IvPAwyKGGuax/ugIcCvw1Ypsx4ZBQ7PiIgd3mTcQjEq8nAdaiDBWdknUsbzSECzEcGgDRRns5eTO4bBsRnjkHnSx18qZuHa8Dg19fzE76UbAENmzYgIYv7Bg6g8y6eKZTNu8omn311VeRkZF01nfffccBFwyDQYMG4c+OTQ41dEnjpplYgHIgjKuJ3U3GQD3CYFP/0zI0a2YEOSJA+Z2HWXvvPSOmfn1b+huflokncjoaeBpJb76WLDE8nXHVqXPzgyC/czWQ/N8sbJDXrVuHbTgOJTkkVzmF0PMVHwyIymkYzMBfcHyDzYbPBy1kUM9Xsuev+1ECgoAgIAj4BIGk8lkFXprAgQAX10xCGmwz+Xtl56+QaDkf7XZ775+OfNwoV8PXQzNkvrRuZtShv0Mz5SD9kSF1M99bL2OJKtHHDQdGSbpmlX40MjxDkrLYJX66VKVqBUqsOb63aoES6lG70lVOXb20YP/mUjnyzdi9YcWRXcciL7xVsWGILaTbymnQapGx0ZVnDDl7LWkHIFUrmFi+nQByaykEIM5SbqrZuUa2sNDE418vXI2f9Kdh0REeGtK8XKZKRTIu3311yfYrutV1SkXUK51px8noKRsuR8Um10ZaFxckAffuxtRTnYZlNgaALNJwO40GGdZAHLVJQ+HLsLxQyXgKxcZpdwoAXJKj9YCdBcZiOFQyo4JuC0c9/vTTTy1btjTHSzjlCNAFoK1OfkBtSiHPyQz4QlJow4bQg8q6M+XVpYISnIKzevVqrITQ4dINnDt3rsWPCzh27BjSok+qZSbAuhTChcNVcaUPZ/TKK4mOGjDm1SwbBry8j+iEKhd4nLoL09q2bdszZ86g5WQuLYBhPReZHYorefQjdQtLiPEpjtu0tHCFXPqW2QxaWd8GRQBTSmxR4XmRlvmTw0CU2OxksF0Ba8YtRDnO12FFlaExHLpKA1fOXoUKO/6lr+GIcRvnamJ3zGLBmAEDjCM1McYsWRJ1aduGDYZmGdcnN9z/GrcFCtgaNrTBi3GBpaPBJkcNQDWfP594kgAO1Dg9gIvjVjgYB296qGmjJDVixAheE2XOjOMC2EnUVI10Ab3sXgQli12kq4HkZ8HZbMO8Gn9n1IuyZO/evRWYer7iFkjRdlcJmIi0Q9WgmK/c4Blip2LnJqndI3T4laq/XbzcCgKCgCAQWAQcZyfHGH9KeHnDvGOfP+l5jXd+ezXEpMNFxnNLPz019fXstV/M1/6LEOwcExIiNy85Orwxj7JWfTpf20/DsuYhHHPm4OFBtWLPJWFLNubOmvULVfNcNseU+TJlXdei75y9f12KudahTPUi44w19qbWb4eE2LKnj0gXGsbt+B1r+62dP6bOM3VuL30u6mreiCxEbj1zrNHCL+ISkmArOrNhJ0g6Rxn8E+M4ihxj/COJquXqxtXHXmnizxo9qWtCs/Ez425oQ3iSxTFNkZzhP3QuCAeGC2GuyKj4KsONUf1r90J5Moedjozj79zNkf0WGbvxQxrnanJP5jORcTkzhcXFJ1T6+Gh0XNLos3Ft81UukiL62JAyuZfjKHKMSW7Z7vJdvHiRx/pzVidlG5k1PL54HB/pNBLwNQLooaBuZkeQ0TXonaXKfoEJQovBParu06RicMywYEPNgRIoGZlX1KgyMTDgwc0p0dViqJjddZufmsNwc7Az5hhXYdyoMTnYrf78M1+5Eim44ukmqEP9XuOojiOSx44dC3cDAaHjU9IoVxN7Ssr0Sl4M+NS5ixwUEWZ8GNpgR3FYVqmScWgmFybInTsn8mLcVq1q+/Zbw8cZFwHIRp6OGmWbNMnGqdGQYhwaMH++DYU1zqSdM8c4GeD6EbVGei6ysz3EgTqqLshH/H7CL6OxSy+gJMVmkmIw8SCJniYMmsqY7L/uJyhVrCdpPBHAbiB5ksUXaZh11XTkSIir6lQCphfrzFcpx0H0zlKOoZQgCAgCgoA7BMKyGydjen7tfsG5jeeFFd/wL32hsjGn9iXEJPrgvPT7VP6ly39H/JULcZdOeV6LSvltsZq26ERfG0nNq9LPavgSdFiv1bP6V35cl5AlXQY8oGGeOX772kFVmz57V7WF+7fki8gaHhp2PupK5+WTn7qjAtpqA6s2hlPTuSSQJAQiKtRIUno/JPYKcYacD5Y0XoEqnxy9HHWDWu1aKzuUWavxJ7Yci4Yva1Yu80e/nI9IF9L4nsyL/7nSa/6ZvFnCVnQr9OpD2YctO++HxgZ7Fa4oGBwq2Wm+BHtLg1H+zJkzO4pN13A5xqeCmGLFiq1du1a553PaHJ6SxukjFZmKwTG3Gj1QR/+G+DUzp1Fh5YreMV5iAoiAq7NxPeQuPZHc1cTuSV6fpsHKXG2G6Vo4SZN/+sKv2eLFNk4fPXDAVrgw2kz6ie2FF4x/6mrXzsY/dTVteqNMqDT+ubo455RLPaUX8BqmU6Kvp8MpCaR8EvO8dlcDyfMSvJKSWRcLcTdFOU0Q7POVcGduelweCQKCgCDgBQTS5TTcP3vrij72j2NRMde9oTnGu4/Z+0CLmSkjzl68u2aJbHl+Pbb7o+pPVMpfjLM1h9V48pO/fr4aG03V+Dvjb4efx//Rsm+zEvcpC81nfx536NLZP07sb1L8vgcL3bRP7l5angZQ6eyWsqXxBIknA6SIhr0BYcXC6a/FJGROH1K3VOa1B66duGSUW71YBpTLIM4ID19xAV2zhmUyoaGGYWfvBWduyx5+JTr+kS+Onb96g267UaLbUACVztzKJQ8FgbSCAB70sKl0cwAF61LSpBU4pJ1pAwHOFXWlsJM2AHDSygwZbEHqy1EmMSfdmRqjhDtLjb0qbRIEBAErIRCeu4iVxEmUJT7nbS9nKpxCwTgf4FJMVPm8RfiXEWNSm+3x4uWm7Fp3+lpkgUyh5sIhzqLiYom5EHVVxYeE2CJjDB5ErmBHwOtHapbOlz5jupBlrxSCF8P88stVF0f+dqFAtvAzVxJ5McWmFc4RXjRXeGx8wspuhXJnNoxAtp+Ibjn2RLDjKfILAmkNAY5g04720lrbpb1pFgE81nGl2eansobLJJbKOtRVc25a27hKJPGCgCAgCAgCKUEgfaEyKcnui7yzS9eLue6MLCWFf/b3sjKT+qt/E3b8Hp+QQHjTqcMjN6/Ilj7i/apNOFtz9MNtqWLO3k0j/l6WYEuY3uBF/J29XekxeLeFBzZ7XrsonTliZQWzTV8cqYlHs3/+ja42/Gjjb/49eSnu5ZrZsMfMlC4EZTQNQnyCLVtEKFacHCBw4Vp8+0knZ/8dWSZ/+rfq5dRpPAmI0pknKEkaQUAQEAQEAUFAEBAE0jgConeWxgeANF8QEAT8gUD628pGH9vuj5o8qyO6bJ1RGdw5KfCsmJtSQZzp+9l7N9a+7U6ODuAf8e+vX7TnguGLre+auYOrNuMkAUi0Xw7vgGLTWSQQjAh45UhNx4ZDhKnIi9fiv1x1YUDDXDWKZ4yMjs+SAUW0xItDOA+cjcmb2dgC7DLj9OHzsRsOR2HFWb1Yxv+SePR/pcCdEuCRfJJIEBAEBAFBQBAQBAQBQcACCAh3ZoFOEBEEAUEgtSOQoXC5y+tnW6eVo4tUt11NPG3AW1INWPc9/3Rp3X6d3mPVrDuz59t+7riOnLxzHf9K5ch38NJZZcKpH90y0LN83VumSWsJcnbsc3VjwI7a9NbJAI69Nrtj/l92XcVUk0fKGBNeDDvNUvnSq8QVbjeOxdx5IiZ/FsNUE70zFY8h8JWYJPs7U3nlryAgCAgCgoAgIAgIAoKAIOAKAbHZdIWMxAsCgoAg4DUEMhZ7wGtlpbig3ZXbzvM2ceZUqNj4ODNxptPsOn8yqcSZzisBiyDgO+KMBmKP+VL1bFWKZqxZImPHKlk5IgC1sq/XXuL0gI+b5b7/tvTDm+e+Ep3wx8Gor9ZcRN1xbJu8UGxvPJKDYzd/2J7oUM9DoCoVTZqemofFSjJBQBAQBAQBQUAQEAQEgdSEgOidpabelLYIAoKARRHIWKKKRSSLy1vipYhCFhEmSWKIvzNHuALi78y7R2o6NoqYvgvPjnwqD4wY4cjohK6zDIPflXuuooyGVSb/IM5em3uaUwLOXkkYsOTcO/Vz/ta9ECQaab5Za2ireX6JvzPPsZKUgoAgIAgIAoKAICAIpFkEhDtLs10vDRcEBAH/IRCWJVeGovdHHfzLf1W6qGl6qaC0fBTizEV/2qDPrm5c7eqp1+O9fqSmUwn3no5pMPp4johQ9MiOX4zTabrNPp0pfUi+LGEHzhpntqprxl+X+VcyT7oj52OjYm843fvvubv/LUucRUfLEbTuOk6eCQKCgHUQkPnKOn0hkggCgoBPERCbTZ/CK4ULAoKAIJCIQKYyDwcci+hyj30bnj3gYiRDAOHOXIGWsXwNV4+8Hu+LIzXdCHn+aryZOFMp0TgzE2c6O3RbUokz8spBARpACQgCgoAgIAgIAoKAICAIuEFAuDM34MgjQUAQEAS8hkCmux/1WlnJKygk5IvCVZOXNeC5qhUsEXAZrClARPma/hHMR0dq+kd4V7WIszNXyEi8ICAICAKCgCAgCAgCgoAZAeHOzGhIWBAQBAQBXyGQ+d76viras3J3VHvu+8uXPEtrrVQonYnemasuwWbTD17POBmgR0QHVzIEb7xlbTaDF1KRXBAQBAQBQUAQEAQEgVSJgHBnqbJbpVGCgCBgRQSyVW8XKLFiC5Z5OV2eQNUu9QY1Aj49UjOAyHStmS2AtUvVgoAgIAgIAoKAICAICAJBhIBwZ0HUWSKqICAIBDcCWSo9GagGTLnjkUBVnfJ6e5YPyvMNUt5wD0vI2bGPhymTmoyTARo3/mtm3H1JzSjpBQFBQBAQBAQBQUAQEAQEgdSEgJyzmZp6U9oiCAgClkYgS/km4TkKxp4/7mcpoyo0Hxeayc+Veqs6Mdi8JZLKbNPrp23650jNW7bOdwm61grKczMAJCYmZsmSJaVKlbrrrrs0PsuWLStSpMgdd9yxbdu2vXv36ngCBQsWrFSpkorZsWNH1qxZb7vtNnMCCVsKgbi4uN27d0dFRd1zzz1hYWFm2a5evbp27dratWuHhqaqze+TJ08uXLjwwIED5saaw8WKFWvcuHG+fPnMkSqckJAAXDo+S5YshQoV4hYYd+3adfbs2eLFi6sYncayAfp3+/btOXPmRGZHIQ8fPkwCFc/AKFmypArzvp84caJs2bI5cuTQuebMmVOiRIn7779fxwQqEBsbu3jxYlV7hgwZ6AumqYiICBWzatUq+qhu3bqZMt34SqE5f/zxR9GiRe+77z67CY1I9V6ojHaNypUrV82aNUHpp59+Ap+7777bLkEAJ8BLly4xIBmuzNuMUjvB7G6vXLmCqEBH4mzZblKRdtMEqmD8UFSZMmWY5+3KlFufIpCSSUwJxpy/bt26WrVqmeW0i9y/fz8fAOYEvBG8VuYYH4VdDbx//vknY8aMzDbmenmFL1y4YI5h9lYTlMXnK7PMTsPCnTmFRSIFAUFAEPAJAphtnl08zCdFuyg0JH3EM+nzu3gYBNHi6cyTTkL17OrGJp6k9DBNqjwZwNz2oDbYvHbt2pAhQ1hgT5s2TS+QxowZ06RJExalv/3224QJEypXrqzby/pZcWfnz5/v27dv+/btn3wyYDqwWioJOEXg9OnTnTt3pqdYDrFqGj9+vJnoHDlyJJwIXew0b/BGQpwxSrt06eKqCTCGpHn++ecdE/z777/PPfccr4N69PDDD/fq1evy5cs9evRgnQlTs2fPnq5du7Zt29Yxr6ViNm3ahJysMFmEw318/fXXdsxpt27daJdaJ7MKnTx5MvIvXbp01KhR1apVGzx48JQpU7JnN7YEiGQSmDhxohUaGB0dzXzF1ASxpcgdOuutt96qUaMG4k2aNGnz5s3p0qVr2LChlnbGjBlTp05t0aIF3Jl5QoNL2rhxIywbrVu/fv3BgwfJsnXrVv5CqPEXvgzubMWKFapGkukyCQRwAvzrr78YlvSaYj8/++wzADHLZg5DsTF6wYRIevyjjz564IEHVAI3TYDaeOWVV6iCNCQeMWIEdKq52MCGz52zzZkTGhJi69Ahnr9ce/aErFwZkiePrWnTeCXbtm0hv/4aumNHSIkSCdWrx1eqlKDiiVm92sgDP/Pww4mJ9+4NWbHCiCxc2FavXvyKFaF6zwjk7rgjoXz5hP8YWlWMb/+mZBJTks2dO5deW7NmjVlQu0i6+BxQmi7eerYWTBE+CboaeEeOHHnhhRc6depkx5198803mjFXAvHtwSeK9eerW8In3NktIZIEgoAgIAh4DYFstTr4mTvbVvWZUyFBvP0oBpueDD7vHheQ6okzIA1epTM9HviA/uKLL958800dowOlS5f+9NNP9S2BU6dO8VEOAYE6gzlewlZDYOjQoSxCPvzwQwR7/fXXYcpgTJSQrKl++OEHqwnsFXnQOHNDnFEF3NC8efOc1gVBhsYlPLL56axZsxjzs2fPhksiI6g+/vjjilcyJ7NOGILpjTfeePnll9u1a3fx4sUnnniC7jZroMChKMWW3Llzm8Vmad29e/dHHnkEDKGTUOBiNTts2LCPP/5YE+vm9IEKwwhXr16d2mnpJ598QmORPH/+xI29BQsWaO4MtZr58+eb5TRPaMxgbdq0IT3skkoDDZc+ffr33ntPZyE73f3999+jhAULSXzAJ8BBgwY9+uijffr0gf5DYEjw4cOHa4HtAp9//jlU4AcffBASEvL+++9DtEEC3rIJo0ePfvDBB99+++34+PgXX3wRSoVK7UoO4O3JkyEvv2zQDs8+G620aTduNGIqVkxQ3NnHH4e99VaYScKw7t3jhgyJI/GaNYl58+ZNOHQoXindjhwZOnKkkb5x43i4s7FjQ6dNu0kbt3jxhAkTYitXTiTgTCX7JJiSSYyZirkdFUuzZE4jFy1apNPwmvMZgN6ZjvFFwM3AQ7d3wIABZo1RLQDjkEvd/vzzzwxOdjW4DZb5SjfEMXDTIHN8LDGCgCAgCAgCXkQgfcHSWSr6T+Mjtsj9r4TcpO3vxbb4oShROvMcZG/RZ6n1SE0zkkGtdKYbwk4vG92slnWMmwC6Kmiivfrqq06t3txklEf+RADSBEs0tvGVwhFL35YtWyoB2PZHsYite3/KExR17du3D36ENeSxY8dgDZTMaO7Uq1dPkWVVq1YlEhsiKzfnzz//RLynn36aJmDQBxV47733mgWGImSNyoXlptloi4GBPhcp8+TJExkZyaP+/fvDLqGxZc5unTA812uvvUZD0EBRUtWvX//vv/+G8lO3v//+OwHYUqcykxFe6ejRo06fEgmFgSLbM888U6FCBU00BHYCpI8YnI899hjihYeHo32DRSo9hWFp79694chgPJs1a4aqDmQEaTZs2NC0aVMmAUyzGb0oThJ5yyZQcvPmzclCAGNVOEpyBcu1cmWoIs569YpbtCh24EADhxEjwqZPv4mpOHUq5I8/DF0zXvQZM256pFrasGH8Z5/F9ewZB8u2f3/IE0+ER0YGAQaw/4yK1q1bm2V1GqkTwLWtXr26X79+EKw60hcBNwMPzVY8QsDYuqmX9/Hdd99FD1RR+UE3Xzk2zcmwc0wkMYKAICAICALeQiBHnZe9VdQty5lQovYt01g5gSided47KT8xAAdnqfVITc9hDKKUmGGinAKfol0gaeHhEdC50BeLKDzm4C6KK2/evDqZBKyGgKIPWFezlkbnCFUabbWH3RYrK5zcWU3mgMsDs4BZX6NGjZ566imoxp07dyISqzVUb5RsK1euJHD77bcHXFQ3AmB+iBEfrzMmh7B+2GNmzpzZnB7uDJWrOnXqtGrV6qGHHtLEE57R8DeEee+WLVvQQMF2G1s/mCO4QseZwVxgAMN4R4LagwRUMtA1MPs//vijusXUCw4Iik1LyAxGc7h4QeCb8O0ISvqpXYDs5cqVo0wU2VDbVCAEdgKE70NIzLGVqCpw5swZxZVDoqGVA584ffp0ZWaLnk6VKlVIDJFKY5XB5i2b8L///Q++Fd7tq6++gliBh7VDxsq3H3xgMBIomg0eHFe3bnyfPnHDhhn02cCBZk00owUYfvJ3/foQeDTj/uarWrWEzp3jPvwwbuPGWBy+kWb27CDgOvg159dZsfy6QU4j1VNsn+luFE7tZgmd14sBVwMPpc6ZM2eiHO2+LlTg+VDRFsrBOF/ZNTAIxpOdxHIrCAgCgkBQI5CpTO3M9zbwQxOuVWk9KcHwlxGkl5wSkKSOUycGJCmLObE6GSAtHKmJ0lkqMNhUfYeZG+sutBXMXUkY2y6W1voKLgUEu7akqVvlXBltAvp03Lhxx48fxyoHBFCfoU9RJkpTaHjYWLzOQxNAMXCARuHChdG6IiPsDBQS9nFjx47F5I1IxV94WKb/k6GOgbYUQmKoNXDgQGgUjJvMYkCjoJ+FqilU4LPPPoupFI7eSABFyPBgdQqhxmyAsSomgThF4i+rcaXDZS7HImF6Db+NWhhMLDHDpI2cG0ADaal+RADeEDaNC26UroQaw/7RnECHQQC+TJl/KnUYfJ/pp4EKwAPChkN2QIrRWdjaIwl0J3/pcZRJYRY4AAR3fpARKhLSnCEBBQwXhr6w55KjcYnjM9IzY3iey58pq1RJV7Wq8a9v30ReLCEBVTuDkWjdOlFvlHCLFkYY3THtcb56dcP6curUUJTzFiww0teufSM9t+YrX74ElDiJ2b7dCcVmThmMYQxysXdmzARKeChppiC03twbwnP0ARf+KLWcQTpfafkJGIbHcgkCgoAgIAj4E4GcDV+P3PKDT2sMy5zz6bA8Pq3C14WL0llSEU72iQFpwcFZUsEMivTsOeNShI1ftFHMAuMeCI855hgJBwUCMD7IicN4dcwixpssTnA3gzoSjsbhVlC9IQFO5T05qi8ompxyIfEhpQoBvZdeeqljx47o9WDAiC0ninvoauFbqnz58imvyKclqK6HJYHywwwKHTq8E2qLXarGpo9LycDA4BAJ1M0KFCiAdzx1aABUC+chQJmhz4VaChpMkIk8slNm8WkrPC/80KFDZqtM9MjgiXCozxl8EElc5qK45eQEYjikkjTMeFClsFHmNCqMkzh6HCVNLhWD7zPtSc0xvd9iIMhgw+kOrObx1MYbrc4c5E3HJk6JwUEHqAzDqRHz66+/QqFinarM4jyXEx6ZC44VagMPU2b1Pc8L8WnKLVvsySwOj710yajTdE6sLWfORD9lp08npi9bNgHWDJtN3J/BoJG+ZUvjiABX0ubJY5Sgs7tKFnTxbAnAseK0IYCnLbMnAf3NxY8Rv1BQvej/2r22AAshjgI1s7EGOUjnKy0/AZcDzpxIwoKAICAICAJeRCBT2UeyVPKt17O5dzW4kC7CizL7uShROksG4Kie5Xy+d1IzpiniLDUpnamOZv2JGx38SSsthqT2vqS3FAIcCok8sCdKKvwWEcBJPAts1JHQW8G2ixi8jGvnUCplmv2L7hKqeUoDCxCUuygCUDOsLTF6QkHD+sQZAqMxx1+9GKbr4YmI0RfMixvnhiTGbRYMFIcGYAzIApWisFtkWatLsE4A81JW2pysqkWCOkStDLKPgx1w9aXjVYCVOQm4WKsz4+Hebvfu3XZp1C1rdUAAK3XBq8I4o7bmNLE/IzkeFOoTxgFSj85F3Uy5f8IPmh60MOPEQ5yhngbXxnEK2LtBpHoiJ3wxRzFoZ/P4OyOXNXv/4sXoyEjj39ixsappmLTinowwJ2/qxu7enRguXPjGi9C2rWHI2bdv+NGjIY0axefLp5M7CezcaZTAiQFOngVzFHQVBLFyvR+odvC9gT47P0lcqEYuX74cVVk7YfjlguN2qiIaXPOVXbuEO7MDRG4FAUFAEPg/e+cBJkWxReHZQBBBgiCKIElFUYxkTBhAREBQDIAKqEhQFMWcEBOKmBEeKohKEBQVDA8jqJgQfCoqUQUDioGcN7x/qKXo7e6ZnWV3Z3p2T398S011dfWtU9W3u07deyseCOzd8daiu832es0eKR/o2C55tn1quz55llEBLwJVLskxwfCe8s0pUcQZCBQbb01nb7IPIxMn5mDOTKWTEQHccPBHe/rpp7Ehok+Z/7NoD3GGuYo52CyVdrHpHnZnydjAQpcZPoWZG05whI7C3Q9fV4gVzBxIYLNDaDCQxHmNA3/AQr97IVZIwC9qo8dhAwkkBAfUqlUrcjCbeuGFF0gwHtg6E06QAsYIy7kbAI6KnCJmFiXZiBObO9oLZxScKG84ICMP0egQFcMr2uu0O0Ps008/HX9GyrisaDnlOqCY6WtXJj/hnjDWg4MjLKA5iIIHGwUl5y0c5xz4DuzIkJBdDmDQ8LE1PCmGZjzvbPJASHVsCXFWhUojsDr+m3C+Zuj6NtbKj4kxfqnsF4FFGzUTOI8xP3bs2MqVK8fIu9mq4pNgRcD+s3fs1CnMcA0blrZhQzhv+/bQXXeFPTpPOy3LEfgO68twsS+/DJNiTgdPfrqOmTNTTaSz1q0D/eC7xI7lJw8RnZvY0KU8wjtfShN4ctkdGBNphLf6irThrFHI3kYFX195ZbY54s4sFEoIASEgBOKHQJlaR1Q5I98mQjHKN7buCTGWDGaxa47y8cUIpqgBlCp207OSsKWms4OKx/aatMi1rxYBR+xm8N6zTgSUDj4CmBCyXI8tIduuYZnCNnxOma1dkjOzhKfZjZRJGvM3YmbhzcRPAIFDIc5O2Mtx5xFwcpmA3DCAsDwYjsGbQPogOA2BFjHUD+6o7LvHfhEUeO+99ygMR2a6HhsuaDWoGcyyyIGWYmpNjCEIxF69epkyCfxr9BUS4lKKPDSHeG2Yq1iRTAG4TkgxWmecGV1n7U8SkESY4FlzLXJMDew2AFNmWEhTHgsvniMm6gwMZw3xT+NHyfap8Hpdu3bFtQ3PYiMDbWGLVWxziGaIz2bfvn1xucWqiD0fdo7csy688MIoAk+aNAnbOjQDFm0QlNwI7QGRinVqEqmL22/PJLQ/zpiHHFLqnHPSGzQo9dprYY7C7Bhgm1+9enbbtjlcGFtq2nybGD487fDDS9WsWapjx7DRLvwauweQgG9i4QECmjSDgTQqwlyFfyurESad2L+uN7sRxpsJzRqotRPnMLP6CuEZhPz1ErgB11d5joEUl0lwnhfYAgsWLEDN2Z9KCAEhIAQCgoBXO3lzAiFqdtaSy8pnZ4TjxRbi8dNRnS+peGAhVhjnqvDWDKbRmXcUeXPijFWk2/1+RcfN8+dEOks+OwNMOvjykrAzgBOE728Ke0UF7fCOInIwmgianJInnghgh2L81OJ50wTe67bbbrMBqiKJgf8dxGKks9ja4OxmyKNIZZIin4bAmdL7vtISohtXKVd8bkyZyHcFG4pej7NyTJ+wWnLmREoTRg0K0jX7k76KBJc3n26CCsFY0pzCEZsNMaE46QKGrs33Xhh7DgaY3MI4hMZ+VRxKLl6c0qhR2Bt906ZtZnRPnZrao0d648bZc+ZsJ/+XX1IGDEjDXswI06xZ9ujRGcQ44+fYsan9+qX36ZP1+OMZEyem9uqVDik2fnzGjBmpEG2dOmVNmZJx8cXpkyfnXMslXN6lS9aVV2aae+EGS0A97Dp5t9IL2DYSNNAwmKxPYKfJJhvmvrv9t+BKbLdvnVwXJlZfFRyrMCmrQwgIASEgBBKAQErqfgMm//5o50K8dXqlGgPK1y7ECuNflbYIKDjm0TcNMFtqhsJhQ0rQUWyMzkpQn5XgprILRIlqfZ06dTADcTnxORHgLGWcOa60y1jJdTaJfkZvCAwLh6s5+++/vyuHn9Hr8ZZXThwQ8PaduSl2goV1dwwYC6uqwq3n4IOziZHlrJOdNLt23ZVDXLPp0zPYfXT58pSaNbMJgmaP3r2zevfOKdmtW1a3bjnpDh2ybJ1QaePH2yvcCXaWsJtL0AvsKWFLPPDAAzZdkETBlVhB7p5E1ya7vhJ3lkSDTaIKASFQ3BAof3THSqdesebd8IblhXK8ePCpW9Jy4kwXSoVxrgRvTezO4nzT4nc7s2nA6md8vghLWoAz07nFb4uA4jdo1aKSjECHDh3wnCJOfCQQmJdSJtJZ5QuBZEQAzzuvR14yNqQQZWbTUVi2QqwwblVJicUN6sTeSNxZYvHX3YWAECjpCOzT49EtSz/d8vO8ggOxvcGJI/eoXvB6ElUDrJmMzgoLfDYN2PLVHJfnZskkzpoeUKZYbhFQWENF9QiBhCNA1CeCfCVcDAkgBOKJALthmg0x43lT3auIEJASKyJgg1btLsfgoEkmeYSAEBACJQSBfXqOKpSWjqkd3pYreY9ghjlLXjxrPDEdAzQrf8kkzmh+/+MrWhCUEAJCQAgIASEgBISAEBACu4GAuLPdAE2XCAEhIAQKE4GydY7d97JxBaxxaePzXt4ajreapIeIs6LoOAKfmWpL2paaFky8NbE7sz+VEAJCQAgIASEgBISAEBACu4GAfDZ3AzRdIgSEgBAoZAT2anXR9lU//vNaxE3Eot8vs0qtvuWCuIdgdLHtWYU5s1AUbsIEPnv0n8NK2paaBkaFOSvc4aTahIAQEAJCQAgIASFQYhGQ3VmJ7Xo1XAgIgWAhsHfnIRVP3M2ALy81aJOVkhKs9sQsDcSZwpzFjFa+CxL4bJ8Wx+f7suS/QMRZ8vehWiAEhIAQEAJCQAgIgaAgIO4sKD0hOYSAEBAC1XuNKd+4S35x2HZYm/+UrpLfqwJSXsRZHDqCSPklzW9RxFkcxpVuIQSEgBAQAkJACAiBkoOAfDZLTl+rpUJACCQBAjWumPrbwx03fv1G7LKOOqB5aNPm2MsHp6SIs7j1xbPd9+k5YdUXK7bG7Y4JvFEx2FizdOnSCQRQtxYCQkAIxI6A9FXsWKmkEBACSY2A7M6SuvskvBAQAsUQgf0HTS9/TMcYG7a4+YWviTiLEaySXQz6DGusYo8BbaSlxb6ZaqAQEAJCQAgIASEgBIRAPBEQdxZPtHUvISAEhEBMCNQY+EqFFt3yLJpZ/aC+ZfbNs1gAC8jiLCGdgvNm8abP5KqZkHGlmwoBISAEhIAQEAJCoNgjIO6s2HexGigEhEBSIrDf5c9XbjMwuuiTDzoleoFgnhVxlsB+Kcb0mYizBI4r3VoICAEhIASEgBAQAsUbAXFnxbt/1TohIASSGIFq3R6udsGDkRqw9agOz6RViHQ2sPkizhLeNcWSPhNxlvBxJQGEgBAQAkJACAgBIVCMEdBeAcW4c9U0ISAEkh6Bym0Hld63wZ/j+mSsWelsTEpa+uM1m4bWr3NmBjzdYt961xx9Kn8DLmdJEA/6rEntsuweUAway84A/UveRqLFoOPUBCEgBISAEBACQkAIJBEC4s6SqLMkqhAQAiURgT2PPOOAIV+sGj9gw1fTbfu/b37xm0lFnMnczPZdQBJQTt/fVGvkR2tHfpxMDKwLPVpRAncGyMjImDFjhoWibNmyTZo0qVq16pIlSxYsWHDyySdXrFjRnv3kk0/WrFlzzDHHfPrppzZz77335pI99tjD5GRmZi5atOiff/6pX79+jRo1bDElEojA7zuOxo0bGxk2b9787bff0vUNGjSg+xIoWFHc+o8//njllVd+/vnnSJXXqVOnc+fO++4bLb4nEM2ZM4fxn5qa41Xz77//Lly48OCDD+bpiFRzcPL/+usvBLby0OQyZcrYnySysrK+++47ynCqdu3a9tTSpUsB8PDDD69UqZLNfOmll+rVq8eDb3MSmHj11VePPfbYWrVqffDBB6gjpyRHHnkkGuzzzz9v1arVPvvkbPOydevWN99886STTqpcubIp/Msvv3z55ZctWrTwjoEffvihQoUKNWvWdFYbwPS6detQs9nZ2YceeigCR5dw06ZNtGv79u0NGzbca69cm/xEaS+3+P7776nZe1X02+lswREouBJj2PMgnHDCCU5hfDN5HH777TeeHfsSd15SROlIAw+lxCPMx4P3vr4aOPj6ytsQZ464MycaSgsBISAEgohAeqUaNa56ZfXMh/+aNBj5MvY/fEB6lSAKGkEmEWcRgEl8NgZoCJGk9FmJ9dPctm3bPffcc9BBB1WpEtYDK1euvO2227p3737xxRc//PDD33zzzR133GHG1vLlywcOHHjdddf9+OOPXML0NT09/OEHy8ZH7QMPPADRsH79esosW7Zs//33J5/0RRddZC7X30QhQBdfe+21zJkNd0Y/XnbZZUYYOu6RRx457rjjEiVbUdwX4uyoo47q169fpMphfikTpQAXPvbYY1OnTmXyaSp57rnnyGFUM8lkSDOwI1UekPzRo0e/9tpr5qFGpKeffvqAAw6wsm3ZsmXAgAE8ofCAK1asuPTSS/v27ctZOKYnnniC8TB06FCab3jzt956a+zYsZMnT7aXJzAB5Xf33Xffe++9cGfjxo37+++/69ata+WhOSkpKWinli1b0l8mH+aInEMOOcRyZzSHBYNu3bpdc8019loSMHE8KT179jz33HOd+UFLz58//+qrr6Y5MLzIRpdB6UYSEortyiuvNLp6w4YNI0aMYKnDFI7SXlgzhgS3WL16NYVHjRp12GGHRbpF/PMR6qWXQikpoUsuCf/lWLIkNGtWCFq7c+cccRYsCOf88EMIHgYN17RpTj45H38cTterFzplZ5jfpUtDH3wQzqxVK3T66aH33w8tWxb+yVGqVOigg0LwxjuXh0x20f4tuBJ7+eWXH3roIThip6CuTJZPbrjhhtmzZxvNZl7izvJFlI408GDx+PBAM3u5M18NHHx9lSeA4s7yhEgFhIAQEAKBQAD/zT0btf3rxRtHVkoat0exZoEYOlGFCIc/O75ichmgyU+TLmUibQkU5sz3339/7969b7rpJni09u3bw7lg4MCHNQYI55xzjiEUhg8fbkwYMDSj2LPPPgt3xrV//vkn81Jm3dOmTWOK26lTJ6flWtTho5NFgsB//vMfGB9rbwIJgrkN9EFaWhpdDMliu75Ibh/3SrE4i86LYXCE7VIUuT7++OM33njDFoBHgIiBfoKSmzdv3uWXXw65HHB7PRiTKDNhGvj1119DilWrVm3KlCmUhEhihDC1hk469dRTf/rpJx7zNm3aMJu97777aL4dPxaWICROP/10F49J0xAMC1nm1WeccYZXSPh9FBR6iTGA3jPmeKtWrWKpAEtDiDbvJUHLufPOO2n4zTffDPdx/fXXP77jiCQk5HijRo3oYljFIUOGwKdMmjQpz/aOHDkSSz3KQ1ZCJj7//PPDhg2LdIv45//5Z6hPn/Bte/UKpaWFE/PmhXNgBQ139sADoRtuCOfbY9Cg0PDh4cJz5uRci2HiypUhY1f6+OPQ5eGynTqFubNnnglNnGgvDScg2shp1ixXZtH9KIgS44nm0cay2ClepEyKvf3225DsT+04eIk7ryr0dJSBx4fE7bffXq5cOe9NI2ngpNNX3qblWDV7TyhHCAgBISAEgoZA6RoN9x80vUO7gUkRNUzEWdDGTxR5wgzacbkcQ6IUTuAp46SJnyaJBIoRtFsfffTRiITTZbt27WBV7rrrLhw93nnnHebS2KBBuLgEJgdnLr714ddwauMqQ5bBUFAS3xNXef2MJwKYqLz77rvGqsjeF8YEOxTm0tWrV7c+ifZsCU9gaIPV1a233mpxeO+991q3bg1xBlWBuSWci+8Ez5ZPeIJZKE8illkwX5ga+cpDE4wdltNvkbYbUzUMuLgQL79bbrnlwgsvpO2+lQQ2E/IUrgezSq+EPA64cw4aNAiaDIsbUwA3sebNm2N0Zj09vRcGJAebHajwM888E3l4is866yzsKOmpmTNnwnsydHHTgzSEE2cYUGbu3Ll4KKOledLRyRgbkplne0uVKsUyCZdwCxx4sV0NSPNjEQMLMkOc8XfmzNA994Qvevjh0KRJua5etSr02WfhnKyskK9VZfv22PSFrrsuBMv244+hjh1DGzfmqiGYP/DCZlTAhjvF882EX8bmlEcezYbBFwQrVKnzqkJPRxl448eP32+//WBsvTeNpIGLgb4Sd+btbuUIASEgBAKNAMTZ1HZ9+BdYBg3W7Ndew9gZINA4SrjcCECfEQEtyAwasok1s53GJBOSC4dNopDgnsMEkk9tzmLawDwNq5MHH3wQcxunJwX2ZVxCHC28QiZOnMhcDi6GmRvFTLVEIyLBBN781N/4I4CJDRwQnVK+fHl7965du2JrACWKPRGeQS5azRYrsQmMJZl2EgnOIoAPMsG/LrnkEugVTLGgI+MZGMiKEXsCyozCGFVBmjAXxdQIXtt5OS6NuGnjyofFEi6Qffr0MWZlPODEG4IrxyoNDTBmzBg4lF69evGwB9MgCyZo+s7jfRztdh741eKGhm3szoxd/2OrgtEZTwQEE+PfnKD5ZHLAJu8qGsiU4W2JZ2ekw2uVBH/Xrl374YcfQoJgIDl48GB0MrbAnCLTLGNAi8AbGofNPNuLtdoRRxwB7/bkk0/CsPTo0cPcLin+3n13WEwMzTCVa9OGt1jooYfCOTvDD4TT5sDxk+OLL0LwaN6jZUseohAmbN98EyKmHGWmTvWWClxO06ZNGck8407JvJlmrQv6icJoNroYWraol1IiDTwsyzCIxoHUKbNNR9LASaevbItsQj6bFgolhIAQEALJhIBh0B766t1P//iRfwERXbZmAemI3RYjgC6cmJg1OaAMgu12o4rlhdArznZBlplvaEg0DE+IFkSwJNalnWUuuOAC+5NiTL/5yaoyf5m/MW3D8IFqA26hY5tQLBNwB/h2EQSaGGe2gRArhLuCdIAPghDBO881y7IlS2ACHgaeCJOlX3/91TYfZhlPQPyJoNXwb2JUQzwRo90WCFoCzhS6pH///jCASI45EjwInphWTpgX5szYJUEh0Toay5Dg4YVIxY0XB73TTjuNp/jFF18kDRuOWROGpcAStKGCaypymnYR4N96nEH50V9YzDH+abtt+OLFi7HIM+oO7uyKK66g7cHfGcDKT6J06dIQuLjV032wnJBcZJLgL8oWdY0XKuHPWAjBkAfC12hgaBG6Dy7sGdwRYz6Al9UUivNQxHxRXAsShmxH1M1d5Bcs8dy5YRmcdlfnnRcitB22Y2vX5ojXqlXYeXPChLAjp3HgxlvRwb7magWEavfuodGjQzv2Tsh1Knl/GDZ8woQJcOsYfOGli90lQRLjvzBA2D7WeHhgI4V3iKSBk05feUeLuDMvJsoRAkJACCQNAsa2C+7MkGiJkhvKjFvL0CxR+Bf6fS2DNnfF1i9WhD/xE3IorlkU2HHWMLNiHLWwQ2Fixje0Kc+iNNwZi9LM2Zw1EFHF7O+G6RlTUJiyG2+8kQIsEfMdzHc5RisB2ZjPKXbJSZuQT9Cg2EmxvQO75pHAIZc5EuZI+KwBBUwQpoWEtHNuqlhyIHK1FD9lWBWGMVZXxteY6GZwZEwmoaI64rIVCkHHYKz0xRdfBJk7I74VBmWmdXjwcdD1Tu4M2ytYM2gUOCbIIzy8OEsx7DiIi8SFMGvnnXces1l2EoCJwySEAHDwaEHjzjB3dcU7s31KB/Xs2RPdRRwnm/n666+Tdm4FQLusqawtFvAECpa+I3o6VnJs3kL4M+N+S/fZ3VRJwzjAqZEza9YsupJRQT/ma+9j1D4H7wIiWp5yyimuV0AQUMIizHWwfcL69eE8xz6xoR0b4YQzd5rrhdj5AJdWfDYNg8ap88+PyJ1xtlq1XJeHfyT5YYYK7KrZQwaGHaKZNRUn1xyfJmIpCcPLgZqC1se/GI7buf1FJA2cdPrKi6e4My8myhECQkAIJBkCxgYtIQwarFmL/eoF1ns0yToyYOIaUy+4s7nLt8R5L06xZnmOBSZFxmSMv0wsmY5u3Lhxzz335EJjgOYNc8aXt7mkXr16kC9mAwHsm/Dw6tKlCzYvAZxo5YlDcSoADUrXGLMUmCDYTAxPXnjhBXg0a0JoyB3Zm3UAAEAASURBVE3ccsWd0fUGMUMekSYHV2XCpeO9aF3kyCQCVFFHBSrgOMQ1D/IL51xTD5ZZLp9NCG6mnRBnFMDqCqtSODJ7Uwrj0nv88cdDqEEUUhIlQLHAGh9ZyV0JYjkRqJFNAEw+tnXEqoMpgyU0OXCIHBTz6jdXVYH6iZv82WefjcstUrGGAelgljF4kDFGM22hs8hHSxt+HFLYd+cE33Yx2glbyTam0G0UIN4Zf4nyHkADvS1bcvYKIGDZhReGW0OsecKT4V9JYLcDDwzncCxebP4POTabDbEFNNzZ4MEhbEw7dAhfFeVYuDB8kh0Dis2BHsOv2SgBGsVP/roURXwaC8NLQD1eT9yO1xPjloTd4Jt0dA2c1PpK3Fl8xpjuIgSEgBAocgQMg8ZtDIlmEkV0V1FmRQRsAKuFxuIfPBp7cSJe0ZFo3IX6+x9f0SQCCEVgRTKxw5l7G+4sFjnx+DB2OlifMd0iXBQuQuZCgo4XdQiVWCQsgWVw7OIwDccVEfsaNtzkJ6ZnbLR34IEHEorOLPjXrVu3BOLjbXLt2rUNccYpWGDoCYCCiYBHgwvG+ZGYQURkZ3YXNPMrb1swHaU59DViY4GI+SFl2PoWIzKYU2L/Mxjatm1LAnYJ4gzHXlsJu+XioYnFFjk8vxBtcIX49kKx2TJJkYDcxyYLasxIS+QvGGTWBqxrGOHwwYRY+8m11SzmkPhj4kGMLy1WdZCkRsdiaEa30r/wXGykC/8FlWbi2WFbRCY4QJGY7SB8e9CEOSNGHm74mLZdffXVsOrcgkvyZbDmW3lRZEL4mN1rjOemucVZZ4XGjAlvEXD88SEiPW7fHhoyJHymbVs8Xk2R8F925Ozf38fBc1eJnam33sqJdHbKKTuzisX/LHqxmkKwM97gxDmll51xHuPWRMLz2XthIIniwhqOHKuvsHmMooGTWl+JO7Ndr4QQEAJCoJgg4CTRaBLunPwtYEw06jTGZbIyKyajJP/NMGZo/DWWaFRQcB7N8mXUJsosX33ipLfMzArvCaeVAfsA2ApNYeclTLCZtjEDnzNnDrNTp4EDdivaLsBCl6gE3Wf7i/V8ju7E7wmFsDtgtmzW+RMlWzDva+FCPCgzHJpwbmVsgxWzu4TMMGMHiqkm7orMNrkEga+//npD9rF9B+ZIcCuE0sdGCVdrUychuo2FET9x2sKDmyhIJuwR82pm14wWHFpHjBgRuwxFWtL2jlMvmTvaU+Yn5CDOpwRu4ye+h8Rxs8QZOfiFYZsJs5xc3Bl+lDCbEB80AdvAyy67zDQWwouIZrBm/DzxxBPZLAKdzKDFfZ7DlEG9Y4lm0t6/9DUrH4SNIwYW4xxukTJE98MF2AWs99rg5Nx5Z3hLTZwx69cPsdvzvHlhyzIOs2OAlXPffUPt2mG4F84AS2+wMwyhxo8PsQxkdhIggJqJv0/IPPZPwH2bDYsx6oSY5jExjxhjjFchxtf2LolK+PaXK7N3797ExTMuzDDjdLqxJU+UzNwXCe1DbfVVFA2cFPoqCp4pu23pt2DBAmMRGqV2nRICQkAIxB8Br3by5sRfqiDc0dBnn6507y3gpNUMQYa0uxJyydzRed5R5M0JQi/HXwZLpXFr4qPx1zdEmqXGCPzfpHY4Pr3Nib/MwbmjdxR5c4IjrSRJIAL45OImY8IkJVCMorg1TBBWV9FrjqWMqwbmOBhjgpid2rkKBO0nRknsvbj33ntHEpgBYAo4Z9SEPyN0N3SJszk0nA3yYnFshJsz5Lvzct80FnCESnTN/qSvfLHyzaSb6FlLdmAyif8mjpZwNxCmNt/32hgzCZJISbo+xvJxK7ZoUeiQQ8J3Y6MIY3cGO0rAsiZNwptmcuCC3LdvDi/Gz+bNQ08/HY5xxkECspGzo0aFXngh7OkJKcamAUT4xGANY7Rp08I7A0ycGC5sDi6HRbz66px7GTdY+GU4dHoB72YcgQ2DCcmOnaaNFrqzgnz/H4uCiqVMLDdGUdCKAPayS3hfDZxYfeWScDd+yu5sN0DTJUJACAiBpEQgx3Bs32IU/iEp+6G4CQ0FJhasuHWq2hM8BHDIjd0nN3jiR5OI4Dh44RHdP1IhzlIm0tlI+fAUMbJCkWqIcz6RjDAIjXJTIhJiOOMq4LQ2taeKJcdqW5ekCWMY6BW+EEdpYPmUBg2IzJWr6eykyT974F785ptsPxr6+edQrVrhIGj2wIV3pxdvqEeP8D9zdOq0q06oNP5FOpzu8PQC4edsSUy3bLogiSJSYr4ioSg4fE8FKtNXAye7vhJ3FqgxJmGEgBAQAkJACAgBISAEhEAJQoBdRPEUJip8pDYzL6VMpLPKFwLJiAD7ikYyMEzG5hSKzGXKhGDZkvGQEkvGXtsNmcWd7QZoukQICAEhIASEgBAQAkJACAiBQkCAzS769etXCBWpCiGQPAgQsc4GrUseqSWpPwJSYv64FLvcQubOiG5IBDt8rQO+FXSx68fdbxARE7DvZbcOxZ3dfRB1pRAQAkJACAgBISAEhIAQEAJCQAgIASFQTBEoTO4M4mzRokXsGJJEUTmLabfmo1mE8SPcIB1H9ETRZ/kATkWFgBAQAkJACAgBISAEhIAQEAJCQAgIgRKAQGFyZ1icGeKsTJky8t9OlsEDd2Y2dqH76rMzsA4hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgZ0IFCZ3hqsmvr4QMc6Nk3feSP8HFwG6rFKlSosXLw6uiJJMCAgBISAEhIAQEAJCQAgIASEgBISAEBACiUAgtRBvSowzEWeFiGfcqoLrpOMUoi5ugOtGQkAICAEhIASEgBAQAkJACAgBISAEhECyIFCYdme0Wa6aydLxLjnVcS5A9FMICAEhIASEQKIQ2LZtW6JurfsKASEgBPKFgPRVvuBSYSEgBJIXgcK0O0teFCS5EBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQ8CJQyHZn3htEymGNYvny5Rs3bqxQocIBBxxQqlSpSCULkp+RkbF69eq9995bIdgKAqOuFQJCQAgIASEgBISAEBACQkAICAEhIASEQMlEIDHc2cKFCz/66CN2eMRVkL8QW6eeemqdOnUKvQ/+/vvv6dOnn3XWWWwAWuiVq0IhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgeKNQAK4s82bN3/44YfsyNmmTRtC1P/xxx8ffPDBu+++27t3b1mHFe/RptYJASEgBISAEBACQkAICAEhIASEgBAQAkIguRBIAHe2Zs0aMMIQDOKMBCTaaaed9vXXX2/ZsqVcuXKbNm3673//+++//6alpdWqVeuUU07BNi0zM/Ptt9/+7bff2AuS/GOOOeboo4+GdKPk6aefPmvWLEi3c889d968eQsWLMAbND09nQJHHXWU6QzM3F5//XX8N7lj27Ztq1evnlydJGmFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBBKCQAK4M6gr+K9vvvnmzz//PPDAA3HVrFq1KhwZ7cd/c+rUqRBksGPr1q1bvHgxmbhzYqf2yy+/HHTQQdWqVfv222/nzp3bqFEjODKON998E6bs4IMPhn2DO6NA7dq1lyxZ8sUXX1SqVMnQc3BnhxxyCCHVvvvuu/fee69bt24JwVo3FQJCQAgIASEgBIRAARFgLZCPH1sJXz6NGzc2cWP5cFqxYgUfTnxiff755+3atbPxZPl8+umnn1q0aEH59evX//DDD9Rw6KGHEnaWhClv66xSpcqxxx67xx572BwSn3766RFHHLHnnns6M5UuIgRW7jj4HqZ+Om779u3OG/GtW6ZMGWdOUqdXrVo1Y8aMn3/+OVIrmCx06NAhegAW/FoYoieddJLxYmHiQI6pkHlH/fr1I1UekHzizGA6YIVxdTFP6Nq1a+1ZEqDBTIfEsmXLONuwYUPz05SZNm1avXr1rBmB88L4p4mfw0iuWbPm7NmzXa1gQsdkjZmdUU1Gtq1bt86cOfOEE06wLfr111/nz5/frFkzpwEEE0b0GKCBFbGz49+ufN0RrYt+ZqrLnLR8+fLRr8WUhNkrqp7Ce+21l7Mw+Sjt/fff35lp0l7F7i2jnCJCoOBKjGEPfXH88cc7JfRmJqqXIw2877//nkcYbeMUmzSWTwx4Hs+6devWqFHDng2+vrKi+iYSwJ3xSjv77LMxFkPRc8yZMwdzs1atWoHsokWLGCLnnHMOH22IW7p0adguPhfIRC22bt3aZHKt1bwoFGrDNu3ZZ59FlXTu3JkyKOLJkyfz1UGd5icamQSmbUuXLiWhQwgIASEgBISAEBACyYgAC4fDhg1j9ZGPJT6QWDvkO+ree+9t2rTpZ599xjQV7gy2hTJ8OBnu7OOPP77++usvvPBCiDO+gK+44gompcYP4LHHHmPWbcozv2U9EkxYg2SrJeqEiTAQ8X187bXXPvfcc9w3GUFLLpn59L3hhhv4xDXcGf1FdzibMHHixKIIE+y8RTzTEGewPP369Yt0U0gxylxyySWRCpA/cuRICCPiKZsyV1555YYNGwzDyGifMGFClGuDcOqpp56ijZUrVzbCjB49Gv8bKxhnnYw5+TfeeGPHjh3hmEaNGsU055577mFUVKxYkVNk8qg+//zz9vIEJmC40EVDhw6FO0Oqf/75xzl02c+NSRwFaMKIESOMnJCe5GAYYbmz8ePHv/HGG+eff/7AgQNNGeZ0V199NdM6LDBYMOjVq9dll12WwGZGv/X//ve/wYMH0xzD5z766KNRFCmMw6BBg4zqZgzff//9rGSY+lHa9DuanMmv646+it1VJoE/UWDTpqWmpIR69criL8fSpSmzZ6dUrRrq1CnLCPbddykffpi6cGFKvXrZLVtmNWmSbfLJmTMnfA38TOvWOYWXLUuZNSucyVPSpk0WTmjLlpniITYgPPDA7KOPzs69+pNztoj+K7gSe+WVV3gdf/LJJ04JXZmJ6uVIAw9S+9JLL+3Tp4+LO2PcMob5roA14yEdMGBA9+7daVfw9ZUTfN90Argz5EB3EL8fNh03TNjHH3/88Z133jnjjDNww+Qssc+MrKhF6HnWYXDMpG94Hf7111/oXGdL+KpA56KX+ZSEmzenUDeoFdKmQng3k88bhQqdlystBISAEBACQkAICIGkQ6Bv374tW7ZEbD6WmJXxhQ135tsKjPeZbjGxZHpJAebkGHTceuutfDuRyXz77rvvNhfed999xgyNFeM777yTuTfcGUb9lIG88K1cmUWBAETJ77//bu1NYA3sXYYPHw6PZr9sbX5SJ7A4i0Kc0TSIlVdffTVKG5lwEsjFFmDmZsxAoGZsZsATMCZOttolLQ8sh8lkosRTbEwKePChk04++WQwxHoL3pwZE4MEHso8y656Ev6TaNf9+/d3imHIAjQM82pC6zhPmTS9ySOA4SELA5dffrnhQ7kKHyZy4M5efvll2guzFswm0wp0LBGKIMSZ/N58883wvA8//LC3pSbn8ccfP/zwwxkMzHDvuusuiDY4R6bAECughEma74VRFLtv+ThnrlqV0r9/mHa4+OJtaWnhm8+fH85p3DjbcGcjRqTdfPOOEzmSpQ0cmDlsWCaFP/kk59pq1bJXrMhKTQ2XGDkydeTIcPkOHbLgzsaNS508eceJnMtDdetmP/dcRtOmcZr4F0SJMYBRXxgM7ZQ9/L9vZvx7OcrAM98JLN05xTbpl156iQtpAtwLqvuBBx4488wzSSedvvI2Ldcg854uihwYU5QjNbO2ybsfdY+hGT/JR6GQwB3AHLzwMEnFDpDxxGILdmQssTZo0MAplVlgMRdaxwRnAdJmEdWVqZ9CQAgIASEgBISAEEh2BPhMgjVj2uzbEEz1Ic4wXDLEGWX4KMJIHycAEocddhhLj94L8XHDDmj58uWsOPLpBYMGPectppyiQOCrr756//33fY1o+B7GXeOWW25hUl0Ut07SOrGJwOqKcW7lx96BGR0Hnpsud1dbJlAJZqE432CZBfMFVRRFNqbot99+O2ZZhiei7cZZBwpp48aNNPaOO+4gOs2RRx4ZpZIAnsJ0BcrP6bVqheRxwEEVQ0JoI2tXyFn61ximRXfmtfUkKkEfQYVjI4IAaF2sBXGop6cwHMEcGI4MxhObEhhzhgFlWK7o1KkTShgt3bx5c+MyBWOInr/qqqsiNTYWxZ4oBPK87+zZqYY4Gzw48403MoYODePw2GNpL76Yi6n466+Uzz8Pq76srNCUKblOmVu0a5f16KOZ11yTCcv2008pXbqkb9yY580TXwCPY0YF5K9TFN/M+PdylIHH6tp+++3HUpxTbJNmJQCW3LA0jGEy8TXkbzHQVwmwO8Nala8xrMnQ8gZfs7DG+ieuBNigwaaZEBtYjRGeAx4NW1xMW8mnPH4EsGzmQv6iWfiLdyf6BXLN5FMVDD3Wg5j72pJKCAEhIASEgBAQAkKgOCHABw8zK5YkTdxYV9OI8cp6L0GCnJFeH3zwQYoxPfvyyy/hYpiv2qv4umV2yvyND7ApU6Yw2YOjIfwFBx9v5kJbWImiQADeBIs/HNz49HXVT5gbugCjFT6MXadK+E+c2ph2Or/54c4YyfahgE7yNWgKDm5QZggDM2Kcc8877zysyXwZ0kceeaRLly7W449nE39qAhcyY8KP75lnnsGS4KKLLuJZZnrlClkYhPYyj2MDNyMJYb+sVzguXXBkWGNhaeWSE1sVjFYojB/Sa6+9BtNEASbkgIBfGG3HKg1/3sAancHxITAzX9Muk8CPitDeeNMzMjEmYgDcfffd9F3Pnj2xKzS2dah3MDEOm/QmlnfUgKmdqcf1N4pid5UM4M977w0TYRia3XNPmDWjh3G3vO66tKFD07p1y3HSNGLj+NmiRebcuSnwaN6GtGiR3bdvuIZBg7IaNixFmZdfTr3oolw1eK9KeE6TJk2QgWBnhJyywvhmxr+XIw08Qg0SpB6bdOwircw2Ab9vKBpyiHLIXxYG+Jt0+sq2yCYSwJ2h6fhiwyuYGGc4wfI1Zox12QqAn3zJcQo7ZBZC+eZD3cCwIi76he8JliPQMuan7RLTGNhZlmKI+kElLNlxOUumkexaCZeGnuWOjEtnGlaOUJRE6XOGGLBgKSEEhIAQEAJCQAgIgSAg4DQEI35F165dvVJBnLHuyOwLYyU+upwFWBY2K5G4ttl8ptw2jXVD79697U8l4oPAQw89xHI9ezJ4uTNmKQRKt1xDfOQJ/l34nmcMwzsQB8ZKC+kAJYG5JRO/sWPHQkdih7XvvvvaAkFLMMeB48YhEQaQpxXnPmI3G6MBp6jMrjms8yansE+87bbbiOYGo4QpE65StJeGk8ZSAViM0YezksSm0TzGWwgx8C6y4xnaCF4YncP4p+1WSNYGsMhjKk4O3BkxzuhoLoSBwoaFquDUIBzJxHsdI1x7YXASWHjQO7AeLEsQofKJJ55ANhL8ZZ6LvSRMGbNjTEBeeOEFuDPDtdE6LmHKDLMWe1t8FXvsl8ehZLNmpXbM7EP2zUM4pXnzwtzZ+efvIrm6ds2CO8N2zO6Q0bJlNs6bkyal4sg5fXq4/EknhcOc+cq8zz7ZF1yQNWZM6g8/+FBsvpckUWbCexn2BqWKBbSxLPNCZ55EHk9s0zCoZPXCjOqk01fepiWAO2NZAN33wQcf4EdgBMJkDDNUmEh+8qqDbmeFgTQvPJaMOIufJnqTjwYyiWgL0wmthm41l5u/KN+33noLI1h+ooP4juRyutZZxqZRWLyl0ErkONOGoYt0lb1cCSEgBISAEBACQkAIJBABPJiMPQIeW9aQ3yUPX7cE2WHayZcrH1FOZ58LdhysVlLGBE7iWiwamIiSYBaHQcfTTz/tZOhcletnoSNAMCMsAYncxBowllN8lJJgJRj7IyYhLPLj18ZXcaHfN3krxHgHb01GKaGvjE8QiBH+GA84DtMuQlkTaR7jrCBzZ3hP2wBYLOEfd9xxRJf3cmc8oZx1Pu842ZhtEJjUYLoF/YSbKiaKTFmZFnEqaNxZ+/btXfHO7PCj4whXjTsqux/YTLhR0ibQuMmkXfQpU0UMMOGVIN0gzlg8QI+59ii0lSQ8AUHGpnZ0B0oY1cqgNd6mTH6NiRkSshUsJCDTUnIIUonxKVHPjFtc7PK7FDu0XezXxqfkt9+6ySwm6+vXh2++Y9vYHCkqV86JU/b33znlGzbMxqUVn03DoFHu3HMjcmecrVo1XIO9PKfeYvFfwnt53Lhx2LRyoG+Ja4Y9Exy3NYY1GONKOGTIEMyYiO539NFHm8yk01fe8ZIA7gwhMNtDOeKWj80XuMNZ2k8BzMdYc4C9ohvsA3/iiScSJZT1BHg3PiAg2lhQgtHkG8I2icI4h8PoU8xas7NA5ywDocbBJWgum+9MUzOHrVMJISAEhIAQEAJCQAgEEAEixjqd1HwlNNPm6667jo8u6DPsHZibEVeFdWDm6lxi/vLtay5nzmaWi5nRscwZKYaa772UWXAE+DAGeWOWYvxnMTwhCAmfxExRmIRYlrPg9yoeNRjEiEhNc0jzFwYKOyw2nGVsG6enpGgpxqGQXzhjGmmZzjDTcUnOqj/FoFRc+fwkLiFhs2DcoNvg15igMrFitmUfbe8lwcwhLCPmEQTFN+Ixp4PfhymjaSYHyoyDYpDLNBPijHzM0Jg/Gr/XYLYLrybIXEMa4mKPDY7xMCUOGn1tXKl45MlHCWNEYhY8Ync0xgrPV7GDTNAAWbcuZ68AApb16hUmInBpJTwZ/pXsvFm/fg5ltmRJDmVWqxY5Oenu3TM//zz9xhvTf/stpX37rH32ida4RYvCV7FjQLRCSXUuOL0Mw4uHn3Eg5Uk0NmWYoVk4sZuGaYGZwZbW8jn2bFLrq0QuXsFw4V/JZsyWOLOYQqi5gOYndmTG8590JKNctI8lzmxtSggBIVDsEcD1mw+sYt9MNTDOCGhcxRnwEnK7OI8rJmnMtwlqgeEDRmosGbJojD8Xc3Xcu/gMI9yvF3mMdMxm5d5TyikiBHDswjLFHPimsdxL2syrcb+gp4gLXES3TtJqYUx2AjbBEC5QjZgvMcnEfI/5G+TLmDFjaF3wY+czEYWtxteS8EBYIBq2iPBe+PGZ3mGOSoLwXt7OmjZtGo1lzHCKndaw+IB6o7yJMeQtH9gc5ndQnyaYD0ISqAfKmDhurBOYgzQLALgZ4deMSRoerHQxCZpPTmDbBfeNEoYdw0QO9QtJaia/tAXzXmhf4g5hHQlZBpWG5R3h2zDVwRmZw3f/BNtSNuXEkSt2xW4vTFQCh037z8rQqVOY4Ro2LM3sk7F9e+iuu8IBzU87LctpOXfWWeFiX34ZJsWcDp78dB0zZ6YS6YzM1q3dHLSrZBL9DE4vYztpFS/LbD169DDEmdVXrM9hNUncRj4zzDB2LgYktb5KjN1ZEg1TiSoEhEBSIIC+5uPDBFJNCoElZFIgoHGVFN2UdEIWcFyZdUTzN1LbzcTMlmFWie0Gk7TGjRsTWJ1ZHE4fXIuTBXY6FHaV5xQzcOZ1+H8pAmwkkIs0n76z3ceNmFpDCRXpHZO9cjOGTSswdmDCZvatY50eUz7Gc5AbiEUhxqHsFYCQGHFcc801xmgUWoSGMDUlH3qIv16mG28piEKYcSwPKIADzaRJkwibhUMrEQ/JCcLh7B2XPM5xzilYThww8VAmza4CRO9xxlRCZWGbST6GtBhtGbqQkvjtGitaV+UB+Ym+hRFjg2PkwTbQhpJkJYNI37Bm5ONw2rdvX1QudCF7PnAY4SHNjeOqb1voa6x4iFzkq9h9Lwlg5u23Z7KlJs6YhxxSqnnz7PnzU7AsQ87hw8OB/+1RvXp227ZZ8GLksKWmN9jZ8OFpzz+fSkwms5MA/Bq7B1CYtQfM/dgzB39n/N+ffPJJjFLxaeMU6+686ZyxPu3t4pxwPQjm7s5MHqIA9rLz0bb6CvafYWwd52kLuw8ZKj8p9FWUrk/heYtyOsqpBQsWQCg6C8CpE9zRhPZ35isdfATQI2zQY72Rgy+wJBQCLgQIrAt3ZlxdvNrJm+O6XD+FgC8CGle+sCizgAjkOa6wsCjgLfK8nFhafJQbv6E8C6uAEChSBDA1Yk4Y/RY8Nd4dGKNfQgQYHIuczEv08gk/i9EZDybWJc4Jc55SYcpES13BhjD34Ok2RovRa4A44I7Ry5izhFGDrnLN/vi+ioO+iiQe/WsQc07gIxVOeD7dRM9a3yl8jYlviIUOXQDvafMLImdgFfvixSmNGoUdbDdtyvHZnDo1tUeP9MaNs+fM2U7+L7+kDBiQZngxfjZrlj16dAYxzkiPHZvar196nz5Zjz+eMXFi2NMTUmz8+IwZM1LPOSe9U6esKVMyLr44ffLkXe50XN6lS9aVV+IPSwUh4wYLv8xYpRcgZFlMMgzm9ddfj50mm2yEyxXgKCIl5itRYHvZV1pXZmL1lUuY3fhZmHZn+FRiVgqDvhty6JLEIkDH0X2JlUF3FwJCQAgIASEgBOKDgF768cFZd4kFgTp16mCnYMxAfMtzljK+p6JkwkdwRCkQtFNE79oN+zjfmFYmFH3QGljo8hDGx7lzQqHXX7gVRhqNWJYV1o0Cq9gPPjibGFnOZrKTZteuu3KIazZ9ega7jy5fnlKzZjZB0OzRu3dW7945Jbt1y+rWLSfdoUOWrRMqbYf1nr0oVwJ3eA6TRS9Yp2ByCss2s4iUWK5m7PwR2F7eKWC0/5NdXxUmd4YVMSaRoIXej2WhIxquOhcvBPCrx6gbDpjNTON1T91HCBQOArz8cH0ydZkgwYVTr2op2QhoXJXs/i+q1mtcFRWyqjf5ESDeAp5Tr776aqSmMC9VTIZI4Cg/SRHACztfBoZJ2sx8iV2mTAiWLV+XBKSwlFhAOqKoxdh97gzjWKK+OU1k8c+Hf2Ffc4gYZ0C4om6D6i8IAvQg7DUdZ/bIKEhVulYIxBkBaF8vZebSS0Ykr76Ks6i6XRIhoHGVRJ2VRKLGPq6SqFESVQgUCgL4rBAcvVCqUiVCIFkQIEBbkGO0JQuMAZFTSiwgHVHUYuw+d1a+fHl86V0e8vAv9evXL2qhVb8QEAJCAARY5OFwQYFeQju5Mn31lauMfgoBg4DGlUZCUSAQ+7gqirurTiEgBISAEBACQkAICIGCILArqF5+a2GfbDYvJ06WTMzyC53KCwEhUBQIoIvQSOgltJOrfukrFyD6GTsCGlexY6WSsSMQZVzFXolKCgEhIASEgBAQAkJACMQHgd23OyPSHpsE//XXX8xURZ/Fp7d0FyEgBKIggGMm9mXoJW80VumrKLjpVHQENK6i46Ozu4dAlHG1exXqKiEgBISAEBACQkAICIGiQ2D3uTNkYjp6wAEHFJ1wqlkICAEhUFgISF8VFpKqx4mAxpUTDaWFgBAQAkJACAgBISAEhECxRGD3fTaLJRxqlBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAELAIFMjuzNaihBAQAkJACAgBISAEhEChIFC6dOlCqUeVCAEhIASKGgHpq6JGWPULASEQEARkdxaQjpAYQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAKBQ0DcWeC6RAIJASEgBISAEBACQkAICAEhIASEgBAQAkJACAQEgQL5bG7evJl9Njds2KB9NgPSnRJDCJRkBMy+ddWqVSN8uxcH6SsvJsoRAkIgUQhE11eJkkr3FQJCQAgIASEgBISAEPBFYPe5MyaiP/30U40aNfbZZx9xZ77gKlMICIF4IsBcdMuWLeilunXruugz6at4doTuJQSEQJ4IRNFXeV6rAkJACAgBISAEhIAQEAJxRmD3fTaxOIM4IzykiLM495luJwSEgC8C6CI0EnoJ7eQqIH3lAkQ/hYAQSCwCUfRVYgXT3YWAEBACQkAICAEhIAS8COy+3RmumrI48wKqHCEgBBKLQNmyZX///XeXDNJXLkD0UwgIgSAg4Kuv8hQsIyNjxowZrmKnnHLKsmXL1q9ff8IJJ9hTK1asmDdvXufOnf/4449PP/3U5KekpJQrV+6www7bf//9yXGe4meDBg0OPfRQythKtm7d+vnnnzurtaeUKDoEeJFxNG7c2HkLbKjnzJlz8sknY7fozE/2NIPwlVde+fnnnyM1pE6dOgzjfffdN1IB8iOB8/HHHzds2LBKlSpRrg3CKRb5/v33XysJTS5Tpoz9SYLHmTaanLS0tAMPPNCkly5dCoCHH354pUqVbPmXXnqpXr16xxxzjM1JYOLVV1899thja9Wq9cEHH6xZs8YpyZFHHokaRMO0atWKeaU5hc558803TzrppMqVK5ucX3755csvv2zRooVzDGRmZi5atOiff/6pX78+66bOagOYXrduHdJmZ2ejYCtUqBBdwk2bNv3www/bt29n6O61117OwuRzec2aNZ2ZJs0tvv/+e9Leq7yFlVO4CBRcifm+an0zkfyTTz7h2dlzzz0LtxVRaos08L777jseYZ5B17UsEC5cuBCddsCOw54Nvr6yovomdp87AxEO30qVKQSEgBBIFAK+qsk3M1ES6r5CQAgIAYPA7qmmbdu23XPPPQcddJCTDmjWrNk777wDfeYkufjYpSSkw48//kiC6Wt6ejo3pRhftGPHjj3iiCOcp9auXcvHLjPY8ePHEzvSCPnyyy8/9NBDTFzVa3FDgC6+9tprmTO7uLPHHnts6tSpEA1xkyQ+N4I4O+qoo/r16xfpdjC/lIlSgAt9wXn33XdvvPHGMWPGOB+WSHdJbP7o0aNfe+01K+fTTz/NlNMpUt++fVkIZJpKJjTZlClTSMAxPfHEE8cdd9zQoUMZGxUrViTzrbfe4umePHmy8/JEpVE4d99997333gt3Nm7cuL///pvAGlaYqlWrwtSjnVq2bEkPmnyYI3IOOeQQy53RHBYMunXrds0115gyrBMMHDgQVcYawJIlS0hfdNFFttqgJebPn3/11VfTHMN+0mUHH3xwJCGh2K688kp0NQXo8REjRjRp0sQUhnlEM/Ts2fPcc891XQ5rxgjhFqtXr+bUqFGjWCBxlUngT4R66aUQizKXXBL+y7FkSWjWrFDVqqHOnXPkWrAgnPPDDyF4mOOOCzVtmpNPzscfh9P16oVOOSUnc+nS0AcfhNO1aoVOPz30/vuhZctyTpUqFTrooBC8sV/045wyhf5fwZWY76vWNxO6igE/adIkPgMKvSG+FUYaeJDaF198MZrZxZ2xwscYnjt3LhLyePbv3793797UHHx95dt8Z+buc2fOWpQWAkJACAgBISAEhIAQiBsCAwYMYMKcr9sNHz7cmDDwXXvhhRfyrQ93Zmqwp5jZnn766ZiHMDdjcs4k/Ntvv83XXVS44Aj85z//+e2331z2JthPvfHGGwWvPIA1YHEWnRfD4AjbpSiS+4KzatWqBx54IMpVgToFY4K0GBX6SgVVRHNmzpy59957OwswtYZOOvXUUwn2Cqnapk0bZrP33XcfPJRr/DivSmAa9cK03ykAfcdP7GiYV59xxhnOUyZN2yHOOnXqxBhA7xlzPIjCP//8k3zowmnTpsHNUcBQh94aEp5z55130vCbb74Z3Xv99dc/vuOIJNUjjzzSqFEjBgOs4pAhQ1i6gCWh9x9++GHMTiEWfS8cOXIklnqUh6yEXHv++eeHDRvmWzIhmX/+GerTJ3znXr1CaWnhxLx54RxYQcOd8aTecEM43x6DBoWGDw8XnjMn51oME1euDBmj28cfhy4Pl+3UKcydPfNMaOJEe2k4AdFGTrNmuTKL7kdBlJjvq9Y3EzaKnuVhKbqGuGqOMvAw/Lz99tsxY3ddws+3334bUfnGgDHntXXHHXe0b9++evXqSaevvE0rVibf3uYpRwgIASEgBIRA/BHAL4PPehwo4n9r3VEI5IkAFg1YrxgDFldhzEAwYfvqq6/Ix2vsrLPOwtbDVUY/ixQBTFSwlsKExHkXbEkwLLr11ludmUobBHzBgUGAsABGa8kVZLiYhWLyyTwT5gtTI6+oUGPMUdkHCc9NzBJtAdpuGsiTy4V4+d1yyy0w49jx2TJJkYA8hetxeq1asXkcMIYdNGgQtNHs2bNNPnC1a9fOkGVQq2TiNGcvCVQCmx2o8DPPPBOp0L0oVewo6SmYUHhPnmuMhSENMTxkGFAG0gFjYdxycc2maZjtkIm6bt68OUZn1rPV1cZSpUqdc845XMItcOB1DhJXyQD+xILMEGf8nTkzdM89YRkffjg0aVIuYVetCn32WTgHzzdfq8r27UNPPBG67roQLNuPP4Y6dgxt3JirhmD+8H3V+mZCnROf4QYXy1iUrYoy8LBP32+//WBsvffHUJ1lABQap2CN+fv111/ztxjoK3Fn3u5WThiBZ555BlVuDlZy7r//foY7+UbF80VS1DC9/vrr3P2bb77J80Ys3lASg/A8S6qAEBACxQkBq6aYS5h2sbRltBZfpb4tZY2aKDC+p8jkZY9HycaNG6mQeljci1QySj6s2fTp0/mm79q1K9/KV111VcE/6KOLHUUYnSrGCDD7YqSZI0bTsP/+97+Uf/HFF/FiYy7HFM6FD9Otzz77DOuV448/nlNNmzblAwBfKlcx/Sw6BDCxgSDjW6t8+fLOu2BWA4lJNDpnptIGAV9wsEuCR4CDSAqUoMyQE6MqBGYuiqkRrxKn5PhWwxzxYurSpQuPJCZa5iyuUjhwERSJqSmTbbxT4VB69erF4k0kAyVntfFPwwTt1FvT38fRbueBxyXelxjA7szY9T+2KiginggIJixZzAmekcsvv9yksZMlYSbquy4LTMoY5thtrLDtRTT+4iP/4YcfYomGf+7gwYMnTpz47LPPcopMwwYy24I3NA6bWBECAgfGO74tw1oNO2J4tyeffJLh0aNHD99iwcw0czgMzTCVa9MmdPPNoYceCkt6xx1uec0X3BdfhODRvAcvqwEDQpiwMX0kphxlpk71lgpcju+r1jeTIIaMAcPDxqcZkQYePsJ4hUdi8dA/9nmEBUZsM/6TTl95QZbPphcT5YQRMOxY69atYbghsLC35G2HE0HHjh1R4s4owkWEFy+Am266yT54ke7Ch75xYcA6lFACrm/NSFcpXwgIgWKAgCXxWYhmmX3Lli3mAzpK0/igxP6ctVnfMv/73/+ee+45aC/0Hvpn9wJJQE88+OCD6K5LLrkEegJfEqqCkvO9Y4yZ0cWOsRIVK2YIMEdiOm0axQIvPj55NpCAaCaGDkHoKcyzYLkYp6fYaaedduKJJ+ZZmwoUBQJwB6zSEwR6+fLltn64BqYiWOX8+uuvNlMJg4AvODwauLCxUhKH79VC6Qg4U+gSogLxSOLDiDkSn8F4YtrKed/BHOHtiKXVU089havU0UcfjdEHhnW8Yljp4bGFhYEZJw2pxAISTmSMmaBx37imIqdpFwHvreaB8qNRvMoZ/9aXnGKLFy/GxAymjDQIXHHFFTwFXGjMZqkKvgmLLQr4+o5ZABOYYAt4fGmxQsCsDJYTkgthSPAXmbETxAuV8GcrV67EkIcvB9MQrNXoPvQ8y4SxCw+8hLmkPBoj9qviWZIwZDsiue0iv2CJ584Ni+A0cT7vvBCh7XjFrV2bI12rVmHnzQkTwo6cxoEb/2YH+5qrERCM3buHRo8O7dg7Idcp/Sg4AoTtY42HBzaSlzT6CsrM3ohHwDz1SaevbBNsQtyZhUIJHwQ6dOhAnFoeANaoWYvGj4ADZ/u2bduyYML80OzbxfueNzfMNPNDFvqIYMobnXUwXg8vvPACgTzZ/gaGiwWxu+66q3bt2rwFMRZjnZwcFsdg6DDS5g3BLXhh8BPDbL57cO+HqGbySZhMKiH+JasomHI4BeUSFtaYM1Ab82e8qTlLghiZvHX4kmAhjm8R7Jz5umJJh0yW02HZ4rk1iVNgpYWAECh0BGAE+OBGOzlrRsnw+LOcSybOIGZLMvQSP1nfZiWA9XliOaFA+GZlyRePGGOShn5g8R/9A8XGVV59RRwZr1qztzZ35O7Gj4b9sLgpdrsYETz66KNY9KB8WDNEBpQbHx+oNb6q33vvPT6asZtDVboqRyorNvGSDfdhb6dEiUWAWbQr3hlWNnYbPgOL6ycvU97U5hTjnzcyVK/5yeTTbP0G7RvYKWix72sT8gkNgzbjKwinbxIEjIcX4DMMhWaMWNk7NZat+oo9XDSQPRZ9wWFSBwUDycLBsgqEAvszBnkrRr5jeWuYLjPW03S9kzvD3IzDFIAa413AewHuDDsOs2kAb5nzzjuPhmM3DROHSQhvE3i0oHFnvP5c8c7sQGVU9+zZk10CIAdtJm4opJ2h8WmXsTjjXck7lJc42iwgO4pasV0J5IQCY3EOq7HrrrsOb2KzDQLdZ3dTJc1sCE6NnFmzZtGVjAr6MV/jli8KDjaduO2223Dug7NwSZLwn16HIjaPXb8+LJdjn9iQ3Rf3r79yRGbnA1xa8dk0DBq5558fkTvjrNntxl6eU4v+KwwE+NDlO4EDNYVBGTQZHLdz+wuocBSvvRUPqRmKSaevbBNsQj6bFgolIiLA57hZF8KknHUMFrIoymMDcYanJB5JJDD34JuPuR9xUthxg58UY4GF7zzCpvDMnH322eTArPFGZ2rK5w4vEpz2mSsyNYXYggUjDZXGS5GqiNpAeV4hfNCzFMNf+C8+LKzNsxGXwiR4Q/AX0wz+Qurxk5AQTIb5WqISPLCQEKaPDbD79OlDfAE2uDGX668QEALJjgCagccc5uujjz6iLfYDGr3Bu9y0jrPoLkh/vkF52fPZjY7CDYSZBhQ/xWD5+UhFfVGe704+aqkTfwpffeVVaxZDFBc6ijVzG2GHNWS8b6gQryJuypobywMsM2CPxocFdzEx0bgRaXK8lTvFRhvbeykhBFwIMJHGjQJrMpvPWhduUPanMwFHxjyN9SSTyVcvBi8cPCDOYkrHEwEUCEuGKCVWE1ER2PuTQEWQCT9C2ny98D1jXPziKVsw72UQ84KDqsSOGMQ4+ASFQuIjM5hNMFLhmscXspUQGw2XzybMyxc4qkU4KAwVztowdBufwUxQQQDjrMAaH0VoR+jSSy/lA56g+KYA70T2B+CVPWHnAYHIu5v5BYaZTBl4a4Obfe9Hqjbh+USEYB7ElwYLGCyAoWbNWgVLaCbGGRLSWeRDnOFJwxQGuhzbyRiJMz48sHWwzvvEO6NC57sg4QhYAWBUCKrBPxsVg3cO4ck4dgR2yym48/Mt5Nxs1uykOnhwCAPcDh1yrrI1uxILF4Yz2DFAR6EjwPQcxy+jYDGNZPWX9WbnXRi3zMFNDmt4EPqsXtgCSa2vZHdm+1GJaAgYo3en6Tv8F1QUfh/mc4TPF14AVMGWtMQr5SHhNWdrJLYl70Ledih3mDK+Y8jBKIz9p5mmUgOXUJi1VtbH4ONYSzdEGJnwaziN8nxi3YY1WTWzjrCjar4PIMWY8fIAM1mlGPc1bg7YdDBBxYWT7WwoywIOfzGgQ0jeTDBukTy0d1SsP0JACCQNAkwV0CGYnsFMYcDlXOlytYGvSTgsPFnQXXytQjTAqXEhxdBgTDOMkzgfoKgUc62vvjKnnGrN3sjMdpyW6sx7ObA1Y9qDJS8RpijDTTGPxS3LXuhKOCt3iu0qpp9CwIkAPDI/4WoJjMV45sXHQ8Fqli0DP2siGzDyIXB5BVuS15ZRIoEIoME4jACsKfKtgoUsP41hEQm+cJiBM0txKpkECpzwW+PK4AsOBj5WNiDlwzL49Ao+fTQHvw18NllC5pOYJkChsrLCU8wXL1/CkEoYLhnrZkraNsIf8XRjsUUOX9QYZPHFy2OO0aItkxQJPDGxyYJBM9IS+YspA0Zn1jUMe3Aw4eOfp4Pvf3SdXQCg4YFdXsJWFPMCltD4AsGqDh8aIyoLGDzj9C88F7aEhIzg44Thyko/nyKG/IJri6KoodqZAREjj+kSBCuzM7x/uAWXxMi7xXlg4LBpnPmM56a5O7E3x4wJbxFApE0iPcKsDRkSPtO2bchpOUf0wv79fRw8TSXOv2+9lRPp7JRTnNlKFw4CcLu2IgwkUVysE5Nj9RXGs/hMYK0Cu23MSI899lh7SVLrK3Fnth+ViIaAMejg2bBrGrzC+SjnfcZWPlhscrH5kjMO/OavrdE4iUBaMWk0Mbwx5uSsMVQmBxNuPvSZAPOtz+sQhynrU4k3KCtpTDuxQOZ7kXcMRt2mZnhuEogEAWdzWDYnbebPdgJsEvB33PGCCy6gAC8nfXoa0PRXCCQ1AkQlg/NiOZdW4KGAAnE2hyedBXw+vp2ZpJmXMgmBYed17rrEWdJXX5kCTrVmL2ElGX4fY1vmOWZJAA2GjjJTHaP3WITgUxilZFYjjLKyX/9RKrd3UUIIgIB3loixyQMPPMDbGRsWAxFxxy+77DJbuHfv3hY6Pm3N56+px1ubLRnllC2jRKEjgH7wIu/NKfT7Jm+FUcBxLv0Gs4G8vHBXJMYI4vG1zLqv8bXkSxtzJLgVIg+QgDmlAMakeGMQl9O0BftEHnxWrPnKJQcOnRdi9+7d8fDAysmUSfhf2zvevrCnjJBMKzAJJ3AbP/nyZ6HdEmfkYCGLGabxVuHNTgQ02zS2ETALYDYnOAnmKTCbJrAMC35GLSMehBdmO7BmpIkyyfQHIwDahRcqh5EfFgxLtEhtoa8hEHEPYtUEFsOEc+W7CE8dF7CRaghC/p13hrfUxBmzfv0Qm6bOmxe2LOMwOwZYCTFdatcuBC/GQZAeb7AzthoYPz6EObUxvyaAmtnqhnVQbDII7sEXGt+EENM8JuYRY4zBYGLDaO+SqIRvf/lmJkpC3/sioX2orb4CWyAllh+XoNCc8SKSQl/5ttRkijuLAo5OEYvxVfgsok4y9+N1xfvMxB0AGozhiVbG69zkQIqxQgK3xXoRxhQmbJAvgsYdGsoZ600zZaVaHBD41uc9AT+NEyjTSMudoen4PrjvvvswEmH+iReVrZZ3J2lMmnluWWHjlYPAZpMaviowMWNzOlOY9UaINiauJKiEpRgRZxZGJYRAsiNAXBvWtXg9O4kw87WNuSs2ZbaB6AqsX1nVNxHE8KDkp/MsaSzCWM02mb76isV8e4k3weIb5AV6jCUBHOhQntRGPfAaxJbi4xhnK27K8rJx2WDBgGUJY/7mrc3kWLGxmLPfKJEKK7/YI8BQxwPIt5nMoDBAYM7M7Av7FBNOm5LMEyJdEuWUuQW7fUW61lcGZRYKApipcriqghpQX7gwsT8jgROFd7DXJjyBYicKPs6JfOXy0Wv1vA2CxmoN5AjPNSs6Ti4JyaHM+Py2m9vwrcsyM+HPuCQI37q8v+ygddoDWsyZZtsCJpOIYByk+cK3xWzCWhranOAnoGwwEMN5jZ61ahmx0dKYAMPdoNVNPr3mQsPVOso7c2xh7IhBBjt3zpq1PWexhKdTUtwiOHMgxRYsCPXtG+bFXnstXBIrasLVNmy46yoTsgI6jDL8ZRtNczhDWRA3zYRO43I2hbr66pwyMJLEvIa7pCN4gkjzAWa4Mz75sNMMAnfm+6r1zeSRt/2+C6AiTrkGnr3bkCFDbNrqK3KYlWM+iUKD/EUJ2DIB11dWzkiJXS2JVEL5JRkBjKVRxMTfQd1go8HQt69hVrOxDyefMkDES5r9N1ko45GgPFNZMu2734khthhYI6OneKIg+zHM5kLs1/hWYFmGCSdfP+zmaa7lL88ehZmFwnmxTm6t7llDYArKjZiXHnbYYfwlKhBiIBUBQRo2bMis1axHITYJ9vTlsac2wp85196dsiktBIRAMiKA+Rhiu8LiojT4GEUbsABggvJSBh3C35tvvtlcwuI2PhScNQwa1BuX8LFuYgmhf3z1VXSIIMXwuIS8wMecbzK+e8yHBQvCLACg4lgq4O4YBbB+QIJoayzNRQpKZe5lxTYbFUUXQGdLOAK88pgeQMg6Z2glHBM1P+AIsBaLF14UITlLmSgFiscpaC9eOr4fz6aBvKFcxBn5+G1Y4sziwHvNfrHbTCUSiwBTJF+1DLngm78b0sKaBZA4oyE4BbGfJv/s7ovspMlPG8QP92LCVhMNjThlGzeG0AdsDmAOXHgpOWpU+BeORqRNWKBOncLpadPC+eSQtv+4/Nprd90Lx23IJuOZZIgna/oHJc1sNFxFwQ4pMS9+RqE5iTPKJLu+SjHBWbytzTNnwYIFGM3mWUwFijEChGBg/Fgdjecm1l7MG5krYn0Gx49JWqQt4bgQnosvAPtqJ8d4LeGo7/puwOuKwig7a4wWBVWWXPBDgTtjakpwAWzTsIU2W+zhJIUzqbH1iFKDTiU7AmxPZuKk2oZIX1koSlSC1UUUgnVsMW03Xpy4b+MsyRo+EwynwsGCFUXBWWemV1/lCSOX4LbJPMeltZCHzYacX8lGuTlzfCu3YvueVWbyIuCrr1waLHlbJ8mFQCwIsOaKwx1rn5EKMy9lydYZbTpSSeXnFwFcQeFuYrmKz37CHLu0E99XrpxYqlIZEMAsHTsAYp8JjWKAgJRYfDqxgPqq4ELKZ7PgGJbcGlwkFHQVfvtmcxymo+z4E4k4AzLmpa5XNTlc5Ysm/JqJHOR71pUJl4cVLnwZAQs5hQG8Ic5IM2XlcJXXTyEgBIorAix5uYgzWoo+4SABXeVlrFgf82Z69VWeiHEJtj/eYnaxwZ6KpPdsAZOwYrvy9VMICAEhkOwIQIoRzyvZWyH5hUC+EMBjhiNfl6hwYBGQEgts1xSuYOLOChfPEl0bE06cMV3GaAlBhHir559/Pr6Z0HPMYBMig24qBISAEBACQkAICAEhIASEgBAQAkJACBQDBArEnbEMjhdJMUBBTShEBFzGaIVYc76qCtuTlC2br0tUuHggYEyKvG2RvvJiohwhIAQSi0AkfZVYqXR3ISAEhIAQEAJCQAgIARcCBdorAHcYV3X6KQSEgBBILAKR9FKk/MRKq7sLASFQkhGQXirJva+2CwEhIASEgBAQAkmEQIG4MxMXRqumSdTfElUIFGME0EUYG7r2c7Htlb6yUCghBIRAwhGIrq8SLp4EEAJCQAgIASEgBISAEHAiUCCfTSpiOsp+ZM4alRYCQkAIBBMB6atg9oukEgJCQAgIASEgBISAEBACQkAIBBmBgnJnQW6bZBMCQkAICAEhIASEQNIhsG3btqSTWQILASFQMhGQviqZ/a5WC4ESiECBfDZLIF5qshAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRAyUFA3FnJ6Wu1VAgIASEgBISAEBACQkAICAEhIASEgBAQAkIgfwiIO8sfXiotBISAEBACQkAICAEhIASEgBAQAkJACAgBIVByEBB3VnL6Wi0VAkJACAgBISAEhIAQEAJCQAgIASEgBISAEMgfAuLO8oeXSgsBISAEhIAQEAJCQAgIASEgBISAEBACQkAIlBwEtM9myelrtVQICAEhIASEgBBIegQyMjLefPNNVzNat269YsWKv/7666STTnKd4ufff//9+eefL168uEyZMocffnirVq3S0tK8xZQTBAT++OOPZcuWlS9fnp6y3bR+/Xq6Lzs7+5BDDuFUEOQsRBlWrVo1Y8aMn3/+OVKdderU6dChwz777ONbAHB++OEHTh166KEVKlQwZbKyssj8999/a9eufcABB/heGMzM77//vmzZsvXq1XOJF6lFjJY///yzYcOGlSpVspdMmzaNGo466iibk8DE9OnTjznmmJo1a86ePXvt2rVOSRo1akRj586d26JFi2rVqplTW7dunTlz5gknnGBb9Ouvv86fP79Zs2bVq1d3Xk564cKFdPr+++9v8rdv306/MyTq16+/7777ugon8Ge+HuFNmzbRLlQ9z/vJJz9QAABAAElEQVRee+3lFNvVXucp3wfBWUDpokOggEoMwRj2X3zxxfHHH+8U0jeTAp9++ukRRxyx5557OgsXaTrSwIuirxYtWrR69epaOw4rW/D1lRXVNyHuzBcWZQoBISAEhIAQEAJCIIgIbNu2bdiwYQceeGCVKlWsfE2aNPnoo48gyLzc2f/+97/BgwdXrVq1cePGkGg33ngj89gRI0bAo9nLlQgIAlBI9913X926dX/66ScYn6eeegpewPQgPMLmzZuR89FHH6X3AyJwoYhBq2F5+vXrF6k2JoqUueSSS7wFmNFdccUVgLNmzRrOPvbYY7BIW7Zsufrqq5cuXcqwh1Pu1avXZZdd5r02gDmQRJdeemmfPn1c3FmkFsExjRo1CuLpnnvumThxYsWKFWkUmc8999zzzz8fhAZC+aGvhg4dCneGVP/88w9MqBVs7733TklJoQBNQCmZfMY5OQcffLDlzsaPH//GG2+cf/75AwcOtNeSoNNRaBdeeOHZZ5/NT8ijAQMG/P777/DLcBnXXXdd586dneUTlc7XIwxLPmjQoFKlSiHthg0b7r///mOPPdZI7mqvszm+D4KzQGLTq1eHpk1LTUkJ9eqVxV+OpUtTZs9OqVo11KlTlpHtu+9SPvwwdeHClHr1slu2zGrSJNvkkzNnTvga+OTWrXMKL1uWMmtWOLNWrVCbNlmzZqUuW2aKh0DuwAOzjz46e489cnLi8F9BlJgR75VXXkF9ffLJJ05pfTOhq6699lqepri9CCINvEj6CtqXMTxv3jwkRA+j0Hr27Em7gq+vnOD7psWd+cKiTCEgBISAEBACQkAIBBeBvn37tmzZMk/5mIVCnGHBccsttxgjJj5hmYJiCdK1a9c8L1eBeCIAPwJxdtNNN2FjBcvZsWPHd955p0uXLnffffdpp512ww03MCG5+eabR44c+fDDD8dTsKK+FxZnUYgz7g6x8uqrr/qKMXr0aIb3rbfeCkcDQQZ/BFzMP7/55hsGOdzZyy+/DCnDmLcmab71BCEzMzPzzjvvLFeunFeYSC1iag2ddPLJJ4Mh1lunnnoqs9nhw4fT5GC2t02bNv3793c20JAF0KPMq9u2bes8ZdLwRxBnPBR06OWXX25If2xsIRq4ChMtewlosLRAMTDkLE/KWWedBTdnCyQqka9H+PHHH8fm9N5770Xyu+66C64clsS3vc7m+D4IzgKJTa9aldK/f5h2uPjibcboef78cE7jxtmGOxsxIu3mm53W0GkDB2YOG5ZJ4U8+ybm2WrXsFSuyUndEnBo5MnXkyHD5Dh2y4M7GjUudPDlXKKq6dbOfey6jadMcAq6om18QJYaO+u9///vdd985hfTNhI1CxTHsnSWLNB1l4EXRV++99x6iTpkyBcb8rbfeYhi3a9cOo9Gk01debHMNMu9p5QgBISAEhIAQEAJCQAgkKQJ8tjK3xAbHev9hzQQ7k56u1dPAdSnuLUceeST0B5Jhj1OjRg2IABb8saM544wzyKTXINSwLsQxLXDSJ0ggMMG2KDU1lcRhhx0GYkYQ2BNjtRTJ0zNB8ka7LZZi++23H1SgbyHfFjE8jP0pLOHGjRsZGHfccUe3bt0YSL6VBDYTyxQoP3xsvRK+//77dOKVV16JKsO61hSAQWvatOlVV13l7F/I5XPPPdeQj5gfUp7pvbfCOOdEeoRhxq+//no4Mh55OD6MTI20kA6dOnVCYzOqmzdvjtkOAvu219mQSA+Cs0xg07NnpxribPDgzDfeyBg6NNxrjz2W9uKLuZiKv/5K+fzzMBOalRWaMiXXKdO0du2yHn0085prMmHZfvoppUuX9I0bA9voXYLxUkaxw+/vygqFfDN52DEtZz3MWbJI01EGXhR9xRhGTogzZIMu5++3337L32Kgr/TlVKTjTZULASEgBISAEBACQqDwEYBAsfNMXPxgDXzvsWTJEmx2TMQcomVhm0MxiJgg2GL4ClySM+FN8L8DAQxwjGUBk2pDBGCGZpAxCRzfAhXLKYG99uCDD3J3pmpffvklthvwL/yEccBXCKchIqBhsoSzZzCNsJy4EaVr6tSpGJVgZ+TMN+lILeLZx4GLZjI1xW/xmWeewdfvoosuIgIaT/0e8XRa8wrtl4NGev31180ZPCutj3n37t3hyDCoxETFdR22KmeeeSaFTz/99Ndee82Qy7QOSzRKYmVmy/fu3Zs0/s6oRyb20G0wSvZsohKRHuF169Z9/PHHmNphMoa1ILZp9B12we+++66xrUNdg4lx2PRtr7NFvg+Cs0CQ0/feGybCMDS7554wa8byASP3uuvShg5N69Ytx0nTyI/jZ4sWmXPnpsCjeVvUokV2377hGgYNymrYsBRlXn459aKLctXgvSrhOYRcQAaCnU2ePNkK45vJ886BObnpblu46BKRBl50fYXHND7XRipYYGTGfo2fJJJLX3mBTbxC8cqkHCEgBISAEBACQkAICIEoCMAU/Pjjj6YAGwVE4s74YDUhkCg5Z84czBzMJdg0vfTSS1Hq16kEIkDnLl++nGk/TBn2RJAFzJSwSSFu9BNPPIFgJBIoXgBvTYgo4j0hGHM2/oIbBg5YIUG4YM3322+/4Q9LTPoASm5EYjKMtyaO1fZpdYkaqUW4qd52220TJkxgkGB0xkM9duxYAsCRZghBx0C6uapK7E96in4xMhDg33Jn0Eb4I0N+YaXCBgJWSKyuiDh+++23kwN3hgktvWl3BrDFnImVK1cuWLCAfufgqbEmt84y8UyXLl060iMMrUa8NpgyqF7EfuGFF+DODNfGAOaphxSGWYtdWteDEPuFcSvZrFkpw2fueFLDt83OhvsOc2fnn7+L5OraNQvuDNsxu7dEy5bZOG9OmpSKI+f06eHyJ50UDnMWrsJz7LNP9gUXZI0Zk/rDDz4Um6e4MvKHQJ76yvXc8QgYm8qk01deXMSdeTFRjhAQAkJACAgBISAEAo0AIbFjiXfGJJNppGkJ9gsvvvgi6bfffpvAxoFuXskWDg4FAAivzrT5kUceYXb97LPPwo/gnoYtFVHhbQz1ko3TrtZfsONgVMNAQSVjqcQOdKAHIwPVQmg/Ml0b2O26OACpcePGYSPG8dVXX8F3Q/fAGTkDgUdqEVsKMDBoAVQLplvQT7/88gsh87G6wl+bU0Hjztq3b++Kd2bhZ09Jov6zS4CxvjT5mA2SoGm2GO1iOwX705tAMXJAHTIo6HQnE+ctHJ+cSI8wZjjGxAwx2BgUsg9anJwPP/yQx5+oZ8YtLnYhXQ8CnEXs18an5LffusksdkBZvz58c8c+saHKlXPilP39d075hg2zccDFZ9MwaJQ/99yI3Blnq1YN12AvJ62jsBDIU18x8JwLPHBtZigmnb7yIuZP1nrLKUcICAEhIASEgBAQAkIguRDA7wPfCmOSw8zc7BaPHUdytaKESIt/FvYpdsrBnB8HN9o+f/58wiExXWHHPYzRMEsJvgdifLoMayxYEhtj21hfwj3htcckDeIMMaCPiRyET1x8RNq9u9DpRGrDzsiYGn3wwQeG5ra1RW8R7tiEzTruuOPYNAB/XtpOqCyCDRk/KVtJ8BPsiIqaIsy/ERVrQfhQmDK4Y3MQCAwa0diweJtDxCicXk1+7dq1eVIC0u+RHmFCGdq24GaLwBBn+GzCtbFJKLw5ftzeZnpzIj0I3pIJz1m3btvGjeF/48blmB+yNwbhyRCMnTeteEuW5KRr1doV7L9797A/5o03pv/2W0r79ln77GOL+yQWLQrXwI4BPueUVTAE8tRXjFtMIM1NIM7Y7JiNAuw9k1pfiTuz/aiEEBACQkAICAEhIASSGAE+Uplj2wMnIKbTzZo1Yz974mdxlrk081KC7CRxI4uv6ASIIbo5IaswP8FqBvaEWHU0F1skjFCYWmNCBYPGzpswI8UXhny0jMjZ2OKBCXBheIW7YuXKlZm2HXHEEdgrET8I8oUEMzdy8lFv3ItiToiNmDnwTOzRo4exPSS8F358iBO9RdOmTaON+DNSkl0m8OYmVBZ6wMTqjntrdv+G+NXigmp23qQWNBVPBHHceDTMQZqng3BmvvdghwSg4Hmh38GNayM5s/teXnSZkR5h2vL000+zyQO7NI4fP57YZ1BpWN4Roe/oo4/GAZnDxrX0FY9NOWfNmhXpQfC9JLGZOGzaf1aSTp3CDNewYWkbNoTz2ArlrrvCe2iedlqW03LurLPCxb78MkyKOR08+ek6Zs5MJdIZma1b7/IDdZXRz91GIE991apVK/hidsNALRunY8azvV1S66t02wwlhIAQEAJCQAgIASEgBAKOgAnz7w32Tw7zZ6dzEyY5mLFgrMTHK/SZaRfTyxEjRpAZ8GaWQPFgOnA5pGsMXXLiiScOHDgQHPDDYjrNbpKkMSwyAdFLID7eJsMhAhfEIhBxFidHgs2TyVOARY/hkshnW7qAcCjeJnhznMQotAjsCVRalBbh3ckTjVkWFlvUxu6TkyZNImwWBmgPPPCAt/6E5Dgb5RLApcpQUPjYsm0CxdhV4JRTTnHGgKOL8XMkH+Xmqoef/fr1I8Qb/uykYVEZGJgceovFPyfSIwzzS3BDWDNEwr20b9++uNwaAh0O3chJQ4zjqq/Y9DVWPISN830QfC8JYObtt2eypSbOmIccUqp58+z581OwLEPO4cPDhmb2qF49u23bLHgxcthS0xvsbPjwtOefT12zJmR2EoBfY/cACmNqzQYs7D9L+EjC7T355JNYZJuVCQwbYTDZXsPeJVEJ14NgxPDNTJSEvvd1PtpWX4EtkLLnL5dgTcngZM8Bc3lS6CvflprMFJ63KKejnCJ8Bm7YUQrolBAQAkIgIQh4tZM3JyGC6aZCQAgIARcCXu1EDhYWrmIF/4kdCmwCcbKInl7w2lRD0SHAlzmWJoTrcsW2x2yQeZQrs+jEiGfNmBoxuYp+xxtuuMG7A6O9hC0LAcflyooXJPmY5Dhnd/aSZEz4tghrRMaGMzgaTcPcAzRiCZMPcQBEsaBBGDV0iGv2V0T6KhZ5IpXB6Az6CcopaLyD6xF++eWX2Rn2qaeeogvgPQvl0fZ9ECIBFc/8xYtTGjUK+1Bv2rQtLWxSFpo6NbVHj/TGjbPnzNnOz19+SRkwIM3wYvxs1ix79OgMYpyRHjs2tV+/9D59sh5/PGPixNRevdIhxcaPz5gxI/Wcc9I7dcqaMiXj4ovTJ0/eZY3L5V26ZF15JTtFUEEIN1i2m4Bf5t1KL0DI4ghsFiHYPwc7zYLvnFNwJRYWtHgd7FjCgORJdGrgxOqrggMsu7OCY6gahIAQEAJCQAgIASEQaAT4eE06B65AA1pkwjHhx+3OW72xKvLmF4OcOnXq4FNszEB8m8NZyvieMpnWqMFZhujUmJk4c5I97dsi300nS/JuEpBQhcJDFfpoifQIQy4U1r18H4TCqrwg9Rx8cDYx/Zw1sJNm1667cohrNn16BhsIL1+eUrNmNkHQ7NG7d1bv3jklu3XL6tYtJ92hQ5atEypth/WevShXgjiSHCaLXrBOweQUlm1mwZVYLomLxQ8iTnrfZcmur8SdFYuxqUYIASEgBISAEBACQkAICIEkRKBDhw54Tr366quRZGdeSplIZ5UvBJIRAfYVDZplXMJhLFMmBMuWcDF2QwApsd0ALRkvEXeWjL0mmYWAEBACQkAICAEhIASEQHFAgKhPBEcvDi1RG4RAzAgQgy+JwvDF3KwSWlBKrIR0/C7H4AQ2mKB9rrvjH+vK0U8hIASEQPwRIO4MUTOc95W+cqKhtBAQAkJACAgBISAEhIAQEAJCoNgjkGDujFkoW6Lg+MqmKtOnTwduYtn2798f59jq1auzi4q3A7buOLz5yhECQkAIFDoCbHl22WWXmWoJeNmrVy/0VYMGDcaMGUOm9FWhA64KhYAQEAJCQAgIASEgBISAEBACQUMgwT6bsGNffPHFTz/99Pnnn59//vnt2rWbMGHCN998w4YXhAVlK9mVK1ey05ATNXbp5qfZ9NSZr3TxQOCxD9f+/K/bDtG3aXWqpA88oaI5dc2r//iW8Wbee2aVsunhbY91CIHoCLBHFbvas0c4isiUHDduHHshoa/YQZwoFWeeeebbb78tfeWCMfaH0Vz40Fk+IbFddeqnEBACQkAICAEhIASEgBAQAkIggQgkmDv7559/2DKGg+1gVq1ahUnZ+++/j2UH2+IQcq9u3bqfffYZ+8gmECDdOv4I/PeHXC5ykQTo22ove+qdRZsys+yvaAm4s2indU4I7ETgo48+KlOmzPDhw7/66iuTR1iKNm3alCtXjp3giWvAvuzSVzvR2vX/9szs9xZv3vU7aiotbPos7iwqRjopBISAEBACQkAICAEhIASEQKIRSDB3dsEFF/znP/+pUqXK+vXrmaOWL18eizMyDSy1atWCUDPpzZtzJmO4ebLxcHbkLThSchsVmZKr166rVHGv3GfCFfsWNnfcsm1bdlbWHmXLmp/RC9syq9etr1ShPNumRKnZFjaJbdu3bc/IKl9u142itM6KsWbdhgrly6WlpsZS2Nxo67bt2zMy9txjD5cA9qevzGs3bES2tNQ0W8wkfAubU/TR5m3bKji2F45S2Fa7fuNGJ9o2P0pie0bm5i1b9iqfyzIxSnlzat3GTXuULl2qVHrs0GVmZq7ftKVi5Bv5NnDj5s2l0tO5k0sk38KmTGZ21oYNmypWKG8viVLYltm4eUup9NTSpUrHUthcRRivNes3VKlYwVYSCxqbNm9JSU3do0zpWArn3CgUWr1mXeWKu4hOe0eTiCSzK991VRx+Qt9zjB071t7L7m/99NNPw/i3bNly6NChXn01f/78b7/91lz19ddfN23WnEfPVuJKlCmda3g4S9Kt5cqWcaqSKIWd1WZkZmZkZFaskOu5cNbsLGzStuZ1GzbtUbZ0VlZkDRsK2cLmWlNzVnb2li1by+1RFj9Wb/1Rcricf6mpKTwsvjVHutYU/pfRVSms22NsoJUZiDKyMsuWLu2tP08xNm/ZigpB/XJtnoWd9aelpW5Ek+zsmnzJjCpOT01LT3erYlN/FDHoy02bNu9Zbpfmj1LYKS1pXhmMwD332PV6il1mXlJlypROjfwkRxIjMytz2/ZM9IxTmEiFnWVIb9m6DYicApMZi8wM4LXrNlSuWCGWwvamtrBLPFtACSEgBISAEBACQkAICIHigUCCubNRo0Zha/bKK68sWrToxhtvxG1z27ZtTmTNvJGQQ/fff7/Jh5e5atA1q9etcxZzpqvknqWbkkwe1vhd4lvY1GbYARg0W3mUwrYMV61Zv56fsRS2NyqTe/4WpXX5qtlbmA99CDRzX+9fX5lp0br1G0Me3tG3sKnTQOdsRZTCVgyu2r5h45atW21O9AQl121gvu0/k4x+rTnrlNBb3iUz9FmU8q7CpiQtAvCUFLcNjm/hHAGyQ4xV542iFd4p9I4bQQRviaXwzov4P1enOm/qKJOTzFfNrsKuFrkqdxW2YrjyXVcl8OeTTz6Jspo9ezZcv6++wotz1qxZRkIcPA9teBgERCSBXVPuXCWzs7dnZjo7KVphxw1gryACHBnhZK6aXec8BNBuFIaN5ZZc6L215265MnZckpUdbmhGjA001xewcCYgZWX7tjTPmmkjCsGwhHkWdrY2LS0XH+R7d1veVTPSZoQyszPcPRsLGqZrItUcRQxa6WLrohSmfpfMvK+d5K8VILrM4eGbleW6katm11lbs+ka+9MkIhWOLoarkuiFXeL5XpuvTNj5fJVXYSEgBIRAohCQvkoU8rqvEBACcUYgwdzZSy+9dP311+OVyTFx4sR33323Zs2af//9t0Fh5cqVBxxwAGmim9kAZ/Bo5MQ+rzYlw7YJfnZnLrid1W7eGrY7w4zCVcb+dBa2mf+uXV95r7Ddmc0xCd/C5tTWbWG7M2f5KIWdxfKs2VuY7/vyDusDbwFnjhFjzfqNFbA7S8uDonLKjHkbdmd77VnOWZsz7Sxs89dtCNudlS3DLC8m+qxsmTJYnGF3ZmvIb8JXjEiVgEClndYikcrYfFPzhk1hu7M851ROMZiOr9+wqdJeu+zObJ0m4SxsT23YFLY7czGwnPUtbK6C6Fi9boOtIXphZzGTjlKztzAPQ+zlYy/pvVEccoYMGUIENKI0Eu+M2/nqq547DiOM0VexP3TOkqu3YyVaNorZjrOws+3w41u3udm6SIWdF9r0bhQ2Q5cL09M22npiSXAJFnbYcJXNbWfEtbshRix3NDXDa0OpxHILb5m161FWZdL9tKK3sFMkTAKdP6MXdpYkDYcViz4xVzlrRqXyknHmuGqOcgozLnrWWT5KYWcxk+YFaqzzvKe8ObZm+mXzlm32p7ckOZHOrt+4uUxp94dNpML5qrnghX1rUKYQEAJCQAgIASEgBIRAUiCQ4H0227ZtS0QzkIImw1ijRYsWhBN69tln165dS7ChJUuWNGzYMClwlJBCQAgUewRwMMdUlv0B2GoTN3NsaqSvin2nq4FCQAgIASEgBISAEBACQkAICAH38mycEbn88svZya5+/frcF4OOBg0aEONs+vTpbB1AKG5INBJxFkm3EwJCQAj4InD77bcTgdES+pMmTbroooukr3yxUqYQEAJCQAgIASEgBISAEBACQqDYIJBg7my//fabMGHCpk2b8NVL3RF3mT3s3nzzzdWrV1esWNHkFBus1RAhIASSDoHeOw4j9p9//umVX/rKi4lyhIAQEAJCQAgIASEgBISAEBACxQmBBHNnBkr4MhemlStXduXopy8C7y12x6H3LWYyj6sTiO6OIqFOCYFkRED6Khl7TTILASEgBISAEBACQkAICAEhIARiREBkSoxABbTYB0s2T/smpsjcd5wOHanuDmg/SiwhIASEgBAQAjEiQLDFGTNm2MLVq1dv3Lixc6s79i6fN2/eihUrCH/BXky1a9em8B9//PHpp5+2b9/eWdJUgvn/J5988v3335cqVerQQw9t1aoVCVu/EnFGgBDAS5curVChQqNGjexOTevWraODkIS4AXvttVecRSrq2zE4X3nllZ9//jnSjerUqdO5c+d9993Xt4AvOOyGzIPwzz//EBmmRo0avhcGM/O7777DHccEtHFKyObOCxcu/Pfff9lIzeylZs4yWgDw8MMPdwa6YTe2evXqHXPMMc4aEpX+P3vnAR9F8cXxS7mEVEABsSCKYsHeBRtYsSBYUMTeFax/e8desWPvvYGKvSGoKEVRFEQpYm8gJb1e/t/LJMNkd+9u95K72wvvPnzC7Oyb9tuZ2Z3fvPfmtdde22abbXDL8/HHHy9btsysxhZbbEFjp06dyrTDfKVuVVdXo9Tfv39/vTf522+/ffnll/jFtvQB0ACTDTbYoEuXLma2PgzTS+mQHNXFHMvojl5D5uQ5c+bU1tbaxzvxJOesKnsOjgPBLiYxiUCglZMYVaLbMxB23XVXXT2mL/oMA4RuwF8VX1lZ+d133/EZgKurVVddVQsnOmDveDim//PPPzEiZAA6lu44PP0/Xzm2RUcKmaKhWFkC/5XX/7asxVlvUVq+5Zo5Ue7KLUFAEBAEBAFBQBBIMgI1NTXXX3997969V1llFcJff/01+vu33HLLjjvuSE0ee+yx++67b7vttmN1/frrrxPmpPJBgwb99NNPpIJKs3Bnv/zyy7nnnltWVsbalaUanhzJ+eabb/b/WjTJsCenOB7ZtddeC+vB8+IJ4vkXpgzW7LTTToNHwKUJ1eDUmk022SQ59UlOKRBnW2655emnnx6pOGhfZBwFHMHhPJ+zzjprwYIFnO3DAo8w/kkjZe6reEiiY489lpZauDOWymeeeeb06dMZnrRoxIgRuJSg5nBM9957784773zNNde8/PLLeLwh8p133mEeeOGFF/zQNCi/66677oYbboA7e/zxxxcvXrzuuuvqijHPZGRkMDX169fv7rvvVvEwR8RwoLnmzmgOGwbDhw//3//+p9M+9dRTJOER//HHHzxfnrK+5bfAjBkzzjnnHJoD8UHdeGSR6AbuQpfwrLOzw4t0ZubRo0czn6sWwTyed955HOd+2GGHqRj913Eg6LspDzB1vfJKICMjcOKJ4b/85s0LTJwYgPM86KCm2s2aFY6ZMyeAI/Sddw5sv31TPDGffRYO9+oV2GOPpsj58wMffxwO9+gRGDgwMGFCYMGCplts/fTuHYA3zstriknCf62ZxFT1xo4de/vtt8MRq0s2tOjSdG+m/cLCQt4FkMu8r08++WQlADN15513MvaT0Dp7x8NP/ZtvvslLil26gQMH8tpiIJs1cRye/p+vzCY4hoU7c4SlnUcOf8rBbZO9zR3zMr84Z017vMQIAoKAICAICAKCQGoRGDlypPporqqqOvvss/nshjtjhQZZxjKVU4BV9Tjk5Oqrr0aDw7G2KEFcccUVrLcVR4MMJ6JA09x222033XSTYxKJTBwCPEpWIDyRwYMHL1q0aN999+Vk50MPPXTMmDE8QdYq0BAsm59++ul29nTQOHPkxTTUKByhu6QvzYAjOLBIuCiFbaFvjxs3jhEBpIpXMtP6LYyuHAPW7sqGetITIM5Yn8NAvfXWWxDi6JCic8rAh07ac889Fy5ciNIKAx/27cYbb4RU8qd+IstsC8n1WSMvAlPAunq//fazPxSYUB4lT5A+wLyXm5uLDFQRbXzkkUdgXVGz5fS5I488MplqOPZ6RolhEqbhl156KRzohRdeeE/jL5I8hAg6p2yHQEYw6uFT2NJgZr7jjjsmT54MseiY0HEgOEqmJBKPwaecEi75+OMDWVnhwFdfhWNgBRV3dsstgYsuCsfr37nnBm69NSw8eXJTWhQT//or0OggPXDPPQHFtQ4eHObOHn008NxzOmk4ANFGzA47tIhM3EVrJrGXXnoJvhttMl09Xs0wzocccsgll1zCE2fahz6++OKLIcRRvSSMSjK3HnjggURzZ44db+bMmRBnVJttnokTJ3LwIzVcf/31df0jDc+0m690i3QgU4fad2CVTsUtuFAXrc3LzcnPa1KPdCHeJLJKxyIL7RozbW5OTmG+54I6FRdmqckjZgHNArk5wcJ8zwx8p6ICbTLQnFOM/4PB7OICqw+7GGkCgeLCgmB242waU7RZAHlSNV+5/Z+6UcPflta5/wcC4OC2gGY50Abz5itX//NMebKuRA0h+g+9yIiIHaSX0ldjy7WUYETQpJZxMa4Yd4y+GEI+vs3by/KZgl6Gpb72GItAfJedOxZlttzDcZlPTjBYVOB5pKvMiwvzg9nx7KnE13VVoQV5HejBLltnEYtjblc5xDcfqrQdiwqy1aefpTaxLklF2lhSzvfjmE9URq0ZgzwXno5zhWLFxvGSUlnSA+mHsbJ3vk/Pp/8734say1hjxEUV8e9NTDlgzVg2U8VnnnkGlkETZ8RAq7GqtNhJ6caw5uQbly9yvcxmZxv9iA8//JDNZC0mgeQggB7BVlttpR4f+jhoHKBXSNGY0MKgcYIWqiiY5qnI5FTJ/6U4goMdH8yjIssYEbQCiyr/t+XJJ5/EAMqR6UYbZffdd4c4oxWwMPxl+cpfdFLQPyVAh0FHiQ+Syy677Oijj4ZRIjKNfpCnMMIMAXudmY6Yl1CP5Xts0qRJSuCjjz4aMGAAzYSNwiAUWs2Rc7TnlvwYpl804w444ACKZggPGTIEPUqe1HvvvQfvicIgZnqQhvAgkKfIQJJioczSgyFP70XNkEg1z6N0pi1bLQ1xHAgWGd9eokGmiDP+vvde4PrrwzW9447A88+3qPK//wamTAnHhEIBR63K/fdHpy9wwQUBWLaffgoceGCg3JVnoxalJP9inXXWoVegVqmLxhYS0oppnxg6tuoz6m7Xrl3pRazjoM6TcKyiY8dbvnw5XDbEGVXCnpS/lrdSpOHZDuareNZI6snJX0EgrRH4YG7lbRNa+FyI1Jxjtiu6eM9Oke5KfPtGgK1Cvlo4DphmsuF/xhlnsDTNy8t79NFH+Qyyx7RvNKR1goAg4DcEmIWYo9iy3muvvajbrFmz2P41K4kihjLxQBvFjFdh0rLwxm2KeWuHxp167N0wxzDjJZxoBHDL9fDDD1MKCjio4RBQjxU9FMKsqPlxS10mujLpkr8jOPARelWJjy3aolgnPzcKFhulEjTmsNGz1xM1OmwYVTysCqtWNBO5xLQT/2jY8EKlDR069KGHHoJDOf7445HHK5YP6STmnPHjx6uGYIkGIajCGF3Ckd16660ozakY/RddFRbqCEMwoXmnyGUmKEiEE088kYYzifF5diBMiS9/6imo50UFsVpVfyEgPvnkExqF9tzvv/+OihnPjhYRqXTrmN7BRBlssr0BCCSMpIDpOBB8iYdDpa67LhyJoplSd0ZtGnNLzHOvuipw1FEt5DH87NcvMG0aKtIt4tUFt0aODAfPOy9stonMyy8HWr4SHVKlPGr7RvPUKVOmPNesO4enM2oFk67qpgyTCTPGUQxHPRn6jPd+EhSQHTsebC8/SGGUzlDsJaxnJ1XhSMMz7eYr1Rzz78qid2a2WcKCgCAgCMREgFfCSSedBHemJTHd//bbb/FBw1cOu0Pl5eX2GC0sAUFAEBAEEooA2mEcEcA3N5ZKuNEZNmwYerJobbDCdF8uxlB2ebVsY13nPh+RbFsE4MiwAIIi0ett8scLEt6aCUCLtG1x7SA3CzjoSuDXD40k3teYvEGl+ZBFMmHHDdbll1+OwWYkw1JoFPqDTqJaxyULaewZcWiIEhbtffHFF8kEVdMLLrgA9TTFwOpUfgjwpFjzqx/qV7pK0EbU/IMPPtCaZerW3Llz0SJUJDI0E0MDmolbzHXwaNBJWLCiZ8cjVqNDZ+ifAA8Lvg8nkhje8rAUyYVjeGpIt0RPEN9ncIg8NR6fiuRZ8xWKjSftNV28xWyUZSDElE++AG7Ittkm/A/tMPVraGBjIBw09K4Chx8ejkF3TL+IdtopHMNeNsp5yoC7mXcNx1t+q60WOPLIcFzjCSuWm2lwyVimlnrI6wB7YJj2Q0BzpAxqmFhqp7AxKLqiDwsXzOxEdzVrEml4pt18ZTZKhUXvzI6JxAgCgoAgEPj0009ZQLIFih9uBceECRPYy8Usgo9UPN2yQWSPwQ+3YCcICAKCQBIQwIIJ7oyC0LlA/0KViKYYxkFm6RgB4TcH10hmpA6zrY1tJtYW5gECiq9ho1uLSSDJCODNihKhEvBhxE+VflTjj8MEcIhmP/MhyTX0W3F2cFB8gI1ieYkqlk+Om4wCGhwfNAo/XBYyAFkqwxmZ7uShllgz6xxolxqz6HHgdYh4jKEOP/xw+gwjGk4cFTZIJVzj4YNfp/JDAJ19i78zXSsOoERtllMClPalisetEgHTNT7tgmbCAgB7RqVrBncGjzZt2jRy0Ln5KkBXxF6BDVfs7KA12ZdVxyDw+NReBbUlDOMAp0YM6jw8Srye8Rw9nRJrHwi+woHKfPuttUYcn1BaGo7sZBj5NBoihyMb1SvDAc5HgTXDZhP3Z43WIIFhw8JHBET6qbeiTh5JzJ/xjHcqRmegnxPQY5/XAfa8vP2JhIqFXeXlbh6wm8zmcNgrTtmoGzMPyqSmznuk4Zl285UdT+HO7JhIjCAgCAgCAQgyfvjj1FigcXbEEUeoS6w/8ERgj+EuX2+opykxPoJ37NuvuqZWZ2IJWDziRZEkYdKEJ84t+3h+eEfU8YcLDjO+vj600WrBgzcLv935sfukAi7/mk1OWgPNQu31lGqYmAgabtCwoGQmSVy4Z8+eFltLykINjTUkVj+aC8P9MBZPrDzZIrZXBj9BRL777rumuRM5sIb37SrU3op2E4N9FuozqN6o5fQWW2zx4IMPwqTguosDCllI01L8nfGXFxDrlnbT8LgbEgkcKGP2ug4++GDOo9RjIe5SkpCQRTIUtrLA4tOCAUihikJVpUOgoFKkwiipQZDhMlxXDJ1TzLh22WUXDg1g/LJAxWSVHpJ2Koro+9P/cYqvmsayHBNFmDKMwlQMsxk/xHARpVh+FY8Jm9fPD41eEgKo5+D3ndMzKQudO54vFrWE2eqgryqtIh4W8Yx9xYkwFaBn57JukQaCD2cJGGClQInDsqOPDrePzo57Muwrceym3c3PndvUdNNzAIflwp2df34A1cNBg8Kpovx++CF8s9ElVxQpn96CY6VmTAWK9yeAmTZdhQDn8KpKq1t0oeRzZ2zIof6pDsZFyReXZxwAakIZfXim9Xy1snBnS5aVdPZ4XEBldU1DKOT1uIAly0s7Fxd6Oi6At2VtXcjrcQHLSsqKPHpTZrlYG1YB9eaTe1lpeVF+B60sag6MSGGKqayp8XpcQElZOR7DI+XpGF9bV19ZVeX1uICS8oo8j571ma1KK6q8HhdQVlGJ02tPa6r6UKi0rMLrcQFlFVXB7ExPxwUwbS0tKfN6XEBFZVVGZqan4wIa2AtdVpLWxwXovmdxhMkwt8cgzNetctpNmFcLpp2N405n0yJg6R6mJBNDTsuzJqIIm5mGQg10JJfCKqEWLimryOuQU1df//JM54OczIJ0+NI9CpnFqHxOMDuE8r2XH6koDjBxiquroTIw0bBnqYT13O5GWGeCMCjxnZ3tdD5JzGowxZFQzfMxhXWhBKAdyyuq9HEBnupcWVXNWigzE9f/Dr/o1WDyNwXMMHlFqQYdiadjHhcQRZiszJx5SfHiiHLchSlsVoMuBCFrOTomkrAFC14K8XUkCl1eUsZxAe4baNbZUj1LrZJ5yVf1K6+8wvkAuJRGB43piOU3iid8dqtVNJaAppEm8SRhkUYld9ttN+Y0eDT0dDg9wBRLZhNW5rIgQ5WeFHa4JSUluNrk2eGxDu/gKK1gpcsaCZUcNA09qaK0Y0gjgYMdHFQj3hW0JRE640ygvoWCg+p03VA4ghmHAScGX0IokTFIIY8Y2niXxx2hUstSxLdKhZc0hjYaW1zSUnTueMFhz5V2LgtZh6OTBTWm2oXnL0YE1L82ZcV7Ophg7InqJcQoZ3SyYQAscAp+07BTTVB/OZWFgyA475UjAnh8eK1SvRFFM/hxni9sOJu1sOSsONDlOeWUU1ArJpLk0IIMeTM3M4wF6Oabb875EukyS3AeleLOzIOphgwJPPRQ+IiAXXYJ4HWAQ7lGjQq3cp99AuaKjRM5R4xwMPA0AVHhd94Jezrjl6bWIDxNPIjRNxgOdBJcLfNS4JuKw2Q4dJUTLdnCV8qq2MHYm5/oGLZwUHdl3MHXoy4A362Ifj1fRR+eaT1frSzcWaL7kOQvCAgC7R4Btu+Uh1daiqMBPkntMdzCdoCfQkO5S3N/vq0pubS2Du4+ylGbprAJPiR5dU3YUYL5iyRsyuhwXm6uDrsJ4FeGQ2VLy+opJTvL25lGJCmvrILvsB+16anOXoXVXoKbVHaZ5aUQ/bmOR23ahU0A4aHMy+jCpiRh2Dr3XLyZM1wmPKwZY8k5yq2q6hroM1M+irAppsL0Cp6sPd4xRucMe1VZVaMvowtb7paWV+bmWD9somdlySFxwpaCWn/pSARgg4nHHL5icX+mikBzAR4NqlfJn3DCCWbRaEPgY5uF2W233aYYNMIosLCEM8UknBwEWA5Bf/BjmU2JeLDC1REPDo9d8CnqzLXevXvfc889jk8/OZX0VSmRwJk8eTKci6m2gzaW/48LUNjSKLU3wyUHHcB6w61ADKFJBydIJNpJnCegz8bF+RFqIDCtyryLU3fVSht3447HDqTkCeoeq5umq6FvqRiOzsQETHn+wkIZT2eaOEMAO1YYfwzEaDKjA5s1HjSAMEDseri6iJQHMKVkXCvbeXQD1fkt1AqKBI9mysSB3YuRI0eiUagIdPYwVLWZk9FEi9QEnjUcMe7S0nqWwLcwR2pijLneegHOxf3qq7BmGb/bb2/RblQteTXBi/HDD4HdYJOjBpg7cb2lThLAgZoyWcZlHqes8HGOfwN2bFGYYpgospU+BjnF4GpRUiouLAOBNzKUujqelb6hVha83PnBo1FB/CrAnNL5k19ZfCnOnz9fTUeUDnqqb+v5CkY70vBMi/kqCqTWT8wooom7RSdmJjU1m2DllaFv4gqVnAUBQUAQ8IQArl6feOIJLJvYY2HqR0XZHuMpQxEWBAQBQSAOBPhWhvOKlBBzLVZTrL6w4sHuAz0OJclSwTEVH2B8iPNjiY6CA4o8kXKW+CQgsM8++/BmYS1XUFCgnx0KCGzyo4lGBTRjkoTKpEURjuCgr5QWlXesJOct6nh4Uh2GWEEdiUM8IFPMZTaUGaookKpKkgUU6iq4P6OrmGsrnU+SA1RVzzw4/LKXDoWhBdRdPILxI6yd/ZmplH83YuCM8KRBS/EdZqfkzCQpD0PZQHNgbEs99bimVkzRmGMz3pnVVTxPzYKGpfLImzFa2HEgmJKpDduV0M0YSLFZszj4IsyLvf56uKY77hh45JFAnz4raq124qDDkOFvo81r+K65Q4ffNOU6jeSHHho455ym5DCSHGEJv8ODgGQgjM2s4s44mwI9TT9wZ/BN+mlSbzhi1LjQnMWMV9HiRLJnjw4yFi0oiSuXeU0tTPx/ZsdjUOO1EMVPRh/TkZ5nzPkq0vD0+XwVE8gUc2ewZhdddBHKq+CI61M2P9Ex5i+ffcTQORTbGrMZIiAICAJ+QGD0x8s+WbDCl62qEuZXWVlWA8B9N84/badiP9TZfR04QJ2tTkxm2CeERCNgj3Gfm0gKAoKAIJA4BFiJYfblKX/lYMVTEhFOBAKsrh0ZzHbMmuEcBys8XL9HwpO7yES6S3w7BsdsNbwYJplmDGGW05YYLpO8rrZXIDkxDBaW7skpq/WlaAbEklUbNsG3A2HDDQMWlx6cpKkO01Ro4Nfs7bfxjh/4+edAjx5hJ2j6hwlvsxVv4Kijwv/Ub/DgFXlyeoA6QECnMgNsSPBTMTwFk6JCX8+UjDvc+knMsWjWGvZ4dlb42eOTHMNmmz6nyLFox+GZ7vNVirkzHEByfgrnraJXjI7ukCFD8JOKTgdW67wmISwxjPJD53DsEBIpCAgCdgTmLXL0i9/CVI1U+/r0KCRrg7Bv0iZOrEVR+WaPBfMBteVrj7Gml2vfIzDi5cWe6njfUOu6xVNyERYEBAFBQBCwIMDJcdhUsiiwxOtL1qXI6EsJCALtAAHOY4FcaAcNacMm4DIEli0dfzKJpeNTi6POKebO0Jm85JJLUEdkCYpTbWiyCRMmoDbJpgon3OH9bsqUKXibi6NhkkQQEAQEgUQgYN/Ltcckotx2n+e/ZfX97/nTfTOP2rbw0r06u5ePJDnzj+qllS1cekWSJH77nt48wUXJSm4JAoKAICAIKAQ4MvL0008XNASBlQoBjrPgt1I1uR03ViaxdvxwzaalmDvjQFPOIcZsEwtwKLO77roLjTNs11UVceqpThjBtBNTZBWJhtq6nNLa0nux2SSLZ2ItScDO7UcSJsPwaXGc9mUUFEXYrEC4INy3mebXgYCZjykcLihkPZYuijDylpwtuUW/5HTFeltxEZM0NFU7fOqzbWPEUg2zzshTUEVN/fmvL1GZW/ZVuGsp9J5DViVOJbTcinRJJuGKJfFnttFSrCMajTVs0YVUKkdhdUu1yCwoirCugy7IjbBKZX8EZqE6Zx1wmbPtweoMrAEqoEqMlLMl3ppergUBQUAQEAQEAUFAEBAEBAFBQBAQBASBpCCQYu4MjTMYdw7uxQEqzh3OPPNMXN+ZDVecC2epaO+ScGfHHnd8aZnVfZJO1am4UIcJKEmomjKnJI7CKjlre1Jx4L3OLYqwliFRWXkll26EmwoKNASzgzoHAlFaZ8/ZTBgzTHPKKiNCZ0kOd0hNIDjKKqvttGOUBoIbOFSVVUycb3V9ZSlCXWZlhpscaghVcKhbjaPFn0M6JJHPyrRXzUG49VEcGh3luTiiAYPIsXo0yVK6o7CSATroM7OgKMI628aCApwb6EZYp7IEzEItt7h0mTMnPNrTOsZQW1VipJwt8Y6ZSKQgIAgIAoKAICAICAKCgCAgCAgCgoAgkGgEUsyd4b+Ac9BxNYeXxP79+2OhiQO5xYubvM/g7GxtPAcGApxAzE9hcTXH2NpW8lFgUivwJctKOhYXxmRZzOV6ZXUNek35eU0HVNmLMIX13SXLSzsWFVjUrKJXOEwX1bXQn3LMWRfRmkBOMLtjdk4gsNxNJrSCmiwrLS/K76BP0IiU0KxzbW1dZU1NfkFeILAskrwlnuQlZeV5HTrk5kJWumLccnNzCvPzKqtcCVuKi+MSBDoVufXLqNAoq6gMZmfn5rQgRu1Fm9DBVMIomTEWecdbZRVVwezM3ByebIufo7CSgNxcWlJmSkcRNsVUOJJwbk6dy8fXITfHMRPHSHsFJEYQEAQEAUFAEBAEBAFBQBAQBAQBQUAQSA4CKebOBg4cOHbs2BNPPPGPP/6YOHEiJ5uWlZVxgN2BBx7IiQHz5s3rYx5OmxxIpBQnBG760C0L1q0o6+itYc3kJwi0TwQwIYfuN9vGcdccfWXGSFgQEAQEAUFAEBAEBAFBQBAQBAQBQaDdINBiBZj8Vo0YMWLq1KnqaN5bb71100037dWr1/jx4zmQtVu3bpBojiezxlHPVToVe02Vl2vV4nGTwyodi9yImTKoC8VRVBzqOShA8a+6fIUVqlmNSGGtbPXU9NJIMmb8+bt3CmKDGsyuqsMA0cOvuNCtVpfONJidFfSeqrjAOPdY5xU14EnpTOeEWpwOuwzg5CuOJ1uYH1E7MlK5KBXG0VejqGFGKqg9xcOaYVc+btw4HIJee+21UPwY2J5xxhnPPPMMJ14/+uijBxxwQBu2t7P3yUSVnhMM8i++mhQXqtGB/qC3X3xdV5VR0KjeW1LrbWpSaeOY21VCNR96a2SzNMrFzUFv/2dnZcWdNo75RFUOheu4UUI/1FsLDek4pjKVGnXdYGGcHydFYX3neH6ZGRlxj7h4ypM0goAgIAgIAoKAICAICAJphUCcn6dt1UYO1nzllVfQNctp/JEtB26+/fbbS5cu7dixY2ZLd/ttVajkIwgIAoJAHAi8+eab06ZNW7hwIYz/sGHDsDd/9tln0ZDlhJMvvvhi+PDhmJkzp8WRsyQRBAQBQcBEwOL71bwlYUFAEBAEfIWAzFe+ehxSGUFAEEgcApmJy9p9zoWFhVBnpnznzp2FODMBkbAgIAikHAEOKlEsP/qwHAFcXV09YcIEDgju0qXLoEGD1l13XTw2prySUgFBQBAQBAQBQUAQEAQEAUFAEBAEBIG2RSDFemdt2xjJTRAQBASBxCFwxBFHPPjgg9iYl5aWYmMO6Y/GGZGqxB49ekCoEZ48efLMmTNV5PTp0/vttDPnx0aqlcUmLookOSRNmONLIlXYMb62rk7XnPMuHGUiReqE1dXeEloy1PlY4tVlFOg82ZaHQg2WgqLkbK+JCJuYtCc0LG0xmylhQUAQEAQEAUFAEBAEBIF2gIBwZ+3gIUoTBAFBIBkI3H///eiavfrqqz/++OPFF1+M2abFTkEdsPvrr7/Onj1bVQg2rbKy0j2d5F6S/BMnHGrwRmOFGhp0ZRo8cVFGK+o9Fmp55LoClnjHS0/CLXIwWtoivvnCU84i3Axb+P92j4bZWAkLAoKAICAICAKCgCAgCKQXAisLd7ZkWUnnTsW4THb/q6yuaQiFvDpHX7K8tHNxoVpCuyyrugYlj9CS6uyf/qt1maT/+nnLSsqKmrx6u0wUqK6pRT0kEGhhHhsz8bLS8iKPrugpprKmJifXm8/mkrLyvA7efN7X1tVXVlV5PWSgpLwir6WNcEwQ6uvrSyuq9LEJMeWVQFlFJU6vcUnuUh4xVo+lZRVefWyXVVQFszM5dMJ9QQ0NDUtLyrweF1BRWZWRmRnfMRru6+ZbSZwzXnjhhXs0/p577rkPP/xwrbXWWrx4saowzs7WXnttwmiiaWW0q6++mhjlC99Nu0zJpctLOxYX4sI8UkJT2JRhoFfXWBXHIgmbCQmXlFXkdcjp4KUvkSo3GETvRnXd7KwyS57RL6lYeWUVRw3kd/AwUnSeem532UCVEGE1H+KAPyK+ugwjkJmVSdrlpeUF+R1w/G/caQpGr0ZdfX15RZU+LiC6sCVzxqz7+cTMGTJz6bKSKMcFmMKWQlGy4+mYAmbYImy5VC8pnqwlPtKlzpn3VGVVTfOxFc7iWthyu7S8MjfH+mETSdhMC/+7vKSM4wLcCOuEnoR1KgkIAoKAICAICAKCgCAgCKQdAtZPzLRrQHuq8IiXmxbh0Ru1RsdsuLPoMnI3QQhM+6XaZc7rdcnO9bQod5mviKUOgX322QePZkceeSQ0GUcE9O3bt6qqiuOAOXCTy3nz5vXp0yd1tVupS777k+Ufz6t0D8HoIauu6flIZPfZi6QgIAgIAoKAICAICAKCgCAgCLQrBIQ7a1ePUxqTaATOfnXx8kpX5myfnrVGoisj+ScZgVNPPfX8889fb731KHfUqFEbbrghPs7Gjx/P0QHdunWDRCOQ5CpJcRqBH/91q7erk0hAEEhTBOrq6jiRfLvttlt99dV1E956662NN964V69eP/zww9y5c7faaismKH23trb2nXfe4SjzPffcU0ViUc6RwZD+uG7cdtttt956a+KV2AYbbLDRRhvptByKglLt+uuvr2M4WXjzzTeXY4U1IG0b+PvvvxcsWMBz2XTTTbOa9VvxszlnzhwK4ikXFbU37p/e+MYbb/z888+RkFxnnXU4k4dXraOAIzihUAgHC0uXLmUgmGPBMQefRM6fP5/NOca1Odx03WgRfWDJkiU9e/ZUeu7qFr3ln3/+YffO/AgZN24cs8GWW26pk6cqYJ+v/vzzzy+//LJ37944kGUW4tTyYHCF4rldnprr+W3SpEkc0LTJJptYmkMfYN5DQZu5i7FjueuHS081rKioYCYHCppTXFxs1p94ZoA111zTjFRhx4FgF5OYRCDQykmMKuEWZtq0abvssouuHmeU0as7dOjAtM9fFY8rmFmzZtE3eFOvuuqqWjjRAXvHiz5fUR+mX1JZ6unz+SomjMKdxYRIBAQBQUAQCCPA5+yzzz7LBw0vMHUQMAtRVrC8Gzp27ChHA0svEQQEgeQggKfFm2666bbbbjO5szvuuGPEiBGsljmu5NFHH91///0vu+wyXR8WqCRhva24M8ivK664YrXVVoNi40gTqH+ICdw4okuLGGedv/DCC5qgITe0a/Vi/vvvvz/vvPOeeuopHaNLkUDrEYBCuvHGGzm4eeHChTyvhx9+mAfB8uOMM86AGVm2bBlF3H333e1MzZlWw/KcfvrpkQCkxyJz4okn2gUcwWFhee6553711Vf0UhZ4p5xyynHHHWdP66uY6667ji8KHjpeU/fee++rrrrK9ADD2DznnHNoC8wRApzxffLJJ1P/9957D2esKMJff/31eJPga0RFMkKffvppPzTQMl/9/vvv7EQyU+21114crMSEM2DAAJM7s8irJuj5jXZts802Fu7sm2++YWuTAQKtgPxdd93lt9nJUw2hS+i9CpOysrKbb76ZJiscmAGYqI8++uhDDjnE8nAdB4JFJoWXS5cGxo0LuyE5/viQckYyf37GpEkZXboEBg9uUkqYPTvjk08yf/gho1evhn79Qttt1+RAl5jJk8O2PL16BQYMaBJesCBj4sRwJJtEe+8dmjgxc8GCpvbBxK6/fsNWWzXkJdFMqzWTmKo3/pSZ2z///HN1yaTHq3aNNdbgoUMHP/TQQ2weMPb1PMnqg8+Afv36NTU7kf/ZO170+Yq6PPPMM/fddx/1hyvHZGfkyJFE+n++3VOQ4wAAQABJREFUiomiW0ckMTMSAUFAEBAEVgYE4MssNBnrTEvMyoCDtFEQEAT8jABqGuXl5bqGXOowugkQZwMHDmRpzYITdubSSy/lux8STcnwRX7vvfdqeR2AieBT/qSTTtIxEmhbBOBHIM4uueQS9mlQamaZ9MEHH1DEAw88sOuuu7700kvvvvsuKlRQJG1bbspzQ+MM9idKNbgbSSvNEZyPPvqI7gpiUC30dpadaGZFyT/lt/D8AHHGc4e2hk56//33OcjbrBUramS4y49h+PjjjzOQEWC9fdZZZ1100UWQbmoIQ05xFDjuVjX9beaT2jBdGuIMJRrW/HltR2ywjIeJwynt66+/Dq02ZsyY1DbTXrqnGt5zzz3onI4dOxblQbSQoALJcNGiRfTkQw89FP0me/7EOA4ER8mURP77b8aIEdmnn56tD2OfMSMcc8stWao+o0dnbb118Jxzsh54IPPCC7N23jl4wQVZ9fXhm59/Hpbk39FHZ+nkY8ZkqsiHHgqzGY8/3nRJ5MknZ8PHbrVVcNq05HnPac0kxrOGCoc4U1DwFw1KONODDjqIXs3bGS1vpjLimdO6d+9OP3/zzTd33313Xt86SYICjh0v5nwFkwtxBq1P/fmcYGZDh44aptd85QipcGeOsEikICAICAKCgCAgCAgCaYkABpVs9n7yySeq9nBhGDpBlqlLxaOxCaxJ//32249LreSCko5JpWkIMLDq378/dJuOkUDbIsCT2mKLLZRuIMY4PER0cCgiOzubRRTPiwDUgIps26LTNzdHcCDO6Kuc50O70OHi73fffefnNi5fvhzdT/QNqSTUEn8xoLZUmK07ZZVp2q6iD8LARBJ9NOhyUqGwNnz4cDqSJXnKL3/55RdUZqgYBHFubm5b1QcEUGxhEiNDOgNKsqjZ2tFrq+LiyCdSDWHGOYHqhhtuYMgPGTIEHoSjycif3jt48GDstRnyO+64I8qGRILY9ttvf/bZZ5tP36yM40AwBfwcnjQp89JLwyTa+efXv/VW3TXXhHG4++6sF19swVQsWpQxdWqYDoNBe+mlFrdU6/bdN3TXXfX/+199164NCxdmHHxwtrGFpET8+Bfim347bNgwXTlstyFJmfaJgWVmcsDhsrrLSOdZ875Gc1y/xHXCNg84dryY8xWuHnbbbTcGOyrAeIR4+eWXmb6oWxrNV5GQFJvNSMhIvCAgCAgCgoAgIAgIAj5FgPUhno905TAn12ECfIijuIQjIcLo4GDBhJUf5pZc4m2Eb1lT6YPvb0wquKU0WfCkxgnCmIA9//zzphgLe35YRaEzgrD82hwBjHDZqCdbDFsw2CGgeDQFOCtqvESheoZWUZsXnb4ZOoLDshMnO6pRcBD0W7Qn/NxG1Iv4sbCE8kapZOedd9b1V9WGQ2EUY8oHswb9jfmqUiujaYxrIiEHsePDwhpbv2OOOQY9O/xkmeM3tc2HAGJKwaMTylOmhWakWkWf38xUak2uDz1XAZRcUM8xxVIYjlTDkpKSzz77jHOoUBlDWxDdNJDBuJhj3BW3iIc7OAhlsMnThEChFUzsjm1xHAiOkj6MvOGGMBF21ln1118fZs1wy4lWInpn11yTNXx4CzfTGH727Vs/fXoGPJq9IX37Npx2WjiHc88N9ekTRGbs2MxjjmmRgz1VymN451IHnJ2hVaoqo7S0dB/GvR0EMbcY49jvwz5Dn/GauPbaaxNdeceOF3O+Qm0Wju+0005DQw3rHEjzAw44gKqmy3wVBVXhzqKAI7cEAUFAEBAEBAFBQBDwIwLQKBarLrOW6NqwHvvjjz/45mathRYDNiBKAOUmi/NpM6EK86X78ccfoweBOZj9rsQkGgEeLko6rI4gAliBqOLwgoQhDOFIRluJrpWf87eAg/6OPmaBaufk5CiNHj83gbqhODZjxgyWzaw2tYKGqjM9gRiUOPB8xBBmaGPhi/dVTL1gozCJgmZF2QoLqccee4zVNWG6EHQMpJsfWs10xHr7008/xdoUz4wxqxR9fjOT83BpO8wRjxhuTtmbEzBlUhuOUkNoNfyXwZRBjKJqhIsouDOtoUOjYMyBzn39LQPBfcKkSe6wQzC7kX7Qtqe8mr76KsydDRu2guQaOjQEd4bu2PLlTVXr168B483nn8+86ab68ePD8v37h92cOda8W7eGI44IYc45Z44DxeaYxFeRar7Sk5gOwLHSt2GiUUFlwyymoWhCGxVlvmKOwswcdxAYj7N7h3IlPZyDL9JlvoqCm1+4M14GKB/qnsGM72ZTIkrD5JYgIAgIAolAwDJZUYTMV4nAWfIUBASB6AhgZWk6CVYKSjoJ+9UY+LAvjQIL39l77LEHugzqLpaA6sRGLUwAFTMINZxqqUi8q1x++eW4VSKhKSbh5CCgznm45pprWDbfeeedqtAjGn+Y07Iawb06C/LkVCYtSrGDY7InKEumBVww3ZziDSl21FFHoVyGS3gNPn6CGJX0B9ZHEGdDhw6lD8BG4XQf4gwxmDW0R1ms/vbbbyiQ4s2Qo3W55RPuDOsz5hN6L16cOKIkuns7mhN9ftOwqAD0Ewee0FjsGVHNQ8FNGbdaxFJ4GamGqOEoFTPqxjHuMA70W2JQP2T44/WM52ieCROzCfaBEDNJkgW++85KZnHAQ6PvvoB5WH3nzk2bPYsXN8n36dOASSs2m4pBo9qHHRaRO+Nuly7hHHTyJDezlcUpGoTOoFRHmRNUhswA7ISdeeaZXPJOv/LKK9EuT1VvjzJfUe0ddthB6ZphQs4GHmw43Fm6zFdRHp8zWRslQSJuMcVvuOGGr732Gpmjnsp2BF4eMOLFDV4iipM8BQFBQBCIAwFYM778cCzCylNtbMp8FQeMkkQQEASSgwCfrXywsgLH8ZP5bY03tNmzZ2O5qavBUW6Yelm8aLG4xYUQJiEmB6GTSCARCGCfBQeqAd9ss83mzZuHwhEkKY9MlahOGPS5BWIiwHHMMxI40A0o4KgkEGe4qGdZ4ZiDTyJvv/12PjBUZdAmwwaTOpt149xVlp1qRc2SFQdJaKBoAbRK0eyAKMd9OGprSGKIjbs3//STnXbaiSphS44XJBTleHC68q0PoKyHvzA02iDm0NZEb8tv5yREqiGGeFojEjNbag5xBicC13bBBRfAm7skziINhNZj2+Y5lJTUlJeH/z3+eJ3KHEdYuCcjzMmburh585rCPXo0kWjcOvLIsD3mxRdn//FHxv77h7p10+IOgR9/DOew7rorkjsI+TVKebXTB6Qw/KFZ6SoEUOBStYaDJqBsOZPcjpjzVc+ePdVkpSqGdhQrJl1J/89Xuqr2gC/0ztgi0HYHnCKBZSyXOHqAp0R/lW0We729xqzSqdhrkrzceDb0VulY5LWg3JwcilpS3TSDuEzeqbjQpaQWy80J8q+6PDzvuP91KvKMfzCYzb+qOm+zVXGh94Kys4LeUxUXhL0Vevox5uPAoTA/fDbyi1+X3f9ZiZvidu7V4br96aqen2xhfgc3+ZsyqHnG0Vfz8zwXZBaa7mH4fQh9dnT5MN1yyy35UOP7JhHzlQKqs/fJRCXMCQb5Fx/axYVqdHibjigLl7ZxdF1VyYLGflVS621qUmnjmNtVQjUfqrDXvx0bZ8V351S8MrPcfdpDtygYuHG+SlvtcXrMzc5Q84n74rQkH49xo9QhrvegKjru/oA5R7Awzo+TooLwrBvHLzMjI+4RF0dxyUnCQppNaXz0Kj84ulDUVfj4ZqHOChYiBqdpfAezYLOrmLG5jXcViyc1nY8E2hwBXFyBNjwmyiP4QnrxxRdhMNmwYR0FL8Dj6NixIxZ5GPS5XFG3eQ39lmEkcGBqOKMQX+w4EgI6qq3WmX6rv64PIxFzSyrMsMVzGVZOSvcQ32doGKCGBuX9yCOPcJokAe7CrBHQyWksMSgkEoP+AcsoVqoss9VpCVos5QE+PjkSFP041OvU8ZFUCYIAQ1Rdt5gsJ8pZNE3LMxy+/vprrB1pPnucPO6DDz4Ynk4L+CEQqYa0hcfK84XlfPLJJ/F9Bj/CQav4s6PHKuts2EB1HIRjQziUE5IdleF0mSUw2MxqPFpTWW6qRuFU4JFHMm66KWunnUL0Bc7JuPbasNBee4VM/dohQxrwIvDll2FSzDTwtCPz3nuZeDojfsCAFZSNXcy3MV27dkWviL7Ba5p3NIrhnCTAahTv+xy1iYoiQ5uuDtkKp5b8VsScr/jMwOEDZpvbbrst0xoD1lSATZf5yhHYOD9PHfOKLxL42BriZaCSs+d2/PHH49xBHTfDoRL2j7n4CpJUgkCqEPi3LB5SIFW1lXIjIfDcc89dcsklmEXwrmK/F1pf5qtIWK0k8Z8vbNKid9NeuDMtdupLi6b94tYbS+e8zMnnrKnTSkAQYP0JCOqvIxrqFtor2ElhtonxpinG9/eYMWNuueUWZffBLZRcWMdis4kCmikJU4PlJlZgZqSEE4cAyyHW/+jOQARQCho6LD9gAYjBgAtCjUh4zzvuuMNv1EDiMImecyRw4Bzxl8+JkyTnlQ2AMX38RS8o0XdZB2FbrSpMWVReHfQxceJECBS4FfgmFEzOOeccVROOu1UaiFyScPTo0RgtKvMuxjsrbdxmsc/HME90zWPmb5mv0IGlMzP50MmZeUh+6qmnmpnADHIZZX5Dl5afTkJWDA34JnUoIZp3J5xwgr7rk0CkGkJ4YcsGa0Y9scDFtzobtIpAh0NXlYccNNtraRHPGi0elIvTepa48sp6jtTEGHOjjYI77tgwY0YGmmW09NZbWyygVlutYZ99QvBi3OJITbuzs1tvzXr66cxlywLqJAH4NU4PQPjHH3/kVYhSDgwDBOt9990Hq64Mh7EjhsFkxFmATf6lpc+z9cWiQ/Vq+oY6z4c3MsrgjG6qhxEM58aoUZ/k2sacr6DMeHnRBDozMzBfEfrwE5/PVzGRTDF3xmuA03bxHKndRrJVoj4OqHqPHj0U4848otyjEonAZptvXlsXUS2iyQNhc9OjSCLiT+Hmurf9/6BRW+eagG8IaPTqDU3L6NVCUqWq86JYoQsyVTqjF4QkqSxPMHqS1txtqmF4Bnb1q62rr21EoL7eLeChhgaNgy7D0kC7gJYkkHLhUMgtQOrxRamzpS1mM1MVxu0uKxa2TDEAgeJnqek4X02aNIkNRlVJ2P+dd9mlqromUp0tej1RJMkhacI1tREnWMeG0C11zd3PFSornbC62u1IcayDp8jqmtqq6qa+6rbLNhZAD9cVjj4Y7fXxKm/JQZdriVeXSesbUg0Tf42GBX9TJhFhvpXZzrXkrN2ZobDAT93F6oefCh/a+FNhDJr4/ua7Fu1+FmbavgntD0vOLMksMY6lWyojl3EjwJoEs00UDdiegf1U+aCAgC8nNNFYXOmHFXcR7SxhJHCgIRgIgEYP9z/VSA3x8AWLxP4cFYbgVo+Jrw4VQDOUZfOFF15Ii9BCMlvEkMRqB1JVSWIqhVsJ8qGr6HxS+NDtMwYHR+pZRQfMGtoj9fymGSVTnjDatXybMUD0qLEIpPYSysaxhqgXcSQL3A0oqZrz1OzNNyuPvHmphSMNBFM4heHGHZ8W5ZsxkGJff107cmQWvNgbb4RZsx12aHjggbqNN17xjaZUCaHDkOFvUbOhl6liiN+00tKm5AcfHDrzzCbqDSaBIywHDhzIg8BJMWFedoo7g4XgY94P3Bl0nn6aIIBCGfVkIDP26R4KO+y1cXmGk35aYbpiaIFsYi7MjhdzvqIKKModfvjh9G0mNJMW9Pl8FRO8VHJnLJ7ZGeDrbZ111tEVtbjbUFjjIFP7PqN/d199jcqqiGtRi61HFEkK9aewRqPNA6BR5XqB2hBo0Ohpa/yYVYKcU6k8GSXpgurqWuwwRCkOSVJZnmAU+VbeUjUEE5f5sKaqzApvjLhfMAOyxkGXYmmgXUBLEki5cB1uPN394BZVWyLV2RLvLtfESvECQzGeM4/4csUmgq1Ox/kKjxWwbKoqJKmprnVPJ7mXJP/ECYcavNFY0L66Ms1H+bl9FjphvcdC3RbgJFcfWlFhp/uR44yWumeKVXZe5S2V0EBZ4h0vRdiExSdomFXySZg1Nl6TfFIZqYZGgE9fXjH6Ugd8rjml6xlHgIUArlrUUtYxOXfNxYJdxhEcergjkvbkPonBOo+1fZTKcOKBXYDltD1JktfV9gokP0bzC8kv2mWJkWoIueAyh5hijgMhZqokCGywQUN1y41kTtIcOnTFch6/ZuPH13E+6i+/ZKy1VgNO0PTvhBNgDJokhw8PDR/eFB40KKTzfPLJukbtPZ2oRYANCX4qiqdgUlRtpZvZ+kmsRY2bLxwHcpv4s2ouIf7/Y85XvMvs5sbpPl+lkjvDASr6kyhooMeBftlnn30Gh4q+uvYfyXao+qpD64+ferbKmL/ZKU/s5+1ekrx8Ihy7VfFK0MDaDNiNpW4yoMdrQHKCzFOurJNyc3NUqkZ/Z0vcFITMioJyagOBSjepcnKCOpUb+VbKqLIyMoDOFX2G253igvC2YYdcaAhXTpHQtIrZopgCZjOTL9zYT8wqRAzjbcqxeo6REXNJ7g3ei9hQKMcT6GKgU+Y4Xx3W+FNVU/OV8uflprLuJcktccIdTPcSLuqdGwzqymRntbD5iplaJyx3TbzGzDOmQH6HnIK8Jpdw4Q1K17/MrExd4dwcbwwjfd51OQ6CulyHe7YoETYh8QkaZpUkLAgIAiYC+GnBckodGmbG6zDvX2T0pQQEgXaAAMcOKh2RdtCWtmpCbm4Alq2tcktmPjKJJRPtFJaVSu6sd+/esGOq8Sw2jz32WI5fQU0Di30CeOCGXOvTp0+boLNkWUnnTsWe1kiV1TUNoZBX5+hLlpd2Li70NBVW12AgxRrM27NYVlJW1OTV2y1CmCk16kB5OwNhWWl5kUdX9BRTWVOTk+vNZ3NJWTn6ym4b0yjXqLtU5fWQgZLyijyP1AAaYaUVVV6PCyirqPRqeIh+RGlZhVcf22UVVcHsTA6dcI8ezhGWlpR5PS6gorIqIzMzvmM03NfNt5Ioe48dOxYbEI6HxwUJ/lnxDZSI+UohsHR5acfiQlyYewWEgV5dUxef0/SSsoq8Dh46kq5bfF1XJS+vrOKogUCgyURF5+kmoOZ2N5IWGTUfxueAf3lpeYHHWVGVjmJmeUWVOi7AUp+Yl2o+iYOA4yN06bKS+I4LQH+WJ+uJe9INUS+pxier41wFeE+hlBofjV5aXpmbkx3HWRnoTi4vKWt/xwW4QlyEBIFUI4DXJ21onOq6SPmCQJIQwGOddlqXpCKlmIQhIJNYwqD1V8be+Jq2rTu2st27d1d5omKKwirO5LA35kh1FBTpgixKHTUV27YakpsgIAgIAm4QwC3j1KlTlfox7jk33XRTjoGX+coNdCIjCAgCgoAgIAgIAoKAICAICAKCQPoikEruzERNuzODPnv77bdxLMfpTqYjTFNYwoKAICAIJB8B/AtwhDy6Zjgc4UcFZL5K/lOQEgUBQUAQEAQEAUFAEBAEBAFBQBBIMgJ+4c4szW5Dp4mWnOVSEBAEBIHWIMDRPJbkMl9ZAJFLQUAQEAQEAUFAEBAEBAFBQBAQBNoTAuFzAOUnCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAnYEfKp3Zq+oxAgCgoAgIAh4QmCfB/5aWuH2LMi1O2e/cvxqnvIXYUFAEBAEBAFBQBAQBAQBQUAQEARWBgSEO1sZnrK0URAQBFZSBMqq3XJnKylA0mxBwJcIKI+KvqyaVEoQEAQEgRYIyHzVAg65EAQEgfaLgNhstt9nKy0TBASBxCBQV1dXX1+v866trdVhCQgCgoAgIAgIAoKAICAICAKCgCAgCLQzBETvLCEP9P0fKtznu1sveQru0RJJQSDFCJSWlm655Za33HLLIYccEgqFzjjjjGeeeSYvL+/RRx894IADUlw5KV4QEAQEAUFAEBAEBAFBQBAQBAQBQaCtERDWpq0RbczvnTmV77mjz249cNWE1EAyFQQEgcQgcO655/70008q76eeeurbb7/l8osvvhg+fPhff/1VUFCQmGIlV0FAEBAEBAFBQBAQBAQBQUAQEAQEgdQgsLJwZ6t0KvYKcF5ujtckyK/SschrqtycHIpaUl3nKWGn4kJP8gjn5gT5V12+wtbMTQ6dijxzAcFgNv+q6hrc5K9ligu9F5SdFfSeqrggXxfqMpCVlRUHDoX5eY35V7ssBbGszMw4nmxhfgf3RSjJjIyMOPpqfp7ngrxWzOfy48aNmzt37l577aXqOWHChOOPP75Lly6DBg1ad911p0yZsscee7RVEzp7n0xU0TnBIP/iq0ZxoRod3qYjyoqv66pKFjT2q5Jab1OTShvH3K4SqvlQhb3+7eh9VlRFZGdlxZ22eT7xWtlABi8m729AVUyHuN6DKm0cU5lKGMzODhbG+XFSVKBmXc8oZWZkxD3iPBcmCQQBQUAQEAQEAUFAEBAE0g2BOD9P062ZUl9BQBAQBFqLwJ9//nn22Wd/+umnI0aMUHmhcXbEEUeocI8ePf7991/CH3300YwZM1Tk559/vsuuu1VW16hL+18LRx9FkrStEbYXHSWmutYbd1ZTV6drXh/ydjqBTliVxGMNqmpqK6ubyH1PHH8o1KArTKujYGi/5VXekoMu1xKvLlvTNyRnE9L40LDgb2YoYUFAEBAEBAFBQBAQBASBdoCAcGft4CFKEwQBQSDhCODa7IQTTrj22mvXWWcdXVhNTQtSDG0+bi1fvnzRokVKpry8vK62tsE1neRekvw9Ces6uwk0NHjjvwINpGhO4omLMlrhuVA3LYkg0xAyKhxBxjnaaCmZOMtEiPUqb8lmBcKWG06XImyi4hM0zCq1MszM89Zbb2244YZ9+vTRWX344Yc9e/bs3bu3ivnxxx+/+uqr33//HZXY7bffnlvEf/LJJ//9959OogKrrrrqrrvuShj2H/NzEhYVFW233XbbbrstkRyN8sYbb+ywww5rrLGGTkgMRa+33no6hn2CLbbYQozWNSCtCWD+P3/+fJ7CZptthtq7yqqiomLOnDkcTQPyxcWebSlaU58kpP37779fffXVn3/+OVJZvHkPOuig7t27Owo4gsNbe/bs2UuWLCGt6v+OaX0VOW/ePHbpVl999Q022MBesV9//bWyslLF0zHWX399Faa3AOCmm27aqVMnneqVV17p1avX1ltvrWNSFXCcQ3Rlfvvtty+//LJv37764YLA1KlTd9ppp27duimx6urqt99+u3///p07dyYGENjF/OGHHzi1ieGw++67Bxt17b/77ju+vrjUmasAYkxrTH1MWeY8ZhFL6OVPP/1kOVeKbpmbmxu9UMd5lXmAyWGttdayp3UcCHYxiUkEAq2cxKgS/Zyer17HqoZ0Wrpuhw4d6Of81dVm1Pzxxx+8c/G2rCMTHbB3vOjzla7PZ599Rv1XWWUVFePz+UpXO1JAuLNIyEi8IJBKBP4p9WC/tlpR07d1Kmvc3svmDfHee+99/fXXF110EStM3gR8BvHtsnjxYtV0Vjtrr7024YMbfyry6quvJuDe1tW9pKdsVWXc/+2Q481iHRNRXfOsrDL3BZmtyDOOLvWUQxzCeR1y8vOazFrDfKfrX2ZWpm5pbk4zXeguOVai7gSdpVS5P/1X+9LX5U4SVU6RgfycjLN27eh4S0fqFumYKAERNsHxhIaZsJXhqqqq66+/ni9RlseaRnnooYeGDBmiuLOHH374wQcf3GabbVgrwnPdfPPNl19+OXf5Llf0BItM6gA1w19W4Hyss0i7+OKLWb6y2J42bRqHnwwePJhU8HSUdeedd5przltvvfWss87S3BkMBZfPP/+8Zu5a2cCVOfnrr7/OJg2sB4tt3ilPPPEEj5jl05lnnpmdHf5oLysrGz16NORme0IJ4oxDeE4//fRIjYLVRcZRwBEcxsjIkSN5ceNUAcrppJNOOu200yJl7pP4UaNGvfnmmzx0Kjxw4EC6gdqQ09WjCTx9tYSGJnvppZe4Bal077337rzzztdcc83LL7/csWN4wn/nnXcee+yxF154QadNYcBxDtH1oZ7MUXiM/d///qci6fnMOf369bv77rtVDJQQMRtttBHcGazBeeedt2zZMsg1tiqfffZZph1mP+gk9gboJxbujPOdmJ0WLFiw5ppr0h8IH3PMMbr0pAV4dtC4ZnE8LDY2zBhL2HFepeE0/7jjjjvssMMs8o4DwSLji8t33gn8/nuAtu+5Z1N95swJfPZZONyrV0B7Ppk/P/Dxx+HIHj0CAwcGtEw4KhBYbbXA9tsHIpDpSiTJf1sziamqjh079vbbb4dKVpe8lOmu9NulS5cWFhbyLoBNholmGTJp0iTioc84uMzS4RPUanvHizlfqZqwq8enBSNUcWf+n69iAugL7ox+oD4IdHVZlKo9BB0jAUFgpULgyKf//XO5K4uwd09bfaVCJlWN5eMMdkyVzifLsccee+CBB/Iu4WVGgBMD+CZjXyVV1ZNyVx4Enppe6r6xp+3U3vRT3Le93UuyErvrrruuuOIKS0th9iHObrjhhr333lvd4lST6667bpNNNrngggtUzIUXXpiTk0OkuiwpKeHrdv/990cgMzOTyPHjx7MUx7fj5ptvrmQc/06fPv3pp5/mE9/xrkR6RQDGB8aEZwpxiQbNvvvu+/777x966KFwlxCdLJMgU1ixsL6CqfSauZ/loXQdeTFdZ/SSXnvtNX1pBhzBYRTMnDkTCqlr165wTEAHO6OJZjO5T8LUFuKMqkKbTpw48fzzz4cf0ZplVBIOiH079vBQFDXrzHob1mnPPfdcuHAh5DijHnbpxhtvhHjyc3tVE2gUxBm9nYcL12nqYTGrsMzeb7/9zMYSZtZCv/WRRx5RrYNo45OM484j9R8oqn/++YdSYBVxWcvESHGKYbTknNBLBrLOn6cDG4LemY6xBBznVZ7+HXfcMXnyZJhEi7y6dBwIjpKpjKTyhx9Obw4UFQVQgm5UGAxMnhw45ZRwrdA05Eu78R0UuOeegCJPBw8Oc2daxqz9m28G9t/fjEhhuDWTGAOfyUrtaakmYORAVz/kkEMuueQSnjizASwzr2kkEaM7QUWxScYv0dyZY8eLOV+pVpCWudd8KGk9X6mGhL+QUvjjcw1P21CnmB5ASVITVKzxJcSLYbXVVuMt0lZ1W7KsxJt1DSrB1TUVlc47+VFqtWR5Kd09ioD9VnVNTVmF54KWlZR59StUXVNbVtGk7G2vRqSYZaXlaDtHuusYX1tbV1LuPLM7yqvIkrLy2jqPBdXVkypKno63qBs1dLwVKRIEwCHS3UjxoA3mke46xvNMebKOt6JE0n/oRVEE7LfopfRVe3z0GEZEdGdA0ZOn9V3Wk2hkqB9fbGx+5ufns3vJpx7bv6xtINFMc4nWN3bp8lIsC+PIp6a2trTc80hXBZWUVdR6dOOlEsbXdVXa8soqenAcLSVJHHO7Kii++VClXV5aXudxVlQJSUVaFfb6N475RBVBHwIlr8UpeZ4LTye+tHG8pFRB9ED6YXyF0vPp/3GkZawx4uJImMIkfCyho4SOmKUOjz/++G677aaJM+4effTRsP+QXBZJfak+t9BsUsQZ8QcccAA73vpSS1oCfK1xQArb4JZ4uYwPAfjQrbbaSj07FKb4PFaeAVhLY7GImR5PBBaJrZr48m+XqSKBwztamfjx4vZ/w1GhgtOBOKOqah9OPXpdc6gxWoSJFlpp5i1YGKXQQYdBKw3lg8suu4whjx6fTuvbADopqNJwfDnsAKo0Zj3hwm666SaLrtb333+PKTpcoaYFQQw6LMojxrQTDlqRZYwdisC2ziwoyWEIQQxOr7rqKnhwmFDawi4Fmr+whA888IBacDnOq+gb7rjjjiidaVNWS80jDQSLWIovx48PE2f8+GvwiU21wmvwlCnhMG5AHLUm2cuBHBg3LjB0aFjshBPCf9P/B5GKYjj8vm4KlssQTywuiGHgcxedSsL0H7RoGfIoHrGLDzMFc6JTJSLg2PFizlfUhIphfIPGpZqgVN3Ser5STUix3hlfeIDI+4BNEnRx+VaDSUWDg20Eugh9CEUPcZ+RiJEgeQoCgkDcCGhan/cZrzEmMT7LYi4y4y5OEgoCgoAgYEcAq72hQ4eihYTlJnOREmBfhA3hK6+80pRnkTZgwACWqWakGYaLwbuZzoRbTGjKsknpOPBJZi5iteIDC1d+uB/CLNTMUMLxIYBhLHoEpOXNorT51LHOGKMplRxWIzzHdmawGR9WOpUjOFj8wRdDB8NDoXN0yimnaLZFJ/RVAPaEH8rsKJ2hHkWYZZFZQ1ZGjDviVSSEi9LJwnQa4z60Shn4TAgoImC4g14CylaYMZqD2szNJ2GUUGAMsUejLZi8mYw/8w9dHfNwtLR0bVkwEsazm44hYKYy41UYoPTn2ceNNoCc7GQXS04MKiMQgszPam0LAUHvpe2o0eGbksmcZ3fiiSc6zqt0YLCinpEUMB0HQnLa5aGUJ54IC2NyPn164MknHbTGXnkl0K9fgD2hxtO3rDl36dKUZLfdAi+/HJZBea2lJqY1STpc45OUak6ZMuW5555T9VWeSXF9qC6VhSbvd7jgXXbZhZ6AwSb9BCXlRPdnx44Xnq2izldUG5VPhh67PpDCqhX8Tev5SrUixXpnzPXMiczsqCXDo8+aNWvChAnM+GyeDBo0CDtwupGGWwKCgCAgCPgQAXa29ZeZD6snVRIEBIH2isAZZ5zB5rP5YYqzYRpr33RkdRpF24INAASioIRGAxYl+hdFUm61CQIAjgUQimbq5Bm+kwlDrFx66aXc0p6h2qSsdM/EERxwo1eju4GWFrQvxAT2sP5vKYpjeDvCjyokKY/brDAxkCzvvvsuRDYnF8G/KD8SqHVADrJogh9nNnjxxRe5deqpp2J/jdM0P9tTz507FyJAscM0jY7NY9JNhkWiIR988IGpj8YzRYfF4gZOJ3EMoDWDfTrIwE9h7AyVlkI+EdtSVORMIzsqg54gR0MQyVPj8Tm2wk2k40BwkzB5Mr/+GnjvvXBxiiGC/DLPrtlpp/CtZ58NoNevDLRtxz6E9dGwsykrC4wdGxbm1+jgTwXb0196LM1h2leNUgG1a4WPPygztM4hT9BD1OeHJL/5UeYruP4xY8ZQT8toTd/5SsObYr0z7PNVVZjRmNrYJmJSO+KII1QkTCr6ioSZT1FGU5H46t5xx75RTOEs/pi1JAG7T+hIwpQVNskJNejkxEQRVnVTf0nCG86MiRKura+vraNe9qpFSRT/Leyqqt0bZDUEaAskdw22lPVuW1RXR4vqQKDGi7liI2gNmFK6LwhJr6aX8QPHaYANjZ3BtQlddS1e/EJ0IwBxWS5PB2sjVRB/XaYKP50gBSFf67KLkrM9f7Or24t2mbOXx1evSoyUsyXeXiWJEQQEAUFgJUcAjgxVBYwr1foTNFgosrZkR9qCDNr9qOFYIvUl6k4coaUvVYBlHhq1mGRySRE4I9cCbDjrsAQSgQAmXWQLd8Dygx9h1JG4xOsZDuDNcxsSUXra5WkHB4Um6GCOvICCgZHB4okVh//77VprrYWTI2i+ww8/HJ+DODnSz8I8hgiS5bHHHkPdDLUU9DhwgYQYvBKp6CTQhfgRo59wGi+W2qytdCa+CigVftPnPRWmabqSG2+8MQhwSoBSxiSe9sKEwiCY/Be+7VgbomOoE1oCHBTAsSekQikvhQePwobwULBFNXdbeXzayxthWsf+h46xNCTmpX0gxEySVAF4MX4nnsgJNYG99gp88EFYd0wf4rHJJmHWDKUZXJspyWHDAhMmtKjhxImB3NwVMddcE8hOMZWxojJtGmLiIj86gzpJU1H/qmOgmahOwWYTBX4ctfHobknbtF4tMosyXzERUTfmXn5UnoMsYI15c6XvfKVbnmK9M1WP++67j+0RXhK850wbfu4qtpJPOrZZ1I/vv7JyHGPVRfqn26YCSgxegTnLnsRRWImF6kP1DSEzSRRhLQYlEaZ/XBMfWLa7J9osFYjjsiHUoJhsN2kbAg20S7UIKNwkQYbm1DUCQFqXSRALFxRoqAN017RjuKCQW1rKfU0iSYKDqmQkAUs8OIN2KBR+wJZbkS5D4acTCnNgjbBHErPENxYEGOG+arlFTKR/YWq45S+SpIpvKRuuoeM/WmyRjHQJLNFzjpRQ4gUBQUAQEAQ0AiyM8XcB1aI0zohH/YQVGm6PtAyGQlhPmOSXvqUCuEbC/TAnx+t49pNZZ1o+yfRdCSQIAYzU4Hf0o9xiiy2UazP8meA8HkfR8GhCnFnAdwQHuoRFmlp/ssBTh1daEvrqEr9FMNSqSjDgmJr+8ssvZg3hAe3ODbUA36icMoExFxQhamu0HYKGhmO5qWV8FWA5je0hTBlKNOoHOQjjafGwjGsnuAN85KvKK09wKMCabSE5CoZmjBkGRuyZdthhB+bAFBJnVGnGjBnwd2o3QteQfQ7dZB4WnGDcxJnjQNAFpT7AauiRR8LVwG/XkCGBBQvC4UYT9XBA/dQRqOefHz6Ic9Cg8NEBlh8nDHDAKP/OPTfwxhsB21E5FvH0vcTtO5VHe0s1gQAWmpysuOaaa6ppjXh10CJjP/nNjDlfMf988803WCjzo9tD4pumhGk3X5kIp56sZctUvQ+UYT8TPZO+qiLayLztCGP0zk9F4naOQGF+nrqM+VdJogNVkJ8XU7nLzBaf6DAfUU6gN4V1NVACKsjrkJ3l1s8xmsS5Odm1dW4ZB11QfIHs7KyCrByXaSEuaSM+8vM7UEe3nrxzcoJ5uTmVNTV5uW6fEfWhILz+5+XmBoMrPvej1zMYzEYeJ+PRxdrqLrMAlWwkc11NUuFukFETzM7OzXElTz2D2Vn5ebmlZfUU1Lgr5apX0EWzs+uC2Zm5OdYnSz6Rms+0xdER5t0owqaYCkcSDmaHzYXc/HKCQcdMHCPdZCgygoAgIAisnAjgaRuLLT5PVfPZlGZteU7jD/19SAQ0WXCLgU5KJHxYzqGVxtKdTyw0m/C0gpshFvBanS1SQolvWwQ4OEtpxxx55JEwnpACcKMsrXmCeOxC10BZY7BkMr0vt20d0iu3SOBAB3Pa7D777EMAuz9UsSAi/dw0fHihPobzePgvTG2os9I9xPcZSmQ4BWdxxCoUFolFNcY6tIVjJXSLIIYw8kVFixj83jDq2VLFO5haRmmxFAagtzQRQDXw+k9XR+lMefEnBrfoNBY9CbOSzEJMSjBoKhK9s2HDhuHtB/qAE1GIZIxgnIQWnhIgT7MUkmPSzpyGB21tAws+jd/YZjnJCKMIwrC1ePpH0YyOyvNlaNMKjjXwWhWO10TtCDT8PktwHLNign77LcA/9ZsxIzBr1oomH3RQYMSIsCs0fobj/BUCOEprhVnrinx8H6KfQIzQN+j/dBLUwHkpUGuOwybMqRGMhfvvv58exVsj+a2JOV9xureuFR4J6Zwmc+3/+UpX3h5IMXdGn+DBo2IKjcq7gb0F8OW4ugMPPJA3B7ttaofBXm+JEQQEAUEgJQig6Ke2enTp6HfoXSAdKQFBwG8I3PDB0onzPWw2PHN0t+Kw0YD8fIeAWvhpNyIsPtmGvPDCC1VF+eZ+/vnnTQ8YLKvQVzKtnJA0V4/4UuF7jBUp9kQqE76+SIKTYNaiFmElIH8TgQBcJ/QHvyfxot2oQohVDrwPTwE1QH6qUNZL6JgkogJpl2ckcNhxR6MHh4CqRRwFC4Hi59Zh34TiJ66vVCVRlWKRTBj39qgjwa0wNgkccsghRLJoghLiNEYlzHIJNRBYJGXexbparbQhwUePHq1kUv6XGpp1YO0NNa+JM27h8wvNGoyQMLA1JWE/4f21IzC2CpiXbrvtNqY4xBgLEP3KZo0pkf5gGoGibKu0vfCnpvPkUIJEu1fXZZkBqEzL+Q/cZbrW3B8T9ciRI80kbsI8a5hBXIT7fZZ44olwcy64IKB7wsEHB159NWyeud56TS3lSFzYQ6VXSP+3GGw2CbXb/8yXMo2kh6NujF45YfoGh+cSwNchqkWqk8OM48IPgjj5iMScryxV0p8rxKfFfGWpv3mZYu4Ma1iIdk2Q8bXH2455s1OnTswmkGgEzOpKWBAQBASBVCGAFsDZZ5/N8WfMS7jnRAuAfV0+zflw4YMV/Vn1hktV9aRcQSAmAr8vs1p2x0wiAj5EAAcXOBQ3K4afaTOGpTVcGFZRLLYx8bOT+5Z1LFmxHIU742Md9RDOP+FS5Q/jZuasIjnQzSydCdAuYwpI2D0CqEqxi4yiAc7s1KKIZyHwRgIQysARHAzfIJQ5WoGjDOGYLIvSSLmlMJ4aovg5YsQI5Q5f+wi/5557VK3oBqyT4UfYrjMpJ+4yAFlAaYeGjHeGP/mQROeTwqY5ziGO9VGO27hleaY49uGnktAivr7QRINBwFM2SmR6WQ56/Bxz9kMkHdJeDbQIH3/8ccY7KFlIkEjzKvJmPhorHTDv+ij86KPhymBuqX/4N4c7Q2fwuuvCcZmNjqRQN4M74y/mmeqn4psu2vN/nLZpPkSoZDQx0ZdkNqMzqJazeY9GKjMbRwTo13RyQDE7Xsz5yqySZafH5/OVWXPHcIq5M77q7NViacqMz4vB/686e+UlRhAQBNorArw2mJrYOcTrIpuHMGW8D9CQxUAAKwP2/fiSsx9vFxONpZXOxrnLqxpCwVBmhoOteee8xi+MmFmLgCAgCKzECLAS69mzpycAWHV7TeIpfxF2gwBcgFYpciPfDmTWWWcdXqN9+/aN1BbuIhPpbqR4uJWuXbtGuuvDeBbG0Sts0R5VTcDdjb0tMOD2yHYTwwqRHYJ20xy059pNWyI2xO6Wa+hQTi5rkj/55KbAUUcF+Kd+gwevEMBut9l0t+muz/5L0CTmqEXEm5pfygGIOV851jDd56sUc2eOmBLZvmf8SK2WeEFAEPAzAptssgnqAHy5ciw0irGzZs2aMGEChhVse+JsiN1vHGFavMC6ac78RbXHPvtvBMnl9vjL9+48fJtCe7zECAKCgCAgCAgC6YjAQQcdhCUdzuMjVZ51KTKR7kq8IJCOCHCQqFaaS8f6S51NBGQSM9Fox2GfcmdtjvgqnZqsD9znjMN798JacpWOzSqmOipWABfvFLWk2pspTadiz4vn3Jwg/6rLrQcsRq9gp6KC6AL2u3jx518VJ2d6+RUXei8oOyvoPVVxQb6XeoVlURGPA4dmt/du3eeHC8rMjOPJFuZ7tnXnbR1HX41ydIZXSNNRnuOrVLVx08tuNi6cTY9CuM9QXpzx9auVrj/99NPd+vevrIrYB/I6GIdtewQlSrbZWXEqphUXhkdHdW2lp7rU1NbW1HAsRpAqcbKsp7QkQbeOwyuqqt0eSGLmH8fcTnLKChUAUiale5qnOAyXJDnB7NraOk6GMWsSM4w8x4F09D6jqpypLTbCVR7fFHV19VVV1XQzqm0/XTd6nUGpKDuTY09MMfIxLy1hsz8zlbkXJh9TmINTzEvumjlbhM068FDDPbDldqwlK1Ne50wn7Nz4+nYjrHPQwpbqaQEJCAKCgBsEunfvrn3tuZEXGUGgHSCADz6fu+FrByAnrQkyiSUN6tQWtLJwZ6lFWUoXBASBdoPAfffdd/HFF0+aNAmXQzU1LegetX9YXV2NGwLVXvySwLZ4YmfcA5WgbKlAHDnHkUS1VCfUAfcIxC1JWXEXpxPqgMtqeJW3ZKuSx5FJHElU0SS0p7XHWOppXoqwiYaEBQFBQBAQBAQBQUAQEATSFwHhztL32UnNBYEWCLw1u+LHRbUtoiJf/K9/x8g35U5EBHD1ypkA06ZNU4clYbTPsfFKGmdn6jB4jgnmpyI5WzrQEMpvhXJZxKoEAgnKlhI7eHSjkBMM6spkeVR50wnz6rypxEZBJuYt1IrzOzTpJjn4k4ucPjMzQ1c4N+itwrnBVr1wVbl5ud40CrNRdWvuftlZ4QMT3f8aUcqyyOvcLPGOlyLsCItECgKCgCAgCAgCgoAgIAikHQKt+pRPu9ZKhQWB9o3AI1+UuGngPht5tlp1k227l+Hgqvvvv3/ixIl4qC0tLeWkGNyfcRwwTBknBnDosj4yuN1DIQ1c2RDYY8yf7pvcp3vOPYd0cS8fh2R9QwBHgZ4SbtitpSWnp8QiLAgIAoKAICAICAKCgCCwciMg3NnK/fyl9YKAIOAagSuvvBKPZpog40j4Y445Zvz48RyCw9EBkGiOp+G4zl4EBQH/IrCoLFQXcmuC2ad7Mhpy0KN/uy9m343zRw9Z1b28SAoCgoAgIAgIAoKAICAICAImAisLd7ZkWUnnTsWebHMqq2saQiGvztGXLC/t7NGLf3UNLqTxru3tWSwrKStq9OptPs7o4Wocetfh3NrbGQjLSsuLPLqip5jKmpqc3Lzo9bHcLSkrz+vQwim1RcB+WVtXX1lV5fWQgZLyirwcbyDU19eXVlR5PS6grKIymO3tsdaHQqVlFV6PCyirqApme7PkwjX70pIyr8cFVFRWZWRmxneMhv3xpV3MP//8Y6/z22+/vXTp0o4dO3Jiuv1uSmIY6NU1dUUF3gagqmpJWUVeB2+jQyWMr+uqtOWVVTjC56iMOOBSc3scCdV82Hygh7cMlpeWF3icFVUBeOsvr6iK77iAOOYTVSiM19JlJXEfqsCTtRwX4BIs9ZJqfLIuUzSJ8Z6qrKpRx1Z4S9kKadwSLi8pU8cFtCIbSSoICAKCgCAgCAgCgoAg0D4R8Lawb58YSKsEAUFAEGgFAp07d25FakkqCAgCgoAVAcs5JNbbci0ICAKCgG8QkPnKN49CKiIICAKJRcAvihKJbaXkLggIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIeEdAuDPvmEkKQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgZUDAbHZjPGcv/+7pqY+hoy+veWa8fgJ0sklIAj4B4HSqtCC//CO1+JXXV0byMzMtZ1WJz2/BUxyIQgIAoKAICAICAKCgCAgCAgCgoAg0I4QEO4s9sMc/pSDg3B7st5dg6+flJTTxexlS4wgkAAEXPb8vGDGV+evlYDyJUtBQBBIewRe+aZ8zGfL3Tfjuv1W2X5t+TJxD5hICgKCgCAgCAgCgoAgIAgkA4F2+IX6+zKrsgxALisJlQfq7OdsrtWpHSKQjI4jZQgCgoAgIAgIAi4Q+KfUtfK2i9xERBAQBAQBQUAQEAQEAUFAEEg+Au2QOXpzdsXdnzjucpda8D1xx6LzBnSyRMqlICAICAKCgCAgCAgCvkWgrq7u7bff1tXr2rXrtttuGwyGzelnz569aNGi/v3767sff/xxeXn5Hnvs8cEHH2y33Xarr766vvXWW29tvPHGvXr1ItWCBQtU/KqrrtqzZ8+11rJqE//www9FRUVrrrmmTi6BBCHw999/8zgKCws33XTTrKwsVUplZSWPoLa2tnfv3u3vcOd///33jTfe+PnnnyNBus466wwaNKhbt26OAhUVFYDDuNhoo42Ki4uR+eeff5Yvb7EWIG2nTn7/5p8/f/5ff/3FIF1//fUdW6oiP//8c0au7gb0Ftrbp08fs4Hjxo1jaG+55ZZR8knarfHjx2+99dbMKpMmTbI8l80226xDhw7Tp0/v27cvU5mqUnV19XvvvbfrrrvqFv3+++8zZszYYYcdVlttNV3t3377jXGhLhkp6623nr7lw0BpaencuXMbGhropYzu6DW0d2ktH2Uqpog5c+YgSfdgutZJJJAEBFo5iVFDuv20adN22WUXXdv//vuPPsMA4YHyV8cToOd/8cUXvOszM5PkvN7e8dzMV/ZU/p+vTJzt4XbIndkbKTGCgCAgCAgCgoAgIAi0DwRqampuuukmVterrLIKX9szZ87Mz8+/4YYbtt9++08//XTq1KmaO7v//vuffvrpm2++mQUbSW677TaTO7vjjjtGjBjBAptUTz31FMnr6+tZjrIG2HvvvS+44IKCggKF2LJlyy6++OKjjz76kEMOaR8Y+rYVUEg33njjuuuuu3DhwrXXXvvhhx9mDQyfcvrpp0Oc5eXl/fnnn3feeScPy7dNiKNitBqWhzZGSssqEZkTTzzRLsDa8txzz1XccVlZGb19m222ATeTXyYVHfjAAw+0J/dPzHXXXUedeei//vorA/Cqq67KyLAbzAQmTJhw+eWX33fffYo7g2NimEM8XX/99c8991zHjh1pEZGMaMa+H1oXCoWYfK655hq4M2oFHQATqisGWU8zEaAJo0ePVvHwAsRssMEGmjt78skn4fqHDRt21lln6bRnnnkmTzw3N5cYJJ999ll9y2+Bb7755vzzz6eSiuy76667otCjjl1atSjKVAxJccYZZ1AEMgjffffd0Kn+wWHp0sC4cZn06OOPD6l+PX9+xqRJGV26BAYPDql6zp6d8cknmT/8kNGrV0O/fqHttmtQ8cRMnhweC716BQYMaBJesCBj4sRwZI8egb33Dk2cmNm8ARRgI2n99Ru22qohL09lkIy/rZnEVP1effVVnhrMuLpk0jvvvPPWWGMNHihk60MPPWRuHowZMwZ+nHd3MtqGAZ/tG8DNfGVP5f/5Kiae6cedMaHw3cBXYKS2fZ+1ZSB7m0h3zfjJkyeXTppuxpjhrOwgnynLM1cN5Awx4yOF+dYcNeqB0vKKbl1W/b6ubyBz3UiSZvzYsWNnBv8oryivzu4cyBlq3ooUpiOOGvVgaVn5mmuu8XX55oGsDSJJmvFvvvnmwvd/ZbenIbc4kDPcvBUpzPw+atQoClpjjdWnVPQJZG0SSdKM/+D99//7ZOGi/xbndigI5B5n3ooU5rVKQeUVlaut1m1K5QYBnqCL3yeffFIzfe4///xbkJ8Xyj0+EHDFu19/3XXVVeVdV+0yo2rdQLarT88pU6bc+e33f/75V1FhQWXuUYFA+CUd83frbbdlVJfwETO7Zu1A9k4x5RH4+uuv75r77R9//ElBy3IOD2TE2JVSeTLP5tYtLcgvmF+7ZiDY301B338/+5a5X/y7+L+igvxI8rWBnEDu0ZHumvEMEx6fivk2e7tA1ubm3UhhPv7+/uCbSHeJRz9i//33jyKQFrdizle0YlFm90DQQ0v5tp77xvdRmp+Xn7906bIOuTlLmVIywjvwbn6szUaNur+8sqpb1y4LKjoGggPdpFIy498Y/+vHf6uu+0Nwz0BmT5dp1dgPNQRygsHS+mAg5wiXCRGDIBg1eQrzRnFR0cyMLVzOGyr/e+8d0zW3im/uYHZ2Rc6RgYwWu3lR6vDzwp9HjbqvrLxi9e7dvy9fNRDcPYqw5dbLL788u+C/v/7+u7Ag/+fgfoHMFeo/FknLJfvPDLHaujo+nhZVdwjkeCAvmCeXT5pZUlrKPPl19o4up3FVAZYxRVm1NbW1mRkB93MsafmCHzXqQ/WS+qaseyB7Z0uLolw+/czTn+cv/3fR4oK8aA+lIZARyD0hSj6WW7Nmzxr1zceWSPMS7umffxcx95qR7sMpma9OO+20fv36UcmqqipWZXx2W/gUVtfPPPOM4lnoRdGbs+GGGyKpZFBDO/vss1mZ8xctNt4yfMTHzCF6/nLXDQI8SoizSy65BB2rxYsXw/WgLXjwwQc/8cQTqFOxdoImQOCWW2555ZVX3GSYLjJonEUhzmgFxMprr73m2Jx77rkHBT24YyiYa6+9FlYCggZ2iZ+S//DDDx944IEBAwY4JvdJ5LfffsvLHfYH2pR5W1HVdkUq1hqaYFI1Z+BDJ+2+++5giPbWnnvuiYrWrbfeGp7AfecyVgMAADKXSURBVKl5BC0IZW/CrsgCJhnW1fvss495S4V5WUOcMSjQXzv11FMVWUak0vSBfbMn8VsMRMNee+110UUXoR156aWXQnywdRGpko5dOuZUTCdHU49uz5fVySefDJFKoZGKSH78v/9mjBgRph2OPbZGadPOmBGO2XbbBsWdjR6ddemlWUbFss46q/6mm+oR/vzzprRduzb8+mtIKVqNGZM5ZkxYftCgENzZ449nvvBCi5Xguus2PPVU3fbbR2QMjLLaINiaSQw24N133+XNq+sB0cE2wEEHHcQmFuvxk046iWmNF70SYMggr4UTGnDseDHnK8dU1DPt5is7tuFOnF4/diF4O/KLVO1NQjM3qZlpucv0wbeIVn1fcTdyPpM/+xTdV6b4w2seWyEfPZSR8cB9Y2D9++XzjR7tM93MZuqUGX/88QevBE8FPf7Yo8ccc8z2nUu2r//MzC1K+KsvZ7GkOfTQQz0V9NyzzzB0t1qtdKv6qVEyN2/NnDkPpdMjjzzSU0Evv/QiRiWbrV22Wf0MM7co4Tnf//rRRx8df/zxh9U8EUWsxa2MwIvjxrHA2Kh3+UY1s1rcinyxYP4/jHYWKgfVeNjUeuWN8Shmb7pp5fo1P0bOu8WdX39ZyuTIcmVQ7UstbkS9eOOdd7CjQR++Z81PUQVX3FxSUkFfvfDCC1dEtQzlBGrtj+/999/nU4xP2JaygUDzONqi/kv+mXfRYuBDX3/Cmrd0qhaRzRdRxnizSBr8H3O+og3dGv6xQ008GiKsJbTeR4vWNgPeIrL54usZM7777rvDDjts/1qPi6uMjHGvvLzzzjujh+JYpeYSHP7/eeF/fKixCbxz3UcOt6NEZWR8+P57sMw77rij10LpQnfecTtrjM2yZ7ifN1Rdpk75mr39wYMHD6l9LkrtHG5lZLzw/HMQu2uvscba7t8OjRmx9cMCgI/aAXXvOOQcJSoj451GfYStttrKK0o1dXWgxBjcun4q/6IUYr81efIUdlxYzAx1P8eqXDIynnzi8eHDh6+3asl6NXPtOUeJmfXdT5999hkvuCgyfAE44vDSSy9hAYR1g0PaqKMGnuKhBx/QH6YOyaNGpXa+wpSDlxrvDl1HPrvvvffe559/nhUafUbHuwxssskmKPiwfhs5ciTLVDLn9+ijj7pMLmJxI7B06dItttgC+oMcYATQOEDHkDAj4oQTTlA2O1Bp7IZio2car8VdYjtI+NVXX/G6VF/4vEfgX8xGsaC98sorGR3+JJJ0VZlpWQhAnBGjZjA2JvVdFYATgSI85ZRTYFH1LTbU0T/lskuXLlhnkwqFNeZeOpKWSYsA7YLyYx9CNcesM7utaNzwgYFqD4o2aoCgmMmeBz9UZbt3764UD81U/gnzjFD72G+//ahSdnY2nDif3zypiRMnQo6jKUYD2Rvju4JhTk927NIxp2JyZr2GBR8/5nCIRf8gELMmkyZlKuLs/PPrBwxo+OqrjCuvzLr77ix0x4YPb1I0I5NFizKmTs3o27chFAq89FILpkwVse++oYEDG375JfD005kLF2YcfHD2jz/WNutPx6xFygTQNqVX8PXywgsvqEqwn80T5IFyiboxk4PeL6E7oWTKpy8zWxJq7NjxYs5XjqmobTuYr9KPO+OVEEdHYfV+2WWX8SDdp2XHho7rtTi+VmGIeYG5LwjFcnaKvBbEdwAbTSxx3RfEgHz99de9FgRnxCaPp3cwW2d8/3ktCBbsuOOOM828YzaNN+i8efO8FgSvx1eFeofFLEIJYBEDx++1IJhKVuZomLssBbGffvqJLwOvBcG98rXBgtx9QSwUH3vsMa8FwSZj74MKsfuCsCdih9xrQe7z97lkaxqOFQZQx7E6Yv+Kjc34imaFhlmW+jD1hC0adqxY4iuUd3CPHj0wuvFUohLGrIO53eIGwk0+9H9Uj+OrMPwXe4AMOjcFmTJM9aiXxlcoTN9OO+3Eh7WZoZswmiwsLOMrlI1xliXxpYVLZScJ+thNJU0ZlERYZsRXKKzx0KFD47ArhIngrRpfoWblkx9mOY3PEUYfm0+qdHYs0BR78cUXUe6wEGdoay5ZskRXMooq2eabhzWIcbyFmRVf7YTR+NAJJZAgBHjDMvOTOQ8UNRwCzMY8Yr6p4NFUodAEBHiOcbwdVA7t7C8zhvq8Byg4CAw2zQaiTQnbGMU+zhROYZivX34sLFE641udTSz75MnLHZqeNbbJnUG3ff/999BtzH5MfXDcsEjsPTCnoavIqjuFjXIsmu92yF91C8JIG5iz487j46WD8qAlISuRAw44AOGBAwcCjubOmMH0vMfs7aizZskqJZcQfJTLh7cqXQWwXS0pKeGji2qjMoa2IHoePDuWQo5dmqcZfSrmRU/+8G5ffvklSkkQkSlpbHyF3nBDmAhD0ez66+sJsH1Az73ggqxrrskyuTNuYfjZt2/99OkZ8Gj2sqDVTjstnMO554b69AkiM3Zs5jHHrGDf7En8EKO+J1mfau6M7kHF1GxPAA0JvotUVdFHY2lpnx8S1BDHjhdzvnJMRQ3Tbr6yo5p+3Jm9DRIjCAgCgoAgIAgIAoLASoWAqSWH3jGkoWo+VBof2TjVxtAPKyGTYWFNxQ6NG5QUGWHXfHGTVmRajwBP6pdffkGRhGW2cmulLSeIJH82SFpfSvvIQRETsE5wBxAH0BC6XaxF+TlrvmshPwVQHMMjPstmHrpW0FAVRM3qwQcfZE/CouXKvukVV1yBsSeMEgMW5RR2hti3IEwXgo5BF89PTQyw06Z7L4yA5s6gjTBmZH8Iix8UcHSdmdB+/PFHpWIDd3bOOeewYUxCqFJYJ1rKKp0mX3311Wzza65BJ/dDICcnh6dD/2Rjgx1l1CyoFQH+0ntRIGK+hd5FYwMre7izKF06ZnOAl517xHyrd7bDDnjLCLdDK8bhh+mrr8Lc2bBhK0iuoUNDcGfojukzP/r1a8B48/nnMzHkHD8+LN+/f9jNWTgv269bt4Yjjgg99FDmnDkOFJtN3HcRdBXqpKd9HWDvlifLuGYUpLzSUearSHVLu/nK3hDhzuyYSIwgIAgIAoKAICAICAK+RgALJqVig4mTqe3Ougu1FNaQ6J6gi4HZpv7sxgZTuUhTDVPqG46NROOMeNMzsaOYRCYIAdRpyRn36jBByi+SWmkTiSYpf1mNJ6jodMwWXS2wwusZ3vHN0zBQlkQ/whwdPm8dlBDeLXnERx11FItk1MB1hWkgVDgLZn50BigSaHEaiwGK8pEP14bqFvQTysJYCQDFO++8wy2/cWdYJlr8nek24ueEJqNOrrQvVTw4EKBpWox2ofo9pPGnIrnkMAH07/zJnVFJCDI2M3gcTKpo2WNzh6km8ajhaKMo3NuhYcrDJSZSl9YgRAoc0fjDhAUyER9/PpwovvvOSmZxVmppabhB5kG4nTs3qDYuXtwk36dPA4QSNpuKQePuYYdF5M6426VLOAedXOWWLn+hkqkqnUGpjqppH1adnsO2Ge7GUCxFAFMGN8e2JqjVUearSCWm3Xxlb4gzWWuXS/cY3iV6bnLZFnZxHfw6xUqMHaXXlzT7JDh3jJWx9T6+SDwZbJKer4ooH8rWApqvMW/kQPTmK1f/ozruySJSZYpRN76KXRXQLIS8MgVvjnD1P3Vz9oYTOTUIgEPk+853QBvMne9FiOWZOh4jFUG8KZr+48molmT0UvOsouj567uMCMaFvnQTYNwx+txIiowFAXZTTYURy90ol/i5YNs2ikCUW1gE8BqOIhDpFt98bJZGuhs9nsW8136lM8SRZRwGmyTHJG233XbT+XgK4EsuPm15UpHWU1lamNoqMzod4zIAPqDkUtgixnMxqRbL3eiX9Ae7o+voSdRdeqCyTHEjbJGh59P/LZFuLhlrjDg3kv6R6dmzJz2Kn+WrAwtoXiUwaJg+8YUdh58yFDowkuJF6exv0T8QtLuaYLPGl4OmydC+wcANPSMMNhc0nyGHAy/aLbSmfvgYuEFM4C8F80yTOMOXPHjCN2lJPwduv/127RCDSZvRh52+WWGcWDGc0V3i9//27jXmlqq8A7iHiwieeEEp+gG8oBWNRFBjq/hBoqVqIWIlYtoqttFq07Q2xkQ+NTEkpgZjYvqBqlisH0gLERUSEBJDhEC81KIpCQHjFxvUhMRWAjVF4PR3XLIYZvaePXvmPfvy7v/OyXvWrFnX/1rzzKz/ep5nsVWkgMYEuybg4pArNJaeDg2wwCYBpGdwzWN3TbMVAS6MkQWszktrUQZoINQY4qn8+EIhnWjluOQGYSs6pZHUCXF9V155JYM7qqPkc3HAR0e4aBhJgw0R77N53pTu6SwFVS/r6my+vAc3c/QfeODhhx46/O/KK3+rPMuk1SEAeufkzdpHkq+ETznltySayz/908PaWJdccsx99x34oz967Hd+pyafEbjnnsMlODFgxr2NjyoSvkh7jaV2imal5+Uv221CoCjY2llh7bv63iyUV/1N2mp5tSt6ZyPc6Y07kYfidP906d4tXni78f0xI/gIPM6yVI429LiTn9dCD7bN7Xl358WP8HZUdm/mFTgvfkTbvMxG4DCCBdPmESNrf3JeZ3viR8zVEV6ENGDE09fT7N25NdrMBPXgNw6oEYxqqYhiy2iCxgEm41or1+jZ9drf/MbV27SVW6oERyKMztvvO7+/GaNRGk0vas/o+WC9N3oe8jbdD0XP3dFPXE+Z671l+U2/w2mbvJ4tpBSd5OXrHGtGe4XZF2dbFNbW2/4drB0TihZBd9Id4QuJ07qyiYsQYcwlTIZcccUVaPRiyLmDELW6jHegpuRzyyQvRmqIieJs3nyWeNm90lb5K7v0hHrueI7Hf/FcxgVw0T3k3osSmc+8JglOdYt62plnnlmbd+211+LaqBqJccoE02zPMgSI05pmKwJ4Q1+n9dBV7sA8ET4+zfzSfmGY4A2xRTxT86hrLYCTcnfZXeRVAkI/yCNsgNirai0ffMhNDaBo5ok2vnguqnOsUHum9MwGO9QFyW4TveBAHxlWjFiJiCaVPDPvWiIZbB59+HhMxyY8Uf873nHoiisO/MM/HH322Y8dPPgU52RceunhRH/wB481VWwvuODQ3/7tU/793w+TYk0DzycKejx0001H8XTm6pxznrADffzmFvx/0kknURAxNzwOvFs6+YePM0cKFCVTHfC8i+GnteqVr7JXC+VVf2O2Wl41pm1/L3M3CASBIBAEgkAQCAJBYN0IFIdHLbdHpVGtSBrT3/72ty2zfYJL0Lpb+yHeh3ixiqLiRNXaF/nmu1ev7d83AUyH1TXNFMtsnUJeFx4ZdY4NoZIj0vYkDaN90+WJHWGfWNjGSi2hDIqVX9Hb2kz6oNtr+nEce9XjSoz42972NskcxYgT7G6RNp9lGbFI9LCKeZcteSttmr8U0FBL3brWElOooplVN/siAQqM98ZrrrlG2KkCTgOoxJkYcskjIN72DGTKeVykFidiSMOZ5W9CJDYcyVtsZVDh9dgfhBfnhlgzjWRf/OEPf7hnSs/siLGmxcNtHLnBsFdFkkGJRlIP5jOLWmPk3//9o//2b0cxxjz99GN///cP/cd/HKBZpj2XXXZY0az+Tj750B/+4WN4MTGO1Ow6O7vssqOdsPk//3P4RE5p8GtODxDgMs8BLF6I1LTRl7aUuOcvOxMUGzGYU7Yna/MmBloPgl1Pk7zMGXOjabasovUO7rLyqonMVsirZoNb4QOet1bUwMu77rprhBLTwMKTLAhsOwI+fG0f2TcY3hGOXS1v6E2sZQ9heDuXSslxr11fintL5ZqYuCudujETq0j2IBAE9hMCa5FUBcCudBJD/2g/wZu+LIWAL3OKBgxmWwbpdAMffvjhJo+wVLGbnJhuhZV/fws//vGPd09g7M+ydXet6il+ov+W+g7kAc3caJHdymEVOKQcxEHR1FsIFw04Noat1d965dV2PRRaix+pzzXjOwdifuELXzAEeM8av3AgehLQV1VFMQjtSbb6W/fee+CMMw778Prf/3246J1dc81Rf/Znx7z2tYduv/3X4v/rvw789V8fXXgxl7/3e4f+6Z8e4eNM+J//+ai/+qtj/vIvH/vHf3zkqquO+vM/PwYp9i//8sj11x914YXHvOMdj1199SMXX3zMv/7rYU6t/GT/4z9+7G/+5tFSFzNYVBR+2bvVKCBkGQIXBpNdkZ0JWp+PZx35/xESYh5kZryFFh/ZsiOWbRvl1XQwnphk08va5RLMHvsJPK1wYDkcB1sNWGTqtYTm8FyefwdUq656xFiYV0oWkSzt6QwvTLz6BOy3nRji9JzhPdJIe+k028vu4pA2+xjl+cL2OyfKwyviLxmfZSdwWegcl2OvYHhFuuBtZ/4ULf0hPZKGcwRt80G51BSyX2eD5eabbx5Yi2S33377HXfcMTx9SWk3jGEc1fFlMyb9aASWfS5GV7QnGcfJQFWPE7mlzVPk4QjxuydAjS7EDjbtgxGW4Ox9nJXOFb2T2paqfZykLVWMlrey20a2ZliqqTVxJFWFIoFNQMDHAA2a7kLa8mlfEmcwf+ELX8hMuAd8d6XpSbA/bjE4pRczhPBq9pfH7hZx5i5X9MuW0yxzW8Lb9VDMI8iwpd3nfdwQOHV0A4kzffnd3z30f//3sH+FzBLjJE2XhThzya/Zddc9whvaf/7nr//7vx++9dZfF+LMrb/4i8MpEWfCf/Inh8OIM+Hzzz8cRpwJixGu/2T/u7/7LXHmLj+SVjFlU8ooCFfVP7qZ04kzVRwhIeZB1mDlb+BvN+XVfubOnNHru59hsFXWUhMOf0FhGCXhNOjiLmFhdrOHpj1vKXYPimXEwiwS2Oehku0geRzQ8JPgeQDl0tj6pHn+Tn91GGvavB/5yEcsorga7U/cvMvTKj+F1re2OpvxPWFoU6J22A0WvydZucWq30lAemQji4rWvJN3WuVoEs7Rj2Yy2ccxRCtB91L5nE8jEIk22YtKczdZK0bbyFmeehmwgM5J560EPZfc1rJUp33dk6Z1yyyirUZ52MZI69a8Sy4hoE2xy/gOnOdIQJ5BoPH5z3++2jjMK7/GA81Cul4uDFh4e4IcJS4lveiF6ZOgIDBaaslujJZ9Lpqw2zGzEcf/KLlnU655qz+MneH4wB6AGcU1SX/i1t1xMlAh40RuqX20PJR9hPitXV5KNtZcJcBfMs0C0puHl9ateZcUBGwVIK+NDu8wTPDmpezG48odzYb1tuPicBVFddPMjBknaUtRU+StEn7wgx9wFTSzVT2RkVQ94ORWEFgZAr5JPMI2Auf93B19hMjKepGKgsBSCHBNWyxzl8q1vxMfd9xhlm21xip7g2iE2N7guPGl7GebTR/xpBKbecs5ak38Mg7fgbFEYcrrqGMaOo6sGj6OfD3Sn2L9MTyLlB/96EdZv5933nlDclmAWTfizmw/qs46cHguq2KaSv1+o1nLYw/5j1ALtpsyNnbGUqrntDVm5Ig5mlZawjrPj4YXV6bQ628bTVTuPHijQHEePHgQ5kQP1qk/FwfGLMARTMaU3hl+CrvXcyKhtb3GO4iEsh7XIfh7pCpN6f49THMGm2BcnAdsGakirg2Hk026QJ9OvazrfQv296h5F3vCV4XqGLc345th6hUOWKGjIdltt93GXQI6TEW4j2ayVphmOMfDcDay/CAgNz0dA+cq+xGEMu8h9sNbxXYvTR5UIyrHZg4dSTpxwx+9bmkjYmbaQLWsDEYUu4IsU6TWss9FqztIBGdXcdvPrwoWbKltLlLl4osvto+H+R3nXX4pGdhs+TiRW6ToEHnYrEt4WfE7WjaqC+HFhc0vf/lLYdvICKxyyFo9i63VttYlDUTnkdldINLNK1KF0LNP00o289LGjNG87rrr8G6EraO7SD9yY2biGjlO0pbs0+Wt1xb1saUowrVLqtL3mfKqbI9XbBMIAkEgCBwhBHykba/N5hHCJMUGgSCwmQhMlFfTO7XgU3h6BasvwQoQrWDBwHMk7owFnB17bJEdeCuB0047bWGT0AQUlHgQpASEaJiXns6UlQxPnKgf+tJoJlWjJ+alL/EWmdYh2CVZZLQaoUfwrW99q5/1UDIOguPMY489loYRY0ALKtmf2jx9pFOx9OgbLIY12GWXXYbecupzP3GmDOq+NIdvvfVWdbGC8VnfT5zJwriActbll1/OapVyhKbCX95Oi56IgB7ixlLnhz/8oVXZu9/9bocKCXDl+ESiTqioPjko00IdR4kzwtDpWifhkyJomdGeoNTGGae/dAOxgf0uRVlmObnmhhtusGRVFxKQ4dKQycMcDG9ojaojxohDU+QUpUJgPqlNT74gCLBgV199dTlx3Ly95ZZbergzq1mUgX4hDYUBroUCTy71SVeYLB2xQFWXriH16Gg0D2l6UurHLygTWV5SqNQRq2g6a69+9asfvzn7/6J4wnyAg1tnCTkRqRBn0ICh08FmZ9vt2IlSa9xz0YLck05P0Axh3zeEIa3Z0UlIUiq6jmDvmo3UZDUwTgbKPk7k1nrHyUPZx4nfUu8I2VgbbD8A9QZVLwtC2FYBoVpPH6vJugEPO6mIS7Wf4VhPYWPqLTDEsyxijstFKmC4dS9Bw2pKkJl2a7oVtWJGSNpSwmh5S0GSW0nvHZsi3oxedguFbW3zhkuq7sudKDagNk5MyNqLBDYZAU+cDyqfaj75NrmdaduOI9Cz99xCBsVGcrYiXUZedTHZupjIq60bst1s8HR5NRG3/al3ZsHP2JC9Cc+jnK26BJNjHYas66xVmKigdWgb9SvL+NynJ+WHm6NMXkaCsgaGjhnmzIGx/ECIoJnOPvtsDNq9997L0pM+FKWhYumJR+PpoJuXCsAll1yCz3ILo0crhCXOhz70IVaB3cQ1xooCn3LOOeeo17m2+Cnsj6WXhfG8ikpeJZOheD00EFIGb1LLnBdgpKmiz372sxg6q7WqO4Zb9K7tLsX1xXKuWHhZCWAeLdVm9r1ZoyrwOIVntDLExdQjhOZVZNzxa3fffbceWWh55KREPKFWmyW3wk6zws1ddNFF4vmm9flrPWmguSSYVxGQr7rqKuozBgVhpNIh881yyMqWdonBQvCptIIwryJzW+PdxcdZJJtRVo8wsXBt9aJ5iTM1EMWIkoNMOiwuEcoAwfexyZ3pH8FC1HyzePZQGCM19nOvCDLqEuaYZACXC5KeJoNLoxAmdbyabdvz8Ew9jg3XO+uRWj0DVKDreS4GYktfjL0nmUnvbGCWksxKnsKj+UOl0dNRIudNXXcXysCe2vtF7kKUeuRhT6Vu9Yvfflkq+zzZ2F9puUsh9zOf+QyBTKjaWuCwo8T3V+qBRWh6uiW2wUOJzBZFydj/t0hI6qUedilNBmxUVYjur3ScpC3tGSFva0fsddkTohFsxwtfTwIj0erdeYENkVSleUPklTeFjxPbRb4xuu/Ted1M/HoRIO48U/bqfBWEPlvvWKT2vUIg8mqvkNy0ciKvNm1E0p7pCHTl1fQyD69kxv04mRqXcQW5LPixCQz0LBgwC7VGXzBWFPWyG7A2wyBQlqm36AHhuejaYHlqZA1YovhMt2YTQ8PLbozy0RnWKtzi+IjHUNTEJUAtC19GKcklNYqyjSwsb/GNRf2qlcUl20ZtKBUxwCn+zvorkovDKQopAlYX+qUK4f6KJPBjDGhuveIVr1CvS2QNngsngv5AgvwmyYw/2D25LL/LvcIKidHgbmraT7VH1oTg8om5sCKMDFU1paGxeNEqI9VfkUHX8jLulClYX4J9YUW4P0SYihQuexmv/oqogGkSC8pmZxdW5FBt1BLljmau/opQjeAykWThPReMAgsrgpW1ZanF04ESFVZUAV+Mp6bc7f418SieWLZJ5q7pYSAcTGNedRNbq5fI4iMJjeLZMf1QhN3ERyimK526MUeo6tHFzpRaAwdo5nMxvCWGxuB+73vfK1kWzqVasnlLShBHdfL0T92ScaYM9FSiPzxB5jM93FpFKzBT5A5EqSsPB1Y6U/xqGI0w8h905WFsNbV52ZKNpIRHyVNsF0TjmylbYYKLyCJF7bWUW0MqpVLqbSK9wj31FE6FF74vSvned1KW9B5buw7CQ94aMyXtwEpHyNvS2vrXdoiXFKBsmIn0vKPphctbryZrBjZBUpX2dKVTNwY1aZvNZ4YpYd7mtxUIGCxDZuAMX3PuJRwEtheBrnTqxkRebYWAajUy8mp7n8q0fB4CXek0L+Xw+P3Jnek/agblgRfAnZV1Hc0IJIV1jr+0b7oYIQKwWi3uw9II6UBTjK1ZlwizMuEbiEZSLU1i6w02cRYStv3pktRbNWDxoxl1LSQezSfmk5/8pG1zAR6vauIaYIiqDVZcypdgSEVWdNx1cXmjzEK3DanIQhF00lvEIj6wh2BxiTVDIVkqk7a1VTVQuBX2Pp/4xCdEombkok9HI09prBFryhqwsJFGC/UIazmkIt+gSqO+JAvDVYJ+YEVyFeiAP7yiMmE++MEPDuxR7VoJDKmolWVgRfQBTQbLYzjg+IZUZP5bQoOOpqRBxFRaFXs6/BBwdBLLDOm2p8bgKwtZBnlVm4HaUEmTmqwGDI3CuR/SSMqYNX4Fga6s7MasoBnLVtGSWsMHqPtcDK/aXPKkK6FkGTKXauHEI/K3Xg55GEvilgxkxakoP8dxELZIJZ6zarHNQFfkDkepJQ+HV6oBLfFLSDJL106bExBA+VkhN9vZDDdlo+eFdSG5x4JSAzyPTndqJm6F6fSBxRNnT2V4pQSjkpFunnSiT8eHvC9K1eaD94W3hscW/SRyyFuj5CXSm5J2eKVlAk+Ut9qAQfOa5l9SF+jrGRrt6b64S2vr3zVKqtKGrnTqxnhkkNozX761IwlsIAKGzMAZvg1sW5oUBEYg0JVO3ZjIqxHAbkKWyKtNGIW0YQ8R6Eqn6YXvW+4MNNZgGIHW2h6fZUmALJiJHXagxLOmRLVYePgEZ8AikirZ1772tW4uehaWKOUWJQKLIhY6JRnayK1uFjHS4x0IKatllzLa7bfSELaUsiRTrHDrZ63FAxfWY3hFSrDowrUtVRHfOjJqHlUUcKEga0ts7PsQrJc1gFkrO/xsYWCOBHR2DNUkkAITjNQma+Ia0DA9suwcXpHa9cjDYEU0vCJjAXN5h1ekfOtbGZeqqHZteEU1y1IVAY0KHophqYpwEwhQuJkP8prVFsmW1kqz/i8zsLanFTCy1sbUWCxHTUV30QdIz1ayesm1nMlj6Kl/1kjzwUrVCNaYIxHoyspuzJGod3qZVWotO0D1uRjRhir3lppLKqoZl5q6pYVVBrIEJGlpZmF8yi1UUdH0nNmXpshdFiUFFnm4bKUyVvGrUk+QfYLCO7uFDiu6XTMbXGUj/toTh7xWQkmJ3GGEPjNXiTQitD7Lm2KpSoGJBqXBJ+9S7wv1Uiz1kNKJLu0c+HoqDa6SdtlKJ8rbUrsJiTT0kkLuF/oVj8bNZbk77+8aJVVpUlc6dWPoNdcHZF5HEr+ZCBg4w7eZbUurgsCyCHSlUzcm8mpZVDcnfeTV5oxFWjIdga50ml7mfubOWuhYt/iexlhxktW61b20vHEQocUSDTVZ8AUogJkWavJaYFifKFaaYu1SClRdz7qISpdkCKbyUeWjH+9m0SKy2pKUclp/l62oZB9REQGKJbGIqg1A1sxjA2saqgqgK5eUJpzcJ0w3xMKvpmkFUlEBZGXQ4Y6Z35ZKLXGLLaelMkKhNTTNSytMiiFFlxNrhvsw4XE9zTTNMDLObFGsSKOPZaOhiU0TjytpptzzcFdWdmP2vNI9LHDcAE1swLjHsFQ6YurKWGSgeYVkr41HtZskFIhqTDdQRO4UlEZUWpqBo7S3UUhD2yRlJpveRFy3na0Y6rf2SOq+CMLay6WHJazZp1Q67n1Rqh7x1igZp1Q6bi6VesvJyF6pXlWGyXuZyGrpkldUa2CNkqq0oSudujE6pTu1zQlsEQIGrnzmbVGb09QgMA+BrnTqxkRezUNv8+MjrzZ/jNLC4Qh0pdPwvPNS7gp3hvZiG4gv4Nm6YkHpaebHqDQWbyUZNsHyhp+spollLaEZYNRTOAiRVqGcnbGEYmXTTNMKW0SpqJJKmlfck7WStS5XVhFNsSbHYeGntU0dolbDymUxvCphJBq38TOTNSNT0Yqh44HO6QSlUp84FpnN4ZgZphVi9BHQ7vJyhedlkFtcwomZyXRweGHNz9kZrRx5PQ5OCcQOW5A7f2BmLXsV2ZWV3Zi9qutIlDNigKY3Y9xjWOod8dTXBpsbdf5oA0KWvK13ewJTUBpdKY1amk2lVTTClFPNXXuaWm7Rg8Y4l7BXErW1Yg++MOOUSke8L0p7xr2eSt7Rlco+ZS6ZPHU4lOOMY0q1pUl1jpXL5t81SqrSjK506sZkLdocsu0KZy26XeOV1vYj0JVO3ZjIq34MN/lu5NUmj07atiwCXem0bAnd9DvBnVFkwA5QbUBpFQgs6a3kKVXRApjpw5W+DHfLEvN8b4XTBa4bY3mjNCsiyz88nQ/3olXRTdmMsStOMwt9ds0111iGzVNta2ZZWUWUOyxFoIfvKD56hvjswI8wk7GqxLLJ7jzNZuNnhlNRgWVl0FFspFmJF7awFOB/fea4NCP5mfIQlYUofR+PT7nb70ScCgmWjQs8VTCCNs+pMSJk65PYrGIPw11Z2Y3Zw+r2vKgRAzS9DeMew1LviKlbG0xU2jOghHX55ZeTgQOJM9mnoDS6UvSxCUzLzEuEoCPqa0cWBpB9FJkZ3dM181zYaxn4IEypdNz7ovRlxOupZJxS6ZS5VPXKbYyZS0WPu19GlQavS1KV2rvSqRuz1FrU+9eL+8477/R5UzY8SkV7+5c859d15gbk3la07aVlLbrtI5j2NxHoSqduTORVE7HtCkdebdd4pbX9CHSlU3/6IXd3gjuzB14PfyQUWJndeOON73rXuwBkDUN9posUH2eWDcgCP++AboKZMXxC+15HyQkM/KDkCofBCP8sPvoLWzez5FbkyiqiNGShiALDPNI8ajVj5qU1sPMT4CCXMxNmpulGpiKYrAw6dVkrGiMTzxEB6u2OSDemdUCBBAudiHsK6oOANaCE6OngRa5b+N7GdGVlN2Zva9zz0kYM0PQ2jHgMS6Xjpm7Jy76PJpfZSORSaVyqF6NRmlIpQU24eXZoCs/cepnXBSjRR5bRLsuQTYVmOaMrVciI90WpetzrqeQdXemUucQwHClpIplOxXvjQhlVWrsuSVVq70qnbszwtShilyX+5z73ufLXQCz7WJVWLfzLHl8txfflwsS7nMDsGv4ZuctApe9bgUBXOnVjIq+2YihnNjLyaiYsidxSBLrSaXpHDijCp/yIH+2qV77ylSMyrjELiXDOOefcc889T33qU1/1qlc5+48nZp6b2GPObBVnwz7EDxw4MPPuHkYy7XzWs561hwXOK2plFbH3OfY3v3kt2av4VDQaSTov1BOe/vSnDy8B2p6aN77xjQcPHjRjrcr44f7ABz7w/ve//ytf+QqClVg4/vjjZxaoOso+lCC4MD/Sz1RXOnVjZjZyoyJHDNB627+yh7HZzbWg5L3p6IzVCO3a2bVUqvaVvTVqTwVGzyU2m17rdqRGyKjSgFVKqlJjVzp1Y9ifnnXWWUcddVQTpW6YViPNzec973nnnnvu0572NJp3t9xyCzrSd87CvN3S+mMU7nVwwQUXFCq5P/Eu36W1TQfwNa95zS6DkL7vGwS60qkbE3m1vcMdebW9Y5eWdxHoSqdummVjjlk2w1an5z/YNyX1Gd/lLDFf/vKXs2twruW8Tp1yyinzbu1t/MrWYCur6IQTTthbiOaVlormIbMw/pjf/BYmayaA9nve8x4xb3/72y+99FLemt73vve95CUv4cKMIiedsnnEmSzWb4zs+Hs60sRZs8FbHR4xQOvt78oexmY314KSObwyWVo7u5ZK1b76nqp09Fyy3eWnhBEyqkC91ZIK0akXyCzEmYAPHlb5/Cc43QKkvny+8Y1vcA1x9NFH+7x585vfbFLZU7z55pvZ41syiWePj6TDi0n51re+lfkt0o2rPoth36A2PzxxEpx55pkFLuaxLPERjmpkmH/yySeX+PwNAkEgCPQjEHnVj0/uBoEgsIEILNjD3MAWT2nSj3/8Y+5pfPk94xnPeMtb3sKag22mNf+UMpM3COwaAlQbGPfR3NTx4447zjrK+kr4jDPO6IHi05/+NDeCHsCeNLkVBIJAEJiOwDgZVerdakmFusJ/2aVgGk+hHg/43Oc+F0eGOKO3yK0qfUns2Ite9CIGud/85jd1mfkwFfvTTjvtDW94g2S09hBhODK/G264wd9TTz0V+4Y78+FEeUqa7373u44UL3DhzmyfEP5SlgJLfP4GgSAQBPoRiLzqxyd3g0AQ2EAEdkvvjBsUPx9/fNwyN7v44otXplm2gWOfJgWBcQhQLqOPQNfgvPPOo0eGOGO5ubAoJt78Qy1MlgRBIAgEgYkIjJNRpdKtllR0xGwHksnckPndfvvtqC7+9ZBlvFUw0r/wwgtPPPFEPeW5ArnmW0jkC17wAu4sSqS8+LUCBbJMaXTTvvSlL3Eh9853vlM8mszhHpydKbNcvv71rxeg2sb9X8mYv0EgCASBhQhEXi2EKAmCQBDYNAR2izvj9p5Xpo997GMOh/riF7/IPf+mjUfaEwS2AoFPfepTF110kaWUh+jaa6999rOfvbDZ0i9MkwRBIAgEgT1BYISMKvVuu6RiY8sHGd0xZph07emXOWiCBSszTB10nELpJqqLJprTlm2EcBJ322233X///Q4Hb4JPQw1xxpaTTtnpp59ebnFk+t73vle4FIh3K/HPfOYzFdjMnnAQCAJBoB+ByKt+fHI3CASBTUNgt7gz6Pv+oy+zacOQ9gSB7UKAxgGznbU4Ed8uoNLaIBAE1oLAbsooFpTOkOV3jG8KrJYf/uvqq68WX0ahHg5TApyU8WvGeYUVLC9pDDxrSunRYf6i4fxFmZUSWn9V1IrJZRAIAkFgCAKRV0NQSpogEAQ2CoF89GzUcKQxQWCbELDc2qbmpq1BIAjsGAK7JqM4esOd0SbDgpWhZncpQHfspJNOooPG42Q50YXWmLPbMWiIMw7LiifKH/3oR03ujOs0eVl3Mq1ipFkKVNSXv/zlF7/4xQ6HKTH5GwSCQBAYgUDk1QjQkiUIBIH1IrBbZwWsF+vUHgSCQBAIAkEgCASBI4QAFgzPdf311ztD/MEHH2SGKayul770pXxWMMB0KZI5p7M1+TUrWmMPPPCAxEg0h8BI7LLVPMcF/PSnP3VigLysPplw1nM2WyldKvaqq65y5kArjJUT71yCbpbEBIEgsIMIRF7t4KCny0Fg2xGI3tm2j2DaHwSCQBAIAkEgCASBp/Doz3/ZLbfcUs4+hggq7XWve13x68+WE/P11a9+VTx9NOdvuvuyl73MMQJYLZH4tbvvvttxmeeee24TzTe96U033njjd77zHZHOVmYHKjudkWaaGnb4ACaORb+YZrgwdPNy1ewJBIEgsCMIRF7tyECnm0FgPyFwYLRv17vuustxVPsJi/QlCASB/YFAVzp1Y/ZHT9OLIBAEth2BrnTqxnz/+98/66yzUF0DO/vQQw/R/2KeyWdZKxf2ijEmS8xaFD0yRwdYx1JME2aVyQ9avVsDjz76qGTVY1qNT6AfAXjeeeedTnjvT5a7QWArEOhKp25M5NVWDOXMRkZezYQlkVuKQFc6Te9I9M6mY5gSgkAQWD8Cd9xxh0Pizj///PU3JS0IAkEgCPQicKTlFYZrHslV/J01W4dHq1RaDTQTlDDGbV6Z3cSJCQJBYN8gEHm1b4YyHQkC+x6BIy2vhu5h7nug08EgEAS2GoGvf/3rN91001Z3IY0PAkFgRxCIvNqRgU43g8A+QCDyah8MYroQBHYEgSMtr8Kd7chESjeDQBAIAkEgCASBrUSA3SVTmq1s+s432sC1zGZ3HpIAsM8RiLza3gGOvNresUvLV4NAuLPV4JxagkAQCAJBIAgEgSAwBgG++X/xi1+MyZk860bAwBm+dbci9QeB1SEQebU6rPe6psirvUY05e03BOLvbL+NaPoTBHYHATbtVHNLf/nG3p2Op6dBIAhsHQJT5NXzn/98p2Hq8nOe8xx+x7au77vZYKcr8MJ53333Ocx0NxFIr7cXgcir7R27cS2PvBqHW3JtAgJT5NWy7R/PnRWN3CiiL4t40geBILBXCFiWdCmzmQrnkVd7hXnKCQJBYBwCU+TVCSecgH/52c9+hoiJ8eY4/Fefy3uHAo6BM3yrrz01BoEpCEReTUFvG/NGXm3jqKXNBYHh8mo6YgcOHTo0rpSf/OQnBw8ePPHEE8dlT64gEASCwJFAgML5gw8+eOqppzYLj7xqopFwEAgCG4JA5NWGDESaEQSCwEIEIq8WQpQEQSAIbAgCM+XV9LaN93d20kkn/fznP9esbIFOH4aUEASCwHQEyCISiVwinVqlRV61AMllEAgC60Ug8mq9+Kf2IBAEhiMQeTUcq6QMAkFgvQj0yKvpDRuvd6buX/3qV/fffz8VD02c3pSUEASCQBCYggCFc8qwaLLjjz++W07kVReTxASBILAuBCKv1oV86g0CQWBZBCKvlkUs6YNAEFgXAv3yamKrJnFnE+tO9iAQBIJAEAgCQSAIBIEgEASCQBAIAkEgCASBILDJCIy32dzkXqVtQSAIBIEgEASCQBAIAkEgCASBIBAEgkAQCAJBYDoC4c6mY5gSgkAQCAJBIAgEgSAQBIJAEAgCQSAIBIEgEAT2JwLhzvbnuKZXQSAIBIEgEASCQBAIAkEgCASBIBAEgkAQCALTEQh3Nh3DlBAEgkAQCAJBIAgEgSAQBIJAEAgCQSAIBIEgsD8RCHe2P8c1vQoCQSAIBIEgEASCQBAIAkEgCASBIBAEgkAQmI5AuLPpGKaEIBAEgkAQCAJBIAgEgSAQBIJAEAgCQSAIBIH9iUC4s/05rulVEAgCQSAIBIEgEASCQBAIAkEgCASBIBAEgsB0BMKdTccwJQSBIBAEgkAQCAJBIAgEgSAQBIJAEAgCQSAI7E8Ewp3tz3FNr4JAEAgCQSAIBIEgEASCQBAIAkEgCASBIBAEpiMQ7mw6hikhCASBIBAEgkAQCAJBIAgEgSAQBIJAEAgCQWB/IhDubH+Oa3oVBIJAEAgCQSAIBIEgEASCQBAIAkEgCASBIDAdgf8HI5aZVdS0K34AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "18e715a7-d625-476a-b31f-05b0f34e5b80",
   "metadata": {},
   "source": [
    "![image.png](attachment:36cf8c63-6090-43a4-ad49-7444e27cddcb.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e542235b-8dc0-46af-9a5e-76bef8332b51",
   "metadata": {},
   "source": [
    "Here, we demonstrate the capabilities of `flexynesis` on a multi-omic dataset of 506 Brain Lower Grade Glioma (LGG) and 288 Glioblastoma Multiforme (GBM) samples with matching mutation and copy number alteration data downloaded from the [cbioportal](https://www.cbioportal.org/study/summary?id=lgggbm_tcga_pub). The data was split into `train` (70% of the samples) and `test` (30% of the samples) data folders. The data files were processed to follow the same nomenclature. \n",
    "\n",
    "- `cna.csv` contains \"copy number alteration\" data\n",
    "- `mut.csv` contains \"mutation\" data, which is a binary matrix of genes versus samples. \n",
    "- `clin.csv` contains \"clinical/sample metatada\", which is a table of clinical parameters such as age, sex, disease type, histological diagnosis, and overall survival time and status. \n",
    "\n",
    "## Data Download\n",
    "\n",
    "The data can be downloaded as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79470b61-d7c0-43c2-b65f-9f281847d3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"lgggbm_tcga_pub_processed\"):\n",
    "    !wget -O lgggbm_tcga_pub_processed.tgz \"https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/lgggbm_tcga_pub_processed.tgz\" && tar -xzvf lgggbm_tcga_pub_processed.tgz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd892ade-8a09-4536-9314-58e747adde33",
   "metadata": {},
   "source": [
    "## Importing Train and Test Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5672014-e1d1-4a79-a8fe-2b69a6f6e1dc",
   "metadata": {},
   "source": [
    "We import train and test datasets including mutations and copy number alterations. We rank genes by Laplacian Scores and pick top 10% of the genes, while removing highly redundant genes with a correlation score threshold of 0.8 and a variance threshold of 50%. By setting `concatenate` to `False`, we will be doing an `intermediate` fusion of omic layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48115ba2-0d2b-4d2e-8ed4-8054626e7a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_importer = flexynesis.DataImporter(path ='lgggbm_tcga_pub_processed', \n",
    "                                        data_types = ['mut', 'cna'], log_transform=False, \n",
    "                                        concatenate=False, top_percentile=10, min_features=1000, correlation_threshold=0.8, \n",
    "                                       variance_threshold=0.5)\n",
    "train_dataset, test_dataset = data_importer.import_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797f40f1-ab8e-4660-b57b-43605792f2d4",
   "metadata": {},
   "source": [
    "## 1. Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0edf92e1-ef70-4dd9-8ee6-6ed7da9d00a1",
   "metadata": {},
   "source": [
    "Before building any machine learning models on the data, it is important to first familiarize yourself with the data you are working with. \n",
    "It is important to know the available data matrices, their sizes/shapes, available clinical variables and how they are distributed. \n",
    "\n",
    "Below you are asked to do simple explorations of the available data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b4fc77-c7b7-44c0-bf8c-9585cbdbe5d2",
   "metadata": {},
   "source": [
    "## 1.1 Print the shapes of the available data matrices \n",
    "\n",
    "- How many features and samples are available per data type in train/test datasets? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03b92b98-f5a6-43f2-a911-c24b343b7afa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b37fe3-d410-4f78-9f94-2eb46ac569cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7779389a-09b4-48f8-9c1d-3278de936e8c",
   "metadata": {},
   "source": [
    "## 1.2 Explore sample annotations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c21ef0-0510-4bec-affa-098e8735ba88",
   "metadata": {},
   "source": [
    "- What are the available clinical variables? Are they available in both train and test datasets? (See <dataset>.ann)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fad5b9d4-d586-4872-aae3-5d3dfba4db60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d753bbb5-f59b-403a-b242-e845242822b7",
   "metadata": {},
   "source": [
    "- Make a histogram plot of the follow up times in months (OS_MONTHS) (use sns.histplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4458f2da-51a9-4114-aee8-b6ebdfa0cf36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f05cb92-2b0b-4594-b441-8d8e8af027f0",
   "metadata": {},
   "source": [
    "- Make a histogram of the age distribution of the patients in the training data; facet the histogram by \"SEX\" variable (see flexynesis.utils.plot_boxplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c15acf-d71f-441f-b707-4a0df1e3d3d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6a58b0fd-f0da-49f4-8e50-12bc57fe0c18",
   "metadata": {},
   "source": [
    "- Make a summary of all available clinical variables (see flexynesis.print_summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75dac2f1-ff27-426b-a96a-677206e5036b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5f75ee87-f099-4f11-a87e-5c0c07267a2b",
   "metadata": {},
   "source": [
    "- Notice that the categorical variables such as \"SEX\", \"STUDY\", \"HISTOLOGICAL_DIAGNOSIS\" are encoded numerically in the \"dataset.ann\" objects. Use dataset.label_mappings to map the STUDY variable to their original labels. \n",
    "Print the top 10 values in dataset.ann['STUDY'] and the mapped label values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1468a277-a1b7-413e-89aa-205844af3054",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c24d216d-f7d2-4d90-9b40-037cbca38220",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "93f73907-2680-44fe-b5b3-f4997fbfdfb5",
   "metadata": {},
   "source": [
    "- Now, let's explore the data matrices. Make a PCA plot of the mutation data matrix and color the samples by \"HISTOLOGICAL_DIAGNOSIS\". See flexynesis.plot_dim_reduced function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a826c1-f848-42c2-bbff-e70d6938ae0c",
   "metadata": {},
   "source": [
    "First create a pandas data frame with the data matrix of interest with feature and sample names \n",
    "> df = pd.DataFrame(train_dataset.dat['cna'], index = train_dataset.samples, columns= train_dataset.features['cna'])\n",
    "\n",
    "Check the data frame contents \n",
    "> df.head()\n",
    "\n",
    "Make a PCA plot of CNA values using the labels from the STUDY variable \n",
    "\n",
    "**Note**: if you couldn't map the labels above, you can also use train_dataset.dat['STUDY'] as labels  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aae61c59-ec46-42b4-9bca-1dd860cfa883",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4846ae8-b74e-44b3-878f-eabc21ffd2c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360c0e0c-f81e-4ea6-aa88-07e3081847c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c54dd4-985e-46d2-b48f-602a3e661c95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56be9595-7ad7-43fa-aab9-be1a65d35a41",
   "metadata": {},
   "source": [
    "- (Optional exercise ideas): \n",
    "    - Make a PCA plot coloring the samples by HISTOLOGICAL_DIAGNOSIS, GENDER, or any other clinical variable \n",
    "    - Repeat the same exercise on the mutation data matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a4f9d8-9dd3-4094-8beb-01b9c0e68426",
   "metadata": {},
   "source": [
    "## 2. Training a single model using manually set hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4785fb02-ea0e-43d8-9ea0-288c667da315",
   "metadata": {},
   "source": [
    "Now that we have familiarized ourselves with the dataset at hand, we can start building models. \n",
    "\n",
    "First we will do a single model training by manually setting hyperparameters. Based on the model performance, we will try modifying individual hyperparameters and build more and more models and see if we can improve model performance. \n",
    "\n",
    "We will need to define the following components for starting a model training:\n",
    "\n",
    "    1. Split the train_dataset into train/validation components \n",
    "    2. Define data loaders for both train and validation splits \n",
    "    3. Define a pytorch-lightning trainer \n",
    "    4. Define a model with hyperparameters \n",
    "    5. Fit the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e564ce0c-f606-4e78-bafe-fbd876c00092",
   "metadata": {},
   "outputs": [],
   "source": [
    "# randomly assign 80% of samples for training, 20% for validation \n",
    "train_indices = random.sample(range(0, len(train_dataset)), int(len(train_dataset) * 0.8))\n",
    "val_indices = list(set(range(len(train_dataset))) - set(train_indices))\n",
    "train_subset = train_dataset.subset(train_indices)\n",
    "val_subset = train_dataset.subset(val_indices)\n",
    "\n",
    "# define data loaders for train/validation splits \n",
    "from torch.utils.data import DataLoader\n",
    "train_loader = DataLoader(train_subset, batch_size=32, shuffle=True) \n",
    "val_loader = DataLoader(val_subset, batch_size=32, shuffle = False) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be618ee5-299f-4979-bf87-bcfd9b0a6e49",
   "metadata": {},
   "source": [
    "Now, we need to define a model with manually set hyperparameters and a lightning-trainer fit the model. \n",
    "\n",
    "**Notice**: Notice the callback we are passing to the trainer which enables us to plot the loss values as the training progresses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228c5467-abbb-4e6c-abe9-02ea172685c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a model with manually set hyperparameters for the DirectPred model \n",
    "myparams = {'latent_dim': 32, 'hidden_dim_factor': 2, 'lr': 0.001, 'supervisor_hidden_dim': 16, 'epochs': 20}\n",
    "model = flexynesis.DirectPred(config = myparams, dataset = train_dataset, target_variables=['STUDY'])   \n",
    "trainer = pl.Trainer(max_epochs=myparams['epochs'], default_root_dir=\"./\", logger=False, enable_checkpointing=False, \n",
    "                     callbacks=[flexynesis.LiveLossPlot(myparams, 1, 1)])\n",
    "# Fit the model \n",
    "trainer.fit(model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84daa082-49ef-4805-8e7b-5d8942778f31",
   "metadata": {},
   "source": [
    "While we can observe how well the model training went based on the \"loss\" values, we can also evaluate the model performance on test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5e29639-4ca2-490c-ba74-d2d2c6994892",
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate the model performance on predicting the target variable\n",
    "flexynesis.evaluate_wrapper(\"DirectPred\", model.predict(test_dataset), test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc154aa-0849-432f-898c-438273b43804",
   "metadata": {},
   "source": [
    "## 2.1 Exercise\n",
    "\n",
    "- Now, repeat the above model training and evaluation by manually changing the hyperparameters (Try at least 5 different combinations)\n",
    "- See if you can find a better hyperparameter combination that yields a better classification performance than the initial setup we provided. \n",
    "- See the default hyperparameter ranges we use for Flexynesis here: https://github.com/BIMSBbioinfo/flexynesis/blob/69b92ca9370551e9fcc82a756cb42c72bef4a4b1/flexynesis/config.py#L7, but feel free to try outside these ranges too. \n",
    "- Also try to observe the impact of the changing parameters on how the train/validation loss curves change. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3931201-3590-4bf4-b889-78a8f218a773",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "    myparams = {'latent_dim': XX, 'hidden_dim_factor': XX, 'lr': XX, 'supervisor_hidden_dim': XX, 'epochs': XX}\n",
    "\n",
    "    model = flexynesis.DirectPred(config = myparams, dataset = train_dataset, target_variables=['STUDY'])   \n",
    "\n",
    "    trainer = pl.Trainer(max_epochs=myparams['epochs'], default_root_dir=\"./\", logger=False, enable_checkpointing=False, \n",
    "                         callbacks=[flexynesis.LiveLossPlot(myparams, 1, 1)])\n",
    "\n",
    "    trainer.fit(model, train_loader, val_loader)\n",
    "    \n",
    "    flexynesis.evaluate_wrapper(\"DirectPred\", model.predict(test_dataset), test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f97c99-86f8-448f-801c-8ff8bd8e5b85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04623801-5a79-4fbc-ad97-420f197588a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08e595f6-65a6-4fde-a27b-85b3519a060d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ee94c3-443a-4de8-9819-dd215b2786c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11042b00-8ede-44de-b677-5d52c8c396fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba03c32e-1037-4402-9b3c-ffbbca20fa42",
   "metadata": {},
   "source": [
    "#### **Warning!!**: In reality, we don't select the best models based on performance on the test dataset.\n",
    "#### The best model is selected based on the validation loss value, where the model parameters that yields the lowest validation loss is selected to be the best model. \n",
    "#### The validation dataset which we use to compute the validation loss is basically a subset of the training dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5efcda5-611a-4420-bb18-cc299b8f99a2",
   "metadata": {},
   "source": [
    "## 3. Automating the Hyperparameter Optimisation Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9c93d9-16c7-4ef5-80d3-a5ccfb6cb438",
   "metadata": {},
   "source": [
    "What we did in the above section was to set random hyperparameters, build a model, evaluate the model and try different hyperparameters based on our previous model performance. \n",
    "However, this process can be quite time consuming and arbitrary. This process can be automated using a Bayesian approach, where the model training is sequentially done for a number of hyperparameter optimisation iterations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e2a088-f4ea-4729-aa25-7c5d9736ee25",
   "metadata": {},
   "source": [
    "Now, we are ready to do a model training using hyperparameter optimisation. \n",
    "- `model_class`: We pick `DirectPred` (a fully connected network) for now. \n",
    "- `config_name`: We use the default/built-in hyperparameter search space for `DirectPred` class. \n",
    "- `target_variables`: 'STUDY' variable contains the type of disease \n",
    "- `n_iter`: We do 5 iterations of hyperparameter optimisation. For demonstration purposes, we set it to a small number. \n",
    "- `plot_losses`: We want to visualize how the training progresses. \n",
    "- `early_stop_patience`: If a training does not show any signs of improving the performance on the `validation` part of the `train_dataset` for at least 10 epochs, we stop the training. This not only significantly decreases the amount spent on training by avoiding unnecessary continuation of unpromising training runs, but also helps avoid over-fitting the network on the training data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1e027f-4f97-45d6-89aa-02ed5aee6a10",
   "metadata": {},
   "source": [
    "**Note 1**: Notice how the hyperparameters using in different HPO steps change at each iteration. \n",
    "\n",
    "**Note 2**: Also notice that we are running the model for more epochs (500 by default) however, by using \"early_stop_patience=10\", we avoid lengthy training when validation performance is not improving. \n",
    "\n",
    "**Note 3**: Try to follow the the loss curves and the used hyperparameters. See if you can spot which combination yields the lowest/best loss values. \n",
    "\n",
    "**Warning!!**: In reality we need to set `n_iter` to higher values so that the optimizer can collect enough data points to learn trends in the parameter space. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3867e7d1-6236-4f97-b9ee-5f78181a5acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a tuner; See n_iter is the number of \n",
    "tuner = flexynesis.HyperparameterTuning(train_dataset, \n",
    "                                        model_class = flexynesis.DirectPred, \n",
    "                                        config_name = \"DirectPred\",\n",
    "                                        target_variables = ['STUDY'], \n",
    "                                        n_iter=5, \n",
    "                                        plot_losses=True, \n",
    "                                        early_stop_patience=10)\n",
    "### Perform Training \n",
    "model, best_params = tuner.perform_tuning()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82add07a-fc7b-4856-b0a6-9202b0f6bc3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## See which hyperparameter combination was the best \n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45eef0bb-6679-48e3-b403-3d72cf5bd724",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Evaluate the model and visualising the results\n",
    "flexynesis.evaluate_wrapper(method = 'DirectPred', y_pred_dict=model.predict(test_dataset), dataset = test_dataset)                            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4a3cc7-da08-4b62-9830-d121c292a968",
   "metadata": {},
   "source": [
    "Let's extract the sample embeddings and make a PCA plot and color by the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0763a8cc-21c7-486e-aaf6-d603ad726150",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embeddings = model.transform(train_dataset)\n",
    "flexynesis.plot_dim_reduced(train_embeddings, train_dataset.ann['STUDY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790e48d1-05b0-48e8-bf90-14ebce4178a6",
   "metadata": {},
   "source": [
    "Repeat the same for the test dataset: extract sample embeddings for test dataset samples and make a PCA plot, colored by \"STUDY\" variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f121cd-13af-4ae0-95c4-0e118a9f6ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_embeddings = model.transform(test_dataset)\n",
    "flexynesis.plot_dim_reduced(test_embeddings, test_dataset.ann['STUDY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed5acc3-e5c5-4aa9-85b4-5d551972cc5e",
   "metadata": {},
   "source": [
    "## 3.1 Exercises\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ee18be-e08e-4003-a46e-dc373350acbf",
   "metadata": {},
   "source": [
    "**Exercise 1**: \n",
    "\n",
    "Look up what Harrell's C-index means and write down a simple description of what it measures. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a62531-9490-49ae-a7ca-af3ba2e9f261",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a0c5237f-3e4d-45be-b0bc-9de647c67a24",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9445a008-55c5-4337-b0c7-c53d0e4d8bb9",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**Exercise 2**:\n",
    "\n",
    "Now, you build a model using hyperparameter tuning (run at least 10 HPO steps) to predict the survival outcomes of patients. \n",
    "Evaluate the final model on test dataset, which computes the \"C-index\". \n",
    "\n",
    "Feel free to cheat from the tutorial available here: https://github.com/BIMSBbioinfo/flexynesis/blob/main/examples/tutorials/survival_subtypes_LGG_GBM.ipynb\n",
    "See how \"OS_STATUS\" and \"OS_MONTHS\" were used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce7b7a48-f061-439e-bb3e-bda8d3a68d71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3102c1c7-7a47-44d4-af76-344ba3497f85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8571c2cd-5494-4106-85e3-20ce81be079a",
   "metadata": {},
   "source": [
    "**Exercise 3:**\n",
    "\n",
    "Again build a model using hyperparameter tuning to predict survival outcomes (as in Exercise 1), however, this time use additional clinical variables as targets. \n",
    "\n",
    "\n",
    "    flexynesis.HyperparameterTuning(train_dataset, \n",
    "                                    model_class = flexynesis.DirectPred, \n",
    "                                    config_name = \"DirectPred\",\n",
    "                                    surv_event_var=\"OS_STATUS\",\n",
    "                                    surv_time_var=\"OS_MONTHS\",\n",
    "                                    target_variables = [], => What other variables can you use here? Try \"AGE\" and/or \"HISTOLOGICAL_DIAGNOSIS\" and see the model performance \n",
    "                                    ...\n",
    "                                    \n",
    "\n",
    "**See if you can get a better C-index using additional target variables.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e59fdb8-79d7-42ec-bf4f-c9b43680c49d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9492b36d-e32c-4454-8693-3d8e4339aa69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba2cba34-4931-4e70-b849-b0bc311bf034",
   "metadata": {},
   "source": [
    "## 3.2 Survival-risk subtypes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693fbe7a-c3e3-4f3c-9fbb-8664293cfb6e",
   "metadata": {},
   "source": [
    "Use the best model from the above exercises to inspect sample embeddings categorized by survival risk scores. \n",
    "\n",
    "Let's group the samples by predicted survival risk scores into 2 groups and visualize the sample embeddings colored by risk subtypes.\n",
    "\n",
    "**Notice**: You can use the code-below to get survival risk groups, however, notice that you must have built a model with \"OS_STATUS\" already. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "287b15d9-454c-45ef-a986-75479cc699ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get model outputs for survival variable \n",
    "outputs = model.predict(test_dataset)['OS_STATUS'].flatten() \n",
    "risk_scores = np.exp(outputs)\n",
    "# Define quantile thresholds\n",
    "quantiles = np.quantile(risk_scores, [0.5])\n",
    "# Assign groups based on quantiles\n",
    "groups = np.digitize(risk_scores, quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b62c56a-32fa-4cfe-ac11-aeea8bdb1c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract sample embeddings \n",
    "E = model.transform(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c9f4a19-151a-40a4-9600-4c0eb60a7050",
   "metadata": {},
   "outputs": [],
   "source": [
    "flexynesis.plot_dim_reduced(E, groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763b226a-552e-4642-88b7-2d840fdc3c35",
   "metadata": {},
   "source": [
    "Let's also see the Kaplan Meier Curves of the risk subtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fdbcc43-e3dd-485e-a1b6-46adeda7213e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove samples with NA values first \n",
    "durations = test_dataset.ann['OS_MONTHS']\n",
    "events = test_dataset.ann['OS_STATUS']\n",
    "valid_indices = ~torch.isnan(durations) & ~torch.isnan(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf37001a-7b26-42fd-8594-aa4382f5c658",
   "metadata": {},
   "outputs": [],
   "source": [
    "flexynesis.plot_kaplan_meier_curves(durations[valid_indices], events[valid_indices], groups[valid_indices]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c844308c-e503-4d39-8bb5-506a7eb5f15b",
   "metadata": {},
   "source": [
    "### Finding survival-associated markers \n",
    "\n",
    "We can also compute feature importance scores for prediction of overall survival. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e82dac8c-8d91-4315-b934-25d28376411a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compute_feature_importance(train_dataset, 'OS_STATUS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01317501-e5f2-48f5-8839-40b3f3a0486b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get top 10 features \n",
    "flexynesis.get_important_features(model, var = 'OS_STATUS', top=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09e03b8-2069-4ee1-84ed-c74692f6123b",
   "metadata": {},
   "source": [
    "### Comparing top markers with clinical covariates \n",
    "\n",
    "Let's build a linear Cox-PH model including the top 5 markers and other clinical variables such as histological diagnosis, disease type (STUDY), age, and sex. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26cbb1ba-04a5-4821-ab20-4d6458f07c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a data.frame with clinical covariates and top markers along with survival endpoints \n",
    "vars = ['AGE', 'SEX', 'HISTOLOGICAL_DIAGNOSIS', 'STUDY', 'OS_MONTHS', 'OS_STATUS']\n",
    "# read clinical variables \n",
    "df_clin = pd.concat(\n",
    "    [pd.DataFrame({x: train_dataset.ann[x] for x in vars}, index=train_dataset.samples),\n",
    "     pd.DataFrame({x: test_dataset.ann[x] for x in vars}, index=test_dataset.samples)], \n",
    "    axis = 0)\n",
    "# get top 5 survival markers and extract the input data for these markers for both training and test data\n",
    "imp = flexynesis.get_important_features(model, var = 'OS_STATUS', top=5) \n",
    "df_imp = pd.concat([train_dataset.get_feature_subset(imp), test_dataset.get_feature_subset(imp)], axis=0)  \n",
    "\n",
    "# combine markers with clinical variables\n",
    "df = pd.concat([df_imp, df_clin], axis = 1)\n",
    "# remove samples without survival endpoints\n",
    "df = df[df['OS_STATUS'].notna()]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895e32b0-23c4-47b5-a313-d26b004effa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a cox model\n",
    "coxm = flexynesis.build_cox_model(df, 'OS_MONTHS', 'OS_STATUS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0a59b53-8903-4146-b297-57cd48e06cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize log-hazard ratios sorted by p-values\n",
    "flexynesis.plot_hazard_ratios(coxm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69da1544-7385-4703-9b39-323cbafdd79e",
   "metadata": {},
   "source": [
    "## 3.3 Final Exercise\n",
    "\n",
    "- Inspect the top 10 markers from section 3.2 and see if they have been characterized in the literature as important markers for Glioma disease progression. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec65747-8d36-4609-a482-f3f8d9cc992c",
   "metadata": {},
   "source": []
  }
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