{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Setting up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "notes" } }, "outputs": [], "source": [ "# activiate inline plotting\n", "%matplotlib inline\n", "from setup import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if 0:\n", " file_name = tutorial_data.get_file('arab107')\n", " geno_reader = gr.genotype_reader_tables(file_name)\n", " pheno_reader = phr.pheno_reader_tables(file_name)\n", "\n", " #the data object allows to query specific genotype or phenotype data\n", " dataset = data.QTLData(geno_reader=geno_reader,pheno_reader=pheno_reader)\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if 1:\n", " #the data used in this study have been pre-converted into an hdf5 file. \n", " #to preprocess your own data, please use limix command line tool\n", " file_name = tutorial_data.get_file('BYxRM')\n", " geno_reader = gr.genotype_reader_tables(file_name)\n", " pheno_reader = phr.pheno_reader_tables(file_name)\n", " \n", " #the data object allows to query specific genotype or phenotype data\n", " dataset = data.QTLData(geno_reader=geno_reader,pheno_reader=pheno_reader)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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68ZdBD2BsU/lpH3YJsNcY+zLicY34d7hPJZkJHAJsVVW3JvkKsFzj8QDc3bO9\niIe+RiOLVulpW9gV0UYa75gA21bVPeOMCQ7593F3S5IkSZKWDMvP2oblZ21z/+Nb5nxugKPRVDbl\n13SqqlOAZbsZSgAk2YShW9P2TrJUkjUYmi10NkNFmG2SzEyyFPDXwC+AlYE7gduSrAm8ZIxT3ptk\nzGLeWONJshMTz4Kqbow7J3lid5vfPsC8CY75VXfMaklmAK/u2f9T4OCesWw2wRgkSZIkSZImNOWL\nTp3dgd2SXJXkYuBw4JvAhcAFwMnAO6rqeoaKNOcAnwYuBX5TVcdV1YXAecDlwDeA0x96GgCOBC4c\nZyHxscZzXbdv3MJTVf0R+BeG1mU6H5hfVT8c79jumNnAWd24L+nZfTCwVZILklwCHDje+SVJkiRJ\nkvoxHW6vo6quA/YeZdc7u6/evvOAncfI2X+M9l16tv+FoaLQwxnPr+mZtVRVbxnjHMcAx4ySu/I4\n4zoKOGqUY25kaLaUJEmSJElT0oylBz2C6Wm6zHSSJEmSJEnSY2hazHQahCRPBH4+yq5dq+qmx3o8\nkiRJkiRJjyWLTo+S7ra1zQc9DkmSJEmSpEHw9jpJkiRJkiQ150wnSZIkSZI0pS1j9WMgnOkkSZIk\nSZKk5iw6SZIkSZIkqblU1aDHoEkuSXF5w/fJcve2yzp/RrssgJXaReVr97ULA/7j6HbX+p4VFjXL\nWulPNzTLArjj1se3C/vdcu2ygJzW7vtg5bf+qVnWreev1SwL4E3bfKRZ1pdue32zLICFJ67WMKxd\nFP/R+HfpwrTLavv2IDPaXeuL5v93s6wT/3H3ZlkAPK3ddW78zvnNsgAuOWOrZllv2OFTzbIAvvim\ntzTLesNn2o3tC3se3CwLIH9p9/548Q+Oa5YFUGn38+PEa1/SLIu3tf2dzK8bZjX+OcmzGmZt1DDr\ngIZZAF9rmPXbhlnAy/7te82yzq9Nm2XdnWWbZQG8jSOaZf1bPk5VNfwDZHJJUvesMuhRPHoedyuT\n9vXzrkZJkiRJkjSlzbD6MRDeXidJkiRJkqTmLDpJkiRJkiSpOYtOkiRJkiRJas67GiVJkiRJ0tS2\n9KAHMD0500mSJEmSJEnNWXSSJEmSJElScxadGkqyVpJjklyVZH6SE5KsP+hxLY4k1yRZbdDjkCRJ\nkiRJSzaLTo0kCXAccEpVPaOqtgL+FVhzsCODJIuzdlc9agORJEmSJEnThguJt7MLcE9VHTncUFUX\nAiT5CPAByhtxAAAgAElEQVRihgo676uq7ySZBcwGbgA2BhZU1d90/f8f8DHgTuBM4GlV9fJuBtKX\ngacBdwEHVtVFow0myWzg6V3f3yZ5K/BfwFO7Lv9UVWcmeSLwLWBt4CwgTZ4NSZIkSZImC6sfA+HT\n3s7GwIKRjUn2BDYFNgHWAM5Jclq3ezPg2cB1wBlJdgDOZag49Nyq+m2Sb/LA7KM5DBWnXpVkF+Cr\nwObjjOlZwE5VdXeXc0RVnZHkqcCJ3bkPA06rqvd1xa7XP4LnQJIkSZIkCbDo1NJYt6XtCHyzqgq4\nPsk8YGvgNuDsqvoDQJLzeWAG02+q6rfd8d8CDuzJ2gOgqk5N8sQkK1XVHWOM5/iqurt7vBuw4dBd\ngAA8PsmKwHOB3bvMHye5edSr+NTsB7a3mQXbzhrjciVJkiRJk9lv5v6O38y9dtDD0DRg0amdS4C9\nxtg38pa14QLV3T1tixh6PUYWr0Yeuzi3v9014rhtq+qeB4UNFaEmznzL7MU4rSRJkiRpslpv1jqs\nN2ud+x+fMudXAxyNpjIXEm+kqk4Blk1ywHBbkk2AW4C9kyyVZA3gecDZjF7oKeAKYL0k63Zte/NA\nIeoXwL5d9izghjFmOY3mp8DBPWPbtNs8DXht1/YS4Al95kmSJEmStGRYZgp/TWIWndraHdgtyVVJ\nLgYOB74JXAhcAJwMvKOqrmeokPSQW/KqaiHwj8CJSeYzdBvebd3u2cCWSS4A3g/83QTj6c0/GNgq\nyQVJLgEO6trnAM/rxrs78FskSZIkSZIeoUleE1uyVNV1DM1MGumd3Vdv33nAvJ7Hb+nZfWpVbQiQ\n5DPAOV2fm+nWX+pjLHNGPL4R2GeUfjcBL+onU5IkSZIkqV/OdJqcDkhyXjcjaWXg84MekCRJkiRJ\n0uJwptMkVFUfBz7eT98k+wFvHdF8+oiZU5IkSZIkSY8pi05LuKo6CjhqwMOQJEmSJGnysvoxEN5e\nJ0mSJEmSpOYsOkmSJEmSJKk5i06SJEmSJElqzrsaJUmSJEnS1Lb0oAcwPaWqBj0GTXJJivkN3yfX\ntItabteb2oUBC49drVnWSnvf0CwL4M41ntgs6z/untEs6z1fXNQsC2CVff/YLOvW+Ws1ywJ4044f\naZb1rUWvbZa1wlJ3NcsCuPaQ9duFbdouCoBVG2Zt3O7n2iHrHd4sC+Bx3NMsa1dObpYFcFy9qlnW\nZ172jmZZD/kc10foFS88plnW8Yfs0ywLaPp9lUVt/w582/7vb5Z1xFb/1ixr3/lfbJYFsBTtfvd9\n9fg3NssC4KSGWa9sF7Xctm3/ZnvRKu0udAOubJYF8FvWbZa1Sm5tlvW1W/6mWRa0/bt5j7//RrMs\ngO+/dN92YS3/nHxpwywgd7T7GV77LUVVpVngJJOkquGfuJNNrmTSvn7eXidJkiRJkqTmLDpJkiRJ\nkiSpOdd0kiRJkiRJU5vVj4FwppMkSZIkSZKas+gkSZIkSZKk5iw6SZIkSZIkqTmLTpIkSZIkSWrO\npbQmkSRrAR8HtgJuAf4E/FNVXTnQgUmSJEmStCSz+jEQPu2TRJIAxwFfqap9urZNgDWBx6zolGTp\nqlr0WJ1PkiRJkiRNTd5eN3nsAtxTVUcON1TVhVV1epKPJLkoyYVJ/hogyawkpyX5UZLLk3yuK1yR\n5I4k/5nk4iQ/T7J61/70JD9JMr879pld+1FJ/ivJL4EPPfaXLkmSJEmSphqLTpPHxsCCkY1J9gQ2\nBTYBdgM+0t2GB7A18Gbg2cDTgT269hWAc6pqY2AecFjXfiTwlqraCngH8NmeU60NbF9Vb295UZIk\nSZIkaXry9rrJo8Zo3xH4ZlUVcH2SeQwVm24Dzq6qawCSfAvYCTgWuA/4dnf814HvJ1kR2AH4bjch\nCuBxPef+bneO0X1+9gPbW86CrWYtxqVJkiRJkiaLumwuXD530MN4bC096AFMTxadJo9LgL3G2JcR\nj2vEv8N97hvj2GJoVtvNVbX5GOe4a9zRHTR73N2SJEmSpCVDNpwFG866/3H94D0DG4umNm+vmySq\n6hRg2SQHDLd1C4nfAuydZKkkawDPA85mqJi0TZKZSZYC9gZO7w5dCnh1t/1a4BdVdTtwdZK9uux0\n+ZIkSZIkSc1ZdJpcdgd2S3JVkouBw4FvAhcCFwAnA++oquu7/ucAnwYuBX5dVcd17XcyVJC6CJgF\nDJet9wVen+R84GLgFT3nHvvWOkmSJEmSpMXk7XWTSFVdx9CMpZHe2X2NdFtVvXyMrENGabsGeMko\n7fsv3kglSZIkSVqCWP0YCGc6LbmKsWcnOWtJkiRJkiQNlLW+JVRVzQPmjbFv5cd4OJIkSZIkSQ/i\nTCdJkiRJkiQ1Z9FJkiRJkiRJzXl7nSRJkiRJmtqsfgyEM50kSZIkSZLUnEUnSZIkSZIkNZeqGvQY\nNMklKa65p1neNuue2SzrvBs3b5YFcO/VDT/475XtogBWuuKGZll3HvPEZlnvPqjtPNUnLDqgWdby\n9edmWQAH7fnVdmFH3NssasZKba/z3mMafh/MbBcF8JT/d2WzrGvfu36zrLyo7e/SJ29zVbOs37/p\nGc2yAGrftAu7tl3UKq/6Y7sw4NbT12oXdnW7KADe0zCr4WUC5H3tvhfe8IJPNcv6woUHN8sCmr53\neVbbnx+rrfuHZlk3zXtys6yX7vq9ZlkAP/71Hs2ydnz6yc2yAE4/crdmWesdeGmzrN/M26hZFsC6\nz7u8Wdb//uyZzbIAdnxBu9f09G+2ez25pl0UAC1/xe8TqqrhL/nJJUnVdoMexaMnv2TSvn7e1ShJ\nkiRJkqa2pQc9gOnJ2+skSZIkSZLUnEUnSZIkSZIkNWfRSZIkSZIkSc25ppMkSZIkSZrarH4MhDOd\nJEmSJEmS1JxFJ0mSJEmSJDU3bYtOSdZKckySq5LMT3JCkvUXM2Nuki0n6HPHIxvp/Tlzkuy6GP3X\nTvLdMfZNOG5JkiRJkqRHYlre1ZgkwHHAV6pqn65tE2BN4MrFiKrua6I+j1hVHbaY/f8AvHqs3TQa\nlyRJkiRJ0mim60ynXYB7qurI4YaquhA4IMkrh9uSfCPJy5MsleSjSS5KckGSN40MTPKaJBd2fT44\nYt/7kpyf5KwkT+ra1kjyvSRnd187dO3/neR13fZBSb7ebR+VZM9u+5ok709yXjdLa4skP+1mbR3U\n9ZmZ5KJue/luVtelSb4PLN8zthcmOTPJgiTfSbJio+dYkiRJkqTJYZkp/DWJTdei08bAglHavwTs\nB5BkFWB74ATgIOCpwKZVtSnwzd6DkqwNfJChYtZmwNY9xasVgbOqajPgNOCArv0TwBFVtQ2wF/DF\nrv1A4N1Jngv8M/Dmrr13dlIBv62qzbvMo4Ddge2AOaNc1z8Ad1TVs4HDgC27ca8OvAvYtaq27J6T\nfx7tCZMkSZIkSVock7wm9qgZ9dayqjotyWe7YsxewPeq6r5uLaXPVdV9Xb+bew4LsDUwt6puhKEZ\nUsDzgB8wNKPqhK7vAuAF3fZuwIZDd/oB8PgkK1TV9UneDZwCvKqqbhnjGo7v/r0IWLGq7gTuTHJ3\nkpVH9H0uQ0UuquqiJBd27dsBzwbO7MbxOODMUc92xHse2N5uZ9h+5zGGJUmSJEma1C6ZC5fOHfQo\nNA1M16LTJQwVlUbzVeB1wN50s546GbX3kJFFrPS03dvTfh8PPOcBtq2qe0bJ2wT4P+DJ45zz7p7M\n3ozec4wc02iPf1ZVrx3nPEPe9u4Ju0iSJEmSlgAbzRr6GnbsaDfMSI/ctLy9rqpOAZZNMnyrG0k2\nSbITQ7eq/dNQt7q82/0z4KAkS3d9n9AbB5wN7JzkiV2ffYB5Ewzjp8DBPeffrPt3G+DFwBbA25PM\nnCBnvGLYsNOA13b5GzNU1Crgl8COSZ7e7VtxcT/BT5IkSZKkSW/Q6y65ptO0szuwW7f49sXA4cB1\nVXU9cCnwlZ6+XwT+F7gwyfnAa3qDquqPwL8ApwLnA/Or6ofDu3u79jw+GNiqW5j8EuDAJI8DjgT2\nr6rrgEOAL09wHSM/iW607c8BKyW5lKE1n+Z34/4/hmZzfSvJBQzdWvfMCc4nSZIkSZI0oUleE3v0\ndEWdvUe2J1kBWB/4Vk/fRQwVgA4ZkbFLz/YxwDGjnGflnu1jgWO77RsZmhE10mY9/X8I/LDb3r+n\n/Wk920cDR/c8Xq/bvImhGU1U1UJGFMp6+p8KbDPaPkmSJEmSpIdrOs90eogkuzE0y+mTVXX7oMcj\nSZIkSZK0pJq2M51GU1U/B2YOehySJEmSJKmhpQc9gOnJmU6SJEmSJElqzqKTJEmSJEmSmrPoJEmS\nJEmSNIUkeXGSy5NcmeTQUfa/Pcl53ddFSf6SZNVu3zVJLuz2nf1IxuGaTpIkSZIkSVNEkqWBTwO7\nAb8HzklyfFVdNtynqj4KfLTr/zLgn6rqluHdwKyquumRjsWikyRJkiRJmtqmV/VjG+CqqroGIMkx\nwCuBy8bo/1rgWyPa0mIg3l4nSZIkSZI0dTwZ+F3P42u7todIsgLwIuDYnuYCfp5kfpIDHslAUlWP\n5HhNA0mK89u9T56/6Y+aZZ1y0suaZQEwv2HWXxpmAbxhYbOoVVa/ZeJOfZoz493NsgBunvHFZlkn\nLzqpWRbAOvW7iTv1abXc2Czrs5e8vVkWwAc2emuzrO/w182yAJZhUbOsVerWZlk/+8MLm2UBMHe5\ndlkrtYsCYGbDrHY/1tr/zL24XdRyez/imekPsnDD1dqF7dMuChiaxN9IVm/3t8fTtr20WVZrv7tx\nnaZ59y58XLuw/2v3s2jdTS9vlgWwdMPfBzfe/cRmWQBPX/aqZllrc12zrB+d/OpmWQD8tmHWrvc2\nDAMWzmiXdUK7qI3/+Zx2YcDjub1Z1lnZlapqMrNlMkpS9YpBj6Kduf839DVszv/woNcvyZ7Ai6vq\ngO7x3wDbVtVbRmYl2Rt4bVW9sqftr6rquiRrAD8D3lJVv3g4Y51eE8wkSZIkSZKWYLNWH/oaNud/\nHtLl90Dv/9VYh6HZTqPZhxG31lXVdd2/NyQ5jqHb9Sw6SZIkSZIkPcT0qn7MB9ZPMhP4A7A38JqR\nnZKsAjyPoTWdhttWAJauqtuTrAi8EJjzcAcyvZ52SZIkSZKkKayq/pLkzcBJwNLAl6rqsiQHdfs/\n33V9FXBSVf255/A1geOSwFDN6BtV9dOHOxaLTpIkSZIkSVNIVf0E+MmIts+PeHw0cPSItquBzVqN\nw0+vkyRJkiRJUnMWnSRJkiRJktScRacBSLIoyXlJLk5yfpJ/TnfDZJItk3yi0XnmJNl1lPZZSX7Y\n4hySJEmSJE16S0/hr0nMNZ0G466q2hwgyRrAN4GVgdlVtQBY0OIkVXVYixxJkiRJkqTF5UynAauq\nG4ADgTfDg2chJdkmyZlJzk1yRpINuvb9kvx3kp8muTrJm5O8vet3VpIndP2OSrJnt/3iJJclWQDs\nPnz+JCsm+XKSX3XHv+IxfgokSZIkSdIUZNFpEuhWh1+6m/XU6zLguVW1BXAY8P6efRsxVDzaGjgc\nuK3rdxbwt8PRQCVZDjgSeFlVbQms1e0DeBdwclVtCzwf+EiSFVpfoyRJkiRJml68vW5yWxX4apJn\nMFQk6n29Tq2qO4E7k9wCDK/RdBGwSU+/AM8Crq6qX3dtX2dodhXAC4GXJ3l793hZYB3gitYXI0mS\nJEnSQFj9GAif9kkgyXrAoqq6oVtPfNh7GZqFtHuSdYG5Pfvu7tm+r+fxfTz0da0RjzPi8R5VdeW4\ng/zc7Ae2t5oFW88at7skSZIkaXK6de753Db3gkEPQ9OARacB626p+y/gU6PsXhn4Q7e9f7+RIx4X\ncDkwM8l6VfUb4DU9+08CDgbe0o1n86o67yGp/zC7z9NLkiRJkiazVWZtxiqzNrv/8bVzvjrA0Wgq\nc02nwVg+yXlJLgZ+BpxYVXO6fcUDM5M+DHwgybkMfRBijdKHUbYfNLOpqu5m6Ha6E7qFxP/U0+e9\nwIwkF3bjmYMkSZIkSdIj5EynAaiqMZ/3qpoHzOu2fwk8s2f3f3TtRwNH9xyzXs/2/fuqav+e9pOA\nDUc530LgjQ/zUiRJkiRJmvysfgyEM50kSZIkSZLUnEUnSZIkSZIkNWfRSZIkSZIkSc1ZdJIkSZIk\nSVJzLqUlSZIkSZKmtqUHPYDpyZlOkiRJkiRJas6ikyRJkiRJkpqz6CRJkiRJkqTmUlWDHoMmuSS1\n2l+ubZa3zlK/a5Z1wa7bNcsC4MCGWe9vmAXwhXZR+Uu77/vP7/i3zbIAvnbf65pl7bLMS5plAbx3\n/b80y9rxip83y1qa+5plAZy24IXNslbfst33O8Da9YdmWRcc3PDnx4J2UQBs1jCr7bdo059tuxx/\nQrOsU894abMsAM5vF7X9m05pFwZcXTObZf3xhPWaZQHw/YZZGzXM+kHDLIBbGmb9S8MsgB3vbZf1\nxxntspZrFwU0/R7ljoZZAB9umNXye6r1ar6rNnyvXdPwvQZwcruov5/zmWZZX/7Um5plAfDKhq/B\nzMdRVWkXOLkkqXrDoEfx6MkXmbSvnzOdJEmSJEmS1JxFJ0mSJEmSJDVn0UmSJEmSJEnNtb6zV5Ik\nSZIkaXKx+jEQznSSJEmSJElScxadJEmSJEmS1JxFJ0mSJEmSJDU3rYtOSRYlOa/n69CuPUkOT3JF\nkkuTvGWcjP2S3NAdf3GS7yZZfjHHsV+STz3S6xkld8skn2idK0mSJEmSNJHpvpTWXVW1+Sjt+wFP\nrqpnAiRZY5yMAr5VVQd3fb8B7A0c1c8AkizTZTRXVQuABY9GtiRJkiRJS4zpXv0YkGk902kcbwTe\nM/ygqm6YoH/g/gLSisBN3eM1knwvydnd1w5d++wkX0tyOvDVLmOdJKcm+Z8k774/ODkuyfxuFtUB\nPe13JPlw1/6zJNslmZfk10le3vWZleSHPef8cneOX/fO3kryN0l+1c3W+q8kvi8kSZIkSdIjMt2L\nC8uPuL3u1V3704F9kpyT5MdJnjFORoC9k5wHXAs8AfhRt+8TwBFVtQ2wF/DFnuOeBexaVa/tMrYB\n9gA2AV6dZMuu399X1VbA1sDBSZ7Qta8AnFxVGwO3M1Qkez6wOz0FsxE2AF7YneuwJEsn2RD4a2CH\nbtbXfcC+41yvJEmSJEnShKb7BLM/j3F73bLdvq2T7A58GXjeGBkFHNNze91ngHcAHwJ2AzZMMtz3\n8UlW7I45vqru7sn5aVXd3GV8H9iJoVvj3prkVV2fdYD1gbOBe6rqpK79ImBhVS1KcjEwc4xxnlBV\n9wI3JrkeWAvYFdgSmN+Nc3ngjyMPvmvOx+7fnrHz9syYtcMYT4ckSZIkaVI7ax78ct6gR6FpYLoX\nncZyLfD9bvu/ga9M0D892z8C3sxQ0SnAtlV1z4M6DxV37uppGrmmU4BKMouhotB2VbUwyanAcl2f\ne3v63wfcA1BV93W3+Y2mdxyLeOD1P7qq/m3MqwNWOOyQ8XZLkiRJkpYU2+889DXsE+8b3FgeK0sP\negDT03S/vW4s/83QrWoAOwNXjNM3Ix7vBFzVbf8UOPj+jsmm42S8IMkTuk++eyVwOrAycHNXcHoW\nsN1iXcX444ShYtfJwF7Di6UnWS3JUx/BeSRJkiRJkqb9TKflu7WYhv2km/HzQeAbSd7G0HpJbxgn\noxha02knhop4v2Po0+9gqOD0mSQXMPRczwP+see43oyzgWOBpwBfq6pzu1vl3pjkUoYKX2eNOGbk\nOMbbrlGOoaouS/LvwE+7BcTv7cb4v+NcsyRJkiRJ0rimddGpqka9/qq6FXhZnxlHA0ePse9GYJ9R\n2uf0k9Hdlvf/xsheeZy8lbt/5wJzx+jznJ7t7wDfGe08kiRJkiRJD8e0LjpJkiRJkqRpwOrHQPi0\n9ynJfsBbRzSfXlVvGcBwJEmSJEmSJjWLTn2qqqOAowY8DEmSJEmSpCWCn14nSZIkSZKk5iw6SZIk\nSZIkqTlvr5Mk6f+zd+dhdlVl2sbvxwpCmGQUHEDQRkRBxDAJKkFUxKFBRAHRJnxOrYK2ra027ZDY\nONvYigMtjUwiCgICDgwikRkkQBgcWlFERBAhDGFOeL8/zi44FFWZWKEqlft3XefK3muv/ey1T1Wq\nwsva60iSJGl8s/oxKpzpJEmSJEmSpOas9WmB3PpfT2uWNevpT22W9cYzj2yWBfCDK97aLOuAmR9s\nlgXwH188sFnWe/7tS82y3rVr26/BHscf1izrP5/1QLMsgI//rt2PzJ9zarOsGXe9sFkWQK19X7Os\nm2eu2ywLYJ1Nr2uW9d2v7tIsa4/fntwsC+CXG27cLOuUvLZZFsArTjqjWdZ2X/hlsyye1y4KYK/3\n/G+zrKNf/fZmWQDs0S5qvX/6dbsw4OTX/GOzrJfzs2ZZN75l/WZZAHVrmmUNPHFOsyyAZzzjmmZZ\nN6zY7t9sm69+SbMsgP/atN2/s35Y7X4fAHz2qZ9qlvVvk9plHfinDzfLAuC0ZZpFPfOdVzfLAljj\nJX9vlnXYhHbzNK6Ys0GzLIDn//x3TfOkxcGZTpIkSZIkSWrOmU6SJEmSJGl8GxjtASydnOkkSZIk\nSZKk5iw6SZIkSZIkqTmLTpIkSZIkSWrOopMkSZIkSZKacyFxSZIkSZI0vln9GBXOdBojksxNclnf\n68Nd+78kmdjXb/bjMJbFfg1JkiRJkjS+WesbO+6uqs2GaX8/cBRwT7dfj8NYHo9rSJIkSZKkccyZ\nTmNYkvcBTwXOSnJmX/sBSS5PckGSJ3dtr0tyYZJLk5zR1z41ybeTnJXkmiT79eX8a5Iru9f7H+/7\nkyRJkiRJ45dFp7Fj4pDH695YVV8FbgAmV9UOXb8VgAuq6gXA2cA7uvZzqmrrqnoh8H3gw33ZzwZe\nCWwJfDLJQJJJwJSubWvgHUk2Xdw3KUmSJEnS427COH6NYWN8eEuVe0Z4vG6o+6vqx932DOAV3fY6\nSY4F1gaeCPyhay/gx1X1AHBLkr91fV4MnFBV9wAkOQF4KTBz2KueMfXh7WdOhmdNXrC7kiRJkiSN\nLZdPh5nTR3sUWgpYdFryPNC3/SAPfw0PAr5UVT9Ksh0wta/f/X3bc7tzCkhfe5jXWk6vmDriIUmS\nJEnSEuQFk3uvQUdNG62RaJzz8bqx705g5QXotzK9R/Gg99jcoDy6KwWcA+ySZGKSFYBdujZJkiRJ\nkqTHzJlOY8fEJJf17f+0qvYHvgWcmuQv3bpO/bORqm9/KnBcklnAz4FnDNPn4ROrLktyOHBx13RI\nVc3sO0eSJEmSpPHB6seo8G0fI6pq2K9FVX0N+Frf/sp928cDx3fbJwMnD3P+tCH7m/Rtfxn48jDn\nLMjMKkmSJEmSpBH5eJ0kSZIkSZKas+gkSZIkSZKk5iw6SZIkSZIkqTnXdJIkSZIkSePbwGgPYOnk\nTCdJkiRJkiQ1Z9FJkiRJkiRJzVl0kiRJkiRJUnOpqtEeg8a4JMXl7b5PlnvGrc2y7v3Cas2yANih\nXVRuaft3a+Wdb2qWNTBhbrOsW69/crMsgH2f8eVmWZfXZs2yANLw5+X2y+zULOs7c69olgUwmenN\nsi5my2ZZAM/hN82yjv3N3s2yBm5q93cKgOvbRdXT22UB5L52WWu94o/Nsm48/5nNsgByV7u/7we+\n4t3NsgAOyn5N81r643nPbZZVq6RZ1sAas5tlATz49xWaZdXx7e4T4E2fOKJZ1t0s3yzrSjZplgWw\nLPc2yyrafg1W5o5mWatXu383n/6GnZtlAbB5u6hn7n91uzDgxZzTLOvII97VLGu3vb/TLAvg+bT7\nN+An8iWqqu1fhjEkSdV/jfYoFp98kDH79XOmkyRJkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpuQmj\nPQBJkiRJkqTFyurHqHCmkyRJkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpOYtOj0GSuUku63utO9pj\n6pdkUpKvjHDs2iSrPd5jkiRJkiRJSweX0nps7q6qzRb2pCQDVTV3cQyoX1XNAGaMdHhxX1+SJEmS\npDFhYLQHsHRyplNjSV6Q5MIkM5OckGSVrn16ki8n+SXw/m7/wCS/TPLrJFskOTHJ/yX5z768E5Nc\nkuSqJO/oa5+d5Atd+xlJtk7yiyTXJHld12dyklO67dWTnN71PwRIX9ZbklzUzdY6OInfF5IkSZIk\n6TGxuPDYTOx7tO74ru1I4N+qalPgSuCTXXsBy1TVFlV1YLd/X1VtAXwTOAn4Z2BjYEqSVbvz/l9V\nbQ5sAbyvr3154Myq2hi4E/gU8DLg9d32UJ8Ezu76nwisC5BkI+BNwDbdrK0Hgb0e8zsjSZIkSZKW\naj5e99jc0/94XZInAU+qqnO6piOA4/r6f3/I+Sd3f14FXFVVN3U5fwDWAWbRmxW1S9dvHWAD4GLg\n/qo6rWu/Eri3quYmuQpYb5ixvoReQYqq+kmSWfRmO+0ATAIuSQIwEbhxgd8BSZIkSZKkYVh0Wrwy\nZP+uIfv3dX8+2Lc9uD8hyWR6RaGtq+reJGcBy3V9HhjS/36AqnowyUhf16HjGXREVe0/4l0AfHPq\nw9ubT4YtJs+zuyRJkiRpbPrj9Ov44/Q/j/YwHl9WP0aFb3tDVXV7kllJXlxV5wJvBab3dRmp6DOc\nACsDs7qC03OArR/D8M4G3gx8OslOwKr0HvE7EzgpyZer6ubuE+1WrKrrHnH2u6c+hktLkiRJksaK\n9Sevy/qTH/7w9enTLhjF0Wg8s+j02Az3CXB7AwcnWR64BthnPv0H24ceK+BU4J+T/Ar4LXDBkOMj\njWW47WnAMUn2BM4H/gRQVb9O8jHg9G4B8QeA9wCPLDpJkiRJkiQtBItOj0FVrTxM20zgRcO0bz/S\nflX9AvjFCH1fPb9rV9W04Y5V1XS6mVZVdSuw4whZxwLHDndMkiRJkiRpUVh0kiRJkiRJ45vVj1Hx\nhBzYTHAAACAASURBVNEegCRJkiRJksYfi06SJEmSJElqzqKTJEmSJEmSmrPoJEmSJEmSpOZcSkuS\nJEmSJI1vA6M9gKWTM50kSZIkSZLUnEUnSZIkSZIkNZeqGu0xaIxLUvym3ffJThue0CzrRXVBsyyA\nO7NSs6wvXvPxZlkA/D3Nop6+1e+aZd10y1rNsgDm3Njua/DS553WLAtgxl2TmmWtNfHGZll7TXhB\nsyyAT02Z2y7sOe2iAJ6w913twj49sVnUgV99d7MsgI25qlnW2zi0WRbAxtVubD8+cbdmWc/a9epm\nWQBH8ZZmWd/gPc2yAP6PZzfLmsUqzbIAns+VzbJexpnNslbhtmZZALNp97vqn888olkWwBd32LdZ\n1oe+8PVmWWt++LpmWQCrMKtZ1s33PblZFsD3l929WdYP2aVZ1q/quc2yAC66Y8umeS1t/qQZzbJW\n5+/NstbglmZZAD/j5c2y/pSNqKp2/8ExxiSpOmy0R7H4ZB/G7NfPNZ0kSZIkSdL4ZvVjVPh4nSRJ\nkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpOZ9qlCRJkiRJ45vVj1HhTCdJkiRJkiQ1Z9FpMUmyVpLv\nJrkmySVJzk8y4meeJlkvySJ9znCSvZM8ZdFHK0mSJEmS1JZFp8UgSYAfAtOr6llVtTmwB/D0xXTJ\nKcBTF1O2JEmSJEnSQrPotHi8DLivqr412FBV11XV17oZTWcnmdG9XjT05Hn1SfKRJFckuTzJZ5O8\nAdgcODrJpUmWS7JDt31FkkOTPLE793NJrk4yM8kXu7Y1k/wgycXda5vF/u5IkiRJkqRxz6W0Fo/n\nAZeOcOwm4BVVdV+SDYDvAlssSJ8kOwH/CGxZVfcmWaWqbkuyL/DBqro0yXLAYcDLqur3SY4A3p3k\nKGCXqnoOQJKVu2t9BfhyVZ2XZF3gVOC5rd4ISZIkSZJGndWPUeHbvnhU/06SrwEvBu4HXg58Pcmm\nwFzg2cOc/0Tga319NujaXw58u6ruBaiq2/ov0/25IfDHqvp9t38E8F7ga8C9SQ4FftS9BjM36j0R\nCMBKSZavqrsX+q4lSZIkSZI6Fp0Wj6uBNwzuVNW+SVYHLgE+APy1qt6aZAC4d5jzR+pTPFxcGqpG\naE83hrlJtgR2AHYD9u22A2xVVffP844Omvrw9paTYavJ8+wuSZIkSRqb7p1+MfdOv3i0h6GlgEWn\nxaCqfp7kM0n+uaoO7ppX6P5cGfhLt/1PwMAwESsD1w/T5wzgE0mOrqp7kqxaVbOAO7tzAH4LrJfk\nWVV1DfBWYHqSFYAVquqnSc4Hrun6nw68D/gSQJIXVNXljxrRflMX7k2QJEmSJI1Jy03ekuUmb/nQ\n/u3Tvj6Ko9F45kLii88uwHZJ/pDkIuBw4MPAN4G9k1xO71G42X3nDM5W+sZwfarqNOBk4JIklwEf\n7PofDhycZHAdqX2A45JcAcwBDqZXlDolyUzgHHqzqaBXcNq8W1z8auCd7d4CSZIkSZLGgIFx/BpG\nklcl+U2S3yX5yAh9Jie5LMlVSaYvzLkLyplOi0lV3QjsOcLhTfu2P9r1vxZ4frf9++H6dMc+D3x+\nyLVOAE7oa/o58MIh1/wrsNUw47wF2GPkO5EkSZIkSUuKbpmer9Fbw/kvwC+TnFxVv+7rswrwdWDH\nqro+yRoLeu7CcKaTJEmSJEnS+LEl8PuquraqHgC+B+w8pM+bgeOr6nqAqvr7Qpy7wCw6SZIkSZIk\njR9PA/7ct39919ZvA2C1JGcluSTJWxfi3AXm43WSJEmSJEnjx0ifbt9vGXrL8uwALA9ckOTCBTx3\ngVl0kiRJkiRJ49s4qn5Mv7L3moe/AOv07a9Db8ZSvz8Df6+qe4B7kpxNb23p6xfg3AU2jt52SZIk\nSZKk8W3yJr3XoGnfe1SXS4ANkqwH3ADszqM/6Owk4GvdwuHL0vvgsQOB/1uAcxeYRSdJkiRJkqRx\noqrmJNkXOA0YAA6tql8neVd3/H+q6jdJTgWuAB4EDqmqXwEMd+6ijsWikyRJkiRJ0jhSVT8Ffjqk\n7X+G7H8J+NKCnLuoLDpJkiRJkqTxzerHqEhV04XJNQ4lKf54f7vA/ZdpFpW2C+tT96Rd2MntogDe\nO+eLzbK+/sF/a5aVf2j7Nfjse97fLOujM77aLAuAte9tFvX/nnpos6zD9nl3syyAjx/Z7u/odx68\nollWa2s8+PdmWb9c7qXNsgCYs8hrNT5KsXqzLABetHyzqCedeWOzrNt/vHazLICc1u5nW72t4e8W\ngHc2zDr5gYZhwDsa/o7fueHvl6+0iwJguXZRdVzb74/c1u59e/2W322WdcL792qWBcAKDbMuapgF\nZPOGPz92bfj98bJ2UQC8vWHW7g2zAJ7e8Gfbee1+rj35zX9qlgUwhcObZX0hU6mqxr+wxo4kVT8Z\n7VEsPnk1Y/br94TRHoAkSZIkSZLGH4tOkiRJkiRJas6nGiVJkiRJ0vg2MNoDWDo500mSJEmSJEnN\nWXSSJEmSJElScxadJEmSJEmS1JxFJ0mSJEmSJDU3potOSWYP2Z+S5KCFOH+9JFe2H9miSzI1yfVJ\nLktyZZLXLcZrTUrylRGOXZtktcV1bUmSJEmSxowJ4/g1ho3x4VHz2V8kSSZU1ZwWWYuggAOr6sAk\nzwHOAdZckBOTPKGqHlzgC1XNAGbMYxySJEmSJEmLxZie6TSMACRZMckfkkzo9lfu9ge62T0zk1wO\nvOehE3uzpE5OciZwRpJVk/yw63tBkk26fmsmOSPJVUkO6Z8RlOQtSS7qZikdnOQJXfvsJAckubzL\nevKC3EdV/QaYk2SNJK9Mcn6SGUmOTbJCl31tks8lmQG8McmeSa7oZkl9ruvz+iQ/67afkuS3SZ6c\nZHKSU7r21ZOcPnhfg2OY131JkiRJkiQtqrFeXJjYFUIuS3IZMA2oqpoNTAde0/XbAzi+quYChwHv\nraoXDJO3GfCGqtoe+BQwo6o2BfYHjuz6fBL4WVVtDPwAWBcgyUbAm4Btqmoz4EFgr+6c5YELumue\nDbxjQW4uyVbA3G73P4AdqmoSvdlJ/9q1F/D3rv0c4HPA9sALgC2S7FxVJwJ/TbIv8C3gE1X1tyGX\n+yRwdndfJy7gfUmSJEmSJC2Ssf543T1dIQSAJHsDm3e7/wt8GDgJmAK8PckqwJOq6tyuz1HATn15\nZ1TVbd32tsCuAFV1VjcTaKWufZeu/bQks7r+OwCTgEuSAEwEbuyO3V9VP+62ZwCvmMc9BfhAkrcA\ndwC7A1sDzwXO77KfCJzfd873uz+3AM6qqlu69+No4KXde7AfcDVwflV9n0d7CfD67r5+0t1X5nNf\nD/vvTz28vfV2vZckSZIkaYlz3fQ/ct30a0d7GI+vsV79GKeWtLf9oUfCqur8bqHwycBAVf2qKzoN\n279z13yOj9Q+uH9EVe0/TP8H+rYfZN7v60NrOj0UnryWXkHszSOcMzjuGjK2/u116M2aWitJqmq4\nNZtGut+R7uth//KJeR6WJEmSJC0Z1p28PutOXv+h/fOn/WIUR6PxbKw/Xjc/RwJHA98G6GYx3ZZk\n2+74vB4TO2fweFe4urmq7gTOo/e4GUleCaxKr9hzJrBbkjW7Y6slWXcRxz20+HMhsG2SZ3XZKyTZ\nYJjzfgls183KGqD3WOH0bm2rQ7v93/Dwo3n9zgbe3OXvtJjuS5IkSZIkCRj7RafhPr2uv+279Ion\nx/S17QN8vVsDqj9j6LlTgUlJZgKfAfbu2qcBr0xyJbAbvUfN7qyqXwMfA07vzjkdWHuYcQ69znzv\nq6r+Tu8RwWO67POBDR91UtVfgY8CZwGXA5dU1SnAv9Nbr+l8egWntyfZcMhYpgEvTXIVvcfs/tRl\nzuu+JEmSJEmSFsmYfryuqlYesn8EcERf04uB46rqjr4+l9JbZHvQR4Y7t6pm0a1xNMTtwI5VNTfJ\ni4DNq+qB7pxjgWPnNc6qOh44fh73NG2E9rOALYdpX3/I/veA7w1p+8++7dnARt3ub4FfdO23AjuO\ncO1h70uSJEmSpHFhYLQHsHQa00WneUlyEL0iyqsbR68LHJvkCcD9LOAn0UmSJEmSJOlhS2zRqar2\nW0y5vwde+FhzkuwPvHFI87FV9dnHmi1JkiRJkjTWLbFFp7Guqj5Db60oSZIkSZKkpc5YX0hckiRJ\nkiRJSyBnOkmSJEmSpPHN6seocKaTJEmSJEmSmrPoJEmSJEmSpOacYKYF89tlmkVtcPTMZlm/e9mm\nzbIA2KNh1gcbZgGH3vG2dmEN37ZaI+3CgGN5U7OsNSdd1ywL4OaZ6zbLuvhpWzbLqo3a/v+Do+dc\n3izrLQPPb5YFcO7ck5pl/fynr22WxfPaRQFw2zrtsvZqFwXApe2ibv/LWu3CTm0XBVC3t/vZtt1W\npzXLAvjF53dsF/addr/fgbH7++Vp7aIA+HvDrIsaZgGv/6fvNsu6mHa/q9i2XRTQ9r9itmqYBdRp\nDb93b2oXxfsbZgGs0jCr3a/3nr3b/Wxbe88/NMu68Z+e2SwL4KsHtfxA96kNs6SHWXSSJEmSJEnj\nm9WPUeHjdZIkSZIkSWrOopMkSZIkSZKas+gkSZIkSZKk5nyqUZIkSZIkjW9WP0aFM50kSZIkSZLU\nnEUnSZIkSZIkNWfRCUgye8j+lCQHLcT56yW5sv3IHnWdaUl2WNzXkSRJkiRJeqx8qrGn5rO/SJJM\nqKo5LbIAquqTrbIkSZIkSZIWJ2c6DS8ASVZM8ockE7r9lbv9gSSTksxMcjnwnodO7M2SOjnJmcAZ\nSVZN8sOu7wVJNun6rZ7k9CRXJTkkybVJVhs6ayrJh5J8sts+PMkbuu1rk0xNMiPJFUk27NpXG+F6\nU5N8O8lZSa5Jsl/fNd6S5KIklyU5OInfF5IkSZKk8WNgHL/GMIsLPRO7gstlSS4DpgFVVbOB6cBr\nun57AMdX1VzgMOC9VfWCYfI2A95QVdsDnwJmVNWmwP7AkV2fTwJnV9XGwInAuiOMrXh45tXQ7Zur\nahLwTeBDXfu0Ea4H8GzglcCWwCe74tlGwJuAbapqM+BBYK95vFeSJEmSJEnz5eN1Pfd0BRcAkuwN\nbN7t/i/wYeAkYArw9iSrAE+qqnO7PkcBO/XlnVFVt3Xb2wK7AlTVWd0Mp5WAlwCv79p/kmTWPMaX\nEdpP6P68dPAa87heAT+uqgeAW5L8DVgb2AGYBFySBGAicOOjrnTU1Ie3nz8ZNp08j+FKkiRJksaq\nueecy4Pnnjfaw9BSwKLT8B4q8lTV+d0jb5OBgar6VVd0GrZ/5675HJ9X+xweOQNtIiOvMXVf9+dc\nHvm1HOl69/dt959zRFXtP8I5PW+dOs/DkiRJkqQlw8BLXszAS1780P7cz39hFEej8czH6xbMkcDR\nwLcBullMtyXZtjs+r8fRzhk83hWubq6qO4GzgTd37TsBq3b9bwKe3K3NtCzw2oUc60jXG64QVcCZ\nwG5J1uzOWS3JSI/6SZIkSZK05Jkwjl9jmEWnnuE+va6/7bv0ikLH9LXtA3y9WwOqP2PouVOBSUlm\nAp8B9u7apwEvTXIVvcfsrgPoHn/7FHAxcDrwqwUc/+A1R7re0HHRXe/XwMeA07tzTqf32J0kSZIk\nSdIiG+M1scdHVa08ZP8I4Ii+phcDx1XVHX19LgX6FxH/yHDnVtUsurWbhlzjVmDHwf0kf+w7dhBw\n0DDn7NO3vX7f9gzgZfO53rQh+5v0bR8LHDv0HEmSJEmSpEVl0Wk+khxErzj06sV8qZHWbZIkSZIk\nSVriWHSaj6ra73G6zjMfj+tIkiRJkrTUsfoxKlzTSZIkSZIkSc1ZdJIkSZIkSVJzFp0kSZIkSZLU\nnEUnSZIkSZIkNedSWpIkSZIkaXwbGO0BLJ1SVaM9Bo1xSYoLG36f3NYuivUaZgEbPHtms6zfvWHT\nZlkA7N4wa7l2UU/f+XftwoCnckOzrPtYtlkWwABzmmWtz7XNsk686fXNsgDWW+vadlkP/qFZFsCL\nl9mlWdYxcy9tlvW76zdslgXAnIb/KpqxTLssYLWd/9Is69ZrntYsi9+0iwJghYZZP2+YBfC8hllr\nN8yCpr9fnrFtuy/q3bV8syyAVZjVLGtu2v4/4JvvWrNZ1qdW+ESzrGPYs1kWwDr8uVnWnMb/JXpL\ntfsanHvyy5tlsUa7KICNt/lls6yrPrhFsyyA1/zXD5pl/Xjn3ZplbXrShc2yAGaevHW7sF1CVaVd\n4NiSpKrtP0vHlDyTMfv18/E6SZIkSZIkNWfRSZIkSZIkSc25ppMkSZIkSRrfrH6MCmc6SZIkSZIk\nqTmLTpIkSZIkSWrOopMkSZIkSZKas+gkSZIkSZKk5lxKaz6SzK6qFfv2pwCTqmq/BTx/PeCUqtpk\nsQxQkiRJkiTNm9WPUeFMp/mr+ewvkiSP67d8koHH83qSJEmSJGnpZtFp4QUgyYpJ/jBYPEqycrc/\nkGRSkplJLgfe89CJyZQkJyc5EzgjyapJftj1vSDJJkkmJLk4yXbdOZ9NckCS7ZOc2Jf1iiQndNuz\n+9p3S3JYt314koOTXAh8Psmzkvw0ySVJzk6yYdfvdUkuTHJpkjOSPHmxv4uSJEmSJGlcc4LZ/E1M\nclnf/mrASVU1O8l04DXAScAewPFVNbcr+rynqs5N8oUheZsBm1TVbUkOAmZU1S5JtgeOrKrNukf4\nfpDkfcCOwJbAXOAbSVavqluAfYBDu8z+2VdDZ2I9FXhRVVVX7HpXVf0+yVbAN4AdgHOqamuAJG8H\nPgx8aNHeLkmSJEmSJItOC+KeqtpscCfJ3sDm3e7/0ivQnARMAd6eZBXgSVV1btfnKGCnvrwzquq2\nbntbYFeAqjoryepJVqyqXyX5DnAKsHVVzemufRTw1iSHA1sDb5nP2As4ris4rQi8CDguyeDxJ3Z/\nrpPkWGDtru2PC/C+SJIkSZK0ZLD6MSp82xfeQxWbqjo/yXpJJgMDXbFolZH6d+6az/FBmwCzgLX6\n2g6jV4i6Fzi2qh4cHEpfn4lDcu7u/nwCcFt/Aa3PQcCXqupH3WN9Ux/V45C+phdOhkmTRxi2JEmS\nJGlMu3I6XDV9tEehpYBFp8fuSOBo4FMA3WNztyXZtqrOA/aax7nndMcP6ApXN3eP7e0KrAJsB/wo\nyZZVdXtV/TXJDcDH6D0WN+imJM8B/g94PXD70AtV1R1J/phkt6r6QXrTnTapqiuAlYEbuq5Thh3p\nO6YuyHshSZIkSRrrNpncew36/rTRGonGORcSn7/hPr2uv+2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ao3bjkiRJkiRpXHCm09h1\nTlVtDZDk7cCHgQ8BHwdmVdXzu2OrVNUJSfatqs26tknAFGBLeoXFi5L8AngW8Jeqek3Xb+UkywAH\nAa+rqluS7A58Gnjb43ivkiRJkiRpnLHoNHatk+RYYG3gicAfuvYdgN0HO1XVbcOc+2LghKq6ByDJ\nCcBLgFOBLyX5HPCjqjo3ycbA84CfJQEYAG54VOJRUx/efv5k2HTyY7k3SZIkSdJouX063DF9tEeh\npYBFp7HrIOBLVfWjJNsBU/uOZT7n1pA+AaqqfpdkM+A1wAFJzgROBK6uqm3mmfjWqfM8LEmSJEla\nQjxpcu816C/TRmskj5s5ruk0KlzTaexamYdnHE3paz8DeO/gTpJVus0HkgwWEc8BdkkyMckKwC7A\nOUmeAtxbVUcDXwI2A34LrJlk8FG+ZZI8dzHdkyRJkiRJWkpYdBoblk/y577XB+jNbDouySXAzTz8\nCXcHAKt2C4FfDkzu2r8FXJHkqKq6DDgcuBi4EDikqmYCm9Bb3+ky4BPAAVX1ALAb8Pku7zLgRYv/\nliVJkiRJ0njm43VjQFWNNM/v5GH63sUjZz4Ntn8U+Gjf/peBLw/pczpw+jDnzgS2W6hBS5IkSZIk\nzYNFJ0mSJEmSNK7NtfwxKny8TpIkSZIkSc1ZdJIkSZIkSVJzFp0kSZIkSZLUnEUnSZIkSZIkNedK\nWpIkSZIkaVyby0gfGq/FyZlOkiRJkiRJas6ZTlow67WL2n3Dw5tlbXPT+c2yAB68foVmWeu88M/N\nsgCydrusHerMZlmffe+nmmUBsGO7qJrSLgvglLy2WVY9Pc2y3sahzbJ6VmsXtVe7KABmLNMsarld\n72mW9c7rv9osC2C1uqVZ1sSBac2yAF4+5+R2Ycu2izrrM69pFwasv/+1zbKWu+LWZlkAKz1pdrOs\nmw9ap1kWwKf2+3izrG9t/v5mWctfcnezLIAVaPc1ePCwdv/2ALh5q4Z5p7WL+vi+B7QLA742sF+z\nrP/OvzTLAtjjoMOa5rVyG6s0zbv2lvWbZT1z06ubZQGcdNGe7cL+o13U5+9/WrswgIMaZm3RMEvq\n40wnSZIkSZIkNedMJ0mSJEmSNK65ptPocKaTJEmSJEmSmrPoJEmSJEmSpOYsOkmSJEmSJKk513SS\nJEmSJEnjmms6jQ5nOkmSJEmSJKk5i06SJEmSJElqblwVnZLMHqbtXUne2m1PSfKUvmPXJlltmHNe\nl+Qji3ms/5TkyiRXJLk0yQe79ulJJg3Tf3KSUxbyGpOSfGWEY8PeuyRJkiRJUgvjbU2nelRD1f/0\n7e4NXAn8ta9/hjnnFGChCjwLI8lOwPuBV1TVjUmeCLy1b0wtrjGhqmYAM0bo0uQ6kiRJkiRJwxlv\nRadHSTIVuBO4FtgcODrJ3cA2XZf9krwOWAZ4Y1X9NskUYFJV7ZfkcOCUqjq+y5tdVSsmmQxMA2YB\nmwDHAVcD+wHLAbtU1R9GGNa/Ax+sqhsBqup+4NC+429M8g1gFeBtVXXukHtaDfg2sD5wN/DOqrqy\nu9dnde3XJfkf4ENV9bokqwPHAE8FLqCv2JbkLd24nwhcBLynqh6c33srSZIkSdKSYI4LiY+KcfV4\n3QgKqK5odAnw5qp6YVXd2x2/uaomAd8EPjTC+SPtPx94F7ARvZlKz6qqLYH/pVfEGcnzGHkGEsBA\nVW0F/AvwyWGOTwNmVNWmwP7AkX3HngPsUFVv5pGzuD4JnF1VGwMnAusCJNkIeBOwTVVtBjwI7DWP\nsUmSJEmSJM3XuJ/pNIyhj9Od0P15KbDrQmb9sqpuAkjye+C0rv0qYPtFHuEjx7TeMMe3pRtrVZ2V\nZPUkK9EriJ1cVfcNc85LgNd35/wkySx678UOwCTgkiQAE4EbH3X2QVMf3t5yMmw1eWHvSZIkSZI0\nFsyY3ntJi9nSWHQaOnNpsEAzl+Hfjzl0M8KSPIHeI2hDz4XeDKH7+rbn9d5eTe9Rv7NGOD6/McEw\na1F17p7HdUc654iq2n8e58F+U+d5WJIkSZK0hJg0ufcadMi00RqJxrml4fE6eLjYciew8kKeey29\nmUAA/0hv7afH6rPAF5OsBZDkiUnethDnn0P3CFy3ttTNVXUnIxeVAM4G3tydsxOwKr0C3JnAbknW\n7I6tlmTdhbsdSZIkSZLGrrlMGLevsWxsj27hLZ/kz337B3Z/Ds5uOhw4eMhC4vT1qWG2DwFOSnI5\ncCowe8g5w6l5HKOqftoVnH6W3jNtxSMXEh+aNXR7KvDtJDOBu+h9Kt9w1+3fnwYck2RP4HzgT91Y\nfp3kY8Dp3UyuB4D3ANeNNH5JkiRJkqT5GVdFp6qa53L0VXUCD6+XBL1PeRs8NgN4Wbd9BHBEt/03\n4EV953y0a58OTO87f/u+7V8Av5jPWA6nVwQb2t6f83fgmUOvV1Wz6NZnGnLutCH7D42jqm4Fdhxh\nLMcCx85rvJIkSZIkSQtjaXm8TpIkSZIkSY+jcTXTaaxJsj/wxiHNx1bVZ0djPJIkSZIkSY8Xi06L\nUVV9BvjMaI9DkiRJkqSl2VzmuRqPFhMfr5MkSZIkSVJzFp0kSZIkSZLUnEUnSZIkSZIkNeeaTpIk\nSZIkaVxzTafRYdFJC+bwdlHfv3pKs6wvnrxvsyyAjy33n03zWqoJaZb1w+zcLIu92kUBsEK7qHyi\nXRbAK08+vVnWAfe2+4yBTbiqWRbAn7Z+TruwGe2iAFab+pdmWXvx3WZZXzy78Tdbw9/OL3/gpHZh\nwIsH2v38+Pnmc5tlLffeW5tlAbyszmyWdehq/69ZFsC9n1utWVa2q2ZZAB/OF5pl/fmSdZpl/ddf\nP9YsC6DuaJd1wAqNP3Pm9w2zGv6O32Gg3d8pgLdxaLOsAdr9LAL43u/3aZb1qg1ObJb1Z9r9nQJY\naZU7m2XdNneVZlkAr9iy3e++M17U7t/ge37qmmZZAAfv/4GmedLi4ON1kiRJkiRJas6ikyRJkiRJ\nkprz8TpJkiRJkjSuzXFNp1HhTCdJkiRJkiQ1Z9FJkiRJkiRJzVl0kiRJkiRJUnMWnSRJkiRJktTc\nqBadkjyY5Ki+/QlJbk5yyiiP66lJjlvM19gyydlJfpPk0iSHJJmYZGqSD45wzuxFuM55I7QfnuQN\nC5snSZIkSdKSZi4Txu1rLBvt0d0FPC/JclV1L/AK4HqgRnNQVXUD8MbFlZ9kLeBYYPequqhrewOw\nEvO+9wV+X5JMqKo5VbXtPLJG9X2WJEmSJEnj11h4vO4nwGu67T2BY4DAQ7OBzu9mAp2X5Nld+/JJ\njk1ydZITklyY5IXdsdlJDkhyeZILkjy5a1/z/7N352F2VXW6x7+vYYgEIq2iOCAICCIyY5glKKIt\noiKK2GgL7RWuE14vKn3bRoN2g4JDI2jbtoqoqGADCiIgg4mMogRCQKCRQWVqEAWDEkgqv/vH2QWH\nojIAKzlF8f08z3lq77XXfs/a59QQfqy9TpL/SnJJ99i2a98xyWXdY2aSSUnWSjK7O75PkqOGB5vk\nx0le3vdchye5MslZSbZOMiPJ9Ul2W8Q1vw/45nDBCaCqTqyqO7rdlyT5WZfzgZEnp+eIJLOTXJFk\nz659apLzkvwIuHJ4jH3nHN3NrDoLeFbf67xFkulJfpXkjCSrL+mbJ0mSJEmSNJqxUHQ6HtgryYrA\nRsAv+o5dDexQVZsDnwAO7drfC9xVVRsCBwNb9J2zEnBRVW0K/Bx4d9d+JPCFqpoCvBn4Wtd+IPDe\nqtoM2B6Yu5jx9s8OWgk4p6peCswBPgm8Ati9216YDYFLF3IswIuBXYApwCeSTBjR503AJsDGwM7A\nEX2Fos2AA6rqxSPGuzuwHrAB8PfAtkAlWR44CtijqrYEjgH+dRFjlyRJkiRJWqxB315HVc1Osha9\nWU6njTi8KvCtJOvSK54Mj3c74N+6869KckXfOQ9U1XDOpfRu2YNecWaDJMP9VkkyCbgA+EKS44CT\nquqWvj6L80BVndltzwbmVtVQkiuBtRZz7sKepIAfV9U84K4kdwDPBm7t67M98N2qKuCOJDOAlwF/\nBi6pqt+OkvvyvnNuS3Ju174+vSLY2d11TxjxXD3nTXto+wVTYc2pi7k8SZIkSdKY9Mfp8Kfpgx7F\nMjXEyLkcWhYGXnTqnAJ8FtgRWK2v/VP0ZhLt3hWmftZ3bGFFm3l92wt46BoDbFVVD4zo/5kkP6Z3\ni98FSV4N3N93fD4PnxE2cRHP9QBAVS1IsqjX9ip6s7NOWcjx/jEO8cj3qXjk9Q/PaPrLQjJHO+fB\n8VTVtgs51rPDtEUeliRJkiQ9QTx9au8x7IZDBjUSjXNj4fY6gG8A06rqqhHtk3lo1s0+fe0XAMPr\nGL2E3m15i/NT4IDhnSSbdl/Xqaqrqupw4Jf0Zv70uwnYtFsTaQ16t7w9XkcD70zyYFaS3YfXn1oC\n5wFvTfKUJKvRm8V0CQsvKkHvVsPhc54D7NS1XwuslmTrbhzLd6+pJEmSJEnSYzboolMBVNUtVXV0\nX9vwrJ3DgcOSzKR329dw+5fpFUquojcb6irgnv7MUbIOALZMMqs7b7+u/YPdgtyz6M0wOn3E2M4H\nbgR+TW9dqP61mEZ++tvI5x79onsLhu8FfLZb2PvX9NZwmrOYc4fHdDJwBTALOAf4SJc52ifS9Z9z\nXXcdxwIXdu3z6K1x9ZkklwOXAdssbOySJEmSJElLYqC311XV5FHaZgAzuu2LefjMo4O7r3OBt1fV\n/UnWAc4Cfjsys6pOBE7stu+iV+gZ+XwHjGyjN7tp474+b1/c+KvqkIUdW8i5F9OboTTSyJyN+rb7\nn++jwEdH9H3wtVvIOY/4JLyufRa9WxslSZIkSRp3XNNpMMbKmk6P1iTg3O6T1wK8p6rmD3hMkiRJ\nkiRJ6jwhi05VNYfep7WNad2i5J8e0XxDVe0xiPFIkiRJkiQtK0/IotMTRVWdCZw56HFIkiRJkiQt\na4NeSFySJEmSJEnjkDOdJEmSJEnSuDbfhcQHwplOkiRJkiRJas6ikyRJkiRJkppLVQ16DBrjkhSf\nbfh9sla7qNzb9vu3np9mWTmg7dhePfuHzbLOeN3uzbJ4Z7sooOn3x05TTmsXBkw/4rXNsp79kRub\nZd1+8trNsgCe9trbm2Xdc/Ozm2UBMNTuZ5Sz20Xl6MZ/S3/bLqomNnzNgKyzoFnWwZcu3yzrk/9n\nqFkWQFZt955mv782ywJYcNqkZlmb/MPFzbIArthkq2ZZtX/Dv8k/avwz2u7XJHVM259RftUuauW9\n72yWde9xqzXLAsgfGr6nM9pFtVYfbPj90fBvCwDPa5zX0jntop5/5HXNsm4+90XNsgCY2DBr+1BV\njX8hjR1J6pu156CHsdTskxPG7Pvnmk6SJEmSJGlcG7L8MRDeXidJkiRJkqTmLDpJkiRJkiSpOYtO\nkiRJkiRJas6bGiVJkiRJ0rg2xIRBD+FJyZlOkiRJkiRJas6ikyRJkiRJkpobk0WnJAuSfLtvf7kk\ndyY5dcDjem6SHyzF/JWSHJfkiiSzk5yXZNJizvlmkj267R2SXJVkZpIVF9L/aUneszTGL0mSJEmS\nNGxMFp2AvwAbJpnY7b8KuBmowQ0JqurWqnrLUnyKDwK3VdXGVbUR8A/AvMUNi4del72BQ6tq86q6\nfyH9/wZ4b5PRSpIkSZIkLcRYLToB/ATYtdt+G/A9IABJpiS5sJvRc0GS9br2lZKc0M32OSnJxUk2\n747dm+Rfklye5KIkz+raV0vyX0ku6R7bdu07Jrmse8xMMinJWklmd8f3SXLU8GCT/DjJy/ue6/Ak\nVyY5K8nWSWYkuT7Jbou45tWBW4d3quq6qnqg/3m7/A8n+UTfeUnyLuAtwKeSfLsb79lJLu1mTr2+\n6/tpYJ3uuj7TnfyR7tpnJZn2aN4kSZIkSZLGuiEmjNvHWDaWi07HA3t1t4ltBPyi79jVwA5VtTnw\nCeDQrv29wF1VtSFwMLBF3zkrARdV1abAz4F3d+1HAl+oqinAm4Gvde0HAu+tqs2A7YG5ixlv/yys\nlYBzquqlwBzgk8ArgN277YX5BnBQV1D7VJJ1F/Fc/c9XVfV14BTgw1X1jm68u1fVFt1zf67rexBw\nfVVtVlUHJdkFWLe7/s2ALZLssJhrlSRJkiRJWqTlBj2Ahamq2UnWojfL6bQRh1cFvtUVZYqHrmM7\n4N+6869KckXfOQ9U1XDOpfRu2QPYGdggyXC/Vbp1lC4AvpDkOOCkqrqlr8/iPFBVZ3bbs4G5VTWU\n5EpgrUVc86wkawO7dOP6ZZJtGL3gtbDBDLc/BTisKyAtAJ7bze4aed4uwC5JLuv2JwHrAuc9rNeZ\n0x7aXmcqrDt1YZchSZIkSRrLZk6Hy6YPehR6EhizRafOKcBngR2B1fraP0VvJtHuXWHqZ33HFlaM\n6V8baQEPXXuArarqgRH9P5Pkx/Ru8bsgyauB/nWS5vPwmWIT+7ZHPtcDAFW1IMkiX/Oq+gtwMnBy\nkgXAa+nN+up/rqey8PWt+td3eiaweVfwunHEGPsdVlVfXdS4ePW0RR6WJEmSJD1BbD619xh2zCGD\nGonGubF8ex30bjebVlVXjWifzENrH+3T134BsCdAkpfQuy1vcX4KHDC8k2TT7us6VXVVVR0O/BJY\nf8R5NwGbpmcNYMqSXNCiJNk2yd902ysAL+me5w7gWUme3t1u+LpFxXRfJwN3dAWnnYA1u/Y5wCp9\n/c8E/mH4U/KSPC9Jf4FPkiRJkqQntEGvu+SaTmNLAVTVLVV1dF/b8Cyew+ndOjYTmNDX/mVgtSRX\n0ZsNdRVwT3/mKFkHAFt2i2hfBezXtX8wyewks+jNVDp9xNjOB24Efk1vXahLR45/IfuL+gS+dYDp\n3W2BM4FfVtVJVTWP3lpQl9Arkv16ERnD+cd113UF8A5662BRVXfRm7k1O8lnquos4LvARV3fE4CV\nF5EvSZIkSZK0WGPy9rqqmjxK2wxgRrd9MQ+feXRw93Uu8Paquj/JOsBZwG9HZlbVicCJ3fZdwF6j\nPN8BI9vozTrauK/P2xc3/qo6ZGHHRjnv28C3F3LsKOCoUdr3Xcj2XcC2C8nae8T+F4EvLmxckiRJ\nkiRJj9aYLDo9DpOAc5MsT+82s/dU1fwBj0mSJEmSJOlJZ1wVnapqDvCyQY9jcbpFyT89ovmGqtpj\nEOORJEmSJElqbVwVnZ4oqupMegt4S5IkSZKkpWz+GF9we7waqwuJS5IkSZIk6QnMopMkSZIkSZKa\ns+gkSZIkSZKk5lzTSZIkSZIkjWtDlj8GIlU16DFojEtS/Ljd98nmu57fLGtFHmiWBfCL27dqlrXg\nyEnNsgC4p2HWG9pFPW3H29uFAfdcunrTvKbubpi1aruotbe9ql0YcMNJG7YLa/2RCbs2zJrbLmqb\nPc9tFwZM5L5mWT87tOWLBhPf98dmWfdPa/eDcPAXl2+WBXDS0MXNsq78YuMP1n1mu6iJr2v3fgKs\nO/n6Zllb5ZJmWb+v5zfLAijSLOu8P2/fLAtg1cnt/ljdfvrazbJWe+3vmmUBrFvtvtcuumFqsyyA\nDda+rFnWrQ88t1nWM1a8q1lWa3cPNfyHEbDqU9r9HNxwSbt/Fz1tk8b/bv5Rw3837xWqqt0vtzEm\nSX2u3jvoYSw1B+bLY/b98/Y6SZIkSZIkNWfRSZIkSZIkSc1ZdJIkSZIkSePaEBPG7WM0SV6T5Jok\n1yU5aJTjb0gyK8llSS5N8oolPffRcCUtSZIkSZKkcSLJBOBoYGfgFuCXSU6pqqv7up1dVT/q+m8E\nnAysu4TnLjFnOkmSJEmSJI0fU4DfVNVNVTUP+D4jPk6qqv7St7sy8IclPffRsOgkSZIkSZI0fjwP\n+H3f/s1d28MkeWOSq4HTgQMezblLyqKTJEmSJEnS+FFL1Knqh1W1AbAb8O0kaT0Q13QasCQLgOOq\n6h3d/nLAbcDFVbXbIs6bChxYVbsl2RF4oKouWhZjliRJkiTpiWRhC24/Ed0w/ffcMP3mRXW5BVij\nb38NejOWRlVV53W1iKd3/Zb43MWx6DR4fwE2TDKxquYCr6L3hi5RZbKzEzAHsOgkSZIkSdI4tvbU\nNVh76kN1oXMO+cXILr8CXpRkLeBW4K3A2/o7JFkHuKGqKsnmAFV1V5J7Fnfuo+HtdWPDT4Bdu+23\nAd8DApBkSpILk8xMckGS9fpPTLImsD/woe6jDrdLslaSc7uPPzw7yRpd37ckmZ3k8iQzurYJSY5I\ncknXf79lddGSJEmSJKmtqpoPvB84E/g1cHxVXZ1k/yT7d932AGYnuQw4EthrUec+1rE402lsOB74\neJIfAxsBXwd26I5dDexQVUNJdgYOBd48fGJV/TbJV4A5VfV5gCSnAsdU1beT7At8EdgdOBjYpapu\nSzK5i3gXcHdVTUmyInB+kp9W1U1L+6IlSZIkSVJ7VXU6vQXC+9v+o2/7cODwJT33sbLoNAZU1exu\n6trbgNNGHF4V+FaSdendcrf8QmL6F/zaGnhjt/0dHvpGugA4NskJwEld2y7ARkmGC1mTgXWBmx6W\nfty0h7Y3mgobT130RUmSJEmSxqarpsOvpw96FMvU/HG0ptMTiUWnseMU4LPAjsBqfe2fAs6pqt27\nW+mmL2HeI1adr6r3JJlC71a+S5Ns0R16f1Wdtci0vact4dNKkiRJksa0Daf2HsNOPGRQI9E455pO\nY8c3gGlVddWI9sn0Fu8C2Hch584BVunbv5Dufkxgb+Dn0FsorKouqapPAHfSW4X+TOC93Ur1JFkv\nyUqP92IkSZIkSdKTm0WnwSuAqrqlqo7uaxv+9LrDgcOSzAQm8PBPtRvePhXYfXghceADwL5JZtEr\nOn1wOCvJFUlmAxdU1Szga/QWB5vZtf87zoCTJEmSJEmPk8WFAauqyaO0zQBmdNsXA+v3HT64a59O\nd6tdVV0HbDIi5pWj5O4xSlsBH+sekiRJkiSNO0OWPwbCmU6SJEmSJElqzqKTJEmSJEmSmrPoJEmS\nJEmSpOYsOkmSJEmSJKk5V9KSJEmSJEnj2hATBj2EJyVnOkmSJEmSJKk5i06SJEmSJElqztvrtGQ2\nndss6rLrt22WddA6hzTLAvjD6s9olrXOoTc0ywI44/Ddm2W9fpfvN8s65dy9mmUB8JuGWUMNs4C9\n3/O1Zlnf/em7mmV9h7c3ywLY9ozL2oXd3S4KgJXbRWXVapb1uRzYLAvgRtZqlrX2x25slgXwijqn\nWdbef3Nys6yT51/YLAvgTctt3Szryi83/mXU8Ofg/h/9Tbsw4NC//3/Nso5ln2ZZu+XUZlkAT+W+\nZllnHf/6ZlkAq+z3+2ZZt1/eLIq7tmj3byyAFz673e+2d63zpWZZALvw02ZZ01ec2izrAVZolgVt\n/1bdO2GVZlkAv/qfLduF3d4uatOtGv5QATMmvqZpnrQ0WHSSJEmSJEnjmms6DYa310mSJEmSJKk5\ni06SJEmSJElqzqKTJEmSJEmSmnNNJ0mSJEmSNK65ptNgONNJkiRJkiRJzVl0kiRJkiRJUnMWnZaR\nJAuSfLtvf7kkdyY5dZDjkiRJkiRJWhosOi07fwE2TDKx238VcDNQgxuSJEmSJEnS0mHRadn6CbBr\nt/024HtAAJJMSXJhkplJLkiyXte+T5KTkpye5L+TfGY4LMm9Sf4lyeVJLkryrK59rSTnJpmV5Owk\na3Ttb0kyu+s/o2ubkOSIJJd0/fdbdi+HJEmSJElL33wmjNvHWGbRadk6HtgryYrARsB4z8+RAAAg\nAElEQVQv+o5dDexQVZsDnwAO7Tu2CbBnd85bkzyva18JuKiqNgV+Dry7az8KOKaqNgGOA77YtR8M\n7NL1361rexdwd1VNAaYA706yVpvLlSRJkiRJT1bLDXoATyZVNbsr6LwNOG3E4VWBbyVZl94td/3v\nzTlVNQcgya+BNYFbgAeqajjnUnq37AFsDbyx2/4OcHi3fQFwbJITgJO6tl2AjZK8udufDKwL3PSY\nL1SSJEmSJD3pWXRa9k4BPgvsCKzW1/4pesWl3ZOsCUzvO3Z/3/YQD71v8/raF/Dw9zMjn7iq3pNk\nCr1b/C5NskV36P1VddYiR/25f3loe5uXw7YvX2R3SZIkSdIYNXs6XDl90KPQk4BFp2XvG8Cfquqq\nJFP72icDt3bb+z7O57gQ2IveLKe96d16R5J1quoS4JIkfwusAZwJvDfJz6pqfreW1M1V9deHJR74\nz49zSJIkSZKkMWGjqb3HsOMPGdRIlpkhyx8D4au+7BRAVd0CHN3XNvzpdYfTu/Xtn+ndelej9Bk1\nc5R+HwCOSfIR4A4eKmIdnuRF9GZBnV1Vs5JcAawFzEySrv/uj/UiJUmSJEmSwKLTMlNVk0dpmwHM\n6LYvBtbvO3xw134scGzfObv1bU/u2z4ROLHb/h3wylGeb49R2gr4WPeQJEmSJElqwk+vkyRJkiRJ\nUnPOdJIkSZIkSePaEBMGPYQnJWc6SZIkSZIkqTmLTpIkSZIkSWrOopMkSZIkSZKas+gkSZIkSZKk\n5lxIXJIkSZIkjWsuJD4YznSSJEmSJElSc6mqQY9BY1ySelbd1CzvZ7VTs6yXHnF9syyA13z05GZZ\np5+7e7MsgJfu9KtmWVce+LJmWWzYLgpg4pv/2Cxrs8mzmmUBXLzr1GZZn//Je5plXcoWzbIAvvOL\ndzfL2nHKmc2yAGb806vbhU1sF5UzG/8tvaVd1Iqz/tQuDLj/GSs2y0rD61xw/KR2YUBWXNAs6+Pv\nazuxfJWhdr8/Pnz5l5plAeT/tvtZqI+lWVYObvwz+oeGWfs3zAK+ceDbmmXt+6PvN8tq+poBtPtT\nBds3zAKybrvvt+2+cXazrPMPeVWzLABObJj17w2zgNzc7j2Yv0q73+HLfXN+syyAw074YLOsf8xR\nVFW7X7xjTJJ6b31u0MNYar6cA8fs++dMJ0mSJEmSJDXnmk6SJEmSJGlcm++aTgPhTCdJkiRJkiQ1\nZ9FJkiRJkiRJzVl0kiRJkiRJUnMWnSRJkiRJktScC4lLkiRJkqRxbcjyx0A40+lxSrIgybf79pdL\ncmeSU5fy804dfo4kuyU5aGk+nyRJkiRJ0qNhqe/x+wuwYZKJVTUXeBVwM1DLagBVdSqwVItckiRJ\nkiRJj4Yzndr4CbBrt/024HtAAJJMSvKNJL9IMjPJ67v2fZKclOT0JP+d5DPDYUm+nOSXSa5MMq2v\n/TVJrk5yKbB7X/s+SY7qttdKcm6SWUnOTrJG1/7NJEcmuSDJ9Un26Dv/I0ku6c558PkkSZIkSZIe\nK4tObRwP7JVkRWAj4Bd9xz4GnFNVWwGvAI5IslJ3bBNgz+6ctyZ53vA5VfWy7viOSTZKMhH4KvC6\nqtoCWJ3RZ1MdBRxTVZsAxwFf7Du2elVtB7wO+DRAkl2AdatqCrAZsEWSHR7PiyFJkiRJ0lgyxIRx\n+xjLvL2ugaqanWQterOcThtxeBdgtyQf7vZXBF5Ar2B0TlXNAUjya2BN4BZ6Bah303t/ngO8BJgA\n3FhV13c53wH2G2U4WwNv7Otz+PAwgR924706ybP7xrdLksu6/UnAusB5/aH3TvvCg9srTN2aFaZu\ns/AXRJIkSZI0Zl0//WZumH7LoIehJwGLTu2cAnwW2BFYbcSxN1XVdf0NSbYC7u9rGgKWS/JC4EBg\ny6q6J8kxwEQeOaspixjLwo49sJA+h1XVVxeRx8rTPrSow5IkSZKkJ4h1pj6fdaY+/8H9cw65ZICj\n0Xjm7XXtfAOYVlVXjWg/EzhgeCfJZsObo2QEWIXe4uR/7mYj/S29gtM1wFpJ1u76vm0h47gQ2Kvb\n3hv4+WLGfSbwD0kmdeN7XpKRRTNJkiRJkqRHxZlOj18BVNUtwNF9bcMzkz4F/FuSK+gV+W4AXj+i\nz4NZVXVFd6vbNcDvgfO7A/cn2Q84Lclf6d3+NmmU5/sAcEySjwB3APuOHOuIcZ+VZAPgoiQAc4C3\nA3c++pdCkiRJkqSxZ6yvfTReWXR6nKpq8ihtM4AZ3fZc4H+P0udY4Ni+/d36tvcd2b9rPxPYYFFZ\nVfU74JWj9Nl3xP7kvu0v8vAFxyVJkiRJkh4Xb6+TJEmSJElScxadJEmSJEmS1JxFJ0mSJEmSJDXn\nmk6SJEmSJGlccyHxwXCmkyRJkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpOdd00hJ5Kvc1y3pNzmiW\nVeunWRbABIaaZW3/inOaZQGcf8HO7cI2bhfFwQ2zgLkff3qzrBtvWbNZFkC9td3329G8v1nWM+qu\nZlkAvLtd1IwjXt0uDOClDbOe2S6qJrb9XdTSKk+7t2ne3EPb/YzWac2imr6fAKzc7j2dPG//ZlkA\nf57w782yVpt/ULMsgDu3e0G7sFXbRdWrG/+MTmqYdXzDLODwAz/aLuzKdlGs1TAL4OMNsy5omAXU\nju2+386/ZYdmWby5XRQA75zXLqvxj2jdvHyzrPf/7RHNsmp22wu9ko2a5o13813TaSCc6SRJkiRJ\nkqTmLDpJkiRJkiSpOYtOkiRJkiRJas41nSRJkiRJ0rg2ZPljIJzpJEmSJEmSpOYsOkmSJEmSJKk5\ni06SJEmSJElqzqLTUpBkQZLP9u1/OMknBjmmfkl2S3LQoMchSZIkSZLGL1fSWjoeAHZPclhV3QXU\noAfUr6pOBU4d9DgkSZIkSVoWhpgw6CE8KTnTaemYB3wV+NDIA0lWS/JfSS7pHtt27U9P8sMks5Jc\nlGSjrn1akmOT/DzJTUnelOSzSa5IcnqS5ZK8IsnJfc/xqiQndduvSXJpksuTnNW17ZPkqG77m0mO\nTHJBkuuT7LH0Xx5JkiRJkjTeWXRaer4M7J1k8oj2I4EvVNUU4M3A17r2Q4BLq2oT4J+Ab/Wd80Jg\nJ+D1wHeAs6pqY+A+YNeqOhd4cZJndP33Bb6eZDV6xa83VdWmwFu64yNnXq1eVdsBrwM+/XguWpIk\nSZIkCby9bqmpqjlJvgUcQK84NGxnYIMkw/urJJkEbAe8qTv3Z0mekWQVegWi06tqKMmVwFOq6szu\n3NnAWt32t4F3JPkmsDXwdmBXYEZV/bbLvXu0oQI/7I5fneTZo13P3dOOfnB74tQpTJw6ZUlfCkmS\nJEnSGHL79Gv5n+nXDnoYehKw6LR0/RswEzimry3AVlX1QH/HrggVRvcAQFUtSDKvr30BD72Hx9Bb\np2kucELXtxaR+Yj8vvE9wqrT3r8EMZIkSZKksW71qeuz+tT1H9yffciPBziaZcM1nQbD2+uWoqr6\nE3AC8C4euqXtp/RmPwGQZJNu8zxg765tKnBnVc1hyYpGVNVtwK3AP/NQkesXwMuTrNXlPn34aR/L\n9UiSJEmSJC0pi05LR/+aSZ8Dntm3fwCwZbdg+FXA/l37NGCLJLOAQ4F39mX1541cj6l//7vA76rq\nWoCquhPYDzgpyeXA95Ygc0x90p4kSZIkSXpi8va6paCqJvdt3wFM6tu/C9hrlHP+BOw+Svshi8g+\nZET37YH/HNH/DOCMEW3HAsd22/suLF+SJEmSJOmxsug0TiS5FJgDfGjQY5EkSZIkaSyZ75pOA2HR\naZyoqi0GPQZJkiRJkqRhrukkSZIkSZKk5iw6SZIkSZIkqTmLTpIkSZIkSWrONZ0kSZIkSdK4NmT5\nYyCc6SRJkiRJkqTmLPVpifz22he3C5vVLupFezYMA9bkpmZZp12/R7MsgHdv+8VmWf95zQHNsli9\nXRQA27eLuv0na7cLA9Z85zXNsoo0y/pTVm2WBcAp89plHbd8uyyArRtmTWyY9ZqGWQDPmNss6s6j\nXtAsC4Cp7aI22eriZlnX3r1esyyAuT96erOsD8/+crMsgNXmHdQs6z3LvbBZFsDRD/yuWdZzJ9za\nLOvuzdv+nrz7r+3yJrx/qFkWwBr1+2ZZV9+0ebMs7m8XBcB+7X5PMtTyDwJs8/c/a5Z10cd2apbF\n89pFAXBPw7/xb612WcD/3vMLzbK+ctiHmmV98p8+0iwL4A5Wa5onLQ3OdJIkSZIkSVJzznSSJEmS\nJEnj2hATBj2EJyVnOkmSJEmSJKk5i06SJEmSJElqzqKTJEmSJEmSmnNNJ0mSJEmSNK65ptNgONNJ\nkiRJkiRJzVl0WkaSDCW5LMnsJCckeeqgx9QvySZJ/nbQ45AkSZIkSeODRadl569VtVlVbQQ8APzv\nJTkpybK6BXIz4LXL6LkkSZIkSdI4Z9FpMM4D1k3yuiQXJ5mZ5KwkzwJIMi3Jt5OcDxybZM0kP09y\naffYpuuXJEcnuaY7/7Qke3THbkry9G57yyQ/67anJLmwe84LkqyXZAXgk8Bbu9lYbxnEiyJJkiRJ\nksYPFxJfxrqZS68FfgKcX1Vbd+3/C/go8OGu64uB7avq/u5WvFd12y8Cvgu8DNgdWA/YAFgd+DXw\n9e78WsgQrgZ2qKqhJDsDh1bVm5McDGxRVQc0vmRJkiRJkgZqvguJD4RFp2XnqUku67Z/Tq84tEGS\nE+gVjFYAbuiOF3BKVd3f7a8AHJ1kE2AIeFHX/nLgu1VVwG1Jzl2CcawKfCvJut3zDH8PpHuM7qhp\nD21PmQpbTV2Cp5IkSZIkjTU3T7+eW6bfsPiO0uNk0WnZua+qNutvSHIU8Nmq+nGSHYFpfYf/2rf9\nIeC2qnpHkgnA3K69eHihqH97Pg/dPjmxr/1TwDlVtXuSNYHpSzT6D0xbbBdJkiRJ0tj3/Knr8Pyp\n6zy4/8tDzh7gaDSeuabTYE0Gbu229+lrHznjaDJwe7f99/DgvMCf01uH6SlJngNM7TvnJmDLbnuP\nhTznvn3tfwZWeVSjlyRJkiRJWgiLTsvOaGssTQN+kORXwJ19fWpE/y8D70xyObA+cC9AVZ0MXEdv\nLadjgYt4qGB1CHBkkl/Sm/U0nHc4cFiSmfSKV8PtPwNe4kLikiRJkqTxZojlxu1jLBvboxtHqmry\nKG2nAKeM0n7IiP3fAJv0Nf1j37EPDG8nOaav/Xx6BaqR2RePaD+4a/8TMGUJLkWSJEmSJGmxnOk0\n/izsU+skSZIkSZKWGWc6jSNVte/ie0mSJEmSJC19znSSJEmSJElSc850kiRJkiRJ49rQgx8Cr2XJ\nmU6SJEmSJElqzqKTJEmSJEnSOJLkNUmuSXJdkoNGOf7iJBclmZvkwBHHbkpyRZLLklzyeMbh7XWS\nJEmSJEnjRJIJwNHAzsAtwC+TnFJVV/d1uwv4APDGUSIKmFpVf3y8Y7HoJEmSJEmSxrUn2ZpOU4Df\nVNVNAEm+D7wBeLDoVFV3Ancm2XUhGWkxEItOWjLLVbOoTGoWxb9wcLsw4KMc3izrZWuf1ywL4Gvv\n+0CzrP/7pX9tlvX5532sWRYA89tF5b/afd8CnPra3ZplbXzBfzfLetO2322WBXDdfpu0C2sYBcDE\ndlHrbXd5s6xrrtm8WRbAn5+zfLOsTx3Q9vfkR+qIZlmrb3JPs6yXzvplsyyAQ/9+z2ZZr3/l2c2y\nAO7Yfq1mWV+ae1OzLIAPrLBGs6z7hj7RLOv/5N+aZQEsP+mBZllb8bjuWniEN/DDZlk/3fkNzbI2\n3/P8ZlkAB+dfmmXN+2S737kA26fdtf76X1/SLOtr+V/NsgCuZoNmWYfz0WZZAF+t/Zpl1cFN/rsb\ngHv/38rNsgCO3q7l6/aIu6/0xPY84Pd9+zcDWz2K8ws4O8kQ8B9V9Z+PdSAWnSRJkiRJkp4g5kyf\nyZzply2qy+P9v+/bVdVtSVYDzkpyTVU9plkVFp0kSZIkSZKeIFaZujmrTH1opv1thxwzssstQP/0\n4zXozXZaIlV1W/f1ziQn07tdz6KTJEmSJEnSSE+yNZ1+BbwoyVrArcBbgbctpO/D7iFNshIwoarm\nJJkE7AIc8lgHYtFJkiRJkiRpnKiq+UneD5wJTAC+XlVXJ9m/O/4fSVYHfglMBhYk+SDwEuBZwElJ\noFczOq6qfvpYx2LRSZIkSZIkaRypqtOB00e0/Uff9u08/Ba8YfcCm7Yax1NaBUmSJEmSJEnDLDot\nZUmGklyW5IokJyVZ7OdkJvlekllJPphk/SSXJ7k0ydpJLlgW45YkSZIkSXo8vL1u6ftrVW0GkOSb\nwP7A5xbWubuvcsuqelG3/4/AD6rqX7su2y3d4UqSJEmSNL7Mf3ItJD5mONNp2boIWAcgyZQkFyaZ\nmeSCJOt1fX4KPK+bHfVx4IPAe5Kc0513b/d1apLpSX6Q5Ook3xl+kiSv7HKvSPL1JCt07Z9OclU3\ni+qIrm21JP+V5JLuse0yezUkSZIkSdK45UynZSTJBHofNXhO13Q1sENVDSXZGTgUeDOwG/DjvtlR\nAeZU1ee786ovdlN6q8vfBlzQFYxmAscAr6iq3yQ5ll7R6tvAG6vqxV3u5C7jSOALVXVBkhcAZ3SZ\nkiRJkiRJj5lFp6XvqUkuA54H3AR8pWtfFfhWknXpFZKW79ozSsZobQCXVNWtAEkuB14I/AW4sap+\n0/U5FngfcDQwN8nXgR93D4CdgQ26j0MEWCXJSlX110d7oZIkSZIkScMsOi1991XVZkmeCpwJvAE4\nGfgUcE5V7Z5kTWD6Y8i+v297iN77WSP6BKCbUTUFeCW9GVXv77YDbFVVDyzymY6c9tD2VlNh66mP\nYbiSJEmSpIG7Zzr8efqgR7FMDVn+GAhf9WWkqu5LcgDw3SQ/BCYDt3aH9231NMC1wFpJ1qmq64F3\nANOTTAImVdXpSS4Eru/O+SlwAPBZgCSbVtXlj0j+4LRGQ5QkSZIkDdTTpvYew24+ZFAj0TjnQuJL\n34Mzj7pizm+APYHDgcOSzAQm8PAZSiNnKy3s2Mh+VNX99IpYP0hyBTCf3i19k4FTk8wCzgM+1J1y\nALBlt7j4VcB+j/oKJUmSJEmSRnCm01JWVZNH7L++b3f9vu2Du+M3ARv39X9YyXk4r6qm03dLXlV9\noG/7XGDzEUO5DdhqlPHdBey1BJciSZIkSZK0xCw6SZIkSZKkcW2ICYMewpOSt9dJkiRJkiSpOYtO\nkiRJkiRJas6ikyRJkiRJkpqz6CRJkiRJkqTmXEhckiRJkiSNay4kPhjOdJIkSZIkSVJzFp0kSZIk\nSZLUnLfXaYlMfOafmmVtueulzbJeyI3NsgC+yn7Nsl5z65nNsgD+15eOapb1hS3/qVnWuy/9YrMs\ngP/8xQHNsmrDNMsC2Jmzm2Vl1WZRvDLtxgVw0hv2bhf2zHZRAGtue02zrOtO2qRZ1sfe9M/NsgDu\nqmc0y/rPLdr9TAH8/tI12oW1+5XLVvyiXRhwbPZpF/axdlEANPz98Zzlb28XBtw37+PNsp464ZBm\nWV8d+kizLIBVmNMs63nc0iwL4D13HNss631zj2mWNSerNMsCmMa0ZlnzsnyzLID9+GqzrJOze7Os\na1mvWRbAyzm/Wdbfnja9WRbAvM3avafLHdru30XPzv80ywKYcsGMZlmXOB1FS4lFJ0mSJEmSNK7N\nd02ngbCeKUmSJEmSpOYsOkmSJEmSJKk5i06SJEmSJElqzjWdJEmSJEnSuDZk+WMgnOkkSZIkSZKk\n5iw6SZIkSZIkqTmLTuNIko8luTLJrCSXJZmSZHqSa7q2q5McleRpizpnkNcgSZIkSZLGB29qHCeS\nbAPsCmxWVfOSPB1YESjg76pqZpLlgcOAHwFTF3GOJEmSJEnS42LRafxYHfhDVc0DqKo/AiQBSNc2\nL8lHgd8k2Xhh50iSJEmSNJ4MMWHQQ3hS8va68eOnwBpJrk3ypSQv7ztWD25ULQBmAesv5hxJkiRJ\nkqTHzJlO40RV/SXJFsAOwE7A8Un+sTucEd2zqHOq6tiR+fMO+8yD20/Zfjsm7LD9UrgKSZIkSdLS\n9ufpl/Hn6ZcPehh6ErDoNI50s5hmADOSzAbeOXxouE+SCcBGwNWLOOcRRafl/99BS3fwkiRJkqRl\nYvLUzZg8dbMH92/95CP+E1BqwqLTOJFkPaCq6rquaTPgt8BL6WY2dQuJ/yvwu6q6ciHn3LRMBy5J\nkiRJ0lLmmk6DYdFp/FgZOCrJqsB84Dpgf+C/gOOS3E/vk+nOAt6wiHP2W9YDlyRJkiRJ449Fp3Gi\nqmYC241yaKfHcI4kSZIkSdLj4qfXSZIkSZIkqTmLTpIkSZIkSWrO2+skSZIkSdK4NrTAhcQHwZlO\nkiRJkiRJas6ikyRJkiRJkpqz6CRJkiRJkqTmXNNJkiRJkiSNa/Pnu6bTIFh00hKZe/7Tm2Vd8Myd\nm2X9z1bPapYF8E8c1ixr9+ee1CwL4Gt7fKBZ1t/96uvNsr42q924ANaeclWzrBs/8pJmWQC3vWPt\nZlkrPPOeZlmr1t3NsgA4smHW8xtmAX9960rNsl6/+/ebZf3rxp9qlgXAa9MsaqVf/bVZFsDnbvnn\nZlkn/OidzbJ+/741mmUBvK5+3CzrxIPf3iwLgNe0i7pni6e1CwP+z1Pa/QL56rwPN8uqpxzeLAtg\n27T7Gf3K0P7NsgC2We3cdmHXt4tan/9uFwb8Hcc1y1quhpplAbz5hac1y/ruTX/XLGtLZjbLAjjh\nt29rF7Zcu58pgH9+zsfahf3Tqc2iXnHQOc2yAA786peb5klLg7fXSZIkSZIkqTmLTpIkSZIkSWrO\n2+skSZIkSdK4NjTf8scgONNJkiRJkiRJzVl0kiRJkiRJUnMWnSRJkiRJktScRSdJkiRJkiQ150pa\nY0SSjwFvA4aABcD+VXXJEp47HTiwqi4d0b4b8JKq+kySNwLXVtXVi8n6JnBqVZ346K9CkiRJkqSx\nZ2j+hEEP4UnJotMYkGQbYFdgs6qal+TpwIpLeO4EoEY7VlWnAqd2u2/sthdZdOqyRs2TJEmSJEla\nUt5eNzasDvyhquYBVNUfq+q2JK9MMjPJFUm+nmQFgCQ3Jfl0kkuBN3cZ70hyWZLZSV7W9dsnyVFd\nUWs34Iiuz9pJNk1ycZJZSU5KsmrfeLLsLl2SJEmSJI1HFp3Ghp8CayS5NsmXkrw8yUTgGGDPqtqY\n3qy093T9i16RaouqOr5re2pVbQa8F/hGf3hVXQScAny4qjarqhuAbwEfqapNgNnAJ5b2RUqSJEmS\npCcPb68bA6rqL0m2AHYAdgKOBw4Dbqyq33TdjgXeBxzZ7R8/IuZ7XdZ5SSYnedooTxWA7tjTquq8\nvuwfLHKQx017aHujqbDx1MVfmCRJkiRp7Ll2Ovz39EGPYplyTafBsOg0RlTVAmAGMCPJbHoFpn7h\n4Wst/WUxkQtGe5qF9F387XR7T1tsF0mSJEnSE8D6U3uPYacdMqiRaJzz9roxIMl6SV7U17QZcD2w\nZpJ1urZ30CtKjRoBvLXL2h64u6rmjOgzB5gMUFX3AH/q+g5nT3+81yFJkiRJkjTMmU5jw8rAUd1i\n3vOB64D96N0y94MkywGXAF/p+o+csVTA3CQz6b2n/9DXPtz3+8B/JvkA8BbgncBXkqxEr8C174g8\nSZIkSZKkx8yi0xhQVTOB7UY5dC6w+Sj91x6xv9NCco+lt14TVXUhsOGILtuMcs6+I9skSZIkSXoi\nmz/PNZ0GwdvrJEmSJEmS1JxFJ0mSJEmSJDVn0UmSJEmSJEnNWXSSJEmSJElScy4kLkmSJEmSxrUF\nQ5Y/BsGZTpIkSZIkSWrOopMkSZIkSZKac36ZlszdDbMmtot6KnPbhQFP455mWc/h1mZZADU/zbKW\ny7xmWXVzu3EBsHHDrJbft0D+WM2yFgxNapZ177NWaZYFwIoNs/7QMAtYlT81y1qe+c2y7ruhWRQA\nT72u3ffaypnTLAsgf243Nm5vF9XaSvy1Xdid7aIAaPfrg7v/smq7MGDFSe3+Lq+Se5tlbdvw2xbg\njIZZc2j7O3yl3NcubEK7qBW5v10Y8EzuapZ1N21/Dm7+Xbus+S3fBNr+IKy25m3Nsu78zQuaZQHc\nwbPahdVNzaJW5IFmWcCY/jsqDbPoJEmSJEmSxrf5LYu4WlLeXidJkiRJkqTmLDpJkiRJkiSpOYtO\nkiRJkiRJas41nSRJkiRJ0vjmmk4D4UwnSZIkSZIkNWfRSZIkSZIkSc15e91SlOQZwNnd7urAEHAn\nUMCUqpq/iHO/CZxaVSc+iue7t6pWfuwjliRJkiRJasOi01JUVXcBmwEk+QQwp6o+P3w8yYSqGlrY\n6Y/lKR/DOZIkSZIkSc15e92ylSTfTPKVJBcDn0myTpLTk/wqyc+TrN/X/+VJLkhyfZI9uoCVk5yd\n5NIkVyR5/WhPkuSIJLO7Pnt27c/pnuOy7tj2XfsuSS7sMk9IMmlZvBiSJEmSJC0T8zN+H2OYM52W\nvQKeC2xTVZXkHGD/qvpNkq2ALwOvBAKsXlXbJdkAOAU4EbgP2L2q5iR5JnBRd6zfm4BNgI2B1YBf\nJvk58HfAGVV1aJKnACt1GR8DXllV9yU5CPi/wKeW6qsgSZIkSZLGNYtOg/GDruC0MrAN8IPkwerk\nCt3XAn4IUFVXJ3l21/4U4LAkOwALgOcmeVZV3dGXvz3w3aoq4I4kM4CXAZcA30iyPPDDqpqVZCrw\nEuDCbgwrABc+YsQnTntoe4Op8JKpj+PyJUmSJEkDc+N0uGn6oEehJwGLToPx1+7rU4C7q2qzhfR7\noG97uCq1N/BMYPOqGkpyIzBxxHnV1//Btqo6rytWvQ74ZpLPA38Czqqqv1vkiPeYtsjDkiRJkqQn\niBdO7T2GzThkUCPROOeaTgNUVX8GbkzyZnhwLaaNF3PaZOCOruC0E7DmKH3OA4eNXiAAACAASURB\nVN6a5ClJVgNeDlyS5AXAnVX1NeBr9BY5vxjYLsk63RgmJXlRkwuUJEmSJGksmD+OH2OYRafB6P+U\nub2BdyW5HLgSeP1C+g1vHwdsmeQK4B3A1SP7VNXJwBXALOAc4CPd7XdTgcuTzAT2BI6sqj8A+wDf\nSzKL3q11/YuZS5IkSZIkPWreXreMVNWo8xWr6ibgb0dp33fE/uTu613AtgvJmty3/VHgoyOOfwv4\n1ijn/QyYsrhrkCRJkiRJWlLOdJIkSZIkSVJzznSSJEmSJEnj2xhf+2i8cqaTJEmSJEmSmrPoJEmS\nJEmSpOYsOkmSJEmSJKk5i06SJEmSJElqzoXEJUmSJEnS+OZC4gPhTCdJkiRJkiQ1l6oa9Bg0xiUp\nzm74fXJBu6g1Pn5duzDg99e8qFlW9mj7s/XqK3/YLOuMU3ZvlsVL217n8qvOaZY174zJzbIAsvWC\nZll1XLuaf7Zr+x7U89Iu7BftogDW/vurmmXd8L4Nm2Wt+vnbmmUBzLl7lWZZQ6dNapYFkJXbZdW6\n7b7XJq77x2ZZAPd//2+aZdWchj9TACe0i3raebe3CwOeueIfmmU9h3Y/V3fUas2yAOZUux+Edy+/\nfrMsgB8NtfuH1rV3r9csa+5JT2+WBfCMd97cLOuBuSs0ywJYeaV2/5a57SfrNMtirXZRzc1tG7fj\nFmc0y5rxmVc3y3rFR09rlgVw7sdf1y7sX0JVNf6DNXYkKS4dx7WPLcbu++dMJ0mSJEmSJDXnmk6S\nJEmSJGl8mzfoATw5OdNJkiRJkiRJzVl0kiRJkiRJUnMWnSRJkiRJktScRSdJkiRJkiQ150Lii5Hk\nGcDZ3e7qwBDw/9m77zDLqjL9+9+bJgoCEgRRgkTFANiAIKjFgGFGXxUT48CAGFBHURkx/FS0QQww\nio4BJAzYYEBQERAFBGkkKLG7yTggMGSQJEjq8Lx/nF1yKKubaljd1VR9P9d1LnZY695rn6qu6n5Y\ne507gQJeXlUjWo4syaHAAVV15XwZ6DxIMhHYuao+NtpjkSRJkiRpvps12gMYnyw6PYGqugvYBCDJ\nF4H7q+qAJ5Hz/tZje7Kq6iLgotEehyRJkiRJGrt8vG7eJckRSd7Wd+CB7r8DSaYkOTbJlUl+2Ndm\nSjfDiCSvT3JRkmlJftsd2zzJuUkuTnJOkvW74+9O8sskpya5LslHkuzZtftDkmclWSfJRX3XWm9w\nP8lmXd60JOclWaYb54nd+UlJDk9yRpJrk+y+IN5ESZIkSZI0tjnTqY3q294Y2BC4FTgnySuq6tyu\nTSVZGTgEeGVV3ZBk+a7fld2xWUm2A74CvL0796IudyngWuCTVfWyJAfQe0zuv5Pcl2SjqpoO7Aoc\nnmRx4GjgnVV1UZJlgIeGGf/6wDbAssDVSQ6sKicfSpIkSZKkJ82iU3vnV9UtAEmmAWsB53bnAmwB\n/L6qbgCoqnu7c8sDRyZZl16Bqv9rc0ZV/Q34W5J7gRO745cCL+22DwN2TfKfwDuBzYANgFu7x+mo\nqsEZWf3jLeCkbm2qu5LcAawC3PIU3wdJkiRJkhYOM0d7AOOTRacnZybdo4lJFgEW7zv3SN/2LP7x\nPS6G9yXg9KraPsmawJQ5ZM7u25/dl/8L4IvA74ALq+qeJM8b0d3Ao08wZpg86bHtjQZg44ERRkuS\nJEmSFirXT4Ebpoz2KDQOWHR6cq4HJgLHAm8CFhthvwL+CByYZK2quj7Js6rqHnqPtg3OLtp1hHl/\nn7JUVQ8nOQU4CHhPd/hq4DlJNq2qC5M8E3hwThlztcukEQ5JkiRJkrRQW2ug9xp01t6jNRKNcS4k\nPu8KOBR4dff43BbAA0POz7lz1V+A3YBfdP2P7k7tD3w1ycXAhL6cGpI5dLt//8f0Zj+d2l3rUWAH\n4DvdtU4BlhzSb2iGJEmSJEnSU+ZMp3lQVf3l3y37tj/TnZ9C32NxVbV73/Y2fdsnAycPyf4jvTWY\nBu3VHZ8MTO5rt3bf9uPOAVsDh1dV9bW5cMhYAc7sXkPviap6CZIkSZIkjSXjbE2nJK8HvkVvUsth\nVbXfMG2+DfwzvSei3l1VU0fad6QsOo0RSY4Dng/802iPRZIkSZIkjY4kE4DvAtsBNwMXJDmhqq7s\na/MvwLpVtV6Sl9NbqmeLkfSdFxadxoiq2n60xyBJkiRJkkbd5sA1VXU9QJKjgTcD/YWjN9E9OVVV\n5yVZPsmq9CazPFHfEXNNJ0mSJEmSpLHjucCNffs3dcdG0ma1EfQdMWc6SZIkSZIkPV1MmwLTp8yt\nxUg/LGxkn2b/FFh0kiRJkiRJY9tYWkj8xQO916Aj9x7a4mZg9b791enNWJpbm+d1bRYbQd8R8/E6\nSZIkSZKkseNCYL0kayVZHNgBOGFImxOAnQGSbAHcW1W3j7DviDnTSZIkSZIkaYyoqplJPgKcAkwA\n/qeqrkzyge78wVX16yT/kuQa4G/ArnPr+2THYtFJkiRJkiRpDKmq3wC/GXLs4CH7Hxlp3ycrVSNd\nX0rjVZLKVbOb5f33Brs1y7qJ5zXLApjIRc2ydnv4kGZZAK9Y4txmWb/58FubZa3wnZubZQHcfduK\n7cJmTmiXBay55rXNsl7Oec2yNuf8ZlkAe573vWZZb335j5plAZz6t9c2y3rgsJWbZW30sT82ywK4\nhdWaZd15+RrNsgC4pmHW7e2iVt3tz+3CgGdyf7Osz/LVZlkA+9enmmWtnhufuNE8eAvHNcv6wB1H\nNsva8tlnNMsCeIQlmmVldru/YwG8adFXNsva5zOzmmU97yv/2ywLYHnubZZ19V0bNMsCeN+KhzXL\nehs/b5b1cb7VLAvgmvvWaZb1weUOfuJG8+AjfKdZ1qfZv1nWK2j393mAG2v1J240Qt9a5LNU1Xxf\nVHq0JClOGsO1jzdkof36uaaTJEmSJEmSmrPoJEmSJEmSpOYsOkmSJEmSJKk5FxKXJEmSJElj28zR\nHsD45EwnSZIkSZIkNWfRSZIkSZIkSc1ZdJIkSZIkSVJzT7rolGTZJDcl+c4czq+V5NIhxyYl+cQ8\nXGMgyYlPdowjyP+HMS4oSR6Yw/EfJHnbE/SdkmTiU7j29UlWeLL9JUmSJEmSnshTWUj8S8CZ89in\nnsL1/i7JolX1dF8GbE7vRc3l3BP1farXliRJkiRp7Hm6VxCepuY60ynJZkmmJ1kiydJJLkuyYTfL\n5tnAqfN4vXS5aye5qO866w3uJ3l9kiu7/e372kxKclSSs4HJSdZM8rtufKclWT3JckmuSrJ+1+cn\nSd6XZNck3+zLen+SA+gVXxZN8sMkVyQ5NslSXZuJ3YyiC5OcnGTV7viUJF9Lcl6Sq5Ns3R0/LMnU\n7nVHkr269+y0JBcluSTJm4Z5j5Pku924f9u9r5nbGDrvGGYMZybZqC/77CQvSbJiklO7r9+hg/ld\nm526nKlJvp/ERy4lSZIkSdJTNtcCQ1VdAJwA7AvsBxwFXAl8HRjJY3Lr9BVipgIf6MXWn4H7+gok\nuwKHJ1kSOAR4Y1VNBFbl8bNyXgBsW1U7At8FjqiqjYAfAd+uqvuAjwA/SPKvwHJVdRhwLPD/JZnQ\n5bwb+B96xZcNgO9V1YbAX4H/SLIo8B3gbVW1KXAE8OXBtwWYUFUvBz4OfLF7r95XVZsAbwHuBH4A\nPAxs393LPwHfGOY92h5YH3ghsDPwCqCSLDaXMWS4MXT39G6ArvC2RFVd2p3/fVW9GDgOWKNr80Lg\nncArurHPBnYcZoySJEmSJEnzZCSP1+0DXAg8COwOfBj4dVXdkiRz7QnXdsUMAJJ8kcdm2RwG7Jrk\nP+kVPjajV1S6rqqu7dr8ENit2y7ghKp6pNvfgl6BZ7Dd/gBVdVqSd9IrSr20O/ZAkt/RKzxdBSxW\nVZcnWQu4sar+0JfzUeBk4EXAad0tTgBu6buvX3T/vRhYq+/+lqRX4Nq9qm7sCkdfTfJKegWd1ZI8\nu6ru6Mt6FfDjqirg1m6c0CuGzWkMNYcx/AzYK8kngffQK1QBvJJu1lhV/TrJPfS+DtsCE4ELu2ss\nBdzGMOo7kx7b2XyAvHxguGaSJEmSpIXcjVP+zE1T/jzaw9A4MJKi00rA0vSKHkvRK/a8Msl/AMsA\niye5H/glcHDXZy/gsifI/QW9GTi/Ay6sqnuSrDmkzdCi1oNPcJ7u8bAXAn8DVuCxQs1hwOfozdQ6\nvK9L/0yqdPsBLq+qV8xh7IOFr1k8/j38PvCzqhosHO1I7/17WVXNSnIdsOSQrMHrDWeexlBVD3aP\n6L0FeAfwsiH3NpzJVfXZOZx7rPPuk56oiSRJkiTpaWD1gbVZfWDtv++ft8/v5tJ6jJgx2gMYn0ay\nfs/BwOeBHwP7VdVOVbVmVT0f2BM4sqo+W1XnV9Um3etXTxRaVQ8DpwAH8diMnKuAtZIMfve/q6/L\n0KLJucC/dts7Ar/vtvcALu+OHdE9KkdVnQ88D/g34Cd9OWsk2aLb/jfgLOBqYOXB40kWS7Lh3O4n\nyYeBZapq/77DywJ3dAWnbYChRTW6ce+QZJEkzwG26Y7P8xg6hwHfBs7vHjccvMa/dTn/DDyLXrHr\ndODtSVbuzq2QZI0RXEOSJEmSJGmunmgh8Z2BR6rqaOBrwGZd8aTf3D4Jbbhz/cd+TO+xs1Ph74Wo\n3YCTuoXEb+9rP/RT3Xan93jedHoFpo8l2QB4L/CJqjqbXrHl8319jgHO7ivGFL3izoeTXAEsBxxU\nVTOAtwP7JZkGTAW2fIJ7/ATw4r41rHajt9bUpkkuAf6d3iyrx/WrquOA/wWuACbTK6bxJMdAVV0M\n3MdjhTyAvYFXJbmM3mN2N3Rtr+zen1O79/FUeutoSZIkSZIkPSVzfbyuqo4Ejuy2Z9N7tK7//GR6\nhZLh+l5Pt6ZS37G9hzTbGji8W89osM0p9B6PG5q395D9/6O3JtFQG/a1GbrY+dbAAX3nbxjuWt25\n6cCrhzm+Td/2X4C1u+21h7btDPt4XFUt27e9e4sxACRZDVikqk7ta3M38Lo5XOMYesU4SZIkSZKk\nZkayptN8keQ44Pn0PtVtfl9reeA8YFpVnTG/rzdauplp+9J7xFCSJEmSJEFvNWQtcKNWdKqq7Rfg\nte6l92lwY1r/zDRJkiRJkqTRNJKFxCVJkiRJkqR5YtFJkiRJkiRJzVl0kiRJkiRJUnOjtqaTJEmS\nJEnSAjFztAcwPjnTSZIkSZIkSc2lqkZ7DFrIJSn2bPd9kovaZdWn0ywLIBc2HNvNbcfG5x5ul3XZ\nku2yWs+XXKlhVsO3DGCxdf/aLOt1K57SLOuk/d/eLAtg+0/9uFnWBWzWLAvgPzmgWdYeV32/WRbt\nvpzttR7bju2ilnnznc2yHjhz5WZZAExvF5UXtf27Vl3W8PfLDe2iALJtw3tt+DO8rm38O7nh774l\nP3Z3uzDgka8u3yxrr/0Xa5b13Rn/1ywLYMKEdp99fud5azTLAuDedlFZqeHfTU9q/OdgYsOsln//\nAzKt4fu2Urv3LTT+ffBww6/pTqGqGn+TLDySFJPHcO1jl4X36+dMJ0mSJEmSJDXnmk6SJEmSJGls\nc02nUeFMJ0mSJEmSJDVn0UmSJEmSJEnNWXSSJEmSJElScxadJEmSJEmS1JwLiUuSJEmSpLHNhcRH\nhTOdniaSLJvkpiTf6Ts2JckFffubJjmjb3/rJOclubJ7vX9I5m59585LstWCuRtJkiRJkjTWOdPp\n6eNLwJnDHF85yeur6uT+g0lWBX4EvLmqpiVZETglyc1V9eskbwR2A7aqqruTbAL8MsnmVXX7/L4Z\nSZIkSZI0tjnTaSGSZLMk05MskWTpJJcl2TDJRODZwKlDuhTwdeBzw8R9GDiiqqYBVNVdwKeAT3fn\nPw3sWVV3d+enApO7fpIkSZIkSU+JM50WIlV1QZITgH2BpYCjgCuB3wE7Aq8ZptsfgO2TDAAP9B3f\nEPjBkLYXAS/qO3/RkPMXArs8+TuQJEmSJGkh5JpOo8Ki08JnH3rFnweB3enNPPp1Vd2SJHPosy/w\neR6bxTRouPaLzeXac8qHcyY9tr36AKwxMJcYSZIkSdJC64opcOWU0R6FxgGLTguflYClgQn0Zjtt\nAbwyyX8AywCLJ7m/qj7bta+qOiPJvl3bQVcAE4ET+o5NBC7pO78pcMaQ85cNO6qtJj2FW5IkSZIk\nLTQ2HOi9Bh2392iNRGOcRaeFz8H0Zi2tDexXVTsNnkiyC7BpX8Gp375d32u7/e8B5yX5RVVN7xYS\n/xrw1e78/sB+3SLkdyfZmN6jdZvPl7uSJEmSJEnjikWnhUiSnYFHquroJIsA5ybZpqr6ZyPVcH2r\n6jdJ7hg8X1W3JdkJOCTJcsBawKer6rju/IlJnttdo4C/Ajv6yXWSJEmSpDHHNZ1GhUWnhUhVHQkc\n2W3P5vGPy1FVk+l9wtzg/jZDzm86ZP8s4OUAST4EvDPJ4VV1f3f++8D329+JJEmSJEka7xYZ7QFo\nwaiqg6pqm8GCkyRJkiRJ0vxk0UmSJEmSJEnNWXSSJEmSJElSc67pJEmSJEmSxjYXEh8VznSSJEmS\nJElScxadJEmSJEmS1JxFJ0mSJEmSJDWXqhrtMWghl6Q4reH3yZLtorbe6rftwoAL75vYLOvRh5Zo\nlgUw+6NLN8ta8tC7m2Vtt9zvmmUBXMqLm2XdMP0FzbIAttyo3b3ewmrNsh6sZzTLArjzY2u0C9u6\nXRTA5u88s1nW1Ls2aZa1w4o/bZYFsCXnNsvaa+a+zbIAtp1werOsYw/duVnWyrv9X7MsgLtuW7FZ\n1uyT2v38Bpr+HuWqhlnAy750drOs+3lms6wN6k/NsgAW59FmWb84YsdmWQDP2/V/m2U9NLPdN9tH\nllizWRbAF96XZllvPuToZlkAz+HWZlnXsnazrMVrRrMsgNPv3rZZ1ior3t4sC2C1uqVZ1vkbvbpZ\n1vOmt/vzCXDTr9drF/bGUFXt/mAtZJIUXxvDtY/PLLxfP2c6SZIkSZIkqTmLTpIkSZIkSWrOopMk\nSZIkSZKaW3S0ByBJkiRJkjRfzRrtAYxPznSSJEmSJElScxadJEmSJEmS1JxFJ0mSJEmSJDU35opO\nSWYlmdq9fjmHNmsleaiv3cVJFkvy7iTfWdBjnpskn30KfQ9N8sKW45EkSZIkSRqJsbiQ+INVtckI\n2l0ztF2SajGAJItW1cwWWcD/A77yZDpW1fsbjUGSJEmSpKevVv9C1zx52s50SrJZkulJlkiydJLL\nkryoYf4Pkrytb/+B7r9J8l9JLk1ySZJ3dscHkpyV5Hjgsm5cR3RtLk4y0LWbkOTrXf/pST6SZJsk\nx/Vd6zVJfpHkq8BS3Wyso7pz/9n1vTTJx7pjSyc5Kcm07vg7uuNTkrxscPxJ9u3a/CHJs7vjKyf5\nWZLzu9crWr2HkiRJkiRp/HraznSqqguSnADsCywFHFVVlydZMslFwKPA16rq+DlErJNkard9dlXt\nDqT/EkMv2f33rcBGwEuBlYELkvy+O7cJ8KKquiHJJ4BZVfXSJBsApyZZH3gPsAawUVXNTvKsqron\nyYFJVqyqu4Bdgf+pqpOSfGRwRlaSicC7gc3pFQzPS3ImsA5wc1W9oWu37DD38AzgD1X1+ST7Ae8H\nvgz8N/DNqjonyRrAycCGc3jPJEmSJEmSRuRpW3Tq7ANcCDwE7N4dW6Oqbk3yfOB3SS6tqj8P0/fa\nYR7DG8njdVsDP66qAu7oij6bAX8Fzq+qG7p2WwHfBqiqq5PcAKwPbAscVFWzu3P3dO2PAv49yQ+A\nLYCd5nDtX1TVQwBJfgG8kl6h6OtJvgb8qqrOHqbvo1V1Urd9EfCabns74IXJ3+ttz0zyjKp6cATv\nhSRJkiRJ0rCe7kWnlYClgQn0Zjs9WFW3AlTVdUmmAJskWQk4uOuzF3DZCLJn0j1+mGQRYPHuePH4\nGVGDxwD+NuT40HZzO34EcCLwMHDMYFFqmOv09w1QVfW/STYB3gDsm+T0qvrSkL4z+rZn89jXPsDL\nq+rROYy1Z/Kkx7Y3GoCNB+baXJIkSZK0kLpkClw6ZbRHsWC5ptOoeLoXnQ4GPg+sDeyXZC/goap6\npCs0bQXsV1VX0Xv0Deh9et0c8voLOtcDE4FjgTcBi3XHzwI+kGQysCLwKmBP/vGRtLOAHYEzusfq\n1gCuAn7b9T+jqmYNPl7Xzc66pbufbftyZvQtTH4W8INuRtMiwFuAnZI8B7inqn6U5D56j/CN1KnA\nR4GvAyTZuKqm/UOrXSbNQ6QkSZIkaaH10oHea9BP9h6tkWiMe9oWnZLsDDxSVUd3M5HOBT4EvCvJ\nbHpFma92BafhDPcoXfUdPxQ4Psk0eo+vPQBQVccl2RKY3rX9ZFXdkeSFQzIPBA5Kcgm9muouVTUj\nyWH0HrO7JMkM4JCuLcCPgZWq6uq+nEO6thdV1eDjd+cPjrGqpid5LfBf3X3PAD74BPfbf58fBb6X\nZDq974czgf8Y7g2TJEmSJEkaqadt0amqjgSO7LZn01sHCeCrI+h7Pb2FwIcenwxM7rbvALbsO/2Z\nvnafAj41pO+Z9Ao2g/uPMMyMo6qaBXyiew21Nb1iV3/7zwy59jeBbw5pcyq9GUtDr7VN3/ayfds/\nB37ebd8F/OswY5EkSZIkSXrSnrZFp7Gm+8S9+4E9RnsskiRJkiSNKa7pNCosOi0kqmriaI9BkiRJ\nkiSplUVGewCSJEmSJEkaeyw6SZIkSZIkqTmLTpIkSZIkSWrONZ0kSZIkSdLYNmO0BzA+OdNJkiRJ\nkiRJzVl0kiRJkiRJUnM+XqeRmdIw69pqFrX1Vmc3ywI4+y/bNct63trXNMsCuOma9ZplvXa5U5tl\nnXDtDs2yANZe54pmWZne7nsN4BsbfaJZ1i78oFnW4jzSLAvgzqXXaBc2oV0UwOrc2Cxr+RXvbZa1\nK0c0ywJ4IVc2y/ruors3ywJ4H4c1yzr2Lzs3y1qXtj9zn7/Kdc2yzn//q5tlATCpXVQ++FC7MGAv\n9m2WNanhjf5bftQsC2BF7m6WNWWXgWZZAMun3c+2Rxd9drOsL76n7e/kfQ5rl/cvh5zULAtgW37X\nLOuzfKVZ1j1ZvlkWwKyZ7X7Jr8YtzbIA1uNPzbIuuOy2Zllf4WfNsgB2PvnYpnnS/GDRSZIkSZIk\njW2zRnsA45OP10mSJEmSJKk5i06SJEmSJElqzqKTJEmSJEmSmnNNJ0mSJEmSNLbNHO0BjE/OdJIk\nSZIkSVJzFp06SWYlmdq9fjmHNmsleaiv3cVJFkvy7iTfWdBjbiHJakmO7bYHkpw42mOSJEmSJElP\nfz5e95gHq2qTEbS7Zmi7JNViAEkWraqnNOkvySJVNXuk7avqFuAdT+WakiRJkiRJQ427mU5JNksy\nPckSSZZOclmSFzXM/0GSt/XtP9D9N0n+K8mlSS5J8s7u+ECSs5IcD1zWjeuIrs3FSQa6do+bTZXk\nV0leNXiNJF9PMg3YMsleSc7vrnVwX591k5yWZFqSi5I8v5u9dWmr+5ckSZIkSYJxONOpqi5IcgKw\nL7AUcFRVXZ5kySQXAY8CX6uq4+cQsU6Sqd322VW1O5D+Swy9ZPfftwIbAS8FVgYuSPL77twmwIuq\n6oYknwBmVdVLk2wAnJpk/bnkAjwD+GNV7QmQ5Iqq+lK3fWSSN1bVr4AfAV+pquOTLA5MAFaZy9sl\nSZIkSdLTnwuJj4pxV3Tq7ANcCDwE7N4dW6Oqbk3yfOB3SS6tqj8P0/faYR7DG8njdVsDP66qAu5I\nciawGfBX4PyquqFrtxXwbYCqujrJDcD6T5A9C/h53/4/JfkkvWLUCvRmUJ0JrDZYTKuqRwGSDM2S\nJEmSJEl6ysZr0WklYGl6M32Woree060AVXVdkinAJklWAgYfT9sLuGwE2TPpHltMsgiweHe8ePyM\nqMFjAH8bcny4dn/P7SzZt/1wV8wiyZLA94CJVXVzki92bZ/aulNnTnpse80BWGvgKcVJkiRJkkbJ\nTVPg5imjPQqNA+O16HQw8HlgbWC/JHsBD1XVI12haStgv6q6it6jb0Dv0+vmkNdfJLoemAgcC7wJ\nWKw7fhbwgSSTgRWBVwF7AhsOyToL2BE4o3usbg3gamA54D/Sm5r0PGDzOYxlsBh1V5Jl6C0SfkxV\nPZDkpiRv7h6vW4J5WdPr1ZNG3FSSJEmStBB73kDvNeiCvUdrJBrjxl3RKcnOwCNVdXQ3E+lc4EPA\nu5LMpleI+WpXcBrOcDOGqu/4ocDx3aLeJwMPAFTVcUm2BKZ3bT9ZVXckeeGQzAOBg5JcQm920y5V\nNQM4J8l1wBXAlcBFw42pqu5Ncii9WVm3Aef1tft34OAk+wAzgLcPc09NPolPkiRJkqSFxozRHsD4\nNO6KTlV1JHBktz0b2KI79dUR9L2e3kLgQ49PBiZ323cAW/ad/kxfu08BnxrS90zgzL79R4D3zOH6\nO83h+LJD9vei9zjg0HbXANsOE/HS7vwUYMpw15AkSZIkSZoXI3+8SpIkSZIkSRohi06SJEmSJElq\nzqKTJEmSJEmSmht3azpJkiRJkqRxZtZoD2B8cqaTJEmSJEmSmrPoJEmSJEmSpOYsOkmSJEmSJKk5\n13SSJEmSJElj28zRHsD4ZNFJIzPQMOuNaRY1nY2aZQFwb7ux3XTFes2yAFi1XdQLuLpZ1t3rnNYs\nC+DSR17SLKvub/f1BPglb2ma18odj6zSNvC8hllbNMwCZjKhWdbrOKVZ1u08u1kWwIMs1SzrW3y8\nWRbAhGq4CueUdlF/3GGgXRiw69oHNcs6f6tXN8sC4KyGWTOXbBgGM/ZZrF0W7bIWbbx67L0s3yzr\n0YcXb5YFcPXDGzTLmnHNss2y3nzoT5tlAfzzISc1y7p9wpHNsgB+M+v9zbKW4qFmWctwf7MsgO1W\nafd3wLW4vlkWwHO4pVlW8cJmWafy2mZZQNPfo9L84uN1kiRJkiRJas6iK18SPAAAIABJREFUkyRJ\nkiRJkprz8TpJkiRJkjS2uabTqHCmkyRJkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpOYtOkiRJkiRJ\nas6i01OQZI0kpya5IsnlSdacS9tfJvnDghzfSCRZLcmx3fZAkhNHe0ySJEmSJDU1Ywy/FmIWnZ6a\nI4H9qmpDYDPgjuEaJVkeeDGweJLnz88BJZmnr2lV3VJV75hf45EkSZIkSQuHJCsk+W2SP3WTaJaf\nQ7vDk9ye5NIhxycluSnJ1O71+rldz6LTCCTZLMn0JEskWTrJZUleAkyoqtMBqurBqnpoDhFvBU4E\njgX+tS93at/rwSSv7K51bpKLk5yTZP2u7buTfKev76+SvKrbfiDJ15NMA7ZMsleS85NcmuTgvj7r\nJjktybQkFyV5fpK1hn4TSZIkSZKkMekzwG+ran3g9G5/OEcAwxWUCjigqjbpXifP7WIWnUagqi4A\nTgD2BfYDjgLWBu5N8vOuQLT/XGYZ/SvwU+AY4F19uZtU1SbAF4ALgHOBq4BXVtXLgC8CXxlsPnRY\nfdvPAP5YVRtX1TnAd6tq86p6CbBUkjd27X4EfKeqNga2BG6b5zdDkiRJkiQ9Xb0JmNxtTwbeMlyj\nqjoLuGcOGRnpxRadp6GNb/sAFwIPArsDbwNeCWwM3EivqPRu4PD+TklWAdatqj92+48meVFVXd7t\nrwfsDwxU1axuatuRSdalV1ga/BrN7Ys6C/h53/4/JfkkvWLUCsBlSc4EVquq4wGq6tHu+iO7+8mT\nHtveaAA2HhhZP0mSJEnSwuWBKfC3KaM9igVr1mgPYKGxSlXd3m3fDqzyJDJ2T7IzvRrJJ6rq3jk1\ntOg0cisBSwMTgKXoFZqmVdX10FsoHNgiyWXA4CNtXwDWAlZIcl137Jn0Zjt9Psky9IpV7+v7on8J\nOL2qtu8WJp/SHZ/J42emLdm3/XBVVTeOJYHvAROr6uYkX+zaDp0pNW92mfSUukuSJEmSFhLLDPRe\ng+7ce7RGovkgyW+BVYc59bn+naqqJPNaKziI3qQc6NUvvgG8d06NLTqN3MHA5+k9Vrcf8DFg+SQr\nVdVfgG2B86vqfGCTwU5JzgVeV1XndftrAad1WYcDR3SPxA1aFril29617/j1wIfSm5r0PGDzOYxz\nsBh1V1fUegdwTFU90C329eaqOj7JEvh4pSRJkiRJTy93ToG/TJnj6ap6zZzOdYuDr1pVtyV5DnP4\nQLS5ZP+9fZLD6K1fPUcWnUagmzb2SFUd3a3bdC7wamBP4PSuEHQhcOiQfmsBqw8WnACq6vok93aL\ngL8NWC/Je7rT76P3qN3kJJ8HTqKboVRVZ3ezpa4ArgQu6rtU9eXfm+RQ4DJ6azad19fu34GDk+xD\n74MV3z60P091RpQkSZIkSZp/Vh7ovQZdNU8z1U4AdqE3mWYX4Jfz0jnJc6rq1m53e2CuH0xm0WkE\nqupI4MhuezawRd/pjebS73pg9WGOb9ptTphD1w36tvfq67fTHK6z7JD9vfr79R2/ht6MrKFe2p2f\nwmOP80mSJEmSNDbMHO0BLDS+BhyT5L30nqh6J0CS1YBDq+oN3f5P6E22WTHJjcAXquoIYL8kG9Ob\nsHId8IG5XcyikyRJkiRJ0jhQVXcD2w1z/BbgDX3775pD/53n5Xqu6SNJkiRJkqTmLDpJkiRJkiSp\nOYtOkiRJkiRJas41nSRJkiRJ0tjmQuKjwplOkiRJkiRJas6ikyRJkiRJkprz8TqNzFXtorJqNcv6\n1ss/3iwL4BkTH2yWtWrd2iwL4HsbfLJZ1vWs1Szr7EP+4dM2n5KXvf+cZllT91ulWRbA1567d7Os\n096ybbOs7y6xe7MsgNdN/H27sFPaRQHc9eaVmmVdVBObZf3oRQc1ywKobdMs613fObxZFsBPrnlP\n07xWXrDOtKZ5r6t237yHr/vhZlkADLSL2mLnM9qFAVtzdrOs3eqQZlnvWOtXzbIAbryp3Z/RZWbd\n3ywLYKelf9gs6/sX7tEs6zlp+/eibdPue/c3M97XLAvgngmHtgubtWmzqBtZvVkWwO+ufmOzrMxs\n9+8DgP/asN3P3bBOs6zJtw40ywL44Tff3y7sNe2ipH4WnSRJkiRJ0tg2Y7QHMD75eJ0kSZIkSZKa\ns+gkSZIkSZKk5iw6SZIkSZIkqTnXdJIkSZIkSWPbrNEewPjkTCdJkiRJkiQ1Z9FJkiRJkiRJzVl0\nWgglWT3Jn5M8q9t/Vre/xhzaDyQ5ccGOUpIkSZIkac4sOi2EqupG4CDga92hrwEHV9X/jd6oJEmS\nJEmSRs6i08Lrm8AWST4OvAL4RpIjk7x5sEGSHyV5E1B9x1ZI8ssk05P8IclLuuOvTjK1e12cZOnu\n+CeTnN+1n7RA71CSJEmSpAVh5hh+LcQsOi2kqmom8CngAODj3f5hwLsBkiwHbAn8Ckhf172Bi6pq\nI+CzwJHd8U8A/1FVmwBbAw8neS2wblVtDmwCTEzyyvl9b5IkSZIkaexbdLQHoLn6Z+AW4CXA6VX1\n+yQHJlkJeDvws6qanfTXnNgKeCtAVZ2RZMUkzwTOAb6Z5EfAL6rq5q7o9NokU7u+SwPrAmf9w0hO\nmvTY9noDsP5Aw9uUJEmSJC0w06f0XtJ8ZtFpIZVkY2A7erOZzk5ydFXdRm/m0r8DO9DNehqu+5D9\nqqr9kvwKeANwTpLXdee+WlWHPOGA3jBpnu9BkiRJkrQQ2mig9xp01N6jNRKNcT5etxBKb+rSQcDH\nukXF/wv4enf6B8DH6RWSrhqm+1nAjl3OAHBnVT2QZJ2quryq9gcuADYATgHe07e+03OTrDz/7kyS\nJEmSpFEw2usujdM1nZzptHB6P3B9VZ3e7R8I7JrklVV1VpIrgOP62hePLSY+CTg8yXTgb8Au3fGP\nJdkGmA1cBvymqmYkeSHwh+4RvfuBnYA759+tSZIkSZKk8cCi00Koe9ztkL792cBEgCTPANYDftJ3\n/kzgzG77HmD7YTI/OodrfRv4dsPhS5IkSZIk+Xjd00mS7YArgG9X1f2jPR5JkiRJkqQ5cabT00hV\nnQasNdrjkCRJkiTpaWXGaA9gfHKmkyRJkiRJkpqz6CRJkiRJkqTmLDpJkiRJkiSpOYtOkiRJkiRJ\nas6FxCVJkiRJ0tg2a7QHMD5ZdNLIrNUu6lVvOKVZ1mW8uFkWwNVs0CzrtEe3a5YF0PJWl+feZllr\n73ZFsyyA1bi1WdbFx6VZFsAnJ+7TLGsl7mqW9cu8pVkWAG9vmHVbwyzg7BNe0ywrEx9qlnXj5as3\nywK4kXZ5Rds/B69f77h2YR9rF3XLI6u1CwOmLDHQLGvrw09rlgVw9s1bN8v64+e2aZYFcOVXXtgs\n67hs3yzrJ9e/q1kWwAwmNMu6/dfPb5YF8PZ/+VmzrO+vtEezrGtYp1kWwOf4crOsJRd5uFkWADM2\nbRa19oQPNMv69aM3NssCyLRqljXzmLb/LF10/3YfU7burEubZb0wFzXLAuDKtnHS/ODjdZIkSZIk\nSWrOopMkSZIkSZKa8/E6SZIkSZI0ts0c7QGMT850kiRJkiRJUnMWnSRJkiRJktScRSdJkiRJkiQ1\nZ9FJkiRJkiRJzVl0GiPSc1aS1/cde0eSk5PMTvL1vuN7Jvlitz0pyU1Jpia5MsmBSTIa9yBJkiRJ\n0nwxcwy/FmIWncaIqirgg8ABSZZIsgzwZeBDwKPA9klWHGze3xU4oKo2ATYEXgK8esGNXJIkSZIk\njUUWncaQqrocOBH4NPAFYHJVXQfMAA4B9phD18GZTUt2r7vn81AlSZIkSdIYt+hoD0DN7Q1MBR4G\nNu07fiBwSZL9h7QPsEeSnYA1gV9X1SULZKSSJEmSJGnMsug0xlTVg0mOBu6vqhl9x+9PciTwUeCh\n/i70Hq87IMmiwM+S7FBVP12wI5ckSZIkaT6Z8cRN1J5Fp7FpNo9ft2nQt4CLgSOGHA9AVc1McjLw\nKuDxRacfTXps+yUD8NKBRkOVJEmSJC1Q06bA9CmjPQqNAxadxpGquifJMcB7gf/pDv/9k+q6T63b\nGrjoHzrvOGkBjFCSJEmSNN9tPNB7DTpq79EaicY4FxIfu4Z+Qt2gbwArDTm3R5KpwKX0ilAHzv/h\nSZIkSZKkscyZTmNQVe09ZH/Zvu07gKWHtLWsLUmSJEkau2aN9gDGJ2c6SZIkSZIkqTmLTpIkSZIk\nSWrOopMkSZIkSZKas+gkSZIkSZKk5lxIXJIkSZIkjW0zR3sA45MznSRJkiRJktScRSdJkiRJkiQ1\nZ9FJkiRJkiRJzbmmk0Zkhdff3Czr9ze/ulnWNs89o1kWwIrc1SzrsrM3a5YFwG7too58+783y3p4\n2grNsgD+PPNF7cJWahcF8I0bPt0sK3u0+/H7yM9PaZYFwDYNsz7eMAvgDe2iaq8lm2VNPPyiZlkA\na3Nts6x7Wb5ZFsCNrN4u7IZ2USsu0e7nN8CjLN4s6+wvbdcsC4C3tYuq57bLAjiM9zXLupr1m2Vt\nmrZ/Rlu6eM00zfs432oXdlK7qCUmPtouDLibZzXLWp0bm2UB3LhIu5+Tv3m43Q/Kjyze8Oc3cOus\ndt9r/987j2mWBfCCTGuWdfXtGzTLet2z2/6d7U97b9w0b8xzTadR4UwnSZIkSZIkNWfRSZIkSZIk\nSc1ZdJIkSZIkSVJzrukkSZIkSZLGthmjPYDxyZlOkiRJkiRJas6ikyRJkiRJkpqz6CRJkiRJkqTm\nLDotYOk5K8nr+469I8nJSWYn+Xrf8T2TfLHbnpTkpiRTk1yZ5MAkmYfrDiQ5cQ7n/ifJtCSXJDku\nyXJP5R4lSZIkSZIsOi1gVVXAB4EDkiyRZBngy8CHgEeB7ZOsONi8vytwQFVtAmwIvAR4daNhfbyq\nNq6qlwJ/BnZvlCtJkiRJ0uibNYZfCzGLTqOgqi4HTgQ+DXwBmFxV19FbT/8QYI85dB2c2bRk97r7\nHxr0ZkQdleTcJH9K8r6+08skObabKfXDvvHc3/UNsBTwl6d2h5IkSZIkabxbdLQHMI7tDUwFHgY2\n7Tt+IHBJkv2HtA+wR5KdgDWBX1fVJQBJPgBQVQd3bV8MbAEsA0xNclJ3fHCW1K3AOUm2qqpzuowj\ngH8GrgE+2vJGJUmSJEnS+GPRaZRU1YNJjgbur6oZfcfvT3IkvcLPQ/1d6D1ed0CSRYGfJdmhqn7a\nV2wabHd8VT0CPJLkDGBz4F7g/Kq6BSDJNGAt4JzuursmWQT4LvA5ekWxv3tw72/8fXuxV2/JYgOv\naPI+SJIkSZIWsHunwH1TRnsUGgcsOo2u2Tx+3aZB3wIuBo4YcjwAVTUzycnAq4CfjvA6AI/0HZvF\nkK9/Vc3uCmGfGhrwjC9+YgSXkSRJkiQt9JYf6L0G/d/ec2o5dswc7QGMT67ptBCqqnuAY4D38lhR\n6u+fVNetvbQ1vUfhhgrw5m6R8hWBAeCC/v7/0CFZty/3TfQe+5MkSZIkSXrSLDqNvqGfUDfoG8BK\nQ87tkWQqcCm9ItKB0FvTaXBdp67dJcAZwB+Afarqtu740FlV1RWafpDkEmA6sALwlRY3JkmSJEmS\nxi8frxtFVbX3kP1l+7bvAJYe0nbYOY9D1nQCuKSqdhnS5kzgzL793ftObz3Pg5ckSZIkSZoLi05j\n03DrREmSJEmSND65ptOosOg0xgydPSVJkiRJkjQaXNNJkiRJkiRJzVl0kiRJkiRJUnMWnSRJkiRJ\nktScazpJkiRJkqSxbcZoD2B8cqaTJEmSJEmSmnOmk0bk7que2y7smnZRe8/8UrswYOmV7msX9kC7\nKAAmt4t6+OcrNMtac9ermmUB3HD4C9qFrdv4f2ecvFizqJrYLIrz/rp5uzCA9zbMWq5hFvDiV1zQ\nLOuy2zZrlnXEI7s2ywJ49hK3N8u6/q7nN8sCeOby97cLa/irpbXraq12YT9rFwXALg1/tt3X7uca\nwBVs2CzrlZzdLOvYG97VLAtg5TVvbZrX0jX3rdMurOHvqtPu2q5dGDB7Zrv/d77tKqc3ywL43dVv\nbBc2vZpF3TbjgGZZAKtN+HizrBNnNfwHArBa3dIsa/auSzfL+s2n39osC4CPNcw6q2GW1MeZTpIk\nSZIkSWrOmU6SJEmSJGlsmzXaAxifnOkkSZIkSZKk5iw6SZIkSZIkqTmLTpIkSZIkSWrOopMkSZIk\nSZKacyFxSZIkSZI0ttVoD2B8cqbTApaes5K8vu/YO5KcnGR2kq/3Hd8zyRe77UlJbkoyNcmVSQ5M\nknm47kCSE+dw7l+STOuyz0qyzlO5R0mSJEmSJItOC1hVFfBB4IAkSyRZBvgy8CHgUWD7JCsONu/v\nChxQVZsAGwIvAV7daFjfA3bosn8MfL5RriRJkiRJGqcsOo2CqrocOBH4NPAFYHJVXQfMAA4B9phD\n18GZTUt2r7v/oUFvRtRRSc5N8qck7+s7vUySY7uZUj/sO34bsFy3vTxw85O9N0mSJEmSJHBNp9G0\nNzAVeBjYtO/4gcAlSfYf0j7AHkl2AtYEfl1VlwAk+QBAVR3ctX0xsAWwDDA1yUnd8cFZUrcC5yTZ\nqqrOAT4CnJrkQeCvXV9JkiRJkqQnzaLTKKmqB5McDdxfVTP6jt+f5Ejgo8BD/V3oPV53QJJFgZ8l\n2aGqftpXbBpsd3xVPQI8kuQMYHPgXuD8qroFIMk0YM0kfwCOAl5fVRck2RM4AHj/4wb8vUmPbW82\nAJsPNHgXJEmSJEkL3GVT4PIpoz0KjQMWnUbXbIZfQ/9bwMXAEUOOB6CqZiY5GXgV8NMRXgfgkb5j\ns+h9/VcCFq+qC7rjxwC/+YeED08awWUkSZIkSQu9Fw/0XoOO3Xu0RqIxzjWdFkJVdQ+94s97eawo\n9fdPqus+tW5r4Jphugd4c7dI+YrAAHBBf/8h7gSekWS9bv81wBVP9R4kSZIkSdL4ZtFp9A39hLpB\n36A3C6n/3B5JpgKX0isiHQi9NZ0G13Xq2l0CnAH8Adinqm7rjv/DrKru0/TeAxzTPXK3I/DJBvcl\nSZIkSZLGMR+vG0VVtfeQ/WX7tu8Alh7Sdtg5j0PWdAK4pKp2GdLmTODMvv3d+7ZPBk5+ErcgSZIk\nSZI0LGc6jU3DrRMlSZIkSZK0wDjTaYwZOntKkiRJkiRpNDjTSZIkSZIkSc1ZdJIkSZIkSVJzFp0k\nSZIkSZLUnEUnSZIkSZIkNWfRSZIkSZIkSc356XUamWsaZr2gXdQOa/6oXRhw9OW7tgtbqV0UAJe3\ni3rr/2v3vv3i1B2bZQGw3Yx2Wdcv1i4LWPsDDb8I1S7qlr8+p10YwA4Ns45vmAVc9onNmmUt+cW7\nm2Xdd9mqzbIA7lu0Xd7aGzX8vgXunbV807xWWo/rgQnPbBd2ULuo1rLD7KZ5+/OpZlmvO+n3zbJa\n/433zmvWaBfW+I/UB5Y7pFnWf6/0mWZZq6x4e7MsgNW4pVnWmtzQLAsgM9v9kp/503bfvG9857HN\nsgBOnPGnZlm7TVivWRbAPpfMahf2X+2ilnxeu797ADx84QpN88a+hv/O0Ig500mSJEmSJEnNWXSS\nJEmSJElScxadJEmSJEmS1JxrOkmSJEmSpDFu5mgPYKGQZAXgp8CawPXAO6vq3iFtlgTOBJYAFgeO\nr6r/N9L+/ZzpJEmSJEmSND58BvhtVa0PnN7tP05VPQxsU1UbAy8Ftkmy1Uj797PoJEmSJEmSND68\nCZjcbU8G3jJco6p6sNtcHJgA3DMv/QdZdJIkSZIkSRofVqmq27vt24FVhmuUZJEk07o2Z1TVFfPS\nf5BrOo0RSQL8HvhyVZ3cHXsH8B7gtcAl9L7eVwK7VNVDSWZ1xxej94DrkcA3q6pG4RYkSZIkSdIT\n+n33Gl6S3wKrDnPqc/07VVVJhv33f1XNBjZOshxwSpKBqpoy0v6DLDqNEd0X+4PAsUnOoFdI+jLw\nemB6VW0CkOSHwAeBbwIP9h1fGfgxsCwwacHfgSRJkiRJ88uM0R5AQ1t2r0FfedzZqnrNnHomuT3J\nqlV1W5LnAHfM7UpVdV+Sk4CJwBRgnvr7eN0YUlWXAycCnwa+AEyuqj8PaXY2sM4wfe8EdgM+Mr/H\nKUmSJEmSRsUJwC7d9i7AL/9/9u48XvO5/v/442lsY50IRWqikH1LtphsJUtUtkRoU9oofOOLQ4Ui\nflEh2SnxjW8kIjkzyG4sY6svMwlZSrYsmfH6/fF+X3Ouc8115lyfz/U+c46Z531u5zbn2p7nfc61\nfD6f9+f9fr1b7yDprZLG5O9HA1sCd3X6+GYe6TT7ORKYCLwKrNt8g6S5ga2B37V7YERMljRK0hK5\nE8rMzMzMzMzMZh/HAhdJ+iwwBdgZQNLSwOkRsQ2wNHC2pLlIg5XOi4hrZ/b4gbjTaTYTES9LuhB4\nMSIa4wdHS5qYv58AnFE5+Jc9fd+vOg5WG9dNM83MzMzMzGy43NULd/cOdytsGETEs8AWba5/Atgm\nf38PsHaVxw/EnU6zpzeA5mJerzRqN82MpOWAaW1HOe3WU6xxZmZmZmZmNozWHJe+Gs47crhaMgtN\nHe4GzJFc08mA6YXETwVOHu62mJmZmZmZmdmbn0c6zb5igO+bNabdzUPq9j2XtKqdmZmZmZmZmVlX\n3Ok0G4qII1suLzLA/fz8m5mZmZmZmdmQcKeDmZmZmZmZmc3mXh/8LlacazqZmZmZmZmZmVlx7nQy\nMzMzMzMzM7Pi3OlkZmZmZmZmZmbFudPJzMzMzMzMzMyKcyFxMzMzMzMzM5vNuZD4cPBIJzMzMzMz\nMzMzK84jnWzWW+jVYlHv5NFiWQCbrvz7Ylnjf/HhYlkA23z7f4plXbrNp4plbfy7PxTLArjhoS3K\nhV1bLgrgrZv+o1jW+3R/sayHF31PsSyAG5YdVy5sr3nKZQHbrnxxsawn9PZiWXOvM61YFsDb+Hux\nrMtu2bVYFsBW611WLOvqa5cpljXmY88VywK4/al1y4U9Vi4KgMfLva/23fnEYlkAp/P5YlnT1ip3\nbvTQpQ8rlgXwNEsWy3qY5YtlAXyVk4pl/eju/yqWtfQHniiWBbACfy6WtQyPF8sCOG6VrxTLmvsH\n5UZmrMRdxbIA3j7Xk8WyvnNX2REoh682qljWd5aYWizr5ZvfWiwL4PDNv10s67vFksz680gnMzMz\nMzMzMzMrziOdzMzMzMzMzGw2V27UmnXOI53MzMzMzMzMzKw4dzqZmZmZmZmZmVlx7nQyMzMzMzMz\nM7PiXNPJzMzMzMzMzGZzZVdJtM54pJOZmZmZmZmZmRXnTqdZSMn1kj7SdN1Okq6UNE3SREn3SrpI\n0uh8e+P6SZLuknSAJA2Qf6akpyTd23J9j6THcs7Elp+/nqQJkh6UdKek0xs/28zMzMzMzMysLnc6\nzUIREcC+wAmS5pO0EPA9YD/g5YhYKyJWA/6T70fT9asCWwJbA0cM8CPOAj7S5voATsg5a0XEVQCS\nlgIuAg6MiJUiYm3gKmDhIr+wmZmZmZmZmc2xXNNpFouI+yRdDhwMLAScExGPtAxeugFYtc1jn5H0\nBeA2oKfN7ddLGjvAj243Omo/4OyIuKUp49ed/SZmZmZmZmZmZgNzp9PwOBKYCLwKrNt8g6S5SaOZ\nftfugRExWdIoSUsA8wCnR8Q2HfzMr0raE7gd+GZEPAesApxd+7cwMzMzMzMze1OYOtwNmCN5et0w\niIiXgQuB8yKiUUJ/tKSJpFFMU4AzOsh5osMOp1OAdwNrAn8Hfth0W9v6UGZmZmZmZmZm3fBIp+Hz\nBqnWUsMrEbHWYA+StBwwLSKe6fQHRcTTTY//OXB5vngfsA5w2aAhv+zp+37VcbDauE5/vJmZmZmZ\nmY0gU3r/yl97/zrczbA5gDud3kTylLpTgZMrPu7tEfH3fHFHoLG63Y+BWyVdERG35vt+HLihuaMK\ngN16umi5mZmZmZmZjRRjx72LsePeNf3y9UfeMIytsdmZO52GVwzwfbPGtLt5SJNQzwVOBJC0NE01\nnST9EtgUWFzS34DDI+Is4PuS1sw/YzLwRUgjoCTtChwvaUnS6KvxwJVlf00zMzMzMzOz4fT64Hex\n4tzpNEwi4siWy4sMcL8Bn6OIeALYpunybgPcb8+ZZNwMbDJYe83MzMzMzMzMqnAhcTMzMzMzMzMz\nK86dTmZmZmZmZmZmVpw7nczMzMzMzMzMrDjXdDIzMzMzMzOz2dzU4W7AHMkjnczMzMzMzMzMrDh3\nOpmZmZmZmZmZWXHudDIzMzMzMzMzs+Jc08nMzMzMzMzMZnOvD3cD5kjudLKO6B1RLOv0ZT5bLOvz\n555fLAtgoz3+UC7sjnJRAHd/ao1iWbGUimXdcMEWxbIAeKpc1D49Py4XBpw5qtxH5m1n7Vssa/s9\nLyyWBcCN8xSLettujxTLAvjt9p8sF3bFUeWy2LZgFsB7y0UdWi4K4OoNy2W9Y+pfimU9cssqxbIA\n9GS57d60hUcVywLYb+vji2Wd8r39i2UBcHi5qLmP/nSxrPj25cWyANCUclHHfLhYFsDBB/2gXNhi\n5aJuXX2TcmHArZOeLJYl3lcsKwW+p1jUe6bdWyzroSdXLJYF8MY+C5YLK/iyBfjO4uU6Fw57utz+\n36j1pxXLAljwkX8UTDu6YJZZH0+vMzMzMzMzMzOz4tzpZGZmZmZmZmZmxXl6nZmZmZmZmZnN5qYO\ndwPmSB7pZGZmZmZmZmZmxbnTyczMzMzMzMzMinOnk5mZmZmZmZmZFedOJzMzMzMzMzMzK86FxGch\nSQImAN+LiKvydTsB+wBbAfeQnpMHgM9ExCuSpuXr5yFVPjsXODEioiV7fmA8MB8wL/CbiPh2vu1C\nYMV81zHAcxGxVr5tPeB4YEngZeAO4GsR8cqQ/BHMzMzMzMzMZrnXh7sBcyR3Os1CERGS9gUulnQd\nqSPpe8BHgLubOoLOB/YFTgRebrp+CeAXwCJAT0v2q5I+FBEvS5oO5P2MAAAgAElEQVQbuEHSxhFx\nQ0Ts2rifpOOB5/L3SwEXAbtExC35uk8ACwPudDIzMzMzMzOz2tzpNItFxH2SLgcOBhYCzomIR9Ig\nqOluAFZt89hnJH0BuI2WTqd8+8v523mBUcCzzbfnkVY7Ax/KV+0HnN3ocMoZv673m5mZmZmZmZmZ\n9XFNp+FxJLA78GHgB8035FFKWwP3tntgREwGRklaQtLSkq5oeuxcku4CngKui4j7Wx7+QeCpiHg4\nX16FNJ3OzMzMzMzMzKwoj3QaBnkK3IXAixHRmFg6WtLE/P0E4IwOcp4Atmm6/AawpqRFgd9LGhcR\nvU0P2Y00Pa+Z6ECc1tN3YZ1xaN1xnTzMzMzMzMzMRpipE25k2vU3DnczZrGpw92AOZI7nYbPG0Bz\nMfBXGrWbZkbScsC0iHhmoPtExPN5BNS6QG9+3NzAjsDaTXe9D1gHuGzQn/vFnsHuYmZmZmZmZm8C\nc2+yEXNvstH0y68fc/wwtsZmZ55e9yaSC4mfCpzc5ra3ShqTvx8NbAlMbLrLFsADeXRUw4+Bz+QV\n7Bo5H5e05FC038zMzMzMzMzmHB7pNLxigO+bNabdzUMaD3guaVU7JC0NnB4R2wBLA2dLmovUmXhe\nRFzblLML8Mt+PzziaUm7AsfnjqY3gPHAlV3/ZmZmZmZmZmY2R3On0zCJiCNbLi8ywP0GfI6aazpF\nxD30nzrXet+9B7j+ZmCTDppsZmZmZmZm9ib1+uB3seI8vc7MzMzMzMzMzIpzp5OZmZmZmZmZmRXn\nTiczMzMzMzMzMyvOnU5mZmZmZmZmZlacC4mbmZmZmZmZ2Wxu6nA3YI7kkU5mZmZmZmZmZlacO53M\nzMzMzMzMzKw4T6+zjsTtKpb1+RvPL5Z1yNf+u1gWwPnsUSxrnkNfKJYF8JrmKxe2bbkoHiqYBaz6\n7duKZZ118n7FsgDunfreYlk9WqhY1hieK5YFsMRujxbLevIzyxXLAljjspuLZS3O2sWy9uLYYlkA\nf2PZYlk/+M8yxbIAdjvq4WJZp/5x+2JZi278ZLEsgDXnvbtY1ty7FB7OP6lc1FGHHlguDHjpkHKf\nbUvpqWJZmx10bbEsgPn4T7Gsr6js5+QG3FQs69f6dLGsd9z9f8WyAI7mf4plXc2WxbIAznli02JZ\nK3Fnsayt3nZ1sSyAqw7esVjW6GX/WSwL4KWblyyWNff6rxfLOuyZsoffNy94SbGssq8Osz7udDIz\nMzMzMzOz2Vy5DkTrnKfXmZmZmZmZmZlZce50MjMzMzMzMzOz4tzpZGZmZmZmZmZmxbmmk5mZmZmZ\nmZnN5lzTaTh4pJOZmZmZmZmZmRXnTiczMzMzMzMzMyvOnU4jhJLrJX2k6bqdJF0paZqkiZLulXSR\npNH59sb1kyTdJekASZrJzxiV7395y/XfkvRAvu1WSXsM3W9qZmZmZmZmZnMCdzqNEBERwL7ACZLm\nk7QQ8D1gP+DliFgrIlYD/pPvR9P1qwJbAlsDR8zkx3wduB+IxhWS9gU2B94fEWvl7wfsuDIzMzMz\nMzMz64QLiY8gEXFfHoV0MLAQcE5EPNIyeOkGYNU2j31G0heA24Ce1tslvQP4KKkj64Cmm74NbBoR\nL+WcF4Fzi/xCZmZmZmZmZiPC1OFuwBzJnU4jz5HAROBVYN3mGyTNTRrN9Lt2D4yIyXkK3RLAPMDp\nEbFNvvlE4EBgkaa8RYCFI2JK6V/CzMzMzMzMzOZs7nQaYSLiZUkXAi9GRGNNx9GSJubvJwBndJDz\nBLANgKRtgacjYqKkcbUadnlP3/crjIMV68WYmZmZmZnZ8Hq2916e7Z003M2wOYA7nUamN2iquwS8\nkustzZSk5YBpEfFMy00bAttL+igwP7CIpHMjYk9JL0l6d0RMnmn4dj2VfgEzMzMzMzMbmRYbtxqL\njVtt+uVHjvzVMLbGZmfudJpN5Cl1pwInt94WEYcAh+T7bQp8KyL2zDcfA/xE0i4R8WIuYL5jRJw3\ni5puZmZmZmZmNsReH/wuVpw7nUauGOD7Zo1pd/OQqqKdS6rdhKSl6V/TqW12RJySO5puk/Q66Z14\nfIH2m5mZmZmZmdkczJ1OI1BEHNlyeZEB7jfg89dc06nl+vHA+JbrjgOOq9VYMzMzMzMzM7M25hru\nBpiZmZmZmZmZ2ezHnU5mZmZmZmZmZlacp9eZmZmZmZmZ2Wxu6nA3YI7kkU5mZmZmZmZmZlacO53M\nzMzMzMzMzKw4dzqZmZmZmZmZmVlxrulkZmZmZmZmZrO514e7AXMkRcRwt8FGOEnxjvhzsbwN4qZi\nWf/zxT2KZQGs/rNbimW9yMLFsgA+yxnFsg4954RiWcxfLgpgg13+WCzrpr9+sFgWgB4u109/5GYH\nFcs6k32KZQHsxMXFsk5+7ivFsgBenbBYsaz1thtfLOu2jTYpllVanKyygd8umHVEwazHCmYBGl1u\n/+iY7b5eLAtgklYrljUm/lUsC+AnGx9YLOv9N04olnXbaYXfo08WzJpWMAv4+lHHFsv6fxcUfMOP\nKRcFoKvKvUdjfNnPSZ1YsG0UbNuR5aIAKPjRpjFlj0kP3fzQYln/76X9i2VtuMCNxbIANpj748Wy\njowgIgrvNIwckgJ+OtzNGEJfHrHPn6fXmZmZmZmZmZlZce50MjMzMzMzMzOz4lzTyczMzMzMzMxm\nc1OHuwFzJI90MjMzMzMzMzOz4tzpZGZmZmZmZmZmxbnTyczMzMzMzMzMinOnk5mZmZmZmZmZFedC\n4iOQpB2Bw1uuXh34aET8vkD+F4GXI+K8brPMzMzMzMzMRr7Xh7sBcyR3Oo1AEXEpcGnjsqQvALuV\n6HDK+aeVyDEzMzMzMzMzG4in141wklYADgP2yJcPlHSrpLsl9TTd7wBJ9+avrzddv2e+712SzsnX\n9Uj6Zv6+V9Kxkm6R9JCkjWfpL2hmZmZmZmZmsyWPdBrBJM0D/AI4ICIek7QV8J6IWE/SXMBvJH0Q\neBnYC1iP1JF4i6TxpPGDhwIbRMSzksbk6Mhfje9HRcQHJG0NHAFsOYt+RTMzMzMzMzObTbnTaWT7\nDnBvRFycL28FbCVpYr68IPBeYCHgkoh4BUDSJcAHSR1KF0XEswAR8dwAP+eS/P+dwNh2d3i+56Tp\n38837gPMP+4DNX8lMzMzMzMzG05TIpgy3I2Y5aYOdwPmSO50GqEkjQN2BNZuuemYiPhZy32/Bqj5\nqgG+H8hr+f9pDPCaWLTnax3EmJmZmZmZ2Ug3Vuo32mB8xEB3NeuKazqNQJLeApwF7BkR/2666Wpg\nH0kL5vstI2kJ4HpgB0mj8207ABOAPwI7SVqsKXf6j5kFv4qZmZmZmZmZzaE80mlk2hdYAjhV6tc3\ndAypxtNN+foXgU9HxERJZwO35vudHhF3A0j6HjBe0jTS9Ll98n0G6sp2F7eZmZmZmZmZdc2dTiNQ\nRBxD6mAayEmtV0TEicCJba4/Fzi35bojm77/UNP3/wCWq9FkMzMzMzMzsxHs9eFuwBzJ0+vMzMzM\nzMzMzKw4dzqZmZmZmZmZmVlx7nQyMzMzMzMzM7Pi3OlkZmZmZmZmZmbFudPJinm195aieU/3PlAs\nK57oLZb1Uu+dxbIAXum9dfA7deiR3r8VywLggd5yWfeVy3q+965iWQDcNL5YVNzVWywLYHLvo8Wy\nSr7WHu2dXCwLYNoNN5QLu7e3XBbwQu/EYlnxfG+xrOJ5txfMerZgFsCdBfMKfhYBRMHX28O9jxXL\nerL3oWJZAI/1Plwsq/T7oOh79KHeclmTy2UBxJRyeX/rfaRYFgD395bLuqdcVjxWLguAl8rlld5f\noGTecwWzACaVyyv5d5vS+9diWQBTr7+xWNazvZOKZQFMCS9Qnkydjb9GLnc6WTGvFe50eqb3wXJh\nf+8tFlW+0+m2YlmTCx6wAPBgb7msgjukL/TeXSwLgJvLdTpxd2+5LGBywY7EVwt2Ov2td0qxLIA3\nbii3o1Zy5xbghZKdnC/0lssqnXdHwax/FcwCmFgwr+TBMRR9vT3S+3ixrKcKdzo9XrKToninU8H3\n6J97y2UV7CQC4K/l8h4r3elU8iRVyRMHjxfMAvh3wbzC+wtF8wq/R4t29hf8Pf9a8MQewLSSnU7j\nC3c6FU0zq8adTmZmZmZmZmZmVtzcw90Ae3P4G+8d9D49LE5PB/dDHdwH6NEj9GjPmd/pZx1F0dMj\nenrUwT3XHzyLq+jp4H6d6mFJelilg3ueMOg9/kMPh9Az6P0O+UwHPw7omQw9Hd530KwHoGeXTu65\n2eBZTKCng/vxrk5+HvSMGUXPu+YpktczQfRs1slr7bgO7gPT6OGwQZ7TwzpKgh6WoIf3dXDPwzvI\neoOeDu73gzEd/DigZ/7R9IxZbPA7bt9B1p3Q08H9YNNO7kSPrqNHg9z3Tx1FVfgsmvV5PUtDzzod\n3PHqDrJ60lcpPddAz0aFsjr+LOrs79pzp+jZfrD7ntRR1ivq4WD1dHTfwfTwOD18fvA7dvjy6dHL\n9OigQe93cgcn+jt/3Xb4HuU6ega77xc7iqLn76Lni4XeUyF6jhiO9/u3B8/iNXo6uN+Ju3fw44Ce\nv0BPh/cdNOs26NmmgztuM/jfovxnbqefbR207XrRs3nBtl0PPZsPcqfBbm9k9RT+DL8Xej4x2L06\n/Mzt+O929OBZ+d9gvrNQBz8O6Jl3QXoWWmKQe+3QWZbuomeuDu77xhud5fX00DPIk3qkyr0ezZop\nPL/TBiHJLxIzMzMzM7PZWETMtj1Pc8Ix7Uh9/tzpZGZmZmZmZmZmxbmmk5mZmZmZmZmZFedOJzMz\nMzMzMzMzK86FxM3sTUHSAsCyEVF2HXCzTNI6QJCqmc4w9zwi7pzljTKbjUhaICJeHu52vJlIGgUs\nRdM+e0SUXefdbISTtARARDxT8/GjgYUj4umW65cEXoyIV4Yzz2x255FONqJIWkDSigXzxkraoil7\nkRoZ80vaXdKhko7IX4Mv2zV47gJdPn6xmX3VyBsl6fhu2tSSt6CkwySdni+/V9K2NbO2ByYCv8+X\n15J0WRdtm0vSHo3nUdI7Ja1XMeMlSS8O8PXCcLatTWZXr7WhaFvhrCUlHS/pSknX5a8/1oj6Yf76\nCXALcHr+uiVfV6dtRZ9PSW+TdIakq/LllSV9dnZrW378ByXtnb9fQtK7u8jqelswVHmlfk9Jn5D0\nF0kvFPos2qmT6zrM2lDS/cBD+fKakn5aI2c9SW9vuvwZSZdJOqnqdi//vT6e/2/9+njVtuXMYtu9\n/PivAk8BfwCuaPqqk1V821JaqW3VSFbi/a5knKTV8+VdJP1E0v6S5quYtaik/5a0r6S5lfZxfyvp\nO0odK1XbtryksyR9V9LCkk6XdJ+kiyWNrZglST2S/gH8GfizpH/kNlYtlnwS8ME2129MJ8tDD2Fe\nfl/OsFx3vv5TNdpmNuK4kLh1RamD6KfA2yJilbwB3D4ivlsja3vSOvLzRcRYSWsBR0ZER4uft8n7\nAvB5YLGIWF7SCsApEdHhgrHTc34PPAfcAUxrXB8RP6zZrg2Bn5POkCwraU3gCxHx5Yo5U+gblfFO\n4F/5prcAf42IOjsyNwMbRIEPBkkXkf5me+bXxoLAnyJijRpZdwKbAddFxFr5ukkRsWrNtp0KvAFs\nFhEr5YOVqyNi3Tp5JZVsW6nX2hC1rWTWNcCvgG+RFkbfC3gmIgZf37193iXAERFxb768KumzaNAF\nn9tkFX2tKXXonAUcGhGrS5oHmFjnvTDC29YDrAOsGBErSFoGuCgiNqqRVWRbMBR5hX/Ph4FtI+KB\nqo8dIG9i4/N2Ztd1mHUr8EngN02f4fdFxCpV2wRsHhHPStqE9L7/CrAWsFJEfLJC1tm0GdHYEBF7\nV2lbziy23ct5DwPrRcQ/6zy+JavkZ+5SwH5A4/mbBPw0Ip6q2baut1WSLp/JzVF1X7J0Xs7socD7\nXanDdjVgflJH7kLAVaQOD0XE7hWyLgUeAUYDqwP3ABcC25M+4/ap2LbrgV8AY4BPk7YJFwFbArtH\nxGYVsg4Atia9Fibn65YDTgWuioiOO3ck3RkRaw9w2/0RsXKnWaXz8ufj5hHxYsv1CwETBvo5HeRu\nDBwBjKVvpGRExHJ18sy64el11q3TgQNJGwCAe4FfApU7nYAe4APAdQARMTFvXOraD1gPuDnn/Vlp\n2GtVy0TEh7toR6v/B3wE+E1u112SNq0aEhFjAZTOqF4aEb/Ll7cGdqzZtruA30i6GGhMgYiIuKRG\n1vIRsbOkXXPIv6ufmJru9Yh4ruXxb9QNAz4QEWvlAxjyAcw8dcMkrQFsQjqAuT4i7h4hbSvyWhui\ntpXMWjwifi7paxExHhgv6faaWZAOXu9tXIiISZLeVzOr6GsNeGtE/ErSf+W81yVNnQ3btiOpI+GO\nnPW4pIVrZpXaFgxFXsnf88kSHU55G/JRYBlJJ5FObAAsDLxeNzciHm35DK/z2pgrIp7N3+8CnBYR\nvwZ+LanS525E7FXj5w+m5HYP4FGg9mi1FkXe75I2InUqnJO/ROpIuVXS7hFxQ422ldhW1ToROAvz\noNz7/UPAyqROp8eBJSNiqqTTSPvhVSwfETsqvVD/DmwSEW/kzqM6+zILRcQpAJK+HBGNEfRnKI3c\nq2JPYMtomlIXEY9I2h24hmojimY2iq7OzJ+SefO0djgBRMRLXW6TzwC+AdxJ00lzs+HgTifr1gIR\ncUtjpyoiQlLdndLSHQuvRcRrjTxJczOTs5oz8SdJq0fEPV20pZ9CO98NG0TE55uyr5R0XM2s+YFn\nSaOKmtXpdHpNTUOzJS0PvFazXfflnYy5Jb0X+Brwp5pZAP9RqpPRaNsS1HytSfo6adTDJaSd7/Ml\nnR4RJw1326D4a61k24pm5f+fVJrK8gRpxF9d90j6OXA+6Tn9FPV2vqHw8wm8JGnxprz1gednw7a9\nlg98GlkLdtGuUtuCocgr+XveLulXwP/S956oc9LgCdJB8cfy/40PkBeA/Wu27dHcWYGkeUmf4XU6\nyEZJmiciXge2AL7QdFutfVpJY0ijATbJV/UCR0VEndduye0ewGTgOklX0P85rTMdqNT7/QRgh4iY\n2HTdb5RGiJ5GOnlYWbfbqojorfNzZ1VeVur9/mpEBPCKpL9GxFSovQ/+RtNjr4yI5st12ialWRCL\nAgtIen9E3Jb33ap+Ts4dbWo4RcQz+XO3iqclfSAibmlp7HrA0wM8ZlblzS9poYh4qSVrYaCbTqfn\nIuLKLh5vVow7naxbz0h6T+OCpE+SzpTUUbpjYbykQ0kbvS2BLwMzGy49kA8Ce0uaTN/OY0TE6jXb\nVWrnu+EJSf9N/wPkx+sEFT7z20Ma7v0OSb8ANiJNe6rjq8ChpL//L0m1nb7TRdtOBi4FlpR0NGna\nx3/XzPoc6QzyvwEkHUsaAVG306lk20q/1kq2rWTW9/KB4zdz7iLUPzgG2Bv4EvD1fHkCcErNrJK/\nJ6Tf8XJgOUl/ApbImbNb2y7OZ+3HKE1n24c0/aaOUtuCocgr+XsuCrwCbNVyfaVOpzxS825Jv4iI\n/wz6gM58CfgRsAxp+3Q1acRYVb8k/f3/QRqNez2k2kmkafB1nEkaHbITaRu6B2lKUJ26Tj2U2+5B\nGun0KDBv/mq7yEGHSr3fF2npcAKmj06qW9us622VpIsjYidJ7Ub6VN5nK52XlXq/L6E09Uwt30P6\n3K3iDkkLR8SL0TSlNO/b1xlldwjpPfAk6bPovPycLkD6fauYWQda1c61bwEXKU2rbXSmrwN8Bti1\nYlbpvDNIr40vRcQUAKVaXz/Jt9V1XT4JfQlNnd/hRVFsGLimk3Uln8X7GbAhqabQZNKc7Sk1shYk\ndSw0dph/D3wnIl6t2bZRwGdb8n4eFV/0GqDwYZ3fMectQdr53oK0kboa+FrUrNmQRxYcQV9Bwwmk\n+jPPDvyoAbOK1ejKeW8F1s8Xb46If9TJGQpK06UaNViurTstJe+Qrhd5pZJ8lvvWiFhtBLSt6Gut\nZNtKZ41kpX9PpeH2jQUXHsqjPmbHtm1F0+d3RFxTM6fItmAI84r8nqUp1ao6mjSNpzF6J2KY64FI\n2gB4G6keUaOzfwXStJ7KB1OS7o6WmkvtrquQN1tv9yQ9AGzUuo+hVCPqTxGxUo3MrrdVkpaOiCdK\n7bOVzmvK7fr9rlQbqvF5o9bvI+LIOm1r83NU93OtKWMu4K2kOotV97+n0VfqodXoiKg0eEIz1iK7\nD/hxtKxANxx5kvYFvk2axgzwEnBMY6pizfb10n4l3g/VzTSry51OVkTuMJqr3ZzkNytJi0TECxpg\nRZw6nTpDSdKCjR3wLjImkGt0Rar9IGBSVCj2qr5l5/td3biuykGB+hfybBRNn345ahaZz9mjSAcu\n06fGRI1lqPMZxr3om163A3B2RJxYMaf1ddb4XRttG7bX21C1reBzUKSztOnM9iRmfA3XOrMt6byI\n2GOw6yrk7UwqoPqCpMNI9UG+W/F9VfT5lPQJ+t6f7XZwK0/PlfT9iDh4sOtq5C4GLBvd1V0bsrya\nbTg4Ir4v6eQ2N0dEfK1m7o2kExonANuRRgCOiojDamQdRxqd+gppFMQawP4RcV7Ntq1OX+fmAxEx\nqU5OzroZODAiGqOmNgaOi4gNamRtDNwVqRbLHqT3548i4q8Vc34UEV9X+2LWlbZ9Q/B+/wJpWuO3\nyLWJgHWB7wNnRsSpAz12VsgjRJ5sORG0VBedRMXy8r7yqxExLW+3VgSu7KZzvqQ82mws/YtOn9tl\n3rua8ugmb07QGC0YEaVquZmNCO50slokfbPpYruDjMr1BvIG+FvMuMHreKWLnDOzIoodHzhKuiIi\ntlHfKnGtQZVWh2s5IJjhAK2LA4PWVV/WAL4YNVYok3R7RKyrphWKJN0VEWtWyOgl/V6jSUONG7Ww\nVgdur7IjL2lc/nZHUudEYwrhbsBTEfGNTrNacr9KOph6mv4rEtYanZQ72jamr5D4DFMPOsiYQvnV\nCIsc6A1R24o9ByU6S3NO8TPbalntS6kOxT1RcaWcpsffGxGr5YPb7wLHA4dHRMdLn5d+PtW3CtiS\npFGvf8w3fYg08qHykvGtf7d83b01Xx/jSZ0mc5MOkp8BboyIWlMwS+blDrtjgaVo6gyIiI6nKUna\nLiIul7QX/bdVjVEP51RtV869MyLWbv67ayYrNg2SdXdErCFpR2Bb4ADSZ2XVKU+LkopNv5NUZ02k\nVbweBT5W50BNaaW0c0nTEyG9Hz5TpyMx73+skdt0NmnbvHNEVCqKLWmdiLijaRvYLCItmNBp1hTK\nf35vCxxMGgUHcD/wg4ioNc1UqRD/55lx/6/qdCwk3UGqdfmffHk+0vuz7qqcxfKUVuLdmPS3vxG4\nDfhPVFhtLuccMcBNjY7Eo2q07XxgOdKCMs3b5KrFv4vl5Q6+fYHlSVNgz4hcv6pGez4SEVfl78eQ\nCsWvl3P3j4orLyoV5P818MuIeLhOmwbJPzci9qz52D0i4rx8rNZum1CnJpxZV1zTyepamPRBtiLw\nfuAy0ofZtsCtNTMvJtVN+Tl9G6g6vaLb5f8bnS7n5bZV2qhHxDb5/7E12tBO44zghqQdtV/ldu1E\nGpJbV+uqL3er/gplXdfoiohx+bGXAJ+PlmXnK2b15sf+MCLWabrpsrwjWNc3SEsWd70MdXYXqX7B\n3EBIemfVETsxNKsRbhURB+YDvSmkGiXXk94Tw922ks9BkQUNIuKJ/K1oc2a7SpakQ0hD5UdLah4B\n+jppSnJdjc/GbYHTI+K3kirVNyv9fEauBSfpGmDliPh7vvx20upWHZP0JdJn9/ItJxAWJh2k1bFo\npJFhnwPOjYgjBjk5MSvzfgBsG11Ma2wc6EfE2XUzBvCq0mjE/5P0FVKB8bqFjxv7m9sC/xMRz0uq\ns33/LnA7sFnkgse5jccA3yPV/6vqhYhYPXdokdtWdwrh1EiFoncAfhJpVc3PVg2JiMb27XbglYiY\nBtN/1/krZo3Njy32+R0RvwV+W+exA/gNqTTANfQVN697VnxUNNUii1T0v5tizCXzFBEv59fETyPi\nB6q46mL2b2b8+yxImvb7VqBypxPpJOHKUW40Qom8c0gF9G8grai5Mn21Fqs6hnTyDVKH099Jxws7\nkgrg71Axb0z+uk7SU6QVHX/VtB/RsTyisXU0/2aS3kK9Uf2NlfUax2pmwy8i/OWv2l+kA9iFmy4v\nTDp7WSfrjsJtu6vNdRNr5FzbyXUV8m4hLY/auDwPcEsXebe2/m7A3TWzlgeuJY2MeYJ0kDe2Ztb9\nnVzXYdYDpGV9G5eXI02pqPs3u675Oejmi3SQ8w/Smd57G19d5E3q5LoOs+7L/58BbN3Na2MI2lby\nObgSeE/jPUAqkHtlF3l3APM2XZ6PNEqvTtaxJX7HprwrSJ1Wk0k7vPN38X4v9nzmxz5IHkGdL88F\nPFgxY1HSaIcLSdMyxuavxbto173A20l1YtbL190zEvJIIyZKvTauAcY0XV6MVDOmbt56pG36sqRR\nO5cA69fMOja/Pu4iFcRekhrbvbwtmOFzg7QdrfRaa3rsDPsF1NwfIXWcHAL8hTQ6d1SX24NbSLWq\nGpcXJo0erJNV8vP7o8B40rbvedK+4I6NNtbIm2F/rYu/2R9Io94alz9Gd/tsxfKAicAGpMVGVsnX\n1X595McvQioIP5k0xXHJmjkXA0sXfB66zmv+25A6rivvwzf/7Zu+v5v+26rK21D69jdEWvnyFNLJ\nx+uAL9R4XVxAGh28KTCO1Cm2KbBpqefEX/4azi+PdLJuLUn/FSRez9fVcbmk/ZhxlYW6tWwkaeOI\nuCFf2Ij+ZxEGe/Bo0tmCJVpqIixCWoGnrjE5ozHCY+F8XV3FViiLNER4c5Wp0VVy2fn9SWeTJufL\nY+m/VHZVkym3DHXpUVPFViMkvaceBF4FvpSnMNQqzD8EbYID6iEAACAASURBVCv5HHyF1BGzoqQn\ncnalkY0tip3Zjoj/ymcr30vTCIWImFCzbTuTRjYeFxHP5dFEB9bMKvl8Qjo4+73Sql0CdiF1hHQs\n0jL1z5NX/8mv2fmBBZXq1lWu+UU66/97UgfPrUoLYPylRs5Q5N0u6VfA/9L/fVC5DhawRERMX8Et\nIp5VKnRbS0Q0Ri2/SHcrsDXeBz8Ano9Uz+bfVB9ZAGkq0gyjGCPidUmvtXvAQJSKaq8MLCrp4/RN\neV+EiqOJmuxCeh/tExFPSnoncFzNLID5omkZ9Yh4UdICM3vATBR5v0v6PPBF4CD6RnCvAxybf98d\nSAfPVfxW0jYRcUXV9rSxL3CBpB/ny4+RViQcCXnfII2AvTQi7sufHdfVCVJaRGZ/0rbuXGDtiPjX\nzB/VNqcxJXIh4H5Jt9J/peZKo2wK502fShcRUxujmWtqXuVv0ZbbagdHRJA6mycolQ3YgvQ5UGVE\n87qkEVyHkurLTZT0alSYRtuOpHOArze2C3lf5IdRY9qqWbdc08m6orRs9C70L6L8q4g4ukbWFArU\nTmrKW4e07HFj4/IcsHd0WHBX0jdIG4GlSaN+Gl4EfhYRP277wMFz9yYtq9ybr9oU6ImaUyOUVso5\niQIrlOWsI2iqTwQcVTNrNGmZ7OZV9U6J+qsRzg+slNv1YERUOsBoyerJ3zZeb7VXfJF0HWkaW5FC\noCq4GmHOW4y+A70FSctdV5oyORRtK/Uc5Okm34+Ib0laiNRZ2lUBTkl/AE6OiN/kyx8jvac2n/kj\n22Z9ntQRvCzpbOb6wE1RsVZdS+aapOegUUOsVmfuELzWRJqqsElu24SIuLRm1vakKRBLk+p+vYs0\nurFSna6RTqkeFrRs+6Jp6fIKWXcAH49ctFqpNtklUaMGU358u4PhqPPazSdEvkR6bUDa/p1a9XMz\nd6J/ir4Ooub/L4gKK6flKXA7kKbYXNZ004vAhRHxpypta8p9G6nsQJBGItdaGStn3Uj67LkjX16X\n9NlUp8h5kfe70up1G7fuF+T8x0n1cSqtuCXpJdJJvv/QdyIzokJtszaZC+eMlwa98yzO6zZL0vGk\nz9qfkabp1T5BqBnrhrVukyt1fJTM04yr140mjcSH6rXveuj/OXtKRDydT9x8PyrWT5J0YUTsWuUx\nHWS+AziRtM3bPiKW7TJvhpqs7a4zmxXc6WRdy507jQOgCVGjiPJQaq7TUPPxX4uIkwq36e3AB0h/\ns1si4smaOXMD50TFIpQzyfsDach885nQcRGxRYn8Gu3ZPCKuVf/VsaCvWGad0QBFSToTWIE07anb\nETtFlTrQG0p555sud5pvJhV6LbJBU6prdgGpwwPyme2I+L8aWZNIB6A3RcSaklYiLYNcq5aKpK+T\nCu42d/SfXvozarhJugfYDLgmUnH4D5GegzqFhZcldcxvnK+aQDr7+1jNthXNK0XSR0gHoePpm/Lx\nhcjFc2vkNRdKnh/4BKlmUeWRdZLOIE2POSe3bY+c9bmKOb3MpEZJ1FgKXNIGEXFT1ccNkLUzaWRT\n48B6E9LIhYtr5r2fNNW0caLg7cAuEXF7jazVItdY7IakByLifQPc9mCVjr+holTofGX6jy6tU+eo\naJ6k1UijkhbPVz1DKlpfafVFSW/Qv4OuWe3Ourxvuh6prtZtdfdNS+VJuhLYLyIe6aYdbzb59bZh\nRBzSZc7dwIcaHcv5JOT4qLlojlk33OlktUhaJFIh1ca0s9pL8LbpWOinaseChmDVBqUi2K07HJWW\nfZX0voh4IHfStetA6XjJ85bcG4DNuxn505Q1KSJWbbmu0opR6lt2vt3ObUSF1YokHRmpSO/ZtH9t\nVBoNoILLUDdl9jQe37iKeiN2hqJtpQ70hqJtRXa+c9appA6ii+k7Kxrddko2n42W9P6IuK1GRmNF\nyLtI9XBelXR/dLF6Xc75d768IHBzxfdo0edT0o0RsVEerdD6Pq11ACTpjohYJ+80rx1ppN49VT4/\nmrL+QOpEPD9ftTuwe0RsWTWrdN4QdIgtQRpN1zih8UydnJnk3xYR76/xuBmeuzrPp6T1gL9FX7H6\nz5A6w/5KGjHc8ahcSQdHxPfVf2XZhogaK8rmztItIo9uys/HtXVet02Z85IWbQF4KJqm/lbMuYFU\nn+4s0qiwuifibiGtkHtXy/VrkEaBf6BCVmO/qO1ovDr7RZJOI42I2Qw4nbRYyy0RUbmge+k8STcB\nh0TEdfnyOODoiNiwYs5VwJciYvKgd+4883PA4fRN9xtHGul+xnDlSdqJtEDAOaTVEYfkhJmkvSPi\nrBqP2xh4NiLuz8/luqRaT9eWbmMdkvYkTdm7CKYvXPS9qscvZiW4ppPV9UtgG+BO2hxkkAo9d2oT\nUvHq7dpkQTqjX0Wj3sFCFR/XVu5U2BRYhTSaZWvSShpVP7QPII1Q+CHtf8/KZ2izycANki6j/wF3\nnZE2V0vajbSyHqQN1NUVMxori2w303t1ICKOyP/v1W1W1njOflgoj4joKRRVvG3A+1sOdq7NB0VV\nzaxtdc9c/Aw4oGXn+2ek1R2rmh94lnRQ0KzbkXDvBHaTtCupztA6g9y/nceU6ij8L3CNpH+RVhLs\nxhsDfN+pxvN5PMxQy6Ly8xkRG+X/i3zmZv/KnX7Xk+qpPA3UndayRMsBxdmS9u+ibSXzziJ1YO2c\nL++er6vTgXUJadGAKyKv7NYN9a9lOBfpgKrudKepkt7TGC2oVMumztLnpwGb54xNSAXKvwKslW/7\nZIWs+/P/7VZCrfu5JlIHesM/mfE91nlYmjJ5JvCLqFGvp1lEbCxpBWAf4E6lWjtnRUTVbfw3gd9I\nOov0txPps3Ev4NMVsxr7RSdQbr9ow4hYLXdqHinph/StWlZHybwFGts8SCv05hMHVZ1Bqp9XsjPm\nIGCtRset0nTJm/LPGpa8iLg4j3Y6nFT/7jz6Xid193PbOYr0udsxSceQXp+jlKYib0I6RjhC0toR\nUauWm6QrIq+e3e5yFRFxbv4MabyPdoyI+2f2GLOh4pFOVpskActGvcKuQ0qpzsvXS2yQlKbHrAHc\nGRFrKBVmvSBqTDmTNBdpGlDdpb/bZfbkb1trgtSpT9Soq9A4YJmLtDRvjqw1YmERmjq4q4yCa8p4\nC7AnqYB4I6vWmejSlAodH0QaCTc6Xx3RRc2eUiTdCezccqB3cdSv8bJktNQnkbRiRDxUI+vuiFhj\nsOtmNUnvJhWx3o00fWEssG5ETCmQPY500H5VF6MVDiAd3DVPrzs7Ik6smDM3cG5EfKpOOwbImxRd\nTq2RNF+kwu0Lkorez0XqiFmE9Llbp77cH0kHFI0C57uS6vtVrtFVOq/k+0DSlsDepJFOF5E6FCq/\nN5vyptC3XZlK6iw9MvLiHBWzNif9zZoXg9g7Iv5YMWf630bST4BnGh3/3X5+KE3FfyO6m+p7HGl/\nobmY/j0RcVDNvPeSntOdgdtJf8Oro4ud9/xe3YE0wu550nvskIj4dYWMtwH7kbZ7kDrwfhJdTscq\nQdKtEbGe0rTrT5A6/iZFxHuGO0/S/5I66s4jvT52B9aJGtOtlWoYHg58OOd11Rkj6U+kqViv5cvz\nAddVHYVVOi8/7mDS3+pCmk60VNnPVfvR9w0rRMR8Fdt1P7A6aTXOp4B3RMTzSvVMb6k7ulHS0hHx\nxECXa+Q1akBCKoFSd0Efs654pJN163fAqoPeqwN5J+Z7wDIR8RFJK5M6aCqfZYk0FWM30tmzbr2S\n86bmndKnSUWBK4uIN/KOcrEifoONtJF0ckR8tcOsYiMVJH0ROJK0akljJ6HqKLiG35HOkN2TsxrF\nY+u2bTvSma2x9O/EqnMW/wLSyLBtSSv67EX/M93D2bYDgT+q/6p/lQsUN7le0uER8avc6XwA8Dmg\nbX2PQUyWdBj9d75r1W3IZ9ybNaasVqr/k6c+zEuaprdDRDwiaXI3HU6SzouIPXJ7ehvXUXP1o4g4\nQdJ4+or97xU16uhFWgnonY1OnjptaZP3kKR3RS5kXdNNwNqk2mN7ANOAs7ts3j7AyfRtD/5Ed++D\nknn/lLQH/Tuw/lEnKCKuIY2mG5NzrpX0KGlK0PlVR0NExNg67Rgg69o8ymZF0uv2oZqvu1GS5sm/\nyxb0X8W01j6tUt2kM8mjuCQ9B3w2atRNIp2A+Dh978/TomYxfYCI+AtwiNKqc9vmdr6hVEvwR1VO\n4ihNf9sr51wDbBsRd0paGrgZ6LjTKXcuHdbxLzJ423YCfh+pbMNhpJFr3416ZQcuzyeqjqNvFNvp\nXTSvZN4+pP2ixijc6/N1dbxOGv05P2kV5G5HNz4M3CzpN/nyx0irEH+Teh1ZXecp1ak7AbicNGrq\n5UEeMjNLklZ+bTdisM6iAf+JiKmkUZwPR56uGhGvKNXcqut1SUtEnhrdZYdTaw3I8yXNdjUg7c3B\nnU5WW0SEpDskrRd9Syt342zSWbxD8+W/kM7W1h3ae4PSEre/om+0Tp0aAbflHY7TSWca/029DVTD\nHyR9Evh1N2crK9h48LskuSOhscP8BnBDFzvMBwKrRkStA6gW80XEAQVyGv4faeWXSdH9NJTFI+Ln\nSgXnxwPjJdU5WBmKtt1IKnI+vR5Il3njgJ/l1+9SwIOkItl1lNz5voK+TsjRpL9fnR21p0id6EuR\ndlBLFC9trZE2N/Wm6TUe3+jEuqPNdVWVnJoLsBhwn9K0neYRklVqRM0naXdgI/Vfxr5RK63OlMll\nIqLfdF9JGwG1RunmTsiupw9nRTvElKaw7EGa5nQnqTNrY+AzpPdvlax2dRan1yKs8VysDbybtO+5\npqTKtRFJU/vHS/oH6TV7fW7re0kr1NZxJvDliGhkbZyvqzxSIW/Tf02FDpzB5M6ivUlT+39N33P6\nR6qdwDqJtD91aPPBe0Q8kTu1Om1Pu1UNm+JqjfI9PNJUqo1J0yePB04lFaHumNJo8j9Gmor4a0lX\nAPNHXjK+qtJ5uZOwo5OAg7SrZGdMw8P5q/Ge/03+vu7JyBJ5hwI7RcR9NdvQ7ApgoXYnafKJnKpe\nk7RA/ttPHz2eO/0r7bvlfe8jSFOFR+XrppG2DUd1cazwOeAD0VcD8lhSB7M7nWyW8/Q664qkh4D3\nkIp4Nh9k1Cn22ii4OzEi1srX1V7aUwOschM1Vrdpynw3acn52sNT1TeFbRpp+khuVv2lgQf5edP/\nnh3c9xRgedKOfWNqwMMR8eUaP/dq0vzxfw9658GzvgW8QNrBmn52vMpZ3pa88cBmETGtQNtujoj1\n8+97Eqmz4+KIWH4EtO3OaJlK1+66iplfAb5Nev3uGjWXFR9K+UDhxqi3rPgYUsfrrqTPtsWAD0fE\nLRVzDiH9nZqXeIZ0dvpnEfFfVduWc/u9n3Mn1j1RozC5ChXBb8obN0BelSWyP0ga9bYT/ZexT8EV\nFw/ImTN8Blb5XGyTtyTp7PFY+o9GrNtpWoSkS4GVSKMHz4pcbDvfdkdEVOrszAfYG5I6N0TqtLqJ\nNNq30nMh6XzSKNe7SJ8d5IzKB+CSNgDeRppm1jiYWoF0QFmn8HS710elz0m1L6LfUHv7rlSP5Xng\n58AlEfFq022XRs1VMLuh/qsaQvq91ydNgXo6Ilpv7yTzrkirex4L3BsRF9R9j3az3zhUeWq/aEND\n1Y55JF0P7FuoM2bEkqRZdHK2MknzN78fm65/K7B0RHRcP1Np2vzWpNVGJ+frliN1vF5V90SQ0pTC\n9SLilXx5NHBrePU6GwbudLKuSBqbv20+yGicCa6a1UuaL/+HSEtkrw98PyI27bqhNahvlbm2ag77\nnuUqdjo9CKzcGGGTD97vjxp1WpRWozmbdJDSqF8TUW9FoK+Qpl4+R9NUvYioM1WP/No6irSqSnPb\n6tRB2I50tn1Z0lmpRUirKM1wwDyr2qa0TPHSpKl/n6JvtMgipGlLteruKK3a9XfSmdplSWfNJ0TE\nt2pkrQh8ixkP3LuuhSVpJeC3UbOGR1POUqRaKruR6tdVnlYr6ZiI+HY37cg5Q9KJlbMXBoguatk0\nZb2NNPotSDu3Tw/ykIFyPhs1V01qytiA1GmyP2lUQGOUzsKkDvFa9X+UpmFOII02a/48qlITZyhW\nTtssKtZIGiTvGmDP6Fsp7u3AORGxVY2sB0jblhGz05m38ZBGho0mnWyBdLLl1Yjopth8EZKWj4iH\nC2VtTBpNMZb+n7m1tqM5cxzw36S/33cj4sqaOVcAj5MK6K9FOiF3S533qKTjydMFS7zeSuS1dMjP\nsHhDlY75nDcknTGSvhgRpw10ebjzZldKq9tuGS2rjSqtfnlNFyffi9SANCvB0+usKxExRX1F6gK4\nvotRQN8kjWRZTqkA4RJUW4mmnzxi4QjSihIAvaRhqp0uEzzQKnMN3YyY+lhuVwDjI2JmZ8Fmpf8j\nrdg1JV9+Z76ujp8BfwDupfs6TN8Elo8yU/UAvgO8SKqFMG83QU3P3XNUnL4ygBJt24q0o7EM/Vec\nexE4pIu2/ST6pls+J2lDUkdIHRcDp5DO4DdGPtTdoW8eZRCkaXIH12zXdBHxFKkj8eSmDnZUoU4a\naXrumMjTMfLn0riI+N+KbTkaOLpUJ1Zuy2qklewWz5efAT4TEZNq5u1MqnvSOID6saQDI+LiChmN\nKV3PKU2v6yeqTemal9TBNCr/3/ACXWxbgNER0e3rayhWTpuQty1j6d+pUHe65LJAc2Hop0jbhDom\nAW+n3rTXodK6jT8i/99VzUCYPhpu/sblqL/gyrNKdVnG0v0iGmcA3yBNu+xqJK3S9K5DSSdGvhtN\nK7LVtDOp3s5xEfFc7uA8sGbWvqR6g9MklRhN3nVe9NXz2550QqSrqfMjqfP2zUgFV4grlDd3a4cT\nQEQ8k0cz1xJ9NSA3ylfVqgFpVoJHOllXNGORuh2ASkXqJO0UaS7/csDfSPVnRCo0WmuFp5x7CanD\n45yctwewekTMcCAzK+Xh4+8njUJpFI69vdsDSaWVTIiIl1qu3ysizu4wY0Ju262kne71gNtIB2mV\nhoB3M32lTVaxqXo5b1JElCqAX3SqTeG2faLK6IsOMxsdpgC9dTtM60z3GSkqjh5stzpZN9OGP06q\nMdJVJ1Z+7E2kVauuy5fHAUdH/dWK7gG2aIxuymdpr40K060lnc3MR5jWmV73ruiuuHlr3neBmyLi\nilKZJSgtLf4KfR39QL2VTHPej0k14ZpXYvtLhQ7X5qxeUv2hW+mbIl1pmzIUlFa6/WRE/KpQ3vak\nzqylSdMQ3wU8EBGr1My7iTRauN/Jm4g4p0bWLRHxgTrtaMm5jXRS8PjcNmh6z0bNUeAFT2COWJIu\nADYA/gc4MyIeHOYmzZFUfoW4rvJmtk/R7b600pT190bEmXmbvFDkKXxms5I7nawreb7w+tFXV2FB\n4OaoMF+48YFaspMi5xZZilppDvSX6VuN5nrglGgzl7vDvHuBNSPX7Mk7vXdV+Zu15PUbrUBaOa3W\naAW1r8nSUGkIuKSjSbW+LqPLOkxKywyvQppy1nzAUudsL5J+QDoY/n2dx7dkdT3VZgjbdgxpimqj\ng+ItwDcjouOisS15xTpMleoJPUPqsO729XEJ6Sz+ld2eQe7w51XpdLqntdNF0r1dvN+LdWKV+oxs\neuy9pI79yJfnAu6u+7t2S4XrqDTlNury/Yc0vbGR1/HIh5a2tU65qdW2dq+1bkgSqSh/Y1TuhKi5\nsETTtqWfxgiQ4VSyAzx3vG5GmhKzlqQPAXt0cRKiqxp8OaPxu+1EGvXX+plbqZModyDCAJ3DUaNu\nZokTmE1Zm7S7PiImVM0aorxFSVO29yL9Dc8CfhkFpjfXbI+ATYFnI+IeSbuQ3vP/B/w0Kq4yWTpv\nKOTOF9qNLhqOPKWi4QMVgx8dEXVX5uwhLVyyYkSsIGkZ4KKI2GjmjzQrz51O1hUVKFKnVCcmSAez\n17fc3M2Bwc3AgdF/RZrjomJxYUkXk0b6nE/aGfoUsGhE7FSzXfcAH4qIf+bLiwPX1T1YGILRCqVq\nskyhfSH3d9fI2qvN1bXO9ua8rg8am7JKFy0d0rZ107lbssO08OtjS9LqTuuTVrw8KyK6XalvZj+v\nSqfTWaQlmn9C+vzYD3hLROxV82cX68TKnbl3kApPi1TAe52oWZxY0nHAGvQfGXNPRBxUI+sImlat\na1wfEUdVyBg3s9uHs8OjqW07kopiN7YvuwFPRcQ3amQeT+rs6LrDek6SO9P/wYwr3dbpAL8jItaR\ndDewdkRM66YzUAUW0VD/RVVmmDpYp5OotBInMJuyfkvf7zg/acT2HVGzXmDpvJz5VtLo+2+Qptq+\nFzipTidbtyT9FFiN9Ls9RFpd7irSiVZFxO7DmVdK7gzrt0IcaZpprRXiSucNhfw5tBbp9dpYoKno\nyQmzTrmmk3XrLOCWPNKgcXbqzIoZHyUtN3oeabh2vzO+XbRtX+DcfFYJ0oHfZ2rkrBL9V4b6o6T7\nB7z34I4B7mw6W7gpULsIMLBANNVTiIjevMNWmQrUZGlqx9g6bRgg6+xSWTmv7hLA7fxW0jZRaKpN\n4bbNpaYVVnKncDc1rAIYA/wzXx5Dzfdo4dfHNcA1SlPNdgWulfQocDpwfkS8PtOAofVV4DDSAS3A\nNaSOp7rukHQC/Tux2tUF6sQ+wJGk0QWQOv27WYHtINLKf41RoafVHRlDOvhvvLZGA9vSVwepI0PZ\nqZTPGL+Lpv2oKiMfoq/Gyw9bRtlcprRiWR1/Ai7NI8y66rDObduAtCLn+4D5SAdWL1Uc0XVjRGyk\n9qu71W5bYbuS2tb8vgzSantV/UupMP/1wAWSngZeGuQxM/MqaZt8KE0jaau0LSLGdfHzOyLp6Ijo\npl4g9F9mvvaI1YjYtvmypGWBH42EPKXp6XuROpnOBd4fEU9LWoD0+TYcS9l/CFiZ1En0OLBkREyV\ndBppWudw55WyP6m20ftjxhXiGotNDEte3jfbl7R69L3AGRExtWJ72nktIt5I/WPTO3PNhoVHOlnX\n8tDt6VPPomKROknnRcQekg6KiB8MQfsWAYiIF2o+/nxSAeWb8uX1gf0iYo8u2rQ0/UcTPTnIQ2aW\nVWy0gsrUZNk8Iq5VX0HgfqJaIeBG5grA0aQdmdF9UV2tuvMW0o5fc7HXjg8aWw6iFqTAyKRSbWvK\nORjYntQRLNJooMsi4vs127UbcCxpmmNjCP1/RcSFFTKKvz5y7uKkM8efJhUr/gXpc2nV0gddqlAn\nrTSl2m2HAZvnq64hFfItUu9spJI0H3B11FjNtOnzYxX63lO1Pz8kfZ80iut+mgoyR8R2NbIeALaN\nvEJZPmi5IiLeVyNrCun9PikKTDPNnV+7kkYPrgvsSZqm0fFJEknLRcQj3bblzSK/P18B5iJtixcB\nLmiMbK6RN5l0UNv1Ihp5FPP3gGUi4iOSVgY2iIqrRKr9iot7kjpRIuqtvDhkq2zlESn313lPlcqT\n9M6IeFTSucDPm7fnkj4YEddL2iIi/lCijVU0j9xtHcVbZ2R06bxSVHiFuJJ5ki4i7UPeAGwNTImI\nr1dpzwC5BwLvIS0ucwzppNIvhmNEnZlHOlktkhZrujiZvtXOQtJiFYelr5M7YT4t6eetN1Yd4i7p\nm80Pb7q+UYCz6tmMdYEbJf0t570TeCgPB49OO2Ry51zzQfZj+f+llYoO1iq+SdnRCiLV2Wn4J8yw\nvO9gNgGuBbaj/SiYOp0KZ5GGMZ9AWiFub/qGM1cm6fPA10irM00kTcu6iVSLoyOFRyQVbVtDpCXZ\n7wG2ID0XR9WdepNHT7xBKoLa6DD9r8jLqVdQ/PUh6VJgJVLH63ZNbbqwzqgRpSm/n4y+WliLkWpu\nfBiqjbxTKjR/EDN2mNaamhFpoYCuVk5T4VpHA4xiac4rMZplQdJqjHU0f358hC4/P0hT4laMMrVJ\n9geuy50LkBYk+ELNrEeB+0p0ODVExF8kjYo0pfasfKBVZWTuxaRt/LURsfmg9x4GkuYFvkTTirLA\nqXVGSEbfQh7TgLMLNO8vpE6sEs4mvRcObcq+iFQPr4odSX+jq/Pl6fX96jYs+lbZapzArL3KVkun\n2FykAvZ1Rw+WyuvNI332aYxgyZ2Ax5NGEq4zHB1O2RK5008t30MqGD/ceaWUXiGuZN77Ik8lzcdB\nt9Voz3SSPhwRv4+I4yRtRVq5eAXgcNIIdbNZziOdrBYNUI+lISrUZZH0NdIO33LMuJxy5bPRSoXz\n2rWt0elUaSUfNS2V3k5ETOkwp3eAdjVyRkJdhWI1WQq3686IWFtNtWvURYFVSZNIHSc3RcSaklYC\njqk5OmyGg6luDrBKtq00jdAV55SK9fZGoQ2a2tfCqlus+xrS1LpvAV8knc1/pu57qkQnlkZwraOG\n3KnfMBewJKnTtN0oi8GySn9+XAnsHIUK/0qan9RpGsCDdTuzJJ0DvBu4knTWHOqdaGnkTQC2BH4O\n/B14krRIRceF5nMn1cWkbfwJMEPB9FptK0nSGaSTsM0r3U6NiM/VyPoEaTToUvT9rrU7XlVwEQ1J\nt0fEui0jUSp/rimNHv8O6T35zYh4QtLkKvt9bTLPi5bR4+2u6zBrL/r2taaSRo3c2EXbGnlqyruh\nYsZbSK+LDUl1nFYjdTgfRyquPeQLYMykbT20r/lVd5+5aF4pMxtl1e2Irm7zSo8IUypMPoG0iMFj\nLbcN22gzm7N5pJPVEmXrsZwEnCTp1IjYt0BeT/et6mcU8HhEvJoPblf7/+3dZ5hkVdX28f89IJIF\nJakvQVBAojCACCgimAFFJAvCo4AoQUUxPAYUBUVR0iMSJEeJEgVEURCUPMAQlCQGgoEgSYL3+2Hv\nM326pmemT+g+1d3rd119Tdepqd2rU3WdddZeCzixqIKoENfbW44LmFa1UJ5+ZFLj0etJPVVmOWVP\n0huARW1/Pr9oLiZbXENKQNWJ62Rg91K1yFKkEcF11oaZSgAAIABJREFUKjyeU2pafY+k3UnJySZ7\n05+z/awklHoe3SVpuSoLKO3Bn5t0Ja9c+Tc/9SsyWomtFGPjniw9LldqbNtGw93GjaJLNiRV+BVX\nkF8BHOKazbqBlyQtaftPeb2lqN9j5FW2j5G0p9P0x19Lql0RQJoceAapx9G0JFaVBUYyqZR/Txdl\ncK+jB2ssVd6q9gLwaJ3Kk6zt549ngVskXUEL0zRJW2mXI239W1UStk+ssc79+W2O/DZd0+iKticl\n/HYnnSD/P2DzimtsRaqMmQ2Yr0EsI2lND65YviJXiNZxIGm75J0txAVwXn4rq/s9fUppGzJA0Srg\niaqLOLUq2CtXb58i6WLSz0kTK5Vv5EqRWhc4bB+vtB13WdLXqtFQiSqVrTNZ4zFgV0mfJm2J/htp\na+Ofm67dVNuvmUfgNXhbVpE0owsFc83g+Git17vWXKXbdZLWtwKnAddK+qxr9GUNoW2RdAqNqWFD\n1dJjPpHXW4TBvWzqnLAU6+xM2q5QxGZXH118Dml7wOuBI4GfkRIx76sTV45tnZ646p5kQDrJWIj0\nB6aoTipKaY8mnTjMysHAl3IcZwNn5zhXAX7I4BPA4bqK1GT+s6QkzOeAvWf+kBn6NCnBsyfpCuv8\n1GsKX/hzvvJ4HimR8hgDW0SHa1dgL+A1DC61/zdweMexFQ5niJ4sDWIbquEupOqKqho3ii6ZDbhO\n0k6khMdhNPse/C9wVa70gLTtpu6Wp6Li5GFJG5NONhZsEFtrSSyliZ5fZ/rnyLq9jvbI6z1KqdcR\nKVE/3DXmAV4oKkhzpd+HSL8Dtfp90f7zx/n5raxWIiBXBaxPqma5iNTP42pSf5xKipM9pUbWNKnE\nyif9+ztNmnoW2LfmUu+x/R1Jc9RMKI+GFyW93vY9AJKWISewa3i4xYRT20M09iZNwVta0jWkrU4f\nbrDes6Rt358iTx6WtIFLg01mRdKXSa89yifZkBLNR9UJKldyngD8KR9aQtJH8/NlnfUa95QsVTqt\nTfodfy9wiaS9bF9RJ6625AtAQzFUvxDU9nptsd1kS/VIr3cZqVdsa/3vbB+ltMPiVEnvI10EHte9\nH0N/i+11oRG121B1U+Ag0kn8o6RE1p22V6wZ27Wk8tIbKU19yUmVKuvcbHs1SfsAz9o+rEl5aq4A\nWhq4hcFfsz1qrneD7TWGOiZp6nC+fkOtUbrvdtsrDXXfMNZ9K/BL0jjq1V29/8+Iyy9Q5wd+bvv5\nWfz3oR6/p0eoKWMLsRXju6eNyK2znWI0qEGj6Pz4jUgnVI8B69v+Y8N4FiadIJg0urtWI19Jm5BO\nyBYnJcPmB/a13Zu0GO56v7O9tqTLSFVsfwPOtL1MjbXuJiVkbmLwc1Hdz/VeYC3XbJqc17iK1Pfk\njznRfz1wMumE73pXaGI9Fihtp10VuMn2qpIWJTWe3qjGWiuTklVFNcvfSdvhbq8Z29XAhm7Qu0rS\nlPx59e2WDkkbknodFX21liT9DP6ywhpFBdjbgMVIFw3KWxzrDkhodYiGpJcxcOHh7gbVg8XP7kmk\n6q65gO+SqsbWrrHWAba/VDeWnrVuAraxfXe+vSxwuutvp/0tAz3hNiH3hLP91Qpr3AccAfzQAz2d\n3pSPPWB7mzqxtSFXL/eeDM4DfAxYyHalqtC212uLWp4Q1+Z6krYgNfk/ATiwye9lXq+8hfZlpIst\nm5EuPP64X5+Lw/gWlU6hqTYbqn6L1KT48pzk2YDhVenMyFy2GzXczZ6XtC3pybpIpr2swXqTgRXc\nXsZ3np6tQEsysHVkuImKmTUWnHMm982QpO1JTQt3AFYBLpa0k+1bKqxxiO29NHTjY7tiw+OetctT\nF6+umtRRnrZo+1BJW5TLl9VwfHS+Kro4aZvkv0lbD+o0mn86J3OmSDqQ1JOlamP4clzzAJ8FlrC9\ns9K2zOVsX1h3zZLajaIlrU9KwOxHqqo5VNLHbf+14jpvtH2nBpr+Fz3mllCaPlT5e2C7+Nl9nNQE\nH0mfqbpOybclLUCqWiiSWHXXe9z2JQ1i6fUg6We2iQVKCcOPkibt7KHU7PkmKjSx1vRbj8tqP3+0\nnAh41vZLkl5U2hb6KOl3v46jgM8WlSY5aX0UqY9MHfcDV0s6H3gmH7Or9WG6Q9IfgddqcJ+uYq1h\nT0Ztm6S1gD87TdNcllTN+EHS9qdh/53KyoMRniVNiyqrW6XXeIiGBiaF9m63XFZpK2fd2N5MSjRd\nQ9o6eSr1f9aWk/R+4BI37280e5FwArD9B9VrFF2Yy/YvJCm/zto3J7aGnXQiXQgZtJXO9i1KVe87\nN4itMdvfL95X6te1J+nn7HTSheBO12vRCQxMiHsf6fm7yYS41tazfaZSr8CvATdIOomB39Wqz7m9\na78AfFHSz0nfgy6buYcJLJJOoal7Sb0j2kg6vWD7H5ImKU3L+ZWkQxqsd6Gk99u+qGFc/0O6mvFt\n2/crjbQ+ucF6twOvZvqm6XXtTdoKVJTlLg18MicIThjmGjdI2sX2oHJ2pUlqdae+bA6sa/tR4DSl\nCWPHkya/DFexxWSoFyq1k3aSvgZswcB45uMknWV7vwrLbEO6wgvwZVKz3MJ787E6se1H6tNzH4P7\nCNVpNL8DzXuylB1H+nkoTiz+BpwFVE46aQaNomvG9T3StLk78tofIlXYVd1K+FnSCcBBDP3z1Vaz\n/8+Stq1W1kYSKyfVIE1N+x7p92Dac3jV5JoGJobeR5rSdCH1G1mXv+4bkr632H5eUtWT0bVJU0JP\nA35fhDvEx6mqzWma1+ck89Gk6V9Pk07i65jbpa1Ntq/Mfwfquje/TQLmpUaPKNvbKE3puoyUmKmd\n9B4BR5J+xgDWIm3z2h1YjZSsG/bWM9fvHzcrbSQ8ioTYIqTn7qKCawPSz1rdpNOLpATb3KSLU/c1\nSBgdQfo9OkxpfPxx5cRRRTcqTQA7mfTzth0NJuvRQk+43oRT6bipuY2wTUq9vj5D+lqdSKpMf6xf\n1mtJqxPiRmC9F4CnSL9L81G/jyQM8Voq/z1YnXQ+E8Koi+11oRYNjJB9DSmJ0LihqtKY8s2AA0g9\nih4F1rBd6cqZBo/vnod08lOUqtrtjO+urFStMy/pRe11DP6aNanaKU8/utvDaB7e8/jFgHNJX6si\nyTSZ1Hx6MzfYFidpbtvP5PfnqFpRVFpnYUjjaOvGUlrrD8Aqxdcpl0lPsb1shTXK5cutTR7Jsa1U\n9+s0kjSwXa/8uU9xhWlWpbWWKt18EXikbkm5pNltvyhpXkhjyyUt5PrbxObs/R0a6lhdkv5su241\nS+P11PIkTc14WlGx3rCnFUk6hTQp7W/AF4ClbT+dEzNXVvlZy9UN7yQliFcm9Uw6zfbU4a4xg3Vb\nnYZXWvd1wHy2azWxVpp0diNpy1Nxsj3ZfTD5sh+Vn7sk/R9pquS+vfdVXHM54EfAYrZXVOqLuKnt\nb9WM8RrgraTk/hWk34sDbFfuzac0SXOH4u+5pFcDJ9jurcoa7npTSL3Nvkl6zXYk8B/bW9RZL6+5\nAKl34FdIlZNHAydX+duQq3t3Z2AgylWkCXF1p0KuCdxFqggvesIdaPt3ddbrN5K+T3rtfRTp69Ro\nKmfb67Wlzddpba8n6T2kixgXAN8oXjOHMJ5E0inUosEjaYc6yRhuhQ15i86iwM3Ac6SrqtsBSwAX\n225yhao2SWfa3mKILQFQY1tA3upQ/ppNWysvWKvJZV57HeB1pOrFYr1KjWgliXTlc6W8xlRX6Gkx\ng5iOIZ1ELa7Uv2AX25+sGNPXSS8gi0qCl4DDqpzIDrHur4APFVfe8gnt2a42dn6kkk7nAp+w/Uid\nx+c1hvqZLdTe0pJPgDYErnHaArsM6SR+rRprvXJm97vCRDy138tmugRCG0mF0lqdJp3alpO28+Wq\nxvLxRYB/2362wlpzk7YoLEaadjklH18HWMb2STVjfDkp+fR9Uk+t2o3m20wE5PXKwziKseKVh3Hk\n36lvMPhke9+6FQb5+7cP028jrDyBVANbEldkYMu2XbM3URuUehKtZvsFpf5muxR/hzXMfohDrPkb\n4PPkvin5b9jtddbK660F3EkLCQ9Jd5GqM5xvTwLusL18zdjWtH19z7Edqr72KD32VaSWCh8h/U6d\nStoCv5KHOf03J5pvr/s5zWDNt5L+5r1UOjbZdt0q8L6SK0jLF2fLKl+obXu9tkh6iYFtwpCe04q/\nTXU+z9bWU+pl+ImmF0RC6GexvS7UdTGwcO8TpKQVqTi+mzw5zQNTFV4Cjs9XCL9Nxclp+YrBfO4Z\nESrpw8ATti8f5lKHKk136v34i5OuxFf1V2BR21f3xLVezfWKxw/ZmJyK04/yC9FfMlB639TBwHtI\n0/6K/gVVm0R/hnQCtabt+wGUtjf+WGkMbKV97qUKvSeAqUrNmCFVQ1xXMbbyiNveyTt1xu8W9gdu\nzidEdSvh6kwbHI59gZ8D/0/SqaTvzY4117qJlFguTogXJF3Zdn6rcjLaSi+bfOX/NcDcSmXoRUJ9\nftIWkiprlSsue1Vaq21K2+Eet/2TnuMfIz13HlxxyUNJPxe9QxrWJfW22W24C+UrvAcMcfwaStvO\nJJ1te5ZbRZWqQN9Pqp5YCjiEVNXZRGvT8DSDYRykIRiV5ETtHpLmcTtTik4BziBNltyV9Ltet9K0\nvCXxPTTbktiW00jTH/9BOnksJrC9gbR9tY65bf8+5ZrSE7ek2k2BbRd/l/5N/efawi+AS/NzdzHp\ndrivh4aKbbrtRA0STueSqrVPAjbxQHX16ZKGndxxqni9W6U+ly24lLQNdsvSxaBjSBXr48FlwG7F\na6w+XK8tbU+Ia3O9txXJ4BDGq6h0CrVIOoNUNvvrnuNvI2Xrt62wVquT0/JV6A8OcdV9YeACD3Oy\niqSLSMmwW3uOr0Lq71Q1GdbqeqXH30m7jclbIek622upwVYsSbcA73TPlrr8vbzcFaewDVGhB6UG\nq1Uq9EZK/n4eQer9VZ66WLsSri2SFsrvFr9DTaa6HQ2ca/vifPu9pK2cu9RYa7qfq6o/a/kxHyWd\n2K3B4B4g/waOd/2Gu43NKonlCuOblXrCrO2eLZxKzbpvdN4yVmW9GVWBSbrD9gpV1hvmx5xlNaFS\nM9YVSRdJzrA9swrATihtp13ZLQzjGKK6dFVgV1eoLu1Zr9hGWJ5+OcO/18Ncq9UtiU1Jegupqu6y\nIlGXq7LmdY3BAUrNgPcgTZRcLV/s+pjt99aM73JgC9uP59sLkiaxvbvGWiJte3ob6bnkN7abJmBb\nIWkDl/qRNVzrKgZaGBTJ16oXbsrr3Uxq8Hwg8HHbvx3O889YofYnp7W6XlsmyucZQr+KSqdQ1+uH\nOgm2/RtJR1Rcq+3JaS/vTThB6gWkak1VF+1NEOV1blXqvVFV2+sV2m5M3pYHJa0L005m9yRtE6hi\n9t6EE0z7XlZ+/rJ9fH7ciVUSo6PsKduHtrFQPqE6lLQ9Zg5SZcFTNcrINwGOJfVeegnYyvZvG4b3\nFtvTpvbYvkSpqXUd90v6KoN72VS++piTjidI+rDts2rGMiJsz9vicrP3Jpzyx3heRYlGNTOr3JpU\nY722bEc66dwL2KvnU6uznWIkpuG1OYyjt7p0So3q0rLiZ+RhSRuT/sYsWHOtxs2YR4Lta4c49ocG\nS+5OqrJcTtLfSBMAt2uw3sJFwinH9pikResslC9MnUP9xuEjaUVJt3jwlvdtbP+oxlpFk/UZtoCo\nyvYFStsTz5B0XJO1+o1bnpzW9nptmSifZwj9KpJOoa75ZnLfyyqu1fbktPkkvaz3qoOkl1EtidV2\nMqzt9QoLk8ZSt9aYvCW7kbayvJa0tfAy4FMV15jZlaNaV5Vy+f0Skl7eRnXBCLhK0gGkBq21J4pl\nh5O2Ff2UVL2zA9UnukHa8vdW23dJejPpim+Tk1mAv0n6CgMThrYl/ZzUsROpmW1xMnUVaepkXVdL\n+gnwWtvvkbQCKUn2k1k9cIyQpMVsP9xzcFHqnZw9KunNtn9fPqjUj2a6CwCjxXbbCa+RmIb3LHCL\npMbDOPIDH+xJrr1YMy6Abyk1dt4bOIy0jbDSpMSS1rYk9qucVNvN9oZKQw0m2X6y4bIvlbeKKQ1g\nqDTVahZVkpWTryNkZ5f6rOXk2i6kpuyVOE3pWgxYk/R5XzfUhcga6/4xV/MfC9Tqi9jH2pycNhLr\ntWWifJ4h9J1IOoW67pH0ftsXlQ9Keh/pym0VnwbOlbQdQ0xOqxHbOcBRkvaw/VSOaz5SAqTKFb62\nk2Ftr1fYN//b2lW9NuQKpabVROW+Sb2a9E26n5RYOJ+BRpD9cmVqddL3r3cbaKWJYoX8Qnk2pyao\nx+Uti1+suMyLtu/K6/1eUhsnKduQerwU2zt+k49VkivXznHFiWuzcDypB83/5tt/JCXuxkvS6XvA\nRUq9nYrnnjXy8YNqrPc54KeSjs/rifQc/lFS0nO8eDUD0/C2oZ1peOfntzaev9uoLi0aw38CeD3p\nosFPPMxGzjPint5EuaJuS2C8TAArJmiuJ0nFa48W/C/pQkTR4+ttQKUtyC1XSY6USZIm2f4vTEvg\nVb2ASX7slqTnsqIa/3BJn3dPn8/hKm+jy9/XLXPyaVzQ4Mlpq7nh5LS212vLRPk8Q+hX0dMp1JJ7\nHlxIauxaPslYB9jY9t0V12ttclquaNoP+DipMTGkhsU/Ab4y3H3X+UrZuaQtBtMlwzzQ6HK4cbW6\n3hBrt3pVrymlyUc7k5r3Fglu225SgdIKpRHv0GC0+0jIL7T3aiv5lU9U3knq8/IQ8DBpqlvVXkd/\nIb24KkooPlO63ShZlxPCuMFY5Vwlsnl5G0oTyn1rNLgf2S2u2EOsnyn10PoSqecRwFTSBLZLaq63\nKKmSsbze4SP1XCTp3bYvHYm1h/nx25yGNzewRJHYbbDOwqSLKxuRfjcvA/a0/c+K6/yU9HfqKuB9\nwAO296oZ07ykJuTLkLaC/xj4AKkXyj19UJHbCg30rPoxaRjBmQy+oFF7S1v+vq5N+ntVu49eab1F\nKFVX235wJv99VEj6Pul12pGkn91dgQdt711jrVuBjYrnnvz1u8I1p7aW1l2R9Du/NWkQQ+XeZv1I\nLU9Oa3u9tkyUzzOEfhVJp1Cb0lSgbRl8knGq7ee6i2pAfiH/+nzznt6rEJLe6VlMsmszGTbc9SS9\n0tXGxfde1XsbUPuqXlskXUuqXrmRwQ2xeydcdUbtTXlqjaTrba/Z0lpLAY+Qrhh/hrSl5Qjb91Rc\nZ18GJ+gGVWPUSdZJWpk0YfFV+dDfSQmx22usdT6pcezlDG4cW2uLkqQrgc2BXzg1A14b+K7tplsK\nxxRJX7I93SS5BusNa+Jc/r+bkLZMLsXgpHWnW4E0/TS884FjbdfaGippU9Lz98ttLyVpNeAbXSZj\nNLjZ9+zA9a7ZNFnSOcCTwLWkSYaLA8+RkmG3tBRy54oEda72m+6Fte2dKq73Rtt3SprM4D5izuvV\naXK+KamS8TWkba9LAnfaXnGmDxwF+YLLLsCG+dDlwDG5QrfqWrcBqzif4EiaBExxxQEJ+bGvI/2u\nb0NKxC4FrGH7gapr9atcmdfayWDb67VlonyeIfSrSDqFESXpWttv6TqOoahPp49UjWukruo11c+V\nIWp5ylObJP2QlCQ6g5RAKaqJhn2SIemDwP8rqi+U+n0tnO/eZ6QSklWSFDkp+WXniUWS3g7sb3ud\nGh93xyEO2zWnEeYTvcNICfWppK/dh21PqbPeWNX2c2SV9STdS9pefXux5aZrGoFpeEqTBN8B/KpU\nVVdpaqukr8/griJB8c2KMQ36PjX5OdDgyXezkSoul7T9bJ31+tUQ1aCD2K60bVXS0bZ3zgnwoZJY\nlbcT59cK7yBNfl1N0gbA9v1QfQzTqgeXzTfvGm5V+hDrfA9YFTiV9P3YCrjV9j4V17mW1OT/TOCn\ntu+TdL/tJoNfQghhQoqeTmGkNWmQHYZHpCqRwj+ZwQvfUXahhuj71SfanvLUptVIJxm9J4pVTjL2\nYXAvnTlIPXvmIfUrGqkquC2B4VbGzO3SiGyn5q+1plnZPr7O42ay3o3552E50u9S7ROgUNtfSJWg\nfZFwylqdhpe9YPvxnrWqfs5PM31iYh7gY8BCTP9cMiu9vfTmKt2u+nlOq1Sx/ZKkv463hFM2GzMf\nsFKJ82RPN+yl1eMF2/+QNEmpz9+vJB3S4vq15YsOJwB/yoeWkPRRDzEleRj2AT4ErEf6vTjS9rkz\nf8iQHiFVpS8KLEKNiaghhBCSSDqFMPb9HLhUUvmqXq2+LG3Q4Ek5X5b0PAOT5jrfHlNwu1OeWtPS\nScYcPX06rnbq6/LPuomdEXC/pK8CJ5F+brej4ov6vI1iRly32k9ppPspxVY/SQtKqju+O9TzBeAS\nSb8ibWuBjpv9u/1peABTlYZozC7pDaTm39dUjOv7xftKTf73JE10PJ0ajeFtz1b1MTPRZgKrnz1c\nZ5vxcOTK3KUovWa3fWKNpR5T6qF3FXCKpEdJk7f6wQ+Adzn3A1XqG3o6abBGJXnL09n5rTbbH1Sa\n3vgh4JuSXg8sqCEmdYYQQpi5SDqFMEblE5RFbX9e0ubAuvmua0hl5Z3w2JiU08qUp5GQX+R+ndSb\nC+BK4Ju2n6iwzILlG7Z3L91cmP6wE6kCo2iwexVQdZvHJvnfYltkOYHVRGvju0Nt+5Emnc1JqtQb\nr/YgTSj7D3AacCnpc69E0qtIfdu2I/VKW932Yy3GWUvLCawJR9LJwNLALZSqxkjf46o+QOqnVfyc\nzA90OjyjZHaXBtDY/kPuJ1ZZz4WvOUjb1Z+qk+B0Gk5xLHCs0rCELYEfSlrc9uJ14gshhIkokk5h\nIru/6wAaOpg0fQqn5txnA0haBfghAyfknZH0AVLyxMCvbV/QcUiF3UhTnl4L/JU05elTnUY04Fjg\nNmALUgJle+A40tXW4fq9pF1sH1U+KOkTQOdXaPPJxDl1+pKUFc1cJb2rp3/YrZJuJlXL1NHa+O4x\nru1tmF+s8H9fbfudLX/8vuM0yODL+a0WpclfmwFHkRoo154EGWrbaITWnQys0FLD4s8Cx9v+M2mb\nNTmZftTMHjRKbpR0DHAyAxcObqizUPnCV24ivilp+l8jth+R9IDtdZSGdIQQQhimaCQeWpFL+sul\n3//Kx1duo9lqzZhmJ00ZWorB0486254BqRmt7e1ndEzSqzyMEdfKY91ncF+lRrQjQdJ3gDWBU0gv\nIrcGbrD9pS7j6neSpthedVbHZrHGosB5pOqJogH56qSqkQ/afriteHs+7pdt7z/M/3sFsHm+ktz0\n404BPmX76nx7XeD/6jayV4vju/uZpKVJlTZLMfg5stbkNLU4cU7SgaSBCJfWiaXfSbqAwVPJyip9\nDyT9l7QFcai+Y+NpC9uEI+lMYC/bf2thrUdJ/R/3cJ6a2y8DVXIT8d0ZqNi+CviR7f+0tH4rg036\n5esVQghjTVQ6hUYk7Uoqz/4PA81PTSoHp6uEU3YB8CypaqSfmtEOSgbl5Njk4vZwEk7ZAjO5rx8a\nuL8feJPzyGOlUdK3kKuzuiBpLlLPq38BFwKfJ1Vi3QPsZ/sfXcVW8qykt9q+CkDSesAzVRbIV2TX\nIU0qWpH0O3lhcaJRl6RFgJ2ZPqnwP/mdYSWcsqeB2yRdnt8v1tqzRmj/Axwn6RX59uOk7Xt1fYE0\nvnu3fPty0rTD8eY80ud1AYOfv+s6mPYmzn0S+Fy/9oRrwdqkZumnMVB9WCSgKn0PRqjXVOgPCwN3\nKE0gLRIwdRPDfwU+CJwp6SzbB7YVZBP5NdAU28tTowfZEOttXro5ifT6ajw2rw8hhDEjkk6hqc8D\nK/XJyXqv19ZtJDwSJH2ZlHApN1KFdEJVp7z9hhlsodoZuLF+pK0xKTFWJNEWoNkJbRtOJFUEzAPs\nDdwOHE6acnM8sHFnkQ34BHBiKYHyGPDRqovk7RhX5Le2/Az4DSkJ0zRJcQ4D/ZwKdde6xfYquR9W\n0YejtpwoPSK/jWfP2T60xfVamzg3RnrDNfFq4J3ANvntIuA021M7jSr0m33bXMz2nyS9DfixpLOA\nudpcv2ZML0q6W9KStv8060fM0iYM/C15EXiA1M+qDbu2tE4IIUwosb0uNCLpMmCz3Jeir+QtMpf3\n2/YMSd+xXaW3yYzWWQw4l5REKZJMk4GXk74nDzX9GE1I2gb4DqkRNsD6wBdtn95hTLfbXilfWf2L\n7cVK91XawjYCsS1RnjhXJJ0qNhAfUW1tUWibpPtIPc2Os31HC+stC+wPrMDASZltL9107X4iaXtg\nGVLz6mnbWGzfNMMHzXy9tUnb6xpPnMsnxtOx/Zs6sfWzvLVoG+D7wL7lJvYhtEXSMbY/Xrr9KWDv\nfnhek3QVsBpwHYMrX2tt9W0ppiWBR20/m3tD7Ujapj4VONp2X0y8DSGEsSCSTqERSauTKkSuZfBJ\nRp0tMq2S9CFSU8pJ9MH2jPy1grSFYrpfvDonepIEbEDasmdSlUGjLVRtkvQaUl8nA9eNVC+hCvFM\n68fQ25uh614NPbGdbXvzWT1mtEn6FnCt7YsarDGzLbeuU52Ye8ptTTopmI3UjP0020/WjPG3pAmC\nPyA1od0RmM32V+us169y37XtSdtLp1Un1W3wnrdK/pueLc2uMUpe0oUMPE/OCawF3Gj7HXVi60eS\n5iRtQ96atGX1fOBY23/tMq7QP9qcxNbPJL09v1t8riL9Pfh1hTUOm8ndlV+XSpoKrGn7mdxjbmnS\nluQNKW0rDyGEMGuRdAqNSLqBtN2mOMkoXiic0GlggKQHSCeMbfQXaUzSlcxk+1DdE71+I2kygz/P\nQX1K6lZRtEHS30k9VETq7XQ6A/FtZXuRDmObYUKsX+QToLkZ3LS4UiK3NPXnk/nfkxiYVoTtuhPn\nivXfTmpevyBp+tp+tu+puMZNtleXdJvtlctaHG0tAAAblklEQVTHmsTWbyTdC7zR9vOz/M/DW2/E\nBhhIWhw4xHaVKY59S9JJpH5rFwNndNz/MIwB5Ulsdaqlc3/ArzN9T77OK51gWvV2+SLVoxUfvyM9\nSavy+1Vfl0q6w/YK+f2bSAmookflrf3UviGEEPpd9HQKTc1m+7NdBzEDD9JSf5E22H571zGMkoOY\neW+eLpNrn2cgthsZ/KK01njmiaSNPju2HwCQ9K6erXq3SrqZ1MS7ktKkyp1IJ1QHAaeSenVdDCxb\nccnnJM0G3CNpd+BvpD5g481tpOTcIy2td7Gkd4/Qlua/AG8cgXW7sh1pG9FewF6paHWa8dQwPdQg\n6WW2B00jzK9lzpO0L1Bni/5PgE+TJpq+1DjIFknaEvgeUFQ2HS7p87bPrLDMKb1fs4b+ImlD21cA\n9wOLAw9IWoju+1OGEMKYEpVOoRFJ+wN/Im0LKPcE+VdnQWWSTgBeB1xCw/4ibZO0MukEatqUOdsn\ndhdRKJN0mO09RvljvsTAlLq5GDxtpy9OQvOV9u2A19n+pqQlgMVsX1djrSnAp2xfnW+vC/xfnZ5R\nuafTlcAxtq/pua/y91LSWsCdpOb3+wHzAwfa/l3V2PqZpF8DqwDX03wyViuVcKW1yltlJgFvAu63\n/ZE6sYUwlpSqLYeaxLa+7bfUWPP3tt/cWpAtknQrsFFR3SRpYeCKKtVE5WrUNv6G579vJ5K2bD8O\nvJU0gXcB4HO2f9Fk/RBCmEgi6RQayVvYhupP9LrRj2awfDUQpi+xrtxfpE05rvVJWysuAt4LXG37\nw13G1bbcpPRU24/l2wsC29j+UbeRzVq/bm/rmqQfk7bRvsP28pJeCVxme40aa00GjgOKKX2PAzvV\n7G02r+2nqj5uGOvObfuZWf/PsanUR2UQ21eObiTTy1tlCi8CDxQJyhDGu+JvkKTjSoeLSWxHV916\nltf8DimBcg4tDA5oU+71t4rzSUm+wDGl2N48zDVGZIt6vki4NKmf1p9JSfr1bf+qjfVDCGEiiKRT\nCKNM0u3AqsBNtleVtCipLHyjjkNr1VDT4Pp1+lmvSDoNrXQiVH5xX2vqn6TZbL8kaQEA24/XWKNc\nDWMG+nPlJesNNJC0DnAMMJ/txSWtCuxq+5OzeOiENpEmzoUwkiT9hTTIQEPcXatie0Z9Jfuhn6Sk\n75FeF53KQM/FW23vU2GNkUo63U7qPXggqQr5u6T+Tmu3sX4IIUwE0dMpNCLpowz9IqbzrWKShroK\nZXc//ejZfLL9oqRXAI+SegWMN5MkTSp6auUeOS/rOKbQzPP5+whM2wJRt2faHyWdDRxn+46aaxR9\nuQR8A/gaPY3razoYeA/wMwDbUySt32C9vjQCk7H2Ka03beIcMOznXEln2t5iBlMOHc17wwQxGzBf\nmwv2eV/JfYAPkfrwGTjS9rkV11i+9LyxTM9zSJPnjjeTEk3XkL4npwLr1FwrhBAmpEg6haaKSSOQ\nrgC9g9SksvOkE6lpdGFOYHNSeXrXrs9bzY4mNa9+mvRiZry5FDhd0pGkRMCuwM+7DSk0dBhwLrBI\n7uf2YeArNdd6E2lU/DE5kXUscJrtJ4e7gO3ji/cl7VV1OtEs1n6wp7lzPzx3tKrcGL48GavBehuX\nbxcT5yous1f+d5O6cYQwDjzcdiuAPB3u28Brbb9H0grAW2z/pM2PU0feVnd2fqtrpAYNvEjqsTg3\n6bXkff0yoCaEEMaK2F4XWpW3ypxh+91dxzIUSdfbXrPrOAp5fPz8tm/tOJTW5ZPYXYEN86HLSY2e\n+2pqzlAk7VhOaIQBkt7IwPf0Ctt3trDm24FTSJPUzgT2s31PxTXa3E5xFvBD4HDSVe49gTVsb93G\n+v2szS2wSlm7O2xXPhmUNA/wXK4KXQ5YDrik5elUIfSlkdjiLennpD56/2t7FUkvA262vVKbH6eO\nEai6LNbd2PaFDdeYQhqW801gIeBI4D+2t2iybgghTCRR6RTa9gxpYlzncpPjwiRgDdIUqk7lE7Fy\nGflVwLhLOpFG2B9p+4iuAylI2gM43fbfJb2eVF2zCnA38HHbt8HgCpowIP9OPcJA3w0PNdp7mGvN\nTvoZ2QlYCjgor7secDGwbEth17EbqULntcBfgcuAT3UYz4iYwWSsZ2fw34ez3lAT526sudxVwHq5\nKvRSUvPerUjTE0MY70aix+NCts+Q9EUA2y9I6osKzrarLkv2AxolnUivDa7P7z8EbCpph4ZrhhDC\nhBJJp9CIpAtKNycBKwA/7SicXjcxcOWsmPrysc6iGfAjYBngNPK2M0nvHIdNircGDslVI8favqvr\ngIDdbBcnxoeSqlnOI00T/DGwbleBjRE3AUsAj+XbCwIPS3oY2Nl2lQTDH4ArgQNtl7eXnjXc/kk9\nV8fnkvTv0t2ue5Xc9t+Bbes8dozZhOmfIz/QYL3y9/9F0nbJuhPnZPsZSR8DfmT7wFxxEMK4Z/uf\nI7DsU5JeVdyQtDbwxAh8nEby1rXz8qTfL3YcDqWEU/lYP7SQCCGEMSO214VGekZuvwj8yfafOwpn\nTJB0F7BCqcH2JNIWlOW7jax9uVH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#choosing which phentoypes to model jointly ?\n", "phenotypes,sample_idx = dataset.getPhenotypes(intersection=True)\n", "phenotype_names = dataset.phenotype_ID\n", "\n", "pl.figure(figsize=[20,20])\n", "Ce= sp.cov(phenotypes.values.T)\n", "pl.imshow(Ce,aspect='auto',interpolation='none')\n", "pl.xticks(sp.arange(len(phenotype_names)),phenotype_names,rotation=90)\n", "pl.yticks(sp.arange(len(phenotype_names)),phenotype_names,rotation=0)\n", "pl.colorbar()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded 804 samples, 3 phenotypes, 1163 snps\n" ] } ], "source": [ "#select subset of phenotypes\n", "\n", "#flowering phenotypes (A. thaliana data)\n", "phenotype_names = ['5_FT10','6_FT16','7_FT22']\n", "\n", "#a larger set of correlated and anti correlated traits:\n", "#phenotype_names = ['Ethanol','Congo_red','Galactose']\n", "#YPD, different temperatures:\n", "phenotype_names = ['YPD:37C','YPD:15C','YPD:4C']\n", "\n", "phenotype_query = \"(phenotype_ID in %s)\" % str(phenotype_names)\n", "\n", "data_subsample = dataset.subsample_phenotypes(phenotype_query=phenotype_query,\n", " intersection=True)\n", "\n", "sample_relatedness_unnormalized = data_subsample.getCovariance(normalize=True,center=True)\n", "\n", "sample_relatedness = sample_relatedness_unnormalized/ \\\n", " sample_relatedness_unnormalized.diagonal().mean()\n", "\n", "if 1:\n", " #subsample for speed?\n", " Is = sp.arange(dataset.geno_pos.shape[0])\n", " #take every 10th SNP\n", " Is = Is[::10]\n", " data_subsample = data_subsample.subsample(cols_geno = Is)\n", " \n", "#get variables we need from data\n", "snps = data_subsample.getGenotypes(center=False,unit=False)\n", "position = data_subsample.getPos()\n", "position,chromBounds = data_util.estCumPos(position=position,offset=100000)\n", "\n", "phenotypes,sample_idx = data_subsample.getPhenotypes(phenotype_query=phenotype_query,\n", " intersection=True) \n", "phenotype_std = preprocess.rankStandardizeNormal(phenotypes.values)\n", "\n", "N = snps.shape[0]\n", "S = snps.shape[1]\n", "P = phenotypes.shape[1]\n", "print \"loaded %d samples, %d phenotypes, %s snps\" % (N,P,S)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#preprocess phenotypes using boxcox\n", "if 0:\n", " phenotype_vals_boxcox, maxlog = preprocess.boxcox(phenotypes.values)\n", " phenotype_vals_boxcox -= phenotype_vals_boxcox.mean(axis=0)\n", " phenotype_vals_boxcox /= phenotype_vals_boxcox.std(axis=0)\n", " phenotypes.ix[:,:] = phenotype_vals_boxcox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation between phentypes\n", "* We start by examining the correlation between phenotypes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GEL3SN6hNAEwDOKZP/Djy8ysr83Z2dw9eyxznLo+7siLE5qZ5I0/1WOp3myKU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\nXSIxhjGS+xrWSO1GExF9HxF9gYj+NRG9RkTPa95T5uJUgu/WJufPz9O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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#pairwise corrrelations of the first 3 traits\n", "pl.figure(figsize=[15,5])\n", "pl.subplot(1,3,1)\n", "pl.plot(phenotypes[phenotype_names[0]].values,phenotypes[phenotype_names[1]].values,'.')\n", "pl.xlabel(phenotype_names[0])\n", "pl.ylabel(phenotype_names[1])\n", "\n", "pl.subplot(1,3,2)\n", "pl.plot(phenotypes[phenotype_names[1]].values,phenotypes[phenotype_names[2]].values,'.')\n", "pl.xlabel(phenotype_names[1])\n", "pl.ylabel(phenotype_names[2])\n", "\n", "pl.subplot(1,3,3)\n", "pl.plot(phenotypes[phenotype_names[0]].values,phenotypes[phenotype_names[2]].values,'.')\n", "pl.xlabel(phenotype_names[0])\n", "pl.ylabel(phenotype_names[2])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Ce = sp.corrcoef(phenotypes.T)\n", "\n", "sample_relatedness = data_subsample.getCovariance()\n", "#set parameters for the analysis\n", "N, G = phenotypes.shape \n", "# variance component model\n", "vc = var.VarianceDecomposition(phenotypes.values)\n", "vc.addFixedEffect()\n", "vc.addRandomEffect(K=sample_relatedness,trait_covar_type='freeform')\n", "vc.addRandomEffect(is_noise=True,trait_covar_type='freeform')\n", "vc.optimize()\n", "# retrieve geno and noise covariance matrix\n", "Cg = vc.getTraitCovar(0)\n", "Cn = vc.getTraitCovar(1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NJKdX1UWdMkcB96iqg5IcBrwXeGg/212w+2hVXZDkDOBVwM7AKVV12YwfsL4M\n3HeWdX+a5PnAN5nRsLXLb2xHbwesAq7rLm9/JftD4NHtrBcBJ083am0dn1zoM0hamWyfJA0j2yZp\nPC3Tg2YOBS6tqiug6QUAPBG4qFPmGOAUgKr6RpLdk+xVVeuXutF5u492rAGeBTwO+OvugvaXqscD\n58+2YlVdDqxKcqckq5N8urPuNkm+DawHzqmqC2es/ghgfVX9sJ2+D3BejzFLGg+2T5KGkW2TNGY2\nsu2ih1nsA1zVmb66nbdQmX37ib2nB8203RROA35dVTe3s3fo9Ff/InBSD/WsA47uTG8CDk6yG3BW\nkomqmuqs8gyaLhRdW/Sxn82P1pxyy/huj3oAu00c3MtqkobIb6bO44apbwEwmc/MWmYU26fLJm9d\nbY+J+7HHxP16WU3SkPjF1Hf55dR3gbkP+lFsmz43+dVbxu82sR8HTuzXy2rSivDDqau5bOqavuro\n5UEzV0xdyZVTV85XpHrc3Mzjutf1ZrWYp49umrGxm6b7v88nyYHAxqr66Vxlqur69lewBwNT7Xrb\nAk8GHtgpegHwIOD0hbZ71zcct1ARSUNu54kHsfPEgwCYzKGsWbNmrqIj1T4dOPnMhYpIGmJ7TNyf\nPSbuDzRnZVesmZmD3WKk2qbHTj58oSLSinX3iX25+8StF9v+Y803F11HL0nhfhMHst/EgbdMf3HN\nl2cWuQbo/iKzH82VwPnK7NvOW7Jeu48uSXuz9D8AfzvLsj2T7N6O7wAcAaztFDkcuKj9hWzae4Dj\nkhzaqecpSe68NeKXtHLZPkkaRrZN0uhapqePngsclOSAJLcDnsaWP+qcDhwLkOShwC/7uZ8QFv+e\nwppjvGu6a8R2wAbgVJona5FkNXBiVR0NrAZOTrINTXL6oar6XKeepwEf3WzjVdcmeTrw9rYx2wR8\nAThzkZ9D0spj+yRpGNk2SWNiOR40U1UbkhwPnEXzMKmTquqiJC9ol59QVZ9JclSSS4EbgOf2u92e\nk8KqWjNjetc5ys1ZZ7dffFV9l827N8wsO+uHq6qvA4/sIWRJY8L2SdIwsm2SxsscD45ZtKo6kxk/\n3FTVCTOmj1+WjbWWJ3JJkiRJGmO93FM4rEwKJUmSJKlPJoWSJEmSNMaW6eX1A2FSKEmSJEl9Wq57\nCgdhq76SQpIkSZI03EY3nZUkSZKkIeE9hZIkSZI0xkwKJUmSJGmMmRRKkiRJ0hjz6aOSJEmSNMZG\n+emjoxs2GH13AAAST0lEQVS5JEmSJA0Ju49KkiRJ0hgzKZQkSZKkMeY9hZIkSZI0xrynUJIkSZLG\nmN1HJUmSJGmMjXJSuM2gA5AkSZIkDY5XCiVJkiSpTz5oRpIkSZLG2Cg/aMbuo5IkSZLUp42sWvSw\nGEnukOTsJJck+fcku89SZr8k5yS5IMn3kvxZL3WbFEqSJElSn7Z2Ugi8Gji7qu4JfK6dnulm4KVV\ndR/gocCLktxroYpNCiVJkiSpT7dBUngMcEo7fgrwpJkFquonVfXtdvw3wEXA6oUqHt2Or5IkSZI0\nJG6DB83sVVXr2/H1wF7zFU5yAHAI8I2FKjYplCRJkqQ+9fKgmRumzuXGqXPnXJ7kbOAusyx6XXei\nqipJzVPPzsAngJe0VwznZVIoSZIkSX3qpTvo7ScO4/YTh90y/bM179tseVUdMde6SdYnuUtV/STJ\n3sC1c5TbDvgk8I9V9aleYveeQkmSJEnq021wT+HpwHHt+HHAFglfkgAnARdW1bt7rdikUJIkSZL6\ntIFVix4W6a3AEUkuAR7TTpNkdZJPt2V+B/gj4NFJ1rbDkQtVbPdRSZIkSerT1n55fVVdBxw+y/x1\nwNHt+JdZwoU/k0JJkiRJ6tMSuoMODbuPSpIkSdIY80qhJEmSJPVplK8UmhRKkiRJUp82bjIplCRJ\nkqSxtWGDSaEkSZIkja2NG0Y3tRrdyCVJkiRpSGz0SqEkSZIkjS+TQkmSJEkaYxtuNimUJEmSpLG1\naePoplajG7kkSZIkDQu7j0qSJEnSGDMplCRJkqQxtiGDjmDJthl0AJIkSZKkwUlVDTqGZZekNr7O\nfLdfB7/xq4MOYUV4yrYPG3QIK8Lkpk0koapG92c4mvZp0zUj/RGGwmP3/vSgQ1gRPnfS0YMOYQUI\n2/zJphXRNp1b9x50GAP3eR496BAG7pWn/92gQxgKeSKLOq6TFBcsIa+6z3Cc29h9VJIkSZL6tWHQ\nASydSaEkSZIk9cukUJIkSZLG2M2DDmDpvPFOkiRJkvq1cQnDIiS5Q5Kzk1yS5N+T7D5P2VVJ1iY5\no5e6TQolSZIkqV8bljAszquBs6vqnsDn2um5vAS4EOjp6TcmhZIkSZLUr62fFB4DnNKOnwI8abZC\nSfYFjgLeD/T0ZFPvKZQkSZKkfm39B83sVVXr2/H1wF5zlHsX8Epg114rNimUJEmSpH4tQ1KY5Gzg\nLrMsel13oqoqyRZdQ5M8Abi2qtYmmeh1uyaFkiRJktSvXpLC86fge1NzLq6qI+ZalmR9krtU1U+S\n7A1cO0uxhwPHJDkKuD2wa5JTq+rY+cIyKZQkSZKkfvWSFN5rohmmnbZmMVs4HTgOeFv776dmFqiq\n1wKvBUjyKOAVCyWE4INmJEmSJGkUvBU4IsklwGPaaZKsTvLpOdbp6emjXimUJEmSpH5t5ZfXV9V1\nwOGzzF8HHD3L/C8AX+ilbpNCSZIkSerXIl9GP0xMCiVJkiSpX1v/lRRbjUmhJEmSJPXLpFCSJEmS\nxphJoSRJkiSNMZNCSZIkSRpjJoWSJEmSNMZMCiVJkiRpjG3l9xRuTSaFkiRJktQv31MoSZIkSWNs\nhLuPbjPoACRJkiRJg+OVQkmSJEnq1whfKTQplCRJkqR+mRRKkiRJ0hjz6aOSJEmSNMZ8+qgkSZIk\njTG7j0qSJEnSGDMplCRJkqQx5j2FkiRJkjTGRvieQl9eL0mSJEn92rCEYRGS3CHJ2UkuSfLvSXaf\no9zuST6R5KIkFyZ56EJ1mxRKkiRJUr+2clIIvBo4u6ruCXyunZ7N/wd8pqruBdwfuGihik0KJUmS\nJKlfNy9hWJxjgFPa8VOAJ80skGQ34BFV9QGAqtpQVdcvVLFJoSRJkiQNv72qan07vh7Ya5YydwN+\nmuSDSb6V5MQkOy5UsUmhJEmSJPVr4xKGGdp7Bs+fZTimW66qCqhZotgWeCDw91X1QOAG5u5mutlK\nkiRJkqR+9HKP4M+m4OdTcy6uqiPmWpZkfZK7VNVPkuwNXDtLsauBq6vqm+30JzAplCRJkqTbQC9J\n4e4TzTDtkjWL2cLpwHHA29p/PzWzQJswXpXknlV1CXA4cMFCFZsUSpIkSVK/tv7L698KfCzJ84Ar\ngD8ESLIaOLGqjm7LvRj4cJLbAT8EnrtQxSaFkiRJktSvrfzy+qq6jubK38z564CjO9PfAR6ymLpN\nCiVJkiSpX4t/7+DQWPLTR9P4UpIjO/OemuTMJBuTrG2flPOxJDu0y6fnfy/Jt5O8LEnm2caqtvwZ\nM+a/IslF7bL/TPLspX4OSSuLbZOkYWTbJI2Brf/y+q1myUlh+xjUFwLvTLJ9kp2BNwMvAm6sqkOq\n6n7Af7fl6My/L3AE8HjgDfNs5iXAhXQet5rkhcBjgYdU1SHt+JwNpKTxYtskaRjZNkljYOu/vH6r\n6es9hVV1AXAG8CrgL4BTquqyGcW+DNx9lnV/CjwfOH62upPsCxwFvJ/NG6/XAH9aVb9p6/l1VZ3a\nz+eQtLLYNkkaRrZN0gq3DO8pHJTluKdwDbAW+C/gwd0FSbal+VXrM7OtWFWXt10d7gRsx+ZPzXkX\n8Epg1059uwK7VNUVyxC3pJXNtknSMLJtklaqIeoOulh9J4VVdWOS04BfV9X0RdAdkqxtx78InNRD\nPbc8NSfJE4Brq2ptkomlxLXmi5tuGX/U/mFif3tKSKPmiiquaMcnJycXte6wtk0Ak++4pWcXEw+D\niYfbPkmjZOr7xdT3p6dqvqJbGOa26YTJW9+D/aCJnXjwxE5LrUoaOVPnw9T3+qxknJPC1iY2bxVv\navutzyvJgcDGtktE18OBY5IcBdwe2DXJqVV1bJLfJLlbVV0+X91veGRfPWMlDYEDEg5oxycnJ1mz\nZlEveIUhbJsAJl9uEiiNsonfChO/NT0V3njGpvmKz2Yo26YXTN55ER9BWlkm7tcM09acNrhYBmFg\nmVPb9eEfgL+duayqXltV+1XV3YCnA5+vqmPbxW8B/i7JLm09O/sULUnLxbZJ0jCybZJGwAg/aGY5\n31NYc4x3TXeP2I7mAuupNH3gSbKazfvGz1p3Vb23fWLXN5NM7863L0P8klYm2yZJw8i2SVpphujB\nMYu1LElhVa2ZMb3rHOXm3F63b/yM+V8AvjBj3v8F/u+SgpU0NmybJA0j2yZphfKeQkmSJEkaYyaF\nkiRJkjTGhugewcUyKZQkSZKkfo37PYWSJEmSNNbsPipJkiRJY8ykUJIkSZLGmPcUSpIkSdIY855C\nSZIkSRpjNegAlm6bQQcgSZIkSRock0JJkiRJGnJJ7pDk7CSXJPn3JLvPUe41SS5Icn6SjyTZfqG6\nTQolSZIkafi9Gji7qu4JfK6d3kySA4A/AR5YVfcDVgFPX6hik0JJkiRJGn7HAKe046cAT5qlzK9o\nnoO6Y5JtgR2Baxaq2AfNSJIkSVLftvo7KfaqqvXt+Hpgr5kFquq6JO8AfgTcBJxVVf+xUMUmhZIk\nSZLUt/7fXp/kbOAusyx6XXeiqirJFs87TXJ34H8DBwDXAx9P8qyq+vB82zUplCRJkqS+9XKl8EvA\nl+dcWlVHzLUsyfokd6mqnyT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(figsize=[15,5])\n", "pl.subplot(1,3,1)\n", "pl.imshow(Ce,aspect='auto',interpolation='none',vmin=-1,vmax=1)\n", "pl.xticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.yticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.title('empirical correlation')\n", "\n", "pl.subplot(1,3,2)\n", "pl.imshow(Cg,aspect='auto',interpolation='none',vmin=-1,vmax=1)\n", "pl.xticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.yticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.title('genetic covariance')\n", "\n", "\n", "pl.subplot(1,3,3)\n", "pl.imshow(Cn,aspect='auto',interpolation='none',vmin=-1,vmax=1)\n", "pl.xticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.yticks(sp.arange(len(phenotype_names)),phenotypes.columns)\n", "pl.title('noise covariance')\n", "pl.colorbar()\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#convert P-values to a DataFrame for nice output writing:\n", "lmm = qtl.test_lmm(snps=snps[sample_idx],pheno=phenotypes.values,K=sample_relatedness)\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_lmm = pd.DataFrame(data=lmm.getPv().T,index=data_subsample.geno_ID,\n", " columns=phenotype_names)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Genome-wide manhatton plots for one phenotype:\n", "for p_ID in phenotype_names:\n", " pl.figure(figsize=[15,4])\n", " plot_manhattan(posCum=position['pos_cum'],pv=pvalues_lmm[p_ID].values,chromBounds=chromBounds,thr_plotting=0.05)\n", " pl.title(p_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Any effect test" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. Training the backgrond covariance with a GP model\n", "Background model trained in 5.22 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/stegle/research/users/stegle/limix/limix/modules/qtl.py:261: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if K1r==None:\n" ] } ], "source": [ "P = phenotypes.values.shape[1]\n", "\n", "covs = None #covariates\n", "Acovs = None #the design matrix for the covariates \n", "Asnps = sp.eye(P) #the design matrix for the SNPs\n", "K1r = sample_relatedness #the first sample-sample covariance matrix (non-noise)\n", "K2r = sp.eye(N) #the second sample-sample covariance matrix (noise)\n", "K1c = None #the first phenotype-phenotype covariance matrix (non-noise)\n", "K2c = None #the second phenotype-phenotype covariance matrix (noise)\n", "covar_type = 'freeform' #the type of the trait/trait covariance to be estimated \n", "searchDelta = False #specify if delta should be optimized for each SNP\n", "test=\"lrt\" #specify type of statistical test\n", "\n", "# Running the analysis\n", "# when cov are not set (None), LIMIX considers an intercept (covs=SP.ones((N,1)))\n", "lmm, pvalues = qtl.test_lmm_kronecker(snps=snps[sample_idx],phenos=phenotypes.values,covs=covs,Acovs=Acovs,\n", " Asnps=Asnps,K1r=K1r,trait_covar_type=covar_type)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues = pd.DataFrame(data=pvalues.T,index=data_subsample.geno_ID,columns=['multi_trait'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OaZZvbNa9Lsm1SQ5NcmKz7QHAbwHPb15/XpKfTvLmZtsTk/y/Zp8fT3Lcvs5Z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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(figsize=[15,4])\n", "thr = 0.1/snps.shape[1]\n", "plot_manhattan(posCum=position['pos_cum'],thr=thr,pv=pvalues['multi_trait'].values,chromBounds=chromBounds,thr_plotting=0.05)\n", "pl.title('Any effect test')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Testing for common effects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A common effect test is a 1 degree of freedom test and can be done by setting\n", "\\begin{equation}\n", "\\mathbf{A}_1^\\text{(snp)} = \\mathbf{1}_{1,P},\\;\\;\\;\n", "\\mathbf{A}_0^\\text{(snp)} = \\mathbf{0}\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. Training the backgrond covariance with a GP model\n", "Background model trained in 4.17 s\n" ] } ], "source": [ "P = phenotypes.values.shape[1]\n", "\n", "covs = None #covariates\n", "Acovs = None #the design matrix for the covariates \n", "Asnps = sp.ones((1,P)) #the design matrix for the SNPs\n", "K1r = sample_relatedness #the first sample-sample covariance matrix (non-noise)\n", "K2r = sp.eye(N) #the second sample-sample covariance matrix (noise)\n", "K1c = None #the first phenotype-phenotype covariance matrix (non-noise)\n", "K2c = None #the second phenotype-phenotype covariance matrix (noise)\n", "covar_type = 'freeform' #the type of the trait/trait covariance to be estimated \n", "searchDelta = False #specify if delta should be optimized for each SNP\n", "test=\"lrt\" #specify type of statistical test\n", "\n", "# Running the analysis\n", "# when cov are not set (None), LIMIX considers an intercept (covs=SP.ones((N,1)))\n", "lmm, pvalues_common = qtl.test_lmm_kronecker(snps=snps[sample_idx],phenos=phenotypes.values,covs=covs,Acovs=Acovs,\n", " Asnps=Asnps,K1r=K1r,trait_covar_type=covar_type)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_common = pd.DataFrame(data=pvalues_common.T,index=data_subsample.geno_ID,columns=['common'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(figsize=[15,4])\n", "plot_manhattan(posCum=position['pos_cum'],pv=pvalues_common['common'].values,chromBounds=chromBounds,thr_plotting=0.1)\n", "pl.title('common')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Testing for GxE (specific effect test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a specifc effect test for trait $p$\n", "the alternative model is set to have both a common and a specific effect\n", "for transcript $p$ from the SNP while the null model has only a common effect.\n", "\n", "It is a 1 degree of freedom test and,\n", "in the particular case of $P=3$ traits and for $p=0$, it can be done by setting\n", "\\begin{equation}\n", "\\mathbf{A}_1^\\text{(snp)} =\n", "\\begin{pmatrix}\n", " 1 & 0 & 0 \\\\\n", " 1 & 1 & 1\n", " \\end{pmatrix}\n", "\\;\\;\\;,\n", "\\mathbf{A}_0^\\text{(snp)} = \\mathbf{1}_{1,3}\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. Training the backgrond covariance with a GP model\n", "Background model trained in 4.94 s\n", "Design(0): \n", "[[ 1. 1. 1.]]\n", "Design(Alt): \n", "[[ 1. 1. 1.]\n", " [ 1. 0. 0.]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/stegle/research/users/stegle/limix/limix/modules/qtl.py:372: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if K1r==None:\n" ] } ], "source": [ "Asnps0 = sp.ones((1,P)) #the null model design matrix for the SNPs\n", "Asnps1 = sp.zeros((2,P)) #the alternative model design matrix for the SNPs\n", "Asnps1[0,:] = 1.0 \n", "Asnps1[1,0] = 1.0 \n", "\n", "# Running the analysis\n", "# when cov are not set (None), LIMIX considers an intercept (covs=SP.ones((N,1)))\n", "pvalues_inter = qtl.test_interaction_lmm_kronecker(snps=snps[sample_idx],phenos=phenotypes.values,covs=covs,Acovs=Acovs,\n", " Asnps0=Asnps0,Asnps1=Asnps1,K1r=K1r,trait_covar_type=covar_type)\n", "\n", "print \"Design(0): \\n\"+str(Asnps0)\n", "print \"Design(Alt): \\n\"+str(Asnps1)\n", "\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_inter = pd.DataFrame(data=sp.concatenate(pvalues_inter).T,index=data_subsample.geno_ID,\n", " columns=[\"specific\",\"null_common\",\"alternative_any\"])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(figsize=[15,4])\n", "plot_manhattan(posCum=position['pos_cum'],pv=pvalues_inter['specific'].values,chromBounds=chromBounds,thr_plotting=0.1)\n", "pl.title('specific')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oUu26rddEE8AUX5oVDOqIiIho9qSCOjbw0SRwQhSaJQzqaLYw5ZKIiAB4ngff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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tests = ['Any effect test','Interaction effect test']\n", "pl.figure(figsize=[15,10])\n", "\n", "#lim = -1.2*sp.log10(SP.array([pvalues['alternative_any'].min(),\n", "# pvalues_inter['null_common'].min(),pvalues_inter['specific'].min()]).min())\n", "plt = pl.subplot(2,1,1)\n", "plot_manhattan(position['pos_cum'],pvalues_inter['alternative_any'],\n", " chromBounds,colorS='k',colorNS='k',alphaNS=0.05,labelS='any')\n", "plot_manhattan(position['pos_cum'],pvalues_inter['null_common'],\n", " chromBounds,colorS='y',colorNS='y',alphaNS=0.05,labelS='common')\n", "plot_manhattan(position['pos_cum'],pvalues_inter['specific'],\n", " chromBounds,colorS='r',colorNS='r',alphaNS=0.05,labelS='specific')\n", "pl.legend(loc='upper left')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }