SHNW_Gut_16S_2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **2019 SHNW 16S v4 faecal microbiota analyses**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Raphael Eisenhofer, July 2020*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate qiime2-2020.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "cd  2019_SHNW_Gut/R1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, let's import the reads into a qiime2 artefact:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mImported ../reads/R1 as CasavaOneEightSingleLanePerSampleDirFmt to demux-r1.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime tools import \\\n",
    "--type SampleData[SequencesWithQuality] \\\n",
    "--input-path ../reads/R1 \\\n",
    "--input-format CasavaOneEightSingleLanePerSampleDirFmt \\\n",
    "--output-path demux-r1.qza "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: demux-r1.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime demux summarize \\\n",
    "--i-data demux-r1.qza \\\n",
    "--o-visualization demux-r1.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    -Minimum:\t1\n",
    "    -Median:\t128215.0\n",
    "    -Mean:\t139647.62886597938\n",
    "    -Maximum:\t593954\n",
    "    -Total:\t13545820"
   ]
  },
  {
   "attachments": {
    "qual.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qual.png](attachment:qual.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Qualities look great. Will choose 150bp as the deblur sequence cutoff. This will let us compare features we find in our studies those found in others. The majority of datasets on QIITA are deblured to 150bp. There's also not a huge amount of information gained in the extra 100bp if we merged R1/R2 to get 250bp (see figure 1 from DOI: 10.1128/AEM.00062-07)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table.qza\u001b[0m\n",
      "\u001b[32mSaved FeatureData[Sequence] to: rep-seqs.qza\u001b[0m\n",
      "\u001b[32mSaved DeblurStats to: deblur-stats.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime deblur denoise-16S \\\n",
    "--i-demultiplexed-seqs demux-r1.qza \\\n",
    "--p-trim-length 150 \\\n",
    "--p-sample-stats \\\n",
    "--o-representative-sequences SHNW-gut-rep-seqs.qza \\\n",
    "--o-table SHNW-gut-table.qza \\\n",
    "--o-stats SHNW-gut-deblur-stats.qza \\\n",
    "--verbose \\\n",
    "--p-jobs-to-start 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: rep-seqs.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table tabulate-seqs \\\n",
    "--i-data SHNW-gut-rep-seqs.qza \\\n",
    "--o-visualization SHNW-gut-rep-seqs.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table.qza \\\n",
    "--o-visualization SHNW-gut-table.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Table summary\n",
    " \n",
    "    Number of samples \t92\n",
    "    Number of features \t8,483\n",
    "    Total frequency \t7,740,835\n",
    "\n",
    "                        Frequency\n",
    "    Minimum frequency \t6.0\n",
    "    1st quartile \t50,267.0\n",
    "    Median frequency \t77,491.0\n",
    "    3rd quartile \t113,561.25\n",
    "    Maximum frequency \t349,258.0\n",
    "    Mean frequency \t84,139.51086956522\n",
    "\n",
    "\n",
    "### 10 samples with lowest # of reads:\n",
    "    K15\t10013\n",
    "    E4-EBC4\t155\n",
    "    E3-EBC3\t154\n",
    "    E6-EBC6\t93\n",
    "    E9-EBC9\t72\n",
    "    E8-EBC8\t43\n",
    "    B12\t42\n",
    "    E5-EBC5\t23\n",
    "    W15\t10\n",
    "    E1-EBC1\t6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 most abundant and prevalent features:\n",
    "\n",
    "                                        Frequency    # of Samples Observed In\n",
    "    3c44df3672100a011a334b67eea24366 \t279,556 \t83\n",
    "    dadb874cf55015dada2a22cc32c1eda9 \t221,891 \t82\n",
    "    6234321dd90179936aa89249d23d256d \t165,345 \t78\n",
    "    ac57d463deafd198f817244255e80019 \t137,292 \t77\n",
    "    8c6accdc08a5bcbdd15cc07420d92e2e \t123,096 \t81"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assign taxonomy using a naive bayesian classifier against the SILVA 132 v4 database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qiime feature-classifier classify-sklearn \\\n",
    "--i-reads SHNW-gut-rep-seqs.qza \\\n",
    "--i-classifier silva-132-99-515-806-nb-classifier.qza \\\n",
    "--o-classification SHNW-gut-SILVA-132.qza \\\n",
    "--p-n-jobs 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SILVA132.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime metadata tabulate \\\n",
    "--m-input-file SHNW-gut-SILVA-132.qza \\\n",
    "--o-visualization SHNW-gut-SILVA-132.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a phylogenetic tree using SEPP: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qiime fragment-insertion sepp \\\n",
    "--i-representative-sequences SHNW-gut-rep-seqs.qza \\\n",
    "--i-reference-database sepp-refs-silva-128.qza \\\n",
    "--o-tree SHNW-gut-sepp-tree.qza \\\n",
    "--o-placements SHNW-gut-sepp-placements.qza \\\n",
    "--verbose \\\n",
    "--p-threads 24 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter out features that couldn't be inserted on tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-filtered_table.qza\u001b[0m\n",
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-removed_table.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime fragment-insertion filter-features \\\n",
    "  --i-table SHNW-gut-table.qza \\\n",
    "  --i-tree SHNW-gut-sepp-tree.qza \\\n",
    "  --o-filtered-table SHNW-gut-filtered_table.qza \\\n",
    "  --o-removed-table SHNW-gut-removed_table.qza \\\n",
    "  --verbose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-removed_table.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-removed_table.qza \\\n",
    "--o-visualization SHNW-gut-removed_table.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like nothing was removed, so will just use the unfiltered table (table.qza) for subsequent analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run alpha-rarefaction analysis to see what a good sampling depth will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-rarefaction.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity alpha-rarefaction \\\n",
    "--i-table SHNW-gut-table.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--o-visualization SHNW-gut-table-rarefaction.qzv \\\n",
    "--p-min-depth 500 \\\n",
    "--p-max-depth 50000"
   ]
  },
  {
   "attachments": {
    "rarefaction_richness.png": {
     "image/png": 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oic2Xy7hEdm42QZEVISvaTYmsOFlQFP7uslWLv8J8kbNYFRatj7ReM+8lnk+oBQAYWzZBkRGyot2UyCAoXl8IisOzF4Es62y3D9m20nTPKctgi1eutIhxNocOrg6GPdvYZ1633cAPFtZtzsp3tFw3sz2DkfVphmweeozwajavCUasHU32oGgbwwbXHW+xJl5b8s2mi1Ibg9eoZsLJLIelcXy8zeHDvJCZ7Xu1WLc8m/WZHZ7Y9mzgdIYGRVZYRLSbEllxuqAo/O01DoMq/BV23Gqsj7ReMx9lsT8AwFgbGhQZFhHtpkQGQfH6QlAc3pAMZExeFmmJ72Dew2pFXDXvbL025NSDqxZ5zOrVLFaMi6aDjSvm3Sw3iVssXsWC8f0Mvh1hZ/O6cKjVkTZv2Eh8BQcvMa2xdjTZg6J4gSleKVquGJettwxegtq5tDQuDV5b2hww5JrTuLdVvcPjLRbFdzX0dDaMu9n7VMYVuxuEN2Q8sbHe6p0Mfl5wRnaD4jDF+YKi+LfW3Db4X2fzdqvGZn2k9RrfUfyL77A5AgBcK7tBcRgIitcXguLwhHRnZUgmMoUlznojP3owilmv2eauwVV+Ppv9RBa1NtVW5x58Qw6nxxBZnISx3c/ygzGWr25m3MfulumOtaNJHxQ58TqSs72CtB5FX2S6JDWyvFq1OJHFARZXnxasL0QF9o9nBl/xmqbH4MRTiVWDO1lP3OHwnYAzmg5B0fRX2/S31bKVbok3rhk3WR9pvWbZBNAIAGC8ICg6FwRFgHHB2tF4B8WGTv21B0WAqe1agiJrYpMwKAIAOC8EReeCoAgwLlg7QlAEuO4QFAEAJg8EReeCoAgwLlg7mqCgqJ5+QdHqvjgruEkObIwyKKoRFAEAxh6ConNBUAQYF6wdjV9Q7DIHxVbddAyKACM2+qDYqhOCImtuCIoAAGMCQdG5ICgCjAvWjsY5KBoahMFsEBQBHLumHkU+mA1vbgiKAABjAkHRuSAoAowL1o7GIyjqDNSnGxCCYmOnXtaqK0NQBHBsdEGRNSvWuFgTE4Iia3Ss6SEoAgBcIwRF54KgCDAuWDsav6DY2W9oNQXF8iYtgiKAI6MLiqxZCUGRNTTW3BAUAQDGBIKic0FQBBgXrB2NcVAcEINiv573KLb1GlSduro2nQRBEcCx0QVF1qxY42JNjDU01txYoxODopAVx6Kt4RcuAExDCIrOBUERYFywdjTmQVFvGNDpB/p0A91aFhT16i69vE1X2YygCODQ6IIia1ascbEmxhoaa268R1E/wBoga2TGxjgGbQ2/cAFgGkJQdC4IigDjgrWjsQyKnDEoGgb69TwotvcZmrsNinZddQuCIoBDowuKrFmxxsWaGGtorLkZexRNQdFIPPs1wC9cAJiGEBSdi1VQlEqlCoWir69PXAeA0WLtiLUm1qbE9WsjXJoaWFY0Dnzaox3o6BMHPpW16tyz2kJK21u6dbjmBLBxtUHxvzdVsAbFmpUw5ClraKy5sUbHmp546+kYNTP8wgWAaehqg2JwcPBYXUrBKFgFxfb29oqKipaWFvzqArgWrAWxdsRaE2tTYtW1MeZEfs+b3sCvWcXxbHoNwt2nhY19B1Jbcut7WVYUDwAAo6sNiq96y1mDEu47ZU1MGMmGB0WD2AbHKijiFy4ATENXGxRTU1PH6lIKRsEqKDIqlYr96lIqlfzCFABGhbUg1o5YaxLb1TXjJzVepPLxbPTi3acdfQZNj76xU8+ubmOqullWDCvvNL4+AIhYUBxh+eumSpYSWVNiDYo1K9a4WBMT7zsd60kUBfiFCwDTDQuKIxQcHMxS4hheSsEo2AZFhgV3qVSaDQCjxVrQ2P4HMP6PK/9puvvUQMK0+229+uZuQ4MxKxY29rlntX1+UYWCgjKKwpoPa0SsKbEGxZoVa1ysifGp9g280Yn3nY5pUGTwCxcAwK4xv5SCUbATFAFgsuFB0Xh9yoq5U7HXdAMqu6ht7OT3oMpa+dg2lc1aSZO2vElbpu4vNRcVCgqKsZgaBWsgrJmwxsKaDGs4rPmwRsSaEmtQwk2nrIkN7U40NkQAAICpD0ERwDkI16fsf7xT0fSkYo+WZ8W2Xn6bnLpL39ChU7Tzi906Y2hkpaZFWyP8REFBMRdjoxDaCGssrMmwhsOaD2tErCmxBsWaFWtc5qcTx6k7EQAAYDJDUARwDjwoGq9SWWFXrsLwp+xCVuhX7OjjfSAtPbx3kV3sqrr4s4vswreB/ezUKzt0KCgo5iK0C9ZAWDNhjYU1GdZwWPNhjYg1JaEvUUyJxv8uI7Q7UxMEAACYFhAUAZyGcJ0q9GyYs6LxHlQ+5EZXP7vAHWjv4x2M7Hq3tYdd+OrZta+xsAUUFBRzEdsFayassbAmwxoOaz6sEbGmxBoUa1ZWKZH3KBrbHgAAwLSBoAjgTIRr1cGsaOAPUGkNJMTFHi0fDdUUGvlPYQEFBcWmmFsH+ym0GtZ8xIho4I8BC+3L3NyQEgEAYLpBUARwJjwoGq9YhYtXg2kcVB4XjYVd5vYZR+BgP4XCrn1RUFBsirmBmJuM0IJ4RDQ2K/5cokVKNDY7AACAaQRBEcDJCNesnE1cNN6MqjP2MeoMbIEX4doXBQVlaDE3E2OTEW80tRsRGbH5AQAATBsIigBOyXz1OnhFa5kYTde7KCgowxfL9mLToEyNDAAAYDpCUARwVsJVLCdc1PIai2KuREFBGabYbTUmYmMDAACYfhAUAZyeeElrZr7YRUFBGWGxJjYtAACAaQxBEWDqEC9yAWBUxIYEAAAACIoAAAAAAABgA0ERAAAAAAAArCAoAgAAAAAAgBUERQAAAAAAALCCoAgAAAAAAABWEBQBAAAAAADACoIiAAAAAAAAWEFQBAAAAAAAACsIigAAAAAAAGAFQREAAAAAAACsICgCAAAAAACAFQRFAAAAAAAAsIKgCAAAAAAAAFYQFAEAAAAAAMAKgiIAAAAAAABYQVAEAAAAAAAAKwiKAAAAAAAAYAVBEQAAAAAAAKwgKAIAAAAAAIAVBEUAAAAAAACwgqAIAAAAAAAAVhAUAQAAAAAAwAqCIgAAAAAAAFhxjqC4ZMmSvxitWrVKrDLx9fUVNrGFkdcAAAAAAACAI04QFF1cXMz5cGjYYzXpRmxh5DUAAAAAAADgiJMFpyVLlrDcKK4Qsew3Y8YMYdmcBq9YIywDAAAAAACAXc4UFBUKBUt67Ke4bryn1BwC2QJbHUmNsMzI5XKPIUJDQxsbG8U9AAAAAAAAph9nCoos5tn0Bw4NgSOpEZaZ3bt3s+Q5lJeXl7gHAAAAAADA9OM0QZHlN8u+RMHQ20pHUiMsO/LMM8+cPn1aXAEAAAAAAJh+nCMosoAnLg1hToPmfUZSMwwERQAAAAAAmOacICiuWrWKBTwz4d5RtiBsZauW9SOsGQaCIgAAAAAATHNOEBTtGjqh4lhBUAQAAAAAgGnOWYOi5SQZYwtBEQAAAAAApjlnDYrjB0ERAAAAAACmOQRFWwiKAAAAAAAwzSEo2kJQBAAAAACAaQ5B0RaCIgAAAAAATHMIirYQFAEAAAAAYJpDULSFoAgAAAAAANMcgqItBEUAAAAAAJjmEBRtISgCAAAAAMA0h6BoC0ERAAAAAACmOQRFWwiKAAAAAAAwzSEo2kJQBAAAAACAaQ5B0RaCIgAAAAAATHMIirYQFAEAAAAAYJpDULSFoAgAAAAAANMcgqItBEUAAAAAAJjmEBRtISgCAAAAAMA0h6BoC0ERAAAAAACmOQRFWwiKAAAAAAAwzSEo2kJQBAAAAACAaQ5B0RaCIgAAAAAATHMIirYQFAEAAAAAYJpDULSFoAgAAAAAANMcgqItBEUAAAAAAJjmEBRtISgCAAAAAEw6nY3UUjNYyoPp/PtWNazoesWd4ZohKNpCUAQAAAAAmHR83qSDs+jgbLEce5y8Xx5cZWX/TKoIE3eGa4agaAtBEQAAAABgXGi7qaeF9w221lJzJTUWkSKbalNJGkcVEVQaREW+lHeGsk9Q+lFK2UcJOyl2K0X8TqE/0eWvKX4jJW43lR2UvMtidTtFraGiC+ILwTVDULSFoAgAAAAAE8SgJ732CmVgQNx5nPR38fDW0UCtMmqSUEMhD2+yFJLGkiScSi9R4QXK86YsD0o/QsmulLDDGN5+o5CfKGglBXxBvh+Rz9vkvYxOLaYTz5Pbk3T4Edr/ALncQ7tuo23/os3/Q+v/g5dN/01bb6Cdt9Ceu2jf/XTwITo6j449TSdfJK9X6exbdOEDCvicLq+k0B8p4leKWkexGyh2I8VtocifKYrVOC7Rv1N1rPih4JohKNpCUAQAAACACeL5spighinZJ6g+l+rSqTqBKiOpLJiK/Snfh3JOUYYbpR6gxN0Ut42i1lPYrxT8AwWuIP9PeeJiuYulL5bBWHhzVP78/3iKY1mOJTqW61i6OzSXJz2PZ3nqY9mPJUCWA1kaZJmQJUOWD6M3Uvx2St7LcyNLjyxDsiTJ8iRLlSxbsoTJciZLmyxzttRQh5K6m6mnmbrV1C4nTQU1FpA8nWriqCKUSvypwJuyj1H6fkraTrHrea9g3AZK3kkZBynHgwrPUtlFqgwnWQKlufKf3U0OS8pufn4YIwiKthAUAQAAAGBcdDaSqpRqkqgkkEesxF20927bWGhTNvxfcrmbjjxOxxbwXHd6CZ15g86/R36fUuDXdPl7CvuFItfxLr6EnfxezfSjPFjmneH3cJYG8fs5pXH83k4W3hqLqKmCdxt2NPAuxP4u8V2NxgAfNqa3hTrqSVNJqkJSZFBNPFWGUWkAFZyhnOOUfoDnvbg/jdnvT77Malg928r2YXuy/dlR7Fh2hg4FPxsfisZx92m+J8X+YVHWUfRa65o/qKVK3BmuGYKiLQRFAAAAALgKAwbqaiJ1Oc9jZZd5R1/SHgpfy3vhzrzOA96++2nbP2nDf9KOm+nAg+TxHO+mu/QN7wNk2e/AbH4H5uFH6cg8OjCHtt/Eb8U0l+NPU32O+ELjTd9Hva3UqeRxS1VE9Zm8B68ynPfpFZ7l/XsZB/ljgXEbeEKLW09JO3hPYPYx3itY6s97CGvieG9hYwHv2WtXUI+GdD3DZb9rwc7cLheXYRwgKNpCUAQAAAAAbmCAujW8F64uncpDKPc0v98y4ne6+BXv1ju+kN+ouf1Gfvcm+7l/Fh1/ht/tGbiCd/Elu1KuF5WHUl0GNVfxHryRPGpYn0uJ28TlMWHQUl87dTZQazWpS6g+i2oTSRo1WDIP8ewXv5E/4xf7B3/1NFfKdqd8LyrxI0kIf+pPnkaN+dTMsl8dv8NTey1dkeA0EBRtISgCAAAATHEss7HkxvIbS3GSMP6UXco+ivyDZzyW9Dye5b18O27iCXDbv3h/4LGneTJk+ZClxCQXyvHkDwrWpvEMyZLkgEE87Sj0tlKXarAoMvmDdpY1rOj7xZ3N+jv5I39ttdRUTspcqkshaTSVB1Hxeco7RVlHKNWFEjaLQ7wkbOGrrJJtKr5AkstUHUN1qdSQR80SfhJ2KnZCAGsIirYQFAEAAACcWG8baaQkz+Jjq+Sf5WO9RP1Jl77ld3t6PEcH5/AhN1kC3HoDuc4k96fI+zV+j2j4Wj4kTPZJ/lyfLIXfR9rVdE0JcIRyjlHM77wrL2ad+NO8IPxkSS/zMO/iS3Pl3X2sktXEb+TdgJmHKNeDCn2oLJCqInhXYX027zZsreEPQ/a1iy8BMCrOFBT/8pe/pKeniysmq1atYvWMi4uLUOPr6yvUsAVHNcNAUAQAAACYjPo6+CiaimyqiORjfqYd4sNvBn1H597lT/odfIiP27nhv2jrP2jvvXzcTq9l5P8ZHwg0cRcfOaYkkI8ioyqlThWflGJiaLuGdP1FUfklKjpHuSd4AozfwHv8UnY7LHF/8hCoqbIY7gVgIjhNUBSSnk1QVCgUrJL9NC+wSmE3hi0Iuw2tGQaCIgAAAMA1CVrJp1gwl/0P0K5/W9WwIs8Ud2b6u/j06/W5VBlFBef5pAsxm/l4nuff51M7sJ13304b/y9t+V/aO4OOPkGnl/IxP0N/5hP6ZR2n4gA+b0RjMR/M06ATzzl+BvQ8snY18o4781N/LMuJI74YR/tM2sHHeon6lfcWJm6htL2U5UYFXnw2CGG0z/pMUhXz5wZZVmS5UVXksCRtx5QPcF04R1CcMWMG+ynkPaFGYM6H5gW2g7AzY86HNjXCsiMIigAAAADX5OQLfP6GhJ0Oy46b6fBjfE52lgDX/wdt+iuffv3IY+T5Cp+yL2Q1nxUww42K/Kg6nhoK+PiZ403fzx8X5JM9VJkmezBP9OdluvNzq3gvaPRait9EqXso8wifs6HYlw/6Igz4ySd7qOIDh/a28YFkrkgSzJ8eNJd0V0rYZFXDCsb2hOvB6W89ZVg9Iyz7+vqaYyFbYKtDa4RlRxAUAQAAAIbT1cQHcZFn8jn6Cs7xDsDYrXwqdr9P+d2ex57mU0Fs+hsfBmarsey8jT8NuO3GwbLh//EhYZR51FYnnnNcDPAZFHqaedBqllBDPh/BpTqGj+YiDPrCYh4LewmbefBj8U+c6n0XZRyi3JN8n/IgcdwXccxPYb6HPvH044G9W5Y2ASYB5w6KbNUcEYWtQ2Ph0BphmamoqFg9xGOPPYagCAAAANOLvp/ft6kq5eO4lAXzeR1SD/AnAC9/Txc+5B19R5/gD/5tvUGcCmLf/XwYGK9Xye8T3gEYu4XSDvPnBiVhfCaJhK38xsv4DWKJ+Z0X8yorbKs8Q3zpqzVg4EN0dqmpTUZNZaTModokqorkA7oU+vDBXcxz/bHgx+PfrxRrnPEv4wCfCVAc+iWSapP5Q4PN5Tyssnim7ebBEgBMnDsouri4LFmyRFhmC2yV7WCOhcL+Q2uEZQZBEQAAAEapp2XipkEfNRaoWApqKOA3cJZcpOwTfCL4iN/5VO/n3uE3iB5+hPbcSZv/hz8BuPNWPhf88Wf4PBABX/AxYBJ2UKY7FflSVTT/sBrpSCcDzDnOb9rUVDgsyTv5T0t8ur82Pt1fSzV/eE+c6j2MSgP4ZO7ZxyhtHyWZxvwUSsw646R/+4wTvp/he1aGm0b+LKVWGQ+T7BuYgJFLAaYi5w6KvsbhTIVltiD0FprzoeUmm5ph4NZTAAAAsCP3NEWtHywXPqAdt1jVsMKi1HgTJgBkL6TI5kO/FF7gz/LFb6ewXyjgc+Ms8M/wvMdS34b/os1/4zmQpUGWCVkyZPkwch2/5zP7JB8CtDqBGgqpTT72c+ixoMhiXneTw5K0nU/tkHXUaro/c2E1fN6/o5R/ms/5zjJnTTx/aFBVxGMkC5OY+AFg/DlrUDRHPvP0GGxBqBHSI2O+y3RozTAQFAEAAMAOz5f5YJuhP1uUn6xWd9xEVTHizlfLoOPdX00Sft+mJIzP/pd2iI/8Gfwj+X7MX9ftSX635/YbjVPA/5Nc7+P3gnq+wu8Lvfw9v0c09QCfNb48hGpTSV3Gp9EbOkv7mGMv0aPhcz+oS3iQq47hA3gWnuGz/EWt4Q/+DVMKvak6lj+P12gc/aWjno/+MgHvGQBGxpmCoiVzLBxzCIoAAABgBwuK+T7isl2HH7ENitpuPvxJYzGfvq80iHI8KdmVdzwGreSDgp56iY/z6XI3n/Vhw3/ygUD3z+IjwXi/Rv6f8hQat43Sj/LpIioi+fTxzVXUrZnAGykHeE8jC5yaSj4MTG0yv7GzxI+PAZNxkD/yF/O7qQ9wDU+GqS58aNCCM/wJwAJvPhZokQ+V+pMkiIrO8ikiWIy0LCxhAsAk5qxB0Ty9/phDUAQAAAA7Tr5A4b/yLjtHZc9ddGwBeTxHB+fwmd/X/wcvu/7NJ4I/sYjOvkUXv6LwNXxyiMxjfOIHaSyfObClhvekTbwBPZ8Nor2OjwdTn8mndpBc5gO95Byj1L187gfzSDBx6/nYMJmH+Y2gpQF8wsC6FD4KKJ8EotF42yrGgAGYgpw1KI4fBEUAAIDpjuWfhkIe5PLP8j5Alu78PuVzPOybyaOgo7LtnxS7mXceNhbxjkQ+iub1w169S8Wf6Gss4LM7sHTHMh5LeizvJe8cHBImZp1xONCDlHeSSnz54DGyRONYoBJ+L2hfG0aCAZi2EBRtISgCAABMcdoefhunLIVqEnmJ/IPnQM+X6dBc2nmL2BPIyt4ZdHwhnX+fP3/I4uLhR/nkELtup9138LLr37T9JnFZKBv+a/TPKF4VFt5YhGNZtLmcTw5RE89ney86x+d+SNtHCVv4E4AsBLKfiVt4DatnWyXBvNuwPot3IbbX8Ts/8UAgADiGoGgLQREAAGAqaJVRXQafEyL9CI+C/p/RqcV0YDZt/cdgFNx1Gx8S5szr/KHB+O2Uc4qPI9pQSN3N4kkssWxZnztYMo6S6/1WNaz0dYg7XwtdLx8XtLWGD/IpTyNpNJVd5E/9ZR2hlN0U96exM3ANvztUeCyw8CwfQqY6hg8MoyrmB7LDdT3i2QAARgVB0RaCIgAAgBMYMPAJ4lk2Kw+hrON8gNDAFeS1jPf77bhZzIEb/pNPDuH2JJ19ky6vovhtfFoISTgp8/jNpdd4U2VLNRVfeTR1W7wzsIPf1dlcwe/wlCXyuR/YeXJPUPoBStxK0b8ZHwv8k2dClgzzvYyPBUby20cbC6iliroaSduFxwIBYLwhKNpCUAQAAJgsupv5836VkXwOw4QdPOyxyOc2n3bfzmeJYFFw4//lo4a6P0U+yyn4B74P27Mikk8x36Ue0dTwI1fiRyl7BkvSDoplcc6ihhVNFfU0G6eLKLaaLiLLjVL3GCeNMD8WeIgPH8rOWRlGtUnGxwIrjI8FtuOxQACYDBAUbSEoAgAATByWi9TlJI3jM08kufA5Ic6/x58MdLmHh0AeBf8ff1bw2NN07l0KWU2Ju/hsgZVRfM4JuzeIjp9sNz4STKGPw8JvCv2F9wpaPRYYP/hYYG+LeCoAgEkPQdEWgiIAAMBY0vWSRspHjiny45PCh6/lM8ifeJ5PH7/5bzwKbvornz7++DPGYWN+oqQ9fKzRqhhSlVLPdU1WfW38eT9lDn9KsPg8JWymdFc+OqijEreBNBXisQAATg5B0RaCIgAAwNXRa6mtjuSZVHqJD/ES9ScFfMEHET04h88YwaIgC4QsFno8Rxc+pLBf+AiiBed4L6K6nE/ld93peqhDwUeOkSXwyeJzT/DnA2N+58OHZh7mHYNVEbxXsMCbMg/y5waFkr6fkrYNrgqlXS6eEwDAySEo2kJQBACAaSTiNz6PvLkce5rPDm9Zw0p9Ln9qrrPROHJMKGV5UOwWuvQNeS+jI4+J80ls+Tvtf4Dv7Psxn3UwZT8VnuczTzRV8JtLJ48uNTWV84FhKkKowIs/OshK3J889bFVSQjfxHZguxl04iEAANMSgqItBEUAAJhGWLQ78wZF/+mwbP8XLywKbv0H7Z9Fp14iv094vEw9QIUX+OTyzZXU3yWebbIYoN42aq2m+mw+Xmjxeco8wmcUjPmddxXmnuDdhrIE3oXYocA0EgAAdiEo2kJQBACAqaxbw+cJrIjkHYMxm2j3XbTnbj6lhKOy7V988BjtZE1T2m5+t2djAR8zpjSADyGTvIsHwsStlHWUii/wxwuVufxRQ96xiSklAABGCkHRFoIiAAA4va4mUubz20Qzj1H0Bgr4nPcE8sFj/od3DB58iE4v5bMOxm6lw4/w2039PuXz0bPi9wld+EBcFgoLilUx4mmvI4OWulR87NC6FD6UaP5pSnOl2D/4+DHpB/jTgxWhfLr5Zgmfa35ALx4FAACjhaBoC0ERAACcwMAAdar4Q4NlwXz8mMg/eNg78Ty5zuTzSbB0d2gun33+0rcUv51yvXjYU5fz/jcbJRcpZd9giVpP596xqmGltVbceQIMGPgcEi1VVJ/Jh5Ap8uHDySRsNt41uodyTxrvGk0kVTGfclDXJx4FAABjDUHRFoIiAABMFiwNdihJkc1HE00/QhG/k+9H5PEsn1dw4/+lHTfxsWTOvM6noU/YSXlnqDqePzE4aW8TtdHfycdKbcin6lgq8aecY3wa+ujfjEOJulGxL5+tnt81KqO+Dtw1CgAwwRAUbSEoAgDAhDLoqV1BdRlUHECpB/mQoRc+MM44fzdt+C8+pujReXT2TQr+kU8wWHCODyWqkTpTZ5q+nzobSF1CtUlUHkR5pyjVhWLWUfxGyjhAhWeoMozfNaqpoJ5m3DUKADBJICjaQlAEAICxZ9DxGzhr0/ik8yn7KPRnOvcuuT9Fu+/gaXDXv8ntSfJ5m0J+ouS9fGIJWQq11PD5CZ0Iy3gs6WkqSZHBs1/hWco4yNNgzO88GeadpPJLPCuqi6lTSXrcNQoAMKkhKNpCUAQAgFFiua6lmmTJPOmxvMdSH8t+bvNp1208DbJMyJLh+ff4jPMp+3liZLmRpcfrO19fex0fIcZcWJDL9bCqYUVrM/vFAL8XtE3G7wutjqESX8p25/eL8rtGt/PlEj9e35BHbbXU3yEeBAAATgVB0RaCIgAADEfXx+/8rE6gfB9K3E3BP/D7Qo/O4/eIsjTocje/a/TCB/wO0tSDfKgYeSa/s9QwWe+ozDnOnwxMPyCWtH2898+8mr6fYn4jaSQfP6YskI8lk7KH9xDGb+K9hYVnqTKc9x9qKqlHw8ehAQCAqQJB0RaCIgAA8PFgmipIGsdHiEnYSUHfkfdrfFLB7TfyUWT2ziCP58j3Yz66TPoRPtKMIpuPOjPghAOuZB7lE9BXhDgsLBNmHjLdNVrCnzbU94vHAgDA1IWgaAtBEQBgqtFIKeu4uGypv4vUZVQVTbmnKX4bn0nCaxkdepi2/ZM2/TefZ+LE83zOicg/KMONz0JRn8tnpHDGNGjDoOWdnA35fDL6xG1XCIqJW/kwMwAAMM0gKNpCUAQAcHpxW/n9n+Zy8EE+0bzrvbT7TtpxC8+BbHXz32jTX/kc9Kde4tPKR/1Jme5UEUHKPD5b/ZQxYOAT0DeVUW0ilQbwBwgTtxhHl9lLBV58yJk0V4r7g09UKJT4TRS/YXCVlei1CIoAANMQgqItBEUAAKd3bAG/R3TnzbTlf2nDf9L6/48nwy3/MFbeyuMii4jFgeLOU0l/Bx9NR5HBewLzTlHKbnGAmZzj/N7RuhRqlvDp7C3nJOzR8LtJhy+41xQAYPpBULSFoAgA4HwMemoooOwTFPg1v3f0z/+PXO+jkNV81sHmSnEfS4cfoaoYcdlJGbTUoaBG4+2jRT584JnY9RS3gT9PWHyBT2GvKuIZD9MSAgDAqCAo2kJQBABwDq21VOxP4WvJ41l+H+n+B/gdpBlH+bgypxbTmTf43aSOys5bnCkoXvH20fpsPlmFtlvcHwAA4JohKNpCUAQAmKT62nm6S9jBQyBLejtv5QtslVWyTZYyj/EZ7c3FZzl/NNGyhhW7PY2Tgfn2UcmIbx8FAAAYawiKthAUAQAmC72Wdw9mHOVdhfsf4A8cejxHEb9RcQDvThy5qmg+s8UkZOf20T9w+ygAAEwGCIq2EBQBAK6n5ioqOE8hP5H7U3wEmsOP0KVvKPskNRaNfj73yRAUcfsoAAA4FQRFWwiKAAATqqeFJOEUu4VOL+WjkrrcTeffo5R9JEvms96PiYkPin24fRQAAJwbgqItBEUAgPGl66O6dEo9SL4f0d57+ayGnq9Q9EYqD6FujbjP2BrXoCjcPipMXo/bRwEAYKpAULSFoAgAMMYGBkhdRnnedHkVHZ3Hbyh1e5KCf6R8H2qq4FvHW08L1eeIy9cCt48CAMC0gaBoC0ERAGAMdKmp7DJFradTL9HWG2jf/eT/KaUfJXmW08zejttHAQBgGkNQtIWgCAAwGtpuqkmk5L107h3acyefvuLM6xS/nd/2aTN3xQSQRvMuPnPJdqOETVY1rHQ2iDszuH0UAADAGoKiLQRFAIARMeipoZCyT1DgCjr0sHHuimf59PfF/tQqE/e5XnKOU/F5qkt1WBK2UJk/bh8FAABwBEHRFoIiAIBDrbV8DkOWBj2eoy1/p0NzeUpkWZElRpYbJ4+sozwBshzoqMRt5HeTCreP9ozPCDoAAADOzJmC4l/+8pf09HRxxYTVsHpm1apVQo2vr69QwxYc1QwDQREAYFBfB1XFUMIOOvMG7byV9txF596lZFeqSZq8fW66HkrbS9FrKHqtVYn5bbBE/UqaCnF/AAAAGMJpgqKQ9IYGRVapUCjYwpIlS4Stwm4MWzDuYqdmGAiKADCt6bWkyKYMN/L/jPbPMs5d8TJFb+BzV3Q1iftMNgYdv1m0NokKz1LyTn4fafpB/mwhq2mR8nFKNZX84UO2YFkMWvFwAAAAGMI5guKMGTPYTyHvCTUCX19flg/FFSO2g7AzY86HNjXCsiMIigAwHRX5UshPdOxpPneF+1MU/APlnaHGYnHrpDNAXSr+MGHZRUrfz3sLU3bzhxLladSu4JNYAAAAwLVx7ltPXYxYPSMkRhYdzbGQLbDVoTXCsiMIigAw9XVrSBJOMZvo9FLa+g/ec3jhQz4Dfl2GuMMk1N9J6mKqDKfsYxS7nuI3Uu5J3k/YLOEz+AMAAMCYcvqgyCqFZbZgNxYOrRGWmfj4+L8N8eCDDyIoAsBUo+0mWTKl7KPz75PLPbTjJvJeRnHbqDKSetvEfSYbg5ZPY1gTTwXelLiVz2GYcZBPYNiQRz3N4j4AAAAwPpw7KLLUZ771lC2w3Dj0RtOhNcIy09nZWTDEggULEBQBwOnxuSsKTHNXzKXNf+O3lYb9QoUXePqapAaoU8knuC/x46PRRK3hDxwW+vCxSdvqMIchAADARHLuoKhQKFilMJgNWxB6C835kC0Y97JTMwzcegoAzoolwCJfCvuVjj/DHzU8MJsCvqBMd6rP5cO9TE69rdRYSBUhfEKLmHUU9yfleFBVJDWV8cFLAQAA4Dpx1qBojnwsHLJlBtNjAMAUV3iBEncNloi15P0qBXxOJ1+ifbNo819p8/+S52JK2EnSWD6txeSk7+PzUlTH8mkMEzbzcWjS9/MxaZQ51KXmnYoAAAAwCThTULRkjoVjDkERACapYwto/X8Yy/8xlv+gP4wLwk9WNvxfPufhZDNgoHY51aXyUUlTdvMJDJO2UcEZkiVSa83k7eoEAACY3pw1KLq4uIhLYw1BEQAmKY9naM+dfGLDDf9Fe+4h94V0Zjmd/2CwsE2TJCj2NPMhZ8qDKPMQH4Qm9g/KdqfKMD5sqbZL3AcAAAAmMWcNiuMHQREAJpfmKkrYQYcf5Y8dHn2cz4PPVhN308WvaOdtfMGytMrEoyaYroeaykkaRbknKG4DH4cmbS+V+JMikzobcUMpAACA00FQtIWgCACTgrqM4rfRoYdp27/o4pdUEUF6rbhJUBXN0+P1YtBRWy3VJvFRSZN38htKE7ZQ/mk+m0WLlM9sAQAAAM4MQdEWgiIAXE+NxRS7hQ48SNtv5DNbVEY6fIpvooPiAHWpqD6bDzyTvp8PQhOzjg9VWhFCqsLJOxkjAAAAjAqCoi0ERQC4DhoKKHoj7Z9FO26mS9/yRw0NV5o2sKeF6nPE5XGi7SJ1CVWGU84xil3Puw1T91CxL8nTqaOej1IDAAAAUxSCoi0ERQCYOMo8ilpP++6nnbfQ5VUkjbtyPhxXBi2fjFGWQAXelLiNJ8P4TXwei+oYPqeFvk/cDQAAAKY6BEVbCIoAMO4U2RTxO+29l3bdRsE/Uk3i9eyd62zgTxWWBlDqXp4MWck4RJJgPmxpj0bcBwAAAKYZBEVbCIoAMF7kmRS+llzuod23U8hPJEuhgesxHKiuh5olVBVJOR584gqWDFN2U+FZqk3m49MAAAAAICgOhaAIAGOJRcHaNAr7hfbcxWdBZAtsdUzyYbucSvzE5SsY4N2Gigwq8aWUPcYbSjdS7kmSRvPEqOsV9wIAAAAwQVC0haAIAGOARUFZMu823H0770IMX8O7E69R+SXKODhYUl143rOsYaW9TtxZ201NZVQVYRyH5g+K+Z0yD5PkMjXk44ZSAAAAuCIERVsIigAwegMG/sBh8A/84cO99/IHERXZ4qZrl3OcJz11icOStJ0kQVR8npJ3GW8o3UPFF0ieRu0KjFAKAAAAVwVB0RaCIgBcNYOeD1ga9B0fvNR1JkX+QfW54qYxxIJiXQr1tTssydsp8xBVx5KmEiOUAgAAwLVAULSFoAgAI8XyYVU0n/Zwx018iovoDaTMFzeNBxYChVFJHZXY9XwSCwAAAIBrhqBoC0ERAK7AoKOKSAr8mrbfSAdmU8wmaiwSN42rLDfeqVgawEvZRSplhS0EDpb4jQiKAAAAMCYQFG0hKAKAfXotScIo4Ava9k86+BDFbiVVibhpXPV38jtOs45Q9Fr+2GGBN1WG8cktis7x2fDZgmXpaRaPAgAAALgGCIq2EBQBwIq+n8pDyP9T2noDHZpL8dupuUrcNK60XXwcmiw3PmBp3ilS5vJ3Yqm5gtJcxWUAAACAMYWgaAtBEQA4XR+VBpHvx7Tl73TkMUrYSRqpuGlcabtJnk7Zxyj6Nz4hfn2Ww3kOr2IeRQAAAICrg6BoC0ERYFpjOa3kIl34kDb/jY7Oo6Q91CoTN40rXQ8pMvkjiNFrKdudZ0VWAwAAAHCdICjaQlAEmI76u6jIj86/R5v/h9yepOS91GaauX5c6Xp5n2HuCZ4Ps45QXSq/4xQAAADgekNQtIWgCDCN9HdS4XnyeZvnQ/enKGU/n5t+Auj6SJlDuSd5Psw4RLVJ1N8hbgIAAACYBBAUbSEoAkx9fe1UcI7Ovkmb/puOL6TUg9ShFDeNK30/NeTxkWmif6P0/VQTT31t4iYAAACAyQRB0RaCIsCU1dtGeWfI+zWeDz2epfQj1NkobhpXBi015lP+aT5+adpeqo6l3hZxEwAAAMCkhKBoC0ERYKrpaaHc0+S1jDb+PzrxPGUcpS61uGlcGXTUWMinPWT5MGUPSaOou0ncBAAAADC5ISjaQlAEmCJYPsw+SaeX8Hx46iXKPEbdEzIZPcuHqiIqPEMx6yh5J1WGU2eDuAkAAADASSAo2kJQBHBuLA1mHadTi3k+9HyZsk/wxDgBBvSkLqFCH4r9g5K2U0XoBI2LAwAAADAOEBRtISgCOKUuNWW608kXeD48vZRyTk1cPmwqo6JzFLueErdQeRC11YqbAAAAAJwWgqItBEUAZ9LZSOlHyeM5ng+9llGu10TlQwM1l1PxBYpbTwmbqSyQWqtZrbgVAAAAwMkhKNpCUARwAh1KSjtEx5/h+fDM63ws094JmWeC50MJlfhR3AaK30il/qSpRD4EAACAqQdB0RaCIsDk1a7gc+IfW8Dntzj7Jp8Lsa9d3DSuWD5kgZDFQhYO4/6kYl8eF1klAAAAwBSFoGgLQRFg0mmro+S95PYkbfor+bxNheepv1PcNL4GqKWKyi5S/CY+RE3ROVKX8ocSAQAAAKY6BEVbCIoAE6o4gJJdB8ulb2jXv8Xl6PXk/SrtvoM2/5XOvUtFftTfJR41vgb4A4dlgfzhw5h1fKILVRGf9AIAAABg2kBQtIWgCDChPF+mY0+T78diufAhH7P08CO07V/053+Sy920+W8kCRV3Hm+tMj5saeIWPkV+vhc1FpBBK24CAAAAmE4QFG0hKAJMqGMLaP1/DCn/x1iMyywoVsWIO4+TtlqSBFPiVor+jfJOkTKX9P3iJgAAAIBpCUHRFoIiwIQ69w5t+ycvm/6b1v//aMN/8YFMhRpzqUkSdx5b7XKqCOGT40evpRwPqs8iXa+4CQAAACbWQE+Poa1Np1ZrFfX91TV9EklvUVFPTk5XampnfHxHZKRi5XfS556reubZyqcXVi5YWPnkU9LnX6h5801zkX34YV9lpXg6uGYIirYQFAEmmrqMjs6jPXdRdTxVRdPhR8X6cdKhoMowStpBUWso253k6aTrETcBAACAhYHubn1rq07Fwpuiv7q6r7y8t7CwOzu7Oz2jMy6uIyKi/XJwm39Ai885jefp5mPHmw4fVru6qnbubNy8Wbnuj/pfflV8/4N8xTd1n30u++BD2bvvVS97Tbr45apFiyoXPF3x+LzyOQ+Vzby/9I47hyll980sn/2gZO4j5fc/UHL7HcOWO7uSk8W3DtfMmYLiX/7yl/T0dHHFmouLy5IlS4RlX19ftifDFhzVDANBEWDi6Hop4jc+lmnUerE3r6eF6nOM28Zap5Iqwyl5F0X9SllHqC6VtBMzNA4AAIBDmlOnlL+vM5f6n3+Rf/OtZQ0r/VVV4t4sufX1GTo69M3NOqWyXybrq6joLS7uyc1jya0rMakzJqY9LKzt0qVWP7+Ws2fZyZvd3ZsOHlTvP+CoGMPbMunixVXPLap8aoHk0cfKH5zDsplVVJt5P6tkmyrmP1m58Jmq51+QvvwKi3w1y5fL3n+/9pNP6r78Uv7tSsUPP7JkyN5ww4aNjdu2q3bvYedvOnyEBUiNpyd7P62+vm2Bge0hIR2RkSxnslDXnZnJ3jz7CH2Siv7qGhZHdSqVvqXF0NnJPqn4mU20cjlLqubSFRen3utqWcOKoatb3BuumdMERSHp2Q2KrJJtMgdFYTeh0lHNMBAUASZIZRTvRXR/ipoqxJrx0NlIVZGUspvnw4xDVJtE/R3iJgAAgPHEu+M0Gn4jZVVVb1ERC0VdiYkdEREsLPEuuJMnWYiqfOrp0rvvKbvnXkel9M67ymfNLntgljm2lc+aJXl4bsW8JyqfXsgz2ytLal5/Q/b2O7UfflT3+RcsZ/LA9usa5R9/NG7erNq505jWDhvT2mn2um3+Ae2Xg9nbYFGNxcvurKye/PzekhKWOftrWFSr16nU+tZWQxf+c+p05xxBccaMGeynkPeEGkts66pVq4SgyHYQdmbM+dCmRlh2BEERYNyx8HbuXT6uafZJGhgQK8dWdxNJoyl1D8+H6fupJp762sRNAAAAjpnTXV9lpaN0p9rj0rhli/L3dYrVq+Vfr6j9+BOW06qXvlq1aFHF/CdZihN65MruvY8nuvlPsvrqJUtrli9ne7L9FT/+qPztd3YGdp6ad96tXPB01TPPVj33nPT556sWPlPxyKPVi1/m5eVXeAh87Y3u9HRD58RMIAwwyOlvPXUxEYKir6+vORayBbY6tEZYZjQaTeQQTz31FIIiwHhhsTDjKG39B/l9St0asXIM9TRTdQyl7uX5MG0vVcdSb4u4CQAApqqBgYGeHn1Li7Ze6Lsr7s7K6kpM4unu0qXWc+c1p041HTmqdmHpbit/cG71T/IV39R+Ykx3ry6rWvR8xZNP8XQ38/7SO+9iGY+nuyefYvUs+9Usf5vtyfZnmZAlQ3YGdh6WFVliZGdm52evwpIke0X2uuzVWcJk74S9n/H6L6EAE8W5g6JCoRDy4eiCYlpa2swhHnroIQRFgHHRWMQHrdk7g2Rj/aA5S4M1cZTmyvNhyh6SRvEeRQAAuF70etJqzUUrlbZ4eVvW8DKSdHf4CNIdwPXi3EGR5UNWabZq1aqhN5oOrRGWHcGtpwBjT9tN4Wv4oDUxm0ln+2z66PW28ntK0/fzfJi8k49V09kgbgIAgAlk6Oxkea+vrKw7I7MjKkr63KKSO+4YppSycvsdNndm8qfshtyZ2XT4sObECf5kXWBgR3hEZ0JCd2Zmb1FRX2WlVqHQazQD3Ri8BGBcOP2tpwJzjyJjzodswVHNMBAUAcZYZRTtvp2OLySNVKy5Rn3tJEukjIM8HyZtp4pQaleImwAAYIzoW1r6a2p6Cwu7kpLbQ0JYVGt2c1Pt3qNc94f8u+9qP/q4etmyyoXPsKTHH8a7/4GKeU9IX1rMwl7dl19WL36ZrVY8+WTVwoWVzzxb+dRTZQ/Mql68WLr4ZekrS6qXvip76+2+8nLxlQBgUnLWoGgT+SyDoi+mxwCYJDqU5PM27biJ8rzFmmvR38HHLM08xPNh4hYqD6K2WnETAABcyUBvr06l6qus7MnJ6YyNa7t0SXPaq+nQocat2+p/XSP/eoXs3fdYkKt48qnyWbNK77q7/KGHWQ6sfnVZ7YcfsWSo/H2dateupqNuLWd9WG7sSkzqKShgSVKv0ZBOJ76GA71FRcq1v4krAOAknCkoWlq1apW4NNYQFAHGwICB0g7Tlr/TxS/5DaIjp+uhdrm4LOjvpLoUyjzC82H8JioLpNZq9gLiVgCAaWtgwNDRoVUoektKutPTOyIiWn19mz1OqPfubdiwUfHjj7Wfflbz5ltVi56XPPpY2Yx7+X2ejz1e9fwLNW+9VffZ54rVqxs2blK7umpOnGj18++IjOxOz+gtLdUq6vkAm3hUD2Dac9ag6OLiIi6NNQRFgGvVWESHHyHXmVR3hUeCuepYKjw7WLLdKWELX8g/zac9TNxGMb9T3J9U6k+aSuRDAJjitFp9c3N/dU1Pfn5XYmJ7cHDL2bNNR46qdu5U/va7/NuVsg8+rF76auXTC8vnPMQ7/WbNrnxqgfTlV2TvvS9f8U39mjWN27Y3HT7c4uXdFhTUGR/fk5PbX1WlU6mHzl0OADA8Zw2K4wdBEWD0tN0U+jNt/hvFbyd9v1g5vOxjlHeKKsMcloSNpBnPGfkBAEZGp1Zr5fLhy9A8Zujq1jU09Ekk3ZmZnTExbQEXNZ6e6v0HGjdvrv/5l7ovv6xZ/rb0pcUV857go3fefY9k7iNVzz5X/drrtZ98olj1fcP69arde5qPHWs9f749LKwrJaW3qFhbW6tvbSWDQXwNAIBxgKBoC0ERYJTKLtOeO+nEImqViTUjkXWEYv+g+I0OS9x6BEUAmAxq33ufBbmye2aIhS3ffQ+vEco9M0pvv1P2Dh+3s3rZa5XPPCuO8nLfzIp586QvvsQCYd0XX9b/9HPDpk3qffs1J0+2+Qd0REezANlXXq5TKg1dXeIrAQBMAgiKthAUAa5ah5JOL6Wdt1DBebFm5PI9eVBkJfo3ivmN/4xeK9aYS0uVuDMAwDjjw72Ul3enp7eHhracOdN08CDLdYoffqz96GPJQw9LHnm0cuEzjkrZ/Q+odu/pTEjoycvrl0r1TZjQFQCcGIKiLQRFgKswYKDUg3zQmqCVfNaKUWBnkCVSzDoqPs/vXG2u4PPmAwCMNUNbG3/2LyenIzq69cKFZjc39f4DQql5/Y3KBU/zoT7vuFPy2ON8jof335evXNmwfr3adZ/m1Km2S5e6kpOVa9fWvv9B7cef1n32OSs1b7xV8djjwrK59JaWiq8HAODkEBRtISgCjJQyjw9as38WKbLFmqvVXkepLpSym9pMd6siKALA1ePdgGVlXamp7cHBmtNeLPs1/Pmn/LvvWN6TvviS5NHHWAIsn/1g5TPP1rzxZt2XX7LIp9rj0nzcoy3gYldiYk9+vra21tDRIZ4OAAAQFIdCUAS4sv5OCvmJD1qTtIcMV5g+yz5dDx/INGYd1cTxTkWzodNjAMA0Zujq0tbVsSDXGRfX5h/Aop1q1676NWv4GDBvvMmCH4t/pffMqHh8nnTxYtn7H/DRXzZsbDpwsMXbuz00tDs9vU8i0anVpNeLZwQAgJFBULSFoAhwBSWBtPsOOvUStY020Slz+IyIuR7U2ybWAMB0YzDom5v7Kiq6MzI7wsL5JBCHDolPA374kfTlVyrmPVE6497yWbMqFzxd/drrdZ99Xv/Lr6odO5rd3Vv9/DpjYnty8/plMkP7qG56BwCAK0FQtIWgCOCQMGjNrn9Tsb9Yc7W61ZR5mBK3kqpIrAGAKWegp0erqO8tKupKTGwLDNScPKna48KnAfx6Rc3y5VWLFpU/9LA4D8QLL8reeVf+rfA0oKvG83T75eCu5OTe0lJdQwNpteIZAQBgwiEo2kJQBLDDoKeUfXzQmuAf+X2no2DQUWU4v9dUEkwGXPwBTFLa2tqWsz7iylADA/rW1n6ptDs7uyMysvXc+aYjRxu3bK1f/VPtJ59UL3214smnyu6bySeEePIptsoq2Sa2A9uN7cwOYQfy4UBbW9mpxHMCAMCkhKBoC0ERwJYyjw7O4YUtjI6mihK3UcZB6lKJNQAwOTSsX1/1/AtiWfR8xRPzy2bNrpj/pGTeE5LHHpc88qjk4YelryxhW4VZAcvnPFT17HM1y5fXffWVcu1vfC54jxNtFy92JSX3FhZqFQpDV7d4agAAcGYIirYQFAEG9XfyLsQtf6fUA7xTcRT6O6jAm+I2kCKDCB0IANffQG9vf1VVV0pKq59f04GDFY89zuLfMKXkjjs0xz36JBV8SBgAAJg2EBRtISgCiIoD+OOIp5fyRxNHY4Bqk/h0+cUX+ASJADCBBvr7+2tqutPT2y5ebDp8RPnHH3WffS5d/HL5nIfK7r2v8plnZe+/X//zL+q9e1vOnO2Mju4rKTE0N5NOZ7/gNlEAgOkHQdEWgiIAH8701Eu0504qDRJrrlaHgk+HaDlBIgCMOa1WK5d3Z2a2BQU1u7k1/Lmh7quvqpcs5feIzri3csHTsnfeVaxerdq9p8XbuzMmprekRN/SIh4LAAAwLARFWwiKMK0Z9JS8l99rGvbLKLsBdX1UdpF3JNpMkAgAo6PXa+vre3Jy2oODm48db9i0Sb7im+ply/gk8vfMqHjyqZrlyxXf/6DasUPjebojMrK3qEjf1IQ+QAAAuEYIirYQFGH6UmTzEWsOP0KNo527oiGPEjZjgkSAq2Yw6Bobe/Ly2sPCNCdONG7dJl+5sub1NyrmzSu9+56KeU/UvPEmq2H1mpMnO8LCe/Lz2f7sKPFwAACAsYagaAtBEaajvna6vIq2/oPSj46yG7CnmbKO8qFN1cViDQDYGBjQqdW9hYUdEREaz9OqHTsUq76veeutivlPlt4zQ/LY49XLlslXfNO4eXPzcY/2kJCenBxtfT3pRzWOFAAAwLVBULSFoAjTTpEf7bqNzr1LnY1izVUx6Kgqkk+QWBGCCRIBGL1G01tc3BEd3eLtrdq1W/Hjj7K336lc8HTpjHslD8+tXrK07quvGjZsbHZ3bwsK6s7M1MrlmFkeAAAmGwRFWwiKMI2Ig9bcRZVRYs3V0lRR0g5MkAjTkL61ta+srDMuruWsj9rFpf6nn2XvvV/5zLNl995XPuch6eKX6z7/omH9+qbDR9ouXuxOT++XyQaQBgEAwHkgKNpCUIRpwaCjpD180JqI30nXK1Zelf4OKjxrnCAxExMkwlRl6Ojok1R0JSa2nj+vdt1X/+ua2g8/qlq0qGzm/eWzZktffKn200+Vv/3edPBgq59/V0pKf3X1QO+oGhQAAMAkg6BoC0ERpj5FNu2fRUfnkbpMrLk6A1SXSnHrqdgXEyTC5NSTk9sRFWUubYGBzR4eljWs6DUaYWc+Ab1U2pWcLExAr1z7W+0nn1S98GL5rFll9z9Qteh5Fg7r16xR79vfeuFCV2JSX0XFQDf+5gMAwBSHoGgLQRGmsr52ClpJ2/5FWcdHOXp+ZwMmSITJj0W7yqcW1CxZKpbFr1QveqH65VdqFr8sfWmx9MWXyu69r2bZa9LFi8sfnGM7Ab3Puc64uL7yckMbBu8FAIDpC0HRFoIiTFmF52nnreT7EXU1iTVXRd9P5ZeMEyTGY4JEmORYCCy9487hyj33Nh040FtcbO5XBAAAAEsIirYQFGEKapXRiUW0dwZVx4s1V6uxgBK2YIJEmJx0anV3enqLt3fDnxtk778vefSxshn3Vjw+T7ro+Zplr8ve/0D29jsVjz1e/9PPlqWvokI8HgAAAIZAULSFoAhTikFHCTv5oDXRG0jXJ1Zeld4Wyj5GSdtIXSLWAFxXOqWyKzFJc/Kk8rffa5YvL5/zUNnM+6uXvsqyX7ObW2dMrLa2dpR3VgMAAIAJgqItBEWYOoRBa9yfoqZR9ZwM6Ekaze81rQjFBIlwfRgMLPWx7Nfs7l7/y6/Vy14re4APMFO9bFn9z7+wys7YOG1dHWIhAADAmENQtIWgCFNBbysFrqDtN1KO5yivoVurKXknJkiECaXT9VdVdYRHNB0+rPjhR+nLr5Tdex9LhiwfspTYfOx4Z1ycVqFALAQAmMy69IZWnb5Jq1P2aWt7+6U9feVdvcWdPXkd3VntXamtnYktHTGajoim9mB1W4Cq5UJDyxmlxrO++bi86Wid+kCtaq+scWd1w1apckNV/e8Vil8l8h/L674rq703ufh/YvKsS+7fYvPM5e9x+UktneL7gGuGoGgLQRGcXsE52nEz+X9G3aMapUPbRUXnKH4j1WdhgkQYPwP9/X1lZe2Xg9Wu++Tfrqx6/oXSe2aUz5pV/drr9WvWNB/36IyP1yrqxb0BAKao6rbqfHX+8KVD2yHuba1bb2gbEsmKOntyO7oz27pSWjuTR1BYJNtUVf9HpWJthfyn8rrvy2q/KZV9WSz7uKj6vULp8vyq1/KqluZWvphd8VyWZEFm+RMZZY+klc5JLbk/pfiepKI7EgtvSSj4V3z+/8TmCeWm+PzbEgruSiq8N7loVkrxQ2klj6WXzs8oW5hZ/ny2ZHFOxau5lW/kV71TIP2gsPrTopovS2QrS2t/KK/7RSJnsfDPyvotUiV7Vy41jftrVUfqVMfk6lOKJm9l87L8SnbC2anFD6aVzEkrnZVSMjOlaF5GmbmwV2GfXfx24JohKNpCUAQnJgxa4zqTZMlizdUZIHkan0O/xA8TJMLYGuju7i0sbLt4UbVrd92XX1Y+82zpnXeVz5pd8/obyrVrmz1OdCUmausRCwFgomkN2h5dT3t/u6ZXo+pWKToVsnZZVWtVeUt5cXNxnjovqzErTZmWqEiMrYuNlEWGVocGVQUFVAScl5w/U3bGs8TzeNHx/fnuO3OPbs4+8nvGwZ/S9q9K2f9VsusnSa7vJbi8GeeyNHbP4pg9z0btXhC554lIl0fCXR4Kd50d6joz7MCM0EM3RMdZ9onZK/n/TshloevOxMJbEwputIhkQ8vN8QUsubGdH0wteTS99MmMMpbuWDZbllf5doH0w8LqL4prvi2t/bG8jsVCFslYHttd0+AqazxUq3KXq08omrzqm32UGr/GlkBVa2hTW2Rze5ymg+XJ9LaunPbugo6e0q7eiu6+mp5+eZ+2sV/XrNW16/TdBgyHPgUhKNpCUASnpO+n+O209R8Uu5Uvj0JnA6Xvp1QXTJAI187Q3t6Tk9t64ULj1m21n3xS8eRTpXfcWT77wZo331Ku/U1z8mRXYpJOqRT3BoApZ21Z+ZLsfHN5NjN/dkr+K9l5L2fnLs7KeSkzm5UQZdkoItnRgqMH8w665rjuztq9LWPbprRN61PWr01a+3PCzz/E/bAyduXX0V9/FvnZR+EfLQ/79NWQjxcHf7wo6OMFgR89EfjxIwEfPxjw8QMBn97r99mdfp/d7vflrb4r/u3//Z0X19x5af1dQZvvCdl9T8i+e0IP3xvuMSPc894on3uifO+Jvnh39OW7YsLvjI26PTb2ttjEW2KTb4xN+2ds5j9is21ymk35V1zOrfE5tydk35uU+0By7kMpuY+l5T6ZnvdsRv5L2YXLcoqW55XMTc79d0LWHYnZdyVl381/5t6akHNfUvb9ydkPJGfPSs56NCXrgEwe3tQWawxsWe1dLK2VdfVW9/Qp+rRNxpzWY8AdQDD2EBRtISiC86lL572IJxaRRirWXBUWLCWXKXY9JkiE0dE3NXWnZ7ScOdOwYaPs/Q8kjz3OY+GDc2reekv52++aU6e6kpJ1jY3i3gAw+egGdN267ta+1sbuxrqOOhbeSppL8lR5GQ0ZLLZF10azzHax8uL58vOnS06ztHY4//DenL07MndsTNv4e/LvLKR9F/vdV1FffRLxybuh775x+Y07owK/Lq09Jm9yVO5Myrsv8Jf5lz57KmjFguAfFoaueTZ8/bMRW56N3Plc1N5nYw4+F+v2XNzJRfFnnos/90yi38LEwKcTLz+VFDY/KXJecsxjSfGPJCXNSUqenZg2MyHjnsTMOxOyb4vPuSk+9x+xuTZp7Ya4vJvj825PzJuRVDA7pejhtKJ56cULM0tfyJa8mlv5Zn7Ve4XST4w3QH5XVru6vO43492PW6XK3TUN+2SNR+vUHsZ+tvMNmgBVS4iad7LFazpSWzuz27sLO3lmk/b0yfu0qn5dq07frTdo8Sg1TAkIirYQFMGZ9LbSxa9ox02Uf1asuVqqQkrcSvme1Ncu1gAMS9fQwIIfi3/KdX/ULH+7/KGHeSyc81DN8uWsRuPp2ZWSolNhDCSAMdNv6O/Udjb3Niu7lLJ2maRFUtRUlKPKSVWmxsvjI2WRl6WX/Sv8fcp8ThWfci9wP5h3cE/2nm0Z2zakblibtHZ1/OpvY779IuqLj8I/ejvk7deCXlt8cfFzfs89df6pR88++qDXg/d53jfTc+Yc7zmP+Ty24MKCRX6LXr748utBr78T8g475MuoL7+O/f6b+N++S966KtXlu7RDq9I9VmWcWZnt+21O0Iqc8C9zYj7NSfgoJ/W9nIy3crKXZectzsy/OT7HJq0NLfck5j2ZUfZsVvmL2fyhNQQ2gMkGQdEWgiI4jbwzfNCawK95XByF3jY+QWLiNmqWiDUANgYGtHV1nbFxzceO1f+6pvq118tnzeKx8KGHWURU/sFi4emu1FSdSi3uDzClBVQE7M3Zay4b0zZ+Hvn5ruxd2zO3b8nYwlbXp66PrYstaCrIasxKrk9my2E1YZeqLvlKfL1LvU8UnThacHRf7r5dWbu2pG/5I+WPXxN//SHuhxXRKz6L+OyDsA/eCn7r1Uuvvhjw4jO+z8w/N3/umbmzvGbNODXj/tP3P3zm4SfOPbHwwsIX/F9YGrj0zctvvhf63qcRn34d/fWquFW/JP6yLnndprRNOzN3uua4Hs4/7FHk4VXqdU5ywacyyKcq0rsq1luaero660R13rHq4iPV5fullburpNultRsr636vUPxQVvdlsYzltGV5lc9nS+ZnlD2UVjIjueiWhIK/GUPd3+Py/p1YODO5+NH00oWZ5a/kVLxdIP20qIYlujUS+eaqepeaRrc6NUtxAY0t4U1txxXqvTLVYRbt6pu9lc0bpcoHUksuqdvMJayprUmrE79ZAJiUEBRtISiCE9BI6fhCPkdiXbpYc1UGDFQdy+815RMk4vc0mOh0/VJpR0RE0+Ejih9/lL6ypOy+mSwWSh6eK3vn3Yb161u8vLvT0vRNTeL+AFNIS1+LvENe3lKeo8pJkCewgOdX4Xeq+BQLXSzX/Zn65+qE1Syqsdg2TJnpOXOR3yKW91jqY9mPJUCWA1kaZJmQJUN2nkN5h1hWZImR5UaWHlmGZC/E8iRLlSxbsoRZpimrbqtWdCrkXU01XS1V3Z2S7t68ju6U1s6o5vZLqlYfpcZD0XSwVrWzuuHPyvqfJfJvSvnolG/lV72cU/FMVvlj6aUPpBTfkVj4z7jBIU/YMquZlVI8L50PbfJqbuW7BdIvimu+L6tlKXGrVOkqa3SXq88oNRdVrZHN7cmtnexF2Usr+rQtOn0/+uUApiVnCop/+ctf0tNtL4uXLFnC6plVq1YJNb6+vkINW3BUMwwERZjU9P18uJqtN1DCzlFmvDYZpeymzMPUjV6g6a6/uppPWnjkqHr/AemLL7FMKNxEKnv3PRYLNZ6nu9PTEQvBuegH9Czy1XXUlWpKWfSKq4sLkgb5lPkcKzzmmuO6KW0Ti23fxHzzUfhHrwW9tsh/0TyfeQ+cfsAy7D3o9eCCCwteCXzlvdD3WNJbk7hmW8a2g3kHT5ecvj/S89aY2MESm3hLXIZlzc3R0Sek6R16Q2O/rrqnr7izJ7OtK17TEdrU5tfY4lXf7Fan3itr3CJV/lahYCGNRbX3CgY78eakltyTVHSzqRPPXG6Kz787qYhtZfuwPdn+7xdWf1ki+7G8bl2lYnu1cn+tiqVHliGD1K3Rze1pbXywk6ruPmWftl2nx6PnADA6ThMUhaRnExRdXFzM+dCcA4XdGLZg3mRTMwwERZi8hEFrTr3Ep8EYBW03FZ83TZAI045WoeA3kbq7K1avlr78ihALJY8+Jnvv/YYNG3lvYUamvrlZ3BtgEtD0asyRL7Yu1hz59ufutxv5LPMeK7O8Zj1x7gm2ie3wQdgHX0d//VPCTxtSN+zO2n0k/wgLfgEVAeE14anK1Hx1fnVbdXOv+Pe/22BQG5Mei1sprZ0Rze0BjS2e9c0PphbPTSt9MbvCUbk5vuCvMVYZjxVWyeIfi3lPZpS9kC15Pa/qg8Lqr0tkq8vr1hsnJzhYqzqhaDrfoLmsbo3VdKS3dRV19kh7+ljaZJlTeEsAABPPOYLijBkz2E8h7wk1Qy1ZsoTlRraDsDNjzoc2NcKyIwiKMBl1ayjgC9p5KxVeEGuuzgApMnhELPEjXY9YB05OW1vbctZHXBlCp1J3paTwIWfW/lbz+htlD/BnCyuefKr2008bt25rvXChJyfH0I4RjGAiGAYMrX2t8k5+YyeLfPHy+GBp8Pny88eLju/L2bclfcuapDXfxnzLIt8bl994wf8Flu6EZ/OEMttr9vxz85/3f/71oNc/DPtwRfSKnxN+3pi2keW9owVHvUq9AioDImQRKfUpeeq8itaK6g5ldVdLTU9vWVdvTnt3UgtPehdVrWeUmmNy9T5Z4/ZqJX8qr7zuyxLZ+4XVr+VVvZRT8URG2QMpxf9OLDQHvBvj8+9MLGSVj6aXPp1Z/nJOxd1JRazytsTC2xIK2Z5s4ZaEArbAyu2JRbcnFv5vbJ67XF3S2Svr6W/S6roR8wDAmTn9racChULBtrKfvr6+5ljIFtjq0Bph2REERZh0cr34uKZB341yYNIuFWUc5BMktteJNeCc6r76iuU9cymdeX/pjHutVu+bKf/qa9k77/KRSO+6u+rZ59ghqt172gIDe4uKBrq7xRMBWBAezBu+dOvEvzws8rX1tSk6FawyuzE7QZ4QUh3CIp9Hkce+3H1b07eyyLcyduXH4R8LkY8FPBbz7Oa9r6O/ZnnP3L/H815FwKWaiJC6lLD6vIgGSURjbbiqMbypLUDV4q1sZgHM1Zzxhoy8Yr5pU8h4wvN4j6eXPptVviSXzzP+aVHNytLaXyXyjVX1e2oajtSpPOubfRv5yJkJLbwTr6Cjp6KbD5jZzAKeg6nD2Q4seQ5f0AcIAFPGFAmKLAEKm4bGwqE1wjJTVFT03hCPPPIIgiJMFsKgNQfnkCJbrLkqBi1JQihuPckSMEHiFCB7+x3Zex8o1/3hqLB8qPhxdXtwcF95uXgMwJU8G/7HvwNW33Hx59sDfmLl1qDtN4T5CcussE23+H//uO+rLOMJvXzs5zyfeYv8Fy0LWma+n/PP1D+3ZrnsyjvmWnjuQOnlQ+UxRyrSjlTmHZKWHKyp2l9TxzLeVulgP57djPc3YyfeXUmFs01jriw1jrnyeXHNqrLatRXi0JpH69Sn65v9GltCm3jGy2rvKunsrenpV/XrOvWYdBwAYMxMhaAo9CUKy2wHcywU9h9aIywzMpls9xDz5s1DUITrT9dHMZtp2z8peS8Z9GLlVVEX86kv8k9jgsQpQ/bWcunLr9R9/oWjUj7noa7kZHFvmPbUPeqK1oqsxqxIWeQFyQX3QvddWbt+S/6NRbt3Qt55MeDFx3weY8Hv35GBM1MKH08vNpWSxzJKhQWh/CM+74W0pO9KylcUV5oHX1mUJXkio+zB1JK7k4puiueja9pkvFdzK98pkH5WzKdPsJvxEls6Mtu6ijt7zBlPfN8AADA5OH1QZJXikok5H5o3Da0ZBm49hetPlkx7Z9DppdQmF2uuSm8b5XpggsSpwdDe3hEV1bh5s3Txy6X33id5bF7ls89JlyytYaFx6asVTy6oWf62ZektKhKPhCmqR9ej7FKWNJek1qeGVoeeLTt7OP/w1vStPyf8/Hnk529efvM5v+cePDP/Lu8nZ55b8kjgpwtCfn0+ascrce5Lk3xeSwt+LSNuaWbqS5nZizIKnkgvfjCl+Ia4/L/G5A5XYnM/yCtiGc+rvtm/sSWsqS2ppdOmH098cwAAMIU4a1AUIt+qVavYgplwWyn7ablqt2YYCIpwPXVryP9T2n07lVwUa67KgIFq4vm9ppVhmCDReRk6OjpjYhq3bhNmMpS9937TgYPdmZkDWq24B0w5AzTQ1tcma5flq/MT5AmXqi6dKj61L2ffn6mbvor79c2I75+5/NWcgE/v8v34Nt8v/x2w+t7grbPCD8+J9p4bF/hQfMTshMSZiWl3JWTfFJf7P7F5f4vNuy2hYHZK8ZMZZS/nVLxXIP26RParRL5VqjxYq2J5L1DFR9fMae+u6O6r69NqdIYew8AwBUEQAGAamrigaO7Qc3FxEWKbOfWNgnlWjDGHoAjXx8AA5XjyQWtCVlN/p1h5VTBBojMzdHV1xsU1bt9RvfTVsnvvk739jnrf/u70jIG+PnEPcE66AV1TT1Nla2VWY1ZwTfSxsoAteV4/ph/7MPHAy9G7nwzf/mDI1ruCtt18aceNl/fdGHr85vDzt0aF3BITd2Ns2t9js4WhWf4Zm3NnQs6clLynM4reyKv4vLjmx/K6jVX1rrJGD0WTX2NLhHHqvNKuXnmfFoOpAADAmJi4oGgeSIZFRLbAUqL50cFRYGlTXBprCIpwHTRVkPtTdPgRUuaJNVdF18PnvYjfRPWjGvMGrhNDV3dnQoJq587qZa+xcFizfLl6796u1NSB3l5xDxgjRe2abZJCcWUsDBA19HVlttRdlpccq0rfWhL7Y27IRxn+SxJ9FsadmRvjfW+k97/Dz/4r7MLfwwP/Hhn1t6jkv8aIqe9/Y3NujE2/MyF1dlLq/NS0lzOz3s/P/760fKu0zq1O7aPUhKj5vZ0FHfzhvWatDrEPAACui4kLiiwfCtNXCAsMWxC3TSYIijChdH0UvZG2/YtSD45yYNL6LOMEif6YINEpDHR3dyUmqXbv4XMbsnD4xpuqPS5dSckDPfjjG0u/SOQLM8vNZU5K/q3xOZY1rOS0i1M+9BoG1P06aU9fXkd3Qkv7hXrlQWnF+rK8bwvS38lKeCk1+smkqNnxEXfHRt4SHf3P6MS/xaQLkY+Vv8bk/C065Z9RsbdGR86IDX84PmxBUviStKiPshN+LEzfJsn3kFWFqFXCTZ6N/boejMoJAABOYkJ7FNPT01etWrVkyRK2yhLjtfQojh8ERZg4wqA1PsupQynWXJVuNb/RFBMkTnosBHalpKhdXGrefIuFw+rXXlft2tWVmIiJDcfPAwmxr+ZVrZbIHZXbkwpvik29KTbTHPmE8reYjL9Fxf49IuSGcL+bw8/eFeE1K+r0/FivlxLOvJN64dvswA2FEYclSQF1+WlN1YruVvH1AAAAppyJC4rCM4oMWxCWFaY5LSYVBEWYCF1N5PcJH7SmMkqsuSoGHR+uJm4DyRIxQeLkNNDX152WpnZ1rVn+Ng+Hry5T7djRGRdn6OoS94CxU9PTn9zaea5B41LTuKKk8qWs/JsTcl/IqviyWOao3J6Yf1/YwVeid32U6PpjutvOAu9T5YExdTF5qrzq9urWPiRAAACY7ibjzZ/XF4IijK+BAco+wQetCV9D2lF1KDVLjBMkemGCxMlmoL+/Oz1Dvf+A7J13y+6bKX1lSePWbZ0xsYaODnEPuAZNWl1eR3ewus2tTv1HpeKd/LJ5qbm3xWeZOgNzb4hKuCHM91+Xj9wUuOWm6Ki/x+bdEJfDSyz7mXtDXB5fMBW2/8nqAvHUAAAAMASCoi0ERRh7PS1Un8MXhEFrjs6jxlFNdseSYf5pTJA4qQxotd1ZWU0HD8ref5+Hw8UvN27Z0hEVZWhHjB+NXsNARXdfnKbDu755e7VyRUn1C1mFM5Ny/s6jHQ+E/4hJ/1dk6L9CTt0YtPvWi789fHnNK5G/r0zasj/3kF+FX0p9SnVbda++V92vq+3tN5fNZRn/io6wrGGFvZb4qgAAADDExAVF4b5TG+K2yQRBEcaA58u0/j/slD/+D61nxbhcFSPuPBIDBn6XadyfVBmOCRKvP622Jyen6fBh2Qcfls28X/riSw0bN3VEROhbcb/iiBiI6vu0mW1dAaqWA7WqNRL5W/nlj6bm3xIvpkE+Lmh03E3hvv8KOnDzxQ0zg9YsDF3zUdyGLVl7TpecjpJFFTcXN/c2i6cbgVadPq8DT4QCAABchesW1ZYsWZJ+DfMojh8ERRgDHs+R23w6955YTr9Km/5Ke+4i79fFmp23XEVQbK/jI9ZggsTrS6fryc1rOupW+9HHLBxWPf9Cw59/toeF6TUacQcYok2nL+nsjWhuP6Fo2lxV/0VJzbOZRTOScv/XOCn8X2Ny/xWTektk8I0hx28K3H5n4NrHQ9a8HrXup9TtRwvcgqqCMhsy5R1yHf7LCAAAwPVw3YIipseAqcxjEfl9TCn7xXJoLrkvGFxlZe/dIwqKuh4q9ecTJCqNd67CBNPrewoKmt3daz/9tOyBWVXPLVL+8Ud7cLC++Sr6sqY87cCAzDiWzPkGzV5Z44/lda/nSR5OKbjRmAaNN4tm3RwdfWvE+ZuCXG8N/GN20JoXw3/7KnHTrhzXc+XnEuQJkhZJez/u1AUAAJhcrltUc3FxQVCEKcvzZcr3EZfrc2nTf1N7vbgqOPzIlYMiC4csIpYGYILECWUw9BYVNR/3qPvs8/JZsyqfeVb52+/tQZd16unenWseS8ZdzseS+aSoZmFGyZ2JwkAyef8Tk3NTbNJtkZdvDTl2U+Cmey6teSp0zbsx69el7zpRdCKsJixPndfY3TjAZ6qHyaVb39Whaxu+6AfQrwsAMO1MXFQzPpNoxdfXV9w2mSAowhhgQXHrDbTzVl42/Be/73TbP8VVsfI/hwuKmCBxgrFwWFKiOXGi7osvy2c/WLngaeXatW2BgbqGBnGHaWNwLBll847qhpWlta/mVcxOKbjB1D34r7jM22Iibw8/e3PQntsu/jb38q9LI9etSt52IPdwQEVAan1qdXt1v6FfPB1MOJboeg09nfr2Fm2zur9B2VtX2yOVdpdLOouKO3Lz2zKyWpPTNHFJzZGxTSGRqsCQRl8P2b4jNTuHKUdrdsp7asQXAACAaWMy9uldXwiKMAZaavi4pqxkutPmv1FlJBVeEGvMpb9T3NkSnyAxnE+QWIsJEsfZwEBfWZnm1Km6r74un/NQ5VML6n/5tS3gorbeuu93Muk2GDr0gyWlreubslrLGlb0I+uxa9Lqcju6g9Sth+tUO6sbWHk6q/S2BFP3YGzerbFJd0Zduj3M/ZbAP2deWvNc2NpP4jduzXLxLvWOro0uaS7R9OLhzDHWb+jr1ne26Vqa+1WNfQqWzWq6Kyq6Sko78gvas3JaUzNaEpI10fFNYVHqoLBG/6CGcwFKr/OKE2fkbp61B4/L9rJEZ1OOyfacrN3vVXfER3HMt/7UReWZ4MYL4aqAaPXl+KbwFE0MO2e0Oii44XxIg19YY0CEKjBCdTFGHZzZkmQuuW1pXXp7/14BAMCUhqBoC0ERxgxLeofmUvJecfWKNFWUtIMKMEHiuGHhUCLRnPaSr/im/KGHK56YX//Tz61+flq5XNxhcnstr8oc5ByVeI3VnI11vf2prZ0XGlr2yhp/KK97I7/q4dQic/fgLbFpd8VGzIzynRt1QhhL5o2oP35N3ele4B4kDcpqzJJ3ynW45/BK2FfEO/F01p14XeXlxk68PFMnXqKxE48lsZBG30DlWb96z3OK4yzCsSDnXrPHOt25nKo94F13hO3AdgsU091Flu4SmoV0l5jTlsrSI8uQLElWd0vqeqqVfXJ1fyN7D+ydsPejG9CK7w8AAGBUJvTWU4VCIa5gMBuYDvJ9aPcdpOsVV4fR30EF3pggcZz0VVa2eHvLv10peXhuxbx5ih9/bL1woV8mEzc7j1dzK7yVzT2GAUflsfTSL4tqVpTIXs6W3J9UwAcXjcm9LS7j3riYB6L97w079O+Lv9x/8btFod9/Eff7zqw9Z8rOxNXFSVok3bqpNnVEUXtOUnOkuUSpL11QeFjWsMIylbETr8tuJ15hexYLY3Y78S7Y68RDugMAgClm3KNaenq68YFEO5YsWSLuNJkgKMLY0Gt5Ssy94t+lAapNoviNmCBxbPVLpS1nzyq+WyV55FHJo48pVn3f4nOuv9q5n7OaGR/7aFrpyzkVjsptCfl3R5y7L2jjHX5fPBLwyWvhK35J/GVfzr4Lkgt8Jvr2aq1h6ueQLn1HU3/jecWJozW73IYrO49W77C5MzNQeXbonZk5rSzdZZZ05Es6i6XdktoeqbK3Tt3fwNJdh66t19CNdAcAAFPSdetRnLQQFGFspB2i/Q9c4TnDDgWluWKCxLHCcmDrufOK73+QPPa4ZO4j8u++Y1mxv6pK3Oyceg0DOe3d3srmdZWKW+My11YoLqtbHZX7knPX5vjJO53jTtpR6DSGwLqeapbZWHjLaEmMbwoLa/S/qDxzVu7uIXM9WrOTBb9ziuNn5Ee96g57y4+eVRzzkbPifrr2oJ/ilL/ytH/96YtK70sNZ5v7VeJ5AQAAYAg8o2gLQRHGQH8Xbb+RyoLF1aF0fVR2kRI2kzJXrIFR0dbWtl64oPjxx4p58yQPz5V/822Ll3dfRYW42dkMEFX39AWr23bVNHxcVD03rfSGuLxZSZlPJUU8GX3k5qjQ2xNz70vKNpXc+5ILLVaz/x6bc7giXTyXUzGQwRwCK7pKWAhMb0kQQmCA0sscAo/LXFgCZEkvtNE/rimM7VPQnsX2l/fUsNTXre/C9BsAAABjZUKDojB3ooAti7WTDIIijIGYTXT0CXGZ0fVQu0UnT0Mej4iYIHG0tHJ5q59f/U8/Vzwxv3zOQ/KvV2g8PfvKy2nA+UJCq06f1NLpLld/X1a7KEtyU3z+jKT8p1NTX0oOeCZi26wLyx4998SnEZ/uyd4TVhMW1VCZ3NqW0toulMjmNs96tXlVKG06vXjqScNA+k59u9rUE5jPQ2A8i3lCCDwjd/MwPunnVrPLs/bgBcWJyw3notWXUzQxuW1ppR0FNd2VjX317bpW3OEJAAAwkSYuKC5ZsoTlQ/Pdp2wZzyjC1NSp4jPs16aJq0xzBb/FlOlppqyjfLnDCW7Dvi60tbUtZ33EFQva+vq2gIv1v/xa+dSC8tkP1n35pebkyd6SEjI40yQi/QMDhZ095xs06yvr38ivuje56JaEgifT85ZmxL+WdPq5kNUPnH38yfNPfh399YHcA9G10coupXjkZKUfEEJgQ22PVAiBaTwEhoaaQqB5uBcPmetZuftFpXe4KiChOSKzJamoPaeqq6y+t7ZF29RrmGqj6QAAADi76/aMIkY9hSnr8iq69CXlew6WrCO8CzFtL8X9Sen7xMrOyZ4BJkZvaWl3Wpq5aNyPVS18RljuCAlR795d9+mnlfOfLJ81u+7zL5qPe/QWFTtROKzt7Q9vatsra/ysuObx9NIb4vIfSStelpXzfmb42wkHFgV9eJ/nfc/4PvNd7HdHC44myBOae5vFIycBHgJ15hBYlN/GQmCcMQT6+defPiM/ag6B7jW7T9cd9q0/Fdx4IUYdnKqJy2vLKO8sYgeyw1mSNBAmBQUAAHAy16dHUUiJ6FGEKailhjb8F6W68GSYvMthiV1PGmd9jm5s1b3/geThhyueWiCW+fMljz7GHzh89DG+8Pjj5ffco/H0FPee3Dr0hrS2Lg9F0+ryuheyJbckFNyTVLQkp+zj3PQvM/3fj9v0rN9LM07NeDHgxdUJq08UnUhVprb3X7c5M7v1Xc39qvreOqGkt8THNoWENPr613t61x09JnMRQiArJ2r3+SiOBSrPRqgCE5sjs1tTijtypd3lyt66Vq2mzzCCCWAAAADA2eAZRVsIinBNzr9PgV9TtjuVB5GqyGFJ3omgKJC9+VbZjHvL73+gbOb9vNw3kxfjMqtkpfSeGV3JyeLek4l2YKC0q9e/sWVTVf3bBdL7U4pvjM9/Nqv8s8LSH/KTvs/0/Dh61fzz8+/zvG9J4JI1SWu8Sr2yG7OvZcZCVZ+ytqfKXAo7skIaz1vWsNJrEB98FXKgvFfG7whty0jVxEarLwc3nL+gOHGq9oA5BB6X7WWxMEDpFdroF9cUmtGSkN+eyQ5hBzb1N3bpO4WzAQAAwHQzGW/+vL4QFGH06nP504nt9ZRz/ArDmaa5IigKOmNiNMePyz//nOXDqucWKX/9VbFqVYu3t2XRNTSIe19Xij5tVHP7/lrVlyWy+RllN8TlP5xW8kGh9OfS4vWFUb+kH/go4uO5Z+bef/r+Ny6/8UfKH+fKzxU0FfTp+8Tjr1lQg8/pukNn5cdMxZ1P/yB3PyN382al7qi7bDf7ac6BJ2v3Cz2BxscCw1kINI8Rqu5v7NRft85MAAAAmPyuf1BMT09ftWqVuDIJICjC6B1/hiLX8QUWFGPWUdwGXmL+4Deasp/CqlCi1iAoMob2drWra/nsB2s//ay3qEisnRy69YbMti7P+uZfJfKXcyr+nVh4Z2Lh0tzKXyW1OyQFO4ou/Z66+a3gt2Z5zXrQ68F3Qt7ZnL45oCKgTFOmM+jEU4ypFm3zecUJD5kry4GOyrEal/y2jBZtU4++SzwMAAAAYFQQFG0hKMIoVUbR1n9Qn7GXpreNulS8SKMo3ZUUmZSyS6wxF32/8bBpSq/RqHbtKntgVt1XX/HBS683/QBJunsDVa1bpcr3CqUPppb8Kz5/QWb5ihKZS3Xd0arsg0U+axLXLAlcMtNz5twzcz8M+3BH5o4gaVBVa5VhYIxHatEP6Jv7VZVdpZmtSRGqwHOK4+41e87I3VhQ9Fd6BjWci1BdjG8OC1P5e9YdSmiOsCwsT4pnAQAAALgGCIq2EBRhlA7NpWTjHBhmuj7eedhRPzg9BrAU1NTUuHVb2f0PyL9d2VdZKdZOuMZ+XZym43Cd6ptS2dOZ5f+My5+dUvxegZQFxdOKujPS5KP5x76P+36R36IZp2Y87vP4Z5Gf7c3ZG14TXttRO7azumsHtKo+paSzKK0lPrTR/6zc3b1mNwuHLA1mtiSxuMhCo35gXHopAQAAABxBULSFoAijUXiedt9BOuvhHytCqPg8X0BQNNKp1A0bN5XNvF/xw4/91dVi7YToMQzktHd7K5t/r1C8mlt5V1LhbQkFi3MqfpbITymaghtqgmqi9uXu+yrqqyfPP8mS4VPnn/o6+uuDeQdj6mIausbyCck+Q29Dn7y0Iz9FExPceMGr7vAx2R7f+pPR6svZrSnSbkmLthmTSQAAAMB1h6BoC0ERrppeS3vvpVzrvza9rRT7h3gnak8zydONtdOUtr5eue6Psvtm1v/8S79MJtaOmwEiaU/fZXXrzuqGj4qqH04ruSEu/4mMsi+LZa6yxvCm1iR1RZA0aEfmjo/CP3rk7CMsGT7n99yquFVuBW6JikRNr0Y80TXr1ncpemVF7TmJzZGXGnxO1R70kO31rz8d2xSS15Yh665q17WObf8kAAAAwJhAULSFoAhXLf0o7X+AbB5Uy/ciabS4PI1p5fL6NWtYRFSu/Y3FRbF2rLXq9EktnW516lVltc9lSW6Kz5+ZXPxWftXGqnrfxpa89vY8ddEFyYWNaRvfCn5rttfse0/d+1LASz8l/HSy+GS6Mr1D2yGe6Np06Npqe6T57ZlxTWEXld4navedrN0fqDyT0Bxe2J4l76np1I/NCwEAAACMNwRFWwiKcHX6u2j7jVQeIq4KWqopcQuNz+iXzqJfJqtf/VPZzPsb1q8f2/kt+gwDBR095xo0f1QqXs+rmpFcdHNCwfPZkh/K647J1Smtncqe9qzGrNMlp9ckrVkauHSm50xW2MLapLXepd45qpwenTjZ4KgN0ECbtqWmuyK3LS1GHexX73lc5nK67lBQw7mk5qjijtz63roew+inTASAMaft6+3t6uxua21vUrc21DfLa1XVVcqKcnlpcV1xoTQnsyIjtSw5oTg+uiA6LDcsKOtyQHrA+ZQL3olnT8V5Hov2OHzs6w93v7mYlzdeMha+sGf5K+ay950l7FTi6wEAOLnrHxQnGwRFuDqxW/msGFYGKNWFlDni2vTTV1mp+G5V2f0PNG7erFOpxVoHDtWqVpbWmsubLPglFVnWsBKn6QhranOpafy0qOax9NIb4vIfTS/9pKhmT01DaFObrKe/qbc5SZHkXuDOh5/x58PPPHD6gTcvv/ln6p/nJeeLmor6Ddc0xqx+QK/Rqqu6yrJakyNVl84rTrjX7PGuOxrS6JuqiS3rLGjsU/Qbxmy+RICprb+np6ejvbOluV3d2KJUNNXWNEor6yWl8pLCmvwcaXaGJD25LDmeB7aowcCW6nsmyccz/vTxmBNHI90PhB1yCd6389KeLQE7NvhtWXf+z1/P/r7a69fvTv34tcd3n7uv+PjIF+8d+mT5/g9e2/3Gi0JxfWfp/g9eZ5VHv3z/2DefnFj1hedP33ivWXVu/S/s8Asb17Lz+G9bz04YuGtz0J6tl/duD9m/K/TgnrDDeyPc9kcdO+i39Y8T3395kpUfvmIv5Ll6Bdst/PBec4n3PMY+l/g5AQCc3LgHxb8MS9xpMkFQhKvQ00Kb/8bn2bdUn0np+8XlaaZPIpGv+KbsgVmN23foNSN60u+1vKr3Cqq3SJWOyr8TC/+dUMh2W1epOKvU5Hf09BkG5J3yKFnU/tz95uFn5njPeTfk3S3pWwIqA8pbynXXMEyobkCr7m+QdBantySEqwJ85Mfca3azn2w5oyWB1av7G9k+4t4ATi7xzEnvNd+zcvrnlZ6rv2Hxye3LD1gcYgvHV37uwcp3nyefO50Teinzkl+a/7mU894JZ07GebpHHz8UcXQfy1EsULFk5b/tzwubfmOh68xvP5z++VsWpY6v/Mztqw8Pf/bOwY/edH3v1cHA9t6rBz56k9Wzrce//ZTtyfb3Xvs9O5adgUU1djZ2ztADu9n52avEnnJnr5h83ivNz4e9h5yQwLyIkKLYyJLE2PLUxMrMtOrcrLriQkVZcUOlRFUjbVbUtTYqO5qbutta+7o6df34jzgAU0STvE0uaTKXmuLGsgy5ZQ0rfd34BT1mJi6qrVq1ysXFRVwh8vX1XbJkibgymSAowlUIWU1n3xKXBQYtxW+itlpxddroLSmp++LL8lmzVLv36FtbxdoReCIt/39i84YpNyXk+9fXVLZWBkmDdmbuNA8/w36y5V1Zuy5LL0vbpKOezLDf0NfYpyjtKEjVxIY0+nrXHXWv2XNecSJSdSmrNbmqq0yjVRtIL+4N4CR6uzrbVA0sNclLi1iUKo6Pzg0NSvU7G3/6OEtfQXu2+m76zXvNKo/vPtv/wWv73lt28OM3HRWX5a8EbFsf7XGEHZt49lTKBe/0gPNZQf7shAVRYcVxUaVJcZL05Krs9Jr8HBbY6stLG6oqmmprNPVy9h46W5p7Otr7e3AnNsCo6PWk0/Gi1VJ/P/X18dLbSz091N3NS1cXdXZSZwd1dFB7O7W1UVsrsV/ELS3UoiFNMzWz0kRNalKzoiJVIzU28NKgJGU91RuLXE7yOqqrpVoZyWRUU0M11VQtJWkVVVVSJSsVVCEhSTmVl1FZGZWWUmkJlRRTcREVFVIhKwVUkE/5eZSXS7k5vORkU3YWZWVSZgZlpPOSnkZpqZSaQinJlJJESYmUlECJCZQQT/FxFBdLcTEUG00x0RQdRVGRFBlBkeEUEUbhYRQWSqEhxZeyCuOlksw6oZRn1JZlsIVac0kPLmmVKsWvDq7ZxAXFv/zlLwqFQlwhYsvoUQTn1lJDG/6Lmq1nAqwMowJvcXl66MnLq/34k/IH56j37TewX1FXaWZc+A1xuf+Kz/tnfD77aSz5N7KauByh/E9s7l3n32HJcJ7PvM8jP3fNcY2QRcg75OLxV6lH31XfW1vckZvUHBXUcM6z7tBxmYtfvWeMOji3La2mu6JN24JhSGGy0fb1dmqam+W19ZLS6tyssuSE/MjQjEBfFtuijx8K3rfTf9ufPut+Ovnj125ffbDv/df2vLl4/wevu3/94anVK3z++Dlgx4aQA7ujPQ4n+XhmXvIriA4rT01koU5ZUa5RyEMP7GYHsj2F4rHqiyOfv2deFYqyokx8KwDTDctpLJ719lBXJ49hLICx3MXiFktZ7LKWJSsWqFiaYiGKxScWnFheYmGJZSQWkFguYomIZaH4WB5+WPJhgSc0mC5fosCLFOBHvufpvA8dc6ejh4YrRw7T0Ykqx9zouDsvHsfoxHE66UGnTtCpk+R5kk6fIi9P8j5NZ7zojDed9SafM3TuLP8I58/RhfP84/hdIH9f8vejAH+6GECBAXQpkIJYuUSXgyg4iEIus29gIDREHxauDYvsDY/qjojtCI9rC09oCU9qCk9Whac2hKfVR2TUhWfIwrKk4TmVYTmSsLzS0PzikPzcwLyUwJJhSlpQaWvleI2cNw1NaI8iS4a+vr5smf1ky5NqDBszBEUYKd+PKHCFuCwQpsRgP6eH7vQM2fvvlz/0cNPhw4auLrF2xCTdvesr62+ISbkxJnZenNfy2M1vRfz4eNDKOy5tvOvMQlbu83n+Eb/X7j/34v5899FNZtipb6/rqS5oz0poDr+oPHOydv8JmetFpXdcU1h+e2Ztj7RD1ybuCjBRBgyG7rbWFmV9Q1WFrDBPkp5cFBuZdTkg5bx37Em3sEMugbs2nd/w6+mfvz32zScHP35rz1uLXd999cgX7534/oszv/3gt2Xd5b3bI90OJHifSA84nxd+uTQxVpqdIS8tbqqtaW9So+8OpqZxCmwsxpgDG0s+Xqd5ImIBiSUlmwRlU1iOYiGKZScWnNiB7HB2EpaRWDpiuSj4MoWF8K4w9lqx0by7LDGB96GlpfKOtews3uHG3l5RIX+rcbH8bfBAxaLUZZ4h2dlCgnkJDeGFnUcq5f2BTWreN8g+uKaZ9xa2tPDvoY2VNv6ddLTzfsXOTv4VdXfxzsaeHv6N9fbyTkj27bGi1fLOSfZlXj2DYUCn1ff36Ho7+7vaejs03a3qLk1DR5O8rVHWqpRqFBVNtWWqmqJGab6yIkdRnikvTastSq4pSKjOi63KjqzIDJOkB5elXCpJvVTCFthqTmQF28R2YLuxndkhlbn10gJlTXFjXZmanZCdlp28SdHOXqhN3eUR473x0g5WNhnLT/6/vXf2o01BOy3Kjsp6qfiO4ZpNaJ9eeno6y4cCy97FEWJHsTOIKyZC5mSECDrCmmEgKMKINBbTpv+mTpW4Kig4QxWh4vKU1pWUXLP8bcncR5rd3QfYr6Kr0azVudWpF2SW35lYuEYiXxLxy8+JP/8Q98Mzvs/M8pr1QegHq+JWbcvYdqr4lK/El5VnfZ9NrU8VD3aMD0Oqa6nprsxrS49tCvGvP31ctvdU7YFLDT6JzZFF7TmKXlm3vlPcG2CsDAz0dnW2qxvVsmp5SWFVVnpJQkxuaFCan0+8lwe/ydNlm+/m373XfO/x3eeHP3tn7ztLXJa/fOiT5ce//dTrl+/Ob1hzaffmsMN74zzdU33PZAcHFsdFVWSk1hblN1ZXtjYqezraB0Z7WzXAGGMBg0e1Xh5COjp4PtFoqKmJ38qoZGlNbkxrNTytVVbw1OR+dLC4sXLE+NNUWGXQJfs9bMMHNlbD6k+doNOevGvL5yxdOMc7sgL86dJF3m3F8lV4KL9rMTqK38qYEM9vcUxN4XGRhUYWHfNy+U2SxUX85klJOX+37D2zd87eP7s6ZZ+FfSL2uVgSY5+RfVL2edmnZp+dfQPjoLGmhYUrc6nIrs+LqbKsYaW3a5RDsvF018/SnbbHmO7am7vbm2xLbamqurChKq9ekq0oy6grSZUVJdXkx0lzoyuzIioyQsvTLpcK/XXpIWVZ4ZKcqEqW7goTq4tTZKXpdZIsId01yFi6K1crKpsbqltUtTzdtTR0tjV1dbT0dLf3sY/Q36vT6+z/g9ar723uba7rqCvTlGU1ZiXIE0KrQ9k1ALsYOJx/eFfWrvUp61cnrJ5/bv5Mz5kPnH7AsrCLB3O5//T9I7lmgBGajDd/2iUkvaFBUahk2MLIa4aBoAgj4vkKRf4hLgvaavnTibopPmpCZ1xczetvVDw+T3Py5AD7xTli/QMDQerWdwukN8bnv19YHaJu69D1BEuDH/d5fMapGY+effTFgBffCn7rzctvLg1cujx4ubk86PXg0H/0DWRo0TZJu8uzW1Oi1EEXFCfda/Z41R0Objifookp7chX9sn7DFfx9gAEgzd5lptv8gwRbvKMOibe5Hl23erR3eTZ1aLBwCowSlot7xTq6eH9RYP9aWr+pJnS+IBZrYyqq/njZOYutaJC3meVm8P7rzLSeV9WShLv14qP5X1cUZG8nyo0hHdhBQXyfjB/38Gc5n2a95Wd9OD9ZpYhTehGYxFO6EY7d9a6Gy1I7P5iJz/jzW9itCwsGbKMd8xYjh/jp8rM4G+YvW325uV1/Ek59nHYh2IfraWFf0z2Ybu7+QdnH3/q4nErrbamuNFRSQ8pZ+mLhS4WvVgAM/bdNbNIxjZJC6z77uKluTHGvjue7soGb8i8XJoZVi5037GAV5Jq7rtrYClRLmmqr2pmeVVd19Zc397S2MnyZGdrT3dHX1+3VtunNzhId0O197c3dDVUtVYVNBWw391RsqjAqsAzZWfcC9335+7fnL55TdKa72K/+zTiU/b7/eWLLy+8sHDumbnsMsCmzPaa/cS5Jxb5L3ot6LUPwj74OvprlhJZVnzB/4Uvo7/ck73HUZl3bh6C4hhyjqA4Y8YM9lPIe0KNgK0KmxhzGrxijbDsCIIiXFl1PG39B/VZP4+Xvp/kV/jb5dQ6oqKql75a8cT8Fi/vgf6r+E+b2e3dP5XX3Z5Y+ExW+TG5ulmrZf+Is18VfJzS0Hd3Z+0OrgmOqYsRSlDNpRPlx8yrQlH3qpv6Gyu6SjJaEsNVF30UfBjSM3K30Eb/tJa48s4iVZ9SO3BNE2DAFJMV5Bewbb1QfDet9fn9x1M/fu31y0rP1V+f/P5Lj5Wfeaz8JHDnpos7N57f8KvnT9+4r/j4wEdvCjMoHPn83ROrvvBZ95PflnVBe7dFuO1P8PJI8z+XGxZUkhhblZ0uLy1Wy6rb1aq+7qu+4xqcW0UFxcaIw2xEhPOeKxaQQi5TcDDvyAq6xJNSXi7vqios4Avm+x5TU3inVkI8v8mQHR4ZYepJC+I3HF70Jz9f3i3mc5bnKy9P/kiYTWcaC1pW3Whn+FNhvBvNj5/h8iX+ZsJC+cgfYjdaHH/FlGT+6iyPsaxo2Y1WXmbqRmMhrZYnNKWSGhv5nY0aoRutnQ+R0tszft1oUxILVNo+HQtXLGKxoNXe1M1CF4teLIA11LTUV/J0Jyvh3XcspEmy5GXpdSwlZoZJUoN4lnNUUlkJYkmPd+Xlx0kLE2vMSY+dip2QJT2WHlmGNMe8rrbe3q5+9mZGnvHM+vR9ml6NvENe3lKe3ZidIE8Iqwnzq/DzLPE8nH+Y/db+M/XPnxJ+YuHtw7APXw96nYW3+efmP+j1oE3Ym+k585Gzjyz0XfhK4Ctvh7z9WcRnLCKuTVq7JX3Lvpx9xwqPnS07e6nqUnRtdKoylQVLaZu0obuhQ9sxzEgBHkUeaxLXmMuK6BUsbVrWsFLZaj14BFyDCQ2KLLCxqMZ+CiPZXO3dp0Njnq+vrzkEsgW2OpIaYdkRBEW4skNzKWWfuCxQ5vC5E6fkICgGQ3toqHTx4soFT7f4nBv5f9Zt6NfulTU+klZ6X3LRhqr6qu6+Mk3ZrqxdT55/kv1SYb9s6rv44+bBjRfcZLstyq6jNTvZT6GwZVbca3adUxyPUF3MbEmq7Cpt7lfpr2H2C5h6WGZTVpRVZqbmRYQkn/di0e7wZ2/vfuOFYcquN16IP328Ij2lrriwUVrZ2lDf044HVqcclnB6e/nzWm1tvJNKpeKdb3W1vAuLxaSyMp6ahMfYMtJ5bxsLVyxlRYTx7HfpIu8oO+/DUxmLbWJmO8qHFRmuHOKhMTGBkoc8jcaiY3ExlZWSRMLHkKyW8rEleTeaghoa+LN2Qjcae6tCNxp751O6G23y0BvTXS9Ld+19nS09bU1dQrpT1bU2VJvTXaM53ZUa011hIn/ujiW3rHBJeshg351lYRkvI7Q8K0KSEy0EPOFezVrTvZrik3i50ZVpxgf2ssIr2NkywiTGZUl2BF9lP1MvlbaqR/Ofpdr62hSdCkmLJEeVI9zMeUFy4WTxSfYr+FDeIXN5N/RdluVYohMGFbcp5s69ZUHLWDL8Kuqr1fG8c29n5k52HnY2ds6Q6pB4eXxWY1apprS2o7a5t7lH1yO+iTGVVpoZkR9hLoFFfqclRyxrWGlobhT3hms2cUFxyZIlLi4u5tjGls35bYTGPChGRkayc9qYPXs2giIMp8iPdt9Beovf3wYtJW6hlmpxdcowGNqCgqqef6HymWdb/fxG+B+Vu/WGcw2aV3Mrb4rP/7JYFqvpaOhWHS86viRwyeM+j29O31zQVCDuanSxwUtIg46KW80ueU+NuDdMS33dXZp6OUt0Zcnx2cEXE7w8Qg7svrDpt5M/fHXwozddlr989Mv3vdesCtixIdLtQMoF7/zIkATvk3Gn3Nly9uWAnJDA9IDz8aePsQXL0tGsFl8Arrv+fuNNlR38jsqmJn7/oULBb0eUVvFYVVrCB9/Py+WhKz2NZzCW6GKiee9ZaAjvTLsYQL4X+G2QLNR5nrS6W5J3xHnwXriz3nxUxgA/fodkaDA/lp0hIZ73ubE4x7JiQT6/XVNSzl+UvTSLlCxYajT8BkgW24TbKNg/gyy8DV8GMGwyxyIWS1bmIslS5MVUWdaw0tM5+jtBRtd3x6IaC2zmdJcaJD56JxS2avkMnuUIKxU5iqp8ZU2R/Rs1WbZkCdP8GJ5Oqx8wXMVfg68jV8zxnmMuLJjd73m/Zc2DXg+GSyNq2muKm4vTlekxdTFBVUFny84K93NuStv0a+Kv38R881H4R68Hvf68//Ms183ymmUZ9thJ5p+bz8Leq5deZbHwi6gvvo/7/rfk33jnXu4+9wJ371Lvi5UXI2WR7Pz56vzK1kpll7K9/6pHMh9vZ8s9LihOhjT4CiW44cLlhvPmVVY8ZHszSrPEveGaTVxQZBlMoVCYY9sopsdg+9sExaG3lY6kRlgWNA6BHkUYjkFPe++lXC9xVVAVSXknxeWpQa9v8w+oeva5qkXPt126xBKjWO8Y+5WY2NKxokR2c3zB4pwK7/rmpv6ugMoA9nuL/Yb7Ie6HuLo4u5Pg57Wls3/lPesOHpftDWw4E6G+eFHpVdyRa1m69B3i3jAVDQwYulpbVNVV1blZRbGRaf7noj2OXNqz5ezvq49984nrO0v3vf+ax3efnVv/8+W922NPuWcE+hbHR8sKcptqa3o62nFdfq36+ngQGr4I/wjwYSeNO7Ps1NLCnyVrUPKn42Q1/EkzFrGEoSZzc/i9jmmp/NbHeOOdlryPLpgntAB//kibzxn+hNupE7ybzu3IYKi72lxXbMx17KVlMj6SCouXzU38tsnOTt4dhxsmryuWykr4c3cqRyUztLy+SnMt6c48cmZ2ZEVuTFVBvLQoid+TySfWy1ZU5Ym3ZbJzsjObh1cRBs/s0PR0tfWypNrXo9X16w36ifhnxOr5PWVqdG00y3s+ZT7PBzz/9IWnlwYudVRYzJt1epbQrSckvc8jP18Vt0pIentz9roVuHmVegVUBITVhCXIE7Ibs0s1pbJ2mbpH3aWdRHfIs3+ttX36ng5te0uPuqFFXqeqqq4rlUhzi0vSC3ITc9NisxMjsqJDMsOCMi5fzLrol+17PuusT673mfxTXgXHTxcf85Ieda/ZfVzm4qh41CAojqWJC4qrVq1asmSJEBSFlHi1E+4PjXmMOQ2yhZHXDANBEYaT6U77HyDLQQj72ilmHfU0i6vOTqttPXe+csHT0pcWt4eEjCQiVvf0bZEq708pnpNasrO6obqnO14evzp+NcuHH4Z96F/hP/S3lIH0tT3ShOZwz9qDZ+RuqZrYhj45Zi+cwnTa/jZVQ315qSQ9OS/8cpKPZ/gRV/9t60///O3hz95xWf4y++n50zd+W9aFHd6bePZUbmiQJC1JUVbc2qjUsRgDY0Wr5Q+esYynauQBT1rFb788c8Yqqtkt7kdta8zlxHE+6skZb/7InL8vf14u5DJPhiwfJsTx3r/0NN4TmJ/H7/Pkj8ZV8uElWa5raOCdh61CrutBrptUBgwDeq1B22u8IZN32fHRMltVxrkQjOOp8Hsyq5rlkqbaUnE6BOvbMmsyQsvNPXV2C4t5LN0Zb8U0D5s5ZFIEeZtG2dGqEsZW6e0Rx1Yxjpx5nX5jOAp7x4uOH8g9sC1j27rkdT/E/fBF1Bfvh77PQt0iv0XzfOYJ/XsPn3l4wYUFLwW89OblNz8K/+ibmG9+SfxlQ+qG5/yfs+z9u/fUvazMODmDFXH51IyJH6CF/R3Q9rJQ19/R3NvS0K2u62DBXlqmLCuSFeZVZWYVp6TnxSVnRCelhCfGX46PDIwP9Uu4dD7R71zyhTMpZ06neXpmnDiVffxE7tEThUc8ig8cK9vnXrHXrXLP0eqdbtW73Kt2H6/ae6Jy/6mKg6crjnhL3M6UHfcpO3Gh1NO32Mu/8OzFwgtBBX7B+ZfC80Mi88NjC2ITi5K8y93danZZFuHOI8tVBMUxNHFBkWEpkUU1gYuLi1g7YuwoFvbMy8KC+Zzme0pHUjMMBEVwqL+Ldt5K5dYTYBT5UPklcdmZDfT3t3h7V8x/snrJ0o6ICLHWsVad/oSiadH/n72/8G/sOvf98ftH/L7fe+73Hrg9cM/pKSRNk2KalNMwZzIZpgxlkgyDx2PPmNmWUZJlkS1ZsiSLmZmZmWWQmfm3tuRxHJikbaDT1p/X56XZa2lb9lj23uvt51nPYwr8q8LxoS+hnZpzjDur9FXgjvji8IsI52d0PlzZXAnNe4VjDFS8ZSiDMU9p8it7uX9/I1qam80n4wmnzaOUGhkUGRbJbqsjld/o+/A07MibLQdeQZw/NlBymdFYKe7t1FGITokgYjGORENzk/nNP+LvEXv6pFaWIb6amIDCaMkEFFXzeqEgG0AyjRqqmCLkQ4VVaFRolx0e+8nalcBIOLT1jtAPRfAQ8C0UomAk9Ai1HygUpSwa3r1ltULBw/ExCDLnZqE0UcCce3oYtFlIwlxZX77f/2B24qNddmPJ6ZHYZLHBHQCwuBvaaLerC8JHPe6K8ToDZ7sRgpbh3R2sKxZQ+RjUQXmYIzt5mLvjdVNj84DrwOfyGVIBUzpgAgCZ9pvSPn0yZMl8ZGsWgN/2/+Ib1MbmBgR7C7nIdMQ57tRldRDsRZiD/kGUCwWzwiDY05Rell8+IzpziHsIgj3qH54afOqH+B9+H/v9nwz85Dfk34DbHIC94/zjFyQXbihv3NPdazQ1dtu7cR4cNUjdCev5JnzJ2eQXbtgjBrE7uy0eZE/WvX3252ptZWNpbhVCu+zCaHw2E5wC7O21JR2miFHjVSvtUplJIjF8wnjuEEYw0CfGoWToXlVvrw6BMHQjLB0IJwzhaUUEmuHhxuKXATAPGWxF+dsxgU6cr7vfhyT4e4nePpKnQHduAt1FYjqHWHYa184U2Dhiu0Bml6ocKoNLb/GY3QF3KBpKp9O5XG5sbGxiYmJqamp2dnZhYWFpaWl1dXXji24HJLkYa9RgjVrIJi1ao+gV8gsz28bo1Z6Af/vsPX1pfaOg+BXq62vWvweKe3qg5LVbvb/bPi5qOrElLdv6enZsf2PaXF6ewGCCT/4i+sabc1LZ9uwDtLa5xR+fPuaK/rPc/pY9TB2ZjM5mwN3xedrzTw8+XWusdec/eTNbWJ/3zNrYI2RkvImRIzpnTHPrD922hz19vjbW1+cm8rlwMGzWO8Q87RBBhOygN1T037oIP3u0ef/L7cf2oS+eId+7xYE1yPEoE4vmVcmSbkc+nVye32tf+WBtbkKZnwC98nmIxBJxKODm9UDxN6MBKq8CkE9QRD4KtAcPh/1YJUxgMMSioWROChnK0uRxoFCeUgFlflrMUCKo3wdFDlNJCCkBWAK8BJ9xd77u9DSURLrjWAzK7dw9A7yHhQV5dQktE6o/+ZEZHx8yvQDSts/+lLb7lS+tLc2vQHVTphYLYbq5iSzUsrwQppv4KP3SlQvvdC3XJ3YyMK3iUKFxuR+AnJYBheZ0LF+hYkrwkzvrDMngRxmYhQZ3wXwxUjeWms5nZwFMzowvfLS5bnGtkIT5tfzhBtwaAH1tD760tmFv/iGCvS8jgr/PNWNZXJ9/kIlJ5LCcxROpGHwJjccnc1kEDh3PoWC4RBQPhxRg4MJeuBwBV3fDDZ1wKwzuau3xtvQEP6I7RKAV5WtHe7swnp5+N5Lg7ht0YcnOfqqTSLOTC3RHYTuGuXYGz8biWzgSi1BulaisCoNDb3VbnR5XMBgMRyLRaDSWSCTS6WQmmxoZSY+Np/P5zMRUZmomMzOXmZ3PLCynF5aT80vxuaXY3GJkdjE0s+CdnHGO5i3pEWM8rYnEFYGI1BMUOn18u4dtddJNtmGDbUhnJmtNgxojQWPqVxmwKgNGZUCrTCi1EaUx92osSK2t1+Doaqjtunl5xx0Xz7Wf2P+xmRuXFEL+9nd2T19a3xwo/o9CvdPtwZfWnxGQ/CO1B4p7+mwtTm5V/sNWxro9LEoP20qoto//CrW5sJBHIgM//Vls//555Rf8RxyziyXB9H+rXL/Q+2CJ0cj8FMlPOsg9+Dj+8WuKa8q0cn1zffvUgqZWJ6zTeloWj4w380ao/jnnXmPDh1mrS4uT2UzK6/ZrlRYOXTmA5nU2D1Xexlw533lif+Nbz/ecPoy/8QGttlwAh6lJeJuAEzJqs0HfzNjo9kv8nQug19IStHkvPw7VQYkD5AtCe/ZsVoi7VEqocx2fB3VQoA5BXAfobjfyfcR7QwXe436c95wF3osUeG8E6gMOeG/l47y3p69fLmUMMFjIlnmQDVwfYDmoWbk8YpOELYDoPr6tDhx/FKaTPyBM53tgmG7+E+mXD7HeFb4LkGzH38NCKZS7Z4AlScknYS/8cdhTfxz2KA8v7AFtbm2ubq4CrpuYn0znM+F01BXxWQIOjcsgtagERilLx6epmSQlbUBBwin6MUocSolCqOD4SE+R6B7k3lgLwdM34O0jerAkTz/FPUD1DA67yEwnle0c5tjpPBtTaOOILDyJWSizyhRWpdqq1drNBofD4HAanG6D22vw+I2+gCkQNgYjxlDMFEmYoklTPG1OZgXhJC+SZkezjGiWFs0NxUZI0RFiONsfyuBCaWww3RfMFI0OpDD+JNaXwHnjOE8U5wrjHUGczYczu3EGO05nhehOpukTyVE8MZLFhQ8zu0mULgKpq3+gC4fv7MN09qI6enq64D3dPXAEAtHbi0T3obAY9AAOS8SgSdg+MhpF7UNS0Uh6H5KBgtMRXQxEJxPeweyGsbpaO88ebjv8RueJdx7kloOvSZlfnD+4pz9S3xwoFvcl7ujrI70vqT1Q3NNni3tti7h/+7ionG1L3bD1cTr6a9HG/Px4V3fgxz+JHzq8oNNtz36WxlfXOhKjTxl8/6V03gykTNMzorjofen7j+EfOyE4QQ/TP3HfHV3O6icVg+nevkSbZIwdWQisbe7FIr4WLc3P5cLB7cEXaWVxcSqXSfs9Qb3GxmdryP1CRDu9oWKw7HrRbYde771wknjnGrOlRoLuMQyT3XJxzG4Zi0ehvhF/dUCSiEOQtmPAWmLhx2aA5x8Q+Vlbg1Iri3G2TBpqNBcIQPvrrBZou51KAVVVASAHcG6IDKVuYvo+2sIHeA+H+RjvgZMB74EPBF+Da4f3UgXemyzw3l4L0L+8NjY2lxdXAYZNjc6Pp6azkYmkbwxgm9+YAmRok4YB3UGYx/KZRUGHIvog69neZGD8s8J0f8tIP786P7IwAniv2GNdEBfQgrRnac8+TXr6OdpzDzK4iQDk+8w9e0XYw3qwlCCFG+XuVGdJzCbGF8e/WtjbobvZtenJ1fGxlVxmKZlYjEQW/IFZt33Cos9plAmZJCrkBdlM/zDVQya7BwacGJyrF+2Go9ydSG8bPNDcHdru59QTbgBDuLcN6elAuXswbiTWjep3Ywnu/kHP4JCbTHFT6G46w81kO1hcJ4dn5RJCyN54Kzreft+wvgRs17AdEW8c1nF4bj/PE+T7wnx/VBBMALoTRdKiaEaSHJWkRmWZcUVuUpHJK9KjikROGUvLQ3G5Lyx1+6UOt8hkFeoMfJWaK5XyRGKeUMgTAPGFfJ6Yy+lvb8U0VPdWlsFvX++8+kHLuWMtp480nzzYfHx/05E3mw6+1vjW80W3H9vXc/pw7/unsFffI9y+Qq4ooVbdGa6/x2yuZrXUctsbeB1NvM5mflcL5O5W8AiGnPYGdmsdo6lquO7uUHUp+d6tgZLL4BXA64BXaz/6VvHFWw+91nliP+Lckb6Lp3DXzg/cvkiuuEqtvcVoLuV03OMjqkToWsz147TacuUA5kHuPLnfoWZuv7t7+tL6i6WeAlAEuLg9eJi0B4p7+gzNZLbu/f+28rtauG6sQS0xxjzbw78ebczOjsHa/Y8/kTh2fNFm2579lJY2Nmkjk/vs4X+W2w87o+yxKX3OXK4t/ynhp6/SX+1z9YG79fap4DW3NlKLMWVegEt24pJdqrwotRQHk9tP7+krkksqVA6gd8xqqek8+U7xWIZFiJAdHFiDjc+yCTiAA4upoeBmDBUOPQTd5lsPvIq8cAKgILidg/ONTGohO9Q5mU2vLv11p09/hhj0rQEcFLsDppChSpvkQnfy4sHQ4BayB+qsIBJCFTUZw1CBzQH8Fhr1p8X30umPxff29LBqeWF1bmpxcmRuNDGVDuXjntGgJQ3t0FPFii6G+4oRP6s4BGYAIkaduVRgfCQ+NZmbAx8OSBIq5uHI7tirTxq4/t0zwF+m5cNfXDMrM9n5bHAqaBu1qdKqYpt1jBvTZe+qM9aVqksvyi6eEp46xD300vBLvyH/5scDP/4u5ruP4x8HTPg87XnAeycEJ4pN1d9TvXtFfx74quE94BLLxXrvneLxNeMF4OvG93Ujmu1P/Cfq8+huzu2etdlnjOYpjX5SoZ4Qy8d5olEWJ0OhJ4nkGJYYQ+HCPehwOzLSAo9sJ2dCjjbAQ01wfxvC04FwdCHtcKQFibL2oa04rI3QbycR7JRBB33IyaI6OMMOPtMu5NjFfJtUaJZJzAqFQakxqLU6rVqnUxmMSqNZYbIorHaF3alwuFVun9ob0AbChnDMFE1aExlXZsQ3Mh7KT8Wn54QKtsQs2jHLQQNf3u4ZYD4RI8f1ggs7t6MJEBe1+s5g2TX8jQ/QF88gzh0FfNW6C+c+4SLdoT6A6I5YenWoogSgHbutXtDTJkXDVUQsrxPiOt59ugNoBz4LuKGwW2sZjZXDtXcpVaWkuzf6b15EXz4H7ildpw60HX6j+OKwI2+AIZgET4GvZ7D8KqX6Fr2xjA2r5HXXCHsbpPhmBbFNRWnXMboM3C6LuMeugDs0cLcB7rXC/c6egKcrFOiMRNpDoaZPOxLpiEbh8XhfIoFLpYgDTa3o6zd23Hvxw67TR3fPoK5dN+n/ilO9HjZ9c6hWLDq6WzuVaR4q7YHinj5D1JNbjPe2j4uKSrZM8O3jvxKtT02NNjX7H3sseerdJZdre/ZT0k3PX/Ql/k3h+J3Jj0yNuaYSbda231N+/yvSrxpNjYHJwPZ5W1vgPh2e94nHWH3x1sF0r2FSAe7W28/t6WsQ8c5VQIaI88d3DD93rPhYdMs7L3edfIdw+wpYRkj6tqOCcactn4wv/a1uFAT/r/ExKEAXDEClXPQ6qB8D4DqAfIhuiAaLj0Uj7hsJhwq0qJQQ70ULvDe6F9/7axXgt20ITH4EgV5dwqGImgXBYmmWorVMr0kQtMsiABHBOTH3SDqYB+g4NbrNgduv+EdrbmopZM1sDx4mFbfwZeYzn+60XqzMeVt9+0PZh6cEp/az9784/OKvyb9+ov+J72G+B8AP4B+AwHc47wAgBFgI4LDWWNth6wAfC6ARoCMASICRACaLffYeVLCalSPxR2nGKdWDjE102KeNn0N34OYCXgG8znC2fyiDIaaQ+GRXX6INHt1FdwUjoo3ISGtvsB0V6EZ5EShPH8qJQdlxKOtAn2WwzzyEMdNwZuaAjTto4w85RFS7hO6Qsxwqrk0jsOokZr3cqFfqDBqdXleQFkhv0BhNGrNFa7VpbHad0613e83+oDUYdkTjrljSn86GsiPRkdF4LpdIp1PJRCoSSnrdUZs5oFeDa69NwDEyqdohgqK/T4zqAvTFbK6h1pQNll3/GOAdeLX10Gst4PHga0XDPjzUVnJ4ZwjccvBV9IdnhipLh+vuAXJjt9ZxYY1Q7K6rRdDdVnQxdscFdNdWx2isotVDdDdYfr3/1mfS3Zvdpw/1XjgJBQZLocAgrbac2VLF7awTIJolGJiC0Kke6tHREUYOyiLC2GVYlwbnNeJ9FnzAgQ/78NEgNpnEA34DFAdYLhRq2UG7cLgtGu2KxZCJBDqZ7E+lBjMZajbLGBnhjo4Kx8dl+bx6clI/NWWZmXHMznrn54MLC7HFxdTy8sjKysTa2sz6+uLmA3KRlJJA1JHLZ2YfZAP4Hbf/zfW1/svpG92jCJQG9+OHW3uguKdPasS9VfH/bM3t2ohVbIkx/1ezNWt9YmK0rs73g8dS584vBz4ivd1KLq3UR3NPaD3fV7vuhjPWqbF+b/8+1j6weripuqnNasHKo3jm4saCd9bOHaEg4020LN42rZ9emyw+taevXLP5MbDsMDGpYAXQeeoA7tqF4bq7D3L70bfijo9vo/3r1cZGAQLHt5LJT0LgMBVK+EQhIeTDYaAgIZsJRfw0Kijc5/VAyaKh0FYkBPVgAAQIDICQz9k+3vEeEz7c2vwoHXRuLDmdgSBwBIAZBIHyqFkIINC3A4G6B0AgAMi5ycXlhdWvqUbL1631zfXJ5cnUbMo34TONmOQpOTvCJvlJKBeqzdJWpa8C1+f3pe8f5x8Hl2uoqBjp6cfxj38f+/2fEn7626HfvkJ/5SD34BnRmSvyK2WasgZTQ5e9C+fB0YI0QVygyWjsY/bwVDi3kPuTuu1tbCyvry+sr8+urU2vrk6srIwtL+eWljIT84H0rDU4pXHkhX2RDmSsGRVvfZB7480Q4IFzYh19kW5MBIkOobBBLNrfj/EOot0UjJuGdTKxTjbWwcU5BP0OCcEpJ7lUFLeG5tIznAaWzcSzWUVmm9RklRvNCr1RpTNo78NeUYD3dEaTwWI12Owmp8vq8dr9AVco7IvEAtFYMBINhUKRoD/i9YRsloBJ71HLnRKhhcvQ00gqIlaKQQjgMHZb/XD9PUBThNtXoITJCye73z0IO/JG074Xi9y12y3vvNxxbB/87JG+D09jr14glFwil18fqrxNrSmn11cwmqpZrTWctnpOWwOnvQFwHQfWgPzgYCHod6hogHDAO0Pg5nde7jn/JiDGzhNvb2dmXn+PeOfSUOV1Wv1tVts9bleVCNUgxbUoiG0aaqeO2W3iI60SlEOFdusxPgsu4MBFAN2F8Ok06dMGmBeJtEejPfF4byIBCHAgnSZnMrRcjjUywh8bE4+PKyYmNJOTxulp68yMa3bWNz8fXlxMgDd9eXl0dXVybW12Y2Np87O6JQOtb26tbGwurm/Mra1Pr65NLK+OLq5k55aTkwvhsdlAbsaTmnTF8/bwuNk/avTk9M6M1ppUmRMKXVSuicqUYak8KJUGpaKAUgQlgX+OtUyv05LY/sR7+tL6RkHx4adEoD1Q3NMnhX9tS1S2fVyUm7TloWwfP9xaHx8fqaryPfqD1AcfLIOl86c0t77Rn8m/aAn+i9x+xh0XjE+wY1ywqvgB7genRafBimRpfbsCzczalH3aOJztR8Sa2CNk96xtYf0hauP7t6H5qcm402bh0MHSBKxIYEfeBMuO1kOv4W98wO1oAisVWm05p6NRjOoCBsOuU+8Uj3c8kUltv9bDLACBc4VIYDIBbf8rQqAMQCAHaufwEQRid0GgGtoi6PVCEDiSgzYQ7mHeX7M2NzdXAARO34fA8C4IVHwSAguRwMBHEOgCEDgOQWDuPgR+U2VdBrT76zg/2PEHw99/Ev+dnWE99wf1nEcDGeoOOC0uJRcXYwsL4fn5QH7GER9Xe7JCfYIqDeOYvh6io7nXUtGqv1mtvlgiO31RdOQU980DzJdeof3ut+Rf/HzgR49ivw/804HHfzP48xeGfvkW7ffHWC+c475ymf/GbdH+atmhFuVRuPYkXn96yHiObbkgtr2vcX5odV92ey673R+6XBecznMO5xm745TNfsJqO2K1HrRY3zFb3jaZ3zSaXjUYXzYYXtAbntXpn9HqfqPR/kqteUqlflKl/qlS9YRC+UO54lGZ/PtS2XfEkn8FFii+w1H/mKF/hmJ6k2Q7NeC8iPPeQvvv9oaqEZG6j0J80XpEpHkgjGTlBpUTggcZHYcxTEye2SEw2sRGm1RvUejNKr1RrdNrtEXK2yW9wWAymaw2q8Npd7kdbrfL7fYAO+w+u9VtMrj0GrtSZpUIDVymdpisIGAlfT1QEK+pupClCQXx+i6ehp892nH87ZYDr+xGu6IB9RWqpOyHnz+K+vBdzJXz+JsfAswbLLtGundzqKKEUn0HvBS1uoxaU0apKqVUlgJ0JJVfJ5ZeHbh1CXftQt+ls8j3ThQYErp0AxfQbj+ARtQH7xY29V0m3btBqbnNaLrLhlXye2qEEN01Y64fEyI7PErpg4w4f1jLK90J2e04HG6LRDpjMUQ83pdM4guxO8r92J1ofFxWoDvD1BSgO+dn0d0cYP7iz/ba5ubS+sb82vrM6vrkytr48urI4kqmUL80NrcYnl30Ty+4J+YcYzO27LQ5NaGP5bXhUVVgROHNypxpiS0ltiRFxrhQHxVqokJlWCAPCaRBoTggFPrFAr+M75NzfEq2T8X0qOhuNd2tYniULK+S41XyfHK+VybwSgUeqcgrFXtkUo9U6pFLPQqpG/oGiN1qkVsrcOrZDoXQo2V7P2bWx4Yatseu9xX/U3v68vrmQPHDDz98aAvY7NYeKO7pY0rotqr/NxRC3NFsBgon/il/fP2LaC2Xy5WX+x55NH358kr0k2kYYFUlmZg97Y79s9z+kiWIz4xL0tpbqls/6v/Rm8w3sR5sfilfPHNsJWeYVJLSqN54i3CUHpr3rty/r+zpS2ppbjbpdtr4LBGyg3jnWsexfWBhAVYq/bcu8rpajExq1GqcGRv5KyskU4TAsSIE+qE2D0UI5AIIpEBt2YsQiAcQSN5is6BaL0UI9AEIjO1B4N+AChC49nEIHIUgUH8fAtkfh0B+EQLjQfM2BBa2Bc7OTi4uLaz+MbU9NzaW1tZmV1cnlpdHlpbSC4sxAGazc56ZGfv0tGlyUjsxoRwfl4yN8UdGWLnccCZLTqf7kylMIoGMxbuj0bZItDUYqg4E7vp8JR7vNZf7Q6fzrN1x0mo7DGGV6XWD8UVAU6+Tfvh7yq/fZLwC/EbBr9C3D14v+Af4R/eRvnuE8sjb5EdfIT3yB+IjT/V//0c4qGE68E/w3/8V4dHnSD98Y+hHByg/OUV/8gLzqevc39wV/KFe/HyH7NVe5ZuD2oMM43Gx9bTGfsHuuezz3fL5S/2BskDwXjBYGQrVhsP14UgT+IKjMVgs3hWP94D/RSLZB/47qXR/OkPIZEjZ7FAuRxsZYYyMskfHeGNjgvFxcT4PsEE5MQnIQTc2ac5O2FIT7viEL5wP+MfDrtGoIe2XxhzciJEWUpCCAnyAiQmSUSE8PIyAR2Dd0R0ObEZEunrDqL5QP9ZP6vcyiE4uxSZimBVcg1qsNSm0ZpXWyHYO07yEYR+xaFIAjYq27gyBqZ4BqUZs1eusWrVZKTdKhHo+R8MaVgwRxf1ofm8nq72RWl9BLLsBmA15YTtz8kFBvPajb3WfOoA4d7T3/ZN9F8+AD8Fdv9BfID3C7SsFXx4ouQyIDhAjYDbM5XO9759CnD/ec/oQuPYCooNe58ArO8VawCsMlFwi3b1BrSlnNtdwO5rAhVqGRaiIWD2NaGKRLTyKXUxzKeheLTNgYods7IiLHfezkxFWMsbIpOiZDDWVIiYS2FgMCXDuE4wHHA7DCkG8PmzJSUFPm0sqfJB7zhx0a0nLy7nF5fzCyszs8vzs6vLULpxLzC9FC40ofNPz7qk5+8ScNT9rHJ/Rjk6rclPyVF4WHxNFRgWBnNCXFXo/aaY6zFVF+IqQUB6SyEJSSUAuDioEASXPr+T41IC+7pftBZin5njVXJ+a71ULfWqRTyX2qSVetRTYo5I4lVKHUuZQyewamU0vt5vkdrPcYpaZrVKTRWywCLRWgcbEkZkYYgNNoKNwVRSGnEKT0Ib4wwQOq5/Jww2L0FRZL1mJHNQgBvQ9eFM3ztKJsXf0OWAodyva0oENde6YbCTK2MbdM8A6o3T70rCnL61vOvX0E9p+7mHSHiju6WPqeWpLA9s+LsrYuRV9qK9Bq5lMtuS27/uPZG7cXE19Mr4UWFgqD2W+p3Y9ofXUR3OKEX+jqfFXpF/9lvzbFktLbCYGztnc2kwvxVV5ET7ZjUm0y8d5icXwJ7pf7OlP1fL8XMrrtgs5YlQ3WHx0FnpOgMUNWLuAJYKZPRyzW2bzY9tnP5xaX4c6/gEITNyHQJ12GwKpAALxUA0YJHwLj4NqwHAKEKjVQP0hfL6tOIDAEah7xF5HvodMm5vrm5urO15dWZqdnN09AwxdFbZP3lpZKkLg/GhyJBWMRV1+n9nm0hmtCqVRJNRw2CrWsIo9pGITVBy8TogzSLEWZZ9Nh3IYES5Lj8fe5XN3+H1tQX9zMNQQCtUEghV+f6nXd9PjuVIgtHN2x7s2+zGL9aDZss9oes1gfEmnf1ar+61G87RK/XOl6gm54gcy+XeLYa6iZfLvgUml6kfgBI32l+BkveFZg/FlwHiA9KzWgzb7cfCyTtd58Ck83qvg0/kDd4LBCoBeReiyBptU3ga+u3bYUYm3lnUbbjZpr5QrLlyVvntOePgw561Xhp//cf8TvyD+4hnKMw/yjweeKFNcYEVYsqTMNGLyTniTs8mJpYnid+8rFHg/VjY259fWAS2MLa0CVIjPLYVnFz0Tc5bctDY5KYuOC4KjLN/IsCdLdmcI7jTeE0X7vX1BW29YhwxLe8McZIjSG+5HhBDwMKwnul20Ex5pRoU6MX4k3oMhu4k05xDTzhBYeWKTQGmUa/XbwT693mA2mW1Wm91mc1gtdrPJZtBbNSqDVKTlsRXDQ7TuNnJLHbkZuJbcVIupv9FedozcVPORW+p6LpxqPfgquAx2nTwAP3sUYBvywknUh+/2XTqDvnQWffks9HjpTN+HpwG5Ic8fh5850v3ugY7j+1oPvQ6IsXn/S22HXwcACT4QnAmwcLDsGqWqlN5QyYE1COAwCbpHOYDWDhGMTKqNzwbE5dcowiZ93GnL+L2j0cBYMpjPhqfzkdmpyMJ8eHbWNzPjmJoyTUxoxsdlo6OCXI71Kd77aDNeAfag/XiFjE1cIaBHy+XY4AOL0TzwUtPTjvuhvOTy8sjq6uT6+jz4nVrbhN6+/PJqdnG5v7Gt79q1HSM+eK/9+P7dM6irVwe5SrIlMWSO001xliHG1ccF+phYG5WqozJlRKmMqORhlTSoEQU0wqCW79dAdLf95xgNy6vj+nR8v0EU0Iv9BknAJA2YFUGbOmjXhFz6kEHh0CtsBoVNJ7do5SZgncqk0xi0Wp1Wr1NrVWq9XGmUSA18sYHL1zN4hmGWgcwwDtLMeJIZTbQi+x1wjLuj19eKCDXd/0Gq7wnU9/gaejwN3Y6GLntDl6W+01TXoa9r19S1KqpbZNXNoqpWUS1MVN8hauwSNXcJWuBCGFLYgRL1YMRIvKSPIMWSZQSagsxU0bhalsjAR9V1tNe07Li1uqm2qmb3THtVs5Ir3P492dOX1sOIan9Z7YHinj6Sh77V+J9b67vWtSNOqNjpxmdn4f/FtRKPZ67f8D3yaPZ2KcDF7dmCJtfWEamx3xj9/6ZwXPQleCPpPlffq/RXfzzw41J1qTFnBHC4trkaXQhIxth9ibaBFFw7Ic0upx5UqGBPn6+VxcVMwOsU86VoOLmipPvdgwALO47tI5ZeFcLbLVwGWKnMTW6Hbf9MLS9DzPZVaRsCR7cS8V0QKClA4NBHENhfKCXKYUNdAYsQ6AcQGIe2/M3uQeBDofX12ZWV/NJSZmEhOjcHFr62qSl9fkIxNi4cGWFms0PpdH8i2ReLd0ciYMlbK5f9TiT+18+xWPItufgFifgxsfh7IvG3d9hMIvlPsfg7UsmjMunjMtlPFYqnVMrfqNXPaLUvGo2vm8z7rLZDAM8cjtNO13tuCM+u+Xy3/IEyCM/CdUU8i8e7E4neVAqbTg9koDjY8Mgoe2yMP56XTkwoJyd109NmsHafm4MqXiwuxpaW0isro6urE+C/ufFFrVlnVmZSsyl33q3NaHkx3qB/EO6A1xvrS9QlFyQXjvGPHeUdLboY8QN+FPfoL0m/BNdG8Owl2aV7unswKwzvxQP2U2fU+9nvPEV66lnqc89SID9D+cOvSb8uHhf9A9wP1Gnt9qf/Ij0I9nzT8/bxWX1mShHPiyJjnMAo3Zcd8mSJnnS/J431ptH+XX3tfOBinsJY4jhDEKe14TSafr2k38DpN9PwDgLWjUb74KhQO+J+ARhkpBkT6ugPwInePoq7n+Eicx10iUOgdinNbqPH5/F5vV6Xy2O3u8xGh05jkQM64KiHh4TYXnZH81D1HfyNDxDnjrQf2S6L0vjWc41vFsJ6B16BHX2z88R+QH1dpw62HHi14c3nP89vPY+8cByqwFl3j9Vax+tqEaO6FHiUhtRvoA+BiyS4fnpVsqBBG7NbUl5XLhzMpxLTo7mF6amVxYXNje1Q8+bm2vr6wurq1PLy6OJian4+AsAM4NmDeQ/xKd5rKSZwJhKYVIpQyN5kFvbmSfJ51eSkYXraNjvrmZ8PFfI2s+D3q7Alb3nnDyi7tbC2MbmylltcScwt+fJzjuyUMTmhDo/KfDmhM821JtnGOF0XZarDPFVErIxI5SGl8GP77uRkEx/F3z2jZXolSo9O5tDJHTqlXaMwa5RmjcqkVhvVKr1arVepdSqlTq3SadV6jUqv0xjVWo1Sp1QYZTKTWGTmCyxsno3JsQ8zHRS6jUS3D1JtAxRb/5ANR7ZgcTo4WtPVq2rvkbV0iBvaBDUt3OomVlUDvbKJVgXe9dahunZyQ8dgYzepFU5q7yV3YshwPBVFoGKG6AM09hBHwBBIuVK1RKVTGY1Gq9XqdDrBj1IwGIxGo8lkMpvNjo2NTU5Ozs7OLi4uLhUEjicmJkZGRlKpFDjN7/a5zA6LyqiXqJVcqYjG4w4ymTgqBUUk9uBw7X2oFnhdZU1lZWXNg1Vxr8KnsG+/GXv60toDxU9qDxT3tK2N9a2W723Z+reHQIAPVbVbIw/jBWglGk1fuuz7/iO58rtruY9Kj65sbrLGpg45Iv8os79lDxMyOXKQcUJwAiyGzonPgZXTysbK0saib87JG6Ei401DGYx5SpNfebjjWg+fVpeXcqGASyqU43opVaU9Zw6DZRNYMA2WXRchO608ZtLtAIub7bP/bAUDW2bTR5aIoRYOu2eAAa19WktLW1NTW7nsVjQK1XqxWqBsT7EIyvxk0LcNOLAIgVwAgRKIEgErAggE3Dg6ujU7C3UX3NNXpALLjUNJkgsRgEDTM1ZARPm8fGxMAFgO8FIKYjlULN5VYLkaf6Dc67vp9lyEom32E1brQbP5rUKc7RmN9pcq9U/lih9IZf+9g3Ay+XcVysdV6p9rtb/WG541ml61WPbbbEdt9tM223t26yWb5YbVeNuqrzBrquWSt2WyR3b5+zLZd3fPSKWPOXSkTDg0mkpOj48tzM6ur35DewI/U/mlfHQ6ahu1yVNyRpiB8+A6bB1V+qprimunRaff4bzzLPXZnxN/vsN+wD8l/PQZyjNvMN84zj/+gfSDUk1po6kR4UAQfUROlKNKqxzjjuRscnZ1dvtzPEAot7HCIvh8K1LJIuw5J+dMozPq9IQkNs4LjzIDOaovO+hND/gyOB+AvdQ27PlTaFcKbU+iTfE+TaRPFu7j+zEsD47uJDIcQywnnW1nc+wCkY0v1/O1Cp5JyLay6E4KxU0Y9KLxAXhfEIaIbIdxeiOtuHDXYLiPGurnhKniCFcdler8MoNNYlQLDUKOhkYSY1HcrtbhBiirE3f1AuL88a5TB2BH3oR27u17vkh9TfteaHr7JeDm/S81vv1Cw5vPgZmWd14BpwEORJ4/jrl8duD2ZXC5Y7XUChHtygG0jkI0s+kOMc+nlodNeiWlWYC+I8SUiXDlItxdJvwqofG0ZKByt/OZj/aSfYr3oPge4L3JSWOB96QF3mMCiivwHqbAex2hUPMO70Ui7bEYPB5HF2qxDGWz9JER7tiYOJ9XFmpsQlVY5uYCCwuxpaXMysrY2tp0obrmZyTLbGytr2yCm+PC/PrsxGJ+bHY0NpbxpxOOeMwUjGg9IaU9KLMEJAa/WBsQKf0iWUAk9osEfgnPJ+d4FSxoGx4APDXdrWS6FRyXnOeUCx1SkU0iNkskRolMK5ErJQqlVKmUqZRaoTcbyS8vrj7IZlFQqtDyDCy2ns7QUobV5CEFYVCG7xeh+/iIXk43gtHeTYV1kVq7Ca1dmOYOeGNPZzOyrRXV0o5t6RpoRpCb+4bb+tkwsqCLJu1lq/AC3aDUwlA7+Qa/yhEx+hLOcDaYymfGZmZmFhYWVj4r/391dRU8NT09PT4+DsAvkUiEw2Gvw203WE0KvUaolLPEIgqXQ6DTMUNk5MBAFxYD60U2d3c3tLdVNzdV1tfeq6ksryj7lCrK7lXfqai/U91UWttW2tBR2tRT2tp7G4a+04kvhxPvgRsrZrgGz24YbLnbsP0xD5ZPaNj+ivf0pfWNguJ3v/vd//E//gd4LDbffzhr2+yB4p62ZUJtwX6wdb/UJ6SYDMo7fci0HAql3n/f98ijI1XV6+Mf9Ta0zCxcC6T+r9L5C72vNZ5jxFVXFFd+iP/h2+y3B3wD08vTs2vTjhkTI0tAxBrBo3PGNLf+WYyxp09pbXVlJBryKCSK/j5qTTni/DGwrgIrLdLdm2JUt03ASXmcS3NfsOL8c8RiQh3/ZNJtA5wD3n3c17vd3l3Ih7r/kYhbGDTUBwKN2iIStui0LT5vSyHbMuih+jEAO5MJKCY5NweFE/+2BFaBUB2Rz/VOFYdPa3Nz5RMsNzNjezDLgaXqDstd2mE508dZTqF4rMBy/3Yf5/69gHM/3I1zZsvbAOccjtMu9wde7zW/vzQYqgpHGmOxjkLYDQ8+78gIA3wN4CuZmtLPzNjn5nwLi7Hl5Qy0Q29xZmF2cXocahlfLA9T7BZol0WK/eKh0ARje0+gWxMPmNNRZ85vTHn1iYApFbZloKEpBU5O+sd2e3nh640VL64t5hZygcmAMWcUxUXUILXP1ddiaSnXll+UXQR0Bxjvd0O/+1H/j4rg9wPcD54mPf3i8IsHOAfOis4CRKzUV7ZZ29BuNC1IA69gyBm8E970XBqw3+ekRSytb0yvro0tQbu84nNLkdnFwMyCZwpivJ0tXsqRKWl2UpSZ4KXGBw3BIU9kx4NmV79QtnuG7I5g+b4+cQjF8SMobjjBhcTZcQQ7gWgfItuZNDuX4RCxHTKBXSV2aGV2g9Jm0trNepvRYNGYNXKLRGDhcO10hpNM8xLIfiwhAMe4W+DWmi5jeZe6FCG9i+ZXYplV/ZRqQn8lAVlOaL8zUH8DV34Jfe088sLJ7tOH2o/vazv0WvP+l7e38xXAr/HN5xr3vdD89kutB15tP/pm58n9iHNH+y6ewV//gFR+g95QwetslmLgetqgXcAJ6jVJtxOK4KWTs/kxcDVb/6K/E21urgLMW1ubWVnJLy+PANILRVAA5wDXPchQ37xw733e24a9QiZndzyOul+apZjJKRwfl09MaCenjJMz1vy0PT/rHp/zjs0HRheCuYVwZimSXIzGFkLheV9gzu2dtbtmLPZpo2Vaa5xU6Sfl6jGpIiOWJUSSqFAU4It8AoFTILDxeWYRTy/na9R8hU4oM4nEVrHQLuU55ByXggVVWyn+1oADBcsr43ilPK9E6BOLPGKJRyx1i2VusdwpUTjkSrtcYVMrrVqlSaPSq9RalVat0MplGolYLRSq+Dw5hymm04U0EptIYPT3D+P6abh+ChZPwgwMYjV8e/ETfY5Fg1xTFdfeIPLA5EGELoY1p8nOMWZgRpKYU2cWzCOLrvHl8NRKcnZtbHF9BnDu+tLS0tzc3NTUFOC6XC6XTCaj0WgwGPR6vQ6Hw2Kx6NU6lVAuZYoEQxwmnkrtGxzsweM70Og2BKKxq7OurbW6qbGyrvZedUX5vSKDAUFQV1bZUFbzEdTdboGg7nYH/nY3oQxBLkfR7qHZVf38aoKkjqxoompa6Lo2hrmDY+/iuXr4/l5RCCWKYSQJrDTbLxsjyEcHpaMk8RhJMDrIGydzxkmMcRJtfHBogkie6B+cxBOnsP3TGNwsGjvZRBivIe04d48WvyXcPTNeM7jqimz/XO7pS+ubA8VXXnmlubl5aGgIgCIYguPiwcOmPVDcE6S1pa36f9vyc7aHQKvzUA2b6Yeo5vKSx5M8e873g8dGauvWJ7b3wORWVlvjIz/Xe/9L6bwZSFFSzhpDzVODT/2B+od2a3tiNpFfGTNPaYYyaGS8iTtC9c05lzb+5pqtf6XaxkKlVDmAHq67h7xwAqy3Ok/sB6srMaoLwkKve/kb6FK4ugo1hS+2g3+QkT1bEsmW27UVDm1l0lvgp2Lx7/TNNZtfA0gmkf47MHQgKT5CB0UDVAMUB22BM76s0z+r0f7qflzuo9RKcCxXPKpU/ViteUqr+53B8ILJ9LrF+o7NfszhPANYDsqlBCwXrAiH6wtZlD3JFCadIeZytNFRzvi4eGJCNTVlLGRO+u/jXH59/c/5aSn2ivioa3xwPObKAdIDvGeThiEOLCxq9WyfRRRyKqM+QzJszyZ9Y7noZD4zM5NfWJpb+SYjgTvsZxoxSZKS4dAw1oMFV6EqfdV15XVAd4DxAOk9Pfj0o7hHi/gHOBDQIGDCE4ITl2SX7mrvtlpaATECbhQnxOB1wKuNLIwUSzED+Fvd2FxY25hZXcsXSnqkF5Zjc0vBmQVvgfQs+VnD2LR6ZEqe28Y8VnKMFh8lRUcGwjlsaDt1s2hMMNMfyhGDWbI/S/ZkKI70kClJUsWI4jCBGcAQ3L0IO7LdJKBwpUyGjM0smtzb1Hr27Z0hsIhO08hVJqPZaDDq9QadTqdRq5RSiULIl4lYQj6JzcbSGT3koWYCsRqDuYNCXu9uf7+98Uxr9bHWssOtNw+1XTzQdv7t1pNvtBx+pent+4Vb3n6xaT/wS8DbM4X4HlTB5d0D4KKEuXqeWHqFWn2H3VYv7u1Sk/otPKZfq0x5XBPZ9OrKZ+fofpzucoDuCunKgdlZz/S0fWrKPDGhy+eVY2PFaB47m6Wn00OFgB5uVwO9+4wXaQ9HuyIxRDiOiiTQgSCi0Ccdu8uY3UPAhNowXp4ji7Mkfo7IzhGGswPUDA7cngbTvYQUAp/sxiY6+hJtvfEWeKwBGW7pC8Iwvi6cBzHgQpPsAxTLEN1A5+i4Aq1YrFZI5Gq5VK8Qm5QCi5JnV3GcKpZLXfi9UAMzPQq2R87xANgTC31CcYAnDXLkQY4ixFEF2ZoAW+PnaP0cnYerc3M0do7azFHo6WL5ME84NEwnEolYLBaNRvcWhEL29iFRaGQfDoHuR2CJcDypG0/tHKDDiOzWQX7zkLiJJquhqSvpunKm6SbTfpPjusHzXxfFypWZWv1Ii3m0yz7a68rhPDmir78VBe/sAe4pGMfg9bsi3Z09H7kDzKN7BuidaEpH72BbN76lHd3Uimhs7Gmo76ivaa2raqquqK++V1NRXnlvF9QVVXmnorasurGsrrWssfNOS09pK+p2O7aki1ACJ5Ugabf6mCU49m0873Y///aAuJQoLR1UlpE0ZSTD3SHLPbKzYshfORStIY/UDY3XD041DCw19y+3YlfaMKuwvvWO3o1O5GZ3z3xXz1Rnz1gnPN0Oj7YjAjCEqw1pbe01tKJULX3S5j5BE5rViKY1YkkN2P56XF8dDl7X31Hb31wzUFdDqKgm3qsZvFNLLqkdul5LuVpHu1g7/H49470G5rlG1ulG7skm3vEWQWMrrbuB0N5AbGsYbK4fRDQSVZ39gyjWjiVDYqi10p6+In1zoFgMIe6AYjGoWHzqodIeKO4JkqoZKmOzWx7qlou4ffyX1pLLlTh50v/YY6ONTetTUELjwvoGKTfxui30TzL7EWd0IBXrdiDBIuxJ4pOV+krbmC23nNJOSAdS8L54q3iMFZn3rz2gm+3fudZWlkcihWjhAJpWdxf5HoSF7cW9hYh2K/crSiL9fG1sQJmisRiU/CmXQVFELBriQBQS2iVYNAoBJYsieqDJ3QZ8+LertbXZxcUE4K78hCI3wgBUFo22+QPlLveHVtsRgHwazdMy+SOA8WSy7wH2M5pff5Dl8u9EY20TE8qpKcN2XG4hWkhIAyD3NUSDv0iry2sLM8vTY1AwMBuZSHhHw7aMV5/8RK8IA9dvk4Td6njAlIq6cqnA/S4RhX7xm39WgVx33q3JaHZMD9Or9dW7Z4Cnl6fBmQtrC7n5bfYD5EYL0TBuDMwKAxeZa4prZ0Rn3uG88wLthacGn3oUu81+38d+/+fEnz9HfW4/ez84ASAiePFOWyfeix8OMYQJuTprtowH/FO5+Pz8n4d5EOOFs4PRHCU2ykiMcVLjgnRekp1QjkypspOa1KQmllf7R5XOEZkxLVEmRcIodzhIx7upcMcw3MZAWNlIS3+Vgtqu5mD0IrJJzjJrxDa9wmbUWI06s8lgNhSQD6jr6onmA6+0Hnqt6JaDr0J90u8PgRvffgFZ9WHH+YNtJ99qPnS/B8PBFxuPv9J85o2WC/taL74Du3Kw/fph2PUjsKuHYBcPtJx7q+nYK+DktkOvd717EPXBu/03PhiquM1qrRUjOzSkfhuf7VXJolZjxu/NpxLzk2MrS9P3q7yOFoLesfn50Oyst1CIxTI5qc/nVYW9ecKREU42y8gU0jWTSXwshorG4OFIB4C0At21hCOwUKQzFO0OxhCBeK8/3udLYDxJjCuJcSQxthTWnMLqU2hNEq1IocRJpCCJYCe66YkOchzWH2/uA/wWq4cXsl4/YZ3EYOB5jPet53p0bO/OEFjHcSl1cq3NoLOYdAaLXmvTq8C33aGXOvVil07g1vHcWo5bw4RaKUCwB5CP6YHYj+tW8twKvlsmcEslXqHUx1cE2MoAQxOk6SNDphjJEh+0xAYtYaLZP2Bw47U2tELXK5TBmbxuCr2LSO7GEeBoPBKJRsF7MT192C70QBdusHuA0jk43EFmtdO4rcOiJoa0nqmu4egquOY7POdNQeCGKHpdmryuiN9QRm6r/He1rgqtpVqnq9XJ6vSces1QoxLbIO1uEDbXc6vrmKW1NAA8H1YTz1biTt5DHb2HOFveebEMdu1OS8mdxrI79XfLau6VVd0rqwAq36VOOIKtUG0PCgKw11baUIza9d/uGSxBUG6h6LfQ3BKcsKRfdpugLCVqSweNd0jWsiHXXYr3Hs1XSQ1VM6K17EgdM9zIDDYz/C0MD4zh6WQ6uxh2OMuGYpsxHCOeox3kaUl8DUWkooiUw1IFXa5gq+UcjYyrlwmNcpFFKrXJFE65yi3T+GT6oMIUVtpiSkdC5U5r/COacF4XmTDGJizxSVtyypmedmdmfLnZ4MhceGw+ll9ITiykpxZzM0tjs8v5+ZWphdWZpbX5lfWlv2gG+57+SH1zqPbhhx++8sorRVAsUiIYbj/3MGkPFPe0tTwDtcRI6LaHQPOjW5Lb0PxfWgt6Q/zwEf8PHx9rbduYmQFXWeXk7HlP/FtyxzOmQGc8jQ/Qj/KOPtH/xBXFFUVaHl0Iysf5mEQHNtmpyAuSi9ENqDXGnra1trycCwfdcrGiv49WW444fxys6toOvzFQconf3WpmD8cd1i9bcuYLtbCwlclAWwe1GqhyDHEAShYFEDhE3hIJt0zGrVAQajn46dSvr7aYzV9OGxsr4H0AqDY5qRsd46UzhFi8Oxiq9niv2R2nTOa3tLrfKZRPSKT/IZN/T635hcH4otV2yOV+3x+4E4k0J5N9udxwPi+dmbEtLsZW1wDGb4JJqK/AfdvtJ5SqH+2eAZ5f+IzOol+H1tc2luZXZicWJ7KzI7HJlH886swB0nOpY1ZJGLBfEQK3uwXKI15dImTNxD0jmXAecCOgR8CQgCS3X+6r1hHekZ8M/OSXpF8+Pfj006Snnxx8EqDdk8QnwUHxGMAeOKEIfjsuZH6+9A7n4Luic5cUN+9oa2qNHTAbFukeHghIqBEjO+4VppOfj3lFxgOT1F2MJy0wnnZ02jg+Y5uYdU3O+abnw7OL8TmARMsjiyujc8vZ8YVUcjbunwxZx9zKjJkX11BCEpyXh3AyYGZKk55YpxqoURDq5bQuFRevEw7pZSyjWmzWys06lUmvNeo/3pZdo1apJWIljyNn0CSkAQEOxYF3DLfUDVaXYks+RHxwov0k1L2m8/oxanWZHI96kDvPHOjFXochzrXBTne2nu1pvYBpvUJsvU3vrBL1tqpICBO73yEd8upZEbsgGZBmYsrRjDY/pp+cNOTz6gLdiUZGuJksPZ0ZiicHYglMJNYbiXYDutuuvxJuCUXagpGOYLTLH+32xeCeGMKdQDriSGscaU4gDAmEJo5Qxrsl8S5BvIMda6PHW8nRJmKsAR9rwMbqP0F3yHhTX6INl+wkpBCkdB81g6NnCewRMn+UJhpjysa5qrxQOyEzTqqs0zrHjMkzawvMucLzvvhCOL2cyMylMhPpZDYVi6XDgWTAEfdaok7dJwu0fNpQgRapWytwaIROtcQlFzskEodA5mDL7HSFg6pyDGqcBL0HZ/ZjHJE+f7IvkO51RREWX4/O0aU0doo1nQIljCmGUQQwIrcdx2rvpXd2UrubKchaMrqa2l8xTLrLYJZz+Le5yhKB6abIe0OWuKZIXVcWHbmmcF5X6K/LpddkrGvSwWui3ut82A1O3U1Gxa3hO7cpN0vJ18qIl+8OXLyHu1iJvliNuliDvF7TXVrVfreypbKisepebeXd6oryivIyiOV2q/xOWVVZRX1ZVfOdWlhpQ3dpM8A87G0oiEcu6R0uQbNL8PySfvmdQXU5hVWCG76DYd3Fc+8SBBWD7IqBgQqkvIYmrx1W1AHTpaWkNEG7EhpbS0yuj85uTC1uruzdyvf0TegbjekBSgR8WNRD21NxDxT3tCW8s0XYt31clAm+Ff4LV1ueV6lj+9/x/+jH452dG7Oz0cXlynDmUY0b+F4oRYwoLskuAT48JTw1HB72zNrBDR4VbwE3ft2kbGT5YxVQ/25VwMIAhIV41M7ewtaDUDt7XmezkUGJWk0zAMm+PgHYA68PwM9s2hILoR4SKCQUKuzHQW3l1SooazSd+ttIm9ncXF9dzS8shKenzePj4mx2KJHoDYcbfL4Sp/OcxXpAb3hOpf6ZVPbfEul/KVU/0emfNVvedjjPen03Q+H6eAKeyZLHxoVTU8b5+eDKyvjm5sNYTQdqF7G7Z2AoH3N/tDMQgB9YDReXxbuTQiOfTgr9RlrGTy9P+yf9yrRy0D/YZm27qbx5jH/spwM/+w3pN68Mv/IgP97/+HlZXbNdCHNpu71OuC9SxLxPx/E+k/FCMws7jJdfXp1ZXVtY21jZ+GTwc3V5fXZiaTw5l/JNhi2jRfxTU0PSfg+v18bqNtNguqEWNblVSelQ0ntVPIJWTNfLeQa11KhVGrRqvVb7UZt2vV5vMpntdrvL5fL5fA6j3qKU6fkcOXmAi+qgwqr6q66jbpyDXzwJ//B4zwfQpaDopsOF0N/l/S13D7e2nYIhzsMwFzoGPuykXIGzbnaWHO8+d7jv6tmiUZfPQL0c7g+BWw68zObcCcdQoSg8FO0MRdshritkZgbDrf5wmz/a4Y12emJdzmiXPd5liXUZ413aWIcq1iGNtomiLdxoEyPSMBxtoMQaSNEGQrS+P1aPjTagY/WoWCO4qmMSHf3JnsF071AGM5ztZ+UGuSNU4ShDMsZW5AWaCYl+UmGe0tinDa4Zi2/OEZr3RheCycVodik5upydWB2bXpucX59b3lhae8DvFPhpXF5YBT/VE7mZbGw8EciFnSmXMWpVhUyygE7g1XI9GrZbw3Crh91qhkvDcqnZTiXbLmNZJRwrn2VkMfVKcSAXnVhdXnuQTeIQxRgCP0hw8L0wBWBqV6vU1swztTD0zWQNrF/V3adEdcsJrXJanUxwT6YvlTtLlIFbqvj1j2APOHZDEbyt9pRpbBVqfbVSWS+XNEu5rSJmu4DWySN3swlw5gByGNtLRfeSUb0EZG9/Tzeqs72nvRnWUt9UX1VbXVF5t/zuNuHtCCK9ew3l1a3ldR13GuF3WlC7Se8WmlPSL7hFkJWSNHeGjOU0eyXTV88Pt4pTXcoxtHGaaJ8Zds/xA/PK2KIxveQeW45Or2bm1/JLG3Ormx8Pps2p01O0wI4nBzzj7abdM8BrowvbZ/99K7K4bJtd2LE2EmWzhnfPAM+u71H0V6aHMfnzL6s9UPx719zoVuX/3MrvCjWMurfkFVsbf7FEzTmpLPrGm4Ef/yQPh0/OzvWmxp8xBf6P3H7OE8fEHJX66ieJT+5j7cP6MKYJDWdkCBFrpGSwlint5OpHtW3+DlWsROqWieQQFpYhzh0Fq8CWA69gr77HgTXoaaSwWT89koNW+l+HwMtOT0MlQx32LYUcKi2Dw2ynj1KHoJIzFvNWJLyVz0OJpn9V2s7/nHV+Ov/TZjtazP+UKx6RSP9DofyhVvcbk/kNu/2Ex3MlGKyMxTrS6f7RUc7kpGZ2zrO8nNl4uLfIrq2sL84uA5YDRAdWvYDuwvYsID2nMgaoT8/5qHG8keu3SQtJoeZ01JVLBwtJoSNzc1NLf3ZS6J+nudW50FRIlVZRgpQOW8dt9e2jvBPPUp9/vP+JYjDwif6f/JbywmusY4cEV85K697ivv8m+92jgovvim+dk5Yd5198jbYfHOz4qPC2KOX9HMb7HBXxbyw5C/AvZB51K9JmblQzHJQQXAKsjYMyMeA6Wrea1qOi9yrZeJVgSCNl6xQinVqu02p2s5/eaDBZTFaH3en1eEOhUNDvD/q8AZfTJBPKWUT+YDcVWYtvuomu/rCn5FTH5cNt773dfPzVxnd29Wc/9FLTmddbLr3dUnIQVnGso/F0N+xCL/IabqCMzKxiS1sVTpTeg7EFBrwRQjA6EI3iIjFUONodisCKsKeT3dOLGs0yeNFiel0/7IOdIbCKV0Y33QV0R4g1EuJtxEQ7KQUvhukY98N04jGWbJynyot0EzLj1GeE6VJL8dxyenxlZHI1P7s2vbg+v7L5GSUoP63F1fWphdWRmaV4fiEwMutMTxtjE6rQuNg7yrVlGMYURRMjySJEUXCQ7ydzPFS2m8FysplOAd0hpjtkdKdy2AWxH92tYrrUHJeS65Jy7UKencuzsfiWYYGFLDARxCa8xIiWmRAqS7fO2W3ywm1BpCuK8iUB9YHHXncM5YgqxAGbPOoxJIv2AusTO0NgLcenGDR/hHwlqsRddaRWFWxWeWEKZ7fcipQZ+8RajFCJ50vwHB6OwcLTaFgyCUPAo7B9PcgeWCesqaWxpq4KIr1PxvTATHV5RX15VUtZbUdZgfRKYdiSzl2khxfeIkhvDqpuU4x3hm33WN4qbqRRnGyTj/ZoJzHmPwn29rS5ubYJvingCrGxtL6+uL6+sL4+B+4Xa2szq6tTq6uTKysTKyvjKytjy8ujy8u5paXs0lJmcTEFbigLC/GFhdjCQmR6wpsfdYymzbm4LhVSxr3ikJ0XsLB8eqpLNWiXDVScPwbVZ7rvtsMv9374duNbL+y44c0XaHLu9te0py+tPVD8pPZA8e9djAtbw2e2j4E216GWGBnz9vCb1ObmLF8QefmVwM9+nutDszPjR5zRf5TZX7WGuuORNhv8edrzf6D8od3eJhsTDGcHAB8yc0TnjPmvunjp0vxcLhzcHvwpWl1a3G5QAbCw+k4RC5v3v4S+fI7VUqulEIIG7WQ2vbm7jO1XqMXFrWx2y+eFukrwuVuDBGj3ILx7i9APZZNqNVBmaTYDZZl+CXm917S63+5YrXlKrnh09wzw9LRl++w/URsby8vL2UL+p3Z0lJtOD8TiXcFQlcd7tZD/+SZ4cYXycWj7n/wR8KkNxpettiMu9wf+QFkk2ppMonM5ej4vn5lxLC7Gwcpg+3X/Qkr6xwDO7RiK78kju2eA56eXlhdW5yYXJ3Pb5WEA4H26PAxwISk0uCspdDQTntiVFPoXqBm7tL4UnY5qs1pykNps6biiKDnEO/EH6ouP368L+kT/T39NfvFFxrF3eFfPyxru6LEdDj4pbFPnRlyTc+HZxczC8sTy6uL6xrA5MuQO75jp8CmNpt0zwO7IxxKwV5fWZ/NLY4nZpHcC4J9LmTLxwxq6T0Z2CQesPKyRjdax0BoWRs0ZUPGH1CKGWi7UQuynhsAPwj+tQacxGLRmk95qNdrsJpvLYvVYLS6j3q5RmhQClZjKZ6NpxBZCbzkKdqWn4Xxn1SlY2bGWmwebL7/d9N6bjSdfazywU+vlhaZjr7Sce6vt8oHO0hOI2vOY9suEvtt0Sq1I0K7W9trdpEiamR5hZjLURLI/GkOGI+3BYGMR/6BwX6jFH4G5o+3WSLs+0qaItggiDexoAzPWzIy3s1JIdhrPH6EYIrBgGBYKt983OG7bNWz3h1qsadX2t+mztLy2MbO0Nja3nJxcDI3NuTMzlvikJpyX+sd4rhzdlh40JbHaGEIRaZeEGvj+Sra3lO66OuR4n2g9jTMf7TO8Dde92ql+rk35TJPi2Ub5Cw3yl+ulbzXIDzbJ34Op7/Rom/t0qH4jZcjCodpEww4F3QWBH92lYLiUHLec55YKPWKxhy/xsGVehsJLVXnJOh/RFOx3xHCeBDoAhYtR/lSfN4FxR/GuyKArSnFGmY4ozxaVmGNqfcykitvEEQcr6CZ5vWhHoMMSrtHH7miSNwohvuuyYIlEwtcp5c4dyyReJce3ewa4v4/Q1tjcUFtXXVm1jXcfFyC9hvLqlvLajvJGeFlzXxkMVwpIrweQHr0EiukJS4iyW2TlzSF9Cc12h+m+xw1VCxIN0pF29QTc8PDAHoAocI0tlg4qIBOAJUBKaQBI8/NhcPGMx3vjcWQsBgyPRnsKfR2hg1gMcjyOGBnhjY/Lx8dlY2PSsTFJweLRUdHoqHB0VAA8MsIH54yMcEdGOLkcu2BWLsfMZhkF07PZ4UwGmAZ+CzIZSjo9VDA5nSYBR1ODgQTRGye64gR7bMASG9CF+zWhfkWwX+xB82wolgkxrOsmqzoI0ja8sAXNaexlNcKptV3k6g5iFQxf0YK+24gsa+i+09BRWt92q7bpRk3D9Zrqq9UVlyrLPqwqeb/yxrnKq2cqL79b9f7xqvNHq04frD61v+bYW7WHXqt/5/6G3ge4bv8r1Qdfqzz8pop92+9t2nHQ1xQGv7y7Znzuer6WvP1939OX1jcHinq9/n8Uqtc0NzcXs0/BTPGph0p7oPh3rcnYVsX/szWzK1EzrtjSw7aPvzFtbMyw2JHnXwj+4hdSAvmGN/5fSudPdN6acALuGz7EPfQL4i9qzJWsDIWc7kPGm3gjVP+cc/mLek//VShmt2Cvvrc9eLBWFhezQf9O30L4WQgLm/a92PfhaUZTlYbUH9Cq8qnExtfR9QG8Zj4PlRW1mKHAIJUCNaXo6YJaUAzToE4VNutWLLo1OfmVhwqt1oPhcD0AuQdZo3l6YuJji9T19XmwFpmddU9MakZHOffxrxowp8Nx2mLZb7a8XbRU9m2V+md6w7MW6ztO5zmf7xb4XIkEMpMdGh8XTU+b5hdCKyv5z+w59hDKpY47FbGAKf0gG9g+9f1g4Ccrhfr/Akmhn6nljRX/VFSU1KK91Bpjxwfy0ne47z5DfflH/T8t0uDj/T/79dBLr7JOnhTduq5uabQSBgIySdoTnJkZXVqZWV37Y6J/KpHRbLJYLbaiZU4xLty1PbRCNugNQopeQDTwCDouQcMlqgRUtZitkQk0aoVOqwHcp9WodBqFXiPTa2V6jUSrlWh0YoVOJNXyBVoOW8WkiGlY9mAnDd9IQN/F9N6EIy939VyAdZxpa3u3rfVUa9PJtoaTbVXHWm4fbLq0r/n0G00H7pf3PPBS66m3Oj48DC95F1v74WBHCQNXLabDdFKU3dgf8gwlImDJS44lsNEYfCf0B7FfsAlgmzcCc0RglgjMEG1TRlqEkQZetJEfb+MnesQZrDRLUo8x9RNS67TeM2sLz/tSi7HR5ez02uTSxkd/1plfWR+dXQ6PzVcbpB1uRbdXVTTOb8YGIjtD4DaX/N0h2X6E7tUuDWC53zTKn6yVPF4h+l4Z//+WcP/lOvsz/Z07/B9WCH9eK/ldk/zNdtVJpP4Sxlw2YG0i2+BUG45uH2LYWCwHj+0Qs1wyllvBcKsZUKc+JbNQxpPvFYl8fImfKw+w1KFhXYRiipOtCaIjOeBK4p1RvCc64IkS3WGKN0r3Rbm+mNiXUPmSBnfa5kj5LNmgNhWVxBLscIrky/S5Rtqs2RpDulSTvqnI3JJFbwn8JWznHbq9athQP6RqGpS1EvitWCasj9KKGGjp7Gts66praqmqrav4iPeqqqo6OjrQ94UZJOPF8u1BQTXVNYTbcOEtAiA91U2q4SbDWsJy3+EG7wnj1dJco3Icpp3o2QV7vMC8PLrwEezNQbA3u/Lld+sVKG4JXC3X1qZXVoolgrKFGrAxQHFzc35wCS0UCrIWmjrqCrWC5ADVCngGqIwJAKwAXaRksj+RwMbjfbEYIhrtglqAFH4a3cFWW6DN4G9X+jol3m6+F8HwICkeFMGFxrr6L/Y0nq4o3/GFW9dKzx7bPXO6ouyqiHzGqj5tVZ+waI4bVce08qMK6WGp8IiQd4jLPshmHqQPH6BQ3iENHhgYOIDFHuhDH0D2HuiGH2zvPNQGO9LcfKix8WBj4zsNjfsbGvc1NL5V3/hq3bY/+OD8lfOnrp8+dvvU4bLj79w7sq/64OuAzRp3oVothGqvVx19u+LEO3dPHSo/e7T8vVNl758pvXi+9OoHt29cLim5VnLn1u27pber7pXUVpU01N1ubrrd1nq7q+N2D/wOsvcOGlOG6y8nEO+RKJU0ejWT2cTntEs4cBUPo+MNmHhkG4/u5HI8HKGfJQsy1OFhXXjIGCFZo/3OCNoTQfrCnSGogQogZ/SDHAy128O7akzs6cvpmwPF7373u0NDQ+AAICI4AJS41x5jTw+dSIe2+Le2j4HWFrekZd9oS4z19WnacPiZP2hfermGzv+5zvMfSuclX7w7KP9A+sHPiD+7rb9BTuD6k919iTaoeOlC4G+seOlngmIBC31OiUCGRRaw8Ah039r3Qu+Fk8P191RErE8tH4tHv7DT15+j2Rmo2aDTuaVSQG0M8TiICRE9UMyQz4Xihz7fVi4HdbT/+mW1vhONts7M2B5kjfZJo+nVYvUXpeqJQoOH/97Bv8L2vxuhUG083pPJDI6O8aem9HNzvuXl7Od0FPyr0Mb6xvzM0uTIXDYyEXPlAO8ZuH49x2/iB+47aBIEwYEZeoQOdExfNja5vPiX/PUBFAdYLj2/oBsNU8LKDgf5trb9jOT2W5x3fzv08hP9PyvS4I8HnnyG+uo73DPvy+9UGDqR7iFuXOPIR6ZXlv6kfNbNjc35qeWx5Gzclfdps2ZhRE33Skh2uVouM0l2zLYPY8Idu2fkZqmUJ5EO88VkhphIEfYTeCgUHQGjdtcS2kv7mi911Z9tbzwFg73bhjwH63+/nXKpk3Klk3qli3K1nXARhn4P1nmmrfZEW+mRtkvvtJ5+s+ngy4Wl5wutR1/vvnC479YZQu2V4c5yPqZOTmkzCRAOZZ/HhAm40dFoXxjg33aJzgL+hVt8hdCfLdJujLapI62SSBMv0siJNXNiMF4SIczgpCMU5ThHPym3TRu8sw7/jD8wFfXkk66REXtqwhCdkBXCdzRrul+fQCqjxdhdKd314aDtFNYEMO/FdtWvG2U/qhL/dylvN9ExxB6NJKiXhrYtCuo4vo+G0pBcGKgfNrWIgl2ycJ86RjAmaNYMx5kROrMiS0Jsikr0Iak2KFH6JTKfVOKTCj1SnkfG8UAt2hlQf3YF0yPleMV8n0AU4EqDbEUYrJfpugjNEKaaIkOm0JAlOGQNDNsDTGeA5/RL3QGtJ2h1B3zOYNwWyBm94yrnpMA0w9AskeWL/aKFPu4CnLXQObwAo8w3k6Ya+nP16ERDb6gB4a7vtDR26hrbFQ2tgoZmRn0DuaYOV1WLqKiG3a1sKK+o+HiNFjCAtu2VVcPK67vLmlF3YPjS7sESBO1WH6cEL7pFVN6i6EqGrXfY3gphz922ynsVNVXVOwboWFNVs+PysnLngOCLYG+jkM24tLY2t7o6VWjjASguU0hWjBZqvfpmZtyFZh6WAsVp83llIewmHh3l3y/6SgMUV6z7mkhg4nFUIVjXFQ7DivWBnMEWSxCmD3Qo/V0iXzfHi6B7eknuPrwLi3YNwF3kDhetxcmoc7Irnfw7duFNu/SyXX7Bqjpr0xyz6A6aDW+ZjC8bzc8ZzL/VW5/UWn+ssf1Abftvpf3f5LZ/FBq+xVH/B0P2GF34Cwr3dyTGSwTq2/3kI5j+U72Y9+DIi51dtacPN7z5/Of6ufqDrzUC75Dbwddbju1rPXkQdu54+/unOi6e7br2PvzWFWTZDVRFKbamHN9YTWxrIHe2UBAdDDSCM4AVkAbEVJKCNazhsU0SgV0lc+vUEYsx6XamvO5s0D8aDeeT8clsZmZsdH5yYnF2Btx8t9+HB2tjY2V9Hbw7ALBz4H0BaD076wXvCHg78nl1obcKv8DSUOld8P0vdM7shCruhlsLgVMkQGvwVCZDzeVYgL3B2zcxoQEfPjlhmRy3TY7aJ3KO8ZRjLGofCdhcFvCxzaFg00cGV4ZPDG3S7S9uT19a3xwoAj4stscoHhQLn24/9zBpDxT/fpWxQsVOd5c29dK2HP3bx1+3wD2QRHL94bn2D6+/LND8o8x+0BFpC9rK9JVPDz59RfMhNgpHJ2C4ZKcyL0wtxv5mipfaBBwJumfHww0VnSf3M1tqyBW3cNcvIM4fazvyOrhHgvsiOKbWlCv6+zwKyUg0tLb6R23a+RMEYG8kt+X3bel1WwIe1K0eACHAQiwaalChkEHNKuJxaOfh17/fbGNjcXExNj1tGh3lplLYcLjB672mVP5QBLVr/7il/w4ZagwItQoEJFmo/uJfXh7Zfq2/Ia2trM9PL+Wzs5lwPurM+fRJmzS8s1HQwPHbZVB+KXgqbMuErJmYeyQdyAN6jNizAA7BwW6vfJ2UCH5EFtc3JpZX0wvL4dlF+8Q0PxlE+5V1FvJlJeyY8ParrNO/pbz244FffAfz3e9hvvcz4lMvDr9xXHD+pupemxU5FGRqs8bUXGpt44/988fGxubc5PJofDbmzHvVWTMvrhoKiPFOLsrMQGrpSBULo+KToUigUqLRanRqhVYhVMnYUhTzXsfgpc7By7v8sWE78SKaX4dQ3EUaqxH26h5TRZf6TifvRvfQ1R70RXjH+z2153runO66cqz97P6Ww1AUouXAK52nD6Cuvku4e5HWXMKDV0r769W0Zj2vzaJscxjbPC7Afq1F9guFm33hNk+k3RFpN0fbtZE2eaSJX8j8ZESa6LH24RicGsOQwgRCiIbzsTEuAcIq7jQomxWaKpH+Nsv0AclyGmc+1Gt4o1v7PAxivJ/WiB+9K/jP21AQ7/+WcL9fLvhJtfhXDbLn2pSvdWkO9uoBEL5PtF6nOsqZbkCJMEkQoYzgdXGKJcVxZqW+UV1kwpac8udmExMLY7PLs0vQG6EUBry6ZMw18iAbuEGBzCfi+6Q8n5ztVRZKGQH2k0Ps5xcK/UJpgCcLcBUBjjoA9e7T+Xl6H8/gFursMp1VozWa1Tq3TBkSi9M83jidPk8c3Orr20IiNnu6weUIPG7C4Ztw5EYPar0LtdjROwXrzbYgoo1wb0OPta5bW9Mpr+3k1bYzattJ1W3YqhZkVXN7RX3jverq8opt4LuvivJ7dVBiZ31XeQvyznalFmoJil2CF9wmyO9S9DVsW6PI16GK9hpzA85JWmiaEy10eE/Pm0YWneNLwcmV+MxyZnp1YmpldnJluRiOg0DOPzjo6iG44YMFE5WNePSddnCwY2d3f8xEKORAkpLJgWJvRoANhcTL7QYe/lCLM9hqDrRr/R1yX7fQ28P2IGhuQHFoQHF9LgLcNdTupDU7mbUOzj0Hv9QhumGXXrLL37Op37Vqjln175gNb5pNLxnNz+gtvy5Q3BMa2yNq+7eV9m/Jbf9bZvufUtu/yG3/oXR8R+V4XOv6mc79K6P3Bb3zLbXtoNx4Uqp5TyC/zBXdYPHKh5mVZEodgdiEw7X2odrh3d0dbd0tDfD6akRVGaLsJvzW5Z4r73VfONV55gjs+NstB14t/i50HH8bfvYI6oN3cdcuEEqvDFXepjdUsNvqBT1tUjRcScBQqkv7Lp1FXzmPu/4+7uYH2GvvIS+cIJZdJZZdH7x7g3TvFr3+Xi4YWFn8GuvZFGh8ZmVlDLx9CwuxubnAzIwL4PfEhG58XDE2JipQNz2dJieT/YUumj0F0m4Cj4UQXx+YB8+Cc0ZGuADUAbGDjwWvAF5nZsozPemeHnPls/bxpHUsas4GjCm3KuGQRC28sJHhU1GcUqJdgFfzsFI2hkvvYzL7KAwMiTvQzyNiBCSkkNItpsKkw2+Lxd+RGnf8fyWmb4ktu2cek+r7lZrt/9WevrS+0YiiXq8vNskAw2KfjOJTD5X2QPHvV9iXtlRN28dA86Nb4pKtpa+5Yx5YUK6s5AcGiEdPHYah/o/E+muDry4UrLfCX6C/8L7qLDLUhow3E1NI3aT8b7J46cDty83vvLzThezTbt7/soXLWF3+SkN2GxtQM/poZMtq2ZJKtoapUOIoYEIkfIs8uCXkbxn0W4HA1ugoAPjtD/mKtbGyMj43583n5dnsUCzeHQjcdbreM1v2abS/lsm/J5X9l1rzC6PpNYfzjM9fGo22pTPEdIaQy9EmJiQzM9bZWedn+s9r5v6waXV5bW5ycTw9kw7lI46sV5ewSsI7vQRNgqBLFQuY0wnv6Ehscmp0bnF2eWP98+h9anQeIOX24KvQ2ubm7Or66NJKYn7JP71gn5jVjU2LM3li2NvqlN3WDb6naD8uKtvHOfcc7c2fEZ4GKPh97PefHvz1G6z95yUf3tNX97nRvBjPOmrNzmcfVHxytzbWNmfzSyPRmah93K3KmDgxBTHARzqZnWYaTD/UqqZ0KBkoJZeoErM0KqlOp9Vp1Tq1TKMWKZUckWyYxaPiqQQYAVuFgF+BdZ6Bod9rJ1/q4t/suHEUfv4o5tp72756Hn3l3EfDa++1HHylu/p893uHWw5BAQ3Y0TeRF47ib50jV15itN7kIcvEA/fkwxVaYZVRWe0w1/g9dcFQky/U4gq1WUNtxhBME26ThZqFkUZutIEZbaCEmwaCbX2+rg4HotbYd0eFvyIaOMckHiGTXkMP/baT8ng95dFK5g8q+D+uEv+yQfZsK0R3B5D6kwW6u0ZxlDHcdTxfke769QmqJc115aT+MX10wpGaDo7MpSYX8/MrCyvrn/NXHfAzs7q0Bn54oM4luZm0Px21hLwaj0XqMIjsGr5NybUrOHY5xylnuxRsj4LlVYigMrY69gOtZnh4CpdI45SpbWqNWa82WNUaj1odUaqySsW4SjWpUM3IVPNi1bJAs8pRLTNUixTVAlE1h1PMohTZDmGsmeutoVvKyLrSQfkdgqAUz7yNptzpJd5FYiq6EffaOypamisaasqr7n28UOfdsnIw2VxeD7vTCC9thRrulfQMlaKYd/GCKpK8nq5r5dq6Jd4+dYRozNBtEwLPlNw3pfVNWzxTTteUzzkdsU8lLZNZw0QeissVYkFCsOjPZpmFoBw5lSLcxzkor7LICcD2YCGp0t8p8nVDIOdBEd3o32pV/1Ni2/FjLMs5jGH3zP8rsx02Sl8zmZ43mH9vsDyts/1EY/2hxv5dle0/FLZ/llkBwgH/H7ntP5WO7wGK07h+rnODW+Sz5sCr1tDb9vBhZ/Rdd+yCN3HFn7wdTFd4Y/XuUIvN02l2IHSmPpUWL5MThKJBLofCGKYOkWnEfjqub7i3e7irjdZWT6mvpFSVEu9cw1//oO/D0/CzRztP7G89+FrjvhdaD7wKjsEMmAfwRiy9Cs6kN1RyYA0COEyKQaiIWB110Mwedoh4HqU0qNdEbeaU1z0SCeXTyZmx0cXZmbXl5T/yr4rT09bxcemOs1laNNqxewZ4dfULGjXdJ718IW82UWytWYi1miYmtOPj8vt9NSHYK+5L3HEw2ByJtEej3eCdBe9vIoFJJvHg7U6nB8GzoSTVkxx2JJmmBFMTZUkCTK6fMeyhk1x0jJWGMNM6TdRmA7XOQL2np5bqaTf19Gt6+gcGxjkD66SBfUTPfUfHe0MrfFkrflYj+a1a9rRK8VOl8nGl+hGF7tty/b/JTP8sNRff7n+UWr4lNf2n1PhdmeFRqe5HMt3PJJqnJarfSVTPS5SvSBS/kW//YDzI/6/U3s5Xb39T9vSl9c2BYnGPIhA4KB6n0w9jY+g9UPw7VUS6Vf9vW2u7aMQE3wrxto+/nKbW1m2zn/GHwM2lJRt+4FJ5/feYiv8Wm274Ii0u6lHBkTOK412hRjhYTmWwlmnt5BfdHv6q1V9yse/iaVL5TVLZDejx7k3wOFR1G9yVi4YdfiPusG6f/edpbm4rlYI6T6hVW2zW1gAeYkJgPHaLxYBySp3OrWRya8IAFsMAAP/0SURBVParbLO+vj6/uBibgkKCnGQKEw43eLzXbPZjBsMLStWPihVBdfpnrbZDbs+lUKg2kUSNjDAmJ3ULC+G/eCWYb0wrS2uzEwvjqelUYDxsy3o0cYs4VOwnoWN5reKQR5sA8+DZsdT0TH4ByhT9s6K5+pD53NAH24MvEvgMS+sbkytrmYXlyOyie2rePD6jGpkSZSaYyTFSNNflcdXZZPdM1JvanguyyqP8919l7H+a9JtHsI8A/5r823fYBy7LLzeYGnAenCAusI/Zcwu59c/d3rm6vD4zvpiLTEdsY25F2siOygl+Xo+T1mgerNb2VygHahTkNgUDpRJSdXKeXi03aDU6YJ1Kr1Wq5Aq+ELCgGEPhd/Yza3rppV3D1zq41zsVt7ut97rtFV3G8k7hrR7aDQT+Ggp5DdN5Hdd0DVdzGVv2fufVQ+jL54Yqbz/IsBP7aKTLFvU9m6XG7m8x+Vu1/laFv0USaBKGG3mRRlYUaucwEGpEuJubbbBKY3eJEnldhrkmGrgmoFzjM27yBLf58nsCfY3Q3irx98jDWG2cbE6xHFmJb1QbyVt3xe7m/tyOkRvrG+Anqgh+Y4l82pMMGwI+hcsttNo5JivDaKYZ9MMW7bBNS3eoCz3cd1vJ8si4PrHALxIFhBK/UOoTyr1ChUeo8oq0HpHOJdU7GOaw1JUx+EaK1juyOl1iZwhMtyRkDPWMODHFiuSI7miv0QtTWOuFuntMWcmQqITILsFTS1CEEji2tKu3rL2zvKWlrL6+vKai7N428BVUXlZWWVbRcKemrbSh63Yz6k47vryHVNFLr8HyGgiSVrKme9jYy7Dj2b4hfoQlTEvEObUoq+dnLOyMczjlHUqFCYk4NhbrvR+gg+K3zmCLOdCu8XdKfd18L5LpRQ95+wc8JLRnGO5hwlycRregyiUuc8qu25UX7Zpzdv0Jq+GgxfSm2fKiwfJ7g/Vpve3HGvujKhsUkVPYi0tzcPBtpfMxjeunAOSMvufMgddtoeMtLe+V3Nzx1Uvv1x17e/fMuVs3WjUKUXZclswqo3FtIKx3u402m9losGrVVrkYMJiZTdfTSIDKAJsJ4e2c9kZAaw8J3f3xKmbPFvZAfqySzeJisphAW2WmvWsWv2uWFv2qXvVtpXlnCHzSyGOb8IWCNwDRe7zhbnuw0xjo0gS65f5uwXagFUn2oPtd2D4HFu7AdzgILXZivW2wykous1JKrLQbZtp10/BNC/2GmXHDxLxhZt80c2+a+TfNot8rVU/K1T+WaR+T6r8n0f9fifFfpeZ/kmzD2z9JLN8Sm/9DZPq20PA9geExvv5HXN1P2dqn2ZrfsNTPMNUv0JWvMlRv0lUHhlVHGdp3mdr3WLqLHMMNnvmOwFIlsTdKbO1SG1Jux8ltgwrrsMLKUZqFSqNcadCq9EaV1qbS+JUqj0LpkssdMpldLrfJ5VaFwqxQmJRKg1pdrH2sU2v4ChVBrf98S6Rfbs2wp116GJM//7LaA8W/U3X+ZMvct30MNOaFWmKsfTUbt2QTs78y+rYHBU3OzrUN0n+Jo/+T0HhUqGnwyC4pL51RHusI1vXE6pk5omvGMr/+VXLLQ6WN9TVwexYi2rtOHYBySs8dodWUKQfQFg5djkMO3LoIDnZ7Nv9H9zZcXd0aHYGCgQb9llCwRSZBQULAhH9M//o/RZub6ysro7Oz7nxelsmSY7FOf6Dc6TxnMr+l0f5KJv+uVPZtteYpk/kNh/Os318ajcEymcHxccnMrBMsER7OloBflza3AOABzBtLTif9YyFrxq2OW4RBLQMCQiPX75BH/AYonS8bmZjIzs5PL62tfNmSOYvrG7Or6ztmx5TPUl8uHk+vrOcWV+Jzy/G5pcDMgmNiTj82Lc9N8lJ5WnyUEMmhg5mBcA4X9CN92jY7s8qEuqGuPSO69Db70O+Gfv8I7tFHcY8+Q3nmMPfwVcXVRlNjv7dfFBe5xl1ji2ObDwDZlaX1qdHFbGg6bBlzylMGZkTW7+N0OSh1pv5SLeq6HF0iw1fKaJ1q3oBOyjCohEaNwqjT6NUG8K9MYhQIDUyOkUTXo4maToyyHqmq7FKXdapKu1SlndKSdsrldsz7nR3nuxrOdt873VNyqufy8a5zh9qPv9X49outh9/oOHuk68N34bfeQ9z7ENV4Bdtxjdh3k0EuEXPvYG8dQ7x3FHfjfNHYa+fQl87sDIFbD76C5dzp8aIRXkKfnzbg5w6FpOyoThy36zMB11g6MT0xtfRVp4IXuvmtLK4uzEDtScbj+aQrHtb7fTKnS2C1s01Wpsk0bDTSTAaaVUeza4Zdu6jPraU71QynkuOSc91ioYcv9bOVIYY6wtCGmbog0xBiG/xsk59n8QrMbrHFIbfYdTaH1enyer3BYDAWi6VSqVwyMxrNjPszk87stCE9I0v2UXmDBhvTFSh62BUkeSI7Q2CkWNnU2lH5ceoDunfnbu2dKsCEHWVNiLttmIoOQnU3pR7BbO7ld6BlCKwKjTYQMDYq2snq9Ul6Ahqky4CwWBFqB1LsR/P8/Qz/4JCfOuBjorycHp+gzSNu8Mgr3crbTs01p+4Dh/GM3XzMatlvsb1mtj1vtP/W4HhS53hc4/iuyv4fSsc/ySCo+xe5/T+VzkfU7h/rPE8bfH8wBV4phOaOOqNnPPGLvsTNQOpuOFMbzbbGR+DJMWwmT8pNMEanBPkZ5eSscXreObcYXFhOLq2Mr65NLS7OTE1Nj+TG4tFMwBt3WENGLQAwu5BrYlLhF050vXsQ9cG7DzKUmfnmc39MZqaOQjSxaOBlPQpJQK+OWk0prysXDuZTiZmxkcWZ6T8j5aSws26xsPXxEwVsoDKkc3OB2VnvzIzr47sfd2rYCEdGeLkcO5ulpdOkVIqQTEKB1ngcypuNRjvDYVgk0mYLwPSBdoWvS+jtZnmQVE/vgLMP7cR2O/CtNgJAuAor+baFetVE+61G/7+ktv8ttT/I4NnrCt6/3A+7QW+lxPwfEvN/iUzfERgf4Rke4xh+xNT/dFj3JE33yyHdb0ja3w9on8VrX8RqXkVr3kRp3oFrDvaoj3crT3XJz3dKP4CJrrQLb7ULyzuFdT0iGFHRQ1JgqKpBhpbO03NFgLXMGgX4rbA7jA6Xxe2xu7x2lw/8gtgdHpvNZTE7TEa70WjV6806nVGr1d/vZGPQaIxqtVmlsigUgPecEolLKHQLhR6B0M0TODh8G5tvZvJ0dJ6KxhcPcTmDHBqBSxzg4XA8NF6IGpAgBuQ9RE3XoB5OMSLpJhTHgueYB/kGqkDLEqpGBD0rnKqPzKhbIcI+NsOt5ohl22/znr609kDxk9oDxb9HuShbsMe2j4E217fUDVtpw/bwTxd/fLo3Nb7jsyLlf9Fl4ADcdz90RJ7kav5BbPklkVvNpN0x1byvOQ0LVvfEGrgjFP+c62+jeOlnanVp0a9VslrrYEfeaN7/0lBFiY3Pnpv8c+OlGxtQZdFYFKoyKpNu0WlbmL7tUOEAHoocfrn+9evrcwsL0akpw8goO5nsC4XrPd6rNttRveE5peqJQkjwCb3hWavtsMdzJRSuSyT7RkZYU1P6hYXI+t8u5H+ONje3luZXpsfmRxNTCd9o0JJ2qWJmQRAs3LUMj1kYdKljYBI8NRKfmhqbX5pb2fxTOvL9SerxaMuNnB2flbb8nPjb3TPlJg4pGuUlE8NRK84n6LDjqwzNl+TXj/CPPUt99jH8Y8B/oP7hKO/oDeWNFksLwUeQJCXuvDu/lP80DS4vrE2NLGSCUyHzqEOa0tEjUpyP3ekg15iwNzXd78ngH0oxpfKhZjUbrRdTTEqeWSe3gMWVwiQXmwVCG4drH2a7SDQnlmjvxpqaIA4UlXRwr3cMX23Hv9/Wfrqt6lhbyWHY5QNt5/a1nniz6cDLDfuebz7yetu5Q7ArZ9puv99acRHWdK2r8wYSU4IbKqVwy3nKCpWlxuFvCIQaA8EmV6jRHGrUhptkoWZBCMYOdTEifYzYAEJQ10kt7aSWdQyVdwzdQ2GuEhtOtQ1WtRGrWwm1wC39tZaQd/u/+iUEtXFfXJ2fWQLgNxIdTTijIb3fK3O4BBY7x2ShGwrgZ9ZTLTqaQz28HfHTDruMVJuJagFMaBw2aRkmFdsiF9jEEhdf4WXrgjRDmGyNE5xJojtBdMdJntiQO0Rz+hl2L8fqkjjcKqfH6Pa6/P5QKBSPx1PxZDaSGgkmxvzxfDA6FQpPhwNZiTZAEThxDCOCJIPhuI29tLpuQnU7urK1u6Kx9W5tXTlUz7OysrK5ubn7waqsqMTUwWn9BMrQEJFBHRCwMQpRt1HR7NCVu3UlbuMVl+W8w3LSZjtss++zOl8xO58xOp/WO36idf5A4/y2yvEtueMfADPIbP+mcHxH5Xpc63lS7/2t0f+CJfiGLXzIETlVSLa86k+WhtLVkWxzbKQrOdqXHh/ITtBGJrnj09KJWe3UnHV2wTe/FFtcya2sTq2tL33qd21lcWF+cmIymxmNRdI+d8xuAQzmlovBZdnIoGjI/XJcrxBRDOJVgMs1sfQq5vI55HsnOk++01pIQi5G8BDnj6Evne2/dZF09+Zw3T12W10xfNd38dRQ9S1xX8eD3HPmQNzxUTufXQ0kZj9etyZ5v/roDrzZpqbMk5OG8THNaE6Ry4izaUEqwU3GmIkYPR6hxMKD0dBAJISLBDHhICoURIQC3cFARzDQFi5UrwmHoYzZ4mMo1Ojwtxn8XQpPt8iNZDt7qU4MwYZHWwd6LAMwC6nBRK4wDpWaaDdM9ItG5lkD64See0DHe1MneFkj/oNa9kul4idy1Q9k2u/I9P8uNe5kUUIsJzZ9R2h4VKh/gqf7GVv7FFPzm2HN78ma54jql/Ca1zHat/v0BxH69zXy3+vNzxi2/Ru9+Rdaw84QskYHp/IHO+WMDgmzW8yGi1gIAadXwOkT8LBCPl4oJIhEZImEKpMyFHK2UsFXK4UalVirkmrVcq1aodWqoXT0bal1WpVOp9Tp5DqtVKuT6ExChVkgs/GlDq7YxRZ6WHwfgxMcZkZp9CSVmqMMZcnk1CA5SiCHBoY8+CErdkgPsBIzLOylMXvpQ70sUi+T0Msc6GMRcGwykUsl84epPAaDx+bw+EK+XMzRSpgWKd0hpfqkgxH5QFyGS8n6sjLEqLQrL2mblDRNS+rmxNWL4rsrotIt0Q3gVdHtRXH5jKgiL6rOieuTkuaIBDYqh20Jr3+eRTdEavn2T9WevrS+UVD87ne/W0g+hVTcqfgQag8U/+60sb7V8r0tz/D2ECih2tI2Q3GQP1cvCzS/13mOWQNFH7YG9ttCB20h8Ah8yBr4V4nlNKsMFqyCxxqFo4zo31zx0t1amJ5yiHnUmrKWd15uO/Q6s7nGq5ItL8xvP/0JLS9vjX1W/HB+fiud3vK4tzTqLS4b6k8Ih0o7QK0pqJSP+tdP/LH968GiZHl5ZHbWNZ6XZjKkWKzDH7jjdJ41md/UaJ6Wyv4bGByAIZgET4ETwGngZPAh4AP/WrpEfB0CdLc4tzI1OjcSm4x7RgOmlFMZNfEDYE2vZ/ts0rBXl4g4sulgfjw9MzuxuFKoAvKVa6lQKiZZ2CJozc+qRqYEaSgk2B/O9gUzL9IP/Iz45K+Hflfwb39J/vVTpKfA4y/Jv3qa9MunSU8/gn3kB9gf/BD/w+eozx3nH7+pvNlqaR30DypSCu+Ed2JpYvvTFLQ0vzqRnU/7J4PGEbskqRsOS7BeVrudVG3E3FB3nZN2nJagrirJ9To20iQhW9U8u0ZpVho0UqtUaOdwHHSGmzzsHRi0wXGaJqTwbjfrZjv5Egz3flvXmdaaYy23DjR/+FbT6dcbj7zctP+Fpv0vNh9/rfns282XjjSWnGqsfK+h9VID4lrrwA044xZedodmquB5auThBl2oyRxsdAWbAsHGQKjZBRVvbDcE4YYI1hgj2VLswLgqNeccW0zMrE0tbSx+ZsCTyWR27lIbqrGXXbs9uK9UKrV99i6tra4vLazOTy9Nj89nQ7mYLRzQeLxSh5NvsbGMFobRRDMaqCY91aobdhapTzPsMlBtForZStbZB9WOQbljUGajyM3DSj1HqxGZZDKrWOPimXwse5jqjhF9SbQ/Bd7QvkAa508OeGMkd5jihGJ3HIdX4vapvT6z3+sKuAI+W8RnjnkMMZc6YZcmzcKkmp2SDCd5Q+6hfgseo0Whpd1IXjt8uLmLWAfDVLXBK5q67jUWXYz7Ad0tK68tr2yprO+oaYY3dSJhCAQc14MhtRHpNcMCeCUC2Q5Ho9HYguBweH19ffG4KHh1Z+kg/zPDdHXRXFt8BJEaw2Xy5Nwkc2xK+Flhurn1L76CbW5ugEvo3ER+MpseiYZSXnfUagroVG6ZyMZnGehDahJehkUAYGO31QN4I9+7BUAOffkcgDooUfPAq437XgBX465TB5AXTmCvvke4XYzjVXLbG0XIDjkeBUDRyKTaBByPQhLUawBGpv2esXh0KpeZn5qEimHez88sbI2b3b01bnLCNZo1lanIJ428Hb+jF/5eo9g9c8LAJYs6A36I3Hbs97Z43W0eZ7vb1uGydjmM3XZ9j1XTY1HBTQq4WoqUiHu5YgxdhCOJ8HjxAEpM7BaT2sTkegmlUkIrk9FL5PSrCuYFOeuMgn1cyTuk4r+tFr6qET+vlv5WLfuFSvEjhfpRhe6/5IZ/lZr+t8TyDxLrP0st/yE1/bfU+KjE8COx/hcS/a+Fuj8ItC8KdK/z9fs5+sNs3XGG4eyw8T2K8SLZeI1guYW3lmHs91C2WoS1CW5t67J0wSzwZjOqQY+r1xMbDaQmPblFT4EZyB06So+egtTT0Hp6v5FBNLHIJjbNyGWYhFyzSGCSiE1GBeC2L9AQUyFRGcVKowSyQSLXi6VaqUQjE6sVQqVKINfxJEa20MrkOYdZ3iFahERJEwZH8fgZNHoFhVxAIqeQqFFkX7IXG0TiHb0DRtSgqm9I1Edj9NG1/SQ9MJFsIA2aKIMWGsnOJDo5/V7+gF+ED0swCWlvVgofk3bmpQWoE9XNiSoXReWA5bahTnh7UVQ2K6qYFFWPiuozoqakqC0qag+KunwiuEvU6xD2WYRYk5BgEBL1ArKOR9Xw6BoeU8Nma1lcPUtkZohMDJmBodINa7R0k45h1DGgPwzpmCYdHTrWM4x6lknEVkkpjM+xhMaxm77KHel/5/rmQBGQ4W44/MTw4dEeKP7dyQDf6nlq+xio2BJjMro9/LP0NJW/kx/ymf6W1FpNb00txf9mipd+WlMjWROTSii9AlYkYDki6GmLWIzrn64Nw+NCeaE7RsIhAiweFI3o2UIWqo9+un/951buBmuXhYXI1JR+ZISZgEKCdW7PZavtsN7wbKFx/L8rVU/oDc/ZbEc93quhcH0yiR4ZZU9NGRYWon8bJWG+pDbWNxdmlidH5nLRiZh7xG9MOeRRI9cPlvtGfsChiAJEBPPg2cncLDjzK+83CNah82tQwZjYHLRR0Dg+I8tNclLjQ7ERTCiDDmbI0RF2chxM8lKB4YiOEOD2ugeazbBSzZ2f43/xGO6xJ/qfeJAfwT5KMTKLn2ZxdiWfnkt6JwKGnE2U0FBDYrSHCbMNVhr6rqk6z0q7zkrBAbnayO6xSEkOhcCi0hjkZqXEIeR7WEz30KABiVe0oAXViOHbncQr7aj32jrOtNQdb7lzuOXS/qZ3X2s6XGgIcfClplOvNV/Y13LtYHPZseaGd1u63mvFf9jBuNopvtmtuQM3VKLsjXhXG9nZznZ1SDydWl+H1d/uCbQEAQoGm32hzkC0L5oiZ0f5+QntzIxrYSG2sjK2vv7FVewfJB3NuE1xD7aOZrDQC+BHs+hpNi1tG/z0VJuZYraR9U6SxkVSuIlSF0HoJgg8gzwPie+mCR18hUWqM+psGotb5goIfRFOIE4PJsmBxEAgsQ2BwQzGn+z3JQjuCMUTorv9PI9X7HEqPTa9W29xyd0uns9G8xtJQS0hrMRHxOgoDxNl4N0ErAWF03ZgZU0Yfi2aUdlLKu/B3ulElLa1lTbU36muANx3XxV379VW1DTWNLU0tre1I1vguCYMqWmI3cAW1ykMb4g0v5IYHykUUPkXuf37atcv9L4XLcEDjsg5T/xWMAUwryc5ZoIZ53XZjYW1ooOeQFdH184QONFoijqz29/ZB2h9bW1pbnY2P5ZPJ3PhYNLtjJgNfo3CKRFYuIziljwJuoff3cpqqaXVlpPKb/Tf/LDvIrQfr+PYvpZ3Xm7a9yLsyJvdpw+hPjiFu3aBeOcapaqU2VTN62wWo7oU/X1aCsHEojlEPK9SGjLq4k5bNugbT8SmR3OLM9PQNryP69OwNzvrmZ62TU4aR3PKdEKYiLKjQVrYPxj04v2eXr+3J+hvDwa2Gc/nbnPZOuyGLpOiRydEqNi9Mhr6GY32GXPgpDv2IP+H0nFLyP5AJjkrk56USY8opG8rZa8p5S8o5b9XKn6pVP1EqX5Mqf2OXP/vMuM/3Q/TfUtm+k+Z8RG54XGZ7mdS7a+lmt9LNC+K1a+KVPsE6oNc1VG28hRDdYaifI+k+ACvvIRSXoErrrcpb9Qpb9xTl5ary8s0dXd1LVW67lpzX4Olv9lKbLeTelyUXi8F56YTPHSSm0XzsBkeLsvD53n4ApdA6BKInSKpQyyzSxQOqdIuU9nkaqtcbVFqgc0qrVGtM2q0eq1Op9XqNDqDWmtQaowqjUmpNsrVRqnSIFboxQqdQKrmS1VcsYIjlHJ5IgFXIGBzgiRSiMUMcdkPcpg+zMFRZpDdE709Y33dI309GXR3Gtudwncn+7vTA10pQmea0JEitaeH2jJDrVlKc5bamKM15IbrRug1I/TKUfq9keGKHK0iO1yRotXEqXURakOQ0uSntHiHYC4yzEHqsJE67eRuOxluHURYib3WwV4rEWUmos0EtGkAYxzAGvqxBjxWh8NpcXgtFnpUY4H71Ri8GoNTYXBqDAZYhUar0WgNuk+NRqkxKA26V92HVPchCoar+3o0fd3q3i51b8cXuK9bi4ZrMQgdtleP79P3o4E1GJQa2f45ViE7RsOR7Z/sPX1pfXOg+D8+Xr1mrz3Gnh4Krcxv1f7LVmJXb1YffcuO3T7+c/WHQSYqlR9bWXuQfy6zwFjo7bP/tjQaDatJeMzlc2BZ3Pv+KTkelfZ7dv72/BkapkFYiEY90OBZswnKMv24NjdXl5ezM7PO8XFJOkOMxmB+f6nDecZkel2teUoq+7ZU9h2N9lcm81tO5zl/oDwW68xkyfm8bHbWvbIy+vccEvxMTebmMuGJaKEJoUMeKfacsIhCbk08ZM0k/WOjiamZ8YWl+ZXtD/iKtLqxOVUoGxOcgWqHakenRZkJRmKMEMkBfsCGMgALqbEYKWTD++UIF63FiqzU119V3DghOPEy/eWfEX72KPbR35B/s4+177z4fLm2vN3SiXcPXh0uuSMtrdFU1mvqGjX193S3SiwXwcGO77DuweopqCvKjtMS+PtyXImWUm/m9tjFFKtcapLrtVK7lO9gMgz9JEUnllOHHLrTjb/SibjQ2nqqpdAXvuXi282n3wDs17Dv+cajLzedfb358tstdw4BPmzrPNOOea998GLL0NUG+q1K9r0yfsNtWc9dFaZBg4MbBgbMBJaDKPMMGPz9rhDGH0aEtgtItsRiiGRyIJtljI2JJyf1XwkK7mhlZWV6ejoZSwctXpfMbOdqLXS1juMxcP1mYfBB1rF8RrrMR+IEaNwgUxATKeIqTdxiDLrtnqDXFgkYYmFFOCAIBJm+ENUXHvRF+/0fQSAaCgYmCN4o2RMc9njYHjvfbZA6FWq72GjiOLSMkJQaZlOiQ7QEjpFAsOIwlr+eYaukGe5SFXfIwtsDrBIcqQSJL+1G3oF1lDU1ltdW3u/0UF5eXnmvqramqbaxvQaGuNeDLUEPXhigHR5iv0wXPM2UfJul+C+B/vtKx8/13mfNgbft4XfdsSv+ZE0k2xYfwaTHaaOTislZ88xCcGEZXJy3v1MPEP9aXe/RE71HjiOPHEMcOdp1+EDboTfhhw/3HD7Uc/hg96ED3QcPcGrrAK0BZgPkBvhtsOwaYDlAdIDrAN1Bfy946/n2o2/1nDmM+uBd/PUPBsuuU2vKmM01vK4WMapbOYDWUYhmNt0h5vnU8rBJn3Q7cqFAPhnfrrby2Z2BNjc2lguwt9PObrvoZbGd3fi4NJfjp5OMRGwoGuoPBfrCQTgEe8HtJiXBYIvfA3PbOx2mHp26VyTGDPMG8PRBBIXSOjhcM8AqxXOv4oTv46Vn+pWnhlRHqOq36ZrXWdoXubpnBNpfi3Q/k+gel+q/KzX8u9T0j1LzbzW676pcP9V5H+Rvye23BNyzLOkHLNlVpqyErigfVlbTNQ3DWhjd1EO39NLtWLqbyAjTWGEOJybmxyWipFSSksrTclVGps5JNVmZbkRmGJEYRmTGnNSUERtTYkNSpI+K9BGhLiTSBQVav1DnAxZpPWKdW6J1ynQOhc6m1FnUOhPAOY0WQjttUWCg0ejUKoNaZVTKLUq5VS62S4UuCc8j4vgEzACfHuRSI+yhMIMYGybEKLgkGZ0i9qUHerN4xAi6axTVNYrsGEF0jsI7c4jODKIzieyIIdujyPZwLyyEggVQMB+qzdvX5ulrdfW1OvrabH1tdmy3qrfjcwzxDw7zSY76HKM61aguDapb09dTYC24FturxaK0uD4tDq3H4/QDOD2h3zA4YCQRjeRB8xDZPDRkpVFt9GErnW5jMhxstoPLdXK5LgHfIxJ5xWK/TBZQKIIqVUijiei0UYMhbjImrOak3ZZy2rNe14jPOxryj4WD49FQPhaZSMQmU/GpdHI6m57JZWZHc3Njo/P5sYXJ/OLU5NLM1PLszPL83MrCwurS4try8vrq6sb6F9+XxyPBlN2844zNMmK07J4BXp77e9z98TXpm0O15ubm3WT43fv99x827YHi35fkNVv417aPgRbzW5Lb0OOX0+8Jw/9Lavv/ZJD/130Xh0X/T6ntbwkUwQol6XZK0XDEuaNgDYS/8YGOOgiWNdtPf47m5nzct3Win+9YI/6xXPbI7hmd4Gcpa2Mi0RsK1bg9l6y2Qzr9HxTKH0qk/6FU/chgeMFmP+bxXguHG5IpzOgoZ2rKuLgYW19/QGrrX7MAxTmV0R3bpGEjz797Bnhu6oF7XKESo8WeE8HxCLit3+85AZjQLot49cmII5cJ5fOZmbnJxdU/t/Lkp7VZKC0zvrwan1vyFiqIKqAY4DglNooLQWmimFAKE/D1enWdTk6rjVBpaL+hKj8nfn8/+8DvKc/8EP/DJ/A/+gPl+beZh87yL96QVdaouzv0JKxJRLGZ+a6Y1JUX2sa55jGGYZSmzpG48SFKuKfrMrLmPXTtxaKRt8+0n9y3MwTuqDqFptQMcBrRlLs96KtdXe/Dmk613jvScuNA84U3m0682rj/hcZ3Xmw+9Vrz+/tabx5srTza2nCiFfZuW/eZVsT5JviFOuSle6ibd3AVJQNNtwg9tymECjanQ6kkmDQ8h0bn17gislCCH08OA/CLxZDFspNfBwpubm4uLi7m8/lUKhUIBOx2u1Gu1HPEWgpXS+ToBrgGgthIVBiHDDqqTTPsNlBt1iGDg6RykaReslgpDoAfhph75EHWcf0cjYPu9g55QgRPBO+LYwLbEAi9ff7kgD9O8oZoHj/b6RHaHHKDVaswWbk6L0Ud7lMkmqWpKlnyjjRxWxa4KXTc4BpvMpQ3KaJbUAlQ8m0UrqwHXt7edre54V59xd37BHj3HgDAuw0tpW1d17p6z/XiDmAHX+qnPk1kPEnh/4qjfEZqfEXvPmAPn/XErwdS1ZFsR2IUn8kzRqfkE7PW2YXwwvL46trutikriwtzE3kojhcKJFz2oEHrloutPOZ2XU00vFhXk9EI1dUEgAcuZeiLZ6B0zZPvtB16HVzcYMdeazn08o473n0dff3A7pmWQy8B3rNwGS6p0K9VRizGlMeZCwcn0qnZ/NjSF+yX/mLYGx0V5HKsTIaaiBOiEUw0jAyHOkM7sBdqsgfata4ekRVF1+MIKiJCSmoV0KrY9FvDrEs03lmK6ChZsm9Q/gpJ9SxZ8+sh3c8phieopkcYpv/mmP6db/5nofkfJNb/T2z9lsj8bZHpUbH+p2LNr2Wq5xTy19WSQ1rhu3r2+0bmVQOj1MisNLIajByYkddjFGBMkkGLgmrWcqxmsd2hcgTfF8reUGrfVG37VY36Ba1qZwj8mkKN4hvEGo9M65Rr7CqNVa0xaTRGrUanB/9AVpk0CrNabtHIIKslFo0U2KyRmTRyqNyTVqXXqAucpzZqlCa1zKqS2JVCp4LvknHcYqZPRPcLaCE+JcQhhVnEKHMgOtwfo+FiQ5joYF+MgIriETEMPILqDiM7QoiOIKLDh+jwIDtcve3O3g5bb7ult93U227obdf1tmt7OzS97Z9Esk8Y1aFBdWr6ujSYHjUWrsQgFDikHIeS4dESPEaAx/LxON7AALufyOwn0QeGhonDNAJDQqEaSLgdGwkoIw7+sRkSTizWzYyOz41PLExNL87M3OerpfWVlY21Pwqx/joEfmNX17eW1zYXVzfnlzdnlzanFzcmFzbycxtjsxsjMyvq4BLPBZlf8Paxe8eLfPfGxN/gAuAvpa8dFKH9iA/W9kkPk/ZA8e9Ii5Nblf+wNeLeHgJZUFsB9vbxl5Dk5FuKnz0J/FpL7x+6+0nPv3L7wvXizI69Usr22X+1WltdCZv0vM7mjuNvN+9/iQwVp2F9cXEacD9LJaGthoOErZ4uk+BXcvkjStWPH2Sp7Ntmy777IcGhfF5eCAn+0XVQ/4bk1sSjzhwAuQfZLAhCRWIWVqfHoYoyHysxyvQaeVC+qN+4ky86tzDzBe0H/3iBl5lZhaqJhmcXnZNQHVFJdoKVHBuMQkVE4f5ou9vSbJfWmCllut4rivozwuvvsE49T331Z4RfPIJ59GcDT/2B/PKbw8dPsK6+z6m+weuuEJFbpGKUwkozJATWcakzr/RM6gJT5vCMIzbrTc3543MO27hRnlYMR3go91C9GX1d3XVeiq9X0AeUndeOwY680XXynQe55cArPQ0X2m4dgVW8C6s7A2s5B2s939Z8qrXxRGv1scbyozXlZ+6Vf3i3oqSypq6mpQfWO4ShyVhqlyEY86fD6TEPWL5PTGhGR4WZDEBBfDTaEwo1f7UouLGxMTc3Nzo6Go/HvV6v2WxWqVQCgYBNo7Lw/bw+vBCBkyEIahTFiOVaiVLbkNZEMWuHXZphl5Fms1ENbrrWx1JF+IqEQp6zKLJuaSokiIQ5Xj/b6uVp3RKpU83Sh9iq8I750qCC5dk9A0xRWNjmkEDllwvcumGHFW93d5hCldrkTVXqmjJ5TRG8IXbd4JluMJW3qILbREZZ/+A9NLoS2VXZ2VzVWl1Rf7dAgHfulN2pqLxd23i1CXa+vedwd+/rSNzLWNKrg4w3GcIDAtUJpfmC2XvLG6uJZLuSo/3ZPGtsSjk5a59djC2uTKxCG/gA783mxz+T95QDaHFvJ7e9cbj+HunuTUB6qA/e7X73YBHzPuGOY/sQ546iL52FKm1WlAA+BFczSV+PmoQ30IdsfLZHIQkZtVBALxwApAcgM+BBFpHsczyWNT0I9sZGVSM5SSbFTcbpsQgpGu6PhNBhqNRKhyvQbgnC1P5Osa+H5UGSHCi0Bd9pINarKXflw9clrPeFvJM8wUGO5HWW/Dmm8tcM9c+ZusdZ+u+xjf/JNX2Lb/nfYuv/lFj/UWj9V571vziWR9jmn3AtT/NMz/CMrwj1b4u1x+WaC2rVFa201CCqMXNbrcMIO7nfgae5eznuXpEHrfDgtF6C2UM2uek6J1vtEKntMqVVpTQDCNMqDTq1Tq/WGLRQJE5l18pdapFPywtpWDHNcEJDSWqGEurBpGowoSQkVUSrSm40Gk33ZTZrrVbp9qAgg16vB++aRmVUK0xKqUUhtsiEVgnPJubaBSwHj2nnDDvZVAdjyDlMdlIHHUMDDvD1DuIcAxgbHmXDIq1ohLmv29jboUd2AIr7JLN93IXQXKeyt1uB6pH3IWToXikGLcXiJPgB8cCgmEgVkxkiCktE5QioPDZVwKCKqGCOIiFQZP1UBZaqwtDUmGEthm7Assz9bAuR7ySJ3DSFn62NiK1ptXvMHJ70Z2YzEwvT0J/a/oRUfPBjvba+uePF5Y387NruGeCv5kr95wl8fZ8Bb/M78LaemVpPTa4nJtZj+bXI2FpodCk4sujJzbuzs87MjD09ZU1OWJLjxsSYITGii2Y1sYw6mlJFk/9/9v7Dva3zyvdH/4P7u/e595w5c+ZMSS9O4sROcxw7iRM7jnsvkixZlmT1YvVGFRax9waiNwIEQPTe694bvffeG3vvwt0gaVqxI0/m2FPi0ff5PtR+X4IgsAltvB+s9a6ljSY10bg6GlVFIopIWBkNKSMBVdSnino1Mbcu4dInnYakHUjbgIwFyliMOaMuxQfzO+aBBS5YuHdGBOQmsv9dWkz9B+i/Iqr95+oBKP43kuhCZfTg9jGssWBFfftLaYnBL9BkJW5jzPyQ3q6bMNqnoU+ZkO7PLCa2b/23pqX5OXj9BC+quva8Vi1O037Hr1PdtzjNjqanqu0KhYLtbYc8TsVhn88aIehZhfKbCuU3Ng0fVK1UfXvHKvV3JyeB7Tv57y2PIZnwFCcKs/ezRRo2fNyPPrzVjz45Ob1ZX3T7Lr6YturHJGeW3ONzYGFanpngJcv0aieJ3IA/2OIEay3CKwDxtKr7kPTWu/yTL4zu/i31T48Sf/4w4Se/Hv79n2hvvcU8coh39YK0o1FDHIJEDIdJHYh50jPh3HyitJgZXypOrUzMrc4uri+tbKx/XKdxaW61EJsJmQoGiVdAB0fIUiKRR2FwaAwyg4akENtwiJvI1rP9Nw51f7S7/YNXYQboPb/3XiT4i+5tOt5+6kTvtRpMawcdg5dxhXajJZuML86Xlpayc3PBqSnb+LiuWBRns4xUCh+L9X8MA18UBbdSQPP5fDQa9Xg8JpNJo9EIhcLR0VH4DQg7NITt6iI0N4+2digH0SbsiJPE8VEl/hGVh653MEwmlrO6b5DjsbLtLp4ZJr6wQh/TK6ImScjOC/qYXi/F5aKZ3VLAY1C5LRK3m+f2j7pDNF9sOJAk3BMPJPgzFKWWavOO2H1bpjqDNGdyZwibbHZxhtiqm2xBLWOkgYJrJPQ3o9uaBu40dd9uaL1RW3/j5s2aW7cv32k63dL+YWfvB/3I/SjCB8SRg3TuMb7srFxfA9oaHf7BSJqSGxeWp/WTc+7ZhdjUTK5cHs9mqrzndsBItllyk1/lPQpegRkQ9bVzWuuqhViunMGeOTx4+L3uvW/c+xfcqrqJOrFZjqXmfHWf3mYCpxKH0FMJm7DHh+8TJsmky54PB7ayN5fmZu/+pZJXq8vrywtrCzMrs+NLU6XFidx8OTWbj41lw+lUIBL3eKIu69ug9jta+3d1ji1/S1vd0LgzhP0drbXDzsRWWyCQOxzUehv9qon9ESg4rBO/p5a9oVC+oND+Xm54XAY8KoEekpq+JbP8k7xaTwXGvH+R2r4ttH6fb/kxy/qzUeuvGdan6NZnGbaXGY63GM49TOchjuOU0H5Far2tNLXpwH5QR7CqRp0SkZuv9TGtQYovhAmGB0KhrkikIxTqCYcRoSAyHMRGg8RYgJzwDye9lLSbkncP52zEohlXAnHjevSkCjGh6B2XdU1K2sZFzePi1pKwNS9szwq7UuKBmBgVFON8ErJXRnPKRu0Krk0ltKgkJo3cqFUZ9brNbp5Q9atOC2lURpUCkkuMEpFVp02n0zP3l81q1TFHP2E5FOwBHWpQixrUoBAqFFKBRkvRWAkGJ8KSBLhhHn6ES2RwSGw2hc+mS1hMJYer5QkAodgoklhEMrtA7hIoPGy5hy73keUBrDSAkAb7pKFuWaRTGYfdo0n0axJIIEUyZ0fteYl/DIpN+XNzybHFsdlV+Jqz+iV9cPZv1UJ+Zjo2/okDpSl75s9mYuNri3+54h18nYQf9vLSGnydn59enp1cnCnPTxXmYFgaS0+XE1Ol+EQpMlYMlYuBUhF+U/AVP2uPMe0CM3YgZQPSVkPKrE+ZtElImwS0Sb02pdOltLq0Rp9R6TIKfVquz0gNWSmQE4N5IZgTgHkeVAU2DlhgQcUtc6ECDyoIjAWRqSg2FSSmotRcVFhLSmtJZS9rHVXrXWOAexzyThh946bAhDU0bY9OO2LVjwI9yVlvas6fmQtk50O5+Uh+IVpYiBcX4bcJ0DcusZYltrHtr7YxsW1M5vjESsf41OxXtjrgf7wegOKn9QAU/7toMlGp/7vKTG57CAtor2Tu2az4BeScMVNysn9UWdqY9YwUTligE9P96jHRvf6ba6M/NzkOr7rgRVv7Oy9+XnGae7W2Vkkmqh3tt3rcD5Pu6lQzQVEygXC6Dmm1j+gNv/J4T2eyZHhRvpmiWJmZdfv9l7Z++oE+pa2tg2Zx6H6G+P6p8v9lyk1peiU7vpQoL3lyc6bklCY+KYlOcMNlRqhICRbwwXS319XoUF8zMj/SoQ8rmvcIL73GOfxH+huPU373I8LDPyc99jTthbc4+w6Lz15TN7ZDKIKTJQ7rbblQZnpiYflfWYFtVDbm1mfHVorhcsgUsSicKqFJzAV4bB2TKSbS6YMjuA5K7x3CnStDV4/3nnyvfV+VBtv2vdJx6q3Om3u72w529p9sGzzT2n2mteFI58V3FPSLLqjFY27fstk0pADp20NLu8fa7gDvOKzUyUnz2Ji6UBBks7RkEhvd3ij4f4mCn00BBUFQoVDweDwajYbD4fr7+9va2hrq6nruNFC6OsWDgxAW6yZRQxRWbESQoEljNHlgROOigxaWDdzsCG/kuGx8q0sEemRqj0ri0vNcFo7bzve4RG6vyuIBDG74dDlFTi/H6WdutvWjBFOfoGAoS/anGc4kzxSXqsJqrs9IdNv7rb5Ws7dW67kqga7y+44fJF46xWy+veURLBpvdu0MYXe99/qda9dP9Q6eRuHOECgXGdwavrRRpe8x2vDuADMQVoajUCji9Pn8DttWfO+v572u916DryfokweJF0+O3LzIbLzJ72yqNtDDIw00kpk7Wi28uVmRBYZJGClLyeR4pjiZm5guz8MsV0rO5iPTaf9E3DkWthQDYN6jzTrkaas4aeLFDKMR7UhYMRyUEANCnF+AdwuINj4BEgzrRTS1aFQhZUtkfKFSwlXLmQYtzQgOW8x4yI5VuzBCN4bpw+DdxF7XcL2NcdHMPQqJf68Ft0qq3M//W+04K5T8ZgR6mmh8FmV8GWN+C23eg7YcRFtP4OwXiM4bw64mmreH5UfzwzR5UKp3Qyab12EIu1VxvzgRYCeD9ESAFPdjY76hqL8vGuiOBjvCofZIuCMS6Ij6OqKuzoitM2rpiEDtEX17WN0akrWGxK0hfluQ0+ZjdLioHU5ih53YaSN0W0j9ZvKQcRgNUfEgnQQyKACLBnJG4f9bgIALiAWgTAwqZKBKCWo1kEEPAlvUB4J6PaTVVOflMkgqMYoEJgHPwuNY2aP2UZqTNuyhkLwkrA+P9mLRbhTSgURZURgjGq9DE1WYYTFmxK7VBgKB5P1lMZlYHDUbzO3QBWyqIY/XZ4e0mX5Nulud6tAkW9XJFm2yRZ9sA1Nd5syALYt2FyiBMUFyUpGeNhbnvJMLqdnlqeX1tX9nzNuMq91dWbu7tLqxuLIxv7Q+u7g+s7A2Nb82Mbc6PrtanlkpTq0UJpdzE8vw5TQ9tpQsV9kmVliAUSecmw9m5wOZeV9yxgPjUGTaGZqywYDkm1AAWSZY+DxDBa0+IwTzfBjJoAIbzO+cMQ7MaZuRNCGQFxlyEkMOpjgZkIXvUwlkNWBWC2T1UNYAZUFjDrbRlLWYc1ZL3mbNO+0Ft73gdRZ8rpLfUwp6x8L+ciQ0Hg9PxKMTqcRUJjWdS8/m87Ol0sLY2OL45NLk9DL8fOFnDT93+AzA5wE+G/A5gc/Mzkd7/65aW9lYWVrf8VzAmsPdvncG9r9f46X/hvoPBcWtbYpbgo+3Z/+L6QEo/nfR6IFqRHFHabDaO/Hul1O2cWnj7uMa69UzlyiqO/BweWOpvFLY+tbfnMYzKXjBB6/w4CXdX1WcBtbERMXpqPC41cghGrkhZk1YiTF/k82+S6X+Pgg9HQhcLRRYS0t/oeD+A+1ofW1jZnwhH5uI2HNOdQzk+U2ioEUecWriPmM6aMt5oDQkDIRdhS1HXIWgIz8xsTS9sDY2s5KfXE6Vl6KFBXhd4k7O2qIzxtCU3jepco9LHWNcW4luLdAcRaqzMOzMw0Z5o50uc61VcgminND2H5DX7RKdfZmz72nGS78gP/4w/sdPUH/3KufNQ9KjV3U3uqy9I8ERRUrhLDtz87nVjc/7vGCjsg5DYHmlmF6Mh+a8rimzsaTVphXSIF/o5QicbIGFyZOR2TQME91H62om1V5HXjzZe2xfx2avtrY9r7Qde6ft8t7OloM96OPd1LPdtAtd1AtdmNPdTR90Xni389Dr/Uf2Ea+dF/a0wmTilAljNrPN2pFMojOZ4fsZZsJwqDuToeTzHBgFJyage1BwYfuhf6ydFNBEInFvCiibzR4eHkaj0T09Pc3Nzbdu3WpsbOzp7iYPDQkJeIBK8TAYCTYzz2aVmOwCnZcfEaQo4ghV6WUADrbFxHbBKFjdNMi1W4WQXap3qjVOg8Zt1nnsBo8TdLnNJpdV53TK7G6ho4qCLG+EEUjci4L4UJbqS7PsKYEhKpeE9DSfAWM19BkMrUrdbb782gj7Bpl8Gz9Yh2pv6K9v6LrZ0Hq9tv7mzZtX6+rPN7V81NHddXifAj0QMGjv58Ej+wbOn/+cfE74AB7C1wcYOGm3LrOabvG7msWD3XL0kJpA0FJGADrbyBSaOAqrwGAVmmwip13is0lCdlnKLEhA7KieHlYPBxV4vwTt4yM8nAE3q99F73FQOx3DMPJ02Ajtdly7A9fhwPe4yEjHCN7CoJg4oxCfbxBLtQq1Sq1X6IwSwCIwOrgWN9PmHXF4yS4fzuMbsgcGDf4BmR/B8qKxDnK3hV4HsS8ahGe0kpMq6Qm5/KhI8bgM+pHM/A1ZtbTm/1HYviW2/JBn+RnT9ATV+Hus8SU0+C4S+hAF1MkY+5XSHe9Ry97QKu6d2a8Q83nDHv6gR9jnkfR6Zd1eVZdX2+UHunzGjoC1M+DoCHraQwEY/Nrgr0FPBzzjs3R6wA63rtOp6nTKu+2ibiuvx8LuMdF7zNReI6kfxPUb8QgjCQNRcNAIycigQUyGkcOCeDyjUASKJaBUDspVkEoDaXSQTg/pDRDwcdc8EIQMAKTVmWEr1Ta50iVTeETSgEAU5vISTFaORisNk6eJ+Dkcdh6Lm8HixzHkHH4kTmQGyFznsMhMUxhGNTDSyXlWidglVgTFugQPyO6wypYp+hxBm0VpM33qVLsqKWQrHVKV62NblAaVwb0zhG0WKdpF1tH0hDg/bRibdc0sJBarDULg/3FbUbIvAcnSc57UnCsx64zP2mMz8GXQEpk2hafhiyEYnDL4J+FLos43ofZMwBdGhWtc7hyDL49iW1lkLQssJZ65xDX92XPkmkp8c0loLonMRYmlJLUU5ZaSwlJUWopqc1FrLujNBYMpB5jyIJQ1glkTkDEDaas+adclHdqEUxVzq6Ieddyni/v1iaAhEQKSESiVUMec6sSO7eqkWZu6d8arjk36Cgu56cXi7PLY/OrE/N2Zxbtzy3cXVuBzVFlZq6xtVL5CdLS2ulEN70+vzIwtTeSrsf1CdDoTmEx6xqk12r4PlZ/nw8q0+54YwAN9Mf3HgeIrr7wC8+FO4VP4+EF7jAf6T1PRW92duPhxIc215WrS6Xhoe/iFdd4dfQY1TOnauz3+m9Pdu/lwQEvGYk4fgleBf1VxmpWVahtDjapCJFQQA6sMbEnfHbJfMplfVqq+a7a8Eg7Xl8vS1bWp7ds/0Ge0vLg6UZhNB8sBU9oqC4Ncn0MVDduyMCvCxAhzo9o7MQoVdszTptWi0L0zTKjIMZVEtjK83KkuffzjiuCELDwmjozxo2V6NDPgsd6xSS+C5GOa7n2yW28JT7zEfe9lzu7X+e89Sv7ZHxjPviPYc1p9ttHUiHKjOFGOIWcITYYmlib+Yv89WOt3YQicgSEwtRiDIdA5Y4YmtZq8RJrkiWJsXoDB9TKEbo7cIVHohVIBS0glsRB9w213sDcuD5451nNwb/uuamyw9d2XGz94p/7kvobrh1o7j3UPn+mTXh7Q3+jX1PRyznejj3c1fdB/8yDh1hmYVZRYhJnHDIK6XMg/NzG+ujKxsJCcmXGNj+sLBQHMfvH4YDT6Z83Z/qITfu3KysrU1NS9KaBqtXonBXRoaKizs7OhoeH27dstLS29vb0YDGZkZETEZEICgU8qTMmFJRl3RsJa5NPn2PTJEcbYMCtL4sVo0gBT62CDFo6tumOQ4wM5HhPfbpVa7Wqr02Bzm+wui8NltTvsTqPTqbFvoyDfE+IG4sxQaiSc2UFBYjBL82TY5rhQHZbwPWKKWYTWiXpl/BY24xaRVINE3OrvuN11p7b19u2GmzdvXb9Vd7mh+Xxr15m+oTNowoVh+g3aaAuD2T86ShgdZTBGBCNkGQkjG+rltjfQbl/uO3Zo6Nj7hIsn7ufuvW8O1rRx2kncDhqvk8XtEnA7pZxOJaddz2o3jrbbaK02SpOVdMdCuGPBNphRdSZMoxXTaoO5DtftJCI8wzgflRSk08IMVmyUG2cKk2xJmquI87VhMRCQGj1yi11pM6vsoNapM7iUgEtidAssXrbNM+L0kt1erNeH9Pv7gsFq1RZXuFsfGBC7MSM28pCZ3g5ybmqF51SSQ3LluxLNC0L9b4Xgz4TQQ2Lz16VV8PsHhf3rYutDPOtPWZbfMi3PMcxvMkwH6cZTDOAGU9fKVqKZIgqLx6UzFCNk2zAavlcrEWHG9xtxfRC2mhUJEvqMlF4zoyfk649Gu6PR3vs5Eun06xFBR2fQ2hU09wagPr9hMKBD+jUYnxLvUw77laN+FT+gVvq1QMBgChhtAYsjaHcHnd6g2xdw+wNev98b8Lq8DqvDZrFZTBYjZKzC3scygqBRbzDrDFaN3qHSOWUqj0QRFEqjfPi0cguM0XEqZZZMXMDjlrGYRSx2DoOfxFNKZHqWwknQheFRmZ+rdfEBi8hiENvlEjdb7CNJgghhpEMYbRGE7/DCdbzQLV7oOj94iRs4zfUf5vn2cr2v8zzPCTx/ELqeFLt/KXU/ofG9YAzuskQPO+IXPOkGf64nWCCEy4zIuDA2qYxP9anTnw6L/blHgUKbOv55SGYpCa1l+IImsVdTCuXOcaVrHCY6jWdC55uEMQ8ITELBKZj6zOFpa2TaFptxxKuJi+74jDc2449NB6LTwehkKDoViUzGwpOx0HgiMJbyl9O+csZbyLjzOWeuYM8WLemSKTlmSo6DsQlDdEobnlIFZ+T+WYl3XuJZknqWFL5lVWBZG1o2hFeg6IopvmJNrjpSq+7Mqje3FiishYtrsfJ6Ynw9PbGem9oozmyMzW3AODe9iXOLK9ss9xUKdK0srS/OrsxOLE0VF8Yyc8X4TC40lfJ+Etv3arNOZdomSZr5cZAV1dHCanJAgfNJkB7hgIvb7WC12RiNFmqdiVwD4i8bMOd1Q6c1MOZtGXlagzmvJ1wByDcg+DbwLVmtFl6nWXShC3uCh/nY6ONCxDE59iR/x4STzKJeuf0oH+gL6z8OFO+lRFgP2mM80H+miK9WNE3bx7BCgoodu338hSUsT39TZOAcfXVm7W9sO/XG+lrCaZOj+gY/3PPXFqcpl6ud7tmsytDgIrE1L7vphw6DwO9U6u/b7Ltj8a6JSWBj4751OP876+7dyvzMUik9FfcUPIaEURiA+AG3Lh5zFUqpqfnppcWl9fLMSqK06E3NwSsheHl0WtW3S3xhxy9xP/jl8K/vnXlHdLbVLur2KK8ZqSe0fe/Lbr8pPP08e8/vGM/9lPzznxAffYr+zNuCPSdVZxtMTVgvThATmAvmxExiYe3TMTRYVQhcgyGwsAmBHuc0DIEa9ZhImudwEyOsCGXUT+Z4aWI3V+WS6R0awKLXaZQyLotPxjMGe0mNDejLl/uOHuvcu6ftnWoXwda3X2rYtevWB+/Xnjly59bJtp6zPSMXYRQc9N8ZDDT26673sc/34U6hBs9SEDeFmE6ATvaoZEm3YzKfXV9dXl2dXlxMzcx4xscNxaIok6HG40NbyBeN9qRS+FyOtZkmap6bC5b5yEXhyJJMsOVJpSSqU+8MYS+TcLyB5rq6uvb29oGBATweT6PR+Hy+SqWygdqQUZUxKcaN4lmAv6BiLoupK2z8IhU/SyRP4kbyw+w4XRhkKd0cvY1rgTjVHYPVTYN8j1nstio9No3HYfA6jV6HyWM2OnRGq8pqh1FQ4g6I/FF+KMkKp2mRLDGchTkQE8qS/BmaI8k0hFkyD4NjppM1VKSI3E3HNeMHbvW232xtuHUHJsCbN2trapuuNnZcaus/34u5gKVcJo3cHh5po44MUCj4YSKDiOPhkLy+DnZzLeX6eczpD/s/eLf97Rc73nm578DuoWMf4s6eJV6qIV9tHK7pGq5BkK/iCJepyJq6geOHBk9se+Do/t4D7+4MYfcdO0ilaPnaHF+f5xnyXCDPAQtsaHs/EseYEZjiUktQZfdrHE7AbTN6TdYA4Ahq3GG5NyL2hbj+EMMfGA4EcIEAMhjoD4W6w+FtXA+H2u2BAZUTwzGTicBIr5p5R867IhYe50v3chSvczR/5Oif4IKP8o3fF1m+LrX9vcr+j3L7tyX2R8S2J0S2P4msbwgt74vMJ0XGK0JDvUDVxZNg2PwR5iiPRlZQcRABAeH6AczHG+HusRHbbcF32/GdLlK7n98elLWHVB0hXXsY6ghbOkP2jrCnMxLYLFAU7IgEumP+wVAAnc2OwC+/+zkW6/MahYVCAV7kpFKpaCQWDIS8Hp/D7rRabCajuUp99wqEjABoMoAWHWBV651KnUum8onlYaEkweFnmazyyMjMMGmegF/ColfRqBU0eh67CX4keobCjtEEPprEylDoGRoxQ0+ng0NUsJkEXcRCh9Gm3QjTHqR5N9qyC2Pejbe8Q7S8Sba+TLE+M2L59Yj5l0zrIyzbw1zbz0TOx2We36n8L+hCuw2R48bYFXPyjjXdZcsiHXmio0BzlljOEs9ZFjjG+NYyzG/VCsPVj6WKMOPBFAcjnNozofdNgsEqs9k3t5mFzVkHmLaDKSeUhm0F0zog4zSmXVVn4K9+Y3qmtLC4srG8uLa6sLY6v7JdJWVi/pMSKTB3Jcaq9VFgEgvkYSpbdWVgQluxJqq0BkaX9eEqv6kCS3JftQamyF39KvMtKf3LmmD1u2B0xRhbsSRW7KlVV3rVm13159dCxbVoaS0xVq2/kp3cKExXf+P4/N2phepjWFipLK9Vy7f8R8XonIq0ihTYsbRLxbpAuHcG9mT+L1ylP1+ry+tLc6tzk8tTpcVxmOUSVZbLhiY/a4gTMzDCGkpQSfBL0V4Rws3vdbI77KPNVlq9efgmRLgKYC/oUR9pB0+ot0Bu6JQGfVaHu6QnXdVTbhrotXpmo57bYhC068SdGnmvWtWv0A5IgEGBCcWxokYdmBEPhuTHYcI4RAzbncQ2Z3F1BezVMvbcJPr4DGr/AvKdZeQr64N/qAw8Vul5pNL5g0rrt6oNzOr/V+Xm/2vbd/6h0vD3lTv/u9LyjS2nb71IPYmv3nLHtf+jEn0Ail+a/nMiiluU+CCi+ED/OUpB1evLysdbuZamvpSWGFvKLa/+i9yM3bvbmf6bqb+yurQYAvWC7uaefW/+VcVplpYq4VBFqagQsLO4mxnBR27tLp3mp1rdo07Xh6kUambG+aBL4Wf16VRSrt8sDvrAVNJXyiamsoX5eHHRnZyFglPwqotjLAqtZY13whqdDmTmM2NLk3OrL3HffYbxzJu8N+/nR0mPPkZ54lXuOx/KT9YA9f3OIW6UC+bAyFRkZuXPPraA12bTa5PF5VxuKb1l46RGNSYUFhnsDJkRJ1BDaHqAyPePyn0irUcJuHSQDYDM1W7Seo1SJeIKR4bpg4O4+qb+85e7Pjzavmd329svbSYivtj0zjsN7x2sO3Ci8ezZttvnunsv9Q9fHZTXDHoaEIkWGAsH9DUI4TUc6+YIo1HEQZhlnIgZLMYji7Pwg7y7tjazuJiemfFOTIDFojiTGUkkkFsFReFVeCpF3EwWVU1NWefnw8vLxY2NpdnZ2Uwm4/F4DAaDQCAYHh52a1QQYNwxqDOBSvu9M7BDcvq8kT2voiyIcEss1Ap1cAU3tIjBTuEpBepoclQY5ii9fINDYDHx3Vs7Bqs0KPCZpH6rKmDT+q06j1nnBHVWHWDT2lxqT0AZiEpDCWEkzY3l6NEcMVRFQSyMgt7UsCVC1nqIYjOeqUETRcghel83tq2pu/Z23e2bt+tuNcLQeutOb00rsqYTe70XdwtBuIMldBAICBwajxoaQfTQO5ro9TXES6eHjn6wtc2v/Z0Xe95/d/DIIdSJ05gzV7DnarHnWtBnelCnkYhj+P6jI4Mf8bG39JRuJwPr49DCfG5cKEuLNVmRoSAEizxjlfT4VqXUw5J/bKVvRBtE7wxhi5xswCGNxrjR6Gg0TImE8OEgKhwaCIe6t2Bvx6FAt9fVB5jRIgOBqqIMShgtPNZ1tuDMqPgQU7GbpX2dCzzLN/1WZP2ZxP6QzP41heN/Ke3/orA/pLD/XGF/SmF7RW7dJTN9KIfOygzXxapGgbSHx0czR8kjw2wyToJH6jD9O6QHoHshbI8Z32MnVGHPO9wWoLSEqU3RkaY4pzkhaItL2uPy9oSmIw50x43dUUtPzNUb8/RG/d2RasWX9mi4KxoeiEexqQQll+GUCvLJSePkpGNqyj81FZ0YT5aKmVw2n05mYpFEwB9xOdle77DPR9my348LhXp3hrC9nmEToDLpQLPGYFPpnXKNR6oIiKRRnjDD5hTpjEkKeY5IWMRhVzGodSRiBYlawOKn8OQSiZYiM8NUrpMqNIxIxRQ5laToxypvIuRHuuQvNIp/fkv8vRrxD29Lf9mk/F2n9gUE8BbevIdi38dwfsBxH+T7DooDBySBA/LAB4rgQVX4uC76kT522ZCoA1LtYGYAymKhPNlYGIHyDGM1+2AU/uubi1xrSWgvS11j2s2qwpbItCM+40lWCwuHc9XyIamxpdzEcml6ZXx2dXphbX5pfWm12o6hsrJerYE5t/xnaJedgtELBjAYw6rwJvPCwHZfiz1LQncV7aTeJYV/WR1Y1oWWgUg1ZGeOr9iSq850NWTny60FC2uRUjVklxyvVtfMT1dDduNzG5ML1QzM+WX4kcCPp7qJ8MvV+t1qGBAmxs0s2OrzXVipPmUYJuHfO7VQbdsAP/et4p+lmeoZyE9X+TY7WUVcmEKT49WzAYMuzKXwU4ABFX4uMPH6c1Xo9WQX7el5U3L0BsBsscqx/vsZdVqrRLm3Q3MjIZgb5VifeOgTnBupN5FvVHEOc14/dGqb5QZPqFCnVdhzKuIlFeWainZLxapXcRtVwjaVpFOl6FNrEGo9Wg3h1ZZegr1vyN3X5+trD/Y1RPtuJHovZnpP53sOl7r3TnS/Od353HzHb5fafr7a/P1K4z9VSzxskVvd/6w0/mN1TdXx/Ur3jyv9v6ggnqyg/1DBPV/tPUZ5p0LfV2EeqnBPVoTnK5KrFcXtalsyfUcF6q+YURU7seKiVXzsSlBUiSoqSUMlY6kUXJVysDIZr1aRWBivLP95U0Q/rwIN7Hia2+rpaLh3purpBxtbvjT9ze9RPHPmzKfukMFgbM3s9Gn87Mzn6AEofvUFX8XMyO1jWE5Ctcn+l6G1u5Xf6+xnLt0Uilu2p/4LC16UuxUSZuOtzl0v/+vFae7erRQLFbNpgzUyhb+aYB1wKF9Sq35oMDzu9X2UzVHmFyLbt3ygj/WpVFKYMar966G031Xw+ceckWkgMCl1jLGNRZGtrPNN2mMzodx8dmIZXo2tb9xdu3u3vLQanlkwj83w05kWh/R3jKff4L9xQnnifn6M+hg9Ilxcn59YHYPZLzYf9M7arVOAflwuL/P4hRF6FkdOIvCRPnIIyQpSxEGOMiDRhVTGiMHkNBot1QiHyWyyO20mCFCLJbwRGnkQOdjQ1HH2UuuhI827321588UqDb75Quvbb7XuPti8/0TbkQudp67037w61HkFhbsyxL46IL86YKsdjDfDTIg01uPUd+iqLpEGYzTyYgH7TLm4WXMSBsLZpaXs7KwPBsJSSZLN0hMJ1BYQxuMD6TQ5n+eNjWmmp+3z89GVlfLa2tLExEQ8Hrfb7Wq1ms1m4/H47u7u2trahoaGgYG+0VGiQsGwWPghj8gs2tz+97l2CNQxoSYoNbkkDqvIC/E//hbXV+1BL/Wb5B6jwgUTJqi1gZAbcvr1/ogmnFDGMuJYlhPNjoQzxM0EUVwwi3NGsaAPrbIMCbT9NFEXgd4yhGnq7Gu509F2q7WttqelbqD5DqLxDqKpabC5ZbCtY6Cnt39ooA/f005qrCVcv4g+fbTvwO6Od6vR1/a3X+x67+3+Ax8MfHgCcfQC4tgNxLE7/Yfbuz/o7fkA3XeEgr4iGm4BmSgvjxIScmISaUqmyUoNeSlUlJhKAnOJU431FcW2stpdBP0ZezTuiQeDSU80Y0lkDamcKpMTp5LMeIz8rEb/f9SOf1LbP7bjz4f2/6VytPD4WD6jj89t4olu8BTnBZpjIuB9seltqfUFmf0puf0XCscPlI6vqarg93WV/WGV/XGV7Y9q66tK83sK6IhMf1akus4TNzA5XXTGEGWYSMKzCGgJDmHA9EHYfhO+30rssxN7XaRu33BHiNoaG2lK0e9kmQ15Vn2BfafAbixwmgqC1ryks6Dqzmn70sBAyjiUtqOSLnTSh04EUIkIMhbpj0RgfO2MRvuTCVwqRU2nWJmUKJ2Qp2LqREQfC5rCPmvAbffa3U6T2wo6LaDdDFjBqs0AsB3uA0EIAI0AYAIMJkBvgnRGkwayqgwGvcvoKzhjU1s2Bsdk9vzOELbKGAZY8hk8aYxMzVJHwyMcO5WvoYo5JAkGK2lGSM73it9rFT/fKHmsVvaLeuWfuvTvIIwHCdaTVOcFpreGF7gtCt+RR5uV8RZNok2b6tClugzpHkNmEMiggSwJzNPBAgMsDIN5IpTHGXNoKIcx53C2PMFeoLiLDO8YLzguj0yCyRl3fj5eXsxPVjFvYm51Zm5lYW51eW5lbfa+dAdjTJVhPFkY0mBUqwIbFF02/KWonfzPszHvRTt/Fe2WlIFlZWBFF4INE2D1K3zjzWPYS9oQfCcbyfG7iytV0tuir+l76GvsM/QFI2KVvsar9BX/DH358zBSbtFX9THAj8SRqj4Fa7IaToQfnjEGexmMwixajTTCD0MTXFYHq88Lplb4qcFYK60+u1mea5rjmmA5yyxngeHK0V0pmjM+4ohRnWGyI0ByegkOF87hwDlsGLsZaYcQNsOATdtnVfVYlV0WWbtZ3GoWNJt4jUZ2PcSshWg3gOGrBtJlPeG8FvORGnVKhTi2nWA5eEyJPCGnnBkeOAZz3SceOK66d9h/WKG9ctvY0GK9U+dsuOatPxtoOB6p/yBR906m9uX87afLN341ce2hmevfWqj5l+Wav1+/8f/bprimf660fbvS9XAV4YZ+U8H8sUJ8ZZPf3q+wj1T4H1XElyuK2mqOlaGzilgWdMVOqrhheONUQpJKTFVJApWstVJwV8ZC1SqAs/nKwkRl5fM7gv5Vuru2cXd5fWNhbWN2ZX1qeX1iaa28uFpYWM3NraRnVxIzy7Hp5fDUUmBi0Te+6B5bcJQXbMVUuzVyWbfl8CVt+LIW/hq5ot9x+IpuMfJgk8uXpv+KyZ9/vbYik/DXnQN4Ej4wbgo+2LrZZ2c+Rw9A8SsuL6vS+cPKxsfxrsl4RXWzsvZ/2ef6U7oVTD1BYvNvvLM9/i8peJlu4bOoNRfa3n7hXy9Os7BQCQbW5Zxx6pUIc5dF9nuV8tsQ8HQgWFMscpeXi9s3e6BNlL43lRQSBECu36qI2gwpszmntxWl1hLMhBL7mME/6YjPRPIL8AJuZhF+q6z+OIyFY8urkZkFy9iMLDeODLiuGkcPKZvf4B95kvb0T4iPvMp76xX2Ky+zX3qT//ou4Tt7RO++QX/p+YEn4YMdvyV4vcXZuMWBzABZGGAr/VK9Vw25AZMDMlm3F8Fmi9nlcnl9fiNklImkYoFELJR31DY1fHTx9oFDDbve3gwMVt36zpttu/Z37DnR8cG57iOXB89cw16qwdddwvZcGBg40UM61Se6NGipRcSaEcEmjL2FaukR2PCAlx9O2eaWtt+q19fnlpZys7P+yUljqSTNZhnJJBpeym8CISKToRQKgvFx3fS0c2EhvrIysbg4XywWg8GgyWSSSqV0Oh2JRHZ2tra13UahmpnMXrkcZTQSQhZS2kwogZgJNWJS1DvL7lmiD6wRB++iqtbKgy5NLGjO3M9GcVAssYNiNyBxQAqHSes2m/xmd9gYTMIoKA0leMHEaDA5HEjiAqlqVNCXRNuCQ3p7v0zfxRW3U5mdWGpfPxHRhkHWIgdv9PVd6+6t6em53tVd09Z7s3WwrnnoVh3y+rWhs6cHjhzo2fc2zH6bZ/WFrj1v9ezb1/fBsb4DH/UeuNK9/3bXvuae93v6DqFRH9HItbLRPpOQHJCwY3JxUqXOKoGCylhUWEpSa1lkKXFMVQKU2EsadxoKxB2RkDfuCSatkZQhkVEnM+JUmpPO0FIpQiyKiES2W7FXHe4MeHs9doQdQBnlGA0LJxilDjPZv1WYfgX5X7KF7+fvaJ3H2bLHleY/yqFXpIZdIvVBvvw0V3RllFNHo3dSKUNk0vAwkUvGqUhoKwlhIw26KINeSm9wpCdK70iOtuRYd8rc2gnerSn+rSle7TS/flrUOCVumZK0j0t7xhWDJTW6COAKJlLeTsm5aVkfPR1kpMK0RIQajxCiEXQ00reZrdoTCSMiIVwkSA75aAE3y+fgeawip0lqg1QWg8aoBwGdGdBbDXoLDH4gWH3Nb1KfSQ+Y9aBNBzo0oEujd2j1TkBrB1Q2UGkD5ZYgV55gCgo05iR5ZBGHW0Miq4WaEQN3UUPreOwKibwwQptksMTGpMYzYQpN3ePpe4c8U2mAa63nhNpFkR5pbEiVRGvSeF2GZMhSDDnax/m6VDCPB3NDYLYLyDQDqVpD8ro+cdGQuAgkLkPJq6bkTXOy3pZqd6YHXCmyJ83zpNTelNOTjPkSY97Ekjuy7Agt24LLZv8S5F0yuBf1nkWNa0ntWlQ6l+TOJZljQeJYlDgXRY5FkXNJCNu1KHQuCh1L8IzQtiC0LPCN8zxwnmuYZ2nnmOpZumJ2RDI7LJgicKdxzEk0bRJBnuzFj3ehx1sHxhp7ynVtpZo7hSu38xeu5T66nDt1IXviXPboR9kjp9IHT2QOHE+/fzj93qHUngOpd/en3t6X6hMHr2N3HL2NzrSj7p2JNDFmhJ5qIBEGNpghYXiDiXQroghDnSlepTtrAoa9ZWty3pKaMSYmwcS4IVHSx/OaWFYVTyticWksIo4GhRE/P+zhhJysoI0RNI8EjMN+kOQzELxajFeFdMsGneJeu7DbzuuwsVuso3fMtFoj5SZEug7gL+kx5/XIM9rB46q+I8r+oyrESRXqjBp7Tk24pCZfVVNr1PSbSuYtGadOwm8Qipr40ia2ooWhbqHoWghAK9rYOmBp6bS3trhabnubrwaaz4Ubj0Ub9sMUl659KXfzD8Ubvxq79qPJa9+HQW7++r8s3fiH1Rv/Y+Pm/2c7KFf/d8UbL6Zq9qduH9ly7OYp37UrO8Oqr+xaJB6rKOsrurZqLM6CqTiGK15mNRAXU1dSYCVnr5QD1SXNbKGaJ7X2b9vxASPZUnByyT+x6BlbdJYX7KV5S3HeWJgDcnO67KwmM6NMzchT05LEtDA+xYtNcSKTzPAEPTQxEhwfDowTfWN47xjGU0a6S4POUp+j2GMvdtoKbZZCiznfaMrVG3O1UPYmkL1uyFzS7RgewpPwt+Ab5JtM8I0L7dZilw3+8VK/o4xwwXcI3y185/CvgH8R/OveuCn/50uCz7cKTG8/sQf6wvqqgSJMgz/4wQ+2vrvDh5+a2Tq+nx6A4ldZMB/ClOjZ6XR/twJ2VJK67dEXk25y9mtSiL/rhaXV/8v+BP+uKiViAI2MP38cXqr+K8VpNjYquewKKC5yrgZZrxmlv1Qqvm0xvBAJ3RkbU6yt/XkSyH9j7aSShmxZmzIKcP0Azw/JowZNQq5NCYCczFEGApOuxGy0sFCcWplb+qRlMnw0vrwanV2EsVCem6DGss1O/TkD7oDixivcPb8Yfuxx6hPviw+2WNoEMUF0KlpeKUTm/XqHxmw2WWyWqq2WIWLjzUPPwQc7gq9vWygIH2+hoNMb1OiNbK4Ej6V2tfbXXau/euLctf2Hbr/71g4Ntu16s23/oY73T3a+d7br/Uv9h6+iTtfgz1/Bnf1o8OyBzku7utsO9OBPDkiuIW0NQ7GWoXAL0dfN9mPVMa6nZCot55c2qh+1rK8vLC3l5+aCk5OmzTb0o5sNJ6ot3RIJZCZT3dk1Pm6YmXEvLqZgIJydnU6n0x6PR6/Xi0Tc0VE8hdJLJDZRKA18fqtO2+OCBqLQYNbQV1J3T0i6Z7j9S0zE6jDiLhZRQQ6uE1Dro+RlEWdGpR43WPPGYMIYDRhiDmXEKAjo5CGHMuo1JHfs0SfuHUKCIFNlIzlDGGcY40tgQlm0J4YweQa0pj6Jto8jGxgWoFAsYgeZWIfEX+5Cn2tBnmscOluH+OgG4vQVxImP+g8f7t6/t2PXZmlWGKrffL7z3Te69rzX9d7hzvdOdey52PXeja69jX2HelGnccRrTHqrgo+2KtkRtTip0+T0YEFnLmntYxrnmMIxJrKWuaYS11iQ2jJaT9zoD9rDbm/MGowDkZQ6npYm0rxkip5MEeGTeU8bj/ZwuDsUGAh6MV4bwQGQjTKqljUiwY8IBkboKDYWJ+odVjQydFfY+tNCcK8EelkGwWT4Q4X5H1X2v1M5HpKbnpGDf7SGDnjj9/O3tC4sgx5jdqfZnXl2ywSvYVZ4a1F0fUVybUl0Y0lye0HSMCtpnpZ2TEh7xmQDYwpUWUMs6WgFkJUzCzMWYcopTbhkMZ8s4hVHfLyIfzTsJ4f8WPiRh0Pd4VB7ONgb8CJ8LozXQfDYqS7rqN3MsZuFFqPEYlIZIQ0EAQAAI58RrGob/HSAVQM61JBLAXrkoF9p8Oj0ToPKZlOa3XIgJNYkBfIyVzjF5MzT6HNU+vzwyCyJMkWklqnsPEuY5iniIm1YAvrlFr3YIhPauUIHXegmC704UQApCg1KokOyOFqZJmmyI/pqFweMNgP/mQSW8v3MhgofCn3v6SJ7tKE92uB72sBedeC42HqRA9bTVV1kMRrNpCFo4n6Krpds6SL5O4mxdkK2k1TuGp7qpU0PcWZw4rlh1TzdsMCxLAmcixwLfAzPwPPTSM5UH22ykzTeih27gyjf7i1eayteulM4eyt/6lru6IXsoY8y+0+kdx9Kv7s/+druxCvvxJ9/Pfbsi7HfPxf57dPR3z4T+e0z0aeejT71p9jTz0WffiH+7EuxP70ce+7V+ItvJF56M/7yG/FX3kq+9m7ijV3Jt95L7NmT2LcndmBP/NDe2NG9sZN742f2xs/ti17YG7m8N3JtX+TmvmjdvlDD+6Gm98Nt74fa9wW79vm7P3D1HLF1nyBfJCNPqTDndqzGnNViz6thb80MHFNw21j8Xien085stdK36pps5k9iL+rRZ7VDpz4pbfLxjjgD8RowfBMcqQUZ9QCrCahuimvTiTs00i6loluu7pPo+gVAP8s0yLAMDjsQOPcg0jvYGxhoCw/Ux/prkr3nMz0n8t0HS127xztemWp9erbx8YU7P16u+9barXv2wm259v9b3RTX8vVK+/c28yp/uRmUe6ZCeKlCfqMysqfaiplzrCI4W5FcqchvVZMqt+NymLsWyl0ba8MhWHfK1936Nbdx1W1f9XpXvMFlb2zZn1ny5pe2A2WlYoM4c1mXubLtLPz1cvXrJ76km+iXTVAC4yR/FcmwnjLKDXMUTFNVJOuywXz1eUh2TZ+tAXK3wFwdlG8w5pvNhVZLocNa7LbBUFflsSFXGe0ew3nHCb5xsn+CGpighSZHw5PsyBQvCsPhtDgxI0tOKFJlRSqnTGXU6aQmHdGkg9qUV5dxalM2fdqoTxn0KY0+pTQkpYakUJfgahNMXZymiQ9r4nh1DKOOIZXRfmWkWxFuk4aaxIF6gf8G13uV5b7IcH1Ec5yk2I+QrAcJlvexpt0o49tD0GsDwEu9hue6dU93aH/Xqv51s+p7N6SfwsJP+WuXBbrw2NZb7QN9cf1tg+KWYPyDtXXMYDB2sBA+gIefndk6hpXP50mf0W9+85sHoPiVlQVdvdDvKGeu6JsqX8ZuutLK2jcVJtS7u0OhLwc7vxTd3djI+NwqPBJ5fP+/XpxmbnbBJc7Krnq5LxkkP1ErvmfXvxYPtExOQhsbX0679r91LS+sljIzIVfBrktAohDA8RkEAa0kIlfEVWAW9Iy7k7Px4mJpemVh+c9eVOt3K/diISNRRAQitVbxOcPAB/ILz7Ff/QnxkT+OPveR+izShdRmtLHZSGIhYpsG5WUeLYtFJtpQiQ56BI+vudCz/+3eD97Zcte+Nzp2v7IzhN39/lu9DfUXz9deOHz20r4PYBpsefOFbYzZ9Ub7/gNdH37U++HVrvevdb53vfeD64hjV5DHz6FPncGdO9u+7+WO8+/2tH6Iol3FahowrtahaCs62k6NIkQpumlCF573wUy4vLG0vr64vFyYmwtNTprLZUUux9wEwmq+XyKBzmbppZJkYgKcnfUuLqZXVqbGx8disYDDoQMArkpFkkoHxeIOiaRJo2m2Q+0RU1cG6izpusdlPVP8gQXW0OoIagOPhFHwLh61QSdtCFgbavma2TJn94+7ojlnKu7I+I0phyoKA+FWsijI8xvFfpPMC8ockMqp17s5QIivj/EN2xZqIzJ5aGdYtT5KZkiHu4dpdajhS+2EYzXYw5fQh84iDhzr27e/a/fb7W9tBQCrBNj+zmvt7+5uf/dAx+4TnXsudO292X+oBXlygHiFSGvgcAe08mGHXpwAdDmjsWi0lSHXOOiZ0HurNfcl9jGeqcQzZ+T2mN4TNAU89pDNGwMDcXU4IYmn+InUaDxBgk9dLNa3g3/RaF8sioqGCGEvJWClO7R0i5ShZ9BVBLp4kMprp8Ae7WNiUZw+Ev8OTXCFJT7Ole3mK54Tqn8l0n1favwHhe3vlbbvyU2PScHnxdo9QuUpgeQ2n9/HZdG4I0ouPsAdKPI7pgRN9QrGhxrxh9ptv6ZTfVdj3hnCPqQRygX4tIqZVItjGmVIrQ9oIbfW5AbMLgNMZnKHge+CWG7ziMdG8juxQd9QKNAbDnWEQl2hQF/AN+TzYLwuottJcdrpNgvLZuWbTSKLWWE0wgSo28rzNABmLWCBwU8FulWQV6F3q3QuldquUVgMQr2Fq3GMSt00YXCYFyKyIyRWhMQJkbkhqiBAE/tYSjdXZ+eDRj4ECMwaoV0mdomkfq4izNYktoJ4nzJVl8OrM2hFEiGODvADA2xP76ijm2btGjG2U4DmEaB2RH9t1HCZYzwvMp+S2o/JnYeVHpU2bgAyO9YCWSlUuHfGoE8bZM4FkXtO6JoXOucF9kWBbZFvrZoH2zLPgea4wBxbN8fWzrI1s2zFDFc+xZdM8kVTIsGEhDsm54wpmWU1owTQC+BwHiLmIHzGhEmbUWkzImUZSFl7EtbuuK09ZmuN2hoj9jsh++2Q40bAfi3gvOx3XPDZP/LYT7ntx932w07bQYdtv836ns22y2p922J53Wx+2WR6ATI+C0J/AMDfGoBf6/S/1Op+ptH+WK35oUr9XYXy67Bl0u9JhY+IOI8JRp/iUf/EJb/Dxn3ARB8fRVxg9N0Y6WyktnaTGxHEWhzuOgV9iTH0EW/whAgmuoFjcuRHIvRFPv0cFnlKiT27bcxZFfqsZmcIe+Cowtp5PU5GpMg9WVJTgXi7TLgygTszjT0yh967iHxjBfFsZeBX1bomHd+v0hrMbPdSXN3/qNz5P5u74x6q3mbgsQrydxXsnyqEl++Sdm8MH9ygnVynX1wbvbnGurPK7ljh9q/w0csC0rKQtiRiL4oEi2Lpglg1L9bPi6A5sXlWZJ8VumeE/mlBYJofhgEJxqRJRmiCFhyHCY3oK+FtRaypgNbnkKosQpYeECb7uYme0Xj3SKSbFOnEhtuRwdb+YFtPoL3T39nm62r2dTd4Buq8yNsezE0P/rqbeMU9fMlFO++in3UwTzk5Jxzco3b+YSrpYB/9xR23Ud64jtt/70w/7dUAj7dgLy26yove8WnvWMk7lvOMJz1jYU/Z5y46XEWzs2Bw5FX2nNSW45szTHN6xJImQkm0Pj6gjnYpwh2y0P38FgJ8ua/KY890ap9qUz/RrHqsUfHTevnDt6UP3ZB8+5roXhK7n79fI4ZvD//UrxqVv2lR/aFDA9/hK30G+M53oYww+x0iWI4N206POC4wXDAc3uR664X+FkkQfmx9qghSF8MDiWFjim7NcBxZoTsv8xXVwTIYGzcnJhzpKW9uJlycY1tTOH2YDMaoUIxhivO0kIiB4NqS97o8+6CE3pemv21QNH4mv/SzWPjZma1jWDab7ZnP6Je//OUDUPxqam2p+o4S12wPN1YrmrpKybs9/AK6W6k8DdgP327WDNdvT/2nan11NWo1igc6+w+8+/nFae6urcyEpSntdZfwea3sIa38Ry7NWylP5+z0g2o0lcWV9Wxuzu8tWYE0KIsYeH4D26cVBPWKGAiknZ6xWG5+bGZ1aeXT5RNgLJzYxELrx1iIDee6vY56K+u8vn2f5NhTjKcfIT7yNv/tm8BNaoAKFcDIbMA1Y1aPiZg5IjrZgUi0kBIIcYxjCGusPrPFtrl3EIJQZ49iTh+m3rh4P/e+/2bfiWP9J8+jztUiTzUMfFjfve9W556avgNX+w+d6t2/b4t82g+8imw4TiTWUKStFEsPxd8/Eh7CxDsZaZyizLdMGbaYcHFtanm5NDcXnpqylMvKXI6VSuE3ewN0wWSYy42WSrLJSePsrH9+PjY1Fcpk7MGgwukctVgIJhPCaOyyWts9jta4oz1r6cxpO0uy3nFe/wwTuTSCWiOgKihEBYu8O0Lc4DErKkXFZKx4PauR+GwsOxYrZ0PlmDPvA1N2ZRTi+7eAEBL4zdKAWe4DFS6D0q7VOcUGJwO0ESAbxuhAWrwoTwwdzAzZQ3iDWVOY9E3N38/kULrz+uW2t15te/vd9l0HOnYd79hzvvfAzaHjbbgLg9Tbw9xOoRSr07JdJm3Oai7ZnGM274QlMGkKVIvyqz0TMAEKLDmBKaq0Bw1ejzlgc4Yhb0QTjEsjCX40wYwnh+NxDIx8WxsvYZCORQZjIWzcT4q4qAEz3aun26V0K58OMeh6IkWNJisHibJenKwboxxA8zAYMgE3OExqHKFdonOOcsTv8pXPibQw+H1PYf7fStvfqRzfVlh+KQefl2n3SmTnJYIWySheQpDJBryylrL85qrsZjXcJ70zK2uZlnVOyvsmFKgxNWFMO1LUsXJ6UUqnimpBr48aiAkjKe2W9WEI43LuDGF7QlSvmRC094Y8XeFg9bmEAp1BT4/f2e+zD7mtGJeF6LSMOKxMm4VvsUhMJgUEavUGk9ZgVQNONehSa20KhVUhhBQcnYKuVFBkcrxAheNrMFwDhgviBABRDFAV4KgO4EI6oVUtcSkUAak6ItIleIYMC9zO2ITNBPIcQ4arTXI0caYyMiILE8VBhCDQyvbdonvPD7uO4GwHMdbDaMsxjPks1nwZb75Gtt6gOW7QnddG7TfZjttcZ63IWStxXlO4L8udlxWuK3JXj9RJkTrFYqdR7PCLnGmxc0LkrG5XEznGxPaCxJyTGDJSVUIErfnz1b1z9/Gc0i8XX9Fpf6XT/FyreVSjeVitfkil+s4Wff01Vqm/r9Y8rNU9qtP/Qm/4NcxyIPS00fQnk/kls+U1q/Utm3233bHP6Tzgch/xeE56fR/5/BcDgavB0I1QuC4SaYxGW+Px7kSiP5kaikVxEf9w0E3zWdlukGfXis0yKSRQ6llq9YhORtCJUAZer57VDtDu6Ek3dLhLWuRpdf8R5eBxFea8jlQD0urN7FaToMso6wfVSD2AUVnwYieR4ydTo8OYNLmnQK6fIF+ZJRxdxr17F/OnKrB1/ehu07ez15DZq+p7rKlGxu6ZyVxVTbQ2zWGRc1jcLI48g6PN4FnTeP4UTjSJF47hBSW8sEQQ5HG8PI6bxbJSmNEUhpbAUOLo4RgKFxlChxGI0GCvf6DT39/q7W/09tV6+m66B667EJddiItO5Dk76pQdfdyKPWLFH7SRD1ip+80j75npu0yj75jYb0KcVyH+yxD/eVD0J0DyjEHylF7ypF72a638F1rFTzWKH6sUP1QqvvepP9D9rFR9X63+kVb7iE73M73hcQPwJAg+BUHPGE3P6aDX5IZdAu0HbPXREcVpgvQSSlTTz6/r4DQ1Mttv0Xp+ewf/9Sv8b13d9jevCmDvDGF/7RL/j03I79WIP8VmsGGKg1muSmh1cpjuYMaDSQ/mved79DCkvTFYbQG6F2P6AG8+TLQe/5jTrrDcNRxvLd/XKArAoNitCMMwidLFCWCCYkoxqqiWE3kKcn9RE6qimiUx6cxM+fIzkfJcamIhP700Nrcys7i6uPrXLhU2NjbW1tZWV1dXVlaWlpYWFxcXFhbm5uZmZ2enp6enxiYnC+PjmdJYsliO5IqBTN6TzDsTWUs0Y4xkgHBKG0iq/EmZt9ApzN3U/Jlv/PnwlmrG/iUs7R5oS3/boNjR0bFTOhU+gIefTTT97MzW8f30IPX0KyttS4Xw8vYxrKi0YhnaPv5iao3lfkETSE++tj3+TxKMgj6ditve0PXea59TnGZjY2kiLY0Zr9jkz6pk3wYkj/gUu3LWroUJ//Yt/vtpcWWjPLMSy887veMgmNUpYhpB0MDxVrNJJWGrPul3l7KZ2cWlaifoT2kLC2MwFo7PKD7GQkw41e8FG63D53R17wr3PUb51RPUJw9JD7VZ2jgRNlQCXDNW/biMk6fgUj0wFmKSXbwkTRtTWMMmu9tmMpuqXGg0aU12ts7ROSz5qAG19/itnnP7qDUXxH0d93PPB291XzjT9d6utrdeaH/7pcEP95PqLrLILVz5IM+KZQax1DgSk+iEfykrT1KWBdYpIDLvh5lwZWN5fj4yNWWFgTCfZ6dShGi0B2bCVAoH82GxKB4bU4+P6yYmgHxeGYvxgsFhLwxlnl6/vyMcakt4O9LWjpSmMyXqznP6xxmIWQpymYjcQCPuIhF3hwkVDrMil1VAoOJ0VCLhSj63UpqcKc2WM9OZ0FjUkfMCSZs8AvK2gRAQ+HUSn1rsUkrsCrlVpbXLjW6O2UmxuPBmD8YRxPiTmGAGbQ9jtHY8V0si8Ia78MQbrajj57o3s2qH+Qy6GWQ6LNv2OBhh/ydDh4Vi0PCpUq9nwhOc8kSnXbEZa3QGDEzBBChzlMSWhNgSUDncgMdmDUDOkMYbkQVjgnCcGYlT4glsLDYYDnSFvB0BZ0/IMRi0YkIWYhAaDhioPtWISzJi51HNjGHjMAEgYPUYlA6F0KO3y3Xqkf0a5BAXQyASqH3DzEYG7zJbdIwv2yVSPS/V/UoOfE9h+ntlNS/0W0rzYwr9ywr5hwruNQWtU04myfBCGRqSIcIyxJhsYFyGHJNjxxXEcTm5LKWUhCN57miayY7RhEGqzEOSO8hKK8VgoplMXKtJANr4Uid31M3GeZl9PnZbgNsQkDbIbAGBpSC2bVtkLQqtpZ0hbLaxYLBoQbNOazKrIJcC8MsBv0wfkOmDcl1IoQ8rDRGlIaYwxJRgUgmmlFBaDqWlUEYEZgVglgPmtxiPDRZ4QFakT0l1caUqpFH6DQovIHdDMqdR5gQkDq3QJuNZuSwLmW4aGDbWYsHjg4bXOrW/vaN8+Lb8ezWSH9WIHrvJ+2Md960m9oGO0bMDzFtodhOB3U3jDzCFKJ4EL1ISJVqqDGDITDypVSpxGiROr9iRFjvGRY4t8CuLLFkxEJcoIhJuSEoOSPoD0ltB2QWf7KRb8aFDtc+u3WU1vGUBXjOanjdbXrXa3rHb9zg2wSzDMC6KPQsfGz6+dwgb/hU+6UihwC6WBOWyZGxMOT6hnZwEpqbM0zP22Vn33FxgYSG6uJhcWsqtrJRWVyfW1mY3NpO3P0ery+sL0ytTxYVScjYTmIw7x4LGgluTsUmSEDuqHQkpcD4Rws3ptDMaLcO3INwlA/K0pu+IEnFSjb2g3+Q9iN0CCjpBWZ9Og1ABSIkVzXej6UEMOYZCZZA9BUTL2MDtqd7Ls52nFto+XLjzwXzt3rkb781e2zd74/jM7UvT9bVTDW1jLW2l9q5cZ2+2pzfd2xvv7432dUUG2gMDzf7Beg/ilnvwqhNx3o46DbGfUSi+8YmVsL9+74xS/i0n8WmN5FG19GGl9Acq+V9AMpXi+2rFDzWKn2iVP9UpH9MrHzconwTUvwc1z0Ca543al8y61yyGN63gLptxr8P0gcv8oct23OM45XWd93kuBXzXQ8Hb4UhDJNocjbVv83MSkUpj0hl8JkvO5UbyeUahwCkW+aXq30s+Pq6emNBPTkJT05aZGUeu7IzmPZ6U1xoP6EMBhS/Id4YYlhARDCE0QZivYNCCies83Qkz2H6c+Z0h6KVewx86NI83KX9SK/vO9e1YHIxzP74thSfhb73cZ4Dh7QDecpJiv8R03eb5YLq7IwrqwmP38xPNSpo5WZjZxrOFlS/5k9yZmRkY1SZy5bFUsRzPl8LZgj9d8KRyjm1USwOhtDaYVPoSUm9C5I7zXVG2PTZqj9FtEYolOmyJEExhjDGKNkaGoPAAEOkHoz1AtAuItOmjLfpoky7WqE/c0SXqdMnb2vQtbfqGLn1dm7mi/SQ/9qo2W6PO3FRnb6lytYp8gyJ/R1pskhaaRcUWYbmdX+7gjnWxE63i9EXt5zh7STdmeNAe40vT3zYoMjbLmW4dwwdb0cIdPrz3W5+a+Rw9AMWvphYnq614ih9/yLQ8U22JMV/aHn4B2WYW/lFuEr30x9X5/5yuiXOT4w6JgF53rePdl+5XnGZ1bapcEIUtF8yq3ysV3zKKfxEU7SmCXStZ/33L2HwVBT/V+aX10vRKrLjgTs6CnnEVmJXJ4xpJxCAIVvmE77eqY0F7Lp+cWphZ/mxz5I27lcmVNRgLbZtYOJoo4cI5fDhHjSWQfnWTFX1affkV7muPkB55jvncWfVZhBPBS3DAsg6cUAmKdGK6H8ZCVLJ9NENQpyTWhNEVcjic9s14IaSHzByNpZdjONVK2fVR66kT1+oPH23b9Wrn/t2oi5eZzf39nUfJjRdZzbe3PFxzfuDQ7p0h7IHbh4jMO8o4W5RjMHI4dLITn+pl58mqMeEWE+YXwjOLselpF0x9hQI/nSbHYtUG4qkUMZOhZLOMXI4JGz5IJEiRCDIc3t4LF/e3x8wdEWVHhNcRo3dmhnvGiAOzOMQqjIJDA+s4zDqdVpGIK3ptxW6rBAOVTLoyOVFZWl5eWJ0ZWyilptLBcsies+njoCQEcLfri6p4XiHPyeFZWTyILQR5CpMIdAidfobDN+wM4j0xTCCNDWWxjghObSNyNUQsC9+ORF2r7Tn8Yds71dTQ9rde6nxnV8+ew4OHLmLPNY3cQgr6WSq6zm7qrRa9TI5sORGlxLyknSHsSKRbq6G6w3JfRBAIs0KB4aAXE3AO+i09HrDDC/R5dAiPGuWSY11SvFNEcPAINibeQsMayUgAO7iFfFWj+vSoQS0SpRrAKPswsi6MshenRRCFBAqNSkcxGB1c9k0h74xE+L5M+pJc+aRS/z2l6X9tcuDXVJafK/XPKdV7FdKPZII6KadfxBjhUWTwL6IgEqi2dGdt+tbl1OnDiX17w+/u9e864N17zHXgrO3odevFFks92tLNNGJlplGtSQxYRDKnmOuWjviU+KAWFTT0h809YWdX2NcRDreHAh1BX4/fPeB24x0uhtnBN9rlBqtWazZKbXGhtSxzVHua/0XzTEUZZOUaCyJjQQLl5EBaZYjrdGFA6zcpXRap1SmBvEJdgK2IMiRJujBD46ZpzBSVHqNQ/GSijYjT4NAsRD+qf+BOD/JMO/atRuLjt0e+d431z5f48Br6a5d5D9+kPd1I3tWBPd2HuIXqbycMDA4PEhloGpvI5Y5IBByNWAqK1TYx6JNYk2LnmMi1KKp2VpgSOzMie1Rq9UtNMMI61Fq7RuU0SD1GccQmyHoE5aBgLCYeS8vGCzAAgNPTFpjW5udDMKotLxdWVyfX1/8N7ekUPfa4KrVenr+fJc3mrOe+zZZWl9bnp5arvJeYgXkv5igHoYJbnbGKN3mPusl7g1Xeo8O8dxPCXwLQpzWoo0rCKc3IBR37ml54Sy+vM2gbdVCz2tqscLVKfK2CUCs72spINo9kmsnZO/hCA6ZUhyzWdRfr27J1zZmGxuSdxlhzU7SlOdLeFOhs9Pc0eHpvu/trXIhL1bAb+rgV96GFuM8yvMtEe9M4+jLIfh7gPa0X/EYnelwj/Zla+iOlfDslVaV4SKN4RK/8pUHxG0j5R6PqRbPmLZt2j0N/0AWc8ELn/ZbrIVtdxNlqo5zRMl7Xjb6hZcB+XcV+ViL+kW70zR0b6HtKTsHSUmZ5Ob+yMrb5t/jXmfmv1OLq+vj8SnpyMVScdaSnwNi4MlDiu/I0SxoPJAbU0TZpqE7gu8pynxlxHCZa30ObXh8EnuvW/bZV/fMGxQ9vSb95VbjFeP9yWfC9GvFP6+Qwzj3bpX11ANiDNn5ItMI/CP94vdDfIQsNaWNEKMm0ZcWegjZctiQmffmZxPhCeXZ5bnnt899m19fX37wjgn92e/yX9EyzijWkzotcOb4jz7FlWZYMzZihQGkymCECaZwujdalkdrUoDY9oE71qpJdqlSHMtmmTLcqU82Kjy1PtygyLYp0szwDu0We3XSuVZ6+tMls1zWZGnX2pip7W7mJavJ8o6zQLCm2iIttolKHoNzJK3dzxnqYE32jkwP0qcGRqaHh6SHSNAo/i8bOYdFzeOQ8AbFA7F8g9SxSupconUu0tmV6y8po88po0yqrYY1Tt86rXeff2hDeuCu8fldyrSK7UpFdrsgvVWSXKoorFcXVivJadYWmqqmoblRUN9e0t5f0dXNAwyTUWDY38z3do2GsIEK5n8mB3kzoyyll/0Cw/rZBEdZOewz4YGtmix5h7WSZfnbmc/QAFL+aEl+qMPZvH8NyUyu+f/3F8K9qem39eypjz74DadvHGa3/UZrIZowsGvnKR21vv/AXi9MsLqULBZbf+RGoeaJajUb4ZJTz3riqaz3srKwsb9/oqyuY6GYX1wuTy9HCgjMxu9mIoszXZ+W6tF6TgORRcHN7m1kS8hlT6UB5Ij8LI832D3+sjcoOFs4q8x9jYSTHTpaYCT/aJ2g09xxVnHhm9JlHSY++zX/7BnAD40XxU2z9mEJSYlEySBgLkYk2WharyAksGdAdd7r9bpPFCmOhAYSEGuMgDzjTxz14C/XRldb6c5c7Pny//d2XESeOUG/W0RuQtDom9rywZz+2e19Tz/5zlHCnsMCAJtRbFumxg2ff3xnCJsJY6OuzTYGhWWdm1jM5652chIpFQSYDA+FAJNIRi/UlEshkEpVMouNxxObGwo83xQW6wubuoLLbz+0IjnRE8J1pTNc4emABWa39uIZGLhEJS8zRNYWsYjZVfN5KMlEpl6t1cWECv3t3ZnY5kpu2BMtaW0auj8kUYYXQr2dXaVDP9oo5bhrTQqIbhhlqOlvNFuk4GiPH6mK6gjRfjOiLY4NpTCiLc0bwWjuBpSBgGai23v6rVzoO72t/96WOt1/v3f3+4PsnMSduDF/rYncOSyhSrcxusqRsoWqDb71vUuEcF1rKAnNUbvPq3RYLEwHg+v/MmD8f4vr1o1SIiPgE+TYNYAcgIsZIIoAkMoAnA9hhPYqoHcBr+7CGPoxxEGnHDPmGERADKeeimEIsVkpol1OuyukfKjivKmS/Vui/ozTBEAj7H5W2H8uh30t1bwoVH7J4l0nUpv4B5J1m1qXLmgMfBl97PfHmW8m9+9KHj6TPX0reaoy3D0QHSGEsO0CR+OhaN8fkFJhsYsgCA50Shh+J18QPOjhhLyMaHI6EsJHwYCTcFQgOeAN4u2fE4uKCdonOqlJbIKXZIjO5pEa/yBgRGJM8KMMBc+ztyF6eC+V4YEpkjMsgvwKwyowpKDjtS8/dzwJrScVXwS+DJVTrFLohh7kdQt+womuUyBom4iZqoK6xu+lsW8fu5qGnaonfvcaCF9Y/uCb65U3JCw3CDzoFFwe4LRgehsxn0kVSlkzHU1lEepcIDEnNCamtKHFOip1b1Ad7XOROiN0eqdssdQEKt1bjVRsCGmNUb0+YfFlnuBBKj+fLs/NzS9W+ef/h8jVIEjc5n2+gGQL6Xdp2m6bJorxllF8FpBf0ko+00tMa5VmN/qIOuqyzXNM5ruvcN7T+G5rgLWm4lh+pGw3XD0fu4MLN6HDzYKi1x9/e4e9s8cJQ13fLPXDZNXTGiT7iwO23EXfbKG9aqK+YGM8bWc+AnKcA/hMG4WM68U810odVsu8rFd9SKL+hVHxXo/iRTvlzveoJUPO0UfeiRf+mDXrPYTzksZzw2s8H3DVhX0M01JGIDaTTuGyOWiiwS2Xx2LhqchKYnrbNzvkWFmJLS7nV1fH19fnNK+K/QbtQxi3Q+hzrPlN0ZH3j7sziamFmKTY2783NwMQFc5fYW2DZs2RjCqmLbQXxbnC9FxiuzwbxHqmVfff6n+Vnfg1+Nd6U/KxeDhMgzIFvDIJ7MaYjJOtZmrOG47kjCsB3CN/tsDHFtmelvqI+MmZLTQXyk+nSRHl8fH52fG22tDKVXZlILZWii4XgQs47n3DOhayzXuO0zTBl1E0aVBNKxbhENi4Ql9nCMkNYovLLJH4Jxy+ieMVBXqGPX+gS5DsE+RZhvlGcqxfnbkuzN+TZ68rsFXXmku7Nm7L3sSaYOe/nn9yUcm8LC/XCYiO/1Mwrt3DL7ZxyJ3u8hzXex5wcZE4MjU6iGFMY+gyBNkOkzg5T5keoc3TSIpO0yCIu8QjLQvyKCLsiwazKUWsq1LoataFDbgDIuyZkxTpU0TVVtA3V/Tjq21U8g1FNDsPb5eqB6mZFc7uia6jWdDC0VqDOiqm3Yh6sWJEVO67iIlU8IxX/aCXAroSElZi0klBXUvpKxlgp2NdKzqVx99y4Z3LKX57x5WYDyVlvdMYZmLF5pkz2KcA8qQPGlNqSSJ7niHMMfobKShNoSQwlicAnetGJ6kYM2LhENzHeR40jGDE0MzQwnEGM5gj3MybZEUuat19PD/SF9TcPil+6HoDiV1Azuequ98nE9nA6VVHWVL6M2qQvme172nrNHZe3x//euns3Hw7qhnGY0x/+peI0d+fmApkM0eM+odf8TK14yCF8JsF4f0rYsWE3Vcb/NoqAra2uz03927ahr29UZhbWchPL4dy8PTYDM4PYVmYbi1Jb2WAtms35aoMKWRjcjGLZFZGQNZuLjE+X5+HftX0Xm9rCwvjson0TC5mbWEiI5DipsjJX5iZsGB/9NtTwvvj9xyiPPTlSTSW9Y2rABTC89Ki0xNmqOjOUaKVmUfDQUgS8GZcr5INsTsNmjX6lHsKLDDVExbluxrUmZMvtpp5zH3Xte7t739u4c+coNa3k6zjMOXbvh3yYDBFH2xDHL/Xt39f57iv482fEhD69kUmNd90Lip81DIpgsG1zR1xnNNoVjXZGIm3RaHs02lHdWxgcjDtQUf1QRDwYZvQF8d0xZGdmoDs32JtHDKwiBubRqGkSYYbNmpPLVm3WSixabZ45O7u8tp5eWnHMLijK08x4ieTNYiwJtDZMkAdoQp+Y6zVshgd1bI+E5WCPmmhUDXVYPEwRjLBkI1ItDbQznEGqJ0zwwkBY7TqIdYRxWiuOI0NjSP2trd1XzncePdC9e8/g3sOowxcJ5++M1A9xBkbFDK1aGzC4SlsbArmmItuYl1hjOrff7Lc5Q4A/pvD72H472WdEuTS9DmmPTTBgZQ0ZqQgzbcjOovok/PvZREKCw3g7os+F6vdie6OkntRIxxizeYZXPyOuTcgarPJWoXwQJyM0yUbPyvnvyeR/kml/rgC/obRsceD/VtoekpmeFAOvCDSHuMprbHkHW0Lhy+QimUupKYLQot2xHAisJJIL6cJsbnyyOFdKT2XD4wlPMWxL+yC/G7DbNQarWuEExB4LL+hiBbxMv3/UE6C5/HSbj2bx8iC3zODSaW2QymaXmd1iU4BvjPKgJAfKsqrgV2BDOQ6U4UEpAZQQQmER6JWAZgmgkOioUl27THNOqtmlUr4KSF+2Cl/2cl8IM99K0PYVKKcnSdfncHeWUZ2gMqw35w224pa11qLCsn28ZR2Q7hyWP1Mrfe2OYl+L8ni7unZAO4ADaRSzlGkz8pw+oScu9mTFnjGJd0bsXhS6tqhvUeieELvTEo9H6tFL3Xype1TuoSg9ZK2fCAbJ5ijNmWT7s5J4UZ+bcE3MJRZXZtb+g/BvZXGtGtwrLY6l5/LR6bR/IgbmQ7K0lxN1UUI2tNc64DS1W82NFlOdyXQDNF81WC/qAPZzVQa7vxXKrxt7T9uxx2z4D6yk3RbyG2bqi2baM2bGk6bRX0DsRwzcH+sFP9SKv6+Wfkcp/+ZWmE4t/65O/pBe/mNA/jOj8tcW1e9tmj85DK+5jHt8tsMBz9lw4EZ152GiN5VCZTKkXJ5RLPLKY7KJCd3UlHlmO0aaWl4urq1Nb2z8538UOD6/8lK38ijZ1qeK3M+P1sl+31aN0f2mRQWDHIxz9wLejn94Q/SLeunvmuXPdyre7FW+P6Q6itGcJ6hrKJpGurqbpUJylSSBbFQkEcklGpXQqOZ49KwowMhB1Gnz8KIFv2jCLgDoRS16QY6alyDn+ENz7KHZUcQsHTkzjJwhoaaxqGkUZhqJmx7AT/URJ7uHJzqp462MsWZmqZFTqOfna0W5m9JsjTxzRZW5rPkkW3LLVzSZ6+rsDVXutjJXr8w1KvJtikKHotijKA2qSih1GaceI2vHR/QTTGCKZ5qWWufVjnmdfRF0LFucK3YHCYeuHSDVIqhbPtQ2+uNrvJ1h1X3YmLivymam/grUVQU2XWNFU1tdzGwR3b2GYU93pwK0VYw9FctgxYapOLdwjlUJ8av7buKqKstlTZWCo1L2ViYilalEZSZbTbNanKiszFbWltfvri1tLM6tz0wulcvzmexMPDkZiIy5AiWrOw/a8zpTVgGkJeoUT5YYFcVHuFESM4ajxZDDsUFCrAcT74DfDVGJNmysixTpHQ71M4JDTD+G78WL3CS5c1htGzFYGUYzy2biuiCRD5SG9IqYVpXU6NJqIKs0FpWOksJTlnmLUn9BGsjJAmlZICkPed99w/fjH3/iH/3I/4Mf/NnMww8HWKztF+IDfWE9AMVP6wEofgXFPFQRnNs+hgVfOuNfQv56byrzMEeuffOP/97Zmxvr60mXXY7qHzz83qeK09y9uzI9bUkkBx3ODzSaH+lUj7glr6ZHDs7S4XcRdSWRqKz9hW11/5U1lp+zKqLbg89obf3u1PxadnwpmJ23RWe03gmRtcqEMscYEJh0hqZ8vrGAo+ABUlZptcE9xPO7NPGoM19ITM5NLm6sf/KXgrFwamUtMVfFQhWMhckqFhIjeW6qrC1MmsolQcKAdOOv6q++xn3tJ8SfPMd87pTqVKu1GR9CsTPUnaozw5khcZFpGtd6Ck5r2Ke1upWA2QBCBgBkyg3dLN2dYWULcrSnC4mqr0eeOdq56+XBD/fjzl3HX+hCnSb0HuL0HhZhPhomXuqBJwcOvd+56xVyzXkhrU9lpimTHH5+BJ/qxaa6OblheqI7NPd5e/RpaaTG1JpzjOR0lIyImKJh0vihHKJvvL93eaBvbbB/dmhwDI0qkIgF5mhZLpu2WtZSqcr0VGV1VTkxS8lP9KZKtyK5M97UCXPktD54Ru69KvZ0Cj00vlvG8cCntAqEHK+MaRPSDKNEKXGIgcMy8FQeSaimGiwUs5tkD+C9MUwoWwVCZxirNaE4ogE0tre1uff61cEz55BHPsKerCFf66S1kVgYAZ9jFGkjAtN2qRKhJad2xU2BgCPs8EWBUFwRDLACDrLPhHRp+hyyXrtgwMJCmEaGQPzAdgwQ3W/EDVixgw5Ej723097dautoYtJxKPoIhsnYci+TeYvD2xnCRlDIYjqxRyqokcqOStVvywx/kEOPKC3/spkUCvt/KR3f07iehPxv2CLHvInb0dxgujRanNRNzgbml8ZXt/9nra9tLM2tzIwvjOdnC/HJdLAcdydDVp/PZHOCgNWgsgAKi1lhdShNTqnRJQNccr1TpXHolXaT3OoUW7wCU4hnjMPgxwRh6stywCQXSvChuACKi6CoGAxLwIAU8khBqxTUSgGBzECU6zuV2is6zX6j6gWL5lWX8v2Q8mRKcSUquBNjd8YZAxkqMk9ETuBQ80jERvVTgMEJJCo2hLcghkUDNFw3va2debWFe6FV9FGbzMVxLesj1b7k9/Gs1MugWbbYb1riLsm9MbnXqfBqlF62woOSu+/IXPVKd63WVwcG6yyRVmeiJ5BBJQrU/IR0fMY6sxBfXJ768ghwm/GKC+X0bD4ynfJORO3VHE6POuMSxO2jEQspYER6oF4n2G4zNFqABh1QJzXUccF6GlhLAOuQxsZeS1uHrbPR3nvbPnDNsbmnzoo7aiLuN1J3g7Q3gNEXDexn9Lzf6gS/1Ih+opb8QCWvFqQB+b/W6X8OQk/dz2rVQ0Hay3Hgg6TpWNp6MeuuLwS6S3H8eJY1WVZNz9jn5vwLC/Hl5dzq6sS/Kev1P0frK5XVhcry7PhEOZ5JO8NRvcsvAJ0UlQUhBFtY+uvD6lNYxfsD0te6JL9vEv3sluA716rpxLC/c13ybKd2H9Z0P//opvhsDwGDG6QSe3jkDiW1xchodDEbItzbWe7tSV7tuuDWGrdulX1nZbR5md66ROmYJ/XMEgdncMgpDG4CRRxDkMv9tGIvvdjFyrdz8q28fIsod0eSrZfmahWZGyqY3/5sF9xlbeaaJnNDm63VZRt0uWZ9rk2f7wYK/WABaSziLCWyrTziHGN5JoWhKXlsRp+ZM+artUY9Y0vByeXY9Ep6djU/vza2uD42tTExfne6WJnOVCZjlbFApeisllJPGaoYFpFUgtyKb7TiHq44cJuA11sB2qt0B1Pcp7gONsx71WTLm1uelta62C2bx7e2Lb+yZiFVktpKGqzkrJWCq1L0VUrhu2Pxu2OZynjh7nh5YwJ+SPMrY1NzheJ4JlNIxdPJUDzqDkfs7qDR5tOb/GrAL1P7RCq/UOkTKnwCuVcg8wqkHj7dj6IEB0nhPlykGxVt34rjYaOdxHAPNdhP8yNYfjTPgxe4SBInRWanKWxMjZWrswgMZglgrJYvNoMGC2CyGqw2ndOqcZtVfpM8AikiOllYKQ2LJWGeKDQqDFEFQRzXP8Ty9TB9LXTPLYrrCtFxGmc/hLa8OQC90AW82Kx4qV70wk3ui9dGX7xAfekjwuun0e+cROw52vfBkc6jh9tMzz6dOLkv137jfvb+8ZcWKnf7BfxAX1gPQPHTegCKXzUVvZWG/13do7ilgrOaQbHxRfHJM7f4T3Ij57k/rZa/hI2Of1Gry0shSC/sae19/617i9Osr8+Nj6uj0Var9S2V6jug5td+xZ484/QivqkiElQ87sr09PZd/C0IAjM6eXTHBkkErDbB2x5qZVGNNGJwlNWeCcFmq3G5cxwKTlUbURQW0tnZTGwi7i549ImtTgkmYdBrSCa8xXJmenH2k02G8L+bWLjkmKhiIetjLOSly7rilHtyzlKKC+Kyfkf/SeXJpxlPP0p69C3+Wxd059sdLYQIkpEhbFWdIaUHhMVqTM9RchpCXrHJw9Na5LrNgKEOGBYb0ELj0KiCQGTQBhGU2pqBg+917H4VcfQ45kzt0PHenoOk/sMS4jU1o4E52oCgXL+JOLK/a/er5PoLPHavxEYWJWi0DAaZaKekEaLcsKHAsGcooTQ6FuuNRNoZm5/RDiVadgw/pE8NTZLW/EBfYmgwisd5GQyrRKoHjEKXj5LIdSSK18LZI97km47oU+bgwwbvt5TOX8ldb6p9R4FQvzLAEHslfLfmYyDUs90KhllA0YyguPhBOhI5gqXySQI1SQGS9XaC2YNzR6s0CNsVQWmhQRZvAIMd6O4ZampF32zC1TSTGwap/XQ6RUkX2Jm6NAssCs0lhbMA+ZPOaMgXd4STUDyljAbZIdew34Rya2EU3IwKMhFGygCA204HBTF9xqFuS2+btanOXnvddvu6vbHW1d0cQHcmKd1FZvs0v2lJfPOu7PKa9Pq8+M6UuPuPSuAf1Y6vfex/UTv+WePcGcL+e6X9sFjxa6P/VXvksDdxPZztThZhsNFMzLrnFosrm5eIu5XVpbX5maWp0nwpPZUIJXxOh9MGmMwqwCjVQWKNUao2y1VmjdIKyC1mqcUlMvsFpijXlGKBeTaU5kJRvtHPN7qFRjtsEWQXG90SyCcF/QrIrwbdWqNTC9q0oEkPGgCDAtCLTDquTTfqBRheYNQNsk2AFFDLDTK1XqDSsxUATWIm85z40SCKnB3CzSCGNgb71xGDhb4hVzda30MW9tDoPSxSH580JKdiNOxhSEyzGtgOF88ZE7iLIs/0x0meW54Xu20854LYMyfd9oLUuyTz7gyrMyLPebH7Q2fsdCBVH831pkrk/Lh4bNo0PR9ZWJ74q8se3t24uxPH+zTjabMOWcosiEOMiIEY0CI92n6TslOtahMpm1mKJrKyCaNq6lc3t2vaGvXdtUD/dePQRRPqjBl31Ew8YCTvNlLfAmkvGUaf1XGe0goeV4t+rpY9vLOzDj7QyB/WSh8FpL+EJL+yiB63C37l4v7Cx/5FkPXzKOtncczfpZB/l0X8j/zg35VQ3xgn/GSK+rsZ5qvzwgOLigsrYMu6i1CJyOOsn7q1f3Jb927ZBr4GqJ/YGVbN++Gcn7D9hP+dtLZczYhZnq4sjldDQLO5aprMZLwyHq6U/ZWSuxomylkqGagaNUpoKjFFlVtCgkqAU/ExK17amosyYSIldESnFKsXYAWjGMowGoHHtKKwNQO4k12E91tJr92h/KGW9vMbrO9e4W6BH+x/ucT/wTXOr24y/1hHe+POyP7W4ZMdpGvdpDu9xJ5+PGaIQEMR+WiCGkc0EUgHbgp/el2y40euS350XXzvzE+vifm1snSLOtOoTddrM7e0mRpt5uo9kboafeY2kGkAs62mbIc512vNI+wFjKtI8JSp/jIjMM4NT4njU/LErC47B+UXrMVFZ3nJN74cnlpJzKxm59aKC+sTSxuzK3eX7nmVbqxWT+DSZGUexrx0NZ4Gnzf4pGVN1TMWV1Yi4irmeRkV13DFjq1YENXPl4G2aphOfau6ie5exlNc+/MIHmLNglqx4JaswwtW2oyZOWnmjJsFRaMkb5RlIXUK0icAMKY3h/W2kM4V1HqC6oDTSNU4+1SuPqWrV+HuFnma6aFaibtT4u4QeWC3STztGitJY2Wo7BS5kyxyEXheHMuLhhGOEhwghHqxkU5UrG0L8DDRTly4hxjqJweHqH4MzY9neMmjHgrbzWA5mWwbl2MTsSwSplHOgNQjBi1RYyCoIazMPCix9AnsnXxPC9NfP+q7QfdcpHku0bzXiOarWOAGSluLVDYMSJp6BW3dnM42Wk8zeeAOAVWPxt0eIt3op9a0j15v5Fy9LbhyXXLlquLiOdW5k/qzRwynD0An95qO77Ice9N6+BXbweft+59xvP97+95fu/b8yrX7Z463Hna8+ZDj1W/b3/i2493v2ff+0HHwEceRnzpPP+Y6/2v3ld+4b/zeVfe0q+lZT/vzetJDCtXXPs/Kr8eVA9t/6wf6wnoAip/WA1D8qon8ekXVsH0M8yFMifD7wRfTwsbGd1XgnSOn8rIv/1OrxdkZt1LKarrVufuVneI0K8tjxZIgGLppND2vVH3bBDwb0h8r8W+sotorNGq1mGQmU22U/zcoSBELWbP52MT9bBSH7J6xsZnV+cX16bH5XHQ8bMs6VNGtVFKrNBwwpdPB8kRhdmVxe5MhjIXTn2Dh5BYWkqJ5frqsL055JueSc/P2so8dYTeaGrdSSZ8YeWKfeN9V4HKnuwUT6cen++C3W0Kqj18Y0Y8rdDmz2OeiA26S3EKXgzATgiAk0oAMpXlUaWHz5KIRuniwB3/+NIyFve/vHjxydvDIne4PBnsPMYdvgPw+swQhEvbhRhvqUMcPdO19jdR+mS3s5Tvx7AQen+zBJjuYGZQsgwPTGEe8LxTpiEZ7t+AwGm2Px/uKQfKEaTRERc7h+3cclhIFAeW9M7C7RczHjf7v6txbYTHYMBC+DgXOmSMd5hhBH+aq/HKxR8N3G3YihGyXctTMJsipKA5+iIbDsfEkPp4uIwi0eKUJB7mx7tgWEGKcEaQGQrD5Q8M0JJqAGkBj+/GEQSqJKKKyQLrUL9Bn5UBeb8pYXXFfJBhJuxJZKJlWJMKcsJPkBxFudY9D0mPj9FpofSZSH4DphTlQj+oFEZ2mniZTQ43p8jnL9cv2lnpnT5cHMxAkIZKMwSKnZ0rYuiip25Bdg2lwRXp7VtwaEg4phRSSiN8mUV1WGg/pna9C/idA37e0LvhZv66z1bmypPDYlim+MtNc2hnCfg8IEc3CUFTi8gksbi5gHdWamWozR2Hky4xSCaQWGyGh0QFjHtcYZ0PZ6tY+Y5JvConMbonZIrMYFBa10ipXW2Vqi1JlVquMBqXRpIScKsitMbph/DOA1eq18EsFBE2gwQwZrAaNVaOyyhU2rtROEdqRAlc719tKs7STdF0Y+QBCgO0dpXdTxZ1EqAsT7kVN9g/eRQytI9GTKGIawwjjeF6ixENRehmGANscFdjTYldR4pmUeOc2621uW+JZUPhn1cExXShlCPvBsAmKSIwRChTuBEOXDf7dGs/jSucPlc5H9J7fmQN7pO5DPMeHPMdRgfOYwHVO4KzlO8+IXGeEznNiD+wLYo+9ML288Kk43njUVg7AjKfJ2mHG48eB0YiG6lYSLDKUSjwgFPcwxJ0kUTtS3N4tbmsRt9XKuq8r+y5qkGd0uGN6wkHD8F6A+raB/irAfF7PekrHfULD/7la/GOV7PsKRZXuqlZ8XSX/vkb+iE76K73s96Di+WrpFN1eJ3DECxwLgKfCwKmY/mRKeySnOVCU7hrjvTpJf2aG9Ot5xA8Wu/55teH/2aj9uO1e/d9V26YPPl7Bv1ih76vwP6oo6ytQf8VJrURklYylMhGrLP0rn7LdJb9RafynHd9t+IdK3f+sHjT+Y+Vjb3iZ1Sy+6WRlMloZD1XKvkrRVcnbK1lzNRyU0lU3ccXkVRrZhrfRalogzCROQsWOqUafzANV5AA7KoYWmEDuqmvvKm9UgeRePrnHa7IrE8KaBK/WOdqmpnRziQNENLofQbjTO3y5c+RIC2N3I/OlWvbvbnF/ep3/ncvbET/Y377Mf+Qq/8lrgj/VCN66JXz/lvDEbfHlOkldnbSzToaqVVBrlfw6lfq22nJb67+hS9/QZ67ptyguDRPdDUPmFpCpA7ONEMxyuU5Lrsea77cVhhxFrDtfr7q3FuVnnb2kmxHY/ixMN728sbB2d/Vz3svuVoOcK3NVSJ4rzJdjM1n/ZNwxHjaWfNqiW150CIpWdsk0UgaJYwBmXDc4pemZUbfPKxuXFXVr8pqK/MrH5+3KurxmRXF7Qd4wK2+elndMyLrHZH0l2VBBhsnJCFkZJSulp6XMtJSbkYoyEklWqshJNVmpviA1ZWVQSmFOKk1xFRhT62MabVSnDBrkXkDiBoVWIw+ycHQWltI6IrXjRc4hvrOP4+xiuVsYngaa+xbVc5XiOU/2nCF7jw97D8ukP99+wd/HSuXXePLXeeCHXOgwFzjM0x0S6g6K1Aclyv1SxX65bK9Kukcl2aMR7dYI3tYJ3tDxX9fxXtZzXzSwnzOwnwVYTwPMPwCsp0DmUyD7KZD1O4jzW4j7G5DzG4j3JMh7Ahj+sWH4h/rhh3TD39cNf1c7/C0t5Vsa6jfV1G+oR76uZnxDzfqmiv1NFe9bKsE3laJvKcXfVEo3y9h+6nHKv6WUfVsl/a5K+j219CGN7Ida+Y+18kd18p/qFb8wKB8DVU8A6t9C2t9D2qeN+mdNhhfM0KtW05s267t2x16Hc7/TecDp+tDlPur2HPd4T3m9Z7y+cz7/RX/gciBwLRis0TMfViq+oVR88xMrv6lSfmvHCsXXCmb89uvlgb6wHoDip/UAFL9SSkGVpn+urHy8HTGuqr77fmG9abG8jMC5r57cHn8ZmikXrQI29cbF9rdf3CpOkw0bcjmaz3ceAH6jUn/Pankrar46Ietcx/VXsOiKTFIJ+LdKifxNy6yOl9OftzizyiN2TdyymUoKcH0OZRQGRRgXYWjc2mRYxcLVteQmFqrzk+xkCR/OkaN5QXrMUJzyTc3nFpZLizPmgpnoI+6kkj47+scPFYduGK92epqGotUkUlyqh5unKEtSSQoYcdqGlI4unnmAC4xI9AYA0gOQRGeSQi6V3gzIZQBtmFF/e+DQ++1vv9S7/0D/oStd+1p7D+JJNWrxkAcY9YJMtZo4zG2/gzpxoOvgG4SBqwx5F9s3REn0IxNtpGQ3J9mvSPSC0XZvrDeRQCeT2/3Wo9GOrB9dMpHLEvIYFTeDGlof7J/o6wkM9HdLXExpYMcKiQ8QfzKEzRL7B3UBkSOlBsM6pdcgduu5LoDjhU+dge1VMWzCYR0TJ6GiucNDbDJaQEDzcEQhnionUVVY0IXxxreB0BVB6kxIkRLJEqJGuFgql0AVkemqUaZJxPWrRUlQGrNr/H6LM+azpGP6bFyS8o5ETCifrtet6naIu22sbvNwlxHfDaB7qiiI7AZ7m02tdeam25bmJmt3l3MI4cJjfVRiiEFMcbAF3uCUuGtB0rgmuw6v4dal1xbEDSVBl42P5vHpKLGkUWE4b3DsN/tftIR+Bvr+Ue3cwuB/0jh/DvletoUPuQJXfe6OIEQMS/lBDtqYYhuLbFNpyxxTifvx8ZaZUKFHrxWaA1KrU261KW2Qxq7VO5QGu8TsElldYqdb4nYrPS6Dy26ym21GyAMAIR0Y0EA+FejSQg49YAZAI8yBesCo1plkahNHClEE4CALqBvWnsZo3u1VPd2qeK5O8Pot5sHbjMv1I22NFHwzUdSBd/YTc0jSAoa0QaCsE+iLZO70iHhyVDXBBSb45imhbVbinJe6F8Ufsx98oPAta4ILhvCMKV60xOPWhMuW0DoSbFscZY3WWsJHjaEXAd+PtNXPCODT8hPA+wdz8G1n9JgvWROpxkvJ+XHJ2LRlZj6+uDyzVm23MJGbz4WnlCN71dXKlh9b/qha+cN7Z1SSXwh6z0rQx+T4gyryXvXIOxrGq1rWC1ruHzT836iFP1dLfqyE6e6zq0YZvFj8iVbyS4PkKVD6nEn2ulWxx6H+0K075QMuB421EVt7woNIB0n5BKuck01OgDNl3XxOsZgUrQRG1x2EKsgp66pQB6MdDHgw5sGwByPfFvvBvvX/VC/yPT+poH5fGX6zwjpckVyp6ForFkzFx67EtdWkktl8lS7+Va3OVzdozRUqU8nKeLBKd9VMQn01IhcWVvd3ualLita7kms7TpBPkxqP3TtzV3J1XnHnYxS5fFd6dUNSuyZpXBW3LYm6lgS9i4LBOS5yloudZuOnRkmTdMo4baQ0TCuSmUUCO4/nZDG8JErg6ReC3SJpu5jZKsE3SfoapE210uu3JB/dlB64Lnn7muT5q+LfXBE9eln0nYvb1Pf1i4IfXRL+6rLwmSuS165K9l6XHbupuFSrqq3XdDTpkO0AtRviD5i1aLuN6IrQ/GVOeFIUm5InZjTpOSA3bywsWIsLzmo/96XAxHKkGpRbyWwiXHlxfXJ5Y3alSnErG9VaYZtaXtuYWVorzy1nJhdjY/P+fLW+qCk+oQuNyXylWQZ+gUFd4HC3PDvCnxwS7wyrJg95eaNSuUoplxgUQrOM7ZLT/HJKRE5IyTF52WBZ3jcl75xTNC/KG1Zlt9Zl26h8V3Z1XXZjVV63Ir+zJG9dlHcuyHvnZYNzMuScFDsrJc5KyHMS2ryEOS/hLEgEixLJjEQ4JeeWleyckplU06LaYb+G4NRiLPoByNCtB1rVQL0SvCWDrkpMl0TG00LTcZ7pQ575A65pL9e8i2N+i2N6hWN6kWP6E8f4DBf6PQ98ig/8VgA8KTI8LtE/JtH+XKb7qUz7iFzzQ7n6+4q/rnOmSv4dtez7GulD+pEfKhTfUiq+vW3lt5Wqj483rVB8Q9PzKND+HaDxG0D9N8G6bxjvfM945wfGOz8yNf7E3PxTS8svba2P29t+42j/nbPzGVf3s56+lzyDr3sRb/lQu/3Y94Okw+Hh4xHqR1H6hTjrepJ3OyVqSsvac+q+AoArGAlF20jZyRz3CyejiqmUfjpnnht3LyzEFhcTi0vppaXc8nJhZaW8ujq+uja1vj67vr5w727YjfX19dWVtaWllYX55bnZxemphcnxubHybKkwU8hNZdOT6cR4IjYWC5ciwWLQV/B7cl5n1mVPOywpqzFhBuNGfQzQRvSqsFYRVMHvcGK/TOAVcz1Ctos/6uTQHSyqbXQYJLXqCfU7Bjsvmk/t1RHqd6wn1U9l77uB5YH+rXoAip/WA1D8SgnxZMU4uH0MLwKUNdUUnS8mZDr5faHa8MIf7n6mReH9tDQ/V4iGtwd/rnIyDtDJhAsnNovTXIW4Q0Fvn8t9VKv7mUb7Y6fzQNLdPA0Q7jKGK4iBCnO0WnCyWPgK9LRYWbubLC9CwSmDNBKx5Xbih9HouDFa2hnCNotDAVO6mJicm6qWTYef+cwmFjonZjWFTSyM5IajBWFmDChtY+Hi+kZ2LqtIKe5NJX2V+8px9ZGblqvt3vqBWBMm2cXOk4VZASuiQZqN9ULLFSp4nazpY2vFGhCCIA1g1JodVk8gYLPaxEJRbx/+3LnOPW907nqz5/3jXXtv9BzoI1zhiodcZkE8ACa9GqORzeB1NKJPHew+t5tMuc4wdoyEerCJDnSilRrv4MfadIleVwqbytIyGWo6jd+KGSb93XkzpijDlemYKczg6mD/TF9vbKAfxBMoLN4theF5wPMNTZWL4LdOjz4RsefvZ6MoJODZFHQLDIQcvIKBEo708ah9fMqggIAT4GkyvEiH0VoxjtAWEKJdYRRgR8kNGKEay1eTeBo6DxDwrRpRFKK5XTRzgKOJCAVJOS2pxsTV/SF1t1fR4ZJ32EWdNmanmdQBYToAVBeMggCyGxpsNw90Wgd6bSiUDT9sp9Ddoywfkxnh0FN8clGEmRD3zombV6S37sqqH+ovi25M8xvjvH4tj0QTcPtkqlqD9bTFv9sWggnnIb1nCwVhf1fnfAJ0vGGxHLXrrztF3TYGwUpgmhHwUloKdAn1CL5hRACpuKCLA6VYUJELJiS2sNheUri2LXeV5c6xnSFsnqnA1asjFrcPCpi0MY06pNCF5IagHPApQY8KdGgBqx4wASAIc6BcC3HkIFEIdjL1l4mafQOK59ukv26Q/PSG4MnrnN0NnMvt3N5eDhvJg3CcEJFbIHNnKdxVCm9tRLg6Il4eVS1xDIsC85LQvrjZxr1qoWtJ6llW+ZcN4RVTfMWRmnVnip5MxJux+rMKf5bqTfd4k5dd8T22yFaq8BYe/4vG+VPQ90dLaJcrdtKfuhWp5oJSNncD2mYW4jNLxdJCOTWb8Y8HTUGHCoLEEi2DqiIh5LgWKaZGgjkjwR6U4t9VjLymYj2r4j2h5z3zqfXrpyxXfF2PP2YW7rKLD7hkJz2KC371rZC+JWrsTdpxGRetGBSOJdRTBevcVHBpKbu6OrGx8eclqdZXK7OFSslXSegrfm7Fiqvo2yvSaxX20Qrl7Qr66Urvo5Xmr1Vu/b8/IcC6/1lp/25l4FcV/AsV2t4K/0xFUVsB+ypOSiUkqWTMlfFodU/BzsVwfbmyPFtZGKumYk4lKuOhjYJzPW1ai+tWw7LVgHDVw1x1jqzaCKtm1CrUvwZ0rmmb19V1d1U3t/BjQ3F9XXV7VVW/omlaUncuKAZmpcgpAW6cSyozR0p0Zn6Y3V4jPXH1E79zVfLjq8J7Z05ckRquaqrRto/zJ9PX9IkbevctPVhnkNXrmQ064h3dwB1tc6PmRqPmXKP60B31u/XKF2sVv70l/WmN5DuXq10Z/uWy4KHrosduy56+o3i9Xbuv13ACabpKtDfR3P1cH0kW4eiTKlvOGhoLp6eKk4tLn4nCLa6uTy2slmaX0xMLkdKcNzdjS01BsQl1sCz1FXnOHMOaGTamsIbEoCbarQi3SIJ1Al8Nx3OR4TpFdRwhWffjzLtQxvfQpt1I0y6kaQ/S9B7K9D7KegJnvUo0to+AGIaBxdHKhQpQLHXJhGEZN61iFpXUkgI/IUe59XuMuud3bND9XqX5yb0zRv0LOfWZGdnNKdnNCdmNMemNkuxGSX6jIKvJyK7HFVcjyks+5Xmn6iO7+pRZexLSHgX1h/SG/Vr9HrX+HbXhTSXwisLwgsLwjNzwlEz/G7n+cZnuF5vA9hOp5gdS9fdkqu/It/o33mvVN+Wqb8tV35erf6BQP6xQP6JU/VSl+qVa9SuN4tc6+W900t8ZxL8HhE+DvGeMnGfNzGcttOesw8/ZCc/aMc85Bp91dD/jbP+D485Tzpu/cVx53Hnul47jP3Me/Iljzw8cr33b8fJ3XK/8wP3aT3xv/cL/zhPB3U+H970QPfh67PCuxPED6Y+OZy+cz1+vKdTVlVpayt09Y4ghc/NREH12x3ryBxruU/fOAMjTKS5hY25u+697jzbWVtdXlleXFrcIbWlmamFqYn58bK5cmi1WIW06n/1XnbKZEiYgDumigCaiU4Y08qBS4peLfFK+V8R1C1gu3qiDTbMzKVY62TJCMFOwRhIaIgwBuHt6Bd1jEIeACEj4NqZhrIWKt9KINgYZ/nH4TpxchpvPhPEPhkD4/v1yYUApDqmlMB/ClAg/APhhJEyGpAWCHxWMkRmXLedx5H0umC1hwgx/sD/46yc2/euqH3ss+MijoSef/MRPPDkDGLbPzgN9YT0AxU/rASh+dQQvRzp/WF2gbKm6rZyyffx/q8jC4j8pTJxn/7QS/7iG6l+SQypU4hA7ZjXf6Dvw1j0zg5z2BnF/O/L4B93vvc5sOq9lXjIb96g1P9AbfuXxns7EMHMOQUUiqqCRFQKuolJWe5R/JXpaLCxvhHMLWu8Ex1QEApOJ0qLelBKrwjvmgHGiN3PvjEQd8aQmnRNzO1hIiRVEmTGwNB2Yni8sriytb6xtrPkn/KwIayuV9BfkX/ya+vg7wrdO647ftF5u9tUgE22MLJ6ZYpN98g6D9swouBepO4BQNtF1owpIBxhhHgDMNpcvGI9EPAa9ijg8crt+4NDBtrdf6tz9XseeM93vN+EvkgV9gEWYiNnLpUQ54bKbOFUyJNYcxfcfZyhqRz3t5Gg7MtGCj7ewEp2q1JAjT4sV+MWiuFDgpFKESKQzFujIWfsLCmRpdGgSN7iCGFgc6EsP9lvwOBaT06zQvW5wfldXTZ78e5XjV0b/fk+8OV7gFSZN/qRG7HNpYyFL5n42ioLDwyIcVYDhyFByPQpyYfwpbCiLtgcxgB2jBPFS/bBUR5eCQoUVkNtsPIWbyvIPUVL9pAixPjRa7+c3esQtTnm7XdhppbWZiO0QpmsrKgiie03YPjMeaSURrRSGlS6wMsUOtsTDEYb43IRgJCfAlvm908KWRfHtDWl1G8+G9Mocv7bEaXWykRIujSCVdGrB6ybXEVvwNXsYfnZf20wThf0PavvDOvMfQP07kOS0kVUHEXsNCLyhm65r4+k6+QYiH5DxQDsHjLGgAgvK8I1Rkckrt1g1NgPokpncAoeX6/PxowFBzi8se1Qy0K0xu7QW95ZVZifPlNgZwlabXCNSsw6AYMm0EEsB4YRA+6iuhqw5iZIfHpQd6Jcf65fdQCkwZJWErrNxdFGutsjRTLHVC2z1Mku1ylKvMLXLbHCZBxOgowp+sMWuZalzWeFa0fpWwNCqLb4WyMPr9HKsFImXLPGSJFYkxfIt4czZYPo9d+x5a/iXkP/bH6cKf13r+hno+5M1tMdV3RBYF80NpEu0woRibMZamvYmEvGgJ2DRuVRsswAHMrv0tNtaynkN5YiKsls58qqS8UcV9wmV6FGl7J7e5YqvK6Xf14h+quM/CfD+ZOS9aRXsd4pPeeXXQsrGmL4/1Nnob2nztbQF2joDbV2+tlZnZ22wo2fHoY6eKet9ys0vTVd3x2Wt1exNF636wZyqoVo8jLG/Qni5gnii0vFQpeHvP8E/2I3/VOn+8d2h394lvbbBOLAuOL+qqF8x9C2b8Qt2+oJXNP//Z+8v3OM6s3x/9C+498y55575zcwZau5w4rDjxIntOLZjiu3YMTNDzJZsMTMzVglKVaUiFTMzMzOrxMyqu7dKkR1PO52ke+Z05ur7fB89+90q2CVV7Xo/e613LRtnzCEedUhGrdwxM2PMQBrTYseU8HF554S0dVLcMCWonuaVznIL59g5C6yMJQaYkAxg3jwzfYaZNcXKG2cVjbBLhzhVMU5thNMQYkKCVJiPiPRgse5egquH7OpmOKFsVwvX1SB014g8FWJvqcSbL/Jnifypq6mVq34iCGSJQiWycI0q0qLdn8443Ch+iNK9yG+kUy8VcM+0SA/UibaWct/NYSRarv/+CfmdHMaWEs7BetGJFtmlTuUdhPYx1pBNMJfQrFUsO0BrrUIPVOzrkvpgcj9SGeiUeFsE7jqOs5xhL6BYsgimJ1jDfaT2Vo/6cofyTJvsaJMEeJYvqwSfl/E+KWK/n8t8K4v+cirlN8mrDQAT/sNj8ttp1E+zGfuLWOeqBfeaJdkQRQVM0oYWozEsGpEioOBUNLiBBrUwGp3MSg+jwMvM9jGf+Oj3/fRbPsYVH+Ocl3HCwzjiYR5wMfY4GLtsjJ0W5k4Tc4eBsUPP3KFlblcxPlcxtypYW6TszWL2JiFnI4BeT9+Ef9q/4TC30/jv0YQfUAQfkkWbSKJP8OLNfeJPcdKteNkOvHQXSbKXLN5PFR6kC75mco6w2ce49ONcygkh4ZQEd0aKOqOEn1V2nFW3ndM1nDXUnDWUnjHnnzRnnLA+Pma7d8R+7ZDt3H7Hsd22r7bbvvjU+slH1rffsbz6mvkPf7S89rr13ffsmz52bNnq3PWl+6sDnm++AdvVXLocuHkreP9++ElKJCu7v7AwVlE5UN8w1NY2DIONoNBjBOI4gzHJ509JZdNaLVjZ2O2eD4YWBwcBkFucnl6YnZmbmpodH5seGQaAbbw/ApDYSMA35HWD8TS7JWIxhoy6oE7lV8sBHHJLhQpYu7SzGQCqF1nUVieHNgHQlcAz8TN4BmwDe/4kkunWkIyKN9OJFiYZwD8bh27ns5xCjkvMc0sFHrkY5DG1PKBVBvXqkFEbNuujViNwnDGnbcDtGPK6hv2ekaAfeBVj0fB4LDo5GJsaHgRe3czYKICmwItdmJlZnJtb+i8pnqc7+lWsqhr4y7/Ilq1bfMz1YjZ/Na2D4vNaB8X/JlpaBJORgIlLQpP9YEmx2b+oJ/7M0vJrXEHK3aRoV/vqrheoM+UCpjCTD4O8yNXnvoZlHaIi9jEZfxBLPrdYHkeCvTNuWVwkiPfA4k0NcTwurlH/Unpa/FkNT8wbfRMM7QBB3q9wjAaHZufnl4Yj405tGG0Kwd1RrCeKcffjvDGsL4b29GO8gGPAT/RKHmmfJyaJrWLh7MpSzIn5CVlEBjVBkwXJX+H2vwl9c1vv1tO0E3eE11PV98usWTB/W48L1aIn57CY57o4m4tYe0vpqTBBJ13GFMrBiKFMZjCa3G6XQ6cTY4l9pTWtt+9XnDhSemR/2bGLlaeftN0BeJ4iJznc2oHR/umZyUmvXiMnwpnQAgz0EYqUjFXnI+zFbe6iZk8R3FNODbTIIhjXEGdwRDY4KAiHcR5Ps9Na6leVR7h1/biGQWjtdGPNfH1NpK5G1w4hobAVNO5pofZVwSokAIRwUufKdYWRwRhLYeOQ5GQ4j9TFZ8DlQiyYPsqhmtQcl1HiW7XYZxR5nw4lPjHR0oPldLAlPXQBiikkCSR8CVdDhRtwjSZ4kak7w4xINaFS9YQ8Lb1MQ6pQIMvkXeXyjgpFZ5WotVYCrZd3tSgR3QokWtZLkmFYUjRHjmHr+hg2AsHd1+Pva4n0VQ3hiyaIWQvU1TSwOXLqCC7Hi66Q4CBYEraJzSkQqe4rTGfUlh0K4+sCzT9xVuuI/oqreI/H3cMnXhQgH/MhRbz6Jl5NN78JI+whCKl4sRwntmMkIYwkipP6iFIHTW5kKRUCjUCipygNfTozzm7t81nxMQtlyMAaVvJjImmQo3Vx7Rq+g8130AR2ssBCE5lYEqNeJAcR8DsJxAqWxLQ6WJFYLBbghcI+noUsCtGlIwz5NF0xT5fPkaVzRNksQT5DUM4QNWDoj6ieIapmSYo5smKOrpxnqxeE+gW5edHgnneERwJD7siIIjJMDg1BAgMlnshje+Cy0XNI4/xMblkLAwL+HV8PMCFAhqf07rsWf54r2Oyxoz0allEglvWpuJ1aWq2SmC/FJonQ1wTIkzzUIS52B2cF/Ji01aIsgFmM33Mob/IIGwV920TYfVLscQXuspZ430jLtHMrvNL2sBoTM7OG3crJqGtufPDPJiAsNHwKhuz4JS904b/Pwk7OEx/N916chx5caNyyWPHWcuGvlp8JAC5n/q/Fgl/Nl785W7NpumnnZPuh8a5TY/ArI+jbQ7hHQ6SMIWreILNsiFs9wqkYY5dMsPKnAT5kZiwwUpYZycuMxwuM1FlmxjQrd5JdOM4pHeVWjvDrh4TNAyJITNLdL0NGlbiohhyRMyNSXlggDvMUIZY6TNOFScZQnymMNIS79OE2bbhBHa5UhopkwWzJ2oK6NYcyxeFCebRC5avTWlt1WohB2m3kwE10lBmNMndgTI1YUznGkI3SJyMAJNOcg4Bt+nZWCjYVst/MpL+SAjZe/7SY8yIDnLYpj7Gzgv9FBQ/gty2l3M1F7I8KWAAlvpZBA3DxWYQDbvxONvGTAvz20r79Vdhv6tFnmpDX2+GPunqy4J1FSEgtprUN39xFbOgl1/TRKqmMUgajkMPKFbAzRewUKSdJzrknZ99SsC8pWGdVrGNK5iEFc5+CuVPG3CpjbpYyPhIz3xcy3xYw3+QzX+Uy/8gG0xp/w2T9isX8LYf5Bx7rJR7zFSHrDRHrLQnrXSlro5y9WcnZpuDuUIJ9F79SiI7IJcflsjNSxQWJ+rJIc12g+5ZjuMMy3mOY7lMt9wnm+zjTPbTlLtxyt8tyG2q502F7AFc/xEmTBA1v8QivrZlLeY3DeuV7e7AvybO+tJ/4xnHkoGPfHvuOHfbPPrN9tMn6zrsAxQEsBxgMFn240f7pZ84dO1x794GtR0+e8l24ELh2PXj3Xig5OZKREc3Lj5WVDdTWDja3DHd2jSCRo31941TaBIczKRZPq9QzRuOswzEfCMyGwnP9/bPDQwDhTA0PTQzEAPIZDQWG/d5BjzPmtPfbzGBKpEELUBNIcXKxWyIAmCqRDAlG2Kh4MBMyEV5DwQAqk4OM1yKGNCawTQRiW5Osq1UBh64AW4+2D6knYozkPhDVwNAZ3SFgr0CaMBExC+rUGkyPBNIohjQAyLfCgQ0AGa4NQQ6ENgHMNjc1OT8zvQh8DS782ISmX5Ym5ycHZwYDIbPDJjGqaGper5jUzOstZ0Ny6HXJ5JKbxKwL5i0fO7Zucx/6+kW2bHhb11Oz+ojr+ou1DorPax0U/5tI2R6v/WB1G5CiEewd9JfpmEq8rQtpPntsdfxitd37GpGRRG+sfpHrLh7hoc7GYrT5QW9cr4sTCfHmxnh3Z5zP+yX2tPiTAman0ZE5tWuMrIxRVDGdZzw2Njc1MRdyDgJ4I8abAZtEXoTN3WZ3djpcCWcpUJ/27l4bAm6xOf2TU4GJANPHrNHUXGde34L8bEPnhi+xu84yT94RX83QPmh110LsiBoV/jGN+nUL7aU08ufFzCSYGMJQ0URqkRRkBrVabbPZ3E6nmsWnNnZ2Ps6pvXC55PC+0m+Olp+81XSrEJUPFyIVHm1sbGAmvhyfnhzyGHlaLkRIKaRzMknqnD5ncZerqMlTBHWX4gMtwijONiqNTVnGx40DA5xgEOFyVPs05RFBVT+hZrC7ZqKpBlxe2FBnbm9nojCNNM41gXqDcDWj8k2R8ajGmaX2QQROLEGNhbIRjeTeFiqxW8BBaROrClkoDQUmwrYQu5t6WupbuQiYsLX2ewamJs8MBa21NFy3gVqso5VoGWVqaoUSXSHvqZZ21IhaQUs6GmWwdhkSLu3FSZBUEYIt6hWJsXI5SWMh9DmwnR5MQwhTFcMVjROy5yhPO3RNEtIH0AUmTC0L39NDo1bzRKki2RWp7IBc/pFI9lvuamvBf2CrX+Xwd3KpZ7moh7zuAn5nHa+nU4BDCpk4oQwnsmLFgUTPd7zUS5bZGEoDT60U6bhSPUFtxJgsGJcVG7bhYibSkJYxIuOM8sQDHHWAb7WJnVqpUyyzcyRWlsTMEuu5Yg1frBSsrBJkCaR9bAmMJmnG8asRzDIoaYSqHGEbR/imhEMCnUYsXxuCZupkvYI5rGAOxZ3HcObx3AUSf5EuWeQpl2S6Jb112uGNBiK6gRHG4Bg8MlTn7892hu5YfP8xDAj4D3z9JpnpsFJxX8EvkVGgEnQfH8pgVPPIuSJisrjvhhR3Roj7mt+3k0f4hPMs+DF+wwIm0MT3eNhPBehdwt6vxcizMuQNJTpZg8s3UWod3K6AkjRoF4+FLDPD0eXFn99kYmFhYWZmZmJiYmRkZDjoHDQLBmTIAVbNcvFv4jn/AK70e5Fz/mGh4Nezle/O1n4y27x9tn3vbNfX84iT86jzi7gry8Sbceq9JcaTBVbGPCd3jlc0I6iYFtVMSZomZW2Tyu5JTe+Evm/CRJmwsiYcwkmPYjKgn47apgd80+HQdKB/2jcyZR+aMg5MqaITotAYyzdKcg+j7YPd5oEWQ3+NJlKiCOVIgynCp8iXLLBmijR5UmmJjFOpoNQqMA3K7mZVa6uqulVVAlFlt6uS2xTftsgvNUlP1EsO1Yh2VIBhtw1ZqyE+wC+lUgD2AxDuw3zWR/msTwrZHxeyNxWA2+/nMjdk09/KIL2Rjn41ref9HOj24vavq5v3lLQ9ROkQCv+L/F4OqRZxg8c7xecfE/C/FnL3CXm7hLztQs5nAu7HPPb7XPYGLvt1NutpyBfAJy7zDR6Ic++JGB+KGZ9IGZ/JGVuVjO0qxg41c5eWsVvP3Gtg7jcx9puZB6ysgzbGASfzkJt50Mc4GGAeCjCPBpmnQ6yLIfaNMOdelP8kIswKS4sCyiq/rsFtbHPZYA4P2hLAG8JkTYwtH2CJBhncAQo91keK9uLC3b2hdligCeKraV7p+gO4xVvW6ahAGSrx8nIat5RDLhZiCmTdOeqmdF3FE2PhI8uTb213Ltsvn3Gc+Ma+f6/9820A0SUAz/rue47PtqjPHtd+e13/8G7Cqpz7wtaktSFg9cWz3obKSaFwSqmc1uunzOYpp2PS454K+icjoYlYP0BxIyH/kM+zQnG2qNUUNutDBk1AowRTJeUil5i/SnFgniTJ+JTi4CpU93cZks0AdyXOkAB6AVAHoJ0C0QHcAIy54VHAXQD8AyAQQEHgoYAHBFMfV+JsAC4CTwdmPNrMwAEAhzHs9wBgCRzYxEA/gJpgSG1yAoS3+fnlX2Y9ub9cY3Nj/VP9vnGfbdimcwllWpJADGfRmiiYEmJ3Fq7xIabiZm/+OWTqMcSDA8gbX/Ze2Io+8Qn26w/79rxN2v4mbfOrnA9fFr79B+Vr4KWBP2nNhtdVH2xQbX5ftf0T7769zg83Oj/6KGHHBx/YN7y9NkzYjG5aPbh1/cVaB8XntQ6K/x20MBMv+k3cwVgdxkxxbjZYEfsvUKff/VuagLfl06Uf0Xyi9e5XfaW5YhTsRa6/fNTQ+gQkw5amX2JPix/Q/OKyf2BGahvpk/WzdIOWwOTIxPxwdMKlCyvpYEEaFd3u0kWAPZNzi46xqb34A69CXv1hb+h8e2PPhweI+8+zT96VXi025DaaO4ql6NsE/K4Gwu9TSdtKOY8QinYmmIMoVelkMrlMJjMYDE6n0yhTc2D43tzaxusPy4+fKDm8t/z42YYrT+AZjWwo06kMjA/OLC/Pz85Ghwa0LjPaqGyQKot55iKSrRBuLWxzFjW5ixDeOnYUox+VhabdY1OekRFVJEJxuyEeXXlYXD5AqxqEV4+3gFg43ljnbG8XIFFQKuueUPmBUP8PKyDxDkN3lmLKwZuberTttZzWEnxLRV9XMx3XI6Kj1fyVejMCjIEBl+BbiIjqjvaKuobmthYio11mbDP7O2VmJEUk6O3Q9vVa2NQXWdLdysEjJF3tEhhMBEOLesgiOEuCFkgxIg2Bb8ATzSi4HdnqRtYEkSUDmLwJQkYiR3QlMPh4FJcdxpQpsK1EUm8rg5jPod/mM4+LWdtF7Dd5gn9mK4EX8o8czSscxXYO9wyXeo+Hz+cT6wT0DqEAKVJhhBasGFwiCIYEJSGC1ENT2Fkqg0ArE+uZCkOf1oSyWHo91t6IBdOvJwwqaSNi9ihbMC7Q9kvsHpnTrHRolA650iaQGbkSHVesEkgUYolUKJKwhTIiR95Nk9XgxIUIfjqEmVxPKGvGwzooHBTHSpEOstWTTO00XTtNARcBjiJkMyT9mgcoVBuj8Nk9M0StFC+n+qPtwT8dBvxHjuoVBu8zGuk0DZ1M7SqjNkIoZWhKDoWYxMZf5+FO8/u+5n0HfqupnitJnhzS21zcJh7qCz5iP6/7uKDrkqj7rrQ7VQEvUqPrDcQeG5viU0gGnPbp4aHVj83P1eLi4uzs7OTk5Ojo6ODg4EDEH/Xawg5t0CSJiOH9jMpBfMow/MoY5JvJhu2zVe/Pl7yylPfPa2FA0Hn/B6zVWf5KvH5TvP7jVdd9BC7wXnPO/56hZ4IVO8OqeL8BrOQ54o1PhMHKk3Pjq3ViluNLUwuLQzPzoYlZ1+i0afCHqc/xRKB7xJM+4rKTOZQ0PjZHAC8QtRWLasskpZXi7EpJSrXkbrX4WrXoTIXwSAV/dzFvayH7w1zGa+nU3z5eTa383RPyaxk0gPQ2FbI+LeZsLeVuL+ftKOd9AbiMvb2M9kUpfnsp8vPizu2FTZ8XVO4pKTxdm/Vta0pm971S9M0G/GUI6QycehTLOIBn7SGzt9E5nzA47zM5b7LYT0O4gNnsl/nct0XcT+42PfmqovFARUvCXxR3vpXZtzYEvK+0Cni/hGhVYVpVlFETY9b2M8Gfg+yaIXbdMLtmlF09yqqcZFVMMytmGEVzzLwFRvoS/fEyPXmRmTHHypvhFE9xKycFjeMSyJASFtX3BkxYr53gdJNtPqopQDdEGZp+hjxGF8Uo3Bj5B2AvwXtQfy3SWYfT11JkNWx+lYBSJsWUKLvydY3ZhvJUS94jW8pt+52rjotnnMe+se/dY9+61brxI/Pb7xjffdeyZYtl317bsWP2yxedd+84U5948nPd5WXehnovtM0H7/ZiUT4S3sekejlMr4jnlQrBqiQSgVPElTdWCVuqf9A1ssriRCAOMBg36wIpTonsXM2cJKAMZJyJSrAwyYk1bOACNqnAKxeDqZI6VcioBSiu324ZcNkHPa7hgHc0HByPRsDcyBGQ4uamJhdmZwCKW/m4/GJkHDSmilJXB/8lkgZFbBOeIuvCcxrQhCIEIr2n/UFnzTVI8en27CNtyfvab+/ouLql89ym7qPv9xzY0Lv7ddy2V0ifvMT68CXROy8p33jJ8PJLz9EdYMMrL+veflOz8R31pxu1Oz/V7dth+Hqv6eTX1gsnndcuer69Fnh0J/Lk0UBm2nBu9nhRwWRJ8XR5+WxlxVxN1UJtzVJtdby+FizQ0N4a7+yIw2FxVO9Ce0schQTX5rzAS21NMbt69YWt6y/WOig+r3VQ/O8gQSlYHSGh5cW4oABsKvUXyD8z868sGXzvV1PyFyzU+b6gdw+XHd1bfmzfd977/eG+kiNfWtpzfrk9Lf6jZuaWnJEpgWkYJ40CP4Ht0ZGZsGvIJP4ueCj2AcOZqfnI9JxiYAzni3U4wvTQ4E7szs3wzbvQu17k97vf/1ZwL0+CvILBbK7EvJVN+apOlIzSdvLMHLVNY7JqtVqpVKpUKi1mi8VgExO4+Kqu9nt51WevFh/+quTIoZoLtzqTiyn1KD1LOzY4PjvbPzFhGx6WhoJEp7XFbC2X2UsoujyEKgdqKmh0FbU7Svs8UPkQ3zVlHZrxT046BwaEHg/CZagNycoHWJXDmOpxSPViY+10Y62/rUWG6IWTGal8xadi46+omjdRyl1dypttyvw6VW2JsKGQWVdMbapndnULsVgVjWDkYPWJVFJer4rWRsNUdXVWtHS0wyA4BlRsaDf7u+R2DFNLowr4LJpCitWrOvSiei2nUt7bBMyfTNS+F1kKbRT3wvRd7ZauBmdPVQBRGkPlTxBWVwwuUB+P92X1o4oCyEo+th2O76mkoh4zsY8EfcmKvo088u/YfAAFf83Rv8k17OQqTvMED/mCAqGoTiDrEOoQQhtG5EOLIwAHAj/xkiBF7mEobTytTqSTSPU0pRGlN8MdVoTfCg8bUf0a/KCMOsxnjgnEE2LtiMIeVDqdGpdR41CrbVKlUSzXC6QqoUQOoKBIIueKFDSBCs1SNhFleb3S+1DhtQbGxXL8k0pcUyuZCmdrCeIQUz3ONUyzDDM0/QxZN0vVzpEV83jhQi9tsRu3BEUsw3rjffg4i6lqSdOQL+l4NxJWsg/zWG+tDcE9Ped6WisbMKk9uPv4vms03Ckm7iAXv5NL/IRDWY34fZfk+SEfu42P3MPvPsxpP8NpvcZuusdpyuC1lAjbgKktXImm6KkSu9gWMA8NBCYmhmcXF37253p5aW5ybjw2PeCdCJnHvepRh2jEzB7Wk4dUmCF5z5AYMixsHuZWj9Fzpgj3Z1EX52HfLEF2LTd+DNaDKftjvPDflrP+9yoEZv7dcu7/WSp9eal242LbriXk6SXivWV+UVzdAS4pjBnik7H47Fh9affDDOia76a3XnpS+zCjA3Q69GF6x8M0qBYheJb6+rvMrkadvlIlKZAwMgV9j7nwR+zWR6z6VE5ZJjcvm5uSy3tQwLtewDtXyD1awN2Xz/4ij/1xNuPtNNpL30+//GMKZUMWfWM+c0sJd0cFf0+VYH+N4ECd6FAd93A983Ad+Ugd7mgd/Ghd+/G6+hO1JWdqsy7UPbzZeCu980Ix/GQN+kgb/isYeReato1A30RmvENjvvYs4yXMYr/E5b0tFGySiD6XiXYrxV9rJKf0kktG0U2z8L5NkOLgZrk5RT5mVZDWFKV29VN7BxmUERZvlC2d4GkmhIoJoWhMyObKLxLVJ4iakwkXEW9vzIOuDUGrjvn592dYuZOcknFe1YiwsV/aHlR2eTRwpxFtsWKM9j6dm6jykeRBsiRMEkaJnH4CvR/7J2GvyVXU7ChusRdDbRU9tjqUtQmva6JJGtncegG5RoyrkvdUKCAl6sZCbU2+rixbV5ihy07VpidrHz/UPLqrvn9befem4vZ1xf3biscPFJkpirxMRUmeorxIXl0qr6uQNVbJm2ukrXWAxW114ta672FbYtkbHKpEfrfmbSX+ZqSsZFEyKYkQnEOQWO0GVh9JLHVbKT2itzIpahRMg+4BjYEre6DAU+j6enW4XnDtHB5lpuAnYv1Li38TGTTz8/Ozs7PT09OTk5Pj4+NjY2MjIyNDQ0MDAwP9/f2RSCQUCgUCAZ/P5/F4XC6Xw+Gw2+1Wq9VsNhuNRr1eD3wTqdVq4JtILpcDX0lisVgoFAoEAh6Px2azmUwmnU6nUqlkMplEIhEIhL6+PiwWi0aje3t789OSM9LTn3N6etqaMzJSmVzs1MIUmJk5EbCP2PUDekVUwQvwqB4q1oHtsfR0Supa6QUN2Md13berGy9UVRyvyPuqImVn1b2t1dc21Z97v+n4htaDr0J3/xHx+R+x21/Ff/EGYceb5J1vU3e9+xzgATa++orhnbe0G9/TfbZJv3Orcf+XlqMH7aePOS+d8d64Grj7bSTpwVB62nBW9mhe3nhx0WRpyXR5xWxV5Srj1dWAgNfcCDJe1wrjoYGTMw68Mk6nxTnsuIAfl0riSkVcp42bjHG7Le52g5OiaCQ+OAheOp+ais/N/YnMeR413odY8zyqY7Kr4dk98T54PPyXli1c15rWQfF5rYPiL16zY+A18tB315N8ArAbFVgs82dqYTn+JpdzJyM7XFy0uuvPaaqne6y1aay7I2FHRwq5ZfPaEHR9zYLHvXrrX7JGJhfMgQmWbrBP1i+1jfhjM7HwuFsfUSWChwwHsD0cnRifXbCOTrHCQ53OMMbbL4uNhaZmEyXW96D3VqmrVP2qF3kHesc1XG2vzC01+2wuL/DFrFKpgK9h4FvZZLCq+Vpqa193Sk3txYelR0+VHtlbdepM652UvpJmWR8r5DRNTjqGh+X9/bRAAO5y1dntpSpjBVNZhBRltUkym80FDc6iLmsN1dOrH1WGZ7wTM8GhIa3Hg3eYWv2yqhincgRfNdZRPd9YO99UG4U0axDwPjK9gCk7TFB/2iXf0yg5Xy5JKxBX58ubCsUt1aJWiASKViFoFjzdTiboaUgpF6kQAWSINbK7BIQGLKa2G9kI70GSO/kaEAtVHhzPRmerhQK+XEbQKzr0YhALFYRqGbxOBKkTttaKOttEMKQLU2JHPLURVi6D1Dy7xw4vdqAKooh8X0+ZA1ZN722HYjsLiZ33qB0nGR07WV3vsjC/YfF/z9G/yzXt4OrP8lUP+KpMgbxIIIWK7AiRNxEPTJgoC9KUHrbaKtRppHqh0khVmxBGC8xphQXM3WE9sl/VNygmDwvZExLZhFw/rnFGdS6P3mXRO3Ram0Jtkil1YqkS5ECxRChR8MQqlkhL4usQTE0NXvkEJj3bJPyqjHkwj/htSV9VIxnXzZbjwIqlw2z9FNs4QzfMUPSzDMMcUzdPUywQ+Iu95CUofLmtIw6BgkWA6dS4RBw3GoMOp9AdQOp1zQJaDQVahy0VEL54DhWeM4v9e2xLCr/jCrv5LqM6iVKSSSkppZY20qp7mPUkHkQkQjlUVK9JGHJpYmHH6FB4cmrsR7RYALUcX5gG+6FP9sfHwHbey/2GBb983s2ftdJmjH3TGsS0snNa1jItqp0RlCeKsswz0pYYa428kxepSfPEO3O4azOIM7Pd38xB9s83fz5f++FS2evL+f/2dHFg/r8uV25Ybt0Bdo+gJsVFlXEdPO5igz0hJgf+5OrE5emFhYHpOe/YjGlwUhEd5/j3pzO3l/NOtMpe5FdTKcefMDan095Lpb36fcxL+CVwrR1jcxEbeJx9NcJDDeKjTeKTrfyzbezzbbQLbYRLbagrrV1XW5tvtFXdbsu/C0lNgt5O7byY132kBL6/Dr0L0vc5jPgJhvo+kf4Gjfn91gLsV5ict1ncTRzBdoFkv1RxTK25YNDdNOrum3SpFm2OTVPiUFU7FU0eGcQrhvlF6BCf0M+lxZjsUbpwiqKYoujHqcZRhmWAZe3nmKJcXZCj8HMlXp7IwRVbeBI9T+rjIMPczn5O6xCnbpxTPs3KX2BmAP+LJWYq8N+Z5pVMCmrHJa1cRKGgq1rcVbdqCPCBr3067KrjdpSyqQV4YR2OV43lVGKYZVhGeR+tvI9STiCVk4hVVEINHV/HwjVwUPU8RJ2gu1bYUSuC1IjaqkUt1cAZRNhcJWwCo20JixsrRbVloqpicXmBqChHUpglKciSFWYrinOVJXnqsgJ1RbG2plzfUG1orjdDWqywDgca4SLiPAyKX8gNKWWJmFvMaR/0OId8YFWSsUhovH8l7DY8ND06slqP5C+OvAGsNTMzMzU1BYDW6OgogEbt7e1tK2ppaWlqaKipqmpqamr8TsBOkUik0+k0Gg1wVlcoFDIZ2HoU2JngKw6Hw2KxGAwGjUajUCjP8hUGg0GhUEgkEg6Hw2Cwrq4uYOYGgUBWn6sJeLaGurq62tra6urqysrK8vLy0tLS4uLiwsLC/Pz83Nzc7OxskMWeUU5OTl5eXkFBQVFRUUlJSVlZWUVFRVVVVU1NDfA49fX1wDE3NzcDTwG8LigU2tnZ2d3d3dPTg0AgANhDIBHwXjgcBe9B9/Rgerqx3V3Yrk5cJxQHbce1t+Jam/uaG/sa6/rqanA1lbjKMlxZKa60AFuQg82BnTvR+O3tpnv3Em64f7/2wYO1IejLl6FHP6n95rX2Q68j97+F3/UWY+tb/I/flL3/huYtsDbPsza8/Zb+w/f0n20yfrHVvG+X5fBXjpNHXRfO+K5fCd3+NvbgwUBKylB21khhwWhF+Xh9/WR723RF+UxlxVx15UJNFRjHAxjv+3G8ZxiPBTIecOIFGE+rARnPlmA8fzwCMN7ACuNNgoz3n6H5qXjrF/Hcf4oX/GrF/x7P/7d43r/EC3/91Bn/M+5krd5+XX+x1kHxea2D4i9e1MdxxMnVbWC6xk4Hi/L9BTql5n7ci7Pu3/MTlg7i++JCYdzlSnjBoR2zUNaGoGFd4JWzX6aAyWdsdE7rGaeoYiRlTO0aC0YnQ84hk9gnwZsBJ4KH4HLEqVmACQEyBPgQoESAFScXwBVWC8sLiqgiT567A/3F/t79+7D7zlPOJ3yj+Vj25b1rQ8DvdL7TymwF5hA6jV4jM7J72cgCaOP13PIT10u/OVz6zVf1V64js4u43Z02BWMwJunvZwSDSI+nyeEo93habLZ2sRqYtBVA2BkN4vQGR0GzpRhpauB6CY4J89BsdHTM6XYzrcYul7Quwq0YIVdNwKvmWmoWG+uG2huNPXACgl4BE95ul56uEN/MFebkShsKFO3lGkiLtrPX2EO193J9KGEAgZUioZS+VgK1ncZDKMBUUrSeCWGRGzD4pl4Mko7g6dpN/i6tF8d30DlavlCkVFB0MohOWKtjVyj6qqU9daK2WmF7naizXdyDkqIoCgxTjcVrezpMXfUOWM09ruYC37HmJIEHIgo9uwdwKr7rPTb+LY7kHa5xO9dwmqe7z9Pn84z1AluHyI0UB9c4EC8NUxR+lsYt0NuVNqvcwFcZCRpzl8kCdVk6A8ausBbZL8cOiMkjUt6EUjGmMo7qPQNGd8DstpucBoNVpTHKVVqJTCEBUVAqkCi5Yg1LoqeLjASBCcYyVOM1DztlB6uFm/LYu/LIlwoJRXVUOJQtQovsZGWMqZtkGadperBLBMc8xzPP8wwLbOUiRbCEIS9Du+NNzeAFaURPnExa5vKnRfx+IdXIQQrpjQxiPhVzl4o+zcDsY+E3sylvMpm/YdJe5hA3crC7uIhj1L69VmtqNIp/kXncjRhsV9Q9Nhqbnp36jx/tFdibeQp78ZgR7JkekMQ9XHDBsxW/ZEAuajoWFc0LkuoFQckiL3+RlbH8TFf0ZXrSIiNlDizQkjPBKhwF63BWDnDrYoKWfiF0QNA8xKkeoReOk1InsXdmEOfnOw4tNG5ZKn9jGZgGrXLgv4BxwvZdYBFR8kMwV0LTFbfT42Et2G1i+ftxy6XlpfG5+fDkrHNkWhubEIfG6N4RnEPfpudWKXAFouZ0buEjZupj5rVU5rF0xu4MxsY02kvJpM9z4ftrhDdh6hd5Qyb5TmXzfRj8Iaw9GVaX2lOS2ZOZi3hYjLxagT7ZgNnXjN3ejtvcjX8fTX6LSHuFzvwtAHgM1m+ozJfIjLdIzI0k9lYSezeV+zVdeJolvsqT3xOpU+X6fI2pSm9pNlg6DSakydBn1lEtGpZNJXIqlC6pNiCxBgWuEMceZdkGGJYRummcYpwm6ybI+mGKIUY1hemWIMMYYGv9bEWQKw7weSEhKyKixkT4IQlqRAwbF7dPihpnhVXz3MJFdlbi/7LEzljkFy5KqmfFzTPyzgkFckyBHVLjgwq8Q9ZnluI0IpSci5CwuoW0Dh6xlYttZCPq2LAaTke1tKpopVrJaomR/2hRS6208omyrVLdWK6tKdGV5+sKMo1ZqYbHD413vjVcvqg/eUJ/6Cvtrh2GfXtNhw+ZTxy3XzjvvHHDff+ePy01WFAYqa4eaG0dQSLHyORJPn9apZ612edDoaWxsTXyfw7JEoGvaDQaDocDgYDX6/V4PA6Hw2azJeJdAIwlgl0JEhMKhXw+H8CwRJgLYDAikYjH4xMBrgR9AQi0hl4AHSW4KwFdAEEliAvAKgCuVjFrRQCAJUALoDJge3XvC5SRkQEQF8B7APXhcDjgAIDDAGgQOB6ADAE+BCiRy+UCxAhwI0CPwJEDxw98FwBUCbAl8KIMBoPJZLJYLMArtdvtTqcTeOHAy/f7/cFgEPhreIIeR8BhCVhMAZPOr1P6lFKvVOQVcd1chotB89IILgLGjoFb4R2mjhZDS522rlJVWSQvypHmpInSkvnJ97j3brJuXmZcPks9e4J04jDh8H7c/l3oXdt6t22Gb/4Q9uHbnW8nlkgAG9tbPjjQsOlkzear5VvulXyelv9FYebOypSdTQ93ddzehby+C3dhB+XUdtaRrfyvPpPs+Fix5UPNxnf1G970bP7U8urr1jc3fOe3rG+8+cxwg/mllwMXzw6mpA5nZIzm5IwV5E8UFU2VlwGAN1tVNV9TtVhbvRrEW3Nrc7wDEu/pBhM1QcwjxRn0OJcN1syTScFqeUZD3GYFJyTAbOSvyHjAaXOiPz7iiw/Y4xF9PKAAm+I46HELIW7oBc9gihawPypwNmPnxelp4JkNfyuOuQRe7YIdiXfsj7ftiDd9Fq/7ECxJWPZyvOjXIBkmTomAc/8RpMHy117ogn9dB8W/otZB8Xmtg+IvW2OhePb/jg9/17vC2hfX/kX/zW6/8d8ZUv6mj+d/EtcR8OA1th8QEvGLA8XFpeXg4IzcMUqQ9zO0A0bvuN87CgYPGY614OFI/8To7IJ5ZJIeGoQ6wlhvv2JgLDI9l5jPDs0MoRy9l5mX3ul65xPkptOsY0mi3Kqsu9X3ztXcO59w+Y1j5WcPrg0BV945U5eWV37qUcWJ8yVH9lafOd6R9JBcV6qidbjNmFAI5fE0rzBhM7AdCdOMVjRT2tjDLGxgptapsxuche36Uoy+VeKlh6Z9g6M+l0to0CEtogY/u3KIUjmBqpptr1lsqAvVt0lr4ehaSl0FN7dYnJ0vrV4BQmiTsQtu6yG7erl+tCiMYNo7epiN1ZDWwjpYfh2uHEprJvFRapAMUWp2J5sBY1IJIpzY3Gn2d5kCWImTxtHx+AK5lKyXQgzCai2rUo6tlnTVCVvBEnniLqgUgZahSGo0XofsNHbWOXsqQ71lw7icecrjGWJKFJ5vayvXNFc2Sf0M3SDfPLxmwTPbgLGyaCODm+BAnDRMVQRZGp9AZ5ca9CqjRGNk6swog7XNamn2WloCxs6wGtEvwwzIaCMKwbhGPaY3j5h9Mas3ZHc7LQ6T0arRGuVKjVS2UkFUphRK1RyJliUx0sRmmtSOF9p62JYqvO5+p+JojfDjfM62HPrZQnJ2Db2rncNBikx4RYSqHWcYZqj6Gbpxlm+bkzrnZfYFqWlRoFpiCpb7yPFusEfocn3THAQ6CofE0M0+bJUDl6vFPZBiLwixX/P7tnGI7zAZYMtpJvVNGv5TKmY/CX6GCL1NaE7HlpfBMxuhDxFdaRx0sZLaZBAg7Rq6DwG9RifuY1L3J8yhfc5lv702BEzG7GYTquOm3riuO65uiysa4pKKZX7BMifrWdhLeImZusDOmuXkTXOLx7nlw4m+C9zGILfVz+/wCnrcIrRHTvSomV690GdRBpymkMs4YJON6qlTCtgsv3KBmraMvRbvOgh2jCj5fTzzf67OeCrfBPPk4SfAlhLcArAKl5UMdpsY9ceXnuLr8sziwuDMnG98xjwEhgG5gVGS2w+36Bo1rFJZb7ag8Qm7MImRnMK8lsE6lsncmcH4MI36h2faJLySSvwgB7+tCLu3svdobdfZpubrbZX3oDmpXQ93lbQ0cJ3ugckXeXMhOa/rfC/9GJp5Hse5hec9JAgyKOJiurSGpWjlqeECTZ9ITxcbhAKDkqc38jV2gcovlIXF4qBU6JdzPRqux8Bxm1lOB9PuZdrCDGuMZh6hGScoxhmyfoJiGKOZhpmWIY5tkGceFOiHxKohmWxELhpVcCZU9Ck1cUaFnVPBF1QAmTctS6rjwqI4NyvOXMmppqfOsvKnOBXjnLoRbssAFxrlwUIchJ/d62GhbEyUgYJQE2FyXKcE1S6Gt4g6G4TtYCEoQXstH1rD76jmdVayoMVsSAGnLZ/XkitsyBHVZkor0pXFKZrcJH3afeODW4ZbV/SXzrVmZxTl5a65IDcXQKVn9xTl5hDv32alpjKzc5jFxYyKClpdHbW5mQKFkru6iHA4HonsQ6P/ukhWXl6eCHwlol4tLS2JkFdXVxcMBkvEuxIwRiAQyGQylUpdw7AEg0ml0gSAabVavV6foK819EpwVyQS6e/vHxwcHB4eHhsbm5ycnJ6ennsxVAAAVqwsXnOSIGl/3/58eX6OLCdLmpUuSc+UZPJDfOOgUdOvkUVkgqCA5WdRPBS8E4+yo2AWGMQIadI31Wpqy1XlhfLCbEl2qig1iZ90l3P3BuvGJfolAN6Ok45/jf96L3bvDvSOrcitH/d8/H73+291vAWQ25vQN9/rem9Tz6YtyC07UDv2YPccxB88Sjx6mnL6Iu3iNea125zbD3kPU4QpWZKsfFl+ubSogV/cQS9GEYqIvfmMrhx+a5asNkNV+kSX/cD05I71/g3b9YuO86ccx484D+x37tzp+GyL7cON1g1vm19+BYzm/fElAPDs73/g/PgT97bPvbt3+w8cCH3zTeTUyf7z5wauXB66dXPk7p2xhw8mHidPpabMZGfOF+QtlpZMZ2SMP3ww/uhhwgNnzjjefXdtCPrhg8XOjhXS48RFQrCj8hrpuQHS84MZm0OD8fGx+Mz0z6mH52KDZx4jJq7tAc9C0oa4sAI8IzEz49TkOOEO2PK091y851i882C8/ct481ZwVXP1OyCYFf8WvKQFTL3WWG7NOf8QL/z3eOlLIPLVbQTvBdkd7zoEXs1HXwT5EKBERnqcmw8+nawBzIrXI0GkdDDiXiF4Juw3xYdc8fFwfGZk9VChe+Plrz5dQQ08bPW7T4eAs/9+HRT/iloHxee1Doq/bGGvxPHfrm5PD4ItMdZOLj9d3qnxf2dLWo4eH8ViV3f9OE11Q//bgOLs/JI7Oi2ygIsPucYhi2fUZx8yS3wSghkwsBF2DU1OzvknZ8T9o72eaJczzA4P28emphdBPFxaXtLGtAXyvC+xu97qeHM3fse3gttPeO1f1OE/KmCl4gwt964jM5MZTTUvcs35o4iCZG5PkZZf67Q1rjBhUzCIjMUYIyPK2KBWYyaThC0dnMIGcWa9Lb/BmN+hLCVqoSovzzfkstkVanWfTtDiZFb1UyrG0FWDTY3BknZ9UTc9H9OTR28pFNUXKQAQ62g2dMGsMJKnlxMAgBDJdCLx6o72voaS2vLUjOrbt9vuPupJK+ir6GRCWAKMfiVmqBbglTyOiab2IozBTmsQp/HSBHqeUCSTkXQSiJ5fraFXyDG1YgALW2qEkAZxV4cMgVH0YvWobnNPnQNeEkTlD+Ey58hPAPcj8xxt5eqGSkFtIb0hCwfJqOmrSKF3XGYxT3DUSIWbqR0Umodf5D55lKgkWex1TluV19wY0ENCqp6oCjeoog9rhcN61aDJPGD1hm0ev93lsjlMJrNOb9TqDIDFEqlErhLJtXyZgSkx0aU2isRJkbqJYg+c52wgm7MQ6ivNot0lnC15rG/yKCkV9NYWNq1HpMPJA2TNKE0/TdFPAzQosM8pPPOG4ILeu6hzLCn1ywLxEoE2D0GM1df11xcFWrMdHSmmnjsaxAUF6ogIu4NH/GB1TSDzDwzK+1T8F1j0EUTPxS7Ig476nI7y6tYMaMMdTONDXleOjFin48KsCqLLwnP7lc4hq23Wq48H5WCgz04G26VqOwDqs8MajE31xuaGhHWV3Y405tpwxfUhesuIsHlA2BYRQv38Tjcf5uAjrQKsSYjXCykGGcegEht1amDG7HA4gOlyIBAIh8OxWAyYKI+Pjc4M+BYCKvBKuaYLvDpOfgBeEW/bAbJf4vo3MHMqewW8NA77BpwSsXPj8mawv6tfCqY5LKx0Rl1aXhxbCQM6vhcG9HYY1bUqZpEYlkWvSenLTsfez0FfyEV/nYveloV5Jw3z2yR8ggB/ndT3elrvxpyObQWNX5WVnqzOvNZ4J6ntXEH3wUrkjkb01lbs5i7Cx5347R3EfZ2kY52USzDabTgzpZedj+FV4wSQ/WWtf3zSt2I84D88xv82mZjY/s595SgaVxQUiwNKcUAv9ltEPqfI4xO6w3xnP88BAN4IyzLJME9RDQD4TdGMUyzzBNc2KXJMSixTCuOMSjOnVSwYpIsG/rKRuWwgxw04sH2RBhpXNsWlVXFB4SIra5aSMkVOHSelj5BzB8ml/eTKEKXOT2nxkNudRKiN2GHGQHSIVjWsVdHRKAHX1NWCV1tawSaf4uZKSWMZt6aAXZHLKE6j5SWRM+8THt/se3ANd/sy9vpFzKVz6HOne0+dgB892v3N0Y4zpyEXL7Zev950+3b9/fv1jx5VP35cmZJalpZWkp5elJGRn5GRk7GyUuwZFScnr269QGlpad1lZf/FSPafKuAcvrY0zjHiMAwY5BE5L8CjeWl9zj6EFdFubK/X1gMsB7AWAHIPeA9usm4CJHaw+4t97Z+s+XztZ/m539uzH7J5b+8uAPMA2AOQDwA/AP8ACARQME2UliPNKZIXVagq6rR1rdommA6CNcDIBiTLiBUaCAojRWekW4wsl4Hr1/MjeuGgXjyq4I/zGJN00hQWM93TM9nSMl5dPVpUOJyZOfgoKfbt7eilS+HTp4NHvvHv3+/ducP92RbnR5vs77xjff0NgPFWUzcB2Hvtdfvbbzs/+ND98Sfebdv8O3eF9n8VOXq0//SpgcuXQNh79HAsPW0iP2+qonymsWG+s2MRhVwmEcGldzxuXCgAMzNlUjA5U60C1+ABRGc2gVDncIBZmj4vOAEIh+LRaHxgID48FIc3xZvqQTevGNhoXNuuA91cF5f0roTmGCBHATSl7ozLm+KiqjivMM7MegbnzoInnERcrvHTeO374Bmp5A8r5Yv/n+9R3HMGI3W/AiN4VRtACGzZFofsiXd/DfbOwVwCZ1aUR3FGBvh0wJMCTw0cgKE3biHGncy4VxQPqeL9ZvAy/UQUDCr+p2nJrFmUC55aQF4kt35vj1ywPBxbvfW6/mKtg+LzWgfFX7AGHeB5cKJ/dahuj9uIq9s/Xcvx+Ft8xsWiEv/Nm6u7fpzsCk6oNH+5pSne3haHtMc7oHEoBNwGNtbc1PA3Dopj0wvW4CTHAHbGF5uHrfYhpy6iZn4veDg8M28YnqAGB6GOEN4XUw2O98/MJVKjxubGME70Vfbl97rf3Qj/8CTj2BNxwS08/rVM0v4aYT3bpnP4gbkRMGFqfni+/e41ZHbKi1xz7ggDUzA8LJ+ctM/NDcTGgjIzFSdtgogLGrQ59Y78JmUuTFxOU3XLzByjVQMQmpTdpidV2VE1zvYWY3mHPBPByMAhc1iQIglAYQAQdsNsMIIbyQmghOFehqsXo+6F0rtLWxtTsivuPyi4frnszDd1F8603b6PzKmkNmD5CBnwwkFTzGKBi6XwYrX+LksIa/JR5Sa2RCSRE7XSdr2gUkWtkKFqRR0r0cKOJgmsUwbv1SAhRmS5ozcriE0b6kubJT+epyQP9GY728tUdTWC6mpmXQ29vYGOgKCImGqmII2hf8BxPOC7skS+FlGwVxxBSiNYRYSutpr8E4Pj8y8yVRXRqKQRkAPdTqvdarYYDAaVSi2TyYDpqUyhkim1EqVBpLJw5DaaxEGWuIkSL1keZOtiGJEfwrCW4bSPoJJTtdz9xaw9uYwHFcz6Ji6xS6hCy7xE9QhVB9IgwzArtM8pvfPG0IIztuiOLRq982LDOJk6gOgIdJS6WlPMbbe0necV8CNi1C5e30cc2quJ9WZsxutsyicM0n4i7jQCcasFklJRX1hSVF+RBqv9llj5LbcmRdhSJOltkPFgMgNe7GHyYkL6lLJvWdcdV7WsEEV+nPW0bwfgZXb6AjdvTlA2KageEzUNitqi4u4JZn6ccnfNC+iUyZaqZ/csk+/41XRgXr7GfsCMfGpqCpiOLyVKTM1NgFeyfWLwKru0AbyyDkzCOg+CU6ji38Yz/g6MCgLTr4aPwTgh7ho4hZLWx41ocDI3YAemSt+FAUcnTd4Rub6fKwxRyF4cQtPVymqtQ9ZUNJQVFZYWJZeXXiupOF5c+2Ve08dZkDfS4L9OwiUg8KUnyHcz2j7Lq95dUny4POdkdeqFukfXmx48bLub3nW/qPdRbd+TdmpOF6MCzmlD8REYIREn5vZJFXiZpU/2vRWnCRMU/VT1AEczINbFNPqYUdfv0EWDOKQH2QbYvfJT3V2BgWav7GldcZsH0TZGFYD/dKlzTmldUJsWdNpFvWLJKF4ycpeN9GUDcVmPWdb2AGfdBWnDLK9iilk4TskewacPYjOjyPQALMPblevsKLS2F5rbS/StZermcmVzpbChgltfQa8ro9ZXUGpKiZXFhLLCvqJcbF5Wb2YqIvVxT/LDrgf3Ou/ebr91s+XmjcZvb9Xd+bbm/p2KB3dLH90vTH5YkJKcm5qSmZq6Cmr/QdmZmXk5OcUFBRWlpXXV1Y319e1tbV1dXQgEAo1G4/F4CoXCZDJ5PJ5YLAbOSODiZ5MJODsB2AYwGwBswHtjYmJidhYEe+AdArxP1gT81mazrQ6+08LfQH+jH6Y7uBX+Iro7QTpxEH/wS8yXW5FbN8I2JkJzgIENYLgNuW0Pds9hwuHTlNNXGFfucO4kC5KzJFklipJaTW2boa3H0oNz4lhOiszB8lRlzxUVzhavevTWLdcHH84VF655vqhgDNYKpkQS8HEcNo5GLcO6Ftta5murZ0uKpnOyJ588Hr9/b/TWzeGrVwYvnI+dPBk9fjx8+HDwwAH/7j3eLwDS+wwkvXffs77xpvmll1dJby2y98EHzk8/c+/Y4f3qK/+x46HzFyK3bvU/ejiQmTlUUjxSXzfW0TGB6p2iUmaEgnmtZtHpXI6E4xPjYBkV4N/9s1dsLszEp4bApICYFcQnrxBMFzdh45pukK+EFWDKJe1JnHg3jrkMxtaAs0rRr57Htuec8T/Bq07A2aZ5K5iL3vEVmKUJIBz6Qhx3HXwogOLoaeDVKH4xCHKyBjAwqIXFDSgQLIEDcHPjPgl4PFEjOFka8YFENz0Mrvr7v6Wl5fjCUnx+MT67sDwzvzw1tzw5uzw+szw2szw6vTQ8tTQ0uTQwsRQbX4qOLUVGF0Mji8HhWZ5thqz7QeuBe60+xbr+Yq2D4vNaB8VfsLoPg9fVEhp2xzmZq9fsf5ZOKUlv46nGTz9dmpxc3fUjFHYYy0/s0lKS472IOLwHvF4Yi4HRRQoZ3HjWf5MFuwHY0HvHaeoBoqJfaR60mWImiX8teBhxD09OzXsnZoTREYQ7CnNFeJFh5/j0zErwEJBhUF+ozN+L2/1Gxxs7cZ/fEdzJ4HUeaGK+lEq52qnAKxxWhwuYhwHcolHoRRR9dza59vHpksNf/rDhqAyUoq5NVdhgK6jX5zQLsnu45SRBJ1dMFghp9L4OFqSFV9cmKOtmZGNw2XRYgbC9YhUI4UQACP0AECKoDiRcga0n4XOaMPczINdv11y9Unj5bMH542UnD5V9s7fxyiVkViG9BS1CS8V9BhAL+0wipp0ncpMUPoTOj7H4KFozRykRK/rUkjYdr0JNrpQha0WQlQ5dABb2QBSIJl1voRX9JIB7NIBPmiEnL9KSh9AZLkiZuqZOUNnCqoHSm3uZ3TQxTsKhWDuotkqWo5DnLhb46kRBhDjcJQ5DpcFeeYir9lhVOo+a6dfB3OY6p72cJreTFKE1ExUhvDzy7B6CLMiXGhVaq1Tr4KtdTIUH4EC8JICVhAnyKFkRJkgDcI69gaDL6ZFdbWCfKmMeKWCcKKDfKqdjO4UylNxNUA+tfNFOMYyzIsecCqTBOVNkzhiZUFsHOKwgoduNLLN0p+i6rilhxyW9ewS4TziUN5hMkANZrN/y6O8IadtFtKNsyjUiNrm7J7+upaa0FFqciq64SS+/zi64zylI55UXc5rrWRgoWYLoDfa1jZIrFoEP7zP4B/blY2fPcosmeRWj/LoBQXOY3+7ldzoFYNDPKCJpxQy1TKDRqHU6HTCzB+brTqfT6/UGAoFIJDJe8+rzM63/4HFpMbjYD5hCqTvB2RXpATjxat2+EhL8x3jG/wDXxtR9CF6bR19Yojxc4OfOKqqmDC2jtu6YDR609LjVTQ5JqYWXqWfe11CuyomnGdgTSPixpq4TJZAzKS0Xv228frbmwdflWV8UlH6U3fhaKuxXK5HAXyf1vZqCeDezY1NO2+eFzfvKmo7VNl1sar3X2ZmOQJbh8S0MFlIow4jMWKkPJ/0e7AFD4OMJ8B5LN8gzDokswzL7qN4+YrEPe+zDAdtgxBIbMEdHTJFxfWhS459WemelbuC/Ocu1zjBMwD8XNNMEDGfFzjmFh1dXwoJBBdjehNm9PaTuzrUhYHJDlbXqkbf2oa3yob78kaIkWVT4mFecwizOIBdl4YtzMSX5yNL8ntLCzrIiaFFee35OW05WS1Z6Q1pKXcqTmpQnlSlPyp48KU55Upiakpuamp2Wlg74TykjIy0zMzUnJy0vP6OoJLukIreitqi2paK5qx6CaINhujEkNJlOYnPZANfJ5XKNRmM0GoH/vsvlArAf+NcPDg6Ojo4CRDc/P7/8p8r5/CWKTkU9Y541GweNDB/j2T2ApxemV2/9E/Xj6S5Pmvdj6A7w69DX3+t6bzNi8w70jgN9B46Tjl+gXbjBugHcN02UBlBipaqySdfUae5E2VEkN4nlZ4nDYk1MYxu2+cf9A9MDU+NDS6Mj8YFYPBSKe9xgWMygj6uUYNCMxwWTIUnEOBaz3N252NS4UF46l587k5sznZfj+Xy7+eWXLS+/8p1fBljumeErlj+85Nu717llq33jRuubbz3FvO9s3fC2fdPHTgAIDx70njrlv3o1eP9+JD29v7BwoLZ2qL0dXMlJIk1wOFNy+YzJNOf1Lg4MLAOY9/O0vAQWwxuPgFeIooa4XwYmZFqIcT0ChC5JbZxXBF4wIj+M990A8zABVIPsiTdvAZs2V7wBXj/K/SfwvJH9v8HwXelL4ALjhk/AyB6AggAQAlhIuAMiIgCKAC4C0AigIwCQdvoS5OxS9cGlum9A1x8Ff9Z+DW6suXJP3MZZPc7/BIFIBvBY/xiAZIvhFSQLDC/6hxa9gwuegQVXbMHZv+DoX7BFFiyReXMY+F6YNwTn9YF5rX9e4wO+KeaUnjm5e07mnpM4gRPLrNAxK7ADUAeeZ9iWGZYZOOfM0I0zNDDp4E+bZgRPUCwzcHvwXnzbrNAOfg1JXeDDKjxzSu+c2gfsB28GPBrTDD4swzxNM87y7WueEzkB2lx9Yev6i7UOis9rHRR/qfJJwFMzcJYHtQxWOvWLV7Z/jrp9yn9lyVibNk9rtau7foSGI76KU1+yUGfAZj4IeHzu52Pqf6UAygsPzSqdYyuzz5jKEDOpQmqWEyAlNdPhMURGYpMD03O6oQlSYADiCBH9A9qh8cHZVdAFZjYoJ/Ia+/IHsPffh713kn4sQ1L8mEx/N5e+MZ9VRNQJdA6T2QzCoVqrEppoUHXDdWTTrYK6y2cqTx+EaPPkg/zIVCBho1GIrc5dGwLu9NQ18zO7KKUoYjsKhkbUI3uLuzB5vegCak+REFqpWgVCggvF8aOFYRTNieqR4StxrORK7qWHlNOXYOcuNF48V3HlXNHFU8WnD5ce3Vd6dG/jjUuY0gpuD15BVihoYAMPkAyJFj7LThM6MQoPzugjGawsjVikIKglrTpepZJUJYXXCgEsbKsVdzbI4HUqZIERk+Tuuz1AvDNFfrhITRrBpXs6ijW1dcLydlYlgtFIZ3Vq+EizsM/GJdmxDGcb290k8LUCTCiJdIhDteJgjcQPlXipIqdVqOiXUMI6pM/S5rCXux31QTey38+MRVShkNXtdolkGrvdHvxORoeXqlzdTkihVOF4Npo8iBM4oFR9KVL2sIVzo5pxtpB6rYRdWMdDQEUipMzZpx4kgc0Gx2mGaaEd+OpdsISnpM4hvijCxHvIbVZ0oR5xXwU/J0UeEGK2cEnvJOKBYEiQ/kcB9UMpZaecekJB/VZCTqdiypCQptbK7up0fPlNduUVZu4dzuMn3Ic53MelzJxaUk0rEgVrl2MrRykZcebjBVbGDDtvglMywq3s59SFuC1+foeL32MToC1iklnONqklJqPearUmkvGeTfgcWwn6zc7OLv65vvMTlb+Pd34FpmOt2IvJ7GytWBsCXi761WjZ3y+U/3628Z1JyMejPdti2F1h8j4Pbb+VuU/D+lLC/0LA+ojDfJvFAtNiGazfYihvQPs21iB3FkKPPmm6eqvm8dmyvAMF1Z/ntL6f3v3yY/S/PwIh8PfJuDfTsB/mYLcV4/dXUo43Mq+2ix7AtVlYaznZ1cT0dfNDAO9hJVGC/Hu8J7ePql1jeu+4JTDpDE36gxORwNiAf2zUOzLhGZp2xmZtUXCKpguA0zKpC5yHATMwYHaVmGYxVtgPmFfJ3cDUbd4YBGd17gFgwrcU8C/77fNu9aiRHZFhnaRWbVeZuD6HVZpGLnhSnZeT9oMC+K079X4C5P6jcjIyCrKzivNyygvzykvyKisKqmuLqpuKqtqKKnpKypFFpai8ElxuKTG3jJxXzShu4lV1SJoQyo4+PZJhIwo9bHVIbh00Bcd9Ywsjc0t/06dNgLI2dG4A0Cvhd7vefbvj7afDzndfg7wGMUL+LN0Bj/MDdJcw8EQf9Xy0rRcM4h0hHDlDOZMI4j0WPM6WZJcoSuq0dcCDA0+Bc+KAp+MH+YqoAmBX96g7MhkZnR2dXfz+H3NhAYyVjYzE+6NgSovLuazTLgoFCxTKHBo1A4VM19dPlpSMZ2eOJSeP3P526OqVgfPnY2fPRE+fDp84EfrmaODrw74DB7x797p37HBu2eL4+BPbe+9Z3njzOcBL2L35MzDK98qr3/kVgBufGb5q/uNLsZSUp7CnUPx82Jufjk8NPg3ieQRxOw3MBdB0xWWNT4N4AKdhLoFLgjsPgMUzAYoDWA4gOmDykPX/BdMEANIDeA+gvroPwdgddC+YxgkwIUCGAB8ClMgvjkvq4irIylI6IkiSAE8CVAmw5UQ0Pjv+fImpH6c5kWWOaQJreoG2zLKNsyzjLBtAJsAmwDMU/YLOBXycQWCzRxesCVpbRTXgHL7KacBpAYQ0gJe+I7QEnq3khP8ZA7cBbplANeAY1lBN7HyKaioveG4B4BB4XuDZzSGAGxdsUZAhAZgEzjbeQRAvAcgMjSxFRkHyHJgAKXR4anl0GowWTsyCkcOZ+fjcAhhL/O4q87r+ZrUOis9rHRR/qQLO+OKa1e2ANC4s/nnna0CeyYHfckW1Fy4N1NWv7voRmh4fqT6/l9B+aJnLALsGTf/Mi8r/ZZpbWPb2T4utIzhZlKOJaTQRg9gnIVrA4KEUDB6OTc66xqd5kWGYK9LjigiiI56J6blEU4t43DhkKFLl7cPveaPjjS+w2x4I7+WLes5BRb9/Qj7aIOrmGVUGi3JFerVZRDIi8iS1VzshD7NrLxytOX8IXXpFwSweiAkghnqIr7o70Pgit3oqoQjEM0DoRq2sIcTSXfheFbmGwHpcLTp3l/f16b6TZyEXLzTdul797bXyK2eLTh8uPrqv5Oje6sunIE/uExtr5SSmRWwySTyKlRxawAKyhcVx4IVOvNJN0lkYeolIjVdKWnScKgW+WtJTBzChqL1G2lWtQBTq0Ek23K0w4f4U+dESLWmCkB6AFenq63jF7cwyDL2GT2vRsbtNXJSFQ7AxaS4y09MrCMBEQaQkAhOFK0SBDKEnU+Aq59nhLLNSqB7U8Pst5KC9x+NscDor/b7uaJTa3y8JhfQej8NsNisUSolUqlDrFFqrSOtS85QqqXzNSqlcLpM9u0clkUN6pNA2IQcus+DU/QTtFEk3RtGPMcwzPMckxxRjcIOkXheuzozM1MK+lcOPi3u/FGN2ift2raIg89dc6mti0kYFYacaf0zdd1WBfiiB5bFbqvvKoJA0fN0dRvUVZuW3zLxHzEcZ7HOFgtPV/EvNzHudpDw0upUEo7Hb9ZzmEKcxwIeCVV4kWJeC6tLwnUaFy25OrMICwK+/v39oaAgAv8nJSQD8fka23sLC+MxMaHJEN+bBjmnLR3kPRoknxuE7Jlvenan8XVfukeKcB8VF6QnfyCnd8BixNgSddoXevJ1FfZ9N2szC7sT3HIO1XW2pe1BWkZVeUnaroOFUTvv+rJ4tGej3Uvpeekz4t0eEXycRXkulfJDD2FrM218tPt4ovwzR3OnWp6MtZWRnO9eHV0SFpu/xniM85emfDg7OREfmhkdmx4Znpgcm5/onFqNjwFwKnPYB7GcMgVM9hQec4fFs4GXytVkd3QjO2ACYB2Zpah+48tMambFHxu3BAas3ZHF6zHarRq2X8mUsIhcPoyJasW3VPTVF7SXZjfmZVTnppVnphZkAy61C3bMCduanpxVnpDXk5eTlAs5NODc3Jzs7e20IOD8nm9CQRTbiCGYUxgJD2iBdjsZ2Z3Wjp3itsTvwKYYHW3Dhbmo/hjtAlQ5ztaNy64TBO+2MzoZGF4Znl/7Wr/EPzw4HJgK2YZu6Xw1AF8VDQdvRUBMU4DGAyjLEGQDjbYZvBqBuC3JLwrshm8+Ug7VS1gyw4qvtr77f/f6niE93oneuBfEAOHzIe5guSi+QF1SqK5v0T4N4bD9bEpZoY1rgqQPjgQHgLTL/Q2ksy3NziyMjC37fnNE4I5FMMxiTGPR4Z+dYfcNIWelQdvZg8sp6vCtXImdOh44cCezf79+507t1q/uTza6PNjnef9+24W3La68/XZWX8MuvWN98y/bhRsfmT107drq/OuA9dtx34ULg+o3g/fvhlNRoTk5/SelATc1gS+twd/cICg0CHos1KRJNq9Qg47nd86HQ4vDwMvD1t7y8gG6bayp56trcufJH39vTVLJs1z0N4kX04PJdJwtMldQ9E8RjZIALgHHXwdV33YefCeK9Hi/6zWrwPxHEK3sZLK/SuDnetvNpEI94N05LiXPyweYx8ua1IB6YERpSxwds8dEAmIG58LPenwsraZMA+YzPLI1MLQ1OAGgERuQAUvIOguwEQJ1lJf6mC8wDOAewFsByopVoG8cKftgBNgM+6TQjAGZzEueLPEs3zss9z+BZGDgVgA8OcCPwLJ4VQvOtEhpwAGCuZv/4d5A2uTwytTy2wmmT33Ha7AqnLSyB+Z/rWteLtQ6Kz2sdFH+RshDBxUKLKzGuhVkw6XTQuvKLn6zl+PK7AvLRxkbv8RN/sgvZn9T87EzTrYPw4l2LCgHYemgsEdj8W9TU7KI9NMUzDuGkUZEqqpMF1ey14GF0dGCqf2pWPThO8McgjhA5MKAfnhieW53ETyxM9Drh17hXNsI/fKf77VP047nysnwmb0cF79V0agZKQZMZVRows9SgN6pFFhpE23iLDXnY0ZXypOrMwYarR/qqr+vFTWOjhsXFaeALyhrWtCjLOjw1Pe6mVXuaevzfba+4yV3SQUDTIRx+SbfoXjbp9DXoyUvlF66VPnhUkfKkMvlhxZ1rpRdOFB/bX/LNnvJzRxvu3UCW5nMQ3XaFKmQP+SwxvcQrpVqB1yjEmXhkC5XjIEmcZCWAhVK+EqcUN2tZVQpctbh7pTBGe5W0u1iNyDCjk3yY5DHSkyXaoyliehhZaGqqFZS00QtRlDI2sUZNb9OzYCYWzsqmOOkMD5Xnx4lCaHEEJY60iYO5It8VgfMyx5rMs9SKbSyNPWDXDvkEER/B7+twOis8npZQCBuNckMhpddrtFisSqVKIpHKlBqp2sxT2ilSd58kCPybaKp+gSZEE9vNxGoHO9PJy05Yy7vJ42xbGwK209Pl3ahQH9LVW2uGZWk7b8m7josxu/nkj9j0VxIoyGG8IqS8Lydu0WJ365BHdD0XDbA7FngWt6qMmt+CSsJ03CXUXWXUXafVPSCXp5Kz8mg3y1mHGkVbO6S7UaJjZN4tHqdAyO6S89haid2qCnpsoVAoUXtjdHR0YmJiZmbmp4Lf4uLU7GxkctI+OqoaHORGo/hgsNvrbXC5yqzWVIvyspV30EHe4kK+6Yf+LtLwT4Pl/2uy8H8uZP+Ppcz/93T+/xwt/39idf8WaP2jvWODvnuzrGfvV9ktn5dxjzWJ13y0SfLs8JVU4oPssvdSqS89BkuD/uEJ+e1M+qdF3C8rhEcbpFeh6icoYzHZ1srzYpQRrnnQ4J9I8N7g+PwV5008AAD/0UlEQVTo1ALwOZqbXwIvtwATL2CCGBsH52e+QXDeZokA8zkw6Cdzg9fjOZYZ+ncJn4kZIdsCTBOnxPYRsTUqNgXFBpdYaxYotTypgisSsrhsGoNCIuNwOAQCDm1vaaytqiorLi3Mz8vJeo74sgHYy8osys4qzQawMKs2M70pLaUtJbnzcRIyJakv/QklJ51bWihrrjegUF4eL2wyRAf9kclgYNrjmDQbx9XKEZHs9jn11QuaW1cTVj28IyvMWhsCVp89ScVkUPrRnAGKBCRAmWVC75lyRGeDo/N/cwQ4NDME4JZlyKKMKnkBHtlNRtqQ7cb2Wk1tobwwTZR2j3vvCuPKKfIpgOK2o7Z/1PPRc6E8wBs6N3zc8/EOFJiueZpy+irj6n3ufeC+ncffF296a82KD94ybng6BCzc+IacBl09lGe0SneRyJzbM2M2T6tUk0LhOJM5RiCOIBBDzS2DlZWxvPxoSmrk7t3QlauB06f9h4949+5zb//C9elnjg8+XKW7Z9DO8uprtrc22N9/H6yxuXWb58svfQcO+o8dC547H7p+PXLvfn9aWiy/YLC6eqhlpd8GnjBOp0/yBWCipsEw63DMBwKLAwNLExPAJ3D1QP+sZkZArhv2xGMWELR8YhDtrCRwLZymCyQxcTVId6zseOnLIL/l/OOqAaLL+QfwZ8LAdsbfxdP+X88H8QAOhB15GsQDHicRxAO4UY8Av+vd3O8H8X7ity3wrQ7Q0fN0N/4f6C4C0h0AZpoVupOtROoAuuNawXRHgO4oetDABssMBt+AX4kcwM2AGwNACGKhcSXaBjyUKwZSHPDgAL/1j4FPB2AbwGxTcyCtAQezvAycKwDwWxqcfJEBqlxfd7eu/ytaB8XntQ6KvzwtL4GVl7Ww1aGdHFc0rm7/dJ2W975FYWo/3LgQ+7FVs5aXliBJJ9qf7Jg3SeMtTWARs789DU/MG30TDO0ASRqVyMIavkcKBg8tZqk/4hkem5xzjE1xIsNdzjDSHRX1j/gmZxa+42T9sLZQnfsVYe8bHW98jtn6UPSgSom636t6O5u+v5LdSlcLlTows1SjNWpsYpK5J1vacIOOzGnrTr1fcXJ/87ffkJsfOA2Y6elAPL40NTcmMBO6FOXggkNpVn3xIzyuSqTsS5im64E6K9eGgOuLb9TnFtwrLs3MyylOvld29WzJiYPFh3eXnPq6+tYlSFYKsa1RxWYMBINTozMx/6hdF1byXGKiBSTDPhOLaqXy7BSJkaEAsVAhblbTK2WYanFXjbClWgytVHQXG5B5LlRmDJe5QH0yQ06LYYpt7bWislZKDoJQQMOVyckNGnqHgYmyMMkOkAm5PrIw1CcOY8RhhCjYJPBm8FzfcK07uebjAlOawgG3BNR+9/CwcWCAGwz2ut31Lld1INATidDCYYnPp7FYzUqVBsBCiVwpVho5chtZ4sJLAhRVjKeNitU+FteAx0lgHayOZiYaKmJ3y70YjZy5m89/XyzamrBItEUk+mxtCJjDeYnb+URIe19G3aYh7jHiDlvgZ03tt3T1KYqKYmFBPS2lF5uE7bpDbLxGb7hKb79LhqaSIXnkzkpKViP7WqdwT6/0pT7VayzNdrH+nNqWZfFCPGFOdNA3+RMi5MvLi/Pzg1PTnrFx/dCwqD9GCYWRPn+ry11hs2eZzA90+isq9TGZfI9I/CmPt4HF/h2L8SsR6d8UmH819f7WA/tDBPLH4cY/TFb9eq7on5Yz/24h5+8ni38zWPl2oGaLteZrTdUlceljbmEFO7On7zETm8RGP2I3JrFyHjPvpTLPpTEPpjO3p9EBUDzRIn2R30qn5jYqNP7R4MgznLO4BNZUAKaPQ5NgZCA4vOgZAKZ686aVoJ/SA17a59vAmeJ3Qb9xomaIoAwT5V6ixEESmchCNZEjxTMFeBoDQyCjcFh4L7yzG9oGaWlqrq2tLSsrKygoyMzMTJBeRkZGbk52UX5uWWFeVXF+XXFeQ2FOS1Fua1Fue3FeR2khvCQfVZiNy0knpiXR7t9m3bwqvnFZefOy+f5Nd9rDYHFOrK1xjIgfEXMHTIpIxOSdctgmDNoxuWyYzxuk0fpx+EgPItjW4a9di/4BbvaWdvjrkME24Le9vCwuo0LNhSSsrU03bvl4bQgYQ34S6Des/on+q/TDvJcqSv2RvPcJ/JMd6B2H8IcA3rvGvPaA9yBTnFmiKKnX1neYOjB2DNVDBVfl9YOr8kKTodHZFxZsXBobc+/e7frqK//Vqy+y9d13fQcPuPftc37xheOTzbZ3v5eZaX3zLft77zo2fuT65BPPlq3ezz/3f7EjsGd36MDB8LFj0bPnYtevDdy5O/Q4eTg7e7S0dKyhfqKjYwqNmqZRZ4WCOa1mwe1ajMWWVyrr/BnNTcQnB8BsTLCdnQ6EKzcPTMg04eI6OMhd0vq4oAwMtdFTwQW6AJihLsThx8FYXPuXq43sKt+Ml/7x+YKZwPZqz4MN8fqPwPKY0L1grA95Biw8DkbwnsTZuct1W5eL314ueX/VpcDPd5dLP3jqgjfi9h/XxuBvku5Wj+2vIeCYweV8awavLhm/t4dnAw5g9dbrWtd/odZB8Xmtg+IvT+oOEBQTiaYzI2BLjMnvCp/+REE9gn9hyWifbZ3g/IRV472FNxvvfD5tk4C1TIPB1b1/AwK+yCIjs2rXGEU5QBeHZCK/YqXnoZrl9BiiI7HJ8NSscmAM54tBHSFqcNA4Mjn6NHg41uuCX+dd/hi56e2uDcep35SpKutE8pMtsjfSKY+6RHiBRiZXKhQKs9GqEzmoLbrGb7mdKey+yoaulBvlx/a2PzjBhmUH3dyFhXHgAV1BPV7e2qLMr7flN3LyW3rbO5r43XB79a3r3I5mt1qesFYmErDFa0PAjdfPlly/Unb5dEPyPXhFCQeHdhj1CwsLS4tL40PTPseQTuEXM+0AEwIvjY83M+lWGs/IBPu/4+XCVhW1SoaqFndUC1urJdBKdXeZBVHsR+eNEdJnyOlDuCJXV62sso2cCcNmklAFkr5qFaVVR4OZGHg7neGmcnxkQRAvCmPFEYIwiOb72gWeDIHzG6H1Vbb+E6HxisZZ44owYzHvqGt4WB6Nknw+iMNR7vW2hUJ9oRDH65OZLVqlSiuRSsVShUCmY0ktVKmLLPezdf0SXVAjs8voKh5KwOzm8WEiQ69ioE8zStAF0eoAUhOCSb0dvbbWUkPnAwllt95w1e2pepGF/I2CynLsQ3zXbXLTdUb9VSbkLh2Rxugr4WAbRT1d8sY+XS7dcp1r2S22vCww/BtX+7HUckLnSrEHG/wx2uCYZXJm+vlMpOWFhdGZmcD4hGlkRDowwAhH0P4AxO2ptjtyzZYkveG6WnNKrtgvlmzlC95jc8AVfWDokvuaQPihRLJdrjig1pwGbmbT3/EpbkeENwaZl8bwJ8Z7vhpv/myq4q25nH9eyvz/TOX8diDvA2/ubkv2SW3WHXFKNvVxPToJAXtEa3nEKnzEvP+YcSGFeSiVsS2V9l4K7Y/J5F8/Ir6eQtmUy9xTLjjZLPsWpsnqM9exXWhV8PNsYjPf7R+efpE/yWfAoHww6LdS5WWcqhvskwPo7+3l2+AsQzdN1UORwIi87j56F4bYgcRAYPDWDmhja1NtfW1ldVlJaX5eXoL3ANjLy8srLS2trq5uqK9vbWnuaG/v6YAiO9rRHa04aDMR2kBqqyU1V5Lryyi1pdS6cmZ9ObemSFqRpyzOUGclqZO+VV89rz52RHf0kOnUEcfF095vr4XSkmJVpf2w9jAVE5DRXDaeOSpVj0olQxzOAJkSRWPDXT3BZojvaQpok6cE6q9FBFtxYRi1H7MSAOQAdzGPa91TttCMf2g+Nrk4sbj8vfARI0pEBzvX3BuEwPzNz+5BBTqC077VW/8UzS/NJ/I5rcNWVb8K4D2Kh9Jr64UYIQDvFcmL0kRp97n3Ad47ST4J8N4XqC829Wx6s+PN53jv7c63N8M370Tv/Br/9RnKmevM6w95DxPFNht0DZ3mTowDQ/PSRCGRJqaxj9jDk+GxubGln7L6YJLPH8MThru7B+rq+wsLw0nJgavXvMeOu3fvcWz+FGyUB2DeG296Ptti/+BD55atq/5si+PTT58Ot2y1vPra8PVr00UFM9WVc20t83D4IgG/zGaBnRL0urjVAna9CwXBemajI+AKhcVFMAtmNUznBhsMgHUyRWDLAQsRbD+Q6IKQCNM97YJwBaQy2JE4dB9Yfqnhk3jNu6sd7fL+OZ71v55CHeDs/x3P/1cw6QagPjBqtwXkQLCX3UrbA8LtOCUJfGR+Mbh8Q9EKXnUFkzNpIFgGFKuxu/HwSn7mT1hBOieygA1UaYanphpmacaEwW2KYV7l+FGZmSuheGAI7AQXziUKnAA3fkp34T9DdzPzYJrouta1rp+odVB8Xuug+AvTwgz4/Wejrg51XXHLT+t5uCbXROh3XGH+w4fh1LTVXT9C9PbMmitfjNjZ8ebGuMu5uvf/quYXl/0DM1LbCEESYQsCcq5HQgKDhxapP+oZHp6YsY5OscJDnc4wyhOVxEYDU7OLKxdHl+KL2mFlvib7AGkfMFfbivrskfhBm66vgGraUcHbW85oIMm5UpVEIjEajRa9S0K0InJkDTc4hCoWsa4c+ug8wIedKWeF2PLBiH55eXF6ZkKsI8JEZfWG3Hp9bhO9EgLHdnXokXQ/mhsgoXTiWgy6EsqBcXgIfsJ8lFCEZKwNATM66BgkHnxdc4uRUFSnsYi4Bj7ZKFxZZMglmplMI5svF/BJMg5USamR9VZLINXClmp5R5Wup8KJLOnHZY8RMof7Cr09NeqGNlpONyqlrydTgC5REus05A4jFWOl091Uto/CD+KFIYw4QhGGgD8dXeyDyXz5Mvcxqf0PPN1LfP1BjSPNEewJD6iGAoOjhliMHQwiXK5awIEAPBikuj0Ck1WhUGskUplYKudLNSs9CR0spV+mCRkVbpvAYCTLdCihESkOYVWTRG0Yo3bCVc5OhbdV5mnE2+vqjNAMDeaKlLKHy9jAZP5exPhAQ91hJRymoXezWK9z2G+vmvMWl/vG0yH7bRbrd7iOarYi1KsPV1pC96z+QxrnexLTv3C0b4mM+9WOby2+ErcfHnQL+i3OAcXwsGTNTleJxfLEYLyl0Z5VKA4CgAdgHocLtrUA2I/Hf1ck3iKT71Wpj+t0l02m+1ZbhstV7vU1BoPd0Sh+cJA9OqqYGDfPxtQzHu6QAhWmVwQQD4LNx/pLt4zlvbKQ8ffzGf8wnPFKKP0Ta+oB4eObpOQsRFJdWxKs9BHhYRL94mPGoRT65ym0d59Q/5hM+k0S8Y0U6sdZrJ1FvMM1kksQdVKvoYRihwp9FF1U7R2Njs0+h7TL0/NLw1OLoRFgvvh5Buq3ycQ1/yaZ+Oukp0NwTxLhXm5dSWFRXk5uIriXl5tXUlRcVVnV2NjY3t4Og8FQKBQej6fRaFwuR8jjSQTg+0zJ56r5HA2HrmcQ9GSUvq9bh4KoYE1SsId7tbilSt5Upqor0JRnaPOTdal39Peu6C+c1B3ap9+x3bxnp+Pr/f6zxwN3bwTyUn0NJW50m50JM6mwKidZOEBnxgjEKBIdgoKrc73lCfwDOBCgQYAJATIkR9HsAZJoiK0aFRvHNc5JS2DGOzAXnVgYm1/+mYWUL9AufND9AUBiCX/c8/FG2Ma1IeA3oG/QvfS/Ou9dY14DeG8tvgfynh3kPWFI+LN5b2lyciEcnrVap+TycSZzFNk71NQUKyyKPH4cun7Df+q056uvXFu32T/4ACA6gP1sG952fLTJ8/nnvj17ggcPRo4ejZ05PXjh/MiN62NJjyazs2YqyuZbWxZ7kYle55PZaaN3bo3eXnX/8eOOd95ZG4K+c3OhOy3OygGjaqT74Mo61PmVNuUHwK4GTZ+Bi+vW+tpl//33iG7Nuf8Err4rfxXEP3DF3Q6wHULPUfCh+m6A0T96GhgJBHuUN4KXShPt7FwcsKhbWAOuuxvxgeHEuZ9QsvtPaPGZIN7wShAvOrYYXEmxdq+tvguCwXaVF6ykIvkufAcAHsB1gFdW2L7QdBNw33W6W9e6/pa1DorPax0Uf2ESlsdbPl/dHvXF2enxn1ua/GMhZhcM7vxix4/K6lmRlFBXdW5H2EKKtzbHLebVvf+XNDO35IxMCUzDJH5QwPfJGODKQw3L6TFGRwamgpMzstgYxtvf4QgzQkPmkcnx+dUv4IG5foSr8zrv8qe9H7/V8eYxyjc12poeteYeUrutiHkfysNxFWKpTK3W2G1OvdhFbTY03eZ1pYnZnSRyfW7rnZPlx/cgcq+r6O2TYyHgAf0+C0nY1iLMrbcXNMoK2git3T0sON6NEoaxeDu7lS0sbcysbHi3u+90ZQ0HzbcItD6dNWEbXyyHQdeGgCU4MbYbzSeCWAjAIZds4jBkAhZFwuhWEOoU8Cpxe7WopUbRUWXqKfP35sdwOf2YQn9Ppb65lVUI603GdD7mInKlfeUqfLOWjDTTKC46yIQBgigMMCFRFOaKg0pFWK4Nk4zhGlPgss71kdQMwNUnMssVo7fK10+PRdwjzqEhSSSC93rbnM4Kr7fdH+hzuplGi1CpBf4+crFEypeo2VIjR+GQKd1mOcCM5gBD4yXIAlj5KEEzStB6e1UGqNzYLHPWSL3lFHtlmw6Sr8XckpMPCRgfspi/EzDfVNE+MRP3muEX9U2piqIG5mM0+h6h7Qa97jKzpABpEAkmJiwJh5xoNff02hBwZxHiEBz1iVB0WEq/JUfnKhpblbl98msy2W6h6BMu760V6nuZL3hfLPkcoEGN5gxAhgAfApTo8dYFAh2RCHZggDEyIhsfN05P++bnhxJvkoSWl+PDU/POUEyvU8rZWAm6Vt6arK846yzYFcl6dyL9V0tpfzeS/htjyme8J8ewyfegyXmVSQ2pj7quJmEPPSZ//oT63mPKH5JIv3lE2pBK/TSbuRuAwErR6SbFrS5tFtZSQ3chJEGOedASnIiNzY1PLy4s/of8rvnFichQxOJySrR6hkiKpbE6sYSmbkRVS3tpbV1hWUleQXZmVmn+Kv4ldD21aFty++rgO/EJpMFY/1A0MhwJjQT9Ay572KT3q+UuMc/GIhmJvVoUVNHdLGkHu2LK2muV7VWa5hJ9fa6x9Ikx56758XXzrXOm00dM+780frzJ/NGHrm2fuQ/tdV8+5X78rbM0zdpeZsDVqrhtQk0nww3HR3qQKymgTZ7SBAG2+SphgSZ0qIMU7QUQUTjEVI6IDGMqx6TZP+2OzUX+U6uAxqZjAIwpoop9uH3HiMce8B68yBt7Nr7a/pT3VtfvoXccxB9cy+fMEGes5XOi7WiqhyoICn5MPuef0dLS4sjIvNs9o1BO0mljCMRIU+NgUVH/kyeRmzeDZ874Dh5yf7HD+fHHAPIlOulZX3vd8f4HYKv0rdsCe/aEDx/pP3du8ObNkcdJ43n5U7U1M50d8wTCkoAPhvgsFvACXyAQj0biQ4NgDz3g/D8eBsNobh7YFVPeFOfmg2zWew4M3+X/Szz9f4DVU15kAPNq3vuTqZhxQenqWjsdIm7uiztW6qkElSttyt1//Tbla7maYyuYFxsHO9EFh0HMSyRqmsNgh4ME5sncc4ksTTCOZ1rtYQD8BEtxgimawG/B26i8wO3Bokrm7yJ4vkHgMcH6lgMTYGXLsafJmcDtV4OBL/b6urt1retvXOug+LzWQfGXpNmxeN7/AdfTJyStint5q9s/Uefk7S8xBcp33pu12Vd3/TmZxOiKUzvcuq54BySukK/u/S/XyOSCOTDB0sSoXJ+I5ZIQLBKixSLzR73DgxNzABDSQ4NQRxhARPnAWGhqNQgDzD5VQ9J8TdYh8v4NnW992rv5ofA+2kZpEznOtcu+qWLU4UUckUwilVmtNpvBI8HbETny+hscUp1CiOkl1qXUXz1ceXo/tuyBUYhdmJuamZpQqChwVmm9LKvelt8qqOzEInqwOjQvjKJ5yJ1yflVPS2X1+wjy7hZoZvqTktOHS04cKLx0WtQDk8HaVb2whJXIbgWiY20IWNRWz+5DSElwBaZRAasEsFDcWq3prLDCi/2o/CC6yIeoMrc280u7UY/R0Pu07jQRulCOa9AQu00UgoPK9JB5PsJKnJAoCnGkYYU6YrcO+v1jysh4T3g4xR48oHb8ga//I18PbADD7lBMPuCLjWhjMUYg0ON0Vrnd9V4fwu4iGyxspU4skclFYglfohJIDXK5VS+zuoWWfrZhkKIeJqqnSNp+rNrVozC0y7W1EnupOJjHMZd2qZpLlcgHCtIxMf1TDvMlLuMlOe0DPWGHEXFG2/xIVlzBfIJE3yUCTFh7mdlxn4XPE/BaVDqK06z2ci1GmFOcm8mmNRvVdN+L3HCf34QttdkyXe4Kn781FEb2x6hDw6Lxcf30tOc56ntOswtLwZFpfXCUa4th1cEulgqGwPY21ZFLU/m5l7WZB91pn4RTX3envqpJfZf6ZF/X48vVyakZSWU3HzV/k9zzRTLu/cfEPyQRf5dEejuFsjWHdbCEf6pafLlZ/m2X7hHCmE+wN7G9OEVEZB22BCY9/dPRkbnRqQXgeVePAHhbzs0NDw8Hg0G7za6RKUUMDg1DwHbCYY3tTeU1lQUlBdkg/mVlZAI0WFtSAalp7G3rJMExXBJNwRdb9Eafzzc4ODgzMyOvLTFS8G6JIGELjy2nktaGgGWQRllJqqitVt7ZoO6u13fWGNpLLI05tvJke/Yte/Ilx81T9lOHrLs/t37wnvnll20b33N8+bnj5EHbrXOWrNv66jRVV56YUMriVxH19XBvA8RXncC/Vm95l78BFYISIghGDM8fpMuHBboxhW3C6EuUAJ0fnlmaXgbXXf31NbUwBYCZadAkColIblK3pbtOW5cnzQNY7iL94mHC4e2o7e91vbdGfYA/gH1wknLysfBxwrWMDAWiaG0IGADFXltvcCL483kvoYWF+PT0cn//gsUyJxZPk0gT3V2j9XXD+QUDjx9Hb94MnT0T+Pqw98svXZ9+6njvfQD5Ekv7LC+/Yn/7bdemj73bv/B/tT908mT0ytWBhw+Hc7LHqqomodBpfN8cn7doNMTDofjwUHxiAuxO9NwqsrkJMM4W1oDFVwy9YCyOWwB2TUi46TMwgpf7jyDpZf99vOT3YDSv5XMwRRN9IU66P88tnpE260mFLDmNrRMmzNRLaEbl2hAwh9cz4hSuPuPPFnDgC4vgctnJWbDz+EqPu++tx0s0SkksxlOuLMYTOcCcTLYFbG+QWEBLe7oSD6ycKV/BPJ1/dRmeox9sZuAbAtsYRFd6GKzF8eYW1psWrGtd60poHRSf1zoo/pJETwVTehIKq+KCgvj31978SDU7CP/KkeMOHh6CQFZ3/Tl5jdzyEzuN4up4DyzOZa/u/a8SMP/pH53TesbpkhCL7RHT7GDwkO30GqNDsUnfxLS4f7TXE+10hlnhIevo1NTKXHwpvhSc9sJc0BuCy1vRn77Z8cY3pMO1ulqiRVdEtZxtEjyEcnBsKYBAGp3e4/IaxB56i6n5Dq/jiUiMU0iIEFzFnZoLB+oufU2qy3Rp+MuLiwG3hcpqa2VkNRjzmvSFUFYzjMBEMnwoThCHNLLqKdiyhi97+jZ29t4rKSq9dq7k6L7iCydK73+L7oQYDFSvFyKDdTqFnJjD+iIrYFA5ocsBL3T3lriQlWZok6SyC5+Baf+W0n6PD88SY6qVfRAjCeug0D0Unp8gCGEBJpRE2PKIQhezOUfC0amx6YXw3AJzcKzSG71i9H4stfwzR7tJar5k9FR4o5RoyDFkHRoShcM4j6fZ6axwuSE2B8Zgpql0fIlcKpZIRBKFTKLTS412kTHE0Y/RdAAQjhE0EbTC3S0zt0iNlWJnvsibyVMVoEQNNeKeJ1L8GRltG5/5Oov5Owl9g4b0uRZ1XNN2R1pWyEzv6L1HbL3BqLnCgt5l43L4nEaJFCPicxlYKbZG231f3nFECPuYi/sdm//PLOm7XOpeASqntL0iC139nZueINvvYNaGgIsyeglMQuJN8pwmZhc8g1MKzzDFEIGKvWV0WwFMWl6LbixpbC6pByx8fEqYcpiXso/2ZE9vyu76x2ezkh/eTi48kdSwK7n7gyT0H5IIv3tEeC+V/EUO65tS/oUa0Z1WZQpcn91nKSI4KqguCMdPUPYLzcNK56jRN+GKTIWGZocn5qfnlhaXliYmJvr7+z0ej8lkksvlXC6XRCL1IpCQ1ra6qprSouKslfV+mQAE5hbUFpZBymoRNa3E1h5OT5+cwDJypF61acAdnB4ZTxDW/PTU5NDgSNAPvEmCeo1XIbFzGUZKnwYDl3W1yKry5d1tqt6uF1nSWqdJPmvf+B6IIm+8bvl0k+XgTvOFI/oHF9T530rrkzg9aRRyNkaUB9XnA/jX7C1bqf7Sjo/00PpxvEGadJinHZUn6n+GZwPD84NTi5PAp2zl7/1XFkCVAKR5x7zamJYX4PU5gbc8pFJVmSnOvMO5c5Z69kDfgc8Qnz3bi+9D2Ic7UDuOEI5col96yHsIsGKtprbL3EVwEQRBgX5A7x/3j82NAY+MZZR3IR+tmVt83XNg37N7ANv8qtVDAbTCe/HR0bUm7EtazTyHO4PFTEGh41WVIznZQ0kPY9evRc6cCR7+2v/ll54tW5wfbgTrdr78SoL9wN7r739o277d8dVBx6nT7pu33E9SvAVFvvqGQDc8SCKHhOKwwRTxBSJjk5HpudDUbHBq1j85452Y8UzMuMannePTjrEp29iUtb/fHHSb3CaDVa438rUamkZBUIl7FfwuOaddymyU0GpEpFIhtYbPaufx4Rwxjq2gMVU8hk5KM6qpFiPZbic73XiXH+sOoTz9SHe0xxXpdkY6HOE2e+hZo42uVnvoBwzcJuhxgQsApufBlnEA5g2uFEYCMM8/BNZGcvaDTfBMKzVXnl2Vx1vBPLpphmJ4WnAF2CkEMO+79XjAXUwhsC+Cox94KPABAcxbSdcE+x8ATzc9H59bXG94sK51reuvpXVQfF7roPiL0UQ/WHtt0AFuLy3E+bnxn1WXzzjieEXAT8nL8505u7rrzykWMJWf3CkiPY6jUXEK+a9b/ewHtLi0HByckVuHaTw/l+ESPw0ejsQmZg3DE9TgINQRwvliyoExYGqVOKzRhWHZkCBPm/E1Zf+73e98DN90j3+X5Kb16TyPezXXWtgNeAFfLBVJ5XaH02UOyIlORJ68/jqHUA1QklBCrEHmX648/VXTjRNMSGnIbpgeH9NIaHBScT0nrcFR0Cor76TDexgatCDci3OQW4WUip7bHcg30LTztbWFj26XHNtXfv5Y4fWLTSW5fG6r0Vis0VzkMy/wqSgeTS/E9PEpHDFLnDCPo6YIHGtDwGwcjY9GNd1gdz3gofJkuBY9EWUjUdwUro8oBOuO4iURliIqMwxYfePBodnRqYXFpfj88rJ+YhoeGUp1BA9qHC8J9L/n6/erHU/swa5Qv3TAHR1S9/fTfL4uh6PS4Wy02nv0ZoJSx5QqBFJAYrlaojaLtF6uNsbQTlH0Y3hVBCl1Q8XWBpGtTBTIFpsf83mZfYzqJkZnJgdzUUTZKWRsYDF/K2S8pqRsVmMOKSDXJVVZrMyW3vuE1hv0mkvMtttMdCaDWkNhd/fiCS1tzLJsQcFFXsluduO7bMK/sqW/YUs2C9jHZfRkHavZIeVErcHp4bW313DxlqWq9xZa93znXUvN254Z7lnO/gdhDzTBgdlwbWaDoKQE2ZxVA3mS3f04uSf5bteTm61Prlc8vpbx+NtbycknkooPJdUcSm468Bjy+0e4D57gdmXhTxRSr9VIHrWr8uD6SpKtke2B8Hw9ohBKHMHJojT1AM84JLeP6r3j9vBUYHAmFJsIRQb9/oDNZtNoNCKRiE6n43A4GAzW0tJSWVmZn5e/kumZDqBgSWFRfXk1pLqxt66dUN/JaUVJOwkGLMdFl/VLzVOW4GJwZGlocnlydm5yYnIwNhzw9dstQZ3KLRXaOHQDGadG9wAcKGypEbXWyjsa1Z0NBkiVqbHYWpluz7nrfHTedflr59Fdyqun9HduGlOTE9ZnpKgKc9aGgDWXzrEarkKkaRBfTaIB4Er7B7J4iKMelZjGta4pa3DGNzjXP7k4vvBzl//9WS0sLwxMDzhGHIqoguFl9Np6m/XNJYqSFGHKdeb1E6QTuzG7N/Vseh36egL/3u16d1vvtkP4QxdoF+5x72VJsirVlVATFOfEcQNcTUzjHnMPzw6/aF3f/NLy9OLSxMLiyNzC4Ox8/wwIYAyxpsPiWzPU4oM8MwTcZfYJMSQGhkRDESjIPiIST+jF9yEJWCQB3UtAo4goDBnZR0cQmUgaH84SI/gKuFjbIzfAVJZurb3L4AIeocPqh1oDz0IXYKgj3OkMdznDiWatCHe01xMFUA3j7cd6In3uIN7lJdpdJKuFYtJRDUqaRsRQslkyCluE4fJgPGargForpNWLmS0SLkTG75aLUUpZn1pF1Rh4WotM79QbfA5TJGwZHrWOTtnHpgC2BCDTPTENAKdvciYwCfIncLYEHJ2YjY1NxUamBoYnhwYnhvrHR6Kjw6GREf/QmHdg3D0w7ugf41rH6MYxxgs9SjdMkwHMM4FNUADM+67sCtimPIF5NrD3HYh5q6vyxsE3/Og0iHkz82Bru3Wta13r+pvROig+r3VQ/MUIdx1c1p+QixGX165u/xRNL0x/JkJuw2FtGz9aHBxc3fuDGh8KVp3bRe+5FCcR4jhMfOk/PUVndn7JHZ0WqqN0pltABYOHSqbDa4oODkx6xmeE0RFgdgXMtDiRYWAOBMwCgbvML8+5p2xdrrbrgovbMVvfgL5xCH+gTlcr8OkhIvcTuDQLxiWyxSKxVKE1BnwhiyxAazG23OVBk0UCtNKqogux+d0ZZ8uP722/f16IbBkIeIM2E43S0tqXVifPbLQWQoQN3Sxar9CLpHgxUDW5ilDa0PM6nnuotS07J63s7Ddlpw6VXDtfmvwtFpkplSUpFN/wmF+J6e0CikpAMglwWiYAqBgEmW9laQdEluGEheYhvnFobQi4TxqhEYUkYQgniQJMyFD2S02DlsAkgCgjkwATrmJU/9wCe2i82td/xeTdLAMDhhul5otGT7k3QooGrIPmwUG+P4ByuhrtjgqLHWIwo9RaokLBlklFMolMI1LY+Co/U9VPUw3hFTG42NcqdFULPYWiQLpQkczDPaYgi9tQTbkkxDUmYZ+A9j6H+Qcu449S6kdK7H5F5yVxTSojtxb1oK8FYMLLrJZbdHgKAVsCw7dWdfakl+Hu36ffO82r2snrfYvL/T9s1as8+S6p9JpOVeK0YaIDmvGpsT9XtsGcs1/KAGh16EWWFx/EPb73KDn7UlLe4eTSbUn1Gx51//pR32tJqE+Su/aldp7L7rpbjMipJ1d2i9pIVjjfh1dH2KYBrXeMrR8kKWMYSZSsinEMQxLriMo5orFH1Raf2mBXqPViiYzD4ZBIJCQSCYFA6urqSkpKsrKyEhCYnZ1dWlraUFvX0dKOgsLI3ShuD16OoBrgDBeSG+mTjbONc3I3MFcGgyGBoZlA/7g/OOzzRG3mgFbplvCtbJqBhFWhuqUdzQAHApa21Spbq7UNpfqKHGNusinpuvnqCcs3ey3bN1s2vGl57VX7xncdOz+1f7Pbevkb46Pzmvybktr7nK4nJFyK+psvnAcOeG5cS9h79Yrv4qW1IWDz2xtchLrVv+x/goBzS3gybBo0iUNispsMs8ASKaAPeQ8v0S8dIRz5AvXFWgrohs4NW5BbvsJ9dZZ69lv2t+mi9DJVZbupC2UnUn18YVinirmsIzHvxBSAN87xadvYlGlkUj88oRkaVwyMSWOjwHmAFxlmhYfowUFyYIDgj2G9/SgAvQAGc4Q6bMEEmHVYAzCrH2Hy9GrtWKURJ1QRWEIsU9juCEHsq24H/MwQMHBHUVGZpKRCWlWnaIaoO3v06D4jkWpl850ypdtg9rt94f7B2NTMwOz80Oz88NzC6NzC2PzC+Pwi8BkFTkozi0tzS8sApi4CJ8zp4figMx6Qg0XItDCw0iYzC6y9iTgVh+yO120E+zFk/z1oYAMYAjuBXwE3AG4G3Bi4C3BH4O7AgwAPlbhON/8fEjUjzyRq/kDZlUTXhMSSudW6mpbv1dX8blXe/MqqPLBMLtcKeA6AQK4NLNay0hUTTO8UOkBLnMtjf1uNJde1rnWt62drHRSf1zoo/jI06ADDiRMrbTBmx8CWGONgGZWfqlOS2t+wxfL3PpgUiVZ3/aDmZsbqru3G1ByJs+jxXmR8/j8rzgBobHrB7BvnigJsqlNIsIhXeh5GvSPR8Rnd0AQpMABM4PD+mHpwPDYDBg+X48vR2ZBwkJ2rST1M++rDng829nx4h3ub5KHwnf4aujETIWwj8oUiMU+qtDrcXltYTnD15ivqr3Ow5Uq9QGbTY5iwx9DHp8uO7ulOuakkoaIuh4ZHgffm15GSG635LfqSTgEMLlL2cgKIHjO+ltNZifwcx/60E55Ukl9280Lpsf1V188X3rkArb/GYl8UCr/gMLdLWfVSqkxANIpwWi6Wz0b1ibqahOA6wzI3ooAocAJ8QtcMvMh4aZRLVA9PzANTwdU/DUjCy8aJaURkKN0ROqRxviww/Jan26uyJ9sCHYGwOOYKDSn8QbLTBXaqsNoazeZuvRarVVCVMo5ULNEK5Xae0s1ShsjKCFIcbOL7yvj+bIH7CV/6mIN8wqzJ7q6vKuqA3u7DHGKTN3Hor7KYvxPR35P1fSntPi2qf8gsLEM/hjffoAFM2HyT2pXci8yrh1U/boDeLe7LeMivPiXp3S5kATT4f9jqd0Waw2pzki0AtqAYGLNOzjxfr/NPaXByTukdRqkC5RRrZpvqSZFgT0rrvz0i/qAJFWl5bbUNmC44k8AQyfQq9wgA1WrXmNg6AuAfeQUFSYoYTRliyN0smZUrN0vUNpXejidRURhsV1dXU1NTRUVFXl5eggABARBYVlbW0NDQ2dmJ7u2l9BH5FKaSwTexJB6Wsp+lHaOvzLOpBnACLXPPaf2Teveo0T6oN0V0Wr8KLA9jYVF0OKSqByKBNCQ4UNxcrWioUFYWaPLTtE/u6a5f1B8/rNu53bjlY9tnG93bP/Id2hY4/5X/7ilP9jV7dZKhO0dBKuULa6m6pl5PU5uvsslT0uVvwIa7aP1YwSBDPSqxTuj90+7BuZgeVaKoubdm2b0j2o1vPbtHUX13yKNZ/Vv/aP3HFFCosbNKXZ8jLX7Iz7jOenSGeusI6fLevrP78Be+Jt08R0+5yStKFjdky2GlGny9kd1iFnfY1D1Oc6/bifb4sN4Q1hvFePt7PWC6Y5czDHEkQm0hYBvujgD7AeQDPukA/tGCgwAKckODgkBM4ovI3UGV3auzOI16i1Wpc0jkHp7AT2eG8MRQW3uoojKUnx9MTQvdvx+4fDlw9Jh3927HJ59Y33ymrd+Gt53bv5BfuKrughvF8oS1UqVMoV0bAhY9Th+USFf/BC/Swmx8LLRaA8aEBbss8IvBXg6Yy/Hur8FG6lVvgXU+M/4ObNVQ+Sa4JrDrYBx9JU5JW+ZWLks6l1XEJaNwyapbcrkXfdEfYjzh9zvjPYt5zy/JW+mdsJKrufC9XM3vB/H+/MdxXeta17r+/1HroPi81kHxl6Geo2B98ISMiLixd3X7p6jM1P2vbBny/MVofsHqrh/U4sJc64OvOjL3LUt4cVhXfOY/5bLxwNi81jzE5nh4ZDB4KKfb3cbowMCEc2yaFxmGuSKAgQ3X+PTsSjBzcnHcPK7tdDdfE5zf1ffF69DX9/ftq9ZUycNaotZfQVBWYvh0npgrkorUpkAgapOHGW2mlnv89kdCHkLtNAotGgil9Xbb/ZPlx/ah8pL0TIpTIaVhm1tgyfWc1AZnEURZ1yUmI6V2OMaJaZahKggXu2nvoEhXa8qLk2+XHf+q5srpynvn64uOk4iH2ZyNbNZWOb9SyRCLCUYhXivo5XFQBH5XG8AGqs4KDyLfjy7WNrWgH2O67nIILD9dM8gzDvGMwys/wQ2BcUhgWDUAih5tZHhhkTM0Xuvrv7YSMPwXjvYDifm8wVPqCRMjPm1E5wuyHC643V7jsFdaTa1mHcKoJGjlDJlEqBHKzHylgy73YqX+VkGwmB9MFdifcKTpbFQaoywLmVZUXdKcBIEfJxG2cWgbmMzf8ulviAlbJfAjguZv6SX56FRo0w0KwIQN1yiQh909udXddcWtPVVlVGiSpO+UgrdFovgdT/MrnvZTmeWswZ3pDEFDg7zhcd/M3DN4+0I9ZUKyNbtVkVooyHwCMHsbMqW84XHek+S8o0kV7z3q+DIdhlb9UKPOrYW04nIYgutGsi1opg5Fk6HIfGQfrae3D9rZ09LaXltbV1xcvMp/K8rJyQEgEIBDAAIxGAyNRhMKhWqVyqY3BUzOAbNnyhKc1weAafcsz5bohDbFMo3zDcNCbb9IGRJKvXy+g0Yy4lCaHqgC2iRurV3lwKYqSXWJojhHkZ6kunNddfqE9ujXpiP7bUd2u4/tCJ3aHr22fyDp5GDhzWhLagBb7OQ1GnQwqR/DimAIETgi2LqCgqXdgQQK4oRDTAAFbROGwLRnaH5gZumH6htX6fF5yr41l2roDUZNgZpUqCIXa6jFGnqxhsEL28LTc4HJWc/EjG1sQjkQ5YY9ZL8F7dZ22eStZlGdgVel55RqWcUaVoGaVajhFmoEJVpZtVFfb7Y1Wdyt9mCrLdBi87Ta3O02F9Thgjk9SJcP5w1SAzF2eJgfHRH1j8hio8rBMe3QuGF4wjI6aV/JfvROzCSSHgdmwBDc+Pzi1NTM3MjoYrQ/7vPGbda4ThuXSeM8bpxOjeNxcSQCrJvV1LBUVTFXVDCVmzOenj6c9Ch2+3b40qXAsWOePXsdn2x+2t79lVftH3/s2rffd/586MHD/sLCweaWUQx2ks+fMRoXolFwqeGK/Fdu2jduWrPtvfeBB3k6/PAj+4cfTeG7wcZ62p64pDbOygZb+SHPxzuOLDfvWa7etlz28VL51qWGbxYh1xd7UhfRpQvElgUacoFJnudw5vmSOaFmTmicE64E356L4yVMMcwwVtI116J5ypV0ze9CeX+iwOZ65ZV1rWtd6/pP1jooPq91UPwFKKQGi53OjoHb46E4KzU+/5O7Raljulf53Lt1da7de5Z/XGCwK+Ob1oe7FnSSOLQdrKr3A1qOLz+jeWAWODy9OvhOq7dcETDPCcVmZKoom+4UEswiglkr8kV9I+HxafXgOMEfg9hDRP8AMNEcnAUPdWF53jvtZMVI2donx+hff4zc9H73+99ybvU5+4zREFxkrcVLkDShQCSmCZVamyfoiClIHlShou4aG1em1PEUAQ9TJy4nNNxsunm88tRX+PJcDZWopODg0Jx62J1mbV6TpRiq7IQpJXCqq6dNj65g5TSR38Fxj7Y05mUnl589Unn2cP2Dc3VZB9CIL5isN9isT1WSChVHJCWaRHitEMnnI3DcTqigpUbeVe1AFPXjchydNbRcGPwBA5cvo/RYKFw/ThK9JrKdEtpPCVZ9U+gqEwfWhsCvTgrsX4lMv+Hpdqvsj4BJuTdA91stboHDhnbYmu22Mru53q7vtKrRegVZLuMqhVIDR24hSJzdIm+5wJ/Ct6exFNl0Qi6lOhf7ML/+dlVGIfQiErOHTv6QyfgDm/4Sn7hRhPxK2HqFXp6BSm9ouUUEmfA6DfKoF1nYBW/phqCxFUxWkkJxTGP4RGb+N672ZYFhp9J21eQtdEeQkWH56GQMmLD+CA1MgEzYqwxUEi05zfLMQn7uYxbica8gpY7yJK8lOS05Kfub5PJ3HnW/lITendr+bV5LRSOst4/F1/s3Z+F+95j0xyer/sMT0u+fGQL+9yTihZTSVQRMTy8oKKiqqmppaenp6enr62MymWKxWKfTORyOUCg0OjoaBzA2kao3NLlgCc+pvMAcfYqhG8HLBkjiCEXgx1OdKIwFBtNDW5WtDbLmWlFzdYIDhU1V4soiaW66LOme6u4Nza0rxuvnbTfOuG+dCN4+Ent4aCT9m/Hyi1Ptj6b6CobETRF7r9OH14cIkgieFcXiI3B4sKXNW9HsBVCwERvupq+ioNQ2YQRQcHh+YHbph67FzC0tTywsAogVnZ7zT4IJmeaRSeAzIh8YA9isze5JJFu+yK32UJVRXarj5yqpqTL0A1HnQ1FHsqQjVdaVJYflqxDlWky9gQi1MnqdAvL/j733cG/qShf17z/wu/ece+8p95wzZ3omBUiAFFIhnZCQkBASEnoChEBCbzY2xjbuvTfZktybumX13rssyerNVrEt9y5ZZf/2thRDCBAyk8xkMvt9vsfP2mtvSVsKAb3+1vq+IYXAbxyYGHLNTc6EwuDrLkeiKw9YMiQaBRYXoVKcfh/UbN04CKhVgEQMcNjQDmccFiqIhWoC6mqA2mrIA+trQ9VVi6Uls3m5UzduBC5f9kMNAA+63ttl27rNtOHxb5KBm2yvv+Hau9dz8pT/+vVAadlUa+sshbIok4ccjsj0dOLV78PcCDA2CLgEIUJfkG8NSZ3xWGYOzHVQ1w7BCFIHVjrqgkRGsI+/TJYt96u/5XjxgFop3KPG5vcm9GBgYGBgfpbAongnsCj+HYB4HWo0HEdeBThZifEDMx2cfkPS+QKFbty4KeR0JWbvC6HqRM3Xby4aJVDLxMn7tRkAMYjcotV28PeJ6cBCKByzOWdEIg+HbBYSBqU0m3VgZCywYJtZ5IxMxReeCUanXfPL4Hdi8GkDoVH1jATprDkp/Ow90jsbW57YiXunWFUsH1FKLJ4Wtra5X8QTivu5ErpM73SPWJWjTORg40VB0yUBt11r14k9wzgZMx1ffrb2xN7Kzz+m1pRIejvobdWIugsNfVfrrPmN+gq0mtgp1rS1m7rLRfUl/e91c99sab1WmF56+rOST3fVnjtUn7m7A7mVzniYy35NKSrRCoWyfqOYMCDs5Qs78bxWFCiHkpZqY3fRBDFjpCdfUtKETybgb0iozUY6e5goGSHLR1WOWc/EMvgJvCaWPSHSb5UZ7xV/5GnrFQKHgWA3oGzmMjDseoRF26lXEVUKllIkGWBKB3tFljqBPZ/jyKTqcsjsfCIyD3O9sO5occ6Z2rMNnXtIxK0M2jom44/c/ieFmB181BFG+VVMZknj+V7QCatPMtBXSD0FpB40C02QlvMGr6qcH2rsT4kH/5Oj2SQy7NbYzpuHy4fGiIFp3fzSwoNlMALzQYVrqlvpqSSZsusVmTn8/GRWezJdndpsuFbISL7ekHw1+Vrax0lFG692PnoFs/N689f5zXlNOARJ0i3ykJWBTpalk6ruIPGbu8mNzZ0ZOYXXCypzKhvjcbKwZWMKbu0QjCuZheQewtjY2Py3f5EBfh1f60S/rHHOCAYCBK4HjbHXN1saUFZki7WlTV5ZKqkuTUjgaohK86S56YqMa+q0q7rrF03Xz9tvnB3OPDWWe2Km6LPFmqPBllMR0jVAWh7WNM2YOv12DKiCA74+ySiROYIljnSAKtgIqWAxqIJ4SAUJokmWJq6Cy+6plYk1FQzHYovh6HQoHFgOeReDzrkl88yibmpeOTErHpvhjUwxfJNkzzjOPdblhMr5Iq0+8GeHw9/p8HQ6XO12c7NV22iUVOpo+eqeFEndZRHqpqo/T0PLVZOzVcQCDavWZM1U9K7FeUF9ra5FPaZ2zDgmlh9of/KdhMPQL4zGA1AXPpsNMOgBpQIQCgAmA+gjAZgeoK0F+usC1L9VA4R+wdTVGelsD6JRi/V1s2Vlk7k5Yymp/tOnhw8fce7aZd26zbjaBd74yKOW55537Nw5dPiI98KF0azsibq66d7eeQ5nWadb8fru97uteBMIjyZm4sRUxKigI8JCRfrrw4SacG/NSndtqKsx1NUS7MUt49nLfQrQ8UK4vmXywDJZ+02A49sPteA1YS4V6or+3YReEE7owcDAwPxigUXxTmBR/LljoQCFfwLCq98vx3RQsdPoA2Vy1ojGogclJb9lS8XbXgG/eyVm7wu350b50TemB9kAoh4Y8Sdm742c7RhxTYVXIvcKBcPGp9sFRJOQaFTyXF7nlHd2STk+ix8KoGw+imcc/Io8tZqhWowsmOf11FHcDe3lQ6xPX+l5+cnWzSeZJ7st3fYJD11la2fKKRwRgyfCcBQCvQN8KmW/C5OvrD7JxhYotRzVsJPtdqFF5BRc8bnKz/bUnzpEqS5mNtV21aQi6k41SrNq7QVNOmSrhtPWZ2ivVrcUML9GsLd1Ec9U5hZfPVX86XvVJz9pSv+wo/YFGvUhJuNVmbBoUCZW0s0ivF6C4QvbCMJmKHMobKkf6KkYJWRNEzMMtZX0zC7idQEVoWPQnH1iP1E6IjFPOUeXFpa/lUDYIZaeGHTXDgfuFY+LBtpktEEVRq3oV0q4aopY1y40Irj2KoqtHKco68KVtRWXV56pyDxefSm/5VA39nVa/0Ym8w9s6gY+7hVeyyf06nOY3Dzk5fbKE8yqE8yWJEZvsaC3ZaClz1rEsZ+V2XcqrY8J9L/iaJ+XGvcNOK5ZvQ2eAGNi1rYYXHmwkraBuaDcNdmt8FQSjLl18qxsXlEyqzOZo0wh+q5VWZNv0K9drEu5eDUl5cOk4sevdq1PwryT0X6iuDMV0Z+PUTXRLTi2rpci7sBS0W09tfWIwsKiGzdupKWlgT+Li4ubmppwOFxrYT6qob69tTUeSWXoTck9a4dg1BYVGHpwMzLNKJHuaemyVTcYS8oGCgqVBXmykgJJZfHtHiiuLJIW5ygKs7RF2YNledbSDFflDX9tyiTq2mL7xQjuHMC4CgXv5jcq2OF3YG3DoApCWcG4CnZ4G0AVRLiL2z31eH87PQCpoHpGNjhnss4PORbG3fPz7vll6+yiYXpePTEnDczwR6dZ/sl+zzhhKNDjGm2zj4DWh7aB1jeCcY2RhgM07wTTP0H3+CjDToJL322Tokz0Kl13rqL+qjDrOOPMB8SPX+p4aQN6wxPoJ17uevkDwgfHaMcu8y7nyHLqBurA/zWYQ8y9pL3gBY+jH4/H/pxNrR9uXDsEYx1qndgnTvz3u4NgEOr9MDp6jyWgnfEloAkDrKsBUKABdgAEHECjRMh9wV7MQmvLTF3tZFHRWHq679z5oc8+c+563/rii8bH1oEeaHr8Cdsrr7r2fDR84oT/WkqguHgSjZ7t61uUSIJWa2Ry8i6FlMGZpSlg1BZzKGM6dlRGinC7IrSWMAm5gkeuYFCh3pZQb3cQQ1om8JbJt2X8yKpgvyxIk4RYkhBPviLRhLWmsHkIWsbph5QPyuwFArGx0djUeCJ05Fjd27cOV2NtnSoMDAwMzD8OsCjeCSyKP2tiUagignr1P1AsAjVOHPnB5ShytDX/zZV3nj3v+fp0Yuq+qJhVZYff9A0QoeSA64HSjxKmPTB8v87USoZVznP7RufM04vgl+YWu7/bOSoemxlaWA7HYpFYeHjJKZpkN9hLvxYf+6h/9+bWzdsx2/PkeSKfyOIdJYoGCGyJQCTuZoh7+ANa24hdNcZEGZsuCRovCtitWuuAxO/rs5rKBPjrvbnnSg980HT+BKk0l1Sa1Zz7Bbr3aoMxv95SgjZgWgXiZqSutUBws5TxRhvjcENFbsb5ss/3VHz+QVPK7vbKF/tJD1Gor/FZuXqpSMuxiQl6MVYgbCcK0WghoorfXC/vbRgiFixRUt2thaJCFCWVRq3Sskh2isiPl47yDZMW38L0wre+ZYL2xZuau27zgmJ2SMreKFBtEqrjsUGg+RNfu3YIxkaBAk1juNBYK7KRU19cX5dztf7qudpz6U3HkF3v9ZG2MOh/YtIe4RBe5LbtptedwhZkIpORVV/Sqk6wmpN4vSXKzubBRrw5l2H9UmR7TW75PW/gD7yB1+Tmo3rXTbuv1TchmJr3gkqYuLvvAXRCmXOyWzZcjRssqFXkZvNLk1m9yRzFNY43qW0iOc+Ueply4+vatNOXrl3dlVy2/mrvuiTsjszOoyWY5CZGIUbWQhZj+lhtXZiGRmRxSWn6av9AkMLCQgQC0dvby2KxVCqV0+mcmpqK3lZWV1WWb+WzRkyGeLi0Kp2Iv3YIhhzdIM9KiXugqLpYVl6oLCsYqCoy1hbbESXDLaVjPSWzfcUrvEKAfT3hgWDwswFZ1YoWygr6nDhQBbU+kniExIQWiHZ0eBCN7iqEq759qKvXQyb6+f0+Jd0/yBqxc0a9LP8o0zdO9U4QhwO9rjFQ9tCrBTPbHVAVFlAFKR6o/opgFNqnp5mc00/PayYCsjE316cjuwRdFlKdDpkrz7vKv3qcfvwj0kev9bz2ZMuToMU90fzEK92v7CbuPkY/doV/JVeeWz9Q32vtZQ2zNGMa96x7bmUu8bncjUBlqffi2bUY/vQTy6ZNvvNn/OdO+05/5f/65MipL5dxmG+WgJJvLQGN61/CAJuAznaouDGVAnA5gFgU5nKCVMoCBjuDRk9UVI7lZPsuXR767HPHu+9Znn/B+MijoAdantli3/G2+8BB79lzI5mZ49U1011dc0zmkkazMjwcW1xM3GKcSBiY8seGjVGjJKpkRISECKs7TO1YIbWv4NtD2O4gFhfE0ZaJwnhmL+5+y2RJsJ8fonFDTP4KT7gikYc1uojZERkORANzUOf0hSC0ee8B/0zDwMDAwMDcDVgU7wQWxZ81mlZIFOMtwlxcQFq+OvsD4Pq4m4TMU+3t1q3bogvfv7PRqu4pOfCWXd4KIBsBqyUx+32I6XYV06YXuuKhFlkkctXaIRgSsomo84BfqWneicHphZkVyKMmQgHtjJzg77gxcOkL7pE3Ma9vbNl4nHG81dhqHbdLDQ4ST8EVivFMEZIqI8qsTuukkuLGFqqqT7J78xRqpnrYwfV42gcH8vmYm50Z50r27UJdPIXJvt5x7VRLwXE0N6vOUdBoqm/VUJoJsuYiRVUO41A988Nm9I2C5LKvPi3+dGfDxffai18kdK7HE15lUDKVfK5e4BATB6U4sbijT4huBuVQgK4TY1CWvqpFyvUpYpa2qoaXiaWWK1g4K13oxUtG2dpxw9B8YCa+YPYWoIyhfRMHdI7fcgeeFxrOi4xYicBozBoebvH5cPHw+/F+P2HtEAy7vURIya9v+xiH30anrmcw/sjp28LtfpfWdAxfktqcVldzGg86IfqqoLNQ2dakr8EY06nmz3jmFyTG/+Jo1wn176isXxndRa4RzOiUanZxYvXTfhDG5oLSVSeswRgKq2WFWfzKZDYuiaO+xvdcpY5drQ2kXR/MPE3OPFF9/cSla1d2Jlc/ehW7IRn7zs2eo+WEq430gg4mopOARDWXl5dnZmbGhTAvL6+urq67u5tOpysUCpvNNj4+Hr4jY7MSWR4dnzba/EyuvbXDUFOjLi1itbX09VPuG/02dEFIWg+IiwDODUgCmcmQB0orAA0ybOyZsRM8Q1SDlyX1c9l+LtnPxnipGC8FjEYHo9kpaXVp252mDqe93eluc0B/RJusvla7r8sJ1d4ke8YZvkneyJQkMKOamNNNzZtnFp3zS97FYGA5NLa04Jrz6yYGBV4B0U5ED6JLVaVporQz7DMH+g/sxO18vuP59aj1oARubN74averHxI/BOUQVMQ8eV69rh5jxbCH2ZqAZmhuaH7lvnuA78PyMjAxAQy5h97ZOXTo8EjajbXwX0+7/dDy1NMLWTchRQRFUSoBNGrAoA8r5Ms83jyZPNPZNVFfP5qd471wwX3osOOdnZYtzxoffsS0bj34V4dz9+7hY8d9V5PGCgonm5pmiMQFoTBoMoUDgUS/nEgsNjsT87miVm1UK4xI6WEuKczArfRjQiRsiEAI4vuDBPYySZZwvz71cp842McJ9TFCFNoKg77CZYXFgohaETENRoc90cn52HwQWuf5gBsjYWBgYGBg/mL+7kVRKpX+j1XOnj0bn+np6YnPgIN7zdwHWBR/vkRWoEWnpj5ovLIAsNOAmaHVEw+Kd8H7jrR1C5OrX7dhSatNzN6bYSu79OBbWlYZ0NoM1Z94YEjyIbJ5hOYMxKPXoWlwNawdgoHVDoutAfDL5HJ0ybowyAr01dgLL8q+2k/75Jm2p1/rfjVDksFys5x+D0ehZ/AlbL64nihsoKk5Op9FGWA3m5BXhIjzfCZqwKqR+339TmeNQV3M7cxtS/m6aO+7oB+2X/66/fz+jrbkJm1RnaOoydjewmegGuSNN7lJxczdTdjLFRlllw4Uf/pOzal3mrNfwqCe6uh4Fd+bLKDTBgQ2KWlQgpVIushCdKsAUSlA1op6UAP9jZPU7CAt1d5WLi9oZRWLOF0mFt9DkIzQlGMa56xvMnh7EwuQlVhMMDWfZvU9LzH+lqP9QGgs4KvUin6nocFqKTHrGxX8QwbDOYs1Ix5W602bLW/tEAy1+gCz+Rq+8kpLemndha7KL1jIS7zWfAUSMVDeZUglm/azTU8K9P/J0TwlHtyjsV8yD1cNjZEDM8aF5aUH/lY9OrsMOaF0qKbHUFwlK77Jq01ika/x9CkCz1Wu/2qnL714NPeyIfeLvswjVamfn7t2bce1+j9dxT1+DbsjC3uklHChjphR111UUZuVlRUXwuzs7Jqamo6ODgqFAv5NZbFYxsbGQqFQ4iVBwtGVybk5u3tcqfNwhHY80YBEqWqqpNVlwtWaMYL6cnFJriw/U52XPVhZ1iOW4BwjFOc34RrtHw7cOnSOoC3DXHa7eEjDGtL3DRkwQ8YOp7HFYUTZTY1Wa6PVuVrEZRhtc7c4nJ1OF25oqN/r5YyMysenBiahgpyOuaXhheWx5dBUKLwQjsTrtURj0YnlCfu0XTGqoLvpXeau2oHaHFnOZd7lY/RjoO+91v3appZNoAGCAQ7Aw49IH33B+CKJn5Qvz0foEVgrljPM0Qa0njnPwg8vPXUn8/PA6AjgsAO6ASglyKRDyz7bWoGGukQyEFE/9PY7U2h0eHT0XuF4/Y2R06e95867Dx6073jb/PQzUIWYJ5+yb9/u/nSf5/TpkRvp4xWVU+0dczTakkoVcrmiUzOxxWBsbCzqsEQMqoicHxbQw+z+FVpfqI8UIpKDRMYykQ9l/CD30ywTRUECM0ggh4iElT78Co0IXhwWMiJKQdSojrptsYlxqJoLvMcPBgYGBuZnyd+9KIL65/VC9eJ37doFfhWLz4ADEHCwesldZu4DLIo/X0TlQO1LibERC+jaE+MHIxwNH5bk/IotF+x8b7yyMjF7byZGBsqObBfiU4GuTkDAT8w+GE2O5lpX/v2DYGf0+FCZuqtfC07sxL/9RPMTR6hHmvRNWq920OpkiZQisaS1X1CIFSG5JpUuoKYN4Yqg5GF3tlxJ0w7Z+V5vl81WYpBXs1sK0Je+LPl0F/Ls8dbP9nbd+KKDnldvLaq3VTWrsSgcpylXlJ/J2FdDPV1fUnL9UOmhd8o/exuZ+lJ3zYsdLW81o87SiUQ5a1BONklBOezuFzW3CRBVwqYaUTdSRUENMyoijGtjffn6uiZhMZXTauCwh8hQTZoxuXXGHVhaCt35TdcfXEF5Jvaq7L/hap/iGU5y9Rgx365ts1nKreYq3UDbgICoJXTKcJeYxO0a7RGrLfteweM+01qdUdSuu0wyfkQ3PCLQ/ZqrfUlmOqBzXLd5m7zj7Mk551Iw/MCJlpHZZYljskcyVNetL62QVdzkt6byuNeF5hTB8BX+UDJtKBPtyc/xFX6lydtPythblXL4q+uZr6Wg/3iV8Pg1/Bs3sZ/m9xzPbzlzszwpDUoS3rx5s7Kysq2tjUwmi8Vio9Ho9/uX17qnRGOx+eXl4dFpnXlEKB+isqw4or6tTYGoFdV/s12wuliakyG/dkmTfGUwI81RXjrS0znLo0SsQsDBnNPhxpRtLgmqzW5aK9p510DbvL0martL1jMkJ/lUzFG9OGDRTrldC4Hx4OLyfYVE4BXg7XikAVmsLE4Vpn7F+mp///4dmB3PtT8XN8C/kgTGCYeB6WnA64G2CKpVgIAHUPuh8jBoZEIF62uhX9/gMACNCtWP0agBiyVqNoc0mkWhcJZIcr72MlQV5t5hfmLjWPKFGQxugc1dUqhDFnvU6YwYDRGVPCzhr3CYKwxaiEIL9jGCJM5ynwQSP/IA5H44ehBLCOF6Q/jOFWJ7uL8jzOiO8PBROS2qF8ac+tjYMLB8v9YdMDAwMDAwfxf8fYtiT08P6IeJg1VAG1y3bl18vOaHd8zEx/cCFsWfKcFZqCXGkAQaL4xBHfbj7TEemOvK/N9xJcisfNeejxLLw+7N3LS76svt5IZj0PYkOi0x+8CgnS36WdVSZCEeo0t+1Zhi7XBsaeSa8tJ+8uHn25/b2rU1RZhCtpPtXodErReIpf0cUW43Nxsr7xa7BqQjnBYT6qqw4RyP0aSzqBR+L83lqnfYK/USBKOpoOGrz0r3f9D42Sdth3f3IFKaleW1jgKEqQnNJzXV86tSWGeL6V/WVBflHq34cmfxp2/XX9jWWfp6F2pPbe1XvW3dQooKKliKkUh7qaKWdmhZaWO1uKtJQUVZWHVLrPRFRrq9t0ZZ2StAawUMF1XiJ0hGRYYJm39xdvHOBZygqrEDc5d0Q08LDP/N0b7DMRSL1Qo50T5Yb7WWDBoadbJeTX+7GneThj1e0fl1fseRDvxLNOw7bPYGLvfJb2ITl/vEbYdPMll/rMTU5Tj8bf5J8fQ8qKCJ1/s+YjHICcWOiR6Bu6HbUFMlR+aL8RlCyQ2hLUXoucJ3pgrt2XhHUa2n4vpo+Wf6wj2kjA9yU04eSSt5KbX9D1eJ65NxL1/vejcN/cm1yi9TC1IyssvLy8G/H4hEokAg0Ov1Xq93Ib6AGbTBxdDKyMSc0RGQqj0Mvp3Qb+zq1bS0yJrq4ulBUX2FtKJIkZOhvHhGffSw4eQJW9IVb0nxJBY/rpD47QMu98CgRaowCPg6AVUnwxoHWy1upMXTbnN3O+zdblOLbeAOM/xuiN2C1XcPtWiZCk45ZhzKUSXDzeiydNUN1N2eBny1+9W1NCAYm1s2v9bz2sekj08wTiQJkgoVhY36RqwNy/VwdeM6z7xnMfztzXV/OYuLQCAA7fs16KFqMWwWQCJCewLXKoWCWtjbDfSToUIySgWg14fV6mWRaJ5On+7pGa+uHknP8Hz1tevjvbZXXjWt32DetMn+5nb3/gPes+fc7+7ynDkzXlt3r7A89/x09k0o9UeSB/HMYA821N0W6kGuYBrD2PowqSFCRUY47VEJLjZAj9mkwIgJmB9L3DkMDAwMDMw/AH/foli8Cqh/IHFjBNVxTQvBAXj43Zn4+F7AovgzhXEDaPsoMVbUATZqYvxgYJ3YZ8TMY8Q+8Ktk2P89ZUtDwam6s291ZH8MJTGIhO+1yu+ym7j76dann25LxFOtT21q3vR061NPtT75ZMvmzc2bwcNT9DMSp8RscwhlSoFIUkvgX27m5fXpaGKPkurGl6irT7G7bsoVlIEhm9Dr7bXbS90utE7YSq3Nq/r8k/J97yM/fq/rwudd/SUN5oo6ezFS29aE66+/yc1IZ5yuqC4oOl51/p2ivW9VnXytNfft3qZDiPqLjTWNDJxQSjEIe8UyDF3S2iFEVAsbqyUdTUoK0shumOAWRhjJfmqJrqNDhhaLKHaG2IeXjHK0AaNnfmLuLkVAHTPLpeaR9yWW37C1Gzm6rwV6jIRpGWi1msvM5iq9ulXH6NBjKxT4lCZManHXqUbMazT6QzTi82TE56j0EkR2OzqtuXktrjejU287TGtGXUezud9Tsgi8K//Mssg+geW5WrqNLXVqTImckSPRZIid16A8of2G2FQgtFZ0D9eXTjRenq77RF7wQVPqp1fSUvdcr92S0v27K6T1Sbjnk9t2JCP2pVZezq+pbUTh8XgejzcwMDA0NDQ3NxeLRkEbjEzMLdk9U2qjnyNxkRmWXoKurV2JahSBnyRogw1VsoZqZXW5tjBvIPmq9sihgZ1vGw8ctKSkmhuRBjpbqx2U2Rx8t5Nqs2HN1lazC2ke7jJZSSYD1TpAs2uobgV5WIz10ts8XfXOitbhOuJIJ2+Cpp2R3VCnJiuuZKgz0lXpYGSr0suU6TdUN9KUaTeUN8A4wj68n7o/vhtwzQDBeLb92R2YHfv793/F+ipVlFqiKkEakAQ7QeQDrd/gnfcGI8HER/njEokAs7NQ/0CbFdBqAJEAKhyKw0DJwHjbwCYEVC+0jwhZokwaU6tWxOIlJnMOh59qbgkUF/uSkoaOHnO8twv0OuOjj0FNI97bNXT0KDgPnp1AIGc6OhcJ2GVq3wqHscJmhqiMIJkVJAnGzl0aPnxiLVx79ttefu32meGDJ5ZbCwGPAph0AMsP0HUQBgYGBgbmH4y/e1EEFTE+Bgd31cLvzsTHICqV6o3vsGXLFlgUf3bMjwE3/w2YsEHjcRPAzQSiD5pWArHP2HfJ0Bs5It3GTXPU70kPRiPLqJSdyORdUQ4NwPT+GUXhQWnZhdm1A7tjX/++e8Vzbc81MhsJbElyM/s0WlTJsAp4w9w2MzpJVH+WR60fMCtVfi/D7W6028uHhzBaTiexKLN03/sVe3e27NvdVXClVVFf5yhusFSjBN0NdbSya4zU8tqCshM113aWHtxefmR7842dOMSJjubMmrJaXCtVRBoQY6RSDE3S1iFA1IB+KGpDqvqQRl6dl1cWYqbOsrJtfUhZN0dKNHOEXrx0hKkKDDhnR6aCkW/v9APf4PjsSo994qTcuZlr+BVb8x5PWyjgieQ4q7HWai0x6hGDwnYjEWkg1HQSKkq7L9Tj3qbQ/0Tpe7oPdbAlK6fuazz6Gie/VrazV/Nrjvb/cTRQsNX/sTr4d7YGHNwenMlb9S3BV/dMLvHM41i2u6vbhGnQUsuVkny58abUnSRwX+VbMyWGEqW5geVta53szA+0nLFVfUK8+Wl+6tdfpBVuT0FuTOr57RXiY1d6n09qfT+9+evitpJmLIXBVqvVLpdremo6shCMTi0Gh8ZmtJYxgdJD49pxfYPt3Ro0ei09KEU1qNBNelTjYHWVKTdr8OIF9dGj0n0HpUnXRXVNXBKNIVGTBx0Ym6fV5kVavF1WF8k0yNJLxSqaWtOvHqQobVTJEJXjJ5D87a3DNfWuwnZPPXm0RzDB0M0q3Ev26ZXJKBAZWRzRjGn6nf2N+sbXul/b2LzxydYn7xXg2WRBcrupneKiSP1Sy5RlfGk88cH9dCTqxwwBRiOgkEPVQcl9QHcXVP8JVMGGOqC9FcDjAAYdEIsiQkGQyVggEmba2yfqVqvFnDvv3n/Avn27edNm0/oNUN+Ij/d6vvp6JD1jvKxsuqF2tqVxsac1SOqFtgJSyKE+apDEXibLocYPOEawpzvUjV7prQ/jqiOkmiijMSpsi2kIMeT+aPGr9w/Awk68BRgYGBgYGJjv8MtZegoOQG/87kLT787ExyB+v7/lO2zduhUWxZ8dxNMA7gQ0iEUBYQHgla/OPhCL4cXDkoz/5ig4Bw75U1ITs/cgFot0FexuOPd2SMqFauWHfnCaJRqJqbnOD3o/2Ny6+Zn2Z+LxdNvT4Jf4tUMwNqA3fI2pbGc6xP0uQqmm+hS7I1MmJemHrBI/VOezzO1G+oapalpnR9LZ4o93Vu15p+XEkY6OoiZzY62jAKlrbCRga7P6Cguriiu+qM/cUXH89aJPdjRdeRdbcwLfUdxUh26t72VjZSKMTNIDymGnsLFGgKgStaDl+FYTt2aIXznLygozUoZZVSoyWdxvAE21TzpCkY8qrTPD48vBlW/lUcPR2Oh0SOSeSVcO7+CbfsvRPsFRfcXhtonIBl2z1VJqNlWZ5C0WaoeViMWQkFXY5Abcrj7qo+T+jaS2T9vz0uvPdbdepteWcPd0KtezBw7rnPWegH5+SagZp/B9dKE/Hp10VzrWEB/TwBD4+znePrqLgLFQmvSCau1AscqSIXEn8V0pQmOWTFehNrYMjBDoAVLjcE+GvuEUq+BAddrxi9fTP0qt3prUvP4KpIUbkjCvpfceKcVnttC6GZJBs23cN7oyMRf2T80PuielOh9d4CLQzF1YXUubEtUkboTSg5KmWlV7swHbY8FhLJ2dgy2tmroGaXE5NzOXmlvc19KNpwu6lYPNgy5IBW0+ksvHcrvFdqPWKLZqSV5J05i40qOpt5tQKncbx9OO9yCbh6vqXUWdXgRlFCOaZBnm1MNLzrHlEduMje/ld1m6ylRlV/lXD1EOvdH7RrzdHxjburbt7dv7AeEDMPaR931G+ewk8+QRypFtnduS+Em3h2169fcpPy7R6N3rx4AGCHpgXQ3QjIZ+q0KlABxWmEFfJhHnu7qnm5rGS0pH0tKGvzzp/HCPddvLxnXrzU8/A7WOOHTYd+HiWEb6RFHuTE3JHKpqsRMRwrevkHEhIjFIoC0TRVAzQAIniMWHMB0rWHSYiIxQUBFOR1SKjxlYwLAamPFABa7uRSQErCzdijk/MCT+1gwY8frJMDAwMDAwMHfj71sUvV4v6H7xYjbxjGJ8EPdDcLB61V1m7gO89PRnx4QNyPwXYNYHjYdFgLgYFLrVEw/EBWnyH3mSmrpG+5vbY8HvEb9+5OHK49sX1XyoofbiDy7LEQlHlSwHi2bFiKg4IQ4vwsejkdOY3J+8dghGLw+DLmLVneFS6gdMcs2IjzM0hAL90OfD+of4ovaGpqP7i/a8XfvxrubrF1q4DfW2yjpbSZMQXdfUW19aXFF1FJG3veb0y0V736r9eie27Di1u6y7vRdd00ntFIgwUlEXVdLeDcqhsKFK1IyWYDr07MZhSckYqyDCuDbJLTBwegUMpZDrokhGSNJRyeCkY3Rx/tut8IPhqHcyqHZOVytch3mWpziaX7FVb7PZOVwaV4kxGWus1hLzQKOF02EhE/uoHfX4NAR+D4mynkxZR+j8sKMwpeFSa/t5Snsu/QKS9wxHd8zgavKOmxa+qfKyir5A4bnCX4tAUslK6uO3z3ivCBzXBLo82UC1xthj8TKkQ9SeQUypuCmZXHQKlfnl9evXDqWWvpNc/0JSy4arvb+7SnwylbCrsP9cE7uWJOfLrbPu8aDVO6s0Bjiy4T6WtZtoaO1Uo9HSpnposWhjlbIFqevtMvX3G0Vig1iiEYilNDaHQOnHknFEegdTjFabm0zD7RorTm2kDZgFtmHt2IR1dNjr0U/ZuMsDHYCoaJlzbVSWZzVUKxxIphuJHW5AussQ7pJuL5I2hpNMcQfnNLppjWxMEk8P3pTeBGUPFL9n259dWyD6Pv59cDJDklGvqyc6iIpRhWfeE47dI609OwsMGhLjv5yVFWBqCvB8Uz+GzwMo/UDvN/VjUE1QkpDcF6X2h4iExe6uWRRqsqpqLCfXd/kK1Ehw507LM1uMj62zvvgS1D3ii+P+i+fGbiRN5qfNVGbPI4qXWmtCuNYQARPEkZaJ3OU+1TJRGMRTQjjsCqEzTO4IM7oiAnxURY9ZpcCIDV4OCgMDAwMD87fl71sUQUA5BPUPBG6P8Yulcx9ATYYG4SDASQemnKuzD0SjseF5CX0fi2dctz5osSZm74GAeKn8yFsTWha0ZG76B39JXQlGFAwbg2Frs4/wuEIJhFgsEUukEikU0tWZW/B5dL+f4HBUuFwNgQDLa+JRr1+p3/te0Z4d9Yc/RVbcbFI21TmKGgxVDZRmZGMBsvEwqvSVhqSXSg68VfH5O505h2gdxf14YicSR2xmCHskws5+SUeXsKlWgKgUolDi7m41rW1IVupnFywybgQZ1+0CpJgvYvMddGjb4QhPO272zE/Nfysns7AccY3NSIyOTrntqtC+kyv7HUu2gSU8xeagJFS1rtlqLbUMVlkkzUYGnsXCofuym4j78JSNFPojuO73OssuI642tZ/tJ6aTSmqon/TLvhx0t/gm7It3V3Tw5XKvZ56+UbIW+67XPJfccfvM6bSizqyUjqIrNTcvZKRdO5Wavy+l/L2UhpdTWp+4hvvdVdJzGZRPK1g32iTd/Xo1d9DPU/koPBem39zRq2tpje8eFDRUyNAN6q4ONYWqEUlUWoPc5hKaHEytqU9pgLKCOjvSNNwm1vZQeSQSndnHENM5eqnSbXdNLiwtL0xAa57dPGAQAyhqFoWZfvEN00CJzFJHd9X3umsb3SVN7tJeH5oRIAonWNwxBsXT12FpL1WVXuFfOdh/8PWe1+PpwU0tm97CvPUZ9bNkQXKFuqLH0iP0Ce3T9qXwD6+T6RkGeroS4wchHAZmZm7tGIwnBokEgICHorEByqIT8RE8LtjVudDcPFNTO15QOHr9uuf0adcnn9pef8P0xEbTxk32N9507/3Ie/zI6KVT46lnp3OT5kpvLNbnLbdUhzBtQQw2iKMv9ymW+2RBAjNEIIWgzhC4MJsQkdCiA4KYXQdM+KDtizAwMDAwMDA/Y/7uRfFHBxbFnxc+NVTsNF7g1NIHaNGrsw+EOqD+SIHcwJNpX3xpEv09D9QIikoPvuWRE6DqGmM/uLZhaGlFRrXQOM4Wm48/NqKQNNnsbVZHr9OFdzkJZlO/Uilw2AlOB3iId7twVmudwdA0PSk39iCxn++v/vDt4o/eqTv7ZUNrEUJfU+sobFTWoAj5bR2H2uueQ2a8UHF8R/G+t9HX9jJabrKJXaReKhZN5XQJBB1UcXsnKIdCUA6RKFEnRkrstcoaRkU5U/SsKD15jFsik9KZIhNN4sWLR9jqMb1rbmwmdHuBnvGZKZ1Dx9ao8DJ7ich8lMt6jsv/FVu5ky3M4nGpKiyUPLSUWFSNg9xeCRvfRS1s7vsM2/8UjfFHLGZ7d/W5xpS65vNkVgoWU4q7iWElDdg6/JNDy7c1DLyNuaWwbmiOwnBz20zqMvWraV3b8lgfVPDi8X4l/90KwdohGH+6Rr6cUrgjo2NTKuEPV/u2ZdOPVnJzmoRtrVxeB8PUGU8PoqTI1fQgqkGK6ZXSGBKxTKjR88wOptPb7x7F2H2tFg/S7GnXWjF8ORHT119ez8wpkhSU6qpqne2d40xW0GKBcs6REDA7DPhVgI0aG2ielxV7xWmDmjyJqZzqqOl2VSJchaihcqy/hezvJXl7e1ztCGNdljyRHtzStgW0wfWo9a90v7KPvO8C90KhorDV2MocYhomDBPLE4lP4c9jYQGYmb4VZiPQ1f6tmckJKCs44gfstsQaURYTUsHOdugP9up2wUh7W7ClebERMVtVNVlUGMjKDuQXBEpKnR/str7wovFPD1ue2eLY8ebQvj2+Lw6Onj86kXJyOuv8XPG1xdqcYGtdqLsjiCEvk6TLfcogiRsk0UJk8gqDHOZRI3Je1KCMDttjs9NwX3gYGBgYGJi/d2BRvBNYFH9eIF4HBEXQYGkCYKVCPx8M8Bv5MVn6f3GU7DMXhg4fSczeA7MWWXroTROvBVpfN/zDmviDLM2HpGQzWeBC2XyyCQ96uArLZPT2K3r7lfHo7KS0FJetHYLRQ1a0F2VX7NlZ8smu2rSL9ZjiemNxvaW4SZjf2f85pu0ZdO7z1WfeLtr7TsOZD/tqL/GIjex+JqmDxergCdqporZOUcKLkMI2jARHGhC0+xSFAXpWiHZ9iZE+KMYwZJp+qQcvGaHJx9SWae/EcuibVoOLSz7bkFiso1NkfJzY1SrSpgnoe/iMP3FkT7CkX7DFCCldqUdbLaUWQ5VJ0qbl9pLopW3UL3v7n2cwf4fBvtJVfwqVVt54qb//Gl6a24FpwTeIFFj/xFjo7oskpxfCavsMheIUoAc1RUp7ssCcKpKVKTQEw7asvhMtqjKm9V7x9E16alEtogZHbuiE0oPIOm4biofH85hsrljB0hqpRgfJ7u11+EFLR1p9nTYfweSiKvQsOp/f3CHKzJUc/lyzZ6/ls899V5PGq2tm+/uXBwdjS0vQGualSWDCAgwJokbMjKZmWJatV6SLBov6beWdrrJ6VwHaXdY53Ng5hETZ68p0hamS5MO0Q6/1vBZPDz7X/txu4u5TzFM3pTcRegTZSVaNqXwLvkjsJ0mXraCQweybwZysb+Jm4hD8GY+szEhVRbC2erGyfLaocPJmZiAlxXfmzPCRI85d71tffCneQtDy9FOON18Z/ugd32cfjp09PJF2arb4ykJJ8nJ9QagDGcQQl0liqG8EWRwks0NU5gqLERZyI0ppxGyIer2xhSAQhvOBMDAwMDAwv3BgUbwTWBR/RljpQP7vgfDqljYtGsooPhjRWPS05OxDPHFZD87yzJbI1FTixN0YdpDKjrypplUCLc3QkrwfyMLMsrjPSBS5UVafctLR4CrdT9lXe+Vs6YEPKo98HI+KQ3vKDuxeOwSj6OOdqIqshr68Wls+Ql/QKvgaS9vaUvpCzcV3Sw/urDz6fnfecT6mSEij0rF8ZgdH0EEVtHQIEpnDRmErRtTTL2fgnarqUW7WPDU9Sk/ysBt4ckmf3EWQjBClI1LDhGss3go/urBo8/j7VIYWlryDIGLjJB6iRIOQMi6LqK9zuas7D2XXhXyiCms2Vq8mD5sMgk6eoLqHfrqXsg2UQzzh+a6mY+iMgtqLfR1pFG12u66hjUljca2OyZW7OEMsBkzOrSjMU3SSXYw06PIVzqt8/XWhuEKlImlVfV2E+qyym1fT0tJeLWbtrZMkYXX3is03GQU4Luh+PVAFUUgFu5yjpOEAyz8pGh5T6C0DXOFgW6f5ZrZl/0Hz5s3mp552frjHe/HieEXlLJG0rNNF5+ehe4quALNeYEQTddCm9Ci3umRAmSHQ5/RZi9ucxfXOfKSzBO2sqLOWFemyr0ouHqTtizeZeLLlyXew7xylHU0VplZpqrBWrNgnds46f6qWEmssLgKjo1BuUKsBBHygn+x+/RXTY+vNGx5fC9O6DbcfGh9+ZPiNZyEV3PyE45Xnht592fvJ9tFjuyZO751OPj6Xc3mxrjyIIy/3xT1QGqQIQjTeCocflkrCanXEao9OzMfmloEQ7IEwMDAwMDD/6MCieCewKP5ciEWBqi2AEgmNp5zQ7sTwg34vL9EUvSrt/5gvM65bvyASJWbvxviIpOLY69yONGh3lm4gMfvAzIwviIlGnHQItET55CDCVbqbtPvd3nerL3/RnnKxv6LwXlF19NM2bHKveGcr4o2qpA/Kj31QvG9nS+phZkuSmN7F6xcyu7jCdhoP3S5srFuVwwZBS6+omybpow1Km/3S/ClaZoSWPEPL1wrofXILXurHS0f4A+NW78L0wtLcnM7n6zQYc3myHLKwniiRYSU+mnyApeQ2SKknuf2PsmTrWMojPFm1nK7SNVvNJVZ9tVnaopTXETiXMdQ36IzfE/qe7EYfRGdlVV8i1KWzKHnY8YomS3evXqaYnVtVr28TjQKB2ZBUP8HE22UIvSFH5rrK16SL+NUqZZ9MSUTjqtJKM68kp2WcSs07kl7zdkb3n5L6Xi/lokSuwHzwXrG1kFPD0g9PzowYjIF+Kqh/oASCKggKoXnTJuf7H3jPnguUls7gcEtqza1fCizPAJPW8LBgwtzp1FdpNLm8gZtES36zvQB0wiZbAcKSX6BLvyI9u5++96XuFza1bHyt57X9/fsv8S4VKYvaTe3sYbZp0jQd/IlLqoCf2uws4PMCZhPUU57DBkgEqJpofS2U3+7tCXd1LqKQ0yXFYykpru1vOHa9P/zlyXuF5Zlnp1MvgAYIeWC/IkiVhNjSFZEyrNCFjY6IdyrhgcEf3PQFBubnSGgeWJoCFgJQVdvpIaglZsAMjBoAvwbqkDkkBlx8wM4CrDTA1AcM4gF9D6DtANTNgLIJkNUBkipAWArwCwFuDsDOhrr10lOhLfGUKwD5IkA6BxXcxp8EsF8AmKNAzxGg6yCAfg/I+neoXVPWvwE3/xWK4keA8o23Avdl4t5gYGBg/v6BRfFOYFH8uQD+cw7+oxuNQOsDxcXAsDgx/32wPKz9ysZH+MqB7TvG8vISs3djbtZa89Ub+LITUGV/yYM+/xpTI/MivLFH5UFb/ZJJRb2j+F3cu7sxH9IE/Oqko32leQoi5l5R/eUBVN2For07Eef2Eaq+EvXVSJhcHoYv6KBxUe2CJkgOBU31wrYeUSddhGOrWNhhVWmAlbVMvb5Cv25nd7OlWozci5WMUpVjA44Jl083NIQeNF4SSI9S+Bcpkl6SzIKXeDiqwUG1hCfGF7PbtrO5v2KrX+eorooFJBXOaqyG/FCF0ClrGKI0LO1tGuMhEvnx7paPm/PSaq9252dw6wr7rNUtC42NPipt2eH4blfJcCQ2Mh2UqAOcHouyVme8KQXlUJ4p5tRplES2EluDLU8uzrh84Xr20esle9MbX7yO/WNS31s3KV9W0W92cZuE6tdzKb++0nf/aNl52PT4E46d73q+Pj1WWDTd27uoUETGv+kQGA0D8/7QmDrgJNqMCKWuiKPLwplykI78WkdeozWnRp+eqbhwhP3pDuIbO3DbPyJ9dJp9OluajTQgKS6KJqAZWRyJ/tSdEsCPbnICGHIDBj30h41Bh/rON6MgIWxthtoMspgRJmMJi5tpbAzk5nrPnHW+9675icehxaIPP2x7ZpP7rRfd774/g8cnnvBuOHa8M9dYBXsgzA/DpwYGuu4SoFCpkICiEdKqUT2kYeOWVQ3TAl4lMCSBNMzBhpZ+mMmrGtYLDHQC6hboenk9IKkGRGWrGpa7qmFpkIP1Xwb6zgOEr6GOR6B9dR+GypW1fQS07AbQ7wJNbwGI14D6l4GaF4CqZ4DyTUDp40Dxo0DBH4G83wA5vwJu/juQ9j9vBShs2f8F5P4aKPgDUPQIULoB+lej8mmg5nmgbhvQ8CrQtB1A7QRaPgDa9gAdnwLdh4Deo9BLE76CPLD/EnRL9OvQ7XFyAF4etNMBVEdxBSCtgUxSgQBUKOgdadsBXVfiDQoKAUHx6mXlkGqCFgoa6VqMGROfKgwMDMzfP7Ao3gksij8LIitA4Z8AIwEa++RQ78QH+x7vW/CdUqT+iqui5RY6d71/n3b5y8t+xOXX29I/BcgkqODHD2RseFpENHZpvW32Ee44G2Evf7339QO4AwSuYH8+t/Lq4eak09jCjHhg8tN7c66vHYJRemh3Q/HXMhaFj+Px26l8dCe/cXVZKaiIbV3CDoYQI5CQaRY5YlSSM0vNiNGSR+kNcoGQKHNiJCMEuZ+tt6nNxAH9BZHkdTp3F0NaRleAZ4dIUo9EY7Up+cOCRjyz5iSX/ihH9QhbdYAvr5LRNFDPwxK7ocqkqRUrbhJYH1IZD/dTH+3ueL+t6GptcvP1TEFaKYPRiA/V1y+0t61IJVD3vG8TCsd8k0GRdITXZVFXDVjSxY4kvjhLzGhQKwkkRXdJb/HFghuXT0LlSSveud78+DXCuhTyB0XMM2h+Ll3dYBhqMTiIBpvE7XXPLTj37IlvnIsH6o2PXzqDun3G+Mijs8Tb7Cg4tzw5ODpMGzQjxYZimiEHY85utOfWO/IQppsV2qRroi8+Y+49zDjwFfdkmjitRluDs+Gkfql71r0SvXfbvR+LxUVg7Lb1ohQyVJi0CQEg6qGKMn0kgMcF1KqoVrPM4cz29I6XV0AJ0g/eN2/eBL1Z0Am3bHbv2Or75O2xr49OZefNIYlQ+VCaNCRQud7/ePjLk4HSsnuF5dnn5siMxJ3A/DIIL0PVvBYnoBZBU65VVdMDPhXkaU4eYGOsShpu1etaIKkDDQfUM14+wM6C3IxyFei7AHkR9jiUEwNlqfXDVSXbDjS8AvkYqFWZ//Yt+/puXP+fQO5vVjVs0y0NA43uloZ9dD8NY2VCKTvQGEG5As0KFDDQJEEL1XZAKT7QME19kG7ZWZB5gq7lUUApQdBIA2bITqeHoIThQgBKHobmoaUl0C8QYWBgYGB+cmBRvBNYFH8WgF8mal+CBuA3e24mMG5enf0eQtHQV6LT64XCfDrP9MTGFY8nceI7hMOz6PQ3ERfejzJokCjGfliFRr9jUtRnatd7Oxz+/hF8k71qa+fWz/Gfd9JFx3J59ed5eGQWq6V8LXjISjGy9vYZZnNpb1Mmf7WPH1SztK1D2EET9EqEeJ6a0+VTFk0yb65QU+boBUYOjSExY6R+MEgqE0vdyRXuYHM38eXJAi2JqTbhpX6qwqdUG1yy/nlOkYFTUiimbufJ/4ut2cZWXhEJ+pQ4mwFKHtoGqtTafArvEIX+KI3+UE/X222l5+tTECkZvC/K+Y1tzOHO7mhDXZRIgFbhxvf1fcPySnQ4sCzi+wTtJm2ZxnZdZE3m83MktEaFGtctb8vuKTiTdSPp85TiPal1L1/v+kNS35Z06qc1/PN4VYHc0aox4eVqnlZnHR5eXknYWmxxcZ7HG9j9qumxx0zr1t2KR799+PDDipZzTEMByZDTZc5G2HPBaBxMr1JfKZCezZScuyG7VqYp7TR38jw8y5RlNrRaI/cn5fvWi0Kt50VC6GN0OmLgW9ZoZvv7x6urfZevuD7cbXly86oTPmLb8qR7xzbfvl2BsyenckvnkZTlPnGILloRK8J6W8Q7FZtZun274FQT2p+athbeU6cd29++fcafkhayOxJX/4MzAn743Fsx0AFQr3xrBgxQPB6c7xG2eFZtTdgQ3xI2UJZuF7buw98StngCDRSwsidWs2d/AHL+G0qdpf8zJGkZ/xeq/Jz3G6DwIaBkPaRq1c8CdVshT0O+DTS/D7R/DHQdAHo/hxY9Es9Aeka7BjDTITcTFAHiylUxQ0I5MdDKjETAQoWUzC0EPHIoNzhmBNwiwCUAhqSAVwWM6CE9m7DfGSs/vH0LDAwMDMzfObAo3gksin97QgvQUiLw6xeIjQoo6lZnv59U6bXtCtJOica0efMMBpuY/Q7RaBBTubP65DtBFhVadPoD+7m5B8eE/cbWQV+304Mb6UDaa59tf/Y04UwVQXAhV9h0ieO0dwjRSLuIO+6w3itkbUhqfbWwgyLCyPi9UgmZbFdUB4RZC9QbQUaGi9ktECoJUg9GOtKjcGLleJr4S7nqmMLQKhlUM7VenHSUpRjSKpVeUVeQmTknqcApaV+I5Y+y1Q+x1J/yZFVShkbTbLOU2Axlg/pivvQUjfEEnfmH3u7X2qu+QlyvSkljf1IpuYmRKCmM5bYWoKkRYDKgPFjoVk+LheWIe2RJxB4WNRv1xSrHNYExRcjMk9CRAg2mSYa+3p136tr1tH0pFTszWp66Tvh9Ut+2fNaBFlkS3YCUDmJ5IgaHM6CQB4ZckW+eNhYKLUqlgdJS1969pvUbXB99TBCn41WlNG1DPHjoa9o3Xlw7BKNdlkmQXUOpryM1WS2DNf3uPm1AO7b4g1uY/DnE14u6v7NetK5mbb0oIJMCxkFgeBiYmgLVN2g2z1FpE3V1/uRk996PLFueTjjhs0+7337Fd2DP2Lmzk/nVc8i+IIkTYnBXJNKIyRYNzEKlROGWEn85oPvNDAMBE1D3IrSXLPe/V+NXQM5/QboVH8Tjxj9BktbxCdC6G0qLNb65KmzPA5VPQcsdix4B8n+3utbxtmxb1n+srnL8I1CyDlriWLUF+n1Ww6sAcgfQvAvKqnXuB3o+g1JqxNMAeTWfxrgBrWnkF0KrGWW1UCZN0wbouqHlEhYKYGdChjYsgxJoY4PAhG01ezaSSJ3BwMDAwMD8TYFF8U5gUfzbw8qEvrqBBGcBVgqw8EBK0GvtOaFt+BNfpTl42PP16cTsd4jFIrT2T8o/e3tOSIcK2IR+WOFKq8orplqbjX6Ma6jT29hsa3yy5clUcuqNdl56tqj1BtfpQJlJuaJmtBhZI0HX3SvEqDpyXYdO0DqizJth3gzR03ysBgWf0Sc2YyX+boWvR62haLsHrM0Gh1xu8dPVY3jpCE/pMMiEo1xEmJMFqJu0BnaWQvU6V/OfLM2LXOVVgYCsINgHq23mYstgiUx1icF+kcH8HRb3Ykft8cb0krTrtP0VkrN9OppINUWhQnLY0QaIRVAH9m9yqrOLYbt3QURzSxoNg4UKd5JgIFVIzZcykDRNd6Wk4VJr1pfn07I/vol+PQv7WAr5sdT+7ZX8Ez3qYoaWKJRRCHgRCWcX8SZc9nBwtVwtSDi8pFKPV1e7Dx40bXjcuev90ZzceQ4nurAYjiz2WgvJoz3iSXY8JC6STIhcOwSjeahcZGtPPNVPxHfXi3bfZb0oYLUAfj+Ua43FYisrQZttjsmcQDT6U6+79+21Pg+VGzU+8ojtuWdcb7/uPbB37MLlyQLEHAoX6mOssFhhqThiNkcn5+A9hN8DqEmgL40ZoZSXnQU5lbYdSosJigDWTShjhvsSyqE1vw+1z6l+Fsqz5f0GyPyXhM6B49INQP5vgMKHgcott8Uz3zrM/FeAcR0WNhgYGBgYmHsBi+KdwKL4N2Z+DPoV/qgBGuvaARNudfZ7sExZTqtSf81VUhEo69Zt0YWFxInvwOv7ovTgW6OCPigvtHjPy74LqAYm2bCYaUMbffgha8twbasV9UTzE0X04hP17JIsCaaQ67DVqBvSy7/4TNLebuMxR036e4WsDSns7JpglQ9wMVShDCPx9cqHOzVeolEvcxtMnoDMOtOvGCFK/SKF0SymTnAqo5IyYBAzbhM1Dwwc4qsfZav/yFZ9wpHViJkaTYvNUmI1FWt117nCnSz2HwjELZ2II8ibeekppMOl4q/6DO0mt0+hjBEJUNd1PA4yommonicoiNMLYatrTtTnlDfozbly91W+Mk1IKpKykRhNZ564+uvG7NMnCxreL8Q9n93/+yTy5mzGhw2iVLyqg6ng0um8FqSyu8Um4Iw7rCvL36xPi0aX9QZQooaOHjNt3GR/a8dIesYcjRaZnh6aULHNdThdVps5q8GZ12rNq3cVgFEHhnP1Z3zwTdQ681nu1d2qfyHx9aLeB1ovCgQCwPIt0Q05XaDZTqJQIzfShw7ss734vPFhyAmtzz7jfme779CBsYupk4VN88iulT7yCpMSlvGjNlNsZh6I/MQ1cn6eRCOJzN4t2SPekj1mxi3Za/ngNtn77S3Zy/g/UO4OnARPNb4BVTrpPgxVQKEmQQk6cQVUYsSAgcqoDEmgJaDTbugVb9+6pm6BsnlrgT0BVD33rRkwJuFlujAwMDAwMPcEFsU7gUXxbwzpHIA5Bg1mhgB2GhD+/o0xs6HZU6IvHxcKMsRK46OPLWm1iRPfQS26UXLgLRe3F0A2xjXpAYlGYnqhS8hxoM1+0vAgeqiy0djwBPqJSnrVhyXMmgxJfz3bbi3n56WWfv5pVeNJaW+7pA29FoLOLhaefvuMuA3FJvfipEPdak/noI/lGpe5ZoTGCZJspF/qkck0dn7PDK8SUDfG7Iz5IQ3TaLomVL/BUf0nW/MiW3WVJyRL8fbBCpulyGDIFEn3c7jrSeSNnchPmnMysq5jjhaLz5EGm5yjLtcQIJUAXR1AYwNApwIWCxAMRmPAxNyK2TItwtlVNQOWLCkoh5IbIlyhgNOEHmhN49WdK6osPF6Ne6eUvimT+vtk8tZC1kmUtIakEnDFGiJW1FSt6ERbecwxmzl0m28HrdbJ5hbPyVPmp562vfKqP/naLJG04HconRiyvrDLcLPRnouyZXcbMvADuSJH52xwgufhdVu614KrqJ6mZNw+A8bo4mjiBR6E764Xxd5vvei31h5HIivDwwt8PvguRjIzhw4fsG190fjwI8ZHHrU//6zrnbd9h4+MXb45VVg/34gOEXFhJikiY0fthhiUZkw8xy+EuOxBmT3wg5JBOTcjAVo2KauFFlIy06GllbgT0GJLKLP3GrQOs2QdlM3L+L8J2QMj/Z+h7XbFj0IVLMFrwCu7DkL9BihXoJ17ojJoKaauG9o15xZCuwqnnMDC+IM3woGBgYGBgYH56YBF8U5gUfxbMuWCUgqzPmgsLQfcvNXZ7+Gc4PS7CvxbcoPl5VfGq6sTs9/Boq8tPfymntwILSkc+wE73CIr0QGuUyB0Is0+okfVPFzVYKh7suXJRhZ6Ry6j4bqI282yW0rJ2Tklhz+qJl2sIV0QSlUkqXctiFIfXjp6+wxZMsyTkJmWSaZ2FC/x02UulVjs5jQviOuBwd7IsGTGZx0wm6tFqkMcxTq26vcs9T62vFbI0mpQNmuR2ZSrVH8lEL5IoW7oavmwNT81L639eL7oEmkQ7RxzzC1COTE2C0qUgWok4AOe4Ug4OjYTMg5MSHos6kqtLUPiTOLzMkS9xQxBU42i8yahtSC9uetgI/u1cs7D1ymPXae8V8rK6FD0MTRWnlBHguRQ3t5k4dBHLcbg3K2CMSG3e7qzy3vuvOW5560vvui9eHG6t8etp7CNNTh9Tpslu8GZ127M7NFm9hsqjGPS2B1G5fMCNuutkAiA5qZvzYCxdLffF/zw9aKJB8aJxVa8vgWRaKq9fTQ7Z/izI/ZXthkffRR0Qtvzz7l37vR99kXgau50fsUiAhEmdISZvRE5I+bQAUs/IBH90zLpgIqmPAjBWSi/N2qAypZYKFCJf1ndap2V7FuBeB2qp1L8GJTKWzO9tcj6D6gQccWT0EY+9LtQLRbsF5Arsm5CrQLk9dBzmslQ1UqfGlq6OT8KrCwmXh0GBgYGBgbm7xBYFO8EFsW/JV0HoVQDyIgWEOQCse8vM9Ogrzutr/8dT6O9dMW9bz+0vPBueFyY8qNvSHuKoVzi8FBi9gFYCUbUTBtP4kZafAQPv91TnyfPfbrlaTS75/VMWv1lgZRCNw+WdeeUFh/aXcNPLqs92V2QzJJZyMoATTN+ryDIRrgixoCQ6eM3h1StgIMR9OkC/mGTyYQVK65xFW9w1f/FVr/AVCVxhWQJzmostVkLB3RXJdJ3mKxNPe0724ovF6U1ncoVXsUPtjoC7qUQsLAA5dBAQWqog4r0qJSRwMToVFCvGJV2WgbK1PZUoe2agJkpxhYTWJ2NnL46NKn7Ug/3Y7TkuXzm7672PZ1BOVLBQmE1BpF+SCzSk3EiZI2stdHMoo6YDEszt3Kw4ZGRGRzOd+Wqdes2yzNbPKdPjzU3qAW15MGibmMWwp6LtmR36tKx2hy+rXM6+E3Dw7tCIkJeR8QnAo8FsL23DsEA345Gc2u9KPHB1ot+m/Do6KJUNt3VNZaf7zn6ueO1V02PrVt1wufd7+7yfX4qcDV7OrdwsaEqjG+OMNqj8v6YUw0szyUe/zOBcAooeSwRxavVVrL+HSj8AzTI/y20dDPvN0DzB1AZzKbtQPVzkPXl/OpbvgceFj0C1Wu55XvHoWqczAyAXwBlCzVt0DJRJw9qlDdugdoSBH9mHwIMDAwMDAzMXwVYFO8EFsW/GT41VBEe2mgUBvhZwJg+MX9vZCOyS5obv+Eqcbg+81NPh0FPuBuBMV71qVf7q69A6w+tlsTsAxBcXFHSrXzFcKPFi/X0YXzoy7xLz7c+38TAv5XBqL/AN0hJJk01Kruk9NieGnlqcfqR/oYyQXMHWzzYJ/OuBUnmI8hGvzUj9SjFjIVRu8/nGxwc5EvklVzZYZ5yA1v1O5b6U6a8VsBUaGpBOTQaMxTK/XzBVkz3mx3lZ8vS685kca5hDV2OwEhotc9EYAyQy6BmfYh6gNIf1g+O+Kb1Yr+sxWgoVjmvCUwpQnKOqAfVRyb3ELh9RQzRKbzm7WrOhhv9v08ivZ7Vf6WaTaUZPVrzsFKm78eLkTXSlgYjo98/OLA4Nbn6SUBEJidnyWR/6nX7G2+aN20e/uILR2WGkJqJ0+e0WnMQjrx2Q0aX5kafoVw3Io48gOQnwOMBGhWSwHsFKIRdnVBRVpkUGDRAnj91v64GkfHxRaVyuhczVlDoOXbM/vprxnXrjY8+ZnvhBfd7H3iPnglcvjGTnb1YVxTG1kYYqKgc9CIl9AfvZ8LiJDBuhbbemUhQVwN+IdRZAXMM2s6X+2sg/X9DceOfEwMwMv7vavwLFFn/D2porkJ/s39PDIzoEus54fYGMDAwMDAwMD8EWBTvBBbFvxmI1wFeHjRwsgF51erU/ZhYnjglPvakiJ+kMJjWb5jncBInvs3crL7uwsudmcehGqcadWL2AViaD8n7zdwBH9Lq6fH0kEa6TjFObW3bWt3fvyedXX+OYxvo0AoR9ak3Ss98VKO6XnT5ILu9l49sEqAaRYoBw+Cg/RssVofO5EwcrCKXK3hyNV4gu8aTb+eqfsVWP89UJbGFeFm72VRoteZrB76SSN8mEt/qqPyyKqP8QiYzpcvQawuMr6wWzAyHAbcL4HEg9W1GRTjccY1VzxlWIAeN+Qp3El+dI+1tlHeSWL0iDkqlu84ZPILmvZxPeSiJ9FgyYXcWMb+Jq5A4p2wuj0Zh6CeIkbUSdN0grc+n1yxM3EoARufm5pjM0Zs3He/sND3+hOvgPl3O10zc5W4DVIem2ZLVMZDWq81m29oCP2gb4Rrz89B6UQIeWiZ6r0CjgHu0xIxMTS2pNTM4XKCo2HP8uGP7m+bHN6w64Yvu9z/yHj0buJQ8c/P6Ym12GFsWpdXHFL2ASwp17v6bEAkBs16oc52dBei6oD57rEyAdBaq6dL0FrTsM/93UM8G0PqKHoYa6zXvgtqXU65AuT7QGE19wLAU6mi3/AO218LAwMDAwMDA/HnAongnsCj+bbAxgPzfQy2tVxYAViowt7pN8d6EY+GvuCd2K3u3SQ3m93b5r19PnPg2S0tuZOoryIufRnu6oW1sD8z81JKUbGIOjjRZ3B3DzfQx4oG+A2+2v5mNpXyWwWs4z7LrGsW0tupzp8uvf1ItvV5++XN+J03YVMdHtQmxMrlYPT5+S7emwxHN3Lf2a2lUmnKO7rcs9acsRYWAKB4os1oLDIPJSuUnDMa7XXWf194suHSjP6VFi7cEwIcnHra8BJVgoZCBhrpod9c0U2Ah6RQNOlOuzJQrYSO1HSRtq0jVbLBWKs1XuxkflROfTMP/9gpxSwr28/y+xk6J0+if83g9AyoDhSBG1YlRteAAPJwPjK5t4YstLi7w+WP5+c4PdoMGbt3zriLtCKX7TMvqmtJ2Q3qn5gZBX6ryC0LRW00XH5RgEBgagvKE/WQAjQSQjbG2zlhnb6yXGMOSwJjLrbBveyM+TgSqDXAPR+fnl3W6WQIpUFTqPfmlc8cO88ZNoBNaX3jJ/cFe37HTgYuXZjIuLVWlRHpyotTymLwdangw50+87k8K+NEtTX2TBuyDCnIKiqD6nNjjULnOum1QlZes/wet/Mz8V6iyS+1LUHoQPEtNhjb4qZsBcz9UHXTSAW0mhIGBgYGBgYH5GQCL4p3Aovi3oWoLIG+ABoO9gKF7dep+FCrzzxvqfstVqwpL7G9uj4H68R1CofHOoldrv9odwvQCdFpi9gGYGV+QkIxUywjSYmsZbhAEWB/iP3yv671zLfTzWUJUEt2qq6bhCeXHD1aUHamkJyHSL/A7GUJEFb+FIO3njHGzqnjKC2LNJZkuHvvEqod5qrVDMC6IVH0M9qAp12LJ0WpP8Ph7cOgjDTlZSWnEa41qonFs7va2ChPjkFlhekA/XOrGebq4A/VqdbVGjNTi+y3NEjPS6G4eMFX2kc9Wt72d1bUuCff7K4RXUnvPlZGxZNWMbwr0QO+AepBKkqDrRMgaPRk3rFHMjvpj32zp/KYPfpnrk0+M69Yb3nlVcvXjvvavGgezms0327XXu9SZDGurZ344fv0PIByG+jRqNdB/grYWaH0sHgc1b7TboGYVAODe/bHxEaiETCIefiReZfS2mUeH3nvf9Ohjthe3uT/41Pf5yfELp2fSTy9VXIx0pcWohYAcBTg4ULGWO8rV/CjcKw3YuX81DfhUIg0ISiCogiXrob1/rR9C5UBpKYCwFNryZ6VB+/2m3UDoZ1MFBwYGBgYGBgbm+4BF8U5gUfwbAH7/Br9hRyNQb31W6vcmVTgeTqo+4w88JVYgA60maLEmTtxGJDJPQL5V/tnORQIGKn9yjyI332XCPyfpM5Fso0jLIMpdrZqUvtXz1kc9Hx2spadniLpymGZdNb6bUnL4w8rOr8razvWWFgra8AJENb+DreXhvNwyktj1ilDyb2zNfeI/2ep6aicF8zmy4Ebaje6kOiV+wL94+02C4+EhQMCLNjdHEIjRNrK6Q8Lr0pMp5la5q8nkQasMvWxSZVPVl3k1L6e0/eEK4bGr2HfTe9PrqDyeITy7tDAR8Oo0g7Q+Cbpe1FStI2GGVLIZvze21g0iHF5Sa8ara1wH9hvXr9e98bz44geE1pPogYx2w412VSpWWyL38pYjdy8Pc0/AOx8bg8rqsFlQWw6osk4PwOdB1WgmJ7/rcs43d0B96u8T69bPXj0U7bgUo2QB0lrARv9x2t99Kw1IgtKA/EIoDQjtBtwN1G29lQYEI/s/gdLHgYZXoDox+FMAIw1q5aftgG7Gp4YcFfRJGBgYGBgYmJ+SpUh0biWyFpPB8NBC8PYZMCI/wS+N/2GBRfFOYFH8axNZgSzRgIHGijrAwVydvSdDc0NnxF88J+acHbBantky2dySOHEbsViI1rO77NA7E8QeoKcbSmo9GGPD0+I+E842grKoUUNV6gnFyx0vH8F+9nYxvSRdTCjr1w00dbWSig/tqu6/WFp6gobs4KNb+E31wh6xWdRo5nXhxZ4epe9tMe8dlfmkwR6PE3rHUb1z7RCMh/jaTGIbVuFbvuPvs2AwZjYF+8hzzc12HFlEVfSxzW3qoSazBy019DDIxPbC4rxrh6+XPZPc+dsrxKeSez7J6i1BMwxaJxCOLkyM+/RaI71P0lwvbKwaIPS4FZJp73B07ROIRpcNUB98++F9hsc3DGx7WnDmXSz6y055SpsmpV2Z3m9uds26Ehc/IKBxTU4AJhNkg709UD1S0A9BSwRdMTB2T0VfXlmxeaYb0b5jX7g++sT1yb54ON/fbX35lbVDaOa93YvY1sSjHhBQ22Y8q2lAJvRrCEn1PdOAYOT8N1C+Edoi2/EJ1NKdmQ5dDz7KzoI6+836oOpKMDAwMDAwfxVC0dh4cLVYHcx3oHonUFYf2nYrULeNwWiy+nyLcDPeHw1YFO8EFsW/NtJaaMsWSMAAFTu975fypfDSMc7hTzS9L0gHrZ8fGz7+ReLEt4gKaIeL9+/wkXugPgr36JfwXXy2CTHFjLGNIM0SlLtaM658rvW5L/Gntt6k1lwXMRtxck1vS1178ZH3qrnJxRnHuG10UBH5SJQIJ3MJiyQCAUk81KMYJUiGqpSIIoui3OmMx9lB63rhwNohGJkafBOnI/HCoNdMTftlGhOFI2MKyFJDi8HdbBhuF1o6+hi0jhJK+cmc1FO7U6rXXcH84QrhpZTOLwowqF7e2DBUP2ZhcsJnGDAyyNKWBiGiUoPrcsmEU8PuyMqtf2aCNtsEusl69BPD5icGXtjMO7WTVHe0m3e5RZncpS0QeTnzK/OJSx+QmRmovaFICHWzQNRDa0rpNGh9qd93Ly2PLQQj/pmQTD9VWus9dsa+7Q3To4+6Xn0h2IuJeO9XnSXIUkcHBIkDUEpvLwp61zTgzX9PGOCN/wWVCa3YDPWKABURFEXWTejPm74HWqo6qod6/UUfuDorDAwMDAzMT8ByJLoQjqyFfW4J4x67fQaMnzpLFo0B4VhsJRoLRqPg/SyGoVuaW4nMroSnQ+GpUHgiuBJYXhlbDo0shfxLQe9icHhheWhh2TW/7JxbAu/ZNrtomV00zywapxcGpxf0U/O6qXnt5Jxmck41MacYn5WPz0oDM5LAjGhsWjg6zR+d5o1McfxTLP8k0zdJ903QvBMUz0S/Z7xveJw0HCAMBXDuMax7rNc11uMa7XKOdjpHWhwjzTZ/q23kXtFi98Oi+CMCi+KdwKL4VyW0AH2VB7/0xyJQ40T/91QlvSy8mGSs+TVHrWppszz/QhTUle+gEJ4tO/SWGd8M1UqZf1D/GTKOSei2bttIk5XeMlwv8Yuebnn6Cin12eu0+iShtKONr2QhcwpLT7xXLUypyDov7GbzG2t4qE5pv8DDy6YLB/sl7h7pCFNqsukVClmZ01nrciHjMeDEUOzytUMwbNaSFjpeaXSzJHqC0og2D3cOOPv4JgJBim1qElWcIqXvvnrtyhvXkL+/QnjsCmb79fYLpVh8v3huClqXuzg16R/UmZgUaQsClEM1pt0p4U+6nZHQrb8cV4bcPlSZ4fiH+mc2Djz9BO/YW9Sygz39p1sUaQQTyjptvbPx/f1ZXABcTqhBRR8RaGqEPtt+MrRzcmgIqk/zXaKx2MxSZHhyZcC93CeYLGj0HT/r2AZ1tHdt3Tx6aOtC8eGYrBkImMOdGeGq3dHqPfGIVL4XK3s9UrU7EdW7o5UfRRsPQq3e836TMMB45P0Wyg0id0BVQ0nnoJbxsjooNe3iA2ODwPxY4k5gYGBgYGB+HoAmthSJzocjoH2B6gV6F2g1+KFAk8X3rbD67zhk+CZBuRKsyhV31a/Yq34FzoN+RYUUa/x2xQKf83bL6l61rA7HSLtjpNXuB20K1K0mK5R/+7MDbfNBzmb3g88JPjMocuALgS8Hvij40uA9EIcD4P2AdwXeG3iHdO8EeLegFrL90FsA3wj4dkBpFI/NgAIpC8yCMqmamFVPzIF6OTA1D6qmYXoB1E7TzAKooNbZRVBHweeEXhe8/28C9e1D8K2BKpv4uGH+YmBRvBNYFP+qgF/um3dBgyE+IC1fnbonneaOjMGMRwUqpFJnfGzdolyeOHEbJn1B2ZHtqq4aqLH+5K0egPfHpvZJWLYOu6/Rhu32NHNc7E3oTWnkoueuUeov8TWEBppEUX/1Quml3VXca83FOcIusgBRyW/tU7GoQ9xSkniIpHTjJCNKhWJArewTyTv6xd0iXbdYB/0U6bo0xlaru1ukX4su4UAvScTmKWQEoQCNZxcmibP3tKftPXEtc0tS+2+vEDdd7dqd3pZRjRMKVEuLUAe8pekpv1FnYlGkrZAcqnpa7SLuhNMeDt5KmS56LHZUjurkB9rnNw9s3iA+/AY995Mu7Ml2dR5vmDkTvItX35NQCPAMAyolQO2HmnA0IQASAZBKoL72d9XvcDQ6sRB2ja9o3EH2wBJOOlVY6//8oOOlp42PPOx86Ymx49vnG1OjDvmdSbyq54C0/+9+cf1/ApgvoVpHgzjALQTdElicSDwWBgYGBgbmJyAcg3JrC2EopTYVCgeWV0D98CwG3fPLoK6A3jI4vaCbmtdMQrky0HNAiwPNB7QgUIdALwJtDZQl0JpAfQI96v5W1mwfQVt9oOG02CB/A3+22EduD6TVB+oWZ1UR+au6GPcrSQBSLPk4pFjKVcUC70c7CaXybrcs8wxkWbZV0XLOLbnml8F3MbywDL4d72IQfF9ggMoKvkfQXcE3C0os+K7nVqBM5mIYSjCCHwWouOBnkvh0YP5hgEXxTmBR/OuxNAWtEhw1AOElgJ0GzAwl5u+GZcpyRfH1Nhn3kMZme+XVsYKCxInbGHK1Vn35GrMmE7KasQdKKMViMaNkSMx3ttr8CFt7nw9PMpM2NW9KJda+nsqov8DV0usJnIGa04fKMvdVkK4QG5oFrR18RI2wh2fgtxt5XQSxB6fyYCV+s0YmkUiqBbKHWcpqjuYax7QWX+C1r1Twb59J5pjkDVXsmx9VpRzee6103VXMQ1fxzye3H7jZUooiDWgMwdU03dLM9IjJYGZRZa2NoBwqu5ptAnbAbl1ZSjTbiMUiAYdoAJ0qPb1L8/LTusfXST95hXpjd3fXSYy+0TBh+AGN76HypH5gQAsw6NCS3YY6AIcBhALAagGm77I0NLa8Eh2bDdvGQkpXkKlbJg8sYxmzOakj+95wvrgBlEPH1k2jpz+Z666LTt3qFAIE56CuFZJqAPcl1Cow4/9AKpjzX0DNc1CdmP6LAPFroPgRaHXo7QGnB2FgYGD+rgCtCXSktehwQGsCb58BY/THyPxEY9CmvniNk9vTdKAIOeeh9ZCgKRmm50F9Uk3MgU4F+hUoWqB0MX2TVO8E2TNOGApg3VDaDbzJVrs/vs/trgGeAi8ALwMvBh9CHAqADwefBHwq8AnBpwWfHHwJ8IXAlwNfFHxp8AbA2wBvBrwl8MbA2wNvcj4cAW8YVK/o6lsADc2zEFwLUORAC719BgxQ1VavhYH5qwKL4p3AovjXo+8C0PMZNDATAF3b6tTdmQ3NHuMcPqTp2iw2WFOvO3e9/92NcGOjtNqzr/TePAPlEofv55xrRCNRvdAlErtQVhfCjmD6mW0DbZvQmy5ikR+lsRvPUFRcdC9VVX7s/Yrq46Ut55kt/TxUI7+xQYSTWITlEoGgX+TqVYwQJUMmrVwsln/BVW5mSfoHct4roDydxXitkB2PlwvYL+YlxvH447X+tLzmDUnYV5LRX+a1Ijv7rRbryuquwuXZ6VGzwcymydqaQDlUdKCsPOaY1RRaSOTxloIBl50sRl8QXfxA8eYWw/rHZB+8SEvehWk/x3KQx5dus7L7A/6rEwgABgPAYQNdnVARmp4ugMuBWjVOjEO7AW8nBsTmgxHfdNjkD8mcyzTdcv9AsJ8baq+ZS9oz+vEm10uPGR9+2L51y8iFE3N9xMiaW04PQa0FOTlQqZiSddCS0fR/BqqegVrJi8qggjFLU4krYWBgYGB+KYD6pJ6YAxXoXtHtGjXOLDx4mq7/22m6NjuUpkN+x+XiAc6DZ9tX10NiXGP4IWgNJMUDLX1k+yfB5xSNTYPPD74K+FrgKxqnocWNjrkl8E7iSTbwDqegxBqUUgMlLQIn02D+IYFF8U5gUfwrMeUCMv8F+rk0AbXEWL5nORPQUL7mnko1V/+Gq5XR2aYnNq54PIlz3zA9o0AkbWtJOhpFI6EM2AMQWYlqOQ6xYhhptdTbq0WjknJJ+dMtTx9vbT+Wxm09jxGK+noJ3JJDOyu7zpVVX+R3s3lNtbymZkmf2MHNZQgHaWIbRjbCkJnVCjlFKHuRpdzDIWhNuZTe/LfSMZ82SFLx+nvFkxnUupwqp9MZXjXe5bnZUfOghUOXtyNBOQQV0cKhgTPgPHg2GguNTWo0pmZ221l28ofyd543PPaobOez9IvvkjpTtF75D2h8PzUFWMyAgA9geqGcYUcbwGQAugFgdARYa5sRJxqLTS9GhiZX9N6gyLZM0S1TtSEKdwWDCFcdWTj7ROCTx1xvPGl89FHbthf8V6/MkvrCoHZGw8CIDmoeSLkC7R7M+W/IDHN+BdUa7b8MqFsAvwaqcwsDAwMD87MkcrcEnX8JStC5vtE5UKv0q3VKlBOzskAiR8ddNTraN0bXah9B23zQKsp7BMqaWI0ZT9N1OqE0HeiBxOEA+Azg84DPBj4n+MyS1dWVoHYOTM2DJmmegVZRur6dpptZTdMtr6bpYKWDgfmxgEXxTmBR/CvRcwQgX4QGaiRgo6xO3Z2agZocU/Z6oap20G7etGkGh0uc+IaFBXNL7gv1pz9ZaUYCalVi9r6sLIfVTBtf7UHaDPWOCk3AlMHJeK71uU8be66kC7BJaKZU1tvaWXL47SrKlbqqTHEvVdBQxUP3KBhsJ6eMJHb3y+0YyYhUpZdKpXXQclNFhrjKaMyqLmwuYtq23uh5KpP+SgE7Hi8XsLcVcNYOwXjoGrmqsmnMYrRyGfIOFCiH0laEiUUZMeqXZiBnnl/0Orx0nqGyv+ccNe1j2a4XDY89onjzGea59zldhd4xW/yNfD+gatptgEQMEPBQedIWNEClQJ8SKNu3lUWFWIlEJ+bDjsCKZijIsyz365Zp2hCVE8Y1ROo/i2atWzz/UODoS+63XzI+9ph161bflaszWFx4yAYVj4GWkp4Aap5fXUr6P6GWg537AG4uYCZDGUUYGBiYXwQLqzVI7h9/nX1ccZcD1QgUpG9cLuRZgFJzjm+73O3rLb/tcuP4oQDGNda9uomuxX7PTXSQ760WLOlyQqVK4i4XX3K5lqAT3qZz4IvqpuaZ3knCcIA0PAFKY59ngjg8gXUHKN4Jqm+KBtVfmWT6pybhJhAwMD9vYFG8E1gU/xqMGqDdiUtTwJQT4GYC0Xv+UyEfkScrz74u436qtbv2fOT5+nTixDcsL/swtS9WHftgsbUZSpQ9AMHFFQXNwtf7kHYVaInGCfcp8qmX215+u6I3K01Ay0KQpaauqoLiz3dWMZM76+tFPd0CRKWwi6rhYAe5vUTxEF7pxkr8arVaJpWf4io2sMQ4bY5annXqRr9mBPpX+XxWUWa3sFHojEcyTv90NmPtEIzPshs78q9LmhuMDLLPMLA4NRGJLI9MyNXOdrKxCEM8T836VPbhVkgOtz3JOfOxuqt6aTqQeAP3Z3kJcLsAuQwgk6BVuGCAA/AQnARP3UZsKRQdnQ1bR0MK1zLbBJkhXRticML4umjDkVj2H2NZv17MfHP83B737rdNj62zvvCi98KF2daqiAAJVSFq3wsUPwZp4c1/A+pfBoinAXk9VMB2JbF/EgYGBuYXBqhG8TKP8QD1qXk1G7YWyG96uIGyuBKNxdse3F4NxbsYHFqA2glYE3vnFgYm50G5UozPgqIVX2kJqhcoYOBr9Q1/d/vct3QuvsCyzT4ST8eBV4LXgwYYz8jFN87xVwufxJdZQkm5b/bOgTcQX2m5lpcbXy1kMrtawiSemovCuTkYmH9sYFG8E1gU/xqg34M2rcWigLgY8MoSk99hdHH0KPfgsYHOx0V6e0WV7ZVXowsLiXOrrKxM9bW8WnZw53T7ah+/B2BxNijvN7PMI0iHqN5WNTg6dAB3YHvHW8/mYkrSRLyyWqLE2ZL1VcmpDyoZyZRWAr8VJUDUijD8QX69iC+hiuy9Cj9B4lEolHSx/FWW/G0OXmnMpWHKD1WIR5dCvc7RLpWzurCgra2N8Q34fnoDhp44WCUvN1fOYszOu6zefralqseU006/TMvZJ/l4q/7xx1QvbJacPmDvRkYmHqC8J1Se1AMlCakUKGGIqAcIOEAsghKJs9DK1QQxIDa3HPFOrRj9IaljmW4AzTDI1K6wuWFiXbTpMyDr10D2f8YQOxarvxhPO+X+9CPT+g3WZ7eMnt+/UPNVpPMEtHw051eQGRY+BDUtZNwA9L1QS8PEbnwYGBiYXwjhGJSvA5VpIgjZHahSoFOZZxZXd8eN4Far/981QJG73eXi2+Rut7j4Tjn6besqxWMzsts2y4HqeLvFeVb3y8VXV06HoEKU8S1zcP1JGBiYvwKwKN4JLIo/OU4u1DsxtAB4pJAo3qObXzgW/px15Ia56tdcjVipNa5bvzw4mDi3SiSyyMS/XbL/bX8rCiASoNIs38f81JKUbKLZRlEOOsJWZ/AP7ere9U73extTcdUpQn5zC0Fkq7/4UcmlveXEJE4PnYes4zUixASJmVNCFxqZElOvbJQqd4glUgS03FR+TVRpNuVWFXeWcuzgP/Boi09ElDoq65vKipElhajyknjUFOZnZWSsHYLRWJTXxs5tZ19l5O/jf7p1YNMG7ZbN2q+OjHa3r/h8idu9F5EItKVQNwBtL+xog4rQYHoAAQ8wm4CpyVtFaCLR6NRixD2xovMEhdZlqn6ZogtydCtcXoSMiKI/B7J+BWT+K4B4DSBdXOouGC+8OXT4iGXTuqE3H5u5+Eqw4LVYyWYg/X9DUbUF6mgvKgccbLj2DAwMzM+cSAxK5cVb1QWWV/yrphdP4g1OLwxMQdUv44VSQFWLl74E/Q0UuS7naOs3KzBBwYtvmSN7xkGv4/ih1ZXgNS1Q5nCk1QH9BMcoG3jlaJtjpH11ASfohN7FIFz4BAYG5pcBLIp3AoviTw5oHbJaIBwEOOnQ0tN7kCPPLrTkbBSpiy1D1q3bxmtqEydWicXCAube0gNv21sQQE/3d4ugfpfpsQVxn4nsHGtyYhutaO2wbXvH9t09n2xOJSLPkLg4Uh9fVXlqZ+nNw1VdaSI8mY+o5qGaZf0iC6uWJHbTZINYyQhPYZJJZBf4inUsYZc6T6vMP5VJUYzMkIYD7bphf1NHXQ7iD1cIzIZkduU1dmVKPFj5Kf0lOWuH0ExlsvzKXt2TG41ffjbZ3h5yuhJ3eVfA7xzj41AxUi4HKkwKmmFXB8BhAQY9EBi7ZcihcDQwF7YHVtRDQa4ZWkpK1YcEgyt8QYSCiDV/DmT/N7SHsG4rQDoLKFBLLMxEbY3v2Mee7X+c+OQ3S1ceiWb+GkoYghqP3AFVo9G0AiMDcO0ZGJhfNqBKgR61Fs75JcP0/O0zYIDelbj6Jyb8jePF12rGa6jEN91Bjrfa4WDN8Ri+SYoH2gIH6hwodaCnNd+WzQOVDxQ/UO2IwwGKZxy8GHwI+EBZYEY9AeXu4q0L3POJhZfgK4Kv+4OaEISisXF4lx0MDMwvF1gU7wQWxZ8WfS9Qsh5quW7tBzSoxOR3oLlpNwYu7VJxdmtsQ1+ccO/b/+1uDTGl9ETZkR1aZCXU8W/5Vsf5ezHunRWRjXi7r8nVhjJ3S22GrW1bP8Ue35LU13Gqg8pWUNn00s/frkCcRLWXiAk90KbEDqyCQdVx8X1iV5/chpGMCOUatkT+Jlv2JpcgMxYwCdUHq0W6SSiRyOofcFQ07ExpXZ+ETS5Baj583fbmG869exPx8cfOPR/dOty717jxCXnFF4mbuyvT04DFArUxxGKg8qRtq2trtRrA71uz4thiKDIyEzaPQJsMmUbIDBmDIYk5LBJFGKhY23Eg/3fAjX+CzBx/EmpY71EsKaUz1Tcmvn5l6uCvlr7612gqVHsmmv8I0Lkf4OUB5n5g5s6KsjAwML9sqN6JbucoYSgQD1C6MKuLJNcCbUvsu7s/a473TR7vu46XaHgQz+Pdx/HAAA87HCO9rlH8aqs6mneC7Z+MF8BUjM9qJ+fA5wSf2TkHlb4cWVqte7kSXghHQHmD03kwMDAwPwqwKN4JLIo/IaAfgpao64aaYbBSoMYYd2NobugY9+AZQ8cjAp2jp9f81NNQ34XbMOhSqr7czq3MBtBIYD7RXfA+jLqnRFRzr3Oo0dXYMtjHGZQ+3/r8x91f70ih4840EoXWfnxD8aG3K3rO93S1CntQ/IZqcS9Dy2kW8aVMkQkj9+KlXrFMiRLIHmNJL4gqTaaCmrLOCpGz3zPeNuh1IvD1ueg/XiHsSGvtp7OW5mY1779s/NPD94nBhx42tibFby/B/BzgsEPlSYl4oLEBemsUMqBUQD0hV5vvQ/0qZpcinqmVQV9IYl+mQZsMl1nGkMwWlkmj7OZY90mg6BEoJVi+CepSKKmGSsvM+kMs9ELxgYXLG4Ln/iWW+j9jqf8cvvlYuGkfIKkFhmVw7RkYmH9wKN4J3dRCvGLnXQMUOVDtQMcDPY2TyONB/Q+w7kTBzDsqrMSjzQ4VybwjoScam5YFoJIqd03oza1ANVTg3XcwMDAwPxNgUbwTWBR/QuT1UHYLZKAVMBNXp+5kKbx0iLE/y1r5G66WbbSa1m+Y53AS51ZxOqvrzrxOyL4CNCGAycnE7L0ZNgeEdGun04Zw1XboBQQV9emWp/e0pX6cyqSk1BHFrr7WtKKDOyv7rhKxRC4awUfUinEiPbOGLjTxRGqMdJQsd8nlyis86cMsYau6QKcuOp3TxxqeaLX5KUyLpaz23dTO9UnYM9nVw8PDo2aDBF0nZZQy+Ck4QTK98Sin8IC46DPNa8+opGW3x6xPBwy5IRXsJwMoJPR2SARAKgGcDiDeWz8cjU4uRFzjKwOeoMAKdTLs1wW55hWlM6xSRQUdQO/XQPlGyAxB/e46CAhLACcPGDMAg/hw55mV3GcjKf8Ong1f/ueli39YzNkZwufF/IN3dtKHgYH5uyL6TWsEUKtAuRr/ppYmaFz21YIra9vw4k0R+KDd+SG7i+/Eww8F1vqVxwWvGSrdCVXOvFc023zE4fG1hJ4SSuhB7eyss4vO1V52awm9xXAUTujBwMDA/GKARfFOYFH8qQgtAPm/B+xMYGYI2p0YvvtCpou886X23C0S7U2rx/HOzpEbNxInVhnxYxFJL7cnfRlD1IMHidl7Y9f4hGxbq8PQ4Kro1ipaRZ2b0Jt2ofJOp9DpZW0kqb23/HjR57uqqSl0Sh8HUctHIiQkiZGB6hO7ODI1RjJCl5t4EvkOtnArmyA2FPOotV80iWneyTaLz4ig1eeh/niF8M6NdnR719zUpL4fL21FOAYodEW+pu6C+4OdoxfPR7TaUF/f5NmzUBlSlQJgMwEcBkA1AYg6AIcFRALAagFWeycCyyvRsbmwbSykcgc5q/0qKDpQEVe0w5GBgaikGyCcB6qfA9L/Gao72r53dbEoBZJDdTNAvhSrfCmW/q9A2j8Fz/3L9KF/mzzx1HTeVwtsSmzpW10xYGBg/pqA1rS2JjPe8m50KeRb7ZHgWC2vYpxe0E3NayYTDRJAGYuvzKR5J8ieRHeEeOIO1DnkdxJ3KCvU467DMQJeA14JXg/aIOiEoBmCfgha4mpRzVnVBLRcU7+ax4MEb24JvAHwNsCb6fOMg4aZuN27gXOPPcjSUxgYGBiYXxiwKN4JLIo/FdwcAPE6NJCWA8Oi1ak7aTO33dQlfarlb1dYvNk59je3x+KrLleZmOCi0l9Anj200lALuO5b/WW1/otF4eHzHCiHotFVgVHparj1m5o3vVlbkXOVROlkUWUq1PUPi776sI6WxaF28hsqhe0tMipby+7rF9kpchNWOsKRatuFsnVM0VeCaqOpqKmho1bhbrP7iSKXoaj2vdSeDUm4UzerBwYGxmxmSXO9gUbgqHNaxKkLRTmxuhrIZhENiWiohwKaWZ2srwU8nthCMOKbhjYZypzLjMF4+Zmg2L5i8EaMxpiSAJCvAA2vApn/AuT9Bmj5AGDdBPQ9gJEAiCugMqRVW4Ab/xRL//dg8kNTh37j2/5r797XxnKy5zmcO/qIwMD8gzMeXAEFLHHwfURiQDCaaH83GVwJQK3Moe12rvkl22rvu3gfc+XqjjvRGNT4juWfpCeamCc23cW73qFst4wu3vIO9L21ruXgxeBD4p0S4i3LwSdca5MQ34MHKtxaj4TAcrzNHbQTL94g4UfJ3YFWCd4MeBvxAAU13r9hLcDbhkURBgYG5h8QWBTvBBbFn4SlKajDvk8N+FWAsOCunfcME4YvBUcuDXb8gTdgE4pN6zcELdbEOQCYndV2lDxTc/zjJdASTcbE7D2IRWODIrdA7GpyCJDuarzKnEPP29y8+cXSuoYrGBJFy5Wzas7sLLn6KZJaxSM0ChCV4h6Cgo4V8OVs4QBOPoSTeqVydQpP8EeWqFFRYtCWnC8hE12BNptfgRbWZzc+dIWwM72zvKZhfHR0kEoELdEi6Wg2Z7FyPtGtfyzU3Bsm8sI0aTxWaLIQRbl2CEUPLUQQQWZIHwxJHWGjP2J3xLT9AD0NQL0DZP8nkP1f0ICeCkhroDo0rEygbQ9Q9DBw43/FitaF8rfOXN7qfecx28Y/QnnXzMw5BiN6e9dEGJh/bMieiUaL9z4BmhseUrVx0JFAbet0xruZ3/I68BCUPVD57pumm7tXmm6t691SBFqNmbitnyXexSB482thnF5QT87dPgPGYvgH1AKFgYGBgfll8AsRxeLi4l27dsXHPT09/2MVcHCvmfsAi+JPQv8loPsQEF0B+FnAuCkxeRvTwelPqHvyrOW/5Wlpbp/lmS1Tra2JcwCwuOjAIbZUHPlguqEG2tR3XyLhqI7v5CuGEI7+5qGGPoXjal/S0y1PP53b1HW1Fc93CCWtpZ/vLCs62kxp4bU18hFVYixXQ2ulC01ioQQjHSHJ3AK5aieH+wKbwDOUCVm1F3rknY5RvMarzW98L7V3QzL+5M0aDocz7rJLWxoG+rrp8oxO1hXla08Ovv7ykkYTYkEFSIMc8zLbBP4M0mVBAgYcxwOap+rDysGYkQlwcoDWD6HypDf/Dcq4ki8C3DyAXwANGt8Asv4DEuyGV6Ntny1Wnwhc+NT6/Bbjnx62b98+kpY2298feYBdmjAwvzCWI1HQwUaWQq75ZdDQQFWTBma4I1OgyOGHAl3OUdD3mh0jSKsXtboyMx5IixcJ/YQG4CGogtqp+bum6RIvAwMDAwMD84/NL0EUpVIpKIFrogiOwZn45L1m7gMsij8+sz5o8eSUC3AwAEVdYvI2YkDsBPN4hSP/ZdlAksUzdPjI8PFbrSOCwdH+rhdK9u0cq68BuOzE7D0IhyIatp2jHm5wdnd6WvsVti/xJ59ve/HJdDQps53AdwiYRUUHdla0nGljdHGa6gXIajFBoqP2ksVOgViMlYzSZOYekXQDi3+M32CyFLe0dtRrh1ptfn6nqj6r9qErxHfSe3KKyodcTjOLKkbVGdg1KH2m8PL7+vWPjVaUAStQT60QiRoe8ETcE/GIirBA2TNrh2AEGZpo8etA3TYAdxKgXgOoyUDv50DVM9D+w6JHoOQhMyPMrptrrvCeO295/gVQDm2vvuZPvjZDJN5RAxYG5hcDKGnz4cj48opnMWibXdRPzSvHZ4Wj00zfZN/weK9rrNUObdJrs49gVtugs/yTorFp5cSsYXrBPrfkXQxOBFfAZwCfBzTJ0aXQ/eNnnuiDgYGBgYH52/JLEMV169adPXs2LoqgDYKH8fk1P7xjJj6+F7Ao/vj0HgVI54DgLNQSY2EsMXkblZrKfGPaUZ3gFbl5pAkJelF0ZiZ+KhyeZfa9VnZwp7O2EqD0379iZ2hpRcWwMQaHGhzNOC+WIrUc6D2wreO1l661UGqIZMEgo/ty0b53qzCXe9nt3IYaUStC0ifUsBgUkY0hG8BIRvjygRs81h9Zwnp5mdFQch3JgHYkmkeUea3vpXZvuIo7lYPAYDCjTpu0FaHFtfTLUrGYM9pnnzDueT8U3za5vAywmSvNeSvttbei6lQs43ffmmmvjTYdhpaSZvwfoOZ5APsFIKkCnLwVm2G6u9t74YL1xRdBObRu3ea7fGUGg13x+VbfIgzM3yXg/7dL304DaibnJLelATudI1Aa0OaPN/SjeSd4I1OywIx2ct48A3VQAL1uJhRe/mu1fYeBgYGBgYH5uxfF4m+Ii2JPT8+aFoID8PC7M/FxnNHvAIvij8yoAUonzo8Bhi7AiE1M3obIJzotPnbD0vkbrtaqMxgfW7col8dPRaNBAefd8iM7DVUlAJEAHsfn78rSfEhOsZBNzgYXgj7C6pcM7O7+8JX2HR8nt/Z1i5liEa7q88KD79WQ0yhUlKChStzVJe1n8HlKjlBLlLuxMp9Apv6QTX2STeboKyTCuiyWocXmZ/eZ6jPK/nSV+H56d1pOoUGns3IZImSNhlzQokqTHduu27hhsqszcRNmE4BshDrj5/0WuPn/gJzfxiOW85tYzq/XDqG48U9A90FgRAc+aMXjme7t9V26bH1pKyiHoCd7z1+Y7uoKud3xZ4WB+TmzEo3NrUQCyyvDC8vxNKBifFYwOs1IpAFH42nAdscI1j3W74G6LIjGplUTUMN0x9ySbzUNuBCOwOk9GBgYGBiYnxV/36Lo9XrjfvjniSKdTv8f32Hz5s2wKP6YQLU6M4E5H8BOA1buLMU5uji6l7a7yF7xsGAA6xm1vfLqWGFR/FQsFlFIDlZ98bawMBPo6QbC4fj8XVmYWZZQzHibtcFVJZ1QkUSSHZ1vv9L+waVrLQSGWaDqR177uOjz96tZxcxuhBBRKcbQ5OQ+mtAiE/AwMh9Z4SKJpZuYzAN8lNVc1NXX22j04G2jgoKuD1Lb1yURzuajEAiEx2qStzepuhB4SSq5/qhu0zrrqeORiQnoDmZmADwOaG2OmhxBvmUl+XexjH+N3fyvRGT9Zyz7P8EBkPWrRNz4p9nM/b4rV63boNb8lme2eE6fnmprC9ntq28IBuZvTzwNOBUK+5eCzvmltTQgxz9F8UB1MjscI6jVSp49rlHicIDum+CPTsvHZ3VT85bZxaGF5bHl0OxKGF7hCQMDAwMD8/fI37cogn6Y0LtVzp49+92Fpt+diY/vBZxR/DEZkgC5v4Y6KMqrABc3MfkN4Wj4EP1AtTP/DYXutHHIdzXJuev9NSHU6c7Vfv0WKf0y0N4Kree8NzPjC2Kyucehr3dVaMdNBAHr5fZXtjXvq8xsw7Ntcg2y8uSHRac/quDVsVAIIbJSjBNoyRSS2C0VcTCSEbZiMI9PfojFL5XUGwdLCvvFaJuPzXPUXi/901XC7hvd17MLuBy2lc8SNVYrcBktwhT1By/pnn1yXiCAXj4aherrIOoBkWRFM7RMN0Rc4yMHX54tvxAWtCaCWhzpOH7rUNDq27XR9vTjw1+enESjg2Yz3AQf5kFYicaC0ehaTITC+un522fAeMClmaC8zUJpwNDIUiIU47Og5oGyRxoOgOLXYodasYMquJYGFI/NqG9LA04GV+BKmDAwMDAwML9gfgl7FEHWMooga34IDu41cx9gUfwxqX0J2no3qgMEuUAskpj8hkxpRpE5/bRRvEViHKXRTE9sXPF44qes1hzE5de7k76KoZqA+fn45F2ZGpkXkk0dTmW9s9w84ezl9T/f+sJLyMM9ZTgiwyATF5ccfr/k2v5yPoqDqBW11IqJEiWDRxHZOFIVVuLny9T7WLjH2TSatlaiRFaq7TjnGLW0//1rzeuSCMml7aWlpVb9gLwDpWytwopS6Xn7DY8/5riRHFtchF7e7wc62gBMb9jgBhVxZcADjDmApu3uV5+wvrTNvvPde4Vp46YFoXD1HcDAPChU78Ra84Z7Bahw8TTg6m7Au6cBwcviuwHX0oCgJeqm5q1wGhAGBgYGBgbmG36BotgDt8f4mTCIAwr/BISXIUscgzbj3Q7NTUtSnsmx9/yGqzW4hs2bNs0SSfFTHk8zKm0b6syRMKIOuG/7h7HhaSHN0uzgIVxVznFfB6f3mZYt2+pOEtu4/RQxvy+t6MC7ZaVfoLkofkOluAMtJgl4PDVHoO6X2YnyYayI9SSLupvbPWAs6+RRm8xensZXk7qaSEzrvllYjsNirUKOEFEp6brWQb+sfWPLwBsvLhtXuzgGg1AJ1iZEVKkL8ixBgTU2MQM1xM/8V6D6ucDu3zhfe9753i7Hm29ZNj1pfmKj5ekt9jffcu76IB6uvZ9AiUQYmB9Cvwdq6S4cm75XtK1KIFQU1HWrKOjabsB4UdCF8J2/soGBgYGBgYGB+S6/EFH8EYFF8cchGgFK1gPaDmjFqbwqMfkNjhnHAebHpY6Kx4Q6lCfg2vOR7+Kl+KmxMXJ70bN1Jz5dqq4ARvzxybvid0zyGVakox/prh8aG2tioTY3b36z7DyRrKdTyWTEucJ9O8vbzveSmgSNFZJuvITIpwktSiEbL/PQFZYCfs/vWMJccbPeXFsjN3bY/H2NnPeTmx69SrhR3JaXl6eRSxUdSDmqBMu/zrv8/uDjjw1VlwKR1S/ZNiuAQgI0RkjhXGYMRoYmATsTKFkHlK4Hcn8FNG0Hxq2RyUnflaumjZsm6utjqz0zYGAehNmVyOhqaVCo7/nEnHC1KgxxtT1gi93PHpnSTM7dKzqdo77FYOKJYGBgYGBgYGD+AmBRvBNYFH8cFAigagsQmoNq2Mx9q7XDYnjxI/KHNY78d1T6wzpnoLTU9sqr8ZWcU1Ointonyw99MFtVDsS7TdwD9+Aoj2tFOLrahlvdPn8lvepx9BP7SlLwLDufj25PP164790yYhq1DSForBRh2AoSjyR2KYUMrMTPUagOslsfY7H6NE0sHbHBOMy1jjVcr374Cn5PWndReV1TU5ORzxaCD+y81os9q39u08CHb614V9/F7CxAxAMtzWGVZZlmWNF7gSkf0LkPyPkvoOQxoPAhQNcNyuQkGm3e/KT37Lnw2F3agcD8wxIDgIUwVCB0aGHZPLMIqp14bIblh6qD9rhGm21+tM3X7RwlDQeYPigZCIqiaWbBPQ8tB51biVA8E4Qh6FQ86N4Jqmdi7RAM0CRhUYSBgYGBgYH5UYBF8U5gUfwRCC8D+b8HrDSoH4ahKzH5DV+zvy6zZiWZZU+KB8fkCuO69cuDg+D83JyB0Lq57MB7Y+XFgGl1eec9sKq8bJG13onGeDF211AuJX8D+vFLFXl4hkUiKqk9ta/w0HtFtBxWY7UIXSXCiuVUEUVk40skRJmHIOp7hkV6j4OTDTa0DWgRxmFKl/SjpPqHrxBzCltzc3JYNKqqs0nSWIhhpYmPbddv3jCKXe1+EY0CahWAqI/yZUGOKSiyxWYWAFEZ1AajYjOQ/r8BahIQml+Uy+073gZjrckHzD8OoAcuhqMTQahfvHV2cWByXrq6ObDfM475pll8p3MElD26b0IwOq2amB2cXnDNL40uQdsCV75vW6BnIQjq5VooJ+ZAw7x9Bgy4wAwMDAwMDAzMjwIsincCi+KPAC8fQLwO9dZnpUB99m8DPYhOVV8osvf+N1c7MBKwPP/CRF0dOL+05Kb0Pl164N2hsiJAcU/FisViJtkwU2qsd9bRRpkWqyO5L2VT85O5TTUkglzKyyz5/MPiE7uLuVW8xjJRa4MIL+dytVyhmiY1UWTWYl7971n8DFG3yIJBDLqJlpHKG02PXsHuut7T1NBSWlKippOFiAp+x3Vi3ReGTesHvzqUaP0/Ogp0tsew+JDYuswcjHimgGEpUPkUUPQQkPUfAOI1IGAKBwLe8xfMm5+cRKESK1RhfnEsr9aJ8S0G7XNL+ql5+fhsvGU81j3WZh8BPbDDMYJzj9G8iQoxhul5x9ySfyk4HQoHH7QiKQwMDAwMDAzM3x5YFO8EFsW/lKUp4Oa/Az41oKgDHMzE5CqagOYQ+5NqV9XjIn3V0Njw8S/c+/aD8hcKjTMpL5Ydem+wMBeqEHMPopGoXuiiqfW1jkrRuEJnGDyBO/VU85aaXiy5l8bFpRYd2lV0bm8DrU7QWC7ubBcR5FShVcln9slcbJnkCLvuERYPr+7oNSrqDUNkhv7jpPo/XcUX5DUXFxXjsRhlW50EUYij3FB+uFX/wuYpIQd61VAI4POApsawYGCZpl8Z9AFzEwDuS+g9Fj4EFPwBGOgCwuGJBoR50ybflauR+1bfgfmZE4rGQKPzL4VAuzNML8Q7RoDWhx8KgAYIeiBog6ATUjzjoB/KAlClUNvsone1V8RSBPZAGBgYGBgYmF8OsCjeCSyKfymUq9CevXETwM8Core65E8Hp3eR3q115n+iNe7W2KY7u8xPPR0OBCKReT5ne9UX74mzrgOU/nt1FIysRLUcB0mnqXOWD0waFRrVIezRZ9DPIahcam8zsfJK0cFdJTcOYXHVwsYKcQ9ZgpeRxG6tgEKUDuEEbc8xsW9xqCwjFmVy1qqd5dlt66/0vpva21GLzMvNFRK6RYgKXkcWNe+Q4fF1lhvnQXmFXtVhB5pRUQo3yDaGJI7Y3DKgRALZv4Lq1qT/M9B/GVprKpHYt2937Hx3aWBg9U5hfr6srDYPXCsVo/p2qRiU1ddi9/eu1gtl+6ckgRntJNQxYnhheWK1ZyDcLwIGBgYGBgbmHwdYFO8EFsW/iFkfkPkvwLgFEBYAfnViEgCisegxxtFyW3amXblOqB+xO03rN8xzOLFYSCbbU/f1e9SUiwAeC+0DvBsry2E104Yxy+pc5dYpl1gp/bDnkxdQ21AMGYOUi046VXRgV0n5SXpbmQhZKerliykKisgmEfH6JaZSTsEf2Pw0UT/RIkIMOtv55n3J9Q9dIRRno6rLypsQCBmqXIwowpNuaN98VvfWC0GLBXrJ+Tmgjxhr7wzxBpdZxohvGhjVA3XbgNzfAFn/nlhr6vd7vj5tfvqZqfaOe905zF8T8L/BPFQqJnRHqRjSaqkYNFQqxg8OwENwEjwFXgBeBl4MPgR8IPyfEAYGBgYGBgZmDVgU7wQWxb8I7HGA8DUwLAKk5YmZVUrVpTd1SXVuwn9ztZKJGcc7O0cyMsEv9hrNscbLb/deOhXr6gDu0UMiuLiiYFg7rJw6Z+XQlJcj4e7ofO819FsdfWIm+XL1FwcL979X2nqF21wkbqkV4BQcro4rVLHEOpaEdZxd9BBL2KmmNFscRWJDSQluw5XuHSndhApETnY2tbVBhCjjduSyUj8Z3PCYrQK8JWgfJKDVAI2NYZZimaYPm0eApTkoeXjzX4H83wF5vwO07bGVlfHqGtPGTf7r1yPT06u3CfNnop6YY/gm16LfA0rd2O0zYEwGoT8b8ZKhE8GV4YVly+yidnJeEphh+yfJnvFe11iL3Y+y+rqco8ShAPgQIVQqZs4IlYpZXi0VE/neUjEwMDAwMDAwMDBrwKJ4J7Ao/vmMGoCb/wZMewBOOjAzlJgEAJFPdJx3qMFd/YxkMN85MnrzJiiKoGsZjVdQadtbTh8NNyOBpaXE1d9mcTYoo5hb7KQGV51n3EcV0l5pf3Nny4eYLgobe6X0yEeF+9+txmQI0aWiNqSAoKYKbSo+gyqzEIWVL7C63+RyMUYJyuzMYegOJjf+8QqhLKe5ubKqvLSEX58rbizq60jRPb9Zt/uVZd/qDQcCQHdnBEcJMgdDMkdsIQjoe6EKrgV/BG78E0C+BARnF/gC22uvOz/cs2wwrN4jzF8ExTvB8E0px+fuFW3OkW7naKvd33SPUjEjSyG4VAwMDAwMDAwMzI8LLIp3Aovin0/rboBxA7CQgIG2xAwA+BZ875OhrYlHdOadKuu8WGxavyHkdDkcxR0FL9d/sS9YXwM1J7wb81NLIoqxydGFdDd7/J4+AfmFlq27Ow8R0W1kRFLJ4d1Fh3Y1E3JEyApRJ0aA15LEbj2fQJNoyzlXH2Lx06WiVoutTjmQVUuCEonX2piltfm5uT21BcL6Uk57vuD0+4NPPu5qKUu8nkgYQ7eE2Log2xQdnQUmHQDqbahBYua/APUvA6P6Fa93+MsvLc8+N92LuddeSpgHZ24l4l0M9rjGmixepNXXZLkV4OFagGcts4twqRgYGBgYGBgYmL8msCjeCSyKfyZDEiD7P4EZN8BKBZYTqzFD0dAn5I+rHXnFTs0jAp0/MGF5ZstUe4fP19VT82z1Zx/NVpYB4+Pxi+9gemyBRzcgnMi2oR6n24UVYLc0P7e/5zSppaAz82rJ5x8WffkhqTdX2FQp6mbxKVqKyKYSMFli3JeszIfZMpROibY4k4miwymNf7iCL8luJlZU5uVks6puCBElFMQV/ZOP6z57Jzy1+uouJ9CCDlPEy1R92DoKhJYBdha01jTnv6FNiZrW2PJyoLzC9MTG0Zs3o/PzqzcI8wNYCEd8i0HTzIIsMMvwTWJcYyirr90xslo2ZlI6PqufmndDewVXJoJ3CXjVKAwMDAwMDAzMXxlYFO8EFsU/k9qXAFE5oEUDNkpiBgBSRam5hlS0p+83XC1/cnbo8JHh41+MjzNw6M2l+98fLy0EvN7Epd9mwj/HZmvrXbVYL81isXQIujahNp/EpFK6kxpOf1ly7MPSy/tZXbkidLUAJ2NxDDyhii+WE/g3trE6PxAom822doP2DIL8xJWuN5PRorK6svz8xqJ0Xl0hu61QcmjH4HObPJQW6JUWF4A+UqSLuEzXhxSu2FIIcHKBwoeB3F8DN/4X0HceCM7OMZm2V15179sftNlW7w7mfiyGoyNLIfPMonx8lumbxLrH0DaomihhKMAdmdJMzjnmlsaDK2E4JQsDAwMDAwPzF+Dz+fB4fOIA5icAFsU7gUXxz8FIBAr/BExYAW4mEA7G54gO4lfCY6ihhhdlxjSbb6IBYXn+hUkft6/ridL97w3lZ0PNJ+7GqHuKzlPWuSpofqler0cJm59AP5HUld3fea7iyL6Sz3dXZB4VtBeLmut5RA1FaFPzGWwpr4b9xSNsQZ5Gj7K6ypiMfWmo318mFGY1csvLszPTCcXJwsZSRvl5w+Pr9Gf3RBbnoLWjA9pYc1uQqg5yzNHAHDDnhxp73Pw3IOP/JNaaDg0Nff659cUXZ0l9iZuDuY2lSHR0KWSdXVSOz7L9kzj3WDNUWdQHyiHLP6kYnwVPgRcsw6tGYWBgYGBgYP5ivF6v5TZ4PF5JSUni4BsWFxcTV8P8xcCieCewKP5gohGgfCOgaQHExYBXFp+zTFl29b1T5yo4bbK9IjfPDxqNj62bkPZSiRvLDr4/mHUDMOjjV97BsDlAlUjqnGVCv06j0VRyqzegNmS1ZPchrpQc+LDo8K6GqjOi5nJRWyuXpCOJ3UYBni2s+5qd8rxA22h0dJsHjiGwm650vZrUJC5vRBTkV2RdY9UWcFqLFe+/anz1aZ9w9TdP4wGgp3eFwIPWmtrHIGlUIKC9iFn/AeT9GlChY4uLY0XFpic2jhUUxO5RaOcfimA0GlgO2eeWVBNznJEpwlCgxe5HWn09rlG6d0IamDHNLPgWgwvhSOIBMDAw/3979x0cR5nnDXyr7p+rutt0d3V3e++xCxvYW/DiRDLYrInLwgIGw8Iu0WtyNF7A2cpZVs45BytLozBKo5xzGsVRlkY5a5T7/Y2edjPu8RhZlo0lfT/1lOrpR909/TSj8Xx5up8GANiuFhYWZmdnp6amxsbGhoeHlUplb29vZ2enQqFobm6Wy+W1tbVVVVVlZWVFRUX5+fkU+TIyMlJTU5OSkhISEmJiYiIjI8PCwoKDg/39/b29vT08PFxcXExNTfX09PSvqrX1yuMQsA4IimIIiteszFcdFCkiUlBUP8KAm1mceSHuOVeFpXdX3S+zqztGx1see7zHyTAz9QHn9w4X6Z3iSvg8KdJa2RtXlk0psVLZUlpaapJqttNvl5XfuYvm5+z//hfr158PDjyT5+OYHybJSK5Lzm+pykuOz/7sicywz8safJs6L5akvGgU9MsTcWb6jjVOTmaG+qEWJ3J9HDPNv6jfeXe93lvc4rz6IRy5OUuhcaqUmoXyDm5ukesq5ux3cmar15omHONUYxPJyc1/ONjx1tvzCgV/ZNvJwvLKkGqhbXK2cmQyq380vmuQzTgapuhP6h7KHxirHZ3qmlaNLyzi4lEAAICNdfMvp2xvb29qampoaKipUf8/evoCVlhYmJeXl5WVlZ6eLpVKExMT4+Pjo6OjIyIiQkNDg4KC/Pz8fHx83N3dnZ2dHRwcbGxsrKyszMzMjI2NKchpo3b6raWl5YULF+zs7BwdHSn10eZeXl6+vr70xZsCIcVC2j+9SlxcnEQiSU5OptxI6ZEyJB0MHVJJSQm9HO2HXkvA9ikwNDREUNxACIpiCIrXZlGlfnpEo0R90ekoH6s+yfjEptEooi+dUmLi4HjviZNtb76Uk/UH909fTvnmCy4rk62maWWFayrtjqpOdlM41fYpiouLj8X9Y4//vQ4BX/l8ddzpk9cpJcaFGuX5OOVG5aZmyrPzyvPyot0z/3Ygu9ipvj2sqe5sePjuE+EHT3gXOXhHWltaGZxJdrGS+VuXP/2I/NmHlJVp6pdpa10JuTiXVD6XJV8enVFPuhP3qXogUe9H6ofp95ZTMqR82PLoY5OpqavHtcUtrqwMzy0opmarRiZzlGOSrqGQtn7KhMGt/QldQ9RC7e1TsyNzC0u4pRAAAOC6zc/Pq1Sq6enpycnJ8fHx0dFRil6UcKwvoeRjYmIipCD2MyUlJT8/PycnJzMzk+IThaiEhATKk5GRkeHh4ZSy6LsrJS5PT09XV1cnJyd7e3vaJ21oampqZGSkmdZoh5TrKN1R6KKoRnmPUl9gYGBISAjtinYYExNDsZDCIb1oWloavaIQ1ShDVlRUVFdX19XVyeXy5ubmtra2jo6O7u7uvr6+gYGB4eHhsbEx6trMzMzcHH8v0gYaGhqi1xLU19fT2eAXLrkRr7ttISiKIShem9wLnNeTXEsKV+nHGrxrvb8q+TSk2+exksavGrsmU6QND+wqzPmjzzeHI499sJIk0X6wxMrySn1BR4g82kPhLu9uLyouejfyo/sCHvQMOOb6wVHX42/avns4PcQy398tO64iKbetPCctK9fwuOzEW6WNPk3difXJz1sE3/FNnNFZixY3N2tDPS/TU1neDtn6H9ft3Sm3/phbWeKmprj4hIWYbFVKzVL7ELe8wlUEcKb/wxn/N2d2G1fmQ0c1YGEh37lr0N5hZSt+xCytcKPzix1TqprRqVzlWGL3UOhqJgxo6YvtHMzsGy0fnmydnB1ULcxjilEAALg+i4uLCxqUSmVRURG/cMnKBv0vSHotygazs7NC+hoZGaFEQbmlv7+/p6enq6uLwkx7e3tLS0tTUxMlHAoYNTU1VVVVFHvKysqKi4spBVESy83NzcrKomjERtIoj1FeYpEsOjqaQtTFixcp11Ewo2Tl7+9P2czLy8vDw8PNzY1yl6OjIyU0W1tblvfMzc0pp4kG2WiRGulXtAJlOVqZYiH/Ox3Onz9PKY4OhsU2iosFBQV0zOXl5dQFIbNRBzs7O3t7e+lss8A2NTVFp4VONX+mAK4RgqIYguI1mJtQPxKjI1/9SIzZYWqoHKx8Kfk5j3brk01tjxTLp3r7Gu/fXZL+J/9zhwI/OrIYdZHTeir60uJyVU5rQHOwp8K/SdGWX1Lwl/A39/sd8PM5YffmKy7H/+Z07PWcILu8QJ9MSW28+kmJEQmyvxzOibOoaw+Qy92lIbtPhT9y0if/gkvaBUszg3OR9uZZ3pZVj+1v/Nvjoy2F6lxaWbkYJlElVy9UdXLzS9ygnHN9WB0R9X/MxX/BqcbG4+Ob9x/o+uDDhe5u/rA2Mwp6Y/OLndOq2tGp/IGxpO6hMIU6E/o190a1D6T1jpQMTTSOz/TPzs8sYpoZAAC4BpTK2IDYxMTE6Ogo5TEWxtjtZ5TEKLRQdKHIRPHmKij/0NctdjVjeHi4kL7YNY0sfbm6ugrpy8bGRkhflKyEUTJGSF9WVlYsfdEmTk5OLi4uFOFoV97e3hTqKNrRS7CrHCnyUfBjFzpSFKRAyK51pIjIxtAoNFJ0pHxbUlJCYZKNpNXW1lLIZMGstbWVukwRlIIoxTM6DxRN6YTQaaGwSpGVzhKdq/n5ef7c6UDrU7QTUIKls8EvXPKdOwG4ERAUxRAUdaoM5jKMvi2Rf+eM/pNz2MOlneHSz1HLcNqZ52L/5N5uFdTbeHtWVdv0bMerfykK3R9m9azHu3+dC/RX3x94ucX5pbKsRp9WH1/FxYbmptzivGdCDz/md9DfXs/2zZccPvuL17kPcn0d84Ij01Lqk/JbinM9vGQvvlNS69HYHVeb/oZTyB0n4vRPGyrcPV2N9O2NzqR72Od9837dvj3NPifUA4kDA8tR8XOSsrks+cr4LLcwwyWfVE9qavBvnNt+rq9yrrGx/bXXWh57fCpTxh/TprLCcRMLi13Tqrqx6YKB8ZSe4YsKJWVCKlShRWqsH5vumZmbXFjCQCEAwJaxxsBG2YYSjnDXGeUf4dpFiUQSGxsbFRXFRsnYjWds1hB2+aKdnR27fFF07xnlNBbJKI+xKxgpjHl6emrebEa5jjbRRNGOr62iXEeHUVlZKaSvxsZGIX1RL7q7u1n6GhwcHB4eFtLXzMzMWtIXAFw/BEUxBEWdvP+onhTU/PZL5Rec3o/V09gkH+es71y0uP1Nr7vsGw0TlVm/yakJ7RsZtLMrunBPtPvjLu+8MuHhxmnNHTo/u1CQVevZ5hLcnlxbV5dekPl48LN/8n/c8/zXzu/91fa9lwKtvszzdsqNkKVkNmXllefk/ONMtolRXbtngyI0L3DP2YgDJ71zzaxLbK1M9M8H2ZpnuVlUP/Jw0wfPjHeWc/PzK1m589E5quTqpa4R9es1xHEWt6vvSDS7jSv3Wx4f7zc0ku/eM+TisrIZ/r2hmEdhjyIfBb/CwXFpz3BEu9J3NROGrj65Plc5VjM61TGlGptfxNWjAAAbLjw8nFKTgA1tUcXCwoK1UIVij1JjgkcKbJR/KAUJgU2Y45ECW1paGgtscXFxmjOF+Pv7+/j4aN5vpjlZiIGBAaU1Q0NDylr0itbW1iywiWYHEd1ylpSUJJVK09PTs7KycnNz2bWLZWVlVVVVlNMaGhqampoopInuN6Nsxi5fpGC2tIR5rQG2FwRFMQRFnUJf50z+n0b5GWfw71zc+5zX47Ro7fjr88UfX+xw+XN50wd17TMlJcXHfxvv+5D9Gy8OOdpyExP8Ti5RTc/Lskrd2uwj23PpXylJjnR/0KPP+z/l/Pn7Xv942+atF2Lc9fJ8XbJjyxJzFUW5UonsmWPFOW6NPUHVRZ94B91xIv7c6bOdzu6BpkbWhucTXWwKjh2tffT+9ggD9UBic8tiRIoqqUp9reniMjfWwfn8ST3+qfcj9byms6NjERFND+7r/vSzxb4+/oBuMVOLS70zc/Lx6eLB8bTekcj2AZYJA1v74jWeXD+MJ9cDwNaysLAwNzc3MzND+YSNlVFcYTebUfpiN5u1tbWxm80o3tTV1bGpGsvLy0tKSiiGUQSiIMSSmOadZhSW2AAau9aRchRFMvoXn2IVS2XsTjPhckeKXnZ2dpTQLly4QCGNItl33ktGKLaxETbRdI6iOULowGQymXCzGQU26gJ1RAhs7e3t1FnRBCEssPFnCgDgBkNQFENQ1GlKyY21f1vqI7nwv3I5ptyYQtYQ9mb6X7zaLxg0t95bUD81Nlb+6e74wJ12rx/qtDThhob4PVwyPa6S5uW6tdtK2svLysojZUkP+O971e85uyOvBpoctXvrRamvZZ6/Z0ZCTXxBZ2GOm2/2m4Y1rc4NnQG54fcZRD100i9bX6/RztRC/7yXtZnM2bz6Dw+3fXVoqr+Wm5xcTkibSyibk9WvTM1xS/Nclqn6WlP9n3Luf+D6KlV1dYqXX2l9+umZwkL+aG68npm5mI4BfkHLzOJy/+x84/hMydBEeu9IdMeAf4s6E9JP2kp4cv2ACk+uB4CNRznK29ubxSR3d3dKR6amphSWiMslFLoohpWWllKkoWDDrmCknENph13EqDnhh3DLGYth7GpG2j9lJ9oVGx+ztbVl1zRe8X4zXdj1imZmZpTZNG9Fo1BH2YwOmI6fXohSn+bdaHQkdDx0VBQRKapp3pBGfWezg7BBNjb/PvW0+tKkjhRHNScIodXYfqib1FnaM50u2puAdkuhjj+zAACbHIKiGIKiTmXuXKYel23MlywjTqZPPzszTjwVedCtzTwxz+G2zMraqVm5wYtJEb+zf/PFJmM9rqeH3/yS8aHphMJ093a7jPZ6+s7hnRZzr9+973i8YvvW4UDLt53efTXLzz43MFgqlSfmt8qyP7MoCXJt7LavqP0qKPCOk/EnT53qumAnsTAxMdCLcbQu/vhI3Z8e6ky04JYWVkor52PyVClVS92r15p25HHWd6oHEs1v48r9l0ZH+86cbdyzd9jTk1tcXD2WG4VCXceUSigVw5PhCiVVKO9VjUwVDqoDYVrvCOVA/xb1Iwrx5HoA+E4Lqw+wnpiYGBkZodDS09PT0dFBSYY9/YziDX2iskefUfhJSkpizz2jjERhidKal5cXRSnKVDY2NpTQhMExqlD6Wr1w8soMDQ0perFpPygd0W4lEgntPyUlRXvmD8padBgUKdnkH1VVVXRsFLroIIU70Ch6sfk/qAt9fX3UF9FNaNPT09TTubk5zNYIAPA9QlAUQ1DUqdSVK7Dlyr01y3yZx0sRjzk3GafVue3ILPJqauiKNE+JudP56MvFZ7/h2sTPPB3tn4oqS3BXOOYp5EVFxfapF3f57frQ7lXHdw576L3m+eWRHG/H3PDEpMzWtLzShOzXLGsaHBq67GVJDxlH3X86KPXc8R6bs/aGBo7mJhn2plWP7u/Qf0011MT1KRdiZarEqsVKBbe0zE0PcSGvqQcS9X7ESY5zMyOjQUGN993f8+XxxQGdI3sbSNI9FNDaF8RKW19gK18CWnupUMW3pY9iYe3oVPf03PjCjU2tAHCTLS8vU8ihtDM2NjY0NERZiEJRW1tbU1MTRSbKTmxmkZycnIyMDKlUyuYUYfen0T9AlMo8PDycnZ3t7e2tra3Nzc3ZgBv9NDMzoxY7Ozv6rbu7u7e3N60fEhJC28bExNB+KLzRPtkFjfQqlZWV9IqU0OjVOzs76UgokrFJ8+kI2S1n/v7+lPGoXRcHBwdKd6xrAACwfSAoiiEo6lTuxbVKL7v6dKz9ZPqnpjWnY7t8Xiqt+VtW2lhZYGrsr93ffznl+CdcXS2/4SXKrtGQ6nB3hVtpcxOlRJMk352+93xq8pLXZ391OfFysP6xPB+n7JgCSW57em6kX4mxi7zrfHnLl0Ghd5xM+MfZs93GVjmW+sb6+qG2lsUfHql/7dGeDDtONb2UUaBKKJ/PqFmZnuNWlrkiZ/WMNQY/5dwOcMra2fKKtueeb33m2ZmSEv44brzknuH8gfHmiRldJVyh7J3BA2EBbpTExETKWgIXF5cLFy7wC5f09PSsrKwsLCzMzMxoDtOxh61dZZguKioqPDycTRHJphuhKCUM0xmsogotUqOjoyOt4OXlRSsHBwfThrQ57YR2RTuk3dLO6SXohdj9aexaRzoMOhg6JDow9hi0jXrinLawsDDKogLT1cdz8wuX0CHxawMAwLaBoCiGoKjLfG3obI6ZUBoLDBzLvno3828+LebmWWE7Mws7Ms9nxv3G+/3nYj5/f6WogN/sku62gYAGf09FQHlDQ35B8ZkYa0qJx049G2H2lv1nh2Otz+X6umZIauIKOlNyjJyr853k3d+kFx4wi9pzJjz57IeD5se9TIysjQxTbEwq/niwy/rtueGWlbrGubjCuaSK5c5B9Wv0VXJ2v1dPsWP6v1xF4OLAQO/X3zTu2Tvi58fdxLnaZpeWozoGItoH4jsHJV2DSd1D9DN+tSKUgJY+BEWADbG4uDg1NTU8PNzb26tQKChrVVZWUnJjc4roYmxszJ7kpj1M5+PjQ/8KhIaGRkRExMbGSiQSqVR6xWE6Nom/aJiOP6xNi3IpnUl+AQAAtjEERTEERV2O5X36evrLQvm04Khds6GrwvzV3FM/yyx9MePz8LwXvL9+Nuj9t5bSUvltLmmp7/Jr8vRuiyirqcvNL/085Ktdvru+/uKpBK83HD58JcXFPNffJyW1MSG/OabQwE3ecaJK8UFA3C9PJnxuYNRxXq/O+pSpgYGfpVnRh39veO9PA/mu3PDIgiRflVi5WNasfsD83AQX9b56xhr9H3MJX3JTQyM+Po177+395sTSyOr9ijdF98xceu+If0svRcGyoYm2yVlWSocmwtr6hUVWKE/ymwHAJZRSRkdH+/v7Ozo6mpqaqqurS0pKcnNzKadRYIuKigoODvb19XV1daVcZ2FhsXqTnR5VaJEaKeOFhITQajY2NuHh4dm6UTKkpMe/KgAAAGhBUBRDUNTFrtEkoNslvNdHs/h2+9+ZU3y0RhLe4+XZbu1h9N5cdAR3+SVSNZVNXi0u/i1JJeWVmXnlRwKP7PHZdfrTP8YGvOL6/t8yPW1zgqMkshZJfr5/VZKLvOtvadWPmEfvOh8VZXh01PBEhIWJuaFBgqVx+fNP9Hq8vzDSspRToZJUzqdVrEytPpuxIpAz+S9O/yec68OcsnampKT16afbnnt+trxi9fVvONXSctWIerqaiwpl1ciU9tykQ3MLucoxfgFge5ibm5uYmBgcHOzq6mppaamrqysvLy8oKMjMzExOTo6JiQkLC/P39/fw8HBwcLCysjI2NtbX1zczM6OM5+Li4u3tHRQUFBERkZCQkJqaStGuuLi4qqpKLpcrFIq+vr6RkZHp6elFHRNTUVa01XDhwgVzc3N+4ZLOzk5+bQAAANCCoCiGoKiLjdzYTWEplK/qve/Jy9lfmPZgQToturdZurVb1sQ6al7kubKyUlpe7dFqH9ScXVRcmppf9beAV+/32mPyzdPhXi96f3okx8shOyorPrctrjDeU956prr9Bb/MX59K+MDSvurMiZYLp6wNDVxNjfM/OCo/fmiozHu5tWMuoWROUrqsWL0yaqiZc76P0/83zux/ucqgRaWy+4svGu+7fzQ4mFu+GeN1PTNzGX0jfi29ab0j3biUFLaohYUF4drOtrY2dm1nUVFRVlaWVCqNi4u7ePFiYGCgl5eXs7MzRTJTU1OKfCYmJtbW1k5OTp6envShGh4eTmumpKTIZLLCwsKKior6+vrW1taenp6hoaHJyUk8HQ4AAOCWgqAohqCoi2N7/fH6MqG8Ulb886yy22QVRwu9j0RavOLicDI2un6sn1+b45aXVrLLCtwVtpGNpYWFxUnZZYcCnt/nvsfT/NkAu1dCT3+R4+2UmVARk99xsVzq0dj1bEbtPrP4XXoxwbYf9+sZSe2sjPT0Is2NS199Rhn46WKXfEFaopJULhZUcysct6jiEr9Sz1ijntf0y5Wp4SFXN/mu3X1nzi6N3fCxO9XScvXI1EWFMkzRXzE8iYtIYROhPDYxMTEwMNDZ2dmuIT09PSEhITIyMjg42Nvb28XFxdbW1tzcnN3FZ2lpaW9v7+bm5ufnFxoaGh0dnZiYmJmZmZeXV1paWltb29zcTDtUKpXj4+MqlYp/MQAAANi0EBTFEBR1ea6i5be5Nbvy61jZmV93T34t/dxFP/NqqeUXWdVZI5Ns5aWF5dSyTEqJCfKagsKiuMSsp3yfOOC0J9LnaU/DN+IszuX4uEpTq2IKWgJqm42qFff75f76dMLfHXwyjY83W1u4GBvZGRlmffZB07lXhit8FgtrVYmVC8lF3Pjq/ptSONP/J1xrOp2T2/LEk4oXX1LV1a2++A3Uy4YQm3ulPcMdU6obNQshwJVERERYaaAUZ2JiQhX1o+4sLS0sLMzMzNLS0tjlnUlJSRTnKNSxO/qE1GdsbEybODo6enp6BgYG0j7j4+MpJebk5BQXF1dXVzc2NnZ0dPT19Y2Ojs7MzPCvDQAAANsMgqIYgqIuzxQX/z6v5qHCWlb2FdTdV1D/UEHVvvzqhwtqHi6svU1WIe1rpjUXVIvxlfEeCkdpbV1+QXF0jOQR74cfddiTGPOE+8m3pA5m2f5BCdk1kUVyH3nX87K6XabJv9eLC/A7XXHOJsvN1VhPL9jUqODvL4xGHl9sqJ1LLJ9LKFlpVKgPYryb835KHRFN/oerDF7o6en66KOmBx4cu3hRdGPkxlItLdeMTkW0K0Pa+kuHJvA0fLgR2BWeQ0NDPT09bW1t9fX1FRUVhYWFMpksOTk5NjaWAt7q1C066evr+/v7U0SkoMgu72xoaBDu6KPUd+MesQAAAABbDIKiGIKiLs7xH52N/eBc3EesfBZ78lCsk7BI5Wzse/XV0aqZ+bCaEE+Fu6yiJje/OCLi4gNee//kuCcx8RmPr97OcrOVRcTH5DeHlbfZ17b+LqDgV6cTXnMMSXA5Vmvt6W9hbmlgkPb1sWbLNybKw+bTK1SSiiVZMbe4yC0vcun66nlN9X7ExX++MjE4aO8g37mrX99geWKCP8QboHdmLrNvxLdZPZGpYmp2jd+yX3MvvP2kRCi/OJFARdSS1zLErw1bDuUxlUo1Pj7OrvBsbm6uqakpLS3Ny8tLT09PTEyMiooKCQnx8fFhs3eam5sbGBhQ0jMxMblw4YKTk5OXl1dQUFBkZGRCQkJaWlpubi5tHh4eTpv4+fnRZxT9ln66u7uHXU6pVPIHAQAAAHAdEBTFEBR18Tn2gv0bhxzfeZkvb7/s8NbhbxffefnCa38u9TgVVO/n0xaYXVyZW1AWGB6w12vnX9z3JEv+6vfV+9ke9umJ0qjCrqDaniOyut+YSe86F+8VYxttbpvjG2ZhYOBjYpz9xWvjCWcWCitViVUL8Tnc4LD6tTvzOYs71AOJLvs4Ze1kamrLo4+1v/ba3A2b3X5umR9CDGrtKx6cmFy4tiHEl1zzf3s2+e7zKXedS/7dWXW561zKjvPfljtOSnKaERQ3h+Xl5enp6ZGRkb6+PoVCIZfLq6qqioqKsrOzpVJpfHy8MJULBTw2lQsb32O39jk4OHh4ePj7+wuzuWRlZdHmtJPGxsb29vb+/v6xsbHZ2VkM9wEAAMCtA0FRDEFRF9/PD/sc+yBc74Su4vjOYYnDu/7N0bmFZbk5pS7+jru8f/9u4B5JxCcXzx7P9nCQZMsii3p9a1ruDiz45WnJK07haTGnUk0jLtpcMNHXl5w92eby7lR+/FxS5Vxs4UpFjfpVZ0a4wEOc3o/V15pWhcwrFB1vvd300MPjMTGrB7Xx+mbnZX2jfs29kq6h1snZ5Wv86t4zNuuT1/60bfbtJxLvPJN8yCnvbEydQ3rLPy5WRZX3aJaBScyS+j1gU7kMDg52d3cLz2zIz8/PyMhISkqKiYkJDQ318/Nzd3e3t7enmEdhj6U+fX199nwFV1dXX1/fkJCQ6Oho2oQ2LCgooJ3U19e3tbX19PQMDw9TsKR4yb8kAAAAwCaEoCiGoKhL0ntvB370TtCnR1nx/ehtt6N/FRap+P/9r7FBbrkFJbnpRRYe5+7x2XEi8t5In88TzPQyAxyiCmsvlvSdz6++3SLtzrMSt0S3IG/zNK8UO0NDZyPD9NN/n0oxn0urUCWUL6VkczMz6nsO8+w4w39XX2sa9yk3PjBgZSW/Z6fSxHR5aoo/pg1VNzYd2a4MaOnLHxgbm7/yw9l0kfdP2qc3P2Of84sTkvuM005H18gaBxeWMED03cbHxym2CSi/UeLiFy654oMTVlZWZmdnx8bGRA9nz8nJSUtLE03gaWNjY2ZmRmGPpT5y9Ys8a2pqmpubu7q6BgYGKFjiyQ0AAACw3Wz6oHjo0KEfrDp27BhriYiIYC1U0dVyFQiKunS+c6Tj9Td6T51ipefEiZ6vv+brJ092nfim5JtzeflFeSlFJ2zf2uF7t3nCgcALn6TZmyfHe0cWdYeVdTwSXHj76cQ/20Xm5Zm4XwiLs3cw0tOLMjjfEXBsJjNdlVS9GJXOdaw+Aru/mrP+rfpaU6f7uUH5RIKk+cAj9OpzLS2rx7KR+mfns/pH/Vp64zoHmyZmltZ8+d/yClfSPmIsaThgkUn58EmbbCtpY3X3OK4fvCZ+fn5GRkaU4oipqSnlN2NjY1ZhKNTRn2SY1sPZWd4TWK4+v8Hd3Z1Wo5VjY2OFizwrKyvlcrkwkyfFS/61AQAAAECHzR0UbWxshHwo5ECq0FdDQhXhV6KWq0BQ1GVCIhlycxdK78nTTQ/tp0qtuUGS6Zsp0Yn5BYU58QXvWTx5j+8OF+mhAOOPszxsowuSIwv7PYrqbrdI+9WZRLsEb6nUJNQpzc3Y8IKBgdTo09kUH1Vy5XxM3kpeoXrSmvkpLvx1Tv/HnMnPuMogSoaUDykl0qvzx7FB5paXa0enItsH2BDiyNwC/4vvMre4nCEf+Cayeo9h6u0nJS+55LtltXaPInusyczMjFKpbG1traioyMnJkUgklPoMDQ0p+OliYGBA2Y89tS8/P7+8vLyurk6hULCLPKduzPAyAAAAwDa3dS49PXToEOVGSoM7duxgLUI+FLWwui4IirpMLU2MLgwLpWWoIrklILM13aPNQVJUnZ9fJIvO/ov1g7v9dgSmfxhs8EVamF14mTyyqPej6NJfnEncb5VYWGHrEOAVa+Nqoq8fYmzYc9FoNqVoTlK2HJPMjY6qX6PUizP6T07/R1zMh8ujSqWxsfyenQNWVisbOgTUNzvH7kKMXx1CXFzbCOCEajG2suejoPLfnUu+80zSu/6l4aVdI9O4IlGMRcGWlpbKykqKghTwwsLCPD096c+TAiEb/aPsd+HCBQ8PD3ZDoK+vb3BwcGRkZHx8PK1sYWGRcbmRkRF+7wAAAABwU2yRoNjT00MhkH5GREQIsZAqtKjdwuqko6PDWsuBAwcQFK8opMXPs+OCV4ctK8HVNpIsNx+FR2pRJaXE5KCIFx323Bfw+/DU41Gmp2JkQeGlveGF7ffZZd5+OvFcZHhVhYWtQ7y/saGpvn6q9RlVSrwqsWoxPJmrWZ20ZqiZs7tHPWmNw25uoGE8Krpp30Mdb709r1h9fOJGYBOZRrYrA1v7CgbGR9d2F6JyQhVQ2PGGV9GvTiXeoy/9MrwyubZftbCt5ykRRUGJRHL1KEgrZGdnV1RU0Ca04fT0NKb3BAAAALjFbZGgSAmQDRVqx0LtFlYntbW1b2rZt28fguIVpZfLCkoLhVJUVFBSVFxUXFZcXFqYny/Lz0zLT09MsY33sA6pKoos6nVKr//luaQdFtKMUqeYDPtAM3drAwNPY6OucPfZxIr5mJyV1HT1pDWLc1zMh+prTY3/myv3U9XVKV5+pfngo5NSKf/C1231WYjqIcSErsHmtd2F2DI45SxrfcEp7xcnJPstMo0k9UWKkaVrnQJ100IUBAAAANjmtkJQZGOJrK59oal2C6vrgktPdSkuKKurq6MkwMjl2ZWVtvzCqpKSUmlGdlBtZ1RBz9HA0p+fTnwjKKWp0doqODBAXx0vkmxMZxPz5hLKlsOiua4u9U7rYziT/1Zfaxr9/tJgT9/58/J7dg7a2a3MbcCjI1RL3z4LsXBw/DsnMqVoU9E1Zp4sf8xaRvnwecc8x8yWZuXWvAVOiILCvYKIggAAAAAg2PRBkbIfX7tEyIfCr7RbrgJBUZfQVH3ftC/80o+xohf//B+CdgiLVOi3ESn+4Xldj1hl32Em9csKLa+ysXHwtTc0sDM06AjyVyVVLoZJuMICbmmJG+/mXB9SDyTa7+SU9aMhoY33P9B59OjCpcx/PVaHEEd8V5+F2KIeQuTbdZE1Dp6KrrnPOI3y4eueRaHFXcoJFf+7TQtREAAAAADWbXMHxWPHjlH2E7DLSumn5uIVW64CQVGXv0Y99kjY/qcjnmTlz9HPv5zwd2GRyu6AXXYRH92ln7LPL7uo1jEqy9nb0NxIXz/a0nw6tmghJouLilZPWrO8xCV9rY6IRv/JlXrPVlcrXnyp5fEnpjJl/Cutl2ppufrSEGLR4Pj4wtWGECdVi9EVPZ+HVt51LpkKVWiRGvlfbwbaUZDyHqIgAAAAAFy/TT+iuOEQFHUxyfP1qCnxq69mJbCmKbS6V1ikYleeZRFp8GVKhrzJxi7Mx8nIyFxfX+7pNycpWg4I4+pq1XtpSefM/ld9renFt5eUXb0nTsp37hpydl65vgea98zMZawOISZ1D7VOzl7lXsLBqTn/go7XPdWT09z6T8bXjIKU8RAFAQAAAODmQFAUQ1DUJbymOE851jwxw0pF32RSpXpuGKGEtnaHxNtW1tra2TmaGxj4mpiMRaQthcZxqVJKPNzsCOf5uHpeU9u7ub6aET+/xr33dn/y6WJfH/8C1061tFw1MnVRoQxu7S8ZmphYWOJ/oaV9eMYps+V5R/XkNOon46fc2Cfjz83NUcYTtLW1RUZG8guXLC0tUYqjCqIgAAAAANxqEBTFEBR1Mc0o+DqlXijnkuTeWd2aLd9I6/zi3dwsLYz09QsdnBeipFxQqHrSGkoy6fqcwU85w//gil1nSkpan3m25YknZwoL+V1fO2EIMbl7qG1y9opPq1he4co7x8yS5I9aye44KXnVvdArV3Fznozv7+/PMt5VmJub87VVFAUpH1IUTEhIoChYXl5OUbC/v5+iIL9TAAAAAICbBUFRDEFRF9f09ujCgasX89hkZyOjfr/QFd8AftKajgLO/Hb1taYhry72KHq+PC7ftXvYw2NlYYHf77VYHUKcvKhQhrT1lw5NTF5pCHF+aTm9YeCbiOo9hqnCk/HHZ9fzcutGkY+yn/5VJScnNzc3IwoCAAAAwC0IQVEMQVEX14wOSelAcvmgUFIqhjQXY4qUhuHShcCLXGS4etKa+SnO71n1pDUX/m+lq5zCIUXEnmNfLg4M8Hu8Ft0zc+m96iHElJ7h9qlZ7astJ1WLMRU9HwaW/e5cMkVECoppDcqb+WT8oaGhqqqqhIQENzc3FgVtbW39/f2pJSsrKzc3t/5yyIcAAAAAcMtCUBRDUNTlzZCKj0KqPw6pYeVstNw/q0dYpPJBcLWlXyY/aU2uDWfwb5zhv3P59tM5uS1PPNn6zLMzJSWre7oGs6tDiOEKZWhbf9nQxNSieAhRc3KaR61kpkny8o7Rm/NgfJVK1dzcnJmZSW8YMzMzPT09R0fHqKiooqKi7u7upSWdN0wCAAAAANziEBTFEBR1+aRQ+nhBgVBeTip3Sm3XbHksPy8oLorrq+Ssfsvp/ZALfHGxs7X7408a9+wd8fVVX4a6ZhT0uqf5IURpz3DHlEoU/VoGptjkNLef5J+M3z48w//uhlleXu7t7S0uLqY0aG9vT8nQxsYmPDw8Ly9PoVDMX9/ErQAAAAAAtw4ERTEERV3KyxyUysSxsTJW2gYqMttrhEUqra0ukhA39bWmVr9ZURQOOTrJ79nZ+/U3i4OD/C7WYHZpufLSEGL58OS0xhDi8gpXrBgxktQfsMj8zemkd3yKg4s6B6fm+F/fGGNjY3V1dcnJyV5eXkZGRpaWlkFBQTKZrLm5eXb2ZsyLAwAAAABw8yEoiiEo6lKUZ9PcbKtZWlouW2xqsqkIMuSyLCbT01see7ztuednyyv4jb/LCsd1TavSVocQU3uHO6e/HULUnJzmHn3pl+GVybX9M/M36sLO+fl5hUKRk5MTEhJCsdDU1NTPzy81NbW+vn58fJxfCQAAAABgS0NQFENQ1CXE0sLE0FAonh4mJSVWmi0mBgY1IR6dR4823nf/aFAQt7ymiWSEIcQwRX/F8OTMIr+V5uQ0+8zSz8fV5bUMLd2Yuw/7+vra29sTEhKcnJyMjY09PT2Tk5OrqqqGhob4NQAAAAAAthMERTEERZ3CTnBubwtlzPmFeYtfcLYvcFZPcZZ/XHF6bdnqcO+f9/adPrM0NsZvopswhOjX3Es/qc4iYP+ESpic5hn7HNu05vq+idXfbKSJiYn6+vq0tDQfHx8TExNnZ+f4+PjS0lJKjPwaAAAAAADbGIKiGIKiTha3c3o/5vR/orv8aOHiV/zKus0sLlcMT4Yp+sMVysqRydkl9RBiQ9+kXXozJUPhyfiUGNn6G2JhYaG9vT0vLy8sLMx6VWhoaG5uLjXSr/iVAAAAAABgFYKiGIKiLiuWv+P0fnTV8sOVPBd+bS0rHNc5rUrtHfZr7s3oG+mZmdOcnOZ355I/DCyLrujZqCfjr6ysKJXK8vLyuLg4Z2dnExMTHx8fqVRaX18/MbHxQ5QAAAAAAFsJgqIYgqIuXW+9Lb/nHvmevaw07t4j37lLfs9O+a7dcqrv2Vv/2/+bzsvj19Yws7hcvjqEeFGhrBqZmlxYEianuc847XR0jaxxcH51XPE6UQJsaGhITU2lTGhqaurk5BQbG1tWVtbf30+5kV8JAAAAAAC+C4KiGIKiLu2vvtb88P7WPz6tqzTef79mUGRDiNKeYb+W3sy+kcaRaWFyGuHJ+NcZ39gFpbm5ueyCUisrq5CQkJycHDzVEAAAAADgeiAoiiEo6tL5zpGu994fsLDQVZoe3MeC4vTiUvnwZGhbf0S7UtY14p7b9qp74a9OJb7kku+W1Xo9T8YXXVBqbGzs5eWVkpJSV1c3toYZdAAAAAAAYC0QFMUQFHUZCw/XjIX9Z850Hjmi2UJFUd8o7Rn2b+n1r+4xSm54xj7nzjNJ7/qXhpd2jUyvc4hP84JSExMTR0fH6OjokpKSvr6+5bU9gQMAAAAAAK4JgqIYguI6TC8ulQ1PBLf2Wxa0fRFZecAiU3gyvmrhmrOccEFpaGiotbW1hYVFcHBwdnZ2a2vr3NwcvxIAAADA5pfZlelR63GVYlNuI+2QlipLb1qJbY11qXK5mcW8xPxIypHvLI+FP7Y7cLdQdgXs2h2we1/IPqE8EfFE3XAdf2bhuiEoiiEo6tI6OVs5MimUosHxtL4RWf9oWFu/ZUnb6fSG96Iqn3bMNZLUFylGrunJ+NoXlLJH3tfW1o6OjvIrAQAAfK88qj2+yvpKKMcyj32Q+oFmC5Wm0SZ+7TWTj8hFX9O/l5LRlSH67n495aX4lx4IfkAo9wfdf2/QvZot+4L3nc8/L9qKlTVmhptQDscf3uG340aXvYF7RS2icrff3Tt8xY1XLC/FvfRO8js3s5gVmzlXOt+08mz0s5QM9wTsYYXqu/x3CYtU9gbsLe4v5v+04LohKIohKOrypEPOz09Irl5iatf6wHrRBaUODg5RUVHFxcW9vb24oBQA4MYRxYPPMz//Y+QfhfJU1FOPRzyu2fJ01NPhTeGirbTLxsaMKxY6VNH3+A0voq/dorI3aMO+0F9PeTD4QdGX9e+xiL7HC+Vw3GHNWEjHTFlRs4WC4rm8c6Kt1l0C6gNK+ktuhdIz1cP/pa1ZQluCa5WrUMyLzT9J/0SzhUr3VDe/NsBNhKAohqCoy9MOub/4RnKV8vNvEpIalPzaWkQXlJqbmwcFBWVlZbW0tOCCUgBYB/pCJsoqQgmRhxgVGl2lWJdZZ/dki7ba2KutRAnkespTkU+JosIGlj1Be0Qt2uWl2I0Zpjide1r0/X7Di+hb+8YWxwrHcwXn7CrsdBXHSsd15AQAgFsTgqIYgqIug1NzXaOzQpHWKz8OKtdsoTK3+O1gILugtKysLG71glIjIyMPD4+kpKSampqRkRF+JQC45Cqxh5XPZJ89HvG4UP5w8Q8Phjx4IOyAUKhFv0BfFFeutdyIQZubc/mWqNzte/fVit/d1xR+Psv4TBRIrqekd6aLEsi6i3xEzr+B1iu7O/ti00WhBDQEUODRbKGinNH5PwEBAGCrQlAUQ1C8HpoXlBobG9vb20dGRhYVFfX09CwtLfErAWhoHmsuU5ZdvUzMT/Br6ybKVN9ZAhsCRQHp6mUd98yIcss6ypMRT2pmlf2h+0UriIoo/IjCyYaUmJYYUVDZkML/VwQAAIBbBoKiGIKiLlKp1FeDt7e3h4cHBUI3NzdHR8cLFy6YmZmZmpoGBgZmZmY2NzerVCp+S9hsvnNoSyjXdFeSKEexsj/sO8KPulz7PT/ruIfnWu/IX8ctMfz5Xa/G0cbi/uKrl7WEagAAAIDvhKAohqCoi5eXl4GBgZFu9NvW1lZ+7e2tbrhO0iYRFY8aD4cKB1acq5yzurNEoUsoa79R6pqGuURRau1FNK519SJKU1cp7Oq7I8lHHgp9SCgPBD+wN2ivZsu+kH1BDUG47QcAAADgZkJQFENQ1EUqlXpqcHZ2Njc35xdW+fr69vf382tvG8K05kK6o/C2L3ifKGtpl6cjnxZFLF3lmm6O0hy/unrB0BMAAAAA6IKgKIagCIwwvifczHYm7wwbmhPNzCEMuAmJ7lTuKbsyO1b86/3jW+O1y+DsIP9KAAAAAAC3GARFMQTFrU3z7jsW/6gI0zyK5qAXHlwrTOmueVsav0cAAAAAgC0HQVEMQXGTEi4B1ZxehcU/KvtCvr0WVHOaE2H+EmEux+ufax4AAAAAYLNDUBRDULzVCAOAwiWgmjO4CPGPiuacKyz+URGeV4bZUAAAAAAA1ghBUQxB8eaYmJ8QEqAwAChcAiq6CVD7ElAqLP5RwaQsAAAAAAAbC0FRDEHxOgk3AV7xElDRTYDCAKBwCShuAgQAAAAA+N4hKIohKOryj6x/PBjyoFDuD75/d+BuoewM2LnTf+fdfndT/NO8CVAYABQuAcVNgAAAAAAAtzgERTEERV2+zvn6QNgBoTwc+vB9Qfc9EPzAvpB9rDwS/kjlYCW/NgAAAAAAbFoIimIIigAAAAAAsM1ti6AYERHxg1VU4Zt0Q1AEAAAAAIBtblsERYqIRauowjfphqAIAAAAAADb3NYPipQPd+zYweosMbK6LgiKAAAAAACwzW39oBgRESEERap859WnCIoAAAAAALDNbeug6Ovr+y9a/umf/unnP//5QbjkZz/72d69e/kFOHjwP/7jPx588EF+AQ4e/MlPfrJ//35+YdujU/Gv//qv/AIcPEh/LPQnwy/AwYP0cUofqvwCHDy4c+fO2267jV+AgwfvuuuuX/7yl/wCHDx45yp+AQ4epLcHvUn4BTh4kD5A6GOEX4DV7+3BwcF81Llu2/rSU4VCYa3lhz/84eHDh2Vwya9+9SsrKyt+AWSyn/70p15eXvwCyGT0ZxUdHc0vbHt0KuiE8Asgk9EfC/3J8Asgk9HHKX2o8gsgk50+ffq+++7jF0Am+/DDD5966il+AWSyV1fxCyCT0duD3iT8Ashk9AFCHyP8Ashk//zP/0z/0PBR57pt/aBIWD4kVOGbdKPvNPSG4xeA43bu3CmVSvkF4Lj/+q//qqqq4hdg9e9LqVTyC9senYq1fM5sH/THQn8y/AJwHH2c0ocqvwAcFxAQQF98+QXgOPqG9+abb/ILwHFfreIXgOPo7bGBMWALoA8Q3DKm6V/+5V8QFK9NxLU8HgNBUQRBUQRBUYT+shAUBQiKIgiKIgiKIgiKIgiKIgiKIgiKIgiKIgiKNxaCogiCogiCogiCoiYERREERREERREERREERREERREERREERREExRsLQVEEQVEEQVEEQVETgqIIgqIIgqIIgqIIgqIIgqIIgqIIgqIIguKNtXfvXldXV34BOO7o0aOlpaX8AnDciy++2Nrayi8Axx08eHB0dJRf2PbGxsbwrVcT/bHQnwy/ABxHH6f0ocovAMelpqYiBmgKCwszMTHhF4DjXFbxC8Bx9PagNwm/AKv/K4E+RvgF4Li7774bs54CAAAAAADAjYKgCAAAAAAAAJdBUAQAAAAAAIDLbN+geOjQoR9c0tPTQy3aT9FYS8vWtt36y+C9oYl6VFRUxOpr6fVaWjYv6oVwNrb5+0To/rFjx1jLWjq7lpZNSvuEbPN3CJ0HUUfW0tm1tGxS2icE/9YQGxsbOg+svpbOrqVlU9M8Idv8HbJR3ddu2aS0T8hNeIds36C4Y8cO4QsfQ6dM/VR+jefyr6Vla9tu/WXw3hBQX1i/hEVRH9fXsknR8bO+sMXt/D6hrzJCHKKOsH9s1tLZtbRsRlc8Idv5HUJngH3ZvXrX1teyGV3xhODfGtYLdmbIWjq7lpbNi/VCOCHb/B2yUd3XbtmktE/ITXiHbO5Ttm4Uu+nssPDN0Pmi083qwhn8zhZW36q2W38ZvDcErEdCd7T7uL4WVt90RGcD7xMBfaehmKTdtfW1sPqmxk4I3iEMdYE6wirr6L52C6tvXtQFdkLwDiHUl2PHjgkReh3d125h9U1K84Rs83fIRnVfu4XVNx3tE7JRp4jVddmmQZGdJjpBhP1v4IiICOHEUYUW19LC6lvVdusvg/eGCJ0H9jmi3cf1tbD6JiWcDbxPGOEfKu2ura+F1Tcv4YTgHUKo49R91gvtrq2vhdU3Kc0TgneIzSUsF2l3bX0trL4Z8afj0gnZ5u+Qjeq+dgurbzraJ+TmvEO2aVAkQgSn83vFE7eWFlbfqrZbfwV4b2iik0AfRlTR7uP6Wlh9kxLOBsH7hFAv8PbQRL3AO0Tkerqv3cLqmxo7IVTZzu8Q6juLQ0Iu0u7a+lpYfdPRPiGskVW252fIhnRfu4XVNyPRCdFuId/Zfe0WVtdlGwVFStt0HonwF8jQIv1ZslzOWmgdWlxLC6tvVdutv9rw3iBCd7T7uL4WVt+krtiFbfs+oS4I/0ppd219Lay+SWmeEE3b/JPkerqv3cLqmxo7IfzCqm34DqH+0vEL6BuadtfW18Lqm472CeF/sQqfIevuvnYLq29qN/MzZJuOKFKApnNKFfpHXThNwhmkyupaa2rZ2rZbfwneGyKsX5p1zT6ur2XzYn2hCt4n2se/ls6upWWTEh3/Nn+HbGD3tVs2I+0Tgs8Qhr7dsvNA1tLZtbRsasIJwWfIRnVfu2Uz+r4+QzbxKbtOdHLpBBEhlNMZZy1UWXvL1rbd+svgvaGJekSfJqy+ll6vpWXzol4IZ2M7v0+ECzQY1pe1dHYtLZvRFU/INv8kEc7JdXZfu2WT0j4h+LeGUN/pPLD6Wjq7lpZNTfOEbPN3yEZ1X7tlk9I+ITfhHbJ9gyIAAAAAAABcEYIiAAAAAAAAXAZBEQAAAAAAAC6DoAgAAAAAAACXQVAEAAAAAACAyyAoAgDAFiRMB0dEDyX7vmhOYr4+rDsCYaa79WHHQz/5ZQAAAA0IigAAsNVQSiT8Asft2LHjVsiKGxIUNSc0X19WpDPDzgaCIgAAXAWCIgAAbDWiQLXGJwvf+kT9Yo/D4hfWDEERAADWAkERAAC2mh2r+IXLsXTECIN7LEkSGxsb+kkBTDNEsTpbWXtz1qL9OHVCx8Aa2fCmsB9dmwiHwX7F9q+JGjWD4loOjPWIsA2FF6WKaAXNIwcAAEBQBACALYiFH6J5DSqh8MYSkeZwHFVYjmKp6SpBUXtz9lvKXUIj24peV3hpqtBWwn50bUIVtnN2GN8ZFInQouvA2DHQrqguHBh7aV2HAQAAQBAUAQBgy2IBSYhDbJH9ilC4ooAkJCuG6tTCQhQLTqxO215xc+016Ser0PpsTaLZyCr0k7Wzumjnos0ZaqRX5BdWsZarHJiwEyFJioKi6DDUqwIAACAoAgDAlsdyFP2k+EQVTZSdqJFCFL/qVYPiFTe/4ppEaBQIv73iJmznbE3CGvmFS6iRVuMXruXACBvVZBVdQVH7FQEAYNtCUAQAgC2FUpNm8GNYCiJU4Zsu0U5o1HLFEHXFza+4plBh6zCi34o2IVRhaxLtzQk1agZF4chF2zKiY1jLiKL2KwIAwLYl/ncFAABgs6NQxLIQQwFJiI4Uh1heYtGI5S6hkX6yRs3fskYWooQ1hRVYhX4KjWxNymOEKoQ2obrwW12bUEXzMLRjGzWyQyJsQ7Y+EeqsXTgwdh5okersFamFNbIVWCOra78iAABsWwiKAACwBVEwo+TDCIGNsETEiEIXoQQltLO0JjSyEKW9OWuhn0JdiFuUTldX/AGLqcJvdW1CP9Vrr0Y+oVET+61ASImE7YfRPDB28ETYGwuN1K7rMAAAAAiCIgAAwLcoLwkB8vuyIbFNMwcCAABcKwRFAACAb31fQVEz17ERRda+bgiKAABwPRAUAQAAvvV9BUUiXOxKrj/gISgCAMD1QFAEAAAAAACAyyAoAgAAAAAAwGUQFAEAAAAAAOAyCIoAAAAAAABwGQRFAAAAAAAAuAyCIgAAAAAAAFwGQREAAAAAAAAug6AIAAAAAAAAl0FQBAAAAAAAAA0c9/8BLFN15tTsam4AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![rarefaction_richness.png](attachment:rarefaction_richness.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Richness seems to plateau for most samples ~25k reads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;32mSILVA132.qza\u001b[0m      \u001b[01;32mqual.png\u001b[0m             \u001b[01;32msilva-132-99-515-806-nb-classifier.qza\u001b[0m\n",
      "\u001b[01;32mdeblur-stats.qza\u001b[0m  \u001b[01;32mrep-seqs.qza\u001b[0m         \u001b[01;32mtable.qza\u001b[0m\n",
      "\u001b[01;32mdeblur.log\u001b[0m        \u001b[01;32mrep-seqs.qzv\u001b[0m         \u001b[01;32mtable.qzv\u001b[0m\n",
      "\u001b[01;32mdemux-r1.qza\u001b[0m      \u001b[01;32msepp-placements.qza\u001b[0m\n",
      "\u001b[01;32mdemux-r1.qzv\u001b[0m      \u001b[01;32msepp-tree.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the soil samples from the table -- we don't need them for further analyses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-no-soil.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"SampleType IN ('Soil', 'Control')\" \\\n",
    "--p-exclude-ids \\\n",
    "--o-filtered-table SHNW-gut-table-no-soil.qza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove samples with low #s of reads (<25,129):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-no-soil-at-least-25129-reads.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-no-soil.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-min-frequency 25129 \\\n",
    "--o-filtered-table SHNW-gut-table-no-soil-at-least-25129-reads.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-no-soil-at-least-25129-reads.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \\\n",
    "--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples removed:\n",
    "\n",
    "    Ksoil\t57671\n",
    "    Bsoil\t44302\n",
    "    Wsoil\t33548\n",
    "    M10b\t11363\n",
    "    K15\t10013\n",
    "    E4-EBC4\t155\n",
    "    E3-EBC3\t154\n",
    "    E6-EBC6\t93\n",
    "    E9-EBC9\t72\n",
    "    E8-EBC8\t43\n",
    "    B12\t42\n",
    "    E5-EBC5\t23\n",
    "    W15\t10\n",
    "    E1-EBC1\t6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create taxa barplots per individual sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-no-soil-at-least-25129-reads-BARPLOT-PER-SAMPLE.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa barplot \\\n",
    "--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \\\n",
    "--i-taxonomy SILVA132.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads-BARPLOT-PER-SAMPLE.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove outlier samples, joey samples, multiple samples from the same individual, and contaminated sample from table (see SI Figures 1 and 2):\n",
    "Outlier:\n",
    "- B10\n",
    "- B11\n",
    "- B13\n",
    "- B14\n",
    "- B15\n",
    "- B16\n",
    "- B17\n",
    "\n",
    "Joey:\n",
    "- M6\n",
    "- M14\n",
    "\n",
    "Same individuals sampled from multiple days samples:\n",
    "- M1b\n",
    "- M1c\n",
    "- M3b\n",
    "- M4b\n",
    "- M9b\n",
    "\n",
    "Contaminated sample:\n",
    "- M7b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where  \"name IN ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14', 'M1b', 'M1c', 'M3b', 'M4b', 'M9b')\" \\\n",
    "--p-exclude-ids \\\n",
    "--o-filtered-table SHNW-gut-table-final.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-final.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--o-visualization SHNW-gut-table-final.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample with lowest number of reads is now W7 (36,346)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create bar plots on final filtered table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-BARPLOT-PER-SAMPLE.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa barplot \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--i-taxonomy SILVA132.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--o-visualization SHNW-gut-table-final-BARPLOT-PER-SAMPLE.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collapse samples by population, then make barplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-POPULATION.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table group \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--p-mode mean-ceiling \\\n",
    "--p-axis sample \\\n",
    "--o-grouped-table SHNW-gut-table-final-POPULATION.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-POPULATION-BARPLOT.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa barplot \\\n",
    "--i-table SHNW-gut-table-final-POPULATION.qza \\\n",
    "--i-taxonomy SILVA132.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_PPN.txt \\\n",
    "--o-visualization SHNW-gut-table-final-POPULATION-BARPLOT.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Core diversity analyses using **36,346** as the rarefaction depth on the final filtered table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-Core-metrics-final-filtered-table-36346/rarefied_table.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/faith_pd_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/observed_otus_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/shannon_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/evenness_vector.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_emperor.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity core-metrics-phylogenetic \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--i-phylogeny SHNW-gut-sepp-tree.qza \\\n",
    "--p-sampling-depth 36346 \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--output-dir SHNW-gut-Core-metrics-final-filtered-table-36346"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/evenness_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/faith_pd_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/observed_otus_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/shannon_vector.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in SHNW-gut-Core-metrics-final-filtered-table-36346/*vector.qza; do qiime diversity alpha-group-significance --i-alpha-diversity $i --m-metadata-file SHNW_2019_Gut_Metadata.txt --o-visualization ${i/.qza/.qzv}; done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PERMANOVA tests for microbial composition differences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_PERMANOVA.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity beta-group-significance \\\n",
    "--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_distance_matrix.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Captive \\\n",
    "--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_PERMANOVA.qzv \\\n",
    "--p-method permanova \\\n",
    "--p-pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_PERMANOVA.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity beta-group-significance \\\n",
    "--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_distance_matrix.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Captive \\\n",
    "--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_PERMANOVA.qzv \\\n",
    "--p-method permanova \\\n",
    "--p-pairwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANCOM analysis to identify differentially abundant features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove low abundance/prevalence features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-FILTERED-ANCOM-ms8-mf200.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--p-min-samples 8 \\\n",
    "--p-min-frequency 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-FILTERED-ANCOM-ms8-mf200.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-visualization SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Composition] to: table-final-FILTERED-ANCOM-ms8-mf200-pseudo.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition add-pseudocount \\\n",
    "--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-composition-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-FILTERED-ANCOM-ms8-mf200-pseudo-CAPTIVE.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition ancom \\\n",
    "--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Captive \\\n",
    "--o-visualization SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANCOM collapsed at family level (i.e. not at the feature level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final-FAMILY.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa collapse \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--i-taxonomy SHNW-gut-SILVA-132.qza \\\n",
    "--p-level 5 \\\n",
    "--o-collapsed-table SHNW-gut-table-final-FAMILY.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-FAMILY.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--p-min-samples 8 \\\n",
    "--p-min-frequency 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Composition] to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition add-pseudocount \\\n",
    "--i-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-composition-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition ancom \\\n",
    "--i-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Captive \\\n",
    "--o-visualization SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unique feature analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate main table to population-specific tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-CAPTIVEonly.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"[Captive]='Yes'\" \\\n",
    "--o-filtered-table SHNW-gut-table-final-CAPTIVEonly.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-CAPTIVEonly.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly.qza \\\n",
    "--o-visualization SHNW-gut-table-final-CAPTIVEonly.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-final.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"[Captive]='No'\" \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A foray into cross-sample contamination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hard to get exact read numbers per ASV per sample, so export to ye old school biom format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mExported table-final-CAPTIVEonly.qza as BIOMV210DirFmt to directory table-final-CAPTIVEonly.biom\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime tools export \\\n",
    "--input-path table-final-CAPTIVEonly.qza \\\n",
    "--output-path table-final-CAPTIVEonly.biom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "biom convert -i table-final-CAPTIVEonly.biom/feature-table.biom -o table-final-CAPTIVEonly.biom/feature-table.tsv --to-tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pull out the EBCs to check if these abundant features spilled over into them as would be expected if cross-sample contamination was happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-EBCs-ONLY.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"SampleType IN ('Control')\" \\\n",
    "--o-filtered-table SHNW-gut-table-EBCs-ONLY.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-EBCs-ONLY.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-EBCs-ONLY.qza \\\n",
    "--o-visualization SHNW-gut-table-EBCs-ONLY.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like we do get spillover of the most abundant ASVs into the EBCs, as we would expect under the scenario that greater abundance = greater chance of cross-contamination."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove features with fewer than 200 reads (0.00015% of total table frequency) and found in fewer than 3 samples. Rationale for 200 reads is to help mitigate effect of cross-well sample contamination. \n",
    "### The most abundant feature in the wild-only table is 3c44df3672100a011a334b67eea24366, with a frequency of 278,742 (total table frequency 5,026,870) or 5.5% of total wild-only table abundance. \n",
    "### This feature is found in the captive-only table with a frequency of only 131 (total table frequency 1,292,572) or 0.0001% of total captive-only table abundance. \n",
    "### At least present in 3 samples because I'm interested in populations here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-CAPTIVEonly-ms3-mf200.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \\\n",
    "--p-min-samples 3 \\\n",
    "--p-min-frequency 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-CAPTIVEonly-ms3-mf200.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \\\n",
    "--o-visualization SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-filter:\n",
    "    Number of features \t2,618\n",
    "    Total frequency \t1,292,572\n",
    "### post-filter:\n",
    "    Number of features \t550\n",
    "    Total frequency \t1,107,746"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering the wild-only table. Using a min-frequency of 750 to keep it the same filter % as the captive only table, as the wild-only table has a greater total frequency.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-ms3-mf750.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \\\n",
    "--p-min-samples 3 \\\n",
    "--p-min-frequency 750"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-ms3-mf750.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-ms3-mf750.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-filter\n",
    "    Number of features \t3,096\n",
    "    Total frequency \t5,026,870\n",
    "### post-filter\n",
    "    Number of features \t642\n",
    "    Total frequency \t4,729,528"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter captive/wild-specific features from tables to look at total abundance of captive/wild-specific features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-WILD-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--m-metadata-file WILD-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-WILD-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in wild-only table = 4,729,528\n",
    "#### Total frequency of wild-specific features = 3,370,268\n",
    "### 71.3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly.qza \\\n",
    "--m-metadata-file CAPTIVE-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in captive table = 1,107,746\n",
    "#### Total frequency of captive-specific features = 816,701\n",
    "### 73.7%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Core microbiome analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-ms3-mf750-CORE-FEATURES.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table core-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-ms3-mf750-CORE-FEATURES.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-CAPTIVEonly-ms3-mf20-CORE-FEATURES.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table core-features \\\n",
    "--i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \\\n",
    "--o-visualization SHNW-gut-table-final-CAPTIVEonly-ms3-mf20-CORE-FEATURES.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge filtered tables to look for core microbiome accross all wombats (i.e. captive and wild)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final-FILTERED-MERGED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table merge \\\n",
    "--i-tables SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza SHNW-gut-table-final-WILDonly-ms3-mf750.qza \\\n",
    "--o-merged-table SHNW-gut-table-final-FILTERED-MERGED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-final-FILTERED-MERGED-CORE-FEATURES.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table core-features \\\n",
    "--i-table SHNW-gut-table-final-FILTERED-MERGED.qza \\\n",
    "--o-visualization SHNW-gut-table-final-FILTERED-MERGED-CORE-FEATURES.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pull out a list of features from each of the core microbiota tables to filter the main tables from (to work out abundance of the core microbiota):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in *.tsv; do awk '{print $1}' $i > ${i/.tsv/-FEATURES.tsv}; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in *.tsv; do sed -i'' 's/Feature/feature id/g' $i; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-ALL-0.900-FEATURES.qza\u001b[0m\n",
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-ALL-1.000-FEATURES.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in *ALL*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-FILTERED-MERGED.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-captive-0.900-FEATURES.qza\u001b[0m\n",
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-captive-1.000-FEATURES.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in *captive*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-WILD-0.900-FEATURES.qza\u001b[0m\n",
      "\u001b[32mSaved FeatureTable[Frequency] to: core-features-WILD-1.000-FEATURES.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in *WILD*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: core-features-ALL-0.900-FEATURES.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: core-features-ALL-1.000-FEATURES.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: core-features-WILD-0.900-FEATURES.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: core-features-WILD-1.000-FEATURES.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: core-features-captive-0.900-FEATURES.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: core-features-captive-1.000-FEATURES.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in *FEATURE*.qza; do qiime feature-table summarize --i-table $i --o-visualization ${i/.qza/.qzv}; done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wild sample analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: Core-metrics-final-filtered-table-36346-WILDonly/rarefied_table.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/faith_pd_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/observed_otus_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/shannon_vector.qza\u001b[0m\n",
      "\u001b[32mSaved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/evenness_vector.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved DistanceMatrix to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_distance_matrix.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_pcoa_results.qza\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_emperor.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_emperor.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity core-metrics-phylogenetic \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--i-phylogeny SHNW-gut-sepp-tree.qza \\\n",
    "--p-sampling-depth 36346 \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--output-dir SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/evenness_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/faith_pd_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/observed_otus_vector.qzv\u001b[0m\n",
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/shannon_vector.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "for i in SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/*vector.qza; do qiime diversity alpha-group-significance --i-alpha-diversity $i --m-metadata-file SHNW_2019_Gut_Metadata.txt --o-visualization ${i/.qza/.qzv}; done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PERMANOVA tests for microbial composition differences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_PERMANOVA.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity beta-group-significance \\\n",
    "--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_distance_matrix.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_PERMANOVA.qzv \\\n",
    "--p-method permanova \\\n",
    "--p-pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_PERMANOVA.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime diversity beta-group-significance \\\n",
    "--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_distance_matrix.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_PERMANOVA.qzv \\\n",
    "--p-method permanova \\\n",
    "--p-pairwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANCOM analysis to identify differentially abundant features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove low abundance/prevalence features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--p-min-samples 8 \\\n",
    "--p-min-frequency 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Composition] to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000-pseudo.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition add-pseudocount \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-composition-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000-pseudo-POPULATION.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition ancom \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANCOM collapsed at family level (i.e. not at the feature level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final-WILDonly-FAMILY.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa collapse \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--i-taxonomy SHNW-gut-SILVA-132.qza \\\n",
    "--p-level 5 \\\n",
    "--o-collapsed-table SHNW-gut-table-final-WILDonly-FAMILY.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FAMILY.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--p-min-samples 8 \\\n",
    "--p-min-frequency 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Composition] to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition add-pseudocount \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \\\n",
    "--o-composition-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime composition ancom \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split samples into population-specific tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Wonga.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"[Population]='Wonga'\" \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Wonga.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Wonga.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"[Population]='Kooloola'\" \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Kooloola.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Kooloola.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table-final-WILDonly.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where \"[Population]='Brookfield'\" \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Brookfield.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Brookfield.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Again, filter features from each table with a per-table frequency of < 0.00015%, and fewer than 3 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \\\n",
    "--p-min-samples 3 \\\n",
    "--p-min-frequency 147"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Brookfield-ms3-mf147.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-filter\n",
    "    Number of samples \t9\n",
    "    Number of features \t1,897\n",
    "    Total frequency \t980,523\n",
    "### post-filter\n",
    "    Number of samples \t9\n",
    "    Number of features \t498\n",
    "    Total frequency \t928,796"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \\\n",
    "--p-min-samples 3 \\\n",
    "--p-min-frequency 245"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Kooloola-ms3-mf245.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-filter\n",
    "    Number of samples \t20\n",
    "    Number of features \t2,281\n",
    "    Total frequency \t1,635,208\n",
    "### post-filter\n",
    "    Number of samples \t20\n",
    "    Number of features \t587\n",
    "    Total frequency \t1,548,744"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga.qza \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \\\n",
    "--p-min-samples 3 \\\n",
    "--p-min-frequency 361"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Wonga-ms3-mf361.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre-filter\n",
    "    Number of samples \t22\n",
    "    Number of features \t2,393\n",
    "    Total frequency \t2,411,139\n",
    "### post-filter\n",
    "    Number of samples \t22\n",
    "    Number of features \t553\n",
    "    Total frequency \t2,288,425"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-no-soil-at-least-25129-reads-outliers-removed-WILDonly-CORE-FEATURES.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table core-features \\\n",
    "--i-table SHNW-gut-table-no-soil-at-least-25129-reads-outliers-removed-WILDonly.qza \\\n",
    "--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads-outliers-removed-WILDonly-CORE-FEATURES.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter population-specific features from tables to look at total abundance of population-specific features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \\\n",
    "--m-metadata-file Wonga-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in Wonga table = 2,288,425\n",
    "#### Total frequency of Wonga-specific features = 249,847\n",
    "### 10.9%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \\\n",
    "--m-metadata-file Kooloola-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in Kooloola table = 1,548,744\n",
    "#### Total frequency of Kooloola-specific features = 117,404\n",
    "### 7.6%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \\\n",
    "--m-metadata-file Brookfield-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in Brookfield table = 928,796\n",
    "#### Total frequency of Brookfield-specific features = 37,753\n",
    "### 4%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brookfield AND Kooloola specific features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table merge \\\n",
    "--i-tables SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza table-final-WILDonly-Kooloola-ms3-mf245.qza \\\n",
    "--o-merged-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Brookfield-AND-Kooloola.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-features \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza \\\n",
    "--m-metadata-file Kooloola-and-Brookfield-only.txt \\\n",
    "--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total frequency in Brookfield AND Kooloola table = 2,477,540\n",
    "#### Total frequency of Brookfield AND Kooloola-specific features = 346,106\n",
    "### 14%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make taxa barplots of these population-specific microbes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table group \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \\\n",
    "--m-metadata-column Habitat \\\n",
    "--p-mode mean-ceiling \\\n",
    "--p-axis sample \\\n",
    "--o-grouped-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table group \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--p-mode mean-ceiling \\\n",
    "--p-axis sample \\\n",
    "--o-grouped-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table group \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--p-mode mean-ceiling \\\n",
    "--p-axis sample \\\n",
    "--o-grouped-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table group \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \\\n",
    "--m-metadata-column Population \\\n",
    "--p-mode mean-ceiling \\\n",
    "--p-axis sample \\\n",
    "--o-grouped-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table merge \\\n",
    "--i-tables SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza \\\n",
    "--o-merged-table SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED-BARPLOT.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime taxa barplot \\\n",
    "--i-table SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza \\\n",
    "--i-taxonomy SILVA132.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata_PPN.txt \\\n",
    "--o-visualization SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED-BARPLOT.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SourceTracker2 analysis -- is soil the source of some gut microbes from wild SHNWs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conda activate st2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter intial table; remove samples with <25,000 reads ('M10b', 'K15', 'B12', 'W15'), outlier samples ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14'), and captive samples (Mx)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved FeatureTable[Frequency] to: table-for-st2.qza\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table filter-samples \\\n",
    "--i-table SHNW-gut-table.qza \\\n",
    "--m-metadata-file SHNW_2019_Gut_Metadata.txt \\\n",
    "--p-where  \"name IN ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14', 'M10b', 'K15', 'B12', 'W15', 'M1a', 'M1b', 'M1c', 'M2', 'M3a', 'M3b', 'M4a', 'M4b', 'M5', 'M6', 'M7a', 'M7b', 'M8', 'M9a', 'M9b', 'M10a', 'M10b', 'M11', 'M12', 'M13', 'M14')\" \\\n",
    "--p-exclude-ids \\\n",
    "--o-filtered-table SHNW-gut-table-for-st2.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mSaved Visualization to: table-for-st2.qzv\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime feature-table summarize \\\n",
    "--i-table tSHNW-gut-able-for-st2.qza \\\n",
    "--o-visualization SHNW-gut-table-for-st2.qzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mExported table-for-st2.qza as BIOMV210DirFmt to directory table-for-st2.biom\u001b[0m\n",
      "(qiime2-2020.2) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "qiime tools export \\\n",
    "--input-path SHNW-gut-table-for-st2.qza \\\n",
    "--output-path SHNW-gut-table-for-st2.biom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sourcetracker2 gibbs \\\n",
    "-i SHNW-gut-table-for-st2.biom/feature-table.biom \\\n",
    "-m SHNW_2019_Gut_ST.txt \\\n",
    "-o ST-analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Answer: no, very minimal <0.0001 % coming from soils"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Bash",
   "language": "bash",
   "name": "bash"
  },
  "language_info": {
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   "file_extension": ".sh",
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