fake_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mechanistic and causal models in clinical trials "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arman Eshaghi, June 2018\n",
    "\n",
    "Queen Square Multiple Sclerosis Centre\n",
    "\n",
    "University College London\n",
    "\n",
    "UK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document aims to demonstrate codes that reproduce the results of the manuscript entitled **Applying causal models to understand the mechanism of action of simvastatin** \n",
    "\n",
    "We will simulate **fake data** that will produce similar (but not identical) results to the real data reported in our manuscript. The information is shared by covariance matrices and simulation methods in R language. In almost all instances the results from original data is different from what you get from data simulation,  because of different distribution of variables in our real data and of the assumption that the data follows a perfect normal distribution in the simulation analyses. This document is intended to help other researchers to use our methods in their own data while preserving ethical and privacy concerns with sharing patient data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: MASS\n"
     ]
    }
   ],
   "source": [
    "require(MASS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Covariance matrix\n",
    "\n",
    "Covariance matrices allow reproduction of many of the reported tests (at the group level) for population of patients without exposing indiviual patients' data. However, note that there are strong assumptions regarding multivariate-normality of data when covariance matrices are later used to simulate **fake data**, that is almost never strictly true in the real data. For this reason many of the results are not identical to what is reported in the manuscript, especially in the mediaton analyses reported below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "cov_mat <- matrix(c( 0.31186188,0.5024700,0.03845738,-0.29656497,-1.034716, \n",
    "                     0.50247004,108.3970770,0.67926848,1.65577687,1.673910, \n",
    "                     0.03845738,0.6792685,0.30155651,-0.03199188,-1.173613, \n",
    "                     -0.29656497,1.6557769,-0.03199188,1.57605450,3.442583, \n",
    "                     -1.03471645,1.6739098,-1.17361292,3.44258328,36.972688) ,  \n",
    "            nrow = 5, \n",
    "            ncol = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "require(MASS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## looking at the rate of change (year 2 - baseline divided by 2 ), as follows:\n",
    "\n",
    "#EDSS: Expanded Disability Status Scale\n",
    "#MSIS: Multiple Sclerosis Impact Scale (only physical subscore is used here, see paper for details)\n",
    "#Cholesterol: Serum total cholesterol level\n",
    "#PBVC: percentage brain volume change calculated with a mehod called SIENA\n",
    "#Block design: a test of spatial cognition\n",
    "\n",
    "id.names =c (\"EDSS\", \"MSIS\", \"Cholesterol\", \"PBVC\", \"Block_Design\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#as reported in the manuscript, means are \n",
    "\n",
    "mu_placebo <- c(0.43, 4.3, -0.0043, -1.4221, -0.5348) \n",
    "mu_treatment <- c(0.1818182, 1.017859, -0.7429688, -0.9158269, 1.422222)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use multivariate normal distribution to simulate data, although this will not produce the exact same distributions, this is sufficient to reproduce the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "placebo_var <- mvrnorm(n=64, mu=mu_placebo, Sigma=cov_mat, empirical = TRUE )\n",
    "treatment_var <- mvrnorm(n=67, mu = mu_treatment, Sigma = cov_mat, empirical = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "placebo_df <- data.frame(placebo_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "placebo_df$treatment <- 'placebo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "treatment_df <- data.frame(treatment_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "treatment_df$treatment <- 'treatment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df <- rbind(placebo_df, treatment_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colnames(df) <- c(id.names, 'group') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 2: showing the scatterplots of simulated (fake) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "require(ggplot2)\n",
    "require(gridExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p <- ggplot(df, aes(group, EDSS) )\n",
    "p <- p + #geom_boxplot(outlier.shape= NA) + \n",
    "    stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,\n",
    "                 geom = \"crossbar\", \n",
    "                 width = 0.8 ) + geom_jitter(aes(colour = group), width = 0.3, \n",
    "                                             height = 0.1, alpha = 5/10) + theme_bw() + \n",
    " ylab('EDSS') +  theme(legend.position=\"none\") \n",
    "edss_p <- p\n",
    "\n",
    "p <- ggplot(df, aes(group, MSIS) )\n",
    "p <- p + #geom_boxplot(outlier.shape= NA) + \n",
    "    stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,\n",
    "                 geom = \"crossbar\", \n",
    "                 width = 0.8 ) + geom_jitter(aes(colour = group), width = 0.3, alpha = 5/10\n",
    "                                            ) + theme_bw()   + ylab('MSIS') +   \n",
    "theme(legend.position=\"none\")  \n",
    "msis_p <- p\n",
    "\n",
    "p <- ggplot(df, aes(group, Block_Design) )\n",
    "p <- p + # geom_boxplot(outlier.shape= NA) + \n",
    "\n",
    "stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,\n",
    "                 geom = \"crossbar\", \n",
    "                 width = 0.8 ) +geom_jitter(aes(colour = group), width = 0.3, alpha = 5/10) +\n",
    "theme_bw() + ylab(' Block Design') +  theme(legend.position=\"none\") \n",
    "block_p <- p \n",
    "\n",
    "\n",
    "p <- ggplot(df, aes(group,PBVC) )\n",
    "p <- p + #geom_boxplot(outlier.shape= NA) + \n",
    "      stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,\n",
    "                 geom = \"crossbar\", \n",
    "                 width = 0.8 ) + geom_jitter(aes(colour = group), width = 0.3,  alpha = 5/10) + theme_bw()  +\n",
    " ylab('PBVC') +  theme(legend.position=\"none\") \n",
    "pbvc_p <- p \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "p <- ggplot(df, aes(group, Cholesterol) )\n",
    "p <- p + #geom_boxplot(outlier.shape= NA) + \n",
    " \n",
    "      stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median,\n",
    "                 geom = \"crossbar\", \n",
    "                 width = 0.8 ) + geom_jitter(aes(colour = group), width = 0.3, \n",
    "                                             alpha = 5/10) + theme_bw()  + \n",
    "ylab('Cholesterol') +  theme(legend.position=\"none\") \n",
    "\n",
    "cholesterol_p <- p \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7H/e+Z0mzv0/PbJ6lo0fG+OQOciEO3d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QzU/we2D9LLiCCixZ3Oei5QqKFZnsqLgF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\n3POC2kVyBIdTW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amHTlJEnilN8dNpB2\n2rGMsT5EZPu25AF+gsesAgaXxtgQLXdcQgelgBr2ve+zNM6IYHgBGaXMxg01xgIR2zqBDSnc\nstmSQB0TyuKo5ggjxEWV61aq+bAf8ofMa69YEqcqkLGvwmDopxMVFc5gCQ9ADw65QEkT556B\n6r0K8gfVQEUGi+qcQsZZAqFEsmJi1lEL5883cI4GETntV1tArZEP5docOnzYVuPcTG1CL9FZ\nnopee5KBOgZjlWiT5SjJadmJkVLG9mU4pVk2UbRpi99vpyj/YnrEbAIGqPqECThN2fTiLd7Z\nCa6oc2tgrLdxn6KRlgiimy1aFmVAs7/rSn9rHsALanjKOC6qRWqVc9RwL00vAdYZkMEINW/w\n+68TWeX8n8vpyWzcaFVPPOaaXxuGdwgJQPqNNyCjucY1wK7bR9aHzPr1VvW3OdRYHqr5vfA7\nFINnszVSWfvJ5z+nMB8sJeOsOSaSAwTCqqj1GRZbpu8EcVuLLnYz1xLKyimC6p3er48tIAM0\nmnlMgRNBfneyzftHjayrc9LGCmqIGvwI85qgdVEvNdUrvZ/6p3fRgkBseHKqVAvlxWug2DTg\nHaRiu6J5dj6XXHKJ/d//+3/thRdeAM11fr3RyTD83//7fzua3tEYzl5yaECF08CxGggQi8So\nIRgc/Sw1+1z3tR5ox+79GU5JjsejapF4eCamTmuwq2iBCruDRQstQ2+TAGifi0Lz8AsxsjNA\n6xJTTqtxvpqJPmcExeOBGTlH2r9IIEIeyGkyUIXsIO2D+S+CrUV603s3zndfrusUX6n2s+Bz\nN2JcqA5JkdtT2FYR4Z44S+8dBdtXjm3asqixwu+27Kurb6O5SlDhNzDcS2F507ymnm75KInR\nY1y2N6SmRDA5Jxi6BllL8qyzO3zIwYxZZnOeJhsExBY6bkH7wl07LTlyFHVMjC0uGOdVz85l\nCfMFTHcS3f+CCKrusuTdt+SsedT3VU8+DkaspIadU9twftVz51AjBSHFqOJ4npxH/6fXmZdX\nMncPwXmUcrZwLXtz3qPJIMWlintUjkq8n1L8e32Wbm8nAyQa8VchihFFd18cHjW1vhDGO4l6\nMP2FgNvjW3c4Ou/BZIhGcCw1tb2UdaJgh6B4TcHx3M78P14DBawB7yAV8MUrhKFfeeWVNnv2\nbLv44ovtve99r82YMcNlXwSF+f3vf29vvfVWuzY+LASdtGaMyfETLPPmG9Dl4rzUwooUMZXR\nkTytfk8pPbjkMKkgu0G9ABHHXBmjemPBiAkovg0x4MWUZzuA4yn7R8ZDfZNcPRIbOErxehvW\n/yMEEhivP4i+DbQvnK1ClnJgSq8AXcuGoqjBYi4WqsbO9Z0YGqUY2C9hpOwEdlRG3dBFLBMF\nr5fmNbANCukVK1bUW1HZI9UoZS/XShMntj1rqZqnO++80zlE06dPd/PWL3/5S/vP//zPTski\n1zvJFvwhB6Hkyquses4zltlGZllmMUGN5CwyyJNPbcEeTnAVnKCSK660qldetnDHdgfvVVbH\nBXIUqImJg8cdAFaXRR0ux86RzAAJlHOafmuZowxPsFxEMenVqx2kOIjVXypzFmLcK6NeLA5S\nb5yXz44rt6eoCV0IvFfX8uZRI+w15o1dBGSG1WayFRzbQMZYlOEiaTlERqgxUQboAzR1VQZI\nTHaayyKnSk1gv796jc2nPlU9knoAGxYRRIpny+8gkBGU73wPpWtMtX55kWmg/mxVZCfnT+fk\na6A3D+snn3zSPv7xj5vqkcRAFYmyRvpbRA1ecmtABkHJFRg7RFlDjELZOoKhpC68iP5HWdFY\nvkpSV1T95BPOyVEGRw9ORVtlPKg3UlMSYkSxtlsloMDb3qb2CZIGaLCAvxxx9QEychL6rgkJ\naEYb0thRQ60nwG4SZWX1FhXaH++kzmXB3v22CGNFhoVY6HYQ0VXvkjObKI7OdZ5q2qiXYGPb\nMYBOASLjpWUa+MY3vmF6ZcsW6vEm5aCy1u+graJAzgjopX/0ox+5XYjk4uabbzYx5n3sYx9r\n6247dDsFMbrdcqtzkNQHSfOIeiF1liTGjbfuZeUEVoC/KfPRWLZNARln9meNDINckgZGl573\nGvMYdVMEaqqB77r6RmWmcu1TwRyRwxSR9CWzfPPIEe4VndZ0oHdq2LoahwbOUnolmZUBo70F\n56mlIudLr7ioP5J6MUlKa50vZaY2McdNY356AoIa9VFrMLfHd+I/ew0UiQbq/zqK5KT8aeSX\nBtSo7le/+pUz1kXGoOxReXm5jaQ2JkrX59eI82s0coS63XobjRyJxvKwClSjkgtGx3cJYCrJ\ns2eD4aeW6AAwOQzDBCx2KbrO5yqcjp+payRL9ihQhgpHIFExyQTTCbdilJSWWbd3vds1pY1v\nk+tzkmh9hihuBtYqVzgNQUOGiGeAE5GcenquTQpmWSWNeJcAP1yHYSLa7O5EVC8ZOtg+Na6s\ngbFxoie1lcit6g8O4ECN4Dd0OnVnYo1qjWxhH4v3HbA9QANHsg8VcEe1BK3ZT76sq9q/zqbx\n7knW8Pbbb69TQQ8CD5MnT7bNGOt5LTjwJzWTonuV/kcibXDzVo57V5kiB79lzonPTy4Lzv2a\nXrzQOUKJ2to+lyFX7za1HuD7qNdb3XVQI9sC7ZWk2qG/UuP4pogqcBBn4NBeWTrEZYTqzq/2\nw2Qyzl+ZXGFLCNQcIHM6BGdxKsEW1RS1VcREpx5qcpriDpD6L4VkyNUzSXTeqjvy0Lq2atlv\nV0ga8A5SIV2tAh6r4C9z58615WQWVLStqLmyS9lsVAV8ih07dBk7TTDHCYKiSGtG8Drh8nFA\nE+Xj6HMEwYOw5VnQllyDVU+kDEXOgSjExXYnljxB+XCUSq6+hqLr2nqGXBvHlsl5Ez1wNRAb\nRYAVvdfYS845t37hdmybQvi4CiPurkXLXM8QGSiiC9iLUfMGBc+LMVTeORQCjHYS0fv+cv0G\n16dEzFFyxiZQYP3x8rE5DaZch1Uh9r3rK90+1LPkKEbOM9t32CeB7Awp0GyVSF3uueeeXKfb\nYcvizpEOspugwYIFC+wzn/lMg2M+/fTTrt4y/oUgYhH7Xnx5sX2O2BEPY2QfI8vmMj3LllhA\nRiIku2HTalkza7ND0fkH1EUG9FtSSwPSqBaomTGQYFMwZTF92EQsg6NfJzgDDjbI3BSKwVPQ\nY/YZMPeJkTMkU364lu2wbps8/7Cfc/lx5SbbynwymLleifzHcMCX0CD2E6NH5AxqKFQyjeyS\n6YUcJUNfk/uRGmrqVt21YJ8tkYM8k6tr6yjTzBVxwpN0Gvgqjm4pAbSj6PlEWDZbMpbWrKNx\nnkiGuDXH8ut2LQ14B6lrXe+Tcrbz5s2zD3/4w7Z48eIGx/+3f/s3u+uuuxos9wtargE5R9Vz\nnq7piwQdeMADMUNtl4yFkiuvdsZDi/eGcXH08iutBzCVBEZJgv5FCeqgXEPZFu+EQ1ILVcJ+\nSmSwqP6JZrWFLg9gwBzEUBD7k0Sx2qFEsTdjDP6mcmO7OUi7MVLu21DporTqNSIRQEwO2p+h\n8f0gRdbNyUbqEX5ALYFqB8bgsIqSXEbEGozXhzZvsU/iaHlpvQaO8dv62te+ZmPHjjX1csuW\n+fPnN6ipHMxvSIZq0QsGdlK9jnBO0uvXWYIgS4agivqoBZpLaB1wDCM+TQ1RPSFDniRrkaSm\nLCDrHZK9rp44yUF7u9O3J11r7Me3SZLVODZjpqVWrbSgts5KTtixs8+1jFj8CkzfcwlmVDLm\ncpw/Zdsko8kGrYF44oXtO+0dMM+1RXS/6tUSkcM1tlvKljN39GDeUPCnD2NQM9k0E9BRrsNs\n2gccybM6Uu8gteTq+nXaogHvILVFa36bFmtgL4b2jTfe6KJR3/3udx1hgzJH69ats5///Of2\nla98BQ6AU+xzn/tci/fpV4xpgIdXtfoNyQgBfuRED1mYttKwySWBojiiBR52jtpb/YyakRCC\nhuSECtd4M1o1BLqSXrjQ0mvXONiemk0mp89wGaponZzvREMDIyJaBKIC5m5BQ4ibIGubiHrL\nAWkPyOhKjMi3ieAOF4SoVgR5EURu/p59dgv1CE1BaVTA/b1Va2wx8LyeqYStOniYWqfeNpYM\noZy7CJbju95H2j3+robWB9F/JHKCND9J9pPhUDBH7//n//yfer+PaH1lm6666qroT/cuSOAg\n9RMrJMGhUc8iET7kIlzJPhXB6I498bhzjGRUBJp3BNeN2globnq7l3XHoUmeeWZNdjq+E+mH\nTFI9wSBPz3/dQjIEcXpw1yuJTGLPM8+yEDix7d2DU8Fvj2OJgKAQZcveA1bKHN4TnUUiKNuh\no1X2O5ynncwxs4DHTgdG15I5RnVycsr1rG0N2+IHWP/Ha9dbdfII5AxHbTvQXK6mjWPeeO/o\nEXYNWfJ8o/RWtkytN7x4DbS3BgpzNmlvLfj9dZgGfvKTn5icJEVW40xSp59+ut1www32iU98\nwn784x97B6mNV0BF0K7nCFHXuAQ8UGW0VD3ylxr4gSAokC0kzzjDkjPPbFUvlBCChqrHHrEM\ncJaaQm8gX/Q5ylRutJLrr89dDxUfTJF8HgosbWGO/jJv46SOJEvTEsOlJapQxJbL1UCUDRLU\nTq/GZB1G0d04R6sPHHQMVdWMd39YbYfIfKluQBTjabYXQ5WXhhp46qmnHGFG9I2cHTlIYrJT\nU+teGLE//OEPrV8jhAeiANcrLrovCqXPm5yiauBu6ZWQrGCgi5pf7HcpMWbmuilrT7R6/jwL\nNlZammatJRjVwS4yn4KKVVaa6oeCU/ib4Ezm0DZLYbwnWhCokYOVoXay6sm/0ux6J7VIOKqM\nz8iGpi6+3JJcCye1Wda4zgvtc5/uJbYVQz/q73UQp/AVOUYEtkZyfivQ2Rv8pq+m2fO7Rg5v\n9vQiiF0Kh7E1994YdP6lUydBAb7X1jKXaK4YT2ZOBDSaO/JR2mvezcdz82M6uRrwDtLJ1X/R\nH30hWYeLL764nnMUP2mx26lPkgqexRTlJbcGZLiol4joel2zVxkfiKBros8OeLjGWZ0UdVVz\n2HBIqSnbI+PGGT+vvALOv4elsqO1uQ/rlqapGwuBdgUwY0UPIxVUa//ppUstNYuIcBeQG6DF\nnbNjl6PXHQjuX7pwTRsxIt5N/6L2EhlEEjlKEf2u/t5Br6RREF1kM0/pu0j+vHmrraGxb1/u\nibeBNirjlQ5CB5dZAURvHH6RepoMgH3PS0MNfO9732uwUJTif//3f2/jx4+3r33ta62KyDfY\nWR4syJBxSQN3DoGmmRwgIG9inSOSYlXPPG2ZtaupWxzqHBy1FFA/IvlGydOyMjy155IRbG7B\nvBqHyK0o8Ck3mgxqDHzVFjkHCYeL4lOwWtQk8a4gTnOSKCuHHOZm1z8to75OZIlSk6dYkBUQ\nam4/+f79LBwQ1R0OzpS4Zq1v4QwdQkc9qR2Vg6JssjJKT+7YYTOAuZWRDe4oETvnFZBDePEa\n6Ooa8A5SV78DOvj8FRFrCgMdfSfSBi+5NZBesdyqX3geY+OoszsEe0lC850cM9YZJTJw0gsX\n1PQSqTU6MhvWu50l1JhRRgviWJ+AuqRpBqseSpGz475s4h8xRlnPHg3Xl5O0qdKsizhI5wDr\n+cz4MvvJ2g22CfiJDMoUDuvto0fZTSPaz0Eah0F0DobgcxRoD8bIVGPabTjI2zEsS/h894rV\n9Dvpa+cPHmSncPy4vAFZhCAwogY+irGleqYSd/0DWw/UbjTO8c2MteaOiG/pPzemge985zuO\nVOaWW25xfdui9fqSGSlX8KGAJCQTVvXwn4G7vW0hGdGQWqE02eAkAZPkGTMt3EDtkHoS1c4j\njlmO+7x63uv0HzoVApjjJoMgpRnYMquA+KbXrHbBmxQBmxCnJoEB7yi+de8xtyubnVm6xNXS\nueMzD6llgKC8zYnaBqTksBWxzIJoYsXgQ/Y8LRmkJMF5kxCrjMGBjTI36kGkxO96aoQ60kEq\nYjX7U/MaaJUGjs92rdrMr+w10DINnAne/Itf/KK9+uqrdvbZZ9fbSA/Yb3/726YiZvVE6kxZ\ntWqVrVlDPU1MxJCl8eaTZDZutGqiuq7GKGoUS4f66if+aomb3wONNhTeZ53tusinOZ+Ah0gD\nGKYAAEAASURBVGooiJYMmVIM4ZhB486LSGS4HxpZMkwOApN9smSpkhs2WGZTb8sMZ3sx56mP\nCTUxDUTL9F0Xkg9AkKAmr/PohaT+IDOpC1D/kfaW20aPdJmeF3CSRK27nexRdyCSJINc36X7\nN26CbvyAfaK8zDlQ0fGd41Pr/QzBuVIGSvS9GSwr9Wz6x4pxRKRroUnRRv69UQ0os/3SSy+5\n7//hH/6h3npqgH333XfXW5Z3f9Q6Jy44wr1Q9dKLzllRvWK4ehWfyUzzOw6fnWsZ6kKN+UMN\noTM4Usr0GI5OIBY54FbuVUu3rfNML3/Lql98wc1BSdapZl5SECdYv9aCceMtZH8hywK2zUA6\nElbxHfNJKKeJmr3qp54gi5RyjJt5p7dOHpCCGn9H89aZZIfm0aT1DWDp1Uyv27g+c3ccdQ2k\nywhSKbJR+/Pu5BH6w3kNdD0NeAep613zTj1jdaAXOcPFwOzUVFFOkiKvImm49957XW2SyBo6\nW37729/a888/b+qrEsm0adPyzkFKQ5MbkjVIqFi6VoJ+/V2PoTQFzykVKUPKoGayYpBycBb+\nFqlC1Zw5NfVHys5FDhGRY0fmAIwiW1yUGIOnGzUEGfZRBTwrQb+XYOwYGr/Cisc+6hwu7VO1\nBBhCXU1GENXVqyNFvUcuoyBar8e2brM/AJ2bgCMWZf0G4uwsAro0D0NKjRsjORuCDfVp2o9T\n1IdrLKidaMKV6foAmS7vHEWaatm7YL/PPfdcy1bOo7UEsU2/QZ0gULoMjo6yzqlpp1NHSE8z\nAi0ZstIh90hALZAzuIHShXv2uMbQIfdMkAL+hvNC8ZWFwLrUX805WbFzzLy50PUucssVSOG+\nIzqDI0V2is8JsqBiwAyZYzNb5pr2C0e6a1yt4IxgfOn5r3kHqVanug4VBC8e5ffem8CTILaC\nux1lrl2MPhWQ6cazoLyVARkFIvdyrTWnNAXPjV1a/9FrwGsADXgHyd8GHaoBNVV84YUX7I47\n7rAf/OAH9Y41gAeoepp85CMfqbe8M/5YAaWsHLb3vOc9nXG4Nh9DXeEdfj9rD6o9CsXeFAlG\ncIIaIdNLAva/ml4tmSXUGigSjOHiDCH6IqXIPGVLZvMmq371FWfMpHmQKnIsCFkaWEyJIC44\nj65ugQe0i2Hy0JbzlJjQPEQm+1j+79ZpYMn+gxhKNTVP0ZZyemTsLKUnSdxBunr4UIq79wDD\nOewyR1pf130aTWavo4bKS9fQgCC56cVv4gwNqGGig8yl6m/PusCJ4HMhwQ0ozuqUQWLSZYJc\nU2go+vWdnPFQGWjNMwqwxLLRylK7Zq61+3BsdzhDBhzXzVnMPyUXXmwJmC6P3vsz7R2yBvZb\nKzp+Zsc2V6eUkqMm58mLLaf2SKyT5w3sb69Tk6TmscociyxhMb/1/3dSRV2bgZaoawX1iI9s\n2GibcFo1Z0yFLlwQWzWW9eI14DXQtAa8g9S0fvy37aABRWEfe+wx2whcbNmyZbYLnLUKnk89\n9VRHQ9oOh2jVLo7iMGwARjZp0qRWbXcyVhaETlCYuDHjxgGUJcF3jQrwKrFHZWQI8XAVuYMi\niXpIBmSXsiVDQ9dAjpEenBgsEpct6gXUjkxVyY03WVBWbiGQIxlHyZEjLUH/Eu3XS8dqIImH\nIwM2W3RZU1m042OA4XyxYrw9sGmzbaCOQd7ROKLSt40aaaXeKMpWYVH+HTK/ZuiNFhEtuJPs\n09f9VjN7IDogi4P3UxMw0ZfKDPO3g9yK0pyXYHHRPRfUZo9CehQpey3RXCFGyxA4XiAyBgn3\nWfXYMkv122Pqv6baSFFyC2bn5pWatdy/AYEzt5y5JO54qV5JNZeac0JgpQHQaxHKuFqo2PbF\n+nG3nEU0r+zvuQMH2EagiHKSBpMxFgzvIuC9LZUNPOd+vWWbdcOxHc28ICdrAdDg7dRPfn7i\nhJzNZ1u6b7+e10BX0IB3kLrCVc6Tcxw1apTpdbJl7dq11GRk7OWXXzaxVqnvySWXXOIyWdk9\nIw4Qtdu6FbanmIhQQs3pIirV2Fft/jGcONkyK1fURHExcuTkBGSVLFVi6bHllql1ZrIPLCMp\nXbnB7PTpNfVGjDnkvzQwmgz1S2loqR08rtbBSR86SG2AfKkas0j6ccJDOaP6A0WQYxkqAHau\ncF0GjZcT00BEUCKd57qnphGlXwz5wgAi/3JwJdWsexAo5GQMn+xtKmCp+/K4MtuOYSWRYyTj\nKns992Un/HOyjtsJp5aXh3COh9ybrKxMQPY4IWioMkEESTL8dgPNJ2SFAxG+UKeW6NbdAhG4\nEFgJoYYPuHcC5ooMMDs5VXFJzjjDqp543M1FEZlDYvcuCwbRXBpHSRJyjya0PZkMZY3kGLnl\nmjeYuxLl5TXOmRbq/p/zDLVNy2ocIuamzLzXLAMBRLfrb3BwQLdxEf+j3mSuRotzVK+zCb1V\nL9jL9jP/HqNWTEQNLZW/7aOWjOsb9VPTHDCO+WIVgZMF1Dm9A5IXL14DXgONa8A7SI3rxn9T\npBpYuXKlOzNlkj7zmc/Y66+/bn/4wx+Ax+92jWvjp/3MM8/YP//zP8cXWVlZmcuC1VvYUX/w\nwHR9SMQitReHB8ngKFVBzJCR06Ji6hySJFtXQqQwU2skB8BkkmR/lE1KkH069qc/WLp8nB07\n93xnSCVhN+uGM5gGuidD6AisaZIkBk/1hIlWleM4SeoZErDlBaybgWijunx8DTQvx3j8ouY1\nIEZH9dvJlgoMxwpqQpbgGKvDvVzYo1zHWRhPo6pyb6N9RPQZewgAnExRMKHO4T6ZA+kqx8ap\nCUV3JueH+0USHgHSRmAolAMDS53qj4gMwWQHVBc4bULZRhyqgB5OgYgWyA5F7lC4kxqkoUPN\nlIWKiWOgg0SgGgKeECp0U+0ifZCCSy+vq1dKKONEJttRem/fBpnMIbffAAp6IwNeohrKWskQ\n0FH2qB6LHsdU/7U0bHmpd1wYrVq075P69LZhOKmCyI4RQQZnegRncitzrKBx6oXWlAhy+ybB\nlN1cz9eB642JwSi1ne6HU3CIN9XO703ty3/nNdDVNeAdpK5+B7Tz+Svbou7zrRVlczpCHn74\nYZchivatsV1xxRWOjGE4LG2SmTNnAstP2r333muf/exnHYlEtH5ZWZmJ3jcub7zxBu1DerhX\nfHmHfa6YaIYzo1qAQMQJFFl3by6SiFGS4EGYVLQYRzBQFkxQGG3HS0XXcnBKVq+yDPS+4A0t\nIZjdti1WTeF2SsYS8Bk1d0xNn26p2shvdI6BCrQX0PtE++fl9i8D552XG100o9WK7v0ozspq\n8PyHMVpElFCOXpo2WXKrQM1eI2NHWTs5pLoHczV1VMz9DoylhRg8KzB8FAmeTKH2VBwkfc53\nkYMUGer5PtZiGF8wtNTBb0XpbfQLUs+haiBrAdnjYBh1aDjiygqFZKEj4oVwx3YrufwKl0lK\nz3kaRwaHiflCvdMSQOfknOS6huqNpDpE1R1Vsd9j1Mv0pLa0TpgbUuecZ9VPP2UhRA5JHDcx\n56kJbUL7HHAcJpxmPoLbumaOqtsBRj3zSRrnris4SGrmfEfZGPslrH+i+tbPW7/wS7mOImxp\nSh6G3OFhIHWi9Vf2bz3z/g70fAl07spGRaJMVD+uvRevAa+BpjXgHaSm9eO/baUGFCk+RJQw\nX+Spp56y7du31w3nqquusv44GJFzFH1xzjnnOAdJDp5Y9iKZjnOgV1xE7KB1tJ9OFbI0LZU0\nkeJj0EObeo8AuXJ5B1F8EzVWk8UkRodjq9pYad0ueafbbXjTzXbg+ecswJgSPXSKvieps86q\nZ8RoxQxU4MeWLa2JNivjVCsZosinUPtQcuVV0aKiet+AY/Tz9Rug3qX+izNTkP50yA9uHzu6\nRXh+sVI9s2OnPbdztx3GaRiLk3N16VCbAOxFDlIJTml0T6kp5BY5TTjEI7l+cqYuw/C8rAA1\nKohdLuO6AE+lIIYswoOSd15mx578q2O7NOBtco4MKu6g3wDLrF/nghgBDk2I4ZyachrMc0Dg\nmCdTbJck6yOHylRzBFwuOXGSBbE5MVsJrlm1eicpU0kGI1vc9jhb1fRbqmn2SnAGRr3U5FPr\nrZpgLLXA3nrLXSYsZuDX/7L4/lK90D9TI7QOB0nNnkuZY4ed0jSpgtZ9FOdoOOvJyVI2+jTm\n+3nAGpVVmln7rFI9k5jwTqePmhevAa+BpjXgHaSm9eO/baUGRMiwaNGiVm7Vcaurxihbfv/7\n39trr71m3/rWt+q+WriQjAhGaLbjVLdCAX2oeu1VS7/2igU8aMMD+8yAqLhaJQz6BJTQiVGj\na85GBglGuOubpKJrshTh7HPsKEXRvQcNshKgOrnEwWnEihdzjrSeutyr7inFQ7iucDvXDgpw\nmWAuv8A52olzNA49SZQFWkDkvC9RcvUwaUqUJbqPqPBLu/faUCK6A8g+yai5Z81a+zg06vFq\ngFd377GHNm+BqpvMCzsdhqHzfvoi1dQjNHUU/53XQI0GFATp/u5bLA3UNrN0MT98WOTKqFkk\nU8Q0J6xVTYCEbI7E1RkpQ81v14DTlQhS146iY3fjJWenZgANdx4MH0nU4fUa+nGcPIl+N6qp\nEsy4K4kCVBOB27VUVgkuycpyjiIZwTxzAGdo3aG3mW9AD7CC4HUfopfbCOYUL14DXgNNa0CB\nUC9eA52qgf30dFis/hxE1E+GnHfeefbKK6/Yn/70J0e2MG/ePPdZ2aV4X6STMbYTPaZgNZn5\n1CtRCyDa78TpM6DjPtUVW6uXUnJ8xXFKXa5DUs1geRjXE4ynAMMps7HSMqpbykUEoadxltRk\nCY7XPWR9XdB/rsaZ2UpNlzrbR6Kszij+fpXo/CEcqKZE27+2Z5+V47Sqt4mKrZUZEkTuz0Bj\nIlkGjO5eHCncVdjnepqaQyrq+59r17sGsdF6/t1roFkNEOBIwhaagE1OwQ/HOCnHI/rtCvqo\nRs/8/gWlc8xytY5Js/tu6wrc741JAgKfxOnTXd8l1T1lyIKHwO6SguZBF+6lcQ1kcuhV83EF\n130GzazVA+2jQPe+euokmzWgk5EPjQ/bf+M1kNca8BmkvL48hTu4bcCt5ICsW7fOrr76arvg\nggsohTlqt912mz3yyCMOAiCYmjrRqx9RZ4qyXCJn+OEPf+h6M4lF7Morr7TPf/7znTmMDjlW\nRoXQYL/EPiVxdLwYGBlgFhmMjoBIY0K1BThHqkXKFZlNUiidXgnVrox+GVMUSpdcfLElRtZk\nSQL6IjmnStFmRSZrRfCZZFlZA/as6PtCfj8kY5ITyIaKnYJxeQx96/te6LMx2SwDlB2oWWNc\nhqC/zVD5Hurd0xTTnQtbWAmwOtU3SXQ8OVKrga3Opy/KlUDyvHgNtEYDCRjqHCU32aMEfZHS\ngtoKCsf9GIhtjt9xuH+fpS64qGGwpDUHaod1U+edzzzDfLV+XU2tFHNXSjWYsXmmHQ5TdLsY\nWxu4EYxX2adIdnBtz4EaPN4rLfrOv3sNeA00rQHvIDWtH/9tGzRQWVlps2fPBtm1xW39zW9+\n037yk5/YekgA5DTJWSrDkFZvpI9//OMgOobajTfe2IYjtX0TES/cdNNNrj5pMLU9uYrj2773\nk7ylLPm4YGQnMYSUrwuAxmUwtpNEa5NnngU7VWl8TTNY6UrmQa1LvUKqTx/3XUiGpOqvj1u3\n99xCLUI/+qvwHcQOVa+/CpQOR0xRZ2oYEqyfAqJXjBI1VqwC8qMi6EjUob4fTVwHNBN5F7Ql\nCtxH2+rd7Q+DJtqnslR9YKzLllMoft/Gd168BlqrARExlFxxtR17BqIEghjOWRJRArTfovlW\nrVGKuSAJtLYzxDWZ5RkR7tnF3EG/NgIvQW2NjAICybJy9+qMsRTLMSZC2HL+4IH27I5dEDBg\n1kHSsIOAZGmvXnYtzIRevAa8BlqvAe8gtV5nfotmNCCnZweRcDlFgrPdf//99qlPfcpt9dWv\nftX+x//4H+7zpk2bbNq0aXbPPfd0uoOkAaR4kCibVEwS4GyK6U7MdfWaM8oImjkLAoWrwfnj\nKsWijPHzD2jkG2K0OEhO7ReqLQq3Use0Zo2p94kkKXpeHKUMlOkhPVUCYDwq9m6qmLt2dwX5\nJqiboCqv4SyOghK9B07MHqKzgr99AEx/dmYo+yQrgDf2wMnZSfPLwdQGSNSjRHS7F9IQMor6\nDiXzt5IsX78sh+sIxdr6Li6qi+rOdczOasXX8Z+9BqSBQEGgm99jjtlOkNm+BD/U64j7V1ml\nTvvdcryqp54kQ7SWBJaoY8h2w6iWuuhiRwbhr1bbNKA5QM2g1edIUN690LpP7tbXriIQVtoM\nwUNjR9zHffLX7TvomUSzX+YqEdJcOWyoDfLZvMZU5pcXmQa8g1RkFzQfTkd9hT760Y/anXfe\n6Ybz9a9/3cHq5s+f7zJG0RhHAqW45ppr7LnnnosW+fcT1ECCzE9y1izXm8T1NSG7EdJTIwFr\nXfLs2uxOI86RDh0cBHqXZYi7IWGwZ/bts3huI1k+zvTqCiID5AMQMShb9ApEC1uJwKvw+YM4\nR+dDfNGcKMP0Idju7l23wcHlZBxmMA4n4zhdN6zU3iayL7kEOMyS/QdsLwyE/WthdmKz65VM\nUTtQQ5+umqfHt263XRibcqQuZZsLBlNzxhi9eA00poGAgJCj+Y5WUI+itggBlszaNZbesAEI\nboaWAQSZqJcz6uzEoAcVY6N7rYLJLly72rUZUJBGd2xIk+rqZ+e6bHaUSWp0B/6LRjWgesbz\nINfRS0yyqvWN5pBGN2rkC0GG71mzzvVjGso8J0j132g+voL9fm7CuAYBnEZ24xd7DRS0BryD\nVNCXL/8Gr0aXelVUVNQb3IQJExy73TD14YjJELIQERQvtth/PAENpM48GxjcUEtD1RtCTZ2c\nOsySImqgp1G2iPY73INxjmET4FyFMprI7DUQoonKJLWXuKaQixdZho7uCYqGBe9JjB7TXrvv\nkP2IIepWorQ3DB/m+iDJOYl6GbXkgNOJwH5l8kRbRv2H+iiV4ohOJZKfIDr7du0OpvC3HLH/\nhsVuLY6tEFBivRNLnmB+c6AJv3/jJgejUf2SyCHuq9xo+zBoNK647CBb9RQR4Lc4nvqgnIWe\nL8SRirJV8XX9Z6+BlmjA9VSa84yll79VUxfEPVr1+KOOBc8wzFVHF9LEOqT3UXZmU1mIjLYj\nYxXPYAe9elMjedAymzZasgnnqiXj8+u0jwZehklzLc7QRAI4kfTBwVZ2+7mdu+y6rLkmWse/\new0Ukwa8g1RMVzMPziXqgdQ9KwshQoaeMOpkPzTVHFN9Ury0rwYS1BzpFYlw/3KYMnKaqGUJ\nBC2sroICeAl4dZExAHVR00aclACqblfELWNFRg0Or4yY5Ljx0e5O6D1Nr6Tquc/UNKqEgCCN\nYZSmKDt1yWX0Xpp0QvvujI3FQKdXW2QIzs6Q7nFSb6m/PgOeCqrlTG3mOiUJsYspT06NnKpH\nYLyTYxRB8NQwWDC7J7ftcJmsCP6ynW2/t3oNEMAqIDElLiP1wMbNtgaj546ysQXRYLYt+vXb\ndKwGMmtW4xwtowcazjjzh2j9Dbp/QfVE+pKmJjGkzUBmUE3/pHqjYQ4Kq4D0dW8YqHHJT+3D\nywlrQI7NaghdjuG8TqfObGQtgUNrdrwCh1XtC7JFDJxv0Vfruuwv/N9eA0WogYa/gCI8SX9K\nna+BbEeo80fgjxjXQPULz1n6zTdphEHTWB581W/hGAHVSpw6hd5Ig/GDcISAUATLlljVGbPs\nFGAwGZrmynBJCrZ34cVkoI5HE+P7btVnaqOqX3rRjH0lcLokKhYPDx6w9EsvUJxdVsfA16r9\nFtnKvbhGFb3rT8/b0V2UeYqfrtYVW5VIHCIHSbUDqpEaT5F2JDJu5mE4zQZ6Mx3IpRevgdZq\nILNubc0cgjMkVkz6JNTMC8BADQiu1fZYy9DGQQ1i4xKwTQLHStTdylZH4kgbcJ6Cgc1DVaNt\n/HtDDVSjw/s3bbYXd+3Gd622Yziwjx84ZLeXj7EzW5n976lnBM+EbNGyeK+l7O/9314DxaSB\n+k/gYjozfy4nVQMHScULahfJER6gMsLjy/Td2xQKe+lYDWQ2b7LMksWm5pGuRoDDKUuUgZ46\nBEphwOrk0KqQWyx2sFdY8r3vsxSRQhk1xsO1Qa+kNg5ZTlhI/U6iPxHomAj+pwa0gvsFw4bH\nvvEfIw2IUlyS5nekeoNIZMZwKV0mKVq2BCcoYt6LlqVoFkoVimsc6R2kSCv+vVUawAh3URNt\npIxP7T3piol0E0pAD2TUoDqHCP5b9cifIYvAuQJVYNVpfvMEasaNy3uIbY7TaddFInyZA3xt\n6b79LmMsSOw7WgGJfR7H6FkguGXqeZWqhqcnsLf5zf9qw0Yaw/awEWTrWyrKYMvROkrWWllq\nSRXX/gAO8Zm1tZAt3Zdfz2ugUDXgHaRCvXJ5Pu677rrL9MoW1Rx56VwNhOqNxCETZBCcYGCH\nZCMSPEjDvXtwXMna1BrcIQx4ARANNZBMDD2efWi3EUN3LYKCbJHz7ASmNy+5NaCapYlk3ZZT\n1F4ObCa6ZhuA0qhX0hgYrCJRLyU5UtkSovpUQ/Vnr+b/9hrIqYGADFEATNf6c2+RjRYjpvvt\nAuW0KCvE/aimr7kkQQ1qybU3WHoeMDxlqIGLJqlZUtuAOmcr14ZFvkyEK99ftca2MS8PYp6W\nI/JbILErCFLdSYPXVOSINqEHOUjKIAuOG4EVRQ5TybVZTMCkVQ4SjJ2XlQ6xZ8hEu/ma50MG\nQo6LCKLN8nViTVwF/1UxacA7SMV0NfPgXHoB6bn11lvzYCR+CMc1kGURK1uEcZNR7yIoduPf\nqk9SGINlHd9H+3wKgPMZ8C41lU3EITUiiuDBW6gwG8HbFhL5VRR4BLS6ZxD9VVFze4ocoveP\nGWn/uWa9rcEIleEiqvAhHO/2LKrxswb2t79s3urqCCJ2O8Hz5DNNFsWzF6+BNmggSdPWzGrg\ntxvWmfUgwCLDfddOYLrU1Q0cZIFgdswhyelnNLp3OUkJnCTVPmr7yNFvdIMu8IXIVLZDqjIh\nNvcOxNlRc+hFsFqegcPSnOwHVteDAFS2qI7xAJm61oieCbeMHOFqIVfhpMkJLmdspzJ3xJ8X\nrdmnX9droNA00L5P8EI7ez/edteAmq6q75GX/NGAqH31UFMhtSK2ksTw4RZSJ2BDgblgeKsO\nAPyjg9ulR+aO/roNT/Af1T+V0POk6q+PEUHe4jJVKtwOgIBoub4vNFmMY/Sz9RvsbQy+bmRu\njqDLp4C6fLK8rFVR25act3ohfXHi+BoqcMhNVEh9GlClXlnNZS8dOsRWY9gsxbhSzyZlk4SA\nunZ4ab26pJYc06/jNRBpQPNHt6uutmrIXUT1HeCch2QnnOdN0COAhCR4xyXA5UZHmzT+DnTL\nG9s16lm874ANrqX1jxQmGK0IWFaT0W+Jg1QO1bp+771jc6gcm2P88Ee1Al4XHV/vYrGLM9nF\nv/OfvQaKXQOFZ40U+xXx5+c10M4aSFDTo4hu9YJ5NU1geYCGbx+xYPx4S2DwZLYBddF/dFzP\nQNCQsw9SO44pMWIkTStvofEskWggfgG0v0nGEvRtPkrajsNol12pX4hotkX3HSdEWEdPmAcp\nmP5Heoa0t4hB70wyVGo6uwjj9PHt262U66hGtpFx1It1Pj2u3N4goi8DS9tM6dPHJvVpB6KN\n9j4hv7/C0gD3WkoNo/VCHMSO+7B6/z47QoDgFGWTvLRKA2o0fSRd3WAb8vnMLQ2zQg1WZMFV\npUNhmDtom5jb+1N7pEDNZmp/J/C7n96CDFSuffplXgNdWQPeQerKV9+fe5s0IINA1MzZ9Mxt\n2llnbQTOPykHiKiv8QBNQPOdEMsUD+Zw317XzV4QtwwPWOOhqnNL8F2HCZFJO326RUcgf1VD\nN95hB+yYHa9GX+o4X0ZNUAaDJJKRRNZX0H9oOyQkEbNc9F32e3QfRfdV9ve5/l4ODe9P11fS\nB6naOWdVRIn/um27fYJ6heGqDUE0uZ9JsbVekUTHiv7ujPeTcczOOC9/jBoNOIicWBFxwo17\n3kvrNTCrf1/745atjr4/gsQe0TOG3/XkFgY1lEH6THmZ289aMkn63Z0DycN7xo6xiOCl9SPz\nW3gNdF0NeAep6157f+Zt1IAM4X1E5lMxKEMbd9W5mylDE68NEDWvBEIGJ5xTZMwewNDxtQE1\namnq391kZ46RyTlCBikuokc4QsH1TjXCzYLOxNfTZxeB5/0YEMg9MHo1J4oM/6xyEwxTGRsR\n7ZtCg0qcpntXrbZPUjuQT1JNli3uPObT2PxYvAbyQQOXAIldefCwLWHeFY12mjouBT2uGDa0\nxQ6SzmMiztQ/95lg28jovc2+Sqnz7BGR8+TDifoxeA0UkAa8g1RAF8sPNT80oOa2A3nwqN6q\n2ESO32EIAPqTTSrxD9acl1ewFdX3qOy5t+p/9u63ENiRYG2RqGfRyD59beKw0mYZqOSUbgcm\np+bKA1rQr2QZjtDbHKsc6ExcJpDFqiRjlWG5apXyRdQIukOzkflyonk0jsyO7ZahIbPIUNQA\nOjlpMj2ISvNohH4ocQ3IKfrUuDJbwPy7krmlGxC5acwtp7YwexTflz73Zn+ZjkQAZB/Q/+01\nUIQa8A5SEV5Uf0peA14DHaOBp4CxCQqTVoqI+gDVbg0gi7MZx6QvDqUMnb04BAR/7QNjRjXr\nHLVllOpHkktU1K3jNvZ9rm38suLTQHr9Oqt+4nFHwW2Qn4SQoaTfWurqhkL1IRNjJNnk1GnT\nLFFeXnwKKNAz6g4D3TkDB7hXgZ6CH7bXQFFpwDtIRXU5/cl4DZxcDYTAOjJbNmOlV7nIdQBb\nXrFA9VaQuXkI6uyhMHVFZAhqpLj+8NuuoeNOIHK7gNtNpuntZaWDO4z9Sf1MRApxiGPHs1Y6\nvvqe5FP26OTejV3v6CG/u+rnnnWw2UB1QRIyEeHGSjv20O9dQ1b1LsqsXWvp11+z5Nmzrdvl\nV7J+bY+0mi38v0WiAfVTmktQZ9n+g3YKDthMyBrUgDbeaLpITtWfhtdAu2vAO0jtrlK/Q6+B\nrqmBNP1Rquc+Y6LtlqgqJ1ExyVIFSt/tTiL2z5vg+lVAHTlH+kpd5vuRQdqDYfqFivGxtTvu\n42DgfFcNHVpb1J1yWSv1QFFt0h2QNIgRy0vX1EC4mwwRxCGJ0mHHFcB94TJHet+7F1IWehWJ\nnAUHO/3M03YUSG3362/scPbK4wPyn3Jp4CDOzD5+x/1KUvXmmFzrtmTZfvb3X+sqbQvzcX/q\nTKupa1pAXyURvHyQ7HZEBtGSffl1vAa6oga8g9QVr7o/Z6+BdtaAmPCq5zxl1q378QawMKwJ\n2hMAG0mJPrzARU6IagOypRtOk4ybzpSrKd4eRCZrDv2Wdh+DRQ8Gq8sp9J7im8B25mXIv2Nx\nL2ZLCPzTaoMWIU1dBa8Lap3o8NBBC9eusfTiNy0566zsTf3fnaABka38ZetWe27nLkfMUMIc\nc+GQwXYdNWOC3bVVnoPJbiP1kpNjLJaDYWB9afcel0maFlve1mP47bwGilkD3kEq5qvrz81r\noJM0kKmsNMNJCAbFiCuSKdfbKL2U+ocicJDG4YTIuMgWZY9O7+Q+I4Itzsbx1MtLfmtALIUi\nPukMCbtD8U7dUdo1fa6F2JEpgiLRZYxCAhhOyCZJAorW0qyfeests1NPc8va+o/IOCRiY4yY\nGW3zJgupiVLrAFNWaxz9zmpp6N3K/h/73ZZt9ixkGiMhVunRLUXD6Yw9vHGzHaYdw7tp7NwW\n0TVYeuhtG0iT6Oi61O2H+2EJ89h4MlXFIA3OrxhOyp9DXmigOH4heaFKPwivgcLVgOA2wQlk\nQUIMIPEWZMevA2obwsOHagrGCxz6dRYMc8/t2m2rYJlSHZBiu1upORI5wyVFyGhYuHdz/o08\nos/v8JHpNzb7HDOgrsH2bRZidAdyTtSSAJiVc1xw2JwcZbl+n3JYqnFu5EidgERU7np357vw\nDbP58xycT8cPVq20EGY9u+wKs169TuBIxbPpLhwZBV1GApvtTtBDuuvOJDqK7PDzLL+AwMug\niMq/Faet/SiZmOFaR9cl2lzLQuB2nXZPRgfuoHedX51D3kHH8LvtmhrwDlLXvO7+rL0GajRA\n1De9YL6lly2xDMZ+Aqro5ORTDQ7zVmkoAe15IMNLrxjMJzx4wBIQNajmodClF9HYT0PF+zAR\n3zeo49BDeUrf3nYj51dKY1gvXgO5NKBsX58sSvZc67XbslOnWDhkKPDWZTDWkfHEyE5cfoVV\nPfmkZdaspqFrwv1WQzK8STI6CmCkTptqqRMc40Fqn5S5OAWH6xR+98eWLLKAWjn4692pOSMW\nKFli+TIrueiSdjvdQt7RFnSWxHnsy7wbF2lsN9m9KnTXp3frnclD9Gc7rWcPe+HwESvF+YqI\nco7hTCSq0zad+b1T78n4ybXzZ2WQovNr51373XVxDXgHqYvfAP70u64GZLBUz51j6RVvOca5\nRM9epiLvbs//zTIwXxm9U1oqwZixlhgx0tKbNlKDNAgjjD4c+/dhiPFx5pkt3U3erzcIY+ND\nY0fb+zMjTSCl7kXg+OW90v0AW62BAAM49Y4L6m0X8Ps89vvfWWbdOgv69bFE3/7OOdLvNXn6\n9HrrnugfGRyhEAM/qHWOtD9nxJKFzaxZY3bBRUURNDlRPfXGSWWKtGocl1RsLqmuDTT1JijT\nVrmAGqNN5PRX4iz1TZW45rOHyRJeTH3Tqb5Wsa1q9dt1IQ14B6kLXWx/ql4DcQ2E27ZZevXK\nmgaSPKidUMCdIcKYee01s4qJLTZiAqKgqSuuMnvtFcsISsM+EjLSZp9LBmlE/LBF8dkzxRXF\nZewSJ+EyNzAwKrvb/QO3W2b5W/zuV7mawcSo0ZaadroF7Q15A8IVONM/S8XKLvOdlxoNDCfz\nPBVndeG+/Vbes6ej305zndYeOuyIFIafQL2WmsV+mmDOQmqZxFwnwocZUL/PwHHiKnjxGvAa\naEYD3kFqRkH+a6+BYtWAmOfU6BSMR71TDDGWQrI/IYXlQe/e9b5r6o8AmEjJhRebnfcOHCQI\nG07g4d7Ucfx3XgNeAy3TQIY6pOrnyAjv2OGM4mDQIEudf4F1wynqSAkGA63DGVJfJtUhRiKa\n8cR46PBj2ZLou674rqza340eRQap0t4CbifHRRmlqWR4bhs98oRVosbV7yRjpJcXrwGvgdZp\noL5l1Lpt/dpeA14DhawBZ7jocZwl0HMHMBwFwMnaJCrI5uXFa8Br4ORpQP2Oqh59xDVtTgwZ\nYqEcFmqSqh57xLq962YYJ4HCdpAkRoywxJTTLL3oTTOgu4LcGlCvoE9vS515dgcdtTB32495\n+LPjy2k4fdj2EVjqx9w5lmzSyexTpAazakbdQ9fNi9dAF9WAt2K66IXvKqctpp777rvPbrrp\nJhrKU1cTkw0bNtiLL75oAyEYOO+886x3K7Ilsd2clI/h29AG49sEPEjbKoK+Bb16W2bPbksM\nGFizG7DwSZoJJs44g55GbXSQ2jogv53XgNdAu2kgDZTO6IEUDBvm9ulgVcx1IVklkbKk3nFh\nux0r145K2L8a1mZWrjCxXCYmVFhy6lRH/Z9r/a68TM5QeXvDHNug0BVksf6waatV8nxJMiZB\n8m4cMcwG+mdBG7TpNyl0DXgHqdCvoB9/kxr40Y9+ZA8++KBdfvnl9RykX//61/bTn/7ULrro\nItu8ebPp7x/84Ac2gCLifJaQfhnVL70AGcImV1OQhEEtde55FsBa1VoRJC516WWwWz1hma1b\nXBF1AJNdmn0mzprd2t359b0GvAbySAPhzh30RILCO1vU92jH9uylLf5b0NvMtq2OFlxkEIn+\njcyZwOiSEL3o5SX/NaC6p3vWrHPtC0bwbEgTLHuZjOOWI0ftcxXjfDYp/y+hH2E7a8A7SO2s\nUL+7/NDANggI7r77bps/f36DASlz9Itf/MK+//3v24wZM+hvWm2f/OQn7YEHHnDvDTbIkwWi\nzD72yF+MrpMWiFabCJ/YorSsBMhMo4ZKE+MX81y3W261zMZKC48etRBIxbF+/XMbVk3sx3/l\nNeA1kGcaEG23GjhnC1TcQZ/aJrLZ3zXzt2jCq56d4+YKx0oX4ATNnGnJWWc1s6X/Ot818MT2\n7c4pGlWLSijhWTCerJb6volE4hzflDrfL6EfXztroPCbk7SzQvzuikMD//7v/+761HzrW99q\ncEKvvvqqjQAjL+dIkgLzfdVVV9mT9AnJZ0nT7T48sN/1FnE1PjzAAmoLDJaizJIlbR66YHrJ\niZMcm1U4cpQvoG6zJv2GXgP5o4FkxSQ3mPDQwbpBKfsjApXkpJrv6r5owYfMrl1W9fRTjtRF\n0LlgaKkZZALVr75i4WpY8VorsLWpJircudMx6rV2c79++2pAGaRsKJ1gmd0SgW0EqunFa6Cr\nacBnkLraFe8i5/vlL3/ZSktLbf369Q3OeMuWLTZyZH2GIDlMO3lQqyt3Isaw9Pjjj9vXvva1\nevvo37+/7YAVKrtDeb2VOuCPktWrXV+hEJx4XFxtwdo1dgyM/4lKdE67MIZchPhEd+i3b1YD\njoaZtY5Qp6HMZ7GJMrTRfVVs55bP55Og9ih1yTut+vm/WXiw9r4iGFRyIbVBo8e0eugZ5hjS\n7SZYXSRBdyB8wLGqlyw2G0ZD6BZKuGsnmai5FgL10/2foBZSUGHVKXk5ORrow72xn+tL3rGe\npKl17UcfJS9eA11NA95B6mpXvIucr5yjxmQrsLRswgZ1FZcRtw/mp3gdkrJL2eQNchyiV2PH\n6Ijlot9OUCvEwevtPoB1LkMWqD0cGu1DBsvJOL96J9XF/ihmnbfHfdnFbod2O11lhhMjRzlH\nxJG6kHFuDXV/fCAuMAO7ZQPp1h2GuvpBmwbrxBYoi3XssUcdVNgGD3EBKWXG/3/2zgNAqups\n/++9M7OwLH1hgaX3pjQRRBDEBrZYsRs1aswXk5hEk1hSPmOqsRKNn4rKX5PYo0bFhgoWBJUq\nvS69LcvSYXfm3v/znt0ZZ4bZPjs75Xl1mLnt3HN+Z+bufe/bSqd/ID60ZXepufIW1jw/1pLA\nmNzW8tzGTaKKUrAA9u6SUvHBgjQQtZooJJBpBGJc7TINAcebygTeeust2R9mUTn//PMRlxwj\nMDlskD6kVdWn2uESXG4SlRXutNNOE32Fy8UXXyxt8BS1rbq3JVAcZJYr3bQBLnBQ0LLLstdp\ndiixPeIddpx48mqeqCG6+6ogHsQNjGb2U06U+iegmRZ3wP+/UaNGEcp5/Z85MWcoRS2ccKts\nYs7KswQJaBFYK6d7cLHW7zbSggeWIn4pugUoR1a3HtFrK1wOII5JULjWQjKYoFjNmouDm3H/\nNwskiwpSEEtC38e0yZUtSMjwaeGushpWeFDWGMVlv4s6TflV/E1NaEd5MhJIEAEqSAkCzdPU\nD4Hp06ebm8tg6xpLVJWCpMpNQUFB8BDzvhd/sNVypDepySr6JNiDIo+B2bPE2bsP3YSlB8qR\nZ+QJ4ulW9xuguozbuIlp8gg8fQTEujTFY0mABJKQgN2zl1ioa6RpwiUXbnbqiozCr7gIiWfw\n4Gr32C1G3BGuE9GKlsZCuruKqt0Od4wvAU3rrcVpRyEZw2Y8ePNhuRdKX7TO4oOy+JJma6lC\ngApSqswU+xmTwEMPPRRzfWUru3fvLhpbpFYjdaFTWYIkB9FxSZW10VDbvMcOEk+XrkizqzEF\n8N1Hem+rgVOTO8iU5VelrQhPHlVh695DvCeMqrUrT0Ox5XlJgATKCLiw+rmayhuZLa3mLUwy\nGFVgfBPPksCsz1BmYJOJh9Rsmt5Ro029I5jyq4XPgjuzBavpUXIEtZLQHqVhCXTLaSL6opBA\nphOggpTp34AMHL+6zD322GPyr3/9S66++mpjTZo2bZrceeedKUHDQvE+D17JIA5qSJW++7bJ\nbGXpU2Xc+ARWrhB3T7H4zj1PLBYYTIZpYh9IoNoENKtc6YcfmALSZfFjlti9e4vvpHFGgbHP\n+Y54VRlyoOQ0hbITltSmOifRByiBuXMjClS7h5ElDdk47YHHVqcJ7kMCJEAC9U6AClK9I+YJ\nko2AutHdc889cvfddxslKRtZmC688EI58cQTk62rSd8f/7yvjb96yIqF1OOC7FlaeNZZXyCe\n3n2SfgzsIAmQQDkB1Egq+eA9cTVGCGm8TdIWWNoDy5YijilHvCNHmR1rm+hBD1aLlG/CmVL6\n8YewUsESrr52Hq94ThwtHmaxM3z5DwmQQMMToILU8HPAHtQjga5du8qnn3561BmGIuHB66+/\nbtIqa7IFBpEfhajKFRp35CLduSBFb7iYp85QlLTGCYUESCB1CAQ2bxaNEbLDUnabuMLWmqBh\nCYrCDhcrDslbbJRZaHTp5eJoPBOszhbar4vSlTqE2VMSIIFUIUAFKVVmiv2sFwKVpQOvlxOm\nUaNGEYL1TfDUGZkxIkbmImW61kehkAAJpBABuLpZSLpwlMBV1kWGS41JQnrLozbXagXa0cQz\nFBIgARJIRgIxroTJ2E32iQRIIBkJeAceY9xxNKg7KFrTRJ8ye5iuN4iE7ySQEgSs5s1NHTQU\nhYvs74EDYiOjmSZqoJAACZBAJhCgBSkTZpljzDwCcFsx7iv6xBc1RrSGSX2IPWCgeHbvEmfp\nMnG0EiXEQrFH36lniNWiZX2ckm2SAAnUEwGrQ77YnTuLs2G9WCjiqg86XC0Ci6QM9snjy1J7\n19O52SwJkAAJJBMBKkjJNBvsCwnEgYCD2B//x9NRr2SHVkoygdZ2n77iG3NSyD3G3bULMUKo\nOeLLEqs9grEbRbrIVbcbFmKNfGPHi9NvoGlPl/UmSwO6KSRAAqlFQDPS+U49XfxfzJLA6lVl\nmergKusdhwx2/Qek1mDYWxIgARKoAwEqSHWAx0NJINkIuMg45UeKXk3VG8pCBfe3wNLFpoCr\nF0VlA198Lv4lWIbBxxRrhDLjO/U0sfM71no4dl6eiL4oJEACKU3AgkLkO+VUU8vMhQXaRt0i\nFIxL6TGx8yRAAiRQUwK86tWUGPcngSQm4KIukVO40yhHMB2ZnpqsU8gS5S5fKn6kOA8sWFBW\n+LG8RpEDS1Lp++9J1qRLaz0yk5QB7aiCZrdCsUfWP6o1y+oeuBl1Y1YjNsRBNsHOuKnt1ZRW\nu+qy435VE9B4o4aKOdKYxnhky6t6lNyDBEiABGIToIIUmwvXkkBKEtCCi5pdzmSYCx8BFCOn\nuFisRVCOUGQ2vICrKjSmbhHiDqQWViRN9V0y82NYrZDyG2I1hkvOCaPE069/eA/4OY4Epu/Y\nKa9v2SYBmAFdB46UtiVjWreWyzp3FE+5YhzH07EpEqh3AvqQxVm+TAILF4ijSSFgufIMPU48\nfVhLrd7h8wQkQAJHEaCCdBQSriCB1CVgobK93jBrFqqICveHDiLOqBEsPAGxmxxtaYBKJXL4\ncI0H7h48ICXvThM5dEhsFJZUq5WLgG7/jI9M6m9Pt+41bpMHVE5gFfi+unmr5DXKkqblrk8l\nmO+P4VbZrUm2jG5TPwk5Ku8Vt5JA3QgE5s0V/5wvxIJipC8X1xT/9PdFH/p4Bw2uW+M8mgRI\ngARqSIBpvmsIjLuTQDITsNu3R3rtzqhQv00E7m4qLpQjt3iPeI4bLjasDC6ezoaLFnzVgCRN\n8VtTCaxdKyatN4rthlz6kA5YXewCsFZR4k9g0d59AoNRSDnSM2QhuL4VMo7NZnHe+ANni/VO\nQB+qBObPw/UJBWORdVMt3GrpFrwCX39VVn+p3nvBE5AACZDAtwSoIH3Lgp9IIPUJ4EbZO/40\n8fTuY7LKOaoowTLkHXWieI8dJF4oSVrY1d27FzoR3LNUidqOavZt88Tu0rXm40fxSMsbo3Ak\n3Oxc3qzXnGc1jtiH+AxfjGKeuu4ALIQUEkg1Am7x7rKMeVEFp00MFK5Xjm6nkAAJkEACCdDF\nLoGwear0IBBAjaFCuDOVWV6SdEyDh4r06iNWyRFxc8osOui0CNzrbChJ3gXzRTTmCDfVTsdO\nEsA6VWic8gKRRUVFR8cxxRiqB/t7NVFAVAFJa+8ecZu3lD1INU6pnEDwe3QEGcN2VINXK9ww\nFsG1sUXAHzFH23D8SLgmVaeNynsU361+KOHB71V8W2ZraUNA6y3hgY2l1uzwGDq9HsFaKthO\nIQESIIFEEqCClEjaPFdaELBhpclF4dW26laWzFJR/7DeHTpMZN8++Gb5kKnq25ikPbAIHYLv\nf6tWrXBPUvVNiZvdWErXrDZxAhbcY4yoSx8++EaNEruiPiQztwT3TRXunUh00QgxYi1bVl1c\n91TMzeKAIxsOHpL2cEWycUO5A0pTGyjC5/XsLm3RTjJJKSxe+puhkEBFBGwUpbXbtBEHvwNT\nMgA7mgcHmpGzXYeyzJgVHcz1JEACJFAPBKgg1QNUNpneBIJZ4o7KFJdCw7Y0uB832tESHFNw\njNHbo5c1XsA38UyTlMHdAVc9ffoLlzvvSePE07NX9O5cjkEgyFw3hX+OsatZ1RSK6w97dJO3\nt22X+Xv2SgBJOY5B/Ni5HdpJ+ygXpYraSOT66owpkf2p67lefvllGTp0qPTqxe93XVmGjteC\n0yefinID74qzdWv57wCPWZBh0zvu5EirUuggfiABEiCB+iNABan+2LJlEsgIAnb7DuK7+FJT\nnFbU7Qs3NQ1VPyUjgGOQrWA5ugrJOC6DCxKckEyShkwZe0OO880335TJkyfLXXfdRQUpzhNh\nwYLku/BicTZsEIELqYVkL1bnLib7ZpxPxeZIgARIoEoCVJCqRMQdSIAEqiKgFikLGfQo8SNQ\nDNe0ubuLZfvhI5KLlN5DkdErr3Gk+5yXrmvxA15FS5s2bZInnniiWq6nVTTFzRUQsGABZd2j\nCuBwNQmQQEIJUEFKKG6ejARqTkALKOoTVXVd0xsISvoTWH/woDy+br0UIbaoEZSgI3Cje2/7\nTrmxe1fp3wxJNygJJaCJJu655x655ppr5JlnnqnQFbIE86WvaMmEJBXBZCP6ngnjjZ7jRC4f\nRtziPnwnm+HBVGO4J0ZLVXOhD18090WLasSZRredbMv8riXbjKRPf6ggpc9cciRpSMBZXyD+\n2bPEgSVBPLZo4VXvqNHG/SQNh8shgYCDG8znN26WvbiJ6ZnzbQKN7chS988NG+XX/fpIdoyb\nIsKrPwL/7//9P2mCTI0XXXSRUZAqOtPDDz8sU6ZMidjcBq5j2zWVfoaIJnrRFyX+BPy4Nsws\n3iuzkGDnIBK1ZOPhyYnNm8q4li2Q+l9VnkiJnouNuIZMKyqWDbBK6+7d8MDt7NYtpQNcdlNV\nmCUzVWcu+ftNBSn554g9zFACzqaNUvruO2WZ5jQbHJ4aBlatMjWMfOeeZ4opZiiatB62KkKa\noa5rk+yIcbZDdrp1sCxtQJbBvlqMl5IQAosXL5bXX39dnn766QotR8GOdOvWTUaPHh1cNO8r\nVqxA3eTUvQGNGEwlC5qNUV9eWDWYtbASUHXY9H7hLvkQiVnaIftou0YeOQDe01E4OoAHJue2\nKc8iivaDc+HBen2paKbLZ3fukhJ4JHRC9lHNNLoB654rLJKbO+aj0HRq3g7qdy3dEsGYCeM/\nDU4gNX8RDY6NHSCB+ifgnzdXXPxxs1uWZ5vTeBPE+bjbtomLGkZWr9713wmeIeEE9Cmxiqbv\njhYLjjGatY5SPwTeeust2b9/f6jxCRMmGNe6W265pVpp/SdNmiT6CpexY8easgDh69Lxs3Lb\nB8tGUyjv2dmRyn06jjfRY9qtMYmbtkhPWIualCs9TdCJplCS5h46IueiBlpuuSJ+ALXp9qIY\neDOsC87Fh5u3SADKa+8wq3RzfF69/4CsQDtno3RFKoqWEaCClIozl/x9poKU/HPEHmYgAY07\ncrUGSJSlQP8QOFCUtLI8K8uk5xdDLUUtERtQeAS1jZCcISgac5BlW5KPp7+U+iEwffr0iEK7\nWhNMi0J/8MEH5qVn1ZvPF198UVavXi0333xz/XSErZJAFIFCWJb14UlQOQpuVndbdcvdBWtQ\nUEEKbgt/L8D3tjniWKNF45gKYJmmkAAJRBKgghTJg0skkBQELHUbyG4irgZ8Rxf+hPKk2yjp\nSSALc39Rxw7yVMEGOewEcFPjNa40B/wBuaRTvlGe0nPkDT+qhx56KKITixYtOspd7Ouvv5b8\n/HxRdzoKCSSKQI4HmUJxsgCUIU+YdVmXNdW/bq9MWvuyZPOhw0ftotcY3UYhARKIJFD5Lypy\nXy6RAAkkkIA98BjxfzJTBIG0Vnm2IRfBzxZSPduoD0JJXwLHtWopOVCMPtpRKFsOH0Y8UhMZ\n1zZXhiDVNyVxBAYNGiT6CpeXXnpJTjrpJFH3OwoJJIpAB1z3+8FlbjFikHrkNDEuuGo5KkC8\noma2rMqyPLJ1K/kayX72wxLdFNcWFU0EE4DH7nFw26OQAAlEEqCCFMmDSySQNAQ8UJCkuFgC\nS5eIq48O8cdQGmeL77QzxGrePGn6yY7UD4F+uOnRF4UESIAE1L36ys6d5Blng4kbUiOSg3jE\nXk1zUDS6k7EuVUbp2BbN5UJYpt/cul12wn1XxQdr9eWwSvfhdaYydNyWoQSoIGXoxGfKsDWb\nzz//+U+54IILpHmYUqHxA2vXro3A0Lp1axk+fHjEuoZcsOBb7h07Tjz9ByDNd5GYYqwd8uFe\nxwDohpwXnjuzCbz99tuZDYCjbzACrZG97pae3WUNYob2wPqjMUW9YE2qbsHoM9rlGSu01lnT\nPN/dcWybDMiw2GATxhOnNAEqSCk9fex8VQT+8Y9/iLrEnH766REK0vPPPy+fffaZyfITbOPY\nY49NKgUp2C8LKb49muabQgIkQAIkkNEEVBmqS5r/PLjq6YtCAiRQOQEqSJXz4dYUJaCFGe+7\n7z6ZN29ezBGsXLlSbrzxRrn44otjbudKEiABEiABEiABEiCBzCTATMGZOe9pP+q//OUvCNlx\n5a9//etRYz2ihTg3bJC+ffsetY0rSIAESIAESIAESIAEMpsALUiZPf9pO/rbb79d2rVrJ+vX\nrz9qjOvWrUNwqyOzZ88WTeurBQ7Hjx8v1113HTJqR7oeaErf5557LqINrY2yB9nktGJ8uokW\n3VPRIoNaoZxS/wRUkVcpQUr33bt31/8JE3wGP7JmBceY4FPzdCRAAiRAAiRQKwLpd4dXKww8\nKN0IqHJUkaxatcpsUkuSFnpUJei1116ToqIiufPOOyMO27x5s7z77rsR67p16yZvvfWWNEHq\nZQoJkEDlBPRhRCYrSHqd0TjIdJfDSEevL70uZjHwv0GnW79z+iAvE+ZCEzFRSKA+CFBBqg+q\nbDNhBFRRUQtQUM4//3yUDWocXIz5fsYZZ5hkDB06dDDbhw0bJh5kjJs6dar86Ec/ikjmoPuO\nHDkyop0ZM2ZIYWGheeIfsSENFjSzn8ZvDR48mApgguZTLUdz584VzaKYrm6f3/ve9xJEM/lO\no2PfuXNn8nUszj3asmWLFBQUmO9wbm5unFtnczUhsHXrVlFPiT59+kibNm1qcmhK7qveHxQS\niDcBKkjxJsr2Ekpg+vTpsmPHjtA5J06cWKWCpG50QeUoeOAJJ5xgFKRt27ZFKEjZSKmtr3C5\n7LLLwhfT6vNvf/tb0TTGv/nNb6Rfv35pNbZkHYx+5x5//HHR764q6JT0InDTTTel14AqGM3T\nTz8tr7zyilx99dXmu1zBblydAALPPvusvPzyy3LFFVfI2WefnYAz8hQkkH4EqCCl35xm1Ig0\nhqimon/Ev/rqq4gEDgsXLkRZCOsoxammbXN/EiABEiDuWQUFAABAAElEQVQBEiABEiCB1CbA\nKOzUnj/2vhYETjzxRJkzZ4688cYbogHk6t6kn/UJfrNmzWrRIg8hARIgARIgARIgARJIFwK0\nIKXLTHIc1SaQn59vkjM88sgjMnnyZNEgzwkTJsjPf/7zarfBHUmABEiABEiABEiABNKTgIXs\nQmU5ZtNzfBwVCVRIQK1HGr+kQazMulSGSbMfadKAnJwcpvmu8JsT3w16CdZEI5o2PjreLb5n\nYmskUH8E9Lqh1w/9DqdjCYT6Ixf/loNzoQmLfD5f/E/AFkkgAwhQQcqASeYQSYAESIAESIAE\nSIAESIAEqkeAMUjV48S9SIAESIAESIAESIAESIAEMoAAFaQMmGQOkQRIgARIgARIgARIgARI\noHoEqCBVjxP3IoGUILBixQp54YUXEtLX1atXy/PPP5+QcyXyJKWlpSaWor7PeeDAgfo+RaXt\nJ2qclXaCG5OGAK8d9TcVifitJ8PvORHjrL9ZYsskEEmAClIkDy6RQEoT0JucF198MSFjWLVq\nlfz73/9OyLkSdZJ9+/bJNddcI9u3b6/XUz7wwAOmqGa9nqSSxhM1zkq6wE1JRoDXjvqZkC++\n+EJuvfXW+mm8vNVk+D0nYpz1CpGNk0AUASpIUUC4SAIkkLkE9EZj48aN9Q5g6dKl9X6Oyk6Q\nqHFW1gduI4FMILB27Vo5fPhwvQ41GX7PiRhnvUJk4yQQRYB1kKKAcJEEkpnAq6++Kh06dJAN\nGzaYArc9evSQM888U7p16xaz25ru9eWXX5bly5eLuj906dJFLrvsMmnfvn1o/3fffVe++uor\nsSxLxowZY17BNL1qJfrPf/4j27ZtM+e48sorTVr00MH4MH/+fHnttddE01WfcsopMn78+NDm\noqIi44an7nitW7eWM844Q0aOHBnankwfDh06JI8//rjp0pQpU+Tcc8+VRo0ayaJFi0RrZ733\n3numXpaOUVmq9Uy5tmzZ0szB8OHDQ8PZsmWL4a7zpG0cc8wxMmnSJJNyV10gleenn35qUqlf\nfvnlcv/998sVV1whb775puiNxrHHHitXXXWVKWis85Obmytnn3229OrVK3SOyuZGvyedOnWS\nwsJC+eyzz0waex2P9jHWOI8//vhQu/yQngSWLFlifuf63frvf/9rvovjxo2Tk046KeaAee2I\niaVGK/XaOHPmTFNO4q9//av88Ic/FP09d+7c2cyFXh+vu+46c12u7PesJ/3888/lk08+MdeO\ntm3bymmnnSYnnHBCzN9zkyZNZN68eTJ06FBTBF1/8/p3Qq+96ha9ePFis+2ss86S5s2bh8ak\nfZs1a5ZxMR42bJhcdNFFJmW7lsTQa9TVV19tvjsrV64015fvfve75u9BrHGy6HoIKz+kKAFa\nkFJ04tjtzCTw5Zdfyp/+9Cf56KOP5NRTTzU30z/5yU9k586dMYGoa8eHH34oxx13nPljOnfu\nXLnlllvEcRyz/1NPPSUPP/ywuQHv27evPPTQQ0bZ0Y267w9+8APzB1iVHrV6qPuZ3nQHRZ9c\n/u53v5OePXsaReGee+4JuY7ptu9973sye/Zso3TpOX/1q1+F2g+2kSzvHo8npICo4qkKnVqT\n1GXxiSeeEP2Dr3Ve9Gbj+uuvD41Lj/vlL39pbnx0LKocKScdvxYg1puhqVOnypNPPmmGqkqq\n1ifRmxxVbLVQ8VtvvWXccGzblgEDBsi//vUvue2220TnR5WrTZs2yZ133hlCVdXc6Pfkb3/7\nm0ybNs3MvbLXQsjr1q2TWOMMNcwPaUtAv8t6c/yHP/xB+vfvb77fd999t3zwwQcxx8xrR0ws\nNVrZqlUr8zvX37sy1wdPc+bMMb9Nfbii1xKtG1XV71kfUulc6YMaVXTUIvWLX/xCli1bFvP3\nrHOtD2Luvfde6d27tznvXXfdZa4pqoiNGDHCPMDR60xQ9O+AFk/XByuqROsDoN/85jdmc/Aa\npdckVer0QZr2OVhcPdY4g+3ynQRSloAWiqWQAAmkBgHciLuwwrgIyA11+JJLLnHxdM8sv/HG\nG+75559vPhcXF7v4A+fipji0L/zEXfxxc3ft2uVCqTKf8fQvtB2Kl/u///u/Lm6oXTzZdO+4\n447QNv2g64Lnws23OV7bDMrf//53F08lXTxxdB999FH39NNPd/EkOrjZrNP+4w98aF0yfdi8\nebMZ0/r16023oLiYZdzMhLr57LPPunh660IBilgHC43hpjygxLq4qQht12U8PQ4tQ8FyoTSZ\nZWWhc/LYY4+Ftusc6DrEQpl1Ol+6DEucWa5qbvR7AgtUqA/aF52Xl156yRwfPc7QifkhbQm8\n88475jsEC0FojPqdg2XSLPPaEcIS1w///Oc/XTwwCbUJxdOF1dhcI4Mrq/o9w6Lt6vwERa//\nEydOdOEdYFZF/56Dcx28bun+sHy7N910U7AJcyys1mZZr3ewJLrvv/9+aDuULPN9gSXKXK/1\n+oOHPKHt8Dow2/XviEr0OEM78gMJpCgButilrGrLjmcqAXWTCrrAKQN9GqgB1tHSokUL+f3v\nfy/6xFAtCerutXDhQrObWkIKCgqMm83gwYNDh6qlSF/qXqNucera9X//93+h7WrhCD+Xuo+p\nG0dQ1FVLLS6a5EDdMLSv4ZXcR48ebZ5ia1/0yWYqiPY/vK/61FatS7ghCHVfLXi7d+82rjTq\n9qJM1MVFGevr66+/NseEDojxoV+/fqG1+qS4e/fukpeXZ9bpXKro01u1SFVnbtQiqPOlou9q\nsdIn1pTMJaDf5fDfq1471IoQnZSE1476/Y7ob1MtuSrVudaqxVot9+quB2XG/P71Gq7HViTh\nlmL9e9GuXbsI92Z1DcaDF3O4XtNxD2tchtesWRNqUq1buk2t2ipqBQtK8NpU3/FVwfPxnQQS\nTYAKUqKJ83wkUEcCGoMULupDHuvGV/+AqlvWN998Y1wm1G0CFh3jf67H6822KjgaexQtGmOj\nfzD1D2T4dlWAwn3L9UZK2wiKulqo6B/u/fv3S9euXYObzHtwe9DFL2Jjki40bdo0pGhoF9V1\nTl1mwrnozYLGDKkiosqLuifpTYkqn8pd50eVwsokPBZA99M4gqCEn6u6c6NzFy5BZSl8HT9n\nFgG9KdbvblCC37no6wevHUFC9fMe5K6tV+f3DMuveVClrr96PdGHWBpTVpnk5OSElLDgfrou\nKOHXFL2mqUKlCnT4eo1BUjfgoIRfU4LXE/07QSGBdCRABSkdZ5VjSmsC6vsdLppgoU+fPuGr\nzGdNAqD7qkVHnx6qaJCviv5R69ixo1Fi1PIRVFzU6qNxL+rvrjfobdq0EbhlmGP0H41tCbde\n7dixwygMQaVJ+6J/OFWJU1929bcPF13WP8RqHUlGCb85qKh/yk3jjG688caQ4gQXF6OI6g3o\ngw8+aIKuNZ4r+JRYEz3ESynUuarO3FTUf11fnXFWdjy3pSYBtXRqfIpaIVXUsqkKk8bF6Xc0\nKLx2BEnU/b2q31pVv2dVVuEKKTfffLNcfPHFpkMaE6TxnkHlpKpzVDUKvVZrm2rhVwVMRZc1\naYN+N6ojde1Ddc7BfUggkQSYpCGRtHkuEogDAbVQvP322yZhgL6rC50G7kaLusfpHzlVgFQ0\nc1owUYD+0dXgf306qMH8esOv+2n2tuBT5gsuuMBkbtMsaNrOggULBDFJsmfPnohTIZZGNMuR\nZkbS/pxzzjnGqnTeeeeJKg4a7Hvw4EFzvGbP0j/CWVlZEW0ky0Lwya66lagFLJbouFQxfOaZ\nZ4xyqK4v6sqo2Z/0Caxy1yeyylhvYPRmc8aMGRHuMGp5U9e78IQXsc5V0brqzk1Fx1dnnBUd\ny/WpTUB/r/pbVwuEJgfRa0fQGhAcGa8dQRJ1f9ffmrqyqQVZr5OxpLLfsz5k0euFWvz1IYu6\ntGlChfDCsHX9PWvGOlWa9eGYJnLRa9fTTz9tFLNwS3asvgfXVWecwX35TgKpQIAWpFSYJfaR\nBMIIaAwBEgXIfffdZ/5wamYhzVIXLbqfpobWrHX6lFgtP5pmVo9TpUqtOJrRSp9EaqppdZ/Q\n+JkbbrjBNIXAYaPY/PrXvzaWEI270f3UvSMo+kdV/3AjYNj8UdVt+qRTRc9/++23C5I1GMVL\n/9Aj0NesCx6fbO/qgqKpcFXhufTSS2NautQP/7e//a1MnjzZxG+oUqSxVj/96U/NcPQ4TdX9\nne98xyiCat1T7hrLpe40eg5Nraw3ORoTppnFairVmZvK2owe549+9KPKdue2NCGg86431uo6\npU/89feqWTCjhdeOaCK1X1Y3W1VAtURCeDxneItV/Z7/53/+xygsSNRg5k8VKs1iqtdxlejf\nc3g5gPDzVPRZ/zb85S9/MRlSNXW3/r3QzKR67dcHZqowVSXR4xw4cGBVh3A7CSQ1AQtPOOlA\nmtRTxM6RwLcENE22xrtojIu6y6gLXFWuDXpDpFYf3bci0e16ox/raaE+9VQlKBiUG6uNvXv3\nmpsAjdeJFr3EqMVFn0qHu+dF75dMy2rx0tiqoItcRX3TOdAbCGUXLZUx1X11XtQyFx4TEt1G\nVcvVmZvK2qjuOCtrg9tSg4C6SyHLpLHy6ndTv3fh8YOxRsFrRywqtVunVuWgK3JFLVT1e9br\nqD6oqug6Go/fs1rO9bqkVqvaSHXGWZt2eQwJJJoALUiJJs7zkUCcCGhWsuqI3rxXphxpG5X9\nMdQ/xpUpR3p80MVDP0eLKnDBGKjobcm6HEtRjNXXyuagMqbals5LLMUq1nkqWleduanoWF1f\n3XFW1ga3pR6Bqr6bwRHx2hEkUff3qpQjPUNVv+eqrsPx+D3HeshVk9FXZ5w1aY/7kkBDEWAM\nUkOR53lJoBYE9I9XPP4I1uLUPIQESCCFCai1SK2dFBIgARIggaoJ0MWuakbcgwRIgARIgARI\ngARIgARIIEMI0IKUIRPNYZIACZAACZAACZAACZAACVRNgApS1Yy4BwmQAAmQAAmQAAmQAAmQ\nQIYQoIKUIRPNYZIACZAACZAACZAACZAACVRNgApS1Yy4BwmQAAmQAAmQAAmQAAmQQIYQoIKU\nIRPNYZIACZAACZAACZAACZAACVRNgApS1Yy4BwmQAAmQAAmQAAmQAAmQQIYQoIKUIRPNYZIA\nCZAACZAACZAACZAACVRNgApS1Yy4BwmQAAmQAAmQAAmQAAmQQIYQ8PwvJEPGymGSAAkkEYHP\nPvtMHn/8cXn66adl+/btMnjwYPn9738vPp9PunTpIsuXL5e///3vcswxx8gDDzwgzz77rDRu\n3Fh69eplRvHVV1/Jgw8+aNr48ssvxePxSI8ePSJG+Oijj8rChQvluOOOi1ivbX344YcyatQo\ns/6JJ56Q1atXS9OmTc25dHnnzp2Sn58vzZs3jziWCyRAAslDgNeR5JkL9oQE0oqASyEBEiCB\nBBP4y1/+4uJC6g4aNMi96KKL3Ly8PHfcuHFm3Z///GfTm9dff90sT5o0ybzbtu3edNNNZts9\n99zjWpblQiFydfvAgQPNPsHtweFAuXJHjhwZXAy9n3baaS6UsNDy8ccf70JBczt06OCOGTPG\nvf76683n1q1bu2vXrg3txw8kQALJQ4DXkeSZC/aEBNKNgDettD0OhgRIIOkJzJgxQ+688075\n8Y9/LA8//LBA0ZHCwkI55ZRTYvb9448/liVLlgiUFykpKZFZs2bJ7373O7n00kuNVUktTrgw\ny2233WasP+PHjzfbYjZWyUq1NGkbf/vb38xeatWC4iQXXnihzJs3z/SzksO5iQRIIIEEeB1J\nIGyeigQykABjkDJw0jlkEmhIAq+++qpkZ2fLH//4x5DS0aZNG4FVKGa3brzxRhkwYIC0atVK\n2rVrZ1zy1J1OlStVjlRUydL2YImSRx55JGY7Va3U9sM9jvVcP/vZz2TBggWyZs2aqg7ndhIg\ngQQS4HUkgbB5KhLIQAJUkDJw0jlkEmhIAnPnzpWePXtKs2bNIroxbNiwiOXgQp8+fYIfzfuy\nZcuka9euRhkK36DxSRrHpLFLtRG46UlOTk7EoXABNMvz58+PWM8FEiCBhiXA60jD8ufZSSDd\nCVBBSvcZ5vhIIMkIaPKDrKyso3qlVqVYkpubG7F6165dFSZO0CQLpaWlEfvHWggEAketbtmy\n5VHrmjRpYtbt2bPnqG1cQQIk0HAEeB1pOPY8MwlkAgEqSJkwyxwjCSQRAc1CV1BQYOKGwruF\nZAjhixV+VuvT+vXrY27XdocMGRLapq54sRSmDRs2hPYJfti0aVPwY+g9eJ6KrFuhHfmBBEgg\noQR4HUkobp6MBDKOABWkjJtyDpgEGpaAJj3QpAwvvPBCREc0pXd1ZPTo0aJWpDfeeCNid3WD\n03ihoUOHhtarVUiVnIMHD4bWqSIWVHxCK/Fh0aJFou574aLpwNWypanGKSRAAslDgNeR5JkL\n9oQE0pEAFaR0nFWOiQSSmMB1111n6hJde+218stf/lKmTJliMsVp0LWKJlyoTDRxgsYg6fFP\nPfWUUWpefvllOffcc6V79+5y6623hg6fMGGCUaauvvpqU/do6tSpous0IUO0OI4j5513nrz7\n7rumdtIPf/hD81nPEcslMPp4LpMACSSOAK8jiWPNM5FAJhJgmu9MnHWOmQQakIDX65WZM2fK\nL37xC3nttdekqKhIxo4dK6+88oqcffbZRyVKiO6qWnQ+//xzQc0j+f73vy+q2GhyhZNOOskU\nje3UqVPokFtuuUVWrlwpzz33nPznP/8xiSF+/etfy9atW81yaEd80Ex5p59+ulG0/H6/KTo7\nefJkufzyy8N342cSIIEkIMDrSBJMArtAAmlMwNLCTmk8Pg6NBEggyQhs27bNKDTRWey03pHW\nQnr++eflsssuq1avDx06ZOKZNB4hmPI71oHqYqdxR7179xaNS4qWESNGyJEjR4zlSPfVPqII\nbfRuXCYBEkgSAryOJMlEsBskkKYE6GKXphPLYZFAshLQuJ7mzZubgq/hffzzn/9s3Os0xqi6\notak/v37V6ocaVuaja5fv34xlaPoc+m+VI6iqXCZBJKLAK8jyTUf7A0JpBsBWpDSbUY5HhJI\ncgKaIOG4444zrnFnnHGGKf760UcfyZIlS+SJJ56QG264IeEjCLcgJfzkPCEJkECNCfA6UmNk\nPIAESKAGBBiDVANY3JUESKDuBDTBgmaMmzZtmrz33nvy5Zdfyqmnnir333+/qMLUEDJx4kTR\nuCMKCZBAahDgdSQ15om9JIFUJUALUqrOHPtNAiRAAiRAAiRAAiRAAiQQdwKMQYo7UjZIAiRA\nAiRAAiRAAiRAAiSQqgSoIKXqzLHfJEACJEACJEACJEACJEACcSdABSnuSNkgCZAACZAACZAA\nCZAACZBAqhKggpSqM8d+kwAJkAAJkAAJkAAJkAAJxJ0AFaS4I2WDJEACJEACJEACJEACJEAC\nqUqAClKqzhz7TQIkQAIkQAIkQAIkQAIkEHcCVJDijpQNkgAJkAAJkAAJkAAJkAAJpCoBKkip\nOnPsNwmQAAmQAAmQAAmQAAmQQNwJUEGKO1I2SAIkQAIkQAIkQAIkQAIkkKoEqCCl6syx3yRA\nAiRAAiRAAiRAAiRAAnEnQAUp7kjZIAmQAAmQAAmQAAmQAAmQQKoSoIKUqjPHfpMACZAACZAA\nCZAACZAACcSdgDfuLbJBEmgAAo7jyDfffCMLFiyQdu3ayfjx46VRo0YN0BOekgRIgARIgARI\ngARIIJUJWC4klQfAvpNAYWGh3HDDDUYhGjx4sHzxxRfStGlTefzxx6V58+YERAIkQAIkQAIk\nQAIkQALVJkALUrVRccdkJfDKK69Ifn6+/OMf/zBdPHTokFx44YXy4osvyo033pis3Wa/SIAE\nSIAESIAESIAEkpAAY5CScFLYpZoRaNKkiXz3u98NHZSdnS39+vWTLVu2hNbxAwmQAAmQAAmQ\nAAmQAAlUhwAtSNWhxH2SmkC4cqQdLSoqkvnz58vNN998VL8XLVokK1asiFh/0UUXiW3zWUEE\nFC6QAAmQAAmQAAmQQIYSYAxShk58ug67pKREbrvtNtmzZ49MmTJFfD5fxFD/9re/mfXhK7t0\n6SJ+vz98VVp+Dg83tCwrLceYKoPKtLlo3769PP/886kyPewnCZAACZBAhhOgBSnDvwDpNPy9\ne/fKHXfcIfr+4IMPHqUc6VgnTJggXbt2jRj2ww8/LNOmTYtYl44LqjwePnxY1CXR6+VPvyHn\nODgX6g4arcQ3ZL/q49z68GHSpEn10TTbJAESIAESIIF6IcC7pHrBykYTTUAz2f30pz+VnJwc\neeSRR6RFixYxuzBo0CDRV7hUtn/4fqn+ef/+/cZSpgqS3phTGo7AgQMHzFzo9zXd56K0tFRo\nsWy47xrPTAIkQAIkUHMCDLyoOTMekWQEtm/fLj/84Q+lc+fOMnny5AqVoyTrNrtDAiRAAiRA\nAiRAAiSQhARoQUrCSWGXakbg/vvvl0AgYNx4li9fHjpYayB17949tMwPJEACJEACJEACJEAC\nJFAVASpIVRHi9qQmoKm8tTCsyi233BLR15EjR8p9990XsY4LJEACJEACJEACJEACJFAZASpI\nldHhtqQnoAViP/3006TvZzp10Nm5UwLLlojs2iUCK52n3wCxO3ZMpyFyLCRAAiRAAiRAAhlM\ngApSBk8+h04CNSXgbNwgpe+9I/BpFGQXEBfKkrNqlXhPHg9FqX9Nm+P+JEACJEACJEACJJB0\nBKggJd2UsEMkkKQEHEf8n38qru0RO7eN6aTVTMTVjGyzPhO7S1exkCGPQgIkQAIkQAIkQAKp\nTIBZ7FJ59th3EkggAbe4WNziPWK3bBlxVgupqqWkVJzCnRHruUACJEACJEACJEACqUiAClIq\nzhr7TAINQcCq/KSWVLFD5YdzKwmQAAmQAAmQAAkkBQEqSEkxDewECSQ/AasFLEetW4tbVBTR\nWXffPpHGjcRq2zZiPRdIgARIgARIgARIIBUJUEFKxVljn0mgIQjYtvhOGiviscXZtg3udrvF\n3bFd3MOHxHvSOLGQtIFCAiRAAiRAAiRAAqlOgEkaUn0G2X8SSCABu0O++C64SJyVK8VFzJHV\nooV4evcVKy8vgb3gqUiABEiABEiABEig/ghQQao/tmyZBNKSgN2yldgjRqbl2DgoEiABEiAB\nEiABEqCLHb8DJEACJEACJEACJEACJEACJFBOgAoSvwokQAIkQAIkQAIkQAIkQAIkUE6AChK/\nCiRAAiRAAiRAAiRAAiRAAiRQToAKEr8KJEACJEACJEACJEACJEACJFBOgAoSvwokQAIkQAIk\nQAIkQAIkQAIkUE6AChK/CiRAAiRAAiRAAiRAAiRAAiRQToAKEr8KJEACJEACJEACJEACJEAC\nJFBOgAoSvwokQAIkQAIkQAIkQAIkQAIkUE6AChK/CiRAAiRAAiRAAiRAAiRAAiRQToAKEr8K\nJEACJEACJEACJEACJEACJFBOgAoSvwokQAIkQAIkQAIkQAIkQAIkUE6AChK/CiRAAiRAAiRA\nAiRAAiRAAiRQTsBLEiSQ6QQcx5Ht27enPQbXdc0Y9+zZI3v37k378SbzADNpLvx+v+hvjEIC\nJEACJEACqUKAClKqzBT7WW8EbNuWvLy8ems/WRo+cOCA7Nu3T1q0aCGNGzdOlm5lZD+Cc9G8\neXPJzs5OawalpaWivzEKCZAACZAACaQKASpIqTJT7Ge9ErAsq17bT7bGM228ycpf5yHd5yLd\nx5ds3y32hwRIgARIoO4E+Fiv7gzZAgmQAAmQAAmQAAmQAAmQQJoQoIKUJhPJYZAACZAACZAA\nCZAACZAACdSdABWkujNkCyRAAiRAAiRAAiRAAiRAAmlCgApSmkwkh0ECJEACJEACJEACJEAC\nJFB3AlSQ6s6QLZAACZAACZAACZAACZAACaQJASpIaTKRHAYJkAAJkAAJkAAJkAAJkEDdCVBB\nqjtDtkACJEACJEACJEACJEACJJAmBKggpclEchgkQAIkQAIkQAIkQAIkQAJ1J0AFqe4M2QIJ\nkAAJkAAJkAAJkAAJkECaEKCClCYTyWGQAAmQAAmQAAmQAAmQAAnUnQAVpLozZAskQAIkQAIk\nQAIkQAIkQAJpQoAKUppMJIdBAiRAAiRAAiRAAiRAAiRQdwJUkOrOkC2QAAmQAAmQAAmQAAmQ\nAAmkCQEqSGkykRwGCZAACZAACZAACZAACZBA3QlQQao7Q7ZAAiRAAiRAAiRAAiRAAiSQJgSo\nIKXJRHIYJEACJEACJEACJEACJEACdSdABanuDNkCCdQLAdd166VdNkoCJEACJEACJEACJFAx\nAW/Fm7iFBEigIQg4u3dLYN7X4m7cKK7HI3av3uIdOkysxo0bojs8JwmQAAmQAAmQAAlkFAFa\nkDJqujnYZCfgFBdL6ZtvSGDVSnEbNRKxbaMslb73jkhpabJ3n/0jARIgARIgARIggZQnQAUp\n5aeQAwgnsHnzZnn55ZfDV6XU58CiBeIeOCB2u/bGYmQ1aSJWh3xxNm2UwLq1KTUWdpYESIAE\nSIAESIAEUpEAFaRUnDX2OSaB/fv3y+233y7vvfdezO2psNKFgmc1axbRVcuyxMpqJM7WrRHr\nuUACJEACJEACJEACJBB/AlSQ4s+ULTYAgTlz5sg111wjW7ZsaYCzx/GUPp+IEziqQRfrrMZw\nuaOQAAmQAAmQAAmQAAnUKwEqSPWKl40ngsC+ffvkzjvvlDPPPFMuv/zyRJyy3s5h9+kr7t59\n4ga+VZLcI4ehNDlid+5ab+dlwyRAAiRAAiRAAiRAAmUEmMWO34SUJ5CdnS0vvfSS5ObmytSp\nUysdz4YNG0TjlMJF02kfOXIkfFWDfXZ79hJ38yZx1qw2CRqCHbGGDZdSjA8dDa6q8bvf7zfH\n6HuyjLfGg0iTA4JzUYrEGzYScaSz6BgpJEACJEACJJBKBKggpdJssa8xCXi9XqMcxdwYtfLF\nF1+UKVOmRKxt06aNFBUVRaxr0IXBQ8WT106sol1QkjwSwGcXfUQn49ItjdWiJAeBA0jIoa90\nFlUGHVhAKSRAAiRAAiSQKgSoIKXKTLGfcSEwatQoUYUqXF599VVp2rRp+KqG/9ysb9z7UFJS\nIvpqjHpK0QzifjI2WCmB4Fw0Qip3n8adpbGogqSJRigkQAIkQAIkkCoEIu8UU6XX7CcJ1JLA\nmDFjRF/h8tprr0mzqMxx4dvT5bNajoIKkrolUhqOgFqNdC50HtJ9LtTFjgpSw33XeGYSIAES\nIIGaE0hv5/ea8+ARJEACJEACJEACJEACJEACGUyAFqQMnnwOnQRIIH4EShFns/XwEXHElXy4\nMWalefKF+JFjSyRAAiRAAiSQXASoICXXfLA3JEACKUhgBdwXn9+4WXZolkFXpFWWTy7t1FEG\ntWiegqNhl0mABEiABEggswnQxS6z5z/tRn/ttdcelaUu7QbJASUVga2HD8vjawtkb6lfuiGm\nqHtOE1Fr0pMFG6Tg4MGk6is7QwIkQAIkQAIkUDUBKkhVM+IeJEACJFAhgTm7dsuhgCMdGjcK\nJSNoi+x0Lv77tDA+qdkrPDk3kAAJkAAJkAAJxJ0AXezijpQNkkDyEHDh8uUe2C9WArPWuXv3\nirN1i0ggIBbqN9mo45TOsvXIYcnxeo4aYlOPR7bAukQhARIgARIgARJILQJUkFJrvthbEqge\nAbh4+efPk8DC+SJHSsTFzbp07y7Sp1/1jq/lXoGVK8T/yUxx/aWhFrwDjhHPmJPEStOkBXlI\nyLBozz6RRqEhmw8H/AHp1yxqZeQuXCIBEiABEiABEkhCAlSQknBSUrlL27Ztk/PPP7/GQ5g9\ne3aNj+EBFRPwz58rgTlg2rKVWK1aC4ruiCxdKj5Yd+Sscyo+sA5bnMKd4p/xkUiTHLFzcsta\nQg0c/6KFYuXmimfgMXVoPXkPHdGypczcWSg7oYi2bZRlOloE3prNbnQu2FNIgARIgARIgARS\nigAVpJSaruTvrAPLhRbBpDQcARcuX4EFC5BKDcpR47KCsFYWbtzbthXvxg3iFhaKdO4clw66\nmG+B0uU6AQmsWyeCZSsn59u2fT6xmjWVwLJlaasgdW6SLTd262qy2K07cBBxSNARYbG7tktn\n6d00jMW3VPiJBEiABEiABEggiQlQQUriyUnFruXn58s333yTil1v+D77/eJfslicVSvFhQXC\nAyXGhnua4Ibb8nrFat6iWn10kXLaLS0Ru3GU9ULd7LQxxCTFQ5zt2yXw2ScS2LlTWxVnP9zM\ncI6jonG8PnEPpXc2t2ORzrsXlKFNhw6L47rSCUpTjuEdD9JsgwRIgARIgARIIJEEqCAlknYG\nn8uPm/8ZM2bIihUrpBRuV0OGDDGvlnBPoqB0jsYMfThdnNUrRZo2M4qGf/YX4rz3rlht86Ds\nIENa+w7iGzuuzGWuEmiakMGyoaaAs8CCExLcuBuLT6PGoVW1/aCJGErfnQZF7giSMOSZZqxD\nB8TZtEnc3DawGmEMQYFF0e7WPbiUtu/ZUIhoMUrb6eXASIAESIAEMogAFaQMmuyGGurcuXPl\n2muvlcWLFx/VhT/96U9yxx13HLU+01a4G9aLs2a1SLv2YuFG2927R5ziYqPkuAE/ssF1Enfb\nVil5Z5pkXXAhstI1qRCRpTFAvftIYOlisYLtQTkSWHocZJVTV7u6il+tXKr4dOgQasrq1EUE\n7ns6DnvAQBFNyrAHY8B4PEOGhvbjBxIgARIgARIgARJIZgJUkJJ5dtKgb8W4yT/vvPNELUgP\nPPCAjBw5Upo2bSoFBQXy9NNPy5133imNkQXsZz/7WRqMtvZDCMBdTTy2UY60FQfJLoyCgXge\na88e89lCumxNn+0g1sejCkgl4h11IhIzHEFc0Fp41dniuogVgqWndOhxJj4m1qHu7iIJwAIk\nSA1uI6mC3aWrUW5i7Su7donAqhUu6gZo9+gJJWkXFDwkg8A5PeizZ+QJIStT+P78TAIkQAIk\nQAIkQALJSIAKUjLOShr16cknnxRVkubNmyd9+vQJjWzQoEHyne98R2666SZ57LHHMl5Bsn1e\nCcDKo7E8Ku5BxOxoggO1/Ki7XLlYWKeKTFViQen0njFRvGo12rfP1EHyQ9ky7cY42KTnnvEx\nNLMAtlrix7unc1fxnTEB6asjFSE93ELMjaz9NpW3ayxUO8SF4utif0+jfLGPOUZ8Q4dh5+Co\nYpyYq0iABEiABEiABEggyQjAB4ZCAvVHYOHChXLyySdHKEfhZ/v+978vq1atki1btoSvzrjP\nVn5HaEX4H9YbFQtKhqklhIx0VliqaBeWOBOjVA1CFhQTC1YjT8+eYiN5hrq6xRKneLf4Z85A\n6rUmZS557dqZ9wDc/vzz5sY6BG32hrULlim1FEHUshVYs0Yc9M/u1AlrXAl88bn4F8wz2/kP\nCZAACZAACZAACaQKASpIqTJTKdpPD27KS5CRrSIJbgsE1HKRuWIjAYP3+ONFdu8Wdwfc7aAg\nWbD8CGKNbMQRqbhQZFRx8tQh4YGNmCD3qzkmlsk/B0kgiuAOt3kzlDHEOcHCFBQt6mq1aimB\nlcuN1Uld/hy41ZkkD9jJQiyT77Qz0CkkftDjNcU3XOw8vXrBPa8sSYPVurUEoGC5hw4Fm+U7\nCZAACZAACZAACSQ9AbrYJf0UpXYHhw8fLrfddpt8+eWXMmLEiIjBqFvWvffeK21ws905TnV5\nIk6QYgve4SOQ9CBfHNQqstSS1KevOFs2G8XEOKnlNBHfyafAva166b6PGv7mTZL1wfvi2pa4\nsBb5168TCynZ7W7dYnrBWR64/W3aLEf+/U+xoEBBFTLKmnfceLGh/Njde0gW+htY8o04GrcE\ny5Gpt1R+Yq3B5O5BjSQoZZpZj0ICJEACJEACJEACqUCAClIqzFIK9/GGG24wyRnUze7GG280\nSlLz5s2lALEqU6dONbFJmqyBUkbA7thJ9BUUdWFTy41JgKDptGFBqo24mvL7i1niajwQ0oZr\nLJMqXS7iwwKrkT0vgCQOsOJpBr2gOJs2ogjsHrE6dhQLCpUWgdXaR/7330UmvYvKrFyIddJk\nDjaUt4iU4to29lelyvJlBZvkOwmQAAmQAAmQAAkkPQEqSEk/RandwWxYDj7//HO5/vrrZfLk\nyRGDadWqlTz66KNy3XXXRaznwrcELCiTHrzqKq5mnYPLnhtlfbJQh0rTh9uIO3K2I3NeC9Sl\n8kJJ2ot9kQzCgrJmlCN0wLjdYT9361bR+CQPUomrWHCps/Pao2AsXANV+YISZpI2FO4UdR0U\nzDOFBEiABEiABEiABFKFABWkVJmpFO3nTmRR05vld955RzYhhfSyZcuQIXqX9ETigP79+5uU\n3yk6tBTrNjJAqMkolkCh8Z5worjbt4of8yMHETOE+kbq5qfxREeJKkD794dWq+LkgYXQff89\no2ThTGWnQqpw4xKI7RQSIAESIAESIAESSBUCVJBSZaZStJ9PPfWU3HXXXbJ06VLp27evdDIZ\nzlJ0MCncbbt1LhI+ZIt1AIoN3OKCoi58aiHSbHcaQ+Q57njjGqdKz5EX/mUSLKg7Xriowhue\n0EG3aftZF15s4qc0lbhutzt3OcrtLrwdfiYBEiABEiABEiCBZCTAR7vJOCtp1Ce1GKkwCUMD\nT2oW4oBGjDJWIWtXobhwt3N27EBR2MPiHT02MrlCucXHO3goLEVwtTtcloVOFSM9xoZbniki\nGz0knMPu2Us8xw4qKxgbpVhF785lEiABEiABEiABEkhGAlSQknFW0qhPN998s+TC1eo3v/mN\nHD58OI1GloJD6d5dSk45TaR7T2Ph8fTqLb7vnG/qJMUajadff+N6JwcOmNTjLhI0aOpuHwrQ\naiFaCgmQAAmQAAmQAAmkIwG62KXjrCbRmDZu3CgDBw40meweeughY0lShSla5s6NXZA0er9M\nWja1iTSrnGaei5M4mkQByRWyqpN2W2OTjhsuqihpDSbNUmdiksIy3ZnCtahzZRSmNI412lfq\nlw8QTzd39x5xYEkb1LK5nJHXVnLVMkchARIgARIgARJIKwJUkNJqOpNvMJqkoRippIcMGRLq\nnIP0z8kk2h9NHJEsYmlWuUULxCoqEsENuNOztzgDBtY5nidYjHc/EiwcRJxQjQQ1jYxgLo2g\nLpK9GDWUVq0UVAIWQcyRA9c6By526SaHkP58ypZtUnD4iOSiGK7qq+9v2i8LduyUmzp2kBZY\nV3DosKxHUosAvktdoXz2zK7cwhaci31wdazxXKQYYD++K8n2m08xhOwuCZAACZBAgglQQUow\n8Ew73Q9+8APRVzKLDctHi6j01w3VXxfFXAOffWqKuQoSH5gCrUsWi42bb89pp9fJmqQ34gfg\nLtcESRka1bKeUpBLYObH4ixdghTercVqlCXuAShcX30pHq2LpMpcGsmiwiLZ5A9Iv5YtQokA\n22J8q8FyfkmpuHhN36kKtlZ9guzbL2NzW8uk/A5iV2D9C5+LxmnurliKGlz6G6OQAAmQAAmQ\nQKoQoIKUKjOVBv1ctGiRrFy5Upo1ayYTJkyQ9evXS9euXZNiZF5YAZJBSuZ+ZeoQeTTrXFBg\nkXAL1omNGCDNNFdbCd6k6ntdxusWFkpg1SrUTmpvCs6a/kDBdNX1bsF88UJB0sQOgYULUC9p\ngwjWe+HWZw88JiIZRG3HkejjVh06JM2yfOKJusnPhZL5OVzuDgT80gkWo8blroclsCLN3LVb\n+uB7fnzr2DWgPOX76ntd5iLRLGpzPlMTqzYH8hgSIAESIAESaCACfKzXQOAz6bSa4nvs2LEy\nePBgmTRpkjzzzDNm+Lr829/+Vo7AOpLJ4h48IP45s6XkhX+Lf948seDK5cKtKyTlN9MmDii0\nsuE+OHuKjaUkOv231bSp6FgC27ZJyX/fEP/ChSI6DiTn8H/+mZSiTpJAmUg1aQL+AcQdRYsf\n64pKjkgjKE5B5Uj3ycJyUxTb/RruiEcCjswu2i1T12+Uf2/cJIv27C2zMkU3xmUSIAESIAES\nIIGkIZAcj82TBgc7Em8Ce1Fn56yzzhJ1s7n11ltl1qxZ5hQagzFx4kS55557ZPPmzaL1kjJR\nXLhplbz1hrgaA4U4Hk16ENhQINbB/eLp0QsFhsqeYZjb84rSZoOls2WzKd6qNY3s/I51jleq\nbC4sxEWhVKwpAGyFuZC5mGMX/XVWrkBSh2KxUWw2JBibs36dBNauFQ+sSakkg1o0l08Ld8kR\nWIZUGVIphXJUjMQNeY0ax1SefNivuMQv/7euQBbv3Sc5Hhv7icyEK974vDZyTquWqYSAfSUB\nEiABEiCBjCJABSmjpjvxg33iiSdkz549shDWhC5dusgll1xiOqGuRS+88IJ07NhRJk+ebF45\nuInONAksgpVlVxGUifyyoUOpcLZuFRdWB6f1blOA1d2HYq6I87HBKlq0TpF/+gcS2LqlLD4J\nN+52blvxnn6G2K1iu3dFt1HTZbt9B7FathIHrnZWW43GQfQNlAe3cKd4+vRFraRtYjVvFtms\nKhaacGLLlpRTkI7FWM5onycfbEMNKJNQ0DJK0Ri4z3WAa92rm7ZI+8aNIsZbjLikHLhtqnLU\nKwdKa7kiqe53H+8olJ5QdmvvLBlxKi6QAAmQAAmQAAnEmQBd7OIMlM1FEpg/f76cfPLJRjmK\n3FK2dNlll4lmuSooKIi1Oe3XOesxbsSqBMWC9UeVDxeuW7JpszjIaCewVHjHnyZW02/3C+5f\n+sknEoDSofFAwZdTVCj+jz8UpA4L7hbfd9zce0/V/sClDu50DmKj3B3bxdO5i3hPHCOWB89d\nYp1b11VkBYtvD+PamlrJLkLChZ/17ilnd2gvE6As/bhnd/lu184yGskYOsNqt3r/AdmP77G+\n1iBhRTvEJx2ARa24tERmwvr08c5CWYbkDWoJ9EHLWoH9KSRAAiRAAiRAAslJgBak5JyXtOmV\nZkz7+uuvKxxPMMVxrNpIFR6UThs0vkjTZJeLBauD3bs3rC0+sWBV8g4ZCstRJ1hkmgd3Ma50\nDixG6sam7myWJm4Iurrh3cprB4Vlhzg7YfGA4lQfYqOeUqOLJomDrHsuYoxUebNg4bIwHrtX\nbwnM+kzcnKZiqeUI4mqtJChIqkSlqvRt1lT0FS5NMV8/6tVd3tm6XRaY+CJXTsxtJePa5Mr/\nzF+EGKVSaWmUQtcoUYWIt+uIrHWlbj0pr+Gd42cSIAESIAESIIFaEaCCVCtsPKi6BEaMGCFT\npkyR1157TS644IKIwzQ+6e6775b8/Hxp375+buQjTpiECx7UDSr94nNjjQkqOZYTMIVXfaPH\niN2la0SvnXXrpBTWIWNhQjIHZ+N6sXX/rt1C8Upq8XDVFSxM8YpoJGzBRR0ejV+CGU+s1q2/\ndfUL26fCj7CS2D16HrXZg2x1qsBpX7UfmqpcM9l5hg0Xu3Pno/ZP9RWtoABd0aWTXK6JHFRB\nxYCmo0aSsRZhoTHij1Q0kcMunRPs0zunHdboHhQSIAESIAESIIFkI0AFKdlmJM36c91114nG\nIV144YUyatQoUaUoG2mrr7zySqM0HUIK5RdffDHNRl394WjqaxtWGGfDehEE/FuwLGiyAw+K\nrtpR1hYXsVylH35g3NTUMmSSIsBK5MDNTVDINZQUATfhxj5he1Cf6IBYFcR2+VevEq1nhAwa\noVt1T59+4hs3DgpN7S8NmsTBO+5k8UP58sPCpWIj0YELa4tm51MrUzpKeMKKZXv3QwlqImsP\nHoIVqUSyMWZNd30Y48/B52NhiToIPhQSIAESIAESIIHkI1D7u6DkGwt7lIQEtMbLtGnT5Pbb\nb5epU6ciNKXMtUjd7jogIYEqT8HEDUnY/XrvkgUrjG/iWeKsWV1myfH6YDXqIh61CMHSEC7O\nxg1GKbJzy2okaZptD2KWHNRIcrZuFqsdrBJHDouLmCR11St58w3Thg0LncYGoTpsqDln924J\nfPwRFKtGsByhIK1ugaUngOKv2r5n8JDQvjX9oIpA4FMUu91VKN6+/YxCZ+oiIZW5KhHe4cfX\ntMmU21+z3XksW0YiW10BlKSdWugXY28N5XEMLHXeqLlNuQGywyRAAiRAAiSQxgSoIKXx5CbL\n0Noi05mm8b7//vtlFQqMFiL7WY8ePczLh5v8TBej6PTrLx68KhPn4MGQpSe4n1qNXLjYuZrm\nG3FHxr1OrTQo3KqKD8wWRvHyv/O2yOkTgofBNW9DmVtd0zahdahYao5TJalOCtLOnRKA0qax\nUOpap2LBwiVQDJxFC0RgHQtX1r7tQPp8GtqqhamD1MbKkn6wFumrFA8H1mEORyBGiUICJEAC\nJEACJJC8BKggJe/cpEXPnn32WVm8eLHce++90rJlSzn++Ejrweuvvy633HKLLF++3LjepcWg\nazEIteg4q1aKg3pINjhprSCrTZny4mzfJgFY3Pwrl4uL2B4/XOK8sAoZ5QOWCAsui55Ro8V3\n6ukoOPuFQAvFsW1DvVBFRVOHC6xUHliR3KWLocAUiLN3j9hQXiNc3tQN7vAh4w4W7jIWaqw6\nH5B6XA0kEe3iOO2nAzdB4/YXZs2qTpOpts+wli1kLBI1fIr5hNppuq+FZU+CdU63HYaiRCEB\nEiABEiABEkhOAlSQknNeUrpXO2FBKNFgdIim+f7yyy9NMdjoQek+6n63YcMGOYxMaBqblImi\nSRJK350G9zgwg8ubFooNLP5GfKhlBChS+tZ/EbuDDHCoPxRA3Iq7bq0EoISoEmVqJOHGW93W\nNNbIFJxF5sBosbROz4J5kgXOLqw5rrriIYV4wB9A7SIoY1CMjKhyo6nG6+ICpvOIPplXWDua\nyc4odRkwz+pOd0XnjjIEsVcrkdIbZXVNBrwBSOkerIkUPUdcJgESIAESIAESSA4CVJCSYx7S\nqhfPPPOM/OpXv4oYUydNRV2BDBkyRFrVU1HTCk6ZNKs1aYH/05kmoZkVlsnPRTKL0k9mGpc3\nVY7U0qOibnjOpo2m4GoAbnKa5c438gSxodSoaG0itQxZEpmOWhM8oGKvBHR/7GNr0gAkyNBU\n4AEoLF5kl1PrjuZg8w4dhqKvhVDKkNkONZk0TqomohYrLSZrrFZ5eWWpvtGWoJCs99jBxpJU\nk/ZSdV9VhI6BgqQvCgmQAAmQAAmQQOoQoIKUOnOVMj392c9+Zoq/lsIV7OOPP5b169fLtdde\ne1T/NYGDKkaTJk06alumrHB3F4m7uxjxOnkRQ9a6R5qdzi3eHVnLCDFbdvcesDQ1Ft9xw8Uz\nclTEcUaBQvwPTHJmH92oLm0CZUg0u5ym9IYlJ6BKT6vWJpGAg2QKjipNUMI8qMHk/xw1jOAa\npmLhPJ4TRlUZH2V2Lv9HXeu8p5yKjHtIRw73QLfcimT36Yv+nhC+Kz+TAAmQAAmQAAmQQNIR\noIKUdFOS+h3SxAt33nmnGUi/fv1k6dKl8rvf/S71B1YPI7DUEw0xPw7c5tyDSMntQ1Y5KCo2\nEhqoW5YmcHCRXU5TZ4eLKcCKQqzRosqTZ8QJEpiL4rwa72PayILbXL7IN4tEkCXPzW5s6ihZ\nmzaKCyVJM835LrnMJG0oefUl9OeIUdhMwof9+8U/A9nuYGUymfWiT1jBstW8hWSdd744O7ZD\nOUMhWbiWBWOqKjiEq0mABEiABEiABEggKQhQQUqKaUjfTlx66aVmcAG4knlgWVDx44b/k08+\nka1IHHDmmWciuVlrsz4T/3EO7IeVZbs46s4Ghcc9iOU1cIcrbiOe3NZi9ewtAc38BkuOUYoA\nyViEwNLqGNtt0auWpV694D630xxjtc2Tkhf+bdz4XLUcQUkybXmR/W4bUoIfcwysSkek5LX/\nSECVqOwmYu3ba9z2jMsetjmLFtVIQTJziVTX6mpHIQESIAESIAESIIFUIlBW4j2Vesy+phyB\nBx98UDp27GgSMWjnr7/+ejn11FPlqquukq5du8qSJUtSbkzx6LCLtM/+L+eIIPbIzoLlCC6J\n0FygyJSl5rYQr6PxRZ7uPctc1eCuZorCHjqIQqzjxa4kbstqgUx4qKWkylTpu29JABYqVbJs\ndbfTBA1ws3PRjuWDdQnpwEvfxj7qmqf9gLXKLc+q56J+jwWFyYErIIUESIAESIAESIAEMoEA\nLUiZMMsNOMZPUTD01ltvlWNgpTiEOBhVhjT199ixY+VHP/qR/P73vzeKkma7yzhBxjgpKhJP\n5y7iIv2zC4uPQGkRxB+phceDdN8aN+SbeKZ4tEgs9hW43KnlqDLlyHCERar0g/fhurdG3EZw\nqdMkCQ4c7tSKp0qRWqBwTmhk4hTtMgqQjUKzrtZH0m2aEQ/udZrEwYZli+5xGfft5IBJgARI\ngARIIGMJUEHK2KlPzMA1jXcHFDNdsGCB2Ljp17pHKvfdd5+piaSJHNSStA/pq5shTiWjxEYm\nOa0XBEuSutfpKyguYndc8DKirmqwBom+qimBNUgFDuVIXdyMO93uPPEXFcJKBUUJRWQ9yGan\nKcIFro9irERIza2WIrg9WprQQVNxa/wTLEkukmn4tLgrhQRIgARIgARIgAQygABd7DJgkhty\niCtXrpQTTzzRKEfaj3feeUfaIgnB8OHDTbcGDhwIjzJXCgoKzHIm/aPxPR6k59YscuHiQlk0\nVh5YfjSOyNQPCt+hGp+dzRtFNN6oXMm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rcLwmoPyvO84DEbC7x2RtDoYTvuJ00f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\nLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <- grid.arrange(edss_p, msis_p, block_p, pbvc_p , cholesterol_p, ncol = 2, nrow = 3, respect=TRUE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The plots looks similar to what is reported. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Structural equation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "require(lavaan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'EDSS'</li>\n",
       "\t<li>'MSIS'</li>\n",
       "\t<li>'Cholesterol'</li>\n",
       "\t<li>'PBVC'</li>\n",
       "\t<li>'Block_Design'</li>\n",
       "\t<li>'group'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'EDSS'\n",
       "\\item 'MSIS'\n",
       "\\item 'Cholesterol'\n",
       "\\item 'PBVC'\n",
       "\\item 'Block\\_Design'\n",
       "\\item 'group'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'EDSS'\n",
       "2. 'MSIS'\n",
       "3. 'Cholesterol'\n",
       "4. 'PBVC'\n",
       "5. 'Block_Design'\n",
       "6. 'group'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"EDSS\"         \"MSIS\"         \"Cholesterol\"  \"PBVC\"         \"Block_Design\"\n",
       "[6] \"group\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames( df )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Figure 3 of the paper for explanation of model B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_B <- '#Atrophy & Cholesterol mediation model\n",
    "Cholesterol ~ a*group\n",
    "PBVC ~ b*group\n",
    "EDSS ~ d*PBVC\n",
    "EDSS ~ e*group\n",
    "Block_Design ~ f*PBVC\n",
    "Block_Design ~ g*group\n",
    "MSIS ~ h*PBVC\n",
    "MSIS ~ i*EDSS\n",
    "MSIS ~ k*Block_Design\n",
    "MSIS ~ l*group\n",
    "\n",
    "#covariance matrices\n",
    "Cholesterol ~~ Cholesterol\n",
    "PBVC ~~ PBVC\n",
    "EDSS ~~ EDSS\n",
    "Block_Design ~~ Block_Design\n",
    "MSIS ~~ MSIS\n",
    "'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df$group <- factor(df$group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "B <- sem(model_B,  data=df,\n",
    "                missing=\"FIML\", fit.measures = T, standardized = T , rsquare = T)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan (0.5-23.1097) converged normally after  84 iterations\n",
      "\n",
      "  Number of observations                           131\n",
      "\n",
      "  Number of missing patterns                         1\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Minimum Function Test Statistic               22.929\n",
      "  Degrees of freedom                                 4\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "Model test baseline model:\n",
      "\n",
      "  Minimum Function Test Statistic              153.435\n",
      "  Degrees of freedom                                15\n",
      "  P-value                                        0.000\n",
      "\n",
      "User model versus baseline model:\n",
      "\n",
      "  Comparative Fit Index (CFI)                    0.863\n",
      "  Tucker-Lewis Index (TLI)                       0.487\n",
      "\n",
      "Loglikelihood and Information Criteria:\n",
      "\n",
      "  Loglikelihood user model (H0)              -1406.430\n",
      "  Loglikelihood unrestricted model (H1)      -1394.966\n",
      "\n",
      "  Number of free parameters                         21\n",
      "  Akaike (AIC)                                2854.861\n",
      "  Bayesian (BIC)                              2915.240\n",
      "  Sample-size adjusted Bayesian (BIC)         2848.819\n",
      "\n",
      "Root Mean Square Error of Approximation:\n",
      "\n",
      "  RMSEA                                          0.190\n",
      "  90 Percent Confidence Interval          0.119  0.269\n",
      "  P-value RMSEA <= 0.05                          0.001\n",
      "\n",
      "Standardized Root Mean Square Residual:\n",
      "\n",
      "  SRMR                                           0.064\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  Cholesterol ~                                                         \n",
      "    group      (a)   -0.739    0.095   -7.755    0.000   -0.739   -0.561\n",
      "  PBVC ~                                                                \n",
      "    group      (b)    0.506    0.218    2.325    0.020    0.506    0.199\n",
      "  EDSS ~                                                                \n",
      "    PBVC       (d)   -0.188    0.035   -5.343    0.000   -0.188   -0.421\n",
      "    group      (e)   -0.153    0.090   -1.707    0.088   -0.153   -0.135\n",
      "  Block_Design ~                                                        \n",
      "    PBVC       (f)    2.184    0.378    5.783    0.000    2.184    0.454\n",
      "    group      (g)    0.851    0.961    0.886    0.376    0.851    0.070\n",
      "  MSIS ~                                                                \n",
      "    PBVC       (h)    1.531    0.849    1.804    0.071    1.531    0.186\n",
      "    EDSS       (i)    2.980    1.772    1.682    0.093    2.980    0.162\n",
      "    Block_Dsgn (k)    0.058    0.177    0.328    0.743    0.058    0.034\n",
      "    group      (l)   -3.431    1.832   -1.873    0.061   -3.431   -0.164\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .Cholesterol ~~                                                        \n",
      "   .MSIS              0.671    0.524    1.281    0.200    0.671    0.122\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .Cholesterol       0.734    0.152    4.843    0.000    0.734    1.116\n",
      "   .PBVC             -1.928    0.347   -5.563    0.000   -1.928   -1.517\n",
      "   .EDSS              0.315    0.155    2.030    0.042    0.315    0.555\n",
      "   .Block_Design      1.720    1.666    1.033    0.302    1.720    0.281\n",
      "   .MSIS              8.657    3.199    2.706    0.007    8.657    0.827\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .Cholesterol       0.297    0.037    8.093    0.000    0.297    0.685\n",
      "   .PBVC              1.552    0.192    8.093    0.000    1.552    0.960\n",
      "   .EDSS              0.252    0.031    8.093    0.000    0.252    0.782\n",
      "   .Block_Design     29.003    3.584    8.093    0.000   29.003    0.776\n",
      "   .MSIS            102.681   12.718    8.074    0.000  102.681    0.937\n",
      "\n",
      "R-Square:\n",
      "                   Estimate\n",
      "    Cholesterol       0.315\n",
      "    PBVC              0.040\n",
      "    EDSS              0.218\n",
      "    Block_Design      0.224\n",
      "    MSIS              0.063\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(B, fit.measures = T, standardized = T , rsquare = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of frequentist and Bayesian mediation models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "med_chol_edss <- '#Atrophy & Cholesterol mediation model\n",
    "Cholesterol ~ b1*group\n",
    "EDSS ~ b2*Cholesterol\n",
    "EDSS ~ c*group\n",
    "#co / variance matrices\n",
    "Cholesterol ~~ Cholesterol\n",
    "EDSS ~~ EDSS \n",
    "# direct and indirect effects\n",
    "ind := ( b1 * b2 )\n",
    "totl := c + ( b1 * b2 )'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "edss_chol <- sem(med_chol_edss, data=df,\n",
    "                 \n",
    "                missing=\"FIML\", fit.measures = T, std.lv=TRUE                  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan (0.5-23.1097) converged normally after  19 iterations\n",
      "\n",
      "  Number of observations                           131\n",
      "\n",
      "  Number of missing patterns                         1\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Minimum Function Test Statistic                0.000\n",
      "  Degrees of freedom                                 0\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  Cholesterol ~                                       \n",
      "    group     (b1)   -0.739    0.095   -7.755    0.000\n",
      "  EDSS ~                                              \n",
      "    Cholestrl (b2)    0.128    0.088    1.447    0.148\n",
      "    group      (c)   -0.154    0.116   -1.327    0.185\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .Cholesterol       0.734    0.152    4.843    0.000\n",
      "   .EDSS              0.585    0.166    3.519    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .Cholesterol       0.297    0.037    8.093    0.000\n",
      "   .EDSS              0.302    0.037    8.093    0.000\n",
      "\n",
      "Defined Parameters:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "    ind              -0.094    0.066   -1.422    0.155\n",
      "    totl             -0.248    0.097   -2.562    0.010\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(edss_chol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model can be done using blavaan package using a similar syntax (not shown here)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atrophy mediation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "med_chol_atrophy <- '#Atrophy & Cholesterol mediation model\n",
    "Cholesterol ~ b1*group\n",
    "PBVC ~ b2*Cholesterol\n",
    "PBVC ~ c*group\n",
    "#covariance matrices\n",
    "Cholesterol ~~ Cholesterol\n",
    "PBVC ~~ PBVC\n",
    "EDSS ~~ EDSS \n",
    "# direct and indirect effects\n",
    "ind := ( b1 * b2 )\n",
    "totl := c + ( b1 * b2 )'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chol_atrophy <- sem(med_chol_atrophy, data=df,\n",
    "                 \n",
    "                missing=\"FIML\", fit.measures = T, std.lv=TRUE                  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan (0.5-23.1097) converged normally after  28 iterations\n",
      "\n",
      "  Number of observations                           131\n",
      "\n",
      "  Number of missing patterns                         1\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Minimum Function Test Statistic               34.035\n",
      "  Degrees of freedom                                 3\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  Cholesterol ~                                       \n",
      "    group     (b1)   -0.739    0.095   -7.755    0.000\n",
      "  PBVC ~                                              \n",
      "    Cholestrl (b2)   -0.106    0.200   -0.532    0.595\n",
      "    group      (c)    0.428    0.263    1.629    0.103\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .Cholesterol       0.734    0.152    4.843    0.000\n",
      "   .PBVC             -1.850    0.376   -4.921    0.000\n",
      "    EDSS              0.303    0.050    6.108    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .Cholesterol       0.297    0.037    8.093    0.000\n",
      "   .PBVC              1.549    0.191    8.093    0.000\n",
      "    EDSS              0.322    0.040    8.093    0.000\n",
      "\n",
      "Defined Parameters:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "    ind               0.078    0.148    0.530    0.596\n",
      "    totl              0.506    0.218    2.325    0.020\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(chol_atrophy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayesian version of the same model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: blavaan\n",
      "Loading required package: runjags\n",
      "\n",
      "Attaching package: ‘runjags’\n",
      "\n",
      "The following object is masked from ‘package:magrittr’:\n",
      "\n",
      "    extract\n",
      "\n",
      "This is blavaan 0.3-2.283\n",
      "blavaan is more BETA than lavaan!\n"
     ]
    }
   ],
   "source": [
    "require(blavaan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in blavaan(med_chol_atrophy, data = df, missing = \"FIML\", fit.measures = T, :\n",
      "“blavaan WARNING: the following arguments have no effect:\n",
      "                   missing”Loading required namespace: rjags\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling rjags model...\n",
      "Calling the simulation using the rjags method...\n",
      "Adapting the model for 1000 iterations...\n",
      "Burning in the model for 4000 iterations...\n",
      "Running the model for 10000 iterations...\n",
      "Simulation complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in as.POSIXlt.POSIXct(x, tz):\n",
      "“unknown timezone 'zone/tz/2018c.1.0/zoneinfo/Europe/London'”"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating summary statistics...\n",
      "Note: The monitored variables 'psi[3,3,1]' and 'alpha[3,1,1]' appear to\n",
      "be non-stochastic; they will not be included in the convergence\n",
      "diagnostic\n",
      "Calculating the Gelman-Rubin statistic for 13 variables....\n",
      "Finished running the simulation\n",
      "Computing posterior predictives...\n"
     ]
    }
   ],
   "source": [
    "ch_atrophy_bayesian <- bsem(med_chol_atrophy,\n",
    "                data=df,\n",
    "                missing=\"FIML\", fit.measures = T, std.lv=TRUE                  \n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blavaan (0.3-2.283) results of 10000 samples after 5000 adapt/burnin iterations\n",
      "\n",
      "  Number of observations                           131\n",
      "\n",
      "  Number of missing patterns                         1\n",
      "\n",
      "  Statistic                                 MargLogLik         PPP\n",
      "  Value                                       -474.439       0.000\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Post.SD  HPD.025  HPD.975     PSRF    Prior     \n",
      "  Cholesterol ~                                                              \n",
      "    group     (b1)   -0.738    0.097   -0.926   -0.548    1.001 dnorm(0,1e-2)\n",
      "  PBVC ~                                                                     \n",
      "    Cholestrl (b2)   -0.104    0.203   -0.501    0.297    1.000 dnorm(0,1e-2)\n",
      "    group      (c)    0.432    0.270   -0.103    0.957    1.001 dnorm(0,1e-2)\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Post.SD  HPD.025  HPD.975     PSRF    Prior     \n",
      "   .Cholesterol       0.733    0.155    0.428    1.035    1.001 dnorm(0,1e-3)\n",
      "   .PBVC             -1.856    0.388   -2.612   -1.091    1.001 dnorm(0,1e-3)\n",
      "    EDSS              0.303    0.050    0.201    0.399    1.000 dnorm(0,1e-3)\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Post.SD  HPD.025  HPD.975     PSRF    Prior     \n",
      "   .Cholesterol       0.309    0.039    0.237    0.387    1.000  dgamma(1,.5)\n",
      "   .PBVC              1.593    0.202    1.221    2.001    1.000  dgamma(1,.5)\n",
      "    EDSS              0.332    0.042    0.254    0.415    1.000  dgamma(1,.5)\n",
      "\n",
      "Defined Parameters:\n",
      "                   Estimate  Post.SD  HPD.025  HPD.975     PSRF    Prior     \n",
      "    ind               0.077    0.151   -0.211    0.385                       \n",
      "    totl              0.509    0.225    0.073    0.954                       \n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(ch_atrophy_bayesian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posterior estimates are very similar to the frequentist approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples<- blavInspect( ch_atrophy_bayesian , 'samples')\n",
    "\n",
    "#beta one is the firs regeression coefficient in the model syntax\n",
    "\n",
    "b1 <- as.numeric(  samples[[1]][,1] ) \n",
    "b2 <- as.numeric( samples[[1]][,2])\n",
    "c <- as.numeric( samples[[1]][,3])\n",
    "\n",
    "\n",
    "\n",
    "ind <- as.numeric( samples[[1]][,10]) \n",
    "\n",
    "\n",
    "\n",
    "total <- as.numeric( samples[[1]][,11]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "direct_indirect_samples <- data.frame( c(c, ind) )\n",
    "\n",
    "direct_indirect_samples$effect <- c( rep('direct', length(c)), rep('indirect', length(ind)) )\n",
    "\n",
    "colnames(direct_indirect_samples) <- c( 'sample', 'effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bZvLeMDAQIECBAgQKCRAmaQGqnv2gQI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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " p <- ggplot(direct_indirect_samples, aes(x=sample, fill=effect)) +\n",
    "    geom_histogram(binwidth=.03, alpha=.5, position=\"identity\") + theme_bw() +\n",
    "#vertical lines for the original data\n",
    "geom_vline(xintercept=c(-0.24, 0.07, 0.09, 0.54)) +  theme(panel.grid.major = element_blank(),\n",
    "                                                           panel.grid.minor = element_blank())\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an overlap between two distributions in the simulated data. Using the original data the two distributions are separate (in the 95% credible interval). Vertical lines show the distribution in the original dataset that is reported.\n",
    "\n",
    "WIP: A non-parametric simulation approach will be introduced in near future to replicate the exact result of the manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "med_atrophy_EDSS <- '#regression models\n",
    "PBVC ~ b1*group\n",
    "EDSS ~ b2*PBVC\n",
    "EDSS ~ c*group\n",
    "\n",
    "#variance and covariacne matrices\n",
    "EDSS ~~ EDSS\n",
    "PBVC ~~ PBVC\n",
    "ind := b1 * b2\n",
    "total := c + ( b1*b2)'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "edss_atrophy <- sem(med_atrophy_EDSS, data=df,\n",
    "                 \n",
    "                missing=\"FIML\", fit.measures = T, std.lv=TRUE                  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan (0.5-23.1097) converged normally after  23 iterations\n",
      "\n",
      "  Number of observations                           131\n",
      "\n",
      "  Number of missing patterns                         1\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Minimum Function Test Statistic                0.000\n",
      "  Degrees of freedom                                 0\n",
      "  Minimum Function Value               0.0000000000000\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  PBVC ~                                              \n",
      "    group     (b1)    0.506    0.218    2.325    0.020\n",
      "  EDSS ~                                              \n",
      "    PBVC      (b2)   -0.188    0.035   -5.343    0.000\n",
      "    group      (c)   -0.153    0.090   -1.707    0.088\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .PBVC             -1.928    0.347   -5.563    0.000\n",
      "   .EDSS              0.315    0.155    2.030    0.042\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .EDSS              0.252    0.031    8.093    0.000\n",
      "   .PBVC              1.552    0.192    8.093    0.000\n",
      "\n",
      "Defined Parameters:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "    ind              -0.095    0.045   -2.132    0.033\n",
      "    total            -0.248    0.097   -2.562    0.010\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(edss_atrophy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "38.3064516129032"
      ],
      "text/latex": [
       "38.3064516129032"
      ],
      "text/markdown": [
       "38.3064516129032"
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      "text/plain": [
       "[1] 38.30645"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percentage simvastatin effect transmitted by atrophy\n",
    "\n",
    "(0.095/0.248) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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