9 方差分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.1 术语速成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.2 ANOVA模型拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2.1 aov()函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aov()函数的语法为aov(formula,data=dataframe)。\n",
    "![R表达式中的特殊符号.jpg](./image/R表达式中的特殊符号.jpg)\n",
    "其中y是因变量，字母A、B、C代表因子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下表是一些常见的额研究设计表达式。\n",
    "\n",
    "小写字母表示定量变量\n",
    "\n",
    "大写字母表示组别因子\n",
    "\n",
    "subject是对被试者独有的标识变量。\n",
    "\n",
    "![常见研究设计的表达式.jpg](./image/常见研究设计的表达式.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2.2 表达式中各项的顺序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表达式中效应的顺序在两种情况下会造成影响：\n",
    "\n",
    "    （1）因子不止一个，并且是非平衡设计；\n",
    "    （2）存在协变量。\n",
    "\n",
    "出现任意一种情况时，等式右边的变量都与其他每个变量相关。\n",
    "\n",
    "此时，我们无法清晰地划分它们对因变量的影响。\n",
    "\n",
    "R默认类型I（序贯型）方法计算ANOVA效应。（参考下面的补充内容）\n",
    "![顺序很重要.jpg](./image/顺序很重要.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "car包中的Anova()函数（不要与标准anova()函数混淆）提供了使用类型Ⅱ或类型Ⅲ方法的选项\n",
    "\n",
    "aov()函数使用的是类型I方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.3 单因素方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windowsWarning message:\n",
      "\"package 'multcomp' was built under R version 3.5.2\"Loading required package: mvtnorm\n",
      "Warning message:\n",
      "\"package 'mvtnorm' was built under R version 3.5.2\"Loading required package: survival\n",
      "Loading required package: TH.data\n",
      "Warning message:\n",
      "\"package 'TH.data' was built under R version 3.5.2\"Loading required package: MASS\n",
      "\n",
      "Attaching package: 'TH.data'\n",
      "\n",
      "The following object is masked from 'package:MASS':\n",
      "\n",
      "    geyser\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "trt\n",
       " 1time 2times 4times  drugD  drugE \n",
       "    10     10     10     10     10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Group.1</th><th scope=col>x</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1time   </td><td> 5.78197</td></tr>\n",
       "\t<tr><td>2times  </td><td> 9.22497</td></tr>\n",
       "\t<tr><td>4times  </td><td>12.37478</td></tr>\n",
       "\t<tr><td>drugD   </td><td>15.36117</td></tr>\n",
       "\t<tr><td>drugE   </td><td>20.94752</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Group.1 & x\\\\\n",
       "\\hline\n",
       "\t 1time    &  5.78197\\\\\n",
       "\t 2times   &  9.22497\\\\\n",
       "\t 4times   & 12.37478\\\\\n",
       "\t drugD    & 15.36117\\\\\n",
       "\t drugE    & 20.94752\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Group.1 | x |\n",
       "|---|---|\n",
       "| 1time    |  5.78197 |\n",
       "| 2times   |  9.22497 |\n",
       "| 4times   | 12.37478 |\n",
       "| drugD    | 15.36117 |\n",
       "| drugE    | 20.94752 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group.1 x       \n",
       "1 1time    5.78197\n",
       "2 2times   9.22497\n",
       "3 4times  12.37478\n",
       "4 drugD   15.36117\n",
       "5 drugE   20.94752"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Group.1</th><th scope=col>x</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1time   </td><td>2.878113</td></tr>\n",
       "\t<tr><td>2times  </td><td>3.483054</td></tr>\n",
       "\t<tr><td>4times  </td><td>2.923119</td></tr>\n",
       "\t<tr><td>drugD   </td><td>3.454636</td></tr>\n",
       "\t<tr><td>drugE   </td><td>3.345003</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Group.1 & x\\\\\n",
       "\\hline\n",
       "\t 1time    & 2.878113\\\\\n",
       "\t 2times   & 3.483054\\\\\n",
       "\t 4times   & 2.923119\\\\\n",
       "\t drugD    & 3.454636\\\\\n",
       "\t drugE    & 3.345003\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Group.1 | x |\n",
       "|---|---|\n",
       "| 1time    | 2.878113 |\n",
       "| 2times   | 3.483054 |\n",
       "| 4times   | 2.923119 |\n",
       "| drugD    | 3.454636 |\n",
       "| drugE    | 3.345003 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group.1 x       \n",
       "1 1time   2.878113\n",
       "2 2times  3.483054\n",
       "3 4times  2.923119\n",
       "4 drugD   3.454636\n",
       "5 drugE   3.345003"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "            Df Sum Sq Mean Sq F value   Pr(>F)    \n",
       "trt          4 1351.4   337.8   32.43 9.82e-13 ***\n",
       "Residuals   45  468.8    10.4                     \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into 'C:/Users/acer/Documents/R/win-library/3.5'\n",
      "(as 'lib' is unspecified)\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'gplots' successfully unpacked and MD5 sums checked\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\tC:\\Users\\acer\\AppData\\Local\\Temp\\RtmpSoqw05\\downloaded_packages\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'gplots' was built under R version 3.5.3\"\n",
      "Attaching package: 'gplots'\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    lowess\n",
      "\n"
     ]
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CUodgAAACpBsQMAAFAJih0AAIBKUOwA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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Mean Plot\n",
       "with 95% CI\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单因素方差分析\n",
    "#以multcomp包中cholesterol数据集为例，50个患者均接受降低胆固醇药物治疗（trt）五种疗法中的一种疗法\n",
    "library(multcomp)\n",
    "attach(cholesterol)\n",
    "\n",
    "table(trt)  #各组样本大小\n",
    "#输出结果看，每10个患者接受其中一个药物疗法\n",
    "\n",
    "aggregate(response,by=list(trt),FUN=mean) #各组均值\n",
    "#均值显示drygE降低胆固醇最多，而1time降低胆固醇最少\n",
    "\n",
    "aggregate(response,by=list(trt),FUN=sd) #各组标准差\n",
    "#各组的标准差相对恒定，在2.88到3.48间浮动\n",
    "\n",
    "#检验组间差异（ANOVA）\n",
    "fit <- aov(response ~ trt)\n",
    "summary(fit)\n",
    "#ANOVA对治疗方式（trt）的F检验非常显著（p<0.0001），说明五种疗法的效果不同\n",
    "\n",
    "#绘制各组均值及其置信区间的图形\n",
    "install.packages(\"gplots\")\n",
    "library(gplots)\n",
    "plotmeans(response ~ trt, xlab=\"Treatment\",ylab=\"Response\",\n",
    "         main=\"Mean Plot\\nwith 95% CI\")\n",
    "\n",
    "detach(cholesterol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3.1 多重比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TukeyHSD()函数提供了对各组均值差异的成对检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  Tukey multiple comparisons of means\n",
       "    95% family-wise confidence level\n",
       "\n",
       "Fit: aov(formula = response ~ trt)\n",
       "\n",
       "$trt\n",
       "                  diff        lwr       upr     p adj\n",
       "2times-1time   3.44300 -0.6582817  7.544282 0.1380949\n",
       "4times-1time   6.59281  2.4915283 10.694092 0.0003542\n",
       "drugD-1time    9.57920  5.4779183 13.680482 0.0000003\n",
       "drugE-1time   15.16555 11.0642683 19.266832 0.0000000\n",
       "4times-2times  3.14981 -0.9514717  7.251092 0.2050382\n",
       "drugD-2times   6.13620  2.0349183 10.237482 0.0009611\n",
       "drugE-2times  11.72255  7.6212683 15.823832 0.0000000\n",
       "drugD-4times   2.98639 -1.1148917  7.087672 0.2512446\n",
       "drugE-4times   8.57274  4.4714583 12.674022 0.0000037\n",
       "drugE-drugD    5.58635  1.4850683  9.687632 0.0030633\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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77jOI7jVCoVc0un01F5Sy/sMzNltVr1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\nlkWyokTUQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"95% family-wise confidence level\n",
       "\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TukeyHSD的成对组间比较\n",
    "TukeyHSD(fit)\n",
    "\n",
    "#作图\n",
    "par(las=2)\n",
    "par(mar=c(5,8,4,2))\n",
    "plot(TukeyHSD(fit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "multcomp包中的glht()函数提供了多重均值比较更为全面的方法，既适用于线性模型也适用于广义线性模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KtImWAHANnyyU9+8jOf+UzaVSzr3nvv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\n+Vl4myn+rLbc/wLxPY7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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(multcomp)\n",
    "par(mar=c(5,4,6,2)) #增大顶部边界面积\n",
    "tuk <- glht(fit,linfct=mcp(trt=\"Tukey\"))\n",
    "plot(cld(tuk,level=0.05),col=\"lightgrey\")  #cld()函数中的level选项设置了使用的显著水平"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3.2 评估检验的假设条件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单因素方差分析，我们假设因变量服从正态分布、各组方差相等，可以使用Q-Q图来检验正态性假设："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>19</li>\n",
       "\t<li>38</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19\n",
       "\\item 38\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19\n",
       "2. 38\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 19 38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KZPxxdfCKnO0RHr1zPVWR99g51UKtUuVqydJyGTybTnIyMjtU9pTVQfERGRFZkx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YYWpqGhsbe+zYsTVr1mzfvl1TU5PD4QiF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\nEgQ7AAAASxDsAAAALEGwAwAAsMT/A9s60kueSKEOAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Q-Q Plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(car)\n",
    "qqPlot(lm(response ~ trt,data=cholesterol), #注意qqPlot()要求用lm()拟合\n",
    "      sumulate=TRUE,main=\"Q-Q Plot\", labels=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBartlett test of homogeneity of variances\n",
       "\n",
       "data:  response by trt\n",
       "Bartlett's K-squared = 0.57975, df = 4, p-value = 0.9653\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#通过以下代码来做Bartlett检验，可以用来做方差\n",
    "bartlett.test(response~trt, data=cholesterol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fligner-Killeen检验用fligner.test()函数\n",
    "\n",
    "Brown-Forsythe检验用HH包中的hov()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No Studentized residuals with Bonferonni p < 0.05\n",
       "Largest |rstudent|:\n",
       "   rstudent unadjusted p-value Bonferonni p\n",
       "19 2.251149           0.029422           NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#方差齐性分析对离群点非常敏感。可利用car包中的outlierTest()函数来检测离群点\n",
    "library(car)\n",
    "outlierTest(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.4 单因素协方差分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单因素方差分析，以multcomp包中的cholesteril数据集为例，50个患者均接受降低胆固醇药物治疗物种疗法中的一种疗法。 其中三种治疗条件使用药物的两种方式（drugD和drugE)代表候选药物，那种药物疗法降低胆固醇（响应变量）最多呢"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'multcomp' was built under R version 3.5.3\"Loading required package: mvtnorm\n",
      "Warning message:\n",
      "\"package 'mvtnorm' was built under R version 3.5.2\"Loading required package: survival\n",
      "Loading required package: TH.data\n",
      "Warning message:\n",
      "\"package 'TH.data' was built under R version 3.5.2\"Loading required package: MASS\n",
      "\n",
      "Attaching package: 'TH.data'\n",
      "\n",
      "The following object is masked from 'package:MASS':\n",
      "\n",
      "    geyser\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "trt\n",
       " 1time 2times 4times  drugD  drugE \n",
       "    10     10     10     10     10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(multcomp)\n",
    "attach(cholesterol)\n",
    "table(trt) #各组样本大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Group.1</th><th scope=col>x</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1time </td><td> 5.78197</td></tr>\n",
       "\t<tr><td>2times</td><td> 9.22497</td></tr>\n",
       "\t<tr><td>4times</td><td>12.37478</td></tr>\n",
       "\t<tr><td>drugD </td><td>15.36117</td></tr>\n",
       "\t<tr><td>drugE </td><td>20.94752</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Group.1 & x\\\\\n",
       " <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1time  &  5.78197\\\\\n",
       "\t 2times &  9.22497\\\\\n",
       "\t 4times & 12.37478\\\\\n",
       "\t drugD  & 15.36117\\\\\n",
       "\t drugE  & 20.94752\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| Group.1 &lt;fct&gt; | x &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1time  |  5.78197 |\n",
       "| 2times |  9.22497 |\n",
       "| 4times | 12.37478 |\n",
       "| drugD  | 15.36117 |\n",
       "| drugE  | 20.94752 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group.1 x       \n",
       "1 1time    5.78197\n",
       "2 2times   9.22497\n",
       "3 4times  12.37478\n",
       "4 drugD   15.36117\n",
       "5 drugE   20.94752"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggregate(response,by=list(trt),FUN=mean) #各组均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "            Df Sum Sq Mean Sq F value   Pr(>F)    \n",
       "trt          4 1351.4   337.8   32.43 9.82e-13 ***\n",
       "Residuals   45  468.8    10.4                     \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit <- aov(response ~ trt)  #检查组间差异（ANOVA）\n",
    "summary(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windowsWarning message:\n",
      "\"package 'gplots' was built under R version 3.5.3\"\n",
      "Attaching package: 'gplots'\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    lowess\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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CUodgAAACpBsQMAAFAJih0AAIBKUOwA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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Mean Plot\n",
       "with 95% CI\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制各组均值及其置信区间\n",
    "library(gplots)\n",
    "plotmeans(response~trt,xlab=\"Treatment\", ylab=\"Response\",main=\"Mean Plot\\nwith 95% CI\")\n",
    "detach(cholesterol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3.1 多重比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TukeyHSD()提供了对各组均值差异的成对检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  Tukey multiple comparisons of means\n",
       "    95% family-wise confidence level\n",
       "\n",
       "Fit: aov(formula = response ~ trt)\n",
       "\n",
       "$trt\n",
       "                  diff        lwr       upr     p adj\n",
       "2times-1time   3.44300 -0.6582817  7.544282 0.1380949\n",
       "4times-1time   6.59281  2.4915283 10.694092 0.0003542\n",
       "drugD-1time    9.57920  5.4779183 13.680482 0.0000003\n",
       "drugE-1time   15.16555 11.0642683 19.266832 0.0000000\n",
       "4times-2times  3.14981 -0.9514717  7.251092 0.2050382\n",
       "drugD-2times   6.13620  2.0349183 10.237482 0.0009611\n",
       "drugE-2times  11.72255  7.6212683 15.823832 0.0000000\n",
       "drugD-4times   2.98639 -1.1148917  7.087672 0.2512446\n",
       "drugE-4times   8.57274  4.4714583 12.674022 0.0000037\n",
       "drugE-drugD    5.58635  1.4850683  9.687632 0.0030633\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TukeyHSD的成对组间比较\n",
    "TukeyHSD(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "image/png": 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77jOI7jVCoVc0un01F5Sy/sMzNltVr1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\nlkWyokTUQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"95% family-wise confidence level\n",
       "\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#成对比较图\n",
    "par(las=2)\n",
    "par(mar=c(5,8,4,2))\n",
    "plot(TukeyHSD(fit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pz8/PHjx+fm5lbZ58UXX4yi6Ny5c6+hSACAddY+2NXr9XK5vOxLcU/gTpe0Tk6e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\n3bYQAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(multcomp)\n",
    "par(mar=c(5,4,6,2))\n",
    "tuk <- glht(fit,linfct=mcp(trt=\"Tukey\"))\n",
    "plot(cld(tuk,level=0.5),col=\"lightgrey\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3.2 评估检验的假设条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>19</li>\n",
       "\t<li>38</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19\n",
       "\\item 38\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19\n",
       "2. 38\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 19 38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DwpNJs1w/79KPY8hzp16qxevXrFihV/\n//03gLp169rZ2fJ7aByxIyIiouJo3RqHDiEhAf36Pb171zR9PniAnj1x5ozQbNEC+/ejevVX\n7dbe3r5+/fqv2ktpYFxo1e4tpsXliImIiMqgs2dRvTo+/BBdusDR0TR93r6NLl10qa59exw6\nZIJUV6YYEexkMlneLdg0fH19ZTKZSUsiIiIia5Gbm7tly5YRI0a888477du3Hzt27NGjRwGM\nGIF795CYiB9/NM0PunkT3brh8mWh2bEj9u7Fa6+ZpvOyw9BgFx0drZlFnG+jWABKpZLjdkRE\nRLbn9u3bHTp08Pf337x586NHj27duqVQKDp37tyr1xRtAmvY0AQ/6OpVdOqEP/8Umt26Yf9+\nVKpkgp7LGkODXWhoKAqbTqE5o/mUiIiIbMazZ8969+598uTJyZMn37lz5/r167du3frzzz+H\nDBmyb58wl6FSJQwc+Ko/KCEB3t74+2+h6eODyEi4ur5qt2WTocFOpVLlXSomr7CwMJVKZbqS\niIiIyPKUSuWFCxemTZv2zTffVKtWTXOyYcOGmzZtdnIaqWkOGADnV1vk5MIFdO6MpCSh6eeH\nyEiUL/9KfZZltjzjl4iIiIpt69atFSpUmDRpUr7z6el22dnCU9KRI1/pR5w9i+7d8c8/QnPw\nYGzeDCenV+qzjDM02InFYs07dgUFBQWJxWLTlURERESWl5CQ0Lx5c+cCI3JubpgyBcC/DRtu\n7dGj+P0fPw5vb9y/LzRHjcLmzShXrvgdEgwPdiEhIQAKbh2mOePv72/asoiIiMg6RUXBwQGO\njg2aNi3+hNjYWPj44PHzfcg+/hirV8Pe3jQVlmWGBjuJRKJ5x67gFrFSqVQul5uxRiIiIipx\nnp6e8fHxGRkZeU8eOAA/P0ybJsrK+t7T07N4PcfEoG9fpKYKTbkcK1bApveDKDlG/FNUKBRR\neTfjBQBERUXp2aONiIiISqnBgwenpqbOmTMn78mFC5GVpTn0GDRoUDG63bkTffvi6VOhOXEi\nFi5EgSeCVEzGbSkmkUi4gSwREVFZIJVKlUrlzJkzs7KygoODK1WqdPcu9uwRPm3d+lqHDka/\niLVtG4YPx7Pn+8rOmYMCczPolRg6Yufl5cUdJoiIiMqOcuXK7dmzp127drNnz3799dfr1q3b\nqNH0nBwAEIlyf/55gLEdrl8Pf3+mOvMydMSOK9URERGVNTVr1jx27NjWrVu3b99+5cqVf//t\nqznfubNd/frGrUqyejWkUjzPhViyBOPGmbxeMma5E7PWQURERFbIzs5u6NChGzduPHny5LBh\nbQE4OODzz43rZPFijB4tpDo7OyxfzlRnLoaO2MXFxYlEIi8vr7i4OLMWRERERNZp0SJ4eaFO\nHbz3nhF3ffcdPv8cmlf0HRzwww8YMcJMBZLBI3aalU1UKpXoJcxZJBEREVnY7Nlwdsbx48al\nuhkzMHGikOocHbFhA1OdeXHRGCIiIirCiRP48ksAWL4cd+8aetfkyZg+XTh2dsa2beCGBuZm\n6KNYrnJCRERUNmVlYdYs4djdHa+9VvQtajWCg7FggdB0ccG2bejd21wVkpZx69gRERFRWTNs\nGCIjheMhQ+BU1HTY3FyMGwft9gVubti1C126mLFC0jLuUWxiYmLel+rCw8NLfnE7Ly8vbQ2J\niYkvu6CEqyIiIrJJx49j2zbhuGpV4YGsHtnZCAzUpTp3d0RHM9WVHCOCnUwmK7grnFKpLMkU\nJRKJ8q6o5+npGR4eXmI/nYiIqKypVAn29gBQpw6OHEG9evouzsrCyJHYsEFovv46Dh6El5fZ\niyQtQ4NdeHi4UqkUi8VqtTosLExzUi6XS6VSzafmKvDFGgBIpVL1cwCCgoK4JQYREZFpJSej\nTx/Y2eGnnxAXB6USJ0+iUSN9t2RmYvBgbN4sNKtWRUwM2rQpgWJJx9BgFxERIRaLCy5ip1Ao\nxGJxRESEqQt7aQ0K7fAuoFarNTvZMdsRERGZyoMH6N4dUVFQqzFvHtq0wZgxqFJF3y2ZmXYj\nR7pqX8V7800cPoy33y6BYukFRmwpph2oy8ff3z8oKMh0JemrQTNAmJcm5ymVyqZNm8rlcmP7\nzM7OPnjwYHJysp5rTp8+3bRpU5FIpP8ym/H06VPtf5PF8euwKvw6rAe/C/O5e9fOz8/1jz+E\noR9v76y0tCL+Od+/nz52bJ2TJ4VQUadO7s6daTVq5Nrqn01HR8eOHTtauorC2cKsWE22CwoK\natiwoUQiMere48eP9+rVq8jLpk2b5u7unpSUVMwSS6GHDx8+fPjQ0lWQgF+HVeHXYT34XQC4\nd+/e4cOHHz9+bG9v36pVq9atW7/iu+8jR9bXprqWLdO//PJ6UlKOnutTU+3Gj697+nR5TbN2\n7WcrVlyzs8uy4b+Z5cuXVyqVp06dsnQhhTA02GmetxY6JBYUFFQyO8mKxeL4+PhCP1IoFPHx\n8b6+vgkJCUb12aFDh8OHD2dmZuq5Zv78+evXr3/jjTdGjhxpVOelVFpa2v3796tWrerq6mrp\nWohfh3Xh12E9+F0AePr06eTJk9evX5+dna092bBhQ4VC0bZt2+L1+fix6Px5IaJ16ZITEZHr\n6lpLz/WPHokCA13OnBGCYJMmuZGRWdWrv1m8n15aXLx4cdy4cYGBgZYupBCGBruQkBBfX9+C\ne8Vq/m9BSEiI6UsrQPPMVyaT5X3NTuuHH37w9PQsOG9XPwcHh86dO+u/ZsOGDXv37n3w4EGF\nChWM6ryUysnJAeDs7FxGfl8rx6/DqvDrsB78LjIzM4cNGxYbG9utW7cxY8Z4enqmpKTExMSE\nhYX5+vru2bOni5FLjNy8iU2b0KMHxo3Dxo3o3RurV9s7O+vLzffvY8AAnD0rNJs1yzl40L5a\nNduP2tnZ2adPny7dwU4ikWimKWgHeLUHUqnU2AegxSOXyyMiIpRKpVKpjIqKyvdDPTw8oqKi\nfH19S6ASIiIiy1qyZElsbOy4ceOWLFmi/Yvs7e09cOBAb2/vDz/88I8//nB0dDSwt7Nn0asX\n7t2DkxOSkrBkSdG33L6NHj1w+bLQbNEiPTIyq1o19+L8MmQ6Rqxjp1AoCj7ojIqKKnT8zEzi\n4uIKzp/Qkkgk3PqMiIjKglWrVtWoUWPBggX53qhr27btxIkT//rrr9jYWAO7OnYM3t64dw8A\nsrKQklL0LUlJ8PbWpbr27XOUyuuVKvFPsOUZt/OEh4eH+kUlM1aXl0Kh0P9ztUvcERER2aSn\nT59evny5e/fuzs7OBT/t06cPgNOnTxvS1dGj8PHB48dCc+xYNGhQxC1Xr6JzZ1y5IjS7d8eO\nHWkVKuibYEElxrhgR0RERBaXkpICoGLFioV+qjmfYsjIGzBpEtLShGO5vOiHsJcuoXNn/P23\n0OzZEzt3onx5jqdYC0ODnUgketn2EtyblYiIqCRVqVLF0dHxr7/+KvTTq1evAqhRo4YhXZUr\nJxxMnYqwMOj/e37hArp1w507QtPPD5GRKF/e0LKpBJhgHbu8m7cSERGRuTk4OHh7ex84cODa\ntWv1CuzeumrVKgDdu3fX08PatQgJQYMGWLYMu3ejUSP071/ED/39d/TqhX//FZpDhmDjRhg8\nPYNKiL4Ru/DwcNFzAIKCgkSFAVAy69gRERGRxtSpU7Ozs/v375+YmKg9+ezZs5CQkK1bt/r7\n+zdt2vRl9y5ejI8+wq1bOHwYR44gOLjoVHf8OHr21KW6UaPw009MddZI34idXC43cK+wklnH\njoiIiDQ6deq0YMGCzz77rEmTJr6+vk2aNElJSdm1a1dSUtLbb7+tVCpfduO33+KLL4RjOzt4\nexf9sw4eRP/+ulfxxo/H4sVFPLQlSyniHTvt7FcAYWFh6pco+bmxREREZVxQUFBsbOx7770X\nHR09d+7cZcuWAQgNDVWpVC+bV7Fjhy7VOTjghx/QsGERP2X/fvTrp0t1QUFMdVbN0HfsuIAI\nERGRtXnvvffee++91NTUe/fuOTk5vflmEXt5bd0qHDg54aef4OdXRP+//IKAAGj33Zw6FTNn\nvmLJZF5GLHcSHR0tEom0z/K179hFR0ebpzYiIiIqWoUKFerXr68n1eXm4pdfsGMHAgPh5oYq\nVRAZWXSq27oV/v66VDdnDlNdKWBosIuOjtbs1qXZfMLLy0v7ka+vL7MdERGRdcrOxgcfwM8P\nAwfi/HmkpOD+ffj4FHHXunUICMCzZ0Jz7lxMmmTuSskEDA12O3bsAJCQkCCRSBITE1UqlVgs\n1r5+p/mUiIiIrEpWFoYNw8aNQvPWLYPuWr0aH32EnBwAEImwdKnuzTyycoYGO6VSKZVKPTw8\nAOzevRuAv7+/5qOwsDA9s2+IiIjIInJyMHiw7r26mjVhyFoXixZh9Ggh1dnZQaHAJ5+YsUgy\nLSMWKNauiHPp0iU834qOiIiIrNPOnYiMFI5r18aBA6hdu4hbFixAcDA0EyYdHLBuHYYPN2+R\nZFrF2StWMz6nGb3D85xHREREVkU776F+fRw6hOd/t19q+nR8/rmQ6hwdsWEDU13pY2iwE4vF\nmsWKNfMkpFKp5nx0dLTmKa2Z6iMiIiKjaOZG1KiB8uWxbBmmTkVcHOrWLeKuyZMxY4Zw7OyM\n7dvx/JUrKk0MfRQbEhLi6+srer4ioZ+fHwCZTKYZvQsODjZTfURERGS4O3fQowc0z9KmTsW5\nc0XfolZjwgQsXSo0XVywfTt69TJjkWQ+ho7YSSSSsLAwzXFYWJhmqwnNW3dqtdqjyOFdIiIi\nMrObN9G1K7RvSHXpUvQtubkYO1aX6tzcsGcPU10pZsQ7dnK5XLO+iVwuz3vGPIURERGREXJz\n0bMnrlwRmt7emDOniFs0S9xpV7aoXBkHDhgUB8lqFWfyBBEREVmbkydx+bJw3Ls3du+Gq6u+\n67Oy8P77uiXuqlTB/v1o1868RZK5GRfsEhMTtTuJAQgPD5fJZOYpjIiIiAzy99+IjMRbb0Gz\nLdSwYdixAy4u+m7JyMCgQYiIEJo1auDQIbzzjtlLJXMzItjJZDJPT898J5VKpXZGBREREZWk\n7GyEhsLTE/37o0cPHDkCtRqbNsHJSd9d6ekYOBC7dgnNN99EbCyeL1ZLpZuhwS48PFypVGq2\nEdPOopDL5ZqFTsLDw81VIBERERXmyhV06oSpU5GVBQA3bui2dtXjyRNIJNizR2jWr4+jR9Go\nkRnrpJJkaLCLiIgQi8VxcXH5zisUCrFYHKEdzCUiIiLzi4/HO+/gxAmhWa4cvv22iIE6ACkp\nkEhw6JDQ9PBAbGzRS9xRKWJosFOpVP4vWanQ399fpVKZriQiIiIqwt69SEsTjps3x4kTRe/o\n+vAhunWDdoimeXMcOVL0JmNUunBWLBERUanx44/o3h0SCd57D7Vro1o1BAfj5Em8/XYRN96/\njx49cOqU0GzVCgcO4I03zF0vlTRDd57QPG/VrmCXV1BQkFgsNmlVRERE9IL79zF2LLZtE5p+\nfvj7b0PvvXULPXrgjz+E5rvvIjoar71m+iLJ4gwdsQsJCVGpVF6aidR5aKbEhoSEmLguIiIi\nei4qCi1a6FKdnR1atjT03mvX0LmzLtWJxdi7l6nOZhk6YieRSKRSad7FTbQHUqlUs8MYERER\nmVxGBgICkJoqNGvUwMqV6NjRoHsTE9G9O27cEJo9emDHjiIWLqZSzYh37BQKRUJCQr6TUVFR\nCoXCpCURERERAFy4gMhIZGfD3l444++PCxfg62vQ7RcvonNnXarr1Qs7dzLV2ThDR+w0PDw8\nuDksERGRuaWkQC7H2rUA8MEH2LcP0dFo3x6GPyE7fx4+Prh3T2gOHIiffkK5cuYolqyIccGO\niIiIzC02Fh9+qJsbcekS2rdH+/ZG9PDbb+jdG//+KzRHjsTatbphP7JhDHZERERWZMsWBARA\n+3jM1RVffWVcD8eOQSJBcrLQ/M9/sHIlU11Zoe8dO5ExSqxiIiIiG7Ztmy7VicU4exb9+xtx\n+8GD8PHRpboJE7B6NVNdGcIFiomIiCwsOxuzZ6NbN8jlCAiAkxPc3PDNNzh8GB4eRvSzbx/6\n9dPtSBEUhEWLwLGXMkXfo1jOkyAiIjK3y5cRGIiTJwEgNhaBgXj0COXLG4ekvRUAACAASURB\nVN3Pjh0ICMCzZ0Lzq68wY4Yp66RSgSN2REREFrN0Kdq0EVIdABcX1K5dnFT388/w99elujlz\nmOrKKOOCXXR0dL5X66Kjo81UGRERkW27dAnjxyM9XWi2aoUTJ1C1qtH9/PADhg1DVpbQnDcP\nkyaZrEgqXYwIdjKZzLfAkoi+vr4ymcykJREREZUJ2ihmb4///Q+//YYWLYzuZNUqfPQRcnIA\nwM4Oy5YhONiURVLpYmiwi46OViqVANQvAqBUKjluR0REZLjDhzFsGOLjsWoVpk7FsWOYPbs4\nqweHh2PMGOTmAoCdHRQKjB1r8mKpNDF0HbvQ0FAUNp1CrVaLRKLQ0FBuF0tERGSIAwfQpw8y\nMxERgb/+Qt26xewnNBRTpwrHDg5Yvx7DhpmoRCq1DB2xU6lUUqm00I/CwsJUKpXpSiIiIrJZ\nR46gXz9kZgKAiwsqVSpmP5Mn61KdoyM2bmSqI4A7TxAREZWYhAQMGqSbLTF/fjGD3aRJmDdP\nOHZ2xtatKPAOPJVRho7YicVizTt2BQUFBYnFYtOVREREZJvmzsWDB8LxV1/hk0+M7kGtxrhx\nulRXoQKio5nqSMfQYBcSEgKg4NZhmjP+/v6mLYuIiMj2VKwoHIwZU5x15nJzIZNh2TKh6e6O\nqCh07Wqq6sgWGPooViKRSKVSpVJZMNtJpVK5XG7qwoiIiGxESgo+/RTXrmHOHNSvjwoVMHKk\n0Z1kZ2PUKGzcKDQrV8bevWjb1rSVUqlnxDt2CoXCz88v31J2UVFRnA9LRET0MhkZ8PNDbCwA\nhITgwIHidJKVhZEjEREhNKtUwb59aN3aZEWSzTBu8oREIuEGskRERAbKzsbw4UKqA+DpWZxO\nMjIwZAh27xaaNWsiJgZNmpimQrIxnBVLRERkLp9+ih07hOPGjREaanQP6ekYNAh79gjNt95C\nTAwaNTJZhWRjGOyIiIjM4v59rFghHNeqhX37UKWKcT08eYK+fXH4sNCsXx8HDhR/QWMqC4zY\nKzY8PFz0cuYrkYiIqDSqVElIclWrYt8+1KqV/4InT54cOnRo3759CQkJBW9PToZEokt1np6I\njWWqoyIYOmIXHh4eFBRk1lKIiIhKnZycnF9++SUmJubevXvu7u6dO3cOCAjYsaP855+jRQsc\nPIj4eHh5oWbNF+5KSkqaPHlyREREdna25kyjRo1mzpypXT7swQP06oXTp4XrW7TA/v2oXr3k\nfi8qpQwNdhEREQDCwsK4sgkREZHGuXPnhg8ffvnyZe2ZNWvWfPZZXErKitxc0Z07OHeukJ2+\nLl686O3tff/+fW9v7759+7q6usbHx69bty4gIODcuXOzZs26cwc9euDSJeH6Vq2wfz+qVi2p\n34pKM0ODnUqlEovFTHVEREQa169f7969e1pa2syZMwMDA+vUqfPgwYPQ0OOLFvVUq0UA7O3R\nvn3+u3JzcwMDA5OTk3fs2DFgwADt+alTp/br12/27Nlt2vT/8st3//hDON+hA6Kji7+lLJU1\nnDxBRERUHFOmTHn48GFkZGTfvn01Z/79t8qqVX2fLwumXr4c9evnfwc9Jibm9OnTX3zxRd5U\nB6BatWoREREeHj1Hjqyr3UzW2xs7d6JCBfP+ImRLDJ08IZVKVSpVYmKiWashIiIqFVJTU7dt\n2+bj46NNdQB+/BGpqdrWpLffPlXwxiNHjgAYMWJEwY+ysurY2x9OTxfepOvRA7t2MdWRcQwN\ndgqFQiwWexZvaUUiIiLbcvXq1czMzM6dO+c92akTHB0BYMiQK8C3l7RvyeXx4MEDAG+99Va+\n8/Hx6NwZmZlCquvfH7t2oXx5c9ROtkxfsMu3oIlKpSp4ksudEBFRGfTs2TMATk5OAO7fR5cu\ncHDAuXO4cgWXL+Pjj//SXpPPa6+9BuCff/7Je/L8eXTvjrt3heagQdiyBU5OZv4dyBYZsY4d\nERERadSpU0ckEsXHxz96BB8fHDmCnBzMmYO6ddG4MS5cuACgbmGLznl5eQHYsmWL9syJE+ja\nFffuCc0aNWIiIlCuXAn8EmSD9AU7tTFKrGIiIiKLq1at2rvvvrtly/6uXTPPnRNO+voCQGpq\n6rJlyypVqtSpU6eCN/r4+DRo0GDevHmal+1UKvTqhX//1X6+ZvHiVHv7EvgNyDZxxI6IiEjn\n2bNnq1at6tOnT7169Tw8PIYOHbpjx47c3NyCV86ePTszc8WFC8IT0w4d8P33SEhIkEgk165d\nmzlzprOzc8G7ypUrt2nTJgcHh27duvXqNa9bt+zkZO2Hi8eO/X3wYD8z/WpUFhgR7BITE0Ui\nUXR0tPaMSCSSyWRmqIqIiMgCbty40aZNm9GjRx88eLBSpUouLi47duwYOHBgz549U1JS8l3c\noYO3SNRHc+zsHO/s7NelS+tGjRrFxcUFBwdPmDDhZT+lXbt2J06caN160r59EzIzhXXHKlRY\nsWxZuaVLvzfTr0ZlhKHBLjExUTMl9sqVK3nPK5VKzpwgIiIb8OzZs969e1+6dCk0NPSff/45\nc+bMhQsXbt68OXbs2AMHDuRdoCQ3F0eO4OFDjB4tAlCjxtWaNT88d+7wP//8M3To0Li4uHnz\n5un/43j5cuNz50IBYdbrhx/eePToP2PHyvgnlV6RoQsUf/vttwCioqIkEon2pFqt1uwhK5PJ\nFAqFWQokIiIqEatWrbp8+fKsWbOmTJmiPVm9evVly5alpaWtX7/+4MGD3bp1y8jAwIHYswfu\n7jh5EgoFgAbA74b/oB9/xIcf4vkmsZgzB5Mm1Tbpr0Jll6EjdkqlUiqV5k11GnK5XCqVKpVK\nUxdGRERUorZu3erq6hoUFFTwo6lTp2ouePYMQ4Zgzx4ASEnBhQtG/5S1a/Gf/wipTiTCt99i\n0qRXq5soDyO2FGvatKlR54mIiEqRa9euNW7cuHxhiwJ7enq6urr+9VfSsGHYvVt7UpgGa7gl\nS/Dpp9CsJGFnh6VLwTfVybQMHbETi8URERGFfhQRESEWi01XEhERkQXY2dnl5OS87NPc3Nyb\nNyXbtwvNevVw4AAKm/b6UmFhulTn4IAffmCqI9MzNNj5+/urVCrNsop5eXl5qVQqf39/UxdG\nRERUoho1anT58uXHjx8X/OjcuXPp6ekODi00zVq1cOAAatUyovOvv8b//Z8u1a1fj5EjTVAz\nUT6GBju5XC4Wi1UqVcF9xsRisVwuN2uVRERE5hYQEJCZmal5nS6vZ8+yhw+PAxZMnOg+ZAgG\nD0ZsLOrVM6LnyZPx1VfCsZMTtm3DsGEmKpoMoFZj+3b06YOAANy6ZelqzMyId+zi4uKio6N9\nX3yhIN88WSIiolLq/fffVyqVS5YsycjImDJlSr169dRq9fnzF/r3v3XjxjgAkZHIsxOYob74\nAt9+Kxy7uGDrVvDPZknaswdTpuDMGaFZqxbmz7doQWZmRLADIJFIuHsYERHZJDs7u507d77/\n/vsrV65cuXKlu7t7Tk5OWtps4FPNBcb+AczNxfjxWL5caFaogF278N57Ji2a9IqJga/vC19c\n/fqWq6ZEGBfsiIiIbNhrr70WFRUVGxu7cePG69evJyaOTksL0HxUubLucaohcnMxdixWrBCa\n7u7YvRuFbR5LprdnD86dQ48eOHNGl+pq1sSXX+KTTyxamfkx2BEREb3A29vb29v777/RoIFw\npmJF7N2L5s0N7SE7G4GB2LRJaL7+OvbuRZs2pi+V8tm7F9On4/hxAHBxQXw84uJw+zaGD8fY\nsXBxsXR95sdgR0REVAgnJ+HAzQ179qBtW0NvzMrC++/r3sarWhV796J1a9NXSHndvo2AABw9\nqjuTlQU3N+zYYbmaLIHBjoiI6AV37+LLL1G3LmJicOIE+vWD4SvxZ2RgyBDdIsY1ayImBk2a\nmKlS0pk374VUV7Uq5s9H1aqWK8hCGOyIiIh0MjLQowcuXgSAjRuN2+8rNRX9+uHXX4XmW2/h\nwAE0bGjyGkkQF4fp0/HXX5gwAdWqCSerVMHEiZgwARUqWLQ4C2GwIyIi0pk0SUh1AOztjbjx\nyRP06YMjR4RmgwY4cAB16pi4PNI4exYhIbqR0S+/xL//okoVlCuHwYPh5mbR4iyKwY6IiEiw\ncycWLRKOO3bEoEGG3vjoEXr1wsmTQtPT0+itKchwSiXGjn1hEZPWreHkBKnUcjVZDUN3npDJ\nZCKRKDExEUBiYqJIJJJxizsiIrIhubn473+FY1dXrFkDB8NGPx48gI+PLtW1aIEjR5jqTO/v\nv3HoELKzsXGjLtVVqoTp07Fnj0UrsyaGBjsiIiLb9uQJbt8WjhcsQKNGBt115w66dMHp00Kz\nXTscOoTq1c1SYZl1+TKGD0f9+ujaFaNHY+RI2NujYkV89RWuXcO0aWX62Ws+fBRLRESE1FSk\npWHuXGzciH79YOBDqZs30aMH/vxTaHbogOhoVKpkvjLLnFu38MUX+Okn5OYKZ377DWvXYtiw\nMjo3okgMdkREVNZdvQofH1y/ju++0z1RLdJff6F7d1y/LjS9vREZCVdX85RYVg0dimPHdE03\nN2H/D6a6l+GjWCIiKtOysjBiBK5dg1qNdesMvSshAd7eulTn44Ndu5jqTCMxERMnYtQoXLuG\nf/8VTlaogMmTce0ahg2zaHFWjyN2RERUps2cid9+E4579TLolvh4+Pjg7l2hOWAANm/W7VRB\nxfbXX/j6a/z4I7KzASAzE2vWYPFieHpi/PiyuNpwMTDYERFR2aVS4ZtvhOOmTYXHfPqdO4ee\nPXHvntAcPBgbN6JcOXNVWHbMnInQUGRl6c5UqoQOHdChg+VqKoX4KJaIiMquxYuRkwMA5crh\nxx+L3iT+xAl07apLdYGB2LyZqe6V5Obi6VOkpb2Q6lxc8NlnmD/fopWVTgx2RERUdjVuLBzM\nmYPWrYu4WKVCr154/FhofvQRVq82bncKyuvGDYwdCxcX1KiBCxfQrBkAuLhALsdff2HBAs6Q\nKA4+iiUiorLojz8QHo4+fbB7N9zd0alTEdfHxMDPD2lpQvPTTxEWBpHI3GXaptu3ERqKVavw\n7BkAPHuGTZtw+DDOnEHjxrpdX6kYDA12CoVCoVBojj08PNR5N/IgIiIqVW7ehJcXHj2CQoGk\nJLz5ZhHX79wJf39kZgpNzVNCprriychA+/a4dUt3plw5+PnBzQ1duliuLFvBETsiIipbcnMx\nahQePQIAtVoYNNJj2zYMH667bNo0TJ9u1gJt0+3biIpC7dqoXVuX6uzs4O+Pr75CkyYWLc6G\nMNgREVHZ8t13OHhQOP7gA9Srp+/i9evx4YfCBAsAc+Zg0iTzlmd77t7F3LlQKJCeDgC7dkEq\nRUwM2rbFV18Jr9aRqTDYERFRGZKUhJAQ4bhuXSxZou/iNWswZoyQ6kQiLF6M8ePNXqEtUasx\nYwa+/RZPn+pO/vMPnr/bRabHYEdERGXIpUvCq3L29li/Hu7uL71y8WLI5dC8Um5nh2XLIJWW\nUJE249AhzJiha4pEGDaMW0eYF5c7ISKiMiEnB7/+ikaNMHgwPDygVOqbCbtwoS7VOThg3Tqm\nOkM9eIBJk9C2LcaMgbOzcFIkwoABOH0aGzeifHmL1mfrOGJHRES278kTdO365PRpNweH5MaN\nh9avL8rM9HvyZKSbm1vBi2fOxLRpwrGjI9avR0BAiVZbSj16JJozB0uWIDUVAE6dwtChiIrC\n+fPw8cE771i6vrKBwY6IiGxccrK6WbO/b92qCyA7u+LDh9X//HPzvn37FixYsG3btpYtW+a9\nePJkzJ0rHDs7Y8sW9O1b4hWXQjdulAsMdHv4UHfGxQWNG6N2bUgkliur7DHNo1iZTGaSfoiI\niEwrORnNm9/SpDoAbdsiKWl9cnLyokWL7ty54+Pjc+fOHc1HajWCg3WpzsUF27Yx1RXh8WOc\nOoUnT0Sxse4PH+pW9pNIoFKhdm0LllZGGRHsZDKZ6CWUSqX5SiQiIiq2KVPSbt58S3Pcvj1i\nYmBvDxcXl//+97+rV6++d+/erFmzAOTmYtw43eakbm6IjuZQkz6PH2P6dNSti7Zt0aVLhXff\nTa1SRQ2gVy8cO4aoKLz9tqVLLJMMDXbh4eF60ltYWJiJ6iEiIjKlY8ceaA5atsTu3ahYUfdR\nQEBA8+bNt27dmpsLmQzLlwvn3d0RFYX33ivxWkuJnByEhqJePcyYgeRkALh2za58+dz4+JTH\nj7FnDzp0sHSJZZihwS4iIgJAQkKCWq0Wi8VSqVStVqvVaqlUCkAul5uxRiIiIiOlpuKzzyCX\no3nzXcAuP78nMTGoUiX/ZW3atLl790FAQPbKlcKZ11/HwYNFbx1bloWFYepUPH6sO+Pvn1W7\n9jNn5xdyM1mEocFOpVJJpVIPDw8A/v7+2tE7hUIhFov5jh0REVmPJ0/QuzcWLsSiRUhIaAH0\nmz//XtWqhVyZk+MAbPv5Z2EqYdWqiIlBmzYlWm2pkJqK+fMhk+HwYTx/KREAunbFoUNQKp++\n/FYqUUbMim3atKnmoGHDhgASExO1OU8znkdERGRxDx/CxwdnzgjNN990AXDixIkGDRrkuzI9\nHTt2BALCzvM1a+LAATRuXIK1lgZnz0KhwIYNePIEAH7+GRcu4MkTpKVh9Gh07QpAeCBL1sDQ\nETuxWHzp0qW8ZxISErTHKpXKlEUREREVS1YW+vTRpbo2bfDNN3XLly8/d+7cp09fGFVKTUWb\nNv+kpgqprlYtxMYy1eUXFoZ33sHy5UKqA+DsjOrVoVDgxx+FVEdWxdBg17x5c6VSGR0dDUAi\nkQAIDQ3VfMThOiIishI7d+LECeG4XTvExMDTs+q0adPOnz/v7e2tHYa4ceNxixZJly9X1zQb\nNMCRI2jY0CIlW51r1zBrFhYsQGYmVqwQtt/Q8PLCrl2wt7dccVQUQ4NdcHAwAF9f3/DwcABS\nqVSlUmnWOtG8fmfGGomIiIpy7Rri41GjhhA7OnTA/v2oVAkAgoODJ02a9Pvvv3t5eVWrVq1e\nvTZ16169fr2W5sZmzXDkCOrUsVzp1iE3F9HR6NsXHh4ICcHnn+OrrxAYCJEI7u745BOcPYuj\nR9G6taULJb0MfcfOw8NDrVaLRMLagwqFAoBmCoVUKtU0iYiISpgmjoSHIyYGajW+/honTuDG\nDUgkeTcqFc2ZM+eDDz5YuXJlXNyfFy8uVKsbaT5q2RL79qF6dYvVbyWys9GjBw4deuHk06eY\nOxdjx8LZGU5OFqqMjGTclmLqPAOyCoWCeY6IiCwoOtpx2jRcuaI7c/AgQkIKn9barFmz4OCF\ne/dC+65d+/bYswevvVYSpVqn337Dr78KK/blTXV2dujTB199BYArmJQy3CuWiIhKpbQ0uw8/\nLJ+RoTtTuTImT37p9TdvokcP/Pmn0OzYEVFRwrPasiY9HZs2YdkynDwJAA4OOHMGDRrg6lVU\nroyPPsLYsSgwh5hKB0PfsROJRJq36wry8vLSPqIlIiIyn4MHMWgQHBzQqpXbvXuOjo7Cc6RG\njfD997hxAz17Fn7j1avo1EmX6rp1072BV9acO4f69fHxx0KqA5CdjbQ0nD+Pq1dx6xa+/Zap\nrhQzwYgd1zohIiJzO30a//kPLlwQmn//bXf8uOuePWlHjlRo2RK9e0PPCENCArp3R1KS0OzZ\nE9u3o3x5s9dsVY4dw+3b8PXFsmW4e1d33sUFn32G9u0hEqF+fcvVRyaib8QuPDxc9ByAoKAg\nUWEAiMXiEqqXiIjKnqdP0auXLtUBqFRJLRanNWuWM2kSJBJ9qe7CBXTurEt1fn6IjCxDqe7J\nEyxfjpYtIRZjyBAMH44uXWBnBwAeHpg/HzdvIjRU3z9AKl30jdjJ5fKgoCBDegkJCTFRPURE\nRPmdOYMHD4TjKlUwZgw++CA1JyezyBvPnkXPnrh/X2gOGYING1CunNkKtTJKJYKDkZKiO/PH\nHxgxAu3aIT0dzZsLCY9sSRFfqfo5AGFhYeqX0CxZTEREZFpPn2LrVrz5JiQSVK6ML79EUhJm\nz0bNmrlF3nv8OLy9dalu1Cj89JPtp7qsLBw+jPh4ZGXhs89eSHVVqyIsDAA8PdGyJVOdbTL0\nHbu8C50QERGVgAcP4O2N+Hi8/jquXYObmxH3xsaif3+kpgrNjz+GUmnjUebWLaxYgRUrcPs2\n7O0RFYWWLXHsGAB07Ijx4zFkCJejs32mWe5EJpNxTTsiIjKhlBRIJIiPB4CHD3HjBpo1M/Te\n/fvh56dbr04ux8KFtvwaWXo6xo/H+vXIzhbO5OTg9GnExEClQo0aRvyjo9LOiP/zIpPJCp08\nIRKJNFtQEBERmUR6OgYM0K3HIZEYEU127kS/frpUN3Gizaa6P/7Ali1ITsaOHVizRpfqALRp\ng//8B+XLo0cPprqyxdBgFx4erie9hWke2hMREZnCvHn49Vfh+N13ERFh6I3btmHoUGQ+n1Yx\nZw7mz7e1VPf4MZYvR8eOaNIE/v7o2BFvvin8jg4OGDQI+/fj99/xxhuWLpQswdBgFxERASAh\nIUGtVovFYqlUqpk2IZVKAcjlcjPWSEREZcyZM8JB8+aIikKFCgbdtX49/P3x7JnQnDsXkyaZ\npTwL+ukn1KyJTz7B8ePCmYQEvP02DhzAypW4dg1bt6JHD1vLsmQ4Q4OdSqWSSqUeHh4A/P39\ntaN3CoVCLBbLZDJzFUhERGXJjh1YtgyTJqFTJ/Tvj337ULmyQTeuXo0PP0RODgCIRPj+e3zx\nhVkrLTm3buH776FUIjsbM2ciPV33kZMTvv4a7u7w9sbHH+OttyxXJVkHIyZPNG3aVHPQsGFD\nAImJidqcF2H4KDkREdFLfPklZs8GgPHjceSIETcuWoSgIGjWb7Czw/LlGDPGLBWWpIwM7NiB\nH37A/v1CYH34EO3b4/JlAGjbFqNGYcQIQ4MvlRGGBjuxWHzp0qW8ZxISEjTBDtxVjIiIXtnc\nuUKqA3SLzxniu+/w+edCqnNwwA8/YMQI05dXwh49gpcX/vjjhZOPH2PVKowahTfeQJMmFqqM\nrJuhj2KbN2+uVCqjo6MBaJYjDg0N1XzE4ToiInpFp05hyhTh2M0N//ufoTfOmIGJE/F8rdUs\nJ6ePli/vsnjx4vS8DyxLicePoVBAJkNkJH799YVUZ2eHvn0xcSLs7eHtzVRHL2VosAsODgbg\n6+sbHh4OQCqVqlQqzVonmtfvzFjjcy9bbOVlO9gSEVFpMXkycnMBwNkZkZF4+22D7po+3Xn6\ndG0ro3nzr9q1u3b+/PlPP/20TZs2f//9t1lqNYP9+zF8OGrUwNixUCoxeDCaNUP9+gDg6Ymv\nv8a1a4iMRLVqli6UrJ6hj2I9PDzUarU2MGmWI9ZMoZBKpSWzOrFUKjX5gnnZ2dkHDx5MTk7W\nc83169ebNm1ao0YN/ZfZjKdPn2r/myyOX4dV4ddhPhcvumnGGv7v/zLefjuzyP+5TUt7Ont2\nzU2bhI0UHByy1q1L9/WdDCAjI0OpVE6fPr13795Hjx51cDDNUvzmM3eu0zffOOc9Y2+PChVS\njh9XP30qqlhRrfnba81/gsravxqOjo4dO3a0dBWFE5WuvcLCw8ODgoJMGCWPHj3auXPnIi+b\nNm3akCFDTPITiYiooP373Vetqtq8efoXX9wpV66IP0y5uQgNrbllizBrwNExY8WKO23apOW9\nZvny5d9///3MmTMHDhxorqJfTU6O6M4dxzfffPb++w0uXHDRnBSJ0K5d2vjx9955J03/7WRx\np06dGjVqlKWryK+UBTsAMplMqVRGRUVpXvV7RdnZ2ceOHcvUrmVZmPnz5yckJAQHB48cOfLV\nf6L1S0tLu3//ftWqVV1dXS1dC/HrsC78OsxBoXCcNatcv37Z33+v73+KtbKzMXas8+bN2nG4\n5C1bnvbunX8f2eTk5Hr16vn4+GzevNmk9ZpGUpJo+HCXc+fsBgzIHjQo+/PPnVxcMGRI1qhR\n2fXr51q6OqOVtX81Ll68OG7cuMDAQCtcx9faB6gLUigUSqUyNDTUJMHOwcGhyBG7DRs27N27\n98GDBxUMXCKzlMvJyQHg7OxcRn5fK8evw6rw6zC5w4cRHAy1GuvWOX7xhWORm19lZWH0aGij\nmoND8uuvDx8yJKrglRUqVKhbt+6tW7es8Ms6eRL9++POHQDYtcthwwaHwEDNJ+WAcpasrLjK\n2r8a2dnZp0+fDnz+tVkVfZMnrHayQlhYmEql0kzRJSKiUiorC+PGCRNa3dxQq1YR12dmYvBg\nXaqrWjW7efNxavWpl1+fWa6c1eWkyEh07SqkOgABASgbg1xUQgydFWtV5HK5Wq02yYgdERFZ\nyrx5uHhROJ49G+7u+i5OT8fAgYiMFJo1auSuWnWtdWune/fu/fnnnwWvv379elJSUhMrWxfk\nyROMGoW056/PjRqFNWssWhDZHH3BTp1HQkJCwZOafWM1J0uiWCIishUZGZg7Vzhu1Qpjx+q7\nODUVEgm0z2nq1cOePWn16mX6+/sD+Oyzz7Kzs/Nen5ub+9lnn6nV6hFWtlTx48d4/Fg4njIF\na9bA+oYUqXQzdMRu1KhRYrG4YICLi4uTSqVeXl6mLoyIiGzZs2d49gwA7OywbBn0rEmSkgKJ\nBIcOCU0PD/z6K+rUyQXQsWPHUaNGRUVF+fr6qlSqrKys7OzsEydO9OvXb/v27f7+/r169SqB\n36VIKSnw90eVKoiOhlKJoUOxaRNmzQIXXSWTM3TyhEqlCgsLK/Sjpk2bmnx5OSIismG5ucjN\nxbp1+PlnDB8OPSuCPXyIXr1w6vl7dM2aISYGb7yhW9RtxYoVrq6uy5cv379/v/YuOzu70aNH\nL1myxHy/guGSktC3L86fB4DgYDx6hNGjLV0T2S4jZsVGREQUOq2XW4oREZHhnj7FwIHYtw/j\nx0P/H5D799GzJ86eFZotW2L//vy7Lzg6On7//feffvrpli1bzp8/uYvdtwAAIABJREFUD8DT\n03PEiBHNipxhWyJOnUK/frqpEl27wt7eogWRrTM02Gl2ffDy8oqLi8t73svLS89gHhERUT5f\nf419+wBg+XIsWAAnp8Ivu30b3bvr9ktt3x579uC11wq/uFGjRiEhIWYo9pVkZKBPH/zzj9AU\ni7FypUULojLA0GCnUCji4+M1+8Pm+0gsFlvhAn1ERGSFLl3Cd98Jx126vDTVJSWhRw9cuSI0\nxWJERaFixZKo0IQuXNCluoAArF0LZ2e9NxC9MiOWO4mLi4uKyr8IZFRUVL4xPCIiokKp1ZBK\nhTkTjo542StwV6+ic2ddquveHfv2laZUl5ODb77BiBGwt8cHH6B+fcyYgU2bmOqoJBi384RE\nIuHKJkREVDynTkE7FDBuHJo2LeSaS5fQo4fupbSePbF9O8qXL6EKX11aGoYNw65dAHDypC6e\nEpWM0relGBERlVIVK0IkglqNmjUxY0YhF1y4AB8f3eNLPz9s3lyaVnpLSoKvL+LjhWajRhat\nhsokBjsiIjK7pUuxZg38/HDgAH7/He+/X8ij1d9/R69e+PdfoTlkCDZuhKMjcnJyIiIifvnl\nlytXrtjZ2bVs2VIqlVrblhIAsrPRs6dutkfduliwwKIFUZnEYEdERGakVmPyZMybBzx/NOnt\nXchlx47B11e3K8OoUVi1Cvb2uHHjxoABA86ePWtvb1+rVq3MzMxTp06tWbNm+PDhX3zxRcn9\nGgaIjdWlujZtsGsX3njDogVRmVQq94olIqJSISsLH3wgpDoA1aujdu1CLjt4ED4+ulQ3fjzW\nrIG9PdLS0nr06HH+/Plp06Y9fPjw2rVrt2/fvnr1av/+/Tdt2jSj0Ke5Je7iRUyYgI8/xhtv\nwNMTAPr1w6+/MtWRZXDEjoiIzGX6dGzYIBy7umLdukLWN9m/H35+ePpUaAYF4bvvhL22li5d\nmpCQMHfu3LyDc/Xr19+2bVuPHj1++eWXCxcudOrUyey/xkvcvYtp07BqFXJyAMDBARcvIi0N\nlSpZqiIijtgREZHZrF0rHFSqhH370LNn/gt++QX9+ulS3dSpWLhQt4Pq1q1bK1eu/H//93/5\n7rK3t588ebJard6lmX1qCRs2wNMTSqWQ6gBUrQpHR6Y6sjAGOyIiMrHMTPzyC65cwfDhAFCr\nFn79FWJx/su2boW/PzIzheacOZg584ULEhISWrVq5ejoWPBHtG7dWiQSXb161fTVGyArCxMm\nIDVVaDo7Y8oUfPWVRWohegGDHRERmUxODk6cQMeO8PPD229jzBj8+y+uXkWrVvmvXLcOAQHC\nYsUA5s3DpEklXKzRzp5FYCAGDMDVq7otawcPxqVLmDWrNC3LQjbsVd+xS0xM9NS8LAqIxWLu\nQkFEVDZlZuKLL7B2LVJShDPp6bh2rfC13FavhlQqPMS0s8OSJfjkk0Iu8/T0PHfuXFZWVsFB\nuzNnzqjV6gYNGpjyd3i5e/cQEoJVq5CbCwDOzoiNxZ49aNYM775bMiUQGcRkI3ZqtTokJEQm\nk5mqQyIismZPnmD7dnzyCSQSHD2Kn3/GokW6VAegXTv4+BRyY3g4Ro/WpbrlywtPdQAGDx78\n6NGj77Sbyz6Xk5MzZ84ckUjUt29f0/wyev30Exo2xIoVQqoD8OabqFkTH33EVEdW51VH7Dw8\nPLSbjEkkEolE8solERGRtYuIgEymW6DkyhWsWqX7tEIFDBuG776DvX3+GxcsQHAwNH83HByw\nbp3wHl6hJkyYsGrVqilTpjx9+vS///1vlSpVAFy+fHnSpEm//vrrkCFDWrRoYdJfqxBqNeRy\nJCcLTScnfPpp/ncBiawHlzshIiLjbNiAkSNfOPPaa+jaFdHRuHABbdvCy6vwF84mT8bcucKx\noyM2bMDQofp+kIuLy969ewcOHDhz5sxZs2ZpFii+c+cOgGHDhpl1geLERMyYgaQkLFyIBg1w\n7x5EIgQE4JtvULeu+X4s0atisCMiIuMsXKg7btQIvXvjf/8DgN690bv3S+/Km+qcnfHzz+jT\np+ifVadOnd9//12zpVhiYqKdnZ2fn19gYGCTJk2SkpJe5bfQ49dfMXgwHj0CgClTsGcPDhxA\nw4Zo1sxMP5DIZAwNdiKRKCwsTC6XF/zIy8tLpVJpH8gSEZFN+uUXbNyIgAAMHYrTp+HujpUr\nMWRI0Teq1ZgwAUuXCk0XF2zfjl69DP259vb2w4cPH/7iI9tk7cNRU1u9Gp98opuu27Ah3N0x\ncKCZfhqRiZlgxE6lUr16J0REZLUyM/H551iyBAC2bsWdO/jkEzg5FbKNREG5ufjkEyiVQtPN\nDbt2/T979x3VRLbHAfwbAoIIKCiIvcGq2EVRgxWxBEURFBt2BTtYWBsi9q5gB13XXrAjRZBi\nI5aVx6qAq4AFFbBgoZeU90fGgIqYxARBfp/zzjtzb2bu/EYO2R93bkH37kqM9mfcuYNJkwqL\nkycX7odGSLlQ0qxYLy8v1mcAXFxcWMUBwPl23UlCCCG/hZgYtGvHZHUADA2hrQ0dHamyOj4f\nY8YUZnV6eggLKyare//+/fr16/v27du+fftevXotX75cPJCu9IWEMAcsFtauxd69Uj0mIWVH\nSYldsS9ei+Xm5qaIYAghhJQtBQUYPBgPHzJFHR3s3g0NDWmvHT0ax44xxRo1cPkyTE2FX512\n8eLFJk2aLFy4kMfjffr06X//+5+Hh4eRkdEByX5kypeaihkzMGsWrK1hbo5mzXD8OBYuLLX7\nE6IwP1jHTvQZAE9PT9F30ConhBDyW/rf//DkCXPcrh3u3oW1tVQX5ubC1ha+vkyxZk1hnz6r\nbGzqs9lsFovVokWLZcuWpaen37lzZ+jQoaqqqgcPHvzw4UNiYuKHDx/Onj1bvXr1SZMmBQQE\nKOWpvvTgATp1wq5d2LEDBw7gxg08fIjhw0vhzoQonrRj7DgcTlxcnFJDIYQQUtaYmmL0aNy8\niYEDsX69tH11OTmwtcWlS0xRXz+fz+91/DivefPmw4YNEwqFt27dWrFixcmTJ6tVqwYgPDxc\nsiKdiorKkCFDTE1NW7duPXfuXCsrK/GYHyU5fx4ODsjKYorF7pNBSDkibWJHMyQIIaRCiY2F\njQ3ev8eZMzhyRIYLMzIwcCCuXWOKDRoI0tM78/kJp0+ftrOzE1fy+fytW7cuWLBAJBKNHDny\n23WG69evP2nSpC1btkRHR7dv314Bz1OcO3cwbBj4fKY4ZAjGj1fSrQgpJdJuKUbTIwghpOIQ\nT3pISMD794XTJqSRng4utzCrMzKCre32Dx+id+7cKcnqAKiqqrq6ug4bNgyAlpZWsU116tQJ\nQGJiorwP8WMHDxZmda6uOH1a2i5JQsosaRO7yMhIHo9nbm6u1GgIIYSUBTt2IDqaOe7WTdqr\n0tJgYYHISKbYsiWuX0dk5HEDA4PRo0d/e744sfveOB/xG1hlLJKalYX167FnDwYPhqYmNDTg\n44MNG6CisO3TCfllZFigGACPx/veWAdaoJgQQn4PT59iyRLmuE0bzJgh1VVv36JvX/z7b+GF\nISEwMMCLFy+MjY1VikuaxO+Cnj9/XmyDUVFRAIyMjGSNv2QpKRg4EP/7HwDs2sXsFaapqdib\nEPLL0J8nhBBCvrB7N7KzmeMtW6AqRQ/Aq1fo3r0wq+vUCRERMDAAAE1NzSzJ3IQv6evrs1is\n5OTkbzvtXr58uWfPnkaNGrVr106+p/hWdDTs7NC8OZPVARAKUaUKZXXktyJtYve9hU6KrodC\nCCHkN1C5MnMwahQsLH58/tOn6NYN//3HFDkcBAdDV5cptmnTJiYm5vXr199eeOXKFZFIxGKx\nLC0tL1y4wOfzAQiFwrCwsF69eqWnp2/ZsuVnpsRmZcHfH25uCAlBfj569cLZs5BsRda5MyZM\nkLttQsoo6rEjhBBSKCEB48Zh3Tps2ID9+6U6v2dPPH3KFC0tERKCqlULTxg/fjyfz58xY4ZA\nICh64adPn+bNm1epUqXdu3fn5OTY2NhUq1atSZMmurq6lpaWycnJ+/fvt7GxkeMR8vPh6QkL\nC+jpwdoaq1ejf388fIiiXRBDhyI8nPrqyG9I2sSOxWJ5eXkV+5G5ublSFxkihBBSOjw90bQp\n2rSBhQVcXX+8m1ZsLLp1Q1ISU+zXD35+qFLli3Osra3t7e3PnDnTtWtXPz+/d+/eJScnHzhw\noH379g8ePFi+fPmUKVMSExPXrFnToUMHXV3d1q1bu7m5xcfHj5d36ZFp0zBnDiIikJ/P1IhE\nKCiAvz8cHbF8OW7fxqlThR2ThPxOpJ08UQJa4o4QQn4DDx9iwQIIhcjMRHAwOnb8wfn376NP\nH7x5wxSHDMGJE6hUqZgzDx48qKent3fv3sGDB0sqNTU1169f/+effwLQ09NbtGjRokWL5A4+\nKwtOTrh7F+vX49WrLz5q1Ajz5qFDB0CWGb6ElFMlJXZeXl4uLi6SoouLS9FiUbTKHSGElGsi\nEWbMYLq42Owf7xt25w7698eHD0zRwQEHDoDNLv5kDQ2N3bt3z5s3LzAwMD4+Xk1NrXnz5jY2\nNvr6+goJXiDAiBHw9weAefPg54c//0SlSrCwQN+++OMPhdyEkPKhpMTO2dn5e5ncV9zc3BQU\nDyGEkF/gwAFERDDHs2ahTZuSTt63L3bGjMb5+cy7zO7dn+zbV5/NVs3MzPzrr7/8/PyePHlS\nqVKljh07Tpw40eLz/AsjI6PZs2crI/iZM5msDkDbtjAxKSwSUtH8YIxd0Umvnp6e35sSy+Vy\nSyVaQgghSrF1K3NQty5WrPjuaQUFBf37b5gypaEkq2Oxdl67ZtS5c8dLly61aNHCxcXl5s2b\nenp6AI4ePdq7d+/Ro0cXFBQoL3JPT+zZwxw3bCjbPhmE/H5kWO7E2dlZqaEQQggpZTduYM0a\n/PMPmjcHABUV7NkDbe3vnj9o0I7g4JkAMz/CxQUfPoxZvtzj/v371tbWr1+/3rJly7t376Ki\noh49evTo0aOBAwceO3Zs/vz5ynuEzZuZAz09BAbC0FB5tyKkHKDlTgghpCLKzISjI7p1w5Il\n6NcP+/bB3x+JiRgw4LuXeHk9v3RpBsCsEeLujq1bUbWqjru7e/fu3fl8vo2NzZw5czQ/LyLy\nxx9/nD9/vlevXjt37nz8+LGSHqRVKwCoXBnnzzPpKSEVGSV2hBBS4dy4gTZtsHcvU+TzoaaG\nAQPQsOF3Lzl9GvPm1QOYWa/r1mH58sJPX7x4wWaznzx58tVVbDZ74cKFAoHg/PnzinwA4PFj\nlXHjGvftq7V2LU6dQnQ0zXglBFDIcieEEELKkehoWFhAMuytalXs2wcNjZIuOXgQkyZBIGD6\nAjZuRNGXq0KhMCkpqXr16gkJCd9ea2pqCuCpZAljRUhNhZ1dlRcvVAB4e2PXLgW2TUj5Rokd\nIYRULHfvFmZ1PXviwAE0aFDS+X/9BUdHCIXiknDbNsGsWWpFT2CxWCwW63t7S4o3nFDgOvbZ\n2Rg0COKsDvjBBF5CKhp6FUsIIRXChQto0gTt26NtW/Tpg06dsGULwsJ+kNV5emLKFCarY7FE\ngFO7dre/OofFYhkZGaWlpTVp0uTbFm7dugWgadOminqQcePwzz/Mcf/+BZMnK6phQn4H1GNH\nCCG/udxczJ+PXbuYzVL9/RES8uOrMjMzhw9/EBjYRVxksQRjx14+fHi/h8fTS5cuqap+8Z+P\nunXrxsXFGX4zJTU3N9fDw0NNTW3IkCEKeZZHj3D6NHPcvHnO/v0FbLZaiVcQUrHI1mPn5eVV\nwqaxhBBCypqnT2Fmhp07mayOxfrxrhIAHj9+XL/+sSJZHZ/FGnXwILdBgwZhYWE2Njb3798X\nf/T27dslS5aEhoZWqlQpKCho0aJFb9++BSAQCG7dumVhYREdHb1gwYL69esr5HFq1mRWY6lT\nR7hjx3NNzeLf/xJSYcmW2Dk7O3M4HCm3oyCEEPLLubriwQPmuGpVHDvG7Jpagk+fPnXoEPbh\ng6O4qKEBf3/V1693Tpgw4enTpw0aNAgICGjTpk3t2rUbNWpUq1atNWvWtGnT5urVq+3bt1+3\nbp2hoaGenp62tnaXLl1u3769ZMmSFSUseSydtDQsXowOHTB/Pm7fhq8vrl/PNDDg/2SzhPx+\nZH4V27JlSx6Pl5CQYGRkpIyACCGEKERKCmrUwPPnTLFLFxw7VtKCJmIiEfr2TcjImCYuamnh\n4kX07Amgxv79+z9+/Hju3Lm//vorKirqn3/+EQqFnTp1GjRokL29vaqqamRk5MWLF8+dO5ea\nmspms9u2bTt27FgTE5OfeYq0NGzZgu3bkZEBAFFRsLPDsGH49EmUlfUzDRPye5I5sYuJiVFG\nHIQQQhQlMhIjR+LFC3TrhoMHsX07mjSBiwtUf/SVLxRi6lTcuWMqLurowN//i/Xh3Nzczp07\nFx8fv3Pnzm8vV1NTs7W1tbW1VdSDxMWhe3ekpRXWaGjQKsSElES2xC4hIYHH43E4HOquI4SQ\nMkgoxPr1cHcHnw8A16+jZk3s3v2Dq548eRIYGPjs2cugoBFxcW3FlXp6CA7++r1tq1atWCzW\ntwsRK1ZyMuLi0LYtAgMLszoWCwMHYsWKH3c6ElKRyZbYbdy4EYC9vb1ygiGEECK/nBzY2uLS\npcKaAQOygFyguqSGz+cnJiZmZ2c3atSoWrVqeXl58+fP3717t0CgAhwBmKyOzf4QGqrbrt3X\ntxAvSqf6w64/eaWmYt06eHsjNxd168LfH/r6SEuDnR2WLKEl6wj5Mdl+OX18fAA4OzsrJxhC\nCCHyCw4uzOpYLL6KintAwPoaNYT169d3cnIaN27cqlWrDh06lJ2dDYDFYnE4HFVV1atXr3bq\n1EMgOHn3bs3P16YIBL0DA0e3a7fkq1vweDyRSKTARemK2rwZ7u7IzmaKL1+iWjUkJUFV9ccv\nkQkhYjL/rnA4HGXEQQghRD4PH37atCklPz+va9cCNbUOBQUA4tnssX376rVq5frhwwd/f/8l\nS5YsX748Pz+/bdu2ffv2rVKlyv379y9cuMDn89u27VKtWkRwMLMzRN26cHAIXrfu4bJly0aO\nHNm4cWPJjfLy8tzd3VVVVYcPH67wpwgL+2KbMhUVODv/YPFkQsi3ZEvsOBwOTYklhJAyIj4+\nzc7uzoMHPYBmAI4cWVS58kw+X6158xw/v5OSfSDy8vIaN26cnJzcqVMn8T4QYn369AkLu3Pv\n3nqRiMnqmjRBaCjq1Bl97ty6R48e2dvbHz58uHnz5gKB4OrVq0uXLuXxeAsWLFBGj93du8wB\nm40RI7BkCU2SIEQesq1jd/DgQQDjxo1TTjCEEEKktWFDZrNmlR484AKa4hpLSysVlRiR6Eb1\n6ppFFwS+fv16cnKysbHxnTt3Hj16JKmPj0/T0IgUiZhZr8bGiIhAw4ZQU1MLCwtjsVhRUVEm\nJibVqlXT0dHp3bv3rVu3nJ2d16xZo9gHCQjA0qXo3x/W1ujWDZGROHKEsjpC5CRbj52RkdH3\ntnkmhBBSal6+xIIFVQCmp43Nxpgx2LSps6FhXv369a9fv7558+aFCxeKPw0PDwfg7Ow8c+bM\nsLAwcX/bu3dITj5YUNBSfE6rVrh8GTWZUXaoU6eOurp6mzZt2rVrl5CQoKKi0qFDBwcHh+aK\nTrh27cLMmRCJEB6OyEjFtk1IRUTjUQkhpNx4/x5z50IkwuTJqYAuoM5iYfBgrFqFFi2QkvKO\nz+cPGjQoKCho69atrq6ubDYbwOvXrwF06dIFQHJyMoCUFFhaoqCg1eeG712+3KpmzcJ3OM+e\nPcvNzW3fvv2uXbuU9zh//cVkdQDy8pR3H0IqENlexRJCCPlVCgoweDAOHsShQ9i8+RPQffTo\nu7dv49w5tGgBAFpaWgAyMjJsbGzevHkjWW1OR0cHQFJSEgBtbe1Xr9CrF+LimGYrV76nrW1d\nNKsDsH37dgB9+/ZV3uMcPQpHRyar09DAxo3KuxUhFQgldoQQUj7Mm4cbN5hjVdUPwJ3x4z92\n7Fh4gra2dtOmTUNDQ/X19QGkfV7bt3PnzgC8vb0B1K7dtWtXSAbaqapez8np2rixnqQRPp+/\nfv36rVu3duzYcdCgQUp6lpwcODtDKASASpVw5gx69VLSrQipWOhVLCGElAN//YXt25njDh1g\nZ5d85gxSUlK+Om3SpEl//vmnr68vgOrVmXWJra2ta9SoERwcXKtWNzc386Qk5mQLC2FOzvKb\nNzPv3bvXokULU1PT/Pz8GzduvHr1qlmzZmfOnFFRUdYf/ywW8+5VTQ0nT8LKSkn3IaTCocSO\nEELKuoICLFjAHFetiuPHoaXFUVFROXr06JgxY4qeOXv27DNnzty+fVtdXT0mJiY9PT03Nzcw\nMDA9PV0kapGaekoy/61162cpKUMePvx38ODBdevWvXjx4uHDh1VUVExMTKZMmTJ37lxtbW1l\nPEtYGJYuhZkZAgIQGAguFz16KOM+hFRQlNgRQkhZJxAw/VssFg4cgJERAEN7e/sTJ05s3LjR\n1dVVciaLxTIyMrp9+3Z+fr6tra2kvkGDQe/fn8jIqPy54uz9+yM1NVXd3d3d3d3ZbPaOHTuy\nsrJUVVXV1dWV9yB+fhg2DPn5uHkTXC7WrVPerQipoCixI4SQMi04GFlZOHsWp0+jb1/Y2DD1\nO3bsiI2N/fPPP8+fP29nZ6elpfXixYtTp049evTI2tp669atV65ceffunZqamqZmr8WL22Vk\nMBd26ZIwdmxanTqne/bsWbRbrkqVKkp9EH9/JqsDwGKB1rknRBlkS+wSEhKMjY3FxyKRyMvL\nKy4uTjwglxBCiMItXw4PDwBYswZffddWr149MjJy4cKFBw8e5PF44kp9ff3Vq1cvWLCAzWaL\nd57g8WBlhU+fmKsmTMDevUZsdmlnVTdvYujQwqxu82Z83heDEKJIMgyMdXJykmR1Ej4+PiwW\nS6EhEUIIAYDAQKxYwRwnJhZzgra29s6dO9+8eXP9+vXLly/fvn07OTl58eLF4uXrAISFoW/f\nwqxu1iz89Rc+f1iqduwofJu8fTvmzPkFMRBSEUib2Hl5efn4+HA4HJFI5OnpKa50dnZ2dHQU\nf6qsAAkhpEK6eRMODsyCIJUrY9as756pqanZtWtXS0tLMzMzVdXC9zDBwRg0CFlZTHHOHHh5\nofT/Ev/wAW/ewNwcANhsbNuGGTNKOwZCKg5pEztfX18OhxP5zYYv3t7eHA5HPLWeEEKIQuTl\nwd4eHz4wRW9vtGkjWwvnzmHQIGRnM8Vly7Blyy/I6s6dQ/36qF8ftWrh4UPEx2PmzNKOgZAK\nRdrEjsfj2dvbF/uRvb29ZHgHIYSQn5eSgpcvmeMFC/DlkiYQivvxvu/IEdjbMwPaAKxbxwzU\nK2UXLmD4cGRmIi8Pvr5o1gyNGv2CMAipUGjnCUIIKSvS0jBoEAwMcOUKNmyApSX27mXWBBEK\nhUeOHOnRo4eGhgabza5Xr97UqVMTixt5d+AAxo8Hnw8ALBY2bSpcA680+fvD3h4FBUxxxIhf\nEAMhFZC0iV0J71tdXFw4HI7iQiKEkIro3j107IiLF/H2LZYuhasrLl/G5MkAkJOTY2VlNWbM\nmKioqF69eg0bNkxTU9Pb27t169Znzpwp2siOHZg4EQIBAKioYPduzJv3C54lJaVwZRMAmzZh\n8OBfEAYhFZC0iZ2bmxuPxzMXD38tQjwl1s3NTcFxEUJIReLrC3NzPH3KFL/aYmv27NnBwcHj\nxo17/vx5UFCQr6/vo0ePQkJCdHR0Ro0a9d9//4lP8/TE7NkQ7y2hqopDh+DkVJoPUejmTeTm\nAgCLha1bf01ySUjFJG1ix+VyHR0deTwei8VycXEBwGKxxFmdo6Mjl8tVYoyEEPJbu3ULI0YU\nTl8dMgSbNxd+mpiYuH///r59+/7999+S7V8B9OnT58KFC3w+38PDA8DKlZgzh8nq1NRw+DBG\njy69RxC7dw9jxmDQINSpA2trGBnB2xsuLqUdBiEVmQwLFHt7e7u6un61lF1gYCBldYQQIof3\n73HzJurVw9OnTELGYmHxYqxYAZUif3QHBgYKhcJZs2Z9u2iomZlZp06dAgMDFy7E+vVMpbo6\nTp2CtXXpPAQjPR3u7ti5kxnbp6ICP79SDYAQIibbzhNGRkYiyQ7ShBBC5MLnw9sby5YhLQ2q\nqoiMxPTpePUKDg4YOvTrk1++fAmgRYsWxTbVsmXLmzeHSLK6ypVx5gxK+c/tiAiMHo2UlMKa\nbxazJ4SUEtorlhBCStXNm3B0REwMU+TzkZKCnTu/e76GhgaAnJycbz8SCnH16gjAQlzU0oK/\nP3r0UHjIPzBrVmFWV6kS5szB8uWlHQMhREyG5U6CgoJYLFZCQoK4yPosKChIObERQsjvJj0d\nffsWZnUA7O3Rv39Jl7Rq1QpAeHj4V/VCIaZMET5+zGR1OjoICiq9rE4gwKZNmDEDsbGoU4ep\ntLDAv/9i3Tqoq5dSGISQr0ib2AUFBVlZWQGIj48HUHR6rJWVFeV2hBAijbg4ZGYyx8bGCAzE\nyZM/SIO4XG716tXXrFmTnJwsqeTz4eCA/fuZ7/Dq1REejq5dlRX2V4RCTJgAV1fs2oUxY+Dr\ni7/+QmgowsLQvHkpxUAIKZa0id358+cBxMfHc7nchIQEHo8n3jdWPORO/CkhhJASREaiZk04\nOKBJE6xahQcPpBoMV6VKle3bt79+/drMzOzvv/9OSkp68+ZD//7vjx9nTqhRQxQaClNTpcZe\nSCTC1Kk4fJgpVq6MqlUxcSJ69y6lAAghJZB2jJ2Pj4+jo6ORkRGAgIAAAJIdxjw9PV1cXLy9\nvZUUIiGElHciEcaNw+HD0NbGP/+gaVPZLh85cqSqquqUKVMmTpwIaACngQHijwwNheHhKqXZ\nT7ZoEfbuZY5r1cLBg6V3a0LID8kwxs7ExER8EBcXB2DAgAEUkrkOAAAgAElEQVRKiYgQQn47\nHh5MF1dGBqKi5Glh2LBhL1682L37cJ069yVZXd26uHq1VLO69HRs28Yc6+khJARGRqV3d0LI\nD8mzV6yPjw8Ao8+/zeI8jxBCSLGOH8fKlcxx7dpf7yohC+1jxxxevWKWEmnSBDdu4I8/fj5A\nGWhqokYNANDWRlAQWrYs1bsTQn5Ihr1ixRtOiOdJODo6iuuDgoLEb2mVFB8hhJRrz59j4kRm\n/WFNTfj5oVo1edp5/x4WFrh+nSkaGyMiAg0aKCzOHzp2DLVqYeBAhIXB1xf378PMrPTuTgiR\nkrRj7Nzc3KysrCTrntvY2ABwcnIS9965uroqKT5CCCnXIiIKd009dEjOKQ7v3qFvX0RHM8VW\nrXD5MmrWVFiQP3TqFMaOhUCA1FT8+y+GDSu9WxNCZCLDXrGenp7iY09PT/E2YuJRdyKRyIgG\nWRBCyJdu38bBg+jYEX/8ATU1eHnBzk6edpKT0b17YVbXsSOuXi3VrC4oCA4OEAgAQE0NnTqV\n3q0JIbKSYecJZ2dnZ2fnkmsIIYR8+oTZs3HoEAB07YpHj+Rv6uVLWFoWttC5M4KC5HyZK5+Y\nGAwdivx8AFBRwd9/o3790rs7IURWtKUYIYQoUkQExo9HUhJTfPVK/qaePEHv3nj2jClaWMDP\nD1Wq/GSAPyAQgMfD+fN48wZubvj7b2RnMx/t2IHRo5V7d0LIT6LEjhBCFObyZfTvD6GQKVar\nVtImsCWLj4elZWGC2KcPzp+HpqYCgvye//7D5s24cAFv3zI1iYlYuhQ7doDPx7p1mDZNiXcn\nhCiEtImdZEux7xFvQUEIIRXZ5cuFWV337jh8WM4XlzEx6NMHqalMcfDgH+889pOys9GpE9LT\nv6isXBlcLl68gFAIQ0Ml3p0QoijSTp5YtWoVAEdHR9F3KDNIQggp0zIzsWwZPDxgYwN9fdSq\nhdWrER4uZ1Z37x569y7M6uzs4Our+KxOJEJYGEaMQI0a6NkTL14gI6PwUwMDzJiBkyeZY8rq\nCCkvpO2x4/F4AGjfMEII+UpeHgYNQkQEAAgEePPmp1q7fRv9++PjR6Y4YkT+kSOV2OyfDbIo\nPh/btmHXLiQmMjVXr+K//7B1K/z9YWSEYcPQowcUe1NCSOmQYecJDoejvDgIIaQ8EggwbBiT\n1QHQ1v6p1iIj0a9fYVbHYv194oRmjRq6I0aMePDgwU81XYSnJ+bNK8zqAOjqon17ODvj8mXs\n3g0LC8rqCCmvZNh5QqlxEEJIebRoES5eZI5btMDP7MITGoq+ffHpE1PU0PCxsfEfP37MH3/8\ncfLkSVNT06NHj/5ktNnZyM0tTBwB1KuHZcsQE4N69X6ybUJImSDtq1h7e3sXF5eEhARai5gQ\nQsQSE7FpE3Ncpw4CA+VfYc7PD/b2yMtjiu3bX7l2zaFKFSZPvHPnjp2d3cSJE1u3bt2qVSv5\nbnH4MKZOhY4Ojh9HWhpycjBsGPr3p845Qn4r0vbYiRciNjY2VmYwhBBSnggEzCawOjoICJBz\nqsTr168nTvQfMoQvyeqaNj0RFdWzSpXCpU3MzMwuXLjA5/NXrlwpX6jbtmHcOGRnIzUVERHY\nvRsHDmDAAMrqCPndSJvYSXaJZX2H0iIkhJAyJzYWo0cjNBTnzmHhQty4gTZt5Gnn0KFD9esv\n+ftvrlAoeX+yMDv7z9jY2K/ObN++fZcuXYKDg+W4y8qVcHZmclBVVdjayhMqIaRckGHyBCGE\nEABXr6qam+PYMcyYgdq1sXYt5Hs7evr06fHjr+bnewNsACwWunY9DqxPSUmxtLRMTk7+6vxm\nzZqlp6enf7XW3I9cvAh3d+ZYXR2+vnLmoISQckHaxO57y9fROnaEkAolKKjqsGFVJFMctLTk\nbCcvL2/SpH9Fon3irE5FBXv2oGfPOAAbN25MTU1dunTpV5dkZGSwWCwNDQ2ZbiSZsVulCi5e\nxJAhcgZMCCkXaEsxQgiR1vnzagsX1pPsLeHiAhMTGS7Pz88/e/ZseHj4+/fv79/vnZ6+EmAB\nUFXFwYMYNQpnzrQFwGazO3XqdPr06T179qipqYmvzc3NvXbtmomJSaVKlaS5l0iEv/9Gaioc\nHHDjBgoKsHs3OneW5WkJIeWQbIndtxuLBQYGcrlchYZECCFllJeXujirY7Gwbh3+/FOGa3k8\n3ujRo589ewYAcAeYjVdVVUVHjrCGDwcALpdbs2bNlStXDh48+Pbt2ykpKfU/z8hYunRpamrq\nvHnzpLmXSISZM7FrFwA8fYo7d2SIkxBSrskwxs7Jyenb7WKtrKycnJwUGhIhhJQtQiGuXcPL\nl+jYUQBAQwPHj8uW1UVHR/fr1y8tLW3btm0zZmQAyz9/kstmD2vc+B9xQVNTc9euXWlpaeIl\n6/77778nT55EREQMHjx406ZNHTt2nDVrljS3c3VlsjoAubkyxEkIKe+kTeyCgoJ8fHzwzWA7\nAD4+PkFBQUqMkRBCfp3379G7N3r0QMuWmDYt79SphH//zRB3sElv7ty5ubm5gYFBSUmzdu6U\njMvLsbc/IRT6zZ49W3Kmra2tn5+fQCAA0K9fvyZNmlhYWPj5+Y0aNSokJERdii1jly/H5s3M\ncfXqWLJEtlAJIeWatK9iV61aBeDbSRIikYjFYq1atYpeyBJCfj95eeBymVeZnz7h4UN2q1a5\nhobCH133heTk5KtXrw4fPvLoUfM9e5hKLS0Rnz/kwYMke3v7o0ePPn36tFGjRuKP9PX1BQJB\nx44d+/bty+fzDQwM+vXr16JFC2nu9fIlVq1ijrW1ERiIZs1kCpYQUr5Jm9jxeDzH7+yV4+np\n6eLioriQCCGkrHBxKRyg1qYN+vQpSE2VuZGHDx+KRKykJLcTJ5gaHR0EBLBu3Oi5aNEicefc\nw4cPGzVqJBKJ/Pz8HB0dVVVV9+7d20b2hUmEQggEAKChgQsXYGYmc7SEkHKNZsUSQkjxQkMh\n6WBr3RoREVCRa+nPnJwC4CiP11xcrF4dwcEwNYW5+YJ3795t2bIFgJOTU82aNVNSUpKTk/X0\n9E6dOiVrVhcSgs2b0b8//P1x7RqGDEGnTvJESwgp16T9luJwOOIxdt9ycXHhcDiKC4kQQsqE\nf5gpDaheHefPQ1dXnkby8rBlS1dghLior4/QUJiaAgCLxdq0adPIkSMBqKqqfvjwoVatWgsW\nLLh37561tbVMdzlzBgMHIiQEc+eiQQOsW0dZHSEVlLQ9dm5ublZWViwW66thduLNxOzt7RUf\nGiGE/DrPn8PeHkFB+PgR3t74PP5NNjk5GDoUERHMbIlatYTh4SpFB729e/fu8uXL9erVe/Lk\niXx7MyYkYN48+PkxRXV1GBjIEyoh5PcgbY8dl8sVj7H7dotYR0dHZ2dnJcZICCGlSCjEqFFo\n2BC2tggOxv376NJFnnYyM2FlhcBAScVTHZ0BeXn3JOUHDx706dPn7du369evly+ry89H9+6F\nWR2Lhe3boa8vT7SEkN+DDGPsvL29bWxsaIFiQsjvzcMDx48DwP37uHdPzt0a0tMxYABu3GCK\nRkYYMSJs3brQtm3bmpiY1K5dOzU1NSYmhs1mr127Vvw2Vno5OQgKgp4ejI0hmcyhowNPT0yY\nIE+0hJDfhmyTJ7hcLm0LSwj5jV28WLhcSMOGzGA4WaWloX9/3L3LFFu0QGgoDA0n29t38vLy\nCg8Pj4qKqlq16vjx452dndu2bStT42fPYu5cPH8OAAcPwsMDwcEwM8PixdRXRwihWbGEEPJZ\ncjLGjoX4r1cNDZw+jc87tcrg3Tv07YvoaKbYujVCQlCzJgC0atVq3759cof3+jWmT8fZs4U1\nCQlYsQLu7nI3SQj53VBiRwghDH9/fPzIHO/aJU93XXIyLC3x8CFTNDPDpUtyTqf9ikiEXr0K\nWwZgbo5p0xTQMiHkdyLbokxBQUFfTZ5ISEhQUmSEEFKaPnxAt25MEjZvnjyD1V6+/CL3at8+\nz8Hh8J49azdu3BgWFlZQUCB3bNnZyMpCYiJTrFsXp07h+nXUqiV3k4SQ35MMPXbFTtoyNjbm\ncDiRkZGKC4kQQkpVTg7s7eHvj5Ej8eQJMjJQr55sLQgEgqCgxxMnNnj7VlNcU7v2f/fudZo9\nO11yToMGDfbv329hYSF9syIR9u/H4sV49w6enti4EadOwcwM7u6oWlW2CAkhFYS0PXZOTk4A\nOByO6EscDofH43l5eSkzSEIIURY+H8OHw98fAI4fh0Agc1YXERHRpEl/a2stSVanohKanGxq\nbt729OnTd+/e5fF4y5YtS0tL69evX3BwsJTNpqWhXz9Mnow3byAU4vBhzJ6N69exeTNldYSQ\n75I2sfPx8XF0dPy2Zy4yMpLD4fj6+io6MEIIUTqRCJMm4eJFptilC6pXl62FQ4cOWVrOSUo6\nCjD5oIEBTygcAGTb2tra2dmZmpp26dLFw8Pj9u3bWlpakydPzs3N/WGzb96gc2dcvswUWSxa\nx4QQIhUZxtiZmJgUW29vb8/j8RQUDyGElJ4tW3DoEHPcoAFOnZLt8levXjk57QOuiETMbg9D\nh6JOnTl6elp//PGHq6vro0ePJCebmJgsWbLk5cuX0nTa7dgByQDm+vUREkLzJAghUpE2sXN0\ndPxet1xcXBztFUsIKY8k32oGBggJQZ06xZyTnp6ekJCQlJQkqYmOjt62bdvatWtHjdqWm+sn\nFFYT148bhyNH+Pfu3bWwsPDx8SkoKNi7d2/Rpvr37w8gKiqqhJCePMGLF6jGNIk+fXDvHiwt\n5X1CQkgFI+3kCW9vb/E02K8WKDY3N+fxePHx8UqI7bu8vLxcXFy+radpHIQQKSUlQSjE6NG4\nexcGBggIwB9/fH3OpUuXVq9efevWLT6fD6B+/fqDBg26evXqgwcPAAC9AD+A2Qd20iT4+CAz\nM1soFFatWrVHjx41a9b86m1GtWrVAGRkZBQbkkiEZcuwejUqVYK/P3x9IRBg6FCo0rJUhBCp\nSfuFIZkS+725sUWLytudQpxHfu9THo/HYrEovSOElGzXLjg7g8WCry8+fIC6OtTVvz5n6dKl\nq1at0tbWtrW1bdSo0cePH/39/Xfs2MFisZycnBo3dlq2rE1uLvPSw9DwpKfnQBWVKjo6Olpa\nWomJiQD09fU/fPhQtE3x38C1a9f+NqS8PEyahKNHASA3F+HhWL1a8Q9OCPntybaO3a/l5eUl\nzuri4+NF3wGAZukSQkrg5YWZM8Hno6AAwcHQ0Skmqzt16tSqVas6deoUHx9/8uTJdevW7d69\nW1dXl81mi0Si2Fgjd/d2kqyuc+fQ1NQRGzasFxctLS1v3LgRGxubnJys/+UmX+JtJyy/ebH6\n8iUsLJisDoCmJk2VIITISdrE7nuJ1PeyK2UQD/ITiURGRkYlxEmzdAkh37N5M1xcmE3D1NUx\neXLxp23cuFFbW/vChQs1xXuBAf/8809MTIyjo2PTpotu3Jidlyc5d+H06SmNGzfev3+/+Ntv\nyZIlIpGob9++79+/7969u/gkPp/v5uZ27Ngxa2vrdu3afXW7+fMheRVRsyauXMH3v+QIIaQk\n5WnsBo/Hc3R0/OFp9vb2xY7AKxafzw8PD//06VMJ5zx79szExKRWrVoln/bbyM7Olvw/+eXo\nx6FADx6w589nhsSpq+PQoSwjI/63v9afPn26e/fu0KFDNTQ0JL/1169fB6CiMj4+viPAjEhZ\nuPCDj8/e2bOF3bp1u3jx4uPHjw0NDY2NjV1cXDZv3gzgypUrM2bMSE9Pj4iIePXqVbNmzby8\nvMRtCoU4e1btv//YgwcXsFjqgBqApk2Fvr5ZDRoIK8aXzU+hX40ypaL9ONTU1Lp06fKroyie\nDIldUFCQlZVVfHy8uMNMMtguMDCQy+UqJTrlu3XrVr9+/X542rJlyzp16vTixYtSCKmMSEtL\nS0tL+9VREAb9OBTiyhVd8VyHSpVEW7YkmZhkFPs7/fTpU5FIpKurW/RX/tWrV8DE3bs7CoXi\nrz6Rm1vK8OHvGzVaO3PmTH9/fwBbtmzR19e/e/duWFiYioqKsbHx7du3xUN+DQwMpk2bNmHC\nhKysrKysrJiYymvW1H7woDKAv/9mnzsXX6OGnoEBn8v9pKIirEjfND+LfjXKlIrz49DU1PTx\n8Sl5hvuvIm1iJ87qAIgTO3Nzc8lHVlZWpZPbcTgcHx8fb2/vkk/z9fWVfvmVzp07X7t2La/I\na5Vvbdq06fDhw4aGhg4ODtLGWp5lZWW9fftWX1+/SpUqvzoWQj8OBcjIYB04oPrsmcqoUXx7\ne/7Tp6zFi/MtLasDxS9GrP55zF3Dhg0lla9e2QGcz1mdcPjwiAULOgE6DRs2NDExsbOze/Pm\njY+Pj/jkzp07r1y5snPnznw+PyUlRUtLS1e8By0gFMLNTX3nTjWhkGmZxWK3alXv8+tZ2lNC\nWvSrUaZUtB9HbGzs9OnTx44d+6sDKYa0id358+fxOatLSEjg8XiSyacsFuv8+fOlkNiJV0L+\ndsmVosT9iJ6enlK2qaqq2q1bt5LPOXr0aHBw8Lt377S0tKSPtvwSCAQANDQ0KsjzlnH04/gZ\nmZnYvh2bN0PciXD3rtrnP7Arl3CVkZFRnTp1QkNDNTQ0VFVVAWzZglOnmL9mWSyBSDR27lxn\nyU/EyMgoJyenXbt227dvFwqFTZs2NTAwkLRWTbIkHQDg/Hls315YbNIE+/ezqlWjH67M6Fej\nTKloPw4+n/+///2vbCZ2sm0pJn4JGxAQAMDe3l78kaenp+TvVKVydnYWd8Wxvk9yZinEQwgp\ny4KD0agRFi+G5NWQmppUF7JYrKlTpz558sTd3R3A8uWYNw+f/5wsYLEc2rf/r2PHjuJyamrq\n4MGDMzIyPDw8zM3Nu3XrVjSr+9b9+8yBpiZWrEBMDD7PryCEEAWQYYydZEuxuLg4AAMGDFBK\nRCUS9xE6OTkVm0o6Ojr+8EUtIaSCWLAA794VFvv2xc6d0l47f/78oKCgtWvXHj3aKilppLiS\nzS4QCm2FQv8XL/SnTZtWrVq1xMREf3//3NxcNze3QYMGldDgs2eYPRv5+Vi+HI8eITMTK1ag\nTRs5H40QQr5Hnlmx4qRKsuaIOM8rTd7e3pTAEUK+lZGBKVNw7x62b4eREe7dAwAOB6tXo2dP\nGdrR0NAICAjkcKIePrT4XJcjENiamr5t2XLclStXxF9BqqqqXbp0WbJkSclzsKKjMWAAUlIA\noG7dwvXqCCFE4aRN7DgcjouLi7Ozc1BQEADJsiNBQUHit7TKCpAQQqSTkQEuF+J9Z5ydERWF\nUaOgpydbSicmFGLhwqqSrE5dPd/JKXDs2FVGRkYvXrzw8vLKzs7OycmpUaOGjo5OyU35+2P4\ncEhWgfj8CpcQQpRC2sTOzc3NyspKMojNxsYGRV6Jurq6Kik+QgiRRmYmrKwg2U2wXTtoaMDW\nVp6m+HyMH1/Yr1a1KgICKpmb2wGQLGtXq1YtaZqKjYWdHfLzmeL48Zg4UZ6QCCFEStJOnuBy\nuZKppp6enuI5sOJRdyVvBUEIIaVg2DDcuMEct2qFLVvkbKegAA4OhVld9eoID0eR9Z1k4+tb\nmNUtWYL9+6WdwEEIIfKRYa9YZ2dn8Y5hkjmn4hrlBEYIIdJKTMSlS8xx27YID0eJM1O/Ky8P\ntrY4eZIp1qqFa9fQvr08TWVkIDoatraoWROVK2PPHqxahc/vPAghRFnK05ZihBDyFYEA9+7B\nyAitWuHBA5iZISgIenryNJWTAzs7BAUxxTp1EBqKZs3kaSoxERYWSEqCgwNSU+VpgRBC5CND\njx2AhISEoivGeXl5OTk5KScwQgj5gcREdOsGU1N06YLISCQmIjJSzqwuMxNcbmFW16gRbtyQ\nM6t7+xZcLpKSAODiRdBbDUJIaZIhsXNycjI2Nv6q0sfHh0VvFwghpUgoxI4daN8eRka4eRMA\n4uKQnIzGjaEq10uI9HRwubh6lSlqaaUOHLgpLU2eXSCzs2Ftjfh4pjh5Mr1+JYSUKmkTOy8v\nLx8fHw6HIxKJJLMonJ2dxQudeHl5KStAQggBACQm4vp1iETYuxezZiE6uvAjS0s0bSpns2lp\n6NlTKJl4AcRmZrbbvt21Q4cOU6ZMKXkj6W/NnYvbt5njAQOwbp2cURFCiHykTex8fX0lm8MW\n5e3tzeFwfH19FR0YIYQw7t8HlwsjI3Tvjpkz8fFj4UdsNmxtcfq0nC2/fQtLS0RHM9+ExsY5\nr1+3EAqTb968aWFhsW/fPlkX6QwJYQ46dsTJk3L2IBJCiNykTex4PJ5kc9iv2Nvb83g8xYVE\nCCGMt28xYQLatSuc9BoWhqlTMXMmRo7Enj149QpnzqBqVXkaT05G9+7491+maGaG27crGxiA\nxWJ17tz58uXLXC730KFD3/5B+5WcHOzeDXt7bNgAV1dUr46uXeHvjypV5ImKEEJ+Bv05SQgp\nu+ztceVKYbFSJSxYgKpVsX37z7b84gUsLfH4MVPs2LEgJEStaIKooqKyadOmoKCgEydOmH9n\nIbu0NOzciR078PYtAJw6BR7viw1qCSGklEnbY1fC+1YXFxcOh6O4kAghFdrHj9i5Ex4eSEtD\ncjJTqa6O2bPx4gUmTFDALcTTaSVZnabmzYgItW+7/UxMTHR1db+3HXZ0NIyMsGwZk9UBYLFQ\nrZoCwiOEELnJtqWYubn5V28lxFNi3dzcFB8aIaTiiYvDwIF4+hQAHj3CwYPYuhUGBpg/Hw0a\nKOYWUVE5PXvyMzO1P1eEqKiMuXfvXLF/oKqpqRUUFBTbjq/vF6P9TEywahWaN1dMkIQQIh9p\nEzsul+vo6Fh0cRPJgaOjo3iHMUII+Rnh4bCzK8yWBAJ07ly4D4R88vLy/Pz8bty4kZ2dXaVK\nFX19Cw8Pcz6/uvhTY+OYjx8nvn37plu3brt27fpqYc7k5OS3b9/279+/aGVCgsbChZqNG4PL\nhZcXcnLQrRtcXTFwIK1sQgj59WQYY+ft7e3q6vrVUnaBgYGU1RFCfl5MDLjcwp1Ve/XC54WV\ninf16tXg4ODExERtbe02bdqMGjVKV1c3NDQ0NDT02bNnurq6pqamRkZGEyZMSBIvFgwAHYFl\ngK64MHQojh1reeDAMkdHRz09vRkzZnTo0MHU1FRyiw0bNohEokGDBklqYmPZEyY0+viRDaBl\nSyQlITcXdesq7l+BEEJ+jmyTJ4yMjGhzWEKIMhw5UpjVzZwJT0+w2cWfmZqaOnLkyCtFZ1UA\nCxcuNDAwePbsWdFKFoulpqa2evXqkSNHxsfXsLXVyMpSE380ZMinEyeqstkYN27ctm3bxAPp\nPDw8Ll68CCA9PX3FihVeXl7m5uZDhgwRXxIVBSurKp8+Mf1yjRujRg2FPDohhCiMtJMnzM3N\nafcwQojC5eZiyxZs2wYuF9raqFQJnp7Yvv27WV1WVlafPn2uXr06efLk6OjovLy8N2/ebN++\nPS8v79mzZ/3794+NjS0oKHj16pWxsbFIJOLz+e3bt3/6tJGtrbYkq2OxduXkjBTfolKlShcv\nXjQyMhIKhQEBAebm5l26dDE0NNy8eXPbtm3PnDmjoqICIDkZAwdCktU5OaF371L45yGEENlI\n22NHK9URQhTuzRvY2DDbgq1di9RUiEQ/WP5t06ZNMTEx69atW7BggbhGX18/Li5OIBDo6end\nuXOnTp06qqqqfD4/ISHBxsbm2rVr48cfzcjon53NtDB5cubHj1fOng1+/fp1zZo1ATRs2DA6\nOtrCwuL27duJiYlsNrtLly7Dhw8fP358pUqVxFcdOIDUVKYFB4f8Xbsq0Yg6QkgZJMNyJ0qN\ngxBS0YSHo0MHJqsDIBRCU/PHi/oeOXKkQYMGrq6ukho+n3/ixAlTU9M1a9a8f/8+KCgIQExM\njEgksra27tdv9+vX+yRZHbByzpyknj17CIXCouuYaGpq1qlTh81mv3r16tWrV2FhYY6OjuKs\nrqAAGRlo0YKZG2Ft/XHbthwVGfbZJoSQ0iPtl1NkZCSPx/veKp2EECKTQ4dgaYkXL5himzaY\nPv3HV+Xm5iYkJHTt2lWlSGKVnJz84cOHHj169OjRA0BsbCwA8R6v9+8bnz49DFAXnzl2bBzg\nHhAQoKGhITlHLDMz88qVK23btmV/+Q740SO0bAldXbx6hX//RXBw5urVLymrI4SUWdJ+P4kX\nN+HxeKzvUGaQhJDfzeHDkEzEGjgQ169LtbRvfn4+AHFaJiHOzzQ0NMT1ubm5AOrXrw+M27bN\nvKCA+XbasAHe3o3r1KmzZs2a69evfz4HAIRC4Zw5c96/fz9x4sSiLf/7L7p3x+PHEAiwZw9a\nt0anTgL6tiOElGX0hychpJSIRPj7bwwfjm3bYGMDFgs6OtixA35+0Nb+8eUAdHR0dHV1xX1y\nErVr11ZTU4uNjY2JiQHQoEEDAP/+2x7YLxKJv+KEkyf/z9UVGhoa+/fvz8rKOnToULVq1T5+\n/Hj37t1jx4717Nlz3759/fr1c3R0lDTL46FXL7x5wxSHDlXEPwEhhCiZtJMnaJUTQsjPSEnB\nxIm4dAkATp3CkycYNgz6+jIv6mttbX348OHr169369ZNXFOlShULC4ugoKBXr16pqKgMGDDA\nywtz5rAAcdNCVdWZGzeuEZ9cu3btBg0aJCQkfPz4UTK2RE1NbdasWRs2bFBVZb4Ss7JgbV24\nVPL48Viy5GeenhBCSols69gRQogcfH0xfTrS0piihgb09KCjI09TixcvPnXqlJ2d3YEDB6ys\nrMSVc+bMCQkJuXv3rrW19dmzjVxdmfe8LJZAJBojEJzo0+efevXqJSUlRUdHA1i1apW5uXlU\nVBSfzzcwMOByuYaGhkXvcv8+3r9njqdNw44doHF1hJByoaTE7tudYaUh31WEkN9VTAxGjCgc\nUVe3Lg4fliGre/PmzbZt20JCQt6+fauqqmpqarpgwYR2bB0AACAASURBVAIvL68BAwYYGxs3\naNAgLy/v7t27IpGIzWZfvGh+8aLk0gKRaLSVVZa2tv3169fv3r1bp04dGxubefPmiaf59+zZ\n89vbRUbC3x8ODhg1CrduwcEBHh60VxghpNwoKbFzc3NjsViOjo7e3t7StGVubs7j8QIDAxUU\nGyGkHCsowK1bYLORl1eY1Y0ciZ07oatb/CXx8fFbtmzx9/fPyMhQV1fv1atX9+7dV6xYIV5w\nrm7dunl5eadOnRIKhUOHDjU0NAwJCYmKilJVVe3evfvYsWOvXOm/d6+euCkWK8/MbMOSJWOs\nra2lj/mvv+DkBIEABw7g5cvvLpJMCCFlVkmJHZfLFYlELBbLx8cHgKenp7Oz87enOTk5iU8A\nDcUjhAAAbt6EoyNiYsBi4dw5rF6NpCT07w8bm+9esn///unTp+fn5//xxx/NmjV79+7d6dOn\nT548qa6ufvTo0REjRoiXOElKSpo6derp06fnz5//6NEj8bUiEWbOxN69TFNaWvDzU+/Va6n0\nAYtE8PDAihWFRYGAEjtCSPnz42EjIpFI3Ann4uJS7EIn4qwuMDCQsjpCyKdPmD4dXbsiJgYA\nRCI8fozFi7FnT0lZXUhIyJQpU+rWrXvnzp3//vvv8uXL0dHRdnZ2APLy8l6/fi1ZuK5+/foX\nLlwwMzPz8vJKTk4GIBRi6lTs2sU0pa2NwED06iVb2HPnFmZ1amrYvBmft5wghJDyRKrxwOKu\nu2LzNg6HI/6Iy+UqOjZCSDkjEsHcHLt3QyhkaszMMHbsjy9cunSpurp6SEhIhw4dPjclCg0N\n7dChQ8OGDVesWFF0MWE1NbU///yzoKAgICCAz8eYMfj8zgB6eggLw+f5stJKTcXOncyxtjb8\n/TF6tGwtEEJIGSHbRC/RN2ieBCFELCcHb99CssactjY8PcHjoWbNH1z4+vXrf/75Z9iwYY0b\nN5ZUfvr06f37923btp02bdrHjx9v3LhR9JLWrVsDSEh4Pno0jh1jKmvUwOXL6NhRhpg/fEBY\nGDQ1mSDr1MH16+jbV4YWCCGkTKEZ/ISQnyIQwNMT1aujalWEhmLqVNSti+HDERsLZ2ephqk9\nf/5cJBK1bNmyaKV4STk+n9+qVSsAz549K/opn88HNM6dG+Pry9TUqoWrV9G+vQyR/+9/aNEC\nlpYYMADR0bh8Gffvo00bGVoghJCyhtaxI4TILzoajo64e5cpHjsGf3/s3i1bI5UqVcKXO7cC\n0NLSqlevHo/HGzRokOQciStXbgPn4uObiot16yI0FE2bynDThw/RsycyMgAgKgpVqsDSUraw\nCSGkDKIeO0KInLy9YWZWmNWpqMDeXp52mjRpoq6uLt6/tagRI0Y8fvx4165dAExMTCT1z5+/\nnzvXBOgvLjZujOvXZcvqAMycyWR1AKZOReXK8kROCCFljQyJnZOTU7GzYsWUFyIhpGzy9ASf\nzxy3aIHr16WaJ/EtbW3tIUOGXL58OSAgoGj9okWL6tatGxoaqqenV6NGDQB5eXnHjwc0a/Y0\nN9dMfI6xMSIi0LChbHe8cAHh4czxpEnYtEmesAkhpAySNrErulgdIaQiu3oVTk6IiEDv3gCg\noYGVK/G//4HDkb/N9evX6+vrDx06dOXKlc+fPwfw7t27kydPZmdns1is9+/fN2zYUE9Pr2rV\nxqNGGebmmoqvatkS166hfn2ZbyfJ6gwNsXUrbRdGCPl9SDvGTrJYHS1rQkhFtmsXnJ3B5+PA\nAbx+jWnTUK+enLu+FlW/fv2QkJCRI0e6u7u7u7tL6mvWrOnn55eTkxMaGhof/zE6en1eXkPx\nR23aICQEBgby3G7oUPz9N4RC7NsHbe2fDZ4QQsoOGSZPcDgcyuoIqbAEAsyfD09PpqipCTYb\nLVoorP02bdo8ePDgwoULoaGh796909LS6tat2/DhwzU1NQFwOMMsLfHxI3Nyp064dAnVqsl8\nl+xsuLlBKMTjx9DWRpUqCoufEELKAmkTO0dHR3oVS0hFNn164TrAqqrw9FR8Xxebzba1tbW1\ntf2q/ulT9O6Np0+ZIoeDwEBUrSrPLcaNw+nTAKClhVWrfiZYQggpi6QdWuLt7Q3AyclJmcEQ\nQsqotDTs388cV62KgACMG1dKt05IQM+ehVmdpSVCQuTM6sLDmawOoH1gCSG/JxnGDAcGBvr4\n+NCsWEIqlCdP4OMDPp9569qwIW7cKL29GWJj0a0bkpKYYr9+8POT8/1pQQFmzmSO9fXh4qKY\nCAkhpEyRYVaslZWVUkMhhJQ158+jbVs4OaFrV0RG4u5dPHiAL3eIUKL792FhgdRUpmhrCz8/\n+RecO3AADx8yx+7u0NVVQISEEFLWSJvYSWbFfrtdrJgygySElLbcXMycCVtbZhXfN2/AYsHU\nFFpapRTAnTvo2RNv3jBFBwf4+uLL7Sdkc/s2c9CqFaZO/dnwCCGkbKJZsYSQr4lEsLREZGRh\nzdKl0NQsvQBu3gSXi0+fmOKECdi7V/5RcY8f4949DB+O8HBUqoQjR6BKmykSQn5TNCuWEPK1\niIjCrE5HB7t3Y9Qoxd8lLy8vLi6Oz+fXqFGjUaNGkvqwMAwejKwspjhzJrZtgxzjeFNScPIk\njh3DP/8AQNOmSEyUpx1CCClHZJgVy+FwzM3NlRoNIeQXys7GihVYvBi1a0NPDwDMzPC//yk+\nq/v06dPMmTMNDAzat29vZmbWuHHj1q1bnzp1CkBICAYNKszq5syRM6vz9kb9+pgzh8nqACQk\nFC6DRwghvytpe+wk816/NwGWhtkRUq7FxmL4cMTGAkBmJv77Dy9eoHVrxb+1TElJ6dmz5+PH\njzt16sTlcjU0NBISEs6ePWtvb29nd/jiRYf8fOZMd3csXy5Dy69f4+RJVK6MCROwYUPhPrYA\nNDTg4UETJgghvz8aaUIIwf79mDUL2dlMsVo16OtDX18p9xozZkx8fPzOnTunT58uqVy7dm33\n7tvOnBkuqVm3DgsWSNvmo0fYvBmHDyM3FwDS0jBoEDw9wWbDwgKjRsHWVgH7nhFCSNknbWJH\nHXKE/K62bsXcuYVFDueLomLdv38/LCxszJgxRbM6AAEBNR4/XiEpbtyI+fOlalAoxJQpOHAA\nQmFhZXo6tm6FszO0tVG9umIiJ4SQcoF67Aip6C5dYg5UVPDnn1i5UvGvX/Pz8zMyMthsdnh4\nOIAJEyYU/XTnTsyahc9/PAq3bxfOnCltBHfuFG6JAUBVFaNGMV19DRsqIHJCCClfZNh5ghDy\n23j9GiNHol49TJiAWbOgpQUjIwQGYu1aBWd1UVFRNjY2enp6NWrU0NXV9fDwAKAnnpoBAPD0\nLMzqWCwR4GRn9/aHzWZnY8ECODlBRwfVqgGAlhacnZGQgIMH5dxwjBBCfgMlfYXLtFEYvasl\npLw4dQrTp+PdOwA4cADLlzOrECvcsWPHJkyYwOfze/bsaWRkVFBQcOHCBQA2NjY3b940NDRc\ntQpLlzInq6qiY8ftN2/uq1ZtW8nNvn8Pa2vweADA5yMmBg8fwtSU5kYQQgi9iiWkIsnPx6RJ\nOHKksKZJE9Spo5R7xcXFTZgwwdDQ8MyZMx06dBBXRkREWFhYPHv2bNSoUWZm4evXMyerq+Pw\n4dxp01a0atWqcom7hiUkoF8/PHnCFOvUYf5HCCEEJb+K/d7uYbSlGCHl1LlzX2R1w4aBx5N/\nR4eSrVu3rqCg4OzZs5KsDkCPHj3atWunoqISEdFfktVpaMDXl3/69Li0tDRnZ+cS2kxPh4VF\nYVZnZoY5c5QSPCGElFPUY0dIhfDoETIzUbs2WCyIRDAwwM6dGDpUiXcMDQ1t166dqalp0UoV\nFZWDBw+Zmt4SCieLaypXFkyYcHbRIo+4uLghQ4Z8Na/iKydO4MUL5rh3b5w/X3p71xJCSLlQ\nUo+dfPtM0O4UhJQ1Bw6gVSt06ID79xEWhjNnEBen3KxOIBCkpqY2adLkq3qhENu3tywomPy5\nIj0np9euXfZJSUlubm4nTpxQUSnmS+n1a3h7Y/9+tG4N8XtaW1sEBFBWRwghXyupx87NzY3F\nYjk6Onp7e0vTlrm5OY/HCwwMVFBshBAF8PXF5MkQCADg6lXMmFEaN2Wz2ZqamhlfTsrg8zF2\nLI4fZ4rq6lkLF16rXduhdu0/u3fvrvPNCsKZmThzBsePIzSUid/dHY8f4/17tGpFu74SQkgx\nSkrsuFyuSCRisVg+Pj4APD09ix3+4uTkJD4BNDeWkDLG3x8ODkxWxGbDyan0bt2+ffsbN25k\nZGRoa2sDKCiAgwN8fSWfv1uy5NbSpQO/d/m7d+jcGYmJX1S+eYO6dVG3rtKCJoSQcu7H69iJ\nRCJxJ5yLiwurOOKsLjAwkLI6QsqUjAyMGoWCAgBgsbBnD3r3Lr27T5o0KTMzc8aMGQKBIDcX\nQ4YUZnUqKql6ejazZ3cr9sJ79xAXh5s3v8jq2GwMGAA3N+XHTQgh5ZlUCxSLu+6Kzds4HI74\nIy6Xq+jYCCE/JSGhcIG69esxeXKJZyvamDFjBg78P3v3HdfU1cYB/BeGgqCCe9YF+opad9Xg\nVtTgwkXrqLMNaq3g1pY6qlZQq9C6iNZZraKtm7i1KnFvxAHiQLAO3MhO3j/uNUEUCEoSxu/7\nRz/3nHtveGKAPpx7znO6rFu3Tipt17Tpg927xX4zs2igrUIxpuiH6ggvWoQGDVC3Ll69Qr16\nANCoERYswL172LWLZU2IiDKRtVWxHJMjyhViYrB1K5yd4eWFY8fQv78JyoKYmZlt2bLF09Nb\noeim0ZR9232rZMl+K1fOd3V1ff+W0FCMHw+1Gmo1LlzAhQvGjJeIKC9guROivObuXTRvjvv3\nUagQbt3CwoUmiyQ2tuD58/O0fw8WL/70t9+ie/U6WrBgwfcvTknBkCFISAAAMzP06WPEQImI\n8oos7BWrVColEkl4eLjQ1M6xUyqVhomNiLLs0SN06ID79wHgzRvExJgskidP4OKCM2fEZp06\nuHq1WL9+LT6Y1QHw88Pp0+Kxpye++MIoURIR5S36jtgplUrh0UlYWJiDg0PqYnWurq5BQUGc\nY0dkci9eoFMn3LwpNvv2Ra1axvvq4eHh69evv3r1qlqtLlGizr59E2/fFjcHq1cP+/ahZMmM\nbl+9WjxwdMSsWYYNlYgor9I3sdu2bRveZnXh4eEqlUoqlQYHBwOQSCTbtm1jYkdkcr6+unlp\nvXph3TojfV21Wv3jjz/6+vq+nYZbHpgNiFld06ZQKmFnl8mLNGmCkBDY2GDVKhQqZNiAiYjy\nKn0fxSoUCrlc7uDgAGD37t0A3N3dhVN+fn7aOnZEZDTx8VAq8d13qFwZBQti+XL89594SirF\nmjWG2gT2fePHj/fx8WnatOmhQ4dCQ+MrV74P1BBO2dtf3LMnJeOs7sIFDB+OL7/EoUO4cgXc\nvIaI6KNlYfGEk5OTcBAaGgqgc+fOBomIiPQwcyZ8fPDmja7H1xeHDwOAjQ1mzoSNjZEiCQkJ\n8ff3b968+aFDh+7etWzXDvfuiacqVLh+/77zjh3Lvv766/RuDwtD8+Z48wYrV+LhQ9jbGyls\nIqI8KQuLJ7SE8Tlh9A5v8zwiMrTnz+Hnh0OHcP8+pk17J6sD0KMHKlbEypX4/ffMn3tmo02b\nNqnVah8fnxs3LFu00GV13brh7NkyVlbqv7SbiL1HrcawYeIbSUkRaykTEdFH0zexk0qlXl5e\nAIQ1sHK5XOhXKpXCU1oDxUdEguhoNG2KMWPQvj3u3EGJEmJ/pUrw9MS//2LePNMEFhoaamFh\nUahQs3btdM+Ce/bE5s0oXdqudu3aGfztt2ABjh0Tj7//HqVKGT5cIqI8Td9Hsd7e3q6urpK3\n2267ubkh1S6xEyZMMFB8RAQgOtqse3eEhQGARoPXr3H+PE6cQLVqaNDA2MHcv39/69atISEh\narW6YsWKT548MTNr1q6d2bNn4gVff41Vq8QZfgUKFEhKZyDuyRNMny4eV66MmTMNHzoRUV6n\nb2Ink8n8/PyEQTs/Pz9hDaww647bURAZ1KNHFt98Y3P7tths1QodOpimhK9Go/Hx8Zk+fXpi\nYmKqbikQpO0YOhQKhZjVJSUlhYaG1qxZ84OvFhqK2FgAkEiwfDkKFzZo7ERE+UIW5th5enoK\n28J6enqm7jFMYEQkWrOmxO3b4o9qmzbYvRtmHzM59iO9fPkyQdgOApg9e/YPP/xQrVq1v//+\n+/Hjx0+fPvXzCzEzOwCIu75+/z1WrNCtxl2xYsXz58+7d++e5jWPHcOUKSheHDIZihXD3Llo\n395Y74eIKE/jlmJEOV1SkjgFon17bN9upBpvERERs2bN2rVr1+PHjwFUr169a9euv//+e/36\n9Y8ePWprawtg715MmWKvVou3fPbZlmnT2kokxQAkJCQsXbp04sSJVapUGTVqVOpXXrUKcjmS\nk/Hnn7h3D2/ndxARUTZgYkeUQ0VF4fvv8eKFzfDhd6tWtbGzsxo1ClZWxvjSBw4c6N2794sX\nL+rUqSOTyeLj448ePfrrr78CmDRpkpDVbd2Kr76C9gmsRPLzvXvTPvusUIMGDSwtLc+dO/fy\n5ctKlSrt2rXLJlXllTlz8OOP0A70p6TAgr+EiIiyT0a/UyVZ+VOaz2SJslFkJNq2RXg4AAsb\nm+Lr1iUULWqUnA6Iiorq1auXhYXFvn37XFxchM7ExMQvvvji0qVLS5Ys+fLLL//8E4MHIyVF\nvMXHB9u377l2za5Ro0YXLlxQq9VOTk5ubm4jR44snGrq3NSpuhUSlpZYuJBZHRFRNuOvVaIc\n5+5dtG2LiAix+b//xWu35zKCefPmvXz5UqlUarM6AAUKFHB0dLx8+fLRo0cnTgxdsMBJyOok\nEsybh3HjsG+ftUQi2b9/f3ov++wZ5s8Xj62tERiILl0M+0aIiPKhjBI7DsIRGV9cHNq0gXYN\nbOfOSX37xgAVjBbA3r17q1Wr1qlTpzT9lSpV0mg0wKh588RVrmZmWLIEHh5Qq9XXrl2rVKnS\nB1/wzh3cuYOGDVG0KOLiULQoduxAy5aGfRdERPmTERfXEZEegoJ0WV2vXli9+o2lpVH/xLp/\n/3716tXf7+/cuTPgBfwGSABYWGDtWnh4AMD69esfPHjg6uqa5pbXrzF2LBwc0KYN3N1x7hwC\nA3HpErM6IiJDyZ7EzkP47U5EnyA6GseOoW5dcQOGfv2wcSMsLY0dhrW19evXr9/vP368DbBQ\nyOosLfHnn+jfH2q1eu3atcOHDy9VqtTYsWNTX79zJ2rVwsKF4lS8CxdQrhz69EE643pERJQN\nsjDHTrvPxAcFBARkRzxE+VFSEmbOxJw5SE7Gl18iLAzPnpksAapfv/6xY8eeP39ul2rH2cmT\n4eurbSVYWg7644+n69dbXbx4MTIyskyZMtu3by9evLj2ij/+wDff6F6zQAH8/LNRoiciyt/0\nHbHz9/fPIKvz8/PLpniI8p2QEDRtipkzkZwMAKdPo0gRUw5rDRo0KC4ubvTo0eq3FeomTkyd\n1cUNHry9ceP/Tpw4sXfvXnt7+ylTply8ePGLL75I/SJHjuiO27TBxYvghtJEREagb2IXGBgI\nICwsTKPRSKVSuVwu7EIhl8sBaPeiIKIsOXgQjRrh/HmxaWmJKVNMGhDw1Vdfubq6rlu3rl27\ndrt3KwcNejNvnvbkay+vvatWuR85cuTVq1cJCQmXLl365ZdfSpcuLZxeuRIFC6J9ewwejMqV\nUb8+1q/HoUNIZ1MxIiLKZvomdiqVSi6XOzg4AHB3d9eO3gUEBEilUs6xI/o4Gzbg7X5dqFsX\nJ0/i229NGhBgZma2ZcuWb7755ujR4126RK1dK+50IZG8mjTpyMKFbundeOwYPDyQmIiDB5GQ\ngNu3cf48+vUzVtxERJSlxRNOTk7CgbBiLjw8XGi6u7uHhIRke2REeVVSEnx80K4dvv8evXvD\n2hq2tpg2DWfOoEEDUwcHALC2tl66dLmr6zNAnChna5tw6BB8fNItPRcTg/79xafJVlaoW9c4\nkRIR0Tv0XTwhlUpDQ0NT94SFhQkDeABUKlU2x0WUR126hCFDcOECABw6hK+/xpMnRtr+VX9J\nSejfH7t22QrNkiWxb1/BevUKZnDL0KGIjBSP581D+fKGjpGIiD5A3xG72rVrKxQKpVIJQCaT\nAZg1a5ZwSph+R0SZmj0bjRuLWR0AW1tUrZrjsrr4eLi5YfNmsVmuHP79F/XqZXTL5cvYsUM8\ndnPDd98ZNkIiIkqPvondhAkTALi6uvr7+wOQy+UqlUoikUgkEmH6nQFjJMoTzp2DtzeSksRm\n/fo4cQIlSpg0pve8fg2ZDEFBYrNiRRw+nPnSh2LFxHp7lStj5UpkZZdpIiLKTvo+inVwcNBo\nNJK3v7CFqnXCEgq5XM4idkQZePgQz57pSg1bWGDKFPz0kwmKD2fs1St07oxjx8RmtWo4eDCT\nwitqNZYsAYBDh6BS4euvYW9v8DiJiCg9WShQjHd3jw0ICGA+R5SpwEAMHoy4OMyfjy1bEBaG\nDh1yyiKJ1J4+RceOOHtWbDo54cABlC2byV3ffy8mdvPnY+JEw0ZIRESZylpiR0RZ8ttvGDMG\nQqHf3btx6JCpA0rHkyfo0EE3+a9OHezfj7fF6dK1ZYuY1QF49MiA4RERkZ6yZ69YInrflCnw\n9MTb7RtMX6AuPdHRaNlSl9U1box//808q7tzR/eO7O0xcqQBIyQiIj3pO2InyWw6dOqntER0\n8iR8fMRjS0usWIG+fU0aUDru30f79rhxQ2w2bQqlEqk2iU3XiBF4/lw8XrHClHugERGRFh/F\nEhmEtqibtTU2bIBbuvs1mFJEBNq1w507YrNtW+zYARsbve49fVo8GDkSPXsaIjoiIsoyfR/F\natIBQNg31pBBEuUaDx+ia1fUro3ChfHjj+jbF4cO5dCsLiwMbdrosjoXF+zcqVdWd/Eirl7F\njBkoVgw9e+LXXw0ZJRERZcWnjtiFhYU5Ojo6OTl5enpmS0BEuVdYGDp1QkQEAEyfjpMnTR1Q\n+kJC0L49Hj4Um927Y9MmFMxoawnR5Mnw9YW5OTZvRkyMQWMkIqIs+9TFEw4ODlKplJtPEB07\nhiZNxKwOgLOzSaPJ0KVLaNdOl9X16oXAQL2yutmz4esLACkpOHHCgBESEdHHyZ5VsdwrlvK5\n58/RtSuePRObrVrhp59MGlD6Tp1C69a66iQDB2LTJhQokPmNixfD21s8trHB4MEGCpCIiD7e\npyZ2/v7+KpVKKpVmSzREudT+/XjxQjzu0wd79+q1sNT4goPRsaNuNevQoVi5Eubmmd948yZG\njxaPraywfTucnAwVJBERfbTsKXfi7u6eHcEQ5T5bt+LCBfTti1q1cO0axozB3Lkwy5EFIg8c\nQPfuePNGbI4eDT8/ffd1vXRJLMhXoAC2bEG7doYKkoiIPkU2lDvx8/PjygnKhyIj4eWFf/4B\nAJUKISGmDihDO3bA3R0JCWJz3DjMmydmdS9fvjx//nxiYmLZsmXr1KmT5sbLl6FUoksXDBiA\nsDBMnQpXV+OGTkREetM3sWNBEyKtlBQsWIAZMxAbK/YkJZk0oMz88w/69kVioticNg3TpwPA\nnTt3Jk+evGXLlpSUFOGUk5PTzJkze/bsCSAhAStXYswYJCRg4UJERsLS0iThExGRvligmCjL\n5sx5Z21EqVK6TSZyoHXrMGQI3mZu8PHBpEkAcOnSpbZt2z5//rxbt24tW7YsVKjQtWvXNmzY\n0KtXr2nTppcsOW3OHERFiXe9eYPERCZ2REQ5HRM7In0lJiIiAhUr4soVsUciweDBmD8fxYqZ\nNLL0rVqFb78VszqJBIsWiZu6JiUlubu7x8bG7tixo3Pnztrrvb29O3XqNGNGRJrXmTJF3x0p\niIjIhDJK7DLdHzY1PqulvO3vvzF6NKKj8fnnWLMGDx7AxgaTJqF1a1NHlr7ff4enJ4QfTYlE\nU7To5OnTV61f7+jm5lalSpWbN29OnTpVm9W9eoUVK3DuXIkhQ3ZfuOAvLJUAULcupk7lpmFE\nRLkDR+yIMnHvHkaNws6dYvPyZZQti6NHTRqTHhYuxLhxePsHV7KFxTfVq1+ztKx+5coVlUpl\nb28PoG/fvgDevIGvL37/XazD9/ffpevXvxoSsrF7968GDECXLvqunCUiIpPLqCpD6j1hw8LC\n3u/UaDRCBTsO11FedfkyatfWZXUABg5E6dKmC0g/P/+MsWO1WV2STPbnw4cLT506dfz48UeP\nHvn6+r548QKAvb391av4/HP8/LOuunKhQqhcuVBiYr+//lJ37cqsjogoN9G33NagQYOkUun7\nCVxwcLBcLnfOydsnEX2CXbvw6pV4XK4ctmzBmjUmDUgPkydj2jRtK97FZWlQ0GBhiA5AwYIF\nJ06c2KpVKwALFy789VfcuqW7t1MnHDmCR4+i7OzszHJmOT4iIkqfvr+4VSpVelWInZycuKUY\n5SVPn2LoUJQvj7Zt0bo1iheHlRW++w6hoejVy9TBZUijwYQJ4nauACwskoAeixfL0lyWmIiK\nFRcCW1essKpZU+xs0QInT0KphJ1d5KlTp5o1a2bEwImIKHtkYY5dYGDgBwsRBwYGZl88RKb0\n4gU2bsTUqeJWqtHRePoU9++jYMFc8ERSrcbIkQgIEJuFC6NWrZ8uXfrX0dEx9WUHD2LUKFy/\nXheoGxPTrUaNU/v2NbGzQ6NGkEjw6tWr/v37JyYmjtbuIEZERLmHviN2crlcpVK9/8jV2dlZ\npVL5+flld2BExnb7NpycMHy4mNUBsLdHo0awssoFWV1yMgYN0mV1RYpAqUSZMjeSk5PVbxe4\nvnqFfv3Qvj2uX9fe97Jnz3bLl7tfvbp68+bAH3/80cHB4dixYxMmTOjYsaPx3wUREX0ifUfs\nAgICQkJCVCrV+zVQpFIptxSj3Oj5c2zfjsBA5YL1jQAAIABJREFU3LiBKVOg0SA6Wne2a1f4\n+6NMGdPFp7fkZHz9NTZuFJvFi2PvXjRsiKAgp23btp05c6ZJkyYAVq7EX3/p7jIzO92gwdJi\nxZw3b968efNmobNixYorVqwYNmyYsd8DERFlhyxMjg4ODg4KCkrTGRQUFBwcnK0hERlcSAi6\ndEHp0hg8GEFBuHULXl5wcYGjIyQSNGuGHTuwYweqVDF1oHpISEDPnrqsTiJ5Ym3dVaHwuHbt\n2ldffWVmZjZhwuSdO5MvXICdnXiNnR2aN9+gVjfz8mq/d+/eyMjIf/75JzAw8PTp0xEREczq\niIhyr6zVsZPJZKxsQrnU8+c4ehRVqqB2bXTpgrt33zlbpw4qVcLNmyYK7mPFxaFXLyiVYtPC\n4mHjxlMePbqmUOxatWrVb7/9NmTIpD/+6HDsmIWZmWbvXqxbJ7l+PfrKlRk7dihatWol1LGr\nUKFChQoVTPk2iIgom7BAMeVxL15g+3Zs3oz9+5GQAEtLHDsG7bYKEgmaNEGfPvDwMGmUH+X1\na3TtiiNHxGb58onHjpWuUmUlgOPHjw8ZMuS779YWK3YIsAKgVktcXOTAcuHirl27rl27lgVN\niIjyGCZ2lJedPQuZDE+e6HqSknDxIrZvx7JlqFEDvXvjs89MF98nePkSnTvj+HGxWbWq+vDh\nAtr30rx582nTVF9/XeTJk4JCT6lSj1u0iDMz61OpUqWuXbu2bNnSFFETEZFhZb5XbFhYmIOD\nQ6b7xvIRLeUQr15h9WpER+Pbb/HXX+9kdQCcndGrF0qU0C0gzY1iYtCpE86eFZslSz4KDi6V\nZp3H4sUltce9e2PlypKFC68zYoxERGQCfBBDeceDB5g8GZ99htGj4eODPn0gk8HKCgAcHfHD\nD7h4EcePo0QJUwf6aR4/houLLqsDLs+de1ab1T14gOXLcesWqlcXOpKHD48IDEThwsaPlIiI\njC2jEbvUg3AckKMcLiAAnp5ISND1pKSgfXvcu4f4eFSsaLrIstWpU5H9+5e+dauA0HRweBoe\n3traeqnQ/Pdf9O6NJ09QpAhu34aNzealS38aOHCVRFLVdCETEZHx5KYRO0lWmDpYMpJr18Sl\nrDNmvJPVubhg0yYAKFkyL2R1MTEx48aNK168btOmcdqsrm7dN3/++Qh4dvz4cQCXL6NTJ/HR\n85s3ePUKDx5sMDcPr/527I6IiPK8LCR2SqVSIpGEh4cLTW0KpdTWWjAwuVxunC9EOZ9ajX/+\nQbNmcHLC//6HdesgLAawsEC/fjh/Hvv2oUYNU0eZTe7du9eiRYsFC7a/eqUExCxNIjl8/XrF\nyMiQunXrrlix4sKF0AEDEB8v3jJ2LG7fPrJjx44OHToUL17cZKETEZFx6ZvYKZVKV1dXAGFh\nYQBS7y3m6upqnNwuICBA2LtMLpdrMmOEeMhUtm3D//6HXr1w8iQAaDQ4dAjr1uHIEdy9i/Xr\nUb++qUPMPmq1evz48Q8fFilW7FJSUjmhs0MHqFTFixUrOHDgwB9//FGj0bRosf7KFfGWkSPj\nSpX6tUuXLoULF54/f77JQiciIqPTN7Hbtm0bgLCwMJlMFh4erlKppFKpNoUSzhqBp6enXC5X\nKBRGGyaknObBA/TqhbAwXY+9Pb79FpaWaNUK5cqZLjLD2Lt377VrFgULnn761EbocXPDzp1o\n2vTzDRs2xMXF/fvvv7t27dJotFVbLi5ZYjd+/PhSpUodOHDAycnJVJETEZHx6VvHTqFQyOVy\nBwcHALt37wbg7u4unPLz8/Py8gowVvWIgIAAhUIxa9YsmUz26a+WnJx86NChFy9eZHDNnTt3\nnJycypYtm/FlecabN2+0/80h7t0zW7Kk4N69Fo6O6hkz4jUaW6G/TBnN8OEJQ4YkFi2qyasf\nzoYNYcCRN2/ERa3duyctX/4mLg5xcahfv36VKlX27dv33XdzgoKcxowJe/IkxslpZYkSfVq0\naNG9e3dra+t88k1rNDnwpyPf4meRo+S3j8PS0rJZs2amjuLDslCgWPunf2hoKIDOnTsbJCI9\nCKmkUqn89Nzu5MmTHTt2zPSyadOmNWnSJDIy8hO/XC4SExMTExNj6ihw86bVH3+U3LvXNiVF\nAuD2bbN27Z7Pnfvs1Cmbzz+P69z5eYECmpcv8fKlqQM1jMuXCymVYwAxke3e/fnPP0f9959u\npkGZMmXOneteq1bhihULrl//ws7OFhgtnHqSpoIfZZ8c8tNB4GeRw+Sfj6NQoUIKheLcuXOm\nDuQDPmbnCYVCAUAYvcPbPM+YPD09PT09s+WlmjZtevTo0YTUyynfM3/+/HXr1pUpU2bAgAHZ\n8kVzuNjY2MePH5csWdLGxsa0kZw9a963r3Vioq6nQAG0bl3MyUk9fDiAAkBRkwVneEePmsvl\nVsnJ4hLvgQOTfv/dwsysUuprHj9umpg4CcC9ewWioqrUq5dsgkDzk5zz00H8LHKU/PZxXL16\ndeTIkQMHDjR1IB+gb2InlUq9vLw8PT2FyW3aBapKpVJ4SmuoAA3MwsKiRYsWGV+zfv36vXv3\nPnnyxNbW1jhRmVZKSgoAKysrk7zflBRs2YILF9ClCy5cgDarMzODmxu8vVG/fiHjR2V8+/ej\nTx9oH2u0axeyenVticQy9TWXL/8XHj5JOLa3R/v2VvnjO9SUTPvTQanxs8hR8tvHkZycfP78\n+ZyZ2Om7eMLb2xuARCIR1sa6ubkB8PDwEJoTJkwwWISUXyQmYsUK1KyJr76Cry86doRMhnr1\nUKIEPDxw/Tr+/jtPLXfNwPbt6NpVl9UVKDD3xg1ZdHRU6muSkpI6drwLlBaaS5eCVU2IiEjf\nxE4mkwmlRgD4+fkJk9uEWXcajUb7WNYkhAJ7/v7+JoyBPtG5c3BwwLff6pa7pqSgXDlcuIDH\nj7FsGRwdTRqfEf39N9zddcWWp0+P//lnTXR0dP369X19fY8fP37mzJlVq1bVq+f2339NhGv6\n98eXX5osYCIiyjmyMMfu/Zlt2TjXjfKnhATEx6NoUfj4IPXSlGrVMHcuihQxXWQmsnYthg5F\nSorY9PWFh0dCZGTnSpUq/fDDD5MnT9ZeWbhwKWvruLg4awcHLFpkmmiJiCin+ZjFE0Sf7vFj\n+Ptj0SK8eIFp06Cttla9On74Af37wyL/fW8uXZowalQBtVoCQCLB4sUYMQJCuRKZTNa7d+/9\n+/eHhISo1eq7d1sWL16/b1+rq1fRrh3s7EwcORER5RBZ+59neHi449tHYhqNxt/fPzQ01GgV\n7ChvePYMP/+M5csRGyv2rFmDiAh88QXMzeHiAnNzk8ZnCleuXOnX72RIyDeAsAZWXb++om3b\nNoBuWzQLCwuZTCaTyWbPxtKlAHDvHtasMU3ARESUM2Vhr1gPDw/H9yY6KRQKiUSSrSFRHufu\nDj8/XVYH4KuvIJGgc2d06pQfs7qgoKAGDf7UZnVmZurPP5938eJ3DRo0OHToUJqLz5/HjBni\n8fPnRo6UiIhyOn0TO39/f4VCIWwjpl1FIWzwJZw1VICUJ4SGYuxYjBuHJ09w44au38UF+/dj\nzhzTRWZqT5486d37SnKyr5DVWVrir7/MLl2adOzYMUtLy379+r169Up7cXw8Bg5EUhIAWFhg\nyhRTRU1ERDmUvo9iAwMDpVJpcHBwmv6AgICQkJDAwEATrqKQyWTClrWUA50/j9mzsW0b1GoA\nePMGCxdi/nzUqAFPz/xSviQDPXvejIsTa9FZWWHLFghbukilUn9//8GDB69du7Zbt27CBT4+\nuHpVvHHyZDRtaoKAiYgoJ9N3xE6lUmk3h03D3d1dpVJlX0iUd3h4oGFD/POPmNUBKFQIvXrh\nxAmsXp3fszqNBqNG4dgxqdC0tsbWrUi9Ud+XX35ZsGDBY8eOaXu0f1g1aICpU40XKhER5Rb5\nb+UhGd6dOyhaFK9fQ6HQdVpbQy7HzJmmC8t0bt26tX///rt37xYuXLhevXouLi7m5pYjRuj+\nfQoXxq5daNnynbusrKxKly796NEjAHfvmsXGYvJkREWhZEmsWQNLy/e+DBER5XtZ2FIsveet\nXl5eUqk0W6OiXEmjwfbtmD0bZ8+iaFEcPIgaNXDjBgoXxsiRGDMGpUubOkSji42NHT169OrV\nq9XaQUugUqVqVasePXy4nNCUSF4qlUWcndPem5KS8vTpUwcHh8OHC0+cWDg+Ht7eMPrOzERE\nlJvom9h5e3u7uro6OzunmWYnLIkVNhyj/GzHDvz4I0JCxOaLFzh+HGfP4sYNVKuWTwutJSUl\ndenS5ciRI+3atRs5cmS9evWeP3++desuX9/ad++KWZ21dWxcXBuJ5Hcg7V9HBw8efP36dbly\nPSZOrBgfDwCpnsoSERF9gL6JnUwmk8vlqYubaA/kcrmwwxjlW6dPw80NqVewVKmCnj1ha4uG\nDU0XlqktWbLkyJEjo0aN+u2334Sfl/h4TJvWQFjWCqBMGc3y5Q979rwil8sPHz5csmRJ7b3R\n0dHfffedlVXt3buHx8ebAZBI8N13pngbRESUe2Shjl1AQECYdiPPt4KCgligOH968wa//YZu\n3eDjg8ePdVld5cpYvBihoahY0aTx5QAKhaJ06dLz588Xsrq4OPTsiV27tOejfvzxYJcuVX18\nfK5evVqvXr3Zs2fv2bNHqVTOmDGjfv364eHhpUsfevZM/Otr2jT06WOaN0JERLlF1hZPODg4\nsLAIvXyJJUuwcCEePQKAnTtx7hymT0doKGQy9O/Pef0AkJiYeO3aNXd394IFCwJ49Qpdu+Lf\nf8Wz5csnRkU1j4kZDLQfO3ZsuXLlpkyZknpKQ7Vq1fz8lg8cKI7hDRiQOG1aAWO/ByIiym24\nKpay5uxZyGR48kTXY2mJsmUxbZrpYsqRYmNjNRpNkSJFALx8ic6dcfy4eMrBAWvXPpZK72iL\nD3/11Ve9evU6c+ZMSEgIgBo1alhZtbC3N5s2DX/8oW7Y8MXChWYAEzsiIspENiR2SqXS1dWV\nI3l5W0QEIiLQrBk2bHgnq2vWDL6+KFvWdJGZTkJCwt69e69du6ZWq6tUqeLq6iqkcQI7O7tC\nhQrdunUrJgYdO+LcObG/dm3s34+QkGsAypcvr73e0tJSKpUKC8xHj8bvv6NAAezbh++/fxUZ\nGWVpme8fbBMRkR4yT+xSbwUbFhbm4OCQ3lnKky5fxpw52LwZKSlo2hQzZ2LJEiQkoG1b/Pgj\n2rY1dXwmsmHDBk9PzyepklxbW9tp06aNGzdO+KGQSCQdO3bcseNE8+bx169bCdd8/jn270ep\nUhg5cgmADh06vP/KPj74/XcASEzE6dOoV88I74aIiPKIjBZPKJXKNHmbo6OjUqkUjp2dnbVn\ng4KCDBQfmVBUFLp1Q7162LgRKSkAcPEiWrZEZCQiI3HwYP7N6hYvXty/f/8CBQr4+/ufPn36\n7NmzK1eurFix4oQJE8aMGaO9bNiwn1JSDmuzuiZNcOQI7O2TpkyZsnXr1h49etSqVSvNK69f\njx9+EI+LFUPfvkZ5P0RElFdklNjNmjULgFwu12g0Go1GyN6ETolEImwjJpxluZM8aeRI7Nyp\nW+5qZYWZM1GgAEqWRIUKJo3MpO7cuTNu3LgaNWpcunRp9OjRjRs3btiw4ZAhQ86dO9e6dWt/\nf/+jR48CiIzE2LH1gf8Jd9nZhdas6TVmzOCqVav6+Pg0bdp01apVaV45MhJDh4r/4FZW2LYt\nX/87ExHRR8gosRNSN201E6GUnUql8vDwACCVSjUaDWud5CUaDXbtQo8eNgMGVD1xwkIYpQNg\na4tx4xARgfHjTRpfzrB+/fqEhIQFCxaUKFEidb+1tXVAQIBEIvnjjz9u3UKLFrh5UzxVosSl\nhIRWq1f7r1mzplChQjNnzjx69GjRokXTvPKZM0hMBAAzM6xdixYtjPF2iIgoL8lkjl2avcKc\nnJwAKBSKoKAgjtLlJWo1Nm/GnDm4dAmABWDx88/JW7di0SIUKYKhQ1G8uKlDzDHOnDljaWnZ\nsWPH909Vr17d0dFRpXrRogUePBA7O3TA1q11LS2jX79+bWFhUbhw4fdvTEnBsWOoVw/duuHa\nNUyZwpJ1RET0MT5mVayfnx+zujymRw/s2PFOT926KaVLW8ycaaKAcrDY2FhbW1tzc/MPni1Q\noOG1a79pBzt79MDGjShQAIClvb39B29JTETXrti3D6VKITSUOTQREX28LOw8oVW9evVsj4OM\n7/lzrF6Nf/5BfDxSr34pX149ZcqD2bPjTRdajlamTJnnz5/HxMS8f+rkSfXVq0tSUsRHtL17\nY9MmIavLyIgR2LcPAB49QmhoNkdLRET5ysckdpTbPXqEH35ApUoYMgS9emH1anh4AICjI/74\nAxcuvOrXLyadASmCi4uLRqNZsWJFmv4TJ9C+fYpGYyc0Bw/Gxo2Zb8Ixfz5WrhSPa9fGu3Mf\niIiIsoaJXb7z88+oUgVz5uDlS7EnMhKLFkGjwc2bGDo08xGmfO6rr76qXr36Tz/9tGHDBm3n\noUNo2zYlNlbM40aNwsqVyDQ5PnMGkyaJxyVLYseOzG8hIiLKQCZz7FQq1fsliF1dXd+/kjtP\n5AqXLqXd+6tzZ3z/vYmiyZ0KFCiwdevW1q1b9+/f/5dffmnfvn10dK2///5arRbr1Xl5YcEC\n6FO6e88eqNUAULAgtm5FlSqGjJuIiPIBjtjlfSEh6N8fxYqhVSuYm4sPB83M0KMHzp7Frl0o\nU8bUIeY2Tk5Oly5dGjlyZHR0tL//3c2bB2qzup9+wsKFmWd1ajUiIuDmhnLlYGODdevg7Gzw\nsImIKM/LaMSOg3C53blzmDkTO3aINW+PHsWNGzh1CmfOoHlzODmZOr7crGzZsosXL27TZnG/\nfpqkJDGP8/HRPVrNQHw8unfHvn1o3x5RUYaNk4iI8pWPKXdCucKRI2jfHtq6GwCKF0fTpihf\nHvXrmy6sPGTNGgwbhpQUMaubOxcTJmR+l0aDoUPFZbAHDiA6GuXKGTJKIiLKT5jY5SnJyQgM\nxOXL6NwZ58/rsjobG8jlGD+eOUS2+eMPyOXiDDkzMyxejOHD9bpx5kz89Zd4LJXyEyEiouzE\nxC6PSEzEmjXw9cWtWwDw22+4dAk7diA6Gu7u8PLCu9tf0Sfx98eYMeIDbjMzLFuGb7/V68a9\nezF9unhcrhw2bTJQgERElE8xscsLzp+HmxsiI3U9ajVKlcKRIyYLKQ/79VdMmCBmdRYWWLsW\nffvqe++ff4o3FiqE7dtRoYKhgiQiovyJq2JzsYQEvHgBAL6+72R1NWtiyxa8t8U8ZYPJkzF+\nvJicWVpiwwZ9s7rkZLx8iS5dYG4OKyusW4dGjQwaKRER5UdM7HKlx4/h7Y3SpWFnh2nTdIsh\n6tfH5s0ICUGXLiaNL4+aPBm+vuKxlRW2bkWfPnrdeP06Pv8c9va4exf37yMyEj17Gi5MIiLK\nv/goNpeJicGsWVi+HLGxYs+6dYiIQMuWMDNDkyZ61cWlrNJoMGoUliwRm7a22LkTrVvrde/h\nw+jVC8+eAcDSpZg40UAxEhERMbHLbdzccPz4Oz3CtH3uMWo4ajVGjIBCITYLF8bu3WjRQq97\n167Ft98iMVFsfvmlQSIkIiISMLHLBVQqLF8OMzPMnKmbSyeRoHt3/PADGjc2aXB5XXIyBg2C\ndlfYYsWwd6++0+Pu3sWwYUhOFpuTJmH2bIMESUREJMgosXt/l9gMcJsKQzhwALNn6xa3Wlpi\n2TIsWIDy5TFuHGrXNmVs+UFSEgYMQGCg2CxRAnv3okEDfW+/cUPM6szM4OOjV/liIiKiT8ER\nu5yrd2/8/fc7PXZ26NQJnTqZKKB8Jj4effpg1y6xWbYsDh5EzZqZ3/j0KUaPxu3bmDUL48bh\nyhV89x26dTNosERERID+e8WGh4c7OjrivZE5Z2dnlUrF4brskpKCy5dRqhQSE9/J6ooVg6en\nXvuQUraIi0PPntizR2xWqIADB1CjRuY33r4NV1dcvw4A48bh/HkDBklERJSGvuVOBg0aJJVK\n30/ggoOD5XK5s7NzdgeW7yQm4o8/ULMmGjSAoyMePxaLmJQpg7lzcecOpk5FwYKmjjJ/ePUK\nnTrpsrqqVXHsmF5Z3fHjaNRIzOoAfRdYEBERZRd9H8WqVCo/P78PnnJyclJoVwxS1mk0WLHi\nnYURcXE4dw4nTiAqCuXKwcrKpPHlMy9fwtUVwcFi09ERBw7gs88yv/H5c3TpIpaMBtCuHWbM\nMFSQREREH5SFAsWB2jnk+vWTng4cgFz+ztYR9eqhVy8ULIiqVZnVGVVMDNq21WV1tWvj6FG9\nsjoAe/fqsrqhQ6FUws7OIEESERGlR9/ETi6Xq1Sq9x+5CnPs0hvMo/Tcu4fvv0ejRhg3Tlfk\nDEDduti0CefOoVQp0wWXXz1+jPbtce6c2KxbFwcPokyZTO5Sq+HvDy8v1KkDqRQlSsDXF3/8\nAUtLQ8dLRESUlr6PYgMCAkJCQlQq1fs1UKRSqaenZ3YHlmfdvAlfX6xbh6QkADh3Dl9/jQUL\ncOMGunRB587cOsI0oqLQvr1uelyTJtizJ/Mht9hYDByIf/4BgFu3dEN9REREJpGFcifBwcFK\npdLV1TV1Z1BQkEwmy+6o8qwDB9C58ztDdLa2qFQJY8aYLiYCbt9Gu3a4fVtsFi9+w8HhtwMH\n2nTr1q1AgQLp3XXjBnr1wtWrYtPGxvCBEhERZSgLc+wAyGQyzbuY1WXJP//osjpLSwwciIsX\nYW9v0pjyvfBwtG6ty+qAAzExDdevX9KnT586depcvnz5g3c9fIgvvtBldZUrY+ZMIwRLRESU\nkawldvSJBgyAvT2srDBiBG7exJo1qFbN1DHlb1evokUL3LsnNuvXj4yKqq/RvL53796cOXPu\n3bvXtm3bW7duvX/j2rV4+VI8btUKp0/D0dFYQRMREaUja4ldeHi45C0A/v7+Hh4ehgksb5JK\n8fQp4uKwZAkqVzZ1NPne5cto2xb//Sc2XVxenTxZsVy54gAqVqw4efLkTZs2xcTETJ48WXvL\n48eYPBm//IJWrVCsGMzMMGECDhxAyZImeQdERETvyMIcOw8Pj/fr1SkUCoVCwZ0nKNc5fRqd\nOuHZM7Hp5HReqWxgbv7ONd26dXNxcdm5c+fr169tbW1Pn0bv3mJhGisr3L+P5GQULmzsyImI\niNKj74idv7+/QqEQNp/QFjfx9PSUy+XCWUMFSGQAJ06gQwddVges+uWX6DRZnaB58+YJCQkR\nEREBAWjZUldu0M4O1tbM6oiIKGfRN7ELDAyUSqXB75VzCAgIkEqlrFFMucjBg3Bx0RUTbtv2\nKjDMyurDdeeEVbH//FNk+HAkJIidvXtj4EBjhEpERJQl+iZ2KpXK3d39g6fc3d1VKlX2hURk\nQPv2oVs3xMaKzTFjMGlSNKBJb/XrlStXJBLJnj3lhaaFBebNQ2AgLLIwi4GIiMhIuCqW8prH\njx//9NNP9erVs7OzK126dNeuXbdt2yZMA922DV274s0b8cqpU7FgAVq2bFGsWLFly5a9fv06\nzUutXRu1aVOPKlVmDh9uaWmJcuWwbx/Gj2cRaSIiyqH0TewyeN7q5eUllUqzLySij3f8+PGa\nNWvOmjUrKiqqUaNGlStXViqVPXr0cHNz27gx2d1dV0fQxwczZgCAlZXVrFmzIiIiXF1dQ0ND\nhbMaDYYOvT1oUNmUlN4RET9+8QUSExEVhTZtTPTGiIiI9KDv8yRvb29XV1dnZ+c00+yEuife\n3t7ZHxpRFkVHR3fv3j0pKWnt2rX9+vUzNzcXOseOHbtpk9XOnWbC6m2JBHPnYvx43Y0jRoy4\nc+fOvHnzateuXbdu3SJFHM+cGR8X94VwtnBhlC5tgrdDRESUVfomdjKZTC6XKxQK7V6x2gO5\nXM79JygnmD179tOnT9Nsc1euXLnmzf/atAkajQSAmRkWL8bw4Wnv9fX1dXNzW7Ro0dGjR69e\n/TUpSczqrK0REIDixY31HoiIiD5BFubYBQQEhIWFpekMCgoKCAjI1pCIPtK2bdtq1aqV5s8M\nPz+MHi0BJAAkEk1AwAeyOkGzZs3Wr19/8WJkUlJboadqVahU6NvXwHETERFlk6wtnnBwcOBe\nsZQzJSUlPXjw4PPPP0/dOXMmxozB2/rZyR06rPnmm3Rf4eJFTJ6MO3fg4QF7e/TogbNnUa+e\nQaMmIiLKTqzZQHmEmZmZmZlZUlKStmfyZPj6iscFCmgSE3s6OTmkd/u+fXBzQ1wcfv8dDx9i\n2TJDx0tERJT9WO6E8ghzc/MaNWqcOHEiOTkZwKRJuqzOygqjRx8BdtasWfOD9wYFoXt3xMUB\ngEajWzlLRESUu+ib2EkkEmdn5w+ecnZ2lrCuF+UAffv2jYqKmj//15EjMXeu2Glriy1bYrdv\n9yhUqFD37t3fv2vPHvTogfh4sTl1KooVM1bERERE2SoLj2JVKpVEItG8na9ElNN4eXn99dem\nKVN0eVmRIvD2Pjlx4rCwsLCFCxeWKlXq/bsmTdIN0f3yCyZPNk6wRERE2S8Lj2KFKsQSiSQ8\nPNxg8RB9PCsr2xo1zgDfvu14+vJl44kTm4WHh/v7+3t5eX3wLltb8WDePEyZYow4iYiIDCQL\niZ27u3tQUBAAR0dHpVJpsJCIPkZSEvr1w9atVkLTyuq1i4tv376Ovr6+169fHz16dJrrAwNR\noQJat8aKFfD1xe7d75QsJiIiyo2ytipWJpNpNBqJROLq6iqXy1nBjnKI+Hj07o3du8VmuXI4\ncMC2Zk3f9K5fvRrffIOUFERFQaXCxIlGipOIiMigPmZVrEajkUqlCoUiveUURMYUFwc3N11W\nV6ECDh1COutfAWD5cgwbhpQUADAzA7+LiYgoz/jIcifBwcFyuVxYTlG7du3sjYlIf69eoVMn\n7N0rNqtVw/HjqFEj3esPHICHB9RqADC6sRoYAAAgAElEQVQzw4oV+N//jBEnERGREXx8HbuA\ngAA/Pz8ACoUi++IhyoKnT9GuHY4eFZuOjjh8GJUqZXTLxo3iRhTm5lizBkOGGDxIIiIio9F3\njt0Hq5x4enpWr17d1dU1W0Mi0suTJ+jYEefPi806dbB/P0qXTvf6M2fw4gX698fmzQCgUODL\nL40RJxERkdF86pZiwnKKbAmFSH8PHqB9e4SGis169bBvH0qWTPd6Hx/88AM0Gvj44MUL48RI\nRERkbNxSjHKfqCi0aaPL6po2xeHDGWV1M2diyhTxCezFi8aIkIiIyCQySuwkEom2HLEkM8YK\nmPK7iAg0b44bN8RmmzbYvx92duleP3Uqpk4Vj62s4Olp8AiJiIhMhSN2lJuEh6NNG9y5IzZd\nXLBrl27riPedP4+ZM8XjIkWwfz+aNjV0jERERCaT0Ry71JPnOJGOTC4kBC4u+O8/sdmtGwID\nUbBgRrc8eiQeFC2K3btZso6IiPI4jthR7nDpEtq102V1PXti8+YPZ3VJSdi4ES1awNERz57B\n3x8jRuDYMWZ1RESU9+m7KlYikfj5+Xl+aIKSs7OzSqXikB4ZzqlTkMnw7JnY/PprrFoFc/O0\nl71+jd9/x+LFiIoSe6ZP183GIyIiyvM+tdwJAJVK9ekvQpQelQqurroaJUOHQqH4QFYHQCbD\n8ePv9DRubPDwiIiIco6MHsX6+/unXvTq5eWV3npYqVRqpHgpnzlwAB066LK60aOxYoUuq0tJ\nwbZtGDAAo0fj6VNdsWIA1avj99+xcqWxAyYiIjKhjEbsPD09vby89HkVb2/vbIqHSGfvXvTo\ngbg4sTl2LObPh1Ba5/FjLF+OgADcuyeeLV8ev/yC336DoyO+/x4yGcw4g5SIiPKZTB7FamfO\nZTDHjsgQtm7FV18hMVFsTpuG6dPF4//+Q8OGiI5+5/pSpTBkCMvUERFRvvZJe8USGciff2Lw\nYKSkiE0fH4wfj61bceoU2rbFy5fvZHWVKmHsWAwebIpAiYiIcpJsWDxBlL1Wr8Y334hZnUSC\nuXNhbo7q1RERAQDz5uHKFdSujatX0aEDRo5E584fXktBRESU3+ib2CmVSldX1wwu4JAeZYsF\nCxLHjy8gfDeZmcHTE7Nm6RZPANBoULQorlwxVYBEREQ5l77Ty2fNmgVALpdr0mHIIClfePjw\nYZMmf40bZ/n2uymlePEJx4/fTZ3VVaqEtWtRvrxpIiQiIsrh9E3shGJ1AQEBhgyG8q+IiIjq\n1f88fbovIHnbZ/76dbMzZ3yERosW2LIFt25hwABTxUhERJTTZaEgBIvVkYFoNJoWLYJfvhyX\npr9Gje5t294Eavr5BR09il69OJeOiIgoI/omdszqyHAGDvwvOvrrNJ2lS2PBAvO//vrL1vZ+\nYOBskwRGRESUu+ib2Lm7u6tUqvDwcINGQ/mNWo0RI/Dnn2XT9FepgmvX0KYNSpUq1a5du5Mn\nTyYlJZkkQiIiolxE38ROKE3s6OhoyGAof1Gr4eGBZcvEpo2NumBBALCywubNsLcX+ytUqKBW\nq2NiYkwTJRERUe6hb7kTYU/Y1AdpcGEsZUlyMr7+Ghs3ajteubs/HzSoolIJNzc0bKi78r//\n/pNIJMWKFTNBlERERLkKCxSTCSQloV8/bNkiNq2s1PHxhVetKlypEnx83rny2bNnBw8ebNiw\nYYECBYwfJxERUe6i76PY9MrXsY4dZVV8PNzcdFldsWJISBC/D0+cuPvulfGDBw9+/vw5Nykm\nIiLSB0fsyKhev0bXrjhyRGyWLYvYWAh/F0gkb/bv79Kzp2Pnzp2LFCly7dq1tWvX3rp1a/Dg\nwQNYvI6IiEgPWUvswsPDtesnNBqNv79/aGgoqxaTnl6+RJcuOHZMbFarhtKloVKJzfnz3xw7\n5rB9+/atW7cKPaVKlVqwYAGH64iIiPSUhcTOw8NDoVCk6VQoFAqFgo9iKVNPn6JjR5w9Kzad\nnLB/P2rWFJvffIOxY0uMHbv1v//+O336dEJCQvny5Rs1asSpdURERPrTd46dv7+/QqGQSqUa\njcbPz0/o9PT0lMvlwllDBUh5wpMnaN9el9XVqYOdOwHgl19QtSr69sVvv4mnypQp061btz59\n+kilUmZ1REREWaJvYhcYGCiVSoODg9P0BwQESKXSwMDA7A6M8o7oaLRsiQsXxGbjxlAo0KoV\nPvsMVla4dQsbNsDa2qQhEhER5Qn6JnYqlcrd3f2Dp4RNKbIvJMpT7t9H27a4dk1sNmuGrVsx\ndCju30dKClasMGlwREREeQtXxZIB3bqF9u1x547YbNsWO3bAw0OX53XvbqLIiIiI8iJ9R+wy\neN7q5eUllUqzLyTKI8LC0KaNLqtzccHOndi4EevXiz2tW2PCBBMFR0RElBfpm9h5e3urVCpn\nZ+c0/cIOY97e3tkcF+VyV66gRQtERorN7t2xcycKFcKSJWJPmTLYsAHm5qYKkIiIKA/S91Gs\nTCaTy+UKheL9TWPlcrlMJjNIdJQ7XboEFxc8fiw2e/XChg0QVrg2bozz52Ftjb/+QtmyJoyR\niIgoD9J3xA5AQEBAWFhYms6goCAWKKbUTp5E69a6rG7gQGzaBAsL/PILOnXCV1/h8GFcuYLW\nrU0ZJBERUZ6UtcUTDg4OrEVMGTh8GN264fVrsTl0KBQKmJtj5kxMnQoA16/rZt0RERFR9srC\niB1Rxg4cQJcuuqzO0xMrVsDcHIcOYcYMsbNECVNFR0RElPcxsaPssWMHunTBmzdic9w4LFwI\niQR376JPH6SkAIC1NQvXERERGVBGj2K1yyP0wUe0+dk//6BvXyQmik0fH0yaJB7/+iuePhWP\n585FvXomCI+IiCifYIFiyoKnT59ev349MTHR0dGxfPnyQue6dRgyRByTw7tZHYC4OPGgb1+M\nGmXMYImIiPKdjBK71INw4eHhjo6OeG9kztnZWaVScbguz7tx48b48eN3796t/awbNWo0Z86c\nyMj2334rZnUSCRYtwsiR4i3R0di1C6NHo3BhFC36TrZHREREhqDviN2gQYOkUmlwcHCa/uDg\nYA8PD2dn5/dPUZ5x8uTJjh07vn79WiaTtWnTxtLS8uzZs1u2bOnQYSfQXsj0zMywdCnkcvGW\nu3fRpAkePkSlSoiIgBkncxIRERmevomdSqXy8/P74CknJyeFQpF9IVHO8ubNG3d395SUlEOH\nDrVq1UrbX6HCrz4+JYVjCwusWYN+/cRTCQno3RsPHwJAdDRevULRosYOm4iIKB/KwkBKenvF\nptdPecPGjRsjIyN9fX1TZ3UzZsDHpxQgAWBmlrJ+vS6rAzB6NM6eFY+HDWNWR0REZCT6JnZy\nufyDe8UKc+zSG8yjPODo0aMA+qVK3CZPxvTp4rFEklCxoqe7u+76PXugHcCtVw8LFxopTiIi\nItL3UWxAQEBISIhKpXq/BopUKvX09MzuwCinePz4sY2Njb29PQCNBhMnYv588ZS1NapV8378\neAuwSHv9v/+KB0WLYvNmWFkZO2AiIqJ8KwuPYoODg4OCgtJ0BgUFcdlE3mZnZ/fmzZvY2Fi1\nGiNG6LK6woWxZw+SknYKOZ/gxQv0748aNVCxIjZuhIODaWImIiLKn7K2WFEmk2neJZPJDBRZ\nepydnSVvhYeHp3eBkaPKw5o1a6bRaDZv3jpoEAICxM4iRaBUomjRSzdu3JBKpULn2LGws8PY\nsbh6FffuoVMnk8VMRESUP+WyKhQSiUSlUmmbjo6O/v7+JownP+jXr1+xYqVHjLD980+xp3hx\nHDqEGjWeDBkyxMzMbMSIEQCWLhWn0+3fjwsXTBcuERFRPpabEjshh5PL5drxQgBeXl4eHh6m\nDi0vs7EpVq3axfh4N6FZpEjCTz/9u2PHtFq1al24cGHq1KmNGjU6fx5jxojXly2L2rVNFi0R\nEVF+pu/iiUwfbhph84nAwECpVBqgfRwIaDQaDw8PoYpe6n7KLnFx6NULZ86UedsR9fJley+v\n6wDKli27evXqQYMGvXqFPn2QkAAA5ubYsIELJoiIiEwjN+0Vq1Kp5NqdDd4S8jmFQuHk5MTF\nudnr9Wt06aJb5VqlCgIC4l6+nKVWqytVqtSoUSMzMzMA27YhIkK85uef0bq1aaIlIiIifRO7\n9AbkJBKJXC437WiZ8NW9vLyqV69u/MUcedWrV5K+fXH8uNh0cMDBg/jsMwcg7UpXR0eYmyMl\nBV27YvJkY8dJREREWp86YhcWFubo6Gic0TKpVBoSEvLBU0KZPVdX17CwMEOHkR88f27+9dc2\nFy+KzVq1cOAAypRJe9m1a5gzB3Xq4MQJREejc2fuCUtERGRKn5rYOTg4SKXSwMBAIyR27u7u\nwlKJDw4QrlmzxtHR0dHRMUuvmZycfOjQoRcvXmRwzZ07d5ycnMqWLZvxZXlGZGS8XF7l2jVz\noVmrVsq2bbHW1po07/6//8xat7Z99EgCYPPmWBeX5NhY4web971580b7XzI5fhw5Bz+LHCW/\nfRyWlpbNmjUzdRQfJvn0RQ/CrmJGWDyh/VoAgoKC3n/qqlQqXV1dhWM94zl+/HiLFi0yvWza\ntGm9e/fOYrC50qNHFt9+WyUioqDQrFMnbtmyO0WKpKS5LCVFMmxY5XPnbITmypW3GzdmWkdE\nRPnIuXPnBg0aZOoo0vrUETt/f3+VSqUtUWtowcHB2mWw7xPqJ2epOnHTpk2PHj2aICzpTMff\nf/+9bt06FxeXunXrZi3c3CYqSuLhYR0RIT5P/eKLlK1b1UWKVHz/yilTCp47Zykc9+uX3KdP\nSaCk8QLNT2JjYx8/flyyZEkbGxtTx0L8OHIQfhY5Sn77OK5evTpy5MiBAweaOpAPyJ5yJ+6p\nN4E3sICAgIzXamRp7NDCwiLTEburV68uW7YMgK2trf6vnOvcuoWOHXH3rths2TI5KMjigz+i\nd+5g8WLxuEYNLF1qkbf/ZUwrJSUFgJWVFf+RcwJ+HDkHP4scJb99HMnJyefPn8+ZiV02zHX3\n8/NjnZHcLiwMbdrosjqp9PWmTW/S+7srJgZC8mxjgy1bUKSIkYIkIiKijOmb2GnSZ/KsTqlU\nSiQS7i320a5cQYsWiIwUm507Jy1adNfa+sMDn/v3A8D8+XB3x5493GSCiIgoB2F1ivzuzBm0\naoWHD8Vm795YvfqNpeWHs7oRI9ChA5o0weefY9MmNG9uvDiJiIgoU/omdhkMiTk7O2dpvQLl\nHCdPokMHPHsmNgcNwsaNsLT88MV+fli2DABSUnDhgpEiJCIiIv1lw4idUH+Ecp3Dh9G+PZ4/\nF5vDhmHlSpibf/hipRLjx4vHxYujXz9jREhERERZklFi5+/vL3kLgJeXl+RDABit3Alll/37\n0aULtCWFvbywfHm6+0a8eoW+fZGSAgCWlti8GRUqGClOIiIi0l9GiZ3+qyK8vb2zIxgyku3b\n0bUrtBXCvb2xcCEyeJx+8SK0204sWoQ2bQweIREREX2ETOrYaWvCSSSSHFvWRKhLbOooco2/\n/0a/fkhMFJs+Ppg0KaPrQ0NRrRpkMpw6hTFjIJcbIUYiIiL6GPoWKGbmlDesXYuhQ8WHqgB8\nfTFxYkbXe3nB3x8lSuDUKVStaoQAiYiI6ONlvnhCmGmXekmsUDdOwOpxucjKlbqsTiLBkiWZ\nZHV+fhA+3idPcPy4MSIkIiKiT5FJYufh4eHl5ZW6R6lUurq6apteXl4eHh4GCY2y1W+/4Ztv\nxKzOzAwBARgxIqPr9++3SL0MViYzeIRERET0iTJK7JRKpUKhAJB6ewkhqwsLCxO2nQCgUCiU\nSqXhQ6WPt2ABvLzEfcAsLLBuHb79NqPrY2Ishg4tlHoZbMmSxoiTiIiIPkVGid22bdsAhIWF\naXuEBE4ulzs4OAg9wlnhSsqZpk/HuHFiVmdpifXrM69Cd+qU7atX4ipZLoMlIiLKLTJK7BQK\nhVQq1eZwAG7evAnAzc1N2yOcFQb2KAeaPBkzZojHVlbYuhXu7pnf1ahRbIUKaokE3t5cBktE\nRJRr6LsqVhAYGAjA0dExdadUKuXmEzmQRoPvv8fixWLT2hr//INOnTK5y9sbvr5FGzY0P3Hi\ndZEiRYoUMXSYRERElG2ytqWYkMClHsOjnEmtxvDhuqyucGHs2ZN5VrdoEWbPRnIyTp2yvXjR\nnFkdERFR7pLRiF2aoTjtBLvU14SHh6tUKm4plqMkJ2PwYKxfLzaLFcOePWjcOJO7Dh/G2LHi\nsZ1dSoMGKRleTkRERDlORiN27u7uALTVTGbNmoV3J9gBmDdvnvZK+nSp9+eVpLPJV8bXJCVh\nwABdVleiBPbvzzyre/AA7u5ISgIACwvMnx9pY8OS1ERERLlM5nvFKhQKIYEQRuZkqQqaeXh4\nCMsmOnfubOhA8wNnZ+c0VQMlEkmaUjLvVxZMfU18PHr2xKZN4qmyZfHvv2jQIPMvvXEjnjwR\nj2fNimvS5PXHvQUiIiIyoUzm2Gk0Gu1jVrlcHhwcLBwLm08IWV1QUBBn3X06pVIpPPjWvOXn\n54e3hQO11wj/5to6gsI1wmBqXBx69sSuXeLF5cvj8GE4Oen11b/4AgULAoCHB4YPT8zsciIi\nIsqJMl88ERwcLOQQAQEBaU5JpVKNRiPjpgR6U6vVt27dunXrVlxcXJpTQnKWumqgp6enMKMx\nPDxc6BHKzaTOpD09PYWpkJcuhctk0I7uVa2K48dRo0bmIfn7w8EBSiVu3sSVK1i27NPeIRER\nEZlO1lbFaslkMo1Gox3Ao0zduXNn6NCh9vb2Dg4ODg4OJUqUGDZs2N27d7UXCAl0mrHPkJAQ\nPV9/1CiHf/8Vjx0ccPgwKlfO/K4//oCXF27dwuzZUKtRu7aeX42IiIhyoo9M7ChLVCpV3bp1\nV69eXa9evbFjx44dO7ZRo0b/b+/+Y6O+6ziOv25ULBNGRUIE98PgHZmkg2U1MbtCgnGY9Jij\n4EAFN2TOu0FM7hJttsRhjBKyrWS5W+LClcDEqJmVP0iEXsZgmM1+15juB+FHTO+YcxkQXbMR\nHWMZjPOPT/n2y11p78q1328/fT5izP34fr99l8++fF98fny/zz333OLFi4e5BWAikXAcx/uc\nDzOXMRaLuX14mUzGcZzZs+N/+9vAXo2NeuUV3XrryFW9/LI2bx54PWuW5s4d9e8HAAACobob\nFGMUPvjgg5UrV16+fPngwYP33HOP+/mLL764evXq1tbWvr6+hoYG9/NCoeDeAjqdTrtP6ZUU\nDofz+XwkEvHeI/pzn4v29w+Mki9erIMHNWfOyFW9846+8x198okkTZmiPXsG5tgBAICJix67\nMbd79+7+/v5nnnnGm+okLV++PJ1Ov/fee7t3777WvqlUqrm52fuJub+M1/nzjlSQ9PWv68iR\nilKdpGefHVwG+8QTuvfeivYCAABBRrAbc4cPH77xxhvXrVtX/tX69evr6+sPHz7s/TAcDrsL\nY+PxuOM47q0Ezf1lzJqVd94pLlhQlLokSZG77iq88II+//lKq3IHXh96SD/72eh+MwAAECwE\nuzF39uzZuXPnfnaokc76+vp58+adPXv2WvualcjmFifui+7u7lOntHSp+voktZhsd+ed7TNn\nVlTPrl361rd0663685/1pz9p586qfyMAABBMzLEbczfddNO777475FfFYvHcuXO33HLLMLu7\nD3YzCyai0eiJE1q+XG4aXLo08sorqqusJXft0sMPS5LjqL9f9fWV/x4AACDo6LEbc01NTf39\n/T09PeVf9fT0vP/++01NTZKu9Qwx91G8Zm2s4zjf/OZgqlu1Sm1t+fK9huRdBtvQoKlTq/1V\nAABAoBHsxlw8Hq+rq9u8efO5c+e8n587d27Tpk11dXUbN240m8nzZF7DrJxwH8Xb2hqX9O9/\nD+S/H/xADz2Uu+++mMqe4Vvun/+8ahnsjh26gcYHAMAuXNvH3O23375t27Y33nhj0aJF27dv\n7+np6enpaW9vv+OOO44ePbpt27bGxkZJ2Ww2Go26T+Z1n88bj8fNHU9efVV//av78I+QFPr9\n70Pf/nZMUjweH/H5H9u2sQwWAADLMcduPLS1tc2cOXPLli1tbW3uh3PmzNm5c+fDZsqbJKm7\nuzuTyaRSKfeTrq4uk9heekn33afz5yUVpYTUUb7N8Nxh3o0bWQYLAICdCHbjJB6PP/DAA0eO\nHDl27JikRYsWLVu2bNq0aSWbJZNJ7x2JjYMHtWqVPvpo4G0qlX366exQ8/GGtn+/Xn1VyaRu\nvlkNDYPT7AAAgGUIduNn2rRpsVgsFotVtde+ffrudwfmxknaskW/+lUVu7vLYPfv19GjVf1k\nAAAwwRDsAm3vXq1fP5jqnnhCjz5axe7eZbCXLtW4NgAAEDQsngiuPXv0ve8Nprqnnqou1b31\n1lXLYJ96qvYVAgCAQKHHLqB27VI8rsuXJemGG/Sb3+iRR6o7wmOPDS6DffJJrVhR4woBAEDQ\n0GMXRJmMfvzjwVSXzVad6iT95z8DLzZu1E9/WsvyAABAMNFjFzhbt2rLloHXdXX63e/0/e9X\nd4S//13/+Ieeflrbt2v+fP3iFzWvEQAABBHBLlgee0xPPjnw+jOf0R/+oDVrqjtCZ6fWrdOn\nn+rBB/XHP9a8QAAAEFwEuwB59NHBJQ719dq7t+qJcb29+uEP9emnkpSv9BGyAADAEgS7QCgW\n9ZOf6NlnB95On66//EXLllV3kDNntHKlLlyQpFCIx0sAADDpEOz8d/myHnlEO3cOvJ0xQwcO\naOnSqo+zaZPOnBl4vWWLVq+uWYUAAGBCINj57NIlbdgwOBlu1iy98IK+9rXRHKq3d+DFmjX6\n5S9rUh0AAJhIuN2Jny5e1Pr1g6lu9mwdOjSaVJfPq7dX7e36yle0dq1++1tV/iRZAABgDXrs\nfPPxx1qzRvv3D7ydN0+HDumrX636OO5jx9JpFQq1rREAAEwk9Nj548IFrVo1mOpuvlkvvTSa\nVNfbqwcfHHhu2JEjtawQAABMOPTY+eB//9O99+rllwfezp+vw4f15S9XfZzTp69aBvujH9Ww\nRgAAMPEQ7CrV29t7wWSo63P+fN3Pf37niRMN5u2XvvTRr3/9eqHw8ShGUdva7jpzZpZ5vX79\nW9OmvXXo0PUXqLq6utmzZz///POnT5+uweFwfebNm7dkyRKaIyBojuCgLQLFNMebb7558eJF\nv2sZDydOnPC7hGsrYiQ7duyo3Z/3F6TXpOKV/x2TvjjaQ02TLl05TqdUs+USS5YsOXbs2JIl\nS2p1QFwPmiNQaI7goC0CZXI2x44dO/xOKEMIFYtFv/9kgu7DDz/cu3fvJ2Yi2/V57bX5//rX\nbPM6FNI3vnG8oeGjUR/t0KFF//1v/ZQpl1ta3pw69dL1l2f09vb29PTcdttt4XC4VsfEqJ06\ndertt9+mOQKC5ggO2iJQTHPcfffdTU1NftcyTqZOnXr//fdPnz7d70LK+J0sETidnZ2SOjs7\n/S4ExSLNETA0R3DQFoFCcwQHq2IBAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAA\nsATBDgAAwBIEOwAAAEsQ7AAAACxBsEOpGTNmuP8P39EcgUJzBAdtESg0R3DwrFiUunTp0uuv\nv97U1DRlyhS/awHNESw0R3DQFoFCcwQHwQ4AAMASDMUCAABYgmAHAABgCYIdAACAJQh2AAAA\nliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgB\nAABYgmAHAABgCYIdAACAJQh2AAAAliDYYWiZTCbkkclk/K4I0pV28buKySiXy3nPCL/LwQDO\nCN9xsQiaULFY9LsGBE4ikejo6Cj5MBqNdnd3+1IPjFwuF4vFJHHajrMhz4h8Ph8Oh32pBwZn\nhO+4WAQQPXYolcvlzImaz+eLxWKxWMzn85Icx+GfYj7KZDLmGoZxVigUSs6IeDwuacOGDX6X\nNqlxRviOi0Uw0WOHUs3NzY7jlPRGFAqFSCTCv8N8Yf7wJUWjUUmO43DajifTJ9HV1dXS0uJ+\nOORpgvHBGREQXCyCiR47lHIcR1LJ5Yqrl4/a29slpdNp/qL0xfHjxyV5U52ktWvXSjpw4IA/\nNU1unBEBwcUimOr8LgCBM+S/fQuFgqTGxsZxLwfKZrPZbNbvKiYvx3FMz5DXggULJJ08edKP\niiY7zoiA4GIRTPTYoSJmOhF/mWKyMVepcmYoEEAJLha+I9hhZIlEwnGcrq4uvwsBAAQXF4sg\nINhNXiX35QqFQrlcrnyz5ubm8pnjqLkKmwMAgomLRUAwxw7X5C49Y+kfJq1r/ZdvbusAQFws\nAoZgN3m1tLQMc48Ac+dPlqyPm+GbAz6KRqNm9Z9XX1+fpIULF/pRERAgXCyChqFYDMHc+TMe\nj3OiAmZ9X8nIeGdnp6QVK1b4UxMQDFwsAohgh1K5XC6VSsXjcZY1AZJaW1slxWIxd4WsmSEe\njUYZdcJkxsUimHjyBEoN80RtOtt9Z271zmk7znhWbGBxRviIi0Uw0WOHq7ASEyiXzWZL7uBQ\nLBZJdZjMuFgEFj12AAAAlqDHDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABL\nEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAA\nACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGw\nAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACw\nBMEOQNBlMpkKt2xubg55VL7jGPEWkMvlvCWZUn2qC4C1CHYAgqtQKIRCoZMnT464ZSaTCYVC\njuN4P0ylUqFQqFAojFmB11R55exEFOUAAAPkSURBVABQQwQ7ABNeJpNJpVKS0ul00SMajUqK\nRCK+ZLsSLS0txWIxmUz6XQgAmxHsAExshULBpLp8Pl8Sm7q7u9PptKQNGzb4UxwAjC+CHYCA\nymQykUhEUkdHxzAT5g4cOCApnU6Hw+Hyb5PJZDQadRzH7bRLJBIl47Nm2DSRSHh3LJmuVzIf\nzj2Id7NcLjdM5SVz7MqZMsqP5jJHCM4MQgABRLADMLF1dnZKGmaIc+3atbqS/yphAlbJdD1J\n5dP1IpGId7NYLDbqMd9EImGyoPdo3qyZyWRisZh3g1Qq1dzcPLofB8BWBDsAAZVMJvP5vKR4\nPD7M7DTHccxcumtZsGCBpMrXMbS3t6tsup75qjwd5vN573w+s2+FlbtyuVxHR4ekkgmCHR0d\nbr+dO9zs3cDbEwkAItgBsF5JT9iIstlseRozc/VKeMd/H3/88VFXuG/fPkkmC7q6u7vdr4bU\n3d1dLBaHHIAGMGkR7ABYriQwVcvMojMdZiVWrFjhvq42PnodP37cHKF8Vp/5SlI8HvduwwQ7\nAEMi2AGY2EYckezr65O0cOHCyo/pXRJRPtmu5ob5Ee5X2WzWZDvD3KKPWxwDKEGwAzCxDbk2\nwruk1Kyu8PauDS+RSLhxqqury0xoG3IotlbM/LziNbibmTFidz6fW+3YFQZgwiHYAZjYTGJL\npVLeTrtYLGbuYJLJZBzHicfjJXPRvOOzJaGwo6MjGo2aCNXS0mI+NOlwjDQ2NurqMDo8M7vO\nZD53rBYARLADMNGFw2HTnRaJRNyZZ6aPraOjI5VKRaPRbDbrbm/GZLdu3Wreuvc39ioZ2x3r\nAdnW1lZJsVjMm+3MQ9JMh5x7izvvXpUHQQCTB8EOQNANf4NiSclk0mQ7d+aZd7mD4zjeDGR6\n+BzHMZtFIhHv3DWVLVPwxqlqn/06YuVGS0uL+aGmo9Fbv4mk4XDYbOBdWmFua7dnz56qSgJg\nN4IdgOBye+M0UqhKJpMlk88kRaPRrq4uXQlM7jG947DpdLqtrc27V8kyBbON2aXycc/KK3d/\nqCnVZe6BN8wGZryY250A8Ap5/+IAACslEomFCxeOeKNgAJjoCHYAAACWYCgWAADAEgQ7AAAA\nSxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwA\nAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxB\nsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALPF/taem\ne3V/oJ8AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Q-Q Plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用Q-Q图来检验正态性假设\n",
    "library(car)\n",
    "qqPlot(lm(response~ trt,data=cholesterol),\n",
    "      simulate=TRUE, main=\"Q-Q Plot\",labels=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tBartlett test of homogeneity of variances\n",
       "\n",
       "data:  response by trt\n",
       "Bartlett's K-squared = 0.57975, df = 4, p-value = 0.9653\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用Bartlett检验做方差齐性检验\n",
    "bartlett.test(response ~ trt, data= cholesterol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windows"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No Studentized residuals with Bonferonni p < 0.05\n",
       "Largest |rstudent|:\n",
       "   rstudent unadjusted p-value Bonferonni p\n",
       "19 2.251149           0.029422           NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#利用car包中的outlierTest()来检测离群点\n",
    "library(car)\n",
    "outlierTest(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.4 单因素协方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dose\n",
       "  0   5  50 500 \n",
       " 20  19  18  17 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(litter,package=\"multcomp\")\n",
    "attach(litter)\n",
    "table(dose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Group.1</th><th scope=col>x</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0  </td><td>32.30850</td></tr>\n",
       "\t<tr><td>5  </td><td>29.30842</td></tr>\n",
       "\t<tr><td>50 </td><td>29.86611</td></tr>\n",
       "\t<tr><td>500</td><td>29.64647</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Group.1 & x\\\\\n",
       " <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0   & 32.30850\\\\\n",
       "\t 5   & 29.30842\\\\\n",
       "\t 50  & 29.86611\\\\\n",
       "\t 500 & 29.64647\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 2\n",
       "\n",
       "| Group.1 &lt;fct&gt; | x &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 0   | 32.30850 |\n",
       "| 5   | 29.30842 |\n",
       "| 50  | 29.86611 |\n",
       "| 500 | 29.64647 |\n",
       "\n"
      ],
      "text/plain": [
       "  Group.1 x       \n",
       "1 0       32.30850\n",
       "2 5       29.30842\n",
       "3 50      29.86611\n",
       "4 500     29.64647"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggregate(weight,by=list(dose),FUN=mean) #获取各组均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            Df Sum Sq Mean Sq F value  Pr(>F)   \n",
       "gesttime     1  134.3  134.30   8.049 0.00597 **\n",
       "dose         3  137.1   45.71   2.739 0.04988 * \n",
       "Residuals   69 1151.3   16.69                   \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit <- aov(weight ~ gesttime + dose)\n",
    "summary(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windowsWarning message:\n",
      "\"package 'effects' was built under R version 3.5.3\"lattice theme set by effectsTheme()\n",
      "See ?effectsTheme for details.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       " dose effect\n",
       "dose\n",
       "       0        5       50      500 \n",
       "32.35367 28.87672 30.56614 29.33460 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#获取去除协变量效应后的组均值\n",
    "library(effects)\n",
    "effect(\"dose\",fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对用户定义的对照组的多重比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\t Simultaneous Tests for General Linear Hypotheses\n",
       "\n",
       "Multiple Comparisons of Means: User-defined Contrasts\n",
       "\n",
       "\n",
       "Fit: aov(formula = weight ~ gesttime + dose)\n",
       "\n",
       "Linear Hypotheses:\n",
       "                      Estimate Std. Error t value Pr(>|t|)  \n",
       "no drug vs. drug == 0    8.284      3.209   2.581    0.012 *\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "(Adjusted p values reported -- single-step method)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(multcomp)\n",
    "contrast <- rbind(\"no drug vs. drug\"=c(3,-1,-1,-1))\n",
    "summary(glht(fit,linfct=mcp(dose=contrast)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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