mamacneil/Lecture_1.ipynb Secret

## Lecture_1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 1 - Getting started with Jupyter and R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to 'The Biologists Toolkit' (what would be in most circumstances called 'Scientific Computing') a course intended to teach you the practical computer skills needed to do Biological science. The course is structured around weekly three-hour lab/lectures using [Jupyter notebooks](http://jupyter.org) (this is one) that allow you to run code in a web browser, making it widely accessible. Along the way there will be small tasks that will teach you how to use them. Not to worry - this week there will no grades but starting next week **these tasks will be worth a large portion of your grades**. So this is a participatory class, intended to teach you useful skills, that should be relatively easy to do well in - provided you attend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Scientific computing\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scientific computing is the practice and culture of using computers in our daily workflow. This differs from *Computer Science*, which is a research discipline that pushes the envolope of what we can do with computers. Scientific computing employs a set of computer applications (tools) to efficiently and effectively pursue biology. Just like an actual toolbox, the ideal set of applications we use will vary by person and the job at hand, but will be based around a minimum set of things needed to get most jobs done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Suggested reading*\n",
    "A recent paper by [*Wilson et al.*](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745) in PLoS Biology gets into detail about scientific computing as a practice, which they structure around several key principles:\n",
    "\n",
    " - Write Programs for People, Not Computers\n",
    " - Let the Computer Do the Work\n",
    " - Make Incremental Changes\n",
    " - Don't Repeat Yourself (or Others)\n",
    " - Plan for Mistakes\n",
    " - Optimize Software Only after It Works Correctly\n",
    " - Document Design and Purpose, Not Mechanics\n",
    " - Collaborate\n",
    "\n",
    "These principles may not mean much now, but they become increasingly important as you move into a scientific career, where publications and reproducible analysis are critical to success. This course is intended to help you understand and develop these principles, using a set of tools and skills that will help you get work done efficiently. We will cover many aspects throughout the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "# Jupyter\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebooks are html mashups of code and text that allow you to easily track, share, and reproduce your work. They are an interactive computing environment, where analysis can be completed in a more than [40 programming languages](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) and data readily displayed in a variety of ways. We will use Jupyter notebooks exclusively for this course, both for lecture/lab material and for submitting assignments. This will be done through Jupyter's server-based platform, [JupyterHub](https://jupyterhub.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "By design, Jupyter notebooks allow you to write code and describe what you're doing as you go along. This is particularly helpful for sharing with others (reproducability) and for writing up methods. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xnew = sin(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.141120008059867"
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       "0.141120008059867"
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      "text/plain": [
       "[1] 0.14112"
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     "output_type": "display_data"
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   "source": [
    "xnew"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ynew = sqrt(345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2.62118781334186"
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      "text/latex": [
       "2.62118781334186"
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       "2.62118781334186"
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       "[1] 2.621188"
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   "source": [
    "xnew*ynew"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By storing things as objects, they can be used and reused as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kernels\n",
    "\n",
    "Jupyter notebooks are powered by what are called [kernels](http://jupyter.readthedocs.io/en/latest/projects/kernels.html), instances of a given program that power the computation behind them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markdown\n",
    "\n",
    "The text in Jupyter notebooks is handled by a great bit of rich text shorthand developed by [John Gruber](https://twitter.com/gruber) called [Markdown](https://daringfireball.net/projects/markdown/syntax). Markdown allows very simple symbols and indentations to create html that can be used by a wide variety of programs and we will use it exclusively for text in this course, including writing your assignments. There are a TON of tutorials and information online about Markdown so feel free to use those resources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### TASK 1\n",
    "---\n",
    "Markdown is a really simple way to make formatted text. It is extremely common in the computer world. You can use a wide variety of special characters to format the text. With this your can make **bold**/*italic* or lists  and tables.\n",
    "\n",
    " - Below this cell create a new cell using the 'insert' command on the menu above. \n",
    " - In that cell use the drop down menu that says \"Code\" or \"Markdown\" and select \"Markdown\" to change the type of cell.\n",
    " - Check out the help menu for Markdown. It will start a new tab where you can select \"Basic writing and formatting syntax\" to view some of the ways you can modify your text.\n",
    " - play around entering some text and then press the play button OR shift-enter to 'compile' your text. By double clicking on the cell you can enter back into the editing mode. \n",
    " \n",
    " - **Write a note to yourself**\n",
    "   - start with a header\n",
    "   - include bold and italic text\n",
    "   - make a numbered/bulleted list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "# R\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "['R'](https://www.r-project.org) is the most commonly-used programming language among biologists today, primarily due to its [object-oriented](https://en.wikipedia.org/wiki/Object-oriented_programming) syntax and its powerful statistical and graphics capabilities. While R may eventually be overtaken by [Python](https://www.python.org), it is widely used and learning it here will serve you well should you eventually make the switch. So to start, let's begin with the [traditional first thing](https://en.wikipedia.org/wiki/\"Hello,_World!\"_program) anyone does in any programming language: `hello world`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"hello, world\"\n"
     ]
    }
   ],
   "source": [
    "# press play\n",
    "print('hello, world')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Let's break down what just happened.\n",
    "\n",
    "The first line started with a `#` which means that R will ignore it. It's called a comment line (and it's not a coincidence that the same symbol is used for hashtags in Twitter!) and comments are your best long lost friends because they tell you what you used to know about your code when you come back to it a year from now.\n",
    "\n",
    "In any basic computer program, one of the most important things you can do is generate output. The simplest way in R is to use the `print()` function. Don't worry, this won't set off any printers - it's only printing to the screen in the format of a typical R function - *i.e.* using parentheses. It works like this:\n",
    "\n",
    "    print('Hello World')\n",
    "\n",
    "The line above is called *a statement.*\n",
    "\n",
    "`print()` is the name of a specific function. Functions are things in any programming language that *do something* and in general they are the name of the function followed by regular brackets or parantheses.\n",
    "\n",
    "The parentheses are there because within them you place so-called *arguments* - an argument is simply an input needed for the function to operate. In this case if you want the computer to print, the only argument necessary is what to print -- which you provide.\n",
    "\n",
    "*There are several different types of brackets in R, so for clarity we will always refer to parentheses\n",
    "when we mean these: *`()`*, square brackets when we mean these: *`[]`* and curly braces when we mean these: *`{}`\n",
    "\n",
    "\n",
    "### An important note on quotes and `strings`\n",
    "\n",
    "In normal writing, we only surround a bit of text in quotes when we want to convey that they are being said by somebody. In R, however, we can surround a little bit of text in quotes to indicate to the program that this is just a  bit of text and not something it needs to execute (run). \n",
    "\n",
    "By programing convention we call text in quotes a `string` - as in a string of characters. Strings are always surrounded by quotes. That is how programming languages are able to tell the difference between the instructions (like the function name) and the text (the thing we want to print). It is also how you tell the difference between the number 1 and the character \"1\". There is no real difference between single or double quotes for strings – R will happily accept either. This can be handy when you want to have a quotation mark as part of your string!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Storing variables as objects\n",
    "---\n",
    "The most common actions you will do in R are to execute commands (run things) and assign objects (store things). If you  want to refer to something, like a string or a number, without writing the whole thing out you can assign that item a name and create an R object. \n",
    "\n",
    "You've been doing it since you first started algebra, when your teacher first wrote \"let x = 7\". Similarly, we can take strings, numbers, data, and functions in R and assign a name to it using either an `=` or `->`. The `->` convention comes from the pre-cursor to R - [S](https://en.wikipedia.org/wiki/S_(programming_language)) - which reserved the `=` for its mathematical definition. R is more flexible and can use either. One further point is that the arrow can go either way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x <- 10\n",
    "10 -> y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 10\n",
      "[1] 10\n"
     ]
    }
   ],
   "source": [
    "print(y)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These actions are called assignments, and once we've done them, we can use the variable names as objects that contain far more than just the letters within them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## TASK 2\n",
    "---\n",
    "Following the instructions below to store/print your name in tasks 2A-C.\n",
    " \n",
    "\n",
    "This is the first time you will see how simple tasks will be graded in BIOL 3872. Questions are generally broken up into 3 parts:\n",
    "\n",
    "1. You will be given a set of instructions. The instructions will tell you the name of the variable to store your answer. Follow this name **EXACTLY** -. e.g. In task 2A the variable used to store your answer is my_name - and like every programming language on earth **it is case-sensitive**. You will also be told what kind of information should be stored in your answer. In this case we want a string. Through the course we will learn to deal many other types of data.\n",
    "\n",
    "2. The second part is where you write your answer. When the assignment is graded the **code in the answer cell will be run to define your answer**. In some instances if you're wrong you may recieve part marks if you've made a valiant attempt. You will see some R code in the cell. This code **must be deleted**. The code is there to tell you that you didn't answer the question when you validate and submit your assignment.\n",
    "\n",
    "3. The last cell is the test cell. You should never alter this cell. When your assignments are graded the test cell will be reset to its original form, so nothing you do in the test cell will be saved. When I am being nice I can have this cell give you hints as to the correct answer. In today's lab I'll be nice - not so often during the tests!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2A\n",
    " - in the cell below store your name in a variable called `my_name`\n",
    " - make sure to store your name as a `string`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2B\n",
    " - in the cell below use `print()` to output your name. You can do this by giving print() the variable `my_name` from 2A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It may seem obvious, but a key feature of Jupyter Notebooks is that once that variable has been assigned it will be available to other cells. If you change the value of a variable by assigning it a new value - you will replace the old value, and any cell that is run from that point on will only reflect the new value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### TASK 2C \n",
    " - In the cell below change the value of the variable `my_name` and then re-run the the print statement from 2B above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK 3\n",
    "\n",
    "Your working directory for the class is your own. You can always make a new notebook to contain your ideas, to practice coding or test your syntax.\n",
    "\n",
    "## TASK 3A \n",
    " - Go to the browser tab with your Jupyter Dashboard and create a new notebook. Use the drop down menu \"New\" in the top right and select \"R\"\n",
    " - Rename this notebook by clicking near the top where it says \"Untitled\"\n",
    " - print hello world using R\n",
    " - Close the notebook by going to `file>Close and Halt`. If you don't Close and Halt, ie if you just close the browser tab, your notebook will staying running in the background. You can re-enter it from the dashboard or alternatively you can select it in the dashboard and clicking the yellow `Shutdown` button. Please, please don't press the Trash icon - I won't be able to help you if you delete your notebook.\n",
    " - If you want you can play around making folders, renaming them, and moving your notebooks.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Graphics in R and Jupyter Notebooks\n",
    "---\n",
    "If you've ever wondered where all the fancy plots you see in scientific papers are coming from - they are made using computer programming. There are a huge number of tools for making graphics to visualize data - we are going to touch on graphics in later labs. But for now, run each of the cells below to see some spiffy graphics generated from real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "Loading required package: tidyr\n"
     ]
    }
   ],
   "source": [
    "# Load graphics packages\n",
    "require(ggplot2); require(tidyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're using R's `require()` command to load two R packages created by [Hadley Wickham](https://twitter.com/hadleywickham) - `ggplot2`, which is an important graphics package for rapidly producing graphics, and `tidyr`, which is an importat package for producing [tidy data](https://en.wikipedia.org/wiki/Tidy_data). By tidy data we mean:\n",
    "\n",
    " - Each variable is in a column.\n",
    " - Each observation is a row.\n",
    " - Each value is a cell.\n",
    "\n",
    "This may seem obvious (or not) but it is an **essential** element of using computers and doing analyis in biology. You will learn why in upcoming lectures.\n",
    "\n",
    "You should also notice that each line is commented; while this is overkill here (we have the Markdown text after all), it is also good practice. At this point you should overcomment, rather than undercomment, but do so as succinctly as possible.\n",
    "\n",
    "Next we can create some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>A</th><th scope=col>B</th><th scope=col>C</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.11721203</td><td>-0.8611675 </td><td>-0.3046940 </td></tr>\n",
       "\t<tr><td> 0.78924000</td><td> 1.4136681 </td><td> 0.3968462 </td></tr>\n",
       "\t<tr><td>-0.25417013</td><td>-1.9237129 </td><td> 1.3203906 </td></tr>\n",
       "\t<tr><td>-0.26901656</td><td>-0.4292704 </td><td> 1.3162218 </td></tr>\n",
       "\t<tr><td> 0.01106806</td><td>-0.2070959 </td><td> 0.7704924 </td></tr>\n",
       "\t<tr><td>-1.08109940</td><td>-0.9977479 </td><td>-0.1774257 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " A & B & C\\\\\n",
       "\\hline\n",
       "\t -0.11721203 & -0.8611675  & -0.3046940 \\\\\n",
       "\t  0.78924000 &  1.4136681  &  0.3968462 \\\\\n",
       "\t -0.25417013 & -1.9237129  &  1.3203906 \\\\\n",
       "\t -0.26901656 & -0.4292704  &  1.3162218 \\\\\n",
       "\t  0.01106806 & -0.2070959  &  0.7704924 \\\\\n",
       "\t -1.08109940 & -0.9977479  & -0.1774257 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A | B | C | \n",
       "|---|---|---|---|---|---|\n",
       "| -0.11721203 | -0.8611675  | -0.3046940  | \n",
       "|  0.78924000 |  1.4136681  |  0.3968462  | \n",
       "| -0.25417013 | -1.9237129  |  1.3203906  | \n",
       "| -0.26901656 | -0.4292704  |  1.3162218  | \n",
       "|  0.01106806 | -0.2070959  |  0.7704924  | \n",
       "| -1.08109940 | -0.9977479  | -0.1774257  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  A           B          C         \n",
       "1 -0.11721203 -0.8611675 -0.3046940\n",
       "2  0.78924000  1.4136681  0.3968462\n",
       "3 -0.25417013 -1.9237129  1.3203906\n",
       "4 -0.26901656 -0.4292704  1.3162218\n",
       "5  0.01106806 -0.2070959  0.7704924\n",
       "6 -1.08109940 -0.9977479 -0.1774257"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some disposible random data\n",
    "tmp = data.frame(replicate(3,rnorm(100,0,1)))\n",
    "# Name the columns\n",
    "colnames(tmp) = c('A','B','C')\n",
    "# Take a look\n",
    "head(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here three things happen:\n",
    "\n",
    " - A [Data Frame]() is created, which is a standard R object for storing tabular data\n",
    " - Names are given to the columns using the colnames() function\n",
    " - The top 6 lines of the dataframe are displayed\n",
    " \n",
    "There are a couple of not-so-obvious things here:\n",
    "\n",
    " 1. A standard throw-away object name `tmp` is used - you will quickly learn that good naming of objects is essential to good code but also that sometimes we create objects we don't care about, just to learn how they work. I typically use `tmp` and `tmp1, tmp2, tmp3...etc.` as disposible names, as well as `poo` which is a self-explanitory bit of code from one of my PhD supervisors, [Steve Rushton](http://www.ncl.ac.uk/nes/staff/profile/stevenrushton.html#background).\n",
    " \n",
    " 2. Three built-in R functions are nested in the first line: `data.frame()`, `replicate()`, and `rnorm()`. \n",
    " \n",
    "To figure out what each of these do, we can pull up information for each object using a `?` ahead of the function name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK 3B\n",
    " - In the cell below, write code to pull up the help information for `data.frame()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can create fancy boxplots of our data using `ggplot2`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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874ct109OU1XZ3NC1/yvGaKQy3ZO5Js4XVZ/BcvRDNWDvfBoYjML1FJxDhRu0Tn\nkmHHoYKc3CHeeDqau3TZJdIV50AABEAABEAABEBAJwIwkHSaDegCAiAAAiAAAiAAAiAAAiCQ\nVQKuWIOUVcLoHARAAARAAARAAARAAARAwDYEYCDZZqqgKAiAAAiAAAiAAAiAAAiAgNkEYCCZ\nTRjyQQAEQAAEQAAEQAAEQAAEbEMABpJtpgqKggAIgAAIgAAIgAAIgAAImE0ABpLZhCEfBEAA\nBEAABEAABEAABEDANgRgINlmqqAoCIAACIAACIAACIAACICA2QT8Zndgtfy5c+fS008/bXW3\n6C9LBJqbm4n/xKas8i9LaqBbELAlAbx/bDltUFoTAur9IzaNj7sXoCaqQg0DCdx1110kNpw3\nUCJE6UjAcQbS1q1bqaioiK677jodeUMngwkcOHCA+I83ok1l01eD1YA4ELAlAbx/bDltUFoT\nAvX19XTw4EF8/2gyH1aocccdd8h7Div6Qh/ZJeA4A4lxsmV/xBFHZJcsereEwP79+2nfvn1U\nXl5OeXl5lvSJTkDAKQT4vcPvIbx/nDKjGIeVBOrq6oiNpIqKCsrNzbWya/SVJQKFhYVZ6hnd\nWk0Aa5CsJo7+QAAEQAAEQAAEQAAEQAAEtCUAA0nbqYFiIAACIAACIAACIAACIAACVhOAgWQ1\ncfQHAiAAAiAAAiAAAiAAAiCgLQEYSNpODRQDARAAARAAARAAARAAARCwmgAMJKuJoz8QAAEQ\nAAEQAAEQAAEQAAFtCcBA0nZqoBgIgAAIgAAIgAAIgAAIgIDVBGAgWU0c/YEACIAACIAACIAA\nCIAACGhLAAaStlMDxUAABEAABEAABEAABEAABKwmAAPJauLoDwRAAARAAARAAARAAARAQFsC\nMJC0nRooBgIgAAIgAAIgAAIgAAIgYDUBGEhWE0d/IAACIAACIAACIAACIAAC2hKAgaTt1EAx\nEAABEAABEAABEAABEAABqwnAQLKaOPoDARAAARAAARAAARAAARDQlgAMJG2nBoqBAAiAAAiA\nAAiAAAiAAAhYTQAGktXE0R8IgAAIgAAIgAAIgAAIgIC2BGAgaTs1UAwEQAAEQAAEQAAEQAAE\nQMBqAjCQrCaO/kAABEAABEAABEAABEAABLQlAANJ26mBYiAAAiAAAiAAAiAAAiAAAlYTgIFk\nNXH0BwIgAAIgAAIgAAIgAAIgoC0BGEjaTg0UAwEQAAEQAAEQAAEQAAEQsJoADCSriaM/EAAB\nEAABEAABEAABEAABbQnAQNJ2aqAYCIAACIAACIAACIAACICA1QRgIFlNHP2BAAiAAAiAAAiA\nAAiAAAhoSwAGkrZTA8VAAARAAARAAARAAARAAASsJgADyWri6A8EQAAEQAAEQAAEQAAEQEBb\nAjCQtJ0aKAYCIAACIAACIAACIAACIGA1ARhIVhNHfyAAAiAAAiAAAiAAAiAAAtoSgIGk7dRA\nMRAAARAAARAAARAAARAAAasJ+K3u0Oz+mpubqbGxkfbt22d2V5BvMYGRI0fSggUL2vTK881/\nPp+PPB5P9Bw/HzFiBF166aXRY3gCAiDQlgB/VnI5ePAgBYPBtifxCgRAICEB9f45cOCAvO9I\nWBknHUGgqamJwuGwI8aCQSQm4DgDiYfLN8d8w4ziLALPPvtsSjdxO3bsoMsvv9xZEDAaEDCQ\ngPpRwev14jPTQK4Q5Q4C/L7hwvcbuOdwx5xjlO4h4DgDiT+wAoEAFRQUuGcWXTDS/fv3S+Po\n+JJienDQYZ2O+Ir5i2nPnj24DjolhQpuJsC/hh46dIhyc3MpLy/PzSgwdhBImUAoFIq+f/g9\nhOJ8ArHRKs4fsXtH6DgDyb1T6eyR7969Ww6wIuCnwiS8g+Wi3gphILErXP1K7mxCGB0IgAAI\ngAAIgAAIgIARBJCkwQiKkGE6AfYGcSnzB5Lqq8zvJ/51HGvRksKFSiAAAiAAAiAAAiAAAi0E\n4EHCpWALAsqDVCY8Q8kUNpC4cLuSkpJkmqAOCIAACIAACLQjsHLlSpo6dWq7xfkcnsqJGvLz\n88nf8p2jGnOoPycJwvePIoJHELAXgeTuNu01JmjrQAJRA8mfXPINZUix56lPnz4OJIIhgQAI\ngAAIWEHgvvvuo2nTpqXV1fXXX59WOzQCARDILgEYSNnlj96TJPCVgZRciF1pKw9Skl2gGgiA\nAAiAAAi0I1BbWyvS4/qo+sx/tDvX0YFDe1bSjs//QbJdRxVwDARAQHsCMJC0nyIoyATUGqTS\nZD1IMJBw4YAACIAACBhAYO/eveTLLaWSPucmJa2huEYaSNwOBQRAwJ4EkKTBnvPmOq2jHiQR\n151MKW1Zq6TaJdMGdUAABEAABEAglgD/QOfLLYs9HPc1G1NcYCDFRYQTIKA9ARhI2k8RFGQC\n6XqQVDtQBAEQAAEQAIFUCXA2VN6Hz5eTfLIfVRcGUqq0UR8E9CEAA0mfuYAmCQgoQ0dlp0tQ\nVZ5S9VS7zurjPAiAAAiAAAjEEqirq5OHlNETe76j195AIXm8fniQOoKDYyBgEwIwkGwyUW5X\nk0PlAh4P5SexSSyzUkkaYCC5/crB+EEABEAgfQJqLz1vS9hcspK8OcWkjKtk26AeCICAPgRg\nIOkzF9AkAQE2dJTRk6Ba9FRJSzIHGEhRJHgCAiAAAiCQIgEVJpeKB4m78AVKYCClyBrVQUAn\nAjCQdJoN6BKXABs6KmwubqVWJ9jTlOP1RNcutTqFpyAAAiAAAiCQFAHlQfIJj1AqxSvWLPHa\npXA4nEoz1AUBENCEAAwkTSYCasQnwLuV85/KTBe/Ztsz7HFCFru2TPAKBEAABEAgeQLKQGKD\nJ5XCBlVzczPV19en0gx1QQAENCEAA0mTiYAa8QmoMDkVNhe/ZtszbCCp8Ii2Z/AKBEAABEAA\nBDonoAwkXyBVD1KkPnuRUEAABOxHAAaS/ebMdRorAymVEDuGxAaS8j65DhoGDAIgAAIgkDEB\nZSB5c4pSkqVC8lT7lBqjMgiAQNYJwEDK+hRAgc4IKC9QiTB4UinK46QMrFTaoi4IgAAIgAAI\nKA+QL5CageRtqa/agyQIgIC9CMBAstd8uVJbZSCVtmSmSxZCqS9iUMFASpYY6oEACIAACLQm\noAwcTtudSlEGEjxIqVBDXRDQhwAMJH3mAprEIaAMnNQ9SBEDSRlYccTjMAiAAAiAAAh0SEAl\nWeDNX1MpvpxIfdU+lbaoCwIgkH0CMJCyPwfQoBMCysBRHqFOqkdPlwV88rlqHz2BJyAAAiAA\nAiCQBAHlQUKIXRKwUAUEHEQABpKDJtOpQ1EGjlpTlOw4ixFilywq1AMBEAABEOiAgPIApepB\nUvVV+w5E4xAIgIDGBGAgaTw5UC1CQIXYcVa6VIqqX1dXl0oz1AUBEAABEAABSUAZON5AQUpE\nvP5IiN2BAwdSaofKIAACehCAgaTHPECLBASUB6k4RQOpuCWpgzKwEnSBUyAAAiAAAiDQjgAb\nSB5vQP61O5ngADxICeDgFAjYgAAMJBtMkttVVB6glLPYtRhUqr3bOWL8IAACIAACqRFgD5Ay\ndlJpqTxO8CClQg11QUAfAjCQ9JkLaBKHAHuQ/B4P5fsiSRfiVGt3WK1ZUh6odhVwAARAAARA\nAAQSEJAGkj+18DoW521po0L0EnSBUyAAAhoSgIGk4aRApbYE2MBR4XJtzyR+pdKCw0BKzAln\nQQAEQAAEOiZw4MBB4UFK30A6ePBgx4JxFARAQGsCMJC0nh4oxwTYwClJ0XvE7QpFG5/wPCHE\njmmggAAIgAAIpErg4MED5PHnp9osalQhxC5ldGgAAloQgIGkxTRAiXgEmpqaiPehUN6gePXi\nHS8WRhI8SPHo4DgIgAAIgEA8Ag0NDRQOh6PhcvHqdXTc22JUwYPUER0cAwH9CcBA0n+OXK3h\nvn375PjTNpBEJrs64YFCAQEQAAEQAIFUCCjvjzJ2Umnr8fpl5jslI5W2qAsCIJB9AjCQsj8H\n0CABARUex56gdEqJ2Cy2ThhZ/CsgCgiAAAiAAAgkS0B5f1TChWTbqXpsWLEXCgUEQMB+BGAg\n2W/OXKWxMpBURrpUB8/t2DjiMD0UEAABEAABEEiWgDJuPP68ZJu0qcft4EFqgwQvQMA2BGAg\n2Waq3KmoMpBS3SRW0Spq2SwW65AUETyCAAiAAAgkQ0AZN+mE2LF89jwpL1Qy/aEOCICAPgRg\nIOkzF9CkAwLKsMkkxI7FqrVMHXSBQyAAAiAAAiDQjoDyIKVrILEHCQZSO6w4AAK2IAADyRbT\n5F4llWGTSZIGpqcMLfeSxMhBAARAAARSIaCMm3QNJPYgBYNB4mysKCAAAvYi4LeXutDWbQSU\nYZO2B6klxE4ZWm7jh/G6l8CWLVvoyiuvTOg9DZNIXsL5S8R+YZ4EqPr170dvvvEmeb34TS0B\nJpxyGAFlIKWzDxKj8Poia5dYTlFRkcPoYDgg4GwCMJCcPb+2H91Xa5DSy2JXLLLYcVFybA8E\nAwCBJAnMmTOHtm3bRs0lwgZKfZ/LaC+eOqLFixbTunXrqF+/ftHjeAICTiegDCRvmkkalOeJ\nQ/VgIDn9asH4nEYABpLTZtRh41GeHyRpcNjEYjimE1ixYoXs4+CVPgoNTN/zkzuhifLGNRPL\ng4Fk+rShA40IfLUGqSAtrZTnSRlaaQlBIxAAgawQSP9bMyvqolO3EVCen3RD7FQ7ZWi5jR/G\n614Cy5cvl4Nv6pEoeK5zPk1VkfbK4Oq8BWqAgDMIfJXFLr0038qDBAPJGdcDRuEuAjCQ3DXf\nthtt1EBqWUuU6gBUcgclJ9X2qA8CdiWwcuVKCosfvsMlMJDsOofQO7sElGHjEckW0ikwkNKh\nhjYgoAcBGEh6zAO0iENAGTZFvnTXIEXawYMUBzAOO5IAX++cpCFT7xHDCZeLvxySIXaOhIVB\ngUAcAtEQu5ZkC3GqxT2sNphVhlbcijgBAiCgHQEYSNpNCRRqTYBv9AqFceQVWbbSKcUtnidl\naKUjA21AwG4EVDicEQYSZ7hr6u6h9evX06FDh+yGAvqCQNoEoiF2gfSynHCaby4wkNKeAjQE\ngawRgIGUNfToOBkCbCCpdUTJ1I+tozxPMJBiyeC1kwlweB2X5pb1Q5mOtblKyGpuplWrVmUq\nCu1BwDYElGGjDJ1UFVchdsrQSrU96oMACGSPAAyk7LFHz0kQYMOmKM31Ryw+IPZtyRV/+/fv\nT6I3VAEBZxD4yoNkzHiUJ0oZXsZIhRQQ0JuAMmzSNpACEQ+SkqP3aKEdCIBAawIwkFrTwHOt\nCDQ2NsqQnkw8SDwgbg8PklZTC2VMJvCVgZReaGqseiqTncqMF3ser0HAiQSUYaPSdac6RmVY\nKU9Uqu1RHwRAIHsEYCBljz167oSA8vqoMLlOqsc9zR4oGEhx8eCEAwmwgdRcJgaWZ4yB1NyS\nKhweJAdeLBhSXALKQPIGCuPWSXTCCw9SIjw4BwJaE4CBpPX0uFs5ZdSoRAvp0mAPUn19vVxD\nka4MtAMBuxDYvn077d2715AMdmrM4SIPhYuI4EFSRPDoBgL8veHx+snrE2kc0yhef8SwYjko\nIAAC9iIAA8le8+UqbVVq7iKfP6NxKw+U8khlJAyNQUBzAsrLo7w+RqnLmezY+FI/XBglF3JA\nQFcCbNik6z3iMam2MJB0nWHoBQLxCcBAis8GZ7JMIGogZZCkgYegkjwoeVkeFroHAVMJKC+P\nSqxgVGdKnlrfZJRcyAEBXQlIA6nFC5SOjspAwo9z6dBDGxDILgEYSNnlj94TEFAGjRFJGrgb\nJS9BlzgFArYnoDxIKrGCUQNSBpKSb5RcyAEBXQmwYeMNiNjSNIuvpS08SGkCRDMQyCIBGEhZ\nhI+uExNQBo0KkUtcO/5Z1V7Ji18TZ0DA/gSkASNyMzR3M3YszT0i8mAgGcsV0vQkEAqFqKGh\ngbw56RtIqi08SHrOMbQCgUQEYCAlooNzWSWgDBoVIpeuMkX+yBomJS9dOWgHAroTCIfDtHLV\nSmrqIjT1G5PBTo1ZeZAQYqeI4NHJBJRRo7xA6YxVbhTr8SF6IR14aAMCWSYAAynLE4Du4xNQ\nX1DKAxS/ZuIzqj0MpMSccNb+BDZu3EgNBxvI6AQNkoxIGc6pw2Eg2f86wQg6J6C+L7w5xZ1X\nTlDDJ9orWQmq4RQIgIBmBGAgaTYhUOcrAipbljJwvjqT2jO1hglfUqlxQ237EVDhb8rbY/QI\nWC6nEN+xY4fRoiEPBLQioL5/fDmlGenFBtbevXUZyUBjEAAB6wnAQLKeOXpMkoDyIGW6D5IK\n0VPykuwe1UDAdgTMNpCaRapvLqof2wGCwiCQJAH+IYALe4AyKWxg7dsHAykThmgLAtkgAAMp\nG9TRZ1IElMcnUw9SodgolouSl1TnqAQCNiSgDBdlyBg9BOWZUv0YLR/yQEAXAlEPUq6IK82g\n+HJL5Sbl+IEuA4hoCgJZIAADKQvQ0WVyBNQXSqYGkmqv5CXXO2qBgP0IyPVB4veAZk7SYEKB\ngWQCVIjUksCePXukXmzgZFJU+927d2ciBm1BAAQsJhBJ72Vxp8l0d+DAAZo5cyZt3ryZhg0b\nRscee2wyzVDHQQTY48MWfH6LByjdoSkDCR6kdAminR0INDU10erVq6mpq9DWZ2wGOzX+5u7i\nmRAND5IigkenEogaSHnlGQ3Rlxtpz/JqamoykoXGIAAC1hHQ0oP04Ycf0re//W0aPXo0LVu2\njG677Ta6//77raOCnrQgwAZNoT8SHpeJQmoNEgykTCiire4E1q1bR8FgkJSXxxR9Ax5qqoCB\nZApbCNWKgPL4KAMnXeX8LQaWkpeuHLQDARCwloB2HqTm5mZ67rnn6KabbqLLL79c0pg2bRr9\n4Q9/oIsvvpgOO+wwawmht6wR4JA45f3JRAm1BgkhdplQRFvdCaxatUqqaNb6IzV+TiFev7ie\ntmzZQlVVVeowHkHAUQRqa2vlePx54heBDIqvpf2uXbsykIKmIAACVhPQzoPEHyInnHACnXvu\nuVEWxxxzjHzO4XYo7iHAHh8jDCS/x0N5Xi/BQHLPtePGkaqwN1M9SAJsEzLZufHyct2YlYHk\ny6vMaOz+lvZKXkbC0BgEQMAyAtp5kLp06SJD6loTmDhxIvnEOpRBgwa1Piyff/DBB9TY2Bg9\nrsJMeA0Tin0JcKgQz2tRbpEhg2BDi7MS4bowBCeEaEiAw5G5mLJJbKvxKg/VkiVL6Pjjj291\nBk9BwDkEeK8vjy9HpPnO7DvIlx/JmLJ161Z8/zjg8uC1nuFw2AEjwRA6I6CdgRSr8Jdffkkj\nR46kq6++mrp35xXCbcuf/vQnah3be/TRR9PgwYPlZoZta+KVnQioBbJGeJB43IU+L9UKj5Ta\n28JOLKArCCRDgDPYhQPCQMosIqjTrpSHig0yvJ86xYUKNiXABpI/nzOeZFb8LQYSh6Ti/ZIZ\nSx1ah0IhHdSADhYQ0NpAWrBgAf3ud7+jESNG0I9//OMOcfz2t7+lhoaG6DkOwzt06BCVlJRE\nj+GJ/QioLxKjDKQin5/WC69icXExeUTIHQoIOIkAe1w3bNhAMsuc19zru7mbICe6YG89Pmed\ndBVhLIoAv5/4R7r8rn3VobQfAy1GFsvD+yVtjNo09Pu1vm3WhpMTFNF2pqdPn05//OMf6Yor\nrqAbb7wxLutLLrmkzTnOfLd06VIqLCxscxwv7EWAk3VwMSKLHcvhTHbKLY5rg4mgOIkAJ2jg\n0A/l3TF1bH6RyU5EDa1es5oKCgrwg4OpsCE8GwTY28PFX8C/BmRWvIFC4j9eg4TvnsxY6tCa\nl3vgR1YdZsJ8HbRL0sBDnjx5Mt1xxx106623JjSOzMeDHrJFQKXkNsqDxCF2XOrr67M1JPQL\nAqYRkBvECulmrz9SA+B+Gg420KZNm9QhPIKAYwjweiEugYL2Yf3pDNIv5Gzdui2dpmgDAiCQ\nJQLaGUj8K8s999xDZ555JvXt25fmz58f/UOazCxdJVnoVmWcUym6M1VBGVrK8MpUHtqDgE4E\nVIpvlWHObN1UPypzntn9QT4IWElAGUj+QmPS2AcKeogEDfWE7x8rZxF9gUBmBLQLsRs7dqzM\n9DJ+/Hjiv9aF1yNdeOGFrQ/huUMJqC8SZdhkOkwlRxlemcpDexDQiYDyIFkSYicGrjxV3O9Z\nZ52lEwroAgIZE1AhdgGjDKSinlInXiPdUTbejBWGABAAAcMJaGcgXXPNNcR/KO4moAwZXjtk\nRFEGkjK8jJAJGSCgCwH25IRziMLl1mikDDHlubKmV/QCAtYQUKGjgaJehnQYKISBZAhICAEB\nCwloF2Jn4djRlcYEogaSWBBpRFGGFgwkI2hChk4EOGvn+vXrIxu4WpShsZm3dhFvTeW50okH\ndAGBTAls3LhRisgxykBqkaPkZqof2oMACJhPAAaS+YzRQxoElCGjPD9piGjTRK1lUoZXm5N4\nAQI2JsB7xXHWx+YeFg7CJzLZiS1iuG/OnocCAk4iwCnzvf48sQ9SZJPXTMeWU1wtRbBcFBAA\nAXsQgIFkj3lynZZGG0jK0IKB5LpLyfEDVl4cFfZm1YCbqjzE+8WsXbvWqi7RDwiYToC3g2BP\nT6CoxrC+AsW9pSz29KKAAAjYgwAMJHvMk+u0NMtAUnJdBxQDdiwBZSCpxAlWDVT1h0x2VhFH\nP1YQ2LZtm9xsPqekj2HdBcR+Sl5/vtxc2TChEAQCIGAqARhIpuKF8HQJKE+PWjuUrhzVTsmp\nq6tTh/AIAo4goAwk9uhYWZTHSvVvZd/oCwTMIrBmzRopOqekr6FdBITBtX79BoSkGkoVwkDA\nPAIwkMxjC8kZEFCeHrV2KANRsmmxL5KwUcnNVB7ag4AuBJYvX07hQpHBrthiA6nFIOP+UUDA\nKQSiBlJpP0OHlFvSn0KhIDZXNpQqhIGAeQRgIJnHFpIzIMCengKRwc5nUFYurEHKYDLQVFsC\ne/bsoe3bt5PV3iMGEi73UDiXCAaStpcHFEuDACce4ZJb2j+N1vGb5LTIU/Lj18QZEAABHQjA\nQNJhFqBDOwL7hIFU5DPu8ixu2U8JIXbtUOOAjQko4yQbBhJj4345M1d9fb2NKUJ1EPiKgDJg\ncksHfHXQgGe5ZRGDC3uHGQATIkDAAgLG3YFaoCy6cA8BDoUrNmgPJKaW4/XKPxhI7rmG3DBS\nHQwk5ox1SG642twxRr6W/fndyJdbauiAc8sOl/KQ1MRQrBAGAqYRgIFkGloITpcApw4+2NBA\nxf7IuqF05cS2Y4MLa5BiqeC1nQksW7ZMqt/U09r1R4pZc0u/Sg91HI8gYEcCu3btotraWsot\njxgzRo4hV6xp8nj9+DHBSKiQBQImEoCBZCJciE6PgDJijPQgsSYsDx6k9OYErfQksHTpUiLx\nKd7cPTv6NfWM9AsDKTv80auxBNR1nFc+yFjBQprHG6AcEbbHIXahUMhw+RAIAiBgLAEYSMby\nhDQDCOzdu1dKUeuGDBAZlcfpw5uamowSCTkgkDUCfJPF4TpN3YQK/ux4kGSqb9G1NNSyRgId\ng4AxBJSBlFsx2BiBMVLyhFyOkFDrnGJO4yUIgIBGBGAgaTQZUCVCQHl5SgwOsVPylIcKvEHA\nzgT4Jotvtpp6Zcc4kuxyPNTUhWjZ8mXU3NxsZ5zQHQRoyZIlkkJexVBTaORVDJFy8YOCKXgh\nFAQMJQADyVCcEGYEAeVBUgaNETJZRklL0gcl3yi5kAMC2SCgbrKytf5IjZkNtEMNh0jtH6OO\n4xEE7EZg8eLF5PXnGZ7iW3HIqxwmny5atEgdwiMIgICmBGAgaToxblZLGTClLam5jWKhDC4l\n3yi5kAMC2SCgbrKaqrPoQRIDVx4svrlEAQG7EuDw69WrV4sEDUPEeiGfKcPIq2QPkofUe9eU\nTiAUBEDAEAIwkAzBCCFGEuDNL7kog8Yo2crgUvKNkgs5IJANAiocKOsepBYDDQZSNq4C9GkU\nAWW05Hc50iiR7eT4AkUiUUM/GcqHRA3t8OAACGhFAAaSVtMBZZiAMmDKDF6DVBqIpA1X8kEb\nBOxKgNf7LF6yWK7/oTw9PEjqBtOuTKG3uwnMnz9fAsjvOtxUECz/0KFDpPYwM7UzCAcBEEib\nAAyktNGhoVkEdu/eLUWXGm0gtciDgWTWzEGuVQQ4QUPDwQZqqsmucSTHmx9J1MAeJCRqsOoK\nQD9GE/jiiy8il7PJBlJB16NlP59//rnRQ4A8EAABAwnAQDIQJkQZQ0AZSEZ7kJQ8Jd8YbSEF\nBKwnsHDhQtlpttcfqZGzoXbw4EGkL1ZA8Gg7Amyw+PO7UE5xjam653c7RsqHgWQqZggHgYwJ\nwEDKGCEEGE1AGTBlLSFxRskvDwSkKN4tHQUE7ExgwYIFUn0tPEhCE2WoKb3szBa6u48AZ2Dk\n7538bseaPvjcssPJK9YizZs3z/S+0AEIgED6BGAgpc8OLU0iwAZMjtdLhS1puY3qprwlxA4G\nklFEISdbBOR6CfHprQyTbOmh+m3qHQn1g4GkiODRTgTmzp0r1S3odpzpans8XioQhtjmzZtp\ny5YtpveHDkAABNIjAAMpPW5oZSKB2tpaKjc4xTerW97ikWL5KCBgVwINDQ1ygXdTlRhBQIM1\nSEINmepbfJuodRx2ZQu93Ulgzpw5cuAFPU6wBEBB9+NlP6pfSzpFJyAAAikRgIGUEi5UNptA\nOByWoQ4VLeFwRvbHXqki4ZWCgWQkVciymgBni2tqaqJQH40+voWhFhIbxq5YsYLq6+utRoL+\nQCAjArNnzyZvThHllQ/OSE6yjQt6nCircr8oIAACehLQ6BtWT0DQyloCHAfON3+VJhhIPJJK\n4UXauXOntYNCbyBgIAG1dqGpjx7eIzU01oez2CHMThHBox0IbNiwQYa6FXQ73rQNYmM55HcZ\nRl5/Ps2aNSv2FF6DAAhoQgAGkiYTATUiBHbs2CGfdDHNQAoQ75jOYUooIGBHAspACvXVzEBq\n0UfpZ0e20Nl9BD755BM56MKqkywbvMcboHwRZsfGGa9FQgEBENCPAAwk/ebE1RpFDaScSMY5\no2F0aZGr+jFaPuSBgJkEOAT1s3mfUXMJUbhCLwNJGWwwkMy8AiDbaAJfGUgnGy06obzCqkh/\nM2fOTFgPJ0EABLJDAAZSdrij1zgEtm3bJs90NclA6pqTI+WrfuKogcMgoCWBVatWUd3eOlLG\niE5KhktFiF05yfTFHCaLAgK6E+AfHGbO/IT8eRWUWz7IUnWLqk6R/c2YMcPSftEZCIBAcgRg\nICXHCbUsIqAMl24thozR3XZrCd1T/RgtH/JAwEwCKutVU3+9vEdqzCGh14EDB2jp0qXqEB5B\nQFsCixcvpj17dlOBMFY8HmvfU7kVg6VhxgYaG2ooIAACehGAgaTXfLhem61bt0oGphlIuREP\nkurH9cABwFYElIEUGqDnR3eof0QvZOey1WXlWmWnT58ux17U6zTLGbBBVtDzVGmgcWZKFBAA\nAb0I6PktqxcjaGMhAbVxXneTQux6tHimVD8WDg1dgUDGBGZ9+imFC4mau2csyhQBTQMiv8J/\nKvREAQHdCUybNk2qWCQMlWwUZZh9/PHH2egefYIACCQgAAMpARycsp4AZ/TJE/sVlZuUxa6q\nxYOEzEHWzy16zIwArz/aJTY55jA2EQ+UmTCTWjdXinVIZUTs6cI6JJMgQ6whBPbt2yc3Ns6r\nGEL+gq6GyExViDKQlKGWanvUBwEQMI8ADCTz2EJyGgQ2bdxIPVqMmDSad9qE91fyi5vLTZs2\ndVoXFUBAJwJqz5TQYXoaR4oV68ebxSJsSBHBo44E2GvDRnxR9RlZU4+TQ+RVHknz58+XG6Rn\nTRF0DAIg0I4ADKR2SHAgWwR27dpFB8X+RL1yc01TwSuMI/YibRSGGAoI2ImASgccOlzvj22l\nn9LXToyhq3sITJkyRQ62qPrMrA66uOYMucEywuyyOg3oHATaEdD7m7adujjgZALr16+XwzPT\nQOIOqoUBxpvF7t6928k4MTYHEQiFQsQeJA5fa+6qvweJ0SN9sYMuQIcNpbm5maZOnUa+vHLK\n7zo8q6Mrqj5L9j958uSs6oHOQQAE2hKAgdSWB15lkYAykKrzzPMg8fBqWuTzLuYoIGAHAhyC\nw2FrocP1No6YZbjEQ01VYj+kz+dJne3AFzq6i8AXX3whs8cV9TpDLOfL7m1QXuUR5M/vRuxB\n4h9CUEAABPQgkN1PBj0YQAtNCKxdu1Zq0tt0AylP9rNmzRpNRg41QCAxARV+Expkj4/s0EAv\nNYWapNcr8chwFgSsJzBx4kTZaXHNCOs7j+mR030X1ZxJnDQC6fFj4OAlCGSRgD2+bbMICF1b\nR0AZLH3zIwaMWT33yYOBZBZbyDWHgMxyJZxHwYH6e5CYQHBwRE9l2JlDBVJBID0CEydOIo83\nQEW9vp6eAINbFfc+R0pUhpvB4iEOBEAgDQIwkNKAhibmEPjyyy9lhrmeJiZpYM2VAbZ69Wpz\nBgKpIGAggZ07d9LixYsp1EcYHfn2MJCa+nooLPZknjJ1ioEkIAoEMifAn/tr1qymwqqTyRsQ\nm4ppUKQu/gKCgaTBZEAFEGghAAMJl4IWBHjRLIfY9RHhdT6T93jhLHa5Yq8l3lcGBQR0JzB1\n6lSpYmiIPYwjqazfQyHh7dqyeQutXLlSd8TQz0UEJkyYIEdb3PtcbUbt9eVQYa/TiDcwX7Jk\niTZ6QREQcDMBGEhunn2Nxs4JGg4dOkT9C/JN14pTffcTYXbrhEHW2Nhoen/oAAQyIaCyWwWH\n2OvjWuk7adKkTIaPtiBgKIGPPvpIyPNQce8RhsrNVFhJn4jBFtEvU2loDwIgkCkBe33jZjpa\ntNeWwPLly6Vuh+ebbyBxR4cJQywkNglEmJ22lwQUEwTYgJ8+fTo1l4v03lU28iAJ3ZXHSxl4\nmFAQyDaBbdu20cKFCym/27Eic1yXbKvTpn/ej4nXRY0fP77NcbwAARDIDgEYSNnhjl5jCEQN\npMKCmDPmvBxYGDHEli1bZk4HkAoCBhD45JNP6ODBgxQcar+P6nCxCLOr8RCnVK6trTWABkSA\nQGYElPGhvDWZSTO2tS+nmAqqTpKh3yphkbE9QBoIgEAqBOz3rZvK6FDXNgRU3PXAAosMpJZ+\nli5dahtGUNR9BNR6ieAR9vIeqZkKDRPJGsJhLD5XQPCYVQLjxo2T/Rf3+UZW9YjXeUnviF4I\ns4tHCMdBwDoCMJCsY42eEhBYvGgRlfn91EMkULCiDG7xVC0S/aKAgI4EOHHJhIkTKCycnU39\n7WkgBYdFvmLUL/c6coZO7iCwa9cumjt3LvHGrDlFvbQcdHEfke5bbFyrDDktlYRSIOASAjCQ\nXDLROg+T48K379hBQ4us8R4xi2JhjFWLdOJLRPpkvhFFAQHdCMybN4921e4S4XXCOPLZ00Bq\n7uahpq5EHCq4f/9+3RBDHxcRYG8sf9aX9DlP21H78yqooPvxMq3/xo0btdUTioGAGwjAQHLD\nLGs+xgULFkgNhxVauyfFsKJCOiDWdyDdt+YXiEvVU2E2ygtjVwzBo7wUDAb7eIJjAABAAElE\nQVQJyRrsOoPO0PvDDz+UAynuq6+BxAoqA069/51BH6MAAfsRgIFkvzlznMa8iJvLsKIiS8d2\nZHHEIFP9W9o5OgOBBAR43Y4MsxERp6FB9vQeqeEFj4x8zagbVHUcjyBgFYE9e/bQrFmzKLd8\nEOWW9LWq27T6Ueuj8H5JCx8agYBhBGAgGYYSgtIlwKFEXI5qMVjSlZNqu+EtBtlnn32WalPU\nBwFTCbDRvnXrVmrk8LqAvQ2k5l4eaq4kmjZtGtXX15vKDcJBoCMCEydOpCaxrYPyznRUR5dj\ngYJuIg35cTR//ny5cawuekEPEHAbARhIbptxzcbLm8PyvhSHif2PeF2QlWWgSNSQ7/MSDCQr\nqaOvZAiMHTtWVuPwNCeURjEO3tMJYXZOmE37jUF5Y0r6nm8L5UtawgCRrMEW0wUlHUrAGd++\nDp0cNwyLfyXj9QnHlFgbXsdsfR4PsRdpw4YNxIkiUEBABwIcXscGUjhXhNcNtrf3SPEMDo98\n1YwZM0YdwiMIWEKgrq6OZsyYSbllh4m/AZb0mWknytMFAylTkmgPAukTgIGUPju0NIAAx4Vz\nOb6k2ABpqYs4rqVfpUfqEtACBIwlwKmIt2/fTnLvI5uH1ykyHGbH2ew4zI5vWFFAwCoCkfC6\nEBX3sYf3iLkECntQftejicPP8eOdVVcK+gGBtgRgILXlgVcWE+D0v1yOLymxuOdIdyeWRgyz\nmTNnZqV/dAoCsQRGjx4tDwWPdtbHM48nFAoR9kSKnXG8NpOA3cLrFAsVDohsdooIHkHAWgLO\n+ga2lh16y5AA74vCIXaDCwqoLGDt+iOl+hCRWrzI56OZM2aoQ3gEgawRYAOCb+jCBR4KDXRG\neJ2CqQy+UaNGqUN4BAFTCezbt4+mT59BOaX9Ka/8cFP7Mlq4CrNT6xGNlg95IAACiQnAQErM\nB2dNJMBeG84sdFJZdrxHPDReh/S10hK5Ue3y5ctNHC1Eg0DnBKZPn06ckrjxKGEc2XRz2Hij\nlJvG9iL69NNPaYfYGBoFBMwmMGnSJOG1DJLyxpjdn5HyA0U9Kb/LUTLMjkNuUUAABKwlAAPJ\nWt7orRWBqVOnylenlpW2Omr9U9X/lClTrO8cPYJAKwLvv/++fBU8xlneIzXExmO81NzcTEjW\noIjg0UwC0fC6PnpvDhuPQbHIusdJWxBmF48QjoOAeQSyE9dk3njkhwmHqRw8eNDEXiA6UwL8\noc8pf4v9PhpebH0Gu9b6KwOJf2289tprW5/CcxCwjMCBAwdowoQJ1FxO1NTPmQZSUBhI+WOa\n6d1336Urr7zSMrboyH0EeM+tjz/+mHLExrB5FYNtCYDD7LbPvU9mtbzssstsOQanKc0/8PD9\nC4rzCTjSQOKwLU4djaIvAd77qLa2lr5ZWSHD3LKpaZecAB0h1iLxeijOGFRRUZFNddC3Swnw\nr8S8L1jjqcKxL0I/nVjCJWJt1WEeWrp0Ka1YsYL69evnxGFiTBoQ4B/g+D6gy2B7eo8YYU5x\nNeVVDotms8N3U/YvLDaQUNxBwHEGktfrpdzcXCrJUlY0d1w2mY9SZY07s6Isc2EGSGA9Fotf\nHGfPnk1XXHGFARIhAgRSI6D2PAke6+zI50YxPv/KJuL0y7fddltqkFAbBJIkoEK4OUzNzoU3\njd1eu4h4K4qrrrrKzkNxhO5+saG9x6E/YDliggwchLO/iQ0EBVHGEuDY8FxhzKrwNmOlpy7t\n7AoR1ySKuklNXQJagED6BNhzyTdATWK/oObuzvQeKTrBI8X4AkTvvf8eQlUUFDwaSoBD7KeJ\n8LpAUTXlVw41VLbVwlQ2O3w3WU0e/bmdAAwkt18BWRj/okWLaOPGjXSKyF6XL1Js61D65OfR\nYQX5xPsy7d69WweVoIOLCHByBo5rbzze2caRnNJcDzUO89DWLVulx9ZF04yhWkSANyQ+1NAg\nstfZN7xOocop6UO55YNk9kfOcIkCAiBgDQEYSNZwRi+tCKiNMM8T6490Kt8Q+vD6NfxSp9Os\nuEMXTlpA4tNY7RXk9FEHj4t89chxO32wGJ/lBNRneLFNs9fFAmMvEn83cSIhFBAAAWsIwECy\nhjN6aSEgU/yOHk0FwnN0Wrke64/U5JzfYrCpVMvqOB5BwEwCixcvplWrVlFwkIfCRS7wIAmY\nocPFWEXCBg61RcZRM68u98lubGwk3rLBX9Bd7iPkBALFfb4hh6EMPyeMCWMAAd0JwEDSfYYc\nph+HsG0Xm0SeLZIi5Ik1SDqVXnm5NLyoiD777DPasGGDTqpBFwcTUF6UxuP1ej+YitwrwuyO\n9RCnNh8/frypXUG4uwhwAiBO8V3c51zHLKbPKz9cpCvvRzNmzJBjc9eMYrQgkB0CLvpGzg5g\n9NqWwDvvvCMPXNilsu0JTV59q2tEL3XTqolaUMOhBDgN8ahRoyicL9JfD3WH90hNZWNLmJ36\nTFDH8QgCmRBQBndJ73MzEaNdWzb4+PNCZefTTkEoBAIOIwADyWETqvNw6urq5PqeqtwcOr6k\nWEtVz60sl9n13n7rLcJ+B1pOkaOU4sXknBSkcbgwjvzuMpCae3hk1j72KnMWPxQQyJQAr9Ph\n9PG+3HIq6H5CpuK0al8iDCQuygDUSjkoAwIOJAADyYGTquuQeG0Px4df3LWLtqEPxWKPg3NE\nyu/NW7bQ9OnTdUUJvRxCQHlPgm7IXtfBnHHWPs7eB49tB3BwKGUC8+bNkz84FNWcRR6vHhlS\nUx5EnAZ5lUfKdVVTpkyV36NxquEwCICAQQRgIBkEEmI6J/Dqq69yoi66qFuXzitnscal3SP6\nvfbaa1nUAl07nQCn7J08eTI1icutqY87P4qDx4hxi38wkJx+tVszPuVdUd4Wa3q1phfenLS4\n9zli3V693I7Cml7RCwi4l4A7v5XdO99ZG/mcOXNo5cqVdLrIXNctJydreiTT8dHFxTRA7IvE\nKVW3CE8SCgiYQWDMmDEUCoUo6KbkDDEgw4UeCg7x0OrVq2nBggUxZ/ESBFIjMH78BPL686mw\n6tTUGtqkNhtIXCZMmGATjaEmCNiXAAwk+86drTR/8cUXpb5X9OhmC71ZT16D9PLLL9tCXyhp\nPwIqvK7xWHd/DKtkDfAi2e8a1knjZcuW0ebNm6iw59eFkZSrk2qG6VLY40Ty5RTTRPHjHYem\nooAACJhHwN3fzOZxheRWBNgLw6EP/YRX5mulJa3O6PuUs+wV+X30+uuv06FDh/RVFJrZkgB7\nTBYuXEihAWINTrm7kjPETlhIeJDCBR7iDaQ5SxcKCKRDgJMzcFFelnRk6N7G4/VTYfWZVLtz\nJzyuuk8W9LM9ARhItp9C/QfwwgsvyF3Av9eju/7KtmiYLzay5WQSvE7kvffes43eUNQeBJS3\nxFV7H8WbGpG9j7P47d27FymM4zHC8U4JSAPJ46Wi6jM6rWvnCiW9z5bqI8zOzrMI3e1AAAaS\nHWbJxjru37+fONlBmcgOd2HLHkN2Gc5VIszOJxbGPv2f/yCcwS6TZgM9OTTmvfeF0R0gCh7p\nbu+Rmi6VxU8Zjuo4HkEgGQKcJn7x4sVU0O1Y8ueVJ9PEtnUKe50mMvQF5BpZ2w4CioOADQjA\nQLLBJNlZRTaO2Ei6vHtXyvPa63Krys2lc0XK7zVr12JRrJ0vQs10nz17Nm3dspUahwnjKBcG\nEk9PU2+vzObHWf3Yk4QCAqkQ4IQ6XIprIt6VVNrara4vUEQFYi3SqlWraMOGDXZTH/qCgG0I\n2OuO1TZYoSgT4D2Pnn3mGWkYXWmT5AyxM3ddzx7y0JNPPhl7Cq9BIC0CvB8Yl+Bx+PhtDZB5\ncFa/sWPHtj6M5yDQKQFlIBX1HtFpXSdUKK6JjFON2wljwhhAQDcC+IbWbUYcpM/bb79N23fs\noIvFvkflARFPZMMysLCATikrofnz52PvCRvOn24qc8KPsR9+SOFiD4UOh/eo9fyobH7KgGx9\nDs9BIB6BAwcO0KxZsyinpB/llvSNV81Rx3kjXC7scUUBARAwhwAMJHO4ul4qZ6P698iR5Bdr\neK6tinhh7ArlJ716StUfe+wxuw4BemtCgG9o6kXIaePRwjjywkBqPS3hCmE09vXQZ599Rhs3\nbmx9Cs9BIC6BmTNnymiF4hajIW5FB53IKepFueWDaPbsOTKE3UFDw1BAQBsCMJC0mQpnKcJ7\nvGzavJkuEpnguufqvTFsZ+SHFxfRCSXFxJvd8voRFBBIl8CoUaNk06DL9z6Kxy94TMRo5JTf\nKCCQDIEpU6bIasqrkkwbJ9QpFum+m5pC9PHHHzthOBgDCGhHAAaSdlNif4XYe/SvJ56Q3qMf\n9bK390jNxg3VES/SI488og7hEQRSIlBXV0d8M9fUVSQlqIb3qCN4weHiK0n8U4ZkR3VwDAQU\nAc4IOXnyFLl5Kmewc1NRBiHC7Nw06xirlQRgIFlJ2yV9vfHGG9J79B3hPeJMcE4oxwoP0okt\nXiQO6UABgVQJjBs3TiYhCB6Nj9147MKFHgoO9MgMXcuWLYtXDcdBQBLg1N47d+6gSOprv6uo\n5HcdTj6R0nzq1GnU3NzsqrFjsCBgBQF8U1tB2UV9NDQ00BOPP045IqX3T6qrHDXy/6rpJcfz\n4IMPOmpcGIw1BMaMGSM7Ch6Dj91ExBUfxStRXZxzNwHlPSmuPst1IDy8KW6v08Vm5rtlEiHX\nAcCAQcBkAvimNhmw28Q///zzMnPdFWLfo2459l57FDt3R4q1SGeUl9HChQvpo48+ij2N1yAQ\nl8DOnTvp008/paZeHmruivC6uKDEieARgo9IegkDKRElnGMCU6dOJbFrqvQguZFIkViHxIVD\nd1FAAASMJQADyVierpbGGzxy5roin49+2NNZ3iM1sbcILxLf3j74wAMyXEodxyMIJCLwoUjt\nzWEwMntdooo4JzfPDQ7x0KZNm2jBggUgAgIdEuAfHfjHqvwuw8kvQs3cWIp6nSYMRB8MJDdO\nPsZsOgEYSKYjdk8HT4jEDPtECuNrxeaqZQFnxoMPKMinb3WtpDVr1xKvtUIBgWQIfPDBB7Ka\nTEKQTAOX12nkZA2iKG4ux4Hhd0Bg2rRp8qib0nvHYvDlFFNB9+OI1+tt27Yt9jRegwAIZEAA\nBlIG8ND0KwIbNmygF194QYbVXV3V/asTDnzGa5FyxRorzmi3XxiEKCCQiADfuPDePqHeHgqX\nI7wuESt1LiQ8SCQidMd+OJY4UxkKCMQSUGFlRdVnxJ5y1WsVZifDDV01cgwWBMwlAAPJXL6u\nkf73v/+dgqEQ/VdNT8oTxoOTC6+tukYYgbt27aKRIqQQBQQSERg/frw8HTwKxlEiTm3OBTzU\nKIykrVu2IsyuDRi8YAK8lcT06dPJX9Cd8ioGuxoK74fERRmM8gX+AwEQyJiAs+9kM8YDAckQ\nmDt3LnEK48EFBfStLpXJNLF9netFGGFlIEDPPvssbdy40fbjwQDMI8Drj7gEj8LHbSqUFS/F\nL5W2qOtsAuyRra+vJ+U9cfZoE48ut2wABYqqacaMmdTY2Ji4Ms6CAAgkTQDf2EmjQsWOCPDC\n87/85S/y1C/71pDH445fyQtEIoqfiVA7/kK69957O0KDYyBAtbW1xD8ghMTGsAivS+2CCA0W\nnyUimx3/+IICAq0JKG9Jcc2ZrQ+79jmHGTY0HKTZs2e7lgEGDgJGE4CBZDRRl8l78803aenS\npXRuRTnxZqpuKt8WyRqGFBbIlN+zZs1y09Ax1iQJTJgwQa6hCSG8LklirarliE1jB0Wy2fFn\nDAoIKAJsIHl8OVRYdbI65OpHFWan9oVyNQwMHgQMIgADySCQbhTDab0fEOmuec3Rz/tUuw4B\ne8t+07e3HDd70UJiDRYKCLQmEF1/NAwfta25JPs8eGSEG/YdS5aY8+utX7+e1qxZQ4U9TiKv\nP9/5A05ihAU9viZZKM9aEk1QBQRAoBMC+NbuBBBOxyfw8MMPi1289xCvx6nKzY1f0cFnjhKb\nx14o1l2tXLmSXnzxRQePFENLlQBnOPzkk0+oqRthc9hU4bXU5/2QSHxLsScOBQSYgPKSuD17\nXeurwevPpQLhTeP1sF9++WXrU3gOAiCQJgEYSGmCc3uzJUuW0CuvvELVebl0nTCQ3Fx+3rua\nCsWaJE77vWPHDjejwNhbEeC0u+xVDMJ71IpKik/zRZjdAA+tWLGCeCsBFBCIGkg1ZwFGKwLF\n1REeik+rU3gKAiCQBgEYSGlAc3sT3pfkj3/8o1xb8es+NZTj8LTenc13ZU6Abq7uKbMq3XPP\nPZ1Vx3mXEJg4caIcaWiY8IKgpE0gdESEH7xIaSN0TEP2ys6eM4dyywZSTlEvx4zLiIEU1UT2\ng5o0aZIR4iADBFxPAAaS6y+B1AG89tprcm+Ss8rL6OviD4Xoih7daGBBPo0ePZpmzpwJJC4n\nwJ6jqdOmUljkLWkSGexQ0icQPCLyNYVfxtNn6JSWH3/8MTWJ91YRste1m9IA7wlVeQR9/vnn\nMvS9XQUcAAEQSIkADKSUcKEypy1+4P77Kc/npV+1JCgAFSKfSNjw+359JIq77rwT+1G4/KLg\n1N779+2n4FDxEeuS1PdmTXm4zENNPYnmCKZ1dXVmdQO5NiCgvCPFNWfbQFvrVSyuGUG89QaS\nNVjPHj06jwAMJOfNqakj+tvf/kZ1+/bRTb16Uo/cHFP7sptwTthwabeutHbdOho5cqTd1Ie+\nBhJQ3o7gUHiPjMDKhmZzUxOxBwHFnQSaxPxPmTKV/HmVlN/1KHdC6GTUynBUhmQn1XEaBEAg\nAQEYSAng4FRbAjNmzKBRo0bR4SKU7HtV3duexCtJ4NbevagiEJAG0urVq0HFpQTkL7h+otDh\nMJCMuARCnM1OFGV4GiETMuxFgL2ydXV7RXjdWcIpi1uXjmYvr3IIBQqrxA8J0xHF0BEgHAOB\nFAjgUyYFWG6u2tDQQHfccQfxbcr/9e9DfoQNdXg5FPv99CuRuCIYDNLtt98uE1l0WBEHHUtg\nnfAgrl27loKHiXdLAAaSERPdVOOhcBHRtGnTZAiRETIhw14EVNKT4t4Ir0s0c0UizO7AgXq5\nxUCiejgHAiCQmAAMpMR8cLaFwKOPPir3WLiiezcaViTuVFDiEjivSwWdUlZC/IvnG2+8Ebce\nTjiTgIr/V14PZ47S4lGJH2SCgzzEm1N/8cUXFneO7nQgMH78BLkZamHVqTqoo60Oxb3Pkbqp\nTaq1VRSKgYDmBGAgaT5BOqjHex4988wz1D0nh34mQshQOifwvyJhAyeyuO/ee7E3Uue4HFWD\n9z/iEhyMj1cjJzY0JMJT8TVSNmTpTYC/gzZv3kSFvU4TRpI7NyVPdoYKe5xIvtxS4nVInLAB\nBQRAID0C+AZPj5trWvHC2D/84Q/Ej7/r15sKxIaoKJ0TqMrNpf+q7kX7xL4df/rTnzpvgBqO\nIHDw4EGaPXs2NXUjClcgvM7ISQ0OFDzFNxaH2aG4i8BHH30kB1zS+1x3DTyN0Xq8fiqqPpM4\n4+y8efPSkIAmIAACTAAGEq6DhASefvpp4l/vvlFZTqdjz6OErGJPfk/sjXREYSHxlzvCHWLp\nOPP1p59+KtefheA9Mn6C8z0U6uORn0c7d+40Xj4kaktg3Lhx5PEGZIIGbZXUSLGSPudJbZgb\nCgiAQHoEYCClx80VrXih+SOPPEKlIvHAb7DnUcpz7hXrJu4Y0FcmtOC9kbCHS8oIbddAeTdC\nYr0MivEEFFek+zaera4SV61aRZwRtLDqZPLliJ2XUTolUNjz6+QNFBIbSOFwuNP6qAACINCe\nAAyk9kxwRBDgD1UOrWtsbJRZ2cpF6mqU1AkcJlKi/6hnD9ohfvHmPaRQnE1AGkhie7BQfxhI\nZsy0WtelDFEz+oBMvQiMHTtWKlTS95t6KaaxNrxOi8Pstm3bhqQmGs8TVNObAAwkvecna9q9\n8sorMgvbqWWldEHXyqzp4YSOf9Srivrn59Hbb79NvJcUijMJsMd1w4YNFGTjyA8DyYxZbu4p\nfrwRSTSnz5iOBehmANZQJhtIvK4G6b1TmxxlUCoDM7XWqA0CIAADCddAOwKbN2+m+//+dyoU\nCRk4GxtKZgQCXi/9UYTa8Zvt/4RXrr6+PjOBaK0lARX2FRoM48i0CWpJ9123t44WLlxoWjcQ\nrAeBFStW0JdffkmFPU+Vmdn00MoeWhT1Ol2G2bGBhGx29pgzaKkXARhIes2HFtrwBqf1Bw7Q\nz3tXU49cES+EkjEB3jvq+1XdafOWLfTAAw9kLA8C9COgwr5Cg/CxaubsKL6Kt5l9QXZ2CYwe\nPVoqUNLvwuwqYsPeOcyO90Tavn27jAax4RCgMghklQC+ybOKX7/O33rrLZo+fTodX1JMl3br\nop+CNtbo5ppeVJOXSy+99BLNmTPHxiOB6rEEeK3ep7M/pWYRjdrcBR6kWD5Gvg5xum/xT3ns\njJQNWXoRGD16DHl9fKN/tl6K2UQbZVgqQ9MmakNNENCCAAwkLaZBDyV4Qeff/vpXyhMhYbf3\n70seEc6CYhwB5nqH4Mrlf//3f6mhoUE+x3/2J8AG76GGQxSE98j0yQwXinTf1R5asGAB7dmz\nx/T+0EF2CPAePps2bRSpvUeQLyAWnqGkTKCIQxPzymns2A9lwqWUBaABCLiYAAwkF09+7NDv\nFKmoeWPTW4Sno1p4OlCMJ3Cs8Mxd0b0brV+/nh5++GHjO4DErBBQ3gysP7IGP3PmTJtIemIN\n72z08v7778tuSwdclI3uHdEnJ7co6XuB2GJiL02dOtURY8IgQMAqAjCQrCKteT+jRo2iSZMm\n0XCxVuYqscEpinkEbu3di6rE2q5nn30WKVjNw2ypZHnz4RfpvQfA62oFeLUfEm76rKBtfR8c\nsjpmzAciMUM5FfU6zXoFHNRj6YDvyNG89957DhoVhgIC5hOAgWQ+Y+17qK2tpb/8+c+Uo7Kt\nIbTO1DnLF9kBOdSOfwH//e9/j9AHU2mbL3zjxo1yI0uZ3jsHBpL5xImaaoQHqSCyDgkbYVpB\n3No+pkyZIr0eJf0vFCm+sQdfJvQLug6nnJJ+NHnyFNq9e3cmotAWBFxFAAaSq6a748Hedddd\ntGfvXrqpuif1Efv1oJhP4MTSErpEJMHgHeIfffRR8ztED6YRUNnUlFfDtI4g+CsCXo9Y7+Wh\nXbt20aJFi746jmeOIMDJgriUDbjEEePJ9iDKDruEQqEgIVlDtmcC/duJAAwkO82WCbqOGzeO\n+O+IwkK6RqShRrGOwC9611C3nBz6z3/+g5s867Ab3hP/2s0lNAQfpxKERf+FBkd4K/4WdYtu\nTCawY8cOmaEwt2wg5XcZZnJv7hBfOuBiMVAPKcPTHaPGKEEgMwLaf6Pzr7Off/55ZqNE6w4J\ncAaou0RiBr8IqeONTH0IreuQk1kHi/w++j+xEW9TU5MMtQsGg2Z1BbkmEeBMhJ/MmhVJ790V\n4XUmYe5QrPTYCeRYh9QhHtsefOedd+RnYtnhl9l2DLopHijsLtdyLV26lJYsWaKbetAHBLQk\noLWB9MUXX9Add9yBN7RJl87dd99NtSJE5ae9qmhAQb5JvUBsIgKnlpfShV0qiXeMHzlyZKKq\nOKchgVnCOGo8JNJ7D9X6o1RDcpmrJNN99/HQwoULib0OKPYnwOvJ3njjDbnuSCUXsP+o9BhB\n2cDLpSKvv/66HgpBCxDQnICW3+qhUIieeeYZuu2227AXj0kXEP/qymlUDxeG0XU9e5jUC8Qm\nQ+BXfWuoMhCgfz3xhDSUkmmDOnoQ4MyPXIJD4T3KxoyEhkS4I8wuG/SN75N/cOAtEIr7nEt+\nsX8PinEEimvOIn9+pfzeP3jwoHGCIQkEHEpASwPpgw8+ECk+x9BfxaalNTU1DkWfvWHtF3sd\n3X777TKkjkPrAiJ7HUr2CJT4/fS7vr0pKH4Y4A1kOeQORX8C/Gs3G0hhkdekqR8MpGzMWPCI\nyGeXMlSzoQP6NI7Aa6+9JoWVD7zSOKGQJAlwNsDSwy6j+vp6JGvANQECSRAQO3foV0499VS6\n4IILyC9uHB9//PGECi5btkxkZwlF6+zcuZOam5sJ6zmiSNo9uffee2nbtm10vfAcDRHJGVCy\nT2BEZTmdXVtOE0W40NNPP03XX3999pWCBgkJqNCu4NHCOPLBQEoIy6STzd09cv3X9BnTRVro\nOsrPR6iwSahNF8vf3R99NF6mpC6sOsn0/tzYQfnAK6h24ZP08ssv08UXc+IGlFQJ8A9j2Fog\nVWr2rK+lgVRZWZk0Tb6RbJ3b/+ijj6bBgwcTf9iitCcwf/584hjkPnl5dINI642iD4HfCi/S\nnLp99Ogjj9Cxxx5LPXtifvSZnfaa8ObKXELD4IFtT8e6I0HB3zu1kT788EM67TRsKmodeWN7\nev7554X3PERdBl1lrGBIixLIKa6RyRqWLJlG06dPl/dK0ZN4khQB3sQYxR0EtDSQUkF/+eWX\nS5exasPepIBYz1FQIHYRRGlD4JBYTP7A/ffLY//Xvw/lIrSuDZ9sv6jMCdD/9K6mu1avpYce\neogee+yxbKuE/hMQ+Pjjj4XnSKw/EvvxoGSPQHCYh3KnEs2YMYPOO++87CmCntMmwN/bo0aN\nJq8/j8oOuzRtOWjYOYHywd+n/ZumybVI/EMcSmoEfGKjdxR3ELC9gfTLX/6yzUzxRmicyrK0\ntLTNcbwgeuCBB2j9hg303e5d6diSYiDRkMBFYvPYD2t30adz5tCECRPossuQ6lbDaaLly5fT\nBvFeCnKSgDwYSNmcoyaRya65hOQv4hxilyP2FkOxFwHei2/Hju3EmdZ8uWIyUUwjUFR9BgWK\nqmn8+PFyLXJFRYVpfTlRMC/98GBLFCdObbsxITakHRJnHuC1WrwhaVdx8/DfNdXOHKRDRvUH\nsTdSnvDu3XvPPVRbW+uQUTlrGBzOxSU4HB+hWZ9ZcbPCYXacfIa9SCj2I8DhdVwqBl9jP+Vt\nprHH4xWcr5brtFVSDJsNAeqCgCUE8O1uCebsdsJJKzhrHWdH+51IKc0blKLoS6BXXi7dKNaH\n7RWLznmvKhT9CHww9gMi4X9Hem895iY4POLF4wyoKPYiwBEfc+fOpYIeJ1JexWB7KW9TbXkT\nXq8/n1566WUktLLpHEJt8wnAQDKfcdZ7eOmll2jBggV0VkUZnVmBvSWyPiFJKHB1VXcaJPao\n4nT306ZNS6IFqlhFYPHixbR2zdrI2qN8hNdZxT1RP5xmvVlEVXPYUENDQ6KqOKcZgWeffVZq\nVDHkWs00c646vtxSKh1wsQxrVN5w544WIwOB9AhobyCx6/3qq69Ob3RoJdN5P/Tgg1QoFhZy\nljQUexDwibCh2/v3JX6D3nnnnYSN/fSZN5W9LniM9h+f+kAzWxMOszvaK98nEydONLs3yDeI\nwI4dO8SePGPkmpji3mcbJBVikiFQMTRikD7zzDPJVEcdEHAdAXzDO3zK//KXv1D9gQN0S00v\nuf7I4cN11PCGFBXSlT260aZNmzrdD8xRA9d4MBymygZSOBfhdbpNU2OLwfree+/pphr0iUPg\nxRdfFPsYBqli6HVi4TtuR+JgMuVwbml/4oQN7BGfI5ICoYAACLQlgE+ktjwc9Wrq1Kli472P\naGhhAV0uMteh2I/AzS2G7dMiwcbKlSvtNwCHacxJAHiPteBRIrQugPA6naa3uZeHmnoQfTz9\nY+yDp9PExNGFveIvv/wK+XKKqVysiUGxnkDlET+SnfLm5CggAAJtCcBAasvDMa84Dv+uu+6S\nIVr/K/Y88ooQFBT7EeDQyF/3qaGQ8FxwqB1Kdgm8/fbbUoHG4/HRmd2Z6Lh3npfmpmaCF6lj\nPjodfeutt6iubq9I7X0VeQOFOqnmGl0Kq04SiTGG0qRJk2j16tWuGTcGCgLJEMC3fDKUbFhn\n5MiRMjTr8u7daEghvnxsOIVRlc+uLKdTykpkpqd33303ehxPrCWwe/dumQSguYvwVPTHR6e1\n9JPrLXicmBfx74033kiuAWplhQCHqvLaF483IMLrfpAVHdBphEDlsB/LJ/Ai4YoAgbYE8C3f\nlocjXq1bt46efPJJqgwE6L9EiBaK/Qn8RiTYyBF7I9137720b98++w/IhiN45513xHqJEDWe\nCG+srtMXLhLJGoZ6aM2aNfIHBV31dLtenDlt48aNVNL/2xQo6O52HFkdf0nf8ylQ2JP4xzdO\nmoECAiAQIQADyYFXAidmCAaD9Ive1djzyCHzW5OXR9eJ1N+1u3bRI4884pBR2WcY4XCY5KaK\n4hOz8QR8bOo8c40nRebnlVde0VlNV+v21FNPyfF3GfYTV3PQYfAer58qjvihvGdQG/bqoBd0\nAIFsE8A3fbZnwOD+J0+eLPfNObq4iC7oWmmwdIjLJoHre1VRVW6O2NzvJVqxYkU2VXFd3zNn\nzqS1a9dS45EeYi8Fir4EQgPFnkjio4+9FLW1tfoq6lLNONHJkiVLqKhmBOWWDXApBb2GXT7w\ncvLllomkGS/T/v379VIO2oBAlgjAQMoSeDO6bWxspL/+9a8yMQOHZKE4i0CeCLG7TSRs4Pj9\nu+++21mD03w0L7zwgtSw8VR8ZGo+VSTyRdOhk70yHFJ6/bRX2F0Kcvg3ly5H/tRdA9d4tF5/\nPpUPvkYaR/C8ajxRUM1SAvi2txS3uZ0999xztH79erqkW1caJFJ7oziPwIiKcjqhpJhmzZol\nEwY4b4T6jYjX9E2ZMoWaehI19cNHpn4z1F6jxhPFPOWQ9LbyD0coehBYsGABffLJJ1TQ7Tjx\nd6weSkELSaBiyDXEhtIzzzxLeM/gogABme8HGJxAgENJnnj8cbnm6OYacSeH4lgCvxLeQb5N\nv+eee/BFZsEs8w8PvAbp0Gk+C3pDF4YQyBdeJJHym/esGjNmjCEiISRzAv/+97+lkMqjbsxc\nGCQYSsCfVy5Srl8hwlJ3ktrOwNAOIAwEbEYAP4fabMLiqfvwww9T/YEDdINYp1IustehOJfA\nYQX5dJnY+JezQGFRrbnzzKm933zrTQqXiOxox2Dtkbm0jZXeeJr4ehNT9h+xyTJK9gl8+eWX\n0uudWz6IiqvPyL5C0KAdgUqRrIGTNnASDQ7lRgEBNxOAgeSA2V+5ciW9+eabVJOXS1eIfY9Q\nnE/gxuqeVCQ2kX3iiSeIb+JRzCHAa48ONRyihtPEnbYPBpI5lM2RyvtVBYd5iD8fOUQSJbsE\nvlp7BO9Rdmcifu+BwiqRev0i2rBhA40dOzZ+RZwBARcQgIHkgEm+7777qLm5mW6tqaaAWMiP\n4nwC7CX8Ua8eclHtY4895vwBZ2GEnM3p+Reep3Ceh1Tq6CyogS4zIHBoRCQskn9IQMkegS1b\nttD774+inOIa4n13UPQlEEme4aGRIyPhkPpqCs1AwFwCuJs2l6/p0nmx/rRp02h4URGNqCw3\nvT90oA+Bq3p0px45OfSKSM3Kv/ihGEuA06nvq9tHh74uPEfCSEKxH4GmauFFGuShL774gjhV\nO0p2CDz99NMiZCtElcN+KkK4sJYvO7OQXK+5pf2puM+5YiuJ5TR16tTkGqEWCDiQAAwkm0/q\n/fffL0fw8z7VNh8J1E+VQK7wFnJCjpCIFX/ooYdSbY76CQjU19cT39RRrvAe8VoWFNsSOHRO\nZP7++c9/2nYMdlZ8l9jc+rXXXyd/flcqPewSOw/FNbp3OfIGOdaRI0e6ZswYKAjEEsA3fywR\nG73+6KOPaOHChXRGeRkNFxvDoriPwAVdKumw/HyZqWvZsmXuA2DSiDn5xZ49e6hBeI/CBfAe\nmYTZErFNfb0UFJvHfvbZZzR9+nRL+kQnXxF48cUXxTq+BqoYej15fSL3Oor2BPK7HEmFVSfL\n9wy/b1BAwI0EYCDZdNZ5zRF7DfjW7ZaaXjYdBdTOlIBXbIp5S+/I/D/44IOZikN7QWDv3r30\n1H+eiqw9Oh0fkU64KA6dF5lHzvaJYh2BAyKz6gsvvEi+nGKxEelV1nWMnjImUNniRVKp2TMW\nCAEgYDMC+Pa32YQpdUeNGkWrV6+m87tU0ACR9hnFvQROFx7EI4sKZbw4r7VAyYwA3xDs37ef\nDp0J71FmJPVp3dRbeJGO8EiP+7hx4/RRzOGavC5C6+rq9lL5oO+TL4AoBztNd1HPUyivcpjM\nAMmZIFFAwG0EYCDZcMZ5fwLOXOYT3gNO94wCAje3eBEfeeQRwMiAwNatWyOZ64rFRqNYe5QB\nSf2aNnxTJAcQLvcHHnyAQqGQfgo6TKNgMEjPPPMMeURYXcXQ6xw2OncMJ5LRjkilaHfHqDFK\nEIgQgIFkwyuBvUfr1q2jC4T3qCYvz4YjgMpGE/haaQkdI9ahzZgxg+bNm2e0eNfI4zDFxkON\n1HCuuJPOwdojJ018c3eRcON4D61bu45effVVJw1Ny7GMGTOG+AeHssMuFQkaKrXUEUolJlDc\n5xsiNXtvGj06MpeJa+MsCDiLAAwkm80nrz3iPT3Ye/STXvAe2Wz6TFX3hhZvIvZFSg/zokWL\n6L333qMmsddy49fw0ZgeRb1bNZwvvEgiT8Cjjz4qQr/q9Fb2/9n7DjgpquT/mjybEyywsOSM\n5IyAoCAqKggo6ikGMJ/hZ8Bw56k/Pe93p+fd/+5Mp5453+mBCZUgSJCcc5QcNucw4V/1Zht3\nYUPPTM/0656qjyuz3S/U+763Pa+66n3L4Nq98QayQKLLLqPXzQYfSeyqb7FYIR3njyja33rr\nrdgFgkcekwjwLsBg007ZrQ8cOAAXZ6RDG7fLYNqzupFEYAh6kfrgWSRi6iJ2Q5bgEHj22WdF\nhYrLcBNtZe9RcOgZo7Q/2QIVY6yCoZBfJERuzugZRHl0ktqOA2dyu8h1xC1HHAHhAXSnAZ0n\no+TZLIxArCDABpLBZpryEtDW7cbWLQ2mOasbDQRmtm4luuH8FcGh/eWXXwpKW0oq6unOj8Xg\n0DNW6Uo0kHypFnj3vXdh7969xlLeINqKHGKoa8Y5Mw2iMavZEAJWuwsZCH8FlBuOjCQWRiBW\nEOCdgIFmmrJa79y5E8amp0IHzH3DwgicicBIZLTrgqyG8+fPFyyHZ97n389GgL74//jHPwKg\n46hiEv6PxdwIONCLdKkVvB4vPP300+Yeqw6jI8YzOgsZ17wvxGf210ED7lJrBMhAIrINyg9H\nJFEsjEAsIMAGkoFm+fXXXxfa3pgV8BIYSHVWNYoI3JDVEvx+PyhvcaPYtSG7olCrkydPCtY6\nX3MOrTPkJAapdHVfK3g6WWDFihVAYcss2iHw9ttvi8Yyet2kXaPckq4I2N3pkNJxEhw7dgwo\nQT0LIxALCLCBZJBZ3rp1K6xatQoGIFNZLzxnwsIINITAeDyf1tLpFIQDeXl5DRXj64gAve1+\n6+23wJ+CXoXx/DiMpUVRfgV6C3HK6ewZn63QZubz8/Nh7ty54EhoieePLtSmUW5FCgTSe84Q\nepAXiYURiAUEeEdgkFlWGGSua8VnjwwyZbqpaUeGw6tbZkJVVRV88MEHuukhe8fkZXviiSdE\nqFXZJHwUMq237FOmqX5E+11xnlV4Dzl/mDbQfvrpp1BZWSnOrFisHK6qDapytOJO6wrxLYeJ\nNBLbtm2TQynWghGIIAJsIEUQXK2aPnXqFHzz9deCtW5UWopWzXI7JkZgcmYziLPZ4EM0kMhQ\nYjkbAdrMrV27Fqp7IDFDb34Uno2Q+a9UjkPChnQkbHj3XSCad5bQEaAUFB98+KE4q5La5crQ\nG+Ka0iKQ3uM6odt7770nrY6sGCOgFQK8K9AKyQi2Q0kNqzHz+/QWmcg+zGckIgi1aZpOstvh\nsmYZkJObC99++61pxqXVQOilwx//hMQM6DUSoVZaNcztGAsBMf9oJOHm/re//S0fQA9j9n74\n4Qc4dvQoJHeYCHakhWYxHwJJ2eeL8Eli/eQ8YuabXx5RXQTYQKqLh3S/edAw+uTjj8FttcJl\nzTkbuXQTJLFCV7VsLrTjMLuzJ4nYy0qKS6B8ggX8afzS4WyEYucK0bpX9bPA9u3bmdgkjGn/\nEL1HJOndrg2jFa4qMwIUNpnadboIo/z8889lVpV1YwTCRoANpLAhjGwDixYtgpP4tvuSZulA\nXgEWRkAtAkQFPyg5ScSMEz08SwCB77//XnjVPNkWqBrJj0BeFwF6d3+8Bf7297+JRNyMSXAI\nHEXP0Y8//gju9B5I790nuMpc2lAIpHaZBhaLDT7GF7csjICZEeDdgeSzS+F1JFNaBLwBkqvL\n6kmGwNSadcNfZoGJKSwshCeefEKwl5VfRSxm7D2SbMnqoo4/EUMtL0dPUmUVPPbYY4ImXxdF\nDNrpf/7zH4FZaterDDoCVlstAo74TEjMHiuSLK9fv15tNS7HCBgOATaQJJ4yeitHCfd6JMTj\nD1N7SzxV0qo2FhPHpjrs8AVS7xK7VKzL73//e8jNyYWKC/DcSUs2jmJ9PdQef/VAK1R3twji\njvfff7/2Lf7cCALEBvmfzz4Dq82FuXIua6Qk3zILAgoJBxnGLIyAWRFgA0nimaUYX/rymdS8\nmcRasmoyI+DAs2uXZGRAUXExzJ8/X2ZVI64bhavOmTMHvK0sUIkGEgsjcCYC5dPQq+i2wHPP\nPweHDh068zb/Xg8CK1euFOQMie0uBJszqZ4SfMlsCCS2HgX2uObwNbLrVlRUmG14PB5GQCDA\nuwSJFwIZSA5krZuA549YGIFQEbg8M0Du8Rm+5Y1VodA6YimjxKBl03ETbGPvUayuhcbGTQmD\nyzDUrqK8Ah599FEOtWsMrJp79NKBJLXzFTVX+B+zI0BkDeQtLC0thQULFph9uDy+GEWADSRJ\nJ37dunXiDeZ5GCKVzOQMks6SMdTqEh8P3eLjYPny5UD01rEozzzzDOTk5ARC61qzcRSLa0Dt\nmKsHB0LtVq9eLfIjqa0Xi+UobJfSCJA3IaHV8FiEIGbHnNJpkhi7YiDHLBA8cNMiwAaSpFNL\neQZIJjK1t6QzZCy1LsGcSJTr5auvvjKW4hpoS6x1c/EMljcLOLROAzxjoQkRahdngef//Dyz\n2jUy4ZT7iLwIlPvIYuHtRCNQme6WO707uFI7w9Kly6CgoMB04+MBMQL8RJNwDXi9Xvjmm2+Q\n1tsGw1OSJdSQVTIaAhSmSX6TWDOQ8vLy4PHfPY4hdRRahzT5HFpntKWri74i1G6SFSorKuHh\nhx/mBLINzAJ9T5GkoIHEEnsIkGHs9XqAXkKxMAJmQ4ANJAlnlA690sbu/LQ0oEP2LIxAuAg0\ndzqhf1IibNq0CQ4fPhxuc4ap/8QTT0B+Xj5UXIisdVkcWmeYiZNAUcFq18sCGzZsgDfeeEMC\njeRSgQ7nL1r0AzgSsjj3kVxTEzVtkttfJPqiMEsWRsBsCPDuW8IZnTdvntBqfEaahNqxSkZF\nYFxGgOwjVr7MKKzuu+++A0oIWzmGH3VGXbd66k2hdv4EC/y///f/gJMt152JpUuXIoNZOSQh\nex1LbCLgSukowuyWr1gBxciUysIImAkB3jVINpt0ToTomCm8bjCH10k2O8ZW5/z0VDEAMhrM\nLidOnID/ffp/ARwA5ddgfB0nhDX7lEdkfJRAtmyqFTweD8yePRuqq6sj0o8RG124cKFQO6nt\nOCOqzzprhADNvxf/PpYsWaJRi9wMIyAHAmwgyTEPp7WgcI7c3FwYlZoCdqT4ZmEEtEKAwux6\nJyaIkCGzs9n95je/geKiYii/BEPrmvPfkVZrKBbb8fS2QtUAC+zYsQNefPHFWITgrDFTfj4i\naLC5UiA+c8BZ9/lC7CCQlH2BGCytBxZGwEwIsIEk2Wwqb+XG4vkjFkZAawTGIG08CSVNNat8\n9NFH8OOPP4KnkwWqzuVHnFnnOZrjKp+MoXbJFnj1n6+Kc3zR7FvGvrZu3Spe5CVkjQLKicMS\nuwi4m/UGuzsdPUg/CqbU2EWCR242BHj3INmM0saVPEfDUpm9TrKpMYU6o2sMpMWLF5tiPGcO\n4tChQ/B///d/AC4MjaKEsOyFPRMi/j0UBJDyu+wq9EZ6fSLUjvL/xLLQ+SOSxNajYhkGHjsi\nYMFnbELWSKT6zgcynFkYAbMgwAaSRDN59OhR2LNnDwxAtrF4G7+Vk2hqTKNKR0wY28rlhOXL\nlkFVVZVpxkUDobCfRx99FMrLy6Hsciv40zi0zlQTrPNgPN2Q9nuYFfbv3w9/+ctfdNZG3+4p\n6TRJYta5+irCvUuBQELNOlDWhRRKsRKMQJgIsIEUJoBaVlfeyp2L549YGIFIITAiJQXK0IhY\nt25dpLrQpd13330XVq9eDdXdLFA9hB9tukyCyTutuBS9SGh4v/XWW7B27VqTj7b+4ZH3jJ4d\nzpROYI9vXn8hvhpTCCS0Gi7G+9NPP8XUuHmw5kaAdxESza9iIA1jA0miWTGfKsNrwjeXoRfJ\nLPLzzz/D839+HgBDocqvZO+rWeZVunGI0E30TqK38pFHH0Ga6wrpVIy0Qhs3bhRsfgmthkW6\nK27fIAg4ElqAM6ktGs7rBeOjQdRmNRmBRhFgA6lReKJ3k75wV+LblwyHAzpjGBQLIxApBAYl\nJwH94a/A3BVmECW0rrKiMhBal8KhdWaYV1nH4O2EoXYjrHDw54MxGWqneM7iWwyUdYpYLx0Q\niG8xWOTF2rZtmw69c5eMgPYIsIGkPaYhtUgUsgWFhTAYN68sjEAkEUiy26F7Qrw4UFtSUhLJ\nrqLS9vvvvy/Cnaq7Y2jdIH6kRQX0GO+kYiKG2qVb4O233zZdqGpTU6uE5jK9d1NIxdb9uMz+\nYsDr16+PrYHzaE2LAO8mJJnaVatWCU0GpbCBJMmUmFqNQcnJgpJ1zZo1hh7n4cOH4bnnnwNw\nY2jdNA6tM/RkGkl5J7LaXRkItXvsscdMR3jS2FRs3LgJzx5lgiOhVWPF+F6MIRBfYyBRCCYL\nI2AGBNhAkmQW6XA5yYAkNpAkmRJTqzEgOVGMz+gG0u9+9zuoKK+AMjw87+fQOlOvWdkG5+2M\nCWSHBljtYiWBLL2QKCwsgLhmfWSbDtZHZwScKR3Bao+HLVu26KwJd88IaIMAG0ja4Bh2KxS2\nkOawQ7s4d9htcQOMQFMI9EMqeRLlPEFT5WW8P2fOHCCiCUoIW40bVRZGINoIlGOoHSWQfe31\n12DXrl3R7j7q/SnnS9wZvaLeN3coNwIWixVc6T2ACHPMELotN9qsXTQQ4F1FNFBuog9Kbpmb\nmwt9EwOb1iaK821GIGwE6BxSRzTG6W2fEfMhFRQUwLPPPgtgBw6tC3s1cAMhI0AJZCdbwevx\nwuOPPy7Y7UJuywAVt2/fLrR040aYhRE4EwF3RmBd0JlqFkbA6AiwgSTBDG7YsEFo0ScpQQJt\nWIVYQaAPGuRkHBnxy+xPf/oTZm4vgIpxeFi+GbPWxcqalXGcnt5WqO5lAXqOf/zxxzKqqJlO\nipfMndZNsza5IfMgoKwLZZ2YZ2Q8klhEgA0kCWZ906ZNQoteCWwgSTAdMaPCOTUGubL+jDJw\nCkf9z3/+A95MC1SO4UeYUebNzHqWT0aCECeIXFx5eXmmHeqePXvA6kgAR2KWacfIAwsdAVdq\nF1GZ1gkLI2B0BDBAxVxCOVF8Pp9IZGeUkdEGld6B90hkA8koc2YGPXvWGOTEOjR9+nRDDIn+\ntp988kmha/kUNI5s7D0yxMSZXEl/KrIojsf1+FUxkHfz6aefNt2Iq6ur4eDBg+BM4/NHpptc\njQbkSukkWiIDidaLGYX2mCyxgYApDSQKGyouLjbEDNKGj+K627pdkGBjmmJDTJpJlOyECYmd\nVos4h2SUv5fPPvsMdu7cCVV9LUAJO1kYAVkQqBplBecqP3z++edw6aWXQs+ePWVRTRM96PA9\nfV85U9pr0h43Yj4EbK5ksLnT4MCBA4bZgwU7Cx6Px/RnDYPFxKzlTWcgWa1WcLvdkJ6ebog5\n27t3L1RWVkL3DGPoawhQWUlVCNgtFugUFwe7cOOTgN4kl8ulqp5ehYqKiuCf//wnhjJZoOIy\nfpmg1zxwvw0ggN7MCiRsSHjNC3//+99Ndx5JCcV1JrVrAAC+zAjg4xnXx8mTGyERz7g6nRh3\najJxOBxgwe9OFvMjwK9gdZ5j5YB8V3ybz8IIRBuBbvHx4PV6Yffu3dHuOuj+XnrpJczBUggV\nYyyc8yho9LhCNBDwdEXChh4BwoavvvoqGl1GrQ9iWyVxJmVHrU/uyHgIOJPaCg8L5cxiYQSM\njAAbSDrPHoULkXRNiNdZE+4+FhHokhAwzJV1KCsG9GX73nvviZwzTMwg6yyxXoRAxaXo3cRv\n1hdeeMGQFPoNzeKRI0fELUdSm4aK8HVGAAk8WgsUjh49ymgwAoZGgA0knadPeXPfmT1IOs9E\nbHbfGUPsSJR1KCsKf/vb38Sh3/KL8JHl4PAGWeeJ9QLwEbviECuQUW8m2u9jx46J6XXEt+Rp\nZgQaRMCR0ErcYwOpQYj4hkEQYANJ54mijWkikjNkmjBWV2douXsVCBBRA4nMtKyk29y5c5HW\nG6B6IBtHKqaVi+iMQCUx2jkAXnr5JSgvL9dZG226P378uGjIkdBCmwa5FVMiYE8IGNAnTpww\n5fh4ULGDABtIOs41se3RW8b2cW4dteCuYxmBNDxwmmK3A5GFyCovvviiiGmvmEChS2wgyTpP\nrNcvCPiT0Ys0wgp5uXnw4Ycf/nLDwJ9OnTqFDGXpYLGi5cfCCDSAgCM+YECfPHmygRJ8mREw\nBgJsIOk4T0SFSZz6HdhA0nEWuOv2yPpI4RAVFRXSgUGG2zfffANe/M719GbjSLoJYoUaRECc\nlUMSr9ffeF0wlTZY0CA3yECyxzU3iLaspl4I2OOaia5pvbAwAkZGgA0kHWePDCSStrhBZWEE\n9EKgXZxLdK2sR730qK/f1157TbxEqByH3iOmVq0PIr4mKQL+RPQiDbNCbk4u/Pvf/5ZUS3Vq\nlZSUCMIJuztDXQUuFbMI2FxpOHYL5ObmxiwGPHBzIMAGko7zqGxIKUksCyOgFwLZNQb6wYMH\n9VKh3n4pRGPuF3PBl2GB6j7sPaoXJL4oNQKVo/ErFm37N998UyRZlVrZRpRTNru2ODaQGoGJ\nbyECFqsNQzFTIS8vj/FgBAyNABtIOk6fsiFVNqg6qsJdxzAC2TUG+s+YMFYm+eCDD8Dr8ULl\nKDSO+OyRTFPDuqhEwJ9igaq+FqAcQosWLVJZS75i+fn5Qim78A7Ipx9rJBcC5EXKywusGbk0\nY20YAfUIsIGkHivNSyqJ1Fq72IOkObjcoGoE2tQYSEoiSNUVI1iQCEw++vgjABduMAfxYyqC\nUHPTEUagahS6kFAoj5dRhRI0k9hcqUYdAusdRQTIQCorKxWpGaLYLXfFCGiKAO88NIUzuMZo\nQ5qMDGKJ9sAXaHC1uTQjoA0CioGuJILUptXwWpk/fz7k4xvISqL1RiOJhREwKgLeNhbwZFtg\n+fLlwpNkxHEUFBQItW2uFCOqzzpHGQFlnRQVFUW5Z+6OEdAOATaQtMMyqJZ8Ph8cx8R7WZz/\nKCjcuLD2CJCRHo+5uGQykP7zn/+IgVYN5UeU9jPOLUYbgSokayD57LPPot21Jv0pG12bM1mT\n9rgRcyNgcyaJASqeR3OPlkdnVgR496HTzBIFpsfrhVYu5IFlYQR0RqCV0yGovnVWQ3RPfxv0\ntt2bBeDLYu+RDHPCOoSHgCAZwfRBc+bMCa8hnWoXFxeLnq1sIOk0A8bqVjGkif2QhREwKgJs\nIOk0c8fQe0TSgg0knWaAu62NQEs8B1dZWSkF89C8efME41fVAH481Z4j/mxgBNx4lq6nRXhp\nN2zYYLiB/OJBSjSc7qxw9BGw1niQFMM6+hqYv0eKQlq3bh18/fXXsHPnTvMPWIcR8g5EB9Cp\ny+PHj4ueMznETqcZ4G5rI9ACPUgkJ06cLEQDDQAAQABJREFUqH1Zl8/ffvut6Le6Dz+edJkA\n7jQiCFT3DaxnZX1HpJMINap4ApSNb4S64WZNgoDNEQixYwNJ+wmlFyyXX345ZGRkwMCBA2Hi\nxInQvXt38XnJkiUhd7h582Z4/fXXQ65vxoq8A9FpVpWNqLIx1UkN7pYREAg0rzHUlXWpFywU\ns7527Vrwtgbwp3F4nV7zwP1qj4CnG65nOxiS7ru0tFQAYnWwB0n7lWG+FpV1ohjW5huhPiPa\nunUrjB8/HtasWQPXXHMNzJ07F77//nu49957gdJ0TJgwARYvXhyScmRsrVy5MqS6Zq2Ej2sW\nPRCgJJgkysZUDx24T0ZAQaB5jQdJWZfK9Wj/u2zZMhFeV92D391EG3vuL8IIODHhcScL7N+5\nX7DZZWdnR7hD7ZpXNro2R4J2jXJLpkXAWrNOFMPatAON4sAOHDgA48aNA0qBQYZMx44dT/dO\n18lwuvTSS+H222+Hbdu2gcUS3AtGj8dzuj3+EECADSSdVoKyEW3mCIQ26aQGd8sICASUdais\nS71gWbFiheja0zW4h7te+nK/jEAwCNC6duz0A61zIxlIykZX8QwEM2YuG3sIWJ0BT6NiWMce\nAtqP+OOPPxZHM95///06xpHSE4XakXGUl5cnQuVbtmyp3BLnlH788UfYvXs3pKamQq9eveCW\nW26BxMREUfall14Cv98vojeeeOIJmDVr1unnExlOb775JqxatQpzW5VB//79Rd2UlLqU/0Su\n9NVXXwGl6GjVqhX86le/ErrQS8/HH3/8tC7l5eUilI+8YF4kKuvbt69oj/RSZMeOHfDhhx/C\nr3/9a3jllVeAjEMa38aNG0X/kydPVoqKf+lMP5UjA3Hw4MF17oXzC7+mDQe9MOrSYiLJYAMp\nDBS5qlYINKvxIOXk5GjVZEjt0EMT8J2Bty0bSCEByJWkRsDTKfCVK9a51JrWVY42uhabEyxW\nfqdaFxn+rT4E2INUHyrhXfvpp5/Ajik5rrzyygYbevnll4EMqdrGERkqZFxQigEygsiIuf/+\n+2HAgAHCG0VGj3J2ic7G02eFlIX2qcOHD4dbb71VXKeyv//974VRQ14qRajcoEGD4O677wZ6\nVtALoBEjRsCjjz4qyivljh49Cj179oQHH3wQyKihkPqnnnoKzjnnHGGcKeWIdOJ///d/4a67\n7oLf/e538NZbb8F3330Hn3/+Odxzzz1iHEpZ+pfuP/300+JcVu3r4X5mAylcBEOsn5ubCy6r\nlZPEhogfV9MWAcVQ19NAoofyvn37wIOJNcHGBpK2M8ytyYCArxWercPMDvQm1EhCHiRl02sk\nvVlXfRCw2QOhmIrnUR8tzNUrGUhdunQBh0N91NGiRYvggw8+gNmzZwumO8ovSEbKHXfcIbxJ\nRBjToUMHcS6SQvLIkKI65GEieeSRR8R5JzKuyGghA4WeXRTmR94qRa6++mogQg46P/zf//4X\nli5dCn/961+F10kpQ//OnDlTeKzIm0UGzxdffCGY+MhLdeONN8KZYX6kC527on0JGVI33XST\nCE8+85zVO++8A6NGjarXs1a7/2A/s4EULGIalScDKQ3fBrAwAjIgoKxFcs/rJfQgJPFms3Gk\n1xxwvxFGwGpBAhKLCBkxUvhRSQkZSEzQEOHVYZrmOcRO26mksDQiUGrRokVQDZPxQwbSb37z\nm9P1yBCaMmWK+F2JZDp9s9aHgoICEVpHHqQrrrji9J22bdvCtddeC2TkbNq0SRgvCxcuFN6j\nrl27ni5HXqd+/fqd/v3w4cNAKTwotG/IkCGnr1Odhx9+GLZs2XIWwQSVJY9TWlqaGDt5w8hA\nfO+9907XX716NVBIHhlYWgvv0LVGVEV75ObMz8+HbnFuFaW5CCMQeQQc5M202YAMd71k165d\nomsvJ4fVawq43ygg4G1lAft+P9B6pzAXI0hpaQlYk4LbnBlhXKxjZBBQvI3sQdIG37i4OOjU\nqVPQ+Y7at28P9ENGBHl3tm/fLn7IG0VCnqCGhM4r0V6VIjuuuuqqOsXI2CGhZ5iSsqa2MaQU\npucb9Umi/FvbOFLKDR06VHwkQ+eCCy5QLkNtg4suZmZmCi/Xv//9b/jHP/4BbrcbyHuUkJDQ\naOjh6QaD/MAepCAB06I4xV1Skq80B9unWuDJbWiDQCp6NPN19CBReB2JL1Ob8XArjICMCPhq\n7Iz9+/fLqN5ZOtEmqrq6GmzsQToLG75QPwKKt9FIXtL6RyLPVTI26NwOvVxvSNavXw8vvvgi\nKAYMGTejR48WHhs6d0SGUufOncUZoIbaUK4r4fZknFnxBWrtH/IiTZ8+HZKSkkDxQjnryelJ\ndRVRXr4mJycrl07/S2QRJPScqS2U6+lMoTA72kN/+eWXovxHH30EU6dOFYQTZ5YN93feoYeL\nYAj1lQVOG1IWRkAWBMhgP4wPVGKWsaE3Kdpy6NAh0aUvg0Psoo099xc9BJT1ffDgweh1GkZP\nyibXWpP8M4ymuGqMIGAVhB4OcS4lRoYc8WESCcInn3wCZBDQGaL65G9/+5sgLCDSgzZt2ojQ\nOgqFe+211+CGG244fX6JPDAk5CFqSBQacfLiEHNebam9R1ByJykvOGuXq32NPGAkxEh3pijX\n6vNCnVn2kksuEZ4kwoIMNDLkaGyREPYgRQLVJtok65eEDaQmgOLbUUUgpcZgV9ZnVDvHzujt\nmN+FD+14NpCijT33Fz0EfDUJkGm9G0Ho8DWJzZlkBHVZR0kQsOJ6UYxrSVQytBpkFJHhc999\n98Hy5cvPGss333wjws169+4N5513nrhPHqP4+Pg6xhHdICY7ktqkCPRStHbIHRlIxIZHxAwK\nq52ohP+js0BEy03JacmzRWF8r776ah0PEIXLURJbRXr06CHOEhHj3JmG2b/+9S9RTI2BREx+\n1113HdB4yUhq164djB07VulG03/ZQNIUTnWNKRvQZPYgqQOMS0UFgWR7wGukrM+odFqrk5zc\nHPDxOfBaiPBHMyLgq7EzlJAT2ceobI5ow8vCCKhFwOZMPmtjrbYulzsbAQpDmzNnjgglu+ii\ni0SIGxkWlC+IyAwuu+wyYYAo3iFqgQwOouYmum06U0Rnj4g6m+qQ1P6uJyIEYo2jnEgUzUFk\nCM899xwQQQTlHSLmODK4HnjgAUElTnTbZJwo5cggGjlyJBDV+B/+8AcR2kf3lIS1pP8zzzwj\nWOuIJIKowOlc1G233SbG9eyzzwqjSyjWxP8ozI6M77fffhtmzJhxuo8mqgV9mw2koCELvwKx\ng5AoG9LwW+QWGIHwEVA8SMqGKPwW1bdAb5SKi4rZe6QeMi5pVASImwedpLU3JzIPRXke0IaX\nhRFQiwAZ1OR9PNNboLY+lzsbAfLqkOdk2LBh8OmnnwrabGKUo0SudA6Hwt1qExuQ0UFJX999\n911xnQwYCu0lY4aSuZJBpMhvf/tbQdJEBtSCBQvEZfLUUF4lovgeM2aMOMtEYXw333wzUHlF\npk2bJrxSRJpAxhgx51FeIjLkiEBBkTvvvBPIqKPksZQnicIGf/jhB3jhhRdEPaVcU/+SJ43q\nUqhfpMLrSAc+BNPUTETgvvKFk8QepAigy02GioCyHvXYuNFbLvFF6ubwulDnj+sZBAGLReRC\nMgrDl/I8sLlSDAIwqykDAjZXqnimk5FU38F8GXQ0og7EAkc5hOj5QZ4e+t6k8z31kSSkp6eL\n80cU/kaMc+TxUYgTKB9SbSGPEBlHlOqjWbNmp28Rgx39EFsdeb0pnK620UNGCukxfvx4mDBh\nwul69IEMpdpt0TXy/ij5jCisLysriy7XkUmTJjVpWFOoHRFQKGeb6jSg0S9sIGkEZDDNKDHd\nSTochA9GTy4bWwgo61FZn9EcvcJe448+N0Q0h8l9MQIBBPCbt3a8v8ywKBEPtOFlYQTUImBz\nBgxqWj9sIKlFTX05MlK6d++uqgIx0KkpSwZL8+bN622TziPRz5lCbZNH59xzzwVKPKsIJYsl\ngghKDlufZGdn13dZ1TVqm8IFa+dDUlUxyEJsIAUJmBbFFQ8S5Z1hYQRkQUBZj3oYSByGIcsq\nYD2igoCBHKUK66rNlRYVaLgTcyBgdwfWC60fooVmMScCdMbo9ttvhz//+c8wePBgQRBB550o\nfK9Pnz7wxz/+UbOBExsfhQvS2SUKsSOq8UgKn0GKJLoNtK0wuyTWHIpvoBhfZgSiioBiICnr\nM5qdk7tciC+avXJfjIBOCHgxvl1Z8zqpoLZbCrkhsbvT1VbhcowA2GrWi7J+GBLzIvCnP/1J\nGETjxo0D8u4QOcMTTzwB8+fPBwrz00rI0KZw/CuvvBLmzp0b8Wcoe5C0mrkg2lFiz5UNaRBV\nuSgjEDEEEmoMdj0MJJcL+b1RLHXzxEVsrNwwI6AnAhYPgLLm9dRDTd/KBlfZ8Kqpw2UYAbs7\nkOTTKGyNPGOhI0BhdmOQxIF+Iil0xunMc06R7I89SJFEt4G2lQ1oPIfYNYAQX9YDgXhr4HGg\nGPDR1IEOmNKPpaLhxHXR1If7YgQihoAH1zi+CKAkh0aQU6dOCTXtcb8c3DaC3qyjvgjY4gIG\nEiXyZGEEjIgAe5B0mDVyEZIoG1IdVOAuGYGzEEioMdj1MJBIGXLFHy05fpZefIERMBMCltLA\naCjviBGEDCSrIxGsduInZ2EE1CHgiMsUBU+ePKmuApdqEAGfzweVlZUN3o/EDYXtLhJtG6VN\nNpB0mCnagNrxYJuj5o29Dipwl4zAWQjE2QIeJMWAP6tAhC8Qe87xE2gg+fANu9VAp9gjjAs3\nby4ErEUBL2lmZmADKfvoTpw4Cfb4VrKryfpJhoA9voXQ6MSJE5JpZjx1Nm/eLKi2o6k55T6K\ndWEDSYcVQJmJlc2oDt1zl4xAvQjEWQOsinoZSJQPgb4ILIUAfmO8XK8XR77ICDSGgCU/cJcS\nNcouxLhaXl4GCWln0/vKrjvrpy8CIiTTYgU2kLSbhxZOB7TFZKyRlG34Ar/Uy2xJhDEbSJFc\naQ20TQaSGx8cLIyATAgoRntFRYUualESOxLbKT940tiDpMskcKcRR4DWN4my3iPeYRgdHDt2\nTNR2JMhvzIUxTK4aAQQs+MLNjmF2yhqKQBcx1+QF6WnwQPvIUqbfuGU7bC6piQOOOYTrDph3\n6XXxiMpvFWQg1YQzRaVD7oQRUIGAqybkkwx4PaRjx46iW+vJwAZSDx24T0Yg0ggo6zuSGeC1\nGsORI0dEU46Es7Pda9UHt2NeBByJrYFIGqJ9fsa8iPLIookAG0jRRLumrwo8bKdsRnXonrtk\nBBpEwI1Gkl5fZl27dhV62Y6ygdTgBPENwyNA65vyhBjBg3T48GGBtyOpjeFx5wFEHwFnzbpR\n1lH0NeAeGYHQEWADKXTsQq5JG1DaiLIwArIh4MR1qVeIXZcuXUTiN9thNpBkWxesj0YIVPnB\nhqRe3bt3j3iSQy00PnjwoGjGmZStRXPcRowh4EgMrJtDhw7F2Mh5uGZAgHfpUZ7Fqqoq0aMT\nWexYGAHZEKB1qZcHifIg9ejRA2zE9M35kGRbGqyPBgjYDqHxj+ef+/Tpo0FrkW/i559/Fp04\nk9tHvjPuwXQIuJLbiTEdOHDAdGPjAZkfASZpiPIcnzaQ2IMUZeS5OzUIOJFeu6LGiFdTXusy\nAwcOFEx29p+RqKEbv0TQGl9uT18E7PsD3lFa50aQffv2gc2ZBHZ3uhHUZR0lQ8CZ0l5otH//\nfsk0M7c6lV4vLEN6/n3FJUBRId1TU2Bwswyw8Iv5oCaeDaSg4Aq/sGIgOXihhg8mt6A5Ak5k\nVyyurta8XbUNDhkyBN566y2w7yEDSW0tLscIGAMBWtckgwcPll5h8iQTSYO7mTG8XdIDGoMK\nOpM7ilGToc0SeQR8fj/8vy3b4P82bYFq/Ez5NkkqMdFsC6QHf27IIJjUjsNl1c4Eh9ipRUqj\nch6PR7Rk50SYGiHKzWiJAD1Qq3U0kIYOHQpWG9LD7uI8DFrOK7clAQJ4/og8o507dwYjJIml\nt/5+3GQ5UzpJAB6rYEQEbM5ETDKcCXv27DGi+obS2YNG0NQFP8AzGzdDGXqQqvH3cvyXfshw\nOobstDcsWQrPbtgU9rhWr14NTz31FOzduzfstmRugA2kKM/OaQOJPUhRRp67U4MAGUgeHQ2k\nxMREGNC/P9iOAiaMDbxtV6M3l2EEZEfAvhvXM74fGzVqlOyqCv127dol/nWndTGEvqyknAi4\nUrtCXl6e+JFTQ3No9dT6jbDk+AmoQsOoIfGiofSnzVvhy4MBdsqGyjV1/cknn4Tnn38eXnjh\nhaaKGvo+G0hRnr7TBhLw+YooQ8/dqUCAPJsefOOkp4wdO1Z079jW8INeT/24b0YgFAQcWwPr\nWVnfobQRzTo7d+4U3bnSONY1mribrS93WiB9w44dO8w2NGnGc7ysHP6+bUejxpGiLBlJD61e\no/wa9L9Hjx6Fb7/9Fp5++ml47733oKSkJOg2jFKBDaQoz5S3ZvNpYw9SlJHn7tQgoDwQKLRG\nLxk3bpzo2r5ZPx30Gjv3a1IEvJj7aBtAamoqDBo0yBCD3L59u9DTndbdEPqyknIi4EoPrB82\nkCI3P18eOgyOIIi/jpaWwaa8/JAUevvtt4GSXN95551gw3D4Dz74IKR2jFBJ2Q8ZQVdT6Oir\ncX+yfWSK6TTdIKw1C1Mx5PUYYPv27aFbt27g2OsHSwkbSXrMAfepLQJEzmAp9cP48ePFpkLb\n1iPT2rZt28Ael4k/GZHpgFuNCQTc6T3FOGk9sUQGgZ2FhUDMdWrFhYYN1QlF/vWvf8GvfvUr\noLQc06dPh5dffjmUZgxRhw2kKE+T8mbeyiF2UUaeu1ODgBL4qRjyaupEoszEiRNFvhjHJg6z\niwS+3GZ0EXCsD6xjsa6j23VIvVEYTX5+PrgzeoVUnysxAgoCrpSOYLW7YcuWLcol/ldjBChs\nLlgJpc6SJUsEMcOMGTNEdzfddBNs2LABfvrpp2C7N0R5NpCiPE2KgaRsRKPcPXfHCDSKgCzr\n8rLLLhN6OtYE/+BvdIB8kxGINgLIXufEcFFiriOWRiOIspmNa3aOEdRlHSVGwGK1gQu9SMSK\naObzKnpOQcekJCCvkFohIgeqE6yQ96hDhw6wceNGmDNnDhw7dgzS09PhlVdeCbYpQ5RnA8kQ\n08RKMgKxhUBWVhYMGzYM7If8YD3BRlJszb65RuvYhOu3CmDSpElgDeKcgJ4o0AaIJI5zIOk5\nDabpW1lHmzaFTzFtGlA0HMgl2a2DCrFLcThgECaODUaKi4vh008/FVUef/xxUH6Sk5Ph448/\nNiVLIRtIwawQDcoqmYx5y6cBmNyE5gjItC6nTZsmxudcyWF2mk80Nxg1BJT1O3Xq1Kj1GW5H\n69evF03ENe8bblNcnxEAZR1ROBaL9giQN2h6x/bgVPEChsgcnhzQD5Tzxmq1ISOIQu/XrVsH\nZOgqPxReR+zMlODdbMIGUpRnVDGQfCDTVjTKIHB30iMgw5vuCRMmQHJKMjjX4t9KNf+9SL9o\nWMGzELAex+SwB/wwZMgQEZpyVgEJL1CiaAqxc+LZEZsrRUINWSWjIRCf2V+orBjeRtPfCPr+\ndegQ6Jyc1KiRRMbRlHZt4aaunYMe0htvvAEU+p6SUveZ0KJFC6Czla+++qpILB10wxJXYAMp\nypOjbDx9vN+LMvLcnRoElIObRN+ptxBLztQpU8FShhTJG/gPRu/54P6DR8C5LOD9vPrqq4Ov\nrFMNYhurrKyE+MwBOmnA3ZoNAUdCK3AktAQykPQmADIbtsp4Ehx2WHjxBOFJoo19HH6HUzoZ\nMopc9GOzwuP9+sBrI4crVVT/S5T/5Cm6/vrr661z8803AyWWXrhwYb33jXrRblTFjaq33R6A\nXNmIGnUcMuidW1EJa3JyIL+qCjLdbhjSvBkkYmwtS+gIYLoWIC+n4ukMvSVtahKdKLnuXUu9\nUD2Y3+dogyq3EhUEyv3gWueD5s2bw4UXXhiVLrXoZM2aQBLJ+BbGyNekxZi5jcgjEJc5CIr2\nfyk20t27c26tSCBORtJLI4bBY317w7zDR2E/nhtyIklG99RkmNCmNaTiS8dQpEePHo16hy6/\n/PJG74fSpwx12ECK8iywgRQ+4IdKSmH26rXwFSZHo5hbiqX1IM0lMQRe37kTPIXxtWmu0B4E\n4Wtn7BY8fh/YJfAeKShmZ2fD2LFjxZsp214feDuxkaRgw//KjYA4e4TkDNdeey04DPTiZvXq\n1QJYNpDkXl9G047WExlIZICzgRTZ2WuTkACzunWJbCcx0Lq0uw1izJg3b55gzTh48KBppkIx\nkKpD4K03DQhhDGRjbh4M++Jr+PbwEXGKqxIPDZZjgrRq/JeMpPf37oMReP8IZopmCR4BWpey\nbeZuvPFGMRDXYiZrCH5G66/hOVgBRW8cg9x798CpWbsg96F9UPLxKfAWeOqvwFeDQwBdsa6l\nPnDiixojhddR+BNtYO3xLcCZlB3cmGO0tNdTGaMjD27YCS2HiAorV64MriKXZgR0QkBKDxLx\n5c+cORM6duwIrVu3Foe/nnnmGUH7qxNOmnXrcrlEW1X4pp4lOAQKMZRu8vxFUIyHiBs6kUL8\n/sfLy2HKgkWw4rJLgmZqCU4j85WuwsNxTndgjcoyOsod06tXL9i6daug/Pa1kCVbkywIqdfD\nj/Nb8vYJKJuTC0Cvx2qSr/tOVYNnfwWUfnoKku/MgrjzU9U3yiXPQsCxHunpMVH9lKuniDwh\nZxWQ9AKdP6KXkymdzpdUQ/3V8uN3zOGNH8C+FX+HgqPrwe+rBpszEZp3ugC6nvcIpLcdpr+S\nEmrgSu0EdncGrFq1WkR7yBLGLSFUrJIkCEjpQfrDH/4AFNP42muvwVNPPSUOhv3lL38xRYzj\naQMJNyoswSHw963bGzWOlNbIk7SnqBg+3ndAucT/qkSADHdnjRGvskpUis2aNUv041pUs6OP\nSq/m66T45aNQ9mUeiDcMZ0JJTIH4U/T3I1A+P998g4/WiPD54/rBJ3Ie0eFlIwkdxCZJaGmM\nhLbRxraqPB9+fG00rPtsJuQfXiWMI9LBW1UCx3d8AUtePRe2fPOQKfYqkcA2vtVQKCjIh507\nd0aieW6TEdAUAekMpNzcXCDGDEqqp7xhuPTSS+Ho0aNAb7fOlCr0KhDjjvLjxXArOosi648S\nvkShYSzBIfAuhs+pxY08Se/t3RtcB1wa8fWDGwkvZPv7Icrvtm3bghPfzFvy+eVCKEu1ckMJ\nGj4FgLGojVfHR1PRy8fAm1vdeDm+Wy8C9q1+sGFyY2XNyva31Jg+K1asEGNKaBU801W9YJjo\nos/rgRVvXoyGEXpAvHi47EyhqBD82bv8b7BjwZNn3uXfEQFlXS1fvly675jG/i5q3+OJjB0E\nIhJiR3HMCp11sFAeP35cVMnKyjpdNSMjA4jy9+TJkyLU5vQN/DB69GjIz//lbWe/fv3EAUCl\nndplZflMXqRyLxtIwcxHJRq+R8vKg6kC2/IxxoUlKAQq8G+3GR4ol/HvZ/r06fDcc8+Ba6EP\nKqbqT0MeFLASFC795BSA6seOH8q+zoOk61tIoLmxVHDPD4BMiWFl/DtqCE3Kf0QEDc6kduBI\n/OX7t6HysXb95zWvQ+ExDKmrzziqBQbd37noGWjT91pIat6t1h3+mNBqhABh0aJFcPHFFxsS\nEHoZTwaTHrKysAieiXBkzGFkB2YJINCkgURGCT00KRGUGjl8+DDMmDEjZD70Y8eOARkQSiia\n0mcSZgqubQgp1wcOHChippXf09LSgHK4kEElq8THxUFZOZMIBDM/RMIQrFTzOa+gICPqefLQ\nxcfHS/n3Q0nq3nnnHTix+gRUXmAFfyqfRVI7wX4kDajeHsQzB7kaKlcXs4GkFuCacvbtPrAd\n8cPIkSOhZ8+eQdbWt/jmzZtFJEZa+8AmVl9t5Ot9z9I/g68J40jR2mK1w4HV/4Tel/xZucT/\nIgLOpDbgQPKPTZs2iQghJaLGSOCE+vJfizHuLa8A+mGJDgJNGkjleOCdNiYPPPAA0NkghYWt\nPvU+++wzuOWWWyAvD2PcQxT6g/F4zmZSotA52ridKS+++GKdS19++aUI0SOvk6ySiMZecUmJ\nrOpJqRflN0rCHyJoUCttkeqSRT0CZfg3RkKZsmX9+7nzzjvhiSeeCHiRprAXSe3s+opxboN8\nx+DLP/s5rLa/WC3n+j4A8v/8z/9I+zfU0NyQgUSSkHVuQ0Vi9np1RRGU5u5RPX7yIp3a/b3q\n8rFUMDFrJOTv/BAOHDhgSOIt2qMqxz+iNW+dOnWC999/P1rdcT81CDRpIFGoGzHKPf/887B0\n6VL46KOPoF27dnUALCsrg/vuu0+QKiQmJsLrr79e534wvzRr1gzIGKI2axtERUVF0KpVq2Ca\nkrZsAm7cj9VsRqVVUkLFLs1uA//efwDPkTft3qbM0VPb112nEg5JKpVKa8I+6W9YVpkyZQq8\n/MrLcHzVcag8n71IaufJGhf8cVNLCHXU6mPGcvZtmEPskF/k7TrnnHMMN0T6fgeLDc+JMAvb\nmZNXVR78S99Q6pzZrxl/JwOcDKRly5YZ0kDSY0724nlqSpoeTWEiDYAmDSSylolNjs763H77\n7dC/f3948803BYkCTda6detEIjwCk+h433vvPejcuXPI89imTRvhpSJK38GDB4t2iLSBzjXV\nPpcUcgcSVExOThY5eyiciTbyLOoQeLjPOfAJGkhqxI1hlrd066qmKJepQaAYDyGTUDirrEKh\ns3fdeRc8/vjj4J7vhfJpTT7CZB1KVPWyuKxga+kE7/Eqdf3iY8nZ82yPvbrKMVgKX9q4vw14\nYO+55x7DAUDkSESCFJc5ECmr5f371wtYV0LzoLt2JWQGXScWKgiiBjTEf/zxRxGZFAtj1mqM\n3tYW8HSJbGi5Yy1yCBRrpbGx21G9u7j++uuBzvtceeWVMHnyZOExIoPlt7/9rfD4/O53vxOb\nlsZC8NRAReE9F154oTDCevToIYwl8khddNFF0Lx58A8pNX1GuwwZSCRFGErYXOKzUtHGpan+\nOiUnwUsjhsKdy1cCnZepT+jRYbdY4MOxoyHZ6aivCF9rAIFiT2CDp6zPBorpfpm8SK+++ioc\nXn0YKsf4wdcssl8Yug9YIwXiJqZDyTsnBJV3k03in1fchPQmi3GBAAL2zchcdxTEd5fRzh7R\nCIT3CP9NbD0yMCD+fx0E7M4ESMrsBcUnt9a53tAvVpsLMrte1NDtmL5OOaPim/fDoxBrIScn\nByhqiEUdAp6OFqiYGNnQcts+zOFWXP/+Sp2W5ikVlPuCHvyUZZtyO/z1r3+F2bNni3A7ehNA\n+YrCNY4UWMlTRW+K6ewTGWPU7t13363cNvy/ZASSFNVsSA0/oCgO4NpOHeHDMaOhGRJ5ONH7\nZkNjiMSB/zqsFmiP4WHfX3whjGrJ7FvBTkthzdk/ZX0GWz9a5U8/D/C4h+u7gFEXrb6N3E/8\nxUhg0wrJa5r6fnVYwDU6BZw92IOkar6JGh+9R3Qu4d5771VVRbZC9B1Okth6lGyqSaNPl9Gz\nMQJRHfmT3++FDkNuk0Z32RRJqFlnimEum36sDyNACKj2IClwLViwAL799lvxK+VLOXHiBNDh\nzuHDhytFwv6XmOjIAKNzR8RIR2d2zCQ0PpKCesgozDTOSI3l4uzWsH3aZPj28FFYeeoU5CAt\nZav4OBiNRtEY/LFx2GJI0CsGkrI+Q2okSpWURNJ7NuwJeJGy2IvUFPQWhxXSnmwH+b87AN4T\nSHZCiWHPFPxGcPZPhJS7meb5TGga+t2xFr1HJwEun3R5WOHlDbUf6etEWUwbVZs7DdwZvSLd\nnWHbz+53HRza8B7k7l+CbHaVDY7DYnVAr4v+CPFp7RosE+s3yBA/tf6vsGTJEvESPNbx4PHL\niYBqD1JpaSnceuutwqtTUVEBn3/+uaBqJHaN2267Da644gqgOGYthUJ9zGYcET7p6YHQlfwg\nGNm0xNUMbdEZo0ntsuHZQQPgnyOHw1MD+sEFWa3YOApjcvOrA2eQjGAgEdUqMYUB7vHd37AX\nSe202zIckPGXTpB4bSZY0+u+H7O1dUHyva0h9bFsIGOKRQUCmHTX/R2SM2CUgxHPHtEIt2zZ\nIlJoJCC7mMXC897QrFvwmTP0us8xdG4CEI03MlrUKUqGEd6Anhf+HjqPxGcTS4MIkCFud6cj\nUcNycb68wYJ8gxHQEYG635ANKEJ5kIhBY/fu3TBu3Dh4++23TxMmUOZtogAnum0q9+677woW\nnwaa4suIgEKhnFezIWVQGAEZEMirMdiNEhNOzyJKDL1hwwaw7fOBtyNv7tSsI4vTCglTmokf\nX5EHfKVesKbYwRrfVOydmtZjq4xzGR5oLvDDNddfA0QwZETh8Dr1s0ZnkYZdPwdO7JoH+396\nCfIOLgdPVQk445tDy26XQKeR93NyWBVwUjgqhdkV7J0jDPQ+ffqoqMVFGIHoItCkgUSZwEeM\nGAH0xpaovu+///46HPCU0PUf//gHnH/++YIOnDYtDz/8MDz77LPRHYmBelM2oLnsQTLQrJlf\n1RyDGUg0Iw899JB4eeP+ygeld7OBFOwqtSajYYQ/LCEgUI7eo4V+kY6C8nMZVU4bSOhBYlGH\nQAskYKAfltARoDC7QjSQKMyODaTQcayvpudgBVSuKgbP0SqMBrCAHaMDXMOSgSIIWNQj0OSO\norKyErp06QIrV64UnqKGEmQRs9T69esFNTcllGVpGAGFje9UFZ4DYGEEJEEgp2Y9KutTErUa\nVWPQoEHCY20/6Af7piAzoTbaMt9kBBpHwL3QB5YyP8yaNet02HTjNeS7S+d8yQMrQp7i5E2u\nLh9yrFG4CAQSElsE3Xe4bXH9AAK+Ei8UPHsQcu/ZCyUfnYKKBQVQPi8fit86ATmzdkHx2yfA\n763n7CkDWC8CTb46bNGiBaxduxbi4uLqbaD2xfbt24vF/uijj9a+zJ/PQIAwJTlZpTInyRn1\n+VdGIBIInEQDic79EfmKkeTBBx+ExYsXQ9w3PijuhecCbHXPBhhpLKyrMRCwYFida6lPUBTf\ndNNNxlC6Hi0pRJ5yDCay96gedPhSJBGgM0hkmG/atAkKCwtBdvbUSGKhRdtkHOXevxd8ufji\nnWyg2iQ8VQGjqGxuLngOVUDqb9rWiQRT0//+/fvhnXfeOV2Uospoz0/5Srt37376upk+NOlB\nos2SGuNIAYUSy1IoHkvDCKSmpgKFJrKB1DBGfCf6CJzEELuWLVtGv+Mwe6TE1FOnTgVrjh+c\nP7EXKUw4uboKBNzzkBgEOU2I1js+3rh06Ep4XUKb0SpGzUUYAW0RoLxbZKCToc4SHgJFfz8S\nMI4CXEv1N4akMlXrS6Hsi7z67zdylQykJ598UrBYU1gksVnT7+eccw689tprjdQ07q0mDSTj\nDk1uzVu1agXHOcRO7kmKIe2IoKEKv6hoXRpRiEHMHecG9/f4pqyCQwiMOIdG0dl6BA1xpPYm\nBlcyzI0sy5YtA6sjARN39jXyMFh3gyLA+ZC0mTjP4UqoXFksXto02SIaSaUfngw51O79998H\nSvdDqQH27t0rjt7QWWCPCdPWsIHU5GqKTIHWrVtDqdeLyWIbM/cj0ze3ygicicCxykC4p1GZ\nuDIzM2HWzFlgKcXQJzwbwsIIRAqBuC8CtPKUKJ3y9BlV6I3w0aNHIb7lUGSn5sPbRp1HI+sd\n37yfMNDJUGcJHYHK1WgcIRmDWvFX+qB6Z5na4o2WGzNmDBQXF4ufRgsa8CYbSDpNmrIRPYIk\nGCyMgN4IHMFkuyRkuBtVZs6cKc6EuH/Ew/P57EUy6jzKrLd9O+Y82uuHYcOGAW0MjCzKpjQx\n61wjD4N1NzAClE8qvsUQYagfOHDAwCPRV3XvcXzBWXPOSJUmdgt4j+NZpRCE8p2ePHlSzBml\n9iFStquuugqMkD8x2OGygRQsYhqVz87OFi0drtmYatQsN8MIhITA4RpDvW3btiHVl6ESnQWh\nMyF0NoSTx8owIybTwYe03l96xeFmSmVhdFHOfSS0Gm70obD+BkYgIWuE0F5ZjwYeim6qE5X3\nGXmLG9cF3x+KOo2XqvcukTIQ0Ri9TB0yZAhs3bpVGEn1Fjb4RTaQdJrAdu3aiZ4PsYGk0wxw\nt7UROFhRIX41soFEA5g2bRp06doFnOv9YDvEoXa155g/h4cAEYDYTgJMmjQJevbsGV5jOtem\ng/ErV60Ce3wmuFI76awNdx/LCCSygRT29NvaIvNsECF2xHBnb+cKqd/58+fDzp07Ydu2beIs\n0mWXXSbyWJGhZDZhA0mnGSV6RBJlYyp+4f8xAjohcLA8EGKnGO46qRF2t0Q9+sjDj4h23HPZ\nQAobUG4ggAASf7i/w/NtbpdIlm50WGhzU4w5kNh7ZPSZNL7+rtTOYHdnYK7NVeD3c2h0KDPq\nHpoEEMTXna2lE5PHhpbOo2PHjtC1a1fo0aMHnH/++fDmm29CYmIifPDBB6GoLnUdNpB0mh7a\niFLS3QPlgTf3OqnB3TICAoED6EHKysoyXA6k+qZv5MiRMHr0aLAf4OSx9eHD14JHwL0Az7Uh\nAQgRgSh57IJvRZ4aq9B7RBLfcog8SrEmMYsAEYUUFOTDrl27YhaDcAZuTbFDwpXNAPBsUZOC\nu/6k21s1WUxtAcWorTJhXk82kNSuAo3LUR4kiuHczwaSxshyc8EikI8U34XIpkhvhswidEaE\nvElxX+NrNaQ1ZWEEQkXAkvdLUthZs2aF2oxU9ehwNUkCbkxZGAG9EVAMdWVd6q2PEftPmN4c\nXMPQk9SYkYQ7/sSbWoCrX2LIQ6Q5ovxp9PPVV1/B1VdfDadOnRJEDSE3KmlFNpB0nJguXbpA\nCVJ9c8JYHSeBu4Z9NUY6rUezCCWPnT59OlhzMWfNsiBiD8wCAI9DMwTcXwWSwt5///2GTgqr\nAEJvfNesWSvOHzmTAmRByj3+lxHQA4GEloNFt2vXrtWje1P0SRFJqQ9lQ9KtLcGShOkHaHfv\nRI8S/aDYWjsh9Yl2kHA5eprCEPpepQgN+rnxxhvh2LFj8N///heIvMFsYjfbgIw0HtqQLlq0\nCPaUlUOm02kk1VlXEyGwpyyQD8FMBhJNDyWPnTN3LsCCMqge5Ad/gorwAxPNKw8lfARsB3zg\n3OSH7t27wxVXXBF+gxK0QMkdi4oKIbn9JRJowyowAriHT+kENlcKGu5rGI4wEYifkA5x49PA\ns7cCvMeQ/hvJG+zZLrC3CY2UQVGHzhsp4XTKNbP/yx4kHWe4W7duovfdaCCxMAJ6IaCsPzp4\naSZJT0+HO++4A6AcQ6S+Yy+SmeY2KmNBT4tC9PHoo4+KkM2o9BvhTtatWyd6iMvsH+GeuHlG\nQB0C5P2Iw6SxSn4ddbW4VEMIWKwWcHSJA/foFHAPTw7bOGqoH7NfZwNJxxmmt5Iku0q1yWis\n41C4awMjsKu0XGz+zOZBoim54YYbxFk/F1I0W0/wWSQDL9Ooq+5Aqnj7IT9ccMEFIjFs1BWI\nUIcbN24ULcdn9otQD9wsIxA8AnHNAwb7hg0bgq/MNRiBCCDABlIEQFXbZIcOHYDIGnbUhDip\nrcflGAGtEPDgW/Ld5eWCoMHtDo32UytdItGOE0NXH3roIUGBSkk+WRgBVQhgnhD3N5j3yG6D\n2bNnq6pilEKbN28Gi9UB7rTACzqj6M16mhuBuOZ9xABpfbIwAjIgwAaSjrNgs9lEbPvPeEi+\nHMkaWBiBaCOwH42jKkwa2atXr2h3HbX+Lr74Yhg4cCA4dqBHYCeH2kUNeAN35FqMHscCP1z3\nq+ugffv2Bh5JXdUrkM5/9+7d4ELjyGLjc6910eHf9EQgLuMc0f2WLVv0VIP7ZgROI8AG0mko\n9PlwzjnnAAX+bOcwO30mIMZ73V4SCO+kdWhmeeSRmuSxX6CB5ONQOzPPdbhjsxSh92iRH5KT\nk+Guu+4Ktzmp6u/YsQN8+EIkrpl5X4hIBTgroxoBImlwJLYBSmLMwgjIgACz2Ok8C3369IH3\n338ftpaUwoBk5LBnYQSiiMAWXHckvXv3jmKv0e+K/s4mTZoEc+bMAedKH1QNRxpUFkagHgTc\n89CbX+WHe2bfAykpKfWUMO6lnTt3CuXd6T2MOwjW3LQI0LosPvg9HD58GNq0aWPacYY6MMc6\nH9j3RTYKwnoqVO3MV48NJJ3nlDZuJMpGVWd1uPsYQ2BzSQnYMdSzZ8+eph855bGZ9+088H9b\nBVX90YvkZtpv0096kAO0HsG8WWv80L5De7jmmmuCrC1/ccVAcqWZi7FSfuRZQzUIuNK6CQOJ\n1ikbSL8gRix/dgdu1yvx2olfrkfqkwWpwVkw5y6DoC8CRNRAoRybcKPKwghEEwE690Y5uHqh\n94jIQswuLVu2hFkzZ8GLL74I7gU+qJjIXiSzz3mw44v7Ar1HaDs/8vAjYLeb7+uRzh+RuFLZ\nQAp2bXD5yCPgTgskK9+zZ49gj4x8j8booVWrVvDQg0g2xBJVBMz3DRBV+MLvjN4M9OvXD5Ys\nWQLHK6ugpYsPzoaPKregBgHyWpKzntZfrMisWbPgk08+gVNLT0HlcCv40/lNWazMfVPjtG/B\n8JW9fhg+fDiMHTu2qeKGvL9v3z6wx2WCzZloSP1ZaXMjQAljSSiZMcsvCBw9ehT+8Ic//HIh\nCp9uvPHGKPQidxdsIEkwP8SwRQbShuJiuMiVIYFGrEIsILChOOC1HDBgQCwMV4wxPj4eHnjg\nASDShjik/S6bwY/AmJn8xgbq9UPcV8hcZ7UCJYU1o5RglEJOTg7EtxxixuHxmEyAgDO5HY7C\nAj///LMJRqP9EBJbj4aUzldo33CtFk+t+ytUFTP+BAnvDmotDL0+koFEsh43rBc1YwNJr3mI\ntX5pvZEMGjQopoY+efJkeOedd2Db5m1g2+8Dbwcm84ypBVDPYJ3L0DjK8cO0q66Ebt261VPC\n+Jfo4DuJM4k2oSyMgHwIWJF63pHQEg4ePCifchJo5EzpACkdLomoJnnb3gJgA0lgzDuDiC41\ndY0TUYPD4YB1RcXqKnApRiBMBChB7EY0kNq2bQuZmZlhtmas6hTW+thjjwml3XMxyBCxYIld\nBCylSOs93w/kXbzvvvtMC8ShQ4fE2BxJzA5m2kk2wcCI6jsvLw/KMUcfCyOgJwJsIOmJfk3f\ndEC+b9++sA8TxuZXV0ugEatgdgS24fmjCsyHMmRIbIbbDB48GMaPHw/2w35wrGMDyezrvbHx\nub73gaXcD7fffjtkZJjXg3/8+HEBgyOhVWNw8D1GQFcEHIlZov9jx47pqgd3zgiwgSTJGhg2\nbJjQZA17kSSZEXOroayzWDWQaHZnz54tmMrivkEvEua9YYk9BKwn/eBa4YOsrCy46aabTA3A\niRMBfmBHfEtTj5MHZ2wE7PEtxACU9Wrs0bD2RkaADSRJZm/o0KFCk9WFHGYnyZSYWo3VhUVi\nfIphburBNjA4Ci+8/vrrwVKIm+TFkU2+14AKfFlnBNxI1EFUjg8++CA4neZmED11KpAB0h7X\nTGfUuXtGoGEE7HHNxU0iFGFhBPREgEka9ES/Vt9Etex2u2FVUWDjWusWf2QENEWgEkPrNmCI\nHeXgatEi8LZO0w4M1Nidd94Jn3/+ORT8UAhVQ/zgT7EYSHtWNRwE7Lt84Njuh/79+8PEiRPD\nacoQdelcB4k9zrxhhIaYCFayUQTs7sD6VNZro4X55lkI+LweOLzpQzi6+RMoztmFzJwOSMka\nAG37XweZXS48qzxfaBgBNpAaxiaqd+jtJbGJLV26FI5VVkKrGEjcGVWAubPTCBA5QxUaSSNG\njDh9LVY/UJLmu+++G55++mlwz/NC+XR+JMbEWvAhMQN5j1DMSut95jwWFBSIS1Zn8pm3+HdG\nQBoEbK4UoUthYaE0OhlFkaITW2HF2xOhsvg4+LyVp9UuPrUDjqDRlNFuJAz51b/BGR/6SxIv\nJphfs2YN/PDDD7Bt2zZB9HTDDTdA586dT/dnlg8cYifRTJ577rlCmxUF7EWSaFpMp4qyvpT1\nZroBBjmgq6++Gjp27AjOtX6wHuGzSEHCZ8jijlV+sOEZ8EsvvVQQ5BhyEEEqTXmQrI5EsFj4\naz9I6Lh4FBGw1RjwRRxNExTqJTm7YfHLQ6G88HAd40g04veC3+eBvIPLYckr54KnqjSotpXC\n1UgidsUVV8CoUaNg3rx5EBcXBx9++CF0794dFi1apBQzzb/8pJRoKkeOHCm0+anmfIhEqrEq\nJkJgBb6Zs9tsoJx7M9HQQhqK3W4XiWMBbaO4uQGvQkgNcSVjIFCBSWG/9YHT5RRJg42hdPha\nlpaWooEUH35D3AIjEEEElDVaVlYWwV7M1/Tqj68BXzV6jdAYakh83iooyz8A2777TUNFGr1+\n3XXXwapVq+DAgQPCIHrllVdgz549cO2118LMmTOBnjFmEjaQJJrNrl27ipw0K9FA8nJuFolm\nxjyqnKqqgt1l5dB/wABITEw0z8DCHMl5550nQg7t+/xg38KEDWHCKXV11yKk9S7xw8033SzY\n66RWVkPlKioqwWJza9giN8UIaI+AxeYSjVZUVGjfuElbzDu4AoqObsCUfp4mR0ihd/t/eilo\nLxIZRZ988gm89tprZz03//KXv4i0GVTGTMIGkmSzSa7LEozx3ITnRFgYAa0RUMLraJ2x1EXg\nkUcewQOtVoj7Cg0kL4fa1UXHHL9Z8vHs0RIfZDTLgFtvvdUcg1I5iip8OWK1mZupTyUUXExi\nBCw1a5TWK4s6BE7u/h5ffgRxfhaTpef9vExd4zWlfvrpJ7Bh5AnlDzxTKH/cq6++Cr169Trz\nlqF/ZwNJsumjN9kkywr4gKJkU2MKdZR1pawzUwxKo0F069YNpk2bBtYcPziXsRdJI1ilasb9\nDYaf4EvW++69DxISEqTSLdLK+P24pi22SHfD7TMCYSFgqVmjfo6iUY1jeeEh8Hl+IWVoqqLV\naofyoqNNFatzf926dZCdnS3YluvcMPEvbCBJNrl0cJ7OhyxlA0mymTG+Oh78wqHzbZmZmeJQ\npfFHpP0I7r33XoiPjwf3fD9YytiLpD3C+rVoO4jnjtb7gUKZyRCOPUEKe950xt60G23EvEaD\nnjFHXBpY0OhRK2R8OlzBsVlSMu2TJ0+CDxlwY0XYQJJspulcyECk+6ZzIicq2cUs2fQYWp0N\nRcUifHPMmDGGHkcklW/WrBncdtttYCnH5LHfx84XQSQxlaVt9xeB+SRabwqljDWhMfsbOcAd\na3jweOVEQHg6UbVY/BsNdUbS2gxGdkr13mGfpxxSsU4wMgDPLRNxRn3njMjgmj17NixcuDCY\nJqUvG3vfEtJPCYCygf2xJm+FAVRmFQ2AwJIar6Syvgygsi4q3nTTTdCyVUtwLfeB9RR7kXSZ\nBI07dWz0gf2AH0aPHh2z+b8cDgdS/VZrjCw3xwhoi4DfF3gxTOuVRR0CLbpNBJsjTlVhMqQy\n2o+C+NRsVeWVQmQgNW/eXOQMVK4p/37wwQfw3HPPwalTp5RLpviXDSQJp/H8888XWi3J53NI\nEk6PYVVakl8AlJCYE8Q2PoUulwsefOBBAHQ4uL9qmDK18Vb4rjQIeJCY4Ws0dm3WAJ27NIpF\nV5H4+Djwe5gZLLqoc2/BIuCrWaMU6syiDgG7Mx76TnpZlRfJYrWJsupa/qUURTd9+umn8N57\n74nk6qtXr4aDBw/CCy+8AA8++KDIjTRlypRfKpjgExtIEk5i+/btoUOHDrAaQ6LKkdGOhREI\nF4H95eVwCGl+hw8fLpK7hdue2etTAtHevXuDYysmFN3HoXZGnm8i3LDm+WH6VdOhU6dORh5K\nWLrThtPnMVeekrAA4cpSIuCrDqxRNpCCm542fa+Gnhf/KUDEUk+4HbED2jAP2rAZX0Jyi9DY\n5ojciRLD7t69G+hFfrt27cRLp8suuwzmzJkDZvP6sYEU3BqMWmlafFV4GG4FJ42NGuZm7oi8\nRyTjxo0z8zA1G5sFaVCJ9ptEnF3hg8OaYRvNhohogwg34pGx7p577olm19L1lZycjIkkSzHM\njl+6STc5rNBpBHxVReJzUlLS6Wv8QR0CXUbeD+fdsQKadxpbx5tE4Xdt+lwNF/zPdsjscjZN\nt7rWA6WI4GbevHmQn58P+/btE8lh//nPf0JaWlowzRiirHraC0MMxzxKXnDBBfDGG2/A4rwC\nOD/dfAvPPDNljJH8gOuIZOzYscZQWAItByFZCuV8+P7778GB7GfVA5AFjMVQCBDRhqXCD7ff\nfxukp6cbSnetlU1NTRVNeisLwB6XoXXz3B4joAkCnsp80Y4ZN9yaANREI0TYcO7N34O3uhyp\nvI8g2YUD3MmtMcRY2+2+3W4XkU5NqGPo2+xBknT6+vfvLyzyH/FgvZffXks6S8ZQK7eqGjaX\nlELfvn3FIUtjaC2Hlg899BDY7DZwf4NhdtVM2CDHrKjTgvJZEdEGEW7ceOON6iqZuBQlcyTx\nVOSaeJQ8NKMj4K1Zn7H+QiPceSSvUWJGZ4hPa6e5cRSubkapzwaSpDNFFJfkRSr0eGA9nkVi\nYQRCRWAxhtfR1p7WE0twCFCM9bXXXAvWAtxsL+WzSMGhp29p99cYSoZT9sD9DwARb8S6EAMV\niafsZKxDweOXGAFPWYAJjfL1sTACeiLABpKe6DfRt3JeZFHN+ZEmivNtRqBeBH6oWT8ULsYS\nPAJ33XUXJCYlgnshJo8tZS9S8AhGv4btgA8cm/3Qq1cvoAPELAAtW7YUMFSXHmM4GAFpEVDW\nZ4sWLaTVkRWLDQTYQJJ4nomOOT4uDpTzIxKryqpJikApsiCuQqKPjh07ih9J1ZRaLYqFv+P2\nOwDwLAsnj5V6qk4r5/4y4O17+OGH8bAynx0jYLKysgQ+1aVHT+PEHxgB2RCoKjksksQqBr1s\n+rE+sYMAG0gSzzWFhYxGWsXjVVWwHc+QsDACwSKwFHNpVeMZNvYeBYtc3fIzZswIJI9dgZTR\neLaFRV4E7JswKezPfpFwe+jQofIqGmXNsrOzRY9VRQej3DN3xwioR6C6+LDwdpqNMlo9AlxS\nFgS0pbWQZVQm0uPCCy8UlIoL8/KhR2KCiUbGQ4kGAotw3ZCwgRQe2pRg9/7/uR9mz56NhA1e\nKLueH53hIRqh2l4/xCGhBp3hJIINll8QaNOmDdhsNqgqOvDLRf7ECEiEgLcSSamQxa59+x4S\naSWPKnnb3gb6YYkOAvwtHx2cQ+6FEnPRm5SFSNN8V9s2IbfDFWMPgUrMo7W0sBBatWolkp7G\nHgLajvjyyy+Hf/3rX7Bj0w6wHfSBty074LVFOPzWnCsDHr6pV06Dzp07h9+giVqg75G2bdvC\nz4f2gR+9yhx6aKLJNclQKgv2iJFQSDjLLwhQ0tx+/fr9coE/RQUBNpCiAnPonSQmJgKdRVq8\neDHsLy+HDngmiYURUIPATwVFUO71wZVMzqAGribL0IaSPEg333wzuL/yQekdbCA1CVo0C1Ri\nUtjv8ZyY2wV33313NHs2TF9kNO7fvx+qS46AM4lfuBlm4mJEUcVA6tKlS4yMWN0wCY+PP/5Y\nXWEupRkC/A2vGZSRa4jC7EgW5AbCpSLXE7dsJgQoLJNkwoQJZhqWrmM599xzYfjw4WDf5wf7\nDqb91nUyzujctQSTwpb44YYZNwAzYJ0BTs2v3bp1E58q8nfUX4CvMgI6IlCRt130rqxTHVXh\nrhkBMKUHicIH6Mcscv7554vY8QW44Z3VJsBEZJax8Tgig0A1htctLiiAjPR0oKTDZvp7iAxi\n6lt94IEHYNq0aUB5dkq6IUMas6SpBy9CJckwci/2Q3JyMtxyyy283hvAuWfPnuJORe5WSG47\nroFSfJLsR8gAAEAASURBVJkR0AcBWpfkqe/atSv/DeszBdxrLQRMZyD5cGNYVlYGJ0+aKxle\n3759Yd26dXC4ohLaYAgJCyPQGAJrMLlwsccLl48cCTk5OY0V5XtBIkAJDMeOHQuLFi0Cx3o/\nVA9gGukgIdS8uGshevMwxO66mddBOYYi0w/L2Qgo1MkVOVvOvslXGAEdEfD7qoE8m5Scu6Sk\nRPzoqE6DXVchqzC/cGwQHlPdMJ2BROxFCQkJpguxoGSHZCCRF+mGrEDCP1OtRB6Mpggo4XWT\nJ0823d+CpkCF2NgjjzwCi5csBvd3PqjuiwaSjY2kEKEMu5olH88dIf06hdXdfvvtQOkRWOpH\ngDAiAz83Z6PY5DFRQ/048dXoI1CRtwP83ioYMGCA1N9ZxGjKfzfRXx969MhnkPRAPYQ+iaaZ\n/iiVjW8ITXCVGEHAi+Gli/ILIAXDjTgPTGQmvX379jB1ylSw5vrBuYrPIkUGZXWtur/3AngA\nfv3rX7NxpAIy2oASnXJV4T4VpbkIIxAdBMpOrhMdUUg4CyMgAwJsIMkwCyp0aN68uXizsgUT\nxp6orFJRg4vEKgLrMbwuv9oDF4wbB3a76ZzE0kwrbcgd+DbRPR/PO1ab58yjNACrUMR6Cg3U\nNX5o264tTJkyRUUNLjJw4EABQtmJNQwGIyANAsp6VNanNIqxIjGLABtIBpp6hY2MwuxYGIGG\nEFiAObNIlPXSUDm+Hh4CdJ7j2muuAUsRbtIxxIsl+gi4yHuEtuk9d9/DLwNUwj9kyBBRsvT4\nKpU1uBgjEFkE6ExP2YnVkJGRAZ06dYpsZ9w6I6ASATaQVAIlQzGF7pvD7GSYDTl1oC+ahfn5\n4hwe5c9iiSwCt956q8i7416Eu/Qq9iJFFu26rVtPoGG6wS8Swk6cOLHuTf6tQQSIISwlJRXK\njv/UYBm+wQhEE4HK/J3grcjnkPBogs59NYkAG0hNQiRPgVatWkGfPn1gQ3EJ5FRVy6MYayIN\nApswBJPWBlHD02FSlsgi0KxZM7juV9eJ/DvO5exFiizadVt3fRfwHlFSWCLnYVGHAGE1fPgw\n8JTnIGvYTnWVuBQjEEEESo8uF61TjjkWRkAWBPhbRZaZUKkHhU3Re+pFHGanErHYKja/Jpkw\nh9dFb95nzZoF7ji3yMPDXqTo4G49jt6jTX6RL4XXevCYU8JjkpLDPwZfmWswAhojUHJkiWhx\nJKalYGEEZEGADSRZZkKlHspmgM8hqQQsxopReF2c2w2jRo2KsZHrN9x0TMZ72ov0E3uRojET\nrvnoPUK56667mHI3BMDPO+88UavkyOIQanMVRkA7BHzVpXj+aI0Ilc3KytKuYW6JEQgTATaQ\nwgQw2tWzs7OhR48esFYwlXGYXbTxl7m/rRhedxwZDsdgElM3Gkks0UPg5ptvDpxF+gH9ux4+\nixRJ5AVzHXqPOnfuzEQkIQJN+ZC6d++OG9O1SPldFGIrXI0RCB+BkqPLgJLEjhkzJvzGuAVG\nQEME2EDSEMxoNXXRRRcBvaf+AXPdsDACCgKKV1Eh81Cu87+RR4DYl66efjVYijH0i/MiRRRw\n18LA2SNKCssJG0OHms4pYmZOYC9S6BhyzfARKD64QDQyFl/ssTACMiHABpJMs6FSF2UDvKDm\nvInKalzM5AiQgeRyufhNnE7zPHPmTEE17VqMHiQfe5EiMQ2WAjRA1/mBPOmXXHJJJLqImTbH\nYZ40kqKfv4uZMfNA5ULA7/PgObhFQGHKlMCYhRGQCQE2kGSaDZW6dOzYEbp06QKrMcyu2IMp\n5FliHoFdpWVwuKJSnD2Kj4+PeTz0AIDCliZPngzWPD84NrKBFIk5cC1B3zn+d8stt4DNZotE\nFzHTZq9evSArqzWU4gF5n6c8ZsbNA5UHgdLjKzHEsxDIWGcmSnnmhTUJIMAGkkFXApE1eDDn\nzWIOszPoDGqrthJep5B4aNs6t6YWAfIikbh+CJAIqK3H5ZpGwFLmB9dKH6RnpMMVV1zRdAUu\n0SQCF100AY2jCnyLz2QNTYLFBTRHoOjAN6JNOjbAwgjIhgAbSLLNiEp9TofZMd23SsTMXWw+\nrgOH3Q4cx63vPJN394ILLgDbUQDbbma003I2nMQQWAVww4wbOMeXRsAqYYqF+7/SqEVuhhFQ\nhwARMxRjeGdaWhoniFUHGZeKMgJsIEUZcK2669atG7Rv3x5+KiyGUi+/rdYKVyO2s7+8HA6U\nV8DwESMgKSnJiEMwlc7EaEciwsFMNTIdB+NF79Ey/HG74Oqrr9ZREXN13bt3b2jbti16kH4A\nb1WxuQbHo5EagZIjP4rwuosvvlic3ZRaWVYuJhFgA8nA005epCqfD5bmFxp4FKx6uAgoyWE5\nTCFcJLWpP2jQIKDzHY4dfiBKapbwEXBs9oOlyA9XTL4CUlNTw2+QWziNwGWXXYZkdlVQdGDe\n6Wv8gRGINAKFe+eKLmj9sTACMiLABpKMs6JSJ2VDrJw/UVmNi5kMAZp/OrBOoV0sciAwY8YM\noYhzGYfZaTEjzqUBHBVctWiT2wggMGnSJPGhcO8choQRiAoC5K0sPrQA2rRpw+x1UUGcOwkF\nATaQQkFNkjr0lro1Zp5eVlAI5RxmJ8msRFeNQxUVsLusXMRw85v16GLfWG90toNi651r0YNU\nxV6kxrBq6p71iB/sP/th+PDh0KlTp6aK8/0gEWjXrh30798fk8auhqriQ0HW5uKMQPAIFOGZ\nN/JaEusnCyMgKwJsIMk6Myr1uhDZ7CowzG5FAWdDVwmZqYopubCYvU6uaXU6nXDllVeCpQIp\nv9ezgRTO7LhWBLxH1157bTjNcN1GEJgyZYq4W7Dns0ZK8S1GQBsECnb/RzTEbJTa4MmtRAYB\nNpAig2vUWlXC7IjFjCX2EKDwOovFAuPHj4+9wUs+4quuukpoKNjXJNdVWvUqMTHseh80a9YM\nzj//fGnVNLpi5PF0u+OgEDeufj+HhRp9PmXWvyJ/F5TnbBIeYQqxY2EEZEWADSRZZ0alXn37\n9gVKUPkjhtkRYQNL7CBwrLIStmGCWCIFyMjIiJ2BG2Sk2dnZMAKZBe2HkazhKHuRQpk2kXAX\nqb2nTZvGTFehAKiyTmJiIlxyycVQXXYCGe2WqKzFxRiB4BEo2PWJqEQedhZGQGYE2ECSeXZU\n6EbeA2KzK8MzSD8VcpidCshMU2RhjdeQw+vknVLa2JM41/DLi1Bmybk6gNvUqVNDqc51gkBg\n+vTponT+zo+CqMVFGQH1CFBS4sK9/0UmyjSOelAPG5fUCQE2kHQCXstulQ2ych5Fy7a5LXkR\nUOi9laTB8moau5qNGzcOEpMSMUwMPUiYy4dFPQLWHCRnOOCHgQMHilw96mtyyVAQ6NevH1B+\nvZIji6G6BDMdszACGiNA5AzEYDd16hRO9qwxttyc9giwgaQ9plFvkTYQzTDE6oeCAqjmMLuo\n469HhyerqmBTSalgn6IQSxY5EXC5XHDxRReDpQQ3+7vZQApmlhx49oiEma6CQS28soIIA88g\nsRcpPBy5dv0I5O34QNzgZM/148NX5UKADSS55iMkbaxWK4zHMLsSjxdWF3E29JBANFglDq8z\nzoRdfvnlQlllw28czfXVlPCy2+2gENHoq01s9E5rlc4j5e/+BHxIw8zCCGiFQNmpjVCRuwVG\njx7NHmGtQOV2IooAG0gRhTd6jSthdkrYVfR65p70QECZZ2Xe9dCB+1SHwODBg6F58+bg2Ioe\nJA97kdSgRqQWtlMgNlPJyclqqnAZDRCIj48Hovz2VuRD0f6vNWiRm2AEAgjkbX9XfLjuuusY\nEkbAEAiwgWSIaWpaySFDhojElD/kF+AejDdhTSNm3BI5VdWwobgEevfuDVmYKJhFbgSISIUM\nWUslgH0X/22qmS3H5kB4HXuP1KClbRllA5u37W1tG+bWYhaB6rKTUHzgG6CkxORBYmEEjIAA\nG0hGmCUVOtpsNsEKU+jxwBpms1OBmHGLUHgdbbN582icOVQ8fcrG3zia66Mp4WSz2zj3kQ7w\n0yZ2zJgxUJG3DUqPr9ZBA+7SbAjk73gf/D4PXH/99SJvn9nGx+MxJwJsIJloXpUNMyeNNdGk\n1jMUSg5Lomy66ynClyRDgIhUUlNTwbEdTVsfe5Eamx5LHobXnQAYNnQYJCUlNVaU70UIgRtv\nvFG0nLftrQj1wM3GCgJE7U2kH3S2jcI3WRgBoyDABpJRZkqFnkOHDoXUlBRYlFeAjMK8CVMB\nmeGK5FVXw1ok4jjnnHOAEpGyGAMB8vCed955YCkFsGHiWJaGEXBsC4TXjR07tuFCfCeiCAwf\nPlxQfhcfXACVRQci2hc3bm4ECvZ8Dt7KAqA8WwkJCeYeLI/OVAiwgWSi6STGJ2KzK6AwO2az\nM9HM/jIUynVF22vFW/jLHf4kOwJkIJHYd7CB1Nhc2XcG8FHwaqws34scAjfffDM27oe8rW9G\nrhNu2dQI+JEyPm/bm2Cz2WHGjBmmHisPznwIsIFksjm9+OKLxYi+z80z2ch4OISAEj7JBpLx\n1sOIESNE/D0TNTQyd8jy59jrFzTAbdu2baQg34o0AhMnTgTKsVaIHgBPBX+fRBpvM7ZffHA+\nVBX9DJdeOhFatmxpxiHymEyMABtIJptcEWaHZx0ozI7Z7Mw1ubnIXsfhdcad07S0NOjRowfY\nD6GHpIK9SPXNpO0g4lINQMYki74IOBwOoLNIPm8legHe0VcZ7t2QCORs/qfQe+bMmYbUn5WO\nbQTYQDLZ/Iswu/HjRZjdamazM9XskveIttWKl9BUg4uRwQwbNgxJGjDM7gAbSPVNuR29RyT0\noodFfwTo3EhSUjIQC5mvGg/QsTACKhEoPfYTVORsFrTe3bp1U1mLizEC8iDABpI8c6GZJpdc\ncolo6zsOs9MMUxkaUsIm2UCSYTZC04HylZHY9rGBVB+C9hpcFJzqK8PXoocAHaq/7rpfgbeq\nCPKQiYyFEVCLQM6mV0XR2267TW0VLscISIUAG0hSTYc2ytDb1/T0dFiUXwjVvgAjlDYtcyt6\nIXCyqgrWY3LYvn37QuvWrfVSg/sNE4H+/fuLFtiDVA+QSH9OIXZ09qhZs2b1FOBLeiBAh+td\nbrcga/B5q/RQgfs0GALl6DkqPbYcBgwYAIMGDTKY9qwuIxBAgA0kE64EohSmQ/zFyGa3gsPs\nTDHD3yN7HYniHTTFoGJwEJQLqUOHDmAnqm/Oh1RnBViPA1hw/92vX7861/kXfRGgl23Tr7oK\nPOWnoGD3v/VVhns3BAI5m14Ret5+++2G0JeVZATqQ4ANpPpQMcE1YiAi+S6H2YdMMJ1A4ZIW\ni4XPH5lgMvv06SOICMggYPkFASU/lMDnl8v8SQIEZs2aBXa7A3I3vwZ+H7JosDACDSBQkb8L\niL2OCGmYqr8BkPiyIRBgA8kQ0xS8kgMHDhQUrT8UFEAFh9kFD6BENQ5XVMKWklIRqkC0uyzG\nRqBXr15iALYjfA6p9kwqBhIlQWaRCwF67kydOgWqS49Cwd45cinH2kiFQM7Gl4U+d955p1R6\nsTKMQLAIsIEULGIGKU/eBgrHKvf64Mf8AoNozWrWh8C3NWQbl156aX23+ZrBEOjZs6fQ2HaM\nDaTaU2c7GsCDGa9qoyLP51tvvVUk/MzF8Cm/zyuPYqyJNAhUFuyFogPfQJcuXWA8sumyMAJG\nRoANJCPPXhO6KxvqeRxm1wRSct+el5MLdjxXNmHCBLkVZe1UIdC1a1dRjg2kunDZjvshOzsb\n4uPj697g36RAoE2bNjB58iSoKj4EhfvmSqETKyEXAqc2voQK+eGOO+4QIeFyacfaMALBIcAG\nUnB4Gao0hap0aN8elhYUCsIGQynPygoEdpeWwb7yCjh35EigRKMsxkcgJSUF/j975wEmV3Gl\n7dM9M9JoZjSSRjPKOeeMslCWAIGQCAIMiGzAROOEza7ZZ83aLL+NzdqAMbYx2IvDWhgDIoic\nBZaQUA4o58k5dbh/nequ1qjVPdPh3u4bvnoeSd33VjjnrTua/vpUnSosKiT3KUSQ1Gy6KjVy\nNREp8aiu419zEeAPvpwEqHTLk4gimWtq0m5NU9V+qj6wlgYPHoy9smmfDRigBwEIJD0omriP\nCy+6iLyaRnzIKIr1CLwajP5dJOYRxT4EhgweQu4a4U8DRBLPakZxgMOgQYPsM8k29IQjfBdf\nLKJI1YcQRbLh/CbjUsnmx0Vzje644w5yu/HRMhmWaGsOAniKzTEPhlmhPli/hmV2hjE2qmNN\nCFvef9RBnEGyaNEio4ZBv2kg0L9/fzlqRlkaBjfhkO7SgEAaICLeKOYmwJvvZRTpy8dFFMlr\nbmNhXUoIyL1HiB6lhDUGSR0BCKTUsU7LSPxBjNPmbqyuoZNNOOQvLZOQ4KA8Z6fEAbGLlyyh\nDh06JNgLmpmRgBJI7jJEkHh+3EGhyIfEopibAEeRVq5cKfciVe570dzGwrqUECjZ/CsxjkZ3\n3nknokcpIY5BUkEAAikVlNM8Bi+J4PKa2OyPYh0Ca4PztXz5cusYDUtjIsAb3rm4yyGQJIeK\nAAfFRcLBX6YlENiLlEmlYlM+zkUy7TSlxDA+96j64Kty/+D555+fkjExCAikggAEUioop3kM\nTvfNWdDUB+40m4PhYyDQJM6ueru8kgq7dqWZM2fG0AJVrESgZ8+e0lwXtgaGOPC+BZzzZY2n\nmIXs5ZdfRp7aY1Sx5+/WMBpWGkKgZNNjst+7774bmesMIYxO00UAAild5FM4bkFBAZ07dy4d\nENnQdooDR1HMT+B9IY7qfD66SESPeL0/ir0IKIHkrkYEiWfWXaVRUVERnnULPeYcRWrXrp3I\naPcE+b0iBSGK4wg0lG6jmsNvEZ/thnOPHDf9tncYAsn2UxxwcMWKFfLFK1hmZ4kZf7m0VNqp\n5s0SRsPImAl0FZFBjpi4qmJuYt+Kfo3ctUTdunWzr4829KxHjx505ZVXkre+mCp2/9mGHsKl\ntgio6NG9997bVlXcBwHLEYBAstyUJWbw/PnzKb9jR+JDYz1i+RaKeQmUNntofWU1DR8+nEaM\nGGFeQ2FZwgRYHBV0LRCpvhFBcnFQW2DgCBKKtQjcdtttlJ3dgcq2PkV+D1YnWGv2krO2/tQG\nqj32AU2cOJHmihUqKCBgNwIQSHab0Sj+8FIIPhOp0uuVB8dGqYbLJiDwqojysYTlTFEo9iXQ\ntUBEkfCZkqRAEtPMS4FRrEWAI6HXXbeavI3lVLbjOWsZD2uTIlD8xc9l+29+85tJ9YPGIGBW\nAhBIZp0ZA+xSH7hfLkE2OwPw6tblyyWlci8GstfphtSUHXXp0oWIj5FpdnYUyVUX8F/yMOVM\nwajWCNx8882UJ1YnlG//LfmasGa0NVZ2uVd77EPiCBInEJo2bZpd3IIfIHAGAQikM3DY+w2f\nhzR48GD6uLKKyj0eeztrUe+21dbSfpFMY968ecTfzqLYl0CnTp2kc656+/oYi2fKf8Ujljao\nYx4C+fn5dIsQSb7mWird+rR5DIMlhhDgA8yLNz4q+77vvvsMGQOdgoAZCEAgmWEWUmjDpZde\nSl7xH9yriCKlkHrsQ71UHIjuXXLJJbE3Qk1LEuAPllxcjZY0Xzejlf+Kh24do6OUEVi9erXY\nU9eVKnb9kTwiaQOKfQnUHHqdGst3yKx1Y8eOta+j8MzxBCCQHPYI8KGxnDb6n2IZF4q5CDSK\n5BlvlJXLvRjY9GquuTHCmry8PNmtq9HhS+waAv4rHkawRp/GEsjJyaE7vvENke67URwe+7ix\ng6H3tBHQ/D4q/uIXMgMn9h6lbRowcIoIQCClCLRZhiksLJTLt3gZ19YakVsXxTQE3imroFpx\n9hGL2KysLNPYBUOMIZCbmys7djn8CBlXc4Cv4mEMbfRqNIFVq1ZR7959qHLv36m5+pDRw6H/\nNBCo/GqNmNuDxMdP8HJ9FBCwMwEIJDvPbhTfLrvsMnkHUaQogNJ0+cVgVO/yyy9PkwUYNpUE\nOnToEBguKBBSObaZxlICkaMQKNYlwJlS77nnbtL8Xire9AvrOgLLIxLg6GDJ5l/KL+/uuuuu\niHVwEQTsRMD0AumDDz6gTZs22Yl52n0599xzqUhEkt4QEYsGEbFAST+BI42NtLG6Rp4pgW/m\n0j8fqbAgOztbDqMiKKkY05RjBPPFKB6mtBFGxUTgInGUxLBhw6j6wKvUULYjpjaoZA0C5Tv/\nJA8Fvvrqq6lXr17WMBpWgkASBEwtkDZv3kw//OEPaccO/EebxByf1TQzM5NWiiQA9UIcrRMi\nCSX9BP5ZHNgTpqJ76bcIFhhNICQIONW3g4sr6H/79u0dTMEervMByN/61rekM8Ubf2YPp+CF\nSN9eLQ8D5mWwt956K4iAgCMImFIgecVhps888wxxCkmXy+WIiUi1k2oZ1z+KS1I9NMYLI8BZ\nBV8SWQVzxRKjCy64IOwu3tqVAC9J4uLyOjtJg0irKTkoHvIN/rIsAT6iYPLkyVR3/COqO7He\nsn7A8NMESrc9LdK4VxOfeYUDnU9zwSt7EzClQHr11Vdp7dq19OMf/5j69u1r7xlIk3f9+vWj\n6dOn09baOvqqviFNVmBYJvBhRSWViXOpLhTLU7APwznPRCgRh9NXuQYjSCEeznkEbOvpt7/9\nbenbqQ0/ta2PTnHMU3+KKnY8K9O4X3/99U5xG36CAGWakcGsWbPkN+m8FOyJJ55o1UReklRR\ncXqZ2MCBA6WoKi7GWQytghM3ly5dSuvXryeOIn1nQL+2quO+QQReCEbxFi9eTHhuDYJswm7r\n6uoCVjlcILn8AQxVVVWEKJIJH9QETOrTpw/Nnj2bPvroI6o++DrlDzgvgV7QxAwEODGD39dE\n163+BtWKg8z5j5NLc3Mz8WG5KPYnYEqB1FUcOBdrqa+vJ/6jCi/P44cXD7AiEv1fTtbQSRxW\nuba0jO7u14fai/XjKKklcKKpiT6trJYbm3lzM57b1PJP52h8HhkXJRDSaUtaxw4KJN6/guc/\nrTOh6+Bf//rX6ZNPPhHn5vycOvZbRC63KT9u6Oqz3Tprqtwn0ravoV69exMn4MDPp91mGP60\nRiCt/2OVl5fTunXrQvZ169aNFixYEHofywtejteyvPLKK7Rz507q3r17y8t4HYXAJZdeKvd7\nvSWSNSwril2YRukOl+Mk8KJIzsDfRXFmIDyzccKzePXQWv6gQLC4O4mbH/S/R48e1KVLl8T7\nQUtTEeD/z1auXElr1qyhij3/RwUjrjKVfTCmbQIsbkXedvq2SLzRW4gkFJJRbuyNd8aTkNaQ\nAS+peOmll0J/OByPkloCfLgflzWnkKwhteSJfCLSyWcf5YjzcC688MJUD4/x0kxARZCkQk6z\nLWkdPrhahSNIKPYicPfdd8sPlKVfPk5+L/a6Wml264s3U83hN2n06NFIHmSliYOtuhFIawSJ\n9ws9//zzujmDjuInMGjQIJo6dSp9/vnntE8kaxicEzy8Mv6u0CJOApycobTZQ5dfvoLy8vLi\nbI3qVicQ+hbS6cvZIZCs/ihHtZ+jgqtXr6bf/va3VLb9D1Q0/vaodXHDXASKN/4/aRAn3Aj9\nX2UuE2ENCBhKAF/ZGYrXGp1feeWV0lBEkVI7X38PRu0U/9SOjtHSTSD0oQMCSU5FiEe6Jwbj\n60qAz83p2DGfykSqaG9jua59ozNjCNQceY/qT20gTpg1c+ZMYwZBryBgcgIQSCafoFSYx9nT\neO3/2rIyavQ7fUNEKogTHW9sovVV1XL5wpgxY1IzKEYBARAAgRQTyBeJgG677Vbye+qo9Msn\nUzw6houXgCb2HBVvDKRn/853vhNvc9QHAdsQML1Aeu655+QGdtsQN6EjnFr3UpGsodbro3Wl\n+IYvFVPEqdU5cIDoUSpom3MMREzMOS+wSn8C1157LfFyu4rdf6bmmqP6D4AedSNQ9dWL1FS5\nV+6LHTlypG79oiMQsBoB0wskqwG1qr1XXHGFNH1N8Eweq/phBbs9Ikr3YkkZ5ebmIjmDFSYM\nNhpLwOlLDI2la4re27dvT5ywQfN7qHjTL0xhE4w4m4Df10wlmx+jzMwsuvfee8+ugCsg4CAC\nEEgOmuzWXO3Xr59ca7ytto52150+V6q1NriXGIH3RXKGco+Hli9fTjk5OYl1glYgYBcCLrs4\nAj9aI8Apv4cMGULV+1+mxvJdrVXFvTQRKN/5R/LUnaSrrrqS+vbtmyYrMCwImIMABJI55sEU\nVqjlXip5gCmMsqERiq/ibUMX4VIMBHDoYgyQUMU2BDiN+7fEeTpcTgX3uNjGORs44muqprKt\nT4kv7XLpG9/4hg08ggsgkBwBCKTk+Nmq9cKFC6mosJBeLyunep/PVr6ZxZnDDY30r+oamjBh\nAo0YMcIsZsEOEAABEDCcAB8EP3nyZKo79iHVnVhv+HgYIHYCpSLLoK+pim6++SYKHWIde3PU\nBAHbEYBAst2UJu5QZmYmXXrZZVIcvYZkDYmDbKXlC8E9XogetQIJt0AABGxLgM/V4XJqQyBT\nmm0dtZBjnvpTVLHjWSro2pVuuOEGC1kOU0HAOAIQSMaxtWTPq1atkofCrTlVbEn7zWx0s0jO\n8LJIzpDfsSOdf/75ZjYVtoEACICAIQQmTZpEvFqhsWwrVR983ZAx0Gl8BEo2/5L8via68447\nsC82PnSobWMCEEg2ntxEXOvduzfNmTOHdtc30HaRsAFFPwJvl1dQpddLK8Rm5ezsbP06Rk8g\nAAIgYCEC9913H/GepOIvfi4y23ktZLn9TG2q2k+Ve9cQJ2riL0hRQAAEAgQgkPAknEXgqquu\nktfWnCo56x4uJE5A8cTyusQZoiUIgID1CXA2uxUrVlBz9UH54dz6HlnXAxapQqXKtN5ZWVnW\ndQSWg4DOBCCQdAZqh+7mzp1L3bt3pzdEsgY+PBYleQIHGhpoU02t3KA8ePDg5DtEDyAAAiBg\nYQJ8LhIfUl7y5a/I7220sCfWNb2hdAvVHFpHo0aNogsuuMC6jsByEDCAAASSAVCt3mVGRgZd\nfvnl1Cj2zLxWWmZ1d0xh/wunSqUdiB6ZYjpMYYTLhQOA5ETgoFhTPI+pNqJnz5509dVXk7e+\nmPj8HZTUEyje+KgclNOv4/+j1PPHiOYmAIFk7vlJm3WXiWx2vEZ8TTDrWtoMscHAnJzhFSE0\nO+Xn09KlS23gEVzQg0DoA4nDBYKSiSEeesBFH5YgcOutt1Jubq44f+c3IsV0tSVstouRtcc/\nEanWP6Vp06bR7Nmz7eIW/AAB3QhAIOmG0l4d8bd7nKxhL5I1JD2xnJyhOpicoX379kn3hw7s\nQYC/gJDF4QKJgv6HeNhjeuFFDAS6dOkizt25mXzN1cTn8KCkjkDxxp/JwdThvakbGSOBgDUI\nQCBZY57SYuUVV1whx/0HokhJ8f9HcWB5HTIEJYXRdo2VIHA5XSD5A1OreNhuouFQqwSuu+46\neTBpxc7nxHI7JAZqFZZON6sPviHSrG+jRYsW0fjx43XqFd2AgL0IQCDZaz519WbevHnUraiI\nXhfJGup9SNaQCNzDDY20sbqGJk6cSJy5CQUEFIGQIAgKBHXdcf8iguS4KW/pMC+xu/3222Wi\nhpItT7a8hdcGEND8Pire9Au55+jee+81YAR0CQL2IACBZI95NMQLTtaw8pJLqMHnp3VCJKHE\nT+DFEkSP4qfmjBb88yULIkgSQ4iHM6YfXrYgwMlrevbqRZV7/kbNNUdb3MFLvQlU7fsnNYuz\nj5YvX05Dhw7Vu3v0BwK2IQCBZJupNMYRzmbH5cXgMjFjRrFnr15No1dKyuQm5PPPP9+eTsKr\nhAmEBIHTg7NYYpfwM2SXhpzu+6477xTH8XioZPMv7eKW6fzQfM0yrXpGRibdddddprMPBoGA\nmQhAIJlpNkxoS9++fWWWm621dbRfJGxAiZ3AxxVVVObx0LJly6hDhw6xN0RNRxDIzMwM+Onw\nJXYun0YhFo6YeTgZiQAfHDtgwECq2v8SNVXui1QF15IkUCEidJ7aY3TFFauIf7ejgAAIRCcA\ngRSdDe4ECXDKby7/DC4XC17GP20QULxUFK6N6rjtMALq1HqX1+Fr7EQELTMrKBYd9gzA3dME\nOKJ6zz13kwgjiSjS/5y+gVe6EODDeEu3/Jo4k+ptt92mS5/oBATsTAACyc6zq5NvS5Ysoby8\nPForzvLhZWMobRMoa/bQR5VVMjHDuHHj2m6AGo4joAQSOX2JnfA/xMJxTwEcbkmAlyIPHz6c\nqg++To3lu1rewuskCZTv+l/yNpTIw3m7d++eZG9oDgL2JwCBZP85TtrD7OxsuUyswuMlXjaG\n0jaBV4WY9Akxeemll7ZdGTUcSYD3XXBxeR3pfshp9r99O5wPFgLi4Bd8WLDKrFb8xS8cTEJf\n1/2eOnEY79NiqXcO3XLLLfp2jt5AwKYEIJBsOrF6u6U+6L+MZXYxoWVOvGSEMwWhgEAkAkog\nkcMFEvuPA5QjPSHOvLZgwQIaO3Ys1R59lxpKtzgTgs5el+14jnxNFXTddavlmVM6d4/uQMCW\nBCCQbDmt+jvFh8kNHDiQPhTLxipFJAklOoGddXW0T5x/NGfOHCosLIxeEXccTUCJAkSQIJAc\n/YMQwfl77rlHXi3ehL1IEfDEdcnXXEPl238vl8nfeOONcbVFZRBwMgEIJCfPfpy+r1y5Uu5B\neqOsLM6Wzqq+VqT25sK8UEAgGgFeuiqLJ1oNZ1x3NROFWDjDZXjZBgH+cmnSpElUd+xDqi/e\n1EZt3G6NQPmOP5CvuZpuuOEG6tSpU2tVcQ8EQKAFAQikFjDwsnUCvFyM14ivLcGhsdFIcRKL\n18Whuh07diReKoICAtEI8BJMTk7ganZw4hOR4ptEmnOkwY/2lDj3ujqnpwRRpIQfAl9TNbFA\n6tgxn66//vqE+0FDEHAiAQgkJ856gj737NlTnom0XSwhOySWkKGcTWC9WILIySw4G1Noj8nZ\n1XAFBCSBDjnifCwRQXFsCfoOgeTYJyCq4zNnzqTJkydT3YlPqP7Uxqj1cCM6gTIZPaqlG2+8\nQS6xi14Td0AABMIJQCCFE8H7VgmopAOviSxtKGcTeLU0EF27+OKLz76JKyAQRiA3J5dcTWEX\nHfSWl9dxycnJCbzA3yDQgsDdd98t35Vs/lWLq3gZCwGOHlXsfJby8zvR6tWrY2mCOiAAAi0I\nQCC1gIGXbRNYunSpjIy8FhQCbbdwTo0Gn4/er6ikXr16yW8+neM5PE2UQG6uwwVSUBwyBxQQ\nCCcwffr001Gk4i/Cb+N9KwTKdjwr9h7Vir1H1yN61Aon3AKBaAQgkKKRwfWIBPjA2Pnz59PR\npibaVlsbsY5TL74rxFGj308XXnih3KvlVA7wO3YCUiBxFMXvzH1IrsaA3/z/CgoIRCJw5513\nysslmx+PdBvXIhBgYcTRI957hOhRBEC4BAIxEIBAigESqpxJgAUAlzcQRToDjOKh+JxxE29A\nIAIBTuYhi0OX2bmCWxkhkCI8HLgkCfBepIkTJ1Ld8Y+ooQTnIsXyWJTvFOceifTefO4RfrZi\nIYY6IHA2AQiks5ngShsE5s6dS/zN97ryCvHFtzO/+Q5HVO310vqqaho8eDANHz48/Dbeg0BE\nAkogKaEQsZKdLwYFUn5+vp29hG9JErjjjjtkDyVfPpFkT/Zv7vfUicx1z8rf0dddd539HYaH\nIGAQAQgkg8DauVs+4HLRokVU2uyhzTVYZsdz/Y4Qi5zie9myZXaeevimMwElDFwNOndske6U\n30ooWsRsmJliAnwu0tixY6n26LvUWL4rxaNba7jyXX8mX1MlXXvttSJBA754sNbswVozEYBA\nMtNsWMiWCy64QFr7pjjzB4XozbIKiYHTe6OAQKwE1MGNrgZnRmKV34pDrNxQz3kEbrvtNul0\n6ZYnned8jB77vU0ievSMOHi5g1heh+hRjNhQDQQiEoBAiogFF9siwOvC+Vvft8srHb/Mrkos\nr/tXdY1cWjdo0KC20OE+CIQIKGHgqg9dctQL5bfi4Cjn4WxcBBYuXEjDhg2j6oNvUFPV/rja\nOqVy5Vd/J29DKV111ZVUUFDgFLfhJwgYQgACyRCs9u+UD0FdsGABlXmwzO49IRJ9Ynndeeed\nZ/+Jh4e6ElDCwFWna7eW6cxdH4icde7c2TI2w9D0EHC5XHTrrbeKwTUq3fp0eoww8aia30tl\n235HmZlZIrX3DSa2FKaBgDUIQCBZY55MaaUSBLz/xsnl7aD/fEYUCgjEQ6BLly6yuhIK8bS1\nQ10lDCGQ7DCbxvvAS5j79u1L1ftfIk/dCeMHtNAIVftfIU/tMVq5cgV1797dQpbDVBAwJwEI\nJHPOiyWsmjVrFuXm5IgEBZWWsNcII2u9PvpMZK/jpXWcwQ4FBOIhoASSEgrxtLVDXeW34mAH\nn+CDcQQyMjLo5ptvJhkt2f6McQNZrGdNrGAo2/a0PH/vlltusZj1MBcEzEkAAsmc82IJqzib\n3bki5fep5mbaUevMNUIfV1bJ7HVLliyxxJzBSHMRUPsEXLUOTdIg/OZzWnjJLgoIxELgkksu\noa5dC6lyz99ktrZY2ti9Tu3R96ip8iu5zLt///52dxf+gUBKCEAgpQSzfQdZvHixdO7dCmdG\nkd4NLq9THOw70/DMCAJdu3aV3bocmi3fLfxWDIzgiz7tR4DFNB+A6vc2UPmu5+3nYAIeqT1Z\nHF1DAQEQ0IcABJI+HB3bCx8am5WZSe85cB+Sx++nj8XyOl7vPWbMGMc+A3A8cQJ8Tkmm+Plx\nOzGC5NOIz0GCQEr8+XFqy6uuuopycnKpYucfhVBqcioG6Xd98WZqKN5IM2bMwO8hRz8JcF5v\nAhBIehN1WH+8PGbqtGm0v6GRjjY66xcVp/au9/nkobkOm3a4qyOBwsJCctXo2KFFulI+QyBZ\nZMJMZCZ/sXDFFavI21hOVfteNJFlqTelbPvv5KA33XRT6gfHiCBgYwIQSDae3FS5xum+ubzv\nsGV2Hwb9Vf6nijfGsReBoqIiEUESPvmdtQ/JXRPwl/1HAYF4CfBBqJy0oUwka+AkBU4szdWH\nqebQm/J8qDlz5jgRAXwGAcMIQCAZhtY5HSuBoASDUzz/oKJKZvGbOnWqU1yGnwYQ4AiSONqF\nVEY3A4YwZZcqggSBZMrpMb1RPXv2JE773Vx9gGqPvmt6e40wsGzHH0S3Gt14441GdI8+QcDR\nBCCQHD39+jjfq1cvGj58OG2qqSVOe+2Esre+nk6K7H0zRapzZOBywowb56MSCK5q48YwY8/K\nX+W/GW2ETeYmoA5E5SiS04qvqZqqvnqBCguLaNmyZU5zH/6CgOEEIJAMR+yMAThZg1csc1hf\nVeUIhz8U0SMu8+fPd4S/cNI4At26dZOdu6udtUxI+av8N44werYrAU6OM2XKFKo/+Tk1lu20\nq5sR/arY81eZye+aa67Gl3QRCeEiCCRHAAIpOX5oHSQwb948+eojcS6QEwqff8Tl3HPPdYK7\n8NFAAurUexVRMXAoU3XtDkbMlP+mMg7GWIbA9ddfL20NLDezjNlJGcoH5Vbs+hPxWYRXXnll\nUn2hMQiAQGQCEEiRueBqnAQmTJhAnURmoU8qq22/YbbG66UtYjnh6NGjCcuD4nxQUP0sAkog\nuKucFUFyBf1FBOmsRwIX4iCwcOFC6tOnD1UfWEvehrI4Wlq3as3hN8lTd5KWL19OXbp0sa4j\nsBwETEwAAsnEk2Ml0zib0KzZs6nM46Hd9eJwExuXT8XZR37hH6JHNp7kFLoWEkgOXGKXlZWF\nD3gpfNbsOJTb7aZrrrmGNL+HKnb/2Y4unuVT2Y7n5LXVq1efdQ8XQAAE9CEAgaQPR/QiCCjB\n8InNl9kp/5S/mHwQSIaAEkiuymR6sV5bt1ilyr67XC7rGQ+LTUXgsssuow4dcoRA+osUSqYy\nTmdjGsp2iINhv6Dp06fL9N46d4/uQAAEggQgkPAo6EZgtoggcVECQreOTdYRR5A6duxI48eP\nN5llMMeKBHiJDGdCdNQSO48m05orcWjFeYPN5iHA/x+vWHGxWGJXQtUH15nHMAMsKd/5R9kr\nR81QQAAEjCMAgWQcW8f1zPtxRowYQVtr66jOZ89035zeu7TZQzNnzpSHFDpukuGwIQR69Ogh\nBJIhXZuyU+Ur+40CAnoQUIKhfNf/6tGdKfvwNVVSjdhrxWdAqfMHTWkojAIBGxCAQLLBJJrJ\nBY4icbrvf4koix3LepGEgouKltnRR/iUegL8gcfFW/eanZGoQSVoYL9RQEAPAkOGDKFp06aJ\n5WcbqbF8lx5dmq6Pir1ryO9roquuugpf0JludmCQ3QhAINltRtPszyxxcCqXz+wqkIJ+QSCl\n+UGz2fAqkuJ2yD4k5ScEks0e5DS787WvfU1aUL7r+TRbov/wmvjisVIkocjMzKLLL79c/wHQ\nIwiAwBkEIJDOwIE3yRLgQ/uys7OJ9+nYrTT5/bRJpPceOHAg9erVy27uwZ80ElBCwV3pjAiS\n8lMJwzSix9A2IrBo0SIqLCyi6v0vk89TayPPiOqOf0zNNUfovPOWUkFBga18gzMgYEYCEEhm\nnBUL28SbzVkkHWlsohNNTRb25GzTNwtxxCKJ9x+hgICeBJRAckomO+Wn8ltPlujLuQQyMzNp\n1arLye+tp6p9L9sKhEphzsvrUEAABIwnAIFkPGPHjaCW2a23WRRJLRtU/jluYuGwYQSUUFCR\nFcMGMknH7opApEz5bRKzYIYNCKxatYr4bCQlKGzgEnnqT1HNkXeI91nxF5AoIAACxhPINH6I\n1I7gF9/w14tMYyUlJakdGKOFCIwcOVK+ZkGxsltR6LrVX3wmEjRkiF+8gwcPxvNl9ck0mf28\nLJWLEg4mM093c1gIcrTZJ7Jd4v9q3fE6ukOOInGyhk8//ZQaSrZQh6JxludRufcFEgc80bJl\ny/DzkubZbG5uJt4PhmJ/ArYTSHzoYIcOHahr1672nz2Tesjro/kPZ7Lj/0jscBBkpcdLu4Xw\nHj9hAvXr18+k5GGWVQm0b99emq6SF1jVj1jtZj95Hx/+n46VGOrFQ4BTfrNAqtjzN8sLJJmc\nYe//yS8UeHldp06d4kGBujoTyMrKssVnGp2x2LI7Wwok/kDOIXaU9BGYMWMGrV27lvbUN9Dw\n3Jz0GaLTyBuqhdgTffH+IzxbOkFFNyEC+fn5lJeXR1WV9tpYHnKw5YsG8aWJ2J7Iy+vws9QS\nDF7rRWD+/PlUVNSNysSZQT2mfp/cWbl6dZ3yfupOfEKe2mO0fPly4kOlUdJLwA5f+KaXoHVG\nh4qwzlxZylIWSFw+t8k+pM+raqQ/SNAgMeAvAwhwRCXDAWm+VZQMmSANeIjQpSSQkZFBl1yy\nMpCs4eBrlqZSuefv0n6k9rb0NMJ4CxKAQLLgpFnB5JBAEpEXO5TPhB+8T2T8+PF2cAc+mJCA\nTFjgJXLV2nt9u0pEgQQNJnwIbWTSZZddJr1RAsOKrvmaKqnm8JtyWffUqVOt6AJsBgHLEoBA\nsuzUmdvwPn36EP/5orqWPCJxhpULpys/KtKWc/Yg3liOAgJGEFARFRVhMWIMM/SpElEof81g\nE2ywHwHeK3rOOeeIRA2bqKlqvyUdrNr/isjN4KFLL73UkvbDaBCwMgEIJCvPnslt5yhSoxBH\n22rrTG5p6+b9K7i8TkXFWq+NuyCQGAEVUXEFU2An1ov5W+EMJPPPkV0sDEWRvvqHJV2q3LtG\nJgRYuXKlJe2H0SBgZQIQSFaePZPbrgTF59WB/TsmNzeqeZ8Hlwkqf6JWxA0QSIKAiqioJWhJ\ndGXqpso/5a+pjYVxliawZMkSkdU2Rxwa+6LIqGqtlQyNFbupsXwH8bl73bt3t/Q8wHgQsCIB\nCCQrzppFbFaCgtN9W7n8Swi8TiLLmDrfycq+wHbzElARJNsvsQsmolD+mndGYJnVCeTk5NB5\n5y0lb30x1R3/xFLuVAajXogeWWraYKyNCEAg2WgyzeYKn4U0bNgw2iqW2DWIAyGtWA40NFBp\ns4emioMHkZLYijNoHZuVYFB7dKxjeXyWsn/8f4M6+ym+1qgNAvERUAKjUkSRrFI0v4+qxf6j\n3NxcWrRokVXMhp0gYCsCEEi2mk7zOcNRJK84LHZTjTXPd1HpvVU0zHyEYZFdCPAyGj5jQ+3R\nsYtfZ/jh18gtAspKDJ5xD29AwAACnP2Nn7faw2+R32ON/bB89pG3oUREv86T2VMNwIIuQQAE\n2iAAgdQGINxOjoASFirRQXK9pb61Wh44ffr01A+OER1FIDMzUxxuWURqj44dnXfxdkSxFQQC\nyY6za06f+EuHiy66SJyJ1EDVh940p5FhVlXte0leufjii8Pu4C0IgECqCEAgpYq0Q8fhNKt8\naN+/LHgekl9EvjbU1FA38aF18ODBDp1BuJ1KAiwc3Bxs9dnzLCQl/iCQUvlUYSwlNKr2v2x6\nGCzkakS0q0ePHoSzj0w/XTDQxgQgkGw8uWZwLS8vj8aMGUO76uqp2itOwbRQ2S1srvH6aMbM\nmRayGqZamYAUDkIbuaqs7EV021UCCgik6IxwR38CQ4YMoeHDh1Ng6VqZ/gPo2GPN4XdEtKue\nli1bJpfc6tg1ugIBEIiDAARSHLBQNTECvMyOvw/fEDxPKLFeUt8K6b1Tz9zpIyrhoCItduPh\nqgxExvjbcRQQSCUBXmYnTl2l6oOvpXLYuMdSUS5pb9yt0QAEQEAvAhBIepFEP1EJqH1ISnBE\nrWiyGypBA/YfmWxibGyOEg5uu0aQgn4pP208lXDNZAQ4IsOl6sBak1l22hxfU7VIR/4RDRw4\nCMdKnMaCVyCQFgIQSGnB7qxBJ02aJFP6fm6h85A8fj9tFpn3BgwYgA3lznpc0+qtiiCpSEta\njTFgcBUZU34aMAS6BIGIBPhg4okTJ1JD8RfkqTsRsU66L9YcflMEuTxied0F6TYF44OA4wlA\nIDn+ETAeQLt27YhF0qHGJipubjZ+QB1G+FKIo0YhklT0S4cu0QUItElARVbsGkHivVWcVaxb\nt25tskAFENCbwAUXBISHWZfZVR14Vbqs7NTbf/QHAiAQOwEIpNhZoWYSBGYGEx1YJYr0eTXn\nIyYIpCTmHE3jJ3BaINk0i12VRl0LuxKnNEcBgVQT4HOFWKBXHzDfPiRvY4VIIvGpTCaBrKmp\nfjIwHgicTQAC6WwmuGIAARWJ+cwiy+xYyPEv0mnTphlAA12CQGQChYWF5Ha77XlYrDoktkfP\nyM7jKggYTIAjl7yaoaF0C3lqjxs8Wnzdc2pv0nzycNj4WqI2CICAEQQgkIygij7PIjB69GjK\nz+9IKjJzVgUTXagVqb2319YR29y5c2cTWQZT7E6AzwyTh8WKSIvdijwkVrjVvXt3u7kGfyxE\n4Pzzz5fWVh9aZyqrqw+9Ie1ZunSpqeyCMSDgVAIQSE6d+RT7zd+KT58+g0qbPbS/viHFo8c3\n3AZxqK1fNFHLAuNrjdogkBwBFhB2PCzWHRR9EEjJPR9onRyBJUuWyA6UIEmuN31a+5prqF4s\nr+PzmrC8Th+m6AUEkiUAgZQsQbSPmYASHGZfZvdZ8LwmtSwwZgdREQR0ICAFhIi0yIiLDv2Z\npQt1+K3aZ2UWu2CHswjwz9f48eNFNrtN5K0vMYXzNUfeEdnrvITokSmmA0aAgCQAgYQHIWUE\nrCOQqogz702ePDllbDAQCCgCSkCoiIu6bvV/lT+IIFl9Jq1v/+LFi4UTGtUcedsUztQcelPa\nEbDLFCbBCBBwPAEIJMc/AqkD0L9/f+otzqLYUFNDfM6QGcuppmaZjnzKlCny7CYz2gib7E1A\nCQgVcbGLtyp1ufLPLn7BD+sRUEKkmhMjpLn4vU3ycNhevXrjcNg0zwWGB4GWBCCQWtLAa8MJ\nzJw1ixp8ftoqkiCYsajlf7OEnSggkA4C6owgd7W9EjW4gv5AIKXjqcKYLQnwAeC816f+xHry\neWpb3kr567oTH5Pf20CLFy9K+dgYEARAIDoBCKTobHDHAAJKeCghYsAQSXW5PpiGXNmZVGdo\nDAIJEFACQkVcEujClE2UP8o/UxoJoxxDYOHChWLfj4fqjn2YVp9rDr8jx2d7UEAABMxDAALJ\nPHPhCEs48QGfL2RGgaRpGn0mMth16dKFRowY4Yj5gJPmI6AEhIq4mM/CxCxif3JzcyknJyex\nDtAKBHQkoARJzZF3dew1vq74d07t0XepY34+9rzGhw61QcBwAhBIhiPGAC0J8LlCY8aMkecM\n1Xi9LW+l/fVukX680uOV6b1ZxKGAQDoIKIGkIi7psMGIMdkf5ZsR/aNPEIiHwLhx46igoEAI\nlPdJ09KzJ7axbCt5G0pp7rnnUmZmZjzmoy4IgIDBBCCQDAaM7s8mwMvX+NfR58F02mfXSM+V\n9ZXiE5wos2fPTo8BGBUEBAGOsOTl5ZGtIkiNGrmaIZDwgJuHAJ/NN3fuXPI1VVJDyea0GFZz\n5H057vz589MyPgYFARCITgACKTob3DGIgNrfo/b7GDRM3N0qe5R9cXeABiCgEwFO1GCnCJK7\nOgBGJaDQCRO6AYGkCMybN0+2rz3yXlL9JNq49uh7xEINX8olShDtQMA4AhBIxrFFz1EITJw4\nkXI6dKBPqwIRmyjVUnq5weejzTW18iRzLANKKXoMFoEAP4MccSERebFDcVUF/MDPlh1m0z4+\n8JdhGRkZVHMsEMlJpWfehjJqLNtGEyZMIF56jgICIGAuAhBI5poPR1iTlZVF06ZPpxPizKHD\nDY2m8HljtTibSWyYxTd5ppgOxxuhIi0q8mJ1ICoaBoFk9Zm0l/0dO3Yk/sKuqXwXeetLUupc\nbTB73rli/xEKCICA+QhAIJlvThxhkRIinwbTaqfbabW8TtmVbnswvrMJKCGhIi9Wp6HOdFJ+\nWd0f2G8fAkqg1B7/KKVOKYE0Z86clI6LwUAABGIjAIEUGyfU0pmA2ufzaTAxgs7dx90dC7V2\n7drROeecE3dbNAABvQkoIaEiL3r3n+r+XMHVtMqvVI+P8UAgGgElUJRgiVZPz+uc3psPiOUj\nJUaPHq1n1+gLBEBAJwIQSDqBRDfxERg4cCD17tWLNtSIpW3+9KRYVRafFEv9DoqlflOmTKHs\n7Gx1Gf+CQNoIKCGhIi9pM0Sngd3Yg6QTSXSjN4GRI0fKdN91Jz4R6b5Ts+evsXwH+RoriL8o\nxJESes8o+gMBfQhAIOnDEb0kQGCWSKfd4PPTltq6BFrr10Qli8DyOv2YoqfkCPTo0UN24KpM\nrh+ztOYIEmfrKiwsNItJsAMEJAEWKHyAOQuWxvKdKaFSd/xjOY5aSZGSQTEICIBAXAQgkOLC\nhcp6ElBLG9K9zG59ZSAHMQSSnrOLvpIhYMcIEosjzhiGAgJmI6CESt3xT1Jimhpn5syZKRkP\ng4AACMRPAAIpfmZooRMB/taOv1VWCRJ06jaubvxiScVn1dVUJD68DR8+PK62qAwCRhFQYsJt\nhwiSTyN3LZGKihnFDP2CQKIE+HcRl7oTnybaRczt/L5mqi/+gniZOX4mYsaGiiCQcgIQSClH\njgEVAU6xOn78eNpZV08VHo+6nNJ/t4nlfTVeH/FyPxQQMAsB/uIgcFhsavZEGOm3iwO0wo2e\nPXsaOQz6BoGECfQS+2H79u1LDcUbSfMb+7uooXgTab4muawvYYPREARAwHACEEiGI8YArRFQ\ny9o+S1O6bxW9Usv9WrMV90AglQRYULhE5IW81hZJKkEDvi1P5dODseIlMF2czef3NlBDyZZ4\nm8ZVv+7kZ7L+tGnT4mqHyiAAAqklAIGUWt4YLYyAEiafBvcBhd02/K3af4S14IajxgBxElCC\nwuqpvtUyQeVPnBhQHQRSQoAFEpe6k58bOl59UCBNnTrV0HHQOQgLn9V8AAA+oUlEQVSAQHIE\nIJCS44fWSRIYM2YM5ed3pPViH1CqS43XS9vq6uQ5FAUFBakeHuOBQKsE1JI0V6W1I0jKfuVP\nq07jJgikiYA6A6/eQIHE+48aSr6kIUOGyNTiaXIVw4IACMRAAAIpBkioYhwBzmo1Y8ZMKm32\n0Ff1DcYNFKHnf1XXkE8kaVAZjCJUwSUQSBsB3hfBRUVg0mZIkgO7KwIdKH+S7A7NQcAQApw5\nUu5DKhF7hPxeQ8ZgccR7nJQYM2QQdAoCIKALAQgkXTCik2QIqH1I6yvFYSkpLGp5nRo/hUNj\nKBBok4CKuLgrrB1BcgcjYBBIbU45KqSZAAsX3ofEB7kaUepPbZDdQiAZQRd9goC+BCCQ9OWJ\n3hIgoCI4KmFCAl0k1ITH65CdTRMnTkyoPRqBgJEEevfuLbu3vEASAi8rKwuHxBr5sKBvXQhM\nmTJF9lN/aqMu/YV3Ui+y5HFR44Tfx3sQAAHzEIBAMs9cONYS/iA4YMAA+qKmlpr9/pRwONrY\nRMeamugcsVG2Xbt2KRkTg4BAPAROC6R4WpmvLi+x4+iRy+Uyn3GwCARaEJg0aZJ8x+cU6V00\nzS/SiG+SPwvqIGi9x0B/IAAC+hGAQNKPJXpKggBHkZqEONosRFIqikorjuV1qaCNMRIhwOeE\n5Yk/rnILL7Fr0MjVSKTEXiIc0AYEUkWAD2/t3LmLEDL6C6Smyr3k99SSEmGp8gnjgAAIJEYA\nAikxbmilMwGVZlsJF527P6u79VWB/U5qed9ZFXABBExAoG+fPpRRKQzxW1MkucsDEPsIP1BA\nwAoEJk2aSN6GUmquOaqruQ3Fm2V/WNKtK1Z0BgKGEYBAMgwtOo6HAJ9B4Xa7KRUCyS8y13EG\nu25FRTLdajx2oi4IpJIAZ9UiH5ErtflLdHPRXRYQdtIP3XpFRyBgHIEJEybIzjnjnJ6lviQg\nkFT/evaNvkAABPQnAIGkP1P0mACBvLw8GjduHO2qq6cqcT6RkYXHqPH6aMbMmUYOg75BIGkC\nSli4LbrMDgIp6UcAHaSYAP8e4tJQqq9AYsHVvn17Gj58eIo9wnAgAAKJEIBASoQa2hhCYMaM\nGcTfN2+oqjGkf9WpilKpZX3qOv4FAbMR6NevnzTJXWo2y2KzJ6MsUE/5EVsr1AKB9BEYO3as\nTCjSULJFNyN8zSIBUdV+GjVqlMzoqFvH6AgEQMAwAhBIhqFFx/ESUILl8+rqeJvGVf9zkd6b\nCwsyFBAwMwElLDJKLboHKWh3//79zYwZtoFAiACvZhg0aJA8C0mvA2Mby7aJ/jW5SiI0EF6A\nAAiYmgAEkqmnx1nG8dpsXoKgBIwR3nMa8S9r64izFSHVqhGE0aeeBDj9PRe3hQVSQUEB8YdO\nFBCwCoExY8aQ5muipsqvdDG5oZQFEhH3iwICIGANAhBI1pgnR1jJ5xFxCtTD4oyi4uZmQ3ze\nKsQRpxPnpBAoIGB2Aj179pTndLlLLBhB8mjkFskl+MsIFBCwEgFeZselQUZ+kre8sWy77AQC\nKXmW6AEEUkUAAilVpDFOTATUsjej9iFtCC6vg0CKaTpQKc0E+HBVjiLJvTwWS/XtLgnAY/tR\nQMBKBEaPHi3NbSwNCJtkbWehlZOTiy8LkgWJ9iCQQgIQSCmEjaHaJjBt2jRZaYNIw21E4fTe\nXKZOnWpE9+gTBHQnICMwIrGji89DslDJCEa9EEGy0KTBVElgxIgRMlFDY/mOpIn4xOGwnprD\nNHJkoM+kO0QHIAACKSEAgZQSzBgkVgK8BCGnQwfaaIBA4qV128QSuyFDhhDvi0ABASsQ4A3j\nXDJOWWuZnTto7+DBg62AGTaCQIhATk6OjNw2VewmTfOHrifyorF8l2w2cuTIRJqjDQiAQJoI\nmFYg1dfX01tvvUXPPfccffHFF2nCg2FTTSAzM5Mmin1IR5v034e0tUZ8kycOiVVRqlT7hvFA\nIBECSmC4iy0mkIL2KvsT8R1tQCBdBFjQ+L0N1Fx9KCkTmiCQkuKHxiCQLgKmFEivv/46XXTR\nRfTKK6/Qrl276L777qOf/vSn6WKEcVNMQC1/0zuKtFEIJC7nnHNOij3CcCCQOIGhQ4fKxlaL\nIGWc1GSCCXXYbeIE0BIEUk+Al9lx4ShSMqWxIhBBUv0l0xfaggAIpI5AZuqGim0kv1gG9eyz\nz9Jtt91Gl19+uWz0wQcf0AMPPEArVqyQy6Ni6wm1rEpgypQp0vRN1bV0fmFX3dzYFFy2p/rX\nrWN0BAIGEuA9PG63m9xCcFim+DTKEIfbDho6SNpuGbthKAgECQwfPly+ahQCKX/AeQlzaSrf\nLX8GeGk3CgiAgHUImC6CVF5eLr/hX7x4cYjixIkT5evjx4+HruGFfQmMGzdOnja+qUa/RA1e\nsbSOU3zzgZVFRUX2hQfPbEeAzwbj51ZGkMRzbIXiFuKIfETDhg2zgrmwEQTOIqCe3aaKPWfd\ni/WCJn5e+Swl/vnNzs6OtRnqgQAImICA6SJIhYWFckldSzZvv/02ZWRkkPpGp+W973znO1TT\n4oN0bm4ude7cmVhooViXAK//3rJlC1V5vdRJ7EtKtuyuq6dGEZ3kJBB4NpKlifapJsAfsA4c\nOEBu8d+aX7+gqmFuZJwICLk+ffrg580wyujYSAIsaDp0yBFL7PYmPIyn9pjYx1RP/fr1w89B\nwhTN1dDj8YjEHdb4ospc5KxnTfKfPA32ed++ffTUU0/R1VdfTd27dz9rtA8//JAqKipC1ydM\nmCBPbW8Sm/xRrEuAhQwLpC1i39CcLp2TdmRzcP8Rn2+BZyNpnOggxQR4md17771HbiE8/F1d\nKR49/uHYTi4s7PDzFj8/tDAHgYEDB9COHTuFyGkid2b7uI3i6BEX/BzEjc60DXgbCIozCKRV\nIPE3+evWrQuR7tatGy1YsCD0nj8g33///fLaTTfdFLre8gUndGj5wL7zzju0f//+iGKqZTu8\nNjeB2bNn0/PPP6+bQGKhxWXu3Ll4Nsw99bAuAoHJkyfTM888QxnHNfKOiVDBZJfYTi58IDP/\nv44CAlYkwIkVduzYITLZHaDsgkDShnj8UAJp/Pjx+L0TDzgT123Xrp08I8vEJsI0nQikVSBV\nVVXRSy+9FHKF/zNSAumjjz6iBx98kFatWkW33nprqE74C15O17JwWJxPn+dNzSjWJaD2nW0R\n+4b0KFtqa6lTfj5xymF+PlBAwEoERo0aJc1VwsPstrOdXbp0oR49epjdVNgHAlEJqMQKTZX7\nEhNIVftk39wPPpNExWypG/j8YKnpSsrYtAokXjbCUYLw8u6779KPfvQjuueee+jiiy8Ov433\nDiDAB7nysoTtR4+QT6z3zUhC1BQ3N1Nxs4fOnT4D4sgBz44dXezduzd1zO9IVcf0S1xiFCdX\nrUbuaqJRswKizqhx0C8IGE1AHdLcVLU/oaGaRTv+QM2fdVBAAASsRcB0YZaysjJ6+OGHad68\neTRgwAD68ssvQ3+wud5aD1ey1vKyhAafn/bXNyTV1baaQBSK+0MBAasSGD1qNLkriVz15t4g\nnHEsYJ+KelmVN+wGASWQWOgkUrhdr169iTNRooAACFiLQFojSJFQvfbaa1RfX09vvvmm/NOy\nDu9HWrZsWctLeG1jAixoeAnmtro6Gpqbk7Cn3J4Lpw9HAQGrEmDBsX79eso4KvYhDTPvMlG2\njwsEklWfNNitCHAWxgyRRZX3IMVbvI3l5GuupkGD8MVcvOxQHwTMQMB0Aumaa64h/oMCAmPH\njpUQdoh9SCu7JX520fbgPibVH8iCgBUJcGZHLgGBZF4PlEDCz5t55wiWxUaAjxfp17cvHTpy\nKLYGLWo1Vx+U73ipOAoIgID1CJhuiZ31EMJiowjwWUiZ4hfUjtr6hIfg8wp2ijOQeA8HbxpH\nAQGrEmgpkMzsAwsk3i/VV3ywRAEBqxNggcNnGXnrS+Jypbn6sKzPWwVQQAAErEcAAsl6c+YY\nizmd5pChQ+mrhgbyJHj2wJHGJqrz+QjfZjvmsbGto3zYJAuPjCOBJWxmdNRVIxI0iH1SY8cE\nor9mtBE2gUA8BFQEqLkmviiSqq/axzMm6oIACKSfAARS+ucAFrRCgA929YooEIukRMouET3i\ngv0QidBDGzMR4GxYLDxkogYhRMxYlHjDfj8zzg5sSoQAfzHBpbnmSFzNVQRJtY+rMSqDAAik\nnQAEUtqnAAa0RkAJm91BodNa3Uj3domEH1xUP5Hq4BoIWIWAysSohIjZ7M44HBBuEEhmmxnY\nkygBtVS0uSawZC7WfjyiPn+pwcu7UUAABKxHAALJenPmKIt5HxKX3XWJRZD2BIWV6sdR8OCs\n7QiEBFJQiJjNQSWQlJ1msw/2gEC8BJRA8tQcjatpc+1ReVByVlZWXO1QGQRAwBwEIJDMMQ+w\nIgqB4cOHyzt7g5GgKNWiXt4rzlDq2rUrFRYWRq2DGyBgFQJKeGQeMuESO7EUNlPsj+rVqxd+\n3qzyQMHONglwqm8uLHhiLX5PPflEmm/VNtZ2qAcCIGAeAhBI5pkLWBKBQF5eHvUWH7gS2YNU\n4fFQqfijRFaE7nEJBCxFoKCgQH7okkvs/OYSSe5icYhtI9GECRMsxRTGgkBrBDhZUGFREXni\nEEieuuOySyyva40s7oGAuQlAIJl7fmCdIDBMRJFqvD4qbm6Oi8c+ET3iAoEUFzZUNjmBiRMn\nkquJyH3SXIZmBKNaEEjmmhdYkzyBPmIfEaf51nyx/Q5qrj0mB0UEKXn26AEE0kUAAild5DFu\nzASGilTfXJTgibXhvgbxdbYoQ4YMibUJ6oGA6QkoAZJ5yG8qWzMPBuxhAYcCAnYiwMtGiTTy\n1J+KyS1PbSCCFGgXUxNUAgEQMBkBCCSTTQjMOZuAEjj7g4Ln7BqRrxwIpgZXAityLVwFAWsR\nmDRpkjRYRWzMYj3b0759e0JCFLPMCOzQi4ASOkr4tNWvWmLXs2fPtqriPgiAgEkJQCCZdGJg\n1mkCgwcPlm8OxnkW0oGgoFLtT/eIVyBgXQK8ZLRDhw6UedA8e5Bc9RpliD1IY8aMIWTtsu6z\nBcsjE1BCx1N3InKFsKuqnmoXdhtvQQAELEAAAskCk+R0EwcNGiQRKMETK4+DQiAViex1nOgB\nBQTsQiAjI0MmQnCXiaQIteYQSSqaNXnyZLtghh8gECLQo0cP+dpTH9vGP29doB4EUgghXoCA\n5QhAIFluypxncE5ODnXr1o0ONQb2FMVCoM7nkxnsBgbFVSxtUAcErEJA7fPJOGASgRS0Qy3/\nswpH2AkCsRBQAkkJn7basJDK79SJsrOz26qK+yAAAiYlAIFk0omBWWcSGDhwIJV7vFQrstnF\nUg4HxdSAAQNiqY46IGApAkqImGWZnbJDCTdLwYSxINAGASWQPMHIUBvVRca7U9QzGHVqqy7u\ngwAImJMABJI55wVWhRHo37+/vHKkKbYo0pFGkQdZFNVOvsFfIGATAjLVt8tFGWbYh+QLHBDL\nS2E7d+5sE8JwAwROE+DDxjMyM6XwOX018itvY4VMB969e/fIFXAVBEDAEgQgkCwxTTCyX79+\nEsLRoPBpi4iqB4HUFinctyIB3lc3bNgwyjwqlth50rvMLoNt8BJh/5EVnyTYHAsBl/gygvez\nehtEJpI2iqrDy8JRQAAErEsAAsm6c+coy5VAOhajQFL1+vbt6yhOcNY5BKZMmUIkjh7KOJJm\ngRTcfwSB5Jxnz4mesuDxNpSR5m99mbe3PiCiIJCc+JTAZzsRgECy02za2Bd1IvmxpthOMj/W\nFFhip9rZGA1ccyiB0D6kNCdqUPuPIJAc+iA6xO2A4NHI21jaqsfe+hJ5v6ioqNV6uAkCIGBu\nAhBI5p4fWBck0Lt3b/nqRFD4tAXmhBBSnfLzkeK7LVC4b1kCSpCkO5MdC6SuhV1JRXktCxSG\ng0ArBJTg8Ta0IZAaIJBawYhbIGAZAhBIlpkqZxvKm79zxOGYLHzaKpqm0cnmZuoVFFVt1cd9\nELAiAT5jhf9kHhJL7PzpWWbnLtbIVSf2H03C+UdWfIZgc+wECsUeJC4qQhStpRcCKRoaXAcB\nSxGAQLLUdDnb2J69eknh0xaFUo+HvEIk9RL1UUDAzgR4mZ1LJHZ0n0qPlyqLnopmpccKjAoC\nxhMIRZDaWmIn9ilxUYLKeMswAgiAgBEEIJCMoIo+DSHA35Y3+v1U7RUps1opp0T0iIs6u6KV\nqrgFApYmoIRJ5kGRrSENJfNAYFy1HyoNJmBIEEgJAU71zcUXFEDRBlV7lCCQohHCdRCwBgEI\nJGvME6wUBJTgUQIoGpTiJo+8pepHq4frIGB1AkogqUhOqv3hcdtnt6dRo0alemiMBwIpJaAE\nT9t7kEopJyeXsrOzU2ofBgMBENCXAASSvjzRm4EE1MF7Jc0BARRtqBJPIIKENKvRCOG6XQjw\nWUi5ubmUmYZMdq46jTLEfvXx48ZTpjhEEwUE7ExARZBUhCiarxxhKhRJS1BAAASsTQACydrz\n5yjrleApbUsgBe+r+o6CBGcdRcDtdtOECRPIXUHkqk5togYVtcLyOkc9co51tqCgQPruaxQ/\nbFGK5veSr7mKVN0o1XAZBEDAAgQgkCwwSTAxQEBtki0RSRhaK5ykgYuKOLVWF/dAwOoEJk6c\nKF1QgiVV/qjzjyCQUkUc46STAEdq27dvLw+LjWaHEk8q2hStHq6DAAiYnwAEkvnnCBYGCSiB\nVNZGBElFmPBLCo+OEwgogSLTfafQ4QxOLy4KR7BQQMAJBDgy5Gsqj+qqN3gPEaSoiHADBCxD\nAAvHLTNVMFQJnvI2IkgVHi9lZmQQn52EAgJ2JzB+/HhyuVyU0giST6PMIxoNGjSIOnXqZHfE\n8A8EJAEWPidObKedz42JSETTAlkdu3TpEvE+LoIACFiHAASSdebK8ZaGBFIbab7LvB4qCKZk\ndTw0ALA9gby8PBo6dCjt2bdHnGIpojqZLsN9zjguxhHZ9lX0yvABMQAImIDAihUriA8iV8Xn\n85FfHD3BSUr4SwouGeLLuQULFqgq+BcEQMCiBCCQLDpxTjSb06Z2EH8q2oggVYoI0uDghlon\ncoLPziPAy9z27NlDGcc08vVPgUAKLq/j6BUKCDiFwOrVq4n/qFJdXU11dXUyKQPvT0IBARCw\nDwHsQbLPXDrCE44MVbYSQaoT3+h5xDd8WOLgiMcBTgYJqH1Aal+Q0WDUOCpBhNHjoX8QAAEQ\nAAEQSCUBCKRU0sZYSRPgfUVVXl/UfqpE9IgLBFJURLhhQwIhgXT49PIfI93MFONwVq/Bgwcb\nOQz6BgEQAAEQAIG0EIBASgt2DJooARZIPhEhqokSRaoKXodASpQw2lmRACdL4L1ILFyMLq5a\njdwikde4ceOIz2FCAQEQAAEQAAG7EcBvN7vNqM39URmzqqNEkZRAUvVsjgPugYAkwBvEx44d\nGzgwVggYI0uGyF7HhcdDAQEQAAEQAAE7EoBAsuOs2tgnJXyqfYGldOGuVos9SFzy8/PDb+E9\nCNiaAEd0uCgBY5Szqn81nlHjoF8QAAEQAAEQSBcBCKR0kce4CRFQwqcmSgRJXVdCKqFB0AgE\nLEhARXSUgDHKBdU/BJJRhNEvCIAACIBAuglAIKV7BjB+XASUQKqNKpACkaWOHTvG1S8qg4DV\nCSjBogSMUf7wAbGFRYXUvXt3o4ZAvyAAAiAAAiCQVgIQSGnFj8HjJaCET21wKV14e3Vd1Qu/\nj/cgYFcCLFi6FnalzKPG7UFyVWjkqhP7j8Zg/5FdnyP4BQIgAAIgQASBhKfAUgQ4UxeXmjYE\nkqpnKedgLAgkSWDM6DHkqiVyVRkjkjKC4mv06NFJWormIAACIAACIGBeAhBI5p0bWBaBgBI+\nfCBspKKuI4IUiQ6u2Z3AmDFjpItKyOjtb8axgPBS4+jdP/oDARAAARAAATMQgEAywyzAhpgJ\n8OGUXOqjCiS/vK/qyTf4CwQcQmDUqFHSUyVk9HZb9avG0bt/9AcCIAACIAACZiAAgWSGWYAN\nMRM4HUEKCKHwhko4QSCFk8F7JxBQwiXjuEFL7EQEqaCgAAkanPAwwUcQAAEQcDABCCQHT74V\nXVfCRwmhcB/qfX7iQzM7dOgQfgvvQcD2BHr16kX5nfJJRXr0dNglDqB11xApEaZn3+gLBEAA\nBEAABMxEAALJTLMBW9okkJOTI+vU+yPvQeLrqk6bnaECCNiQwMgRI8ldKRI11OsbRVKia+TI\nkTakBpdAAARAAARA4DQBCKTTLPDKAgRUZKhBRIoiFb4OgRSJDK45hcCIESOkq+4T+gok1Z/q\n3yk84ScIgAAIgIDzCEAgOW/OLe2xEkiN/igCSVxXdSztKIwHgQQJKAGTobNAUv0NHz48QcvQ\nDARAAARAAASsQQACyRrzBCuDBHh/UXZ2NkWNIEEg4VlxOAElYJSg0QsH95eZmUkDBw7Uq0v0\nAwIgAAIgAAKmJACBZMppgVGtEeAIUaQIkl/TqBkCqTV0uOcAAkOGDCG3203uEzo669Moo5ho\n8ODBUiTp2DO6AgEQAAEQAAHTEYBAMt2UwKC2CEQTSE3BZXdYYtcWQdy3M4H27dtTv379KOOU\n2IMkvjTQo7hLRS8iL8qwYcP06A59gAAIgAAIgICpCUAgmXp6YFwkArzELlIESV3j+ygg4GQC\nQ4cOJVezyGRXoQ+FjJMBocX9ooAACIAACICA3QlAINl9hm3oX1sCCREkG046XIqLgBIyMooU\nV8vIld0cjRJF9Ru5Fq6CAAiAAAiAgD0IQCDZYx4d5QULJK9YOuQLWz6kIki8xAgFBJxMgPch\ncVHCJlkWSmhBICVLEu1BAARAAASsQAACyQqzBBvPIKAEkNpzpG42+QPfcmOJnSKCf51KgJMp\ncFHCJlkOLLT45653797JdoX2IAACIAACIGB6AhBIpp8iGBhOQAmgcIHEGey4qPvh7fAeBJxC\nYNCgQcQp8d2ndPBYfPGQIZI0cHpvzo6HAgIgAAIgAAJ2J4DfdnafYRv6pwSQihgpF9USu3bt\n2qlL+BcEHEmAfwb69OlDGSXJZ7FzlwmEIoOdiko5EiicBgEQAAEQcBSBTLt56xdRhMbGRqqo\n0Cl9k90A2cifaBEkTexNwvzbaKLhSkIE+vbtS0eOHCFXtUZaviuhPriRuzggsnr16oWfq4Qp\noqEdCXg8HulWTU0N1dfX29FF+BRGgOecP2Og2J+A7QQSLyvJysqi3Nxc+8+eQz3My8uTnqsl\ndQqDiih17NgR86+g4F/HEuCECp988gm5RRTJl4xACkah+Awk/L/q2McJjkcgwKKooaGBOHMq\nf+5AsT+BjIwM+zsJDyUBWwokfoCxzMq+T7hK462W1ClPm7TAHqScnBzMv4KCfx1LQGWyyxAR\nIF8gZ0NCLLg9FxZc+H81IYRoZFMCvFqFS2ZmJn42bDrH4W7xPkz+Ih7F/gSwB8n+c2w7D1UW\nu+awMLeKKKn7tnMcDoFAHAQ4qQIXt0iwkExR7QcMGJBMN2gLAiAAAiAAApYhAIFkmamCoYqA\nEkDhe5DUEjt1X9XHvyDgRAJK0PASu2QKJ3ro3r27XEaUTD9oCwIgAAIgAAJWIWC7JXZWAQ87\nEyeglvn8+MAhynWfXg9c6fXKTiGQEmeLlvYhUFRURLzctKY0ic3jjRq5akWK7zGBaJR96MAT\nEAABEAABEIhOABGk6Gxwx6QExowZQ5yIodLlpqNimd1RkbmQ/60Te8+6du1Kau+FSc2HWSCQ\nMgL9+/enDE7THTxEOd6B+fwjLtwPCgiAAAiAAAg4hQAiSE6ZaRv5OWvWLNqwYYP0qLa2ljjF\napcuXXBArI3mGK7oQ4CX2e3cuZPc4tQDf9f4+3SXBpbnQSDFzw4tQAAEQAAErEsAESTrzh0s\nBwEQAIFWCfTr10/eV0Kn1coRbqp2EEgR4OASCIAACICAbQlAINl2auEYCICA0wkoYeMuSyxR\nAwSS058g+A8CIAACziQAgeTMeYfXIAACDiAQEkgJpvp28/4lUfr27Rt4gb9BAARAAARAwAEE\nsAfJAZMMF0EABJxJQAmbrC1+sQ8p/ihSxnGNunXrhv19znx84DUIgAAIOJYABJJjpx6OgwAI\n2J0Ai5sePXvQyRMnyV0Vv0BiPuPHj7c7JvgHAiAAAiAAAmcQgEA6AwfegAAIgIB9CLhcLlr3\nxjqqqqqK6FRdXR3xn06dOlG088M4dT4KCIAACIAACDiJAASSk2YbvoIACDiOAAsfjiRFKpwi\nPzs7G2nyI8HBNRAAARAAAccSQJIGx049HAcBEAABEAABEAABEAABEAgnAIEUTgTvQQAEQAAE\nQAAEQAAEQAAEHEsAAsmxUw/HQQAEQAAEQAAEQAAEQAAEwglAIIUTwXsQAAEQAAEQAAEQAAEQ\nAAHHEoBAcuzUw3EQAAEQAAEQAAEQAAEQAIFwAhBI4UTwHgRAAARAAARAAARAAARAwLEEIJAc\nO/VwHARAAARAAARAAARAAARAIJwABFI4EbwHARAAARAAARAAARAAARBwLAEIJMdOPRwHARAA\nARAAARAAARAAARAIJwCBFE4E70EABEAABEAABEAABEAABBxLAALJsVMPx0EABEAABEAABEAA\nBEAABMIJQCCFE8F7EAABEAABEAABEAABEAABxxKAQHLs1MNxEAABEAABEAABEAABEACBcAIQ\nSOFE8B4EQAAEQAAEQAAEQAAEQMCxBCCQHDv1cBwEQAAEQAAEQAAEQAAEQCCcAARSOBG8BwEQ\nAAEQAAEQAAEQAAEQcCwBCCTHTj0cBwEQAAEQAAEQAAEQAAEQCCcAgRROBO9BAARAAARAAARA\nAARAAAQcSwACybFTD8dBAARAAARAAARAAARAAATCCUAghRPBexAAARAAARAAARAAARAAAccS\ngEBy7NTDcRAAARAAARAAARAAARAAgXACEEjhRPAeBEAABEAABEAABEAABEDAsQQgkBw79XAc\nBEAABEAABEAABEAABEAgnAAEUjgRvAcBEAABEAABEAABEAABEHAsAQgkx049HAcBEAABEAAB\nEAABEAABEAgnAIEUTgTvQQAEQAAEQAAEQAAEQAAEHEsAAsmxUw/HQQAEQAAEQAAEQAAEQAAE\nwglAIIUTwXsQAAEQAAEQAAEQAAEQAAHHEsi0o+dr1qyh999/346uwacwAn6/n/hPRkYGuVyu\nsLt4CwIg0BoB/Py0Rgf3QKB1Avj5aZ2PHe8ePXrUjm7BpwgEXJooEa5b9pLX66XGxkbL2g/D\n4yPwl7/8hZ588kl66KGHaM6cOfE1Rm0QcDiBZ599ln7/+9/TI488QtOmTXM4DbgPAvERePrp\np+lPf/oTPfbYYzRhwoT4GqO2ZQnk5ubiC1nLzl7shtsugpSZmUl5eXmxE0BNyxOora0lzLvl\npxEOpIEAfz+Gn580gMeQtiDAEST++cnKysLnDlvMKJwAgdMEsAfpNAu8AgEQAAEQAAEQAAEQ\nAAEQcDgBCCSHPwBwHwRAAARAAARAAARAAARA4DQBCKTTLPDKggSGDh1KF198MfXs2dOC1sNk\nEEgvgREjRsifn+7du6fXEIwOAhYkMHr0aPnzU1hYaEHrYTIIgEBrBGyXpKE1Z3EPBEAABEAA\nBEAABEAABEAABFojgAhSa3RwDwRAAARAAARAAARAAARAwFEEIJAcNd1wFgRAAARAAARAAARA\nAARAoDUCtkvz3ZqzuGc/Aq+++ipVVFTQ1VdfbT/n4BEIGEDgk08+oV27doV6bt++PfXv35/G\njh1LnTp1Cl3HCxAAgegEjh8/Tps2baKtW7dShw4daOrUqTRjxozoDXAHBEDAUgQgkCw1XTC2\nJYGamhr62c9+Rm63m0aNGkUTJ05seRuvQQAEIhBggbRu3ToaOXKkvFtXV0cnTpyg7Oxs+fM0\nYMCACK1wCQRAQBF45ZVX6L//+7+pX79+8vfO7t276YUXXqAVK1bQN7/5TVUN/4IACFiYAASS\nhSfP6abzhzz+5nvQoEH04osvQiA5/YGA/zET4A92jz32WKh+fX093X777fT888/TD37wg9B1\nvAABEDiTwNtvv02PPPIIPfjgg7Ro0aLQzY8//pjuv/9+mjt3Lk2aNCl0HS9AAASsSQB7kKw5\nb7BaEFi7dq1c0rBw4UJ6//33qby8HFxAAAQSIJCTk0Oc8ru0tDSB1mgCAs4h8Oc//5mWLFly\nhjhi72fNmkU33HADlZWVOQcGPAUBGxOAQLLx5NrZtT179tDevXtp8eLFdM4551B+fj7xsgcU\nEACBtgl4vV65d4/37/HyOv6CgZfeXXTRRW03Rg0QcCiBpqYm+uqrr2jatGkRCdx4443yd1LE\nm7gIAiBgKQJYYmep6YKxigBHj3gPhdovwd/ovfTSS3TNNdfIPUmqHv4FARA4m8C+ffto+fLl\nZ9yYOXMmzZkz54xreAMCIHCawP79+8nn81GfPn1OX8QrEAABWxKAQLLltNrbqebmZnrzzTeJ\nP9B9+OGH0tnOnTvTqVOnaP369fK6vQnAOxBIjsDAgQPpoYcekp3wzxNn5Hr66afpjjvuoKee\neiq5ztEaBGxKoLCwUHqG5dw2nWC4BQItCEAgtYCBl9Yg8MEHHxBvKucldvxHlby8PJmsgYUT\nCgiAQHQC7dq1kxm4VI0hQ4bIFN933nmn/JkaOnSouoV/QQAEggSKioqoS5cudODAAbnnKBzM\n66+/TtXV1bRq1arwW3gPAiBgMQIQSBabMJgbSM4wffp0evjhh8/AwZnsHn30UTp58iT16NHj\njHt4AwIgEBsBj8cTW0XUAgEHEuDzjvh3zYUXXki8ckGVyspK+vnPfy73xEIgKSr4FwSsSwBJ\nGqw7d460nJfRbdy4kZYuXXqW/5xyNSsrS+5FOusmLoAACIQIcAT2yy+/lH/4sMs33nhDnuvC\nafOHDRsWqocXIAACZxLgc474cGVO6c2pvTnRybvvvksPPPCArHjTTTed2QDvQAAELEkAESRL\nTptzjebkDLm5uRGXN/ASu3nz5slsdpxNKDMTj7dznxR43hqBI0eOEC+n45KRkUEFBQU0YcIE\nuuWWW/Bz0xo43HM8Af79w4fEPvPMM/Jg5ZKSEslk9OjR9Itf/IJ4fx8KCICA9Qm4NFGs7wY8\nAAEQAAEQAAEQAIHUEuBVDfzlHAsnFBAAAfsQgECyz1zCExAAARAAARAAARAAARAAgSQJYA9S\nkgDRHARAAARAAARAAARAAARAwD4EIJDsM5fwBARAAARAAARAAARAAARAIEkCEEhJAkRzEAAB\nEAABEAABEAABEAAB+xCAQLLPXMITEAABEAABEAABEAABEACBJAlAICUJEM1BAARAAARAAARA\nAARAAATsQwACyT5zCU9AAARAAARAAARAAARAAASSJACBlCRANAcBEAABEAABEAABEAABELAP\ngUz7uAJPQAAEQMB6BPx+P23evJlOnjxJgwcPpuHDh1vPCVgMAiAAAiAAAjYigAiSjSYTroAA\nCFiHAIui5cuXU9euXWny5Mm0bNkyGjFihHz9wQcfJOzI1q1b6be//W3C7dEQBEAABEAABJxO\nAALJ6U8A/AcBEEg5ge3bt9PixYtpw4YNdNVVV9FLL71Eb775Jt1zzz106NAhWrp0Kb3//vsJ\n2cVi67PPPkuoLRqBAAiAAAiAAAgQYYkdngIQAAEQSCGBgwcP0qJFi6i5uVkKmUGDBoVG5+ss\nnC688EK67bbbaMeOHeRyuUL3Y3nh9XpjqYY6IAACIAACIAACUQhAIEUBg8sgAAIgYASBv/71\nr3K/0f/+7/9SS3GkxuKldiyOysvL6dSpU9SjRw95q6SkhJ577jnatWsXVVRUyP1KLKTmzJkj\n73PdJ554gjRNo40bN9KDDz5IN998M/Xt21fe56V3f/vb32jnzp3Ur18/KcIWLFighg39+9FH\nH9Frr71GLORmzZol+/iv//ovKdxmz54dqtfQ0CCX8nEUzOfz0fjx4+mWW26hzp07h+o8+eST\n1K1bN2nD448/TiNHjqTevXvTV199Rffddx916tQpVJdfPP/883T06FH67ne/e8Z1vAEBEAAB\nEACBlBIQv0xRQAAEQAAEUkRgxYoVWmZmpiYiSDGPKESLVlRUpOXl5WkiwqQJoaJlZGRoIrqk\n/e53v5P97N+/X5s3b54mfoFoPXv2lK+3bdsm7/3617/W2rVrJ/9cdNFF2qRJk2S9b3/722fY\n8PDDD8vr48aN0y699FJNiBtt7ty58tpPfvKTUN1jx45pAwYMkP2xPUKoabm5uZoQP5oQTKF6\nEyZMkHaw7W63W/75zW9+I/t7+umnQ/X4hRBcmhBM2nXXXXfGdbwBARAAARAAgVQT4G8bUUAA\nBEAABFJEQESENBFJiWu0c889V+vYsaMmMt2F2h05ckQKLRY7LQuLJhE5Cl3au3evFDLz58/X\nRBQqdP2BBx6QQuWtt96S1959910pYO666y5NZNaT17j+2LFjzxJI5513ntahQwdN7HUK9bd7\n926te/fu2pgxYzSPxyOvs0BiwSaiRVp9fb0mol9aU1OTVlhYKIVXqLF48Ze//EXWfeedd1pe\nxmsQAAEQAAEQSDkBJGlIabwOg4EACDiZAC9L46VwQkjEjEH8VqAf/OAH9PLLL5/Rrk+fPjRt\n2jTipXetFV52x/ud7r//fhLCJFT1W9/6FmVlZREvfeOyZs0aEqKHeDmd2vfE9X/0ox+F2vAL\nXgL3+uuvy+V0U6dODd0bNmwYfe973yMRtTojwYSIHMk+uW9OYS4iWXT11VcTZ+o7fPhwqD0v\nHxRRKRJRsNA1vAABEAABEACBdBDAHqR0UMeYIAACjiTAIoHPOhLRlpj9Z7HCWe143xGLGE7c\nwO03bdokX7cltrgu9yGWtp2V/jsnJ4f27NkjbeF9S2ybiFSdYZuIUJ3xnvcwcWkpjlQFFmxc\neJ/UwoUL5WveA5WdnS1fq79uuOEGeuyxx+SeIxZuLBrXrVsnhaASZ6ou/gUBEAABEACBVBNA\nBCnVxDEeCICAowmw4Dhx4oQUPNFAsPjhyA5Ha7j88Y9/lIkOLrvsMvr9739PYrkaib068syk\naH2o66WlpdS+fXsS+56Iozkt/4ilcjR9+nRZlSNRHN0JLyzqWpaysjL5Nj8/v+Vl+VrskZL/\niiV2oXt8zlN44YQOEydOpD/96U/yFidn4EQP7BMKCIAACIAACKSbACJI6Z4BjA8CIOAoAlOm\nTJHZ5MSeG7r99tsj+v4///M/9Ic//IHEfh65DO7GG28ksZ9HRpA4A50qvCyNl+C1VjhT3uef\nf07/+Z//SbwMrmXhlOAsnLgMGTJEnsvE/bWM4ojkDy2byCgTX+Asd+FFXWNb2yocRbr77rtl\nFIyz63GGvEhZ/drqB/dBAARAAARAQG8CiCDpTRT9gQAIgEArBFgUsfC599576ZNPPjmrJqfY\nZuEjkiOQyCAnl9KxkFm5cqVMz60aiOQLcnlc+LlHIrud3HOk6nGqbi7cZ8uyZcsW4ogPH07L\n5ZJLLiGONrFwa1l++ctftnwrU3V36dJFCrhwccbRLS6xCKSvfe1rMmIlMuzJ86Cuv/562RZ/\ngQAIgAAIgEC6CbjEL7jWv35Mt4UYHwRAAARsRoCjMueccw7xUrTzzz9f7jHipWwigxs988wz\n8iwhFk8c8RHZ6mTUhvfy/OpXv6JRo0bRp59+KhMi8N4dkXGORGa4UNSHzx3iPT+8t0ek9Jbn\nKPGSNk6IwAkY+DDaL7/8kh566CEqLi6W5yKJNNzEQouX2/F5SSyaeOxXX31VJmTg5BIiBbgc\nk6eCEz/ccccdJFKWyzOLeGke73HiPz/+8Y/p+9//vpwxXkbHS/p4f1OkwksGX3zxRbkEUGTo\nO2v/U6Q2uAYCIAACIAAChhNggYQCAiAAAiCQWgKcIpvPEBLL2WR6a/GfvTzbaNWqVZo4SPUM\nY8QSNE1EgmQabq7H5xPxeULiIFbZVmSEC9UXyQ80kXxBXhdiS14XQki7/PLLZVpwbs9/hg4d\nqonECKF2/KK2tlYTES5NLLfTCgoKND6zae3atbK+iCSdUVdEi+TZTKo/Iai0Rx999Iw6nOY7\nPA15ywqvvPKK7Pvaa69teRmvQQAEQAAEQCCtBBBBEr/dUUAABEAgXQTq6upklEj8JpCRokiJ\nEpRtHDHiqBOn+G6tcMKD8vJymda75X4iTvctxBeJA1mpV69eoagT98URHHHY61lRHHE+Ei1Y\nsID+/Oc/05VXXnnWsBzh4mV93F+85Y033iBOFMGRM3FOU7zNUR8EQAAEQAAEDCEAgWQIVnQK\nAiAAAtYi8Mgjj8gldB9//DHNnDkzZPySJUtIHCZLhw4dkpn0QjeSfMFLA3l5ISd24LTgLYVc\nkl2jOQiAAAiAAAgkRQACKSl8aAwCIAAC9iDAAmjy5MlyTxOLIj5fiSM727dvl3uLbr75Zl0c\n5UgZHwbLqc45msVnO3ECChQQAAEQAAEQMAsBCCSzzATsAAEQAIE0Ezh+/LhMzMBL3/gMJj74\n9YILLiAWTHoWsc9KLunj9OXIXqcnWfQFAiAAAiCgBwEIJD0oog8QAAEQAAEQAAEQAAEQAAFb\nEMA5SLaYRjgBAiAAAiAAAiAAAiAAAiCgBwEIJD0oog8QAAEQAAEQAAEQAAEQAAFbEIBAssU0\nwgkQAAEQAAEQAAEQAAEQAAE9CEAg6UERfYAACIAACIAACIAACIAACNiCAASSLaYRToAACIAA\nCIAACIAACIAACOhBAAJJD4roAwRAAARAAARAAARAAARAwBYEIJBsMY1wAgRAAARAAARAAARA\nAARAQA8CEEh6UEQfIAACIAACIGByAl//+tfpnHPOoUOHDkW09Nxzz6Wf/vSnEe/pfXH+/Pn0\ny1/+Uu9u0V8LAh988EGLd4m/vOaaa+j73/9+1A4eeeQReaB01Aq4AQIWJACBZMFJg8kgAAIg\nAAIgEC+Br776ijZs2EA333xzxKZbt26lY8eORbyn98Vt27bR8ePH9e4W/QUJfPOb36Rvfetb\nuvDYs2dPVFHNAzQ1NVF9fb0uY6ETEDALAQgks8wE7AABEAABEAABgwm0a9eO3nrrLXrqqacM\nHgndp5NAcXFxyob/93//d3rvvfdSNh4GAoFUEMhMxSAYAwRAAARAAARAIP0Epk+fTh06dKBv\nf/vbtHTpUhowYEBUo37wgx/Q3LlzZT1V6d1336VXXnmFfvazn8lLDz74IC1btoxOnjxJa9as\noYyMDLryyitpyZIl9M4779Dzzz9P7du3J16mNWPGDNVN6N+//OUv9I9//IPy8vLokksukX2F\nbooXHL14+umnadeuXdS/f3+6+uqrz+jn73//O1VUVEg/fvOb3xD7d99999GXX35J/P7gwYPU\nr18/Ou+882jFihUtuz7rNUdCXnjhBXr99delH9deey2VlpbKP7fffnuo/j//+U/JgEXI+PHj\n6e6776bCwsLQfWZywQUXUFlZmeyvqqqK5s2bR9yH2336e+lEffP5fJIJRwOZO3O59NJLaeHC\nhdIG5vXFF19ILsziG9/4Bg0ZMkTea8t2ZsBMX3vtNTkn0aKNIWfFC54/jgiyUOISq/+ycou/\n/u3f/o2WL18uBfz27dvp+uuvp5qaGiopKaHFixdLUb9z504aMWKEnOMePXqEWmuaRvws8dwx\n77Fjx9Jdd91F3bp1k3X27t1LTz75JN17773Svs6dOxNH2ZghzzE/57/97W9p//79NGfOHNn2\nxIkT9Pjjj8tr/GxefPHFlJ2dHRoTL2xOQDxUKCAAAiAAAiAAAjYnIPb9aGKfkXbkyBGtU6dO\n2oIFCzS/3x/yWnxo1MQHyND7/Px87Yc//GHoPb8Q+0008SE/dE18ANWmTZumCaGl3XbbbdrQ\noUO1rKws7f7779e6dOmi3XDDDZoQRpr4KKWJD9GhdkJQaD179pTtxP4WTQgNWefhhx8O1Xnj\njTe0nJwcTQgA7Xvf+5522WWXaUKAaeKDbKjOrbfeqo0cOVL6M2bMGI193Lx5syZEoCZEiSY+\ntGviw63mcrm0ln2HOmjxQnwgl/XEh3RNiCktMzNTjs2cVBFiSNrJHMUHem3cuHGa+KCuiQ/g\nqorGTGbNmqUxv9WrV2sXXnihbHPHHXeE6iTqm9frlby7d++uCfGiiQ/5kjnzFeJH9v/rX/9a\nGzZsmLRDiARNCDF5PRbbb7zxRslAiAGN63ft2lXO41VXXRWyPfwFjyFEaOhyLP6HKrd4UVBQ\noE2dOlXOpRB02mOPPaYxM36m+FnhuWWf+dnt27ev1tjYGGr9ta99Tc4X2//AAw9ow4cP1/gZ\n27Jli6zz9ttvyzng/gcNGiT9Onr0qOyfx+L+uA/xpYH0n/sZOHCgJoSZnEN+5oXwC42HF/Yn\nQPZ3ER6CAAiAAAiAAAgogcQkfve738kPjE888UQITKICiT8Qi2/bZT/l5eVSQLEgOXz4sLwm\nohIaf6B/6KGHQmPxh1cWUqdOnZLXWKixOGNhw31xG/6AyuJKRExC7cS+Gik8RPRGXmOBxOLg\n//7v/0L9sCjiD/YsJlThD/v84TpaEdEv2c9f//rXUJW1a9fKa0ogffTRR/K9SGQRqsMf0vkD\nPAsKVZgH/2EWqoiomiaiZPJtMr6JCInkyx/4Vamrq5NC8rrrrlOX5If9KVOmhN7HYvuLL74o\n/WO/VRHLMeW1eAVSa/6rvsP/ZYFUVFSkiYiRvMXPBAsknl8RpQpVV3Mlolzymoj6yToi8hWq\nw0xYuLJQ5aIE0qpVq+R79cWA6r/lvIvop+zv5z//uazLf4kEJ1LEhy7ghe0JnI71iicQBQRA\nAARAAARAwP4ExDfkctnZd7/7XTpw4EBSDs+ePZvUcicRNaLBgwfLbHniW3nZL+974qV8vOyr\nZeF24oO0vCQElVzW1NDQQJx9jZfIsV1XXHEFCRFFvNyJ//CywOrqahIf+ENdiUhPaPkc9yOi\nB3J5m4hekYjUkBAxJKIRcllaqFHYC95Dw0sPL7rootAdHkt8aA+95+Vp7Asvn1P2CBEkOfKS\nNPGhO1SXlxgyC1WEWKHa2lpqbm5Oyje2iRkJ0Sa75uVk//rXvyRHXo4WrcRiOzMVovSMjHS8\nbI+XtMVbWvO/tb54KaJarshzyYWXtfHyNlU4EyMXXsLIZd26dTRAPF+8zFAVEXmUyzU//vhj\nueROXRdRSPlS9c1veFnoypUrVRX57PIbXlanCj9T/Ezy8kYUZxDAHiRnzDO8BAEQAAEQAIEz\nCPBeFbEsjW666SYS37CfcS+eN2IJ3BnVWUSMGjXqrGviK+czrvG+kpaF9wpx4X1DSmzwnhH+\nE1727dsXutSnTx9ikaSKWCpFvOfk0UcfpT/+8Y9S+LCw+MlPfhL1wz5n8BNLBWVd1Q9/cOYP\n7JWVlfISZwFkgRPum6ovli7K/UD8XglGdS83N1e+FFEtUrYn4ht3wqLvV7/6lfzAzuKRxQEL\ntXC+amz+Nxbbed+PErUt27Joire05j8/H9EK+xJeRPSRWgoaFj9cmCUXnutINvKci0jpGV8A\nROqfbRXRTNkX/8X28Xyp55GvtbzP71HsT+D0/yj29xUeggAIgAAIgAAIBAmwsBDLiIijSWKp\nXUQuHo/njOucECG86PXhkaMhXPgDsdi/I1+LpU9yA7180+IvTuqgSvj4/GH6P/7jP+TZPR9+\n+KFMOPDcc8/JBA4c+eFIUXjhaA8LnPAilmqFLom9LzKiJPa1nCHIVAUV+eD3LZMxqPvq32R8\n+//tnTFO5EAQRXsPwAXgBIgEiYyYhANwGTKQICHhBEhIIIghJCICLkBOAEcg3n4leVXjnfUY\nj1aC6dfS7nhmPO2u58Sfqvpdy+Ai21FLxWIfqVpCVtbX10PoDgmkMWtnHkwL+oOMFaYGXxlD\n8Q/N07+XnLtoLtY2LwuK4QQDUYSAZMybf0iwxY/8r0kCltg1edsNWgISkIAEJFAKZWj7+/ul\nmiD8tZcNIgSHrzzItAw9iOdzFx0/PT3NnNJlsXCG29raiqwBpWsIpu4fmZBqBlFqf9PMb/Mb\nNqCtvUnhnre3txeOe+fn51FqRZZk3kBoIHzyvDxU5zXijMZnlFp16+H14uIinNFylmPeNbrP\nlokNwVgNCwqvBwcHIY4orSOLksu/WEuXheO6Y9a+s7MTboGIyG5QFki833kQ2+vra5Q95nVi\nZ09m6aviLs/hcbsEFEjt3nsjl4AEJCABCURvDn9Zp3wsj+r4Va6vr8v9/X2UhR0dHS1Vipfn\n5hi78OosF6KDviMsqek12d7ejlI1xBtCgOuS3Xl8fCzVaS5K3ob6YnhgxuL77OwsNqPlAf/q\n6ip6gqrrXH8Z8R7RhdhBKFGaR0aN+BEI3aiN+iFI2IAVLth8cx3sqbnmokxHNw8liVNjq+55\nIQTIJHG/EHXVJS+Oc7ZrbW0t7Kmr4UKI3DFrx56dsjKs1LG7fnl5iWzVvKxhF8t3eMXOm5I4\n+tWwAWe99Jxxz7HydkhgCgEF0hRq/kYCEpCABCSwIgQ2Njai1K4fTnVri54d9qapTm2xrxG9\nL2MzJf35+u/Zi+nm5qZUx7lSHfbK7u5uuby8/HMamSD24Tk9PY0Hd/YxwuyAvpKhNdA3dHx8\nHHMRG4ILgcUeTv8qp+IB+/n5ORr0EW3EzrXZE6freSGjRpaL9bJHEoKK8zC6YK+hr4ypsVXb\n8BACvLIu1odpRLVVDxOMrqysOtqF+QDi6e7uLvY0WrR2ygwRVAgv7jf7VlE2yB5S33lgpMGe\nW5+fn9EfxnsEEuWjiCeHBKYQ+FVT5bNdk1Nm8TcSkIAEJCABCawkATYjJcOEMPgfA/FCGRRZ\nj3mDZvy3t7fIKmUzhnnn9j+jXAwXtOwo1z+H98xPiVrdI2fma0wsyCRRRpcHZW1kKnIjf/5+\n7PHU2BBCHx8f0V8zJBZxekMw5HPGrJ3SSlj/tPI0DDXI+tFf55DAMgQUSMvQ87cSkIAEJCAB\nCfx4AmSuKN/DFppMFgMHPDI1t7e3kbX58UEagAQkMJqAAmk0Kk+UgAQkIAEJSGAVCbBXEuVo\nlGptbm6GocP7+3s5PDwsJycnqxiyMUlAAgMEFEgDcPxKAhKQgAQkIIF2CFBO+PDwEGV59OBY\nqtXOvTdSCWQCCqRMw2MJSEACEpCABCQgAQlIoGkCutg1ffsNXgISkIAEJCABCUhAAhLIBBRI\nmYbHEpCABCQgAQlIQAISkEDTBBRITd9+g5eABCQgAQlIQAISkIAEMgEFUqbhsQQkIAEJSEAC\nEpCABCTQNAEFUtO33+AlIAEJSEACEpCABCQggUxAgZRpeCwBCUhAAhKQgAQkIAEJNE1AgdT0\n7Td4CUhAAhKQgAQkIAEJSCATUCBlGh5LQAISkIAEJCABCUhAAk0T+A2HzYJ6u2oT8wAAAABJ\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp %>% \n",
    "  gather(\"Category\", \"X\") %>%\n",
    "  ggplot(aes(x = Category, y = X, fill = Category)) +\n",
    "    geom_violin() +\n",
    "    stat_summary(fun.y=mean, color='black', geom='point', size = 3) +\n",
    "    labs(title = \"Violin plot\",\n",
    "         subtitle = \"Means indicated as black dots\",\n",
    "         captions = \"Numbers generated in rnorm\") +\n",
    "    theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if you want to figure out what the commands above do, you can use the popup help function in Jupyter by adding a question mark before the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pull up ggplot documentation\n",
    "?ggplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What have you learned and what's next?\n",
    "\n",
    "The point of today's lab was simply to get you familiar with the structure of the course and Jupyter notebooks\n",
    "\n",
    "**You should at this point be comfortable:**\n",
    " 1. Logging into JupyterHub\n",
    " 2. Making a new notebook\n",
    " 3. Making a new cell\n",
    " 4. Changing the type of cell between Markdown/Code\n",
    " 5. Writing a tiny bit of markdown\n",
    " 6. Assigning a variable\n",
    " 7. Printing something to the screen\n",
    "\n",
    "Next week we will actually do some scientific computing - so come prepared!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Task 4\n",
    "---\n",
    "\n",
    "If you're happy with the state of your notebook and you're done all the tasks:\n",
    "\n",
    "1. Click on the title of the notebook and add your Banner number to the end of the name with an underscore, i.e. \"Lecture_1_Getting_started_B001XXXXX\"\n",
    "2. Save your notebook by clicking the save icon (above left)\n",
    "3. Email your saved notebook to [a.macneil@dal.ca and taylor.gorham.dal.ca](mailto:a.macneil@dal.ca,taylor.gorham.dal.ca)\n",
    "4. Exit the notebook by selecting `file>Close and Halt`\n",
    "\n",
    "\n",
    "---\n",
    "# ** A bientôt ** !"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}