3. R for data analysis
3 steps to Basic Data Analysis
- In this short section, we show how the data manipulation steps we have just seen can be used as part of an analysis pipeline:
- Reading in data
read.table()
read.csv(), read.delim()
- Analysis
- Manipulating & reshaping the data
- perhaps dealing with “missing data”
- Any maths you like
- Diagnostic Plots
- Writing out results
write.table()
write.csv()
A simple walkthrough
- We have data from 100 patients that given consent for their data to use in future studies
- A researcher wants to undertake a study involving people that are overweight
- We will walkthrough how to filter the data and write a new file with the candidates for the study
The Working Directory (wd)
0. Locate the data
Before we even start the analysis, we need to be sure of where the data are located on our hard drive
- Functions that import data need a file location as a character vector
- The default location is the working directory
getwd()
[1] "/Users/dunnin01/work/git/r-intro"
- If the file you want to read is in your working directory, you can just use the file name
list.files()
- The
file.exists
function does exactly what it says on the tin!
- a good sanity check for your code
file.exists("patient-info.txt")
[1] TRUE
- Otherwise you need the path to the file
- you can get this using
file.choose()
- If you unsure about specifying a file path at the command line, this online tutorial will give you hands-on practice
1. Read in the data
- The data are a tab-delimited file. Each row is a record, each column is a field. Columns are separated by tabs in the text
- We need to read in the results and assign it to an object (
patients
)
patients <- read.delim("patient-info.txt")
In the latest RStudio, there is the option to import data directly from the File menu. File -> Import Dataset -> From Csv
- If the data are comma-separated, then use either the argument
sep=","
or the function read.csv()
:
- You need to make sure you use the correct function
- can you explain the output of the following lines of code?
tmp <- read.csv("patient-info.txt")
head(tmp)
- For full list of arguments:
?read.table
1b. Check the data
- Always check the object to make sure the contents and dimensions are as you expect
- R will sometimes create the object without error, but the contents may be un-usable for analysis
- If you specify an incorrect separator, R will not be able to locate the columns in your data, and you may end up with an object with just one column
# View the first 10 rows to ensure import is OK
patients[1:10,]
- or use the
View()
function to get a display of the data in RStudio:
View(patients)
1c. Understanding the object
- Once we have read the data successfully, we can start to interact with it
- The object we have created is a data frame:
class(patients)
[1] "data.frame"
- We can query the dimensions:
ncol(patients)
[1] 10
nrow(patients)
[1] 100
dim(patients)
[1] 100 10
- The names of the columns are automatically assigned:
colnames(patients)
[1] "ID" "Race" "Sex" "Smokes" "Height" "Weight" "State" "Pet" "Grade" "Age"
- We can use any of these names to access a particular column:
- and create a vector
- TOP TIP: type the name of the object and hit TAB: you can select the column from the drop-down list!
patients$ID
[1] AC/AH/001 AC/AH/017 AC/AH/020 AC/AH/022 AC/AH/029 AC/AH/033 AC/AH/037 AC/AH/044 AC/AH/045 AC/AH/048 AC/AH/049 AC/AH/050
[13] AC/AH/052 AC/AH/053 AC/AH/057 AC/AH/061 AC/AH/063 AC/AH/076 AC/AH/077 AC/AH/086 AC/AH/089 AC/AH/100 AC/AH/104 AC/AH/112
[25] AC/AH/113 AC/AH/114 AC/AH/115 AC/AH/127 AC/AH/133 AC/AH/150 AC/AH/154 AC/AH/156 AC/AH/159 AC/AH/160 AC/AH/164 AC/AH/171
[37] AC/AH/176 AC/AH/180 AC/AH/185 AC/AH/186 AC/AH/192 AC/AH/198 AC/AH/207 AC/AH/208 AC/AH/210 AC/AH/211 AC/AH/213 AC/AH/219
[49] AC/AH/220 AC/AH/221 AC/AH/225 AC/AH/233 AC/AH/241 AC/AH/244 AC/AH/248 AC/AH/249 AC/SG/002 AC/SG/003 AC/SG/008 AC/SG/009
[61] AC/SG/010 AC/SG/015 AC/SG/016 AC/SG/046 AC/SG/055 AC/SG/056 AC/SG/064 AC/SG/065 AC/SG/067 AC/SG/068 AC/SG/072 AC/SG/074
[73] AC/SG/084 AC/SG/095 AC/SG/099 AC/SG/101 AC/SG/107 AC/SG/116 AC/SG/121 AC/SG/122 AC/SG/123 AC/SG/134 AC/SG/139 AC/SG/142
[85] AC/SG/155 AC/SG/165 AC/SG/167 AC/SG/172 AC/SG/173 AC/SG/179 AC/SG/181 AC/SG/182 AC/SG/191 AC/SG/193 AC/SG/194 AC/SG/197
[97] AC/SG/204 AC/SG/216 AC/SG/217 AC/SG/234
100 Levels: AC/AH/001 AC/AH/017 AC/AH/020 AC/AH/022 AC/AH/029 AC/AH/033 AC/AH/037 AC/AH/044 AC/AH/045 AC/AH/048 ... AC/SG/234
Word of warning
Like families, tidy datasets are all alike but every messy dataset is messy in its own way - (Hadley Wickham - RStudio chief scientist and author of dplyr, ggplot2 and others)
You will make your life a lot easier if you keep your data tidy and organised. Before blaming R, consider if your data are in a suitable form for analysis. The more manual manipulation you have done on the data (highlighting, formulas, copy-and-pasting), the less happy R is going to be to read it. Here are some useful links on some common pitfalls and how to avoid them
Handling missing values
- The data frame contains some
NA
values, which means the values are missing – a common occurrence in real data collection
NA
is a special value that can be present in objects of any type (logical, character, numeric etc)
NA
is not the same as NULL
:
NULL
is an empty R object.
NA
is one missing value within an R object (like a data frame or a vector)
- Often R functions will handle
NA
s gracefully:
length(patients$Height)
[1] 100
mean(patients$Height)
[1] NA
- However, sometimes we have to tell the functions what to do with them.
- R has some built-in functions for dealing with
NA
s, and functions often have their own arguments (like na.rm
) for handling them:
- annoyingly, different functions have different argument names to change their behaviour with regards to
NA
values. Always check the documentation
mean(patients$Height, na.rm = TRUE)
[1] 167.4969
mean(na.omit(patients$Height))
[1] 167.4969
2. Analysis (reshaping data and maths)
- Our analysis involves identifying patients with extreme BMI
- we will define this as being two standard deviations from the mean
# Create an index of results:
BMI <- (patients$Weight)/((patients$Height/100)^2)
upper.limit <- mean(BMI,na.rm = TRUE) + 2*sd(BMI,na.rm = TRUE)
upper.limit
[1] 30.9533
- We can plot a simple chart of the BMI values
- add a vertical line to indicate the cut-off
- plotting will be covered in detail shortly..
plot(BMI)
# Add a horizonal line:
abline(h=upper.limit)
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ACCCCAAAIIIIAAAggggAACCCCAAAJOFyDAG8c9qMFbTS1atAgL4j56yoCAAFm7dq28+OKLZpdO01CnTh3RuXtJCCCAAAIIIIAAAggggAACCCCAAAIIIIBAVAIEeKOSiaXte/fuNUcqXbp0tEdMkyaN/Pjjj1K+fHmT748//pCmTZua0b/RFmQnAggggAACCCCAAAIIIIAAAggggAACCHitAAHeOO7627dvmzOkSJEixjMlT55cFi5cKPny5TN5ly5dKu+++26M5ciAAAIIIIAAAggggAACCCCAAAIIIIAAAt4p4OedzY6/VufPn182bdokf/31l62TZsqUSZYtW2ZG8p47d050Dl8N+Hbr1s1WeXfPpAvJDRs2TO7cuePuVaV+CCCAAAIIIIAAAggggAACCCCAAAJxLKBrVA0YMED0N+nxBAjwPp6b7VJWgHfGjBkSFBQkGTJkiLFs3rx5zUjeKlWqSEhIiHTv3l008NumTZsYy7p7hn//+98yceJEd68m9UMAAQQQQAABBBBAAAEEEEAAAQQQiCcBnbL01Vdfjaezed5pCPDGcZ/q4mozZ86Us2fPmoXWpkyZIv7+/jGe9fnnnxcNCjdu3FhCQ0Olffv2oouv6Wsnp7t375rqN2/eXGrWrOnkplB3BBBAAAEEEEAAAQQQQAABBBBAAIEnEBg/frxs2LBBrHjRExzKq4sS4I3j7q9Tp45Uq1ZNfvrpJ1m+fLkULlxY6tevL7ro2jvvvBPt2Rs2bCj/+te/5K233jKB3X79+omvr2dMm1y2bFlp27ZttO1nJwIIIIAAAggggAACCCCAAAIIIICA5wpovEwDvKQnE/CMaOGTGcR5ab0b8cwzz5jzBAcHyzfffCPjxo2zdd6OHTvK5MmTxc/vf7F4p4/gtdVoMiGAAAIIIIAAAggggAACCCCAAAIIIICALQECvLaYnixTnjx55Pfff5dOnTpJypQpzcGyZctm+6Dt2rWTP//8UwIDA22XISMCCCCAAAIIIIAAAggggAACCCCAAAIIeL4AUzTEUx8nS5ZMvvrqKxk5cqRs3rxZrly54tKZixUrJmvXrpWpU6fK2LFj5a+//nKp/JNk3r59u8yfP1/u37//JIcxZbUNmi5fvmx+8w8CCCCAAAIIIIAAAggggAACCCCAAAIIPL6Az3+Ddk8etXv881PSAQJ169aVJUuWxGpNdQ5iDXSTEEAAAQQQQAABBBBAAAEEEEAAAQS8U6B169Yyffp0mTZtmrRq1co7EWKh1YzgjQVETz/EoEGDRBdFi435f3U+4r///luyZs3q6Wy0DwEEEEAAAQQQQAABBBBAAAEEEEAAgTgXIMAb58SPd4LTp0/LgQMH5Pr161KyZMkEDYgWL15c9Cc20sKFC02AN3HixLFxOI6BAAIIIIAAAggggAACCCCAAAIIIICAVwuwyFo8d/+tW7dk7969cvHixUjPvGPHDtEpEXQRtooVK0qtWrUkICBAsmfPLl988UWszIMb6YnZiAACCCCAAAIIIIAAAggggAACCCCAAAKOEyDAG09dFhwcLB07dpT06dNLoUKFJEuWLBIYGGgWTLOqoMFdDepGNt/tqVOn5P3335caNWqYEbBWGX4jgAACCCCAAAIIIIAAAggggAACCCCAgPcKMEVDPPR9SEiI1KtXT3799dews927d0/WrVtnfnTjW2+9JS1atJBLly6ZPClSpJBnnnlG/P39ZdeuXXLo0CEzevenn34SnYB65cqVYcfiBQIIIIAAAggggAACCCCAAAIIIIAAAgh4pwAjeOO43zWQ26xZs7Dgbrp06aRSpUpSunRpseah7dy5s3z66aeyc+dOUxsN9B4/flw2btwoCxYsMHPxbtiwwYz81Qwa5NXVBUkIIIAAAggggAACCCCAAAIIIIAAAggg4N0CBHjjuP9nzpwpixYtMmdp06aNCdyuXr1aNm/ebF6XKlVK7t69K0FBQSZP7dq1ZcaMGZIhQ4ZwNStXrpysX7/ezM2rO3r27BluP28QQAABBBBAAAEEEEAAAQQQQAABBBBAwPsECPDGcZ9rsFZTiRIlZNKkSZIqVaqwM+r0C8uWLROdjsFKw4cPt15G+K3z944YMcJsP3nypJw9ezZCHjYggAACCCCAAAIIIICAawI64GLq1KlmWjVd4NjPz88MuChfvrwMHjw4ygWSXTsLuRFAAAEEEEAAgbgRIMAbN65hR9X5czU1b97cfFEM2/H/LzJnziytWrUy71KnTi0FCxZ8NEu495UrVw57bx07bAMvEEAAAQQQQAABBBBAwCWBP/74Q4oVKyZt27aVxYsXmwWNdZo1XSRZp0zr1auX5M2bVyZPnuzSccmMAAIIIIAAAgjElwAB3jiWPn/+vDlDjhw5ojxTmTJlzL6UKVOKj49PlPl0hwaEkyVLZvLoKF4SAggggAACCCCAAAIIPJ6Arm0RGBgoe/fuNQMtJk6caKZR0xG9Fy9eNFOt1apVyyyE3KFDB/n4448f70SUQgABBBBAAAEE4lCAAG8c4uqhc+fObc5w4MAB8zuyf3TUrj4KdubMGbl8+XJkWcK2HTt2TG7evGne60gCEgIIIIAAAggggAACCLgucOTIEWnSpImEhITI66+/Ljt27BAN4urAjESJEolOj1a3bl1ZunSpTJkyxSyQPGDAAPn2229dPxklEEAAAQQQQACBOBQgwBuHuHroQoUKmTPo/LvXrl2L9GwVK1aUU6dOyY0bN8LN0RtZ5lGjRoVtLlKkSNhrXiCAAAIIIIAAAggggIB9AV20+NKlS9KwYUMZP368CeBGVVoXS7a+h3fr1s0EhaPKy3YEEEAAAQQQQCC+BQjwxrG4zuWl6fjx41K/fn2JbloFnXpBRwtElfRL5Zdffml2V6tWTdKmTRtVVrYjgAACCCCAAAIIIIBAFAK6WLGOxE2aNKmMHj06xmnS9DAdO3YUnVpNv8/PmzcviiOzGQEEEEAAAQQQiH8BArxxbN6gQQOpVKmSOcuqVaukQIECZnVeXczBTtJpG4YPHy5ly5aVLl26iM4Hljhx4rARBHaOQR4EEEAAAQQQQAABBBB4ILB8+XIJDQ2VGjVqSPbs2R/siOaVrpVhDd748ccfo8nJLgQQQAABBBBAIH4FCPDGg/fs2bOlQoUK5kw6DYOuzquLNthJa9eule7du8umTZtMdj8/PxkxYoQULlzYTnHyIIAAAggggAACCCCAwCMChw8fNlueffbZR/ZE/7ZUqVImw6FDh6LPyF4EEEAAAQQQQCAeBQjwxgO2v7+/rF69WoKCgiRnzpzmjMWLF7d15jx58oTl09EFOgq4U6dOYdt4gQACCCCAAAIIIIAAAq4JWIsW6xRpriQrv1XelbLkRQABBBBAAAEE4kqAAG9cyT5yXJ1WYeDAgXL06FHZuHGjaNDXTsqXL5/07dtX1q1bJ7rSb2BgoJ1i5EEAAQQQQAABBBBAAIEoBAICAsweayRvFNkibLZG7mbLli3CPjYggAACCCCAAAIJJeCXUCf21vPq3F3lypWz3XxdSK1///6285MRAQQQQAABBBBAAAEEohewBk0sWbLErHGh06DZSQsWLDDZrPJ2ypAHAQQQQAABBBCIawFG8Ma1MMdHAAEEEEAAAQQQQAABtxIoWbKkFC1aVE6dOiVjxoyxVbedO3fKrFmzzILHTZs2tVXGGzLdvn1bjh8/LpcuXfKG5tJGBBBAAAEE3FKAAK9bdguVQgABBBBAAAEEEEAAgbgUGDJkiDn8Bx98ID///HO0p9JAcMOGDeXevXvSsWNHyZ07d7T5PX3n9evX5bPPPpPSpUuLzkucK1cuSZ8+vWTOnFnat28v27dv93QC2ocAAggggIBbCRDgdavuoDIIIIBA3Avs2LFD9KL2jTfekFatWkmPHj1k/vz5cuPGjbg/OWdAAAEEEEDATQTq1q0r7733nugI1Fq1akm/fv3kypUr4WqnAV0dtVumTBk5cOCA+a2BTW9Ov/zyi+TPn18+/PBD2bJlixnRrAtJp0uXTs6fPy/ffPONPPvss8ZW/UgIIIAAAgggEPcC9iabivt6cAYEEEAAgTgW2LZtm3Tt2lXWrFkT4Uyff/65ZMyYUfr06SOdO3cWnS+chAACCCCAgKcLDB8+3IxAHTx4sFn3Qm+Ali9fXnQRtcuXL5vFkS9cuGAYqlatKt99950kT57c01mibN/ixYvllVdekTt37hinoKAgqVatmiRJksSU2bdvn0yYMEFGjx4tI0eONItEf//99+Lry7iiKFHZgQACCCCAQCwIEOCNBUQOgQACCLi7wNy5c6VNmzYSEhJiArmNGzc2j1WmSJHCXHwtXLhQfv/9dxMAXrVqlcyePdtxF7DHjh0TXfxm69atcu7cOcmQIYOZX7F+/fpSoEABd+8i6ocAAgggkAACekNz0KBBUq9ePfn4449F/wY+eiO0cOHCZrRq69atvTpQefjwYWnRooUJ7nbv3l2GDh0awUP/3upN4yZNmoiOkNYnhD755BPp27dvAvQup0QAAQQQQMB7BHzu/zd5T3NpaUILlCpVSv78809p0KCBzJs3L6Grw/kR8AqBtWvXmtE1OtpG5w3UR0vTpEkToe06Kqddu3aiI5WaN28uM2fOjJDHHTdcvHhRdP5EfSQ0qkdB9ULziy++kBw5crhjE6gTAggggICbCOjfQP2uevbsWUmbNq0ULFhQ8uXL5ya1S9hqtGzZ0nw30AXm5syZE2NlNFBepUoVM0Jap7fQUdEkBBBAAAEEHhXQG6jTp0+XadOmmSkEH93Pe3sCPCtjz4lcCCCAgCMFNKiri53obw2Cjhs3LtLgrjZOR9poMFiDvzrf4A8//OD2bd6/f7+ULVtWJk2aZEYRNWvWzDwaqiOSv/76axOw1lHK+kitzp+4efNmt28TFUQAAQQQSDgBna5IpxzQkap16tQhuPv/XXHp0iXztzRx4sSi01rYSZUqVZJGjRqZp4f0wp2EAAIIIIAAAnEnQIA37mw5MgIIIJDgAnpBdejQIXnmmWdE5xeMKRUpUsSM8NV8AwcOjCl7gu4PDg42F98HDx40Qd7du3ebqSVef/1186itjkbWIK+OGqpZs6acOXPGBLGPHz+eoPXm5AgggAACCDhNYPXq1eZm8YsvvujS0zAaKNe0YsUKpzWZ+iKAAAIIIOAoAQK8juouKosAAgi4JmCNwtXF1ewucNKhQwdJnz69mctWg8PumnQ+Px3BW7p0aTNfYt68eSOtakBAgCxZskReeuklE+Tt0qVLpPnYiAACCCCAAAKRC+g895qKFi0aeYYothYrVszsscpHkY3NCCCAAAIIIPCEAgR4nxCQ4ggggIA7C2zfvt1UT+fAs5v8/PwkMDDQZLfK2y0bX/nOnz8v48ePl0SJEpm5mmJa0Vzz6Ry9qVKlMvN///XXX/FVVc6DAAIIIICA4wWsOe7176krybq5bJV3pSx5EUAAAQQQQMC+AAFe+1bkRAABBBwnoIFQTVmyZHGp7pkzZzb5rfIuFY6HzLognM4rrKNydXVzO8nf39/Mqah5WeTRjhh5EEAAAQQQ+J+AtUjp3r17XSLZt2+fyW+Vd6kwmRFAAAEEEEDAtgABXttUZEQAAQScJ6CLxWhyNVBr5bfKu1vLd+7caaqkC7i4knTuQE1WeVfKkhcBBBBAAAFvFdC/tzoad9WqVXLx4kXbDLrIqSZXniSyfXAyIoAAAggggECYAAHeMApeIIAAAp4nYM19t2bNGtuN08co161bZ/Jb5W0XjqeMFy5cMGfKlCmTS2e08lvlXSpMZgQQQAABBLxUQJ8EqlWrloSEhEhQUJAthW3btplplHTqp1atWtkqQyYEEEAAAQQQeDwBAryP50YpBBBAwBECDRs2NPX88ssv5f79+7bqPG3aNNEAqC6kkj9/fltl4juTFag9e/asS6c+d+6cyW+Vd6kwmRFAAAEEEPBigSFDhkiSJElk7Nixot8roksHDhyQ+vXry927d6VTp04S1UKo0R2DfQgggAACCCBgX4AAr30rciKAAAKOE2jdurXkypVLtmzZIgMGDIix/gcPHpT333/f5LM7QifGg8ZBhuLFi5uj6qOiriQrv1XelbLkRQABBBBAwJsF9KmecePGGYKuXbtKkyZNZMeOHeFILl++LCNHjpSyZcvK0aNHpUKFCvLZZ5+Fy8MbBBBAAAEEEIh9AQK8sW/KERFAAAG3EUiWLJlMnDhRdNXrfv36Sffu3c3jlZFVUKdxCAwMNHPrNWjQQJo1axZZNrfYVqdOHUmaNKmsXLkywsVlVBU8deqUzJ49W3x8fMQa2RxVXrYjgAACCCCAQESB9u3by6xZsyRlypQyd+5c0RumOXPmNIFcfa2LtL733nsSHBwsjRo1kuXLl5tRvxGPxBYEEEAAAQQQiE0BAryxqcmxEEAAATcUqF69ukyePNlcYA0fPlzy5ctnAr16gbZgwQLzmGXVqlWlcuXKcvr0aalWrZqZM08Doe6aMmTIYB75DA0NlZYtW8rVq1ejreqdO3fM/H83btyQpk2bSsGCBaPNz04EEEAAAQQQiFzg1Vdflf3798s777wj/v7+cuLECVm/fr254arTQel3iqVLl5oAsAaCSQgggAACCCAQ9wJ+cX8KzoAAAgggkNACbdq0kQIFCkjnzp1l06ZNooHeR1Pq1Knlww8/lI8++siM+H10v7u91xHJy5YtMxeU//jHP2TmzJlm3uBH63nkyBFp27atrF27VrJnzy4jRox4NAvvEUAAAQQQQMAFgYCAABk9erS5Sax/Z//++2/R7xG5c+c2v104FFkRQAABBBBAIBYECPDGAiKHQAABBJwg8Pzzz8tvv/0mGzZsMCNrDh06JDqiVS/SdI68unXrSrp06ZzQFFNHvZBcsmSJ1K5dW7Zv3y4lSpQwr3XkkI4o0oXifvnlFzNK+fbt2+YRUs2v7SUhgAACCCCAwJML+Pr6Sp48eczPkx+NIyCAAAIIIIDA4woQ4H1cOcohgAACDhTQaRdeeOEF8+PA6keoso4U2rhxo/Tp08cs/LJo0SLRn4eTXny2a9dOdPVvDfySEEAAAQQQQAABBBBAAAEEEPAkAQK8ntSbtAUBBBDwQoE0adKYR0R79+5tgrvbtm2Tc+fOic7TW7RoUXn55ZclR44cXihDkxFAAAEEEEAAAQQQQAABBLxBgACvN/QybUQAAQS8QCBLlizSoUMHL2gpTUQAAQQQQAABBBBAAAEEEEDggYDvg5e8QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSgJ+TKktdPUfg7t27cv36dc9pEC1BAAEEEEAAAQQQQAABBBBAAAEEEHBJQONDpCcXIMD75IYc4TEEFi9eLKlSpXqMkhRBAAEEEEAAAQQQQAABBBBAAAEEEEAAAUuAAK8lwe94FUiUKJEkS5YsXs/JyRBAAAEEEEAAAQQQQAABBBBAAAEE3Efg5s2bcu/ePfepkENrQoDXoR3n9GrXq1dP5s2b5/RmUH8EEEAAAQQQQAABBBBAAAEEEEAAgccUaN26tUyfPv0xS1PMEmCRNUuC3wgggAACCCCAAAIIIIAAAggggAACCCCAgMMECPA6rMOoLgIIIIAAAggggAACCCCAAAIIIIAAAgggYAkQ4LUk+I0AAggggAACCCCAAAIIIIAAAggggAACCDhMgACvwzqM6iKAAAIIIIAAAggggAACCCCAAAIIIIAAApYAAV5Lgt8IIIAAAggggAACCCCAAAIIIIAAAggggIDDBAjwOqzDqC4CCCCAAAIIIIAAAggggAACCCCAAAIIIGAJ+Fkv+I0AAu4jcPToUVm+fLkcO3ZM7t69Kzly5JDKlStL0aJF3aeS1AQBBBBAAAEEEEAAAQQQQAABBBBAIMEFCPAmeBdQAQQeCOzYsUN69OhhgrsPtj54Vbp0afn888+lUqVKDzbyCgEEEEAAAQQQQAABBBBAAAEEEEDAawUI8Hpt19NwdxOYOXOmdOjQQW7evClp0qSR2rVrS7FixcTPz0/27dsnixcvlj/++EOqVKkin3zyifTq1cvdmkB9EEAAAQQQQAABBBBAAAEEEEAAAQTiWYAAbzyDczoEIhNYunSptG7dWkJDQ6Vjx44yePBgSZ8+fbisGvgdNmyY9O/fX3r37i2pUqWSzp07h8vDGwQQQAABBBBAAAEEEEAAAQQQQAAB7xJgkTXv6m9a64YCV65ckbZt25rg7sCBA2XcuHERgrta7WTJkkmfPn1kxowZ4uPjI927d5f9+/e7YYuoEgIIIIAAAggggAACCCCAAAIIIIBAfAkQ4I0vac6DQBQCX331lZw/f97MqxsUFBRFrgebmzVrJq+//rrcuXPHTNXwYA+vEEAAAQQQQAABBBBAAAEEEEAAAQS8TYAAr7f1OO11O4HvvvvO1Omjjz6yXbeePXuavAsWLDCBXtsFyYgAAggggAACCCCAAAIIIIAAAggg4FECBHg9qjtpjNME7t69K9u2bTMLqeniaXZT7ty5JX/+/HL58mU5cOCA3WLkQwABBBBAAAEEEEAAAQQQQAABBBDwMAECvB7WoTTHWQLnzp2T+/fvS6ZMmSRx4sQuVT5btmwm/9mzZ10qR2YEEEAAAQQQQAABBBBAAAEEEEAAAc8RIMDrOX1JSxwokC5dOlNrHYnrarp06ZIpYh3D1fLkRwABBBBAAAEEEEAAAQQQQAABBBBwvgABXuf3IS1wsEDy5MklV65cEhISIlu2bLHdEg3u7tq1y4z6zZMnj+1yZEQAAQQQQAABBBBAAAEEEEAAAQQQ8CwBArye1Z+0xoEC9evXN7UeM2aM7dpPmDBBdP7eypUrS+rUqW2XIyMCCCCAAAIIIIAAAggggAACCCCAgGcJ+HlWc2gNAs4T6Nq1q4wfP16++eYbadKkibz00kvRNmLnzp0ycOBAk+fDDz+MNi87EUAAAQQQQACB2Ba4du2arFy5Uvbv3y9Xr16VrFmzSrly5aR06dLi4+MT26fjeAgggAACCCCAAAIxCBDgjQGI3QjEtYBOsfDxxx9L7969pVGjRjJp0iR59dVXIz3t2rVrpWnTpqIXVm3btpUqVapEmo+NCCCAAAIIIIBAbAtcvHhRBgwYIOPGjZNbt25FOHzevHnNTejmzZtH2McGBBBAAAEEEEAAgbgTIMAbd7YcGQHbAr169ZLjx4+bCya9KNLpGlq2bCnFihUTPz8/2bdvn8ydO1cWLVpkjlmrVi0z6tf2CciIAAIIIIAAAgg8gcD27dvl5ZdflqNHj4qvr69UrFjRjNpNkyaN+Q6zfPlyOXjwoLRo0UJ+/PFHmThxoiRJkuQJzkhRBBBAAAEEEEAAAbsCBHjtSpEPgTgWGDt2rHm0UYO969atMz+PnjJlypTywQcfmNG+iRIlenQ37xFAAAEEEEAAgVgX0JvQNWrUkDNnzsgLL7wguhZA0aJFw50nNDRUpk6dKp07d5Zp06aZILBOP0VCAAEEEEAAAQQQiHsBArxxb8wZELAt8Nprr0mzZs1k3rx5oiNhdJTMvXv3JHv27GY6hsaNG0uWLFlsH4+MCCCAAAIIIIDAkwp06NDBBHdr1qwpCxcujHRkro7qbdeunZQsWVICAwNlypQpUqdOHbO+wJOen/IIIIAAAggggAAC0QsQ4I3eh70IxLtA6tSppU2bNuYn3k/OCRFAAAEEEEAAgYcE/vOf/5gF1TJkyCCzZs2KNLj7UHYT4B0xYoS8/vrr0rdvXwK8D+PwGgEEEEAAAQQQiCMB3zg6LodFAAEEEEAAAQQQQAABhwt8++23pgVvvfWWpE+f3lZr2rdvLwEBAbJnzx7ZsWOHrTJkQgABBBBAAAEEEHh8AQK8j29HSQQQQAABBBBAAAEEPFpg06ZNpn0vvfSS7XbqOgHVq1c3+a3ytguTEQEEEEAAAQQQQMBlAQK8LpNRAAEEEEAAAQQQQAAB7xA4e/asaaiuB+BKsvLrwmwkBBBAAAEEEEAAgbgVIMAbt74cHQEEEEAAAQQQQAABxwqkSZPG1P3y5csutcHKnzZtWpfKkRkBBBBAAAEEEEDAdQECvK6bUQIBBBBAAAEEEEAAAa8QKFiwoGnnhg0bXGqvlb9AgQIulSMzAggggAACCCCAgOsCBHhdN6MEAggggAACCCCAAAJeIVCvXj3TzvHjx0toaKitNm/cuFH+/PNP0dG7FStWtFWGTAgggAACCCCAAAKPL0CA9/HtKIkAAggggAACCCCAgEcLNGnSRHLnzi3btm2ToUOHxtjWa9euyRtvvGHydenSRZIkSRJjGTIggAACCCCAAAIIPJkAAd4n86M0AggggAACCCCAAAIeK5A0aVL517/+Jb6+vtK7d2/59NNPoxzJe/LkSalRo4bs2LFDihQpIj169PBYFxqGAAIIIIAAAgi4k4CfO1XGG+ty6tQpuXjxoty4ccP8JEuWzDzOpgtaZMyYUfQ9CQEEEEAAAQQQQACBhBKoVauWjB49Wt59910JCgqSWbNmyWuvvSblypUT/c56/PhxWbp0qUyePFmuX79uRvwuWrRIUqVKlVBV5rwIIIAAAggggIBXCRDgjefuvnr1qkydOlVmzJghO3fuFH0fVfLz85NnnnnGfHmuW7eu1K5dW3x8fKLKznYEEEAAAQQQQAABBOJE4O2335a8efNKp06dZNeuXfLee+9Feh6d0kFH/GbKlCnS/WxEAAEEEEAAAQQQiH0BAryxbxrpEc+cOSMDBgyQadOmRRvUfbjw3bt3zQIVukjFuHHjpFixYjJkyBCpU6fOw9l4jQACCCCAAAIIIIBAnAvUrFlTdu/eLfPnz5fFixfL/v375cqVK5I1a1YzIKFp06ZSokSJOK8HJ0AAAQQQQAABBBAIL0CAN7xHnLwLDg6W6tWrm/nIrBPoSNyAgADJlSuXZM6cWZInTy46x5kGdW/evGm+LOvjbkePHpVbt26ZYjri9+WXX5bhw4dL165drUPxGwEEEEAAAQQQQACBeBFInDix6Chd/SEhgAACCCCAAAIIuIcAAd447gedh0xH3OpiE5qee+456datm1StWtUEdmM6/Z07d+T333830zp8/fXXou/1kbgCBQqYKRtiKs9+BBBAAAEEEEAAAQQQQAABBBBAAAEEEPBcAV/PbZp7tOzbb7+VDRs2mMq8+uqrsnHjRtHfOmrXTtJREhUqVJDx48ebx+H0vaaPPvooyhWM7RyXPAgggAACCCCAAAIIIIAAAggggAACCCDgfAECvHHch+vXrzdnKF68uBmF6+v7+OS6yNrnn39ujqcjgg8fPhzHtefwCCCAAAIIIIAAAggggAACCCCAAAIIIODOAo8fbXTnVrlR3X799VdTm3r16ok1+vZJqteoUaOw4vv27Qt7zQsEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8D4B5uCN4z4/ceKEOUPOnDlj5UwZM2Y0gWKdizckJCRWjslBEEAAAQQQQAABBBBAAAF3EQgNDZUzZ87IxYsXzdR2Or2dLlJNQgABBBBAAIHIBQjwRu4Sa1vz5s0rW7duNfPwduzY8YmPq1M+aHBX07PPPvvEx+MACCAQuYBeWOic2WvWrJFTp06ZGyu5c+eWl156SQoWLBh5IbYigAACCCCAAAIIPLbAnj17ZOjQobJo0SK5cOFC2HGyZcsmr7zyinzwwQcSWwNnwg7OCwQQQAABBDxAgABvHHdi6dKlTYB3zpw50r59e3nxxRcf+4yXLl2S999/35TPkCGDPP300499LAoigEDUAvPmzZMPP/xQ9u/fH2mmypUryxdffCElS5aMdD8bEUAAAQQQQAABBOwL3L9/XwYMGCADBw6Ue/fumYJZs2YVfXpRR/LqzfavvvpKJk6cKCNHjpTYGDhjv3bkRAABBBBAwP0FmIM3jvuoZ8+eZuTfzZs3pX79+jJ+/Hi5ffu2y2fVUcA1atQwwWIt/Oabb7p8DAoggED0Anpx0bVrVzNCRIO7ehNF3+sFhQZ027VrJ+nTp5fVq1fL888/LzNmzIj+gOxFAAEEEEAAAQQQiFHg7bffln79+pl8nTp1El1r5PTp07Jz5045d+6cbNu2TVq2bCl6TaXXQYMHD47xmGRAAAEEEEDAmwQYwRvHva1TNAwaNEh69Oghly9fNl9I9LWO5NXRfxpA8vf3l+TJk0uyZMnk7t275ovLlStX5Pjx43LgwAFZu3at+XJjVVUDvXp3m4QAArEr0LdvX/nyyy8ladKkJqCro0MSJUoU7iT632avXr1kzJgx0rZtWzOyRKdtICGAAAIIIIAAAgi4LvDNN9/IuHHjJEWKFDJ//nypXr16hIMUL15cpk+fLrVq1TLfv3r37i3PPfecVKtWLUJeNiCAAAIIIOCNAgR446HXu3fvboJAejdaF0a7evWqLF682Py4enoNJOmoQV9fBl+7akd+BKIT+OOPP+TTTz8VPz8/899mVBcMadKkMSN69caMBoR1VK/eiEmVKlV0h2cfAggggAACCCCAwCMCem2kN8416ZOOkQV3Hy6io3iPHj0qGuDVqev0KUcWX3tYiNcIIIAAAt4qQJQwnnpe59/VLyP6BUbnk3Il6WhCnd5BFxv48ccfReffJSGAQOwK6LxvOkWD3pCJKrj78Bn79OkjFSpUMPPC6agTEgIIIIAAAggggIBrAkuXLjVTMZQpU0ZatWplq7B+V8uRI4ds375dNm3aZKsMmRBAAAEEEPB0AUbwxmMPZ86c2YwQ1FGCR44ckY0bN5pFnPSRb52+QUf2Jk6c2IwE1FGCOr1DkSJFpESJEgk6OnDz5s0yd+5cCQ0NfWKtkydPmmPcuXPniY/FARCILYFr167J8uXLzXQM1kKGdo6tKznrzZfvv//eBIbtlCEPAggggAACCCCAwP8EfvrpJ/OiWbNmtkmSJEli1ksYNWqUaPmyZcvaLktGBBBAAAEEPFWAAG8C9Wzu3LlFf5yQdMGDJUuWxGpVdSVcEgLuIvDXX3/JrVu3pHTp0pIpUybb1bJG+v7555+2y5ARAQQQQAABBBBA4H8CuuaIpsKFC/9vg81/rfxWeZvFyIYAAggggIDHChDg9diujb2Gff7551KpUiXz+PqTHnXkyJGiwd3s2bM/6aEoj0CsCejqzJpcnT5FFwNJnTq1GX2vI/D1NQkBBBBAAAEEEEAAAQQQQAABBBCITwECvPGp7cK5Tp8+bRZuun79upQsWdLlwJMLp4oxa6FChUR/YiPNmjXLBHh1ISsSAu4ikD59elOVixcvulQlnWpEp3ewplZxqTCZEUAAAQQQQAABLxfQuXQ17dmzR+rUqWNbY/fu3SZvzpw5bZchIwIIIIAAAp4swCJr8dy7+hj43r17JapA0o4dO6Ru3bqSLVs2qVixotSqVUsCAgLMiNcvvvgiVkbRxnOTOR0Cbi9QsGBB8fX1FZ1qQW+q2E3r1q0z/01qeVZwtqtGPgQQQAABBBBA4H8C1nRXs2fPtk1y+/ZtmTdvnslvlbddmIwIIIAAAgh4qAAB3njq2ODgYOnYsaPoSEEdDZslSxYJDAyUsWPHhtVAg7sa1I1svlud1kAXf6pRo4b8/fffYWV4gQACTy6QMWNG89/jzZs3ZcKECbYPOHr0aJO3QYMGtsuQEQEEEEAAAQQQQOB/ArVr1zZPKuqizjNmzLDFooNedO7dZ555Rp577jlbZciEAAIIIICApwsQ4I2HHg4JCZF69eqZwJG+1nTv3j3R0X9vv/12WJC3RYsWcunSJbNf5/YsV66cvPzyy5I3b96w0YG6Umzr1q1NHv5BAIHYE+jZs6c52Mcffyy7du2K8cB6EaKjR1KlSiXvvvtujPnJgAACCCCAAAIIIBBeQK95Bg0aZDa+8cYb8vPPP4fP8Mg7ne4tKCjIXBsNHz487BrpkWy8RQABBBBAwOsECPDGcZdrILdZs2by66+/mjOlS5fOLFhWunRpM2+nbuzcubN8+umnsnPnTpNHA716V3rjxo2yYMECMxfvhg0bwubB1SDvtGnTTF7+QQCB2BGoWbOmtGrVyiyYVqVKFVm1alWkB75//7589dVX0q5dO7NfR5HoiHwSAggggAACCCCAgOsC7du3N0863rhxQ1566SVzbXTw4MFwB9LrpDZt2oheJ+n11cCBA6V69erh8vAGAQQQQAABbxbw+W+w4r43A8R12zUQq19GNOnvMWPGmBF/+v7MmTOijyVt2bJF35qk7yObokF36jQPxYoVM4uUZc+eXU6cOPG/Qg76t1SpUmaeU32k3Zo7y0HVp6oeLqBTNDRq1EiWLl1qWqpTouj7fPnyic73tn37dpk+fbrodCqa+vbtK/379zev+QcBBBBAAAEEEEDg8QT0krRfv35m0IsGcDXpmiQ6jZZeM509e9ZsS5o0qYwYMULeeust855/EEAAAQScL6BPqet1tsbPdNAV6fEE/B6vGKXsClhzSZUoUUImTZokfn4PyP39/WXZsmWSO3du0TvWmvRRo6iSzt+rX2h0RPDJkyfNFx1GDkalxXYEXBdIliyZLFq0SIYMGWJ+VqxYIfrzaNL/ZvW/1VdeeeXRXbxHAAEEEEAAAQQQcFFAF6vVm+Z6nTN06FDzfUzXINEfTVmzZjXfuz788EPJlSuXi0cnOwIIIIAAAp4v8CDa6PltTZAWWnN5Nm/ePFxw16pM5syZzR0KXdgpderUUrBgQWtXpL8rV64ctl2PTYA3jIMXCMSKgK+vr/Tq1UvefPNNmT9/vqxZs8bcUEmSJIm5GaOPDtaqVUv0PQkBBBBAAAEEEEAg9gSKFCkiU6ZMMdMw6MLSFy9eFL1e0oExGgQmIYAAAggggEDkAgR4I3eJta3nz583x8qRI0eUxyxTpoxZgC1lypQxfnHRLzg6ylAfJddRvCQEEIgbgQwZMsg///lP8xM3Z+CoCCCAAAIIIIAAApEJJEqUSHRKOv0hIYAAAggggEDMAiyyFrPRE+XQR7k1HThwwPyO7B8dtRsQEGDml7p8+XJkWcK2HTt2zAR3dUPevHnDtvMCAQQQQAABBBBAAAEEEEAAAQQQQAABBLxPgABvHPd5oUKFzBl0/t1r165FeraKFSua+aV0Ht5UqVJFmsfaOGrUKOul6CNMJAQQQAABBBBAAAEEEEAAAQQQQAABBBDwXgECvHHc923btjVnOH78uNSvXz/aaRV06gV9HCmqpMHdL7/80uyuVq2apE2bNqqsbEcAAQQQQAABBBBAAAEEEEAAAQQQQAABLxAgwBvHndygQQOpVKmSOcuqVaukQIECUq9ePfnjjz9snfnMmTMyfPhwKVu2rHTp0kXu3r0riRMnlodH8to6EJkQQAABBBBAAAEEEEAAAQQQQAABBBBAwOMECPDGQ5fOnj1bKlSoYM6k0zAsXrzYrAhr59Rr166V7t27y6ZNm0x2Pz8/GTFihBQuXNhOcfIggAACCCCAAAIIIIAAAggggAACCCCAgAcLEOCNh8719/eX1atXS1BQkOTMmdOcsXjx4rbOnCdPnrB8uoqsjgLu1KlT2DZeIIAAAggggAACCCCAAAIIIIAAAggggID3ChDgjae+12kVBg4cKEePHpWNGzeKBn3tpHz58knfvn1l3bp1cuTIEQkMDLRTjDwIIIAAAggggAACCCCAAAIIIIAAAggg4AUCfl7QRrdqoo+Pj5QrV852nXQhtf79+9vOT0YEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8B4BRvB6T1/TUgQQQAABBBBAAAEEEEAAAQQQQAABBBDwMAECvB7WoTQHAQQQQAABBBBAAAEEEEAAAQQQQAABBLxHgACv9/Q1LUUAAQQQQAABBBBAAAEEEEAAAQQQQAABDxMgwOthHUpzEEAAAQQQQAABBBBAAAEEEEAAAQQQQMB7BAjwek9f01IEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8DABArwe1qE0BwEEEEAAAQQQQAABBBBAAAEEEEAAAQS8R8DPe5pKSxFAAAEEEEDAnQSuX78uq1atkoMHD8qNGzckICBAKlSoIAUKFHCnalIXBBBAAAEEEEAAAQQQQMCtBQjwunX3UDkEEEAAAQQ8T+DcuXPSv39/mTRpkty8eTNCA0uVKiWDBw+WGjVqRNjHBgQQQAABBBBAAAEEEEAAgfACBHjDe/AOAQQQQAABBOJQYPPmzVK/fn05deqU+Pr6SmBgoJQuXVpSpEghR44ckRUrVsiWLVukZs2a0qNHDxk6dKj4+PjEYY04NAIIIIAAAggggAACCCDgbAECvM7uP2qPAAIIIICAYwT2799vRuUGBwdLpUqVZOzYsVKoUKFw9b9165aMGjVKgoKCZNiwYeLn5yeDBg0Kl4c3CCCAAAIIIIAAAggggAACDwRYZO2BBa8QQAABBBBAIA4FWrVqJRrcfeWVV2TlypURgrt66qRJk5qRu0uWLJHEiRObqRr+85//xGGtODQCCCCAAAIIIIAAAggg4GwBArzO7j9qjwACCCCAgCMEFi1aJL///rtkz55dpk6dakbmRlfxatWqSZ8+fUwW63d0+dmHAAIIIIAAAggggAACCHirAAFeb+152o0AAggggEA8CsyZM8ec7b333pOUKVPaOnO3bt3M3Lxr166V06dP2ypDJgQQQAABBBBAAAEEEEDA2wQI8Hpbj9NeBBBAAAEEEkDgt99+M2etXbu27bNrILhixYpy//590cXZSAgggAACCCCAAAIIIIAAAhEFCPBGNGELAggggAACCMSywJkzZ8wRc+TI4dKRc+bMafL//fffLpUjMwIIIIAAAggggAACCCDgLQIEeL2lp2knAggggAACCSiQJk0ac/YrV664VAsrv1XepcJkRgABBBBAAAEEEEAAAQS8QIAArxd0Mk1EAAEEEEAgoQXy589vqrBx40aXqmLlt8q7VJjMCCCAAAIIIIAAAggggIAXCBDg9YJOpokIIIAAAs4W0Dlo9+zZIz///LPogmPHjh1zXIPq1atn6jx+/HjbdV+2bJkcPXpUsmfPLs8++6ztcmREAAEEEEAAAQQQQAABBLxJgACvN/U2bUUAAQQQcJTAxYsX5YMPPpBs2bJJ4cKFpVq1avLiiy/KU089JUWKFJFRo0bJ7du3HdGmf/7zn5IhQwZZuXKlTJkyJcY6BwcHy7vvvmvyvf/+++Lj4xNjGTIggAACCCCAAAIIIIAAAt4oQIDXG3udNiOAAAIIuL3AqlWrpECBAjJs2DDRBcY0qFu1alUJDAyUjBkzyu7du6VLly5SunRpOXTokNu3J126dDJ8+HBTzzfeeEMmTpwYZZ11hLIGsw8cOGDa16lTpyjzsgMBBBBAAAEE7AvoU0Fnz56Vffv2yaVLl+wXJCcCCCCAgFsLEOB16+6hcggggAAC3iigwd2aNWvKhQsXTKBz8+bNcuTIEfnpp5/MFA1nzpyRhQsXSqFChWTnzp1SoUIFOXHihNtTtWvXToKCgsyo49dff93Ue/LkybJt2zbZv3+/Gd3buXNn064tW7aYAPeCBQskSZIkbt82KogAAggggIA7C+zatUv073DmzJnF399fChYsKOnTp5e8efPKRx99JOfOnXPn6lM3BBBAAIEYBAjwxgDEbgQQQAABBOJTQIO6TZs2lbt378p7770nK1asMKNYH65DokSJROe03bRpk1SpUsWM8G3WrNnDWdz29cCBA2XGjBnm4nL9+vXSoUMHKVmypAnm1qhRQ0aPHi03b96U5s2by2+//Wbm33XbxlAxBBBAAAEE3FwgNDRUevbsKcWLFzdTJOkfZQ+AAABAAElEQVT3jCxZspi/u2nSpDFPAQ0dOtQEer/99ls3bw3VQwABBBCISoAAb1QybEcAAQQQQCABBAYPHmxG7lavXt1MaRDd3LOpUqWS77//3szRq8HSH374IQFq7PopW7RoYaZf0GkaGjZsKCVKlJD8+fOb6Sd0FNHWrVtl5syZotM6kBBAAAEEEEDg8QV01O6QIUNEbw7r1E76xIw+CbR3714zRYN+f9CbxlevXhW9WTxhwoTHPxklEUAAAQQSTMAvwc7MiRFAAAEEEEAgnICOspk+fbrZphdj0QV3rYIaBO3Vq5e88847MnXqVHnllVesXW79W4PTOnpXf0gIIIAAAgggEPsC+lTMtGnTREfqLlq0SCpWrBjuJPo9o3z58mbaJ124VQPA+n2iVKlSUqZMmXB5eYMAAggg4N4CjOB17/6hdggggAACXiSwZ88eM6omd+7c5uLKbtMbNWpksq5Zs8ZuEfIhgAACCCCAgAcL6AJqH3/8sWnhlClTIgR3H226zoHfrVs3uXPnjnTv3v3R3bxHAAEEEHBzAQK8bt5BVA8BBBBAwHsETp48aRqr0xW4krJmzSo6Ivby5cty7do1V4qSFwEEEEAAAQQ8UECnbQoODpbKlStLgwYNbLWwX79+ZuG1X375xUylZKsQmRBAAAEE3EKAAK9bdAOVQAABBBBAQCRx4sSGQUfPuJqsMtYxXC1PfgQQQAABBBDwHIGVK1eaxuiipXZT6tSppW7duia7Vd5uWfIhgAACCCSsAAHehPXn7AgggAACCIQJPPXUU+b1rl27ROfjtZv27dsnt27dEh3JmzRpUrvFyIcAAggggAACHipw/Phx07IiRYq41EIr/7Fjx1wqR2YEEEAAgYQVIMCbsP6cHQEEEEAAgTCBp59+WgoUKCDnzp2TZcuWhW2P6YUuoKKpZs2aMWVlPwIIIIAAAgh4gcD9+/dNK+0s2Powh5XfKv/wPl4jgAACCLivAAFe9+0baoYAAggg4IUCb775pml1jx495Pr16zEK6OjdESNGmHxW2RgLkQEBBBBAAAEEPFogZ86cpn26gKsrycpvlXelLHkRQAABBBJOgABvwtlzZgQQQAABBCIIvP3226KPR/7111/SsGFDuXLlSoQ81gYN7tauXdsEglu1aiXPP/+8tYvfCCCAAAIIIODFAlWrVjWtnzNnjm2FGzduyKJFi0x+q7ztwmREAAEEEEhQAQK8CcrPyRFAAAEEEAgvoHPoLliwQLJkySK6wEnRokVl3Lhxcvbs2bCM+/fvl759+0rp0qXl4MGDUrZsWRk/fnzYfl4ggAACCCCAgHcLNGrUSNKkSSMrVqywPe3Tp59+KhcuXJDy5ctLoUKFvBuQ1iOAAAIOEyDA67AOo7oIIIAAAp4vkC9fPtm0aZOUK1dOTpw4IW+99Zb4+/tL2rRpJUWKFGae3oEDB8q1a9ekZcuWsnr1arPd82VoIQIIIIAAAgjYEciQIYP06dPHZG3RooX8/vvv0RabNGmSDB48WBIlSiSff/55tHnZiQACCCDgfgIEeN2vT6gRAggggAACkitXLtmwYYN8++23UrduXUmVKpWZriEkJMSM7m3Tpo2sX79epk+fTnCXzwsCCCCAAAIIRBB4//33pXHjxhIcHCwvvviiBAUFyalTp8Ll27Fjh2gA+LXXXhNdWE2Duy+88EK4PLxBAAEEEHB/AT/3ryI1RAABBBBAwDsFdCXrJk2amB8V0Pl4EydOLMmTJ/dOEFqNAAIIIIAAArYF9HvErFmzJCAgQL766ivRKRgGDRokuXPnlvTp05tg799//22Op98txowZI+3bt7d9fDIigAACCLiPACN43acvqAkCCCCAAALRCuhcegR3oyViJwIIIIAAAgg8JODn5yejRo2S3377zYzm1ameDh8+LFu2bBEN7mbNmlU6deokunArwd2H4HiJAAIIOEyAEbwO6zCqiwACCCCAAAIIIIAAAggggIArAs8995x89913cvv2bTO//6VLl8yUT9mzZxcd6UtCAAEEEHC2AAFeZ/cftUcAAQQQQAABBBBAAAEEEEDAlkCSJEkkT548tvKSCQEEEEDAOQJM0eCcvqKmCCCAAAIIIIAAAggggAACCCCAAAIIIIBAOAECvOE4eIMAAggggAACCCCAAAIIIIAAAggggAACCDhHgACvc/qKmiKAAAIIIIAAAggggAACCCCAAAIIIIAAAuEECPCG4+ANAggggAACCCCAAAIIIIAAAggggAACCCDgHAECvM7pK2qKAAIIIIAAAggggAACCCCAAAIIIIAAAgiEE/AL9443CCCAAAIIIIAAAggggAACCCCAAAKOEjh8+LCsWbNGTpw4IX5+fvLUU09JtWrVJEuWLI5qB5VFAIHHEyDA+3hulEIAAQQQQAABBBBAAAEEEEAAAQQSVGDjxo3y4Ycfytq1ayPUw9fXVxo0aCBDhgyR/PnzR9jPBgQQ8BwBArye05e0BAEEEEAAAQQQQAABBBBAAAEEvERgxIgR0qNHD7l3756kS5dOatasKQUKFJC7d+/Kzp07ZeXKlfLDDz/I8uXLZfr06SbY6yU0NBMBrxMgwOt1XU6DEUAAAQQQQAABBBBAAAEEEEDAyQITJkyQbt26iY+Pj3z00UfSu3dvSZUqVbgmnTlzRrp3726Cu02aNJEVK1ZI5cqVw+XhDQIIeIYAi6x5Rj/SCgQQQAABBBBAAAEEEEAAAQQQ8AIBnW/33XffNS39+uuvZfDgwRGCu7rT399fpk2bZqZw0FG9LVu2lOvXr3uBEE1EwPsECPB6X5/TYgQQQAABBBBAAAEEEEAAAQQQcKjAwIED5fbt29KuXTtp27ZtjK3QAHD58uXl9OnTMnbs2BjzkwEBBJwnQIDXeX1GjRFAAAEEEEAAAQQQQAABBBBAwAsFdCTuvHnzTMuDgoJsCeg0Dr169TJ558yZY6sMmRBAwFkCBHid1V/UFgEEEEAAAQQQQAABBBBAAAEEvFRg//79cunSJcmfP7/kzZvXtkL16tUlUaJEsmXLFrMom+2CZEQAAUcIEOB1RDdRSQQQQAABBBBAAAEEEEAAAQQQ8HYBXThNU/bs2V2iSJo0qWTKlElCQ0Pl/PnzLpUlMwIIuL8AAV737yNqiAACCCCAAAIIIIAAAggggAACCEiaNGmMwpUrV1zWsMpYx3D5ABRAAAG3FSDA67ZdQ8UQQAABBBBAAAEEEEAAAQQQQACBBwJ58uQxUy3s3LlTrl69+mBHDK+2bt0qISEhkjNnTkmePHkMudmNAAJOEyDA67Qeo74IIIAAAggggAACCCCAAAIIIOCVAunSpZOKFSvK7du3ZfLkybYNxo0bZ/LWq1fPdhkyIoCAcwQI8Dqnr6gpAggggAACCCCAAAIIIIAAAgh4uUCPHj2MQL9+/UQXXYsprVq1SiZOnCh+fn7StWvXmLKzHwEEHCjg58A6U2UEEEAAAQQQQAABBBBAAAEbAjdv3pQVK1bIzz//LMePHxcfHx/JlSuXVK9eXapVqyZJkiSxcRSyIICAOwnUqlVLmjRpIt99951UrVpVfvjhBylTpkykVVy4cKG0atVK7t27J3379pX8+fNHmo+NCCDgbAECvM7uP2qPAAIIIIAAAggggAACCEQqMG3aNOnZs6ecPHkywv6RI0fKU089JcOGDTOBoggZ2IAAAm4t8M0338jZs2fll19+kXLlykmzZs2kcePGJoB79+5d0Tl6p0+fbm7waEPatm0rOuKXhAACnilAgNcz+5VWIYAAAggggAACCCCAgJcK3L9/Xzp16iRjx441AiVLlpSmTZtKoUKFJDQ0VHbv3i2zZ8+WXbt2me36uPdnn33mpVo0GwFnCqRIkUJWrlwpvXv3llGjRsmsWbPMz6OtSZs2rfTv31+6dOny6C7eI4CABwkQ4PWgzqQpCCCAAAIIIIAAAggggICO0tPgbvLkyUUXVmrTpk0EFA0KjR8/3gR9dBRvlixZpHv37hHysQEBBNxXIHHixObmzDvvvGNu2qxevdqM2Ne5dnUqlpo1a5qRvRkyZHDfRlAzBBCIFQECvLHCyEEQQAABBBBAAAEEEEAAgYQX0FG5n3zyiVlMSefe1Hl2I0s6F++bb74pAQEB0rBhQzMKsFGjRvL0009Hlp1tCCDgxgIazP3ggw/MjxtXk6ohgEAcCvjG4bE5NAIIIIAAAggggAACCCCAQDwKDB482EzDoFM0RBXcfbg69evXl/bt28vt27fNfLwP7+M1AggggAACCDhDgACvM/qJWiKAAAIIIIAAAggggAAC0QrowkqLFy82ebp27Rpt3od3Wnnnz5//8GZeI4AAAggggIBDBAjwOqSjqCYCCCCAAAIIIIAAAgggEJ3AiRMn5PLly/LUU09J7ty5o8sabt8zzzwjOkfn6dOn5cKFC+H28QYBBBBAAAEE3F+AAK/79xE1RAABBBBAAAEEEEAAAQRiFLCCs5kyZYox76MZrDLWMR7dz3sEEEAAAQQQcF8BArzu2zfUDAEEEEAAAQQQQAABBBCwLWAFac+ePWu7jJXRKmMdw9rObwQQQCAmAZ0eZsaMGfLKK69Ivnz5JH369JI3b17ROb6nTJli5viO6RjsRwCBJxPwe7LilEYAAQQSVuDMmTPmS8OyZcvk8OHDcuvWLcmePbtUrFhRWrZsKaVKlUrYCnJ2BBBAAAEEEkhAL7h/+eUX2bp1q5w7d85ccBcpUsQsvJU8efIEqhWnjUuBHDlymKkWjh8/LgcOHDCBFjvn27Jli1y6dEms8nbKkAcBBBBQgfXr15uFGvft2xcORP+fcujQIVm4cKEMGDBAJk6cKJUrVw6XhzcIIBB7AgR4Y8+SIyGAQCwK6IXojz/+aC5Orl69KlmzZpXy5ctLhQoVJFGiROZMw4YNk/79+8v169fDnVnnj9u8ebOMGDFCXn31VRk7dqykTZs2XB7eIIAAAggg4KkCGtj96quv5NNPP5Xz589HaGaqVKlEF9X66KOPJGXKlBH2s8G5AvodSUfMff311/L555/LuHHjbDVm+PDhJp+OviMhgAACdgXmzZtnrrdu374thQoVMn9bqlSpIpkzZzZ/f9asWSOjRo2SHTt2SI0aNWTq1KnSvHlzu4cnHwIIuCDgc/+/yYX8ZEXgiQR0NOWff/4pDRo0EP1jQELgUYFTp05Jr169ZNq0aRIaGvrobjOypF+/fmZEkubRpJ+ntm3bSsmSJSVp0qRy8OBB8/nSi5obN26YLxv65cLf3z/C8diAAALOErhz547cu3dPkiVL5qyKU9tYFdALyYsXL5qRikmSJInVYzv9YMHBwdKoUSNZvXq1aYounlW1alVzo1TN1q5dKxs3bgzbt2jRIrMgl9PbTf0fCOzfv190pLZ+j/rhhx9MwPfB3oivZs6caZ560lHdOgJPR/GSEEAAgZgENGj7/PPPm+ut9957T3TwjTUQ5+Gy+v+iPn36yKBBg8y12rp166RMmTIPZ+G1lwu0bt1apk+fbmIArVq18nKNx28+I3gf346SCCAQywIbNmwwwVqdA04v2PUub9myZSV16tRy7NgxWbp0qezdu1dee+01c2bdPmfOHKlVq1a4mgQEBMg//vEP6dy5s7mo2bZtm5kPSi9qI/vSEa4wbxBAwO0EfvvtN5kwYYIsX75c9CaQ3pvOmDGjVKpUydzcqVevntvVmQrFvoD+bRg5cqQJWGkQSj8HPj4+UrBgQWnYsKEZNZQlS5bYP7GDjqg3QNRCp2XIli2bTJo0SV566aUILfj999/ln//8pxlRVbNmTRPwTZcuXYR8bHCmQP78+U0g5YMPPpAmTZqIjs59++23I3wH0s+LPu2kN9Y1ffHFFwR3ndnl1BqBBBHQJ0F0ME2HDh3M/z+iqoSvr695ouTatWtmNO+7774ret1HQgCB2BVgBG/senK0GAQYwRsDkBfv1ov1cuXKmfnf9GJz/PjxEUYU6cW8PnKqgVtNOiLpp59+ilZNp3rQO8QaIP73v/8dFhyOthA7EUDALQT0ouHNN980d/OtCiVOnNgEKW7evGltkhdffFFmzZolenOH5JkC+ri5/r9fLw416U3ADBkymFG8OppXk97008dA27VrZ9574z+DBw82wTodgamjdHVO+qiSTn+k/+3ok1V641T/RpI8S0Cn4Bg6dKhplC56pCO79RFq/T71119/ydy5c+XIkSPmRsnAgQOld+/engVAaxBAIM4Edu7cKfqEiP4t1nl27UyHp9/rdOG1v//+2/yN0ms/EgIqwAje2Pkc+MbOYThKbAvowlH6aN3s2bPNwhh6h52EgCcL6AW5TsSvI02WLFkSIbirbdeRWhqo1aRBnp9//lm+++478z6qf3T+J52DTpOOUiEhgIAzBPQiQBfi0KlYdI5QHWG2e/dus5BiSEiI6AJC+t+0Tr2ioxWfe+65sP8/OKOF1NKugAaodLSpBnd1Sh59GkM/Azrfun5O9L1u14Bl+/bt5bPPPrN7aI/KpxZWME/nOIwuuKsN14D4t99+a/6eagBdFyoleZbAkCFDZP78+aIjenXBNf186H8j+t+TfjfS4K5O5aBrHhDc9ay+pzUIxLWAXq9paty4sa3gruZNkSJF2Py7VnndTkIAgdgRIMAbO462jqJzz+zZs0dmzJhhFj7QEROPphMnTkibNm3Ml3KdnFwnIH/22WdFF8PQANiVK1ceLcJ7BBwvsGzZMvOYjo6+04vM6KZRsL4MdOvWzbT7448/jrH9umCIPs6to1W4gI2RiwwIuIXA66+/LvoYuY700NXddbEoHXmmN3o06QhFfTRQ/7uuWLGinDx50kzJootLkTxHYMWKFdKzZ0/RxzsnT55s5lcPDAw077WV+vdC3+u8/jodgebTUYsrV670HASbLVGry5cvmwVJ7a5SrqM6mzZtaua11rlaSZ4noAuu6c2xNf9diyAoKMhcT2iQV78/6TyYOoemPjlFQgABBFwR0DVPNLk6l66V3yrvyjnJiwAC0QswB2/0PrG2d+vWrdKiRQvzBevhg1avXt3MIZo+fXpzcaqPyukjDo8mffxwypQp5suZPk5l/Y/x0Xy8R8CJAvqZ1qTzMcW0mrf1ZUAvTL755hvz39SuXbukaNGiUTZdAwB6o0Snc9DyTz/9dJR52YEAAgkvsH79etFFf/Tmpo4s09FnUSV9NHDhwoXm76L+rdXHzN96662osrPdQQJ6Y1wXbdHHyXVhFg1KRZd0VKI+AaWjvbXc9u3bwwLB0ZXzlH2bN282TXE1WKf5dfCBVd5TPGjHAwH9HqTXGPpDQgABBGJD4Pr16+YwMV27PXouK79V/tH9vEcAgccXYATv49vZLqkXnjq/jN49fzTpCBNdIEovYtr9d4SuFdzVhTF0RIWOQtFH1vURVE1Hjx6VV1991Tya+OixeI+AUwX++OMPU3W94RFd0hsd+qNzL+pKzzoHryYd3RdT0kCRJmv+xpjysx8BBBJO4F//+pc5+fvvvx9tcNeqoc77Zj2aPnbsWGszvx0u8J///MeM0NabcvpZsJO6d+8uuXPnFr3xp6MTvSnpInSa9DukK8maysEq70pZ8iKAAAIIeKeAte6BxidcSTo1jCarvHnDPwggECsCBHhjhTHqg+h8cDqSyFoARAO9+qihrmpbvHhxU1BXB9fHTK3FojS/Ljg1Z84c0cUydH40fa8BYE06AvGTTz4xr/kHAU8QsC4qY7ootRbV0f+edHJ+64uBVT46C2vu3qxZs0aXjX0IIOAGAjptiyadsshuevnllyVdunTmceNTp07ZLUY+NxZYvny5qV2zZs3MPLF2qqrzs2t+TTplgTcla4EbnabBlaTz32uyyrtSlrwIIIAAAt4p8MILL5iGL1q0yCWAxYsXm/xWeZcKkxkBBKIVIMAbLc+T7xw2bJhYF5oarN2wYYN5zFBHGumoxbffftucZPTo0ea3zpOlI5esRxesGqRJk8bMPWc9WqWrROsjiyQEPEFApyjRFBwcHGNzypcvb/LolwMrvwZ1oks6N6fOea0T+5coUSK6rOxDAIEEFtBH9i5cuGD+DubJk8d2bfz8/MxiQVrAGh1iuzAZ3VLAujFXrFgxl+pn5Xd1VJFLJ3HDzAULFjS10ilOXElWfqu8K2XJiwACCCDgnQI6vY9ew2l8w7oxH5OEPpmjg9r0ycq6devGlJ39CCDgogABXhfBXM2uo3M1VatWzUy3YC0Oo9v0YlRXANcFZKz0xRdfWC8j/NayOgedJn3MXFcQJyHgCQK6cJImO4/T6hQlmvQmiX5J0GSVN28i+WfAgAHmhoiO8NOpHUgIIOC+AtYTLzpi39VklbGO4Wp58ruXgLVgnn5fciVZ+a3yrpR1ct46deqYOYeXLl1q+yaHfp+cNm2aabb+jSQhgAACCCBgR0AHpOnCjZr0iSt94ji6pDffdQF5Tfo0szXAJ7oy7EMAAdcECPC65uVybl3dW1NUd6j0YrRSpUomT8aMGSWm0UolS5YMWzDEOrYpzD8IOFhAR65r0rkzdT7q6JJ+MdDRWQcOHJD9+/dL5syZzYrhUZXRhdgmTJhgHu/t379/VNnYjgACbiKgX/j1RoyO0L948aJLtdL/L2iy5hR1qTCZ3U7A6kf9f70rybrIzJEjhyvFHJ9Xpy1q3bq13Lp1S1577TWxE+Du0qWL6DRHgYGB0f4tdTwODUAAAQQQiHUBnWaydu3acu7cOXn++efl66+/lnv37oU7j17b6UKeZcuWNYvKV65c2UxZGS4TbxBAIFYECPDGCmPUB7HmQYtuXjNr8Se7d7F0JVxNLBYVtTt7nCWg8yXqojjbtm0LG6UeVQv08z9+/HixRsPr9CV79uyJkF3nFOzWrZvoquqadFqTAgUKRMjHBgQQcD8BazqiH374wXblNm3aJCdOnDALTOXLl892OTK6r4BeBGqaO3euS5X8/vvvTX6rvEuFHZ5ZpwPTQO/PP/8sDRo0CJvK6NFmhYSEyBtvvGGm/9JRWGPGjHk0C+8RQAABBBCIVsDX11e+++47adSokfl7o9dd+jeocePGZipKXTT+/9g7E3ibyu6Pr7c3TRoNRZNUkhRKMkURUmSsEBlCgyFzREihQhGSNMsUFYWSpCJDmkQZCpEm0YDm9O7/+q3++3TuvWfY+95zzj3Db30+l3P2fp699/M95+xhPWv9FmqstGnTxpzACHp78cUXLZM54oa5kgRIIFcE6ODNFTbvnVw9M+jthrMPPvjAVm3btk1+/fXXcM1s+YoVK+Svv/6y12XKlInYlitJIFUIIJIdTls4b4cMGWJ/7vc8+xgQyXXzzTeb5ALScFF0EBG9KGCIh9Xu3bvbTDJuJiCBghsPOHfRh0YCJJA4Aoi+/fzzzwWaun4NUYiwESNGeJrMhCY9CpjC8BDhTgDZAv6TsgTq1q1rD4aY/Js2bZqncUBuYN26dRbFfdlll3nqk06N8GCNgjfIblm4cKHJgCEVFgXrwPGNN94QZLOUKlVKHn30UdOmR1Hf8847L50wcCwkQAIkQAIJIoAaJ3Dyzpo1y2ohIJoXE63IzMTyXbt2WZDN1KlTzbl71FFHJejIvO8GgXO5uV/1vge2JIEEEdCHIlocCehDKiqhOYceeqijkgo59jRv3jxbjzb4GzduXI42wQs05c7aqWPL0RS84FUp8fr888+349eokpQ4Xh5kYgnow6aD7zZ+CxqB56hzx3nttdccFe93Zs+e7bRr187RCum2vmzZso4+xDvq0HU0+siWub8j/K+OXad+/fqOTqAkdhDcGwlkMIFPPvnE0egNR51LWX6TqpPt6OSNo05fT3Q0vc+54IILbBua+ufo5GfYfpr65/Tq1cvaqtSR532E3SBXJBUBTfe0zxbn+bfeeivisb355puOPmha+6effjpi23RfqZMrjhbAMRbB18bg15UqVXK0AGm6o+D4SIAESIAEEkhAMyvtuU0Lxzs6gejg3jAZTfXqHY00drRYd+BaqRnVjkYdOzopmoyHnNbHpAEa9jnoRH1ajzPeg0MUHC2OBNasWWOOJtxQ4+SBEx0cvatXr3YGDRoUcFZVrFjRvtB4MAn3AIP27o25pjfE8ajjt2k6eOPHNl22rNFFzjnnnBP4rrvfefd/jfJ1Onfu7Ozbty8wZE01dRYvXuyo1q4zYcIER2eNHZ0tDqznCxIggfgSgENWI2gd/D7d32qhQoWckiVLBhxuWI4bZ03N83QwGp3vwGGLfuXKlbPfOJy5wQbnVL169awNJoeWLl0avJqv04RAx44d7TPGBB++Z9nP799++60zYMCAwD0VrhG0fwjgfrNv376OFvt1ypcv72jdB6dLly4hf09kRgIkQAIkQALpTkAjjAP3ju49q0pm5ggYuuKKK5zvv/8+3XEkzfjo4I3NR/EfbEa/2LQ4Erjllltk8uTJYfegD8GmIYr0OKQwILUU+jVILUSauTqJRZ1XsmTJEtsG0iB0Jsw0S8NuNElXaESW6AO56cLNnTs3SY+Sh5XfBCDOv2jRIlmwYIEVU0PaTLFixUy8H5pOZ5xxRn4fIvdPAiQQRACSCkihVwec4JqnDiRxJYrwe16+fLnpa2tEvsmmoPihK8MQtJkcL6GvjSKMbtEsnAcgyQJ5Fsi1QKIFhuuoRvnbdTPHRrgg5QmgQAskOEaPHm3yPJDe0YlAkyFAgbCNGzdagU7cP0GOYOTIkYGCtCk/eA6ABEiABEiABEggJgRwz1C1alWBNGaRIkVEJ4cFtWDcoqw7d+6UmTNnyqhRo0SduyZntHLlSmsbkwPgRsIScJ8lILUFuTVa7gjQwZs7br56/fnnnzJw4EB54IEH7MEkuDM0aODIqlatmp1MWrdunaNNcHs81EycONEeoIOXp8prOnjz75NCRXro8sHR8s0331iVeo2uM73amjVr8mE4Dx8NKpajoA1uADSSTFCwBkWeGjRoIKeffnoetsyuJJD8BB588EFBFWUUPMQ5BueTcHbvvfeao05li2zyUiNzwzUNLP/9999NRxs63bghDzbojOKGEM4/3KjT0psA6hkMHz7c9GRRJMy1ww8/XFSSRzTTSTQjyl3M/0mABEiABEiABEjACCCuUbNYZNmyZaISRfLSSy9ZAFEoPF9//bWgIBwC02rXrm3PeaHacVnsCNDBGxuWdPDGhqOnraiOqDlz3aJqKldgUU6ISHLtscceE02lk71797qLAv+fdtppoppyER+eA42T9AUdvIn/YBA9B6fKfffdJ/v37w95AHCyoBCZW7k+ZCMuzEEANwoqu2IFa1BQIJQ1atRIxowZYzPAodZzGQmkMgFMHGES46effrIb5auuuirqcBDdi8Ibqg9q18SoHYIaIGp3x44dcuDAASuipVrcnJwK4pMpL1GQFtHbe/bsMcc+JtTg5KWRAAmQAAmQAAmQQCgCKCjasmVLy5Beu3atZQGFaucuQ9BOhQoVLMMaReOaNWvmruL/cSBAB29soNLBGxuOMd0KHpSR6oqUQ6QG4AEWJ5datWqJ6sPEdF9eNrZixQqrihkLNQ+cWPFApkVzrLqzl/2zTe4JoBpo06ZNBWnRMNXgs3RnRO4i+gmzktOnTzeHiWpnykMPPSQ33XRT7neYQT3Br1WrVlYNFsPGhA1mekuUKCGQlHjvvfdsHZzqiGxE+jgcWjQSSCcCqntt54y6deualJCXscEpjN8JfhtffPGFnHLKKV66sQ0JkAAJkAAJkAAJkAAJ5IoAfClajFW0qLdo4XpP20BAAgIT/NznetowG+UgQAdvDiS5WkAHb66wZVYnRGRBCzWWBmeYG8kcy+1yW1kJQK8WM46IEoeeENJSshskRO6++24ZMWKErZo3b54g6pQWmQBmgDFhoUWg5PHHHzfHefYeiOrt3r27tYN2NuQxEMVOI4F0IeCeY5588klp376952FphWKZM2eO/XagOU8jARIgARIgARIgARIggVAEEGiGwKQtW7YIApjwbHvRRRfZc1io9tmXIfMHATcIaEKwGWQyvRgC7yAHBo1/BCZAYowWHwJ08MaG68Gx2Qy3ks4EoK+IiFsUOcmrQSrgyy+/DAiZ53V77B+ewHPPPWfOXa1ab1pDpUqVCtn4kEMOMQcvLnooToMIXhT4g44sLTQBROPCuXvMMceY07ZMmTIhG+KGYNasWXZDgdliOMCQEgQtbRoJpAMBFKOAoeCVH0NmChy8iOClkQAJkAAJkAAJkAAJkEB2Am4dBvgjoIsbbHDWIrIW2vzR9Pfhf4BsIQp1e3XuYl/HHnusZZp9/vnntn9kwdJIIJkJ0MGbT58OijLlZgYIkg1uYRG32mO8hwB9RVRFj4Uh0hEnWJyQafEl4EbkogpoOOdu8BH069dPEL2LQmFPPPGERZ4Gr+frfwkMGzbM3qBwYjjn7r+txQojogjb+vXr5YUXXhBEPdJIIB0IuBN/fict3PZu/3RgwTGQAAmQQKIJoCI8ZLigTf7XX3+ZNjmytaBLTSMBEkgsAdzTQKLt008/lX379lmUKYp5UYoqd5/D9u3bLasUz08wOFfBE0FIOOdBRhLF6hcvXiwjR46U/v37h90Rzo+wAgUKhG0TbsXBB//jMnO3Ea4dl5NAMhBgGFkCPwVEVF5xxRVywgknWDEQOIbatm1rJyevh4EIQFwkeKHwSiwz2+GCiEhRzDq2a9fOM4QePXpY27lz53ruk2kNN23aJBs2bJDjjz/eM1tESbtsIZlBI4F0IeBONG7evNnXkNz2bn9fndmYBEiABDKcAIoMXnPNNVK8eHFp06aNDBo0SO68807p3LmzTepXq1bNMowyHBOHTwIJIYAo09GjR9sES+XKlQWp5l27dpXmzZvLqaeeKvg9Ll26NCHHki47weQVJqvg3IXPZMmSJbJt2zbLoEQgEgJnUAQN2aewAQMGyD333BN2+CeddJKtcyfDwjbMtgJBeQhOg0TDiSeemG0t35JA8hGggzcBnwl0YuBkw40YZplwwoKODBxFzzzzjNSsWVN69+4diMxNwCFxF2lO4JNPPrERVq1a1ddM5SWXXGL93P5pjilXw4PjHIabDj+R6JC9gLn97Q3/IYEUJ1C7dm0bAWRLvBqKEL788svW3P1deO3LdiRAAiSQ6QTmz59vhV0ROIIJZNTKGDhwoAwdOlRat24thQoVklWrVtl9CqTRaCRAAlkJQMv1jjvukAYNGlhEaL169aRnz57yxhtvZG3o4R2kqvC8BUcjHI6InsekCwpz4bcJOQD8HnG/07dv35hIHno4rJRv0rFjR4vSrV69urzzzjvGL/ugEMh03333meQXMsMw0YW2oQxty5cvb4WwX3311VBNQi7D/Sqypy+88MJ8KXYf8qC4kAQiEVBHIy3OBG699VZHP4PAn6YVOJpi4OhMUGAZ1p911lmOzkxFPJqGDRsG+kRsmKQrtbiaHX+TJk2S9AjT47CmTp1qnHUG2deANPXE+mkqiq9+mdR4/PjxxkiLp/kathZcs35FihTx1Y+NSSCZCejDjINrGq5hWpnY06FqCp21r1Gjhqf2bEQCJEACJPAPgWXLljmaYmzn0FatWjnffPNNDjQaWOIMGTLEUYeHtVPtyhxtuIAEMpGARmI6jRs3tt9F8LN58OsqVao4GozhCc8PP/zgqEPXtle6dGlHo0xz9FPnoKMTLY5Oxlg7zejL0YYLshJQ6QVjpbVOHNxnejGd5LI+GngQtrlK61mbChUqOBqZG7adu0Ijs51zzz3X+kyYMMFdzP/jREAnRoy1BkDGaQ+ZsVlG8OoZPZ6GaL2HHnrIdgFphhdffNE0eZBi8OOPPwr0UVGoCQa9HkQFQsSbRgJ5IaBOROu+a9cuX5tBdDnM7e+rc4Y0RmQMDHrYfkxvAq05it7RSCBdCOC65mqeXXvttfLRRx9FHBp02HHdQ/Q7/qf9QwDnanWQC+Rx3n77bUHVZhoJkAAJBBNAGvh1111nWrvI/JsxY4ZpfAa3wesjjjhCUCsA65FW3KdPH3FlcbK35XsSyBQC69ats2hdPIsjqlYDsOSll16Sd999VxDRiYheSJ6sXr1aEDXqZhpF4oMo3S1btlh0Z7go08MOO8zuk6ATi/o7KBbmZduR9pvu63DugkHmAveZXgwSDTj3IQpbJ75CdsHnBR1f+Gc6dOhg59KQDXXhn3/+aVKaH3/8scne3HjjjeGaJnS5uihFg4bsnO73WTShB8qd5R+BzPBj598o9WRgMxGIiAw3G6gVIR1NGbB2+k2w6N5ws1WM4M2/zzKV9owZakSIH3nkkQ5mjr2aG/l7+eWXe+2Sce0++OAD+62qDravsT/66KPWj9HrvrCxcQoQ0KrEjqY52vcb0bx33323o5NFWY5cb5AddUwErnOMKPsHj6ZaO5ramSOjB/cMOA9r0cssHPmGBEggcwmMGzfOzqGaKuzgvOvFbr75ZuuD8y+NBDKVAO5JVAvXfgt16tRxdFI1JApEv6s0gLXD/Yw6hUO2w0I81+NZS52KzhdffBG2XfCKsWPH2rbLlSsXvJivsxFw/SLqbM+2JvJb915U652EbYjPzc08u+iiiyz7TIvjBdrjteolOzjPwi+jkwEO7mHz23QiwYFfSR3edlw4NvxpLQsH2eIqFZLfh5jn/TOCN88IbQPQgqXFkQBSUPHj02JqEfeyd+9eR/VPAz/YihUrOqpTmKMPHbw5kHBBGAJIMcJ3DzcTXgwPC0hZQZ9HHnnES5eMbVOiRAnjFOkGIhgO2OJmDmyfeuqp4FV8TQJpQQCpbp06dbLvOL7nSA1GuiJunnHziWX400gWR4tjpMWY8zKIX3/91WnRokWACx4QcQ/QtGlTc/i6Kdhg1q9fP8/OnLwcE/uSAAkkNwGNKrRzhtd7D4wGjid3wh/pxjQSyEQCbsDVxRdf7GhkZlQEGt1pvzW0D2caMWpt4Fzzati3Fmm2flrvxFM3OCS7detmUo64h4LUw+mnn273XJrx42kbqdbIZRTOER9uPBrxa2yjySm8//77DgJ13HtTjdx2atWq5WgmtVOsWLHAcjzvhQvQC3cM8ViuxeMC0jw4Zsj94R5bs0IDx3r44Yc7kydPjsfuE7ZNOnhjg5oO3thwDLsV9wTlJVoJD3yI5HFPNldeeaVz4MCBLNumgzcLDr6JQEDTf+y7hFlKLSYQoeU/q26//XZrj4sZHwIi44IDHL9TrabqfPXVV5Eb61po4aE9tLe9aD5F3SAbkECSEli+fLmjRUXMketey/A/roW33HKL5yiXJB1eTA4L13VE54IL9OXwIJI900IlnBwtmBTQ7EMUHo0ESCCzCbganqECQCKRcTUkvdwLRtoO15FAKhJADQxkxWDi9LPPPvM0hP379wciJcM5UbVIul3HVXrB0zbdRqiPguv/Y4895i4K+T/uCzBxjgma4Pup7K+bNWvm4J4hney0006zMatspa9h3XDDDdZP5cCi9sNnrFI2Dp57szPF89rw4cMdRHTnt2nxPzs+BE507tzZyT4xgMxS6LG7YxgxYkR+H3Ku908Hb67RZelIDV79NcTToN8CgyZMNNOZF9MCQvVNGPR5tJBTtG5cTwIhCVxxxRWiFzrRi5NpO6Pacijbt2+fqPNAdHZQ9AZINMLUNKJCteWyfwigsquK+IvKq4jO7ovOBIdEA708jb6Tu+66y9iqTINVvA7ZmAtJIA0I4PcATTtoTm/atMmqGUNXHnpokyZNEo2YSINR5m0IONdC7w+6ctD608gcgUZfsKHa85133imvvfaa4N5AozLk2WefDW7C1yRAAhlEALrceKbAuUEn7n2N/KSTTrL2fusy+NoJG5NAkhJYuHCh6MSq1KtXT9xn7GiHqhJ30q5dO2uGe5pQpnKKtlgdhKFWh13mtg+nE4uOGvQlGlEq6gS2Z7JevXrJe++9J+r0FQ0UkfXr18vgwYPl6KOPlhdeeEE0QEz27NkTdp+ptkIjlO2Qwz1fhRuP2/6MM84I1ySwHJ+xBuDI9u3bRR3/orIMpt8LTWXUSho0aJAn/01gg3F4MXPmTFFpHvsO4Hs4ZcoUOeecc7LsSQvYm966Fiaz+hbQksa9Iy1zCRycuUNPzMhLlSpl4u0bNmzwtEMUt1q0aJGdqCGg/fDDD9vFCMUUaCTglwC+P3DwwjFwzTXXiEowSKNGjQQXTtwk6KyfaKqfOWPgYHj66afNGex3P5nWHgWi5syZI6r1ZA6aSpUqGVeNsBfV+DLmKNowbdo0UU0k0agBu0m77LLLMg0Vx5uhBOCU1PSxDB19+GGjIMZ9991nDXBePvvss8M31jUaISTjx48XjdoQzbKQq6++2m7gI3biShIggbQjAGeERnCJRu+KakTaa6+DVBk4a+oWdfbaj+1IIB0IYLIZhsJpfsxt7/bP3tedaMFzlh9z27v9Q/XFNR8TwCgItmDBghxOPY3KF/yhnWZNWYFbPOfBSakRv6E2mVLL8HyFsaiklzRv3tzTscO5i0K/OM9Vq1bNUx+3ERz/Xp3/bp94/w9HvlvE+KGHHrJnzkj71OhXe+YcOHCgwG+EooLp8F2INGauC02AEbyhucRsKRy8sOnTp5sTzcuGMeuEWRo8IMP69u0rWvzKS1e2IYEsBDSdT2bNmiUqKWCVllVHyKJJ27dvL5ouLYgoRaQdZolxI3Httddm6c834QkUKlTIqt7fdtttNrOKqry40dLUa9F0KYuIhnP3ggsukLfeessqsYbfGteQAAlkAoF58+aZgwbnCdXc9TRkZGLgwQOR0CtWrPDUh41IgATSiwAyrDA5j0hETCB7NUQCwukB53CyOTC8joHtSCAvBNwJDkS/+zG3PaLnQ5kbJYrIWj/m/n7d/tn7qtSVRWRqcS8L+soesRncHllRCAxTDVl7JkmXTB9ET8NRi2xmBCJFMzhDu3TpYs3wfIvAmlQ3ZHrhOVJruFhGrpfxwGeEQCMtCmfP9V76sE36EaCDN86fqVattT1o9U7Ba6/pUVogy5zCuCFTUQ1RsXdRnRibtY/zIXPzaUhAiwtYCgrSlBAFhgsnZBlGjRplFwHMkmrF0jQceXyHdOihh1o0HtJ7EC2txRQtBUwLJdnEzJtvvmkpVUidopEACZDAsmXLDAImgbwa7gMaN25szd3+XvuyHQmQQPoQQKQeDPcbXg2ZWcjYwn0IsgRp6UtAC/qKFtWz7LwdO3YI3qe64RkYDtH7779f+vTpI4hORHAKHF9eTWsAWNNIkgihtgUZNpjbP3sbRJnCVO81+6qw7zdu3CgrV640WaZwWX0TJ060/pB4O+uss8Juy12hRcFE9WLtrdvXXZeq/xcuXFhUS9YOH89Wc+fODTsUSA3inmrNmjUW8azF78K2TaUVrsxCy5YtPUfiwrHtRjwvWbIklYbLY40lgSyKvHwTFwJ16tQJCF+j2qFGT1pRFS87QzXEYHF1fdALbMtL/2RrozoxdvxNmjRJtkPj8ZAACZAACZBAXAnoA51dA/XG29d+VL/Y+qFQHY0ESCAzCaDgkE4sO3gWQCHdaLZ161ZHs43s3KHZA9Gac32KEti8ebOjgUCBz1r9BPaZ45lTnWOOOhVTcmT4zqqMkY3FHVPw/yjoqvIJUcemGXa2jcqVK0dtG9wATLE/1c0PXhx4rVILVmwZbXCNjmaqoe24hdm0xk7I5iq/4mjkru1XnfUh24RaiGNRqT07N6RTwbWuXbsGPn+d6HZUrsLRYDkHhSZV/tLRQCVHHdzWpmjRoo5qE4fCk5LLMF58t/yeu3UCxPqhQF+qGYusxeYTYwSv/nLibUiPP++882w3etKVp556ygqmeNnvTTfdZPozSM2CQXeLRgIkQAIkQAIkkHoEEPUPQzqhH3Pbu/399GVbEiCB9CCgleVl6NCh9iwAvU3UAghnqLGAYrCQ4dKgikAWQLj2XJ6aBKDpXrZsWXnyySftsz755JNNGgyp+3jmhMQftFrdCM9UGKW6OARFxfC9hf4tUs6Rfo8o3pEjR5qcHDSp58+fLxUrVhRIpEWyunXrWnHCd955x3RdI7V11yECesaMGSZtEi7jBgXUx44da11uvfVWiyx2+2f/f//+/VYLBVk4+IxQ3CuUQacfbRFt76coLY4FdQ/gJ8Cxp4shIvnBBx8UfN74nFHrBAVq8R7SFZDJQ7E76CUjghff9XQx/A5gfnV03fZu/3ThwXF4J0AHr3dWuW4JzSycdHQWKlD59sQTT/S8Peilfvjhh1KjRg3PfdiQBEiABEiABEgguQjAQQODHrofg4YmzO3vpy/bkgAJpA8ByGwh+AOFmlA3AU5cOPdQYAhFdV544QVp3bq1oPgrHD0XX3yxoLo6Lf0IwLmFdHQ49SC79tlnn5l0Ab4LkGrQCG7p1q2bDXzw4MHSo0ePlICAYx03bpzVt5gwYYKNA0WmUDgK33/ozEKT/vrrr7ffASY7oFsbzlDTxk3bh0SdK70Qrj10q1u0aGETsfgtRZJJwG/wrrvuMm1syOFdeumlVvsE0m2YXMFvEk54bAMOStTvwP/h5FJ+//13OywUvvZrbh93G377J2t7OM/xXYZkA4qnQTIDBeqgYYzCYpAffPvtt9Pu/sh18Icr8hfu84IMCMztH64dl6cxgdgEAnMrXgn89ddfzqpVqxwVzvbaJUs71dJyVJ/XOfroo7MsT5U3lGhIlU+Kx0kCJEACJBBrAhpxZKlzZcqUcVQf0dPmtcCLg1RbvRX1lI7qaaNsRAIkkNIEVIc3R0o+zhHunxbZdVSz1NHo/5QeJw8+NAEtPGWfNT5nLcwdutH/L33llVcsfR/fDY1Kjdg2v1dqlK2NSzNXHdUgjXo4+I5jXCVKlHDUsRm2PeQRtLCptdUIWmfx4sUh22pAleM+q2qAlrNnz56Q7bIv1GLqDiQC3N9fqP/VOels2bIle9cs73GckGDB+CG74NUg7aCOT9u/H2kHr9tnu8QT0Mk6+zy1WLfnncPPVLJkSeunkx6e+yVLQ0o0xOaT+A82oychGgkkhICepCwaGWk3kQTTE3Iw3AkJkAAJkAAJJJCA3nyLOnctGgXppohIimaoCK16/IKCLCyaEY0W15NA5hDYu3evzJ49WxYtWmTRuji/nHTSSRZFiMI8SG2npR+BAwcOWDr+tm3bBJGtkC+IZigEppqc9p1ApK86hqN1yZf1V155pahDWgYNGuRJVgLRy5BpQFaM6uAKrpfhDBG1KECFAsgwdeSK1skRFCmDnAWigCGhANcI5A5U71XOPPNMa+vlHxT7QlFDSEeAMd5DTuCiiy6yiOArrrjCy2ZMbgCF2CDBcvXVV3vqo8FjFt2qjm5B9DAt9QmgOCailFEcEFkYiFaOZpAMwX2lalfLJ598YhIj0fok03pE5U+bNs3zeJPp2JPqWGLjJ+ZWSMAbAXdWlEXWvPFiKxIgARIggfQigGgrFE/973//6+hDd9jBISJHU1UtEgPFU/QBNmxbriABEiABEsgMAijSqc4ER52QnjNBcD1RfVLrpyntSQkKxcEKFChgf+qM9XyMiEoGD5UridoHEbJjxowJG/2Oa23fvn0dnTyJuq14NVCnvY0Hn5eXCHx8thg7GAwcODBeh8Xt5gMBld+xz1U1lp1oxXkR1Y/Ib3wPUIwuFY0RvLH51KjBq78CGgmQAAmQAAmQAAkkgoBW/jYtOZVokI4dO0qDBg1EZZsEun8wlWSQ5557TlSOSe6++25RR7BpbJYvXz4Rh8d9kAAJkAAJJDGB119/3Y4O0Z2azu/pSFF4CXqxMLe/p44JbLR+/XpBFPqFF14oKkvkec/16tWzttAejmbqQBaVdbCoSES+33PPPRbxCB1dXHd3794to0ePFpVCjLapuK1HpDUihz/++GO7R0DEdiSDFvPSpUtN17dfv36RmnJdihFAHSZoruP+sH79+tKzZ88cEdqbN2+Wzp07W7Q3vit33HGH3Vem2FB5uDEkcHAMt8VNkQAJkAAJkAAJkAAJRCGAQjFIpUbxkJdfftn+0OXQQw+1wi5udxRkRcon0khpJEACJEACJLBz506DgDRsP+a2d/v76ZuItt99953txk8hcnQoXLiwwHELyRKN0PUkPwGJissvv9z+EjE2P/vAscHZXL16dUtXR7FEpN5DiiLY4NjTaGOTktDITSs+d+yxxwY34es0IKB661ZYDkXmHnzwQfuD/A6+9/jNfPXVVzZK/AZQ0K9Xr15pMGoOIS8E6ODNCz32JQESIAESIAESIIFcEGjbtq00bNjQdAPnzZsnGzZsEGiuHXXUUVKhQgWLxkBUBiqA00iABEiABEgABBCNC9NkXvvf6z9ue7e/136JaudG7UIP149pMTKL/NU0dk/OXT/bzq+2yNhBVC5q1kAbGFHN0AUuW7asZfXAubtu3To7PDh1Z82aJSrTkF+Hy/3GkQB+r4gwb9Gihdx7772m8ayF9AR/sCJFitj3ZMCAAabZG8dD4aZThAAdvCnyQfEwSYAESIAESIAE0otAoUKFLJ0OKXUw1duzKN70GiVHQwIkQAIkECsCbvE8TAr6Mbe9299P30S0hQMT9u6773qOxEX7FStW4D8rLGUv0uQfFGfDZwYZiSeeeELg1MWfa5CRaN26tQwZMsQKxbnL+X96EoBzH8XWIMOAqF0UDYRzF9lgXqVa0pMMR5WdgDfhnuy9+J4ESIAESIAESIAESCCmBCDRQCMBEiABEiCBcARcyZ45c+aYsydcu+DlWojLUvixrG7dusGrkuY1HFWIVN2/f79JE3k9sIkTJ1rTxo0be+2SMu0QnYu0+2+//dYc31pIS2bPni0rV66UPXv2WAZQsWLFUmY8PNC8E4AcR4kSJUQL18spp5xC527ekabdFujgTbuPlAMiARIgARIgARIgARIgARIgARJINwI1atSwdP2tW7fK+PHjPQ0POp6bNm2S008/XWrVquWpT340Qpo5DDr127Zti3oI06ZNs5R1RLN26dIlavtUbYBiq3B+N2vWTK655hqpWrWq6Q6n6nh43CRAAvEjQAdv/NhyyyRAAiRAAiRAAiRAAiRAAiRAAiQQEwJw9o0ZM8a21b9/f4vojLThuXPnBgovjR49Oqkdg82bNxdE4n7//fdy6aWXypo1a0IODXrCkyZNkhtuuMHW33///ZauHrIxF5IACZBABhGggzeDPmwOlQRIgARIgARIgARIgARIgARIIHUJoEAntFehx9myZUtB0c6PPvooy4DWr19vDlA4Tf/66y9BdCwiQJPdEJVbs2ZN2blzp1SpUsWOGdqjkCV44403ZOzYsXLBBRdI165dbVzg0KlTp2QfFo+PBEiABBJCgEXWEoKZOyEBEiABEiABEiABEiABEiABEiCBvBMYNmyYFC1aVPr27WvFl+AEReFOLNu9e7cVYcJeDjnkECvU1bt377zvNAFbOPLII2XJkiUydOhQc+YiAhl/2Q36ow888IBcffXV2VfxPQmQAAlkLAE6eDP2o+fASYAESIAESIAESIAESIAESIAEUpFAt27dpFGjRgLphRdeeEG+/vrrgGO3ePHi0qRJE+nXr5+ULFkypYZXoEABGTlypHTv3l2ee+45WbZsmY0NhUgxlvr168tVV10lhx12WEqNiwdLAiRAAvEmQAdvvAlz+yRAAiRAAiRAAiRAAiRAAiRAAiQQYwKnnnqqTJgwwf6gXbtnzx4pXLhwWmjSwkkNJy/+aCRAAiRAAtEJ0MEbnRFbkAAJkAAJkAAJkAAJkAAJkAAJkEDSEoBjF380EiABEiCBzCTAImuZ+blz1CRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAmlAgA7eNPgQOQQSIAESIAESkDxRwwAAQABJREFUIAESIAESIAESIAESIAESIAESIIHMJEAHb2Z+7hw1CZAACZAACZAACZAACZAACZAACZAACZAACZBAGhCggzcNPkQOgQRIgARIgARIgARIgARIgARIgARIgARIgARIIDMJ0MGbmZ87R00CJEACJEACJEACJEACJEACJEACJEACJEACJJAGBOjgTYMPkUMgARIgARIgARIgARIgARIgARIgARIgARIgARLITAJ08Gbm585RkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpAEBOnjT4EPkEEiABEiABEiABEiABEiABEiABEiABEiABEiABDKTAB28mfm5c9QkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJpQIAO3jT4EDkEEiABEiABEiABEiABEiABEiABEiABEiABEiCBzCRAB29mfu4cNQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQBoQoIM3DT5EDoEESIAESIAESIAESIAESIAESIAESIAESIAESCAzCRycmcPmqEmABEiABEiABEggdQj89ddfMnfuXFmwYIF89tlnsn//filWrJhUrlxZWrRoIeXKlUudwfBISYAESIAESIAESIAESIAEYkqADt6Y4uTGSIAESIAESIAESCC2BBYtWiRdu3aVbdu2ZdnwJ598Iq+//rqMHDlSrr32Wpk0aZIULlw4Sxu+IQESIIFkILBx40Z5/vnnZe3atbJ792457rjj5JxzzpHGjRvbRFUyHCOPgQRIgARIgARSmQAdvKn86fHYSYAESIAESIAE0prAQw89JLfeeqv873//k3PPPVc6depkzpCjjjpKdu7cKS+//LI8/vjjMnv2bHn33XfN4VuyZMm0ZsLBkQAJpA6BXbt2SY8ePewc5ThOlgN/8cUX5Z577pHatWsLznVnn312lvV8QwIkQAIkQAIk4J0AHbzeWbElCZAACZAACZAACSSMwMKFC825ix0iSrd///5y0EH/lk8oW7as1K9f35ZfffXVsnr1arnqqqvknXfekYIFCybsOLkjEiABEghFYMOGDXaOwmTUEUccIa1bt5Y6deqYvMwPP/wgy5cvl6efflqWLl1qE1eQoYGzl0YCJEACJEACJOCfwL9PCf77sgcJkAAJkAAJkAAJkEAcCPzxxx/SpUsXi9xFhNvtt9+exbkbvMuTTjpJXnvtNYvwhWzDmDFjgldn5Ot9+/bJ2LFjpVatWnLiiSeacwmRzXCEI9oZEdE0EiCB+BH4/vvvpWHDhpZpULNmTdMOnzJlisnJ4H2TJk3k/vvvl61bt0qrVq0Ev9mmTZvKpk2b4ndQ3DIJkAAJkAAJpDEBOnjT+MPl0EiABEiABEiABFKTwLPPPitffPGFnH/++dKvX7+ogzjyyCPlkUcesXbjxo0TFGXLVJsxY4acccYZ0rt3b3nzzTflm2++kd9++022b99uGqAoSlehQgVZt25dpiLiuEkg7gQGDx4sn3/+uVSpUkUWL15sEy2hdnrMMccIfrOuk7dbt26hmnEZCZAACZAACZBAFAJ08EYBxNUkQAIkQAIkQAIkkGgC8+fPt13edNNN8p///MfT7qtVqybly5eXn376yVKfPXVKs0aIdkYa+J49eyx6d86cOfL111/Lr7/+ahGEEydOlNNPP13Wr18v1atXl7feeivNCHA4JJD/BPD7e/TRR+Xggw+Wp556Sg499NCoB/Xwww9bkUgUjlyzZk3U9myQugRwjYL+Ms7H0F7G9W7v3r2pOyAeOQmQAAkkCQFfDl4I4x84cCAuf0yVS5JvBA+DBEiABEiABEgg3wls3rzZjqFq1aq+jsVt/+mnn/rqlw6N582bJ4MGDTKnEqKZoesJSYbixYvL4YcfLmeeeaZ07dpVoAvarl07+fnnn6V58+aWQp4O4+cYSCBZCKD4I54Z69WrJ6VLl/Z0WIjkbdu2rbWF84+WfgS2bNliEh1FixY1iY7u3bsLIrYbNWokWIbJOUR900iABEiABHJHwJeDF7pJBQoUiMvf3XffnbsRsBcJkAAJkAAJkAAJpBkB6FHC4PTwY277TIuGgmZxjx49BMEI0CC+8cYbw2JDNOGTTz5pBemgEzpw4MCwbbkivQigsNfvv/+eXoNKwtFgEgVWo0YNX0fntt+4caOvfmyc/ASQTYEME/yPrBQU24NzF5Nu0ErHuRtSHeXKlZOXXnop+QfEIyQBEiCBJCTgy8GbhMfPQyIBEiABEiABEiCBtCNw/PHH25i++uorX2Nz259wwgm++qV6Y0TvQrMY2rq33npr1OHAwYDUYDh7Z86caZIOUTuxQcoRQBQppAJq164thx12mEkAIJr75JNPtkkASHXQYk/gxx9/tI0WKlTI18aPO+44a+/299WZjZOWwMKFC6Vly5YmlXP99dfbuRqFQSdMmGAyDci22K4a6dBHdzMrlixZkrTj4YGRAAmQQLISoIM3WT8ZHhcJkAAJkAAJkEDGErjooots7IsWLfLM4O+//xY8NMMqVarkuV86NERKOKx9+/aeNYtPOeUUiyIDNz+c04FXJowBRfTKli1rjtw33nhDEOUNhyMcvZgIgeMXEwI9e/Y0OYFMYJKoMboTVChw6Me+/fZba450fVp6EEDUfJs2bQRyjMOGDZOpU6dKsWLFcgzupJNOklmzZsmAAQPs93jdddeJm8mSozEXkAAJkAAJhCTgy8GLdLd4afAOGTIk5AFyIQmQAAmQAAmQAAlkGoFrrrnGhjxp0iTxGs322GOPya5du+Scc84xx1YmMdu2bZsNt2LFir6G7bZ3+/vqzMZJS2DVqlWCooPQoi5TpoxMnz7dijhBkuO3336Tjz/+2By7KAL24IMPmh4oHP202BA4//zzbUN+ozDd9m7/2BwNt5KfBEaPHm2FPy+//HLx8rw/cuRIueSSS2T37t0ybty4/Dx07psESIAEUo6ALwcv0tn++9//xuXPa4XolCPMAyYBEiABEiABEiABnwTwgHvZZZcJHFIoPPPnn39G3MIHH3wgffr0sTaZWNfg119/tbEfccQRETllX4l0fRicfrT0ILBnzx5p1qyZ/PLLL4J08LVr1wqiAY8++ujAABHZO3bsWHn77bcF0aZIIR88eHBgPV/kjQCceUceeaTxXbFihaeNffnll6bBimdCfH609CCAqFzY0KFDPQ0In7/rCHb7eurIRiRAAiRAAuLLwUteJEACJEACJEACJEACiSHwxBNPmPPplVdeMQ1Rt3BR8N6R9op2cAjDodWhQ4eMdI4UL17csPitwA7dR5jb397wn5QmMHz4cEGqPwo3oZjeIYccEnY8kDKBfvNBBx0k999/v/j9/oTdcIavKFiwoPTr188otG3b1qIxIyFB4TtotGKipVWrVlK6dOlIzbkuRQjgd4hzbOHChaVKlSqejxrXM0wQoNgeZRo8Y2NDEiABEqCDl98BEiABEiABEiABEkhGAqeeeqosXrxY8D+i4M477zxz5N52222CKF1IZ5UsWVI6duxohWnatWsnkydPTsahxP2YLr74YtuHn+rrSMl3tXurV68e92PMxB0gzRqO0zp16sgZZ5wh0NmEU7V3797y/vvvxxwJIt3h1IWNHz/esg6j7aRq1aoW6RvcN1ofro9OoH///lK5cmWB/An+DxfJCyceHHpYX6JECablR0ebMi1cTWUUNfSTrYuM4RNPPNHG6W4jZQbNAyUBEiCBfCRwsJ994wS7ZcsWP108t8XDC/5oJEACJEACJEACJEAC/xAoX768QH7hrrvukkceeUSWLVtmf8F8zjzzTHP4IgIuU+3aa6+1FPsZM2ZYkR7orkazKVOmyM6dOy1a0NXijdaH670TgH4mUq3379+fpdPXX38t7733nkkk4HPD9/rYY4/N0ia3b6C9i4g/aLiee+65njcDKYenn37aiu3ht0bLO4FDDz1UMOHSuHFjWb16tWASBhMpcPajyBaKb+F8Bt1dTLZgAmDBggXCAmt5Z58sWzjqqKPsUH7++Wffh+T2QSQvjQRIgARIwBsBXw7eF198UW6++WZvW/bZ6s477/SszeNz02xOAiRAAiRAAiRAAilLAOmtKAQ1YsQIee211+Szzz6ziN0TTjjB0l7z4pyEsw1OUWz3iy++EMdxBNFW0P+F9m+qTL6ffvrp0rlzZ3n44YdNouLNN98U8Alny5cvtyhSrL/nnnvCNePyXBLo1KmTPP7449a7YcOGJh1SoUIFOeywwyyiE7IIiDafPXu2aeTi84qFTMaOHTtsn4h292Nue7e/n76xbovfNzSBt27darIrcIaiYBwco+CXSgZ9Y3y29913n4wZM8aidLNH8hYoUEBuueUWm8Q67rjjUml4PNYoBBCxj+8spE+gjV2kSJEoPf5ZDT1mXJvg3I10Hve0MTYiARIggQwi4MvBm0FcOFQSIAESIAESIAESSCoCeNht2rRpTI4J2r2QeYDjJXuBsc2bN8vrr78umHzv2bOnOZYPPjj5bxlRrR0RnCiqBRkARJCCV3BqMIqxIXUfBX+Qkt+lS5eYMY3JB5MGG8F3Cs5dfF+nT58ujRo1yjIqpF4jmrNHjx7SpEkTi1DH/3D85fV79tdff9m+4DT0Y+5+Dxw44KdbTNtu2rTJCiW6siHZN47IVkREd+3aNct3Onu7ZHuPSF4cN4pAQnLmww8/NE3eQoUKCSLtr7zySsFrWvoRgHMXk4WYsMA5AbIdXuyxxx6zZldccYUnmRUv22QbEiABEsgEAnm6W8csHHSroJOTVzv77LPzugn2JwESIAESSEMCiChEZOGuXbsE6X7Q6DviiCPScKQcEgkkhgCcWKhSP3/+fHMUNW/eXK677jo555xz7D0cvM8++6yggvmoUaMsnR4P6MkePYjCTnAgXX311Zb6jXEhKhT3qpAAQEQYIndRjA7Wq1cvgVOYFjsCiLwbNmyYfY8QnQsHTTg75ZRT7POCM37NmjXy6KOPWiRnuPZeliNiEIYoWD/mtnf7++kbi7b4fUFiBWnpRx99tE06XHjhhYLvNKKK8VuFVEv37t1t8gVR94cffngsdp2wbWAsmHCJ1SRVwg6cO8oTAeht4/s9cuRIm9CJVkBv/fr1Fu2NneIcTSMBEiABEvBBQB+cPZtqUzm66Sx/Ogvv6MnXeeeddzxvhw0zl4Bqotn3RyM1MhcCR04CJOCJgDpjHH2YdTQ9Nct1RyuiO+o0cDTC0NN22IgESCArAY0AtN+URgQ6mj6ddWXQO9zbqcPL2qpGadCa5H6pUZyO6ro6KtuQ5dyBe1iN5nU0etRZunRpcg8iRY9u0KBBxrxVq1aeR6ByDdZHtaQ99wnXcO/evQ6uERqR6+Aa4tX69etnx4BrTqINvzOdPLH9t2nTxtFU9pCHoFJ5jsq1WDt1Bodsw4X5Q+C7775znnvuOUeldJyHHnrIUS1hR7Wn8+dgknCvOB/g/KuTOo5OVIQ9QvwWdFLO2t5www1h23EFCZBA+hHA9Q/niWeeeSb9BpfAEUFrzZdp+pCjKXuORtzaBxDs8FVxfAc3dh9//LGvbbJx5hCggzdzPmuOlATyQkCjkxyN0g1cZzCZeNFFFzmazulo1khguWqEOppenpddsS8JZBQBLW5lTk44lN59992oY//kk08cjZy339wbb7wRtX2yNcA9KRwvmh7saBSkL6dfso0lFY5HiwLad8WPA10LbDmqs2n9NHo8z8PUwm22rfbt23va1vbt2x2NLrU+Xn4TnjbqsZFG09t1Dc9TKhcStdeGDRucY445xo51zpw5UduzQXwJrFu3zlGNaeeggw6yzyT4uVilKRw4KTWqPb4HkQJbV2kcp2bNmsYIky8dOnRwXn31VUej0x38/lSWxMEkont/p3rTzu+//54CI+MhkgAJxIoAHbyxIenbwRu8W9VQclRLxznttNNyXNS0WIGjqRjOtm3bgrvwdYYToIM3w78AHD4JeCCgabqBa4qmWjsfffRRll7ff/+9oxqPjqaxWrvatWs7iNijkQAJRCegUgz2uxkwYED0xv/fQouQWZ8GDRp47sOGmUnAdZSq1IAvAKrDat8xTeX21S9U408//TQQEasSHKGaBJZ9++23Dp5Z4JjLj6jYmTNn2r5LlSrlqCZ04LgivXCvkeeee26kZlwXZwL4HFTr2T4/TJghs+jWW281R32NGjUCzkrVF3aWLFkS56NJ/s3/8ccfjmq6W3R9sCM8+DWi72+77Tbe0yX/x8kjJIGYE6CDNzZI8+TgDT4ELWrhaLGEQFpF8Mm6SpUqjha6cL755pvgLnydgQTo4M3AD51DJgEfBLQ4UuCBacKECRF7IpLJTR/HZCONBEggMgFESqoWrTkktKp55MZBa3fv3m3OCjgxGDEfBIYvsxDA9wuRjPjDaz/mRt2q9rOfbmHbTps2zSLV8TyCiUJcL4IN0YFTp04NXENUf9qBvEOiDceGY0Rav1dD1K/WQbF+sYh49rpftvuXAFKI3WddRF5j4jm7IcgJ0b1oh3NnqskZ4vcwadIkBxN7yNw9+eSTHdXLtud9PPeHMvyuMG5k/O7bty9UE0f1rh0tuudUr17dOfXUU50SJUo4cIirdrfj57oUcuNcSAIkkLIE6OCNzUcXMwevezi4oXtDU/huuummgE6UewHEDR8irTDj+cMPP7hd+H8GEaCDN4M+bA6VBHJB4PLLL7eHIa86iHhgwrUFqZBI9aORAAmEJ4BoRdyTIR3eryHCEH0RHUkjgXAEXM10v+djBIPg+/XWW2+F27Tv5VrkLSC9gG1DSu6yyy5ztOiec+SRR9r+sPySSy5xMImRH+b+ruAQ82NaQNCOH2OkJZaAFn11tMCd8Z84cWLEnf/vf/9zbr75ZmuLjNdUkR1A3R1otOP3Ee6vUaNGjha/tfG/8MILTt26dQMT9G6fihUrWpBXqow74ofJlSRAAnElQAdvbPAepCfgmJo+aMull14qkydPFn2QkFdeeUXatm1rFWH1IieqySWdO3cWvQEUvTCIpiYFqhnH9EC4MRIgARIggZQigAr3ixcvtqrhd911l6djV11eadGihWjqn6CqOI0ESCA8AS36YytVUzd8ozBrVBLF1mhUVpgWXEwCIuqoNQwqteAZhxaoEtW+FZ2oEw0E8NwvWsNrrrlGdEJCbrnlFtHiZLJ161bR4pyi0YeiEhJy4YUXikZiigamiEbERttcXNarY9m2q5MuvrZ//PHHW3u3v6/ObJwnAsOHDxfNZBAtHCZasDLitrSoo2g2klSoUEFUa1Y0yCli+2RYqfV0pF27doLvlk5+2G9k48aNsnPnTnn77bdFM6bsuf6ll16SCy64QDQSV5o1ayavvfYaAsekZMmSUrp0aVEnuLz//vuisgxStmxZUb3iZBgej4EESIAE0ppAzB28wbRURF3q168vOgsouHnT2T1RfSt7eFedKdFiF6JacIKbFLShkQAJ5I4AHto1TU+2bNkiWsggdxthLxLIZwJ4yMbDgUaBiKaRez4aTe21tphApJEACYQn4DqRVDJLNOMqfMMQa/BwD8MEPY0EwhGA0ws2atQoc4KFaxe8HA4zfB81FVxyM/kQvK3sr7VAp2iauWikoWjBQFEtVFm5cqUFocCprBFDAidcfpnqs9quNcXf1yG47eG4piWOgMpjiBZttB1q0XFPO8bzMJymsFmzZnnqk1+NnnrqKdEaOqJauPLkk0/Km2++ab8RlWgQlWgwZ+69995rEyeVK1eWr776yn5PeJbH7wxOYZVoEI1Il59++knmzZsn5cqVs8kVOIK1fk9+DY37JQESIIGMIBBXB28wQczKN23a1CJ2cZPVp08f0UqZ1gQOKdXcCW7O1yRAAlEIaFEpefjhhwU3WFpRWXDzpal+NquOKHpEM8JZRiOBVCHgOpDwXfZjiBSBaaVqP93YlgQyjgCcZ2XKlLHMqWXLlnkeP6KwMFGPB3zVvfbcjw0zj4BqygaiFREFCIdYJEMmn6a52zOB18yNSNsLtw7PHKqzKyrRICrRIO5kR7j2iVqOyEaYSlN43iXu7ZYvX27tMSZa4gggIlxlBuXMM8+Us846y/OOtQCbTSSsWbMmae/NVXPXns8xKEQat2/fPuz48Ps57rjjAus7dOhgkfLBk/NwEjdu3Nii8zHxg6h5vMf/NBIgARIggfgQSJiDF/IMiM5CKgsuivfff7/v6JH4IOBWSSD1CCBat3z58qKFHQQ3i1q8wdKh8NvCQwweFFq3bm0z7Uh7p5FAKhBwJ/38Rhbi+gKDRBCNBEggMgFImsDuvvvuyA2D1rpt3Wj5oFV8SQJZCOA8DKctJp7nzJkjderUscjZLI30DaQ+kOqNexU4LPFc4Do7s7dN5/cIfoEhjd/rpLzq7gqi8FVTWM4777x0xpN0Y4P8IEyLg/k6toIFC5pMCIIz3OhrXxtIQGNE78J5jUkQyCtGMkTCL1q0SFTL2po9/vjjYZ/r4ehFpi4CUjCR/8ADD0TaNNeRAAmQAAnkgUBcn4bx0I3UDjihkCKlBdYsfcO9OCIlqmbNmqKVY6Vbt255GAa7kkDmEIAOFjTu8D8isfAAhRsypENpZVq7cXziiSfklFNOMZ05RKq4v7nMocSRpiIB94FJq537Onyk3cLc/r46szEJZBgB6CFCbxST7kOHDo06ejjeXnzxRUudh0OORgLRCCALAw4g3PtjwhlOSOilawFm6dGjh1x11VVSvHhxk3HAs8CYMWNsebTtpuN6SNWVKFFC1q5dK8OGDYs6RDjIwBDmpv1H7ZTmDaDBD/1+RILfc889gntg974g1kN3JURcPXM/23cjV12nqJ++iWi7YMEC282NN94YdXdw6MLwHcTvfc+ePaJFb8P2K1CggP3e0cDtG7YxV5AACZAACeSegM4Wx9Q08spRp66jTl2r0qxHlqX6pt7IWfXacePGOarbE9N9c2PJT0CLZ9j3oUmTJsl/sEl4hKhCqylhxlDTnByVNwl7lBoh4FSrVs3a1qhRI2w7riCBZCGgKeCORn85GpHu6KSE58PCbwHXGo0K8dyHDUkgkwlo5JWjEfP2u9FIrZC/N31gD1R/x72bTiZmMjKOPRcE8B1SZ6Sj0Yv2XQt+JsB3SqN7Hc1CysWW06uLFqcK/B779u3raAGvkANcvXq1oxOZxrJevXoOnrky2VRSwLn99tsddbrm+H7hu6YR4Y7Wf4kpInyncZ+iBcScX375xfO2tcCYHaNK3Hjuk+iGp512mh2jyiZG3bVOEgba4hoC3hqlG7Ufxo+2mokYtS0bkAAJZBYB1cS384MWP82sgcd4tAfrSTbPhkhdaEEhkvD5558PGS2ISrVIC0RFW8xU00iABPwTQAED6H+de+65lgKJCrXhDIU7UOEWxQ3w+8Rvs3nz5uGaczkJ5DuBokWLik7+WEHO2267zVPxTUSJIboQ6Y+IhKKRAAlEJ3D55ZdbsR9opE6dOlWeffZZy7KCnidS7JERgt8WKsWjhsIjjzwi0FalkYAfAigApgEdgqJMuA9BvQ2dqLbI3osvvpgF+/4fJmQsENXYuXNni2ZGIS4Uf8OzE65t27dvF0RXvvzyyybjAHb4zeK3mqkGqTJEgiNzDQZWyG5DhC30+NVpblG8zZo1M67IFkUUaV4N32nsB4X6pk+fbtv2ss0pU6ZYs4YNG3ppni9t3KhkN0o53EHguoCIXTyDqFM4INPgRiiH64fliPZFYTZEovvRMI60Ta4jgVQngOsinu9R6wDa1pBchMwRjQRyRSC3DmPMGmvalaPSCo5WVDZvux5Alv9VI9TRSpzOli1bcrsb9kszAozgzdsHqo5d+43pjb7nDalT2PpogQfPfdiQBPKLAKI6jjjiCPvO3nHHHY5OIIY9lFWrVjk6kWFtR4wYEbYdV5AACYQmgN+bTvwFogeD7+MQpdagQQNn/fr1oTtzKQmQQEwJqNPQqVixol3Tgn+L7mtEjWpKvKOSBDHdb6ptTPWHHS34aJwuuOAC57333ssxBNW6dXD/695PdOrUKUeb3C547rnnbN/HH3+888UXX0TdzIoVKxx1Ltt5VqUjorbPrwaIeMZ37YMPPoh4CMgeRDt8H2HqaLf34BLNVN/X2uoEYrSmXE8CaU/g448/drS2QY4sl4MPPtgyXF5//fW0ZxA8QEbwBtPI/ev/oKuepD0ZInX1IiUQ90c0IAT+sxuiPxCpiz+3snn2NnyfuQT0Rkw+/PBDi9KbO3du5oLIxcihowvNOlSo3b17t+jJ39NWUMwBkZEocqA3ZRkd8eEJGBvlOwFcX3ANQbG1Sy65RNTRK5deemngO4+IHUQUoijNn3/+aZHpyCCBliONBPwSQNEbTZ+1exoUrCxZsqQVL/K7nVRuj2ispUuXWlQV7vWg4V6rVi1BpXQaCZBA4gjgsQzRzgsXLpStW7eKygDYvZ9KbonKEdn9XOKOJjn3hMhdRDTj/uCVV16xSNJwR/ruu+/a/QPuf1WuQdyiduHae12uQRNWZAxRqMgiQmRqKMN5FdmrqJXRr1+/gA5tqLb5vUyd4BZJjqKauO+KZNBxx/MF6oFUqlTJvqeIylUJhkjdRB3zFsGLCGxG8EZExZVpTgB64fid4Z4Lzy/woeE5H+cKnVgX3JvCOnToIA8//LBlU6U5Ern++utl2rRpohINlsWS7uON2/j8+Ib1gdpm3fRgsvxfqlQpB5FWjPLwQzMz2zKCN/efO3Tq8NvTNDTfG9GHdOvrR9fU907YgQRiSODVV191dGIicK2BLq+mAgYidvFbQIRhr169nAMHDsRwz9xUphDQh1FHU6IdTYMLfM/c+xvc14wfP97RCYRMwcFxkgAJkEDSE4AOMc7TRx99tLNr1y5Px6vOEeujDhRP7b00+umnnxxED+NYNIDC9MoRlYqIXnXMO+r0dVTWxoHWNNogUyLZ71WgBY1jxb3Xjz/+GBGDTsJb27p169r/KhsSsT1W6sSFtdUJxKhtc9sAtRyQ5aiSJ47KmFg0cqRMsNzuh/1IIC8EBgwYYL8FPMdApz57XSqcX+677z5HCzJaO5V2yQjNdUbw5uVb9W9fbyGAeraHabd/Xvz/v5rKYVFW6rSzmYcdO3YI/nJjmMXTB6rcdGUfEkh6AtCbevTRR00/bdu2bRZJi+rS1atXN91Q6CFGM0YnRiPE9elEQAvIiMr7iDrZTGtQ05hMhxBjhAYedOx69+5tGtPpNG6OJTEENJVU2rdvb1FH2CMiJ04//XTTB0U0L6LEb731VotmQnQWawck5nPhXkiABEggEgFoD8NuvvlmUYmESE0D6xCZOmzYMNmwYYPgXgJ1LPJq0MdEpHXPnj3tOjF58mTBX3aDTu3AgQNFpTWSPssIWtDIltJi6dKyZUuLTIYGeyjr2LGj3ZtB6ximMlmhmgWWqXPbIpix4IYbbggsj9WLd955x6IhETGNiMhgQ1SxBgOIykpmRBRk8Nj5OvkIQEsduvTQBEdWQShdbpxfUIsEmQIqa2IZC6NGjRJ1DCffgHhEyUfgX19v9Fd64bJZBB1FzP+/8847ox8AW6Q8gUyM4NUUDNOpivS70RsqR4tCRPx8oTmGbahEgwNtMa+Gir+IINCbtIyY/fPKhe1SiwA031DZGd9nGgnkhYDKTAWiqjR11tHCFlk2hxoD8+fPd3Ti2c650HrE+ZdGAiRAAiSQvwRUqsLOy2+88YavA2nbtq31e+yxx3z189IYurq33367U7VqVQfRqTpZ6KjEjYP7/+yReV62l59tNFDLgbYwnjcuuuiikHq8WmTNauy40cnIEoyUIQjNaDcyD9fTffv2xWyIiM5V533gmo5sL0QVqxPZtE2R+eU+f1WoUMGTZnLMDo4bIoFsBPB9RSYBvpP3339/trWh3yIzAO21+GHaPwO55wmVaAgNg0s9EfAVwatfLhoJkIAPAm6FckTfQlMUs9ZaQMP0wlAV+aWXXpIHHnjAZstRAVgLF4bVpNJihhZ1gOiDRYsWhZzxC3Vo0MzWs4FVSPdacRnap4hMUFkI0RQ40VQ4i7DHTCKqe9JIINEE3GrNid4v95deBJBlhMhdnBOhfxYqGgLnSURU1KxZ0/7HuRC6YG6kUnoR4WhIgARIIHUI4J4UBp1wP+a2Rz2LWBsyQLSoeKw3my/bO/XUU0ULO0mjRo3sGQC1U/DcgmcUTRe3TF11OImmkNvx4ZkAnwmyeTVYyyJ/8cwAg4aoym3J4MGDZe3atVKwYEGZN2+eqKPK1sfiH+zzrrvukv/+9792Pe/fv3+O7S9evFi6d+9ux4AIZTzbIBOMRgKJJqAFIS2TAOcjfCe9GCJ4kemL3xLqFyEjgUYCkQj4cvCiaBoejOJhOqsWj81ymySQbwSQfjF16lRzjsLJml2GATeE+LvpppusAIN7Q6XVa0Wr/oY8bjiIkZaukQKWsgGnVyRDAYThw4dbE6RTRTM4PSAlgRumUEUUUdgN28HNlNfUuGj75HoSIAESSBQBnNtQbKdVq1YhnbvBx4GHVKTPlSlTRvBAi4dESIfQSIAESIAE8oeA6xzUKFBfB+C2d52PvjpnWGNIWOBZBJOgKO70/vvv218whsqVK5tT+7zzzpNrr73WAlXwPAMZBEgaobAzJlRRJBCG4qW4nsbyeX/ZsmWCgnB4NoHjuEGDBsGHGHiN6zacuu7/OE7INNFIINEEIH8CwwQKJBq8mmp4m4MX/eng9Uotc9v5cvBi1gt/NBIggcgEoLkLJygid0M5d4N7Y/YbkbyokPzRRx9ZRG+46rVdunQxjS9E8cJBMXPmzLDVg1GFExeQr7/+WmrUqCG4OEQyOD1at25tN0loB6dG/fr1reItboy1sIXN6muxRavujGPGjD2NBEiABFKBwO+//y5z5syx87LXaCtUCu/Tp49NqqGqLx28qfBJ8xhJgATSlQBqtiAaFJqrfu5B0R7Gei/evhkqByda5MkcqHCkauE4+fnnn6V48eKiUhTmsHW3pHIZ5jCFMxhtUT/BNTh0kQGD5xeVT3AXx+R/BLsgMGXIkCFhnbvujqBpCgfz2WefLc8//7wgkhJRyTQSSCQBlWO03eE85sfc8xb8CzQSiEbgoGgNuJ4ESMA/AdX4EtWoslnt7JG7obaGiF0Uk4JNnDgxR4EAtw+KHWCWGjdeKPyDtCnMQsNx4RpuwJ588kmbJV+5cqUg3QpO5kiGGyTcgGHbhQoVsm2iGAXkIxAxjKg3yEJg2SWXXCK4QGFcuS2qGOlYuI4ESIAE4kEAUUiIJoJTQHX5PO+iWbNm1hYSOjQSIAESIIH8I+AWJJoyZYo597wcCaJR3333XZMYYKCSF2L/tkEkLoqvIeoVk53XXXddFueu2/Lqq6+2IBAEhKBIqeoSm4zDhx9+aM8RsXbu4vkDzzhw3Pbt29c9jIj/o9gaivPBECBDI4FEE3CjdiFf4sdQpBCGaHUaCUQjQAdvNEJcTwK5IIAKmTA/lWKh93jmmWealhVuRsMZImsRTYv/N27caPIOcMpiVhozfNCVwn537txps+yrVq0S6PdGsqefftpmthFNjBumcNG+2D50KK+88krZvXu3dO7cOdJmuY4ESIAEkoaAG/lwxhln+DomLZhjUb+QrcFkGI0ESIAESCB/CGhhTAtcgOMQQQjRDAEQcE7i3N21a9eYR5FG23+mrYd0HJ5lIEEH52u8bMWKFbZpOJ+jydUFH8NVV11lb99+++3gxXxNAgkhAPkS2Pr1633tz23v9vfVmY0zjgAdvBn3kXPAiSCwbds22w2KE/gxt73bP1xf6GFDzmHSpEmiVW4tgnfz5s2WFoUCaYiynT59uuAG6MQTTwy3GVuOm15E6MIeeughwbYjGWYfp02bZpG+cPbyJikSLa4jARJIFgLIgID9+eefvg4J7XGeRCQTZHdoJEACJEAC+UMAkaDIdMO5+LbbbhOtRB924g2BCAhIQDo+nI5I6aelBwG3ToifbByM3G3v9k8PGhxFMhJAJi8CspBxizo7iDp3s3ohc+jqgns5djx3w9z+XvqwTeYSSEic9/bt2+3L/emnn8oJJ5xgWqMQZ3eF8jMXP0eergSgZwsLVywt3LjdWWhcFKIZHK233HKL/e3fv9+0dlFFFg5dP/tF2houOrjpadmyZbTd2npE+iIiAgUYIBFx8cUXe+rHRiRAAiSQXwTcBztMjvmxdevWWXNGTvihxrYkQAIkEB8CiMJ0JcSQnj9jxgy58cYbpUqVKvZsCRkxZNJNnjxZ9u7dK0jNnz9/flwjSuMzUm41HAH3Ocd93grXLvty9/nK7Z99Pd+TQF4JIOAKgVNw4mb/fqKAITJuN23aJKi348ozRtonZB+R2YvzmCtRE6k915FAnhy8n3/+uc1IYIYUEYNw2sLB5BoiXgYMGCDjxo3LETEDDZF7773X9Hzc9vyfBNKFAJysKDKA30jZsmU9DwvtYShi4McwWRIt8jbc9qBLCatbt66v6DTMIsLB6/YPt30uJwESIIFkIFCuXDmbAMOkMwrBQBbHi0HCBnbFFVd4ac42JEACJEACcSbQs2dPgXxOt27dzPnhaqtm3y0ieOEg8XtfnX07fJ9cBPDZw/w+g7jt/Uo1JdfoeTTJSgCZtT169BBo5iLLAJm5p5xyihUohJMWRdJhWDdhwgQ7L0XKLEBhQJzjYKNHj/YlR2Kd+E9GEsiVRAMct4jegx4nNDgHDhwo1atXl+OPP15GjBgRANmvXz8ZNWpUDucuGuCLj1nXFi1aCFLKaSSQTgTwe4Bh9s6r7dmzR6CXi8hcyC4kyr777jvbVTQph+zH494su/2zr+d7EiABEkgmArihdp0AcA4EF6cMd5zIcIBzAJPSiBCjkQAJkAAJJAeBRo0aCbJDMQkHbd7y5cub07dq1aqCczxqSixcuJDO3eT4uGJ6FAgsK1iwoBXPc/VJvezg8ccft2Zw/NNIIJYEENAInW/4uOAnQyYBJhRQwHzJkiWC5+VZs2bJySefHJCVgQ+tVq1aVsjclQ/73//+Z/4AFDRE8cI//vhD4FNr1apVLA+X20pjArly8ELzCNVLsztmf/jhBws3x5d1w4YNWcTv4Tzq2LGjfTnxxXZt9uzZ8vzzz7tv+T8JpAWB1q1b2ziQQobfhRe76667BFU1cdNx7LHHeukSkzbuvn788Udf2/vpp5+svdvfV2c2JgESIIF8INC7d29B5A4K9KCYJORtwhl0G+FAwM36rbfemussiXDb53ISIAESIIG8EYAmb9u2bQXPk2vXrpWtW7eaY3fs2LFWaDhvW2fvZCWAzx2Trgg6g1wdnp+iGWqTLF261IpRt2nTJlpzricBzwTgyEXgIgIJpk6davIw2QOnECiAwEa0Pe+882zbqA3x5ptvWoYYZBqLFi1qhSCrVasmM2fOtNdwHCNgkkYCXgn4dvDCsTtmzBjbPr7EFSpUMJkFaHdClxOGL6FbsRTvEa6OWQxEwUAnCXqfWO8aBPJp/xKA4xwz0tCPQkTnt99+++9KvkoJApA7qF27tiAqF1EF2TV4sg8CFwOkauDkP3z48Oyr4/oeWkAwtyKt1525xdXc/l77sR0JkAAJ5BcBRPxAi7FIkSJ2jYWEDu5Rvv76azskXH/h/IVDF5kYuP7Wr19f7rvvvvw6ZO6XBEiABEiABEggGwFomCL9Hc8viHT8+eefs7X49+2zzz4rnTp1sgW4nh9zzDH/ruQrEsgjAcgs4P6xT58+cv3110fcGjLeUXgNDl1E53bv3t2kHOBXg98AQQXIkofDGP4gSD7QSMAXAZ358mUq8OzoDuxPHVJZ+qoTyznrrLMC69FOHb9Z2gS/qVixYqCtzrgGr0rL11pB0dEfsaORzCHHp4VcHHUKOjqbE+DistaUfUed646ePEL2TZWF559/vo2tSZMmqXLIuT5OndRwVMbAxqvaj47O0OXYlupX23fC/Zyz/6ZydIjDAk1Tdo4++mg7Tp1Q8LQHnSkP/Na1yJqnPmxEAiRAAslCYNu2bU6lSpWyXGtx7T3ooIMCy7SmgKM31g7OdzQSIAESIAESIIHkIqATsk7hwoXtuo1nLtUpdTSL2FFnr/PNN9846khzGjRoELiuqwMuuQbAo0l5AvieqXPW0cJ9jma3eh6PBhLY91Iz460PfDzwC6hUg+dtpFtDjaw3Js8880y6DS2h4/FVZA2yC1999ZX6okRQYMkVfbYF+g9mIhClG1y4BJoh4Qxp7K7Y+c6dO003KVzbVF4OnT9EAz366KM2DGhEZbfBgwdb0TnM2oSyNWvWCP4Q6YmQ/WCZi1DtuSz/CaDa5VtvvWUpvqjCfumll1pqMATXUb0VBdWgD4bPHJG7rnZPoo8c6SG9evWSYcOG2ew2osZRtC2SDRkyxGYVEb2rzvpITbmOBEiABJKOQMmSJeWdd94xiahp06bJ8uXLTU4HhWJRvAUF1ZD26adIZtINMgkO6KOPPrLoKkRC47oHeYx69eoxeioJPhseAgmQAAmkOgFkEuP5GD6F1atXm1ZpKN8DInZR3N3V4U/1cfP4k4cA7h/Ve2eZu34iw5s2bSrjx483XwFGowEGll2WPCPjkaQqAV8OXlSedq1OnTruyyz/16hRw7Rtvv/+e1t+7rnnZlkf/EajOQNvXcdxYEEavcBFB1UQXdu3b5/70v6HUzw4LR+po7hgnXnmmQJd1M8++8x0pSC6jbR4nYk0xyBSTWnJTQApFpjEgBbYxIkTTRsM+mCuoaBa48aN7fOP9Ftx28frf9wMQQsb1T0vu+wymTNnjpQoUSLH7iAAj8kIyLDg2B955BGBQ4RGAiRAAqlGAOlwSOvEHwypcjiv4SabljcCKCqCegwbN27MsSEwbt++vUB3vlixYjnWcwEJkAAJkAAJeCWASVkEpyxYsMCKWMHRi0lFBKvgWbphw4YWwKKRvl43yXYk4JmA68PCd82PYcIb5kqE+enLtiQQiYAvB6/rtMUGNQ0i7HaxDm0R0XvIIYeEbYdoDtfS9cutsgwB5y7EtqH7gxkb17Zs2WKVXvEejjJouOAvmA3WoXAANFrg4EU06NChQwNayFhPS14C+CwHDRpkD7sffPCBaGqwafLi+6DSG0kRyeTqUkI7GFXjEZkLDSFoT0Lfau/evTYz/sQTT1jkMR7Q8To4Wj95PwEeGQmQAAlEJ4BsBlreCECDDhlL0DWG4TqHaGhMGP7yyy824YmCIshoghYyHMGVK1fO207ZmwRIgARIIOMJwJGLPxoJJJKAe++IIAE/5raP5Cvzsz22JQGXgC8Hb3ChqOwOSHeD+B+OXVikNlgfHPmHG/90NPchByzg7M1ekAqRku7YERkJx20oQ0Tva6+9ZhVh4ex9/PHHLfolGuNQ2+Ky/CGAaDHVnba//DmCyHs97bTTLGUZYu6oNIsHcFdWJLgnKn8+/PDDVoAoeDlfkwAJkAAJZDYBFBjBfQ8qnKOALqqcQ4Io2DCxDfmLJUuW2CQioq1Kly4d3ISvSYAESIAESIAESCDpCKAQGgIT4diFZCaen2Hwz/gxSFjBIBlGI4FYEsh61+1jy5FmG1zHLRxamW6bNm0yBKjcmd25ixWuBjFkGVANNJLhgQlaLYiaVBFvi+StUqVKpC5cRwK+CBQqVEhU2NwijrV4mulaIc1Ji7CJFlCUq666yvS3mcLsCysbkwAJkEDaE4DD9sEHH7SHHkxIX3zxxSHHjDTGRYsWSatWrUwOCNki0EPmPWNIXFxIAiRAAiRAAiSQjwQQ5AgfDJ6RUZPKNUxgV6tWzTLWcR+zefNmzxPWTz/9tG0G2bI0EoglgVw7eCEmHc28tIm2jVRfv2PHDhtCsN5w8JhQaAuGyE7XMR68PvtrpPQjPV6regu2TQdvdkJ8HwsCmIyINuEQi/1wGyRAAiRAAulBAFlIsLvvvjusc9cdKe53nnrqKasnAFkgaCdiApGW2QTwEA3pDkwQfPHFF4LaE5CJQm2ARo0a2WRzZhPi6EmABEiABBJJAEX8mjdvLl9++aXtFkFPiNqFxAL8OMuWLQscDrKWli5dGtWns3DhQrvWQScak9w0EoglAVYSiSXNENtyBbfXr18fYq3IhRdeGHJ5uIVIC4BzF3b88ceHa8blJEACJEACJEACJJAQAjt37jSddjz4oF6AF4PEVK9evazp7NmzvXRhmzQmAIc/is60bNnSZMjg5IW0GZbjARhprBMmTEhjAhwaCZAACZBAMhFYuXKlXHLJJebcRZDd4sWL5YcffhDIKyBLG34ZFBwvWrSoHTacvdddd505f8ONA9c2ZDDB7rzzTvpzwoHi8lwToIM31+i8dXQduHPnzg3ZwS1SheJbKE4SzRDlAkMq4wUXXBCtOdeTAAmQAAmQAAmQQFwJIAoXhgchyEl5NTc10e3vtR/bpReBrl27SocOHazyPR6ix44daw/SkP2YOHGiSZPhoRoF/Fq3bu3pfjm9CHE0JEACJEACiSTw448/SrNmzeT3338XSG2uWLFCUIw8OOMaEbiI2l23bp2UKlXKDg8T1qhX8+STT8p3331ny/7880/r3759e6s9sH//fmnXrp307t07kUPivjKEQK4lGjCrjlmLULZ7925bjNB1zLyHs+3bt4dblTbLcaOKgmgI4R89erT069cvy9iQdnbMMccImN17772mfZqlQdAbpKvhpheGyGD0o5EACZAACZAACZBAfhJwH2JOOukkX4fhtt+1a5evfmycPgRGjBhhhflQoHnKlCnSpk2bLIPDfTIcwC+++KI9EM+YMUNOOOEEeeCBB7K04xsSIAESIAESiBUB+GVwb3LppZfK5MmTszh2s++jWLFiJs1wzjnnCJy3n332mdxwww3WDMXY4OB1pUvxHjKIgwYNyr4ZvieBmBDItYN33LhxUQ/g559/thn5qA3TuAFmZx577DFBdMptt91murnDhg2TwoUL26hxQkBUbr169QTLcYPbo0ePHCcRCHc3btzYTjTo2L9//zSmxqGRAAmQAAmQAAmkCgFIM8D27t3r65Dd9pyw9oUtbRpv2bLFUlQREfXCCy9YZFO4weEe+JVXXpFatWoJnkEQyYv6FTQSIAESIAESiCUB6L+7QYpjxozJ4ZcJta+TTz7ZAvmGDBlihddwX7Nq1Sr56aefrAgbJIgaNmxoE5YlSpQItQkuI4GYEKBEQ0wwht8IZmmef/55KVKkiDV66KGHTGMMemII3f/www/l1FNPtdeY2enTp4+ULVtWOnbsKMOHD7dIBpwQUEzNjXCB0xjraSRAAiRAAiRAAiSQ3wRKly5th4CHGT/mtj/rrLP8dEu5tpAXQArnxx9/7NsJnnKD9XHAyGw7cOCA3dO6ch2RuletWtVSWnG/jOgqGgmQAAmQAAnEmgCu1chMgg/Gz0RiixYt7FCQpf7yyy8LZB4g8YC/DRs2yKhRo4TO3Vh/WtxedgK+Injd4gfZNxKL9+eee24sNpOU20AFYETpwqmLkH1ErEybNs3+Qh3w5s2bBX+hDJq9kyZNCrWKy0iABEiABEiABEgg4QRQEwByC3ioWbRoUcRIzOCDc+9nGjVqFLw4LV4jJRPFV9zJfHdQqKFQqVIl6dy5s2W5Bev5uW0y5X/ILsCgrevVunXrZs5dRPOC8SGHHOK1K9uRAAmQAAmQQFQCKBwLO/vss6O2DW4ACc2DDz7Y9OQxeYnXCPajkUAiCfhy8NapU0fwR/NPoHLlyrJ+/XpBBO/UqVOt+qKfrZQpU8ZugG+66SYrsOanL9uSAAmQAAmQAAmQQLwIwGnZt29f6dWrl8ABt2bNGilUqFDE3UHTbvny5VZBGplJ6WSort2kSZPAZP2RRx4pJUuWNA2+bdu2GR8wwj0hivCedtpp6TR8T2NBVDMy04477jjLXPPUSRudeOKJFlUFeQfUpsADNY0ESIAESIAEYkUA9zQwVzfXz3bdPu42/PRlWxKIBQFfDt5Y7DCTt4EZHFRLxN+nn34q77//vqXr4WZ/3759As1iRCMULFhQUJURWi6owgjncPny5fMN3dKlS2X69Om5OsllP+gdO3bYIqQq0EiABEiABEiABNKDQJcuXQQFsFBzoHbt2qapevrpp+cYHB5+JkyYEKgePX78eLvnydEwRRfAuQspAejuQXILRcSuuOKKQKQp7n8QuYoiK2vXrjUJLjh7IdeVSYbUVVi0iYBQTOAUhrnbCNWGy0iABEiABEggNwSQfQ2DrIIfg3/n77//Nh9OJmfn+GHGtrEnQAdv7Jl62iL05lJFc+7BBx+Ul156ydO4vDZy9YS9tmc7EiABEiABEiCB5CWAVPl58+ZZptdHH30kqCaNyNyrrrrKIlR/+eUXee+99+SJJ56QDz74wLKR7rnnHnE165J3ZN6PDJP0KAYG5y4ieOHwRvHcYDvssMNszFdeeaU0bdpUXn/9dWnevLmgmO5BB2VOaYzjjz/esOB+EE5/P9FO3377rfUtWrRoMFq+JgESIAESIIE8E4B0aPHixU12avXq1TYR62WjM2fOtGbMePdCi23iRYAO3niRTaPtIoWwWbNmgoqSebU777zTUurcmbG8bo/9SYAESIAESIAEkoMA0ufxMARN1WeeeUamTJlif9mPDvcAmDyGgzOdDLITiOBB5G4o527wWJGphSK8FSpUMMc3MqVQqyFTDONHbY+tW7fadwZRz14MfKGPWLhwYeG9pBdibEMCJEACJOCHACYcb7jhBsvA6dOnj7z11lumpxtpG6hBMHbsWGvSqVOnSE25jgTiSoAO3rjiDb/xP/74I1ei299//7389ttvtmFIOCTCsJ9Y6eMhLROaaZkUpZKIz4j7IAESIAESIIFkIHD00UfLU089Jf3795dnn31WVq5cKd98843JT8Gh16BBA5s0RiRruhmik2EjR47MEbkbaqzHHHOMDB061IqtoRhbJjl4wQORy6gqft9991n0dyhG2ZehLQyTA0yBzU6H70mABEiABGJB4LbbbrN7GdzDdOjQQR5//PGA1FL27WPSEfc2+/fvl2uuuUaqV6+evQnfk0DCCPxH06KchO0tw3f03HPP2ckBqYm7d++W0qVLWyVlFE7zeiJAquOCBQuMZCp+dKi0/eGHH1rqIgqL0EiABEiABEiABEgg1QlgAr5IkSKmJ4zXBQoU8DQk1GBANCrs119/9dzP08aTvNF3331nRdLwUDxp0iS55ZZbIh4xJgxatWpljDZu3CihNJ4jboArSSAJCMAZ5E58HXHEEfY9rlGjRq4Cf5JgODyEPBBA0NaXX35pwVvIgME1hJY8BFBToFatWgKJKdRDGjZsmNSvXz/wW92zZ49MmzZNhg8fLrjuo3bSihUr0qquQCI/DUxygycywNq0aZPIXafVvjJH7CsfPzacFBABixmdRYsWCW5o4ZxFIQ58gWvWrGnFRtzI3Hw8VO6aBEiABEiABEiABEjAJwE8pMPgdPTq3EV7RDzjwf7AgQMW6YxlmWLQ4YWsBaxr164ycOBAc3JnHz+0jaHX3Lp1a7t/HjNmDJ272SHxfdITWL58ucCRi4KKLVu2lF69egmCfOrWrSvQk0bEIPS7aelPYP78+VKvXj1BFgdq8sB5iO9AmTJlzFmIwuu0/CdQqVIlc9jiuo7aAtDWP/bYY6VUqVL2Oz7hhBPsdwznLiJ48RuH/BCNBPKTACUaEkAfN6xTp04N7KlgwYKCm1potcDRC21baLYsXLjQHMAlS5YMtOULEiABEiABEiABEiCB5CbgFgnLTXaVW+MgE+WrrrvuOvnhhx+kZ8+e5sRFGiweolGkDzwQDPHiiy/KV199ZYXY7r77bunevXtyfxl4dCSQjQBqkNx112rwP8sAAEAASURBVF323AcH0GWXXWbFJxG1//7779vf6NGjZfbs2VbYuly5ctm2wLfpQGDv3r2Cc97LL79swzn44IPNWYhinDt27LDz3eDBgwX1b+bMmSMXX3xxOgw7pccA5/uGDRvkkUcescA8/F63bNliYzr00EOtsGyXLl0EhVNpJJAMBOjgjfOnsHbtWjtJYzeY5UHBkYYNG9pNK07yeD9ixAjBaxSOuPTSS+XNN98UOnnj/MFw8yRAAiRAAiRAAiQQIwIo+AUnL4qG+amzgIg9aBQj6hf3iZlo3bp1E0h49e7dW955552Qhfng8Lr//vvtYToTGXHMqUsAjl2kdkMzGs67fv36CaQZgg3Pi3ASrVq1ypy/a9as4bNgMKA0eI2oXGTtrlu3zqQYoL+ONHREhML+/vtvK+Y1ZMgQixrFJMArr7witWvXToPRp/YQ4MhF8Vj8ITMb12wsK1asmK+MndSmwKNPFQKUaIjzJ/Xwww/bCRszdK+++qo0atQoUGAMaRm4yENHDLNDMBQgwwl9165dcT4ybp4ESIAESIAESIAESCAWBI477jipWLGiPfy99NJLnjeJiD082GOC34+0g+cdpEjDatWqyerVq835AUdujx49LFIXRdXee+89S4+tU6dOioyGh0kC/xDAhAWid+HcnTdvnhVVzO7cRcsKFSpYgA+CgKDrmWkFFzPh+wI5Djh3IcOAejyY2HKduxg/viNw5i5btszOfZCmgbwjpB1pyUMAmdhnnnmmYFI3k6/ZyfOJ8EiyE6CDNzuRGL+H8xaGdAzXiZt9F8WLF7eT+SWXXGKrPv/8c9NxwQwRjQRIgARIgARIgARIIPkJdOrUyQ5y0KBB4kVDEbp9iO6DuX3tTQb/gyI1iOQdN26cjB8/3nRJ4TinkUAqErjjjjtMlmHAgAGWwRlpDIcccojMmDHDogJRqMlN44/Uh+tSgwCKdeGzhXMQxdLhHAxnkKZ58MEHrZgX5GsgS0MjARIgAa8E6OD1SiqX7TZv3mw9o92cosgG0jCqVq1q7aHvcu2111pURy53zW4kQAIkQAIkQAIkQAIJItCxY0eBlMBnn30mzZs3l/3794fdM5y7yOqCtix0FhGpRSMBEkgfAoi8XLp0qRx22GHSv39/TwODPi+Kr8FmzZrlqQ8bJT+BJ554wg4SUbso2BXNIPfzwAMPWDMUZP/rr7+ideF6EiABEjACdPDG+YuA9ApYqHSc7LuGwDrS+hD2D8PMLQtJZKfE9yRAAiRAAiRAAiSQfAQgxzV37lwrpLt48WJLu8bDeXA0L2ouPProo5bVtXLlSqvEjWI6bpG25BsVj4gESCA3BKCjiwKK0F2F49arNWjQwJpCsoSWHgRef/11G0jLli09DwhSDshowDUDMjU0EiABEvBCgA5eL5Ty0KZUqVLWG9UXvViRIkVk0aJFUrRoUWsODV93Bs9Lf7YhARIgARIgARIgARLIHwKIzoLu5vnnny/btm2Ttm3bSqFChaxgUokSJay4zo033miRu9WrVzfdWRRqoZEACaQXgW+//dYGdOqpp/oamNve7e+rMxsnJYEvv/zSjqt06dK+jg9OXtjOnTt99WNjEiCBzCVAB2+cP3vXwTt9+nSBjo4XO+OMMyySFxG9sL59+8rUqVO9dGUbEsgXAkgd+vTTTwXRSJs2bZLff/89X46DOyUBEiABEhDBwyS0XSH7BOchCrmULVtWbr75Znn77beJKM4ETjvtNIu4euqppyx6D1F827dvt0K6iNRFsTCkXy9fvlxQh4FGAiSQfgTcqN1IUi2hRu1G/B955JGhVnNZBhFwHMdGywyPDPrQOVQSyCMBOnjzCDBadxRXg0GHCa937doVrYutr1KlisApDKF1nNw7dOggw4YNs1QfTxtgIxJIAAEUEWzTpo1FJGFWGtFImG0uXLiw6Q9CS5qWOAJwInzzzTfmZP/xxx8Tt2PuiQRIICkI4H5h+PDhctZZZ1m1dqT44r4DKZ7IJHrkkUekRo0apv2KSu20+BHA/Vu7du3krbfeEhTNRQHdHTt22OvXXntNWrRoQVmG+OHnlkkg3wm4knsosOXHIO0Ac/v76cu2yUnALarmNaPXHYXb3o3qdpfzfxIgARIIR4AO3nBkYrQcOkqI1IC9+uqr5vyCs3bixIlR99C0aVOZNGmSPQDAcXPnnXeafEPUjmxAAgkgcM8995g2FCYi9u3bZzei1apVEzh6f/vtN3nhhRekUqVKFoGO7y8tfgTWrl1rjnY41k888UQ7zyAlGA8Ht99+u9CREz/23DIJJAsBOHcxkTx48GDLooDWHySfkOb7008/CSbcsO6YY46R+fPn2/nZTRtNljGk63EceuihgqhePKQXKFAgXYeZkePCpCoc9jNnzrT/v/7664zkwEHnJHDBBRdYhD6kWlwN1pytci6ZMmWKLWzYsGHOlVySkgTq1q1rx+2ncN769evlk08+keOOO06iFWtPSSg8aBIggfgQ0AcCWpwJbN261VGRdORYBP40VdLzXp988klHC3cE+rrb8byBJGqomnQ2jiZNmiTRUfFQ/BLo16+ffY4aoeRoRVhHU0+zbEIfeBx1LDqHHHKItVMNwizr+SY2BA4cOOD07NnT0dStwPlB032ds88+21EnTmAZXj///POx2Sm3QgIkkJQEhg4dar95lWNwlixZEvYYcX7WLCFrqw4IR4vBhm3LFSRAAqEJaCFkRyVQslx/cX+O6zF+XwsWLAjdkUszisCoUaPsXIv7Ms2kiDp2dQBae9y36eR81PZskBoEdILVzg0qv+hs3rw56kH//fffjgaI2XcB9/k0EsgEApoVbN95LU6bCcON2xgZwRsfv3mWraLgBtJtunbtKgULFrR1iLLzau3bt5cPP/zQ0iq99mE7EogXgXnz5sno0aNFnbemFT1hwgRB4Zhgg+bjyJEjZenSpVY5GBrSbkRCcDu+zj0BvSoIIvTGjRtnn4U63S0FGNFDkM6ARMOKFSvkyiuvtPTsq6++WqAHSSMBEkg/Akj/R1YFZAHmzp0rl112WdhB4vz8yiuvWIT/Bx98IJMnTw7blitIgASyEvjjjz8E9+W4tq5atUqOOOII+721atXKMvZwnw9pFERfosAe2tMyl0D37t2lXLlyJp11+eWXm4xWOBozZsyw7xbW33vvvSZ3Fq4tl6cWAURz43yADEecG3DNDmcavCG33HKL6EStFV0fNGhQuKZcTgIkQAI5CcTNdcwNhySgxagcvSF0VK4h5PpoC59++mmLDPg/9u4DPKrie/z/AUIJXTpILyIo0pv0XkKvUgVbKCJIExUUlCJIVXoTKYJ0UJpKkaY08SO9S5Pee0v+OfP9X34EssndkE22vOd5QrJ7Z+6d+7phs3vuzJmkSZNGVNUttzOC1y0vi+1O6YjRkIUDzd21kMCurXZz58419dOkSRMcsnCErTZUilhg8ODBxjVk6pZ5TQmvxdChQ01dHVEdks4hvKpsQwABDxSwZlWEBJ5s9z4kTYN5XQhZ2NV2Gyoi4MsCIemmghs0aGD+34QsgBUccoM1OCRgE4pEH3/zzTfBIQtsmXo6Y03bUXxXQGe5haRoMb8P+nvRs2fP4JDFLoNDUuQEhyxQHByS3uPxaM2QT+rBXbt29V0sLz7zkFzswUWKFDG/BzrTJmSwTHBIjvzHZ6yzaZYtW/a4ToIECYLXr1//eDs/IODtAozgjZorHEt382zYl2cQcI2A3sHU0cghb3jNKCPXHIW9ukog5I2GlCtXTkICAhIyxUjixIlj61C6aOCWLVskJNgrjRs3ttWGSo4FNKeuzgzQlZk1x6aOComohKTSkDFjxkjVqlVNPvCI6rMdAQQ8R+DVV181ufo2bNggpUuXttVxffuns4k0R+/+/ftN/nRbDamEgI8K6IylDz74wOTEXLdunRmZ6Yhi9+7d5v3S5cuXzUybzp07O6rK8z4goO/b3n33XdFZcI6Krp0QcvNe3nnnHUdVeN7DBUIGupjFN3WdEi066+bFF1+UkNQNojnxb9++bZ7XRdnmz58vxYoVM4/5BwFfEGjVqpXMnDlTQlI0mLVlfOGcXXGOpGhwhSr7RMBLBTTlgpaQESy2g7ta3wrqWu31OUrkBTRQrsFdnSJqJ7irR+rfv79Jl/HLL7/IiRMnIn9wWiKAgNsJ6CI+WpxZiCUkV6iEzKox7az25gH/IIDAMwIaePniiy/M85ruSKfdh1f0poump9Ki7UJG74VXnW1eLpAqVSozsEXTd2iwP2QkpwnshcyKk+rVq0vIqG8JWbOF4K6X/x6EjPyXkDUxRN+L165dW3QBzpMnT0rISG4T3C1QoIBJg6eDaAjuevkvA6eHgIsE/Fy0X3aLAAJeKGCtuJ47d26nzu6ll14y9a32TjWm8jMCumK3Fs35Z7eETAczHyLmzZtnVnNu27at3abUQwABNxYIWYzF5PnUGRX6YdGZYq0LQPDJGTXq+qJASGo10VGYGnSpU6eOLYKAgAAJWYjN5OrVvNeaC5/i2wLFixcX/aL4tkCVKlVEv0JSN5pZNJqbV2fUaACYggACCDyPACN4n0ePtgj4mICVkkEDCs6UkPxzprrV3pm21H1WQO/2a8mTJ8+zG8N5Jm/evGYrI3jDQWITAh4moK+rqVOnFn1ddvYmWkhuSHO26dOn97CzprsIRK+Apj/RoinGnClWfau9M22piwAC3i0QN25c0XQMOhCG4K53X2vODoHoEiDAG13SHAcBLxDInDmzOYs9e/Y4dTaai06L1d6pxlR+RsBKna5TrCkIIIBAqVKlDELIwmm2MU6fPi1//fWXyf1npWqw3ZiKCPiYwH///WfOWPPfO1Os+lZ7Z9pSFwEEEEAAAQQQcEaAAK8zWtRFwMcFdIEuLZo/6t69e7Y0NBg5Z84cU9dqb6shlRwK6N1+Lfv27XNYJ6wNupCSlowZM4a1mecQQMBDBax0LbpAj910C3379hWdXaEjDBMmTOihZ063EYgegZAV7c2B7t6969QBrfpWe6caUxkBBBBAAAEEEHBCgACvE1hURcDXBXRRCB3ppSO/Bg0aZItj/PjxoiN4s2bNKgR4bZFFWKlSpUqmzo8//hhhXavC9evXRXMAarHaW9v4jgACni3QsGFDs2iPpm9p0aKF3L9/P9wTmjRpkkyePFnixYsn/fr1C7cuGxFAQCRbtmyGYefOnU5xWPWt9k41pjICCCCAAAIIIOCEAAFeJ7CoioCvC2hKgOHDh0vs2LHNqtAaJAiv6EjfLl26mCpff/21CSaEV59t9gSaNm0qujiSTsdes2aNrUY6Wk+DvBUrVjTBdluNqIQAAh4hoK/Ns2fPlhQpUsiSJUukbNmysn379mf6rotEdezYUd577z2zbcyYMaKruFOcE9B8x1u2bJG5c+fKrFmzZN26daKL5FC8V6BGjRrm5PSa273WOnrXmsFUs2ZN78XhzBBAAAEEEEDALQQI8LrFZaATCHiOQPny5WXo0KGiqRc0SFC/fn3ZvHmzmeprncWOHTukVatW0rhxYzOSrFevXqwebeFEwfc0adLIxx9/bPakwV7NoxleGT16tIwYMUJ0MYchQ4aEV5VtCCDgoQI5c+aUtWvXSpYsWUzwsWjRovLqq69Ky5Yt5Z133jE3d3SV7rFjx5rXAp1doc9T7AtcvXpV9O9Z2rRppUSJEqKvv+pboUIFSZUqlbz55ptiLVxnf6/U9ASBYsWKiX6dOXNG+vTpY6vLn3/+uWjuXZ39VLx4cVttqIQAAggggAACCERWIFZIkCY4so1ph4CzAoUKFRKdrqY5/xYtWuRsc+q7kcCMGTOkQ4cOcvPmTdMrf39/s5L7pUuXHueA1JxzX331lXTu3NmNeu4dXdHcmRpcX7p0qVkkqXv37tKuXTvRAI5VNPA7YMAAWbhwoXlqwoQJj0fuWXX4jgAC3iVw7do10Vy8+v/98uXLoU7Oz89PatWqJf3795dXXnkl1DYehC+gr6d169aVU6dOmYp58uSRfPnymWD5oUOHzIhpfV3WfMbff/89NzXD5/TIrX/++acZHf/gwQPzt/WTTz5xeB763kdvxOqNVR3h/frrrzusywYEEEAAAQR8XUAHh82cOVM0xqA3zymREyDAGzk3WkVSgABvJOHctNn58+dNygYNIOoHXKtkzpzZBB+7du0q+jPFNQL6IVOD5+PGjTMH0GnaOnovWbJkZtTQhQsXzPOazkHTaVgLMbmmN+wVAQTcSeDhw4dmJK+OKNWp4nrzp2TJkpI8eXJ36qZH9GXv3r3GTtPclCpVSkaNGiWFCxcO1ffjx4+bgJ6mytDXYv27qDezKd4l8N1335mR7xrM11Hc+j5HR3CnTJlS9Aa3BnM1ldUff/xh0llNnDhR3n77be9C4GwQQAABBBCIYgECvFEDSoA3ahzZi00BArw2oTyw2u3bt0XzO2oOyMSJE3vgGXhulzUXpOY4Xrly5ePR03o2GTNmNKPIPvroI0mXLp3nniA9RwABBGJIQPPtvvbaa6JB3iZNmpjRJToq01HRWRO9e/eWJEmSyMGDB3ntdQTlwc8vX77cBHk1XYNVNKj/5KRI/ZurCxkGBARYVfiOAAIIIIAAAg4ECPA6gHHyaT8n61MdAQQQCFNAp6UyWjdMGpc/qbn95s+fLzqiV6cP6ygzzdObPn16lx+bAyCAAALeLDB9+nQT3H3ppZdM6oXwgrvq8Omnn5p0DYsXLzbT+L/99ltv5vHJc9MF0w4fPixTpkwxI7V37dpl0qHoDW5N29GgQQMzaldnz1AQQAABBBBAAIHoEiDAG13SHAcBBBBwsYAGHrJly+bio7B7BBBAwHcEfvjhB3OyGrjVvPJ2iuY41gDvnDlzZOTIkRInThw7zajjQQJ6U7tTp07mS7uto3d1FC8FAQQQQAABBBCIKYHYMXVgjosAAggggAACCCCAgDsLbNy40XSvTp06trupC9hlz57dpC06cOCA7XZU9FwBgruee+3oOQIIIIAAAt4iwAheb7mSnAcCCCCAAAIIIIBAlAlcuXLFLFD3wgsvOL04nc6mOHr0qGie1rx580ZZn9hRaAEdOauLm+kMFl1glIKAIwHNp7127VpZsWKFHDt2TO7cuWMWnyxTpozoDRxNsUFBAAEEEEDAkwUI8Hry1aPvCCCAAAIIIIAAAi4R8Pf3N/vVQJCzxWpj7cPZ9tR3LKBB3dmzZ4vmR163bp3cu3fPVNYAb7Vq1aRdu3ZSoUIFxztgi88JrF69Wjp37ix79ux55tynTp1qFkXs2bOnfPzxx6RUeUaIJxBAAAEEPEWAFA2ecqXoJwIIIIAAAggggEC0CWjOXV2s8u7du2ahNbsHvn//vuzevdtUJy+6XTV79f79918pWrSotGjRQlatWiVqnSpVKjN699q1azJ37lypWLGiNGzYUG7cuGFvp9TyagFd6LBq1aomuKupUz777DOzOJ6O5B0zZoxUqVLF/K706dNHatSoIbdv3/ZqD04OAQQQQMB7BQjweu+15cwQQAABBBBAAAEEnkOgZs2apvV3331ney8LFiyQ69evS8GCBU2A2HZDKoYrcPjwYSlWrJjs2LFDsmTJIpMmTTLpGS5cuCBXr16VEydOyIABA0ywd+HChaJT7wnyhkvq9Rv1/6KO3NVR3/369ZP9+/eb7/Xr15fq1atLhw4d5JdffjEjwdOlSye//vqrvPnmm17vwgkigAACCHinAAFe77yunBUCCCCAAAIIIIDAcwq8//77ogtojR49Wv7+++8I93bx4kXp1auXqdepU6cI61PBnoCO1NU8qRrM1TQMu3btknfeeUc0P7JVMmXKJJ988om5Tnny5JH//e9/0rZtW2sz331MQIP77du3N8HdESNGmJG7mqs5rFKuXDlZv369+X2aP3++GeEbVj2eQwABBBBAwJ0FCPC689WhbwgggAACCCCAAAIxJlCgQAGT01XTNOho3u3btzvsy+nTp82oQB1J+vrrrzMS0KGU8xvGjh0r+/btMwvW6ajMJEmSONxJ1qxZZfny5WYkr9b9/fffHdZlg/cK6AhvvSFQtmxZM4o3ojPNlSuXfPXVV6aajgSnIIAAAggg4GkCBHg97YrRXwQQQAABBKJZQEfP6TTWYcOGmRFyw4cPN1NZHzx4EM094XAIRL/AqFGjTJ7OM2fOSMmSJU3Ad+PGjSYNg+br1JGimtczb968Jn1Azpw5zQjA2LF5mx1VV2vChAlmV0OGDJFEiRJFuFsN8nbv3t3Us9pG2IgKXiWwePFicz5dunSxfV5t2rSRpEmTyl9//SUnT5603Y6KCCCAAAIIuIOAnzt0gj4ggAACCCCAgPsJ6Or0GszVoIrmuHy6pEiRQj766CPRD9Dx4sV7ejOPEfAKAZ3WrSNCe/bsKbpgkwYMHQUN69WrJ1OmTBH9v0GJGgEdEa25U1OmTGlGSNvda8uWLUUXztKbUxTfE9izZ485aR3Ba7fo3zG9iaML+Gl7TftBQQABBBBAwFMECPB6ypWin24p8OjRI9m8ebMcPHhQdPXmtGnTmtWdX3rpJbfsL51CAAEE7AqcPXtWNFi1ZcsW00QXjCpfvrykTp1azp8/L2vXrjUjFzXAu2TJElm0aJGkSZPG7u6ph4BHCfj5+ZmbHYGBgSaAu3LlSjl+/Lg8fPhQXnzxRfN/o3Xr1lK6dGmPOi9P6KwGeLVoXt04ceLY7rKO4tXRvpcuXZJbt27ZGvlre+dUdGsBXVRNb0pq/uwn8zTb6bR1c+by5ct2qlMHAQQQQAABtxEgwOs2l4KOeJLAnTt3RBds0JFt+sHh6ZI/f3758ssvpXbt2k9v4jECCCDg9gI67TwgIMBMU82ePbsJaGlw9+myZs0aefvtt82Nrlq1aplFahIkSPB0NZ98rItA6aJcmgNSAwavvPKKFClSxAQcfBLES046d+7cZkS7jmqnRI+ABtG1aJDd2WK1sfbhbHvqe6aABnZ1xLe+/uqXDsCwW86dO2eq6s1MCgIIIIAAAp4k4Pw7JU86O/qKgAsEdMSOruT8zz//mL1rzj2dzpUsWTLRBVZWr15tRrVpnffee8+svO1o1V4XdI9dIoAAAs8t8Pnnn5vgrs5G0FkK+kE5rFKxYkUzwlcXlNq2bZt88cUXMnDgwLCq+sRzOmrshx9+kH79+smhQ4eeOWcd6fnxxx+LjgK1Ak/PVOIJBBAIJaD/b7SE9X8qVMWnHuhMA51dlThxYvMe7anNPPRyAR1s8dtvv5n35c2bN7d1tnpz888//zQ34l577TVbbaiEAAIIIICAuwiw+oO7XAn64RECOl2rcuXKJrirUwV1ZWbN0TV58mSz+NCcOXPkv//+Mzn6EiZMKBMnTpT27dt7xLnRSQQQQEAFdLST5hnVBaJ+/PFHh8FdS0vTMsyePdt8IB45cqT46rRWDQw0btxYNO+nBqLSp08vGlTo2rWrvPnmm5ItWzZzE/D999+XSpUqycWLFy1CviOAQDgCuXLlkowZM5r/P3/88Uc4NUNvmj9/vnmiQoUKoTfwyCcEGjZsaM5z6NChojff7JQxY8aIvpZrqhVnRv3a2Td1EEAAAQQQcLUAAV5XC7N/rxLQD+aHDx82eXb1Dn9YCzfoaF2tt27dOpPvTRdbmTdvnlc5cDIIIOC9AkuXLhVdXK1mzZpSoEABWydatGhRqVq1qmj6mp9//tlWG2+qFBQUJE2bNpUFCxaYfI/ff/+9nDp1SmbNmmVu/k2bNk2OHj0qaquL9qxfv15q1KhhvLzJgXNBwFUCepNES69evUT/v0VUNP+qNZvAahtRG7Z7l0Dbtm0lc+bMsnPnTvn0008jPLnt27eLzl7R8tlnn0VYnwoIIIAAAgi4mwABXne7IvTHbQU0n6KO0PX39zcf4pMmTRpuXzXgoXl6tfTu3TvcumxEAAEE3EVAUy1oqVatmlNdsurrh2RfK6NHjzaB7VSpUpnpvbrYlo6AfrpoXnb1zZkzp6hTnz59nq7CYwQQCEOgR48eZkSl3hzp0KGD6CK3jsqNGzekQYMGZsSvpo+xRnI6qs/z3ikQP3580Ztrmg5n0KBB0q5dO5OyI6yz1VkomnJIb1JqPZ2tR0EAAQQQQMDTBJ799OFpZ0B/EYgmgblz55opXm+99ZYZgWXnsFpXpxUePHjQ5LO004Y6CCCAQEwKaN5KLVbeS7t9sepbC9TYbefp9e7evWsW1dTz+O6770TzFodXdNqvzurQALAGhs+cORNedbYhgECIgK5zoCPkNWg3YcIE0bQLmzZtCjX1XhdSW7RokRQuXFjWrl1r0qRomhmK7wro74nOpNDFP/X3RhcNfeedd0RTMegMOx2AkS9fPpNOR28MaIodTVFEQQABBBBAwBMFWGTNE68afY4RASvvm64sb7fEiRNHqlevbnL0akqHQoUK2W1KPQQQQCBGBDSQokUXJ3KmWPWt9s609eS6urCm5tPV1/datWrZOhVNfVG3bl0TjNKAlI5IpCCAQPgCpUqVkjVr1pgRuRs2bDB5UtOlS2dGxD948ED279//+HVLF9havHixucke/l7Z6u0CTZo0kdy5c5t86Pr7o4Hdp0uGDBmkf//+omkdKAgggAACCHiqAAFeT71y9DvaBc6ePWuOqfm8nClWfau9M22piwACCES3gDUCdfPmzdKmTRvbh9f6WvSDtC8V6+af3eCuZaM3CzW4qzf/CPBaKnxHIHwBTblw4MAB0YWzpk+fLsePH5cn31+99tprZor9u+++a6bmh783tvqKgAb89Wbcvn37ZPny5XLs2DHR2Re6GGaZMmXMiHBdQ4OCAAIIIICAJwsQ4PXkq0ffo1UgSZIk5ng6hcuZcvPmTVM9ceLEzjSjLgIIIBAjAnXq1JFPPvlENC2N5i1MmTJlhP3QtA46fVqL5pn1pWIFl6ybeXbP3apvtbfbjnoI+LqAroHwxRdfmC8N1J0+fdoEc7NkyWICdr7uw/k7FsiTJ4/oFwUBBBBAAAFvFCAHrzdeVc7JJQK6KI6WrVu3OrV/q77V3qnGVEYAAQSiWeCVV16RmjVrmqnOdkaWBgcHmxFzevNL0w5YI4Cjudsxdjhu/sUYPQdGQLJly2ZSNZQoUYLgLr8PCCCAAAIIIODTAgR4ffryc/LOCFjTbzV3V1BQkK2mmg9O88TpoiBVqlSx1YZKCCCAQEwLjBw5UpInT25G8bZq1Upu3boVZpd0hkLz5s1NqoEUKVLI8OHDw6znzU9aN++2bdvm1Gly888pLiojgAACCCCAAAIIIIBAOAIEeMPBYRMCTwrUr19fsmbNKv/8848MGzbsyU1h/qwLfgQGBsqjR4/Mir3WKK8wK/MkAgj4rIDmAbxz545bnX+uXLlMygV93Zo5c6ZZxOjzzz83K9Pv3r3bLHTUp08fyZEjh8yZM8escK/5ZHWFcl8r1sKbS5YskXPnztk6fb3m6qrFunloqyGVEEAAAQQQQAABBBBAAIEwBAjwhoHCUwiEJRAvXjwZPXq0xIoVS3r16iUjRowQnZocVrl+/bpZ5Xn9+vVmBee+ffuGVY3nEEDARwV0Je8WLVpImjRpxN/fXxImTCipUqWSpk2byqpVq9xCpWLFimYBMF3USPPEas5LfS5fvnxSqVIls+K45t7VBWp0obCyZcu6Rb+juxN6409vAOoo5/fff9/h34Un+6U5jk+dOiVFihQxfk9u42cEEEAAAQQQQAABBBBAwFmBWCEBqrAjVM7uifoI2BAoVKiQ7Ny5U+rVq2em9Npo4nZVdOpy165dzYf4okWLmtyTGgDRRT90oY+VK1fKt99+KxcuXDABm19++UUKFizodudBhxBAIPoFrl69Km+++aYsXbr08cETJEhgbhw9OYq3WrVqMmvWLFsLnD3ekQt/0IC0jtDdtWuXXLp0yby26Wr1DRo0kHLlyrnwyJ6x66NHj0rhwoVFr2+bNm1k3Lhxotf16aIzOj777DMZOHCg6E1DvQlYvHjxp6vxGIFwBbZv3y4//vijbNmyxdx80fcfOupeR4M3atTIpIUKdwdsRAABBBBAAAEE3EhAU8Lp7LYZM2ZIy5Yt3ahnntUVAryedb08vrfeEODVi6BTcTt27GgCuo4uio5mmzZtmlkAxFEdnkcAAd8RuHz5slkMaN++fSa/bY8ePaRZs2aPXyNOnDhhgjaDBw82QVRNf7B582Yzytd3lDz3TFevXm0WmdORvJkzZ5YPPvjAjHROly6duZ4azNVZIHv37hU/Pz/z90FHcVMQsCugKUDatWsnixcvdtgkS5Ys5veM1B8OidiAAAIIIIAAAm4mQIA3ai4IAd6ocWQvNgW8JcCrp6uj7X744Qf56aef5MCBA6JpGXS6tY7qbdKkiVSuXNmmCtUQQMAXBKpXr27SL+jI12XLlpn0LWGdt6ZDqF27tugoPU1/8Pvvv5sRvmHV5Tn3EtAc7TqCV2eqOCq6KNukSZOkfPnyjqrwPALPCBw6dMjcMDh58qRobuz33ntPatasKZkyZTLvP3Q0r/5e/f333+b1QtNIde7c+Zn98AQCCCCAAAIxLaAzmnTAlH7pouQ6A0o/R2usoHHjxmZAREz3keNHrwAB3qjxJsAbNY7sxaaANwV4bZ4y1RBAAAFzI6hOnTqSOnVqE4DJkCFDuCqa4kVTu2jal9mzZ8sbb7wRbn02uo+AZr7SFBwLFiww11qvZYoUKSRv3rwmV69+cIkbN677dJieuL3AjRs3zM1jvZmsN33mzZsnadOmfabf+runi8B+9NFHJo2UfnDWm0UUBBBAAAEE3EVgw4YN5ialBnYdFR0opTctdZ0Dim8IEOCNmuvsFzW7YS8IIIAAAggg4EhAc7Jq6dOnj0QU3NV6GgjWRc3efvttk8+VAK+qeEbRhTjr1q1rvjyjx/TS3QWGDBliZgoVKFDA5PnXRRnDKvq71717dzOCV79rKqkqVaqEmQ86rPY8hwACCCCAgCsFdH2Jtm3byoMHD+Sll16SwMBAM1o3efLkcubMGdG1a8aPHy+//fabFCtWzPzN0wFiFAQQsCcQ2141aiGAAAIIIIBAZAT0TawuUhYnThxp3ry57V1oqpf48ePLpk2b5ObNm7bbUREBBLxH4P79+/LNN9+YE5owYYI4Cu4+eca6EKx+INZ0DnPnzn1yEz8jgAACCCAQIwK6rsRbb71lgru9evWSPXv2mIXLNZCrwV5dtHfAgAFy5MgRCQgIMAuW6+y38+fPx0h/OSgCnihAgNcTrxp9RgABBBDwGAEdkXDv3j0zcjdlypS2+504cWKzAJvmKTt16pTtdlREAAHvEdAc3JrjXwO2+iHYTtGRvDoqSouuE0BBAAEEEEAgJgWCgoKkQ4cOojctdYbJoEGDzIKzYfVJR/PqYqK6VoGmKtPZbxQEELAnQIDXnhO1EEAAAQQQiJSAjuDVEi9ePKfbW230DTEFAQR8T0AXV9NSokQJp07eqn/w4EGn2lEZAQQQQACBqBb49ddf5X//+59kDcmpq6N0Iyp+fn4mB6+uWTB16lS5fPlyRE3YjgACIQIEePk1QAABBBBAwIUC6dOnNzkxdRTu3bt3bR/p4cOH8u+//5r6L774ou12VEQAAe8R0NG7WpImTerUSVn1rfZONaYyAggggAACUShgzSbRFA3W4IWIdp8zZ06pVKmS6PvhFStWRFSd7QggECJAgJdfAwQQQAABBFwooDkzixYtatI0LFu2zPaRdIEJDc68+uqr4kxqB9sHoCICCLi9QNq0aU0fnU3Tovl3tVjtzQP+QQABBBBAIAYErNkk1uwSu10oWbKkqWq1t9uOegj4qgABXl+98pw3AggggEC0CbRu3doc6/PPPzeB3ogOrGkdevfubaq1atUqoupsRwABLxWw8u7q9FYdxWS3rFy50lQtXry43SbUQwABBBBAwCUC1mwSa3aJ3YNY9a32dttRDwFfFSDA66tXnvNGAAEEEIg2gffee09y5cplVgzWYG94OXU1iPPuu+/Kjh07JHPmzNKpU6do6ycHQgAB9xJ45ZVXRL/OnTtn8hHa6d2lS5dk7NixpmqTJk3sNKEOAggggAACYQroe9ZVq1ZJ//795YMPPjADEGbMmCH6t8ZusWaTODsbxapvtbd7POoh4KsCfr564pw3AggggAACdgRu3bolmi5BFzu6ceOGpEuXzix4VLBgQTvNTR1dJGLRokWiU83mzp0rx44dk6+//lrKli1r8vNaO9q8ebP06NFD9Lumdli4cKH4+/tbm/mOAAI+KPDll19KgwYNzMrjRYoUMSlfHDFonu833nhDrl69KtWrV5dSpUo5qsrzCCCAAAIIOBR49OiRjB492gR2L168+Ew9XQhNc+rq36g0adI8s/3JJ3Q2ytKlS00u3YYNGz65Kdyfrdy71myWcCuzEQEEJFZwSMEBgegSKFSokOzcuVPq1atngh3RdVyOgwACCDgroCMTvvjiC5k4cWKYi6O99NJL5k1v48aNbe9aVxCuW7euHD9+3LTJkCGD5MmTxwR59+/fL9ZIBX1+8eLF4QZybB+Uiggg4PECOqp/8uTJkihRIhk2bJi88847EidOnFDnpa8vOltg69atoos7btu2TVigMRQRDxBAAAEEbAjogAZ9f6sjd7UUKFBAqlWrZv6mXLt2Tf744w/R1EGaUixjxoyii6hpHUflwIEDkjdvXtEBD/p+N2vWrI6qPn5eBzloMFgHVmheeQ0oU7xXQFPSzZw5U3R0eMuWLb33RF18ZvwvcTEwu0cAAQQQ8DyBf/75R2rXri0nTpyQ2LFjS5kyZURzWSZOnNi8ydQ3vLrgg05/1tEL48ePN29aIzrT/PnzmzQNw4cPlylTpphA73///fe4mb5Jbtu2rRmpZ+Ude7yRHxBwIwEdIaof1DTgSHG9gKZc0DEZ+rrRrl07c/OpatWqkilTJjOz4M8//5QtW7aYOtmzZzcftgnuuv66cAQEEEDA2wSCgoLMTBB9r6upEfTvTkBAwDOnefjwYXOz8ffffzfBX72pqKnFwiq5c+c2Qbvp06ebwPGaNWskSZIkYVU1z+l7bL1hqUXXryC4ayj4B4EIBRjBGyERFaJSgBG8UanJvhBAwBUCGtQtWrSonD9/3kxvnjBhgsmB+eSxdNra1KlT5cMPPxRN4aCj6SZNmvRkFVs/a9oHPZ4GbjRQo2+AKQi4o4D+js6bN0/0w9m6devM7732M1WqVOaDnQYdS5cu7Y5d96o+LViwwOQ/1BFQTxdN69KhQwf59NNPJXny5E9v5jECCCCAAAIRCuighfbt25u0C3rzMFu2bA7b6AjeOnXqiC7sqWmBrJQKYTW4cuWKGSyh7301t7y+b9bUZU8Wfa/x448/SseOHeXy5ctm3zqjLVasWE9W42cvFGAEb9RcVAK8UePIXmwKEOC1CUU1rxTQxbN++OEHEyTRBbQuXLggyZIlM1P09c2RTsHlQ3nMX/pKlSqJjiyoUaOGSZMQL148h53S0Qrly5eX27dvm7qafoGCgLcJaEoRHa2uU/+toq9V+sFOb3BYRXO/WmkErOf47hoBnWWgI3bPnj1rRkHpIo4VK1YkZ7druNkrAggg4BMCOoBBBxycOXPGpFPUtIoRFQ3E5siRw+R+19QNJUqUcNjk33//NUHbXbt2mToaG9Cbwy+88ILojDZd80LXqdBSv359M12fmUKGw+v/IcAbNZeYAG/UOLIXmwIEeG1CUc3rBDQQqPmEdMqRo5IiRQr55ptvpEWLFo6q8LyLBXRkYoUKFSRlypSiU8/sBNx16rSONMiXL59o0IWCgDcJ6P8DXahLR7Trhz4dHao58XTkrpajR4+aD2CaF1Zz9hUuXFh0uiYfyLzpt4BzQQABBBDwBYENGzaYBYB1RllYM0UcGfTs2dMsHtytWzcZOnSoo2rmeb0xrAsNjxgxQq5fv/5M3SxZspi0DG3atGHk7jM63vsEAd6oubaxo2Y37AUBBBBAwJGA5rAqW7asCe6+/PLLZoSbTsvX0W86ilenHlWuXNlMRdIg8KBBgxztiuddLDB37lxzBA3Y2gnuamXNEaarB+tohH379rm4h+wegegTuH//vlkUUIO7mu919+7dEhgY+Di4qz3RfK+aH08XUNVRpDo7QVOWUBBAAAEEEEDAswT++usv0+EqVao41XGrvtU+vMZ6A7hv377mM5B+Rho5cqR5rAMmdGaKjuDV9ShIyxCeItsQCFuAAG/YLjyLAAIIRInAkSNHpGnTpnL37l2Tz0qDgG+//bYZCacLBugoOJ3WryvRai4qXRVdR8gtWbIkSo7PTpwT0JHWWjQ9g92i11HTOmix2tttSz0E3FlAP2zt3bvXrHytq1mHt/CfTs9cvny5SRcwZ84c2bRpkzufGn1DAAEEEEAAgacE9IaulvTp0z+1JfyHVn0duGK3aAo0vXncuXNnc6NY8/4WK1aMwK5dQOohEIYAAd4wUHgKAQQQiCqBXr16ybVr18yKsRosCW8VWB31ptOadIGBLl26iI6eo0SvwLlz58wBnV19PmPGjKad1T56e83REHCNgC60omXIkCG2Ui7kzJlTunbtatpYbc0D/kEAAQQQQAABtxfQXLhaNK+uM0UXUNNid/abM/umLgII2BcgwGvfipoIIICAUwK6WICueO7v729y69pp/MEHH8hrr70mugjBTz/9ZKcJdaJQwBqhGFZOsPAOo0F8LbpoHgUBbxDQhdUOHDhgZhk4M6Jdc6hp0WmXFAQQQAABBBDwHIE8efKYzm7cuNGpTlv1rfZONaYyAghEmQAB3iijZEcIIIBAaIGVK1ea0bgaHEmXLl3ojQ4exY4dW958802zVac7U6JX4KWXXjIH1FWAnSlWfau9M22pi4A7CmiAV0vevHlFX5fsFk3VoDe1dJrmnTt37DajHgIIIIAAAgjEsICmHEucOLFs3brVdtoxnXE4ceJE03NNO0dBAIGYE7D/jj3m+siREUAAAY8U0NXltRQqVMip/hcsWNDU10UGKNErUKdOHXNAnV6uqTLsFM01qrmVdVpb6dKl7TShDgJuL6CLQGqJGzeu0321UtE8fPjQ6bY0QAABBBBAAIGYEUiQIIHJiavvgXXNkJs3b0bYkZ49e5qZh/nz53dqDYsId0wFBBBwWoAAr9NkNEAAAQTsCejCalr0zZIzRUe/aWH0mzNqUVP3jTfeMAvg7dixQ4YPHx7hTvWNb7t27Uw9zZtsBbYibEgFBNxcwMorfejQIad6qnmob9y4YRZbS5IkiVNtqYwAAggggAACMSug64e88sorZvBCxYoVTfA2rB7p55xOnTrJqFGjJH78+DJhwgSnZvyEtU+eQwCB5xPwe77mtEYAAQQQcCRgrSjr7Ehcq77V3tH+eT7qBTQYP3r0aKlXr57oiIRHjx5J9+7dw3zDevr0aWnUqJHs3r3bvBHu1q1b1HeIPSIQQwKabkRfg06cOGGmaRYtWtRWTzTvuJby5cub7/yDAAIIIIAAAp4joCkadB2QqlWrmr//L7/8srRo0UKqV68uugixrjuxefNmmTZtmpw6dcoMZJkxY4YUL17cc06SniLgpQKM4PXSC8tpIYBAzAuUKVPGdOLnn382gUK7PVqyZImparW32456USOgaRpGjBhhUjR89NFHoikzvv32W5OPbN++fWbxqM6dO4sGwP7880/Jli2beSOcKFGiqOkAe0HADQRixYolrVu3Nj3R0TxBQUER9urq1asycOBAU8/KJR5hIyoggAACCCCAgFsJ6HvbLVu2mPcBmrJp6tSp0qRJEylVqpTUrFlT+vfvb4K7RYoUkQ0bNpgBD251AnQGAR8ViBWSX8VekkEfBeK0o1ZAc5Hu3LnTjI5btGhR1O6cvSHgZgL68po7d27RKc6a0zUwMDDCHv79999SuHBhM9X/8OHDJl1AhI2o4BKBZcuWyfvvv+9wapoetHHjxjJ27FhJlSqVS/rAThGISYErV66YGxkXL1400zBHjhwZ5mh27aOmK9HFVdasWSMlS5Y0o3tisu8cGwEEEEAAAQSeX+DgwYMyf/582b59u2gapmTJkpn3BjogokKFCqI3hCkIPK9Aq1atZObMmaKjwVu2bPm8u/PZ9qRo8NlLz4kjgICrBfQNz6BBg8xd7Q8//NCsRh/eqFyd5tSwYUMzUq5jx44Ed119gSLYf0BAgFSpUkUWLlwoOgpbA/WaWzRdunRSokQJadq0qeiCEhQEvFVAFw7UD3X6/0BHsWs6kq+++kqKFSv2+JQ1jYneDNGUJgcOHDD/P+bOnft4Oz8ggAACCCCAgOcK6Iy1Tz75xHNPgJ4j4EMCBHh96GJzqgggEP0CGrDt0KGDGeVZuXJl+eyzz0QX43pyOr8GSH788UfRHK5nz541Oaysac7R32OO+KRAvHjxRBde0y8KAr4oUK5cOfn111/NaPW1a9ea1yddgC1Xrlyi0zb37t0rly9fNjT58uUTTTFjLdDmi16cMwIIIIAAAggggAACMSFAgDcm1DkmAgj4lICOfNPFu4YPHy69e/eWAQMGyOuvv24WMNKclZrHVadAa9EFDTTYq/UpCCCAgDsIaJBXR+cOGTLETJ3T2Qb6ZZW8efNKu3btzFfcuHGtp/mOAAIIIIAAAggggAAC0SRAgDeaoDkMAgj4rkDs2LFl2LBhJj/l559/LuvXr5fVq1eHAtEAiS7opTmHtD4FAQQQcCcBTdegKWd0doGmKzl9+rRoMDdr1qyM2HWnC0VfEEAAAQQQsClw5swZ0a+ECROa1HBPzjC0uQuqIYCAGwkQ4HWji0FXEEDAuwXKli0rOsX5/Pnzooup6ffkyZPLyy+/LDlz5vTuk+fsEEDAKwQ0t7jm49MvCgIIIIAAAgh4loDOHtSBJz/88IMcPXr0cef1pq3O2Pnggw+kdu3aj5/nBwQQ8BwBAryec63oKQIIeIlAmjRpTCoGLzkdTgMBBBBAAAEEEEAAAQTcXOC3334z60pcunTJ9DRlypSSJUsWuX37thw5ckR0u37VqlVLZs2aJUmTJnXzM6J7CCDwpADzgJ/U4GcEEEAAAQQQQAABBBBAAAEEEEDAiwSWL18uNWrUEA3uVqxYUTZs2CAXLlyQHTt2yL59+8zPum6IBn1//vlnKV++vNy6dcuLBDgVBLxfgACvm17jR48eybp168zXsWPH3LSXdAsBBBBAAAEEEEAAAQQQQAABBNxVQBdGbdasmTx8+FB69uxpRumWLl1aNO2SVZIlSybvv/++/PXXX5I7d27ZuXOndOzY0drMdwQQ8AABArxuepFu3rwpFSpUMF9Tpkxx017SLQQQQAABTxU4d+6cTJw4UQIDA6V+/frStm1bGTx4sBnF4annRL8RQAABBBBAAAEEQgv07dtXrl+/bt7v6Xu9JwO7oWuKZM6cWX766Sfx9/eX6dOnm3VDnq7DYwQQcE8BArzueV3oFQIIIIAAAi4R0BuIXbp0MW/gNbirQd7FixfLtGnTpFevXpI3b16pU6dOqIU3XNIRdooAAggggAACCCDgUoE7d+7I7NmzTVB36NChto6VK1cuad++vQQHB5v3h7YaUQkBBGJcgEXWXHwJDh8+bKZCOHuYGzduPG6ieXL279//+LH1w8svv2z9yHcEEEAAAQQiFDh9+rTJv7Zr1y6JHTu21K1b1yz4lyFDBtFVlTdt2iRz5841Izf050WLFknZsmUj3C8VEEAAAQQQQAABBNxP4M8//zSLqBUtWlSyZ89uu4NNmjSR4cOHm3QOthtREQEEYlSAAK+L+cuUKSNnz559rqOMHz9e9OvponfUKAgggAACCNgR0BEctWvXFg3u6ijdH3/8UV599dVQTdu0aSMDBw40aRs0uKsB4C1btshLL70Uqh4PEEAAAQQQQAABBNxfQPPvanF2cJhV32rv/mdKDxFAgBQN/A4ggAACCCDgAwKac00XzNBgrY7OfTq4axGkTp1aFixYIDpyQ0f1tmvXztrEdwQQQAABBBBAAAEPErDy7To7OCwoKMicpc74oiCAgGcIMILXxdepT58+0q1bN7l79645UpIkSaRmzZrhJjbXivfv35eFCxeaNjrS6rXXXjM/8w8CCCCAAALOCujo3WHDhplmmms3efLk4e5CPwxMmjRJ1qxZI2vXrjUB4VKlSoXbho0IIIAAAggggAAC7iWgi6Zp2bNnj1Md27t3r6mfKVMmp9pRGQEEYk6AAK+L7Tt06CDly5eXFi1amBUoNbeujoiaOnWqaM5DR+XatWuPA7y6unn//v0dVeV5BBBAAAEEwhX49ddfRRdXK1GihJQsWTLcutbGpEmTyttvvy068lfTNRDgtWT4jgACCCCAAAIIeIaAvvfTQWY6i0vX9bFSL0TUe12YTUuVKlUiqsp2BBBwEwHG20fDhdARuJrDsEePHmZRm1WrVkm+fPnMQjbRcHgOgQACCCDg4wK7d+82AnrD0Zli1de8vRQEEEAAAQQQQAABzxKIFy+etGrVynT6ww8/FDupGvR9n87k0vQMb731lmedML1FwIcFCPBG08XXF9YhQ4bI6tWrRac5XL58WZo2bSotW7YUHa1LQQABBBBAwFUCly5dMrvW/LrOlFSpUpnqVntn2lIXAQQQQAABBBBAIOYFPvvsM0mZMqWsXLlSOnbsKI8ePXLYqQMHDphFeTVlZGBgoFmY12FlNiCAgFsJEOCN5suho6H++ecfadasmTnyrFmzzGhezXNIQQABBBBAwBUCVqD2/PnzTu3+woULpr7V3qnGVEYAAQQQQAABBBCIcYG0adPK/PnzJX78+DJu3DiTrmvZsmVm3R+rc//9958MGDBAihYtKsePHzepuUaMGGFt5jsCCHiAAAHeGLhIurjNDz/8IDNnzpRkyZLJyZMnpXLlytK1a9fHi7HFQLc4JAIIIICAlwpoWiAtumCaM8Wqz0KfzqhRFwEEEEAAAQQQcC8BHWi2bt06yZgxo2zbtk1q1aplYhE5c+Y0awPp87179xZdM6h58+ai6zdoQJiCAAKeI0CANwavlS68pqN5y5Yta3Lh6B2yIkWKmAToMdgtDo0AAggg4GUCehNRF03bunWrbNy40dbZ6YKgU6ZMMXUbNGhgqw2VEEAAAQQQQAABBNxTQBdcO3jwoHz99ddSsGBBM7jsyJEjcubMGUmYMKE0bNhQfv/9d9FZxv7+/u55EvQKAQQcChDgdUgTPRsyZ85sRlQNGjRI4saNK3v27JHixYvLV199FT0d4CgIIIAAAl4vkCBBArPQp55omzZtJKKcukFBQWZRDc0Xr8Fh/UBAQQABBBBAAAEEEPBsAQ3cdu/eXf766y+5deuWHD58WDQ9w/Xr100aBx18RkEAAc8UIMDrBtdNV6fs1auX/Pnnn/Lyyy/LgwcPCPC6wXWhCwgggIA3Ceibec2rpiM1Xn/9dYezRc6ePSt169aVRYsWSYoUKWT8+PHexMC5IIAAAggggAACCIQI6KjdHDlySPr06UVjEhQEEPBsAT/P7r539b5QoULmTlqPHj1kzJgxbnNyK1askOnTp4uO6HrecuzYMbOLO3fuPO+uaI8AAggg4ISAjuJdunSpBAQEmL81hQsXlurVq0vVqlVN7rUrV67Ipk2bZOHChWZEhy6stmTJEvPG34nDUBUBBBBAAAEEEEAAAQQQQCCaBQjwRjN4RIfTKROjR482H8D1g7WWYsWKRdTMpdsnTpwoixcvjtJjWCuzR+lO2RkCCCCAQLgC6dKlkw0bNki/fv3km2++Eb2Bp19Pl0aNGsnQoUMlS5YsT2/iMQIIIIAAAggggAACCCCAgJsJEOB1swtidadGjRqiX+79DArSAABAAElEQVRQdHqurqQZHBz83N356KOP5N9//5VMmTI9977YAQIIuF7g9u3bsn79ejl+/Lg8fPhQXnzxRbMwpE7dp3imgE7HGzx4sPTs2VN++ukn2bFjh5w/f16SJ09u0gTVqVOHUbueeWnpNQIIIIAAAggggAACCPioAAHeGLrw9+7dk/jx4zt9dF0Yx0pvkDFjRqfbR6ZB2rRppXHjxpFp+kwbXTxOA7yxYsV6ZhtPIICA+wjoYgt9+/aVGTNmmBV2n+xZnDhxpFatWjJw4EDJmzfvk5v42YMEUqZMaRZc00XXKAgggAACCCCAAAIIIIAAAp4rQCbtaLx28+fPN6NyNWCqqRjy5MkjrVu3NjkP7XZDP4jr6FdGwNoVox4CCDgrsHbtWsmXL59MmjRJ7t+/L6VKlZLAwEDp2LGjVKlSRTTAqylkChYsKNOmTXN299RHAAEEEEAAAQQQQAABBBBAAIEoFGAEbxRiOtrVrVu3pEOHDmahsifr7N+/X/Rr1qxZ0rlzZxkwYIAJ/D5Zh58RQACB6BTYunWr1KxZ04za1cW4Ro0a9cx0/YsXL0rv3r1lwoQJ0rZtWzMboVmzZtHZTY6FAAIIIIAAAggggAACCCCAAAL/vwAjeKPhV+GTTz4JFdxNlCiRZMuW7XGagqCgIBkxYoQUKFBAjh07Fg094hAIIIDAswKaOqZp06YmuKsjdjU/a44cOZ6pmCpVKtHc3CNHjjTb3nnnHTl9+vQz9XgCAQQQQAABBBBAAAEEEEAAAQRcL0CA18XGf//9t4wZM8YcRVMz6LTm69evy9GjR+XKlSsyZMgQSZYsmdl+8OBBKV++PEFeF18Tdo8AAmELaEoGzZGtN5tGjx79+CZU2LXFzDxo0qSJ6EJs/fv3d1SN5xFAAAEEEEAAAQQQQAABBBBAwIUCBHhdiKu7HjdunDx69Ej8/Pxk1apVoquTx479f+wa2O3Ro4fs27dP8ufPb3py4sQJqVSpkpw7d87FPWP3CCCAQGiBOXPmmCc0/YK+ZtkpuhCblnnz5pnXOvOAfxBAAAEEEEAAAQQQQAABBBBAINoECPC6mFqDt1qaN2/+OIj79CHTp08v69evl3LlyplNmqZBc19q7l4KAgggEB0Cmipmy5Yt5gZUjRo1bB9SF4vMnj27XLp0SQ4dOmS7HRURQAABBBBAAAEEEEAAAQQQQCBqBAjwRo2jw70cOHDAbCtcuLDDOrohadKksmLFCilZsqSpt2PHDtGpzzr6l4IAAgi4WkAXTnv48KGkTJlSEiZM6NThMmfObOqfPXvWqXZURgABBBBAAAEEEEAAAQQQQACB5xcgwPv8huHu4f79+2a7nYCJv7+/LF26VHLmzGnaLF++XDp16hTu/tmIAAIIRIVA4sSJzW4iM3Pg5s2bpq21j6joD/tAAAEEEEAAAQQQQAABBBBAAAF7AgR47TlFulauXLlM271799rah65Ov3LlSkmdOrWprzl8hw8fbqstlRBAAIHICuhNKE0Xowum7dmzx/ZurPqaWzxbtmy221ERAQQQQAABBBBAAAEEEEAAAQSiRoAAb9Q4OtyLFeCdNWuWXL582WG9JzfkyJHDjOTVEb1aunfvLtOnT3+yCj8jgAACUS5Qq1Yts89JkybZ3re+tt25c0dKlChh0jvYbkhFBFwksH37dnn//fclb968Jv1R8uTJJV++fNKtWzenbl64qHvsFgEEEEAAAQQQQAABBBCIcgECvFFOGnqHurialvPnz5uF1s6dOxe6goNHGizRwImOigsODpa2bdtKv379RBdCoiCAAAKuEPjggw/Ma47OHNAF1yIqJ0+elN69e5tqH374YUTV2Y6ASwU0VUjLli2laNGiMmbMGNFFTm/cuCHXrl2T3bt3m9kwr732mnTo0EHu3bvn0r6wcwQQQAABBBBAAAEEEEAgOgUI8LpYOyAgQCpXrmyOsmrVKtEV5zVYO3r06AiPXL9+fRk7dqzEihXLBHb79u1r0jdE2JAKCCCAQCQEXn31VenSpYto7nAdzbt+/XqHe9EFJPW1TW9eVa9eXRo1auSwLhsQcLXA1atX5fXXXzc3RjXdSI8ePWTbtm1y/fp10W2bN282o3r9/PxEb2Do766OPKcggAACCCCAAAIIIIAAAt4gQIA3Gq7ihAkTzPRQPdSVK1dk2rRpMn78eFtHDgwMlKlTp4p+KNXCCF5bbFRCAIFICgwZMkTq1q0rFy9elPLly0uzZs1k2bJlcvz4cTl9+rSsW7fOBMry588vBw8elIIFC8rs2bMjeTSaIRA1Am+88Ybs2rXLpGXQ7/p7XKRIEUmSJIkkS5ZMSpYsKd9++61o+oYsWbLIxo0b5b333ouag7MXBBBAAAEEEEAAAQQQQCCGBQjwRsMFyJ49u2zdulU6duwoiRIlMkfMkCGD7SO3adNGdu7cKWXKlLHdhooIIIBAZATixIkjCxculM8//1zix48vc+bMMaN5s2bNKhkzZpQKFSqY6e8PHjyQt99+2wTKNMcpBYGYEli0aJHoDJk0adKY7/o311HRXLy6kGnixIll5syZ5vfXUV2eRwABBBBAAAEEEEAAAQQ8RYAAbzRdqQQJEpi0DDpV9I8//jALpzlzaJ06rdOlv//+e7OYUdKkSZ1pTl0EEEDAtoDm/taUMIcOHZL+/fubm0s66vHFF1+UYsWKyUcffST//POPTJ48WXQ6/PMUTQehecYpCERW4JtvvjFNNU+93oSIqLz88svSs2dPU81qG1EbtiOAAAIIIIAAAggggAAC7iwQK+SDNZ+s3fkKeVnfChUqZEYj16tXT3TUFQUBBHxLQP/k6AhhXURyw4YNJhWEpqDREcI1atSQ9u3bm1zlvqXC2UZWQBdRe+GFF0RHnmtaEU3JYKecOnVKMmXKZOrrImya656CAAIIIIAAAggggAAC0S/QqlUrM7tuxowZZtHk6O+BdxyREbzecR05CwQQQMDtBY4dO2ZmIOiCbHqDRwNy8eLFk0ePHsnhw4dNjlSdQt+tWzd5+PCh258PHYx5gRMnTpjfn1y5ctkO7mqvdaSvpnTQAPGFCxdi/kToAQIIIIAAAggggAACCCDwHAIEeJ8Dj6YIIIAAAvYE9u7da9I7aD5yDa6NHj1aTp48Kffu3RNN07Bt2zbp0KGDaHqI4cOHi47y18AvBYHwBG7fvm02RyZViL+/v2l7586d8A7BNgQQQAABBBBAAAEEEEDA7QUI8Lr9JaKDCCCAgGcL3Lp1S+rUqWNG7NasWVM02KuLTlr5UjVFQ5EiRczibRs3bjQjK5ctWyYff/yxZ584vXe5gLVg6b///utULme9sXDmzBmTmiFdunQu7ycHQAABBBBAAAEEEEAAAQRcKUCA15W67BsBBBBAQIYNGyZHjhwRzcG9YMGCcKfS6yJuS5cuFQ36jhgxwiz0BiECjgR04T/N36xpFv78809H1Z55fuXKlWbkuN5YiB8//jPbeQIBBBBAAAEEEEAAAQQQ8CQBAryedLXoKwIIIOBhArqo2vjx402vR44cKQkSJIjwDIoXLy5t27Y1eXgnTpwYYX0q+LZA8+bNDUCfPn1sjeLV/M79+vUzbay2vi3I2SOAAAIIIIAAAggggICnCxDg9fQrSP8R8CABzXX5008/yZAhQ8z0e821unbtWhbU8qBr6GxX//77bzMVPnv27FKmTBnbzd98801TV0daUhAIT6B79+6SMmVKWb16dYRpPYKCgqR9+/ayc+dOyZIli7Rr1y68XbMNAQQQQAABBBBAAAEEEPAIAT+P6CWdRAABjxa4efOmDBo0SEaNGiWaj/XpkjZtWundu7cJvMSJE+fpzTz2YAHNjaolf/785rvdf6z6Vnu77ajnewIvvPCCzJ49W2rUqCGDBw+WgwcPytdffy05cuQIhbFnzx7p0qWL/Pbbb6ILrM2fP9/WiPJQO+EBAggggAACCCCAAAIIIOCGAgR43fCi0CUEvEng2LFjUqtWLbOwVqxYsaRkyZJmJKcGZc6ePSu//vqr2dapUyczunfu3LmSLFkybyLw6XPRxay0OJvn1KpvtfdpRE4+QoEqVarIkiVLRFMuLFq0SBYvXiyFCxeW3Llzi47a1eDuP//8Y/aTOnVqWbhwoVnYL8IdUwEBBBBAAAEEEEAAAQQQ8AABArwecJHoIgKeKnDlyhWpWrWqHD58WPLlyydTp04NM6jy888/y7vvviu//PKLNGrUSFasWGEW2fLU86bf/09AF8HSor8DzhRdlE1LhgwZnGlGXR8WCAgIMDeL+vbta0b0bt++XfTLKnrjSFN/6GwBDfJSEEAAAQQQQAABBBBAAAFvESDA6y1XkvNAwA0FPvzwQxPY05XqNddu4sSJw+yljvDdsmWLlChRwkyf1lQO3bp1C7MuT3qWgF77hAkTyo4dO+TEiROSOXNmWyegozC1lCtXzlZ9KiGgAnpDYdKkSfLtt9+a15RTp05J7Nixze9dsWLFJG7cuEAhgAACCCCAAAIIIIAAAl4nwCJrXndJOSEE3EPg0KFDMmPGDDM1f968eQ6Du1ZvNfA3bdo083DgwIHC1HxLxrO/a67Thg0bSnBwsHz66ae2TubixYsybNgwU7dly5a22lAJgScFEiRIYG4OtGjRQpo1ayalSpUiuPskED8jgAACCCCAAAIIIICAVwkQ4PWqy8nJIOA+AgsWLDC5LzUnZtasWW11TNM5aN7My5cvy+rVq221oZL7C/Tr188sajVz5kwZOnRouB2+fv261K9fXy5duiSaV1W/KAgggAACCCCAAAIIIIAAAggg4FiAAK9jG7YggMBzCFi5L6tVq+bUXqz627Ztc6odld1XIFu2bDJlyhTTwR49epiFsI4ePRqqw7oQ1rJly6Ro0aKyceNGyZQpkxkBHqoSDxBAAAEEEEAAAQQQQAABBBBA4BkBcvA+Q8ITCCAQFQLnz583u3F2kSxrUS6rfVT0hX3EvIBOk9dcqG3btjULYM2ePdssvKfB37t378r//vc/OXfunOlo/vz5ZcmSJZI2bdqY7zg9QAABBBBAAAEEEEAAAQQQQMDNBRjB6+YXiO4h4KkCumK9lmvXrjl1ClZ9q71Tjans1gJNmzaV/fv3y1tvvSVJkyaVXbt2ydKlS+WXX34xwd2cOXOKLrC3detWyZIli1ufC51DAAEEEEAAAQQQQAABBBBAwF0EGMHrLleCfiDgZQK5c+eWn3/+WTZv3iy1atWyfXabNm0ydbU9xfsEdDE9Tdcwbtw4M2r3zJkzogtiZc+eXTTAS0EAAQQQQAABBBBAAAEEEEAAAecEGMHrnBe1EUDApkCdOnVMzWnTpsnt27dttTpy5IgZzRknThypWbOmrTZU8kyBePHimXy7+nuii+sR3PXM60ivEUAAAQQQQAABBBBAAAEEYl6AAG/MXwN6gIBXCpQtW1ZKlCghOkKza9euEZ7jgwcP5O233xb9rnlaU6dOHWEbKiCAAAIIIIAAAggggAACCCCAAAK+LkCA19d/Azh/BFwoMHbsWPH395cJEyZIx44dzWJaYR3u0qVLoiM5f//9d8mYMaMMGDAgrGo8hwACCCCAAAIIIIAAAggggAACCCDwlAA5eJ8C4SECCESdQMGCBeWHH36QZs2aiQZ7NSfvu+++K6VLl5YUKVKY0b2//fabTJ48Wa5evSpp06Y1i26lSZMm6jrBnhBAAAEEEEAAAQQQQAABBBBAAAEvFiDA68UXl1NDwB0E6tWrJxs2bJDAwED566+/pE+fPmF2q0aNGjJ+/HjRRbgoCCCAAAIIIIAAAggggAACCCCAAAL2BAjw2nOiFgIIPIdAkSJFZPv27WYBtcWLF8u+ffvkypUroiN1dZRvo0aNpFixYs9xBJoigAACCCCAAAIIIIAAAggggAACvilAgNc3rztnjUC0C8SKFUuqVatmvqL94BwQAQQQQMCtBe7evSsHDhyQ8+fPS7JkySRXrlzywgsvuHWf6RwCCCCAAAIIIIAAAu4iwCJr7nIl6AcCCCCAAAIIIOBjAvv375fmzZtL6tSppUCBAlK1alUpXry4eVy+fHlZsWKFj4lwuggggAACCCCAAAIIOC9AgNd5M1oggAACCCCAAAIIPKfA8OHD5dVXX5XZs2fLrVu35JVXXpEqVapI0aJFxc/PT37//XepWbOmvPHGG3Lnzp3nPBrNEUAAAQQQQAABBBDwXgECvN57bTkzBBBAAAEEEEDALQW++OIL6datmwQFBUnHjh3lxIkTsnv3bpOrfevWrXLx4kUZOXKkSdfw448/Su3ateXhw4dueS50CgEEEEAAAQQQQACBmBYgwBvTV4DjI4AAAggggAACPiSwZs0a6du3r8SJE0fmzZsno0ePlowZM4YSSJw4sXTu3Fm2bNkiGTJkkNWrV0u/fv1C1eEBAggggAACCCCAAAII/J8AAV5+ExBAAAEEEEAAAQSiTaBHjx4SHBwsOoq3YcOG4R43d+7cMn/+fNGFOocNGyZnzpwJtz4bEUAAAQQQQAABBBDwRQECvL541TlnBBBAAAEEEEAgBgT27Nkjf/31l6RJk8akaLDThZIlS0q9evVMHl4d8UtBAAEEEEAAAQQQQACB0AIEeEN78AgBBBBAAAEEEEDARQJr1641e65Vq5bEjx/f9lEaNWpk6q5bt852GyoigAACCCCAAAIIIOArAgR4feVKc54IIIAAAggggEAMC5w+fdr0IFeuXE71JGfOnKb+qVOnnGpHZQQQQAABBBBAAAEEfEHAzxdOknNEwFmBw4cPi04jvXTpkqRKlUpee+01yZo1q7O7oT4CCCCAAAIIPCEQN25c8+jBgwdPPBvxjw8fPjSVrPYRt6AGAggggAACCCCAAAK+I0CA13euNWcagYAu+DJ37lwZMGCA7Nq165nahQoVkj59+pg8gM9s5AkEEEAAAQQQiFAgS5Yspk5Yf2fDa/zPP/+YzdxsDU+JbQgggAACCCCAAAK+KkCA11evPOcdSuDWrVvSqlUrWbRokXk+RYoUUrp0aTN69/z587Jx40azKEz9+vWlRYsWMnnyZEmQIEGoffAAAQQQQAABBMIXqFatmqmwYsUKM0smZcqU4Tf4/7fOnDnT/GS1t9WISggggAACCCCAAAII+IgAOXh95EJzmo4FHj16JBq41eCuBnanTp0qGtRdsmSJTJkyRX766SfzeOzYsZI0aVKZNWuWNGvWTHTELwUBBBBAAAEE7AtkzJhRAgIC5ObNm9KjRw9bDfXv7qZNmyRNmjTSoEEDW22ohAACCCCAAAIIIICALwkQ4PWlq825hikwePBg+fXXXyVdunSyZcsWadu2rcSJEydUXc351759e9m8ebMJAi9evFhGjx4dqg4PEEAAAQQQQCBiga+//loSJkwo3333nfTu3TvcG6Y///yzvPvuu2angwYNksSJE0d8AGoggAACCCCAAAIIIOBjAgR4feyCc7qhBa5evSpfffWVeXL27NlirdIdutb/e/TKK6/ItGnTzBNffPGF3Llz5/9t5CcEEEAAAQQQiFAgT5488v3334ufn5/Je1+qVCkzW8b6mxoUFGRuuLZu3Vrq1Klj/ta+//778tZbb0W4byoggAACCCCAAAIIIOCLAuTg9cWrzjk/FtCRQTdu3JCKFStK+fLlHz8f3g+1a9eWokWLyrZt2+SXX36RunXrhledbQgggAACCCDwlECjRo0kefLkJv/9H3/8YQK5sWLFkhdeeMH8XX7w4IFpET9+fPnyyy9tp3N46jA8RAABBBBAAAEEEEDAJwQYwesTl5mTdCSgi6dp0RFCzhSrvuYEpCCAAAIIIICA8wKVK1eWw4cPi6ZsKFasmBnRe/nyZXn48KGZUdO1a1c5ePAgwV3naWmBAAIIIIAAAggg4GMCjOD1sQvO6YYWOHPmjHkiW7ZsoTdE8Chr1qymhtU+gupsRgABBBBAAIEwBBIlSiTdu3c3X7p46ZUrVyRJkiSiue8pCCCAAAIIIIAAAgggYE+AAK89J2p5qYAu8qLl9u3bTp2hlSfQ39/fqXZURgABBBBAAIGwBTRFQ4oUKcLeyLMIIIAAAggggAACCCDgUIAUDQ5p2OALAtmzZzenuWPHDqdO16pvtXeqMZURQAABBBBAAAEEEEAAAQQQQAABBBCIIgECvFEEyW48U6BmzZqm47Nnz5a7d+/aOonr16/L/PnzTd2AgABbbaiEAAIIIIAAAggggAACCCCAAAIIIICAKwQI8LpClX16jMDrr78uRYoUkdOnT0ufPn1s9btnz55y6dIlKV++vOTLl89WGyohgAACCCCAAAIIIIAAAggggAACCCDgCgECvK5QZZ8eI6D5/r755huzmMvQoUPlyy+/FF3kJawSFBQkvXr1kgkTJojm3h05cmRY1XgOAQQQQAABBBBAAAEEEEAAAQQQQACBaBMgwBtt1BzIXQVKliwpEydOlDhx4shnn30mRYsWlZkzZ8rJkydFF1M7fvy4fPfdd1KgQAEZPHiwCQZ///33kj9/fnc9JfqFAAIIIIAAAggggAACCCCAAAIIIOAjAn4+cp6cJgLhCrRp00bSp08v77zzjugCaq1atQqzfrZs2WTatGlStmzZMLfzJAIIIIAAAggggAACCCCAAAIIIIAAAtEpQIA3OrU5llsLVKtWTQ4dOiQ6OnfRokWye/duuXz5sqRMmdKM1m3QoIG0bNlS4sWL59bnQecQQAABBBBAAAEEEEAAAQQQQAABBHxHgACv71xrztSGQIIECSQwMNB82ahOFQQQQAABBBBAAAEEEEAAAQQQQAABBGJUgBy8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBXwi9Gj+/DB7927J/Hjx3da4NKlS3Lnzh3TLmPGjE63pwECCCCAAAIIIIAAAggggAACCCCAAAIIeI8AI3ij8VrOnz9fatSoIWnTphV/f3/JkyePtG7dWjZt2mS7F23atJFMmTKZL9uNqIgAAggggAACCCCAAAIIIIAAAggggAACXilAgDcaLuutW7fkzTfflMaNG8vKlSvl/PnzEhwcLPv375cZM2ZI2bJlpWvXro9H5kZDlzgEAggggAACCCCAAAIIIIAAAggggAACCHiBAAHeaLiIn3zyiUyfPv3xkRIlSiTZsmWTWLFimeeCgoJkxIgRUqBAATl27NjjevyAAAIIIIAAAggggAACCCCAAAIIIIAAAgiEJ0CANzydKNj2999/y5gxY8yeNDXDkiVL5Pr163L06FG5cuWKDBkyRJIlS2a2Hzx4UMqXL0+QNwrc2QUCCCCAAAIIIIAAAggggAACCCCAAAK+IECA18VXedy4cfLo0SPx8/OTVatWSZ06dSR27P9j18Bujx49ZN++fZI/f37TkxMnTkilSpXk3LlzLu4Zu0cAAU8QOHv2rKxZs0bmzZsna9euNSlePKHf9BEBBBBAAAEEEEAAAQQQQAABBKJHgACvi501eKulefPmj4O4Tx8yffr0sn79eilXrpzZpGkaAgICRHP3UhBAwDcFVqxYIaVKlZIMGTKYmz5NmjSRihUrir5e6GvFb7/95pswnDUCCCCAAAIIIIAAAggggAACCIQSIMAbiiPqHxw4cMDstHDhwuHuPGnSpKIBnZIlS5p6O3bsEA3o6OhfCgII+I7AvXv3zKKMNWvWlM2bN4u/v79UqFDBLNKogd348eObG0JVqlSRwMBAefDgge/gcKYIIIAAAggggAACCCCAAAIIIPCMAAHeZ0ii9on79++bHSZMmDDCHWsgZ+nSpZIzZ05Td/ny5dKpU6cI21EBAQS8QyA4OFjeeOMNsyhj4sSJZeTIkXLx4kWTomHu3Lmybt06uXDhgnz99deirykTJ06U1q1be8fJcxYIIIAAAggggAACCCCAAAIIIBApAQK8kWKz3yhXrlym8t69e201SpUqlaxcuVJSp05t6msO3+HDh9tqSyUEEPBsgVGjRsnixYslZcqUZvRu586dzQjeJ88qUaJE0r17d9mwYYNZoHHOnDkyYcKEJ6vwMwIIIIAAAggggAACCCCAAAII+JAAAV4XX2wrwDtr1iy5fPmyraPlyJHDjOTVEb1aNJgzffp0W22phAACnilw8+ZN+fLLL03n9f97vnz5wj2RQoUKyeTJk02dzz//XO7evRtufTYigAACCCCAAAIIIIAAAggggIB3ChDgdfF11cXVtJw/f94stHbu3DlbRyxRooRoUDh27Nii07bbtm0r/fr1k6CgIFvtqYQAAp4loClZ9CaQLqym+XftlEaNGokGevV15ddff7XThDoIIIAAAggggAACCCCAAAIIIOBlAgR4XXxBAwICpHLlyuYoq1atkjx58phg7ejRoyM8cv369WXs2LESK1YsE9jt27evSd8QYUMqIICAxwmsX7/e9LlevXpO9d2qb7V3qjGVEUAAAQQQQAABBBBAAAEEEEDA4wUI8EbDJdT8mNZ06ytXrsi0adNk/Pjxto4cGBgoU6dOFT8/P1OfEby22KiEgMcJ/Pfff6bPmqLFmWLVt9o705a6CCCAAAIIIIAAAggggAACCCDg+QIEeKPhGmbPnl22bt0qHTt2FF0gSUuGDBlsH7lNmzayc+dOKVOmjO02VEQAAc8SSJAggemws7l0rfpWe886a3qLAAIIIIAAAggggAACCCCAAALPK/B/w0Kfdy+0j1BAgy+almHkyJGyfft2uX79eoRtnqzw6quvik7B1sWXxo0bJ3v37n1ys0t/Xrx4sXz33XdRkv/3yJEjpq+3b992aZ/ZOQKeJpA1a1bT5b///luaNWtmu/taX0u2bNlst6EiAggggAACCCCAAAIIIIAAAgh4jwAB3mi+lppqQRdQi2xp3bq16Fd0lhkzZsjSpUuj9JA3btyI0v2xMwQ8XaBGjRoyaNAgmTNnjnzxxRcSP378CE9Jb5TMmzfP1NP2FAQQQAABBBBAAAEEEEAAAQQQ8D0BAry+d82dPuNJkybJW2+9JcHBwU63fbqB5iP++eefpWHDhk9v4jECPi1QunRpKVCggOiI3P79+8uXX34Zocdnn30mZ8+eNTeNChcuHGF9KiCAAAIIIIAAAggggAACCCCAgPcJEOCNoWt67949WyP0nu7epUuX5M6dO+bpjBkzPr3ZJY9TpEghAQEBUbLv1atXm/3EiRMnSvbHThDwFoFYsWLJqFGjpGLFijJgwADR/3cffvihw9MbOHCgDBs2TOLGjWtSvzisyAYEEEAAAQQQQAABBBBAAAEEEPBqARZZi8bLO3/+fNFp1GnTphV/f3/JkyePSbewadMm273QBdcyZcpkvmw3oiICCHiEQNmyZeXbb781fe3atauUK1dONAf21atXzXNXrlyRBQsWSKlSpeTTTz+V2LFji46KL168uEecH51EAAEEEEAAAQQQQAABBBBAAIGoF2AEb9SbPrPHW7duSYcOHcwCaU9u3L9/v+jXrFmzpHPnzmbUngZ+KQgg4LsC7du3l3Tp0klgYKBZWFEXV9Si+bsfPnz4GEbrTJkyRWrWrPn4OX5AAAEEEEAAAQQQQAABBBBAAAHfE2AEbzRc808++SRUcDdRokRmxXudkq0lKChIRowYYfJvHjt2LBp6xCEQQMCdBerXry9HjhyRoUOHmtG6SZMmNcHdZMmSSZkyZczrxeHDhwnuuvNFpG8IIIAAAggggAACCCCAAAIIRJMAAV4XQ+uCSWPGjDFH0dQMS5YskevXr8vRo0dFp1sPGTJENGij5eDBg1K+fHkhyGs4+AcBnxZIkiSJdOvWTTZu3CjXrl2TBw8emFQNOqK3S5cuojeKKAgggAACCCCAAAIIIIAAAggggAABXhf/DowbN04ePXpkplevWrVK6tSpY/Jm6mE1sNujRw/Zt2+f5M+f3/TkxIkTUqlSJTl37pyLe8buEUDAkwQ0RQMFAQQQQAABBBBAAAEEEEAAAQQQeFqAAO/TIlH8WIO3Wpo3b/44iPv0IdKnT29ybeqCSlp0BG9AQIBo7l4KAggggAACCCCAAAIIIIAAAggggAACCCDgSIAAryOZKHr+wIEDZk+FCxcOd4+aY3PFihVSsmRJU2/Hjh3SpEkTM/o33IZsRAABBBBAAAEEEEAAAQQQQAABBBBAAAGfFSDA6+JLf//+fXOEhAkTRngkf39/Wbp0qeTMmdPUXb58uXTq1CnCdlRAAAEEEEAAAQQQQAABBBBAAAEEEEAAAd8UIMDr4uueK1cuc4S9e/faOlKqVKlk5cqVkjp1alNfc/gOHz7cVlsqIYAAAggggAACCCCAAAIIIIAAAggggIBvCRDgdfH1tgK8s2bNksuXL9s6Wo4cOcxIXh3Rq6V79+4yffp0W22phAACCCCAAAIIIIAAAggggAACCCCAAAK+I0CA18XXWhdX03L+/Hmz0Nq5c+dsHbFEiRKiQeHYsWNLcHCwtG3bVvr16ydBQUG22lMJAQQQQAABBBBAAAEEEEAAAQQQQAABBLxfgACvi69xQECAVK5c2Rxl1apVkidPHhOsHT16dIRHrl+/vowdO1ZixYplArt9+/Y16RsibEgFBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAJAQK80XCZJ0yYIPny5TNHunLlikybNk3Gjx9v68iBgYEydepU8fPzM/UZwWuLjUoIIIAAAggggAACCCCAAAIIIIAAAgj4hAAB3mi4zNmzZ5etW7dKx44dJVGiROaIGTJksH3kNm3ayM6dO+X/a+9O4K+q6gSAHxaRTXBfwQ1TB3AXXBIpY3DXaiydUCp1cit1TEZFUz9NiKZmJWpag1KjmWVquDKmKSUuiQvuW5poIWLuIAhv3rnNvfPen//26D14793v/Xz+vLuce+853/M/3Pv/vXPPHTFiRKf3kZAAAQIECBAgQIAAAQIECBAgQIAAgeYXEOBdTnXcs2fPEIdlePvtt8OMGTOSF6dVcuqhQ4eGe++9N0yZMiXE8Xn79etXye7SEiBAgAABAgQIECBAgAABAgQIECDQhAJ/f+6/CQtWr0WKQy3EAO2yTmPHjg3xx0SAAAECBAgQIECAAAECBAgQIECAAAE9eP0OECBAgAABAgQIECBAgAABAgQIECBAoEEFBHgbtOJkmwABAgQIECBAgAABAgQIECBAgAABAgK8fgcIECBAgAABAgQIECBAgAABAgQIECDQoAICvA1acbJNgAABAgQIECBAgAABAgQIECBAgAABL1nzO0CAAAECBAgQIECAAAECBAgQINCwAu+99164/vrrw+233x5efvnlsHDhwrDBBhuEkSNHhoMPPjgMHDiwYcsm4wQ6IyDA2xklaQgQIECAAAECBAgQIECAAAECBOpOYPLkyeHUU08Nc+fOLcvbI488Em6++eZw+umnh2984xvhnHPOCT169ChLY4FAswgYoqFZalI5CBAgQIAAAQIECBAgQIAAAQI5EjjhhBPCEUcckQR3d9ttt3DFFVeE+++/P8ycOTNcd9114ZBDDgkff/xxuPDCC8Mee+wR3n///RzpKGqeBPTgzVNtKysBAgQIECBAgAABAgQIECBAoAkEvve974Uf/vCHoVevXuHHP/5xGDNmTFmptttuu/CFL3whjBs3Lnz+858Pf/jDH8Khhx4abrzxxrJ0Fgg0g4AevM1Qi8pAgAABAgQIECBAgAABAgQIEMiJwOzZs8MZZ5wRunTpEq699tqlgrulDNtvv324++67wxprrBFuuummcMMNN5RuNk+gKQQEeJuiGhWCAAECBAgQIECAAAECBAgQIJAPgYsvvjjMnz8/GYLhgAMO6LDQm2yySZgwYUKS7rvf/W6H6SUg0GgCAryNVmPyS4AAAQIECBAgQIAAAQIECBDIscDUqVOT0h977LGdVvjyl78c+vTpEx544IEwZ86cTu8nIYFGEBDgbYRakkcCBAgQIECAAAECBAgQIECAAIGwZMmS8Pzzz4euXbuGnXfeudMiPXv2DHFc3kKhkOzf6R0lJNAAAgK8DVBJskiAAAECBAgQIECAAAECBAgQIBDChx9+GD7++OPQu3fv0L1794pI+vfvn6R/5513KtpPYgL1LiDAW+81JH8ECBAgQIAAAQIECBAgQIAAAQKJQN++fUOvXr3C+++/H959992KVF577bUk/dprr13RfhITqHcBAd56ryH5I0CAAAECBAgQIECAAAECBAgQyASGDRuWzN9xxx3Zuo5m/vrXv4bHH388CQ4PGTKko+S2E2goAQHehqoumSVAgAABAgQIECBAgAABAgQI5FvgC1/4QgJw7rnnJmPydkZj4sSJSdp99903Gd6hM/tIQ6BRBAR4G6Wm5JMAAQIECBAgQIAAAQIECBAgQCAceeSRYcMNNwwzZ84MJ598cociN9xwQ5g0aVLo1q1bOPvssztMLwGBRhMQ4G20GpNfAgQIECBAgAABAgQIECBAgECOBXr27Bmuvvrq0KNHj3DRRReFL33pSyEOwdByWrBgQZgwYUKIPX6XLFkSYi9ewzO0VLLcDAKVvW6wGUqsDAQIECBAgAABAgQIECBAgAABAg0tsNtuu4Xrr78+Ce7+/Oc/DzfeeGMYPXp02HrrrZPA7wsvvBBuvfXWMHfu3NClS5ek5+64ceMauswyT6AtAQHetmSsJ0CAAAECBAgQIECAAAECBAgQqFuB/fbbLxmm4dRTTw1xGIabbrop+SnN8I477hjOO++8sMcee5SuNk+gqQQEeJuqOhWGAAECBAgQIECAAAECBAgQIJAfgc022yz86le/CnPmzAnTpk0LL7/8cli4cGHYYIMNwsiRI8M//dM/5QdDSXMrIMCb26pXcAIECBAgQIAAAQIECBAgQIBAcwiss8464bDDDmuOwigFgQoFvGStQjDJCRAgQIAAAQIECBAgQIAAAQIECBAgUC8CArz1UhPyQYAAAQIECBAgQIAAAQIECBAgQIAAgQoFBHgrBJOcAAECBAgQIECAAAECBAgQIECAAAEC9SIgwFsvNSEfBAgQIECAAAECBAgQIECAAAECBAgQqFBAgLdCMMkJECBAgAABAgQIECBAgAABAgQIECBQLwICvPVSE/JBgAABAgQIECBAgAABAgQIECBAgACBCgUEeCsEk5wAAQIECBAgQIAAAQIECBAgQIAAAQL1IiDAWy81IR8ECBAgQIAAAQIECBAgQIAAAQIECBCoUECAt0IwyQkQIECAAAECBAgQIECAAAECBAgQIFAvAgK89VIT8kGAAAECBAgQIECAAAECBAgQIECAAIEKBQR4KwSTnAABAgQIECBAgAABAgQIECBAgAABAvUi0L1eMiIf+RKYOHFiuPzyy5d7oT/44IMwe/bs0K1bt9C1q+83lnsFOGEuBD7++OOknN27u8TkosIVcrkLLF68OCxZsiTENtalS5flfn4nJNDsAoVCIcRrmfvFZq9p5VuRAu4XV6S+c+dBIN4rxp+BAweGXr161XWRX3/99brOX6Nkzl/fjVJTTZLPjTfeOCnJG2+8EeLPiprSG4oVdX7nJZAHgYULF+ahmMpIYIUJLFq0aIWd24kJ5EHA/WIealkZV7SA+8UVXQPO3+wCr7zySkMUMXZa2GijjRoir/WayS7Fb6gL9Zo5+WpOgRdffDGsqD9Kf/nLX4YzzzwzjB49OpxwwgnNCaxUBFagwPz588NBBx0UevbsGa6//voVmBOnJtC8AmeffXZ46KGHwllnnRWGDx/evAVVMgIrSOCuu+4KF154YfjUpz4Vxo0bt4Jy4bQEmlcgfnly4IEHJk9UTp06tXkLqmQEVqDAhAkTwn333Rd+8IMfJPGPFZiVTp26b9++YcCAAZ1KK1HrAnrwtu5ibQ0FBg0aVMOjt3/o9dZbL0mw4YYbhn322af9xLYSIFCxwPvvv5/sEx8d18Yq5rMDgU4JXHrppUm6YcOGaWedEpOIQGUCb731VrJD/EPTtawyO6kJdEYg7R0fh8zTxjojJg2BygUmT56c7LTBBhuELbfcsvID2KPhBAxC2nBVJsMECBAgQIAAAQIECBAgQIAAAQIECBD4u4AAr98EAgQIECBAgAABAgQIECBAgAABAgQINKiAAG+DVpxsEyBAgAABAgQIECBAgAABAgQIECBAQIDX7wABAgQIECBAgAABAgQIECBAgAABAgQaVECAt0ErTrYJECBAgAABAgQIECBAgAABAgQIECAgwOt3gAABAgQIECBAgAABAgQIECBAgAABAg0qIMDboBUn2wQIECBAgAABAgQIECBAgAABAgQIEBDg9TtAgAABAgQIECBAgAABAgQIECBAgACBBhUQ4G3QipNtAgQIECBAgAABAgQIECBAgAABAgQICPD6HSBAgAABAgQIECBAgAABAgQIECBAgECDCgjwNmjFyTYBAgQIECBAgAABAgQIECBAgAABAgQEeP0OECBAgAABAgQIECBAgAABAgQIECBAoEEFBHgbtOJkmwABAgQIECBAgAABAgQIECBAgAABAgK8fgcIECBAgAABAgQIECBAgAABAgQIECDQoAICvA1acbJNgAABAgQIECBAgAABAgQIECBAgAABAV6/AwQIECBAgAABAgQIECBAgAABAgQIEGhQge4Nmm/ZJrBMAv369Uv269+//zLtbycCBNoX6NGjR1h55ZWDNta+k60E/hEB17J/RM++BDoWSNtY+tnxHlIQIFCJQLdu3UKfPn2Se8ZK9pOWAIHOC6TXsPSz83tK2agCXQrFqVEzL98EKhVYvHhxuP3228OIESOC/+gq1ZOeQOcEHn744dCrV68wePDgzu0gFQECFQnMnTs3zJw5M+y5554V7ScxAQKdE1iyZElyv7jrrruGVVddtXM7SUWAQEUCjz76aOjevXsYOnRoRftJTIBA5wTmzZsXHnrooeR+sUuXLp3bSaqGFhDgbejqk3kCBAgQIECAAAECBAgQIECAAAECBPIsYAzePNe+shMgQIAAAQIECBAgQIAAAQIECBAg0NACArwNXX0yT4AAAQIECBAgQIAAAQIECBAgQIBAngUEePNc+8pOgAABAgQIECBAgAABAgQIECBAgEBDCwjwNnT1yTwBAgQIECBAgAABAgQIECBAgAABAnkWEODNc+0rOwECBAgQIECAAAECBAgQIECAAAECDS0gwNvQ1SfzBAgQIECAAAECBAgQIECAAAECBAjkWUCAN8+1r+wECBAgQIAAAQIECBAgQIAAAQIECDS0gABvQ1efzBMgQIAAAQIECBAgQIAAAQIECBAgkGcBAd48176yEyBAgAABAgQIECBAgAABAgQIECDQ0AICvA1dfTJPgAABAgQIECBAgAABAgQIECBAgECeBQR481z7yk6AAAECBAgQIECAAAECBAgQIECAQEMLCPA2dPXJPAECBAgQIECAAAECBAgQIECAAAECeRYQ4M1z7Ss7AQIECBAgQIAAAQIECBAgQIAAAQINLSDA29DVJ/MECBAgQIAAAQIECBAgQIAAAQIECORZQIA3z7Wv7AQIECBAgAABAgQIECBAgAABAgQINLSAAG9DV5/MEyBAgAABAgQIECBAgAABAgQIECCQZwEB3jzXvrITIECAAAECBAgQIECAAAECBAgQINDQAgK8DV19Mk+AAAECBAgQIECAAAECBAgQIECAQJ4FBHjzXPvKToAAAQIECBAgQIAAAQIECBAgQIBAQwsI8DZ09ck8AQIECBAgQIAAAQIECBAgQIAAAQJ5FhDgzXPtKzsBAgQIECBAgAABAgQIECBAgAABAg0tIMDb0NUn8wQIECBAgAABAgQIECBAgAABAgQI5FlAgDfPta/sBAgQIECAAAECBAgQIECAAAECBAg0tIAAb0NXn8wTIECAAAECBAgQIECAAAECBAgQIJBnAQHePNe+shMgQIAAAQIECBAgQIAAAQIECBAg0NACArwNXX0y3xmBxYsXh//6r/8Ko0aNChtttFFYbbXVwp577hm+/e1vhz/+8Y+dOYQ0BHItcMstt4QxY8aE4cOHJ+1nnXXWCbvvvns46qijwqOPPtopG+2wU0wSEcgELr300rDuuusmPy+88EK2vr2ZF198MRx55JFhu+22C/369QuDBw9Olq+88srwwQcftLerbQSaXuDhhx8OZ555Zthnn31CvI7Fn5EjR4bTTz89vPPOO50qv2tZp5gkyqnAgw8+GMaOHRuGDRuWXIMGDhyY/M0V2917773XKRVtrFNMEuVEYO+99w5dunQJkyZN6nSJq3UvWK3jdDrjElZHoGAi0MQCs2fPLgwdOrRQbC2t/nTv3r3ws5/9rIkFFI3AsgsUL+yF4hcjrbadtE1169atcPzxxxfef//9Nk+kHbZJYwOBVgWefPLJQs+ePbO29/TTT7earnTl+eefX1hppZWyfdI2mn7usssuhbfeeqt0F/MEciPwwx/+sN32sdZaaxVuvvnmdj1cy9rlsTHHAh999FHh6KOPLnTt2rXNa9D6669fuOmmm9pV0sba5bExZwKXXHJJ1p4uvvjiTpW+WveC1TpOpzItUVUFusSjFW/+TQSaTuDdd98NI0aMCI8//nhStm233Tbsv//+YYMNNgj33ntvuOGGG8L8+fOTb8WK/2mG4447rukMFIjAsgosWLAg6bE7a9as5BBrr7120os39gj88MMPQ+wJdc0114SPP/442R57bEyZMmWp02mHS5FYQaBdgYULF4addtqprHd8McAbttxyyzb3iz10Dz/88GR7MTAcDj744FAM6IZXX301FINW4bHHHku2bbXVVuF//ud/kp6LbR7MBgJNJBD/zPnyl78cil/mJ6Xq0aNH8kRXvB+MPQrjveCMGTOSbX379k2e7Npiiy2WEnAtW4rECgKZwMknnxwuvPDCZHnllVcO//Zv/xZ22GGHENvN1KlTw5133plsi+3vgQceCPFvspaTNtZSxHKeBeLfWIcddlhYsmRJwhBjFV//+tfbJanWvWC1jtNuZm2snUBVw8UORqCOBL75zW9m33p98YtfLMRvl0un6dOnF/r375+kiT15X3vttdLN5gnkWqD4hUfWfopDmhTmzZu3lEdxeIZC8RHXLN0vf/nLpdJoh0uRWEGgXYFx48Zlbap495fMt9eD94033ij06tUrSRevab/73e/Kjl8MGBcOOeSQ7JjHHnts2XYLBJpZoDhEV/a7v/nmmxdiD8HSqfg4eKG0zRU7BpRuzuZdyzIKMwTKBO67775C8RHypJ0Vh8ErPPXUU2Xb48LEiROzdljsKFCI7a7lpI21FLGcR4FiJ5rkmhSfkEzvAeNnRz14q3UvWK3j5LHu6qXMoV4yIh8EqikQg1HFnhjJf4wbbrjhUsHd9FzFb5Wz/zzPOuusdLVPArkWWLRoUaFPnz5J24g363/961/b9IiP26U3IHvttVdZOu2wjMMCgQ4F7r777uwR11VWWSVrW+0FeMePH5+l+9GPftTqOeIf08UxEZN08dpY7CnVajorCTSTQPFJlMKaa66Z/N737t278Morr7RavOKTKIUY/I3XsviIefwDt3RyLSvVME+gXKA4vm52DSqOHV++8f+Wir0QC8UnU7J0La9p2lirbFbmTOCee+4pfOITn8jaSfr3VWcCvNW6F6zWcXJWdXVVXC9ZK7YYU/MJFHsShuKYoEnBimNChfhIUGvTfvvtF9JH8a644opQDGy1lsw6ArkSiC8fTF/IdOCBB7b7OHdsQ8VgcOIzc+bMMiftsIzDAoF2Bd5+++3k5TTxcbz4ItD4Iqh0ii/YaGuaPHlysqkYEA5f/epXW01WDFqFk046KdkWr40//elPW01nJYFmErj11lvDm2++mRQp/v4Xv/BvtXjFnlLhK1/5Sij+YZ0Mj/Lcc8+VpXMtK+OwQKBMoPTe71Of+lTZtnQhXsP22GOPdDEZ5itbKM5oY6Ua5vMmUIwOJkNFxvbz/PPPJ8WPw3J94xvf6DRFte4Fq3WcTmdcwqoLdK36ER2QQB0IpOOpxayMHj263RwVXyKVbP/LX/4Sir2n2k1rI4E8CMRx0P75n/85DBkyJBlDrb0yx8BRHJ83TnPnzg3FoVCy5NphRmGGQIcCxxxzTDJm7uqrrx7iDXZ7Qd30YC+//HIo9rBPFkeOHNnml5kxwWc+85nsmFdffXV6CJ8EmlYgBo3SKQZw25tOO+20EAO7xcfNwyc/+cmypK5lZRwWCLQpEO8f25qKT5Jkm+JY86WTNlaqYT5vAvGL/WLv9/hkfVL0I444IvkSZJtttsko2rsnrNa9YLWOk2XazAoREOBdIexOWmuB+++/PzlFDD5tvfXW7Z6u9D/PJ554ot20NhLIg0D8UmTatGkhtoeOBvR/5513QrwhiFPsDR9frpFO2mEq4ZNA+wIx4HrttdcmiS677LJQfNt4+zv839a0jcXF1l5aU3qQtdZaK6y33nrJKte6UhnzzSoQXwYap9hzd9CgQctczLSduadcZkI7NrHAdtttl5UuvlCttSm+kPf222/PNrW8XmljGY2ZHAvEv79+//vfh5/85CehOKxQpyXS9hN3aNm2Wh6kvXvBah2n5TktL18BAd7l6+1sy0nghRdeSM60wQYbhJVWWqnds5Y+svfMM8+0m9ZGAgTKBeLQJuk3zsOHDy/bqB2WcVgg0KpAcVzQ5NG8uHHMmDGh+FLQVtO1tjJ9lC9u22STTVpLUrYuvd6999574fXXXy/bZoFAMwnMnz8/pNeg9A/e2Esq/vF8zjnnhOKY8UlP3eOPPz75ciVua2tKj+Oesi0h6/MsUHwpb4hDBMXpu9/9bii+m6GMI/bWPeGEE8Ljjz+erI9PiJUGheNKbayMzELOBOKXhzG4escddyz1BElnKKp1L1it43Qmz9LUTqB77Q7tyARWjEAcOzR9DGidddbpMBPxm6x0euutt9JZnwQIdCAQhzWZMGFCkireeTDeAwAAIldJREFUnHzta1/L9tAOMwozBNoUiEGlsWPHhtgTfuDAgWHSpEltpm1tQ+njsMtyvetsT+HWzm0dgXoWiF+cpEHbNdZYI3nHQmxraU/5NO9xSIbi28nDj3/84zBlypQwYMCAdFPy6VpWxmGBwFIC8doTe+fuv//+If4d9dnPfjaMGDEibL/99sn7UH73u9+FF198Mdmv+KK18Itf/KLsGNpYGYeFHArE4Rdi21jWqVr3gtU6zrKWw37VERDgrY6jo9SRQPxDOZ169eqVzrb5WZrmww8/bDOdDQQI/L9AbGd77713EpiKa2PvjNJxC7XD/7cyR6AtgfPPPz/ce++9ydi4V111VVh11VXbStrqeu2sVRYrCYTYSz2devbsGQ444IAkCNW9e/cwePDg5IVqsbfSU089FeLj43fddVfSq3DWrFlh3XXXTXfNrnFxRen9YpagxUxpGveULXAsNq3ArrvuGp599tmkZ3wcGmX69OnJT2mB4wujvv/974fYIaB0ch0r1TBPoHKBarWhah2n8hLYo5oC5f/DVvPIjkVgBQm0vKnvKBulY4a6Ge9Iy3YCIcRHXw888MDw2GOPJRybb7551pM39dEOUwmfBFoXiG8e/9a3vpVsjF+QlL5hvPU9ll6rnS1tYg2BKFDaNmLv3NjDMD4WHq9b8edXv/pV8vnII4+EoUOHJmhvvvlmOPHEE8sAS48TA8UdTe4pOxKyvRkFfv7znyftKB33OgZxN9tssxB7z6dT7Cm/5557htdeey1dlXxqY2UcFghULFCtNlSt41RcADtUVUCAt6qcDlYPAqVj7sZeGR1NpWk6c/Pe0fFsJ9DMAvEP4M985jPhnnvuSYoZx/SMfziX9lqKG7TDZv4tULZ/VCB+SRLH2120aFHSm3DixInLdEjtbJnY7JQDgTjuZzrF+7w4fm68bsXeu6VTDO7efffdWe/5+Ph47FWfTtpYKuGTQOsC8YVQ8Xo2Z86c5MVQsZduDBTFHvLxnjEOzxA7BcTpzjvvDLvvvnuYPXt2djBtLKMwQ2CZBKrVhqp1nGUqhJ2qJiDAWzVKB6oXgb59+2ZZWbBgQTbf1kxpmv79+7eVzHoCuReIL8HYZZddwowZMxKL+FKnOLZaay930g5z/+sCoB2BcePGhfhSz3gz/d///d9hWb9c1M7aQbYp1wKlbSNCnHfeedmLoFrCrLnmmuG0007LVpcGeEuPU3q/mCVuMVOaxj1lCxyLTSfw9ttvh5NPPjl52W4cR/S2225Lhuzq3bt3VtZNN9003HjjjeGYY45J1r300kth/Pjx2XZtLKMwQ2CZBKrVhqp1nGUqhJ2qJiDAWzVKB6oXgfRNrjE/pYOFt5W/0jT9+vVrK5n1BHItEN/uGsdYS990vMMOOySB3taCuxFKO8z1r4vCtyMQ/wC+5JJLkhTf/OY3ky9I4h/JLX9KeyDG61S6vfSpk9JrVum1rK3Tl6Yp3bet9NYTaFSB0mtQLEPsNdjeVDqGfByXN51Kj1PaftLtLT9L02hjLXUsN5tA/IIyHbfz8MMPb7edxTHn0xd7XnPNNUmP3+ihjTXbb4XyLG+B0mtN6TWorXyUpindt3S+NE2lx2krvfXLR0CAd/k4O8tyFIiPiqc3EK+++mqHZy5NU/pijQ53lIBATgRuuOGGZHzQuXPnJiXeZ599kkdd45uT25q0w7ZkrM+7wG9+85uM4Nxzzw2rrbZaqz+//vWvs3TDhw/P0vzhD3/I1g8aNCibL72WZStbzKRp4oumSsdGbJHMIoGGFxgwYEBWhthTvnQ521AyE8cLTaf0i8y47FqWqvgksLTA008/na2Mw3e1N/Xp0yfstttuSZLFixcnLziMC9pYe2q2EehYoFr3gtU6Tsc5lqKWAgK8tdR17BUmkI6xFr99SoNSbWUmjhGVTsOGDUtnfRIgUBS4/PLLw0EHHZS8WC2CxEfsYoAq3qh3NGmHHQnZTuAfE0jbWDxKaVCqtaPG8X5feeWVZNNWW221zMNCtHZs6wjUm0D8AmO99dZLshV/9+M4oO1Nf/vb37LN8UuX0iltZ+4pS1XMEwhh3rx5GUPauSZb0cpMHK4hneKYvemkjaUSPglULpC2n7jnP3IvWK3jVF4Ce1RToHs1D+ZYBOpFYKeddkoG8o/5iWOp/cu//EubWZs+fXq2Le5nIkDg7wJXXXVVEtAtFAohjq12wQUXhJNOOqnTPNphp6kkzJHA3nvvHeKYnx1NsQdv+qj40Ucfne2z0UYbZbtus802YeWVVw4fffRR2YuhsgQlMw8++GCSLq5yrSuBMdu0AvH3PI79Gac4dnxpL92WhS79sr/l0EOuZS21LBP4u8CQIUMyiniNGTlyZLbc2kzpFy1bbrlllkQbyyjMEKhYoFr3gtU6TsUFsEN1BYp/uJsINJ3AI488Uii2lOSn+ObWNstX7M1UKD66l6Tbcccd20xnA4G8CcyaNatQfIw7aRtdu3YtTJkypWIC7bBiMjsQyAQOOeSQ7DpWfCFbtr7lTLzGpde7mTNnttycLX/ta1/L0t18883ZejMEmlXgF7/4RfY7XxxaqLBkyZI2i/q5z30uS3vrrbeWpXMtK+OwQCATKD7RlbWbUaNGZetbmymO1VsoDu2VpO/Ro0ehOM58lkwbyyjMEMgEfvKTn2Tta9KkSdn61maqdS9YreO0lkfrlo+AIRqqGy93tDoR2HbbbUN8CVSc4uPk8SUALaf58+eHI488MsRH9+J0yimntEximUBuBY499tiQvszprLPOCmPHjq3YQjusmMwOBCoWiNexdDrqqKOSl7Gly+lnMWAVrrzyymRx6NChIY6jbSLQ7AIHHHBASHu8xzYwfvz4Votc/MIjxLHm47ThhhuG0aNHl6VzLSvjsEAgE/j0pz8dNt5442T5zjvvDGeccUa2rXSmGNYIJ5xwQvZitX/9138NcWzsdNLGUgmfBJZNoFr3gtU6zrKVwl5VEVg+cWRnIbD8BYqP4xWKj5Un33zFHojf+c53Ci+99FLyjXFxWIZC8Y3K2bdiO++8c6E44P/yz6QzEqhDgdhbt3iBydpO7Pm07777durnzTffLCuRdljGYYFApwU624M3HnCvvfbK2mzxD+XCb3/720LxS8zC7NmzCz/4wQ+yJ1XitVDv3U5XgYRNIHDHHXcUunXrlrWP4jjyhbvvvrtQ/HK/8Oc//7kwYcKEsu0xfWuTa1lrKtYRKBTuueeeQry2pPeNxQBRYdq0aYUPPvgg+Sm+GLRQHLoh21780qUQe/O2nLSxliKW8y5QSQ/eaFWte8FqHSfv9beiyh9W1Imdl8DyEIiP5xVfBpXdVMSbj3RIhvRGpDgmW6H4IrblkR3nINAQAvELj7R9VPr56quvLlVG7XApEisIdChQSYC3+KKbQrHXYVm7bXmti235oosu6vC8EhBoNoG77rorezQ8vabFR8TT+fgZA1QdtQ/Xsmb7zVCeaglceumlhd69e5e1qXgNKg38xnY2aNCgwkMPPdTmabWxNmlsyKFApQHeat0LVus4OayyuiiyIRqKVxtT8wp88YtfDPfff38ojq8bij04koKmQzIUb+7DiSeemGzvzAtvmldJyQiUC6Qvdipfu+xL2uGy29mTQGcEVl999XDbbbclj6DH+Til17o4v9VWW4VbbrkluebFZROBPAnEx8iLY3yG4tiCYZVVVkmKXhz/MyPYbbfdQrHHYYftw7UsIzNDoEyg2DM+PPnkk2G//fYLxfc3JNviNag47nUy37dv3+T69MQTTyR/k5XtXLKgjZVgmCVQoUC17gWrdZwKsy95lQS6xDBzlY7lMATqWuDDDz8Mjz76aCg+khc23XTTsMUWW4T+/fvXdZ5ljkCzCWiHzVajylOPAn/605+SgFavXr3C5ptvHjbZZJNQ7ElVj1mVJwLLVSD+2fPcc8+Fxx57LPTr1y9pH/GesNLJtaxSMenzIhC/PHn++efD008/HWJgd/DgwWHgwIGhOGxeRQTaWEVcEhNYSqBa94LVOs5SGbSiJgICvDVhdVACBAgQIECAAAECBAgQIECAAAECBAjUXkB3jtobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIrGCBxYsXh3XXXTf7icsreho1alSSnwEDBqzorDg/AQIECBAgQIBAAwt0b+C8yzoBAgQIECBAgACBTgvMmTOn02mXR8J58+aFmKfu3d2SLw9v5yBAgAABAgQINKuAHrzNWrPKRYAAAQIECBAgQIAAAQIECBAgQIBA0wsI8DZ9FSsgAQIECBAgQIAAAQIECBAgQIAAAQLNKiDA26w1q1wECBAgQIAAAQIECBAgQIAAAQIECDS9gABv01exAhIgQIAAAQIECBAgQIAAAQIECBAg0KwC3ujQrDWrXAQIECBAgAABAhUJnHbaaeFvf/tb2GmnncJXv/rV8Oabb4Zrr7023HfffWHGjBnJy9C23377sOuuu4Zjjjkm9OjRo93jP/nkk+H73/9+eOihh8Ls2bPD4MGDw4gRI8KXvvSlMGTIkHb3Ld24aNGiMHny5OQ4jz76aHjllVfCFltsEbbddtswatSo8NnPfrY0eTZ/9dVXh+nTpyfLq6++epgwYULo0qVLtr10JubzmWeeSVbttttu4dBDDy3dbJ4AAQIECBAgQKCOBboUilMd50/WCBAgQIAAAQIECPzDAosXL04CtOmBPv7449CtW7d0MfkcOHBgEoiNwc3//M//DKNHjw7PP/98WZp0YdiwYeG6664LG2+8cbqq7PP0008PEydODK3davft2zcJHJ9xxhkhBmy7d+8eYhC3tenpp58OY8aMCY888khrm5N1Bx98cLjsssvCaqutVpYmBpW33nrrJGgdN0yaNCkcd9xxZWniwo033hg+97nPJevXXHPN8Nhjj4X1119/qXRWECBAgAABAgQI1KeAAG991otcESBAgAABAgQIVFGgkgBv7MH76quvhtdffz184hOfCLvvvntYY401wu9///vw4IMPhhgcjtPOO++c9Oxtmc3/+I//COeff36yumvXrknwNPb6fffdd8Ndd92V9KqNweWePXuGDz74oM0A7z333BP23nvvMH/+/ORYO+64Y9hzzz2ToPITTzwRbr/99vDss88m2zbaaKMkMNu/f/+y7MQgdAwAxykGluN+MW06xTLGIPC8efOS3r1Tp04N++67b7rZJwECBAgQIECAQAMICPA2QCXJIgECBAgQIECAwD8mUEmANz3TOeecE8aNG1fW8/eOO+4I++23Xxbk/eMf/xh22GGHdJcwa9assN1224V4vn79+iU9dWOQtnS64IILwimnnBKWLFmSrI7B3jRonKaLy3EIhjjMQ5xiwPikk04KMWCcTgsWLEiGirjqqquSVf/+7/8evve976Wbs8+xY8eGn/3sZ8ly7JUcyxCn2Ls4Lt95553Jclv7Jxv9Q4AAAQIECBAgULcC/3+HWLdZlDECBAgQIECAAAECy1dg//33D3FM3jh8QukUe9CeeOKJ2arYi7Z0ikHhGNyN05lnnpn0wC3dHudPPvnkcNhhh7VcXbb8ox/9KAvuHn744ck+pcHdmDj2AL7yyivDLrvskuwbh2BIx9EtPVhcnw4lMW3atDBlypRk80UXXZQFd2OQ+txzzy3dzTwBAgQIECBAgECDCAjwNkhFySYBAgQIECBAgMDyE4jDLLQ1xZeQpVN8EVs6xV63acA3vtTsqKOOSjct9RmDxy0DtqWJLrnkkmzxO9/5Tjbf2kzs2RunOI7vNddcs1SS2JP4pz/9aXa+GGCOwz+MHz8+SbvKKqskPY07emncUge2ggABAgQIECBAoC4EBHjrohpkggABAgQIECBAoJ4ENttsszazE4O36bRw4cJ0NhkD9+23306WY4/YOOZtW9MWW2wR2jpH7AH80ksvJbtusskmYb311mvrMMn64cOHZ9vbeinciBEjwqmnnpqki0HpUaNGhY8++ihZji9oaysv2YHNECBAgAABAgQI1K1A+TNndZtNGSNAgAABAgQIECCwfARWXnnlsM4667R5stKeruk4ujHxX/7yl2yfAQMGZPNtzcQ0zz333FKbX3nllZAGjuNL0DpzrPQgbQV44/azzz47GX/34Ycfzsb8/cpXvhLGjBmT7u6TAAECBAgQIECgAQUEeBuw0mSZAAECBAgQIECgdgJxyIIuXbpUfIK5c+dm+6y//vrZfFszAwcObHXTCy+8kK2PvWxfe+21bLmjmRdffLHNJCuttFLyUrYjjzwySxNfGGciQIAAAQIECBBobAEB3sauP7knQIAAAQIECBCoE4EYGE6ndKiGdLm1zzhmb2tTnz59stVx+IWOXsiWJS7OxCBuW1MMQJ9++ullm4877riw++67h7XWWqtsvQUCBAgQIECAAIHGERDgbZy6klMCBAgQIECAAIE6Flh33XWz3P35z3/O5tuaiUMxtDZtvvnmZau//vWvly0v68IRRxwR5syZk+y+/fbbh5kzZybLsUfvTTfdtKyHtR8BAgQIECBAgMAKFvCStRVcAU5PgAABAgQIECDQHAJDhgwJ3bv/vf/EU089FQqFQpsFi9vaGk4h9qZdddVVk31nzZoVFi1a1OZx4oYPP/wwGVv32WefDfPnz2817RVXXBGmTp2abIu9gu+7774Qg7xx+s1vfhPidhMBAgQIECBAgEBjCgjwNma9yTUBAgQIECBAgECdCay22mrh05/+dJKrGLz99a9/3WYOr7/++rKXsrVMuPPOOyerYsD24osvbrm5bPnyyy8Pe+21V9hyyy1DfGlayym+eO2kk05KVscXyF155ZUh/UyHdIjb23tBW8tjWiZAgAABAgQIEKgfAQHe+qkLOSFAgAABAgQIEGhwgTimbTrF8W7feOONdDH7fOutt8K3vvWtbLm1mfPOOy9069Yt2XTWWWeFJ598srVkIfbanTBhQrbt+OOPz+bjTBzn99BDDw0ffPBBsv7ss88OgwcPTua33nrrbEzeuD2ma2tc4GQH/xAgQIAAAQIECNSlgABvXVaLTBEgQIAAAQIECDSiwIEHHhjiT5xi8HXHHXdMxrd95513wrvvvhtuueWWsMMOO4RnnnkmdOnSpc0ixuDrsccem2x///33k+NccMEFSS/bOLxDfIlb7IkbewzPmzcvSXfQQQeFT37yk2XH/Pa3vx0efPDBZN2wYcPCuHHjyraPHz8+xHPFKaaL6U0ECBAgQIAAAQKNJdCleIPY9uBgjVUWuSVAgAABAgQIECDQqsDixYuz8XFjgthTNe0hm+4wcODAMHv27LDmmmuGuXPnpquX+nzggQdCOoTC0UcfHS677LKyNDGQ+/nPfz789re/zdZ37fr3fhVLlixJ1u29995hlVVWCdddd12Sr9bG2Y29auPQCS3Hx+3Ro0dYuHBhduw4M2rUqHDzzTcnQy+kG2bMmBFGjBgRYtnjkAzxpWpp7900TfyM63faaafMZPr06WGXXXYpTWKeAAECBAgQIECgjgX04K3jypE1AgQIECBAgACBxhPo169fuPXWW8M555wT1l577aQAMbAbf/r06ZOMk3vjjTeG3r17t1u4mDaOrztt2rSw1VZbZQHp0uDuoEGDwuTJk8Ntt91WFtyNvX7jkAsxuBunOMxDa8HduC2+bO2UU06Js0n6ww47LMT9TQQIECBAgAABAo0hoAdvY9STXBIgQIAAAQIECDSgwEcffZSMn/unP/0pbLzxxmGbbbYp60lcSZEWLFiQDO0QX4a21lprhU033TQMGDAgpL2DKzmWtAQIECBAgAABAs0jIMDbPHWpJAQIECBAgAABAgQIECBAgAABAgQI5EzAEA05q3DFJUCAAAECBAgQIECAAAECBAgQIECgeQQEeJunLpWEAAECBAgQIECAAAECBAgQIECAAIGcCQjw5qzCFZcAAQIECBAgQIAAAQIECBAgQIAAgeYREOBtnrpUEgIECBAgQIAAAQIECBAgQIAAAQIEciYgwJuzCldcAgQIECBAgAABAgQIECBAgAABAgSaR0CAt3nqUkkIECBAgAABAgQIECBAgAABAgQIEMiZgABvzipccQkQIECAAAECBAgQIECAAAECBAgQaB4BAd7mqUslIUCAAAECBAgQIECAAAECBAgQIEAgZwICvDmrcMUlQIAAAQIECBAgQIAAAQIECBAgQKB5BAR4m6culYQAAQIECBAgQIAAAQIECBAgQIAAgZwJCPDmrMIVlwABAgQIECBAgAABAgQIECBAgACB5hEQ4G2eulQSAgQIECBAgAABAgQIECBAgAABAgRyJiDAm7MKV1wCBAgQIECAAAECBAgQIECAAAECBJpHQIC3eepSSQgQIECAAAECBAgQIECAAAECBAgQyJmAAG/OKlxxCRAgQIAAAQIECBAgQIAAAQIECBBoHgEB3uapSyUhQIAAAQIECBAgQIAAAQIECBAgQCBnAgK8OatwxSVAgAABAgQIECBAgAABAgQIECBAoHkEBHibpy6VhAABAgQIECBAgAABAgQIECBAgACBnAkI8OaswhWXAAECBAgQIECAAAECBAgQIECAAIHmERDgbZ66VBICBAgQIECAAAECBAgQIECAAAECBHImIMCbswpXXAIECBAgQIAAAQIECBAgQIAAAQIEmkdAgLd56lJJCBAgQIAAAQIECBAgQIAAAQIECBDImYAAb84qXHEJECBAgAABAgQIECBAgAABAgQIEGgeAQHe5qlLJSFAgAABAgQIECBAgAABAgQIECBAIGcCArw5q3DFJUCAAAECBAgQIECAAAECBAgQIECgeQQEeJunLpWEAAECBAgQIECAAAECBAgQIECAAIGcCQjw5qzCFZcAAQIECBAgQIAAAQIECBAgQIAAgeYREOBtnrpUEgIECBAgQIAAAQIECBAgQIAAAQIEciYgwJuzCldcAgQIECBAgAABAgQIECBAgAABAgSaR0CAt3nqUkkIECBAgAABAgQIECBAgAABAgQIEMiZgABvzipccQkQIECAAAECBAgQIECAAAECBAgQaB4BAd7mqUslIUCAAAECBAgQIECAAAECBAgQIEAgZwICvDmrcMUlQIAAAQIECBAgQIAAAQIECBAgQKB5BAR4m6culYQAAQIECBAgQIAAAQIECBAgQIAAgZwJCPDmrMIVlwABAgQIECBAgAABAgQIECBAgACB5hEQ4G2eulQSAgQIECBAgAABAgQIECBAgAABAgRyJiDAm7MKV1wCBAgQIECAAAECBAgQIECAAAECBJpHQIC3eepSSQgQIECAAAECBAgQIECAAAECBAgQyJmAAG/OKlxxCRAgQIAAAQIECBAgQIAAAQIECBBoHgEB3uapSyUhQIAAAQIECBAgQIAAAQIECBAgQCBnAgK8OatwxSVAgAABAgQIECBAgAABAgQIECBAoHkEBHibpy6VhAABAgQIECBAgAABAgQIECBAgACBnAkI8OaswhWXAAECBAgQIECAAAECBAgQIECAAIHmERDgbZ66VBICBAgQIECAAAECBAgQIECAAAECBHImIMCbswpXXAIECBAgQIAAAQIECBAgQIAAAQIEmkdAgLd56lJJCBAgQIAAAQIECBAgQIAAAQIECBDImYAAb84qXHEJECBAgAABAgQIECBAgAABAgQIEGgegf8FOcnSozas694AAAAASUVORK5CYII=)
- It is also useful to save the variable we have computed as a new column in the data frame
round(BMI,1)
[1] 22.9 25.1 26.4 30.6 26.5 27.9 26.3 25.6 23.4 28.2 28.2 NA 30.0 27.9 24.5 22.0 25.6 31.5 23.8 NA 23.5 26.7 31.4 NA
[25] 24.6 NA 24.8 29.2 NA 24.1 25.1 28.0 29.4 28.2 23.6 26.4 NA 25.0 27.7 27.0 25.6 26.7 24.5 26.1 23.1 28.2 26.9 NA
[49] 25.4 25.9 NA 24.8 28.2 NA 30.4 26.8 26.0 25.2 26.9 31.7 25.6 NA 26.7 27.8 28.4 NA 31.5 27.0 30.0 26.5 25.2 NA
[73] 26.7 25.8 NA 27.6 29.1 26.6 26.6 26.9 27.6 26.4 27.8 NA 27.8 25.8 27.7 28.7 24.2 24.6 28.3 24.8 27.8 21.4 28.0 26.0
[97] 26.2 26.4 27.7 NA
patients$BMI <- round(BMI,1)
head(patients)
- To actually select the candidates we can use a logical expression to test the values of the BMI vector being greater than the upper limit
- if the second line looks a bit weird, remember that
<-
is doing an assignment. Thevalue we are assigning to our new variable is the logical (TRUE
or FALSE
) vector given by testing each item in BMI
against the upper.limit
BMI > upper.limit
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE TRUE FALSE NA
[21] FALSE FALSE TRUE NA FALSE NA FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE
[41] FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE NA FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE TRUE
[61] FALSE NA FALSE FALSE FALSE NA TRUE FALSE FALSE FALSE FALSE NA FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE
[81] FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA
candidates <- BMI > upper.limit
We have seen that a logical vector can be used to subset a data frame
- However, in our case the result looks a bit funny
- Can you think why this might be?
patients[candidates,]
The which
function will take a logical vector and return the indices of the TRUE
values
- This can then be used to subset the data frame
which(BMI > upper.limit)
[1] 18 23 60 67
candidates <- which(BMI > upper.limit)
3. Outputting the results
- We write out a data frame of candidates (patients with BMI more than standard deviations from the mean) as a ‘comma separated values’ text file (CSV):
write.csv(patients[candidates,], file="selectedSamples.csv")
- The output file is directly-readable by Excel
- It’s often helpful to double check where the data has been saved. Use the get working directory function:
getwd() # print working directory
list.files() # list files in working directory
To recap, the set of R commands we have used is:-
patients <- read.delim("patient-info.txt")
BMI <- (patients$Weight)/((patients$Height/100)^2)
upper.limit <- mean(BMI,na.rm = TRUE) + 2*sd(BMI,na.rm = TRUE)
plot(BMI)
# Add a horizonal line:
abline(h=upper.limit)
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ACCCCAAAIIIIAAAggggAACCCCAAAJOFyDAG8c9qMFbTS1atAgL4j56yoCAAFm7dq28+OKLZpdO01CnTh3RuXtJCCCAAAIIIIAAAggggAACCCCAAAIIIIBAVAIEeKOSiaXte/fuNUcqXbp0tEdMkyaN/Pjjj1K+fHmT748//pCmTZua0b/RFmQnAggggAACCCCAAAIIIIAAAggggAACCHitAAHeOO7627dvmzOkSJEixjMlT55cFi5cKPny5TN5ly5dKu+++26M5ciAAAIIIIAAAggggAACCCCAAAIIIIAAAt4p4OedzY6/VufPn182bdokf/31l62TZsqUSZYtW2ZG8p47d050Dl8N+Hbr1s1WeXfPpAvJDRs2TO7cuePuVaV+CCCAAAIIIIAAAggggAACCCCAAAJxLKBrVA0YMED0N+nxBAjwPp6b7VJWgHfGjBkSFBQkGTJkiLFs3rx5zUjeKlWqSEhIiHTv3l008NumTZsYy7p7hn//+98yceJEd68m9UMAAQQQQAABBBBAAAEEEEAAAQQQiCcBnbL01Vdfjaezed5pCPDGcZ/q4mozZ86Us2fPmoXWpkyZIv7+/jGe9fnnnxcNCjdu3FhCQ0Olffv2oouv6Wsnp7t375rqN2/eXGrWrOnkplB3BBBAAAEEEEAAAQQQQAABBBBAAIEnEBg/frxs2LBBrHjRExzKq4sS4I3j7q9Tp45Uq1ZNfvrpJ1m+fLkULlxY6tevL7ro2jvvvBPt2Rs2bCj/+te/5K233jKB3X79+omvr2dMm1y2bFlp27ZttO1nJwIIIIAAAggggAACCCCAAAIIIICA5wpovEwDvKQnE/CMaOGTGcR5ab0b8cwzz5jzBAcHyzfffCPjxo2zdd6OHTvK5MmTxc/vf7F4p4/gtdVoMiGAAAIIIIAAAggggAACCCCAAAIIIICALQECvLaYnixTnjx55Pfff5dOnTpJypQpzcGyZctm+6Dt2rWTP//8UwIDA22XISMCCCCAAAIIIIAAAggggAACCCCAAAIIeL4AUzTEUx8nS5ZMvvrqKxk5cqRs3rxZrly54tKZixUrJmvXrpWpU6fK2LFj5a+//nKp/JNk3r59u8yfP1/u37//JIcxZbUNmi5fvmx+8w8CCCCAAAIIIIAAAggggAACCCCAAAIIPL6Az3+Ddk8etXv881PSAQJ169aVJUuWxGpNdQ5iDXSTEEAAAQQQQAABBBBAAAEEEEAAAQS8U6B169Yyffp0mTZtmrRq1co7EWKh1YzgjQVETz/EoEGDRBdFi435f3U+4r///luyZs3q6Wy0DwEEEEAAAQQQQAABBBBAAAEEEEAAgTgXIMAb58SPd4LTp0/LgQMH5Pr161KyZMkEDYgWL15c9Cc20sKFC02AN3HixLFxOI6BAAIIIIAAAggggAACCCCAAAIIIICAVwuwyFo8d/+tW7dk7969cvHixUjPvGPHDtEpEXQRtooVK0qtWrUkICBAsmfPLl988UWszIMb6YnZiAACCCCAAAIIIIAAAggggAACCCCAAAKOEyDAG09dFhwcLB07dpT06dNLoUKFJEuWLBIYGGgWTLOqoMFdDepGNt/tqVOn5P3335caNWqYEbBWGX4jgAACCCCAAAIIIIAAAggggAACCCCAgPcKMEVDPPR9SEiI1KtXT3799dews927d0/WrVtnfnTjW2+9JS1atJBLly6ZPClSpJBnnnlG/P39ZdeuXXLo0CEzevenn34SnYB65cqVYcfiBQIIIIAAAggggAACCCCAAAIIIIAAAgh4pwAjeOO43zWQ26xZs7Dgbrp06aRSpUpSunRpseah7dy5s3z66aeyc+dOUxsN9B4/flw2btwoCxYsMHPxbtiwwYz81Qwa5NXVBUkIIIAAAggggAACCCCAAAIIIIAAAggg4N0CBHjjuP9nzpwpixYtMmdp06aNCdyuXr1aNm/ebF6XKlVK7t69K0FBQSZP7dq1ZcaMGZIhQ4ZwNStXrpysX7/ezM2rO3r27BluP28QQAABBBBAAAEEEEAAAQQQQAABBBBAwPsECPDGcZ9rsFZTiRIlZNKkSZIqVaqwM+r0C8uWLROdjsFKw4cPt15G+K3z944YMcJsP3nypJw9ezZCHjYggAACCCCAAAIIIICAawI64GLq1KlmWjVd4NjPz88MuChfvrwMHjw4ygWSXTsLuRFAAAEEEEAAgbgRIMAbN65hR9X5czU1b97cfFEM2/H/LzJnziytWrUy71KnTi0FCxZ8NEu495UrVw57bx07bAMvEEAAAQQQQAABBBBAwCWBP/74Q4oVKyZt27aVxYsXmwWNdZo1XSRZp0zr1auX5M2bVyZPnuzSccmMAAIIIIAAAgjElwAB3jiWPn/+vDlDjhw5ojxTmTJlzL6UKVOKj49PlPl0hwaEkyVLZvLoKF4SAggggAACCCCAAAIIPJ6Arm0RGBgoe/fuNQMtJk6caKZR0xG9Fy9eNFOt1apVyyyE3KFDB/n4448f70SUQgABBBBAAAEE4lCAAG8c4uqhc+fObc5w4MAB8zuyf3TUrj4KdubMGbl8+XJkWcK2HTt2TG7evGne60gCEgIIIIAAAggggAACCLgucOTIEWnSpImEhITI66+/Ljt27BAN4urAjESJEolOj1a3bl1ZunSpTJkyxSyQPGDAAPn2229dPxklEEAAAQQQQACBOBQgwBuHuHroQoUKmTPo/LvXrl2L9GwVK1aUU6dOyY0bN8LN0RtZ5lGjRoVtLlKkSNhrXiCAAAIIIIAAAggggIB9AV20+NKlS9KwYUMZP368CeBGVVoXS7a+h3fr1s0EhaPKy3YEEEAAAQQQQCC+BQjwxrG4zuWl6fjx41K/fn2JbloFnXpBRwtElfRL5Zdffml2V6tWTdKmTRtVVrYjgAACCCCAAAIIIIBAFAK6WLGOxE2aNKmMHj06xmnS9DAdO3YUnVpNv8/PmzcviiOzGQEEEEAAAQQQiH8BArxxbN6gQQOpVKmSOcuqVaukQIECZnVeXczBTtJpG4YPHy5ly5aVLl26iM4Hljhx4rARBHaOQR4EEEAAAQQQQAABBBB4ILB8+XIJDQ2VGjVqSPbs2R/siOaVrpVhDd748ccfo8nJLgQQQAABBBBAIH4FCPDGg/fs2bOlQoUK5kw6DYOuzquLNthJa9eule7du8umTZtMdj8/PxkxYoQULlzYTnHyIIAAAggggAACCCCAwCMChw8fNlueffbZR/ZE/7ZUqVImw6FDh6LPyF4EEEAAAQQQQCAeBQjwxgO2v7+/rF69WoKCgiRnzpzmjMWLF7d15jx58oTl09EFOgq4U6dOYdt4gQACCCCAAAIIIIAAAq4JWIsW6xRpriQrv1XelbLkRQABBBBAAAEE4kqAAG9cyT5yXJ1WYeDAgXL06FHZuHGjaNDXTsqXL5/07dtX1q1bJ7rSb2BgoJ1i5EEAAQQQQAABBBBAAIEoBAICAsweayRvFNkibLZG7mbLli3CPjYggAACCCCAAAIJJeCXUCf21vPq3F3lypWz3XxdSK1///6285MRAQQQQAABBBBAAAEEohewBk0sWbLErHGh06DZSQsWLDDZrPJ2ypAHAQQQQAABBBCIawFG8Ma1MMdHAAEEEEAAAQQQQAABtxIoWbKkFC1aVE6dOiVjxoyxVbedO3fKrFmzzILHTZs2tVXGGzLdvn1bjh8/LpcuXfKG5tJGBBBAAAEE3FKAAK9bdguVQgABBBBAAAEEEEAAgbgUGDJkiDn8Bx98ID///HO0p9JAcMOGDeXevXvSsWNHyZ07d7T5PX3n9evX5bPPPpPSpUuLzkucK1cuSZ8+vWTOnFnat28v27dv93QC2ocAAggggIBbCRDgdavuoDIIIIBA3Avs2LFD9KL2jTfekFatWkmPHj1k/vz5cuPGjbg/OWdAAAEEEEDATQTq1q0r7733nugI1Fq1akm/fv3kypUr4WqnAV0dtVumTBk5cOCA+a2BTW9Ov/zyi+TPn18+/PBD2bJlixnRrAtJp0uXTs6fPy/ffPONPPvss8ZW/UgIIIAAAgggEPcC9iabivt6cAYEEEAAgTgW2LZtm3Tt2lXWrFkT4Uyff/65ZMyYUfr06SOdO3cWnS+chAACCCCAgKcLDB8+3IxAHTx4sFn3Qm+Ali9fXnQRtcuXL5vFkS9cuGAYqlatKt99950kT57c01mibN/ixYvllVdekTt37hinoKAgqVatmiRJksSU2bdvn0yYMEFGjx4tI0eONItEf//99+Lry7iiKFHZgQACCCCAQCwIEOCNBUQOgQACCLi7wNy5c6VNmzYSEhJiArmNGzc2j1WmSJHCXHwtXLhQfv/9dxMAXrVqlcyePdtxF7DHjh0TXfxm69atcu7cOcmQIYOZX7F+/fpSoEABd+8i6ocAAgggkAACekNz0KBBUq9ePfn4449F/wY+eiO0cOHCZrRq69atvTpQefjwYWnRooUJ7nbv3l2GDh0awUP/3upN4yZNmoiOkNYnhD755BPp27dvAvQup0QAAQQQQMB7BHzu/zd5T3NpaUILlCpVSv78809p0KCBzJs3L6Grw/kR8AqBtWvXmtE1OtpG5w3UR0vTpEkToe06Kqddu3aiI5WaN28uM2fOjJDHHTdcvHhRdP5EfSQ0qkdB9ULziy++kBw5crhjE6gTAggggICbCOjfQP2uevbsWUmbNq0ULFhQ8uXL5ya1S9hqtGzZ0nw30AXm5syZE2NlNFBepUoVM0Jap7fQUdEkBBBAAAEEHhXQG6jTp0+XadOmmSkEH93Pe3sCPCtjz4lcCCCAgCMFNKiri53obw2Cjhs3LtLgrjZOR9poMFiDvzrf4A8//OD2bd6/f7+ULVtWJk2aZEYRNWvWzDwaqiOSv/76axOw1lHK+kitzp+4efNmt28TFUQAAQQQSDgBna5IpxzQkap16tQhuPv/XXHp0iXztzRx4sSi01rYSZUqVZJGjRqZp4f0wp2EAAIIIIAAAnEnQIA37mw5MgIIIJDgAnpBdejQIXnmmWdE5xeMKRUpUsSM8NV8AwcOjCl7gu4PDg42F98HDx40Qd7du3ebqSVef/1186itjkbWIK+OGqpZs6acOXPGBLGPHz+eoPXm5AgggAACCDhNYPXq1eZm8YsvvujS0zAaKNe0YsUKpzWZ+iKAAAIIIOAoAQK8juouKosAAgi4JmCNwtXF1ewucNKhQwdJnz69mctWg8PumnQ+Px3BW7p0aTNfYt68eSOtakBAgCxZskReeuklE+Tt0qVLpPnYiAACCCCAAAKRC+g895qKFi0aeYYothYrVszsscpHkY3NCCCAAAIIIPCEAgR4nxCQ4ggggIA7C2zfvt1UT+fAs5v8/PwkMDDQZLfK2y0bX/nOnz8v48ePl0SJEpm5mmJa0Vzz6Ry9qVKlMvN///XXX/FVVc6DAAIIIICA4wWsOe7176krybq5bJV3pSx5EUAAAQQQQMC+AAFe+1bkRAABBBwnoIFQTVmyZHGp7pkzZzb5rfIuFY6HzLognM4rrKNydXVzO8nf39/Mqah5WeTRjhh5EEAAAQQQ+J+AtUjp3r17XSLZt2+fyW+Vd6kwmRFAAAEEEEDAtgABXttUZEQAAQScJ6CLxWhyNVBr5bfKu1vLd+7caaqkC7i4knTuQE1WeVfKkhcBBBBAAAFvFdC/tzoad9WqVXLx4kXbDLrIqSZXniSyfXAyIoAAAggggECYAAHeMApeIIAAAp4nYM19t2bNGtuN08co161bZ/Jb5W0XjqeMFy5cMGfKlCmTS2e08lvlXSpMZgQQQAABBLxUQJ8EqlWrloSEhEhQUJAthW3btplplHTqp1atWtkqQyYEEEAAAQQQeDwBAryP50YpBBBAwBECDRs2NPX88ssv5f79+7bqPG3aNNEAqC6kkj9/fltl4juTFag9e/asS6c+d+6cyW+Vd6kwmRFAAAEEEPBigSFDhkiSJElk7Nixot8roksHDhyQ+vXry927d6VTp04S1UKo0R2DfQgggAACCCBgX4AAr30rciKAAAKOE2jdurXkypVLtmzZIgMGDIix/gcPHpT333/f5LM7QifGg8ZBhuLFi5uj6qOiriQrv1XelbLkRQABBBBAwJsF9KmecePGGYKuXbtKkyZNZMeOHeFILl++LCNHjpSyZcvK0aNHpUKFCvLZZ5+Fy8MbBBBAAAEEEIh9AQK8sW/KERFAAAG3EUiWLJlMnDhRdNXrfv36Sffu3c3jlZFVUKdxCAwMNHPrNWjQQJo1axZZNrfYVqdOHUmaNKmsXLkywsVlVBU8deqUzJ49W3x8fMQa2RxVXrYjgAACCCCAQESB9u3by6xZsyRlypQyd+5c0RumOXPmNIFcfa2LtL733nsSHBwsjRo1kuXLl5tRvxGPxBYEEEAAAQQQiE0BAryxqcmxEEAAATcUqF69ukyePNlcYA0fPlzy5ctnAr16gbZgwQLzmGXVqlWlcuXKcvr0aalWrZqZM08Doe6aMmTIYB75DA0NlZYtW8rVq1ejreqdO3fM/H83btyQpk2bSsGCBaPNz04EEEAAAQQQiFzg1Vdflf3798s777wj/v7+cuLECVm/fr254arTQel3iqVLl5oAsAaCSQgggAACCCAQ9wJ+cX8KzoAAAgggkNACbdq0kQIFCkjnzp1l06ZNooHeR1Pq1Knlww8/lI8++siM+H10v7u91xHJy5YtMxeU//jHP2TmzJlm3uBH63nkyBFp27atrF27VrJnzy4jRox4NAvvEUAAAQQQQMAFgYCAABk9erS5Sax/Z//++2/R7xG5c+c2v104FFkRQAABBBBAIBYECPDGAiKHQAABBJwg8Pzzz8tvv/0mGzZsMCNrDh06JDqiVS/SdI68unXrSrp06ZzQFFNHvZBcsmSJ1K5dW7Zv3y4lSpQwr3XkkI4o0oXifvnlFzNK+fbt2+YRUs2v7SUhgAACCCCAwJML+Pr6Sp48eczPkx+NIyCAAAIIIIDA4woQ4H1cOcohgAACDhTQaRdeeOEF8+PA6keoso4U2rhxo/Tp08cs/LJo0SLRn4eTXny2a9dOdPVvDfySEEAAAQQQQAABBBBAAAEEEPAkAQK8ntSbtAUBBBDwQoE0adKYR0R79+5tgrvbtm2Tc+fOic7TW7RoUXn55ZclR44cXihDkxFAAAEEEEAAAQQQQAABBLxBgACvN/QybUQAAQS8QCBLlizSoUMHL2gpTUQAAQQQQAABBBBAAAEEEEDggYDvg5e8QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSAAFeJ/UWdUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBB4SIMD7EAYvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwkQ4HVSb1FXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgIQECvA9h8BIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCSgJ+TKktdPUfg7t27cv36dc9pEC1BAAEEEEAAAQQQQAABBBBAAAEEEHBJQONDpCcXIMD75IYc4TEEFi9eLKlSpXqMkhRBAAEEEEAAAQQQQAABBBBAAAEEEEAAAUuAAK8lwe94FUiUKJEkS5YsXs/JyRBAAAEEEEAAAQQQQAABBBBAAAEE3Efg5s2bcu/ePfepkENrQoDXoR3n9GrXq1dP5s2b5/RmUH8EEEAAAQQQQAABBBBAAAEEEEAAgccUaN26tUyfPv0xS1PMEmCRNUuC3wgggAACCCCAAAIIIIAAAggggAACCCCAgMMECPA6rMOoLgIIIIAAAggggAACCCCAAAIIIIAAAgggYAkQ4LUk+I0AAggggAACCCCAAAIIIIAAAggggAACCDhMgACvwzqM6iKAAAIIIIAAAggggAACCCCAAAIIIIAAApYAAV5Lgt8IIIAAAggggAACCCCAAAIIIIAAAggggIDDBAjwOqzDqC4CCCCAAAIIIIAAAggggAACCCCAAAIIIGAJ+Fkv+I0AAu4jcPToUVm+fLkcO3ZM7t69Kzly5JDKlStL0aJF3aeS1AQBBBBAAAEEEEAAAQQQQAABBBBAIMEFCPAmeBdQAQQeCOzYsUN69OhhgrsPtj54Vbp0afn888+lUqVKDzbyCgEEEEAAAQQQQAABBBBAAAEEEEDAawUI8Hpt19NwdxOYOXOmdOjQQW7evClp0qSR2rVrS7FixcTPz0/27dsnixcvlj/++EOqVKkin3zyifTq1cvdmkB9EEAAAQQQQAABBBBAAAEEEEAAAQTiWYAAbzyDczoEIhNYunSptG7dWkJDQ6Vjx44yePBgSZ8+fbisGvgdNmyY9O/fX3r37i2pUqWSzp07h8vDGwQQQAABBBBAAAEEEEAAAQQQQAAB7xJgkTXv6m9a64YCV65ckbZt25rg7sCBA2XcuHERgrta7WTJkkmfPn1kxowZ4uPjI927d5f9+/e7YYuoEgIIIIAAAggggAACCCCAAAIIIIBAfAkQ4I0vac6DQBQCX331lZw/f97MqxsUFBRFrgebmzVrJq+//rrcuXPHTNXwYA+vEEAAAQQQQAABBBBAAAEEEEAAAQS8TYAAr7f1OO11O4HvvvvO1Omjjz6yXbeePXuavAsWLDCBXtsFyYgAAggggAACCCCAAAIIIIAAAggg4FECBHg9qjtpjNME7t69K9u2bTMLqeniaXZT7ty5JX/+/HL58mU5cOCA3WLkQwABBBBAAAEEEEAAAQQQQAABBBDwMAECvB7WoTTHWQLnzp2T+/fvS6ZMmSRx4sQuVT5btmwm/9mzZ10qR2YEEEAAAQQQQAABBBBAAAEEEEAAAc8RIMDrOX1JSxwokC5dOlNrHYnrarp06ZIpYh3D1fLkRwABBBBAAAEEEEAAAQQQQAABBBBwvgABXuf3IS1wsEDy5MklV65cEhISIlu2bLHdEg3u7tq1y4z6zZMnj+1yZEQAAQQQQAABBBBAAAEEEEAAAQQQ8CwBArye1Z+0xoEC9evXN7UeM2aM7dpPmDBBdP7eypUrS+rUqW2XIyMCCCCAAAIIIIAAAggggAACCCCAgGcJ+HlWc2gNAs4T6Nq1q4wfP16++eYbadKkibz00kvRNmLnzp0ycOBAk+fDDz+MNi87EUAAAQQQQACB2Ba4du2arFy5Uvbv3y9Xr16VrFmzSrly5aR06dLi4+MT26fjeAgggAACCCCAAAIxCBDgjQGI3QjEtYBOsfDxxx9L7969pVGjRjJp0iR59dVXIz3t2rVrpWnTpqIXVm3btpUqVapEmo+NCCCAAAIIIIBAbAtcvHhRBgwYIOPGjZNbt25FOHzevHnNTejmzZtH2McGBBBAAAEEEEAAgbgTIMAbd7YcGQHbAr169ZLjx4+bCya9KNLpGlq2bCnFihUTPz8/2bdvn8ydO1cWLVpkjlmrVi0z6tf2CciIAAIIIIAAAgg8gcD27dvl5ZdflqNHj4qvr69UrFjRjNpNkyaN+Q6zfPlyOXjwoLRo0UJ+/PFHmThxoiRJkuQJzkhRBBBAAAEEEEAAAbsCBHjtSpEPgTgWGDt2rHm0UYO969atMz+PnjJlypTywQcfmNG+iRIlenQ37xFAAAEEEEAAgVgX0JvQNWrUkDNnzsgLL7wguhZA0aJFw50nNDRUpk6dKp07d5Zp06aZILBOP0VCAAEEEEAAAQQQiHsBArxxb8wZELAt8Nprr0mzZs1k3rx5oiNhdJTMvXv3JHv27GY6hsaNG0uWLFlsH4+MCCCAAAIIIIDAkwp06NDBBHdr1qwpCxcujHRkro7qbdeunZQsWVICAwNlypQpUqdOHbO+wJOen/IIIIAAAggggAAC0QsQ4I3eh70IxLtA6tSppU2bNuYn3k/OCRFAAAEEEEAAgYcE/vOf/5gF1TJkyCCzZs2KNLj7UHYT4B0xYoS8/vrr0rdvXwK8D+PwGgEEEEAAAQQQiCMB3zg6LodFAAEEEEAAAQQQQAABhwt8++23pgVvvfWWpE+f3lZr2rdvLwEBAbJnzx7ZsWOHrTJkQgABBBBAAAEEEHh8AQK8j29HSQQQQAABBBBAAAEEPFpg06ZNpn0vvfSS7XbqOgHVq1c3+a3ytguTEQEEEEAAAQQQQMBlAQK8LpNRAAEEEEAAAQQQQAAB7xA4e/asaaiuB+BKsvLrwmwkBBBAAAEEEEAAgbgVIMAbt74cHQEEEEAAAQQQQAABxwqkSZPG1P3y5csutcHKnzZtWpfKkRkBBBBAAAEEEEDAdQECvK6bUQIBBBBAAAEEEEAAAa8QKFiwoGnnhg0bXGqvlb9AgQIulSMzAggggAACCCCAgOsCBHhdN6MEAggggAACCCCAAAJeIVCvXj3TzvHjx0toaKitNm/cuFH+/PNP0dG7FStWtFWGTAgggAACCCCAAAKPL0CA9/HtKIkAAggggAACCCCAgEcLNGnSRHLnzi3btm2ToUOHxtjWa9euyRtvvGHydenSRZIkSRJjGTIggAACCCCAAAIIPJkAAd4n86M0AggggAACCCCAAAIeK5A0aVL517/+Jb6+vtK7d2/59NNPoxzJe/LkSalRo4bs2LFDihQpIj169PBYFxqGAAIIIIAAAgi4k4CfO1XGG+ty6tQpuXjxoty4ccP8JEuWzDzOpgtaZMyYUfQ9CQEEEEAAAQQQQACBhBKoVauWjB49Wt59910JCgqSWbNmyWuvvSblypUT/c56/PhxWbp0qUyePFmuX79uRvwuWrRIUqVKlVBV5rwIIIAAAggggIBXCRDgjefuvnr1qkydOlVmzJghO3fuFH0fVfLz85NnnnnGfHmuW7eu1K5dW3x8fKLKznYEEEAAAQQQQAABBOJE4O2335a8efNKp06dZNeuXfLee+9Feh6d0kFH/GbKlCnS/WxEAAEEEEAAAQQQiH0BAryxbxrpEc+cOSMDBgyQadOmRRvUfbjw3bt3zQIVukjFuHHjpFixYjJkyBCpU6fOw9l4jQACCCCAAAIIIIBAnAvUrFlTdu/eLfPnz5fFixfL/v375cqVK5I1a1YzIKFp06ZSokSJOK8HJ0AAAQQQQAABBBAIL0CAN7xHnLwLDg6W6tWrm/nIrBPoSNyAgADJlSuXZM6cWZInTy46x5kGdW/evGm+LOvjbkePHpVbt26ZYjri9+WXX5bhw4dL165drUPxGwEEEEAAAQQQQACBeBFInDix6Chd/SEhgAACCCCAAAIIuIcAAd447gedh0xH3OpiE5qee+456datm1StWtUEdmM6/Z07d+T333830zp8/fXXou/1kbgCBQqYKRtiKs9+BBBAAAEEEEAAAQQQQAABBBBAAAEEEPBcAV/PbZp7tOzbb7+VDRs2mMq8+uqrsnHjRtHfOmrXTtJREhUqVJDx48ebx+H0vaaPPvooyhWM7RyXPAgggAACCCCAAAIIIIAAAggggAACCCDgfAECvHHch+vXrzdnKF68uBmF6+v7+OS6yNrnn39ujqcjgg8fPhzHtefwCCCAAAIIIIAAAggggAACCCCAAAIIIODOAo8fbXTnVrlR3X799VdTm3r16ok1+vZJqteoUaOw4vv27Qt7zQsEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8D4B5uCN4z4/ceKEOUPOnDlj5UwZM2Y0gWKdizckJCRWjslBEEAAAQQQQAABBBBAAAF3EQgNDZUzZ87IxYsXzdR2Or2dLlJNQgABBBBAAIHIBQjwRu4Sa1vz5s0rW7duNfPwduzY8YmPq1M+aHBX07PPPvvEx+MACCAQuYBeWOic2WvWrJFTp06ZGyu5c+eWl156SQoWLBh5IbYigAACCCCAAAIIPLbAnj17ZOjQobJo0SK5cOFC2HGyZcsmr7zyinzwwQcSWwNnwg7OCwQQQAABBDxAgABvHHdi6dKlTYB3zpw50r59e3nxxRcf+4yXLl2S999/35TPkCGDPP300499LAoigEDUAvPmzZMPP/xQ9u/fH2mmypUryxdffCElS5aMdD8bEUAAAQQQQAABBOwL3L9/XwYMGCADBw6Ue/fumYJZs2YVfXpRR/LqzfavvvpKJk6cKCNHjpTYGDhjv3bkRAABBBBAwP0FmIM3jvuoZ8+eZuTfzZs3pX79+jJ+/Hi5ffu2y2fVUcA1atQwwWIt/Oabb7p8DAoggED0Anpx0bVrVzNCRIO7ehNF3+sFhQZ027VrJ+nTp5fVq1fL888/LzNmzIj+gOxFAAEEEEAAAQQQiFHg7bffln79+pl8nTp1El1r5PTp07Jz5045d+6cbNu2TVq2bCl6TaXXQYMHD47xmGRAAAEEEEDAmwQYwRvHva1TNAwaNEh69Oghly9fNl9I9LWO5NXRfxpA8vf3l+TJk0uyZMnk7t275ovLlStX5Pjx43LgwAFZu3at+XJjVVUDvXp3m4QAArEr0LdvX/nyyy8ladKkJqCro0MSJUoU7iT632avXr1kzJgx0rZtWzOyRKdtICGAAAIIIIAAAgi4LvDNN9/IuHHjJEWKFDJ//nypXr16hIMUL15cpk+fLrVq1TLfv3r37i3PPfecVKtWLUJeNiCAAAIIIOCNAgR446HXu3fvboJAejdaF0a7evWqLF682Py4enoNJOmoQV9fBl+7akd+BKIT+OOPP+TTTz8VPz8/899mVBcMadKkMSN69caMBoR1VK/eiEmVKlV0h2cfAggggAACCCCAwCMCem2kN8416ZOOkQV3Hy6io3iPHj0qGuDVqev0KUcWX3tYiNcIIIAAAt4qQJQwnnpe59/VLyP6BUbnk3Il6WhCnd5BFxv48ccfReffJSGAQOwK6LxvOkWD3pCJKrj78Bn79OkjFSpUMPPC6agTEgIIIIAAAggggIBrAkuXLjVTMZQpU0ZatWplq7B+V8uRI4ds375dNm3aZKsMmRBAAAEEEPB0AUbwxmMPZ86c2YwQ1FGCR44ckY0bN5pFnPSRb52+QUf2Jk6c2IwE1FGCOr1DkSJFpESJEgk6OnDz5s0yd+5cCQ0NfWKtkydPmmPcuXPniY/FARCILYFr167J8uXLzXQM1kKGdo6tKznrzZfvv//eBIbtlCEPAggggAACCCCAwP8EfvrpJ/OiWbNmtkmSJEli1ksYNWqUaPmyZcvaLktGBBBAAAEEPFWAAG8C9Wzu3LlFf5yQdMGDJUuWxGpVdSVcEgLuIvDXX3/JrVu3pHTp0pIpUybb1bJG+v7555+2y5ARAQQQQAABBBBA4H8CuuaIpsKFC/9vg81/rfxWeZvFyIYAAggggIDHChDg9diujb2Gff7551KpUiXz+PqTHnXkyJGiwd3s2bM/6aEoj0CsCejqzJpcnT5FFwNJnTq1GX2vI/D1NQkBBBBAAAEEEEAAAQQQQAABBBCITwECvPGp7cK5Tp8+bRZuun79upQsWdLlwJMLp4oxa6FChUR/YiPNmjXLBHh1ISsSAu4ikD59elOVixcvulQlnWpEp3ewplZxqTCZEUAAAQQQQAABLxfQuXQ17dmzR+rUqWNbY/fu3SZvzpw5bZchIwIIIIAAAp4swCJr8dy7+hj43r17JapA0o4dO6Ru3bqSLVs2qVixotSqVUsCAgLMiNcvvvgiVkbRxnOTOR0Cbi9QsGBB8fX1FZ1qQW+q2E3r1q0z/01qeVZwtqtGPgQQQAABBBBA4H8C1nRXs2fPtk1y+/ZtmTdvnslvlbddmIwIIIAAAgh4qAAB3njq2ODgYOnYsaPoSEEdDZslSxYJDAyUsWPHhtVAg7sa1I1svlud1kAXf6pRo4b8/fffYWV4gQACTy6QMWNG89/jzZs3ZcKECbYPOHr0aJO3QYMGtsuQEQEEEEAAAQQQQOB/ArVr1zZPKuqizjNmzLDFooNedO7dZ555Rp577jlbZciEAAIIIICApwsQ4I2HHg4JCZF69eqZwJG+1nTv3j3R0X9vv/12WJC3RYsWcunSJbNf5/YsV66cvPzyy5I3b96w0YG6Umzr1q1NHv5BAIHYE+jZs6c52Mcffyy7du2K8cB6EaKjR1KlSiXvvvtujPnJgAACCCCAAAIIIBBeQK95Bg0aZDa+8cYb8vPPP4fP8Mg7ne4tKCjIXBsNHz487BrpkWy8RQABBBBAwOsECPDGcZdrILdZs2by66+/mjOlS5fOLFhWunRpM2+nbuzcubN8+umnsnPnTpNHA716V3rjxo2yYMECMxfvhg0bwubB1SDvtGnTTF7+QQCB2BGoWbOmtGrVyiyYVqVKFVm1alWkB75//7589dVX0q5dO7NfR5HoiHwSAggggAACCCCAgOsC7du3N0863rhxQ1566SVzbXTw4MFwB9LrpDZt2oheJ+n11cCBA6V69erh8vAGAQQQQAABbxbw+W+w4r43A8R12zUQq19GNOnvMWPGmBF/+v7MmTOijyVt2bJF35qk7yObokF36jQPxYoVM4uUZc+eXU6cOPG/Qg76t1SpUmaeU32k3Zo7y0HVp6oeLqBTNDRq1EiWLl1qWqpTouj7fPnyic73tn37dpk+fbrodCqa+vbtK/379zev+QcBBBBAAAEEEEDg8QT0krRfv35m0IsGcDXpmiQ6jZZeM509e9ZsS5o0qYwYMULeeust855/EEAAAQScL6BPqet1tsbPdNAV6fEE/B6vGKXsClhzSZUoUUImTZokfn4PyP39/WXZsmWSO3du0TvWmvRRo6iSzt+rX2h0RPDJkyfNFx1GDkalxXYEXBdIliyZLFq0SIYMGWJ+VqxYIfrzaNL/ZvW/1VdeeeXRXbxHAAEEEEAAAQQQcFFAF6vVm+Z6nTN06FDzfUzXINEfTVmzZjXfuz788EPJlSuXi0cnOwIIIIAAAp4v8CDa6PltTZAWWnN5Nm/ePFxw16pM5syZzR0KXdgpderUUrBgQWtXpL8rV64ctl2PTYA3jIMXCMSKgK+vr/Tq1UvefPNNmT9/vqxZs8bcUEmSJIm5GaOPDtaqVUv0PQkBBBBAAAEEEEAg9gSKFCkiU6ZMMdMw6MLSFy9eFL1e0oExGgQmIYAAAggggEDkAgR4I3eJta3nz583x8qRI0eUxyxTpoxZgC1lypQxfnHRLzg6ylAfJddRvCQEEIgbgQwZMsg///lP8xM3Z+CoCCCAAAIIIIAAApEJJEqUSHRKOv0hIYAAAggggEDMAiyyFrPRE+XQR7k1HThwwPyO7B8dtRsQEGDml7p8+XJkWcK2HTt2zAR3dUPevHnDtvMCAQQQQAABBBBAAAEEEEAAAQQQQAABBLxPgABvHPd5oUKFzBl0/t1r165FeraKFSua+aV0Ht5UqVJFmsfaOGrUKOul6CNMJAQQQAABBBBAAAEEEEAAAQQQQAABBBDwXgECvHHc923btjVnOH78uNSvXz/aaRV06gV9HCmqpMHdL7/80uyuVq2apE2bNqqsbEcAAQQQQAABBBBAAAEEEEAAAQQQQAABLxAgwBvHndygQQOpVKmSOcuqVaukQIECUq9ePfnjjz9snfnMmTMyfPhwKVu2rHTp0kXu3r0riRMnlodH8to6EJkQQAABBBBAAAEEEEAAAQQQQAABBBBAwOMECPDGQ5fOnj1bKlSoYM6k0zAsXrzYrAhr59Rr166V7t27y6ZNm0x2Pz8/GTFihBQuXNhOcfIggAACCCCAAAIIIIAAAggggAACCCCAgAcLEOCNh8719/eX1atXS1BQkOTMmdOcsXjx4rbOnCdPnrB8uoqsjgLu1KlT2DZeIIAAAggggAACCCCAAAIIIIAAAggggID3ChDgjae+12kVBg4cKEePHpWNGzeKBn3tpHz58knfvn1l3bp1cuTIEQkMDLRTjDwIIIAAAggggAACCCCAAAIIIIAAAggg4AUCfl7QRrdqoo+Pj5QrV852nXQhtf79+9vOT0YEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8B4BRvB6T1/TUgQQQAABBBBAAAEEEEAAAQQQQAABBBDwMAECvB7WoTQHAQQQQAABBBBAAAEEEEAAAQQQQAABBLxHgACv9/Q1LUUAAQQQQAABBBBAAAEEEEAAAQQQQAABDxMgwOthHUpzEEAAAQQQQAABBBBAAAEEEEAAAQQQQMB7BAjwek9f01IEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8DABArwe1qE0BwEEEEAAAQQQQAABBBBAAAEEEEAAAQS8R8DPe5pKSxFAAAEEEEDAnQSuX78uq1atkoMHD8qNGzckICBAKlSoIAUKFHCnalIXBBBAAAEEEEAAAQQQQMCtBQjwunX3UDkEEEAAAQQ8T+DcuXPSv39/mTRpkty8eTNCA0uVKiWDBw+WGjVqRNjHBgQQQAABBBBAAAEEEEAAgfACBHjDe/AOAQQQQAABBOJQYPPmzVK/fn05deqU+Pr6SmBgoJQuXVpSpEghR44ckRUrVsiWLVukZs2a0qNHDxk6dKj4+PjEYY04NAIIIIAAAggggAACCCDgbAECvM7uP2qPAAIIIICAYwT2799vRuUGBwdLpUqVZOzYsVKoUKFw9b9165aMGjVKgoKCZNiwYeLn5yeDBg0Kl4c3CCCAAAIIIIAAAggggAACDwRYZO2BBa8QQAABBBBAIA4FWrVqJRrcfeWVV2TlypURgrt66qRJk5qRu0uWLJHEiRObqRr+85//xGGtODQCCCCAAAIIIIAAAggg4GwBArzO7j9qjwACCCCAgCMEFi1aJL///rtkz55dpk6dakbmRlfxatWqSZ8+fUwW63d0+dmHAAIIIIAAAggggAACCHirAAFeb+152o0AAggggEA8CsyZM8ec7b333pOUKVPaOnO3bt3M3Lxr166V06dP2ypDJgQQQAABBBBAAAEEEEDA2wQI8Hpbj9NeBBBAAAEEEkDgt99+M2etXbu27bNrILhixYpy//590cXZSAgggAACCCCAAAIIIIAAAhEFCPBGNGELAggggAACCMSywJkzZ8wRc+TI4dKRc+bMafL//fffLpUjMwIIIIAAAggggAACCCDgLQIEeL2lp2knAggggAACCSiQJk0ac/YrV664VAsrv1XepcJkRgABBBBAAAEEEEAAAQS8QIAArxd0Mk1EAAEEEEAgoQXy589vqrBx40aXqmLlt8q7VJjMCCCAAAIIIIAAAggggIAXCBDg9YJOpokIIIAAAs4W0Dlo9+zZIz///LPogmPHjh1zXIPq1atn6jx+/HjbdV+2bJkcPXpUsmfPLs8++6ztcmREAAEEEEAAAQQQQAABBLxJgACvN/U2bUUAAQQQcJTAxYsX5YMPPpBs2bJJ4cKFpVq1avLiiy/KU089JUWKFJFRo0bJ7du3HdGmf/7zn5IhQwZZuXKlTJkyJcY6BwcHy7vvvmvyvf/+++Lj4xNjGTIggAACCCCAAAIIIIAAAt4oQIDXG3udNiOAAAIIuL3AqlWrpECBAjJs2DDRBcY0qFu1alUJDAyUjBkzyu7du6VLly5SunRpOXTokNu3J126dDJ8+HBTzzfeeEMmTpwYZZ11hLIGsw8cOGDa16lTpyjzsgMBBBBAAAEE7AvoU0Fnz56Vffv2yaVLl+wXJCcCCCCAgFsLEOB16+6hcggggAAC3iigwd2aNWvKhQsXTKBz8+bNcuTIEfnpp5/MFA1nzpyRhQsXSqFChWTnzp1SoUIFOXHihNtTtWvXToKCgsyo49dff93Ue/LkybJt2zbZv3+/Gd3buXNn064tW7aYAPeCBQskSZIkbt82KogAAggggIA7C+zatUv073DmzJnF399fChYsKOnTp5e8efPKRx99JOfOnXPn6lM3BBBAAIEYBAjwxgDEbgQQQAABBOJTQIO6TZs2lbt378p7770nK1asMKNYH65DokSJROe03bRpk1SpUsWM8G3WrNnDWdz29cCBA2XGjBnm4nL9+vXSoUMHKVmypAnm1qhRQ0aPHi03b96U5s2by2+//Wbm33XbxlAxBBBAAAEE3FwgNDRUevbsKcWLFzdTJOkfZQ+AAABAAElEQVT3jCxZspi/u2nSpDFPAQ0dOtQEer/99ls3bw3VQwABBBCISoAAb1QybEcAAQQQQCABBAYPHmxG7lavXt1MaRDd3LOpUqWS77//3szRq8HSH374IQFq7PopW7RoYaZf0GkaGjZsKCVKlJD8+fOb6Sd0FNHWrVtl5syZotM6kBBAAAEEEEDg8QV01O6QIUNEbw7r1E76xIw+CbR3714zRYN+f9CbxlevXhW9WTxhwoTHPxklEUAAAQQSTMAvwc7MiRFAAAEEEEAgnICOspk+fbrZphdj0QV3rYIaBO3Vq5e88847MnXqVHnllVesXW79W4PTOnpXf0gIIIAAAgggEPsC+lTMtGnTREfqLlq0SCpWrBjuJPo9o3z58mbaJ124VQPA+n2iVKlSUqZMmXB5eYMAAggg4N4CjOB17/6hdggggAACXiSwZ88eM6omd+7c5uLKbtMbNWpksq5Zs8ZuEfIhgAACCCCAgAcL6AJqH3/8sWnhlClTIgR3H226zoHfrVs3uXPnjnTv3v3R3bxHAAEEEHBzAQK8bt5BVA8BBBBAwHsETp48aRqr0xW4krJmzSo6Ivby5cty7do1V4qSFwEEEEAAAQQ8UECnbQoODpbKlStLgwYNbLWwX79+ZuG1X375xUylZKsQmRBAAAEE3EKAAK9bdAOVQAABBBBAQCRx4sSGQUfPuJqsMtYxXC1PfgQQQAABBBDwHIGVK1eaxuiipXZT6tSppW7duia7Vd5uWfIhgAACCCSsAAHehPXn7AgggAACCIQJPPXUU+b1rl27ROfjtZv27dsnt27dEh3JmzRpUrvFyIcAAggggAACHipw/Phx07IiRYq41EIr/7Fjx1wqR2YEEEAAgYQVIMCbsP6cHQEEEEAAgTCBp59+WgoUKCDnzp2TZcuWhW2P6YUuoKKpZs2aMWVlPwIIIIAAAgh4gcD9+/dNK+0s2Powh5XfKv/wPl4jgAACCLivAAFe9+0baoYAAggg4IUCb775pml1jx495Pr16zEK6OjdESNGmHxW2RgLkQEBBBBAAAEEPFogZ86cpn26gKsrycpvlXelLHkRQAABBBJOgABvwtlzZgQQQAABBCIIvP3226KPR/7111/SsGFDuXLlSoQ81gYN7tauXdsEglu1aiXPP/+8tYvfCCCAAAIIIODFAlWrVjWtnzNnjm2FGzduyKJFi0x+q7ztwmREAAEEEEhQAQK8CcrPyRFAAAEEEAgvoHPoLliwQLJkySK6wEnRokVl3Lhxcvbs2bCM+/fvl759+0rp0qXl4MGDUrZsWRk/fnzYfl4ggAACCCCAgHcLNGrUSNKkSSMrVqywPe3Tp59+KhcuXJDy5ctLoUKFvBuQ1iOAAAIOEyDA67AOo7oIIIAAAp4vkC9fPtm0aZOUK1dOTpw4IW+99Zb4+/tL2rRpJUWKFGae3oEDB8q1a9ekZcuWsnr1arPd82VoIQIIIIAAAgjYEciQIYP06dPHZG3RooX8/vvv0RabNGmSDB48WBIlSiSff/55tHnZiQACCCDgfgIEeN2vT6gRAggggAACkitXLtmwYYN8++23UrduXUmVKpWZriEkJMSM7m3Tpo2sX79epk+fTnCXzwsCCCCAAAIIRBB4//33pXHjxhIcHCwvvviiBAUFyalTp8Ll27Fjh2gA+LXXXhNdWE2Duy+88EK4PLxBAAEEEHB/AT/3ryI1RAABBBBAwDsFdCXrJk2amB8V0Pl4EydOLMmTJ/dOEFqNAAIIIIAAArYF9HvErFmzJCAgQL766ivRKRgGDRokuXPnlvTp05tg799//22Op98txowZI+3bt7d9fDIigAACCLiPACN43acvqAkCCCCAAALRCuhcegR3oyViJwIIIIAAAgg8JODn5yejRo2S3377zYzm1ameDh8+LFu2bBEN7mbNmlU6deokunArwd2H4HiJAAIIOEyAEbwO6zCqiwACCCCAAAIIIIAAAggggIArAs8995x89913cvv2bTO//6VLl8yUT9mzZxcd6UtCAAEEEHC2AAFeZ/cftUcAAQQQQAABBBBAAAEEEEDAlkCSJEkkT548tvKSCQEEEEDAOQJM0eCcvqKmCCCAAAIIIIAAAggggAACCCCAAAIIIIBAOAECvOE4eIMAAggggAACCCCAAAIIIIAAAggggAACCDhHgACvc/qKmiKAAAIIIIAAAggggAACCCCAAAIIIIAAAuEECPCG4+ANAggggAACCCCAAAIIIIAAAggggAACCCDgHAECvM7pK2qKAAIIIIAAAggggAACCCCAAAIIIIAAAgiEE/AL9443CCCAAAIIIIAAAggggAACCCCAAAKOEjh8+LCsWbNGTpw4IX5+fvLUU09JtWrVJEuWLI5qB5VFAIHHEyDA+3hulEIAAQQQQAABBBBAAAEEEEAAAQQSVGDjxo3y4Ycfytq1ayPUw9fXVxo0aCBDhgyR/PnzR9jPBgQQ8BwBArye05e0BAEEEEAAAQQQQAABBBBAAAEEvERgxIgR0qNHD7l3756kS5dOatasKQUKFJC7d+/Kzp07ZeXKlfLDDz/I8uXLZfr06SbY6yU0NBMBrxMgwOt1XU6DEUAAAQQQQAABBBBAAAEEEEDAyQITJkyQbt26iY+Pj3z00UfSu3dvSZUqVbgmnTlzRrp3726Cu02aNJEVK1ZI5cqVw+XhDQIIeIYAi6x5Rj/SCgQQQAABBBBAAAEEEEAAAQQQ8AIBnW/33XffNS39+uuvZfDgwRGCu7rT399fpk2bZqZw0FG9LVu2lOvXr3uBEE1EwPsECPB6X5/TYgQQQAABBBBAAAEEEEAAAQQQcKjAwIED5fbt29KuXTtp27ZtjK3QAHD58uXl9OnTMnbs2BjzkwEBBJwnQIDXeX1GjRFAAAEEEEAAAQQQQAABBBBAwAsFdCTuvHnzTMuDgoJsCeg0Dr169TJ558yZY6sMmRBAwFkCBHid1V/UFgEEEEAAAQQQQAABBBBAAAEEvFRg//79cunSJcmfP7/kzZvXtkL16tUlUaJEsmXLFrMom+2CZEQAAUcIEOB1RDdRSQQQQAABBBBAAAEEEEAAAQQQ8HYBXThNU/bs2V2iSJo0qWTKlElCQ0Pl/PnzLpUlMwIIuL8AAV737yNqiAACCCCAAAIIIIAAAggggAACCEiaNGmMwpUrV1zWsMpYx3D5ABRAAAG3FSDA67ZdQ8UQQAABBBBAAAEEEEAAAQQQQACBBwJ58uQxUy3s3LlTrl69+mBHDK+2bt0qISEhkjNnTkmePHkMudmNAAJOEyDA67Qeo74IIIAAAggggAACCCCAAAIIIOCVAunSpZOKFSvK7du3ZfLkybYNxo0bZ/LWq1fPdhkyIoCAcwQI8Dqnr6gpAggggAACCCCAAAIIIIAAAgh4uUCPHj2MQL9+/UQXXYsprVq1SiZOnCh+fn7StWvXmLKzHwEEHCjg58A6U2UEEEAAAQQQQAABBBBAAAEbAjdv3pQVK1bIzz//LMePHxcfHx/JlSuXVK9eXapVqyZJkiSxcRSyIICAOwnUqlVLmjRpIt99951UrVpVfvjhBylTpkykVVy4cKG0atVK7t27J3379pX8+fNHmo+NCCDgbAECvM7uP2qPAAIIIIAAAggggAACCEQqMG3aNOnZs6ecPHkywv6RI0fKU089JcOGDTOBoggZ2IAAAm4t8M0338jZs2fll19+kXLlykmzZs2kcePGJoB79+5d0Tl6p0+fbm7waEPatm0rOuKXhAACnilAgNcz+5VWIYAAAggggAACCCCAgJcK3L9/Xzp16iRjx441AiVLlpSmTZtKoUKFJDQ0VHbv3i2zZ8+WXbt2me36uPdnn33mpVo0GwFnCqRIkUJWrlwpvXv3llGjRsmsWbPMz6OtSZs2rfTv31+6dOny6C7eI4CABwkQ4PWgzqQpCCCAAAIIIIAAAggggICO0tPgbvLkyUUXVmrTpk0EFA0KjR8/3gR9dBRvlixZpHv37hHysQEBBNxXIHHixObmzDvvvGNu2qxevdqM2Ne5dnUqlpo1a5qRvRkyZHDfRlAzBBCIFQECvLHCyEEQQAABBBBAAAEEEEAAgYQX0FG5n3zyiVlMSefe1Hl2I0s6F++bb74pAQEB0rBhQzMKsFGjRvL0009Hlp1tCCDgxgIazP3ggw/MjxtXk6ohgEAcCvjG4bE5NAIIIIAAAggggAACCCCAQDwKDB482EzDoFM0RBXcfbg69evXl/bt28vt27fNfLwP7+M1AggggAACCDhDgACvM/qJWiKAAAIIIIAAAggggAAC0QrowkqLFy82ebp27Rpt3od3Wnnnz5//8GZeI4AAAggggIBDBAjwOqSjqCYCCCCAAAIIIIAAAgggEJ3AiRMn5PLly/LUU09J7ty5o8sabt8zzzwjOkfn6dOn5cKFC+H28QYBBBBAAAEE3F+AAK/79xE1RAABBBBAAAEEEEAAAQRiFLCCs5kyZYox76MZrDLWMR7dz3sEEEAAAQQQcF8BArzu2zfUDAEEEEAAAQQQQAABBBCwLWAFac+ePWu7jJXRKmMdw9rObwQQQCAmAZ0eZsaMGfLKK69Ivnz5JH369JI3b17ROb6nTJli5viO6RjsRwCBJxPwe7LilEYAAQQSVuDMmTPmS8OyZcvk8OHDcuvWLcmePbtUrFhRWrZsKaVKlUrYCnJ2BBBAAAEEEkhAL7h/+eUX2bp1q5w7d85ccBcpUsQsvJU8efIEqhWnjUuBHDlymKkWjh8/LgcOHDCBFjvn27Jli1y6dEms8nbKkAcBBBBQgfXr15uFGvft2xcORP+fcujQIVm4cKEMGDBAJk6cKJUrVw6XhzcIIBB7AgR4Y8+SIyGAQCwK6IXojz/+aC5Orl69KlmzZpXy5ctLhQoVJFGiROZMw4YNk/79+8v169fDnVnnj9u8ebOMGDFCXn31VRk7dqykTZs2XB7eIIAAAggg4KkCGtj96quv5NNPP5Xz589HaGaqVKlEF9X66KOPJGXKlBH2s8G5AvodSUfMff311/L555/LuHHjbDVm+PDhJp+OviMhgAACdgXmzZtnrrdu374thQoVMn9bqlSpIpkzZzZ/f9asWSOjRo2SHTt2SI0aNWTq1KnSvHlzu4cnHwIIuCDgc/+/yYX8ZEXgiQR0NOWff/4pDRo0EP1jQELgUYFTp05Jr169ZNq0aRIaGvrobjOypF+/fmZEkubRpJ+ntm3bSsmSJSVp0qRy8OBB8/nSi5obN26YLxv65cLf3z/C8diAAALOErhz547cu3dPkiVL5qyKU9tYFdALyYsXL5qRikmSJInVYzv9YMHBwdKoUSNZvXq1aYounlW1alVzo1TN1q5dKxs3bgzbt2jRIrMgl9PbTf0fCOzfv190pLZ+j/rhhx9MwPfB3oivZs6caZ560lHdOgJPR/GSEEAAgZgENGj7/PPPm+ut9957T3TwjTUQ5+Gy+v+iPn36yKBBg8y12rp166RMmTIPZ+G1lwu0bt1apk+fbmIArVq18nKNx28+I3gf346SCCAQywIbNmwwwVqdA04v2PUub9myZSV16tRy7NgxWbp0qezdu1dee+01c2bdPmfOHKlVq1a4mgQEBMg//vEP6dy5s7mo2bZtm5kPSi9qI/vSEa4wbxBAwO0EfvvtN5kwYYIsX75c9CaQ3pvOmDGjVKpUydzcqVevntvVmQrFvoD+bRg5cqQJWGkQSj8HPj4+UrBgQWnYsKEZNZQlS5bYP7GDjqg3QNRCp2XIli2bTJo0SV566aUILfj999/ln//8pxlRVbNmTRPwTZcuXYR8bHCmQP78+U0g5YMPPpAmTZqIjs59++23I3wH0s+LPu2kN9Y1ffHFFwR3ndnl1BqBBBHQJ0F0ME2HDh3M/z+iqoSvr695ouTatWtmNO+7774ret1HQgCB2BVgBG/senK0GAQYwRsDkBfv1ov1cuXKmfnf9GJz/PjxEUYU6cW8PnKqgVtNOiLpp59+ilZNp3rQO8QaIP73v/8dFhyOthA7EUDALQT0ouHNN980d/OtCiVOnNgEKW7evGltkhdffFFmzZolenOH5JkC+ri5/r9fLw416U3ADBkymFG8OppXk97008dA27VrZ9574z+DBw82wTodgamjdHVO+qiSTn+k/+3ok1V641T/RpI8S0Cn4Bg6dKhplC56pCO79RFq/T71119/ydy5c+XIkSPmRsnAgQOld+/engVAaxBAIM4Edu7cKfqEiP4t1nl27UyHp9/rdOG1v//+2/yN0ms/EgIqwAje2Pkc+MbOYThKbAvowlH6aN3s2bPNwhh6h52EgCcL6AW5TsSvI02WLFkSIbirbdeRWhqo1aRBnp9//lm+++478z6qf3T+J52DTpOOUiEhgIAzBPQiQBfi0KlYdI5QHWG2e/dus5BiSEiI6AJC+t+0Tr2ioxWfe+65sP8/OKOF1NKugAaodLSpBnd1Sh59GkM/Azrfun5O9L1u14Bl+/bt5bPPPrN7aI/KpxZWME/nOIwuuKsN14D4t99+a/6eagBdFyoleZbAkCFDZP78+aIjenXBNf186H8j+t+TfjfS4K5O5aBrHhDc9ay+pzUIxLWAXq9paty4sa3gruZNkSJF2Py7VnndTkIAgdgRIMAbO462jqJzz+zZs0dmzJhhFj7QEROPphMnTkibNm3Ml3KdnFwnIH/22WdFF8PQANiVK1ceLcJ7BBwvsGzZMvOYjo6+04vM6KZRsL4MdOvWzbT7448/jrH9umCIPs6to1W4gI2RiwwIuIXA66+/LvoYuY700NXddbEoHXmmN3o06QhFfTRQ/7uuWLGinDx50kzJootLkTxHYMWKFdKzZ0/RxzsnT55s5lcPDAw077WV+vdC3+u8/jodgebTUYsrV670HASbLVGry5cvmwVJ7a5SrqM6mzZtaua11rlaSZ4noAuu6c2xNf9diyAoKMhcT2iQV78/6TyYOoemPjlFQgABBFwR0DVPNLk6l66V3yrvyjnJiwAC0QswB2/0PrG2d+vWrdKiRQvzBevhg1avXt3MIZo+fXpzcaqPyukjDo8mffxwypQp5suZPk5l/Y/x0Xy8R8CJAvqZ1qTzMcW0mrf1ZUAvTL755hvz39SuXbukaNGiUTZdAwB6o0Snc9DyTz/9dJR52YEAAgkvsH79etFFf/Tmpo4s09FnUSV9NHDhwoXm76L+rdXHzN96662osrPdQQJ6Y1wXbdHHyXVhFg1KRZd0VKI+AaWjvbXc9u3bwwLB0ZXzlH2bN282TXE1WKf5dfCBVd5TPGjHAwH9HqTXGPpDQgABBGJD4Pr16+YwMV27PXouK79V/tH9vEcAgccXYATv49vZLqkXnjq/jN49fzTpCBNdIEovYtr9d4SuFdzVhTF0RIWOQtFH1vURVE1Hjx6VV1991Tya+OixeI+AUwX++OMPU3W94RFd0hsd+qNzL+pKzzoHryYd3RdT0kCRJmv+xpjysx8BBBJO4F//+pc5+fvvvx9tcNeqoc77Zj2aPnbsWGszvx0u8J///MeM0NabcvpZsJO6d+8uuXPnFr3xp6MTvSnpInSa9DukK8maysEq70pZ8iKAAAIIeKeAte6BxidcSTo1jCarvHnDPwggECsCBHhjhTHqg+h8cDqSyFoARAO9+qihrmpbvHhxU1BXB9fHTK3FojS/Ljg1Z84c0cUydH40fa8BYE06AvGTTz4xr/kHAU8QsC4qY7ootRbV0f+edHJ+64uBVT46C2vu3qxZs0aXjX0IIOAGAjptiyadsshuevnllyVdunTmceNTp07ZLUY+NxZYvny5qV2zZs3MPLF2qqrzs2t+TTplgTcla4EbnabBlaTz32uyyrtSlrwIIIAAAt4p8MILL5iGL1q0yCWAxYsXm/xWeZcKkxkBBKIVIMAbLc+T7xw2bJhYF5oarN2wYYN5zFBHGumoxbffftucZPTo0ea3zpOlI5esRxesGqRJk8bMPWc9WqWrROsjiyQEPEFApyjRFBwcHGNzypcvb/LolwMrvwZ1oks6N6fOea0T+5coUSK6rOxDAIEEFtBH9i5cuGD+DubJk8d2bfz8/MxiQVrAGh1iuzAZ3VLAujFXrFgxl+pn5Xd1VJFLJ3HDzAULFjS10ilOXElWfqu8K2XJiwACCCDgnQI6vY9ew2l8w7oxH5OEPpmjg9r0ycq6devGlJ39CCDgogABXhfBXM2uo3M1VatWzUy3YC0Oo9v0YlRXANcFZKz0xRdfWC8j/NayOgedJn3MXFcQJyHgCQK6cJImO4/T6hQlmvQmiX5J0GSVN28i+WfAgAHmhoiO8NOpHUgIIOC+AtYTLzpi39VklbGO4Wp58ruXgLVgnn5fciVZ+a3yrpR1ct46deqYOYeXLl1q+yaHfp+cNm2aabb+jSQhgAACCCBgR0AHpOnCjZr0iSt94ji6pDffdQF5Tfo0szXAJ7oy7EMAAdcECPC65uVybl3dW1NUd6j0YrRSpUomT8aMGSWm0UolS5YMWzDEOrYpzD8IOFhAR65r0rkzdT7q6JJ+MdDRWQcOHJD9+/dL5syZzYrhUZXRhdgmTJhgHu/t379/VNnYjgACbiKgX/j1RoyO0L948aJLtdL/L2iy5hR1qTCZ3U7A6kf9f70rybrIzJEjhyvFHJ9Xpy1q3bq13Lp1S1577TWxE+Du0qWL6DRHgYGB0f4tdTwODUAAAQQQiHUBnWaydu3acu7cOXn++efl66+/lnv37oU7j17b6UKeZcuWNYvKV65c2UxZGS4TbxBAIFYECPDGCmPUB7HmQYtuXjNr8Se7d7F0JVxNLBYVtTt7nCWg8yXqojjbtm0LG6UeVQv08z9+/HixRsPr9CV79uyJkF3nFOzWrZvoquqadFqTAgUKRMjHBgQQcD8BazqiH374wXblNm3aJCdOnDALTOXLl892OTK6r4BeBGqaO3euS5X8/vvvTX6rvEuFHZ5ZpwPTQO/PP/8sDRo0CJvK6NFmhYSEyBtvvGGm/9JRWGPGjHk0C+8RQAABBBCIVsDX11e+++47adSokfl7o9dd+jeocePGZipKXTT+/9g7E3ibyu6Pr7c3TRoNRZNUkhRKMkURUmSsEBlCgyFzREihQhGSNMsUFYWSpCJDmkQZCpEm0YDm9O7/+q3++3TuvWfY+95zzj3Db30+l3P2fp699/M95+xhPWv9FmqstGnTxpzACHp78cUXLZM54oa5kgRIIFcE6ODNFTbvnVw9M+jthrMPPvjAVm3btk1+/fXXcM1s+YoVK+Svv/6y12XKlInYlitJIFUIIJIdTls4b4cMGWJ/7vc8+xgQyXXzzTeb5ALScFF0EBG9KGCIh9Xu3bvbTDJuJiCBghsPOHfRh0YCJJA4Aoi+/fzzzwWaun4NUYiwESNGeJrMhCY9CpjC8BDhTgDZAv6TsgTq1q1rD4aY/Js2bZqncUBuYN26dRbFfdlll3nqk06N8GCNgjfIblm4cKHJgCEVFgXrwPGNN94QZLOUKlVKHn30UdOmR1Hf8847L50wcCwkQAIkQAIJIoAaJ3Dyzpo1y2ohIJoXE63IzMTyXbt2WZDN1KlTzbl71FFHJejIvO8GgXO5uV/1vge2JIEEEdCHIlocCehDKiqhOYceeqijkgo59jRv3jxbjzb4GzduXI42wQs05c7aqWPL0RS84FUp8fr888+349eokpQ4Xh5kYgnow6aD7zZ+CxqB56hzx3nttdccFe93Zs+e7bRr187RCum2vmzZso4+xDvq0HU0+siWub8j/K+OXad+/fqOTqAkdhDcGwlkMIFPPvnE0egNR51LWX6TqpPt6OSNo05fT3Q0vc+54IILbBua+ufo5GfYfpr65/Tq1cvaqtSR532E3SBXJBUBTfe0zxbn+bfeeivisb355puOPmha+6effjpi23RfqZMrjhbAMRbB18bg15UqVXK0AGm6o+D4SIAESIAEEkhAMyvtuU0Lxzs6gejg3jAZTfXqHY00drRYd+BaqRnVjkYdOzopmoyHnNbHpAEa9jnoRH1ajzPeg0MUHC2OBNasWWOOJtxQ4+SBEx0cvatXr3YGDRoUcFZVrFjRvtB4MAn3AIP27o25pjfE8ajjt2k6eOPHNl22rNFFzjnnnBP4rrvfefd/jfJ1Onfu7Ozbty8wZE01dRYvXuyo1q4zYcIER2eNHZ0tDqznCxIggfgSgENWI2gd/D7d32qhQoWckiVLBhxuWI4bZ03N83QwGp3vwGGLfuXKlbPfOJy5wQbnVL169awNJoeWLl0avJqv04RAx44d7TPGBB++Z9nP799++60zYMCAwD0VrhG0fwjgfrNv376OFvt1ypcv72jdB6dLly4hf09kRgIkQAIkQALpTkAjjAP3ju49q0pm5ggYuuKKK5zvv/8+3XEkzfjo4I3NR/EfbEa/2LQ4Erjllltk8uTJYfegD8GmIYr0OKQwILUU+jVILUSauTqJRZ1XsmTJEtsG0iB0Jsw0S8NuNElXaESW6AO56cLNnTs3SY+Sh5XfBCDOv2jRIlmwYIEVU0PaTLFixUy8H5pOZ5xxRn4fIvdPAiQQRACSCkihVwec4JqnDiRxJYrwe16+fLnpa2tEvsmmoPihK8MQtJkcL6GvjSKMbtEsnAcgyQJ5Fsi1QKIFhuuoRvnbdTPHRrgg5QmgQAskOEaPHm3yPJDe0YlAkyFAgbCNGzdagU7cP0GOYOTIkYGCtCk/eA6ABEiABEiABEggJgRwz1C1alWBNGaRIkVEJ4cFtWDcoqw7d+6UmTNnyqhRo0SduyZntHLlSmsbkwPgRsIScJ8lILUFuTVa7gjQwZs7br56/fnnnzJw4EB54IEH7MEkuDM0aODIqlatmp1MWrdunaNNcHs81EycONEeoIOXp8prOnjz75NCRXro8sHR8s0331iVeo2uM73amjVr8mE4Dx8NKpajoA1uADSSTFCwBkWeGjRoIKeffnoetsyuJJD8BB588EFBFWUUPMQ5BueTcHbvvfeao05li2zyUiNzwzUNLP/9999NRxs63bghDzbojOKGEM4/3KjT0psA6hkMHz7c9GRRJMy1ww8/XFSSRzTTSTQjyl3M/0mABEiABEiABEjACCCuUbNYZNmyZaISRfLSSy9ZAFEoPF9//bWgIBwC02rXrm3PeaHacVnsCNDBGxuWdPDGhqOnraiOqDlz3aJqKldgUU6ISHLtscceE02lk71797qLAv+fdtppoppyER+eA42T9AUdvIn/YBA9B6fKfffdJ/v37w95AHCyoBCZW7k+ZCMuzEEANwoqu2IFa1BQIJQ1atRIxowZYzPAodZzGQmkMgFMHGES46effrIb5auuuirqcBDdi8Ibqg9q18SoHYIaIGp3x44dcuDAASuipVrcnJwK4pMpL1GQFtHbe/bsMcc+JtTg5KWRAAmQAAmQAAmQQCgCKCjasmVLy5Beu3atZQGFaucuQ9BOhQoVLMMaReOaNWvmruL/cSBAB29soNLBGxuOMd0KHpSR6oqUQ6QG4AEWJ5datWqJ6sPEdF9eNrZixQqrihkLNQ+cWPFApkVzrLqzl/2zTe4JoBpo06ZNBWnRMNXgs3RnRO4i+gmzktOnTzeHiWpnykMPPSQ33XRT7neYQT3Br1WrVlYNFsPGhA1mekuUKCGQlHjvvfdsHZzqiGxE+jgcWjQSSCcCqntt54y6deualJCXscEpjN8JfhtffPGFnHLKKV66sQ0JkAAJkAAJkAAJkAAJ5IoAfClajFW0qLdo4XpP20BAAgIT/NznetowG+UgQAdvDiS5WkAHb66wZVYnRGRBCzWWBmeYG8kcy+1yW1kJQK8WM46IEoeeENJSshskRO6++24ZMWKErZo3b54g6pQWmQBmgDFhoUWg5PHHHzfHefYeiOrt3r27tYN2NuQxEMVOI4F0IeCeY5588klp376952FphWKZM2eO/XagOU8jARIgARIgARIgARIggVAEEGiGwKQtW7YIApjwbHvRRRfZc1io9tmXIfMHATcIaEKwGWQyvRgC7yAHBo1/BCZAYowWHwJ08MaG68Gx2Qy3ks4EoK+IiFsUOcmrQSrgyy+/DAiZ53V77B+ewHPPPWfOXa1ab1pDpUqVCtn4kEMOMQcvLnooToMIXhT4g44sLTQBROPCuXvMMceY07ZMmTIhG+KGYNasWXZDgdliOMCQEgQtbRoJpAMBFKOAoeCVH0NmChy8iOClkQAJkAAJkAAJkAAJkEB2Am4dBvgjoIsbbHDWIrIW2vzR9Pfhf4BsIQp1e3XuYl/HHnusZZp9/vnntn9kwdJIIJkJ0MGbT58OijLlZgYIkg1uYRG32mO8hwB9RVRFj4Uh0hEnWJyQafEl4EbkogpoOOdu8BH069dPEL2LQmFPPPGERZ4Gr+frfwkMGzbM3qBwYjjn7r+txQojogjb+vXr5YUXXhBEPdJIIB0IuBN/fict3PZu/3RgwTGQAAmQQKIJoCI8ZLigTf7XX3+ZNjmytaBLTSMBEkgsAdzTQKLt008/lX379lmUKYp5UYoqd5/D9u3bLasUz08wOFfBE0FIOOdBRhLF6hcvXiwjR46U/v37h90Rzo+wAgUKhG0TbsXBB//jMnO3Ea4dl5NAMhBgGFkCPwVEVF5xxRVywgknWDEQOIbatm1rJyevh4EIQFwkeKHwSiwz2+GCiEhRzDq2a9fOM4QePXpY27lz53ruk2kNN23aJBs2bJDjjz/eM1tESbtsIZlBI4F0IeBONG7evNnXkNz2bn9fndmYBEiABDKcAIoMXnPNNVK8eHFp06aNDBo0SO68807p3LmzTepXq1bNMowyHBOHTwIJIYAo09GjR9sES+XKlQWp5l27dpXmzZvLqaeeKvg9Ll26NCHHki47weQVJqvg3IXPZMmSJbJt2zbLoEQgEgJnUAQN2aewAQMGyD333BN2+CeddJKtcyfDwjbMtgJBeQhOg0TDiSeemG0t35JA8hGggzcBnwl0YuBkw40YZplwwoKODBxFzzzzjNSsWVN69+4diMxNwCFxF2lO4JNPPrERVq1a1ddM5SWXXGL93P5pjilXw4PjHIabDj+R6JC9gLn97Q3/IYEUJ1C7dm0bAWRLvBqKEL788svW3P1deO3LdiRAAiSQ6QTmz59vhV0ROIIJZNTKGDhwoAwdOlRat24thQoVklWrVtl9CqTRaCRAAlkJQMv1jjvukAYNGlhEaL169aRnz57yxhtvZG3o4R2kqvC8BUcjHI6InsekCwpz4bcJOQD8HnG/07dv35hIHno4rJRv0rFjR4vSrV69urzzzjvGL/ugEMh03333meQXMsMw0YW2oQxty5cvb4WwX3311VBNQi7D/Sqypy+88MJ8KXYf8qC4kAQiEVBHIy3OBG699VZHP4PAn6YVOJpi4OhMUGAZ1p911lmOzkxFPJqGDRsG+kRsmKQrtbiaHX+TJk2S9AjT47CmTp1qnHUG2deANPXE+mkqiq9+mdR4/PjxxkiLp/kathZcs35FihTx1Y+NSSCZCejDjINrGq5hWpnY06FqCp21r1Gjhqf2bEQCJEACJPAPgWXLljmaYmzn0FatWjnffPNNDjQaWOIMGTLEUYeHtVPtyhxtuIAEMpGARmI6jRs3tt9F8LN58OsqVao4GozhCc8PP/zgqEPXtle6dGlHo0xz9FPnoKMTLY5Oxlg7zejL0YYLshJQ6QVjpbVOHNxnejGd5LI+GngQtrlK61mbChUqOBqZG7adu0Ijs51zzz3X+kyYMMFdzP/jREAnRoy1BkDGaQ+ZsVlG8OoZPZ6GaL2HHnrIdgFphhdffNE0eZBi8OOPPwr0UVGoCQa9HkQFQsSbRgJ5IaBOROu+a9cuX5tBdDnM7e+rc4Y0RmQMDHrYfkxvAq05it7RSCBdCOC65mqeXXvttfLRRx9FHBp02HHdQ/Q7/qf9QwDnanWQC+Rx3n77bUHVZhoJkAAJBBNAGvh1111nWrvI/JsxY4ZpfAa3wesjjjhCUCsA65FW3KdPH3FlcbK35XsSyBQC69ats2hdPIsjqlYDsOSll16Sd999VxDRiYheSJ6sXr1aEDXqZhpF4oMo3S1btlh0Z7go08MOO8zuk6ATi/o7KBbmZduR9pvu63DugkHmAveZXgwSDTj3IQpbJ75CdsHnBR1f+Gc6dOhg59KQDXXhn3/+aVKaH3/8scne3HjjjeGaJnS5uihFg4bsnO73WTShB8qd5R+BzPBj598o9WRgMxGIiAw3G6gVIR1NGbB2+k2w6N5ws1WM4M2/zzKV9owZakSIH3nkkQ5mjr2aG/l7+eWXe+2Sce0++OAD+62qDravsT/66KPWj9HrvrCxcQoQ0KrEjqY52vcb0bx33323o5NFWY5cb5AddUwErnOMKPsHj6ZaO5ramSOjB/cMOA9r0cssHPmGBEggcwmMGzfOzqGaKuzgvOvFbr75ZuuD8y+NBDKVAO5JVAvXfgt16tRxdFI1JApEv6s0gLXD/Yw6hUO2w0I81+NZS52KzhdffBG2XfCKsWPH2rbLlSsXvJivsxFw/SLqbM+2JvJb915U652EbYjPzc08u+iiiyz7TIvjBdrjteolOzjPwi+jkwEO7mHz23QiwYFfSR3edlw4NvxpLQsH2eIqFZLfh5jn/TOCN88IbQPQgqXFkQBSUPHj02JqEfeyd+9eR/VPAz/YihUrOqpTmKMPHbw5kHBBGAJIMcJ3DzcTXgwPC0hZQZ9HHnnES5eMbVOiRAnjFOkGIhgO2OJmDmyfeuqp4FV8TQJpQQCpbp06dbLvOL7nSA1GuiJunnHziWX400gWR4tjpMWY8zKIX3/91WnRokWACx4QcQ/QtGlTc/i6Kdhg1q9fP8/OnLwcE/uSAAkkNwGNKrRzhtd7D4wGjid3wh/pxjQSyEQCbsDVxRdf7GhkZlQEGt1pvzW0D2caMWpt4Fzzati3Fmm2flrvxFM3OCS7detmUo64h4LUw+mnn273XJrx42kbqdbIZRTOER9uPBrxa2yjySm8//77DgJ13HtTjdx2atWq5WgmtVOsWLHAcjzvhQvQC3cM8ViuxeMC0jw4Zsj94R5bs0IDx3r44Yc7kydPjsfuE7ZNOnhjg5oO3thwDLsV9wTlJVoJD3yI5HFPNldeeaVz4MCBLNumgzcLDr6JQEDTf+y7hFlKLSYQoeU/q26//XZrj4sZHwIi44IDHL9TrabqfPXVV5Eb61po4aE9tLe9aD5F3SAbkECSEli+fLmjRUXMketey/A/roW33HKL5yiXJB1eTA4L13VE54IL9OXwIJI900IlnBwtmBTQ7EMUHo0ESCCzCbganqECQCKRcTUkvdwLRtoO15FAKhJADQxkxWDi9LPPPvM0hP379wciJcM5UbVIul3HVXrB0zbdRqiPguv/Y4895i4K+T/uCzBxjgma4Pup7K+bNWvm4J4hney0006zMatspa9h3XDDDdZP5cCi9sNnrFI2Dp57szPF89rw4cMdRHTnt2nxPzs+BE507tzZyT4xgMxS6LG7YxgxYkR+H3Ku908Hb67RZelIDV79NcTToN8CgyZMNNOZF9MCQvVNGPR5tJBTtG5cTwIhCVxxxRWiFzrRi5NpO6Pacijbt2+fqPNAdHZQ9AZINMLUNKJCteWyfwigsquK+IvKq4jO7ovOBIdEA708jb6Tu+66y9iqTINVvA7ZmAtJIA0I4PcATTtoTm/atMmqGUNXHnpokyZNEo2YSINR5m0IONdC7w+6ctD608gcgUZfsKHa85133imvvfaa4N5AozLk2WefDW7C1yRAAhlEALrceKbAuUEn7n2N/KSTTrL2fusy+NoJG5NAkhJYuHCh6MSq1KtXT9xn7GiHqhJ30q5dO2uGe5pQpnKKtlgdhKFWh13mtg+nE4uOGvQlGlEq6gS2Z7JevXrJe++9J+r0FQ0UkfXr18vgwYPl6KOPlhdeeEE0QEz27NkTdp+ptkIjlO2Qwz1fhRuP2/6MM84I1ySwHJ+xBuDI9u3bRR3/orIMpt8LTWXUSho0aJAn/01gg3F4MXPmTFFpHvsO4Hs4ZcoUOeecc7LsSQvYm966Fiaz+hbQksa9Iy1zCRycuUNPzMhLlSpl4u0bNmzwtEMUt1q0aJGdqCGg/fDDD9vFCMUUaCTglwC+P3DwwjFwzTXXiEowSKNGjQQXTtwk6KyfaKqfOWPgYHj66afNGex3P5nWHgWi5syZI6r1ZA6aSpUqGVeNsBfV+DLmKNowbdo0UU0k0agBu0m77LLLMg0Vx5uhBOCU1PSxDB19+GGjIMZ9991nDXBePvvss8M31jUaISTjx48XjdoQzbKQq6++2m7gI3biShIggbQjAGeERnCJRu+KakTaa6+DVBk4a+oWdfbaj+1IIB0IYLIZhsJpfsxt7/bP3tedaMFzlh9z27v9Q/XFNR8TwCgItmDBghxOPY3KF/yhnWZNWYFbPOfBSakRv6E2mVLL8HyFsaiklzRv3tzTscO5i0K/OM9Vq1bNUx+3ERz/Xp3/bp94/w9HvlvE+KGHHrJnzkj71OhXe+YcOHCgwG+EooLp8F2INGauC02AEbyhucRsKRy8sOnTp5sTzcuGMeuEWRo8IMP69u0rWvzKS1e2IYEsBDSdT2bNmiUqKWCVllVHyKJJ27dvL5ouLYgoRaQdZolxI3Httddm6c834QkUKlTIqt7fdtttNrOKqry40dLUa9F0KYuIhnP3ggsukLfeessqsYbfGteQAAlkAoF58+aZgwbnCdXc9TRkZGLgwQOR0CtWrPDUh41IgATSiwAyrDA5j0hETCB7NUQCwukB53CyOTC8joHtSCAvBNwJDkS/+zG3PaLnQ5kbJYrIWj/m/n7d/tn7qtSVRWRqcS8L+soesRncHllRCAxTDVl7JkmXTB9ET8NRi2xmBCJFMzhDu3TpYs3wfIvAmlQ3ZHrhOVJruFhGrpfxwGeEQCMtCmfP9V76sE36EaCDN86fqVattT1o9U7Ba6/pUVogy5zCuCFTUQ1RsXdRnRibtY/zIXPzaUhAiwtYCgrSlBAFhgsnZBlGjRplFwHMkmrF0jQceXyHdOihh1o0HtJ7EC2txRQtBUwLJdnEzJtvvmkpVUidopEACZDAsmXLDAImgbwa7gMaN25szd3+XvuyHQmQQPoQQKQeDPcbXg2ZWcjYwn0IsgRp6UtAC/qKFtWz7LwdO3YI3qe64RkYDtH7779f+vTpI4hORHAKHF9eTWsAWNNIkgihtgUZNpjbP3sbRJnCVO81+6qw7zdu3CgrV640WaZwWX0TJ060/pB4O+uss8Juy12hRcFE9WLtrdvXXZeq/xcuXFhUS9YOH89Wc+fODTsUSA3inmrNmjUW8azF78K2TaUVrsxCy5YtPUfiwrHtRjwvWbIklYbLY40lgSyKvHwTFwJ16tQJCF+j2qFGT1pRFS87QzXEYHF1fdALbMtL/2RrozoxdvxNmjRJtkPj8ZAACZAACZBAXAnoA51dA/XG29d+VL/Y+qFQHY0ESCAzCaDgkE4sO3gWQCHdaLZ161ZHs43s3KHZA9Gac32KEti8ebOjgUCBz1r9BPaZ45lTnWOOOhVTcmT4zqqMkY3FHVPw/yjoqvIJUcemGXa2jcqVK0dtG9wATLE/1c0PXhx4rVILVmwZbXCNjmaqoe24hdm0xk7I5iq/4mjkru1XnfUh24RaiGNRqT07N6RTwbWuXbsGPn+d6HZUrsLRYDkHhSZV/tLRQCVHHdzWpmjRoo5qE4fCk5LLMF58t/yeu3UCxPqhQF+qGYusxeYTYwSv/nLibUiPP++882w3etKVp556ygqmeNnvTTfdZPozSM2CQXeLRgIkQAIkQAIkkHoEEPUPQzqhH3Pbu/399GVbEiCB9CCgleVl6NCh9iwAvU3UAghnqLGAYrCQ4dKgikAWQLj2XJ6aBKDpXrZsWXnyySftsz755JNNGgyp+3jmhMQftFrdCM9UGKW6OARFxfC9hf4tUs6Rfo8o3pEjR5qcHDSp58+fLxUrVhRIpEWyunXrWnHCd955x3RdI7V11yECesaMGSZtEi7jBgXUx44da11uvfVWiyx2+2f/f//+/VYLBVk4+IxQ3CuUQacfbRFt76coLY4FdQ/gJ8Cxp4shIvnBBx8UfN74nFHrBAVq8R7SFZDJQ7E76CUjghff9XQx/A5gfnV03fZu/3ThwXF4J0AHr3dWuW4JzSycdHQWKlD59sQTT/S8Peilfvjhh1KjRg3PfdiQBEiABEiABEgguQjAQQODHrofg4YmzO3vpy/bkgAJpA8ByGwh+AOFmlA3AU5cOPdQYAhFdV544QVp3bq1oPgrHD0XX3yxoLo6Lf0IwLmFdHQ49SC79tlnn5l0Ab4LkGrQCG7p1q2bDXzw4MHSo0ePlICAYx03bpzVt5gwYYKNA0WmUDgK33/ozEKT/vrrr7ffASY7oFsbzlDTxk3bh0SdK70Qrj10q1u0aGETsfgtRZJJwG/wrrvuMm1syOFdeumlVvsE0m2YXMFvEk54bAMOStTvwP/h5FJ+//13OywUvvZrbh93G377J2t7OM/xXYZkA4qnQTIDBeqgYYzCYpAffPvtt9Pu/sh18Icr8hfu84IMCMztH64dl6cxgdgEAnMrXgn89ddfzqpVqxwVzvbaJUs71dJyVJ/XOfroo7MsT5U3lGhIlU+Kx0kCJEACJBBrAhpxZKlzZcqUcVQf0dPmtcCLg1RbvRX1lI7qaaNsRAIkkNIEVIc3R0o+zhHunxbZdVSz1NHo/5QeJw8+NAEtPGWfNT5nLcwdutH/L33llVcsfR/fDY1Kjdg2v1dqlK2NSzNXHdUgjXo4+I5jXCVKlHDUsRm2PeQRtLCptdUIWmfx4sUh22pAleM+q2qAlrNnz56Q7bIv1GLqDiQC3N9fqP/VOels2bIle9cs73GckGDB+CG74NUg7aCOT9u/H2kHr9tnu8QT0Mk6+zy1WLfnncPPVLJkSeunkx6e+yVLQ0o0xOaT+A82oychGgkkhICepCwaGWk3kQTTE3Iw3AkJkAAJkAAJJJCA3nyLOnctGgXppohIimaoCK16/IKCLCyaEY0W15NA5hDYu3evzJ49WxYtWmTRuji/nHTSSRZFiMI8SG2npR+BAwcOWDr+tm3bBJGtkC+IZigEppqc9p1ApK86hqN1yZf1V155pahDWgYNGuRJVgLRy5BpQFaM6uAKrpfhDBG1KECFAsgwdeSK1skRFCmDnAWigCGhANcI5A5U71XOPPNMa+vlHxT7QlFDSEeAMd5DTuCiiy6yiOArrrjCy2ZMbgCF2CDBcvXVV3vqo8FjFt2qjm5B9DAt9QmgOCailFEcEFkYiFaOZpAMwX2lalfLJ598YhIj0fok03pE5U+bNs3zeJPp2JPqWGLjJ+ZWSMAbAXdWlEXWvPFiKxIgARIggfQigGgrFE/973//6+hDd9jBISJHU1UtEgPFU/QBNmxbriABEiABEsgMAijSqc4ER52QnjNBcD1RfVLrpyntSQkKxcEKFChgf+qM9XyMiEoGD5UridoHEbJjxowJG/2Oa23fvn0dnTyJuq14NVCnvY0Hn5eXCHx8thg7GAwcODBeh8Xt5gMBld+xz1U1lp1oxXkR1Y/Ib3wPUIwuFY0RvLH51KjBq78CGgmQAAmQAAmQAAkkgoBW/jYtOZVokI4dO0qDBg1EZZsEun8wlWSQ5557TlSOSe6++25RR7BpbJYvXz4Rh8d9kAAJkAAJJDGB119/3Y4O0Z2azu/pSFF4CXqxMLe/p44JbLR+/XpBFPqFF14oKkvkec/16tWzttAejmbqQBaVdbCoSES+33PPPRbxCB1dXHd3794to0ePFpVCjLapuK1HpDUihz/++GO7R0DEdiSDFvPSpUtN17dfv36RmnJdihFAHSZoruP+sH79+tKzZ88cEdqbN2+Wzp07W7Q3vit33HGH3Vem2FB5uDEkcHAMt8VNkQAJkAAJkAAJkAAJRCGAQjFIpUbxkJdfftn+0OXQQw+1wi5udxRkRcon0khpJEACJEACJLBz506DgDRsP+a2d/v76ZuItt99953txk8hcnQoXLiwwHELyRKN0PUkPwGJissvv9z+EjE2P/vAscHZXL16dUtXR7FEpN5DiiLY4NjTaGOTktDITSs+d+yxxwY34es0IKB661ZYDkXmHnzwQfuD/A6+9/jNfPXVVzZK/AZQ0K9Xr15pMGoOIS8E6ODNCz32JQESIAESIAESIIFcEGjbtq00bNjQdAPnzZsnGzZsEGiuHXXUUVKhQgWLxkBUBiqA00iABEiABEgABBCNC9NkXvvf6z9ue7e/136JaudG7UIP149pMTKL/NU0dk/OXT/bzq+2yNhBVC5q1kAbGFHN0AUuW7asZfXAubtu3To7PDh1Z82aJSrTkF+Hy/3GkQB+r4gwb9Gihdx7772m8ayF9AR/sCJFitj3ZMCAAabZG8dD4aZThAAdvCnyQfEwSYAESIAESIAE0otAoUKFLJ0OKXUw1duzKN70GiVHQwIkQAIkECsCbvE8TAr6Mbe9299P30S0hQMT9u6773qOxEX7FStW4D8rLGUv0uQfFGfDZwYZiSeeeELg1MWfa5CRaN26tQwZMsQKxbnL+X96EoBzH8XWIMOAqF0UDYRzF9lgXqVa0pMMR5WdgDfhnuy9+J4ESIAESIAESIAESCCmBCDRQCMBEiABEiCBcARcyZ45c+aYsydcu+DlWojLUvixrG7dusGrkuY1HFWIVN2/f79JE3k9sIkTJ1rTxo0be+2SMu0QnYu0+2+//dYc31pIS2bPni0rV66UPXv2WAZQsWLFUmY8PNC8E4AcR4kSJUQL18spp5xC527ekabdFujgTbuPlAMiARIgARIgARIgARIgARIgARJINwI1atSwdP2tW7fK+PHjPQ0POp6bNm2S008/XWrVquWpT340Qpo5DDr127Zti3oI06ZNs5R1RLN26dIlavtUbYBiq3B+N2vWTK655hqpWrWq6Q6n6nh43CRAAvEjQAdv/NhyyyRAAiRAAiRAAiRAAiRAAiRAAiQQEwJw9o0ZM8a21b9/f4vojLThuXPnBgovjR49Oqkdg82bNxdE4n7//fdy6aWXypo1a0IODXrCkyZNkhtuuMHW33///ZauHrIxF5IACZBABhGggzeDPmwOlQRIgARIgARIgARIgARIgARIIHUJoEAntFehx9myZUtB0c6PPvooy4DWr19vDlA4Tf/66y9BdCwiQJPdEJVbs2ZN2blzp1SpUsWOGdqjkCV44403ZOzYsXLBBRdI165dbVzg0KlTp2QfFo+PBEiABBJCgEXWEoKZOyEBEiABEiABEiABEiABEiABEiCBvBMYNmyYFC1aVPr27WvFl+AEReFOLNu9e7cVYcJeDjnkECvU1bt377zvNAFbOPLII2XJkiUydOhQc+YiAhl/2Q36ow888IBcffXV2VfxPQmQAAlkLAE6eDP2o+fASYAESIAESIAESIAESIAESIAEUpFAt27dpFGjRgLphRdeeEG+/vrrgGO3ePHi0qRJE+nXr5+ULFkypYZXoEABGTlypHTv3l2ee+45WbZsmY0NhUgxlvr168tVV10lhx12WEqNiwdLAiRAAvEmQAdvvAlz+yRAAiRAAiRAAiRAAiRAAiRAAiQQYwKnnnqqTJgwwf6gXbtnzx4pXLhwWmjSwkkNJy/+aCRAAiRAAtEJ0MEbnRFbkAAJkAAJkAAJkAAJkAAJkAAJkEDSEoBjF380EiABEiCBzCTAImuZ+blz1CRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAmlAgA7eNPgQOQQSIAESIAESkDxRwwAAQABJREFUIAESIAESIAESIAESIAESIAESIIHMJEAHb2Z+7hw1CZAACZAACZAACZAACZAACZAACZAACZAACZBAGhCggzcNPkQOgQRIgARIgARIgARIgARIgARIgARIgARIgARIIDMJ0MGbmZ87R00CJEACJEACJEACJEACJEACJEACJEACJEACJJAGBOjgTYMPkUMgARIgARIgARIgARIgARIgARIgARIgARIgARLITAJ08Gbm585RkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpAEBOnjT4EPkEEiABEiABEiABEiABEiABEiABEiABEiABEiABDKTAB28mfm5c9QkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJpQIAO3jT4EDkEEiABEiABEiABEiABEiABEiABEiABEiABEiCBzCRAB29mfu4cNQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQBoQoIM3DT5EDoEESIAESIAESIAESIAESIAESIAESIAESIAESCAzCRycmcPmqEmABEiABEiABEggdQj89ddfMnfuXFmwYIF89tlnsn//filWrJhUrlxZWrRoIeXKlUudwfBISYAESIAESIAESIAESIAEYkqADt6Y4uTGSIAESIAESIAESCC2BBYtWiRdu3aVbdu2ZdnwJ598Iq+//rqMHDlSrr32Wpk0aZIULlw4Sxu+IQESIIFkILBx40Z5/vnnZe3atbJ792457rjj5JxzzpHGjRvbRFUyHCOPgQRIgARIgARSmQAdvKn86fHYSYAESIAESIAE0prAQw89JLfeeqv873//k3PPPVc6depkzpCjjjpKdu7cKS+//LI8/vjjMnv2bHn33XfN4VuyZMm0ZsLBkQAJpA6BXbt2SY8ePewc5ThOlgN/8cUX5Z577pHatWsLznVnn312lvV8QwIkQAIkQAIk4J0AHbzeWbElCZAACZAACZAACSSMwMKFC825ix0iSrd///5y0EH/lk8oW7as1K9f35ZfffXVsnr1arnqqqvknXfekYIFCybsOLkjEiABEghFYMOGDXaOwmTUEUccIa1bt5Y6deqYvMwPP/wgy5cvl6efflqWLl1qE1eQoYGzl0YCJEACJEACJOCfwL9PCf77sgcJkAAJkAAJkAAJkEAcCPzxxx/SpUsXi9xFhNvtt9+exbkbvMuTTjpJXnvtNYvwhWzDmDFjgldn5Ot9+/bJ2LFjpVatWnLiiSeacwmRzXCEI9oZEdE0EiCB+BH4/vvvpWHDhpZpULNmTdMOnzJlisnJ4H2TJk3k/vvvl61bt0qrVq0Ev9mmTZvKpk2b4ndQ3DIJkAAJkAAJpDEBOnjT+MPl0EiABEiABEiABFKTwLPPPitffPGFnH/++dKvX7+ogzjyyCPlkUcesXbjxo0TFGXLVJsxY4acccYZ0rt3b3nzzTflm2++kd9++022b99uGqAoSlehQgVZt25dpiLiuEkg7gQGDx4sn3/+uVSpUkUWL15sEy2hdnrMMccIfrOuk7dbt26hmnEZCZAACZAACZBAFAJ08EYBxNUkQAIkQAIkQAIkkGgC8+fPt13edNNN8p///MfT7qtVqybly5eXn376yVKfPXVKs0aIdkYa+J49eyx6d86cOfL111/Lr7/+ahGEEydOlNNPP13Wr18v1atXl7feeivNCHA4JJD/BPD7e/TRR+Xggw+Wp556Sg499NCoB/Xwww9bkUgUjlyzZk3U9myQugRwjYL+Ms7H0F7G9W7v3r2pOyAeOQmQAAkkCQFfDl4I4x84cCAuf0yVS5JvBA+DBEiABEiABEgg3wls3rzZjqFq1aq+jsVt/+mnn/rqlw6N582bJ4MGDTKnEqKZoesJSYbixYvL4YcfLmeeeaZ07dpVoAvarl07+fnnn6V58+aWQp4O4+cYSCBZCKD4I54Z69WrJ6VLl/Z0WIjkbdu2rbWF84+WfgS2bNliEh1FixY1iY7u3bsLIrYbNWokWIbJOUR900iABEiABHJHwJeDF7pJBQoUiMvf3XffnbsRsBcJkAAJkAAJkAAJpBkB6FHC4PTwY277TIuGgmZxjx49BMEI0CC+8cYbw2JDNOGTTz5pBemgEzpw4MCwbbkivQigsNfvv/+eXoNKwtFgEgVWo0YNX0fntt+4caOvfmyc/ASQTYEME/yPrBQU24NzF5Nu0ErHuRtSHeXKlZOXXnop+QfEIyQBEiCBJCTgy8GbhMfPQyIBEiABEiABEiCBtCNw/PHH25i++uorX2Nz259wwgm++qV6Y0TvQrMY2rq33npr1OHAwYDUYDh7Z86caZIOUTuxQcoRQBQppAJq164thx12mEkAIJr75JNPtkkASHXQYk/gxx9/tI0WKlTI18aPO+44a+/299WZjZOWwMKFC6Vly5YmlXP99dfbuRqFQSdMmGAyDci22K4a6dBHdzMrlixZkrTj4YGRAAmQQLISoIM3WT8ZHhcJkAAJkAAJkEDGErjooots7IsWLfLM4O+//xY8NMMqVarkuV86NERKOKx9+/aeNYtPOeUUiyIDNz+c04FXJowBRfTKli1rjtw33nhDEOUNhyMcvZgIgeMXEwI9e/Y0OYFMYJKoMboTVChw6Me+/fZba450fVp6EEDUfJs2bQRyjMOGDZOpU6dKsWLFcgzupJNOklmzZsmAAQPs93jdddeJm8mSozEXkAAJkAAJhCTgy8GLdLd4afAOGTIk5AFyIQmQAAmQAAmQAAlkGoFrrrnGhjxp0iTxGs322GOPya5du+Scc84xx1YmMdu2bZsNt2LFir6G7bZ3+/vqzMZJS2DVqlWCooPQoi5TpoxMnz7dijhBkuO3336Tjz/+2By7KAL24IMPmh4oHP202BA4//zzbUN+ozDd9m7/2BwNt5KfBEaPHm2FPy+//HLx8rw/cuRIueSSS2T37t0ybty4/Dx07psESIAEUo6ALwcv0tn++9//xuXPa4XolCPMAyYBEiABEiABEiABnwTwgHvZZZcJHFIoPPPnn39G3MIHH3wgffr0sTaZWNfg119/tbEfccQRETllX4l0fRicfrT0ILBnzx5p1qyZ/PLLL4J08LVr1wqiAY8++ujAABHZO3bsWHn77bcF0aZIIR88eHBgPV/kjQCceUceeaTxXbFihaeNffnll6bBimdCfH609CCAqFzY0KFDPQ0In7/rCHb7eurIRiRAAiRAAuLLwUteJEACJEACJEACJEACiSHwxBNPmPPplVdeMQ1Rt3BR8N6R9op2cAjDodWhQ4eMdI4UL17csPitwA7dR5jb397wn5QmMHz4cEGqPwo3oZjeIYccEnY8kDKBfvNBBx0k999/v/j9/oTdcIavKFiwoPTr188otG3b1qIxIyFB4TtotGKipVWrVlK6dOlIzbkuRQjgd4hzbOHChaVKlSqejxrXM0wQoNgeZRo8Y2NDEiABEqCDl98BEiABEiABEiABEkhGAqeeeqosXrxY8D+i4M477zxz5N52222CKF1IZ5UsWVI6duxohWnatWsnkydPTsahxP2YLr74YtuHn+rrSMl3tXurV68e92PMxB0gzRqO0zp16sgZZ5wh0NmEU7V3797y/vvvxxwJIt3h1IWNHz/esg6j7aRq1aoW6RvcN1ofro9OoH///lK5cmWB/An+DxfJCyceHHpYX6JECablR0ebMi1cTWUUNfSTrYuM4RNPPNHG6W4jZQbNAyUBEiCBfCRwsJ994wS7ZcsWP108t8XDC/5oJEACJEACJEACJEAC/xAoX768QH7hrrvukkceeUSWLVtmf8F8zjzzTHP4IgIuU+3aa6+1FPsZM2ZYkR7orkazKVOmyM6dOy1a0NXijdaH670TgH4mUq3379+fpdPXX38t7733nkkk4HPD9/rYY4/N0ia3b6C9i4g/aLiee+65njcDKYenn37aiu3ht0bLO4FDDz1UMOHSuHFjWb16tWASBhMpcPajyBaKb+F8Bt1dTLZgAmDBggXCAmt5Z58sWzjqqKPsUH7++Wffh+T2QSQvjQRIgARIwBsBXw7eF198UW6++WZvW/bZ6s477/SszeNz02xOAiRAAiRAAiRAAilLAOmtKAQ1YsQIee211+Szzz6ziN0TTjjB0l7z4pyEsw1OUWz3iy++EMdxBNFW0P+F9m+qTL6ffvrp0rlzZ3n44YdNouLNN98U8Alny5cvtyhSrL/nnnvCNePyXBLo1KmTPP7449a7YcOGJh1SoUIFOeywwyyiE7IIiDafPXu2aeTi84qFTMaOHTtsn4h292Nue7e/n76xbovfNzSBt27darIrcIaiYBwco+CXSgZ9Y3y29913n4wZM8aidLNH8hYoUEBuueUWm8Q67rjjUml4PNYoBBCxj+8spE+gjV2kSJEoPf5ZDT1mXJvg3I10Hve0MTYiARIggQwi4MvBm0FcOFQSIAESIAESIAESSCoCeNht2rRpTI4J2r2QeYDjJXuBsc2bN8vrr78umHzv2bOnOZYPPjj5bxlRrR0RnCiqBRkARJCCV3BqMIqxIXUfBX+Qkt+lS5eYMY3JB5MGG8F3Cs5dfF+nT58ujRo1yjIqpF4jmrNHjx7SpEkTi1DH/3D85fV79tdff9m+4DT0Y+5+Dxw44KdbTNtu2rTJCiW6siHZN47IVkREd+3aNct3Onu7ZHuPSF4cN4pAQnLmww8/NE3eQoUKCSLtr7zySsFrWvoRgHMXk4WYsMA5AbIdXuyxxx6zZldccYUnmRUv22QbEiABEsgEAnm6W8csHHSroJOTVzv77LPzugn2JwESIAESSEMCiChEZOGuXbsE6X7Q6DviiCPScKQcEgkkhgCcWKhSP3/+fHMUNW/eXK677jo555xz7D0cvM8++6yggvmoUaMsnR4P6MkePYjCTnAgXX311Zb6jXEhKhT3qpAAQEQYIndRjA7Wq1cvgVOYFjsCiLwbNmyYfY8QnQsHTTg75ZRT7POCM37NmjXy6KOPWiRnuPZeliNiEIYoWD/mtnf7++kbi7b4fUFiBWnpRx99tE06XHjhhYLvNKKK8VuFVEv37t1t8gVR94cffngsdp2wbWAsmHCJ1SRVwg6cO8oTAeht4/s9cuRIm9CJVkBv/fr1Fu2NneIcTSMBEiABEvBBQB+cPZtqUzm66Sx/Ogvv6MnXeeeddzxvhw0zl4Bqotn3RyM1MhcCR04CJOCJgDpjHH2YdTQ9Nct1RyuiO+o0cDTC0NN22IgESCArAY0AtN+URgQ6mj6ddWXQO9zbqcPL2qpGadCa5H6pUZyO6ro6KtuQ5dyBe1iN5nU0etRZunRpcg8iRY9u0KBBxrxVq1aeR6ByDdZHtaQ99wnXcO/evQ6uERqR6+Aa4tX69etnx4BrTqINvzOdPLH9t2nTxtFU9pCHoFJ5jsq1WDt1Bodsw4X5Q+C7775znnvuOUeldJyHHnrIUS1hR7Wn8+dgknCvOB/g/KuTOo5OVIQ9QvwWdFLO2t5www1h23EFCZBA+hHA9Q/niWeeeSb9BpfAEUFrzZdp+pCjKXuORtzaBxDs8FVxfAc3dh9//LGvbbJx5hCggzdzPmuOlATyQkCjkxyN0g1cZzCZeNFFFzmazulo1khguWqEOppenpddsS8JZBQBLW5lTk44lN59992oY//kk08cjZy339wbb7wRtX2yNcA9KRwvmh7saBSkL6dfso0lFY5HiwLad8WPA10LbDmqs2n9NHo8z8PUwm22rfbt23va1vbt2x2NLrU+Xn4TnjbqsZFG09t1Dc9TKhcStdeGDRucY445xo51zpw5UduzQXwJrFu3zlGNaeeggw6yzyT4uVilKRw4KTWqPb4HkQJbV2kcp2bNmsYIky8dOnRwXn31VUej0x38/lSWxMEkont/p3rTzu+//54CI+MhkgAJxIoAHbyxIenbwRu8W9VQclRLxznttNNyXNS0WIGjqRjOtm3bgrvwdYYToIM3w78AHD4JeCCgabqBa4qmWjsfffRRll7ff/+9oxqPjqaxWrvatWs7iNijkQAJRCegUgz2uxkwYED0xv/fQouQWZ8GDRp47sOGmUnAdZSq1IAvAKrDat8xTeX21S9U408//TQQEasSHKGaBJZ9++23Dp5Z4JjLj6jYmTNn2r5LlSrlqCZ04LgivXCvkeeee26kZlwXZwL4HFTr2T4/TJghs+jWW281R32NGjUCzkrVF3aWLFkS56NJ/s3/8ccfjmq6W3R9sCM8+DWi72+77Tbe0yX/x8kjJIGYE6CDNzZI8+TgDT4ELWrhaLGEQFpF8Mm6SpUqjha6cL755pvgLnydgQTo4M3AD51DJgEfBLQ4UuCBacKECRF7IpLJTR/HZCONBEggMgFESqoWrTkktKp55MZBa3fv3m3OCjgxGDEfBIYvsxDA9wuRjPjDaz/mRt2q9rOfbmHbTps2zSLV8TyCiUJcL4IN0YFTp04NXENUf9qBvEOiDceGY0Rav1dD1K/WQbF+sYh49rpftvuXAFKI3WddRF5j4jm7IcgJ0b1oh3NnqskZ4vcwadIkBxN7yNw9+eSTHdXLtud9PPeHMvyuMG5k/O7bty9UE0f1rh0tuudUr17dOfXUU50SJUo4cIirdrfj57oUcuNcSAIkkLIE6OCNzUcXMwevezi4oXtDU/huuummgE6UewHEDR8irTDj+cMPP7hd+H8GEaCDN4M+bA6VBHJB4PLLL7eHIa86iHhgwrUFqZBI9aORAAmEJ4BoRdyTIR3eryHCEH0RHUkjgXAEXM10v+djBIPg+/XWW2+F27Tv5VrkLSC9gG1DSu6yyy5ztOiec+SRR9r+sPySSy5xMImRH+b+ruAQ82NaQNCOH2OkJZaAFn11tMCd8Z84cWLEnf/vf/9zbr75ZmuLjNdUkR1A3R1otOP3Ee6vUaNGjha/tfG/8MILTt26dQMT9G6fihUrWpBXqow74ofJlSRAAnElQAdvbPAepCfgmJo+aMull14qkydPFn2QkFdeeUXatm1rFWH1IieqySWdO3cWvQEUvTCIpiYFqhnH9EC4MRIgARIggZQigAr3ixcvtqrhd911l6djV11eadGihWjqn6CqOI0ESCA8AS36YytVUzd8ozBrVBLF1mhUVpgWXEwCIuqoNQwqteAZhxaoEtW+FZ2oEw0E8NwvWsNrrrlGdEJCbrnlFtHiZLJ161bR4pyi0YeiEhJy4YUXikZiigamiEbERttcXNarY9m2q5MuvrZ//PHHW3u3v6/ObJwnAsOHDxfNZBAtHCZasDLitrSoo2g2klSoUEFUa1Y0yCli+2RYqfV0pF27doLvlk5+2G9k48aNsnPnTnn77bdFM6bsuf6ll16SCy64QDQSV5o1ayavvfYaAsekZMmSUrp0aVEnuLz//vuisgxStmxZUb3iZBgej4EESIAE0ppAzB28wbRURF3q168vOgsouHnT2T1RfSt7eFedKdFiF6JacIKbFLShkQAJ5I4AHto1TU+2bNkiWsggdxthLxLIZwJ4yMbDgUaBiKaRez4aTe21tphApJEACYQn4DqRVDJLNOMqfMMQa/BwD8MEPY0EwhGA0ws2atQoc4KFaxe8HA4zfB81FVxyM/kQvK3sr7VAp2iauWikoWjBQFEtVFm5cqUFocCprBFDAidcfpnqs9quNcXf1yG47eG4piWOgMpjiBZttB1q0XFPO8bzMJymsFmzZnnqk1+NnnrqKdEaOqJauPLkk0/Km2++ab8RlWgQlWgwZ+69995rEyeVK1eWr776yn5PeJbH7wxOYZVoEI1Il59++knmzZsn5cqVs8kVOIK1fk9+DY37JQESIIGMIBBXB28wQczKN23a1CJ2cZPVp08f0UqZ1gQOKdXcCW7O1yRAAlEIaFEpefjhhwU3WFpRWXDzpal+NquOKHpEM8JZRiOBVCHgOpDwXfZjiBSBaaVqP93YlgQyjgCcZ2XKlLHMqWXLlnkeP6KwMFGPB3zVvfbcjw0zj4BqygaiFREFCIdYJEMmn6a52zOB18yNSNsLtw7PHKqzKyrRICrRIO5kR7j2iVqOyEaYSlN43iXu7ZYvX27tMSZa4gggIlxlBuXMM8+Us846y/OOtQCbTSSsWbMmae/NVXPXns8xKEQat2/fPuz48Ps57rjjAus7dOhgkfLBk/NwEjdu3Nii8zHxg6h5vMf/NBIgARIggfgQSJiDF/IMiM5CKgsuivfff7/v6JH4IOBWSSD1CCBat3z58qKFHQQ3i1q8wdKh8NvCQwweFFq3bm0z7Uh7p5FAKhBwJ/38Rhbi+gKDRBCNBEggMgFImsDuvvvuyA2D1rpt3Wj5oFV8SQJZCOA8DKctJp7nzJkjderUscjZLI30DaQ+kOqNexU4LPFc4Do7s7dN5/cIfoEhjd/rpLzq7gqi8FVTWM4777x0xpN0Y4P8IEyLg/k6toIFC5pMCIIz3OhrXxtIQGNE78J5jUkQyCtGMkTCL1q0SFTL2po9/vjjYZ/r4ehFpi4CUjCR/8ADD0TaNNeRAAmQAAnkgUBcn4bx0I3UDjihkCKlBdYsfcO9OCIlqmbNmqKVY6Vbt255GAa7kkDmEIAOFjTu8D8isfAAhRsypENpZVq7cXziiSfklFNOMZ05RKq4v7nMocSRpiIB94FJq537Onyk3cLc/r46szEJZBgB6CFCbxST7kOHDo06ejjeXnzxRUudh0OORgLRCCALAw4g3PtjwhlOSOilawFm6dGjh1x11VVSvHhxk3HAs8CYMWNsebTtpuN6SNWVKFFC1q5dK8OGDYs6RDjIwBDmpv1H7ZTmDaDBD/1+RILfc889gntg974g1kN3JURcPXM/23cjV12nqJ++iWi7YMEC282NN94YdXdw6MLwHcTvfc+ePaJFb8P2K1CggP3e0cDtG7YxV5AACZAACeSegM4Wx9Q08spRp66jTl2r0qxHlqX6pt7IWfXacePGOarbE9N9c2PJT0CLZ9j3oUmTJsl/sEl4hKhCqylhxlDTnByVNwl7lBoh4FSrVs3a1qhRI2w7riCBZCGgKeCORn85GpHu6KSE58PCbwHXGo0K8dyHDUkgkwlo5JWjEfP2u9FIrZC/N31gD1R/x72bTiZmMjKOPRcE8B1SZ6Sj0Yv2XQt+JsB3SqN7Hc1CysWW06uLFqcK/B779u3raAGvkANcvXq1oxOZxrJevXoOnrky2VRSwLn99tsddbrm+H7hu6YR4Y7Wf4kpInyncZ+iBcScX375xfO2tcCYHaNK3Hjuk+iGp512mh2jyiZG3bVOEgba4hoC3hqlG7Ufxo+2mokYtS0bkAAJZBYB1cS384MWP82sgcd4tAfrSTbPhkhdaEEhkvD5558PGS2ISrVIC0RFW8xU00iABPwTQAED6H+de+65lgKJCrXhDIU7UOEWxQ3w+8Rvs3nz5uGaczkJ5DuBokWLik7+WEHO2267zVPxTUSJIboQ6Y+IhKKRAAlEJ3D55ZdbsR9opE6dOlWeffZZy7KCnidS7JERgt8WKsWjhsIjjzwi0FalkYAfAigApgEdgqJMuA9BvQ2dqLbI3osvvpgF+/4fJmQsENXYuXNni2ZGIS4Uf8OzE65t27dvF0RXvvzyyybjAHb4zeK3mqkGqTJEgiNzDQZWyG5DhC30+NVpblG8zZo1M67IFkUUaV4N32nsB4X6pk+fbtv2ss0pU6ZYs4YNG3ppni9t3KhkN0o53EHguoCIXTyDqFM4INPgRiiH64fliPZFYTZEovvRMI60Ta4jgVQngOsinu9R6wDa1pBchMwRjQRyRSC3DmPMGmvalaPSCo5WVDZvux5Alv9VI9TRSpzOli1bcrsb9kszAozgzdsHqo5d+43pjb7nDalT2PpogQfPfdiQBPKLAKI6jjjiCPvO3nHHHY5OIIY9lFWrVjk6kWFtR4wYEbYdV5AACYQmgN+bTvwFogeD7+MQpdagQQNn/fr1oTtzKQmQQEwJqNPQqVixol3Tgn+L7mtEjWpKvKOSBDHdb6ptTPWHHS34aJwuuOAC57333ssxBNW6dXD/695PdOrUKUeb3C547rnnbN/HH3+888UXX0TdzIoVKxx1Ltt5VqUjorbPrwaIeMZ37YMPPoh4CMgeRDt8H2HqaLf34BLNVN/X2uoEYrSmXE8CaU/g448/drS2QY4sl4MPPtgyXF5//fW0ZxA8QEbwBtPI/ev/oKuepD0ZInX1IiUQ90c0IAT+sxuiPxCpiz+3snn2NnyfuQT0Rkw+/PBDi9KbO3du5oLIxcihowvNOlSo3b17t+jJ39NWUMwBkZEocqA3ZRkd8eEJGBvlOwFcX3ANQbG1Sy65RNTRK5deemngO4+IHUQUoijNn3/+aZHpyCCBliONBPwSQNEbTZ+1exoUrCxZsqQVL/K7nVRuj2ispUuXWlQV7vWg4V6rVi1BpXQaCZBA4gjgsQzRzgsXLpStW7eKygDYvZ9KbonKEdn9XOKOJjn3hMhdRDTj/uCVV16xSNJwR/ruu+/a/QPuf1WuQdyiduHae12uQRNWZAxRqMgiQmRqKMN5FdmrqJXRr1+/gA5tqLb5vUyd4BZJjqKauO+KZNBxx/MF6oFUqlTJvqeIylUJhkjdRB3zFsGLCGxG8EZExZVpTgB64fid4Z4Lzy/woeE5H+cKnVgX3JvCOnToIA8//LBlU6U5Ern++utl2rRpohINlsWS7uON2/j8+Ib1gdpm3fRgsvxfqlQpB5FWjPLwQzMz2zKCN/efO3Tq8NvTNDTfG9GHdOvrR9fU907YgQRiSODVV191dGIicK2BLq+mAgYidvFbQIRhr169nAMHDsRwz9xUphDQh1FHU6IdTYMLfM/c+xvc14wfP97RCYRMwcFxkgAJkEDSE4AOMc7TRx99tLNr1y5Px6vOEeujDhRP7b00+umnnxxED+NYNIDC9MoRlYqIXnXMO+r0dVTWxoHWNNogUyLZ71WgBY1jxb3Xjz/+GBGDTsJb27p169r/KhsSsT1W6sSFtdUJxKhtc9sAtRyQ5aiSJ47KmFg0cqRMsNzuh/1IIC8EBgwYYL8FPMdApz57XSqcX+677z5HCzJaO5V2yQjNdUbw5uVb9W9fbyGAeraHabd/Xvz/v5rKYVFW6rSzmYcdO3YI/nJjmMXTB6rcdGUfEkh6AtCbevTRR00/bdu2bRZJi+rS1atXN91Q6CFGM0YnRiPE9elEQAvIiMr7iDrZTGtQ05hMhxBjhAYedOx69+5tGtPpNG6OJTEENJVU2rdvb1FH2CMiJ04//XTTB0U0L6LEb731VotmQnQWawck5nPhXkiABEggEgFoD8NuvvlmUYmESE0D6xCZOmzYMNmwYYPgXgJ1LPJq0MdEpHXPnj3tOjF58mTBX3aDTu3AgQNFpTWSPssIWtDIltJi6dKyZUuLTIYGeyjr2LGj3ZtB6ximMlmhmgWWqXPbIpix4IYbbggsj9WLd955x6IhETGNiMhgQ1SxBgOIykpmRBRk8Nj5OvkIQEsduvTQBEdWQShdbpxfUIsEmQIqa2IZC6NGjRJ1DCffgHhEyUfgX19v9Fd64bJZBB1FzP+/8847ox8AW6Q8gUyM4NUUDNOpivS70RsqR4tCRPx8oTmGbahEgwNtMa+Gir+IINCbtIyY/fPKhe1SiwA031DZGd9nGgnkhYDKTAWiqjR11tHCFlk2hxoD8+fPd3Ti2c650HrE+ZdGAiRAAiSQvwRUqsLOy2+88YavA2nbtq31e+yxx3z189IYurq33367U7VqVQfRqTpZ6KjEjYP7/+yReV62l59tNFDLgbYwnjcuuuiikHq8WmTNauy40cnIEoyUIQjNaDcyD9fTffv2xWyIiM5V533gmo5sL0QVqxPZtE2R+eU+f1WoUMGTZnLMDo4bIoFsBPB9RSYBvpP3339/trWh3yIzAO21+GHaPwO55wmVaAgNg0s9EfAVwatfLhoJkIAPAm6FckTfQlMUs9ZaQMP0wlAV+aWXXpIHHnjAZstRAVgLF4bVpNJihhZ1gOiDRYsWhZzxC3Vo0MzWs4FVSPdacRnap4hMUFkI0RQ40VQ4i7DHTCKqe9JIINEE3GrNid4v95deBJBlhMhdnBOhfxYqGgLnSURU1KxZ0/7HuRC6YG6kUnoR4WhIgARIIHUI4J4UBp1wP+a2Rz2LWBsyQLSoeKw3my/bO/XUU0ULO0mjRo3sGQC1U/DcgmcUTRe3TF11OImmkNvx4ZkAnwmyeTVYyyJ/8cwAg4aoym3J4MGDZe3atVKwYEGZN2+eqKPK1sfiH+zzrrvukv/+9792Pe/fv3+O7S9evFi6d+9ux4AIZTzbIBOMRgKJJqAFIS2TAOcjfCe9GCJ4kemL3xLqFyEjgUYCkQj4cvCiaBoejOJhOqsWj81ymySQbwSQfjF16lRzjsLJml2GATeE+LvpppusAIN7Q6XVa0Wr/oY8bjiIkZaukQKWsgGnVyRDAYThw4dbE6RTRTM4PSAlgRumUEUUUdgN28HNlNfUuGj75HoSIAESSBQBnNtQbKdVq1YhnbvBx4GHVKTPlSlTRvBAi4dESIfQSIAESIAE8oeA6xzUKFBfB+C2d52PvjpnWGNIWOBZBJOgKO70/vvv218whsqVK5tT+7zzzpNrr73WAlXwPAMZBEgaobAzJlRRJBCG4qW4nsbyeX/ZsmWCgnB4NoHjuEGDBsGHGHiN6zacuu7/OE7INNFIINEEIH8CwwQKJBq8mmp4m4MX/eng9Uotc9v5cvBi1gt/NBIggcgEoLkLJygid0M5d4N7Y/YbkbyokPzRRx9ZRG+46rVdunQxjS9E8cJBMXPmzLDVg1GFExeQr7/+WmrUqCG4OEQyOD1at25tN0loB6dG/fr1reItboy1sIXN6muxRavujGPGjD2NBEiABFKBwO+//y5z5syx87LXaCtUCu/Tp49NqqGqLx28qfBJ8xhJgATSlQBqtiAaFJqrfu5B0R7Gei/evhkqByda5MkcqHCkauE4+fnnn6V48eKiUhTmsHW3pHIZ5jCFMxhtUT/BNTh0kQGD5xeVT3AXx+R/BLsgMGXIkCFhnbvujqBpCgfz2WefLc8//7wgkhJRyTQSSCQBlWO03eE85sfc8xb8CzQSiEbgoGgNuJ4ESMA/AdX4EtWoslnt7JG7obaGiF0Uk4JNnDgxR4EAtw+KHWCWGjdeKPyDtCnMQsNx4RpuwJ588kmbJV+5cqUg3QpO5kiGGyTcgGHbhQoVsm2iGAXkIxAxjKg3yEJg2SWXXCK4QGFcuS2qGOlYuI4ESIAE4kEAUUiIJoJTQHX5PO+iWbNm1hYSOjQSIAESIIH8I+AWJJoyZYo597wcCaJR3333XZMYYKCSF2L/tkEkLoqvIeoVk53XXXddFueu2/Lqq6+2IBAEhKBIqeoSm4zDhx9+aM8RsXbu4vkDzzhw3Pbt29c9jIj/o9gaivPBECBDI4FEE3CjdiFf4sdQpBCGaHUaCUQjQAdvNEJcTwK5IIAKmTA/lWKh93jmmWealhVuRsMZImsRTYv/N27caPIOcMpiVhozfNCVwn537txps+yrVq0S6PdGsqefftpmthFNjBumcNG+2D50KK+88krZvXu3dO7cOdJmuY4ESIAEkoaAG/lwxhln+DomLZhjUb+QrcFkGI0ESIAESCB/CGhhTAtcgOMQQQjRDAEQcE7i3N21a9eYR5FG23+mrYd0HJ5lIEEH52u8bMWKFbZpOJ+jydUFH8NVV11lb99+++3gxXxNAgkhAPkS2Pr1633tz23v9vfVmY0zjgAdvBn3kXPAiSCwbds22w2KE/gxt73bP1xf6GFDzmHSpEmiVW4tgnfz5s2WFoUCaYiynT59uuAG6MQTTwy3GVuOm15E6MIeeughwbYjGWYfp02bZpG+cPbyJikSLa4jARJIFgLIgID9+eefvg4J7XGeRCQTZHdoJEACJEAC+UMAkaDIdMO5+LbbbhOtRB924g2BCAhIQDo+nI5I6aelBwG3ToifbByM3G3v9k8PGhxFMhJAJi8CspBxizo7iDp3s3ohc+jqgns5djx3w9z+XvqwTeYSSEic9/bt2+3L/emnn8oJJ5xgWqMQZ3eF8jMXP0eergSgZwsLVywt3LjdWWhcFKIZHK233HKL/e3fv9+0dlFFFg5dP/tF2houOrjpadmyZbTd2npE+iIiAgUYIBFx8cUXe+rHRiRAAiSQXwTcBztMjvmxdevWWXNGTvihxrYkQAIkEB8CiMJ0JcSQnj9jxgy58cYbpUqVKvZsCRkxZNJNnjxZ9u7dK0jNnz9/flwjSuMzUm41HAH3Ocd93grXLvty9/nK7Z99Pd+TQF4JIOAKgVNw4mb/fqKAITJuN23aJKi348ozRtonZB+R2YvzmCtRE6k915FAnhy8n3/+uc1IYIYUEYNw2sLB5BoiXgYMGCDjxo3LETEDDZF7773X9Hzc9vyfBNKFAJysKDKA30jZsmU9DwvtYShi4McwWRIt8jbc9qBLCatbt66v6DTMIsLB6/YPt30uJwESIIFkIFCuXDmbAMOkMwrBQBbHi0HCBnbFFVd4ac42JEACJEACcSbQs2dPgXxOt27dzPnhaqtm3y0ieOEg8XtfnX07fJ9cBPDZw/w+g7jt/Uo1JdfoeTTJSgCZtT169BBo5iLLAJm5p5xyihUohJMWRdJhWDdhwgQ7L0XKLEBhQJzjYKNHj/YlR2Kd+E9GEsiVRAMct4jegx4nNDgHDhwo1atXl+OPP15GjBgRANmvXz8ZNWpUDucuGuCLj1nXFi1aCFLKaSSQTgTwe4Bh9s6r7dmzR6CXi8hcyC4kyr777jvbVTQph+zH494su/2zr+d7EiABEkgmArihdp0AcA4EF6cMd5zIcIBzAJPSiBCjkQAJkAAJJAeBRo0aCbJDMQkHbd7y5cub07dq1aqCczxqSixcuJDO3eT4uGJ6FAgsK1iwoBXPc/VJvezg8ccft2Zw/NNIIJYEENAInW/4uOAnQyYBJhRQwHzJkiWC5+VZs2bJySefHJCVgQ+tVq1aVsjclQ/73//+Z/4AFDRE8cI//vhD4FNr1apVLA+X20pjArly8ELzCNVLsztmf/jhBws3x5d1w4YNWcTv4Tzq2LGjfTnxxXZt9uzZ8vzzz7tv+T8JpAWB1q1b2ziQQobfhRe76667BFU1cdNx7LHHeukSkzbuvn788Udf2/vpp5+svdvfV2c2JgESIIF8INC7d29B5A4K9KCYJORtwhl0G+FAwM36rbfemussiXDb53ISIAESIIG8EYAmb9u2bQXPk2vXrpWtW7eaY3fs2LFWaDhvW2fvZCWAzx2Trgg6g1wdnp+iGWqTLF261IpRt2nTJlpzricBzwTgyEXgIgIJpk6davIw2QOnECiAwEa0Pe+882zbqA3x5ptvWoYYZBqLFi1qhSCrVasmM2fOtNdwHCNgkkYCXgn4dvDCsTtmzBjbPr7EFSpUMJkFaHdClxOGL6FbsRTvEa6OWQxEwUAnCXqfWO8aBPJp/xKA4xwz0tCPQkTnt99+++9KvkoJApA7qF27tiAqF1EF2TV4sg8CFwOkauDkP3z48Oyr4/oeWkAwtyKt1525xdXc/l77sR0JkAAJ5BcBRPxAi7FIkSJ2jYWEDu5Rvv76azskXH/h/IVDF5kYuP7Wr19f7rvvvvw6ZO6XBEiABEiABEggGwFomCL9Hc8viHT8+eefs7X49+2zzz4rnTp1sgW4nh9zzDH/ruQrEsgjAcgs4P6xT58+cv3110fcGjLeUXgNDl1E53bv3t2kHOBXg98AQQXIkofDGP4gSD7QSMAXAZ358mUq8OzoDuxPHVJZ+qoTyznrrLMC69FOHb9Z2gS/qVixYqCtzrgGr0rL11pB0dEfsaORzCHHp4VcHHUKOjqbE+DistaUfUed646ePEL2TZWF559/vo2tSZMmqXLIuT5OndRwVMbAxqvaj47O0OXYlupX23fC/Zyz/6ZydIjDAk1Tdo4++mg7Tp1Q8LQHnSkP/Na1yJqnPmxEAiRAAslCYNu2bU6lSpWyXGtx7T3ooIMCy7SmgKM31g7OdzQSIAESIAESIIHkIqATsk7hwoXtuo1nLtUpdTSL2FFnr/PNN9846khzGjRoELiuqwMuuQbAo0l5AvieqXPW0cJ9jma3eh6PBhLY91Iz460PfDzwC6hUg+dtpFtDjaw3Js8880y6DS2h4/FVZA2yC1999ZX6okRQYMkVfbYF+g9mIhClG1y4BJoh4Qxp7K7Y+c6dO003KVzbVF4OnT9EAz366KM2DGhEZbfBgwdb0TnM2oSyNWvWCP4Q6YmQ/WCZi1DtuSz/CaDa5VtvvWUpvqjCfumll1pqMATXUb0VBdWgD4bPHJG7rnZPoo8c6SG9evWSYcOG2ew2osZRtC2SDRkyxGYVEb2rzvpITbmOBEiABJKOQMmSJeWdd94xiahp06bJ8uXLTU4HhWJRvAUF1ZD26adIZtINMgkO6KOPPrLoKkRC47oHeYx69eoxeioJPhseAgmQAAmkOgFkEuP5GD6F1atXm1ZpKN8DInZR3N3V4U/1cfP4k4cA7h/Ve2eZu34iw5s2bSrjx483XwFGowEGll2WPCPjkaQqAV8OXlSedq1OnTruyyz/16hRw7Rtvv/+e1t+7rnnZlkf/EajOQNvXcdxYEEavcBFB1UQXdu3b5/70v6HUzw4LR+po7hgnXnmmQJd1M8++8x0pSC6jbR4nYk0xyBSTWnJTQApFpjEgBbYxIkTTRsM+mCuoaBa48aN7fOP9Ftx28frf9wMQQsb1T0vu+wymTNnjpQoUSLH7iAAj8kIyLDg2B955BGBQ4RGAiRAAqlGAOlwSOvEHwypcjiv4SabljcCKCqCegwbN27MsSEwbt++vUB3vlixYjnWcwEJkAAJkAAJeCWASVkEpyxYsMCKWMHRi0lFBKvgWbphw4YWwKKRvl43yXYk4JmA68PCd82PYcIb5kqE+enLtiQQiYAvB6/rtMUGNQ0i7HaxDm0R0XvIIYeEbYdoDtfS9cutsgwB5y7EtqH7gxkb17Zs2WKVXvEejjJouOAvmA3WoXAANFrg4EU06NChQwNayFhPS14C+CwHDRpkD7sffPCBaGqwafLi+6DSG0kRyeTqUkI7GFXjEZkLDSFoT0Lfau/evTYz/sQTT1jkMR7Q8To4Wj95PwEeGQmQAAlEJ4BsBlreCECDDhlL0DWG4TqHaGhMGP7yyy824YmCIshoghYyHMGVK1fO207ZmwRIgARIIOMJwJGLPxoJJJKAe++IIAE/5raP5Cvzsz22JQGXgC8Hb3ChqOwOSHeD+B+OXVikNlgfHPmHG/90NPchByzg7M1ekAqRku7YERkJx20oQ0Tva6+9ZhVh4ex9/PHHLfolGuNQ2+Ky/CGAaDHVnba//DmCyHs97bTTLGUZYu6oNIsHcFdWJLgnKn8+/PDDVoAoeDlfkwAJkAAJZDYBFBjBfQ8qnKOALqqcQ4Io2DCxDfmLJUuW2CQioq1Kly4d3ISvSYAESIAESIAESCDpCKAQGgIT4diFZCaen2Hwz/gxSFjBIBlGI4FYEsh61+1jy5FmG1zHLRxamW6bNm0yBKjcmd25ixWuBjFkGVANNJLhgQlaLYiaVBFvi+StUqVKpC5cRwK+CBQqVEhU2NwijrV4mulaIc1Ji7CJFlCUq666yvS3mcLsCysbkwAJkEDaE4DD9sEHH7SHHkxIX3zxxSHHjDTGRYsWSatWrUwOCNki0EPmPWNIXFxIAiRAAiRAAiSQjwQQ5AgfDJ6RUZPKNUxgV6tWzTLWcR+zefNmzxPWTz/9tG0G2bI0EoglgVw7eCEmHc28tIm2jVRfv2PHDhtCsN5w8JhQaAuGyE7XMR68PvtrpPQjPV6regu2TQdvdkJ8HwsCmIyINuEQi/1wGyRAAiRAAulBAFlIsLvvvjusc9cdKe53nnrqKasnAFkgaCdiApGW2QTwEA3pDkwQfPHFF4LaE5CJQm2ARo0a2WRzZhPi6EmABEiABBJJAEX8mjdvLl9++aXtFkFPiNqFxAL8OMuWLQscDrKWli5dGtWns3DhQrvWQScak9w0EoglAVYSiSXNENtyBbfXr18fYq3IhRdeGHJ5uIVIC4BzF3b88ceHa8blJEACJEACJEACJJAQAjt37jSddjz4oF6AF4PEVK9evazp7NmzvXRhmzQmAIc/is60bNnSZMjg5IW0GZbjARhprBMmTEhjAhwaCZAACZBAMhFYuXKlXHLJJebcRZDd4sWL5YcffhDIKyBLG34ZFBwvWrSoHTacvdddd505f8ONA9c2ZDDB7rzzTvpzwoHi8lwToIM31+i8dXQduHPnzg3ZwS1SheJbKE4SzRDlAkMq4wUXXBCtOdeTAAmQAAmQAAmQQFwJIAoXhgchyEl5NTc10e3vtR/bpReBrl27SocOHazyPR6ix44daw/SkP2YOHGiSZPhoRoF/Fq3bu3pfjm9CHE0JEACJEACiSTw448/SrNmzeT3338XSG2uWLFCUIw8OOMaEbiI2l23bp2UKlXKDg8T1qhX8+STT8p3331ny/7880/r3759e6s9sH//fmnXrp307t07kUPivjKEQK4lGjCrjlmLULZ7925bjNB1zLyHs+3bt4dblTbLcaOKgmgI4R89erT069cvy9iQdnbMMccImN17772mfZqlQdAbpKvhpheGyGD0o5EACZAACZAACZBAfhJwH2JOOukkX4fhtt+1a5evfmycPgRGjBhhhflQoHnKlCnSpk2bLIPDfTIcwC+++KI9EM+YMUNOOOEEeeCBB7K04xsSIAESIAESiBUB+GVwb3LppZfK5MmTszh2s++jWLFiJs1wzjnnCJy3n332mdxwww3WDMXY4OB1pUvxHjKIgwYNyr4ZvieBmBDItYN33LhxUQ/g559/thn5qA3TuAFmZx577DFBdMptt91murnDhg2TwoUL26hxQkBUbr169QTLcYPbo0ePHCcRCHc3btzYTjTo2L9//zSmxqGRAAmQAAmQAAmkCgFIM8D27t3r65Dd9pyw9oUtbRpv2bLFUlQREfXCCy9YZFO4weEe+JVXXpFatWoJnkEQyYv6FTQSIAESIAESiCUB6L+7QYpjxozJ4ZcJta+TTz7ZAvmGDBlihddwX7Nq1Sr56aefrAgbJIgaNmxoE5YlSpQItQkuI4GYEKBEQ0wwht8IZmmef/55KVKkiDV66KGHTGMMemII3f/www/l1FNPtdeY2enTp4+ULVtWOnbsKMOHD7dIBpwQUEzNjXCB0xjraSRAAiRAAiRAAiSQ3wRKly5th4CHGT/mtj/rrLP8dEu5tpAXQArnxx9/7NsJnnKD9XHAyGw7cOCA3dO6ch2RuletWtVSWnG/jOgqGgmQAAmQAAnEmgCu1chMgg/Gz0RiixYt7FCQpf7yyy8LZB4g8YC/DRs2yKhRo4TO3Vh/WtxedgK+Injd4gfZNxKL9+eee24sNpOU20AFYETpwqmLkH1ErEybNs3+Qh3w5s2bBX+hDJq9kyZNCrWKy0iABEiABEiABEgg4QRQEwByC3ioWbRoUcRIzOCDc+9nGjVqFLw4LV4jJRPFV9zJfHdQqKFQqVIl6dy5s2W5Bev5uW0y5X/ILsCgrevVunXrZs5dRPOC8SGHHOK1K9uRAAmQAAmQQFQCKBwLO/vss6O2DW4ACc2DDz7Y9OQxeYnXCPajkUAiCfhy8NapU0fwR/NPoHLlyrJ+/XpBBO/UqVOt+qKfrZQpU8ZugG+66SYrsOanL9uSAAmQAAmQAAmQQLwIwGnZt29f6dWrl8ABt2bNGilUqFDE3UHTbvny5VZBGplJ6WSort2kSZPAZP2RRx4pJUuWNA2+bdu2GR8wwj0hivCedtpp6TR8T2NBVDMy04477jjLXPPUSRudeOKJFlUFeQfUpsADNY0ESIAESIAEYkUA9zQwVzfXz3bdPu42/PRlWxKIBQFfDt5Y7DCTt4EZHFRLxN+nn34q77//vqXr4WZ/3759As1iRCMULFhQUJURWi6owgjncPny5fMN3dKlS2X69Om5OsllP+gdO3bYIqQq0EiABEiABEiABNKDQJcuXQQFsFBzoHbt2qapevrpp+cYHB5+JkyYEKgePX78eLvnydEwRRfAuQspAejuQXILRcSuuOKKQKQp7n8QuYoiK2vXrjUJLjh7IdeVSYbUVVi0iYBQTOAUhrnbCNWGy0iABEiABEggNwSQfQ2DrIIfg3/n77//Nh9OJmfn+GHGtrEnQAdv7Jl62iL05lJFc+7BBx+Ul156ydO4vDZy9YS9tmc7EiABEiABEiCB5CWAVPl58+ZZptdHH30kqCaNyNyrrrrKIlR/+eUXee+99+SJJ56QDz74wLKR7rnnHnE165J3ZN6PDJP0KAYG5y4ieOHwRvHcYDvssMNszFdeeaU0bdpUXn/9dWnevLmgmO5BB2VOaYzjjz/esOB+EE5/P9FO3377rfUtWrRoMFq+JgESIAESIIE8E4B0aPHixU12avXq1TYR62WjM2fOtGbMePdCi23iRYAO3niRTaPtIoWwWbNmgoqSebU777zTUurcmbG8bo/9SYAESIAESIAEkoMA0ufxMARN1WeeeUamTJlif9mPDvcAmDyGgzOdDLITiOBB5G4o527wWJGphSK8FSpUMMc3MqVQqyFTDONHbY+tW7fadwZRz14MfKGPWLhwYeG9pBdibEMCJEACJOCHACYcb7jhBsvA6dOnj7z11lumpxtpG6hBMHbsWGvSqVOnSE25jgTiSoAO3rjiDb/xP/74I1ei299//7389ttvtmFIOCTCsJ9Y6eMhLROaaZkUpZKIz4j7IAESIAESIIFkIHD00UfLU089Jf3795dnn31WVq5cKd98843JT8Gh16BBA5s0RiRruhmik2EjR47MEbkbaqzHHHOMDB061IqtoRhbJjl4wQORy6gqft9991n0dyhG2ZehLQyTA0yBzU6H70mABEiABGJB4LbbbrN7GdzDdOjQQR5//PGA1FL27WPSEfc2+/fvl2uuuUaqV6+evQnfk0DCCPxH06KchO0tw3f03HPP2ckBqYm7d++W0qVLWyVlFE7zeiJAquOCBQuMZCp+dKi0/eGHH1rqIgqL0EiABEiABEiABEgg1QlgAr5IkSKmJ4zXBQoU8DQk1GBANCrs119/9dzP08aTvNF3331nRdLwUDxp0iS55ZZbIh4xJgxatWpljDZu3CihNJ4jboArSSAJCMAZ5E58HXHEEfY9rlGjRq4Cf5JgODyEPBBA0NaXX35pwVvIgME1hJY8BFBToFatWgKJKdRDGjZsmNSvXz/wW92zZ49MmzZNhg8fLrjuo3bSihUr0qquQCI/DUxygycywNq0aZPIXafVvjJH7CsfPzacFBABixmdRYsWCW5o4ZxFIQ58gWvWrGnFRtzI3Hw8VO6aBEiABEiABEiABEjAJwE8pMPgdPTq3EV7RDzjwf7AgQMW6YxlmWLQ4YWsBaxr164ycOBAc3JnHz+0jaHX3Lp1a7t/HjNmDJ272SHxfdITWL58ucCRi4KKLVu2lF69egmCfOrWrSvQk0bEIPS7aelPYP78+VKvXj1BFgdq8sB5iO9AmTJlzFmIwuu0/CdQqVIlc9jiuo7aAtDWP/bYY6VUqVL2Oz7hhBPsdwznLiJ48RuH/BCNBPKTACUaEkAfN6xTp04N7KlgwYKCm1potcDRC21baLYsXLjQHMAlS5YMtOULEiABEiABEiABEiCB5CbgFgnLTXaVW+MgE+WrrrvuOvnhhx+kZ8+e5sRFGiweolGkDzwQDPHiiy/KV199ZYXY7r77bunevXtyfxl4dCSQjQBqkNx112rwP8sAAEAASURBVF323AcH0GWXXWbFJxG1//7779vf6NGjZfbs2VbYuly5ctm2wLfpQGDv3r2Cc97LL79swzn44IPNWYhinDt27LDz3eDBgwX1b+bMmSMXX3xxOgw7pccA5/uGDRvkkUcescA8/F63bNliYzr00EOtsGyXLl0EhVNpJJAMBOjgjfOnsHbtWjtJYzeY5UHBkYYNG9pNK07yeD9ixAjBaxSOuPTSS+XNN98UOnnj/MFw8yRAAiRAAiRAAiQQIwIo+AUnL4qG+amzgIg9aBQj6hf3iZlo3bp1E0h49e7dW955552Qhfng8Lr//vvtYToTGXHMqUsAjl2kdkMzGs67fv36CaQZgg3Pi3ASrVq1ypy/a9as4bNgMKA0eI2oXGTtrlu3zqQYoL+ONHREhML+/vtvK+Y1ZMgQixrFJMArr7witWvXToPRp/YQ4MhF8Vj8ITMb12wsK1asmK+MndSmwKNPFQKUaIjzJ/Xwww/bCRszdK+++qo0atQoUGAMaRm4yENHDLNDMBQgwwl9165dcT4ybp4ESIAESIAESIAESCAWBI477jipWLGiPfy99NJLnjeJiD082GOC34+0g+cdpEjDatWqyerVq835AUdujx49LFIXRdXee+89S4+tU6dOioyGh0kC/xDAhAWid+HcnTdvnhVVzO7cRcsKFSpYgA+CgKDrmWkFFzPh+wI5Djh3IcOAejyY2HKduxg/viNw5i5btszOfZCmgbwjpB1pyUMAmdhnnnmmYFI3k6/ZyfOJ8EiyE6CDNzuRGL+H8xaGdAzXiZt9F8WLF7eT+SWXXGKrPv/8c9NxwQwRjQRIgARIgARIgARIIPkJdOrUyQ5y0KBB4kVDEbp9iO6DuX3tTQb/gyI1iOQdN26cjB8/3nRJ4TinkUAqErjjjjtMlmHAgAGWwRlpDIcccojMmDHDogJRqMlN44/Uh+tSgwCKdeGzhXMQxdLhHAxnkKZ58MEHrZgX5GsgS0MjARIgAa8E6OD1SiqX7TZv3mw9o92cosgG0jCqVq1q7aHvcu2111pURy53zW4kQAIkQAIkQAIkQAIJItCxY0eBlMBnn30mzZs3l/3794fdM5y7yOqCtix0FhGpRSMBEkgfAoi8XLp0qRx22GHSv39/TwODPi+Kr8FmzZrlqQ8bJT+BJ554wg4SUbso2BXNIPfzwAMPWDMUZP/rr7+ideF6EiABEjACdPDG+YuA9ApYqHSc7LuGwDrS+hD2D8PMLQtJZKfE9yRAAiRAAiRAAiSQfAQgxzV37lwrpLt48WJLu8bDeXA0L2ouPProo5bVtXLlSqvEjWI6bpG25BsVj4gESCA3BKCjiwKK0F2F49arNWjQwJpCsoSWHgRef/11G0jLli09DwhSDshowDUDMjU0EiABEvBCgA5eL5Ty0KZUqVLWG9UXvViRIkVk0aJFUrRoUWsODV93Bs9Lf7YhARIgARIgARIgARLIHwKIzoLu5vnnny/btm2Ttm3bSqFChaxgUokSJay4zo033miRu9WrVzfdWRRqoZEACaQXgW+//dYGdOqpp/oamNve7e+rMxsnJYEvv/zSjqt06dK+jg9OXtjOnTt99WNjEiCBzCVAB2+cP3vXwTt9+nSBjo4XO+OMMyySFxG9sL59+8rUqVO9dGUbEsgXAkgd+vTTTwXRSJs2bZLff/89X46DOyUBEiABEhDBwyS0XSH7BOchCrmULVtWbr75Znn77beJKM4ETjvtNIu4euqppyx6D1F827dvt0K6iNRFsTCkXy9fvlxQh4FGAiSQfgTcqN1IUi2hRu1G/B955JGhVnNZBhFwHMdGywyPDPrQOVQSyCMBOnjzCDBadxRXg0GHCa937doVrYutr1KlisApDKF1nNw7dOggw4YNs1QfTxtgIxJIAAEUEWzTpo1FJGFWGtFImG0uXLiw6Q9CS5qWOAJwInzzzTfmZP/xxx8Tt2PuiQRIICkI4H5h+PDhctZZZ1m1dqT44r4DKZ7IJHrkkUekRo0apv2KSu20+BHA/Vu7du3krbfeEhTNRQHdHTt22OvXXntNWrRoQVmG+OHnlkkg3wm4knsosOXHIO0Ac/v76cu2yUnALarmNaPXHYXb3o3qdpfzfxIgARIIR4AO3nBkYrQcOkqI1IC9+uqr5vyCs3bixIlR99C0aVOZNGmSPQDAcXPnnXeafEPUjmxAAgkgcM8995g2FCYi9u3bZzei1apVEzh6f/vtN3nhhRekUqVKFoGO7y8tfgTWrl1rjnY41k888UQ7zyAlGA8Ht99+u9CREz/23DIJJAsBOHcxkTx48GDLooDWHySfkOb7008/CSbcsO6YY46R+fPn2/nZTRtNljGk63EceuihgqhePKQXKFAgXYeZkePCpCoc9jNnzrT/v/7664zkwEHnJHDBBRdYhD6kWlwN1pytci6ZMmWKLWzYsGHOlVySkgTq1q1rx+2ncN769evlk08+keOOO06iFWtPSSg8aBIggfgQ0AcCWpwJbN261VGRdORYBP40VdLzXp988klHC3cE+rrb8byBJGqomnQ2jiZNmiTRUfFQ/BLo16+ffY4aoeRoRVhHU0+zbEIfeBx1LDqHHHKItVMNwizr+SY2BA4cOOD07NnT0dStwPlB032ds88+21EnTmAZXj///POx2Sm3QgIkkJQEhg4dar95lWNwlixZEvYYcX7WLCFrqw4IR4vBhm3LFSRAAqEJaCFkRyVQslx/cX+O6zF+XwsWLAjdkUszisCoUaPsXIv7Ms2kiDp2dQBae9y36eR81PZskBoEdILVzg0qv+hs3rw56kH//fffjgaI2XcB9/k0EsgEApoVbN95LU6bCcON2xgZwRsfv3mWraLgBtJtunbtKgULFrR1iLLzau3bt5cPP/zQ0iq99mE7EogXgXnz5sno0aNFnbemFT1hwgRB4Zhgg+bjyJEjZenSpVY5GBrSbkRCcDu+zj0BvSoIIvTGjRtnn4U63S0FGNFDkM6ARMOKFSvkyiuvtPTsq6++WqAHSSMBEkg/Akj/R1YFZAHmzp0rl112WdhB4vz8yiuvWIT/Bx98IJMnTw7blitIgASyEvjjjz8E9+W4tq5atUqOOOII+721atXKMvZwnw9pFERfosAe2tMyl0D37t2lXLlyJp11+eWXm4xWOBozZsyw7xbW33vvvSZ3Fq4tl6cWAURz43yADEecG3DNDmcavCG33HKL6EStFV0fNGhQuKZcTgIkQAI5CcTNdcwNhySgxagcvSF0VK4h5PpoC59++mmLDPg/9u4DPKrie/z/AUIJXTpILyIo0pv0XkKvUgVbKCJIExUUlCJIVXoTKYJ0UJpKkaY08SO9S5Pee0v+OfP9X34EssndkE22vOd5QrJ7Z+6d+7phs3vuzJmkSZNGVNUttzOC1y0vi+1O6YjRkIUDzd21kMCurXZz58419dOkSRMcsnCErTZUilhg8ODBxjVk6pZ5TQmvxdChQ01dHVEdks4hvKpsQwABDxSwZlWEBJ5s9z4kTYN5XQhZ2NV2Gyoi4MsCIemmghs0aGD+34QsgBUccoM1OCRgE4pEH3/zzTfBIQtsmXo6Y03bUXxXQGe5haRoMb8P+nvRs2fP4JDFLoNDUuQEhyxQHByS3uPxaM2QT+rBXbt29V0sLz7zkFzswUWKFDG/BzrTJmSwTHBIjvzHZ6yzaZYtW/a4ToIECYLXr1//eDs/IODtAozgjZorHEt382zYl2cQcI2A3sHU0cghb3jNKCPXHIW9ukog5I2GlCtXTkICAhIyxUjixIlj61C6aOCWLVskJNgrjRs3ttWGSo4FNKeuzgzQlZk1x6aOComohKTSkDFjxkjVqlVNPvCI6rMdAQQ8R+DVV181ufo2bNggpUuXttVxffuns4k0R+/+/ftN/nRbDamEgI8K6IylDz74wOTEXLdunRmZ6Yhi9+7d5v3S5cuXzUybzp07O6rK8z4goO/b3n33XdFZcI6Krp0QcvNe3nnnHUdVeN7DBUIGupjFN3WdEi066+bFF1+UkNQNojnxb9++bZ7XRdnmz58vxYoVM4/5BwFfEGjVqpXMnDlTQlI0mLVlfOGcXXGOpGhwhSr7RMBLBTTlgpaQESy2g7ta3wrqWu31OUrkBTRQrsFdnSJqJ7irR+rfv79Jl/HLL7/IiRMnIn9wWiKAgNsJ6CI+WpxZiCUkV6iEzKox7az25gH/IIDAMwIaePniiy/M85ruSKfdh1f0poump9Ki7UJG74VXnW1eLpAqVSozsEXTd2iwP2QkpwnshcyKk+rVq0vIqG8JWbOF4K6X/x6EjPyXkDUxRN+L165dW3QBzpMnT0rISG4T3C1QoIBJg6eDaAjuevkvA6eHgIsE/Fy0X3aLAAJeKGCtuJ47d26nzu6ll14y9a32TjWm8jMCumK3Fs35Z7eETAczHyLmzZtnVnNu27at3abUQwABNxYIWYzF5PnUGRX6YdGZYq0LQPDJGTXq+qJASGo10VGYGnSpU6eOLYKAgAAJWYjN5OrVvNeaC5/i2wLFixcX/aL4tkCVKlVEv0JSN5pZNJqbV2fUaACYggACCDyPACN4n0ePtgj4mICVkkEDCs6UkPxzprrV3pm21H1WQO/2a8mTJ8+zG8N5Jm/evGYrI3jDQWITAh4moK+rqVOnFn1ddvYmWkhuSHO26dOn97CzprsIRK+Apj/RoinGnClWfau9M22piwAC3i0QN25c0XQMOhCG4K53X2vODoHoEiDAG13SHAcBLxDInDmzOYs9e/Y4dTaai06L1d6pxlR+RsBKna5TrCkIIIBAqVKlDELIwmm2MU6fPi1//fWXyf1npWqw3ZiKCPiYwH///WfOWPPfO1Os+lZ7Z9pSFwEEEEAAAQQQcEaAAK8zWtRFwMcFdIEuLZo/6t69e7Y0NBg5Z84cU9dqb6shlRwK6N1+Lfv27XNYJ6wNupCSlowZM4a1mecQQMBDBax0LbpAj910C3379hWdXaEjDBMmTOihZ063EYgegZAV7c2B7t6969QBrfpWe6caUxkBBBBAAAEEEHBCgACvE1hURcDXBXRRCB3ppSO/Bg0aZItj/PjxoiN4s2bNKgR4bZFFWKlSpUqmzo8//hhhXavC9evXRXMAarHaW9v4jgACni3QsGFDs2iPpm9p0aKF3L9/P9wTmjRpkkyePFnixYsn/fr1C7cuGxFAQCRbtmyGYefOnU5xWPWt9k41pjICCCCAAAIIIOCEAAFeJ7CoioCvC2hKgOHDh0vs2LHNqtAaJAiv6EjfLl26mCpff/21CSaEV59t9gSaNm0qujiSTsdes2aNrUY6Wk+DvBUrVjTBdluNqIQAAh4hoK/Ns2fPlhQpUsiSJUukbNmysn379mf6rotEdezYUd577z2zbcyYMaKruFOcE9B8x1u2bJG5c+fKrFmzZN26daKL5FC8V6BGjRrm5PSa273WOnrXmsFUs2ZN78XhzBBAAAEEEEDALQQI8LrFZaATCHiOQPny5WXo0KGiqRc0SFC/fn3ZvHmzmeprncWOHTukVatW0rhxYzOSrFevXqwebeFEwfc0adLIxx9/bPakwV7NoxleGT16tIwYMUJ0MYchQ4aEV5VtCCDgoQI5c+aUtWvXSpYsWUzwsWjRovLqq69Ky5Yt5Z133jE3d3SV7rFjx5rXAp1doc9T7AtcvXpV9O9Z2rRppUSJEqKvv+pboUIFSZUqlbz55ptiLVxnf6/U9ASBYsWKiX6dOXNG+vTpY6vLn3/+uWjuXZ39VLx4cVttqIQAAggggAACCERWIFZIkCY4so1ph4CzAoUKFRKdrqY5/xYtWuRsc+q7kcCMGTOkQ4cOcvPmTdMrf39/s5L7pUuXHueA1JxzX331lXTu3NmNeu4dXdHcmRpcX7p0qVkkqXv37tKuXTvRAI5VNPA7YMAAWbhwoXlqwoQJj0fuWXX4jgAC3iVw7do10Vy8+v/98uXLoU7Oz89PatWqJf3795dXXnkl1DYehC+gr6d169aVU6dOmYp58uSRfPnymWD5oUOHzIhpfV3WfMbff/89NzXD5/TIrX/++acZHf/gwQPzt/WTTz5xeB763kdvxOqNVR3h/frrrzusywYEEEAAAQR8XUAHh82cOVM0xqA3zymREyDAGzk3WkVSgABvJOHctNn58+dNygYNIOoHXKtkzpzZBB+7du0q+jPFNQL6IVOD5+PGjTMH0GnaOnovWbJkZtTQhQsXzPOazkHTaVgLMbmmN+wVAQTcSeDhw4dmJK+OKNWp4nrzp2TJkpI8eXJ36qZH9GXv3r3GTtPclCpVSkaNGiWFCxcO1ffjx4+bgJ6mytDXYv27qDezKd4l8N1335mR7xrM11Hc+j5HR3CnTJlS9Aa3BnM1ldUff/xh0llNnDhR3n77be9C4GwQQAABBBCIYgECvFEDSoA3ahzZi00BArw2oTyw2u3bt0XzO2oOyMSJE3vgGXhulzUXpOY4Xrly5ePR03o2GTNmNKPIPvroI0mXLp3nniA9RwABBGJIQPPtvvbaa6JB3iZNmpjRJToq01HRWRO9e/eWJEmSyMGDB3ntdQTlwc8vX77cBHk1XYNVNKj/5KRI/ZurCxkGBARYVfiOAAIIIIAAAg4ECPA6gHHyaT8n61MdAQQQCFNAp6UyWjdMGpc/qbn95s+fLzqiV6cP6ygzzdObPn16lx+bAyCAAALeLDB9+nQT3H3ppZdM6oXwgrvq8Omnn5p0DYsXLzbT+L/99ltv5vHJc9MF0w4fPixTpkwxI7V37dpl0qHoDW5N29GgQQMzaldnz1AQQAABBBBAAIHoEiDAG13SHAcBBBBwsYAGHrJly+bio7B7BBBAwHcEfvjhB3OyGrjVvPJ2iuY41gDvnDlzZOTIkRInThw7zajjQQJ6U7tTp07mS7uto3d1FC8FAQQQQAABBBCIKYHYMXVgjosAAggggAACCCCAgDsLbNy40XSvTp06trupC9hlz57dpC06cOCA7XZU9FwBgruee+3oOQIIIIAAAt4iwAheb7mSnAcCCCCAAAIIIIBAlAlcuXLFLFD3wgsvOL04nc6mOHr0qGie1rx580ZZn9hRaAEdOauLm+kMFl1glIKAIwHNp7127VpZsWKFHDt2TO7cuWMWnyxTpozoDRxNsUFBAAEEEEDAkwUI8Hry1aPvCCCAAAIIIIAAAi4R8Pf3N/vVQJCzxWpj7cPZ9tR3LKBB3dmzZ4vmR163bp3cu3fPVNYAb7Vq1aRdu3ZSoUIFxztgi88JrF69Wjp37ix79ux55tynTp1qFkXs2bOnfPzxx6RUeUaIJxBAAAEEPEWAFA2ecqXoJwIIIIAAAggggEC0CWjOXV2s8u7du2ahNbsHvn//vuzevdtUJy+6XTV79f79918pWrSotGjRQlatWiVqnSpVKjN699q1azJ37lypWLGiNGzYUG7cuGFvp9TyagFd6LBq1aomuKupUz777DOzOJ6O5B0zZoxUqVLF/K706dNHatSoIbdv3/ZqD04OAQQQQMB7BQjweu+15cwQQAABBBBAAAEEnkOgZs2apvV3331ney8LFiyQ69evS8GCBU2A2HZDKoYrcPjwYSlWrJjs2LFDsmTJIpMmTTLpGS5cuCBXr16VEydOyIABA0ywd+HChaJT7wnyhkvq9Rv1/6KO3NVR3/369ZP9+/eb7/Xr15fq1atLhw4d5JdffjEjwdOlSye//vqrvPnmm17vwgkigAACCHinAAFe77yunBUCCCCAAAIIIIDAcwq8//77ogtojR49Wv7+++8I93bx4kXp1auXqdepU6cI61PBnoCO1NU8qRrM1TQMu3btknfeeUc0P7JVMmXKJJ988om5Tnny5JH//e9/0rZtW2sz331MQIP77du3N8HdESNGmJG7mqs5rFKuXDlZv369+X2aP3++GeEbVj2eQwABBBBAwJ0FCPC689WhbwgggAACCCCAAAIxJlCgQAGT01XTNOho3u3btzvsy+nTp82oQB1J+vrrrzMS0KGU8xvGjh0r+/btMwvW6ajMJEmSONxJ1qxZZfny5WYkr9b9/fffHdZlg/cK6AhvvSFQtmxZM4o3ojPNlSuXfPXVV6aajgSnIIAAAggg4GkCBHg97YrRXwQQQAABBKJZQEfP6TTWYcOGmRFyw4cPN1NZHzx4EM094XAIRL/AqFGjTJ7OM2fOSMmSJU3Ad+PGjSYNg+br1JGimtczb968Jn1Azpw5zQjA2LF5mx1VV2vChAlmV0OGDJFEiRJFuFsN8nbv3t3Us9pG2IgKXiWwePFicz5dunSxfV5t2rSRpEmTyl9//SUnT5603Y6KCCCAAAIIuIOAnzt0gj4ggAACCCCAgPsJ6Or0GszVoIrmuHy6pEiRQj766CPRD9Dx4sV7ejOPEfAKAZ3WrSNCe/bsKbpgkwYMHQUN69WrJ1OmTBH9v0GJGgEdEa25U1OmTGlGSNvda8uWLUUXztKbUxTfE9izZ485aR3Ba7fo3zG9iaML+Gl7TftBQQABBBBAwFMECPB6ypWin24p8OjRI9m8ebMcPHhQdPXmtGnTmtWdX3rpJbfsL51CAAEE7AqcPXtWNFi1ZcsW00QXjCpfvrykTp1azp8/L2vXrjUjFzXAu2TJElm0aJGkSZPG7u6ph4BHCfj5+ZmbHYGBgSaAu3LlSjl+/Lg8fPhQXnzxRfN/o3Xr1lK6dGmPOi9P6KwGeLVoXt04ceLY7rKO4tXRvpcuXZJbt27ZGvlre+dUdGsBXVRNb0pq/uwn8zTb6bR1c+by5ct2qlMHAQQQQAABtxEgwOs2l4KOeJLAnTt3RBds0JFt+sHh6ZI/f3758ssvpXbt2k9v4jECCCDg9gI67TwgIMBMU82ePbsJaGlw9+myZs0aefvtt82Nrlq1aplFahIkSPB0NZ98rItA6aJcmgNSAwavvPKKFClSxAQcfBLES046d+7cZkS7jmqnRI+ABtG1aJDd2WK1sfbhbHvqe6aABnZ1xLe+/uqXDsCwW86dO2eq6s1MCgIIIIAAAp4k4Pw7JU86O/qKgAsEdMSOruT8zz//mL1rzj2dzpUsWTLRBVZWr15tRrVpnffee8+svO1o1V4XdI9dIoAAAs8t8Pnnn5vgrs5G0FkK+kE5rFKxYkUzwlcXlNq2bZt88cUXMnDgwLCq+sRzOmrshx9+kH79+smhQ4eeOWcd6fnxxx+LjgK1Ak/PVOIJBBAIJaD/b7SE9X8qVMWnHuhMA51dlThxYvMe7anNPPRyAR1s8dtvv5n35c2bN7d1tnpz888//zQ34l577TVbbaiEAAIIIICAuwiw+oO7XAn64RECOl2rcuXKJrirUwV1ZWbN0TV58mSz+NCcOXPkv//+Mzn6EiZMKBMnTpT27dt7xLnRSQQQQEAFdLST5hnVBaJ+/PFHh8FdS0vTMsyePdt8IB45cqT46rRWDQw0btxYNO+nBqLSp08vGlTo2rWrvPnmm5ItWzZzE/D999+XSpUqycWLFy1CviOAQDgCuXLlkowZM5r/P3/88Uc4NUNvmj9/vnmiQoUKoTfwyCcEGjZsaM5z6NChojff7JQxY8aIvpZrqhVnRv3a2Td1EEAAAQQQcLUAAV5XC7N/rxLQD+aHDx82eXb1Dn9YCzfoaF2tt27dOpPvTRdbmTdvnlc5cDIIIOC9AkuXLhVdXK1mzZpSoEABWydatGhRqVq1qmj6mp9//tlWG2+qFBQUJE2bNpUFCxaYfI/ff/+9nDp1SmbNmmVu/k2bNk2OHj0qaquL9qxfv15q1KhhvLzJgXNBwFUCepNES69evUT/v0VUNP+qNZvAahtRG7Z7l0Dbtm0lc+bMsnPnTvn0008jPLnt27eLzl7R8tlnn0VYnwoIIIAAAgi4mwABXne7IvTHbQU0n6KO0PX39zcf4pMmTRpuXzXgoXl6tfTu3TvcumxEAAEE3EVAUy1oqVatmlNdsurrh2RfK6NHjzaB7VSpUpnpvbrYlo6AfrpoXnb1zZkzp6hTnz59nq7CYwQQCEOgR48eZkSl3hzp0KGD6CK3jsqNGzekQYMGZsSvpo+xRnI6qs/z3ikQP3580Ztrmg5n0KBB0q5dO5OyI6yz1VkomnJIb1JqPZ2tR0EAAQQQQMDTBJ799OFpZ0B/EYgmgblz55opXm+99ZYZgWXnsFpXpxUePHjQ5LO004Y6CCCAQEwKaN5KLVbeS7t9sepbC9TYbefp9e7evWsW1dTz+O6770TzFodXdNqvzurQALAGhs+cORNedbYhgECIgK5zoCPkNWg3YcIE0bQLmzZtCjX1XhdSW7RokRQuXFjWrl1r0qRomhmK7wro74nOpNDFP/X3RhcNfeedd0RTMegMOx2AkS9fPpNOR28MaIodTVFEQQABBBBAwBMFWGTNE68afY4RASvvm64sb7fEiRNHqlevbnL0akqHQoUK2W1KPQQQQCBGBDSQokUXJ3KmWPWt9s609eS6urCm5tPV1/datWrZOhVNfVG3bl0TjNKAlI5IpCCAQPgCpUqVkjVr1pgRuRs2bDB5UtOlS2dGxD948ED279//+HVLF9havHixucke/l7Z6u0CTZo0kdy5c5t86Pr7o4Hdp0uGDBmkf//+omkdKAgggAACCHiqAAFeT71y9DvaBc6ePWuOqfm8nClWfau9M22piwACCES3gDUCdfPmzdKmTRvbh9f6WvSDtC8V6+af3eCuZaM3CzW4qzf/CPBaKnxHIHwBTblw4MAB0YWzpk+fLsePH5cn31+99tprZor9u+++a6bmh783tvqKgAb89Wbcvn37ZPny5XLs2DHR2Re6GGaZMmXMiHBdQ4OCAAIIIICAJwsQ4PXkq0ffo1UgSZIk5ng6hcuZcvPmTVM9ceLEzjSjLgIIIBAjAnXq1JFPPvlENC2N5i1MmTJlhP3QtA46fVqL5pn1pWIFl6ybeXbP3apvtbfbjnoI+LqAroHwxRdfmC8N1J0+fdoEc7NkyWICdr7uw/k7FsiTJ4/oFwUBBBBAAAFvFCAHrzdeVc7JJQK6KI6WrVu3OrV/q77V3qnGVEYAAQSiWeCVV16RmjVrmqnOdkaWBgcHmxFzevNL0w5YI4Cjudsxdjhu/sUYPQdGQLJly2ZSNZQoUYLgLr8PCCCAAAIIIODTAgR4ffryc/LOCFjTbzV3V1BQkK2mmg9O88TpoiBVqlSx1YZKCCCAQEwLjBw5UpInT25G8bZq1Upu3boVZpd0hkLz5s1NqoEUKVLI8OHDw6znzU9aN++2bdvm1Gly888pLiojgAACCCCAAAIIIIBAOAIEeMPBYRMCTwrUr19fsmbNKv/8848MGzbsyU1h/qwLfgQGBsqjR4/Mir3WKK8wK/MkAgj4rIDmAbxz545bnX+uXLlMygV93Zo5c6ZZxOjzzz83K9Pv3r3bLHTUp08fyZEjh8yZM8escK/5ZHWFcl8r1sKbS5YskXPnztk6fb3m6qrFunloqyGVEEAAAQQQQAABBBBAAIEwBAjwhoHCUwiEJRAvXjwZPXq0xIoVS3r16iUjRowQnZocVrl+/bpZ5Xn9+vVmBee+ffuGVY3nEEDARwV0Je8WLVpImjRpxN/fXxImTCipUqWSpk2byqpVq9xCpWLFimYBMF3USPPEas5LfS5fvnxSqVIls+K45t7VBWp0obCyZcu6Rb+juxN6409vAOoo5/fff9/h34Un+6U5jk+dOiVFihQxfk9u42cEEEAAAQQQQAABBBBAwFmBWCEBqrAjVM7uifoI2BAoVKiQ7Ny5U+rVq2em9Npo4nZVdOpy165dzYf4okWLmtyTGgDRRT90oY+VK1fKt99+KxcuXDABm19++UUKFizodudBhxBAIPoFrl69Km+++aYsXbr08cETJEhgbhw9OYq3WrVqMmvWLFsLnD3ekQt/0IC0jtDdtWuXXLp0yby26Wr1DRo0kHLlyrnwyJ6x66NHj0rhwoVFr2+bNm1k3Lhxotf16aIzOj777DMZOHCg6E1DvQlYvHjxp6vxGIFwBbZv3y4//vijbNmyxdx80fcfOupeR4M3atTIpIUKdwdsRAABBBBAAAEE3EhAU8Lp7LYZM2ZIy5Yt3ahnntUVAryedb08vrfeEODVi6BTcTt27GgCuo4uio5mmzZtmlkAxFEdnkcAAd8RuHz5slkMaN++fSa/bY8ePaRZs2aPXyNOnDhhgjaDBw82QVRNf7B582Yzytd3lDz3TFevXm0WmdORvJkzZ5YPPvjAjHROly6duZ4azNVZIHv37hU/Pz/z90FHcVMQsCugKUDatWsnixcvdtgkS5Ys5veM1B8OidiAAAIIIIAAAm4mQIA3ai4IAd6ocWQvNgW8JcCrp6uj7X744Qf56aef5MCBA6JpGXS6tY7qbdKkiVSuXNmmCtUQQMAXBKpXr27SL+jI12XLlpn0LWGdt6ZDqF27tugoPU1/8Pvvv5sRvmHV5Tn3EtAc7TqCV2eqOCq6KNukSZOkfPnyjqrwPALPCBw6dMjcMDh58qRobuz33ntPatasKZkyZTLvP3Q0r/5e/f333+b1QtNIde7c+Zn98AQCCCCAAAIxLaAzmnTAlH7pouQ6A0o/R2usoHHjxmZAREz3keNHrwAB3qjxJsAbNY7sxaaANwV4bZ4y1RBAAAFzI6hOnTqSOnVqE4DJkCFDuCqa4kVTu2jal9mzZ8sbb7wRbn02uo+AZr7SFBwLFiww11qvZYoUKSRv3rwmV69+cIkbN677dJieuL3AjRs3zM1jvZmsN33mzZsnadOmfabf+runi8B+9NFHJo2UfnDWm0UUBBBAAAEE3EVgw4YN5ialBnYdFR0opTctdZ0Dim8IEOCNmuvsFzW7YS8IIIAAAggg4EhAc7Jq6dOnj0QU3NV6GgjWRc3efvttk8+VAK+qeEbRhTjr1q1rvjyjx/TS3QWGDBliZgoVKFDA5PnXRRnDKvq71717dzOCV79rKqkqVaqEmQ86rPY8hwACCCCAgCsFdH2Jtm3byoMHD+Sll16SwMBAM1o3efLkcubMGdG1a8aPHy+//fabFCtWzPzN0wFiFAQQsCcQ2141aiGAAAIIIIBAZAT0TawuUhYnThxp3ry57V1oqpf48ePLpk2b5ObNm7bbUREBBLxH4P79+/LNN9+YE5owYYI4Cu4+eca6EKx+INZ0DnPnzn1yEz8jgAACCCAQIwK6rsRbb71lgru9evWSPXv2mIXLNZCrwV5dtHfAgAFy5MgRCQgIMAuW6+y38+fPx0h/OSgCnihAgNcTrxp9RgABBBDwGAEdkXDv3j0zcjdlypS2+504cWKzAJvmKTt16pTtdlREAAHvEdAc3JrjXwO2+iHYTtGRvDoqSouuE0BBAAEEEEAgJgWCgoKkQ4cOojctdYbJoEGDzIKzYfVJR/PqYqK6VoGmKtPZbxQEELAnQIDXnhO1EEAAAQQQiJSAjuDVEi9ePKfbW230DTEFAQR8T0AXV9NSokQJp07eqn/w4EGn2lEZAQQQQACBqBb49ddf5X//+59kDcmpq6N0Iyp+fn4mB6+uWTB16lS5fPlyRE3YjgACIQIEePk1QAABBBBAwIUC6dOnNzkxdRTu3bt3bR/p4cOH8u+//5r6L774ou12VEQAAe8R0NG7WpImTerUSVn1rfZONaYyAggggAACUShgzSbRFA3W4IWIdp8zZ06pVKmS6PvhFStWRFSd7QggECJAgJdfAwQQQAABBFwooDkzixYtatI0LFu2zPaRdIEJDc68+uqr4kxqB9sHoCICCLi9QNq0aU0fnU3Tovl3tVjtzQP+QQABBBBAIAYErNkk1uwSu10oWbKkqWq1t9uOegj4qgABXl+98pw3AggggEC0CbRu3doc6/PPPzeB3ogOrGkdevfubaq1atUqoupsRwABLxWw8u7q9FYdxWS3rFy50lQtXry43SbUQwABBBBAwCUC1mwSa3aJ3YNY9a32dttRDwFfFSDA66tXnvNGAAEEEIg2gffee09y5cplVgzWYG94OXU1iPPuu+/Kjh07JHPmzNKpU6do6ycHQgAB9xJ45ZVXRL/OnTtn8hHa6d2lS5dk7NixpmqTJk3sNKEOAggggAACYQroe9ZVq1ZJ//795YMPPjADEGbMmCH6t8ZusWaTODsbxapvtbd7POoh4KsCfr564pw3AggggAACdgRu3bolmi5BFzu6ceOGpEuXzix4VLBgQTvNTR1dJGLRokWiU83mzp0rx44dk6+//lrKli1r8vNaO9q8ebP06NFD9Lumdli4cKH4+/tbm/mOAAI+KPDll19KgwYNzMrjRYoUMSlfHDFonu833nhDrl69KtWrV5dSpUo5qsrzCCCAAAIIOBR49OiRjB492gR2L168+Ew9XQhNc+rq36g0adI8s/3JJ3Q2ytKlS00u3YYNGz65Kdyfrdy71myWcCuzEQEEJFZwSMEBgegSKFSokOzcuVPq1atngh3RdVyOgwACCDgroCMTvvjiC5k4cWKYi6O99NJL5k1v48aNbe9aVxCuW7euHD9+3LTJkCGD5MmTxwR59+/fL9ZIBX1+8eLF4QZybB+Uiggg4PECOqp/8uTJkihRIhk2bJi88847EidOnFDnpa8vOltg69atoos7btu2TVigMRQRDxBAAAEEbAjogAZ9f6sjd7UUKFBAqlWrZv6mXLt2Tf744w/R1EGaUixjxoyii6hpHUflwIEDkjdvXtEBD/p+N2vWrI6qPn5eBzloMFgHVmheeQ0oU7xXQFPSzZw5U3R0eMuWLb33RF18ZvwvcTEwu0cAAQQQ8DyBf/75R2rXri0nTpyQ2LFjS5kyZURzWSZOnNi8ydQ3vLrgg05/1tEL48ePN29aIzrT/PnzmzQNw4cPlylTpphA73///fe4mb5Jbtu2rRmpZ+Ude7yRHxBwIwEdIaof1DTgSHG9gKZc0DEZ+rrRrl07c/OpatWqkilTJjOz4M8//5QtW7aYOtmzZzcftgnuuv66cAQEEEDA2wSCgoLMTBB9r6upEfTvTkBAwDOnefjwYXOz8ffffzfBX72pqKnFwiq5c+c2Qbvp06ebwPGaNWskSZIkYVU1z+l7bL1hqUXXryC4ayj4B4EIBRjBGyERFaJSgBG8UanJvhBAwBUCGtQtWrSonD9/3kxvnjBhgsmB+eSxdNra1KlT5cMPPxRN4aCj6SZNmvRkFVs/a9oHPZ4GbjRQo2+AKQi4o4D+js6bN0/0w9m6devM7732M1WqVOaDnQYdS5cu7Y5d96o+LViwwOQ/1BFQTxdN69KhQwf59NNPJXny5E9v5jECCCCAAAIRCuighfbt25u0C3rzMFu2bA7b6AjeOnXqiC7sqWmBrJQKYTW4cuWKGSyh7301t7y+b9bUZU8Wfa/x448/SseOHeXy5ctm3zqjLVasWE9W42cvFGAEb9RcVAK8UePIXmwKEOC1CUU1rxTQxbN++OEHEyTRBbQuXLggyZIlM1P09c2RTsHlQ3nMX/pKlSqJjiyoUaOGSZMQL148h53S0Qrly5eX27dvm7qafoGCgLcJaEoRHa2uU/+toq9V+sFOb3BYRXO/WmkErOf47hoBnWWgI3bPnj1rRkHpIo4VK1YkZ7druNkrAggg4BMCOoBBBxycOXPGpFPUtIoRFQ3E5siRw+R+19QNJUqUcNjk33//NUHbXbt2mToaG9Cbwy+88ILojDZd80LXqdBSv359M12fmUKGw+v/IcAbNZeYAG/UOLIXmwIEeG1CUc3rBDQQqPmEdMqRo5IiRQr55ptvpEWLFo6q8LyLBXRkYoUKFSRlypSiU8/sBNx16rSONMiXL59o0IWCgDcJ6P8DXahLR7Trhz4dHao58XTkrpajR4+aD2CaF1Zz9hUuXFh0uiYfyLzpt4BzQQABBBDwBYENGzaYBYB1RllYM0UcGfTs2dMsHtytWzcZOnSoo2rmeb0xrAsNjxgxQq5fv/5M3SxZspi0DG3atGHk7jM63vsEAd6oubaxo2Y37AUBBBBAwJGA5rAqW7asCe6+/PLLZoSbTsvX0W86ilenHlWuXNlMRdIg8KBBgxztiuddLDB37lxzBA3Y2gnuamXNEaarB+tohH379rm4h+wegegTuH//vlkUUIO7mu919+7dEhgY+Di4qz3RfK+aH08XUNVRpDo7QVOWUBBAAAEEEEDAswT++usv0+EqVao41XGrvtU+vMZ6A7hv377mM5B+Rho5cqR5rAMmdGaKjuDV9ShIyxCeItsQCFuAAG/YLjyLAAIIRInAkSNHpGnTpnL37l2Tz0qDgG+//bYZCacLBugoOJ3WryvRai4qXRVdR8gtWbIkSo7PTpwT0JHWWjQ9g92i11HTOmix2tttSz0E3FlAP2zt3bvXrHytq1mHt/CfTs9cvny5SRcwZ84c2bRpkzufGn1DAAEEEEAAgacE9IaulvTp0z+1JfyHVn0duGK3aAo0vXncuXNnc6NY8/4WK1aMwK5dQOohEIYAAd4wUHgKAQQQiCqBXr16ybVr18yKsRosCW8VWB31ptOadIGBLl26iI6eo0SvwLlz58wBnV19PmPGjKad1T56e83REHCNgC60omXIkCG2Ui7kzJlTunbtatpYbc0D/kEAAQQQQAABtxfQXLhaNK+uM0UXUNNid/abM/umLgII2BcgwGvfipoIIICAUwK6WICueO7v729y69pp/MEHH8hrr70mugjBTz/9ZKcJdaJQwBqhGFZOsPAOo0F8LbpoHgUBbxDQhdUOHDhgZhk4M6Jdc6hp0WmXFAQQQAABBBDwHIE8efKYzm7cuNGpTlv1rfZONaYyAghEmQAB3iijZEcIIIBAaIGVK1ea0bgaHEmXLl3ojQ4exY4dW958802zVac7U6JX4KWXXjIH1FWAnSlWfau9M22pi4A7CmiAV0vevHlFX5fsFk3VoDe1dJrmnTt37DajHgIIIIAAAgjEsICmHEucOLFs3brVdtoxnXE4ceJE03NNO0dBAIGYE7D/jj3m+siREUAAAY8U0NXltRQqVMip/hcsWNDU10UGKNErUKdOHXNAnV6uqTLsFM01qrmVdVpb6dKl7TShDgJuL6CLQGqJGzeu0321UtE8fPjQ6bY0QAABBBBAAIGYEUiQIIHJiavvgXXNkJs3b0bYkZ49e5qZh/nz53dqDYsId0wFBBBwWoAAr9NkNEAAAQTsCejCalr0zZIzRUe/aWH0mzNqUVP3jTfeMAvg7dixQ4YPHx7hTvWNb7t27Uw9zZtsBbYibEgFBNxcwMorfejQIad6qnmob9y4YRZbS5IkiVNtqYwAAggggAACMSug64e88sorZvBCxYoVTfA2rB7p55xOnTrJqFGjJH78+DJhwgSnZvyEtU+eQwCB5xPwe77mtEYAAQQQcCRgrSjr7Ehcq77V3tH+eT7qBTQYP3r0aKlXr57oiIRHjx5J9+7dw3zDevr0aWnUqJHs3r3bvBHu1q1b1HeIPSIQQwKabkRfg06cOGGmaRYtWtRWTzTvuJby5cub7/yDAAIIIIAAAp4joCkadB2QqlWrmr//L7/8srRo0UKqV68uugixrjuxefNmmTZtmpw6dcoMZJkxY4YUL17cc06SniLgpQKM4PXSC8tpIYBAzAuUKVPGdOLnn382gUK7PVqyZImparW32456USOgaRpGjBhhUjR89NFHoikzvv32W5OPbN++fWbxqM6dO4sGwP7880/Jli2beSOcKFGiqOkAe0HADQRixYolrVu3Nj3R0TxBQUER9urq1asycOBAU8/KJR5hIyoggAACCCCAgFsJ6HvbLVu2mPcBmrJp6tSp0qRJEylVqpTUrFlT+vfvb4K7RYoUkQ0bNpgBD251AnQGAR8ViBWSX8VekkEfBeK0o1ZAc5Hu3LnTjI5btGhR1O6cvSHgZgL68po7d27RKc6a0zUwMDDCHv79999SuHBhM9X/8OHDJl1AhI2o4BKBZcuWyfvvv+9wapoetHHjxjJ27FhJlSqVS/rAThGISYErV66YGxkXL1400zBHjhwZ5mh27aOmK9HFVdasWSMlS5Y0o3tisu8cGwEEEEAAAQSeX+DgwYMyf/582b59u2gapmTJkpn3BjogokKFCqI3hCkIPK9Aq1atZObMmaKjwVu2bPm8u/PZ9qRo8NlLz4kjgICrBfQNz6BBg8xd7Q8//NCsRh/eqFyd5tSwYUMzUq5jx44Ed119gSLYf0BAgFSpUkUWLlwoOgpbA/WaWzRdunRSokQJadq0qeiCEhQEvFVAFw7UD3X6/0BHsWs6kq+++kqKFSv2+JQ1jYneDNGUJgcOHDD/P+bOnft4Oz8ggAACCCCAgOcK6Iy1Tz75xHNPgJ4j4EMCBHh96GJzqgggEP0CGrDt0KGDGeVZuXJl+eyzz0QX43pyOr8GSH788UfRHK5nz541Oaysac7R32OO+KRAvHjxRBde0y8KAr4oUK5cOfn111/NaPW1a9ea1yddgC1Xrlyi0zb37t0rly9fNjT58uUTTTFjLdDmi16cMwIIIIAAAggggAACMSFAgDcm1DkmAgj4lICOfNPFu4YPHy69e/eWAQMGyOuvv24WMNKclZrHVadAa9EFDTTYq/UpCCCAgDsIaJBXR+cOGTLETJ3T2Qb6ZZW8efNKu3btzFfcuHGtp/mOAAIIIIAAAggggAAC0SRAgDeaoDkMAgj4rkDs2LFl2LBhJj/l559/LuvXr5fVq1eHAtEAiS7opTmHtD4FAQQQcCcBTdegKWd0doGmKzl9+rRoMDdr1qyM2HWnC0VfEEAAAQQQsClw5swZ0a+ECROa1HBPzjC0uQuqIYCAGwkQ4HWji0FXEEDAuwXKli0rOsX5/Pnzooup6ffkyZPLyy+/LDlz5vTuk+fsEEDAKwQ0t7jm49MvCgIIIIAAAgh4loDOHtSBJz/88IMcPXr0cef1pq3O2Pnggw+kdu3aj5/nBwQQ8BwBAryec63oKQIIeIlAmjRpTCoGLzkdTgMBBBBAAAEEEEAAAQTcXOC3334z60pcunTJ9DRlypSSJUsWuX37thw5ckR0u37VqlVLZs2aJUmTJnXzM6J7CCDwpADzgJ/U4GcEEEAAAQQQQAABBBBAAAEEEEDAiwSWL18uNWrUEA3uVqxYUTZs2CAXLlyQHTt2yL59+8zPum6IBn1//vlnKV++vNy6dcuLBDgVBLxfgACvm17jR48eybp168zXsWPH3LSXdAsBBBBAAAEEEEAAAQQQQAABBNxVQBdGbdasmTx8+FB69uxpRumWLl1aNO2SVZIlSybvv/++/PXXX5I7d27ZuXOndOzY0drMdwQQ8AABArxuepFu3rwpFSpUMF9Tpkxx017SLQQQQAABTxU4d+6cTJw4UQIDA6V+/frStm1bGTx4sBnF4annRL8RQAABBBBAAAEEQgv07dtXrl+/bt7v6Xu9JwO7oWuKZM6cWX766Sfx9/eX6dOnm3VDnq7DYwQQcE8BArzueV3oFQIIIIAAAi4R0BuIXbp0MW/gNbirQd7FixfLtGnTpFevXpI3b16pU6dOqIU3XNIRdooAAggggAACCCDgUoE7d+7I7NmzTVB36NChto6VK1cuad++vQQHB5v3h7YaUQkBBGJcgEXWXHwJDh8+bKZCOHuYGzduPG6ieXL279//+LH1w8svv2z9yHcEEEAAAQQiFDh9+rTJv7Zr1y6JHTu21K1b1yz4lyFDBtFVlTdt2iRz5841Izf050WLFknZsmUj3C8VEEAAAQQQQAABBNxP4M8//zSLqBUtWlSyZ89uu4NNmjSR4cOHm3QOthtREQEEYlSAAK+L+cuUKSNnz559rqOMHz9e9OvponfUKAgggAACCNgR0BEctWvXFg3u6ijdH3/8UV599dVQTdu0aSMDBw40aRs0uKsB4C1btshLL70Uqh4PEEAAAQQQQAABBNxfQPPvanF2cJhV32rv/mdKDxFAgBQN/A4ggAACCCDgAwKac00XzNBgrY7OfTq4axGkTp1aFixYIDpyQ0f1tmvXztrEdwQQQAABBBBAAAEPErDy7To7OCwoKMicpc74oiCAgGcIMILXxdepT58+0q1bN7l79645UpIkSaRmzZrhJjbXivfv35eFCxeaNjrS6rXXXjM/8w8CCCCAAALOCujo3WHDhplmmms3efLk4e5CPwxMmjRJ1qxZI2vXrjUB4VKlSoXbho0IIIAAAggggAAC7iWgi6Zp2bNnj1Md27t3r6mfKVMmp9pRGQEEYk6AAK+L7Tt06CDly5eXFi1amBUoNbeujoiaOnWqaM5DR+XatWuPA7y6unn//v0dVeV5BBBAAAEEwhX49ddfRRdXK1GihJQsWTLcutbGpEmTyttvvy068lfTNRDgtWT4jgACCCCAAAIIeIaAvvfTQWY6i0vX9bFSL0TUe12YTUuVKlUiqsp2BBBwEwHG20fDhdARuJrDsEePHmZRm1WrVkm+fPnMQjbRcHgOgQACCCDg4wK7d+82AnrD0Zli1de8vRQEEEAAAQQQQAABzxKIFy+etGrVynT6ww8/FDupGvR9n87k0vQMb731lmedML1FwIcFCPBG08XXF9YhQ4bI6tWrRac5XL58WZo2bSotW7YUHa1LQQABBBBAwFUCly5dMrvW/LrOlFSpUpnqVntn2lIXAQQQQAABBBBAIOYFPvvsM0mZMqWsXLlSOnbsKI8ePXLYqQMHDphFeTVlZGBgoFmY12FlNiCAgFsJEOCN5suho6H++ecfadasmTnyrFmzzGhezXNIQQABBBBAwBUCVqD2/PnzTu3+woULpr7V3qnGVEYAAQQQQAABBBCIcYG0adPK/PnzJX78+DJu3DiTrmvZsmVm3R+rc//9958MGDBAihYtKsePHzepuUaMGGFt5jsCCHiAAAHeGLhIurjNDz/8IDNnzpRkyZLJyZMnpXLlytK1a9fHi7HFQLc4JAIIIICAlwpoWiAtumCaM8Wqz0KfzqhRFwEEEEAAAQQQcC8BHWi2bt06yZgxo2zbtk1q1aplYhE5c+Y0awPp87179xZdM6h58+ai6zdoQJiCAAKeI0CANwavlS68pqN5y5Yta3Lh6B2yIkWKmAToMdgtDo0AAggg4GUCehNRF03bunWrbNy40dbZ6YKgU6ZMMXUbNGhgqw2VEEAAAQQQQAABBNxTQBdcO3jwoHz99ddSsGBBM7jsyJEjcubMGUmYMKE0bNhQfv/9d9FZxv7+/u55EvQKAQQcChDgdUgTPRsyZ85sRlQNGjRI4saNK3v27JHixYvLV199FT0d4CgIIIAAAl4vkCBBArPQp55omzZtJKKcukFBQWZRDc0Xr8Fh/UBAQQABBBBAAAEEEPBsAQ3cdu/eXf766y+5deuWHD58WDQ9w/Xr100aBx18RkEAAc8UIMDrBtdNV6fs1auX/Pnnn/Lyyy/LgwcPCPC6wXWhCwgggIA3Ceibec2rpiM1Xn/9dYezRc6ePSt169aVRYsWSYoUKWT8+PHexMC5IIAAAggggAACCIQI6KjdHDlySPr06UVjEhQEEPBsAT/P7r539b5QoULmTlqPHj1kzJgxbnNyK1askOnTp4uO6HrecuzYMbOLO3fuPO+uaI8AAggg4ISAjuJdunSpBAQEmL81hQsXlurVq0vVqlVN7rUrV67Ipk2bZOHChWZEhy6stmTJEvPG34nDUBUBBBBAAAEEEEAAAQQQQCCaBQjwRjN4RIfTKROjR482H8D1g7WWYsWKRdTMpdsnTpwoixcvjtJjWCuzR+lO2RkCCCCAQLgC6dKlkw0bNki/fv3km2++Eb2Bp19Pl0aNGsnQoUMlS5YsT2/iMQIIIIAAAggggAACCCCAgJsJEOB1swtidadGjRqiX+79DArSAABAAElEQVRQdHqurqQZHBz83N356KOP5N9//5VMmTI9977YAQIIuF7g9u3bsn79ejl+/Lg8fPhQXnzxRbMwpE7dp3imgE7HGzx4sPTs2VN++ukn2bFjh5w/f16SJ09u0gTVqVOHUbueeWnpNQIIIIAAAggggAACCPioAAHeGLrw9+7dk/jx4zt9dF0Yx0pvkDFjRqfbR6ZB2rRppXHjxpFp+kwbXTxOA7yxYsV6ZhtPIICA+wjoYgt9+/aVGTNmmBV2n+xZnDhxpFatWjJw4EDJmzfvk5v42YMEUqZMaRZc00XXKAgggAACCCCAAAIIIIAAAp4rQCbtaLx28+fPN6NyNWCqqRjy5MkjrVu3NjkP7XZDP4jr6FdGwNoVox4CCDgrsHbtWsmXL59MmjRJ7t+/L6VKlZLAwEDp2LGjVKlSRTTAqylkChYsKNOmTXN299RHAAEEEEAAAQQQQAABBBBAAIEoFGAEbxRiOtrVrVu3pEOHDmahsifr7N+/X/Rr1qxZ0rlzZxkwYIAJ/D5Zh58RQACB6BTYunWr1KxZ04za1cW4Ro0a9cx0/YsXL0rv3r1lwoQJ0rZtWzMboVmzZtHZTY6FAAIIIIAAAggggAACCCCAAAL/vwAjeKPhV+GTTz4JFdxNlCiRZMuW7XGagqCgIBkxYoQUKFBAjh07Fg094hAIIIDAswKaOqZp06YmuKsjdjU/a44cOZ6pmCpVKtHc3CNHjjTb3nnnHTl9+vQz9XgCAQQQQAABBBBAAAEEEEAAAQRcL0CA18XGf//9t4wZM8YcRVMz6LTm69evy9GjR+XKlSsyZMgQSZYsmdl+8OBBKV++PEFeF18Tdo8AAmELaEoGzZGtN5tGjx79+CZU2LXFzDxo0qSJ6EJs/fv3d1SN5xFAAAEEEEAAAQQQQAABBBBAwIUCBHhdiKu7HjdunDx69Ej8/Pxk1apVoquTx479f+wa2O3Ro4fs27dP8ufPb3py4sQJqVSpkpw7d87FPWP3CCCAQGiBOXPmmCc0/YK+ZtkpuhCblnnz5pnXOvOAfxBAAAEEEEAAAQQQQAABBBBAINoECPC6mFqDt1qaN2/+OIj79CHTp08v69evl3LlyplNmqZBc19q7l4KAgggEB0Cmipmy5Yt5gZUjRo1bB9SF4vMnj27XLp0SQ4dOmS7HRURQAABBBBAAAEEEEAAAQQQQCBqBAjwRo2jw70cOHDAbCtcuLDDOrohadKksmLFCilZsqSpt2PHDtGpzzr6l4IAAgi4WkAXTnv48KGkTJlSEiZM6NThMmfObOqfPXvWqXZURgABBBBAAAEEEEAAAQQQQACB5xcgwPv8huHu4f79+2a7nYCJv7+/LF26VHLmzGnaLF++XDp16hTu/tmIAAIIRIVA4sSJzW4iM3Pg5s2bpq21j6joD/tAAAEEEEAAAQQQQAABBBBAAAF7AgR47TlFulauXLlM271799rah65Ov3LlSkmdOrWprzl8hw8fbqstlRBAAIHICuhNKE0Xowum7dmzx/ZurPqaWzxbtmy221ERAQQQQAABBBBAAAEEEEAAAQSiRoAAb9Q4OtyLFeCdNWuWXL582WG9JzfkyJHDjOTVEb1aunfvLtOnT3+yCj8jgAACUS5Qq1Yts89JkybZ3re+tt25c0dKlChh0jvYbkhFBFwksH37dnn//fclb968Jv1R8uTJJV++fNKtWzenbl64qHvsFgEEEEAAAQQQQAABBBCIcgECvFFOGnqHurialvPnz5uF1s6dOxe6goNHGizRwImOigsODpa2bdtKv379RBdCoiCAAAKuEPjggw/Ma47OHNAF1yIqJ0+elN69e5tqH374YUTV2Y6ASwU0VUjLli2laNGiMmbMGNFFTm/cuCHXrl2T3bt3m9kwr732mnTo0EHu3bvn0r6wcwQQQAABBBBAAAEEEEAgOgUI8LpYOyAgQCpXrmyOsmrVKtEV5zVYO3r06AiPXL9+fRk7dqzEihXLBHb79u1r0jdE2JAKCCCAQCQEXn31VenSpYto7nAdzbt+/XqHe9EFJPW1TW9eVa9eXRo1auSwLhsQcLXA1atX5fXXXzc3RjXdSI8ePWTbtm1y/fp10W2bN282o3r9/PxEb2Do766OPKcggAACCCCAAAIIIIAAAt4gQIA3Gq7ihAkTzPRQPdSVK1dk2rRpMn78eFtHDgwMlKlTp4p+KNXCCF5bbFRCAIFICgwZMkTq1q0rFy9elPLly0uzZs1k2bJlcvz4cTl9+rSsW7fOBMry588vBw8elIIFC8rs2bMjeTSaIRA1Am+88Ybs2rXLpGXQ7/p7XKRIEUmSJIkkS5ZMSpYsKd9++61o+oYsWbLIxo0b5b333ouag7MXBBBAAAEEEEAAAQQQQCCGBQjwRsMFyJ49u2zdulU6duwoiRIlMkfMkCGD7SO3adNGdu7cKWXKlLHdhooIIIBAZATixIkjCxculM8//1zix48vc+bMMaN5s2bNKhkzZpQKFSqY6e8PHjyQt99+2wTKNMcpBYGYEli0aJHoDJk0adKY7/o311HRXLy6kGnixIll5syZ5vfXUV2eRwABBBBAAAEEEEAAAQQ8RYAAbzRdqQQJEpi0DDpV9I8//jALpzlzaJ06rdOlv//+e7OYUdKkSZ1pTl0EEEDAtoDm/taUMIcOHZL+/fubm0s66vHFF1+UYsWKyUcffST//POPTJ48WXQ6/PMUTQehecYpCERW4JtvvjFNNU+93oSIqLz88svSs2dPU81qG1EbtiOAAAIIIIAAAggggAAC7iwQK+SDNZ+s3fkKeVnfChUqZEYj16tXT3TUFQUBBHxLQP/k6AhhXURyw4YNJhWEpqDREcI1atSQ9u3bm1zlvqXC2UZWQBdRe+GFF0RHnmtaEU3JYKecOnVKMmXKZOrrImya656CAAIIIIAAAggggAAC0S/QqlUrM7tuxowZZtHk6O+BdxyREbzecR05CwQQQMDtBY4dO2ZmIOiCbHqDRwNy8eLFk0ePHsnhw4dNjlSdQt+tWzd5+PCh258PHYx5gRMnTpjfn1y5ctkO7mqvdaSvpnTQAPGFCxdi/kToAQIIIIAAAggggAACCCDwHAIEeJ8Dj6YIIIAAAvYE9u7da9I7aD5yDa6NHj1aTp48Kffu3RNN07Bt2zbp0KGDaHqI4cOHi47y18AvBYHwBG7fvm02RyZViL+/v2l7586d8A7BNgQQQAABBBBAAAEEEEDA7QUI8Lr9JaKDCCCAgGcL3Lp1S+rUqWNG7NasWVM02KuLTlr5UjVFQ5EiRczibRs3bjQjK5ctWyYff/yxZ584vXe5gLVg6b///utULme9sXDmzBmTmiFdunQu7ycHQAABBBBAAAEEEEAAAQRcKUCA15W67BsBBBBAQIYNGyZHjhwRzcG9YMGCcKfS6yJuS5cuFQ36jhgxwiz0BiECjgR04T/N36xpFv78809H1Z55fuXKlWbkuN5YiB8//jPbeQIBBBBAAAEEEEAAAQQQ8CQBAryedLXoKwIIIOBhArqo2vjx402vR44cKQkSJIjwDIoXLy5t27Y1eXgnTpwYYX0q+LZA8+bNDUCfPn1sjeLV/M79+vUzbay2vi3I2SOAAAIIIIAAAggggICnCxDg9fQrSP8R8CABzXX5008/yZAhQ8z0e821unbtWhbU8qBr6GxX//77bzMVPnv27FKmTBnbzd98801TV0daUhAIT6B79+6SMmVKWb16dYRpPYKCgqR9+/ayc+dOyZIli7Rr1y68XbMNAQQQQAABBBBAAAEEEPAIAT+P6CWdRAABjxa4efOmDBo0SEaNGiWaj/XpkjZtWundu7cJvMSJE+fpzTz2YAHNjaolf/785rvdf6z6Vnu77ajnewIvvPCCzJ49W2rUqCGDBw+WgwcPytdffy05cuQIhbFnzx7p0qWL/Pbbb6ILrM2fP9/WiPJQO+EBAggggAACCCCAAAIIIOCGAgR43fCi0CUEvEng2LFjUqtWLbOwVqxYsaRkyZJmJKcGZc6ePSu//vqr2dapUyczunfu3LmSLFkybyLw6XPRxay0OJvn1KpvtfdpRE4+QoEqVarIkiVLRFMuLFq0SBYvXiyFCxeW3Llzi47a1eDuP//8Y/aTOnVqWbhwoVnYL8IdUwEBBBBAAAEEEEAAAQQQ8AABArwecJHoIgKeKnDlyhWpWrWqHD58WPLlyydTp04NM6jy888/y7vvviu//PKLNGrUSFasWGEW2fLU86bf/09AF8HSor8DzhRdlE1LhgwZnGlGXR8WCAgIMDeL+vbta0b0bt++XfTLKnrjSFN/6GwBDfJSEEAAAQQQQAABBBBAAAFvESDA6y1XkvNAwA0FPvzwQxPY05XqNddu4sSJw+yljvDdsmWLlChRwkyf1lQO3bp1C7MuT3qWgF77hAkTyo4dO+TEiROSOXNmWyegozC1lCtXzlZ9KiGgAnpDYdKkSfLtt9+a15RTp05J7Nixze9dsWLFJG7cuEAhgAACCCCAAAIIIIAAAl4nwCJrXndJOSEE3EPg0KFDMmPGDDM1f968eQ6Du1ZvNfA3bdo083DgwIHC1HxLxrO/a67Thg0bSnBwsHz66ae2TubixYsybNgwU7dly5a22lAJgScFEiRIYG4OtGjRQpo1ayalSpUiuPskED8jgAACCCCAAAIIIICAVwkQ4PWqy8nJIOA+AgsWLDC5LzUnZtasWW11TNM5aN7My5cvy+rVq221oZL7C/Tr188sajVz5kwZOnRouB2+fv261K9fXy5duiSaV1W/KAgggAACCCCAAAIIIIAAAggg4FiAAK9jG7YggMBzCFi5L6tVq+bUXqz627Ztc6odld1XIFu2bDJlyhTTwR49epiFsI4ePRqqw7oQ1rJly6Ro0aKyceNGyZQpkxkBHqoSDxBAAAEEEEAAAQQQQAABBBBA4BkBcvA+Q8ITCCAQFQLnz583u3F2kSxrUS6rfVT0hX3EvIBOk9dcqG3btjULYM2ePdssvKfB37t378r//vc/OXfunOlo/vz5ZcmSJZI2bdqY7zg9QAABBBBAAAEEEEAAAQQQQMDNBRjB6+YXiO4h4KkCumK9lmvXrjl1ClZ9q71Tjans1gJNmzaV/fv3y1tvvSVJkyaVXbt2ydKlS+WXX34xwd2cOXOKLrC3detWyZIli1ufC51DAAEEEEAAAQQQQAABBBBAwF0EGMHrLleCfiDgZQK5c+eWn3/+WTZv3iy1atWyfXabNm0ydbU9xfsEdDE9Tdcwbtw4M2r3zJkzogtiZc+eXTTAS0EAAQQQQAABBBBAAAEEEEAAAecEGMHrnBe1EUDApkCdOnVMzWnTpsnt27dttTpy5IgZzRknThypWbOmrTZU8kyBePHimXy7+nuii+sR3PXM60ivEUAAAQQQQAABBBBAAAEEYl6AAG/MXwN6gIBXCpQtW1ZKlCghOkKza9euEZ7jgwcP5O233xb9rnlaU6dOHWEbKiCAAAIIIIAAAggggAACCCCAAAK+LkCA19d/Azh/BFwoMHbsWPH395cJEyZIx44dzWJaYR3u0qVLoiM5f//9d8mYMaMMGDAgrGo8hwACCCCAAAIIIIAAAggggAACCCDwlAA5eJ8C4SECCESdQMGCBeWHH36QZs2aiQZ7NSfvu+++K6VLl5YUKVKY0b2//fabTJ48Wa5evSpp06Y1i26lSZMm6jrBnhBAAAEEEEAAAQQQQAABBBBAAAEvFiDA68UXl1NDwB0E6tWrJxs2bJDAwED566+/pE+fPmF2q0aNGjJ+/HjRRbgoCCCAAAIIIIAAAggggAACCCCAAAL2BAjw2nOiFgIIPIdAkSJFZPv27WYBtcWLF8u+ffvkypUroiN1dZRvo0aNpFixYs9xBJoigAACCCCAAAIIIIAAAggggAACvilAgNc3rztnjUC0C8SKFUuqVatmvqL94BwQAQQQQMCtBe7evSsHDhyQ8+fPS7JkySRXrlzywgsvuHWf6RwCCCCAAAIIIIAAAu4iwCJr7nIl6AcCCCCAAAIIIOBjAvv375fmzZtL6tSppUCBAlK1alUpXry4eVy+fHlZsWKFj4lwuggggAACCCCAAAIIOC9AgNd5M1oggAACCCCAAAIIPKfA8OHD5dVXX5XZs2fLrVu35JVXXpEqVapI0aJFxc/PT37//XepWbOmvPHGG3Lnzp3nPBrNEUAAAQQQQAABBBDwXgECvN57bTkzBBBAAAEEEEDALQW++OIL6datmwQFBUnHjh3lxIkTsnv3bpOrfevWrXLx4kUZOXKkSdfw448/Su3ateXhw4dueS50CgEEEEAAAQQQQACBmBYgwBvTV4DjI4AAAggggAACPiSwZs0a6du3r8SJE0fmzZsno0ePlowZM4YSSJw4sXTu3Fm2bNkiGTJkkNWrV0u/fv1C1eEBAggggAACCCCAAAII/J8AAV5+ExBAAAEEEEAAAQSiTaBHjx4SHBwsOoq3YcOG4R43d+7cMn/+fNGFOocNGyZnzpwJtz4bEUAAAQQQQAABBBDwRQECvL541TlnBBBAAAEEEEAgBgT27Nkjf/31l6RJk8akaLDThZIlS0q9evVMHl4d8UtBAAEEEEAAAQQQQACB0AIEeEN78AgBBBBAAAEEEEDARQJr1641e65Vq5bEjx/f9lEaNWpk6q5bt852GyoigAACCCCAAAIIIOArAgR4feVKc54IIIAAAggggEAMC5w+fdr0IFeuXE71JGfOnKb+qVOnnGpHZQQQQAABBBBAAAEEfEHAzxdOknNEwFmBw4cPi04jvXTpkqRKlUpee+01yZo1q7O7oT4CCCCAAAIIPCEQN25c8+jBgwdPPBvxjw8fPjSVrPYRt6AGAggggAACCCCAAAK+I0CA13euNWcagYAu+DJ37lwZMGCA7Nq165nahQoVkj59+pg8gM9s5AkEEEAAAQQQiFAgS5Yspk5Yf2fDa/zPP/+YzdxsDU+JbQgggAACCCCAAAK+KkCA11evPOcdSuDWrVvSqlUrWbRokXk+RYoUUrp0aTN69/z587Jx40azKEz9+vWlRYsWMnnyZEmQIEGoffAAAQQQQAABBMIXqFatmqmwYsUKM0smZcqU4Tf4/7fOnDnT/GS1t9WISggggAACCCCAAAII+IgAOXh95EJzmo4FHj16JBq41eCuBnanTp0qGtRdsmSJTJkyRX766SfzeOzYsZI0aVKZNWuWNGvWTHTELwUBBBBAAAEE7AtkzJhRAgIC5ObNm9KjRw9bDfXv7qZNmyRNmjTSoEEDW22ohAACCCCAAAIIIICALwkQ4PWlq825hikwePBg+fXXXyVdunSyZcsWadu2rcSJEydUXc351759e9m8ebMJAi9evFhGjx4dqg4PEEAAAQQQQCBiga+//loSJkwo3333nfTu3TvcG6Y///yzvPvuu2angwYNksSJE0d8AGoggAACCCCAAAIIIOBjAgR4feyCc7qhBa5evSpfffWVeXL27NlirdIdutb/e/TKK6/ItGnTzBNffPGF3Llz5/9t5CcEEEAAAQQQiFAgT5488v3334ufn5/Je1+qVCkzW8b6mxoUFGRuuLZu3Vrq1Klj/ta+//778tZbb0W4byoggAACCCCAAAIIIOCLAuTg9cWrzjk/FtCRQTdu3JCKFStK+fLlHz8f3g+1a9eWokWLyrZt2+SXX36RunXrhledbQgggAACCCDwlECjRo0kefLkJv/9H3/8YQK5sWLFkhdeeMH8XX7w4IFpET9+fPnyyy9tp3N46jA8RAABBBBAAAEEEEDAJwQYwesTl5mTdCSgi6dp0RFCzhSrvuYEpCCAAAIIIICA8wKVK1eWw4cPi6ZsKFasmBnRe/nyZXn48KGZUdO1a1c5ePAgwV3naWmBAAIIIIAAAggg4GMCjOD1sQvO6YYWOHPmjHkiW7ZsoTdE8Chr1qymhtU+gupsRgABBBBAAIEwBBIlSiTdu3c3X7p46ZUrVyRJkiSiue8pCCCAAAIIIIAAAgggYE+AAK89J2p5qYAu8qLl9u3bTp2hlSfQ39/fqXZURgABBBBAAIGwBTRFQ4oUKcLeyLMIIIAAAggggAACCCDgUIAUDQ5p2OALAtmzZzenuWPHDqdO16pvtXeqMZURQAABBBBAAAEEEEAAAQQQQAABBBCIIgECvFEEyW48U6BmzZqm47Nnz5a7d+/aOonr16/L/PnzTd2AgABbbaiEAAIIIIAAAggggAACCCCAAAIIIICAKwQI8LpClX16jMDrr78uRYoUkdOnT0ufPn1s9btnz55y6dIlKV++vOTLl89WGyohgAACCCCAAAIIIIAAAggggAACCCDgCgECvK5QZZ8eI6D5/r755huzmMvQoUPlyy+/FF3kJawSFBQkvXr1kgkTJojm3h05cmRY1XgOAQQQQAABBBBAAAEEEEAAAQQQQACBaBMgwBtt1BzIXQVKliwpEydOlDhx4shnn30mRYsWlZkzZ8rJkydFF1M7fvy4fPfdd1KgQAEZPHiwCQZ///33kj9/fnc9JfqFAAIIIIAAAggggAACCCCAAAIIIOAjAn4+cp6cJgLhCrRp00bSp08v77zzjugCaq1atQqzfrZs2WTatGlStmzZMLfzJAIIIIAAAggggAACCCCAAAIIIIAAAtEpQIA3OrU5llsLVKtWTQ4dOiQ6OnfRokWye/duuXz5sqRMmdKM1m3QoIG0bNlS4sWL59bnQecQQAABBBBAAAEEEEAAAQQQQAABBHxHgACv71xrztSGQIIECSQwMNB82ahOFQQQQAABBBBAAAEEEEAAAQQQQAABBGJUgBy8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBUgwBuj/BwcAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIvAAB3sjb0RIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhRAQK8McrPwRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgLEOCNvB0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGBXwi9Gj+/DB7927J/Hjx3da4NKlS3Lnzh3TLmPGjE63pwECCCCAAAIIIIAAAggggAACCCCAAAIIeI8AI3ij8VrOnz9fatSoIWnTphV/f3/JkyePtG7dWjZt2mS7F23atJFMmTKZL9uNqIgAAggggAACCCCAAAIIIIAAAggggAACXilAgDcaLuutW7fkzTfflMaNG8vKlSvl/PnzEhwcLPv375cZM2ZI2bJlpWvXro9H5kZDlzgEAggggAACCCCAAAIIIIAAAggggAACCHiBAAHeaLiIn3zyiUyfPv3xkRIlSiTZsmWTWLFimeeCgoJkxIgRUqBAATl27NjjevyAAAIIIIAAAggggAACCCCAAAIIIIAAAgiEJ0CANzydKNj2999/y5gxY8yeNDXDkiVL5Pr163L06FG5cuWKDBkyRJIlS2a2Hzx4UMqXL0+QNwrc2QUCCCCAAAIIIIAAAggggAACCCCAAAK+IECA18VXedy4cfLo0SPx8/OTVatWSZ06dSR27P9j18Bujx49ZN++fZI/f37TkxMnTkilSpXk3LlzLu4Zu0cAAU8QOHv2rKxZs0bmzZsna9euNSlePKHf9BEBBBBAAAEEEEAAAQQQQAABBKJHgACvi501eKulefPmj4O4Tx8yffr0sn79eilXrpzZpGkaAgICRHP3UhBAwDcFVqxYIaVKlZIMGTKYmz5NmjSRihUrir5e6GvFb7/95pswnDUCCCCAAAIIIIAAAggggAACCIQSIMAbiiPqHxw4cMDstHDhwuHuPGnSpKIBnZIlS5p6O3bsEA3o6OhfCgII+I7AvXv3zKKMNWvWlM2bN4u/v79UqFDBLNKogd348eObG0JVqlSRwMBAefDgge/gcKYIIIAAAggggAACCCCAAAIIIPCMAAHeZ0ii9on79++bHSZMmDDCHWsgZ+nSpZIzZ05Td/ny5dKpU6cI21EBAQS8QyA4OFjeeOMNsyhj4sSJZeTIkXLx4kWTomHu3Lmybt06uXDhgnz99deirykTJ06U1q1be8fJcxYIIIAAAggggAACCCCAAAIIIBApAQK8kWKz3yhXrlym8t69e201SpUqlaxcuVJSp05t6msO3+HDh9tqSyUEEPBsgVGjRsnixYslZcqUZvRu586dzQjeJ88qUaJE0r17d9mwYYNZoHHOnDkyYcKEJ6vwMwIIIIAAAggggAACCCCAAAII+JAAAV4XX2wrwDtr1iy5fPmyraPlyJHDjOTVEb1aNJgzffp0W22phAACnilw8+ZN+fLLL03n9f97vnz5wj2RQoUKyeTJk02dzz//XO7evRtufTYigAACCCCAAAIIIIAAAggggIB3ChDgdfF11cXVtJw/f94stHbu3DlbRyxRooRoUDh27Nii07bbtm0r/fr1k6CgIFvtqYQAAp4loClZ9CaQLqym+XftlEaNGokGevV15ddff7XThDoIIIAAAggggAACCCCAAAIIIOBlAgR4XXxBAwICpHLlyuYoq1atkjx58phg7ejRoyM8cv369WXs2LESK1YsE9jt27evSd8QYUMqIICAxwmsX7/e9LlevXpO9d2qb7V3qjGVEUAAAQQQQAABBBBAAAEEEEDA4wUI8EbDJdT8mNZ06ytXrsi0adNk/Pjxto4cGBgoU6dOFT8/P1OfEby22KiEgMcJ/Pfff6bPmqLFmWLVt9o705a6CCCAAAIIIIAAAggggAACCCDg+QIEeKPhGmbPnl22bt0qHTt2FF0gSUuGDBlsH7lNmzayc+dOKVOmjO02VEQAAc8SSJAggemws7l0rfpWe886a3qLAAIIIIAAAggggAACCCCAAALPK/B/w0Kfdy+0j1BAgy+almHkyJGyfft2uX79eoRtnqzw6quvik7B1sWXxo0bJ3v37n1ys0t/Xrx4sXz33XdRkv/3yJEjpq+3b992aZ/ZOQKeJpA1a1bT5b///luaNWtmu/taX0u2bNlst6EiAggggAACCCCAAAIIIIAAAgh4jwAB3mi+lppqQRdQi2xp3bq16Fd0lhkzZsjSpUuj9JA3btyI0v2xMwQ8XaBGjRoyaNAgmTNnjnzxxRcSP378CE9Jb5TMmzfP1NP2FAQQQAABBBBAAAEEEEAAAQQQ8D0BAry+d82dPuNJkybJW2+9JcHBwU63fbqB5iP++eefpWHDhk9v4jECPi1QunRpKVCggOiI3P79+8uXX34Zocdnn30mZ8+eNTeNChcuHGF9KiCAAAIIIIAAAggggAACCCCAgPcJEOCNoWt67949WyP0nu7epUuX5M6dO+bpjBkzPr3ZJY9TpEghAQEBUbLv1atXm/3EiRMnSvbHThDwFoFYsWLJqFGjpGLFijJgwADR/3cffvihw9MbOHCgDBs2TOLGjWtSvzisyAYEEEAAAQQQQAABBBBAAAEEEPBqARZZi8bLO3/+fNFp1GnTphV/f3/JkyePSbewadMm273QBdcyZcpkvmw3oiICCHiEQNmyZeXbb781fe3atauUK1dONAf21atXzXNXrlyRBQsWSKlSpeTTTz+V2LFji46KL168uEecH51EAAEEEEAAAQQQQAABBBBAAIGoF2AEb9SbPrPHW7duSYcOHcwCaU9u3L9/v+jXrFmzpHPnzmbUngZ+KQgg4LsC7du3l3Tp0klgYKBZWFEXV9Si+bsfPnz4GEbrTJkyRWrWrPn4OX5AAAEEEEAAAQQQQAABBBBAAAHfE2AEbzRc808++SRUcDdRokRmxXudkq0lKChIRowYYfJvHjt2LBp6xCEQQMCdBerXry9HjhyRoUOHmtG6SZMmNcHdZMmSSZkyZczrxeHDhwnuuvNFpG8IIIAAAggggAACCCCAAAIIRJMAAV4XQ+uCSWPGjDFH0dQMS5YskevXr8vRo0dFp1sPGTJENGij5eDBg1K+fHkhyGs4+AcBnxZIkiSJdOvWTTZu3CjXrl2TBw8emFQNOqK3S5cuojeKKAgggAACCCCAAAIIIIAAAggggAABXhf/DowbN04ePXpkplevWrVK6tSpY/Jm6mE1sNujRw/Zt2+f5M+f3/TkxIkTUqlSJTl37pyLe8buEUDAkwQ0RQMFAQQQQAABBBBAAAEEEEAAAQQQeFqAAO/TIlH8WIO3Wpo3b/44iPv0IdKnT29ybeqCSlp0BG9AQIBo7l4KAggggAACCCCAAAIIIIAAAggggAACCCDgSIAAryOZKHr+wIEDZk+FCxcOd4+aY3PFihVSsmRJU2/Hjh3SpEkTM/o33IZsRAABBBBAAAEEEEAAAQQQQAABBBBAAAGfFSDA6+JLf//+fXOEhAkTRngkf39/Wbp0qeTMmdPUXb58uXTq1CnCdlRAAAEEEEAAAQQQQAABBBBAAAEEEEAAAd8UIMDr4uueK1cuc4S9e/faOlKqVKlk5cqVkjp1alNfc/gOHz7cVlsqIYAAAggggAACCCCAAAIIIIAAAggggIBvCRDgdfH1tgK8s2bNksuXL9s6Wo4cOcxIXh3Rq6V79+4yffp0W22phAACCCCAAAIIIIAAAggggAACCCCAAAK+I0CA18XXWhdX03L+/Hmz0Nq5c+dsHbFEiRKiQeHYsWNLcHCwtG3bVvr16ydBQUG22lMJAQQQQAABBBBAAAEEEEAAAQQQQAABBLxfgACvi69xQECAVK5c2Rxl1apVkidPHhOsHT16dIRHrl+/vowdO1ZixYplArt9+/Y16RsibEgFBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAJAQK80XCZJ0yYIPny5TNHunLlikybNk3Gjx9v68iBgYEydepU8fPzM/UZwWuLjUoIIIAAAggggAACCCCAAAIIIIAAAgj4hAAB3mi4zNmzZ5etW7dKx44dJVGiROaIGTJksH3kNm3ayM6dO+X/a+9O4K+q6gSAHxaRTXBfwQ1TB3AXXBIpY3DXaiydUCp1cit1TEZFUz9NiKZmJWpag1KjmWVquDKmKSUuiQvuW5poIWLuIAhv3rnNvfPen//26D14793v/Xz+vLuce+853/M/3Pv/vXPPHTFiRKf3kZAAAQIECBAgQIAAAQIECBAgQIAAgeYXEOBdTnXcs2fPEIdlePvtt8OMGTOSF6dVcuqhQ4eGe++9N0yZMiXE8Xn79etXye7SEiBAgAABAgQIECBAgAABAgQIECDQhAJ/f+6/CQtWr0WKQy3EAO2yTmPHjg3xx0SAAAECBAgQIECAAAECBAgQIECAAAE9eP0OECBAgAABAgQIECBAgAABAgQIECBAoEEFBHgbtOJkmwABAgQIECBAgAABAgQIECBAgAABAgK8fgcIECBAgAABAgQIECBAgAABAgQIECDQoAICvA1acbJNgAABAgQIECBAgAABAgQIECBAgAABL1nzO0CAAAECBAgQIECAAAECBAgQINCwAu+99164/vrrw+233x5efvnlsHDhwrDBBhuEkSNHhoMPPjgMHDiwYcsm4wQ6IyDA2xklaQgQIECAAAECBAgQIECAAAECBOpOYPLkyeHUU08Nc+fOLcvbI488Em6++eZw+umnh2984xvhnHPOCT169ChLY4FAswgYoqFZalI5CBAgQIAAAQIECBAgQIAAAQI5EjjhhBPCEUcckQR3d9ttt3DFFVeE+++/P8ycOTNcd9114ZBDDgkff/xxuPDCC8Mee+wR3n///RzpKGqeBPTgzVNtKysBAgQIECBAgAABAgQIECBAoAkEvve974Uf/vCHoVevXuHHP/5xGDNmTFmptttuu/CFL3whjBs3Lnz+858Pf/jDH8Khhx4abrzxxrJ0Fgg0g4AevM1Qi8pAgAABAgQIECBAgAABAgQIEMiJwOzZs8MZZ5wRunTpEq699tqlgrulDNtvv324++67wxprrBFuuummcMMNN5RuNk+gKQQEeJuiGhWCAAECBAgQIECAAAECBAgQIJAPgYsvvjjMnz8/GYLhgAMO6LDQm2yySZgwYUKS7rvf/W6H6SUg0GgCAryNVmPyS4AAAQIECBAgQIAAAQIECBDIscDUqVOT0h977LGdVvjyl78c+vTpEx544IEwZ86cTu8nIYFGEBDgbYRakkcCBAgQIECAAAECBAgQIECAAIGwZMmS8Pzzz4euXbuGnXfeudMiPXv2DHFc3kKhkOzf6R0lJNAAAgK8DVBJskiAAAECBAgQIECAAAECBAgQIBDChx9+GD7++OPQu3fv0L1794pI+vfvn6R/5513KtpPYgL1LiDAW+81JH8ECBAgQIAAAQIECBAgQIAAAQKJQN++fUOvXr3C+++/H959992KVF577bUk/dprr13RfhITqHcBAd56ryH5I0CAAAECBAgQIECAAAECBAgQyASGDRuWzN9xxx3Zuo5m/vrXv4bHH388CQ4PGTKko+S2E2goAQHehqoumSVAgAABAgQIECBAgAABAgQI5FvgC1/4QgJw7rnnJmPydkZj4sSJSdp99903Gd6hM/tIQ6BRBAR4G6Wm5JMAAQIECBAgQIAAAQIECBAgQCAceeSRYcMNNwwzZ84MJ598cociN9xwQ5g0aVLo1q1bOPvssztMLwGBRhMQ4G20GpNfAgQIECBAgAABAgQIECBAgECOBXr27Bmuvvrq0KNHj3DRRReFL33pSyEOwdByWrBgQZgwYUKIPX6XLFkSYi9ewzO0VLLcDAKVvW6wGUqsDAQIECBAgAABAgQIECBAgAABAg0tsNtuu4Xrr78+Ce7+/Oc/DzfeeGMYPXp02HrrrZPA7wsvvBBuvfXWMHfu3NClS5ek5+64ceMauswyT6AtAQHetmSsJ0CAAAECBAgQIECAAAECBAgQqFuB/fbbLxmm4dRTTw1xGIabbrop+SnN8I477hjOO++8sMcee5SuNk+gqQQEeJuqOhWGAAECBAgQIECAAAECBAgQIJAfgc022yz86le/CnPmzAnTpk0LL7/8cli4cGHYYIMNwsiRI8M//dM/5QdDSXMrIMCb26pXcAIECBAgQIAAAQIECBAgQIBAcwiss8464bDDDmuOwigFgQoFvGStQjDJCRAgQIAAAQIECBAgQIAAAQIECBAgUC8CArz1UhPyQYAAAQIECBAgQIAAAQIECBAgQIAAgQoFBHgrBJOcAAECBAgQIECAAAECBAgQIECAAAEC9SIgwFsvNSEfBAgQIECAAAECBAgQIECAAAECBAgQqFBAgLdCMMkJECBAgAABAgQIECBAgAABAgQIECBQLwICvPVSE/JBgAABAgQIECBAgAABAgQIECBAgACBCgUEeCsEk5wAAQIECBAgQIAAAQIECBAgQIAAAQL1IiDAWy81IR8ECBAgQIAAAQIECBAgQIAAAQIECBCoUECAt0IwyQkQIECAAAECBAgQIECAAAECBAgQIFAvAgK89VIT8kGAAAECBAgQIECAAAECBAgQIECAAIEKBQR4KwSTnAABAgQIECBAgAABAgQIECBAgAABAvUi0L1eMiIf+RKYOHFiuPzyy5d7oT/44IMwe/bs0K1bt9C1q+83lnsFOGEuBD7++OOknN27u8TkosIVcrkLLF68OCxZsiTENtalS5flfn4nJNDsAoVCIcRrmfvFZq9p5VuRAu4XV6S+c+dBIN4rxp+BAweGXr161XWRX3/99brOX6Nkzl/fjVJTTZLPjTfeOCnJG2+8EeLPiprSG4oVdX7nJZAHgYULF+ahmMpIYIUJLFq0aIWd24kJ5EHA/WIealkZV7SA+8UVXQPO3+wCr7zySkMUMXZa2GijjRoir/WayS7Fb6gL9Zo5+WpOgRdffDGsqD9Kf/nLX4YzzzwzjB49OpxwwgnNCaxUBFagwPz588NBBx0UevbsGa6//voVmBOnJtC8AmeffXZ46KGHwllnnRWGDx/evAVVMgIrSOCuu+4KF154YfjUpz4Vxo0bt4Jy4bQEmlcgfnly4IEHJk9UTp06tXkLqmQEVqDAhAkTwn333Rd+8IMfJPGPFZiVTp26b9++YcCAAZ1KK1HrAnrwtu5ibQ0FBg0aVMOjt3/o9dZbL0mw4YYbhn322af9xLYSIFCxwPvvv5/sEx8d18Yq5rMDgU4JXHrppUm6YcOGaWedEpOIQGUCb731VrJD/EPTtawyO6kJdEYg7R0fh8zTxjojJg2BygUmT56c7LTBBhuELbfcsvID2KPhBAxC2nBVJsMECBAgQIAAAQIECBAgQIAAAQIECBD4u4AAr98EAgQIECBAgAABAgQIECBAgAABAgQINKiAAG+DVpxsEyBAgAABAgQIECBAgAABAgQIECBAQIDX7wABAgQIECBAgAABAgQIECBAgAABAgQaVECAt0ErTrYJECBAgAABAgQIECBAgAABAgQIECAgwOt3gAABAgQIECBAgAABAgQIECBAgAABAg0qIMDboBUn2wQIECBAgAABAgQIECBAgAABAgQIEBDg9TtAgAABAgQIECBAgAABAgQIECBAgACBBhUQ4G3QipNtAgQIECBAgAABAgQIECBAgAABAgQICPD6HSBAgAABAgQIECBAgAABAgQIECBAgECDCgjwNmjFyTYBAgQIECBAgAABAgQIECBAgAABAgQEeP0OECBAgAABAgQIECBAgAABAgQIECBAoEEFBHgbtOJkmwABAgQIECBAgAABAgQIECBAgAABAgK8fgcIECBAgAABAgQIECBAgAABAgQIECDQoAICvA1acbJNgAABAgQIECBAgAABAgQIECBAgAABAV6/AwQIECBAgAABAgQIECBAgAABAgQIEGhQge4Nmm/ZJrBMAv369Uv269+//zLtbycCBNoX6NGjR1h55ZWDNta+k60E/hEB17J/RM++BDoWSNtY+tnxHlIQIFCJQLdu3UKfPn2Se8ZK9pOWAIHOC6TXsPSz83tK2agCXQrFqVEzL98EKhVYvHhxuP3228OIESOC/+gq1ZOeQOcEHn744dCrV68wePDgzu0gFQECFQnMnTs3zJw5M+y5554V7ScxAQKdE1iyZElyv7jrrruGVVddtXM7SUWAQEUCjz76aOjevXsYOnRoRftJTIBA5wTmzZsXHnrooeR+sUuXLp3bSaqGFhDgbejqk3kCBAgQIECAAAECBAgQIECAAAECBPIsYAzePNe+shMgQIAAAQIECBAgQIAAAQIECBAg0NACArwNXX0yT4AAAQIECBAgQIAAAQIECBAgQIBAngUEePNc+8pOgAABAgQIECBAgAABAgQIECBAgEBDCwjwNnT1yTwBAgQIECBAgAABAgQIECBAgAABAnkWEODNc+0rOwECBAgQIECAAAECBAgQIECAAAECDS0gwNvQ1SfzBAgQIECAAAECBAgQIECAAAECBAjkWUCAN8+1r+wECBAgQIAAAQIECBAgQIAAAQIECDS0gABvQ1efzBMgQIAAAQIECBAgQIAAAQIECBAgkGcBAd48176yEyBAgAABAgQIECBAgAABAgQIECDQ0AICvA1dfTJPgAABAgQIECBAgAABAgQIECBAgECeBQR481z7yk6AAAECBAgQIECAAAECBAgQIECAQEMLCPA2dPXJPAECBAgQIECAAAECBAgQIECAAAECeRYQ4M1z7Ss7AQIECBAgQIAAAQIECBAgQIAAAQINLSDA29DVJ/MECBAgQIAAAQIECBAgQIAAAQIECORZQIA3z7Wv7AQIECBAgAABAgQIECBAgAABAgQINLSAAG9DV5/MEyBAgAABAgQIECBAgAABAgQIECCQZwEB3jzXvrITIECAAAECBAgQIECAAAECBAgQINDQAgK8DV19Mk+AAAECBAgQIECAAAECBAgQIECAQJ4FBHjzXPvKToAAAQIECBAgQIAAAQIECBAgQIBAQwsI8DZ09ck8AQIECBAgQIAAAQIECBAgQIAAAQJ5FhDgzXPtKzsBAgQIECBAgAABAgQIECBAgAABAg0tIMDb0NUn8wQIECBAgAABAgQIECBAgAABAgQI5FlAgDfPta/sBAgQIECAAAECBAgQIECAAAECBAg0tIAAb0NXn8wTIECAAAECBAgQIECAAAECBAgQIJBnAQHePNe+shMgQIAAAQIECBAgQIAAAQIECBAg0NACArwNXX0y3xmBxYsXh//6r/8Ko0aNChtttFFYbbXVwp577hm+/e1vhz/+8Y+dOYQ0BHItcMstt4QxY8aE4cOHJ+1nnXXWCbvvvns46qijwqOPPtopG+2wU0wSEcgELr300rDuuusmPy+88EK2vr2ZF198MRx55JFhu+22C/369QuDBw9Olq+88srwwQcftLerbQSaXuDhhx8OZ555Zthnn31CvI7Fn5EjR4bTTz89vPPOO50qv2tZp5gkyqnAgw8+GMaOHRuGDRuWXIMGDhyY/M0V2917773XKRVtrFNMEuVEYO+99w5dunQJkyZN6nSJq3UvWK3jdDrjElZHoGAi0MQCs2fPLgwdOrRQbC2t/nTv3r3ws5/9rIkFFI3AsgsUL+yF4hcjrbadtE1169atcPzxxxfef//9Nk+kHbZJYwOBVgWefPLJQs+ePbO29/TTT7earnTl+eefX1hppZWyfdI2mn7usssuhbfeeqt0F/MEciPwwx/+sN32sdZaaxVuvvnmdj1cy9rlsTHHAh999FHh6KOPLnTt2rXNa9D6669fuOmmm9pV0sba5bExZwKXXHJJ1p4uvvjiTpW+WveC1TpOpzItUVUFusSjFW/+TQSaTuDdd98NI0aMCI8//nhStm233Tbsv//+YYMNNgj33ntvuOGGG8L8+fOTb8WK/2mG4447rukMFIjAsgosWLAg6bE7a9as5BBrr7120os39gj88MMPQ+wJdc0114SPP/442R57bEyZMmWp02mHS5FYQaBdgYULF4addtqprHd8McAbttxyyzb3iz10Dz/88GR7MTAcDj744FAM6IZXX301FINW4bHHHku2bbXVVuF//ud/kp6LbR7MBgJNJBD/zPnyl78cil/mJ6Xq0aNH8kRXvB+MPQrjveCMGTOSbX379k2e7Npiiy2WEnAtW4rECgKZwMknnxwuvPDCZHnllVcO//Zv/xZ22GGHENvN1KlTw5133plsi+3vgQceCPFvspaTNtZSxHKeBeLfWIcddlhYsmRJwhBjFV//+tfbJanWvWC1jtNuZm2snUBVw8UORqCOBL75zW9m33p98YtfLMRvl0un6dOnF/r375+kiT15X3vttdLN5gnkWqD4hUfWfopDmhTmzZu3lEdxeIZC8RHXLN0vf/nLpdJoh0uRWEGgXYFx48Zlbap495fMt9eD94033ij06tUrSRevab/73e/Kjl8MGBcOOeSQ7JjHHnts2XYLBJpZoDhEV/a7v/nmmxdiD8HSqfg4eKG0zRU7BpRuzuZdyzIKMwTKBO67775C8RHypJ0Vh8ErPPXUU2Xb48LEiROzdljsKFCI7a7lpI21FLGcR4FiJ5rkmhSfkEzvAeNnRz14q3UvWK3j5LHu6qXMoV4yIh8EqikQg1HFnhjJf4wbbrjhUsHd9FzFb5Wz/zzPOuusdLVPArkWWLRoUaFPnz5J24g363/961/b9IiP26U3IHvttVdZOu2wjMMCgQ4F7r777uwR11VWWSVrW+0FeMePH5+l+9GPftTqOeIf08UxEZN08dpY7CnVajorCTSTQPFJlMKaa66Z/N737t278Morr7RavOKTKIUY/I3XsviIefwDt3RyLSvVME+gXKA4vm52DSqOHV++8f+Wir0QC8UnU7J0La9p2lirbFbmTOCee+4pfOITn8jaSfr3VWcCvNW6F6zWcXJWdXVVXC9ZK7YYU/MJFHsShuKYoEnBimNChfhIUGvTfvvtF9JH8a644opQDGy1lsw6ArkSiC8fTF/IdOCBB7b7OHdsQ8VgcOIzc+bMMiftsIzDAoF2Bd5+++3k5TTxcbz4ItD4Iqh0ii/YaGuaPHlysqkYEA5f/epXW01WDFqFk046KdkWr40//elPW01nJYFmErj11lvDm2++mRQp/v4Xv/BvtXjFnlLhK1/5Sij+YZ0Mj/Lcc8+VpXMtK+OwQKBMoPTe71Of+lTZtnQhXsP22GOPdDEZ5itbKM5oY6Ua5vMmUIwOJkNFxvbz/PPPJ8WPw3J94xvf6DRFte4Fq3WcTmdcwqoLdK36ER2QQB0IpOOpxayMHj263RwVXyKVbP/LX/4Sir2n2k1rI4E8CMRx0P75n/85DBkyJBlDrb0yx8BRHJ83TnPnzg3FoVCy5NphRmGGQIcCxxxzTDJm7uqrrx7iDXZ7Qd30YC+//HIo9rBPFkeOHNnml5kxwWc+85nsmFdffXV6CJ8EmlYgBo3SKQZw25tOO+20EAO7xcfNwyc/+cmypK5lZRwWCLQpEO8f25qKT5Jkm+JY86WTNlaqYT5vAvGL/WLv9/hkfVL0I444IvkSZJtttsko2rsnrNa9YLWOk2XazAoREOBdIexOWmuB+++/PzlFDD5tvfXW7Z6u9D/PJ554ot20NhLIg0D8UmTatGkhtoeOBvR/5513QrwhiFPsDR9frpFO2mEq4ZNA+wIx4HrttdcmiS677LJQfNt4+zv839a0jcXF1l5aU3qQtdZaK6y33nrJKte6UhnzzSoQXwYap9hzd9CgQctczLSduadcZkI7NrHAdtttl5UuvlCttSm+kPf222/PNrW8XmljGY2ZHAvEv79+//vfh5/85CehOKxQpyXS9hN3aNm2Wh6kvXvBah2n5TktL18BAd7l6+1sy0nghRdeSM60wQYbhJVWWqnds5Y+svfMM8+0m9ZGAgTKBeLQJuk3zsOHDy/bqB2WcVgg0KpAcVzQ5NG8uHHMmDGh+FLQVtO1tjJ9lC9u22STTVpLUrYuvd6999574fXXXy/bZoFAMwnMnz8/pNeg9A/e2Esq/vF8zjnnhOKY8UlP3eOPPz75ciVua2tKj+Oesi0h6/MsUHwpb4hDBMXpu9/9bii+m6GMI/bWPeGEE8Ljjz+erI9PiJUGheNKbayMzELOBOKXhzG4escddyz1BElnKKp1L1it43Qmz9LUTqB77Q7tyARWjEAcOzR9DGidddbpMBPxm6x0euutt9JZnwQIdCAQhzWZMGFCkireeTDeAwAAIldJREFUnHzta1/L9tAOMwozBNoUiEGlsWPHhtgTfuDAgWHSpEltpm1tQ+njsMtyvetsT+HWzm0dgXoWiF+cpEHbNdZYI3nHQmxraU/5NO9xSIbi28nDj3/84zBlypQwYMCAdFPy6VpWxmGBwFIC8doTe+fuv//+If4d9dnPfjaMGDEibL/99sn7UH73u9+FF198Mdmv+KK18Itf/KLsGNpYGYeFHArE4Rdi21jWqVr3gtU6zrKWw37VERDgrY6jo9SRQPxDOZ169eqVzrb5WZrmww8/bDOdDQQI/L9AbGd77713EpiKa2PvjNJxC7XD/7cyR6AtgfPPPz/ce++9ydi4V111VVh11VXbStrqeu2sVRYrCYTYSz2devbsGQ444IAkCNW9e/cwePDg5IVqsbfSU089FeLj43fddVfSq3DWrFlh3XXXTXfNrnFxRen9YpagxUxpGveULXAsNq3ArrvuGp599tmkZ3wcGmX69OnJT2mB4wujvv/974fYIaB0ch0r1TBPoHKBarWhah2n8hLYo5oC5f/DVvPIjkVgBQm0vKnvKBulY4a6Ge9Iy3YCIcRHXw888MDw2GOPJRybb7551pM39dEOUwmfBFoXiG8e/9a3vpVsjF+QlL5hvPU9ll6rnS1tYg2BKFDaNmLv3NjDMD4WHq9b8edXv/pV8vnII4+EoUOHJmhvvvlmOPHEE8sAS48TA8UdTe4pOxKyvRkFfv7znyftKB33OgZxN9tssxB7z6dT7Cm/5557htdeey1dlXxqY2UcFghULFCtNlSt41RcADtUVUCAt6qcDlYPAqVj7sZeGR1NpWk6c/Pe0fFsJ9DMAvEP4M985jPhnnvuSYoZx/SMfziX9lqKG7TDZv4tULZ/VCB+SRLH2120aFHSm3DixInLdEjtbJnY7JQDgTjuZzrF+7w4fm68bsXeu6VTDO7efffdWe/5+Ph47FWfTtpYKuGTQOsC8YVQ8Xo2Z86c5MVQsZduDBTFHvLxnjEOzxA7BcTpzjvvDLvvvnuYPXt2djBtLKMwQ2CZBKrVhqp1nGUqhJ2qJiDAWzVKB6oXgb59+2ZZWbBgQTbf1kxpmv79+7eVzHoCuReIL8HYZZddwowZMxKL+FKnOLZaay930g5z/+sCoB2BcePGhfhSz3gz/d///d9hWb9c1M7aQbYp1wKlbSNCnHfeedmLoFrCrLnmmuG0007LVpcGeEuPU3q/mCVuMVOaxj1lCxyLTSfw9ttvh5NPPjl52W4cR/S2225Lhuzq3bt3VtZNN9003HjjjeGYY45J1r300kth/Pjx2XZtLKMwQ2CZBKrVhqp1nGUqhJ2qJiDAWzVKB6oXgfRNrjE/pYOFt5W/0jT9+vVrK5n1BHItEN/uGsdYS990vMMOOySB3taCuxFKO8z1r4vCtyMQ/wC+5JJLkhTf/OY3ky9I4h/JLX9KeyDG61S6vfSpk9JrVum1rK3Tl6Yp3bet9NYTaFSB0mtQLEPsNdjeVDqGfByXN51Kj1PaftLtLT9L02hjLXUsN5tA/IIyHbfz8MMPb7edxTHn0xd7XnPNNUmP3+ihjTXbb4XyLG+B0mtN6TWorXyUpindt3S+NE2lx2krvfXLR0CAd/k4O8tyFIiPiqc3EK+++mqHZy5NU/pijQ53lIBATgRuuOGGZHzQuXPnJiXeZ599kkdd45uT25q0w7ZkrM+7wG9+85uM4Nxzzw2rrbZaqz+//vWvs3TDhw/P0vzhD3/I1g8aNCibL72WZStbzKRp4oumSsdGbJHMIoGGFxgwYEBWhthTvnQ521AyE8cLTaf0i8y47FqWqvgksLTA008/na2Mw3e1N/Xp0yfstttuSZLFixcnLziMC9pYe2q2EehYoFr3gtU6Tsc5lqKWAgK8tdR17BUmkI6xFr99SoNSbWUmjhGVTsOGDUtnfRIgUBS4/PLLw0EHHZS8WC2CxEfsYoAq3qh3NGmHHQnZTuAfE0jbWDxKaVCqtaPG8X5feeWVZNNWW221zMNCtHZs6wjUm0D8AmO99dZLshV/9+M4oO1Nf/vb37LN8UuX0iltZ+4pS1XMEwhh3rx5GUPauSZb0cpMHK4hneKYvemkjaUSPglULpC2n7jnP3IvWK3jVF4Ce1RToHs1D+ZYBOpFYKeddkoG8o/5iWOp/cu//EubWZs+fXq2Le5nIkDg7wJXXXVVEtAtFAohjq12wQUXhJNOOqnTPNphp6kkzJHA3nvvHeKYnx1NsQdv+qj40Ucfne2z0UYbZbtus802YeWVVw4fffRR2YuhsgQlMw8++GCSLq5yrSuBMdu0AvH3PI79Gac4dnxpL92WhS79sr/l0EOuZS21LBP4u8CQIUMyiniNGTlyZLbc2kzpFy1bbrlllkQbyyjMEKhYoFr3gtU6TsUFsEN1BYp/uJsINJ3AI488Uii2lOSn+ObWNstX7M1UKD66l6Tbcccd20xnA4G8CcyaNatQfIw7aRtdu3YtTJkypWIC7bBiMjsQyAQOOeSQ7DpWfCFbtr7lTLzGpde7mTNnttycLX/ta1/L0t18883ZejMEmlXgF7/4RfY7XxxaqLBkyZI2i/q5z30uS3vrrbeWpXMtK+OwQCATKD7RlbWbUaNGZetbmymO1VsoDu2VpO/Ro0ehOM58lkwbyyjMEMgEfvKTn2Tta9KkSdn61maqdS9YreO0lkfrlo+AIRqqGy93tDoR2HbbbUN8CVSc4uPk8SUALaf58+eHI488MsRH9+J0yimntEximUBuBY499tiQvszprLPOCmPHjq3YQjusmMwOBCoWiNexdDrqqKOSl7Gly+lnMWAVrrzyymRx6NChIY6jbSLQ7AIHHHBASHu8xzYwfvz4Votc/MIjxLHm47ThhhuG0aNHl6VzLSvjsEAgE/j0pz8dNt5442T5zjvvDGeccUa2rXSmGNYIJ5xwQvZitX/9138NcWzsdNLGUgmfBJZNoFr3gtU6zrKVwl5VEVg+cWRnIbD8BYqP4xWKj5Un33zFHojf+c53Ci+99FLyjXFxWIZC8Y3K2bdiO++8c6E44P/yz6QzEqhDgdhbt3iBydpO7Pm07777durnzTffLCuRdljGYYFApwU624M3HnCvvfbK2mzxD+XCb3/720LxS8zC7NmzCz/4wQ+yJ1XitVDv3U5XgYRNIHDHHXcUunXrlrWP4jjyhbvvvrtQ/HK/8Oc//7kwYcKEsu0xfWuTa1lrKtYRKBTuueeeQry2pPeNxQBRYdq0aYUPPvgg+Sm+GLRQHLoh21780qUQe/O2nLSxliKW8y5QSQ/eaFWte8FqHSfv9beiyh9W1Imdl8DyEIiP5xVfBpXdVMSbj3RIhvRGpDgmW6H4IrblkR3nINAQAvELj7R9VPr56quvLlVG7XApEisIdChQSYC3+KKbQrHXYVm7bXmti235oosu6vC8EhBoNoG77rorezQ8vabFR8TT+fgZA1QdtQ/Xsmb7zVCeaglceumlhd69e5e1qXgNKg38xnY2aNCgwkMPPdTmabWxNmlsyKFApQHeat0LVus4OayyuiiyIRqKVxtT8wp88YtfDPfff38ojq8bij04koKmQzIUb+7DiSeemGzvzAtvmldJyQiUC6Qvdipfu+xL2uGy29mTQGcEVl999XDbbbclj6DH+Til17o4v9VWW4VbbrkluebFZROBPAnEx8iLY3yG4tiCYZVVVkmKXhz/MyPYbbfdQrHHYYftw7UsIzNDoEyg2DM+PPnkk2G//fYLxfc3JNviNag47nUy37dv3+T69MQTTyR/k5XtXLKgjZVgmCVQoUC17gWrdZwKsy95lQS6xDBzlY7lMATqWuDDDz8Mjz76aCg+khc23XTTsMUWW4T+/fvXdZ5ljkCzCWiHzVajylOPAn/605+SgFavXr3C5ptvHjbZZJNQ7ElVj1mVJwLLVSD+2fPcc8+Fxx57LPTr1y9pH/GesNLJtaxSMenzIhC/PHn++efD008/HWJgd/DgwWHgwIGhOGxeRQTaWEVcEhNYSqBa94LVOs5SGbSiJgICvDVhdVACBAgQIECAAAECBAgQIECAAAECBAjUXkB3jtobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIECBAgAABAgQIECBAoCYCArw1YXVQAgQIECBAgAABAgQIECBAgAABAgQI1F5AgLf2xs5AgAABAgQIECBAgAABAgQIECBAgACBmggI8NaE1UEJECBAgAABAgQIECBAgAABAgQIECBQewEB3tobOwMBAgQIECBAgAABAgQIECBAgAABAgRqIiDAWxNWByVAgAABAgQIECBAgAABAgQIECBAgEDtBQR4a2/sDAQIECBAgAABAgQIECBAgAABAgQIEKiJgABvTVgdlAABAgQIECBAgAABAgQIECBAgAABArUXEOCtvbEzECBAgAABAgQIrGCBxYsXh3XXXTf7icsreho1alSSnwEDBqzorDg/AQIECBAgQIBAAwt0b+C8yzoBAgQIECBAgACBTgvMmTOn02mXR8J58+aFmKfu3d2SLw9v5yBAgAABAgQINKuAHrzNWrPKRYAAAQIECBAgQIAAAQIECBAgQIBA0wsI8DZ9FSsgAQIECBAgQIAAAQIECBAgQIAAAQLNKiDA26w1q1wECBAgQIAAAQIECBAgQIAAAQIECDS9gABv01exAhIgQIAAAQIECBAgQIAAAQIECBAg0KwC3ujQrDWrXAQIECBAgAABAhUJnHbaaeFvf/tb2GmnncJXv/rV8Oabb4Zrr7023HfffWHGjBnJy9C23377sOuuu4Zjjjkm9OjRo93jP/nkk+H73/9+eOihh8Ls2bPD4MGDw4gRI8KXvvSlMGTIkHb3Ld24aNGiMHny5OQ4jz76aHjllVfCFltsEbbddtswatSo8NnPfrY0eTZ/9dVXh+nTpyfLq6++epgwYULo0qVLtr10JubzmWeeSVbttttu4dBDDy3dbJ4AAQIECBAgQKCOBboUilMd50/WCBAgQIAAAQIECPzDAosXL04CtOmBPv7449CtW7d0MfkcOHBgEoiNwc3//M//DKNHjw7PP/98WZp0YdiwYeG6664LG2+8cbqq7PP0008PEydODK3davft2zcJHJ9xxhkhBmy7d+8eYhC3tenpp58OY8aMCY888khrm5N1Bx98cLjsssvCaqutVpYmBpW33nrrJGgdN0yaNCkcd9xxZWniwo033hg+97nPJevXXHPN8Nhjj4X1119/qXRWECBAgAABAgQI1KeAAG991otcESBAgAABAgQIVFGgkgBv7MH76quvhtdffz184hOfCLvvvntYY401wu9///vw4IMPhhgcjtPOO++c9Oxtmc3/+I//COeff36yumvXrknwNPb6fffdd8Ndd92V9KqNweWePXuGDz74oM0A7z333BP23nvvMH/+/ORYO+64Y9hzzz2ToPITTzwRbr/99vDss88m2zbaaKMkMNu/f/+y7MQgdAwAxykGluN+MW06xTLGIPC8efOS3r1Tp04N++67b7rZJwECBAgQIECAQAMICPA2QCXJIgECBAgQIECAwD8mUEmANz3TOeecE8aNG1fW8/eOO+4I++23Xxbk/eMf/xh22GGHdJcwa9assN1224V4vn79+iU9dWOQtnS64IILwimnnBKWLFmSrI7B3jRonKaLy3EIhjjMQ5xiwPikk04KMWCcTgsWLEiGirjqqquSVf/+7/8evve976Wbs8+xY8eGn/3sZ8ly7JUcyxCn2Ls4Lt95553Jclv7Jxv9Q4AAAQIECBAgULcC/3+HWLdZlDECBAgQIECAAAECy1dg//33D3FM3jh8QukUe9CeeOKJ2arYi7Z0ikHhGNyN05lnnpn0wC3dHudPPvnkcNhhh7VcXbb8ox/9KAvuHn744ck+pcHdmDj2AL7yyivDLrvskuwbh2BIx9EtPVhcnw4lMW3atDBlypRk80UXXZQFd2OQ+txzzy3dzTwBAgQIECBAgECDCAjwNkhFySYBAgQIECBAgMDyE4jDLLQ1xZeQpVN8EVs6xV63acA3vtTsqKOOSjct9RmDxy0DtqWJLrnkkmzxO9/5Tjbf2kzs2RunOI7vNddcs1SS2JP4pz/9aXa+GGCOwz+MHz8+SbvKKqskPY07emncUge2ggABAgQIECBAoC4EBHjrohpkggABAgQIECBAoJ4ENttsszazE4O36bRw4cJ0NhkD9+23306WY4/YOOZtW9MWW2wR2jpH7AH80ksvJbtusskmYb311mvrMMn64cOHZ9vbeinciBEjwqmnnpqki0HpUaNGhY8++ihZji9oaysv2YHNECBAgAABAgQI1K1A+TNndZtNGSNAgAABAgQIECCwfARWXnnlsM4667R5stKeruk4ujHxX/7yl2yfAQMGZPNtzcQ0zz333FKbX3nllZAGjuNL0DpzrPQgbQV44/azzz47GX/34Ycfzsb8/cpXvhLGjBmT7u6TAAECBAgQIECgAQUEeBuw0mSZAAECBAgQIECgdgJxyIIuXbpUfIK5c+dm+6y//vrZfFszAwcObHXTCy+8kK2PvWxfe+21bLmjmRdffLHNJCuttFLyUrYjjzwySxNfGGciQIAAAQIECBBobAEB3sauP7knQIAAAQIECBCoE4EYGE6ndKiGdLm1zzhmb2tTnz59stVx+IWOXsiWJS7OxCBuW1MMQJ9++ullm4877riw++67h7XWWqtsvQUCBAgQIECAAIHGERDgbZy6klMCBAgQIECAAIE6Flh33XWz3P35z3/O5tuaiUMxtDZtvvnmZau//vWvly0v68IRRxwR5syZk+y+/fbbh5kzZybLsUfvTTfdtKyHtR8BAgQIECBAgMAKFvCStRVcAU5PgAABAgQIECDQHAJDhgwJ3bv/vf/EU089FQqFQpsFi9vaGk4h9qZdddVVk31nzZoVFi1a1OZx4oYPP/wwGVv32WefDfPnz2817RVXXBGmTp2abIu9gu+7774Qg7xx+s1vfhPidhMBAgQIECBAgEBjCgjwNma9yTUBAgQIECBAgECdCay22mrh05/+dJKrGLz99a9/3WYOr7/++rKXsrVMuPPOOyerYsD24osvbrm5bPnyyy8Pe+21V9hyyy1DfGlayym+eO2kk05KVscXyF155ZUh/UyHdIjb23tBW8tjWiZAgAABAgQIEKgfAQHe+qkLOSFAgAABAgQIEGhwgTimbTrF8W7feOONdDH7fOutt8K3vvWtbLm1mfPOOy9069Yt2XTWWWeFJ598srVkIfbanTBhQrbt+OOPz+bjTBzn99BDDw0ffPBBsv7ss88OgwcPTua33nrrbEzeuD2ma2tc4GQH/xAgQIAAAQIECNSlgABvXVaLTBEgQIAAAQIECDSiwIEHHhjiT5xi8HXHHXdMxrd95513wrvvvhtuueWWsMMOO4RnnnkmdOnSpc0ixuDrsccem2x///33k+NccMEFSS/bOLxDfIlb7IkbewzPmzcvSXfQQQeFT37yk2XH/Pa3vx0efPDBZN2wYcPCuHHjyraPHz8+xHPFKaaL6U0ECBAgQIAAAQKNJdCleIPY9uBgjVUWuSVAgAABAgQIECDQqsDixYuz8XFjgthTNe0hm+4wcODAMHv27LDmmmuGuXPnpquX+nzggQdCOoTC0UcfHS677LKyNDGQ+/nPfz789re/zdZ37fr3fhVLlixJ1u29995hlVVWCdddd12Sr9bG2Y29auPQCS3Hx+3Ro0dYuHBhduw4M2rUqHDzzTcnQy+kG2bMmBFGjBgRYtnjkAzxpWpp7900TfyM63faaafMZPr06WGXXXYpTWKeAAECBAgQIECgjgX04K3jypE1AgQIECBAgACBxhPo169fuPXWW8M555wT1l577aQAMbAbf/r06ZOMk3vjjTeG3r17t1u4mDaOrztt2rSw1VZbZQHp0uDuoEGDwuTJk8Ntt91WFtyNvX7jkAsxuBunOMxDa8HduC2+bO2UU06Js0n6ww47LMT9TQQIECBAgAABAo0hoAdvY9STXBIgQIAAAQIECDSgwEcffZSMn/unP/0pbLzxxmGbbbYp60lcSZEWLFiQDO0QX4a21lprhU033TQMGDAgpL2DKzmWtAQIECBAgAABAs0jIMDbPHWpJAQIECBAgAABAgQIECBAgAABAgQI5EzAEA05q3DFJUCAAAECBAgQIECAAAECBAgQIECgeQQEeJunLpWEAAECBAgQIECAAAECBAgQIECAAIGcCQjw5qzCFZcAAQIECBAgQIAAAQIECBAgQIAAgeYREOBtnrpUEgIECBAgQIAAAQIECBAgQIAAAQIEciYgwJuzCldcAgQIECBAgAABAgQIECBAgAABAgSaR0CAt3nqUkkIECBAgAABAgQIECBAgAABAgQIEMiZgABvzipccQkQIECAAAECBAgQIECAAAECBAgQaB4BAd7mqUslIUCAAAECBAgQIECAAAECBAgQIEAgZwICvDmrcMUlQIAAAQIECBAgQIAAAQIECBAgQKB5BAR4m6culYQAAQIECBAgQIAAAQIECBAgQIAAgZwJCPDmrMIVlwABAgQIECBAgAABAgQIECBAgACB5hEQ4G2eulQSAgQIECBAgAABAgQIECBAgAABAgRyJiDAm7MKV1wCBAgQIECAAAECBAgQIECAAAECBJpHQIC3eepSSQgQIECAAAECBAgQIECAAAECBAgQyJmAAG/OKlxxCRAgQIAAAQIECBAgQIAAAQIECBBoHgEB3uapSyUhQIAAAQIECBAgQIAAAQIECBAgQCBnAgK8OatwxSVAgAABAgQIECBAgAABAgQIECBAoHkEBHibpy6VhAABAgQIECBAgAABAgQIECBAgACBnAkI8OaswhWXAAECBAgQIECAAAECBAgQIECAAIHmERDgbZ66VBICBAgQIECAAAECBAgQIECAAAECBHImIMCbswpXXAIECBAgQIAAAQIECBAgQIAAAQIEmkdAgLd56lJJCBAgQIAAAQIECBAgQIAAAQIECBDImYAAb84qXHEJECBAgAABAgQIECBAgAABAgQIEGgeAQHe5qlLJSFAgAABAgQIECBAgAABAgQIECBAIGcCArw5q3DFJUCAAAECBAgQIECAAAECBAgQIECgeQQEeJunLpWEAAECBAgQIECAAAECBAgQIECAAIGcCQjw5qzCFZcAAQIECBAgQIAAAQIECBAgQIAAgeYREOBtnrpUEgIECBAgQIAAAQIECBAgQIAAAQIEciYgwJuzCldcAgQIECBAgAABAgQIECBAgAABAgSaR0CAt3nqUkkIECBAgAABAgQIECBAgAABAgQIEMiZgABvzipccQkQIECAAAECBAgQIECAAAECBAgQaB4BAd7mqUslIUCAAAECBAgQIECAAAECBAgQIEAgZwICvDmrcMUlQIAAAQIECBAgQIAAAQIECBAgQKB5BAR4m6culYQAAQIECBAgQIAAAQIECBAgQIAAgZwJCPDmrMIVlwABAgQIECBAgAABAgQIECBAgACB5hEQ4G2eulQSAgQIECBAgAABAgQIECBAgAABAgRyJiDAm7MKV1wCBAgQIECAAAECBAgQIECAAAECBJpHQIC3eepSSQgQIECAAAECBAgQIECAAAECBAgQyJmAAG/OKlxxCRAgQIAAAQIECBAgQIAAAQIECBBoHgEB3uapSyUhQIAAAQIECBAgQIAAAQIECBAgQCBnAgK8OatwxSVAgAABAgQIECBAgAABAgQIECBAoHkEBHibpy6VhAABAgQIECBAgAABAgQIECBAgACBnAkI8OaswhWXAAECBAgQIECAAAECBAgQIECAAIHmERDgbZ66VBICBAgQIECAAAECBAgQIECAAAECBHImIMCbswpXXAIECBAgQIAAAQIECBAgQIAAAQIEmkdAgLd56lJJCBAgQIAAAQIECBAgQIAAAQIECBDImYAAb84qXHEJECBAgAABAgQIECBAgAABAgQIEGgegf8FOcnSozas694AAAAASUVORK5CYII=)
patients$BMI <- round(BMI,1)
candidates <- which(BMI > upper.limit)
write.csv(patients[candidates,], file="selectedSamples.csv")
Exercise: Exercise 3
- A separate study is looking for patients that are underweight and also smoke;
- Modify the condition in our previous code to find these patients
- e.g. having BMI that is 2 standard deviations less than the mean BMI
- Write out a results file of the samples that match these criteria, and open it in a spreadsheet program
### Your Answer Here ###
---
title: "Introduction to Solving Biological Problems Using R - Day 1"
author: Mark Dunning, Suraj Menon and Aiora Zabala. Original material by Robert Stojnić,
  Laurent Gatto, Rob Foy, John Davey, Dávid Molnár and Ian Roberts
date: '`r format(Sys.time(), "Last modified: %d %b %Y")`'
output:
  html_notebook:
    toc: yes
    toc_float: yes
---

# 3. R for data analysis

##3 steps to Basic Data Analysis

- In this short section, we show how the data manipulation steps we have just seen can be used as part of an analysis pipeline:

1. Reading in data
    + `read.table()`
    + `read.csv(), read.delim()`
2. Analysis
    + Manipulating & reshaping the data
        + perhaps dealing with "missing data"
    + Any maths you like
    + Diagnostic Plots
3. Writing out results
    + `write.table()`
    + `write.csv()`
  
## A simple walkthrough

- We have data from 100 patients that given consent for their data to use in future studies
- A researcher wants to undertake a study involving people that are overweight
- We will walkthrough how to filter the data and write a new file with the candidates for the study    
    
##The Working Directory (wd)


- Like many programs R has a concept of a working directory 
- It is the place where R will look for files to execute and where it will
save files, by default
- For this course we need to set the working directory to the location
of the course scripts
- In RStudio use the mouse and browse to the directory where you saved the Course Materials

- ***Session → Set Working Directory → Choose Directory...***

## 0. Locate the data

Before we even start the analysis, we need to be sure of where the data are located on our hard drive

- Functions that import data need a file location as a character vector
- The default location is the ***working directory***
```{r}
getwd()
```

- If the file you want to read is in your working directory, you can just use the file name

```{r eval=FALSE}
list.files()
```

- The `file.exists` function does exactly what it says on the tin!
    + a good sanity check for your code

```{r}
file.exists("patient-info.txt")
```

- Otherwise you need the *path* to the file
    + you can get this using **`file.choose()`**
    
- If you unsure about specifying a file path at the command line, this [online tutorial](http://rik.smith-unna.com/command_line_bootcamp/?id=vczhybjhtyt) will give you hands-on practice
    
##1. Read in the data

- The data are a tab-delimited file. Each row is a record, each column is a field. Columns are separated by tabs in the text
- We need to read in the results and assign it to an object (`patients`)

```{r}
patients <- read.delim("patient-info.txt")

```

In the latest RStudio, there is the option to import data directly from the File menu. ***File*** -> ***Import Dataset*** -> ***From Csv***

- If the data are comma-separated, then use either the argument `sep=","` or the function `read.csv()`:
- You need to make sure you use the correct function
    + can you explain the output of the following lines of code?

```{r }
tmp <- read.csv("patient-info.txt")
head(tmp)
```
- For full list of arguments:
```{r}
?read.table
```

##1b. Check the data
- *Always* check the object to make sure the contents and dimensions are as you expect
- R will sometimes create the object without error, but the contents may be un-usable for analysis
    + If you specify an incorrect separator, R will not be able to locate the columns in your data, and you may end up with an object with just one column
    
```{r}
# View the first 10 rows to ensure import is OK
patients[1:10,]  
```


- or use the `View()` function to get a display of the data in RStudio:
```{r}
View(patients)
```

##1c. Understanding the object

- Once we have read the data successfully, we can start to interact with it
- The object we have created is a *data frame*:
```{r}
class(patients)
```

- We can query the dimensions:

```{r}
ncol(patients)
nrow(patients)
dim(patients)
```


- The names of the columns are automatically assigned:

```{r}
colnames(patients)
```

- We can use any of these names to access a particular column:
    + and create a vector
    + TOP TIP: type the name of the object and hit TAB: you can select the column from the drop-down list!
```{r}
patients$ID

```

## Word of warning


![](images/tolstoy.jpg)



![](images/hadley.jpg)

> Like families, tidy datasets are all alike but every messy dataset is messy in its own way - (Hadley Wickham - RStudio chief scientist and author of dplyr, ggplot2 and others)

You will make your life a lot easier if you keep your data **tidy** and ***organised***. Before blaming R, consider if your data are in a suitable form for analysis. The more manual manipulation you have done on the data (highlighting, formulas, copy-and-pasting), the less happy R is going to be to read it. Here are some useful links on some common pitfalls and how to avoid them

- http://www.datacarpentry.org/spreadsheet-ecology-lesson/
- http://kbroman.org/dataorg/

##Handling missing values

- The data frame contains some **`NA`** values, which means the values are missing – a common occurrence in real data collection
- `NA` is a special value that can be present in objects of any type (logical, character, numeric etc)
- `NA` is not the same as `NULL`:
    - `NULL` is an empty R object. 
    - `NA` is one missing value within an R object (like a data frame or a vector)
- Often R functions will handle `NA`s gracefully:

```{r}
length(patients$Height)
mean(patients$Height)
```

- However, sometimes we have to tell the functions what to do with them. 
- R has some built-in functions for dealing with `NA`s, and functions often have their own arguments (like `na.rm`) for handling them:
    + annoyingly, different functions have different argument names to change their behaviour with regards to `NA` values. *Always check the documentation*

```{r}
mean(patients$Height, na.rm = TRUE)

mean(na.omit(patients$Height))
```

##2. Analysis (reshaping data and maths)

- Our analysis involves identifying patients with extreme BMI
    + we will define this as being two standard deviations from the mean

```{r}
# Create an index of results:
BMI <- (patients$Weight)/((patients$Height/100)^2)
upper.limit <- mean(BMI,na.rm = TRUE) + 2*sd(BMI,na.rm = TRUE)
upper.limit
```


- We can plot a simple chart of the BMI values 
    + add a vertical line to indicate the cut-off
    + plotting will be covered in detail shortly..

```{r}
plot(BMI)
# Add a horizonal line:
abline(h=upper.limit) 
```

- It is also useful to save the variable we have computed as a new column in the data frame

```{r}
round(BMI,1)
patients$BMI <- round(BMI,1)
head(patients)
```

- To actually select the candidates we can use a logical expression to test the values of the BMI vector being greater than the upper limit
    + if the second line looks a bit weird, remember that `<-` is doing an assignment. Thevalue we are assigning to our new variable is the logical (`TRUE` or `FALSE`) vector given by testing each item in `BMI` against the `upper.limit`
    
```{r}
BMI > upper.limit
candidates <- BMI > upper.limit
```

We have seen that a logical vector can be used to subset a data frame

- However, in our case the result looks a bit funny
- Can you think why this might be?

```{r}
patients[candidates,]
```

The `which` function will take a logical vector and return the indices of the `TRUE` values

- This can then be used to subset the data frame

```{r}
which(BMI > upper.limit)
candidates <- which(BMI > upper.limit)
```


## 3. Outputting the results

- We write out a data frame of candidates (patients with BMI more than standard deviations from the mean) as a 'comma separated values' text file (CSV):

```{r}
write.csv(patients[candidates,], file="selectedSamples.csv")
```

- The output file is directly-readable by Excel
- It's often helpful to double check where the data has been saved. Use the *get working directory* function:

```{r eval=FALSE}
getwd()      # print working directory
list.files() # list files in working directory

```


To recap, the set of R commands we have used is:-

```{r}
patients <- read.delim("patient-info.txt")
BMI <- (patients$Weight)/((patients$Height/100)^2)
upper.limit <- mean(BMI,na.rm = TRUE) + 2*sd(BMI,na.rm = TRUE)
plot(BMI)
# Add a horizonal line:
abline(h=upper.limit) 
patients$BMI <- round(BMI,1)
candidates <- which(BMI > upper.limit)
write.csv(patients[candidates,], file="selectedSamples.csv")

```

##Exercise: Exercise 3

- A separate study is looking for patients that are underweight and also smoke; 
  + Modify the condition in our previous code to find these patients
  + e.g. having BMI that is 2 standard deviations *less* than the mean BMI
  + Write out a results file of the samples that match these criteria, and open it in a spreadsheet program


```{r}
### Your Answer Here ### 



```

