tag:blogger.com,1999:blog-734431969787914172024-02-20T06:55:31.436-05:00Deciphering life: One bit at a timeAnonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.comBlogger32125tag:blogger.com,1999:blog-73443196978791417.post-26531471570557015062013-09-04T12:51:00.000-04:002013-09-04T12:51:28.202-04:00Moved!For those who didn't know, previously I was working with Hunter Moseley at the Center for Regulatory and Environmental Analytical Metabolomics (CREAM) at the University of Louisville. In July, the principal investigators associated with CREAM were offered an opportunity to create the Center for Systems Biology in the <a href="http://ukhealthcare.uky.edu/markey/">Markey Cancer Center at the University of Kentucky</a>. I was offered the chance to move too, and I did. We officially moved our computing facilities on August 30, and are temporarily housed in a wet lab while we await the completion of renovations in our new space. <br />
<br />
With the move, I'm also planning to attempt a move of my blogging platform to a github hosted version written completely in markdown, probably using <a href="https://github.com/DASpringate/samatha">samatha</a>. This will take some work, but I think it will be better overall for what I want in a blogging platform overall. By generating static HTML, I shoudn't be dependent on what Blogger is doing, and it integrates better with the other things I am trying to do as well (especially with keeping the source of blog posts hosted on github too, you did know I was doing that right??).Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-23835709647836957412013-08-16T12:52:00.001-04:002013-08-16T12:53:34.691-04:00Reproducible Methods (or bad bioinformatics methods sections)<p>Science is built on the whole idea of being able to reproduce results, i.e. if I publish something, it should be possible for someone else to reproduce it, using the description of the methods used in the publication. As biological sciences have become increasingly reliant on computational methods, this has become a bigger and bigger issue, especially as the results of experiments become dependent on independently developed computational code, or use rather sophisticated computer packages that have a variety of settings that can affect output, and multiple versions. For further discussion on this issue, you might want to read <a href="http://bytesizebio.net/index.php/2012/08/24/can-we-make-research-software-accountable/">1</a>, <a href="http://ivory.idyll.org/blog/anecdotal-science.html">2</a>.</p><br />
<p>I recently read a couple of different publications that really made me realize how big a problem this is. I want to spend some time showing what the problem is in these publications, and why we should be concerned about the current state of computational analytical reproducibility in life-sciences.</p><br />
<p>In both the articles mentioned below, I do not believe that I, or anyone not associated with the project, would be able to generate even approximately similar results based solely on the raw data and the description of methods provided. Ultimately, this is a failure of both those doing the analysis, and the reviewers who reviewed the work, and is a rather deplorable situation for a field that prides itself verification of results. This is why I'm saying these are <strong>bad bioinformatics methods sections</strong>.</p><br />
<h2>Puthanveettil et al., Synaptic Transcriptome</h2><br />
<p><a href="http://doi.org/10.1073/pnas.1304422110">Puthanveettil et al, 2013</a> had a paper out earlier titled <a href="http://doi.org/10.1073/pnas.1304422110">“A strategy to capture and characterize the synaptic transcriptome”</a> in PNAS. Although the primary development reported is a new method of characterizing RNA complexes that are carried by kinesin, much of the following analysis is bioinformatic in nature.</p><br />
<p>For example, they used BLAST searches to identify the RNA molecules, a cutoff value is reported in the results. However, functional characterization using Gene Ontology (GO) was carried out by “Bioinformatics analyses” (see the top of pg3 in the PDF). No mention of where the GO terms came from, which annotation source was used, or any software mentioned. Not in the results, discussion, or methods, or the supplemental methods. The microarray data analysis isn't too badly described, but the 454 sequencing data processing isn't really described at all.</p><br />
<p>My point is, that even given their raw data, I'm not sure I would be able to even approximate their results based on the methods reported in the methods section.</p><br />
<h2>Gulsuner et al., Schizophrenia SNPs</h2><br />
<p><a href="http://dx.doi.org/10.1016/j.cell.2013.06.049">Gulsuner et al</a> published a paper in Cell in August 2013 titled <a href="http://dx.doi.org/10.1016/j.cell.2013.06.049">“Spatial and Temporal Mapping of De Novo Mutations in Schizophrenia to a Fetal Prefrontal Cortical Network”</a>. This one also looks really nice, they look for <em>de novo</em> mutations (i.e. new mutations in offspring not present in parents or siblings) that mess up genes that are in a heavily connected network, and also examine gene co-expression over brain development time-scales. Sounds really cool, and the results seem like they are legit, based on my reading of the manuscript. I was really impressed that they even used <strong>randomly generated</strong> networks to control the false discovery rate!</p><br />
<p>However, almost all of the analysis again depends on a lot of different bioinformatic software. I do have to give the authors props, they actually give the <strong>full</strong> version of each tool used. But no mention of tool specific settings (which can generate vastly different results, see <strong>Exome Sequencing</strong> of the methods).</p><br />
<p>Then there is this bombshell: “The predicted functional impact of each candidate de novo missense variant was assessed with in silico tools.” (near top of pg 525 of the PDF). Rrrreeeaaaalllly now. No actual quote of which tools were used, although the subsequent wording and references provided imply that they were <a href="http://www.nature.com/nmeth/journal/v7/n4/full/nmeth0410-248.html">PolyPhen2</a>, <a href="http://sift.jcvi.org/">SIFT</a>, and the <a href="http://www.sciencemag.org/content/185/4154/862.long">Grantham Method</a>. But shouldn't that have been stated up front? Along with any settings that were changed from default??</p><br />
<p>There is no raw data available, only their reported SNPs. Not even a list of <strong>all</strong> the SNPs that were potentially considered, so that I could at least go from those and re-run the later analysis. I have to take their word for it (although I am glad at least the SNPs they used in later analyses are reported). </p><br />
<p>Finally, the random network generation. I'd like to be able to see that code, go over it, and see what exactly it was doing to verify it was done correctly. It likely was, based on the histograms provided, but still, these are where small errors creep in and result in invalid results.</p><br />
<p>As above, even if the raw data was available (didn't see an SRA accession or any other download link), I'm not sure I could reproduce or verify the results.</p><br />
<h2>What to do??</h2><br />
<p>How do we fix this problem? I think scripts and workflows used to run any type of bioinformatic analyses have to become first class research objects. And we have to teach scientists to write them and use them in a way that makes them first class research objects. So in the same way that a biologist might ask for verification of immunostaining, etc, bioinformaticians should ask that given known input, a script generates <em>reasonable</em> output. </p><br />
<p>I know there has been discussion on this before, and disagreement, especially with the exploratory nature of research. However, once you've got something working <em>right</em>, you should be able to <em>test</em> it. Reviewers should be asking if it is testable, or the code should be available for others to test.</p><br />
<p>I also think we as a community should do more to point out the problem. i.e. when we see it, point it out to others. I've done that here, but I don't know how much should be formal. Maybe we need a new hashtag, #badbioinfomethodsection, and point links to papers that do this. Conversely, we should also point to examples when it is done right (#goodbioinfomethodsection??), and if you are bioinformatician or biologist who does a lot of coding, share your code, and at least supply it as supplemental materials. Oh, and maybe take a <a href="http://softwarecarpentry.org">SoftwareCarpentry</a> class, and look up <a href="http://git-scm.com/">git</a>.</p><br />
<p>Posted on August 16, 2013 at <a href="http://robertmflight.blogspot.com/2013/08/reproducible-methods-or-bad.html">http://robertmflight.blogspot.com/2013/08/reproducible-methods-or-bad.html</a>, raw markdown at <a href="https://github.com/rmflight/blogPosts/blob/master/reproducible_methods.md">https://github.com/rmflight/blogPosts/blob/master/reproducible_methods.md</a></p>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-29311545193820040082013-07-11T09:24:00.003-04:002013-07-11T10:21:00.789-04:00R Interface for Teaching<p><a href="https://twitter.com/intent/user?screen_name=kaythaney&tw_i=354635159447941120&tw_p=tweetembed">Kaitlin Thaney</a> asked on Twitter last week about using <a href="https://twitter.com/intent/user?screen_name=ramnath_vaidya&tw_i=354599459868508160&tw_p=tweetembed">Ramnath Vaidyanathan's</a> new <code>interactive R notebook</code> <a href="http://ramnathv.github.io/rNotebook/">1</a> <a href="https://github.com/ramnathv/rNotebook">2</a> for teaching.</p>
<blockquote class="twitter-tweet"><p>Liking the look of interactive R notebook by <a href="https://twitter.com/ramnath_vaidya">@ramnath_vaidya</a>. Any success stories in using to teach? <a href="http://t.co/wmVuFM2Rst">http://t.co/wmVuFM2Rst</a> (HT <a href="https://twitter.com/_inundata">@_inundata</a>)</p>— Kaitlin Thaney (@kaythaney) <a href="https://twitter.com/kaythaney/statuses/354635159447941120">July 9, 2013</a></blockquote>
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>
<p>Now, to be clear up front, I am <strong>not</strong> trying to be mean to Ramnath, discredit his work, or the effort that went into that project. I think it is really cool, and has some rather interesting potential applications, but I don't really think it is the right interface for teaching <code>R</code>. I would argue that the best interface for teaching <code>R</code> right now is <a href="http://www.rstudio.com/">RStudio</a>. Keep reading to find out why.</p>
<h2>iPython Notebook</h2>
<p>First, I believe Ramnath when he says he was inspired by the <a href="http://ipython.org/notebook.html"><code>iPython Notebook</code></a> that makes it so very nice to do interactive, reproducible Python coding. Software Carpentry has been very successfully using them for helping to teach Python to scientists.</p>
<p>However, the iPython Notebook is an interesting beast for this purpose. You are able to mix <code>markdown</code> blocks and <code>code</code> blocks. In addition, it is extremely simple to break up your calculations into <strong>units</strong> of related code, and re-run those units as needed. This is particularly useful when writing new functions, because you can write the function definition, and a test that displays output in one block, and then the actual computations in subsequent blocks. It makes it very easy to keep re-running the same block of code over and over until it is correct, which allows one to interactively explore changes to functions. This is <strong>awesome</strong> for learning Python and prototyping functions.</p>
<p>In addition to being able to repeatedly <code>write -> run -> modify</code> in a loop, you can also insert prose describing what is going on in the form of <code>markdown</code>. This is a nice lightweight syntax that generates html. So it becomes relatively easy to document the <em>why</em> of something.</p>
<h2>R Notebook</h2>
<p>Unfortunately, the <code>R notebook</code> that Ramnath has put up is not quite the same beast. It is an <a href="http://ajaxorg.github.io/ace/#nav=about">Ace editor</a> window coupled to an R process that knits the markdown and displays the resultant html. This is really cool, and I think will be useful in many other applications, but <strong>not</strong> for teaching in an interactive environment. </p>
<h2>RStudio as a Teaching Environment</h2>
<p>Lets think. We want something that lets us repeatedly <code>write -> run -> modify</code> <strong>on small code blocks</strong> in <code>R</code>, but would be great if it was some kind of document that could be shared, and re-run.</p>
<p>I would argue that the editor environment in <a href="http://www.rstudio.com/">RStudio</a> when writing <a href="http://www.rstudio.com/ide/docs/authoring/using_markdown">R markdown (Rmd)</a> files is the solution. <code>R</code> code blocks behave much the same as in iPython notebook, in that they are colored differently, set apart, have syntax highlighting, and can be easily repeatedly run using the <code>code chunk</code> menu. Outside of code blocks is assumed to be markdown, making it easy to insert documentation and explanation. The code from the code blocks is sent to an attached <code>R</code> session, where objects can be further investigated if required, and results are displayed.</p>
<p>This infrastructure supplies an interactive back and forth between editor and execution environment, with the ability to easily group together units of code.</p>
<p>In addition, RStudio has git integration baked in, so it becomes easy to get started with some basic version control.</p>
<p>Finally, RStudio is cross-platform, has tab completion among other standard IDE goodies, and its free.</p>
<h2>Feedback</h2>
<p>I've gotten some feedback on twitter about this, and I want to update this post to address it.</p>
<h3>Hard to Install</h3>
<p>One comment was that installing R, RStudio and necessary packages might be hard. True, it might be. However, I have done multiple installs of R, RStudio, Python, and iPython Notebook in both Linux and Windows, and I would argue that the level of difficulty is at least the same.</p>
<h3>Moving from Presentation to Coding</h3>
<p>I think this is always difficult, especially if you have a powerpoint, and your code is in another application. However, the latest dev version of RStudio (<a href="http://www.rstudio.com/ide/download/preview">download</a>) now includes the ability to view markdown based presentations in an <a href="http://www.rstudio.com/ide/docs/presentations/overview">attached window</a>. This is probably one of the potentially nicest things for doing presentations that actually involve editing actual code.</p>
<p>Edit: added download links for Rstudio preview</p>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-67400934367455688922013-06-05T14:09:00.000-04:002013-06-05T15:20:51.292-04:00Tim Horton's Density<p>Inspired by this <a href="http://www.ifweassume.com/2012/10/the-united-states-of-starbucks.html">post</a>, I wanted to examine the locations and density of Tim Hortons restaurants in Canada. Using Stats Canada data, each census tract is queried on Foursquare for Tims locations.</p><br />
<h2>Setup</h2><br />
<pre><code class="r">options(stringsAsFactors = F)
require(timmysDensity)
require(plyr)
require(maps)
require(ggplot2)
require(geosphere)
</code></pre><br />
<h2>Statistics Canada Census Data</h2><br />
<p>The actual Statistics Canada data at the dissemination block level can be downloaded from <a href="http://www.data.gc.ca/default.asp?lang=En&n=5175A6F0-1&xsl=datacataloguerecord&metaxsl=datacataloguerecord&formid=C87D5FDD-00E6-41A0-B5BA-E8E41B521ED0">here</a>. You will want to download the Excel format, read it, and then save it as either tab-delimited or CSV using a non-standard delimiter, I used a semi-colon (;).</p><br />
<pre><code class="r">censusData <- read.table("../timmysData/2011_92-151_XBB_XLSX.csv", header = F,
sep = ";", quote = "")
censusData <- censusData[, 1:17]
names(censusData) <- c("DBuid", "DBpop2011", "DBtdwell2011", "DBurdwell2011",
"DBarea", "DB_ir2011", "DAuid", "DAlamx", "DAlamy", "DAlat", "DAlong", "PRuid",
"PRname", "PRename", "PRfname", "PReabbr", "PRfabbr")
censusData$DBpop2011 <- as.numeric(censusData$DBpop2011)
censusData$DBpop2011[is.na(censusData$DBpop2011)] <- 0
censusData$DBtdwell2011 <- as.numeric(censusData$DBtdwell2011)
censusData$DBtdwell2011[is.na(censusData$DBtdwell2011)] <- 0
</code></pre><br />
<p>From this data we get block level:</p><br />
<ul><li>populations (DBpop2011)</li>
<li>total private dwellings (DBtdwell2011)</li>
<li>privale dwellings occupied by usual residents (DBurdwell2011)</li>
<li>block land area (DBarea)</li>
<li>dissemination area id (DAuid)</li>
<li>representative point x coordinate in Lambert projection (DAlamx)</li>
<li>rep. point y coordinate in Lambert projection (DAlamy)</li>
<li>rep. point latitude (DAlat)</li>
<li>rep. point longitude (DAlong)</li>
</ul><br />
<p>This should be everything we need to do the investigation we want.</p><br />
<h2>Dissemination Area Long. and Lat.</h2><br />
<p>We need to find the unique dissemination areas, and get out their latitudes and longitudes for querying in other databases. Note that the longitude and latitude provided here actually are weighted representative locations based on population. However, given the size of them, I don't think using them will be a problem for <code>Foursquare</code>. Because areas are what we have location data for, we will summarize everything at the area level, summing the population counts for all the blocks within an area.</p><br />
<pre><code class="r">uniqAreas <- unique(censusData$DAuid)
summarizeArea <- function(areaID) {
areaData <- censusData[(censusData$DAuid == areaID), ]
outData <- data.frame(uid = areaID, lamx = areaData[1, "DAlamx"], lamy = areaData[1,
"DAlamy"], lat = areaData[1, "DAlat"], long = areaData[1, "DAlong"],
pop = sum(areaData[, "DBpop2011"]), dwell = sum(areaData[, "DBtdwell2011"]),
prov = areaData[1, "PRename"])
return(outData)
}
areaData <- adply(uniqAreas, 1, summarizeArea)
.sessionInfo <- sessionInfo()
.timedate <- Sys.time()
write.table(areaData, file = "../timmysData/areaData.txt", sep = "\t", row.names = F,
col.names = T)
save(areaData, .sessionInfo, .timedate, file = "../timmysData/areaDataFile.RData",
compress = "xz")
</code></pre><br />
<h2>Run queries on Foursquare</h2><br />
<h3>Load up the data and verify what we have.</h3><br />
<pre><code class="r">load("../timmysData/areaDataFile.RData")
head(areaData)
</code></pre><br />
<h3>Generate queries and run</h3><br />
<p>For each dissemination area (DA), we are going to use as the location for the query the latitude and longitude of each DA, as well as the search string "tim horton". </p><br />
<p>Because Foursquare limits the number of userless requests to <a href="https://developer.foursquare.com/overview/ratelimits">5000 / hr</a>. To make sure we stay under this limit, the <code>runQueries</code> function will only 5000 queries an hour.</p><br />
<pre><code class="r">runQueries(areaData, idFile = "../timmysData/clientid.txt", secretFile = "../timmysData/clientsecret.txt",
outFile = "../timmysData/timmysLocs2.txt")
</code></pre><br />
<h3>Clean up the results</h3><br />
<p>Due to the small size of the DAs, we have a lot of duplicate entries. Now lets remove all the duplicate entries.</p><br />
<pre><code class="r">cleanUpResults("../timmysData/timmysLocs2.txt")
</code></pre><br />
<h2>Visualize Locations</h2><br />
<p>First lets read in the data and make sure that we have Tims locations.</p><br />
<pre><code class="r"># read in and clean up the data
timsLocs <- scan(file = "../timmysData/timmysLocs2.txt", what = character(),
sep = "\n")
timsLocs <- strsplit(timsLocs, ":")
timsName <- sapply(timsLocs, function(x) {
x[1]
})
timsLat <- sapply(timsLocs, function(x) {
x[2]
})
timsLong <- sapply(timsLocs, function(x) {
x[3]
})
locData <- data.frame(description = timsName, lat = as.numeric(timsLat), long = as.numeric(timsLong))
hasNA <- is.na(locData[, "lat"]) | is.na(locData[, "long"])
locData <- locData[!(hasNA), ]
timsStr <- c("tim hortons", "tim horton's")
hasTims <- (grepl(timsStr[1], locData$description, ignore.case = T)) | (grepl(timsStr[2],
locData$description, ignore.case = T))
locData <- locData[hasTims, ]
timsLocs <- locData
rm(timsName, timsLat, timsLong, hasNA, locData, hasTims, timsStr)
.timedate <- Sys.time()
.sessionInfo <- sessionInfo()
save(timsLocs, .timedate, .sessionInfo, file = "../timmysData/timsLocs.RData",
compress = "xz")
</code></pre><br />
<h3>Put them on a map</h3><br />
<pre><code class="r">data(timsLocs)
data(areaDataFile)
canada <- map_data("world", "canada")
p <- ggplot(legend = FALSE) + geom_polygon(data = canada, aes(x = long, y = lat,
group = group)) + theme(panel.background = element_blank()) + theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank()) + theme(axis.text.x = element_blank(),
axis.text.y = element_blank()) + theme(axis.ticks = element_blank()) + xlab("") +
ylab("")
sp <- timsLocs[1, c("lat", "long")]
p2 <- p + geom_point(data = timsLocs[, c("lat", "long")], aes(x = long, y = lat),
colour = "green", size = 1, alpha = 0.5)
print(p2)
</code></pre><br />
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAM1BMVEUA/AAA/gAA/wAB+QEB/wED8wMD/wMG5gYH/wcMzAwP/w8ZmRkf/x8zMzM//z9//3////9c+nxPAAAACXBIWXMAAAsSAAALEgHS3X78AAAfRElEQVR4nO2d6YKrqhKF08n2emzbxPd/2uuszFVQIIRaP87ZUaCK9TkgDv0YWVXqcXcCrHvE4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDj61hGO5OQScGH1/DRfLvQV6ZSgyeUhs6BaURtQl//A2BwZPKQdJHkTJl8FRywMuNPIOnkj/ZW9AzeF/JPCKCj8GewXtKAyQuemL6DN5HCpCdSjHcGbyvVPJD4CAuJXYGj9aVQDTIkaHPYvBQJaMcH/osBg/QXcxjTt0xeIe+EfosBm/RF3FWxOCd+k7yDB6hxPj5HH+/EiC/UFe3AXoxeI0U0/WIABiR0DUbQSwxeEWq95YlUI6OwtrqUcXgRbn8h7P0Pz8k6SiDP2VloFkVh3uizjL4Uw4MUK6uJnPAzuAVWUmcC5QiSnE085TYGbwsGAw9dldL+VAfGfxFWCIuWjljZ/CniLjgcN+BfBWDXwTCY2GEx30zdwa/CcTn5KQwK448gxcFoqTlVgTuU3WCV4xH49ItLop8teAH3yO1vga2ldvx1wn+ero+fiJQwRfnyr1a8PIYnYY88K5d3EcsYKoXvE6IvRS+RRgauFkM/hQIe+j7FPd1TxSDP+SBsEzmixi8WRCQJTJf9NXgg3wH4iwR+qxvBh/uPoQnYAPJkX8t4HG+wxmCSecG/5vBj/LjUjDrsbvuuSz3vfyqLwc/C80Cx91W5VhD2iESVQB+NB/09Uhs0LX7dkHzNofKBw/w1IzARMTODoI5d/SFg4d4akNgq26q5oudwRMJYqqLgam+pU75zBeVCh7qqYODBQ4t+OzIlwoeLRMHG53rCg133EaQrqsgVQN+kZaDlc0G+LpkXz7Cwcfsk6e+BLzHc+/ybw9Axe7uY3ngZRM9nJWr+MIpGXuR4DU3xaCVz0bIkimO+KbSwO/kfbCHoxBbuCRQHPfywG9yOxxyxnaXuf4w7/rIDFLqK8Cbivg2aG5FLmPnnjP5UsEDZmt9fNdwt5MXlxWDvQzwvj6SWA+FvBcrgvqYOfjQPYiIvCMrObsSsGcNPvzAGYdAkUd2RfmCz9JW65E+v3QtyhV8lk7CseeQrV2Zgk/sJSgKAnsB+LMEbzMxhp8OXNgbsEWQzxG86BzUwyCfrbwYfBJJxgEtNJbRLB2k/ytRj5sBunVmxCVxzw+83k1YLcMaXdlROURoNzc7fGGNXArd87TKDLzeWKmEuZapxeOHEsQSXV9A267ahKujtysr8HbXbabC/JZ3UmMNWxZqc2pNexpZKCPwDuxnCUtlS9Py/6013LmUrgzBawfoCgn98QAUAnIu/lrgu7IHrzk6a6GAODm3Hk1R38741UumjMCfOk0bLNz1Y/Ljl75lMHjHycPVg9y55wn+kGS8lZoM3joWAIAv6drMQzmDFxzX7/PW7ULTHGKPBw3ty904Mgcv/7aCszIVF5g2HWO4UT18I7ejzJQzeAsHkMztaku5tiOhvE/YrJQ1+FN46uftPGtjo/yNC0BIZz7x/QhXCeCxvgv222Z49Qd/fVSconsSrALAB3LX3V9XCelrfi/3EsCvMpjq5m5pykTZ0fQ3cC8HvEZ6z4Hmi6W04L6Ze7ngtXaHeE4Hvgj0ZYKPYjQVeJJkoqtA8PGMlluTwxhJF0i+QPCp50mPMJZdvDjuRYIPvHNWYNwIyg081NavMP9O3Q9ef+xkRVaW4IWp1Psy+2plCn6/e8L7fyzdDx5wuRwxtiWdeFFzUAbgIRMlkaK6E4kQOA/lAB40RUYd0HWj/tvZFwPew3/8g7aQwF+ySeQAHsQd67OxirFdUFT9qsvvUjaJksDD7bRWMbXqzV1Y4pXvDcoAPJi7D3jE+C2cu3q/Jl/694Onx35tFBDOloilqrYO6l2dW1UO+KM4plVASFsilkTNNcogfzt4BHaAkWcRS1GlXWMm5kSN2esWhjkUR7eDh17EA30Ua0jr1JDbT1Mixjwt+RdytC8BvFxoq3e2oG1NOSucNfVA0Nxh2TJ4rfDYz8P+pRFte5pIupjGVIyJ2jMvY5fPHbymzGXJtRVNe7pI8Ot4c572zOMYRa28wetKaPfq859mBPYw42XTcOZpa06qReQTuW4G7+RuLzAIe5/SIjyYsN6dp6U1Uy1a38KVMXjXerWQUgMcTVzvztPSnKkStXWBuhe8C6mTvAraajYsGCBPc3vLYu1qYu8CdSt4K1FnEX0tu9f2aEZCjgq6FDQFSL0LVabg3SWM1VxeW8KB61gaO1uR8qGzjUR3grcDtJewVNQSsoe9roFUMedmWkHrHIGyBm8uYKvo9NuMBXRXZ62Az82Yzy3Kc1RvXeky1u20CQe0/OBNnto/f909gWM753o6C3DaUARWXh8ImFw2uh88aswM8BZktna9uYqBIj4548kkuXIAj5glx8sa0hDImeCJEZmLOaHUyhG8tMpQCmy2LaghCUeCZynfjGyJpVEW4EHXuj4eW9uU11krGZr2TciRWgLlAd58BX1d7yVMAvZamqb900IlF0OZgLcOehJ4C6wllwjKjPd4qwK9pQVPkA86tUjKHXwidxHVgjO6hrgPfu7gCXz2ihIhD00AYHYxVAF4iLfuSgR5SO3vYe5hnz14Esd94sTIQxdjuAd9/uA18nMY22Z4VEhWmBwpVST4WT4m71VhzYXH9M4wgdKDJ+whoa+wCphgqOTcCVLrhj2epotoX9Ezg0HB8RlSmILQHXt8SCeRbkLNJSov1UUmlhR+GvAXEx2O2tsIF7zx4GTgWW/MXUFJlQT8xUjFVUBnQeaBBQ4B7plUR6gOzwoelETRwV9t0LgK6SvUPKjFsBjoTi7/OBdhcr+Uw4QNUVzw197ofb38y90MiaSWTUHcfUN64JFdTMUEf+2NoYfivzCnYLT2puR29WHg3dMtFmMhM0wjH/Cugc9R7OyPqZvSv4zdB+PF2XpdoTkbIR4JEpcLPvkmF1d+4G2pbiuFLpk6ayhhCBmgsyFNq+tiw3DMYYO7GCh9e/UY8gZvSndbcSnh7jTICad5LmfNt1q3f+tDuW2wlhpBHriboVYQeDnTY+GxEk4GZgK4qm6JuTFoB225qOv3UojErQFJFQreNDga/OarYSaAqjlb068zVILM3MtrhZq+fY2lcPAXc+Slzs4CBMtAW8PalnGVfgXsno2wDpGyqZ8RRQIeMz3pIVAG2sLgVYNp61WvOy2NCGvAGVv6GVFE4AflyE67HYyj4TOGAOeUdfqTrrHly2KHEXIMoVxmR3s68FvfxiHanm85iAtrtyKXf2vX6Ovr29WGcJzkhXJ246y144gUfAIZU9Dkp+Zt7Yupf9clDhvCnIO5T6TSwBv2HW1uqN6Y+zfqwZtSMweyewfLlkzlgQdOo65Gw+y0D8RGLXhcXmcU355R67vAXwttJ2dnX5zd08/0ILI6qkDcA7RDogLBOyfTzn+COoLqnLEclX+IdoJUHHhXYmKKwJ4A+2YzwREG7J8jYzIVBt4jQ0g5YNeOQrihnVqAoo+BKgu8b3LusqB+HdV8wQ9DNuf5LwJv9dTVEVi/9mraok67zkGFK8qwX5XE0xeAF9egKp/XarBu7dUw4KUC4FiRd/yywNvkmRwK/GC1wOUW0kIPMgh9D3jzXRZgZXgMz/OJh4cedID6IvAD9EwdUtHqgMMtHw898MD0beB97w1C+2V3wOqWj4cedID6LvBpdnl7K1q7vE30wAPTl4EHj5i1VYNLkZu4FvVg5BSDJ45O6+FS1AORW98F/ub8Ilnogcit7wIPmhAljaYER1mImDWixv994H0regWTZ32RDkLcPIp4kLLoy8Cn1tUNvIk4Oz1IWcTgw+S3O0qjNnAsQjH4QCmPWUD901pqs9eDlEUM3kOyDb72aZZZ7PUgZRGD95DgQ4h7moVmfz1IWcTgfSQ4EeCdssTksAclhxi8l3ztVprRN6yu9A1oFIP30+EFsXdnOf2xgEwM3kubD2Ms8tKp3gOSSwzeR7sR435JTmKeraAHJrsYvFNqp9ff+94eh7x5DY0YPERSv7cfhx9k7ukK4g8qIDF4mK79Pkzws9wRRSnnF8UlBg/V0fPDAx+/IVGkoj5h3GLwYG2dv3iAPAojoghBoojBI6T1AGIWwjTVaA9CADF4pFQPiL1SqnsQAojB46V4QGuV3IIHIYAYPFoaC2id8oaJEYNHS2cBrVP+OOFi8FjpLaA2KgApTAweKZMFxEYFIIWJwSNltgDgFMK8AKYgMXicbBa4rRrHEfoEvz9SmBg8TjYLbF55+OdBBiEGj5LVAr1Zvv55gMGIwaNkt4DUQA8wGDF4gNTbJaZyhA56gMGIwavSjcb1t8r3tdJDGkQWeqCBi8Hr5JiLlwsLywgt9GADFoPXS+imvf/yQk8T+16TQzwxeJPOnqrdv7qg2OJpogqe9/hbNDqeojyLCYvUckAjoTcAiMTgrXK6AHEL6GNa8gzeLpcLVp+QNqoL8WzAYvAOWV1wG4XxUVqKflUDJQbvktEFiFE4I8WlGCR4MXiXyC2/GKk9tKeIzODdIrfc5qWwMGpgBm8VxhIPJ1U3haWeLYLE4E1CG+LhpMbPc6l3eyAxeKOwfng4OWr+gpK4JpoYvFk4OzyMXGorlgoroqk48Jop7SjC2mFejfYUHjpADN4gpB0e68yuwmP7qzjwqYSzQ14iOSWWdvoKj+0vBm8Qzg5lieyU8NvpLBKIjxi8VkGeng3gm6eIDxKD1ynM07MFnwDh8UFi8BoFejoo53hchPAMAGLwGgV6OrjBg57gCEzDLgavUaip6nN4EYIEisFrROivfxCeuUsuSn8D4vDducQi9TdVHLQYvCJKe5MFQovByyK1N2EopBi8LFJ7U8bCicHLonQ3cTiMGLwkUndvCQoTg5dEae4tQYEiBt80Tbj3d4rY3nuiQkS9xxfOncHbFNX5e0Xu7z1RAWLwgsj9RUVNuQUweEHk/mKiJt31fcAfikzhBlHZirOIPCpADF4Qla04i4x1ydM5FQT++8gTuQq2yFGVPp1DYeC/jjyNqSiHrDXp89kVCP7byJN4ijbIXJM+n10MXhCJp3iDjPXo89kVCl5624+cRGJRWGq0Bxc2UkK7gsFLb3xFgJFSwW6YzMGHjZPQIWrwhZMPdsNkDjpsrIx2hYOXXveih5FS4W5ovfEIGyujXWTgpZ+FKtwNjTVeYSNldIgAvPyuVxQiaURghp8TtrrESW2iAC/fVaIHkkoUZvgYYatLnNMuEvCSYiBJpJt8sNclTWpXDPAFk7/JhlE+akbMalMU8Ewe5cIoT8/GTGpXHPAFk191iwnGiiTZSGLwWt3igqkaTTKSIoEvnfxNLugrESUjKhb4wsnf5YKuSpwnMhi8Vne5YKhBlc5FDF6vzGygSudUNPCFk8/MBrJ0DjF4vTLzgS6dXfHAl00+OxvoElrF4A3KzQe6fFYxeIOy84EuoUURwZdNPjsfCBOaxeANyswHwnRWxQQfn3zEP1OSmw2E+Sxi8CblZgNdPouigo9OvuvitX30IRMXgtMQxeANunYgExtC0xAUF3x08vH+GJmQfCYuUBDZxeBhysIFIiiLSgcf8SQv6XYXyKAsigw+NvmYoztZt5pA/kms2ODLnsWRdKcHlExmRQdfJ/kIL5URY4kP/pvII/pM7gAxlQTgsR1vpfN2spE7QPAO+/c/MDZQScDjet624u+cwAPdFwsmDQ1VGvDfc7iHdjdK5ymJJAJfF3mpVNLYUDF4tCCdjdN1SiB5gk83LeM1fojZdcLAVmUGfhvIdanGc54Dxwg9jxHapjzB5y/qjtMHdigz8AXppn5TAWHw3rqp20RACgOf1akgXbeRUSHKGfzroSzKDPz+f8peu4OSAMkZ/GMDnxNsvQg7HRAOpVTgA0zoU+3mYWEou+wTDamc9/jUCtvA0nY5GMgt4LM6U1PJ1uE48YLoM3gqmbubJCJWfFuWSjd0N4RIWvApH4pNLU130wbFqQzwjnNDFtuTrr9pg6KUBvwn8KRuBd/nMWYQOnxDSKTKAG9TFtQHmULygFilncAJYpTFAd0sXX/ThUMr5nfuxuNSc9h80O6d/frYhXuj0FbOZH+/AbwuKlyxP3C4/dNmxAq+ebbmIpbKdYIX/fVREvD7T5sd3RN+JPc/6Ef/koLU/Xix9FHhSgae0om2ld+2AesbwAvN++KJ/IHD84fjeO+SAOz59DsvRJWm9/ECXX54ok8FfiY/+tvRX567bZoJfD8k/SoCQJreRwtjDw1S5A8cHv8cxQ0VIWX41s/gl38kewobIk3vI8VxxwYoEfhx9N7h1XF7/3MdCM7rG8uRP9W4X9P7GNL2xgNOKvDrDh/sxtrt10taJCzY1DQ2q+il6X0M6bdjPJzY37kTf4a6gdl7D/CJJPc3eXCcYn/SVP5tEQJq1/feF3SxpIxm0wbHKu0eb5V2ckW/NbTt0Ma84eejUdrQPZvxerTUA01k8NJP/KW824ZrCfUKwFiffsy39/DsrZd88vJAk3iPpzkCCnv7laB60Eh4wbd28drbtKFxiv6BQ/kXRT/lr+RkIrnrt0WGKC34kcCOfO7HyaLuqW9gkOJ/2VL6OQQd7/sZfLZPZKhdvSUwSKnBj/5zt7Nm5v1jvkK3Hu5vmsuVOnpbZIgSfNJUu9BTy72ZZabeCr5ZjwmpzwpEnQyPDFCyZ+5OmQ/1QFA/6uvTotr1Jo5umj/qpiD1M6HwFG4Ab3YEhsVdqtvA+0bwFqybsQODlBw8wY2abIf1MT5kig0L1R2H+kDlOqZfRNdNr6BwlQc+24u5VVTd9AmJUVaDu1nOA3nOR/pZRzeTR0Qpuz3e772KjATrZoSAOGU1qodp3eU1N+IC2yUSRReR4QYP+kWAF5mawHt7Ryu/LgYG6z9YCMWB77UP2AGU6o6eVw+9I63Bxk8J4L07ujxHPz9c7VO5uzyrlctZIUiCn3gKme/xvfjIxfxrfpvC55LO9qCOqaCnUuzyopt4CHeAR9iiQeQLPiBkjhK9xDO4BTzwSRzDg5bGOzA16bRS+AXXPeBnWXrV6cft+9qAe67fsrWILvrYnxr8JU9zt5Z92ng4DznOfwl40c8iwF/OSeZ+PXTj9hP3QT71yzK5SLbTh8Nto3rsyPfyauwJ3n5t3s/Hjty2DYJDjuylF4T04OUOhBth0Lx95PcFBYKTjeymF4Y7wdu/leB3HIcOAAo+1ytmemG4d+ZO+rlq2ydaGTyMlRX82cS542X4uLbJMoOXXhTunbmbp5iVbq9MVBqGKde+AXPTf2bPBP6mK4AeAXKp4AfhpgmcTe2f8VDfKY/SXnfS6+Lnj6/HC/GpvoHwTeAR91uWCn4n+nvBdx/jZ25jDctm2N3r/Pc8BZzXoR7rYzn348fLs7bjdrSX8XcP237c9/PVndexeIbdXG/sbp/QOtde/3+DRJ/kBcpaTSWQbgO/Zj0f61fwUIjbbO784StgHUex/jqI9ABPPRSQjFKWSCt1lSC6ea6+b49/Ou08pujXu7PgMwHgBYwgdrTgtTbZLLSst+k28ELSGjdfCthpL+zX7xpSj7pyup7TG2W3EP/g1Z17/JH4lPXn02+cj92zMdPNcfKFams02WTxbzMEaf7d4KdhWv87ju/pnP1afyuW5shZFUGWvYnevNKw+NQHd1V/P/j5yYoF/LNpLnflDt4k3A2fvc3qo6hd16++LAduwIy8/sNPQPPvBL9vso9pl3+uuvSju3YodLc3Tc6J4Ale9gjIc3xvtnyAO6/8+DGo0q5bwY+b01NPPwt3eQPu+6SHegd4wCfogsALnghL9NZdpiOWDXt+jhhsfA7g+/7mJE45sNLdRFa3kCW+dO/CnuvlBYPl+Dh/JATc05vBrx40f/dmcZEdl7sEeEhiAC+9M4TOFNzRu/f4Zdaubd7ugolkpeUuAz3W67ljMFozdOt28EsPuj+PKYhYMs/nXMr40T3X9NKVxtTm+w/B0biFQXuZAfhxflb+9+4krnqrYDvx/Kk3vXmINCy7/7Rqn4nceP39aSahTRka2x4NnxlTlAP4cfyZLuUzk2SonKDW9dcDPqifS76e+0MHU4vqo2b2Qa+xZVj/8gA/5jO6O+QBXgbrUvN8dQcrTRO2818g91zAZymbnS7mqO8sqw06Z9+NoaGdY/AWDeecjm6dEfwxcoPd9hvnyTqxfu8iaAo9HyRA8Bk8RAbwzms34P3eCfyFXfPrvsSxbkUgMXhfQcDDNAog5yeCIMGNjZU0qi9UJNjn5w7f8qJ+eUjBI/LI4BOIBPvMuJMPHl03bQ1vM3mZ9rU92J0PBh8gKvCDctZoXr0N4FL6+mzOBX7XDs9/7twZfIBMLDHP8PVCO+eTZ9bLePkssI0Tlv9085/edefO4ENkgol5OPuz35F7NdKTFSJd11B/2EeJ/Q9kGpTBhwi+Yxv1GfsL+KvEUB/HqXu4zANBUmfwQSIgfzlfi+8Eom5YbmO6D/iBWwYfpGDs60Bta+3Tiaswiazge3BNBh+kMOr9/rTn3tw5Kny9UOCHY3TP4JMoiHv38+y6/gLq8uDV/EIvLo/1/9rpZZ0YfJBCuPev57O/7vDCnZp1ueauoDslUOYMPkxGqu4nsbvX/h7B1tb7fak1P58zHfvlhxEZfCaSYB4fYHN8J3m+cdt227c89rbe78su3y1Td51AcT4kADICJc7gg/RP+HxD3wrvhAgSZvOWxzSO13gu7QlbzTKpJ8zSQcADxeCD9L8fEZXpW/r9/EzNyX55CXx93lIALz5W303I++Y6/BsYfD46we9XZ93yly6lT+0PwpJ9A+kllJ08hfNu+uWTHUcsqrQZPIEG+QNt8xHfeJafVz2b/ngx8NqS+BJnP+3z72kravrzKv3v338kOTN4Cq1fcFJfXZUetm3b9Yse3fJU3fyBvWmEdz3S/7aXWs3z2fXje/w8t+nbZcXz+T+SlBk8oTS4hf2+ndRNV3Gv9QAxU+8E8O/tpdflYLC+Nb6sOHb4frr4pznaM3g6HTdYr7jFO7RN27ePRzPfuO10D1EP5wCx79rjGxnim3UkyTJ4Eh1ElDO68E29oX88uvnt5sf53U6lnedPu288TSMUIOTO4Kl0IJHAd+KbFet3P9rL93eVVuanb7aR4jQoWMb973Wnh8/Eu8XgqXS55FJfoNo+zPxqF/CPR7v/vcxBbmTfXpavW+zXh9NFQtt+/jrCT0gweHKdoDfNkzXNDLttXz8L+WbfNBSSlw3lfCd2nttt2+Hvjf+DkkYxeHJJO/s8lu/mIfp8zG9W7vNTVttHs7WV2+XK/fwGUEt5dl/F4Mn1mXZO4e/fdt18hP9pm3ae05v/+Xzsg3e58rq/P3+2puZn7s9pYMosGTy92umMfAE/z8nPX/RqXm33eDU/0/9f7fz9Fx3J/a+o7r+nK8TLV3sJk2TwhLoO7wTw7fPVz9My00l+OXTPQzd5tnbVZ/57GePOvV8v5yPs8AyeUufAfpu3P7+C3k0XZt2E/7pBaEl+/s7vHL4ezeUWAG2uDJ5SB/ht2nW/5zKd3LtpkAd5tenQ52+qNbXTwJ+cxYjBU+rY5YUPG01n/PnvabSoJ+U/+0Fj+X47faoMnlIr2v/+03wBp+9w9M5HO+J8+JPBU+rdza+4vX565Qms5tFhZl/ex+2e5/NflG8AMnhK9Y9X//73mu+kb7OtO8Cf/7lfe7zoHBw+n3G+CMbgCfW3XK7NEzTd+phFP4/OFoJIevvg4A38vgVeDJ5Q7/VTV838sFTfvJ5t325PZSG/1XucH+J96ZXBU2rbT5/PRz/M92OWS7ppO/jzA99G/LYzg6fTu+vnG+h993i+5svv5/rcXIv+XuvxJE+UNFcxeDq9+wX8eqx/NOtzcz8+R+ttRB/zk94MnlDDMlfXPibwr4n9cqj3ugpn8GVp2dv71/Ox3Hx9eX+S+7eJfaBn8JSaLt/aZr6QW/+iln9Dv038P9TD4OnUzQ9XDU2zPF4VdpiOfJwfGTyllrm67hm6v69txT3QM3hKLbO0K/bgaVbCxyr1YvB0mq7gt6dos/tDK6oYPJk+M/jtyfn4e2yoGDyVPsttmddrO8c/f/P5W3o6MXgivSfq3XYFv4rBf72m4/rz+dN10wn+9eq6punzP9EzeBr9Ptvl+2XtY0Lf9z8MvgZ9Pn+/7fomxMT91Q+fz39UX66IJQZPoM90Ql+et/h5dvuc22/g3F1sMXgCvee77v32avOm3K/nGDyB5vN51zj/SGBWYvDh+jeDn3b6zPdxUQw+XMvN9+YZ+T4qsRh8uJZvHbQlHedHBk+h3+dPWxp3Bk+hv7+muTsHrBh8uN55z8rrxeDDVdRofheDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlIx+ErF4CsVg69UDL5SMfhKxeArFYOvVAy+UjH4SsXgKxWDr1QMvlL9H99RQs4MVogaAAAAAElFTkSuQmCC" alt="plot of chunk mapIt"> </p><br />
<h3>How far??</h3><br />
<p>And now lets also calculate the minimum distance of a given DA from Timmys locations.</p><br />
<pre><code class="r">queryLocs <- matrix(c(timsLocs$long, timsLocs$lat), nrow = nrow(timsLocs), ncol = 2,
byrow = F) # these are the tims locations
distLocs <- matrix(c(areaData$long, areaData$lat), nrow = nrow(areaData), ncol = 2,
byrow = F) # the census centers
allDists <- apply(queryLocs, 1, function(x) {
min(distHaversine(x, distLocs)) # only need the minimum value to determine
})
</code></pre><br />
<p>From the <code>allDists</code> variable above, we can determine that the maximum distance any census dissemination area (DA) is from a Tim Hortons is 51.5 km (31.9815 miles). This is based on distances calculated "as the crow flies", but still, that is pretty close. Assuming roads, the furthest a Canadian should have to travel is less than an hour to get their Timmys fix. </p><br />
<pre><code class="r">totPopulation <- sum(areaData$pop, na.rm = T)
lessDist <- seq(50, 51.6 * 1000, 50) # distances are in meters, so multiply by 1000 to get reasonable km
percPop <- sapply(lessDist, function(inDist) {
isLess <- allDists < inDist
sum(areaData$pop[isLess], na.rm = T)/totPopulation * 100
})
plotDistPerc <- data.frame(distance = lessDist, population = percPop, logDist = log10(lessDist))
ggplot(plotDistPerc, aes(x = logDist, y = population)) + geom_point() + xlab("Log10 Distance") +
ylab("% Population")
</code></pre><br />
<p><img src="data:image/png;base64,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" alt="plot of chunk percPopulation"> </p><br />
<p>What gets really interesting, is how much of the population lives within a given distance of a Timmys. By summing up the percentage of the population within given distances. The plot above shows that 50% of the population is within 316.2278 <strong>meters</strong> of a Tim Hortons location. </p><br />
<p>I guess Canadians really do like their Tim Hortons Coffee (and donuts!).</p><br />
<h2>Replication</h2><br />
<p>All of the necessary processed data and code is available in the <code>R</code> package <a href="https://github.com/rmflight/timmysDensity"><code>timmysDensity</code></a>. You can install it using <code>devtools</code>. The original data files are linked in the relevant sections above.</p><br />
<pre><code class="r">library(devtools)
install_github("timmysDensity", "rmflight")
</code></pre><br />
<h3>Caveats</h3><br />
<p>I originally did this work based on a different set of data, that I have not been able to locate the original source for. I have not compared these results to that data to verify their accuracy. When I do so, I will update the package, vignette and blog post.</p><br />
<h2>Posted</h2><br />
<p>This work exists as the vignette of <a href="https://github.com/rmflight/timmysDensity"><code>timmysDensity</code></a>, on my <a href="http://robertmflight.blogspot.com/2013/06/tim-hortons-density.html">web-blog</a>, and independently as the front page for the <a href="rmflight.github.io/timmysDensity">GitHub repo</a>.</p><br />
<h2>Disclaimer</h2><br />
<p>Tim Hortons was not involved in the creation or preparation of this work. I am not regularly updating the location information obtained from Foursquare, it is only valid for May 31, 2013. All code used in preparing these results was written by me, except in the case where code from other <code>R</code> packages was used. All opinions and conclusions are my own, and do not reflect the views of anyone else or any institution I may be associated with.</p><br />
<h2>Other information when this vignette was generated</h2><br />
<h3>Session Info</h3><br />
<pre><code class="r">sessionInfo()
</code></pre><br />
<pre><code>## R version 3.0.0 (2013-04-03)
## Platform: x86_64-unknown-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=C LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.2 markdown_0.5.4 geosphere_1.2-28
## [4] sp_1.0-9 ggplot2_0.9.3.1 maps_2.3-2
## [7] plyr_1.8 timmysDensity_0.0.3 devtools_1.2
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.2-2 dichromat_2.0-0 digest_0.6.3
## [4] evaluate_0.4.3 formatR_0.7 grid_3.0.0
## [7] gtable_0.1.2 httr_0.2 labeling_0.1
## [10] lattice_0.20-15 lubridate_1.3.0 MASS_7.3-26
## [13] memoise_0.1 munsell_0.4 parallel_3.0.0
## [16] proto_0.3-10 RColorBrewer_1.0-5 RCurl_1.95-4.1
## [19] reshape2_1.2.2 RJSONIO_1.0-3 scales_0.2.3
## [22] stringr_0.6.2 tools_3.0.0 whisker_0.3-2
</code></pre><br />
<h3>Time and Date</h3><br />
<pre><code class="r">Sys.time()
</code></pre><br />
<pre><code>## [1] "2013-06-05 14:04:49 EDT"
</code></pre>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com2tag:blogger.com,1999:blog-73443196978791417.post-77660313020462965642013-05-30T10:07:00.002-04:002013-05-30T10:07:53.121-04:00Storing package data in custom environmentsIf you do <code>R</code> package development, sometimes you want to be able to store variables specific to your package, without cluttering up the users workspace. One way to do this is by modifying the global <code>options</code>. This is done by packages <code>grDevices</code> and <code>parallel</code>. Sometimes this doesn't seem to work quite right (see this <a href="https://github.com/cboettig/knitcitations/issues/14">issue</a> for example.<br />
<br />
Another way to do this is to create an environment within your package, that only package functions will be able to see, and therefore read from and modify. You get a space to put package specific stuff, the user can't see it or modify it directly, and you just need to write functions that do the appropriate things to that environment (adding variables, reading them, etc). This sounds great in practice, but I wasn't clear on how to do this, even after reading the <a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/environment.html">help page on environments</a>, the <a href="http://cran.r-project.org/doc/manuals/r-release/R-intro.html">R documentation</a>, or even <a href="https://github.com/hadley/devtools/wiki/Environments">Hadley's excellent writeup</a>. From all these sources, I could glean that one can create environments, name them, modify them, etc, but wasn't sure how to work with this within a package.<br />
<br />
I checked out the <code><a href="https://github.com/cboettig/knitcitations">knitcitations</a></code> package to see how it was done. When I looked, I realized that it was pretty obvious in retrospect. In <code>zzz.R</code>, initialize the environment, assigning it to a variable. When you need to work with the variables inside, this variable will be accessible to your package, and you simply use the <code>get</code> and <code>assign</code> functions like you would if you were doing anything on the command line.<br />
<br />
To make sure I had it figured out, I created a very <a href="https://github.com/rmflight/testEnvironment">tiny package</a> to create a custom environment and functions for modifying it. Please feel free to examine, download, install (using <code><a href="https://github.com/hadley/devtools">devtools</a></code>) and see for yourself.<br />
<br />
I have at least two projects where I know I will use this, and I'm sure others might find it useful as well.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-85127203469210960092013-05-10T10:40:00.001-04:002013-05-10T11:07:49.571-04:00Writing up scientific results and literate programming<p>As an academic researcher, my primary purpose is to find some new insight, and subsequently communicate this insight to the general public. The process of doing this is traditionally thought to be:</p><br />
<ol><li>from observations of the world, generate a hypothesis</li>
<li>design experiments to test hypothesis</li>
<li>analyse results of the experiments to determine if hypothesis correct</li>
<li>write report to communicate results to others (academics and / or general public)</li>
</ol><br />
<p>And then repeat.</p><br />
<p>This is the way people envision it happening. And I would imagine that in some rare cases, this is what actually happens. However, I think many researchers would agree that this is not what normally happens. In the process of doing steps <strong>3</strong> and <strong>4</strong>, your hypothesis in <strong>1</strong> will be modified, which modifies the experiments in <strong>2</strong>, and so on and so forth. This makes the process of scientific discovery a very iterative process, often times right up to the report writing. </p><br />
<p>For some of this, it takes a long time to figure this out. I'll never forget a professor during my PhD who suggested that you write the paper, and then figure out what experiments you should do to generate the results that would support or disprove the hypothesis you made in the paper. At the time I thought he was nuts, but when you start writing stuff, and looking at how all the steps of experiment and reporting can become intertwined, it doesn't seem like a bad idea. <a href="#note1">note1</a></p><br />
<h2>Literate programming</h2><br />
<p>What does this have to do with <a href="http://en.wikipedia.org/wiki/Literate_programming"><code>literate programming</code></a>? For those who don't know, <code>literate programming</code> is a way to mix code and prose together in one document (in <code>R</code> we use <code>knitr</code> & <code>sweave</code>, <code>python</code> now has the <code>iPython</code> notebook as an option). This <code>literate programming</code> paradigm (combined with <code>markdown</code> as the markup language instead of <code>latex</code> thanks to <code>knitr</code>) is changing how I actually write my papers and do research in general. </p><br />
<h2>How that changes writing</h2><br />
<p>As I've previously described <a href="http://robertmflight.blogspot.com/2012/10/writing-papers-using-r-markdown.html">1</a><a href="http://robertmflight.blogspot.com/2012/08/loving-markdown.html">2</a>, <a href="http://rstudio.org"><code>RStudio</code></a> makes the use of <code>knitr</code> and generation of literate documents using computations in <code>R</code> incredibly easy. Because my writing environment and programming environment are tightly coupled together, I can easily start writing what looks like a shareable, readable publication as soon as I start writing code. Couple this together with a CVS like <code>git</code>, and you have a way to follow the complete providence of a publication from start to finish.</p><br />
<p>Instead of commenting my code to explain <strong>why</strong> I am doing something, I explain what I am doing in the prose, and then write the code to carry out that analysis. This changes my writing and coding style, and makes the interplay among the steps of writing scientific reports above much more explicit. I think it is a good thing, and will hopefully make my writing and research more productive.</p><br />
<h3>Notes</h3><br />
<p><a name="note1">1.</a> I am not suggesting that one only do experiments that will support the experiment, but writing out the paper at least gives you a framework for doing the experiments, and doing initial analysis. One should always be willing to modify the publication / hypothesis based on what the experiments tell you.</p><br />
<h3>Sources</h3><br />
<p>Published 10.05.13 <a href="http://robertmflight.blogspot.com/scientific-writing.html">here</a></p><br />
<p>The source markdown for this document is available <a href="https://github.com/rmflight/blogPosts/blob/master/writing_up_results.md">here</a></p>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-31955516002204159172013-04-09T21:23:00.001-04:002013-04-10T08:17:25.601-04:00Why I wear barefoot shoes<br />
Two weeks ago I ordered <a href="http://www.softstarshoes.com/adult-shoes/runamoc/dash-runamoc-all-smooth-chocolate.html">SoftStar Shoes Runamoc Dash</a>, and this evening, I laced up a new pair of <a href="http://xeroshoes.com/">Xero Shoes huarches</a>. Why would an overweight, non-runner, have ordered two pairs of "barefoot" shoes?? In the spring of 2010, a lot of publicity was given to the barefoot running phenomenon. I was interested, because I had a lot of knee problems growing up, and of course one of the claims of barefoot running was to help with various foot, leg, and knee problems. I was doing a lot of reading about it, and found "Invisible Shoes", a newly created enterprise out of Denver, Colorado. For $20, they would send a sheet of <a href="http://xeroshoes.com/shop/diy-kits/diy-vibram/">Vibram sole</a> that you then cut out to make your shoes, keeping them on with the included nylon string.<br />
<br />
That first summer, I didn't do a lot of walking, but mostly drove to work, and any time I had to walk any distance in my new shoes ended up with a lot of stretched skin on the balls of my feet. But I still really liked wearing them. It required me to walk in a completely different way, and seemed to help with some foot issues.<br />
<br />
By the second summer I had figured out the bus system in the city, and was walking 0.7 miles each way to the bus stop (1.4 miles each day, sometimes more). Very quickly I got used to the shoes, and I started to notice that I was able to walk further, without pain, and stand for much longer periods. Seriously, prior to this I could maybe be on my feet for an hour before developing serious pain in my knees, but by mid summer I could easily be on my feet for 3 hours without experiencing major pain.<br />
<br />
I put so many miles on those shoes that I started to develop a much thinner spot on one where the ball of my foot struck the ground first, and I broke the laces on each shoe twice where they are held on by a knot on the bottom. I wore them everywhere, and really enjoyed them. It was amazing to actually feel the ground you were walking on. The one time I went hiking on nature trails with them was awesome!<br />
<br />
Unfortunately, winter came, and my feet do not like the cold (even the little bit of cold we get here in Louisville, KY). I reluctantly put up my Invisible Shoes, and went back to my sneakers. The following summer, I remained in my sneakers, dealing with sweaty feet, and sore feet, legs and knees. Last fall, I resolved to find a solution that would enable me to wear barefoot type shoes most of the year.<br />
<br />
I went to my local outdoor gear stores and looked at options, finding nothing I really liked, or considered truly barefoot (no more than 6mm of contact, and no drop from heel to toe). I did however find <a href="http://www.softstarshoes.com/">SoftStar Shoes</a>, and their various options for barefoot style shoes. This past Christmas season, I had some extra money, and four weeks ago I finally ordered the Runamoc Dash. It took about two weeks to have it made and ship, but it was worth the wait. I have an extremely comfortable, good looking barefoot shoe, that I can wear pretty much anywhere. This includes the gym, as my gym has a no open toe shoe policy.<br />
<br />
I had also kept abreast of the changes at Invisible Shoes, with their switch to Xero Shoes, and a more curved sole that kept one from catching the toe on the ground (a problem I had, especially when I got tired). Last week I ordered a pair, and I have just finished punching the toe hole and lacing them up. This is good, because the temperature just started climbing into the 80s today, and it will be nice to have a very cool pair of barefoot shoes.<br />
<br />
If you want to try barefoot shoes, why not give the Xero option a try. It is relatively inexpensive, and you might like them so much you want to get a pair of SoftStar's. Oh, and they don't tend to develop a funky smell like some other brands.<br />
<br />
<h4>
Some Caveats</h4>
<div>
As with most things, there are always some warnings, things to take note of that I forgot to mention. If you look around at other barefoot sites, you will probably see some of these, but these are </div>
<div>
<ul>
<li>If you don't modify how you walk in these, your feet and legs will probably take a beating from heel striking on pavement. Without any cushion, all the force gets transmitted up your leg, and it will hurt.</li>
<li>If you do change how you walk to a mid- or fore-foot landing, it will take time for your legs to adjust. Man did my feet and my upper calves (just behind my kneecap) ache the first few days in my Dash's, and my calves would start to cramp after just 1/2 mile of walking. But after a week or so, I don't have any problems. Don't expect to walk 2 miles or run a marathon immediately after switching.</li>
<li>Some people may develop more problems. As usual, as more people are trying barefoot running / walking, some people are finding it is probably not for them. Whether this is really true, or they just never figure out how to change their stride, is open. But that's why I would try the inexpensive option first.</li>
<li>You will probably walk slower, and / or take more steps to move the same distance. If you are coming down on your mid- to fore-foot, you can't take as big a step. So it may take effort to keep up with others who are walking in regular shoes. On the other hand, more steps means more energy, which probably amounts to a higher rate of caloric consumption (or those of use with a spare tire to lose can hope anyway).</li>
</ul>
</div>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-34151842685389788482013-04-05T09:43:00.001-04:002013-05-30T09:28:14.124-04:00Reproducible publications as R packages... and markdown vignettes!I hadn't really considered it before, but <code>R</code> packages are an interesting way to distribute reproducible papers. You have functions in the <code>R</code> directory, any required data files in <code>data</code>, a <code>Description</code> file that describes the package and all its dependencies, and finally you have the vignette, generally in <code>inst/doc</code>. Vignettes in <code>R</code> traditionally had to be written in Latex and used Sweave to mix the written and code parts.<br />
<br />
It is very easy to imagine that the vignette produced could be a journal article, and therefore to get the reproducible article and access to underlying data and functions, you simply need to install the package. I don't know why I hadn't realized this before. It is actually a really neat, and potentially powerful idea. I would not be surprised that this was actually part of the logic of incorporating the Sweave vignettes in <code>R</code> packages.<br />
<br />
One wonders why this isn't more common, to distribute papers as packages? I am guessing that it is likely from the requirement to use Latex for writing the document. I've written a vignette, and I can't say I really cared for the experience, basically because getting the Latex part to work was a painful process.<br />
<br />
However, with the release of <code>R 3.0.0</code>, it is now possible to define alternative vignette engines. Currently the only one I know of is <a href="http://yihui.name/knitr/"><code>knitr</code></a>, which currently supports generating PDF from <code>Rnw</code> (latex with an alternative syntax to Sweave) and HTML from <code>Rmd</code>, or R markdown files. Markdown is so much easier to write, and in my mind the HTML generated is much easier to read. In addition to that, customization of the look using CSS is probably much more familiar to many people who are doing programming nowadays as well, another big plus.<br />
<br />
In addition to having to write using Latex, the process of changing, building, loading, and documenting packages was pretty cumbersome. However, <a href="http://had.co.nz/">Hadley Wickham</a> has been doing a lot to change that with his <a href="https://github.com/hadley/devtools"><code>devtools</code></a> package, that makes it quite easy to re-build a package and load it back up. This has now been integrated into the latest versions of <a href="http://rstudio.org/">RStudio</a>, making it rather easy to work on a package and immediately work with any changes. In addition, the <a href="https://github.com/hadley/test_that"><code>test_that</code></a> package makes it easier to run automated tests, and <a href="https://github.com/klutometis/roxygen"><code>ROxygen</code></a> makes it easy to also document your custom functions used by your vignette.<br />
<br />
So, I know I will be using Yihui's <a href="http://yihui.name/knitr/demo/vignette/">guide</a> to switch my <a href="http://bioconductor.org/packages/release/bioc/html/categoryCompare.html">own package</a> to use a markdown vignette, and will probably try to do my next paper as a self contained <code>R</code> package as well. How about you??<br />
<br />
Edit: As Carl pointed out below, pandoc is very useful for converting markdown to other formats that may be required for formal submission to a journal.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com6tag:blogger.com,1999:blog-73443196978791417.post-33979627227453452072013-01-10T08:46:00.001-05:002013-01-10T08:47:17.472-05:00Jan 10, 2013 RoundupHere are some things I noticed today that others might benefit from:<br />
<br />
<h2>
Probe to Probe Mapping</h2>
Neil Saunders highlighted a problem in software that is supposed to work with different array types, CNAmet:<br />
<blockquote class="tr_bq">
"the problem of mapping measurements between different array types is not dealt with by CNAmet": or anything else in my experience (<a href="http://twitter.com/neilfws/status/289153271891914752">link</a>)</blockquote>
There is a software package that does these type of interconversions, Absolute ID Convert, which uses genomic coordinate mapping to interconvert between array IDs. The paper was published in BMC Bioinformatics last year (<a href="http://dx.doi.org/10.1186/1471-2105-13-229">link</a>) by a PhD student from my group, who has gone on to PostDoc at Harvard.<br />
<br />
<h2>
Sequencing Errors due to Sample Processing</h2>
<div>
Nick Loman (<a href="http://twitter.com/pathogenomenick">twitter</a>, <a href="http://pathogenomics.bham.ac.uk/blog/">blog</a>) has an interesting <a href="http://pathogenomics.bham.ac.uk/blog/2013/01/sequencing-data-i-want-the-truth-you-cant-handle-the-truth/">post</a> highlighting <a href="http://www.nature.com/nbt/journal/v31/n1/full/nbt.2472.html">recent developments</a> in errors in Next-Generation Sequencing arising from sample preparation protocols. Casey Bergman is maintaining a <a href="http://www.citeulike.org/user/cisevol/tag/sequencing_error">collection of papers related to sequencing errors</a>.</div>
<div>
<br /></div>
<h2>
Protein Annotation Biases</h2>
<div>
Iddo Friedberg has a new <a href="http://arxiv.org/abs/1301.1740">arXiv preprint</a> examining the effect of biases in the experimental annotations of protein function, and their effect on our understanding of protein function. I haven't read it yet, but from the abstract, it looks like it is definitely something to check out.</div>
<div>
<br /></div>
<h2>
Free Statistical Methods Ebook</h2>
<div>
If you work with any amount of data (likely if you read this blog regularly), then you might be interested in the <a href="http://www-stat.stanford.edu/~tibs/ElemStatLearn/">free ebook</a> of "The Elements of Statistical Learning" (Hastie, Tibshirani, and Friedman), which is a book on a wide variety of machine learning methods, written by experts in the field. I downloaded my copy, you should too.</div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com2tag:blogger.com,1999:blog-73443196978791417.post-18648868808694456142012-11-30T13:38:00.000-05:002012-11-30T13:39:29.345-05:00First Calibre RecipeNot long ago I <a href="http://robertmflight.blogspot.com/2012/11/calibre-python-reading-papers-in-e-ink.html">posted</a> that I was going to learn some Python and write <a href="http://calibre-ebook.com/">Calibre</a> <a href="http://manual.calibre-ebook.com/news.html">recipes</a> for reading journal articles on e-readers. After some work, I thought I would let you know that I have gotten my first recipe completed, for the <a href="http://bioinformatics.oxfordjournals.org/">"Bioinformatics Journal"</a>. You can find the recipe <a href="https://github.com/rmflight/calibre_rssJournal/blob/master/bioinformatics.recipe">here on Github</a>. I hope others find it useful!<br />
<br />
Its not perfect, and there are still plenty of times I can imagine wanting to go to the HTML or PDF of an article, but the convenience looks great. Now I just have to start working on some other journals!Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-73519107607992771842012-11-28T16:12:00.002-05:002012-11-28T16:25:14.730-05:00Hive Plots Using R and Cytoscape
<h2>Hive Plots??</h2>
<p>I found out about <a href="http://www.hiveplot.net/"><code>HivePlots</code></a> this past summer, and although I thought they looked incredibly
useful and awesome, I didn't have a personal use for them at the time, and therefore put off doing anything with them.
That recently changed when I encountered some particularly nasty hairballs of force-directed graphs. Unfortunately, the
<a href="http://academic.depauw.edu/%7Ehanson/HiveR/HiveR.html"><code>HiveR</code></a> package does not create interactive hiveplots (at least for 2D), and that is particularly important for me. I don't necessarily want to be able to compare networks (one of the selling points made by Martin Krzywinski), but I do want to be able to explore the networks that I create. For
that reason I have been a big fan of the <a href="http://db.systemsbiology.net:8080/cytoscape/RCytoscape/versions/current/index.html"><code>RCytoscape</code></a> <code>Bioconductor</code> package since I encountered it, as it allows me to easily create graphs in <code>R</code>, and then interactively and programmatically explore them in <a href="http://cytoscape.org"><code>Cytoscape</code></a></p>
<p>So I decided last week to see how hard it would be to generate a hive plot that could be visualized and interacted with in
<code>Cytoscape</code>. For this example I'm going to use the data in the <code>HiveR</code> package, and actually use the structures already
encoded, because they are useful.</p>
<h2>Load Data</h2>
<pre><code class="r">require(RCytoscape)
require(HiveR)
require(graph)
options(stringsAsFactors = F)
</code></pre>
<pre><code class="r">dataDir <- file.path(system.file("extdata", package = "HiveR"), "E_coli")
EC1 <- dot2HPD(file = file.path(dataDir, "E_coli_TF.dot"), node.inst = NULL,
edge.inst = file.path(dataDir, "EdgeInst_TF.csv"), desc = "E coli gene regulatory network (RegulonDB)",
axis.cols = rep("grey", 3))
str(EC1)
</code></pre>
<pre><code>## List of 5
## $ nodes :'data.frame': 1597 obs. of 6 variables:
## ..$ id : int [1:1597] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ lab : chr [1:1597] "pstB" "hybE" "fadE" "phnF" ...
## ..$ axis : int [1:1597] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ radius: num [1:1597] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ size : num [1:1597] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ color : chr [1:1597] "transparent" "transparent" "transparent" "transparent" ...
## $ edges :'data.frame': 3893 obs. of 4 variables:
## ..$ id1 : int [1:3893] 932 612 932 1510 1510 413 528 652 1396 400 ...
## ..$ id2 : int [1:3893] 832 620 51 525 797 151 5 1058 1396 1559 ...
## ..$ weight: num [1:3893] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ color : chr [1:3893] "red" "red" "green" "green" ...
## $ desc : chr "E coli gene regulatory network (RegulonDB)"
## $ axis.cols: chr [1:3] "grey" "grey" "grey"
## $ type : chr "2D"
## - attr(*, "class")= chr "HivePlotData"
</code></pre>
<h2>Process Data</h2>
<p>So here we have the data. The <code>nodes</code> is a data frame with the <code>id</code>, a <code>label</code> describing the node, which <code>axis</code> the node
belongs on, and its <code>radius</code>, or how far out on the axis the node should be, as well as a <code>size</code>. These are all modifiable
attributes that can be changed depending on how one wants to map different pieces of data. This of course is the beauty of
hive plots, because they result in networks that are dependent on attributes that the user decides on.</p>
<p>In this case, we have a transcription factor regulation network. I am going to point you to the previous links as to why
a normal force-directed network diagram is not really that informative for these types of networks. I'm not out to
convince you that <code>HivePlots</code> are useful, if you don't get it from the publication and examples, then you should stop
here. This is more about how to do some calculations to lay them out and work with them in <code>Cytoscape</code>.</p>
<p>Bryan has implemented some nice functions to work with this type of network and perform simple calculations to assign
axes and locations based on properties of the nodes. For example, it is easy to locate nodes on an axis based on the total number of edges.</p>
<pre><code class="r">EC2 <- mineHPD(EC1, option = "rad <- tot.edge.count")
sumHPD(EC2)
</code></pre>
<pre><code>## E coli gene regulatory network (RegulonDB)
## This hive plot data set contains 1597 nodes on 1 axes and 3893 edges.
## It is a 2D data set.
##
## Axis 1 has 1597 nodes spanning radii from 1 to 434
##
## Axes 1 and 1 share 3893 edges
</code></pre>
<p>And then to assign the axis to be plotted on based on the whether edges are incoming (sink), outgoing (source), or both (manager). These are the types of decisions that influence whether you get anything insightful or useful out of a
<code>HivePlot</code>, and changing these options can of course change the conclusions you will make on a particular network.</p>
<pre><code class="r">EC3 <- mineHPD(EC2, option = "axis <- source.man.sink")
sumHPD(EC3)
</code></pre>
<pre><code>## E coli gene regulatory network (RegulonDB)
## This hive plot data set contains 1597 nodes on 3 axes and 3893 edges.
## It is a 2D data set.
##
## Axis 1 has 45 nodes spanning radii from 1 to 83
## Axis 2 has 1416 nodes spanning radii from 1 to 11
## Axis 3 has 136 nodes spanning radii from 2 to 434
##
## Axes 1 and 2 share 400 edges
## Axes 1 and 3 share 21 edges
## Axes 3 and 2 share 3158 edges
## Axes 3 and 3 share 314 edges
</code></pre>
<p>We also remove any nodes that have zero edges.</p>
<pre><code class="r">EC4 <- mineHPD(EC3, option = "remove zero edge")
</code></pre>
<pre><code>##
## 125 edges that start and end on the same point were removed
</code></pre>
<p>And finally re-order the edges (not sure how this would affect plotting using Cytoscape).</p>
<pre><code class="r">edges <- EC4$edges
edgesR <- subset(edges, color == "red")
edgesG <- subset(edges, color == "green")
edgesO <- subset(edges, color == "orange")
edges <- rbind(edgesO, edgesG, edgesR)
EC4$edges <- edges
EC4$edges$weight = 0.5
</code></pre>
<h2>Calculate Node Locations</h2>
<p>In this case we have three axes, so we are going to calculate the axes locations as 0, 120, and 240 degrees. However, we
need to use radians, because the conversion from spherical to cartesian coordinates involves using cosine and sine, which
in <code>R</code> is based on radians.</p>
<pre><code class="r">r2xy <- function(inRad, inPhi) {
x <- inRad * sin(inPhi)
y <- inRad * cos(inPhi)
cbind(x, y)
}
tmpDat <- EC4$nodes[, c("id", "axis", "radius")]
tmpDat$radius <- tmpDat$radius * 3 # bump it up as cytoscape coordinates are small
tmpDat$phi <- ((tmpDat$axis - 1) * 120) * (pi/180)
nodeXY <- r2xy(tmpDat$radius, tmpDat$phi) # contains cartesian coordinates
</code></pre>
<h2>Create GraphNEL</h2>
<p>Initialize the graph with the nodes and the edges.</p>
<pre><code class="r">hiveGraph <- new("graphNEL", nodes = as.character(EC4$nodes$id), edgemode = "directed")
hiveGraph <- addEdge(as.character(EC4$edges$id1), as.character(EC4$edges$id2),
hiveGraph)
</code></pre>
<p>We also want to put information we know about the nodes and edges in the graph, so that we can modify colors and stuff
based on those attributes. For example, in this case we might want to modify the node color based on the axis it is on.
Using attributes means we are not stuck using the colors that we previously assigned.</p>
<pre><code class="r">nodeDataDefaults(hiveGraph, "nodeType") <- ""
attr(nodeDataDefaults(hiveGraph, "nodeType"), "class") <- "STRING"
nodeTypes <- c(`1` = "source", `2` = "man", `3` = "sink")
nodeData(hiveGraph, as.character(EC4$nodes$id), "nodeType") <- nodeTypes[as.character(EC4$nodes$axis)]
edgeDataDefaults(hiveGraph, "interactionType") <- ""
attr(edgeDataDefaults(hiveGraph, "interactionType"), "class") <- "STRING"
interactionType <- c(red = "repressor", green = "activator", orange = "dual")
edgeData(hiveGraph, as.character(EC4$edges$id1), as.character(EC4$edges$id2),
"interactionType") <- interactionType[EC4$edges$color]
</code></pre>
<h2>Transfer to Cytoscape</h2>
<pre><code class="r">ccHive <- CytoscapeWindow("hiveTest", hiveGraph)
displayGraph(ccHive)
</code></pre>
<pre><code>## [1] "nodeType"
## [1] "label"
## [1] "interactionType"
</code></pre>
<pre><code>## interactionType
## TRUE
</code></pre>
<p>Now lets move those nodes to their positions based on the Hive Graph calculations.</p>
<pre><code class="r">setNodePosition(ccHive, as.character(EC4$nodes$id), nodeXY[, 1], nodeXY[, 2])
fitContent(ccHive)
setDefaultNodeSize(ccHive, 5)
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<p>And set the colors based on attributes:</p>
<pre><code class="r">nodeColors <- hcl(h = c(0, 120, 240), c = 55, l = 45) # darker for the nodes
edgeColors <- hcl(h = c(0, 120, 60), c = 45, l = 75) # lighter for the edges
setNodeColorRule(ccHive, "nodeType", c("source", "man", "sink"), nodeColors,
"lookup")
setNodeBorderColorRule(ccHive, "nodeType", c("source", "man", "sink"), nodeColors,
"lookup")
setEdgeColorRule(ccHive, "interactionType", c("repressor", "activator", "dual"),
edgeColors, "lookup")
setNodeFontSizeDirect(ccHive, as.character(EC4$nodes$id), 0)
redraw(ccHive)
</code></pre>
<pre><code class="r">fitContent(ccHive)
saveImage(ccHive, file.path(getwd(), "nonScaled.png"), "PNG")
</code></pre>
<p><img 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" alt="nonScaled", width=700/></p>
<p>This view doesn't help us a whole lot, unfortunately. What if we normalize the radii for each axis to use a maximum value
of 100?</p>
<pre><code class="r">useMax <- 100
invisible(sapply(c(1, 2, 3), function(inAxis) {
isCurr <- EC4$nodes$axis == inAxis
currMax <- max(EC4$nodes$radius[isCurr])
scaleFact <- useMax/currMax
EC4$nodes$radius[isCurr] <<- EC4$nodes$radius[isCurr] * scaleFact
}))
tmpDat <- EC4$nodes[, c("id", "axis", "radius")]
tmpDat$radius <- tmpDat$radius * 3 # bump it up as cytoscape coordinates are small
tmpDat$phi <- ((tmpDat$axis - 1) * 120) * (pi/180)
nodeXY <- r2xy(tmpDat$radius, tmpDat$phi)
setNodePosition(ccHive, as.character(EC4$nodes$id), nodeXY[, 1], nodeXY[, 2])
fitContent(ccHive)
redraw(ccHive)
</code></pre>
<pre><code class="r">fitContent(ccHive)
saveImage(ccHive, file.path(getwd(), "scaledAxes.png"), "PNG")
</code></pre>
<p><img 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jtD9qN7p548I103N6LcWMKXjuRxYGUzd9V046wV21kIGchXruS4qIz9yVSWsTezl+whgpcGR6bGFQMH6l0kv4HNAlDNR/qs2ZcN5EVL5tViaoVrhax6vfmTJ6lqP1PaZlMCSzXjJ0MvvOmB2uwRQiFkbjUx5MNGdYRSTdTpcSSVOkKhbsjTGlymK8ppX9R8cTp7axMnGpE36zmBx3OvguVvqc2X3PBLZvhOlLLXOzidmMJrKDyKVWIKO68N/IaP491JVukgd/xRA3pPJxbVZLlU6ejVE4uQjo4KBeOgPPs7lu0fIFXzb3PHwWWmqTHSSVNjU8I2wnHOjtSTsWlR3JGCFwi4z/4hvEyhLfvSnjwIr28ks2vYeB3OrjqHZ7KqUgxfKv5VnZvX+VpE+VRR+5nSNlt2alQw8v3FrDDvD5gbf6UR+8rPh401xm4PrtI3Vrtpq0mZvGmAJsZ0ElotWc+JLTvMh28A6NbCT6qHXzbg7yoKb8uO0iAsvIslDDp8gLnjDxzQG4wU7amIXqOjNwV0JKKjQg57lGcvyZMDCxhCrieUUAa6FFPZEcA+tkzZEWdynCsspklEtFDxiC4ZmV3AAvl4jExl8ByFkxtdpGpOut6NH33wFi9tcSk9lI2bGpsaRaYoPdyLG8rY2Il/iX93g2WJ0JYNDwPy3sgcrqZ8rBRNRPtmci//7dy/+N8Uv/1vtH8ofDsMOUUeC6xQf1n3UFBHa5GRJ4zaz5q2yw2UqaGake+LWL+aEaerpGVc7sYmRDsdpwmlD5sA7Ba9WjGGEDlgt6ek0uSauUdyQ/CwcjvwrewtJTSQ7zlpn7+rw7+b8nfnpnAhhXdiR2kDxztWx0WcKiyOPwqnSueMzqWhs4wumuXSOx1dCOg1E4sQoKNC9UBke5RnD+XIzaSi1g2v48juEU4vF/ne1Yp9dOUOwpsr+b9s6S3jW7E15DArY2+6LWIydujMRezFDjkZm768i9qyzaFj/+Jm9nKZ2F+NT7O8BJw9jQ8BZ//0Z599/sWPb+uzP3X+0a2uJyci1o1cI33qSSaQINoWrzw2UJMSGI/1q0VaG2nm1uMKUanbnlEBVevooEBR7Jaz2A1PZ5xXqptat3EZfw9Z2VtNSGi1JF/WeE7q8jchhb/rNl/2jsK7awrvRitnsEMcZ9VxPo4/JXW8jbxFPqMLdfR7B3QUiI4K1UOT7T1UxEKYLPiUNTdpy+qtmbczudGZXFmlnsqOGAg5V1rmRszVfYE43PVyocl1j/mD1d0Yr29MgY9WD1wA88Epd3K8wb2yUSst+UZPvdZSKjrygZGx5/0zcLkThoq8d/rXw0kCX+R7chrYsgG+l/LfLuMl1jSyFnqRjA9M4TTDDAT+mMfZFeBWJn7uSb5dS+6KWrSfG2oj2q4ycy/XJHOHXrjjY6LM7ceMlqyintRtLBklYjeUrihX1xeea6wmfNlbhzdvtVQznhOh7M3n70r4YFvh361WFykci8103Y7SGMercse741QJ1kzlRDguBdCFI0VZQO/dSNGaVlEE6KhQ3UWrYnClpbDqK2usJV91d/PsdeSwPz5BBukZ5mdWzyeTv+GFxX9kd224LemwAW6C3eL72exIJmzYcVmFV8IjuxMPTbrEUN6RGgMur4nThsdnw4a53ChrNGLQRaYnhrTVxPLPBv+xGG3/+I+G/mCRp5c/c9RGtC1i5l4VNFBOlUwaQsjNcjMx6iFVDaRu2CBQ9SSp1NRZofKxu6nVBNhaS8o4qwlf9paSMMjFe4vK3vfI3x1S+N2YwqU7VVgcFxjHuzyVE+H4owZ0NFIUFaLqhxOr13atkV/BDtBNjdaolDy+c3vkwBtbiufj89vdyLMvK3pwEwzH6OkzQhkbrkXL3jksSvdEikrvQg83MDeQN/C3RL7/nZ//RJS2f/8//MNmqH30fFAb0bZ4YcmBGjN3EeuHn2sFWjXttcAV8CtRqZumW0bqZrF7ClbAErCbJW8t2US65pu8hZ4Tia2WDWTvLvB3/fDB+6LwXttRpOO4QB3vwhggPo4/6mSVewd04UjRmlbRHgF6o4lFSERH9QhLOKu8pQCQS3vyIVA1V1uZ8w/+0qUjKSHuOnhjCV840rDza8QvurLP5MJXwWKetWKzgjSwsqiMDbt34snNJ4jE/KbQk+Mno3jUdeD01Wjta15bMDmlJ1/pqBEdqdTTt9HmHV+/+/O/I0rbf/Af/6uGnT9PPIEE0XaLZm6+zh1+ARROM7dgog3H3KJSt4YBUFbqZrHbQCjV0rCbZzUZqqdYC60mtZ6TplMtbxMGPaRBypFh+Fvq8J1K+GDD8O92KDw2czez6+/NrNKLqZw8HH8sueMI0NsZWiSmo6O8RVR3RtV3M6u8t73g89+/i+2deqgrW0yKY/DKEj135t5F9+pNamyPsLnMPjV9mx5hh6gLZWw6ks+WOAqu1NNZWFs2HGdxWzYTzBecx2dy4zqiBaf7Px/+70Rp+z8aXkhD7fln8hlBtN3EzL1aLXJD2alR+JWerM3+o6k6V6ZeWuoWO1+NhJyVujns1oglbYtiN5Qab0LMsCntfF2cleg54U+1lHiguOGXUobv9Ii/S52FhQspPEFEH94trdzKKYbjKHf80QC6yEjR6iCXnoYt1jC6iI6OAB2VNPvHmYd6AlRd+foxdw37BjspJe76xhK+ssdPfIs7qZPuyu1sooi3kGUJm4vto2VsgRf8xlIeor5U3yQtmpZ94UiWsJCNMGgphZpq0yauJeQJTPe7P6uVt//Jn/8jKai9ks08n48Pou3mFYgN1xhLlsN99Iic0t8AXguSjOjx72WepqVusVE1REXqZsuYrZOIBGBKKP3YRM0ucSZvTX2ihQcY62jerOzd3lRLqfzdyvBLjr9Z/7c7q+ruO1ih8EdrCu9GskoA5Y4/xqoJW6xi9DscWlRXROcBOppYhEzVj4KqOel6L37wwVtgpOvmCwZA8HNn5jC82S1niJCwucw+tt+RlbFrmhdZcYQdot74CkynZUe24S7A/9tTm5uxZb9uMb+8Fjbgrh2KTuT9U2TIeDRj/Zd/8Vs//eKzz5n6P4f+KVX8FqE2ou12rQuxgfVwFXNno4Psr6Zyw8LcEo6560ndcLLOEMOc1F3eFDnSEnZXWU2Iup8TLS5vYpuREu9NsFj8coZQJvKhlo5eq+HfWlKmJ2T08B1G/+46fz+Z1sxOcLynuePIqfIAdXTRwaK909ERoD/0hVzxr9aIr7bTH/dj796H1ln7x9Oj6kpTI/XNO2znwpWV2NQIDzv1FPbjByv5X/ZolzijiI4qx5I4U6+L/mn41Iip6c1lbPZtXUhkD5weYbukN/FGWz8PUSOY/gilx+XCe6IzOVbyzSQi+nOzl40vXCO+liAcPF/URrTdcs1j/TXJ3KHUEPurSVzY8yvXkopQabksYJeMk7hCtK3QUp3mYSyZ6JZKojXspq0meTr1r56RWsNAuZ4ckuAMYVstJXhOcABiZa500OqRZPlbevMlPGCS5u/y8PkQpunhyoqlcEzfqh3FiCsdqdeexDhtR4nqo/FJ2hRe+voRneG9yR3nOVWYtqcTf+l9ZBPh+MPU0ZsCeu9EdBbQRUaKVreKIkBvbd01//0a+c1W5mIv8f59ZOODb+GjM1vjK3hKVM1J15uZ8w/e4oU9JW1SY/jSnjoOrsKr62kjJi1jLxx5C+lpysjF9kWjmiXv7EeLtw0Z27eAT+V1BnLYn3i76jXxW0SuTf4dlzWOaQ0i7DGkJV8aCJk33Z+JKWfI8doBkLhSJ+DsqZySDE1lwoYPt7sKqL2Jf9n87aCnRT5f1Ea03U6FijMr1caStXAf/PCWuWtlbHb8O8fcULOZYY2Y1M13dXPe7noBgo2x21jyqPG6/Yua8idtyDjfPLOP85yoJRhCDITcQ0y0d2CBv+2pEVNGbmT4uzndMvw9i8ssGYUtNSJx+OW9mMLhSgcUHoxPwB8CxGOxmWjqkVF43VbOLuaOW6pwHJ5iP3aIzCqPCNC5vMUNgcvlvZ0G9DOr59rkR4Deu88mq1XzWxVbidWLPkaq5kvX+9G9cxdOS9fN8PrGHIQjc+ah3ke2eo3XDGH/kJk/4xtFtIQiGJsQnT7DytgfXbhQxi6WvnUWIgbqDZOBXb6/WNKjheDUafV2juxOYOJpLviPkmlJ5TQ5Hsy94dNLJjk+Q74VdjFN5mqzHPSEkghO5oKTi1Hi2oK1hNqL+VMu7O95ojai7fbLl5bXjny/NXND0ctEIXPjcj5ze0jVJC4+/sYiFlxtzCrawG7WaqIhGsaSEHXzBIXlIQ1T1ZPt622TnWrZqueEX+7omDOtAJiewYHj4WIxJAH3ZVz4d7B0F83OfAqv2FEkTC+Dq+1MdgSQHSgci2npeT1xU4KIPlIKr3QaMUGHXXWqlC/rDJHT3nFgAtg4wvHH7kRfDUVWg8F1v7NGQd9z8hV07x2MFGUBHRYJ677KxKJVLPMwwxZvtepzoDFOq76hG/taiNWDBS0Tq/d+M3sFW3tEVF0+i0o/bGYvjwPLF87MDVxhJOTxMT0nBXjJd/ZiiYUvw8VFW95ruFWO5zKqOKZd9ZqEbuwbU6gmVIQqXZopu44cU5OsxiyvScsmg0b4mPBXsLdp2a+15OgMNRUvMWfv/K+WswQ7BrLcexYajGQ0k2RtqPZsZmRGkMytJeTJiK7on1rAIrA1uAJzR1XK5feZG0gQbXenstHBGubORvu539qjasE4STndW8BjblrqFh9LKSJ1l+XzbCO120IMN9hh2mqCDzVgZQ29hJVLPwKWrErfrNWSldLhYabsSIcHnNW/57Jl/7dWQm8H8PcMy9+E6i7PDQLfjCZttKU7qg22SOFwRXamXvvjb6NRTTRqgO08ClO4VAmK+Ho7+eF9ZJOfrEJ3YXaEUMxIzttWTtY7jlo5n0wtUB/5raKsgs4HdP7Eop72ib630zZ0WA/UiOhlBR3DaQU9e9IlrVokVk+iA4Sl6jMPRecLRXd3kicbuZuV/C/vMay6w1qhfrkfPzh1U5e22E3T1bs5cGMNf3Th+9juGvVN4+Hh3a186S9ZKzZH2AZc4Y+/AQg+rSNjn7mI7dRpsrQzQ04BJasb3iaMuBJ4Hc7AqgWkyf/O5dtMbbFBfpWFWeF6JRVaj8grnB1RRHN2vYCz4fYk5GzAFSyq2XSZV/zu5dJf06jN67nczpwi1Ea0fbcV7lurMpZUzNxQrpxeBI6rdW6ouZzcIBrwR8jnMuJ+D9pkUicXU4vLTYSi8W7TVpP6yjTj/34pxWrC95zMEEod269JNJpqqSU78pxUGXswjSPNDt+hgV5N3E/4oPSKZIK0kwQoPKYFnralX8E1TiKFP9ik8O7WJv4lZ1b56MrB9f3aggE90H6VzrzjKFnl2Wa58AFdOFK0F4B+afbzR4ruOK11R4omCusxYiO+315Y9bkrB39yGNmi7R+Pn6o51/VW9uI4uHzhhAVGREpo0qU9ceJf3EnfQzdIceHX8fkddyE+TRluG/3lztTrdFi/6bbcCGP7TMFdjw+LTxmIEenpe97Em2WvCc4r/qZgQbUfe1cD2fQBJPdWE6b1UKXTbDn61kfYtVSt+9GRHDNlVMKncybHNtzmJb91OV8OGLlAqI1o+97LTo3WmLlXbpO5y1BYWlaLZV37sVr1ejqjFCVUI+ApqRKzaJvqY7ccPvNTuSbY7SmpOAW6QZ6gRKsJV7bsMCB1U88JE4QC+KuUHu/d1H8C/G3OyqYk+L8fAn/TdpTS10Dh0biRdnUzpnCgcLG+lkc2r6d3ZpX92CGw8pm3cO7Iwl32uktTOfk4jnLHn22Qy30Beo3LhU1Dh9p0wT5YtzyWdY913Wtb83pWfb7VSGolOf9kpoquUL/cTX344C1e2mKfLBLy+KzYuQt/H1q/A9d1HcL+jknFTs/lTawVmzE3K7CouuSbPbN6hO/spsuSwHSW9Csp8gqtcFNygGMPblqPZGr895f25AG2IxKWsvDdEr7CN41AzSenvaRDR6lq7rzuxLgtLTKh3ZUc36MdVnZ+0ghcaVtC7cXCJUJtRNs9a6BMDdWMwinxzNz1mFtDymfIcYE9WmUQdXUTMhMxWqcz0mTMKuoTrbyxyaS8kXkA95eNrwJSUk1qKlc6AJ7WSlrBD7UR792Ev7MqZ2qY7r9safglE37S6+bLphROm8KBwqO6cEwtPaMQaiY78hgzCrvoHa+Zytl1HEfqOCpRQL+dWGTdrQb03o0UZRm9ZmKRuI7+YJzo8Kk5wLaZPD4Jo2TMoStbDD5xe8mj3uXxSbKPzp+Hi4smyspl9gE6W9Kj2ZDhwOG4MdfK2Md215J3Fq7e+mbXbQ2p0JOvzXlbqnRMO+7Ib+i0bEeqppMVFhgb+CcRb0zxFyuZKN80Av/Op93uvLdGz4Yd9sffOpIinD2XHVnzmPdc9p3EEX/jZ94Ctw/vYhIiyIq/WIuMItRGtN3jJS/WXzXyPdRHRQaqoLy0rCUF42wIuVkMo2cz4qkXk7i41F3B7jpqN5D3bE4Oj2n6QjSwMMCZ4fB14v8aDI1v2mo5QygltFoOsSPluyV7c8UNv5TK3/gQHf79APibrWeYFN7bZJXuBB2i3PHnBNkdjIA5dLoPPO5tr2/D49rwu9ZpJrYJRXQuDb0XjN4A0IUjRTsH9KXCr2jp2r945UjcWCSNkrmwp4AsNzNX9+6KYazYq9a808CLyWOnz6x7zMJ+x2uTf9NtSYb109kRcdGarmEDqXYX4sXStzUtsHBGwbl0w7sQwWLj1FvYypwviiVtL1FHKwkHF/pB21mxcSKHWSl7TeeYhpCHYxPB+FuhuG7AlcDZ+077QXilZvvHwRVuT/Zjh9IMJCMItRFt30U5S/iywMyNJQdqHqbNiYTJizI3gLVO6PSgx83UdXVzLZV1sJtWu5t6uyvknWtsyRhqw2rC95xo8SEJUy27L3s/Gf4WUngwPiE9o5Cl8GdlR2mK473NHUdOlWdA1d1NqhYNW7zV0WlWrplY1AtG508sagzoq4nFA2z73IVf0WlxTT8yoWsbduqhDrEtONoPwXTOzp1xFaIGXkAeN0T9yC4yfQZ+SAaN8AC+CZAdGaOhhqfz+lAxX8PWldOs9BuA6RN/qcavf+7KwYkntGUz9cMyPr8a1fK/Tl+NT6dJ0pKv5WwtqYhH6C9IhZytI+SFwNSx3fXeJyK7HAVXKy8wuIq82oi2H2IJR74Dc/PN3A2YGyBJdJvSs7qlYzd8/KYlp4XQJu/GeYKkrBPyljTV8vYqBo/0kIYevX0tD59/kPxd6iwp/BmawttUx7uTO17G8RrjOFLHnxVV98Lowp9YdL+AvlvOcmFt6BbYn5VAgAX0ew9EZ8enewtZI6WuysVjZOxNt0V4fD5avKteUzKsn8uU2580lExPjc4VzLEFSV8IrJNfv49sXNriVbZsR+pddG+N/Eb8PZ3/GkCWs2rQaBEaXE35UoUVU94suDfJgbOTEb2wEQh+FcW0x1bXqTPFRo40ULXfRzaRgQTR9oMuYUrgYrhP+DAx5pbXY24P9lYn2tTYTOqmeT0n15S/yRJpqZzMthBCUhka322rCU9yljzVkpDNZOVd95y0z98knVE4jctMGbkzrXhQ/F2PwqWbwhGFS/SOdz13XFQdRziOqLrrcS4igF49seguFfSqiUU8Bb1zQGcJ21NMGag3/NsiK2PXmz6z57TjoUln6rWOGNaTry15F2u2ll4A2YeRrSt6hjzflh2DU4vu+6wTWbiY/7iSdPGTRtaw8aUsHi+sT1E6wTwaBR4y5kIG0YZ7eHXw0i6s4RXqK9Hn2o/uczt24l+Ugtrr2BhCbUTb932JF4x8p2IDwodN43IBWcoN9UNFzJkRjZjdWY/LGkvdDbGbpnb4rfRXZyx5GltNWPK2UB1F/nFTLSV4TmS985x0xt9D3PCdB3uutm0KRxTeJadKl3D8eXvHRcOqpcfqPQeqboG/57+nR8mEVs9dOdFRMpcW76HTvu+0b7mYOUFu84bbsumhNWwhoPcizoUP6CIjRRlA50aKZgpbprxNS4rE3s1mRzJhw47LKlxFnNrcSz5TAjNGcuF6hpDm0SuFX+3FDuEw1vRen/hLsAKsZ6FZLH2/TGysxQx8zl6NTy8Rq7ArU5Sm1oRNKMigEZYKc2LRfubM6Aeb+9Ic3Mh9qref+7FDbvc+eJvPiVssXCPURrT9YBJLijNr4Rc1zO1Li0DtJC7MLYH/KmoiurmyR8e0uLylrG4hdmtEk0wI+XR2uKWX6SmpaPJu7HvuwGoi9JzU6+OsyN7MVMueyt41/M0O32kqyes4/r6/8MG2KdyZei3RjoIovEMcZ9XxLuF4UIjjbLLKI8VxRNU9wmuAwlPf/JUtLuVg3lixC2fmKLS6kbuReACFCnrNxKJeA7owbLHM6G7a5cIsGMzMysGy7fVshZNLua1OvlGBswvOtJvqhcqZh9pJnjQ6YuwYSIFpZD5/FiwWDYKh68acshCcWvTNinI2rCW23LCQ8O9F3zXY2+3kh8oyxk1IM5DctkVmkujjg2j7QRTg9Xq4NpmbnxLYgLlZo1U95i4xWd31pO56ASb8MhEKXT3DNNGayUSK1YSxjL/s0GpS4zlpQvk9brUUVpSyO9MKU6Y8/FJDPILw7zszhSMK7y6Od8msIpKscu9Bh53YP9gv6OHx8Ffwt4iq6xmf3ofWz125a0v4RkpTozV26s7vJo7usqlRIqDfQSC6uI5+m7dYHixK6+gLq6kTWMvVTABlbdlw6Bq3G66kAvxEvzVsHMg7P3/tLxI14dn0tTQ7XPLNbrosv/fzv/PTLz77/Isf/84XP/5T1x9xanchMAWcfdJMqIZPegW1Xbnmn835rzlVG5YBpdIP6NOEaPtBm7mLWL94d6MYc6sbZonQru46UrclKymEpOvYzZI3sHVjq0m3yJueapmVS/ScTBGKO4Nvlr+tKXb4Jd1Y+UjDT9qjcIl2FJbCnannHlPYC+94uZWzGzjei9xxIOBuUTVsAbbzQAIuHqx0fWmXlMd3YwlfODNwIm1lLu437ppfxdK3rkLCQE6o6eEy4q7IyZwqkHgTjqlzwUmoBf/ssmdu02NmLS67dwLoNTp6M0AvLaXw1fh0TdLIMrFBLNw4CyENWXsVnc2olryzOy6rKzn+OcPZvPrsf3zxD6KY9tzsPfNQTSfYb+Jf8gboJCSp2reTdBBqI9p+wFUVy/0C+FuYEsiWHzOKXEoIRZh0N9i+uNRNyiRK3WXsxut6u9vD7rLVpFHMdnesJiKeE7KR+H3HnpNq/n704YN3YApHYeE9wnE2WaUKxzsSyJvnjnfL/nEQ3UVULfGNfhfd++jGy2OepOTxuYn96N59TWoUVqp0bM279ORrIW42vW4AhpLBqfcO143IvKoQrTTH9pduo6xFkxb5CvodjBQVBXSodY8ZatE3W/LNUKEpImBc8sxtuMx4RD9FDv/OFz+pRu1yLRjVq8VfNV/r5r9FqI1o+xmZuYG5/fExqczNmIOF49+FUremA6kbykIMdx276c1SE+pmCnR3yZuXczKkufOplpL3sBz+/djDB++YwpEdpac4Lgg6DPaIM5CpunPpejdxfBxYvJAoXZvDF/bkcWDpgUjXxdK3keLSdH5ST6k0lKwltuZ7lJOYccfjuhbMd2QHv5/4l7pljlrOf7mJZfd8gS2XFWrDban0iTKA3nWXy0eLF0D8HU3h1mXv3KJvrhDUfP6zz0Rp+7//+d+V4kzj1thwcFpKIEGojWj78TB3arCGuZerR74317lJeWPmZqVudWdSd++wu3QneYINZG9NQ639DuK9uxl+wvD3Qw4fRAEpT8k+vpt4f+xfoueY2BLX5sinskTdEYv3yKnyxKVrD3lFxzYHpTQ1wvt1GNncyN3cu3SdKh27CgkjpWUMIVJhmk221ggW3u7k24Wg9aPVJ3ZShc5cxF7scElsImM77QSl38ABFNqyYZUI68OVQlM5+YelFLEamV3zGlY9xjX/DBmcIkPGbGgyjRkSmDaGaUMxdTim9iXeeBJvHCn66mfPDHtT8lR0uBhWrIRk68GB9WDfeoj+nlwUtRkD90+aqtrcNx7wuRPN3kaojWj76RQVGag2c9eOfO8Kc3dF6u4pdje1mjDk/bK7grf0Vsuy5wSX3b3npH3+fvDh33dP4UFkR7nbVsUbc/CTJXRtDtP/6HgGEB/H2dzx54PjbB7fUWD53JG5sYQlHLH7aWoUrdjCjr3g01OjjCFELhWvabCW60jVJDmhFTQIzmZHspjpncv7ySQiY1/aEkfBle6eG3AY4SNQO5LGFocfruNf8h/pLPpsuNaUGTPjr+cyo1AzOdV0VjWFKw24Qk8otIRcIzZ4Di7acK1OxobzEeVSSLYWfMnE/1W5T2uqHm031rZhwdwSai/Of40MJIi2n6KZO1zXzE2L4qVlNSFso5SLjn/vhdTdBLtJmTGrMJZMnZJ37/ME68neQNVNWy3v0XOC+LtbFI5M4V2h6lr7R/Kksam6Ye5419RxbgzQI8XxNeqbvdhhC9L1w3CGFEvfegvZ6fyMllTVne1Qt4YNpNqRD4aKC85CqGa4I/uBDSQMKwHXhcUv1tYZOfNQXZSx2Vop/GoxmgsmNb7ka2ty1JoZowE6PToD9JwbbgrQ9cqRVgbjChxTLjJUvREcoME6+KIBWLO1FO4jsb50rB8gwZ0e/OnPanzbZWOJP73S4EVxCzb4yDSPAZ3/FTcrHqE2ou1HX/742JqAueuZuesxN41TeHOtuq7UTcpakrp7jd0lCXmCGrz7VhO+7D1FKJpYvR+A54TffFkVPtgKfz/k4TvdovCZ7EjLrZlPy47yKKYq0k4VvnGcwfHuGMcf6lROTrr+6MhIy+N7EM6QVOnYnY8ZSa2GUrZC1fQtQ0MNT+f1oWIeAJ0d7ugvElOUXvh4U/YVFXG+dwZEZGxzEN7KNmTs3MKuPT8rVKA7AWjRmiQU/oQiiykXwvLVkGw9NLghjaqhNkJ968G+lVAfFe0LpAZnci+1/NsrLptLKyc8XwgzSX7/53+/wWu/uvXASELt4i/Wom8RaiPafmoFq9UaM3epvpm7Q+auJ3VraKlb0ZLUfQfYDaUnm4+u7B15tzLV8v49J23zt+6RDL9suzoJC39EpvCnPaucU8f5ON4VdbxmDFDvcJyVrj/4Fq5sCWnBLPfvDGENIQZqTEMp1JS8JbO1nhqdK5hhC9zWOMI25S0agXtbR6iwpHnD77+0BET7HcsyNg/+hABtyo30AqD53TKwNdgslCmrisWGi+HR1aByIyjfoOXqJiYQvoMUwHozOLgdkO34lVu+keWgLI8NJuID9kwVYTMp2kPTuFxLyueyL4vBKfaSziSTAHN/9tMvfvxvhv+vBm/ipT3BnepNz2pkIEG0/fTN3DWfUtGR73zm1gr8cNKZu4tSN2Mun9ARyno+kK5gd/M8wZ5ZTYStlo2nWj4cz0kn/P24hu/cI4VjMe0d21HYWL2nStWd47iYWSXQRRwXBh02aGp8H96Ad0eqdH2vzhA2IcSUN9Nma6pVPJXrydeWvCtVOhZumVz4CiuuW/J2jVh/pDs3WwoHz2zl47PvtkUSE3wLhyk7ImbhkPUCoI04/SzTGRU8KSN+q4DjTdnxpcjbLd/4vnd026/YCsg2gwOSqfoFQ9UDW0Gaqve8qkOP9tLmoPNGrO5l/0QxIl+IDKTi/ULIhmv1VE7B7qElN+hODW25XaxfDv576HDsJk6avq1Xtuitqh1YI75GBhJE26j+htazq40l9Ua+80ubkwmZW5eTKvcCW3dL6r4b7IYyziul5AkaS57e+qeltVoCmrOek4cge9fwdwvDd1j+fszhg4/OFI5mlfcOx9kxQF3C8Uru+LU1SicnmiM3Zkl/eI9x12WzNWXUtGy2lmtIxST51pnHyOKX9bbPEjZrxYbPxTQgbG4Y4JWp+wDorMqUuwXo9Jg5NRZMahIxA/8jAwvXfe/0geftvnds16fcpqmaMYFIo2parg72bwVebvvle76Rfc/4sXvy1FobAnjoMC2ERouRwaVwXzI+YMnWZtTCbcVwK8YDf7vSL53pQSfpOggUHakx9udUcArOn6bvMizebleMgU38y6aovRbTIdRGtP18zdz1Rr43Zm74X/ihVOWYVOlxRbek7jvDbp7J+36sJnzZW/JUS1r2hgc/KPgu1Qy/fOrDd+6Zwnl2FNb+sZG7QVT9EGoDv+Hj+KU13h11vPxm0TL5lYV2RLwPrt5lK2fFbE2OtEyupHKS0nkLaQB0UQtHGaAFFg5NTwE6NwxPVwFoRoGGXYIda2C1gg8IfFjo7xk87iOv+sAzvudT7fgVm8GXrVA1K1e/pE0gvuE93+iud3QlPruZ3DyI7n50ZYVrrWuLZ9NrKEbki+F+gOwYYxfRCV4g/2IySQzFkgOe9OA0qU4vHG3kPmUjBvZX3sSba5P/fXi78Zt+7sS5dd1W5qoV1PYg1Ea0/ZzM3NUf78VwX3P0zAnNIS0wNyt1a7oRYCIduz14d4wK95gn2MhzQjYTvwnZg/KcVIv3qhaGXz7R8JM7MoVjmhimTUT1ubD+2OGSMqscUfXdNzXuJo4/+IqX1pi0PL57yx1Pld458yED+balRkYtqZjGh2fL8vPjAGgpte+frZarWzKBQA1sB4aAyPd9qj3vqzXv+HpEzv4Wfn7g1Rx7Ilc2TPRtPXJYlwJvipHBVUDY0ItUvN8msIvQkF2tlE3lZIl4fzg5MEkM+4tEcYE2LG277eyRhzfio8XbVNiG5SK3G9uZ02Ze7V9W2iLjs6WF79DnHdH28zZzh1/EG5q52ZoUUalbY25jySgudbeY1S0Ru2lQI5Xdwm4eeQ/do8lbxHPSNGHwQXpOql4ICh+UbP9Yii2mYqZ4zAAYDTDdNoVHMZQUfj/FNjWeevNXtoS0PL7QpS1+5iGOAisf/AtnbqJL6njFqcIZx1kcXy5+Vyx9GyqWTJRZR45qSLlwCsw0rpzNjHAAPXdr4QC6ZTG3RwANUAiMTgN0tuyBNmVU1uSoL/k6nJhY9Xq6H9uXWTisyNXyFk0gL2gTCC1Xy3f9w3te1Yb31crCVmWtRR2tJkzrwcFd38iRW/PBPnctlvnNytjbHuNCaLQUoZ+dhWxXWgSy1UStBXQu95LEBoCzdcSQJW8nFsrLrWtLxJgrL5zWPRba3R7fk4ja+7HDFlRthNqItp+zmXu5dTN3PeYG2A2Vlu9R6qZRGFcBdmvuBLtLTfMEiTuymtTI3lLivTUPrNWyAX+3ET742Pm7bVP1scOVCOtiUW0sipLCH5yN5LapMSIxj+/CmTkKrW7kbiR+z9B57ngiZogkJuzJV0CupmoP9N0BdE41d9tECDWdG6Zt2aTKTQWL8dyZM3VjCvV0+kxFrvYrt4JDmy0kgdDuarpnMTC041Ps+lS7vrG1mKWu6Fv6fi1TeuebPnLqr6yOBu/Lid227J8oRGQrzP0aIDsX63enB3U1LfUE68yuWhfBv22ZoUKkLx6nOdtAjsfnK0EuH90khmnZR0ajGvbpGhycD955hNqItlF1YObmx3IzZu5QcUYKcwsX0GpC0RJzN5C65zIdzJLEVZMAGXWwG65H3cXu0n3nCdZxzBskek4eSLx3d8NPNA87fJDL1NtJnvTaVN3F1kw0uF66M+QosHxhT97vpEZn0WfPz5qy4xUPdHZkOkOPIewdQGsFAM1aOMw4nHhjNlzLWjgap2LrqTFHIZjGc0d+vGaqOZey8tGFty1j8+TqkVZ7FjeaydUNPvWwfDrAVk88kRuzt/FZse2ZzkeGC1j/2u2tmYj2eYWQzVzrtKTCWB3/Dz8EIl9h2iWn8SENqXAX4kUe8u4mTzc9FrYpC96mC4uvsbB9GFzn9u19ZLPJi134DqE2om1UUszcfUWsX5qLwyjMLWmVuetK3cSQHpcBkbefMVIy6QlZHbVb3gvs5kzemjoIeMdWE77nZJKQSxop/yBbLTvn73sJ/+54VnkPWxUTRPTJJ4XfpTOkPKnRLCmP79KROg4uw4nRah6fCEDnRtg53ncH0LcWDmtmjLVwRBIT+26bFO/4iTu1E4hnYw53akL4XHN5U7hQ2I7AyjMpJmMH4QjTMnb2ui0TiAIQuRW5uk8oV7/3vL20Jw+wHYlvHDwMVtTw2YcP/qdmeTJXFs9CcJyKDi7yvnamMBqyJ3EhZMv1pCKBaSbxKg+9nhgKJweAs1MMZ9Nz6fPT+ELVEdvIfboweQ3MkhuunLtOG5suUu9VwP5Xkk+C600ueqXvVxOWSlskQm1E26hqCgi75nLTYOR7L5i7R1J3M7W7J9hdkjC6ki5cdvdvNJD0bFauI4ckwLfs4XtO6vI3cXf8/ZCp+h5aMxkKx2IzT9WOQjtDQusf3bi0uOvgtTX60UUAoolOahQC9ExOJQTobtEzA9ByoQLNTkA04/R/OQW65lVXTeW0xulQwu5M5Qxc09HjIVEehZ9/dOH7sXeNZWzWBLLrHaVHurRG1SJy9bZXdeV1VPcTR4/ovJdPi6XfNBWw4VN8HFy5tCWkvPZd7wweleWjfav8sRhYXyA5aBSDbLhub3pNCUxX0wSpJ2TRBC2c4dF+lrMN5FusuL5QHQOyUvoentebGGf/Ko7pyla08JLoK3oXO+D29oN3XoKBxIDC/hBto2rZzL0a7pNiLGGZW4PXmsa0pAx+3upu0FndQiG2Y6n7Vu1W1A/xUPqxiV4cWJa86w2vYa0m2vn7mfYST9Mih4Y2mjfxw+jIlw+51VKUvyvhg83Cv9kMWgPD37bsiDs69oSpum0KxzBdOKb2Jt6Yn40pnHWGHAcWL+yJG0tIQgxIKB43+lM6c+q1Of3q3gA6y/UvjnIe6Nu3DC7UqmnK6C1kRafGtNty8B2H4x+duXN79MIauDT7bsz0AJTujAFypD54pzoJrq6Rqzd8Ks4EslL4FXwq4RNaI8zDx3w7/bEBZLMCNnyozzyUJDOYOXDh8FKhsUxsYLH6hjuP9QVpyBa5PakJBemfOba7AJQ11Zw9nZPlovRBKGB0zHZ5uE8hni99I9zba2skG54spwHiSq4pU/SlbWWuuKUUvK0tGEgQaiPaRtW0ArHhmpTAVQnJ3GyJjn+Hi3sbzA1gbcDFCLVjqZvFbmNWIT55oZfY3ThPkPlOAJhvyEJN3MtbnysdWLIqgwRjhvoxtFp2zt/wGGOObr60p0b8IcWTp+qWl2qsKTw+GeW3ZkoYaCJK4Q/EjrJGfXuIbX30UFfWKIcalSZCZgzhXHoUcJbvge7uHG8hQAOscwq0GEDXHRmjJhVaanQmPwNszU+27no1GZ9OqTyEt5jMHnly17wDW7eY4Op37tddoeoGPYtVadm8HYCF9F7ivehcz7IDO7oLf3XJzXlpVhe2YCk6HY+9xGP9K9WQXYr0hZIDk7jYxB9cTvinb0z+HZfVnnpVo2eZskN4lN5UKfzSesvZs/npzPwH0RcLO3xm8WhvT9Qju7Pc9Sjm2N7OnPKPhnQDyUrShVAb0TYqyffRWP96uOoSRkUGJP5tF5m7gdQ9TSg7lLrZmsqJB6Sw2G0hhnvVyFhSackGVpMhxu0gv3uTN99zMkMotWx6QLOEQQOhiKe9j+skh7tmMmNrgb+Z4wCrkRnGf+LOjpy7cif+0mFkC27Mm9mrVepbFFZdNoUnbWxrZjA+4Uy9nslKmn5ixJW29Ctv4k04po5GddGoAbbTIwrnLBzm7P0DNNdECHsF+xZb2LEXfEZygk62bmHauRwebKDG7AVvpLjUU7bmqvH49GnKCPxN4WuHwaVLR0rE124OfvAaD9wTe7QJRNGpCcSv2vOPCrXksjpui8My+MxNHgVXDyJbh1H666kaNZq2ZUd34YMsKmAfB1ekdzPTW7M4Dv2mVGI4He/PR/tqZCyAbF9qcAoXkV0Asn2p8Wuz/9rkX/LOwdK05jHWzOB8hD5Ka0GZO6XgljTBYnGhDunuJml6nr79JDqT45zZSWTNSXxdWSfYky14tRMm+F90DUS0japFHKlegq+FpZq5b5lbZPz7TFv8Wk/q1pBye6YLPNpI7SblOlzeO+wuW03wukZqZk7BS11O1uuh8U1zTgxMA5ammdtbiw9ZssO50sFDOY3nv18jv5HoACG949aUYpYZvqOVgDg6UjZFyOayckda6cM16KLRwI4CFB6Pz9FR3zGtP/4WwNqIK6Wbwn2JN5Gohqbw+CRQeCa3KARodgwhwCtLsZyFgzfHuycAzc3xrgfQdRtmSt8CGQNbAyVrWgBrmYaSaSklEDmwNdD5nTb5LHyXmH/nKSSNlFqMsA3OQihV2NmJ7VWZKzzu9x5NeyYQobV6x6u69Lq6NpXTHLimo8ejl44ki+PvsL29+CGQt1R/CLORG7P31Da77pMvh14kYi9zsf6aeyjUQqQvkBqawmtTzFnvpS3zip0PemHxFf3TNV0TGnLImxpcZDh7IzAUiw1zG7Hn/aLWEc4nc2MKYFENd1OrxPkJhO0N/OamsvxINDWQrCZsCLURbaPqtELFmZXqiwX8r50alb4FsfHv9O2zvf1p4OpuO6u7BbUb4D7aQ/sE8LQab8KytIX6bvMERcuWHb4dKT/UONNgkpB7iLvwxiwV/2qN+Gondfouuvc+tHbiL310Zm8sEalClC0ON9rjwNIBtrObPGa16sWFHxyEypYdmcVlUvhbQ5aHz1syClta+XyGX7ZdeH6PFsIT061mFLKXEUfqNYA7YASGaYLxt6bsaG8BmhlD2BigG7O1t5Cdzs9oqVENWW95L65ba0mVkdK487Eumq1bMYr8kJ4/9hVyc3kTs+c1p/2wNe+MFJeX8ZNTb+HYpTnwvulCEoh3WGK+Xtk7nv92O3P6PrJ55i2cO7KX9uS1BQNWZlwiwe7O6awQtiVwarMAYcP+pxJ98fggiVW1PJY92ZGBQFIxlRMXdGbxilHt1OZORvTa6mWhlpAFky9ZC8qWX5YPvdbdvguw5oG3pvGRgSPwzmnnzrddl5Pb/waq9rUVa65qx4wItRFto+oec6eGaowlUka+N2FuQqYnle1SqXESFwRdMeMSOnd1czWZVdZTwnqtdtPrCmpCSw5pGroaNMRL4wMwT3Mj5ZsnYRND8MhEPtRhAMhG7mY7/XE/9u4ouHrqyQMo30i7m5ZN1Z78cXDlILq7kzyBTa1Qf9lSznGr/G1ghu88zPDvu+4NWNgFWhUq0Pw53qwCDdzsi7/BaDFb3WpSuDkz6kmMM3YUTSSq9sTfTOVUFYDOqOgmwvTobYzdK39K7yfs6+TXjRMnWi3AYoDjKUqnJZWtEP/dma2bFrHwJTC0Ne/WUaNCwt7z6druWeTL1Tu+4V3vqwbjYDr5XmsvcQSr6Jrrw40Vu7ZFaQe5OdI5iKejinS8v4jVrihW6XSRl/6E0phTiF7DJ3HZpbUyPPLQ4YzE1Gznw/8h/+3Pv/js8y9+TNfPPhtT/+/sUPcV/8RMbvT2LVAEilSxWcoeQPOlyWe43QdtYbgSnh3erofaNxZsufTXyECCaBvVPVQ2OsiXKNbgUhIbaJW5NQJviS7XvlI7l1GJArEBl83l5F3EbnV97DYRil4feXWe1jYaW4oBze/R5F0No8xIeQmhv/r68d7APSuFXwEKw83yfWgd4JimakdKIlVfWWMf3cSxf/Egsg1b4ITqXrzeYGnenhoxZ2VTOZn+bsMHHxdAd8W8UaZwTNPJ4PoYpovHpxJpd7dM4awhxJy3TVLjGkqhaYmtKbmeemXKm+/MbN3MKPLrxPyeuxDnjCKJ6KtD79suBFf7ldLHwXQE2aXfwEdeaMuGa8JhcP0wskUn9ElucBStc7c1ERvAxbwiS+H+QkQeTIzMirYrEHJtTv7ebr7iJR7uOm2O5Bh32vzPL//LMmfz6s2b/yeQrOSU82ewN6gP3iJs35Ucr3hIrJFb6aHKsQ1szXlybsxhCahtQqiNaBtVr0qYEgjMHYi1pvKKjX8HWJS3GtHNl7r1uHjE3lymmwBaH7tpE95UrufYLcVqwpq8H8gJ08JIeYLOOfGn3547M9LtH3dJ1dLLh2seNX8LAdqUG+kdQPPneItZOFSwG6yFw5X37cUOT/yLl/YEAAF/cH0urE9E9TFM2z6FS57XkyodewvZOWpWR766VaMfutm6qVEkM382n3QfurWHnrd73lc7vmHgY6DkNnoWt/zybd9I4+nlPap18muAaSZPhj84Bvvoyn1049IbHGkntxU79i7Amr+88eIvVjLxXFyWiA/UxGPfdj0OFEOqcHxMpCeYuQJoc7I9k6UqTdzkX/eYhU2Qn//sMyFtQ5W1JPJttLixICH3Yy/6Dp6lEJjithwqLtyICdvLxe+4g3NjCYuGsSBVG9E2qrsuOzW6Vm0sgf9tyczdgLk72bG5jBgNd9XV3RS74SXcgdrNlgagh3xZNyqbGNLgQHvyh3DOsA4QZ/bVFNFM+SPYnJOhmYx80zvzNDL1gL9bCx8kaZvKXFZuTSkchOoOAVrWU4Bm53jzAbqxB5oeJRPeoCc1WmNSvtagZ3o7s+/Dm9vpj8BJ/Hk9bZjCOQqPAsTHte7Uaz0+0pIn5H7N1g1q3z976NG8c7/e843s+OWbwZcboYE2Iva4cTBr5PI9vhy4GuzH3gnTsq8lr9vFCZttCKGIROxlOt6/IOYVKUYGS4HXKUwzWzd4R0H69LWBgBYfGTROC/5kLjf078Z+TxS1P//iMw2pcBYixQVJlz5mkE3wko78K5/wk3ktvEAuDabK2M2tXc3BxqgNVWmLjE8j1Ea0jeoOzNyDVWbusNSR7zXMLcpbnQnAjNRNiri6zZmR7h6EetitYfL7eu3trgSGlFTwdA1QiZ2hcwftldJHwABJO9IyHSFB9n5UUy0lVkvh3xpChL/5AM3N8X74AN2oz5UZJXNE56ylbixhCYQUvLLFTr35vdih6KTGelVOCmcyCjujcE04rnYyFK7hTY15CIaQClW7tIeecs/iZuAlI1e/kA7Wm8F+YHH4W256Ods/d+ahNrOX976G30meMGnZ7RlC6O7JG3Pw0pG4dKQu7Um2rqzYjQXb8uhZr8iKwCuyGO4vRuSrPh0eNsyJQTY7PIFTsk+tng82zw0zSubU5k6HDYbqG5+WlLnT8nmG5v/14G/Voe0f4wstHPBPjFZd+YaHou+q3Gs/iFcyo64tmGTU/mE15SujdlS7OP9LBEKItlHdUc1j/ev8i1H4RTzWMnOLjn/X4PIw6e6o0ZANMBFxdcs92Nu7wW713WI3bY6fV6kbYTfz31ynJu9OBiuKatXzeZJtteS+eG3wEuCRHtLwxD5KVfxNylribzuh6g5AM3O8hQBdM8e7W7WB02Ouz+hRMrFPkqTr0Lkj/T68DufMSr7TO/2tIcSiI8fY6w/Xmom1OLi+PUdKF2sls3Do1h3REXuvdn3D2wHZVmBQugmE8YEAVQ/u+OmeRUDzXe/wJ4+1ZkwmICm8X42HqHd+SmxnTtmCCwtts2bq1Fs8c5NQ9Nx4e/LaFqXlWHP3Y0bmw69S8YEiVhuPzYT3DSyERrfcU4XAlDkzWu+rOWcKPjgqY04JHyUiZDyxuStNkFG1prZ3RR6OjSxF+m/fhb6c7S/qadvSD+OlnZ4bn4houSfKli5vLCLC9pUtfvv+BjbxL5ucZglHRdVGqI1oG9W9mLnXqowlfb60vHPmZhIGOmVuD6nSi3qdCTngeNePRgPs1uJ3ZzKRZDVhC5c1/ooWUBjgBu6yAMedU7VEBwjbaqlpkMrCjZSv32r5cJoIgVkbKNCaOgo0MDSQ9BwT/t0Gf98LQEuRrj86c5Kl6ygnXXfoHWITQmYoo5YabmVkjMxADDtTryNxxk9y3xRepmq37p3n9a5PtR2QA1VvBPtblKvLPYt0xJ5PtYVNHweXL2g+E1nwXFuxE/8SQLBEGoYS6sTlgu2bb+tWWu5VNl8r9c49nY4PZWL1vCKyZf/EgcPMQraUL4vghFn2zl2afez21z0Wb+JNzaVMR8gTsZHV2xURcPaBZ/zK6tx2W373T/+2kLb/+eDvSzxJPrpJeNItt5V7LjcZXy/9utKUmTi5hfKkZNT+YSXpukXt2VKzIBRUiLZR9ar88bGa6xQguMSR702Ymw7ckLU3ilKK1N11Vzdbszk5E8Ai7jOZzd2pnbrx0PiK1YSQs1QNiCx9alqvfdXlVkvy5QPxnHBjCIUAzY4hbADQHSrQnfB3V/zfLdUa9Q2AMuDylS0haQSJOXRpTx0HlnaSJ510u7IJIY5CaIpSa6iWYq1bMFvzTeFA0i3ZUURn18epvNAE8t7DzllU3vYsvmhFrgaqfrlDR+wpacHbqzj0Wo7csQtH4sqeuLZGYc1zY25itHgINNxeEjacclemcJn7HSluMXAcXGFXCMuYtV7L42K4fz48vOWdvLZ4FnyzEiEb3lYsqj5wOMoLFbNv0TdnTb+qnZmaU6aio6s8o/w772vg7D2H3Xz74N/52U8qCYBf/Pif/od/LPHkZ2dG8iP/NIyHhOfYDtSgNvykqapdMZAg1Ea0jeohVDbaX/0FXF8eG2hjO6Lj3wE4OmduRuoWadTrhau7MXYz+SHy6ezInb07rP1Dx4wHakiuQ7TVxKsSBoA8hG5FgOnmrZas7E2+BExvKns3oOdejiEsAzQ7x1uoQNtwrRQF2tEef2cU1lSXh+8szX+/mb08Cq6cuXBJ0rU5cG2JnLnIA2y3E+maS9/TU29aHRmjJuFEUs1Rs100W3diCmcp3JN4HY1N5CIThdDbxcCr1ZCiGVX3bdBytWybdlePbPuHWXf1g6lbajdX6toc5jTvS2uc08IvHBlYcV3aYi3khzRrc6wYh4qp6YIxGh9MxQbyYl6RYmSgFBx755yDrQFk21OvJX7q59KqNe8c68ymowYt3qJ/GqhakF85QoTH13nfRcD7dW6zHNmdpnTFmqKjVIEiJTep//DFb/1Pfb9tixJSv/pgOiNhBzzJN3y7Nt+xzUaR8AMQ92OHUr3a8WmE2oi2UT2gKmL9bY98b87c9EBHdec7SUvdYtdNIyE3lox3jN3AQF00mUg3VQN5a/BmXAjkTanuPVavQ8+JmvGcWLPyHtEzy7LaOgDNzfG+AwuHI6u0ZpWzOJ0qqJOwz+zwyzb4+1a6LsCdWxpeV6RrWKe1d0YJzdZS3x16cavQU2PSk62765qYD8yueA0LfgMR1kajak+LFO5IjYXib5MRDRXULAY16/7xbe+o0F3daxo+d+JCkRhqN3nEHaumoRbVXrXvNnPXh5Et2GBNcp+kPTeHLuypveQBe27YCv7p/KSeHNOQVX6hOVIWiw8S0T5hyyP8pBAZWvZPfLA5biF7TOziUPsTeDt2nNZzi49vHz+xuXOhSb3gbXUlXi8FJ6o5exg4G7g8EH/Lv3aZKCu58FV7n322M3LJO1vZyTxFC9vmKsc2nLQV1I7uS/Vqo7ZIRNuoHqyZu8OUwAbMrSbkM91oPWwgdXdxLGWPsLvGVA3c05KpGu6dXFi1J9HUaiK7m1STTswbltywpKmWdMIg3WppxocfBUB3hb+naf6WMu+zLn+zeXxnHuraEpGWxxf56MzC2QULv/aaGhubrSdzKiAetojgZNE/w9aW2woYtOO07Todp47AuR27Yw/xR+v0e5f20D3B9iwyJpDBesr0akixGHhVCL3NRSaisQlPewEp9MgebTyqz0YMe94w002YubZGr01iCjGdwpE6Cq5wbuw7rnXy6/3YO7hk8bFPel1a/Dtueyg+0fT4BBIDyXi/6Ch1Vsbe8UxdWzx0RJLb7KyjZGt5eL3kn/tgc9+Y/cK9OnA4IjGNRnBVD8YmNr0aOAe4p97xK88cs2cWTzqk1/Ge0UipJQZpi3dGOmi5+oyO/CtvU0/SX5zyHdv78b0zxtUtFbVTAYTaiLZRPYLCkgNrnY18r0rbEBn/Lp8hx7uyq6JSt4YNMCFVd4zdzLgcOeftFlJ1T03VFmqisdWENXkb5ztdjUQKRUBVc27Cgo8DQ89mVNNZFUBtt9zP1pxcJ4UvGdnbkpV5sqrHAtAdhp840wpTRl7mb6I1/ia943WkayaPz1fYSxwJ8/g4nXg3eVQvaAKw+MwePLf6AKfYujb5gGyYohu52HpAHgmP+9ilOXRp33nGd8sRe4Prwb7WexY5d/XItl+1mybhQGWyMSw+154jZSY7UjaFR9VA4Ymwbj4wy8b27cUOl0p3/T0VrLW2MhfvojTq3bSUgV2+iPnPLZ51t9kbfztJKpuYOkhZvM6Ux5VwX54nY9P9gi6bL/mm3tdcelwGizfA60uzr96Jd23yb7otruS4cExyIP523avfCL7kc/YH+/SVyVcIThmqn5T2MnVg0jgKb7L7M5PlIv/Kt48bbkKkKXjiX0SojWgb1RM2cw9WOeTCLY98b8LcpGwS744No57U3aMAE+nYfV+tirpiU5N3JU+QpWcoS+b1XO7VXGZ0llGg4d4PDD2JK3tjgJaz8jNsH55lhpGfZ1n5OfeKpuecBnbJTzls2eFnG+/dQvighOE7Gnr4pQxgHZAdwJ3m7weZNdFF18T7kP29x3DoebPvecX0LLYaXP2CoerB7YB8z6fY9g1vB1VL8Vi9d4Rc+AorrjsLIR1VmzRnzr4OxDVYVNsqhUtpzeyama30m1Xq273E++PAEj0dVuxkYNZO/k91KdZ3anMv+edmm8W/sJdrb7Y/GR8g6sVjh5Vsy+OtDu0MxCZ0IgeNvt5O54Zhpdd0Xcc0Qc7OZWrDN2Gz0ag2H37D74Pc8ss+OKZv6OGRlpo/mc1PZ+bPOjnaW9lzdpcyET13TLKlyxrH9jlP1T4OriDURrSN6olWzUUw/KKNlMAq5iYEBIB3DYinM8p6akeHUndjUzWL3Rrx4fDyyazyjgcrchYOS6aJ1eT2+Ax1fYTKTJYGaDPdPjhqwWFPXpuJSU/BlitddJhz0tRzormN9773hME7Tl4DgJbefHnL35Xh8x0yMSdj05I2o21fm/zXlhDtYOEDsVjQRCce4pIguLqcBNK6XL0ZfLntV+wEFPuBkY3Q65WFraZPzRG2kVKL9JNQam8hm54/XmCm960TXx0FV+AI3Nx6ReYDs4mwLoZpY1F1MD4hPaaQpfDOMwpX8r/cSZ1uRwoHrsCVmNcCWPO6YUI2/NWR3ZEM6yZzqmrDtJy5ACq05LCeGp8rzLqLyVTpeKm0l0jK0vH+mk4hNrkvjw3Mh169d1aGpZ9ZPHB8hJDNXATkczll43OSNwnSSwWnprK1hxcuWa7kayw+shipcPZ2YOjEabxhJHBzWsVc3hW33ZCj8I4vdPZVw0rhV+V5YW4ztye2ko397dVtFAl/wXPiX5SK2nSu9reIXhBto3p8KYFr1cy9EnrRRkogVyLj3wkZgHjXgvOwtzpc3p7U3cn8FygTrtDVY0GCxu4ueqCBZYUWjka3Z0bPVjcdXXmb883Jz3z3MxO+8VDcz2yrpZqQ5DlpLHsD3gmxGFiwMRNf06M6hED8gFRhB65qib91HH+nFPC37xx2xktt3XJbOYN1NKplXdeu1Hh5SUkppqgJRyEUW9jprbehNriaHgezEWoluDr4YjM0sBkY3AnItn3yHb9yM/hKClXzC9AZANpfJKYovShhA3wDkLFtc0vz3+/FDmnfPJfm1mz6TE1MYUth4aJJ4eT8Ibawas27Jym1jhyFDy8WVc/7Z+D9vRYTqq/MvmuavOtp2P4Lsw/+Fs6EMk9TwwbyjTlvChXz9TpZszlzPD5IxPqFbuylcB+FyZYDbz9a3Tyftyce1cKVXKzxURyybxh3+KnNfWJzX9/GZtOSsN0Vx7TCJkg9Dhc3pTulnI/08zmb1bMPHU42b1vHs8E4C5F86S87P5M/3VoKeauI8r2J79huRdUOlVutYgY0mB3RNqonZeZuY+R7E+Ym5V1k7sZSt4941QlVN9WqTURz7OYAGuCVpdgagO6df0NqnmBJ9XDOwMY68ZLXRsd745I8J1OEwp1WPnTXhLTdu6Ej+cKXtviZhzz2L71vphM7Ohu+Qy/bqOFZytg02brD4oKr99scB/NiI8i4q+kB5vJtqIByJTbV9v5whG3KW+jojNqTahh+HizmuWAKOF1ZGVs0s7xm+ozEElJ4S62ZsDqKRjVkaHLZO7fntJ9ZPTx/hf/S7G2oYfvPLJ59h30l6F/KfVgs/UbiPscSI+l4fz4qktw3j/WTEcW618B5RWjjhNkTik3UzEjnrpw1kE3jtdn3weaGRSAsIZJhPSAy/wHwMmFdoRVpbad/4kkp89gAzzX08thpuDH74MjAgYKr5SRRSd2epgwdWke4opPsmd1zpsa4/eF+eyM4Yc68hWar0CgykCDaRvWkiqK/a6vo3HABbS8l8M6Ye3Hhh2hIC7elBKYlg0bhbaZxAX8DhUt3gAgBmsZuXF4v9cyYVRhJVVcAmlWgAdaFCjTsVWOTNz26smmeYDPyflDz6gCmW4r33vTOtMXEAaGNGF6X9OS1d4mDvcg+m8cnbZRMEM7JM2/+ILorMTOkabI1N3yn1+GDjajapd73jvGSQPpakqsBxDcDg0x2NQ3WEk0gUoo1itjz/nqEDfzNGUU4GVu0GfrGFDpzEXS/Y1tD1GFtQ7+PBbuBnNBTKg0dOi5nI1+42fXRqLoNU7g38Sbe8PIIH084ReHqJzH/kfWK1Gt5JKODxdCrfZepxk6NxTR6QikK2Tp+D4zZ/4Gxhofib8t91elX8/6Za54NhmuCrHfN9CaHc9F+vjuf5eyPFi/ORAHqiREtOcK90fAuF7sUWX0Y3mZ3csFXifyzlHz1hG24ASGvNqJtVCglsMzc/nhH3WnCUZRMdJ0iVFruiq+6aZ1bPVysHmwBtsNNxQNUBWAFbBUq0C2FQFuIYYnYzQE0a4BuA6AlYnFN0ITHK3V05eOZV0fvJJC0Iy1r2mqpufWcuLIq9pjAydC2k1icQiqjZIgbq8SQh+AFk/smMe4a2Lq9ZGt2oQtEbqYsvQj/5kwgB97Xu/5bqm7NWk3PWYS/2mZMINs+5UZQtRazdP0Sly9909iKzRpFigu/5svYV2JGEdoRYUtIjO1jU8mBp43khJbh6ZbevtnsCOsP2XFZWR8FcDPQMzA0kDTwtCs13pIpHKidySjUxRJTUkzhqZwxHh8UTe5bwPrwqKwUmLjkeUVYJdufeGuAhYHYdyzanGzPZLl1XXtyYUPNTiai2jOrV9AEOSdsgmTLkFP6kyO5WBVnH7m012YvADoVnIJL+iQ+aqQ0let23o4vXHfr1GI6I4OsGZ37eLKRf6LCdnPUziQRaiPaRvXUzdzdGPneKnMvzn+/Tn69lTnfix2+D61/8M1fONLSw6rhwYd2pys1Yc2MzKVU5iyNsHPp0ZlsOwDdhgINAN0Uuz24qgc6cWucasHeSskThIc9ugkdXKsl2zDQmL/hkR7S0PYp3fIoGRNtCzl1kxLjrhsnWzcbda40UG9sBY+UqTGd8Hfaq2rVBAIUDn+yFXhJe0j8ii3/SC+our5RpBZzDeRbvhW7ImPDqkksG/vGEinH9vFkbDjIsYUd4Onp/IyBfKMhRzRkRxcZLd3eN46HJjfdlhObW/x0skav6fC+EJ9Hj+xO+BPg8kzYEIqpHan2TeFY2paM0l4R4GlhyyMFkB0Z3vBN813UUEc2ZyiuFp+ZwMwz4iAbChYPNSq1LTUufL0XFl8uNCluQWHSFV3p4VS8n2c06uc4GxYksFbRE0pLwc69+1OUNjl/2M2ug9L33Eidydv95HtIaoTtS3sCqdqItlGhoiseG1ivMnP3URHaWJKlvntp3ftXb6h/Z1xxJ37RFebedrjrDS3b9lp8yXFX6pW5GqB7PMS7KoJjOqOCZ4TnpSM4smPW1FgwqYnHpxrQsB+b0BFKUeCjwyJIZUYwhv1e5OFbk3czq4lX1fWsiTtrtWw61ZKVvWey8gY5J6x0fTtKBpMySoYeWWJP1Iu7rhatvwNWc9CC64SGUrQ4kVGupZRTeT38ear0rjvHrXX+5sIHU94RkeDqgGI7oOyiCUSiUSRYzDewYt8aRX5oLmObgx/cGBH3u3EXPRmRDgEc7pCnBVbjYUtWnY47tvzhjw7xMen0ACxHktnDUN1ltjlwY8XOHZkLR7rSh+dw5cKGeGyS35op3RQOyB6J0bN+qNAEGRzPh94eeJw1LZiAyDFMaxCHbFj+KTa9Jr4bZNE3Z8pUshTD8Ylju0vYuwk/jEVFmiDZC+lcdtSUlcfjA3zF/dA9cWVxMZEgFkt6FP7WnvfoqNtvF0lFF60jFd3aUn6//PE33B5mS3uiwjbcKZZLfy3Fq70WGS0Vf4FoBNE2qqdfNUFOHtvQP/z55N/642m2fvxvZ+T2ujf4peJfrRFfbac/slPKuBEw215zzShKRvOQ2aJvGYBW3iVAmzIqa3LUnnwVSUwkYoauE22XsLuRTixqJj71FltiYk+peZ6glnx5X6Mru2AhkJYwWIn3xsf2Yocn/qVLe0qidH1jjpw70geR7Z30WQPpmix+GSqWzHnbJLB1i44CxhCimslPMo2M3WHrff/se6fu0PNmz0uPgwE+rpGrAaCrhu+QrYV/d+7/btWKLWoUEVqxa2TsLbet5J8G7INF9SR9gaqXuN8mT2tJlYF8M5Of8RaysLgqlr5dLv4almEH0d16dnDWrAJFn4EN2hzNTGumd34Pe0dvigfisNkGtuw4lcfiJnZeT7iVqZmcKRxQOIXpolGtL/7GlB2tXUIQMj5kc5zNukHg6lf0z5xaxZX7bZeFiQ0RmyVJyC2Z0SlcFosPrPBO1H3v6IXVxk6RdCfo6TaeQnKaqnhU5vImfOGy62fdhStbXnJYXZWd5M1pXk9e8r8baYzay5k0T9VGYX+ItlE9JzM3e0VbCfb9N3+q51CbA24fdg5U7cvN2jIac2rUnGWmqKRVjIWj7hQVIzksMv6dlFuSr9oLgb4zgG7PNcFit+jNg8E7eTSoeiA68S15N7GaPF7y5snenbRaBq9s0VNvHnANmKlefy0wMW0IyU/qyNEW0U3OeEJez1Iz/gIJjN6FiD2uZ7E9d/WtCQSWiIDRbQ/f6S5/s0YRQNimVmzWPG0v+KYI+DAO3/J0t5b0Ci2lMpITpoLj/2fvzX4bWdM0v3/Exqmesd0wbNi+MmDDRvVie4C5GGAufGEY9njgBYZv7MF4MHWq+8YnlSmuoraUuIqUKDK4RgSD+05GBDeJ2vddSkmp1HJqap2uqtPV7e+LIIPB2BiUqDxZVQG8SCgliqSCsfy+N573ecCrSJq6AOSFGbTFD1eJTTg7KyOKu4tWwLINRjzKEnYCIDUg7Ct8dbP5b/eqD1fEmoDXP6L0ceFyvfXbASOPeUMl91Yw8riVNK4kpunUfDljzeSseGHWW50YShTOpWZWUwtHYYRTg4ArwgJpWSeW7xFCzuG7lvBESjPS4mzK5KtaFihdpviWH5pzFJu4C8JXuQxFMlmro2ZEG6loM8utZp316XRrY/UVMjuv470wSG53svM0JBuNX/BvkgzoanfN/jQBiUbbWv2RVqxsWPIaBKjN1v86YYeS5fKUvzwVLc3iOViZ3FwuN1/qn32RuBbKMvc0l6EgLjujPxmm9A7yOWXvK4ODNPlpKygf7Vimnc8rrGQBbG2TaYCBH2HUF+TNNzi6ktZ/aX6CKocajwpXl4nNu0hlF19WM2rJmicyo5aT3KCt2CEEaUQ8dRsUWw/ZtLbVje76fLBJAFZ7CVtzM4sK7Wq1M4uEHvy6GhFIX/il2vCdDn+Hh/eqH+DZVzfZaxZbfYKR5YyEpBkurxudNQv4cENNHHzQKk0SYcRM+XZAAztU/IhSjJpOXiICF/PJT6HScfGs0zGt/ewiuX0fzAue6ix9APZPhbfE2mNTUimPrcybcv4tlZrYjyICA5NHf2wX9VdSrmzG/gyncD/TCwe/mE/baynPYTR4zxN8P/njl+GIwhCkmzSDctCQs9dTb/6f6f/yz7/+6h//+B8excwsZwOaL2QcDtoYbSbA4spVm+WkI0wG+6vEje2S33J/wlJ1QhwbudH6Dd+P6KSgtM9sVsmOgCRr1VBbo22t/njr68UdSdr+yzE355D64CPAOXobQ/y1ObmLFsOURidtcJHQfBpWedYu5V/mpEYpJvnSyls2uGiTVQG7y5ZRv6jhJaUmlhKQt6Ob+MjVy1/6uaus56y+ghWzKs0JrWNHLcOkhb3HMrDsnCNkfTTO1ueEE1I1OneCd9LLh6fqMVZdDcB6tE4gQ5l/94XvMOGXks9Jrn6MNBOemmPIsVHVPA2TEaeWGu5II6FmwFTOQ2mXfDrNnSg3sAF83xBNyMr+IQibxffj3Dlfls2afAPy3qGUFmml4ntJe+yt5Dd0bqyQe7eanDuKBgG2CiD7MBqIF+acUj0C8B1XdSJefF/IOnOAwjPDUbibMoVL0/H8XCpnzWRsqbw1UppZ6ouENCySE+ClAWenmH72Pzf95z/8+qsf/oirH/zTH/8ntYR7gTL66aXK6sflho8nHfGO0HVEXNxWolIL3IuitTyH2n0DHv6EGgHJTmZa02prtK3VH3VNRi8kaft/Mb6PlQ3eirT+GJys7/z4bsQvw99CWyjxGGWXuftInRWQgPKVJ/3lqWBpCs/NxrOwoQ6KwKd/j7AbULWnNiHXOXbQJiwz+UW9YTubXjnYT1D3B7AoCpAG9amW4MG/D38UJ0eWWdjQwvWScqlcTQ01fMnyd1/4zjCrKRupquykwU4a7aT52beq2EJoR6RqT5W8yg3sD8T6bZhUlIh0RNhX+Op+tc+Vb23lu4Pyxxu82SfLHuSWrWCPDb5Tyb2tZowHGBM86RdC9h6K4MVZh8ytCXtd76XfS4403AXw/WjgBA1Xkgv5jD2btQKYBhTOziyqpHDWICWThRRezcyvJybAytDn/h8AXvNQu1P/fP4vmu1f4c0qP4P9laQjXD36O0afH30E775KR0Oy0f5bmEfL2yxHhQ8Dzf401NZoWyut/r6x8rf/8dchMW0HfHo28t1DQ1nImS8kO0YGi3jyE4C/j9CAHH8rMvczR5Rg0XoG0w0LJMT0JdLsZ9QvWGlOQOr912Njr2ojaawabZQ0jrhrcDyLxR057B4Kg0RlGEmP2dZH3jobrRtE3uP2P4jbEX7KvFg1qNKc0IApDUHS9Ad8c+blNbT5CcvfjPkgOvo7P7+/JT5In/VbtHgRJWldP5rVV989K3Xv+c9//Cdi1Ab1Zz/+ylO3cw+LNrOvJB3hisuM/NaXsPbmkQw8Fk8JbG1ku9pVmhOQaKit0bZWWsHypu//9F8jfNT+6/dTgn6JnTl7pgvzSv0bXiMH9nIY1d1lIJLGrAB8g0y72kMJb1zamCjKznmZ/kxXsn5Sl2iocyL1TME1qu2MUZYFubhEBrsR2vwF7h7uFZONYj4a+kXRlXJFruxy3UQ/bUWo94HqTLA6Fayoq+pUgJxB6Lklaqrb1ARlGNwBlVll+WEWEutiqTBRqmeF74vgU4PKTjnmGGa37NgacsWBjm4kqyzbsIcJ/f3xN8/8W+PvP+xCypOSqM3qSXgZ7NevfaI7T+5wlzA31dvrkq3VLovnBVc6ucZ2z+xP02prtK2VVn3jUOSv/m/n2j8xFv7nGdqThPc6BZHvO6lv8rm3nbuNpLGZ9l4F0Hs/8QThm1Dp8gEe/MmPX4ciS1WTeIbSQeq5l8sUnJB0c7A/jZXm2Hb1EtO9XiDNbDMbGpiM2H9ADXwMlr4MJHWA3bIiE/DMlOHLxG44XkkOypehhnA1WW/+zVHh6gO+yppIqjSKuQ8Vr4gNvt01GxnjrS8xlslD7g+DxNasz8kg9IRmL05aH6roh1FXv9lPvNsndPtsevnr5CyqdQVlhkHBRvA3gp66w1GbGtYjXK6clFmgdHfRRpa/n2E+GCxNuMmpvntTcqV4z8oG/ZRAmeQLPgY+jBrNvSMro5BWXTrbc5rfL1vvfZbVF9gB0MJsPmPPp+2p3PxidUJSRsLS9meQjnT4uP5TTme/kYr3/FLqM+wD7gN5sV/7gLHI9ORa61uNLjTa1kqrgS6BOXHkezxr7kAnrXfzJmyqcftFMPzJj6nnb1/FYpOYajK2k++UMWUr+c1KZozMjpXybzOFd4nieKyoQ4umGDldJoKsdCSeVU3q309DnZW+zLirckzzRWM3Up/vtmOH8BPcrv/8NHdyjbfuw6UndXmZT0j6Llo9S+0dFj+wniFsZAzrEDL8WsvgqE0v1xFoaKBiWq7nBMLMLK7gFn8F4JeSkWLXyBJqTvCykU2E2Y+/3YuP78f1oD5/IkzXtfCK4+mF+pytxiaNj4TJWOUAeLYJR91shxkxxv6dwbhYd6HNfGHlWEBO6yvfnaf2aNw2lPm3HWAQrfd2+TvRXlEecFTj0PcRpcEDwMPOcgefQiWlMUdGhH2DN/ljjnLpBND5G60JXusD0QYvtMbLrexYZefNciOPtcybfO5dKzV1Fg4fRoPX/dYirE0elVwIlqZlP1BajxVmmpn3a/H5rZj1ALcfYvZT1HWGLlxGFj9Elm6Cvo8h5D4QuUei3/YLvtXXoz92HoocR0JHkcAeipxGwldB9EMQuwlgn5DYeTiySiylclZ+hORf/OgfSCtJvv6TRvuXn+HQ2Gx/x52OHoIFbgPa6/oOaocKPFtYpca2htoabWul1TOLSE9v9zM3QHBuLAZc9tDMfLBidNM6ez+CpPNTJ6HQHYLCfDI/lHdLnp1jpVmJywOtbybcdHYMUPXWUAnSjO5FksUJys39UcsA9EU5O2leQx1KX0oQ033lSbZ7zbTlYEu7LyBjdKTugbnQcoCo97GvKyVSfz3pi8qyNlSMGzLRlSrx+hpfAVC+X/m0Xr8HgMjQodVeMw3J1nDlBrAS/LoaJ4oTwisyrn6rvKcVk+9c7IJTaXJUx0pEvFUD2T57bZ4GqwiwFFmuexfqs8xqxGgbnYzYXjc6apOeuhNsUkDtYKs22/+msnKbaDWWG15xpo+7bg01iHRrQyC3XW//jr2nAScLJXaAJFhfVVPLw4bv8Pk706ircei7ItbAY/bLd1fYCgBoNWOOm82/UWNgwrplP/ZnWEJAL1zyTdw3Wr85TBQLubdyI4/g++W87hYPHKLRQzQisBa5DoYPcM8W7lwjbLsx+z5mPxk1PctTNQqe9iLiP8R8++jySSR4FYx8knqVRwQ/jCL5jF1s7w1Op+BU7JVsb3/9gzG/8fOcwbjJSMDctnrv7bE//RgleX91QqGxraG2RttaafXSyhTf9rVbUm/AlYB/qfMw845LpCVSNIILpOT8nL82s0t4b0P4k1/Y/K4RS5Lx7yf+wJM//uCLXQXQ9YQPcHOq+A4wdDn/tpF5006/eQaLg18ELF7Iv00XIYsTZQNaNAYpMz+iElwDhts+BZccqQ8lfXHXoMhEMnGTvcXsIY2fX/qisrbrv7CxLSs5+gRLMrBgoHQ8iEkC6LlI78PWNXUbb1SW64iz9n7YwL9O1Hl9HrCmMluLjauHjoNhRSCidnWganbQA00GO5oTpGp5CU8v1d3O+jRYgVhV5GgO1Z/meJoLRxS/DWr1ETB0oBERu2I76hZfPRBv1WurPxXvHhep3U+hwpNPjL9MqktiY696r2D+DfjbXwWHANyAAyd0HV3+DlRNgL/XfW7WoQ/GghY/7NA/AeisSNgdn77T7JEawu5aNT9dJHcEkH0XqXBu2QCvzxM7dxGqlZoqMW1s7gy2Rej245ZdYmonNr9H2A4w+zHqOEPd59FXp2cWne+CQfD812EveK2LKHzdc9R1gjpB7aP2TWyuFbc1ks5GYnEXhb7d4tO4IPV9LbaMF96LYyydlJGfBg9OCN+g/xPA6x5zf/3Vf////def59J2i9Lcew7VF3uC8nYM/PQGb3E/Pc2dcb1tcWOb56s9p41FarStlVYvE3dmxnZSAjH3GP8Guh1acHR8+rykIVI0e6vgmqdTuCLy+ZthbgnKbOIL/CYTx98biSDsvlNuvGQmykaiqAcsDjB6JCwOngo8IWBx+OS8vvhoq56ISEpf3CQUmcgE3RscNYOPNn4mkbo615cWHuBfazHc0tFUyHOnjda564ZniK0ZqzKLt76k4GwtEIE8z7h6J/HNbuLtbuLdfhyQkE69urrQSC5Wwe49PqDrD+8IQc1Jvozz9R6v05/u8LStZlmov+f60yr9pwE951b2I80klzAijk+vrFxz8elcG5uVUjxKNpgB/qK109zJukhToaYK1SDAaF+FMf8eJvwSqRgjJTPgbzVO2OpUv788zR7fh0vcU7XSrlba1kzYN2L2bcKxjzmOUOcZuvA5G8/99MygM2Y/xGx7xPx2bH49PrOSmtnEbRfRcIvwhirTK8QS2zt/9Mc+BKMnkfAhGjiIBi/CkU8IrlJGchgNkMkFX2VSYj0sOmUlcvNIaXqB6vw3Vxz/7/7qP/xsLW1Ql6k97s0fZ097UqUmlPBd4yu9CJvsybcB7lZMUoTaFc3sT6NtrbQaqZi7TgjF3Kk3sbJB0IINFYROAoC8fRWjh5ZNxGCkrjrwGDrmEjYImV/pY24J5SUBqP0o5hMqYbosniiOpxkWBzANkFp833agWBzge4NJZeOxuPFVWZytBYmh0t7IUSg7/b1IX4YSqduUFN7MuovUKTzAWZtlxdaSbM2JQM7wmaPYxEFcPzRVy7SrR6WuZuHbPigOBtp7UzBbx0+Zn73ZmQBLs6s+A3h9YKd/IGFnWjtoM++pO8VCEW9jEW+WCyunAsIGtUP/9CqxcR8s8CP3eHOu+Q/E2nb958O+n4EKbCJp8FeMXkrP8rdVRfiOe1D4DuzlFyO17GIj7VrvoLOTQWf3WdR9FVl6bXSWpOfzKAB31xEG1SNb8fk1Yp5Kz9DpyWZ8YjM1sVEpnyfWrhD8LBR+kOpAg7PlRShCZr2u+mQzsXQUCYICYH0cCV4H0Uc/MdTbY9vYqZzVrroFAM5Ue1HEQ5rc3V5MtDx+kdz+nNeyg+o99yd8RGs8d229ALUv0ns77V9z/wVfS3a1NdTWaFsrrUZcAK+3RGJuu6hDyR+F4cNTtGwCVA3YWoG83bRJIv6dNsRKs6otUKT5W8DiUNI9ChZfyYzR2bEyIxYHLB4r6vAC7IvXVlqjceJr+xZqsthtpwwAu0cifZEi9c9qMzyM9MU5rAhEsl092plF/jyiuz5rr00J5hGRKhcprxsYKS/K1oEbx16DPO2tL6KtjPq8cTXFxqeDhY27bpXA07ol0IgkWi1qVSj5WF/57iR3ehuln5CknBr7OHe+WR/OEG2z8au96sNgBTYz4LhV/wV4fEciwpB9x3ywanCTejX28L3wy5IxTFleFZ3vIDovXTON5wvUxcg2HKeMcuM0unCCLWzEXLHClJN1b6zL3OSp6Smu8ZFz7RZXDrDIhwAqKfN4QIgD1LeLBw9QBLD1WSR8p65pzStC0MYOl4YLHfNWJ9Zj3kJmwdad/GGCb3RQQqOYmjny6k1GBrJOnklUsrUK8Lq3liDW4IO768anfsX2BrWiobZG21pp9bpVzb7bSfWhTD39VsDcNiZP203PSKkFIDdghY7U2yGtYzZLqSkM+YzzKoA++GKPvqEsCIlPSOw4GtxMlwf+deGVyWgVSroBNwMWBwxdYlgcUPVIWJw1UXkGi0PfbnnshjcWhsFuPi3tVe8vUrsAax6RjJxnSAsPDO368r36M8pJX+qJyHPN8n5Tap8SzRrSiCzVPS6o95iwPcPQmkuirhodlGK2Dt35ZBereqw2+ilYlrCJFu1rIGIpNisUibfqYqEIVCpTT1eJDWhFLN/GBo8ZYQObwaMcVGBnj3bJJ7oQaWS9e+jiccRzgnqOmZbzedQtJ9iA/F2C/W+nmuFLui/8EvJ3nxImBp62h84hgM6LrGbjFLUdY9Zj3HqAz+7j0wdx83bCXMu+AeeQWjXXXv3NSmV/J5G8DuKS1iJdhDXIQDZUBFG9M8zYZiGwh2OCIcW7ALYfRfZRUIGjSPADbFoPsXi4C8Qe2FMrT9p+w7SxM1mrZCdFucCv1BOeQzTopjr5UK5utyVRfPsRpT/nxesR6U1GZlor3OUJLGL5qA12QnivRqaxvVFtgo3PjEVOaKit0bZWWr2+mLtPWMKIuWmRoQFtwDLzMvgFCWmxPBGj/Rx5C9redtGNbKjCpHoKPwDQl4HIJz+unr+ZCMz4QP5GqhLC2WXKLGBxQNKjNVEZyOLuto8xEJRzSRtgIAjw+jh3/oFYvw+VJLOapdpyyctIfCURzmVdvsqkfSjteN1A4NNiUmcb6vZRumd8jmjSF76cvW5y1Kc9NeE8Ypie9tBGFRYcsO29VDU0XnDnhBWKRJpJR31STooNKFxM2OyecxepSM07wp3kBmscFa7WVdsk8xvYN6HIh3DwLOI7jSypoeeXF9v/9pAQ/lTxNy98h2ScdlZSU8X8W/7II1erUHs2Vsgbmu17ANmrle09IiGwFoGi4UioknJ1XDvq0iPCNiYYkkr2zrcbyamLcITVipyg0b1oYD8aOIgGTsMhsUXgwKb1bRD/EI48+mJP/YTNvj0yuRAtzdiGm13unYsAoF+GolhpvicP62pI0Mq7j77IQfnjZ7tm8Scjd8lvOR8SW93AjEJyXe12pwsu1djeoNpd1J5cb95pJKDRtlZavXolW67N/msM+G+oJHVeBhxDmfxVs6zpBA2xm33aaNksEJw4oQmDQczc4JESl/CR8re77QYvJFxF0KyBiVDxCSgZL5nxgoFvovJCFueZqBgFhobK2O1gJlbZNuTA1rWovxXfwcJ0ahEvzokNvJQvtHyxdd/MYmzygDDsJcYlRSAOesyqFKLO6ChII/IFidRfy/UFkHSw43MyOFIePAyQupo2NjvsKCkUAd8EPwIILhmRDfafG7wpMNzgDNHugwWwd23XfyFWPK/GHWsxxzbh2MOcR5jztKN49n4IewE6v7bPBr/rfIGCV7cdYdYD4v1u7P1WbHYtNdnMzNVK0TpVb9S2OfOTIcwHGf5mw3fAL5bwiY49dv5diYQH6WrzF+18Yz9GiFvLh9FAOeVkZwqddRnOhixoDOend7qcvYG591D/PoocoMgRM8X4OPSmI55goxo7QANXkfCT1K+D0+BabDmdtXpIswJJy88PdL4m8u8/BKN0con1jYVTvwzdssORLlrXSo8/BHJsUtVnqOPsac9mJHtkb5q5v2gzt8NTcnd67ZKNbV5XWzP702hbK60+M3OXdNv9wpK1zFg845E8UzsY/mOx2yaDa4sVU6K9KiBvBwyzNAhAirEg7AxZSpI3x98Ao8FV5An6f6vUnxAwgt6PA3Bn+Tsk5dXNZv0AIlfVU2RYfOQmKoDF3UUzxG5a7gaCfrE6oRz2eR5GN2L+QsYRLc6qbl0b7DXTQt3KtmlPiEVuZvGQMB/EdbuDjKslpdXuxDsbOT5YaEvq3W1MSu/RCUckcO+XL32R66kLYD1dcNoGBvvROlZzwrW9lYUiHGGLPfvYNjZgaCaNKNlKu1ZS1tW4HaDzLu4C6HyAuQA6n0Y9AJ2vXhOdZek56jpD7Seo/Ri37sbm1uNTq+mZWtZVrxQAPa+tDoFBG63fHBSvPxDt+0BOkGtTw2eHNf/mwi/DVZMAhR99xA7mz2Sti8zy1dEw2juOMWKcBb/u2kq7dqKLuwCvmab1sIIQoQjHH9vC/Eco8iTzJJdgrZFyBcpT0sfCwMFT3sknWJ4Gy4mTCLpc6QzNLze89hr82t19nmTxHXjRs8zh57lI7ZLfPvHsz9Fanne7cpZn2kj15N2ixjZfQKKhtkbbWmn1/Ym5+5y5vwHfKVJpqdxBCM3F9g6j1rA4ZPolHeymQr0ZTUZw4hL5NkBkYeyoOXsTBfIeCX+vJ/zi3ry41T1UyRkaDisWX6iaZLAbdrtZ7L7zx06ioVrCA6796pUh9poZsHWl4D9H7Zeo4wybOY5ZFNrV6p1A9okJSX89rG2xkTpleIWh8eDvrRtGaJYnKX0RkPr35foiKYBR7qwDWOcLRUrFxVrWzTaeWXo+RhcAPZ9HlgA93wT9AJ0/vZLPRkfxHL4LIuLeM6DnExjU4jhDnecRz23IDx7Z9zaGyZpRcqWo3NzgLRnReV/6YzM9w7fHLuETgL+XK0YPqVdjPtgbvmTMT2w1o7sxYZc64y1XJ/DC+1Z88SAaOIuEPyLYMxcnCCGwFnlgUsYk/cUBvm/hfrQ441StxhazuIt39vBWJ8CK4g7BUzk7O8nDZrD76yHOtwp8EaiMn6NzT77EZv2Xn+fy1PuTA2nwX37GQq+pH+7d1RQ3tjkByU7asNb4qF3xNdrWSqvvtURomCzpwPedpLB9yI7VJzPerkhaCbs91AQfu0EtUsLWOLT97rdxgHBJ6QGgA0wfLCF9Bn/zLMA7/E3rnJReZat7WBYf1kRFAbvZbjdraMiaqGAlKEnnc2euMHWGWi9RG7gunuLTh4Tp2e3qnhOIOn89LszFW19aqM/b6xPQa5zWK0pN4I/sNTUGFH3hiAphLs+roVxfPrfxy4v91GXROcj4bIQXLyIQnZnGs41BZ4aeo85z1HMRRQ4T8e1ccbPZMTY5Lp59CpUekaT8UdZxwr6Mb7yEsFm8hsOX/sQgUVniA44Wc3pSPuWxkHu73j5mnzldWAyUTM/m7w18+TIUeXzRqibxKVzYS+T3sWDf88gQNtPGjhbTDg85IeuIXxPeXLLTBvEc/CLvGcCKnUwufPLH2gTCTVJGmslG+5fp9io79OnsTv2Wc2/hJCJj+vE5JiO7mZHg099sf8fdTwDv5Knnb1Po8y3pb2yvU5s9AYmG2hpta6XVl1DiyPft1DfJ6gw0kKak+Q98nzebaHFQRrlpISdl5GO3n56U0lMa3OKsuy55R6qT6v+Wo5hPMgJTjQXhYWx5NPdAqafT3MkN3rwPleRYIVWYwYpGomSQNFHxkgZJuTwU9tQMHtI40NAwVtSvxN8N0a5WjIMBaAsAF2AugF3A087ae3vdZB0mRdJB62zU4ND4pbpl2DCX77e4zCM5WJcVwNBf3FApWHV8CpcBUR0XLnepbzl57mn2CJANYGjFvPTnZM0IjprzxA7Aa2ZQIaHimE20MvZ8/h2dkxixaPFHHuHz/62ctchVKJrFLS/hb/XnGfDXrVBXjXz+JBIZrCRB8HrcHSxNyyRngWWqTuyODz5rN2kSNkpoA1Ke5uCb5eyPCP4hhAfKnQikxbqrsnINhxDaT+x9Jy7sjCjC4UhmTvHpMxxT4MzJLUsOqveLTW5uwXCCBboN78xG+2/lGtt81NYcSDTa1kqrL86ZW+ASCDAu0oaquGTGaxN1pq2MYyD/GULVaUXsNsUznRZyKGsVX0LspLS34FCCk5Hwt0oLFL4f32Vi8zZKP/YyzAbUTQDbQwNUciGVswqGGvkmKnLYzTY7wU/VGBqKTFSMsZKh2YpJiqcB7HrqNkdtZlieVoF9RkdtGvD0IKkJzDpxtAzfy1HAHxbkazY4xfNtCPkYDLJ2ciORbXyJfur06w6VcvJriNfh0hOSUcJrEeKXcqYyoxXZFu3tgLy5kUdYXWuRm6BQ5nEaDtUTnmhx2leZTGetjcTCWmz5OBJim801fLbH34OijrjwHWnzQaZBe4vW2rXr23B8wInIH9/FkFBxgKOIU1LZUrEsdoba+xyi0MI03wQwk7UygnKinPZ0pSOWRKux2vWlsXZNpdibTouUbitpEMg2Xq/A0o7bGtfxtWr7nnvnnoq5ayuZEclOktwajEPtnbRBcyDRaFsrrb7UXl36rUDMnc+NsT8qtnfsNTFMG6y0sUilVWI3uJBz2E1k3BKNc9roK1rkXL355B0pmtUITuT4+8EHJShK7Tq5EcxY/jh3DlvX4dK3/qTK3vlZOAwu5/mMXXKokc6bTlErHFjEZpj0cuN+QseXVrPYbZPx2ViomkZnaGgMPisfkXl7RnvdAmB9uY6w4YjKYS5Y22Klx5WlJmCHca+YRk7Pn1nx/DHouw4vgrqMuC6izjPUeozNHxHv92KzW3HLesrSSE/Vi55a3l+vFBokvZ/dIsrvF6t6hVFLNVL13ws/daH0xZ94QlIP/W48V75QKW+oymhFKrm3hbyB04rIWYvcBvBtzNeILzaJxT0UuQxF1QtCAEMDkgY87WbmvwdEjdKD+Fskb0tkbOIQUIltK3pdN2nG83MLXWNs/hAkXpzlph5hBmRx9igSBK94gIY59xKkEWq0e1mhC/X33V/v3Ewr5cc63oK589e++mw2/4Y7Id8yTiPcRQSgf1e5nuJ3teFv1X7Gbcw9HmprAhKNtrXS6gt3CVwSRr4nvyHS0woKbPb6ioh8zQBVO6EWRVqMC4gccHmUdLJTmH0z/rQx2d5ghyyXq3qFMMuhpN6ybbZEkI3gGYK/eRLwrYRfsnVdS7jFQ417hPU8aj9H55mZxQk4sxgfYmbRRxnl5PLQtLFqwkpGgaHhSExUGEND2HEPUROApzm9x6jCER0tg3WQg56N1GFFy/dDz8OgcyszUc7qsnlDsmCIVPUB0qjsis1JKS4Tmw+Stn2+eLRsYgBIp8beGy2b+iNmsjBiJn1wWL5db/2WL4AR+6l/3y6Nw/mpZ3Bbbxuu/ma1vH6IdSD7JoieRsKH0cBhNFHj0igAAIAASURBVHgaCQ2cYnxS7xbiIy5C0U74JeOw5Bg+fOeTH6OS82pN38XfoQ2p/Hw8P2cXNSzAxxcpzkRKM/y2N1hdwGFrJEYUbJ1ZydpsYeV4lWe1vlxbFq7oSu/OMJRVbqytfPfal57e+sqfhKjdMHFXhBM0wLQthKjNiLy5xna850CiobZG21pp9Xsj5u7HL4DgkTrRg/KMV1pYTEpoAAZiN5az2PohkmXuVA3lnkTs6i1J3khF/wzBCefVHSpPFVOOjYT/Jfy9nvAHajOwXY1amZnFmSPCtA9nFodML4f1Zjc+th8f30/oQR3EDa2sabnu9tEDsFv8/SBlxkp6AM2jNTQETwWekCgboUML5X5243kXcwF0dqPuAVITFiko3Whc6oLIDeOzMbDrzHd65rtis559nrpT0hU72swqELZy+swTkgaUDB6w3g86K43aYlU/INWyJp1qeZw9fQgWYGa77F6dANCjIMIeUvry5fqpDzfaAS3t8f1oCPwrnArtD99RaT4Iz1dM+I6vYvBXjFE5j3xaJ/hAwR9CJhbw/HubiL8dlMFXthD5uZ76gjSvxZYfGdPuNcLHZUOizbzAqb3SuOT1xTsG26sZHUvAF+ndV5+M5K0zN9vf8S3/wqVpdhxWjNr8xvZx0sigtkkz+9NoWyutfs8qnxvrZ+431ew7/gOK7R2rRKJezzFQErvlgirdtUkrJaFRJjJChgM8Ha6Y1AhOBpI3FyXzoBgls4nhw0bw8Ecwj2LLg8xAAIjDscX9hG4/rj8gDNsJY6JmsA0ST0PspmQf464a3TVLv7/HxEJ9zltfCjeTfH8PztBQvYmKnFgc4HsDjqm97bF4CSC+IVWcBowLSFflpsNwRuRN65WlJg7a8Ax6fobT88tdsbldTil9Jly6TGwKQtS3az8/LlxcJLe7c4S9X0HLpiFSLSuGPXxJQhvtT95FyP3q0DpXdeYhkOBbePD3S/oiIHVVri8K5t+0Wv72VwFSm0M0/7DVL5JmKrnQjHuQyhT3zb/4669++PUPfvj1V3/+9Vf/aPxPYaubCzanGMsRJgvzKoSGutOQyw1vZeVW8CE22r+y9fwBO18UC2Pn6X32bwG736teaPiZkexOyNcWssfFRus3Uu4lXGMb1wQkGm1rpdXvd631R76DShffCpUApF7ZMVBQCSrklNF2Q+aWIngxc7d5rt4DydtLjoOHxUk/m4J+F6kMMdQYRDfwZSahg1VXz57iM5vYHMffT8/ib2ixx1A1mR1/OTcgtHkAdrd9qj7xxm4tuyjoOseLE2jXROWFLC5pokIUjTscBYpkGx8ibmjRPUBqooNSk1XLKx0FbHw60giJCZsTitCrTwrTtFz6jESvPZDptLG7d/bXW7/dr3w6yxzeYI0nJC3ncXGV2IQWIuQTZyESpqfZVEubilTLaHVis/7TVzEPAcuGUBGAOMDxvpPJ6u8Oy7dgscGuGao5qAxuic4wYD9pZN+Ucu/o7NwuFmzii1+sn7oa6Usma2NuCMwEix3zbyet6h3y+HvCTc7wf/RnP/rBD2F91a0f/MVf/wMuff2WCYR/9MfLKTdnpJ1qrfGlI1zZ6kLJeKgyvpVzsQtC8GG96vWFnxl5ntxh3k/vIsLuS3vUT8S/yLciOU7qNdTWaFsrrX7vK9LOCYQl4L+hrFA0osYxUIjd7dUlyiIx/TMkc3PkjZVMagQngLzbuFNaZoCh5xF717h65piYOCD0eyqMqwF/H0cDQ0bwdPibsUAJpPA5FbfL9fb6hKsG+9OS/tMDsbuIW05Qz3nUexlZvg75OZ8N9V1nfgFKTpTMGGOiImlo+EIWR4umGDld6wYusiJve218oJ8g1raMhLDl4tOXG15lKTYcyS1cAYyWbGMD7Gbb2Jv1X0EMbf/dZv2Xh6UbyQY2l94CFdjZo73qw2bjV33IwjhhM2O7vV/cw5eg5qQ2PkBzIkq1fJZ5SOIBrBnQmgCvO5Dd/jvwnsHaAGyKw7CnmB+nRCOPmyndamKilZhvx9wnkeDALMZ7f+wugIN6UHzYFyx96fTUmZdT+7osf//513/Sj9od4P5n7n9wFYqyf/gBGlzsClRYI23JXZSbjOQMth20rpV9e1j80NmLqg+vd2XZJb/lRE1gBwbf8a56OdRexZeffPGNhrQZKOex/S1CaKit0bZWWv1huQSK9LsAxIVzluocA8XY7aEmxA05OylMS4Z5y5RRroMI+AYQDNu6xstmf8XgJmVllOzoEqBz8Ej1VC2lAxnbSzDqalG7+jC2/AILwkCaiAj8p6Xkzm5Iz5Gly4iXpWd2UpDIzDtoabk83IY1UwW3PEPuLOg6X0YdF6jtFHt/gs0dETP7senduHkzaWqnDY2spZSx4CXDqAY3BSYqWFFnI/UKShsbu+MN4yfICkWizawkYasRiuzQPwV7oHIbe696z4LsLvl0ljm8jq0oN7AB+mzVf8E1sBUIWzJrhhVhh+npoTQnu/iy4qBCAvA3wOuj4qXkXX62wNs+Sx/cB/NUylTKj9W6KY+bSf16YnqLsO/iiyfRwMApxjsEvwmgHxH8QXEdexvADqOBFWKJ9f/pmG/QxmLaLXkMggPtJohu4y5Wh3BBZJ4QaVIfSvoCXjdUnoqWpgUVz8/l0/Zi2ikoKukG77kdE9YWvnyAwkFPrk4jIcDTUEAiRG1YgMLZbRXPz3eNtN2skbZkITVEvAOAg6tNbn0Kl+HuF8yDldJnmIx8ROBkZLV93Mu5rICzk3RXW9DY3sFnNdTWaFsrrf7QXQKT31Szb8UPU+8YKC5/ZVJgyC3H3Nv1n3f8+IJMAIfMlfgac0erk0hFv0jq7IOk3ljZKJ8IA6l6LzHORC3q9+PGdnIcpY1DNbTW+SOY6lrgfP7exPBh289qsJtD50sGnUEdx6YPY1O7cdNO0tBOGVpZHaDnWm76hbrnvmFcXuLmS5rioJCMbmB0pZyfoLIUG3wHaYSUCZudd1RuY5/mTjZWfgvo87hweZHcBgtCacuLQI5tYPP1IZxF2hW+2n2JhNy0rpqw9MZKK1g1O2hVo5ZdnxOI13cR6jyxo4DX3FsFf+8Zmivmx8nsu5XkxHp8divmOkCXr4IDshjBT5upxfXEgrj2o77jcPBUVNeh6C2C3iKYoB6Q2KOfENeTX70djXqf0M9XkqgNaftHf1JLLjqZM5KtZuQbaYur2roRO58glfHNtGGH+gn7QmA/fL1LyUMgzy0L2d1VYPknmF7ob2xze0tcQ22NtrXS6o9FzL2d/CaRnpJ8pHrHQInfrfRJUxyk8GYreGZfxcKn6ouoXcG4mq1UxhSpmL3kuILU20HpWMFJFZ9Qqa4OVKYq2Vk69X4tZge1I9N4Fss2njWCybMAZ/hbOCkILeqcgJ7P0HmIzgREZyIzCbDbJp1ABMcNMdLyhexgtZUWXjJzTfGhHFQc9JgKP0F9lLKkWxtyhD3Qs4+z7WOc1xOSbewPxPp+9Q42sLNHcg3sJ3/yI0qzDWyBPqSPsJWiHIWErUCrh+EAV8c8Wr0JoqAEzAppFSEkgVV9Ho1Wo60nBdr+8Z90fDyaCYXFoWAykgmp7XyxAg6x9t9/INqsK87AZdWzi58ZeVSE6iN708y9nzNfSFKSxNZe7bB38qxcaJdjjba10uoP2iWwML3dHz8J/hvISd+1l3QMBN9xkUaFl9iu//wkdwrOy9dB9A7p6DEAXkvKVHae4a+XeBNPAp4ze6Glmk4hK44lb0qkeGY1G89TPCsUAGhWAs64dxHqCKbH320ME9vV8QujLICtZbDbALGbsnzR+x7TFI+V9AI3w83+pjiW/MZGvhtkZjwO6LzPQaVsUEmrXF2Foh+CEVDXDLNydR/AHxDiwS+sR9kPlOg0UwWlUeb3VYrb/4nRoogLrEbAkfjJj4G6DaCXwchZKHwSDu1gSItYohLuSsLFV5Kksza0OCtWnrBu/R7SHC5No4VZtDjDamMAVUvR9g/+0fifuuvW8urlwCOIC9PhRhLZkPb13OJG69dPjNfHVXz9lY5ffmbkZXwDfAda/tE9y7+T7InsfSRq+8mPdxvbCe1CrNG2Vlr9UVSy9E4Q+b6WHmu37ztWfcWfjAePY+WfKzoGwjn6TtOien+Z3LlAC+dhqNEEoPkQXbjEXOJ29VJV3DI3QOZWEIHEJw+IqX3cuo/aDzHXAdptPPPomZV6u+SHLKH0oiv1fp7Bs4TiGX9/GJsSKJ4Fmg2AzqfByG0A6/C3qhZ4j783EsHvEbs3sLAacuWYlStAKveAVpGYoB4RaCEsVbK0mod+gnrJKV6+xbuDdRzT6jPjbH/BA98fE9enQOwGwa4DqKDOQ6EjFDmI+gS1hi6txJ0rKYeg1rEI/zABe851ECtk7OJDwKrGPJvWV/MLlYwtUbAGS1Oe6oSTGbu0DuNjCKgasDVeeJ/P2GsJ91psmQm8hKqbJz/RjnmXqn13YCQb27Em2Vod3I121t4LRVbQZFC3lvoGzgPkzjt6aBnN9Atrs/0dp4O6Dxe79N99M5ThqHipgNrHyd4Ruln7mXYJ1mhbK63+qMXcy1HDf/pX4X/nXyyy9af/GglmnxQcA9mLVqI4fYrNncSmjmMT+3H9bmJMjp7344b9uGWpapRkbkHv+XnJglhlGlC1i9YrSL3BT5er+mhxog1WBaziGZ89BKsCRvG8nTSspvSNzFsyO17J6qp54wsVz4Ke6xHu61mg+IeL4Nklgnx+reCWhZrJJuNxBrAbjlT+ofRcAVJblecFwQqE1OeHnCL9XkQFksAK9geB8IOjVbCI5YojV0Crp5EAKAGzAlpdxRfEtAoqi1kHjFrWxheregl7b1F9DGDruA+Apr0rOPY1kHRrY6m9bK3rrXWJFC1wSAYrU+KtAdZmd+E4I+pNDEB8X+w8ggarsxL3smijvSYxfZvEpzmkXiItC5SJiXIczhp8gWlUp3LWcsq1QixxSN23OPfFwIeyjyLpnG2BlkiqAjgOHvbnX3/1Q9YH8Ouv/vKvvqJW79WcQ4J1TLAl2a/J3Nt262St/XfsRMFdlHyl+chHJMnPjIQaku42BGfUreyG7KKd3t1OvuMa209IUrvyarStlVZ/jLXevY9PEcYf/MtFDrXZ+nf/xdKsf7WdK1zgrivMfobOxUsWqV6jYbk6cYgunKAeRvHsvQh7b0IIh84PfqF9QY1YkhzHPPEJcyierRDIZOaQit5NKrl6O6Grtw6Q97bf3bmnzO+5SoLRSNnr0hc6xny3Aaxrv6DWCOXBBy3Awe+CZwBU7a4aB2D3H1Bv1UmxXCXHTPBHgLxzuAUyKxK/9xNcffR3sPU6iH4IR0Cdh8MstrK1iyJb+HIb94hLsufKtl3VjDmyliZn6YNbtMZ48w02DwEPlpThrpfa21h0Hw3sRwOn4dBA6z3w07NI6ABdXsEtKlMtrVKR8uCFADVGi7MsbHHTqMttl7VmFEM2+1kskyaxr8gTtP1hCVvWngU+DCEOcJ8kYXtIs43qIXUqMx8tzviqEz2kpoe29nPSxkB5KpexrxHLZ6HQTUDJd+WRCYRvxZf8VYvyk7toE+9vj4kz2BWKyYzsom2tx/GRyvhObg48YL9yxz7zYfn2dScj/XF2D8eoni9KjFxQQO2d5NhxUs99oDvtX2vXXI22tdLqj9UlMG/eSr35JzqXALXZ+pN/ubS5HGb5FVx77pEYqAMcsYptPWjAtcaeDtIXk2TTJ0XmZrUBYuZ+YbVxJ0yyJHUORVtcRuptziVnv1+a3I0Fr4PQN+1pmAk2sM2hmRqK+Gszw2L3k4RIgHjqFmC1TrdVRK4cs3J1EYnuob5tbFlc64R3SIrFlRaKK99hOYtyaHzHT5A2CMTiz3EzTL9pMW6G4EnSRehmSJQM0J6Fcivj9VHxEnDzAxyaVMxulDEP2ag/bCUzu1jwAEWOosGL8AB7ENh4RrDjaGArtrgdcxziznPUeRNaugsiD0hU8EiVqZacvbefMvMhO9bIWWtGuQWPGLIfO3qqhII9ixxhA6SmMrZM2spHatuwSM14/wGk9lQngqUpsGzIpW0ryaVDLHgTxMAp60nFgXYXiNUTHl9l0qb2RQ3gMOltBCTeXP23Kk/OjfaveDvzBCNfYfMQdGCHZB9zHVtljXEEfjgjqWu8xb1zdjLyIr3HbXMnKRvLsF473U6+AwcOT7GtNbY12tZKqz/6+o++XpCkbVD/u3emy8QGO2Vw1UxL5MQyNQFOoAtVo/hqZ6MMq8q+v4P63K/E3Bx5s0mWCuHMdkYTCQAdq0yPQDwAGYIQVfzeR/Dhla27AP4hGBUg7E4iwPA3Y0HoVyVXYC0IAX/vEt4vYaRyoGcfK0VQtmVgTUU+EOti2z43Y5io1LKFWYDjbp6vDgBlvGQmykYBiz8j4gfgO/jFvsHNEozbTBVm5PD60Z+6DxUFeL2VLe8SsGl9wDStrwMDPK3BDnMVih5FgvvRwErCvpqY38XnriKuj0Hfo1/xd/3J+2DhFqufJ7d2ss29WALgJoBvNamWrL13gDRIkqUYsgfeFIJaqUB8D/cD/K3mFzik7smpn4XUgMjBk7BIvZr0dwTf/vhezDcUXsMYFyQGPpFsxuamTCL1tsFOi05fvXFtY6T8nv9UZ/kz9UcNNxlprettvIVNKT+2mfe3GXN01gD7Ir078mN2v3onmIw8yZ339IS0QR61D3eSUFLIV2xrjW2NtrXSSqu/5yu2hbRtnV0ilZSO8M6+WF5CG2LF9+qRFLC1ZBQlYPFvpafrYKtMXA9I7C6A3QWFdRqKHkeD/DpCA9vYMlY2KydZgrfkJvXBiiGVmWN7rs2MGzpwUQYsM/d9fV4biWDPAnyI/jfUn3T4GzZ9TURm/nVtAVXEpyt79rW73th3kYpk+oyUyFtxVI7WgXLUDJ64WyFrBoAyVjQCaE4Ux9MMi5PZsZGweKyogz6JJXML9+xiQcDWAJfVNK3B2uksHDmMBvZRZC8WPYqlzvDsUXTxOhQYNOqQAOuTu2j1MrF5ULrZrP9qtfmLdr6xHyOE+mN/7DAazGfsWNmiKtWSOUDAIxHarN5YkMxApBZNKA6N1ABzOaROZ6x0ynkVjvQM7xm8vg8Q24T3JBIaCq8fGdkM2BSFtMMhpcNmGsxmZ/8tPjut56k+9P7yFPjikx/nL4PVHzv8zEhH3chxfKgyvpnsNLbP0gfM0yY26798hcnIrm4tWADfOSpe+itT3Fuqto/lUJvtau+kDT2X9EBau8hqtK2VVlr9/f9pXZGjbZ3Nsp56kym8U2ZuFyV1Sxo6/bERx7Cl6qwZ3aRxmZpIp9ySU1xuwGRSzI0Up4a6GDto8H5gLVD6JVK/zFSgoo9U9NGKHi0biKIpWYCVyZvTuQlQ4IsoQ94eeam3tZung5ZM7q6GcrFi+hI+wc10eVgLcC6C5xBFUphtVO+ktfrrwspxpJlWCHfMrewrEzbbxr7Bm5LpM9IJjsSaIGIGaw+QmjBNU52V0g+VNQPeWLw4z7H4S1J+AIuD36Kz0NAQPA/H4kTRuIa7ASPuRwN7aGAnHj9KrAJWvsXq9+HSx2D0SZ6tPwWiV+HAfahwHVsFq5Ttei9Ge7X5CCD7CMXFkL2HIpmsFRzFncO5Ngc+puLKebG9bm8aA6TBPUyq5R6+BJCab/rh6vSADcMmpXNIHS7OJAt9SC2g6uNoKFSYY+KoPCxeD5UI+whvFKDt2FK0NK38rryVCVd/kxuc3LhNx95B8hWgEiabnhcYuqs8iHiZkYbFhovzIbHX9KuZsXYL2u2B/fw+CEXVH4j2601GgiUu+O9B5WYVX+b+wMWmVU5Awna1AXDvNi60xrZWGm1rpVW/RcnK7/7hv/KJUfs/+FceEuu5lwAsiFR0nRu7tGwKifhHzP1Wowh0uveIaahRsQNErpq8tWmWuYXyEnpo5n5JLZEWVnCiIPV20jp/xRAtTuBFCyT4kjFRMKVBMfgOCb4wkcqaQcVh/qIZFFad+r3gbxjBky4PQ9i/Ka9cxZqkt7EobmOz6KYc7si1sU9zJ0yIemJQnz4JExzTB5IRM1wdF8/uIiSgHAy3MGYOOlmFN9wPxwGgS8qvzxM78F3B6caEwnTjQyCzjodipQm0aATcPBIWB79bYlgcPBt4TkD5iZJ5p+sc8gR9S4LnkaVd3LWHO69jLbEN3Erj00ameAIgu383AB/0emw5lbNyxyb4pKLNLPgoi+0r2G+uS2wrhDIjVT2rORmJ6QeL1ADHWTm1GKk5EQgzThC/RqLNpJfVV3TTXp+D18wWwA+jwWLa6aTMPNG57FtdrkwIxCTgnXurE3wxCTh9cZYdglAhldAJFkTc2/A2+qLak8W3WzkX+zCw57NPu1d9GO2pA97z4Tz76j8Fy0uw2/feRl0vi9opHYva4Oun7qDCk+axrdG2tgm00qp3iqf+5j/r15P8e//v4o9QS7Q8TufGBBNjbKvbU7Us0kaAyI5az4GrI8mgJC9aBjdtVnkNtknFvwMuX6KNCDnur+qWSKaYHraTZkv3euS9XNUrCE4clM5LjoOHgQer/OvY9+yiYRDPElP+qj5UgRUtGWIlI8D3eNGQKJgF7J7IT7DsDqpEh57H30/DR2AKLMBX27+rrFwnWi1Gim1UFIr8bnAbG2s+SgU3ChvYic3jwqU4I11A2AoSEVD22oQN7ipyewuUmlhJPQD0AeYhvvh1IHaIRvYxqJw+joYuQpFHxQ0LKJkoGrjETXAclfNvaYbFhx7cTL0Rszh/cHOFPN5MF65CQgH3TRBdIZagtUgXst11K96ssp+UtW4SQPaITD/6JhQBUvPk1KIlH9eujoT2MBT8m8la+cv1Z+M12JnZBjZeeO/sDH12dCnKb365PLlAmQXrf1950kWa+B0ExjBHD7dhTb+J+4RDIyonI7vuLs7arKM+ya0GfaQOCpO6j/yI0owBdmm0xn+XqT3uDe9X7zYavwBf8Mfiq20J48L1+nlHQJLSrdcvwbpCa2xrpdG2VlrJFlb6N//HZOn/ehuYMIebyaOV9mWoOGNjpgYzxbeAs/nXe7bV7agZ5BjUDX0DJBS0dtI4RIoEKYGq3i6125mYCTi7SRm9tRl7zeCvTMJbsfQsQplAAYT1lXWdqoz7KnpQSEWHVGEFq7oA+IIcR6rjvqqeI3gPj+AF7z9aNilLvdkkS6QCH/nZmvGsfoYjeA+P4IMMwYfLeowheKYHb0zlIMSXsLlDJgKzYwHuG84CfD3hF0JJw6tGis22sc8z+w/B7JNitxg2sLNHh+VbhQb2ZvNvLuMbyoTNjgaCxwAW537R3cbscFmokwVH8CmDPYE0ID7ol3wYgZrp3e4U4+2gKcZHf+wiHIFTjGhgFwtuJTMb9Yft+s+Pc+c3eJNplqdFLG4UsPgzTVRSUCwuMlExRskJrvPKQnZlBTrHAUDkTygCpH7JhCLf9EMSqcXtalYEwgyzGllnbrD11mLLYUbX8Wy85hrY1dQCJxFh57BV/mmAs9k8yD7OrligBSGf0bvtbX8tALctJZwWvUPrqiYj69xrQe8XDrXBv02wJCtH2Ift1H7GPi1YfL7SZOQ13gKoDQ7PWLGnIPeuehW02kxXG+q5tca2Vhpta6XV4GLlgLdojf2vv2pdZHLR7MyMTi3zRtDqBmTgI037vsVFElpD2GnhRdouMkhmL3he0gRA2d4Jp4CXajltqLNmEjwneKSHNio00sQs/sLNUm2mWYIH5a1AiI+WjdBbkFJy9Wal3oC8IcFTkIkddMfJ60srD2nE8nOHKDIcf/NGMI+jgSI2J27DsxKatdXfnWaOP0WqbNC0NKEimWtidWADGxD2Fb4KVd1+pYWBggj7oHID9vD7QJ7z5oMRToqfi40ed9AG+T43BhB8H04xBrax6HoJqmlZtv5ArN9FKowGXUkhc4egl2H/Geo4xWcOCeNONysKoDOVG8MqJqJoYNxOnm9oSOZe1fRDXqHUbVfvxZYlHQYXaEMq27EAugpF1+PPx2u2gQ3WRXyJCHvCcdJDrPMXqxZBHiRc55cnFkgTkZ/jbzQbg9qeuh2sM1kl1Xko8ozGtqs+11VrGLAWyX/pePHddnc4EnagE1usEHy99dvXmIwE61K2q33mC3GnbnvTrKzVZlFba2xrpdG2Vlqpqov0ble01xt1z+ccrm7/xkuO5/JvN3k61O1uqzvld3OnWio57yThfKSDgp0kG2W2SXoLiJS+VtrgqVlcVYbFqR6pO0mJjpSTNAx5R5snGGU8DRm9+EtZPEb7laXeLHmzghPw4N5ElHwbHmEmO4NMDz5U0QfYljwJ4zAXSVgeSuekOYIfjZDGXbN4oNais1VRhr9vYRzJMPDdlYAfoQEiMyfowbNteDdzD2GRacAjTIXLcIYVFF4yxPMmULANnzXx5O+wavj7ZxA2wOurxOZ9qCi2vj5Bo3tRGBbDNq0R5g0rkDc0pakZ3EnDAYpwTWu2YX9UuILjjFH6IZAZZKICrUJOUWQ9Pr+WnNjqz2EFB1Qj+6acH2skTZyQBhyYO9RPesqB1d+0iyustUiqMEN0BzfZLvULTT+siqYfyu1q8Cl8CuCbmM8BF8mdlw4UDIu0Sc7k5CXda7hxkJh4xpEl7EXaiJQtwx0CcDltEazefeVJsCXR4gwCQ74E+4MRbeZbq7/xNJxQ2l6elPAeHXQC8dfD3BMSq3VWQ8LqndyUbjM91m7/jBskYNerF8ntUU5G+tPdgzex0/oVe5hwH59NSq7NCUggatc7XfZvNSsSrTTa1korNQWu6B132OyR4Efp7Dx35bZDWcV4Q9DqTr1JFWCr+4aX6cBVIjvtrpgke2k2yiwf88Z0qWtGN20E72GRMsgw99BaUulsDtHs5jI1QaQ96jdguKpC6s0ITgTkPaoSt+FB+Uk9K6SBwpLqOEPwumBZz4pqfFAKzxA8Cc3IWSb2MJka4tolvMPyN18CLuDvl5S9+1ZdXXz3kDofqWPxPVgxRBl8x0qGWMHIEXyiICR4rCod/cPsVzqlnjdYMZI6KPIeZFD4hKTvw6UbvHmQ26pkJwBJtzJvxJORdG4sn3u3nuikLD0EspeJrT3ykYNs1lqkjUdGaPqxRFrkJxSVqPqjP7od82IlfaxoqCXtV2E0lXzfgWxaLnLImMt4AV6fhsI3yDPxGrwHqRnHTrYO2BPA+SeTXLAPuUEAZ/srk8LJjSqU3/jKE+BF+esW9mukEaJWH8Hnkmy12O/fIigv0EeVzTY/MzJYRxfrLv4bIHNj2+lZ7sEnuVP2abd4hjMvnYyMVrlFIFipPiEp8DVn+Qc+tWSjroTatVP2m0eFD5zdodbY1kqjba20GlB3kQo7giP501Bxpv9qpM8W3m32d+aq2TFAdZ7Mwhbu+4jgwnExv1vqYgyv0AMukF1TW5dUp9xO6jHKs8TMbgIOg31xtjdD64cHkQEhhYxMRe8iDUuUUZLFEZj+Y1Ap9XZ3BCfmz/xBr7Z/R6/+JNPaCjWIbjutr2nnayDupttdNcp9HGAje0jjQWz5Jfy9mlz+chQ1HME74O0IhuApaLvOjhDIk7fBRhkctJHRz3BteNdFanever++8t16+7iQe0vlJMxJwHcqubfljO4ODXD9xVus3sbTLFKHBBOKozf9GCACefBhl5jnGYaGUCxeNYD1qlq89g+ecRSZ9MFtYiP16/givCMX8djIAfOaUpxtluRseBODNN/4sVpyUXDKctXmciv77HFUa/2EfdpUfp6/9dQ0tvmZkc7ae6JF9w+KjG+nvulT+oWKUOmH1Ud1EuBPRh4XPzz6U8wNnwB3gnXVZ9SgNtPYTnSHFjLaZVQrjba10mpAsbkJEItrP5MWTmTeg4t3X9wDaYgX3zUzfTfE211Vd7A4W026zsNC0wYXKfTxZTvZAJfVNKo9Yq9ARkbJQ16Lo9/YS1KjYhsVq9E9lwOuL47hFmtX5jGQvHlS71ckbzZ3JtxMOOsS7sKeujPSTGZbu4KUaXfb5+4uISTXDL5ar0O8lfAPHcHDG8FcSyyHK/pICcpLQkwFKnpfFdYyqYP4y5SL1jmYstM6W+1LVMMPbMPnPq/ph3R0PMP38ItA5gNazFc6qZ/LlBmwMt/QUL2JynpcLV4/qTDpQypTkoQdLFp2GMgGJxaHoqjMIW2UBNbtpkB5ShTZA/vZgLNXcQ94crF6BBw7/KPDXrOwAyoPXS/zh26uzRMygDutXSmdrW6orNyyDj/s+QHs2GuZN5vVnh3nfuUT+7SH5duRnAoOqvfcOMFHtPbY9eHhjia7SEPSj9q9mBuusQ3XPDIXDq002tZKK616tV37eeceaPpA4WHZgkMQXIwWZn1lfV6k6u60umueYHUmlbWuxZY/8KQmCDUp2bED5OGtWRwkzI+wyzOxu2axifOTAdOQZqzYcVAOVaehfFzqVeDAJTWRoKCbHmDxRUbMzZ/dtI6YxQ0ci3trU8pSbwelG5XgZLX9XXHlHGC0ZO6MqzYbbODp1kaj/XPpIarGrw6LHzrSZ198oWqSw27AHABWLhObm/U+IxEuAlO9BTifv1kLwpPc+TW+chOJ70eRfRRhsxjBvsRPbFnFnQHKwBYrpAmU9dGKAeA7S/DRUofgg12CZxxpOgQPVjuMGh5mtdhHR9ujNf1QOaEoJwJ5CBAXkVCLwPirL7BvuGpzEmaO9aUE5aVzepawHdCqBcIuYHGspOdMVIbC6yfFBvYmviySiPQI21MdQofNz50Rc3ZQxNngpOGowVETtPCeNcwWnAHcDTdr5MLVcr3jil1N9qZWvkUINdzJn4yst3+2WHfzXwucSPnDkaBusAa86xjMj8r4j+ewWXjohkm5a73pUoHl33r9UhK1+xrbAa2xrZVG21pppa4+RqrsbNbARyayc7b+Hls6Ox8qzcWK4yuiVne8+A6gbbv1bS4fClQmqynXYTTAolIq5QBXcZtEIGVfc2WJsRnp2nr0LoRe2mwT8x9lYOJyOjIVN22MZ9xOyiQnKnVSRha7pR0SKQ9kcRKysp2j59GxeEeQQAPch2KYpZrlhVJvQNjllSui1WlVSoFUINFq0KtPYlfstdXf7ZJPZ9mjW6yu4CKigN02xudEwUhkE8OHtgBn+Ju1QDmK+SQf8xHBziLhA8YeZJeIbmXL4i2zSz1dpHZhOmOwoPDXcWE64GEhyhKgjAhl5OZZIb4z/7IEb+04xBtevCTrM/3Ipm2tpFul9kMgAnnyx3pxMMqtccYqhy0nI6FhVTReLo0V+sEbQEWgDt6IF43q8foBUj6h0MA+joRkchw7hJ1PGMu5sWTxbZDULVA626CZYAdtdJLSFpz/7Y///R9+/YMf/ugrWF9/9Y/G/7SD0aTZxaTeLlBGthcgUI/Y6vpMa0twpKRb6xzWc1nl9wGulZBQORmZam9Hm1n+rTakqmNQuwfrm/Vfso6Zyk2Q501GfmJW0aAyeUev39Fy9Z0TGjc7aQN7Mt+gd/k/4je2NcW2Vhpta6WV2jrOnXdOnaJ0OnEF2mGB6hHgQjENA34DJV0Jdmj69J3l/FvwffZ3W9QlmnuPFmZrCTe4yO343PaauAkNWNmYKkhQZr4ZBgxqo01WZnoSRk6K4A9iX9UiFJxQBjupd9Us0jflFbFbjsU5yTjbtrTJh24+k8WhtYu+q1ExYxmLpOCEbVUijZA0YTO5M5WVW7ErtqCBrQLpkvfhEtvG9pIGmwxrQnlM1SQnY+A7YW/GssfDWoDzLAgBf7P2IMJ7NfIu19J/VLAAQJyTXAuejcx7pEw/nh9LHi3OpDNWMuW4DEeG+qt77er+9PJXKtXaa+LeTzz4Y0+yWxiuiKRmHPsIu5AwifUqy9QbRbmOYYmckPsswMf0F38NILuL2t0C/A0IGx71pGE17u3ccOtXj7gbs432L4UHWusn3GttYdzaj3jgXB2DJdlR5tYNbzISY9wDjbwbRPqV9Bv+cCTnFgXImO8W9YLhHJL7OB5CWe545ERu9oZRaPbXTYsUoDa/sf2gNba10mhbK63UF5xMZ/ooV/F1lb+STk4t9CcbA5KIF6HX8nJhKl58t9qfj7OSGUsW34VKc72rF9kIVSYzWdtabHmRkrTfNvgpC5Z/r/A2AH+7SwZJt0F3VR6JoIuCcYHSYyVLfz6cKZ5xv3x7cizuqpl696ZHw+K9Z2OVD+zsJtSLc6xQt0aaydzKvoCwVTawhbf7AxnweECikn+pMnZ7qxOs+d0VvrqRa2zj2C6K7DPWewJBCFeAodkRzCEk4D5+/zuhzNaAD26jNFgzHBWuemydLo/C9INddxltyrGpNWMet6hY27B//oBY+wc/HqIsCGUIV41oGfbdQeFFA1rUR8G/JWOkbAhWoIomAEXwsHsN7Wgo3QLNlt7J0F4iNrse8wK8vgsMmH8FP71HYvd+XJmwP0jPOPZth6FUIoJTjZtSiqq11wyR4owYtZn6AVhdx3JzYA3QVS3zP2gDVitL7ur27mDxItyru87rQd/ALq9gMrK1+mtXbZZtn7PfzBTf7vQPR4JDlbFsj4NF48tPR+fJnZ7ReyDHobad18jv72p/ZFF7JznGH4vUrEi00mhbK61eWoCoWA+ytZXv1P9WJmMViLnjhXnup8GyvixqdZfyb33l/lmc1reAcdH8LJGZFyMONDwmDYDIsxl7rGKTeydR0ilBtDTUeauKjmeSLLvNbyjlDFWnX2lThyoWTjLOtbhGKRnvzYP24n5UNrD5beyL1K641yuXNcNit01at2AAPx2Ui97Ni4kGtrEImxcjkICzbd2hLMCPo8FNDP9ArB/nztvJ4ghMPzpDsb1GdSZta6YWxe/BAbCbUnj+rmoC+gkyLdJA/D5AwK6hYogPKLA1/MXZfNLRrqyqUhAx9zfo1afeDEYjGcfV43X8ASEAm94qPvIBiR3JNrB7hoCuYUJnRJFM5kB5yk4bBnK2gzb8Y8NXUqgNafufWf4r9j0L1CP2mlkuGJWTa4M6Dge53exDbIULQ5U73m01Ex9qwUq4zwuFHAdnSP5wJCTa4hX7tHvVhxeebXbJb3sr52C2pwojeyr2ZGu119Vu3m2nTZ3hV2pTQvzNNbZDBe26qZVG21ppNVwdlm7Yc+h+5dOwv4vnBZkmhnzGzv3UR5qSUq3uWHGc3+pmq06uu/NuqQYhVGvs+NzHkRCVXIjl3tdqq+J3kqqhdkltCXhCWh3RdmTfsElpJw1YwYJULa+xwd1t9yJtskppUnsszuHg6AIp+RuB7Yvn8HnlNvZ+9U4hzfHRHzsPRY4jwQM0CP71UUaHzJArKzLhh5zvgYphG9RHWVaQklzzJeDD8rc4gkfe+r3n+wEYHZD6Bu6/80dZ5FXfegc8bVMWedMwT142upJH2PaakSb87dZPWAVRotVQJmxA4Rw+bmRI9Xh9jxCXoch1IAq+UGhg3waxdswbK7xXQ9hE0YJC+x1YsYKBLbwIre5tik5B4FOIlGaWBzXCIWcXAGd3aP6/+bEcbX/1l3/1D6HDNE89ApMj6165nbDUvrJ2+9DgnfRmBsIIl1t+GWtLH+bcmHLd0Gg/5Vb2BW+bzr7ZTr2VHqQJl14+H/nE3R5BevdJtrIb/DVGD7UbH7pa7bH12qH42bTGtlYabWul1YsKhpYx5+Xr2Oozfj3WDCClPjE3uMRmkz0loqedC1eEre7N1Ju8uNXdLRivLbL8s9f0qZSDhbw9FJFseCfbG5JtSw9pZicvHTVQBsHk5aBMSgOXuTPyVrfkMgAqwusSs1+LXVvD12BxFgrtUizO1k0QPY2ED6OBw2jwLBzmbseLSwG7wWIGYLckW5/mTtRIrslMn+lHIj037Agmx9/gF5OpuW6j2krmFnaw6Y/BwFOA+LZrQf2sTBZObIA3EwhanLOzG5YelwtZZPa0Lnn7CUjYpVn28w3nFvh2IpJWM6yCqLhyzhK2erxmAhqJy2DkPBhRbmCDH51FwiuErESET9jNpHcvtnyNRD/6cWbaNcam5LDzhcmCzSatH+vKRSqTuawtkZ9TaGa7asZK2rmOLfI5m1N1y/W2Le5/KrAGytR3FG37es98052JfPTjvNxy6cZ2sI7xJyMb7V+yGhKu8DJ0/OAPR8LBA/qn7NOCY+GlqI0kxdbmJ9kTTsQCDnZ+V5sbi5TsamtWJFpptK2VViMowNlsDN5667fPe4Z8xtZ3p5jWB0tTgXaY/xgfaUoV3631t7obmTdoSRdrOsXPCdXhtKRjoJG7fnwIomux5XhhXtDwFhuJ2JhflJy8tFFDyDn4k5cjanUvuOGmk+YwFqQAaQHeYpdG+5VP5+n9G6yBl82cq7enalnkZjcpPoWP1NmwN7s5wWdxsP4BHwRsWkcDe1CfjdwG8ftQEWK3nKaCwe67SOWx60cmidR9E4rqlhZEZu4IHXYEsxfBc4biKi1TmN5z8tEvfPxVCK2n/OESDIdai3seRA9w0OxwrU5+gcdEV7YtAwmb3TGG6F4zeH0RihyEg2ch9A5uJUL2kf7YZShaSjmd5ITSrQDKsESbB240tDSj7O2zWJ3IZe3llNMnCqDhb5xQcfLOh9/7Ymh+2im0BDV4qrDX3nMj4dWf/fgrW19HfEr5wFxqLPRM+jJ27g+5SGYe/ZmuzXZSZjKy84vLtWXwHaQR6j+D6TZTwuFIUFfEGuuN8+zzMFtcZiT/7sRVYtPe7N2IqLaPRQKSsQ1KenqHb0WiXS610mhbK62eWQflj51JteLVS54nlbH2hx7r09l5wWPYVjeZHZNodVd04udMZrxyjoECVcNhNEglF/izlfzWlBxz903+sbbf3Rav6hD4rvikqJa/lRW3/YaAhlh+tpVZAFdi2PeVGp4DwMqSt4eUdvXuY/ER98UNYhb31vpC6dZXvkMoszJ2v8D0Q5hMzmo/2umOHPYss3OE4cfR4LP5+7TL3/eB1HH8YK28s5XMXgfxfgNp4hiN0unl5eqkJGFLjjm6425A1YP/wO5jWKFIurXRTBeH6l5fh6K7YCEUjV4HGYmIX0FJEjsM+7DSrE3J+LIv1lG5SnEX+HTkszn1gJjjxYlqxkrk3zvlbbPBM8QKc13OnhHPSi52c7icNdP/aPkvfvi1sLH9v7n/rGckKq8e6WjkmlXuj3VRxk/dud5HBGdEGkrxLvzJSDji0trpSko63y/lx5jGtuAe469ZxRTA4pechPmZkb2uBLGG1vK9bdW0Smi1+321pRvbWnikVhpta6XVs2tt5Tv29v1IgoLxQp8zN6CoQkZixlGy1U1nx6KlcXGru9jekXMMrIsu+WzDm5OaSKTEM/Hvgxv2TPMb3s6mhwgo6SAjY/v9DM8+14otQE4mc/O1BJSq30vZdwDmZiz5ts6zhx9iq+Br7nIIm0+4M1I0KufpsEmWkaK5jUNxjkekURnp7CbbAdXbaybGX3xSAbtVJpMLkJql6vtA7lt/ilMsKNQZCvl7yBR6govg2Ur0smYuwugKBqc5v1XWc3Mi7NKsSHasX1qxYi2LMnmzOg0HbRz4nh/9EK8PsOAW7j+NBD8GYo++mHxSeuw2iG6jHi9lttEDhEYCwn70o/dI9C4YvAsiN6Gl6/DiZcR5GXWco3Nk0qDI63AHixVmGgkXnVzwVieUk2vWYtCwD3B2pDDlkedssKJrxN0rxKKbhI/5y7+C/WxQf/Gjr/rmudsDJHN8yz9QrcRSL/M8UTrLn3Gd44GTkeCQdzCWJtwBFS2PQ9QmtwS/eJo9Vm/GqmYyko/a8I3Ve1tejNpyXe3+xnZCU2xrpdG2Vlq9qK4Sm51uTfNvXv5sRGp+ieybbXJXzemkxK1buVZ3pvBusSoBxHKOgQg1yc+t7EkV4WylO5Z7byfFsgqDGubmCqlaEIA7pMEBEygNtiHGEwXOJ0I9QLa1s0k/HOfOYQM7mJekopsguoX7KikXkZ9bTXnkvPyefMnbKA2eaoP+tJXMVPFZQN7LVb1CjDyXpwPIm5ti3EeRo2jgLoCxLM75qNhro8h26e+sM5pmg50xUYFZPwx2A6TGCrNQTp339FmjUD+5JRoPMHo68eQfXk7t56QgsXskC55qr/1vwT5/mjvhLAjVP5Ukf6sh7G6qiBN8+o76pDi0RdlPkJGa6Dk/wXs4rgrxGiwy9yPBy1D0LoAriM4/QYlI+ABzhcome23cOuiF6rjlDJs9jk0exSYO4oaDuG4nwQRaJcb247pDwnQUsxzHZg7xuWjFbKOUlqYA9SKlGfA+dzB/Kmd1KhqVRIozd4xaGnwo0dL7JRGUL3YlLmCVCJ4tm7EdRYPL3XGIYHn6Mhzqy+SqG+vtXww80u29xbARKU9yWxIsLfiSaLHN9mLDwZ+M7NeQGDoh7WmJ4ci19t/BNTOMVaefb+fa/k4iBIp5wt4BW+/ERqoRkGhWJFpptK2VViOuXfKpg6e501E9ZzZj7XPmpvXh0nSonZV8MGBrQNj8Vjfgb5JpdQMiF8tL5BwDC2k7l1spkJrsoYidFHdth2NuKOisTNi7ughPTeciDU56ePUFIz6J4RZwOZScCwQwDZC6mQ7lsrZF0hwtTbdjS3KziccxfDcaHOhpzZd6K5A3UtGDRzKy1MwtWjtLH2y0ftPuBr7ECu+RyqSbMjmYdPqlsoWN3uxwM38dQr/EQYUvU4GDqkukcZmeUW1l2ElgeYJ3/2MPSHQPsxaL72p8y7PWb48Ll3DdwtPngM1+HqtupYpHKP4S/l5P+LntoDw1CGHOH9/EsO2o7zQUFig9VPoJpnCL5G2QHnUF8JOwfz0+jVTHFIY1O8qomjGSnQZgfUiYj2JTR/jsIT6/h1t3cNta3F5LWNP5aQ9vOe0tGWyK9nxgPwHH/gqxdB6ONBMeRF6ZzY45gn3sKhC9CYDdOA5tSfofz0Svd/zswb+hyjRWeA+O+lrCDeCYe1i01BfYPlA90rnnVl/ugilcCWxjvXXUJn2kICOJNDL8yUjwnUSrIRBc5fNvxcORfC3fYfHDs8+3j6LTyKcwvPmz2Ozp/oPtoLCrLYqw0RTbWmm0rZVWryYmaf8d21V9SXNFsjLZeYGYO5W2yj0YgDXAa0GrGyB4qvjOR5rE8hLJFiBAyWXGxptKui9DElHYTKSc/oXMzXa7XZSJT67LtNEDJROM7SBteIb4BMDfWeZwv3K30fr1+sp3x7nzu0jlydfXxj5FA3tY8ABFDqKBs4iSPQh/ihFa9UWD+8wU410AU5Z68wUn0bLZ9ixcdtKmYMHCxv3AbzLUODIW50zcGM2MqwpZ/BGJXkTmOIv3RuZNMT+WK0w02/f8XX2v+nBFrAluDpxhue1k9hjFH/u7wk88it1K+HsW4ENG8Bxjwgj6TdR/Fg0rCKl72mt/LD7IT5BN2AF0zr3oh2B4H/VsJnQI+aZ/dlb643ZShkx+1kNaZNX5Nf0CaaETC/to4DqIFhIu5U42TCYvT1aTrotQ+CgSymStdsVmtos2ldPO82DoEermiVB5VszlYAHf4Wxav0Sag8UZANnXAZTIzxUzDt6fY+TvLdhqUZUKjifXBv+CFQLH1p8CkFMvY21uZcX/xUb7qWek3YR3Y+jVp46GpHvvwkfqwGltPbcoNae+wg5grK3+7nmnWS4zUoDa1fZxbxnTNKsXkAga2wp5mVpppdG2VloNUefp/Q4ENH412mdeaf+bcL9xmJ02ZNM2hV+Ra3WHK3pxq9sh0bFm2mlZR7v1bbibW3nTLzWRZG7AK8n2hvo/jTU/dkmZNgCwAH91LAMl0bD53fHzVtv8Zp1P0rgFwDFEZDRwFAnKNa3/f/be/MeR9Mzz+0fWLmkWM+tfvAPDMNbwwJC02lnMjoEdGAsDtgFjsYDXP6wBGyPtjNSaWY2nqyoryWQm806eyZtBMsi4yCCDNxm8j7zvzMqrKo/KozWSWlqpp6cFvxFvRDDICDKZVZnVRwXwoJDFIxgMBsnP++X3+T5dKiaCc4aQSHAXCxxE0KNQCBpCunjd5y4zFobhcj88RWOgYBzG6u1hX4CbdTxC/DOylvWO4jjYCMzRa8a8gwxLiZ0rJHmLRKVvcVdlEoAyZ8ov6WWJ1w/Qu8kTOT95p8y9Ft7SRIJwHtPrPV6do0jsECcv0fCNn7j2QclZRXvud7Q3yeDb8TccwdMvAuUyQJxw3qHACj67Ex1dxWe20eBZSIjBRpiFu0ZXcnmCpvIdLhFwzBcrM632F/46ogrxdHphmfK+RMEpBBYhwgjJOukx3ZVhD86HXMJ+hIYv/USDcgXyU4P3xFOc2I74JdMUWMcG89NKA7cE63Z2DMlNxVOm01BkNxJAczM74YDqAbFw7pGrod7LMru2pcK9o8FSVhpTLyTrSfSJVft0Rk7zbdBfuKp2OGfKVBXOxgaj0hzJBf9Vfgl/XTlM7LzlR7dsZqSwPAtlxR3Td0X+Nf5uPTExJGp3hG2/JmxrpdG2Vlo9lJmkJHTYHDK7j7F9Km72dTsvAbeRUcuAu6hK3a3EU1pN6lZNDDSLYdugcmwST3GCt9xqslgaU21HG8DcA8KPATTEk6YSvajqZoEVLk7aWa5r0Fy+l8uZI/XFko7pnv4NZzHuRIKAxQ9RlEtN5qTZGMzRy8bNcWYBplPby2PDzFAEL8pg8pZbvcmyX91CWvrZOVa64R2ufBLIvdzVsVu+E5RiFpwVo5vlWbzzyj5U+6aeN6jwLbNlLl+8RNjeJV1b6KT0URJ/v8UInpNAeBUXtPktyrmPoedBfFAMNm814cl7dNgTqaS3l3T2yjRg0FirCrDSrBCtVU0pALLvVLLBe5BJmsB5eOOjwL9pxjLYmQ3eAkRm7tJHwFyXVcLrZA3B3HQPyjtYA3yfmvkRWp78FHijbWEIuBc38SozD3bYXjYon6yrYh/+A8oitlVYqxzogyciPfdzXtjmtF41W0VPZyT3WdcoS1NdhdbM7Ihqc6Qkc4ATfrX6qwfpjLxBmJX2P3Qb0PV0rcqr2sOitiZsa6XRtlZaPVbBKd9XwfS7TzLrb+Y222Tfi+Drk8jMh9vpwfeCUvdqvEvqzqeehQujPVI3UXCpJgZaKvpsW5xkwU+MpzILy6QX9lbGFKPjIXO7Sg4psy/V3FYStqU8FsnOlhPeLSp2EUz0M3JAUyn4/ubiF7if7A1xyr1GM1sYZwjhcql5a/JbdF4uVoycSi0b+PI2Y8n7zFAk40Y8Z7jT6u3gDSdEfpyn6ti9CFUMro5fBVKramkM9fYVwG4rN31TLdmwpLfw6xDYamm6z+iiu5wqXbo4S1huEOwSCV0GA+ch7yvUcYxZjrCFfXJylzIIXYMAp0jvDh48Q/BrGSIfYoRsBE/0vhGEAN/lh+sN5wtCNwnbJs2ZgEPFETG9e3QYyd9WGW00LjJJ5zLlPQ2F78wQbOEO6119seC0iaUWNnAEnOpgzdCk3Gj/ITiiOG0oxRbFhPIIkZm1lwyhbC9n2/i3htQ86igYwctdiNsBXp8HcCwzC/7mPCdqD5cuYsN/LvnFCe1mzq4NU/+knx0EYRtAp1LrdcpiuQu1E3BJoXkOIdteWxBWC3zA9gatU82DgpHzp3xyyFt0Rvakgt6KqI20yc4qqGZZqt+sJyYF1C7W79zyQXpfE7a10mhbK60epQ6Z3XdPoRqm6MSCudv1kWQsd96LrNsiuRfl5LOeUfDx7IizNNFj6ea+8MoqFAVwvEuoZmuhwhSTNG9hSD/mRrJG+eWAmAE3NynvAUb0c9yCdctpbHU/c8KylUV2PJmytgn3JubfGtjFKBx8wikznwyvf3M2mMWSDs8ZB3vE5UjNhX4wi7dI4grNvaKW91NH69Vf9jk3Nu5sspSs3rDJUilXc38E4rdo/E0g8QZJfOKnRdqOCdcOtG30w24x92as6nNINy4RC+7yuOCh55Vs6PY2P7RHxVOeOQvi18jdudfHofBGBFnBbY2oeT/CjeCBIzBv79OCeR4idkjvgPk48IUwlXSm/p0DUp5gv4crUy5TeWzwGQieO5GZa1AuqIXvh0NpxmIvjQ0+gOHszGkgAlehTcIFeNpRGkc5zu71jUjDa8ChtvMyLXg4gObgvkuk18GOg3+vEDKSm1Se5Ce+e6B2qrXbWcJVLeBfsDbuzIXBEFHrFS7Zywi9jFg92dMZCZblrqoDZnpKKwc29Wwj/rHqQ+9njgVLEnv7Np2R/oQqavOKe+foLdWvZF7t9jBbvtWEba002tZKq0eqteqn4ry0jcd+rGb7EMvMditkhnTSNlRuQGGUyYwsx7uyutnks1BB3yN1I+VpFebgh6EoNxtj3Fh6FlcwN6SEOLMAiPx1AO+j0XIRIofJva3i9SaT2aQib9HF2CbCBabj/ZCEaph8ci/ziVlICBnzJqchUsu+kjm2PiPqL1MHm6VbJVUPE1wNydtRHh1g9baWRsVswXc3adwPuwuE8U0IOwsETyK+o8jiHm7ZIxb2iNk9cmqXGt+J6rdiL1xcyjg/ZL4km5dZeeDeTcDiKwzLtMtSyspy1HnEZ4/In9GJL3QSigD+vubtKPdtwdzDAhQzD7jKyuoBwr4JEDd+oflyyDxBmjCuEr54fPrONZ6FDxgBkA32lm8fJJuU604xG5zJmYQwkfEIDUE1GvB0ODvbo2dbS2P2TpaR3lHmONvJjq8QXvhmAWtd8F+wan0dxPis7q67e0pG/iQfVpFttD+VtuCpebhe54JRmoJ04yf6pXPU2rfm7s5IUBGRv211YQ2A8UPaVZsjQV2Ei1JH430LvIW7z4S4hNqdwV5VPVvf32Cmh1e1NWFbK422tdLq0esCK3PRqoHk45lJ5JWgpxzFrnEVvqKRHthA2eXqzo9WmC7mbiWeRhWu7mwpYeozkFJmxV5D6zF7ZRbyhCs/p8Yo+grVGXVxjhCHeHIHi2xjweG7GAGjsMm3n0wuKHBg57n063uaT8p6a8UQYBagtXr4cTCD6855OjxCjbpLOixnuJu8/YLdWZS9hckyN7wcfoblT8nsajKzHI+sx3wDsJubG1oxpIkJ+ekxVMWeMQQXpdKri7+bR6X7lQKUP+ZgdfJMwyuEPESRjagARtvR0S3MBUganFG397SAX/qJHTSgbMHko+J1A/MEBz0FgNRl2nEWwrjE7mC4FLPR6YW7xGwdOMOF8TQInkyY+QUkb8HKzFi6V8LgQhlnc6NJ4ZuxELdDsj8JhZH8ZCQ7ex7A98MhZ2nSUut6dFdZ6Gq4JBtDfgTZK/MCH1enrHxzJMR6aZyN6NgWE2wCCdXOSFD55in0kPhqTngw4ZD2jfjTwbmr+5mT+35yvkzudb9rYjCmsyfyD2mEO6g9hFdbE7a10mhbK63eRx2kXsLP2S325r09aCpp6jFzY5nZcHvYLEJfXpdJP5dL3Wv0x8Xks0BuVNEIpfLjOPjKB1/zAHnBg1Zijg3cD7/aufRxX0CVubMxOyetBQaJ1iyzWGDMsYypC6nvhWvlzlhyX3EilJ2BduoNIqw0n3gK9zKf6MWZ8zpLSf+wwjORM3jYF7byaL+dsVRG7eXRQEFH5iZPI+5VfLoz2IgaXaYnl6mFtah5kzDvkqZdYmGfnNkjjbuUYVuaq6Ioipm8G7vFaSxSjPQesbBDWLYJ2xa+uIO79jCPO29cjjk573UQl1I4ekrpUXkYp3jnDw707WWw271jcVq0F/L38BbwW74BkdO/CV8l7gSo6mTHhydsaJ3Kx+1bEX8xvugsTrmL40nG7BuYmQ2fSCg3fcwL9uBt5RAxmm91mLF2d08CpJaPbbeK0izg+yNUONuXSC94EzFJE3j6O1gAKfuUO7+BI/cKh0brtGS3CNZxLqU7OytrNyQ6LYOiPRrOUzSJDYhSZ2Sj9Vu4VueD//RdQ9rVmiP54L8WDP5bbvz9fe3aXb9++GPr5U6GN0xBgZF/Mq/2sB+nG6VLTdjWSqNtrbR63FppfAbbbk5jq+/5oeOpXjP3gGRuVVc3lhuFMVtyV3eP1J1vnlpLasM4yjombZVNtKagu2MXC6xHHKrMTeZmN3jvB0RqN2t82yZFAanBFqQOxXUirHT0cpEjnHx+d1YdZ/5m9ZbS2PA7Y+Yj4cCRcfS383KPxWeecCeJPwYHOkqq82uqtNptTQnnxpGCzt7fcGLhUpYheU/0I+lhaiv2Ak5jwWh9vxwMmGiBF43FZOQVikmK/nL0DryWOhSvEOplCF2P+FYYtvf0Kzq3I/6jEFok5l2lsU7v5gPlqHRM52XO+AFYHOfTaQ6QwGtAtNzcHOp2WP2b859c+jn/iV9Udk135Qlay6Nkei6WXhicmQ2Xr0zKfBxAm5THKxs3A44Jlp21dmvh3Kga+Upb6omsGJqUG2afA14HkC3ZqbdwFG1gKnE6xXHR/hG7r12bqOe5t2FFf4yGlcL2Kz4Pmz8HYt2dkXrYGcmd6iK4S9cGCy/4k1PX55P2tzDx/Zhev+9HZdc8Jl9UFbXBMmCdmb2XgUT0gtOasK2VRttaafXodY5Xobt3qfn5e37oZnu3xwBqL+vzcfu9NoIWdLn08zUZiq3TH4NLOFd3yYRlZsu0YwtD6qTb0vv7PvdfV3GCM4TIwEVCauX4d26GS0n/1qEfXIficPT8jrXtc7lZLvbEOrRlxSzar+2sAc8aBxzwpcZNK19cjkdWY8FN0rlNWvfIXpM0eBViyfEgnKdTGh1sOAlnxwrUqOTrkI8H3yPm94iFbdyyTS5u4o49zHPZHaMOD9ppAPMx+sU+fX5mPiY8TxjfBa85ibF4vI0G7wjp83dZRFL0i8Uyl8ZokiLYHybQUIo11NnYcSZpfhkOXSP34+9rH5llrPeyJykTAGtRJ6DkQG6qZyOR7IxdwdmLapzNpe+lzNLvS7uRgJMdB/fdwQLcmUwlHXWz6s8C66ID5BJJDvNBIZmb/bUg9JPIU/+uEFRJt5doAWvkpYemWhUB3Jvb8JJQjYK8Cz5bmomn6/THfVvSRSvIWvXTe32+XQeS8hduS9bRLo/8Y5iJ+6ramrCtlUbbWmn1/moveyp8z+XOvpQdyEYtXSPfeTM3xTrutRFvZorMvmgnulzdDeYZuDCRmD8Ih3Yjwb0Il1FtrxiUujX47oeTyVWcJ2rMbS+Ni212HaSGA184pH5oer5/xB5944+e46nV0q23wAWT+3KjNpaf73hX9IRc/ofaqr2kS5Dzg60dgwvAdLgwDsCaM5zcleqNF6YlI2n/p0nukEhvu2TVRLBBmjCKNh517C6IEeZyvF5miuo4QmOwx/F2cOdiEOPmOIprhu2ofidm2SMRD2d36YuzjspEL4uXHwDHwclJZubYuGMXC3DxKUPMor/24y3SRWTmHKXxYRZmAK/zCZsyic9S0YN3gaPUE4atd3S5WbibiQ0JU7vhQMe0Qy+Cq3yFSegnOYi3pARrZVfuvTBxUYzytFaNdKMNU1AupYZmf3SzfSm84u3fStE6xcaZ9N6XOiNr7V9BDwn4V8reZrJcPuNaPqy+QG3/DsatnuPVe32svSYa8pdJjtr82Ei9uOwZfwvUbmsZ21pptK2VVu/VTML/mHhG1L/E3WAYUw8hEZn5Rut3P/Vu/8lo8r//G+LfzVWJ3M/ld2m1vyg2L1OVYi6NlmnXXjj4BiFCBX2+W+pe46VuJM8NQbRApK7obKzKdAyeucfUoJMb9NgDqeD73lWcfM8wLY/Y474p+eDqQ+buSJlMbj6XmGtEzeukeYMw7xALaGncUhm33sdcDp3olsqYozzWz9ohc0ibNknzEmVqJOwNtpMujLVL4ey4t6hb7O87V8sW5ITbfSISZO29bXlV23LUCRZUZ92qM6BqfgaKXnUhYS0bKL5/tJ+APUAIvxV7HLdxm0DYsac7lOGAmD0NOwcTtpTH5y6Pg2d06SfPEfw0GK7HXKu4/zIgdDq+CmIrhDdE660lg5ntsLj5bdXxSG66GF/cxpDTEHaFkIMzc7YiSCw971C+R1QaQLu6KmHX45/qn3zvoyff/6sn3G9H3ZxtlcY3VifSyUXpN6ULBIeZRZHc7AXCHYRMHlc+kEXUy4u0XXX0jGpR9Yp0ApcbN3zGtg6QvXKcDaclhzLiq0ybRZ+GtTreCT6qhQTrSJMWR0S94AZy9WmOBLWbPxdwuXg9/EfifvZQ/pbfKXSpIeZqx50ldEEUC/fTWbThkVpptK2VVu+zYLYUYO77tu88eBHZOekrdrak/x+e2f7Rn7uk+v2/9NljF9XGDcs2WzSxFw7tR0K7kcAy6k4yVjH0YxwQs481xLtHwYOq81J3KDchT/lVWrrN3K/zeiZhKjOWng5FX8GojDqxs/q7fM9S+IbwxXkPrVowc1NXAQo82VAG+m65JrMoI8j/gGVbKWawr+POqtFPXazBxk3oHLsfyZXHLKweJ4xwO0vxp4XU80xav9ze7/cqb7X/82WoeItw2IoXpoMFvb08aq7cQd6RfAf+lmMqeN3PHILRemdlop9+D7H7KIReIuRAiwgFUFieIrIRe7ZHTpxELJdBBKDzkIR9FiReReKvsSrU74+4GfIkqP1waIn0phlLJDurtEpHMwvrGN/OK/u1xMOC5YTOxEq/QtwvSkXB3yrPHUA/2CsmaXIXJwZsKpA3uljuBn9m/MPv/PhJpz568s//+olsqSYtkBZe8SHcQqB1JAhbOfP88BpwibMsm/0OkLfapYhzwrYoyr5B2cEfKQCvpQMCsNtTdcNO0M6SQ5zTLtpIxGmaUW9PZyQnCjQ24IXhOi09o3Ly6YDmSL4/kjOCX6G54aOfVqs/H4Danci/ir7+Vqq2JmxrpdG2Vlq975Kkl7eIpnrwCrRCviI3seV/tczLURvWH/7EiWSmhw/94KTuVJf5YZX+OM1L3VLoR5WwKFHJzH+pH/pCSnpWYW7uZ2XDbaAz/mZIB203WMNxMMw5VoJzFqFDukFZl6MWKEjvEvNiZMc9SLpf8p3cIQ22vEssbBGmDdIMwB3gO5qctlQMJji+sXKv8BOdozymJGw+gjA6wF2TomfRgsHBSu2GgwwnqtmCkIlfhtBl0hMlZ0UZVedscdOUHG2fysRvGXYH2PHBFhFwwCXCXiacnOo8BGGLJw99HUjd8tFycOBok3IDkEVz00q8dhanAF6/DmD94hoXi4bBthOcFlmciwMX3yllnanS9ycFpDCVTdhWCS9YA7zhBObujPBQmKUXVRcDsOMQ/AHwuou2+foXP3kie5vMhHPz8iOc4bzjXExQmxJ82F3PqzomnzQpiMr5GeksuvPzxCoutJxVc659Cv+Wp/69DvmVci9gbulFLDbOpORQa9UIf07ho0g6Q9o3En1bHTYqv4BG8Jepg3ugsGxN3oPanbGRZV2o9PwtVG1N2NZKo22ttPoSaqn1BZwnfIGVvyK7lGZs//Q/LSppG1QsODo8Uy7Hn7YTTxHWQGdH2kqpO/eCSujB5bBsisRAODUmyYxKt5EKZY1qurheecu3ribzrJx8xGKTz3Lp51/fAqumRHbk61vx7AiV1X1ZhedHsdwI/+8LLD/CVW5E+EMsJj1eiy1s4c7DsP8igMI1JIzky8dtSL4rFvD7f6WC2rD4t5LBXp05CXUk7ddBDG7ByY5vR7h59aX4ovwN6K7bLSIrw6k3MALlJpySZ4YMKGlCO6D2RvszSN7y1L/bbmFbitl+4yeElsoqKvnWJA8J2SiJhunRpYHNkaCO6fX7/nJ464tLe3hAbyvaPaUl4ou3Q21N2NZKo22ttPpy6jS2Cj98V2u//ors0rd/6FWlbWLRthMdfQs1t0w/j+ReAMRcl0vd8adQ6pYuwQvq0dG+ok65zUjRqDL+vaSvkvq3V5210uqrWlux57uU4RCfOwnbX6G+4xC6hSEZxuotGL/70bf70bajYnKXZuVi+Truh9GNSH7qLMgZtb3spNw9Em238FZWImypoSKYn7kRSfEqMCiNJCG6PkCBv6Ww7V2e7HvmtPcoyniOs7K4ataO+btRFqZF1tOSZ7rI/262mvb32wdA2FDIGD5i9SqY6fyeEOt1p1iqnaBGzkBSyL9NZ7wmbGul0bZWWn0ptVW8hp+/9/q581HrX49lVGnbmJpIM6bzYHiVsuRSYxgziiZG+8mHaTVN1M8alKPga8xTLDdKMmPwNmTqhepAShurU26QZiZUc4gTifF30W7z6eePqm2Dp/yASvz7r6XuV1CrxyjwNgHnSS79LJV+TmVHQoUXbvbFoizV0VIeC+Wmgrnp/rT9rf2IzJHlp9KMBd43lja/8VOnvohZtr4FLFttX8lF3HB+SjJ/80x8t42k0viZtAz214LSwHZ56p80px2WFLP9CW8jMVc6sf3l1s+gh8RVdTirdtGlxknL6/2bI7lOx8yJkCsqixMZUCf0srQPF5FSz7XysZFJZvTtULutDY/USqNtrbT60swk7d9dBdN811H+K7JLc/grJWr/8bhZVJonKK/rf////L/3A+7y//GvPUFL8HUAe4MQN37qZgif9KbPGS6MqkrdvsIo2A7Y2nbIy9lLeo3LXI7eJuHckI1FBFUl9SZlGndJn6H1CpP0wi5p2iRsK1Frm7KuRh3nIUw58p1rUGMsZGHhzmMVK4VcBUOfLj29tTQWKk73bSNj2XR6pJR6Bp57JD8XKUzKK1yYRAuTrvwE2L6ywJatJYNK8WCkVjrVErOoVUv3TS1LRecojT5KseqXO7l/depVHgUMbQPrybLOWhm19O9b7VcDnCQdggwQARGdC4zn2het0C5z1zGZEvzW1Slxig0fkggBN7fwmqoMQ9uSXRtQclsc2G4vjZ0juHKcjejfEBj0MBgCu1Rr30oeEm/Nx/P3WLJ5IL12XBM2QO3GIHkCfJwO79A7ESf78iMn4z3X8pF/UtK57u0MJKAO0vuasK2VRttaafWlFfQXvsX8hcerv3ZvPvmBW0Ltf/XcYcwKXW5/E5v4vb9w9LB4eLaLWXfCwWXSs0R41gn/NhZ4GUaPUfSVSOS3naSR0WRmZLVbKC0nn0VyL5YIISYMzRvV4EMfy86/DIX3I8ENzL9MeZuko0TNK5kbgGYoMxYpzPaUKsWqIexQ5KpkVvM3F1W/9DJXlEdbdTmh8tqBF1R1leIojzsLBmUhhYlAcVKqYGEyVJiKFKaWCISN261lA8zHcHBJ27NEehbNzmDZmUh2NpzjgnoQsF4qTljumzZz/1LrkvzWv/xIoO1VwgebKcGeLMc5tHWxRvm7KVATxGamKjOBpOblwrZkI7npD4tILSzefazR/hQGbPOpfw7ZnHasq5exE7PNCdvp2p50FV4XDC10o2URMwFTaa49cT0xO+Dja5O9FUYZZF/d+Vn3MnUo78ZW3sBU7Rzn1UL2rT9UNWFbK422tdLqy6yN0s8EaSex89XZqyT764/9Oz9yrnkSV40Wl58VzXDJ3H80YlVJLPlLzxsvtRMOpBMWT35CVXm1qJOrHnzxA7wGkL3RLXUzmRFXUeAqPk5Yr0JdrF4xuARme/f2UCLZqQ8aUsvDkutAfoVpdw9DsShHrpNylvXmOS++mdWxhJWlF+MpU/8AED04nZDy+A6GXvqJm8HzjPzRN37yJBjOJM3cDw7VcapVabW/cDZslupQR89VMhLZmWh6gUwv4N0w7eyTjf3eylOcaJMecAS+2wXc3/oXP4GoTaUZq/CrVGFyi2bBhfKsG3NVl28cKxsBM4zVKh75SN4is5HEhrFrFxsXbS6aegzu4RtZ0Lg0zgbWZSguvUxYvWMHzzdP4YQdT82FtgTnt58d5QO2P76jGYZa4lVqZqn1xeBb7mRfy9PcV+u/6bVrVzqvb6UA1iRfaMK2Vhpta6XV17Xg755XwfTwubBfSjXbO//4L5yqru6KVfil+MpPrhHgS37+vtzgK4wy3VI3+GZleanbwX/nWXgniVltjLYyLM+iMKKYORY3Pga5qjBrWfdAtKruJAnlJ/DcNCgiNyOvLQLpidI7Q3AumC/mRPKADqeUOqurOAEPqas8BgBLPG76e8UOSi/ZSvx5Nv0cy47kKglwwkTy477C2GJ51FzuGyzoEFK9xwHFOln1kYr9sNvMb8GjSF6/9UcBXhfji86i6Dyu6mHMha/lB5AtP4scpXF/fjKYnw53K9OeovH9wrR+2GmjwlCbmVXSK86pEfog//gnv/+n+ifiUHQSvOJiyPfcy2i9QrtMau4RWDYxXM9RMhQSNiljm4fFzTtpWwoNROs0+C8M2OZT/3yqc9rb3HjIW+mqNbwz6abR+szBD6EEwF1u3UqvdZ3h1uRLBWbAZxQgZmhNuVO82CmcyfP+9pMve6X6Rlh6RYIV87t8cmrCtlYabWul1Zdfh8k9Qfhhb7/iu/r7f+lTpe2fUsZIbrZFemSD4vAl0svlBA9Gt27CcLJGLDdaY7rsJUuJp/HsiI81CJorqzMrSNrCuSoNPRSrHP8OXaSu0oS7MN5TWG4qlp2HFeVqjklbXgcxaFlZJ/xLhGeZ9KyTAtEC0NmNBEq0g0jNVioteHzq7atMWp9PP2swva2ES/GnpdSzdHoEzb2Ae7JF+m795KDJO1xOduwKSS7HXACaATrfNVamg9dcUnJ5zMVOWBScCpibSVvBi3UUCl8E8ROU2OXnOMo3vkE47YBxWf3QY3f0MGEavDp41qhcF7lZYzgLjjzHx8PM0/EWjGRmvhZ1vA7hN8Kwof5x15zvecxXMUp4Lb7WE2jRFM36wvl5CNOApAGnBvLvH6Z75qS+/d2JzNwmjgzujjhEQ4tixjmTdpySTS57pKziHoEVbbek7R+HcGlVBoVtKX38BlGnbWdNoHNbZQb8VwrYxjKzXT0b3cI2kpeu7QoEDNcTYqTJipRFSMGAbfrZMJ+lALhXq78aHrWPaMVc2MbfdQ4XF2P/xVt/Zh7HdzRhWyuNtrXS6suvteqn8OP4mF7/iu/qn4wmlaj9Bz9yzIsMZC3rg7npddwvfZOdBfA10sfQ5vtK3en0c6XUjRZ0aNLOxhe9hQlV8ALMLd/hai5hZXVKbdVVnLx5pynu1I2fgj2dJ6HIQRjdxpA1wtcinY2Yqc7MSrvdTjwtpJ4BBAcgHmNMF36ia9SlYjg8rOWo8y3wWiCesl6aSMIdkIqhELdvY4HXociVn4QPvUP4j0LoFXLHxs+D2B7ugXMcg6VRKH6bK8PDIqfX2ko6PGeEQ2dg490w5G0tjQ6YpwOxG0ChmxWU6YgoS4ffDaa5nxrKenP5kWXst2nu1CdSZrDA6zl5lAPhVwivZNSupOgDsjzAPQJLCtcjMgs12t2JsYcJfSJtH7R/rvxMIGo56Xk12p/KeyXB3qrOaefbMcel0+8WoaXLM8196CHx1RC60oa2aUdplP8oeLravhim6fyMqA+42Xr5F/LDda0INFxq/lpaZILz9l1Quy3LN9SEba002tZKqy+5LrDykF7DL7ec9KW8gRLWv/fMqCXxjeHZOTl2XwaIvXCwHnUmk2YmaSIys5HcrLdg5DrJ+vCHqtTdSjylealbGAKvlhgICBtsHzxKKmkBaF6hF+GM6x5F3MmOL5VfNhi0SXqWKf8G5t+PBF+Gwq+D2AWCAxp4QCJ/yRF5AGrkS4R7jfS+Dgn2m1UcXyY9AJovubmYw+K10p7OtZSl5xuU+yUaOg8QNz5SNrpPELDBhbd9x2pyE9dfh8LyOY6w1rgG1qfx7HOkOGqrGIKlyd38ua9gsJV1PJcMG2Ni4U0+jvKYrTwWyk/TaRMRM+I5g6rhRIJpNDeDZWaw7CwnTmcFmH677kOwGHOw487ihJudcJbGreWvbPqKvuu3iKTpCEVPkFDvS4b0voIZxiYZQhqZ+nKcNvd3j8BarM1Ji5xbX6zHsb2afTPARlJu3Eg/NNGNNrhECthOJi3y/EH5vbwVbjy7ZIBZrfxC9Jb8SogxqczW2r+UtszyAduDmyPbsum8W8XrfrdZaXwmjZiBo0Z7b9P8DVjmSUeMrlXe5QNTy9jWSqNtrbT6CpUUEAu+ML7iu2qhzv67n2KQs3//L33/r33RPJC0wJc3menC7j6WCWqLCu/ivvWIbw3zLVGeZtRdohfzcTtADVWpO59+HsiNQtnP0icxsIfJHAq9E+y8nR3rQfMmBWjYD3ZjC/ftkr5lIt2IIy0hZQU5CKOAnqXcw0/uPy5eXjc84wK4l1wrAPpXKD8k8lcoLuB11JnLEd5qx3oL1ioQrM8C+LWfUg4b3yH8hyjaYxFRWpzBox+GAlDAllc78ZQzYedHfAVduDDFJC1bJNkiEDOrU8VrLzjalXHe5zOcWFs2eIoTgewUmp0hUrMU14M4+45RHk4WbNPoL3BqN9hUKDcN/vAVJ+2lsa9LS6vc+QN2m6UXLwNkLeq6VnsR3wQI+eoOPFnJjt8u7PlLM6rZI/KS55CsxQjJsW3pCNvCoMdPEFrFrl0RDqyn6uYs12LA9iLblfonn9Meb6+DG9Qot7RCkK4K1DC4tXRz116fF6H/hXBC3vXRdI5XObEfzfVrgFkv/+JWZiBR6Yxs/qaWspoqQq65pT7+jp+W2vBIrTTa1kqrr1BJzT2n1NLXYoexzN/5mOta8x/gfyO5uTsxws6r3XuykXKDRFy+PkGi5wHsHEKtPxpKzJPZFz1Sd5N5RmdHqMQ8oGQ3q5p+rZcGUPdjbq7VktXfRXLjvsIkYFwlmi+RnnXMv4X5dzFkKxKEuYePSuRKH7lE2NAicnuXRUQpYEuEHc3oY+npWsy5gSNnQWyJcPUjbHnHJLiNs9jpQ/WxUzgzBWE68kC5eBCmA/kpNDcTycyS2Vk6bYonTdGkiUrPo7npx7Jil3XvIYPc3N29ynE2Qpaii1nGqvo6glVW91uGEiacZ8yrpQvzXe6RHg9JkF0AG7F1HNsOscNPHABJLveza5sr4432Z20xYLsn9U8+p73WvoVP8xNx8SBhaKKxIsR716hy+8ws2KI4Dwk3pJ1dG9KP129S2Erjs9vuBomezsil5qfrSZN8suY7fkhqwrZWGm1rpdVXrs6IOv/NRC83/v7ruP/l1s/4qL4ugLDxSRdK9S6WWgBIem/u5I0Zlwjhy+t6pO41+uNc+jm4HNwsHreqJgYuFg2cak56AB8DSg4WZxXOb9hY+QAF0NxTnAD8p4LmhHcDQ3YjgcNI8BAQ+SO7ViCR9xOwwXGrJJ+xydEqPd2KLu6HQ1d84+byXYTtKE1wynRmKgKjPDhf0APANLg72Ajg8ojoG4llFvJxK6C3BuXKx+10aoGn6vFvUIK4Xu6k8haMS3zYyDLB+afXZZkePXXFO4VuRBrej0S48L6kr1TdkDcfq7pHejwkoF6Hs2nG1CNsH7R/rlSgYVH1Sk/knxSw7SsY5al/8jntEtxL7pSN9m/B5ZXWz+HYSFt1utb+lUkM2M7yAdtr9Iu7mxH5wQX9Pj851JZEetXOyOZv1pIWCzsioXaRn6ypCdtaabStlVbfqJJMh/uZk6/pU2i1v6AbLaUuGM7MRXKzSgKzlw3RzPzLcEiuz/EtWcMowVQ/qRtcCK7a8Dn6JQZmS4kONDAONQuKrhVxyNEcsDIgZoB64IkA2gOg+YDA1wfNHW3KvUJ6tyLIbjh4Ggq/CZA3D29Z8Ukm8m2fRyJsR4nbJagiP1QunqRMA0An0vMUnAiTn/EUJgC7J1PWJuFqUR5wtMERAIfCV5h859Ew+mEUZZhpo7Bz6B87mYTPhtfLQv2m5ZwNrjpDIv1ezXMEh+2S1yLXeitTtXQmWAnI9lzdPQIr3ziWHrqUT4ItyBzborAdiKv6myuNn0l7DrBbJGnhxQLPQjbOprMDtopgdDkMhnpEXzg2ksPr5lGgHoJ/I8VR6Blrt38x+JMHEPZNgOHIPraqeoMbf7y7MzLdg9rrKWuSGZVWKYsNuyZsa6XRtlZafQNrqfUFVF/O8erX+onU2r/kJ5X0Mk2StrSjER679QoJ3BBPmg7RcOdL2h+/CsVveD/3nRyplLqFUfC81G1VJAZCY4C3NPMKxQ5xRJW5YYZGmx5ZwycvAfT4oz0jVGBr4zKB7eKcvVuJ5rAT9PHQHBBwPGVKMyKakx6A5ruRwH449CqI3fjJAUfsMoBt4P5K1JliLImkGXqmQ5wyPfXWyjS4F7gv2AKAabA1sE2w5ULCXok5Nwj/ZZAbOHoexMAerpNImz9QD0fV74jgHPV6WH0P/t6nRoe3i9jYMXv3A4GXEsZ3QM4GFUvP3Q5eO/Hvi86r7I82c8vWyqT8SfVzj4hwrBcXvZNXwXQ6YZEaLZR5GqvZN92JIsIb3F1bhJdIAdt4ZvZGtlqW5rTDzkhYkjvlFdkEV8UaNXh5mM/qhjkkYP3T5BfS66m7wVfqe9ko/Ux57TUfVCJb0gMk/7wHtcEDmTsv4ti7fwxqGdtaabStlVZf0YJT0LgvA8VUs69dMY01i4Jxbaxhqb3JLS2oUDA3raJ2lwxJxiJXu68DzFFsY6X9D6uJ/asALVKvCoJDqbvJdI2lrDFPodS9WBpTJgaa+XauQur5Ek/qWcagytwHvgDE6/WIb5jkkBsfeRnA99BQk3Cv4Pi1iETHYWzvfaE5HNqC5jlZmshyNg8pGu+t0ZZ3ekwBKMd4cRoskJikORu3snH7MuGV+chjVwh+EAlukEjz4e0f+rcO0ZNSvR0lONpdp4rdXLzguw3p7HNjfTA3aZUdfPAGAeulHs4GxL8VucNkdc41R3Jn4F5EeqdQ3dkjY1U+jK9fBWuoaJnQ7SXWVIVtyUbySbeNJFinxHfHGLRrSwHbZi71r9OYce2PyDsjhYVEnpBvttS6Mle4E8NZtTRavzWL6YFxPmB7Pf50mE8bOCDsAiuroDaveXetk+WfriJqSy+luap7908/bXikVhpta6XVV7e2iteDG32+diK3FDIg59dIZqZzG8oTys2YFRwMwA6Q3Otgpw/sKpDumQ8H+PvGD8CXvO0Wnn15XS79fK17FDyUuuNxq7mkPh5lg5i/9XMcUKHcPamCZn5uS0VMUbgTr4dqA/Vzczdfc92KgXJscZHlxMLlqPMohJ4jHM3L0XyN9PegOReHl5uJ5Gahv/ndnR6iMs3ZpiNc4t4cwenTHFLnEtYS7ZRU83NZcx74G1y4QngrMUchbgfs+GBU/W4jYIahYS7VuzxqeXw13V42oNmZHs4GL+JpiDOK7IQDNjHZw18w3vjvPnlgPsmNj9oX0XYpKgWu6/3sHWJwrnYg7QleJm+RhKqwfe2jpXO1c9/2S5PovQZ/C1K3iMgpxqIcZyN1RnKHojp/i0j4S7fan7uqdgjuheY50a7AjTtLOvj+vbM5EtQmK8yk3M2+6rkKfGj0HLq97JkStX3lZ7LIvwf4aVETtrXSaFsrrb7SZhLoPlQVab6mlW7uWqsTSgyKJ+e7miyjrnBuWkWjZTm1uwu7Q9m9rLq1/QaN3gYE8wmUuluJpz1SN5YbLVCjEt/02Evw7PxLPtNj3x9QtX2LzE0BvD4KRZZJz+oQeH3tJwFD74WDAKwBIsu3uR5BuBQRf1cM9g4WqkSdhYSdYSx0ep5Iz0ey7zS0RQ7TsAcRbFCcqjjhKN3Bx49jqlbxT78FYS9WjGZW96jQbGPf8pnGGDPgbHN3qB9YlkDO3pVxNjj9KjHn7RAdC7timOYG7pdM29L6IV+4+6ND8pCYK+On0WVVYbtfGom5e0J7WxawvViePAlFlHPapc5IS3WCd6eIs3LS+1SjLEB/Pct5SMSjBJsx1hJDOTpgf/lVMN0T/PcmlO05dCfRFSVqy4Xtd4/8a2vDI7XSaFsrrb76BTvrQa1VP/3GPKlK6+dILaQmLo7RcUvvkiMaCOSmlGq3CnajuZ3C2YDHXU3snxIIlLpVXN2FUTRvNKvZSzwFowAEvsHMrZ6vd4WQFwh+FAm3KG4JYe0Oe2bjjgbtLMXsBUaAaZxn3weBaThMsQeme7A4FrVskd5NmWreoFxsfDEfdayQnhXKB//78FRdhgZ63rJc1g8Vq1cWWhgd3DScmURqoUW4apQzw1iI/Lz86VOZ+S0MgYDIxdiVBVvIe0juU600Y/UVJ8yK8GwY1nEQCtll6z07azhFwkP0uVJpxnIpZmyHs5256JwsXdENmOoilatil2I3lktnt76YqrDdMW3LkNFWEQK87ZU5eIkUsA2qLEv9k4RtW6UTe1Jr/7pDor5osXkJ/d/emqfV/lzykJD5kSEDtrn3ePVXMNfvMLknvxzOC+vKJg/lBqP2g3hI5Gb3mwCjfaNppdG2Vlp9FWuj9DP4Sd3jmvgGVKq5ba/MKnHKwU6stm97brze/qQRQ/yFSVXsziQsV9LsDH/sMlzaLnRxRq5oyaSfV5ina/THR7jtjT+cTJji2RGlqxvLjWaJWVV7ib1igOp1A/eqoaGezM1e+0nAT+cB/CzANSa+CkbyUSuRnYmkZ7DcHJ6ZxbJznGf63SYggntJaR5YdhaQZSy9QAFMT88G+W1aK9yywTzYUwGK1TuKM8vFV7t45HFM1V3zz60lTm213Fe0Bk+kxI0c8hUn4ykTkzQV4naWD09cIbwKQ4tq9KGvTjozSbOzOMXpplkjeOLczMv3MjmyTnrcbFeXsLdgBDsPOfsoGHZ1H2oya7od2NIqpGsHsUhuNp42Cwp3JFCKCXT7xk942cn18s/vfA9WOTgWHjfUSEAkVRW2ZYYWYYQkUgv3TGhvywK20cKCPPUPzmmXd0Ym2q22bAj8VYBxVi3QQwKwO1PbFpsj4ZD2j9sFaphPFfA5qex1eUW1FU2lCVXUtnVS9vXF9v67f8qdkJ2HhuGGWmml0bZWWn0V64r/ART8+817ao3Wb9F6TJVR8PS86l0AdpejLlXs9hQn5Nh95SOXo6YSM9pgngHCvkIw3ptBNQn3Hhq65MzQfDNlQZ9PP19XuroLowCvlU5fS1kvTtuhrKxO2Ua5WDKEczMPMrQFQDmWmyEzs7H0TDI9mWWmmvTsS9x+hAIydmcSNnmQojM7bYEicZ/N5pL2JdyzRHqh/eORkqoBT9vKY7bKGDg4lpLeLOrK72vyIliHTHBTh/iEFjmagye+RHqK9CJYmYCXRnVqo72sf0flnltX8Cq1szLOxhfByfBnxj/83k+efPcnT8C//9bxBIb6wWE0dHq2u5dRvxJVlbR7/SSHYRS+cBfibzvglDsVbRuH4cgwqM3HfYxJDY7bhUtw377Ctug8vgmnBLu25GzmJ7S3ZQHbYGvLstS/Wx/Z0xmJ1ZOi7ivcJlpPiFtryd0muTQ/pJ0eqjlSst7Jh4Kd88aSnlG1nRCS1mcSalczDunlcNbmH1bYfhPMaN9lWmm0rZVWX92Cak2/NKtvisg9r+rc7TFzvx12vw5ggGz45JC+SqGf1dPZkXa3q7ucfBbJvUBzRlUh1pGfgmRmUWNuW0k/ZM50LD1PAy5MzWSS4+Wkbjkmk9tjz/bIiWPMvIXbarFF2HQox0Fn0Wjl1TizbFWQ7abqR8nUK0O5eszGGpzFcUfJYHkL0VpRy7RfxdASt8OEFrC0AE/E87ZrmH4FnoW3MEll5iGaxzLzDnbifukiPa9vcSqTsNrKnAXoex89+c6Pu+pf/OQJOBtzcXPPmKdQyX0RoJS9s8pzdR33Q9R2F6aFLmo0ZC8bRCin1iu/HOZ9h9ZJ6XQtNI6u0Fw/YVuGxYKwbRZ9L94qovRwU2mLPPXvFENq7VtpQo1NtJ1cogXpacINemquVvsLZ1Uwt/iLL+AyuN0Yqk18NytkWm+ywo9jp7FV5QHcLl5KqL2WdglznTI+qd3zQSL/eoRt7YtMK422tdLqK13SCOJjev2b+hwbrd9G6klzRYWibGV9rO7hfqSOXfwHc/P/WmjM4a8ard/JsZtNuLxqamUPdislw9chrBZ1pOJmOgVqgUrPoQUdgOyNbqmbyYz4CqOqIXFmsaHQoojxBjcGKEyn5xnGwuVMR507GBfQdomQB2hgB3dsxXQ9oxxB7URHd8mpDWqmSi8kUvNKUAaEbWZ1/txMM+paoXzQ/gF59B5UfQ+a1EO52lHivOBgqWCrGMyl+wfedZlY9OqDyqsAxYyDhXBf0bhMuW/81BYV3uqD5mRmDhwKe2nM/B68ImW9mzUwKXM8aUokzOWYC3I2KADWPagN6986nnRp6pWJUiapDJG8RVRaJEtxF0zSBP9eisI2F0STmZGcVMO846rtKwkugzX0IPVygLB9FUjK+/wWq8KMSWvVKN0GYLcwZL7qkKf+wTntklYtn38urSVehiNwa+XWzyrtX0jnQ5Nf/a4nhm1VhE6YN2hegN3YmvIAdmZGNn+zlvYIqJ32SK2iDzI2skfYvgprUSRaabStlVZf+YLfIjcBZqn1xTf4aWabR7aqSg7JVEH/R89s/+jPXVL9z4Zsqfb5cnt/M2o9Djg3IshpMPw6FCnTjjux+xIh1nF/ImWy9QdTN6sHhL2kJnW7ikY1HtUvkS4A076CUgjXW1ndeRDfwzzb5PxG7JmSsLeio2sxYyU+HU1zXYzyuwtUzQHlIw8q75arfexkMD9hZjv9heZ7xomogqnKhVVuULm1MjV4m4H81AbuWyeQTcrbUxuUZ5Vy1Wl7MmVC81OLJZW+UvAQSGEyllooxhfrlLNGucAfubjdA57jQ7RORjMLdNpk7l5+fOcjddr+3k86tI3WozsErdJcq3CPvAmQRGZO6rD0VOYFfEQ5//RhKCzG3QzVimcSV7aW6pg0ebGvsC2zkWC1lHRilxs3Ql+EGLANLkwmu1L/XtJkpzMSgGxD6GPeaP9W3tPJdes2Gry5RdgHJvucD9j+eMhPD6nFBQ7ffZk6lM+v6emMXGp/vp6yS6q2p+WWXpFgO6gJ21pptK2VVh9ivUwdwE/tYUIGvu4id7ie6BG5/3TSJEdtWD8y+E/RCCCwStSZTdgYxhJNLxB8+Aaens0y9jux+zyA12POSHZWGrsD/gAg6xdDpp0ZU6i/1K2KaPGkGfCfuzihytwrmK9fbZKBzQiyIrN/PBZVy+RqZ8kAlgdkam4fQ5sxD3hQzmte0d9DtC7rrCUDeL5gI0uU98ZP3fAz4YesT96tXgWxJdLLJE0edR/2GHhxWXpxNxKQj9I89+FobrYnGeY+wrwOzRklQwug9jjgbNnJYOGI03yIoqqoDeq7P37CT2+xMtXaRZAY5pm+DpFoXggAQQpGsG45CQp4DZYQDtbQmdXCfnLnu8wv6tDgLK22Pz1ObA4QtuUarYyqO3ZtUJaKcKKGatFXstS/G4RQdkbCug5lRL2cG46D1EKt9hdkOysYVNjRVeghGa45UjKNgGUDWDzsFM5k4nqsd9p867O1lENUtV2AvCWZ/6FySOQH7RItaF9hWmm0rZVWX4Narf8GyjPy7p9vcBWar2FAAajp4ti3fuhU0vYf/Mgxf5dPwF2YzCStqtgNLizTDojdbxDiGEVfB7C+o+ALo8nMyHJcRep2VFSA2J+f5u9lVKi5XAvdRtgB57+kGUDVc/c0VevfQq62c9PdjZHcbIUPZTsPRsq0HclPOkpjlqGd1nz0nt5eMoA7gruDjXzyzrj88IRd0YNVipKw4bMGe67qVhrmsIMDlWesXRv0YWh2Rv6KgAVMPm57FRROpH60/b2PnsQa9XyGvB3uKW8REWnd5WWnzDLH9mkoAp6yLz8libjDCdviG6RuX6t+Cj9b+gnbN+GU5FGRxtY4q2Ylu1sqxlLc3mUxj3WmWQHslm9Weu41ym2tGiutn0s7xq0B4JD24ZojQQHCvvXT0G53kDqSNUTGO52RMKVE7tVOe9rN31g6LpcHQ21N2NZKo22ttPpa1jle5XUa+pttJpGJ3J9BJ/dfUUYlasPSpQyKSe+ct5hvQJzlGhBTnGeaTSwuET424bgTuwfXps+J5V7AQRtSLSU4qdtVVInmsJT1LOWgmQXV8e+qmC7d13xvb4MgV7tKBn9hspCwvBatBaukP86YvYVJa8lwj3gQrutR7y5OYLnZGu28Qshhios+DHbqHFRAKgLWhaxeBfDXIYyroFiy/54HiQsu2o84RNEG5U6kTO4+GjaWmQEv4jrul6fOCRHpKMrZe96h69GfmxRZVjAic+Cem5KvUhwlA9gBaawmOBS1mOt7H31bjba/9S8/erKJIyrnmFpPZDXmlxZjtopwwh+IE9qTSTO4dp9KDM92ZunE4+HyNdm6Q9gW9wrNzYkCsDChHVSl8TPpXKVK2JVshXONRKTj46pZ5dvcy7ySeie4kZPNbX7HhGdHCAHbT1fbF/f99W8/81ISla8DKUVn5BdrGZ+kagPUtldnHjbyr1fYxqral5dWGm1rpdXXpvYzJ0Kwbv78w3nW+dbJVMb+X/5ARdv+vR+4G5iP4QeJl2OONdILKA1QzhuEkANfT52gkVLC4b4Lu6/95FkAPwlFjtCwvPbCwe0IguT1ycyIfD7OOv0xC6XuskGhB3NrACQ71Ye57+kblrmrwboimJnKphflcEZn5h3lcYtokx3aZs3vQ1kXKIxtEPZLNPQmFLn0E5d+8pJPEH/jx0Fd+8hbP5cgcdOTT+dXJ8XH07DBugKsqfJx2xaG3KilUzejLldp/N3CBPV41hgodBnKm6QLKUzKnTbgVWhQLokyr31Ug3Avkc4iM+VkDSrC9o+fKJcEfSpWZBBpRSF/UBg/coHgfPeqQWo3fBPMD35D4a2stJF841iaBdNP2JZou011nM259ql0ra06JTVHrhI++f7nckaRzscVMEqLyxIqzA+hlDwktvLo0j2bI5fav4OBKpeRYifAuzMQPnpG1KFXWzKQrKfs4L90rWp66Mg/TdjWSqNtrbT6Gpf0Uyn85vhwqtH67I91QSVt/z8jAV4/I6sxZ4f5hoCY41B4NxLYjURSjEUVu31FYzFuv/KRUqjCMRbZivhXSW8txo0tJNOc/cOd15PZXqm7nXgaV5O6+fQSPcJFYnc5N8zC5VODw0D8hSk6PXuIheRPZC3i8eQnLPedksg7rQHCRlNzq6T/S3GDvKNLBKx5IGHfiqM6QYH7noaweMpkf+fAbGWXp7M41Y55nd17BTh7mfBIrA/+WCG9B5hlhR7B8iPSFr73429/58ffEj0k3/5kiGHs3FMLRPGMEIsJlk8d1K7qDsIovE02buNaEaoeyZWx3f71wBySziwbP5/cd6ew/QZlOYoNYLK2Tlq6lqpXJLWbzrjlqX9vkJDUGVlT7JUk/Yby5lr7l7ywrZcHbG8M3RwJaqt43XP0roNJ2d+ZXq920gL+2y8pRRO2tdJoWyutPug6pZaUY9I+hEqyv/5vfxqSo/b3/8Z94u58tb8OYuUoN/J9Dn/1QwP2g9EQbYrc+kkY7HDL/0Z/iKJbGLJC+pYx7wka5uK3+WuvEDyTsHjUWyo57D5HcIARG7gf/mqvvFkwr0unn6/KmHud/riYfBYujDorhl5lmtVxHhK18e9Iehb2LDZinms/AQqgJNSSz/x4M+rCs3Ou0rh1yC5Gach5aezLdVoPrtMg1ia9/VwiAMLAMUFzM1RmHs/MRXLTSGHqfU5cp7JgTeJzdOecBPJTcs4GL9M64d2mZjbpp/Hsc2u5azY7vNlJGJxyqpJ2THnheZgGyzlpUkxXG27aBrMC3yAkOBsXS4a2Igy7r4ekahDhmIPLLfYG3lGy2UQrUVVwtIvK92LV1O3/1ksdkwfhkDxQxSeEzOjTtb2ebb4iG1LMdr7JyeTWqvBkkeIL4U1UrA3/+XDWPb/mxh+/DSS6OiPlXu2UY6n5a/5oPHzknyZsa6XRtlZafe1rN38ueBNTRx/acy/VPh8J7P7JWOj7BvP/6Z6ZLenD2RlAzB3gdkf/p7/19KYEVn/dyq+sxpBNLHQeBNBMQrw+QEMbhK8Wc7LxRSZpiuRmney4uzDJJM3uPthdjzrBHQE5bWJIPGXiYqe5Ael6eWggmXvRMwqek7qzI97cVG+nY3kskJzh06B72ygrlBvsZCphkTmth+ue5EVrcK84Y37PojU/WRNQDnXNO3nA4udVEDtA0b1IcBtD2qQbVDG+mE3YkowZEHOIGwxkuGeTot5c1r9nzi7Riz0xkeHczAaOyERcajuCbFFzgLPZ5DN3Z+63DpwkR2j4hA/ouBrWOsJH5uFE50FlT9lc1R0SnMEJ3izPC9u2yvSbYF64L0IP6SFhqlzm9GWE063RzLS4NjCoyLT+aDg7q7Rrg3JVHcIdK/NMqiv17ywgGGDCNZU4QimP7yTE8Wi2cSCuK0abQubm0+E/GSQzjEjw8XO8A9+bXEJLx6vNGUh41MYqaem3goeK/OsRts+xuvadpZVG21pp9fWrpdYXsO/nHKt8sAcBqYVkwudYJeqAptX/8AxRuk3+zd965q3ePOnMJqxkeg4pTA6TqYelZ+KpBY9KrnbHZHKBEMuEh0sPVKjdvrwul36+1i1151PPwOWOHqm7ok9kFlR4uqz3lMfvMjyMATzCs3MPL1pzajp1zdu1LwIE4EVQO5HAGu5dJr0F2p6P28HBBNBsKz1YQCFn3uAGp4/ZulXkUH46zsybH3YQ5l2zfsDCoBhb7Jn1SKXn98JdQ1vWcd8eYd+Oji7FnwYLBilH0lOc2MECYPlBp0w3w/lGOkp/OCxtR97Z6ajMwve+1KkJzmRwy3zj+EZMuHtNDAoskkz89hqnT+/mhIw8ycJRKJZ7f0wjl9cIn6pdO9XalS4v1DbOgphc2O6ZGdnlZmkeS7ds8HYOacGZyjy/b3MkqKPEtjxXez8r9V9G97OHnFc7g3QMJM3f9KSyWGpjD/jpJAnbQ4bDaKWVRttaafVVrGN6XfDsVj/9MI9ApvlS5DODONBuuo76vvUDV7/okm/90PnvPTN9OuHG3MUJwNB+HsQBv3bJ1YXJJGN2sgZVtZtL6vBxKRwtyk2nF7DcnDwJJJkwk9leqbvBPItmR0K5id52SVZvURivweUic3PXuooTAlv78GGhGaJex85OXSJcBshuOLCG+epRVz5pjgLcf2h4hdxsL3PucDc7AfacG+uodvDD2RkiM0em54n0nHzdAv6OpRe8hUeKGxds2cpdaka9ZGa+Jzw7kTIfoWG5kL+J+TdIC+DslfjTbHJS+j3EwicAwo7JNGMBf98LtbcjqHBKV7p6IrESeoskYEK8EFQSdcKbdWwk/kG+BWtV+nWF85AsNT+/CqbvFLbBOtYiLjnkE9q5DYpRgME6VaK7+nSPIo5+TuhG67cXIUq+wz5x8ewujcIh7euJ2XsI2+3P5Q+9Wv7kVt4Z2e3Vhqo2qMeI/Ot1bGsZ21pptK2VVl/f2qj8Qvi5OXXwYR6BRusz6PIM1nBvzQO/Nf+W7psSKNWfe+ZDuelAfgqAsqNksFW4sd5DRk0jhfE417rXO1TcXOaSTKDJRMV9G4ykEpZAbjTfLXWDv8ElCqmbs4LY2LGerDouXJk1Su7zTzgnN8lVIH7to28AUqPRaxSvxHKhGu2vBx0Vm6NiAnRlqRo4y2/1HjMg3wVebeXxaGb+GA3f+MkdLFCI27GMivAPJ84ADK1Fnbm4NZU0SwZl4ZcBzgmz8C6BfYON7LbyqOq1AKATaXMXZ5fHsrLwbMGZgPvXSfs2pVuNP23Ep8Ky9lbwvE7FwS5nQRxcchIKD4/aO2hQ5Hud3D1SZteu+cHpt77YDe/YvvVTsM0gUCO227++k7Y7HpKq4CE5ZHbvFLZBucWfd2zVCfnl/pq0q+OpAiVP/QOvPnwgaWZk157Us9KMzOP4TqX9C2nZMc0cBgAAgABJREFU0xDXpcN/FKy0/wE2jsNaqX0qWUq4sbvdqraE2s7a/CN5SDqObb/m2NZKo22ttPqaFxSlwL9L7d99mEcArcd4SXuy1f481mgAppws6FVTAuX1z36yeByxHEYcqdTcnakUXHB1ccJbmCTSs82Y67UPE2PFuJZKpdoN6JA3mSxe+ciTUASwZk/bZSg3QWdHeqTuOvOMzL6QS91CYiA71uNtANsHFxLZ2XBuxls0OtlxKwey78HHfMdDBHNTW7j/LEBsYsEibUdz0/0IG06cAQdwifRm41YqPa8UswP5yYfe/1FLZdTBjqouOdzFiUzSdoJEcNGaPICzdyKBVcKxE9WvxJ+uUPMpxizpvmA7a/LwO3/Uxk6AE2B41N4M+5WeFnvNtJO/gKo2wMrjiICSK7y7A5wAfGZIVho907c5UtwgWINxkNr4Ldwmmh0kbONFQjmhnVvuyoJNUq3dte7Uv4OInXPdtFSsbvnmKZMwyzsILaK5hcwJzZFtdm34z4Fr/pNQ+Fmg9AkcPi/0kbc/6+Rqy1C72N7vnAk1wyMJ25pjWyuNtrXS6mtfki61Xv75h2om2Yffl5nmS66BsnXlqjpUp7vL68kPnBLm7kbHjjDzAe7056cW2TFfgZuzmGIs+6Fgvy5A3o9BQWXuAEUrCZfYUqnvZzLZD4fSjMUlYjf8mleVutMqUjevcyukdHDh0HYOLjEagJSDNXiK46b7tRjqB4vEvsLUCu7bwJF83BbMqxA22PNQvmum42koUoktqorZScZsZR9Wxh41l9RnZIKXg0maAPEDmL7wYaHcdM90JLBmuEC6pqkfoehSdHGXNKzEn61Ts03KKb2mloo+zVivugO/N0muR7BFuYZE7XXSo0RtTrcuXELtFsDxduGNIGz7ogi/JrFUpvguRoHwXsb3VN8slkpHfRck2NgaRNIBwjY3oV3cmVqsawakvSJow0gtHO9O/btBCD5bEFX1kIA7SsL2LcIgDUIM2NbB6Po1+sU9RAdp8LsvekKvvSYandGb7LWqqs2b13VyR43m2NZKo22ttNJKvdaqnwo/xdIbH+YRaLU/h/PwQjVKvOQLtFL6oxHbANr+J3/h2Ig9rZPP/pcXpn/6Y/t/85H9/56Y3Yg92yeNpxHLZTB44yOvfRSoSz92hYZbscVhTSapBXtZRe32FybrUecbBHsZDrG03VuYsJfHYLAGlLpbiaeDpW5APA5OR1dhbjufmQ0egpuamZlNMpYy7WiRni3cvUnYr/2Cs4XKzN+Hs++4pa9ozNO2cmwRPCgAU+U4GBc7TmbmNrsnzgAoVxWzwSWR9NyDu7GV5WDH6fQCJGwxcz3i7u6CdbLjlZijZ+4MWFatkK49chJw9gY5d8SPpZTnk6wQHiU928r6RnRY1N7BAoojr8/VDnazr6AvAgD3Jnt7QAvBI9v87cGhrrY/bUuDHvtYF+Q5JNF2C1wCluhws6GBwrYUOxjOzq5m30iXE7Wc1KTbaH8mbxvlZ7BPWSuTqnsC3qr8UB7RYF35hbRjbFrykPxiyE+AN6Gs7JeE2AGz11kaxbdVVW1eSu8s8+hG65GE7atwTvuS0kqjba20+ibUGzT/gZtJwnWat5NOw1gDWKXW1Y9C7n8zP/8HP3YoafuPx81jsclvdzdTAuxeigpf9luxkT1q4hiz7ESc3nuGOpv5JBO+pXJc1WRSo52XfnyLTw+UoNNRMaAFXT79fF0hdSP5LvzylMeV+XfgkmW/Q5qWchH0XYSQWz7lcAdFODvKA1lNbJXJcHERyy2oukQA8btZI5GZ28D9cqXzAiFqMae6mJ00WcuP64EBEAkWIW3K3WMIaZOexe7YE7A/DcrVM5AS4PgK4dzHpwFnbxML5wHMW5yQ0kJs5bF8wvYSVfkl5HUofIIOGxFzjIZ7skcgrXZQG0lsFa85KVeU2xHhYHKIvMp+MthGIqnX0EMC6hyvQoIfIGw7q2bpad7KQqMBXnfyQ1q7yWRX6t91IGyu6mpq43WyzSNwF3B6SDHblqrYZFkQPCTLKdeQ7/1LtCh/XF6qFy0cRL0T9pc0yVG7E/lX0S3WLI8kbGuOba002tZKq29O7aeOhN9M+W/iD7DSzV3RTLLfc1W8sfRTauZbP+yycf+THzmexsf/q58sKin8X39s24h12alBbcae7ROTxxFrM2pzVSbuxXnR9FwiaVZTu/Xh7HSL9Nz4qd1IoERbEmk9k3mWSz3zsQal1F2Go+Bl9hJHxagiVJd16yELYB1JzH6otkhreczFTqgSdjg3F82YfXmjPz8JkPqsm2j3w4FifJETs2W4D5jSXHpcoznY4WB+qhp19hA2IOkLBGtTHnv30QM3XiG8PSF9gLOXSOdLfHaNHgGcfe3H87RNfkjBeqkWcx336YC88lNDjrA5C2E9PywEa5wH42VyH6L2TYCBbrHd5KowazMUgUu7UCPBGZf9glMZ/KHS41i3SxuHQvh24Q28fbC/sJ1oLEvn1TYWFKbD8OWreoXzsGqpNY57XnRfZarWvlVta4bmEykb5yKYlgK2l8Rzfsg3/jlWUWTv0FJn5FomKKF2u/F3quZ1y0N7SNra8EitNNrWSqtvZHF9Tvzn+zG9/sGaSWAyCVpXUfVq7V+O05HvG8zf/qHrD/6j6989Q2KFTyyYRdVh8l/80OUtTO1iziPMskuNbdJPe8h7K/biEJ89ijjXSJeSvM1CVjQXdmEv6Zwlna84ihZeYPmRGm1JMxa7wmkNsDuUmwHYDa2up+jiNs0lDXvpEaXUDXCE6R4F781Om7vHSXIkwQ14Nz4Is1qEOfDqnY4AT/157oHAimK9W8wGUNuiPOmk2Vswmt7XDBpwML3FiSRjkYf0CTJzAN/CkcNQOJOw9AypAc8FLHh6bn+IoquEm+Ps+PNd3AKezusAZpEF1yD5yXXMT6dM4PJ3DDU/CYR7UDtX41KGpPToq2Baasx4Iw6hjKUW4LoFXn4r8usqN8Ol6y3AVDckj3KszanXS+3fwd/ErgOpfsI23wEpXMWkuI5GyUaSa0vJm3pws0rM0bXGQNB4W/2zKFgT/NnSjT2ioyOVHrlXc+QrqjOm8SqY4k+5TibJVo4U3jXMtFzV7rZr6x5wbKQmbGul0bZWWn3DCw4rvkUSH6yZBH6L88kkX6jegGms1WNe+EXoKC/8p0Cpn6UbzXLTDRdZA+AYKjUNwPoIm9+J6nuwmyfv5wfE1Elk8TTk74LdKhcwzMft6c1VLriNAxrxklJ8MZpaGIzd1whxELG2E0/X6I+VUjfgb7Zb6nYWjebuvjrI3M7ig5GuPEsEoCcbc5o5tXu8HHOchLrMElnG5igaLP2d049gFBmLphdWFco0+O/LcGiJ9FJZ00mQ42z5ygH8zSRNymC+wxBaizleYpbV2OgeYblFSACygdyk/OGalHsvHAzlpi8C+FsRdqwfapurY1B7ltL0r0LZ1ZrAi8vsgTigERek5bpFLqneqk0Il/hSMlJLP4gFcn2FbWvVKKasGHtmj1sqgkUKq6WYQrinMRSvOPu8BzfgvU6ChNRwLM5eHRVPb919UVv+N6zdeFTm1e6aRbDY6Gj8WCX9eMI2wG7ti0krjba10uqbZSbJnAhdU4XLDzyZJN866XebraxAKmhu2pA1qKL2t/+ji0jPR1hhDHUxYUmkYLS2rpwwHYXth/j8Hjmu1Lx3oqOH+OyrsOMi4h+SEQF2x5MqLZWQa7cw/y0PVckkYMRRJ+/qBpA9QOp2QeZWNDsi2al3JOydSOAyEDkO+zZIs68wAbAey86t4/43CHmKoqEsP0gSTp4vvy8Zm/dYl2lHj1EEyurbWAAsAwL5aVdx6mUwiGdmlZx9Gup1VJ8FsDbpBpy9Fhs9wOy3fuoTf7QedVjKXTPYzwM4eFxwBHrs3UPWDRKX/r4MkvIn5eenxoA18ym1JNwgXJBQG9SF2LhZjC/CFRpE8wuyLNmge077xZrYe1oVbrzc+HsYkAc23k/Y9lYR6Vhd+gj5loN1XPzdw8htjfD2zGlXfffVW/8ZekgAxEsy/AYuvFmEIe3xoTwkMEdFGLcZW31NNrt+KMCjooFkfqnZ1WpJN1qyH23GH/xTSBZFognbWmm0rZVW37haan4OQ3PBl/SHeQT4MTccE4frdL/bSMOAUkmOVP7rv7YraftPJ028SWAqllqQ39fSNpZjLiI7y43ILuup1PTLiOMIX9iNjg0g71chdBhwrMXcg7Gbj8wL02kOVnysIZ4dWeovdTsqAHyVPZHDMnePhn3lD59EbFuxkTr9sZPV2SpjaH7aXZqwCtK1/r25RKREkVh6fqs750TyiiwTviRjgegfSU29QrBwdtbcZebWg6d2pgB0wNlN0n0cdq7HdBxn8xr5Ph7wyH4c8BQnwOMC1AbHpxC3D8nWYs4dJcrtcZmrG5cvHuCsGQ61Y4Iz+yJcXGl0Gn/X25/eCs5sUjDBV8d6bCQAu+Xnba520El55+3doA4TO4OF7Y5du6LbxoXuz1NyGVxVbtxI42CKjYtoLiD/PQE8WdXOSN7nHRBWsKUV6fZCb0Pu+fDNkceJLZmqvbRe+eWt7IeCK1RE7cRkj6rd4yF5jE8hzbGtlUbbWmn1Da/XZAvGC8i/nj+oEsfcGFvtz9XXJO3fwS/mw/hasEYYsobf+4uuuJJ/9lOHfFRNODeTSVhVN7WcjCeSZoBi5rI+nYSa9yyA7N72SvrpHjVxglnOgv5V5hBSu7U1BgqOdRRsJ1zC2pilKmH3WD/svkGIayTyiT/qSVtUpW4A4j5WYu5hde4ewgZoeISG83E7h609mYBl3XsI6VMJxi6Pl+jF/XBIKWO/DIfalDucn4L0byvp2BhY50RUOVsK9ftT/ZPv/vhb3/noyXd+DOpb3/+rf7xJGSTOBpgeycxJui9AW4DXV34S0LabnZDyNO6sVwFM5GOK/zcm2/Oo7DiMS8tmSak9w2s972XJIN4i3fDoQRO2jPNi/fjSVp0U2zw+g8L2GVFXFbbldm20TgtbFoVtKWAbDm/fiiBdJhwipPp+kURl8Ca9RaS5MxQfdSIMad+IP73zPf4yddhJucHKcms7b6WTqdqNXvO6TdZl8eB2bc2xrZVG21pp9UHUTv5CiIDInHyYRyDV3BbMJM3Tfre5DBe4aDC8Cs0njqrpf7POfXfU+n98jFinwtcIsx8JEZk5qYvOXhrDsrMV2j7gcZsxPJpa8BYnAM+JbhMV8t6IPesh7wG1FieI7Gw/7N7AfACLuZ7L0pirqGMyKlI3YHEodZvVArZtJc5BDpYT8okzELKD+anhkfptndmj5sqLIabS6F0lw7IsGFuq8wC2hSPlmMNdBOsBvaWkt5bH3MWJOuk6UoRnu4oTTcolD8/+45/8PiBsnrM79c//+gm46g0SpdLz8i2guWlwWG58VDZu8xYnhx/AvoH5EylTZyKSXPBGqE4WSk2Y/7Lc+Ht4fkIUXmp9oZBOBZhz8KmFZlmehipt26szPR4S3g6+Aaeu9BO2JZ62Vadu/B2jObiKqBclc3mj/VkiZe3OAyFUT+Zy6xYm4oMt19q/km7PJLguz1Ly6ZDNkTuFsx7UBkdMEvXBYdmhR2BbZE8CCSikTcoiLCceVdjWhkdqpdG2Vlp9c80k7d9dB5ISSn6YySTwSz1cTwxurgIHSvKfBGpc8tpRCIXflMuF46P41grlDmdnpMQ6gGsAwUvtnbtfhUw5kTQD8jbz5H0ctgPCVkYKDk/eazQ6CLtxLwztdpYnIrkX5WTXA8mlbpsib5v3PRtPUdSfM1qEML47ZkYKNyiPmd4askt3AzqASFtlbD3iVnpFXgUjS6SX84pUuGn2ttJ4IW53sOOAs3fR4FEo3MPZ4HJw+57WSVCipN1dHz1h4w40OyObcWOAdwe4D1YmeGauZ97NJz6qH2qDTfHtgOEBqG0Ws0d6UPuUWlKi9isUFxMVgz39kReRgtK0LfeQwFk2oFarv4I/74A3gmRGlwvbWC0ln9AumTTeoCynlItnCN1o19q3r7qN7ycErnoOe2ouqafiOL4jF7YDxRdDNkfKUfsNmocXnuP1zuwhckZMIPlUeXe5a/8xPnw6wrY2PFIrjba10uqbXdKPqiuN336YRwDOqOOTST7v80v0gXiIPpN3WKbSwrS//QhKN1qr1V9fB9MZxuqXjWIJ5qYYxjz8zkC3CSBvX35qJ+I8xiz3Ie8vNvOb10GyQrusPCgw6YV+2E1kZncxZIXwgiWBrzDKZEbg7Gul1G1hdUrmtvXNvea8LhauxixvT9i6oZVyvbmkb5AeVa9ILeoM56fEHR47RkNN0mUXOXtJEZ6NFCZVOfvKT/yZ8Q9VUJuv7//VE2kLKcYC2RocWEfJUI06b/qzdbdROxrJzXJjU9gxgQ7ljO6XUHtc0ptXa7++QnNS259qspCE7Bi/HgD4KN1dGtguN21LHhKLTMoVwov8NJpbUArb3IT2znjFdkev5SHeU3ULd6nMg/+Wup3rN4i6sB1rNISYxXqyLU279EXPQ4RZHNK+flfA9nbhujOdMZQRLixedpZh6KLYFqmC2iZxFibnNX8ED4nm2NZKo22ttPqAarMkDJMDTPlhHgFpzM3/z96bODeS5eeB/4hHbY03pI0NO7SK2NjYjdhYSWOv7fVGrDWyZO0qwhu2ZyRZssLe6RlJ03Nora6T4FksFosAAQIgCWTivu/7SlzJ+2bxrOINVB/T3TPd0+rxvsyX7+XLxEGQXWT3dL2ILxhVIJBIJEDye9/7ft/XyUyCGz3W8g3VkCXakvbrC4MWji3Wm8/9c6dWbzD+lKyEBFwnGzFc9cR6ZN5rwXtwvPKItc7mR9rKwJ1otwHSbg8DWKYvNuxOPyxHFeOb9cidkCh1G1sEZkDdJCJe1GiL/YZC3+ei11eDBhD6lMqTgOKxk1E98o4/NOb6Nz3CTF7Fb9IXBmYKwy9Y95xPzbPZzKPW8GzonK76p9c8M//kB290Ytu/85bAtmdzw7CE/NzqE+w6xf5VN9N75IgNZQUWglMt3SsS1TaXDOTw7gUbl3zPkY22VHvf7sfqvsSh0XwkQWFlVRUcv9VDsppvSpU9kVXcWBnPJggBuJ/0ZJ+Le2WQbUdQeB/4qJSq7+QKdZXMv5ovt552oX4OZ5fNnBEugOWY7dJIBpW0L2WcXX+tNfFKA1PtxcrPiPWYG1LtVgMJr4z8A/++UWGbRpFQULZNQfFa4NyeEvQVV/41TiYZwUJaKxbLH0n7zvGdFpPJvBQXHdHDactIdWG10GhaQ5tOu4swlkwVBjypsUho6non2SPzhsEme05DMTIxcXXave611PzTluzDROKeSurOJO45sn2mwqC2lfjmbzVgxJoeUfHRPYcTrBbCsadYFzfnHqRDk4d2V8Pqgzzbm3h8wniSER0ZzKcVDdbtebbFXwgaV3zTzzwTB05T0+JvNW3DWcl/+tYbubABati7DgeTGQELrRNUk94LsM0DXMxjVRQ3GouE2SME1ZZI7W5sq9NnBvunExGpRx1bUHBhexOxbY4/xzsYOIcEkHjoVAHM3pYfkz4qeZmyT3ETqNd9WCWZN5xxLQrY9ldKwqyC36y4woyrrbMLkGzJrFJ7IUxNOLKYlU4X+lZ6GI5cLr6PqfaFLUYsMIgim+C9tgkkADnhUvRhYfuGfu3QjG0KyrYpKF4v7EY3pT457sPX8wrAmhs9NwqYd3viIgYyHATVI1nL3AfSHrctKDc1lmdK1XNYEF3xm9m0HOgxmxv2JZ4sdsg7ux7z3nNr24xXXsa8E1GtMz2m60C717wWb2Tcl3rQSeq+Zc6tLWkmC5p9Il0Ex2Njr4iwh5C9sxh+yAeMgOyeMT4uaJoELyfx+Jzxt5TU9PmST1rDsyGrW/TMzvund9yTxzY5SOS3O7BteBDAtktBo77Y70qPnl8xURu/C6bigOpbhdAUKUhDsRlT7S77UQdOPyK1fnh8cj6ytbAdGyfIp9tMPZeaX1LPMRfHwrbKrq0YyrQE3dUo8pA8Abf70lqVqaatsO2vFuGjvBXJZk3GbPORy4cjF6qfyIyfjS7wfye9ZFtSjm50mNsmkCA7jeamqTZ1bFNQtk1B8dphufge3pJ+Pa8ArrlJ1nba3uHYw+FMAxUubAl49SrFZyZOh/21wWp5O74DFaxUeHImN6Q0c09cd7D100Nf1paSjAdsdsSZegwOXgxrOwabdGXe3Wn3qm/WkulLJ+4tEUfDUrex1Cbsr+XGzxX2pytoltxW0iuy7LFK8diQHRb6/BlB418KaOZ9JkCyAfKhqcmSJh3SH1s9Yqgf6RvRXMqz91zGE1b2gZwx3mRkMhGdVMvbb73xL4d/Q7RquF0pQfrNh4xXra15YXHjc1t1qc0n8YSFfPfX8sK2CfzWVuqwy+cEi7uVgKnViIL5KCxsn+Fm8AXHHpL56s+hWeXUmWWxsI0c23KENrJrC4tSp2RuObTJL6pUfafMNzeUGwindntbDwmUw8F6FXpIFlHaPUAgJZW0r0SGO1Lt8gf4pTUZmWofeuuK2sigtq2BhCcjWYTXVafCNgVl2xQUFK8Mp668YHC0JV7PFvdq/RP4Z76TmURKQLOG2u0MbODWDHAcTyWFnaxmzlAtHcFRtmOr258cxymBQjxIcqwSnO79JBe5i3x0erJzBkjPzFuz79ZtuEyz2UdCS3xJA05mqvw0H57qQrtLYb0v9aAWvdsqddvTw+qAv2L/5+XcRY0/+ZgceZzzzbpTY+i7D3XFvjDqN1kJaDbcwnQj4tn9xdDUiU1dUgP+HY/pWhJCJJ4975spho077imSZ18w/lLAlIjqFjyz0+J66Z/84I3ffuuN3/7BG9/4/hvwPktei7EwoC1q1q9g1JYjtA15aRkWC0+0xpWQs8sbmRMc99GdamPH9kur3yLN7GoUbM8qZzxzhHGCLctji7uxLXgQ8LzaFmEbN7RPcRMtXvCACa0tmbLgro5FtSonetufQROnF9+mfkC7JZMbIzfgLKOVXkeqzf8dvj4NaxhT7R30QqSpUJujk6pN1kYC2k2FbQrKtikoKF4lcOzGSuGd1/MKwGQSPTda5z9r/e52cr+T2Wae/wzKaU0mgiU67D0F1MFfLR4E5+F9tlwOT3KMTAn0pMZ8VaOsshd/Wiiro1FW4jVjfuRKHTE9MW8x2AQz7x7V7kz0aUYpdS+JUjebfvj5OTd4lLkwtG9xQpK96bKheGx8zIf+1L0l5ClfRTz70O4CnBjw7GU3c2pzM0QsDHxgLKbtxrNDpn3nzAkr668NS2DVbS2EjEs+Syj+VEt4Wmr+aXQffzI6KZbg9Kv91r3AGljI7SAz+thLOaY6sO61iON9KdnUkT6CVBIs+dazZ90/zNix/Qw5wsmMkRfeOXQCwU4eksXyR9A9deIuWnIjKmFbbmgXI7RVbLsSmCb7d0yFR6rUvxd2a+s5OysR+KhgtQpvKfHv4+XHmm+s+3Ak4NaAYePXheODFis/U2UpbqS2O123m66NVK1JqLBNQdk2BcXrBaHxQfxb3mpNfk0QRfkJyVqbP8ZgESLRoOxpG5+JqyjtUGdO0LDXZ75KnhS557I7kL4AAAbJZBRm7mDi6aB9/X/6/7x/7zvmN96c/uZAKpb/aIH/uT1tnCh+np5zjbaosWUeeRJPZhHzfuYd7RIpuOeYccfHjMX+Ra9lxW11pTrQ7tRYMTJejyhc3bXoXVHqHlJYQUp9l9e/F/uwLfsF61nyWsKxp2QnpeDbLj4Mp+5jnr3pG951MJBnR2MT5tLgnsO57rGZCMeOWDrYDxhzo4ORettl54Jqni3kbzicpaCx7DdX/WZyqnI2O7KPemoO7E5WfBOd6dGG1XtVqg0+DIv8p7ryI0i1m4oWHs+JMwcjtPFiD1PttdzFJdKpO4YXEuH4hGo+knRCX1ijrDixAIE9JIL1QuyBB0+6xH0g5xuKwjbZ0J7mZYn9zCHsj51avHhlEqmuGLmJQkiRstJsJ2zjXCALx+LlLmDtWNhGFqaOw5G4bJKk2qrJSHHcM9qaSg6hJah2jt++qV1EN0eFbQrKtikoXl/Aqb4LNv7amklgMom7kmjjlq59Cv2yba3ta7lzyY3qzJK352qngGdjkTtYLZ64CvgPP2BCZkQNvz39mGyDB/jvfjg1lL5moJ622G/JP8qE9amI3pt8MlUYUGnenuTjS8O8oeZtETXvRFRrSz9uLZiEtLsSGV9uJ3XDWsqu9e9CkF8mOgnYMODKsEqdzO025Qa04n0CCp49dOAQyDHi2UMHNicg6K08Ox+a6hR3DXh2Oqx75po9Y23k7Uc2T81vTkZ1ix4LQ+j94DSyEQM+Gu+bgaagVGTyypI2WFF4q2g2UU21AZZzF5Bb78S2RSPENvwvIOjLxfcu/SRfoKXFEeuGuyha5bQf9rEs5k/aekiWSz+Bzwg4N3Zs6/OCsF3lPyEb2lsPa0dlk2bO6K7EmPxjaUsBUfxDN6M64TL/oaE0BoNNivUmvNHHp15a0aAn65WGI/n3277kczYhU+2yvGZo2GKqK78b3Wx7BLI28oYi/6iwTUHZNgUFhZw/sIEE2tcNbNnZxUxy5sgIPMlX6yCtRaBeNV9T+EDAoQJVDptcAfleTiziQa5jq9ubHJvID/w3f2VSsW2AP5gY/3Fg6G50sDOrVt8ymxsCdGc2O6IrXaKIm/IDXNgQiWm9lzHvbd8wHq9MhnVtabdgQ0+P1YJPycdWond9qQek1C1z7qLGmh55wXrW3SzgxNgrAnmtPznOBU2QZ/tT9xfCwlmthu488z4+dNgwz3alRgGTaw3PNhT6qwHzhaW9nr3ntMcj2h2n5Yy1k7efMd66fzoSnfCkxxKRSdLwPZUf2HCzqOnGBwVjcIdVj/Ua7pH13Km4fvuZQXzVrQnfOOIacGtcPtUj1d70prCwDS4sPP8ZbqYN27YGdBxezvW3zgSD5eVi5WcqYVtfeowb2lt5JFjz4Dc6Xd0DX+d9s8rWoTZ1NhaOlZ6iti6bOsCVkdNIRroMR+IxZXD/5aJMx8nJyBPWAaV68Ipaj5Djz0mn0M39hnmW2KbCNgVl2xQUrzWwfPvCV389rwBOJmlbc3MYmIeDpO2378Wdd1E8ayN+F+oN3EQNEOYyZ/Y0pgKTU5ZWqg3wtTelf/zaW8b/bHuEIjg0U6UBFZnWdXabTBUGnOlRwKrLAeOGi9lzOAHHxc4KQFW9iXFvetSWeXSp5k0Gm3Sn3TxBuxdDb6dVru6iZjo3JFWpo2XDZElTCgl5zOtumzEnpHq7M/fnMc/2jJ3YXJhnR8XKRsDIVS8cvNhF72wnpgt4dig28czFnIvci6CA/lU3E0o8daTHAklFLZEQTBGfwOHZ2077bHZYFPU1+w7nld0jTGQR9pUW9w0FFdX2I9IffCZE2QjUEH+ozu2p5dJPevkMY2H7whqAOydapQX5xFfEzygzY8Jnspa7kOw0oRXZsS1mbDsqIUxJq7wiqfrQJ/x0TCLTUUgsg3Qnn6j2FraD6RYH15IU8l2WHSY6bmjDyeJlg+AhCbcfjjyzpzDVXiNmTsjOyCObrcFGSGeO2kNSRlSb67vR3zA0ioSCsm0KCor/Cv4aQU2rk7Xxq24m+RgmkzgrkS6DpKQrVDa+1z7FxtC2B6/znwWrVSxymzj9ZqQG//o6x9xt2bYK/5d2/FIPCWCfluxwMP4UMNE1N9OlYyUZ0c3mBrWdJyxl5u0b6BIpuOSZ7pF2l6J3SKkb1r8bCgOQZAs828OaxFPyEDx7B/Bs1gWZLuDZ6ZC+aQ0EExMqng0euOzpyLP37a54dHLTZT1nHEoPcWDNzQYT40x2FDBLW0bhdZksaua9cic875uBFnZLZuSc8V2Vah+j+MhaZhleLnJS85iRuPuz6A5Y7sIPEqbai+WeAtrX4nI94YJ3tnU+kjRtzwXMrcmAc/wvYIQOeFLwOdcSwjbZ0B6vq/0Y4LDgE4UDtic54edo02WDJ9Opp71S/6mhNA573cG/pUVveZ0Uts9Zm7Bmy2VaX++JmKQk7chlj+QRT6EzEnW2M56txDpK+G62HkTHjeAP5M1F/vE0ioSCsm0KCgpJEEIV5Zup56/nFXBUguLf/jEY99v24qwW2seHQZoibHx33vEv1pukyJ3JphpsdNvk+/qb070Q7u86R9rmeEwW+meyQ+7U43RYX/NPb7jZ1jzpY9a9b3eseqzGfP9k6eFE6WHv2SbVkK5LpOBmoH/PPX7onOKDhktp92Lo7WjyviX7kPC0DAI2DF3XzuyDWuQerKM/cE2eMW7h0+hiXamxOe/0idULeLZqhTCTG17rHL33gvXEY9o19+wZa28qddZDu9OfeGIqDDnTY06i9RN3uWP1GrtHJop9zsRYs4MXvBOaggNbanzMxKUgasx6Aa3kUEo3eCLhg2RLyRYIZ44MAbxkAg8J2w2r35t80jofyRMt6G0LXLaTB/g3wGzuEdoxEKJFiJFfY+tTg3cQXzpjRQjyi8W0aq98i7ANvVu4NpIngkHkR4X62g5HnriLbak2T6SJg3eKr74L401PndnWiZQcv41t6HrlsoQK2xSUbVNQUNyMmYT/BfQfH3nKr+cVwMkkrWYSUTAT//wnD9o+dit5KO1ceyvdnyVUreNSaxOnP/Rm7/bbe2Hbv6PR9Z4DqBUdzDO5QXfySSk4bShqtMU+7efIwO6Fea/7+3Y9T3ZdJn/scXfaXY7ecacfTuc1UuRIXkPy7AtRPIY8e9ltPW0JzxY05uzwM4etE8c9YtyRqHbRO9MQKTvJfQHPDibGpwr94OU40o9U1hFxKlR3gUb0tpw2yZJR7EvFJsmcvt6M2qFFTlp9WfLSzgY+SFNggdOpsB7dObzE/URJtT/pdakcWydeoEtaPCjJNC5sx2yb5OLz1Z/D2JwzRwb8KsAZ29lccbo8hZTyQTLyTzps6syIJnGnKybwpFOlAZj6h1cm54yj08+ak5i2NFWEVc0Lmwcnc68IVPv9tiPdban2mQ3nAAaWirtr+UaXhHJMtXU37CGhwjYFZdsUFBQynvt56BntXVH7KqHOf9ql5gY2Zu+HljsHDoTE4O3QPH+JFadYf8dSZuTEsWSkb9D5a9+7hG3/xo+nbq4dvXvXTFvmnY3q9lyGHe/jteB9Fe1eDd3Z8g/tucf3XaZ4/ImKdntTY0vBcXC3ufCdWPK+OYfMG/m+C9H9Ani2LzVxYHMesq7p3LDq2T2psYN2ZZBIxfcmotqa33zGqK3Vp4w3Epsw5Ydns8OOzCiTeaRi8PpiP6mUZ8MG6B7RFwfWXEwn6brTmZzbk9I6lntXizzNpDS+7p2Jp+4e2qX1wGK2gXdITl353qm2IGyjwO8Liw+cNooN8Sld3biw3SuK0Dryu2SdzQy65lOFQX+l1MmuDcGFzPgOkL8WwgbVbOgqf6r6/ENXlZGbqNalXzWp6jOtcjXy3GlajoypDW/Izi4Yb2IK5f65K46/tROZB7eApS/Mwm91x02UB4jIv/ObFbZpFAkFZdsUFBSEX0KaLtqJP3s9rwBTtkMjaWsyCVTUwNdOj4VhDgIJiO/08lzBahk7ua1F3YE9ltF5wk/db3Rg29+4b4hFtfD+Pw4M/RvDkz+eeaxJDNwEw9aV+rXFK2jeC74pwLy3vSNrwQetmveWb2DfrVXV6AAi60yP8n4duEMxdteR7TOJ6q8hrwE8e92tDs8GVCwcnzi3dDSjnzCeZGRyzjd7rtSzIc+ORrVM3qAraVypMWfysb6gXkKA208QZz1hvNiMYSoMN6xXjvk7CK9JZDRbwZyepNpz7lnwwvXFvqaoozcsvlNnFtNTVbhNdywWL7B3omH1GQr9rfORpMViLmBSTQTKdTbOnChsSyecyqWxAOyt5NpMOwiZgNKdmZRwxZjMMEz9w26ZY9ammmGYLVtgLKbCQyI+UTTyFC9jWocjSaqtKgfYTMjxms89RfiiYJThfmhF/WNORv6VdTf6K4VmbFNQtk1BQaE0k9Q/wx1y1EyiNqeGlsXxtVinSPLVgpTddoZEzUtRrL+D3asA8+EQYFpv9dnasu3/yIwCsqgPPPlf/ka+8VfeNP/hxNOnRc2VyiZfCVpc2hp7ajQcf7rlMe27Jrd9w63BJqvBu1u+wT3P012CeU8W+wPJJ4uhsbnwHcHVnR8QfbQDKnfHJTw7Oln1my9aeHbD6i8Gjd6McZIbsmVGXenH00oSD1EIGbFKveZmsDXCXvafMlftiQxu5CSt1F6QtyNwgDTku7O5PnBB0hE9HtmU5XlP6UofWjwOe2H1gcUGWi896qSwtvq5yTob0rGtK7VpaCcxXdSh9ElpqKCOujY79bQD1g7v6a/Kv2SsZSk9sIEmUC8Yj2o4cie+20nVXijUcK7LuS2mUutbE12IVaXmpn+lUGGbgrJtCgqK/9qWUwKAv7uv4cvHySStZpKtlOTM7pIRgQojg/Ptkn07IVJdwCK3M2c4Z6M/6rP/ipJq/9EjPTTj/g9/a2gl4r/2ltGltTUZX9Pif+FwRGM6Z3J0Oj9gKPbrRB58k7RbYywM8oHpA7sTZgs+c9qzYUMmZsz5jRse055b2znMe+jAObnuNZlLQ9hkshAaK8buOrNCPw4g9LmwoVN4trCwsfrKQVMxZDxl2/Dsmm86mmYtnBWwZ2/iidi9or4UM7mhbacNNx2mwno0MalJVXbljLnecMFEF9GIra4oO8KbjHyfFY91gutbCd/xpx/sOu3KmJRwd7dSK5b5D8gjOFC/DKd0R5CmbZWHZKXwDq6zId3MxtIT3M3UatcWpgyrJ3jb4dTmJlP/sIqv6mnP1o7hwOVseQZvH5X49+U1CXrgsme0E9WG50lS7SZayYAlE55CORejuFunUHDk3y14SI7laU4qbFNQtk1BQUH86ZWio9v1Jr7OZpLu/e3SOFRoBW1zL17pScv8T+DzQmz4ggfT/rv99j+/x/5QYzc7VgVja1ivcY11MXb/Vp+urbHkyOk6tbobVkDmfLXgNObiOsjF4fTkK5LGdcihoS8MABK85mYisaez+ZF8SLvjngbMezPQvxq6o2Lea8H7QoGlS7ftMQPmrS8OhGNPq4HxcPK+qTCkK2o2vNaWIhjEs1v0bDG3mwlkZ7zVNFjGOFOP3anH+kKbbs5oTNuQU0pcluwISqgYrPMfyiWFVyyJLPMnpCOcrI4HSxFBKi4JuYqG4gMoqDfE5QTMdL9qKNALmxdldXu2nfa2hTUC7bNL53Bs97Z1QME6G+zYJp02ZEO7graiYd90eDIQ1supf5j7KoXtOv+pmTOK7HywUG/g23Wc9FzzbiJykRiO3EodyC4RZWw2oNpysKM1sJqXIoM2M8fSToWy695cmcCvy11KUGGbgrJtCgqKLwbnop4HvlIzCXn7fPXnUALsYsuer30K79NkItd66qVJFAAcjk81kdEWM7m/Ni11n6QUKt8zXSrfNZBbk1xQXxgEAITYkhmcyQ5P54cmi/3G/IC22I/m3l6BNA4Ys7kwEE6M5UNTcwHzjsu05x7fCGhaTd5rwQfPvKP7QqTg5CQshw+NC/04xX7IuQWe7TfnwlP/9K03/tmDX1dR3i2XLR4zpmp7s2XLdG7IlRybaZmzhDr6kteCHzXvm4Vl7OD6+FLjC9VPmpbg1YzaqX34Jga4NPlEZ0Q497ZLSsqbiwg2kkhcjsnbjW3hPtceA7YlPkd4wRNRXdv5yAAvFysm84rMHMxKoblZPnOufUO77P3gGFwq1BS7ciIJLWa9bXvaXZUYfEikuoBvdNbDEy2+9jPGie+wkT3C8XmnzoKKau+7dfJkJEpaxEuIc0ea9H2RkX8T5YEbHzr31aiwTUHZNgUFRXvsRjakLW+ino2aSXgUqt2plA4CdryLydzNazw7KXKbCgOHTkLNZaP9M0uXBgX+8yHd/zk68W+NT16lRVvF1EnH9ucQxadzg+XwFGTe6/6+Vs0b1ugcuKYWfFPJuL4YGbenhiPB8d/5wRu/9f2//1vfl75+40f/QFBtbZ5AQpeuHQSrZQM34kk+tqdHte2WCpbsyAmLvc7+aFTWO+f9M+vZ51el2odByfthzo+TF+SMkanwEetFF3MQvLRwcmDP7kAkMo9/7prW0BX4nBNH7PkArYfzkYAot4jQGsyDFXMayjqb1mWJidO2/xnhP8Dv/rbD3rT6jNwITP3D/ZGqnnawdoUeEkuZIXeN8HM5sg/ktnb+Y2IWQnovwE+Wimo/843Jb4FX7qZZ4j5oO+2NPww3XRuJFkLSmT9LbNM/KxSUbVNQUCiA/1ZdyT/6OphJYELiqTPb5bGbmRNUH1i69gkkaptY5M6HZ/AI3c504OvfneklnBviD5+OvxJafAvQFwDzNuy7pnY9T9oyb0nzdhl++61fRVRbxj/+4RuAzxXqDTNnYDMj3uQTPOmoQjIySfo6cN62oTBwbPHs+3Mvr0i1zzwZRBw15AjpBTH4eG6L4W/V40+eeUcMxYELkYtjuwWUY8HX3j8n2LMhmNT909LUKaeYjzRWdHMBE2LbQaUZ+hn2rpT585Y4dk1buzaAgZPc4cGEECEy7zGpU/+UdTZgBWsojYnnNlLmPyQ8JHK36AXrUC0J1rMXcqqMStXOVVZCd7Gu37DFWodPwLplvvpzfONkRV5L3GhtpFrYtlJhm4KybQoKis4C7bkt0Sl/46uNeG1dSkCr7SlU/+hmp/heJQcKwb+ylwZvd0Gl/lN72S/lA+aGT2wS7TCPOt9409w74f6HP5r6jv3Rl5lnY8xmHhfDBhFGPmjad07teB+vK2t0/tmDX2+l2hD+anGqMAh4tjU70vb4pvzQjkOeSiyGjLhC0pYZFTnrlZP+zm1xaNUgFzOTRU2TkSn7hSU0VRnDodS73idNiz8W0ZI2A/CJgh+bXcIOcclAs10WtsFXJjMCG23I+ch0+Rm48cKKhfwo6Ywi62xmcuqWn1C1vdUYh4roippTixeQ42guCg0zuBWowbjJhzgrIckoX5XD+GBJO0QpdhcvKaESLFLtoCq5XDrzwjKg2g08O2sNLhLlr/h1kcOU7lICtlQK1peq4TaEbfRytpLP6R8UCsq2KSgo2gCLXqoZo9cEuObGqTStYpNr98CWF96qZMaNft5J01h11VCS9Om5AAP5R2nS83/82NI74Qb4Fz+eObS6jxn3qdXbsPq7oju/9As0ohMsrx7HjGfXMbvvMmz7hlOJe7/zg19tS7UBBXemxlypxwrrCMGAAQvHtuALq8+THMMKbilo7N5T0xGM8PEwlybJJ7Lnp14ycs1Kkw2LHgbpu/EMC04jHdHDgUJx7G+R3FACn7GrC9vBA7uz7Xykqgh9EQ0RkoaxjcyJwrENo1q46U7PiyX8NQ8cXQ2qU/8sijobvHYFq8fWcxMMRYWH28EhUtheLr6P344LW0JJtRcB1W47GQmB++dJIxzhR78ND4mcsU2FbQrKtikoKDphsfIzKRSsq0f5KwyoK09yI3VCNuuREi2j/m0VUbi2yG0rezFlvEBZcu5x92/+9RU499e/O31PY78JQnxDaFj8c/4ZT/KJoTRgzT5KRnUrbmtntv0GaR1R2bVXvHIT5K7Dgd0jk0XNrs1xzTMUidRESTGTms7nWqm2mdOrBgH1hf4G5MrINXHVEcl9h5yBCL5mwvrW+UhzyYBi9dSm7UXuQ/gDfuwuicL2sLLbaLjT805xE8j7PgwPG45qoVf7AkWvkD3tZf5D6CEBX0kPiYkzYA/Jcvjtl+ixTSYKqDYR4xhe4P9OpWqTk5Hbqd22+3JkY4CWkz8Mt/PbgwrbFJRtU1BQ9AScC9bdNfFamUl63+6/sEnhcYB5v5LzSdQ29cgsu+2SO8kfj7j+0fdZQKa/1hvn/tO77OflwZ21bbIrsX0ctaWbrH7ocOZDU67UmDE/qBPVYnv6cS5s2HHY8ezdv9C80Yltk5UlskabH7ogAvgK4Sn83en80JHV3dPrbVfYnuIrCit8sZ/nzk5sSZXyTQ7nwS6bhtXrToypGDDsl+k9CKiBhO1Tqw/3R5LzkRx/DkXotqZtsIrGdTYqxzb4d7HaaPukERTXA3AKy4asAWjOaRLdPaSwjUd+yZ8jMmA7nhLKR2UnffIQj6gKVJuor18orQOqve0bkDNPvGr79Wq+qUpRnKoa5LZL3ncbwrYjS4VtCsq2KSgoegLekO19a/t1MJOciHUVR95K94fvRqVt+ufeVzaPRYrcyaiOaB8MzrvrQ87dHkXu/3SPNY86D6b912Pbp6ybTQtGcG9Ks+4feO40QpJ9ZHPv2sAywJ8OT6pa33VFTTY41cpcD+2ust/kBvSaCMO2pEeS0ckNN3tuVZfagPtXgqbfeqsN1f7Gj74u1VsWZRtJIWQk8rn9THoEfysR1R06w9e7AoBqp0Mmkmqb86Nz/KfnbrkKp8lINNpamcVyO6Ta/lQ/tpEc+8tX+lxJQrg3hYcjhWZK1B9p4B63+jRwh+WJK6uipNDZbFYK2/5Kx+leHQrYTkQkdTke1am6MMmedhymqfohwgHbkyUhCfHQ/RSvPaSZh1ZVu7QJqLbIy5F/ho22niFcRYBvQY0gJyw5kJm+MkyFbQoKyrYpKL5cEOKlxb98PTKA18RM0qMGOc9/9lJMy75SoFuPojvcmjcVBo5sHmJcL/nf/8DWu7HkH/3ldGzCfTWebfE6U8JTB5MDa/7hFw4z5NkHdqd4Jv5gYkKVa2Eo9lcDZlgu2LD4dxz2fHDKmRqdEsKtNQQdH3Anx0pB46GYIqcYQ7T61twM4N+W3JA7e9+evQ8o2m8Lg5LyrOTvvPWrupJGR+QSThUGTomg6y2nDbfbANYLDrjMnb+0XHO9kY+YlBU5VvGDUW2l2qKNQbpbIP70nHFv+vt1pYdYbr/qnolK2D4SZ2dtGbjpocHzkTPcDBaqEfkLtm5bQdeKIhuRm+30pLMoYHsCHXPX5QBLLIHHo+p4sqe9VH8P5uoYuYlq/WN5YpII2J4L3xEJNLKR4IB5a5BUtQHVXg7dB/dsMu0nI1X+N9zMJX/GuFvykJza0lTYpqBsm4KC4grA282LV+kh/2qbSfD86KUGm2NXEUlch6/2xMr8hziupAg1YyRy/+93vV/7znTvnPu//d70nwneEn/TKrS+X4g4sXgPXLYVHwuIKYCuAIisRI5dmYFN39ixYwbxbO8p40UsXGGVNhYGav5pwGsL4SlLdsRQ7G8XEjIYjD9d8VgbLTL2GeNb8lr8iXHw1MbCQ1/6vid7X0uMPP7L4d/4xve//o0fCQYSc26QlLSjsQnyUCnU+QLP6tTm3kwfN65rpMENNVCzr6aKEtW2Blrt0cFSBZ/VuT25Hng4F3rblRpVETJcU9rLULJC2LYGcH+krjREeEgQxc96VQUr2CC+HxEKSskokk4N7Twsaef6iH514Qgl5FFpIrpM9rS39ZCQ5N6W74c5M2ggsj3VXiitQ6p94rDJ457tfiOBtYr0+4oTDOIG7vFtRv5RYZuCsm0KCorrAEdwbCcPqJkEAvChHqt/1nLnKCo4exOnB0gMFLmZ7KMzVmarp67cg0njlRJLfv0tU19KEQC34XLt2uyzQpSexKGDScCzH5/apHHDQ8Zxwfixq0Q5Yzcwkx2aLHZsoGQyI7mQYdfhaLSoy+DGUtDoTD8W+X2fJf8wkrrnyjzQKrtpcEEjIP16gseDf+/ZZVN70+pnM3IaIFgSgBv3AksnbLhn04j/GAu3lsAJ65Fl+/xAPS+M6O0HlgmHTJB0+WNHjS09uh5lAGU05x8su60okb1wjRHJc/TaXzgEORwsLSRTePkZorPSBdFx/RfWKHZ6wFOSwz1rn6oc253s2qL3Q3abHDmcUOCHVh/cE0T2tAerZWldVMkpj4MDtqUa0TPGprrma8RP1nxpeyUsREDuu6e6TEbysKlHHJaAe3GAXuOPrqk8fju/Mc6wa58K2xSUbVNQUPQI8LcZJteSA/6vuZkE8CHVGFYXNJmIFLxd+/QmTq9a/xgnGS8SPeSA9wSnJ/7nH16Nc//Km+Yf+QcPHPZwVKsjSJg3NbDtGT+1SazowOFuML5D1mXODV2hvKbY70+Mz3tnoPdAkfHHeuZ8s4A14gOCZ3dn7qfj923Zh/jhWJw+IPg0AH4Kd/IJefuGiyWJOBcUhNhTR+YwON+7kr3sYYhxST8+mis5Nse9q6LapIEEcNltbxzbORaqn6yEBdsx+Demy1e1JwGsx9bxW9y0hgDfhcsSXI7IIme/8JL5D14ib8aJr4jVX/zpNebk6+Muxzs9KfgW2egOXy/xefOretqL9Sb0kJg4PekhkQO2ub5a9D5k2y+J8Upw5I3Ui1av9opvoCnX3OS7SwNwfwAbeLTlvlv7dUGFbQrKtikoKK4D2Mp2acL0a2UmuWDjPRZtPvdLxG4/vHpzJ4lF7kB8nDRInNjY8FP3P/zL6Stx7v9tSDeGLM6+5PCOW3/OSF7qHbsdUNhYDHDx/l4La7LDhdDUpott9YoA0lz1mx3pUTJCxFwQxOxC9KEzI99oQ/I5YM8wAeOE9WB+BoXSZ05FkF84NkHUOgrt4sKgJBM/8lauHcaCDxiMG+fE1ZdAtZWBgHiZeuQp64t4FnBoobQEWGM59DaTfdR6f6g395K2iTcx9m0CSQUXUOo/4hg0HCk9KaDdBP8L8i11NqSwbeI6qr9V/hOSaueqR+AIsagO7kucIuG/wXjRjtBnZjHdT1vqL9QVYrncL1McgFR7K9RH+qCUqrbk1QZf5extJtrRuCWa0c8dafDSdBV5oyZHdP3crGObZmxTULZNQUFxPeD4gt4r7r7yZpLeG7YXOUkIv2CjN3qeWOQ2FQZUvBPgHjvxV6bFX/srpkfC/fe/Z/pr05N9l3nHYVvxWPOhKWdydCp/OcOGUXeAPQfjTwER3HM4VV6RhjjyGIlrW3VxJvcwGwc8u8+de6wlsvwyEb22KB12BSnNR3bs7vDb048uyGex+s35IXJM85B1QTvvqUhqe2uIVORsAM5qTYttOEVNKR6WFqIdqPZC9eMTd/EFqpsRAvX4d1eijwBxnMk/bLWR9D4iqRC2GYE3W6TWTA3yafSTHTeL+Zekv2UvIj18PXsG/juJhf9iXye7tnA3TrbiRPj6oW8eNt5Db31rT7u/WpSyTaqKDTEyYHsJNYNeMPKY70b2iKDaO5Bqr4QfNrHtp91kJAQOwt+JP2N4H6b1M/UZKmxTUFC2TUHxpTeTIDfkmSNDzSSk3n/Bxnt5+Dmycq4U37vpU03V9kycMBFYCE+RgRt133Sp/t5M5PzX/5rpXeT+2nfM3zI/uYxey32Ns9nhRFS35LWcEEZnCEC7uaDJn3iia5mVnCw+9Kfuc9G7hZgmlBk3c1OkP/uZw2ZALB/QdzwdeIbsKE2lZL7vtJNKuSv9GGf2NZkevdrB5+6MapGQixjhTCeflcIutlKbHaj2J1CoxgU6utKAMAcZugNtJGfo4rQuaME/ehW2xc72TRcrPUV5UPigViO4HTNT3Rc2YQjT9mL5Iyhsn7ry4Ic6Uq/Kidd8R03dRXhIrBwLOSVO/btgcCG8C94/WzvWivses+WZOi+PEZf4E3ycWHpwBbFtLFrvheVG93luD3q1wdfn7nz3yUjyRxIsWuarP5c/XeWBW/stce6kUSQUlG1TUFB8DuCG50vnAl8TMwlOIl+ofnzpw3GGye0EKYIlgasSA4xnMTTQsMrC4YEnPc9/Fst/9KcT1a+9eQVjyde/Z/ovQaUOLdBrDRxBA+zZlRorhoxbTlurjL3hZnOhKTbzqC1TNxceJhL35sJ3srH+YN5QqDV1nGwAiMW051bfLJLAE1G5PrDqnya7VORgvrCWPH4mPNWlnqYTFrkPVryOc1ZeMGw4BVJrzozw+S2ZahNjkVu8VGAE6OCJM4dsJxpoUC7y7y6mo4A75mP3belRxP6DrZ+Q7ik3pLB96iqAf/gT43g+kuM/wBsC5pJBGhtAJ7kT3oLWcMGJXnyvyn9Axqp0esYy35yQ3c8Sc8Wpf0esR1VnAz57Zs4orsEG1R4StAQCn0y48BBitj1mvBYiqTY2kOzE5FDFg/Bap/PEDhnwGmW7Ntd3m78lqLBNQdk2BQXF5wKOJ8Mptq+5maR3MZK/yeDtLsjUDvNxzWrwriJeg4ms5gQO9MTz/I3vzlzJzA0IOjRAk8l9c75Z0kuARx5r/ml3cmw6O2JoZz4BpNCefZCL361H7maj/e6Shau9768UyHnKBe8sOJQ9IyW4AULfEBoT/ZBrOjOj6uQQq382p3iueZ/r6p01wWXRUFEJ6OWXY/dK1J+RAjcAt25a2lDtJe6Dc1Qgai48ImOelyN9MI1kzjcjOVWIjZEeRyTxjsELmxdwO/BfqOJDWil7SLh+mQLCTD1rYKX0PgyihnU2emQOAe9FubOtWS6lR8z11FcthA1I2Fb3tINlnmQ4qS6Qx7GgKEDAuRfEUVERd+TiIeSzwlR7JXR3MbeOr/NJh8lI1XLFWJwg7Nrbt/bjRjO2KSjbpqCgeAWQU8P4X1AzyVz9M2wS7eXhNxe83QUL2RQuxG7ISnDwILgIFgBc7bPvGpevRLjF0BJTMjq57bS3lbGzYYMjPWbNDjOZR7pSm/i/yeJDX+Y+H7lTi9zLxAaYCgN4NjhVY0mP72PPjMKCm0hMEqqnc0NHNjeug1l3sduiaRjjyGbTlR6ST1QNOq9MtW0xeN1s6adk/J+wpxHWg9d7LjqpFFTbGlBQbbsUg70b3cSjivHy8gL/wbLIL8Fa5RjpwQoTgvhAyIM7YZn/QHbLxLfBVy5owvORjopP5SER2LmviCmgVGcjBlHbKnKyijnXsV5RR4RVJ8qSrpyPSql/hw6nStgGCzzoIbGUGdJDohjczD+Fl2I19PaJU47QWSy9T3q1wdf58nOivz1yidXNIZDdFa/8uqbKui9E2N5N7NI/FhSUbVNQUFwTWD3qpX3jdTCT9MKQMG46eLs92y6uAuLSsLphszcgqfIIoD2xyAm5zpbI+RtvzlyVc//j78+qkvtmcyPu9ONwfMJYGGhrGpnJP0gk7i2G71Qj9xKJIaZiLdeEiJt85Zi8WxbwWpHMhWJPEUHv3xOJ3Tkj83tcey4O5wHSqaDa2WxI7knpsSNTZNIcf67l+ppoIdG0BvTF/kWvNJd5EJiX1WLIETPSz8Jq4eUFG8MLsKnKGE7wEIhydAKmkUznBlttJAvVj3uJtH/BSj518I+mJQTY/2x2GIrTPNEXM8PJQ4HYRnJhkQzr+6HlNL9DbjJ0EradZTmAZao0Jk/9iql/4NnxVYI97dX6xzAVB6xIy/yH5KG0pQFs7VgJS3bt1cBdMvVPZSABtPuCTeD1YafJSPTDJeXfT6AEGN3tekiwsN2kwjYFZdsUFBSfBzhkukd++dUzk8BkBntZshPAILneY8hvOni7jeZXfSlUh7CCDLzisQqqZ9gg252twWfxHXhPvf/sV787e1XO/UNmcjo3HI/quGBHZzY0jZSid5YB3Yw+iCYHmYoF8mxxAi+G7byGQv+am4VMLhbTQcuKtqRZdTNi5Y1CqybZtuoZA/XklVzaAgeNCkmOnnqKKDkXKKAz+7QeCuK86kUh9aIN1d7InMBEEYExx/dI7uvhI3g+0pTvqweQjYSJXWlEkhS2n2X30Rsq9UdCmxOZTKKwkQC2bYtDHxFf/YlWDi3pKGxH+DoZ+YfjSmIxA9zT2LM7cIWkxM5R6Hu0ukIeysuX8KEqkQE8HHliceKS9lM3p1C1S9uH3rps1Ekddf+cw59E2bPEaW4t8o8K2xSUbVNQULxiAGYpkCE2emlj+VcSkE8AZgPNJFJHtDXU49W4neBtJT5biWhg9SMMrzBxWlNhAMbhSXZYVwGz/x9Olt5486rGErOuQ2GkofgwkLpfiwg8m4s+DCUHWc5dQyJlhf/ZZEnuJmSyI3DwDpA5X2IcV9JwYjH4GeM7VYacPCOcJOSTWjnmilQ7+CwleHtgxfcFI3+rFjS704/nqx8hzkqK5UFMtdezZ5BqNy3B9axgqLBWZskpvYU8D/mlUMGIclTW+J/KI8g9fJAUwraYrCLNR3J9tqqb6LI5b8u2sXY+xU30ImyTlzTN7+Db5dQ/q6LOBu/84LUoIWxLHw99eRQPR+76Rk+dBbyqWS3vwwQSqGoDet3LZKSkAnAfgitf987iYByG930hwjZU6CkoKNumoKD4XMBBHJuZ49fw5adqe5ITt7bOE8V1PZb+3FrwNomlhPnYPgsD+EQbzDNvJact9WORVeR54bXcOSQuxzOB3/yrKxtLvvYdk8o0kk7cXRbI09vFaJ8/OegsAZ4tZ7elK/vYyAs4Xyg+ARXTY9bjSI8aUXCeJ/kE3l4NmFUs+YWy2oag2lcykAgzkYChwtiNbZe8CHnOutY8Nld6rJWzNsF5BubQT8S+RLWtIfxDgdX6mbJFELbFmO108p41N0w2qKuk2e6bJHI6Cv8OvFDSfGSpH2eG2MoOxdqYMG3D+E53OaYIhOkgbE/ISjlYnk3I71pYj1p1nNiszwu5JR9CDwn4qvKQGCrjeOGxilTt1dCdc3vqgkXRhJYQQbW3V/Mv8XrpuIeNIxiXJJfP366HhKdRJBSUbVNQULxazFd/DrnF7STZfcnNJLhNo/e1x20Gb0sUPxt74RCo6nOboIAGq2VeTEQ2cwYm++iEKBZ54a3O8b+A/9bNmL52RcIN8OPAiCvzgIsK+uVi6E4hpnGmhwDPxr4RiNmyFRNEXVEz751BvZKumdzQNMr7m8kOQQP3stdyrBS2ubhJntFEbBtQ7d6bayAHBVQ7yEs9LLmIUfZwi8/LBY3Z6AzJdFuptkQ62Sg2gXj4iMrUAQVdY76vLOr0KhtJLwMAMFpbYMysDwb/5UNTaK0ySHbZKJxLSo1/Pr2mcvi0Fbb1pVHFsCnykOQKdRg+I+aiSOdz4BeEbQbljZBlq7wUsC0tPBLZpytBaTjy0C4sQvCk6XPnNKba4u0h1Ex5eYDPXP0zcPEn0E6I9tapNnVsU1C2TUFB8eoBdThYIfGam0l67/9rnTS9teXKQmH+0GmAmSTkRn+d/8xfLWpL/VuEAfqCjTatYVwG/j/+F+ZqhPtNk97cPx++k4tpmGy/J8+oeHaheq4rybndU4WBPbsT+bBZY2HAkh1Gk5GaU8Tt4jEdyRqtucfO1GNVm/oMN3MYnuudap/bEuB8pspScY8n/eilPBkpJZ+wmVE+mt1O7SnCDRHVfhbfgYkZQqgi4bfGYc9TnFZY7aSDko2ESCMhbSR4HGIz1VEZxVZ7IW/bKijKuDcHr1vS5Wed5HAonOtKA5cK20yJIe/jLssZhSW0VDiyucie9mh1Bd6ZbFpVafy6Qv+KLGzfB9/aSj7HPaAw7A9S7YYt1kuRDQa4aL4Ubl/ShMrcLf9CwML2s8Q2/etAQdk2BQXFqwG2T3TPT3hNzCQwFfHQP9fr5oAQvC1Kd2C5wt+G932ufHTgmpQU65LGzBkV9LfeMHMGwcihbIpZ89hFF4EOkPIridxf+65JHx3y5C21lhAJf7VCMrnZ7BAO6l7xWPXF/pmcbOPeRzOR/sT4CSKppzY3tE/kQ1MK/s0xz/29U+3gsbfG8R9gnjpBTEY2hY5GJzbezFc/wlKrwFltSUlsjqyi9Ul8mdimiJeX8TGRsH0f2kj0hUGyQb31B6qTH4kUtvfCK8I9vRa1Rb48od7TkAvbBTBFi1z82UHYzlWPyGPquUf4W4E0CzcTdh0OOfUvXy7V34O7PUZuolpX1DzhgG2xpP0+ZtsL/N+JKwF0Va0+QLUXisIkw7G7iA++njvtcZIE27V1pUEqbFNQULZNQfFVABZ0jz3ca/jyVWaSHktJWjcHeg/q/rzvF//pnntc6lvJDQmObSXLhyK3uTB4YJNdyxdMSC8GJ8N8iX+jTVzBTzLbZgbUWJogmZwz9fgcScg1v1lYBhC5gVzQjFl4LKpFSqofe3N5/zQZHnfiLvSuau8FloJ8UUuUYjYJl8hOdhvSykWvBbD/BuECP0WMaje6iQ0kK2JKdKuaq688FjYWuD1IMQ35h9hG0lS69i8dkSSFbRji4U88UbLtNk2QuLAdIBNj1RWeLcJ2mf9IdZ9iVW6CXHNJGYinaOVzwTq7eEh4ImA7nBzFju2FuFk1vrnieQSp9l54A5/wobfey2d7pfAOjvyDSYvUsU1BQdk2BcVXBIeijijUZPSw1fvVNpP0WLitoAjF95CZIXk7J7zjlxzJTEZYJxTqbdy6UOTGNYcCnbL6XelRcCOg42X+J4Cm/+bfTPXCtv+fx4rJtgr/7oSy5iYjNsXAZ0mH9YBq64r9OIQkhurZTxivKT+Io0iYNFa+Netu9qrNNXgmUsgekVVtzYXVi1n7Znp72etEJzaJLSU4/m+O/8VzP48Cy1OLnGIiUBa2EfNbjo5BlgnI/XOURrJYeId8FCyd6bR23XfIVvL1rBDZfmh3aZXXM8qttCOCQZzQR1SmdxS2tZzimNayDX8rjJw864hzC6+iuBWsluGdvZWc+miya0WzGnwbN0fC777w5vC06HxhWRzPfQ8z13NHr2vXqbyce3jLkX8KYdtCo0goKNumoKB41cBdErejzn6ZzST4UqwoKVR34DQG2C9z4+afsMQgXSkhOCJZ2+4g238G+NOCj1FQTK8lUdsE3wW0G+b0/YPvXBLL/T2jPO1nL4e0SsW0hmTphtUXiWuxgVvKj0vLfezguzLztnkQXxRI4Y7DfmWqLSjTL1Wc8hDF6on17GnhWjkFHn9u9auoNqx+PAzMY9t361JTh0g8FLaFlVVYmI+Mp+4SNpJAi3QdgqUzbd8U7PDZ9KaajPCxSYX1CkNOydRddp1CMYtaJAO3Ctv60hPlAGU/oXk3pWohq69hlXvai/Um3OQxcXqVh4QM2F72DmIPCV8Vfl2IDZFoCWGLqtYGDSbS46c6XJY3KKaqBipsU1BQtk1B8dUyk/C/AGwDZoq9zmYSW9nby4hbG8EyLLl+n/vnb2N1lCxgD7RQ/lLtZgFaS6yqWOYZ41vi3vNWMpDZnDDe8nS2E9V+483pQEZqMgdXiaTaulL/htuGj+lOjaFWdkmhNOT7L5DmveKx6kqaC2Ts9opnrkcO3RNlREkvM5Geegp7G6DTFzwFcQdpHBAOMjYYTLVlJ8kLn9S3cuLMgfdddd2K/LtYOQb/Fmwk2YxkIyn15cKS0bzJKNh2988PYNhoZeJf4n8OlyimvGy50XawTyyiuhwcRi6sUlBbkErYdpajpIUdIELU0yQiUurfKr5c1sAKfwxXX4CXF+qNFmFbus6WzBCm2suRQUi1tyOPVAuPhjUip5j3vF0mC/zF/tv/JUAztiko26agoLhx7IeWrxQ1/RWDu5KAVKNS/6kUSxLZ6P3h87VPoZjX7FnJ+zxYzq3BNyse07ftH2k7tMcFZi6QlinslcdzkNzMe2dgjuHvvz37974zrWi6+X9N3/l3f5N3r8T5LX2hnyRwptzgrlMasHtuc1uyI2hWcgRFAfYf2TzYQ2IsDCQRzztlPBOlh9PogPnw1JWo9pk7P8mRPZcCS4tGnhKdkVKYxny0qKbp9oTqlhN3sW0aD5FRPYiE7X5UaqM5tEsi+m5U4foAJFvaQyh/1F3YhsF/vG+GqHjsC/Dt/c0XaAbxwupFTUMSR2fTo+Q9E+UtlV3bJEapSHOThfqxmBEJ3pEmstyc2Oz+qhSbCP6hemo5YLuoWQ3ck6h2+G3h8lZOVyKaC9ZL8lQ8xiCIxJd1Rrb6XrSlvi/kNwAVtiko26agoLhxrBTekdjDVVjmV89MEq0udffddtTGnFkp/SN3437ThfK78LlKYYGrmTlDL+/sSnbfVmJxQfe51acTrdWxmBbWgEvy5JT3n//F6P/6549+/9sPNP/6P83+7rdmf/fbpj/6E1l/LfZFktOHyLW863AYkW9EpNoSbVpzy0pzOD4hCNuIa6ZDemdeosve5JM9ZYV7V/dIYDvAK13j0r/lvD/C2rHqYZWKeEwd/+fhFqqftL1u+MiSsM1/AIlmOH0f20haDb5w1dq0hroL2zyK83OhDQEhJJHIDFG83dVPsDFjLiC0Ds0UtVjhVk9GKq01WiJgmydS/zadDHoJ/lztVCtO0M6WZ1QTt8nyOj7Ugvup7CHJ+gHVXhZr25voyu+HN9Zzp/K17aHIRrJrVw14jyKVSnyBwjZ1bFNQtk1BQXGzgOF357bEHP+L189M8pmeG4U6MSRMF2z8SkfYSh5KJMNVvIUTlvpNggzZPN/ehIC8DVtin7mjHKwGpkmqZ8mOkH2NW6MGkWGrMTH0l6JLZLiWzOIOnRWP1YAk6pncoBY5Q3DBjXgf4SQjOIrE6g8WJHZVDArjnltOW49su5SOTBDZIzA9EHxtEnl/m7y8OdOUDSTggy0QqRNnTm6X9POdZmFhk6JiPjJuhURTV+rHNpKX1rDqgV2WaqSwvRNex6GEqoTBNvtOkVXSRmLgRg2F9sI2ocdrWgO2ceqf6JKXTubEwSAPyWCxrp5VwI6UqcIgORw5X34OqfauW/blL/KfYoX44ioTw9ieZCgNfCG/fKiwTUHZNgUFxS0BJpeJsbvN1/DlOysRUV8c3kzswOuwUP34SkdoSsHbwVsI3r5gBLa04ZG6S4r1ZhdTPixt2Y1uQpO6kRufzg0es144n6cr9pPh3Nbf+1Zbtj37e9+KZoO1aPQc3XnON6tDwSOm3NAkGtoLxp/KAifrgcr3GfItHNl9gmO71L/tlCYjN1w9se2ZgiJ7ROJnhf6XVlnV3kzLA6MvGTky79zqXcht4q5QaVVZ67hEwQpxvCwNOy6H77XaSJ5FdxSXunM7ktBig7RkHtUrJqOTmGp38pAsch82CWkfPHs8l2grbE9xT1QXR68Uyxe9FtTxiS641QuWl9K7JpaSkpjhzHg3Y9s9jKn2YmUZUm0A7INvMtErdUa2EvqJoqb3VqkbcWxTYZuCsm0KCoqbBuYineIUvtpI1nakcOjs3PVWHbcZvH3OigTaOQvPuVMsiXRncQQWF4njssBcWGoLP7DLydyzv/vt9mz7d789F3TjpL9iyCjXppT6p9CoH5t9RFJkwLwFhTX1BBcNCtOo6dELIiEE0m5VLbkKOtKLjGj9bHakwRK2EHeFoNqyN6bJBmZyw+Djfe6QedUS95OOxgZOq8q9nuO2Ibl0ZDTgxeLA7E6mnc3MsXrjiJVe777dD4P/zhgfDkns5CEBeO7ncZrKIev2V0pthW0r51DZtVUB2zj1b947i70fz11SRc5s2aLykJT49+WEx+gk9pAshYcw1V4ozGO2fWZPE9GK7/X4MdZxI/hS172zt59AusJ/jIXtRWXUOgUFZdsUFBQ3ghOx+63BRntPm/4KmUk+hf4Bd9FzvXLN2wzePnHGRZ3Y2mm4TXnnnBjBUcO2GTNnFDfuH8MgiCWPBVMlyze/1YltQ6oNviaiOhz2LMQIZkaRmWTo3CLT6HmvNAJ4igbpDh3eamC6qWTS+/Zuvu1zxqfIHEQM1ZMcP3XEW0NIRM9GXNkM/6gUMl2wMVX8X+eJPRTGV7VL72z0kRSzXexPoVnPRkvdYKcRSVnYFh3bMPivEjRd6iFZLr7XJEJU/Gl7NldsFbZz1SOtbNTWtAZsk6l/p6wTFR65jNwE3M9p3RvBhY7gyCuoOXJZiCLBVJt/7qvhmG18kjs969M5/lyOA8+M9l7g+grRJDxU9Pc/BWXbFBQUtwHc7bKWu3gtzSSw5qb/TMzowGLwFQwetxW8/cJbEKu/bZOiVddW9na7sxh1d+rMtgr5UyLfyoUMmHME/t332rJt1x/9Z0jXvMknJNUOi+q1QNqK/cfIzw09JCaxrCQR0eEbnzkdbTJGJHe1v/Vbz1k3pn0TJY0WUe14fPowVJXj/IjUthPxvZPJn8Nhy4w2ZDoY7E6tAMNGYXyYBH+CgjjugdvxTKcqjYTvXER6hhYb+3Z5PnIW1dqznd87sEBi04p8vbbCNhEg2N8asM0TqX8VVOopxpLMoslg9QtJJqfxMedjASL1T7LTLOSKoh8m2BLwUrqCXRutarQFzRdiYBOEbQt1bFNQtk1BQXG7WKz8DP4F/UJ0pi8cmdoh/PO/50nCbLirHuHWgrclYse4rMWnl8aSHASXWuc+Z8szIj8TCHE0NvGSaDtvw7a/+S1BSre52cyjyaJGi8YT8yEDpmWwDxJbwGEWuD3b95IYYWzl00co26T1u7tOB5mtgUK+NaV4cDHbaEu1z90p+YWIZ7LhsuFK+WfxHfRcwd6F7aWUT7KRZEe62EiEZ7cLz37krSh+pooXpLB95CoJVhMXe2ngHVjxFoJTuYgRv8y2wray30fTGrBNpv6dMbKwveER1hVM2a7ykBx5q3iFY8vpYKGPuNi4g6g2p6Kq15iM1FXkgc5di/0LSfpvyvWcVNimoGybgoLiFgHNx01rqG0I8VfeTAJrbuZCwU45bt1xa8HbMKeiYfW681J6Rr3zaCaef1X61LflqnCCbR/pHG3NJPv62dnsMJN5JFHtoqYcMOIQkppYEY8NwXM+QTc15/v2nePdZx93HY62t297bETmYD82avPZNVFVlbVqWdV2ccTQnh9HoOBIFjn+ryVLBEKuaidI8HL4gWQj4TTYRtLagTJX/0x6RUorxTEStl/YvFjYDqENgWi544zEoTMFmCju5blgoq3CNjEZ2TdZetwasM0TqX8Lvml8eU9FYVvPjZb5D1WrOB1K8tYWNUsZJx6OJKm2ariw+wKmFaEyp0XBMtHE5DVcW1TYpqCgbJuC4pcY2Ht6pTLFrwxclZhgVIjpu3SUdMftBG9jAh1LS4aEXO200523k/ttU1ZmymbRAaLxJ2RObPlXf9LWSTL9zW8H0rNEYbsJB5IkUB87VJFPGK85NwSI1JwQxhzszra32gWSbMhUWzNZkERQV/oJz53xRCs4oLw4hOQwWJWZHxOAhe3yATMnAvF1SWU35x2UVBxFYiiNSTdy55BlzoeFb+EclWN3QfXYVVT3SDoilnk5BQX8Gwb/nQlOdA2ss+n4Y5g+glkueGegEs2rhG23+FmFx2GrnrYB2zj1b8/hxD3tJzZwzYVkmHhtXbUHUvHKHpJU9dlyCKf+QapdIYRhRQ5675ORPBH5py8NwEGR21/bU2GbgrJtCgqKLwzgzx5MMVNtiL8mgDU3QgR1h3CJS3E7wduYQFeiDyXKW9vsdOf17JkUfV14p9U5M5MddqXG5EySb3bMJMFhbUteC/RkC/U06LEvkCcEeki86fvb/siluX6bItsmm9u3nbLLwlCQ6G8ooZ+r/Uyk2qHWEBKRaiuMJedIEgYUc8UnGaMbTFjyT0eXuwnbRH36clwPiaZV3ENodJ6ow+sfcknzwoaEbdbNo+C/UlCKc3FUfJ1+Br3JCdTaIzFClbCdqx5h34ipPIUd22TANk+k/i37TOj6+EVXvRcsLMl77gVXwO2YB0+Wx5ZQ6GEr1V4sva/YiIjtXIVqy7WR8FfN7YcgkcL2SuGU/tqnoGybgoLitnHon4Nbw7cfyPUlMJN8NsmN4Ajq6wUA30LwNt6CWAqhwOZqx/JLnEwHaLfqW2zZyWRGSLbdKZNkBrBtaLHwzdjTyLSQHzyzeEW6LM1HAiIuuhr6NuL1l9bLU7TFppXAKUqwblp9qPh9QIdoWSpunRPrey4sYXm20iG5hPcDy0Rnu+Bn2HfFsFXan3iykJbIXLPriCTsUxS97APypYMOivAdLaeJoYKeJhPo9FOjGpEkhW1YM9Sw+AXhv6tju5RKwTNZ8eA+Tp9K2J5AZ6vjhma46bYB23Lqn2+2ibJijpwsuFyG0li1Lq8KnsUFj81UAXup+1ZyZSXVVny6XlrlddSpM9/751aujSxpsrEQSmP84IsSthtXd4tRUFC2TUFB8Qqwlru4tdzoLyFgzQ2MoL7etOhz/xyKrdi86fdoyzegLQ3AFsxOd16ofowyDfdV3yrUz1kl24786Q/as+0/+Y+QajtTUt6foTgAM0aaFv+pKE4fsW6oefORQI/1kM9FRRybm2GBy0xuBJfXVFPSFsGZJyMP5FnCMtW2KKj2Tmwb35IOT4KF03z1I8S2OyrTRf5dLRJcYVW7cN3yvFRUHhXOZxOZXp77FzuNSJIfmOcOD0r/EMT1E1ZgqGtuBo17dsjYLl9gyotPeBU9CgrbRGdkf5zfbhuwTab+7bokX01D5NzgsMmarEZvpQ5eWoKrTgv2pfj41Eq4I9UGi3B8hRtXmU8IVetYj58q62D8edvezVsTtmnGNgVl2xQUFF8M5uqfNcQku2uEcnwFANPxFryzbdPcenLjADoiimcXtvhNMQYkV2/7RizFx+JsnL6bmEfUSargKLIBwrfdKZMEUu1c2IC0Z82cZxZ1E0pJF+G44H9wpB/3SLUbFj90FZ+h8coLi9+SliwTluwIn5f2FhazjabM8KSBvMXMhTwWKU7p7UXWibQTj+j2HiOU5o7aNsFfB+WLHO6HdHOaE0ofG53TSKBurdoMwekl6zHBHg0f7kpJ2wIc397Wr0dDiobSI6zZk8K2gRvH9DpR3posDbUGbPNE6l85YMbTqyd2wViy440pqXaA9JBoS0NLMR1BtdUed2zmaYpt7b1/aDHVnuA0eLl4DbMWFbYpKCjbpqD4KmA/tPxFbfJ+GcwkhtJ4ITQlxWtcq+jnzCEJscudOws/D2R65xlz50ZFkjRY70x9JOW1XYJ4tvYiG5HzthtmH/v7f6oWtn/vW7zLjBVWMcZEslXsoMFB2GWjLWrI1O3uwB2WeOTuiHVL/uPUkznu3VZLBg4DEag2kaK9xf8EBh3C+wgqslhrn43OqA7SNj0DE0FZ2OY/QMl3D7Rcnzf5pFMaiWDsyRyrRiSfOyWCe8YK5uxiUKC5h3YXIrX9bd8mR8mFr/Bi/gSXvWNh218vyY0wnMMhJsS3BmyTqX/PbSwZiQgoMv6crBaacAUC1iT4sFztxbIcsD3cMgScl3cY7IkrUO3yAKbaOf4cZh+d2xJz/C+osE1BQdk2BcXrCCyd7kY2XkszSQizq+uVbuCeoBsaNsXmkD33eCT1QGKK9Y6neuwuddm1n/fLXZK5795t6ySJv/m3OITEkR6FARcnrAdKtqeMD2ZoJIkum0uBIz5wtc2Kj5WM2rWfEWKkOu9PVLXlG7f59zHVPkd3zoeEXno+KnX6dIn/gx2iLfORVslGknIobCSuUpcRSbw2w3Lyemyd4z+AI6SZsL7LfGSaP8SUN80tNlnJG31h9UKLS7Z8gO9g4Mar/AdtA7YFch+SUrqzkSm8Jjl2msjVwnLxffitfYsTH9ZStiyH5IBt9acavUzpzeI/7tVDUubwiC3D+xa5D6XNlmvNRVBhm4KCsm0Kiq8IoBp6PSvFLzsytcOZ3ND1+tsl/sp/BvmEkFx+M7OSkjzsmizG7kgNi8pANxKwTvLEmWv73T1fTq4H/7ffbe/b/vd/LoVe54ah2RoQygs0ewdzSMzoovUITGExI4TlNcqXKYeNLGYFX/IW/xOSam/V33uBKsTBC8SRgtbcI9K0feKQXiP4h1p2RcF/cSL9ehklckDnRhcbibCY8XDkD8u+XSFs29Jj0DYzlR9QcXrFmGZRg67wBDnTmYsYxRsfTchFNoPiImG8bcB2NOuE9Zy7DscZ40D2G8kZ33DGJaqNrqGWMHisRB5gDwlfVYxtrOZfXjtgW34KUYMHa/gvZA6bRpFQULZNQUHx5QL8i9gaG/d6mEmEmhso3147nuzEVUDdGYc3o9IJ9tkDp34uIrFtf7Wjz75tnSTGYWgBs5Don36/Lds2/8VfiHx6uOo3k+naoodEyuFuG57dBevi9N4J48Ge7FpKQYWJEJLgQaiudpVYAs+qL6FsD5X7heoncCWw53CqTdtW6VDbqT3yKaY4LU7JkEcXuG2JdEaHAVN0pUfJQcxO7wU26mBheytUDvJFuKhYQFdpkmszH2nmpFjASWGF8HP5ZQpjo4Jj21h6hKcYy3wzVOXbBmzzROpfITwppyW6ZvBqYaEsp4Cn4wb5ONFhTLWXQg/aru7k8VZf7ap2ba2YL47HQm6/sFbO2Gai9Dc8BWXbFBQUXzyWOOlP8u2n4X4Z4KrEoM/hyHPNUdHVXAN1qaRvbvPhhWMacKOp4lD3WBLsbGlrQ3+W2JXrZgYNbdm23vBjQ6E/EJ+Ac43nSOsFnBt6SOw9D0dKxDowu293kM5vlXJ85kjKFmEmgYisfITl5OmpS7IRH3nK4KUtJsqSJVcMIiRN253i/1qr2gXpNzoGSWctnxdL6RnpxbZbrmAPPdwG2fSmcI2lOPUo6f1s5hGaj1TPQvgrJUx5F5PCj9tOeItk25bcMDZ7RPi6eNrtA7bl1D/vbMMqGejPkcINVgtgQdJEY44Naxgf1pjvkyP/wm+rzhDy45ftKjy7g4j86wuVBSMTDq+8nkfr1QjbPXtgKCgo26agoLhZwFG/259k+jIgVdub882KVpDgtQ+COEpwvvbpDb07L5w2oXulMNo9lgRTnLbtmLj+BiL4x99rif/7c11Rw6ZHoUUBa7cA7uSYOBzZd2h3N3um2vnwVDLphUdbFlKl/SoOdxCqy3kjbBjp04RpOHECI+SEi+Crz4lXeMUjieuxqI40bfMd4v8AwyZlV5mZhaF3+Q60keCunDX+p12uLaSPF0jyB7QbcGJe7Es/sDsnOsxHVvlPsPqbi8zAnzXcl3lm9QIKbuSkyBRnWRBlOwVsCycj6ugNq28pZMFDlsdWO1otxJpI4wece5IbwSyfTNdeihsUuzTEZCRCT6bnHL+No050lUHyc3v7eUdY2H7JRujvdgrKtikoKL4swILoWu7i9TOTfJaKTl27v13y74ZXJXd1aOWVnyH0Cp84fIAeebOD3WNJOtVJ8sqhWEFFNnot7eokzcmhXackkZ7ZfKoc6GzY0LuqnYrpEvHZ6cKAJFF7LSptezHbeNkyGUnGkghU255C6dc8Fuy3ENfUFwZJ0zYvB5IElI5tlO9RmZUnULMZaUwwrhftKKNd0kh4FOADyCs4DVLY9tRTmKnDbERhB6AaafE0D6LEw2Ech4epYTYyOYuEbXN5UjVMSQZs80TqXzVgxkbtM5ZFFzPYZDDVDkdKPH75wYxcG6kajlRNRiJz1POe7Nro+NiqjvvtwRKFCtsUFJRtU1BQCDUWcP/99h2WXwYkMxI1WcldMxIYz0pesK/eJyqVF9pCgCElEve6x5JgX1DbeOOF6ieYi4Q7+La93/4usiV4kYfErxdTSqbyA71T7XJwNpQQWGw49lSibm47krGDqsnIJiK4suMWLDD8Naxq70dW8cbLAv/TY1bwTmy4WJVpm28X/9e2ql1gZhFJ6M1yi6SN5KKD2Vda9ohiLb44z3wJ8FhYPwnYvw5NQKoeayrrcX75vqegNklbhRhsYwkupaSGy04B22Tq35bXjMNeDuwsfu2knwRT4cniA1LY5vNLislItOw5Z5NtVyydoKsMYuE8h8LF4Ye2wUavF6x5/V0mnBH+ObaqKCgo26agoLgRQCYBdbvX7bVny5uSghstXfsgp86sdJDCK3apHgYXRbYdAwzp0lgSok6yfcQKJrj2f/1nbdm27Q/+TCRqsockFHsKn3TNbe2Raq95fZas5F6oBiRG+Az5tiG3JmrYA5uixblJNO+c+Kok1VasKHzSuRWDRpVpu238H474MJQIXs6dI+9yv770BHz3DBHotjYS8HOBRyS3QmWcbG3ghBabQ7GRnvfPSN7oio58bKhaxSr1utuGfcx7kUVsArHmpctV5gWdvlPANk+k/iUielys88I2S+QnStdwrfDOZFkO2OYFPRt5SCKKwxIEPYTflwt78tIPJ8P78PGnyjrV6v2Wc0UXS+9TYZuCsm0KCoovLwA5+6Iq374MZpIXNoFprfl81z7IVvIQJ2a82tOTYtSsQUCSeoklgaSwE9HBk3POP/qLtmzb/n//uTK8j5WsEdho0Q6kk3vTHZ1BpghXemwDuT4WikdYc8U9hcLgY3BBJHxyc+Gpl8Pjeq1RzWseScR1ivOIpGm7Nf6vk7C9HNdLHYrZDPiuDb26ToF3pDXiFDm2t8VEFPxYbAUh5yPJtGxfcpzcPmqg4+w47cbSIEzaVj1EFbCNU//A0uWFDTtz/EzZrppuXM9elPgTfBx79u4ijiJRDkdesDG5CIb/9EqEFVNt0qcOU8mF4D/uw9v8Qb5AnxkqbFNQtk1BQfFlxHz155CH3VBLy5ccz9xS5WGdv/6YI9zFBqzx1QZv40aV5dB9QJWMl8WSdKmTFE9S6lLh/3akLdvmftBHcGi/odAvmhA0L2w9NUduur24HCcZNQNCueUU2PZzm/vYg1ldUBVCQo5FnnlLeEmwHd9rc0FEW/kL1iMaM5Sm7Zb4P22pH81HDihsJLDbJXwnwteFQkev1E5/as91X46upNawsA1VcziwuO20I1qvkI31nBRRMp0bVhFQPITqyI3Dzkh4Ow7YNnNG9c+pTzrPcEKP9x92XFP7viq58oEbLHKDeunhQja6gupsljJOeQbXhSPYgxu581NHtveYbZyurSVDFflfnNsSt/+bhDq2KSjbpqCg+CUAtFrefhXFlwEbkQrUX1PV65fePRcvoKA7xnduYtthJTYDqBKbH+oeS3LiLnbhOg1Gjniz/eF/UFFt9g/+A0mdA3HJQ1L3zVxOta0BPmjViQwMkGAuEYVsEgZj7zjsTYf6IReWsLgAwOQ72HDEMdXeSrXJL18ovwMl4RWPVcjFS461dURIpgv+XS2yFOOqduEgeV6aFIyO6UujpI2ku58HnNsZi3JLXDYhLaTUD+0cvuQTaRKRl7cdbBUP5qOnFi+54ZDNFcnCdiM3gWwnPGbtqoBtwLBhLOOS13LOojobqzdZ2yFtJOtZYdaZrcrl8NVcbCV0t3U4ci+0gh91EF4jLyCg3d0/lubKBD6+uyRXu+Ny+1seuZYd/1TYpqBsm4KC4ksL/Gfyeq2Kv9yvPSUx2kjGdf39gcrPYKDbmSPzSs8NBc8lBDNJIPWweywJXDV1OocLNt49kwTcLlk4nFKY3UxuGPutW0i2bO8uBaW4OkOhv5aeP7NJo5aQHK+7bA2VsVh0b5Nt7U1HrCntDwQ7OZqwaRtm/+Uis60jkngKcwLFgMBSRlkHRZ6KOW57gtPYLksjwTsGR54sPnnJDC0G/50xPij0kipvpLqC+WglYG6wUVhnI22nOF1YisaTkaRg7K+opwig8R0Q7mhaXvzMB2aPvRU5RcQjzD7O1T7AT23Njy/mMmg48s4ifyK5nLn35P0EV04lD3f/TOb4bXx8nfLaSnWbjvRtxolSYZuCsm0KCopfDuDut9uPyP3CgaPx4jH95zGTwIzhl4Jh4KNXdW5ruQtJs0xygDClLoslgUF1neokT5257pkkgX//l5ACmvNDZHNkk4m3eLX9mC+mowZMzfn81nJqB1F2yU+84WSbbFjVB064t0WqLQqrTSaykTnpuAvhZnH2H3i6hXihlW3jMA0cAl3kXxB3+wSpvPegjQQyZuGw9linHw3JlM9KS5EDm7QUOWeEi5AN61XzkVX+E8ybg4kJQd2PywXp8WyiaZE08mOrG05G8kTAtq40rDoHnPpXDpjPiZ72XV9JVUYzV/sIm3kmihrRNtNmOPKlHP8ihWpf2JOXrjrQMkYjD1+SayEUiUO+2FsAjSKhoGybgoLil8dMElz8nMnTv7wrDbgTXQoak7Xtax8HJ5cfipN/r3YlsJFaBYSJi0qDktHqSnefdwefSaGXTJJURKKPsagOcLhTZ1pFtRsE1Q4lxtFM5JP5UoP0YYupf7C83U2aszf5DxpMkDhaFFPttXyjy9V4IWb/7Tod8BkXCyft2HZQdD+PtZ2PXEr5UMy2dVJskzlhPF3SSPCIJKD4eBjULMbzweA/gKkCDO+T5yOx99qUG2rVeg35frwtUCpL5qU0v4ODsXNVxesqV/ef2wUtHHxdD0bwddtxsor1Dxvmaz8LEoWUPj4FGDZaXcjDkQ02gon1Ilpe4le3H+6WJaKTdwz6cvx260qvaQ2RKv5Ng0aRUFC2TUFB8csELKPesjT1ZQCUpTddrLMSuvZBcPB2wxp6ZWQCFYZvpQ4EeTL0NiQ63sol83wL1TbM49hTujST5EQYQBTES0Ox/8ziv5AE+/aqtj8p0cp4fHquJjj+jxxYNPVvuiU6eGT34ofvsVWSap/bItJFY6PdW77lwnafUNg+m1UIwPvRZaT4CnZwHPwXLy+Td1sOSw0vvCA/903nBlWZ350WMCc2pbDN9UEP94ZLqv7RoflI0q59yLpUUT++jMWaHmv1bExyEksGD1edQCEkFTDFYjpcZyP3tONEkcTz5fgk4fEY4vn3sbA9l5VyxI8I58l67hTeuJV83ouNhIz8M1cmeOWkNdwc6zShe0N4iVYOVNimoGybgoLil0HiRXkCr9Z5/Es0JHrMevTc6Ocxk5y4Cq82eJuM0F6JjoqxJIK4yJTt3ZdMbeskoa22eybJTE4SL2tB62l3qp16AmciS3Ep33oltSynbifXccG4SoIlqbZEkdnocvG9S2R+VNjuFUcSo7FJhbUaneqxqzjFaREpVAjbc9y2xD6jw/56ibSRnDOxLp+NBuFQh8L2bFZyezvSj+FzpcvCGrVUfQfz0UzIoAqFzJYPDIWBXRsK7GOkVRkZsK0ajoxmnedWKfVvM8hiTfrQalOFMC7FdIbSQ/zsJf79ZZRDshK6K7Hq1JG87CGqT1/iBEamW0OTVvaQ9Hcc5233waOObQoKyrYpKCiQjCemO9/yn8wvxQtHBoyZ3FCqtnft46zmGhLnc5de1bnh5OmlpAUwJ1uun0yx6OQ8aRsKcYiCUwCYP/gTFdVm/tUfQ9lYUC6Lj5u2WJcQEki1TfnBWnqecANL3z1lhPDyXYdTcCaIX1uBqfa5PdVLNjN0kMPsPyFqI6rIRyfj/3CBoq3uVQjb0TEpZjvPQxsJjEwRhOHOH3hwekcoAPHE5oGi9YUozx/Z3FplCp6uJEnUlpxUokkK9vasQM3xVTpxZVUEPc2rA21w6l8wbsB1Nicso166WAM5tO8BwFbsc0LqH2qO5N/nlZ2RZ/a08jMm7TYslt7vbNfub438U+0O3fLUB40ioaBsm4KC4pcPy6WffCE9cF84cCSLN/nk85hJBFMsE/n/2XsT3jayNUvwL069nu5BDQboRvWr7prqARpVU1XdXUAPUM6XiyXvdjolSrJW7vu+iRRJkUFSDEmUaGu1rX2hSOdSzqxMpzuzJiLuvV9cBoMUSVGyJX/AgSFRZGyk5HNPnO8c4h7uV/A2FNaU0ymJOQUTX9BgPj0Nvn2dJFjzT8dcxj/T0bbHY4PEJrFrc7XpsiFU2z73WBROYePgIYExu2PF9rClRG7XNRthNLFqjXdCtZfz35Jil3UXLdwpp3carhLjizNFm66wLS9FQkTr/YyM+o3O39XEmLS+npTmeuIyXZ4VVsDaQRcnuTHpyTOCGVwc5Hj4Opuh3N2x+Tsy22ZbozZu4RGdMRWmNHsPzU4RWb3iMr1yTrDLS1++7xOBJVfcQ0MgbAsNw5Gr4Sf8so0GfXIfnn3vwrl2mlaRf7y1nbT/oLCNQCDbRiAQ54CYDc4ssU+qxR3s0anQ2Ijw4CJmktehF23KWXoAFNYsZ+VByWT8T4T0pBaO2mvhbdi2vYVve+rP/7Ni0hhuk6vtn5VzuIPxUWLU1vWQSI+sxdbofKfTWucabRqotj2p6y9vBmT/FX2TspEm9UD3rCX2CcL2ZGMq+XJqjs1HjroKUaVVfoodT0v7hMQjD5lj+8Tikbt+hIfkHZF4sCE7ALSeb2h/4Ytq+hSNhWlbQmbVkfA3PLXlXjWg8ZDIi0BixTH6PMlxiDE5tEwRFXlbhBQ/32T6SyY8yx6SSvC2pjlSnYw0+jWZ+p3EbIMPXhP5x98zqVkiV/lHA6NIEMi2EQjEtfdUtE+HuHkgEvKid0aiFBcxkywtvKfB27bZPk5wyipp8TuJPy2cF0tCQrV3gqtt2LZJL2xbwvT/9seR7EDVqDqtd628D8Q3kZbLETMx12LjggT02lMr1Ud3mYFErpM0R5up9pEr1yHVFrnCdqfik56Njumy7UJgXDeKRBG2aTqHKFSHO7aRSL8O4N8gwrYgviV+jAU/BPbdK4pvIXAwlKEsEG4QuYpyP/xYRr5vcGJ2QYO9wmIHW429RqMG8swF3+QhWEeUg5FWKfIbDWZrkzq8aMzbyuIxU/FviRk5gfvYkWmejGxWiM+l2prIPyb/vyO/O6/Da1e3PMYoEgSybQQCcY1VXuFHehP8arMFPjhIC+OuTS7hu6CZ5IQVYq8K/9yvuw1k2G41+MVK8JZBcdC2iiVpUydJMtpktv2H/6TPtv/NHyVup5qzlaxogCPxUC6vSQja8zV7NYxN+ghtOekY3ysnz9cDHNV+18UiUClsf22nQdcL4YAu2zYwb/F47mkDIxTfsiC8Lzu3kUg4sDHHttlLqiJfh2gF40zqHsxHQkP7ZH5EosJE6CVBeHmxLhFxImxLgEKfcubNVP4ZeVB6uWa/0qsg9c+TtYF75MA2QYeYF36GTS25J3jzDFDtSlAmx1vRl+raya/Npjw3ZtsgPGgV+UffmsjG1SeH1lkrKgrbCGTbCATiWuLQnb/63NwPDqL71kx+Q3bwgmaSjbljSm76sWIhkW1k/mw1NiaxqMn5drEk/PNb3biY+9tbumzb9Dd/wzI6PDYWu0EwkX4o+zcyWoMK7yFZiyzCrOQGY9tQCgOQPmBdfbSgsH3VY9JN2ibxf1s2aysJdjVmog0vCS+xkaRDzxjLDLXZ9RmT+TP+MZLxRxYMO4z3Dwu3wa49nBtYn32pidEkzhYibCumbVYJJO5BwHauqBXXs8FxaM3ke9prxPSy8PNqbARYMpz1bH5tKTZJ1xXycKQyGQljqXo3W9rHbPO1kZrIP3onhwX/XeV8JDq2EQhk2wjEzRkZXEudfDpnDd00xtSDC5pJFO1NKSFvy+S6WgZUbQnZvpyUy1kcc1+1iSVpUycJbLuVmeRACd/YszlDswaeH7vi33hnh0sFncAKiLk4cMone+qmSXykaRy6YwAH3oVu3b2rfkp504FnuqZtEv83lrlDeOFI/n7r+ch3xEayZzvfRrLpmWU010c8JGupKnOV0KBxLm3w9lxxk1BPqLMxCLL+TaJIZAdOeBzmFCFg25S3avYLqX+bDqtRGIFLd2w2L4v/i1BtiXmDvYdsZ6zwVDlT1hw55+A92ZrJSHqC58Vsgwl+SC+HRBbOY6/pfOTc0RWavjCKBIFsG68CAnHNsVz8hfwnLRGjT+esIVchFpV7yG1530W2duAp0hnB5OFFnfRKLGNdacxZnhfPjSVpUycJPzob9xibte0/+4+HBtuO07FtNzdI0Vb3bNS4qLevqsnH96VvJLfgVXuKEUIT/yc9jW9V7BCbLLf7Wea+vmnbFDowuoAXNla1/+tyRqRyb+SJxkZSb1vmUmNnV/JM+cWS4olXyDTnk4YIalc+BpeXRHMYC9OUB7Mq9TMWPX5o9jFpfLB5OHLFYyRPC8a/OTM5WGuPC6i2dC6wyDm02yCVjxuOlHNXamqGY0AzGcnsN+1itkF6l77Q9ZDIJiJHiiwFe3hb0bGNQCDbRiA+bTOJ4kaQY+w+GTMJZOctBQMKfbl7ETNJJUvzIiQ6clG2zbHnJWH33FiSNnWSsKn4f/mfuk4Sx1//HR94R9Vfj0P/HDkPyWriZTlVAyVV4qmEiG86rdp28R6WLkph+7bd1sq0XTf5jamHLYP/Ivdpn6KwZRLsHdpINjwJGEyczDxk9FQ+QYHNYgLVHs09Lud/Ak+FRD3DYon8CITt4extsK2bk7RYPlxc0uw3PDtGUv8kzp33PYRLty4eAtWW2TZ7nA5Zijkxs6IGbGdW+M7IzYT+kq9NzDZfGzldmtZfoM6/ufr2WYwiQSCQbSMQNwRrqZOrv0H8wUHTPNxztPR74UIZC4R7SYRmaeH9RbbDs+fFBXnaTwy3iyUBI9CK8LbVpmz/7v/WZdvWf/dXIJqem8EMlt8TJYfkDWdagLiPDaeF39qZsevxUyhsX/ROKwEgAxrTNhmRBGqYFb9r/Ok7Nh/5uSJsd2ojqbLRzz2bWxDlK3nEikInWN2mGrpX/BbyXp5n6mQyUiNs25Lf0EvEnNbNHhLZ0WS3kdQ/W/IR39POU+0qWOEVi8tw7o5mOHItfaJad/yL+g6l0FKbOVH17ITbLT1O/iX2yXx3Nb+hlfkTFLYRCGTbCMQNAbS4H7rzn85Zk/SPU9vsiJLDcEEzCQRvS19cyEaf2OczH0h/u0FRVXVjSdrUScKmHC3ytm1//tcam/W+U78UE3JI6kafwryDfPDISwcd7CPVNm+Y0bZq6ZptQ2F7NCYXrFjnHjU/B4Tt4bxW2F5JeFnMtomQyNHcIIT6taT4USh88W24vcDpFbXexlNtok9L15yvswG7szFHAz0M2cFjbxauWCsPySxL/Sv6J7e8qmP7hXhABmRlQ3bSC9dTtpEIAznxB344slz4GZZMZ5aWjfTqc5qmJw3n2bVFEvynFDkdeEtX9huqWl9Q2EYg20YgEDcAkBan6/i8kYDiel8ueHEzCQRvn1ljFzkqiTRTMU8RYkl/uzEz0CqWBJp6mov9gG0X/nFQl23n/uELome3T4VbiyzyXTZnxpAa8GdqkLT3rE5FUGfBf+5ob6btA4vboIjEiciE9nwFNes6ltemjK+GvqRt7eJbYiNJhEfPle1PmHh8aHG/jMqW5QP/Mi0GSjzhqbY574B1GqmzIZORJPMbhG13agTY7aJfDuwLFkXNTlP55UPlnsC+1WWeuw9LgiOLFVTtlVmzKP4G11M+gKJHMxz5hnktpC/KrT/ArURivjYymBfE86aK+Wr6yxW2MYoEgUC2jUDcODPJaZsO8BsJIKPFVKUvZpJj5j1YbTJ1dA4Y3yS0ppxKSKTKNfdFq1iSxdJvrdy0cIISrP+H1kxi/bd/RcVOplvXbNH2suiBM3RmjjfHaZP4v6rJ9zLxUnl+oL2W3FJAZYXt0gbpYGgkrHmOc24YjNHaLQhVNjU4CDaS12xwczvyUnenyagFZiiVZJ5TkeVg1EzeYY5qk5xsWA5JC1RC6Am886z+JjvAXzTZXpL7unm/ef8kS/0b3naOgVdkJT7JqLZVoto1RwzWQgalxGcl+CU0R55wRTZt1skn9hTYfvjH+ci/0dy9Nm8NKdS8+FhC5yA5PyhsIxDIthGIG4Wr/w/1wwI8GJuzr/tiJnmRpoFxul0zHWJFeMt76JfmyxKviiQ+bxNL0qpOEizdtUmv+Q9/qQ3b/sNfHj9zrnl87YXtU6uqfO8GS28aW9lpo43dTmJJYIqxN7YNhe2LPpq0vZxcbeUwTsW1w3yrsVEqbKfm4Jm1tmcniG9VYdvqqZuCy8V3m+y6JcKjnLA9kC3WZM8Vq7OJc+XtU4WJEU7YLnO517oB2+H5KKT+PcvdgZ72A8sUUG0SC/NG1bw9OfEHfjhyI6D6eV5GN9sxV/aO7DfmDnHQ9ornAACAAElEQVQ5JAOdzHVsJvau5ncTo0gQCGTbCMTNxMvoVqt5u5vpVi/9RjqoJZ7qKPTBTKIIcmHI7+sNEJZCbjIsFmoSr8rE2sWSqGXvLWRy4b/f1nWShP6/B3XVY61jgOFzSF54ljUjlVwytJtEbjPTQuDc1kb95YpS2F4zeSOREelkJzJ3l8SGkJyZ/ASMKm4ltBHpXMz2vxLVORoZbl8hOZN5DGuJmtF35JJt6zUTbS4cZQQarCAweLoZWxlWVeFHEsPmhe0ztoUzk0d3OHLJM0Oe4I19zfe0ExpdnrWLpXeaGwvjOfnOBlDttdBdWPm0moxsYq4NF8GQu3tu5B8BKcCSFnWLF5sA7hwYRYJAINtGIG4mVnP/TKXKtjrZTQLhqUduIbHwui9mkn3fEtPLe1cB+XdhUXy/GvyifSxJqzpJYNu+f/83umzb+R/+vj0Z5T0kuqo2GYWEQJJGi3DXPIkUtkv/TqflpO1AXOucgQy+ZHhUu0pJzbH5yFFlclF+5pbSuSNruj6dmk9r3r5rpT6TY6uHrLtWxF9pBp9T7aqcFqZErkmxak+OsNJ4EuJh4KJI+Ou25J9qHo4MxGjqX9ljlPsmmXq95xglVBvCznesbj4pvBwzgIcE3o4z6+x5K8AIe0fURWAnkX/wZ4GMhO6En1/VTSeMIkEgkG0jEDedfUpk4hM5X5LjVjcFS+L7vphJysJPFw/eJor7q/A6M0iMtY8l4esnddl28I//oMu2/f/x72DATsfsy+WQ1I36qra003UnZYRrKtvuxbcNhe3P3RbacB5umAp1inNAEJupfCU0SMOnhWpe/OlcG4kgvp3M3QMuS+jvZmLv1JpgJaP3wUNCGDPkl0diz1RDS37XOUcDuUcUYTtX/BbYdrOHRGxM/auaWU+72aMYSIyganvEHESh15RTgOHI196v23RGtpqP3JzdbzbktIn8ExuHiSu5H67IsY1RJAgEsm0E4iabSVjywJX9z/qRnG85/1O/zCRV6yxJwes5eJsY6CUOTd3MSn+7MfNVq1gSvn5Sl22vfv61LttevvWwFS3mPSTQYqNxklSt8bLw47rLyti26yJOkoqH8s6Sd0bXtA3Cdjg2rNn4sviWxWx/KftDcjPn2kgMwiAI2yescH5VbikKEAfIENtdtlgTlewXImzv2L0G9qOQKB+hRLLJtxLtlr61zw21scLHIjT1b8E3acypdTYvnQ/Bqw3n+4ZzwK+GPqfCduAzdTJS+L79Vd0JrTf3aA7lBs+N/AO3FTnrKyuaxSgSBALZNgJxwyGn6lIrc+VTOF/Il5C+6JeZBBj8TqjHO+8aHzbpb28TSwJ7lLiRqDcGKsH/F/+vhmr7/v3ftGlYBFW1ag21UrVJIviGi2Z6rMXWeDG13iXbhuy/SMwg2xvS9xvcF7kCJYhyR6O8mGmcjzTR4cKEF2wkG6zY8sinTZG3F7wgbMvvlH+eLFcg+C8Q+4b6sIU7/A0EObw8+RgmI2XFvVHYDhaLexZaplM1aQvSV90Jkvon/WuZe1Qzu5i928F7tZXFwL0l94SaXQjDkYFbJ0Zn+85IXS8QxGx3GPlHALE2zWnulwTM2EYgkG0jEDcfx64snYhqpG43EhBWLRHWfplJlsTfaPC2JdLbFkics/Qv3aDS394mlqRVnSScHUHyv/7TzP/+R4lnG//wn3z/8LA26W3VsFg1qQYG/bFIRxqK4glLPjZ76E7dm+1DTlqBFLZLWyOmbYlz8z8FpdmYeqhEgjTMdILuK4rvVBsJPQu96kRhQBW2LW5ywU8cKfDADDO5mvRKloUficBfcZtgMpJsSiNsDwmDEABSzrzhd/o6UMn5JyD1r+D/Bq7nxuw0r2rP5tekDdZNXqjkVHNI/Le7yr3R6MRpsdph5B/9KLpy5CbGovj7Vfw+YhQJAoFsG4H4FLAVe33FNRYfFiQ+j9g2+mUmOXLSHsFK9vseXq6ZeiT97W1iSTQR3a3Ytmw4jg7t2hwSO6+ZAq2E7c2I8KaFS5uj2qoKu6MEWkv/UtOt/fwSeB2axQrbV91mcpqFkBt+mhUPOMe2Ilc71ZHQRWGLktGILIdPFeTcEs/s161Iv0EYVLgsPc7l9CZh0ode2ihJhhd5owV5R0DYBq+zRtieyj+DI9Ts91V4veiZ4lP/GnraS+8aFwPkTOmaZyc2AWyb3ViY7eSqNsdsDwkDnUT+0c/V/JtWUe7o2EYgkG0jEIg+mEn2/EufwvnS/nb7nPR1v8wkFwzebp56rEQetYkl0UR0t2HbmdDYhtPyOvGKKwn/l1YeEt3JSOlToYmBI/F/m06acwdyeN0S6sKtywrbFwIsaTu9wTmYqc94Jv2AGaxVY8Nq5AmN2c6IIutRb2UjcZcS0k+fO2no3qnFq3i1CcOjVG98/g4NGCmG+cXMIjWUD6Tyu83CdlLck1jyIlOvea8LMdaXfFM09W/26avGnnb+CCeEMWmD/tAomM4rQZJseGvfPtqVRUcTs20o3IX8xLRYPf9z6FsklwXuY1wqNmf3UdhGIJBtIxCfCg68JdLf8SmYSUhlvUQppJPtl5lEvFjwdvPUI+lvbxVL0qpOspltL/gmlz0zPI3WqqEsh0QX0jJA4yioxEs0L89lpvcKmDx5Zuzi3LeVmI59q6vZtJ0VvwOOuG9yNR32O0pGQ3LMdl6st7eRkJ9CTfpmMM81bgZI+Tzo6Px6rGbyTqblknZfKacrbBtyMkc/M3mYFZv6iLaVm0WRyAiJPXnuMo/mBvmedv7wcuIPVMJXFy1+fjiy3sFkZJMrI6AYyktwXpOFoQ5W3b+QzzBM6166Y5vdb6mbg/hHGIFAto1A3HCszx1TCjV3fONPFsgWMT07C1FiJimWLqSuXSR4G5LmVOaUTrWPJdGEBmpYOGDJ+Cw98nh/yKzr2OZzSJqxG1hpNu+uudwsnZpV2zCFuGrplDNB9t8Lt6XZtD0EJSy5u82LhJWEl8Vsm8BG4kw+ahaYFUJ8jxe2q4rXnCi4ANfsE7K7MeEx//GYDz6DychmYdtVjPBGF3lmVJRHSNdSZ3y5vUS4XclH+xY2/mj0NrlcBjTbqbiHpLN7HvyMDEduRV92vN5riNnmSjEHO3k5jN4+n39z1cL2/An+EUYgkG0jEDccEPvVXE948wDBHRKvkr6dW9hr0yPThRH5AsHbUFgId/CXs8/bx5K0qpMEBnM0agv95X+DQJLgH//h0GBt9WSdbvbIhu6c3JqTBZJ4/GwjVKE8ckc7PF8obF/2OjSm7az43TDrO8yKB83ZgquhL6mNRBlnJDaSNRfl00eueY2HhBe2NzyyV0cZjgTbjA9YqSC+lX4RqtYoEd0N2cER4S5sjRe2c8VvIZ2Ql5PXU4fkgPnUP/fcF2qdDbshQOAohchGwEZSN9J2yQ3voGLlz3UhFXMx28OMxHfoIRFZDOWJM3PFwnbNFMG/wAgEsm0E4pPAbmCFmkmuqqv5Ay4tKKFUhOGS+NtY7mkrCbkr9By8DXoqidiTUfyufSyJJsZEQ7nq0z7/X/ydJgHQ9Rf/fUFQ9XvIIdGh2q27RdeYPXpVfKuhTV3Y3FlheyHg1pi2RwuPWQnLwGr2TNvFI1RZt+IgbyM5M/uaj4H8qOyeYU4PL3n3wa4tIRsYZ7u7I3LFLuHYsKYFhhe2yQdGVs19NpgQfcFWca+c9l2bLEsfmd2OxL0j1tNeM3l1XS7yesDih1FLRdi+RX4Zu/BHcTHbrlwcGtrNorejdyRTowuS5OEV/A6iYxuBQLaNQHyKgCDqq/nv9sOCyHgw0dgvM0nPwdtw8Suc02M1+EWbWBJihyCzns1se23AoNtukxinE4TrSuC0Ll5Gt9rxJIdVMR+7myfzOj/f10qqySuHvdm0DRkagVzhyEMzQ+pWqpqvxkapsJ2aa28jMeQeUiLeKGzDpSYYzdL5yICYXS7+QlYgezanITcIk5EaYTtYLDLZWM3+O3GnVQYfHCdfxMMjS5FBMGTv+hqE7WHF5UICT+D+ALGRyMORXQ4AQGp1zRRRPST5O11NblxZDCjn2EZhG4FAto1AfDpmEvH3M0v0EzGTEKoKGSD9MpP0HLytm+i3Ep9sE0vSqk6S8L/5v/tcl20H/mmCkTN9YXszcY7pfF/pannF4v/kPRq7Y9uQ/dds2h7LPeGT+M7MMfLMA7Yu4ucjRVY22Wwj8Yt0QDDjH+OL0EV5hPElnOy2w8bPRy4GPNB0A5ORGmHbNjc+zF4SLlb4aUsaAhOZPFUGT6UVhSdxW+1pbxS2PWIOdl3Kr7GX+xRh+09vOp6MbLaRWJJPOmxoV9+Rws8kEvFVZAOFbQQC2TYCgbhE7AQrJG6MD1e+kYCpRKLk9dFMQgweMm9O1zp/lW6iXzkZVGJJbuvGkrSqk6zZZIaa/x8Dumw7dNvWxkOyNbt77qGS6cYNp4XTKburtoHsv3IwQHhhPmTn2bMEa8nDK6CrovyBXM6IdD4y8oS3kVRprEqg2aEBwvaOjWbObPgTKjFNUf17vGBI5fI1lo09zU1GaoTtifwo+XpCGC5n3mjYdt0UWvJS40og+nQ9OqG2VwYjjcL2AKuuvHemZk57nwfl5sitxKvubGBe8Y3Whj5wbm2k+osffk5+8cvCj1cpbL+xhPGvLgKBbBuB+LQA44PnCpzXHdDFCFpyv8wkFZblzLexnIvl4i/NfJf0t5vTciyJMW9udQqaOknCtl8/ntJl2/PurVY5JGup03OPcz2cpE92WrVO8Y61bShsL4SoaVuMyHOlM0UbuI3FxvlLem0j9wnbXhRkr8uYIC+QrMxGAqHUI8JDJmw/YyyWUu2i+PbI4mZis2+YmyNc8RhJhIg19URzwCBsmzPDzEMyUBTfnZki/AWsm0OBGE39K3uM2cQY9LRLX/AbHCs85TV1sKMcmcc2vIM9RLbX2YXacjoZiX/Q+RgDmZDe94lXLGyXcz/gX10EAtk2AvHJoWpP6s7e3TBALjWw236ZSWT/g8Jd5FlJsQsLbPN4Iulv9yixJGO5J62WRi/SZ81sW0Luv32podrBPxl54wcPzUZarlLcThr/53ZyRx5o1ZeubxFmhe3EtG3IDi4V5dlQki4iN9oUbQ2qOWXb76iwHfqcl4fLnhmWPxjjPSSc5u3fd1C2bco8hlNORMaY42LQmXxCWPKSdyYv/tRK2B5hTmtfIdeg0dI7Qj9D6p9n9sl2QBXRy1n1bc2Jx1BKby56VBOIyV8J3KpZevExwxamcw86j/wTGyd0X2RqV/Crh45tBALZNgLxqQMyGdRwjBsKEkyxE1wl3/bRTLITet5D/XVzfvai+F4il/E4jSXJiz/qLhhIjqHK9e1xleR99sT3H/7W9Ie/tP3Hf4wPJcSF306szVQ70ImqTbVtB43/q8RLTdp2R2x71R9k44BWYtqWmK70uFsMg2ysq5prYrZb2UjAoeGLDUOmHvnRjGBW292N/uEsJeWjuUcvlaqdqsmbm99uJWxPZmhYyqjwULMYkBjkcvFdlKX+5fwT0tqgxlpvTs0Nn6gh1UMyIHJNnG+MvkPLdFnsOhEI2tql4+E7ejr1PrlyZNxWN/ARHdsIBLJtBALRZ4CBeCv2+mafaXOCXr/MJEsL74mAV7XOdnFXQYlJ0dT4VSKPslE6KDm3sKcxAOhy+hNnWsOnD62ubEmOOtHNIVnNdjGNR7VbbuCv7N4EqtfJFkj2n2yAidLojFRkWuSiSEDF5+L/ZCatidkmaSSqjUTZO3hIJM59aqHC9oFV1o/DxSXp8SXvNJ2PdDngmb44peALYe3IqSX5NWXYmTsgmWeLsgZcU6tk/M/n64XZyX0l9e/U7E2mk3tscFP+qahWtxjz6lxmTvyBv9VQtTjX0r2UvIDfJu+bkjbrysW7MD6x+snt2Msr+KVDxzYCgWwbgUDIILUpx67szT5N0t9+ptgP+m4mIf0pSrLET11ddk0gTDnhXGSxJOHicpNlJcbL863YtoTUwq7YlEMiT8V1eQeDaMB7NtVGUrf7eb57/s0Th53kdfCm7Vh+lTm2VWEb4v/OzLFl4YDNR94VuTlI0TvFSiKjyfw2ENn5eFhdTohvi+JboijD0OT4/H3KtrO3ScrKqcW3VPxVc7SglI9kqYfElLdqqPaZMVRO+udDNPwkGRpV7lT42FLH2CBss0XFeE5uLHoRmYa1yutQL33pXFu7PB9pyN3t4begbgpewWA0CtsIBLJtBALBGBULu9CM390wQH0jMM4+mkk2Z/eoY9i31LPWLrL+9mfz8qCksxDthKDrsu1sdv3U3PCIRI6ht7JzEGK66VBHJMEIUbeEzn05X9jOm7abhW2Ri//b9S9VIo8J2y6n5UVgPL9Jnn9ipiOPL8R/Adu3IfcQhO0jmyxsjyqa91Sa9sBXjV7g5ZHISKubOZbkIypsz9+BgO2i+I7chVDtOoGgP/EQUv/yYl1i2MCAGz0kg3w233q6WmUjm29Mvl4NUQG23vDxJpyObsIUfyXzkTvh51fwG4fCNgKBbBuBQDCGB+G7nIf45gEirvkBwX6ZScCHXTd3SiwOPYXmWwpLuZdtYkl0X3LsyvBccOfpTOEfB5PPnmn4dw9a5nL2jHBlPpBEItmaQOt2PiVW2C59AabtrPgdn7HdTM62E3s0ZjtIY7afCV/z7FlaOYwXDECg16Jr3HjimSsfo4+zzvlQ3ACt5oQlV+3JZtcyCNvwRay0caRccx6L4c+41D+DcuSsp91pgq15xQQc4Wx+Tbbl+G6D87vmiPVIYdlhRMJDaXHro11Xo7CNQCDbRiAQOjorlL/cSOj6nvtoJjlktOxFutrJ8/cCZZ1rrvS3t4ol0X3Jnm+ZehgMNsu//SsIJDH9m/8sMe+eqbZi+6b2iXWXhVtUMG3bfj7bll5IbN+LqXUwbQ/l7jIaelfXjlxOZ9l85DPGgxtsJCfmEBBZv1g6NtPxxBOLOynusVHIweb5yCgTttdSJ62EbeiyGcs9JQOFjbcIvJ741yTP5IVTXg4dWazMqONt9JCw7RSeysq901kJ3IJB1d4+Y+vxFVhvPMsbuvv8i79fZQCRGkViCuAfWAQC2TYCgVBdFnyR+M1D82BiH80k3QZvN/vICVaDX7SKJdGtkyQUXILxD3+pSQA0/tkfZS124X1vZwTxf2seP8eimIrs3jx3CyT775XDLkZSdOIwEoHQ66z4XSM/Y1JoaJDORwoH0uPp4iF57Slj1aa5J8xDcm9VfAtUeCmzPczMG6LPRI/TMw2KNZHqdekm6Nmgu7/0CbwPhzqFnEZI/fMnLc/FE7Wn3aOW208Uhvgo8WNH5nnwVt1ubS6c7wo1djx7Fle3r4UGe75QCYVtBALZNgKBuCIsFX/VBOTdSOg6MfpoJukqeBvqLbXui/hkq1gSWBTxDmzCtuP/5X/qttt4/v5Rz6ez4aJOjLXYWrNmeb5DSS1sNyuStmzaHhZUS3QrjwSh2pXgF7o2EgkwYSmIbw8stFDm1OJ9JlCOOyLcB6V8jKWL2JIPW6WygLA9xFTwxKwTdgcpmRKi0WEu9e9fuZ52lWorAdt0O6Hc4lbi1UvnI+mMwHBStUR7eDuG8nfgMNJitbcPf9Uav4LgP3RsIxDIthEIxAf+z/hDgRBcaV3Bl5/30UzSVfC2LnUWlf72VrEkunWSRCO3/flf67Jty5//197ZtiLiHpvdjfZ0f4dsWy1sj+edySfEtA3zkbF8w7puI3nEWhjddD4yQdvdNWkkRzYPZc/FMC9sp6JukKVfedIs+toN/hBi/9AtUNQI22Pz92Gz0mJGekfgW0j9S8+XytlNeHwrkOQ8JAOwHigL39eN/ufKGTEVvBdh25WLbzmdXWUvNnyohB/pbEZ087J/y0DYrqOwjUAg20YgELpMrsOWwWt9jjxb7aOZpKvgbaj006TyLc2XW8WS6NZJkiWE4//8f3TZtvn/+tveFydKeJ/sA+HZtrFTtg2F7UvFnwzZQSXEeqA5+I96Tpjr/cg2Rdi2KMpec18pp7WRKMTdkLunOFVUYRu4crAogrDtSNCGmsn0fZKB2DwgqArbDDs2ymsPPUWJap/aZ5iA7eVT/2rskKpm9RKZ8kY+YFs6khf+L6TTqVqcXbUCNa8H6izPcTu+1fU6kAT/GQPlws9XJmz3VpOJQCDbRiAQN3mIkKSDaQLmbhKgykdjXe2jmaTz4G3w0Wq88ov5w1axJLp1koRtF/5xUJdth7+y9Hwux2bauK7jrj5PXoXsP/nlzLQNrYoaYVtmrhYa/7fjGpHZduQ+eXw0/0CJ5GuwkQznbgtK5Q0I7b74N7QEXjBvx3a4J9M9HlmdilGqcq6wLRF08tpjV7acikoHU2cku86l/m0Fkrp1NjAcKdFu6RfqxG6mwjbjyrXulWnpLKQ1xpuO1znNPrEr+9VGYRuBQLaNQCDagTiANUaLG7aioH3pjffT+2gm6Tx4GxIJpS8aDlJ8vxr8QjeWRDdWBfzfzWaSi9hIllmz44bL0uyurp/H+SD7r+K2MtP2bX5wUIM6U6PXAl/J85EZkbeRFH1QaiNXuljycqj2jo3u4szkA7u2vCkzzSgs+SbZMOWAMpAabc5m0QjbEvM+MXoo1U6GK41EmeYJKql/oHMfW9VbIgbOQ3LiyCge9M8qF7ORTCoDlydmV88RH+BZ0nzSLlXYPrPN4l9UBALZNgKBaKm2XkFqwYeCxKIUh0CBf7CPZhKx4+DtVkK7hNXo01axJM11khCiLMH/F38/82eUatv/6p9Eofe+wDUX7WHZcDsb2XagkyJJKGwH0zYk680UdS4yeD/k4D8Wsx0SaeXkESuFMSWfGIRBdp0pCc74x4hwni3WVsRf4WoYmGidDY5pbgi0ErZF3ySl2krNkHww7glYYEDqH/S0140+ELYdJTWXcDFalC0fnvtkI4fmsa4KOAFpcYuI5cD4N2f3u30rSSmS9O+VCdsYRYJAINtGIBAtpF/x96o13kxGb55+35ws3kczSYfB27q2EPqjhLNVLElznST4v/urX6676Izjuj/SrG2fm2EHhe1g2uZbFfXYNu1iVGK2aUfMaO4RbyORqC14SJqF7WBRVFYjUZa97QL7ikygHenm8V+NsD2leLurtllQtVfDD6rGEFxbkvrHE/0ji7WZuE8KI0TJfk7DVVQvypkx2K1dW9pgOjzec1A33ELZmt1FxzYCgWwbgUB8eNBE5yuZpvogACVY45bpo5mkLFAafeJItbe1tAowKadTrWJJmkMML5Vt10ze5aw6kbkr7nUSi1GOL9P+F7eFM20rbubCTBu2XTN7ZBuJ+JZ5oGWuWWI2kjOTh3hIFOdJg7D9TBjSaOTGuYdkj+a5B/KRZGrnCtsnRk/dHKmAqh19Ki58B3HakPoHPe11rn3dINyD5QQ5hheBP9Eow9BnsIVt8fvO3wIDqwE6Y8fQgz1DWpgR+rtU/PWqhO0ACtsIBLJtBALREpB6cQVK2Id1y2hmE/trJqlaZ2nwdttmGWoiD69rHif97bqxJM2dOJfEtjcdMtvetza0qNTt/k4cEZD9t+L1EdN2qygSegos/q9qdlRC1CjiUu428DaSSGSYPt+TYPzbqxDcwaISYHLgKYIIDcaVI6Nb916NRtieDRtkqp1KgKotFr+TLj6wbZL6J3I97cd2OsM6m1+D7cyHZ5QfjdPtSP/68j0o01bRCgOXPdszpDUzMcRfQY4+CtsIBLJtBALRnctTUwFzY9DGv9FHMwko6Duh5+1IeVO3JYXS364bS9LciQOxhv1l2zt2h6y72xvWHlULTZ6uW0LtmLqT6eL5b4lpm3WhP9F9vhr/Z50pJwO8WsylkfgEpnnXGN997jRLz0mKexphOxgZYsnZd3TrbDTC9lh2oGYK8VR7sfhmbmHPGR+CC0tS/05sNpZP4lE3xWjx1PwDemDMQ1IJD7wxdXTRNIBj42K2u7aRwKelOfcQHdsIBLJtBALx4b0Wl/0/9IdC86Bh380kS+JvROo7a6vzNZuwAa1iSZo7ccCY21+2TTI31hrj/86MwU5i7KCwvcG03ULYFrn4v1fur0nMNtBNzkbi1QjbdcWxbS9QJ3Qlsgj2bpiPXPZN6yxmmoTtFx5zOckMJOF7S4UTacUlXfmqhbnDjb68WJd72tl1fhmkxzNWeEo9JFkqQq/77jMPyS2RSygvJ047ptqq010t1vEudDeGwTI9dZvqL0nYPkNhG4FAto1AIDpXf6+gdu6DQCIfuhSkJL4fEeR0Z1ve14e9OLNU6st+3+2RiEp/u24sSbMT5jLYdiVeosTR0Rj/BzKtvSXbXonSxPHnjabt0cLj1sye1sRsBujSwlWcYzYS+qPtyMtmYRvs2vzjy24T1LBLdFNT1dksbDsST1YTs5QfRx6Jxe+kJzgKQZ7pJoPTIt/TzoTtnPgDbCcZGZHN+kYnpP6V46Zt8ftuCyAn8k/Vpp5cvMPIRR1/DrMYXXbEEDq2EQhk2wgEomuQmLwriAz7IGi2PgMIxxrO3ZWY94UN4lXWR9gy4KV55FFd8ySDurEkzXWSl8G2W8X/QZvMkTva8sS57L9IZByK07Pid63965QoLwpb+jYSRlXXomsgbINdW8J0Wi1/GZ2nA4vuxNe6K0Ze2B7O3l5MgoHkHqHasQXZh+2a/Zrfe0OdTSav8XsY5gfpKijwOd1a6DP+boC0UOnkyqfFKjjODYW7Z7ZZttNgb7/FVWu8OYwFHdsIBLJtBALxgQF2Bc0o4Q07u2bVM7HwmpYdLqxdfEd1c5i0BbV6Qqs4QlHpb9eNJZGOWTPGCvy7n2zbSRnzejjZcEYd1BmS7L8Di1smo2r2393WL3lHN8tFfNCJw5A2+e7U4gVhG+zaY8LTIzNUuIdIf6TEWfdsId0gDl7YDs/NqF7t/LH007z441hO9pofK851YhkXG3ra6QoEArblfRmdynDkRIU5tsXiNm8l3/MudXLl1WNTohJhRrPbmG34VFz2HSp0bCMQyLYRCERPZhIWZdAcl3ED0KrEse9mkn3fEqNKe92q7KS/XTeWRMOiwPnTV7ZNxxxbqZitXsgXtgvFo+EOhO3VmImZJejuTIJdYyPZ95V5YVsC2LVJdAk8bsrSCJTx+UHdOhtT+iFQ7Wfz9xnVvrNYfEOeYM7bZBYeH4ZtHnuzuj3tsB1f/BvtcGTwNv9mdVi3PpS/AxnhwWLpVfwVuzJdz0dCL+xlB/+hsI1AINtGIBA9gpgcruA29NWjTdB1f80k5wZvNweMqAcpvm8VS6KZ8rwMtq0b/8crtS1N21xh+2juHitOv91uX76Hmk5yMiPYbCM5YeR71+ogj+TFuvTMDbuV5YSEwIax6XA3f3RPbYkhTtguMwPJYoGmcUeKFZmF5+7s25w8UYae9lMzDWkZYiXtw9kB6u0JDEDqH32nmI2kbj6fbQfzgroMyBuUOwnB3kYPJYZNwiV1J0QvRdg2+fFvJgKBbBuBQHQ5YsVy5cAffJPQMnqv32aS9sHbbTwtshkg8kg3lkRTPn8ZbFvi2bI+7bA2se1zqNULl4XEXZfjy8BrZ9Pxllq48HrXYaAODTYnQG0kwQYbSWIeUqv9xeIu/0xouhFjecbvB5rfXIlqW1OqY9ub/Jwk9BEDiYRc6XtyZ2M+NMZd0gD0tIOwbS56YDvrdvmnh9YQeEiWYpPMeEMXJ6f2zPnCNrtcw3mtLt6tQwNihS7bCVZnwvaJNYl/MxEIZNsIBKJrAfhq5LEPqNzrjif210zSPngbUiMkxqwjjSecurEkmoMHJ3cfV0e68X+8jbjVCyH7j1ifiSmizY5Www+PbbSacdcvO5shvO+YKdlHvnxRfLtrpS5qUNyHBblqMcGYcd3oH2HBef7Y15pVx4ljXnqOOoCYu02pdkHtmCQeEvPc/VOzF65n3SRbXOjAK1t+ANWeyTwgE5CVIGuODH7W+a0A1a4tQAHQ7bRYlRc83oXebCSL4u9Ve/IKIvMr8ycobCMQyLYRCMSFAJ3PmpLzGwBi4ZCWE7qn1kczSfvg7VbFlpRtp1O6sSTNbm+ebeualbvCeph6lNecFn1tuwX/K8ep/PzCbQE+6o59046xBT87M1PPRll4O1fcoXGBaqmNvK9p4TGc4KrScTNVmCDPhOC/Q2eOycNyoQz/zu4rn+Tp9D1IBlyIfM6r2hICxbwm4ZtV0jg0Pe0j+ScwHEkF77gbhO2yeMxua4Q7JKNybSS7XK5cXOOHPnF1l5YNd6Uu/kk4R9hmlwiFbQQC2TYCgegRwAUvO6/3A/pkdBt8+msmaRO83WZeUxats891Y0ma3d79Zdsbbkp/11xuXbbdSm2FwnZbUk2MLoTcLW0kqYxET+vMEi1LvOxVqeAYUFVT3grC9rESdA2l7lOpR0DKjZkR8qDEqvnwyn2fSJ4DhzSdGdBQ7WypTm5oeGaHasSXQlX8AFzYPZfsng/lFmE7ueCkHA6TeLUaolR7JTwoNo0Pnpijba52WqyqFnBWAFQRf+lqvLLh86aEuF/2Ihkd2wgEsm0EAtEPM4n4e9Uab58YfU0hkew2C4n+mknaBG+3PwzS394cS9Ls9u6ztu2g8X+VeKmFb1ufbUNh+zBX07ic3mgpbIe/kk4QjvxZ/mt41aGVBn6/9sUnhXvwnLXoGpmMpKElZjebI4yCBWXbbgf7E4nmkDCeuQvCtoZql8TfJoUxRase3CCnwIzggJrJo7F8kE546T2VtsYNR/7QtDI5h4waOA8JPKjGbHfJZeETddlpQmBJR2EbgUC2jUAgLgRiWriCHLEP5UpvlUbcRzOJ2Dp4GwYcW1Hk1eAXzbEkzf4TsFP3R9tWGOexWatJ1x2R9k4SYtoOxb5RVeT0/ZY7Kn5LSCpVcN1GGBMczQ3C6UgP7rDc61OLl/dMDwuDoD3PJYMwHwkXYTewQhmhUR1q9MYbqLYEXzFLbyDMGtjzvUB2abp5QOaUE4UhoOx7RmfNGi2Lx+AhWUypZqETc7T9yoRR7Qd85F+zSWMntN7LL6wxUBZ+vLxfn+34FgrbCASybQQC0a9BqG81XSo3Bqf2Odke4FvU/Wl/zSS7TGHdir3WkP42WYQy246NNceSNNdJ9pdtv7TbZJ5nd2ger1ra1SKu+ulPh7JqxF4kZmi1l9XYM4mhbvruk1fxjTO8jUSOHGHO7LXoGpmMVJTggQNnGoL/xlmNTlZ5rXSJtqPbcE1G2MYNudtLQsMnObVwNKzMVk7mJ0jq37HZw19P6GnnS9p9s0/JRYCS9lVuOJKny1VruLWHZEutouTaf/i+mO6GBIq/1iyRK7gZhY5tBALZNgKB6CdIWN6RW7hh59Wmx7HvZpKlhffExVu1zmqJC5HYW9z3X0l6EyyWJFeitu/mOsn+sm0iUW82BZJAevSZXos4KWxfd6gDf+1N24Snkvg/W+IR/6oji5uFdpueO80gbM/kZtQOSLEEnO+1JzvMjS1Kl3Qn9FwlrEF1ZDOR9Gve5UlhnNzEgNS/dGhUYyMhwrYB3NWKfP4880ZM+ZqHI5mNJNDmJoDGlGIQbje+FlK6u4vZBovRpaZ2omMbgUC2jUAg+gyJCNJ704Wfb9J5QTZfq2Gy/ppJThwplrzxU/NiplXM4vK8KETooGRi4XUrjt5Htr2cZaOxrqZAEraXqkWHbZPC9hFO2G5j2l5OzRGSemKxSnyafwlfDMkL20LYP8ToqUS7t2M7YIx2saHMqbScx3fqmIMtnNpCQ1n6qpF5bcmOk01bRtJ+kvq3a3PueWI81SZ1NkYlHJBYPtbt5q3EK3nBwIYjK7GnjbdNMuzYgq2p9oAm8q+JqfvLuR96uF1TtScvtZHqDWZsIxDIthEIRH8Bc1et3A7XFO3T9/puJtmc3WMl5EudG1qWhN2V4C1yGIFivhVH7yPbXvdTc/a6y9okw7NQPLtW1CSF7QdGF0TjtTdtV0KDhKfWjL6hRoLujz9l0qkPhO2ayQdPGM19rVjhQ2w+MgZ8et/h5YnymTVkTn0BL5wTivwxzC3sEQ+JMW+G1L/w7Fidu5h1o4/U2cBGnLNPiE+jEvqCUu3QLS0fNZ6TRmIWvbByeFYc43+kxmx3qRxDvs2l/p6iYxuBQLaNQCAuBaS8kE9VuwE4d0IRzCTWvKc/bldFkK6bG4y8JK+tlVFnceGtxOfGlVgSRyGoeUfAngtyYx/Ytoum+Em0u5Vbt+zWjpaSwvYJlvthUAh0K9P2svgWimCsiQcaYXuXVaa/strOWDjgonuG8fg7YkN9ZmDJx+wl2cFGqh1eiHwJRHxs/g5/DMXSL6R/R3qL/UkjCSF5rmT88al/h1ajrEPn7sDh1ZTc9LJ4vKo3HMkvftqkkUBmy3BT9Y+a9WFPdfXGkc6jyx5oxvJIBALZNgKBuFzThW469fVFe8+0KOc6x0kwXKH0LxffHeFDiq22qnmwTe1fJfLImvpStk/kp1XTuVLXAuufPrLtDReN/1vOnrWyEOhxdMuB0TXcyJtbmbZXYybCU8XYqIZqKzXs9ERy/imtsC0M5EXZh3NmiTIBOCDRaPLTUNKiUm1LqBL802juK3XyMp1ttgnJS4JiZdNBU/8CiRmRi1OsG2XLvldMQA6J6JkpK7Yi8JBohiPlt8a71D6NBOZBhwWts6XnmO2y8COh6Zfa/Poq/gqFbQQC2TYCgbgcGbjwM/m//LJDfK8Y7XVl3kzCl8v0jEr2e1pF7lSZX/thTVk2njX6E18QBzk8qKmT7CfbbhH/J7ad/DuwuE0pplLPD7Q3ba+GPidUFeYORxgn9sS/Bh8FlEQuuqcpnxZX2ZHQkz0yuoEKqyzZHFwLfhlIfA5Ue3x+kD8AiWGTx215XzRKU//y/kn5poE3qwrbSk/7MKjjmUF5MlIJioHhSD5gW2MjqRl1ZhyfFcfgqIJ57Qev55htMlzRxhaFjm0EAtk2AoH42EEk2Ko1fqkDWFeM5gr0ZowKj/poJjlTAtok1rIk/tbhMZTTqeZYEs2IZx/ZNhl2lP7VuRXQImybFLZDWrYr9qiNaXtR2CI8dUYR7Akm0/fJF69sNFp7j/lJQNg252ji+KEzB+KxeY5S/JnMA+CpEtUuB28Nc8I279jOiz8Sg5D0zmaKa4dWlzIK6U3PlxSu7GP7lbO9J3OPgc2vh8rMdM6E7fATvTVJS3Gaj/wbEx63vsLdxWxLnwES/HfsSKNjG4FAto1AIK4rJA7XpmP8mqK5lLEZjkKY6MrF0i994PcsmQ5G2Zpr2LUu5+zz5lgSeDvK+Z/6y7aPlXbGjab4P7AjN5Otitvqh0YbYWBaoc7B2LC+sB15IvHUEhv9JHOH4K6Gs6iaqWN7y25VxO8HzZRUyfamL9wzUna+75SF58n5Lzlhu8GxbWbpIvGFDUj9S4bGNFx51+OO5Aqw/WhiSiPMK8J209IocdqGkqqGGWGg+ac9x2zDh2Frdgcd2wgEsm0EAnFdsVT8lbicd4KrN+akIMahzRJirrTbx2SS5uDtc4MIFwu15lgSTaDKG1OwL2y7Tfyf2LqKfNNpBbuFO+ekPunItL6dJiTHbKtjgtmBbbudvjbxmIXu+ZifxMfbteXLFVqD+chcYJwPwKbLGM/jufgXvBGcd2yHi8vkQUchGJ6PVpUpzFd2e16UPwDHVg9spyy+h7CUUWZEKafnVKqdWdGjpOBm0d4BMOTUXh6+NpK7vD3GbBNDVN0cbvURujj2QksdFtEjEAhk2wgE4gL/4yqTeTVL5PL+U79iNNfENKMkvifhFea8rS871QRva1RqHbYtvl8NfqGJJYFYxo25I5GNe16cba+5aK3MhtvZyretSaZbFv8lGB2GLOpCiBqpxYhOpEY5GZF4qiv1GVDhPbt9hhm+wUZyaqHC9rbVIT1OqDATttUzHWHzkdnQGGQgrvkH+VpKXtjOlurEQzIuDBVLv5Q9Rkj9k5de4onq/DZ5ZcWdbaQovtF4SFaCX+oLwGwL2+L3/OPBvABOG03kX7Phu6uY7dXcP5OZip1g5TKFbSb5e0X8S4hAINtGIBCXZiaZO7qCprorRvtyGQLSgTKcG8yL/3zxPWqCt8+N/RaVsTxNLIlmnVA3R/rCttvE/6kui8aojRWvD3jkTH4iEjMoCYCDS0WdxcNq6EvFQ0Id1dbEA94NQjzTp8xDUlfabXylnErW1eA//5HVCcK2RDePbUbFf+Iy5cZVYTvb4Ng25mmNztzCXmh2iqT+VVwm+kkwO1W2bfPDRqYyQ3TvMUOb4UjF2fy8VVQ2bK05h4Sg55jt3cAKW7z9eOmO7bbVmAgEAtk2AoG4KGAYq1UVy3XEuQF8olKD0sdkErExeLsTN8tK0tscS8LHF/aXbddM3ub4v1NzXJdyzQXVkA3pW2LadiZ1xgdFocp7SIYE2aW97KN5I+bUA42wvWtzmoSGYU3+NEERn8k82PTdObHKR77vivEekqn0PXitr5gFD4nMIB02cqaRlENm0tlNPqt7hDO6sC38QKrmJSzFJlsI2wFd0zY0tEvQ1EY2v7armO2l4q/kVxKS1y/VsY3CNgKBbBuBQFw6SIDGZTdoXCVeRTao57V11kpJ/G0s97SPySR88LbGE6LveElnm2NJeFW+xvKnL8i2SfL0vtWlQ7nsfl3xFRzb03njcnqjjWl7NTYKHhKJc88V5er1cVaIs2d1kNBrIKzGnKGV10LCMNtOxXevEvjTG8WBnQ1N8sJ2vkBHUedLNVIbOSEYiqVfIPUvFxgnTzjjhG3SGE82PptfY6p8u+FIrRUkcQoPTpem20T+Nb22O/EYxnw3koeX7dhGYRuBQLaNQCCudKzwgsEXHw/WUqed3Ijvb80NH7x9bqWlqPS3N8eS8HWS/WLbO3aZ8r606zjUq9YwZduWEDw4KxTAsS192960XQmqdu3J3NhW4vWJ0QOPEBvJkdlF9nJg0TL+I+c8nOM8E9RHJLYdvLXhfUgedyaeNDu2S+L7SWGcvH3phZN8cYek/h2bPST1b9fj5ql23eSlIX2Fp9RDct5wpNg6jQSOxyA8aGnlt6eYeaY7RnvizMj+GXvy8nI5YQmBwjYCgWwbgUBciZlE/L1qjV/2neurRLnw87nSsoTUwgGrHlzpy34heHu59F6TCahz2RfeNseS8HWS/WLbNUUhXtOL/5NINq1pNKoF8oZGatvGtL2cEQ3zDd7lY3t6PEuFbdvcY17Yrhv9i+JuG2HbwJzi85GvJAb8Mpwnj0N1PB9FQmz3EryFjPRtzj9BnhwPjzIXh5dn2+nACBG2Obv5Lca2b7e+dGx80xzkqPZAq4b2RqsG3fW+d6HzNwsc/6+im5d15wfKI1HYRiCQbSMQiKuzXiitdXVjQOKpN+OMWKTDOcmGpObGmDf3x5PDBW93wplWw/c0sSR8LU5f2HYlXqIjkg5LG0ZYM1JtO15cV0vaU8+lR4yKl1rXtO1KqwaPiCCnZyRDquF7xyEbOY4tNIDv2OxtugVRhRPcttvA6VEJ/mkpt0X6OPetLr4uBzz3xEMynZ8sie+bU//2nCaear8xURuJR8yxK/+EUu3QrbYaMN3CgY++cCL/9Fy7trzey/3QG6Pd94n0N/HS5iPRsY1AINtGIBAfAOAzbqPFXi9U7UnZw+DKtX+au5DQOKcvgiXxNxK8fWaJSIz5XLq/Ep/UxJLwQd19YdtrTurikL5o4yio26lTwiAMQqW5fEbFnwzZwRam7XdDWZpDMlWQ8++S4h4/zkjy++pM217KaD9adXMIThCs3lPpLySqLfvg7bLLZdkzAxvMK+y2WPqFpDeOCPezJZlbQ+pfKGrQFbYZ24am9/OHI3m2DXHUwWJJpf6FoXaMVh39DHb+Zi0XfyFjsnv+pcsa0gito7CNQCDbRiAQHwan9rlzczyuEYgl48wSbf80SCaJFPsTbHzkzFJvhj1+btJLOZXQxJJAIKO0/jmzxi7OttcdFppFGNfrXmEyZ9m9qXEkZ4NKz2Ik1cq0PZYFQ8VtZkG5Ay83zt2XVzsWyvVPzNpR1BXx18YRRrq15awsqMuLDUWung8+0zi2HYUgeSRUlK9tKD5KzCrPXfQGxZHFCl5tunejd0gYyLGAv1VGtSuhz9o6cICsU8bM5ZAMnmeMpq/dnO3ijduKvaaDtpkaOrYRCGTbCATipgFUVYnn3YDT2Y69PLe/nWBcGOpjzc3G3DH1Zige7iO30E4Lny9rYkn46ECebbdp6jlP26bxf+29xZQu5x9SYTs7uOL1ydb2yLSuaTsgZoFYB4SIYrEY5YXtVza7smUqbK9FtZ2dbyxhODtb4j4dOmSrjmDCxI9ISoSeRJHEFtZYCrixJL6XP7d2OfWvavL6kxYN0wU44t+Y8ka665Tv3OFIzcXZ8y4pVHuAT0XsRD/uqqNxUfyd3A85dqQvaT5SYtgobCMQyLYRCMQHQ7nwM/E6k7Dn644XmRqlrfNv2j8Tam76kkyisDTVINH+XsFioaaJJeGjA3m23fPMHCGjO3ZHC3d7A/caYoRy025ezsu9PM7kE13TNvDOkazcvxguLvFUm6SRgLBdbRK2d8Iv+LQQ6IlM5reJIyUVGqOjk4qPhTi28+KP4CEhi5NEeJTmmYRoleMpE7ZrZg/YSAyCOs7YyXCkvFoTv4cjlL41i14Q8l25+Dn6MaPpZ7bZLtZFqZMLrqxQ2EYgkG0jEIiPHUduQeZGtsQNOJel4q9k8XAud0kvnNDg5GKpPwqiMt5HiWbbi7kovi8HvyB79xWzxEEBBvq+sO19JRdv02Ftzwulr58VaNCeITuwqQSYtDJtjwmPgVWXEvZssTbE1aorTY13ecf2ZjDfeBflFS88l4JPNI2MI7l7i94ZGJEcZo5tc55OUkaLstskX9zZtcmDmIdWVyovVxTxPe3HdjOwbfCQVML3CdVeDd5qf93OWJk8SSIfYh4SQ+FuB6SWHkNF/KXzd4rktddNweXiu0u524PlkQgEsm0EAvHBAbUabfrGrxHIoOS+7xwZD2puYFTxokSf5Q9CukgbrMbGSCyJLe9j0jitkzyzxi/OttvE//FiJy9XFwMTFbe1lWmb95DMRr8QxXejwkPmA6GPv7bZjyw07vrM5OP3uJnY5al2zeIYYZXv04J8/e2KLZssEiTODcJ2pFihjvC8WXrLpEeywXFN6h/0tFfNds60DQfwA3hIVuYc59ygYFfm1J4Z4mojz73gvcVsV3I/kD2+Dq+hsI1AINtGIBA3FrIerFC9c4PzrgUO3XJg84nj/NJsT2Guj8kkMu2zxXkfQhusJL0klmRCGKFeCGVcdc+3eGadvSDbXg8nWSCJpS0DC5gKM+CUkIcm4/lWpm1wfYxlv6pE7pvyVka170B/e83ogxHDDU+ikWoHeLZd8d4Z5kYti+LbodzAhCKNyzQ6YiDCdrZUHxEeKB6SB9mSvBSMpBynZplPv7Y7SOrfViAJYrY581i1jIv04Dscjmy4Mib/ZGGIi/zb6sBH1EvM9k74+aVOTWAUCQKBbBuBQHw0DFW5nV21xi+vx+7KsBtYIbfmzz2XvtfcvIoAuZGz/No8czmdZbEkd8jYH/HzSP9enG1vuKnWu+Zyt/M8mPwg30aihgMLfXKzaXtUobxKwPZXi8FbsXn1hY6CF2wkR1YX03dVYfv5fF0zvFg3eydyjxlZvyd7VJSbDM7EExiRJMK2MW8mT4svbNCVoWI1kRCYnaAqPjNqH1nMjtg3WnLZ8XCkyKeRGAOwhHiWN5w//NBTzLa0yiUztSfOzKUJ234UthEIZNsIBOKjACTQvUif3RhjTCdFIYTn9avmRg7eZvxmNd9OrVwSdiGWhKRHQ51kH7TttvF/ZfcmcGIoqZF7cIjtJP+txrQ9V9wBlTec/Lwsd7ZTnduTT8LXm05bzUR15deuMNWVsz80R4WsJ6EiXp6PDBZF2iUZfEZyVEayg3mx6itS74qzQPMcA7Exkvq35JmhXvCglzuXgROWt11njeurjGpXwvfP56YsGPGQde6cG/lHhe2eYrbhg7qWOr2UZSdGkSAQyLYRCMTHA6VUJXJuUPS1wGqWxkqszx2f++T+1tzI/l1Hms47RtrZDxYX3kIsSWxB9uzSXk9T8NR2YW1bif87NusL23U7aMyUbbvjX8vLA4/saV6OzWtM2+AhMczflmjr2DxN157Ij5pzZuDNhyyKpM4c2zzVrpugyD2oWsCV+chhVqyz5pQHHLcctsn0/flSjdRGSsshCI0h5yURbkj9gzqbfYcJinXkc/fKs6edD0dqlODO7dqaF3YVs33izJBs+Eu6oQTunb3QEv6JQyCQbSMQiA8P0pgtGzDaWiCuxcqB9reHn5/75NTCEd+ZcnFszu6xMbvkOdbtyEM+lgSCz49d8xdk2+3j/6qWYIO2rQjbEoUtp3aaTdtjedW+vBi8ZVe85goG3KUM/GgmcxeE7V2HzLaXi+9AKj4zuesqHz0cYvTaXvBOC1M0UlC4T0Ykl7wzgng0KYyTx+cW9shhxyH1L/iM2p8sRqDyRGJnbFuWcsvicSX0WYfDkbKNxBHT9L2bRW9X3uiuYrbXUqcXHITtUNiuo7CNQCDbRiAQHwlepM8g8vm6nwtRDQ89hU6eTGpu+pVMAsnZEvdaEtutW1ZmjXwsCZh5TpzpC7LtAyUYZLNFIAmE3J0pbNuUeijvyGEnP3XNqqbtiFDhPSRC5HPqGxFuh0srfPbfa4cNGPyq+Fai2mTullDhqoX91BQQ5IFIOh/JV77bk4/JcxKhUU8hTZ0qhTQ5qvR86cgsn9S+zZkvyquCspwAwwYi3WOygzzeYNoGqr0a/KyTiwa6+JHF02Hkn+aFXcVsE+OQRIUvKfgPhO2upjYRCASybQQCcZmSsPh7Vcme65CkftQ6vX+p8wRx4HZ9MZOU8z8BV34detHumekUiSUZF74RuTpJyJLrjW0vZ9mqydUikITx4KrZJ1HeA6NL6T+nTx7lTNvQWG7IyR6S4Sz91lrwDQkNSdskJ0TCgVVutIGin7rR+8I7AKezE1odUXMDH47k7rGGyJlQbIg8J5NdIR4SaRVULFEmmvNPkJ9Go3Rm8dhi5oRteSO8aXtlzqEOR7LU7Q7pKVhcurWRdB6zXRZ+JLdfLsm4hY5tBALZNgKB+EhBrcPGQLnw87U+kdfhtU6CQahuympu+mImgZ4a0uLelhY/J7EkclmM+B5EcZIX3jPbXvfTib11l762DVl1u06LYZ5S4XJQnkRcjAlg2h4vGJi7emAxeGtynnpIRnOPR3Nf81Tbm/iaF7ZrbGSwLhFQ31f7zgk+FRFourUAGYJ3ZaM2G5Ecz35NOj5TCwfkgP1JCxmO3GBq/YHFA4ryaoAeJ2/a7mo4sslGMhDMCx1e7SNXQTOX2ZHVJ7p1qQn3sHKQDg//rCEQyLYRCMRHBOn//ssukb4abCQPu2IzE4Khj2YSPn/j+Xy95TOL30EsSWrhCGj62cWmJCWSTdm2P9Le+eANG1Y8RmWe0rMsypOI82ETMW0XilWVTM8bAonPwa49k5vUtLWfqMK2+8zC0jlMvk3f/ReBL9+ofTFzdhYXOJQbVD0qpeJw7vaGU85Reeny8l52qgR7qD87pAjbe55S1WyHnnZYEoCaWwneph6S0K1u6WnN5Okk8o+7mAG4I9H5eozcRDp2ZVHYRiCQbSMQiE8OpGPlknjAlQF04pex7c7MJLTmBuIvLoIzi1q9fuRup5LmIl+ywO+KyOokqxdLANxw0fi/5ax+mCMMLA4LA8fmBoe3L/4NsVAPMwXaIAwWI19BhIhLTGiotmPuMQjbZ9YwUO2K7yuJ7+57onAuK+KvkNI9KtyladbC0GT6vtyMo3RA5gLjSuPPaIlZ3iWGTV6+5J1RVOHNI6Mdtpn3UV/KvgfE6QB4SMrpuW6914ZuPCQV8Re+lbNDbCboHK30BTq2EQhk2wgE4tMzk0RpGHM5/9O1PpG6WWZ+u4GOamvmS1TKDRSL/Vqx1InqaWoXwFyOjRq4cUDywjNL9CJse1OJyYOqmjbSezpEaetzr438aFxJ93uWuQtKtiCsG5hdeyZvVsvMGR1/bXOw4UI30O5N3x2J7K7F1HCVmikiiFXNa6Xtp4vbw7nb0+l75GmR6NCIIMf/kePJi/XXdnn7p2ZveD5aLvxcl/VsF/S00yxCYQzsMWdGb1fDkRJ2rHDk/rRY7fxS11Qhvwu2TWqMpJXVUvHXyxO2uwpIQSAQyLYRCMTHqwp/tCCxJEeuXIfP72MyCaFTYF/enG0pYa4kvZMZWTk252W+Sxo9a+bwRdj2jkJPX9ptuj8dyz1hUR6+50wFL8eXZR/5bBnSQiChbypzh7mrB4AlK08gvPy+yqeVzQLVXk4L5C1gBS5VQ+4haOTkC1c+RoRtiWSTp0m0m+/1hNS/bHBcUaCDJ+YZ2KaRbFBRo0HQrbgfK2z7sw6HI82it27yQkhLdys6dfpzvcOXwCzsq/D6ZXzs4TpgeSQCgWwbgUB8vDh2ZYnL9lqfBelvP7NEO3w+NBde3ExCSPOxi3LNE0dLR+/yvOicU2JJck9FNfI8dBG2TcwhGy3i/9RpQpOfyMY7NppFnQ/ZG1wiwmC4uATfPhMeA9VOFw/J1y/tVNg+ZsL2tueRYuHIQJ40kflFuSWnwYIyJjzKi1XyYC4wTkYkLXkHeEjU1D+rS/q6Zo0SXzW9qjYrodqz+TWVZZr8VNgOP+nwcskXhCUJbotdhNJU5k/46c8OQYL/LuneEefYRmEbgUC2jUAgPmJAofQlBSZcDbZjL6l9ubM8Y6i5CReXL7hrQpqrtsSZMgz3xhRYWniv+8wlYTfIxZKQTBge3bLtSrxERyQdOvF/M0WbQi6p04Co0WssuiQYG+bZcKp4CPx4mvVHykbtfMQkyLz8We4OHGdVIcGvvA8Vqp1aFH8nrhiWhFiZFqY1hu9ssTY+T2clXzrlQO5Nl42U2BNkg+Pk5fHw6J5HPq8D2wT4whn1vycvMLxZcHR0NRw5nBtYck/0ECqi8HsWKG6OdPiS5eIvxOB0cDmOanRsIxDIthEIxPUAcIJOuhg/WkBZz1rqpCsziTFvvuCud4Kriqwe2w1WaHV2QJ/BL4rvk/E/QWkirHN6ZttrLqoxb7idOuRSsXBodrHipfaJccXUQWArhkcE+u34vEqRpUukbEe2lKy4qanj1OJR8gQNhGpLT9hI7PO7ELnobhb/506l6c2EkewdMiJZCc/DoUZSDpLhve2wraS3NPXvhxYz36zOmbY9nQ9HThbkt5uzkQS7o7Zsp+XcDx2+BIL/XmRq6NhGIJBtIxCITxoH3gXCFxfF36/xmkGR+l5FNjp8Cam5Gc4N5sV/vsiuYdJ0aeE9kRul1UurJy/EHoCmDnWSPbPtNvF/RNhW2baJaNK+5bx8B6M8u8ix4QGJDVPxOPvVUJZmkgwLcr1iXvyJbYdyX4kWH5unZK92ak658u/4VJZT+1wyv63xqBTFd2NML/fGvm4uMeVT/8g1PLIaNXU2HjGn0XQr7seroc87uVBpcYtspM7Oopw47YXadqyIy+1RSpL6iSN1Gb9W6NhGIJBtIxCI64S11Em3wvBHCDKlt+/rlHz0y0zyMrYN3TpQw/4irR92sZRwkFgSZyEKeecXZNs1k7c5/o+Urle8FrZxRTlmw5TGFBTWDHgKcfp1Vp2YlL6QeLbM2nMz0rdrbMJSUaB9oGrL0n74uXoKJjn4zyAM8mw7XFwCYXs4NwAjkmBl5lP/oPnyDRO2t51jygvvUF6eeQNZKJVQp8OR5Gr4Q6O9RVPDfGTnMduwlLqMMHsUthEIZNsIBOKaYXHhPUk3O/CWru9ZkP72Y0e685f0xUwCPgqJPm7N7tA5Que87pMlkkpiSYx5E9/63hvb3nRYyVih5vFYfpWw212ntaF8xy3buwXxrerxyFuGczR7ZISj2qn8LrOjDPB2lCOLp27yANWGTnLmaQ4BtWVpfcPSIyBsm5L3F70zxOauXkAnXTNEIyNsCtMM7TPkeHKMVZ+ZWHWlydthc6Qhd1dTP1m3hLoUkrseRqRJNZbIZQT/gbC9F1rCP18IBLJtBAJxnahq3RTspPz84wQZlKybw53fuO+LmQRUzBXhrfRtTVFnJQ66JOpcyeXscxJLMpZ7yLe+98a2W8X/QXjfGcv0oLXnwTnlp0x7zg6MCY9YQ/tXQJFdBRrtkhfr0rdF3xRLvJYF8hOLujsyIQrYmNufzI3xHpWi+JYXtqV/d5wyUzz00I5xPvWPEnqjHWgxqbMx5m3N/ukT+0wnl8gsQp+lumboykZyYk91G7MNwZo7wdX+38Nhx4PCNgKBbBuBQFwvMwlNcNtI7F/3U+g8XKUvZhLIVJa+kL7d99CcEIn96zy/+B3EkuTFH3nHcw9sm4wbrjXG/2XFA5Co6xY1XnDPJk9Scm3qt02pxzwPpmp0/hlsyjI/okwWsmlFs0tinDVLjPy0kvuhQdimwX/qpnyFnEbYnswNkwZNcqZ86l/ZaYIWG9bT7iKUXT27zAqwbTHVUVo2HIw/NNYbSYWhzM5jtsngLCzA+ixss+NBYRuBQLaNQCCulZmk9Bthfofu/DU9hXLh5x6cshevuQEhk4z9VbLfU4uzbVb3+cn4lxBLwgfndduB0ir+bygH3ZB3gSnKT3NZeNm7AWwyckSZjGT3Cl4bOGG7xjj3kTNLnnDgLfEH/zpU8YslNWBbyRTXCNsLqTV+ZcKl/lEPyZnbo6mzIQHb1CIfugum7c6o9iDs/Qwi/LrJ/ivnfujWRrJU/JX4so7cQv+FbWuyhz5LBAKBbBuBQHwU2GEBdsvFX67pKZAow9fhtc5fQswkcuVK6fseWT6zX8NtAYlnU+eGoGNQKSTGQFAnzTiq/Bwod6HlO2mlufQFJ2x/N8yEbenrNxzbXvU4hhvnFzVdkhIlJZOREtZTh0dWp1bYJjYMRa/dSB422GAU8mfI3eEDtjXCtqcwB6GH0meMT/3bMdpJ733N7OF72scKT9VVTWysamVc3HR+fh+J/CNQKiTZRfZ2IQnXTYFuY7bhHPnQFRS2EQhk2wgEAiEbA5g2vHNNT4H0YoInuCszSaDYo6gP9uuXsW0qwbKUkn2PztTpUtJPYkkchaDG99wV21530JyQSrykK2wr5CwARevOvF1Dr4lPg33xVVqgWuwLOSwlYJ57EGVpIWcspppIvNIpV20J/shPlKlQbjhyUiNsTwijxdI7aAISudS/cGSIGDy2/WFe2B7mPSTS5zN4iySrKEOo52SDpMUqHIyhcHdb/L632kWwynQes03uV1St8b4H/4GwjY5tBALZNgKBuK44caTad49/5ODJ3FWaSTSu6yXxN8LSanoS7NJ8mcSSzAjjmjrJLrVtGuXBPwhMOiseNHiObQ49hq3COfcV2cJqli66DLmBqhn6Zdx8dh5Er6ilOeKvo4rxg6AovtMI29KqRnqEcHTpNBtS/4hCnz6pMU5P6mwgYFs+qtDnEtuud8yYOcOMTNl7s5G8ir/qlt2Cib9bCz4K2wgEsm0EAvFJAIpaKh0reR8VeKPCVZpJiOWdD6A4cuWoWzp5qNXC84cklmQ4d0dTJ9kV235pl/vPd+wOeGQs94QVygwwaZZu2TCvZ9eGzsj5r1ZjJlGpqiFy+JHVGYoMaTodCdteXHhfVTrq6+YQBP/lit8C0beXPBph218UNDcBuNQ/mXYfeIp7LjOjtl4+YFtWtdN5iWpX3I/5ccw2MKgC/0AwL/AS9ak90+0iSpb2W1jwddZ7vkWSSFMu/IzCNgKBbBuBQCBaWpA7b2T8qKCJB7kyMwlItvDIi3SVWiyabhQsiu+DCTqVWElu98y2962ykXrTYW0WdGP51QY11OTnBiK1VNuQvS0XQ4pvJapdZ+Uy5tQDPWFb2lQAlmQgta6lqmPCQ15L1gjbJfE9/+7k2eQlmZKsKly2ztXZ8AHbxEMiC9sWFn7StptGotdwas/yBsabAz1MFsIJVsSOFm8Swya0XuLclyds73sX8C8VAoFsG4FAXGMcO9Kkf/s6Hvxy8ZfeCvwuaCbR9YuTkU2Jni4tvNc8P5mg3DSfed4b217OnhHfxbqLsu1nwrCG73LqrA80b+2IZPb2ksRlQ4PL4v8Cql0zejyxp3rCtr9uiZLAjao1BkpzuLgEG5zKjcknmE6BsJ0t1TV29iNlFJKm/ilmm0PW0y6dlCZgezU2Rti2mpadedPmygwL6tAneaSmjop2USHJxVp3+ipYh3S12OtK2O62BROBQCDbRiAQHx1IR8xlMIarQbOp4wrMJCRaROLcDdqzb4nlND/XPD+fNJPdRYRsb2x73R9huX5WDcucKdqavRAtPCQDc9EvZGE7I9ZMITBy5ENfHVhpAsmhraEf58wSBZJNbSGhspqxLdxuErYfalz1JIeEpf5RxwX0tCt1Nvxw5A+V0Gcatt3Ors0i/6R1RVqsMlWYjYo6Yp2/p3CCHWrJi+LvxGBzGStVdGwjEMi2EQjEzYFsJ1Duhl9GDd4VgDRmdxt1fEEzie50Zln4iTFUbXjccjpNXM72vK83tr3hdlIXh8stfSsxbCroMr4rYSWqlg7qsm3fHCGyn9XMEXhmxfeVa/YJHFKtcRqSfDaOXAKYSaaFKdWUkrsneznmPKAul7hCTeK3IUozSf1bT8uE+MRmY3U2Hk3AdiX0BaHap/YZsLK0uiaayL+mJUcXqjAXs93pq6BStNv7KujYRiCQbSMQiE8OpLXkzBLre4TZFYCkfNQ7yGPuo5mEdAeeWbTSadVKg7cr2QbJfCm3MZX5ksSSUMNJt2zbReP/lrNnrYTt59AUY/Kpoi/DZO4BIbJQW1M3+jZ9d8pJPwjbBxaXxqJNCHfdTD0ne+4Ev8254k629O04E7Zn0g+bTT6Q+renxCM+F0/gwbzvIR+wXU7PkSOUTdvsAE7tLQNnwCdjKKgdPVUmxndl2j5jielvjMGulnk1S2Sp+Cs6thEIZNsIBALRkUr3IlO7fgef2Kfu3vxPXb3QWYj2bCYBw67mcXAqayzdiwtvHSmZbY/k7vLB1V2wbSXT49gsC9tuMaxxKlPF3WFlrg9vU6PNnUr4K4nFnpmdjVQ7PJ9Q2T8QcZ5t74bX4OtxdTjy9rAShOLMTJNBTKUrp6pSfzYiKb81HmOVkWboaZe+0AZshyjVXgkPQrjKmvhTe6rNq/s920jgfDdn9zt5PvSJ9v2OEDq2EQhk2wgE4gZiceE9GZjrKiLjI0FFbmZRlgrps65eOLew17OZpFXy4JL4G2F7zVp7IEH724/tsyrb9i93uEc+/g+I5ljuCTzBL5aqJtC2G50k2dt5YUdisVWzA3a96bu/kvSK4m+v7JT+vrTZGekMqPzbEgHdd9PJZ3jfNgnmUHFxYp6m75kyj3UXJCT1r6yklGwF1B5yY+5hQ8B2+AkI2+AGaTWw2Bz5x9lIAt2W2vQQs01ubsi540rR5mUI20euAv5pQiCQbSMQiJsDGhtsCvb9tvilLxWamh07REn8bSz3tDczSRtB/ciZZSrpHv94IklNxvsuEC/9+z6xU7ePksq36bTG8quajG2C4dwAROadQSaJglTEvxp7xlPtV96H5VRUotqr4luV+vtE3qsNpmTgf1Ppu3z9e7ZUtya/gW95YVvmozYHpP5BFjX0tB9azAbhHvf8HypBOhy5Muc4cGQYU9dh22lxC/p6IPKPbt8RO9ftrUNwWRVOhzHb0u8IyWnpdloAhW0EAtk2AoH4dLGWom7ajbmja3fwzenXl20meZE+o/7s+W+bftQieDtF7R+v3NFu2fZylu5uw2XRFbZHFINHnVUzHlhdfLr2UvGnE6sFdrrteURUbemFu4wT78tfBIDjAmfdCVERd9fmbAjtzj2UVikQRWLJPGngo6FcjTbA+woh6ujghG15McA/fxWoduhzUc5S9LcpbAfPuma9wQvDVUu0G7Yd6CpmG/xC3d5OQWEbgUC2jUAgPmEzSek3kvV24C1dU2G+hyC2ns0k7e0rusHby/MLz+bljpt1t7dbtg3xfy8ktt2UsZ3Mb5MHIVav4oWn3XbNPjm2h1Sq7XoCVJsXtpMJB9hI6qYw43w5YKLSdni27S7GrQnVw80L2+XCz0X/JO1p98yowjNbDOxaJ81Fj/p8dTjyM1HpuKm31neH8ndgp+lGNZ1jq10Iw7tesauYbTn4zy4vG6R/+ztVfGqdRWEbgUC2jUAgbjJ2AyvETLJcfHe9jpxYhKUjl9YMV2MmgQ7OjYTOUJ1u8PZi/tiUkdl22Wfrmm276PijN/6Ucc27zVovKKM8Ld6zu3WptuxOsXpAC9/3lZttJJuze1RjNrsbZy4HhnN3RzN3dB3bqejIGeP9i8IBXRFBT7vJZ2jUpFeZXXs1fJeJzfqhIlbRCscwXdK+Zaf2TLf1NCI3VdlhAAjc1ujWudS5sC0tAPDPEQKBbBuBQNxAQIjE1uzO9TpyyFTpYWQNzCTQgNiRtaNth6Vu8Pbiwk/euS+kHRX9U0BnD90daeoQ/zemjCTKnmnxO/Kj8YIBRiHrDWxbNpyUvNOwrx3XCE+1Ra473Tr3GF57ZlSF8LqZfp0OPYNtyskqwiNZ2M7qCNtyIp53Bog1rH/qRi/0tPMl7Svhh5RqK8ORCmlOsJdr2TbnYxnQuaWgnkIXcZDdysmk2KjvEw7QZInCNgKBbBuBQNxknNrniH/geh12e6W5QzOJr5jtTokk44bRTX0ruV7wdjR5T/Y/hJ4BnT1xpjvZ16YS/3dgcTNpeVDjISGoGyl7lui4K/Z10afS+n3HWCV8n6faOzYfCNuuQlRTZ8OHAEqkebQpvVvXsX3kyoYi39TYYcAQ4ZHFCnU247kh9Y0TjyGHZDEVYaSZHsDrcIMR38C4/nBuoNlDwvFm/7bYqQufM290RNDLwo/k+vQ9vQeFbQQC2TYCgfgk8CqyoRts9/GDJBi+Cq93+0Iwk0wIo33co27wdio1Le0oGhnulm3v2OVZxi0H9VHE8jTjeUhoYMDA2Bx5vxCYhL0cOKbktnZOUeaFbcvc/e3IS5CT1UoaE6RlzxCOC2QXHNt8YPZaWh60JbngdCnCLo5aZ+N/2OghocORq8pwZBsbybPimOpEz8V1vPvepVaKeNslU6CrmG1SpaQ7HXsRcN06KGwjEMi2EQjEjQZ0dryMbl2vIz92ZZs7ZS7VTNI+CEU3eHshLceSOJOPumXbx2bZe73qNvFBHJO5MY3eDDx11zevmlXs0zKjDQ020HcmbFfNvhHhbgPJbmLbU+m7w7nBYZb01yBsJx8x0fd76ZmxiIHfArnVcGahfTpnZidf0r4y5+ACtulK4GVoE14Oz0yLVfU083daLH66tpFIDLt5X22wWPqNBP9JH7Y+C9tGFLYRCGTbCATik8GxI91bvseHhcR6Ze5oS/Tw2t7MJOfy++bg7aXcxoiGbTvOZ9uVeIkJwxMgbAsyAR3QZ9scTqwWQmeXUw1vKHhOQpEhXynHvwSMHNTOYZcbbaby47AXLopkgDNCBHeM9n2bTKxPWfYICSOHfQWTDSmBq9AcGRvjtkP3XreEObs2O9PG2sjGM2JKfOK0Y47bXcw2hKz3YFhqt2xzYMY2AoFsG4FAfEoAC0Ql98N1POxuY0l6NpMcuYX2/SbP0zWqH9uTVBwtVGcyX/Fsu5NVzZqLhorMRkaBbmo8JLps+8Rio+Jx6LMG1uhJgLA9nLtdZmmGmslICP6bEIb53YGwPZ2+TzZYs8q27/nQGF1dOGzKhKgcsw097WcWZwPVDt9lHpJbjRI1SwhhcS5qbaSgb9eWlfXEaQ+EFWwk5c4+6mR9VbNEeviMobCNQCDbRiAQCEZc8j+RObBXkY1rdNgQqCJ9cTVmkk5Cvs8sNCe7LJDKyd+cqdtds20nDSRxzD2cKdqkR6aF6Waq/dxr4VnymdlZCn9OGW2kQVSumVRhezT36I1JpddykjSnbR+b3RIdnxcPYS+2xGNNeeSRwkEz4YmqImnv25z7rhhZh5SzEGXtcxct6meMDUeuBm+V03N6EjUlza5cHFRtq2htSVhNQaaIhzp8+/a9C61yBvXvMLA1SQ+zAW1QY58QFLYRCGTbCATiEwJRba+XmaR9JN9lmEl2gqug4LZ8Tug5y9VeIo+E5x51y7ZXlaTqmsk7rAjbAm9i5nBkd3BuEPdc7E9LoT/RuA9hS0fYNnmlV4XFUp13WstZiirbToeeufIxQ+4e7AUytoljm0xGKpOURvJFJD5G50ejm9DTfmJrIMpQ0l4JNjhDXofLmt71TjwkDSp1xzYSiF7pMGabWJWkV5WFH1HYRiCQbeNVQCAQF4JEWC+iE38oSMRXTpUOrvbw2h7MJKRSp/2A3dLCezoraaYu5ETKxLPtM/v5juE1JWx73+o0FWYUAjrYTLXHM/fqKtX2ZmJfPsvdbo774IXtom9qqNFGQlwxSpckDf6bEOTammG2F3PuGR9FUi78TGhuNGIgqX8bzv+fvTd9buNcszz/xYob05+qY7qib03NTHR1T0z1RNdUT1RMl31v+YqyFq9XpBZLlrARC4l9IcEVC0EmSZCUKIqkxEXigoVu6dpqy3LYM5l4Mx8kcnnzTSCpxToRv3BQYCKRgCjHycPznCdEHvBh1Ed72nsKtrXhSCrYNivgpj/XeV0hqU0xkrZPtI1kQ3rlaj5ypfaa3UL0N4Zrx0kIiW0AoLYBAB8kq7VXTPf0p1zfriXPCVJ7GyYRTIqfRGa0He9K7mKxVNKr7UZogv8qheq6rLPlI2XNbZch6SS2E11jO6j0bU8U/qjNIHZ3Lm7mN6lCWzGqq1fb3SSDenP1NKTGxOtj90q1lr8aIMlLie3Q9BeSbqJxpxPUlgV3ZvJbGiWkdTbPwj1LHzeshiMNRu+m9PLuEr3ToXRtiedSB7UkjF+0jUTXuJd09Xf9YLbpqbGdgrENANQ2AOADhS3Mk7XgsvTr+3LNItEOD8MkJCtZ84YdtPz8KKqcdqWynyp+1vW2g3n+qwxXhlqdPPTqmK8ifW8ltYe2R3sS2y3/uPz4aob845/obKfBuN7Ynqg+IsF3OKYIvnrl+1Mt/jE+o+h7mo8cn/ZRYlsRrNpemHx+WKtMuUOJi6Z/nDS3/u2sZS9qjvvH9rGKlP51RypXBTWrmxiJtl8z4xzClv8JKHH2Tqjdw38O6NgGAGobAPBBQ7vQvTXzzveaxeSvV2ES+ZMRXHTCQghn/tSK9Mvy4suJwtWuMtatdjdTkv67f1aV5puR4LWKKUNSurDY3QOv2tvtcOTe3CdqMDp32c7YNrSRsDjyUkJdur4dVsYxg7VRMrYpsX139srT5LI6MhgNPwvFlAB6ID47rzjQbB0pXczOuC6xPbdGBdsbs8aV9bSw/cyflO8xNKl9gf/Z7kjPXbnUkvuabfqL3i3sILENAIDaBgB4Ay3yOEgsvy/XTInhvu8QXIVJxFtQ2C8KSK7N5b/qqu1AlvPEa+VLeW1fzFzqttnYThb+TFKb/Nq5sfBcXp2PXJ2T7IztG+XP28GsPoNRk74/GI2p4jsRm19qjWgG853yl5TYflC8T9c/n76lzVPeYD82ba0ehE12WmZIDMORBvX5dDROb3BWctiy1NQ6s8VXSFLN9lkwK3L80057ify+VmqvkdgGAEBtAwA8Qy1h8Ke9bRc+1zuEAT1IV2ES2ru5NX3kcBtQeq6ltJWZyGLxG325NS9GUr5QTd5mR/pnPzNI7Vtzl6hfbyvjp3PK31rPfGyo2a6XmvoDFCFb3e9mMGZa8jGBGTUTchIYW628+Ea30Ua/PJLOM5P/lrX+7Y9Gq1Jbfwei3Yd0DeyVwh3z5sgeEawtgwyU1ZLBW7WbzpkQ7VkH8RW3aWmRmm0qxLRbGgpjGwCobQAA6BP6Bbqjmnx34G9T9zZM4qpzUF+8PT87pqvqsx3su7Wg2MkrnWa9lj9ukNojpYsktR/Fv1qWfqZzjk2rMZL1wq2uQRtUTeutSJCdYX9yTx+oSNekrWhIvSvwJeILc/Ra1+cvkrFNzdatQHZNa/3L50dUvz+3oDO2x6yN7cLXFqJ27ozejvpaThmSHuksbGx3a7bFYiRPspuCeSFxTrXIexvGNgBQ2wCADzpMIv3aCE143np2rrDMxnGs1PcZXIVJ1Oxy/pHjkfri7cX5pa4B7LfVW9c62eXtSIjWp+t56lPV8+Ox4frEst4bXsqqbdarlafsVOvS9/SKt8qKS32z/FW7N4Nxs3Kp3SkHlPXudmR0WNczSIntUW2+s+1L5ibvsNa/h9Hu2pr1eMDS2F5Lf8IZjlTuRvw5qi807ITn0PIn3eYxqGTwJDQldP/WmY8c5CeK4+I/mjzE/2cAgNoGAHzQsJYPbxOr5wrrwB4k/TKzeCQeJlHXuAgsF9QXb68snHS9bRuZ+E3HYpeh+j/9ZGQpo6al92PDj6OjPeEEf1K1kLMXzMb2bjjMTrKaq9A17OR271a+remmLe/NXNG93JB5M/x28fGjzm2ALLhTk7fZq9yu3Gx3u1Z0e9rn1ta1BfJ16dhGgKoieGtcaT6JlSfEZKv6rFZEqIhGX7Mtf+14/OaM+je1VfRMFnerSPwwtgGA2gYAfPDQ3GF/CxrfPFSlslb5vu+TfFO5JhgmcRVc0RVvn+jDzZalcmyH4o35S6z+bzl+jyRvbOIL1SYP32r5xx6MBfVqu+1LMLVdn8qZU8K35q4wva5bGJ6ckpS0OhX/LcXv6E30G/PqF3dnrqgJ6fGlfF6d3ax1Wv9kytKLB4mRrrEtnegyJKrXvp695Gj3dsY3L7tMP6fOSekexcqsN8bD0QX6iGBsAwC1DQAAnYxpp9DN29+knx/ik4scxhdmBcMk7MMRrG3RF2/r1ba5r/Ce1rsXK36p1oyk1SWON+c+VYuiIzfbHSG+Fk8YZFxH2n5MNdt7owmt6yOq6ua5L3TB8eT18uV8bpgiIiPlIX2ZN33x2DfaMa0nqrU91vrX8MeLs3PsVYYrF9ta699pMNI19bXhSPPmSHNom6lt0Yx+ME92vrDSTYnXbK9rs60iv7sQpFtFAmMbAKhtAADQZzP6LrF+01lzrYFOJEs9eJjE7fZKKt7uLe5oWxrbMpO5G+yYTP7PrCGb/fFJPPwopk5Prla/61xJnuRyp2b7C92LqiL4G63VZCcc6Wr9hDKVSMV/Cx1je4RC26ULWhXJFZZ7qUs/l1Nqp8pMRu0M8VVHn0S7RSs6J/jFutZDIstuWw1KTeH++KzUEE4/u4uR1MsvXKlztZNHfsudJnIktgEAUNsAgPN1i71d7XF+HMdKg092CoZJDhLKkpfT8LTgaal4W8/mzIn+mKg03V3ZmFCz1DfmLykTk6NKVPowFlqWfn4aVIpNHkfCmtGrdUj7lSTJckWtqX4SUxu1T/xxtp1R6RXpzmgmZWX/7ewlSmxbroVXn+JP1ivPczMR1vr3LBSr1vbU24PKUEvb097WdWxvZP5Va/372O4zSdeWqMWvlLrtJpKRcrXUph3IaWUpzgveV2s/tQNZb0eEu+kdGNsAQG0DAIBZv4pryrcLsyQbo8VBTiIYJnH7WlS8rWe78ER/zLAuyLE+prRoHwfG5K8Xk3cV8RcZXV58WZ+oqr64FtrWLXmJr6f/0FV4mpBNTd1g5wxMX9XNaCryejMW0BZDKs0n385eNsxHhovyU1IPZxXXeWVcXTaZnVRvRW5UP3sWpgnLbop9Y7ZKBdt2w5EdpX7BbSBEeV+Rglvl6iowLd9ber5LtY3ENgBQ2wAAYMn25L7nlcPnB+mkQSbbBMMkj7MP+Z3ZFmnj0QmD2tbHglPlBb2pvBtRotLyfyMzStL6aTQiLf4oH/ZgTNXHsuxWlVwwowroYHKtcFe9vJgqSZv+BIn4nXCYMhjLyds3yhcpanKnfPn23CV9CQnjsW90P/NAPmFyKmho/ZusbipyWTuDfqCWCrY5w5EjC5e2tNJuV44vRTIawbzI8XuZh5S0Eb/DbISnLGdYkdgGAGobnwIAwEvqCz+ylmIPx8XOD1rK47hQffAwSR/KfrvwxKC29UOWw9UepXscUOIiGx2H+2kkvLyoRucfR8LKI8ExXThbq97zJVal7zVjWxXB9fSEuhNH1+LHRhKp+O/EP65MKI7fMSS2785cOYmoo5APogFD699w5cJpMGiOoa8VbjoOR4akkHx+NuvZuSXIuVfbKbfqvDk66fwrCK2Kx8P0FOVeYGwDALUNAAC2geNGaMIrq+8c7w2qLz1pLRQJk2wVD/sYIaW1MmpFyViZPV6SnvZW76lx6ofRwMloZHlR1dCr1e9YLeCjaMjK6x1nj+zEJ7SYcuJG+St2zsjUF01dwlt+pKEV/41PfJHLX2MBcX0nyWO/WjCSLVxnR67E76k3JOVrvvJn+veiNZ2/2EirrX/STMI2Q9Lb4V2fOxP8AE/Dc253MXZfRWBb+0FyxduaeSS2AYDaBgAAB6jH2sMY6/nRDBYG2d8uHibpr977ODqvV6jHkUktsX1Rr3Qz+T+r22diIXK1Fds4rjrWG2Mhcyb4aExV242A6hnvpKfpnEehqD5GMj7xlabIk9/OfXYaiA/3ZkiCU1fqlefy2apS+3A0ylr/Ch0BXZZeyAc3/Vq9SecGgDVFrmtSeyNjOxw5XO3dmOMqtK29haZPKMNzEp4Rf5XV2itWHTPgzw8S2wBAbQMAgAuoWc9DCXJ+sGK+wTvCWZjkXvWu3QH359p9pFbuz7b0arsRTOu9XqKkFe1tJnsM0QexIOv+q8/smb1b9sfN/KamR+NV6aUWIxli61ooRnIY0or/knfuj49mC18bqki287vshPOZm+oqHK3171p5aKp4Rb+Cnv141Genu1K7Zp3EuFW7yV6iz9A2bcGUnguJXc34P4wvOh7MsvgeTikgsQ0A1DYAAAjB2u683at3TtDC+QHPQ2GS8tJzfsDXreVPKV6KfY9UPzOo7Xonri2zWuo5uaH7T7FjZ6Zps4yq8LQw9JNYlt0zsBjJyfgcuby0G1L+Wn6nbV/qmtHY/oydbXZ+6bTjlB9qrX/+qrJfvaWlUFqBGG04px6Stexnlu99Vtqml+gjtH0QXzHcWjgki9zUbC8v/sxSHx6uc8LySACgtgEAQAiaPhxkTeObgUpUBtzIQ2GSVK3Kz4jLKtNltCBlWCdpiHDcKA09ioQMo5DKK2rdf7SwXUluZP5EuW35jytzO1qKWol8qPOOpQuP42U6bGPM/3RU1cp70fHa0qv70YDB2KbzLybuaK1/mrFduVBNfEY1go+LG6oNnxomtc2Paytee6Ub2t6UXrr+6MTCJ1Sz3RaInTwqHvT3F2obanK5Kx4AALUNAPigwyTM9hNcVP4WoYDH4DcG/DDJau1VfxOZhkHJzJTPMJt44IuwmMdueLTHTR8LqUlxbWH7qvS9LG31AvRYS2zvR+IT1UcUI6FFRTJ3K9fp64UlpQz7xvzFXmP7c3Z++doMrX/XOvnylmZLH4dGWe+4LCg30n9Uje3piOUbH1nQpjArF5bm9rRr6Kf7TzBGQm9TZFu7t9PAG9IrLI8EAGobAAD6SWh41dVwfjcGapX1APvbBcMk/b2QQW3nCsN6pXsYjtQX1eKRLV3xiGK+RkP6he2KsV3wk9pu+5JkbJ/5FDl+u/QFO2dobmQ7kSVRvh/NaCGQJOWwLY1tWWSz1j9Zdst/jCxllK2W8etkbEtKSci00iEYCmitf9bDkbHyBL1ESAo1/ZrrLDwiWS+euhLoVLMtEjuR70bUpsuBf2y0vHhKe4MwtgGA2gYAABGvTospD1iu9wZojBY9seEdwySs/0S+D+lDba//8avkv/+/Av/T3wX+/d/f/Pqf2Avlc8OrlfbDrDpa9zDSTYysSv/jqBPa3tEZ3huZj7veti91FFTzIYfhsWXpV1pqs1j777SDZismn0T9+kFnb3xw6itLYzufH2GHLSW+1edAaE/7cThAE7T7kRv8zZHdDMnCpY5LrYrR47hoSLoVSGk3CUL6lazllkAufC+9wVoF6ws/emNs0zTnxDb+7wEA1DYAALhQsUdjlXf8OtVIwGD72xm3Kzc4YRJV1rusapHVdvkfP7n3V7/X882//Oc701fqHdN6ayyqKuOxaNd8NXX/rc5JisDVVku2uw0hipVbmShRNOVu5VuSngtJv94hvj93NmyV2K7W9ljr33FgPDuvrGy8Xv1SPoD2tLd9cX1u5/HYl4rULoxYZ0g03T9cudArhVNuPjct6V48dRUj2Zg/cfh9iDYf6VVQioxtJLYBgNoGAAAXqLvKPfL/zvE6848G39/OSNRKnDAJW/Eti3t3l/fFPd/v/tagtmUq0RU1vxFRFzQ+zE7Rsx6MBQ0L2zdyV2SB2w5T6bUqx49Gx1dqr/0zas/JnfLNaoIkstKurTaWZDbq0s/3o0FLY3s+fYsdNtNp/SvWdrSGbPVV9tI5/Uf9MPmnjYz1cOTt6tf6DImSCZk7cxva1sVIhJInrmq2tyf3PFlBaja2HYU+AABqGwAAerKtni+1Pg9o78zgrcmlpTYnTMK6vd2a/dX/etEstWUyF9SIyJaWz+7R6MaF7T+x5EbLP06ykunpzfzm49wWbV+/Xr5MFYGHgYheg7aC2etWxnZ2Ps9a/56EI1VJEaDDFeWAZ8GAluVQ1+g8TZQ6lxqTr2R5xiKwMSs1ul0r5ctqAsd9aLsdzGhXLtTtSDXbjciUg7Et/doIK9Gd0/C0x8a2L4n/aQAAtQ0AAO5gbq5XuuS87wo8qXLjhElYDbnbT6P0fw9Zqu30H3zsgN3wKOu37oa2TQvb14pxprabmpJWje3QWL368kEsTMkNxZDWpOeJJs0bo8XT6OyBLzpsZWyvxO+pfSl5JRlyt3JH/q6yp107z3ZKlbCNQFQdkbQxtq9VuufXJapdh7appFwkRqJ3lx0P3pw58XAgAcY2AFDbAAAwELuFnT7Wlb9haHTP7fyi2zAJK79zGxC//+lNS7U9Let5Zhh3piEfRUKc0PZ65hOmtkkBM1bmduSrCkxfpdC2PkZCh+13+jruzV01G9up4j3W+vcgGpA6S9rZAY1AWFXqgbCm+NWek/3odcvNkTfKl7XKv6FZqWFKVPezQlLkYF3RtfPxB8kVtrnJk7IdnbGdxv8uAIDaBgAA19QXflSL0rIP3+XrZLV0ngx0csIkapDd5d5KWdhl/pd/NEjt6H/8k7SopMxXS+oioU1d/d9Dw8L2SoNJ7cXsx2c6b/s0OL5eeq4shix1HeWm5mc3SW0H1ApC/WHBqas9ORZfIjmlNKKMVJQBx1TxS3qV+5Jq2W7Hr6qh7VTA/DbTtSU6+a1qd3ryOF5yO0HYDqRcJU/awjXbq7VXHt6YwdgGAGobAAA8gIWVPWn8OD+Y69wMFjw5m12YhJx+V+OYstpu3hmf/j/+G81Kxv/p69qc+ruCTaWhj6ntbv2fYWH7ekGt24tM/6ve297Mb8p/Ow96Bx91cjyhmdyKeC1lbuqN7TN/RtK1/pWTt+U/Bmrjauuftqf9ZFQNl69lL+5Hr3PWdg5XLAq89Ylq8YXtZBifxGuOBz+aPBQ3wmnK05Pf1cDYBgBqGwAAPIBWow8+g/gGEi8D7m/nh0m2iod9vAorm1Os6NuxZV9lYbrnY3wYUxdGyrJb/X2CaWE7q9leS38kXxKJ19NAYnNGKe64O3uZZLR/6ktyhSm/wb4Y1m20CU5dOQ7PVqU2a/07DcRn55c6qWvlGNrTfuZPaNf5YiP98XHI15mYTFtJ7e7J9RmSngT23JnLGIlYzba2PKgdyDkmjlhjuie/A9Eb21geCQDUNgAA9M9K7bWHv3w/Jx7MqnkM+YvzC5NQ+YkrZ5TUtuUcJ6ntjYklVcb1LmxfnZlmxvbUxB861rV6qt30NMvP6BdDngRUp7zl64l3H/hiPca2L7ldfDKfucm+O5W5oShmLXVNe9qPgmq4RZba8gWwEclnY8aATUgK0ckDUtykhlOCiWr14/LlXKptTc2XXzi44MUD9W9h+gjGNgAAahsA8C7lNDpdHLJqHLzQ+rzy5dWXXu1vZ9ypfCNrx7vVO/oHabeLq55mvdo2dyk+ioQ6O2XGuo/0LmzfyFxkavvW/CfrYz46Ffudw2rirk2MpIcbpa7/PT7xZ/mR2fkl1vp3OBqtSu1MeZnViexEb2rueELz2pV18Q+Tf7L7hId710bq6Se0rannVsQ5F3QYXxRPeLOCnUZoYln61UNj21HlAwCgtgEAwAHydD1xjs8JZsB7tR0wVasxBTm/1M1F0DZ7V5+DXm2bpepeZ1uN/F/2R1lhMxHMuv9Wpe+Z1F7NftxxnRNUviGrRvmLb0qfkoyOT//ZUmqfRObI/75eGqL17OyL7CQzttUD2tqe9oNYQDO2lQvYGVdHJDdneir5rnUzJENWfymuQ9uuvHCS5ifhGQd9XH6hzvt6cT9GxnbLD2MbAKhtAAAYmOWlX7zddH0esGlOr6rBS0vt4fJFQ5iEHHRXxd58ta32amv1f4buv/WCn4ndwsSf5rK3zUq66yuXPz0OjpkPaPmz4YV4V5FPfC0/mMtd17f+3V64xr7bXWfjV9fZrGcvsQugEcnV2iu6+JHKVS2dciFdW+LFPMRC22eBnPjIo6uabTZH68li1J4qEukV/v8AANQ2AAB4wF56nZXfedJSfK5X6FXc5W71jqGZRBaafSxG4ajtjYkltVMvpqptw8L29cwfmdgdUQqw4wYlPZu/p/Vby3r3YtvK2K5LP5PUvl5SY9/61j9a0t5Zi5PQr7OpS8dsQFPmaayoxDDC3U2N6WrFsvKve3OiLWxvCy9Z7MZIfM5euHjNNhVZHiYkJLYBAFDbAIB3EYose7Kv8TzwvDuFwiSlpe90Zq3rdHiP2u6tLd+MxgxqW7+wfbmyzZRuPfPJXOaGWUmPdI3toW4bifZd+YuHs42KbpX62JTiT+cLIxQm0WdIGsGwYU87Se2N9IVmMG9Qq8PdtZFD1mq4j4XtbpbaiNdsU/Hfw+ljGNsAAKhtAMA7GSaRfmVB4WfjCx/I/YAsspmaTNRKhnS4q10/erV9kKrrv/UwEtTX/xkWtq/nvmRit5T56jRoNLanCqP6+UhzjORR8ZkS9ihdVI8pDR2HZ/d8YX3rH1vSrlQHlj/rzl92jO2Vwh326uuy3NciNNuFJ+zKry9csav8696ZuFzY3ugIesGRSvGabfrRPYnMDD4ficQ2AFDbAABwXqibFL1Ivp4HFPPwcO0lC5PcqdzsKsLRolk0OwQe7NU2S3QoYeiSMna5nlQl9cZ4WJJ+Yr178n/LyW5PCJ1K1+jHQiC9ce1AVnWgtcOi09fP/Bl9619ZejHc3UAZMexp38gwV/ujtenIw+ljfRkLv/JPp7bdLWxvUUIj4BwjEa/ZphlfV/kfGNsAAKhtAMCbhho5Blct5wSTwp7sLtHCJFWmKavSX9gjrEXOlcHfCE/Zqe3d8Ki+/k/f/bdWjKvWcn6EjO0mrYcMJvXGtq/ytcHYZie8NXeJjG0lvhz161v/qE6kmvicAtBsT/tG+oKaIcl8TDdatERT99IXbaPSWmhbXG2r6lwsdiJes83GZz0ZOSBju43ENgBQ2wAAcB6wdSqy4nw3L08WwR7ub5eRRbahmYRJN1eCnqO2DRva9X9cz3zC9O5mfNxsbDf84z0xkkBaL7WbwTw7IRX/3Zn7TP6jvvUvuNANotCednWdzdyaFtf+SP6a3vVJZEZ/zmuVCzxH32Vo+2miLDjyKLmp2V6rfG/5ycPYBgBAbQMA3kVoQbqrZYpvOOtiaKkbELbmhsIkbNGPq55BvdrWm+KrpSY1hEiGhe2VhmpsZz45DapS+Gm4G93ejwa7WY7qqMHYXisrdXvBKS1aXVJkcapwU9/6R0/fTtww7Gknqb2RvqBPq8uC9Xb1a3qiZeVf36HtM+2GoSWgtunkJ6Ep/pGsqUbRxwPvoNFVkSTxvwIAoLYBAOBc8Hxlo7dsFQ/72PXIR9aU+jU3rLa5MVrsT23rTfGHSdX93YoFpd6F7euFG0zvPix2Zx9PQ3n6Op4bUdtIKpfuFx/rpXY7kDEktq+XPqXUiiy487mR6xU1YTJcuUCW+dNRv/JXXBih4Uj1L73yg3phxfsktWXZLRQLEY+RuNvWLuQxr9ResxHVo1jZO2M7heWRAEBtAwDAOcKCy14tkfEWig2YF6T3TWnpO32YRB0VddNHYau2Y6q83hqLGkLb2nzkR0+DakXgs84XbdOI5MziI0owq1Uk00cdY/tzOqYiNfL5buvfRixA33oW8hn2tOuHI9kj25N77JjQzOfaXOaQQyxkfNnVwvadifviavskPKOFpx0OplLIralnSGwDAKC2AQDvB57XWnvI8tIvlHnw8LT3qr5OmOQbSdfcPLja3oqp9X8Pkzn9wvbVOYnp3Ue57ib2Zm/liKZ6P20Fsr0xkpRmbKvp6uHSULW29ywUY61/9aifpPZI5TKts2F72mmZjqy5dWGMDfbcEe2c/AyJ5H5heyuQ0sZA0wLCVz35YXxRZMygEZpYXvwZiW0AANQ2AOD9YKX2minavfT6O3h55zHHSc0kpaU23WyIR8P1alt/YV0zu9TUL2zfyF2xNLYXknfaWicJu56HsXFDYvsguWYwtpPSUjl5m1r//FNX6Vsno6P6Pe312WnDcKT624zIrJL21hzxW7WbAkkPdwvbaYXkjvTcIctUfiHoglP/+uC/6ICxDQDUNgAAvFFY9YdiGQ68K8RzWK667enmkfLScyY0xxdmqbxZfE5Ur7b1ge+9sFJxfRiKST0L22epeq9rbPsU23u4PEQdeWz20bxd0mBsy8dk5/MNv9r6t+27R2HusVrQsM6GMiQb2Ss9vy7o2Ofz6Vv8yj8LtS3WRiIrbPEF7+1ATnBrOptnHbweXm9sI7ENANQ2AAC8CUhxPphtvmvXRq0p9epLT8Mkd1kzCTmm4oOYdmr7OKBMQO6GRyXdwvb1gp9J3r1QVB2O7JRtB2YUT1pTpQn563szVw1SuxmeNBjbNypX6+NqMjtfGKHyvpHKUCNAe9oVud8djtRlSPQOcbYwLCt1oWR/vOQqtE17edoCoe1uPH2StzFUVtjML38aX/LK2D6DsQ0A1DYAALyxeDSrejhILL9r19bd394ZFvQKaiZZnN9ze6dhqbY3JpbUne3RkH5hOyWnqSrkNBB/MKaGralkWv56Jxxm/nG3+E963ZPYLl9ITH7DWv+2oqHkRLe872Gqu96ys87mBc1l0nAkYyavVnSHpj9PVyti8lRT/wGh0DaNftaLp/wj9zIPBV1witcPeEOoryLZmD/Bv30AoLYBAOBNBzbYcsF3h+7+dk87CstLz9Vmksq0quaLh4Oo7c3YmKa2gxvjqs28lgxoxnZEb2yPlIb0altW2CPlISa7DcV/emP7WuXiTkRt/ctM3iVj+5vyNVpnw/a0r2tSW/5Cf+WzUmNl/C5T/DcWLgu+Xy20LWRsywpbvI2Ebi2ao5O8u0Hp10ZoQknJR2Y9S2z7U/hXDwDUNgAAvDkezDbPw0L2hGawcB6+O2smuV25ztxi8fX1erVNey6p/k+W3TQuWc98YTa2UxN/Zio5nhuhNTSB6assqkFn3po+7Elsly/Epr6k1r/rpU9phftuOt5jbM8kusORUk8uWRbojzsO+oNOY8l5qO12MKO9qbTAmYWKQSjptD25B2MbAAC1DQB4P8Mkmn2oX474jsDWjHveCE5hkied6UZx71yvtmmQkdT2xsQSLWzfyHzcMbbDJLUPglFSz/vRoOazxo9C0YezjXZv8V9w7suusV26QK1/qfkxelxKxUnKsz3t61bDkYrUrly4OX+R3VqsZUU/zGbA3cL2bnuJU4xEvGab/QC0/emV2mtvEtvBLP7JAwC1DQAAbxq2E3twTeM5tIDG25QLNZNUUncUcZx9OIjafhRR1PZxYGwtr4rI++N3mOpt+lXv+SQY/2b2Egnlpl+Nf7SDyf3k6klkulssOK5s3hkudY1t3/QV9q35zM3hivrgnfnLB5F7+j3tssK2HI4MSHFZbUeLqju+OXMqamy7WdhOMRKhNhKxmu2N+e/YYXvZ+zC2AQBQ2wCA9xiSNeKZijcD7W/3fP8OayYJzHzhaoGOpdpm9X/yf2lh+2ryc1n1boe/1TqwE5XUbVLPt8qfyiJbWxkzrte1rGgvNP0FHTxcvnDgj7LWvzvVL2kBZEfXxmlPe106Zm66TH12ujdDojxlOnODHbxe/ouw2naxgL3pSwsa4eI122yiwFVFo7W4D6SR2AYAQG0DAN4y57FKxsPbAPFBRkFStRqToY/DYfEIzUlkxqy2qYRkuzPIeBwYZ60gLS3m8SwQ04ewjwLxtuZ5t8Pj+hDzSXTekNi+Xrqo1vZNXKeC7ZnsN8fBgH5POxVsG4Yj6VSsPbAVzAkWq9fnzsRHHvV29UF8xUn7CtVs0+ol/c7O/qCIC5ZHAgC1jU8BAPD2Mhtaz5q35daDZsq1/e3iYQ9BqtIPrJlkLn1LXM+xGDGxWntF9X9bsWCjI683oz6DsR2c+ozU86PY6JFSv63lrcfyR7F5ffGf3tiWWU58y1r/SDePzA+x9Eh3T3t3OPJj/XDknYVrdJ5no0lX0fymXwttiwWdxZfgCNZsU9v61tSzgaR2MAtjGwAAtQ0AePusVb73ajn2eZjugxucZu5W73Q2y1wRn8I0qG35zmQzqq5kp1nJeuKa3tjeCgdJ8hYy15XtNtNXqf9Orz6ZHLymM7ZHOkWBLV9ifHKYgiV7vohunY1ija9rPSTr2S/pUmelbTrPt+XP3HYpUrhFZGF7y58UNMIFa7aVyd1OaKcxWhxky6l+eSSMbQAA1DYA4C1zHCudRwHIgLCt3VS35yHZ2ioTo7uxVN9q+2EkSN62+mD6wk7sjhbLjt8oXVS7sefUTMi10oW2ldp+NH3UU0VSvpAofs1a/+iR0ZmbR76wfk/7RvqCth/+o964Nqn2oQdzLbcLYmixvFCMRItqnIbnxLLgDjXb1Eq5m98eLEOCxDYAAGobAPDOsD25f04jiZ5EXFZrHnuTpaXvWDyjmL0xgLetWtrbUaX7b380IgtftsimY2yPdntIAorbvRMOd1fb+FKnowW9K6xPbA+XLxz5xo4D48E5LVtSGWqEp5vBiLbOJirNrVHBtn44cqRM/SdD6WrlSXaTPUWwc6Yb2hZc2C4cI+kumyy/4Bz2bHyB9QPWF34cyNj2w9gGAEBtAwDeGWRlw0xKz0PSg0A25/25tucnD83dlCXp3dnLS9Ivfajttcr3u2FlMvJpcIy1Wd8fv7M99hXNL5J0PhhVJfK9masdtc1yJql2N0aSuTf7md7Y9s0oxX+l9Df0SFl6cRK4111nM1elHpL1zB/pIpXKP+o/qY7IjzyNL7IRSWFjO615885PaQTz+kIVDlSzfcaNkdQrP7CfwwG3GqGKBAAAtQ0AeOdgnmIjNDFIWNbje4Dqy/NrJ5yYzTJVOruw24falm8ADjt7Z5jmVgz41GdkbD+IqInt0NTnFFa+VtKtbdfxQPofemNbZicclk8emL5CS9olrf+E7WlfKdyx3BxJ55G/UHPVwZyrEcnunhqB0DYlvFuRguCR/JptVrKulBWWnvf/Y9PtGYSxDQCA2gYAvDNQv/V5GMn9m5Qdq1W8FUIQ8osAAE+lSURBVFuc9ZndkY42TZQLfajtB5O7TP6yPEnDH98e+9psbJ/41F0207NF9gilR+hUBmN7uKyEvHOFYS1DckF+9f1EQL+nvdv6pxuOvFa+SLGTWanBfmXhekSyc2Eie2p0ufOUqIjnWuDLS7+we4PjyOxgPzMpGNsAAKhtAMA7B5Uc76XX352rYhr3PLrA5ZuK2KQymHir9KVImMSgtnfH1HY5Niu5HfGfdja3KyOPMTWxvZS4zR7ZT67eKH/eq7aTmlGd10ttNh/5IBqgP05WNzsKUu05OQ0G19Kf6Ixt9fL0lX8BKd7fHdRxvCS+sL0VKQgmvJmG7oj4tMj93vbk3mDGNjq2AQBQ2wCAdxLWAaKsQfF0Wfog0GJ5z8+8Mf/dotb4Mbt44lZtP9bq/7bCire9qQlrmTvly51dlVfVAHRIqeC4VhmyVNvBqc8NavupLxab/ErtAaxcVv5eYgHa0/40qMuQzK11jW3t/CMLl8zBDMG/UOpLEVnYTmOIjWBe8Mi9DG8qgBXjyD9+ggOd1srej8Q2AABqGwDwrkJTiVvTR+9avsXzzTvyCZ+FYixMMr4w61Zt74XVMr4n4WhnVlIV3/uhCCsVOek80pavXPo5Ky0xKXw/HtKfRFa3w71S+97M1WryDv1RVZDanvbjUd9G6iPzcCRJbRY7MVxzY7ToMrTtpo3EJ76tPcm/+VE/z+xm/3dQ6NgGAEBtAwDeZZTFIqEJZZQtIb0jl0QiTLwuWpDVmqLMokUlTPJN5Zpbtb3faRph0e19rZiPjO3p9E1V8xWVxPn1itrKdxIc15/kMDimquSSKq+3wqGA1voXqCn7a06DIW0zZXwzeck8HGmo/NPHoN2m3sXVtniMhBY6trglJ/JFUtlL/4ltVJEAAKC2AQDvOHvpDdZ2PMhv8728AVj6xe2cnzjyaSlMMrN45EptHyo72JN74YjZ2P529hL7YzV5hz1XM7CHmtrKFXU+snxVb2yPlIeo9W+kMqRdpJrYfhq5dT+ttv6tFNQzd9ZGDukr/whaESrY6OIqtE3Gdsvp4G7v9fyJ48zAIHtDUUUCAIDaBgC8B9yfPxt8Us1bGqPFwQuYbWLK6Weh2LBYmMSgtk8Ciqv9KBJqaDEPMra3O52Aj6KhXEGRv4mlslo2UrlEbdZM1KqPl1S5PDb55d25T1kQpSwdKwo4pNYLtgPjG6k/mIcjhyuaUu/NkEjuRyRJFj8dXxY+2MFCPsisiCj43cLO4L/BQGIbAAC1DQB4P2Dq9ihWfkeuh81unsdWefZOR2evi4RJDhMr5qrsh7HQ01G1iuQ4pMRCxopfdJpGlC0z1UxYHyOJLeRJ0Sr3D8GYIUaSnFBb/24vKBdzXzrpHhwYMQ9HXtOkNlX+mbMZ8h2F4Iik+ML20/Cc1jHioGvbmpffiEzx8kvhKeWY8FTfXe+oIgEAQG0DAN4byGisV354F66H7W8XV43iyApePvN8Pi4SJtnXgsV6tqIh6uYbn/jieukiE6y3O1lqKTejj5GsSa/1z/VPKZHx6/Ofslf/duYyxa/ZKzb9US2tMUZSeyOtetj6tZFU+deTDOlUfAiWJ7pa2E7VJU1f2jGr4zgfSbO58g9e/7+m0GR9c3QS/4QBAFDbAIB3Gllkn19Uug+2po8Gn5/jhEMeJ6aHO3th+GESS7W9HVGTHifBcfkMa+O+ti9ZSt1Soh2liyu1l/oYyUl0Xv9c0taq8z2lbo4s1nY6AfocHbk+9qU5Q9JNe+sq//R5d1cjkk1/TnxhO13YjsTb+NiITIkoeLbEVL7avkcFHk0eCi6QBwBAbQMAwDvBSXSO/Wb/nVD/2v72reLhOWVUfNWAY5jEUm03AnEytn2zlxX9nd+9O6vo5ujUl8YYie6JR6OKOr87rfrZw/PqGkj5eC2FrJ75NDC6oQ1HbiWvsO+OaBmSYVNcW83fz7VdfWht4YXt9eKp4ApJinZwtrXLt3bspQdZF0qJ7ZPQFP7xAgCgtgEA7wE7hV01Alt+8S5cD7NpH2cfeqy2OwK6MVrM1lYdwyRmtf1UWx55EhwfLg+dBGPHsfLq7JZ6qtzdjgM9xKYedzObhhjJrfKnI5qx/e38pc5haoZkN92dvHyUvqIZ2x/vJ1eknrWRPZV/erYn9131lIuHtmnQsx3McA7TV1/zYkLa/p2N+e8GSGzD2AYAQG0DAN6vMEn1JXMcPRe4/cEiyM/GFzxOhHeknizlq9JfHMMkZrW9G1ZjJJmJP0uJO61gVj5sIaNWaEu5mdhCnhzrtj9liJFM5IYNU5KsYLvjNGvrbEIhypAcR3z1yvedyr8LlpV/5hsJwR2ch5n7LkLb2luoF0950RRtW7v8hd0xy0u/sKXuguFy/vXA2AYAQG0DAN4nno1VXa0hPFfIhPZYbXfmL5n56hgmMavtrWioE3RO3OwMR9YXfpQPyxVGKLQ9rMVIJuYmelZI+hKBuS++nb2sV9tUsN3d0+5LrGtSezP9r+w69T0kvMw0K5YRq68+0+4EHEPbbmIkmjc/aRtloY7CvjNCMLYBAFDbAID3FVJCff+K30MoF7Fae3VOp3UMk9ip7bnkze3w6H0t7qwPbVOMpB3IGRa2r8Tv0XykVrD9wmBsH0RuMal9P/3RsS8iC8ru2kiryj+9Z+xqzpV6CTclh9hJO5jR1C3PNd+Z2Bap2Wa/smgFc323zVAVCYxtAADUNgDgPYPW+x10ssJvF5r5E1zU4vaOol596RgmYWp75+o3k3//z/H/+f8s/O//dfGLr5r+RGriz/vJVfVDKz2l0LY+RmIcr/QnM7lr+i2SdyvqbsiTUW2djT+xkVGHI7djV+RHEsVr/Mo/88e1NX0kpLaFF7bT4CM/RkKxmSP7iIh8F6feEvSbVkIVCQAAahsA8H7zNL7Uyd3mlxd/frtXslp75WoJuSDU9Mz8e36YRFbby//tiu93f3vvr35P5P/wx5PITPew3AyFtilGIqXGTGo7MaKT2td0vSJkMx+MaetsMh+pUe8SPeUi/311G9MFRiTFF7ZTjKTtc9rWrqVNOItm1GS5L9V3rTsFYGBsAwCgtgEA72eYZOqZGjCYOX3rF9MMFuQr2Uuve3hOslfZwnB+mGTTP+//3d/ppTajnH1Gx8zk7lJom2Ik5t7Atj/emyE5VoWvfk+7ltjeS2c7Uvsiv/Kv75g72dWy7Hb4K/ClRXT5YXzRUZTTb076nnxFYhsAALUNAHjvWV76hWncp/Glt34xbBON4NifqGLrbfLmh0lmPvnWLLVlsp+O0THRznpI+b/+SpiJ49GpryzUdpBW26hL2pWLKT3qtp3EPmNSey17sRUs+GcvOVb+6WEjkgeJZW9jJLRC8iC+ImI578clR/ed3ef0ldhGFQkAAGobAPD+o3bkBbJ97/nz+ErEKu36DqhwwiST/++IpdpOfXRX82tfjnQc6I7Drerjx+GoxU4c/zipbZ15H1H3tAdi+s2RO/5Qd21k+ZL4mxIZkdyUXgp2jOxIz0X6s0VqtpelXxvhKbY+Sf4aiW0AANQ2AODDZb2kaqxHxYO3HGvRTTR6eFqDME3VqkzXlpaM45iVLxOWanvy+pw5tK2K49KnZqmtmL7RIItrxyU17LGdmqJywJ3MRdXYno5IWrGJY+Uf4Wqi9DRcFAxtC8ZImqOT2mEpx7j8bmGnX2MbVSQAAKhtAMBvBWZDehvhGDxj7RWGLZXlpedM2iZqxhDzdu5x4m/+wSC1R3///yxprj+Ftv2VIDtJMXPDUm1vxJUDhqvdYcd2YFy3p50Z23+QH+9W/pV5lX89vwTQSsRFavVoKNMxtE2r3U/DcyIBD07N9rPxBfZriv5+YbIxfwJjGwAAtQ0A+A2FSTT1xra3vMUQ+YCGKC/inKrTI/eqimi+XblhVNuFJ8c3w+m//S8ktaP/4Y+VQsMc2qYYyWkgbqm22XepYHs/0V1ns5Me0jIkL9LVCmVI7s1cEXxHTMuKjkj6k+66/7gCl2q2OSesV35gwl3/mcPYBgBAbQMAPlzWKt8zcbNT2H3LLrtJGQ/OaXjaMFBoFyaR1Tb7HPa+ule6XpyPP5YWu+YxhbYnct+wp0cnv7SU2kxtj1Su6uSjKsqPggFtOPIz+fFhWhtZuiiyU93tp1SfOxNc2N7yJ0Xi3WeaDm6OTtods5deZ8fIP1cwtgEAUNsAAKBwHJlVwgax0tu9DFkTd5IM0x6e01x1YhcmIbVtGYlezk+xZ/lmv2Zf3O8uYO8ubCe13f1swwEq4aaC7Y7U7m6a7AwUCr1ralkRKSZvahsuHRe2U81IK1JwDMErURmbmu2V2utWMDdIMImu5DQ0iX+YAACobQDAb4Td/PZ5TCj2l2lp+9N9L/oWVPCWYRK+2p7P+rU+bEUl35m93NZmCvUrJNl/h3XVIvTdp8HbNBx5q3ZTc7UvJIrXlMFKsY2eW9NH4iOSFMWua2vnnWQ0z9g+ipUcXWf5HsDVkksDVEXSthf0AAAAtQ0AeP+QRTZTZo9zW2/xMkhK9hdCsFbbVotgLMMkfLUdm1RC2/emP2dPnE/fsouRnPkSU1U1en4UDGkKVV1ns57546zUIFf7ZlltNamLvWVXNySCkYxWpCASOCHtfhRb4Od2GqGJ/or/uha7kxkPAABQ2wCA9y1M0nEu+9ZJXol+/TIab/xyqxpvyzAJR22vSK9ZaPvOzCVWS8LmIxuBrsiez9w0FFHfl06oEuQw+iUNRw53K/8uiK+esQvG2LE580QwtE0X2QjmbePUSs22wxglVROKFIFzjO0zGNsAAKhtAMBvj53Crip05r97i5dhKOzzKp1iXsViDpM8mjywU9v1yWVyo2Vy+WtqaYa/20kia2jDPvNGIKyts1GN7bXCzZuVL7prI2tLtFbd1ecjst/+zJ8WDW0LXEPLn3NcasNCO21fqr9yGxjbAACobQDAbzpMsvAjiwrspTfeusX+bHzBqxNSkni19oofJrk/37ZT27PZe51Fj6onvR0ZPesNbT8OB9mwI3nJtM5G6TSMqnvaZXk9TBvdq19T2KMt1r/hyvunV+eHtoVjJOrZ9jIP+T8/gvvkkdgGAEBtAwA+OA6SK4oVGsy/xTCJZcx6oCy4zYpKc5iEo7bjE0oPyfWyEiaJFr/UNPQoHX+9dGEjHtTPGra0dTaNQFiJa6c/Wp7JUeXfSEUpLdkuUtgjM8h74ajttpNrTsZ2y17x6zMejr9DEBnfhLENAIDaBgB8iNDC7Qdzrbd1DbuFHfFFia7elDkhYwiT2KntFen1zXlFZzNbenX8njFG4lcq/56GYtof03pjm62zWUt/MlK9SFkUtjbyKKq2fAjW/7Eqa0MG3fpXBPGSoGsusv6GarbbAWspLN+eNUITfbc3wtgGAEBtAwA+CGTN1AzmPd8v4wqatOvPIjXDWQhvCJPYqe3lQnfj453Zy61OqfazYIxiJPn8sPytpla23fSlW5oQPxkNsYLtu0vfXKuok5F3Fq6pKY6gmuIQrP8TH5Ekx5q/sP00PKfJ3JTI2erlF9amu1YmI9ICzjG2OUtzAAAAahsA8FtgP7XWsTCzy4s/v5ULWK29GkS3uco6G8Ikdmq7nA2S2qbiv51Y0LCnXdfsMW5YZ7NWuNkdsqxc0KnMtHj93/LSL+oIqUDjh+DCdrphkO8Q7I45jC862uTsNqAVzK3UXvdtbGN5JAAAahsA8NuHrOWtqWdv6xqaHcdXpHZjcPmuD5PYqe3xya9YjGSkPPSsExdp+xPkZB+NRpmMbmt+tnFPe+ajES2uPVxRMySq2HUKQ+tZq3wvuDhmU3opsq1G0k1S7kjPbRW5ZmyfhGf4F9bfX1k3sR1EYhsAALUNAPgAYGLXw1YQt4jnJUT1nH0JtD5MYqe2WWhbX/y3H4rQkXdmL2vedqJni7tmbN+uXCVjO11bskxoiLwL8RFJWtjOD20fxFccD+vUbDvcErA0eX87ifSJbfzTAwBAbQMAPghoFK+PVICHFyCLfq9OyOnwLi89H+40jSRqJUu1vTpZJ61MxX8tXc32tc4i9+HyhXbvRsmDyA2liiT7KT19ZOGS5W2AYP2feFsLucX80LZIjKQ5OsmPecg/JK1gru+7o25iG8Y2AABqGwDwgUBjhV4lp93iqudOBFmhckY/71V9LExiqbYXMmNMK4enPldj2TqpPZ++qTX6XSSjuiPHxztL2j+iyj9ZlBte1239n3gTuWCMhG4P6sVTx2Psarapztwx38JLbAuv0gQAAKhtAMBvgdPwtOKMxkpvV+6bW0QGeTt2W1dStRoTxCvzj81qO1MYYd9djt8763WvZWjfzVR158yXMKyzmZz6g6Hyrycw46b+T3xEkha282tGZIXtqHRFarbZZ9sITfTR0Y6ObQAA1DYA4AOFNpV45S67QlaWqmYt7LyBIDiFSWZnp81q+/bcJflbN+cvsvRIWyepj0LqfORweUjq1oCoe9rX0x9d04ztW7Wb5td1Vf8nXozYzWZwJSzVobSDGXuP3KFmm65KpCaFI+XRsQ0AgNoGAHxYUMuEV3rX2+yHW56NL/Ct+rvVO7ImTk7fNqjtlalNQ/Gfnnsz6vjjnfJN0572j2+VqYdkiK93Rer/7PbP28dIHBa2dyu07WMkjjXbB4llZqI7XhUnNY4qEgAA1DYA4EOEpYT7Ww34BvSxKxznC9O1JVkWB2Y+M6jtaiZ8TVf8R/OC7IthVU8rYpr2tMsiciP9US37R06GxBCJ9uQtGNU2d/KSYiScJhDHmm1ZYbNywMOE5PZvZD8uwdgGAEBtAwA+aGiDeh+1bl5FWdr+tCf72x9nH/IXnlelH1iYhCQgi4zn8tflB1OFP5uN7UJ2RIuRXNpNx/VafD3zES2ySVcrjs6xh2a/bmE7L7TdpIiIvSh3rNnentxTP6u5luvEtg/LIwEAUNsAgA+b+sKPTG9ZFuedN7QJ3BOtTzF0zjGB6qhebbPFk6xL+1Ek1M1qB8e0+cgLNB/Z7mnaTiS14ciR6kVHE7otUMchHmTvxjP8WZHDDuLWkXHHmu1l6ddGaKK/336Qsd2GsQ0AgNoGAHzIsDhHf3UTXgXH++iV6y/0zMIkerXNQttU/KdIWF+CxUgeh8KUyT6IBXqX2sT1o5O2l+Sm/s/FiKTmGW9KtuOtO9Jzx6aRk5AWQ7fxyDdn1CzKduEJjG0AANQ2AAD0ZTBrvdeOIs9zxAvvXL0RTsVKVfrLcPmiXm3XsklZNK+Od4v/ysnbhv2Rd8o3aU+7VvGRZGFuw9pIA67q/8RzNUzI8v1yoRiJZn4fxhc5d2LtQNbtfCSMbQAA1DYAAKis1F4zybuXXn/zr87GND3Z3/5gtqkO5M1/xznMVw3o1Xa6MHyjdJHWRh4Fxu7H76r7I7UYyZOgj5SrtvtmXKn8q47wL6kVyKhbY3LOny2r/nDMbNDCdn5mhgLZp+E56xBR+QW/ilu+aWEneZLdhLENAIDaBgCA/mE6rxXMeTKt6O6lOy0czWB+8FMJJjEytWW92r41PzSRG6FHJrLXWYwknf+zGssuXz7TEtttTZTvR4PXuBkSg+QVuX7BEUlq2j4NTTvrXY6x3VXt1nOlbOq0j1R9t4pEbFk9AABAbQMAfuOQK+xJftoVVIqyUnv9ZlLgVekH0tY7mZXh8tDBaFRZlh6KPQlH7lS+YGqb5iO3Ij7a006yO54dsav861XbovV/qzV1YHF7ct9JbScdQ9stf9JxhSR9CI8mDy1DPs3OXh6RHfJ2xjY6tgEAUNsAAKBKK1kYcdaenx9kSPPjHyK4EKya0NwcG09MfE1/zBauHSYUr/2BYl2zIciL+nU2pHQDUtyV7vTKmK/PnTnKaL3/3YoULA/YyzzkV3FvTT1TP6KZkz6NbR+MbQAA1DYAAJD8Sq+zEb3BPeb+JLIn+ywFF4yTHJzO3tiKBjuVf7GtSDAuFZk+vjn/KVPbmfzX2p722Eb6I8ptu7oekfo/wRHJpl+LfwTSAm8w5WiQ2+WqWZ5eaapxGS7qJrZhbAMAoLYBAIDYmP9O0Bj2HDWvnFwZ/FRqw4lTd7iSNrl0Y/Lv/zn47/5D9n/9p82hke1oMFL8gmQozUce+dTi7Z30UC37x7YvIa62XdX/Ca7VJNP66bjtbyEawbxjbJq/4nFDG6B8nNtCYhsAALUNAABeql5P6kFcwYTmSXTOM+HuNGi4+M+Xfb/723t/9Xti4g9/lB9nMZJE8Wsmte/NXFEbSALhjbSyOVK1hMWk5HF4lj1d/sKrKxe5AAqL28VITsIzfNN9P7XGjPb6wo8wtgEAUNsAAOAN6uZzX8qtxvLkdT2ZqDsNTzumz6uTLYPUZpSzz1hQZLikGduhKBnbo9Of0Fqctpi3feZX6/+2iw6rYerVl/rdlo6hbf7CdsewOL9me7X2UzuQFZH+nMQ2OrYBAFDbAABghDo9PIlQi0NbadyuUDFzNFZxtOfzn2fMUlsm+6kSGpnO3GRS+/r8p9oW98Ba5mNlYrJ6wXFfTK/HLDoiKbjB/syvLawJ2i5sb0UK/PWQjjXb2wU1APNgrtWfsd2ITOFfEwAAahsAAGy9Ycf08Dmp/MH7B0XSz6mP7lqq7cI/fa6fjyylb3Y0a2Ij/dGtziOzUoMvZO0SHc7uvtiIJMnZ+tyZ/YuqirxhU2HuWLPdCCvr3I8js8vSrzC2AQBQ2wAA4CXUfu12ockgdPe3O003OsJ25TRGi5xjJkemLdX28kefHfnGaD5SHUYM3p4ufHStciEgxY/G8iLte0a1LWCEi1jyklifoGP7Hr9mm5rXHxUPXH3ydGtxEoKxDQCA2gYAALtk8MKPLP8wuPDtw1M/TEjeRM/9vHa8xcqr6F//R4PUDv71P5zejt2buarNR17tnCfBhiNlta0IynDSVZJEvJ1D5GbjOF5yTI0/TZT57rtjzfbT+BLL0LsqgtyZ2EYVCQAAahsAAFxEn/n2sOcwT/okMjOo2u5EMhzDGwfXQrF/+59Iaof+5h+r4TV98d/jUFgZE4zcGJ3+ZLgjtSXdgsZ20LnR73FuR/BgwRFJGm08DRcdze+Wjerl12yvVb5n91pPspv9JbZhbAMAoLYBAMCB7cl9r5Y79pFgWa395MnF8wcu2750+25ic2hk7k/fTn9bW6y8OvNnSml1PvLm/MXOOpvx1bQyHJmuqgEPWTfztWyPFa3V/zVDE/wjaUhUlt0iinY388gpJeK8rd0yWs02HClX4qaU5ii2AGMbAAC1DQAAoqzUXrNgg6y93tiLUlyYv7fcEUHlSqOEtHWy7Uten7/I1PZq4q4iamOfKcq78oXZXW6HnWVlK6BK8yfJNf6RTOM2g4UBQ9sUI7Fr0ebXbMt/762gMkB5FCu7S2z7kdgGAEBtAwCAq1xHYpmFd91u7e4/L66lKQbcZEmqnW/MG9R2ff67B9GgGiMpKfORTX+sMxw5ZBnDqI89ctag1B/iNG8qMiJJoW1OHUo7kORb73RJsuy2+rXAHvvu5syJ+Ad+GF+EsQ0AgNoGAACXDrFW/yyL1zf2os1goY/EsIH7c20Rj9ygts/8mW80YzvQ2R+pGNsVpfLPUq0KOb5i9X/dPpY8T8GT0G+Hsv3FSDakV/y6Ejao2hgtuir+Q2IbAAC1DQAArpElIAsV8JcyeguzeAes+has7jaqbV/qWnmIGdtHoWgjEB6d/uRW7aaNskyJyVBeqKMrgue/E7mx6drqNk3bB/EVfl9Kc3SSY0LTXQpf9BvodmzD2AYAQG0DAIC7MEmnJMRx34qHsPiy8opuvFUDq7VXIokUvdquz3+Xzv9Zm4/89MyXeJi+ONybIRExj/tT2yJB8+7Cds74Y0B9R02bnTXkte9lLHoGDxMS+/BdTamiYxsAALUNAACDBqAH3+8oGl8h3Vn5YZDziHi0erXdCOSHmbFdvlCP+5vByLXyEM9gFvBxt4vq8vNWICNyV8PvW2z6c5raTjsK3x3pufm7jyYPOTESKll31XcOYxsAALUNAAADhEmkXxuhCVlLPRtfeDOvKJipcDZcBTbF6NX2SvIuk9oj5SH5kVLxYrq2xLWrncXlaWxOm0ecdjiyk5bmf8hnfjUbfRqyPlu9eMr33c+099sO5MzfpZJyV4UwMLYBAFDbAAAwEOpeRl/KVfvyIGFxJpS3J/cGOU9jtKgkzlN1QbV9Z/4SU9uh6c+PR78dqVx1CIf4XdT/7eXWRd6y2IikfRuJNqpot0mHZhnr5Rfm2yo2n+oqMX8Sq8DYBgBAbQMAgDdm84CtfOIwo3fA0UyRk5DaPgmkhrX9kc980dHpTxwzKpwOPpM+dlCiIg0qFNrmqm1thrJ4apHJ1kr6LO8TqH+Gv8nSTr7D2AYAQG0DAMCgynXAnhBxWM93IzyQgBOpr5bV9uG1QPkfP4n//X/55l/+80j8o1vzl45Hv52Vtu1PmxefkhTsCtRt0HzlGNo+C1iHtnek5/wL49dss4+rFcyt1F6LfsJYHgkAgNoGAABPIDnIX83oWXalEyCW1eEgRSjPxhcc7xDuXxjx/+7v7v3V7xl3f/f7+U8v31m4xguoBNOaxEwLWL9C3rbIiCRtajzM3Lc+QNsnbxkj4dds068v+DF3GNsAAKhtAAA4F2jFoys11jdUhLJeet6/Qe4kYRemvwv9m/+NpLbK735fnWxxTtv0OVTsWSW8U4NHzJm05YxmdjPZVjESfs222vPoS4n3wGB5JAAAahsAALzkOFZy9F89F/dbU8/6Pgnr7W4GC3YH5D/PGKV2h9zlODeKrartRtBBbVP9n93MIoOqwXcLO7Yfvraw3U64U4zETo5zarZXaq/ZjKar2hmKpB/FFvCvAwAAtQ0AAIOyPbmvTvLNn72Bl2P6b5D97dRnZ3dA+g8+S7Wd+pfbIomOdtjB0z3phN2VNEt4lpdmERiR7L5o0Hphe8vPy7fwa7YpJiReudjt2BZb8QMAAFDbAADg5Ddrq0/2U2tv4OXY0N6zsergtwd2o4eTI9OWanviWlHE062POew2b/uE6v/UkDp3W2c3JWKzsP2M20bCqdlW+tTDU2wmVXx5JznlsuzGPw0AANQ2AAB4A5s7bAbzg+xUd5kDyfd9Bsdd6IuVV5G//k8GqR3863+olV5yhaZQzYhel69LPzneV5xy19+oatufdAiI21wVp2abIvKcHIvxgqmKBMY2AABqGwAAPIQqmQfc8uhKK3NK8fiQjtyY/842EXE9HPu3XcEd+pt/nE/sOWhoVbmK1v85rpxkO2U4I5IU2rZr+G6Espwi7YPMCkccszuotj8tXvwHYxsAALUNAADnAq08PEycu8y6P3/WxxZxt3noVjDXvpvYHBqZ+9O309/WFivOyl7c1hWp7OjOg9rvlKHQdtOf48dIDuIrHIu9OTppfPXKD+yWgF+H0hNGp+WRMLYBAFDbAADgOWzvTDuQXV78+VxfSCnK6AhB8YSDgbXK96qQnT7iqG3a3C542jOxJElde3X+ykmy8DkGPInpTellH5dET9+QjPcSj7MPHV/dTrvD2AYAQG0DAID3UDxjkG4+Qdj0nrjt2odtPIDadnB295NqfqNlX0FIepczIum4sP0skOPESE7CM1qkO2X+TQV7++IrQpHYBgBAbQMAwDmHSaRfG6GJAdtCBHna2Z/S97p4WU06zv/1pbZtpW3vrYJa/3c0vsjTr50RSc57pIXtdq/Y9qc4MZLuBsr4op2tzrkbsbPJYWwDAKC2AQDgvGBtIZ35xZ/O9YUe57aY79v3GVjKnLP/stmv2j5zWg/ZCjjX/1EOnuPfn2li+jhe4kU7rLR4vfyCY0WzdUXy/QaneVBPd3kkjG0AANQ2AACcHxvz3zHVtT25f64v5Fjh55xFcdqIzsIq4hvpj8bygoqTLGdO/R99khx3WctJW79cS7OuLQ9oayGTM9OSeXppwTeOxDYAAGobAADeHKedmMRJdO5cX2W9/BfHMUeR6zxILDuqbcF0eCPIW9loGboQuZ1Yq3zfX2ibyr9bkQLnGh5NGtW8/H5ZmLte+UHkjVP+G8Y2AABqGwAAzh1ait636ywC1ZKIxzwsU9Hyf71S201tKWPT54HaViWv/YikY2ibM7K5l3lot/JG+WA7CZZn4wuimRwktgEAUNsAAPDGoHK93fz2ub6QWktib07zYatbODOIbtV2O6imsVvcKcl16SdNJaccrXfOzQA/tN0I5u36RiRuzfZuYcfVliJ0bAMAoLYBAOBNw2bsZL14rlvcDxOS41ZzR/O4MVr0TG1rErYd5qntvZw6SNoKZOyOoRFJTnLaoftP0+LtQIpjrhtqtpVWmc67lv8r+HeHKhIAANQ2AAC8acgfFV+M0k9kRaslEezNMKreTn1K077x2q3aJsO4PvaIa8lr9X9RW1udVl3ajUg6LmwnEbwjPbeLWZtt763pI1dDrrLChrENAIDaBgCAN0194Ue3S7/74OH08SCanvLlnnnbYoskyXWu24w/6m9X7LLv/IXtrUiB10ZiX7PNsuytYG6l9lrM2E7B2AYAQG0DAMBbgKWim8H8+YVJurUkwhtY9GxP7mvV4K+88bbFFkmS2HVMuXB8d5K59bkzXizbNK/JqdmmwP1eet2lsZ3EDzwAAGobAADeKFRgd3+ufU4voYSbWS2JcC205RXa+cfuvW0h6SkSvWBd4LwRSaa27dpI/LYxEk7NNm0mWrM33S0V/0msgh94AADUNgAAvFGoSG4/tXZ+r3ISmeGrUg4PZpv8IEqfuW1/Ukyk2qptWf3zd1jyQ9un4TmO5WxXsy3/fbFN9YIfJhnbjmvqAQAAahsAAM6Fg8SyqxBw3y/BSVxwoElEO/edSXk3SRLrBuseJa2lNTjb3ek2wK6Djx/aJsu5ZWoj4dRsU65GcFsQvYo5/A0AAFDbAADwJiDVuDX17JxeYje/zc9ec6CYsp2+ZCOD3ua295MrqhS2v0NwXA8kGNq2iJFo+3fkWyCTI640pTRCEyI5e1SRAACgtgEA4O2zvPSLq3BCH2zOnPadDqfAht2QpXu1bbtNhpBFtlrikVyxO4a9rl0ROC1stzTRD+IrnO92a7bnTyxtfsHFnCT39zIP8XMOAIDaBgCAtwYbvJMF6Grtp/M4PylmwX5ow82AuvOysDO42q6PPdIiIkluttu5/q/ZUeR2OzL5C9spZGK+DI4hzQI58l9TfeFHF4ltVJEAAKC2AQDg7UKmaR9qWBAWgeiv2Ju/r9GV2m6HkyJTg471f44jkvyF7eReH8RX7J541LusvluObqPv7YxtdGwDAKC2AQDgbYdJpF8boQlFGsZK5/QSbH97f+dnRXt2YtqV2m7R2vZghuttO/jfmzMn/GyMFsu2yKvUi6ccf50MacO2doqJi6RxUEUCAIDaBgCAd4vH2Yf8mb8BYYOSbX+6f1/cxtN1pbab2gCieaGMheS1l6r0cVnOfVJo21Jtn/nTmto2XsNJaMqyFpBuh+SPwpWxfRRbwM82AABqGwAA3j5U/bFdeHIe56f97X2oeaan7YY4XaltUrqNoK3aFqn/ezZW5YxIOoS2NT1dL57aOOLGucat6SPxqA+WRwIAoLYBAOBdhBVXn1OYhNS8XTs1B7Ze3u7C3KltMn3H8rZq1an+b1n6tR3Icl6RE9ruxkhMUnhDemX3LfYeBTvRUUUCAIDaBgCAd5Hdwo6rleBuo+HqsKNYe50eWdRyjGSX3nbK0fR1rP+jOwe7mhROaLsdzNjZ3t1t7cGs5cvtpdfdGNvo2AYAQG0DAMC7RL36krVe2LV/DOqdR+fEKzX0sIJCu1WU7jpJ/M4RC8f6v0fFA87A4tPxZU6lNxnPTxNly7D4mWk+ci97nz2+Xv4LjG0AANQ2AAC8xxzFyhwXeUAOOgkNwTk/PVTH4V2SJDWIImcjkm1/ennpF87TzQvbOTESO096Wfq1GczLjz8bq7owtlFFAgCA2gYAgHeQ7ck9bYvhd56ffKewyxGpIhEXyye6VNvOKQvH+j92T2J328BZ2E6NKG1TIwoZ6ifhGUsfXSTvTlIeHdsAAKhtAAB4F1mtvWLpapGIsFtof7vbXPhW8ZDTZ8JmKDsZlVXxoIWjZuXU/zmMSLKX4K6QPA3P2Qllw+Os/bAZzDveoti1BwIAANQ2AAC8QzDx2ghNLEu/epwLX/iRaUFZPbt64oPZJsdx3+/MUCo1I+NVYbVt62071v9tlF9w3sVxvMR5up3WPwnPWHre8vtVN1YKJOmpPfDxxGP8GAMAoLYBAOAdhYqx+6jqc4S5wm6nMGmxvOVUIqntk+isYNaiba+2j6KqXG7YBEXIaLeU/pzQNq2xNKvttk3NNhsPla+2XvlB0NjG8kgAANQ2AAC80ywv/cLG8vooD3HkOKZo2Wfj7hYcUgXe1vSRJ2qbs7bGsf5vP7XGSZ9zQttt7VsngZ6q727Ndq9Qri/8yFI9hwnnELbdWhwAAIDaBgCAdw5ZZ/c3zuh8Zm5ztm26o/qSE94QV9tHY3nHKUnH+r/jyCxnsSVT222r5LTdt1q0eDLQY4dTE4vjxGrX2EbHNgAAahsAAN59Hsy1OF7yIGxP7rMzr9ZeubLbOdtkxNV2I5jWXOR0f8Hu5cWfmUVtOUXaDNgubG8E83bdfN2a7fmTnqeEJtidiWOAHsY2AABqGwAA3qswifQrk3puIx8DJrAdEyCWeyjF1TYV8DV9jmrbOv3MH5HkLGynb7V8vQb2xGNLgU4f1E5hVzixDWMbAAC1DQAA7wnqAhdfqr7wo4enXa2pGeXtyX1XT2x24tSWjrK42qat6S37UUL+rCF585YlhpyF7WRs70jPex9XbwAakSn940/jiyzMs1r7SdDYRhUJAABqGwAA3hvIxHUriwV1s8gmGj2sdtpycNOF2taEaTtsLaYd6/84I5L1uTO7UHgrUrDLVVM5t35bO334e+kNQWMbyyMBAFDbAADwntEIK0ruOFby9rRs9aPb07JnWc4miqttsoHrY4+sX8Wp/o+JfsvLaPptQ9v0uo1gTxvJUaxkqZWfZDc523wsz/xo8hA/sQAAqG0AAHivwiRaJ4bb1Y9CGRWXhSds546lRnfhbTstkuTX/9GIpGVfOCWzn44vC2bB2z6L6cZl6VfWwHgSnRM2tpHYBgBAbQMAwPsG9e5Zzib2DW2HcSXiOdWBLrxtp0WSZ/40p/6vW9ViPSJpneigGIlBE2/Mn1guu3lUPFDt6uKBoLGNKhIAANQ2AAC8l7D8xqlNrKLPRLi2jdxVvSA54oN520m+t80/gEYkzQGPbmjbn7Iztg2jmW1qKendOsn6vJvBAt/7h7ENAIDaBgCA957twhPBBSviUHm2K8ucYi0e5LZ5hSS8qMlBcoXpYI4pbl7YbldUYlmzTbciu/ltJLYBAFDbAADwG2e19hMnqdw3jdGi2zJvzlocN0mSpN2ix54DbOT4SWTGbkTSbmH7aXjOso1kL/PQ8rWo84S//QfLIwEAUNsAAPAb4Wl8SQlMhyYcNxq6CGF3NsO72t++NX1kl/Z2rbbturSLT7RsRsb83ZXaa/XGw8qSpyI/YzRFc6AN+3TImT6JdbV7feHHVjAnUo9IvjiMbQAA1DYAALzf0FDjg9mmV+ekWIh4LQlnCeVe5r6mtmeERKpN0Jlf/0cXYP4cjuMl+9C2KosNS20sA+L0sWzOnPKM7fAMEtsAAKhtAAD4jbBSey1ouPZhVIvvb6dAs1nsUricb5bXxx7xF8Hw6/9289t2I5JkYB/3yv168dRy3023ZrtXbbOATSM8xb8JQWIbAAC1DQAAvynYdKCsuV01ZPOEr9YtKL6okp5ibt8jtd0M5DlnaIeT/CQJv/5PTdRYCXq7YkE6YTuYsZTL+uY+qhfkfyaNCKpIAABQ2wAA8NviwWyzj84+Pu2OEt1Lrwsev1p7ZSdGBdV2i9a2BzPWl8St/1MXzlutjrdT23TCerGbDNmQXlka28/GquyWZqX2GsY2AABqGwAAPiwaoQm3LSJ8jjtpCst+D37q2jykKKi2m7605cCiSTQnOc66+dUptG2wzO1iJM3RSfX4QE5/L8FGMJ9kN3k3DMEcjG0AANQ2AAD8BtlLr7NmOr7z6iKd0ikSsdxWYyuXO+6y2Q4XVNuU62gE0xw1b9mpxwmak9ksy+4eYzuY0ZRx2trwLr/ofrxZddBzvfQcxjYAAGobAAA+OGhIUTxpzWe3sGM3dGjrr3eGCM3DmqJqW7Ouj8YsDuPX/3FKVOyX16TMMRJZJZuNcJpDPYqVeTcbmikOYxsAALUNAAC/QU7D026zHxw4hXr88Ik5zSLsbfP2RPLr/9gGe/OIJC1sNzjiFCMxbNI509IsJ+Fuewkt7nk4fQxjGwAAtQ0AAB8orAJPlpX1hR8HPxtNPYrvb5d1thLYiJX6U9vU02cdiQ6owY+9nDGpsrz0C5vpNNvqTX/OcmE7ZcQNYW7LaPhxZNZxfxAS2wAAqG0AAPiNU6/8wATfbmHHkxNyWj44UW+zwewySWKtVtv2I5Jrle/tUjTklxsWtpOyP4h3q7sP44vmwu/782cidx1YHgkAgNoGAIDfPifROVnwnVplLfrALp5hB5vUlDV6v2o7yVXbts43Z5ummu7oNbB3pOeWHX8k6PU126zLXP7Wau0nJLYBAFDbAADwYYdJtNHGNav9L25h6ll8fzuNKg7mbSfdfletT/GlDH0sFNo2iGDLGIllzTYV//ENfhjbAACobQAA+DDCJAs/MnX4OPtw8LORZ7wx/53I8TRNKItUz5MknPo/Np15Ep0TDG2Th30anjP70/qabSr+43wCSGwDAKC2AQDgA4KNKvJH+gShVkHzMna+OjeUBrpKkljqaU79H41IysrYGD7RwtmG0LYq6/1Jy6QK1WzLZ2ZK2jz3CWMbAAC1DQAAHygkec17Xtwiy001xyy2v50WyBuc4AezDfZ4K5Bzlq1WDjGn/o+aCmVNL2KW0354/eOWNdv0SXJuNmBsAwCgtgEA4MNipfZa9XrFJDIftrBGsMObvHDDtOL9+bamtjN2zz0ay3OmJDn1fxRfMWx5tFvYTi0lrUhBp8starZZQEXW05zYOl0zjG0AANQ2AAB8KBwklh1loqtcirlmxDo1Xn1paQaLqO1GMG25R92QtLZ4s2xEMpA1JGfaNgvbLTW9eQST7hw4CfimZmy3YWwDAKC2AQDgAwqTTB+5XQNpB9WMiOxvp4U4ht5rUtttKyWtKlfNXZa/sFLbtvV/zH03j0hadpi0IgVz8MOyZpt6TuqVHxwT2xvSK/zUAQCgtgEA4EOBxvvEF9N4JdwtF8GQ2rZr9+sEPFS13Qim7U7bNj2dkuVmB9p6MbtmeDeCebNxTjXblMYxb6HX5b9hbAMAoLYBAOBDhVVly5LRUEHtFlrTKLifkolUg/YVUdsU/GiHk/Zq26hraURSvivQP34amtbq/JwXs5srSqi2nHOPQcIdxjYAAGobAAA+OLoytDjQ9B716x2k6iLHs1yH4WAxb1sr4BszLkjn1P9R0GW9/Jfe5InqlD8dX+bHSE7CM9q1qZ76svRrIzzV6T+ZsmtRRGIbAAC1DQAAH3aYRJaMoQl+FkIQVs0huA2eHWx4USFv234O8jg8q847hmcN32LzoM1gwSCLSbtbGtstnY1NnjqVilCPIcfOR2IbAAC1DQAAHzqPsw/VOb+FHwc5jzov6E+LNJwcjVXMjYFC3rb9Iskzv1r/t118Ymmlm+8obLr/jG0k9fILc7yb1bBwQjhIbAMAoLYBAAC4jlzbQSFm+YTO0rxjNhuMcDFv2/YAO9ubKlAMMfF2KGtOjJyG58zh73ZAW+0+Oqnq78oP7OU4yRkY2wAAqG0AAABMYk47Lh53EQHvnUS0hE1nGvq51yt/Ec1t+5PiQpwuzLA1k9T55swTc2JE3zBo1s3sFwLmdZhIbAMAoLYBAAAMZEvb0bWQ84+c4yva2KKdH8w3jNu+pPi36LVWaz85Hk8XsCOpKyf3Mg8Nbjc1J9rdn8iiHMY2AABqGwAAgJZLXviRGb2chYgi2MWjOfreEPIWVdt+F/V/lnsu63Nn5uPrxVPz+WmS8jC+yB7ZKh7yi1za2tZ3GNsAAKhtAAAACmxsUZbLg5yE6VqRk5BgNeyeFFTbZyYVu5dbt9v6btk22A6qoe1mIGfOlljFSLqvyApVOBvvYWwDAKC2AQAA9LA9uc8PIotAaWbHWhLaPWnIrvDVdn3skXl3uiqpw+qemqNoT7pDVvNqMqSw2/tCqrCuz52ZX71ePGWPmGu25c/Hbi0lEtsAAKhtAAAA1tAS8r30et8nIcfaUbLbTS7y1XY7nLRLktjV/9mtlDe/0I703Gxjm2u21aJDX6pe+QHGNgAAQG0DAIDLHEhowm4zoiNUJui4mZIcYkcRrKdFa9uDxriIXf0fG5E0tIA/yda10HbanM+mk5trtingfpBYtpbaWkAFxjYAAGobAADA/ydiA4tD+9sdDXIKeBh0uYO3HcyYtzzapav1efSTyEyvEa4ubD8NF3V63RgjMddsU72JwZJXbyF0VSSyUsdPFAAAahsAAECPVmbFdnbGrQisutuwJNLytSxX6jiobbKfw9Zq21z/x9S/4R2Rjb0pvTTESPRnMGRCaMu93XZ6qiLhTHkCAADUNgAAfLi42r7OOYOhbo+TbzaUc5NatbwAGm08GsvbqO2UCwddZ5C3AylDItxcs03e//bkPoxtAACA2gYAANc8mG2K74O0hIq0DdV+ZmRFbs6cdAWr1dPP/NbhbLv6P5raXC8977rvIbW9pB1ImnX8QXzF4H9TzTYLpbSCuZXaa66xncYPEgAAahsAAIBVwEMLS4hsqOHrdcfwt2UNNl9tU5LEeCqb+j+2H95g1dNLyLKbk2AxBMFpANQykq43tqm9BAAAoLYBAAAYYZ3ZSsPdwo99PJ3CG4ZAthm2I8Yg66laxNrbVr+bMnne1vV/zI22e4nu0wM5Q4zkML5oSJsw4W633B6JbQAA1DY+BQAAEIK6+SzTySKwiIjBtDbDpLBhntJJbVsXj1h63lSQ8iS7SQ9uSi8tdrOT2x2eM1zGXkZZYbNSe83mRy2nP3XGdmpj/gQ/QgAAqG0AAAA8WK/IcazU39OZjHZ8+kFi2dzvIeZtC9X/0W2DfkSy6c9Zqe2U3sbWx0LYI7Ro0zLODmMbAACgtgEAwE2YRGuVdpx0tMQyLW13mKG9pM8kiVX9H41IrtZe6TIn6vmfjqudgCeBvEGsN0cntTHKnP72w3Lvj16aw9gGAEBtAwAAcIYmAh2z15aQzLWMOJs1vdskibHmT7vaM39PGQjrImyMFq1ccF33n/ZIK1IwPMKK/GjJvKGs0GBst/yoIgEAQG0DAAAQg40w2q1xEUx+82sEqStQb4EL5bZ7l6LvJ1dUvdtrk5unMDdnnpibtrWmP/WcjyYPDVETlnixHBvtMbalV/ixAQBAbQMAABCCksqydHb7XBpPtDSDzRa4Xlhz1PbRWN4yn21Z/2d5DS1/VqvlVs/QihQM+vvMpy51Pwkry95lhc0uyXK/JhLbAAAAtQ0AAP2wWnvVDmQ79dIbfTydBZ35G+BpNaM+cMJR241g2jIxYln/R/EP+QuzON6ceaK9XNoQIzEMYlLcRX8ek7GNKhIAANQ2PgUAAHDJ0/hSZ4ox38cWd5a+MGSmDVgLYnu13dSUcbN3WaNl/R958z0jkiZr3OBMG2q2adePZaIGyyMBAABqGwAA+mdr6pnmBJ+6fS5ZwhylTvFu/dZJjto+86tquxFMW4S5e+Ml5hHJw8x9w5Dl00TZEAQ31GyT+26uHkcVCQAAQG0DAMBALC/+zMIk/ECIJbS/nRP7pq2T+j5sjtomD7sdTprVtqH+z7wWns7c1Hr9Wpo53QjmJauabVYc3grmVmqv7YxtVJEAAADUNgAA9AlziJvBgrlnmo+lkjZHw83OMdfb1or5xrqDj5b1f8tLv5gbDElJ1+fOLE1xQ8021SDupdcNV0K9JUpiG1UkAAAAtQ0AAP1BFvXmjOuwhFoJkn3IOcbcY03bZyy8batFkpb1f5aJcMP440F8xRAsMdRss+U7lq3hdJH6JkEAAIDaBgAA4DJMIv3aDCq9e4cJye1zWQxD/i/nmGawYDCP24GMrbdttUjSsv6Ppcb1yyyP4yVDi/ZZoGfm0lCzvVJ73QrmLK9fZ2wjsQ0AAFDbAAAwGI+zD5kBvFr7ydUTLRezG7BIV/PUtsU0ZNcL1znQrBFFXyRCmW9ZdhtyKTvSc8lUs02VJuYFPfSKbX8KPx4AAAC1DQAAA0GpDHMvBx/LDj4D5nWPPLXttwhvWNb/mUW8wRffmbhvtLp7N0qyvvBGaMIQWEdiGwAAoLYBAMBjmPQ8jsz2J9PNe2E4aRNHb9vQPWI2vM0DmvW5M8NhrUBKHyPZyzzUZ7jpys27MM+wPBIAAKC2AQDgPMIklvOCvMy3VTGIAYvIh6Pa9if5YW5z+WDTn1OfG8yqr0LNJ8VTfarkML5IVyUfU1/40S6xzSYpAQAAQG0DAMCgyCKbydOdwq6rJ5oTHUa1bdpB45zb1qWldfV/3QfNI5LdbHen+09W2AarW/9HWWGzN2tuGe8a25pqBwAAALUNAAAecBRT1i4ex0qunvVsfIH/LHUEU1eVbae2j8by5tCIZf2f+UUNge+WtpOyHczIfzwJz+gXsNMWTEMABlUkAAAAtQ0AAOfFbmGnjzCJWUzbnZZ8aDu13Q4nzUkSy/o/g6HeDW3TenbaktOJkdAfZT29LP3aCE0Ywi0GYxtVJAAAALUNAAAeQ/kK/rYaA1vFQ7tYiN0BpLYNsr5Fa9s7hrQxIqIdbB6RpNC2/IX8xx3puX7asl5+of/j1vSRZQFLj7GNKhIAAIDaBgAAz3kaX7QsxeMHvu1aq1W1ranb9fJfDGrbEOSQRbYaGtF52+b6PzohifXuQGQntE3nYYmUdkDT4qOTktaR0grmVmqvrY1tXxo/CQAAALUNAADeQ/3ZD2abgk9ZXvpF3d9uqtJjmFsCbdU2edvhpF0gW7IckWRqm3q1NfF9Eq9J3flIxbGmewP9bksJVSQAAAC1DQAAb4DlxZ+ZdOZ0jJhhXd3mfg/Gxvx3TMVuzpzw1TaV9NXHusLdnOQ2FHjTwnYW2qYYCRPfhppttvzSHGLpGtuBHH4MAAAAahsAAM4LVkTdGC2Kh0nMjdo9cXBTzNrW29Y86e5zrer/VCtdC5d3F7ZHlX3sTW09OwuE6Gu2V2qvW8GcYdUOEtsAAAC1DQAAb46H08dqmGSuJfgUc7RDz2rtFTshNXnbetumLTbm+j+zdjc8i8R3K1KQemu2qR3FkJPRLY9EYhsAAKC2AQDgXMMk0q/NoNJ7fZBcEXyKebOjAcOOdHu1bSzblkU2e3Bfuxhzw4lBbZ/pDHJ9zbZS/BeeUmz78JTetn888RjGNgAAQG0DAMCbYy+90UkwZw2tHXaY/WYDzY5opsHEViDLy237k2bXmer/WPC6qVndFNpmwe5uh2BHbetrtumWwLBknqIm6NgGAACobQAAeBNQi8ij4oHgUwxZagPqMhrNn26GJjnedtvHq/8zjEiSVpZlt16dn8Rrhppttn5Svk79LQQS2wAAALUNAABvIUzCQhdHsbLgUwwi2MBxTHGgn41VhdS231T/p6VEzG2DhgPsarbrlR/YCKahawXGNgAAQG0DAMBbC5PIrNZeiR3fE/Cw1uKaduerbX39iCHJTWWCbMyxu7C9c0CjEzcnM1tvWrP18oZk+c7ENoxtAACA2gYAgLfAhhbDMKw3t4PW4liqc2oV5Kjto7G8QVub6/8MI5K0sJ3Z4RQjafllVX1C31pe+oUV/x3HSjC2AQAAahsAAN4JjiOzZoXqGPU22NWq2k7V2UJ4jtpuBNOatlZr+I6iJUP9n3oeTbWTvN7NPJJ0bSQ70vO29q1GZIo0un6IE4ltAACA2gYAgLcJa9FWxKjAJnMq1bb0winIwVHbtJWmqZVeU/2fLLvZI4allfoOk1akoDfCybeWlTRLjbeCOX0dOIxtAACA2gYAgLdJvfqSTRbaNY0YUItHrFa+01oZpnct1faZX1XbjWDaYF2z+j/DiOTT8WVLeX0WyO3HJVreTlFv/btAYhsAAKC2AQDgHQiTdFxhu9lHA6xizzJ5YsxbW6ltKvtrh1UL/Kx3kbshrELHN/05vdrekZ6TTJdlNwufyLcN9coPOmX//7d3bz1trFcYx79ipX6AVq2q9rpqrypVrbRVaUu96U1baV90X9TGYBKH4AOYUziEgzN2zCHhfDABAraJsrt3lJQoqDOzZpbfeceGsCEqhr/0UzQe2wFNbp4srXetLIVtACBtA8D/mabkpaf/ubzzxG8XaSbbLD9fmzgMq9Q/dK5tB3G5mlmPpu0gEFsHMTWLV6dOj9NTesJy2fmgX6wsnEk53P2fQOv3ZHkkAJC2AeA2KJc+SlrdLax8fjTXnerKKku3r21HK9lObNmNfURSPi/TSBKtaST1VDiopCenHSwyMZDCNgCQtgHgdpH+kJPekbJzfvEnrWHYbd96+eS4Y207Wsk2xv/l4n3hurDdatr220jCGvnTt7Kjx/1Tf3k6tgGAtA0At6mZJGwCiWdouxAeW/SoqnPvzBl8HdJ2ZNi2Nf7P/cuDYX/FTatpuzZQabWRhGO23Wv3Fza/QmEbAEjbAHD7mknC1TA6d+8C1oQ+Zc0HvLC2HfSNWOP/7COSYX36pfNOk3ejJ3uaKsj16/STVwPPpI+8snBGxzYAkLYB4JYKxnokc+a86vaf9HdGupk7/lYwhs8ve7tR+NJOEi1Cb4ztOOHwb/kdrIXtGqA3R1b0enXujTSCm9GfwjYAkLYB4NbRloy1icOLP2lmYuut4LRlbslL2w+mOnWSNDVtR0vdZo43F7ZXx471upav6JjtreGgP3tp5m2sYztLYRsASNsAcGuaSZzzk94Ra47exU3ei/4+GpN5xvGCtH2azLYtdZtf1xK1d1YyqfveB7W9ZO/RM/mFj/on44Xt173j/JsCAGkbAG6R3dyS1QPdlnUa0iSLcl4NlMy0rScvDzPD1ilJc/yf9deGU0eyZgncbCPR0K+/RquwnRzkXxMASNsAcLvoCD855niBYCxJbNn7YWbWO/KYmdXkbabhk1TOnPe3MbZjNpZoK4v7a5hN29pG4o356x8PrlOFg8ycP2+7UC59pLANAKRtAOgCMm+k7WL2eA073nMijdfS2hFP2/VEkLbdC8cc/9eTt9rBzabtZiqv11rk3s6vBhdh4qewDQCkbQC47STytl0VGUnV0Y2P9v3ekbZpW9uvT1Je2rbG/5l18UjTti5vz9e0Fh5MUElkZUs8hW0AIG0DQBdYDJc7msti4jZG9+Rjzxcicz+2C6vaGdImbYe5+TAzbIZvGf9X98O3zDPRNhJvbWTYRtLsCQre9b4RaWXRwX+7YambwjYAkLYB4FaTlNx2nLay1tDES+MdatuReX86XcSJHpE0F7Zr80kj/K43jaS4Y/30ZoLCNgCQtgGgG2jdenn6TafPWGsj29a842m7GZ2ubY7/M6cKmgvbWyskw7YT6WDxgnX/Ezq2AYC0DQBdpjr/XmJxfOSISRo/ZDa2Whs/0NB8lHkq1+thIrcXSRrj/8wjkq1GbW8ySVaTdNCFEoZ4DfpNOrYBgLQNAF3k4NG8HHYsO+edPiOHGq3pJWaHyf5jR653hl6a8VrStjX+L3JEUhJ2crARJuxW5vYHofil7iEZCk7HNgCQtgGgy2hfhy6miQsOREb3t69Mn+oX26XtVieJjv87TeYdY4C32bStnSfatN1I5uTmXnaRwjYAkLYBoCu5AToY+hFtFIkk8rH9+P5287Bj57TtFbMbPcEU7aP0pPkts2lbi9xa2D4amDd7yunYBgDSNgB0JdlT00gNmaVrky6eXJs41Jvm6clOnSRNPxlr3XpjbEeDux+7s+FM7iGrY1t+H92e4zBjGwBI2wDQpXSPuhmmrfp3sM1xeN28rzc79m0ns050/N9ubskrZqeKurC96X8suNZdkongYn2s5n5rfXSfwjYAkLYBoFubSaSQrBtk4mQSn7W/vdlTkA5sK20fZobNU5KRHu7wiKS5sF0/rLlcRgrWU8Pl0ke/sD1IYRsASNsA0K2k5NxM5mT6R6duE2t/u0TwWrZqpe2TVC4sReccY/yf9oh7QwC1np0e1EK4FrCl+WS3sEJhGwBI2wDQ9bQz21pho3RzpNnb/br/iRS8rbStWyHdC3P8n/4Ur3clGc7709p2eHHSOxL0ds/+QMc2AJC2AeAukEK1jMGO00GB5v72g8ycfKU2WA7SduGF12EStl83koPm+D89Irk8ftyarp20x2xL/fvVQInCNgCQtgHgjgiGaiey1fn38Xd1cp9Z/K5lq+6d4/TEnn/hd5U899J2WKVupgfN8X/yeTfWx5u2tWNbL1amT83C9mF0tw4AgLQNAN1kcfZ7ybVbxc22H5CS825uyer2dtPzbn4lSNuDFcc41FjNrJvj/9xcLmcxWy3arXp2eNEXDP4rO+da2JaV7wAA0jYAdDGZBOJm4gveNVtNpJnbTeEbxZ1w1/qCWZB2jPF/0SOSHdtIwgr6rvn3HPeO8q8DAKRtAOhuW8XN+M5Iq2+k6Y8ZsT5vpW0tVDs6eDuRXZlqhj0hc3pu0h4C6Mdxb89O6SMd2wBA2gaAO6U6/14S8HZh9YIsXp17J3f01OPm8EYkbSfstN30G1Qu7dWWn76XXaRjGwBI2wBwB8n2GWuuttDi9IvJutxZnTgK1kn6JyyNTpKgJm2O/9MjkvG+ESuFL828bRW26dgGANI2ANwZG6N7wZC+6TfWW88XPlj72zV/72SXI2k7rFgfpSeDUYA9eV2FE6RtY4WkjguULZIOM7YBgLQNAHdSZeEsPntE1VNFc8H70sxbycS72aVo2s6GhyCD8X+HD6aDfTf9I/bmyGipe21sn8I2AJC2AeDOkiXt3lFFY21k2z4THcItX4nVtrN6DnKnsNypadtcJCk/lI5tACBtA8CdpWsjtT9byYBt3d9eWTgL0vZAK21XM+saoDVS66zAWNN25OV2YZXCNgCQtgHgLnOTdCM1ZHaMtIK4rl4Pu7o1ZOtFM22fenQj9cGjeZnkHWvaNk5JJrLV2R/o2AYA0jYA3HFSw24mc5WFM/O+G7K1u1ruNHsKXsgOR2i7abuhvSKpvDaNyBHJRodebXnphnsK2wBA2gaAu0+HjWiq1rJ3cCwyPEN5kh73Bok8mNG0XU/kggOR4UWrRTu6sN1q3fZ+KB3bAEDaBoC730zinJ/0jvjped56S6rUur9dzk0epadaLSXJIGSfpMK0Hd5pu6ddGk6O0xO1fIXCNgCQtgHgXpCFNV4v9fx78750YNdTxeBlZs7PyuNB2s7Mt8J0OP7vpHc0krnb8ed807ENAKRtALgftEXbzcGRFD68bu5vrw16Bel6Opii/bp/SrtBdPyfdJtoY0lcIzVUe1TSb/HwAYC0DQB3nz9CJFjuqKz5gLKPvdFX1LTdTMZXsufiTdsm/1AmHdsAQNoGgPtkq7gpCXhx9nu96Q3p82+677ovdwovzWF/Xm1bF0la40eSbaeR+OO6vf6T4Nwkjx0ASNsAcC/oqsjt4XW9WXbOZeqfTOPeLG6ZudlP25FUbe+MjKTtrBy4bHVsP5zlsQMAaRsA7oujhzMyMMS8+fqBF6mP+icdY99NrLYdjdfJjucjX/VP07ENAKRtALiP/FEhkeWRjrm/3TlfGz/okLa1gSTXqYHEH1cyQsc2AJC2AeCeqiycyRlHXWfjR/DdIII//U734NidJMkLGkhajvyDmPIBnjYAkLYB4N6RAdsnvSNl51zutDZNjh/ooMBOfdsX8EcEBh/bHyjxqAGAtA0A94418s/1fOG/Mkt7u7CqJymDydnJy0N2K5qninRsAwBpGwDutfKzT43UkA4hEbK//eDR/POFD/E17Jc2kFjvMooEAEjbAHB/yQqbZjLnJu9Ie0naW7EebQ753LR9Eu51p2MbAEjbAHCvvZisB43aE4dyJ1hqk8iWSx+DVZHtD0deXtiu5Ss8YQAgbQPAPW4mcc79UX1e60jQzB2O2V6aeStdJVei7d10bAMAaRsAEMzYbiZzlYUz9+Xy0+8kLq+P1Y5bU/yuXNg+fDjPswUA0jYA3Hc66W9jdE+q3TKWZDe37K9ev0LUbhptJzxYACBtAwA8UsM+Cjc+HvVPer0lmbna4/JnDyGJ2BtweKoAQNoGAHi2h9clJVfn3rkva4MV97qeKsrEkqtqJihsAwBpGwAQWpz9XoLyVnHTfen+KS+lpZuObQAgbQMAruXo4YwblI/TE+71i6lGsHQ9+/xHnJLkYQIAaRsAELExuidxeXn6jW6RfPX42VX7tilsAwBpGwBgq86/l1Ek24VV92U9NexF5wczFLYBgLQNALgBwc723pGycx5c941eKW3vD5R4jABA2gYAtKFbJFemmrK/nVEkAEDaBgDcjMrCWTOZk1EkmryZsQ0ApG0AwM2QdTaN1NDyzJurpW2WRwIAaRsAcLEXk3VJz+tj+02WRwIAaRsAcIPKzz7VU0U3Pdcel2WdOx3bAEDaBgDcmN3Cihegk7lXAyW/RSTHjG0AIG0DAG7GyvSpZOgDSdvM2AYA0jYA4Aad9I54w7bTY5+TtnfzqzwxACBtAwA+13Zh9fPPR/K4AIC0DQC4gqWZt8Hxx8uathlFAgCkbQDAlR31T3qDt5P5i0aRJOnYBgDSNgDgRzSTDK8zYxsASNsAgC+iOveO5ZEAQNoGAHyxZpKHMxek7c2RDR4RAJC2AQA/0kZxhxnbAEDaBgB8mWaS+fedZpJQ2AYA0jYA4LoOHz6lsA0ApG0AwBexNrbvzR75R2LpL3/f+tu3LI8EANI2AODGlEZPsr/4/b9/8kvx+Oe/Pfiml8cCAKRtAMC1lT6lf/VHjdqi/2e/K81+4OEAAGkbAHAtE4mKFbXF2L/meDgAQNoGAFxL7qtE27Sd/fO3PBwAIG0DAK6l8NdM27Sd/7qPhwMApG0AwLVMP95tm7anMwzbBgDSNgDg2vJf91lRO/dVgscCAKRtAMDNGP1mKv3rPyV++pu+X/5h5J9jTukTzwQAbr//ASTM1NnFIoygAAAAAElFTkSuQmCC" alt="scaledAxes", width=700/></p>
<p>This looks pretty awesome! And I can zoom in on it, and examine it, and look at various properties! And I get the full
scripting power of <code>R</code> if I want to do anything else with, such as select sets of edges or nodes and then query who is
attached to whom. </p>
<h2>Disadvantages</h2>
<p>We don't get the <strong>arced</strong> edges. This kind of sucks, but from what little I have done with these, that actually is not that
big a deal. Would be cool if there was a way to do that, however. I do see that the web version of Cytoscape does allow
you to set a value for how much “arcness” you want on an edge. </p>
<p>This does mean that any plot with only two axes would need special consideration. Instead of doing two axes end to end
(using 180 deg), it might be better to make them parallel to each other.</p>
<p>With more than three axes, line crossings may become a problem. In that case, it may be worth looking to see if there are
ways to tell Cytoscape in what order to draw edges. I don't know if that is possible using the XMLRPC pipe that is used
by RCy.</p>
<h2>RCy Tip</h2>
<p>If you want to know how the image will look when saving a network to an image, use <code>showGraphicsDetails(obj, TRUE)</code>.</p>
<h2>Other Visualizations</h2>
<p>Of course, I had just wrapped my head around using HivePlots in my own work, when I encountered ISBs <a href="http://www.biofabric.org/"><code>BioFabric</code></a>. Given how they are representing this, could we find a way to draw this in Cytoscape??</p>
<h2>Cleanup</h2>
<pre><code class="r">deleteWindow(ccHive)
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<h2>Session Info</h2>
<pre><code class="r">Sys.time()
</code></pre>
<pre><code>## [1] "2012-11-28 16:16:41 EST"
</code></pre>
<pre><code class="r">sessionInfo()
</code></pre>
<pre><code>## R version 2.15.1 (2012-06-22)
## Platform: x86_64-pc-mingw32/x64 (64-bit)
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] HiveR_0.2-7 RCytoscape_1.6.5 XMLRPC_0.2-4 graph_1.34.0
## [5] knitr_0.8
##
## loaded via a namespace (and not attached):
## [1] BiocGenerics_0.2.0 bipartite_1.18 digest_0.5.2
## [4] evaluate_0.4.2 formatR_0.6 grid_2.15.1
## [7] igraph_0.6-2 lattice_0.20-10 MASS_7.3-18
## [10] plyr_1.7.1 RColorBrewer_1.0-5 RCurl_1.95-1.1
## [13] RFOC_2.0-02 rgl_0.92.892 sna_2.2-0
## [16] splines_2.15.1 stringr_0.6.1 survival_2.36-14
## [19] tkrgl_0.7 tnet_3.0.8 tools_2.15.1
## [22] vegan_2.0-4 XML_3.9-4.1 xtable_1.7-0
</code></pre>
<h2>Where</h2>
<p>Published on <a href="http://robertmflight.blogspot.com/2012/11/hive-plots-using-r-and-cytoscape.html">Blogger</a>. Source hosted on <a href="https://github.com/rmflight/blogPosts/blob/master/hiveplots_example.Rmd">Github</a></p>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com3tag:blogger.com,1999:blog-73443196978791417.post-11932840429111689752012-11-17T15:01:00.004-05:002012-11-17T15:01:52.521-05:00Calibre, Python, reading papers in e-inkYesterday I set up the excellent <a href="http://ipython.org/">iPython Notebook</a> on my windows machine. This is essentially an interactive web-interface to the Python shell, that lets you record everything you have done, and mark it up with lots of stuff using Markdown and mathjax. In many ways, it is very similar to using <code><a href="http://www.rstudio.com/ide/">RStudio</a></code>, <code><a href="http://www.rstudio.com/ide/docs/authoring/using_markdown">RMarkdown</a></code>, and <code><a href="http://yihui.name/knitr/">knitr</a></code> to generate <a href="http://robertmflight.blogspot.com/2012/10/writing-papers-using-r-markdown.html">Markdown and html reports</a> from <code>R</code>.<br />
<br />
I don't actually do any of my scientific coding in <code>Python</code>, but that may change. My motivation for wanting to learn some <code>Python</code> comes from the fact that <code><a href="http://calibre-ebook.com/">Calibre</a></code> is written in <code>Python</code>. Calibre provides a nice method for taking RSS feeds, parsing them, and spitting out the results as something an e-reader can understand (and really, it supports many different e-reading platforms, including ePub and Kindle formats).<br />
<br />
Although I have been using my first generation iPad to read scientific publications from PDF for 2 1/2 years now, the recent experiment of Genome Biology providing the ENCODE publications as <a href="http://genomebiology.com/content/epub/gb-2012-13-9-r53.epub">ePub</a> made me try reading scientific publications on my 3rd gen Kindle. I loved it! Even without the color figures, and the rather small screen, the experience was simply amazing. Especially given that many papers I am reading more for information intake than for marking up, it really works. And if I really need to mark up a Kindle doc, I can use the Kindle app on my iPad, or my computer. But highlighting works well. I can even retrieve the highlights and notes from the text file that holds them on the Kindle itself.<br />
<br />
Most scientific publications are made available as <a href="http://genomebiology.com/2012/13/9/R53">HTML pages</a>, or PDF. So in theory, we should be able to easily generate an ePub or Kindle format using <code>Calibre </code> from the raw HTML. However, for some reason the powers that be in the e-journal publishing world decided that figures and tables should not actually be part of the document. I really don't know why, because they are in the PDF, and it is not that hard to do in the HTML (see an example paper I did <a href="http://rmflight.github.com/affyMM/">here</a> in HTML).<br />
<br />
What this ultimately means is that to generate an e-reader compatible document, we need to actually modfiy the HTML. We need to go in, find the elements that tell us where the figure and table pages are, parse them, and get the actual files. I actually <a href="https://github.com/rmflight/res_html2azw">figured out</a> how to do this for at least one journal in <code>R</code> using the <code>XML package</code>, and was going to create a package that would take a series of links or DOIs and process them.<br />
<br />
But I get many of my papers by <code>RSS feed</code> for specific journals. And <code>Calibre</code>, as I already mentioned, has some nice functions for <a href="http://manual.calibre-ebook.com/news.html">automatically processing RSS</a> feeds and generating e-reader compatible docs from them. And <code>Calibre</code> is written in <code>Python</code>, and new RSS processing recipes are written in <code>Python</code>. Therefore, I guess I'm going to learn me some <code>Python</code>!Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-87915122399566223292012-10-10T14:13:00.001-04:002013-05-30T09:28:30.377-04:00sfLapply vs lapply in R<p>I've been using the <code>snowfall</code> package for a while to enable parallel processing in <code>R</code> on my Windows machine, where I have an 8 core processor. I discovered today that the function <code>sfLapply</code> will only work with with an object that has a class of <code>list</code>. This is really important, because there are many things that are “list-like”, and are actually lists at heart, but <code>sfLapply</code> probably won't like it.</p><p>Lets whip up an example. </p><pre><code class="r">require(snowfall)
require(GenomicRanges)
gr <- GRanges(seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-",
"+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score = 1:10, GC = seq(1, 0,
length = 10))
gr
</code></pre><pre><code>## GRanges with 10 ranges and 2 elementMetadata cols:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 [ 1, 10] - | 1 1
## b chr2 [ 2, 10] + | 2 0.888888888888889
## c chr2 [ 3, 10] + | 3 0.777777777777778
## d chr2 [ 4, 10] * | 4 0.666666666666667
## e chr1 [ 5, 10] * | 5 0.555555555555556
## f chr1 [ 6, 10] + | 6 0.444444444444444
## g chr3 [ 7, 10] + | 7 0.333333333333333
## h chr3 [ 8, 10] + | 8 0.222222222222222
## i chr3 [ 9, 10] - | 9 0.111111111111111
## j chr3 [10, 10] - | 10 0
## ---
## seqlengths:
## chr1 chr2 chr3
## NA NA NA
</code></pre><pre><code class="r">
class(gr)
</code></pre><pre><code>## [1] "GRanges"
## attr(,"package")
## [1] "GenomicRanges"
</code></pre><pre><code class="r">
grList <- split(gr, seqnames(gr))
grList
</code></pre><pre><code>## GRangesList of length 3:
## $chr1
## GRanges with 3 ranges and 2 elementMetadata cols:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 [1, 10] - | 1 1
## e chr1 [5, 10] * | 5 0.555555555555556
## f chr1 [6, 10] + | 6 0.444444444444444
##
## $chr2
## GRanges with 3 ranges and 2 elementMetadata cols:
## seqnames ranges strand | score GC
## b chr2 [2, 10] + | 2 0.888888888888889
## c chr2 [3, 10] + | 3 0.777777777777778
## d chr2 [4, 10] * | 4 0.666666666666667
##
## $chr3
## GRanges with 4 ranges and 2 elementMetadata cols:
## seqnames ranges strand | score GC
## g chr3 [ 7, 10] + | 7 0.333333333333333
## h chr3 [ 8, 10] + | 8 0.222222222222222
## i chr3 [ 9, 10] - | 9 0.111111111111111
## j chr3 [10, 10] - | 10 0
##
## ---
## seqlengths:
## chr1 chr2 chr3
## NA NA NA
</code></pre><pre><code class="r">class(grList)
</code></pre><pre><code>## [1] "GRangesList"
## attr(,"package")
## [1] "GenomicRanges"
</code></pre><p>So we have the <code>GRanges</code> object <code>gr</code>, and a <code>GRangesList</code> in <code>grList</code>. Now lets try to do some parallel execution of finding overlaps of itself.</p><p>This is the function we will use in parallel:</p><pre><code class="r">returnOverlaps <- function(inObj1, inObj2) {
findOverlaps(inObj1, inObj2, type = "any")
}
</code></pre><pre><code class="r">sfInit(parallel = T, cpus = 2)
</code></pre><pre><code>## Warning: Unknown option on commandline: options(encoding
</code></pre><pre><code>## R Version: R version 2.15.0 (2012-03-30)
</code></pre><pre><code>## snowfall 1.84 initialized (using snow 0.3-10): parallel execution on 2
## CPUs.
</code></pre><pre><code class="r">sfLibrary(GenomicRanges)
</code></pre><pre><code>## Library GenomicRanges loaded.
</code></pre><pre><code>## Library GenomicRanges loaded in cluster.
</code></pre><pre><code>## Warning: 'keep.source' is deprecated and will be ignored
</code></pre><pre><code class="r">
overlap <- sfLapply(grList, returnOverlaps, gr)
</code></pre><pre><code>## Error: 2 nodes produced errors; first error: no method for coercing this
## S4 class to a vector
</code></pre><pre><code class="r">
sfStop()
</code></pre><pre><code>## Stopping cluster
</code></pre><p>Ok, we get the error <code>no method for coercing this S4 class to a vector</code>. Seems kind of cryptic, at least it did to me. What about using normal <code>lapply</code>?</p><pre><code class="r">overlap <- lapply(grList, returnOverlaps, gr)
overlap
</code></pre><pre><code>## $chr1
## Hits of length 7
## queryLength: 3
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 1
## 2 1 5
## 3 2 1
## 4 2 5
## 5 2 6
## 6 3 5
## 7 3 6
##
## $chr2
## Hits of length 9
## queryLength: 3
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 2
## 2 1 3
## 3 1 4
## 4 2 2
## 5 2 3
## 6 2 4
## 7 3 2
## 8 3 3
## 9 3 4
##
## $chr3
## Hits of length 8
## queryLength: 4
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 7
## 2 1 8
## 3 2 7
## 4 2 8
## 5 3 9
## 6 3 10
## 7 4 9
## 8 4 10
</code></pre><p>This works without any errors. Odd. It was only when I was trying to get this to work using <code>llply</code> from the <code>plyr</code> package that I saw a message about <code>as.default.list</code> or something like that. So maybe we have to convert the <code>grList</code> to a good and proper <code>list</code> first?</p><pre><code class="r">sfInit(parallel = T, cpus = 2)
</code></pre><pre><code>## Warning: Unknown option on commandline: options(encoding
</code></pre><pre><code>## snowfall 1.84 initialized (using snow 0.3-10): parallel execution on 2
## CPUs.
</code></pre><pre><code class="r">sfLibrary(GenomicRanges)
</code></pre><pre><code>## Library GenomicRanges loaded.
</code></pre><pre><code>## Library GenomicRanges loaded in cluster.
</code></pre><pre><code>## Warning: 'keep.source' is deprecated and will be ignored
</code></pre><pre><code class="r">
grList <- as.list(grList)
overlap2 <- sfLapply(grList, returnOverlaps, gr)
sfStop()
</code></pre><pre><code>## Stopping cluster
</code></pre><pre><code class="r">
overlap2
</code></pre><pre><code>## $chr1
## Hits of length 7
## queryLength: 3
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 1
## 2 1 5
## 3 2 1
## 4 2 5
## 5 2 6
## 6 3 5
## 7 3 6
##
## $chr2
## Hits of length 9
## queryLength: 3
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 2
## 2 1 3
## 3 1 4
## 4 2 2
## 5 2 3
## 6 2 4
## 7 3 2
## 8 3 3
## 9 3 4
##
## $chr3
## Hits of length 8
## queryLength: 4
## subjectLength: 10
## queryHits subjectHits
## <integer> <integer>
## 1 1 7
## 2 1 8
## 3 2 7
## 4 2 8
## 5 3 9
## 6 3 10
## 7 4 9
## 8 4 10
</code></pre><p>It works! I'm not exactly sure why there is this difference, but I thought perhaps I can save someone else a few hours time figuring it out.</p><p>SessionInfo:</p><pre><code class="r">sessionInfo()
</code></pre><p>R version 2.15.0 (2012-03-30) Platform: x86_64-pc-mingw32/x64 (64-bit)</p><p>locale: [1] LC_COLLATE=English_United States.1252 [2] LC_CTYPE=English_United States.1252<br />
[3] LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C<br />
[5] LC_TIME=English_United States.1252 </p><p>attached base packages: [1] stats graphics grDevices utils datasets methods base </p><p>other attached packages: [1] GenomicRanges_1.8.13 IRanges_1.14.4 BiocGenerics_0.2.0<br />
[4] snowfall_1.84 snow_0.3-10 knitr_0.8.1 </p><p>loaded via a namespace (and not attached): [1] digest_0.5.2 evaluate_0.4.2 formatR_0.6 plyr_1.7.1<br />
[5] stats4_2.15.0 stringr_0.6.1 tools_2.15.0 </p><p>Posted on <a href="http://rflight.blogspot.com/2012/10/sflapply-vs-lapply.html">Blogger</a>. <a href="https://github.com/rmflight/blogPosts/blob/master/sflapplyVlapply.Rmd">Rmd</a>, <a href="https://github.com/rmflight/blogPosts/blob/master/sflapplyVlapply.md">md</a></p>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-81917745947548767162012-10-09T10:02:00.000-04:002013-05-30T09:27:39.771-04:00Writing papers using R Markdown<p>I have been watching the activity in <a href="http://rstudio.org"><code>RStudio</code></a> and <a href="http://yihui.name/knitr/"><code>knitr</code></a> for a while, and have even been using <code>Rmd</code> (R markdown) files in my own work as a way to easily provide commentary on an actual dataset analysis. Yihui has proposed <a href="http://yihui.name/en/2012/03/a-really-fast-statistics-journal/">writing papers</a> in markdown and posting them to a blog as a way to host a statistics journal, and lots of people are now using <code>knitr</code> as a way to create reproducible blog posts that include code (including yours truly).</p><p>The idea of writing a paper that actually includes the necessary code to perform the analysis, and is actually readable in its raw form, and that someone else could actually run was pretty appealing. Unfortunately, I had not had the time or opportunity to actually try it, until recently our group submitted a conference paper that included a lot of analysis in <code>R</code> that seemed like the perfect opportunity to try this. (I will link to the paper here when I hear more, or get clearance from my PI). Originally we wrote the paper in Microsoft® Word, but after submission I decided to see what it would have taken to write it as an <code>Rmd</code> document that could then generate <code>markdown</code> or <code>html</code>.</p><p>It turned out that it was not that hard, but it did force me to do some things differently. This is what I want to discuss here.</p><h2>Advantages</h2><p>I actually found it much easier to have the text with the analysis (in contrast to having to be separate in a Word document), and upon doing the conversion, discovered some possible numerical errors that crept in because of having to copy numerical results separately (that is the nice thing about being able to insert variable directly into the text). In addition, the Word template for the submission didn't play nice with automatic table and figure numbering, so our table and figure numbering got messed up in the submission. So overall, I'd say it worked out better with the <code>Rmd</code> file overall, even with the having to create functions to handle table and figure numbering properly myself (see below).</p><h2>Tables and Figures</h2><p>As I'm sure most of you know, Word (and other WYSIWYG editors) have ability to keep track of your object numbers, this is especially nice for keeping your figure and table numbers straight. Of course, there is no such ability built into a static text file, but I found it was easy to write a couple of functions for this. The way I came up with is to have a variable that contains a label for the figure or table, a function that increments the counter when new figures or tables are added, and a function that prints the associated number for a particular label. This does require a bit of forethought on the part of the writer, because you may have to add a table or figure label to the variable long before you actually create it, but as long as you use sane (i.e. descriptive) labels, it shouldn't be a big deal. Let me show you what I mean.</p><h3>Counting</h3><pre><code class="r">incCount <- function(inObj, useName) {
nObj <- length(inObj)
useNum <- max(inObj) + 1
inObj <- c(inObj, useNum)
names(inObj)[nObj + 1] <- useName
inObj
}
figCount <- c(`_` = 0)
tableCount <- c(`_` = 0)
</code></pre><p>The <code>incCount</code> function is very simple, it takes an object, checks the maximum count, and then adds an incremental value with the supplied name. In this example, I initialized the <code>figCount</code> and <code>tableCount</code> objects with a non-sensical named value of zero. </p><p>Now in the process of writing, I decide I'm going to need a table on the amount of time spent by post-docs writing blog posts in different years of their post-doc training. Lets call this <code>t.blogPostDocs</code>. Notice that this is a fairly descriptive name. We can assign it a number like so:</p><pre><code class="r">tableCount <- incCount(tableCount, "t.blogPostDocs")
tableCount
</code></pre><pre><code>## _ t.blogPostDocs
## 0 1
</code></pre><h3>Inserting</h3><p>So now we have a variable with a named number we can refer to. But how do we insert it into the text? We are going to use another function that will let us insert either the text with a link, or just the text itself.</p><pre><code class="r">pasteLabel <- function(preText, inObj, objName, insLink = TRUE) {
objNum <- inObj[objName]
useText <- paste(preText, objNum, sep = " ")
if (insLink) {
useText <- paste("[", useText, "](#", objName, ")", sep = "")
}
useText
}
</code></pre><p>This function allows us to insert the table number like so:</p><pre><code class="r">r I(pasteLabel("Table", tableCount, "t.blogPostDocs"))
</code></pre><p>This would be inserted into a normal <code>inline</code> code block. The <code>I</code> makes sure that the text will appear as normal text, and not get formatted as a code block. The default behavior is to insert as a relative link, thereby enabling the use of relative links to link where a table / figure is mentioned to its actual location. For example, we can insert the anchor link like so:</p><pre><code><a id="t.blogPostDocs"></a>
</code></pre><h3>Markdown Tables</h3><p>Followed by the actual table text. This brings up the subject of <code>markdown</code> tables. I also wrote a function (thanks to Yihui again) that transforms a normal <code>R</code> <code>data.frame</code> to a <code>markdown</code> table.</p><pre><code class="r">tableCat <- function(inFrame) {
outText <- paste(names(inFrame), collapse = " | ")
outText <- c(outText, paste(rep("---", ncol(inFrame)), collapse = " | "))
invisible(apply(inFrame, 1, function(inRow) {
outText <<- c(outText, paste(inRow, collapse = " | "))
}))
return(outText)
}
</code></pre><p>Lets see it in action.</p><pre><code class="r">postDocBlogs <- data.frame(PD = c("p1", "p2", "p3"), NBlog = c(4, 10, 2), Year = c(1,
4, 2))
postDocBlogs
</code></pre><pre><code>## PD NBlog Year
## 1 p1 4 1
## 2 p2 10 4
## 3 p3 2 2
</code></pre><pre><code class="r">
postDocInsert <- tableCat(postDocBlogs)
postDocInsert
</code></pre><pre><code>## [1] "PD | NBlog | Year" "--- | --- | ---" "p1 | 4 | 1"
## [4] "p2 | 10 | 4" "p3 | 2 | 2"
</code></pre><p>To actually insert it into the text, use a code chunk with <code>results='asis'</code> and <code>echo=FALSE</code>. </p><pre><code class="r">cat(postDocInsert, sep = "\n")
</code></pre><table><thead>
<tr> <th>PD</th> <th>NBlog</th> <th>Year</th> </tr>
</thead><tbody>
<tr> <td>p1</td> <td>4</td> <td>1</td> </tr>
<tr> <td>p2</td> <td>10</td> <td>4</td> </tr>
<tr> <td>p3</td> <td>2</td> <td>2</td> </tr>
</tbody></table><p>Before inserting the table though, you might want an inline code with the table number and caption, like this:</p><p><code>I(pasteLabel("Table", tableCount, "t.blogPostDocs", FALSE))</code> This is the number of blog posts and year of training for post-docs.</p><p>Table 1 This is the number of blog posts and year of training for post-docs.</p><p>Remember for captions to set the <code>insLink</code> variable to <code>FALSE</code> so that you don't generate a link from the caption.</p><h3>Figures</h3><p>Oftentimes, you will have code that generates the figure, and then you want to insert the figure at a different point. This is accomplished by the judicious use of <code>echo</code> and <code>include</code> chunk options.</p><p>For example, we can create a <code>ggplot2</code> figure and store it in a variable in one chunk, and then <code>print</code> it in a later chunk to actually insert it into the text body.</p><pre><code class="r">plotData <- data.frame(x = rnorm(1000, 1, 5), y = rnorm(1000, 0, 2))
plotKeep <- ggplot(plotData, aes(x = x, y = y)) + geom_point()
figCounts <- incCount(figCount, "f.randomFigure")
</code></pre><p>And now we decide to actually insert it using <code>print(plotKeep)</code> with the option of <code>echo=FALSE</code>:</p><p><img src="data:image/png;base64,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" alt="plot of chunk figureInsert"/> </p><p><strong><a href="#f.randomFigure">Figure 1</a>. A random figure.</strong></p><h2>Numerical result formatting</h2><p>When <code>R</code> prints a number, it normally likes to do so with lots of digits. This is not probably what you want either in a table or when reporting a number in a sentence. You can control that by using the <code>format</code> function. When generating a new variable, the number of digits to display when printing will be saved, and when used on a variable directly, only the defined number of digits will display.</p><h2>Echo and Include</h2><p>This brings up the issue of how to keep the code from appearing in the text body. I found depending on the particulars, either using <code>echo=FALSE</code> or <code>include=FALSE</code> would do the job. This is meant to be a paper, a reproducible one, but a paper nonetheless, and therefore the code should not end up in the text body. </p><h2>References</h2><p>One thing I haven't done yet is convert all the references. I am planning to try using the <a href="https://github.com/cboettig/knitcitations/">knitcitations</a> package. I will probably post on that experience.</p><h2>HTML generation</h2><p>Because I use <code>RStudio</code>, I set up a modified function For generating a full <code>html</code> version of the paper, changing the default <code>RStudio</code> <code>markdown</code> render options like so:</p><pre><code>htmlOptions <- markdownHTMLOptions(defaults=TRUE)
htmlOptions <- htmlOptions[htmlOptions != "hard_wrap"]
markdownToHTML(inputFile, outputFile, options = htmlOptions)
</code></pre><p>This should be added to a <code>.Rprofile</code> file either in your <code>home</code> directory or in the directory you start <code>R</code> in (this is especially useful for modification on a per project basis).</p><p>I do this because when I write my documents, I want the source to be readable online. If this is a <code>github</code> hosted repo, that means being displayed in the <code>github</code> file browser, which does not do line wrapping. So I set up a 120 character line in my editor, and try very hard to stick to that. </p><h2>Function source</h2><p>You can find the previously mentioned functions in a github gist <a href="https://gist.github.com/3858973">here</a>.</p><h2>Post source</h2><p>The source files for this blog post can be found at: <a href="https://github.com/rmflight/blogPosts/blob/master/papersinRmd.Rmd"><code>Rmd</code></a>, <a href="https://github.com/rmflight/blogPosts/blob/master/papersinRmd.md"><code>md</code></a>, and <a href="https://github.com/rmflight/blogPosts/blob/master/papersinRmd.html"><code>html</code></a>.</p><p>Posted on October 9, 2012, at <a href="http://robertmflight.blogspot.com/2012/10/writing-papers-using-r-markdown.html">http://robertmflight.blogspot.com/2012/10/writing-papers-using-r-markdown.html</a></p><p>Edit: added section on formatting numerical results</p><p>Edit: added session info</p><pre><code>R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_0.9.2.1 knitr_0.8.1
loaded via a namespace (and not attached):
[1] colorspace_1.1-1 dichromat_1.2-4 digest_0.5.2
[4] evaluate_0.4.2 formatR_0.6 grid_2.15.0
[7] gtable_0.1.1 labeling_0.1 MASS_7.3-21
[10] memoise_0.1 munsell_0.4 plyr_1.7.1
[13] proto_0.3-9.2 RColorBrewer_1.0-5 reshape2_1.2.1
[16] scales_0.2.2 stringr_0.6.1 tools_2.15.0
</code></pre>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com6tag:blogger.com,1999:blog-73443196978791417.post-38641748860138623262012-09-12T11:02:00.000-04:002012-09-12T11:05:27.030-04:00AbsIDconvert: New method for converting genomic identifiers<p>Today our paper <a href="http://dx.doi.org/10.1186/1471-2105-13-229">“AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities”</a>
finally hit BMC Bioinformatics. I'm really excited, because I've been wanting to tell people outside our primary
collaborators about it. The website for the tool is <a href="http://bioinformatics.louisville.edu/abid">http://bioinformatics.louisville.edu/abid</a>. There will eventually be
a downloadable virtual machine for local analyses, with no restrictions on the number of items submitted.</p>
<p>The basic premise is that every genetic type of identifier, whether it is an Entrez Gene, Refseq, Ensembl (gene,
transcript), and microarray probe (or probeset for Affy) can be reduced to a DNA sequence that can subsequently be
placed on a reference genome as a genomic interval. Conversion between different types of identifiers then becomes
a problem of finding overlapping genomic intervals. </p>
<p>We have a large number of different types of identifiers for different organisms and genome assemblies stored as
genomic intervals, including many Affy and Agilent microarrays. However, if your favorite array is not present, or your
identifier doesn't seem to work (as of Sept 12, 2012 there is at least one Agilent array that seems to be missing IDs),
you can submit the sequences and find corresponding genomic intervals and translate to other identifiers.</p>
<p>Note that there is a limit on how many sequences / IDs can be uploaded at one time (you will get a message to “Select
Genome Version!!!!!!” when you try to upload too many sequences for example). This is removed in the virtual machine
version.</p>
<p>The code behind the website uses <a href="http://r-project.org"><code>R</code></a>, <a href="http://rapache.net/"><code>RApache</code></a> and the <a href="http://www.bioconductor.org/packages/2.10/bioc/html/GenomicRanges.html"><code>GenomicRanges</code></a> package for storing and querying intervals. Alignment is carried out using <a href="http://bowtie-bio.sourceforge.net/index.shtml"><code>Bowtie2</code></a></p>
<p>I hope others find this resource (and or approach) useful!</p>
<p>Next week I hope to put up a post with more examples, although you can probably get a good idea of how it works and
the possibilities from the publication and the website.</p>
<p>Source hosted at <a href="https://github.com/rmflight/blogPosts/blob/master/absidConvert_live.md">https://github.com/rmflight/blogPosts/blob/master/absidConvert_live.md</a></p>
<p>Posted to <a href="http://robertmflight.blogspot.com/2012/09/absidconvert-new-method-for-converting_12.html">http://robertmflight.blogspot.com/2012/09/absidconvert-new-method-for-converting_12.html</a></p>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com1tag:blogger.com,1999:blog-73443196978791417.post-39136396924625079782012-08-16T16:29:00.001-04:002012-08-16T16:30:36.619-04:00Show me yours<p>romunov at <a href="http://danganothererror.wordpress.com/">“danganothererror”</a> recently posted about his personal
setup for working with <a href="http://danganothererror.wordpress.com/2012/08/09/show-me-yours-and-ill-show-you-mine/">R</a>, and challenged others to post as well. <a href="https://dl.dropbox.com/s/si6z10l55eebjk1/rstudio_screen.png">Here
is my setup.</a></p>
<p>I use <a href="http://rstudio.org">RStudio</a>, maximized on one monitor (I have a two monitor setup).
This gives me multiple editor windows for scripts / function writing / package development,
an integrated R command window, workspace & history browser, as well as files, plots,
packages, and help. For working on multiple projects, I use the RStudio project
feature, that keeps project specific information (directory, saved sessions if you
want it, integrated git repos), and multiple desktops using <a href="http://dexpot.de/">dexpot</a> on
Windows.</p>
<p>RStudio also has markdown to html support directly, and they are adding a bunch of
package development support, using a lot of the work Hadley Wickham has done with
the excelent <strong>devtools</strong> package.</p>
<p>I like it a lot, and much prefer it over my previous Notepad++, npptoR, R gui setup.</p>
<p>Source markdown at <a href="https://github.com/rmflight/blogPosts/blob/master/showmeyours.md">https://github.com/rmflight/blogPosts/blob/master/showmeyours.md</a></p>
<p>Posted at: <a href="http://robertmflight.blogspot.com/2012/08/show-me-yours.html">http://robertmflight.blogspot.com/2012/08/show-me-yours.html</a></p>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-75223077952307777612012-08-15T16:02:00.000-04:002012-08-15T16:07:22.220-04:00Loving Markdown!<p>Ok, so for those who don't know, the guys from RStudio recently teamed up with
Yihui to add some really nice report authoring options in RStudio using the
packages <a href="http://yihui.name/knitr">knitr</a> ability to turn a combination of <a href="http://daringfireball.net/projects/markdown/">markdown</a> and
<a href="http://www.r-project.org">R</a> code into html. </p>
<p>I have to admit, this has really changed how I work. Previously, I generally had
R scripts, that I would then run, and summarize the results in a separate document
as a report on what I had done. I know, many like to talk about <a href="http://www.statistik.lmu.de/%7Eleisch/Sweave/">Sweave</a>,
the language that R uses to generate vignettes demonstrating package functionality,
but have you ever tried to write a Sweave document?</p>
<p>You need to know a fair amount about Latex, and even then it can be difficult to
get the output you want. In addition, reading the raw file can be quite painful
(I know, I have my own <a href="https://github.com/rmflight/categoryCompare">Bioconductor package</a> that I wrote a Sweave
vignette for).</p>
<p>Writing R Markdown documents just feels different. When I read the raw source
of a Markdown document, I can actually read it, code and all. What is really
sweet is that instead of writing about what I am doing in the comments, I write
it out in full in the document, and then have the code blocks doing the actual
calculations. What is really great is to regenerate the report, I simply re-knit
it to generate a new html file.</p>
<p>It is so much easier to work with, that I am probably going to switch even how
I write my blog posts, using a Markdown document as the source. For right now,
that means writing a .md file, and then converting it to html using the R Markdown
package, and then writing in the html to Blogger. You can see a good explanation
of that process from Jeffrey Horner's blog <a href="http://jeffreyhorner.tumblr.com/post/25804518110/blog-with-r-markdown-and-tumblr-part-i">here</a> and <a href="http://jeffreyhorner.tumblr.com/post/25943954723/blog-with-r-markdown-and-tumblr-part-ii">here</a>.</p>
<p>When I combine this with a <a href="https://github.com/rmflight/blogPosts">github repo</a> for storage, it also means I have
some other place to keep the raw source of my blog posts, as well as easily read
and edit the text. For example, you can read the raw text that was used for <a href="https://raw.github.com/rmflight/blogPosts/master/rmarkdown_post_150812.md">this
post</a>.</p>
<p>Source of this post is at <a href="https://github.com/rmflight/blogPosts/blob/master/rmarkdown_post_150812.md">https://github.com/rmflight/blogPosts/blob/master/rmarkdown_post_150812.md</a>.
Published at <a href="http://robertmflight.blogspot.com/2012/08/loving-markdown.html">http://robertmflight.blogspot.com/2012/08/loving-markdown.html</a></p>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-53769714789136880242012-08-15T15:13:00.001-04:002012-08-15T15:13:55.958-04:00Journal Club: 15.08.12<p>I just came back from our Bioinformatic group (a rather loose association of various
researchers at UofL interested in and doing bioinformatics) journal club, where we
discussed this recent paper:</p>
<p><a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002511">Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes</a></p>
<p>Besides the catchy title that makes one believe that perhaps Google is getting into
cancer research (maybe they are and we don't know it yet), there were some interesting
aspects to this paper. </p>
<h2>Premise</h2>
<p>The premise is that they can combine gene expression data and network data to find
better associations between gene expression data and a particular disease endpoint.
The way this is carried out is through the use of the TRANSFAC transcription factor -
gene target database for the network, the correlation of the gene expression with
the disease status as the importance of a gene with the disease, and the Google
<a href="http://en.wikipedia.org/wiki/PageRank">PageRank</a> as the means to transfer the network knowledge to the gene expression
data. They call their method <strong>NetRank</strong>. </p>
<p>Note that the general idea had already been tried in this paper on <a href="http://dx.doi.org/10.1186/1471-2105-6-233">GeneRank</a>.</p>
<h2>Implementation</h2>
<p>Rank the genes with disease status (poor or good prognosis) using a method (SAM,
t-test, fold-change, correlation, NetRank). Pick <em>n</em> top genes, and develop a
predictive model using a support vector machine. Wash, rinse, repeat several times
to find the best set, varying the number of top genes, and the number of samples used
in the training set.</p>
<p>For <strong>NetRank</strong>, the top genes were decided by using a sub-optimization based on
varying <em>d</em>, the dampening factor in the PageRank algorithm that determines how
much information can be transferred to other genes. The best value of <em>d</em> determined
in this study was 0.3.</p>
<p>All other methods used just the 8000 genes that passed filtering, but NetRank used
all the genes on the array, with those that were filtered out had their initial
correlations set to 0, so that they were still in the network representation.</p>
<p><img src="http://www.ploscompbiol.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pcbi.1002511.g001&representation=PNG_I" alt="Monte Carlo cross-validation"/></p>
<h2>Did it work?</h2>
<p>From the paper, it appears to have worked. Using a monte-carlo cross-validation,
they were able to achieve over 70% prediction rates. And this was better than any
of the other methods they used to associate genes with the disease, including SAM,
t-test, fold-change, and raw correlations.</p>
<p><img src="http://www.ploscompbiol.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pcbi.1002511.g002&representation=PNG_I" alt="NetRank feature selection performance"/></p>
<h2>Issues</h2>
<p>As we discussed the article, some questions did come up.</p>
<ol>
<li>What was the variation in <em>d</em> depending on the size of the training set?</li>
<li>How consistent were the genes that came out as biomarkers?
<ul>
<li>It would be nice to try this methodology on a series of independent, but
related cancer datasets (ie breast or lung cancer) and see how consistent the lists
are. This was done <a href="http://www.biomedcentral.com/1471-2105/13/182/abstract">here</a>.</li>
</ul></li>
<li>What happens if the genes that don't pass filtering are removed from the network
entirely?</li>
<li>Were the problems reported with not-filtering genes due to having only two
disease points (poor and good prognosis) to calculate a correlation of expression
with?</li>
<li>How many iterations does it take to achieve convergence?</li>
<li>The list of genes they come up with are fairly well known cancer genes. We
were kindof surprised that they didn't seem to come up novel genes associated
directly with pancreatic cancer.</li>
<li>Why is <em>d</em> so variable depending on the cancer examined?</li>
</ol>
<h2>Things to try</h2>
<ul>
<li>Could we improve on this by instead of taking just the top-ranked genes, look for
the top ranked cliques, i.e. take the top gene, remove anything in its immediate
neighborhood, and then go to the next one?</li>
<li>What would happen if we used a directed network based on connected Reactome
or KEGG pathways?</li>
</ul>
<p>The markdown source of this post is <a href="https://github.com/rmflight/blogPosts/blob/master/jc_150812.md">here</a>.</p>
Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-88084447014786558152012-07-13T22:28:00.000-04:002013-05-30T09:29:14.894-04:00Creating custom CDF for Affy chips in R / Bioconductor<head><br />
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<h2>What?</h2><p>For those who don't know, <strong>CDF</strong> files are chip definition format files that define which probes belong to which probesets, and are necessary to use any of the standard summarization methods such as <strong>RMA</strong>, and others.</p><h2>Why?</h2><p>Because we can, and because custom definitions have been shown to be quite useful. See the information over at <a href="http://brainarray.mbni.med.umich.edu/brainarray/Database/CustomCDF/cdfreadme.htm">Brainarray</a>.</p><h2>Why not somewhere else?</h2><p>A lot of times other people create custom <strong>CDF</strong> files based on their own criteria, and make it subsequently available for others to use (see the <a href="http://brainarray.mbni.med.umich.edu/brainarray/Database/CustomCDF/cdfreadme.htm">Brainarray</a> for an example of what some are doing, as well as [PlandbAffy][linkplandb]) </p><p>You have a really nifty idea for a way to reorganize the probesets on an Affymetrix chip to perform a custom analysis, but you don't want to go to the trouble of actually creating the CDF files and Bioconductor packages normally required to do the analysis, and yet you want to test and develop your analysis method.</p><h2>How?</h2><p>It turns out you are in luck. At least for <strong>AffyBatch</strong> objects in Bioconductor (created by calling <strong>ReadAffy</strong>), the <strong>CDF</strong> information is stored as an attached environment that can be easily hacked and modified to your hearts content. Environments in R are quite important and useful, and I wouldn't have come up with this if I hadn't been working in R for the past couple of years, but figured someone else might benefit from this knowledge.</p><h2>The environment</h2><p>In R, one can access an environment like so:</p><pre><code class="r">get("objName", envName) # get the value of object in the environment
ls(envName)
</code></pre><p>What is also very cool, is that one can extract the objects in an environment to a list, and also create their own environment from a list using <strong>list2env</strong>. Using this methodology, we can create our own definition of probesets that can be used by standard Bioconductor routines to summarize the probes into probesets.</p><p>A couple of disclaimers: </p><ul><li>I have only tried this on 3' expression arrays</li>
<li>There might be a better way to do this, but I couldn't find it (let me know in the comments)</li>
</ul><h2>Example</h2><pre><code class="r">require(affy)
require(estrogen)
require(hgu95av2cdf)
datadir = system.file("extdata", package = "estrogen")
pd = read.AnnotatedDataFrame(file.path(datadir, "estrogen.txt"),
header = TRUE, sep = "", row.names = 1)
pData(pd)
</code></pre><pre><code>## estrogen time.h
## low10-1.cel absent 10
## low10-2.cel absent 10
## high10-1.cel present 10
## high10-2.cel present 10
## low48-1.cel absent 48
## low48-2.cel absent 48
## high48-1.cel present 48
## high48-2.cel present 48
</code></pre><pre><code class="r">
celDat = ReadAffy(filenames = rownames(pData(pd)), phenoData = pd,
verbose = TRUE, celfile.path = datadir)
</code></pre><pre><code>## 1 reading J:/R150_libraries/estrogen/extdata/low10-1.cel ...instantiating an AffyBatch (intensity a 409600x8 matrix)...done.
## Reading in : J:/R150_libraries/estrogen/extdata/low10-1.cel
## Reading in : J:/R150_libraries/estrogen/extdata/low10-2.cel
## Reading in : J:/R150_libraries/estrogen/extdata/high10-1.cel
## Reading in : J:/R150_libraries/estrogen/extdata/high10-2.cel
## Reading in : J:/R150_libraries/estrogen/extdata/low48-1.cel
## Reading in : J:/R150_libraries/estrogen/extdata/low48-2.cel
## Reading in : J:/R150_libraries/estrogen/extdata/high48-1.cel
## Reading in : J:/R150_libraries/estrogen/extdata/high48-2.cel
</code></pre><p>This loads up the data, reads in the raw data, and gets it ready for us to use. Now, lets see what is in the actual <strong>CDF</strong> environment.</p><pre><code class="r">topProbes <- head(ls(hgu95av2cdf)) # get a list of probesets
topProbes
</code></pre><pre><code>## [1] "100_g_at" "1000_at" "1001_at" "1002_f_at" "1003_s_at" "1004_at"
</code></pre><pre><code class="r">
exSet <- get(topProbes[1], hgu95av2cdf)
exSet
</code></pre><pre><code>## pm mm
## [1,] 175218 175858
## [2,] 356689 357329
## [3,] 227696 228336
## [4,] 237919 238559
## [5,] 275173 275813
## [6,] 203444 204084
## [7,] 357984 358624
## [8,] 368524 369164
## [9,] 285352 285992
## [10,] 304510 305150
## [11,] 159937 160577
## [12,] 223929 224569
## [13,] 282764 283404
## [14,] 270003 270643
## [15,] 303343 303983
## [16,] 389048 389688
</code></pre><p>We can see here that the first probe set 100_g_at has 16 perfect-match and mis-match probes in associated with it. </p><p>Lets summarize the original data using RMA.</p><pre><code class="r">rma1 <- exprs(rma(celDat))
</code></pre><pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre><pre><code class="r">
head(rma1)
</code></pre><pre><code>## low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel
## 100_g_at 9.643 9.741 9.537 9.354 9.592
## 1000_at 10.398 10.254 10.004 9.904 10.375
## 1001_at 5.718 5.881 5.860 5.954 5.961
## 1002_f_at 5.513 5.802 5.571 5.608 5.390
## 1003_s_at 7.784 8.008 8.038 7.835 7.926
## 1004_at 7.289 7.604 7.489 7.772 7.522
## low48-2.cel high48-1.cel high48-2.cel
## 100_g_at 9.571 9.476 9.531
## 1000_at 10.034 10.345 9.863
## 1001_at 6.021 5.981 6.285
## 1002_f_at 5.495 5.508 5.630
## 1003_s_at 8.139 7.995 8.233
## 1004_at 7.600 7.456 7.675
</code></pre><p>Now lets get the data as a list, and then create a new environment to be used for summarization.</p><pre><code class="r">allSets <- ls(hgu95av2cdf)
allSetDat <- mget(allSets, hgu95av2cdf)
allSetDat[1]
</code></pre><pre><code>## $`100_g_at`
## pm mm
## [1,] 175218 175858
## [2,] 356689 357329
## [3,] 227696 228336
## [4,] 237919 238559
## [5,] 275173 275813
## [6,] 203444 204084
## [7,] 357984 358624
## [8,] 368524 369164
## [9,] 285352 285992
## [10,] 304510 305150
## [11,] 159937 160577
## [12,] 223929 224569
## [13,] 282764 283404
## [14,] 270003 270643
## [15,] 303343 303983
## [16,] 389048 389688
##
</code></pre><pre><code class="r">
hgu2 <- list2env(allSetDat)
celDat@cdfName <- "hgu2"
rma2 <- exprs(rma(celDat))
</code></pre><pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre><pre><code class="r">head(rma2)
</code></pre><pre><code>## low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel
## 100_g_at 9.643 9.741 9.537 9.354 9.592
## 1000_at 10.398 10.254 10.004 9.904 10.375
## 1001_at 5.718 5.881 5.860 5.954 5.961
## 1002_f_at 5.513 5.802 5.571 5.608 5.390
## 1003_s_at 7.784 8.008 8.038 7.835 7.926
## 1004_at 7.289 7.604 7.489 7.772 7.522
## low48-2.cel high48-1.cel high48-2.cel
## 100_g_at 9.571 9.476 9.531
## 1000_at 10.034 10.345 9.863
## 1001_at 6.021 5.981 6.285
## 1002_f_at 5.495 5.508 5.630
## 1003_s_at 8.139 7.995 8.233
## 1004_at 7.600 7.456 7.675
</code></pre><p>What about removing the <strong>MM</strong> columns? RMA only uses the <strong>PM</strong>, so it should still work.</p><pre><code class="r">allSetDat <- lapply(allSetDat, function(x) {
x[, 1, drop = F]
})
allSetDat[1]
</code></pre><pre><code>## $`100_g_at`
## pm
## [1,] 175218
## [2,] 356689
## [3,] 227696
## [4,] 237919
## [5,] 275173
## [6,] 203444
## [7,] 357984
## [8,] 368524
## [9,] 285352
## [10,] 304510
## [11,] 159937
## [12,] 223929
## [13,] 282764
## [14,] 270003
## [15,] 303343
## [16,] 389048
##
</code></pre><pre><code class="r">
hgu3 <- list2env(allSetDat)
celDat@cdfName <- "hgu3"
rma3 <- exprs(rma(celDat))
</code></pre><pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre><pre><code class="r">head(rma3)
</code></pre><pre><code>## low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel
## 100_g_at 9.643 9.741 9.537 9.354 9.592
## 1000_at 10.398 10.254 10.004 9.904 10.375
## 1001_at 5.718 5.881 5.860 5.954 5.961
## 1002_f_at 5.513 5.802 5.571 5.608 5.390
## 1003_s_at 7.784 8.008 8.038 7.835 7.926
## 1004_at 7.289 7.604 7.489 7.772 7.522
## low48-2.cel high48-1.cel high48-2.cel
## 100_g_at 9.571 9.476 9.531
## 1000_at 10.034 10.345 9.863
## 1001_at 6.021 5.981 6.285
## 1002_f_at 5.495 5.508 5.630
## 1003_s_at 8.139 7.995 8.233
## 1004_at 7.600 7.456 7.675
</code></pre><p>What if we only want to use the first 5 probesets?</p><pre><code class="r">allSetDat <- allSetDat[1:5]
hgu4 <- list2env(allSetDat)
celDat@cdfName <- "hgu4"
celDat
</code></pre><pre><code>## AffyBatch object
## size of arrays=640x640 features (22 kb)
## cdf=hgu4 (5 affyids)
## number of samples=8
## number of genes=5
## annotation=hgu95av2
## notes=
</code></pre><pre><code class="r">rma4 <- exprs(rma(celDat))
</code></pre><pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre><pre><code class="r">rma4
</code></pre><pre><code>## low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel
## 100_g_at 9.463 9.555 9.449 9.402 9.448
## 1000_at 10.183 10.010 10.010 9.970 10.102
## 1001_at 5.944 6.005 5.944 6.090 6.237
## 1002_f_at 5.787 5.846 5.817 5.815 5.763
## 1003_s_at 7.751 7.769 7.913 7.864 7.861
## low48-2.cel high48-1.cel high48-2.cel
## 100_g_at 9.458 9.401 9.431
## 1000_at 10.010 10.197 9.890
## 1001_at 6.148 6.189 6.207
## 1002_f_at 5.763 5.741 5.755
## 1003_s_at 7.918 7.863 7.929
</code></pre><pre><code class="r">dim(rma4)
</code></pre><pre><code>## [1] 5 8
</code></pre><h2>Custom CDF</h2><p>To generate our custom CDF, we are going to set our own names, and take random probes from all of the probes on the chip. The actual criteria of which probes should be together can be made using any method the author chooses.</p><pre><code class="r">maxIndx <- 640 * 640
customCDF <- lapply(seq(1, 100), function(x) {
tmp <- matrix(sample(maxIndx, 20), nrow = 20, ncol = 1)
colnames(tmp) <- "pm"
return(tmp)
})
names(customCDF) <- seq(1, 100)
hgu5 <- list2env(customCDF)
celDat@cdfName <- "hgu5"
rma5 <- exprs(rma(celDat))
</code></pre><pre><code>## Background correcting
## Normalizing
## Calculating Expression
</code></pre><pre><code class="r">
head(rma5)
</code></pre><pre><code>## low10-1.cel low10-2.cel high10-1.cel high10-2.cel low48-1.cel
## 1 6.938 6.851 6.831 6.742 6.894
## 10 6.663 6.610 6.615 6.612 6.610
## 100 5.256 5.499 5.420 5.205 5.352
## 11 7.529 7.807 7.552 7.539 7.728
## 12 6.163 6.216 6.230 6.091 6.071
## 13 6.311 6.404 6.264 6.290 6.257
## low48-2.cel high48-1.cel high48-2.cel
## 1 6.636 6.862 6.576
## 10 6.572 6.685 6.481
## 100 5.256 5.281 5.258
## 11 7.792 7.682 7.590
## 12 6.058 6.204 6.159
## 13 6.249 6.275 6.313
</code></pre><p>I hope this information is useful to someone else. I know it made my life a lot easier.</p><pre><code class="r">sessionInfo()
</code></pre><pre><code>## R version 2.15.0 (2012-03-30)
## Platform: x86_64-pc-mingw32/x64 (64-bit)
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_0.6.3 hgu95av2cdf_2.10.0 estrogen_1.8.8
## [4] BiocInstaller_1.4.7 rat2302cdf_2.10.0 AnnotationDbi_1.18.1
## [7] affy_1.34.0 Biobase_2.16.0 BiocGenerics_0.2.0
##
## loaded via a namespace (and not attached):
## [1] affyio_1.24.0 DBI_0.2-5 digest_0.5.2
## [4] evaluate_0.4.2 formatR_0.5 IRanges_1.14.4
## [7] markdown_0.5.2 parser_0.0-16 plyr_1.7.1
## [10] preprocessCore_1.18.0 Rcpp_0.9.10 RSQLite_0.11.1
## [13] stats4_2.15.0 stringr_0.6 tools_2.15.0
## [16] zlibbioc_1.2.0
</code></pre><p>Edit: added session information.</p>Source of the code that generated the output is on <a href="https://gist.github.com/3108891">Github</a>
</body>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-82539140712583201982012-04-16T10:13:00.000-04:002012-04-16T10:13:16.867-04:00Firefox Keywords + Javascript = Magic!Here is something particularly useful that I keep rediscovering for various reasons. Firefox allows you to refer to any bookmark with a keyword, and use that keyword as a shortcut in the address bar to access it. For example, if I create a bookmark that points to "http://www.google.com", and go to "Show all Bookmarks", and click on that bookmark, then I can modify the "Keyword" field, and I will then be able to simply type "google" in the address bar and go there. This has been in Firefox since a very <a href="http://www-archive.mozilla.org/docs/end-user/keywords.html">early version</a>. <br />
<br />
This gets cooler in that you can easily set up these keywords to actually bookmark search services that will then take a search string following the keyword and redirect you to the <a href="http://support.mozilla.org/en-US/kb/Smart%20keywords">search results</a>. <br />
<br />
From a Bioinformatics perspective, I could set up a custom search on the UCSC Genome browser that uses this string as the location:<br />
<script class="brush:html" type="syntaxhighlighter">
<![CDATA[
http://genome.ucsc.edu/cgi-bin/hgTracks?hgHubConnect.destUrl=../cgi-bin/hgTracks&clade=mammal&org=Rat&db=rn4&position=%s&hgt.suggestTrack=refGene
]]>
</script><br />
And then give this bookmark the keyword "ratgenome". I could then search for genes in the Rat genome from the Firefox address "ratgenome 'genename'". For example, I could do the query 'ratgenome brca1' to search for "brca1" in Rat.<br />
<br />
Now, how often do you look in just one place for something though? What if I wanted to search NCBI's gene database, Ensembl, GeneCards, and UCSC Genome browser? Now we create a simple javascript command, and save that as our bookmark with a keyword:<br />
<script class="brush:javascript" type="syntaxhighlighter">
<![CDATA[
javascript:void(window.open('http://www.ncbi.nlm.nih.gov/gene/?term=%s'));void(window.open('http://useast.ensembl.org/Multi/Search/Results?species=all;idx=;q=%s'));void(window.open('http://genecards.org/index.php?path=/Search/keyword/%s'));void(window.open('http://genome.ucsc.edu/cgi-bin/hgTracks?hgHubConnect.destUrl=../cgi-bin/hgTracks&clade=mammal&org=Rat&db=rn4&position=%s&hgt.suggestTrack=refGene'));
]]>
</script><br />
If I supply the keyword "genesearch", now I can simply do "genesearch brca1" and have all four windows open as tabs with my search results. This can be set up for almost any site as long as you can figure out what the search string parameters are, replacing the part of the URL that defines your query with "%s".<br />
<br />
Inspiration from <a href="http://sergey.marechek.com/blog/2008/09/25/31/">here</a>.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com2tag:blogger.com,1999:blog-73443196978791417.post-25105282022684337542012-04-02T21:06:00.003-04:002012-04-02T21:06:29.073-04:00Bioconductor, Git and SVN multiple branchesSo the Bioconductor SVN repo is set up in the standard trunk / branches way, but as a developer, you only have write access to your package directory, which is either in "trunk/x/x/packageName" (dev) or "branches/releaseNum/x/x/packageName" (release). This is not what Git is expecting if you want to enable keeping track of release and dev in the same Git repository. How do you set it up so you can keep track of everything in a single repository like you might normally want?<br />
<br />
I found some suggestions <a href="https://community.jboss.org/wiki/DevelopingOnMultipleSVNBranchesWithGit" rel="nofollow">here</a>, and show them below:<br />
<br />
Clone your SVN repo:<br />
<pre><code>git svn clone https://svnRepo/trunk/x/x/packageName packageName
</code></pre>Now add information about the branches:<br />
<pre><code>git config --add svn-remote.releaseNum.url https://svnRepo/branches/releaseNum/x/x/packageName/
git config --add svn-remote.releaseNum.fetch :refs/remotes/releaseNum/
</code></pre>Now fetch and create a local branch tied to the remote:<br />
<pre><code>git svn fetch releaseNum
git checkout -b local-releaseNum -t releaseNum
</code></pre>From these two branches, you should be able to create normal Git branches, work and modify them, and then go back and merge changes, rebase, and dcommit as usual. Although I'm sure you could clone each of these into separate git repos, you would then lose the ability to do a diff between them. I hope this saves someone else from searching over the web for a couple of hours.<br />
<br />
Originally posted on my <a href="https://github.com/rmflight/general/wiki/Git-SVN-resources">Wiki</a>.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-1460635465240084492012-03-14T08:35:00.000-04:002012-03-14T08:35:09.638-04:00Tweetdeck odditiesI've started using <a href="http://tweetdeck.com">Tweetdeck</a> to keep track of multiple twitter accounts and Facebook, and it's pretty cool. One thing that seems to be odd though, is that if you add multiple accounts, the "Home" column will show the feed from the first twitter account added and the Facebook feed, but not a second twitter feed. It took me a while to figure this out. I decided the easiest thing to do then was to have three columns, a timeline for each twitter account, and the facebook news feed.<br />
<br />
I didn't see anything about this in the help, and there don't appear to be any options to change what appears in the "Home" column.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-42384007475436615142012-03-09T11:59:00.000-05:002012-03-09T12:16:58.012-05:00Pushing to GitHub as two different usersSo I recently decided to start using GitHub for both some personal and professional-related stuff. As these two areas can be very different, I decided that I wanted two separate GitHub accounts, but I wanted to be able to clone and push changes from either my work computer or my home computer (i.e. I might work on stuff either at work or at home, especially the professional-related items).<br />
<br />
This can be done, but it took a bit of work. First, <a href="http://help.github.com/set-up-git-redirect">install and set-up Git</a> on your machine. Test it, and make sure it works. Assuming this is the machine that you will be using two different accounts on, go back and generate a new set of ssh-keys, using the email address for your other GitHub account, and a different file name.<br />
<br />
<script class="brush:py" type="syntaxhighlighter">
<![CDATA[
ssh-keygen -t rsa -C "emailaddress@whatever.com"
fileNameOfKeyPair
]]>
</script><br />
Probably try to give it some descriptive name so you know what it is<br />
<br />
Now, in the ".ssh" directory, generate a file "config" if it doesnt exist, and set up a new host:<br />
<script class="brush:py" type="syntaxhighlighter">
<![CDATA[
Host descName
Hostname github.com
User git
IdentityFile ~/.ssh/fileNameOfKeyPair
]]>
</script><br />
When you clone the repository you want to work on, use the ssh command like so:<br />
<script class="brush:py" type="syntaxhighlighter">
<![CDATA[
git clone ssh://descName/pathToRepo.git
]]>
</script><br />
cd into the directory, and also set your username and email address to correspond with that GitHub account for that local directory:<br />
<script class="brush:py" type="syntaxhighlighter">
<![CDATA[
cd repoName
git config user.name "userName"
git config user.email "email"
]]>
</script><br />
Now when you commit, the changes will be committed with the correct user, and more importantly when you "push" your changes back to GitHub, it will now use the alternative key that is set from the hosts file.<br />
<br />
Based on information I found here: http://superuser.com/questions/232373/tell-git-which-private-key-to-use<br />
<br />
Edit: GitHub assigns which user made which commit based on the user email address attached to the commit, so make sure you set the "user.email" correctly. If you are using GMail, you can add a "." anywhere in your email address and mail will still get to you, but GitHub (and Twitter, and others) will see it as a different address.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-21339078147555452782012-03-06T15:18:00.000-05:002012-03-06T15:18:11.795-05:00VizBi 2012 - Visualizing Biological DataIf you are interested in visualizing biological data, the <a href="http://vizbi.org/">2012 Visualizing Biological Data</a> conference is currently on. On the website they have videos of talks from previous years. In addition, there is an issue of <a href="http://vizbi.org/Nature_Methods/">Nature Methods from the 2010</a> conference that is probably worth checking out.<br />
<br />
Follow the hashtag <a href="https://twitter.com/#%21/search/realtime/%23vizbi">#vizbi</a>Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0tag:blogger.com,1999:blog-73443196978791417.post-38238115873079674092012-02-23T09:30:00.000-05:002012-02-23T09:30:18.921-05:00Software CarpentryAn article in this weeks edition of Nature on providing source code for journal articles that depend on new or original computer programs to analyze data (<a href="http://www.nature.com/nature/journal/v482/n7386/full/nature10836.html">link</a>) led to the discovery of two new resources:<br />
<br />
1 - An article on scientists ability to write code (<a href="http://www.nature.com/nature/journal/v467/n7317/pdf/467775a.pdf">link</a>) <br />
<br />
2 - An actual course focused on teaching scientists how to write computer code, known as "<a href="http://software-carpentry.org/">Software Carpentry</a>". The materials for the course are posted on-line, with <a href="http://software-carpentry.org/3_0/">full lecture content</a>, as well as videos. If all you have is a basic introduction to programming, this might be useful.<br />
<br />
My own programming experience, I have one intro to programming course from my Masters, and then did a lot of Matlab programming during my PhD (including GUI development), and then learned R, object-oriented and R packages during my PostDoc, and I think I am going to work through this course. I have finally been implementing unit-tests and using version control, but I am sure there are lessons to be learned from this course.Anonymoushttp://www.blogger.com/profile/11732353840939638830noreply@blogger.com0