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<h1 class="title toc-ignore">VoltRon</h1>
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<p><br></p>
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<!-- ## VoltRon: An R package for Spatial Data Analysis and Integration -->
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<p><br></p>
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<div style="width:76%; margin-left: auto; margin-right: auto; ">
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<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/voltron_framework_box_io.png" class="center"></p>
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<p><br></p>
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<p class="maintext">
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<strong> VoltRon </strong> is a spatial omic analysis toolbox for
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multi-omics integration using spatial image registration. VoltRon is
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also capable of analyzing multiple types of spatially-aware data
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modalities.
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</p>
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<ul class="maintext2">
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<li style="padding-bottom: 10px">
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<strong> Unique data structure </strong> of VoltRon allows users to
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seamlessly define tissue blocks, layers and multiple assay types in one
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R object.
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</li>
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<li style="padding-bottom: 10px">
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<strong> End-to-end downstream data analysis </strong> for distinct
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spatial biology technologies are supported. VoltRon visualizes and
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analyzes regions of interests (ROIs), spots, cells, molecules and tiles
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<strong>(under development)</strong>.
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</li>
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<li style="padding-bottom: 10px">
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<strong> Automated Image Registration </strong> incorporates
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<a href="https://opencv.org/">OpenCV</a> (fully embedded into the
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package using <a href="https://www.rcpp.org/">Rcpp</a>) to detect common
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features across images and achieves registration. Users may interact
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with built-in mini shiny apps to change alignment parameters and
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validate alignment accuracy.
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</li>
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<li style="padding-bottom: 10px">
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<strong> Manual Image Registration </strong> helps users to select
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common features across spatial datasets using reference images stored in
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VoltRon objects. In case automated image registration doesn’t work, you
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can still align images by manually picking landmark points.
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</li>
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<li style="padding-bottom: 10px">
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<p style="padding-bottom: 3px">
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<strong> Spatially Aware Analysis </strong> allows detecting spatial
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patterns across cells, spots, molecules and other entities.
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</p>
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<ul class="maintext3">
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<li style="padding-bottom: 10px padding-top: 12px">
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<strong>(Niche Clustering: Spots)</strong> VoltRon allows integration to
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single cell RNA datasets using
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<a href="https://satijalab.org/seurat/">Seurat</a>,
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<a href="https://www.bioconductor.org/packages/release/bioc/vignettes/SingleCellExperiment/inst/doc/intro.html">SingleCellExperiment</a>
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and <a href="https://github.com/dmcable/spacexr">spacexr</a> for spot
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deconvolution. Estimated cell type abundances are then used to cluster
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spots into groups of cell type niches which are defined as spots with
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distinct composition of cell types.
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</li>
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<li style="padding-bottom: 2px">
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<strong>(Niche Clustering: Cells)</strong> VoltRon creates spatial
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neighborhoods around cells to cluster local cellular compositions around
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all cells which in turn informs users on cell types that are likely
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within proximity to each other.
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</li>
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<li style="padding-bottom: 10px">
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<strong>(Hot Spot Detection)</strong> VoltRon detects region of locally
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spatial patterns of cells/molecules/spots that are abundant in
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biological events and/or features.
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</li>
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</ul>
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</li>
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<li style="padding-bottom: 10px">
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<p>
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<strong> Support for Big Data </strong> for VoltRon objects enables
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storing large feature data matrices and large microscopic images of
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tissues on disk without overloading memory, thus allowing analysis on
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large datasets with ease. VoltRon stores large images as pyramid
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structures to speed up visualization and data retrieval.
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</p>
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</li>
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<li style="padding-bottom: 10px">
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<p>
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<strong> Interoperability across R/Python frameworks </strong> allows
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users to convert VoltRon objects to a large number of objects used by
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other spatial omic platforms such as Seurat, Squidpy (AnnData),
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SpatialExperiment (BioConductor) and Giotto.
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</p>
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</li>
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</ul>
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</div>
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<div style="width:70%; margin-left: auto; margin-right: auto">
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<div style="float:left; margin-left: auto; margin-right: auto">
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<p><br></p>
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<div id="staying-up-to-date" class="section level2">
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<h2>Staying up-to-date</h2>
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<p>To ask questions please use VoltRon discussion forum on google
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groups.</p>
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<ul>
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<li><a href="https://groups.google.com/forum/#!forum/voltron_discussion"
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class="uri">https://groups.google.com/forum/#!forum/voltron_discussion</a></li>
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</ul>
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<p><br></p>
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</div>
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<div id="installation" class="section level2">
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<h2>Installation</h2>
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<p>Install from the GitHub repository using devtools (with R version
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4.3.0 or higher):</p>
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<pre class="r"><code>if (!require(&quot;devtools&quot;, quietly = TRUE))
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    install.packages(&quot;devtools&quot;)
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devtools::install_github(&quot;BIMSBbioinfo/VoltRon&quot;)</code></pre>
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<p>Depending on the number of required dependencies, installation may be
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completed under a minute or may take a few minutes.</p>
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<p>On <strong>Windows</strong> and <strong>MacOS</strong>, OpenCV will
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be downloaded automatically upon installation. However, <a
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href="https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html">Rtools</a>
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may be required to be downloaded too, hence this may take some time!</p>
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<p>On <strong>Ubuntu</strong> we provide a set of instructions that may
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help users to build OpenCV with necessary headers [here](<a
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href="https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md"
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class="uri">https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md</a>.</p>
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<p>On <strong>Fedora</strong> you may need <a
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href="https://src.fedoraproject.org/rpms/opencv"><code>opencv-devel</code></a>:</p>
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<pre class="sh"><code>yum install opencv-devel</code></pre>
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<p><br></p>
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</div>
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<div id="dependencies" class="section level2">
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<h2>Dependencies</h2>
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<p>VoltRon incorporates <code>RBioformats</code> package to import
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images from <code>ome.tiff</code> files, which requires <a
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href="https://www.oracle.com/java/technologies/downloads/?er=221886">Java
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JDK</a> to be available in your system:</p>
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<p>See <a
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href="https://cran.r-project.org/web/packages/rJava">https://cran.r-project.org/web/packages/rJava</a>
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for more information.</p>
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<p><br></p>
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</div>
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<div id="docker-hub" class="section level2">
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<h2>Docker Hub</h2>
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<p>You can also run VoltRon from a container already available in <a
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href="https://hub.docker.com/repository/docker/amanukyan1385/rstudio-voltron/general">Docker
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Hub</a>. The docker image is based on the <a
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href="https://rocker-project.org/">Rocker Project</a> and can be run
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from the terminal like below:</p>
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<pre><code>docker run --rm -ti -e PASSWORD=&lt;yourpassword&gt; -p 8787:8787 amanukyan1385/rstudio-voltron:main</code></pre>
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<p>Then, start the RStudio session from the browser at
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<code>http://localhost:8787/</code> and enter <code>rstudio</code> as
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username and <code>&lt;yourpassword&gt;</code> as password.</p>
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<p>See <a
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href="https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/docker_desktop_instructions.md">here</a>
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for more instructions on how to run the container using <a
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href="https://www.docker.com/products/docker-desktop/">Docker
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Desktop</a>.</p>
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<p><br></p>
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</div>
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<div id="references" class="section level2">
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<h2>References</h2>
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<p>Manukyan, A., Bahry, E., Wyler, E., Becher, E., Pascual-Reguant, A.,
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Plumbom, I., … &amp; Akalin, A. (2023). <a
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href="https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1">VoltRon:
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A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics
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Integration using Image Registration</a>. bioRxiv, 2023-12. d</p>
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<p>To ask questions please use VoltRon discussion forum on <a
551
href="https://groups.google.com/forum/#!forum/voltron_discussion">Google
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groups</a>.</p>
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</div>
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</div>
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</div>
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<script>
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// add bootstrap table styles to pandoc tables
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function bootstrapStylePandocTables() {
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}
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$(document).ready(function () {
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});
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</script>
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<!-- tabsets -->
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<script>
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$(document).ready(function () {
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  window.buildTabsets("TOC");
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});
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$(document).ready(function () {
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</script>
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<script>
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    var script = document.createElement("script");
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