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authorJohannes Ranke <jranke@uni-bremen.de>2014-10-10 22:41:34 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2014-10-10 22:41:34 +0200
commit99f93922f42af6cadd5c7ff4b0f1a96c830f9fb7 (patch)
tree00e2a6fc4e0c301721bc8307097a2b76917c9a57 /vignettes
parent813b2bba907bf2a62e1d5d58e9f362c09da7dfc1 (diff)
Added a manual, small GUI improvements
Diffstat (limited to 'vignettes')
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+<!--
+%\VignetteEngine{knitr::knitr}
+%\VignetteIndexEntry{Manual for gmkin}
+-->
+
+```{r, include = FALSE}
+library(knitr)
+opts_chunk$set(tidy = FALSE, cache = TRUE)
+```
+# Manual for gmkin
+
+## Introduction
+
+The R add-on package gmkin provides a browser based graphical interface for
+performing kinetic evaluations of degradation data using the mkin package.
+While the use of gmkin should be largely self-explanatory, this manual may serve
+as a functionality overview and reference.
+
+For system requirements and installation instructions, please refer to the
+[gmkin homepage](http://kinfit.r-forge.r-project.org/gmkin_static)
+
+## Starting gmkin
+
+As gmkin is an R package, you need to start R and load the gmkin package before you can run gmkin.
+This can be achieved by entering the command
+
+```{r, eval = FALSE}
+library(gmkin)
+```
+
+into the R console. This will also load the packages that gmkin depends on,
+most notably gWidgetsWWW2 and mkin. Loading the package only has to be done
+once after you have started R.
+
+Before you start gmkin, you should make sure that R is using the working
+directory that you would like to keep your gmkin project file(s) in. If you use
+the standard R application on windows, you can change the working directory
+from the File menu.
+
+Once you are sure that the working directory is what you want it to be, gmkin
+can be started by entering the R command
+
+```{r, eval = FALSE}
+gmkin()
+```
+
+This will cause the default browser to start up or, if it is already running, to
+pop up and open a new tab for displaying the gmkin user interface.
+
+In the R console, you should see some messages, telling you if the local R help
+server, which also serves the gmkin application, has been started, which port it is
+using and that it is starting an app called gmkin.
+
+Finally, it should give a message like
+
+```{r, eval = FALSE}
+Model cost at call 1: 2388.077
+```
+
+which means that the first kinetic evaluation has been configured for fitting.
+
+In the browser, you should see something like the screenshot below.
+
+![gmkin start](img/gmkin_start.png)
+
+The statusbar at the bottom of the gmkin window shows, among others, the
+working directory that gmkin uses.
+
+Note that the project file management area described below can be minimized by clicking on
+the arrows on the right hand side of its title bar. This may be helpful if the vertical
+size of your browser window is restricted.
+
+## Project file management
+
+At startup, gmkin loads a project called "FOCUS\_2006\_gmkin" which is distributed
+with the gmkin package. A gmkin project contains datasets, kinetic models for
+fitting, and so-called fits, i.e. the results of fitting models to data. These
+gmkin projects can be saved and restored using the project file management area in the
+top left.
+
+![projects](img/projects.png)
+
+If you would like to save these items for reference or for the purpose of continuing
+your work at a later time, you can modify the project name and press the button below it.
+The full name of the project file created and the working directory will be displayed
+in the gmkin status bar.
+
+For restoring a previously saved project file, use the Browse button to locate
+it, and the "Upload" button to load its contents into gmkin.
+
+## Studies
+
+The "Studies" area directly below the "Project file management" area can be expanded by clicking
+on the arrows on the right hand side of its title bar. Studies in gmkin are
+simply a numbered list of sources for the datasets in a project. You can edit the titles
+directly by clicking on them. If you would like to add a new data source, use the "Add"
+button above the table containing the list. If there are more than one studies in the list,
+you can also remove them using the "Remove" button.
+
+![studies](img/studies.png)
+
+Note that the user is responsible to keep the study list consistent with the numbers that are
+used in the list of datasets described below.
+
+## Datasets and Models
+The project loaded at the start of gmkin contains two datasets and four kinetic models. These
+are listed to the left under the heading "Datasets and Models", together with a button for
+setting up fits as shown below.
+
+![datasets and models](img/datasetsnmodels.png)
+
+For editing, adding or removing datasets or models, you need to click on an
+entry in the respective list.
+
+For setting up a fit of a specific model to a specific dataset, the model and
+the dataset should be selected by clicking on them. If they are compatible, clicking
+the button "Configure fit for selected dataset and model" will set up the fit and
+open the "Plotting and Fitting" tab to the right.
+
+## Dataset editor
+
+The dataset editor allows for editing datasets, entering new datasets, uploading
+data from text files and deleting datasets.
+
+![dataset editor](img/dataseteditor.png)
+
+If you want to create (enter or load) a new dataset, it is wise to first edit
+the list of data sources in the "Studies" area as described above.
+
+### Entering data directly
+
+For entering new data manually, click on "New dataset", enter a title and select
+the study from which the dataset is taken. At this stage, you may already want
+to press "Keep changes", so the dataset appears in the list of datasets.
+
+In order to generate a table suitable for entering the data, enter a comma separated
+list of sampling times, optionally the time unit, and the number of replicate measurements
+at each sampling time. Also, add a comma separated list of short names of the
+relevant compounds in your dataset. A unit can be specified for the observed
+values. An example of filling out the respective fields is shown below.
+
+![generate data grid](img/generatedatagrid.png)
+
+Once everyting is filled out to your satisfaction, press the button "Generate empty grid
+for kinetic data". In our example, this would result in the data grid shown below. You
+can enter the observed data into the value column, as shown in the screenshot below.
+
+![data grid](img/datagrid.png)
+
+The column with title override serves to override data points from the original
+datasets, without loosing the information which value was originally reported.
+
+If everything is OK, press "Keep changes" to save the dataset in the current
+workspace. Note that you need to save the project file (see above) in order to
+be able to use the dataset that you created in a future gmkin session.
+
+### Entering data directly
+
+In case you want to work with a larger dataset that is already available as a computer
+file e.g. in a spreadsheet application, you can export these data as a tab separated
+or comma separated text file and import it using the "Browse" and "Upload" buttons in the
+dataset editor.
+
+As an example, we can create a text file from one of the datasets shipped with
+the mkin package using the following R command:
+
+```{r, eval = FALSE}
+write.table(schaefer07_complex_case, sep = ",", dec = ".",
+ row.names = FALSE, quote = FALSE,
+ file = "schaefer07.csv")
+```
+
+This produces a text file with comma separated values in the current working directory of R.
+
+Loading this text file into gmkin using the "Browse" and "Upload" buttons results in
+an import configuration area like this, with the uploaded text file displayed to the left,
+and the import options to the right.
+
+![upload area](img/uploadarea.png)
+
+In the import configuration area, the following options can be specified. In the field
+"Comment lines", the number of lines in the beginning of the file that should be ignored
+can be specified.
+
+The checkbox on the next line should be checked if the first line of the file contains
+the column names, i.e. the names of the observed variables when the data are in wide format.
+
+As "Separator", whitespace, semicolon or comma can be chosen. If whitespace is selected,
+files in which the values are separated by a fixed or varying number of whitespace characters
+should be read in correctly. As the tabulator counts as a whitespace character, this is
+also the option to choose for tabulator separated values.
+
+As the "Decimal" separator, comma "," or period "." can be selected.
+
+In the next line, it can be specified if the data are in wide or in long format.
+If in wide format, the only option left to specify is the title of the column containing
+the sampling times. If the data is in long format, the column headings specifying the
+columns containing the observed variables (default is "name"), the sampling times
+(default is "time"), the observed values (default is "value") and, if present in the data,
+the relative errors (default is "err") can be adapted. The default settings appearing if
+the long format is selected are shown below.
+
+![long](img/long.png)
+
+In our example we have data in the wide format, and after adapting the
+"Separator" to a comma, we can press the button "Import using options specified
+below", and the data should be imported. If successful, the data editor should
+show the sampling times and the names of the observed variables, as well as the
+imported data in a grid for further editing or specifying overrides.
+
+After editing the title of the dataset and selecting the correct study as
+the source of the data, the dataset editor should look like shown below.
+
+![successful upload](img/successfulupload.png)
+
+If everything is OK, press "Keep changes" to save the dataset in the current
+workspace. Again, you need to save the project file in order to be able to use
+the dataset that you created in a future gmkin session.
+
+## Model editor
+
+The following screenshot shows the model editor for the model number 4 in
+the list of models that are in the initial workspace.
+
+![model editor](img/modeleditor.png)
+
+In the first line the name of the model can be edited. You can also specify "min" or
+"max" for minimum or maximum use of formation fractions. Maximum use of formation
+fractions means that the differential equations in the degradation model are formulated
+using formation fractions. When you specify "min", then formation fractions are only used
+for the parent compound when you use the FOMC, DFOP or the HS model for it.
+
+Pressing "Copy model" keeps the model name, so you should change it for the newly generated copy.
+Pressing "Add observed variable" adds a line in the array of state variable specifications below.
+The observed variables to be added are usually transformation products (usually termed metabolites),
+but can also be the parent compound in a different compartment (e.g. "parent\_sediment").
+
+Only observed variable names that occur in previously defined datasets can be selected. For any observed
+variable other than the first one, only the SFO or the SFORB model can be selected. For each
+observed variables, a comma separated list of target variables can be specified. In addition, a pathway
+to the sink compartment can be selected. If too many observed variables have been added, complete lines
+can be removed from the model definition by pressing the button "Remove observed variable".
+
+If the model definition is supposedly correct, press "Keep changes" to make it possible to select
+it for fitting in the listing of models to the left.
+
+## Plotting and fitting
+
+If the dataset(s) to be used in a project are created, and suitable kinetic models have been defined,
+kinetic evaluations can be configured by selecting one dataset and one model in the lists to the left,
+and the pressing the button "Configure fit for selected dataset and model" below these lists.
+
+This opens the "Plotting and fitting" tab area to the right, consisting of a graphical window
+showing the data points in the selected dataset and the model, evaluated with the initial parameters
+defined by calling `mkinfit` without defining starting parameters. The value of the objective function
+to be minimized for these default parameters can be seen in the R console, e.g. as
+
+```{r, eval = FALSE}
+Model cost at call 1: 15156.12
+```
+
+for the example shown below, where the FOCUS example dataset D and the model SFO\_SFO were selected.
+
+![plotting and fitting](img/plottingnfitting.png)
+
+### Parameters
+
+In the right hand area, initially the tab with the parameter list is displayed. While
+name and type of the parameters should not be edited, their initial values can be edited
+by clicking on a row. Also, it can be specified if the parameters should be fixed
+in the optimisation process.
+
+If the initial values for the parameters were changed, the resulting model solution
+can be visually checked by pressing the button "Show initial". This will update the
+plot of the model and the data using the specified initial parameter values.
+
+If a similar model with a partially overlapping model definition has already be fitted,
+initial values for parameters with the same name in both models can also be retrieved
+from previous fits. This facilitates stepwise fitting of more complex degradation pathways.
+
+After the model has been successfully fitted by pressing the "Run" button, the optimised
+parameter values are added to the parameter table.
+
+### Fit options
+
+The most important fit options of the `mkinfit` function can be set via the
+"Fit option" tab shown below. If the "plot" checkbox is checked, an R graphics device
+started via the R console shows the fitting progress, i.e. the change of the model
+solution together with the data during the optimisation.
+
+![fit options](img/fitoptions.png)
+
+The "solution\_type" can either be "auto", which means that the most effective solution
+method is chosen for the model, in the order of "analytical" (for parent only degradation
+data), "eigen" (for differential equation models with only linear terms, i.e. without
+FOMC, DFOP or HS submodels) or "deSolve", which can handle all model definitions generated
+by the `mkin` package.
+
+The parameters "atol" and "rtol" are only effective if the solution type is "deSolve". They
+control the precision of the iterative numerical solution of the differential equation model.
+
+The checkboxes "transform\_rates" and "transform\_fractions" control if the parameters are fitted
+as defined in the model, or if they are internally transformed during the fitting process in
+order to improve the estimation of standard errors and confidence intervals which are based
+on a linear approximation at the optimum found by the fitting algorithm.
+
+The dropdown box "weight" specifies if and how the observed values should be weighted
+in the fitting process. If "manual" is chosen, the values in the "err" column of the
+dataset are used, which are set to unity by default. Setting these to higher values
+gives lower weight and vice versa. If "none" is chosen, observed
+values are not weighted. Please refer to the documentation of the `modFit` function from
+the `FME` package for the meaning of options "std" and "mean".
+
+The options "reweight.method", "reweight.tol" and "reweight.max.iter" enable the use of
+iteratively reweighted least squares fitting, if the reweighting method is set to "obs". Please
+refer to the `mkinfit` [documentation](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html)
+for more details.
+
+The drop down box "method.modFit" makes it possible to choose between the optimisation
+algorithms "Marq" (the default Levenberg-Marquardt implementation from the R package
+`minpack.lm`), "Port" (an alternative, also local optimisation algorithm) and
+"SANN" (the simulated annealing method - robust but inefficient and without a
+convergence criterion).
+
+Finally, the maximum number of iterations for the optimisation can be adapted using the
+"maxit.modFit" field.
+
+### Fitting the model
+
+In many cases the starting parameters and the fit options do not need to be modified
+and the model fitting process can simply be started by pressing the "Run" button.
+In the R console, the progressive reduction in the model cost can be monitored and will
+be displayed like this:
+
+```{r, eval = FALSE}
+Model cost at call 1 : 15156.12
+Model cost at call 3 : 15156.12
+Model cost at call 7 : 14220.79
+Model cost at call 8 : 14220.79
+Model cost at call 11 : 14220.79
+Model cost at call 12 : 3349.268
+Model cost at call 15 : 3349.268
+Model cost at call 17 : 788.6367
+Model cost at call 18 : 788.6366
+Model cost at call 22 : 374.0575
+Model cost at call 23 : 374.0575
+Model cost at call 27 : 371.2135
+Model cost at call 28 : 371.2135
+Model cost at call 32 : 371.2134
+Model cost at call 36 : 371.2134
+Model cost at call 37 : 371.2134
+```
+
+If plotting of the fitting progress was selecte in the "Fit options" tab, a
+new separate graphics window should either pop up, or a graphics window previously
+started for this purpose will be reused.
+
+### Summary
+
+Once a fit has successfully been performed by pressing the "Run" button, the summary
+as displayed below can be accessed via the "Summary" tab.
+
+![summary](img/summary.png)
+
+The complete summary can be saved into a text file by specifying a suitable file name
+and pressing the button "Save summary".
+
+### Plot options
+
+In the tab "Plot options", the observed variables for which the data and the model fit should be
+plotted can be selected as shown below.
+
+![plot options](img/plotoptions.png)
+
+### Confidence interval plots
+
+Whenever a new fit has been configured or a run of a fit has been completed, the plotting
+area is update with the abovementioned plot of the data and the current model solution.
+
+In addition, a confidence interval plot is shown below this conventional plot. In case
+a fit has been run and confidence intervals were successfully calculated for the fit (i.e.
+if the model was not overparameterised and no other problems occurred), the
+confidence intervals are graphically displayed as bars as shown below.
+
+![conficence](img/confidence.png)
+
+<!-- vim: set foldmethod=syntax ts=2 sw=2 expandtab: -->
diff --git a/vignettes/gmkin_manual.html b/vignettes/gmkin_manual.html
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+<!DOCTYPE html>
+<html>
+<head>
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+<title>Manual for gmkin</title>
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+
+<h1>Manual for gmkin</h1>
+
+<h2>Introduction</h2>
+
+<p>The R add-on package gmkin provides a browser based graphical interface for
+performing kinetic evaluations of degradation data using the mkin package.
+While the use of gmkin should be largely self-explanatory, this manual may serve
+as a functionality overview and reference.</p>
+
+<p>For system requirements and installation instructions, please refer to the
+<a href="http://kinfit.r-forge.r-project.org/gmkin_static">gmkin homepage</a></p>
+
+<h2>Starting gmkin</h2>
+
+<p>As gmkin is an R package, you need to start R and load the gmkin package before you can run gmkin.
+This can be achieved by entering the command</p>
+
+<pre><code class="r">library(gmkin)
+</code></pre>
+
+<p>into the R console. This will also load the packages that gmkin depends on,
+most notably gWidgetsWWW2 and mkin. Loading the package only has to be done
+once after you have started R. </p>
+
+<p>Before you start gmkin, you should make sure that R is using the working
+directory that you would like to keep your gmkin project file(s) in. If you use
+the standard R application on windows, you can change the working directory
+from the File menu.</p>
+
+<p>Once you are sure that the working directory is what you want it to be, gmkin
+can be started by entering the R command</p>
+
+<pre><code class="r">gmkin()
+</code></pre>
+
+<p>This will cause the default browser to start up or, if it is already running, to
+pop up and open a new tab for displaying the gmkin user interface.</p>
+
+<p>In the R console, you should see some messages, telling you if the local R help
+server, which also serves the gmkin application, has been started, which port it is
+using and that it is starting an app called gmkin.</p>
+
+<p>Finally, it should give a message like</p>
+
+<pre><code class="r">Model cost at call 1: 2388.077
+</code></pre>
+
+<p>which means that the first kinetic evaluation has been configured for fitting.</p>
+
+<p>In the browser, you should see something like the screenshot below.</p>
+
+<p><img 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qf6nJ8nrEDhFBwt0NTnXQoZaTfgon1DsQwsOxix/rj7H8e4oQNX3T0dijGoq4KWzaJDbaJFUUwMJbghSMACAJY8DIX3aKgJa9GJ56kSKnCH38Tp2zUU8rlGxOd4yk2JolWrHFsbIYQQQkhvqFxmL+J37LXfJxabSHJNp0/qPV3d7iQBJNd02sSe075ONzSx+3W+8ssF+0/s27/oY6muqC1ZbZBq11sP+P78gMLEUz2K3F1ttl0rnQGAkuV+HwwkySmqmsXGUKK91LaPvrIyh9RMTVsZVfSVczQVdTMKAFr1aAQAEqZDkhXLzOMQymgBi2ARIKPhg0iqbiunUWOHRo0dxLn1pm24VpCVCgD97A0Pvsk7/VI4yOKj/977NWwaBUxsDgDycmobFP5Rq83Z0YT9zyfJxficzpJnakOWlzOwnuH+uLyL8blesmgAMLVvXqFN1dlVsOJ2Rt3ObRUT1cagtgpFNYsS3WoTtaY2Ml7xVLg/JnuI7D8AqOHUnklnvjZx+RJHQ458XXNRfS24Rz+KAqOzh0jvqm4VMR3o9MfMvKdMqfr+dF8L7pGPogMvsgaIIrWvRblHixBCCCGEtKT6LIDyJBVlAgDJMijfYquILetPD5p78FKiKPnFYwAQ2LpWdkR6xbkWb09wihkJTjWKXMxI2BOc4lyLp7p7xf3NaL61RtGkFX8ciAYvny7jGxkBwIQ2NjwW6/gdmO9ZR8DjLN91BQCGN6nBIomArZdUtz64Gbls65nJK475bQq1tLYaOswLADqM9PJpav3w7N+z99yvVaf2uGGFT9yUlyM/utqcPYe71xFwrh+/eiahDo8kAED0dV3k5XgM9+rWxOrO8YtLTr5r2MR57sj6FddQAGDTui4AdGwpUExUG4PaKhTVLEp0q03U6uE/eERru783Hhi+9WWLNu6/zy2s6U9tbDgkMWnOQaV1Dcb95OlkYXBm87HfY+qqbl3qY2/EZm+8BDsHNDTnc0cvPqq2kLE/eTY0Nzi18fiuuAZGLAIA8rWoYLlHq1al34CHCy644IILLrjgUolLUYjdwYU3eU72tgaAsOgsLX9daeP7GYEUi9ve2+uXvnblWGwVkRX/KuD4w8cfhBTHoKFTvRm+Ho0MWZUdlL6JTijQnKGxHU9tesfGpgCwfMHc5Ws2REc91lxI2I3QKbNmf3j7+lIs38fFRHHT1afZPs2+pFyNym7ryAOA60+F/r1rv0kv58nP+sGhBrnx4gdvFwFgQ31WldtEJhW2H7+XZHEjDsyo7FiKp020iq2NEEIIIVQ9RcYVKHVkmBXVZwGU5+XTq9snlWNpVY1ZPae1C50qOwo916S2YWWHoEzDiBpShA2lqkq1ydrlB64nZE9dOql1RiQACGq1ruyINClFtFWqtRFCCCGEqghtnwWAECqE/QotYUOpqkpt8vNy35+ZtTpdIw93rdxgilWaaKtSayOEEEIIVRGqb92rlDAQ0hnYrdASNpQqbJNvCVsbIYQQQkhVxd4IgJD+oXF6sXawoVRhm3xL2NoIIYQQQqrwRgCESua/tyIA4LLJPddTKjuWqovDJrGhlMjbpFFtQdV5FqBeepNONaptzLQ2QgghhBBSpDwE8PBtXqXEgVD5au1YUQ8D79bCsoJKRgiVl8Z1TBrXqewgEEIIIYQqz7VHH9WmKw8BMK8GRAghhBBCCCGEkJ5RHgJACGlW1HAaQgghhBBCCFVxRQ4BZOVJw2KEqVkSKYVPVEJVHZskLE3Z7Rua1BDgqBZCCCGEEEJIDyVeW6995h+m+UnARDVdfX8pK0967l5G3ZoGDZ2MDTj4hEBU1YkkdOIn8V8PMga0qWFqyKrQY438rkaFlo8QQgghhBBCSo6FpwPA8jUbtMlMEMQP0/zUblI/BBAWI7Svya1nyS11fAh9SwYcwsHSAABuR2f3cjev0GMlZIgrtHyEEEIIIYQQKkp01GPNGcJuhGrYqn4IICVL0sHZuPRBIVQZaplxw1/lVPRR8MYYhBBCCCGEkI5SPwQgo2gDNs7/RzrGgENIZRXeQ8eHYyCEEEIIIYR0FD47DaGSoWkcA0AIIYQQQghVPplMlpqUaGVbi8XS9oFoOASAUMngCABCCCGEEEKo0tEUlfzhfUFBfsbHVOtadhRFabNXCYYAnj98UtrYUFXUxK15ZYegk3AIACGEEEIIIVTpUpISCwryASA76xOXxzO3sKC0uGm5ZLMAGje1L2V0qIqJfvauskPQVTgCgBBCCCGEEKpcH1OSc3OE8o9pKclcLtfQyLjYK5YlGwKwMBOUJjqE9Ag+CwAhhBBCCCFUuSytbZq5tTKvUUMxMTM9PTnxg+YdSzYEwCLJEoeGkH4pagSg9fjdAEAQLAtLix7dWv/Ytc43DQsAAGRi4aFjETtvvaFpuBc0RW0eJk6uUe3w33sxKf4zA8NypABF7iJHy8RtJgVpyMkUrrg1692rXw8/vP82K58mbe2sx4zyHuRkyGSTY/Lfu/7v1qsv49LEJjUtenX3mNnFVrtKI4QQQgghVB0lJ34otsOvqmRDACQOAaBqT/P9NRH7fIOPn11y9HK6YOTyNupnzVwPPLEoPCtyfzH97WKplrP+4NPuA73om4Ga4yRZBraSxL+yJL1MOCJh3B0xX0AKhRRd7K1D8u2acypuXbzuVmSudMVq35bi1/0DIo8dvztgqSezSR45RUPivZDpR17Zd/C8utLx6oHTAYcufDIZudgdpx0hhBBCCCFUnnAIAKGSeZ0i0rD1TarMvr07BAeHHvtvbN32ty7fPPM4MU0o5pua9+zn1b+R8YiFB5icbSfsntbRQWlr6vOnu4NfxKXmSIFlambi7u460auuTJx17M87/77OyBKDfb26E0e2d+SRiuUcD/Bl1gf3cQeJrNg4aUrSy4Z7+FJ6Yy/z97cfGVs2z068zewik2QfOxUe9iItH9j1GzhMGtamDpfMT3+zNujf2Gxw92grL1xtVKqHfpovA4DMDElWjfoHl9dX3KqYLfDoGwAY2Nk+OZ1q5OUGd25cO/RwhF3b4s8HQgghhBBCSGtlGgKQCJOev4jPzi2QUYShwMyhSVMrfpEFhl6/3sXbW/tjxd+5Wbe9Z4nCq5jj0gmxj2Pff/Ls0lleVEnLRNUKh2cLAKKceID2nXp6duoJMnH26GVnTp8I7b+8z/EAX6b3zvTblbauPP7go5Ravni0s/GXP6XQoCuX3+ZN+2m4KyRM3Ry2PtB8l19TpXJKhKapRl419p+PAS+Pm+FZ9frYPD5cuOn2oSuXY3N9fAf2Zr+Yte/Z6kCzndMaXTgQ8SJd3GPCYB/Zk3u3NEWleqzJLS023Uvf9NuJOnVtXBs79PrO0ZRFMJvkoxjHA3wjcmQA0ILPAgAOvzYAFOS+BsAhAIQQQgghhL4SdiO0LLuX8FkAxFdDAE8evrRq3trV1IikpZnJL588eGXbUU0fgEEQhNLummVI6folyV+Url27lSi/0nFpKu/Vu4z2nb1YBCkvqqR1QfrEhKfp1JvwSJqmAIAgCBMeGRd2+9d/4tLyZAAgFaUr7qt2awcb7tkPBWu3X2pR36pRfdvObR1qsokz7/MBYOfmE8yOwqQoE16zYuPRHGfdpm6iI5feip3+yZJuaGI8+/Mup97mAcD4ZqYCaAHwLDvhkQmvyfVMCQCMayowJtoAvGRyaohK8dBdR/Zr0PrN7acJdx7E/fV3QvhLyeEZhf9KXNo0QZ6NTQAAGPNIQwJomgAAAgjNVUAIIYQQQqj6yMgFACAIoozllGwIgCC/Op4UaA7fkM0mAbg16rh0qg1MPJQ069mj6PSsPK6pVTO3pgLmoh9BMLur3SorSH34IFooZtk3aeloZfgu/EaWjL4ect2lUxcbLgsAgKauh9zycKn93/N4mmPs5N7Sls8Gmroe+m+LOoZP3mV28faiJJlRD5+nZxdwBRZN3JpbcFkAEBwc3LVr11If9/6tf4Eg7twM7dq1q7wozXVB+k3zHx1BgDj3PQAYCBoQBAT8FZsmoTauHu2/6IjSvmq3TpozpE3U2yevP0bHxgc9eHHqZuLJxR2YP7sTG8ebkGqOXVQ8muPk8Ky6CVh7wyK4gsbOhqTyLgQQNAUAQJAEUfgERIIgCKDkOTVEpXRoBycHByeHEb2b9p1/NvP1fYJoqpqtiwn7XKYkMk/axZgtzv0AAIamTmX+9w0hhBBCCCG9ouXryTSMFJTwRoCvC3JtYnsnIuKjrU3NmjWtappzPvcEUp48EVk08mxZMzP+ftTTtA4trACA+Ly72q2JT2KMGrZx42fefvCyoXWLeh26vLp2rVs3hQv4BAto6atc0w6dvYQJjx8/TrBrXw8IFkGLPnAbdfF2Iwki6fFTmWXjLq1rZH14FPUo2bNt7bIft02Xrtc+p8iL0lwm0m+kun74l60Edf/qQwDoOLgpSRIiigYA8tMLDgESGrJpMGMRbAKkNLwTy9Rt5bq1cHJr4STKadhv0V/5n5JJkhjjYLj5Vd6B59njrZJHrY+0snPZ798SAOTl1OOp+UNm4uw+KxAA/tk6UXVrn9YmM4PTGvTsIK8RSRLjGvA3xuT+8Tqvr+wJAFg4uJMk0d2M82eG+I83ub1Ej+Q5i4pKqYl2bjp9Iylnwk9D25FpAMA1slc8nDzbgMH1z+19cejP2PZjG4b99YAgwGdEU81NjRBCCCGEUDUUHfVYcwbNdwqUbAhAiaFNU0/T7NSP6RmJb2Kfx9Rr3rKuhQEAvMkSu7lZskiihn1j0a1ogK86xmq3xudIWtY05JD8Lp3sNByxQT1LNkmY2TlLX94HqAcANC1rWMeC6ZDHC8Wu7jVZJGFu5yyJfQxQu7yOWxTNNUV6SfOl6R4/HTQyMek74Hs/V2MAmNvZbuXNpJ3BsL5nveUhCX5rzh9b0n9KC8vdj9L8fz29RGXr9FaWh/+N/5CRT5Es29q2fft0Igjwmdbz3eHwkCPnr0qgnmPdqWNdmRjk5ZwNGMYc/fuZgfJImM4/ABAEqRozQUBtT0e4ntGzval8K0FA18k93hwKC97950WC4+LmMntMQ4KA4VPaPNhx98Lu82lenfjkyzyKFkGRUSk10eghLZJPPty5/vhWgmXvUHf0yO/UZrNp3mHdUO7u6w+HzIk0sbQYMarvuEZ8rc8JQgghhBBCSCtlGgIAALahSS17k1r2DtL8lJuRT+t2bgkA+RR9+3pwYQ6Ve+bVbs2naK4WV/yMWQQAECSPpr88S9zo8/T7fIrmMxlYfJrKL8fjFkVzTXUMTYuFKeKCXIqiKjsUrZAkyeUZcQXWxXTKy/24RRzt2u/KV9oBoPWA788PKFw/1aNwpf/Evv0/Z1Dd2qnHd8pH5JpOm9hzmkrhiuUUFYOk4OOApQ8VY5bnMbZofu335kqJJNd0+qTe078uxNTOefcq58IPvT/fwK8uKtUAzOwdV851hOKyAYBbpza7OrVRTUcIIYQQQgiVlzINAeRmZRiaWDC9C5aBKS19zqTzSaJlF29uER0ztVt5JFFA0fzieuN5MtqIRdBUAUHw5InyffgkkSujjVkELcslSMNyPG5RNNdUx4gyOSyo5+TE4XAqOxStSCSShIQEEGUCz+JbHrfsT+D4lm6dvtVpcDfdihkhhBBCCCFUQco0BPA+6nG+XRMXe0sWLU15/YTNr8Ok1zczePYu09XeLPdj3KM3VMe2zop7qd1qz+fEpuY1FmSF/Zfk5dkSAIxYRJ5MxmexFPd9/SGjqb15VsIL+bEU1TPlxrzLcKtr9un9C65JPaWtZTluUTTXVLfk5WQ1aNBAV/r/AMDhcOzs7GJjY/nfdghAt+5P9xkz2KeyY0AIIYQQQghVEWUaAmjY2jX66cvbr6NkwBKYW7m2rs+kW7m2+PjwaeirXDZP4NDUTWkvtVvtWjRK+u/eLSnLvkkrJsWtae07t8K9u3RS3LeBYebtG48ojqBxSzW37lu7Nk+5/zQ0TsQzqdnczaYcj1sUzTXVLVKplM1ma/mEySqCzWZLpdJvfFC8oo4QQgghhBDSUWUaAmAZWLi0bPC89AAAACAASURBVKeaTrAELq08XL5OlD9mX+1WlqF1247WiimG1o28rRsplWxo1aCTVQO1xQIAyTZr0baD4lZKmk0Q7DIeV34I1RW1Zeou3er/VxbdmgWAEEIIIYQQQnJlfRxgFRd6415Nh+aVHQXSK6SOP/YRIYQQQgghVG3p0hCA4gV/LXl39a6ISPQVzgLQBt4IgBBCCCGEENJRujQEgCqa6hCAh4cHs0JyDKxqO/We+L+J3moexFit4I0ACCGEEEIIIR2FQwCoGBEREQBASQvePr481n/mRO9zlR1RZZrsbV18JoQQQgghhBAqV60dBcuvlUM5eFsz+oJWIU8EAJk4j2/jQcnyO3QeErphhmcHT5qmcxJCZ47u1+m7jn1HTr/yNlta8Lbn91NEMpqmaZn00zCfIRkSGU3TNCX2697zbYH08Yn1Q3v7fPddh15DJ524n0bTdF5q+GzfoV6dPPuO9LsWL1SNQbPKbjOEEEIIIYQQ0hklmwVw+UpkBcWBqqz27dszKzyLxmuP7CRILiVJDXFcFHzTBQCO/BRgMnLNlT4ucVdWzvY/7HN6un+TlE3PMn5uZpEasSaVlxZwJ2VdJ9vMZ5sTGvvXNWD12nHulxOX3a0NY2/vn7Vq47DTq47N+tX1x9/Xtrb/cP/4lNlBXU/PrNTqIoQQQgghhJDeKsEQQBM3fLS+nktJSVFNPHv2LADIRMLH1wOXTP390JZRNCUe3to6/WM6AJxOyt3exiYrLcOixei8VL+UlEGO47uvW3ncd+vgwPV3+87p99eGwBTnH/5cEdx9yZCUlJT5E7oumzPPw725i6t70I7+KSkpf3zIyfEfuxsAAEiWYUrK0G9aZ4QQQgghhBCqNvBZAEgrLAOBW/cpuYETAUYBQA124S0kBMDnufiF/29o1b9Vzsgb7/jBYrcTzsMTJMMvv3c+k9PqmJUhADTqPm1n24QnUc/uX9526v6YjePdWQBBf542Y+E9KQghhBBCCCFUsbDfhb4gVMgTaWnB89C9BubtFRMJguhtw98b8lIsE7+8sZdv3YtJHDah8Z7VR5zGjCIIYtQYpyOrdjWeMIzZtHGG7+k4qVvHbmMmTor/ewdBEL1rGW27/FRKyd78e+iHeftVY9CsstsMIYQQQgghhHQGzgJAxejfvz8AsAyMbB2azV71o9LWvstmBKzYMHLXJ3N7lxnL+zOJVh5+Rtvn+HnaAoCNp5/BviV+HlbMJt+J/VbvWnZ6tZBvXqv3pMUA0HfFnFerdo88mGxYs/7QWUvkBz137pzaFYQQQuWLoqj3798LhUKKoio7liqKJEmBQFCnTh2S/HLtpKq1m04EqUQXY9aSHlet1NS2CT5oTP/0/L5tZYeAioFDAOgL1Yvq58+fV82mmMi39ljxu4dSBhanZuAfBwvXubYH/wiUb6rZctDGvYMUM/PM3RZv2FnUIVRXEEIIla+UlBSKopycnDgcTmXHUkVJJJKEhISUlBRbW1t5YlVrN50IUokuxqwlPa5aqaltEwBo3NS+skJCxYp+9u77bm20z/8qNv7ylUgcBajicAgAFTKxaSip7BhKwcTGorJDQAgh3ZaZmdmgQYPq2SfREofDsbOzi4uLU+y6VLV204kglehizFrS46qVmto2AQALM0FlhYS0kZsj1DKnkbGgYYO6r9+oeb44qlJwCAAhhBCq1qRSKZvNpmm6+KzVGJvNlki+Giqvgu2mE0Eq0cWYtaTHVSs11TYBABaJzybTE7k5QhYbe5c6AE8SQgghhBBCqHKQOARQtZVo0MqAy624SFB5wSEAhBBCqLqrzpcly0In2k0nglSiizFrSY+rVmo4BFDF4ZdW/+AQAEIIIVTd4S+80tGJdtOJIJXoYsxa0uOqlRoOAVRx8i8tc6ZoAPrzKy1IkmS20dXyJRe6C4cA9A5Ni4Up4oJcPXvfDEmSXJ4RV2ANKq8tQAghhBBCOopFfDUEwGazFD9KpTImkVlZ29Di51cZihniwv9cExBw4MpjiURa8cFWXywWy8jIiCAImqZzc3NlMplqSmXHiLSFQwB6R5TJYUE9vXvfDPMiGRBlAg9fAYAQQuWssq5MengUvlaWINk17JwGz1o1roNNpURSOqrt5uHhERERwaxT4sTNc2fmf7do8bCWZTzQ5cAF6w7evXErRLH8sgQJACSLa2nv3GPotCn93dTuWIpjKSrfmDOfXlyx+ejzN4k5EpatY7Nx85b3bmJexggVFVWUUrqW2TRQ+7cW88+BLUfPx8Sn0lwzJ7cO0xbMdqvBqyJhy8+jNvGUDkEqX92RqVzHkqf8lV6w4Ov8vZeeX7bsj6B/nFXLQeWCpmmCIAwNDT2Gb5FKKZIkIk/+lJeXx+fz2w7dTFE0m02GH5uZk5ODk1x0BU680Td5OVl2dnZ61v+Hzy+SycvJquxAEEIIlaeIiIiIiIjwW8Hbfu6xb6l/ZYdTbvJT780dNcN8yOay9/9pKn/FvpvHr/wDAEyHbUUvrzKWGRERcfvmtS0LRz4PnLMyNKlE+2pz9HKPeeHs9e7jl5/9J/Rm8F9LRjod3nSwLKWpKqonrBR8eY04KEq989vUrXcHzdn4T0jY36f3D26as+J/f6jNqdqGFR224nmsOCRBKC6qKSRBsEiSJIjNDSz+zRaxSPL4x3z5ppc3jo7q4qR2L1zKvgAATdMUReXm5kac+JEkCYqi2w7dbGRkxPT/SZKIOPFjbm4uRVE0TeMogE7AIQB9I3/fjP5hs9lSKU7xQgih8ldZ/7DLDw1AUpSUJI1omvbw8JBn8PDwoGUFHbsMe3VhU/9unp28ex54m1U5sar7Xas2D03THx+cnui7rduK/b4datM0nZcaPtt3qFcnz74j/a7FC9WkUNLvOnR7euzXnt6ePn1GnXicoVhmQJ9eADDQy5OmaQ8Pjxsje1zOyPfw8EgUycoSJJAcexfPX7cOD/3tkNogmWxK6YpHV92l4mJOl8hs7O14bILk8Jt3nX5iz480TRMkO+7i5gHdPD29e77Mk9A0nZMQOnN0v07fdew7cvqVt9nSgrc9v5/CHFMm/TTMZ0iGREbTNE2J/br3fFsglZdf1DdNKXjmy3luw/x+Pbp+99133/cZvvncS1rhm1yKqh1dc3nI5tXervW4LIJnYtVt3MrT28dqbvnyDfvxifVDe/t8912HXkMnnbifpvY8UrL8Dp2HhG6Y4dnBU7WdaVlBB8+Bd3fO+b6n77HQdyGbZnl36bn72gst20R7/nGZTMyjrPhlKQeVSFZWVlZWVkZGRlJS0p3jM5lRgFaDNjL9/zvHZyYlJWVkZDDZ8Le6TsAhAD2k9h9c/VDZTYsQQqictW/fvkPH74eNGb/u0P0f161Rk4M0kIlTDue1PXop5OTmsYdnrf/mMZZM1LmNQ2du9Ny0pbuzKZNybNavrlNW/RNydfOPrdfNDlKTQrAoWc5pbo/T/wRvmdV07+I9igUuvHgdAO7cucN89Dx6mfloyy2HX3GCOsMKPoWoDVJt8IpHL2qXioj5t0Ujd4wfNGX24p0H/3wYm/k5mTyS0+bIpZCTWyYsW3EMAI78FGAyaNmVm9dXj6q5wf8wy6Cuf5OUTc8yACA1Yk0qLy3gTgoAZD7bnNDYv67BV/ecF/VNUw2+35y15y5fu3371rFts05tmlu6Gsn9k1kwqr5ANV1Dy5dv2At3nJu359StWyEb/VoFrdqoWLb8PBIkj5KkhjhODL4ZDCrtDKQBJU3/t6nfyd0jdiyf8bDV7DOHfjy0ovSTeggFpS4ElRfZZ/n5+UlJSeHHZpAkAQAkSYQfm5GUlJSfny/PU9nBIq3gswAQQgih6i4lJaWyDn327NkP139/Un9cTwcBgISJRDGelJQUmhL5dnIQZnyEmh3zPm5JSZlRWdEqUdtumyKouXMGbv5xftPtyxoYsQHgjw85Of5jdwMAAMkyTEkZqpoCAMM87LLS002bDCn4NCUlxbeoY6k2USmClCdS0kwgiJSUFLUhaUhXW6+Ki5nbfODvB31in0VHR0dtnbPTYc7OKc6mNCUeW/jFaP/2xqCUlO9PJ+Vub2OTlZZh0WJ0XqpfSsogx/Hd16087rt1cOD6u33n9PtrQ2CK8w9/rgjuvmSI0lGK+qYpBZ+SkpL++K9NBy7Hp2TkiyQURZeodqrZCBqSU1IKWCQADBgwgEk8e/ashpYv37DnT+i6bM48D/fmLq7uQTv6qz0EANCUeHhr6/SP6QCg2s40Je7dwDCfbCYTfxzoyMsnm1GSjFL/w4JXfaoUxdMhEomSkpLDj83oOGp72FG/pKRksVhUibGh0sEhAD2E/24ihBDSIbW9ZzzdtDZ26pwGhoU/S2Q0sAigZbnyPHzmQV8ES20JVUqA/xgACJDsW+C/fu22+XZcFgsg6M/TZqwvF29VU+DLbzLqW07SFL49zbPwLiokDemaN5U7SpJDcIydmrd2at66dz+v4RNWTDm2AQAMC58AVxgDAfD5N1Dh/xta9W+VM/LGO36w2O2E8/AEyfDL753P5LQ6ZmWo9kDafNPWrD3UdnbAApe6hlzR4MFjylg17xq8U7HCH5xNAeDs2bPweSCgRM1blrAbdZ+2s23Ck6hn9y9vO3V/zMbx7kWVUINdZDt/joGAwpOCV+/1h9JbxgoK8hMTE0MPTklMTBSJsP+vk/BGAD1Ulpn27dq10/Cx2PQSFV4Kld20CCGEKoTPjAlnVx/Mp2gAsOSw/olLp2QFDy/v4ersI75re05a2pe3YO7v6VKqdy2jbZeiJDLpm4hDP8wLBADVFIIgD9x4KaWo+DuHDMw8NRduyCI/isVljJCmRB9eRKz95aq3Xy+1ITFU0+VHL2qXioh5y+Qftl6+nyOSUZL8V//dIjnqXw/Uy4a/5/oLkVT0MnQP37oXkzh0fOM9q484jR4JACNHOx1ZtavxeOUJCyUKvkUto7q1bXiQd/uPQFM2+T6/TOeip5/3tZWrbz57L5FR+dmp4RcDWRxL0Njy5Rv2xhm+p+MkLTp0HT1hYvzfO7QpWW07fxuN+ZzY3LxveUSk+oO8oKAgISGhoKAAf6vrKJwFgBBCCFV3lXjDLXNoFtfKb5rDL3vC1k7zXDrHd8vaWQdz+T4TlliwwykglCKsOrcHq41Enujc239Ozq9z/he0cbn/qzV7Rh1KNqxZf+isJQRB9F0x59Wqr1JomuoqvDJp1P8kfLsxC39VLVmeQhDE2ild/HynnDp+oNRBDhgwgCBIEyuHbuNWjHatAQCqITH7qqbLj652lwqKeVrA7C1bD00+ECAGbq0Grj+u+kkeoeJe/ZbNCFjx26jdn8ztXWYs789stW7vZ7Rjjl/nWgRB2Hb2Mwhc4tfeuqhoVb9pSsETBDFowYhpM3xFvFqDZi5d0PvD7HETimpnbapW03Ximh9OBe5dueNDOstQ4Nis3dLft2pu+fIN23div9W7lp/+KOSb1+o9abHm88isqG1n1WwV8ad6+cCUxraO+dlfXmOhdDjshZY7SuUdjUUlIl1B7A5OZtYme1vLU/dcT/FpZlJJIaEyyU5+1ahRo1Lv3r59e/nDewo/hod16NRj9/SO8wOvS/m1J6zcNqy5OZMtN/HGwp83P36TYVa3md/qNT51Bed/+zno+oO07Hxjizo9Jv76Y7+GWS/Ozvp5d3wOq9uktZe3TQkPDy9L7WJiYkxsGmrIcDUqW/GbvHzB3OVrNkRHPdZcbNiN0CmzZn94+/pSLN/H5atv/tWn2Yp/C/WKmDeIEEK668mTJxYW6q+pIkUZGRnNmzeXfyzfduvfv/+5c+fKXk6FBllBdDFmLelx1UpNqU0uX4ns+X3bSowHaXb5SqS9nZH2+R0dHUNvPcFzWnFK2ruRGNrIE69GZTMrOAsAFefLY4oXvr2xfsbiPcP++pnZcuSnAJORa670cYm7snK2/2Gf09P7zVnbbw4AUBnxkX3Hzf2x3/kDP293nLZtf1fHqMsb/q7UeiCEECpK1bmurlvKt90q6Czo4snVxZi1pMdVQ/oKJ1boHxwC0ENlfLCz6gNyQd1jilNSUlSfB6v6sNlL6QU7m5mlpWVatxxK0Rcr8aHTCCGEUJV1/vz5yg4BIYTUwCEA/YNDAOgr5mzyo4Sy5JAAIBMnsDhWTLraxxSrPg9W9WGzMoDPbwjFN4UihFAVhVcmS0cn2k0nglSiizFrSY+rhhDSFfhGAD1ElMGIRub7Qx5m5UlFOel3L++t4TaWIAiCIA/cfCmj6fiIQwZmnvKHvvS24e8NeSmWiV/e2Mu37kUQRItaRnXr2BoS+bf/2G/KJj8USDqZGByJfEfT0pjrB8iyREYQ+F9NhBBCCCGEECoLnAWAvuL9v+Xxm3bMPrKugGXq5Oa5el57AKBpqmv2lYkjCx9TLM/cd9mMgBUbRu4qfB4sAAxaMGLajHGfHzb7/qex4/f/OmLJukVDtnE7DlsoIMOYVz0jhBCqOkiSpGmaJPGqgCYURSk1URVsN50IUokuxqwlPa5aqam2Car6PiTllyTzk4qLBJUXHALQQ2W5Ws7m2U9euGaySnqTgbMPDZwt/8jcssi39ljxu4diNr5Vj4MnexR+aP3bmfEAAL8f6FeYMvBsqQNDiKH26x0aGtq5c2dm/cKFC9u2bXv8+DGLxWrWrJm/v3/37t3lOSmK2rJly59//hkVFWVvb9+mTZuff/7Z2dlZXnJ+fj6Px1M8FpNCUdS+fft2794dGxvL4/EaNWo0e/bs/v37V3B1S6+goMDQ0BBKewuflrsnJycDgLW1mtdrlYXmYjWcRFQ6JiYmQqFQIBDgT/OiUBQlFApNTL56ZUxVazedCFKJLsasJT2uWqmpbRNU9eHj/fUPDgEghHSPm5ub4u8ngUDArCxatCggIAAATExMaJq+du3atWvXfvnll6VLlwIATdMDBgy4cOECAFhZWb18+fLZs2enTp26ceNGy5YtNR9x1apVTCFNmjQhSfLWrVu3bt1av3793LlzK6iOOsHW1ha+Hjep6GLLchJRUerUqZOcnPzp0yeJRFLZsVRRHA7HzMzMxsZGMbGqtZtOBKlEF2PWkh5XrdTUtgkAXL4SWSnxIFRt4RCAHir3e+aZX9sIVR137txR7Rzeu3dvzZo1RkZGQUFBgwYNomn61KlTEyZM+PXXXwcMGNCsWbMzZ85cuHDB0tLy/Pnz7dq1y8vLGz169Llz5+bNmxcSEqL5iLt27WKO6+HhAQCnTp0aOnTob7/9Vs2HAL69spxEVBSCIGxtbZmRF6Q9nWg3nQhSiS7GrCU9rlqp4RVmhL696jgNCSGklwICAmiaXrx48ZAhQ0iSZLFYw4cPnzNnjkwmW7duHQBs374dAH755RcPDw+CIIyMjDZs2ODt7c3n8zVPd6coKjU1FRSmGwwYMCAoKIg5olLmDx8+DBo0yNLS0srK6ocffvj06RMA7NmzhyAIS0tLoVD48eNHgUBAEMTJkyfT0tKmT59er149AwMDKyur/v37v3z5EgAKCgqYR2Bu2bKlZs2a7u7u9+/f3717d9OmTfl8frt27R49eqSY7dixY3Xq1GnYsOG2bdvU1kVtVEpu3LjRrl07gUDg7u4eHBwsTy8qSPloo6GhYUxMTFHZAOD169e9e/c2NTW1tLQcOnTohw8fNESlVKxSkKU+iQghhBBCCHAWgP7R4yfN4CNkkGaPHz8GgCFDhigmDhkyZOXKlcwmpkfas2dP+VZHR0fFvm5RSJLs1KlTSEiIq6tr586dvby82rVrN2rUKA6Ho5QzPz//u+++S0xM7NWrl0gk2rdvX3R09K1btyZNmnT06NFbt25t2bIlMzMzJyenT58+Q4YM6du378WLF62srHr06PH69evz58/Hx8c/fPhQXuCSJUssLCwePnzYvn17AGjZsiWHw4mMjJw0adJ///0nzzZlyhQ3N7fIyMhZs2aZmpqOHTtWm6gU/6Du3r3r7e1NUVSLFi1omu7Xr5980/jx49UG6ezs/OLFCwBo2LAhl8stKhsAjBkz5s6dO97e3llZWadOnUpLSwsJCSkqKqVilVq41CcRIYQQQggBzgLQP1yesVAopCiqsgMpZ8wjZLg848oOBFUJhoaG8ldF+vr6AgBFUfHx8QBgZ2enmLN27doAEBcXR1FUYmIiAFhbW5fiiEePHh01apShoWFISMiSJUu6du1at27d8PBwpWyHDh169+7d5MmTz5079/fff/v4+ISHh4eGhpIkuWfPHgMDg/Xr12/fvl0gEOzYsYOZETpq1KjLly+fO3fu6NGjAPDo0SOxWCwvMDg4+O7duwAgFosvXbp0586diIgIJpvicbdt23br1i3mboVt27ZpGZVinmXLllEU9fPPPz948ODBgweKNzgUFaT8Ev2TJ0/q169fVDaKopjRisWLF1+7ds3Pz6958+Y0TRcVlVKxikGW8SQihBBCCCGcBaBvDARWtCgzKytLKpVWdizlic1m841NCQNznOmLAKBhw4byK9jMTZUkSdatW/f169eJiYmOjo7ynEyPsV69eiRJ2traJiYmpqWlMeMCJWJjY3PkyBGxWPzo0aOrV68GBga+fft26tSpUVFRitmY6QY7duzYsWOHPDE6Otrb29vZ2Xnp0qWLFy8GgI0bNzIx7Nix4/jx45s2bXr06FF0dDSTX3EIr1WrViwWi1lnJgIwvWKZTKZ43AEDBjD/O2HChGfPnilNidcQlfwj00ufMWMGMw/fz8+PuXtCmyA1ZyNJslu3bpcuXfLy8nJ0dBw6dOiCBQsIgtAmKiVlPIkIIYQQQgiHAPQNTRDAs+DzLCo7kAqB/X/EePLkierjAF1dXV+/fn3y5MmFCxfKE0+dOgUAbm5uAODk5JSYmBgSEiKfJx8bG9uuXTsjI6O4uDg+n5+Xl5eXl8eUnJubCwAcDofD4dA0vW7dOpqm/f3927Rp06ZNm4kTJ9aqVSs6Olrp/pSsrCwAsLW1VXzpUX5+4Qt18/LylFImTJhw+PDh1q1bT5gwoWXLlvJXG8rJ+/8AUOyNMEzvXTWb5qgYTJdefh++4nGLDbLYbGfPnj148ODBgwdv374dEBAQHh5+8+ZNbaJSpfkkstn4HzWEEEIIIU3w1xJCSE8sWLDg3Llzq1atcnZ27t+/P03TJ0+eXLduHUmS8+fPB4CpU6feuHFjyZIlrVq1atKkSV5e3sKFC9PT01u1asVmsxs3bnz//v3AwEBmDnxgYCAAODs7M53hvXv3xsXF2drajhs3DgBevXoFnycXKMbAvJ1+woQJK1euBIDIyMikpCRXV1cAePLkydq1ay0sLEQi0f/+978BAwbUr1//9OnTAHDkyBEnJ6ewsLBS1/3s2bO+vr5nzpwBgGbNmim9FkRDVHItW7a8du3azp07mTw7d+6Ubyo2SGZKQlHZmGc02tjYhIWFPXz40N3dPTIykqKoYqNSmunA0HwSS9puCACy8qRhMcLULImUwlFWhBBCSP/hDyaEkJ5o06bNggULAgICBg0aZGJiQtO0UCgEgF9++aV58+YAMGTIkEOHDl2+fLlp06b16tVLS0vLyckxNjbeunUrACxfvrxPnz7z589fv349QRDMKwCWL1/OFD5v3rypU6f6+vquXbuWzWYz8/9//PFHpRh++OGHDRs2rFu3Lj4+XiQSnT592tzcPCoqSiqVTpw4USqVLlq0KDMzc9WqVZMnT7527Zq9vX1MTEzv3r1dXFyuX7/OFCKTyRQvwmtj1qxZ+/fvj4yMZELVMirFPAsWLAgODl61atXff/8NAA8ePJBvKipIADAyMsrNzZ0yZcpvv/1WVDaCIEJDQ//777/r168zZ6Rdu3YkSWqISrFYpdv+NZ9EVFJZedJz9zLq1jRo6GRswCnnF8oihBBCqHwduFYOhaifVsoiCZEUrwYgHSOS0GwW/oSt1lavXn3u3DkvLy8ul2toaOjt7X358uWlS5cyW0mSvHDhQkBAQOvWrVNTU62srMaOHRsVFeXk5AQAvXv3vnnzppeXF0VRUqm0c+fOISEhgwYNYvadPHnyiRMnPDw8UlNT37x54+bmFhQUNGPGDKUAbG1tw8LCPD09//rrr5CQkIEDB0ZERNja2m7duvW///6zs7ObPn363Llzzc3Nr1+/HhQUdPDgQXd396SkpNTU1IsXL9atWxcALl26VNKK79q1682bN3Xq1Nm9e/fAgQO1jEoxj5eX16VLl9q0aRMTE5Ofn79v3z75Jg1BLl++3NTU9NKlSzk5ORqyHT16tHv37uHh4Y8ePerRo8eBAwc0R6VYrFJdNJ9EVFJhMUL7mtx6llzs/yOEEELVBLE7OJlZm+z95UrLXw8yjA1IB0uDSooKodJ481GUK6J7u5vLU5YvmLt8zYboqMeadwy7ETpl1uwPb19fiuX7uJgobrr6NNun2ZeUelaG5RszQmVRUFBgaGgIAErP/0NIS4GhqR2cjQ3Y2P9HCCGEdMCBjf8rUe9GYmgjT7walc2sqJ8F4NHQJD5N/OajSCTBn5VIB4gk9JuPovg0sYeToLJjQQghnSGjaOz/I4QQQtWK+mcB1BSwB7S2uB0jDH+VI5XhKACq6tgswsqE06elRQ1jfLwFQgiVkvz6AEIIIYT0VZH9JVM+u5fChGqEEEJVDY/Hw1sAUPnybmpc2SEghBBCSL3D5fE4QLxkihBCCKFC8iGlfAn97H1+apa4mkwGZLMISxNu49o8Y4Mv90jqcSPofX31voJaUtsOCKFqDocAEEIIIfQZDQAgkdHhMdmt6gt6u1sY80r2ikodlVMgi07I+/el0LOxCYdFAECuiAqLEbo7GPdsYW7C17ffS9l50qj3ebejhZ0aC/hcEvSuvnpfQS2ptgNCCFWXfwERQgghVCzm2mh0YoG7g6BlfWMZDVKqWlwv5XHJVvWNaRpiEgtc6hgCfB3/ZQAAIABJREFUwNP3+W4Oxi72xu/TJZKP0soOsJxxWERze2MAiHqX36aBEehdffW+glpSbQeEEMIhgHJ2+UpkZYdQ3fX8vm1lh4AQQrqKebpEQrrIx9VcKoNq0fv/jAZoXJt/Nza7aW0eAKQJJX1aWSRkSmU0kKS+vTdBRsOHTGmzOvz7r4XMSdez+up9BbWk2g4IIYRDAOWvcVP7yg6h+op+9q6yQ0AIIR3GdBEkMtrYgFVQ/V4MLOCxxNLCfpKMok0M2e/SRfraW5RRtAmfLZXpbX31voJaUmoHhBDCIYDyZ2GGr6ZHCCGkmxR6CdXjDgB1FCpeLS4X63199b6CWqq2f9EIoa/hEED5Y5H4tBWEEEI6SfFCoaTaPDVdAQFfN0J16DHqfX31voJawmkACCEGDgGUPxKHABBCCOkmWuFCoVimPk/ToRuZFZLFsraxGTWm53h3vZr+ptgIJKG+xzh11QnFj7sWD2dSdi0eXo6RVESZqrSpb27K24P/PH+ZJBTTLAvLGj7fe3Sqw6vQqMpRiU4oQbLMzc27dGvfzZH/bdofKuNEI4SqMxwCKH84BIAQQkhHKV4nFGt8aPrDY/7C1Nedfjq3edPZUUFjKzowRcG7gubdynx4zL+Cytf+ovGe/41Qu37//KXdT7IVU0pEvnupSygRbeobePTe83zZpOmDnKXvFh54fD04qvPENt8gtnJRohOa9ynhp223zp2+8f2CXsXuorsnGiFUnRU5BEB8HiUlOYZOLbsFHDnc39FEmxIJgijRE0fX1Ddb8PqT9vmrPqUhAAMOR/GjSCJhVqKOr/JbuefRm4waju5T/7f956Eu8jxv/9kxfdmWiKh4MLJq6zN0zfbVLcy4TFHy3eWFMynpDw9PnLn+7vM3WWK2Y4vOywKDBjmbFRVhdty50SPmhUYlmNVz/9+BM5PbWgGArODttD79TkZ8cP5u0MkLO+sasIpKBFq6zW/AqqMhZM3Gs3eem+dTu0ztpUJDNRFCCH0zRc0CkG8Vy2QAQBBssQwuHrsUFBGflCUyrmE5clxfX1d+27FbOTyrtZ4GPwcn3j7wY1EZ9g6z+fnU8yyS33/EwI5Zd5ddeCFkGQ0eO2hWe3OZOOP3PdeDn6dkFECDRg3nz+jalM9qO6ZwDoLbyI13An2VMxjSSsdNePDfqtNPnidmiYFdo4Z5hw5tf+7fUPtG0DxtXHHrpF+PA8C+pSOYFQCYvOL4vqWFXTtKmn3q3L2I2PR8mlWvft0xA9zt2NQPK0+RLKMp7cwP30ukDc2GjejS3oqruDuzsm/pCNXda3PoSStOsrhm87vU2H3zbQ7B69K9y5BmgrQX0UE34t6m5UiAZWIqcHVtOqZjnbLXN15EAYABSZjbNNi1oIFSlb+s/28oE9VCb8sdoa9zCF6nbp1dc54H3n6Xx+J16d5liAu/mAzNBOHXwi9FJaflSgxNTX26d+rVkDdpxUkW19zPjbvj3sddS4YF7zl7JqvmjnkdASBy/7kD6aY753UpYwXlW0mgAAAItjwnSag5fbW5pE6faIRQdabpejVN0zRNS3JStk+wGNPpJy1LLOkbR86nF5Qof9XHIkjFBQCkUpl8YRIz7q/qOPPirH3XM4QZwbunn5naPuBhOrMpK3qb+/Df+/9yIikr/8OzG8PrPx/ne1helGrhzMrY76f3XHH8dYpQ+PHNlnEWk7v5K+VUXOZ4jbebeeBjdubFla3n9/djEh8uHpA87Pf3aW83DksasOSBhsQ3xwf/af9DTHJ2xMFxFzZv1XCg0i2q1ZS3mzb7IoQQKjWa1vZSYdsxG7vNvWhqaT11Zh8A6D2y1+lt08P2+malpe7bfhEINgBIRRnX63QI3Te96Azpl8zdAud7FORknwo6ec2m1YEF7UW52SeD/gGA8+v+PBb5ftqiCRd/9YqJejZnzWMAiDxcePE/8rC/mgwqx53+++377z5t3TLjTtDMvzaM1qb/r9gIRBGYrZN+Pc4s8hSCIAKXjWTWA5eNlOf/90ToteepbQb3XDWiftyLV5uOxpEsDgDQVO4rB7clwxrmZmUc/+O50u7yMlV3J0g2AFASYaSZ8+LRzcT5ucGXIgiCWH/68YsUof/sIXuXDP1tZo+xneyLqoI8Zm3q69vCHAB2bD+7/MDN03feCKkv14oUi5JHdcfEiYkq5NL1ezUb/2/s5wiLzUAQHXw6BMwZvHth79xPny6cCf+8S/Z9a9ftiwYTBNGudx1x/oe7+RSA7ExyQa12bmWvoPyE/vh7uLGZxcAhnRQrqKb9dfZE44ILLjq9lIvibwQguYIuE7bmTbMF2E9T+Vzj5n/5NRi45UaeOL8g7ebwHhP+fvjBtpn37r///N6GDwqzAMRZd8f2nvj3vTirlv1OXjnkZswBgJz357p7Tb7/ke+//8aqgfU2Opr/my0iCOJISu5oayP9eGEpoTLCrJpydOK2vn88HN6hNgA4dxx59tjZ1hNP/O/xLAD4Y2yAV9C/03s5AADYNpy0+sokLQrPlFJm9esZ81gANbpNPZA4iVbNKed//mpj93ZsAtwHrRKNsmNybj/xZuqzduZGvLYDJ7912UVsbFdU4rFltydG/FHTmFvT86cIT01NIXxzsqfPjPsfuT9s/OfkdLfYPLERITYwavxk39A+s7Yl0jUn/XZ1rGT3kAW7k2VmY9de2TnFRbFSkVuGjbvZJPLUUgsOS0ZRQIt4ghYPdvTuO3t7ksxi3Np/dkxx0XR4hBBCJURrfbewvCvOiLl6dd6ZmNRcKQBICpILS6OlP3aw4bCIojPIprSoISDNAMJk0rwZ7jUFpDlAmFSUBAB743IB4JeFu5jM2e/vArgrHrSoDIrH9bLjHXub9+PS462dbF2c7bt7Olmxi3kgnPaNIO+8FetMfD4A9HM0NgIXgBhh4lMAJwAAIIfUF7DoZgAvCrLiANxKtDtNywY4mfEJE4BHlCQdAFpaGlxNKti092oT+xoO9jYebvbmrHKor3uf7r+4vPvvZeKDqPi/gxMjXop/G9+oyAJp2QAnUz4hAHhEyQqGOH8VYbEZ4u9GbrvxNjNfBgAy8ZddhrnWYC7NC2q51zd4feV+drOm7zJkxMQ2xUxTLfsJLfr0aZ9TN040Qqg6KP6qKSXJuXlgpnGdiQBAkIaUOPH3Gj99ys8GgKP9R3zquzktLytwnGRc38NKO/4xYOC7zssTstO39Hw/cNQlJvFw/ykN199+d3vx1hmzAMA/LhMAaJoeZcXXj/4/AJAEobioppAEsed19tKOtvKPdl2WZcXtYtZ3vPy0omcd1V3UFiVP+WPXqMkujQdPmnPgbGimhBZwSLUlMItrKw8uSZAEkX5/tZmTP5N4/VNBBzMDkiB4ph0KMq9pSDyRktfy8XZnS2NbZ88zb4QaDhTYfwbf7+SnlCfN/5uZIaEELJIkeTJx0vyYlvcTs6NOj9/p13vB+/aPkrKf/p+9O4+L4f0DAP7MXt1bCRUp6XCFCCGKoghb5JaULmckJGfOilBukaMcOUrlTjm+5L5zhpxJdN/ttju/P5b9Za+urdnq837Ni9lnnnmezzP7bLvzzBXrfniJa+VGfY719X5p/jLGX5VC+tNMkiy7/PuS932fZhS+jHU79Dc/3wYBAABQa7U+yLAs6vWv4orw3XP40pX/7paIyqBERggj881zY+CmJhz2vh+58H7kwjuHZvKtKyYDr16vNTN2eQ2f3LdN8Y+0XYcvTPG7WmVbJHikRVjpHIQQ99ED3BnS30SsOt9i/KsjedLfMnCEEJrgNnrxhP7DurQq/f0j+kLyqj0Pqi6yeu3V0tW2t+m3er4NQij/+wuEEDdeNo5wDv/FeryoBCOsMsOeq2m5pexli8fxlanAO7aBkaf2of98kJqe/E2+ZfeOsmSJNLBaBLZ/tXNKyxsNE0wwNepJIsQNAXDPHaLKt/I8kH/y1kZuIoddsmGeJY1MRQgFP83a4zNCiSZr4RGa82ob3+oB938dX2GvSJOz9t6TcSOYm7j1TY7/CL1WRu6FP+Il04LGQPBcrC/lFfpy/z8FgyrfsaLsM3f+U1lFJ3mqYCHi6U05mPEybnSPljci1nXR6Lw56UeVqxR/Txg58sTOS4u4L3MqOH92tqktOBXZYhK/lFcsv637OD37+GxFj+HBYqrY9T4v1HOQrKyaU+Dqir99FueUb1vuoCxD6WCxpKL0Y9jysdx5VtET3opZj3YtfWhy98Bc2r/fkhx28fYVDoL5AQAAEKuc+0c++zkVQwih3Ar+3ylVZhDkbqiAENr9LDf/e4rZjB0Oa5K56dyj+B9LWaIyVIaRaL37dPGYMnTLktEIoZLs77VqX81wxx++l///hopj28shhM5/Kf79KQUhpKzdnZuO4+zoz0VZn18ihGSVDWq6uiCMRO3UWdfeuo+X40CEUFn+L4m06MTBC/MDT97KLCvNz0EIUeXaIIR0qCSEUNKvsozU5zTJDcKzuF0j/wP3jS4U9nBKLTOTiuKPESlFXUe0l1jFoona/k3vjQYANAfiLgQQdVi+m8KfHdRPZRUGchSEEEXOkLcHy5NWVqEr+6d8jPTnyTFfytiatCoGa5sewS2pL0d5WczqpfhnS7JK3lDk/nwfdJSnPili9leiCZZDxjD234MeCCGEMzHS/wcLlPX6uHj1cfFaVpB2St3EaXFuopiQCj7GDjH1co57OFnnz5OcWlJIuSxOSyqJw8omUVuKSaSTSV4LR9NlZIfMOZi/uDNCa0XV8q2cbShHQQjJ0M3+ab4sGSGEkRQQQh3+zuP4/288ZTZ0nfmOG0J/TugLyw8AAEAian2EYZW1tl9C+tYLaM8Yg8WXvzguP1bTDIJGLRr/eV/ipb1Hz1VgHQz1Fsz6cwv6BX01Qh9muvkcSQwVnqGyG7FXw259/JJVgpOobbXbjZ9gU2W9dT/MMqmLWtSbnIDtF3YttuOm9J9k+e3sgzsnzl9DVIMuHZ3HdOCmYxhZJ/XRmocZiiotp0zpzLc6r0BRqwt6cvN+3PP0jPwyHKO0Um9tNbR/ldFWp72jbbv+uvDyxP6zEYjcpl1bxui+CCF3W8MtV97HHL78c8ggDeqHr0yOsL31GpvRV2P3g18n7qAlFu123Pvpv/eKYB6KbGt7VdrZAtpSHfkqC6z7Gypq+ze9NxoA0Bxg+xL/XI/nYaX+zwIRN/avnG6kSIv6VWIkT6koeSnfagKz+HXlDN0VaQm5ZRrUf0400JOnXsouNax0AFxURY3UxSv3bW1MK6cIbeDeXq0vrbgfN1aX+/LrxfF9Vg3LfOSBEDrUX/Ooy/UkDyGX2JmryC76mMdQ+zOeUvwzolXXUyXZ5xFCrx9c69DbUpaEEELs8m80hS7sikJRQTILH1nojpoS/2LegNa8RFdNxVEvf49RkyvNim7d7XJhxn5RiU4aigOeZs7UVOAwf9DofSrK0kVV1E6WeiOvTE+WzCy4JaNszt0OlTeI0HkMw658LXzi0kd1a7Jn9xZ8i4Suy8O3/f2XLvIPDH6T8lxUhFy3blz39PL+/jntwgd5a6N/LilMeFlg3e3/Ke1by4kvCgAAGpGwpMzKf+ISUgpM9WQRQkkvCxeO0vqUzSEuNGLoqpG2nv9uZaSEGmQjuK45jmpyQwGJa+D2SgqO46yS3A2hCbiJ5Vqb1mJySkkDpeqNBgA0amf3rqvR3g1LToOXmJBSwJ2p0x3Uffu0nhuSWMwsurZjXiuTJXxLlw/UmLEjqayC+TRmiXafPw8UmKuvsiI2Nfv1IUX1odyUzvLUD8UldQmjMZpwZNHV6Yxjt1OZFcwPd6LGTL7sGzGeu2hspP/DBZbBMXeLmOyinC+ngj31LQO5iw5sd5xpv+TRpyx2RUna8yTPYd6O+3ZwF+2ZPHZswJnfxczy/Myjy8cr688TU3u4nX3LTdcr7/8jhGY56+88fqegLP/uqT0GbrPEJHq5GOzdeSWntDBxu6taN18xFc3QUlwY+aC8LOfI0jU12j7W7RR9Yo/tGTb8bl55jVYEAABQFxK81LDxam4bodG19/fLa3O2XuW00/UZ2qo6+RtdA+sJ4dcwwwQTTHWfJKLqJwKIMSEm6sywaaqrfmibjDx5aTLf0rGn486Pnqbq+1ZRu9fKg3+u/HeL23li8MC2eYoLDtzkplw87NlZU6+0IKOJnQ4gXotuSx4eYLq6Dp7xMVvNwMTr4MOFXVS5i5T1PVPOslz9pq6e9BUpqA8YMel0jDd3kaHTgWuqobPteyW/+qncrrPTopjgcX/OIwi6Fe0+bZH++smlSKGHuX3srZViave68aPiehfM9c/LC9mlti1ke/qf6zDSTtv3k7755IvxxtxFQhN7rb1sbj9Ciz6ppdGw41fcxVV0ZqPFiOF0H7rXrgtYWL8abSKqYq9zh/p37+f+/uXhGq0IAACg9prL97BYDbURCDws/I/G9qa37ma1v1tNViC6gfBGAwCkisghAFF745XTZVQHxj36VHlpWe5jElmRO09V6hl54yXfcwKUdCc++DKxckr78TtKx+8QU2NjJ6pdXSeuuDdxhdBFOjZzE23mCl3UafT8a6PnC6bLt7E6lvS0miGxOELOgiPLtNuf+GR/NRJJVPXtF55sr0ZF73Pan36W3rEl5dHJBfs0pnMTK28QofO8mXa2obm2whehptthAACAQPCHFTW/jdDk29vkG1hNsB0AAFx1OgtAkErrAbbLT0m2TNB4fTi+bVns+PRcplbXQXsSookOp1krLCyMioqCcRMApJa8vPzUqVOr9bSw+gR/JVDz2whNvr1NvoHVBNsBAMAl4SGAMhZcuS1FhP6UrI8vAFEVOR5IcDwg8dpAbVy6dOnGjRsWFhZEBwIAEG7v3r39+/fX09MjNoxHn8sRQp20lBrFbeEk7lM2p5OWIncj0CiksKRMoiOqd1QKqWm3t8k3sJp42wEAACQ8BACkSoMN98K4cqNgZmbm4eFBdBQAAOEePHhAdAgIITTMuFr3V2vCOrejd25HdBAAAACAMEeuSqCQOj0RAADQNLx9+3by5Mk2NjYODg5PnjwhOhwAAAAAAABAvYCzAABo7jIyMjw8PCIjI3V0dDIzMydPnrx3715DQ0Oi4wIAEODqs99EhwAAAACAegRDAJJ38cp9okMAoAbOnTvn6empo6ODEFJXV/fz8zt58uTKleKeKwkAAAAAAABojGAIQMJsbUyJDgGAmsnPz2/Xrh1CyM3Nbc+ePWpqanl5eUQHBQAgxhQzNaJDAAAAAIBwwZK4FwAMAQDQ3FlYWOzcuXP48OGKioopKSlHjx61srIiOijQxDGZzGPHjrFYLKIDqV8DBw7s0qUL0VHUTHoOk+gQAAAAAFCPYAgAAKmWm5u7adOmmJiYjIwMNputpaXFYDBWrlxJp9MlVUXfvn27detmY2NDpVKnT58+evTokSNHSqpwAIR69epVdHT09OnTiQ6kHv348WP79u179+4lOpCagQe8AAAAAE0bDAEAINWcnJzat28fFxeno6OD4/j3798PHjzo5OQUGxsrwVoWL148c+bMxMTE+Pj4jRs3SrBkAEQxNDQcP3480VHUo3fv3r1584boKGqMQ3QAAAAAAKhXMAQAgFS7fft2dHQ0jUbjvjQ0NAwICNDW1pZ4RUpKSgwGIzQ0VOIlAwAaERxOAwAAAACaNBgCAECqmZmZ+fj4zJ07V1tbG8Owb9++hYeHGxsb10ddZDKZRCKVl5fLyMjUR/kAAOkHIwAAAABA0wZDAABItcjIyMDAQAaDkZ6ejuN4mzZtGAzG0aNH66m6bt26vXr1qlevXvVUPgBAysEQAAAAANC0wRAAAFJNVVU1KCgoKCio+qv8+vVr69atfImvXr3q2rVrleuamJg8evQIhgAAaLbgQgAAAACgaYMhAACaGjqdLniXta9fv75//77KdU1MTLZv314/cQEAGgGOsBGAPi77uDMkMrm1eqtJE62mdlds0LAAAAAAICEwBACAVLt58+a0adOUlJSOHDmydOnS+/fv9+jRIzw8vGPHjqJWkZWVNTEx4UtUV1fPyMiosrpOnTq9ffu2rkEDABotMecAPDjkWfj7i9WSyzt3X5yyd0LDxYRQ0oEov+T8B4c8G7JSAAAAoEmCIQAApJqXl9fWrVvz8vL69OmzZMmSkydPnjlzxs3N7datW/VRHdwREIBmTsx1ADiOcJyDEMIwCo6jCycTD97/npHPVGyhNsXR2rm7nKlrOFWmZZC5zNKkn7cOuInKsH98a9+Y1HxMzm6i7aD8p/4XPhaS5R2mjpzfT5nNzNsZ/l/im6wcJtI30PWdZd5Fnmw64885CH1d9t3ZO5E/gxzOV++Pp8/Wxb15k17IxMhqLVQGmvXyHa3bMFsPAAAAkH78QwAcDufbt2+FhYUcDjwbGDQ+JBJJSUmpXbt2JBKJ6Fgk4+PHj3Z2dkVFRe7u7gsWLFBTU3N2dl6yZEn91Qh3BASgOUvLLBe1yHTGPgzDlFTVGPaD0zLLOw8etHkwYjMLHFfH7N97xdx/NEKogpkbpzz8wFpV0RlyjmMDV7vIzd71+PSRuN+Th691VZq96/GpI4mjdUcl7jt3/HPJrAWTeqD0mSG3FgQq753T9USA82S/wwihEwHO4YGnBDPw1eu15+HvCo7/cseOipQqGwUAAAA0N/xDAJmZmRwOx9DQkEqlEhIQAHXBYrHS09MzMzM1NTWJjkUy9PT04uLi8vLyEEKhoaELFiwIDw/v3r17/dUIdwQEoDlTkhU5fnp+24zKLz/eur328sfsEjZCqKI8m7sijrNn9m+tRMJEZ+DMMG6hiCkj9JjDLvPo+WeezcxSkiXFfCtFCO0JieJWUZiRoiTbrXJsojJUrtdMgxb7vSxo1wXjDq07dtAcYqqrRsEkt4UAAACAxo1/CCA3N1dfXx/2/0EjRaVS27Zt+/HjxyYzBLB9+3ZHR0c6nf7w4UNfX199ff0ePXocPHiw/mo0MTHZsWNH/ZUPQPU9f/48PDw8Ly/P3Nx8+vTp0vzdNHTo0MTERKKjkACS6J1lvkWB5z5kszhbNzouXHa08lJlMiY+A52MECLzzeM4ImF/8kRtdaELi0N8Bl697j7jTVM+v0j7/ebDl8NP3p25+ePU8oHVaTsAAADQHPAPAVRUVFAoFHgmEGi8KBQKi8UiOgqJsbCw+PbtG3c+KSmpAWqEOwICKZGcnLxixYqdO3e2bNnyxIkT06dPP378ONFBidQwH88GgIkeAuBbVM7BEUJY7lsqhlg4yv/7LAFetiozCM5P05ULeV9y+FWBc+ufjsH3W7U1OujdCyFEwVAFjr4wK0RlqFwUhlGNexgY9zAoL9K3X36+NO+nmEYBAAAAzY2Q8/1wABq5hv8g3b9/38jIiE6njxkz5u7duyYmJoqKimQyuU2bNg0fTB1RKBQKhVJSUkJ0IKC527Jly7Fjx7p27aqurr5gwQI2m/358+dal4Zh2KZNm1q3bj1o0CBeOfHx8cbGxioqKpqamsHBwbyckZGRGhoaP3/+HD9+fOvWrTt06ODk5MR7pgaGYVFRUT169FBTUwsJCUEI2dvbI4SMjY3r0FxpQcIwwUnookWD28pTyHuTsM0j2qvIUecGxfNlE59B6Lz1TNuxPTXvHYubsum+dnsdb9ce3Ayexq0oGOazJkZoBr5671y5M2vtidELDo5ddUlTS9PNdYTQRsEEE0wwwQRTo5sk8l0PTwQAQAJmzZrl4uLi6ekZFhY2cODArVu3enp6ysrKEh1XLfXt2/fhw4cWFhZEBwKahV27dsXGxgqmP378ePr06byXaWlp48aNU1VVFcy5fv16U1PTKisqKir68ePHqlWrvL29z549ixBauXLltGnTvL29X7x40a9fv0WLFnFz3r9/Pykpyd3d3dHRMSIiory8PDQ01N3d/fz589wMX79+ffbs2fXr10eNGrVgwYLY2FgMw549e1aL5ksbTNitABJ2ugom9h5jEzvmz/wpWyFric9QuUzePEVGeaar7UyBouxcGXZ/5wUz8IU3yNZskK2ZkIAAAAAAIHQIgJCDqAA0ai9fvpwzZ46srKynp6ePj8/MmTMb9UP1+vfvf/fuXRgCAA1jzpw5c+bMEUx3c3ObNWuWiYkJQgjHcWtr66ioKDU1tVpX5OzsTKFQFi5c2LFjR27K06dPHz58eOTIkZs3bzKZTF7O1atXt2rV6vr167x9foRQq1atePOzZs3CMMzS0rK0tLTW8UgnSR1hAAAAAIB0kuohgNyX59eFHHv96UcRi6yp1236Yv9RXYQc/2liuHtfREcBaobD4XCP+SsoKCCEGvX+P0JowIAB4eHhREcBmrs1a9Y4ODhMnjy5ZcuW0dHRDAajLvv/PCQSic1mc+cnTJhAo9EmT54cEBAQERHBy8Pd21dVVX3x4kWHDh0QQsXFxdnZ2bwMSkpKdY9EOom5HSAAAAAAmgCpvhDAz3vzQP+wDX0NaHjpy5uHN247Mmr/AqKDqsK6kZYrL1yrSwnV3/+ve121KEdSlQJp1rJly1+/fuE4jsHxQECctm3b3rhxIyEhIS8vb/PmzXp6enUs8NChQ6tXr962bZu5uTk35erVq3fu3OnSpcuhQ4fQ3xvi8vKPHTs2ICAgNDS0qKhoypQpBgYGe/bsEVM+i8WS5mcWVBN86gEAAICmTapvB5jNYmtot5WlYCSqfPehs6PC5uM4Hhu8xG7EUDMzM5vRk0JiU1klH4ZaTSxhc3Ac57BLJloN/VDCKvmV7O08wdLcgjFlztUvhZXLLM99snLmlMGDBtlO8Ix/nYvjeP/+/XlL+/fvz2GXDhw8/nrwXIuBFpXncRznL5ZdNmjIxPfx2+yHWZhb2R7+nH9jyoiLOaX9+/f/Uc7+UyKnwmzgsJfH19paWViPnhr1PIevzKL06/Pr7WBNAAAgAElEQVQc7czNBjGmzL7yuYAXj2AT+CIXUpew1vGXLzbmGjXwedTmCaOszcwGjpzgFvU4q/66QS00/Aep6enYseOHDx+IjgI0d7KysgwGw8nJqe77/wghFouloaFx7dq10NBQbsrGjRsHDx7crVu37OxsGxsbV9d/Lilfv349m83W1dXt2rVr+/btefcLFMrW1lYiQRKO++A9mGCCCSaYYIJJCieJkOqzALYsm7LAxeGUkYmxcc9+ZlY99VURQnY+QXY+CCFOzpf7jOmL5tvFzdQq3PupwFtPueDTvsK2nnpylPAZa3vM3xnUR/v74xOe3oeGRs/jlXli3jI0buOVnUbpDyM9lm4dHbeWr1KMJMth/bqmtyzxphFGovDmEULHvfiLZTMzI0tMj13wKnx3eqrX5sS4i2jAgDt37lQqjsxhF0XTRkRf9vt8Y/Pc5WETz/lWLvPoggD6lMAro40+XlnvvTDSOno2dz3BuvgivypYl7DWCZYvJubwmjTQbXfsmqiLvdTlPtw+6LVh68ToDZLvAY0Hm82ufOis8nwjHZLgXpBiYGBAdCAASExgYGBgYGDllMq3IVi8eDF3hveZVVJSOnjwoGA5lT/UvPkLFy5IPGBCwEkAAAAAQNMmZAggMzOz4eMQitZ97M4j1h9evXnzJmW7zx5dnz2eHZWzn5/bdvjil8yc0nIWh4NnZmZ2nd49YtvNKStNH2y73n16YGZm5snvRUULnfYhhBAikeUyMyfwyjz2uXBnH43crBx53ZFHw/40tnKTMzMzcQ5zUh/17N/ZCKHK84LF4pxyZ3PdwpzfqOWgkt+hmZlzkbANOLF/2/zsbOUu48vyPDMznSuXGZ1RvKuvRn5WTgtjx5JfczIzHbglCNZVZeRCWydYvpiYa9TAJTOGrvZZ3L9Xd6MevQ7ttpeebkOIRrqfL8aAAQN2797t5OREdCAAgAZFktQhBgAAAABIJak+C4DDKsKoiobd+xh27zPKznLSjHWex4MDgyJMvQOWGunI0crHjZuGEFLp6EZ+t6SkovuRt1hgJxWEEBmhQ2eiVchCLnMgI1QhsLPGxhEZQzi7mJeiRiEJzgstVp77awkji2nI363M4V15wSsTQ+hvOP+EJViX0Mj5COYRWr6omGvUwE7DZ+0xTX+R8urxxR2nH0/b6tKriuCaGRzHX716dfr06TVr1hAdS2107dr11atXREcBgMScOHGC6BAaBxgBAAAAAJo2IUMA0nMroFAPd/KExa5WxvIk5vvH/5GoLTAMM26joNNOUw4rvX3ysDKF9L2M1U5Oyc2IHfYwrKKrG51CRgiNaqOw4+JLv5Fdvz04tjG64kDw/y/vHN1WIezKy8UjjH6+jFmyLfXE4eWtqOTLaTm2ugrPLobRSBi3+UJP6hZaLF9OOTIpi8VqRaNVSiQdvpk6a0jHr3cjZFQs+MofpSG//1qql2WHtBv75dVH8pYK1iUYuWBdgnlElS805ho1cMuc6RrOayYMGtalc1unOduwGXADeYQq7fmfOnXq69evVlZWREdUSyQSSUFBIT8/X1lZmehYAJCASZMmER1C4/D0cwnRIQAAAACgHkn1WQCzArxDtx9xP7SRiWht9HvM37AAIeSwdPKsudPLZds4zFu1dNS3BU4u0aePdXEZutErauw2N+6KjHU+7zfsm3Lkp1zLDhO8VlQuc/Qan+ANe6ccypRvZeDstwwhtMrHOTRw3pFieesZK1pQktmij7SLKZYnyHPI7Okep08c5qXgOGdowRXXKStZ8m2n+fHfeoCxem7AuuApe/NUtY3m+tuLqUswcsG6BPOIKl9ozDVqoLOr3ca9q6M3FsqrthnltlzkVmseKu/55+fnZ2RkXL582cLCgvukwEbK1NT04cOHQ4cOJToQAEDDmT5QhegQQFPz+PFjExMToqPgJ51RiSedMUtnVAA0VQFXJVAIti/xJ3fOw0odIfTixQuJPHgZ8NjZ2cXFxVUzc0XJ13FTF8aePVOvITV52dnZ3bt3Rwj5L13kHxj8JuW5+Py3blz39PL+/jntwgd5ayN65UUJLwusu/0/pX1rOaEldO7cOSsra9y4cRMnThw0aBCFQpG2uwN4e3tnZGRERUVVf5WEhIR79+6tWrWq/qICzdbTp08jIyO3bt1KdCD16N27d9u2bdu7d28187u5ufn5+TXwYwXCkjIr/4lLSClwMoMTf4CEPXnypFcvqbtaUDqjEk86Y5bOqABoqgL9V9Ro74Ylp8FLTEgp4M5I9VkAzdAkx4VDPIUffgfSLCcnp3Xr1rq6ugYGBmSyuBtDNCJ9+/bdsWMH0VEAABqUtA1fgqZBOvuVdEYlnnTGLJ1RAQBEkep7ATQN8fHx1c8cfTa6/iIB9Sc9Pf3mzZsnT57s1asX9wSEr1+/amtrEx1XnaioqBQXF7NYLCqVSnQsAIAGAr/kQX2Qzn4lnVGJJ50xS2dUAABR4CwAACSAQqFYWVlZWVnt2rXr2rVr7dq1MzY21tbWHj169Lp164iOrvZ69+79+PHjfv36ER0IAKCBwNE8UB+ks19JZ1TiSWfM0hkVAEAU/iEAEomE4ziJJORxegA0ChwOh8AOTKVSbWxsbGxs9u7de/Xq1VOnThEViUQMGTLk2rVrMAQAJE5GRiYxMdHT05PoQOpRXl6euro60VHUGPyUB/VBOvuVdEYlnnTGLJ1RAQBE4R8CoNPphYWFSkpKMAoAGiMOh1NYWEin06vOWs9oNNrIkSNHjhxJdCB1MmjQILgdAKgPXbp0OXfuHJvNJjqQ+gVDAABwSWe/ks6oxJPOmKUzKgCAKPxDAO3atfv582deXh6LxSIkIADqgkqlqqioaGhoVJ21kcjNzd20aVNMTExGRgabzdbS0mIwGCtXrmyYYQ5FRcXy8vLy8nIZGZkGqA40Kzo6OkSHAIQQ/CmvrEz3PPduk7km72V+fkGDxwUaN6H9qvJLQjqV0KiERsKXXs1s9aE6W7J2YdQleKFDALfCVy3fcfzN1zwV7c6T565f62ZRlwAavlEANGH8QwAYhmlqampqahISDQCAj5OTU/v27ePi4nR0dHAc//79+8GDB52cnGJjYxsmAFNT0/v375ubmzdMdQAAYgn+lCdR6OV+i/JuRCpTMFF5ABBPsM/k5eUjhFRUlLkzhHQqoZUKTczLy8dxfJah5p7UDN7L6pcpQaLCq5xBTHi1KLx2K36JnTVx86/wE4lDjDQyUpK8J030bHlvr52Qkd/qbNUql9YoNgAAnO0PgFS7ffv2li1bOnXqJCcnJy8vb2hoGBAQ8Pjx4wYLYPDgwdevX2+w6gAAxMIFsYtXhfWeEPqM+4qb5/DiyV31tNRatGjfqc+ywyk4u7SlRrdraybo6pvviP8Qu9ReS1NvffRzHMeLf1weZ9GrTevWnfuNik7NF1I+aOqE96tKi1glb7Tb9iqo4OA4zmEX92mr87a4tEXLdg93eBpqtdbp1G/XvUxc0n1JaFQIIZxd2kqz98sIXyNt9dZtDYJTc3EcV1FRPmfa4cSvEhUV5c+lFSoqyrjgp0BsSyVCZMz/UlFRZjN/OXbVj/yQj+N4Xuohw54zsphswQ2Y/Sx8UFcddS2D2TsfYBgmwahCfONmxuy36aFNI9N0jEcciHE/57sd57D43lb+rSr6Lwl3m9/bvbB3R221FmodTax2/5eBC+sVEmkUAFJLIt/1MAQAgORdvnxZV1dXU1PzwoULdSzKzMzMx8fn3bt3paWlZWVl79+/9/PzMzY2lkic1TFw4MA7d+40WHUAAGIJ+73BVunkNfnBgvsF5fjfn/tOQcdS3n/9nfXrXtz6fb7jcUyGzcxMMvF/dHnuGvfRd8wDX9wK2DZrPI7j2+1m9lt+JO37l5gNg73HbWrQH0pAOojoV/9fRJbtuK5L0bKnv3Acz361usBovaGcDKeiYL/spKcfv51d33vj9A24pPuS0KgQQjgmU8H8tq3Q8k7q90dn52+z8+amj7z7ASGUk5OrLUMS/ikgbghAVVWFN3FTMErLPZeCAka5pRd9dx+zd++VnS0omOAGDJ66qsuqmG9pz6fSD5NqG7zQqM5ml87Wo/NeKhvMK806gyMy39vKv1VF/yXh1jLN//Dmy08zf6Wf9DffNNcXF9YrJNIoAKSWRL7r4aGAAEiem5vbjh072rdvP378+DreETAyMjIwMJDBYKSnp+M43qZNGwaDcfToUUmFWiV5efmKiorS0lI5ObkGqxQAQBShPy5whCbsXWcz+/SNyKnclxk39niu3P/qc0ZRaTmbg+MI4ZzyRTYd6WSDinL3ZcMMlMh6bKY7jlDY5/zcCQPXI4QQIlGUcNSIn5MKak3Mj1buouFBozcsScATHG+tvOCw2ZebuMZpkCyV1I3hXzrTGEchEu9Lono7zi4L9hiqQMYUTNyLM5bj6CAvM14pm+CnQHxLJUJo+dk5uXwZcITktcacWXZuwOABnjvvW7SUFfphPJFZ8mhMDzKF1HfSWrbXsVoHL7giGcMq8EqbC+dgGEXo24r+3aqi/pJwl0ZudJzuOH30MIuBFozUZz3qtVEANGH8QwBFZRWExAGAZCnKNvTw1urVq5csWaKgoIAQ4j1Qg8Ph1LFYVVXVoKCgoKCg6q9SUFBw5coVvsT3799TqdTaxTBgwIA7d+5YWVnVbnUAQCMi9AgDjuM0utnmzv673tqSMAzHcUcn/5H7Lh0Z1EWRVqqh0YG7lhIJ4TgJIaT4dwbHcRLCXv/Mak0liSkfNHli3nfuIrVu69q8N/pcZL3mVft7XVpwEyncI7gcNkKk+uhLono7+tOZce7vZPzvIejKq4j6FNR3DxcTs2BKTvpPZQ36h6/5OK6OEBLcgGyEKjjcjcyqS/CCK05oLRfyOntd95bcl7mpO+XVJwl9W9G/WxWJ+EvC/be3y7ZHth/+u3X76v45wVf9b6yzqr9GAdCE8e8mpecwCYkDAMnq2KahhwCmTZvm7e3t4OBgY2Nz4MABDw+PsrKysLCwBg4DIcThcHJzc/kSy8rKqlyRzWbv2bPn4sWLGIYxGAw3NzcymYwQsrCwuHnzJgwBANAciNnB6OlzPMhqrRIZw3F8iB69s6GOHJ4fvXl1Kxr5bUEpEvgdz52Zqac8L+zmEY8BqZc3TgthPkvc2FBNAVKkyiEAhNE2jm81c6er4tR1FITjOMIw0uqoB5sn934Xv1q+9fj66EtienvlRbwUJQrpW2mplqws96X4T0E9qf4QQP77Iy6ntB8kn1jUf1CsRbKdloLgBhyjJrfuwsstowzvH1tJxjAJDgG4B9oPGOfSL2rH0O5tfr25sXDigYnb7+M4Lvi28m1VJOIvCfdfz34d26+N8bF37NNHv3M/T3zty/prFABNGP9uEnxKAKgdfX39ffv2RUREzJ8/f/ny5Z8/f5ZIsenp6atXr541axaVSvXw8Hj58mWfPn3279+vr68vahUVFRUPDw++xDdv3mRkZIivy9fXV0ND48KFC2w229/fPyAgYMWKFQghMzOzgICAurcFACD9xOxgYBS1LctZvaZycByfd3hp3wGdSxX1vEKjDrmnDu7UBYn44e4Rve/xtMUd1n5RbNvdZ/sx+DnePFU9BICQ0SKfR51nnkjt/Hd/jzMl+3D39vYsuv7yI2froy/VdAjgwqbJ/Tr1/PbpNfel+E9BPanmEACb+WvWmKCAKw8UqAqbz/qZjpo14MEhwQ3oG+471nV0u3k0u0WRKpQYFgf/+9yPukalNTzkuK+/v5uN2/c8Fe2uU/2il1lqcg/N872tfFsViR0CWLVhttPiMaHfcpXU9T0CjgvtFZJqFABNGLYv8Sd3zsNKHSH0Or2E0HgAkIwubeURQv5LF/kHBr9JeS4+860b1z29vL9/TrvwQd7a6J/n6ya8LLDu9v+U9q2rvh7+169fGzZs6Nmz5/Tp0zGsrl87AwYMMDMzW7VqlYWFha2t7Zw5c8LCwq5fv37jxo0alePt7Z2RkREVFSUqA5vNtrKyqlzskCFDeM8CYDAYBw4caN26dW3aAACoBjc3Nz8/Pz09vYasNCwps/KfuISUAvvu5IYMADQHH96n6hsYis/DZubfPrZ8XmzPF3Gu3BT11q0yf/0mNippI50x1yiq+n5bAWjydm8JrNHeDUtOg5eYkFLAnRE4CwCG5wGoldjY2JkzZ8rJyYWFhYWGhl65csXNzW3p0qUGBgZ1Kfb9+/dJSUlycnKfPn2aM2eOpqamr6/vtm3bJBU2T0lJCZ3+z/AHhmEcDod7X4Phw4cnJCQ4OjpKvF4AgFSBnwGgPlTZrzrodGphZBMRM03w8DuBUUkh6Yy5RlFJZxMAaFYEhwAICQMA4e68KxKfYUBHxYaJpEre3t6XLl36/Pmzq6vr169fbWxsBg4cGBgYuG5dnW5ZPG7cOA8Pj8WLF3t6eoaEhCxYsODw4cN9+/aVVNg8SkpKBQUFP378aNOmDULo/fv38vLyvPsajhgxYsWKFTAEAECTB7/OQX2osl99Sk/ny5nxMxOGAARJZ8zVj6oB3lYAQJVgCABIr7upRR5D1cXnCUvM7G8oFaMAguf8Kygo1HH/HyG0Y8eOkJAQZ2fnL1++FBQUREZG2tjYRERE1LFYoYKDg8ePHz906FAWi/Xff/8dPHiQt0hXV/fz589sNpt7g0AAQFMFPwNAfZDOfiWdUYknnTFLZ1QAAFHgdoBASt2rxv4/QshjqHpYYmY/KRgFCAkJGTFihJycXHh4uASLpVAoixYtWrRokQTLFKV3795JSUkvXrwgkUhr1qzhe4igqanpw4cP+/Xr1wCRAACIAgfoQH2Qzn4lnVGJJ50xS2dUAABR4F4AQKrlcarIoEJCSDr6LYPBYDAYREdRV7KysqKuMhgxYsSlS5dgCACApk0a/pyCpkc6+5V0RiWedMYsnVEBAESp7oUA+V/fr418+vhzfilO0myrPm2qlYNh1bdG/3+xbGZft0MIoYeHPPu47OPO1DJk0JywKhBCyCMgDiEU5mfHTfznJQ0hOAOtQVhYWMCjAQFo8uCnPKgP0tmvpDMq8aQzZumMCgAgCv8QAEfER3j5pv/uF1es2+hswkyzD7h//MSDMassEEJJ4VHLkvPvH6xif55XKgdH3MyiKgKgsgo2QgjtXmI3e1OcR0Acd4abwl3EBd2pAdBoNDqdnpmZqa5e9QUaAIBG6uuXT0SHAJog6exX0hmVeNIZs3RGBQAQhX8IIC2zXGi+l6VshFBuDitfrcMR/w7cnJP9DnOXms7Yx505EeCMEOKmnwhwLs3+FHTo3ocC1Ku/Ka983lI2M//4mTv30nLymUi7vY7rlAF6sqRfr1/uS3z38VdRBSIrq9B79erhaqkj0SaDxoT1dz8/1Mdu/pY47v5/qI8di/1PNsF+20uX+LsDND3Dhw+/cuWKk5MT0YEAAOrLgAEDiA4BAAAAAMIlxMfUvRBSNfN5mLRACG3bEuW77+rx/z7ks3H0d4e/8gyf+MN332WXW08bPUX3p+DS64euXEzJnOjusGtOv0/vP2wOf4MQWn/iyeuMQr+lU4+ud9y1iAH7/81cTn4Fb1rtNhIhtNptZOVEogPkt23bNnl5eawSoiOSJO7tAIiOAgAAAAAAAFBL/GcB0GWFDwoMnWKn3+fT7Zfpd558PHcpPTmVFTm3q+BalVeny5KSclkIoeldlRSxvgil8uWM+VaKENoTEsVNKcxIoct2G6hBO/u9LGjXBeMOrTt10BxsqtuS0qR2okA15RQhhBCJhCGEVoedRwit8Ri1xmNU5Ze8zKL6bcNbt27d+fPnLS0tiQ6kXujo6Pz69au0tFROrga3AgEAAAAAAABICf4hADHHLHUNdXUNdSeP6spYcjY37TGGda28FoYhHEdshMiIyUvk3hwEwzAMcfjKxzBEwhBCKGqrC530/1rdfMb3Tfn8Iu33mw9fDj15d/rmj1PLB9axkaDxIpNICKH1Mxkr9savDjvPneGmVM4mPcfa27VrN2TIEKKjqEfW1tYJCQl2dnZEBwIAAAAAAACoMf5jpyQSJnTaFxozeWnE1Z9lpXlZCCGagjY3nXuE/iuTrS9DRgid/1me/uqJDPanqOEqVITQyU/FP98+5pXPm5mmK4cQOvy6oPDXO8biCLeQJyQSRibTehobTh9rtmb2UIRQad5PUSHB1LQnvg65cbYdQoi7/79xtp2obHzpDW/p0qXBwcGFhYVEBVDfHBwcoqOjiY4CAAAAAAAAUBvVPQvAcbzxz1NP92w+sR0ja+vqOE4x4+b0NG6171nWwrXRO8Z19ot5czg09vsoKy3au4/lbDZCkzz7Ptn9IH5fXJaluTwptYSDl1eqyHqW7dfI5GtH4xJYqL2ezkynHhiGbl9Ojrz35XtOKYdE1tTSZIw2l54DvKDhkSsNUgXNtfPdGRc0V8jxZ+npJFOmTEEILVmyhJfSxJ6Uo6+v/+XLFyaTSaPRiI4FAAAAAAAAUDP8QwCijp6qaOutX6QnmG7vyrD/O3+s35/b/qPBztz/aW077tvQ8U/iqBnc/6/udP1TF015lqvtrH8LNB9hZj7CrLrhg6autSr6lfv/l3z7/61V/8wQd9SfXxPb4RfK0tLy+vXrNjY2RAcCAAAAAAAAqBn+CwEwAKRDxzayYYmZKiRkqCZyUiGhsMTMjm1kBVcn5OPUTMC1AACA+sMqejphgKGcjJLRwEnPillEhwOal0+xAaaGmjQKTdOg77ozHxFCvtrKvJ8WBpP/48v/+8Wl2Yy+JNKfn9OCvbd2KTUi9PdP/se1FBnKghfZlbPxB3/voG2/Li3pclQZRYPe1gcf/BZVGgCgiRG4FwAGE0zSMnVuKxuWmCl+6txWVui6DQzDsKVLlyJhg2gNHUr9MzIyevPmTUWF1D2REQDQWDhrKIpalDDdIW3Ipuyi3wGWaQ7OiQ0ZFSCWmF4hZpFk2UxZ7RaRXMIsSY5wW+s0HCH0tbzidn45juM4jr8/Yc6X33LR6RFrzvFOABTsvbVLqSm8Em7KLZ9DDgfHnvG+KmatscNnD1559EtWcWn+j0M+PTfN3yCqNABAEwNnAQCp1kVLTvwkasUG/iDhOB4YGIj+/eJswl+fgwYNSk5OJjoKAED9wyvIVJX7W6Zr0GVV23bblpyJENo/z167lTKFTGnRtvPC/c9xTglV3iBmrpUcVQ4hVJx+wdbEUFFGVsvIMupdPuKU0hQ6vQhfoKMiJ6OkseFt7tmurY5kFmMY9qmMLVhh8LWMTYtHyFNlrRcG/EgMbuj2goaSHDK7YxtVCpmqadgv5HpG5V7B18EqL+LvXZLWU5nGZuMIYWQKTVbVBCH0rZytLUMWlT8l4eDonuq8l4K9t3YpdcSpyPa6zt7tsJf8wDudyRGV7SeT3b6jvgKNRJGlD5wc9PZuSN2rBgA0CnAWAExNcwL1ys7O7syZM0RHAQCofxiFU5G/U84pLbswcYvpqvErEULuO2K//s6vYDNfJ23ZPm8kRpJnl3873c2/oKwAIRQ81Gng+jM5xfkJW4fOHL4OkeQqyr8GFli//FX0/qpv4NCZY179RgjhOK4rK2TP6lEhsz9dBiEkQx/ALHzQsK0FDWesb9iu5I9MVvHFTVZrZ3hV7hV8HazyIv7eJWn7k/dsHtaJSqYYDF676/Z+hNC3cvYc804yVFrbrkOOvcoVv7pg761dSk3xHQL5edurvHugmmyLTSZkr6R0UWtdDF/ka9J+wPDxfht33HiRKao0AEDTI/hQQJhgagoTqFempqZPnjwpLy+vOisAoPHb7D5EnkrtOS6o5PdJhFD61ZDBPfRbKCm26zqKXZ6OEMI55cEuA6hkKkJo+8e85bY9ZKiyXW2WF37fjxDC2aV7vEYo0cjapnOL0k9Vs1IcIYRgD6TJig2Z4WQ/bv7yzWmKDr8/na68SLCD8Qj2LskKcVg44tDdclb5vQh771EBCKEp87xddl4tKs075q3jaTW3muUI9t7apVS3un/PPTw1P8EqyBIhNHiTzQ2fY6LWMpgc8DH747bF01oy0/xGG8y+lym0NABA0wMXAoCmiZCPU7Nia2t74cIFoqMAADQEGvdvKs7h/mywd/C1Xh/1MSOntOz/NxtrS/tzSJ+MsAwmm7v/wGb9OVVbmYwhhBBGrbKufnTag0ImQoiZf4tG7y/RdgAp0n9W2IfLe6y7tUzc5WLic7nyIqEdjEto75KgoJScdQ4mNAqtp/3qvHehCKGATZvG9GlPpchbuOwozYoXv7pg761dSl2wip4sfZkdaaaJYZiG6cHcd6vuFTKF5uSU52IUVVMrho//tv9e3w63HlnHqgEAjQVcCABT05xAfZs2bdqxYyKPLQAAmgwMIy2OuMvksJ+fWaKg4YgQsjFU7ta5gwLKP7F2gTqN/LqgtHL+BYYqM3YklVUwn8Ys0e6zQGiZdArpa2mp0EVLRmgFn3lQUJoXv2GJNsNX4s0BUmJKp9YBj8utJ7pt2Bb6cpcbqtQrBDsYb1F1eldd2LaQXRX7lMWpeBHvL9tiFEJoRk/9DVdeV1SU3Do0R1HLRfzqgr23dil18fHY/BYDDvAO4x+xaO0T8VFozmnt2znvuphTwmKXFT44d5Qs07aOVQMAGgsK3+s+ekqExAFAoybmvIM6nkeXm5u7adOmmJiYjIwMNputpaXFYDBWrlxJp9PrUmzdaWtrFxYWZmVltWzZkthIAAD1Csc5M7L2adEtmcodN55NQgj5xqzV76xRomS4NPxitNebHuptKudfkHj8gd0cFd80Je1eKw8KP2qavNu5o3qH0oIMwUVD9p89PHxyu3lfdU3t4y7y34AdNBmbQnzsZg8N+JxN1+w4f/s5VKlXCHawp38XVad31cXeKzvtp4xWmPS7pX7f7ZdjEULrg2fbeQxZ/SWvXXfLw3mA6bwAACAASURBVNd2cbNhGMb9cud9+3NnWMUpfL1XsD9XJ6UuNvk/nZEUx3s5eq/HLPNNaM4h9O9vFRzH99w66uzi297HrhTJduwz7MjNI5XbwstWx3gAAFKIfwgAAFALvO/I0NDQZ8+ecZ8OsHTp0sGDB9exZCcnp/bt28fFxeno6OA4/v3794MHDzo5OcXGxtax5LqbNGnSyZMn58yZQ3QgAID6ZbYk4teSCN5LJe2ZmcUz/7wY+YC1+Z/M8hrWsfdTK6dU3ovgzhu5h5e6C6+LIm907L8UiYQNpJnWcN/Haf8c8eb1CqEdjLtIsHdJllpPl1tv/jnU38Zq4cOPC/my8bq04B4yX+8V7M/VSakRvhgOZhRVfqliuKrop/BQ6fr2MbfsxZcGAGiSYAgAAEnavn37ixcvFBQUEEI7d+40NjaePn16XQq8fft2dHQ0jUbjvjQ0NAwICNDW1pZArHU2btw4e3t7GAIAANSa4ClUsAcCAAAA1Cu4czoAkpSXl8fh/HkGL5vNzs2t4gFCVTIzM/Px8Xn37l1paWlZWdn79+/9/PyMjY3rHKkE0Ol0DQ2Nt2/fEh0IAKAe1es+OS6g/uoCAAAAABI8CyAsKVNoPgAaFw8rdULqtba2njVrVnBwMELIx8dn+PDhdSwwMjIyMDCQwWCkp6fjON6mTRsGg3H06FFJBCsBTk5OERERGzduJDoQAAAAAAAAQNWEXAgwxUyt4eMAQIKOJ/M/RqjB7Ny5c/78+V27diWRSCNHjty6dWsdC1RVVQ0KCgoKCqr+KiwW69u3b3yJ+fn5vNMTJMjGxmbt2rVlZWWysrISLxwAAAAAAAAgWUKGANJzhD8+FABQJTU1tXo9RH/79u2BAweKz5OVlSU4ZPDo0SNNTU2Jx4Nh2NixY6Ojo6dOnSrxwgEAAAAAAACSJWQIAK7DA6DWfvz44eHh8eDBg0ePHgUEBGzZskVeXl6C5Q8aNKjKa2U1NTX37dvHl+jt7Z2RIeT5W3Xn4uIyceJEGAIAoGnIyckhOgQAAAAA1CMhQwAcGAMAoLZ8fX379u174cIFNTW11NTU+fPn79+/vy4FKigolJSUVE7h3kBbem6apaampqmp+fTp0549exIdCwCgrpSUlIgOAQAAAAD1SMgTAQRvzwtA49LwHySe5OTk+fPnI4QUFBSOHz8eFxdXxwLv379vYmJy+vRpXtMIb6MgT0/PsLAwoqMAAAAAAAAAVEHYhQDStXMBQGNSVFQkIyPDnVdSUiKTyXUs0MjIKCkpydnZ+d69ewEBAXUOsF4MHDhwyZIlBQUFdDqd6FgAAAAAAAAAIgk9CwAmmBr3RCALC4v4+HiEUFpampeX16hRo+peprKycnR0tJqa2rBhw+peWj2ZOnWq9DyqEAAAAAAAACAU3A4QAEnavn27h4eHgoKChYWFg4PD+vXrJVIsiUTy8/Pr169fUlKSRAqUODc3t0GDBrm7u1OpVKJjAQAAAAAAAAgn9EIAGAQAoJZ27NgREhKip6dXH4UPGTJkyJAh9VFy3cnIyNjb2586dQoeDQAAAAAAAIDUqsGFAL2d9/V23tfH5YDNkpiQq98IP9m71hOngsltS0OWKfEaYRIzEaioqMjMzGzo0KEnT54sLy8nMpQGN2fOnL1798IYIgBNDI1GIzqEKoiKUPojB1zS806xip5NNu9CV2xhPHjq82IW39LfKZfnjRnAu+OPYObapdQR7V/cxPy09XKKcj4p2ZWz8Tfn/mHGwO6aanR5RdXO/WwPP/wtqjQAQBMjZAiAgwufuO4ecPbuzj567OKq+4Wicl49ENXHZZ+opdWfJFUO/1RVS+ujTMnXCJPoiUDbt2///v27j49PfHy8oaHhwoULiYymYSkrK5uaml65coXoQAAAjZ5rO9XqZ2YymfUXCWhWrs6Y8Glw4M/cHxuGfJrgyn/lnbVv9PDVZ3kj3YKZa5dSd8xKuCm3Fx8Zs98+epG48ieMmme+7MiHjLyCrC8HvI2DFwaKKg0A0MQIuRAgLVPcoctPv9jaA3qhxMTrxx856Qz47+LNmOc/sgqZ8sqqtnaW9p0UJ/sd5uY0nbFv1iBdvqW/Xr/cl/ju46+iCkRWVqH36tXD1VKHzcw/fubOvbScfCbSbq/jOmWAniypcjmh03oLrsULiT+GjnKTl0VSaC1WWbcMTfxYiMkNY1g7GtNLsz8FHbr3oQD16m/K31KczV1lzfBWWxM+FGFyQ2yHmRS92H3jUzFJnrs6m1Vw/HTyrXdZpYjSQV/XbWLfdjSS0DKFNoe3VOgWqOW7B6QShUIxMTH5/Pnz+/fvT506tXXrVqIjajje3t4zZswYPnw40YEAACQs62mI8fSUZ0/2t6T8e/AAr5BV0Pxvw2iH9afLFfWXn7g8f4B6+AKHDaduZuQU09X1nVYd2+Sir6BqctxZ22n/3YKSguIfFyePWXTr1Tdl/f6bTp2eoE9TaNHn3jYb+yX7M9nKy+++6DKha2RmcSSNllpQ2l6W/7kqd7bPc99yMi2zqFWHXot2n/YarEmj0Zhl+XyF+HVUrSJyQKjsp2HDHVa+zadNXHkGwzCEEH/HMFCoWdcqypRX6R4/v6vjwQzfXcd0bnm5hz+ft/+C/3jj6ke17cbPgDAbearM0PnrMwyDEPrn6+zpxf3iM9cuRSLbszJORbb3TfbdY7v6zjH+wRzXhia85/9kstt31FOgkRCim03c+HKixAMBAEip2nwdUmU1EULlRV8QQua2FiF+k4/4jynMy4mOuo4QOhHgzM12IsBZcOn6E09eZxT6LZ16dL3jrkUM7t7v9UNXLqZkTnR32DWn36f3HzaHv+ErR+haPPy1YGSEEJuVd1ulyzpXY2Zp0ZXY/xBC8Yfvvssut542eoruT/4m/V3lpnLnda7G5aVFCbGX7rbqtsGtJ2/12xFXLqZk9p9it9VJP/X1243hqaLKFNocHvFtAY3dgQMHhg0b1qlTp4cPHwYGBn758oXoiBpU27ZtNTQ0Hj16RHQgAABJqih9N2FMdPStPUL2ojEKpyJ/t5zju585lzf3XTPZHyHkGhKd9iOrtKz4ecKmnQsYGEmeXf79jNHK7MJshNBWmxkD1pzMzMu6tNlyzsiNiCRXUf5tU8HQZ+m5by4t2mwzx+75D4QQk8kU3P9HCE1YdmD7zbfFJXnxgZYb3L3/pAoUUnXkgFABDn5GGy7mZr53Vj7AfW/4O0ZNuxZJjs38ebXvxtf/eS+fNuyW5dYPj7cEuTBqFNXjQmY/ugxCSIben1n4sKaZa5dSd3yn7mcme5d326gm2yKwF2nBtR+i1joXttDP1MB81KQVgbtupmSKKg0A0PQIOQuALivua5IuS8JxDkIIwzC6LOnjrdtrL3/MKmEjhCrKsyuvK3TpQA3a2e9lQbsuGHdo3amD5mBT3ZYULOZbKUJoT0gUd8XCjBS6bLfK5Qhdi5dBaAw4znHt2UIRU0boMZuZRZclJeWyEELTuyopYn0RShVsKY5zXI3/rMJhl3n+u/rpzyUIIZduykrIGKFXBenP6LJdhJYppjlVtgVIRE4xYVXHx8e7u7vHx8fLyckRFoTk3Lp1KykpSUVFZdKkSRoaGtVZZe3ate7u7gkJCfUdGwCgwQSPsel84p6pssj9gUDXwfJUkvHYjSXOHRHa8yNxu5Pv7pRPGYUlZWwOjhDCOeWbpvenkskIoV1peTkMk1UIIYRIFDpCQTi7dOfc4UpkTKnv7KIfixE6LiaYM1ucJzpMGjNiiMWQMT/e9+SlCy2kysgBUSJ/Fr8Z35NCIQ2YFsj2PIKEdQxUw66Fc8qX2XZWJnesKHdaM7wjnWzAZjrVLjwcIYSq+/NMMHPtUmqN74z90wsTLXduQgiZB1jPcz2Ohi8Rupb+xPXvHHwe/ZecnHxjhX0X4+Ovd5iqC5YGAGh6hOztY5jwibeUVfINISSjpI9hKODch6wS9taNjpXX5c0LLnXzGR8wY5C9sXrpr6+HztyYsykZwxAJQwihqK0uF0NmXAyZcX7LFL5yhK7FmwRr4VIiI4xERgjhOMKwP3eJwzAM+3vlvmDreKsIrv43GvRndYwkqkyhzalmW2CSyESgBw8eGBkZNY39/3Xr1p08edLGxkZPT8/e3v7Vq1fVWUtHR8fAwCAxMbG+wwMANJhPZibnV4rbLadx//DiHO7vCoeJy4auPfb2a2ZB4f9PkWtD+/P1SkbY1+Iy7pXGZSVZ3ERlMoYQQljVTxXt57nnzfmdw4xaJu1xM13yz81HBAupMnJAFDZCFdxr6vE/t8QT2jFq1LXQnz5ARgjR/87UiCmd9rCQiRBi5t+m0fvVNHPtUiSLVfR0+avsYxbaNBqtndnh3NQ19wuF79JzynMximpfy1HeK4OvvbhxyNZO4sEAAKSTkCEAEgkTOv1ZinEeJzxFCA0a15VEwso5OEKIlPeOiiGEUAGOSCSMe1T7K5MtuJRMpvU0Npw+1mzN7KEIodK8nyQSNk1XDiF0+HVB4a93jMURbiFPuDXyyhG6Fm8SrKVyQ3jzw1WoCKGTn4p/vn0s2FKhq1Sen64vjxA6mVaS+eEZQqiFbi9RZQptDm+p+LbAJJGpdh8Gidi8efPu3bt///5NYAwS8fv379u3b+/cubN///6jR48+evTomjVrqrnuihUr1q1bB48GAKDJ2Lcyekr25kXXMoQuxTDS0qP3mBz2ixg/BfUpCCFrA+VunXQVUP7JDT7qNPKbgtLK+ecZqHjsulZWwXwW69ehv4/QMukU0rfSUqGLphm13fSkfOj4GWu3bH21x7MukQMCTWglvzI+hc0uu33Yl4xhSFjHqGnXqjsfm7bbYh4WlOadD/RrN2pRTTPXLkWy0k4sbNF/H+9+fgfNWy05miY0p7NBB9c9l3JKWOyywocXjpNl2kg8GACAdBJyIYD4g6gjFhxRoNMZY2zm9FBECC0a3Hb9zYw9iWizbXv/a+lzAuOOr7D3NG6171nWwrXRKwSWzu7dKvLel+85pRwSWVNLkzHaHMOQ9Szbr5HJ147GJbBQez2dmU49uDHwyvGx0BJci0cwBsGGYBia5Nn3ye4H8fvisizN5UmpJRy8HCHZfxvLt0rl+aEeIz5F3Ercd+Y8RjXqaeQ9zUBUmaKawy3n9uVkMW0BjZ2TkxNCaNeuXbyURron/P79+969e/Ne6uvrV39cQ1NTs0+fPrGxsWPGjKmf6AAADW35uS3te032+X5NU+DWYjjOcc7ar6tmzaQbrjtzBSG06PTqzt3alSgZLN537uTctyZa/9z1xutyxEMHr1bLPim167lsf4zQ6m7ucOqq1bEg+6vgosAtC8bOGx40NpuuYThvW2xdIgcEWn18lc0UK7qbzPjlZ1pQTlXgQjpGTbtW3Q3eeyZilGOHBd/a92XExA/iJtJoNO658bxr47kzJXlP+TILrl6dFMkKXvfM+Uo07+XIXW5zLYPRrAPo3+cCMpnMndePuLotM1jiUIpkDXtbHUw6WLl1vGwSjxAAQDhsX+KfM6k8rNQRQmFJmYYaMoSGBEBdpf4s5/Zn/6WL/AOD36Q8F5//1o3rnl7e3z+nXfggb21Er7wo4WWBdbf/p7Rv3VjP8Pf29s7IyIiKiqpm/t+/f7u4uJw/f5778suXL4sXLz516lQ1V8/MzBw7duytW7dIJPjNDUC1uLm5+fn56enpNWSlYUmZlf/EJaQUuJi3qGkhvB0kACQLuhYAAPDZsNKvRns3LLn/38wrIaWAOyP0LAA4Kg1Ac9eqVatu3brNnTt36tSpWVlZGzZsCA8Pr/7q6urqw4YNO3LkiIuLS/0FCQBoeA18k3DB6mCfEAAAAKgLIUMAhF5JDUDj9uPHDw8PjwcPHjx69CggIGDLli3y8vJEB1VLAQEB169fv3LlioqKSmxsbDWfCMDj5+c3ePBgBoOhpqZWTxECABpeA++Bww5/cwbvPgAA1Ac4CwAASfL19e3bt++FCxfU1NRSU1Pnz5+/f/9+ooOqvSFDhgwZMqR268rIyPj5+fn7++/YsUOyUQEAAAAAAABqR9gTATCYYGrcE4GSk5Pnz5+PEFJQUDh+/HhcXByR0RCNwWB8+fLl+fMqrlYCAAAAAAAANAxhFwLA3bsAqK2ioiIZmT831FRSUiKTa/xQ4iYmMDDQx8fn0qVLRAcCAAAAAAAAgAsBAJAoCwuL+Ph4hFBaWtrGjRtHjRpFdEQE69Kli7GxcUREBPdxiQAAKVdYWEh0CAAAAACoR3A7QAAkafv27R4eHgoKChYWFg4ODuvXryc6IuKtW7du6NChgwcP1tbWJjoWAEAVWrSo8UMBAQAAANCI8A8BcJ+mDkCj1kdPiaiqNTU1z507J8ECc3NzN23aFBMTk5GRwWaztbS0GAzGypUr6XR61StLBwqFEhIS4unpefHiRTjJCAAAAAAAAALBdf8ASFJoaOiwYcNYLNawYcNatGhx8ODBOhbo5ORUVFQUFxeXmZn5+/fvc+fOkcnkRndSvbGxcc+ePcPCwogOBAAAAAAAgGZNyIUACKGwpMwGjgMASSH2TJa1a9fevXs3Nja2ZcuWycnJlpaWM2bMqEuBt2/fjo6OptFo3JeGhoYBAQGN8Yz6lStXWlpaWltb6+rqEh0LAAAAAAAAzZTwIQCE0PSBKg0ZB9fjx49NTEwavl4gKcS+g48fP35ZqkdU7VxUKrW8vPzIkSNeXl5kMpnFYtWxQDMzMx8fn7lz52pra2MY9u3bt/DwcGNjY4lE25Dk5OQiIiKmTZt29epVOTk5osMBAAAAAACgORJ5IQBOBKLqBZJC7DvYcJ8b0TZs2GBubk4mk4cNG2Ztbb1u3bo6FhgZGSkvL89gMFq1aqWmpmZra8tisY4ePSqRaBuYgYHBvHnzFi5cSHQgAAAAAAAANFMizwIgaodKSnbkQK0183fQ1dXV1dWVO//58+e6F6iqqhoUFBQUFFT3oqTBxIkTExMTjx496ujoSHQsAAAAAAAANDtihgAaMgzi6wWSAu8g4b5+/cobhuBJTU01MDAgJB4+ISEhtra2vXv37tSpE9GxAAAAAAAA0LzAWQBAwpr5O/jhwwcvL6/k5GQqlTpkyJBt27ZpaWnVpcDU1FQXF5d3795NnDgxNDSUQqEUFRUpKSmJ2c7a2tpXr17lS/T29s7IyKhLJJKioKAQFRU1YcKEM2fOqKvDU0gBAAAAAABoOHAvACBJxL6DDfe5Ec3R0bFXr14fP35MTU01NDSs+9P7XF1dx44d++bNGzabvXLlSokESThNTc3t27dPmzatvLyc6FgAAAAAAABoRuAsACBhzfwd/PDhw+3btykUCkLI39+/TZs2dSwwJSUlISFBTk5u165dffr0cXFxqXuZ0qBnz57Ozs7u7u6HDx8mkUSORQIAAAAAAAAkqAZnAdDpSlUehq1OHvFHcYWWWXkSrKWOlQIJEvoO/n4Q4WDZu32bVi1aqhubMyIe/a5+gTV6cxvucyPa7Nmz16xZk5aWlpaWtnbtWi8vrzoW2LZt2/+xd9/xOZ3/H8evO3f2NoIYQYhQQSo1ipIgokbU3rEp3xipGqH2iJjR2iSovaV+VBGrKEVbRIuqESIiJCSRfef8/rg1IqvCnZw77tfz4dHHOdcZ1zv3OVLnc1/nnCtXrgghlEqlv7//4MGDVSqVJpLKr1evXm5ubv37909PT5c7CwAAAKAT8jcK4G2ust7zSiz75s+fvxBCWFtbqSckSXr+/IUkScOr2a68FaGRTqFB2Y9F705ffb726Ea3WobpLy/938KRExb0OTov8+HL7w61kEKhyDw7e/bsjOn3HL0/Z84cd3f3Hj16rF271t3dPSQkpEGDBu+zQ01JT08/ePDgrVu3nJycPDw83m0nAwYMiI2NHTly5PLlyzUbDwAAAEB2+RgFIISQVIk2tp+Efj/Bya50qXIOC2/FSJL07I/Az2pWLF3eYcSyXxUKhSRJLx8d7tKsbtlSpWo0bLfn1gtVypM+Natuuv1CkqTnt9ZX+3jg0xRVbt/i5vEFb8a0tbXVgQb2254kWFtb3UtMUy/K0unbf3UMDcrxCD5JUVWoWtnEQKE0Mm/QefqvR/wyHz5ra6uMNdXTWc6o1IS/7MrVjU1LlyQpXfWyXrmKNxJSc+tdLnl/Ju/jiy++uH79eu/evdWzfn5+gYGBmUsMskhNTe3YseOFCxfKly9/7NixPn36vPNPOnr0aDMzsw/mMQcAAACANstnCUBhlJbyYElc83O3Hl7aN3pJBx9Jkhb2nvrR1L0P7lzpbblBTwhJkr7t8GXDyRvvPLy/d46rT5f5Cv2SK3/092s3ODz+4ZCOq1b9tKy4vuLtLyCzLxJCtP3lthAiOjrGzkhP5NRpHpdkKDg5HsEd33lPd63VqnO/GYtW/xwaKUlS9sOXefMsZ5TS2HHWR/GTfn8iSdKz69NinWZXM1bm1ruM4uLixo8fX6dOHXNzcxcXl6+//vrp06ca2bOdnZ2rq6t6WqFQNG7cePLkyRrZ8zvbvHmzm5vb7Nmzu3fvvmDBgqpVqx48ePCd9zZ//nyVSjV69GjuCAAAAAAKVO4lgGx/XjWqkhYObWlmqCznMuRlxD5JiG2RCXM61lHqG9fvMVMlSZIQa+69mN2tSZlSZT7tPCv+0UZJCNPyHXdPMmvk2sh52b5mJY2z7/yNLv5rkXizRT2RvVP+FP6fHI+gfedpl//5fc7I7sVT783s6fL1pSdStsOXeTr7GdXav/0R3yOSED9POdh5Qes8epdLXFxc3bp1nz59+v333z9+/Hj9+vUxMTENGzaMioqSNVdBCQ0Nbdq0acZss2bNfv/99/fZ4dy5cx0dHbt06ZKUlPTe6QAAAADk7F2eBWChp57QV7eohEhLlyRJktJT1S16QvHn46elDPQybxUd/tiqjOXtsBeSlNebwPP4LjfzooxvjDNmc+wUhS/7J5+e/FxhZF23aeu6TVsP8e5VsUb3+fePZ14zNV3SVwgp7dWzHrKfUSVqzSr7t9O9+FYzrlc6/1FxLTy406dPd3NzW7NmjXq2du3agYGBgwcP9vPzW7x4sbzZCkLlypVv3rxZt25d9exff/1VuXLl99zniBEjlEpl586dt2zZYm1t/d4ZAQAAAGSVzxsBsg3IlySpYwmTWQdD09KSftkyRalQSJL0ZRWrkWtOJaUmXz0wrU4LX0mSnt/aMGCn3eldZ/QWd93/ID5fw8izL1JPW+jrPUhMzJjN3ikKX45HcLizk/e6I9EJKWlJcb8d3qlnZCtlOnzljfSDfn+kSn15fO04Yz2FlNMZJSkM53a1+XLZIPPes/Rzv+++MP7G5OKnn37y8fHJ0ujj43PgwAFZ8hQ0Ly+vlStX7tu37/bt29u3b9+zZ0+XLl3ef7fDhg2bNGnS559/fv369fffGwAAAIAsNFACmBA44dKk9hUq19r0YoC1vl5qujR0z2qD/ePsy5fvMu3MiBmjVSlPhnf099s/38zAfME+X992w6Pe73GA6umD83s2rP5xxmyWTnO9TERByvEIzv+/VXF7Z9StUr585Y++WndnWfAyKdPh2756xq7+jSo4NDxqNrKMoTI1PYczSpIkp6/HXlrwyxTvGnn3Lpe7d+9m/xq8SpUq4eHhsuQpaJaWlsHBwVeuXJk6deqdO3eCg4ONjY01sufGjRtv3Lhx8ODBP/30k0Z2CAAAACBDPm4EiHwSJUmS+r+ZW0rWH3762vBXK3k/FkJS2rhuPHwh87abr4aq92lWvmvob11z3H9u/WbuK/O0Y5+AsD4iI5Lxm53Ke0Goy7J/8uaVPl9/4PMs62QcPoc2Qw+3Gapun9M7Qogczqi05Bfng4+VajjHzcpAO49s5cqV7969W6NGjcyNd+7cqVq1qlyRClqxYsWmT5/+DhvGxcVZWFjksUK1atUOHz48bNiwkydPzpo1S18/119TAAAAAPIlH6MACoFc/UJTCugI2lesPmZr/Peb+/5n73Lx8PAICAjI0rh48WIPDw9Z8min1atXf/LJJx06dGjcuPGJEyfyWNPKymr79u329vYeHh53794ttIQAAADAhy1/jwMsBPJeyOH9FcQRvPvvcHqtPT2mT5/u4uIyZMiQUaNG2dvb37lzJyAg4MyZM5cuXZI7mrb48ccfL168eOHCBaVSGRcX17Fjx+rVq9va2uaxyZAhQ5o0aTJs2LC2bdt6e3srlcpCSwsAAAB8kPIYBSDDH7n65Y+m/sh7BGVkYWFx+fJlS0vLnj17lipVqmfPnsWKFbt8+XLeI951yoEDB8aOHau+jLewsOjTp8/Ro0f/c6saNWocPnxYT0+vdevWV65cKfiYAAAAwIeMUQDQMJ09ghYWFosWLVq0aJHcQT40enp6I0eObNu27ZgxY2xtbWfNmlWqVCm5QwEAAABFEiUAaBhHEDnq3Lnz4sWLV61apb4RYMuWLd9///3bb25vb//DDz8cPnz4iy++aNu27ahRo3IbYZGamnr8+PGEhITatWtXqVJFQ/EBAACADwElAGgYRxA5atGixe3btxs0aGBpaZmcnDxnzpy8HwSQo9atW7u7u2/bts3Dw6N9+/be3t5ZCgHR0dGdOnVydXUtWbLkypUru3XrNnjwYM39EAAAAEDRlmsJIOy+PE/hlqtfaIq8R9DJ5B8hSssYAHkYNmzYsGHD/vOlgHlTKpV9+vTp2bPn9u3b27Vr9+mnn44ePTqjmjBjxoxZs2Z99tlnQojhw4e3atWqY8eOJUqU0MwPAAAAABRxuZYAGjVqVJg5AI1YExLJiavlNPKIRKVS2bt37169eh05cmTo0KHm5uYDBw5s0aJFaGhoxtsZlUqlq6vrlStXmjdv/v49AgAAAB+AXN8IAABaTqFQeHh4HDhwYObMmYcPH65Xr154ePiJEycyVrh7927ZcUT/sAAAIABJREFUsmVlTFj4/vrrr++++y4wMDAmJkbuLO/rzp07ISEhYWFhcgcBAAD4cOQ6CgAAigoHB4dFixalpKQEBAT07du3ZMmSn3/+uaGhYWxsrKOjo9zpCs/69ev379/ftWvXuLg4T0/PoKAgBwcHuUO9o8mTJ4eGhjZs2HDdunWWlparVq1SKBRyhwIAACjyKAEA+EAYGhqOHz++RYsWixcvPn78uEKhkCRJ/USAFi1aWFlZyR2wYCUnJ69Zs+bMmTNKpVII0aZNm7Fjx+7evVvuXO/i7NmzUVFRwcHB6tnp06fv2bOnS5cu8qYCAAD4AFACAPBBcXFx2bJli3pakqQ//vjjp59+WrNmTVJSUv369Zs1a/bpp58WL15c3pAF4Z9//qlbt676+l8IUbFixWfPnskb6Z2dO3euY8eOGbOdOnUKDAykBAAAAPD+KAEA+GApFIqPP/74448/njhxYkpKyq+//nr69OlVq1Y9f/68atWqderUqV27trOz84dREahQocLt27czZmNjY42MjGTM8z5KliwZERGRMfvo0SMbGxsZ8wAAAHwwKAEA0AmGhoZNmjRp0qSJEEKSpLt37/7xxx+nT59etmxZdHS0hYWFo6NjlSpVqlSpUrVqVTs7O339Ivbr0cLCom7dupMmTerbt29cXNyMGTMmTpwod6h35Onp+cUXX9SvX9/JyenevXt+fn4bN26UO9S7S09P/+WXX549e1a/fv0yZcrIHQcAAOi0/L0RIOrqjyM86+vpFep7BBRvUjeeWDnBpWoZI32jMlVdxq84nrHyX3vn1ChtoW9kUad5r+MRCerNs+xNCBF1PqhNw49KWpoYGJk7fNIq6NeoQvyBskbS4MrvKfunpymynDlAbhQKhb29fadOnaZPn753796TJ09u2bKlZ8+eJUqUuHz58ty5c9u0adO8efOWLVv2799/2rRp69atO3z48J9//vn06VO5s+dl7ty5tWvX9vf337Bhw+zZs11dXeVO9I5KlCixdevWpUuXenh4fPPNN2vWrKlUqZLcod5RbGzs559/vmPHjqtXr/bt23fbtm1yJwIAADotf19zNf9611z/AysPFPaXGJIkZZ69u6u/55zIbcG/uNcpG/7HT1+29+xX6trGLpXv7R3ZxPdp8Jk7Dcob/LJ3wcA+QbdDvHPcYafWI9pvObfL3dkoPf78vjmDR88Z+EtAofwoGtO/jPmGx/Ea3PztP713INeZ8wGIiYmZP3/+3r17IyIiVCpV+fLlPT09p0yZYmlpKXe0D4qlpaWLi4uLi0vmxqSkpLt37/7zzz+RkZGXLl06dOjQ48ePo6OjhRCSJJmYmJQqVap48eLFihXL8b+F/1MoFIoePXr06NGj8LvWuAoVKqxdu1buFBowffp0Hx+f1q1bCyEmTZrUqlUrDw+PD+PeEwAAUBTlrwRw7UhQAeXIl/mj94w+er9dzeJCCPtPPLcd+V+l1gs3dlk+83/rx54Jb1LFSgjRtPec271z3cPjFFUlx6pmhnpCWDbp6X+j5xtLX4Yf7Orpczo0zNqh0cI9+7rZJ3epUqtdyK2BDlbPb6yp3u5E6F9b9n3Vadb2E4+iX1qWceg/ffviQdUMzD465FOzx7qISau2Vzz9v4Fr/xgd9NOsbrWUhiXPzevQYcaOZAuHqTuP+TQunWMvPRxfP6782W+rWn4x6a/nhj2n71ePAlg78ovM3X0W0HJj5MuNCsWdxLRj4zq/kWRInbMBIwbO3/ZPZLxNFZcJq/eNcbPN0pdRp6oZm1c2fvXwsLf/9N6Blpw5RZGXl1elSpWCg4MrVqwoSdLDhw+DgoK8vLz2798vd7QPn7GxcY0aNWrUqJHj0qSkpCdPnkRHR8fExKj/GxYWpp6Ojo5+/vy5unapfjGBesLCwsLExMTc3FwIYWRkZGpqam1tbWJiYmJiIoQwNTVV371vZmZmaGgohLCwsFDfj2BlZaUeRGNtbZ3jyCB9fX0LC4sC+hzwPq5evbpgwQL1tJ6enqur6++//96iRQt5UwEAAJ1VNG52zfxPXkmSdkQl3Ha0zmgpVmNcwpNqQizf9zTxbsW3+mr0UODXrVwqBXzaolnTph7turnWLp156cKWXk0Wn9jv7nj7+KJGrWf1uLvw+zPf1fi0d6ubK4e1+nbT5YslDfSGfLd/yHdCiPTHNw6Xd267eMhDVUrETw3+7++BV0o7NR++68h9799taraZ1T0iPe3FMhOvO8/W/bVnuGvXKT6P1uTWS0aA2V+Mr+V3/GJ3p182jtgkhBAia3dJUeLf64rsSTpNWLPlxpPmFc2v/DCjxcBRY+7uytLX87uvN8/w9p8eCtOZM2f27NmjviAUQlSrVs3Pz8/Ozi6PTcLCwgYNGpSl8d69e3Xr1i2olDrJ2NjYzs4u72ORRVxcXGJiYnx8vBAiOTk5ISHh+fPnCQkJSUlJQoiEhISYmBghxIMHD1JTU9Xrp6WlCSFevHiRnp4uhMioLKipVKrY2NjMXaSkpLx8+VL8W2LIvOjly5cpKSlCCHNzcwMDgxwTqgNkUCqV7zDeJCN20ZLHx/I+rl271rx5c0NDw169eg0YMCAiIqJ06dL/vRkAAEDBKBolgCwXq0qFIi1zg5SuUOgLIRRCpEiSEP9987xDT79/uo6/eOrnMz+f8G3v8PGOv1c0fP1vsm//eR7dps5kIYQQevqWQiw0t+v208w9TnVrjV7/p7uNiRAi/GhA76+XXb3zKC4hSZUuCSGk9OQp7T6yUlZPS+49u011S2U1Vcqrb9IXDHEzNdD7uIt/Ql97Idbk1ktGgI0RL293r6uvr9e43wLV4PU5dpch+6L9AQM7f9Glc5sWri06R92tm3dfGd7+00Nhaty48dixY729ve3s7BQKxYMHDwIDA52dnfPYxM7O7ujRo1kad+7cqeX3sesCCwsLCwuLUqVKyR0EhefUqVMBAQHLli0rVarUvn37Hj58WLNmTblDAQAA3VUkH8/Wt4zpvGuv33cdc2ORmW0/IUTnkqaLbsRkWdnWUHk/WaWeTku8oW9cSQiRnhyj0C/WoIXn2OlLTv95JrBV28ybKIUiIkUlSZIkSarUF+rG6IcR1mWtbt17NftF5wmtZm//JyI6Mel1EiulQgilEMLy3wk1Q/VltZSe+QPPsRc1lRCvahxSah7d5bbo0+Frbh9e2apWyWPLB7iMPZx3Xxly/PQgu02bNpmamnp6etrY2JQoUaJNmzapqambN2+WOxeAt9KsWbNRo0YNHz7c1dX10qVLmzdvLsyHvAIAAGRRJEsAo7/rtqp1t/0X76aoUsP+ONTLY7nX6tFCiClrhqxuO+DEX4/TUhJ+O/idc4NvhBAzGpXxCTr8+HnSy2cP9i8bWd7DTwjRt1KF/ssPRSekqpLifj2wWWlULvP+x1SzHvhdSFJayu97x9vVGyOEeH5jTZfNla8duaY39/OdYfFCCI9qVrVq2JuJF9tmjiltqPwzNjG3tAqF3rjvf0lJV13ZPd6sTJ88esnQw8Z00v6rKlXSz0FfKxWKHLuz1NcLS0zMcVGv6qX8Lie36j54zpKlocsH59hXxuYZcvz0ILtixYr5+/vfvHkzPj7+5cuXf//996JFi6ysrP57SwDawc3N7Ycffjh79uycOXN4ZAMAAJBX/m4EyPjuQj2RZXx+oanYYd3BR+O/7vFpt/vRxSvXGTTr6JzW5YUQdu0XX0ib08G16t8xombj9gt3rxVCDDh45FqfoXUnd43Xt2nYuu/pHV2FECt/3tx/wIRKYzskCmPHeu4bT73xxukxx7b+2uF/1hPuWNjVnRL0Q3pqZJ8W07+7cMvC0HxVyCyHz7xcb++esHdm1RplEiyqTQw8tGfUX3VKl80trSSlD3y6urxl8xQrx7n7QnLrJfMmM3fOaNGtmckAo+5T9xfX354miezd/b6iv2Np+8TYiOyL7u6b2GFES797zyxtHUd/eyDHvs7+u3lGpzl+epqiJWcOAAAAAOgyxepjj9VTQ1u8vhl+TUhk5lm8D0W2B++h4GQ5dadP/Hr6vIV/XbuS91Y/nzwxbJTPw3t3Dt42beX0xsPPjoTGtqr1uqVSKRPNBi40p06dmjp1avXq1fNY588//3z2LOudJjJKTU3V09NTKpX/vWqhkCQpJSVF/dB+LZGUlGRsbCx3itdSU1MVCoX6LQbaQJKk5ORkbfuIXF1d1e93yOL06dNHjhypUKFCYeZZExKZ+VfckWux/N8fAACtld+rm1ST1y9lP3Lt1TOkteUfagAKVLNmzTZs2JB3Nap79+4rV64stEj/acuWLQ4ODvXr15c7yCuPHz9ev369r6+v3EFeGzNmTEBAgNwpXtu/f7+VlZWbm5vcQV6Jj4/38/ObM2eO3EFe8/X1HT16dJaXNah98803hXz9DwAAdBAlgALHEABoicqVK+e9grm5ebNmzQonzNs4d+5c7dq1tSfS/fv3f/zxR+3JI4SwtrbWqjzXr18vWbKk9kR68eKFVuURQhQvXrxy5cpmZmZyBwEAADqqSD4OEAAAAAAA5BclAAAAAAAAdAIlAAAAAAAAdEKuzwKIjo4uzBwAAAAAAKBA5VoCsLCwKMwcgIZQunp35ubmckd4g4mJiYGBgdwpXtPT08vxQe4y0rZDZmRkZGhoKHeK1xQKhbY9eM/c3FyhUMidAgAA6C7eCADglQMHDsgd4Q1jxoyRO8IbKlSosGLFCrlTvEHbDtmgQYPkjvAGS0vLLVu2yJ3iDdu3b5c7AgAA0Gk8CwAAAAAAAJ1ACQAAAAAAAJ1ACQAAAAAAAJ1ACQAAAAAAAJ2QjxJA9LWtrevam5mYlqvWcP6hhwWXKQvDN6kbT66e1KB6BXMT8wrVG/iuOpGx8o39frXKFzcxL+7Squ+JiAT15ln2JoSIurDBs0lt2xKWpubFajRss+FiVKH9ONkjaXDl96H+eI1NLas4N5227rRmdx517fDIjo2MjIw0u1sAAAAAwNvLRwlgYKvhdSZvfxIbHbKy88y+3QouU3YpmQgh7u0Z1Mnv2tStPz+Le3Z6y5QrczsN3HtPCHF//xjXyddXnbwZG3UnYEDl4f035LbDbu1GNp208XbE89in99f5OC/8al5h/SgaM6hCMY1vnpKSkhj75NDKry7O6jB477332X8WrSbsaT1tnyRJG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" alt="gmkin start"/></p>
+
+<p>The statusbar at the bottom of the gmkin window shows, among others, the
+working directory that gmkin uses.</p>
+
+<p>Note that the project file management area described below can be minimized by clicking on
+the arrows on the right hand side of its title bar. This may be helpful if the vertical
+size of your browser window is restricted.</p>
+
+<h2>Project file management</h2>
+
+<p>At startup, gmkin loads a project called &ldquo;FOCUS_2006_gmkin&rdquo; which is distributed
+with the gmkin package. A gmkin project contains datasets, kinetic models for
+fitting, and so-called fits, i.e. the results of fitting models to data. These
+gmkin projects can be saved and restored using the project file management area in the
+top left.</p>
+
+<p><img 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" alt="projects"/></p>
+
+<p>If you would like to save these items for reference or for the purpose of continuing
+your work at a later time, you can modify the project name and press the button below it.
+The full name of the project file created and the working directory will be displayed
+in the gmkin status bar.</p>
+
+<p>For restoring a previously saved project file, use the Browse button to locate
+it, and the &ldquo;Upload&rdquo; button to load its contents into gmkin.</p>
+
+<h2>Studies</h2>
+
+<p>The &ldquo;Studies&rdquo; area directly below the &ldquo;Project file management&rdquo; area can be expanded by clicking
+on the arrows on the right hand side of its title bar. Studies in gmkin are
+simply a numbered list of sources for the datasets in a project. You can edit the titles
+directly by clicking on them. If you would like to add a new data source, use the &ldquo;Add&rdquo;
+button above the table containing the list. If there are more than one studies in the list,
+you can also remove them using the &ldquo;Remove&rdquo; button.</p>
+
+<p><img 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" alt="studies"/></p>
+
+<p>Note that the user is responsible to keep the study list consistent with the numbers that are
+used in the list of datasets described below.</p>
+
+<h2>Datasets and Models</h2>
+
+<p>The project loaded at the start of gmkin contains two datasets and four kinetic models. These
+are listed to the left under the heading &ldquo;Datasets and Models&rdquo;, together with a button for
+setting up fits as shown below.</p>
+
+<p><img 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" alt="datasets and models"/></p>
+
+<p>For editing, adding or removing datasets or models, you need to click on an
+entry in the respective list. </p>
+
+<p>For setting up a fit of a specific model to a specific dataset, the model and
+the dataset should be selected by clicking on them. If they are compatible, clicking
+the button &ldquo;Configure fit for selected dataset and model&rdquo; will set up the fit and
+open the &ldquo;Plotting and Fitting&rdquo; tab to the right.</p>
+
+<h2>Dataset editor</h2>
+
+<p>The dataset editor allows for editing datasets, entering new datasets, uploading
+data from text files and deleting datasets.</p>
+
+<p><img 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" alt="dataset editor"/></p>
+
+<p>If you want to create (enter or load) a new dataset, it is wise to first edit
+the list of data sources in the &ldquo;Studies&rdquo; area as described above.</p>
+
+<h3>Entering data directly</h3>
+
+<p>For entering new data manually, click on &ldquo;New dataset&rdquo;, enter a title and select
+the study from which the dataset is taken. At this stage, you may already want
+to press &ldquo;Keep changes&rdquo;, so the dataset appears in the list of datasets.</p>
+
+<p>In order to generate a table suitable for entering the data, enter a comma separated
+list of sampling times, optionally the time unit, and the number of replicate measurements
+at each sampling time. Also, add a comma separated list of short names of the
+relevant compounds in your dataset. A unit can be specified for the observed
+values. An example of filling out the respective fields is shown below.</p>
+
+<p><img src="data:image/png;base64,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" alt="generate data grid"/></p>
+
+<p>Once everyting is filled out to your satisfaction, press the button &ldquo;Generate empty grid
+for kinetic data&rdquo;. In our example, this would result in the data grid shown below. You
+can enter the observed data into the value column, as shown in the screenshot below.</p>
+
+<p><img 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" alt="data grid"/></p>
+
+<p>The column with title override serves to override data points from the original
+datasets, without loosing the information which value was originally reported.</p>
+
+<p>If everything is OK, press &ldquo;Keep changes&rdquo; to save the dataset in the current
+workspace. Note that you need to save the project file (see above) in order to
+be able to use the dataset that you created in a future gmkin session.</p>
+
+<h3>Entering data directly</h3>
+
+<p>In case you want to work with a larger dataset that is already available as a computer
+file e.g. in a spreadsheet application, you can export these data as a tab separated
+or comma separated text file and import it using the &ldquo;Browse&rdquo; and &ldquo;Upload&rdquo; buttons in the
+dataset editor.</p>
+
+<p>As an example, we can create a text file from one of the datasets shipped with
+the mkin package using the following R command:</p>
+
+<pre><code class="r">write.table(schaefer07_complex_case, sep = &quot;,&quot;, dec = &quot;.&quot;,
+ row.names = FALSE, quote = FALSE,
+ file = &quot;schaefer07.csv&quot;)
+</code></pre>
+
+<p>This produces a text file with comma separated values in the current working directory of R. </p>
+
+<p>Loading this text file into gmkin using the &ldquo;Browse&rdquo; and &ldquo;Upload&rdquo; buttons results in
+an import configuration area like this, with the uploaded text file displayed to the left,
+and the import options to the right.</p>
+
+<p><img 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" alt="upload area"/></p>
+
+<p>In the import configuration area, the following options can be specified. In the field
+&ldquo;Comment lines&rdquo;, the number of lines in the beginning of the file that should be ignored
+can be specified.</p>
+
+<p>The checkbox on the next line should be checked if the first line of the file contains
+the column names, i.e. the names of the observed variables when the data are in wide format.</p>
+
+<p>As &ldquo;Separator&rdquo;, whitespace, semicolon or comma can be chosen. If whitespace is selected,
+files in which the values are separated by a fixed or varying number of whitespace characters
+should be read in correctly. As the tabulator counts as a whitespace character, this is
+also the option to choose for tabulator separated values.</p>
+
+<p>As the &ldquo;Decimal&rdquo; separator, comma &ldquo;,&rdquo; or period &ldquo;.&rdquo; can be selected.</p>
+
+<p>In the next line, it can be specified if the data are in wide or in long format.
+If in wide format, the only option left to specify is the title of the column containing
+the sampling times. If the data is in long format, the column headings specifying the
+columns containing the observed variables (default is &ldquo;name&rdquo;), the sampling times
+(default is &ldquo;time&rdquo;), the observed values (default is &ldquo;value&rdquo;) and, if present in the data,
+the relative errors (default is &ldquo;err&rdquo;) can be adapted. The default settings appearing if
+the long format is selected are shown below.</p>
+
+<p><img src="data:image/png;base64,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" alt="long"/></p>
+
+<p>In our example we have data in the wide format, and after adapting the
+&ldquo;Separator&rdquo; to a comma, we can press the button &ldquo;Import using options specified
+below&rdquo;, and the data should be imported. If successful, the data editor should
+show the sampling times and the names of the observed variables, as well as the
+imported data in a grid for further editing or specifying overrides.</p>
+
+<p>After editing the title of the dataset and selecting the correct study as
+the source of the data, the dataset editor should look like shown below.</p>
+
+<p><img 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" alt="successful upload"/></p>
+
+<p>If everything is OK, press &ldquo;Keep changes&rdquo; to save the dataset in the current
+workspace. Again, you need to save the project file in order to be able to use
+the dataset that you created in a future gmkin session.</p>
+
+<h2>Model editor</h2>
+
+<p>The following screenshot shows the model editor for the model number 4 in
+the list of models that are in the initial workspace.</p>
+
+<p><img 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" alt="model editor"/></p>
+
+<p>In the first line the name of the model can be edited. You can also specify &ldquo;min&rdquo; or
+&ldquo;max&rdquo; for minimum or maximum use of formation fractions. Maximum use of formation
+fractions means that the differential equations in the degradation model are formulated
+using formation fractions. When you specify &ldquo;min&rdquo;, then formation fractions are only used
+for the parent compound when you use the FOMC, DFOP or the HS model for it.</p>
+
+<p>Pressing &ldquo;Copy model&rdquo; keeps the model name, so you should change it for the newly generated copy.
+Pressing &ldquo;Add observed variable&rdquo; adds a line in the array of state variable specifications below.
+The observed variables to be added are usually transformation products (usually termed metabolites),
+but can also be the parent compound in a different compartment (e.g. &ldquo;parent_sediment&rdquo;).</p>
+
+<p>Only observed variable names that occur in previously defined datasets can be selected. For any observed
+variable other than the first one, only the SFO or the SFORB model can be selected. For each
+observed variables, a comma separated list of target variables can be specified. In addition, a pathway
+to the sink compartment can be selected. If too many observed variables have been added, complete lines
+can be removed from the model definition by pressing the button &ldquo;Remove observed variable&rdquo;.</p>
+
+<p>If the model definition is supposedly correct, press &ldquo;Keep changes&rdquo; to make it possible to select
+it for fitting in the listing of models to the left.</p>
+
+<h2>Plotting and fitting</h2>
+
+<p>If the dataset(s) to be used in a project are created, and suitable kinetic models have been defined,
+kinetic evaluations can be configured by selecting one dataset and one model in the lists to the left,
+and the pressing the button &ldquo;Configure fit for selected dataset and model&rdquo; below these lists.</p>
+
+<p>This opens the &ldquo;Plotting and fitting&rdquo; tab area to the right, consisting of a graphical window
+showing the data points in the selected dataset and the model, evaluated with the initial parameters
+defined by calling <code>mkinfit</code> without defining starting parameters. The value of the objective function
+to be minimized for these default parameters can be seen in the R console, e.g. as</p>
+
+<pre><code class="r">Model cost at call 1: 15156.12
+</code></pre>
+
+<p>for the example shown below, where the FOCUS example dataset D and the model SFO_SFO were selected.</p>
+
+<p><img 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" alt="plotting and fitting"/></p>
+
+<h3>Parameters</h3>
+
+<p>In the right hand area, initially the tab with the parameter list is displayed. While
+name and type of the parameters should not be edited, their initial values can be edited
+by clicking on a row. Also, it can be specified if the parameters should be fixed
+in the optimisation process. </p>
+
+<p>If the initial values for the parameters were changed, the resulting model solution
+can be visually checked by pressing the button &ldquo;Show initial&rdquo;. This will update the
+plot of the model and the data using the specified initial parameter values.</p>
+
+<p>If a similar model with a partially overlapping model definition has already be fitted,
+initial values for parameters with the same name in both models can also be retrieved
+from previous fits. This facilitates stepwise fitting of more complex degradation pathways.</p>
+
+<p>After the model has been successfully fitted by pressing the &ldquo;Run&rdquo; button, the optimised
+parameter values are added to the parameter table.</p>
+
+<h3>Fit options</h3>
+
+<p>The most important fit options of the <code>mkinfit</code> function can be set via the
+&ldquo;Fit option&rdquo; tab shown below. If the &ldquo;plot&rdquo; checkbox is checked, an R graphics device
+started via the R console shows the fitting progress, i.e. the change of the model
+solution together with the data during the optimisation.</p>
+
+<p><img 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" alt="fit options"/></p>
+
+<p>The &ldquo;solution_type&rdquo; can either be &ldquo;auto&rdquo;, which means that the most effective solution
+method is chosen for the model, in the order of &ldquo;analytical&rdquo; (for parent only degradation
+data), &ldquo;eigen&rdquo; (for differential equation models with only linear terms, i.e. without
+FOMC, DFOP or HS submodels) or &ldquo;deSolve&rdquo;, which can handle all model definitions generated
+by the <code>mkin</code> package.</p>
+
+<p>The parameters &ldquo;atol&rdquo; and &ldquo;rtol&rdquo; are only effective if the solution type is &ldquo;deSolve&rdquo;. They
+control the precision of the iterative numerical solution of the differential equation model.</p>
+
+<p>The checkboxes &ldquo;transform_rates&rdquo; and &ldquo;transform_fractions&rdquo; control if the parameters are fitted
+as defined in the model, or if they are internally transformed during the fitting process in
+order to improve the estimation of standard errors and confidence intervals which are based
+on a linear approximation at the optimum found by the fitting algorithm.</p>
+
+<p>The dropdown box &ldquo;weight&rdquo; specifies if and how the observed values should be weighted
+in the fitting process. If &ldquo;manual&rdquo; is chosen, the values in the &ldquo;err&rdquo; column of the
+dataset are used, which are set to unity by default. Setting these to higher values
+gives lower weight and vice versa. If &ldquo;none&rdquo; is chosen, observed
+values are not weighted. Please refer to the documentation of the <code>modFit</code> function from
+the <code>FME</code> package for the meaning of options &ldquo;std&rdquo; and &ldquo;mean&rdquo;.</p>
+
+<p>The options &ldquo;reweight.method&rdquo;, &ldquo;reweight.tol&rdquo; and &ldquo;reweight.max.iter&rdquo; enable the use of
+iteratively reweighted least squares fitting, if the reweighting method is set to &ldquo;obs&rdquo;. Please
+refer to the <code>mkinfit</code> <a href="http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html">documentation</a>
+for more details.</p>
+
+<p>The drop down box &ldquo;method.modFit&rdquo; makes it possible to choose between the optimisation
+algorithms &ldquo;Marq&rdquo; (the default Levenberg-Marquardt implementation from the R package
+<code>minpack.lm</code>), &ldquo;Port&rdquo; (an alternative, also local optimisation algorithm) and
+&ldquo;SANN&rdquo; (the simulated annealing method - robust but inefficient and without a
+convergence criterion).</p>
+
+<p>Finally, the maximum number of iterations for the optimisation can be adapted using the
+&ldquo;maxit.modFit&rdquo; field.</p>
+
+<h3>Fitting the model</h3>
+
+<p>In many cases the starting parameters and the fit options do not need to be modified
+and the model fitting process can simply be started by pressing the &ldquo;Run&rdquo; button.
+In the R console, the progressive reduction in the model cost can be monitored and will
+be displayed like this:</p>
+
+<pre><code class="r">Model cost at call 1 : 15156.12
+Model cost at call 3 : 15156.12
+Model cost at call 7 : 14220.79
+Model cost at call 8 : 14220.79
+Model cost at call 11 : 14220.79
+Model cost at call 12 : 3349.268
+Model cost at call 15 : 3349.268
+Model cost at call 17 : 788.6367
+Model cost at call 18 : 788.6366
+Model cost at call 22 : 374.0575
+Model cost at call 23 : 374.0575
+Model cost at call 27 : 371.2135
+Model cost at call 28 : 371.2135
+Model cost at call 32 : 371.2134
+Model cost at call 36 : 371.2134
+Model cost at call 37 : 371.2134
+</code></pre>
+
+<p>If plotting of the fitting progress was selecte in the &ldquo;Fit options&rdquo; tab, a
+new separate graphics window should either pop up, or a graphics window previously
+started for this purpose will be reused.</p>
+
+<h3>Summary</h3>
+
+<p>Once a fit has successfully been performed by pressing the &ldquo;Run&rdquo; button, the summary
+as displayed below can be accessed via the &ldquo;Summary&rdquo; tab.</p>
+
+<p><img 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" alt="summary"/></p>
+
+<p>The complete summary can be saved into a text file by specifying a suitable file name
+and pressing the button &ldquo;Save summary&rdquo;.</p>
+
+<h3>Plot options</h3>
+
+<p>In the tab &ldquo;Plot options&rdquo;, the observed variables for which the data and the model fit should be
+plotted can be selected as shown below.</p>
+
+<p><img 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" alt="plot options"/></p>
+
+<h3>Confidence interval plots</h3>
+
+<p>Whenever a new fit has been configured or a run of a fit has been completed, the plotting
+area is update with the abovementioned plot of the data and the current model solution.</p>
+
+<p>In addition, a confidence interval plot is shown below this conventional plot. In case
+a fit has been run and confidence intervals were successfully calculated for the fit (i.e.
+if the model was not overparameterised and no other problems occurred), the
+confidence intervals are graphically displayed as bars as shown below.</p>
+
+<p><img 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fffm28bjUZ1wyjQyDa9NLp8+XJKSsru3buVp+3YseO5554bPny4TdkkSZI0GhsTAs7s66+/vnLlyl//+lfbdq+qqmrTphgxYsSwYcO6E6mkpOSLL75YvXq18rS4uLgNGzYEBgZ28bBiX8V0/TNK7f6xL8UFD6qpU6dOnTrV5t379+9v978aHTVqlKWjYkcqXyhZdFRNPvroIwcnAWBHzv6tdy+99JK9wgBwPL71DoBAD/tnlAAIxbfeARCIb70DIBAXSgAEcvaOEoAejY4SAIG4UAIgEB0lAALRUQIgEBdKAASiowRAIDpKAATiQgmAQHSUAAhERwmAQFwoARCIjhIAgegoARCICyUAAtFRAiAQHSUAAnGhBEAgOkoABKKjBEAgLpQACERHCYBAdJQACMSFEgCB6CgBEIiOEgCBuFACIBAdJQAC0VECIBAXSgAEoqMEQCA6SgAE4kIJgEB0lAAIREcJgEBcKAEQiI4SAIHoKAEQiAslAALRUQIgEB0lAAJxoQRAIDpKAASiowRAIC6UAAhERwmAQHSUAAjEhRLg1DQajdoRuoWOEvBQMJlMo0ePdvy6YktMXFzc0KFDleds3ry5pqbm11auXr0qSdKPP/545MgRofGAh0R6erpOpyspKXH80mKb1l2Rm5t74sSJ8ePHW0YKCgq2b99+79694uLiuXPnqpgNcBKVlZWRkZGvv/56bGxsuxM0Gs2GDRv27t27dOnSlpaWDz/88ObNmwkJCYmJiZIkBQcHBwYGRkVFOTS0JEmiS8yOHTuee+654cOHK09zd3d/7733LHe3bdt29+7dnn4JCtiLwWCIjIzcsGHDwoULFaaNGzfu1KlTwcHB77zzTlFRUW5ubnR0tLnEPP30044K25bYEtOVjtKtW7du3bq1fft2y0hubu6lS5ckSfLw8BAaD+gRZs2aZTAYIiIilKfNmzevT58+kiStWLHCxcUlIiLi7t27DgmoRP0LJVdXV61WO3HiRMvIpUuXAgMDNRrNrVu3VAwGOIl33333448/Xrly5QcffKAwzdXV1XzDxcVFcppWlNgS05WOUt++ffv27du6Ql+4cCE0NFSj0eTn5wuNB/QIU6dODQkJeeKJJ/773/8+++yzasexjlN8RqmxsXHGjBmWu2VlZQcOHOAlDGCh1Wp37ty5ZMmSoqIirVardhwrqH+htHDhwldfffX+8dLSUjc3N8fnAZyKLMvmG7NmzSorK+t0muVGm9v333UM9T+j1G59kSQpKCho2rRp9s8E9GSa9qgdSon6HSUAXafKK5Hu4DNKAATiM0oABHKKjhKABxUXSgAEUr+jBOABxrfeARCICyUAAtFRAiAQHSUAAnGhBEAgOkoABKKjBEAgLpQACERHCYBAdJQACMSFEgCB6CgBEIiOEgCBuFACIBAdJQAC0VECIBAXSgAEoqMEQCA6SgAE4kIJgEB0lAAIREcJgEBcKAEQiI4SAIHoKAEQiAslAALZ+Havj4/P77//PmPGDOVpFy9e3LNnj4uL2DeV7ejevXtubm5qp+iqpqam3r179+rVY/6d4McrlIuLS3BwsOhVGhsbPTw8rNhBFmnVqlX5+flCl7Ajo9E4a9YstVNYITEx8ZtvvlE7RVeZTKbp06erncIKb7zxRlZWltoprDBt2jS1I7Sjx1RoAD0RJQaAQJQYAAJRYgAIRIkBIJDYEhMSEuLp6Sl0CTtycXGZOnWq2imsMHbsWB8fH7VTdJVGo3nqqafUTmGFMWPG+Pn5qZ3CCs7549XIsqx2BgAPLC6UAAhEiQEgECUGgECUGAACUWIACESJASAQJQaAQJQYAAJRYgAI1N0SU1tbGxUV5e3tHR0dXVtb25WtyrsIZUPazz//fOzYsZ6enuHh4aWlpY5Ma1tgs6KiInd3dwcm7SRPR1ubm5uXLVvm6+s7ZcqU69evO3/g3Nzc0NBQrVYbGhp69uxZ50krSZLJZBo9erRVuzhAd0vM9u3bhw4dajAYhgwZkpqa2pWtyrsIZW3a8vLymJiYXbt2GQyG6OjoV155xZFpbQhsduvWrdjY2IaGBseGtSVtWlpafX19WVmZTqdLSkpy/sAxMTF6vb6mpiYxMTEmJsZ50qanp+t0upKSkq7v4iDd/Na8oKCg4uJiWZaLi4uDgoK6slV5F6GsTZuTkxMXF2feWllZ6ePj48i07UbqdGtLS8vcuXMzMzO7/8t1QNrx48cXFBTIslxfX3/hwgXH5rUl8JgxY3bt2lVTU7N79+7HH3/cedJmZ2cfO3aszS9dxeeaRXcfhe7u7g0NDbIsNzQ0aLXarmxV3kUoG9KaNTc3L1myZNmyZQ6L2mmkjrampKSsWbNGlmXHlxgb0np7eyckJHh5eU2cOLGwsND5A+fl5Vn+ec7Ly3OetGZtfukqPtcsunuhJMuyRqMx3zCZTF3ZqryLUDaklSTpyy+/nDx5soeHR3p6uiPTKkTqaGtOTs7JkydTUlIcnLOjPJ1ura+vl2X54sWLzzzzTHx8vPMHTkhIWLduXUVFxdq1a9evX+88ae21i911t8QMHDjw2rVrkiRdv3590KBBXdmqvItQ1qaVZXnDhg1vvvnmoUOHtm3b5vj/rcXawFlZWbm5ua6uruYHlkaj+frrr502rSRJvr6+K1eu9Pf3X7FiRVFRkcOi2hz4/Pnzq1at8vf3T0hIOH/+vPOktdcudtfdEhMVFZWRkSHLckZGxpw5c8yDZ86cUdja7qBjWJv23Llzhw8fPnr06MCBA41Go9FodGRaGwInJydbXqBKkiTLsiO/ZMuGB0NkZOS+ffvu3bv3/vvvT5o0yWFRbQ4cHBy8Z88eo9F44MCBkJAQ50nb9V0crZsXWrW1tTNnzhw0aFBUVFRdXV3rB3dHW9sddAxr0yYnJ9v3xyU6cGs9Iq3BYIiIiPDw8AgPD7906ZLzBy4uLtbpdP369dPpdOZ3Up0kbbt3VXyuWfCtdwAE4q97AQhEiQEgECUGgECUGAAC2f6HHufOnXP8p2AAqOuRRx7R6XRdn29jR+nq1asLFiyYN2+eDfsC6Lk+/fTTjz/+eNiwYV2cb+OrGJPJFBwcnJCQoDzt+PHjTz75ZP/+/W1bBYDDVFVVfffdd7Nnz1aedunSJas+iyD2vZjs7Gzz3y8DcHLXrl3Lzs62+2F5uxeAQGJLTO/evR3/0UEANnBxcendu7f9D2v3I7aWnJzs6uoqdAkAdjF27Nj7P5TXfWJLjJubm9DjA7AXjUYj4gkr9kLp+PHjVVVVQpcAYBdVVVXHjx+3+2HpKAGQJDpKAHoiOkoAJImOEgCh6CgBEIiOEgCB6CgBEIiOEoCeh44SAEmiowRAKDpKAASiowRAIDpKAASiowSg56GjBECS6CgBEIqOEgCB6CgBEIiOEgCB6CgB6HnoKAGQJDpKAISiowRAIDpKAASiowRAIDpKAHoeOkoAJElYR0lsiUlOTh47dmy7mzZt2jRgwAChqwPoOkEdJbElxs3NTaPRtLtp586dRUVFQldXEBERYfNW25hMptGjR9v9sIC9PGgdperq6v79+wtdXUFWVpbNW22Qnp6u0+lKSkrse1hxfv/999raWrsftq6urrKy0u6HhV08UB2luXPnSpIUGhra0Y4ajSY1NdXPzy8sLOzq1avmwaNHj4aGhnp6evr7+7/99tuWmQcPHhwwYMCNGzfmz5/v5+cXEBCwaNEig8FgmXDo0KGQkBAfH5+0tLROV7dsrampWbRokb+//8CBA19++eWamhqFM9VoNImJif7+/m+++ebmzZuDgoI8PDy2bt1q3hocHLxp0yaF3Z3NJ598curUKeU5WVlZC/6/Tz75RHmXnJycjz76yH4xYU8PVEfpyJEjkiQVFBQozDEajRUVFWFhYatWrTKPbNq0KSYmprq6+sSJE3q93jLz/PnzWVlZ8fHxL7zwQllZWX5+fmBgYHx8vGVCeXl5QUFBZmZmYmJip6tbtq5cudLV1fXXX3+9fPmyq6vrmjVrlE9q3Lhxp06dSkpK8vT0LCoq+uSTT7Zs2WLe9PTTT8+ePbuzn0oPk52dvXjx4vf+R6/Xf/HFF2qHgtMR2+7pTkcpNjbWxcVl9erVo0aNMo/88MMPeXl5+/fvz83NbWxstMxMSkry9fXNyclp/TLP19fXcnvp0qUajWb69Ol37tzpeoATJ078/PPPffv2lSQpOTk5ODhYef68efP69OkjSdKKFStcXFwiIiLu3r3b9eV6Iq1W6+XlZb5969YtdcOgmx7Szyj16tXLZDKZby9YsMDV1XXhwoUpKSkHDhywzDFXEy8vr8LCwoCAAEmSbt++XV1dbZmg1WptW93yXrVGo7HE6IjlTM1VtaP3uXuE5ubmt956a/fu3QpzLl++fPr0aQ8PD/PdO3fuXLlyZcaMGQq7VFZWPvXUU/YMCvt56D6jtHfv3qSkpHfeeSc8PNw8cvr06XPnzo0ZM2bv3r2SJDU3N7d+ifT888+npKSkp6cbjca//OUvI0eO3Llzp8Lxm5qazC86Otr67LPP6vX6d999V5ZlvV4/c+ZMm8+lx3FxcdHr9S+++KLCHL1eP2vWLJ1OZ7579erVN954w/yr6cjhw4fLy8vtGRT286B1lDrV1NQ0YMCA7Ozs9PR088jWrVunTZs2bty46urqyMjIxYsXt56fnJxsMpmGDx/+xBNPDBs2zPJ+cLtmzpwZGBiovDUtLe3OnTvDhg0LCAhobGw0v1WM1i5cuPDl/3zzzTdqx0G3COooiX0Vk52dPWjQoHab07IsK++7bdu2bdu2tR5Zvnz58uXLzbfXrl3b5jharTYjI0N5Ictt5TcmLVsPHjyoHPL+I7e7XLt3e7p58+adPn36+++/t4z87W9/UzEPusncUbJ7X0Llv+5v9w0Lhz0VbVhd3cAOs3Tp0k7nTJgwYcKECVYd9rnnnrM1EXoqlTtKHT05HfPXEzaUhgevmgBmD1dH6aWXXrJ7GAAKHrqOEgBHeug6SgAc6YH6jBIAZ/NAfUYJwEOCb70DIEkPW0cJgIPRUQIgEB0lAALRUQIgEB0lAD0PHSUAkkRHCYBQdJQACERHCYBAdJQACERHCUDPQ0cJgCTRUQIgFB0lAALRUQIgEB0lAALRUQLQ89BRAiBJdJQACEVHCYBAdJQACERHCYBAdJQA9Dx0lABIEh0lAELRUQIgEB0lAALRUQIgEB0lAD2PRpZlG3arqKiYPXv2iBEjlKd999139+7d66Hv+DY1NfXp00ftFDZqbGzsoT92qYeH77kPm8bGRo1GEx4erjzt+++/P3PmzODBg7t4WBtLjCRJFRUVd+/eVZ6zefPmsLCwoKAg25ZQ18aNG9evX9+vXz+1g9hi5cqVaWlpaqewUc8NbzQat23bJqIv4wClpaVnzpzZsmWL8rQ+ffp0vb5I3ekoDRw4sNM5/fv3nzRp0vjx421eRUX9+/efMmWKp6en2kFs4enp+dRTT6mdwkY9N3xdXV3//v17aPhHH320uLg4ICDAvoflvRgAAlFiAAhEiQEgECUGgEBiS0zfvn1FfLDKMdzd3TUajdopbNRDG2FmPTe8RqNxd3dXO4WNevfu3bdvX7sf1vamNQB0igslAAJRYgAIRIkBIBAlBoBAlBgAAlFiAAhEiQEgECUGgECUGAACUWIACGS3ElNbWxsVFeXt7R0dHV1bW2vVVtUpx/v888/Hjh3r6ekZHh5eWlqqSkIFXfnZFhUVOednZ5TDNzc3L1u2zNfXd8qUKdevX1cloQLl8Lm5uaGhoVqtNjQ09OzZs6okVGYymUaPHt3uJjs+Ye1WYrZv3z506FCDwTBkyJDU1FSrtqpOIV55eXlMTMyuXbsMBkN0dPQrr7yiVsiOdPqzvXXrVmxsbENDg+OzdUo5fFpaWn19fVlZmU6nS0pKUiWhAuXwMTExer2+pqYmMTExJiZGlYQK0tPTdTpdSUlJu1vt+YSV7SQoKKi4uFiW5eLi4qCgIKu2qk4hXk5OTlxcnPl2ZWWlj4+PCvkUKf9sW1pa5s6dm5mZacfftdPh6mgAAAJbSURBVB0phx8/fnxBQYEsy/X19RcuXFAhnyLl8GPGjNm1a1dNTc3u3bsff/xxNQIqyc7OPnbsWEePCjs+Ye32sHN3d29oaJBluaGhQavVWrVVdV2J19zcvGTJkmXLljk2WueUw6ekpKxZs0aWZecsMcrhvb29ExISvLy8Jk6cWFhYqEZAJcrh8/LyLP+Q5+XlqRGwcx09Kuz4hLXbhZIsy+ZvV5Fl2WQyWbVVdZ3G+/LLLydPnuzh4ZGenu7wdJ1QCJ+Tk3Py5MmUlBSVonVO+SdfX18vy/LFixefeeaZ+Ph4NQIqUQ6fkJCwbt26ioqKtWvXrl+/Xo2AtrPnE7Y79am1ESNGlJaWyrJcWlo6cuRIq7aqTiFeS0vL+vXrw8LCSkpKVErXCYXwer2+za/7q6++Uilm+5QfGP7+/hUVFbIsGwwGd3d3FfIpUg7v7u5uMBhkWa6qqurXr58K+bqgowpgxyes3V7FREVFZWRkyLKckZExZ84c8+CZM2cUtjoPhfDnzp07fPjw0aNHBw4caDQajUajmkHboxA+OTm5zYNp6tSpKka9n/LDJjIyct++fffu3Xv//fcnTZqkWsoOKIcPDg7es2eP0Wg8cOBASEiIaimtIeQJ25361Fptbe3MmTMHDRoUFRVVV1fX+mHd0VbnoRD+/v92S9Wk7VD+yVs4YXK5s/AGgyEiIsLDwyM8PPzSpUvqxWyfcvji4mKdTtevXz+dTmd+69QJtXlUiHjC8sWaAATir3sBCESJASAQJQaAQJQYAAJRYgAIRIkBIBAlBoBAlBgAAv0fZSjcN8LhEd4AAAAASUVORK5CYII=" alt="conficence"/></p>
+
+<!-- vim: set foldmethod=syntax ts=2 sw=2 expandtab: -->
+
+</body>
+
+</html>
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