Selection procedure : will be based on through the written examination, Practical / skill test and Viva Voce Test. Written exam will carry 100 marks, skill test 50 marks and interview will carry 50 marks.
Basics of Computer Applications, Sports, Analytical Reasoning, Mental Ability etc.
in High Court Website www.gujarathighcourt.nic.in OR http://hc-ojas.guj.nic.in, in the prescribed format from 01/03/2014 to 31/03/2014.


text:
Q: Ler código de barras de boleto bancário Como posso ler código de barras de boleto ITF (tamanho 44) codificada no padrão Intercalado 2 de 5. Usando alguma biblioteca como zxing, zbar, etc.?
Ja utilizei as bibliotecas zxing e zbar, mas não reconhece o código de barras do boleto.
Ja tenteis os exemplos:
https://github.com/TheLevelUp/ZXingObjC
http://zbar.sourceforge.net/iphone/sdkdoc/tutorial.html
De todos os exemplos que encontro eu consigo ler todos os tipos de código de barras (QR Code, Code 39, 93, 128, ITF, etc) até os ITF de tamanho pequeno. Menos o de boleto bancário.
Alguém tem alguma ideia do que posso fazer?

A: É possivel ler código de barra de boletos Brasileiros com a biblioteca ZBar. O que você tem que fazer é setar a qualidade do vídeo para alta. Segue o trecho de código de configuração do ZBarReaderViewController.
// ADD: present a barcode reader that scans from the camera feed
ZBarReaderViewController *reader = [[ZBarReaderViewController alloc] init];
reader.readerDelegate = self;
reader.supportedOrientationsMask = ZBarOrientationMaskAll;
reader.videoQuality = UIImagePickerControllerQualityTypeHigh;

ZBarImageScanner *scanner = reader.scanner;
// TODO: (optional) additional reader configuration here
[scanner setSymbology: 0
               config: ZBAR_CFG_ENABLE
                   to: 0];
[scanner setSymbology: ZBAR_I25
               config: ZBAR_CFG_ENABLE
                   to: 1];

Desde a versão 7 do iOS o framework AVFoundation oferece um método nativo para leitura de código de barra. Porem, o suporte ao padrão intercalado 2 de 5 (AVMetadataObjectTypeInterleaved2of5Code) foi introduzido recentemente no iOS 8.
Modifiquei o exemplo disponibilizado neste tutorial e realmente funciona bem para ler os códigos de barra de boletos tanto título quando convênio.

redpajama_set_name: RedPajamaStackExchange


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text:
\section{Introduction}\label{introduction}}

Visualization is increasingly important for accurate guidance and interpretation of neuroimaging results, as current research is able to generate a high amount of data and outcomes.
For Magnetic Resonance Imaging (MRI), neuroimaging software provides whole-brain information by using many small units of space (\textgreater100.000).
Nonetheless, this data is often grouped and summarized into a limited number of regions using predefined brain parcellation atlases.
Brain parcellations segment the brain into a finite set of meaningful neurobiological components, which reflect one or more brain features either based on structural or connectivity properties (\citet{eickhoff_2018}).
The use of brain atlases is widespread as these facilitate interpretation and minimize the amount of data, hence reducing problems with multiple comparisons.
This enables replicability and data sharing in otherwise computationally expensive analyses, which are often performed in specialized software environments such as R (\citet{R}).

MRI data provides good spatial resolution and thus an optimal representation has to respect spatial relationships across regions.
Results from brain atlas analyses are most meaningfully visualized when projected onto a representation of the brain, thus it is desirable that any visual representation takes this relation into account.
The projection of data onto brain representations provides clear points of reference - especially when the reader is unfamiliar with the atlas - eases readability, guides interpretation, and conveys the spatial patterns of the data.
Adopting the grammar of graphics implemented in \emph{ggplot2} (\citet{ggplot}), one can plot neuroimaging data directly in R with several tools such as ggBrain (\citet{ggBrain}) and ggneuro (\citet{ggneuro}; see neuroconductor \citeyearpar{neuroconductor} for curated neuroimaging packages for R).
Yet these tools display whole-brain image files and are not well-suited for representing brain atlas data.

In this tutorial, we introduce two packages for visualizing brain atlas data in R.
The \emph{ggseg} and \emph{ggseg3d} -- pluss the complimentary \emph{ggsegExtra} -- packages include pre-compiled data sets for different brain atlases that allow for 2D and 3D visualization.
The two-dimensional functionality in \emph{ggseg} is based on polygons and \emph{ggplot2}-based grammar of graphics (\citet{ggplot}), while the 3D functionality in \emph{ggseg3d} is based on tri-surface mesh plots and \emph{plotly} (\citet{plotly}).

Both packages present compiled data sets, tailored functions that allow brain data integration and plotting, and other minor features such as custom colour palettes.
The data featured in the packages are derived from two well-known parcellations: the Desikan-Killany cortical atlas (DKT; \citet{dkt}), which covers the cortical surface of the brain, and the Automatic Segmentation of Subcortical Structures (aseg; \citet{aseg}), which covers the subcortical structures.
Both atlases are implemented in several neuroimaging softwares, such as FreeSurfer (\citet{fischl_99}, \citet{dale_99}, \citet{Fischl2000}), and are commonly used in relation to developmental changes, disease biomarkers, genomic data, and cognition (\citet{amlien_elaboration_2019}, \citet{WALHOVD20051261}, \citet{Pizzagalli}).
The \emph{ggsegExtra} package contains a collection of precompiled atlases (currently 15 additional atlases) and it is frequently updated.
A summary of all available atlases compatible with the \emph{ggseg}-packages as of December 2019 can be found in Table \ref{tab:atlasTab}.

\begin{table}

\caption{\label{tab:atlasTab}Table of currently available atlases in the ggseg, ggseg3d, and the ggsegExtra R-packages. Polygon and mesh refer to 2D and 3D brain atlas representations, respectively}
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}[t]{lllll}
\toprule
Title & Item & Mesh & Polygon & citation\\
\midrule
Automated probabilistic reconstruction of white-matter pathways & tracula & None & ggsegExtra & \citet{tracula}\\
Desikan-Killiany Cortical Atlas & dkt & ggseg3d & ggseg & \citet{dkt}\\
Desterieux cortical parcellations & desterieux & ggsegExtra & None & \citet{desterieux}\\
Freesurfer automatic subcortical segmentation of a brain volume & aseg & ggseg3d & ggseg & \citet{aseg}\\
Genetic topography of brain area morphology & chenAr & None & ggsegExtra & \citet{chen}\\
\addlinespace
Genetic topography of brain thickness morphology & chenTh & None & ggsegExtra & \citet{chen}\\
Harvard-Oxford Cortical atlas & hoCort & None & ggsegExtra & \citet{ho}\\
HPC - Multi-modal parcellation of human cerebral cortex & glasser & None & ggsegExtra & \citet{glasser}\\
ICBM white matter parcellation & icbm & ggsegExtra & None & \citet{icbm}\\
JHU parcellation & jhu & None & ggsegExtra & \citet{jhu}\\
\addlinespace
Local-Global 17 Parcellation of the Human Cerebral Cortex & schaefer17 & ggsegExtra & None & \citet{schaefer}\\
Local-Global 7 Parcellation of the Human Cerebral Cortex & schaefer7 & ggsegExtra & None & \citet{schaefer}\\
Parcellation from JHU & jhu & ggsegExtra & None & \citet{jhu}\\
Parcellation from the Human Connectome Project & glasser & ggsegExtra & None & \citet{glasser}\\
White matter tract parcellations & tracula & ggsegExtra & None & \citet{tracula}\\
\addlinespace
Yeo 17 Resting-state Cortical Parcellations & yeo17 & ggsegExtra & ggsegExtra & \citet{yeo2011}\\
Yeo 7 Resting-state Cortical Parcellations & yeo7 & ggsegExtra & ggsegExtra & \citet{yeo2011}\\
\bottomrule
\end{tabular}}
\end{table}

\hypertarget{tutorial}{%
\section{Tutorial}\label{tutorial}}

This tutorial will introduce the \emph{ggseg}, \emph{ggseg3d}, and \emph{ggsegExtra} packages and familiarize the reader with the main functions and the general use of the packages.
The tutorial will focus on the two main functions: \texttt{ggseg()} for plotting 2D polygons and \texttt{ggseg3d()} for plotting 3D brains based on tri-surface mesh plots.

\hypertarget{plotting-polygon-data-ggplot2}{%
\subsection{Plotting polygon data (ggplot2)}\label{plotting-polygon-data-ggplot2}}

\emph{ggseg} is the main function for plotting 2D data.
By default, the function automatically plots the DKT atlas (see Figure \ref{fig:init}).
The \texttt{ggseg()}-function is a wrapper for \texttt{geom\_polygon()} from \emph{ggplot2}, and it can be built upon and combined like any \emph{ggplot}-object.
The image plot consists of a simple brain representation containing no extra information.
Hence, \emph{ggseg} plots can be easily complemented with any of the available \emph{ggplot2} features and options.
We recommend users to get familiarized with \emph{ggplot2} (\citet{ggplot}).
The package is currently only available through github, but we expect to submit the \emph{ggseg}-package to The Comprehensive R Archive Network (\citet{cran}) in 2020.

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# remotes::install\_github("LCBC{-}UiO/ggseg")}
\KeywordTok{library}\NormalTok{(ggseg)}
\KeywordTok{library}\NormalTok{(dplyr)}
\KeywordTok{library}\NormalTok{(tidyr)}

\CommentTok{\# Figure 1}
\KeywordTok{ggseg}\NormalTok{()}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{init-1.pdf}
\caption{\label{fig:init}By default \texttt{ggseg()} will plot the dkt atlas in grey shaded polygons.}
\end{figure}

In addition to the standard options for \emph{ggplot2} polygon geoms, the function also has several options for plotting the main brain representations.
These options are atlas-specific.
For cortical atlases, such as the \texttt{dkt}, one can stack the hemispheres, display only the medial or lateral side, choose either one or both hemispheres, or any combination of hemisphere and view (see Figure \ref{fig:collection} for examples).
For subcortical atlases, such as the \texttt{aseg} atlas, the options are more limited but one can often choose between axial, sagittal, and coronal views.

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# dkt dark theme}
\NormalTok{p1 <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{position =} \StringTok{"stacked"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{theme\_dark}\NormalTok{() }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title=}\StringTok{" "}\NormalTok{)}

\CommentTok{\# dkt classic theme}
\NormalTok{p2 <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{position =} \StringTok{"stacked"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{theme\_classic}\NormalTok{() }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title =} \StringTok{" "}\NormalTok{)}

\CommentTok{\# dkt medial view}
\NormalTok{med <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{view =} \StringTok{"medial"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title =} \StringTok{" "}\NormalTok{)}

\CommentTok{\# dkt left hemisphere}
\NormalTok{left <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{hemisphere =} \StringTok{"left"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title =} \StringTok{" "}\NormalTok{)}

\CommentTok{\# aseg default theme}
\NormalTok{p3 <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{atlas=}\NormalTok{aseg) }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title =} \StringTok{" "}\NormalTok{)}

\CommentTok{\# dkt left medial alone}
\NormalTok{combo <{-}}\StringTok{ }\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{view =} \StringTok{"medial"}\NormalTok{,}
               \DataTypeTok{hemisphere =} \StringTok{"left"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title=}\StringTok{" "}\NormalTok{)}

\CommentTok{\# Combine plots to Figure 2}
\NormalTok{cowplot}\OperatorTok{::}\KeywordTok{plot\_grid}\NormalTok{(p1, med, combo,  p2, left, p3,}
                   \DataTypeTok{labels =} \KeywordTok{c}\NormalTok{(}\StringTok{"A: dkt {-} dark"}\NormalTok{, }\StringTok{"B: dkt {-} medial"}\NormalTok{,}
                              \StringTok{"C: dkt {-} combo"}\NormalTok{, }\StringTok{"D: dkt {-} classic"}\NormalTok{,}
                              \StringTok{"E: dkt {-} left"}\NormalTok{, }\StringTok{"F: aseg"}\NormalTok{),}
                   \DataTypeTok{hjust =} \FloatTok{{-}.05}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{collection-1.pdf}
\caption{\label{fig:collection}\emph{ggseg} plots can be used with any \emph{ggplot2} feature such as standard scales and themes. For cortical atlases, one can supply special \emph{ggseg} options to determine, for example, hemisphere, view, or position. \textbf{A:} \texttt{dkt} atlas, stacked with dark theme ; \textbf{B:} \texttt{dkt} with medial view only; \textbf{C:} \texttt{dkt} atlas with only left medial display; \textbf{D:} \texttt{dkt} atlas, stacked, with classic theme; \textbf{E:} \texttt{dkt} atlas with left hemisphere only; \textbf{F:} complete \texttt{aseg} atlas}
\end{figure}

\hypertarget{using-own-data-with-fill-and-colour}{%
\subsubsection{Using own data with fill and colour}\label{using-own-data-with-fill-and-colour}}

\texttt{ggseg()} accepts any argument you can supply to \texttt{geom\_polygon()}, and therefore is easy to work with for those familiar with \emph{ggplot2} functionality.
Standard arguments like \texttt{fill} that floods the segments with a colour, or \texttt{colour} that colours the edges around the segments are typical arguments to provide to the function either as a single setting value or within the \emph{ggplot2} mapping function \texttt{aes}.
To use color palettes corresponding to those used in the original neuroimaging softwares one can use atlas-specific `brain' palette scales (Figure \ref{fig:fill}).

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Figure 3}
\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{mapping=}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{fill =}\NormalTok{ area), }\DataTypeTok{colour=}\StringTok{"black"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale\_fill\_brain}\NormalTok{(}\StringTok{"dkt"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{theme}\NormalTok{(}\DataTypeTok{legend.justification=}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{),}
        \DataTypeTok{legend.position=}\StringTok{"bottom"}\NormalTok{,}
        \DataTypeTok{legend.text =} \KeywordTok{element\_text}\NormalTok{(}\DataTypeTok{size =} \DecValTok{5}\NormalTok{)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{guides}\NormalTok{(}\DataTypeTok{fill =} \KeywordTok{guide\_legend}\NormalTok{(}\DataTypeTok{ncol =} \DecValTok{3}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{fill-1.pdf}
\caption{\label{fig:fill}Supplying `area' to the fill option in \texttt{ggseg()}, will use the column `area' from the accompanying dataset to create a discrete colour palette over the segments in the atlas. The \texttt{dkt} atlas default palette corresponds to the FreeSurferColorLut scheme.}
\end{figure}

Most users will use \texttt{ggseg()} to display - using a color scale - some descriptive or inferential statistics, such as mean thickness or brain-cognition relationships across the different brain regions.
Yet, before projecting the statistics onto the segments, one should explore the structure of the atlas data sets.
The atlas data set structure will help users understand what incoming statistical data needs to look like.
Note that each atlas corresponds to a unique data set.
All data sets have a similar structure and contain key information regarding the atlas, the region names, and the coordinates for the segment polygons.

\small

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Look at the top 5 rows of the dkt dataset}
\KeywordTok{head}\NormalTok{(dkt, }\DecValTok{5}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\normalsize

In any atlas, the column `label' is particularly useful for combining the data of interest with the \emph{ggseg}-polygons.
The column `label' contains the label (region) names as in the original neuroimaging software.
For example, the DKT atlas label column matches the region names from Freesurfer statistics table outputs.
Yet, the data in \emph{ggseg} is in a long format - that is each region has a single row - and any data of interest needs to be in this same format.
Often data sets are organized in wide format, in which subjects are represented by rows and each different data variable is represented in a separate column, and thus need to be rearranged to work with ggseg.
See below an example of wide-to-long conversion.

\small

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Create a mock freesurfer output}
\NormalTok{freesurfer\_stats <{-}}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}
  \DataTypeTok{id =} \KeywordTok{c}\NormalTok{(}\DecValTok{10}\OperatorTok{:}\DecValTok{12}\NormalTok{),}
  \DataTypeTok{lh\_superiortemporal =} \KeywordTok{c}\NormalTok{(}\FloatTok{3.32}\NormalTok{, }\FloatTok{4.1}\NormalTok{, }\FloatTok{3.5}\NormalTok{),}
  \DataTypeTok{lh\_precentral =} \KeywordTok{c}\NormalTok{(}\FloatTok{2.3}\NormalTok{, }\FloatTok{2.5}\NormalTok{, }\FloatTok{2.1}\NormalTok{),}
  \DataTypeTok{lh\_rostralmiddlefrontal =} \KeywordTok{c}\NormalTok{(}\FloatTok{3.3}\NormalTok{, }\FloatTok{3.2}\NormalTok{, }\FloatTok{3.1}\NormalTok{)}
\NormalTok{)}
\NormalTok{freesurfer\_stats}

\CommentTok{\# Wrangle wide format data into long format}
\NormalTok{freesurfer\_long <{-}}\StringTok{ }\NormalTok{freesurfer\_stats }\OperatorTok{\%>\%}
\StringTok{  }\KeywordTok{gather}\NormalTok{(label, thickness, }\OperatorTok{{-}}\NormalTok{id)}
\NormalTok{freesurfer\_long}
\CommentTok{\#\#   id lh\_superiortemporal lh\_precentral lh\_rostralmiddlefrontal}
\CommentTok{\#\# 1 10                3.32           2.3                     3.3}
\CommentTok{\#\# 2 11                4.10           2.5                     3.2}
\CommentTok{\#\# 3 12                3.50           2.1                     3.1}
\CommentTok{\#\#   id                   label thickness}
\CommentTok{\#\# 1 10     lh\_superiortemporal      3.32}
\CommentTok{\#\# 2 11     lh\_superiortemporal      4.10}
\CommentTok{\#\# 3 12     lh\_superiortemporal      3.50}
\CommentTok{\#\# 4 10           lh\_precentral      2.30}
\CommentTok{\#\# 5 11           lh\_precentral      2.50}
\CommentTok{\#\# 6 12           lh\_precentral      2.10}
\CommentTok{\#\# 7 10 lh\_rostralmiddlefrontal      3.30}
\CommentTok{\#\# 8 11 lh\_rostralmiddlefrontal      3.20}
\CommentTok{\#\# 9 12 lh\_rostralmiddlefrontal      3.10}
\end{Highlighting}
\end{Shaded}

\normalsize

Data in long format can then be used directly with the \texttt{ggseg()}-function, as the `label' column corresponds in name and content with the `label' column in the atlas data of \texttt{dkt}.
The data \textbf{must} include a column that has the same column name and at least \emph{some} data matching the values in the corresponding column in the atlas data.
In the next example we create some data with 4 rows, with an `area' and `p' column, representing the results of a hypothetical analysis.
The \texttt{ggseg()}-function will recognise the matching column `area', and merge the supplied data into the atlas using \emph{dplyr}-joins.
We use the `p' column as the column flooding the segment with colour.
The appearance of the plot can then be modified similarly to any other \emph{ggplot2} graph using functions such as scales, labs, themes, etc., as seen in Figure \ref{fig:datasupp}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Make some mock data}
\NormalTok{someData =}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}
  \DataTypeTok{area =} \KeywordTok{c}\NormalTok{(}\StringTok{"transverse temporal"}\NormalTok{, }\StringTok{"insula"}\NormalTok{,}
           \StringTok{"pre central"}\NormalTok{,}\StringTok{"superior parietal"}\NormalTok{),}
  \DataTypeTok{p =} \KeywordTok{sample}\NormalTok{(}\KeywordTok{seq}\NormalTok{(}\DecValTok{0}\NormalTok{,.}\DecValTok{5}\NormalTok{,.}\DecValTok{001}\NormalTok{), }\DecValTok{4}\NormalTok{),}
  \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}\NormalTok{)}

\CommentTok{\# Figure 4}
\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{.data=}\NormalTok{someData, }\DataTypeTok{mapping=}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{fill=}\NormalTok{p))  }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{title=}\StringTok{"A nice plot title"}\NormalTok{, }\DataTypeTok{fill=}\StringTok{"p{-}value"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale\_fill\_gradient}\NormalTok{(}\DataTypeTok{low=}\StringTok{"firebrick"}\NormalTok{,}\DataTypeTok{high=}\StringTok{"goldenrod"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{datasupp-1.pdf}
\caption{\label{fig:datasupp}Supplying data through the `.data' option in \texttt{ggseg()} enables use of columns in the supplied data to aesthetical arguments (such as `fill'). The \emph{ggseg} plot can be used with any other polygon compatible function from \emph{ggplot2} or \emph{ggplot2} extentions, for instance adding title, changing the legend name and the colour scheme with standard \emph{ggplot2} functions.}
\end{figure}

If the results are only in one hemisphere, but you still want to plot both of them, make sure your data.frame includes the column `hemi' with either `right' or `left'.
In this case, data will be merged into the atlas both by `area' and by `hemi'.
For more information about adapting data and viewing only one hemisphere or side, the \href{https://lcbc-uio.github.io/ggseg/articles/ggseg.html\#single-hemisphere-results}{package vignettes} contains more elaborate information.

\hypertarget{creating-subplots}{%
\subsubsection{Creating subplots}\label{creating-subplots}}

There is often the need to plot a statistic of interest in different groups (e.g.~thickness or brain - cognition relationships in young or older adults).
This may be obtained also with \texttt{ggseg()}, using \emph{ggplot2}'s \texttt{facet\_wrap} or \texttt{facet\_grid}, with three guiding rules:
\textbf{1)} as before, data needs to be in long format with a column indexing which group the row corresponds to (group data should appear in seperate rows, not in separate columns).
\textbf{2)} The data needs to be grouped using \emph{dplyr}'s \texttt{group\_by()} function \emph{before} providing the data to the \texttt{ggseg()}-function.
The \texttt{ggseg()}-function will detect grouped data, and adapt it to \texttt{facet}`s requirements.
\textbf{3)} Apply \texttt{facet\_wrap} or \texttt{facet\_grid} to the plot having used the above two rules.
An example of this can be seen in Figure \ref{fig:datasupp3}, where a mock data set including summary statistics for two groups ('Young' and `Old') is used when faceting a \emph{ggseg}-plot.

All the concepts described above also work with the \texttt{aseg} atlas for subcortical structures, except for `hemisphere' and `view' arguments that are superfluous in subcortical atlases (Figure \ref{fig:atlases}).

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Make some mock data}
\NormalTok{someData =}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}
  \DataTypeTok{area =} \KeywordTok{rep}\NormalTok{(}\KeywordTok{c}\NormalTok{(}\StringTok{"transverse temporal"}\NormalTok{, }\StringTok{"insula"}\NormalTok{,}
               \StringTok{"pre central"}\NormalTok{,}\StringTok{"superior parietal"}\NormalTok{),}\DecValTok{2}\NormalTok{),}
  \DataTypeTok{p =} \KeywordTok{sample}\NormalTok{(}\KeywordTok{seq}\NormalTok{(}\DecValTok{0}\NormalTok{,.}\DecValTok{5}\NormalTok{,.}\DecValTok{001}\NormalTok{), }\DecValTok{8}\NormalTok{),}
  \DataTypeTok{AgeG =} \KeywordTok{c}\NormalTok{(}\KeywordTok{rep}\NormalTok{(}\StringTok{"Young"}\NormalTok{,}\DecValTok{4}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"Old"}\NormalTok{,}\DecValTok{4}\NormalTok{)),}
  \DataTypeTok{stringsAsFactors =} \OtherTok{FALSE}\NormalTok{) }\OperatorTok{\%>\%}
\StringTok{  }\KeywordTok{group\_by}\NormalTok{(AgeG)}

\CommentTok{\# Figure 5}
\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{.data=}\NormalTok{someData, }\DataTypeTok{colour=}\StringTok{"white"}\NormalTok{, }\DataTypeTok{position =} \StringTok{"stacked"}\NormalTok{,}
      \DataTypeTok{mapping=}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{fill=}\NormalTok{p)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{facet\_wrap}\NormalTok{(}\OperatorTok{\textasciitilde{}}\NormalTok{AgeG, }\DataTypeTok{ncol=}\DecValTok{2}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{theme}\NormalTok{(}\DataTypeTok{legend.position =} \StringTok{"bottom"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{datasupp3-1.pdf}
\caption{\label{fig:datasupp3}To visualize data corresponding to different groups, one needs to use \texttt{group\_by} or similar \textbf{before} plotting with \texttt{ggseg()} for facetting to work.}
\end{figure}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Figure 6}
\KeywordTok{ggseg}\NormalTok{(}\DataTypeTok{atlas=}\StringTok{"aseg"}\NormalTok{, }\DataTypeTok{mapping=}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{fill=}\NormalTok{area)) }\OperatorTok{+}\StringTok{ }
\StringTok{  }\KeywordTok{theme}\NormalTok{(}\DataTypeTok{legend.justification=}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{),}
        \DataTypeTok{legend.position=}\StringTok{"bottom"}\NormalTok{,}
        \DataTypeTok{legend.text =} \KeywordTok{element\_text}\NormalTok{(}\DataTypeTok{size =} \DecValTok{5}\NormalTok{)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{guides}\NormalTok{(}\DataTypeTok{fill =} \KeywordTok{guide\_legend}\NormalTok{(}\DataTypeTok{ncol =} \DecValTok{3}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{atlases-1.pdf}
\caption{\label{fig:atlases}The \texttt{aseg} atlas showing subcortical structures has some distinct differences from the \texttt{dkt}. For instance, there is no option to show only a single hemisphere. Furthermore, rather than showing lateral and medial surfaces, it shows an axial and sagittal slice.}
\end{figure}

\hypertarget{plotting-3d-mesh-data}{%
\subsection{Plotting 3D mesh data}\label{plotting-3d-mesh-data}}

Representing brains as 2D polygons is a good solution for fast, efficient, and flexible plotting, and can be easily combined with interactive apps such as Shiny (\citet{shiny}).
Yet, brains are intrinsically 3-dimensional and it can be challenging to recognize the location of a region as a flattened image.
This problem is exacerbated in atlases that represent subcortical features as they are 3-dimensional, while cortical structures, such as grey matter structures, can be flattened to 2-dimensions.
Hence, here we also provide the \emph{ggseg3d} package to plot, view, and print 3D-atlases in R.
\emph{ggseg3d} is based on tri-surface mesh plots using \emph{plotly} (\citet{plotly}).
The data structure is more complex than the \emph{ggplot2} polygons, and includes additional options for brain inflation, glass brains, camera locations, etc.
As \emph{ggseg3d} is based on plotly, the resulting brain atlases are interactive, which guides interpretation, and is useful for public dissemination.
We recommend users to familiarize themselves with \emph{plotly} (\citet{plotly}) when using this function.

Out-of-the-box, \texttt{ggseg3d()} plots the \texttt{dkt\_3d} atlas in `LCBC' surface, but there are two more surfaces available for cortical atlases (Figure \ref{fig:ggseg3d-1-out})
The `LCBC' surface consists on a semi-inflated white matter surface based on the \emph{fsaverage5} template subject.
All \texttt{{[}...{]}\_3d} atlases include a \texttt{colour} column that based on the color scheme used in the source neuroimaging software.

\begin{figure}[H]
\includegraphics[width=0.3\linewidth]{ggseg3d_white} \includegraphics[width=0.3\linewidth]{ggseg3d} \includegraphics[width=0.3\linewidth]{ggseg3d_inflated} \caption{The three surface options provided in \emph{ggseg3d} atlases. \textbf{From left to right:} the `white' surface is the white matter surface, `LCBC' surface is the semi-inflated white matter surface (inflated over 10 iterations), and the `inflated' surface is a inflated grey matter cortical surface as provided by the FreeSurfer software.}\label{fig:ggseg3d-1-out}
\end{figure}

The 3D-atlas data is stored in nested tibbles.
Each cortical atlas has data sets for three different surfaces (see Figure \ref{fig:ggseg3d-1-out}) and the two hemispheres.
Only one surface is available for subcortical atlases as inflation procedures are irrelevant.
The `ggseg\_3d' column includes all necessary information for \texttt{ggseg3d()} to create a 3D mesh-plot, and should not be modified by the user.
The additional 3D-atlases in \emph{ggsegExtra} have the same data structure.
It is important to note that the coordinates in the plot (X, Y, Z) are \textbf{not} any type of radiological coordinate system, but arbitrary Cartesian plot coordinates.

\small

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# remotes::install\_github("LCBC{-}UiO/ggseg3d")}
\KeywordTok{library}\NormalTok{(ggseg3d)}
\end{Highlighting}
\end{Shaded}

\normalsize

\hypertarget{external-data-supply}{%
\subsubsection{External data supply}\label{external-data-supply}}

Similarly as in the 2D-atlas, the user will use \texttt{ggseg3d()} to display through a colour scale some descriptive or inferential statistic.
If the data is not already in the correct long format or uses similar naming as the atlas, the users should inspect the atlas data for a specific surface (and hemisphere, if desired), and then \texttt{unnest(ggseg\_3d)} it to see how the atlas data is organised.

\small

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Select surface and hemisphere, and then unnest to inspect the atlas data}
\NormalTok{dkt\_3d }\OperatorTok{\%>\%}
\StringTok{  }\KeywordTok{filter}\NormalTok{(surf }\OperatorTok{==}\StringTok{ "inflated"} \OperatorTok{\&}\StringTok{ }\NormalTok{hemi }\OperatorTok{==}\StringTok{ "right"}\NormalTok{) }\OperatorTok{\%>\%}
\StringTok{  }\KeywordTok{unnest}\NormalTok{(ggseg\_3d) }\OperatorTok{\%>\%}
\StringTok{  }\KeywordTok{head}\NormalTok{(}\DecValTok{5}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\normalsize

Note the \texttt{mesh} column, which contains lists.
Each list corresponds to a region and contains 6 vectors required to create the mesh of the tri-surface plot.
It should also be noted that the `label', `annot' and `area' columns could provide matching values for your own data.
Similarly to the \texttt{ggseg()}-function, the `label' column should match the region names used in the original neuroimaging software while `area' and `annot' provide alternative/secondary names.
It is thus important to match your regional identifiers with those used in the atlas.
To colour the segments using a column from the data, a column name from the data needs to be supplied to the \texttt{colour} option, and providing it to the \texttt{text} option will add another line to the \emph{plotly} hover information.

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Figure 8 left}
\KeywordTok{ggseg3d}\NormalTok{(}\DataTypeTok{.data =}\NormalTok{ someData, }\DataTypeTok{atlas =}\NormalTok{ dkt\_3d, }\DataTypeTok{colour =} \StringTok{"p"}\NormalTok{, }\DataTypeTok{text =} \StringTok{"p"}\NormalTok{)}

\CommentTok{\# Figure 8 middle}
\KeywordTok{ggseg3d}\NormalTok{(}\DataTypeTok{.data =}\NormalTok{ someData, }
        \DataTypeTok{atlas =}\NormalTok{ dkt\_3d,}
        \DataTypeTok{colour =} \StringTok{"p"}\NormalTok{, }\DataTypeTok{text =} \StringTok{"p"}\NormalTok{,}
        \DataTypeTok{palette =} \KeywordTok{c}\NormalTok{(}\StringTok{"\#ff0000"}\NormalTok{, }\StringTok{"\#00ff00"}\NormalTok{, }\StringTok{"\#0000ff"}\NormalTok{)) }\OperatorTok{\%>\%}\StringTok{   }
\StringTok{  }\KeywordTok{pan\_camera}\NormalTok{(}\StringTok{"right lateral"}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{ }
\StringTok{  }\KeywordTok{remove\_axes}\NormalTok{()}

\CommentTok{\# Figure 8 right}
\KeywordTok{ggseg3d}\NormalTok{(}\DataTypeTok{.data =}\NormalTok{ someData, }
        \DataTypeTok{atlas =}\NormalTok{ dkt\_3d,}
        \DataTypeTok{colour =} \StringTok{"p"}\NormalTok{, }\DataTypeTok{text =} \StringTok{"p"}\NormalTok{,}
        \DataTypeTok{na.colour =} \StringTok{"black"}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{   }
\StringTok{  }\KeywordTok{pan\_camera}\NormalTok{(}\StringTok{"right lateral"}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{ }
\StringTok{  }\KeywordTok{remove\_axes}\NormalTok{()}
\end{Highlighting}
\end{Shaded}

\begin{figure}[H]
\includegraphics[width=0.3\linewidth]{ggseg3d_data} \includegraphics[width=0.3\linewidth]{ggseg3d_colour} \includegraphics[width=0.3\linewidth]{ggseg3d_nacolour} \caption{\textbf{Left:} Supplying data to \texttt{ggseg3d()} works similarly to \texttt{ggseg()}. Since \texttt{ggseg3d()} is based on \emph{plotly} rather than \emph{ggplot2}, most aesthetic adaptations must be set in the main function. Here we set the parcellation colours with the `colour' column and accompanying mouse-hover text with the `text' column. \textbf{Middle:} The palette for \emph{ggseg3d} needs to be specified directly in the main plot call. The `palette' option takes vectors of colours either as HEX-codes or R-colour names. \textbf{Right:} The colour of the \texttt{NA} values can also be changed through the option `na.colour'.}\label{fig:ggseg3d-data1}
\end{figure}

\hypertarget{customizing-colours-and-the-colour-bar}{%
\subsubsection{Customizing colours and the colour bar}\label{customizing-colours-and-the-colour-bar}}

You can provide custom colour palettes either in hex or R-names, as seen in Figure \ref{fig:ggseg3d-data1}.
Colours will be evenly spaced when creating the colour-scale.
A palette may also be supplied as a \emph{named numeric vector}, where the vector names are the colours that users wish to use, and the numeric values are the breakpoints for each colour (e.g.~\texttt{c("red"\ =\ 0,\ "white"\ =\ 0.5,\ "blue"\ =\ 1)}).
This way the users can control the minimum and maximum values of the colour scale, and also how the gradient is applied.
If another colour than the default gray is wanted for the \texttt{NA} regions, supply `na.colour', either as HEX colour or colour name.
This option only takes a single colour.

\hypertarget{adding-a-glass-brain}{%
\subsubsection{Adding a glass brain}\label{adding-a-glass-brain}}

Subcortical atlases include cortical surfaces and other landmark structures for visualization purposes only.
One can control the opacity of the these \texttt{NA} structures, to improve visualization.
Glass brains can be added to provide a frame of reference for the subcortical structures with the function \texttt{add\_glassbrain()} (Figure \ref{fig:glassbrain1}) , which takes three extra arguments: hemisphere, colour, and opacity.

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# Figure 9}
\KeywordTok{ggseg3d}\NormalTok{(}\DataTypeTok{atlas =}\NormalTok{ aseg\_3d,}
        \DataTypeTok{na.alpha=} \FloatTok{.5}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{   }
\StringTok{  }\KeywordTok{add\_glassbrain}\NormalTok{(}\StringTok{"left"}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{ }
\StringTok{  }\KeywordTok{pan\_camera}\NormalTok{(}\StringTok{"left lateral"}\NormalTok{) }\OperatorTok{\%>\%}\StringTok{ }
\StringTok{  }\KeywordTok{remove\_axes}\NormalTok{()}
\end{Highlighting}
\end{Shaded}

\begin{figure}[H]
\includegraphics[width=0.6\linewidth]{ggseg3d_glass} \caption{For subcortical structure visualization, one can add a glass brain to the plot. This will help with locating the structures relative to the cortex, and make the plot easier to interpret. The glass brain is controlled by three options: opacity, hemisphere, and colour.}\label{fig:glassbrain1}
\end{figure}

\texttt{ggsed3d()} is based on \emph{plotly} and thus additional \emph{plotly} functionalities can be used to modify and improve the 3D atlas representations.
In addition to Carson Sievert's book on \emph{plotly} in R (\citeyearpar{plotly}), we recommend resources for modifying axes in 3D plots (\citet{plotly-ax}), the basic introduction to tri-surface plots (\citet{plotly-tri}), and this tutorial on tri-surface plots with \emph{plotly} in R (\citet{plotly-trisurf}).
Finally, we recommend \href{https://github.com/plotly/orca\#installation}{orca} command line tool to save \emph{ggseg3d} atlas snapshots.

\hypertarget{additional-atlases}{%
\subsection{Additional atlases}\label{additional-atlases}}

The \emph{ggseg} and \emph{ggseg3d} packages have two atlases each, which are 2D and 3D variations of the same main atlases: the \texttt{dkt} (\citet{dkt}) and \texttt{aseg} (\citet{aseg}) atlases.
These are, however, only two among many meaningful ways of segmenting the brain into different regions.
Thus, the \emph{ggsegExtra} package is a repository containing additional with additional data sets for plotting with the \emph{ggseg} and \emph{ggseg3d} packages.
There is an ever-increasing amount of new atlases being created, as research and methods in neuroimaging analysis progresses.
The \emph{ggsegExtra}-package is intended to be expanded as a community-effort, as new and informative atlases are published.
A small collection of the atlases currently in the \emph{ggsegExtra} package may be viewed in Figure \ref{fig:ggsegExtra}

\begin{figure}[H]
\includegraphics[width=0.8\linewidth]{ggseg_extra} \includegraphics[width=0.4\linewidth]{ggseg3d_extra1} \includegraphics[width=0.4\linewidth]{ggseg3d_extra2} \caption{Four example data sets from the \emph{ggsegExtra} library, plotted with the \texttt{ggseg()} and \texttt{ggseg3d()} functions.}\label{fig:ggsegExtra}
\end{figure}

\hypertarget{discussion}{%
\section{Discussion}\label{discussion}}

The main aim of the \emph{ggseg}, \emph{ggseg3d}, and \emph{ggsegExtra} packages is to ease and streamline visualization of brain atlas data in R, by gathering a collection of atlases from several scientific sources and providing customized plotting functions.
In this tutorial, we introduced the packages to the readers by presenting some use examples and highlighting the main functions and options that are available.
As visualization tools, these packages add up to manifold functionalities such as ggBrain (\citet{ggBrain}) and ggneuro (\citet{ggneuro}) in R, and software-specific image viewers such as FSLeyes (\citet{fsleyes}) and Freeview (\citet{dale_99}).
In this regard, we do not aim to compete with software-specific visualizations or advocate for the superiority of the \emph{ggseg}-packages as visualization tools.
After all, flattened 2D polygons do not rely on a meaningful brain coordinate system and the units of information in 3D meshes are limited to the number of parcellations.
On the contrary, we believe the \emph{ggseg} niche among visualization tools resides in its simplicity and its ability to be combined with statistical analysis pipelines.
The possibility to serve as an interactive tool for dissemination and reproducibility when combined with other technologies, such as Binder (\citet{binder}) or Shiny (\citet{shiny}), is an added benefit.
This is exemplified in the online supplementary information of Vidal-Piñeiro et al.~(\citeyearpar{vidal_2019}).

The three \emph{ggseg}-packages contain three main features:

\textbf{1)} a collection of 2D-polygon and 3D mesh brain parcellation atlases. The atlas data include the necessary coordinates for plotting, and include other information that should be recognizable for users.\\
\textbf{2)} \texttt{ggseg()} and \texttt{ggseg3d()} functions for visualization. Both functions are flexible and well-adapted to their environment and can be combined with any additional argument from \emph{ggplot2} and \emph{plotly}, respectively.\\
- \texttt{ggseg()} is a wrapper function for \texttt{geom\_polygon} from \emph{ggplot2} and it can be built upon like any \emph{ggplot2} object.\\
- \texttt{ggseg3d()} is a \emph{plotly} wrapper function for tri-surface mesh plots which prints 3D atlases.\\
\textbf{3)} Complimentary features -- e.g.~color scales - and functions such as \texttt{as\_ggseg\_atlas()} and \texttt{as\_ggseg3d\_atlas()} to convert data in the correct atlas format.

These functions provide users with the possibility of adapting the plots to their wishes, and also makes it possible to create and contribute to the atlas repository in \emph{ggsegExtra}.

The foundations of the \emph{ggseg}-packages trace back to the necessity of visualizing and exploring the lifespan trajectories of cortical thickness across different brain regions (see supplementary information in Vidal-Piñeiro et al.~(\citeyearpar{vidal_2019}) ) .
That is, \emph{ggseg} appears with the need to inspect and display brain information over time - i.e.~including a spatial dimension and a time-varying factor - overcoming the constrains of printed journals and classical 2D plots (e.g.~bar plots).
The current state of science requires researches to share the results of studies in both high detail and in an intuitive manner, as it permits communication to wide audiences and facilitates reproducibility.
Hence, we believe this tool conforms to the essence of open science and invite users to improve the code, provide examples, or tutorials, and contribute to the atlas collection according to their own interest and needs via the public \href{https://github.com/LCBC-UiO/ggseg}{ggseg GitHub repository}, \href{https://github.com/LCBC-UiO/ggseg3d}{ggseg3d GitHub repository}, and \href{https://github.com/LCBC-UiO/ggsegExtra}{ggsegExtra GitHub repository}.

Finally - while the \emph{ggseg}-packages are circumscribed to brain parcellations - we believe that the structure and functions of the package can be easily applied to any scientific field that benefits from data being displayed across the spatial dimension.
We encourage readers to borrow the package functionalities and adapt it to their respective fields and structures of interest, such as has already been done with the \emph{gganatogram}-package (\citet{gganatogram}).

\hypertarget{planned-package-improvements}{%
\section{Planned package improvements}\label{planned-package-improvements}}

In the \emph{ggsegExtra} \href{https://github.com/LCBC-UiO/ggsegExtra/wiki/Contributing\%3A-polygon-atlases-new}{github wiki}, we offer a pipeline to create and supply atlases for 2D plotting.
At the moment, the creation of atlas for ggseg is convoluted and difficult and requires manual intervention.
We are in the process of designing a simple, straightforward pipeline to facilitate the creation of new ggseg (2D) atlases.
The aim is to create a set of functions that will call specialized tools like FSL(\citet{fsl}), Freesurfer(\citet{fischl_99}, \citet{dale_99}, \citet{Fischl2000}) and Imagemagick(\citet{magick}) to detect the polygon vectors or the mesh segments given an MRI image containing a parcellation specification, and organize these into valid \emph{ggseg} and \emph{ggseg3d} atlases.
We encourage users to contribute to the \emph{ggsegExtra} brain atlas repository by including additional brain atlases.

\hypertarget{conclusion}{%
\section{Conclusion}\label{conclusion}}

Visualization is a fundamental aspect of neuroimaging to explore and understand data, guide interpretation, and communicate with colleagues and the general audience.
In this tutorial, we have introduced the \emph{ggseg}-packages, tools for visualizing brain statistics through brain parcellation atlases in R.
This visualization tool easily combines with interactive routines as well as with diverse statistical analysis pipelines.
We hope this tool and tutorial proves useful to neuroscientists and inspires others to apply the functions in a wide variety of fields and structures.

\hypertarget{author-contributions}{%
\section{Author Contributions}\label{author-contributions}}

Didac Vidal-Piñeiro generated the idea for the tool, and the initial scripts for plot visualization.
He has also been responsible for converting images from neuroimaging data to ggseg-like data (e.g.~polygons and mesh data).
Athanasia M. Mowinckel adapted the initial scripts and made the functions into package format and has continued developing the functions with the aim of increasing user-friendliness.
She is also responsible for conceiving and adding the mesh-plot functionality through plotly, and developing the pipeline for making that possible.
A. M. Mowinckel wrote the first draft of the paper, and both have since critically edited it.

\hypertarget{conflicts-of-interest}{%
\section{Conflicts of Interest}\label{conflicts-of-interest}}

The authors declare that there were no conflicts of interest with respect to the authorship or the publication of this article.

\hypertarget{acknowledgements}{%
\section{Acknowledgements}\label{acknowledgements}}

We thank John Muschelli for package code comments and its adaptation to neuroconductor ({[}2018{]}), and Richard Beare for the first community created atlas, and base code comments.
We are indebted to A. M. Winkler scripts for converting neuroimaging data (Freesurfer) into .ply files, a necessary step for neuroimaging to mesh-plots conversion, and to Inge Amlien for the `LCBC' surface files and helpful contributions to the ggseg3d pipeline.
We are indebted to those users that actively contributed to the package and all the individuals that worked on the framework on which the package is sustained (e.g.~R-project, neuroimaging software).
Finally, we thank Anders M. Fjell (AMF) and Kristine B. Walhovd (KBW) for their encouragement and financial support.

\hypertarget{funding}{%
\section{Funding}\label{funding}}

This work is funded by EU Horizon 2020 Grant `Healthy minds 0-100 years: Optimizing the use of European brain imaging cohorts (Lifebrain)', with grant agreement 732592.
The project has also received funding from the European Research Council's Starting (grant agreements 283634, to A.M.F. and 313440 to K.B.W.) and consolidator Grant Scheme (grant agreement 771355 to KBW and 725025 to AMF).
The project has received funding through multiple grants from the Norwegian Research Council"

\hypertarget{prior-versions}{%
\section{Prior versions}\label{prior-versions}}

Athanasia Monika Mowinckel also has several tutorials on her blog regarding \emph{ggseg} creation and functionality (\citet{ggsegAnim}, \citet{ggsegIntro}).

\renewcommand\refname{References}

redpajama_set_name: RedPajamaArXiv


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Mechanical: Thermo pane windows, updated wiring (no knob & tube) including circuit breakers, copper plumbing & upgraded from the street, forced air gas furnace, central air conditioning, wood burning fireplace updated & chimney checked 2001, decks 2001, central vacuum (most outlets work, a couple don t), there is minor water seepage in spring during heavy rains on the north east corner of basement walls. The water line to kitchen is insulated with heat tape.
Inclusions: Gas stove, fridge, built-in dishwasher, microwave, washer, dryer, custom window coverings including California shutters & blinds, electric light fixtures, security system.
Excellent location -- walk to subway station or take the twenty-four hour streetcars along Spadina, Dundas and Queen to quickly connect to subway stations on the Yonge/University/Spadina and Bloor-Danforth lines. You are already downtown, and easy access to the Gardiner Expressway and Lake Shore Boulevard provide exits from the city.
An ideal executive home, 50 Beverley Street is located in a residential oasis of the lovely Grange Park neighbourhood. This district is home to a large number of artists and such Toronto landmarks as the Art Gallery of Ontario and the Ontario College of Art. Kensington market & Chinatown shopping districts are within walking distance, providing an exotic escape from the everyday.
Interior is luxurious, with a mix of contemporary and classic touches including refinished hardwood or pine floors. The living room, renovated in 2001, is perfect for entertaining as it is spacious, bright and open with extra high ceilings. Exposed brick and a wood-burning fireplace add to the fashionable charm. There are windows at the front & the back of this huge room.
The formal dining room is refined, with hardwood floors, sliding glass doors to a courtyard that provides light from the outdoors, as well as a beamed ceiling, track lighting and has ample space for a full dining suite.
The sun-filled cooks kitchen was completely renovated in 2001 and is well appointed. Features include granite counters, ceramic backsplash, an abundance of tasteful wood cabinetry, undercabinet lighting, ceramic floors and black appliances including a gas stove. It opens onto the patio via French doors.
The 2nd floor features three good size bedrooms, hardwood floor landing, and two full bathrooms. You have a choice of which room to use as your master as the front room has a 4 piece ensuite, and the back bedroom features a lovely walkout & deck -- both have double closets.
The 3rd floor is a spacious loft currently used as an office/family room with 2 skylights and a spectacular walkout to the rooftop deck. This could be converted into a fourth bedroom, as it also has a double closet.
There is a full basement with a separate walk out to rear yard and is clean with good height.
There are two (rare & valuable downtown!) parking spaces via right of way behind the fence. Professionally landscaped rear garden features a private flagstone patio, flowers beds, as well as a mature tree and ivy-covered privacy fence.
The neighbouring homes are all owner occupied by mature singles or couples, who have generally lived there for 20+ years.


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Our Lady of Endor Coven, also known as Ophite Cultus Sathanas, was an American Satanic cult founded by Herbert Arthur Sloane (born September 3, 1905, died June 16, 1975) in Cleveland, Ohio, with a claimed origin in 1948 though definitive documentation of the group does not appear until the 1960s.

The group was heavily influenced by gnosticism (especially that found in the contemporary book by Hans Jonas, The Gnostic Religion), and worshipped Sathanas, their name for Satan (Cultus Sathanas is a Latin version of Cult of Satan).  Sathanas (or Satan), was defined in gnostic terms, as the Serpent in the Garden of Eden who revealed the knowledge of the true God to Eve.  That it called itself "Ophite" is a reference to the ancient gnostic sect of the Ophites, who were said to worship the serpent.

The "Lady of Endor" is a reference to the Witch of Endor, a woman in the Hebrew Bible said to summon spirits of the deceased. Sloane's Coven was first publicly documented in the middle of 1968, when British occult writer Richard Cavendish said that he had received a letter from a Satanist "lodge" in Toledo, Ohio and a 1967 interview with Sloane with a Toledo newspaper about his occult and fortune telling business made no mention of it. While current scholars of Satanism point out that there is no substantial evidence showing Our Lady of Endor Coven existing prior to 1966, some also point out that it is likely that his group did have roots prior to that time:

It seems probable the group was in existence before 1966, although I have not found any traces of it in literature prior to that date. Sloane himself suggested that he was already operating in the 1940s, but given the many parallels with Wicca the group displayed, it is more likely its date of origin must be located sometime after 1953, the year Gerald Gardner's neopagan cult of witchcraft came into the open.

Beliefs

Sloane had a Protestant upbringing. Sloane referred in his June 1968 letter to his group as "Our Lady of Endor Coven, The Ophitic Cultus Sathanas" and clarifies that it was Sathanas in the form of the Serpent who brought the knowledge (gnosis) of the true God to Eve when she ate of the Tree of Knowledge. This true God is above the creator god of this world. He writes that Cain was the first Satanic priest and performed the first Satanic Mass (which explains Cain being punished by the creator god). According to Sloane, True Satanists then, are following the way of the Serpent and of Cain, as the early ophitic gnostics were.

This terminology is summed up in Hans Jonas' book within a few pages, titled "Eve and the Serpent", and "Cain and the Creator":

... it is the serpent that persuades Adam and Eve to taste of the fruit of knowledge and thereby to disobey their Creator ... Indeed, more than one gnostic sect derived its name from the cult of the serpent ("Ophites" from the Gk. ophis; "Naassenes" from the Heb. nahas--the group as a whole being termed "ophitic")

This general Serpent is also the wise Word of Eve. This is the mystery of Eden: this is the river that flows out of Eden. This is also the mark that was set on Cain, whose sacrifice the god of this world [the creator god] did not accept whereas he accepted the bloody sacrifice of Abel: for the lord of this world delights in blood.

Sloane believed in a horned god, which he said was revealed to him in the woods when he was a child. This horned god, according to him, was the original and most ancient god ever worshipped by humans (he explains that anthropology has proven this). After seeing Margaret Murray's book The God of the Witches (published in 1931), he said he realized that the horned god was Satan (Sathanas). Sloane had become very active in the Spiritualist Church movement, being a minister in a Spiritualist Church since the 1930s, and traveling to various locations in Ohio to perform services. He describes his coven of Sathanas as developing out of his occupation with Spiritualism.

Sloane also corresponded with his contemporary Gerald Gardner (the founder of the Wicca movement, who died in 1964), concerning Sloane's interest in portraying true witches as worshiping Satan, or "Sathanas", a view which Gardner himself fought against in his writings. However, Sloane did believe that, while "gnosis" referred to knowledge, and "wicca" referred to wisdom, modern witches had fallen away from the true knowledge, and instead had begun worshipping a fertility god, a reflection of the creator god.

When the Church of Satan appeared in the 1960s, Sloane began corresponding with Anton LaVey, with an interest in finding more Satanists to participate in his own organization. Sloane highly recommended the book The Gnostic Religion, and selections from it were sometimes read at ceremonies.

References

Citations

Works cited

 James R. Lewis (2001). Satanism Today, ABC-CLIO Incorporated, ISNB 9781576072929

Further reading
  Contains 1970 interview with Sloane.
  Contains interview with Sloane.
  Quotes Sloane's mimeographed material, and describes his Coven.

Cults
Magical organizations
New religious movements
Organizations based in Cleveland
Religion in Ohio
Satanism in the United States
Witch of Endor

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HomeFamilyParenthood
Meghan Markle and Prince Harry Are Expecting Child Number 2!
Meghan Markle – Prince Harry – Gettyimages
*There's good news from Royal couple Prince Harry and Meghan Markle or should we say, the Duke and Duchess of Sussex. On Sunday, representatives for the couple announced the news, via a statement, that they are looking to be parents for the second time. The news was first reported by People and was confirmed by Business Insider.
"We can confirm that Archie is going to be a big brother. The Duke and Duchess of Sussex are overjoyed to be expecting their second child," a spokesperson for the couple told Insider via email.
If you recall, Markle, 39, and Harry, 36, welcomed their first child, Archie Harrison Mountbatten-Windsor, on May 6, 2019. He'll be 2 years old this coming May.
MORE NEWS ON EURWEB: Janet's in 'Control' Again! 1986 LP Back on the Charts After Timberlake Apology / Watch Singer Thank Fans
A post shared by Misan Harriman (@misanharriman)
Markle previously shared in an essay for The New York Times titled "The Losses We Share" and published in July 2020 that she suffered a miscarriage.
"Losing a child means carrying an almost unbearable grief, experienced by many but talked about by few," she wrote in the essay.
Markle and Harry ended their official duties as senior royals in April 2020 after announcing their choice to "step back" as working members of the royal family that January. Since leaving their roles in the royal family and dropping their official HRH titles, they've been residing in California with Archie.
Markle and Harry's second child will presumably be the Queen's 10th great-grandchild.
Meghan Markle and Prince Harry Are Expecting Child Number 2
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See your family dreams become reality in this exquisite all brick 5BR/5BA home on .43 acre lot in beautiful Heron Knoll of Hamilton Proper. Formal sweeping entry opens to office, formal DR and 2 story great room w/FP and built ins. Gourmet kitchen features granite countertops, SS appliances, Jenn Air gas range, ctr island w/bkfst bar, eat in kitchen and cozy hearth room w/FP. Main floor MBR w/ensuite bathroom featuring dual vanities, dual WIC, jetted tub. 3 BR's and large bonus room upstairs! Finished basement includes wet bar, 5th bedroom w/full bath and extra bonus room could be used as exercise or craft room. Lovely backyard patio! Home features security system, surround sound and backup generator.


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HomeWiki
Biography, Career, and Net Worth of Jeff Goldblum in 2022
Jeffrey Lynn Goldblum (born October 22, 1952) is an actor and musician from the United States. He has been in some of the highest-grossing films of his era, including Jurassic Park (1993) and Independence Day (1996), as well as their sequels The Lost World: Jurassic Park (1997) and Jurassic World: Fallen Kingdom (2018), and Independence Day: Resurgence (2001). (2016).
Goldblum also appeared in films such as Invasion of the Body Snatchers (1978), The Big Chill (1983), and Into the Night (1985), before gaining greater recognition for his role as Seth Brundle in David Cronenberg's The Fly (1986), for which he received a Saturn Award for Best Actor.
Facts about Jeff Goldblum:
Full Name Jeffrey Lynn Goldblum
Birth Date October 22, 1952
Birth Place West Homestead, Pennsylvania, United States
Nick Name fashion daddy
Religion Jewish
Education Graduated
Horoscope Libra
Father's Name Harold L. Goldblum
Mother's Name Shirley Goldblum
Siblings 3
Build Not found
Profession actor and musician
Wife/Husband/Boyfriend/Girlfriend (Name) Emilie Livingston
Net Worth $40 million
Facebook https://www.facebook.com/goldblumofficial
Twitter https://twitter.com/goldblumlooks?lang=en
Instagram https://www.instagram.com/jeffgoldblum/?hl=en
Jeff Goldblum's Net Worth:
Goldblum is an actor from the United States with a net worth of $40 million. Jeff Goldblum has starred in some of the most well-known blockbusters of all time, portraying unusual characters in limited release indies. He is gaining new acclaim for his unique demeanour in talk show interviews and other appearances.
Jeff Goldblum Early Life
Goldblum's parents had a non-professional interest in show business when he was born on October 22, 1952, in West Homestead, Pennsylvania, which he considers as one of the influences that led to his chosen vocation. In an interview with The Guardian, he also cites his older brother Rich's sudden death at 23 with his increased focus as a young man and his drive to "save me and survive."
Jeff Goldblum's Career
Goldblum made his Broadway debut in the musical Two Gentlemen of Verona, then starred in the 1974 film Death Wish as a drug-addled punk. Goldblum spent years seeking that one breakthrough job that would launch his career into the stratosphere, and he landed memorable tiny roles in films like Nashville and Annie Hall. Finally, the kind of significant job he'd been waiting for appeared: he played a seedy magazine journalist who reconnects with his college classmates after a funeral in 1983's The Big Chill. Goldblum continues to make notable supporting roles in films such as Invasion of the Body Snatchers, The Right Stuff, and The Adventures of Buckaroo Banzai (in full cowboy costume).
In the 1980s, he began to amass prominent roles with Into the Dark and a genuinely star-making central role in filmmaker David Cronenberg's The Fly (1986), in which he played Seth Brundle, a crazy scientist who accidently hybridises himself with a housefly, earning him significant critical acclaim. Jurassic Park and its sequel Jurassic Park: The Lost World (in which he reprised his role in two further Jurassic Park sequels, Jurassic World: Fallen Kingdom and Jurassic Park: Dominion in 2021), as well as Independence Day, were other huge box-office triumphs (1996). He featured as Detective Zach Nichols on the USA Network's crime drama show Law & Order: Criminal Intent for the eighth and ninth seasons, cementing his place as one of Hollywood's top actors.
But then he took a step back to focus on lower-profile independent films like 2002's Igby Goes Down, which is nothing from the ordinary for a nonconforming performer.
Other Works:
In 1984, he played legendary TV comedian Ernie Kovacs in a TV biopic. He has appeared in cameo roles on a variety of shows, including Sesame Street, The Larry Sanders Show, The Simpsons, King of the Hill, Mr Show with Bob and David, and several projects from comedians Tim Heidecker and Eric Wareheim, beginning with Tom Goes to the Mayor and culminating in Tim and Eric Awesome Show Great Job! He also appeared on multiple episodes of the IFC sketch comedy show Portlandia, and in 2019 he was given his documentary series.
Over the years, Goldblum has offered his acting talents to several video games. In 1996, he played Count Dracula in Goosebumps: Escape from Horrorland, and in 2015, he appeared in Call of Duty: Black Ops III. Dr Ian Malcolm (first in 1997's Chaos Island: The Lost World and then again in Jurassic World Evolution more than 20 years later) and David Levinson (in the 1997 Independence Day video game) are two of his most famous cinematic characters who have appeared in video game adaptations.
Goldblum has mentioned in interviews that he could have gone into music instead of acting at a young age because of his early passion for music. He's a talented jazz pianist, and his first professional recording was on the 2001 compilation CD Hollywood Goes Wild!, which featured music from several movie and television stars. But it would be another 17 years before he and the Mildred Snitzer Orchestra released a full album together: The Capitol Studios Sessions in 2018, followed by I Shouldn't Be Telling You This in 2019.
Jeff Goldblum's Family Background
Jeffrey Lynn Goldblum was born to Jewish parents on October 22, 1952, in West Homestead, Pennsylvania, just outside of Pittsburgh. His mother, Shirley Jane Goldblum (April 25, 1920 – February 23, 1983), was a radio broadcaster who later ran a kitchen equipment and appliance sales company, and his father, Harold Leonard Goldblum (April 25, 1920 – February 23, 1983), was a physician and a major in the United States Army during World War II. His ancestors hail from Starobin and Zolochiv in Russia and the Austro-Hungarian Empire. Pamela is his younger sister, and Lee is his older brother. Rick, who Goldblum has referred to as a "sort of hero of mine," died in 1971 at 23 while travelling in Morocco after contracting dysentery that led to kidney failure.
Jeff Goldblum's relationship status
From 1980 to 1986, Jeff Goldblum married Patricia Gaul, an actress who costarred with him in the film Silverado. Then he was notably married to actress and three-time costar Geena Davis from 1987 until 1990. (with whom he appeared in Transylvania 6-5000, The Fly, and Earth Girls Are Easy). Goldblum married his third wife, Canadian Olympic gymnast Emilie, in 2014.
Jeff Goldblum's Background in Education
At the age of 17, Goldblum travelled to New York City to pursue a career as an actor. He worked on stage and studied acting under the direction of renowned acting instructor Sanford Meisner at the legendary Neighborhood Playhouse. Jeff made his Broadway debut in the Tony Award-winning musical Two Gentlemen of Verona. He is a seasoned jazz pianist.
He was nominated for a Primetime Emmy Award. Little Surprises, a short film he directed, was nominated for an Academy Award for Best Live Action Short Film.
Jeff Goldblum's net worth is?
He has a net worth of $40 million.
Is Jeff Goldblum a Native American?
Jeffrey Lynn Goldblum was born to Jewish parents on October 22, 1952, in West Homestead, Pennsylvania, just outside of Pittsburgh.
What's Jeff Goldblum up to these days?
His mother was of Austrian Jewish origin, and his father was of Russian Jewish descent.
Is Jeff Goldblum a vegan?
Jeff Goldblum isn't a vegan.
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