dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic study To define genes suitable for haplotype-mapping, we selected a panel of 73 genes from the BCAA pathway and immediate regulators (S1 Table ). To detect the most associated genes with IR, we applied the sliding window procedure with a window length of 4 SNPs after identifying LD blocks by Gabriel et al. method  [38]. Thus, a number of 109,754 short haplotypes were tested against IR and significant associations (P � 9. 39 x 10 -5 ) were obtained for 21 haplotypes in 6 genomic regions of Chr 6, 12, 16 and 17 (S3 Table ). Among genes of BCAA metabolism defined in the KEGG database, associations were found with downstream genomic regions of the MUT and AACS. All other genes in BCAA metabolism including BCAT1 and BCAT2, BCKDHA, DBT, DLD displayed hits with at least one order of magnitude lower P values and therefore were not further investigated in fine-scale haplotype mapping.
Discussion In this paper, we present for the first time evidence for the association with IR of four genomic regions close to genes involved in BCAA catabolism or its regulation. The MUT gene operates in the distal steps of BCAA degradation, while AACS, albeit included in the KEGG database, is involved in ketone bodies metabolism. The SLC6A15 gene encodes for a specific BCAA transporter operating in the brain and under the regulation of PRKCA. The association of these genes with IR was not previously reported and none of corresponding effective SNPs were reported as being associated with IR. These findings reiterate the potential relationship between IR and BCAA metabolism and add stronger evidence for common genetic susceptibility, although the causality relationship cannot be established from these studies.
08 and 0. 07, respectively) suggest that they are low-frequent variants but with major effects, situation which is typical for complex conditions. Comprehensive review of all effects described in this study, indicate that MUT gene would be influential concordantly on both BCAA and IR by the haplotypes B2_H12 (frequency 0. 12) located in block B2. The effect of this haplotype was synergistic to haplotypes in the next blocks (B3_H3 and B4_H8 with frequency of 0. 22) covering at least 10% of the population (Fig  2 ). The second genomic region was located close to AACS (acetoacetyl-CoA synthetase) on Chr 12q24. 31. The gene was included in this study because of its annotation in the KEGG database for BCAA metabolism, but the enzyme encoded by this gene is a ketone body-utilizing ligase (EC 6. 2.
This study is limited by the size of the population at the discovery stage, involving only two ethnic populations. Extension of our study over other Mediterranean populations available on MEDIGENE program would considerably complicate haplotype mapping. Another limitation was the unavailability of a metabolomics profile that would better characterize potential gene defects by measuring metabolites at various steps of BCAA metabolism. Despite these limitations, implication of these genomic regions in IR is novel since none of SNPs described here were previously reported in GWAS of other complex diseases. Thus, our study drives the attention towards these potentially regulatory regions that warrant further biological investigation in relation with IR. The PPM1K gene identified by MR was not associated with IR in our study.
Acknowledgments This work belongs to workpackage 8 ( WP8 ) of the  MEDIGENE  project of the  European Union ,  Seventh Framework Program  for research, technological development and demonstration under grant agreement no  FP7-279171-1. FG is " Chargede Recherche Hors Classe" at Institut National de la Santeet de la Recherche Medicale (INSERM ) and affiliated to the  Institut Convergeances Migrations (ICM) ,  Collège de France, Paris (France). We extend our thanks to  Struan Grant  and  Michel Koenig  for reading the manuscript.
Characteristics of the adopted recommendations The previous result only provides information about what criteria the participants considered most relevant for their playlists. Our next goal is to determine to what extent the participants actually relied on these criteria when adopting recommendations. However, not all quality criteria could easily be compared with the actual choices of the participants. For instance, our dataset did not contain the lyrics of the tracks, nor the audio signal to assess the quality of the transitions. Still, we could analyze the following criteria. First, more than 45% of the participants of the RecUsed group created playlists in which each artist appeared only once (this value is 57% across all participants). Although these participants are not the majority, the value can still be considered high as the RAGH recommender always recommended a few tracks from the artists that were already in the playlist.
In our future work, we plan to study this particular aspect in more detail, especially by proposing algorithms that are able to infer the context and the purpose of a playlist from the available data. One prerequisite is to build a rich dataset in which information about these two particular elements is provided. For example, the "InCarMusic" data set made available by  Baltrunas et al. (2011) , despite some limitations (low number of tracks, user ratings of genres instead of individual tracks, one very specific type of context, etc. ) can be considered an important step in that direction. Another interesting finding is the importance of the order. This criterion was ranked comparably high, and one third of the participants reordered their tracks during the creation of their playlists at least once.
Acknowledgements Open access funding provided by  University of Klagenfurt. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Materials In this study, test images are chosen from the Simulated Brain Database of the Mc-Connell Brain Imaging Centre at McGill University  20. As we are not interested in performance of classifications but merely the speedup of generation of GLRLM and features extractions, a simulated MR image would be sufficient for the goal. The database contains a set of MR brain images produced by an MRI simulator  21. Simulation settings can be configured from 3 modalities, 5 slice thicknesses, 6 levels of noise and 3 levels of intensity non-uniformity. In general, different configurations try to simulate practical artifacts which can be used to evaluate performance of some classifier, they do not influence significantly the speed of GLRLM construction and features extractions, as for images of a fixed size, they act as the same containers of similar data, which require approximately the same amount of work, i.
Nvidia's CUDA platform, which may be the earliest widely adopted software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, makes the general purpose programming on GPU relatively easy. Generally speaking, CUDA enables custom functions to be executed in parallel on GPU's SMs. These custom functions are called compute kernels, which can be directly defined in a normal C program as CUDA provide an extensions to the C programming language. Hence parallel primitives are generally implemented in this way and can be called as a function. Nowadays there are abundant literatures  [22] [23] [24]  talking about how to program GPU with CUDA in great details, therefore, we suggest readers to refer to them.
CUB library. Though CUDA provides the tool set to write general purpose program to accomplish complicated tasks, it doesn't mean that one can easily implement an efficient program to solve a particular problem, as parallel modeling is quite different from the sequential counterpart and flexible management of memory also makes it difficult to be handled appropriately. Hence there exists various libraries based on CUDA that implements common collective primitives that take care of low-level details and leave user to deal with main logic of problems. Example primitives like parallel sort, prefix scan, reduction, histogram, etc. are essential for constructing high-performance, maintainable parallel programs. CUB is such a library that provides state-of-the-art, reusable software components for every layer of the CUDA programming model, including warp-wide "collective" primitives, block-wide "collective" primitives and device-wide primitives  25.
Environments. Our experiments were conducted with two configurations of different GPUs, so that the results is not specific to a particular GPU and reveals how our parallel program adapts to different devices. Here we list some main specifics of hardware in Table  3. For the aspect of operating system and software, we use Ubuntu 16. 04. 4-x86_64 with kernel version 4. 13. 0-37-generic, and the sequential program is compiled by a GNU C-complier of version 5. 4. 0 with optimization flag -O3, while the parallel counterpart is compiled with CUDA-7. 5 toolkits.
They would also save considerable time and resources currently required to re-code published methods. In addition, re-coding presents challenges in code verification relative to a published method, raising possible concerns about the validity of the end results and limiting the long-term value of the effort. Here we deploy and share open-source software (called Invasive Neurophysiology-Brainstorm, or IN-Brainstorm) that integrates multiple aspects of data analysis for most modalities and signal types for basic electrophysiology: from single cells to distributed channel arrays, from spiking events to local field potentials, from ongoing recordings to event-related responses, and from in vitro preparations to free-behaving models. We also emphasize the importance of an extensive graphical interface for user-friendly access to advanced analytical methods, of flexible scripting features for high-performance computing, and of traceable code execution.
The proposed tool is accompanied by extensive online documentation and support from a user community web forum. This free application builds on the foundations of the Brainstorm platform  6  , which is well-established (21,000 user accounts), free open-source software for magnetoencephalography (MEG) and electroencephalography (EEG). Brainstorm can integrate multimodal data volumes in addition to scalp electrophysiology e. g. , magnetic resonance imaging (MRI), CT-scans and functional near-infrared spectroscopy (fNIRS). It also features advanced source modeling for electrophysiological signals. The IN-Brainstorm application provides a comprehensive suite that interoperates with other, more specific and constantly evolving IN tools available from the open-source community e. g. , for performing spike sorting.
Results The IN-Brainstorm functionalities described here offer comprehensive solutions for data importation and analysis, including spike-sorting, extraction of local field potentials, and correlations among these measures across multiple channels. Importantly, thanks to an intuitive graphical user interface, no programming skills are required for accessing and using the advanced methods available, including for assembling and sharing advanced data analysis pipelines. A summary of these software features is provided in Table  1 , and a schematic of the workflow enabled by the toolbox is shown in Fig. 1. The bedrock of the present developments is the Brainstorm platform. Brainstorm 6 is written in Matlab (Matlab2008a and higher) and Java. It is therefore independent of the operating system (Windows, MacOS and Linux).
Community code management is via GitHub. Users without access to a Matlab license can use a fully executable version of the application compiled for the above operating systems. Extensive documentation is freely available online, with specialized tutorials, datasets and videos (https://neuroimage. usc. edu/brainstorm/e-phys/ Introduction). A thorough comparison of Brainstorm and other open source toolboxes was published recently  21. In the following sections, we describe a broad spectrum of analysis options for multiscale electrophysiology that are enabled by IN-Brainstorm and illustrate these features with the processing of an example raw data file. Importing, reviewing and pre-processing raw data. Raw data importation. Data to be analyzed must first be imported into the software. Brainstorm can read raw electrophysiology data from 80 different file formats.
Synopsis of the e-Phys toolbox and the tools that can be used for LFP analysis. The e-Phys toolbox provides a working framework for every step of the e-Phys analysis and each module can easily be enriched with future additions. depending on the visualization parameters set by the user (e. g. , virtual page length, selection of a subset of channels or montages for review, keyboard and mouse shortcuts for navigating and marking events). Task events (e. g. , stimulus types and presentation times, behavioral responses) and ancillary recordings (electrooculograms, electrocardiogram, eye and body movements, video recordings of behaviour, etc. ) are readily registered to the electrophysiological data in IN-Brainstorm, for multimodal data review, quality control and event-related processing. We emphasize that when a raw file is reviewed, the physical data is not duplicated as a Brainstorm file.
Unsupervised spike sorting. 2. Supervised spike sorting. As WaveClus and UltraMegaSort2000 have built-in supervised spike sorting graphical user interfaces, we synchronized their GUIs with IN-Brainstorm's. For Kilosort, we developed specific GUI bridges via Klusters 9. The user-selected supervised clustering tool is called from Brainstorm's main window after an unsupervised spike-sorted file is selected (Fig. 4a ). The user then switches to the GUI of the selected supervised spike clustering tool (Fig. 4b-d ). Once supervised spike clustering is complete, the spike events are updated accordingly and registered into the software's file system. Double-clicking on the link to the raw data file lets the user review the updated spike events along with the raw electrophysiological traces as shown in Fig.
The resulting LFP traces and experimental events are automatically registered in IN-Brainstorm's data repository for further review and analysis with a vast library of tools and pipelinesas described belowor for easy exportation to other software or plain files. LFP extraction produces a new IN-Brainstorm down-sampled time-series binary file (Fig. 5b ) with all the corresponding metadata, such as channel description (e. g. , electrode labels and locations), and spike and experimental events. This file is easily sharable among researchers since its size is typically ~20-30 times smaller than the original raw file. Figure  5c  shows a segment of the LFP file created. Epoching. Once the relevant neural signals (LFPs and spikes) have been extracted from the raw data, they can be divided according to experimental epochs.
Epochs are typically comprised of experimental trials, with the time window selection defined around a stimulation or behavioral event of interest. These can be imported directly into the IN-Brainstorm file system. To illustrate these functions, we make use of the example visual cortex recording described previously (Fig. 2 ). The experiment involved presentations of moving stimuli while the animal maintained fixation; we defined the relevant epochs as segments of [-500, 1000] ms around the onset of each visual stimulus (Fig. 6  Left). In total we considered 8 different directions of the visual stimulus moving pattern; each stimulus condition was repeated 4 times (one condition was repeated for 96 trials for usage in the raster plot, and noise correlation functions). Imported trials to the database are shown in (Fig.
) of the motion stimuli, and its 95% confidence intervals. The tuning curve shows the preference of this neuron for stimuli moving in the right direction (Stim On -1/4 pi condition). Topographical plots. When multichannel recording devices are used, neurophysiology data can be shown as topographically registered to structural anatomy. IN-Brainstorm can show neuronal firing at the 3-D locations of the recording probes/arrays. To illustrate this feature, we used a separate dataset that was collected from two 96-channel Utah arrays and one 32-linear probe  38. A structural T1-weighted MRI volume was acquired preoperatively. The head and brain surface envelopes were segmented with Freesurfer  39  and directly imported in IN-Brainstorm. The electrode contact locations were co-registered to the 3-D anatomical volume by specifying the distance of the electrodes along the probe and locating the tip of the probe and the entry point through the skull, using Brainstorm's MRI volume viewer.
Neuronal firing was binned in 10-ms segments and displayed on the animal's anatomy as shown in Fig. 9a  (a single bin is displayed in the figure). This figure shows IN-Brainstorm's ability to overlay the segmented cortical surface, MRI orthogonal slices, the implanted devices with actual geometry, and color-coded displays of raw or processed electrophysiology data (here instantaneous firing rates). Figure  9b  shows a zoomed-in version of Fig. 9a  over the Utah array implanted in the prefrontal cortex. Spike-spike analysis: noise correlations. While tuning curves capture neuronal sensitivity to stimulus properties, the fidelity of a population code is thought to be limited by noise that is common across neurons  40  ; for example, neurons would be noise correlated if for each stimulus their activities are correlated  41.
Such noise correlations are typically quantified as the Pearson correlation coefficient between the firing rates of two neurons across trials. Such correlations strongly influence the accuracy of population coding  [42] [43] [44] [45] [46]. Noise correlation statistics are displayed with IN-Brainstorm from the correlation of the spike trains that each neuron elicited within a given epoch, for all neuronal combinations. The end result is a nxn matrix (with n the number of unique neurons that produced spikes during the selected trials) that shows noise correlation estimates between the selected neurons. Figure  8c  shows the noise correlation profile across the 32-channel array of the example dataset, for 53 unique neurons that elicited spikes across all trials at the 8 conditions of presentation of the moving stimulus in the original data set from Fig.
2. Spikes included in the correlation computations were selected in the [0,300]-ms time range of each trial. The computed noise correlation showed 2 pairs of neurons with abnormally high noise correlation (above 0. 8). After further inspection, it was revealed that this was due to the fact that the spike-sorter that was used was not taking into account the relative position of the electrodes, and the same neurons were picked up from neighbouring channels: Neurons: AD01 |1| -AD02|2| and AD08 |1| -AD09 |1| were the same neuron. (c) Noise correlation. The function selects all the neurons that elicited spikes within the trials imported and displays a nxn figure where the noise correlation is computed for all combinations of neurons. Specifically for the dataset illustrated, there were 53 unique neurons picked up by the electrodes (according to the spike sorting step).
Statistical inference and machine learning. Once measures have been extracted from spiking or LFP data, tools to conduct inferential statistical analysis in the multiple dimensions of electrophysiological data (space, time, frequency, connectivity) are available from Brainstorm's library. Parametric (one-and two-sample tests) and nonparametric permutation tests, descriptive and distribution statistics from histograms (Q-Q plot and Shapiro-Wilk test for data normality) are available. Here too, the software architecture emphasises interoperability with other toolkits, for expanded resources. For instance, multidimensional and nonparametric cluster statistics can be run on LFP and time-frequency data, from Brainstorm, via calls to FieldTrip  12. In addition, statistical learning tools for decoding and multivariate pattern analysis (MVPA) are also available (see e.
g. Cichy et al. 57  ). The Brainstorm library also includes support vector machine (SVM) and linear discriminant analysis (LDA) classification of LFP time series based on experimental events and conditions. additional features. Processing power. Hardware acceleration in the processing of long recordings is enabled by Matlab's standard parallel computing (e. g. , multi-core) features, which are controlled directly from Brainstorm's GUI. Flexible management of memory resources is also accessible to users, with the specification of the amount of RAM allocated to data manipulations while executing the LFP extraction process. Moreover, GPU acceleration computations are enabled through Kilosort for the spike-sorting step. Data management. Generally speaking, formal data management plans are seldom adopted by electrophysiology labs.
As with all features in the application, user interactions with Brainstorm's data organization are facilitated both by the application's GUI and direct access via scriptable functions using Matlab code. Another important aspect of Brainstorm is its capacity for importing entire data repositories at once, with associated metadata, when those datasets are organized according to the emergent Brain Imaging Data Structure (BIDS). Originally driven by the neuroimaging community, BIDS is a grassroots effort to harmonize data organization and documentation  59. BIDS has recently been extended to MEG electrophysiology  60  and is presently integrating EEG  61  , and invasive neurophysiology  62. Batch processing. The software has a specific GUI for assembling data processing pipelines in an intuitive manner, choosing elementary processes from the (IN-)Brainstorm library and assembling them together into a logical progression along the workflow.
Discussion We provide a free, extensive open-source software application for invasive electrophysiology. IN-Brainstorm is built on the foundations of Brainstorm, which was originally designed for human multimodal electrophysiology and imaging. IN-Brainstorm supports multiple data formats of raw signals from a variety of acquisition systems. The recorded traces and their LFP versions can be reviewed, quality-controlled and processed within a unique analytical environment, with easy GUI interactions, rich visualization, intuitive pipeline editing for scripting and sharing. We have built bridges for IN-Brainstorm to interoperate seamlessly with established, free spike-sorting tools. A specific emphasis was put on providing versatile solutions for multidimensional data visualization, including 2-D and 3-D topographical plots registered to structural anatomy from co-registered MRI data.
Source modeling of array data is also available using boundary element modeling of head and brain tissues  63, 64  and a variety of source modeling techniques available in Brainstorm  65. Videos synchronised to electrophysiological traces can also be imported and visualized simultaneously in synchrony, for marking behavioral events. The software is supported by an expansive online documentation (with tutorial data) and online user forum. The active Brainstorm user community contributes to an efficient peer-reviewing/debugging process, and daily updates deliver bug fixes and software improvements that are readily available to the users. With IN-Brainstorm, electrophysiologists are provided a free, integrated software environment that promotes and facilitates harmonized principles of data management, methods, documentation, code verification and reproducibility of data analyses.