dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The Brain Imaging Data Structure (BIDS) is an emerging standard for the organisation of neuroimaging data (https://bids. neuroimaging. io)  1. The significance of BIDS is timely: there is increasing availability of open neuroimaging data resources, and strong interest in aggregating large, heterogeneous datasets to harness a new range of scientific questions, and augment statistical power. This also poses new challenges in terms of data organisation, harmonisation, (re)processing and sharing, especially without a standard format for digital neuroscience data between or within data modalities (e. g. , in electrophysiology). Consequently, present data management practices do not generalise between labs, or even between members of the same research group. This leads to suboptimal usage of human (time lost retrieving data), infrastructure (data storage space) and financial (limited longevity and value of disorganised data after first publication) resources.
Poor or lacking data management strategies also negatively affects the reproducibility of results, even within the lab where the data were collected. BIDS was initiated with magnetic resonance imaging (MRI) data. It provides a simple, hierarchical folder structure, with key study parameters documented in text-based metadata files. MRI-BIDS can manage multiple MRI modalities, with minimal curation overheads. This reduces the likelihood of datahandling errors. Another key benefit is the augmented interoperability between BIDS-compatible software tools for data analytics. Here we introduce a significant extension of BIDS to electrophysiology data. The technical sophistication of magnetoencephalography (MEG) makes it the most challenging electrophysiology data type for standardisation  2. MEG-BIDS can therefore readily be generalised to electroencephalography (EEG), multiunit recordings, and local field potentials.
Further to strengthening and rationalising data management in MEG labs, MEG-BIDS provides a common structure for present and future large MEG open-data repositories  [3] [4] [5]. The new standard handles files collected from all types of MEG instruments, regardless of their raw data file format (e. g. , CTF/VSM, Elekta/Neuromag/MEGIN, Yokogawa/KIT, Neuroscan/KRISS, etc. and more can be readily added to accommodate the newest MEG manufacturers and as format versions evolve. ) The absence of a unique data file format in MEG is indeed compensated by MEG-BIDS's standard data organisation and metadata principled reporting: the sharing and processing of large and complex data hierarchies is simplified, and made compatible and reproducible across tools for data analytics.
MEG-BIDS specifications The MEG-BIDS specifications were defined in accord with best-practice guidelines for conducting MEG research  6  , with direct inputs from MEG investigators, technical support staff and data managers. The teams of leading academic data-analysis software were also directly involved 7 : Brainstorm 8 , FieldTrip 9 , MNE  10  , and SPM  11  , which all have adopted MEG-BIDS. The design process was initiated at the 2016 International Conference on Biomagnetism (Seoul, South Korea), where the first incarnation of MEG-BIDS was introduced, and open group discussions ensued. A first version of the present manuscript was shared as a preprint  12  , and received comments and suggestions from the community to refine the MEG-BIDS specifications. In July 2017, we published an open poll survey for the MEG community to express their potential interest in MEG-BIDS.
The poll received 78 international entries, with most salient results indicating i) strong interest in a common standard for MEG data organisation (98. 7%), ii) affirmed willingness to try a MEG-BIDS solution (97. 4%). MEG-BIDS shares common data descriptors with BIDS, such as Subject, Session, Technique, and Run. In MEG terminology, Subject refers to a scanned participant. We acknowledge that MEG is not strictly a scanning technique. Yet, this is for convenience, affinity with other neuroimaging modalities, and to reflect the language used in most MEG labs. 'Session' defines a non-intermittent period of time during which the subject is in the scanner. 'Run' is a period of time during which brain activity or empty-room (for noise characterisation) is recorded continuously, with no interruptions.
It is typical with MEG that a Session consists of multiple Runs: task instructions can change and/or participants can take a break between runs. The notion of Task refers to the instructions (and corresponding stimulus material) that are performed by the participant. Responses is a feature for continuous recording of behaviour. Note that BIDS also supports storage of stimuli files (such as videos), and annotations of events via a hierarchical event descriptor extension. MEG-specific metadata elements can be reported with the widely used JavaScript Object Notation (JSON) and Tab Separated Value (TSV) text file formats, both human-and machine-readable, which contributed to the versatility and practicality of MRI-BIDS. The comprehensive MEG-BIDS specifications are openly available elsewhere  13  , and include links to main BIDS specifications and JSON schemas.
Note that there are no JSON LD context files, but with attention to backward compatibility with present BIDS specifications, they could be added in future versions. There is also ongoing work in the BIDS community to reach to the NIDM (Neuroimaging Data Model) efforts to expose experiment metadata as linked data. In the same vein, with MEG-BIDS extending from original MRI-BIDS, it does not support Resources Description Format (RDF) representations. The original decision for BIDS to not rely on RDF was driven by the complexity of that format and relatively low familiarity among scientists and software developers outside of the semantic web circles. Yet, current investigations of BIDS to NIDM conversions may lead to RDF views of BIDS datasets. The MEG-BIDS file organisation also extends, with full compatibility, that of MRI-BIDS: file names are constituted by a series of key-value pairs, with multiple possible file types, while retaining a few typological aspects.
MEG-BIDS can therefore register data of any kind, including but not limited to task-based, restingstate, and empty-room data collection. We emphasise that all of the above notions apply also to EEG and all other modalities of electrophysiology, for which MEG-BIDS serves as template for standardisation. Such flexibility was crucial to retain for MEG, as we already mentioned there is no common, open or standard file format in the field. MEG-BIDS stores unprocessed (raw) data in the native vendor file format. This means that users can still rely on their preferred data review application, possibly provided by the system's vendor, for files stored in the MEG-BIDS folder structure. Free, open-source BIDScompliant software (see below) can also extract most required meta information elements from these raw data files, such as data collection parameters and other study and task descriptors.
Free, open MEG-BIDS data samples We provide four publicly-available data repositories in MEG-BIDS format, for a total of 200GB. They are freely accessible for download in the public domain via OpenNeuro. Note that for educational and demonstration purposes, the International Neuroinformatics Coordinating Facility's GitHub also hosts a lighter version (data structure only, no actual MEG data provided) of the MEG-BIDS dataset examples (https://github. com/INCF/BIDS-examples). 1) Resting-state  14  (https://openneuro. org/datasets/ds000247): Five minutes of eyes-open, resting-state MEG data (5 subjects from OMEGA  4  , with individual T1 MRI; 10. 26GB). This dataset is described in Brainstorm's specific MEG-BIDS tutorial (http://neuroimage. usc. edu/brainstorm/Tutorials/ RestingOmega). 2) Auditory task  15  (https://openneuro.
org/datasets/ds000246): MEG-BIDS version of an auditorystimulation study (1 subject with T1 MRI; 2. 3GB). The data is described in detail in the main online tutorial of Brainstorm (https://neuroimage. usc. edu/brainstorm/Tutorials). 3) Visual and auditory tasks  16  (https://openneuro. org/datasets/ds000248): MNE data sample (1 subject; 185. 85MB) from a previously published study  10  (includes T1 and FLASH MRI). This dataset is described in detail in MNE's online tutorial (https://martinos. org/mne/stable/manual/sample_dataset. html). 4) Visual group study  17  (https://openneuro. org/datasets/ds000117): A multi-modal (MEG, EEG, fMRI) human neuroimaging dataset of 16 subjects from a previously published study on face processing  18  (60. 88GB): This dataset is described in detail in the SPM12 manual (www.
Open-source and publicly-available MEG-BIDS compatible software and tools Widely-used free and open MEG (also for EEG and electrophysiology) software packages have already added functionality to support MEG-BIDS. All feature basic preprocessing tools and advanced solutions for sophisticated data analytics e. g. , source imaging, time-frequency decompositions, statistical inference, multivariate decoding, and measures of functional connectivity: 1) Brainstorm 8 : Matlab and Java application, with rich graphical-user interactions and analytic pipeline designs for MEG, EEG, NIRS, and electrophysiology recordings. BIDS-formatted MEG/EEG datasets can be imported automatically into the Brainstorm database, as described in the above-mentioned MEG-BIDS Resting-state tutorial using the OMEGA sample.
2) FieldTrip 9 : Matlab toolbox for MEG, EEG, and other electrophysiological data full analysis pipelines. Among others, FieldTrip has been used for the MEG part of the Human Connectome Project. 3) MNE 10 : a Python software package with MEG analysis tools and workflows. MNE provides code to read and write files in MEG-BIDS-compatible format. Eventually, the MNE team has committed to distribute all its tutorial datasets in MEG-BIDS, along with and the relative analysis scripts. A preliminary version is available at https://mne-tools. github. io/mne-bids/. 4) SPM 11 : software written in Matlab, where many widely-used methods for the analysis of PET, fMRI and for computational neuroanatomy were originally developed and implemented. SPM has been extended to MEG and EEG analyses and SPM12 includes the spm_BIDS library to parse and query BIDS-formatted datasets, as well as low-level functions to read/write JSON and TSV metadata files.
A complete pipeline for the analysis of a group MEG-BIDS dataset is also available. 5) MEG-BIDS JSON file generator: Python scripts to produce the JSON sidecar files and reduce the overhead of adopting MEG-BIDS: publicly-available (https://github. com/INCF/pybids). 6) MEG-BIDS validator (http://incf. github. io/bids-validator/) : Developed in JavaScript using Node. js, it can be packaged to work in the browser (Google Chrome). A command line version is also provided so that it can be used in scripted analysis. The validator performs several sanity checks on datasets with ensure they are compatible with BIDS. This includes the use of regular expressions to check filenames, and JSON schemas to ensure that the metadata files are standardised by data types.
Discussion MEG-BIDS is a proposal to establish a standard framework for the organisation of electrophysiology data. It does not impose the standardisation of the data file format per se. Most present and future tools for MEG data analytics can be readily equipped with the necessary readers for all existing vendor formats: these libraries are available in open source from the above-cited software. We believe the capacity of MEG-BIDS to organise data without requiring a common data format is actually a strength: the standard is flexible enough for any data parameters to be extracted and stored as metadata in sidecar JSON files at the time of creating a new MEG-BIDS data entry, regardless of the original file format of the raw data. Therefore, any new data format for electrophysiology is by design compatible with MEG-BIDS  19.
Acknowledgements This work was supported by the  ERC Starting  Grant  SLAB ERC-YStG-676943  to Alexandre Gramfort, by the  EU-H2020   Marie Curie "ChildBrain" Innovative Training Network  grant no. 641652  to  Robert Oostenveld , by an  NWO-VIDI  Grant ( 864. 14. 011 ) to  Jan-Mathijs Schoffelen , and by a  Discovery Grant  from the  Natural Science and Engineering Research Council of Canada  ( 436355-13 ), the  NIH  ( 2R01EB009048-05 ) and a  Platform Support Grant  from the  Brain Canada Foundation  ( PSG15-3755 ) to  Sylvain Baillet. The UK MEG community is supported by  Medical Research Council  grant  MR/ K005464/1  and The  Wellcome Centre for Human Neuroimaging  is supported by core funding from the  Wellcome  ( 203147/Z/16/Z ). Guiomar Niso received financial support from the  AXA Research Fund.
3A ). Hence, some fluorescent bacteria were detected in the suspensions of nasopharyngeal swabs at D9 for K921A and D20 for K937B by confocal microscopy, confirming colonization of both baboons by the B1917-GFP strain carried by the co-housed infected baboon (Fig. 3C, D ). Only K937B showed an increase of circulating WBCs from the beginning of nasopharyngeal colonization (Fig. 3B ). These data indicate that juvenile baboons can transmit B1917-GFP from one animal to another. Ex vivo imaging of baboon airways using pCLE. Ex vivo imaging was performed using baboon tissues to establish pCLE technique in airways. APC detection was assessed using this technique as they have been shown to play a key role in the early immune response against B. pertussis  [15] [16] [17] [18]. APCs were selectively labeled and detected in the lower respiratory tract, from the trachea (Fig.
We used the GFP derivative strain to investigate natural B. pertussis transmission to naïve co-housed baboons and confirmed previous published data from younger animals  14. However, the efficiency of transmission to naïve baboons and the intensity of associated clinical symptoms may vary due to social interactions and inter-individual variability. Nevertheless, effective clinical infection of the challenged animals validated B1917-GFP as an appropriate strain for the study of pertussis pathogenesis by imaging. This allowed us to investigate bacterial-host cell interactions using ex vivo and in vivo imaging technologies. We used this baboon infection model to investigate whether cell populations of interest, such as APCs, and bacteria could be labeled and detected in vivo during a B. pertussis infection by pCLE imaging.
Quantification of coughing. Animal cages were monitored with a video-recording system. Cough occurrences for each animal were assessed daily during a 6-h period (6:00-9:00 a. m. ; 5:00-8:00 p. m. ) using Windows Movie Maker software. Antibody labeling. Mouse anti-human HLA-DR antibody that targets APCs (BD, clone L243) and its isotype control (mouse IgG2a (BD)) were labeled with AlexaFluor647 using the Microscale Protein Labeling Kit (Invitrogen). Antibody and fluorophore concentrations were measured after coupling by nanodrop spectrophotometry. Ex vivo imaging of the baboon respiratory tract. Lower respiratory tract explants were obtained from euthanized baboons. Immediately after euthanasia, lung lobes were isolated and tracheal rings cut and preserved in complete medium (DMEM containing 10% SVF, NEAA (1X concentration), 1 mM sodium pyruvate, and 0.
B. pertussis was detected in 10-µm tissue sections using anti-B. pertussis-LOS-A (ThermoFisher, clone D26E) and goat anti-mouse IgG3-AlexaFluor594 (Life Technologies) antibodies. APCs were detected using anti-human-HLA-DR and goat anti-mouse IgG2a-AlexaFluor488 (Life Technologies) antibodies. Cell nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI). Image acquisition was performed using a SP8 confocal microscope (Leica, Germany). Image analysis. Films obtained using pCLE were first selected using IC-Viewer software and then exported to ImageJ (National Institute of Mental Health, USA). Intensity thresholds were fixed to minimize the background to signal ratio. Bacterial colonization of the lower airways was assessed using GFP area quantification. Briefly, the total area of GFP-positive signal was calculated for each imaging time-point using Visiopharm ® Software (Visiopharm, Denmark) in 30 images randomly extracted from the pCLE acquisition.
Acknowledgements We warmly thank the  IDMIT infrastructure  staff for excellent technical assistance, especially  Laetitia Bossevot ,  Naya Sylla  and  Sebastien Langlois. The IDMIT facility is supported by the  French government  " Programme d'Investissements d' Avenir" (PIA) , under grants  ANR-11-INBS-0008  and  ANR-10-EQPX-02-01. This work was supported by  PERISCOPE. PERISCOPE has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking , under grant agreement no. 115910. This Joint Undertaking received support from the  European Union 's  Horizon 2020 research and innovation Program  and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  and  Bill and Melinda Gates Foundation (BMGF).
Background: Here, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue. Results: Our package allows the user to visualize evolutionary variability by locating the positions most likely to vary and to assess mutation candidates in protein engineering.
However, it is essential to evaluate not only neighbouring amino acids in primary sequence, but also dispersed ones that can be seen as neighbours in tertiary structure and can provide intrinsic functionality e. g. tunnels and gates  [2]. We provide an R package that performs the unique analysis of protein evolution that summarises entropy or variability scores in an MSA. Residues responsible for protein functionality are often dispersed across the sequence but are in close proximity in the tertiary structure (we called those a functional compartment of protein structure). We have designed BALCONY (Better ALignment CONsensus analYsis) to facilitate the analysis of dispersed key amino acids, by fast and convenient data extraction, manipulation and visualization. The package has the flexibility to select amino acids and make it possible to analyse evolution of any protein structural feature constructed by residues i.
Thus, the package can be used in thermostability optimisation, binding properties enhancement, ligands filtering analysis by channel and tunnels or in identification of hot spots for directed mutagenesis. This tool is unique in the number and kinds of analyses it implements, and also in its convenient combinability with other packages within the R environment. The BALCONY package implements multiple entropy/ variability scores that may be used to locate evolutionarily variable or preserved residues. Moreover, we are proposing a new score for the measurement of a residues conservation.
Results of the analysis can be saved as Coma Separated Values (CSV) file and/or can be readily plotted with dedicated functions. BALCONY can process three different types of data: MSA of homologous sequences; specific structure/feature of protein of interest; structure of protein of interest. MSA of homologous sequences is required for all types of analyses, the other data is optional. Details of data formats used by BALCONY are provided in the BALCONY manual file. The middle section of Fig. 1  illustrates main data processing workflows in BALCONY. For example, MSA of homologous sequences can be submitted to validation routines finalized in analysis of outliers and noteworthy sequences. It may further result in the improved MSA. Next, altered MSA data can be used in the remaining types of data processing and analysis procedures.
BAL-CONY data processing workflows are very flexible; all functions can be easily combined, rerun, or even, if necessary and possible, run in any order. Analysis functions allow convenient preparation of different types of plots and statistical analysis. Detailed results of entropy analysis for each alignment position can also be saved as CSV file (Additional file 1: Table  S1 ). In comparison to other R packages  [7, 8]  BALCONY facilitates summarization and conservation-variability analysis in context of protein structure. Solution presented in BALCONY package aids the comparison of functional compartments across one protein and facilitates multiple proteins statistical analysis. Also, a unique feature is the Fig. 1  Flowchart illustrating the BALCONY package components and workflow possibility to add details to investigated residues for integrative analyses.
Results and discussion In order to perform full evolutionary analysis coupled with structural data, the input is minimal: an MSA and a plain text file with any structural/physicochemical data provided by the user (Additional file 1: Figure  S1 ). Both are described exhaustively in the BALCONY manual. Initial data analysis and validation: The package allows to primarily extract, analyse and summarise information contained in the MSA. Undeniably, the quality of the alignment is essential for the downstream analyses, therefore we equipped BALCONY with features improving verification of the MSA. BALCONY aids detection of the outlying sequences. Managing outliers may be iterative process, requiring realigning sequences. Outliers that contain additional domains or large insertions and may be detected using the pairwise similarity of each sequence in the dataset to the consensus sequence.
Other outliers may be detected using pairwise distances between all sequences. The user decides if outlier sequence should be removed, trimmed or left for further analysis. The variability-conservation oriented investigation of some functional compartments of structure (residues responsible for protein functionality are often dispersed across the sequence but are neighbouring in tertiary structure) facilitates rational protein design  [9, 10]. When the residues are adjacent in the sequence, obtaining this information is easy. The BALCONY package aids the entropy/ variability analysis for sectors of the protein structure, which are dispersed in the primary structure. For this purpose, the special (but simple) text file containing table with numbers, names and additional numeric features of certain residues in protein structure may be used (Additional file 1: Figure  S1 ).
This functionality promotes the investigation of the evolutionary background of some functional residues in protein structure. Therefore, it may improve the process of rational protein design by easily comparing the variability of different functional compartments of a structure (like cavities, tunnels, surface, binding sites) with additional features obtained from different methods. For example, the additional numeric feature can serve information about physiochemical properties of particular amino acids, its accessibility for solvent exposure, β factors, RMSF (Root Mean Square Fluctuation) data from MD (molecular dynamics) simulations etc. This is also suitable to compare in this way functional parts of multiple protein. Since other software for structural analysis of protein sometimes lacks the functionality for missing residues detection, BALCONY allows to correct the numbering of residues in file described above using information from the REMARK465 section in PDB file  [11].