dataesr/scientific-paragraphs-categorization huggingface dataset:
20 s in all regression-based methods, the parametric g-formula relies on correct model specification. To explore the validity of our parametric assumptions, we compared the observed means of the outcome and time-varying covariates with those predicted by our models. We used a nonparametric bootstrap procedure based on 500 samples to obtain percentilebased 95% confidence intervals (CIs). All analyses were conducted with the publicly available SAS macro GFORMU-LA (http://www. hsph. harvard. edu/causal/software/).
aegypti populations collected from South America and South-East Asia together with a laboratoryselected strain. This study evidences several gene copy number variations (CNVs) and point mutations affecting detoxification enzymes associated with resistance to the pyrethroid deltamethrin. In this context, the present study aims at complementing the genomic data generated by Faucon et al. [28]  by combining a RNA-seq approach with a targeted DNA-seq approach focused on regions upstream ATG in order to better understand the genomic bases of metabolic resistance to pyrethroids in Ae. aegypti. The same biological samples as those used in Faucon et al  [28]  were used in order to combine results obtained from both studies. The importance of CNVs as a cause of over-production of detoxification enzymes was investigated by comparing RNA-seq expression data with targeted DNA-seq data.
Mosquito populations Eight Ae. aegypti populations of distinct geographical origins were used in the study. These populations were identical (same generation and egg batches) as those used in Faucon et al. 2015  [28]  in order to cross link genomic data previously obtained with transcriptomic data generated by the current study. These included three laboratory strains fully susceptible to all insecticides (S populations) and five populations showing elevated resistance to the pyrethroid insecticide deltamethrin (R populations). Field resistant populations were collected in Thailand (NakhR and PhetR) and French Guiana (CaynR and StGeR) during 2014-2015 and maintained in insectaries for 2 generations. In addition to field resistant populations, one laboratory strain selected with deltamethrin for five generations at the adult stage was included in the study (DeltaR population selected from the 'Bora-Bora' strain, 80% mortality at each generation).
Only read pairs mapping at a unique location (mapQ > 50) were retained. Quantification of transcription levels was performed using the Cuffdiff2 module of Cufflinks implemented into a Galaxy pipeline (http://galaxyproject. org) based on Fragment Per Kilobase exon Model (FPKM) values obtained for each gene across all samples. Transcription ratios between each resistant and each susceptible strain were computed across all biological replicates using Cuff-diff. Genes showing an FC ! 3 (in either direction) and a q value 0. 001 between a given resistant population and all three susceptible strains were considered differentially expressed (DE genes). Polymorphism analysis was performed with Strand NGS V2. 2 (Strand Life Sciences). SNPs were called against the reference genome with the following parameters: ignore spill overs at locations around homopolymer stretch greater than 10; ignore reference locations with homopolymer stretch greater than 10; ignore reference locations with coverage below 10; ignore reference locations with variants below 2; confidence score cutoff 50; error rate 0.
Variations upstream ATG showing a significant correlation (Pearson's r ! 0. 7 or -0. 7 and p 0. 05) with the transcription level of their respective genes were identified. Then, the effect of these upstream variations was examined in silico by comparing the occurrence of transcription factor binding sites (TFBS) at each locus between the reference and the variant alleles. For each gene, all possible variant haplotypes were compared with the reference haplotype. Transcription factor binding sites were screened with Promo V3. 0 (http://alggen. lsi. upc. es/cgi-bin/promo_v3/promo/promoinit. cgi?dirDB=TF_8. 3) using Transfac V8. 3 database restricted to animal and factors of at least six bp. DNA patterns showing 5% matrix dissimilarity with known TFBS were retained and compared between reference and variant haplotypes.
Acknowledgments The authors thank  Dr. F. Chandre  from the  Institut de Recherche pour le Developpement of France  for supporting the French-Thai partnership and helpful discussions. The authors also thank  Dr. P. Sirisopa  from  Kasetsart University Thailand  and  Dr. K. Thanispong  from the  Bureau of Vector Borne Diseases of the Ministry of Health of Thailand  for their support regarding mosquito collections.
This project was primarily funded by the  French Institut de Microbiologie et Maladies Infectieuses  (grant IMMI  109764 ). FF was supported by a PhD fellowship from the  Grenoble-Alpes University (UGA). We acknowledge support from the  federative structure Environmental and Systems Biology (BEeSy) of Grenoble-Alpes University. This work was also supported by the  French-Thai cooperation programme-PHC Siam project  ( RESA 2013-2014 ) funded by the  French embassy and the Office of the Higher Education Commission of Thailand  and the  Center for Advanced Studies for Agriculture and Food, Institute for Advanced Studies, Kasetsart University. WJ was supported by the  Thailand Research Fund  (senior research scholarship  RTA 5558002  and grant for new researchers MRG 5380102) and  Kasetsart University Research and Development Institute (KURDI).
We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI.
Introduction The Brain Imaging Data Structure (BIDS) is an emerging standard for the organization of neuroimaging data  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 machine learning techniques and address a new range of scientific questions, with greater statistical power. A single neuroimaging study by itself can represent a large and intricate volume of data, with multiple protocols and modalities, and several categories of participants possibly enrolled in repeated sessions. These aspects are challenging to data organization, harmonization and sharing. The situation is aggravated by the lack of a unique neuroscience standard for digital data across, and sometimes within, modalities such as electrophysiology.
An important secondary outcome is the facilitation of interoperability between tools for data analytics, provided that software and pipelines adopt BIDS for data inputs. We describe here a key extension of BIDS to electrophysiology data. The technical sophistication of MEG makes it the most challenging electrophysiology data type for standardization  2. For this reason, MEG-BIDS can readily be generalized to electroencephalography, multiunit recordings, and local field potentials. Further to strengthening and rationalizing data management in MEG labs, MEG-BIDS can provide a common structure to present and future large MEG open-data repositories, such as the Human Connectome Project (HCP 3 ), the Open MEG Archives (OMEGA  4  ), and the Cambridge Centre for Ageing Neuroscience (Cam-CAN  5  ).
The absence of unique data file format in MEG is compensated by MEG-BIDS's standard data organization: the sharing and processing of large and complex data hierarchies is simplified, and made compatible and reproducible across tools for data analytics. To derive the MEG-BIDS specifications, we have combined perspectives from investigators, technical support staff and data managers, with the best-practice guidelines in the field  6. We also involved the expertise of leading academic software developers for MEG science  7  , including Brainstorm 8 , FieldTrip 9 , MNE  10, 11  ), and SPM  12. The proposed MEG-BIDS specifications are presently compatible with these software applications and toolboxes.
Material & Methods The MEG-BIDS fields and data organization were defined, bearing in mind the best-practice guidelines for conducting MEG research  6. The initiative fostered contributions from multiple MEG experts, with a face-to-face discussion at BIOMAG2016, the International Conference on Biomagnetism, where the first incarnation of MEG-BIDS was introduced  13. The first set of feedback comments and the minutes from further group discussions are publically available ( https://groups. google. com/d/msg/bids-discussion/xTHBsGhu0hk/MN25xbxRBwAJ ). A first version of the present manuscript was shared via the preprint server bioRxiv, from where more comments stemming from the community were collected and considered for improvement of MEG-BIDS. A poll survey to probe the interest of the concerned community was also conducted (LINK TO RESULTS).
MEG-BIDS builds on the BIDS hierarchical data structure. For instance data descriptors such as subject , session, technique, run are BIDS notions that were re-used in MEG-BIDS. Similarly, the simple although extensively used human and machine readable file formats (JavaScript Object Notation [JSON], and Tab Separated Value [TSV] text files) that contributed to the versatility and practicality of documenting metadata elements in BIDS, were expanded with MEG-BIDS. MEG-BIDS employs a straightforward terminology, cautiously defined in line with the general BIDS specifications, although adapted to the unique requirements of MEG. For further reference, the MEG-BIDS specifications are detailed in an open-access online document: https://docs. google. com/document/d/1FWex_kSPWVh_f4rKgd5rxJmxlboAPtQlmBc1gyZlRZM ) The terms used refer to notions that were defined by reaching a consensus amongst the MEG-BIDS contributors and the MEG community.
For example, ' Subject ' refers to the scanned participant. Note that from a technical standpoint, MEG is not a scanning technique. Yet, we used this terminology for convenience, affinity with other neuroimaging modalities, and to reflect the language used in most MEG labs. A ' Session ' defines a non-intermittent period of time during which the subject is in the scanner. A ' Run ' is a period of time during which empty-room (for noise characterization) or brain activity 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 to indicate the recorded behaviour of the subject in relation to the task.
As with the general BIDS specifications 1 , MEG-BIDS file names are constituted by a series of key-value pairs, with multiple possible file types. Some typological aspects are mandatory, while others remain optional, although required to abide to the BIDS guidelines. MEG-BIDS can therefore register data of any kind, including but not limited to task-based, resting-state, and empty-room MEG recordings (e. g. , for noise estimation purposes). We emphasize that all of the above notions apply also to EEG, and all modalities of electrophysiology, for which MEG-BIDS serves as template for standardization. There is no common, open or standard file format in MEG, equivalent to DICOM or NIfTI in MRI. MEG systems manufacturers (CTF, Elekta/Neuromag, BTi/4D Neuroimaging, KIT/Yokogawa/Ricoh, Tristan Technologies, ITAB, KRISS/Compumedics Neuroscan, York Instruments) all cater a vendor-specific format.
With MEG-BIDS, unprocessed (raw) data is stored in the native file format (see Discussion for further consideration of common MEG format initiatives) and users can still rely on their preferred data analysis application, possibly provided by the MEG vendor, to browse and read in the MEG file contents. Software can also extract meta information elements from the raw data files. They concern e. g. , data collection parameters and other study descriptors, which are eventually transcribed into sidecar JSON files by said MEG-BIDS compatible applications. One major benefit of metadata extraction is the facilitation of subsequent data searches and indexation, without the handling and repeated parsing of large raw data files. Additional relevant files can be included alongside the MEG raw data: some propositions are detailed in the online specifications.
For a given investigator or research group, MEG-BIDS describes a hierarchical structure that descends from a 'Study' folder. Multiple 'Subject' subfolders contain the data from the participants enrolled. They are arranged by 'Session' , each session subfolder containing 'Run' folders and eventually, data and metadata files. One issue that required special attention was the multiplicity of coordinate systems and units between MEG systems. To impose a unique coordinate system for BIDS based on the subjects' brain anatomy (e. g. , MNI coordinates or equivalent) was an appealing solution, which however would lack generalizability in the MEG practice. MEG data can be collected without anatomical information, such as empty-room noise recordings, which are important to optimal source modeling 6. MEG-BIDS therefore associates all recordings with a coordinate file defined according to the MEG system used.
Again, MEG-BIDS compatible software can read and interpret this information properly. Akin to MRI, we anticipate that the systematic data organization enabled by MEG-BIDS will be supported by an increasing number of neuroimaging tools, and that more shared data repositories will be organized accordingly. The straightforward design of MEG-BIDS makes it an interoperable common exchange format for transferring data between investigators and community repositories e. g. , OMEGA  4  and OpenfMRI  14, 15. It also facilitates multimodal integration (between MRI, fMRI, MEG, etc), as the data from multiple modalities follow the same organization scheme.
OpenfMRI study ds000117: A multi-subject, multi-modal human neuroimaging dataset of 19 subjects participating in a visual task  16  (~178GB): https://openfmri. org/dataset/ds000117/. This dataset is used in one of the SPM tutorials, for training purposes: http://www. fil. ion. ucl. ac. uk/spm/doc/manual. pdf#Chap:data:multimodal.
Software Widely used MEG software packages have already added functionality to support MEG-BIDS: Brainstorm 8 : Brainstorm is an 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 OMEGA tutorial: http://neuroimage. usc. edu/brainstorm/Tutorials/RestingOmega FieldTrip 9 : FieldTrip is an open-source MATLAB toolbox for the analysis of MEG, EEG, and other electrophysiological data. Like most other tools listed herewith, FieldTrip can implement full analysis pipelines, starting from coregistration, preprocessing, time-and spectral analysis, source reconstruction, connectivity and statistics. Among others, FieldTrip has been used for the MEG part of the Human Connectome Project.
Since a FieldTrip analysis pipeline is represented as a MATLAB script, its application on BIDS structured data implies that the BIDS details are represented in the analysis scripts that users write. MNE  10, 11  : MNE ( http://martinos. org/mne ), whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data. It is a software package that provides comprehensive analysis tools and workflows including preprocessing (Maxwell filtering, ICA, signal space projectors), source estimation (eg. using MNE, beamformers or mixed-norm sparse solvers), time-frequency analysis, statistical analysis including multivariate decoding, and several methods to estimate functional connectivity between distributed brain regions. The core of MNE is written in Python and is distributed under the permissive BSD Licence.
MNE will use the BIDS data structure to distribute all its tutorial datasets and the documented analysis scripts. MNE provides Python code to read and write files in BIDS compatible format, as well as summary reports automatically generated via the MNE report command. A preliminary version is available at https://github. com/mne-tools/mne-bids. SPM  12  : SPM ( http://www. fil. ion. ucl. ac. uk/spm ) is a free and open source software written in MATLAB where many widely used methods for the analysis of PET, fMRI and for computational neuroanatomy have been originally developed and implemented. More recently SPM has been extended to perform M/EEG analyses, including topological inference for neurophysiological data, empirical Bayesian framework for source reconstruction and Dynamic Causal Modeling (DCM), an approach combining neural modeling with data analysis.
Discussion Although MEG-BIDS is a proposal to establish a standard framework for the organization of electrophysiology data, it does not impose the standardization of the data file format per se. Some initiatives are aiming towards the definition of a new common binary file format for electrophysiology (MNE python group). Akin to DICOM in MRI, one single file format would beneficial, when considering the diversity of native raw file formats in electrophysiology. Yet, MEG data volumes are typically very large (several GBs), hence their duplication into a standard format may be unpractical. Our position is rather to promote MEG-BIDS and ascertain that tools for data analytics continue to be equipped with the necessary readers for all existing vendor formats. We believe the capacity of MEG-BIDS to organise data without requiring a common data format is actually a strength: the standard is flexible in the sense that any data parameters can be extracted and stored as metadata in sidecar json files at the time of creating a new data entry, regardless of the file format for the raw data.
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  National Science and Engineering Research Council of Canada , the  NIH  ( 2R01EB009048-05 ) and a  Platform Support Grant  from the  Brain Canada Foundation  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.
Open MEG-BIDS datasets We provide four different publically-available datasets in MEG-BIDS format (~200GB). They are freely available for download from the International Neuroinformatics Coordinating Facility (INCF)'s GitHub: https://github. com/INCF/BIDS-examples/tree/bep008_meg The MEG-BIDS sample data release includes: OMEGA Resting-State samples: Five minutes of eyes-open, resting-state MEG data is available for 5 subjects from The Open MEG Archive (OMEGA)  4. The data are available from the Brainstorm Tutorial: tutorial MEG resting state & OMEGA database. The first release of data in MEG-BIDS format (~10. 5GB) available here: https://box. bic. mni. mcgill. ca/s/omega?path=%2FContributions%20(in%20BIDS%20format)%2Fsample_BI DS_omega Brainstorm Auditory Example dataset: Brainstorm Auditory tutorial dataset 8 (~2.
Phase analysis was performed after synthesis by X-ray diffraction at room temperature using a BRUKER D8 ADVANCE powder diffractometer working in Bragg Brentano geometry with Cu-Ka 1,2 radiation and Lineal LinxEye detector. The diffractometer was operated over the angular range 2q ¼ 10e70 for qualitative analysis and 2q ¼ 10e140 for Rietveld analysis, using a step size of 0. 015 in 2q and acquisition time of 1 s per step. The data analysis was performed by the Rietveld method using the Jana program  [22]. The background was interpolated between manually selected points. To model the peak profiles, a Cagliotti-type function with three parameters was used for the Gaussian part, and two parameters for the Lorentzian contribution. Additionally, an anisotropic broadening correction along [001] was taken into account, probably related to a decrease of the coherent domain size in this direction.
The samples were place in an alumina sample holder. Data were collected in the range 2q ¼ 13e64 (total counting time ¼ 10 min per scan), with a 0. 010 step, every 25 C from room temperature (RT) to 850 C. Cyclic Full Pattern Matching refinements were carried out with the FullProf software  [24]  and its graphical interface WinPLOTR  [25]. The background was fitted with a 6-coefficient polynomial function. A pseudo-Voigt function was used to model profile shapes, including the Cagliotti function variables U, V, W, the Gaussian-Lorentzian mixing parameter h and two asymmetry parameters. The values of standard deviations were corrected according to Berar and Lelann's description  [23]. To probe the stability of the pure materials in reducing atmosphere, characteristic of the SOFC anode, ~0. 3 g of each powder was heat treated at 800 C for 8 h in a tubular furnace fed with a wet H 2 / N 2 ¼ 3/97 gas mixture (Q ¼ 5 L h À1 e pH 2 O~0.
Acknowledgments We gratefully acknowledge the financial support of the  Vicerrectoría de Investigaci on y Extensi on of the Universidad Industrial de Santander (UIS)  (Projects #  5465  and  1333 ). G. G. and Z. M. express their personal thanks to  Dr. Mario Macías  and  M. Sc. Monica Sandoval  from  UIS  for their useful advices, the  Laboratorio de Difracci on de Rayos X (Parque Tecnol ogico Guatiguara-UIS ) for XRD measurements at RT. We also present our thanks to  Dr. Liliana Mogni ,  Dr. Alejandra Montenegro ,  Dr. Jesús Vega  and  M. Sc. Diana Garc  es for all the support at  CNEA. The  Fonds Europ een de D eveloppement R egional (FEDER) ,  CNRS, R egion Nord Pas-de-Calais and Minist ere de l'Education Nationale de l'Enseignement Sup erieur et de la Recherche  are also acknowledged for X-ray diffractometers' funding.
BIOINFORMATICS ANALYSIS. To identify any association between the novel loci and gene expression traits, we performed a systematic search of cisexpression quantitative trait loci (eQTL) (described in the Online Appendix). To identify candidate causal SNPs at the new loci, we annotated each of the lead variants as well as SNPs in high linkage disequilibrium (LD) (r 2 > 0. 8) on the basis of position, overlap with regulatory elements, and in silico SNP prioritization tools (Online Appendix). For both the novel loci and all previously reported CAD loci (1,2), we tested the association of the lead CAD-associated variant (or, if unavailable, a proxy) with traditional cardiovascular risk factors using publicly available GWAS meta-analyses datasets for systolic, diastolic, and pulse pressures  (7, 8) ; lowdensity lipoprotein (LDL) cholesterol level; highdensity lipoprotein (HDL) cholesterol level; triglycerides level  (9, 10) ; type 2 diabetes mellitus  (11) ; body mass index (BMI)  (12) ; and smoking quantity  (13).