dataesr/scientific-paragraphs-categorization huggingface dataset:
As in the previous section, the FC values of GOIs were calculated as the ratio of normalised transcript levels between the xlnR + and the wild-type strain, in each condition. About xlnR transcript levels themselves (data not shown), an approx. 8-fold increase of transcript levels could be observed in the xlnR + strain incubated on xylose and xylan, which correlated to the copy number of xlnR in this strain. Its overexpression was still significant for the remaining polymeric substrates as for cellobiose (FC around 6), and less pronounced for arabinose, starvation conditions and glucose (2 < FC < 4), confirming the strong dependency of transcript levels upon nature of the carbon source. Not surprisingly, data presented in Fig. 5  showed that the consequence of xlnR overexpression was also highly dependent upon nutritional environment.
Gene selection and primers design The choice of a total of 62 genes of interest (GOIs) that encode representative glycoside hydrolases of different GH families is described in the results section. This list of GOIs, manually curated (genome annotation from A. Llanos Ph. D. thesis, (http://www. these s. fr/2014I SAT00 29), proprietary data from ADISSEO France SAS), is reported in Table  2. Besides GOIs, 6 other genes, (ubc6, sac7, psm1, npl1, spo7 and spt3), were used as reference genes for RT-qPCR data quantification and normalization  [59]. Primers were designed using Vector NTI advance v11 (Life Technologies) with melting temperature of 58─60 °C, length of 18─25 bp and GC content of 50-60%. Most of the genes possess one to several introns in their sequence, which allowed designing the primers at the exon-exon junctions to minimise the amplification of contaminant gDNA.
Fold-change calculation and statistical analysis Quantification of the transcripts by RT-qPCR was performed from three independent biological replicates (samples) for each of the 13 culture conditions. A technical duplicate was carried out for each sample, except for the glucose samples that were analyzed in technical quadruplicates due to the low transcript levels of most of the GOIs under this condition. Raw qPCR signals were analyzed using the Real-Time PCR Analysis software (Fluidigm ® ) to determine Cq values. These Cq were then used to calculate the Fold-Change (FC) values, taking into consideration each primer pair efficiency. Data normalization was performed using a multiple reference genes procedure, with the geometrical mean of 6 validated reference genes for T. versatilis, i. e.
The number of hexagonal spatial pixels captured by CAM2 was 198, which is roughly equivalent to a 15 × 13 rectangular grid area. It is noteworthy that the wavelength range can be tailored to an application, and pulses with bandwidth as broad as, e. g. , 100 nm can also be shaped with nanometer resolution. To calibrate the HSI setup, we performed a wavelength scan with the laser in CW mode to acquire spot patterns at different wavelengths. The patterns were processed by a custom Matlab script to yield a conversion matrix between pixels of the camera and spatiospectral pixels of the resulting hyperspectral image. This matrix was used to convert raw images from CAM2 into arrays of spectra. Additional information on the calibration can be found in Supplement 1.
The European Pressure Ulcer Advisory Panel, the Pan Pacific Pressure Injury Alliance, and the National Pressure Ulcer Advisory Panel are updating the 'Prevention and Treatment of Pressure Ulcers: Clinical Practice Guideline' (CPG) in 2019. The aim of this contribution is to summarize and to discuss the guideline development protocol for the 2019 update. Methods: A guideline governance group determines and monitors all steps of the CPG development. An international survey of consumers will be undertaken to establish consumer needs and interests. Systematic evidence searches in relevant electronic databases cover the period from July 2013 through August 2018. Risk of bias of included studies will be assessed by two reviewers using established checklists and an overall strength of evidence assigned to the cumulative body of evidence.
Identifying the evidence As the guideline builds on a previously published body of evidence, the evidence search dates for this 2019 update are 1st July 2013 through 31 December 2017. A last update is planned to include evidence published until 31 August 2018. Some SWGs, particularly those that address evidence in topics newly introduced to the guideline, may extend the search to ensure previously published literature meeting the inclusion and exclusion criteria has been represented. Several electronic databases will be used, such as Medline and Embase, using a sensitive search strategy. References must meet the following general inclusion criteria to be considered eligible: • The articles must be primarily focused on PI prevention, risk as- sessment, or PI treatment in human subjects. • The articles must have been published in a peer reviewed journal.
Data extraction The full papers of included references will be obtained and made available to the relevant SWGs on a web-based platform (LineGuide) that facilitates critical appraisal and data extraction. A data extraction template will be used to extract relevant data from individual papers, including study design; description of participants; study groups and interventions; outcome measures; length of follow up; study results; and comments and limitations. The technical documents summarizing data extraction of included studies will be made available at the guideline website after the guideline has been published.
Coseismic crustal displacement fields based on the static solution. The coseismic crustal deformation in terms of horizontal and vertical displacement fields are computed based on daily position estimates of the permanent GPS sites. The GAMIT/GLOBK software package is used to estimate the daily positions of GPS sites  24, 25. In the GAMIT processing, a loosely constrained daily solution is computed which further processed with GLOBK to obtain an accurate solution. The analysis has been performed using GPS measured positioning time series over 41 days (20 days prior to the event, event day and 20 days after the event) in each case. The difference in position coordinates on the day before and day after the earthquake provides the respective coseismic displacement fields. coseismic ionospheric perturbations (cip) from GpS-tec observations.
GPS data in RINEX format from permanent GPS sites have been analyzed based on the following formula to estimate the slant Total Electron Content (sTEC), sTEC f f f f L L 1 40 3 ( ) ( 6 ) 1 2 2 2 1 2 2 2 1 1 2 2 λ λ =.       -        - where f 1 and f 2 are the carrier frequencies (1575. 42 MHz and 1227. 60 MHz respectively), L 1 and L 2 are the carrier phases and λ 1 and λ 2 are the corresponding wavelengths. The data sampling interval is 30 s. An elevation mask of 30° is applied to exclude the low elevation satellite measurements. The sTEC is further converted to vertical TEC using the mapping function M as follows  26  , vertical TEC M sTEC (7) = × =      -       +            M E h R 1 cos( ) 1 / E 2 1/2 where R E denotes the Earth's mean radius, E is the satellite elevation angle, and h is the IPP height.
Acknowledgements A. S. , D. T. , and S. N. sincerely thank the  Department of Science and Technology (DST), Govt. of India  for the research fellowships. This work is part of the  interdisciplinary program  ' Coupled Lithosphere-Atmosphere-Ionosphere-Magnetosphere System (CLAIMs)' of Indian Institute of Geomagnetism  and supported by  DST, India. LR acknowledge the support of  French national space agency (CNES)  and  Université Côte d' Azur (UCA).
The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.
iEEG is relatively rare compared to EEG or MRI, and researchers sometimes have to wait several years before completing a study. This makes it important to have a consistent way of archiving and documenting iEEG data within a lab. In addition, because of its unique spatiotemporal properties  [5] [6] [7] [8] [9]  and the high cost of acquisition, it is important that these unique data, contributed by rare patients, are made maximally useful and reusable to the scientific community. Currently, iEEG datasets are stored in many different structures, with each lab adopting their own methods for data and metadata organization, which imposes a substantial barrier to collaboration, reproducibility, and scientific progress. In addition, there are many electrophysiology file formats, which do not generally include all information needed to understand, analyze, and reproduce scientific results.
iEEG-BIDS Data Structure The general BIDS specification is designed to modularize data such that it can gracefully handle multiple modalities and recording devices that belong to a single dataset. Metadata are stored either as a Tab Separated Value (TSV) file for tabular data (. tsv) or as JavaScript Object Notation (JSON) file for "key-value" data (. json) (Fig. 1 ). These file formats have the advantage of being both human and machine-readable. Metadata fields that are not specific to the iEEG extension are shared with the broader BIDS specification. There were several new types of metadata files created for iEEG-BIDS, as well as additions to pre-existing metadata standards in BIDS. These include developing the file content and naming of the metadata for the iEEG data files (_ieeg. json, Fig.
Unofficially supported formats. There are three "unofficially-supported" formats. Their use is discouraged, though allowed for reasons stated below. These unofficially-supported formats are: Neurodata Without Borders (. nwb) due to its shared goals in facilitating sharable and reproducible neurophysiology datasets, EEGLab (. set) due to its prevalent use in the iEEG community, and Multiscale Electrophysiology Format version 3 (MEF3) due to its suitability and future potential for clinical iEEG recordings and device-related applications (such as HIPAA-compliant multi-layer encryption of sensitive data). Unofficially-supported formats will pass the validator, but are not guaranteed to become supported in the future, and unofficial support may be dropped. There is no hard rule about the number of compliant data formats allowed, though the number will be kept as low as possible.
The BIDS validator. A validator (written in javascript) determines whether a given dataset adheres to the BIDS specification (bids-standard. github. io/bids-validator/). It parses a folder, checks whether its hierarchy and naming structure conform to BIDS, and checks text files for the proper metadata and file types needed for the BIDS specification. The BIDS validator was modified in order to validate the new metadata fields and folder structures introduced by the iEEG-BIDS extension. Online iEEG datasets organized according to iEEG-BIDS. As a part of creating the iEEG-BIDS specification, five datasets were converted to BIDS format and added to the BIDS-examples github repository (github. com/bids-standard/bids-examples). These examples are meant to demonstrate how the BIDS specification maps onto different scientific use-cases.
The neural data of each dataset is removed in order to make it easier to download, though instructions are provided for accessing the full datasets. The datasets available include: an auditory filtered speech experiment, a motor-movement dataset, a visual stimulus dataset, seizure sEEG recordings from a focal epilepsy of the left temporo-occipital junction, and a multimodal dataset with fMRI and ECoG per subject. the BIDS starter kit. The BIDS starter kit is a collection of community-driven guides, tutorials, helper scripts, and wiki resources to help researchers get started with the BIDS data structure (github. com/bids-standard/ bids-starter-kit). These resources cover the two main languages supported by iEEG-BIDS (Matlab and Python). A tutorial that describes how to create a BIDS-compatible iEEG dataset has been provided on the starter-kit wiki (http://github.
com/bids-standard/bids-starter-kit/wiki/Creating-a-BIDS-compatible-iEEG-dataset) and Matlab and Python scripts are also available to produce iEEG sidecar. json and. tsv files. pybv and pyedf. While many neuroscience toolboxes exist for the Matlab language, there are relatively fewer for Python. To improve Python support for the primary data formats supported by iEEG-BIDS, two new python packages were created for writing BrainVision (pybv, github. com/bids-standard/pybv) and European Data Format (github. com/bids-standard/pyedf) files. These will be maintained by the BIDS community.
MNE-BIDS and MNE-Python. The MNE-Python package is an open-source tool for electrophysiology analysis, visualization, and data representation in Python, and has recently been extended to include functionality for iEEG. Alongside the creation of the iEEG-BIDS specification, the community worked alongside those in the EEG and MEG communities to create a new package for moving between MNE-Python and the BIDS specifications in electrophysiology: MNE-BIDS (github. com/mne-tools/mne-bids). MNE-BIDS facilitates converting electrophysiology workflows in MNE-Python into the BIDS dataset framework  11.
FieldTrip. FieldTrip is an open toolbox for electrophysiology analytics in the Matlab language  12, 13. It supports importing data from a large number of iEEG formats and can export data to BIDS recommended formats. Furthermore, FieldTrip includes the data2bids. m function to help users to organize their iEEG and MRI data and to provide proper metadata annotation (www. fieldtriptoolbox. org/example/bids). iElvis. iELVis (github. com/iELVis/iELVis) is an open source Matlab package for localizing iEEG electrodes and visualizing their data overlaid on neuroimaging  14. It can read and write iEEG electrode coordinates and associated neuroimaging files using BIDS-iEEG conventions. See the BIDS-iEEG compatibility page for more information (http://ielvis. pbworks. com/w/page/130759893/BIDS-iEEG%20Compatibility).
Brainstorm. Brainstorm is an open-source Matlab application for the analysis of multimodal neurophysiology data  15. It provides user interfaces for positioning electrode contacts in post-implantation CT or T1 scans as well as reviewing/annotating long data records such as clinical iEEG signals (neuroimage. usc. edu/brainstorm/Tutorials/Epileptogenicity). It supports most file formats commonly used in iEEG and can convert them into BrainVision or EDF files. Studies in Brainstorm's database can be imported and exported automatically as BIDS-formatted datasets. OpenNeuro. Open Neuro is a repository for public neuroimaging data, currently supporting MRI and MEG (OpenNeuro. org). It heavily capitalizes on the BIDS standard -each dataset is validated prior to upload using the bids-validator. OpenNeuro now supports, validates, and accepts iEEG-BIDS data.
BIDS Extensions The BIDS specification is an ongoing process with a growing community. It uses the BIDS Extension Proposals (BEP) process to discuss and integrate improvements in the BIDS specification. Several outstanding questions will require further community input and discussion, such as formally describing coordinate systems, merging metadata across modalities, and handling derivatives of raw iEEG data. In particular, further discussion is warranted to adapt iEEG-BIDS for non-human subjects and clinical iEEG recordings. As projects such as BIDS make datasets easier to discover and use, it is particularly important to protect the privacy of subjects. BIDS does not directly address privacy concerns -it only provides a structure for how the data should be stored. It is possible for data to be stored with BIDS in a manner that does not divulge subject identity (for example, by removing identifiable images and stripping any patient information from the data file), but this is up to the researcher.
Future extensions to the BIDS specification, and documentation from the BIDS community, could help guide researchers in ensuring the privacy of the individuals that have provided their data in a BIDS dataset. This process will be further improved as the BIDS community continues to grow. Data sharing, reproducibility, and quality control are priority areas of large research consortia, such as the US Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative as well as the Human Brain Project (HBP). Within these efforts it has become clear that community-wide accepted data standards are essential  16. Within the HBP, iEEG-BIDS has been adopted in the Medical Informatics Platform (where it is intended to be deployed in several hospitals for conducting multicenter studies) (http://humanbrainproject.
Conclusion iEEG-BIDS specifies a structured way of storing iEEG data and metadata. BIDS is a community-driven project, and the iEEG-BIDS specification was created after many months of open discussion with the broader iEEG community. One of the core benefits of adopting community standards such as iEEG-BIDS is that it provides a common point that can be the basis for future collaborations. It facilitates reproducibility and cross-modal integration across datasets, experiments, and recording sites.
Acknowledgements We are grateful to  Cyril Pernet  and the  EEG-BIDS team  for sharing their article draft of the EEG-BIDS specification and to  Elizabeth Dupre  and  Patrick Park  for their help with the bids-starter-kit. This work was supported by:  The National Institute of Neurological Disorders and Stroke (NINDS)  under award number  R37NS21135. The  Gordon and Betty Moore Foundation  through grant  GBMF3834  and by the  Alfred P. Sloan Foundation  through grant  2013-10-27  to the  University of California, Berkeley  to CH. The  Netherlands Organisation for Scientific Research (NWO ,  016. VENI. 178. 048 ) to DH. The  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement no. 616268  F-TRACT to OD. The European Union's  Horizon 2020 Framework Programme for Research and Innovation under Specific  Grant Agreement No.
785907  ( Human Brain  Project  SGA2 ),  720270  ( Human Brain  Project  SGA1 ) to OD and LZ. ERC grant  320708 , 'iCONNECT' to  NFR. Marie Sklodowska-Curie Global Fellowship  ( 658868 ) to AS. National Institute of Mental Health  Award  R24-MH114705-01  to KG, The  Van Wagenen Foundation  to KJM, The  NIH ,  NIMH ,  BRAIN Initiative  grant #:  R01MH111417  to NP, NFR, and  JW ,  ERC  Grant Agreement no. 616268  F-TRACT to FT. NSF  ERC EEC-1028725  to JO. The  Sloan Research Fellowship  ( FG-2015-66057 ), the  Whitehall Foundation  ( 2017-12-73 ) to BV. The  National Science Foundation  under grant  BCS-1736028  to BV.
This study examines the effects of puberty and sex on the intrinsic functional connectivity (iFC) of brain networks, with a focus on the default-mode network (DMN). Consistently implicated in depressive disorders, the DMN's function may interact with puberty and sex in the development of these disorders, whose onsets peak in adolescence, and which show strong sex disproportionality (females > males). The main question concerns how the DMN evolves with puberty as a function of sex. These effects are expected to involve within-and between-network iFC, particularly, the salience and the central-executive networks, consistent with the Triple-Network Model. Resting-state scans of an adolescent community sample (n = 304, male/female: 157/147; mean/std age: 14. 6/0. 41 years), from the IMAGEN database, were analyzed using the AFNI software suite and a data reduction strategy for the effects of puberty and sex.
Participants The IMAGEN consortium recruited over 2000 youths. Only five sites opted to collect resting-state scans. IMAGEN data are available from a dedicated database: https:// imagen2. cea. fr. Adolescents (n = 381) from the IMAGEN sample  39  underwent fMRI scanning in a resting state. A sample size of n = 381 subjects was expected to provide sufficient power to detect reliable interaction effects of puberty by sex on iFC measures. Indeed, previous studies have reported significant clinical group effects on measures of intrinsic connectivity in pediatric samples smaller than n = 60 (e. g. , ref. [65] [66] [67]  ). Recruitment and assessment procedures, and exclusion and inclusion criteria are described elsewhere in detail  39. In brief, participants and their parents were recruited via middle-schools in five European sites.
Two participants lacked pubertal scores. The excluded group (n = 77), compared to included participants (n = 304), had more boys (T = 3. 58, p < 0. 001), but was similar in age, puberty status, and IQ (all p's > 0. 1). Characteristics of the sample are presented in Table  1. Of note, participants overlapped slightly with those of our previous structural studies (i. e. , n = 21 in common with  40  , n = 31 in common with Vulser et al. 41  , and n = 5 in common to all three samples). Behavioral data were collected again in this sample 2 years later, at age 16 years. The attrition rate was 17% (53 subjects were not tested at follow-up), leaving a sample of 251 16-yo adolescents.
Pubertal status Pubertal status was assessed via self-report using the Pubertal Development Scale (PDS)  71. Previous research has demonstrated that the PDS exhibits good internal consistency (median α = 0. 77) and is highly correlated with physician ratings (Pearson's R = 0. 61)  71, 72. BOLD fMRI data acquisition, preprocessing, and analysis MRI data were acquired at five sites using 3 T scanners: Phillips (Dublin), General Electrics (London), and Siemens (Paris, Dresden, Mannheim). BOLD fMRI signal was acquired across 40 interleaved slices using the following parameters: TR = 2,200 ms; TE = 30 ms; flip angle = 75°; acquisition matrix = 64 × 64 × 40 with 2. 4 mm slice thickness and 1 mm slice gap yielding an acquisition resolution 3. 4 mm isotropic; 187 volumes collected over 6. 5 minutes.
Acknowledging the debate as to whether global signal should be regressed out of resting-state data sets, we decided not to adopt this strategy. This decision was based on Saad et al. 76  who suggest that global signal regression can introduce spurious correlations into resting-state data sets, and advise against its use in preprocessing. In addition, we employed several strategies to minimize motion-and physiological-related variance, thus mitigating the need to apply additional measures, like global signal regression, to minimize variance related to motion and cardiac/respiratory processes (Power et al. 77  ). The following nuisance signals were regressed from the functional volumes: (1) six head motion parameters and their derivatives, (2) average time-series extracted from the ventricles, (3) time-series from local white matter within a 25-mm radius sphere surrounding each voxel using the ANATICOR approach  78  , and (4) individual regressors corresponding with a frame-to-frame Euclidean norm motion derivative ≥ 0.