dataesr/scientific-paragraphs-categorization huggingface dataset:
The last module is Independent Component Analysis (ICA). These four modules are performed by AnyWave. The JSON files describing the processes were written by an engineer but the processes are launched by the technician. This sequence of pre-processing on the data was done using the pipeline processing option to run the processes one after the other. The technician used the graphical interfaces of BMP (Fig. 7. b). As data are acquired and imported throughout the year, the processing pipeline description was saved the first time it has been created and it is loaded each time the nurse wants to pre-process the data. The four processes take approximately 2 hours, therefore the technician can launch it and come back later to get the results as BMP runs each process one after the other. Fig. 7. B illustrates the results of this pipeline: injection of EEG in MEG file, automatic cleaning, marking of bad channels, ICA components.
Discussion We developed versatile solution for running processing module on BIDS dataset. BMP can select subjects based on criteria, specify parameters of the modules, and launch the modules. Results are saved in BIDS hierarchy in derivatives folder. We chose the BIDS format, which has been developed specifically for organizing and sharing data across centres, in a well-structured manner that is easily understandable. Before BMP, the BIDS Manager software can be used to upload/convert and store the data in BIDS  [8]. Here, we presented an extension to this work, BIDS Manager-Pipeline, which can process the data across all subjects of the data structure. BMP has been developed for windows system and will be adapted to Linux and Mas OS system in the future. Over the past 20 years, the neuroscience community developed several tools to organize and analyse large datasets: XNAT  [16] , COINS  [17] , CBRAIN  [18] , LORIS  [19] , WeBrain  [20].
XNAT is a platform to manage, store in secure repository and distribute neuroimaging and associated data. COINS is an open-source tool to manage different studies, subjects, imaging, and clinical data. , based on the two. tsv tables of the Bids Dataset, participants. tsv and statistical_table. tsv. The boxplots show the spike rate (Delphos on 217 patients) according to the epilepsy type (axe X) grouping patients in seizure free (SF) or not seizure free (NSF). We also looked at the correlation between the age at the first seizure and the spike rate (Male and female). CBRAIN is a web-based research platform to manage Big Data research. Loris is a project management system integrating data from neuroimaging, behavioural scalars, and genetic biomarkers. WeBrain is a web platform-based platform to store, explore and analyse big EEG data.
The four first platforms are web-based developed initially for Neuroimaging data (and have now been extended to electrophysiology data) with their own specificity, while the last is specific to EEG data. The main purposes of these platforms are to centralise and share data. With the creation of BIDS format and following its increasing popularity, neuroscience tools have become BIDS compatible. Thus, many pipelines are now able to read in BIDS Database (e. g. , Brainstorm, Fieldtrip, SPM12)  [5] [6] [7]  and the BIDS App has been developed to work on the BIDS structure. Still, it can be difficult to launch several processes in a pipeline. BIDS App is interesting to process data from any OS. It is also convenient to share pipelines with the community. Nevertheless, creating BIDS App requires some computer abilities and knowing BIDS format because the pipeline must be able to read BIDS database, then it must be converted into a Docker image.
We wanted to create a tool that is dedicated to electrophysiology, that can handle data and module locally. The Neuroimaging in Python: Pipelines and Interfaces (Nypipe)  [21]  library inspired this project in its approach to facilitate workflows and including user interfaces. However, Nypipe requires describing the module in object-oriented manner to include it in the package and is more for neuroimaging data. Boutiques  [22]  is a framework for automated application integrated in different platform such as CBRAIN and Loris. Boutiques allows to publish, integrate, and execute command-line applications. JSON files are used to describe the module containing the command line template, the inputs, and the outputs. To be callable, the module must be saved in the repository of Boutiques named Zenodo.
Inputs can also be described in a "config" file, but they have to be described in a JSON way and the subject ID must be mentioned. The plat-form builds and executes the command line. Boutiques also have implemented a validation part to check the conformity of the JSON files and the validity of the input's files. Boutiques is an interesting framework because it can be integrated to any platforms. However, to use one module it has to be saved in dedicated repository so local module cannot be launch without that. Boutiques has also been integrated to Nypipe and BIDS-App to handle the process of BIDS dataset. Creating Bids Manager-Pipeline was a way for us to integrate existing module to BIDS Manager and our homemade software without modifying them to be compatible with BIDS structure or BIDS Manager, which can be appealing for other research groups as well.
The goal of BMP is to be able to run any pipeline using modules, which can be Matlab scripts, AnyWave plugins, BIDS Apps, and executable files. A common point with Boutiques is to describe the pipeline in JSON file in a specific way and the pipeline must be callable by command line (i. e. , <pipeline-name> <argument-name> <argument-value>). BMP should be considered as a bridge between BIDS Dataset and pipeline. It can read and write in a BIDS structure instead of the pipeline, it manages the input, output, and parameter selection for the pipeline. It allows launching processes for all selected subject and files without any other action from the user. Modules created locally or before existence of BIDS format can be run without modifications. The graphical interface allows to choose the subjects, parameters and run the pipeline.
The possibility to create processing pipeline will hopefully facilitate the process and save time for users. BMP can run neuroimaging and neuro-electrophysiology pipeline so only one framework can be used to analyse all the data in the BIDS dataset. For now, only some module types are accepted by BMP. The types are BIDS App (running through Docker), Matlab script, and executable software (. exe for windows system), AnyWave  [13]  and its integrated plugins. AnyWave has been developed in order to visualize and process electrophysiological data. This software integrates plugins to process data such as ICA and nonlinear connectivity measure (h2), AnyWave can be launched from the command line and its plugin can be used this way also, so it is fully compatible with the BMP principle. A few limitations remain.
Firstly, BMP is running the module in local and not in remote access and does not integrate parallel computing. Secondly, when the BIDS dataset is large, there is a long processing time due to the reading of data. These limitations could be solved by using threads in Python and will be the target of future developments. We would also like to provide more module types and in particular to integrate Boutiques. We would like to provide different types of statistics table, for example using atlases in order to focus on brain area instead of channels.
Conclusion BMP is a flexible way for running processes on a BIDS structure. Each process can be an executable file or a Matlab script. Processes can be applied in series, each one taking the output of the previous processes. Subjects within a BIDS structure can be selected based on criteria (as defined in a requirements json file). Finally, the output is saved in the derivatives section of BIDS. BMP can have applications in clinical studies, for example for computing and comparing biomarkers in large epilepsy databases, but also offer new opportunities in basic neuroscience. Further development will include statistics table by anatomical region in addition to channels results to compare the different modalities of electrophysiology in a common framework.
Human and animal rights The authors declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans as well as in accordance with the EU Directive 2010/63/EU for animal experiments. d'Avenir' French Government] and by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 and 945539 (Human Brain Project SGA2 and SGA3). Research was also supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and ANR-11-IDEX-0001-02 (A*MIDEX)". This work was performed on a platform member of France Life Imaging network (grant ANR-11-INBS-0006). We would like to thank the users Anthony Boyer, Sophie Semidei, Aurélie Ponz, Matteo Demuru, Emma Acerbo, Sara Simula and Elodie Garnier for their feedbacks on the use of BIDS Manager-Pipeline.
Funding This work was performed on a platform member of  France Life Imaging network  (grant  ANR-11-INBS-0006 ). It has been supported by:  RHU EPINOV [A* MIDEX  project ( ANR-17-RHUS-0004 ) funded by the  'Investissements d'Avenir' French Government ]. The European Union's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  and  945539  ( Human Brain  Project  SGA2  and  SGA3 ). ANR-16-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  ( BLRI ) and  ANR-11-IDEX-0001-02  ( A*MIDEX ) grant  ANR-11-INBS-0006.
Data availability statement The datasets analysed during the use case one are not publicly available due to their belonging to Human Brain Project but are available from the corresponding author on reasonable request. The datasets analysed during the use case two are not publicly available because they are clinical data. The source code of BIDS Manager-Pipeline is available at https://github. com /Dynamap /BIDS _Manager /tree /dev. We provide a simulated example dataset to test the BIDS Manager and BIDS Uploader at https://figshare. com /articles /Example _Dataset _for _BIDS _ Manager /11687064. We also provide the BIDS dataset resulting of the simulated example dataset to test BIDS Manager-Pipeline at https://figshare. com /articles /dataset /BIDS _dataset _for _ BIDS _Manager-Pipeline /19046345.
We provide a compiled version of BMP at https://figshare. com /articles /software /BIDS _Manager-Pipeline _compiled _verison /19062312. We provide a module example named "filter_data" to test BMP on the GitHub in the folder software_to_test or in software_needed on the folder containing the compiled version in Figshare (see link above). The json file describing "filter_data" is present in the folder SoftwarePipeline. BMP also appears in the Human Brain Project software catalogue.
Variants ltering strategy We aimed herein to select pathogenic or potentially pathogenic variants associated with the development of LSII by removing neutral variants and sequencing errors. We prioritized rare functional variants (missense, nonsense, splice site variants, and indels) and excluded variants with a Minor Allele Frequency (MAF) > 0. 01 in dbSNP137, and 138, in the Exome Variant Server (http://evs. gs. washington. edu/EVS/), 1000 Genomes Project (http://www. 1000genomes. org/), or Exome Aggregation Consortium database (ExAC), Cambridge, MA (http://exac. broadinstitute. org) and the genome Aggregation Database (gnomAD) (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA398795). We also removed all synonymous and homozygous variants considering that LS is an autosomal dominant syndrome.
728 G > A p. R243Q, classi ed in class 3 'VUS' in Clinvar, has been selected for Sanger sequencing validation and for structural model effect prediction analysis. Hence we tried to perform several in silico prediction analyses by using online prediction tools and databases (15 prediction tools) as well as structural modeling of MSH2 known complexes. This mutation has been con rmed for the index case "JI-021" in an heterozygous state and not found in the index case's relatives (index case's daughter, brother and her niece). This variant has been forward searched in three other GC cases "JI-002", "JI-030" and "JI-036" belonging to unrelated families suspected with LS II and ful lling the rst criterion of Bethesda guidelines for the rst case "JI-002" and the fth criterion for both "JI-030" and "JI-036" cases based on their family history of cancer.
Electronic academic databases We searched the seven following electronic academic databases to the specified date: The Ovid MEDLINE search strategy was presented in the protocol  (Pega et al. 2020a ). The full search strategies for all databases were revised by an information scientist and are presented in Appendix 1 in the Supplementary data. We performed searches in electronic databases operated in the English language using a search strategy in the English language between March and May 2020. When we neared completion of the review, we conducted a search of the Ovid MEDLINE and PubMed databases on 14 March 2022 to capture the most recent publications (e. g. , publications ahead of print). Deviations from the planned search strategy are documented in Section 8.
Organizational websites The websites of the seven following international organizations and national government departments were searched in May 2020: 1. International Labour Organization (https://www. ilo. org). 2. World Health Organization (https://www. who. int). 3. International Agency for Research on Cancer (https://www. iarc. fr). 4. European Agency for Safety and Health at Work (https://www. osha. europa. eu). 5. Eurostat (https://www. ec. europa. eu/eurostat/web/main/home). 6. China National Knowledge Infrastructure (https://www. cnki. net). 7. Finnish Institute of Occupational Health (https://www. ttl. fi/en). 8. United States National Institute of Occupational Safety and Health (NIOSH) of the United States of America, using the NIOSH data and statistics gateway (https://www. cdc.
Hand-searching and expert consultation We hand-searched for potentially eligible studies in: • Reference lists of previous systematic reviews. D. Loomis et al. • Reference lists of all included study records. • Study records published over the past 24 months in the three peerreviewed academic journals with the largest number of included studies. • Study records that have cited the included studies (identified in the Web of Science citation database). • Collections of the review authors. Additional experts were contacted with a list of included studies, with the request to identify potentially eligible additional studies.
Selected studies Study selection was carried out in Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). All study records identified in the search were downloaded, and duplicates were identified and deleted. Afterwards, at least two review authors, working in pairs, independently screened titles and abstracts (step 1), and then full texts (step 2) of potentially relevant study records. A third review author resolved any disagreements between the first two review authors. Study records were not assigned to reviewers who had been authors of that study. The study selection was documented in a flow chart in the systematic review, as per PRISMA guidelines.
Types of populations We included studies of working-age (≥15 years) workers in the formal and informal economy. Studies of children (aged < 15 years) and unpaid domestic workers were excluded. Participants residing in any Member State of WHO and/or ILO and working in any industrial sector or occupation were included. Occupational exposure to welding fumes may potentially have further population reach (e. g. , as an environmental exposure, through the release of welding fumes from the workplace into the community); the scope of our systematic reviews did not capture these populations and impacts on them. Appendix A in the Supplementary data for  Pega et al. (2020a)  provides a briefer overview of the PECO criteria.
Types of exposures We included studies of occupational exposure to welding fumes in accordance with our standard definition (Table  1 ). Occupational exposure to welding fumes may be measured in several ways: • Directly with quantitative measurement (e. g. , by means of technology, such as air monitoring). • Directly by observation of the work process. • Indirectly by proxy of occupation (or job title), such as relevant codes and/or titles of the International Standard Classification of Occupations (ISCO)  (International Labour Organization 1966; 1987; 2012)  (Table  2 ). • Indirectly by job task of welding. • Indirectly by classification in a job-exposure matrix (JEM) based on expert judgment or data external to the study. • Indirectly by judgment of scientists with subject matter expertise.
Data extraction and data items We used the standard data extraction sheet that WHO and ILO have developed for their series of systematic reviews for the WHO/ILO Joint Estimates. The data extraction sheet was trialled until data extractors reached convergence and agreement. At least two review authors independently extracted data on study characteristics (including study authors, study year, study country, participants, exposure, and outcome), study design (including study type, comparator, epidemiological model(s) used, and effect estimate measure), and risk of bias (including source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest including statements of declarations of interest and funding sources, and other sources of bias).
We prepared the data for entry using Excel and conducted the meta-analysis in RevMan version 5. 4. 1 (Nordic Cochrane Centre, Copenhagen, Denmark). We input RRs (log-transformed) and their standard errors into RevMan with a precision of two decimal places. Standard errors were calculated from the lower confidence limits, which sometimes results in small discrepancies between the upper confidence limit reported in the original study record and that displayed in the forest plot of the relevant meta-analysis. If a study reported two or more estimates of the effect of different levels of exposure, as compared with no (or low) exposure (reference group), the estimates associated with these comparisons were not independent, as the study participants in the reference group were shared across the comparisons.
Therefore, these estimates could not be included in the same meta-analysis as if they came from separate studies. In such cases, we computed a composite (average) study-level effect estimate for the comparison of each exposure level versus the reference group, by taking within-study correlation into consideration as suggested in the Cochrane handbook for systematic reviews of interventions  (McKenzie and Brennan 2021). We followed the principles outlined by  (Borenstein et al. 2009). We then entered this pooled effect estimate for this study in the meta-analysis and reported more detailed methodological information and data inputs in an appendix. We neither quantitatively combined data from studies with different designs (e. g. , did not combine cohort studies with case-controls studies), nor unadjusted effect estimates with adjusted ones.
We only combined studies that we judged to have a minimum acceptable level of D. Loomis et al. adjustment for confounders (i. e. , a study must have adjusted for at least one of the two Tier 1 confounders of age or sex). In instances where two or more studies of the same data source (e. g. , the same study cohort) were eligible for inclusion in the meta-analysis, we prioritized in this order: i) the study with the most informative assessment of exposure to welding fumes; ii) the study with the longest follow-up period; and iii) the study with the most complete control of relevant potential confounders. If our pre-specified rules for selecting a study's result did not allow us to uniquely identify-one for inclusion, we randomly selected one study. If quantitative synthesis was not feasible, we synthesised the study findings narratively and identified the estimates that we judged to be the highest quality evidence available.
4. 1), we judged that case-control studies generally have a lower risk of confounding from tobacco smoking. Additionally, more of the case-control studies consider exposure to asbestos for this outcome. Moreover, the pooled effect estimate from the meta-analysis of the case-control studies for the outcome is based on a much larger cumulative sample size and has better temporal coverage, as we have many more case-control studies in our dataset, including recently published ones. Our main meta-analysis for this outcome is consequently that of the relevant case-control studies as the prioritized evidence. We consider the evidence from the cohort study as supporting evidence.
Limitations Our systematic review and meta-analysis has several limitations. First, even though the search strategy included many academic and grey literature databases, potentially eligible studies may have been missed (e. g. , those published in languages other than those we collectively covered, or those only indexed in additional databases like CINAHL, which could be included in future updates of the systematic review). However, this is unlikely given that consultation with subject matter experts did not lead to the identification of any additional eligible studies. Given the large number of included studies and consequently large number of included study participants, the overall findings would not have been affected by eligible studies that our literature search (which would likely be relatively smaller in sample size).
Acknowledgments We thank  Dr Paul Whaley  (Editor,  Systematic Reviews , Environment International; and  Lancaster University ) for the editorial review and guidance. Dr Kurt Straif  (at the time:  WHO IARC)  contributed to the early stages of this systematic review. Dr Yuka Ujita  and then  Dr Halim Hamzaoui  were the ILO focal point for the  WHO/ILO Joint Estimates. Dr Nathan DeBono  ( WHO IARC ),  Bayan Hosseini  ( WHO IARC ),  Dr Ann Olsson  ( WHO IARC ), and  Professor Lesley Stayner  ( WHO IARC ) provided comments on an earlier version of the manuscript.
The NMR experiments were processed using NMRPipe  [36] , and SPARKY  [37]  was used to measure the intensity values of cross-peaks for the different relaxation delays. CurveFit (AG. Palmer Lab) and homemade Python (www. python. org, accessed on 14 February 2022), R (www. rproject. org, accessed on 14 February 2022) scripts were used to determine the R 1 and R 2 relaxation rates, as well as the associated uncertainties from the single-exponential decay. The 15 N-CPMG peak intensities were converted into relaxation rates using procedures previously described  [30, 38]. The effective transverse relaxation (R 2eff ) at the lowest frequency CPMG and the R 2eff at the highest frequency CPMG was calculated using the equation: R 2eff = 1 T cpmg × ln( I(0) I(ν cpmg ) where I (0) is the intensity in the absence of relaxation time and I (ν cpmg ) is the intensity at CPMG frequency with T CPMG relaxation time.