dataesr/scientific-paragraphs-categorization huggingface dataset:
1752 could be detected at this sample size with 80% power at a conservative alpha set to 0. 005  60. For correlational analyses looking at brain-behaviour associations, we examined a subset of patients with the data from the SRS (Nautism_male = 421) and ADOS total scores (Nau-tism_male = 505). With the same power and alpha levels, the minimum effect for SRS is r = 0. 1765 and r = 0. 1651 for the ADOS. There are likely many variables that contribute to variability in CT between individuals and across the brain. In order to visually assess the contribution of some prominent sources of variance we adopted a visualization framework derived from gene expression analysis (http://bioconductor. org/packages/variancePartition)  61  and included the most commonly available covariates in the ABIDE dataset: age, sex, diagnosis, scanner site, full-scale IQ, verbal IQ, handedness and SRS.
Given that ABIDE was not designed as an integrated dataset from the outset, it seems plausible that the scanner site might be related to autism or autism-related variables (e. g. , some sites might have different case-control ratios or only recruited specific subgroups). Figure  5  shows the ranked contribution of those covariates. Perhaps unsurprisingly, scanner site and age proved to be the most dominant sources of variance (each explaining on average around 15% of the total variance). Our initial conventional analysis was aimed to delineate potential broad case-control differences, as has been done in previous studies  14, 27. We used a linear mixed effects model with scanner site  as a random effect. Given the potentially strong contribution of age we chose to include this as fixed effects covariates in the model.
Acknowledgements This work was supported by a  European Research Council (ERC)   Starting Grant  ( 755816 ; AUTISMS) awarded to M. V. L. R. R. -G. was funded by the  Guarantors of Brain. J. S. was funded by the  National Institutes of Health Oxford-Cambridge Scholars Program. R. A. I. B. was funded by a  British Academy  Post-Doctoral Fellowship and Autism Research Trust. Data were curated and analysed using a computational facility funded by an  MRC research infrastructure award  ( MR/M009041/1 ) and supported by the  NIHR Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the  Department of Health and Social Care.
Data Data were abstracted from the health section of a survey developed by students and researchers at the Ecole Nationale Supérieure de Statistique et d'Economie Appliquée (ENSEA -national school of statistics and applied economics in English) and administered to a sample of 15 to 65 years old in Dabou and its vicinity, a small town at 60 km from Abidjan, Côte d'Ivoire, in May 2017. A multistage sampling strategy was used to collect data on the living condition, resilience, and health among the elderly in Dabou and its vicinity. We randomly selected households stratified according to the following categories: 1) group 1: Dabou (the town itself), 2) group 2: 4 villages (Dabou's vicinity) with more than 700 households, 3) group 3: 3 villages with 400 to 700 households, 4) group 4: 8 villages with 150 to 400 households, and 5) group 5: 1 village with less than 150 households.
We used the following respective sampling rate: 5, 20, 33, 50 and 100%. This drawing allowed us to obtain a sample of 2776 households, among which 663 from group 1 (Dabou), 794 from group 2, 427 from group 3, 823 from group 4, and 69 in from group 5. Then, at the level of each household, a simple random draw was used to capture one individual among those 15 and above in age. Our sample size (n = 2775) was calculated to ensure that with an alpha of 0. 05 and a power of 0. 9, a rate of resilience of 50%, the minimum detectable variation is under 3%. However, due to a 9% nonresponse rate, the final sample size was n = 2523 for the entire survey. We extracted information from this dataset regarding individuals aged 50 to 65 (n = 267). Two pre-tested questionnaires were used in the survey. The first questionnaire was administered to the head of the household to gather information on the members of the household, including demographic characteristics (e.
g. gender, age), education and employment status, and dwelling/living conditions (Additional file 1). The second questionnaire (Additional file 2) gathered information about the participants' health, including overall self-assessed health, conditions, physical functioning, health behaviors (e. g. smoking, alcohol, diet, physical exercise), and self-assessed survival probabilities for four target ages (70, 75, 80 and 85). Informed consent was obtained from all subjects (parent and/or legal guardian for participants under 18) before the administration of the survey. From the dataset created following the administration of questionnaires, we extracted information about all individuals aged 50 to 65 to target elderly populations.
Descriptive statistics The dataset used consists of 267 individuals with an average age of 56. 4 and predominantly males (55. 4%). 60% of the individuals had a low education level. Table  1  provides additional statistics on the dataset. From these average values in Table  1 , we observed that the obesity rate grows slightly with age (12% on average for individuals aged 50-54, 13% for individuals aged 55-59 and 19% for individuals aged 60-64). Moreover, alcohol consumption declined slightly with age, while the smoking rate only declined after the age of 60. Besides, the employment rate decreased with age (from 71% for those aged 50-54 to 36% for those aged 60-64). It is also notable that only a relatively small proportion of older people perform physical activity (only 21% of those aged 50-65 perform physical activity).
These results suggest that promoting a healthy lifestyle, improving health and living conditions, as well as employment status, are key to increasing elders' SLE. However, even though the results are promising, they lack generalizability. The dataset used covers only one region of the country. Furthermore, due to its geographic proximity (under 50 km and accessible by paved road) with the largest urban center of the country (the economic capital, Abidjan), this region benefits from the health facilities provided in the capital. This might affect individuals' perception of their health and this region could differ from others. Finally, Subjective expectations of outcomes such as life expectancy depend on the subject's psychological profile or personality traits. However, we did not collect information on the subject's psychological profile or personal traits and therefore did not adjust for them in our analyses.
Acknowledgements The research leading to this publication was supported by the  project CALIPSOplus  under the grant agreement  730872  from the  EU   Framework Programme for Research and Innovation HORIZON 2020. A major part of the experimental work was carried out at beamline P64 at  DESY , a member of the  Helmholtz Association (HGF)  supplemented by work at beamlines  B18 ,  B16 , as well as the optics lab at DLS. The authors would like to thank  Wolfgang Caliebe  for help and advice during the experiments at  P64. A. W. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant agreement (GA)  no  665593  awarded to the  Science and Technology Facilities Council. V. S. acknowledges financial support by the  Austrian Academy of Sciences  via  DOC fellowship  and DG by  DST/INSPIRE  Fellowship/[IF160579], awarded by the  Government of India Ministry of Science and Technology.
These outputs are then used to forecast outflows at the outlet in three watersheds in northwest Algeria to show the sensitivity of the region's hydrology to changes in climate factors by  2039, 2069 and 2099. This study could be a decision-making tool for all actors working in water management. These are the actors working in the mobilization and distribution of drinking water, water use, the risk and pollution related to water, as well as the protection of natural environments. It could also constitute a database for potential studies on adaptation measures and the development of future water resource management methods.
Data The rainfall data used in this study come from two different sources. On the one hand, the SIEREM system (Environmental information system on water resources and their modeling) of the Mixed Research Unit HydroSciences Montpellier giving monthly spatialized rainfall on a grid of 0. 5°resolution and covering the period from 1940 to 1999  (Boyer et al. , 2006; Rouché et al. , 2010; Dieulin et al. , 2019). In the other hand, rainfall stations data from the National Agency for Hydraulic Resources (ANRH) have been included in the study area (Table  1 ). Monthly temperature data are from the Climatic Research Unit CRU and are available on the website (WWW. CRU. UAE. AC. AC. UK). The recovered data corresponds to raw series of temperatures from different measurement stations located in the study area (Table  2 ).
Acknowledgments The authors would like to thank the  TASSILI program  (project code  18MDU109 ) for awarding a fifteen month internship at the  University of Montpellier. We would like to thank the  Directorate-General of Scientific Research and Technological Development (Algeria)  and the  Mixed Research Unit HydroSciences and IRD of Montpellier (France). Finally, we would like to thank the  National Water Resources Agency (ANRH) , and the  French Foreign Ministry  for subsidizing the study.
Data collection and analysis An ethnographic approach was used to collect the empirical data. It included field notes and a total of three hours of transcribed video and audio recordings from four group discussions. The groups were divided by the teacher according to the students' consent regarding participation. Two groups were video recorded, one group was audio recorded and the last group, of which four students had declined to be recorded, was not documented at all. The video recordings were done with one camera facing the group gathered in a half-circle at a table; equipment for additional audio recording was also used. The audio-recorded group had the equipment placed in the middle of the table. For the third group, the researcher took field notes while sitting outside the group circle.
Introduction: Lung cancer (LC) has the highest cancer mortality worldwide with poor prognosis. Screening with low-dose computed tomography (LDCT) in populations highly exposed to tobacco has been proposed to improve LC prognosis. Our objective was to perform a systematic review and meta-analysis to evaluate the efficacy of screening by LDCT compared with any other intervention in populations who reported tobacco consumption for more than 15 years on LC and overall mortality. Methods: We searched randomised controlled trials (RCTs) studying screening by LDCT compared with any other intervention in a population who reported an average smoking history greater than 15 pack-years from inception until the 19th February 2018 using Medline and Cochrane Library databases. Publication selection and data extraction were made independently by two double-blind reviewers.
Search strategy The search was made using the Medline and Cochrane Library databases. The research terms were covering lung cancer, screening by LDCT compared with any other intervention, RCT and their synonyms, from inception until the February 19th, 2018, as follows: ((((" It was completed by a Medline search with the names of the trials identified, the reference list of the eligible articles, a clinicaltrials. gov database search, a free-text Internet search and a regular update Medline search. The search was limited to articles published in English and French. The initial search was updated on February 12th, 2020.
Quality assessment and data extraction The critical appraisal of each eligible RCT was made by two reviewers (AS, JF) using a CONSORT checklist including random assignment, complete diagnostic work-up planned, respect of inclusion criteria, valid measurement of mortality and blinded outcomes assessment. The contents of the checklist and the inclusion or exclusion of each study for the meta-analysis were then discussed with two epidemiologic experts (PFP, BO). Data were collected and managed using REDCap electronic data capture tools. The extraction of the data for the meta-analysis was made independently by two double-blind reviewers (AS, JF), and the differences were resolved by an epidemiologic expert (PFP). When several articles were available for a single study, data from the most recent publication were used, unless unique data werefound in a previous publication.
Statistical analysis Results on all-causes and lung cancer-specific mortality of the studies included were combined for meta-analysis. In the collection of studies considered the effect size and the true effect size were estimated. A general model was then specified by both standard and random effects models to incorporate the between-study variance  [15]. The inverse variance weighting method was used for pooling, on the hypothesis of common 'effect size' normality and weights assigned to individual studies. This method allows maximising the accuracy of the estimate of the common effect, minimising the variance of the pooled result. The binary outcome data as mortality rate were extracted in a 2 Â 2 table for all selected studies. A relative risk and 95% confidence interval (95% CI) were used as summary statistics.
Study selection Of the 891 publications identified with the search strategy in the Medline and Cochrane Library databases, 799 were excluded on titles and abstracts because of topic or study design: 449 were not related to lung cancer screening, 142 reported results of lung cancer screening without using LDCT, 40 were not RCTs and 168 were systematic review or general considerations. The additional search identified 22 other publications which were added to the 92 studies. Reading the full-text of these 114 publications, 107 studies were excluded: 11 were pilot study, 31 did not report mortality data in the control group, 59 did not assess mortality as primary end-point, and six presented intermediate results. A total of seven RCTs were finally included, including a total of 84,558 patients (Fig.
Results of the meta-analysis A total of 84,558 participants were included in the metaanalysis. There was no heterogeneity in the data (I 2 Z 0%, tau 2 Z 0, p Z 0. 67). A relative reduction of overall mortality of 4% was observed in the experimental screening group versus control group (risk ratio [RR] Z 0. 96, 95% CI: 0. 92e1. 00) (Fig. 3 ). Concerning lung cancerespecific mortality, a significant relative reduction of 17% was observed in the experimental screening group (RR Z 0. 83, 95% CI: 0. 76e0. 91) (Fig. 4 ). To prevent one lung cancererelated death, 294 patients needed to be screened.
Immunofluorescence, Immunohistochemistry and Morphometry. Immunofluorescence and immunohistochemistry were performed exactly as described previously  (Annicotte et al. , 2009; Rabhi et al. , 2016). Pancreatic tissues were fixed in 10% formalin, embedded in paraffin and sectioned at 5 µm. For immunofluorescence microscopy analyses, after antigen retrieval using citrate buffer, 5-µm formalin-fixed paraffin embedded (FFPE) pancreatic sections were incubated with the indicated antibodies. Immunofluorescence staining was revealed by using a fluoresceinisothiocyanate-conjugated anti-rabbit (for E2f1; Santa Cruz), Alexa-conjugated anti-mouse (for glucagon co-staining with E2f1) or anti-guinea pig (for insulin co-staining with E2f1) secondary antibodies. Nuclei were stained with Hoechst. For morphometric analysis, three to ten animals from each genotype were analyzed, and images were processed and quantified using ImageJ software by an observer blinded to experimental groups.
RNA quality was verified using RNA 6000 nanochips on the Agilent 2100 bioanalyzer. Purified RNA (50ng) with RNA integrity number ≥6. 5 was subsequently used for library preparation (TruSeq Stranded mRNA Library Preparation Kit, Illumina) and sequenced on a HiSeq2500 system (Illumina). 3 biological replicates per condition were sequenced using paired-end mode. A mean of 54 million paired-end reads of 75 bp were generated for each sample. After initial checks and validation of sequence quality, RNAseq reads were aligned to the mouse reference genome (mm10) using TopHat2. Subsequently, both quantification and annotation of the reads were performed using Bioconductor package Rsubread. Finally, the differential gene expression analyses were performed using Bioconductor package DESeq2. Using a P<0.
1 ng of eluted and purified DNA was used to prepare DNA sequencing library with the Nextflex rapid DNA seq kit 2. 0 (Perkin Elmer, NOVA-5188-01) on the NextSeq 500 system (Illumina) using single read 100 base pairs mode. The demultiplexing of sequence data (from BCL files generated by Illumina sequencing systems to standard FASTQ file formats) was performed using bcl2fastq Conversion Software (Illumina; version 2. 20). Trimming of residuals adapters and low quality reads was performed using TrimGalore (version 0. 4. 5). Subsequently, sequence reads from FASTQ files were mapped to the mouse genome (mm10) using Bowtie2 Aligner (version 2. 3. 5. 1). Finally, peak-calling was performed with MACS2 software (version 2. 2. 7. 1). Further bioinformatic analyses of the public data sets and those of this study were performed using the open web-based platform Galaxy Europe (https://usegalaxy.
The digested peptides were loaded on a 100 micron x 25 mm Magic C18 100Å 5U reverse phase trap where they were desalted online before being separated using a 75 micron x 150 mm Magic C18 200Å 3U reverse phase column. Peptides were eluted using a 90 minutes gradient with a flow rate of 300nl/min. An MS survey scan was obtained for the m/z range 300-1600, MS/MS spectra were acquired using a top 15 method, where the top 15 ions in the MS spectra were subjected to HCD (High Energy Collisional Dissociation). An isolation mass window of 1. 6 m/z was for the precursor ion selection, and normalized collision energy of 27% was used for fragmentation. A five second duration was used for the dynamic exclusion. Database Searching: Tandem mass spectra were extracted by Unspecified version Unspecified. Charge state deconvolution and deisotoping were not performed.
All MS/MS samples were analyzed using X! Tandem  (The GPM, thegpm. org; version CYCLONE (2013. 02. 01. 1) ). X! Tandem was set up to search the mouse-F-_20150428_KK0vMm database (unknown version, 90758 entries) the cRAP database of common laboratory contaminants (www. thegpm. org/crap; 114 entries) plus an equal number of reverse protein sequences assuming the digestion enzyme trypsin. X! Tandem was searched with a fragment ion mass tolerance of 20 PPM and a parent ion tolerance of 20 PPM. Carbamidomethyl of cysteine was specified in X! Tandem as a fixed modification. Glu->pyro-Glu of the n-terminus, ammonia-loss of the n-terminus, gln->pyro-Glu of the nterminus, deamidated of asparagine and glutamine, oxidation of methionine and tryptophan, dioxidation of methionine and tryptophan and acetyl of the n-terminus were specified in X! Tandem as variable modifications.
Criteria for Protein Identification: Scaffold (version Scaffold_4. 5. 3, Proteome Software Inc. , Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they exceeded specific database search engine thresholds. X! Tandem identifications required at least -Log(Expect Scores) scores of greater than 1. 5. Protein identifications were accepted if they contained at least 1 identified peptides. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. List Filtering: Final list generation was done by taking all proteins with a spectral count of five and above from each replicate reaction and comparing them in a venn-diagram against IgG control replicates.
Proteins unique to both experimental replicates were then applied to the PANTHER database for protein ontology results. Statistical Analysis. Data are presented as mean ± s. e. m. Data are derived from multiple experiments unless stated otherwise. Statistical analysis was performed using a two-tailed unpaired t-test or one-way or two-way ANOVA with Tukey's post hoc test comparing all groups to each other, using GraphPad Prism 7. 0 software. Differences were considered statistically significant at p 0. 05 (*p < 0. 05, ** p < 0. 01, and *** p < 0. 001).
05). Interestingly, a vast majority of the genes were upregulated in E2f1 β-/-isolated islets (493 genes, Figure  3A  and Supplementary Table  S4 ), with only 199 down-regulated genes associated with the loss of E2f1 expression in β cells (Figure  3A  and Supplementary Table  S4 ). This first observation suggests that E2f1 expression is not only necessary to activate, but also to repress gene transcription within the β cell. Analysis of the RNA-seq data of downregulated genes with metascape software revealed an enrichment of gene networks involved in signal release, regulation of exocytosis and negative regulation of secretion (Figure  3B ). Conversely, upregulated genes in E2f1 β-/-isolated islets were associated with the regulation of cell adhesion, inflammatory response and cytokine production (Figure  3C ).
Acknowledgements. We thank  Dr Patrick Collombat  and  Dr Raphael Scharfmann  and members of the INSERM  U1283/CNRS UMR 8199  for helpful discussions, and  Céline Gheeraert  for excellent help with ChIP experiments. Human islets were provided through the  JDRF award   31-2008-416  ( ECIT Islet for Basic Research program ). The authors thank the Experimental Resources platform from  Université de Lille , especially  Cyrille Degraeve ,  Yann Lepage ,  Mélanie Besegher  and  Julien Devassine  for animal care. We thank the Department of Histology from the  Lille Medicine Faculty , particularly  M. H. Gevaert  and  R. M. Siminski , for histological preparations. This work was supported by grants from  « European Genomic Institute for Diabetes » E. G. I. D ,  ANR-10-LABX-46  and  Equipex   2010 ANR-10-EQPX-07-01 ; ' LIGAN-PM ' Genomics platform, a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir I-SITE ULNE / ANR-16-IDEX-0004 ULNE  (to P.
F. , A. B. and J-S. A),  Agence Nationale pour la Recherche  ( BETAPLASTICITY ,  ANR-17-CE14-0034  to J-S. A. ),  European Foundation for the Study of Diabetes (EFSD , to J-S. A. ),  European Commission, European Research Council  ( GEPIDIAB 294785  to P. F. ),  INSERM ,  CNRS ,  Institut Pasteur de Lille ,  Association pour la Recherche sur le Diabète  (to J-S. A. ),  Université de Lille  (to F. O. , C. B. , X. G. , N. R. and J-S. A. ),  I-SITE ULNE  ( EpiRNAdiab Sustain  grant to J-S. A. ),  Conseil Régional Hauts de France  and  Métropole Européenne de Lille  (to X. G. , N. R. and J-S. A. ),  F. E. D. E. R. ( Fonds Européen de Développement Régional , to N. R. , P. F. and J-S. A. ) and  Société Francophone du Diabète  (to S. A. H. and J-S. A).