dataesr/scientific-paragraphs-categorization huggingface dataset:
We first assessed the population structure in our data set of Roma samples together with non-Roma (European and South Asian groups, supplementary table 1, Supplementary Material online). Principal component analysis (PCA) results are compatible with Roma being admixed between European and South Asian samples (fig. 1A ) and ADMIXTURE analysis (supplementary fig. 7 , Supplementary Material online) at k ¼ 2 shows the Roma as a mixture of two cluster components found in European and South Asian samples. At k ¼ 3 (lowest cross-validation error value), the Roma individuals display membership in a specific component (colored in orange) and a blue component found in Europe, which reproduces previous results  (Mendizabal et al. 2012; Moorjani et al. 2013). A set of metrics were calculated to describe the genetic diversity in Roma compared with non-Roma.
2019)  with available high-coverage exomes. We also genotyped the Iberian Roma samples with Affymetrix Axiom Genome-Wide Human Origins 1 array. Genotype calling was performed with Axiom Analysis Suite 4. 0 software with default threshold settings. Genotyping errors were filtered out with PLINK/1. 9b (Purcell et al. 2007)  using the following quality control filters: SNP missingness of 5%, individual missingness of 10%, SNPs failing Hardy-Weinberg exact test with a P value of 10 À5 , and minor allele frequency (MAF) threshold of 0. 01. After filtering, the genome-wide array data set contains 486,009 SNPs in a subset of 62 of the WES Iberian Roma samples. The Iberian Roma samples were merged with IBS, TSI, PJL, GIH, and ITU from 1,000 G (1000 Genomes Project Consortium 2012), keeping 474,632 genome-wide SNPs and 487 samples (supplementary table 1, Supplementary Material online).
Sequence Preprocessing The WES preprocessing was performed following the GATK Best Practices recommendations  (Van der Auwera et al. 2013). Reads were mapped to the human reference GRCh37 with bwa 0. 7. 15  (Li and Durbin 2009). Then, duplicates were marked with Picard 2. 8. 3 (http://broadinstitute. github. io/picard, last accessed March 24, 2021) and indel realignment and base quality score recalibration were performed with GATK 3. 7  (McKenna et al. 2010). Variant calling steps were performed with HaplotypeCaller, GenotypeGVCFs, and VariantRecalibrator from GATK 3. 7  (McKenna et al. 2010 ) (see supplementary note 1, Supplementary Material online for more details). We removed indels, nonautosomal chromosomes, and nonbiallelic and fixed sites. Sequencing errors were filtered out with VCFtools 0.
The mean genotype concordance between genome-wide array and WES is 99. 81 6 0. 35% for the 6,828 common SNPs in both data sets. We assigned the ancestral state of each variant based on the six primate EPO (Enredo, Pecan, and Ortheus) multialignment Ensembl Compara v59. The genome-wide array data set (474,632 variants) and the WES data set (410,225 variants) were merged, resulting in a data set of 878,162 variants and 487 samples. In supplementary note 3, Supplementary Material online, we assess the quality and potential sequencing biases in the new Roma WES (supplementary figs. 2, 3, and 29, Supplementary Material online and supplementary table 9, Supplementary Material online).
2016). For PolyPhen-2, three categories were used: benign (<0. 446), possibly damaging (0. 446-0. 908), and probably damaging (>0. 908)  (McLaren et al. 2016). For CADD, since values are Phred scaled, variants were split in score changes of 10 into four categories: <10, 10-20, 20-30, and >30. Finally, we also annotated highconfidence LOF variants using the LOF Transcript Effect Demography on Functional Genomic Variation. doi:10. 1093/molbev/msab070 MBE Estimator (LOFTEE) VEP plugin (available at https://github. com/konradjk/loftee, last accessed March 24, 2021).
Population Structure Analysis PCA and ADMIXTURE were performed using the merged data set of genome-wide array and WES variants. Linkage disequilibrium pruning was applied with PLINK/1. 9b (Purcell et al. 2007 ) (window size of 200 SNPs, 25 SNPs shift at each step, and r 2 threshold of 0. 5) and MAF >1%, keeping 405,814 variants. PCA was performed with the SmartPCA program implemented in the EIGENSOFT 4. 2 package  (Patterson et al. 2006) , and ADMIXTURE  (Alexander et al. 2009 ) was run 10 independent times with different random seeds for ancestral components k ¼ 2-5. Pong  (Behr et al. 2016 ) was used to identify modal ADMIXTURE results. In addition, ADMIXTURE was run, independently, for k ¼ 2 for the genome-wide data set (202,724 variants) and the WES with MAF > 1% data set  (42,381 variants).
Identification of ROH Segments ROHs were identified from the merged data set of genomewide array and WES variants using PLINK/1. 9b (Purcell et al. 2007). ROHs with minimum 50 SNPs, 500 kb, and a maximum gap of 100 kb (between a pair of SNPs) were considered  (Kirin et al. 2010). ROHs were partitioned in two length categories (0. 5-2. 5 and >2. 5 Mb), representing "short-medium" and "long" ROHs. Short and medium ROHs are pooled together, whereas long ROHs are classified into its own category because the distribution of lengths is shifted to longer ROHs in the Roma: they show a larger number of longer ROHs than non-Roma, as previously shown  (Font-Porterias et al. 2019). Derived missense variants within ROHs were stratified in GERP, PolyPhen, and CADD deleterious categories. ROHs and ancestry-specific segments inferred from merged data set with genome-wide and WES variants were matched to the derived variants from the WES data.
2016). Genomewide associations from the GWAS catalog and eQTLs within the top regions were identified (GTEx Consortium 2017;  Buniello et al. 2019). For PBS and XP-EHH statistics, we performed a gene annotation enrichment analysis with genes within the top 1% selection signals using DAVID 6. 8  (Huang et al. 2009 ). Gene Ontology  (Ashburner et al. 2000)  and KEGG  (Kanehisa and Goto 2000)  pathways were used as functional databases, and the genes present in our data set were used as the background gene list. Maeztu" (AEI, CEX2018-000792-M). N. F. -P. was supported by a FPU17/03501 fellowship.
Acknowledgments We would like to thank all the DNA donors that made this study possible and the invaluable collaboration of the  Federaci o d'Associacions Gitanes de Catalunya (FAGiC). We also thank  M onica Vall es  ( Universitat Pompeu Fabra ) for technical support. This study was supported by the  Spanish Ministry of Science, Innovation and Universities (MCIU)  and the  Agencia Estatal de Investigaci on (AEI)  (grant numbers  CGL2016-75389-P ,  PID2019-106485GB-I00/AEI/10. 13039/ 501100011033 ) and " Unidad de Excelencia Mar ıa de Demography on Functional Genomic Variation. doi:10. 1093/molbev/msab070MBE.
Objectives: We aimed to systematically review and meta-analyse estimates of the prevalence of occupational exposure to ergonomic risk factors for osteoarthritis and other musculoskeletal diseases. Data sources: We searched electronic bibliographic databases for potentially relevant records from published and unpublished studies, including Ovid Medline, EMBASE, and CISDOC. We also searched electronic grey literature databases, Internet search engines and organizational websites; hand-searched reference list of previous systematic reviews and included study records; and consulted additional experts. Study eligibility and criteria: We included working-age (≥15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (<15 years) and unpaid domestic workers.
Electronic bibliographic databases We searched the following four electronic bibliographic databases: 1 The Ovid MEDLINE search strategy was presented in the protocol  (Hulshof et al. , 2019). Based on this search strategy, the strategies for the other databases were developed by a clinical librarian and information scientist and are presented in Appendix 1 in the Supplementary data. We performed searches in electronic databases operated in the English language using an English-language search strategy. Consequently, study records that did not report essential information (i. e. title and abstract) in English were not captured. Just prior to completion of the systematic review, the search of the Ovid MEDLINE database was updated on 27 August 2019 to capture the most recent publications (e.
Organizational websites. The websites of the following nine international organizations and national government departments were searched in December 2018: 1. International Labour Organization (www. ilo. org). 2. World Health Organization (www. who. int). 3. Eurofound (https://www. eurofound. europa. eu/en) 4. European Agency for Safety and Health at Work (https://osha. eur opa. eu/en). 5. Eurostat (www. ec. europa. eu/eurostat/web/main/home). 6. International Ergonomics Association (http://www. iea. cc/). 7. China National Knowledge Infrastructure (http://www. cnki. net/). 8. Finnish Institute of Occupational Health (https://www. ttl. fi/en/). 9. United States National Institute of Occupational Safety and Health (NIOSH), using the NIOSH data and statistics gateway (https://www. cdc. gov/niosh/data/).
Hand-searching and expert consultation. Hand-searching for potentially eligible studies was undertaken in: • Reference lists of previous systematic reviews. • Reference lists of all study records of all included studies. • Study records published over the past 24 months in the three peerreviewed academic journals from which we obtained the largest number of included studies (Occup Environ Med, Scand J Work Environ Health, and Int Arch Occup Environ Health). • Study records that have cited an included study record (identified in Web of Science citation database). • Collections of the review authors. Additional experts were contacted with a request to identify potentially eligible studies. This included invitations to the Scientific Committee on Musculoskeletal Disorders of the International Commission on Occupational Health and the International Ergonomics Association to submit potential additional eligible studies.
Selected studies Study selection was carried out using Covidence  (Babineau, 2014; Covidence Systematic Review Software). All study records identified in the search were downloaded and duplicates were identified and deleted. Afterwards, two review authors independently and in duplicate screened titles and abstracts (step 1) and then full texts (step 2) of potentially relevant records. A third review author resolved any disagreements between the two review authors. If a study record identified in the literature search was authored by a review author assigned to study selection or if an assigned review author was involved the study it was re-assigned to another review author for study selection. The study selection is presented in a flow chart, as per PRISMA guidelines  (Liberati et al. , 2009).
However, it is very unlikely that studies on the effect of occupational exposure to ergonomic risk factors on osteoarthritis of knee or hip would capture occupational exposure to hand-arm vibration, and that studies on the effect of occupational exposure to ergonomic risk factors on epicondylitis would include occupational exposure to kneeling, respectively. Therefore, we included studies that had measured occupational exposure to at least five of the seven relevant ergonomic risk factors. To approximate the two risk factor levels (Table  1 ), we differentiated between no exposure (in the majority of studies proxied by duration of exposure ≤2 h per working day) and any exposure (a proxy of duration of daily exposure >2 h). We included studies with exposure data for the years 1 January 2005 to 31 July 2018.
For optimal modelling of exposure, WHO and ILO require exposure data up to 2018, because recent data points help better estimate time trends, especially where data points may be sparse. The additional rationale for this data collection window is that the WHO and ILO aim to estimate burden of disease in the year 2015, and we believe that the lag time from exposure to outcome will not exceed 10 years  (Haibing et al. , 2006). Both objective and subjective measures of exposure were included. Examples of objective measures are real-time observation of posture by video analysis, force measurements or time-weighted acceleration measurements of hand-arm vibration. Subjective measures may be selfreported duration of exposure to the risk factors in a typical working day. If both subjective and objective measures were presented, then we prioritized objective ones, as an estimate based on this is likely more resilient to potential risk of bias in the study.
Types of studies This systematic review included studies of any design reporting quantitative results, including cross-sectional studies. The studies had to be representative of the relevant industrial sector, occupational group or the national population. We excluded qualitative, modelling and case studies, as well as non-original studies without quantitative data (e. g. letters, commentaries and perspectives). Records published in any language were included. Again, the search was conducted using English language terms, so that records published in any language that presented essential information (i. e. title and abstract) in English were included. If a record was written in a language other than those spoken by the authors of this review or those of other reviews  (Descatha et al. 2018; Godderis et al.
Extracted data WHO and ILO developed a standard data extraction sheet and all data extractors piloted this sheet until there was convergence and agreement among them. The majority of the data extractors participated in WHO's online training for the use of the data extraction sheet. At a minimum, two review authors independently extracted the data on occupational exposure to ergonomic risk factors, disaggregated by country, sex, age and industrial sector or occupation. A third review author resolved conflicting extractions. Data were extracted on study characteristics (including study authors, study year, study country, participants and exposure), study design (including study type, exposure measurement and type of prevalence estimate), risk of bias (including missing data, as indicated by response rate and other measures) and study context.
The prevalence estimates from included studies were entered and managed with the Review Manager, Version 5. 3 (RevMan 5. 3). Data on potential conflict of interest were also extracted from the included studies, such as financial disclosures, funding sources, and authors' affiliated organization. A modification of a previous method was used to identify and assess undisclosed financial interests  (Forsyth et al. , 2014). If no financial disclosure and conflict of interest statements were provided, other records were searched from this study published in the 36 months prior to the included study record and in other publicly available repositories  (Drazen et al. , 2010a; Drazen et al. , 2010b).
Population studied The included studies captured 150,895 workers (81,613 females and  69,282 males). One study examined only male workers, one study only female workers, and three studies examined both female and male workers. The most common age groups in the included five studies were workers aged 50-54 years, followed by those aged 45-49 years and 55-59 years, respectively. Almost all included studies examined populations in the WHO Europe region (four studies in 35 countries). There was one study of workers in one country in Africa (South Africa). For three countries (France, Denmark and Netherlands), data were available from two included studies each. These countries were captured in the Eurofound study (2017), as well as in  Andersen et al. (2016) , the Constances Cohort Study (2018), and  Van der Molen et al.
Limitations of this review Our systematic review and meta-analysis have several limitations. First, although our inclusion criteria were broad, the systematic review found only studies from two WHO regions, namely Africa and Europe. Of the five included studies, four were from Europe and one from Africa. The one study from Africa could not be included in the meta-analysis. Consequently, the analyses of our systematic review were based almost exclusively on data from countries in Europe. This already indicated indirectness but, moreover, the indirectness of the evidence for the global population of workers must be prominently noted. Second, our searches may have missed studies published in languages other than English. However, we searched many electronic bibliometric and grey literature databases using a comprehensive search strategy and consulted additional experts who also did not identify any additional eligible studies.
We have some confidence that we identified most if not all studies eligible for inclusion in our systematic review. Third, assessment and measurement of occupational exposure to the eligible ergonomic risk factors was generally with questionnaire and, consequently subjective data from self-reported exposure. Important aspects of exposure such as frequency and intensity of exposure was often not measured or otherwise included in the exposure assessment (or assignment). Objective measures for occupational ergonomic risk factors collected using observational methods are often also not reliable or validated  (Van der Beek and Frings-Dresen, 1998) , and this suggests that the evidence from exposure data presented in this systematic review may be acceptable, even if collected subjectively using self-reports from workers.
Fifth, although in general the prevalence estimates found in this review show a reasonable consistency and comparability, also indicated by relatively narrow confidence intervals, the data from the two French studies (the French part of the Eurofound Study and the Constances Cohort Study) are rather different. One of the reasons for this could be the absence of data from agriculture workers and self-employed workers  (Goldberg et al. , 2017)  but this may probably not explain totally its much lower prevalence. Future studies on the Constances Cohort and on forthcoming waves of the Eurofound Study may shed some light on this.
Acknowledgments We thank  Paul Whaley  ( Associate Editor for Systematic Reviews, Environment International ;  Lancaster Environment Centre, Lancaster University, United Kingdom ) and  Tim Driscoll  ( University of Sydney, Australia ) for the editorial guidance and support. We thank  Ivan Ivanov  (WHO),  Nancy Leppink  (ILO) and  Yuka Ujita  ( ILO ) for their coordination and other support for this systematic review and  Barbara Gerstenberger  ( Eurofound, Ireland ),  William Cockburn ,  Xabier Irastorza  and  Lorenzo Munar  ( European Agency for Safety and Health at Work -EU-OSHA, Spain ) for their help with the Eurofound data. The authors alone are responsible for the views expressed in this article, and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.
ACKNOWLEDGMENTS The authors would like to thank  Ines De Domahidy ,  Magali Quillico  and  Esther Richez  ( La Boite d'Impro, Le Mouvement d'Improvisation AMiénois ) who designed and ran the improv workshops, all the teenagers, teachers and parents who agreed to enter this study. We would like to thank  Nabila Terchani  for dealing with all administrative issues.
Data Availability Statement The "GLOBAL-REANALYSIS-PHY-001-031" product was provided by the Copernicus Marine Environment Monitoring Service. Available at https://resources. marine. copernicus. eu/?option=com_csw&view=de-tails&product_id=GLOBAL_REANALYSIS_PHY_001_031 (Accessed: April 28, 2020). The code to project vertical profiles on a B-spline basis and carry out the functional Principal Component Analysis is freely available on GitHub for R (https://github. com/EPauthenet/fda. oce) and Python (https://github. com/EPauthenet/fda_oce_python/blob/master/fda_oce_python. ipynb) environments.
Pu, Y. , Liu, H. , Yan, R. , Yang, H. , Xia, K. , Li, Y. , et al. (2020). Cas FGOALS-g3 model datasets for the CMIP6 scenario model intercompari- son project (ScenarioMIP). Advances in Atmospheric Sciences, 37(10), 1081-1092. https://doi. org/10. 1007/s00376-020-2032-0.
More recently, computers and tablets have emerged in the classrooms as teaching aids, and specific software becomes available to teachers of all disciplines. Geometry benefits from visual tools, geography from Google Earth, language from orthographic correction and voice helpers, etc. With the advent of the digital revolution, progressively impacting all sectors of human activities and professional practices, an unescapable question emerges: will teachers disappear, or have to adapt to completely new schemes of professional activity? The physician profession is interesting to observe. In its practice, the personal relation with the patient, as for teachers, has always been considered essential. Yet, the profession is currently confronted to this same question and already encounters significant evolutions, such as telemedicine, robotics helpers.
What does it mean for education, in order to remain human? In her excellent paper, Sharkey develops an in-depth analysis of the ethical concerns about robot teachers, covering the various uses mentioned above  (Sharkey 2016 ). • First, she discusses the threat to privacy, with robots exerting personal surveillance, collecting personal data on children, monitoring teacher performance or classroom activities. To assess performance of children, emotions could be assessed with various sensors, measuring facial expressions or physiological reactions. The results may be used without control of the parents or imposed on them as criteria for judging their child's behavior. When undertaken with research aims, such actions could be done with adequate ethical protocols, 14  but their generalization may easily turn into a "Panopticon" to control the classroom and even to provide data for commercial production of educational material.
Conclusion In the classroom, the replacement of teachers by robots could be extremely diverse in its modalities, from full substitutes to teaching or learning companions. It is still in infancy and sufficient research of the impact is not yet available. The technical possibilities combining artificial intelligence and 20 See Climate Change Policy Simulator C-Roads, from the Massachusetts Institute of Technology. https://www. climateinteractive. org/ tools/c-roads/ 21 See Software SimClimat (2019, in French): http://education. meteofrance. fr/lycee/animations/logiciel-simclimat-un-modelesimple-de-bilan-radiatif-de-la-terre 22 See https://play. google. com/store/apps/details?id=org. socratic. android&hl=fr teaching needs are probably immense, but the opportunities and costs of such investment remain today questionable.
SAGs possessing these genes were less abundant than those missing that genomic region. Overall, patterns in nucleotide variation indicated that the population dynamics of Methanothermococcus were not governed by clonal expansions or selective sweeps, at least in the habitats and sampling times included in this study. Together, our results show that although specific lineages of Methanothermococcus co-exist in these habitats, some outcompete others, and possession of accessory metabolic functions does not necessarily provide a fitness advantage in these habitats in all conditions. This work highlights the power of combining single-cell, metagenomic, and metatranscriptomic datasets to determine how evolution shapes microbial abundance and diversity in hydrothermal vent ecosystems.