dataesr/scientific-paragraphs-categorization huggingface dataset:
For the bioinformatics pre-processing, IDAT files from both cohorts were imported and processed using R software. Quality-control graphs and bimodal distributions for each dataset are shown in Supplementary Figs. 4, 5. We excluded cross-reactive probes and XY chromosomes, leaving a total of 459,761, 459,770, and 459,768 probes for the analysis in BCH-Cutaneous, BCH-Acral and TCGA cohort, respectively. The data were further normalized using the FunNorm function of the Bioconductor Minfi package  44  (Supplementary Fig. 4 ), which was shown to perform equally good or outperform existing normalization methods  45. Inferred beta values were used to predict sex as a quality-control step using the Minfi function getSex. All samples were correctly predicted. The DNA methylation level β-values were logit transformed to M-values to map the range (0, 1) to (-inf, +inf) as it is more suitable for running regressions.
Surrogate variable analysis (SVA) was performed on the methylome data to correct for potential batch effects, to adjust for differences in cell type composition as a reference-free method  46  , and to adjust for latent variables, a choice validated by the findings of our benchmarking  47. SVA also increases statistical power by removing (unwanted) variability through aggregating information at the data level and constraining the data's variability to the phenotype of interest  48. For the statistical analysis, we used robust linear regression (robust to outliers) to test four statistical models in the discovery cohort 1 (BCH), including one crude and three adjusted models, comparing the DNA methylome of UV-mutant versus non UV-mutant cutaneous melanoma patients (Supplementary Figs. 5, 6 ): 1-Crude Model (Supplementary Data 2); 2-Adjusted Model 1 adjusted for sex (Supplementary Data 3); 3-Adjusted Model 2, adjusted for sex + age at diagnosis (Supplementary Data 4); and 4-Adjusted Model 3, adjusted for sex + age at diagnosis + tumor type (primary or metastasis) (Supplementary Data 5).
Supplementary Fig. 5  shows quantile-quantile (Q-Q) plots of -log 10 P values, which deviate from their expected values under the null hypothesis across all models. Although the adjusted models yielded a larger number of significant findings relative to the crude model (Supplementary Fig. 6 ), we preferred to take a conservative approach and focus our analysis on the crude model, especially that it showed the least genomic inflation and risk of false positives, with a lambda of 1. 20 (i. e. , approaching to 1. 0 being the no inflation limit) (Supplementary Fig. 5 ). Moreover, the predominant proportion of significant findings in the crude model was actually common with any of the adjusted models (Supplementary Fig. 6b ). We also compared for each model, two approaches of methylome-wide analysis: the Differentially Methylated CpG Probes (DMPs), analysing individual CpGs using the Bioconductor limma package  49  , and the DMRs, analysing regions of genomically proximal CpGs using the Bioconductor DMRcate 50 package with the default proximity-based criteria (±1000 base pairs).
At least 90% of DMP-based genes overlapped with those derived from DMRs across all models (Supplementary Fig. 6a ). For this reason and because DMR analysis represents a dimension reduction approach with higher statistical power than DMP analysis, we focused downstream analyses in BCH and TCGA data onto the DMR approach applied to the crude model. This pipeline was equally applied to the DNA methylome comparison between non UV-mutant acral and non UV-mutant cutaneous melanomas. Statistically significant DMPs and DMRs were defined as those with FDR-adjusted P value < 0. 05. We complemented the cohort-specific analysis by a meta-analysis across the BCH and TCGA cohorts comparing UV-mutant versus non UV-mutant cutaneous melanoma patients. We used the Metal tool  51  and the Dmrff package in R to perform DMR fixed effects inverse variance-weighted meta-analysis  52  , using the crude model as prioritized in the cohort-specific analysis.
The meta-analysis lambda value was 1. 16, showing low inflation. Statistically significant DMRs were defined as those with FDR-adjusted P value < 0. 05. Due to the larger number of hits expected with the increased statistical power afforded by the meta-analyses, we also reported the more stringent Bonferroni-adjusted p values, especially considering the higher likelihood of false positivity due to clinicopathological, ethnic and methodological differences between meta-analysed BCH and TCGA. In addition to generating DMPs and DMRs, methylation data from BCH and TCGA samples were further investigated using Partial Least Squares Discriminant Analysis (PLS-DA)  53. This approach performs classification of samples using partial least squares regression of the categorical outcome Y (cancer subtype) on the predictor variables (DNA methylation).
PLS-DA is a clustering technique that allows the quantification of the discrimination relevance of a given variable (CpG) and to predict the phenotype of new samples (independent of DMPs or DMRs). This method is especially suited to deal with a much larger number of variables than samples, as in next-generation microarray and sequencing data, and we aided this method further by a filtering step using median absolute deviation (MAD)  54. We selected the 100 most variable CpGs and applied PLS-DA on the methylation matrix on this subset of sites to assess the discriminative potential of the DNA methylome between UV-mutant and non UV-mutant cutaneous melanomas (as well as acral melanomas in the case of BCH samples). Pyrosequencing methylation analysis. For the quantitative measurement of DNA methylation levels in individual CpG sites of the TAPBP (7 CpGs) gene (Supplementary Data 22), we pyrosequenced the bisulfite converted DNA using the PyroMark Kit (Qiagen catalog no 978703) as per the manufacturer's instruction.
Briefly, DNA was immobilized onto streptavidin-coated beads in binding buffer for 10 min. The biotin-labeled PCR template was isolated and denaturated using the pyrosequencing vacuum prep tool and incubated with 0. 4 μM sequencing primer in annealing buffer (20 mM Tris-acetate, 2 mM MgAc2; pH 7. 6). The reaction was incubated at 80 °C for 2 min and cooled down to room temperature for 20 min to allow sequencing primer annealing. The methylation levels at the target CpGs were evaluated by converting the resulting pyrograms to numerical values for peak heights and expressed as the average of all patients for a given CpG site analyzed. RNA expression data and analysis. Transcriptome data, measured by RNA sequencing (RNAseq), were downloaded from TCGA-SKCM project  9  and normalized with DESeq package  55.
As with the DNA methylation data, the normalized RNAseq data was first filtered by MAD for the 100 most variable transcripts and then analysed by PLS-DA to assess the discriminative potential of the transcriptome between UV-mutant and non UV-mutant cutaneous melanomas. We used quanTiseq package  56  to estimate the fractions of ten immune cell types using the RNAseq from TCGA-SKCM project  9  , comparing UV and non UV-mutant melanoma patients. Then Mann-Whitney U Test was performed to compare the two conditions as this non-parametric test is robust to outliers, which were detected in some data points of the various cell types. Gene ontology and pathway analysis. Gene ontology and pathway analysis were performed using the Jensen Disease ontology and KEGG pathway databases available on Enrichr website  57, 58  (https://maayanlab.
cloud/Enrichr). Given that genes with larger numbers of probes are more likely to have significantly differentially methylated CpGs, potentially biasing gene set analysis, we implemented the gometh function of the missMethyl package  59  in R to adjust for the number of CpGs per gene, which ranges on the 450K array from 1 to 1299 CpGs. Cross-OMICs and integrative analysis. Regarding eQTMs, we applied Pearson correlation between RNA expression and DNA methylation data of the 169 genes that are common between BCH and TCGA DMRs, while limiting the analysis of a given gene to its constituent CpGs. For the integrated methylome-transcriptome analysis, we filtered each of the 450K and RNAseq datasets by MAD and applied PLS-DA independently to each OMICs, as described in previous sections. Next, we applied sparse PLS-DA  60  that uses LASSO 61 penalization technique to select the 25 most informative CpGs and transcript probes in each dataset (Supplementary Data 23).
We then applied an integrative analysis on the subsets of methylation and transcriptomic data, together with UV exposure outcome. We used the DIABLO method to gain a better understanding of the interplay between the different levels of data that are measured  62. All these analyses were done using mixOmics R package  63. For the prediction of multi-OMICs driver score, genome, transcriptome and DNA methylome data were downloaded from TCGA-SKCM (473 cases)  9. Then, we calculated for each gene: [CNV: Copy Number Variation], being the number of tumors affected by a deep insertion (≥ +2) or a deep deletion (≤ -2), and this number was divided by the maximum CNV value obtained across the analysed genes in order to generate the CNV score; [MUT: Mutation], being the number of tumors affected by at least one single-nucleotide alteration, and this number was divided by the maximum MUT value obtained across the analysed genes in order to generate the MUT score; [EXP: Expression], being the number of UV-mutant cases presenting variations in RNA expression (|logFC| > 2) relative to non UVmutant patients, and this number was divided by the maximum EXP value obtained across the analysed genes in order to generate the EXP score; and [METH: Methylation], being the number of UV-mutant cases presenting variations in methylation (|delta betas| > 0.
Accordingly, statistical power is estimated based on the methylome as such: the overall mean standard deviation (SD) of methylation probes in the BCH or TCGA data is 0. 11 (for methylation values ranging 0-1). Given an effect size ≥10% methylation difference (a threshold used in our prioritization filters as reported in Supplementary Figs. 1a, b, 2a ) and based on an alpha of 0. 05, we will have >80% power with at least 20 exposed cases and 20 controls. Our sample size is larger and encompasses 21 UV-mutant cutaneous and 91 non UV-mutant cutaneous melanomas from BCH and TCGA. In addition to single-OMIC analysis, we performed integrative OMICs analysis, which can depict small effects shared between OMICs and not detected in the individual analyses and, hence, could be performed on smaller sample sizes than single-OMIC analysis.
The various plots in the manuscript were generated using ggplot2 package  66  , except for the heatmaps, which were generated using Heatmap plus package. All analyses were performed on R. P values ≤ 0. 05 were considered statistically significant. Adjustment for multiple testing was based on FDR < 0. 05. Systematic literature search. We performed a systematic literature search on PubMed to select papers published until May 2021 that analyzed DNA methylomewide data in clinical melanoma samples. To this end, we used the following syntax: ((melanoma) OR (melanomas)) AND ((Global DNA methylation) OR (methylome) OR (methylome-wide) OR (DNA methylation)). In total, we found 867 studies of which 20 (Supplementary Data 1) were included in our analysis since they covered methylome-wide profiling rather than targeted DNA methylation assays, and they were also conducted on clinical samples rather than cell lines or animal models.
Acknowledgements We thank all melanoma patients who contributed to the study. This work was supported by  Public Ministry of Labor, Campinas (Research, Prevention, and Education of Occupational Cancer) ,  Barretos Cancer Hospital  and  IARC. A. L. S. A. V. was supported by  FAPESP  (grant no  2016/15941-3  and  2017/09612-0 ). We thank the  BCH  biobank for DNA isolation and sample processing and storage. The initial development of the multi-OMICs driver score was partially supported by the grant from the  Institut National du Cancer (INCa, France) ,  La direction générale de l'offre de soins (DGOS) , and  INSERM (SIRIC LYriCAN ,  INCa-DGOS-Inserm_12563 ) to Z. H. Where authors are identified as personnel of the  IARC/WHO , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  IARC/WHO.
Data availability The 450K data generated in this study have been deposited in the GE0 database under accession code GSE202750. The WGS data are available in the ICGC database [https:// dcc. icgc. org/projects/SKCA-BR]. The 450K, RNAseq and WES data on melanoma samples from TCGA were downloaded from the GDC Legacy Portal [https://portal. gdc. cancer. gov/legacy-archive/search/f] and cBioPortal [https://www. cbioportal. org/ datasets]. All other data is available within the Supplementary Data. Source data are provided with this paper.
Coherent Weather Structures One of the most powerful aspects of data assimilation is to retrieve quantities that cannot be observed, such as winds in the case of Mars. Both Figures 2 and 3 illustrate this point. To demonstrate the assimilation of coherent weather structures, Figure  2  shows an example identified in EMM Emirates eXploration Imager imaging (EXI, Jones et al. (  2021 )), and our corresponding LETKF analysis. We searched the EXI ultraviolet 320 nm images for examples containing water ice clouds indicative of atmospheric fronts or vortices, and in visible images for similar dust features. We identified 23 such observations during the assimilation period, many with dust or cloud NW or NE of Alba Mons. The best example was captured in eight observations over four hours on 16 August 2021 (L s = 86.
Funding for development of the EMM mission was provided by the UAE government, and to co-authors outside of the UAE by MBRSC. This work was supported by a Joint Research Agreement between MBRSC, Dubai, and NSSTC, UAE University. RMBY acknowledges funding from UAE University Grant G00003407. RMBY and MAJ acknowledge funding from UAE University Grant G00003322. Supercomputing resources were provided by UAE University High Performance Computing, with technical support from Anil Thomas and Asma Alneyadi. The authors thank Luca Montabone for access to processed MCS temperature observations and the MY36 GCM dust scenario file, Sandrine Guerlet, Claus Gebhardt, Bijay Guha, and Hessa Almatroushi for useful discussions, Bryan Harter for help with the assimilation dataset, and two anonymous reviewers whose comments improved the manuscript.
Data Availability Statement Data from the Emirates Mars Mission (EMM) are freely and publicly available on the EMM Science Data Center (SDC, http://sdc. emiratesmarsmission. ae). This location is designated as the primary repository for all data products produced by the EMM team and is designated as long-term repository as required by the UAE Space Agency. The data available (http://sdc. emiratesmarsmission. ae/data) include ancillary spacecraft data, instrument telemetry, Level 1 (raw instrument data) to Level 3 (derived science products), quicklook products, and data users guides (https://sdc. emiratesmarsmission. ae/documentation) to assist in the analysis of the data. Following the creation of a free login, all EMM data are searchable via parameters such as product file name, solar longitude, acquisition time, sub-spacecraft latitude and longitude, instrument, data product level, etc.
Data products can be browsed within the SDC via a standardized file system structure that follows the convention: /emm/data/<Instrument>/<DataLevel>/<Mode>/<Year>/<Month> Data product filenames follow a standard convention: emm_<Instrument>_<DataLevel><Start-TimeUTC>_<OrbitNumber>_<Mode>_<Description>_<KernelLevel>_<Version>. <File-Type) Emirates Mars Infrared Spectrometer (EMIRS) data and user guides are available at: https://sdc. emiratesmarsmission. ae/data/emirs. Emirates eXploration Imager (EXI) data and user guides are available at: https://sdc. emiratesmarsmission. ae/ data/exi. The data produced by our assimilation can be obtained via the EMM SDC at https://data-sdc-publications-mbrsc. s3. eu-west-1. amazonaws. com/Young-EMIRS-assimilation-MY36-first-results. zip. Mars Climate Sounder observations are available from the NASA Planetary Data System Atmospheres node at https://pds-atmospheres.
nmsu. edu/data_and_services/atmospheres_data/MARS/mars_reconnaissance_orbiter. html. MOLA topography data are available from the NASA Planetary Data System Geosciences node at https://pds-geosciences. wustl. edu/missions/mgs/megdr. html. The Mars PCM is available from http://www-mars. lmd. jussieu. fr/; we used GCM subversion revision r2533. The assimilation uses letkfmars git revision 9421d1c2a78abbd03eafc2cf3c1b0719e7586660.
Sociological survey design To understand how local forms of organisation influence the reporting of health information, we needed a sample composed of two departments with similar farming profiles, but levels of reporting for surveillance that were as different as possible. We categorised the farming profiles of the departments based on the available information: number, size, and production type of the bovine farms in each department. We relied on the French national cattle and veterinary databases (managed by the French Ministry of Agriculture and Food) for information about herd sizes and abortion reporting, and on the typology of cattle herds developed by  Sala et al. (2019)  for production type classification (dairy versus beef herds). We first selected three departments that met our criteria.
Data analysis We analysed the data collected during the survey using inductive reasoning. This reasoning is carried out through the analysis and crosschecking of the data from our investigation (interview content and grey literature). We identified "consolidated facts" based on the cross-checked interpretation of the actors' discourse, which we then sought to compare again with the data in order to test their robustness. We did not make any a priori hypotheses to confirm or refute in the field, but only analytical hypotheses drawn from our interpretations and therefore from the field. For example, during our investigation, following crosschecking of the discourse of farm animal veterinarians and the staff of the departmental health-support association (called the "GDS"), we found that there were contradictory signals of collaboration and competition.
Co-opetition between veterinarians and the GDS We observed in Departments A and B that both veterinarians and the GDS aimed at being the farmers' reference regarding health issues and cattle follow-up for prevention and control measures. This aspect generated ambivalence in their relationship, which could be considered co-opetition, i. e. involving "cooperative competition"  (Brandenburger and Nalebuff, 1996). We observed collaboration between veterinarians and the GDS in the two studied departments, as the GDS had administrative software that was useful for veterinarians for the follow-up of herds. Veterinarians did have software that monitored productivity and health indicators in their client herds, but this software was not designed for individual follow-up of the animals and the veterinary acts performed.
The GDS used their management software to register for example their visit reports, the referent clinic of each farm, the analytical results, and the health statuses of the individual animals and herds for all diseases of interest in member-herds and for regulated diseases in all the herds in the department. In both Departments A and B, most veterinarians could not organise the follow-up of their client herds, because they lacked time and logistics. The GDS software enabled them to implement long-term measures in the herds, such as repeated screening for a contagious disease in a given period, or the vaccination or treatment of animals according to their health statuses and changes in these statuses. For example, if targeted screening and treatment of a herd was needed, the GDS could provide the veterinarian with the list of the bovines and their individual screening results before each visit, indicating whether they were already treated or not.
Acknowledgements We are grateful to all the professionals who took part in our sociological survey; to  Christelle Philippon , the secretary of our research unit, for her help in transcribing the interviews; to  Anne Touratier  and  Kristel Gache  from  GDS France , for their help in selecting the departments for the study and contacting the main local health actors; to the  French Ministry of Agriculture and Food , for access to the national veterinary and cattle databases; to the  Translation Unit of ANSES , as well as  Coup de Puce-Expansion and CM Stevens  ( MA, DESS, ELS ) for English language editing of the manuscript. This research received support from the  "complementary training through research" (FCPR) program  of the  French Ministry of Agriculture and Food.
Sequencing and phylogenetic analysis Eight out of the 78 human samples and 6 out of the 94 animal samples that were RTqPCR-positive for RVFV were used to sequence the NSs gene of the S segment by full length S-Segment PCR amplification  [25]. To compare the genetic relatedness of the sequenced viruses, phylogenetic analyses were performed against a panel of 157 published NSs RVFV nucleotide sequences. Before phylogenetic inference, datasets and multiple sequence alignments were thoroughly checked to eliminate misalignments and ensure correct framing of the coding sequences. Sequences were aligned by ClustalW, edited using MEGA X software  [26]. The evolutionary history was inferred using the maximum likelihood (ML) method and general time reversible model (GTR)  [27]  implemented in the above mentioned MEGA X.
Genetic diversity RNA was extracted from 14 RVF RT-PCR positive samples originating either from humans (n = 6) or from animals (n = 8) followed by the amplification and sequencing of the S segment. No differences in amino acids (aa) were found between the S segments analysed. Since only partial S segments, specifically from the NSs gene, were available for West Africa (Senegal and Mauritania) in GenBank, the phylogenetic analysis was based on the NSs gene of the RVFV S segment. One hundred percent identity was found between the human and the camel sequences that were amplified during the outbreak of RVF in Mauritania in 2020. The sequences consistently clustered with the West African strains, specifically found in Senegal and Mauritania in 2013 and 2015, as well as Niger in 2016, in the genetic lineage H as the North African Egyptian strains (Fig.
A discrete Gamma distribution was used to model evolutionary rate differences among sites. (5 categories (+G, parameter = 0. 3199)). The rate variation model allowed for some sites to be evolutionari-ly invariable ([+I], 35. 26% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Classification of the isolates followed the lineage terminology of  Grobbelaar et al. , 2011 [46]. The GenBank accession numbers for the NSs gene of the virus S segment are ON052829(2020 Camel Mauritania) and ON052830 (2020 Human 7 Mauritania). 83 out of 357 of the camels. All animal species were affected, in particular in the Assaba region, where the outbreak started with a cow that aborted early on September 4, 2020 which coincided with the clinical signs observed in the index human case.