dataesr/scientific-paragraphs-categorization huggingface dataset:
The land cover dataset used in this study consists of a three-way table (69 districts, 21 years, and 12 PFTs) giving the fractional cover as a function of district, time, and PFT. Applying PTAk to this dataset shows that 86. 26%, 5. 83%, and 4. 38% of its variability are captured by the frst three best principal tensors, respectively. In Figures  3  and 4 , the weights of each triple of components of the frst two principal tensors are displayed as a signed relative contribution (CTR) to the variability, which is defned as the squared component weight divided by the average contribution (equivalent to an uniform equal contribution) and multiplied by the sign of the weight (Appendix C). The larger the relative CTR, the greater the contribution of that variable to the component. The spatial component of the frst principal tensor (Figure  3  left) indicates that some districts (the dark green ones, some in Sweden and some in Finland) make much larger contributions to the overall variation in fractional cover than others.
Therefore, the observed differences in the importance of abiotic and biotic explanatory variables in areas of increased disease incidence could be attributed to the ecology of the diseases, such as local interaction between ticks and their vertebrate hosts  [64] , and where and when humans are exposed to ticks. and Komi, making up 69 districts (www. clinf. org, accessed on 15 October 2021, for the whole spatiotemporal dataset collected during the project  [65, 66]  ).
Funding: This work was carried out under NordForsk funding to CLINF, a Nordic Centre of Excellence (NCoE) led by Professors Birgitta Evengård and Tomas Thierfelder (https://www. nordforsk. org/projects/clinf-climate-change-effects-epidemiology-infectious-diseases-and-impacts-northern, accessed on 13 October 2021) under grant agreement no. 76413. The APC was funded by the CLINF project.
Data gathering We used published data on home-range sizes and species traits such as body mass, thermoregulation (endotherm, ectotherm), trophic guild (carnivore, herbivore, omnivore) and locomotion mode (running, flying, swimming) of terrestrial and semi-aquatic amniotes by combining the most extensive databases to date  (Tamburello et al. , 2015; Tucker et al. , 2014). These databases included home-range estimates from different tracking and analysis methods. As a prior study showed that these methodological differences do not affect the scaling relationships  (Tucker et al. , 2014) , and we have put the highest priority on estimating the slopes accurately (which is at risk in mixed models with insufficient replication of the independent variables such as body mass or temperature within each random block such as methodology), we have refrained from using them in our study.
We then extracted the corresponding data on temperature, precipitation, Normalised Difference Vegetation Index (NDVI) and Human Footprint Index (HFI) from global databases (Table  S1 ). The Human Footprint Index cumulates different proxies of human pressure on the environment, such as the number of built environments, croplands, pasture lands, population density, nightlights, railways, major roadways and navigable waterways  (Venter et al. , 2016). We used the CHELSA database for temperature and precipitation  (Karger et al. , 2017 (Karger et al. , , 2018) )  and MODIS data for NDVI (MOD13C2;  Didan, 2015). We extracted the annual mean data of the corresponding study year. While temperature extremes should also have an effect on the metabolism, behaviour and feeding performance of animals, we focused on the annual averages of temperature to test for relationships with the average NDVI and the long-term averages of metabolic rates because (1) all of these measures integrate over time periods, and (2) we could establish causal relationships for our SEM analysis (see below).
For HFI, only maps from either 1993 or 2009 were available. Thus, we used the HFI from 1993 for studies before 1999, the mean HFI from 1993 and 2009 for studies from 1999 to 2003 and the HFI from 2009 for studies after 2003. As we lacked data for high and low latitudes (i. e. low temperatures), we specifically searched for and added these types of studies. Including only data points where all environmental variables were available resulted in a total of 682 recorded home ranges of 395 species (Table  S2 ) from across the globe (Figure  1 ) with a dominance of mammals (n = 536) compared to birds (n = 65) and reptiles (n = 81).
Structural equation model on the effect of environmental and species traits on home-range size We used a 'piecewise' approach to structural equation modelling (SEM, R package 'piecewiseSEM' version 2. 1;  Lefcheck, 2016)  and constructed a model to determine the relative effect sizes (given by range-standardised path coefficients representing the relative importance of a predictor) of both direct and indirect effects of environmental variables on animal home-range size (Figure  2a ). To disentangle the relative contribution of the body-mass-dependent mechanisms metabolism  (West et al. , 1997) , trophic guild  (Riede et al. , 2011)  and movement speed  (Hedenström, 2003; Hirt et al. , 2017b) , we employ these effects as composite variables (variables that arise from the combined influence of measured variables;  Lefcheck, 2019)  according to three non-mutually exclusive hypotheses.
The 'movement hypothesis' is based on animal movement capacities. Movement rate and locomotion mode (walking, flying, swimming) should determine how far an animal can travel on a given day and thus also constrain the homerange size  (Haskell et al. , 2002) , with faster animals having more extensive home ranges. We used exploratory speed (a voluntary speed used for exploring habitat) as the best proxy for animal space use. As accurate empirical allometric scaling relationships of exploratory speed for all locomotion modes are lacking, we defined the potential exploratory speed as 3% (Peters (1986), p. 88) of the maximum speed, for which high data quality is available  (Hirt et al. , 2017a , Table  S3 ). The 'metabolic hypothesis' is based on body-mass-dependent energy demands. Larger animals have higher metabolic rates and, thus, higher energy requirements  (Jetz et al.
Full regression model for home-range size prediction To obtain a full model for home-range size prediction, we used linear mixed-effects models (R package 'nlme') for effects of log10 body mass [g]) on log10 home-range size [m 2 ] with species traits and environmental variables as co-factors. We did not include thermoregulation and locomotion mode as species traits, as (1) thermoregulation is fully correlated with taxonomic class (i. e. all reptiles are ectotherms) and (  2 ) not all locomotion modes cover a sufficient fraction of the body mass range. We included taxonomy (class, order, family) as a nested random effect. We did not include the study as a random effect in the regression models since it is generally associated with geographical location. With mostly one or just a few data points per study, such a random effect would be correlated with the environmental predictor variables, resulting in an overfitted model that would not allow a meaningful inference on the fixed effects.
Structural equation model on extinction risk We used data from  Hoffmann et al. (2010)  on genuine transitions in IUCN Red List status across birds, mammals and amphibians  (Hoffmann et al. , 2010, Table S6  therein; Figure  3a ). The data include the years and respective threat status referring to the transition (time period ranges from 1980 to 2008 in total). We considered the following IUCN Red List categories: Least Concern (LC); Near Threatened (NT); Vulnerable (VU); Endangered (EN); Critically Endangered (CR); Extinct in the Wild (EW); Extinct (EX). We associated each species with its average body mass and diet (carnivore, omnivore or herbivore) obtained from publicly available databases  (Jones et al. , 2009; Kissling et al. , 2014; Myhrvold et al. , 2015; Oliveira et al. , 2017; Trochet et al.
For these species, we downloaded occurrence data from the GBIF database (filtering for basis of record as "preserved specimen", "human observation", "observation" or "machine observation" and excluding entries with geospatial issues, https://doi. org/10. 15468/ dl. f87zwu). We extracted the corresponding environmental variables for each geographical location and the respective year in which the animal was categorised into its historical or current Red List category. We calculated the median of all environmental variables by species (resulting in a database containing 447 species). We used the full regression model developed in this paper (see Table  S5  parameter estimates) to calculate the historical and current optimal (assuming minimal human pressure everywhere) and realised (with the respective level of human pressure) home-range sizes.
Global prediction of space loss We used current environmental and anthropogenic conditions (Table  S1 ) to predict the global distribution of the discrepancy between optimal and realised home-range sizes using the full regression model. For illustration purposes, we predicted the distribution of home-range sizes for a 2. 8 kg carnivore (the median body mass of all carnivores in the database; Figure  3c ). However, the global map projection can be conducted for all different kinds of species trait combinations. For predicting the optimal home-range sizes (only based on abiotic conditions), we set the HFI to zero. All statistical analyses were performed using R version 4. 0. 2.
I G U R E 1 Global distribution of home-range estimates. (a) Geographical locations of 682 studies recording home-range sizes of animals plotted on the global mean annual temperature (1979-2013, CHELSA) to illustrate environmental variability across sampling sites. (b) The database includes animals of different taxa spanning six orders of magnitude in size as well as ectotherm and endotherm, terrestrial and semiaquatic animals from cold and warm regions (from 70° south to 80° north).
Extinction risk is directly affected by the change in human-driven space loss. (a) Final selected structural equation model (χ 2 = 5. 19, d. f. = 3, p = 0. 158) showing how anthropogenic pressure (HFI) increases species extinction risk via changes in space availability (i. e. the discrepancy between optimal and realised space use). Red and blue arrows denote significant negative and positive effects respectively. (b) Home-range size of large animals is most strongly affected by human pressure. Barplot on top gives the distribution of size classes across the gradient of human footprint values (x-axis). (c) Predicted (using the full regression model) human-driven space loss for a 2. 8 kg carnivore (median mass of all carnivores in the database) using global distributions of temperature [°C] of 2018, NDVI of 2018, and Human Footprint Index of 2009.
F I G U R E 1 Global distribution of home-range estimates. (a) Geographical locations of 682 studies recording home-range sizes of animals plotted on the global mean annual temperature (1979-2013, CHELSA) to illustrate environmental variability across sampling sites. (b) The database includes animals of different taxa spanning six orders of magnitude in size as well as ectotherm and endotherm, terrestrial and semiaquatic animals from cold and warm regions (from 70° south to 80° north).
F Extinction risk is directly affected by the change in human-driven space loss. (a) Final selected structural equation model (χ 2 = 5. 19, d. f. = 3, p = 0. 158) showing how anthropogenic pressure (HFI) increases species extinction risk via changes in space availability (i. e. the discrepancy between optimal and realised space use). Red and blue arrows denote significant negative and positive effects respectively. (b) Home-range size of large animals is most strongly affected by human pressure. Barplot on top gives the distribution of size classes across the gradient of human footprint values (x-axis). (c) Predicted (using the full regression model) human-driven space loss for a 2. 8 kg carnivore (median mass of all carnivores in the database) using global distributions of temperature [°C] of 2018, NDVI of 2018, and Human Footprint Index of 2009.
Cell cycle analysis Hundred thousand mESCs were fixed in 70% EtOH overnight at -4 °C. After fixation, cells were treated with RNase A (100 μg/ml) (Thermo Fisher Scientific, #EN0531) and stained with propidium iodide (40 μg/ml) (Sigma Aldrich, #P-4170) for 30 min at 37 °C. The acquisition of the DNA content was analyzed on FACS CALIBUR (BD Sciences) flow cytometer. Quantitative results were analyzed by FlowJo software (BD Sciences).
Apoptosis analysis using annexin-V staining At the indicated incubation time, floating cells were collected in culture supernatants and adherent cells were harvested by trypsinization. After collection, cells were washed twice with cold 1× PBS, and about 2 × 10 5 cells were resuspended in 100 μl binding buffer (FITC Annexin V Apoptosis Detection Kit, Biolegend). Subsequently, 5 μl FITC Annexin V (FITC Annexin V Apoptosis Detection Kit, Biolegend) and 10 μl propidium iodide was added to the cell suspension. Cells were gently vortexed and incubated in the dark for 15 min at RT. Thereafter, another 400 μl Annexin V binding buffer was added to each tube. Cells were analyzed using a FACS CALIBUR (BD Sciences) flow cytometer. Dot plots were generated using the FlowJo software.
Statistics Statistical analysis of WT versus mutant samples comparison (cell proliferation and H2Bub1 density) was performed using nonparametric two-sided Wilcoxon ranksum test with continuity correction using R (version 5. 3). No multiple comparisons were performed therefore no multiple correction were applied. Statistical results were expressed as P value or P value ranges (ns, p > 0. 05; *, p ≤ 0. 05; **, p ≤ 0. 01; ***, p ≤ 0. 001). Bar plot graphical data were represented as mean ± SD and individual data points. Proliferation graphical data were represented as mean ± SD. H2Bub1 density data were represented as box plots. Statistical analysis of the RNA-seq datasets was performed using a Wald test (DESeq2, see below).
After Sep Pak desalting, peptides were analyzed in triplicate using an Ultimate 3000 nano coupled in line with an Orbitrap ELITE (Thermo Scientific, San Jose California). Briefly, peptides were separated on a C18 nano-column with a 90 min linear gradient of acetonitrile and analyzed with Top 20 CID (Collision-induced dissociation) data-dependent acquisition method. Data were processed by database searching against Mus musculus Uniprot Proteome database (www. uniprot. org) using Proteome Discoverer 2. 2 software (Thermo Fisher Scientific). Precursor and fragment mass tolerance were set at 7 ppm and 0. 6 Da respectively. Trypsin was set as an enzyme, and up to two missed cleavages were allowed. Oxidation (M, + 15. 995), GG (K, + 114. 043) were set as variable modification and Carbamidomethylation (C) as a fixed modification.
RNA-seq and chromatin immunoprecipitation coupled high throughput sequencing (ChIP-seq) analyses For RNA-seq, total RNA was extracted from mESCs and MEFs (3 biological replicates of WT and Atxn7l3 -/-for each cell types) using the NucleoSpin RNA isolation kit (Macherey-Nagel), according to manufacturer's instructions. Libraries were generated from the purified RNA using TruSeq Stranded mRNA (Illumina) protocol. After checking the quality of the libraries with the Bioanalyser (Agilent), libraries were sequenced on the Illumina HiSeq 4000 at the GenomEast sequencing platform of IGBMC. The raw sequencing data generated reads were preprocessed in order to remove adapter, poly(A) and lowquality sequences (Phred quality score below 20), then were mapped to the mouse mm10 genome using STAR  [46].
After PCA analysis, one WT ESCs dataset was excluded because it did not cluster with the other biological replicates. Differentially expressed genes were measured using the DESeq2 package  [47]. For the downstream analysis, only the transcripts which base median over 10 normalized reads (DESeq2 normalized reads divided by the median of the transcript length in kb) were considered. Using these criteria 16269 transcripts were expressed in mESCs, and 15084 transcripts were expressed in MEFs. ChIP-seq experiments were performed using the protocol described in  [48] , with some minor modifications, including the use of 10 mM N-ethylmaleimide (NEM, Sigma-Aldrich) in all buffers and the use of either the anti-H2Bub1 antibody (MediMabs, NRO3), the anti-RPB1 C-terminal domain (CTD) Pol II antibody (1PB 7G5  [49] ) from control and Atxn7l3 -/-mESCs and MEFs (n = 1, each) or the anti-Ser2P CTD Pol II antibody (3E10  [50] ) from control and Atxn7l3 -/-mESCs (n = 1, each).
Normalization between ChIP-seq datasets For H2Bub1 ChIP-seq samples to correct for the bias introduced by the differences in the sequencing depth among samples, the total reads present at 3115 intergenic regions far away from genes and larger than 100 kb were selected as described previously  [19]. These intergenic reads were used for the normalization of H2Bub1 ChIP-seq samples. We calculated the size factor of these intergenic regions for each sample using DESeq2 (version 1. 16)  [51]. These size factors were used to normalize the H2Bub1 ChIP-seq data. For Pol II ChIP-seq and Pol II Ser2P ChIP-seq samples, the total number of mapped reads were used for normalization. Twenty million of total reads were used to generate. bed files for the seqMINER software analysis. The. bigwig files for the Integrative Genomics Viewer (IGV) visualization were generated with makeUCSCfile program in Homer package, the total number of reads was normalized to 10 million and the fragment length was normalized to 100 bp.
Calculation of density values Density values were defined as follows: density = [(number of aligned reads in a region of interest)/(length of the region of interest in bp)]/(size factor × 10 -8 ). For H2Bub1 datasets, we considered only the gene bodies of expressed genes containing at least 1 ChIP-seq read. Out of 16269 expressed genes in mESCs, 15467 contain at least 1 ChIP-seq read. Out of 15084 expressed genes in MEF cells, 14500 contain at least 1 ChIP-seq read (Supplementary Table  S4 ).
Generation of average profiles and heat maps Average profiles and k-means clustering were generated with the seqMINER program  [52]. The end of each aligned read was extended to 200 bp in the direction of the read. For the analyses around promoters, the tag density was extracted in a 2 kb window centered on each TSS. For average gene profiles, each gene body was divided into 160 equal bins (the absolute size depending on the gene length), 5 kb upstream and downstream were added. Moreover, 20 equally sized bins (250 bp/bin) were created upstream and downstream of genes. Densities were collected for each dataset in each bin.
Deregulation of gene expression is more severe in Atxn7l3 -/-MEFs than in Atxn7l3 -/-mESCs To further characterize ATXN7L3-dependent DUB activity, we measured changes in steady-state mRNA levels between Atxn7l3 +/+ and Atxn7l3 -/-mESCs, as well as between Atxn7l3 +/+ and Atxn7l3 -/-MEFs by carrying out RNA-seq analyses. We first verified whether the Atxn7l3 -/-MEF-like cells still belong to the MEF lineage in spite of their unusual morphology by comparing our MEFs RNA-seq results with 921 RNA-seq datasets from 272 distinct mouse cell types or tissues  [56]. This analysis indicated that the Atxn7l3 -/- MEF-like cells clustered together with Atxn7l3 +/+ MEFs or fibroblasts (Supplementary Fig. S3C ), suggesting that the Atxn7l3 -/-MEF-like cells belong to the fibroblast lineage. Differential gene expression analysis between Atxn7l3 -/- and WT mESCs, or Atxn7l3 -/-and WT MEFs, showed that in both Atxn7l3 -/-samples there are significant numbers of genes which expression was up-or down-regulated (Fig.
On the contrary, it was reported that the effect of H2Bub1 on nucleosome stability is relatively modest  [68]. If H2Bub1 deposition (by RNF20/RNF40), or H2Bub1 erasure (by the ATXN7L3-containing DUB modules) would carry out opposite genome-wide actions, their gene regulatory actions would result in mirroring effects. However, when comparing RNA-seq data from Rnf40 knock-out MEFs  [12]  with our RNA-seq obtained from Atxn7l3 -/-MEFs, we did not observe any anti-correlation between the regulated genes in these two datasets (the Pearson correlation coefficient is -0. 042, Supplementary Fig. 8 ), suggesting that the H2Bub1 deposition and erasure are not (only) carrying out opposite functions on the transcribed genome. Contrary to H2B monoubiquitylation, it is much less well understood whether H2Bub1 deubiquitylation would be a process significantly impacting Pol II transcription.
Acknowledgements We thank all members of the  Tora lab  for protocols, thoughtful discussions, and suggestions, especially  V. Hisler  for help with mice dissection,  JC. Andrau  for advice on  Ser2P ChIPseq ,  C. Hérouard  and  M. Jung  from the  GenomEast platform [France Génomique consortium  ( ANR-10-INBS-0009 ], for library preparation, sequencing and analyses;  C. Ebel  and  M. Philipps  for help with FACS, the IGBMC histology platform, the IGBMC cell culture facility, and  S. Falcone ,  M. Poirot  and  F. Memedov  of the IGBMC animal facility for animal care taking.
Funding This study was supported by grants from  European Research Council (ERC)  ( ERC-2013-Advanced  grant  340551 , Birtoaction),  Agence Nationale de la Recherche (ANR)   PICen-19-CE11-0003-02  and  EpiCAST-19-CE12-0029-01  grants,  NIH   1R01GM131626-01  grant (to LT), and  ANR-18-CE12-0026  grant (to DD), by  IGBMC International PhD program  LABEX fellowship (to FW); and by the  IdEx-University of Strasbourg PhD program  and by the  "Fondation pour la Recherche Médicale " (FRM) association (FDT201904008368) (to VF), and an  ANR-10-LABX-0030-INRT grant , under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. Compliance with ethical standards.
Background: The purposes of the study were first to determine the incidence rate of medical unfitness for work at sea among French seafarers, second to identify the conditions (diseases or accidents) causing such incapacity so as to set up prevention measures where possible and third to ascertain whether there were any overrepresentations of diseases according to category of unfit seafarers (fishers, merchant seafarers, shellfish farmers and professional sailors). Methods: An exhaustive, observational, descriptive, retrospective epidemiological and nosological study was carried out based on the medical coding of files stored in the Aesculapius® national database, which registers all medical data regarding seafarers presenting at the French seafarers' health services. The increasing rate of permanent medical unfitness for work at sea was calculated in relation to the annual number of registered seafarers.
Data collection Information was collected from the computerised medical records of professional seafarers listed in the Aescu-lapius® database. Only SDs and the doctors at Toulouse's Centre de Consultation Médicale Maritime (which is France's TMAS (Telemedical Assistance Service) centre) had access to this database. The database listed all the French seafarers who had been seen by French SDs and, more importantly, all those referred to one of the four CMRAs between 2005 and 2016. It was housed in the Centre Administratif des Affaires Maritimes (administrative centre for maritime affairs) in Saint-Malo. There were 4978 files recorded in this database by the different CMRAs for the period in question. The CMRAs' decisions concerning fitness for work at sea for each case (which were categorised as fit without restriction, fit with restrictions, temporarily unfit or permanently unfit) were also accessible.