dataesr/scientific-paragraphs-categorization huggingface dataset:
10. 1. We used the "anviprofile" program to compute the coverage per nucleotide position and statistics for each metagenome assembly using the BAM files. We merged the resulting anvi'o profiles using the "anvi-merge" program. After the metagenomic snakemake workflow, we performed an automatic genome binning from assemblies using the "anvi-cluster-contigs" anvi'o program and the CONCOCT  [52]  algorithm ("--driver CONCOCT" flag) with a specific number of cluster ("--clusters X" flag) by sample (See Supplementary Table  1 ) to obtain the best possible ratio of completion and redundancy for bacterial bins (checked with "anvi-estimategenome-completeness" program). Finally, we manually refined the bacterial bins obtained from each sample with the interactive program "anvi-refine". In addition, we ran the "references-mode" of the metagenomic snakemake workflow to perform read recruitment of the quality-filtered reads from all samples to the refined Wolbachia Metagenome Assembled Genome (MAG) and anvi'o profiling for inter-organ comparisons.
Variants (SAAVs) We used the "anvi-gen-variability-profile" program to extract the variability of Single Nucleotide Variants (SNVs, "--engine NT" parameter), Single Codon Variants (SCVs, "-engine CDN") and Single Amino Acid Variants (SAAVs, "--engine AA") from the anvi'o profile databases of i) quality filtered reads from each sample mapped on the corresponding Wolbachia MAG (for intra-sample variability), ii) quality filtered reads from all samples mapped on each Wolbachia MAG (for inter-sample variability). We filtered SNVs, SCVs and SAAVs by removing the ones with entropy < 0. 2 (20%) and departure from consensus < 0. 2 (20%) in order to discard those that could be due to sequencing errors and therefore considered as noise (https://merenlab. org/2013/11/04/oligotyping-best-practices/). We also removed the SNVs identified as coverage outliers based on mean split coverage by anvi'o (cov_outlier_in_contig = 1, cov_outlier_in_split = 1).
We used the "anvi-script-snvs-to-interactive" program to generate a visualizable SNV database from the intra-sample filtered variability table using the "anvi-interactive" program. We then focused our SNV analyses on a set of wSCG (Single-copy Core Genes from Wolbachia, that is genes that occur only once in each genome of the genome set) as defined by the pangenomic analyses of our MAGs and Wolbachia reference genomes described in the section below. We added the wSCG assignation in the visualizable Single Nucleotide Variants database using the "anvi-import-misc-data" anvi'o program. Finally, we edited the SNVs figure using the "anvi-interactive" program.
Pangenomics We performed pangenomic analysis for all of the Wolbachia MAGs and 3 selected Wolbachia reference genomes: wPipPel isolated from Culex quinquefasciatus (NCBI Accession ID NC_010981. 1), wPipMol isolated from Culex molestus (NCBI Accession ID NZ_CTEH00000000. 1) and wPipJHB isolated from Culex quinquefasciatus (NCBI Accession ID NZ_ABZA00000000. 1). To do that, we downloaded the fasta files of the three selected Wolbachia reference genomes and reformatted them using the anvi'o "anvi-script-reformatfasta" program. We then generated a new contigs database from the reformatted fasta files using the "anvi-gen-contigs-database" program. We identified HMM hits using the "anvi-runhmms" program to assign functions but also taxonomy to genes using the "anvi-run-ncbicogs" and "anvi-run-scg-taxonomy" programs, respectively.
We created an external genome database including all these Wolbachia reference contigs databases. We created an internal genome database including all the Wolbachia MAGs contigs databases (M11, O11, O03, O07, O12) stored in the profile and contigs databases generated during the snakemake workflow with references mode. We then generated a genome storage database from both external and internal genome databases using the "anvi-gen-genomes-storage" program. We ran the "anvi-pan-genome" program (using '--use-ncbi-blast' and '--mcl-inflation 10' flags and the "genome-name" flag) to identify gene clusters and compute the pangenome for all the Wolbachia MAGs to study the gene similarity between MAGs and 3 Wolbachia reference genomes to study the similarity between MAGs and Wolbachia reference genomes.
To note, only highly similar genes are added in a gene cluster during the anvi'o pangenomic workflow with almost no chance for two highly similar genes to end up in distinct gene clusters (which was essential for our study when we compared SNVs occurring in Singlecopy Core Genes (SCGs) shared by multiple Wolbachia genomes). We finally used the "anvidisplay-pan" program to display the pangenomes and visualize the distribution of gene clusters across genomes from our different genome sets. We identified SCG shared by all Wolbachia MAGs and reference genomes (hereafter wSCGs) using a custom R script among the gene clusters identified during the pangenomic workflow. To note, we also mapped each metagenome on each corresponding Wolbachia MAG (metapangenomics) in order to compare the mean coverage between the gene clusters identified as wSCG and the other ones.
Metapangenomics for inter-organ variability We used a custom R  [53]  script (based on https://merenlab. org/data/wolbachiaplasmid/#recovering-coverage-values-for-gene-clusters-of-the-wolbachia-pangenome-in-cpipiens-metagenomes) to recover coverage values of M11 and O11 MAGs and compute their means by gene cluster. We used the available results of blastn that identified wPipPel genes that match to WO prophage regions ("WO_in_wPip_best_hit. txt" file from https://merenlab. org/data/wolbachiaplasmid/#identifying-genes-that-correspond-to-wo-prophages detailed in  [42] ) together with the same custom R script to detect "phage like" gene clusters. We imported coverage values and WO prophage assignation to the pangenome database using the "anvi-import-misc-data" anvi'o program to build the metapangenome.
MLST, wsp, cidA and cidB putative hits in wSCGs We used blastn to identify MLST (MultiLocus Sequence Typing -gatB, coxA, hcpA, ftsZ, fbpA) and wsp genes from the PubMSLT  [54]  (Public databases for molecular typing and microbial genome diversity) in our Wolbachia MAGs. Similarly, we identified best hits using blastn for cidA (NCBI Accession ID from MF444963 to MF444981 for the 18 cidA variants) and cidB (NCBI Accession ID from MF444982 to MF444996 for the 14 cidB variants) genes from  [55]. We finally used a custom R script to tag SNVs occurring in these hits and added them to the SNVs database using the "anvi-import-misc-data" anvi'o program.
Reconstruction of Wolbachia metagenome-assembled genomes (MAGs) in one midgut and four ovaries of Culex pipiens individuals Our quality filtering of raw reads sequenced from midgut and ovaries samples from individual mosquitoes resulted in 94,024,472 and 75,040,983 paired-end reads on average, respectively (Supplementary Table  2 ). Individual sample metagenomic assembly generated averages of 135k and 214k contigs > 1kb (mean numbers) recruiting 60% and 47% of filtered reads for midguts and ovaries, respectively (Supplementary Table  2 ). To estimate the extent of eukaryotic contamination in our metagenomes, we used phyloFlash  [56]  that annotates short reads based on the SILVA rRNA databases  [57]  (see Supplementary Note 2 for further details). Results suggested that the vast majority of our reads (over 99% for each sample) originated from the host genome, especially in midgut metagenomes (Supplementary Figure  1 , Supplementary Table  3 ).
This wSCG gene was annotated to an "Ankyrin repeat function" by COG20 database, involved in proteinprotein interactions. Among all SNVs, we chose to highlight the most reliable ones (i. e. filtered and not flagged as coverage outliers) by a star in Figure  2. Of note, a SNV can be flagged as a coverage outlier in one sample and not in another, since both positional coverage and mean split coverage are specific to a sample. For instance, some SNVs colored in red are present in M11 but not in O11 because they have been flagged as outliers in M11 but not in O11. However, by looking at the distribution of SNVs without filtration (that is, including SNVs identified as coverage outliers), we observe the occurrence of additional SNVs shared by multiple metagenomes at the same positions (Figure  2 ).
The distributions of all SNVs (with and without coverage outliers) on the entire split and/or full gene are provided in Supplementary Figure  8. In gene 589, we reported three filtered SNVs including two shared by O03 and O12 at positions 17,065 and 17,068 and one shared by O07 and O12 at position 16,917 (indicated by a star in Supplementary Figure  9 ). This gene was identified as wSCG and annotated to an "ATP-dependent 26S proteasome regulatory subunit" by COG20 database. This subunit is involved in the catalyzation of protein degradation. We also observed more SNVs shared by multiple metagenomes by looking for the distribution of all the SNVs on the entire split or gene (with and without coverage outliers, Supplementary Figure  10 ). To discard the effect of putative PCR errors, we manually checked for the presence of homopolymer regions upstream of the reported SNV positions using the anvi'o interactive interface and did not identify any.
Overall, making good use of Wolbachia requires information on the genetic variation of the host, the pathogen and the endosymbiont at fine scale as distinct variants can alter pathogen virulence as well as the efficiency of the protective or reproductive phenotype. Wolbachia is widely used in antivectorial programs worldwide to fight diseases and knowledge of distinct bacterial cells within single individuals and egg stage is critical. midgut and ovary metagenomes (10. 5281/zenodo. 7183277), the FASTA files for the five Wolbachia MAGs (10. 5281/zenodo. 7183304), the anvi'o merged profile databases for the Wolbachia MAGs used for the SNVs and metapangenomic analyses (10. 5281/zenodo. 7183324) and the same profile databases including additional egg metagenomes (10. 5281/zenodo. 7924651). A reproducible bioinformatics workflow including scripts used for all computational analyses is available at the URL https://github.
Acknowledgments We are thankful to  Valentin Loux ,  Cédric Midoux ,  Véronique Martin  for their support on the MIGALE high-computer during the data analysis,  Corinne Cruaud  from the  Genoscope  for her assistance with sequencing, as well as  Ewa Chrostek  and  Wolfgang Miller  for helpful discussion about Wolbachia variants. We also thank  EID Mediterranée  and  Ignace Rakotoarivony  for mosquito sampling and dissections, respectively. This work was supported by the  ERC   RosaLind Starting Grant  " 948135 " to JR, and the  INRAe Animal Health Department and Occitany Region  to HS and JR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The starting point of natural hazard loss modelling Before the 1990s, actuarial methods were used to extrapolate the limited loss experience to estimate extreme losses. This loss experience gathered all data available along with historical macro-economic information, which was used to estimate as-if losses (i. e. what would be the loss if the event were to occur today). It constituted the state of what was known at that time by insurers and public authorities. The occurrence of Hurricane Andrew in 1992, whose impact was unexpected, was a game changer for modelling losses generated by natural hazards  (Grossi et al. , 2005; Mitchell-Wallace, 2017). One of the reasons for this event to be unanticipated was the increase of exposure in coastal areas where storm surge generated by Hurricane Andrew led to massive flood damages  (McChristian, 2012).
, 2007; Kakooei and Baleghi, 2017). A point of attention is the need for coordination among data collectors and the use of common definition of damages' scale to avoid the creation of multiple, overlapping and not comparable datasets. Research initiatives aiming at gathering various data sources already exist at country level, as for example the HOWAS database for flood damage in Germany  (Kreibich et al. , 2017; Kellermann et al. , 2020). Could this type of work be extended at the scale of Europe or even more globally? In terms of modelling, the occurrence of natural disasters feeds research that is integrated into the loss modelling framework every 2 to 5 years, mainly in the hazard and vulnerability components. For example, the successive occurrence of the two extreme European windstorms Lothar and Martin at the end of 1999 triggered the introduction of clustering effect in the modelling of the frequency of European winter windstorms.
Statistics All statistical analyses were done using R (version 4. 2. 2). A two-tailed exact binomial test was performed to test preference for Roger and Graham (null hypothesis set at 50%, "stats" package in R). To include the other individuals with fewer trials in the analysis, we checked for a consistent preference at the group level using a generalised linear mixed-effect model (Count of first removal ~ Type of object + (1|Subject)) with a Poisson family. We then conducted a model selection to compare with the null model based on AICc (function "dredge", "MuMin" package in R).
Inter-individual differences We investigated inter-individual differences in the general success in the task (number of trials needed to remove the stone first). We ran a generalised linear mixed-effect model (Number of trials needed to remove the stone first ~ Subject + (1|Individual session)) with a Poisson family distribution. We then conducted a model selection to compare with the null model based on AICc (function "dredge", "MuMin" package in R) before doing post hoc analyses [function "lsmeans", "lsmeans" package in R  (Lenth 2016) ]. We then investigated inter-individual differences using two behavioural indices: the time taken to remove an object from the bower (either object) and the number of objectoriented actions inside the bower before making a choice. Decision time is a parameter that is often used in reversal learning tasks with longer decision times indicating a less automatic response  (Olton and Samuelson 1974).
Collaboration can occur at any step of the surveillance process, from the governance to the implementation of operational surveillance activities (e. g. , sample collection, data analysis)  (5). However, little is known about the factors that can foster collaborations, especially between the various surveillance programmes composing a surveillance system  (6) (7) (8). A recent study pointed out that the French antimicrobial resistance surveillance system was resourceful and varied yet complex and fragmented, involving 48 surveillance programmes [targeted the human (n = 35), animal (n = 12), food (n = 3) and/or the environment (n = 1) sectors] from different domains [AMR, antibiotic use (AMU) and antibiotic residue] (9). Furthermore, collaborations among several programmes were observed, including cross-sectoral collaborations [among human (hospital and community), animal, food or environment sectors] and cross-domain collaborations (AMR and AMU).
Given the travel restrictions related to the COVID-19 pandemic, all interviews were conducted remotely, by videoconference, using Microsoft Teams ® software. In order to ensure the comparability of the information collected, one of the interviewer (L. R. ) was systematically present at all the interviews and was assisted by one or two other interviewers (other co-authors) depending on the number of people interviewed. Interviews with key-informants were systematically individual ones, whereas the number of respondents for the interviews with coordinators varied from one to four, depending on the main coordinator's willingness to be accompanied by other co-coordinators (from the same programme). At the beginning of the interview, the aim and background of the study were explained, as well as the interview's confidentiality rules, and the roles of the respondents were collected.
Data analysis The interview recordings were manually transcribed and compiled with the notes. At first, data analysis involved reading through all of the transcripts to get a sense of the dataset as a whole  (12). Then, the transcripts were subjected to thematic analysis, as described by Beaud and Weber  (13). Specifically, thematic analysis is a method of examination of the content of discourses to identify, analyse and interpret meanings gathered in themes. The analysis was conducted inductively in a circular process and used a constant comparative method (  14 ): repetitions of forward and backward movements from transcripts, gathering of text fragments, attribution of codes and introduction of inferences  (11). Before making any inference, evidence to the contrary was sought. The data were examined in regard to the research questions, significant text fragments were identified, coded and grouped into categories, i.
Data availability statement The datasets presented in this article are not readily available because the data used for this study were obtained through semistructured interviews of programmes' coordinators and key informant experts. Conditions of approval (respecting the anonymity of respondents) do not allow us to distribute or make available data directly to other parties. Data analysed are included in this published article and its supplementary information files. Requests to access the datasets should be directed to lucie. collineau@anses. fr.
[62] , investigated the dissemination of heavy metals in aquatic system of Fayoum Governorate (Egypt). Sampling was carried out for a period of two years from 1997 to 1998 and 1998-1999. Water of Qarun Lake and Sanhour River were found to contain less metal contamination as compared to fish organs (head, flesh and viscera), which have higher levels of heavy metals (species includes Solea aegyptiaca, Penacus sp, Mugil sp. and Tilapia sp. ). Amongst the studied metals (e. g. Zn, Fe, Mn, Fe, Mn, Co, Cd, Cr, Ni, Pb, Co) the concentration of Pb and Cd were found to be above the tolerable range anticipated by FAO  [61]. Data were assembled from marine and freshwater African ecosystems including East, West, Central and Southern African sub regions. Contamination of heavy metals were paralleled among plants, animals, sediments, and water.
Samples collected from Boolaroo exceeded the standard food limits. Samples collected from Cowra have heavy metal concentration below the International Food Standard guidelines. It was concluded that vegetables rising near smelter are found to be more contaminated  [64]. Garcia et al. investigated the heavy metal content of soil and grass by collecting grass and soil samples at different hotspots near to roadsides  [65]. Location situated near A-8 highway was found to have high concentrations of Pb, Cu and Zn while correlations between the metal content in soil and grasses were also obtained. Factor analysis was smeared to heavy metal data and the link among Cd, Pb and Zn appeared to have the same pollution source. In addition, Barakat et al. studied heavy metals in soil irrigated with wastewater in Beni-Mellal city (Morocco)  [66].
Result Historical LSAT trend: observation and CMIP6 simulations Figure  1a  shows the 50-year running mean LSAT trend from 1950 to 2014 in the CRU dataset. Almost the entire global land surface has experienced signi cant surface warming. The warming trend is remarkable over the mid-and high-latitudes of the Northern Hemispheric continent and northeast Africa, weakest over the Tibetan Plateau and its downstream area, and the Andes Mountains along the western edge of South America. The global warming features can be generally well captured by the ensemble mean of the 35 CMIP6 models (MME, Fig. 1b ). The spatial correlation coe cient between MME and GISTEMP is 0. 57, higher than the correlations for 31 of the 35 models. However, the LSAT trend in MME is spatially much smoother than in CRU. As shown in Fig.
, 2020)  serves as the observation reference. The dataset was derived from historical weather stations and presented as interpolated variable on a 0. 5° grid over land surface. It is highly reliable, especially in mid-latitudes where there is a dense observation network. The CRU data and all the model outputs are interpolated to a regular 1° × 1° grid to facilitate the subsequent operation. Acronym of each region are introduced in Table  2.
In addition, we also used analysis provided by DUALEX R , a leaf clip sensor measuring light absorption spectra, to get access to epidermal chlorophylls and flavonols (anthocyanin precursors) content. The data indicate that this species is very sensitive to Pi deficiency. A strong differential growth was observed between 0 to 500 µM Pi where an optimal growth was observed (Fig. 2a  and b ). Interestingly, both root and shoot reacted to Pi supply. Unlike Arabidopsis, the shootto-root ratio could not be discriminated between the ranges of applied Pi (Fig. 2b ). Chlorosis promoted by Pi deficiency was very mild (Fig. S2a ) as illustrated by the 15% reduction of chlorophyll content observed in the range of 0/100 µM Pi. The accumulation of anthocyanin precursor was also observed but remained very limited (Fig.
Acknowledgements We specially thank Maëlle CABANNE and Marine FOYER for their excellent technical support. We sincerely thank Dr. Nusrat ALI for providing the housekeeping genes for the three plants crops used in this study. We would also like to thank Dr. Frank JAMOIS as Director of the Research Analytical Platform at Agro Innovation International-Timac Agro. We would like to thank our colleagues Eva DESCHAMPS and Yann BRANDT in charge of the coordination, piloting and realization of the field trial and the trainees and interims who participated in the work at Arvalis. We would like to dedicate this article to the memory of Damien De Craieye. He actively participated in this work during his master and despite a very courageous struggle was taken by a devastating illness this summer.
Secondly, by developing different solutions for mapping the rapeseed fields when there are constraints on ground sampling, by using a model trained with ground samples from one year and then applying it to other years without having to retrain it, and by testing the use of small training sample size in the classification. Also, the effect of the phenological shift in the rapeseed growth cycle between the training and test years, and the effect of the number of images in the S1 time series on the accuracy of the transferability of models were investigated. The approaches proposed in this paper could enable users to detect the rapeseed fields regardless of the inaccessibility of the study site and to obtain the accurate classification of rapeseed over large regions. This paper is organized into six sections: introduction, study site and dataset, methods, results, discussion, and conclusions.
B. Dataset 1) Ground data: The ground samples were taken from the RPG data which is the database of the farmers' declarations of agricultural plots in France. It contains the boundaries of each declared agricultural plot and information such as the crop type and the size of each plot. The RPG is available for download for the whole of France via (https://www. data. gouv. fr/en/datasets/registre-parcellairegraphique-rpg-contours-des-parcelles-et-ilots-culturaux-etleur-groupe-de-cultures-majoritaire/). To map the rapeseed fields in the current paper, the RPG data of the study years including 2018, 2019 and 2020 were used in order to create the large annual sample dataset for the training and testing. The cultivation period of rapeseed in the study area starts from September and ends in July of the next year.