dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface Soil Moisture (SSM) Products Acquired by the SMOS Satellite The first satellite mission to focus primarily on the collection of soil moisture data was the European satellite SMOS (Soil Moisture and Ocean Salinity), launched in November 2009. Here, we use the term surface soil moisture (SSM) to refer to the volumetric soil moisture in the first few centimeters (0-5 cm) of the soil. The soil moisture data acquired by SMOS are used as alternative surface boundary conditions (Appendix A). The SMOS-CATDS (Centre Aval de Traitement des Données SMOS) provides either a 3-day product or a 10-day SSM product (that contains median, minimum, and maximum values of soil moisture). Ascending and descending overpasses of the SMOS satellite are bound to show different values of the retrieved parameters that may not always be comparable, and they are, thus, separately retrieved.
The data are presented over the Equal-Area Scalable Earth (EASE grid 2)  [51] , with a sampling of about 25 km × 25 km, and the studied area is included in two grid cells, called L1 and L2 (Figure  A1 ). Using the Berzème station during the year 2019, we found a bias of -12. 3 vol. % and a correlation coefficient of 0. 49, as already observed in southwestern France by others  [52]. As the residual water content, θ r , is almost zero for all three-day SSM products, the effective saturation, Se, is calculated following Equation (2) (Figure  S6 ): Se = SSM ω (2) where ω is the porosity at 5-10 cm (about 50% in the Harmonized World Soil Database). Another SSM product, called SMOSMAP-IB, is available at INRAE BORDEAUX  [53]. It fuses SMOS and NASA's SMAP satellite. We found a much better correlation coefficient of 0.
Seismological Data Analysis Using the Vibration Sensor at Clauzel House (CLAU) A vibration sensor was installed by the quarry owner in the private house Clauzel (CLAU, red triangle in Figure  1 ) to monitor the vibrations due to the quarry blasts. The sensor is a three-component, short-period seismograph (sampling rate at 1024 Hz). The recorded data of the mainshock include visually unnatural jumps in velocity, and this leads to unexpected levels of acceleration. The on-site visit leads us to think that the CLAU sensor has not been correctly fixed on the house floor and probably may have been impacted by the fall of miscellaneous objects around it. Although the whole waveform may not be exploitable, the first movement at the beginning of the signals could be informative. In order to verify the polarity, we checked the blast signal of 25 September 2019, for which the source location is well-known.
For the given records, we removed the linear trend, applied the Butterworth bandpass filter (order of 8) between 1 and 10 Hz, and integrated once using the software SeisGram2K Seismogram Viewer v7. 0. 0 × 10 (www. alomax. net, accessed on 16 January 2021) for data viewing and processing. Then, we manually exploited the particle motion of this blast for a selected time window. We obtained a back azimuth of N98 • E ± 20 • for the true value of N111 • E (Figure  S7 ). Thus, the particle motion approximatively indicates the event direction with a margin of error of around 16 •. The sensor data were used to estimate the azimuth of the mainshock of 11 November 2019, assuming the same margin of error.
Conclusions We compared two potential triggering factors for the earthquake of 11 November 2019 at Le Teil (France). We carried out two separate numerical approaches to simulate the impacts of water and stress transfers from the ground surface on the fault system at depth. We considered the double-surface rupture found by our DInSAR interpretation (BRF in addition to LRF) and a newly processed local seismic cross-section in order to shape the 3D geometry of the local three-fault system. The soil moisture data at the ground surface were used as surface boundary conditions of the hydraulic model during the period 2010-2019. The near-vertical BRF geometry became the major drain path of rainfall during the month before the earthquake, thus increasing the pore pressure at depth, enough to possibly trigger a very shallow earthquake on LRF.
The open-source software platform under the LGPL license, named SALOME (http://www. salome-platform. org, accessed on 16 January 2021), has been used to generate the mesh for the whole domain, in order to, ultimately, simulate fluid flows in the faulted region using the ComPASS platform. The geological units and faults (Figure  5 ) are meshed by a tetrahedral conformal meshing using the SALOME code. The unstructured mesh is composed of more than 140,000 tetrahedral elements, where the mesh size has been constrained for specific boundary elements (top surface, faults, intersection of faults). The fault is meshed as a two-dimensional (2D) surface with triangular elements, which are interconnected with the surrounding matrix using conformal meshing. The finest elements are localized at the fault top (triangles' side lengths, around 18 m).
Data Availability Statement: Sentinel-1 satellite acquisitions were provided by the European Space Agency (https://sentinel. esa. int/web/sentinel/sentinel-data-access, accessed on 15 June 2020). The SMOSMANIA soil moisture data are freely available from the International Soil Moisture Network website (https://ismn. geo. tuwien. ac. at/en/, accessed on 16 January 2021). The SMOS Level 3 SSM products were downloaded through the website of the SMOS-CATDS (Centre Aval de Traitement des Données SMOS, https://www. catds. fr, accessed on 16 January 2021). The SSM product that fuses SMOS and NASA's SMAP satellite (SMOSMAP-IB) is available at INRAE BORDEAUX. The A059 unwrapped interferogram image is provided in the Supplementary Materials. The datasets generated or analyzed in this work are available from the corresponding author upon reasonable request.
Overview and scope This project was a multiorganizational, multidisciplinary consensus to produce a practical document to guide clinicians involved in the care of patients with synchronous colorectal cancer and liver metastases. The scope of the project was to review and, where necessary, update terminology and to describe current management pathways. The MEDLINE®, EMBASE, Web of Science and Cochrane databases were queried in July 2022. The search terms 'colon cancer', 'rectum cancer' and 'liver metastases' were used in combinations. A search was carried out to inform the construction of each statement before circulation. Separate searches were undertaken for terminology, diagnosis, composition of multidisciplinary team, considerations for 'upfront' synchronous surgery, chemotherapy for synchronous metastases, the 'bowel-first' and 'liver-first' approaches.
The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species.
It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis.
Data type-specific databases allowing the deposition of these data have been created, e. g. , GenBank  (23)  for genomes, Gene Expression Omnibus  (24)  and ArrayExpress  (25)  for microarrays, European Nucleotide Archive  (26)  and Sequence Read Archive  (27)  for RNA-Seq, and ProteomeXchange  (28)  for mass spectrometry. However, integration of these data in a genus-or species-dependent manner is currently restricted to a few model organisms  (29) (30) (31) , and pathogenic-microorganism-specific integrated data found in databases like PATRIC  (32)  have not been updated with recent experiments. Here, we present a unique curated multi-omics database gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets originating from Yersinia spp. since the beginning of the omics revolution.
This database was constructed using the Bacnet platform  (33) ; we contributed to improvements of this platform. Indeed, with omics technologies evolving rapidly, new data formats, such as LC-MS/MS shotgun, which was absent from the first Bacnet-based website, were implemented  (31). For several reference genomes, we implemented integrated views at the gene level of the Kyoto Encyclopedia of Genes and Genomes (KEGG) for biochemical pathways  (34) , UniProt for protein information  (35) , InterPro for protein domain signature  (36) , IntAct for molecular interactions  (37)  and the Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) for protein interactions  (38). Raw reads from 425 RNA-Seq runs were consistently processed and analyzed with our bioinformatic pipeline, and a quality control and differential analysis report is available for each experiment which encompassed replicates.
Processed omics experiments are easily browsable, linked to their Gene Expression Omnibus (GEO)  (24) , European Nucleotide Archive (ENA)  (26)  or Proteomics Identifications Database (PRIDE) (39) repositories, and directly viewable at the gene level or according to experimental conditions on the dedicated Yersiniomics website that we have implemented (https://yersiniomics. pasteur. fr/).
RESULTS From public databases, we collected genomic sequences, raw RNA-Seq data, microarray data, and mass spectrometry data that we processed and integrated into the Yersiniomics website (https://yersiniomics. pasteur. fr/). Yersiniomics relies on 6 principal tools (Fig. 1 ): (i) three omics browsers ("genomics," "transcriptomics," and "proteomics") in a table format allowing navigation among the different genomes and biological conditions of the experiments implemented in the database; (ii) two omics data set viewers, called "genome viewer" and "heatmap viewer," allowing navigation among transcriptomics and proteomics results; and (iii) a gene viewer allowing navigation among the genes of a specific genome, to quickly access associated entries in external databases and to browse associated omics data.
Database functionalities. (i) The omics data set browsers. From the Yersiniomics home page, three omics browsers are available: genomics, transcriptomics, and proteomics (Fig. 2 , middle panel). From the genomics browser, 200 complete Yersinia genomes are sorted according to their phylogenetic relatedness, based on the 500 genes of our recently proposed Yersinia core genome multilocus sequence typing (cgMLST) scheme (40) (Fig. 3 ). Displayed information related to genomes includes species name, the most recent assignation proposed by the French Yersinia National Reference Center and determined via our cgMLST, the number of chromosomes and plasmids, the chromosome and plasmid total size, the lineage and sublineage (when applicable) based on single nucleotide polymorphisms (SNP) analysis for FIG 1 Yersiniomics database construction pipeline and tools.
Genomes were collected on the RefSeq database (GenBank). RNA-Seq raw data (. fastq files) were collected on the ENA browser and then processed as read counts and fold change. Fold changes from microarray experiments were collected on GEO or from tables and supplemental tables in publications. Fold changes from proteomic experiments were collected from tables and supplemental tables in publications. Data sets were integrated into Yersiniomics and can be consulted via three omics browsers (genomics, transcriptomics, and proteomics) and three viewers (gene, genome, and heatmap). Y. pestis  (41, 42)  and on our cgMLST  (40)  for the other Yersinia species, the biotype and serotype (when relevant), the isolation source, year and country, the number of genes, rRNAs, and tRNAs, and the assembly ID, linked to GenBank, and its FTP link.
A search bar can be used to explore the biological conditions of each experiment, and some filters are accessible, such as the reference genome used, the data type ("Gene Expression" for microarray experiments or "RNA-Seq"), as well as the selection of a specific mutant or growth phase. The proteomics browser also implements a "protein localization" column (i. e. , supernatant, whole cell, or soluble or insoluble fraction), as well as the number of proteins detected in each experimental condition. After selecting one or several biological conditions, their associated transcriptome or proteome values can be visualized in two ways: as a heatmap viewer or as a genome viewer. The heatmap viewer is a sortable table with each line corresponding to a gene in the genome and columns displaying expression comparisons of two biological conditions using a color code proportional to the fold change, facilitating tracking of highly differentially expressed genes.
(ii) The gene viewer. From the home page, a quick access to the gene viewer for reference strains is implemented (Fig. 2 , top). In the gene viewer, each gene is accessible via a gene list or a graphical view after a specific Yersinia genome is selected (Fig. 7 ). A search bar makes it possible to quickly filter the list by gene name, gene locus, or any field present in the "General information" panel as described below. For a specific open reading frame (ORF), information such as gene locus, name, product, position, strand, and size (in base pairs and amino acids) can be found under the "General information" panel. A link to the GenBank protein and the number of genomes presenting a homolog in the Yersiniomics database is also present, in addition to the graphical view presenting the genetic environment of the gene.
Other general information extracted from the "features" field of the GenBank GFF file is also displayed. A synteny based on the SynTView software (50) was also implemented for some reference genomes: Y. pestis CO92 and KIM, Y. pseudotuberculosis IP32953 and YPIII, and Y. enterocolitica Y11 and 8081 (Table  1 ). As synteny data are heavy to download and the SynTView software requires a significant amount of memory and computational power, loading is optional and conditioned to the "Show synteny" button in the "General information" panel. After the loading of SynTView, the software displays the beginning of the genome. When clicking on or searching a gene in the Yersiniomics gene viewer panel, SynTView automatically centers on the corresponding gene. The software allows the user to browse the genetic environment and conservation of each gene among other closely related genomes present in Yersiniomics.
The "Homologs" panel represents the 200 genomes present in the Yersiniomics database next to the phylogenetic tree (Fig. 8 ). For each genome, the homologous locus, old locus, and linked GenBank protein ID are displayed, next to the BLASTP results, such as the percentage of coverage and percentage of similarity on the covered region, or the BLAST E value and bit score. The "Bidirectional" column indicates if the best BLASTP hit of the homologous gene is reciprocal. Genomes are highlighted in blue in the table when searched with the search box, and genomes selected in the table are highlighted in red on the phylogenetic tree displayed on the left. The product of the percentage of coverage times the percentage of similarity is displayed alongside the gene locus in the phylogenetic tree, which can be exported in SVG format.
For most of the RNA-Seq data, if replicates were measured, the P value and adjusted P value were computed and are displayed with the comparisons. The results can be filtered using a cutoff on the P value. For the microarray experiments and the RNA-Seq experiments without replicates, the P value and adjusted P value were set to 0. 0 and the experiments cannot be filtered using the P value. Each comparison can be selected and displayed in two different ways, in the genome viewer or in the heatmap viewer, as detailed above. For RNA-Seq data, an additional panel called "Transcriptomes" displays the transcripts per million (TPM) normalized value (51) of the raw read counts mapped to the gene, allowing the user to assess whether the gene is highly transcribed. For genomes presenting semiquantitative proteomic data, a similar panel called "Protein differential expressions" displays the fold change for the abundance of the gene-encoded protein in the available comparisons, very similarly to the "Transcript differential expressions" panel.
When a specific gene is selected, these tabs are automatically linked to the corresponding entries in each website. This Nucleotide and amino acid sequences can be visualized and downloaded. A search bar to find other genes in the genome is available in the left column. A switch between the chromosome and the plasmids is available in the upper banner next to the "Chromosome/Plasmids" label. For several reference strains, synteny can be shown in the SynTView software. Selection of a gene on the Yersiniomics gene viewer automatically searches for the corresponding gene in SynTView. Genomes are aligned on the searched gene, which is shaded in yellow. Homologous genes are displayed with the same color. Genes of the reference genome can also be searched in the "Gene List" tab. Genome rearrangement between the reference and another genome can be visualized in the "Dot plot" tab.
The "Circular" tab allows the user to map all the genomes on the reference genome on a circular plot. allows very quick access to diverse information, such as the implication in biological pathways in KEGG (Fig. 10A ), the Alphafold prediction structure in UniProt (Fig. 10C ), the protein domains and Pfam families in InterPro, protein-interacting partners identified with methods such as a yeast two-hybrid screen against human proteins (52) accessible in IntAct (Fig. 10D ), or other metalinks such as co-occurrence in abstract computed in the STRING database (Fig. 10B ). A button allows the user to directly open the corresponding website in a new tab of the web browser. Due to cookie policies, we recommend using Mozilla Firefox to access the STRING tab embedded in Yersiniomics. Data loading.
excel). The TPM-normalized count can then be used by each user to compare gene expression within the same sample or compute coexpression networks. Genomic data set description. We collected 200 assemblies of Yersinia genomes, among which 10 were sequenced twice (annotated "bis") by different laboratories with different technologies (such as Sanger or combination of Illumina and Nanopore or PacBio sequencing). Of the 190 unique genomes, we gathered 61 Y. pestis, 24 Y. pseudotuberculosis, and 37 Y. enterocolitica genomes. In addition, 37 strains of the fish pathogen Y. ruckeri were also collected, as well as one complete genome of the insect pathogen Y. entomophaga and 30 genomes of nonpathogenic Yersinia (Table  2 ). In the genomics browser, the "Name (GenBank)" column refers to the GenBank taxonomic assignation, but particular attention should be drawn to the "Most recent assignation (cgMLST)" column, where 9 strains were reassigned by the cgMLST scheme developed in our laboratory  (40).
Of note, the RefSeq-reannotated locus name, recognizable by its ". _RS. " pattern, was used to access the genes differing from the initial widely used locus name, such as "YPO. " for Y. pestis CO92 or "YPTB. " for Y. pseudotuberculosis IP32953. We added this previous name in a column named "Old Locus" in the heatmaps and this old  FIG 8  The gene viewer homolog panel. The table of the fyuA homologs in the Yersiniomics database is displayed on the right, with the gene locus, the BLASTP results (such as percent coverage and percent similarity on this coverage), the E value and bit score, and the name of the corresponding strain. The product of the percentage of similarity times the percent coverage, as well as the name of the homologous gene, are also displayed on the phylogenetic tree on the left and highlighted when selected.
A multi-fasta protein file of the selected homologs can be downloaded. locus is present after the RefSeq locus name in the gene list of a genome, separated by a dash, and followed by the gene name in parentheses. For example, the mioC gene in the CO92 genome can be found as "YPO_RS01005 -YPO0001 (mioC). " The Y. pestis community widely uses the CO92 and KIM locus names to refer to genes, using the formats "YPO. " and "y. ," respectively. However, the KIM "y. " locus can be found only in the KIM101 genome annotation in GenBank, a strain lacking several plasmids (pCD1/pYV and pPCP1/pPla) compared to its parental strain, KIM5. To map the RNA-Seq data to the most complete genome but conserve the known locus numbers widely used by the Y. pestis community, we reannotated the old locus of the KIM5 genome with the KIM101 old locus using a systematic BLAST search between both genomes' open reading frames.
Of note, the YPIII genome in GenBank lacks the pIB1 plasmid (the YPIII pYV virulence plasmid coding for a type III secretion system). Thus, this plasmid was not included in the database. Transcriptomics data set description. Transcriptomic experiments were collected for Yersinia reference strains such as Y. pestis CO92, KIM, 91001, and Pestoides F, Y. pseudotuberculosis YPIII and IP32953, Y. enterocolitica 8081 and Y11, Y. ruckeri CSF007-82, and Y. entomophaga MH96 (Table  3 ). Of note, some strains' data are mapped on genomes of other strains, such as Y. pestis 201 Microtus, whose microarray data were generated using CO92 microarray, or RNA-Seq data that were mapped to strain 91001 Microtus strain, as the strain 201 sequence is assembled only at the scaffold level. This is also the case for Y.
DISCUSSION Over the last 2 decades, the omics revolution has generated an impressive number of data sets which are often difficult to analyze in depth without any prior bioinformatic knowledge. In addition, only a fraction of generated and processed data is generally accessible and/or used in research articles. Here, we aimed at exploiting Yersinia omics data published over the last 20 years to make them accessible in a user-friendly way to biologists without familiarity with deep bioinformatics. We thus constructed and processed a database gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets of Yersinia species, which are browsable at the gene level and according to experimental conditions on the custom-made Yersiniomics website (https://yersiniomics. pasteur. fr/). Notably, three previous studies  (60) (61) (62)  did not measure differential gene expression under their biological conditions.