dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome data. We retrieved from all the complete genomes available in the NCBI non-redundant RefSeq database in March 2021 (22,255 genomes, 21,520 plasmids) those of Escherichia coli and Staphylococcus aureus species. These resulted in a set of 1,585 genomes of Escherichia coli and 582 genomes of Staphylococcus aureus, including 3,409 and 462 plasmids, respectively. The accession numbers and further information on the plasmids is available in the Supplementary Table  1. The information on the chromosomes and the relevant data is available on the Supplementary Table  2.
Collection of the oriT database and its identification in the complete genomes. We built a collection of experimentally validated origins of transfer. First, we retrieved the 52 oriTs with a status 'experimental' from the already published oriT database by Li and collaborators  31. We expanded this collection by consulting the literature, using as a query "oriT" in the PubMed database (available in September 2021). Among the 708 entries, we screened for experimentally validated oriTs not included in the aforementioned database. This resulted in the retrieval of 47 additional oriTs. However, 1 oriT from the published database and 7 oriTs from the literature were discarded from the collection as only the nic-site sequence was available. This resulted in a final dataset of 91 origins of transfer.
Information on this collection is available in Supplementary Table  3. We used BLAST, version 2. 9. 0+, to identify oriTs  48. The complete genomes of E. coli and S. aureus were indexed with makeblastdb. Then, we used blastn to search for occurrences of each of the 91 oriTs (query) against the database of complete genomes. Due to the short length of the origins of transfer, blastn was used with the option -task blastn-short and an E-value threshold of 0. 01 following the developer's instructions. In cases in which two different oriTs were identified in the same region of a plasmid (overlapping), the oriT hit with the best E-value was retrieved. We identified during this screening an exceptional case of a ~50 kb plasmid with 23 identical oriTs. This plasmid (NZ_CP019265. 1) was discarded from further analysis as we considered it to be a sequencing artifact.
Characterization of conjugative systems and relaxases and plasmid classification on the mobility We used the module CONJscan of MacSyFinder, version 2. 0  49  to identify all the complete MPF systems. The individual hidden Markov model (HMM) hits that were not associated with MPFs deemed complete were used to identify incomplete MPF systems. Relaxases were identified using HMMER version 3. 3. 2  50 , and the HMM profiles employed by the software MOBscan  51. We used the tool hmmsearch (default options) to screen for relaxases in all the proteins annotated in the dataset and kept the 2,195 significant hits with >50% coverage on the profile. A careful analysis of the results revealed that this version of the RefSeq annotations sometimes missed genes encoding relaxases, especially when these genes overlapped others (Fig  S13 ).
To correct for this artifact, we introduced a preliminary step of re-annotation to ensure a coherent annotation of the genes throughout all the genomes, which was then used to identify the MPF and the relaxases. For the annotation, we used the software Prodigal, version 2. 6. 3  52  , with the recommended mode for plasmids and viruses to identify all open reading frames. Hits were then identified as mentioned above. When two different profiles matched the same protein, we kept the one with the lowest E-value. Following the previous characterization, plasmids were classified in different mobility categories depending on their composition in terms of oriT, relaxase, and MPF genes. Plasmids encoding a putatively complete MPF system (including a relaxase) were considered to be conjugative (pCONJ).
Identification of Rolling Circle Replication Proteins For the identification of Rolling Circle Replication (RC-Rep) proteins involved in plasmid conjugation, we first retrieved the RC-Rep of the Staphylococcus aureus plasmid pC194 (NC_002013. 1), a pNT plasmid known to be mobilized through in trans conjugation  36. We used its Pfam profile  53  , Rep_1 (PF01446), to look for related RC-Rep proteins in all the plasmids of E. coli and S. aureus using the HMMER tool hmmsearch (default options, E-value < 0. 001), version 3. 3. 2  50.
Identification of phage-plasmids For the identification of phage-plasmids (P-Ps), we retrieved the E. coli and S. aureus P-Ps recently unveiled  54. The database used in the cited work corresponds to the same RefSeq database (retrieved on March 2021). This way, we were able to identify 222 P-Ps among the 3,409 E. coli plasmids and 1 P-P among the 482 S. aureus plasmids.
Analysis of the pangenome of E. coli and S. aureus plasmids The pangenome of the plasmid-encoded genes of E. coli and S. aureus was identified using the module pangenome of the software PanACoTa, version 1. 3. 1  55. Briefly, gene families were built with MMseqs2 , version 13. 45111, with an identity threshold of 80%. This is the typical threshold for the determination of the E. coli pangenome  30. This way, the 227,428 plasmid-encoded proteins in E. coli were grouped into 11,530 gene families. In S. aureus, the 7,902 proteins were grouped into 1,010 gene families. Some plasmids were not used in the analysis because their annotations lacked protein coding genes: 32 of the 3,409 plasmids in E. coli (0. 94%) and 20 of the 482 in S. aureus (4,15%). Rarefaction curves were performed with the R package vegan, version 2.
Clustering of the oriTs We clustered the oriTs in families, by searching for sequence similarity between all pairs of oriTs in the reference dataset using blastn  48 (Fig S16). BLAST was used with the option -task blastn-short and an E-value threshold of 0. 01. Only matches with >80% identity and >70% coverage of the smallest oriT were kept for the clustering analysis. The clustering was performed with the hierarchical method available in the R package pheatmap, version 1. 0. 12 (default options)  60. The clusters were named after well-known oriTs contained in the cluster: F-like, R6K-like, R64-like, ColE1-like, RP4-like and R46like. The association of each oriT to their oriT family is available in the Supplementary Table  3  and Supplementary Figure  16.
Determination of antimicrobial resistance genes For the identification of antimicrobial resistance genes encoded in the plasmid dataset, we used AMRFinderPlus 61 , version 3. 10, with the default options. This tool combines BLASTP and HMMER to identify the 6,189 resistance determinants available in the NCBI Pathogen Detection Reference Gene Catalog (April 2022). The latter is the result of the curated merging of various widespread-used databases, including CARD  62  , and ResFinder 63 databases, among others  61.
Statistical analysis Except where explicitly stated, all statistical analyses were done with R, version 3. 5. 2. Additionally, all visualizations were performed with the R package ggplot2 64 , version 3. 3. 5, occasionally supported by the R packages ggsignif 65 , version 0. 6. 0 and ggridges 66 , version 0. 5. 3. For the construction and visualization of the networks, we used the R package igraph 67 , version 1. 2. 4. 1 and the software Gephi 0. 9. 2  68  , respectively.
Acknowledgements. We would like to thank  Eugen Pfeifer  for providing the wGRR and PTUs data,  Fernando de la Cruz  and  Maria Pilar Garcillán Barcia  for discussion along the years on plasmid mobility. Microbial Evolutionary Genomics Unit for scientific discussions. INCEPTION  project  [PIA/ANR-16-CONV-0005 ]. Fédération pour la  Recherche Médicale  [Equipe FRM/EQU201903007835]. Labex IBEID  [ ANR-10-LABX-62-IBEID]. HORIZON-MSCA-2021-PF-01-01 EvoPlas-101062386  to  Manuel Ares-Arroyo.
A. B. and V. F. W. acknowledge funding by the  state of North Rhine Westphalia (NRW)  and the  "European Regional Development Fund (EFRE) " , Project  " Cluster Industrial Biotechnology (CLIB) Kompetenzzentrum Biotechnologie (CKB) "  34. EFRE0300095/1703FI04. L. P. , M. H. C. , and F. P. acknowledge financial support by  ANR  (project  O2-taboo ,  ANR-19-CE44-0014 ), the  Université Grenoble Alpes (UGA) , and the  Centre National de la Recherche Scientifique (CNRS). Support for the article processing charge by the  Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University  is acknowledged. The funding bodies had no role in the design of the study or the collection, analysis, or interpretation of data or in writing the manuscript.
5B ). In addition, among the 518 candidate MDV circRNA clones, we analyzed 289 sequences devoid of internal splicing events and processed by noncanonical back-splicing. These circRNAs possess a high variability in size, ranging from 50 to 1428 nt, and in localization (unpublished data). Identification of viral circRNAs during in vivo infection. To demonstrate MDV circRNA expression during tumorigenesis in the natural host, we used a complementary approach because our inverse PCR approach was limited to targeted regions. We scanned the fulllength viral genome to detect circRNA expression by taking advantage of high-throughput RNAseq data obtained after MDV in vivo infections. A bioinformatic pipeline was applied to extract viral sequences from a circRNA enriched data set (accession number GSE138600) that was previously exploited to characterize specific host circRNA signatures during Marek's disease virus infection  (20).
6 ). TA 1 sample analyses revealed circRNAs processed from noncanonical back-splicing at the pp14 locus, with a differential rate according to the tested samples. Three of the TA 1 samples presented a low number of pp14 circRNAs while one TA 1 sample harbored a high noncanonical circRNA expression from this gene corresponding to 39% of all viral circRNAs (Fig. 6C ). In contrast, we found  noncanonical back-splicing sites at the vTR locus at low levels but consistently in the different spleen samples (Fig. 6 ). The bioinformatic pipeline was also applied to another set of high-throughput RNAseq data obtained from an independent experiment investigating MDV in vitro productive infection and focusing on host circRNAs dysregulation  (47). This RNAseq data set (accession number GSE166240) from infected chicken embryo fibroblasts (CEF) was processed to map back-spliced junctions over the MDV genome.
ACKNOWLEDGMENTS We thank  Ann Reum ,  Laetitia Wiggers  and  Denis Rasschaert  for outstanding assistance. This research was funded by a  Fonds pour la Formation à la Recherche dans l'Industrie et l'Agriculture (FRS-FNRS)  grant and a  Fonds Spécial de la Recherche (University of Namur ) grant, both awarded to A. C. , and the  Volkswagen Foundation Lichtenberg  grant  A112662  awarded to B. B. K. We declare no conflict of interest.
ACKNOWLEDGMENTS OI acknowledges support by  CNRS ,  INSERM  and  University Clermont Auvergne. KC acknowledges the support of  National Research Foundation of Korea (NRF ,  NRF-2020R1A2C2007763 ). MP acknowledges the support of the  Ministry of Science and Innovation of Spain  ( PID2020-118038GB-I00 ) and of the  European Union  ( Marie Curie ITN ,  MEICOM 765212 ). CL acknowledges the support of the  BBSRC  grant-aided support as part of the  Institute Strategic Program Designing Future Wheat Grant  ( BB/P016855/1 ).
Acknowledgments: The authors thank  Entraide aux Malades de Myofasciite à Macrophages   E3M  (financial support for L. A. ),  I For Lyme  and  Agence Nationale pour la Recherche  (financial support for J. -D. M. ), and BioSanté éditions. We gratefully acknowledge  R. K. Gherardi  for his careful proofreading and advice as well as the other members of our labs for their contribution to discussions on this subject. Financial support for M. N. B. 's lab was received from  INSERM  and  Sorbonne University  and the  Région Ile-de-France. M. N. B. is supported by the  CNRS , and was associated with the  BioPsy Labex  project.
Finally, Microsporum species distinction appeared less controversial  [19, 20]. To improve MALDI-TOF mass spectral fungal identification, an online application named MSI was launched in 2017. Even if it allowed for an improvement of dermatophyte identification, mistakes between closely related species were still observed with MSI  [21] [22] [23] [24]. Sharing most of its references with MSI, MSI-2 (https://msi. happy-dev. fr/ (accessed on 12 December 2021)) is a new application typified by more accurate algorithms and an extended fungal database  [25]. The aim of this study was to improve MSI-2's library of references for the identification of dermatophytes, investigating two improvements: increasing the number of reference strains per species and including more spectra per reference strain obtained from cultures of various ages.
Species identification was obtained through the alignment of the ITS sequences of the isolates with those of the reference strains  [1]  available in GenBank in MEGA X software version 10. 1. 8 (Kumar, Stecher, Li, Knyaz, and Tamura 2018) to compare the base-to-base sequences. Indeed, it has been demonstrated that database results by automated comparison may be inaccurate  [26]. The genotype determination of isolates belonging to three complexes of closely related species (T. rubrum complex  [15] , T. interdigitale/T. mentagrophytes complex  [18]  and T. tonsurans/T. equinum  [27]  complex) was carried out via comparison with the ITS sequences of published strains also available in GenBank.
(c) Genotypes Genotypes for the TOU-A population were obtained from Frachon et al. [37]. Genotype data for the RegMap  [16]  and 1001G  [17]  datasets were obtained from Arouisse et al. [43]. For the 1001G dataset, this consisted of SNPs that were directly genotyped through whole-genome resequencing (WGS). For the RegMap panel, this consisted of SNPs that were directly genotyped with a 250 K SNP chip and supported by WGS in resequenced accessions, and SNPs imputed by intersecting the RegMap chip genotypes and 1001G WGS genotypes. Of SNPs, 2. 8 M with greater than 95% imputation accuracy were retained, which primarily excludes SNPs with low-frequency alleles.
(d) Broad-sense heritability of glucosinolates We fitted linear mixed models for log-transformed ion counts per milligram of leaf tissue using lme4  [44] , including random intercept effects for the plant accession identity and for the plate containing the sample during extraction and HPLC-MS/MS quantification. The models included all biological replicates per accession (mean N = 3. 93 for TOU-A, see above; mean N = 3. 69 for Brachi et al. [32] ; mean N = 2. 00 for Katz et al. [13] ). Variance components were extracted from the model, and heritability was estimated as the proportion of total variance explained by accession identity, after excluding variance explained by sample plate identity. This approach leverages the independent biological replicates per accession to estimate variance components without the use of SNP genotypes or the matrix of genetic relatedness among accessions and thus is not biased by potential differences in patterns of population structure among mapping panels.
(e) Genome-wide association mapping To standardize comparisons across datasets, analyses were conducted identically for the TOU-A, 1001G and RegMap datasets. First, best unbiased linear predictors (BLUPs) were extracted from the linear mixed models above; for one dataset  [6]  that pooled biological replicates, abundances from the single technical replicate per accession were used directly. Values were converted to z-scores so that GWAS would produce effect size estimates in units of phenotypic standard deviations. Second, GWAS were implemented as linear mixed models in GEMMA v0. 98. 1  [45] , including a centred genetic relatedness matrix (-gk 1) to account for population structure. Significance per SNP was assessed by Wald Tests (-lmm 1). Finally, to estimate the proportion of variance explained by a given SNP, we fitted a linear mixed model using lme4qtl v.
Because peaks of association at known GSL biosynthetic loci in previous GWAS reside tens or even hundreds of kb from the causal genes  [13, 32, 34] -which may arise from extended causal haplotypes  [34] , structural variants, or intergenic regulatory variants-we defined candidate SNPs as those within 30 kb of known biosynthetic genes. For the three loci with significant SNPs in our re-analysis of the 1001G and RegMap datasets, for which the causal genes are well-established, we further extended these windows in 10 kb increments until they captured 90% of the SNPs within 0. 5 Mb of the known causal loci (AOP2/3, GS-OH, MAM1/3) that harboured significant associations with single GSL molecules or precursor:product ratios in those datasets.
(i) Significant associations The identity of associated loci in TOU-A depended on how GSL phenotypes were represented. First, we performed separate GWAS for the abundance of each molecule. This approach cumulatively uncovered significant associations at five biosynthetic loci (figure  3a ). By contrast, only four cumulative associations (three per dataset) were recovered using the same approach in a re-analysis of three previous GWAS datasets, which consisted of mapping populations spanning the European continent (N > 300 accessions; electronic supplementary material, figure  S3a ). When traits are correlated, as expected for pleiotropic traits such as metabolites from the same biosynthetic pathway, GWAS approaches that use information from multiple traits in a single analysis may increase power  [50].
The association at CYP79F paralogs was recovered in our re-analysis of one European Arabidopsis dataset (electronic supplementary material, figure  S3 ), strengthening the evidence that CYP79F is a broadly important determinant of GSL profile variation across populations and species. Two distinct loci harbour paralogous GS-OX genes that catalyze the S-oxygenation of methylthioalkyl to methylsulfinylalkyl GSLs with broad substrate specificity. While natural variation in the locus containing GS-OX2, GS-OX3 and GS-OX4 had been detected through QTL mapping with biparental RILs  [51, 52] , neither locus had been detected in the three large, European GWAS panels. In addition to harbouring a significant association when considering common variants (MAF > 0. 05; figure  3a ), GS-OX1 harboured the strongest genomewide association for many molecules when slightly rarer variants were considered (MAF > 0.
Even in cases where various genotyping and statistical approaches do largely succeed in mitigating specific confounding factors, integrating them to address many factors simultaneously is challenging. For many research questions, the use of local mapping populations in which these Bolded genes are novel associations, defined as those significantly associated in TOU-A but not in our re-analysis of three datasets with geographically broad European mapping panels. A '+' indicates that multiple paralogous genes at a locus could contribute to the association (e. g. CYP79F1 and CYP79F2 are represented as CYP79F+). The pathway and enzyme positions are based on Harun et al. [31]. Note that additional steps producing GSLs that accumulate only at very low levels in leaves are omitted. (Online version in colour.
Acknowledgements. We thank  Xiaohao Guo  for assistance harvesting samples;  John Zdenek  and  Tommy Clark  for assistance with plant care; and  Bader Arouisse ,  Ella Katz ,  Daniel Kliebenstein ,  Arthur Korte ,  Baptiste Mayjonade , and members of the  Bergelson Laboratory  for sharing data and/or helpful feedback. Amélie Vergnol  was a student in the  Magistère de Génétique Graduate Programme at Université de Paris  when the work was conducted.
Interestingly, MTM-HCC is characterized by increased levels of alpha-fetoprotein and activation of angiogenesis; the antitumour effect of Ramucirumab or other anti-angiogenic drugs in patients with this subtype of HCC should be assessed. 41 uring trans-arterial chemoembolization (TACE), the ischemic damage caused by the arterial occlusion induces the production of the hypoxia-inducible factor (HIF)-1α, a known modulator of PD-L1 expression, decreased the percentage of T-regs and increased the CD4+/CD8+ ratio. 44 n particular, Montasser et al in their series with 11 cases of MTM-HCC demonstrated an increase of PD-1 and PD-L1 expression in HCC following TACE that supported the use of TACE in combination with immunotherapy. 45 his rationale guided the design of trials testing the combination of TACE and anti-PD-1 mAb (like, in NCT03397654 trial -pembrolizumab after TACE-or NCT03143270 trial -nivolumab with drug-eluting bead-TACE).
9D  and Supplementary Figs. 10-11 were drawn using the Huttenhower Galaxy web application (https:// hutte nhower. sph. harva rd. edu/ galaxy) via the LEfSe algorithm (Galaxy Version 1. 0)  39. Briefly, P values were calculated based on an alpha value for the factorial Kruskal-Wallis test among classes an alpha value for the pairwise Wilcoxon test between subclasses (both set at 0. 05 or 0. 01 as reported in figures) and a threshold on the logarithmic LDA score for discriminative features, set to 2. 0 or as reported in figure legends. Diversity indices were calculated using the software PAST 4 (Hammer, Ø. , Harper, D. A. T. , and P. D. Ryan, 2001. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 9 pp). The mean of each diversity index can be found at the following link https:// www.
A total of 256 transients were collected into 65,536 data points using a spectral width of 20 ppm and an acquisition time of 2. 7 s. 1  H NMR spectra of fecal samples were acquired using the Carr-Purcell-Meiboom-Gill (CPMG) spin echo pulse sequence with presaturation, with a total spin-echo delay of 320 ms to attenuate broad signals from macromolecules. A total of 256 transients were collected into 65,536 data points using a spectral width of 20 ppm, a relaxation delay of 5 s and an acquisition time of 2. 7 s. Prior to Fourier transformation, an exponential line broadening function of 0. 3 Hz was applied to the Free Induction Decays (FID). All NMR spectra were phase-and baseline-corrected and referenced to the chemical shift of TSP (0 ppm) using Topspin (V3. 2, Bruker Biospin, Germany). Original sources of method description as already published  [41] [42] [43].
Statistical analysis. Data for diversity indices and metabolome analyses are presented as mean ± SD. Statistical analyses were performed by 2-way ANOVA followed by a 2-stage linear step-up procedure of Benjamini, Krieger and Yekutieli to correct for multiple comparisons by controlling the False Discovery Rate (< 0. 05), by using GraphPad Prism version 9. 3. 0 for Windows Vista (www. graph pad. com, GraphPad Software, San Diego, CA). Values were considered significant starting at P < 0. 05 or as reported. For the taxonomical and predictive functional analysis of gut microbiota significant values were considered starting at P < 0. 05 or as reported. Principal Component Analysis graphs were drawn and related statistical analyses were performed using PAST 4 0. 08 (https:// www. nhm. uio. no/ engli sh/ resea rch/ infra struc ture/ past/) and calculating Bray-Curtis distance with 1-way PERMANOVA analysis with Bonferroni's correction.
Patients and samples For our discovery series, we used The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) public dataset. It consisted of patients with primary HCC treated by surgical resection in more than 20 different clinical centers. 16 The inclusion criteria were as follows: i) unequivocal morphological features of HCC (all slides were reviewed by a pathologist specializing in liver disease [JC], and cases with features suggestive of combined hepatocellular-cholangiocarcinoma were excluded), ii) > -1 available digital histological slide(s) from formalin fixed-paraffin embedded material and iii) available gene expression profiling (obtained by RNA sequencing). Data and slides were accessed and downloaded in March 2020. The validation series consisted of primary HCC samples developed in patients treated by surgical resection in Henri Mondor University Hospital (Créteil, France).