dataesr/scientific-paragraphs-categorization huggingface dataset:
Data description Young differentiating xylem and cambium tissues were harvested in June 2015 from 480 Populus nigra trees from a common garden located at Orléans (France) (241 genotypes, 2 clonal trees per genotype, Data file 1  [10] , Data set 1  [11] ). RNA from the xylem and cambium were extracted with RNeasy Plant kit (Qiagen, France) according to manufacturer's recommendations. Treatment with DNase I (Qiagen, France) was carried out to ensure elimination of genomic DNA. RNA was eluted in RNAse-DNAse free water and quantified with a Nanodrop spectrophotometer. RNA from xylem and cambium of the same plant were pooled in an equimolar extract (250 ng/ μL) and sent to the sequencing platform. The sequencing platform POPS (transcriptOmic Platform of Institute of Plant Sciences-Paris-Saclay) prepared the RNAseq libraries from polyA-RNA selection using the TruSeq_ Stranded_mRNA_SamplePrep_Guide_15031047_D protocol (Illumina, California, U.
S. A. ). Identity of each sample was checked with the following procedure: a first round of variant detection was performed with FreeBayes (v. 1. 0. 0)  [12] , then IBS (Identity By State) was calculated between samples of the same individual, as well as between the samples of the present study and the 852 individuals that had been previously genotyped with the Illumina 12 K Infinium Bead-Chip array  [4]  (Data file 2  [13] ). After removing sampling errors and correcting identities, 241 unique genotypes were considered corresponding to 461 FASTQ files (Data set 2  [14] ). The sequences for each sample were processed with the pipeline defined in Rogier et al. [7]  with small modifications to detect SNVs. All experiment steps (from growth conditions to bioinformatic analyses) are available in the CATdb database (Data set 3  [15] ).
1. 0. 0)  [12]  in a multi-sample mode and (iii) the mpileup tool from SAMtools (v. 1. 3. 1)  [23]  in a multisample mode followed by bcftools (v. 1. 3. 1)  [24]. The resulting 3 files (one per caller) were filtered with VCFtools (v. 0. 1. 15)  [25]  to obtain only the intra-specific (Populus nigra) biallelic SNV with a variant quality score (QUAL) threshold over 30. These filtered files were then combined together with the vcf-isec tool from VCFtools and we only kept the genotype calls that were detected by at least 2 variant callers. Otherwise, the genotype call was set as a missing value for this particular individual. As there remained some missing data (Data file 3  [26] ), genotype imputation was performed using the Fimpute program (v. 2. 2)  [27]  for the SNV located on the chromosomes/scaffolds that contain at least 2 SNV.
Limitations • The SNVs are limited to expressed genes: 26,909 vs a maximum of 41,335 P. trichocarpa protein-coding genes. We found a correlation between expression level and SNV density at gene level, which is likely due to an increase in coverage for highly expressed genes (Data file 7  [32] ). • RNA were extracted from young differentiating xylem and cambium tissues, which might not be representative of transcriptional activity of other tissues of the tree. The found SNVs are therefore specific to the genes expressed in these two tissues. Nevertheless, this makes this SNV dataset clearly appropriate to study the genetics of wood formation. • The SNVs are limited to the gene space: 92. 55% of the found SNV are exonic, intronic, in the 3'UTR (UnTranslated Region) or in the 5'UTR (Data file 7  [32] ).
Availability of data and materials The raw RNAseq sequences are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive with the SPR188754 identifier  [14]. The 241 associated accessions can be found in the GnpIS Information System under the collection "POPULUS_NIGRA_RNASEQ_PANEL"  [11]  or with the DOIs in the table of the studied genotypes  [10]. The steps of the experiment (from growth conditions to bioinformatic analyses) are described in the CATdb database  [15]. The other data described in this Data note can be freely and openly accessed on the Recherche Data Gouv repository under https:// doi. org/ 10. 15454/ 8DQXK5 [33]. Please see Table  1  and references  [13, 26, [28] [29] [30] 32]  for details and links to the data.
Methods Design. The study used a sequential mixed-methods design with three stages: a survey, rapid evidence synthesis, and stakeholder focus groups (Fig. 1 ). Ethical approval was granted by Lancaster University Faculty of Science and Technology Research Ethics Committee (FST19070), and all methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants prior to data collection. Participants for the survey and focus groups were purposively sampled from three pre-defined stakeholder groups: • Farm-level o Farmers, farmer associations or cooperative representatives o Farm-or agri-environment-focused non-governmental organisation representatives o Farm-or agriculture-focused policy makers To present stakeholders with the discussion topics, we developed crop improvement "option cards", which displayed 15 potential crop improvement options organised into three categories: yield, nutrition and sustainability.
Participants were asked to fill in a blank option card (#16) with a crop improvement they thought was missing from the 15 option cards and this additional input was discussed at the end of the focus groups. Participants were encouraged to discuss the relative merits of their suggestions and agree on the most important. Prompts were used to probe participant choices-or why certain options had not been mentioned. Focus group analysis. Adopting a Framework Analysis approach  33, 34  an initial coding framework was developed by open coding of transcripts associated with each stakeholder group by the moderator responsible for that group. After these were agreed through consultation with at least one other member of the research team, the transcripts were fully coded and analysed using NVivo software.
Acknowledgements This research was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 817690. We would like to thank all the participants of our focus groups, pilots, and online surveys for their invaluable input, and  Branwen Miles  from  COPA COGECA  for her assistance in recruiting farm-level participants. We also thank all the members of the CropBooster-P consortium who gave feedback and attended events throughout this stream of work, particularly  René Klein Lankhorst ,  Gijs Kleter ,  Jean-Pierre Cohan ,  Remi Duchesne ,  Christian Kohl ,  Dörthe Krause ,  Nick Vangheluwe , and  Martin Parry , as well as those colleagues at who provided useful insight to our work, notably Ian Dodd at  Lancaster University  for his contribution to contextualising the water use evidence synthesis.
Results Concord is a polyphyletic Salmonella serovar found in three diverse super-lineages High-quality whole-genome sequence data was used to reconstruct the population structure of the 284 confirmed S. Concord isolates by inferring a neighbour-joining tree based on core-genome multilocus sequence typing (cgMLST) distances. Three polyphyletic Salmonella super-lineages, i. e. genetically diverse groups with up to 2000 allelic differences  32  , were identified, namely HC2000_750, HC2000_141 and HC2000_177997, and are hereafter referred to as super-lineage A, B and C respectively (Fig. 1A ). Querying the Enter-oBase database for isolates that were part of these three superlineages resulted in identifying a set of 1430 isolates comprising eight different serovars, all with the same O:7 serogroup (formerly known as group C1) (Fig.
12). Reference genomes were constructed from Oxford Nanopore reads and polished with Illumina reads. The size column reports the size in base pairs (bp) of each chromosomal integration or plasmid. Only AMR genes that were exact database matches and contributed to the MDR, XDR or PDR genotype were listed. Reference genomes for three susceptible isolates (with laboratory identifiers 679052, H044240362 and H04340470) were not shown. Plasmids displayed unique structural arrangements within and between lineages (Table  1 ). IncHI2 plasmids, occurring in L3, L8, and chromosomally integrated in L5, had a conserved backbone, and differed in those regions harbouring AMR markers (Supplementary Figure  13 ). Each AMR region harboured multiple insertion sequences (Table  1 ). A 415 kb circular contig of a complete L5 genome contained backbone elements originating from both IncHI2 and IncA/C2 plasmids present in L3 and L5 (Supplementary Fig.
High concordance between MDR, PDR and XDR genotype and phenotype A selection of 56 isolates covering multiple S. Concord super-lineage A lineages and different clinically relevant genotypic AMR combinations (susceptible, MDR, XDR and PDR), were subjected to antimicrobial susceptibility testing (AST) to assess the association of AMR genotype with its phenotype. Of the 56 isolates tested, 32 showed phenotypic MDR, nine showed XDR and three showed PDR (Supplementary Data 2). Overall, the AMR genotype was a good predictor of phenotypic MDR, XDR and PDR in S. Concord with a high concordance between genotype and phenotype, ranging from 98% to 100% (Supplementary Table  2 ). The contribution of each combination of resistance genes to the minimum inhibitory concentration (MIC) of an antimicrobial compound is shown in Supplementary Fig.
IncHI2 plasmids carrying genes encoding XDR and PDR are responsible for high AMR in the ongoing invasive S. enterica serovar Typhimurium (S. Typhimurium) epidemic in SSA  7, 40, 41. IS1R is a known mediator of chromosomal integration of AMR in Enterobacterales  42, 43  and IS26 mediated structural differences in the AMR regions of Salmonella genomic island 1 in S. enterica serovar Kentucky  44. Chromosomal integration of AMR, which we observed in L3, L4, L5 and L8, was reported previously in S. Concord  20  and other salmonellae including S. Typhimurium DT104  45, 46  and other Enterobacterales  47, 48. Chromosomal integrations are considered an effective mechanism for the stable dissemination of AMR genes  47. However, in our dataset, there is no indication that the chromosomal integration of AMR has facilitated the expansion of resistant S.
Ethics statement This study relied exclusively on bacterial isolates and associated metadata collected under local mandates for laboratory-based surveillance of bacterial pathogens and organisms. In the event that the listed institutes have collected personal identifiable information, it was deleted during the preliminary phase of data collection for this project. Therefore, in all instances, neither informed consent nor approval from an ethics committee was required. Isolates or genome sequences were obtained from the following institutes: Institut Pasteur (France), Technical University of Denmark (DTU Denmark, Denmark), the United Kingdom Health Security Agency (UKHSA, United Kingdom), Jimma University (Ethiopia), US Centres for Disease Control and Prevention (USA), Institute of Tropical Medicine Antwerp (Belgium), Sciensano (Belgium), The Center for Food Safety and Applied Nutrition (CFSAN) of the Food and Drug Administration (FDA) (USA), and the National Institute of Public Health Czechia (Czechia).
S. Concord sequence data collection Short-read sequencing data of 310 S. Concord isolates were initially included in the study, of which 126 originated from the public domain and 184 were generated as part of this study. Metadata was collected in Microsoft Excel v16. 0, listed in Supplementary Data 1, and  The isolates were recovered in 12 different countries, including France (n = 116), UK (n = 53), USA (n = 39), Belgium (n = 17), Ethiopia (n = 27), Austria (n = 9), The Netherlands (n = 4), Denmark (n = 4), Turkey (n = 4), Czechia (n = 2), Ireland (n = 2), and Israel (n = 2). For five isolates the country of isolation was unknown. The great majority of the isolates were from humans (90. 5%, 257/ 284). For 63. 8% (164/257) of the human isolates the specimen types were known and included stool (n = 148), blood (n = 10), urine (n = 3), pus (n = 2), and meconium (n = 1).
Short-read sequencing and quality control Illumina (San Diego, California, USA) short-read sequencing data was obtained for 184 isolates sequenced for this study. In brief, all isolates were cultivated on tryptic soy agar (TSA) plates or BD Columbia Agar with 5% sheep blood (Becton Dickinson GmbH, Heidelberg, Germany) and incubated overnight at 37 °C. To ensure purity, a single colony was sub-cultured and again incubated overnight. Next, a single colony or swipe was added to 10 mL Tryptic soy broth (TSB) or Lysogeny broth (LB) for overnight incubation with agitation. From this liquid culture, 300 µL was added to a 1. 5 mL Eppendorf tube for DNA extraction with the Gentra Puregene Yeast/Bact. Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Sequencing was performed at the Wellcome Sanger Institute (Hinxton, UK) on the Illumina HiSeq X10.
For Illumina data of the 126 isolates that were sequenced by other institutions, isolates from UKHSA were sequenced as described in Chattaway et al. 2019  58  , isolates from Jimma University were sequenced as described in Perez-Sepulveda et al. 2021  59  , seven isolates from Institut Pasteur (lab ids: 156 K, 202109373, 202101195, 201804751, 202205355, 202001598, 202005029) were sequenced as described in Jones et al. (  2019 )  60  and isolates from the CDC and FDA collections were sequenced as part of the PulseNet surveillance system  61. All short-read data were made publicly available. Accession numbers are listed in Supplementary Data 1. Raw sequencing reads were trimmed with Trimmomatic 62 v0. 39. Read quality was assessed with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) v0.
11. 9 and MultiQC 63 v1. 8. Sequencing quality was further assessed in terms of estimated read depth and mapping-based coverage. The estimated read depth was calculated as the number of sequenced bases divided by the length of the S. Concord reference genome. Coverage was calculated as the fraction of the reference chromosome that was covered by at least 10 reads with base quality 20 and mapping quality 60. For this purpose, reads were aligned to the reference genome using minimap2 64 v2. 17-r941, clipped alignments were filtered out using samclip (https:// github. com/tseemann/samclip) v0. 3. 0, and bam files were further processed using samtools 65 v1. 9. Nine isolates in total with estimated read depth <40 and coverage <85% were discarded. Taxonomic read classification with Kraken2 66 v2.
Annotation and AMR determinant detection Reference genomes and draft assemblies were annotated with Prokka 69 v1. 14. 6 and subjected to in silico serotype determination using SeqSero2 70 v1. 2. 1. In addition, all genome sequences were compared to the PlasmidFinder 71 database (retrieved on March 1, 2020) with Abricate (https://github. com/tseemann/abricate) v0. 9. 9 to type replicon genes. Only hits with 90% identity and 90% coverage were retained. The presence of known antimicrobial resistance genes and chromosomal point mutations was assessed using AMRFinder 72 v3. 10. 30 with the 'organism' option set to 'Salmonella' (database version 2022-05-26. 1).
Phylogenetic analyses A neighbour-joining tree of the entire set of S. Concord isolates was constructed based on core-genome multilocus sequence typing (cgMLST) distances. This analysis was caried out using Enterobase platform and relied on the cgMLST V2 plus HierCC V1 scheme, the NINJA algorithm and the GrapeTree 73 visualisation tool. The resulting tree is publicly available via https://enterobase. warwick. ac. uk/ms_tree? tree_id=81010 and can be inspected alongside the HierCC clusters. Sequence type, cgMLST clusters at the HC0, HC900 (eBurst groups) and HC2000 (super-lineages in Salmonella) levels were also included in Supplementary Data 1. Enterobase was searched on 21/07/2022 for additional Salmonella isolates that were part of three HC2000 clusters that harboured S. Concord isolates.
This dataset can be accessed via https:// enterobase. warwick. ac. uk/species/senterica/search_strains?query= workspace:79432. A detailed S. Concord super-lineage A (HC_2000_750) SNP-based phylogeny was inferred from genome sequencing data of the 245 S. Concord isolates part of this super-lineage. In brief, snippy (https:// github. com/tseemann/snippy) v4. 6. 0 was used to produce a core single nucleotide polymorphism (SNP) alignment of 20641 polymorphic SNP sites. A large proportion of these sites were flagged by Gubbins 74 v3. 2. 1 as recombinant. The remaining 7148 non-recombinant variant sites were extracted using snp-sites (https://github. com/sangerpathogens/snp-sites) v2. 5. 1 and used to infer a phylogenetic tree using RAxML-NG 75 v0. 9. 0, employing the general time-reversible model with gamma-distributed rate heterogeneity and the Lewis ascertainment bias correction (--model GTR + G + ASC_LEWIS).
The tree with the best maximum likelihood score out of 100 trees was retained and midpoint rooted. Ggtree 78 v2. 2. 4 was used to construct phylogenetic tree visualisations in R. Pairwise SNP distances for S. Concord super-lineage A were calculated using snp-dists (https://github. com/tseemann/snpdists) v0. 7. 0 from the same SNP alignment used for phylogenetic inference, but without recombination removal. The resulting SNP matrix was visualised using ComplexHeatmap 79 v2. 8. 0 in R.
Read length distributions and yields per barcode were inspected with PycoQC 81 v2. 5. 2. Taxonomic classification with Kraken2 confirmed that all reads originated from the genus Salmonella and were not a contaminant. FASTQ files were filtered using filtlong (https://github. com/rrwick/Filtlong) v0. 2. 0, retaining 95% of the highest quality reads of at least 1000 base pairs (bp). FASTQ files for each isolate were submitted to the ENA and accessions are listed in Supplementary Data 1. Candidate assemblies were generated using Canu 82 v2. 1. 1, Redbean 83 v2. 5, Raven 84 v1. 3. 0 and Flye 85 v2. 8. 2, and used to build one consensus sequence for each replicon per sample with Trycycler 86 v0. 4. 1. The resulting consensus assemblies were polished with ONT reads using Medaka (https://github. com/nanoporetech/medaka) v1.
2. 2, utilising the r941_min_high_g360 model. Consequently, three rounds of Illumina read polishing were performed using Pilon 87 v1. 23 with the minimum depth, minimum mapping quality and minimum base quality set to 10, 60 and 20 respectively. Nanopore reads were mapped back to the resulting references using minimap2 to assess the mean sequencing depth per contig as calculated with Mosdepth 88 v0. 3. 1. The mean depth of coverage for each contig per reference genome is reported in Supplementary Table  3  and ranged from 43 to 1881. Genomes were annotated and queried for AMR genes and mutations as described under 'Annotation and AMR determinant detection'. Prokka annotation files were searched for insertion sequences. Mauve 89 v2. 4. 1 was used to align reference genomes and detect large chromosomal insertions or deletions which were visualised with the genoplotR 90 package v0.
Several other antibiotics, were included, namely meropenem, gatifloxacin, tigecycline, colistin, temocillin, and combination agents ceftolozane/tazobactam, ceftazidime/avibactam, piperacillin/tazobactam and meropenem/vaborbactam. Resistance was interpreted according to CLSI M100Ed31E  91. Azithromycin resistance was interpreted as a MIC higher than the epidemiological cut-off for (i)NTS, which was reported to be 16 mg/L. For temocillin and tigecycline, the epidemiological cut-offs of 16 mg/L and 0. 5 mg/L reported by the EFSA  92  were used. arising from this submission. M. A. C. is affiliated the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Genomics and Enabling Data at University of Warwick in partnership with the UK Health Security Agency (UKHSA), in collaboration with University or Cambridge and Oxford.
M. A. C. is based at UKHSA and the views expressed are those of the authors and not necessarily those of the NHS, UKHSA, the NIHR or the Department of Health and Social Care. The laboratory of F. X. W. belongs to the "Integrative Biology of Emerging Infectious Diseases" Laboratory of Excellence funded by the French Government "Investissement d'Avenir" programme (grant no. ANR-10-LABX-62-IBEID). The French National Reference Center for Escherichia coli, Shigella and Salmonella is co-funded by Santé Publique France and the Institut Pasteur. Genome sequencing performed at the Earlham Institute as part of the 10KSG consortium was supported by the Global Challenges Research Fund data and resources grant (BBS/OS/GC/ 000009D), and the BBSRC National Capability in Genomics and Single Cell (BB/CCG1720/1) grant via members of the Genomics Pipelines Group.
Acknowledgements This work was financially supported by the  Research Foundation -Flanders  (FWO:  SB Ph. D. fellowship  1S40018N  to W. L. C. ). The computational resources and services used in this work were provided by the  HPC core facility CalcUA of the University of Antwerp , and the  VSC (Flemish Supercomputer Center) , funded by the  Research Foundation -Flanders (FWO)  and the  Flemish Government. We acknowledge the support of  BIOMINA (Biomedical Informatics Network Antwerpen). The work by S. V. P. and G. D. is funded in part by a grant from the  Bill & Melinda Gates Foundation  ( OPP1151153 ). This research was funded by the  National Institute for Health Research [Cambridge Biomedical Research Centre  at the  Cambridge University Hospitals  NHS Foundation Trust]. K. M. and N. R.
Data availability The sequencing reads generated using Illumina, Nanopore, and PacBio technologies can be accessed through ENA/SRA, and the corresponding accessions for each isolate can be found in Supplementary Data 1. Furthermore, assembled short-read data can be obtained from EnteroBase (https://enterobase. warwick. ac. uk/species/senterica/ search_strains?query=workspace:79416). The data underlying Fig. 1  are accessible via Enterobase (https://enterobase. warwick. ac. uk/ species/senterica/search_strains?query=workspace:79432), while the data underlying Fig. 2 , including metadata, AMR genes, and replicon genes, are available through Supplementary Data 1. Antimicrobial susceptibility testing data, which are the basis of Supplementary Figures 15 and 16, and Supplementary Table 2, can be obtained from Supplementary Data 2.
The PlasmidFinder database (retrieved on March 1, 2020) is available at https://cge. food. dtu. dk/services/ PlasmidFinder/, while the AMRFinder database (used database version: 2022-05-26. 1) is accessible via https://www. ncbi. nlm. nih. gov/ pathogens/antimicrobial-resistance/AMRFinder/. Enterobase was searched on 21/07/2022 for all isolates included in HC_2000, and the search result can be accessed at https://enterobase. warwick. ac. uk/ species/senterica/search_strains?query=workspace:79432. Source data are provided with this paper.
, 2014; Solomon et al. , 2008). Some degree of temporal variation can be accounted for, like the long-term decrease in oceanic 13 C following industrialisation (Suess Effect:  Gruber et al. , 1999) , while tissue type can also improve temporal inferences. For instance, BFT vertebrae retain multiple years of isotopic foraging signatures across their growth axes  (Andrews et al. , 2023)  and therefore bone isotope values dampen out intra-and inter-annual variation, providing an average across years of foraging prior to capture. In this study, we examine a long time-series of isotopic data on Atlantic bluefin tuna, including 98 archaeological and archived bones from 11 eastern Atlantic, Mediterranean and Black Sea locations ca. 1st century to 1941 CE, and 20 modern samples. First, we analysed their δ 13 C, δ 15 N and δ 34 S isotope values to characterise the foraging ecology of Black Sea BFT, whose distribution and feeding habits remain unknown.