dataesr/scientific-paragraphs-categorization huggingface dataset:
Metatranscriptomics and RNA sequencing: Cell pellets were re-suspended in TRIzol reagent and subjected to three cycles of 1min bead-beating (0. 5mm silica/zirconia beads) followed by RNA extraction using the Direct-zol RNA Kit (Zymo research) according to the manufactures' protocol. DNA was removed by TURBO DNase (Ambion) until no genomic DNA was detected by PCR. The quality and integrity of the total RNA were checked on the Bioanalyzer system (Agilent). Because RNA yield is low, only 6 samples containing the highest amount of RNA and best RIM values (>6) were selected for RNA sequencing and meta-transcriptomic analysis (data not shown). Ribosomal RNA depletion was performed using the Bacteria RiboZero kit (Illumina). From rRNA-depleted RNA, directional libraries were prepared using the TruSeq Stranded mRNA Sample preparation kit following the manufacturer's instructions (Illumina).
Libraries were quantified on Bioanalyzer DNA chips (Agilent) and Qubit ® dsDNA HS Assay Kit (ThermoFisher). 150 bp paired read sequences were generated on the Nextseq 500 (high output) sequencer according to manufacturer's instructions (Illumina). The multiplexing level was 10 samples per lane. Reads were cleaned for low-quality and adapter sequences using Cutadapt version 1. 11. The level of ribosomal RNAs (rRNAs) remained high in our environmental samples, even after both in vitro rRNA depletion step and optimized RNA extraction methods (see above). We tackled these technical shortcomings by including an in-silico rRNA depletion step to improve mRNA read assembly and subsequent analysis (Supplementary Fig. 2  and Supplementary Tables  2  and 3 ). Ribosomal RNA reads were filtered out by mapping on the Silva database with BWA-MEM v0.
1. 17 (default parameters) and Samtools v1. 9  (36). Then, unmapped reads (mRNA) were subjected to de novo assembly using SPADES v3. 13. 1 (k-mer length 21). From 31% to 42% of trimmed reads coding for rRNA per sample were fast assigned to bacterial families with Kraken  [39]  against the Silva SSU/LSU-Ref database (Release 132)  [40]. 27% to 31% of reads corresponded to not classifiable metagenomics sequences, related to microbial dark matter  [41]. 0. 68% to 12% of reads were classifiable on the 16S rRNA Silva database. (Supplementary Figure  6 , Supplementary Table  3 ). After sequencing, trimming and in silico ribosomal RNA depletion, we obtained 4. 137. 706 putative mRNA reads (57. 77 % of the initial sample) for the Hospital in situ biofilm, and 35. 861. 826 putative mRNA reads (29. 6% of the initial sample) for the Urban in situ biofilm, leading to a reasonable number of contigs to analyse, with 866.
Metatranscriptomic analysis: Mapping the mRNA assemblies to specific reference databases enables to assess the expression levels of functionally viable ARGs within biofilms. A specialized database of the genes targeted by the high-throughput qPCR for the targeted resistome analysis was built (accession numbers available in Supplementary Table  1 ) and used for mapping assembled contigs with BWA-MEM v0. 1. 17 (with default parameters) and SAMtools v1. 9. Then, the transcriptome activity and abundance of ARGs was assessed by mapping reads on assembled contigs  [42]. Reads were mapped against each ARG-like sequence identified from meta-transcriptomic assembly contigs, using Bowtie2 v2. 3. 4. 3 with default settings. The ARG-like sequences with > 100 mapped reads were assigned to ARG transcripts.
To increase the chance of detecting active ARGs in hospital and urban wastewater biofilms, transcripts coding for ARGs were searched against the CARD database (release 08/21/2019) by using the Resistance Gene Identifier (RGI 3. 1. 0) retaining perfect and strict hits only  [43]. The ARG-like sequences were identified as ARG transcripts when their coverage was up to 80% with a minimum of 100 reads mapping. The expression level of a particular ARG transcript in each sample was calculated by summing the number of hits by ARG class (see list of categories in Supplementary table  3 ). In addition, we weighted the identification with the mean of read coverage by ARG class. In addition, we detected ARG-carrying MGEs in WW biofilms, namely the active resistance mobilome. First, assembled contigs were mapped against the ACLAME database [44], using BWA and Samtools.
Subsequently, identified MGE-carrying contigs were again mapped against the CARD database to identify ARG-carrying MGEs. If one MGE-carrying contig mapped to different ARGs, only hits with the best CIGAR (extracted from the SAM file) were considered. All steps described above were implanted in python-based modules, and all modules formed the framework of the pipeline AROM, for ARg On Mobilome (Supplementary Figure  2 ). Lately, we added the module get abundance to normalize gene expression levels according to the sample size (Gb), the length of reads and the length of each ARG  [45]. All changes to AROM (https://github. com/sophiaachaibou/ARGmodules) are tracked in GitHub and the versions managed using bioconda.
Confocal scanning laser microscopy settings and analysis: Z stacking acquisitions were acquired with a confocal microscope (ZEISS LSM880) using a x20 and x100 magnification. Five Z stacks were performed per condition. The following settings were used to measure the fluorescence from the samples: autofluorescence (λexc 405nm/λem 460nm), FITC (λexc 488nm/λem 525nm), tetramethylrhodamine (λexc 561nm/λem 580nm), Nile Red (λexc 641nm/λem 650nm), Syto60 (λexc 641nm/λem 678nm) and TOTO-1 (λexc 514nm/λem 533nm). 3D view analysis was performed with the Imaris software (Oxford Instrument) using acquisitions with the x20 magnification. Surfaces were created using a manual thresholding. The quantitative analysis was performed with the open-source software BiofilmQ (https://drescherlab. org/data/biofilmQ/) on the acquisitions made with the x100 magnification.
Biofilm matrices To test for the effect of sample origin (hospital vs urban WW), environment (in situ vs in vitro) and addition of ciprofloxacin on the composition of the biofilm matrices, the relative abundance of three features were measured, the eDNA, lipid and polysaccharides. For each of these features three models were built. The first type of model focused on data obtained without ciprofloxacin and analyzed for each measured feature, the effect of the variables 'sample origin' and 'environment', as well as their interactions. The second type of model focused on data obtained in the in vitro environment and analyzed for each of the three measured features, the effect of the variables 'sample origin' and 'ciprofloxacin', as well as their interactions. The last type of model is devised to perform a pairwise comparison of each sample, hence, a different model was built for each pair of samples, with one explanatory variable and two levels, corresponding to the sample ID.
Pairwise comparison of the normalized abundance of the resistome data using the Tukey HSD test To compare the relative abundances of genes classes between samples, for each class and biological replicate, we summed the normalized abundances of individual genes per class. Then for each gene class, we fitted the relative abundance with a log linear model with one explanatory variable, and one level for each sample. Finally, we applied pairwise comparisons of samples using the Tukey HSD test implemented by the multcomp R package and p-values were adjusted using the single step method  [47].
Pairwise comparison of biofilm communities, mobilome and resistome For the six types of multivariate description of the biofilms, communities (family and genus levels), mobilome (plasmid and gene levels) and resistome (targeted and non-targeted), we compared each pair of samples by measuring the proportion of the Simpson's diversity that was between samples. Simpson's diversity measures the probability that two entities taken at random from the dataset are of the same type. It is calculated as 1 -∑ 𝑓 𝑖 ², where values of 𝑓. are the frequencies of the entities. To compute the proportion of this Simpson's diversity that is between a given pair of samples A and B, we computed the total diversity using the average of the frequencies of the two samples and the average diversity within the two sample: 𝐷 𝐴𝐵 = 1 - (1 -∑ 𝑓 𝑖𝐴 ²) + (1 -∑ 𝑓 𝑖𝐵 ²) 2 ⁄ 1 -∑ ( (𝑓 𝑖𝐴 + 𝑓 𝑖𝐵 ) 2 ⁄ ) ² This divergence index turns out to also be the formula of the between population fixation index (𝐹 𝑆𝑇 ) used in population genetics, using alleles frequencies  [49].
The microbiota of hospital and urban WW in situ and in vitro biofilms As described in methods, a relatively large proportion of the RNA reads from the metatranscriptomic RNA sequencing approach of the biofilm communities were ribosomal RNA (rRNA) reads. The 16S rRNA reads were filtered out by mapping on the Silva database, allowing for the assignment of a small proportion of those reads (between 0. 68% and 12%) on family level. Hence, this step allowed for additional analysis of the "active microbiota" of these biofilms. Among the classifiable 16S rRNA reads from the metatranscriptomic analysis (methods), we identified a large majority of Enterobacteriaceae in hospital biofilms and a mix of Flavobacteriaceae, Enterobacteriaceae, Streptococcaceae and Francisellaceae in urban in situ biofilm (Supplementary Figure  6  and Supplementary Table  3 ).
Assessing the expression of ARGs based on the CARD database To identify additional ARGs that were not included in our targeted resistome approach, mRNA reads of our samples were mapped against the Comprehensive Antibiotic Resistance Database (CARD) that archives thousands of gene sequences encoding resistance determinants to antimicrobial agents. ARG expression was higher for in situ hospital WW biofilms compared to their in vitro counterpart (10. 4% for hospital in situ biofilm vs about 0. 5% for in vitro of total mRNA reads in the respective samples (Supplementary Table  1 )). The detected expression levels of ARGs in the in situ urban WW biofilms was lower compared to their in vitro counterpart (0. 04% of mRNA reads for the urban in situ vs urban in vitro 0. 4% biofilm). A tenfold higher expression level of ARGs was detected in the in situ hospital WW biofilm (10.
The active resistance mobilome To study the expression of MGEs and more specifically MGEs associated with ARGs, assembled contigs were mapped against the plasmid sequence collection v0. 4 available at the ACLAME database  [44]. Subsequently, plasmid associated contigs identified by this approach were mapped back to the CARD database to identify specifically plasmids that carry ARGs. These ARG associated plasmids are referred to as the "active resistance mobilome" in the respective samples. Global expression of plasmids was very low in all the samples studied (0. 48% of all assembled contigs for hospital in situ biofilm and 0. 14% for urban in situ biofilm (Supplementary Table  1 )). In total, we counted respectively 115 and 66 plasmid-ARG associated contigs for the hospital and urban in situ biofilms and identified a diverse range of expressed plasmids (71 individual plasmids identified across all samples) expressing a diverse range of ARGs that could be grouped into 10 classes conferring resistance to antimicrobials, quaternary ammonium compounds and heavy metals.
The highly active proportion of Enterobacteriaceae in hospital in situ and in vitro biofilms might indeed explain the higher expression levels and the types of MGEs and ARGs detected in these samples, and their response to antibiotic stress. This further highlights the need to study the 'active biofilm community' and might point towards the fact that the key players for accumulation and transfer of ARGs and MGEs in highly anthropized sites such as WW are indeed human-gut associated bacteria. In a recent study, the Plascad tool was designed for plasmid classification, AMR gene annotation, and plasmid visualization  [74]  and they showed that: i) most plasmid-borne ARGs, including those localized on class-1 integrons, are enriched in conjugative plasmids in their datasets, and ii) transfer between AMR plasmids and bacterial chromosomes was mediated by insertion sequences, mainly those belonging to the IS6 family, IS26 and IS6100  [74].
Mosaic hepatocytes displayed high expression of IGF2 pathway partners (IGFBP1, IGFBP2, IGFBP3) and genes involved in lipid metabolism (APOC1, APOC2) (Fig. 4e ). Gene-set enrichment analysis between mosaic 11p15. 5 cells from all 3 patients compared with non-mosaic cells revealed enrichment in fetal hepatoblast, platelet activation, transport of small molecules and lipid metabolism (Fig. 4f ; Supplementary Data 5 and 6). Extracellular matrix was more abundant in the mosaic area in patients #4001 and #3115 but not in patients #3559. Of note, mosaic 11p15. 5 areas did not show overexpression of Wnt/β-catenin target genes (TBX3, GLUL, AXIN2, LGR5, LEF1, Supplementary Fig. 13 ). Only GPC3 progenitor marker gene identified in bulk RNAseq was overexpressed in mosaic hepatocytes in spatial transcriptomics analysis.
Whole-genome and whole-exome sequencing Tumor and non-tumor DNA were extracted using either the Maxwell 16 tissue DNA purification kit (Promega, ref # AS1030) or the AllPrep DNA/RNA/miRNA Universal kit (Qiagen, ref #80224). WGS from 116 samples (51 non-tumor and 65 tumor samples) were performed at the Centre National de Recherche en Génomique Humaine (CNRGH, Evry, France) on an Illumina HiSeqX5 with paired-end reads and an average depth of 30× for non-tumor samples and 90× for tumors. Among them, 3 non-tumor samples were not previously published. Reads were then aligned on hg19 reference genome using the Burrows-Wheeler Alignment tool (BWA)  34. Picard tools (http://broadinstitute. github. io/picard/) were used to remove PCR duplicates, GATK tool (https://gatk. broadinstitute. org/hc/en-us) for local indel realignment and cgpBattenberg (https://github.
5 locus in the non-tumor sample, and N min and N maj are, respectively, the number of minor and major alleles at this locus. In the specific case of cn-LOH, since N min = 0 and N maj = 2 we have: % cells altered = 100 x ð2 xGAF NT À 1Þ ð 2Þ Then, using RRBS and/or MS-MLPA methylation values, the percentage of mosaic 11p15. 5 cells in non-tumor livers was calculated with the following formulae: %cells altered = 100 × IC1 À IC2 IC1 + IC2 ð3Þ When FFPE slides were available, non-tumor liver areas with high IGF2 and low H19 expression in singleplex or duplex RNAscope stainings were annotated as mosaic 11p15. 5 areas (Mosaic area NT ) with QuPath software. The total non-tumor slide area was determined with QuPath software as well (Total slide area NT ). Then, the percentage of mosaic cells was determined as follows: % cells altered = 100 × Mosaic area NT Total slide area NT Methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) To assess IC1 and IC2 methylation levels, we used MS-MLPA ME030-C3 BWS/RSS kit (MRC-Holland) in which ten probes were methylationspecific (4 IC1, 4 IC2, 2 targeting IGF2 promoter methylation).
Reduced representation bisulfite sequencing (RRBS) IGF2 promoter methylation was assessed using RRBS methylation data from 17 non-tumor samples and 87 tumor samples. RRBS was performed with 100 ng of genomic DNA from each sample at IntegraGen (Evry) according to Gu and colleagues' protocol  36. We used BS_Seeker2 to perform alignment on the hg38 genome version. IGF2 promoters (adult promoter P1 and fetal promoters P2-P3-P4) were defined as follows: first, we displayed on the IGV software the distribution of CpG sites detected with RRBS (that are, the CpG sites detected in at least one of the 110 samples from the RRBS cohort). We then defined the promoters so that they contained CpG clusters in the neighborhood of the beginning of the corresponding exon and had a size of around 1000 bp. P1-P4 promoter coordinates (hg19) are available in Supplementary Table  2.
Acknowledgements We thank  Isabelle Janoueix  (U830),  Olivier Delattre  (U830),  Yves Lebouc  (U938),  Maya Arnould  (U1138),  Quentin Bayard  (U1138),  Samantha Schaeffer  (U1138),  Sandrine Imbeaud  (U1138). We would like to thank  Claire Gandon  from  Necker Hospital Tissue Bank and Biobank ,  Julie Tisserand  and  Christophe Avignon  from the  Centre de Ressources Biologiques AP-HP Paris-Saclay  for providing the study materials as well as HEPATOBIO database. FunGeST team (FUNctional GEnomics of Solid Tumors) is supported by  Ligue contre le cancer (équipe labellisée) ,  SFCE  ( Société Française de Lutte Contre les Cancers et les Leucémies de l'Enfant) , the  SIRIC CARPEM ,  PeLiCan. Resist InCa  ( Pediatric LIver CANcer  database to combat RESISTance to treatment,  Institut National du Cancer) ,  France Génomique  ( GEPELIN  project), association  Etoile de Martin ,  Fédération "Enfants, Cancers Santé ", association  Hubert Gouin "Enfance et Cancer ,"  INSERM ITMO Plan Cancer ,  CisMutHep InCa High-Risk High-Gain  ( Institut National du Cancer , grant number  PEDIAHR22-009 ),  LABEX Onco-Immunology (investissement d'avenir ), and  THRIVE EU Horizon funding Mission Cancer  ( EU-HORIZON-MISS-2023-CANCER-01 ).
Data availability The raw data from this study, including WGS, WES, RNAseq, RRBS, snRNAseq, and visium datasets have been archived in the EGA database under accession codes EGAS00001005108, EGAS00001003837, and EGAS00001006692. Raw sequence data (FASTQ files) access is available upon successful application to the 'Data Access Committee' EGAC00001002924 according to the EGA general guidance. Access to these datasets is restricted to comply with European data protection regulations and to protect the privacy and rights of individuals whose data may be included in these datasets. When the application is successful, access is granted for the duration of the approval project. Any utilization of the data for a different project will require prior approval through a new agreement. A response to all initial requests should be done in 6 weeks.
The processed spatial transcriptomics and snRNAseq data can be found in the Figshare repository (https://doi. org/10. 6084/ m9. figshare. 23552595), and source data are provided along with this paper. The fetal liver methylation publicly available data used in this study are available in the GEO database under accession code GSE61278  21. The remaining data are available within the Article, Supplementary Information, or Source Data file. Source data are provided in this paper.
Data sets We extracted spatially explicit (1 • × 1 • grid) historical  and future projected (2015-2100) time-series of simulated physical, biogeochemical, and ecosystem variables within the focus region from CMIP6 and Fish-MIP outputs (see below). Physical and biogeochemical variables were selected based on their role in shaping marine ecosystem responses and included: sea surface temperature (SST, • C), sea bottom temperature (SBT, • C), dissolved surface oxygen concentration (mol m -3 ), dissolved bottom oxygen concentration (mol m -3 ), surface pH, bottom pH, and net primary productivity (NPP; mol C m -2 ; diazotrophs excluded). Ecosystem variables included: total consumer biomass (TCB; g C m -2 ; all vertebrates and invertebrates of trophic level >1, excluding zooplanktonic species), phytoplankton carbon concentration (PHYC; mol C m -3 ), and ZOOC (mol C m -3 ).
ABB acknowledges financial support from the MEOPAR Postdoctoral Fellowship Award 2020-2021, 2021-2022, and the Ocean Frontier Institute Modules G and H funded by the Canada First Research Excellence Fund. HKL acknowledges funding from the Natural Sciences and Engineering Research Council (NSERC) of Canada. Funding for Hubert du Pontavice was provided by the NOAA NEFSC's "New England's Groundfish in a Changing Climate" program. MC would like to acknowledge partial funding from the Spanish National Project ProOceans (PID2020-118097RB-I00), the EU grant agreement No 101059407 (MarinePlan project), and the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000928-S) to the Institute of Marine Science. OM acknowledges the EU H2020 Grant No. 817578 (TRIATLAS). CMP acknowledges support from NOAA grants NA20OAR4310438, NA20OAR4310441, and NA20OAR4310442.
Image credit: Josh Wood. 3 × 3 μm 2 at ID15a using a Kirkpatrick-Baez mirror system (KB) and to 10 × 10 μm 2 at ID15b using Compound Refractive Lenses (CRLs). Diffraction data were collected with a Dectris Pilatus3 X CdTe 2 M detector at ID15a and a MAR555 image plate detector at ID15b. Pilatus3X and MAR555 detectors were placed at 250 mm and 260 mm from the sample. A 20 µm pinhole was mounted at the entrance of the DAC to cut background air scattering. The detector-sample distance and the detector tilt angle were calibrated using LaB 6 and silicon standards for ID15a and ID15b, respectively. Contrary to the MAR555 detector, which was centered with respect to the transmitted direct beam, the Pilatus detector was offcentered, allowing a higher Q-range to be detected. The typical collection time was of ~8 min at ID15a and 30 min at ID15b.
A comparison example between structure factors measured at ID15a and ID15b at ~30 GPa are displayed in Supplementary Fig. 9. As one can notice, the two curves nearly coincide, and most importantly, the onset of the first and second sharp diffraction peaks, FSDP and SSDP respectively, coincide. Because of the larger high quality Q-range obtained from structure factors measured at ID15a (larger detector placed off of the sample center, higher energy), we decided to calculate the x-ray Pair Distribution Functions (PDFs) only from those data. The structure factors collected at ID15b were used, together with those from ID15a, to perform analyses on the FSDP and SSDP, of the S(Q) (Supplementary Figs. 6  and 10 ). Data for PDF analysis were collected at each pressure in 128 exposures of 4 s each.
Particular care was taken during cell loadings to ensure reproducible positions of the diamond Bragg reflections on the detector between sample and background measurements. For each sample dataset the median image was calculated and we subtracted the corresponding median background image. Azimuthal integration to yield I(Q) diffraction patterns was done using pyFAI  49. From each I(Q) the PDF, g(r), was calculated using PdfGetX3  50. The range of Q for g(r) calculations was 0. 8 ≤ Q ≤ 12 Å -1. In order to obtain a reliable PDF from weak, noisy signal, ad-hoc intensity weighting and smoothing of the Q-space input functions were used to remove artifacts such as e. g. ripples, associated with parasitic scatting, particularly generated by the tails of diamond peaks. While peak intensities might not be very precise due to the difficulties of estimating against an unknown background, i.
e. consequence of DAC setup, the peak positions are considered to be correct. Thus, we explain the distinguished intensity drop of the C-O peak of the PDF at 104 GPa (Fig. 3 ), attributed to the PDF generation process. Ab initio molecular dynamics simulations. First-principles molecular dynamics (FPMD) calculations were performed using the Vienna Ab initio Simulation Package (VASP)  51  based on the density functional theory. Effects of the core electrons were replaced by atomic projected augmented wave (PAW) potentials  52. Valence electronic configurations of 3s 2 3p 6 4s 1 , 3s2 3p 6 4s 2 , 2s 2 2p 2 , and 2s 2 2p 4 were used for K, Mg, C, and O atoms, respectively. The Perdew-Burke-Ernzerhof (PBE) exchange-correlation function  53  was used. The wave functions were represented by a plane-wave basis set with an energy cutoff of 495 eV.