dataesr/scientific-paragraphs-categorization huggingface dataset:
H. ), and the  Foundation of the University of Strasbourg  (HEPKIN to T. F. B. and Y. H. ) and the  Institut Universitaire de France  (IUF; T. F. B. ). M. H. is supported by an  ERC   CoG grant  (HepatoMetaboPath) and  EOS  grant and by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project-ID  272983813-TRR 179 , and Project-ID  314905040 SFB TR209. This work has been published under the framework of the LABEX  ANR-10-LABX-0028_HEPSYS  and  Inserm Plan Cancer  and benefits from funding from the state managed by the  French National Research Agency  as part of the  Investments  for the future program. We thank  Prof. R. Bartenschlager  ( University of Heidelberg, Germany ) for providing plasmids for production of HCVcc Jc1 strains,  Dr. F. Chisari  ( The Scripps Research Institute, La Jolla, CA ) for the gift of Huh7.
Data availability All data generated in this study are provided in the Supplementary Materials, in Source data file or have been deposited at NCBI Gene Expression Omnibus database. For specific questions, contact the corresponding authors and lead contact, Prof. Thomas F. Baumert (thomas. baumert@unistra. fr). The genomic dataset generated in this study and presented in Figs. 1, 2, 4g, h, 6, and 8a, as well as Supplementary Figs. 1  and 3  have been deposited at NCBI Gene Expression Omnibus database as a super series under accession code GSE66843 "A cell-based model unravels drivers for hepatocarcinogenesis and targets for clinical chemoprevention". Subseries: GSE66841: gene expression profiles of Huh7. 5. 1 and HepG2 cells infected with HCV, HBV, HDV, and alcohol, and clinical liver tissues treated with various compounds of coculture with LX2 cells.
GBS data analysis and linkage mapping Illumina sequences were quality filtered with the process_radtags program of Stacks v2. 3  (Catchen et al. , 2011)  to remove low-quality base calls and adapter sequences. We followed the Rochette & Catchen protocol  (Rochette & Catchen, 2017)  to obtain a de novo catalog of reference loci. Briefly, the reads were assembled and aligned with a minimum stack depth of 3 (-m=3) and at most two nucleotide differences when merging stacks into loci (-M=2). We allowed at most two nucleotide differences between loci when building the catalog (-n=2). Both parental and all offspring FASTQ files were aligned to the de novo catalog using Bowtie2 v2. 2. 6  (Langmead & Salzberg, 2012) , the option 'end-to-end' and 'sensitive' were used for the alignment. At this step, one.
bam file was obtained per individual to construct the linkage map with Lep-MAP3  (Rastas, 2017). A custom python script was used to remove SPN markers with reads coverage <5. After this step, the script calls Samtools v1. 3. 1 and the script pileupParser2. awk (limit1=5) to convert. bam files to the format used by Lep-MAP3. We used the ParentCall2 module of Lep-MAP3 to select loci with reliable parental genotypes by considering genotype information on parents and offspring. The Filtering2 module was then used to remove non-informative and distorted markers (dataTolerance = 0. 0000001). The module SeparateChromosomes2 assigned markers to linkage groups (LGs), after test, where the logarithm of odds score (LodLimit) varied from 10 to 50 in steps of 5 then from 20 to 30 in steps of 1 and the minimum number of SNP markers (sizeLimit) per linkage group from 50 to 500 in steps of 50 for each of the LodLimit.
The two parameters, lodLimit = 27 and sizeLimit = 250, were chosen as the best parameters to obtain the 23 linkage groups (as expected in members of the Oleoideae subfamily;  (Wallander & Albert, 2000). A custom python script removed loci with SNPs mapped on two or more different linkage groups. The last module OrderMarkers2 ordered the markers within each LG. To consider the slight stochastic variation in marker distances between executions, the module was run three times on each linkage group, first separately for the meiosis that took place in each parent (sexAveraged = 0) and then averaged between the two parents (sexAveraged = 1). To produce the most likely final father and mother specific maps and a final sex-averaged maps (De-Kayne & Feulner, 2018), we kept for each map the order of markers that had the highest likelihoods for each linkage group.
Acknowledgements We thank  Sylvain Bertrand  and  Fantin Carpentier  for technical help for the phenotyping and  Jos Käfer  for scientific discussion and help in applying Sex-DETector to our material. We thank  Jacques Lepart † ,  Mathilde Dufay ,  Pierre Olivier Cheptou ,  Xavier Vekemans ,  Sylvain Billiard  and  Bénédicte Felter  for scientific discussions. Field and laboratory work for phenotyping were done at the  Platform Terrains d'Expériences (Labex CeMEB   ANR-10-LABX-0004-CeMEB ) of the  Centre d'Ecologie Fonctionnelle et Evolutive (CEFE, CNRS ) with the help of  Thierry Mathieu  and  David Degueldre  and at the platform Serres cultures et terrains expérimentaux of the Lille University with the help of  Nathalie Faure  and  Angélique Bourceaux. We are grateful to  Marie-Pierre Dubois  for providing access to the microscopy facilities at the  SMGE (Service des Marqueurs Génétiques en Ecologie) platform (CEFE).
This work was funded by the  French National Research Agency  through the project ' TRANS'  ( ANR-11-BSV7-013-03 ) and by a grant from the  European Research Council  ( NOVEL  project, grant # 648321 ). A. C was supported by a doctoral grant from the  French ministry of research. The authors also thank the  Région Hauts-de-France , and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development  for their financial support. We also thank the  HPC Computing Mésocentre of the University of Lille  which provided us with the computing grid. We thank  Tatiana Giraud ,  Ricardo C. Rodríguez de la Vega  and two anonymous referees for constructive reviews of an earlier version.
Data, script and code availability Fastq files for all 204 offspring and both parents are deposited in the NCBI BioProject PRJNA724813. All scripts used can be accessed at https://github. com/Amelie-Carre/Genetic-map-of-Phillyrea-angustifolia or https://doi. org/10. 5281/zenodo. 5616347 (Carré & Gallina, 2021).
Multidrug-resistant Streptococcus pneumoniae emerge through the modification of core genome loci by interspecies homologous recombinations, and acquisition of gene cassettes. Both occurred in the otherwise contrasting histories of the antibiotic-resistant S. pneumoniae lineages PMEN3 and PMEN9. A single PMEN3 clade spread globally, evading vaccine-induced immunity through frequent serotype switching, whereas locally circulating PMEN9 clades independently gained resistance. Both lineages repeatedly integrated Tn916-type and Tn1207. 1type elements, conferring tetracycline and macrolide resistance, respectively, through homologous recombination importing sequences originating in other species. A species-wide dataset found over 100 instances of such interspecific acquisitions of resistance cassettes and flanking homologous arms.
Blue bars indicate recombination events on terminal branches of the tree, identified in only one isolate. In the bottom half of the graph, the line represents the frequency of recombination events along the genome's length. The online version of this article includes the following source data and figure supplement(s) for figure  1:  Source data 1. Sequence and epidemiological data for S. pneumoniae samples. the overall ratios of base substitutions resulting from homologous recombination relative to point mutations (r/m). Consistent with its more rapid serological diversification, r/m was higher in PMEN3 (13. 1) than PMEN9 (7. 7). This difference in r/m could be due to the two lineages differing in three ways: (i) in the number of recombinations, (ii) in the length of recombination events, or (iii) in the sources of their recombination events, with more divergent sources increasing the r/m.
By contrast, within the ST143 clade, only 12% of isolates were trimethoprim resistant and 36% were sulfamethoxazole resistant. The 15 isolates from South Africa in the PMEN3 lineage were all resistant to both trimethoprim and sulfamethoxazole. The high levels of resistance to both antibiotics in South Africa across PMEN3 and PMEN9 could be driven by widespread cotrimoxazole consumption, as it is commonly used as a prophylactic treatment against secondary infections in HIV-positive individuals  (Daniels et al. , 2019). This was tested using all genomic data from the GPS collection. Most isolates were resistant to sulfamethoxazole (11,594 of 20,043; 58%), with fewer isolates resistant to trimethoprim (7770 of 20,043; 39%). The combination of resistances, conferring full co-trimoxazole resistance, was identified in 7666 isolates (38%).
, 2016). Therefore, the 103 isolates collected prior to the introduction of PCV7 were used to test whether this atypical pattern of antibiotic consumption could explain the success of the clade. There was significant evidence of a molecular clock, based on the correlation between the root to tip distance and the date of isolation for this clade (Pearson's correlation coefficient; R 2 = 0. 15, n = 103, p value<1 Â 10 À4 ; Figure  5 -figure supplement 2). This estimated the clade's MRCA existed in 1970. Generating a time-calibrated phylogeny using BactDating  (Didelot et al. , 2018)  suggested a relatively slow clock rate of 5. 30 Â 10 À7 substitutions per site per year (95% credibility interval of 3. 98 Â 10 À7 to 6. 75 Â 10 À7 substitutions per site per year). The Skygrowth package  (Volz and Didelot, 2018)  was then used to reconstruct the effective population size, N e , and the growth rate of N e through time of this clade.
The flanking regions immediately adjacent to the insertion have a low percentage identity when aligned to other pneumococci, ranging between 92 and 94% (Figure  5 -figure supplement 5). The immediate upstream 500 bp region most closely matched to a S. mitis reference genome (accession code AFQV00000000). Therefore, other acquisitions of common MGEs, related to either Tn1207. 1 or Tn916, were analyzed to determine whether they had also been recently imported from related commensal species.
For Tn1207. 1-type elements, S. mitis was the most likely donor (Figure  9 ). In the regions upstream of the Tn1207. 1 insertion, S. mitis was the top match for 92% of 500 bp long flanks. Even at longer flank lengths, S. mitis was still the leading match for upstream regions, although for Figure  9. Identification of likely sources of Tn916-type and Tn1207. 1-type elements. Flanking regions upstream and downstream from mobile genetic element (MGE) insertion sites were compared to a reference streptococcal database. Lines represent the proportion of matches, across all element acquisitions reconstructed as having occurred through homologous recombination, that correspond to the four species present in the reference streptococcal database. The dashed unmodified locus lines represent data from the orthologous regions of isolates without the MGE insertion.
mitis sequence disappearing 4 kb from the insertion site. However, for the upstream flanking regions (Figure  9 -figure supplement 1A), the median g score remained low with increasing flank length, with a median of 0. 83 at 7. 5 kb upstream of the insertion. This upstream region, replaced by S. mitis sequence in many isolates, extended into the uvrA gene, another component of the nucleotide excision repair machinery within the pneumococcus. The consistency of top matches for this Tn1207. 1-type element insertion into tag was high across the 66 independent acquisitions within recombination events (Figure 9-source data 1). For the 500 bp upstream region, 85% of the insertions (56 of the 66 within recombination blocks) had the S. mitis 21/39 (accession code AYRR00000000) reference as their top hit.
1-type elements were only found at 27 sites. Precisely determining the species of origin for observed interspecies recombination events is challenging, given the diversity of oral streptococci and their ability to exchange sequence. While our reference database is sufficient to split likely non-pneumococcal from pneumococcal DNA, it is not detailed enough to fully delineate the networks through which AMR genes spread. Though S. mitis is clearly a crucial source of antibiotic resistance genes for S. pneumoniae, the much greater diversity of this commensal means the few available samples are spread thinly across the population structure  (Kilian et al. , 2014). As such, greater sampling of more streptococcal species is needed to assess the most likely donor for these interspecies transformations.
Isolates that could be cultured were sequenced as paired-end 24plex libraries on Illumina HiSeq 2000 machines, generating 75-nt reads. Sample identity was checked through comparing serotype, inferred by seroba v1. 0. 0  (Epping et al. , 2018) , and ST with those determined by sample providers. Samples were checked for contamination through assessing their mapping to the reference sequence, as described previously  (Croucher et al. , 2011). After these tests, 215 PMEN3 and 263 PMEN9 isolates passed for use in the described analyses. These datasets were combined with isolates from the GPS project, which generated a database of 20,043 high-quality pneumococcal draft genome sequences from 33 countries collected between 1991 and 2017  (Gladstone et al. , 2019). In total, 49. 7% of these isolates were collected from locations before the PCV7 vaccine was introduced.
Most isolates were sampled from cases of IPD in children under the age of five. PMEN3 and PMEN9 corresponded to strains GPSC6 (454 isolates associated with information on country and date of isolation) and GPSC18 (312 isolates associated with information on country and date of isolation) in this collection, respectively  (Lees et al. , 2019). Hence, the final dataset sizes were 669 isolates for GPSC6, containing PMEN3, and 575 isolates for GPSC18, containing PMEN9. Among these PMEN collections, 64. 7% of isolates were collected before the PCV7 vaccine was introduced. Raw and processed sequence data for the 478 isolates not within the GPS collection are publicly available in the EMBL Nucleotide Sequence Database (ENA; project number PRJEB2255). All accession codes for reads, assemblies, and annotations are listed in Figure  1 -source data 1.
Generation of annotation and alignments De novo assemblies were generated using an automated pipeline for Illumina sequences  (Page et al. , 2016). Briefly, reads were assembled using Velvet with parameters selected by Velve-tOptimiser. These draft assemblies were then improved by using SSPACE and GapFiller to join contigs  (Zerbino and Birney, 2008; Boetzer and Pirovano, 2014; Boetzer and Pirovano, 2012). The final assemblies were annotated using PROKKA  (Seemann, 2014). Whole-genome alignments were generated for phylogenetic analysis through mapping of short read data against reference sequences. For the PMEN3 and PMEN9 analyses, the reference genomes were S. pneumoniae RMV4 rpsL* DtvrR (accession code: ERS1681526)  (Kwun et al. , 2018)  and INV200 (accession code: FQ312029. 1), respectively.
Antibiotic consumption data Selection pressures on specific clades were inferred using macrolide and penicillin consumption data from Europe. Two data sources were used for macrolides: a study looking at macrolide resistance among pneumococci isolates in Germany by  Reinert et al. , 2002 , which recorded data from 1992 to 2000; and the European Centre for Disease prevention and Control (ECDC), which recorded data from 1997 to the present day. The ECDC data for Germany is from the primary care sector for outpatients, with a population coverage of 90%, while the Reinert et al. paper takes data from both prescriptions in hospitals and from community general practitioners. The macrolide usage data were combined using the 3 years of overlap between the two datasets as a scaling factor. This was the mean transformation that mapped the Reinert et al.
, 2002 data to the ECDC data. It was applied to convert the data from 1992 to 1996 into the same units as the ECDC data (defined daily doses per 1000 individuals in the population). For b-lactam consumption, data were also taken from the ECDC for 1997 to present day. For blactam consumption from 1992 to 1997, data were taken from  McManus et al. , 1997. These data reflect hospital and retail sales of oral antibiotics in West Germany for the years 1989 and 1994 in the same DDD units as the  ECDC data. A linear trend between 1989 ECDC data. A linear trend between , 1994 ECDC data. A linear trend between , and 1997 , the first year of data from the ECDC, was used to impute the missing values between 1992 and 1997.
Phylogenetic and phylodynamic analyses Gubbins v2. 3  (Croucher et al. , 2015b)  was used to identify recombinations and generate phylogenies for both the PMEN lineages and the GPSCs. Gubbins was run for five iterations. The starting phylogeny of isolates was constructed with FastTree 2  (Price et al. , 2010). Subsequent iterations generated phylogenies with RAxML v8. 2. 8  (Stamatakis, 2014) , with a generalized time reversible (GTR) model of nucleotide substitution with a discretized gamma distribution of rates across sites. Time-calibrated phylogenies were generated from the Gubbins outputs using the BactDating R package v1. 0. 1  (Didelot et al. , 2018). Isolates without dates of collection were pruned from the phylogeny, and the root-to-tip distances used to test for a molecular clock signal.
Where one was detectable, BactDating was run with a relaxed clock model and a Markov chain Monte Carlo (MCMC) length of 50 million iterations. Chain convergence was checked through visual inspection of trace plots. The Skygrowth R package  (Volz and Didelot, 2018)  was then used to formally test the link between antibiotic consumption and population growth rates. The timed phylogeny generated by BactDating was input into Skygrowth, where it was analyzed in combination with the b-lactam, macrolide, and macrolide-to-b-lactam consumption data. Consumption data were each separately rescaled prior to analysis with default settings and priors. Four sets of analysis were run: one for each of the consumption datasets, and one without consumption data. In each case, the MCMC was run for 100 million iterations, which visual inspection suggested was sufficient for the chains to converge.
Antibiotic resistance analyses The MIC for penicillin had been determined for most isolates in the PMEN3 and PMEN9 collections (65. 5 and 80. 9%, respectively). The MICs of the remaining 341 isolates were predicted using an RF approach analogous to that developed in  Li et al. , 2017. In this approach, the TPD of three penicillin binding proteins (PBPs; PBP1A, PBP2B, and PBP2X) were extracted and each amino acid position used as a predictor to train an RF model on the continuous log 2 MIC value. The training data came from 4342 isolates previously characterized by the CDC, 2018. This model predicted the MIC for all 341 isolates with unknown MIC values. The continuous MIC values predicted by the model were then converted into categories based on the pre-2008 CLSI meningitis breakpoints for resistance, with an added intermediate class of isolates for those with 0.
Ancestral state reconstruction The penicillin resistance categories inferred from the metadata and the RF model were reconstructed on the time-calibrated phylogeny using the phytools R package v0. 7. 7  (Revell, 2012). The make. simmap function was run using an equal rates model and an MCMC chain sampling every 100 iterations. The input was a matrix of character states for the tips, in which each isolate's observed or predicted phenotypes were assigned a probability of 1. After the reconstruction, each node's state was assigned as that with the highest posterior probability. Starting at the root, the number of lineages of each state at each coalescent event in the timecalibrated tree was recorded. Every time a node was reached, an extra lineage was added to the total. If there was no state change between two nodes, the count for the state was increased by 1; if there was a state change not on a terminal branch, the count for the new state was increased by 2; else if there was a state change on a terminal branch, the new state count was increased by 1.
MGE identification Reference MGE sequences were used to search all the genomic datasets for intact and partial representatives. For the Tn916 element, the 18 kb reference given by the transposon registry  (Tansirichaiya et al. , 2019) , extracted from Bacillus subtilis (accession code: KM516885), was used. For Tn1207. 1, a 7 kb reference extracted from the S. pneumoniae INV200 genome (accession code: FQ312029. 1) was used. BLASTN was used to detect Tn916 and Tn1207. 1 among the assembled genomes in the collections, with hits filtered using an empirically determined tthreshold alignment length of 7 kb and 2 kb, respectively, for each element. BLASTN results were merged if they represented continuation of an element's sequence split across multiple contigs, to enable detection of elements in isolates that were fragmented in draft assemblies.
pneumoniae and other Streptococcus species, building on the database collated in  Mostowy et al. , 2017b. BLASTN was used to compare each flanking region to this database. The orthologous regions to these flanks were also extracted from isolates not containing the insertion, to act as a control. The statistic g was used to determine the species of origin for an insertion. This used the BLAST bit score, which is a normalized form of the raw score of an alignment. The bit score measures sequence similarity irrespective of query sequence length and database size. The g statistic was calculated as the bit score of the top-ranked S. pneumoniae hit (b) divided by the bit score of the topranked hit (B): g ¼ b B (1) Hits where the top match was S. pneumoniae, indicating the insertion originated from an intraspecies transformation event, had a g score of 1.
0. Any score below 1. 0 indicated a potential origin from outside of S. pneumoniae. This pipeline was also applied to detect the origin of pbp genes involved in the acquisition of resistance. Here, using phenotype predictions from the RF model described above, ancestral node resistance states were reconstructed. The descendants of nodes where resistance was acquired, or lost, with the fewest base substitutions subsequently accumulating in the three pbp genes then had their gene sequences extracted. These sequences were then compared to the reference database using BLASTN. The same g statistic as above was used to detect the likely origin of these pbp genes. The code for both the altered pbp pipeline and the MGE detection pipeline is available at https:// github. com/jdaeth274/ISA (copy archived at swh:1:rev: c3873d851fdfb01efd8bb1f8a18f33acb06b6fc5, D  'Aeth, 2021b).
For murM, where the effects of alterations on resistance levels are less well understood, a different approach was taken. The regions corresponding to the murM genes in the annotated references were extracted from the PMEN3 and PMEN9 whole-genome alignments. To enable the detection of possible interspecies recombinations, murM sequences from S. mitis 21/39 (accession code: AYRR01000000) and Streptococcus pseudopneumoniae IS7493 (accession code: CP002925) were added to the dataset. All murM sequences were then aligned with Muscle v3. 8. 31  (Edgar, 2004). Sequences were clustered into lineages, and recombinations inferred, using fastGEAR  (Mostowy et al. , 2017b). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.