dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Screening and Authentication of Y. pestis DNA in Human Skeletons. The presence of Y. pestis in the samples from Saint-Laurent-dela-Cabrerisse (SLC) and Bergen op Zoom (BER) has been confirmed by PCR in a previous study by  Haensch et al. (13). A similar approach was used to screen 36 individuals from Abbadia San Salvatore (BSS) and nine individuals from Oslo (OSL) (see Methods for more details on the experimental work). Positive samples from Abbadia San Salvatore and Oslo were enriched without initial metagenomic screening. Genomic Data Analyses. We systematically reanalyzed all available raw sequencing data from ancient Y. pestis metagenomic datasets from the Second Plague Pandemic. Additionally, we reanalyzed the raw data from two Bronze Age samples and two Justinian Plague samples from the First Plague Pandemic.
As previously reported, two Bronze Age samples from Bateni and Kytmanovo (Russia)  (17) , as well as the Justinian samples  (9, 18) , can be found on branch 0 (Fig. 3A ). As expected, the five described ancient genomes cluster with all other known Second Plague Pandemic genomes (Fig. 3 ). Divergence Dates and Tip Dating. The reconstructed maximum likelihood tree shows a high correlation between the root-to-tip distance and the sampling time (correlation coefficient r2 = 0. 74). The date-randomization test (DRT) showed a different result. In fact, the outcome of the DRT showed that the 95% highest posterior density (HPD) of the substitution rate estimated for both the original and 20 date-randomized datasets is overlapping (SI Appendix, Fig. S11 ).
Discussion As mentioned in the Introduction, two scenarios have been proposed in past studies for the persistence of plague during the Second Plague Pandemic. However, the few currently available ancient Y. pestis genomes have proven insufficient to explain the mechanisms underlying the recurrent plague outbreaks in Europe during the Second Plague Pandemic. To provide further insights into the events driving the Second Plague Pandemic and to properly address which of the two proposed scenario has the highest likelihood, we have evaluated the available data along with five sequenced Y. pestis strains stemming from four European archaeological sites of the 14th century, using an integrative approach. By combining historical, archaeological, and genomic data, we attempted to reconstruct the spread of plague in a multifaceted context and to define the routes of transmission following the initial introduction of plague during the Black Death, accordingly.
These data support an account by Agnolo di Tura  (26) , who reported the desolation of the city of Siena and its surrounding countryside by the plague in 1348  (27). Starting from the port city of Pisa in mid-January 1348 (4), plague made its way through Tuscany and was subsequently reported in Florence (March-April 1348) before reaching Siena and its countryside in May 1348, likely over the Via Francigena, a well-frequented route by pilgrims  (4). Considering this chain of events, it is reasonable to assume that the Abbadia San Salvatore strain reached Pisa over sea and continued to spread over land by infected individuals or goods. Given the number of subsequent outbreaks recorded over this short period of time, it is plausible that the additional two SNPs were acquired through the large transmission chain in Italy, rather than having been gained before the strain's arrival in Pisa or within a newly established local wildlife reservoir.
Shotgun Metagenomics Data Analysis. The presence of Y. pestis in shotgun metagenomic datasets was investigated using two methods. The first method is based on read abundance of specific genes and was implemented in Metaphlan  (50). The second method is based on the exact alignments of kmers and was implemented in Kraken  (51). This procedure was only applied when the first screening using qPCR was ambiguous. Mapping-Based SNP Analysis. We designed a custom pipeline (SI Appendix, Fig. S5 ) to analyze raw sequencing data from modern and ancient genomes using snakemake as the workflow manager system  (52). The genome of the Y. pestis strain CO92 was used as reference. Paired-end reads in fastq format were trimmed and merged using ClipAndmerge (https://github. com/apeltzer/ ClipAndMerge). Thereafter, we used bwa-aln  (53)  to align the merged reads against Y.
pestis strain CO92 by disabling the seed option (-l) and setting the option -n to 0. 1 (for modern data, the -n option was set to 0. 01). Duplicated reads were identified and marked using picard-tools (broadinstitute. github. io/ picard/). Local alignment of reads was performed around gapped regions before SNP calling using the GATK IndelRealigner module (https://software. broadinstitute. org/gatk/). DeDup (https://github. com/apeltzer/DeDup) was also used to remove duplicated sequences as it has been specifically designed for merged reads. Aligned Binary Alignment Map (BAM) files of ancient DNA samples were analyzed using MapDamage2 (54) to assess and recalibrate aDNA damage patterns in the form of by C-to-T or G-to-A conversions. Edit Distance. To verify that the generated reads belong to Y.
pestis rather than Y. pseudotuberculosis, all reads from each sample were mapped to the genome of Y. pseudotuberculosis, strain IP-32953, as described above. The edit distance, which defines the minimal number of substitutions, insertions, and deletions to transform the read sequence to its homologous sequence in the reference genome, was calculated using BAMStats (bamstats. sourceforge. net/). SNPs Call. SNP calling was performed using samtools  (55, 56)  and bcftools (https://samtools. github. io/bcftools/). SNPs located within a frame of 10 bp from indels were excluded with samtools. For each sample, all identified SNPs were filtered and annotated using a freely available tool, snpToolkit (https://bitbucket. org/Amine-Namouchi/snptoolkit); snpToolkit allows one to filter and annotate SNPs from vcf files by providing the genbank file of the reference sequence used during reads alignment.
The generated tree was visualized using FigTree (version 1. 2. 1, tree. bio. ed. ac. uk/software/ figtree/), and the substitutions leading to each SNP were mapped in the phylogenetic tree using Fastml v3. 1  (62). Divergence Time and Tip Dating Analysis. Temporal signal detection in the sequence data was performed using TempEst  (63). As previously described  (17, 64, 65) , the lognormal relaxed clock model and constant population size models implemented in BEAST  (66)  were applied to the alignment sequence and phylogenetic tree to determine the divergence dates at each node. To evaluate the reliability of the Bayesian inference, we applied the daterandomization test (DRT) as implemented in the R package tipdatingbeast  (67)  to generate 20 BEAST input files in xml format with randomized dates.
ACKNOWLEDGMENTS. We thank  Lars Walløe  for useful comments and discussion; and  Joachim Burger  for providing access to the aDNA facilities at the  University of Mainz, Germany. This project was funded by the  European Research Council (ERC)  under the  FP7-IDEAS-ERC Program  (Grant  324249 ). Part of the data analysis was performed on the Abel Cluster, owned by the  University of Oslo  and the  Norwegian metacenter for High Performance Computing (NOTUR)  and operated by the  Department for Research Computing at the University Center for Information Technology Services (USIT) ,  University of Oslo. C. O. is funded by  Satsningsmiljø Grant Comparative Infection Biology (COMPI) ,  Department of Biosciences, University of Oslo.
openstreetmap. org). OpenStreetMap is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). GCRMN stands for Global Coral Reef Monitoring Network. The map was generated using QGIS 2. 18 software (GNU licence). The mean annual SBIR for surfers from 1988 to 2016 was 6. 5 ± 1. 7 per million hours of surfing (pmh; Fig. 2d ). The SBIR decreased from 19 pmh in 1988 to 0 pmh in 1993. During the 1993-2010 period, the mean annual SBIR remained relatively low (1. 3 ± 0. 5 pmh), but it increased from 2011 to reach a maximum of 42. 8 pmh in 2015 (1 event per 24,000 hours). Over the 1988-2016 period, the SBIR per surfing sector (i. e. , surf spot or group of surf spots) ranged from 0 (no event) to more than 1,000 pmh. Highest SBIR per surfing sector occurred where a single shark bite event was associated with a very low ICS (Fig.
S6 ) with rare and sporadic incidences reported by local authorities and in newspapers through 1980. Since 1980, local archives suggest that newspapers have consistently documented shark-human interactions (particularly bites). Accordingly, shark bite occurrence data were extracted from the Global Shark Attack File (GSAF) on-line database, as well as from local scientific reports or publications  36, [58] [59] [60]  for subsequent analyses. Additionally, these data were supplemented by conducting a systematic search of records in newspaper articles during the entire study period (1980-2016). Each shark bite report was documented using a compilation of scanned local paper press releases (published before 2010) and several informal interviews with local ocean-users (divers, surfers, spear fishers) completed with a continuous on-line local press survey undertaken from 2011 to 2016.
Modifications of the original GSAF dataset were also undertaken, including the addition of missing events, the removal of double counted events, and the correction of dates and locations. The following minimum set of basic descriptors were documented for each reported shark bite event: time-date location, injuries (including fatal injuries), victim age, gender, and activity. Data were anonymized and a unique ID number was attributed to each event. The CFR  61  was calculated as the proportion of fatal cases. The activities practiced by the victims were categorized as follows: (1) surfing (and other surfing sports, except windsurfing), (2) spearfishing, (3) bathing/swimming/wading (including after a fall at sea), (4) windsurfing, (5) diving, (  6 ) fishing and (  6 ) kayaking. The presence of other ocean-users in the water within a radius of approximately 100 m during the shark bite event was also documented.
Where shark species information is mentioned (victim and/or witness account), only cases providing a positive species identification are assessed. Mapping of shark bite events was based on interviews with victims and witnesses, and the local knowledge of authors of the present study (EL, NL). Google Earth aerial photography and a 5 m resolution bathymetric model Litto3D ® -HYDRORUN (provided by Institut Français de Recherche pour l'Exploitation de la Mer, IFREMER) were used as background information for mapping in QGIS  62  Version 2. 18 software.
Daily rainfall data (in mm per day) were extracted from the online Meteo-France database using the closest weather station with a complete data series over the 1988-2016 period (seven meteorological stations distributed along the coastline, within a 10 km distance range from surf sectors). A set of cumulative rainfall period from 1 to 30 days before the day of shark bite event were calculated together with the corresponding mean over the same period per year from 1988 to 2016. The Supplementary Fig. S1  provides an overview of the major intense rainfall events that occurred during the 1980-2016 period. Atmospheric conditions of the day were extracted from the online Meteo-France (French National Meteorological Service) database and assigned to one of the following broad categories: (1) Sunny, (2) Partly cloudy, (3) Cloudy variable, (4) Rain shower and (5) Rain -heavy rain.
Acknowledgements Our deepest thoughts go to the victims of shark bites incidents in La Réunion, and to their relatives. The data collection phase of this research was partially supported by the  FEDER   CHARC  project ( 2011-2015 ) funded by the  European Union 's, the  Regional Council of La Réunion  and the  French State (DEAL). This research received additional contribution from the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 6417 , ECOPOTENTIAL). We kindly thank the following persons and institutions who provided insight, data and expertise that greatly assisted the research:  Olivier Bielen  ( Centre de Ressources et d' Appui pour la gestion du risque requin CRA ),  Antonin Blaison  ( Institut pour la Recherche et le Développement IRD ),  Robert Boulanger  ( Fédération Française de Surf, Ligue de Surf de La Réunion ),  Jean-Christophe Buyat  ( LETGA Saint-Paul ),  Pascale Chabanet  (IRD),  Philippe Charvin  ( LETGA Saint-Paul ),  Eric Chateauminois (CRA) ,  Frédéric Chiroleu  ( Centre de Coopération Internationale en Recherche Agronomique pour le Développement CIRAD) ,  Geremy Cliff  ( Kwazulu-Natal Shark Board ),  Emmanuel Cordier  ( Université de La Réunion UR ),  Virginie Cordier  (UR),  Estelle Crochelet  (IRD),  Gilbert David  (IRD),  Patrick Flores  ( Conseil Régional de La Réunion, Ville de Saint-Paul, Fédération Française de Surf, CRA ),  Pierre Giovannangeli  ( Mickey Rat Surfshop ),  Arnold Jaccoud  (consultant),  Sébastien Jaquemet  (UR),  Jean-Lambert Join  (UR),  Matthieu Le Corre  (UR),  Lola Massé  (RNNMR),.
2 ). The ICGC-JP cohort comprised one HBV insertion in CCNA2 intron 2 and one fusion between the first untranslated exon of ANXA5 and exons 3-8 of CCNA2 (Fig. 1a, d ). In total, we identified 10 HCC with CCNA2 activation events in the LICA-FR series (6. 2%), 2 in the ICGC-JP series (0. 8%) and 18 in the TCGA series (5. 4%), associated with a significant increase of CCNA2 mRNA expression, but also generating a truncated cyclin A2 protein lacking the N-terminal destruction box and the ubiquitination site. Viral insertions and enhancer hijacking activate cyclin E1. In our series of 160 HCC, we identified 5 AAV2 insertions (three previously described in the ref. 16 ) and one HBV insertion in the 5′ region or upstream the transcription start site (TSS) of CCNE1 (Fig. 2a , Supplementary Data 3).
In total, we identified 10 HCC with CCNE1 activation events in the LICA-FR cohort (6. 2%), two in the ICGC series (0. 8%) and 10 in the TCGA series (3. 0%). Across the three data sets, 52/751 tumors (6. 9%) displayed an activation of cyclin A2 (n = 30) or E1 (n = 22) due to viral insertions or structural rearrangements. These are later referred to as CCN-HCC. The proportion of CCN-HCC varied between the cohorts (12. 5% in our series, 8. 4% in TCGA and 1. 6% in ICGC-JP) due to differences in etiological backgrounds (Supplementary Data 2). It was particularly high in our series enriched in cancers developed in a non-fibrotic liver, and low in the IGGC-Japan series dominated by HCV-related cases. Cyclin A2 or E1 activation defines a homogenous HCC subgroup. We next explored the molecular and clinical characteristics of CCN-HCC.
Gene expression analysis of the LICA-FR and TCGA showed that CCN-HCC defined homogeneous transcriptional clusters (Fig. 3a ). They were characterized by an overexpression of cell cycle genes, in particular E2F targets, and an activation of the ATR pathway in response to replication stress (Fig. 3b , Supplementary Data 5). The most significant downregulated pathways were oxidative phosphorylation, suggesting a metabolic switch to aerobic glycolysis (Warburg effect), and MYC targets. We also compared the alteration frequencies of known liver cancer driver genes 10 between CCN-HCC and others. CCNA2 and CCNE1 activation events were remarkably exclusive from CTNNB1 and TERT promoter mutations, but frequently associated with PTEN and RB1 inactivation in both the LICA-FR and TCGA series (Fig. 3b , Supplementary Data 6).
4 × 10 -11 , Fig. 4b ). We validated this association using WGS data from the ICGC-JP series and a subset of 48 samples from the TCGA series (Fig. 4c , Supplementary Data 7). In absence of WGS data for the rest of the TCGA series, we used SNP array data to estimate the number of focal gains (<200 kb) in each tumor as a surrogate marker of the RS1 signature. With a median of 120 events, CCN-HCC displayed a significant increase of focal gains as compared with other HCC in the TCGA series (median = 6, P < 2. 2 × 10 -16 , Supplementary Fig. 5 ). Thus, CCN-HCC have a relatively low mutation burden but a large number of structural rearrangements with a specific signature.
CCN-HCC share a similar signature with BRCA1-altered cancers. To investigate the prevalence of the RS1 signature in other cancer types, we applied our method to 2606 tumors from the ICGC PanCancer Analysis of Whole Genomes (PCAWG) dataset  23, 28, 29. In this pan-cancer series, we identified 9 rearrangement signatures (Supplementary Fig. 12 ), including one signature (RS1-pancan) highly similar to the RS1 signature that we identified in liver cancers (cosine similarity = 0. 91). The RS1pancan signature was detected at low frequency in several cancer types (e. g. bladder, lung, esophageal and gastric cancers), and was highly active in breast (18% of samples with ≥ 50 RS1 events) and ovarian (33%) cancers. However, this signature was associated with CCNA2/E1 rearrangements only in liver cancer (Fig.
Discussion Here, we report the characterization of a homogeneous HCC subgroup driven by the activation of CCNA2 or CCNE1 gene. CCN-HCC represent 7% of HCC across the 3 data sets analyzed here, but up to 14% of HCC developed in a non-fibrotic liver. These patients often have atypical clinical presentation, without any history of primary risk factors, and can be remarkably young, exemplified by tumor FR-3880T developed in a 32 year-old woman without any risk factor, due to a TET2-CCNA2 fusion. CCN-HCC are usually large tumors of poor prognosis but share molecular characteristics, in particular high proliferation and replication stress, that could provide therapeutic opportunities  36. First, conventional chemotherapies mainly affect actively dividing cells by generating DNA damage or blocking DNA replication, and the tandem duplicator phenotype was identified as a marker for chemotherapeutic response in breast cancer cell lines and patient-derived xenografts  37.
P-values were obtained using one-sided Wilcoxon rank-sum tests. d Number of RS1 events in liver, breast and ovarian cancers with or without CCNA2, CCNE1 and BRCA1 alterations. The middle bar, median; box, interquartile range; bars extend to 1. 5 times the interquartile range. e, Violin plots representing the distribution of tandem duplication sizes across liver, breast and ovarian cancers with or without CCNA2, CCNE1 and BRCA1 alterations. f Violin plots representing the replication timing of duplication and inter-chromosomal translocation breakpoint loci in liver and breast cancers with or without CCNA2, CCNE1 and BRCA1 alterations. Replication timing was determined using Repli-Seq data from the HepG2 cell line for liver cancer and from the MCF-7 cell line for breast cancer. g Proposed connexion beween rearrangement signatures in CCN-HCC and in BRCA1-inactivated breast and ovarian cancers and PTEN.
Methods Description of the LICA-FR cohort. A series of 160 hepatocellular carcinoma (HCC) samples and their non-tumor counterparts were collected from patients surgically treated in four French hospitals located in Bordeaux and Paris region. The study was approved by institutional review board committees (CCPRB Paris Saint-Louis, 1997, 2004, and 2010, approval number 01-037; Bordeaux, 2010-A00498-31). Written informed consent was obtained in accordance with French legislation. All samples were immediately frozen in liquid nitrogen and stored at -80 °C. HCC were enriched in cases developed on a non-cirrhotic liver (107/160, 67%): 75 tumors developed in non-fibrotic (METAVIR F0-F1), 32 in chronic hepatitis (F2-F3) and 53 in cirrhotic liver (F4). Clinicopathological data were available for all cases.
Sequences were aligned to the hg19 version of the human genome using BWA  52  version 0. 7. 12. We used Picard tools version 1. 108 (http://broadinstitute. github. io/ picard/) to remove PCR duplicates and GATK 53 version v3. 5 for local indel realignment and base quality recalibration, as recommended in GATK best practices  54. We obtained an average depth of 119-fold for tumors (range 104-126) and 41-fold for matched non-tumor liver samples (range 38-43). Whole exome sequencing. Whole exome data from 156 tumors of the LICA-FR series were analyzed in this study, comprising 96 previously published  10   Somatic mutation calling. We used MuTect2 to call somatic mutations from WES and WGS data by comparing each tumor sample with its matched non-tumor counterpart and a panel of normals (PON) file.
We excluded mutations belonging to the ENCODE Data Analysis Consortium blacklisted regions (http://hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/ wgEncodeDacMapabilityConsensusExcludable. bed. gz) and regions covered by < 6 reads in the tumor or normal sample. We then selected only single nucleotide variants (SNVs) with a MuTect2 flag among "PASS", "clustered_events", "t_lod_fstar", "alt_allele_in_normal" or "homologous_mapping_event" and small insertions and deletions (indels) with a MuTect2 flag among "PASS", "cluster-ed_events" or "str_contraction". To improve specificity in the calling of mutations with low variant allele frequency (VAF), we quantified the number of high quality variant reads in the tumor (mapping quality ≥ 20, base quality ≥ 20) and the number of variant reads in the non-tumor sample with no quality threshold using bamreadcount (https://github.
com/genome/bam-readcount). Only variants matching the following criteria were finally retained: VAF ≥ 2% in the tumor with ≥ 3 variant reads, VAF ≤ 5% in the non-tumor samples with ≤ 2 variant reads, and a VAF ratio ≥ 5 between the tumor and non-tumor sample. Copy-number and structural rearrangement analysis. We used MANTA  55  software to identify somatic structural rearrangements in WGS data. To keep only the most reliable events, we selected only rearrangements supported by ≥ 10 reads and with a variant allele fraction ≥ 5%. We used cgpBattenberg  56  algorithm to reconstruct copy-number profiles from WGS data. We used the circular binary segmentation algorithm implemented in the Bioconductor package DNAcopy  57  to reconstruct copy-number profiles from WES data. RNA sequencing. RNA samples from the 160 tumors of the LICA-FR series were sequenced in several batches with slightly different protocols.
RNA samples were enriched for polyadenylated RNA from 5 μg of total RNA, and the enriched samples were used to generate sequencing libraries with the Illumina TruSeq or Illumina TruSeq Stranded mRNA kit and associated protocol as provided by the manufacturer. Libraries were sequenced by IntegraGen (Evry, France) on an Illumina HiSeq 2000 or 4000 as paired-end 75 or 100 bp reads. Full Fastq files were aligned to the reference human genome hg19 using TopHat2  58. Sequencing details for each sample and the parameters used for TopHat2 are indicated in Supplementary Data 1. We removed reads mapping to multiple locations, and we used HTSeq  59  to obtain the number of reads associated to each gene in the Gencode v19 database, restricting to protein-coding genes, pseudogenes, antisense and lincRNAs (n = 42540).
We used the Bioconductor DESeq2 package  60  to import raw HTSeq counts for each sample into R statistical software and apply variance stabilizing transformation (VST) to the raw count matrix. FPKM scores (number of fragments per kilobase of exon model and millions of mapped reads) were calculated by normalizing the count matrix for the library size and the coding length of each gene. We used the area under the ROC curve (AUC) to identify and remove 2724 genes with a significant batch effect (AUC > 0. 95 between one sequencing project and others). Gene fusion detection. Fusions detected by TopHat2 (--fusion-search --fusionmin-dist 2000 --fusion-anchor-length 13 --fusion-ignore-chromosomes chrM) were filtered using the TopHatFusion-post algorithm. We kept only fusions validated by BLAST and with at least 10 split-reads or pairs of reads spanning the fusion event, and we removed fusions identified at least twice in a cohort of 36 normal liver samples.